fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

7907f9f16e61e6d3d77348c8c036aab74d3205b1

723

py

Python

renovation_service_provider_manager/api/__init__.py

leam-tech/renovation_service_provider_manager

353125e3d332d841495f93bf154b76f2cef75d3f

[ "MIT" ]

null

renovation_service_provider_manager/api/__init__.py

leam-tech/renovation_service_provider_manager

353125e3d332d841495f93bf154b76f2cef75d3f

[ "MIT" ]

null

renovation_service_provider_manager/api/__init__.py

leam-tech/renovation_service_provider_manager

353125e3d332d841495f93bf154b76f2cef75d3f

[ "MIT" ]

null

import frappe, re from renovation_service_provider_manager import invoke_mediator @frappe.whitelist(allow_guest=True) def get_service_provider_client_id(provider): k = f"client_id_{re.sub('[^0-9a-zA-Z]+', '_', provider.lower())}" client_id = frappe.cache().get_value(k) if client_id: return client_id client_id = get_client_id_from_mediator(provider) frappe.cache().set_value(k, client_id, expires_in_sec=18000) # 5hr return client_id def get_client_id_from_mediator(provider): try: r = invoke_mediator("/api/method/renovation_mediator.api.get_service_provider_client_id", {"provider": provider}) r.raise_for_status() r = r.json() return r["message"] except: frappe.throw(r.text)

31.434783

117

0.749654

import frappe, re from renovation_service_provider_manager import invoke_mediator @frappe.whitelist(allow_guest=True) def get_service_provider_client_id(provider): k = f"client_id_{re.sub('[^0-9a-zA-Z]+', '_', provider.lower())}" client_id = frappe.cache().get_value(k) if client_id: return client_id client_id = get_client_id_from_mediator(provider) frappe.cache().set_value(k, client_id, expires_in_sec=18000) return client_id def get_client_id_from_mediator(provider): try: r = invoke_mediator("/api/method/renovation_mediator.api.get_service_provider_client_id", {"provider": provider}) r.raise_for_status() r = r.json() return r["message"] except: frappe.throw(r.text)

true

7907fafcb079d191c0e965019b8286b4c02cb7f0

2,514

py

Python

virtual/lib/python3.8/site-packages/setuptools/extern/__init__.py

ShaviyaVictor/nyumbakumi-

933d825844da139998867594c1e21b09ba5c8e63

[ "MIT" ]

3

2022-03-02T12:13:02.000Z

2022-03-02T12:38:46.000Z

virtual/lib/python3.8/site-packages/setuptools/extern/__init__.py

ShaviyaVictor/nyumbakumi-

933d825844da139998867594c1e21b09ba5c8e63

[ "MIT" ]

1

2022-03-15T12:10:47.000Z

virtual/lib/python3.8/site-packages/setuptools/extern/__init__.py

ShaviyaVictor/nyumbakumi-

933d825844da139998867594c1e21b09ba5c8e63

[ "MIT" ]

null

import importlib.util import sys class VendorImporter: """ A PEP 302 meta path importer for finding optionally-vendored or otherwise naturally-installed packages from root_name. """ def __init__(self, root_name, vendored_names=(), vendor_pkg=None): self.root_name = root_name self.vendored_names = set(vendored_names) self.vendor_pkg = vendor_pkg or root_name.replace('extern', '_vendor') @property def search_path(self): """ Search first the vendor package then as a natural package. """ yield self.vendor_pkg + '.' yield '' def _module_matches_namespace(self, fullname): """Figure out if the target module is vendored.""" root, base, target = fullname.partition(self.root_name + '.') return not root and any(map(target.startswith, self.vendored_names)) def load_module(self, fullname): """ Iterate over the search path to locate and load fullname. """ root, base, target = fullname.partition(self.root_name + '.') for prefix in self.search_path: try: extant = prefix + target __import__(extant) mod = sys.modules[extant] sys.modules[fullname] = mod return mod except ImportError: pass else: raise ImportError( "The '{target}' package is required; " "normally this is bundled with this package so if you get " "this warning, consult the packager of your " "distribution.".format(**locals()) ) def create_module(self, spec): return self.load_module(spec.name) def exec_module(self, module): pass def find_spec(self, fullname, path=None, target=None): """Return a module spec for vendored names.""" return ( importlib.util.spec_from_loader(fullname, self) if self._module_matches_namespace(fullname) else None ) def install(self): """ Install this importer into sys.meta_path if not already present. """ if self not in sys.meta_path: sys.meta_path.append(self) names = ( 'packaging', 'pyparsing', 'ordered_set', 'more_itertools', 'importlib_metadata', 'zipp', 'importlib_resources', 'jaraco', 'typing_extensions', 'nspektr', ) VendorImporter(__name__, names, 'setuptools._vendor').install()

32.649351

84

0.604614

import importlib.util import sys class VendorImporter: def __init__(self, root_name, vendored_names=(), vendor_pkg=None): self.root_name = root_name self.vendored_names = set(vendored_names) self.vendor_pkg = vendor_pkg or root_name.replace('extern', '_vendor') @property def search_path(self): yield self.vendor_pkg + '.' yield '' def _module_matches_namespace(self, fullname): root, base, target = fullname.partition(self.root_name + '.') return not root and any(map(target.startswith, self.vendored_names)) def load_module(self, fullname): root, base, target = fullname.partition(self.root_name + '.') for prefix in self.search_path: try: extant = prefix + target __import__(extant) mod = sys.modules[extant] sys.modules[fullname] = mod return mod except ImportError: pass else: raise ImportError( "The '{target}' package is required; " "normally this is bundled with this package so if you get " "this warning, consult the packager of your " "distribution.".format(**locals()) ) def create_module(self, spec): return self.load_module(spec.name) def exec_module(self, module): pass def find_spec(self, fullname, path=None, target=None): return ( importlib.util.spec_from_loader(fullname, self) if self._module_matches_namespace(fullname) else None ) def install(self): if self not in sys.meta_path: sys.meta_path.append(self) names = ( 'packaging', 'pyparsing', 'ordered_set', 'more_itertools', 'importlib_metadata', 'zipp', 'importlib_resources', 'jaraco', 'typing_extensions', 'nspektr', ) VendorImporter(__name__, names, 'setuptools._vendor').install()

true

7907fb1a03f455a7370cb9f215000397fc06da34

2,382

py

Python

python/atlassian/config-report.py

oldD0g/code-snippets

68325d63122a5bbbab68dd726ea3add096380e12

[ "CC0-1.0" ]

null

python/atlassian/config-report.py

oldD0g/code-snippets

68325d63122a5bbbab68dd726ea3add096380e12

[ "CC0-1.0" ]

null

python/atlassian/config-report.py

oldD0g/code-snippets

68325d63122a5bbbab68dd726ea3add096380e12

[ "CC0-1.0" ]

null

#!/usr/bin/env python """ Object-oriented implementation of backup reporting code. Defines a class called 'Backup' that records all backups of a device """ import os, sys, argparse import glob from configparser import ConfigParser from atlassian import Confluence class Backup: def __init__(self, device, backup_root): self.device = device self.root = backup_root config_pattern = "{}/*/{}".format(self.root, device) configs = glob.glob(config_pattern, recursive=True) # Remove the full pathname, we only want the directory and the filename bkps = [dir[len(backup_root)+1:] for dir in configs] self.backups = bkps def name(self): return self.device def latest(self): if len(self.backups) >= 1: return self.backups[-1].split('/')[0] else: return "NotFound" def main(): parser = ConfigParser() parser.read('config-demo.ini') device_list_file = parser['backups']['device_list'] apikey = parser['confluence']['apikey'] username = parser['confluence']['username'] url = parser['confluence']['url'] page_ID = parser['confluence']['page_ID'] confluence = Confluence(url=url, username=username, password=apikey) # Read in all the devices from the nominated file with open(device_list_file) as file: lines = file.readlines() devices = [line.rstrip() for line in lines] wiki_table = "||Device||Date||" for device in devices: device_bkp = Backup(device, parser['backups']['path']) latest_bkp_date = device_bkp.latest() print(f"Latest backup for {device_bkp.name()} is {latest_bkp_date}") wiki_table += "\n" + f"|{device}|{latest_bkp_date}|" print("Wiki text for table is:") print(wiki_table) result = confluence.update_page( page_id=page_ID, title='Config Retrievals', representation="wiki", body=wiki_table) #pprint(result) print(f"Title of page set to '{result['title']}'") print(f"Confluence revision for page is now {result['version']['confRev']}") if __name__ == "__main__": main()

32.630137

82

0.585642

import os, sys, argparse import glob from configparser import ConfigParser from atlassian import Confluence class Backup: def __init__(self, device, backup_root): self.device = device self.root = backup_root config_pattern = "{}/*/{}".format(self.root, device) configs = glob.glob(config_pattern, recursive=True) bkps = [dir[len(backup_root)+1:] for dir in configs] self.backups = bkps def name(self): return self.device def latest(self): if len(self.backups) >= 1: return self.backups[-1].split('/')[0] else: return "NotFound" def main(): parser = ConfigParser() parser.read('config-demo.ini') device_list_file = parser['backups']['device_list'] apikey = parser['confluence']['apikey'] username = parser['confluence']['username'] url = parser['confluence']['url'] page_ID = parser['confluence']['page_ID'] confluence = Confluence(url=url, username=username, password=apikey) with open(device_list_file) as file: lines = file.readlines() devices = [line.rstrip() for line in lines] wiki_table = "||Device||Date||" for device in devices: device_bkp = Backup(device, parser['backups']['path']) latest_bkp_date = device_bkp.latest() print(f"Latest backup for {device_bkp.name()} is {latest_bkp_date}") wiki_table += "\n" + f"|{device}|{latest_bkp_date}|" print("Wiki text for table is:") print(wiki_table) result = confluence.update_page( page_id=page_ID, title='Config Retrievals', representation="wiki", body=wiki_table) print(f"Title of page set to '{result['title']}'") print(f"Confluence revision for page is now {result['version']['confRev']}") if __name__ == "__main__": main()

true

7907fb69542584c044bb901f0348ed8fd6ad0055

2,662

py

Python

nnunet/training/network_training/competitions_with_custom_Trainers/MMS/nnUNetTrainerV2_MMS.py

nasyxx/nnUNet

92d5f2352349eed278e22f7a38cb86b0fccd7c75

[ "Apache-2.0" ]

72

2020-10-30T08:55:17.000Z

2022-03-30T03:15:55.000Z

nnunet/training/network_training/competitions_with_custom_Trainers/MMS/nnUNetTrainerV2_MMS.py

nasyxx/nnUNet

92d5f2352349eed278e22f7a38cb86b0fccd7c75

[ "Apache-2.0" ]

16

2021-01-13T03:39:47.000Z

2022-03-31T21:35:32.000Z

nnunet/training/network_training/competitions_with_custom_Trainers/MMS/nnUNetTrainerV2_MMS.py

nasyxx/nnUNet

92d5f2352349eed278e22f7a38cb86b0fccd7c75

[ "Apache-2.0" ]

20

2020-10-29T20:47:28.000Z

2022-03-26T07:18:00.000Z

import torch from nnunet.network_architecture.generic_UNet import Generic_UNet from nnunet.network_architecture.initialization import InitWeights_He from nnunet.training.network_training.nnUNet_variants.data_augmentation.nnUNetTrainerV2_insaneDA import \ nnUNetTrainerV2_insaneDA from nnunet.utilities.nd_softmax import softmax_helper from torch import nn class nnUNetTrainerV2_MMS(nnUNetTrainerV2_insaneDA): def setup_DA_params(self): super().setup_DA_params() self.data_aug_params["p_rot"] = 0.7 self.data_aug_params["p_eldef"] = 0.1 self.data_aug_params["p_scale"] = 0.3 self.data_aug_params["independent_scale_factor_for_each_axis"] = True self.data_aug_params["p_independent_scale_per_axis"] = 0.3 self.data_aug_params["do_additive_brightness"] = True self.data_aug_params["additive_brightness_mu"] = 0 self.data_aug_params["additive_brightness_sigma"] = 0.2 self.data_aug_params["additive_brightness_p_per_sample"] = 0.3 self.data_aug_params["additive_brightness_p_per_channel"] = 1 self.data_aug_params["elastic_deform_alpha"] = (0., 300.) self.data_aug_params["elastic_deform_sigma"] = (9., 15.) self.data_aug_params['gamma_range'] = (0.5, 1.6) def initialize_network(self): if self.threeD: conv_op = nn.Conv3d dropout_op = nn.Dropout3d norm_op = nn.BatchNorm3d else: conv_op = nn.Conv2d dropout_op = nn.Dropout2d norm_op = nn.BatchNorm2d norm_op_kwargs = {'eps': 1e-5, 'affine': True} dropout_op_kwargs = {'p': 0, 'inplace': True} net_nonlin = nn.LeakyReLU net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) if torch.cuda.is_available(): self.network.cuda() self.network.inference_apply_nonlin = softmax_helper """def run_training(self): from batchviewer import view_batch a = next(self.tr_gen) view_batch(a['data']) import IPython;IPython.embed()"""

43.639344

117

0.657776

import torch from nnunet.network_architecture.generic_UNet import Generic_UNet from nnunet.network_architecture.initialization import InitWeights_He from nnunet.training.network_training.nnUNet_variants.data_augmentation.nnUNetTrainerV2_insaneDA import \ nnUNetTrainerV2_insaneDA from nnunet.utilities.nd_softmax import softmax_helper from torch import nn class nnUNetTrainerV2_MMS(nnUNetTrainerV2_insaneDA): def setup_DA_params(self): super().setup_DA_params() self.data_aug_params["p_rot"] = 0.7 self.data_aug_params["p_eldef"] = 0.1 self.data_aug_params["p_scale"] = 0.3 self.data_aug_params["independent_scale_factor_for_each_axis"] = True self.data_aug_params["p_independent_scale_per_axis"] = 0.3 self.data_aug_params["do_additive_brightness"] = True self.data_aug_params["additive_brightness_mu"] = 0 self.data_aug_params["additive_brightness_sigma"] = 0.2 self.data_aug_params["additive_brightness_p_per_sample"] = 0.3 self.data_aug_params["additive_brightness_p_per_channel"] = 1 self.data_aug_params["elastic_deform_alpha"] = (0., 300.) self.data_aug_params["elastic_deform_sigma"] = (9., 15.) self.data_aug_params['gamma_range'] = (0.5, 1.6) def initialize_network(self): if self.threeD: conv_op = nn.Conv3d dropout_op = nn.Dropout3d norm_op = nn.BatchNorm3d else: conv_op = nn.Conv2d dropout_op = nn.Dropout2d norm_op = nn.BatchNorm2d norm_op_kwargs = {'eps': 1e-5, 'affine': True} dropout_op_kwargs = {'p': 0, 'inplace': True} net_nonlin = nn.LeakyReLU net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs, net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) if torch.cuda.is_available(): self.network.cuda() self.network.inference_apply_nonlin = softmax_helper

true

7907fb94af616116405a175738be8418ab426188

1,294

py

Python

7.py

Nikolas-01/Lesson_7

dfbc8306bf9858b85253e5bf2066bb147b93ece0

[ "MIT" ]

null

7.py

Nikolas-01/Lesson_7

dfbc8306bf9858b85253e5bf2066bb147b93ece0

[ "MIT" ]

null

7.py

Nikolas-01/Lesson_7

dfbc8306bf9858b85253e5bf2066bb147b93ece0

[ "MIT" ]

null

from docxtpl import DocxTemplate import csv import json import random #случайный авто with open('Car_info.txt') as file: car_rand = [] reader = csv.reader(file) for row in file: car_rand.append(row) report_car = car_rand[random.randint(0, len(car_rand)-1)] car_info = report_car.split() #О авто def get_data (Brand, Model, Fuel_cons, Price): return { 'Название': Brand, 'Модель': Model, 'Объем': Fuel_cons, 'Цена': Price } def from_template(Brand, Model, Fuel_cons, Price, template): template = DocxTemplate(template) data = get_data(Brand, Model, Fuel_cons, Price) template.render(data) template.save('О_машине.docx') def report(Brand, Model, Fuel_cons, Price): template = 'О_машине.docx' document = from_template(Brand, Model, Fuel_cons, Price, template) report(car_info[0], car_info[1], car_info[2], car_info[3]) #csv файл car_list=[] with open('Авто_инфо.txt', 'r') as file: for row in file: inner_list = [x.strip() for x in row.split(',')] car_list.append(inner_list) print(car_list) with open('car.csv', 'w') as file: writer = csv.writer(file, delimiter = '*') writer.writerows(car_list) #json файл with open('Авто_json.txt', 'w') as f: json.dump(str(car_info), f)

30.809524

70

0.665379

from docxtpl import DocxTemplate import csv import json import random with open('Car_info.txt') as file: car_rand = [] reader = csv.reader(file) for row in file: car_rand.append(row) report_car = car_rand[random.randint(0, len(car_rand)-1)] car_info = report_car.split() def get_data (Brand, Model, Fuel_cons, Price): return { 'Название': Brand, 'Модель': Model, 'Объем': Fuel_cons, 'Цена': Price } def from_template(Brand, Model, Fuel_cons, Price, template): template = DocxTemplate(template) data = get_data(Brand, Model, Fuel_cons, Price) template.render(data) template.save('О_машине.docx') def report(Brand, Model, Fuel_cons, Price): template = 'О_машине.docx' document = from_template(Brand, Model, Fuel_cons, Price, template) report(car_info[0], car_info[1], car_info[2], car_info[3]) car_list=[] with open('Авто_инфо.txt', 'r') as file: for row in file: inner_list = [x.strip() for x in row.split(',')] car_list.append(inner_list) print(car_list) with open('car.csv', 'w') as file: writer = csv.writer(file, delimiter = '*') writer.writerows(car_list) with open('Авто_json.txt', 'w') as f: json.dump(str(car_info), f)

true

7907fb9f3bf5ec2da1ef4ed0d8d5e4c7860ac719

1,228

py

Python

examples/undocumented/python/structure_discrete_hmsvm_bmrm.py

gf712/shogun

ca2afb8f092288455701539aa58952dbf6743378

[ "BSD-3-Clause" ]

2,753

2015-01-02T11:34:13.000Z

2022-03-25T07:04:27.000Z

examples/undocumented/python/structure_discrete_hmsvm_bmrm.py

gf712/shogun

ca2afb8f092288455701539aa58952dbf6743378

[ "BSD-3-Clause" ]

2,404

2015-01-02T19:31:41.000Z

2022-03-09T10:58:22.000Z

examples/undocumented/python/structure_discrete_hmsvm_bmrm.py

gf712/shogun

ca2afb8f092288455701539aa58952dbf6743378

[ "BSD-3-Clause" ]

1,156

2015-01-03T01:57:21.000Z

2022-03-26T01:06:28.000Z

#!/usr/bin/env python import numpy import scipy from scipy import io data_dict = scipy.io.loadmat('../data/hmsvm_data_large_integer.mat', struct_as_record=False) parameter_list=[[data_dict]] def structure_discrete_hmsvm_bmrm (m_data_dict=data_dict): import shogun as sg try: _ = sg.create_machine("DualLibQPBMSOSVM") except: print("DualLibQPBMSOSVM not available") return labels_array = m_data_dict['label'][0] idxs = numpy.nonzero(labels_array == -1) labels_array[idxs] = 0 labels = sg.SequenceLabels(labels_array, 250, 500, 2) features = sg.RealMatrixFeatures(m_data_dict['signal'].astype(float), 250, 500) num_obs = 4 # given by the data file used model = sg.create_structured_model("HMSVMModel", features=features, labels=labels, state_model_type="SMT_TWO_STATE", num_obs=num_obs) sosvm = sg.create_machine("DualLibQPBMSOSVM", model=model, labels=labels, m_lambda=5000.0) sosvm.train() #print sosvm.get_w() predicted = sosvm.apply(features) evaluator = sg.create_evaluation("StructuredAccuracy") acc = evaluator.evaluate(predicted, labels) #print('Accuracy = %.4f' % acc) if __name__ == '__main__': print("Discrete HMSVM BMRM") structure_discrete_hmsvm_bmrm(*parameter_list[0])

27.909091

92

0.754886

import numpy import scipy from scipy import io data_dict = scipy.io.loadmat('../data/hmsvm_data_large_integer.mat', struct_as_record=False) parameter_list=[[data_dict]] def structure_discrete_hmsvm_bmrm (m_data_dict=data_dict): import shogun as sg try: _ = sg.create_machine("DualLibQPBMSOSVM") except: print("DualLibQPBMSOSVM not available") return labels_array = m_data_dict['label'][0] idxs = numpy.nonzero(labels_array == -1) labels_array[idxs] = 0 labels = sg.SequenceLabels(labels_array, 250, 500, 2) features = sg.RealMatrixFeatures(m_data_dict['signal'].astype(float), 250, 500) num_obs = 4 model = sg.create_structured_model("HMSVMModel", features=features, labels=labels, state_model_type="SMT_TWO_STATE", num_obs=num_obs) sosvm = sg.create_machine("DualLibQPBMSOSVM", model=model, labels=labels, m_lambda=5000.0) sosvm.train() predicted = sosvm.apply(features) evaluator = sg.create_evaluation("StructuredAccuracy") acc = evaluator.evaluate(predicted, labels) if __name__ == '__main__': print("Discrete HMSVM BMRM") structure_discrete_hmsvm_bmrm(*parameter_list[0])

true

7907fc90c49114bc8b1bda121717a23b62b78812

5,390

py

Python

topi/python/topi/cuda/conv2d_hwcn.py

peterjc123/tvm

d6dcd6c56febfbb12efe67884c188f045f435893

[ "Apache-2.0" ]

48

2020-07-29T18:09:23.000Z

2021-10-09T01:53:33.000Z

topi/python/topi/cuda/conv2d_hwcn.py

peterjc123/tvm

d6dcd6c56febfbb12efe67884c188f045f435893

[ "Apache-2.0" ]

9

2021-04-02T02:28:07.000Z

2022-03-26T18:23:59.000Z

Fujitsu/benchmarks/resnet/implementations/implementation_open/mxnet/3rdparty/tvm/topi/python/topi/cuda/conv2d_hwcn.py

lablup/training_results_v0.7

f5bb59aa0f8b18b602763abe47d1d24d0d54b197

[ "Apache-2.0" ]

42

2020-08-01T06:41:24.000Z

2022-01-20T10:33:08.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # pylint: disable=invalid-name, too-many-locals, too-many-statements """Schedule for conv2d_hwcn with auto fusion""" import tvm from .. import tag def schedule_conv2d_hwcn(outs): """Schedule for conv2d_hwcn and any element-wise operations. Parameters ---------- outs: Array of Tensor The computation graph description of conv2d_hwcn in the format of an array of tensors. Returns ------- s: Schedule The computation schedule for conv2d_hwcn. """ outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs sch = tvm.create_schedule([x.op for x in outs]) def schedule(Apad, W, B): """Schedule conv2d_hwcn""" sch[Apad].compute_inline() AA = sch.cache_read(Apad, "shared", [B]) WW = sch.cache_read(W, "shared", [B]) AL = sch.cache_read(AA, "local", [B]) WL = sch.cache_read(WW, "local", [B]) if B.op in sch.outputs: Out = B BL = sch.cache_write(Out, "local") else: Out = sch.outputs[0].output(0) sch[B].set_scope("local") BL = B tile = 8 num_thread = 8 block_factor = tile * num_thread step = 8 vthread = 2 block_x = tvm.thread_axis("blockIdx.x") block_y = tvm.thread_axis("blockIdx.y") block_z = tvm.thread_axis("blockIdx.z") thread_x = tvm.thread_axis((0, num_thread), "threadIdx.x") thread_y = tvm.thread_axis((0, num_thread), "threadIdx.y") thread_xz = tvm.thread_axis((0, vthread), "vthread", name="vx") thread_yz = tvm.thread_axis((0, vthread), "vthread", name="vy") hi, wi, fi, ni = sch[Out].op.axis bz = sch[Out].fuse(hi, wi) by, fi = sch[Out].split(fi, factor=block_factor) bx, ni = sch[Out].split(ni, factor=block_factor) tyz, fi = sch[Out].split(fi, nparts=vthread) txz, ni = sch[Out].split(ni, nparts=vthread) ty, fi = sch[Out].split(fi, nparts=num_thread) tx, ni = sch[Out].split(ni, nparts=num_thread) sch[Out].reorder(bz, by, bx, tyz, txz, ty, tx, fi, ni) sch[Out].bind(bz, block_z) sch[Out].bind(by, block_y) sch[Out].bind(bx, block_x) sch[Out].bind(tyz, thread_yz) sch[Out].bind(txz, thread_xz) sch[Out].bind(ty, thread_y) sch[Out].bind(tx, thread_x) # Schedule BL local write sch[BL].compute_at(sch[Out], tx) yi, xi, fi, ni = sch[BL].op.axis ry, rx, rc = sch[BL].op.reduce_axis rco, rci = sch[BL].split(rc, factor=step) sch[BL].reorder(rco, ry, rx, rci, fi, ni) fuse_index = sch[BL].fuse(ry, rx) fuse_index = sch[BL].fuse(fuse_index, rco) rx = fuse_index sch[AA].compute_at(sch[BL], rx) sch[WW].compute_at(sch[BL], rx) sch[AL].compute_at(sch[BL], rci) sch[WL].compute_at(sch[BL], rci) # Schedule for A's shared memory load yi, xi, ci, ni = sch[AA].op.axis ty, ci = sch[AA].split(ci, nparts=num_thread) tx, ni = sch[AA].split(ni, nparts=num_thread) _, ni = sch[AA].split(ni, factor=4) sch[AA].reorder(ty, tx, yi, xi, ci, ni) sch[AA].bind(ty, thread_y) sch[AA].bind(tx, thread_x) sch[AA].vectorize(ni) # Schedule for W's shared memory load yi, xi, ci, fi = sch[WW].op.axis ty, ci = sch[WW].split(ci, nparts=num_thread) tx, fi = sch[WW].split(fi, nparts=num_thread) _, fi = sch[WW].split(fi, factor=4) sch[WW].reorder(ty, tx, yi, xi, ci, fi) sch[WW].bind(ty, thread_y) sch[WW].bind(tx, thread_x) sch[WW].vectorize(fi) scheduled_ops = [] def traverse(operator): """Traverse operators from computation graph""" if tag.is_broadcast(operator.tag): if operator not in sch.outputs: sch[operator].compute_inline() for tensor in operator.input_tensors: if tensor.op.input_tensors and tensor.op not in scheduled_ops: traverse(tensor.op) elif operator.tag == 'conv2d_hwcn': Apad = operator.input_tensors[0] W = operator.input_tensors[1] if isinstance(W.op, tvm.tensor.ComputeOp) and 'dilate' in W.op.tag: sch[W].compute_inline() B = operator.output(0) schedule(Apad, W, B) else: raise RuntimeError("Unsupported operator: %s" % operator.tag) scheduled_ops.append(operator) traverse(outs[0].op) return sch

37.957746

79

0.601299

import tvm from .. import tag def schedule_conv2d_hwcn(outs): outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs sch = tvm.create_schedule([x.op for x in outs]) def schedule(Apad, W, B): sch[Apad].compute_inline() AA = sch.cache_read(Apad, "shared", [B]) WW = sch.cache_read(W, "shared", [B]) AL = sch.cache_read(AA, "local", [B]) WL = sch.cache_read(WW, "local", [B]) if B.op in sch.outputs: Out = B BL = sch.cache_write(Out, "local") else: Out = sch.outputs[0].output(0) sch[B].set_scope("local") BL = B tile = 8 num_thread = 8 block_factor = tile * num_thread step = 8 vthread = 2 block_x = tvm.thread_axis("blockIdx.x") block_y = tvm.thread_axis("blockIdx.y") block_z = tvm.thread_axis("blockIdx.z") thread_x = tvm.thread_axis((0, num_thread), "threadIdx.x") thread_y = tvm.thread_axis((0, num_thread), "threadIdx.y") thread_xz = tvm.thread_axis((0, vthread), "vthread", name="vx") thread_yz = tvm.thread_axis((0, vthread), "vthread", name="vy") hi, wi, fi, ni = sch[Out].op.axis bz = sch[Out].fuse(hi, wi) by, fi = sch[Out].split(fi, factor=block_factor) bx, ni = sch[Out].split(ni, factor=block_factor) tyz, fi = sch[Out].split(fi, nparts=vthread) txz, ni = sch[Out].split(ni, nparts=vthread) ty, fi = sch[Out].split(fi, nparts=num_thread) tx, ni = sch[Out].split(ni, nparts=num_thread) sch[Out].reorder(bz, by, bx, tyz, txz, ty, tx, fi, ni) sch[Out].bind(bz, block_z) sch[Out].bind(by, block_y) sch[Out].bind(bx, block_x) sch[Out].bind(tyz, thread_yz) sch[Out].bind(txz, thread_xz) sch[Out].bind(ty, thread_y) sch[Out].bind(tx, thread_x) sch[BL].compute_at(sch[Out], tx) yi, xi, fi, ni = sch[BL].op.axis ry, rx, rc = sch[BL].op.reduce_axis rco, rci = sch[BL].split(rc, factor=step) sch[BL].reorder(rco, ry, rx, rci, fi, ni) fuse_index = sch[BL].fuse(ry, rx) fuse_index = sch[BL].fuse(fuse_index, rco) rx = fuse_index sch[AA].compute_at(sch[BL], rx) sch[WW].compute_at(sch[BL], rx) sch[AL].compute_at(sch[BL], rci) sch[WL].compute_at(sch[BL], rci) yi, xi, ci, ni = sch[AA].op.axis ty, ci = sch[AA].split(ci, nparts=num_thread) tx, ni = sch[AA].split(ni, nparts=num_thread) _, ni = sch[AA].split(ni, factor=4) sch[AA].reorder(ty, tx, yi, xi, ci, ni) sch[AA].bind(ty, thread_y) sch[AA].bind(tx, thread_x) sch[AA].vectorize(ni) # Schedule for W's shared memory load yi, xi, ci, fi = sch[WW].op.axis ty, ci = sch[WW].split(ci, nparts=num_thread) tx, fi = sch[WW].split(fi, nparts=num_thread) _, fi = sch[WW].split(fi, factor=4) sch[WW].reorder(ty, tx, yi, xi, ci, fi) sch[WW].bind(ty, thread_y) sch[WW].bind(tx, thread_x) sch[WW].vectorize(fi) scheduled_ops = [] def traverse(operator): if tag.is_broadcast(operator.tag): if operator not in sch.outputs: sch[operator].compute_inline() for tensor in operator.input_tensors: if tensor.op.input_tensors and tensor.op not in scheduled_ops: traverse(tensor.op) elif operator.tag == 'conv2d_hwcn': Apad = operator.input_tensors[0] W = operator.input_tensors[1] if isinstance(W.op, tvm.tensor.ComputeOp) and 'dilate' in W.op.tag: sch[W].compute_inline() B = operator.output(0) schedule(Apad, W, B) else: raise RuntimeError("Unsupported operator: %s" % operator.tag) scheduled_ops.append(operator) traverse(outs[0].op) return sch

true

7907fcd9d8b5f016e33c1b2eeafd3a39be62d79e

3,299

py

Python

data/make_hdf5_files.py

pisalore/pointnet_shrec17-classificator

4c2288d16b953f466967a3deb569bba059a156f8

[ "MIT" ]

3

2019-11-13T09:16:47.000Z

2021-02-17T08:48:48.000Z

data/make_hdf5_files.py

pisalore/pointnet_shrec17-classificator

4c2288d16b953f466967a3deb569bba059a156f8

[ "MIT" ]

null

data/make_hdf5_files.py

pisalore/pointnet_shrec17-classificator

4c2288d16b953f466967a3deb569bba059a156f8

[ "MIT" ]

null

import h5py import numpy as np import os from plyfile import PlyData, PlyElement HDF5_DATA = 'hdf5_data' print('Generating .h5 files...', '\n') if not os.path.exists(HDF5_DATA): os.mkdir(HDF5_DATA) filenames_training = [line.rstrip() for line in open("filelist_training.txt", 'r')] filenames_testing = [line.rstrip() for line in open("filelist_testing.txt", 'r')] print((len(filenames_training))) print((len(filenames_testing))) f_training = h5py.File("./hdf5_data/data_training.h5", 'w') f_testing = h5py.File("./hdf5_data/data_testing.h5", 'w') a_data_training = np.zeros((len(filenames_training), 2048, 3)) a_pid_training = np.zeros((len(filenames_training), 2048), dtype = np.uint8) labeldata_training = [] a_label_training = np.zeros((len(filenames_training), 1), dtype = np.uint8) a_data_testing = np.zeros((len(filenames_testing), 2048, 3)) a_pid_testing = np.zeros((len(filenames_testing), 2048), dtype = np.uint8) labeldata_testing = [] a_label_testing = np.zeros((len(filenames_testing), 1), dtype = np.uint8) # ====== GENERATING TRAINING FILES ====== #======================================== for i in range(0, len(filenames_training)): print(filenames_training[i]) plydata = PlyData.read("./ply_dir/" + filenames_training[i] + ".ply") piddata = [line.rstrip() for line in open("./seg_dir/" + filenames_training[i] + ".seg", 'r')] # labeldata = [line.rstrip() for line in open("./label_dir/" + filenames_training[i] + ".seg", 'r')] for j in range(0, 2048): a_data_training[i, j] = [plydata['vertex']['x'][j], plydata['vertex']['y'][j], plydata['vertex']['z'][j]] a_pid_training[i, j] = piddata[j] # a_label_training[i, j] = labeldata[j] for i in range(0, len(filenames_training)): labeldata = [line.rstrip() for line in open("./label_dir/" + filenames_training[i] + ".seg", 'r')] a_label_training[i] = labeldata[0] data = f_training.create_dataset("data", data = a_data_training) pid = f_training.create_dataset("pid", data = a_pid_training) label = f_training.create_dataset("label", data = a_label_training) # ====== GENERATING TRAINING FILES ====== #======================================== # ====== GENERATING TESTING FILES ====== #======================================== for i in range(0, len(filenames_testing)): plydata = PlyData.read("./ply_dir/" + filenames_testing[i] + ".ply") piddata = [line.rstrip() for line in open("./seg_dir/" + filenames_testing[i] + ".seg", 'r')] # labeldata = [line.rstrip() for line in open("./label_dir/" + filenames_testing[i] + ".seg", 'r')] for j in range(0, 2048): a_data_testing[i, j] = [plydata['vertex']['x'][j], plydata['vertex']['y'][j], plydata['vertex']['z'][j]] a_pid_testing[i, j] = piddata[j] # a_label_testing[i, j] = labeldata[j] for i in range(0, len(filenames_testing)): labeldata = [line.rstrip() for line in open("./label_dir/" + filenames_testing[i] + ".seg", 'r')] a_label_testing[i] = labeldata[0] data = f_testing.create_dataset("data", data = a_data_testing) pid = f_testing.create_dataset("pid", data = a_pid_testing) label = f_testing.create_dataset("label", data = a_label_testing) #======================================== #======================================== print('HDF5 files generated.')

41.759494

113

0.631403

import h5py import numpy as np import os from plyfile import PlyData, PlyElement HDF5_DATA = 'hdf5_data' print('Generating .h5 files...', '\n') if not os.path.exists(HDF5_DATA): os.mkdir(HDF5_DATA) filenames_training = [line.rstrip() for line in open("filelist_training.txt", 'r')] filenames_testing = [line.rstrip() for line in open("filelist_testing.txt", 'r')] print((len(filenames_training))) print((len(filenames_testing))) f_training = h5py.File("./hdf5_data/data_training.h5", 'w') f_testing = h5py.File("./hdf5_data/data_testing.h5", 'w') a_data_training = np.zeros((len(filenames_training), 2048, 3)) a_pid_training = np.zeros((len(filenames_training), 2048), dtype = np.uint8) labeldata_training = [] a_label_training = np.zeros((len(filenames_training), 1), dtype = np.uint8) a_data_testing = np.zeros((len(filenames_testing), 2048, 3)) a_pid_testing = np.zeros((len(filenames_testing), 2048), dtype = np.uint8) labeldata_testing = [] a_label_testing = np.zeros((len(filenames_testing), 1), dtype = np.uint8) for i in range(0, len(filenames_training)): print(filenames_training[i]) plydata = PlyData.read("./ply_dir/" + filenames_training[i] + ".ply") piddata = [line.rstrip() for line in open("./seg_dir/" + filenames_training[i] + ".seg", 'r')] for j in range(0, 2048): a_data_training[i, j] = [plydata['vertex']['x'][j], plydata['vertex']['y'][j], plydata['vertex']['z'][j]] a_pid_training[i, j] = piddata[j] for i in range(0, len(filenames_training)): labeldata = [line.rstrip() for line in open("./label_dir/" + filenames_training[i] + ".seg", 'r')] a_label_training[i] = labeldata[0] data = f_training.create_dataset("data", data = a_data_training) pid = f_training.create_dataset("pid", data = a_pid_training) label = f_training.create_dataset("label", data = a_label_training) for i in range(0, len(filenames_testing)): plydata = PlyData.read("./ply_dir/" + filenames_testing[i] + ".ply") piddata = [line.rstrip() for line in open("./seg_dir/" + filenames_testing[i] + ".seg", 'r')] for j in range(0, 2048): a_data_testing[i, j] = [plydata['vertex']['x'][j], plydata['vertex']['y'][j], plydata['vertex']['z'][j]] a_pid_testing[i, j] = piddata[j] for i in range(0, len(filenames_testing)): labeldata = [line.rstrip() for line in open("./label_dir/" + filenames_testing[i] + ".seg", 'r')] a_label_testing[i] = labeldata[0] data = f_testing.create_dataset("data", data = a_data_testing) pid = f_testing.create_dataset("pid", data = a_pid_testing) label = f_testing.create_dataset("label", data = a_label_testing) print('HDF5 files generated.')

true

7907fd6c2e4e2f3c44e4478178b44dc4ccb98a8e

1,390

py

Python

tareas/3/ManzanaresJorge-SalazarJesus/spn.py

jorgelmp/sistop-2022-1

5c3b7e5215247533446aa006affe6cc64a48d989

[ "CC-BY-4.0" ]

6

2021-08-30T19:11:57.000Z

2021-09-05T01:30:59.000Z

tareas/3/ManzanaresJorge-SalazarJesus/spn.py

jorgelmp/sistop-2022-1

5c3b7e5215247533446aa006affe6cc64a48d989

[ "CC-BY-4.0" ]

13

2021-09-07T22:24:47.000Z

2021-11-23T05:26:38.000Z

tareas/3/ManzanaresJorge-SalazarJesus/spn.py

jorgelmp/sistop-2022-1

5c3b7e5215247533446aa006affe6cc64a48d989

[ "CC-BY-4.0" ]

33

2021-09-01T00:44:27.000Z

2022-02-09T06:17:55.000Z

from scheduler import Scheduler from collections import deque class Spn(Scheduler): name = "Shortest Process Next (SPN)" ejecutados = [] ejecutados_visual = "" def __init__(self,procesos=[]): self.spn_queue = deque() self.t = procesos[0].arrvl_time self.procesos = procesos self.getMaxT(procesos) self.check_for_new_process() def check_for_new_process(self): list=[] for i in self.procesos: #print(i.id+" "+str(i.exec_time)) if i.arrvl_time <= self.t: list.append(i) # self.procesos.remove(i) list.sort(key=lambda Process: Process.exec_time) for i in list: self.spn_queue.append(i) self.procesos.remove(i) def execute(self): while self.t < self.max_t: if self.spn_queue: ejecutando = self.spn_queue.popleft() self.ejecutados_visual+=ejecutando.id self.t +=1 while not ejecutando.execute(1) : self.ejecutados_visual+=ejecutando.id self.t +=1 ejecutando.compl_time = self.t self.ejecutados.append(ejecutando) else: self.emptyExec() self.check_for_new_process() #print(self.t)

32.325581

57

0.539568

from scheduler import Scheduler from collections import deque class Spn(Scheduler): name = "Shortest Process Next (SPN)" ejecutados = [] ejecutados_visual = "" def __init__(self,procesos=[]): self.spn_queue = deque() self.t = procesos[0].arrvl_time self.procesos = procesos self.getMaxT(procesos) self.check_for_new_process() def check_for_new_process(self): list=[] for i in self.procesos: if i.arrvl_time <= self.t: list.append(i) list.sort(key=lambda Process: Process.exec_time) for i in list: self.spn_queue.append(i) self.procesos.remove(i) def execute(self): while self.t < self.max_t: if self.spn_queue: ejecutando = self.spn_queue.popleft() self.ejecutados_visual+=ejecutando.id self.t +=1 while not ejecutando.execute(1) : self.ejecutados_visual+=ejecutando.id self.t +=1 ejecutando.compl_time = self.t self.ejecutados.append(ejecutando) else: self.emptyExec() self.check_for_new_process()

true

7907fdd7386241163ed033444bcaddf34a68b930

5,414

py

Python

tests/test_visualizer.py

bjtuyxc/detectron2

ebb9f8c9166765c508f8ac53d9ed2004739b28d1

[ "Apache-2.0" ]

null

tests/test_visualizer.py

bjtuyxc/detectron2

ebb9f8c9166765c508f8ac53d9ed2004739b28d1

[ "Apache-2.0" ]

null

tests/test_visualizer.py

bjtuyxc/detectron2

ebb9f8c9166765c508f8ac53d9ed2004739b28d1

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved # File: import numpy as np import unittest import torch from detectron2.data import MetadataCatalog from detectron2.structures import Instances, RotatedBoxes, BoxMode from detectron2.utils.visualizer import Visualizer class TestVisualizer(unittest.TestCase): def _random_data(self): H, W = 100, 100 N = 10 img = np.random.rand(H, W, 3) * 255 boxxy = np.random.rand(N, 2) * (H // 2) boxes = np.concatenate((boxxy, boxxy + H // 2), axis=1) def _rand_poly(): return np.random.rand(3, 2).flatten() * H polygons = [[_rand_poly() for _ in range(np.random.randint(1, 5))] for _ in range(N)] mask = np.zeros_like(img[:, :, 0], dtype=np.bool) mask[:10, 10:20] = 1 labels = [str(i) for i in range(N)] return img, boxes, labels, polygons, [mask] * N @property def metadata(self): return MetadataCatalog.get("coco_2017_train") def test_draw_dataset_dict(self): img = np.random.rand(512, 512, 3) * 255 dic = {'annotations': [{'bbox': [368.9946492271106, 330.891438763377, 13.148537455410235, 13.644708680142685], 'bbox_mode': BoxMode.XYWH_ABS, 'category_id': 0, 'iscrowd': 1, 'segmentation': {'counts': '_jh52m?2N2N2N2O100O10O001N1O2MceP2', 'size': [512, 512]}}], 'height': 512, 'image_id': 1, 'width': 512} v = Visualizer(img, self.metadata) v.draw_dataset_dict(dic) def test_overlay_instances(self): img, boxes, labels, polygons, masks = self._random_data() v = Visualizer(img, self.metadata) output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image() self.assertEqual(output.shape, img.shape) # Test 2x scaling v = Visualizer(img, self.metadata, scale=2.0) output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image() self.assertEqual(output.shape[0], img.shape[0] * 2) # Test overlay masks v = Visualizer(img, self.metadata) output = v.overlay_instances(masks=masks, boxes=boxes, labels=labels).get_image() self.assertEqual(output.shape, img.shape) def test_overlay_instances_no_boxes(self): img, boxes, labels, polygons, _ = self._random_data() v = Visualizer(img, self.metadata) v.overlay_instances(masks=polygons, boxes=None, labels=labels).get_image() def test_draw_instance_predictions(self): img, boxes, _, _, masks = self._random_data() num_inst = len(boxes) inst = Instances((img.shape[0], img.shape[1])) inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) inst.scores = torch.rand(num_inst) inst.pred_boxes = torch.from_numpy(boxes) inst.pred_masks = torch.from_numpy(np.asarray(masks)) v = Visualizer(img, self.metadata) v.draw_instance_predictions(inst) def test_draw_empty_mask_predictions(self): img, boxes, _, _, masks = self._random_data() num_inst = len(boxes) inst = Instances((img.shape[0], img.shape[1])) inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) inst.scores = torch.rand(num_inst) inst.pred_boxes = torch.from_numpy(boxes) inst.pred_masks = torch.from_numpy(np.zeros_like(np.asarray(masks))) v = Visualizer(img, self.metadata) v.draw_instance_predictions(inst) def test_correct_output_shape(self): img = np.random.rand(928, 928, 3) * 255 v = Visualizer(img, self.metadata) out = v.output.get_image() self.assertEqual(out.shape, img.shape) def test_overlay_rotated_instances(self): H, W = 100, 150 img = np.random.rand(H, W, 3) * 255 num_boxes = 50 boxes_5d = torch.zeros(num_boxes, 5) boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-0.1 * W, 1.1 * W) boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-0.1 * H, 1.1 * H) boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H)) boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H)) boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800) rotated_boxes = RotatedBoxes(boxes_5d) labels = [str(i) for i in range(num_boxes)] v = Visualizer(img, self.metadata) output = v.overlay_instances(boxes=rotated_boxes, labels=labels).get_image() self.assertEqual(output.shape, img.shape) def test_draw_no_metadata(self): img, boxes, _, _, masks = self._random_data() num_inst = len(boxes) inst = Instances((img.shape[0], img.shape[1])) inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) inst.scores = torch.rand(num_inst) inst.pred_boxes = torch.from_numpy(boxes) inst.pred_masks = torch.from_numpy(np.asarray(masks)) v = Visualizer(img, MetadataCatalog.get("asdfasdf")) v.draw_instance_predictions(inst)

40.402985

96

0.602143

import numpy as np import unittest import torch from detectron2.data import MetadataCatalog from detectron2.structures import Instances, RotatedBoxes, BoxMode from detectron2.utils.visualizer import Visualizer class TestVisualizer(unittest.TestCase): def _random_data(self): H, W = 100, 100 N = 10 img = np.random.rand(H, W, 3) * 255 boxxy = np.random.rand(N, 2) * (H // 2) boxes = np.concatenate((boxxy, boxxy + H // 2), axis=1) def _rand_poly(): return np.random.rand(3, 2).flatten() * H polygons = [[_rand_poly() for _ in range(np.random.randint(1, 5))] for _ in range(N)] mask = np.zeros_like(img[:, :, 0], dtype=np.bool) mask[:10, 10:20] = 1 labels = [str(i) for i in range(N)] return img, boxes, labels, polygons, [mask] * N @property def metadata(self): return MetadataCatalog.get("coco_2017_train") def test_draw_dataset_dict(self): img = np.random.rand(512, 512, 3) * 255 dic = {'annotations': [{'bbox': [368.9946492271106, 330.891438763377, 13.148537455410235, 13.644708680142685], 'bbox_mode': BoxMode.XYWH_ABS, 'category_id': 0, 'iscrowd': 1, 'segmentation': {'counts': '_jh52m?2N2N2N2O100O10O001N1O2MceP2', 'size': [512, 512]}}], 'height': 512, 'image_id': 1, 'width': 512} v = Visualizer(img, self.metadata) v.draw_dataset_dict(dic) def test_overlay_instances(self): img, boxes, labels, polygons, masks = self._random_data() v = Visualizer(img, self.metadata) output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image() self.assertEqual(output.shape, img.shape) v = Visualizer(img, self.metadata, scale=2.0) output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image() self.assertEqual(output.shape[0], img.shape[0] * 2) v = Visualizer(img, self.metadata) output = v.overlay_instances(masks=masks, boxes=boxes, labels=labels).get_image() self.assertEqual(output.shape, img.shape) def test_overlay_instances_no_boxes(self): img, boxes, labels, polygons, _ = self._random_data() v = Visualizer(img, self.metadata) v.overlay_instances(masks=polygons, boxes=None, labels=labels).get_image() def test_draw_instance_predictions(self): img, boxes, _, _, masks = self._random_data() num_inst = len(boxes) inst = Instances((img.shape[0], img.shape[1])) inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) inst.scores = torch.rand(num_inst) inst.pred_boxes = torch.from_numpy(boxes) inst.pred_masks = torch.from_numpy(np.asarray(masks)) v = Visualizer(img, self.metadata) v.draw_instance_predictions(inst) def test_draw_empty_mask_predictions(self): img, boxes, _, _, masks = self._random_data() num_inst = len(boxes) inst = Instances((img.shape[0], img.shape[1])) inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) inst.scores = torch.rand(num_inst) inst.pred_boxes = torch.from_numpy(boxes) inst.pred_masks = torch.from_numpy(np.zeros_like(np.asarray(masks))) v = Visualizer(img, self.metadata) v.draw_instance_predictions(inst) def test_correct_output_shape(self): img = np.random.rand(928, 928, 3) * 255 v = Visualizer(img, self.metadata) out = v.output.get_image() self.assertEqual(out.shape, img.shape) def test_overlay_rotated_instances(self): H, W = 100, 150 img = np.random.rand(H, W, 3) * 255 num_boxes = 50 boxes_5d = torch.zeros(num_boxes, 5) boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-0.1 * W, 1.1 * W) boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-0.1 * H, 1.1 * H) boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H)) boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H)) boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800) rotated_boxes = RotatedBoxes(boxes_5d) labels = [str(i) for i in range(num_boxes)] v = Visualizer(img, self.metadata) output = v.overlay_instances(boxes=rotated_boxes, labels=labels).get_image() self.assertEqual(output.shape, img.shape) def test_draw_no_metadata(self): img, boxes, _, _, masks = self._random_data() num_inst = len(boxes) inst = Instances((img.shape[0], img.shape[1])) inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) inst.scores = torch.rand(num_inst) inst.pred_boxes = torch.from_numpy(boxes) inst.pred_masks = torch.from_numpy(np.asarray(masks)) v = Visualizer(img, MetadataCatalog.get("asdfasdf")) v.draw_instance_predictions(inst)

true

7907fdda48e28d327e2b86a5e1a99f1449f54f4a

1,778

py

Python

Scripts/mybar.py

jovanzac/Captain

3e410aa22eec4f72274b9bf4f0f2b3c91936356d

[ "MIT" ]

null

Scripts/mybar.py

jovanzac/Captain

3e410aa22eec4f72274b9bf4f0f2b3c91936356d

[ "MIT" ]

null

Scripts/mybar.py

jovanzac/Captain

3e410aa22eec4f72274b9bf4f0f2b3c91936356d

[ "MIT" ]

1

2020-12-25T08:21:37.000Z

import tkinter as tk from PIL import Image, ImageTk # The Custom Variable Widgets class MyBar(tk.Canvas) : def __init__(self, master:object, shape:object, value=0, maximum=100, bg="#231303", trough_color='#8a7852', bar_color='#f7f4bf'): """Creating the alpha mask and creating a custom widget of the given shape and dimensions.""" # open shape mask with PIL im_shape_alpha = Image.open(shape).convert('L') # create bar shape image with the choosen backgroound color im_shape = Image.new('RGBA', im_shape_alpha.size, bg) # apply shape as alpha mask to "cut out" the bar shape im_shape.putalpha(im_shape_alpha) width, height = im_shape_alpha.size # create the canvas tk.Canvas.__init__(self, master, bg=trough_color, width=width, height=height, highlightthickness=0) self._value = value # bar value self.maximum = maximum # maximum value # bar width and height self.height = height self.width = width # create tkinter image for the shape from the PIL Image self.img_trough = ImageTk.PhotoImage(im_shape, master=self) # create bar to display the value self.create_rectangle(0, height, width, height * (1 - value/self.maximum), width=0, fill=bar_color, tags='pbar') # display shape on top self.create_image(0, 0, anchor='nw', image=self.img_trough) @property def value(self): """Return bar's value.""" return self._value @value.setter def value(self, value:int): """Set bar's value.""" self._value = value # adjust bar height to value self.coords('pbar', 0, self.height, self.width, self.height*(1 - value/self.maximum))

40.409091

120

0.644544

import tkinter as tk from PIL import Image, ImageTk class MyBar(tk.Canvas) : def __init__(self, master:object, shape:object, value=0, maximum=100, bg="#231303", trough_color='#8a7852', bar_color='#f7f4bf'): im_shape_alpha = Image.open(shape).convert('L') im_shape = Image.new('RGBA', im_shape_alpha.size, bg) im_shape.putalpha(im_shape_alpha) width, height = im_shape_alpha.size tk.Canvas.__init__(self, master, bg=trough_color, width=width, height=height, highlightthickness=0) self._value = value self.maximum = maximum self.height = height self.width = width self.img_trough = ImageTk.PhotoImage(im_shape, master=self) self.create_rectangle(0, height, width, height * (1 - value/self.maximum), width=0, fill=bar_color, tags='pbar') self.create_image(0, 0, anchor='nw', image=self.img_trough) @property def value(self): return self._value @value.setter def value(self, value:int): self._value = value self.coords('pbar', 0, self.height, self.width, self.height*(1 - value/self.maximum))

true

7907fed5bbfcfa6e7035b885995f9b21d8943f56

610

py

Python

v_python/fixture/admin_catalog.py

spcartman/selenium_full_course

673f25dcf2340c0c14666c7a91f774fd7659f0b1

[ "MIT" ]

null

v_python/fixture/admin_catalog.py

spcartman/selenium_full_course

673f25dcf2340c0c14666c7a91f774fd7659f0b1

[ "MIT" ]

null

v_python/fixture/admin_catalog.py

spcartman/selenium_full_course

673f25dcf2340c0c14666c7a91f774fd7659f0b1

[ "MIT" ]

null

class AdminCatalogHelper: def __init__(self, app): self.app = app def go_though_each_product_and_print_browser_log(self): for i in range(len(self.app.wd.find_elements_by_css_selector('.dataTable td:nth-of-type(3) a[href*="&product_id="]'))): self.app.wd.find_elements_by_css_selector('.dataTable td:nth-of-type(3) a[href*="&product_id="]')[i].click() [print(log) for log in self.app.wd.get_log("browser")] self.app.wait_for_element_to_be_visible('#tab-general') self.app.wd.find_element_by_css_selector('button[name="cancel"]').click()

50.833333

127

0.678689

class AdminCatalogHelper: def __init__(self, app): self.app = app def go_though_each_product_and_print_browser_log(self): for i in range(len(self.app.wd.find_elements_by_css_selector('.dataTable td:nth-of-type(3) a[href*="&product_id="]'))): self.app.wd.find_elements_by_css_selector('.dataTable td:nth-of-type(3) a[href*="&product_id="]')[i].click() [print(log) for log in self.app.wd.get_log("browser")] self.app.wait_for_element_to_be_visible('#tab-general') self.app.wd.find_element_by_css_selector('button[name="cancel"]').click()

true

7907fee4677a36aa4ecc2aa9a88cbdfe69077ec6

687

py

Python

setup.py

arthurcgusmao/py-mcc-f1

d1b7cb856fbf03faad6a9eeeaea08da049c603c0

[ "MIT" ]

7

2020-10-26T21:33:40.000Z

2022-02-14T10:56:06.000Z

setup.py

arthurcgusmao/py-mcc-f1

d1b7cb856fbf03faad6a9eeeaea08da049c603c0

[ "MIT" ]

1

2022-02-13T19:17:15.000Z

setup.py

arthurcgusmao/py-mcc-f1

d1b7cb856fbf03faad6a9eeeaea08da049c603c0

[ "MIT" ]

1

2022-02-14T10:56:08.000Z

import setuptools with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name="py-mcc-f1", version="0.1.0", author="Arthur Colombini Gusmão", description="MCC-F1 Curve", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/arthurcgusmao/py-mcc-f1", packages=setuptools.find_packages(), install_requires=[ "numpy>=1.14.0", "scikit-learn>=0.22" ], classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], python_requires='>=3.6', )

26.423077

53

0.630277

import setuptools with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name="py-mcc-f1", version="0.1.0", author="Arthur Colombini Gusmão", description="MCC-F1 Curve", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/arthurcgusmao/py-mcc-f1", packages=setuptools.find_packages(), install_requires=[ "numpy>=1.14.0", "scikit-learn>=0.22" ], classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], python_requires='>=3.6', )

true

7907ffa028fa0010a06fc93cf03a76362f49f5c9

1,127

py

Python

udemy-data-structures-and-algorithms/15-recursion/15.8_string_permutation.py

washimimizuku/python-data-structures-and-algorithms

537f4eabaf31888ae48004d153088fb28bb684ab

[ "MIT" ]

null

udemy-data-structures-and-algorithms/15-recursion/15.8_string_permutation.py

washimimizuku/python-data-structures-and-algorithms

537f4eabaf31888ae48004d153088fb28bb684ab

[ "MIT" ]

null

udemy-data-structures-and-algorithms/15-recursion/15.8_string_permutation.py

washimimizuku/python-data-structures-and-algorithms

537f4eabaf31888ae48004d153088fb28bb684ab

[ "MIT" ]

null

''' Given a string, write a function that uses recursion to output a list of all the possible permutations of that string. For example, given s='abc' the function should return ['abc', 'acb', 'bac', 'bca', 'cab', 'cba'] Note: If a character is repeated, treat each occurence as distinct, for example an input of 'xxx' would return a list with 6 "versions" of 'xxx' ''' from nose.tools import assert_equal def permute(s): out = [] # Base case if (len(s) == 1): out = [s] else: # For every letter in string for i, let in enumerate(s): # For every permutation for perm in permute(s[:i] + s[i + 1:]): # Add it to the output out += [let + perm] return out class TestPerm(object): def test(self, solution): assert_equal(sorted(solution('abc')), sorted( ['abc', 'acb', 'bac', 'bca', 'cab', 'cba'])) assert_equal(sorted(solution('dog')), sorted( ['dog', 'dgo', 'odg', 'ogd', 'gdo', 'god'])) print('All test cases passed.') # Run Tests t = TestPerm() t.test(permute)

23

96

0.573203

from nose.tools import assert_equal def permute(s): out = [] if (len(s) == 1): out = [s] else: for i, let in enumerate(s): for perm in permute(s[:i] + s[i + 1:]): out += [let + perm] return out class TestPerm(object): def test(self, solution): assert_equal(sorted(solution('abc')), sorted( ['abc', 'acb', 'bac', 'bca', 'cab', 'cba'])) assert_equal(sorted(solution('dog')), sorted( ['dog', 'dgo', 'odg', 'ogd', 'gdo', 'god'])) print('All test cases passed.') t = TestPerm() t.test(permute)

true

7908015094df0f7d24b375510cc3e85e33122519

11,743

py

Python

PNet/train_pnet.py

mangye16/ReID-Label-Noise

89aa11f68c275a0bcff232d9a5c3ae152c9276af

[ "MIT" ]

11

2020-04-03T09:01:36.000Z

2022-03-11T08:12:16.000Z

PNet/train_pnet.py

mangye16/ReID-Label-Noise

89aa11f68c275a0bcff232d9a5c3ae152c9276af

[ "MIT" ]

null

PNet/train_pnet.py

mangye16/ReID-Label-Noise

89aa11f68c275a0bcff232d9a5c3ae152c9276af

[ "MIT" ]

3

2020-12-18T11:53:05.000Z

2022-01-12T16:35:45.000Z

# -*- coding: UTF-8 -*- from __future__ import print_function, division import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.optim import lr_scheduler from torch.autograd import Variable from torchvision import datasets, models, transforms from tensorboardX import SummaryWriter import sys import json import scipy import os, time import argparse import numpy as np import torchvision import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt from PIL import Image from shutil import copyfile from model import ft_net from test_eval_cython import get_test_acc, extr_fea_train from utils import * import loader, loss import pdb version = torch.__version__ # ##################################################################### # argsions # -------- parser = argparse.ArgumentParser(description='Training') parser.add_argument('--gpu',default='0', type=str,help='gpu ids: e.g. 0 0,1,2 0,2') parser.add_argument('--seed', default=1, type=int, help='rng seed') parser.add_argument('--model_dir',default='.checkpoint/', type=str, help='output model name') parser.add_argument('--data_dir',default='/home/comp/mangye/dataset/', type=str, help='data dir') parser.add_argument('--dataset',default='duke',type=str, help='training data:Market1501, DukeMTMCreID') parser.add_argument('--pretrained',default='',type=str, help='path of pretrained "model:./model/baseline/net_8.pth"') parser.add_argument('--batchsize', default=32, type=int, help='batchsize') parser.add_argument('--noise_ratio', default=0.2, type=float, help='percentage of noise data in the training') parser.add_argument('--lr', default=0.01, type=float, help='initial learning rate') parser.add_argument('--alpha', default=2, type=float, help='beta distribution: alpha') parser.add_argument('--beta', default=6, type=float, help='beta distribution: beta') parser.add_argument('--LabelWt', default=60, type=int, help='label refinment weight') parser.add_argument('--weighttype', default=0, type=int, help='weight type: instance weight, class weight') parser.add_argument('--stage2', action='store_true', help='training stage 2') args = parser.parse_args() torch.manual_seed(args.seed) start_epoch = 0 if args.stage2: start_epoch = start_epoch + 20 best_acc = 0 test_epoch = 2 lr = args.lr data_dir = args.data_dir + args.dataset suffix = args.dataset + '_noise_{}_'.format(args.noise_ratio) if args.LabelWt > 0 or args.stage2: suffix = suffix + 'batch_{}_wt_{}'.format(args.batchsize,args.LabelWt) else: suffix = suffix + 'batch_{}_baseline'.format(args.batchsize) if args.stage2: suffix = suffix + '_beta_{}_{}_lr_{:1.1e}'.format(args.alpha, args.beta, args.lr) suffix = suffix + '_w_st2_new' else: suffix = suffix + '_lr_{:1.1e}'.format(args.lr) suffix = suffix + '_w_st1' print ('model: ' + suffix) # define the log path log_dir = './new_res/' + args.dataset + '_log/' checkpoint_path = './res/checkpoint/' vis_log_dir = log_dir + suffix + '/' if not os.path.isdir(log_dir): os.makedirs(log_dir) if not os.path.isdir(vis_log_dir): os.makedirs(vis_log_dir) writer = SummaryWriter(vis_log_dir) test_log_file = open(log_dir + suffix + '.txt', "w") sys.stdout = Logger(log_dir + suffix + '_os.txt') # define the gpu id str_ids = args.gpu.split(',') gpu_ids = [] for str_id in str_ids: gid = int(str_id) if gid >=0: gpu_ids.append(gid) # set gpu ids if len(gpu_ids)>0: torch.cuda.set_device(gpu_ids[0]) print ('using gpu: {}'.format(gpu_ids)) # ##################################################################### # Load Data train_transform = transforms.Compose([ #transforms.RandomResizedCrop(size=128, scale=(0.75,1.0), ratio=(0.75,1.3333), interpolation=3), #Image.BICUBIC) transforms.Resize((288,144), interpolation=3), transforms.RandomCrop((256,128)), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) test_transform = transforms.Compose([ transforms.Resize((256,128), interpolation=3), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) # load training dataDatasetFolder print('Starting loading training data: ', args.dataset ) train_dataset = loader.DatasetFolder(os.path.join(data_dir, 'train'), transform=train_transform) class_names = train_dataset.classes dataset_sizes_train = len(train_dataset) use_gpu = torch.cuda.is_available() # Define a model model = ft_net(len(class_names)) if use_gpu: model = model.cuda() # Load a pretrainied model if args.pretrained or args.stage2: # model_name = 'market_noise_0.2_batch_32_lambda_0.4_lr_1.0e-02_st1_epoch_best.t' model_name = '{}_noise_{}_batch_32_wt_60_lr_1.0e-02_w_st1_epoch_best.t'.format(args.dataset, args.noise_ratio) print('Initilizaing weights with {}'.format(model_name)) model_path = checkpoint_path + model_name model.load_state_dict(torch.load(model_path)) else: print('Initilizaing weights with ImageNet') # generate noisy label if args.noise_ratio >= 0: trainLabels = torch.LongTensor([y for (p, y, w) in train_dataset.imgs]) trainLabels_nsy, if_truelbl = gen_nosiy_lbl(trainLabels, args.noise_ratio, len(class_names)) print('Finish adding noisy label') # generate instance weight if args.stage2: print('Generating sef-generated weights......') weight_file = './new_res/' + 'new_{}_{}_weights.npy'.format(args.dataset, args.noise_ratio) label_file = './new_res/' + 'new_{}_{}_label.npy'.format(args.dataset, args.noise_ratio) # if os.path.exists(weight_file): # all_weights = np.load(weight_file) # pre_pids = np.load(label_file) # else: tansform_bak = train_transform train_dataset.transform = test_transform temploader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batchsize, shuffle=False, num_workers=8) model.eval() # Set model to evaluate mode print('Start extract features...') start = time.time() train_feas, pre_pids = extr_fea_train(model, train_dataset, temploader, use_gpu) print('Evaluation time: {}'.format(time.time()-start)) indexs, ori_weight = gen_weights_dist(train_feas, trainLabels_nsy, class_names, args.alpha, args.beta) order = np.argsort(indexs) all_weights = ori_weight[order] np.save(weight_file, all_weights) np.save(label_file, pre_pids) train_dataset.transform = tansform_bak all_weights = all_weights.astype(np.float32) for i in range(len(trainLabels_nsy)): train_dataset.imgs[i] = (train_dataset.imgs[i][0], int(pre_pids[i]), all_weights[i]) else: print('Setting same weights for all the instances...') for i in range(len(trainLabels_nsy)): train_dataset.imgs[i] = (train_dataset.imgs[i][0], trainLabels_nsy[i],1) dataloaders_train = torch.utils.data.DataLoader(train_dataset, batch_size=args.batchsize, shuffle=True, num_workers=8) # 8 workers may work faster # load testing dataDatasetFolder test_dataset = {x: datasets.ImageFolder( os.path.join(data_dir,x) ,test_transform) for x in ['gallery','query']} dataloaders_test = {x: torch.utils.data.DataLoader(test_dataset[x], batch_size=args.batchsize, shuffle=False, num_workers=8) for x in ['gallery','query']} # Define loss functions # if args.LabelWt>0: # criterion = loss.LabelRefineLoss(lambda1=args.LabelWt) if args.stage2: criterion = loss.InstanceWeightLoss(weighted = 1) else: criterion = nn.CrossEntropyLoss() # optimizer ignored_params = list(map(id, model.model.fc.parameters() )) + list(map(id, model.classifier.parameters() )) base_params = filter(lambda p: id(p) not in ignored_params, model.parameters()) optimizer_ft = optim.SGD([ {'params': base_params, 'lr': lr}, {'params': model.model.fc.parameters(), 'lr': lr*10}, {'params': model.classifier.parameters(), 'lr': lr*10} ], weight_decay=5e-4, momentum=0.9, nesterov=True) # Decay LR by a factor of 0.1 every 40 epochs exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=20, gamma=0.1) def save_network(network, epoch_label, is_best = False): if is_best: save_path = checkpoint_path + suffix + '_epoch_best.t' else: save_path = checkpoint_path + suffix + '_epoch_{}.t'.format(epoch_label) torch.save(network.state_dict(), save_path) def sigmoid_rampup(current, rampup_length): """Exponential rampup from https://arxiv.org/abs/1610.02242""" if rampup_length == 0: return 1.0 else: current = np.clip(current, 0.0, rampup_length) phase = 1.0 - current / rampup_length w = float(np.exp(-2.0 * phase * phase)) return min(w,0.5) def train_model(model, criterion, optimizer_ft, scheduler, epoch): scheduler.step() lambda1 = sigmoid_rampup(epoch, args.LabelWt) train_loss = AverageMeter() data_time = AverageMeter() batch_time = AverageMeter() model.train() correct = 0 total = 0 end = time.time() for batch_idx, (inputs, targets, weights) in enumerate(dataloaders_train): if use_gpu: inputs = Variable(inputs.cuda()) targets = Variable(targets.cuda()) weights = Variable(weights.cuda()) data_time.update(time.time() - end) optimizer_ft.zero_grad() outputs = model(inputs) if args.stage2: loss = criterion(outputs, targets, weights) else: loss = criterion(outputs, targets, lambda1) loss.backward() optimizer_ft.step() train_loss.update(loss.item(), inputs.size(0)) # measure elapsed time batch_time.update(time.time() - end) end = time.time() _, predicted = outputs.max(1) correct += predicted.eq(targets).sum().item() total += inputs.size(0) if batch_idx%10==0: print('Epoch: [{}][{}/{}] ' 'Time: {batch_time.val:.3f} ({batch_time.avg:.3f}) ' 'Data: {data_time.val:.3f} ({data_time.avg:.3f}) ' 'Loss: {train_loss.val:.4f} ({train_loss.avg:.4f}) ' 'Accu: {:.2f}'.format( epoch, batch_idx, len(dataloaders_train),100.*correct/total, batch_time=batch_time, data_time=data_time, train_loss=train_loss)) writer.add_scalar('training acc (train)', 100.*correct/total, epoch) writer.add_scalar('loss', train_loss.avg, epoch) for epoch in range(start_epoch, start_epoch+41): # training print('Start Training..........') train_model(model, criterion, optimizer_ft, exp_lr_scheduler, epoch) # evaluation if epoch%test_epoch ==0: model.eval() # Set model to evaluate mode start = time.time() cmc, mAP = get_test_acc(model, test_dataset, dataloaders_test, use_gpu, max_rank=10) if cmc[0] > best_acc: best_epoch = epoch best_acc = cmc[0] save_network(model, epoch, is_best = True) print('Epoch {}: R1:{:.4%} R5:{:.4%} R10:{:.4%} mAP:{:.4%} (Best Epoch[{}])'.format( epoch, cmc[0],cmc[4],cmc[9], mAP ,best_epoch)) print('Epoch {}: R1:{:.4%} R5:{:.4%} R10:{:.4%} mAP:{:.4%} (Best Epoch[{}])'.format( epoch, cmc[0],cmc[4],cmc[9], mAP ,best_epoch), file = test_log_file) test_log_file.flush() print('Evaluation time: {}'.format(time.time()-start)) # if epoch%20==0: # save_network(model, epoch, is_best = False)

38.755776

154

0.664311

from __future__ import print_function, division import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.optim import lr_scheduler from torch.autograd import Variable from torchvision import datasets, models, transforms from tensorboardX import SummaryWriter import sys import json import scipy import os, time import argparse import numpy as np import torchvision import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt from PIL import Image from shutil import copyfile from model import ft_net from test_eval_cython import get_test_acc, extr_fea_train from utils import * import loader, loss import pdb version = torch.__version__ sys.stdout = Logger(log_dir + suffix + '_os.txt') str_ids = args.gpu.split(',') gpu_ids = [] for str_id in str_ids: gid = int(str_id) if gid >=0: gpu_ids.append(gid) if len(gpu_ids)>0: torch.cuda.set_device(gpu_ids[0]) print ('using gpu: {}'.format(gpu_ids)) , pre_pids) train_dataset.transform = tansform_bak all_weights = all_weights.astype(np.float32) for i in range(len(trainLabels_nsy)): train_dataset.imgs[i] = (train_dataset.imgs[i][0], int(pre_pids[i]), all_weights[i]) else: print('Setting same weights for all the instances...') for i in range(len(trainLabels_nsy)): train_dataset.imgs[i] = (train_dataset.imgs[i][0], trainLabels_nsy[i],1) dataloaders_train = torch.utils.data.DataLoader(train_dataset, batch_size=args.batchsize, shuffle=True, num_workers=8) test_dataset = {x: datasets.ImageFolder( os.path.join(data_dir,x) ,test_transform) for x in ['gallery','query']} dataloaders_test = {x: torch.utils.data.DataLoader(test_dataset[x], batch_size=args.batchsize, shuffle=False, num_workers=8) for x in ['gallery','query']} if args.stage2: criterion = loss.InstanceWeightLoss(weighted = 1) else: criterion = nn.CrossEntropyLoss() ignored_params = list(map(id, model.model.fc.parameters() )) + list(map(id, model.classifier.parameters() )) base_params = filter(lambda p: id(p) not in ignored_params, model.parameters()) optimizer_ft = optim.SGD([ {'params': base_params, 'lr': lr}, {'params': model.model.fc.parameters(), 'lr': lr*10}, {'params': model.classifier.parameters(), 'lr': lr*10} ], weight_decay=5e-4, momentum=0.9, nesterov=True) exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=20, gamma=0.1) def save_network(network, epoch_label, is_best = False): if is_best: save_path = checkpoint_path + suffix + '_epoch_best.t' else: save_path = checkpoint_path + suffix + '_epoch_{}.t'.format(epoch_label) torch.save(network.state_dict(), save_path) def sigmoid_rampup(current, rampup_length): if rampup_length == 0: return 1.0 else: current = np.clip(current, 0.0, rampup_length) phase = 1.0 - current / rampup_length w = float(np.exp(-2.0 * phase * phase)) return min(w,0.5) def train_model(model, criterion, optimizer_ft, scheduler, epoch): scheduler.step() lambda1 = sigmoid_rampup(epoch, args.LabelWt) train_loss = AverageMeter() data_time = AverageMeter() batch_time = AverageMeter() model.train() correct = 0 total = 0 end = time.time() for batch_idx, (inputs, targets, weights) in enumerate(dataloaders_train): if use_gpu: inputs = Variable(inputs.cuda()) targets = Variable(targets.cuda()) weights = Variable(weights.cuda()) data_time.update(time.time() - end) optimizer_ft.zero_grad() outputs = model(inputs) if args.stage2: loss = criterion(outputs, targets, weights) else: loss = criterion(outputs, targets, lambda1) loss.backward() optimizer_ft.step() train_loss.update(loss.item(), inputs.size(0)) batch_time.update(time.time() - end) end = time.time() _, predicted = outputs.max(1) correct += predicted.eq(targets).sum().item() total += inputs.size(0) if batch_idx%10==0: print('Epoch: [{}][{}/{}] ' 'Time: {batch_time.val:.3f} ({batch_time.avg:.3f}) ' 'Data: {data_time.val:.3f} ({data_time.avg:.3f}) ' 'Loss: {train_loss.val:.4f} ({train_loss.avg:.4f}) ' 'Accu: {:.2f}'.format( epoch, batch_idx, len(dataloaders_train),100.*correct/total, batch_time=batch_time, data_time=data_time, train_loss=train_loss)) writer.add_scalar('training acc (train)', 100.*correct/total, epoch) writer.add_scalar('loss', train_loss.avg, epoch) for epoch in range(start_epoch, start_epoch+41): print('Start Training..........') train_model(model, criterion, optimizer_ft, exp_lr_scheduler, epoch) if epoch%test_epoch ==0: model.eval() start = time.time() cmc, mAP = get_test_acc(model, test_dataset, dataloaders_test, use_gpu, max_rank=10) if cmc[0] > best_acc: best_epoch = epoch best_acc = cmc[0] save_network(model, epoch, is_best = True) print('Epoch {}: R1:{:.4%} R5:{:.4%} R10:{:.4%} mAP:{:.4%} (Best Epoch[{}])'.format( epoch, cmc[0],cmc[4],cmc[9], mAP ,best_epoch)) print('Epoch {}: R1:{:.4%} R5:{:.4%} R10:{:.4%} mAP:{:.4%} (Best Epoch[{}])'.format( epoch, cmc[0],cmc[4],cmc[9], mAP ,best_epoch), file = test_log_file) test_log_file.flush() print('Evaluation time: {}'.format(time.time()-start))

true

79080274d654c3494c58716d1acbc6511f150845

2,436

py

Python

tools/mo/openvino/tools/mo/front/kaldi/extractors/tdnncomponent_ext.py

ryanloney/openvino-1

4e0a740eb3ee31062ba0df88fcf438564f67edb7

[ "Apache-2.0" ]

1,127

2018-10-15T14:36:58.000Z

2020-04-20T09:29:44.000Z

tools/mo/openvino/tools/mo/front/kaldi/extractors/tdnncomponent_ext.py

ryanloney/openvino-1

4e0a740eb3ee31062ba0df88fcf438564f67edb7

[ "Apache-2.0" ]

439

2018-10-20T04:40:35.000Z

2020-04-19T05:56:25.000Z

tools/mo/openvino/tools/mo/front/kaldi/extractors/tdnncomponent_ext.py

ryanloney/openvino-1

4e0a740eb3ee31062ba0df88fcf438564f67edb7

[ "Apache-2.0" ]

414

2018-10-17T05:53:46.000Z

2020-04-16T17:29:53.000Z

# Copyright (C) 2018-2022 Intel Corporation # SPDX-License-Identifier: Apache-2.0 import numpy as np from openvino.tools.mo.front.common.partial_infer.utils import mo_array from openvino.tools.mo.front.extractor import FrontExtractorOp from openvino.tools.mo.front.kaldi.loader.utils import read_binary_bool_token, read_binary_integer32_token, collect_until_token, \ read_binary_float_token from openvino.tools.mo.front.kaldi.utils import read_binary_vector, read_binary_matrix from openvino.tools.mo.ops.tdnncomponent import TdnnComponent class TdnnComponentFrontExtractor(FrontExtractorOp): op = 'tdnncomponent' enabled = True @classmethod def extract(cls, node): pb = node.parameters collect_until_token(pb, b'<MaxChange>') max_change = read_binary_float_token(pb) collect_until_token(pb, b'<L2Regularize>') collect_until_token(pb, b'<LearningRate>') collect_until_token(pb, b'<TimeOffsets>') time_offsets = read_binary_vector(pb, False, np.int32) collect_until_token(pb, b'<LinearParams>') weights, weights_shape = read_binary_matrix(pb) collect_until_token(pb, b'<BiasParams>') bias_params = read_binary_vector(pb) collect_until_token(pb, b'<OrthonormalConstraint>') orthonormal_constraint = read_binary_float_token(pb) # used only on training collect_until_token(pb, b'<UseNaturalGradient>') use_natural_grad = read_binary_bool_token(pb) # used only on training collect_until_token(pb, b'<NumSamplesHistory>') num_samples_hist = read_binary_float_token(pb) collect_until_token(pb, b'<AlphaInOut>') alpha_in_out = read_binary_float_token(pb), read_binary_float_token(pb) # for training, usually (4, 4) # according to Kaldi documentation http://kaldi-asr.org/doc/classkaldi_1_1nnet3_1_1TdnnComponent.html#details # it looks like it's used only during training (but not 100% sure) collect_until_token(pb, b'<RankInOut>') rank_in_out = read_binary_integer32_token(pb), read_binary_integer32_token(pb) biases = mo_array(bias_params) if len(bias_params) != 0 else None attrs = { 'weights': np.reshape(weights, weights_shape), 'biases': biases, 'time_offsets': time_offsets, } TdnnComponent.update_node_stat(node, attrs) return cls.enabled

40.6

130

0.719212

import numpy as np from openvino.tools.mo.front.common.partial_infer.utils import mo_array from openvino.tools.mo.front.extractor import FrontExtractorOp from openvino.tools.mo.front.kaldi.loader.utils import read_binary_bool_token, read_binary_integer32_token, collect_until_token, \ read_binary_float_token from openvino.tools.mo.front.kaldi.utils import read_binary_vector, read_binary_matrix from openvino.tools.mo.ops.tdnncomponent import TdnnComponent class TdnnComponentFrontExtractor(FrontExtractorOp): op = 'tdnncomponent' enabled = True @classmethod def extract(cls, node): pb = node.parameters collect_until_token(pb, b'<MaxChange>') max_change = read_binary_float_token(pb) collect_until_token(pb, b'<L2Regularize>') collect_until_token(pb, b'<LearningRate>') collect_until_token(pb, b'<TimeOffsets>') time_offsets = read_binary_vector(pb, False, np.int32) collect_until_token(pb, b'<LinearParams>') weights, weights_shape = read_binary_matrix(pb) collect_until_token(pb, b'<BiasParams>') bias_params = read_binary_vector(pb) collect_until_token(pb, b'<OrthonormalConstraint>') orthonormal_constraint = read_binary_float_token(pb) collect_until_token(pb, b'<UseNaturalGradient>') use_natural_grad = read_binary_bool_token(pb) collect_until_token(pb, b'<NumSamplesHistory>') num_samples_hist = read_binary_float_token(pb) collect_until_token(pb, b'<AlphaInOut>') alpha_in_out = read_binary_float_token(pb), read_binary_float_token(pb) collect_until_token(pb, b'<RankInOut>') rank_in_out = read_binary_integer32_token(pb), read_binary_integer32_token(pb) biases = mo_array(bias_params) if len(bias_params) != 0 else None attrs = { 'weights': np.reshape(weights, weights_shape), 'biases': biases, 'time_offsets': time_offsets, } TdnnComponent.update_node_stat(node, attrs) return cls.enabled

true

790802f077454ad281ac4d77e36901e0b7c8bf8b

70,452

py

Python

rasa/nlu/classifiers/diet_classifier.py

mukulbalodi/rasa

3126ef1148c165f2402f3c7203138d429e46c68c

[ "Apache-2.0" ]

null

rasa/nlu/classifiers/diet_classifier.py

mukulbalodi/rasa

3126ef1148c165f2402f3c7203138d429e46c68c

[ "Apache-2.0" ]

null

rasa/nlu/classifiers/diet_classifier.py

mukulbalodi/rasa

3126ef1148c165f2402f3c7203138d429e46c68c

[ "Apache-2.0" ]

1

2022-02-22T12:35:19.000Z

from __future__ import annotations import copy import logging from collections import defaultdict from pathlib import Path from rasa.nlu.featurizers.featurizer import Featurizer import numpy as np import scipy.sparse import tensorflow as tf from typing import Any, Dict, List, Optional, Text, Tuple, Union, Type from rasa.engine.graph import ExecutionContext, GraphComponent from rasa.engine.recipes.default_recipe import DefaultV1Recipe from rasa.engine.storage.resource import Resource from rasa.engine.storage.storage import ModelStorage from rasa.nlu.extractors.extractor import EntityExtractorMixin from rasa.nlu.classifiers.classifier import IntentClassifier import rasa.shared.utils.io import rasa.utils.io as io_utils import rasa.nlu.utils.bilou_utils as bilou_utils from rasa.shared.constants import DIAGNOSTIC_DATA from rasa.nlu.extractors.extractor import EntityTagSpec from rasa.nlu.classifiers import LABEL_RANKING_LENGTH from rasa.utils import train_utils from rasa.utils.tensorflow import rasa_layers from rasa.utils.tensorflow.models import RasaModel, TransformerRasaModel from rasa.utils.tensorflow.model_data import ( RasaModelData, FeatureSignature, FeatureArray, ) from rasa.nlu.constants import TOKENS_NAMES, DEFAULT_TRANSFORMER_SIZE from rasa.shared.nlu.constants import ( SPLIT_ENTITIES_BY_COMMA_DEFAULT_VALUE, TEXT, INTENT, INTENT_RESPONSE_KEY, ENTITIES, ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_GROUP, ENTITY_ATTRIBUTE_ROLE, NO_ENTITY_TAG, SPLIT_ENTITIES_BY_COMMA, ) from rasa.shared.exceptions import InvalidConfigException from rasa.shared.nlu.training_data.training_data import TrainingData from rasa.shared.nlu.training_data.message import Message from rasa.utils.tensorflow.constants import ( LABEL, IDS, HIDDEN_LAYERS_SIZES, RENORMALIZE_CONFIDENCES, SHARE_HIDDEN_LAYERS, TRANSFORMER_SIZE, NUM_TRANSFORMER_LAYERS, NUM_HEADS, BATCH_SIZES, BATCH_STRATEGY, EPOCHS, RANDOM_SEED, LEARNING_RATE, RANKING_LENGTH, LOSS_TYPE, SIMILARITY_TYPE, NUM_NEG, SPARSE_INPUT_DROPOUT, DENSE_INPUT_DROPOUT, MASKED_LM, ENTITY_RECOGNITION, TENSORBOARD_LOG_DIR, INTENT_CLASSIFICATION, EVAL_NUM_EXAMPLES, EVAL_NUM_EPOCHS, UNIDIRECTIONAL_ENCODER, DROP_RATE, DROP_RATE_ATTENTION, CONNECTION_DENSITY, NEGATIVE_MARGIN_SCALE, REGULARIZATION_CONSTANT, SCALE_LOSS, USE_MAX_NEG_SIM, MAX_NEG_SIM, MAX_POS_SIM, EMBEDDING_DIMENSION, BILOU_FLAG, KEY_RELATIVE_ATTENTION, VALUE_RELATIVE_ATTENTION, MAX_RELATIVE_POSITION, AUTO, BALANCED, CROSS_ENTROPY, TENSORBOARD_LOG_LEVEL, CONCAT_DIMENSION, FEATURIZERS, CHECKPOINT_MODEL, SEQUENCE, SENTENCE, SEQUENCE_LENGTH, DENSE_DIMENSION, MASK, CONSTRAIN_SIMILARITIES, MODEL_CONFIDENCE, SOFTMAX, ) logger = logging.getLogger(__name__) SPARSE = "sparse" DENSE = "dense" LABEL_KEY = LABEL LABEL_SUB_KEY = IDS POSSIBLE_TAGS = [ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_ROLE, ENTITY_ATTRIBUTE_GROUP] @DefaultV1Recipe.register( [ DefaultV1Recipe.ComponentType.INTENT_CLASSIFIER, DefaultV1Recipe.ComponentType.ENTITY_EXTRACTOR, ], is_trainable=True, ) class DIETClassifier(GraphComponent, IntentClassifier, EntityExtractorMixin): """A multi-task model for intent classification and entity extraction. DIET is Dual Intent and Entity Transformer. The architecture is based on a transformer which is shared for both tasks. A sequence of entity labels is predicted through a Conditional Random Field (CRF) tagging layer on top of the transformer output sequence corresponding to the input sequence of tokens. The transformer output for the ``__CLS__`` token and intent labels are embedded into a single semantic vector space. We use the dot-product loss to maximize the similarity with the target label and minimize similarities with negative samples. """ @classmethod def required_components(cls) -> List[Type]: """Components that should be included in the pipeline before this component.""" return [Featurizer] @staticmethod def get_default_config() -> Dict[Text, Any]: """The component's default config (see parent class for full docstring).""" # please make sure to update the docs when changing a default parameter return { # ## Architecture of the used neural network # Hidden layer sizes for layers before the embedding layers for user message # and labels. # The number of hidden layers is equal to the length of the corresponding # list. HIDDEN_LAYERS_SIZES: {TEXT: [], LABEL: []}, # Whether to share the hidden layer weights between user message and labels. SHARE_HIDDEN_LAYERS: False, # Number of units in transformer TRANSFORMER_SIZE: DEFAULT_TRANSFORMER_SIZE, # Number of transformer layers NUM_TRANSFORMER_LAYERS: 2, # Number of attention heads in transformer NUM_HEADS: 4, # If 'True' use key relative embeddings in attention KEY_RELATIVE_ATTENTION: False, # If 'True' use value relative embeddings in attention VALUE_RELATIVE_ATTENTION: False, # Max position for relative embeddings. Only in effect if key- or value # relative attention are turned on MAX_RELATIVE_POSITION: 5, # Use a unidirectional or bidirectional encoder. UNIDIRECTIONAL_ENCODER: False, # ## Training parameters # Initial and final batch sizes: # Batch size will be linearly increased for each epoch. BATCH_SIZES: [64, 256], # Strategy used when creating batches. # Can be either 'sequence' or 'balanced'. BATCH_STRATEGY: BALANCED, # Number of epochs to train EPOCHS: 300, # Set random seed to any 'int' to get reproducible results RANDOM_SEED: None, # Initial learning rate for the optimizer LEARNING_RATE: 0.001, # ## Parameters for embeddings # Dimension size of embedding vectors EMBEDDING_DIMENSION: 20, # Dense dimension to use for sparse features. DENSE_DIMENSION: {TEXT: 128, LABEL: 20}, # Default dimension to use for concatenating sequence and sentence features. CONCAT_DIMENSION: {TEXT: 128, LABEL: 20}, # The number of incorrect labels. The algorithm will minimize # their similarity to the user input during training. NUM_NEG: 20, # Type of similarity measure to use, either 'auto' or 'cosine' or 'inner'. SIMILARITY_TYPE: AUTO, # The type of the loss function, either 'cross_entropy' or 'margin'. LOSS_TYPE: CROSS_ENTROPY, # Number of top intents for which confidences should be reported. # Set to 0 if confidences for all intents should be reported. RANKING_LENGTH: LABEL_RANKING_LENGTH, # Indicates how similar the algorithm should try to make embedding vectors # for correct labels. # Should be 0.0 < ... < 1.0 for 'cosine' similarity type. MAX_POS_SIM: 0.8, # Maximum negative similarity for incorrect labels. # Should be -1.0 < ... < 1.0 for 'cosine' similarity type. MAX_NEG_SIM: -0.4, # If 'True' the algorithm only minimizes maximum similarity over # incorrect intent labels, used only if 'loss_type' is set to 'margin'. USE_MAX_NEG_SIM: True, # If 'True' scale loss inverse proportionally to the confidence # of the correct prediction SCALE_LOSS: False, # ## Regularization parameters # The scale of regularization REGULARIZATION_CONSTANT: 0.002, # The scale of how important is to minimize the maximum similarity # between embeddings of different labels, # used only if 'loss_type' is set to 'margin'. NEGATIVE_MARGIN_SCALE: 0.8, # Dropout rate for encoder DROP_RATE: 0.2, # Dropout rate for attention DROP_RATE_ATTENTION: 0, # Fraction of trainable weights in internal layers. CONNECTION_DENSITY: 0.2, # If 'True' apply dropout to sparse input tensors SPARSE_INPUT_DROPOUT: True, # If 'True' apply dropout to dense input tensors DENSE_INPUT_DROPOUT: True, # ## Evaluation parameters # How often calculate validation accuracy. # Small values may hurt performance. EVAL_NUM_EPOCHS: 20, # How many examples to use for hold out validation set # Large values may hurt performance, e.g. model accuracy. # Set to 0 for no validation. EVAL_NUM_EXAMPLES: 0, # ## Model config # If 'True' intent classification is trained and intent predicted. INTENT_CLASSIFICATION: True, # If 'True' named entity recognition is trained and entities predicted. ENTITY_RECOGNITION: True, # If 'True' random tokens of the input message will be masked and the model # should predict those tokens. MASKED_LM: False, # 'BILOU_flag' determines whether to use BILOU tagging or not. # If set to 'True' labelling is more rigorous, however more # examples per entity are required. # Rule of thumb: you should have more than 100 examples per entity. BILOU_FLAG: True, # If you want to use tensorboard to visualize training and validation # metrics, set this option to a valid output directory. TENSORBOARD_LOG_DIR: None, # Define when training metrics for tensorboard should be logged. # Either after every epoch or for every training step. # Valid values: 'epoch' and 'batch' TENSORBOARD_LOG_LEVEL: "epoch", # Perform model checkpointing CHECKPOINT_MODEL: False, # Specify what features to use as sequence and sentence features # By default all features in the pipeline are used. FEATURIZERS: [], # Split entities by comma, this makes sense e.g. for a list of ingredients # in a recipie, but it doesn't make sense for the parts of an address SPLIT_ENTITIES_BY_COMMA: True, # If 'True' applies sigmoid on all similarity terms and adds # it to the loss function to ensure that similarity values are # approximately bounded. Used inside cross-entropy loss only. CONSTRAIN_SIMILARITIES: False, # Model confidence to be returned during inference. Currently, the only # possible value is `softmax`. MODEL_CONFIDENCE: SOFTMAX, # Determines whether the confidences of the chosen top intents should be # renormalized so that they sum up to 1. By default, we do not renormalize # and return the confidences for the top intents as is. # Note that renormalization only makes sense if confidences are generated # via `softmax`. RENORMALIZE_CONFIDENCES: False, } def __init__( self, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, index_label_id_mapping: Optional[Dict[int, Text]] = None, entity_tag_specs: Optional[List[EntityTagSpec]] = None, model: Optional[RasaModel] = None, sparse_feature_sizes: Optional[Dict[Text, Dict[Text, List[int]]]] = None, ) -> None: """Declare instance variables with default values.""" if EPOCHS not in config: rasa.shared.utils.io.raise_warning( f"Please configure the number of '{EPOCHS}' in your configuration file." f" We will change the default value of '{EPOCHS}' in the future to 1. " ) self.component_config = config self._model_storage = model_storage self._resource = resource self._execution_context = execution_context self._check_config_parameters() # transform numbers to labels self.index_label_id_mapping = index_label_id_mapping or {} self._entity_tag_specs = entity_tag_specs self.model = model self.tmp_checkpoint_dir = None if self.component_config[CHECKPOINT_MODEL]: self.tmp_checkpoint_dir = Path(rasa.utils.io.create_temporary_directory()) self._label_data: Optional[RasaModelData] = None self._data_example: Optional[Dict[Text, Dict[Text, List[FeatureArray]]]] = None self.split_entities_config = rasa.utils.train_utils.init_split_entities( self.component_config[SPLIT_ENTITIES_BY_COMMA], SPLIT_ENTITIES_BY_COMMA_DEFAULT_VALUE, ) self.finetune_mode = self._execution_context.is_finetuning self._sparse_feature_sizes = sparse_feature_sizes # init helpers def _check_masked_lm(self) -> None: if ( self.component_config[MASKED_LM] and self.component_config[NUM_TRANSFORMER_LAYERS] == 0 ): raise ValueError( f"If number of transformer layers is 0, " f"'{MASKED_LM}' option should be 'False'." ) def _check_share_hidden_layers_sizes(self) -> None: if self.component_config.get(SHARE_HIDDEN_LAYERS): first_hidden_layer_sizes = next( iter(self.component_config[HIDDEN_LAYERS_SIZES].values()) ) # check that all hidden layer sizes are the same identical_hidden_layer_sizes = all( current_hidden_layer_sizes == first_hidden_layer_sizes for current_hidden_layer_sizes in self.component_config[ HIDDEN_LAYERS_SIZES ].values() ) if not identical_hidden_layer_sizes: raise ValueError( f"If hidden layer weights are shared, " f"{HIDDEN_LAYERS_SIZES} must coincide." ) def _check_config_parameters(self) -> None: self.component_config = train_utils.check_deprecated_options( self.component_config ) self._check_masked_lm() self._check_share_hidden_layers_sizes() self.component_config = train_utils.update_confidence_type( self.component_config ) train_utils.validate_configuration_settings(self.component_config) self.component_config = train_utils.update_similarity_type( self.component_config ) self.component_config = train_utils.update_evaluation_parameters( self.component_config ) @classmethod def create( cls, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, ) -> DIETClassifier: """Creates a new untrained component (see parent class for full docstring).""" return cls(config, model_storage, resource, execution_context) @property def label_key(self) -> Optional[Text]: """Return key if intent classification is activated.""" return LABEL_KEY if self.component_config[INTENT_CLASSIFICATION] else None @property def label_sub_key(self) -> Optional[Text]: """Return sub key if intent classification is activated.""" return LABEL_SUB_KEY if self.component_config[INTENT_CLASSIFICATION] else None @staticmethod def model_class() -> Type[RasaModel]: return DIET # training data helpers: @staticmethod def _label_id_index_mapping( training_data: TrainingData, attribute: Text ) -> Dict[Text, int]: """Create label_id dictionary.""" distinct_label_ids = { example.get(attribute) for example in training_data.intent_examples } - {None} return { label_id: idx for idx, label_id in enumerate(sorted(distinct_label_ids)) } @staticmethod def _invert_mapping(mapping: Dict) -> Dict: return {value: key for key, value in mapping.items()} def _create_entity_tag_specs( self, training_data: TrainingData ) -> List[EntityTagSpec]: """Create entity tag specifications with their respective tag id mappings.""" _tag_specs = [] for tag_name in POSSIBLE_TAGS: if self.component_config[BILOU_FLAG]: tag_id_index_mapping = bilou_utils.build_tag_id_dict( training_data, tag_name ) else: tag_id_index_mapping = self._tag_id_index_mapping_for( tag_name, training_data ) if tag_id_index_mapping: _tag_specs.append( EntityTagSpec( tag_name=tag_name, tags_to_ids=tag_id_index_mapping, ids_to_tags=self._invert_mapping(tag_id_index_mapping), num_tags=len(tag_id_index_mapping), ) ) return _tag_specs @staticmethod def _tag_id_index_mapping_for( tag_name: Text, training_data: TrainingData ) -> Optional[Dict[Text, int]]: """Create mapping from tag name to id.""" if tag_name == ENTITY_ATTRIBUTE_ROLE: distinct_tags = training_data.entity_roles elif tag_name == ENTITY_ATTRIBUTE_GROUP: distinct_tags = training_data.entity_groups else: distinct_tags = training_data.entities distinct_tags = distinct_tags - {NO_ENTITY_TAG} - {None} if not distinct_tags: return None tag_id_dict = { tag_id: idx for idx, tag_id in enumerate(sorted(distinct_tags), 1) } # NO_ENTITY_TAG corresponds to non-entity which should correspond to 0 index # needed for correct prediction for padding tag_id_dict[NO_ENTITY_TAG] = 0 return tag_id_dict @staticmethod def _find_example_for_label( label: Text, examples: List[Message], attribute: Text ) -> Optional[Message]: for ex in examples: if ex.get(attribute) == label: return ex return None def _check_labels_features_exist( self, labels_example: List[Message], attribute: Text ) -> bool: """Checks if all labels have features set.""" return all( label_example.features_present( attribute, self.component_config[FEATURIZERS] ) for label_example in labels_example ) def _extract_features( self, message: Message, attribute: Text ) -> Dict[Text, Union[scipy.sparse.spmatrix, np.ndarray]]: ( sparse_sequence_features, sparse_sentence_features, ) = message.get_sparse_features(attribute, self.component_config[FEATURIZERS]) dense_sequence_features, dense_sentence_features = message.get_dense_features( attribute, self.component_config[FEATURIZERS] ) if dense_sequence_features is not None and sparse_sequence_features is not None: if ( dense_sequence_features.features.shape[0] != sparse_sequence_features.features.shape[0] ): raise ValueError( f"Sequence dimensions for sparse and dense sequence features " f"don't coincide in '{message.get(TEXT)}'" f"for attribute '{attribute}'." ) if dense_sentence_features is not None and sparse_sentence_features is not None: if ( dense_sentence_features.features.shape[0] != sparse_sentence_features.features.shape[0] ): raise ValueError( f"Sequence dimensions for sparse and dense sentence features " f"don't coincide in '{message.get(TEXT)}'" f"for attribute '{attribute}'." ) # If we don't use the transformer and we don't want to do entity recognition, # to speed up training take only the sentence features as feature vector. # We would not make use of the sequence anyway in this setup. Carrying over # those features to the actual training process takes quite some time. if ( self.component_config[NUM_TRANSFORMER_LAYERS] == 0 and not self.component_config[ENTITY_RECOGNITION] and attribute not in [INTENT, INTENT_RESPONSE_KEY] ): sparse_sequence_features = None dense_sequence_features = None out = {} if sparse_sentence_features is not None: out[f"{SPARSE}_{SENTENCE}"] = sparse_sentence_features.features if sparse_sequence_features is not None: out[f"{SPARSE}_{SEQUENCE}"] = sparse_sequence_features.features if dense_sentence_features is not None: out[f"{DENSE}_{SENTENCE}"] = dense_sentence_features.features if dense_sequence_features is not None: out[f"{DENSE}_{SEQUENCE}"] = dense_sequence_features.features return out def _check_input_dimension_consistency(self, model_data: RasaModelData) -> None: """Checks if features have same dimensionality if hidden layers are shared.""" if self.component_config.get(SHARE_HIDDEN_LAYERS): num_text_sentence_features = model_data.number_of_units(TEXT, SENTENCE) num_label_sentence_features = model_data.number_of_units(LABEL, SENTENCE) num_text_sequence_features = model_data.number_of_units(TEXT, SEQUENCE) num_label_sequence_features = model_data.number_of_units(LABEL, SEQUENCE) if (0 < num_text_sentence_features != num_label_sentence_features > 0) or ( 0 < num_text_sequence_features != num_label_sequence_features > 0 ): raise ValueError( "If embeddings are shared text features and label features " "must coincide. Check the output dimensions of previous components." ) def _extract_labels_precomputed_features( self, label_examples: List[Message], attribute: Text = INTENT ) -> Tuple[List[FeatureArray], List[FeatureArray]]: """Collects precomputed encodings.""" features = defaultdict(list) for e in label_examples: label_features = self._extract_features(e, attribute) for feature_key, feature_value in label_features.items(): features[feature_key].append(feature_value) sequence_features = [] sentence_features = [] for feature_name, feature_value in features.items(): if SEQUENCE in feature_name: sequence_features.append( FeatureArray(np.array(feature_value), number_of_dimensions=3) ) else: sentence_features.append( FeatureArray(np.array(feature_value), number_of_dimensions=3) ) return sequence_features, sentence_features @staticmethod def _compute_default_label_features( labels_example: List[Message], ) -> List[FeatureArray]: """Computes one-hot representation for the labels.""" logger.debug("No label features found. Computing default label features.") eye_matrix = np.eye(len(labels_example), dtype=np.float32) # add sequence dimension to one-hot labels return [ FeatureArray( np.array([np.expand_dims(a, 0) for a in eye_matrix]), number_of_dimensions=3, ) ] def _create_label_data( self, training_data: TrainingData, label_id_dict: Dict[Text, int], attribute: Text, ) -> RasaModelData: """Create matrix with label_ids encoded in rows as bag of words. Find a training example for each label and get the encoded features from the corresponding Message object. If the features are already computed, fetch them from the message object else compute a one hot encoding for the label as the feature vector. """ # Collect one example for each label labels_idx_examples = [] for label_name, idx in label_id_dict.items(): label_example = self._find_example_for_label( label_name, training_data.intent_examples, attribute ) labels_idx_examples.append((idx, label_example)) # Sort the list of tuples based on label_idx labels_idx_examples = sorted(labels_idx_examples, key=lambda x: x[0]) labels_example = [example for (_, example) in labels_idx_examples] # Collect features, precomputed if they exist, else compute on the fly if self._check_labels_features_exist(labels_example, attribute): ( sequence_features, sentence_features, ) = self._extract_labels_precomputed_features(labels_example, attribute) else: sequence_features = None sentence_features = self._compute_default_label_features(labels_example) label_data = RasaModelData() label_data.add_features(LABEL, SEQUENCE, sequence_features) label_data.add_features(LABEL, SENTENCE, sentence_features) if label_data.does_feature_not_exist( LABEL, SENTENCE ) and label_data.does_feature_not_exist(LABEL, SEQUENCE): raise ValueError( "No label features are present. Please check your configuration file." ) label_ids = np.array([idx for (idx, _) in labels_idx_examples]) # explicitly add last dimension to label_ids # to track correctly dynamic sequences label_data.add_features( LABEL_KEY, LABEL_SUB_KEY, [FeatureArray(np.expand_dims(label_ids, -1), number_of_dimensions=2)], ) label_data.add_lengths(LABEL, SEQUENCE_LENGTH, LABEL, SEQUENCE) return label_data def _use_default_label_features(self, label_ids: np.ndarray) -> List[FeatureArray]: feature_arrays: List[FeatureArray] = self._label_data.get(LABEL, SENTENCE) all_label_features = feature_arrays[0] return [ FeatureArray( np.array([all_label_features[label_id] for label_id in label_ids]), number_of_dimensions=all_label_features.number_of_dimensions, ) ] def _create_model_data( self, training_data: List[Message], label_id_dict: Optional[Dict[Text, int]] = None, label_attribute: Optional[Text] = None, training: bool = True, ) -> RasaModelData: """Prepare data for training and create a RasaModelData object.""" from rasa.utils.tensorflow import model_data_utils attributes_to_consider = [TEXT] if training and self.component_config[INTENT_CLASSIFICATION]: # we don't have any intent labels during prediction, just add them during # training attributes_to_consider.append(label_attribute) if ( training and self.component_config[ENTITY_RECOGNITION] and self._entity_tag_specs ): # Add entities as labels only during training and only if there was # training data added for entities with DIET configured to predict entities. attributes_to_consider.append(ENTITIES) if training and label_attribute is not None: # only use those training examples that have the label_attribute set # during training training_data = [ example for example in training_data if label_attribute in example.data ] training_data = [ message for message in training_data if message.features_present( attribute=TEXT, featurizers=self.component_config.get(FEATURIZERS) ) ] if not training_data: # no training data are present to train return RasaModelData() ( features_for_examples, sparse_feature_sizes, ) = model_data_utils.featurize_training_examples( training_data, attributes_to_consider, entity_tag_specs=self._entity_tag_specs, featurizers=self.component_config[FEATURIZERS], bilou_tagging=self.component_config[BILOU_FLAG], ) attribute_data, _ = model_data_utils.convert_to_data_format( features_for_examples, consider_dialogue_dimension=False ) model_data = RasaModelData( label_key=self.label_key, label_sub_key=self.label_sub_key ) model_data.add_data(attribute_data) model_data.add_lengths(TEXT, SEQUENCE_LENGTH, TEXT, SEQUENCE) # Current implementation doesn't yet account for updating sparse # feature sizes of label attributes. That's why we remove them. sparse_feature_sizes = self._remove_label_sparse_feature_sizes( sparse_feature_sizes=sparse_feature_sizes, label_attribute=label_attribute ) model_data.add_sparse_feature_sizes(sparse_feature_sizes) self._add_label_features( model_data, training_data, label_attribute, label_id_dict, training ) # make sure all keys are in the same order during training and prediction # as we rely on the order of key and sub-key when constructing the actual # tensors from the model data model_data.sort() return model_data @staticmethod def _remove_label_sparse_feature_sizes( sparse_feature_sizes: Dict[Text, Dict[Text, List[int]]], label_attribute: Optional[Text] = None, ) -> Dict[Text, Dict[Text, List[int]]]: if label_attribute in sparse_feature_sizes: del sparse_feature_sizes[label_attribute] return sparse_feature_sizes def _add_label_features( self, model_data: RasaModelData, training_data: List[Message], label_attribute: Text, label_id_dict: Dict[Text, int], training: bool = True, ) -> None: label_ids = [] if training and self.component_config[INTENT_CLASSIFICATION]: for example in training_data: if example.get(label_attribute): label_ids.append(label_id_dict[example.get(label_attribute)]) # explicitly add last dimension to label_ids # to track correctly dynamic sequences model_data.add_features( LABEL_KEY, LABEL_SUB_KEY, [FeatureArray(np.expand_dims(label_ids, -1), number_of_dimensions=2)], ) if ( label_attribute and model_data.does_feature_not_exist(label_attribute, SENTENCE) and model_data.does_feature_not_exist(label_attribute, SEQUENCE) ): # no label features are present, get default features from _label_data model_data.add_features( LABEL, SENTENCE, self._use_default_label_features(np.array(label_ids)) ) # as label_attribute can have different values, e.g. INTENT or RESPONSE, # copy over the features to the LABEL key to make # it easier to access the label features inside the model itself model_data.update_key(label_attribute, SENTENCE, LABEL, SENTENCE) model_data.update_key(label_attribute, SEQUENCE, LABEL, SEQUENCE) model_data.update_key(label_attribute, MASK, LABEL, MASK) model_data.add_lengths(LABEL, SEQUENCE_LENGTH, LABEL, SEQUENCE) # train helpers def preprocess_train_data(self, training_data: TrainingData) -> RasaModelData: """Prepares data for training. Performs sanity checks on training data, extracts encodings for labels. """ if self.component_config[BILOU_FLAG]: bilou_utils.apply_bilou_schema(training_data) label_id_index_mapping = self._label_id_index_mapping( training_data, attribute=INTENT ) if not label_id_index_mapping: # no labels are present to train return RasaModelData() self.index_label_id_mapping = self._invert_mapping(label_id_index_mapping) self._label_data = self._create_label_data( training_data, label_id_index_mapping, attribute=INTENT ) self._entity_tag_specs = self._create_entity_tag_specs(training_data) label_attribute = ( INTENT if self.component_config[INTENT_CLASSIFICATION] else None ) model_data = self._create_model_data( training_data.nlu_examples, label_id_index_mapping, label_attribute=label_attribute, ) self._check_input_dimension_consistency(model_data) return model_data @staticmethod def _check_enough_labels(model_data: RasaModelData) -> bool: return len(np.unique(model_data.get(LABEL_KEY, LABEL_SUB_KEY))) >= 2 def train(self, training_data: TrainingData) -> Resource: """Train the embedding intent classifier on a data set.""" model_data = self.preprocess_train_data(training_data) if model_data.is_empty(): logger.debug( f"Cannot train '{self.__class__.__name__}'. No data was provided. " f"Skipping training of the classifier." ) return self._resource if not self.model and self.finetune_mode: raise rasa.shared.exceptions.InvalidParameterException( f"{self.__class__.__name__} was instantiated " f"with `model=None` and `finetune_mode=True`. " f"This is not a valid combination as the component " f"needs an already instantiated and trained model " f"to continue training in finetune mode." ) if self.component_config.get(INTENT_CLASSIFICATION): if not self._check_enough_labels(model_data): logger.error( f"Cannot train '{self.__class__.__name__}'. " f"Need at least 2 different intent classes. " f"Skipping training of classifier." ) return self._resource if self.component_config.get(ENTITY_RECOGNITION): self.check_correct_entity_annotations(training_data) # keep one example for persisting and loading self._data_example = model_data.first_data_example() if not self.finetune_mode: # No pre-trained model to load from. Create a new instance of the model. self.model = self._instantiate_model_class(model_data) self.model.compile( optimizer=tf.keras.optimizers.Adam(self.component_config[LEARNING_RATE]) ) else: self.model.adjust_for_incremental_training( data_example=self._data_example, new_sparse_feature_sizes=model_data.get_sparse_feature_sizes(), old_sparse_feature_sizes=self._sparse_feature_sizes, ) self._sparse_feature_sizes = model_data.get_sparse_feature_sizes() data_generator, validation_data_generator = train_utils.create_data_generators( model_data, self.component_config[BATCH_SIZES], self.component_config[EPOCHS], self.component_config[BATCH_STRATEGY], self.component_config[EVAL_NUM_EXAMPLES], self.component_config[RANDOM_SEED], ) callbacks = train_utils.create_common_callbacks( self.component_config[EPOCHS], self.component_config[TENSORBOARD_LOG_DIR], self.component_config[TENSORBOARD_LOG_LEVEL], self.tmp_checkpoint_dir, ) self.model.fit( data_generator, epochs=self.component_config[EPOCHS], validation_data=validation_data_generator, validation_freq=self.component_config[EVAL_NUM_EPOCHS], callbacks=callbacks, verbose=False, shuffle=False, # we use custom shuffle inside data generator ) self.persist() return self._resource # process helpers def _predict( self, message: Message ) -> Optional[Dict[Text, Union[tf.Tensor, Dict[Text, tf.Tensor]]]]: if self.model is None: logger.debug( f"There is no trained model for '{self.__class__.__name__}': The " f"component is either not trained or didn't receive enough training " f"data." ) return None # create session data from message and convert it into a batch of 1 model_data = self._create_model_data([message], training=False) if model_data.is_empty(): return None return self.model.run_inference(model_data) def _predict_label( self, predict_out: Optional[Dict[Text, tf.Tensor]] ) -> Tuple[Dict[Text, Any], List[Dict[Text, Any]]]: """Predicts the intent of the provided message.""" label: Dict[Text, Any] = {"name": None, "confidence": 0.0} label_ranking = [] if predict_out is None: return label, label_ranking message_sim = predict_out["i_scores"] message_sim = message_sim.flatten() # sim is a matrix # if X contains all zeros do not predict some label if message_sim.size == 0: return label, label_ranking # rank the confidences ranking_length = self.component_config[RANKING_LENGTH] renormalize = ( self.component_config[RENORMALIZE_CONFIDENCES] and self.component_config[MODEL_CONFIDENCE] == SOFTMAX ) ranked_label_indices, message_sim = train_utils.rank_and_mask( message_sim, ranking_length=ranking_length, renormalize=renormalize ) # construct the label and ranking casted_message_sim: List[float] = message_sim.tolist() # np.float to float top_label_idx = ranked_label_indices[0] label = { "name": self.index_label_id_mapping[top_label_idx], "confidence": casted_message_sim[top_label_idx], } ranking = [(idx, casted_message_sim[idx]) for idx in ranked_label_indices] label_ranking = [ {"name": self.index_label_id_mapping[label_idx], "confidence": score} for label_idx, score in ranking ] return label, label_ranking def _predict_entities( self, predict_out: Optional[Dict[Text, tf.Tensor]], message: Message ) -> List[Dict]: if predict_out is None: return [] predicted_tags, confidence_values = train_utils.entity_label_to_tags( predict_out, self._entity_tag_specs, self.component_config[BILOU_FLAG] ) entities = self.convert_predictions_into_entities( message.get(TEXT), message.get(TOKENS_NAMES[TEXT], []), predicted_tags, self.split_entities_config, confidence_values, ) entities = self.add_extractor_name(entities) entities = message.get(ENTITIES, []) + entities return entities def process(self, messages: List[Message]) -> List[Message]: """Augments the message with intents, entities, and diagnostic data.""" for message in messages: out = self._predict(message) if self.component_config[INTENT_CLASSIFICATION]: label, label_ranking = self._predict_label(out) message.set(INTENT, label, add_to_output=True) message.set("intent_ranking", label_ranking, add_to_output=True) if self.component_config[ENTITY_RECOGNITION]: entities = self._predict_entities(out, message) message.set(ENTITIES, entities, add_to_output=True) if out and self._execution_context.should_add_diagnostic_data: message.add_diagnostic_data( self._execution_context.node_name, out.get(DIAGNOSTIC_DATA) ) return messages def persist(self) -> None: """Persist this model into the passed directory.""" if self.model is None: return None with self._model_storage.write_to(self._resource) as model_path: file_name = self.__class__.__name__ tf_model_file = model_path / f"{file_name}.tf_model" rasa.shared.utils.io.create_directory_for_file(tf_model_file) if self.component_config[CHECKPOINT_MODEL] and self.tmp_checkpoint_dir: self.model.load_weights(self.tmp_checkpoint_dir / "checkpoint.tf_model") # Save an empty file to flag that this model has been # produced using checkpointing checkpoint_marker = model_path / f"{file_name}.from_checkpoint.pkl" checkpoint_marker.touch() self.model.save(str(tf_model_file)) io_utils.pickle_dump( model_path / f"{file_name}.data_example.pkl", self._data_example ) io_utils.pickle_dump( model_path / f"{file_name}.sparse_feature_sizes.pkl", self._sparse_feature_sizes, ) io_utils.pickle_dump( model_path / f"{file_name}.label_data.pkl", dict(self._label_data.data) ) io_utils.json_pickle( model_path / f"{file_name}.index_label_id_mapping.json", self.index_label_id_mapping, ) entity_tag_specs = ( [tag_spec._asdict() for tag_spec in self._entity_tag_specs] if self._entity_tag_specs else [] ) rasa.shared.utils.io.dump_obj_as_json_to_file( model_path / f"{file_name}.entity_tag_specs.json", entity_tag_specs ) @classmethod def load( cls, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, **kwargs: Any, ) -> DIETClassifier: """Loads a policy from the storage (see parent class for full docstring).""" try: with model_storage.read_from(resource) as model_path: return cls._load( model_path, config, model_storage, resource, execution_context ) except ValueError: logger.debug( f"Failed to load {cls.__class__.__name__} from model storage. Resource " f"'{resource.name}' doesn't exist." ) return cls(config, model_storage, resource, execution_context) @classmethod def _load( cls, model_path: Path, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, ) -> "DIETClassifier": """Loads the trained model from the provided directory.""" ( index_label_id_mapping, entity_tag_specs, label_data, data_example, sparse_feature_sizes, ) = cls._load_from_files(model_path) config = train_utils.update_confidence_type(config) config = train_utils.update_similarity_type(config) model = cls._load_model( entity_tag_specs, label_data, config, data_example, model_path, finetune_mode=execution_context.is_finetuning, ) return cls( config=config, model_storage=model_storage, resource=resource, execution_context=execution_context, index_label_id_mapping=index_label_id_mapping, entity_tag_specs=entity_tag_specs, model=model, sparse_feature_sizes=sparse_feature_sizes, ) @classmethod def _load_from_files( cls, model_path: Path ) -> Tuple[ Dict[int, Text], List[EntityTagSpec], RasaModelData, Dict[Text, Dict[Text, List[FeatureArray]]], Dict[Text, Dict[Text, List[int]]], ]: file_name = cls.__name__ data_example = io_utils.pickle_load( model_path / f"{file_name}.data_example.pkl" ) label_data = io_utils.pickle_load(model_path / f"{file_name}.label_data.pkl") label_data = RasaModelData(data=label_data) sparse_feature_sizes = io_utils.pickle_load( model_path / f"{file_name}.sparse_feature_sizes.pkl" ) index_label_id_mapping = io_utils.json_unpickle( model_path / f"{file_name}.index_label_id_mapping.json" ) entity_tag_specs = rasa.shared.utils.io.read_json_file( model_path / f"{file_name}.entity_tag_specs.json" ) entity_tag_specs = [ EntityTagSpec( tag_name=tag_spec["tag_name"], ids_to_tags={ int(key): value for key, value in tag_spec["ids_to_tags"].items() }, tags_to_ids={ key: int(value) for key, value in tag_spec["tags_to_ids"].items() }, num_tags=tag_spec["num_tags"], ) for tag_spec in entity_tag_specs ] # jsonpickle converts dictionary keys to strings index_label_id_mapping = { int(key): value for key, value in index_label_id_mapping.items() } return ( index_label_id_mapping, entity_tag_specs, label_data, data_example, sparse_feature_sizes, ) @classmethod def _load_model( cls, entity_tag_specs: List[EntityTagSpec], label_data: RasaModelData, config: Dict[Text, Any], data_example: Dict[Text, Dict[Text, List[FeatureArray]]], model_path: Path, finetune_mode: bool = False, ) -> "RasaModel": file_name = cls.__name__ tf_model_file = model_path / f"{file_name}.tf_model" label_key = LABEL_KEY if config[INTENT_CLASSIFICATION] else None label_sub_key = LABEL_SUB_KEY if config[INTENT_CLASSIFICATION] else None model_data_example = RasaModelData( label_key=label_key, label_sub_key=label_sub_key, data=data_example ) model = cls._load_model_class( tf_model_file, model_data_example, label_data, entity_tag_specs, config, finetune_mode=finetune_mode, ) return model @classmethod def _load_model_class( cls, tf_model_file: Text, model_data_example: RasaModelData, label_data: RasaModelData, entity_tag_specs: List[EntityTagSpec], config: Dict[Text, Any], finetune_mode: bool, ) -> "RasaModel": predict_data_example = RasaModelData( label_key=model_data_example.label_key, data={ feature_name: features for feature_name, features in model_data_example.items() if TEXT in feature_name }, ) return cls.model_class().load( tf_model_file, model_data_example, predict_data_example, data_signature=model_data_example.get_signature(), label_data=label_data, entity_tag_specs=entity_tag_specs, config=copy.deepcopy(config), finetune_mode=finetune_mode, ) def _instantiate_model_class(self, model_data: RasaModelData) -> "RasaModel": return self.model_class()( data_signature=model_data.get_signature(), label_data=self._label_data, entity_tag_specs=self._entity_tag_specs, config=self.component_config, ) class DIET(TransformerRasaModel): def __init__( self, data_signature: Dict[Text, Dict[Text, List[FeatureSignature]]], label_data: RasaModelData, entity_tag_specs: Optional[List[EntityTagSpec]], config: Dict[Text, Any], ) -> None: # create entity tag spec before calling super otherwise building the model # will fail super().__init__("DIET", config, data_signature, label_data) self._entity_tag_specs = self._ordered_tag_specs(entity_tag_specs) self.predict_data_signature = { feature_name: features for feature_name, features in data_signature.items() if TEXT in feature_name } # tf training self._create_metrics() self._update_metrics_to_log() # needed for efficient prediction self.all_labels_embed: Optional[tf.Tensor] = None self._prepare_layers() @staticmethod def _ordered_tag_specs( entity_tag_specs: Optional[List[EntityTagSpec]], ) -> List[EntityTagSpec]: """Ensure that order of entity tag specs matches CRF layer order.""" if entity_tag_specs is None: return [] crf_order = [ ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_ROLE, ENTITY_ATTRIBUTE_GROUP, ] ordered_tag_spec = [] for tag_name in crf_order: for tag_spec in entity_tag_specs: if tag_name == tag_spec.tag_name: ordered_tag_spec.append(tag_spec) return ordered_tag_spec def _check_data(self) -> None: if TEXT not in self.data_signature: raise InvalidConfigException( f"No text features specified. " f"Cannot train '{self.__class__.__name__}' model." ) if self.config[INTENT_CLASSIFICATION]: if LABEL not in self.data_signature: raise InvalidConfigException( f"No label features specified. " f"Cannot train '{self.__class__.__name__}' model." ) if self.config[SHARE_HIDDEN_LAYERS]: different_sentence_signatures = False different_sequence_signatures = False if ( SENTENCE in self.data_signature[TEXT] and SENTENCE in self.data_signature[LABEL] ): different_sentence_signatures = ( self.data_signature[TEXT][SENTENCE] != self.data_signature[LABEL][SENTENCE] ) if ( SEQUENCE in self.data_signature[TEXT] and SEQUENCE in self.data_signature[LABEL] ): different_sequence_signatures = ( self.data_signature[TEXT][SEQUENCE] != self.data_signature[LABEL][SEQUENCE] ) if different_sentence_signatures or different_sequence_signatures: raise ValueError( "If hidden layer weights are shared, data signatures " "for text_features and label_features must coincide." ) if self.config[ENTITY_RECOGNITION] and ( ENTITIES not in self.data_signature or ENTITY_ATTRIBUTE_TYPE not in self.data_signature[ENTITIES] ): logger.debug( f"You specified '{self.__class__.__name__}' to train entities, but " f"no entities are present in the training data. Skipping training of " f"entities." ) self.config[ENTITY_RECOGNITION] = False def _create_metrics(self) -> None: # self.metrics will have the same order as they are created # so create loss metrics first to output losses first self.mask_loss = tf.keras.metrics.Mean(name="m_loss") self.intent_loss = tf.keras.metrics.Mean(name="i_loss") self.entity_loss = tf.keras.metrics.Mean(name="e_loss") self.entity_group_loss = tf.keras.metrics.Mean(name="g_loss") self.entity_role_loss = tf.keras.metrics.Mean(name="r_loss") # create accuracy metrics second to output accuracies second self.mask_acc = tf.keras.metrics.Mean(name="m_acc") self.intent_acc = tf.keras.metrics.Mean(name="i_acc") self.entity_f1 = tf.keras.metrics.Mean(name="e_f1") self.entity_group_f1 = tf.keras.metrics.Mean(name="g_f1") self.entity_role_f1 = tf.keras.metrics.Mean(name="r_f1") def _update_metrics_to_log(self) -> None: debug_log_level = logging.getLogger("rasa").level == logging.DEBUG if self.config[MASKED_LM]: self.metrics_to_log.append("m_acc") if debug_log_level: self.metrics_to_log.append("m_loss") if self.config[INTENT_CLASSIFICATION]: self.metrics_to_log.append("i_acc") if debug_log_level: self.metrics_to_log.append("i_loss") if self.config[ENTITY_RECOGNITION]: for tag_spec in self._entity_tag_specs: if tag_spec.num_tags != 0: name = tag_spec.tag_name self.metrics_to_log.append(f"{name[0]}_f1") if debug_log_level: self.metrics_to_log.append(f"{name[0]}_loss") self._log_metric_info() def _log_metric_info(self) -> None: metric_name = { "t": "total", "i": "intent", "e": "entity", "m": "mask", "r": "role", "g": "group", } logger.debug("Following metrics will be logged during training: ") for metric in self.metrics_to_log: parts = metric.split("_") name = f"{metric_name[parts[0]]} {parts[1]}" logger.debug(f" {metric} ({name})") def _prepare_layers(self) -> None: # For user text, prepare layers that combine different feature types, embed # everything using a transformer and optionally also do masked language # modeling. self.text_name = TEXT self._tf_layers[ f"sequence_layer.{self.text_name}" ] = rasa_layers.RasaSequenceLayer( self.text_name, self.data_signature[self.text_name], self.config ) if self.config[MASKED_LM]: self._prepare_mask_lm_loss(self.text_name) # Intent labels are treated similarly to user text but without the transformer, # without masked language modelling, and with no dropout applied to the # individual features, only to the overall label embedding after all label # features have been combined. if self.config[INTENT_CLASSIFICATION]: self.label_name = TEXT if self.config[SHARE_HIDDEN_LAYERS] else LABEL # disable input dropout applied to sparse and dense label features label_config = self.config.copy() label_config.update( {SPARSE_INPUT_DROPOUT: False, DENSE_INPUT_DROPOUT: False} ) self._tf_layers[ f"feature_combining_layer.{self.label_name}" ] = rasa_layers.RasaFeatureCombiningLayer( self.label_name, self.label_signature[self.label_name], label_config ) self._prepare_ffnn_layer( self.label_name, self.config[HIDDEN_LAYERS_SIZES][self.label_name], self.config[DROP_RATE], ) self._prepare_label_classification_layers(predictor_attribute=TEXT) if self.config[ENTITY_RECOGNITION]: self._prepare_entity_recognition_layers() def _prepare_mask_lm_loss(self, name: Text) -> None: # for embedding predicted tokens at masked positions self._prepare_embed_layers(f"{name}_lm_mask") # for embedding the true tokens that got masked self._prepare_embed_layers(f"{name}_golden_token") # mask loss is additional loss # set scaling to False, so that it doesn't overpower other losses self._prepare_dot_product_loss(f"{name}_mask", scale_loss=False) def _create_bow( self, sequence_features: List[Union[tf.Tensor, tf.SparseTensor]], sentence_features: List[Union[tf.Tensor, tf.SparseTensor]], sequence_feature_lengths: tf.Tensor, name: Text, ) -> tf.Tensor: x, _ = self._tf_layers[f"feature_combining_layer.{name}"]( (sequence_features, sentence_features, sequence_feature_lengths), training=self._training, ) # convert to bag-of-words by summing along the sequence dimension x = tf.reduce_sum(x, axis=1) return self._tf_layers[f"ffnn.{name}"](x, self._training) def _create_all_labels(self) -> Tuple[tf.Tensor, tf.Tensor]: all_label_ids = self.tf_label_data[LABEL_KEY][LABEL_SUB_KEY][0] sequence_feature_lengths = self._get_sequence_feature_lengths( self.tf_label_data, LABEL ) x = self._create_bow( self.tf_label_data[LABEL][SEQUENCE], self.tf_label_data[LABEL][SENTENCE], sequence_feature_lengths, self.label_name, ) all_labels_embed = self._tf_layers[f"embed.{LABEL}"](x) return all_label_ids, all_labels_embed def _mask_loss( self, outputs: tf.Tensor, inputs: tf.Tensor, seq_ids: tf.Tensor, mlm_mask_boolean: tf.Tensor, name: Text, ) -> tf.Tensor: # make sure there is at least one element in the mask mlm_mask_boolean = tf.cond( tf.reduce_any(mlm_mask_boolean), lambda: mlm_mask_boolean, lambda: tf.scatter_nd([[0, 0, 0]], [True], tf.shape(mlm_mask_boolean)), ) mlm_mask_boolean = tf.squeeze(mlm_mask_boolean, -1) # Pick elements that were masked, throwing away the batch & sequence dimension # and effectively switching from shape (batch_size, sequence_length, units) to # (num_masked_elements, units). outputs = tf.boolean_mask(outputs, mlm_mask_boolean) inputs = tf.boolean_mask(inputs, mlm_mask_boolean) ids = tf.boolean_mask(seq_ids, mlm_mask_boolean) tokens_predicted_embed = self._tf_layers[f"embed.{name}_lm_mask"](outputs) tokens_true_embed = self._tf_layers[f"embed.{name}_golden_token"](inputs) # To limit the otherwise computationally expensive loss calculation, we # constrain the label space in MLM (i.e. token space) to only those tokens that # were masked in this batch. Hence the reduced list of token embeddings # (tokens_true_embed) and the reduced list of labels (ids) are passed as # all_labels_embed and all_labels, respectively. In the future, we could be less # restrictive and construct a slightly bigger label space which could include # tokens not masked in the current batch too. return self._tf_layers[f"loss.{name}_mask"]( inputs_embed=tokens_predicted_embed, labels_embed=tokens_true_embed, labels=ids, all_labels_embed=tokens_true_embed, all_labels=ids, ) def _calculate_label_loss( self, text_features: tf.Tensor, label_features: tf.Tensor, label_ids: tf.Tensor ) -> tf.Tensor: all_label_ids, all_labels_embed = self._create_all_labels() text_embed = self._tf_layers[f"embed.{TEXT}"](text_features) label_embed = self._tf_layers[f"embed.{LABEL}"](label_features) return self._tf_layers[f"loss.{LABEL}"]( text_embed, label_embed, label_ids, all_labels_embed, all_label_ids ) def batch_loss( self, batch_in: Union[Tuple[tf.Tensor], Tuple[np.ndarray]] ) -> tf.Tensor: """Calculates the loss for the given batch. Args: batch_in: The batch. Returns: The loss of the given batch. """ tf_batch_data = self.batch_to_model_data_format(batch_in, self.data_signature) sequence_feature_lengths = self._get_sequence_feature_lengths( tf_batch_data, TEXT ) ( text_transformed, text_in, mask_combined_sequence_sentence, text_seq_ids, mlm_mask_boolean_text, _, ) = self._tf_layers[f"sequence_layer.{self.text_name}"]( ( tf_batch_data[TEXT][SEQUENCE], tf_batch_data[TEXT][SENTENCE], sequence_feature_lengths, ), training=self._training, ) losses = [] # Lengths of sequences in case of sentence-level features are always 1, but they # can effectively be 0 if sentence-level features aren't present. sentence_feature_lengths = self._get_sentence_feature_lengths( tf_batch_data, TEXT ) combined_sequence_sentence_feature_lengths = ( sequence_feature_lengths + sentence_feature_lengths ) if self.config[MASKED_LM]: loss, acc = self._mask_loss( text_transformed, text_in, text_seq_ids, mlm_mask_boolean_text, TEXT ) self.mask_loss.update_state(loss) self.mask_acc.update_state(acc) losses.append(loss) if self.config[INTENT_CLASSIFICATION]: loss = self._batch_loss_intent( combined_sequence_sentence_feature_lengths, text_transformed, tf_batch_data, ) losses.append(loss) if self.config[ENTITY_RECOGNITION]: losses += self._batch_loss_entities( mask_combined_sequence_sentence, sequence_feature_lengths, text_transformed, tf_batch_data, ) return tf.math.add_n(losses) def _batch_loss_intent( self, combined_sequence_sentence_feature_lengths_text: tf.Tensor, text_transformed: tf.Tensor, tf_batch_data: Dict[Text, Dict[Text, List[tf.Tensor]]], ) -> tf.Tensor: # get sentence features vector for intent classification sentence_vector = self._last_token( text_transformed, combined_sequence_sentence_feature_lengths_text ) sequence_feature_lengths_label = self._get_sequence_feature_lengths( tf_batch_data, LABEL ) label_ids = tf_batch_data[LABEL_KEY][LABEL_SUB_KEY][0] label = self._create_bow( tf_batch_data[LABEL][SEQUENCE], tf_batch_data[LABEL][SENTENCE], sequence_feature_lengths_label, self.label_name, ) loss, acc = self._calculate_label_loss(sentence_vector, label, label_ids) self._update_label_metrics(loss, acc) return loss def _update_label_metrics(self, loss: tf.Tensor, acc: tf.Tensor) -> None: self.intent_loss.update_state(loss) self.intent_acc.update_state(acc) def _batch_loss_entities( self, mask_combined_sequence_sentence: tf.Tensor, sequence_feature_lengths: tf.Tensor, text_transformed: tf.Tensor, tf_batch_data: Dict[Text, Dict[Text, List[tf.Tensor]]], ) -> List[tf.Tensor]: losses = [] entity_tags = None for tag_spec in self._entity_tag_specs: if tag_spec.num_tags == 0: continue tag_ids = tf_batch_data[ENTITIES][tag_spec.tag_name][0] # add a zero (no entity) for the sentence features to match the shape of # inputs tag_ids = tf.pad(tag_ids, [[0, 0], [0, 1], [0, 0]]) loss, f1, _logits = self._calculate_entity_loss( text_transformed, tag_ids, mask_combined_sequence_sentence, sequence_feature_lengths, tag_spec.tag_name, entity_tags, ) if tag_spec.tag_name == ENTITY_ATTRIBUTE_TYPE: # use the entity tags as additional input for the role # and group CRF entity_tags = tf.one_hot( tf.cast(tag_ids[:, :, 0], tf.int32), depth=tag_spec.num_tags ) self._update_entity_metrics(loss, f1, tag_spec.tag_name) losses.append(loss) return losses def _update_entity_metrics( self, loss: tf.Tensor, f1: tf.Tensor, tag_name: Text ) -> None: if tag_name == ENTITY_ATTRIBUTE_TYPE: self.entity_loss.update_state(loss) self.entity_f1.update_state(f1) elif tag_name == ENTITY_ATTRIBUTE_GROUP: self.entity_group_loss.update_state(loss) self.entity_group_f1.update_state(f1) elif tag_name == ENTITY_ATTRIBUTE_ROLE: self.entity_role_loss.update_state(loss) self.entity_role_f1.update_state(f1) def prepare_for_predict(self) -> None: """Prepares the model for prediction.""" if self.config[INTENT_CLASSIFICATION]: _, self.all_labels_embed = self._create_all_labels() def batch_predict( self, batch_in: Union[Tuple[tf.Tensor], Tuple[np.ndarray]] ) -> Dict[Text, tf.Tensor]: """Predicts the output of the given batch. Args: batch_in: The batch. Returns: The output to predict. """ tf_batch_data = self.batch_to_model_data_format( batch_in, self.predict_data_signature ) sequence_feature_lengths = self._get_sequence_feature_lengths( tf_batch_data, TEXT ) sentence_feature_lengths = self._get_sentence_feature_lengths( tf_batch_data, TEXT ) text_transformed, _, _, _, _, attention_weights = self._tf_layers[ f"sequence_layer.{self.text_name}" ]( ( tf_batch_data[TEXT][SEQUENCE], tf_batch_data[TEXT][SENTENCE], sequence_feature_lengths, ), training=self._training, ) predictions = { DIAGNOSTIC_DATA: { "attention_weights": attention_weights, "text_transformed": text_transformed, } } if self.config[INTENT_CLASSIFICATION]: predictions.update( self._batch_predict_intents( sequence_feature_lengths + sentence_feature_lengths, text_transformed, ) ) if self.config[ENTITY_RECOGNITION]: predictions.update( self._batch_predict_entities(sequence_feature_lengths, text_transformed) ) return predictions def _batch_predict_entities( self, sequence_feature_lengths: tf.Tensor, text_transformed: tf.Tensor ) -> Dict[Text, tf.Tensor]: predictions: Dict[Text, tf.Tensor] = {} entity_tags = None for tag_spec in self._entity_tag_specs: # skip crf layer if it was not trained if tag_spec.num_tags == 0: continue name = tag_spec.tag_name _input = text_transformed if entity_tags is not None: _tags = self._tf_layers[f"embed.{name}.tags"](entity_tags) _input = tf.concat([_input, _tags], axis=-1) _logits = self._tf_layers[f"embed.{name}.logits"](_input) pred_ids, confidences = self._tf_layers[f"crf.{name}"]( _logits, sequence_feature_lengths ) predictions[f"e_{name}_ids"] = pred_ids predictions[f"e_{name}_scores"] = confidences if name == ENTITY_ATTRIBUTE_TYPE: # use the entity tags as additional input for the role # and group CRF entity_tags = tf.one_hot( tf.cast(pred_ids, tf.int32), depth=tag_spec.num_tags ) return predictions def _batch_predict_intents( self, combined_sequence_sentence_feature_lengths: tf.Tensor, text_transformed: tf.Tensor, ) -> Dict[Text, tf.Tensor]: if self.all_labels_embed is None: raise ValueError( "The model was not prepared for prediction. " "Call `prepare_for_predict` first." ) # get sentence feature vector for intent classification sentence_vector = self._last_token( text_transformed, combined_sequence_sentence_feature_lengths ) sentence_vector_embed = self._tf_layers[f"embed.{TEXT}"](sentence_vector) _, scores = self._tf_layers[ f"loss.{LABEL}" ].get_similarities_and_confidences_from_embeddings( sentence_vector_embed[:, tf.newaxis, :], self.all_labels_embed[tf.newaxis, :, :], ) return {"i_scores": scores}

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from __future__ import annotations import copy import logging from collections import defaultdict from pathlib import Path from rasa.nlu.featurizers.featurizer import Featurizer import numpy as np import scipy.sparse import tensorflow as tf from typing import Any, Dict, List, Optional, Text, Tuple, Union, Type from rasa.engine.graph import ExecutionContext, GraphComponent from rasa.engine.recipes.default_recipe import DefaultV1Recipe from rasa.engine.storage.resource import Resource from rasa.engine.storage.storage import ModelStorage from rasa.nlu.extractors.extractor import EntityExtractorMixin from rasa.nlu.classifiers.classifier import IntentClassifier import rasa.shared.utils.io import rasa.utils.io as io_utils import rasa.nlu.utils.bilou_utils as bilou_utils from rasa.shared.constants import DIAGNOSTIC_DATA from rasa.nlu.extractors.extractor import EntityTagSpec from rasa.nlu.classifiers import LABEL_RANKING_LENGTH from rasa.utils import train_utils from rasa.utils.tensorflow import rasa_layers from rasa.utils.tensorflow.models import RasaModel, TransformerRasaModel from rasa.utils.tensorflow.model_data import ( RasaModelData, FeatureSignature, FeatureArray, ) from rasa.nlu.constants import TOKENS_NAMES, DEFAULT_TRANSFORMER_SIZE from rasa.shared.nlu.constants import ( SPLIT_ENTITIES_BY_COMMA_DEFAULT_VALUE, TEXT, INTENT, INTENT_RESPONSE_KEY, ENTITIES, ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_GROUP, ENTITY_ATTRIBUTE_ROLE, NO_ENTITY_TAG, SPLIT_ENTITIES_BY_COMMA, ) from rasa.shared.exceptions import InvalidConfigException from rasa.shared.nlu.training_data.training_data import TrainingData from rasa.shared.nlu.training_data.message import Message from rasa.utils.tensorflow.constants import ( LABEL, IDS, HIDDEN_LAYERS_SIZES, RENORMALIZE_CONFIDENCES, SHARE_HIDDEN_LAYERS, TRANSFORMER_SIZE, NUM_TRANSFORMER_LAYERS, NUM_HEADS, BATCH_SIZES, BATCH_STRATEGY, EPOCHS, RANDOM_SEED, LEARNING_RATE, RANKING_LENGTH, LOSS_TYPE, SIMILARITY_TYPE, NUM_NEG, SPARSE_INPUT_DROPOUT, DENSE_INPUT_DROPOUT, MASKED_LM, ENTITY_RECOGNITION, TENSORBOARD_LOG_DIR, INTENT_CLASSIFICATION, EVAL_NUM_EXAMPLES, EVAL_NUM_EPOCHS, UNIDIRECTIONAL_ENCODER, DROP_RATE, DROP_RATE_ATTENTION, CONNECTION_DENSITY, NEGATIVE_MARGIN_SCALE, REGULARIZATION_CONSTANT, SCALE_LOSS, USE_MAX_NEG_SIM, MAX_NEG_SIM, MAX_POS_SIM, EMBEDDING_DIMENSION, BILOU_FLAG, KEY_RELATIVE_ATTENTION, VALUE_RELATIVE_ATTENTION, MAX_RELATIVE_POSITION, AUTO, BALANCED, CROSS_ENTROPY, TENSORBOARD_LOG_LEVEL, CONCAT_DIMENSION, FEATURIZERS, CHECKPOINT_MODEL, SEQUENCE, SENTENCE, SEQUENCE_LENGTH, DENSE_DIMENSION, MASK, CONSTRAIN_SIMILARITIES, MODEL_CONFIDENCE, SOFTMAX, ) logger = logging.getLogger(__name__) SPARSE = "sparse" DENSE = "dense" LABEL_KEY = LABEL LABEL_SUB_KEY = IDS POSSIBLE_TAGS = [ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_ROLE, ENTITY_ATTRIBUTE_GROUP] @DefaultV1Recipe.register( [ DefaultV1Recipe.ComponentType.INTENT_CLASSIFIER, DefaultV1Recipe.ComponentType.ENTITY_EXTRACTOR, ], is_trainable=True, ) class DIETClassifier(GraphComponent, IntentClassifier, EntityExtractorMixin): @classmethod def required_components(cls) -> List[Type]: return [Featurizer] @staticmethod def get_default_config() -> Dict[Text, Any]: return { S: {TEXT: [], LABEL: []}, SHARE_HIDDEN_LAYERS: False, TRANSFORMER_SIZE: DEFAULT_TRANSFORMER_SIZE, NUM_TRANSFORMER_LAYERS: 2, NUM_HEADS: 4, KEY_RELATIVE_ATTENTION: False, VALUE_RELATIVE_ATTENTION: False, MAX_RELATIVE_POSITION: 5, UNIDIRECTIONAL_ENCODER: False, H_SIZES: [64, 256], BATCH_STRATEGY: BALANCED, EPOCHS: 300, RANDOM_SEED: None, LEARNING_RATE: 0.001, DENSE_DIMENSION: {TEXT: 128, LABEL: 20}, CONCAT_DIMENSION: {TEXT: 128, LABEL: 20}, NUM_NEG: 20, SIMILARITY_TYPE: AUTO, LOSS_TYPE: CROSS_ENTROPY, RANKING_LENGTH: LABEL_RANKING_LENGTH, MAX_POS_SIM: 0.8, MAX_NEG_SIM: -0.4, USE_MAX_NEG_SIM: True, SCALE_LOSS: False, 2, NEGATIVE_MARGIN_SCALE: 0.8, DROP_RATE: 0.2, DROP_RATE_ATTENTION: 0, CONNECTION_DENSITY: 0.2, SPARSE_INPUT_DROPOUT: True, DENSE_INPUT_DROPOUT: True, _EPOCHS: 20, EVAL_NUM_EXAMPLES: 0, ENT_CLASSIFICATION: True, ENTITY_RECOGNITION: True, MASKED_LM: False, BILOU_FLAG: True, TENSORBOARD_LOG_DIR: None, TENSORBOARD_LOG_LEVEL: "epoch", CHECKPOINT_MODEL: False, FEATURIZERS: [], SPLIT_ENTITIES_BY_COMMA: True, # If 'True' applies sigmoid on all similarity terms and adds # it to the loss function to ensure that similarity values are # approximately bounded. Used inside cross-entropy loss only. CONSTRAIN_SIMILARITIES: False, # Model confidence to be returned during inference. Currently, the only # possible value is `softmax`. MODEL_CONFIDENCE: SOFTMAX, # Determines whether the confidences of the chosen top intents should be # renormalized so that they sum up to 1. By default, we do not renormalize # and return the confidences for the top intents as is. # Note that renormalization only makes sense if confidences are generated # via `softmax`. RENORMALIZE_CONFIDENCES: False, } def __init__( self, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, index_label_id_mapping: Optional[Dict[int, Text]] = None, entity_tag_specs: Optional[List[EntityTagSpec]] = None, model: Optional[RasaModel] = None, sparse_feature_sizes: Optional[Dict[Text, Dict[Text, List[int]]]] = None, ) -> None: if EPOCHS not in config: rasa.shared.utils.io.raise_warning( f"Please configure the number of '{EPOCHS}' in your configuration file." f" We will change the default value of '{EPOCHS}' in the future to 1. " ) self.component_config = config self._model_storage = model_storage self._resource = resource self._execution_context = execution_context self._check_config_parameters() # transform numbers to labels self.index_label_id_mapping = index_label_id_mapping or {} self._entity_tag_specs = entity_tag_specs self.model = model self.tmp_checkpoint_dir = None if self.component_config[CHECKPOINT_MODEL]: self.tmp_checkpoint_dir = Path(rasa.utils.io.create_temporary_directory()) self._label_data: Optional[RasaModelData] = None self._data_example: Optional[Dict[Text, Dict[Text, List[FeatureArray]]]] = None self.split_entities_config = rasa.utils.train_utils.init_split_entities( self.component_config[SPLIT_ENTITIES_BY_COMMA], SPLIT_ENTITIES_BY_COMMA_DEFAULT_VALUE, ) self.finetune_mode = self._execution_context.is_finetuning self._sparse_feature_sizes = sparse_feature_sizes # init helpers def _check_masked_lm(self) -> None: if ( self.component_config[MASKED_LM] and self.component_config[NUM_TRANSFORMER_LAYERS] == 0 ): raise ValueError( f"If number of transformer layers is 0, " f"'{MASKED_LM}' option should be 'False'." ) def _check_share_hidden_layers_sizes(self) -> None: if self.component_config.get(SHARE_HIDDEN_LAYERS): first_hidden_layer_sizes = next( iter(self.component_config[HIDDEN_LAYERS_SIZES].values()) ) # check that all hidden layer sizes are the same identical_hidden_layer_sizes = all( current_hidden_layer_sizes == first_hidden_layer_sizes for current_hidden_layer_sizes in self.component_config[ HIDDEN_LAYERS_SIZES ].values() ) if not identical_hidden_layer_sizes: raise ValueError( f"If hidden layer weights are shared, " f"{HIDDEN_LAYERS_SIZES} must coincide." ) def _check_config_parameters(self) -> None: self.component_config = train_utils.check_deprecated_options( self.component_config ) self._check_masked_lm() self._check_share_hidden_layers_sizes() self.component_config = train_utils.update_confidence_type( self.component_config ) train_utils.validate_configuration_settings(self.component_config) self.component_config = train_utils.update_similarity_type( self.component_config ) self.component_config = train_utils.update_evaluation_parameters( self.component_config ) @classmethod def create( cls, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, ) -> DIETClassifier: return cls(config, model_storage, resource, execution_context) @property def label_key(self) -> Optional[Text]: return LABEL_KEY if self.component_config[INTENT_CLASSIFICATION] else None @property def label_sub_key(self) -> Optional[Text]: return LABEL_SUB_KEY if self.component_config[INTENT_CLASSIFICATION] else None @staticmethod def model_class() -> Type[RasaModel]: return DIET # training data helpers: @staticmethod def _label_id_index_mapping( training_data: TrainingData, attribute: Text ) -> Dict[Text, int]: distinct_label_ids = { example.get(attribute) for example in training_data.intent_examples } - {None} return { label_id: idx for idx, label_id in enumerate(sorted(distinct_label_ids)) } @staticmethod def _invert_mapping(mapping: Dict) -> Dict: return {value: key for key, value in mapping.items()} def _create_entity_tag_specs( self, training_data: TrainingData ) -> List[EntityTagSpec]: _tag_specs = [] for tag_name in POSSIBLE_TAGS: if self.component_config[BILOU_FLAG]: tag_id_index_mapping = bilou_utils.build_tag_id_dict( training_data, tag_name ) else: tag_id_index_mapping = self._tag_id_index_mapping_for( tag_name, training_data ) if tag_id_index_mapping: _tag_specs.append( EntityTagSpec( tag_name=tag_name, tags_to_ids=tag_id_index_mapping, ids_to_tags=self._invert_mapping(tag_id_index_mapping), num_tags=len(tag_id_index_mapping), ) ) return _tag_specs @staticmethod def _tag_id_index_mapping_for( tag_name: Text, training_data: TrainingData ) -> Optional[Dict[Text, int]]: if tag_name == ENTITY_ATTRIBUTE_ROLE: distinct_tags = training_data.entity_roles elif tag_name == ENTITY_ATTRIBUTE_GROUP: distinct_tags = training_data.entity_groups else: distinct_tags = training_data.entities distinct_tags = distinct_tags - {NO_ENTITY_TAG} - {None} if not distinct_tags: return None tag_id_dict = { tag_id: idx for idx, tag_id in enumerate(sorted(distinct_tags), 1) } # NO_ENTITY_TAG corresponds to non-entity which should correspond to 0 index # needed for correct prediction for padding tag_id_dict[NO_ENTITY_TAG] = 0 return tag_id_dict @staticmethod def _find_example_for_label( label: Text, examples: List[Message], attribute: Text ) -> Optional[Message]: for ex in examples: if ex.get(attribute) == label: return ex return None def _check_labels_features_exist( self, labels_example: List[Message], attribute: Text ) -> bool: return all( label_example.features_present( attribute, self.component_config[FEATURIZERS] ) for label_example in labels_example ) def _extract_features( self, message: Message, attribute: Text ) -> Dict[Text, Union[scipy.sparse.spmatrix, np.ndarray]]: ( sparse_sequence_features, sparse_sentence_features, ) = message.get_sparse_features(attribute, self.component_config[FEATURIZERS]) dense_sequence_features, dense_sentence_features = message.get_dense_features( attribute, self.component_config[FEATURIZERS] ) if dense_sequence_features is not None and sparse_sequence_features is not None: if ( dense_sequence_features.features.shape[0] != sparse_sequence_features.features.shape[0] ): raise ValueError( f"Sequence dimensions for sparse and dense sequence features " f"don't coincide in '{message.get(TEXT)}'" f"for attribute '{attribute}'." ) if dense_sentence_features is not None and sparse_sentence_features is not None: if ( dense_sentence_features.features.shape[0] != sparse_sentence_features.features.shape[0] ): raise ValueError( f"Sequence dimensions for sparse and dense sentence features " f"don't coincide in '{message.get(TEXT)}'" f"for attribute '{attribute}'." ) # If we don't use the transformer and we don't want to do entity recognition, # to speed up training take only the sentence features as feature vector. # We would not make use of the sequence anyway in this setup. Carrying over # those features to the actual training process takes quite some time. if ( self.component_config[NUM_TRANSFORMER_LAYERS] == 0 and not self.component_config[ENTITY_RECOGNITION] and attribute not in [INTENT, INTENT_RESPONSE_KEY] ): sparse_sequence_features = None dense_sequence_features = None out = {} if sparse_sentence_features is not None: out[f"{SPARSE}_{SENTENCE}"] = sparse_sentence_features.features if sparse_sequence_features is not None: out[f"{SPARSE}_{SEQUENCE}"] = sparse_sequence_features.features if dense_sentence_features is not None: out[f"{DENSE}_{SENTENCE}"] = dense_sentence_features.features if dense_sequence_features is not None: out[f"{DENSE}_{SEQUENCE}"] = dense_sequence_features.features return out def _check_input_dimension_consistency(self, model_data: RasaModelData) -> None: if self.component_config.get(SHARE_HIDDEN_LAYERS): num_text_sentence_features = model_data.number_of_units(TEXT, SENTENCE) num_label_sentence_features = model_data.number_of_units(LABEL, SENTENCE) num_text_sequence_features = model_data.number_of_units(TEXT, SEQUENCE) num_label_sequence_features = model_data.number_of_units(LABEL, SEQUENCE) if (0 < num_text_sentence_features != num_label_sentence_features > 0) or ( 0 < num_text_sequence_features != num_label_sequence_features > 0 ): raise ValueError( "If embeddings are shared text features and label features " "must coincide. Check the output dimensions of previous components." ) def _extract_labels_precomputed_features( self, label_examples: List[Message], attribute: Text = INTENT ) -> Tuple[List[FeatureArray], List[FeatureArray]]: features = defaultdict(list) for e in label_examples: label_features = self._extract_features(e, attribute) for feature_key, feature_value in label_features.items(): features[feature_key].append(feature_value) sequence_features = [] sentence_features = [] for feature_name, feature_value in features.items(): if SEQUENCE in feature_name: sequence_features.append( FeatureArray(np.array(feature_value), number_of_dimensions=3) ) else: sentence_features.append( FeatureArray(np.array(feature_value), number_of_dimensions=3) ) return sequence_features, sentence_features @staticmethod def _compute_default_label_features( labels_example: List[Message], ) -> List[FeatureArray]: logger.debug("No label features found. Computing default label features.") eye_matrix = np.eye(len(labels_example), dtype=np.float32) # add sequence dimension to one-hot labels return [ FeatureArray( np.array([np.expand_dims(a, 0) for a in eye_matrix]), number_of_dimensions=3, ) ] def _create_label_data( self, training_data: TrainingData, label_id_dict: Dict[Text, int], attribute: Text, ) -> RasaModelData: # Collect one example for each label labels_idx_examples = [] for label_name, idx in label_id_dict.items(): label_example = self._find_example_for_label( label_name, training_data.intent_examples, attribute ) labels_idx_examples.append((idx, label_example)) # Sort the list of tuples based on label_idx labels_idx_examples = sorted(labels_idx_examples, key=lambda x: x[0]) labels_example = [example for (_, example) in labels_idx_examples] # Collect features, precomputed if they exist, else compute on the fly if self._check_labels_features_exist(labels_example, attribute): ( sequence_features, sentence_features, ) = self._extract_labels_precomputed_features(labels_example, attribute) else: sequence_features = None sentence_features = self._compute_default_label_features(labels_example) label_data = RasaModelData() label_data.add_features(LABEL, SEQUENCE, sequence_features) label_data.add_features(LABEL, SENTENCE, sentence_features) if label_data.does_feature_not_exist( LABEL, SENTENCE ) and label_data.does_feature_not_exist(LABEL, SEQUENCE): raise ValueError( "No label features are present. Please check your configuration file." ) label_ids = np.array([idx for (idx, _) in labels_idx_examples]) # explicitly add last dimension to label_ids # to track correctly dynamic sequences label_data.add_features( LABEL_KEY, LABEL_SUB_KEY, [FeatureArray(np.expand_dims(label_ids, -1), number_of_dimensions=2)], ) label_data.add_lengths(LABEL, SEQUENCE_LENGTH, LABEL, SEQUENCE) return label_data def _use_default_label_features(self, label_ids: np.ndarray) -> List[FeatureArray]: feature_arrays: List[FeatureArray] = self._label_data.get(LABEL, SENTENCE) all_label_features = feature_arrays[0] return [ FeatureArray( np.array([all_label_features[label_id] for label_id in label_ids]), number_of_dimensions=all_label_features.number_of_dimensions, ) ] def _create_model_data( self, training_data: List[Message], label_id_dict: Optional[Dict[Text, int]] = None, label_attribute: Optional[Text] = None, training: bool = True, ) -> RasaModelData: from rasa.utils.tensorflow import model_data_utils attributes_to_consider = [TEXT] if training and self.component_config[INTENT_CLASSIFICATION]: # we don't have any intent labels during prediction, just add them during attributes_to_consider.append(label_attribute) if ( training and self.component_config[ENTITY_RECOGNITION] and self._entity_tag_specs ): attributes_to_consider.append(ENTITIES) if training and label_attribute is not None: training_data = [ example for example in training_data if label_attribute in example.data ] training_data = [ message for message in training_data if message.features_present( attribute=TEXT, featurizers=self.component_config.get(FEATURIZERS) ) ] if not training_data: return RasaModelData() ( features_for_examples, sparse_feature_sizes, ) = model_data_utils.featurize_training_examples( training_data, attributes_to_consider, entity_tag_specs=self._entity_tag_specs, featurizers=self.component_config[FEATURIZERS], bilou_tagging=self.component_config[BILOU_FLAG], ) attribute_data, _ = model_data_utils.convert_to_data_format( features_for_examples, consider_dialogue_dimension=False ) model_data = RasaModelData( label_key=self.label_key, label_sub_key=self.label_sub_key ) model_data.add_data(attribute_data) model_data.add_lengths(TEXT, SEQUENCE_LENGTH, TEXT, SEQUENCE) # feature sizes of label attributes. That's why we remove them. sparse_feature_sizes = self._remove_label_sparse_feature_sizes( sparse_feature_sizes=sparse_feature_sizes, label_attribute=label_attribute ) model_data.add_sparse_feature_sizes(sparse_feature_sizes) self._add_label_features( model_data, training_data, label_attribute, label_id_dict, training ) model_data.sort() return model_data @staticmethod def _remove_label_sparse_feature_sizes( sparse_feature_sizes: Dict[Text, Dict[Text, List[int]]], label_attribute: Optional[Text] = None, ) -> Dict[Text, Dict[Text, List[int]]]: if label_attribute in sparse_feature_sizes: del sparse_feature_sizes[label_attribute] return sparse_feature_sizes def _add_label_features( self, model_data: RasaModelData, training_data: List[Message], label_attribute: Text, label_id_dict: Dict[Text, int], training: bool = True, ) -> None: label_ids = [] if training and self.component_config[INTENT_CLASSIFICATION]: for example in training_data: if example.get(label_attribute): label_ids.append(label_id_dict[example.get(label_attribute)]) model_data.add_features( LABEL_KEY, LABEL_SUB_KEY, [FeatureArray(np.expand_dims(label_ids, -1), number_of_dimensions=2)], ) if ( label_attribute and model_data.does_feature_not_exist(label_attribute, SENTENCE) and model_data.does_feature_not_exist(label_attribute, SEQUENCE) ): model_data.add_features( LABEL, SENTENCE, self._use_default_label_features(np.array(label_ids)) ) model_data.update_key(label_attribute, SENTENCE, LABEL, SENTENCE) model_data.update_key(label_attribute, SEQUENCE, LABEL, SEQUENCE) model_data.update_key(label_attribute, MASK, LABEL, MASK) model_data.add_lengths(LABEL, SEQUENCE_LENGTH, LABEL, SEQUENCE) def preprocess_train_data(self, training_data: TrainingData) -> RasaModelData: if self.component_config[BILOU_FLAG]: bilou_utils.apply_bilou_schema(training_data) label_id_index_mapping = self._label_id_index_mapping( training_data, attribute=INTENT ) if not label_id_index_mapping: return RasaModelData() self.index_label_id_mapping = self._invert_mapping(label_id_index_mapping) self._label_data = self._create_label_data( training_data, label_id_index_mapping, attribute=INTENT ) self._entity_tag_specs = self._create_entity_tag_specs(training_data) label_attribute = ( INTENT if self.component_config[INTENT_CLASSIFICATION] else None ) model_data = self._create_model_data( training_data.nlu_examples, label_id_index_mapping, label_attribute=label_attribute, ) self._check_input_dimension_consistency(model_data) return model_data @staticmethod def _check_enough_labels(model_data: RasaModelData) -> bool: return len(np.unique(model_data.get(LABEL_KEY, LABEL_SUB_KEY))) >= 2 def train(self, training_data: TrainingData) -> Resource: model_data = self.preprocess_train_data(training_data) if model_data.is_empty(): logger.debug( f"Cannot train '{self.__class__.__name__}'. No data was provided. " f"Skipping training of the classifier." ) return self._resource if not self.model and self.finetune_mode: raise rasa.shared.exceptions.InvalidParameterException( f"{self.__class__.__name__} was instantiated " f"with `model=None` and `finetune_mode=True`. " f"This is not a valid combination as the component " f"needs an already instantiated and trained model " f"to continue training in finetune mode." ) if self.component_config.get(INTENT_CLASSIFICATION): if not self._check_enough_labels(model_data): logger.error( f"Cannot train '{self.__class__.__name__}'. " f"Need at least 2 different intent classes. " f"Skipping training of classifier." ) return self._resource if self.component_config.get(ENTITY_RECOGNITION): self.check_correct_entity_annotations(training_data) self._data_example = model_data.first_data_example() if not self.finetune_mode: self.model = self._instantiate_model_class(model_data) self.model.compile( optimizer=tf.keras.optimizers.Adam(self.component_config[LEARNING_RATE]) ) else: self.model.adjust_for_incremental_training( data_example=self._data_example, new_sparse_feature_sizes=model_data.get_sparse_feature_sizes(), old_sparse_feature_sizes=self._sparse_feature_sizes, ) self._sparse_feature_sizes = model_data.get_sparse_feature_sizes() data_generator, validation_data_generator = train_utils.create_data_generators( model_data, self.component_config[BATCH_SIZES], self.component_config[EPOCHS], self.component_config[BATCH_STRATEGY], self.component_config[EVAL_NUM_EXAMPLES], self.component_config[RANDOM_SEED], ) callbacks = train_utils.create_common_callbacks( self.component_config[EPOCHS], self.component_config[TENSORBOARD_LOG_DIR], self.component_config[TENSORBOARD_LOG_LEVEL], self.tmp_checkpoint_dir, ) self.model.fit( data_generator, epochs=self.component_config[EPOCHS], validation_data=validation_data_generator, validation_freq=self.component_config[EVAL_NUM_EPOCHS], callbacks=callbacks, verbose=False, shuffle=False, ) self.persist() return self._resource def _predict( self, message: Message ) -> Optional[Dict[Text, Union[tf.Tensor, Dict[Text, tf.Tensor]]]]: if self.model is None: logger.debug( f"There is no trained model for '{self.__class__.__name__}': The " f"component is either not trained or didn't receive enough training " f"data." ) return None # create session data from message and convert it into a batch of 1 model_data = self._create_model_data([message], training=False) if model_data.is_empty(): return None return self.model.run_inference(model_data) def _predict_label( self, predict_out: Optional[Dict[Text, tf.Tensor]] ) -> Tuple[Dict[Text, Any], List[Dict[Text, Any]]]: label: Dict[Text, Any] = {"name": None, "confidence": 0.0} label_ranking = [] if predict_out is None: return label, label_ranking message_sim = predict_out["i_scores"] message_sim = message_sim.flatten() # sim is a matrix # if X contains all zeros do not predict some label if message_sim.size == 0: return label, label_ranking # rank the confidences ranking_length = self.component_config[RANKING_LENGTH] renormalize = ( self.component_config[RENORMALIZE_CONFIDENCES] and self.component_config[MODEL_CONFIDENCE] == SOFTMAX ) ranked_label_indices, message_sim = train_utils.rank_and_mask( message_sim, ranking_length=ranking_length, renormalize=renormalize ) # construct the label and ranking casted_message_sim: List[float] = message_sim.tolist() # np.float to float top_label_idx = ranked_label_indices[0] label = { "name": self.index_label_id_mapping[top_label_idx], "confidence": casted_message_sim[top_label_idx], } ranking = [(idx, casted_message_sim[idx]) for idx in ranked_label_indices] label_ranking = [ {"name": self.index_label_id_mapping[label_idx], "confidence": score} for label_idx, score in ranking ] return label, label_ranking def _predict_entities( self, predict_out: Optional[Dict[Text, tf.Tensor]], message: Message ) -> List[Dict]: if predict_out is None: return [] predicted_tags, confidence_values = train_utils.entity_label_to_tags( predict_out, self._entity_tag_specs, self.component_config[BILOU_FLAG] ) entities = self.convert_predictions_into_entities( message.get(TEXT), message.get(TOKENS_NAMES[TEXT], []), predicted_tags, self.split_entities_config, confidence_values, ) entities = self.add_extractor_name(entities) entities = message.get(ENTITIES, []) + entities return entities def process(self, messages: List[Message]) -> List[Message]: for message in messages: out = self._predict(message) if self.component_config[INTENT_CLASSIFICATION]: label, label_ranking = self._predict_label(out) message.set(INTENT, label, add_to_output=True) message.set("intent_ranking", label_ranking, add_to_output=True) if self.component_config[ENTITY_RECOGNITION]: entities = self._predict_entities(out, message) message.set(ENTITIES, entities, add_to_output=True) if out and self._execution_context.should_add_diagnostic_data: message.add_diagnostic_data( self._execution_context.node_name, out.get(DIAGNOSTIC_DATA) ) return messages def persist(self) -> None: if self.model is None: return None with self._model_storage.write_to(self._resource) as model_path: file_name = self.__class__.__name__ tf_model_file = model_path / f"{file_name}.tf_model" rasa.shared.utils.io.create_directory_for_file(tf_model_file) if self.component_config[CHECKPOINT_MODEL] and self.tmp_checkpoint_dir: self.model.load_weights(self.tmp_checkpoint_dir / "checkpoint.tf_model") # Save an empty file to flag that this model has been # produced using checkpointing checkpoint_marker = model_path / f"{file_name}.from_checkpoint.pkl" checkpoint_marker.touch() self.model.save(str(tf_model_file)) io_utils.pickle_dump( model_path / f"{file_name}.data_example.pkl", self._data_example ) io_utils.pickle_dump( model_path / f"{file_name}.sparse_feature_sizes.pkl", self._sparse_feature_sizes, ) io_utils.pickle_dump( model_path / f"{file_name}.label_data.pkl", dict(self._label_data.data) ) io_utils.json_pickle( model_path / f"{file_name}.index_label_id_mapping.json", self.index_label_id_mapping, ) entity_tag_specs = ( [tag_spec._asdict() for tag_spec in self._entity_tag_specs] if self._entity_tag_specs else [] ) rasa.shared.utils.io.dump_obj_as_json_to_file( model_path / f"{file_name}.entity_tag_specs.json", entity_tag_specs ) @classmethod def load( cls, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, **kwargs: Any, ) -> DIETClassifier: try: with model_storage.read_from(resource) as model_path: return cls._load( model_path, config, model_storage, resource, execution_context ) except ValueError: logger.debug( f"Failed to load {cls.__class__.__name__} from model storage. Resource " f"'{resource.name}' doesn't exist." ) return cls(config, model_storage, resource, execution_context) @classmethod def _load( cls, model_path: Path, config: Dict[Text, Any], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext, ) -> "DIETClassifier": ( index_label_id_mapping, entity_tag_specs, label_data, data_example, sparse_feature_sizes, ) = cls._load_from_files(model_path) config = train_utils.update_confidence_type(config) config = train_utils.update_similarity_type(config) model = cls._load_model( entity_tag_specs, label_data, config, data_example, model_path, finetune_mode=execution_context.is_finetuning, ) return cls( config=config, model_storage=model_storage, resource=resource, execution_context=execution_context, index_label_id_mapping=index_label_id_mapping, entity_tag_specs=entity_tag_specs, model=model, sparse_feature_sizes=sparse_feature_sizes, ) @classmethod def _load_from_files( cls, model_path: Path ) -> Tuple[ Dict[int, Text], List[EntityTagSpec], RasaModelData, Dict[Text, Dict[Text, List[FeatureArray]]], Dict[Text, Dict[Text, List[int]]], ]: file_name = cls.__name__ data_example = io_utils.pickle_load( model_path / f"{file_name}.data_example.pkl" ) label_data = io_utils.pickle_load(model_path / f"{file_name}.label_data.pkl") label_data = RasaModelData(data=label_data) sparse_feature_sizes = io_utils.pickle_load( model_path / f"{file_name}.sparse_feature_sizes.pkl" ) index_label_id_mapping = io_utils.json_unpickle( model_path / f"{file_name}.index_label_id_mapping.json" ) entity_tag_specs = rasa.shared.utils.io.read_json_file( model_path / f"{file_name}.entity_tag_specs.json" ) entity_tag_specs = [ EntityTagSpec( tag_name=tag_spec["tag_name"], ids_to_tags={ int(key): value for key, value in tag_spec["ids_to_tags"].items() }, tags_to_ids={ key: int(value) for key, value in tag_spec["tags_to_ids"].items() }, num_tags=tag_spec["num_tags"], ) for tag_spec in entity_tag_specs ] index_label_id_mapping = { int(key): value for key, value in index_label_id_mapping.items() } return ( index_label_id_mapping, entity_tag_specs, label_data, data_example, sparse_feature_sizes, ) @classmethod def _load_model( cls, entity_tag_specs: List[EntityTagSpec], label_data: RasaModelData, config: Dict[Text, Any], data_example: Dict[Text, Dict[Text, List[FeatureArray]]], model_path: Path, finetune_mode: bool = False, ) -> "RasaModel": file_name = cls.__name__ tf_model_file = model_path / f"{file_name}.tf_model" label_key = LABEL_KEY if config[INTENT_CLASSIFICATION] else None label_sub_key = LABEL_SUB_KEY if config[INTENT_CLASSIFICATION] else None model_data_example = RasaModelData( label_key=label_key, label_sub_key=label_sub_key, data=data_example ) model = cls._load_model_class( tf_model_file, model_data_example, label_data, entity_tag_specs, config, finetune_mode=finetune_mode, ) return model @classmethod def _load_model_class( cls, tf_model_file: Text, model_data_example: RasaModelData, label_data: RasaModelData, entity_tag_specs: List[EntityTagSpec], config: Dict[Text, Any], finetune_mode: bool, ) -> "RasaModel": predict_data_example = RasaModelData( label_key=model_data_example.label_key, data={ feature_name: features for feature_name, features in model_data_example.items() if TEXT in feature_name }, ) return cls.model_class().load( tf_model_file, model_data_example, predict_data_example, data_signature=model_data_example.get_signature(), label_data=label_data, entity_tag_specs=entity_tag_specs, config=copy.deepcopy(config), finetune_mode=finetune_mode, ) def _instantiate_model_class(self, model_data: RasaModelData) -> "RasaModel": return self.model_class()( data_signature=model_data.get_signature(), label_data=self._label_data, entity_tag_specs=self._entity_tag_specs, config=self.component_config, ) class DIET(TransformerRasaModel): def __init__( self, data_signature: Dict[Text, Dict[Text, List[FeatureSignature]]], label_data: RasaModelData, entity_tag_specs: Optional[List[EntityTagSpec]], config: Dict[Text, Any], ) -> None: super().__init__("DIET", config, data_signature, label_data) self._entity_tag_specs = self._ordered_tag_specs(entity_tag_specs) self.predict_data_signature = { feature_name: features for feature_name, features in data_signature.items() if TEXT in feature_name } self._create_metrics() self._update_metrics_to_log() self.all_labels_embed: Optional[tf.Tensor] = None self._prepare_layers() @staticmethod def _ordered_tag_specs( entity_tag_specs: Optional[List[EntityTagSpec]], ) -> List[EntityTagSpec]: if entity_tag_specs is None: return [] crf_order = [ ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_ROLE, ENTITY_ATTRIBUTE_GROUP, ] ordered_tag_spec = [] for tag_name in crf_order: for tag_spec in entity_tag_specs: if tag_name == tag_spec.tag_name: ordered_tag_spec.append(tag_spec) return ordered_tag_spec def _check_data(self) -> None: if TEXT not in self.data_signature: raise InvalidConfigException( f"No text features specified. " f"Cannot train '{self.__class__.__name__}' model." ) if self.config[INTENT_CLASSIFICATION]: if LABEL not in self.data_signature: raise InvalidConfigException( f"No label features specified. " f"Cannot train '{self.__class__.__name__}' model." ) if self.config[SHARE_HIDDEN_LAYERS]: different_sentence_signatures = False different_sequence_signatures = False if ( SENTENCE in self.data_signature[TEXT] and SENTENCE in self.data_signature[LABEL] ): different_sentence_signatures = ( self.data_signature[TEXT][SENTENCE] != self.data_signature[LABEL][SENTENCE] ) if ( SEQUENCE in self.data_signature[TEXT] and SEQUENCE in self.data_signature[LABEL] ): different_sequence_signatures = ( self.data_signature[TEXT][SEQUENCE] != self.data_signature[LABEL][SEQUENCE] ) if different_sentence_signatures or different_sequence_signatures: raise ValueError( "If hidden layer weights are shared, data signatures " "for text_features and label_features must coincide." ) if self.config[ENTITY_RECOGNITION] and ( ENTITIES not in self.data_signature or ENTITY_ATTRIBUTE_TYPE not in self.data_signature[ENTITIES] ): logger.debug( f"You specified '{self.__class__.__name__}' to train entities, but " f"no entities are present in the training data. Skipping training of " f"entities." ) self.config[ENTITY_RECOGNITION] = False def _create_metrics(self) -> None: self.mask_loss = tf.keras.metrics.Mean(name="m_loss") self.intent_loss = tf.keras.metrics.Mean(name="i_loss") self.entity_loss = tf.keras.metrics.Mean(name="e_loss") self.entity_group_loss = tf.keras.metrics.Mean(name="g_loss") self.entity_role_loss = tf.keras.metrics.Mean(name="r_loss") self.mask_acc = tf.keras.metrics.Mean(name="m_acc") self.intent_acc = tf.keras.metrics.Mean(name="i_acc") self.entity_f1 = tf.keras.metrics.Mean(name="e_f1") self.entity_group_f1 = tf.keras.metrics.Mean(name="g_f1") self.entity_role_f1 = tf.keras.metrics.Mean(name="r_f1") def _update_metrics_to_log(self) -> None: debug_log_level = logging.getLogger("rasa").level == logging.DEBUG if self.config[MASKED_LM]: self.metrics_to_log.append("m_acc") if debug_log_level: self.metrics_to_log.append("m_loss") if self.config[INTENT_CLASSIFICATION]: self.metrics_to_log.append("i_acc") if debug_log_level: self.metrics_to_log.append("i_loss") if self.config[ENTITY_RECOGNITION]: for tag_spec in self._entity_tag_specs: if tag_spec.num_tags != 0: name = tag_spec.tag_name self.metrics_to_log.append(f"{name[0]}_f1") if debug_log_level: self.metrics_to_log.append(f"{name[0]}_loss") self._log_metric_info() def _log_metric_info(self) -> None: metric_name = { "t": "total", "i": "intent", "e": "entity", "m": "mask", "r": "role", "g": "group", } logger.debug("Following metrics will be logged during training: ") for metric in self.metrics_to_log: parts = metric.split("_") name = f"{metric_name[parts[0]]} {parts[1]}" logger.debug(f" {metric} ({name})") def _prepare_layers(self) -> None: self.text_name = TEXT self._tf_layers[ f"sequence_layer.{self.text_name}" ] = rasa_layers.RasaSequenceLayer( self.text_name, self.data_signature[self.text_name], self.config ) if self.config[MASKED_LM]: self._prepare_mask_lm_loss(self.text_name) if self.config[INTENT_CLASSIFICATION]: self.label_name = TEXT if self.config[SHARE_HIDDEN_LAYERS] else LABEL label_config = self.config.copy() label_config.update( {SPARSE_INPUT_DROPOUT: False, DENSE_INPUT_DROPOUT: False} ) self._tf_layers[ f"feature_combining_layer.{self.label_name}" ] = rasa_layers.RasaFeatureCombiningLayer( self.label_name, self.label_signature[self.label_name], label_config ) self._prepare_ffnn_layer( self.label_name, self.config[HIDDEN_LAYERS_SIZES][self.label_name], self.config[DROP_RATE], ) self._prepare_label_classification_layers(predictor_attribute=TEXT) if self.config[ENTITY_RECOGNITION]: self._prepare_entity_recognition_layers() def _prepare_mask_lm_loss(self, name: Text) -> None: self._prepare_embed_layers(f"{name}_lm_mask") self._prepare_embed_layers(f"{name}_golden_token") self._prepare_dot_product_loss(f"{name}_mask", scale_loss=False) def _create_bow( self, sequence_features: List[Union[tf.Tensor, tf.SparseTensor]], sentence_features: List[Union[tf.Tensor, tf.SparseTensor]], sequence_feature_lengths: tf.Tensor, name: Text, ) -> tf.Tensor: x, _ = self._tf_layers[f"feature_combining_layer.{name}"]( (sequence_features, sentence_features, sequence_feature_lengths), training=self._training, ) # convert to bag-of-words by summing along the sequence dimension x = tf.reduce_sum(x, axis=1) return self._tf_layers[f"ffnn.{name}"](x, self._training) def _create_all_labels(self) -> Tuple[tf.Tensor, tf.Tensor]: all_label_ids = self.tf_label_data[LABEL_KEY][LABEL_SUB_KEY][0] sequence_feature_lengths = self._get_sequence_feature_lengths( self.tf_label_data, LABEL ) x = self._create_bow( self.tf_label_data[LABEL][SEQUENCE], self.tf_label_data[LABEL][SENTENCE], sequence_feature_lengths, self.label_name, ) all_labels_embed = self._tf_layers[f"embed.{LABEL}"](x) return all_label_ids, all_labels_embed def _mask_loss( self, outputs: tf.Tensor, inputs: tf.Tensor, seq_ids: tf.Tensor, mlm_mask_boolean: tf.Tensor, name: Text, ) -> tf.Tensor: # make sure there is at least one element in the mask mlm_mask_boolean = tf.cond( tf.reduce_any(mlm_mask_boolean), lambda: mlm_mask_boolean, lambda: tf.scatter_nd([[0, 0, 0]], [True], tf.shape(mlm_mask_boolean)), ) mlm_mask_boolean = tf.squeeze(mlm_mask_boolean, -1) # Pick elements that were masked, throwing away the batch & sequence dimension # and effectively switching from shape (batch_size, sequence_length, units) to # (num_masked_elements, units). outputs = tf.boolean_mask(outputs, mlm_mask_boolean) inputs = tf.boolean_mask(inputs, mlm_mask_boolean) ids = tf.boolean_mask(seq_ids, mlm_mask_boolean) tokens_predicted_embed = self._tf_layers[f"embed.{name}_lm_mask"](outputs) tokens_true_embed = self._tf_layers[f"embed.{name}_golden_token"](inputs) # To limit the otherwise computationally expensive loss calculation, we # constrain the label space in MLM (i.e. token space) to only those tokens that # were masked in this batch. Hence the reduced list of token embeddings # (tokens_true_embed) and the reduced list of labels (ids) are passed as # all_labels_embed and all_labels, respectively. In the future, we could be less # restrictive and construct a slightly bigger label space which could include # tokens not masked in the current batch too. return self._tf_layers[f"loss.{name}_mask"]( inputs_embed=tokens_predicted_embed, labels_embed=tokens_true_embed, labels=ids, all_labels_embed=tokens_true_embed, all_labels=ids, ) def _calculate_label_loss( self, text_features: tf.Tensor, label_features: tf.Tensor, label_ids: tf.Tensor ) -> tf.Tensor: all_label_ids, all_labels_embed = self._create_all_labels() text_embed = self._tf_layers[f"embed.{TEXT}"](text_features) label_embed = self._tf_layers[f"embed.{LABEL}"](label_features) return self._tf_layers[f"loss.{LABEL}"]( text_embed, label_embed, label_ids, all_labels_embed, all_label_ids ) def batch_loss( self, batch_in: Union[Tuple[tf.Tensor], Tuple[np.ndarray]] ) -> tf.Tensor: tf_batch_data = self.batch_to_model_data_format(batch_in, self.data_signature) sequence_feature_lengths = self._get_sequence_feature_lengths( tf_batch_data, TEXT ) ( text_transformed, text_in, mask_combined_sequence_sentence, text_seq_ids, mlm_mask_boolean_text, _, ) = self._tf_layers[f"sequence_layer.{self.text_name}"]( ( tf_batch_data[TEXT][SEQUENCE], tf_batch_data[TEXT][SENTENCE], sequence_feature_lengths, ), training=self._training, ) losses = [] # Lengths of sequences in case of sentence-level features are always 1, but they # can effectively be 0 if sentence-level features aren't present. sentence_feature_lengths = self._get_sentence_feature_lengths( tf_batch_data, TEXT ) combined_sequence_sentence_feature_lengths = ( sequence_feature_lengths + sentence_feature_lengths ) if self.config[MASKED_LM]: loss, acc = self._mask_loss( text_transformed, text_in, text_seq_ids, mlm_mask_boolean_text, TEXT ) self.mask_loss.update_state(loss) self.mask_acc.update_state(acc) losses.append(loss) if self.config[INTENT_CLASSIFICATION]: loss = self._batch_loss_intent( combined_sequence_sentence_feature_lengths, text_transformed, tf_batch_data, ) losses.append(loss) if self.config[ENTITY_RECOGNITION]: losses += self._batch_loss_entities( mask_combined_sequence_sentence, sequence_feature_lengths, text_transformed, tf_batch_data, ) return tf.math.add_n(losses) def _batch_loss_intent( self, combined_sequence_sentence_feature_lengths_text: tf.Tensor, text_transformed: tf.Tensor, tf_batch_data: Dict[Text, Dict[Text, List[tf.Tensor]]], ) -> tf.Tensor: sentence_vector = self._last_token( text_transformed, combined_sequence_sentence_feature_lengths_text ) sequence_feature_lengths_label = self._get_sequence_feature_lengths( tf_batch_data, LABEL ) label_ids = tf_batch_data[LABEL_KEY][LABEL_SUB_KEY][0] label = self._create_bow( tf_batch_data[LABEL][SEQUENCE], tf_batch_data[LABEL][SENTENCE], sequence_feature_lengths_label, self.label_name, ) loss, acc = self._calculate_label_loss(sentence_vector, label, label_ids) self._update_label_metrics(loss, acc) return loss def _update_label_metrics(self, loss: tf.Tensor, acc: tf.Tensor) -> None: self.intent_loss.update_state(loss) self.intent_acc.update_state(acc) def _batch_loss_entities( self, mask_combined_sequence_sentence: tf.Tensor, sequence_feature_lengths: tf.Tensor, text_transformed: tf.Tensor, tf_batch_data: Dict[Text, Dict[Text, List[tf.Tensor]]], ) -> List[tf.Tensor]: losses = [] entity_tags = None for tag_spec in self._entity_tag_specs: if tag_spec.num_tags == 0: continue tag_ids = tf_batch_data[ENTITIES][tag_spec.tag_name][0] tag_ids = tf.pad(tag_ids, [[0, 0], [0, 1], [0, 0]]) loss, f1, _logits = self._calculate_entity_loss( text_transformed, tag_ids, mask_combined_sequence_sentence, sequence_feature_lengths, tag_spec.tag_name, entity_tags, ) if tag_spec.tag_name == ENTITY_ATTRIBUTE_TYPE: entity_tags = tf.one_hot( tf.cast(tag_ids[:, :, 0], tf.int32), depth=tag_spec.num_tags ) self._update_entity_metrics(loss, f1, tag_spec.tag_name) losses.append(loss) return losses def _update_entity_metrics( self, loss: tf.Tensor, f1: tf.Tensor, tag_name: Text ) -> None: if tag_name == ENTITY_ATTRIBUTE_TYPE: self.entity_loss.update_state(loss) self.entity_f1.update_state(f1) elif tag_name == ENTITY_ATTRIBUTE_GROUP: self.entity_group_loss.update_state(loss) self.entity_group_f1.update_state(f1) elif tag_name == ENTITY_ATTRIBUTE_ROLE: self.entity_role_loss.update_state(loss) self.entity_role_f1.update_state(f1) def prepare_for_predict(self) -> None: if self.config[INTENT_CLASSIFICATION]: _, self.all_labels_embed = self._create_all_labels() def batch_predict( self, batch_in: Union[Tuple[tf.Tensor], Tuple[np.ndarray]] ) -> Dict[Text, tf.Tensor]: tf_batch_data = self.batch_to_model_data_format( batch_in, self.predict_data_signature ) sequence_feature_lengths = self._get_sequence_feature_lengths( tf_batch_data, TEXT ) sentence_feature_lengths = self._get_sentence_feature_lengths( tf_batch_data, TEXT ) text_transformed, _, _, _, _, attention_weights = self._tf_layers[ f"sequence_layer.{self.text_name}" ]( ( tf_batch_data[TEXT][SEQUENCE], tf_batch_data[TEXT][SENTENCE], sequence_feature_lengths, ), training=self._training, ) predictions = { DIAGNOSTIC_DATA: { "attention_weights": attention_weights, "text_transformed": text_transformed, } } if self.config[INTENT_CLASSIFICATION]: predictions.update( self._batch_predict_intents( sequence_feature_lengths + sentence_feature_lengths, text_transformed, ) ) if self.config[ENTITY_RECOGNITION]: predictions.update( self._batch_predict_entities(sequence_feature_lengths, text_transformed) ) return predictions def _batch_predict_entities( self, sequence_feature_lengths: tf.Tensor, text_transformed: tf.Tensor ) -> Dict[Text, tf.Tensor]: predictions: Dict[Text, tf.Tensor] = {} entity_tags = None for tag_spec in self._entity_tag_specs: if tag_spec.num_tags == 0: continue name = tag_spec.tag_name _input = text_transformed if entity_tags is not None: _tags = self._tf_layers[f"embed.{name}.tags"](entity_tags) _input = tf.concat([_input, _tags], axis=-1) _logits = self._tf_layers[f"embed.{name}.logits"](_input) pred_ids, confidences = self._tf_layers[f"crf.{name}"]( _logits, sequence_feature_lengths ) predictions[f"e_{name}_ids"] = pred_ids predictions[f"e_{name}_scores"] = confidences if name == ENTITY_ATTRIBUTE_TYPE: entity_tags = tf.one_hot( tf.cast(pred_ids, tf.int32), depth=tag_spec.num_tags ) return predictions def _batch_predict_intents( self, combined_sequence_sentence_feature_lengths: tf.Tensor, text_transformed: tf.Tensor, ) -> Dict[Text, tf.Tensor]: if self.all_labels_embed is None: raise ValueError( "The model was not prepared for prediction. " "Call `prepare_for_predict` first." ) sentence_vector = self._last_token( text_transformed, combined_sequence_sentence_feature_lengths ) sentence_vector_embed = self._tf_layers[f"embed.{TEXT}"](sentence_vector) _, scores = self._tf_layers[ f"loss.{LABEL}" ].get_similarities_and_confidences_from_embeddings( sentence_vector_embed[:, tf.newaxis, :], self.all_labels_embed[tf.newaxis, :, :], ) return {"i_scores": scores}

true

79080353a2b4abcea79550828a093d0dd73b34c5

2,964

py

Python

src/imagedata/transports/abstracttransport.py

erling6232/imagedata

69226b317ff43eb52ed48503582e5770bcb47ec4

[ "MIT" ]

1

2021-09-02T07:20:19.000Z

src/imagedata/transports/abstracttransport.py

erling6232/imagedata

69226b317ff43eb52ed48503582e5770bcb47ec4

[ "MIT" ]

3

2018-02-28T09:54:21.000Z

2022-03-22T10:05:39.000Z

src/imagedata/transports/abstracttransport.py

erling6232/imagedata

69226b317ff43eb52ed48503582e5770bcb47ec4

[ "MIT" ]

null

"""Abstract class for image transports. Defines generic functions. """ # Copyright (c) 2018 Erling Andersen, Haukeland University Hospital, Bergen, Norway from abc import ABCMeta, abstractmethod # , abstractproperty # import imagedata.transports class NoOtherInstance(Exception): pass class AbstractTransport(object, metaclass=ABCMeta): """Abstract base class definition for imagedata transport plugins. Plugins must be a subclass of AbstractPlugin and must define the attributes set in __init__() and the following methods: open() method isfile() method walk() method """ plugin_type = 'transport' def __init__(self, name, description, authors, version, url, schemes): object.__init__(self) self.__name = name self.__description = description self.__authors = authors self.__version = version self.__url = url self.__schemes = schemes @property def name(self): """Plugin name Single word string describing the image format. Typical names: file, dicom, xnat """ return self.__name @property def description(self): """Plugin description Single line string describing the transport method. """ return self.__description @property def authors(self): """Plugin authors Multi-line string naming the author(s) of the plugin. """ return self.__authors @property def version(self): """Plugin version String giving the plugin version. Version scheme: 1.0.0 """ return self.__version @property def url(self): """Plugin URL URL string to the site of the plugin or the author(s). """ return self.__url @property def schemes(self): """List of transport schemes supported by this plugin. List of strings. """ return self.__schemes @abstractmethod def walk(self, top): """Generate the file names in a directory tree by walking the tree. Input: - top: starting point for walk (str) Return: - tuples of (root, dirs, files) """ pass @abstractmethod def isfile(self, path): """Return True if path is an existing regular file. """ pass @abstractmethod def open(self, path, mode='r'): """Extract a member from the archive as a file-like object. """ pass @abstractmethod def close(self): """Close the transport """ pass @abstractmethod def info(self, path) -> str: """Return info describing the object Args: path (str): object path Returns: description (str): Preferably a one-line string describing the object """ pass

23.52381

83

0.592105

from abc import ABCMeta, abstractmethod class NoOtherInstance(Exception): pass class AbstractTransport(object, metaclass=ABCMeta): plugin_type = 'transport' def __init__(self, name, description, authors, version, url, schemes): object.__init__(self) self.__name = name self.__description = description self.__authors = authors self.__version = version self.__url = url self.__schemes = schemes @property def name(self): return self.__name @property def description(self): return self.__description @property def authors(self): return self.__authors @property def version(self): return self.__version @property def url(self): return self.__url @property def schemes(self): return self.__schemes @abstractmethod def walk(self, top): pass @abstractmethod def isfile(self, path): pass @abstractmethod def open(self, path, mode='r'): pass @abstractmethod def close(self): pass @abstractmethod def info(self, path) -> str: pass

true

790803d1ca6e878f6c564a575b45f035b7ac69cb

4,789

py

Python

tests/pools/test_wallet_pool_store.py

duderino999/ceres-combineharvester

f63ab6c4d0e33c3b6550c1f5641f28ab2c68b001

[ "Apache-2.0" ]

39

2021-08-04T14:49:27.000Z

2022-03-29T16:30:19.000Z

tests/pools/test_wallet_pool_store.py

rickguo216/ceres-combineharvester

e93b26a77b1fc4fe9de80d10f745b09a13f9c288

[ "Apache-2.0" ]

30

2021-08-19T22:44:31.000Z

2022-03-29T19:09:26.000Z

tests/pools/test_wallet_pool_store.py

rickguo216/ceres-combineharvester

e93b26a77b1fc4fe9de80d10f745b09a13f9c288

[ "Apache-2.0" ]

23

2021-08-07T07:33:20.000Z

2022-03-27T11:15:00.000Z

import asyncio from pathlib import Path from secrets import token_bytes from typing import Optional import aiosqlite import pytest from clvm_tools import binutils from ceres.types.blockchain_format.coin import Coin from ceres.types.blockchain_format.program import Program, SerializedProgram from ceres.types.blockchain_format.sized_bytes import bytes32 from ceres.types.coin_spend import CoinSpend from ceres.util.db_wrapper import DBWrapper from ceres.util.ints import uint64 from ceres.wallet.wallet_pool_store import WalletPoolStore @pytest.fixture(scope="module") def event_loop(): loop = asyncio.get_event_loop() yield loop def make_child_solution(coin_spend: CoinSpend, new_coin: Optional[Coin] = None) -> CoinSpend: new_puzzle_hash: bytes32 = token_bytes(32) solution = "()" puzzle = f"(q . ((51 0x{new_puzzle_hash.hex()} 1)))" puzzle_prog = Program.to(binutils.assemble(puzzle)) solution_prog = Program.to(binutils.assemble(solution)) if new_coin is None: new_coin = coin_spend.additions()[0] sol: CoinSpend = CoinSpend( new_coin, SerializedProgram.from_program(puzzle_prog), SerializedProgram.from_program(solution_prog), ) return sol class TestWalletPoolStore: @pytest.mark.asyncio async def test_store(self): db_filename = Path("wallet_store_test.db") if db_filename.exists(): db_filename.unlink() db_connection = await aiosqlite.connect(db_filename) db_wrapper = DBWrapper(db_connection) store = await WalletPoolStore.create(db_wrapper) try: await db_wrapper.begin_transaction() coin_0 = Coin(token_bytes(32), token_bytes(32), uint64(12312)) coin_0_alt = Coin(token_bytes(32), token_bytes(32), uint64(12312)) solution_0: CoinSpend = make_child_solution(None, coin_0) solution_0_alt: CoinSpend = make_child_solution(None, coin_0_alt) solution_1: CoinSpend = make_child_solution(solution_0) assert store.get_spends_for_wallet(0) == [] assert store.get_spends_for_wallet(1) == [] await store.add_spend(1, solution_1, 100) assert store.get_spends_for_wallet(1) == [(100, solution_1)] # Idempotent await store.add_spend(1, solution_1, 100) assert store.get_spends_for_wallet(1) == [(100, solution_1)] with pytest.raises(ValueError): await store.add_spend(1, solution_1, 101) # Rebuild cache, no longer present await db_wrapper.rollback_transaction() await store.rebuild_cache() assert store.get_spends_for_wallet(1) == [] await store.rebuild_cache() await store.add_spend(1, solution_1, 100) assert store.get_spends_for_wallet(1) == [(100, solution_1)] solution_1_alt: CoinSpend = make_child_solution(solution_0_alt) with pytest.raises(ValueError): await store.add_spend(1, solution_1_alt, 100) assert store.get_spends_for_wallet(1) == [(100, solution_1)] solution_2: CoinSpend = make_child_solution(solution_1) await store.add_spend(1, solution_2, 100) await store.rebuild_cache() solution_3: CoinSpend = make_child_solution(solution_2) await store.add_spend(1, solution_3, 100) solution_4: CoinSpend = make_child_solution(solution_3) with pytest.raises(ValueError): await store.add_spend(1, solution_4, 99) await store.rebuild_cache() await store.add_spend(1, solution_4, 101) await store.rebuild_cache() await store.rollback(101, 1) await store.rebuild_cache() assert store.get_spends_for_wallet(1) == [ (100, solution_1), (100, solution_2), (100, solution_3), (101, solution_4), ] await store.rebuild_cache() await store.rollback(100, 1) await store.rebuild_cache() assert store.get_spends_for_wallet(1) == [ (100, solution_1), (100, solution_2), (100, solution_3), ] with pytest.raises(ValueError): await store.add_spend(1, solution_1, 105) await store.add_spend(1, solution_4, 105) solution_5: CoinSpend = make_child_solution(solution_4) await store.add_spend(1, solution_5, 105) await store.rollback(99, 1) assert store.get_spends_for_wallet(1) == [] finally: await db_connection.close() db_filename.unlink()

36.557252

93

0.640426

import asyncio from pathlib import Path from secrets import token_bytes from typing import Optional import aiosqlite import pytest from clvm_tools import binutils from ceres.types.blockchain_format.coin import Coin from ceres.types.blockchain_format.program import Program, SerializedProgram from ceres.types.blockchain_format.sized_bytes import bytes32 from ceres.types.coin_spend import CoinSpend from ceres.util.db_wrapper import DBWrapper from ceres.util.ints import uint64 from ceres.wallet.wallet_pool_store import WalletPoolStore @pytest.fixture(scope="module") def event_loop(): loop = asyncio.get_event_loop() yield loop def make_child_solution(coin_spend: CoinSpend, new_coin: Optional[Coin] = None) -> CoinSpend: new_puzzle_hash: bytes32 = token_bytes(32) solution = "()" puzzle = f"(q . ((51 0x{new_puzzle_hash.hex()} 1)))" puzzle_prog = Program.to(binutils.assemble(puzzle)) solution_prog = Program.to(binutils.assemble(solution)) if new_coin is None: new_coin = coin_spend.additions()[0] sol: CoinSpend = CoinSpend( new_coin, SerializedProgram.from_program(puzzle_prog), SerializedProgram.from_program(solution_prog), ) return sol class TestWalletPoolStore: @pytest.mark.asyncio async def test_store(self): db_filename = Path("wallet_store_test.db") if db_filename.exists(): db_filename.unlink() db_connection = await aiosqlite.connect(db_filename) db_wrapper = DBWrapper(db_connection) store = await WalletPoolStore.create(db_wrapper) try: await db_wrapper.begin_transaction() coin_0 = Coin(token_bytes(32), token_bytes(32), uint64(12312)) coin_0_alt = Coin(token_bytes(32), token_bytes(32), uint64(12312)) solution_0: CoinSpend = make_child_solution(None, coin_0) solution_0_alt: CoinSpend = make_child_solution(None, coin_0_alt) solution_1: CoinSpend = make_child_solution(solution_0) assert store.get_spends_for_wallet(0) == [] assert store.get_spends_for_wallet(1) == [] await store.add_spend(1, solution_1, 100) assert store.get_spends_for_wallet(1) == [(100, solution_1)] await store.add_spend(1, solution_1, 100) assert store.get_spends_for_wallet(1) == [(100, solution_1)] with pytest.raises(ValueError): await store.add_spend(1, solution_1, 101) await db_wrapper.rollback_transaction() await store.rebuild_cache() assert store.get_spends_for_wallet(1) == [] await store.rebuild_cache() await store.add_spend(1, solution_1, 100) assert store.get_spends_for_wallet(1) == [(100, solution_1)] solution_1_alt: CoinSpend = make_child_solution(solution_0_alt) with pytest.raises(ValueError): await store.add_spend(1, solution_1_alt, 100) assert store.get_spends_for_wallet(1) == [(100, solution_1)] solution_2: CoinSpend = make_child_solution(solution_1) await store.add_spend(1, solution_2, 100) await store.rebuild_cache() solution_3: CoinSpend = make_child_solution(solution_2) await store.add_spend(1, solution_3, 100) solution_4: CoinSpend = make_child_solution(solution_3) with pytest.raises(ValueError): await store.add_spend(1, solution_4, 99) await store.rebuild_cache() await store.add_spend(1, solution_4, 101) await store.rebuild_cache() await store.rollback(101, 1) await store.rebuild_cache() assert store.get_spends_for_wallet(1) == [ (100, solution_1), (100, solution_2), (100, solution_3), (101, solution_4), ] await store.rebuild_cache() await store.rollback(100, 1) await store.rebuild_cache() assert store.get_spends_for_wallet(1) == [ (100, solution_1), (100, solution_2), (100, solution_3), ] with pytest.raises(ValueError): await store.add_spend(1, solution_1, 105) await store.add_spend(1, solution_4, 105) solution_5: CoinSpend = make_child_solution(solution_4) await store.add_spend(1, solution_5, 105) await store.rollback(99, 1) assert store.get_spends_for_wallet(1) == [] finally: await db_connection.close() db_filename.unlink()

true

7908048c54c17fc631ebe3c58b705e6febe60f67

5,593

py

Python

google-cloud-sdk/lib/googlecloudsdk/command_lib/crash_handling.py

bopopescu/searchparty

afdc2805cb1b77bd5ac9fdd1a76217f4841f0ea6

[ "Apache-2.0" ]

null

google-cloud-sdk/lib/googlecloudsdk/command_lib/crash_handling.py

bopopescu/searchparty

afdc2805cb1b77bd5ac9fdd1a76217f4841f0ea6

[ "Apache-2.0" ]

null

google-cloud-sdk/lib/googlecloudsdk/command_lib/crash_handling.py

bopopescu/searchparty

afdc2805cb1b77bd5ac9fdd1a76217f4841f0ea6

[ "Apache-2.0" ]

3

2017-07-27T18:44:13.000Z

2020-07-25T17:48:53.000Z

# Copyright 2013 Google Inc. 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. """Error Reporting Handler.""" import sys import traceback from apitools.base.py import exceptions as apitools_exceptions from googlecloudsdk.api_lib.error_reporting import util from googlecloudsdk.api_lib.util import apis as core_apis from googlecloudsdk.calliope import backend from googlecloudsdk.command_lib import error_reporting_util from googlecloudsdk.core import config from googlecloudsdk.core import http from googlecloudsdk.core import log from googlecloudsdk.core import metrics from googlecloudsdk.core import properties from googlecloudsdk.core.console import console_attr def _IsInstallationCorruption(err): """Determines if the error may be from installation corruption. Args: err: Exception err. Returns: bool, True if installation error, False otherwise """ return (isinstance(err, backend.CommandLoadFailure) and isinstance(err.root_exception, ImportError)) def _PrintInstallationAction(err, err_string): """Prompts installation error action. Args: err: Exception err. err_string: Exception err string. """ # This usually indicates installation corruption. # We do want to suggest `gcloud components reinstall` here (ex. as opposed # to the similar message in gcloud.py), because there's a good chance it'll # work (rather than a manual reinstall). # Don't suggest `gcloud feedback`, because this is probably an # installation problem. log.error( ('gcloud failed to load ({0}): {1}\n\n' 'This usually indicates corruption in your gcloud installation or ' 'problems with your Python interpreter.\n\n' 'Please verify that the following is the path to a working Python 2.7 ' 'executable:\n' ' {2}\n' 'If it is not, please set the CLOUDSDK_PYTHON environment variable to ' 'point to a working Python 2.7 executable.\n\n' 'If you are still experiencing problems, please run the following ' 'command to reinstall:\n' ' $ gcloud components reinstall\n\n' 'If that command fails, please reinstall the Cloud SDK using the ' 'instructions here:\n' ' https://cloud.google.com/sdk/' ).format(err.command, err_string, sys.executable)) CRASH_SERVICE = 'gcloud' ERROR_SERVICE = 'gcloud-user-error' CRASH_PROJECT = 'cloud-sdk-errors' CRASH_API_KEY = 'AIzaSyA45D7bA0Y1vyLmQ_Gl10G149M8jiwwK-s' def _GetReportingClient(): """Returns a client that uses an API key for Cloud SDK crash reports. Returns: An error reporting client that uses an API key for Cloud SDK crash reports. """ client_class = core_apis.GetClientClass(util.API_NAME, util.API_VERSION) client_instance = client_class(get_credentials=False, http=http.Http()) client_instance.AddGlobalParam('key', CRASH_API_KEY) return client_instance def ReportError(err, is_crash): """Report the anonymous crash information to the Error Reporting service. Args: err: Exception, the error that caused the crash. is_crash: bool, True if this is a crash, False if it is a user error. """ if properties.VALUES.core.disable_usage_reporting.GetBool(): return stacktrace = traceback.format_exc(err) stacktrace = error_reporting_util.RemovePrivateInformationFromTraceback( stacktrace) command = properties.VALUES.metrics.command_name.Get() cid = metrics.GetCIDIfMetricsEnabled() client = _GetReportingClient() reporter = util.ErrorReporting(client) try: method_config = client.projects_events.GetMethodConfig('Report') request = reporter.GenerateReportRequest( error_message=stacktrace, service=CRASH_SERVICE if is_crash else ERROR_SERVICE, version=config.CLOUD_SDK_VERSION, project=CRASH_PROJECT, request_url=command, user=cid) http_request = client.projects_events.PrepareHttpRequest( method_config, request) metrics.CustomBeacon(http_request.url, http_request.http_method, http_request.body, http_request.headers) except apitools_exceptions.Error as e: log.file_only_logger.error( 'Unable to report crash stacktrace:\n{0}'.format( console_attr.EncodeForConsole(e))) def HandleGcloudCrash(err): """Checks if installation error occurred, then proceeds with Error Reporting. Args: err: Exception err. """ err_string = console_attr.EncodeForConsole(err) log.file_only_logger.exception('BEGIN CRASH STACKTRACE') if _IsInstallationCorruption(err): _PrintInstallationAction(err, err_string) else: log.error(u'gcloud crashed ({0}): {1}'.format( getattr(err, 'error_name', type(err).__name__), err_string)) ReportError(err, is_crash=True) log.err.Print('\nIf you would like to report this issue, please run the ' 'following command:') log.err.Print(' gcloud feedback') log.err.Print('\nTo check gcloud for common problems, please run the ' 'following command:') log.err.Print(' gcloud info --run-diagnostics')

37.039735

79

0.734311

import sys import traceback from apitools.base.py import exceptions as apitools_exceptions from googlecloudsdk.api_lib.error_reporting import util from googlecloudsdk.api_lib.util import apis as core_apis from googlecloudsdk.calliope import backend from googlecloudsdk.command_lib import error_reporting_util from googlecloudsdk.core import config from googlecloudsdk.core import http from googlecloudsdk.core import log from googlecloudsdk.core import metrics from googlecloudsdk.core import properties from googlecloudsdk.core.console import console_attr def _IsInstallationCorruption(err): return (isinstance(err, backend.CommandLoadFailure) and isinstance(err.root_exception, ImportError)) def _PrintInstallationAction(err, err_string): # installation problem. log.error( ('gcloud failed to load ({0}): {1}\n\n' 'This usually indicates corruption in your gcloud installation or ' 'problems with your Python interpreter.\n\n' 'Please verify that the following is the path to a working Python 2.7 ' 'executable:\n' ' {2}\n' 'If it is not, please set the CLOUDSDK_PYTHON environment variable to ' 'point to a working Python 2.7 executable.\n\n' 'If you are still experiencing problems, please run the following ' 'command to reinstall:\n' ' $ gcloud components reinstall\n\n' 'If that command fails, please reinstall the Cloud SDK using the ' 'instructions here:\n' ' https://cloud.google.com/sdk/' ).format(err.command, err_string, sys.executable)) CRASH_SERVICE = 'gcloud' ERROR_SERVICE = 'gcloud-user-error' CRASH_PROJECT = 'cloud-sdk-errors' CRASH_API_KEY = 'AIzaSyA45D7bA0Y1vyLmQ_Gl10G149M8jiwwK-s' def _GetReportingClient(): client_class = core_apis.GetClientClass(util.API_NAME, util.API_VERSION) client_instance = client_class(get_credentials=False, http=http.Http()) client_instance.AddGlobalParam('key', CRASH_API_KEY) return client_instance def ReportError(err, is_crash): if properties.VALUES.core.disable_usage_reporting.GetBool(): return stacktrace = traceback.format_exc(err) stacktrace = error_reporting_util.RemovePrivateInformationFromTraceback( stacktrace) command = properties.VALUES.metrics.command_name.Get() cid = metrics.GetCIDIfMetricsEnabled() client = _GetReportingClient() reporter = util.ErrorReporting(client) try: method_config = client.projects_events.GetMethodConfig('Report') request = reporter.GenerateReportRequest( error_message=stacktrace, service=CRASH_SERVICE if is_crash else ERROR_SERVICE, version=config.CLOUD_SDK_VERSION, project=CRASH_PROJECT, request_url=command, user=cid) http_request = client.projects_events.PrepareHttpRequest( method_config, request) metrics.CustomBeacon(http_request.url, http_request.http_method, http_request.body, http_request.headers) except apitools_exceptions.Error as e: log.file_only_logger.error( 'Unable to report crash stacktrace:\n{0}'.format( console_attr.EncodeForConsole(e))) def HandleGcloudCrash(err): err_string = console_attr.EncodeForConsole(err) log.file_only_logger.exception('BEGIN CRASH STACKTRACE') if _IsInstallationCorruption(err): _PrintInstallationAction(err, err_string) else: log.error(u'gcloud crashed ({0}): {1}'.format( getattr(err, 'error_name', type(err).__name__), err_string)) ReportError(err, is_crash=True) log.err.Print('\nIf you would like to report this issue, please run the ' 'following command:') log.err.Print(' gcloud feedback') log.err.Print('\nTo check gcloud for common problems, please run the ' 'following command:') log.err.Print(' gcloud info --run-diagnostics')

true

790804b88401ab86c218f1bb24640bb1070e042d

353,326

py

Python

src/sage/graphs/graph.py

cffbots/sage

226937dfa9b8b335e873c3c65a796ae1b0924ff2

[ "BSL-1.0" ]

null

src/sage/graphs/graph.py

cffbots/sage

226937dfa9b8b335e873c3c65a796ae1b0924ff2

[ "BSL-1.0" ]

null

src/sage/graphs/graph.py

cffbots/sage

226937dfa9b8b335e873c3c65a796ae1b0924ff2

[ "BSL-1.0" ]

null

# -*- coding: utf-8 -*- r""" Undirected graphs This module implements functions and operations involving undirected graphs. {INDEX_OF_METHODS} AUTHORS: - Robert L. Miller (2006-10-22): initial version - William Stein (2006-12-05): Editing - Robert L. Miller (2007-01-13): refactoring, adjusting for NetworkX-0.33, fixed plotting bugs (2007-01-23): basic tutorial, edge labels, loops, multiple edges and arcs (2007-02-07): graph6 and sparse6 formats, matrix input - Emily Kirkmann (2007-02-11): added graph_border option to plot and show - Robert L. Miller (2007-02-12): vertex color-maps, graph boundaries, graph6 helper functions in Cython - Robert L. Miller Sage Days 3 (2007-02-17-21): 3d plotting in Tachyon - Robert L. Miller (2007-02-25): display a partition - Robert L. Miller (2007-02-28): associate arbitrary objects to vertices, edge and arc label display (in 2d), edge coloring - Robert L. Miller (2007-03-21): Automorphism group, isomorphism check, canonical label - Robert L. Miller (2007-06-07-09): NetworkX function wrapping - Michael W. Hansen (2007-06-09): Topological sort generation - Emily Kirkman, Robert L. Miller Sage Days 4: Finished wrapping NetworkX - Emily Kirkman (2007-07-21): Genus (including circular planar, all embeddings and all planar embeddings), all paths, interior paths - Bobby Moretti (2007-08-12): fixed up plotting of graphs with edge colors differentiated by label - Jason Grout (2007-09-25): Added functions, bug fixes, and general enhancements - Robert L. Miller (Sage Days 7): Edge labeled graph isomorphism - Tom Boothby (Sage Days 7): Miscellaneous awesomeness - Tom Boothby (2008-01-09): Added graphviz output - David Joyner (2009-2): Fixed docstring bug related to GAP. - Stephen Hartke (2009-07-26): Fixed bug in blocks_and_cut_vertices() that caused an incorrect result when the vertex 0 was a cut vertex. - Stephen Hartke (2009-08-22): Fixed bug in blocks_and_cut_vertices() where the list of cut_vertices is not treated as a set. - Anders Jonsson (2009-10-10): Counting of spanning trees and out-trees added. - Nathann Cohen (2009-09) : Cliquer, Connectivity, Flows and everything that uses Linear Programming and class numerical.MIP - Nicolas M. Thiery (2010-02): graph layout code refactoring, dot2tex/graphviz interface - David Coudert (2012-04) : Reduction rules in vertex_cover. - Birk Eisermann (2012-06): added recognition of weakly chordal graphs and long-hole-free / long-antihole-free graphs - Alexandre P. Zuge (2013-07): added join operation. - Amritanshu Prasad (2014-08): added clique polynomial - Julian Rüth (2018-06-21): upgrade to NetworkX 2 - David Coudert (2018-10-07): cleaning - Amanda Francis, Caitlin Lienkaemper, Kate Collins, Rajat Mittal (2019-03-10): methods for computing effective resistance - Amanda Francis, Caitlin Lienkaemper, Kate Collins, Rajat Mittal (2019-03-19): most_common_neighbors and common_neighbors_matrix added. - Jean-Florent Raymond (2019-04): is_redundant, is_dominating, private_neighbors Graph Format ------------ Supported formats ~~~~~~~~~~~~~~~~~ Sage Graphs can be created from a wide range of inputs. A few examples are covered here. - NetworkX dictionary format: :: sage: d = {0: [1,4,5], 1: [2,6], 2: [3,7], 3: [4,8], 4: [9], \ 5: [7, 8], 6: [8,9], 7: [9]} sage: G = Graph(d); G Graph on 10 vertices sage: G.plot().show() # or G.show() - A NetworkX graph: :: sage: import networkx sage: K = networkx.complete_bipartite_graph(12,7) sage: G = Graph(K) sage: G.degree() [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 12, 12, 12, 12, 12, 12, 12] - graph6 or sparse6 format: :: sage: s = ':I`AKGsaOs`cI]Gb~' sage: G = Graph(s, sparse=True); G Looped multi-graph on 10 vertices sage: G.plot().show() # or G.show() Note that the ``\`` character is an escape character in Python, and also a character used by graph6 strings: :: sage: G = Graph('Ihe\n@GUA') Traceback (most recent call last): ... RuntimeError: the string (Ihe) seems corrupt: for n = 10, the string is too short In Python, the escaped character ``\`` is represented by ``\\``: :: sage: G = Graph('Ihe\\n@GUA') sage: G.plot().show() # or G.show() - adjacency matrix: In an adjacency matrix, each column and each row represent a vertex. If a 1 shows up in row `i`, column `j`, there is an edge `(i,j)`. :: sage: M = Matrix([(0,1,0,0,1,1,0,0,0,0),(1,0,1,0,0,0,1,0,0,0), \ (0,1,0,1,0,0,0,1,0,0), (0,0,1,0,1,0,0,0,1,0),(1,0,0,1,0,0,0,0,0,1), \ (1,0,0,0,0,0,0,1,1,0), (0,1,0,0,0,0,0,0,1,1),(0,0,1,0,0,1,0,0,0,1), \ (0,0,0,1,0,1,1,0,0,0), (0,0,0,0,1,0,1,1,0,0)]) sage: M [0 1 0 0 1 1 0 0 0 0] [1 0 1 0 0 0 1 0 0 0] [0 1 0 1 0 0 0 1 0 0] [0 0 1 0 1 0 0 0 1 0] [1 0 0 1 0 0 0 0 0 1] [1 0 0 0 0 0 0 1 1 0] [0 1 0 0 0 0 0 0 1 1] [0 0 1 0 0 1 0 0 0 1] [0 0 0 1 0 1 1 0 0 0] [0 0 0 0 1 0 1 1 0 0] sage: G = Graph(M); G Graph on 10 vertices sage: G.plot().show() # or G.show() - incidence matrix: In an incidence matrix, each row represents a vertex and each column represents an edge. :: sage: M = Matrix([(-1, 0, 0, 0, 1, 0, 0, 0, 0, 0,-1, 0, 0, 0, 0), ....: ( 1,-1, 0, 0, 0, 0, 0, 0, 0, 0, 0,-1, 0, 0, 0), ....: ( 0, 1,-1, 0, 0, 0, 0, 0, 0, 0, 0, 0,-1, 0, 0), ....: ( 0, 0, 1,-1, 0, 0, 0, 0, 0, 0, 0, 0, 0,-1, 0), ....: ( 0, 0, 0, 1,-1, 0, 0, 0, 0, 0, 0, 0, 0, 0,-1), ....: ( 0, 0, 0, 0, 0,-1, 0, 0, 0, 1, 1, 0, 0, 0, 0), ....: ( 0, 0, 0, 0, 0, 0, 0, 1,-1, 0, 0, 1, 0, 0, 0), ....: ( 0, 0, 0, 0, 0, 1,-1, 0, 0, 0, 0, 0, 1, 0, 0), ....: ( 0, 0, 0, 0, 0, 0, 0, 0, 1,-1, 0, 0, 0, 1, 0), ....: ( 0, 0, 0, 0, 0, 0, 1,-1, 0, 0, 0, 0, 0, 0, 1)]) sage: M [-1 0 0 0 1 0 0 0 0 0 -1 0 0 0 0] [ 1 -1 0 0 0 0 0 0 0 0 0 -1 0 0 0] [ 0 1 -1 0 0 0 0 0 0 0 0 0 -1 0 0] [ 0 0 1 -1 0 0 0 0 0 0 0 0 0 -1 0] [ 0 0 0 1 -1 0 0 0 0 0 0 0 0 0 -1] [ 0 0 0 0 0 -1 0 0 0 1 1 0 0 0 0] [ 0 0 0 0 0 0 0 1 -1 0 0 1 0 0 0] [ 0 0 0 0 0 1 -1 0 0 0 0 0 1 0 0] [ 0 0 0 0 0 0 0 0 1 -1 0 0 0 1 0] [ 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 1] sage: G = Graph(M); G Graph on 10 vertices sage: G.plot().show() # or G.show() sage: DiGraph(matrix(2,[0,0,-1,1]), format="incidence_matrix") Traceback (most recent call last): ... ValueError: there must be two nonzero entries (-1 & 1) per column - a list of edges:: sage: g = Graph([(1,3),(3,8),(5,2)]) sage: g Graph on 5 vertices - an igraph Graph:: sage: import igraph # optional - python_igraph sage: g = Graph(igraph.Graph([(1,3),(3,2),(0,2)])) # optional - python_igraph sage: g # optional - python_igraph Graph on 4 vertices Generators ---------- Use ``graphs(n)`` to iterate through all non-isomorphic graphs of given size:: sage: for g in graphs(4): ....: print(g.degree_sequence()) [0, 0, 0, 0] [1, 1, 0, 0] [2, 1, 1, 0] [3, 1, 1, 1] [1, 1, 1, 1] [2, 2, 1, 1] [2, 2, 2, 0] [3, 2, 2, 1] [2, 2, 2, 2] [3, 3, 2, 2] [3, 3, 3, 3] Similarly ``graphs()`` will iterate through all graphs. The complete graph of 4 vertices is of course the smallest graph with chromatic number bigger than three:: sage: for g in graphs(): ....: if g.chromatic_number() > 3: ....: break sage: g.is_isomorphic(graphs.CompleteGraph(4)) True For some commonly used graphs to play with, type:: sage: graphs.[tab] # not tested and hit {tab}. Most of these graphs come with their own custom plot, so you can see how people usually visualize these graphs. :: sage: G = graphs.PetersenGraph() sage: G.plot().show() # or G.show() sage: G.degree_histogram() [0, 0, 0, 10] sage: G.adjacency_matrix() [0 1 0 0 1 1 0 0 0 0] [1 0 1 0 0 0 1 0 0 0] [0 1 0 1 0 0 0 1 0 0] [0 0 1 0 1 0 0 0 1 0] [1 0 0 1 0 0 0 0 0 1] [1 0 0 0 0 0 0 1 1 0] [0 1 0 0 0 0 0 0 1 1] [0 0 1 0 0 1 0 0 0 1] [0 0 0 1 0 1 1 0 0 0] [0 0 0 0 1 0 1 1 0 0] :: sage: S = G.subgraph([0,1,2,3]) sage: S.plot().show() # or S.show() sage: S.density() 1/2 :: sage: G = GraphQuery(display_cols=['graph6'], num_vertices=7, diameter=5) sage: L = G.get_graphs_list() sage: graphs_list.show_graphs(L) .. _Graph:labels: Labels ------ Each vertex can have any hashable object as a label. These are things like strings, numbers, and tuples. Each edge is given a default label of ``None``, but if specified, edges can have any label at all. Edges between vertices `u` and `v` are represented typically as ``(u, v, l)``, where ``l`` is the label for the edge. Note that vertex labels themselves cannot be mutable items:: sage: M = Matrix( [[0,0],[0,0]] ) sage: G = Graph({ 0 : { M : None } }) Traceback (most recent call last): ... TypeError: mutable matrices are unhashable However, if one wants to define a dictionary, with the same keys and arbitrary objects for entries, one can make that association:: sage: d = {0 : graphs.DodecahedralGraph(), 1 : graphs.FlowerSnark(), \ 2 : graphs.MoebiusKantorGraph(), 3 : graphs.PetersenGraph() } sage: d[2] Moebius-Kantor Graph: Graph on 16 vertices sage: T = graphs.TetrahedralGraph() sage: T.vertices() [0, 1, 2, 3] sage: T.set_vertices(d) sage: T.get_vertex(1) Flower Snark: Graph on 20 vertices Database -------- There is a database available for searching for graphs that satisfy a certain set of parameters, including number of vertices and edges, density, maximum and minimum degree, diameter, radius, and connectivity. To see a list of all search parameter keywords broken down by their designated table names, type :: sage: graph_db_info() {...} For more details on data types or keyword input, enter :: sage: GraphQuery? # not tested The results of a query can be viewed with the show method, or can be viewed individually by iterating through the results :: sage: Q = GraphQuery(display_cols=['graph6'],num_vertices=7, diameter=5) sage: Q.show() Graph6 -------------------- F?`po F?gqg F@?]O F@OKg F@R@o FA_pW FEOhW FGC{o FIAHo Show each graph as you iterate through the results:: sage: for g in Q: ....: show(g) Visualization ------------- To see a graph `G` you are working with, there are three main options. You can view the graph in two dimensions via matplotlib with ``show()``. :: sage: G = graphs.RandomGNP(15,.3) sage: G.show() And you can view it in three dimensions via jmol with ``show3d()``. :: sage: G.show3d() Or it can be rendered with `\LaTeX`. This requires the right additions to a standard `\mbox{\rm\TeX}` installation. Then standard Sage commands, such as ``view(G)`` will display the graph, or ``latex(G)`` will produce a string suitable for inclusion in a `\LaTeX` document. More details on this are at the :mod:`sage.graphs.graph_latex` module. :: sage: from sage.graphs.graph_latex import check_tkz_graph sage: check_tkz_graph() # random - depends on TeX installation sage: latex(G) \begin{tikzpicture} ... \end{tikzpicture} Mutability ---------- Graphs are mutable, and thus unusable as dictionary keys, unless ``data_structure="static_sparse"`` is used:: sage: G = graphs.PetersenGraph() sage: {G:1}[G] Traceback (most recent call last): ... TypeError: This graph is mutable, and thus not hashable. Create an immutable copy by `g.copy(immutable=True)` sage: G_immutable = Graph(G, immutable=True) sage: G_immutable == G True sage: {G_immutable:1}[G_immutable] 1 Methods ------- """ # **************************************************************************** # Copyright (C) 2006-2007 Robert L. Miller <rlmillster@gmail.com> # 2018 Julian Rüth <julian.rueth@fsfe.org> # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # https://www.gnu.org/licenses/ # **************************************************************************** import itertools from copy import copy from sage.rings.polynomial.polynomial_ring_constructor import PolynomialRing from sage.rings.integer import Integer from sage.rings.integer_ring import ZZ import sage.graphs.generic_graph_pyx as generic_graph_pyx from sage.graphs.generic_graph import GenericGraph from sage.graphs.digraph import DiGraph from sage.graphs.independent_sets import IndependentSets from sage.misc.rest_index_of_methods import doc_index, gen_thematic_rest_table_index from sage.graphs.views import EdgesView from sage.misc.lazy_import import lazy_import from sage.features import PythonModule lazy_import('sage.graphs.mcqd', ['mcqd'], feature=PythonModule('sage.graphs.mcqd', spkg='mcqd')) from sage.misc.decorators import rename_keyword class Graph(GenericGraph): r""" Undirected graph. A graph is a set of vertices connected by edges. See the :wikipedia:`Graph_(mathematics)` for more information. For a collection of pre-defined graphs, see the :mod:`~sage.graphs.graph_generators` module. A :class:`Graph` object has many methods whose list can be obtained by typing ``g.<tab>`` (i.e. hit the 'tab' key) or by reading the documentation of :mod:`~sage.graphs.graph`, :mod:`~sage.graphs.generic_graph`, and :mod:`~sage.graphs.digraph`. INPUT: By default, a :class:`Graph` object is simple (i.e. no *loops* nor *multiple edges*) and unweighted. This can be easily tuned with the appropriate flags (see below). - ``data`` -- can be any of the following (see the ``format`` argument): #. ``Graph()`` -- build a graph on 0 vertices. #. ``Graph(5)`` -- return an edgeless graph on the 5 vertices 0,...,4. #. ``Graph([list_of_vertices, list_of_edges])`` -- returns a graph with given vertices/edges. To bypass auto-detection, prefer the more explicit ``Graph([V, E], format='vertices_and_edges')``. #. ``Graph(list_of_edges)`` -- return a graph with a given list of edges (see documentation of :meth:`~sage.graphs.generic_graph.GenericGraph.add_edges`). To bypass auto-detection, prefer the more explicit ``Graph(L, format='list_of_edges')``. #. ``Graph({1: [2, 3, 4], 3: [4]})`` -- return a graph by associating to each vertex the list of its neighbors. To bypass auto-detection, prefer the more explicit ``Graph(D, format='dict_of_lists')``. #. ``Graph({1: {2: 'a', 3:'b'} ,3:{2:'c'}})`` -- return a graph by associating a list of neighbors to each vertex and providing its edge label. To bypass auto-detection, prefer the more explicit ``Graph(D, format='dict_of_dicts')``. For graphs with multiple edges, you can provide a list of labels instead, e.g.: ``Graph({1: {2: ['a1', 'a2'], 3:['b']} ,3:{2:['c']}})``. #. ``Graph(a_symmetric_matrix)`` -- return a graph with given (weighted) adjacency matrix (see documentation of :meth:`~sage.graphs.generic_graph.GenericGraph.adjacency_matrix`). To bypass auto-detection, prefer the more explicit ``Graph(M, format='adjacency_matrix')``. To take weights into account, use ``format='weighted_adjacency_matrix'`` instead. #. ``Graph(a_nonsymmetric_matrix)`` -- return a graph with given incidence matrix (see documentation of :meth:`~sage.graphs.generic_graph.GenericGraph.incidence_matrix`). To bypass auto-detection, prefer the more explicit ``Graph(M, format='incidence_matrix')``. #. ``Graph([V, f])`` -- return a graph from a vertex set ``V`` and a *symmetric* function ``f``. The graph contains an edge `u,v` whenever ``f(u,v)`` is ``True``.. Example: ``Graph([ [1..10], lambda x,y: abs(x-y).is_square()])`` #. ``Graph(':I`ES@obGkqegW~')`` -- return a graph from a graph6 or sparse6 string (see documentation of :meth:`graph6_string` or :meth:`sparse6_string`). #. ``Graph(a_seidel_matrix, format='seidel_adjacency_matrix')`` -- return a graph with a given Seidel adjacency matrix (see documentation of :meth:`seidel_adjacency_matrix`). #. ``Graph(another_graph)`` -- return a graph from a Sage (di)graph, `pygraphviz <https://pygraphviz.github.io/>`__ graph, `NetworkX <https://networkx.github.io/>`__ graph, or `igraph <http://igraph.org/python/>`__ graph. - ``pos`` -- a positioning dictionary (cf. documentation of :meth:`~sage.graphs.generic_graph.GenericGraph.layout`). For example, to draw 4 vertices on a square:: {0: [-1,-1], 1: [ 1,-1], 2: [ 1, 1], 3: [-1, 1]} - ``name`` -- (must be an explicitly named parameter, i.e., ``name="complete")`` gives the graph a name - ``loops`` -- boolean (default: ``None``); whether to allow loops (ignored if data is an instance of the ``Graph`` class) - ``multiedges`` -- boolean (default: ``None``); whether to allow multiple edges (ignored if data is an instance of the ``Graph`` class). - ``weighted`` -- boolean (default: ``None``); whether graph thinks of itself as weighted or not. See :meth:`~sage.graphs.generic_graph.GenericGraph.weighted`. - ``format`` -- if set to ``None`` (default), :class:`Graph` tries to guess input's format. To avoid this possibly time-consuming step, one of the following values can be specified (see description above): ``"int"``, ``"graph6"``, ``"sparse6"``, ``"rule"``, ``"list_of_edges"``, ``"dict_of_lists"``, ``"dict_of_dicts"``, ``"adjacency_matrix"``, ``"weighted_adjacency_matrix"``, ``"seidel_adjacency_matrix"``, ``"incidence_matrix"``, ``"NX"``, ``"igraph"``. - ``sparse`` -- boolean (default: ``True``); ``sparse=True`` is an alias for ``data_structure="sparse"``, and ``sparse=False`` is an alias for ``data_structure="dense"``. - ``data_structure`` -- one of the following (for more information, see :mod:`~sage.graphs.base.overview`) * ``"dense"`` -- selects the :mod:`~sage.graphs.base.dense_graph` backend. * ``"sparse"`` -- selects the :mod:`~sage.graphs.base.sparse_graph` backend. * ``"static_sparse"`` -- selects the :mod:`~sage.graphs.base.static_sparse_backend` (this backend is faster than the sparse backend and smaller in memory, and it is immutable, so that the resulting graphs can be used as dictionary keys). - ``immutable`` -- boolean (default: ``False``); whether to create a immutable graph. Note that ``immutable=True`` is actually a shortcut for ``data_structure='static_sparse'``. Set to ``False`` by default. - ``vertex_labels`` -- boolean (default: ``True``); whether to allow any object as a vertex (slower), or only the integers `0,...,n-1`, where `n` is the number of vertices. - ``convert_empty_dict_labels_to_None`` -- this arguments sets the default edge labels used by NetworkX (empty dictionaries) to be replaced by ``None``, the default Sage edge label. It is set to ``True`` iff a NetworkX graph is on the input. EXAMPLES: We illustrate the first seven input formats (the other two involve packages that are currently not standard in Sage): #. An integer giving the number of vertices:: sage: g = Graph(5); g Graph on 5 vertices sage: g.vertices() [0, 1, 2, 3, 4] sage: g.edges() [] #. A dictionary of dictionaries:: sage: g = Graph({0:{1:'x',2:'z',3:'a'}, 2:{5:'out'}}); g Graph on 5 vertices The labels ('x', 'z', 'a', 'out') are labels for edges. For example, 'out' is the label for the edge on 2 and 5. Labels can be used as weights, if all the labels share some common parent.:: sage: a, b, c, d, e, f = sorted(SymmetricGroup(3)) # optional - sage.groups sage: Graph({b: {d: 'c', e: 'p'}, c: {d: 'p', e: 'c'}}) # optional - sage.groups Graph on 4 vertices #. A dictionary of lists:: sage: g = Graph({0:[1,2,3], 2:[4]}); g Graph on 5 vertices #. A list of vertices and a function describing adjacencies. Note that the list of vertices and the function must be enclosed in a list (i.e., [list of vertices, function]). Construct the Paley graph over GF(13).:: sage: g=Graph([GF(13), lambda i,j: i!=j and (i-j).is_square()]) sage: g.vertices() [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] sage: g.adjacency_matrix() [0 1 0 1 1 0 0 0 0 1 1 0 1] [1 0 1 0 1 1 0 0 0 0 1 1 0] [0 1 0 1 0 1 1 0 0 0 0 1 1] [1 0 1 0 1 0 1 1 0 0 0 0 1] [1 1 0 1 0 1 0 1 1 0 0 0 0] [0 1 1 0 1 0 1 0 1 1 0 0 0] [0 0 1 1 0 1 0 1 0 1 1 0 0] [0 0 0 1 1 0 1 0 1 0 1 1 0] [0 0 0 0 1 1 0 1 0 1 0 1 1] [1 0 0 0 0 1 1 0 1 0 1 0 1] [1 1 0 0 0 0 1 1 0 1 0 1 0] [0 1 1 0 0 0 0 1 1 0 1 0 1] [1 0 1 1 0 0 0 0 1 1 0 1 0] Construct the line graph of a complete graph.:: sage: g=graphs.CompleteGraph(4) sage: line_graph=Graph([g.edges(labels=false), \ lambda i,j: len(set(i).intersection(set(j)))>0], \ loops=False) sage: line_graph.vertices() [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)] sage: line_graph.adjacency_matrix() [0 1 1 1 1 0] [1 0 1 1 0 1] [1 1 0 0 1 1] [1 1 0 0 1 1] [1 0 1 1 0 1] [0 1 1 1 1 0] #. A graph6 or sparse6 string: Sage automatically recognizes whether a string is in graph6 or sparse6 format:: sage: s = ':I`AKGsaOs`cI]Gb~' sage: Graph(s,sparse=True) Looped multi-graph on 10 vertices :: sage: G = Graph('G?????') sage: G = Graph("G'?G?C") Traceback (most recent call last): ... RuntimeError: the string seems corrupt: valid characters are ?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~ sage: G = Graph('G??????') Traceback (most recent call last): ... RuntimeError: the string (G??????) seems corrupt: for n = 8, the string is too long :: sage: G = Graph(":I'AKGsaOs`cI]Gb~") Traceback (most recent call last): ... RuntimeError: the string seems corrupt: valid characters are ?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~ There are also list functions to take care of lists of graphs:: sage: s = ':IgMoqoCUOqeb\n:I`AKGsaOs`cI]Gb~\n:I`EDOAEQ?PccSsge\\N\n' sage: graphs_list.from_sparse6(s) [Looped multi-graph on 10 vertices, Looped multi-graph on 10 vertices, Looped multi-graph on 10 vertices] #. A Sage matrix: Note: If format is not specified, then Sage assumes a symmetric square matrix is an adjacency matrix, otherwise an incidence matrix. - an adjacency matrix:: sage: M = graphs.PetersenGraph().am(); M [0 1 0 0 1 1 0 0 0 0] [1 0 1 0 0 0 1 0 0 0] [0 1 0 1 0 0 0 1 0 0] [0 0 1 0 1 0 0 0 1 0] [1 0 0 1 0 0 0 0 0 1] [1 0 0 0 0 0 0 1 1 0] [0 1 0 0 0 0 0 0 1 1] [0 0 1 0 0 1 0 0 0 1] [0 0 0 1 0 1 1 0 0 0] [0 0 0 0 1 0 1 1 0 0] sage: Graph(M) Graph on 10 vertices :: sage: Graph(matrix([[1,2],[2,4]]),loops=True,sparse=True) Looped multi-graph on 2 vertices sage: M = Matrix([[0,1,-1],[1,0,-1/2],[-1,-1/2,0]]); M [ 0 1 -1] [ 1 0 -1/2] [ -1 -1/2 0] sage: G = Graph(M,sparse=True); G Graph on 3 vertices sage: G.weighted() True - an incidence matrix:: sage: M = Matrix(6, [-1,0,0,0,1, 1,-1,0,0,0, 0,1,-1,0,0, 0,0,1,-1,0, 0,0,0,1,-1, 0,0,0,0,0]); M [-1 0 0 0 1] [ 1 -1 0 0 0] [ 0 1 -1 0 0] [ 0 0 1 -1 0] [ 0 0 0 1 -1] [ 0 0 0 0 0] sage: Graph(M) Graph on 6 vertices sage: Graph(Matrix([[1],[1],[1]])) Traceback (most recent call last): ... ValueError: there must be one or two nonzero entries per column in an incidence matrix, got entries [1, 1, 1] in column 0 sage: Graph(Matrix([[1],[1],[0]])) Graph on 3 vertices sage: M = Matrix([[0,1,-1],[1,0,-1],[-1,-1,0]]); M [ 0 1 -1] [ 1 0 -1] [-1 -1 0] sage: Graph(M,sparse=True) Graph on 3 vertices sage: M = Matrix([[0,1,1],[1,0,1],[-1,-1,0]]); M [ 0 1 1] [ 1 0 1] [-1 -1 0] sage: Graph(M) Traceback (most recent call last): ... ValueError: there must be one or two nonzero entries per column in an incidence matrix, got entries [1, 1] in column 2 Check that :trac:`9714` is fixed:: sage: MA = Matrix([[1,2,0], [0,2,0], [0,0,1]]) sage: GA = Graph(MA, format='adjacency_matrix') sage: MI = GA.incidence_matrix(oriented=False) sage: MI [2 1 1 0 0 0] [0 1 1 2 2 0] [0 0 0 0 0 2] sage: Graph(MI).edges(labels=None) [(0, 0), (0, 1), (0, 1), (1, 1), (1, 1), (2, 2)] sage: M = Matrix([[1], [-1]]); M [ 1] [-1] sage: Graph(M).edges() [(0, 1, None)] #. A Seidel adjacency matrix:: sage: from sage.combinat.matrices.hadamard_matrix import \ ....: regular_symmetric_hadamard_matrix_with_constant_diagonal as rshcd sage: m=rshcd(16,1)- matrix.identity(16) sage: Graph(m,format="seidel_adjacency_matrix").is_strongly_regular(parameters=True) (16, 6, 2, 2) #. List of edges, or labelled edges:: sage: g = Graph([(1,3),(3,8),(5,2)]) sage: g Graph on 5 vertices sage: g = Graph([(1,2,"Peace"),(7,-9,"and"),(77,2, "Love")]) sage: g Graph on 5 vertices sage: g = Graph([(0, 2, '0'), (0, 2, '1'), (3, 3, '2')], loops=True, multiedges=True) sage: g.loops() [(3, 3, '2')] #. A NetworkX MultiGraph:: sage: import networkx sage: g = networkx.MultiGraph({0:[1,2,3], 2:[4]}) sage: Graph(g) Multi-graph on 5 vertices #. A NetworkX graph:: sage: import networkx sage: g = networkx.Graph({0:[1,2,3], 2:[4]}) sage: DiGraph(g) Digraph on 5 vertices #. An igraph Graph (see also :meth:`~sage.graphs.generic_graph.GenericGraph.igraph_graph`):: sage: import igraph # optional - python_igraph sage: g = igraph.Graph([(0, 1), (0, 2)]) # optional - python_igraph sage: Graph(g) # optional - python_igraph Graph on 3 vertices If ``vertex_labels`` is ``True``, the names of the vertices are given by the vertex attribute ``'name'``, if available:: sage: g = igraph.Graph([(0,1),(0,2)], vertex_attrs={'name':['a','b','c']}) # optional - python_igraph sage: Graph(g).vertices() # optional - python_igraph ['a', 'b', 'c'] sage: g = igraph.Graph([(0,1),(0,2)], vertex_attrs={'label':['a','b','c']}) # optional - python_igraph sage: Graph(g).vertices() # optional - python_igraph [0, 1, 2] If the igraph Graph has edge attributes, they are used as edge labels:: sage: g = igraph.Graph([(0,1),(0,2)], edge_attrs={'name':['a','b'], 'weight':[1,3]}) # optional - python_igraph sage: Graph(g).edges() # optional - python_igraph [(0, 1, {'name': 'a', 'weight': 1}), (0, 2, {'name': 'b', 'weight': 3})] When defining an undirected graph from a function ``f``, it is *very* important that ``f`` be symmetric. If it is not, anything can happen:: sage: f_sym = lambda x,y: abs(x-y) == 1 sage: f_nonsym = lambda x,y: (x-y) == 1 sage: G_sym = Graph([[4,6,1,5,3,7,2,0], f_sym]) sage: G_sym.is_isomorphic(graphs.PathGraph(8)) True sage: G_nonsym = Graph([[4,6,1,5,3,7,2,0], f_nonsym]) sage: G_nonsym.size() 4 sage: G_nonsym.is_isomorphic(G_sym) False By default, graphs are mutable and can thus not be used as a dictionary key:: sage: G = graphs.PetersenGraph() sage: {G:1}[G] Traceback (most recent call last): ... TypeError: This graph is mutable, and thus not hashable. Create an immutable copy by `g.copy(immutable=True)` When providing the optional arguments ``data_structure="static_sparse"`` or ``immutable=True`` (both mean the same), then an immutable graph results. :: sage: G_imm = Graph(G, immutable=True) sage: H_imm = Graph(G, data_structure='static_sparse') sage: G_imm == H_imm == G True sage: {G_imm:1}[H_imm] 1 TESTS:: sage: Graph(4, format="HeyHeyHey") Traceback (most recent call last): ... ValueError: Unknown input format 'HeyHeyHey' sage: Graph(igraph.Graph(directed=True)) # optional - python_igraph Traceback (most recent call last): ... ValueError: An *undirected* igraph graph was expected. To build an directed graph, call the DiGraph constructor. sage: m = matrix([[0, -1], [-1, 0]]) sage: Graph(m, format="seidel_adjacency_matrix") Graph on 2 vertices sage: m[0,1] = 1 sage: Graph(m, format="seidel_adjacency_matrix") Traceback (most recent call last): ... ValueError: the adjacency matrix of a Seidel graph must be symmetric sage: m[0,1] = -1; m[1,1] = 1 sage: Graph(m, format="seidel_adjacency_matrix") Traceback (most recent call last): ... ValueError: the adjacency matrix of a Seidel graph must have 0s on the main diagonal From a list of vertices and a list of edges:: sage: G = Graph([[1,2,3], [(1,2)]]); G Graph on 3 vertices sage: G.edges() [(1, 2, None)] Check that :trac:`27505` is fixed:: sage: Graph(Graph().networkx_graph(), weighted=None, format='NX') Graph on 0 vertices """ _directed = False def __init__(self, data=None, pos=None, loops=None, format=None, weighted=None, data_structure="sparse", vertex_labels=True, name=None, multiedges=None, convert_empty_dict_labels_to_None=None, sparse=True, immutable=False): """ TESTS:: sage: G = Graph() sage: loads(dumps(G)) == G True sage: a = matrix(2,2,[1,0,0,1]) sage: Graph(a).adjacency_matrix() == a True sage: a = matrix(2,2,[2,0,0,1]) sage: Graph(a,sparse=True).adjacency_matrix() == a True The positions are copied when the graph is built from another graph :: sage: g = graphs.PetersenGraph() sage: h = Graph(g) sage: g.get_pos() == h.get_pos() True The position dictionary is not the input one (:trac:`22424`):: sage: my_pos = {0:(0,0), 1:(1,1)} sage: G = Graph([[0,1], [(0,1)]], pos=my_pos) sage: my_pos == G._pos True sage: my_pos is G._pos False Or from a DiGraph :: sage: d = DiGraph(g) sage: h = Graph(d) sage: g.get_pos() == h.get_pos() True Loops are not counted as multiedges (see :trac:`11693`) and edges are not counted twice :: sage: Graph({1:[1]}).num_edges() 1 sage: Graph({1:[2,2]}).num_edges() 2 An empty list or dictionary defines a simple graph (:trac:`10441` and :trac:`12910`):: sage: Graph([]) Graph on 0 vertices sage: Graph({}) Graph on 0 vertices sage: # not "Multi-graph on 0 vertices" Verify that the int format works as expected (:trac:`12557`):: sage: Graph(2).adjacency_matrix() [0 0] [0 0] sage: Graph(3) == Graph(3,format='int') True Problem with weighted adjacency matrix (:trac:`13919`):: sage: B = {0:{1:2,2:5,3:4},1:{2:2,4:7},2:{3:1,4:4,5:3},3:{5:4},4:{5:1,6:5},5:{6:7}} sage: grafo3 = Graph(B,weighted=True) sage: matad = grafo3.weighted_adjacency_matrix() sage: grafo4 = Graph(matad,format = "adjacency_matrix", weighted=True) sage: grafo4.shortest_path(0,6,by_weight=True) [0, 1, 2, 5, 4, 6] Graphs returned when setting ``immutable=False`` are mutable:: sage: g = graphs.PetersenGraph() sage: g = Graph(g.edges(),immutable=False) sage: g.add_edge("Hey", "Heyyyyyyy") And their name is set:: sage: g = graphs.PetersenGraph() sage: Graph(g, immutable=True) Petersen graph: Graph on 10 vertices Check error messages for graphs built from incidence matrices (see :trac:`18440`):: sage: Graph(matrix([[-1, 1, 0],[1, 0, 0]])) Traceback (most recent call last): ... ValueError: column 1 of the (oriented) incidence matrix contains only one nonzero value sage: Graph(matrix([[1,1],[1,1],[1,0]])) Traceback (most recent call last): ... ValueError: there must be one or two nonzero entries per column in an incidence matrix, got entries [1, 1, 1] in column 0 sage: Graph(matrix([[3,1,1],[0,1,1]])) Traceback (most recent call last): ... ValueError: each column of a non-oriented incidence matrix must sum to 2, but column 0 does not Vertex labels are retained in the graph (:trac:`14708`):: sage: g = Graph() sage: g.add_vertex(0) sage: g.set_vertex(0, 'foo') sage: g.get_vertices() {0: 'foo'} sage: Graph(g).get_vertices() {0: 'foo'} """ GenericGraph.__init__(self) from sage.structure.element import is_Matrix if sparse is False: if data_structure != "sparse": raise ValueError("The 'sparse' argument is an alias for " "'data_structure'. Please do not define both.") data_structure = "dense" if multiedges or weighted: if data_structure == "dense": raise RuntimeError("Multiedge and weighted c_graphs must be sparse.") if immutable: data_structure = 'static_sparse' # If the data structure is static_sparse, we first build a graph # using the sparse data structure, then re-encode the resulting graph # as a static sparse graph. from sage.graphs.base.sparse_graph import SparseGraphBackend from sage.graphs.base.dense_graph import DenseGraphBackend if data_structure in ["sparse", "static_sparse"]: CGB = SparseGraphBackend elif data_structure == "dense": CGB = DenseGraphBackend else: raise ValueError("data_structure must be equal to 'sparse', " "'static_sparse' or 'dense'") self._backend = CGB(0, directed=False) if format is None and isinstance(data, str): if data.startswith(">>graph6<<"): data = data[10:] format = 'graph6' elif data.startswith(">>sparse6<<"): data = data[11:] format = 'sparse6' elif data[0] == ':': format = 'sparse6' else: format = 'graph6' if format is None and is_Matrix(data): if data.is_symmetric(): format = 'adjacency_matrix' else: format = 'incidence_matrix' if format is None and isinstance(data, Graph): format = 'Graph' from sage.graphs.all import DiGraph if format is None and isinstance(data, DiGraph): data = data.to_undirected() format = 'Graph' if (format is None and isinstance(data, list) and len(data) >= 2 and callable(data[1])): format = 'rule' if (format is None and isinstance(data, list) and len(data) == 2 and isinstance(data[0], list) and # a list of two lists, the second of ((isinstance(data[1], list) and # which contains iterables (the edges) (not data[1] or callable(getattr(data[1][0], "__iter__", None)))) or (isinstance(data[1], EdgesView)))): format = "vertices_and_edges" if format is None and isinstance(data, dict): if not data: format = 'dict_of_dicts' else: val = next(iter(data.values())) if isinstance(val, (list, EdgesView)): format = 'dict_of_lists' elif isinstance(val, dict): format = 'dict_of_dicts' if format is None and hasattr(data, 'adj'): # the input is a networkx (Multi)(Di)Graph format = 'NX' if (format is None and hasattr(data, 'vcount') and hasattr(data, 'get_edgelist')): try: import igraph except ImportError: raise ImportError("The data seems to be a igraph object, but "+ "igraph is not installed in Sage. To install "+ "it, run 'sage -i python_igraph'") if format is None and isinstance(data, igraph.Graph): format = 'igraph' if format is None and isinstance(data, (int, Integer)): format = 'int' if format is None and data is None: format = 'int' data = 0 # Input is a list of edges or an EdgesView if format is None and isinstance(data, (list, EdgesView)): format = "list_of_edges" if weighted is None: weighted = False if format is None: raise ValueError("This input cannot be turned into a graph") if format == 'weighted_adjacency_matrix': if weighted is False: raise ValueError("Format was weighted_adjacency_matrix but weighted was False.") if weighted is None: weighted = True if multiedges is None: multiedges = False format = 'adjacency_matrix' # At this point, 'format' has been set. We build the graph if format == 'graph6': if weighted is None: weighted = False self.allow_loops(loops if loops else False, check=False) self.allow_multiple_edges(multiedges if multiedges else False, check=False) from .graph_input import from_graph6 from_graph6(self, data) elif format == 'sparse6': if weighted is None: weighted = False self.allow_loops(False if loops is False else True, check=False) self.allow_multiple_edges(False if multiedges is False else True, check=False) from .graph_input import from_sparse6 from_sparse6(self, data) elif format == 'adjacency_matrix': from .graph_input import from_adjacency_matrix from_adjacency_matrix(self, data, loops=loops, multiedges=multiedges, weighted=weighted) elif format == 'incidence_matrix': from .graph_input import from_incidence_matrix from_incidence_matrix(self, data, loops=loops, multiedges=multiedges, weighted=weighted) elif format == 'seidel_adjacency_matrix': weighted = False self.allow_loops(False) self.allow_multiple_edges(False) from .graph_input import from_seidel_adjacency_matrix from_seidel_adjacency_matrix(self, data) elif format == 'Graph': if loops is None: loops = data.allows_loops() if multiedges is None: multiedges = data.allows_multiple_edges() if weighted is None: weighted = data.weighted() self.allow_loops(loops, check=False) self.allow_multiple_edges(multiedges, check=False) if data.get_pos() is not None: pos = data.get_pos() self.name(data.name()) self.set_vertices(data.get_vertices()) data._backend.subgraph_given_vertices(self._backend, data) elif format == 'NX': from sage.graphs.graph_input import from_networkx_graph from_networkx_graph(self, data, weighted=weighted, multiedges=multiedges, loops=loops, convert_empty_dict_labels_to_None=convert_empty_dict_labels_to_None) if weighted is None: weighted = self.allows_multiple_edges() elif format == 'igraph': if data.is_directed(): raise ValueError("An *undirected* igraph graph was expected. "+ "To build an directed graph, call the DiGraph "+ "constructor.") self.add_vertices(range(data.vcount())) self.add_edges((e.source, e.target, e.attributes()) for e in data.es()) if vertex_labels and 'name' in data.vertex_attributes(): vs = data.vs() self.relabel({v:vs[v]['name'] for v in self}) elif format == 'rule': f = data[1] verts = data[0] if loops is None: loops = any(f(v,v) for v in verts) if weighted is None: weighted = False self.allow_loops(loops, check=False) self.allow_multiple_edges(True if multiedges else False, check=False) self.add_vertices(verts) self.add_edges(e for e in itertools.combinations(verts,2) if f(*e)) if loops: self.add_edges((v,v) for v in verts if f(v,v)) elif format == "vertices_and_edges": self.allow_multiple_edges(bool(multiedges), check=False) self.allow_loops(bool(loops), check=False) self.add_vertices(data[0]) self.add_edges(data[1]) elif format == 'dict_of_dicts': from .graph_input import from_dict_of_dicts from_dict_of_dicts(self, data, loops=loops, multiedges=multiedges, weighted=weighted, convert_empty_dict_labels_to_None = False if convert_empty_dict_labels_to_None is None else convert_empty_dict_labels_to_None) elif format == 'dict_of_lists': from .graph_input import from_dict_of_lists from_dict_of_lists(self, data, loops=loops, multiedges=multiedges, weighted=weighted) elif format == 'int': self.allow_loops(loops if loops else False, check=False) self.allow_multiple_edges(multiedges if multiedges else False, check=False) if data < 0: raise ValueError("The number of vertices cannot be strictly negative!") if data: self.add_vertices(range(data)) elif format == 'list_of_edges': self.allow_multiple_edges(True if multiedges else False, check=False) self.allow_loops(True if loops else False, check=False) self.add_edges(data) else: raise ValueError("Unknown input format '{}'".format(format)) if weighted is None: weighted = False self._weighted = getattr(self, '_weighted', weighted) self._pos = copy(pos) if format != 'Graph' or name is not None: self.name(name) if data_structure == "static_sparse": from sage.graphs.base.static_sparse_backend import StaticSparseBackend ib = StaticSparseBackend(self, loops = self.allows_loops(), multiedges = self.allows_multiple_edges()) self._backend = ib self._immutable = True ### Formats @doc_index("Basic methods") def graph6_string(self): r""" Return the graph6 representation of the graph as an ASCII string. This is only valid for simple (no loops, no multiple edges) graphs on at most `2^{18}-1=262143` vertices. .. NOTE:: As the graph6 format only handles graphs with vertex set `\{0,...,n-1\}`, a :meth:`relabelled copy <sage.graphs.generic_graph.GenericGraph.relabel>` will be encoded, if necessary. .. SEEALSO:: * :meth:`~sage.graphs.digraph.DiGraph.dig6_string` -- a similar string format for directed graphs EXAMPLES:: sage: G = graphs.KrackhardtKiteGraph() sage: G.graph6_string() 'IvUqwK@?G' TESTS:: sage: Graph().graph6_string() '?' """ n = self.order() if n > 262143: raise ValueError('graph6 format supports graphs on 0 to 262143 vertices only.') elif self.has_loops() or self.has_multiple_edges(): raise ValueError('graph6 format supports only simple graphs (no loops, no multiple edges)') else: return generic_graph_pyx.small_integer_to_graph6(n) + generic_graph_pyx.binary_string_to_graph6(self._bit_vector()) @doc_index("Basic methods") def sparse6_string(self): r""" Return the sparse6 representation of the graph as an ASCII string. Only valid for undirected graphs on 0 to 262143 vertices, but loops and multiple edges are permitted. .. NOTE:: As the sparse6 format only handles graphs whose vertex set is `\{0,...,n-1\}`, a :meth:`relabelled copy <sage.graphs.generic_graph.GenericGraph.relabel>` of your graph will be encoded if necessary. EXAMPLES:: sage: G = graphs.BullGraph() sage: G.sparse6_string() ':Da@en' :: sage: G = Graph(loops=True, multiedges=True, data_structure="sparse") sage: Graph(':?', data_structure="sparse") == G True TESTS:: sage: G = Graph() sage: G.sparse6_string() ':?' Check that :trac:`18445` is fixed:: sage: Graph(graphs.KneserGraph(5,2).sparse6_string()).size() 15 Graphs with 1 vertex are correctly handled (:trac:`24923`):: sage: Graph([(0, 0)], loops=True).sparse6_string() ':@^' sage: G = Graph(_) sage: G.order(), G.size() (1, 1) sage: Graph([(0, 0), (0, 0)], loops=True, multiedges=True).sparse6_string() ':@N' sage: H = Graph(_) sage: H.order(), H.size() (1, 2) Sparse6 encoding of canonical graph is unique (:trac:`31026`):: sage: G = Graph([(0,1),(1,2),(2,3),(3,0),(0,2)]) sage: H = Graph([(0,1),(1,2),(2,3),(3,0),(1,3)]) sage: G == H False sage: G.is_isomorphic(H) True sage: G.sparse6_string() == H.sparse6_string() False sage: G_ = G.canonical_label() sage: H_ = H.canonical_label() sage: G_ == H_ True sage: G_.sparse6_string() == H_.sparse6_string() True The method can handle vertices with different types (:trac:`31026`):: sage: G = Graph([(1, 'a')]) sage: H = Graph(G.sparse6_string()) sage: G.is_isomorphic(H) True sage: set(G) == set(H) False """ n = self.order() if not n: return ':?' if n > 262143: raise ValueError('sparse6 format supports graphs on 0 to 262143 vertices only.') if n == 1: s = '0' * self.size() else: try: V = sorted(self) except TypeError: V = self v_to_int = {v:i for i,v in enumerate(V)} edges = [sorted((v_to_int[u], v_to_int[v])) for u,v in self.edge_iterator(labels=False)] edges.sort(key=lambda e: (e[1], e[0])) # reverse lexicographic order # encode bit vector k = int((ZZ(n) - 1).nbits()) v = 0 i = 0 m = 0 s = '' while m < len(edges): if edges[m][1] > v + 1: sp = generic_graph_pyx.int_to_binary_string(edges[m][1]) sp = '0'*(k-len(sp)) + sp s += '1' + sp v = edges[m][1] elif edges[m][1] == v + 1: sp = generic_graph_pyx.int_to_binary_string(edges[m][0]) sp = '0'*(k-len(sp)) + sp s += '1' + sp v += 1 m += 1 else: sp = generic_graph_pyx.int_to_binary_string(edges[m][0]) sp = '0'*(k-len(sp)) + sp s += '0' + sp m += 1 # encode s as a 6-string, as in R(x), but padding with 1's # pad on the right to make a multiple of 6 s = s + ( '1' * ((6 - len(s))%6) ) # split into groups of 6, and convert numbers to decimal, adding 63 six_bits = '' for i in range(0, len(s), 6): six_bits += chr( int( s[i:i+6], 2) + 63 ) return ':' + generic_graph_pyx.small_integer_to_graph6(n) + six_bits ### Attributes @doc_index("Basic methods") def is_directed(self): """ Since graph is undirected, returns False. EXAMPLES:: sage: Graph().is_directed() False """ return False ### Properties @doc_index("Graph properties") def is_tree(self, certificate=False, output='vertex'): r""" Tests if the graph is a tree The empty graph is defined to be not a tree. INPUT: - ``certificate`` -- boolean (default: ``False``); whether to return a certificate. The method only returns boolean answers when ``certificate = False`` (default). When it is set to ``True``, it either answers ``(True, None)`` when the graph is a tree or ``(False, cycle)`` when it contains a cycle. It returns ``(False, None)`` when the graph is empty or not connected. - ``output`` -- either ``'vertex'`` (default) or ``'edge'``; whether the certificate is given as a list of vertices (``output = 'vertex'``) or a list of edges (``output = 'edge'``). When the certificate cycle is given as a list of edges, the edges are given as `(v_i, v_{i+1}, l)` where `v_1, v_2, \dots, v_n` are the vertices of the cycles (in their cyclic order). EXAMPLES:: sage: all(T.is_tree() for T in graphs.trees(15)) True With certificates:: sage: g = graphs.RandomTree(30) sage: g.is_tree(certificate=True) (True, None) sage: g.add_edge(10,-1) sage: g.add_edge(11,-1) sage: isit, cycle = g.is_tree(certificate=True) sage: isit False sage: -1 in cycle True One can also ask for the certificate as a list of edges:: sage: g = graphs.CycleGraph(4) sage: g.is_tree(certificate=True, output='edge') (False, [(3, 2, None), (2, 1, None), (1, 0, None), (0, 3, None)]) This is useful for graphs with multiple edges:: sage: G = Graph([(1, 2, 'a'), (1, 2, 'b')], multiedges=True) sage: G.is_tree(certificate=True) (False, [1, 2]) sage: G.is_tree(certificate=True, output='edge') (False, [(1, 2, 'a'), (2, 1, 'b')]) TESTS: :trac:`14434` is fixed:: sage: g = Graph({0:[1,4,5],3:[4,8,9],4:[9],5:[7,8],7:[9]}) sage: _,cycle = g.is_tree(certificate=True) sage: g.size() 10 sage: g.add_cycle(cycle) sage: g.size() 10 The empty graph:: sage: graphs.EmptyGraph().is_tree() False sage: graphs.EmptyGraph().is_tree(certificate=True) (False, None) :trac:`22912` is fixed:: sage: G = Graph([(0,0), (0,1)], loops=True) sage: G.is_tree(certificate=True) (False, [0]) sage: G.is_tree(certificate=True, output='edge') (False, [(0, 0, None)]) """ if output not in ['vertex', 'edge']: raise ValueError('output must be either vertex or edge') if not self.order() or not self.is_connected(): return (False, None) if certificate else False if certificate: if self.order() == self.size() + 1: return (True, None) if self.allows_loops(): L = self.loop_edges() if output == 'edge' else self.loop_vertices() if L: return False, L[:1] if self.has_multiple_edges(): if output == 'vertex': return (False, list(self.multiple_edges(sort=True)[0][:2])) edge1, edge2 = self.multiple_edges(sort=True)[:2] if edge1[0] != edge2[0]: return (False, [edge1, edge2]) return (False, [edge1, (edge2[1], edge2[0], edge2[2])]) if output == 'edge': if self.allows_multiple_edges(): def vertices_to_edges(x): return [(u[0], u[1], self.edge_label(u[0], u[1])[0]) for u in zip(x, x[1:] + [x[0]])] else: def vertices_to_edges(x): return [(u[0], u[1], self.edge_label(u[0], u[1])) for u in zip(x, x[1:] + [x[0]])] # This code is a depth-first search that looks for a cycle in the # graph. We *know* it exists as there are too many edges around. seen = {} u = next(self.vertex_iterator()) seen[u] = u stack = [(u, v) for v in self.neighbor_iterator(u)] while stack: u, v = stack.pop() if v in seen: continue for w in self.neighbor_iterator(v): if u == w: continue elif w in seen: cycle = [w, v] while u != w: cycle.append(u) u = seen[u] cycle.reverse() if output == 'vertex': return (False, cycle) return (False, vertices_to_edges(cycle)) else: stack.append((v, w)) seen[v] = u else: return self.order() == self.size() + 1 @doc_index("Graph properties") def is_forest(self, certificate=False, output='vertex'): """ Tests if the graph is a forest, i.e. a disjoint union of trees. INPUT: - ``certificate`` -- boolean (default: ``False``); whether to return a certificate. The method only returns boolean answers when ``certificate = False`` (default). When it is set to ``True``, it either answers ``(True, None)`` when the graph is a forest or ``(False, cycle)`` when it contains a cycle. - ``output`` -- either ``'vertex'`` (default) or ``'edge'``; whether the certificate is given as a list of vertices (``output = 'vertex'``) or a list of edges (``output = 'edge'``). EXAMPLES:: sage: seven_acre_wood = sum(graphs.trees(7), Graph()) sage: seven_acre_wood.is_forest() True With certificates:: sage: g = graphs.RandomTree(30) sage: g.is_forest(certificate=True) (True, None) sage: (2*g + graphs.PetersenGraph() + g).is_forest(certificate=True) (False, [68, 66, 69, 67, 65]) """ connected_components = self.connected_components() number_of_connected_components = len(connected_components) isit = (self.order() == self.size() + number_of_connected_components) if not certificate: return isit else: if isit: return (True, None) # The graph contains a cycle, and the user wants to see it. # No need to copy the graph if number_of_connected_components == 1: return self.is_tree(certificate=True, output=output) # We try to find a cycle in each connected component for cc in connected_components: isit, cycle = self.subgraph(cc).is_tree(certificate=True, output=output) if not isit: return (False, cycle) @doc_index("Graph properties") def is_cactus(self): """ Check whether the graph is cactus graph. A graph is called *cactus graph* if it is connected and every pair of simple cycles have at most one common vertex. There are other definitions, see the :wikipedia:`Cactus_graph`. EXAMPLES:: sage: g = Graph({1: [2], 2: [3, 4], 3: [4, 5, 6, 7], 8: [3, 5], 9: [6, 7]}) sage: g.is_cactus() True sage: c6 = graphs.CycleGraph(6) sage: naphthalene = c6 + c6 sage: naphthalene.is_cactus() # Not connected False sage: naphthalene.merge_vertices([0, 6]) sage: naphthalene.is_cactus() True sage: naphthalene.merge_vertices([1, 7]) sage: naphthalene.is_cactus() False TESTS:: sage: all(graphs.PathGraph(i).is_cactus() for i in range(5)) True sage: Graph('Fli@?').is_cactus() False Test a graph that is not outerplanar, see :trac:`24480`:: sage: graphs.Balaban10Cage().is_cactus() False """ self._scream_if_not_simple() # Special cases if self.order() < 4: return True if self.size() > 3 * (self.order() - 1) / 2: return False # Every cactus graph is outerplanar if not self.is_circular_planar(): return False if not self.is_connected(): return False # the number of faces is 1 plus the number of blocks of order > 2 B = self.blocks_and_cut_vertices()[0] return len(self.faces()) == sum(1 for b in B if len(b) > 2) + 1 @doc_index("Graph properties") def is_biconnected(self): """ Test if the graph is biconnected. A biconnected graph is a connected graph on two or more vertices that is not broken into disconnected pieces by deleting any single vertex. .. SEEALSO:: - :meth:`~sage.graphs.generic_graph.GenericGraph.is_connected` - :meth:`~sage.graphs.generic_graph.GenericGraph.blocks_and_cut_vertices` - :meth:`~sage.graphs.generic_graph.GenericGraph.blocks_and_cuts_tree` - :wikipedia:`Biconnected_graph` EXAMPLES:: sage: G = graphs.PetersenGraph() sage: G.is_biconnected() True sage: G.add_path([0,'a','b']) sage: G.is_biconnected() False sage: G.add_edge('b', 1) sage: G.is_biconnected() True TESTS:: sage: Graph().is_biconnected() False sage: Graph(1).is_biconnected() False sage: graphs.CompleteGraph(2).is_biconnected() True """ if self.order() < 2 or not self.is_connected(): return False if self.blocks_and_cut_vertices()[1]: return False return True @doc_index("Graph properties") def is_block_graph(self): r""" Return whether this graph is a block graph. A block graph is a connected graph in which every biconnected component (block) is a clique. .. SEEALSO:: - :wikipedia:`Block_graph` for more details on these graphs - :meth:`~sage.graphs.graph_generators.GraphGenerators.RandomBlockGraph` -- generator of random block graphs - :meth:`~sage.graphs.generic_graph.GenericGraph.blocks_and_cut_vertices` - :meth:`~sage.graphs.generic_graph.GenericGraph.blocks_and_cuts_tree` EXAMPLES:: sage: G = graphs.RandomBlockGraph(6, 2, kmax=4) sage: G.is_block_graph() True sage: from sage.graphs.isgci import graph_classes sage: G in graph_classes.Block True sage: graphs.CompleteGraph(4).is_block_graph() True sage: graphs.RandomTree(6).is_block_graph() True sage: graphs.PetersenGraph().is_block_graph() False sage: Graph(4).is_block_graph() False """ if not self.is_connected(): return False if self.is_clique(): return True B,C = self.blocks_and_cut_vertices() return all(self.is_clique(vertices=block) for block in B) @doc_index("Graph properties") def is_cograph(self): """ Check whether the graph is cograph. A cograph is defined recursively: the single-vertex graph is cograph, complement of cograph is cograph, and disjoint union of two cographs is cograph. There are many other characterizations, see the :wikipedia:`Cograph`. EXAMPLES:: sage: graphs.HouseXGraph().is_cograph() True sage: graphs.HouseGraph().is_cograph() False .. TODO:: Implement faster recognition algorithm, as for instance the linear time recognition algorithm using LexBFS proposed in [Bre2008]_. TESTS:: sage: [graphs.PathGraph(i).is_cograph() for i in range(6)] [True, True, True, True, False, False] sage: graphs.CycleGraph(5).is_cograph() # Self-complemented False """ # A cograph has no 4-vertex path as an induced subgraph. # We will first try to "decompose" graph by complements and # split to connected components, and use fairly slow # subgraph search if that fails. self._scream_if_not_simple() if self.order() < 4: return True if self.density()*2 > 1: return self.complement().is_cograph() if not self.is_connected(): return all(part.is_cograph() for part in self.connected_components_subgraphs()) P4 = Graph({0: [1], 1: [2], 2: [3]}) return self.subgraph_search(P4, induced=True) is None @doc_index("Graph properties") def is_apex(self): r""" Test if the graph is apex. A graph is apex if it can be made planar by the removal of a single vertex. The deleted vertex is called ``an apex`` of the graph, and a graph may have more than one apex. For instance, in the minimal nonplanar graphs `K_5` or `K_{3,3}`, every vertex is an apex. The apex graphs include graphs that are themselves planar, in which case again every vertex is an apex. The null graph is also counted as an apex graph even though it has no vertex to remove. If the graph is not connected, we say that it is apex if it has at most one non planar connected component and that this component is apex. See the :wikipedia:`Apex_graph` for more information. .. SEEALSO:: - :meth:`~Graph.apex_vertices` - :meth:`~sage.graphs.generic_graph.GenericGraph.is_planar` EXAMPLES: `K_5` and `K_{3,3}` are apex graphs, and each of their vertices is an apex:: sage: G = graphs.CompleteGraph(5) sage: G.is_apex() True sage: G = graphs.CompleteBipartiteGraph(3,3) sage: G.is_apex() True The Petersen graph is not apex:: sage: G = graphs.PetersenGraph() sage: G.is_apex() False A graph is apex if all its connected components are apex, but at most one is not planar:: sage: M = graphs.Grid2dGraph(3,3) sage: K5 = graphs.CompleteGraph(5) sage: (M+K5).is_apex() True sage: (M+K5+K5).is_apex() False TESTS: The null graph is apex:: sage: G = Graph() sage: G.is_apex() True The graph might be mutable or immutable:: sage: G = Graph(M+K5, immutable=True) sage: G.is_apex() True """ # Easy cases: null graph, subgraphs of K_5 and K_3,3 if self.order() <= 5 or ( self.order() <= 6 and self.is_bipartite() ): return True return len(self.apex_vertices(k=1)) > 0 @doc_index("Graph properties") def apex_vertices(self, k=None): r""" Return the list of apex vertices. A graph is apex if it can be made planar by the removal of a single vertex. The deleted vertex is called ``an apex`` of the graph, and a graph may have more than one apex. For instance, in the minimal nonplanar graphs `K_5` or `K_{3,3}`, every vertex is an apex. The apex graphs include graphs that are themselves planar, in which case again every vertex is an apex. The null graph is also counted as an apex graph even though it has no vertex to remove. If the graph is not connected, we say that it is apex if it has at most one non planar connected component and that this component is apex. See the :wikipedia:`Apex_graph` for more information. .. SEEALSO:: - :meth:`~Graph.is_apex` - :meth:`~sage.graphs.generic_graph.GenericGraph.is_planar` INPUT: - ``k`` -- integer (default: ``None``); when set to ``None``, the method returns the list of all apex of the graph, possibly empty if the graph is not apex. When set to a positive integer, the method ends as soon as `k` apex vertices are found. OUTPUT: By default, the method returns the list of all apex of the graph. When parameter ``k`` is set to a positive integer, the returned list is bounded to `k` apex vertices. EXAMPLES: `K_5` and `K_{3,3}` are apex graphs, and each of their vertices is an apex:: sage: G = graphs.CompleteGraph(5) sage: G.apex_vertices() [0, 1, 2, 3, 4] sage: G = graphs.CompleteBipartiteGraph(3,3) sage: G.is_apex() True sage: G.apex_vertices() [0, 1, 2, 3, 4, 5] sage: G.apex_vertices(k=3) [0, 1, 2] A `4\\times 4`-grid is apex and each of its vertices is an apex. When adding a universal vertex, the resulting graph is apex and the universal vertex is the unique apex vertex :: sage: G = graphs.Grid2dGraph(4,4) sage: set(G.apex_vertices()) == set(G.vertices()) True sage: G.add_edges([('universal',v) for v in G]) sage: G.apex_vertices() ['universal'] The Petersen graph is not apex:: sage: G = graphs.PetersenGraph() sage: G.apex_vertices() [] A graph is apex if all its connected components are apex, but at most one is not planar:: sage: M = graphs.Grid2dGraph(3,3) sage: K5 = graphs.CompleteGraph(5) sage: (M+K5).apex_vertices() [9, 10, 11, 12, 13] sage: (M+K5+K5).apex_vertices() [] Neighbors of an apex of degree 2 are apex:: sage: G = graphs.Grid2dGraph(5,5) sage: v = (666, 666) sage: G.add_path([(1, 1), v, (3, 3)]) sage: G.is_planar() False sage: G.degree(v) 2 sage: sorted(G.apex_vertices()) [(1, 1), (2, 2), (3, 3), (666, 666)] TESTS: The null graph is apex although it has no apex vertex:: sage: G = Graph() sage: G.apex_vertices() [] Parameter ``k`` cannot be a negative integer:: sage: G.apex_vertices(k=-1) Traceback (most recent call last): ... ValueError: parameter k must be a non negative integer The graph might be mutable or immutable:: sage: G = Graph(M+K5, immutable=True) sage: G.apex_vertices() [9, 10, 11, 12, 13] """ if k is None: k = self.order() elif k < 0: raise ValueError("parameter k must be a non negative integer") # Easy cases: null graph, subgraphs of K_5 and K_3,3 if self.order() <= 5 or (self.order() <= 6 and self.is_bipartite()): it = self.vertex_iterator() return [next(it) for _ in range(k)] if not self.is_connected(): # We search for its non planar connected components. If it has more # than one such component, the graph is not apex. It is apex if # either it has no such component, in which case the graph is # planar, or if its unique non planar component is apex. P = [H for H in self.connected_components_subgraphs() if not H.is_planar()] if not P: # The graph is planar it = self.vertex_iterator() return [next(it) for _ in range(k)] elif len(P) > 1: return [] else: # We proceed with the non planar component if P[0].is_immutable(): H = Graph(P[0].edges(labels=0, sort=False), immutable=False, loops=False, multiedges=False) else: H = P[0] elif self.is_planar(): # A planar graph is apex. it = self.vertex_iterator() return [next(it) for _ in range(k)] else: # We make a basic copy of the graph since we will modify it H = Graph(self.edges(labels=0, sort=False), immutable=False, loops=False, multiedges=False) # General case: basic implementation # # Test for each vertex if its removal makes the graph planar. # Obviously, we don't test vertices of degree one. Furthermore, if a # vertex of degree 2 is an apex, its neighbors also are. So we start # with vertices of degree 2. V = {} for u in H: d = H.degree(u) if d > 1: if d in V: V[d].append(u) else: V[d] = [u] apex = set() for deg in sorted(V): for u in V[deg]: if u in apex: # True if neighbor of an apex of degree 2 if deg == 2: # We ensure that its neighbors are known apex apex.update(H.neighbor_iterator(u)) if len(apex) >= k: return list(apex)[:k] continue E = H.edges_incident(u, labels=0) H.delete_vertex(u) if H.is_planar(): apex.add(u) if deg == 2: # The neighbors of an apex of degree 2 also are apex.update(self.neighbor_iterator(u)) if len(apex) >= k: return list(apex)[:k] H.add_edges(E) return list(apex) @doc_index("Graph properties") def is_overfull(self): r""" Tests whether the current graph is overfull. A graph `G` on `n` vertices and `m` edges is said to be overfull if: - `n` is odd - It satisfies `2m > (n-1)\Delta(G)`, where `\Delta(G)` denotes the maximum degree among all vertices in `G`. An overfull graph must have a chromatic index of `\Delta(G)+1`. EXAMPLES: A complete graph of order `n > 1` is overfull if and only if `n` is odd:: sage: graphs.CompleteGraph(6).is_overfull() False sage: graphs.CompleteGraph(7).is_overfull() True sage: graphs.CompleteGraph(1).is_overfull() False The claw graph is not overfull:: sage: from sage.graphs.graph_coloring import edge_coloring sage: g = graphs.ClawGraph() sage: g Claw graph: Graph on 4 vertices sage: edge_coloring(g, value_only=True) 3 sage: g.is_overfull() False The Holt graph is an example of a overfull graph:: sage: G = graphs.HoltGraph() sage: G.is_overfull() True Checking that all complete graphs `K_n` for even `0 \leq n \leq 100` are not overfull:: sage: def check_overfull_Kn_even(n): ....: i = 0 ....: while i <= n: ....: if graphs.CompleteGraph(i).is_overfull(): ....: print("A complete graph of even order cannot be overfull.") ....: return ....: i += 2 ....: print("Complete graphs of even order up to %s are not overfull." % n) ... sage: check_overfull_Kn_even(100) # long time Complete graphs of even order up to 100 are not overfull. The null graph, i.e. the graph with no vertices, is not overfull:: sage: Graph().is_overfull() False sage: graphs.CompleteGraph(0).is_overfull() False Checking that all complete graphs `K_n` for odd `1 < n \leq 100` are overfull:: sage: def check_overfull_Kn_odd(n): ....: i = 3 ....: while i <= n: ....: if not graphs.CompleteGraph(i).is_overfull(): ....: print("A complete graph of odd order > 1 must be overfull.") ....: return ....: i += 2 ....: print("Complete graphs of odd order > 1 up to %s are overfull." % n) ... sage: check_overfull_Kn_odd(100) # long time Complete graphs of odd order > 1 up to 100 are overfull. The Petersen Graph, though, is not overfull while its chromatic index is `\Delta+1`:: sage: g = graphs.PetersenGraph() sage: g.is_overfull() False sage: from sage.graphs.graph_coloring import edge_coloring sage: max(g.degree()) + 1 == edge_coloring(g, value_only=True) True """ # # A possible optimized version. But the gain in speed is very little. # return bool(self._backend.num_verts() & 1) and ( # odd order n # 2 * self._backend.num_edges(self._directed) > #2m > \Delta(G)*(n-1) # max(self.degree()) * (self._backend.num_verts() - 1)) # unoptimized version return (self.order() % 2 == 1) and ( 2 * self.size() > max(self.degree()) * (self.order() - 1)) @doc_index("Graph properties") def is_even_hole_free(self, certificate=False): r""" Tests whether ``self`` contains an induced even hole. A Hole is a cycle of length at least 4 (included). It is said to be even (resp. odd) if its length is even (resp. odd). Even-hole-free graphs always contain a bisimplicial vertex, which ensures that their chromatic number is at most twice their clique number [ACHRS2008]_. INPUT: - ``certificate`` -- boolean (default: ``False``); when ``certificate = False``, this method only returns ``True`` or ``False``. If ``certificate = True``, the subgraph found is returned instead of ``False``. EXAMPLES: Is the Petersen Graph even-hole-free :: sage: g = graphs.PetersenGraph() sage: g.is_even_hole_free() False As any chordal graph is hole-free, interval graphs behave the same way:: sage: g = graphs.RandomIntervalGraph(20) sage: g.is_even_hole_free() True It is clear, though, that a random Bipartite Graph which is not a forest has an even hole:: sage: g = graphs.RandomBipartite(10, 10, .5) sage: g.is_even_hole_free() and not g.is_forest() False We can check the certificate returned is indeed an even cycle:: sage: if not g.is_forest(): ....: cycle = g.is_even_hole_free(certificate=True) ....: if cycle.order() % 2 == 1: ....: print("Error !") ....: if not cycle.is_isomorphic( ....: graphs.CycleGraph(cycle.order())): ....: print("Error !") ... sage: print("Everything is Fine !") Everything is Fine ! TESTS: Bug reported in :trac:`9925`, and fixed by :trac:`9420`:: sage: g = Graph(':SiBFGaCEF_@CE`DEGH`CEFGaCDGaCDEHaDEF`CEH`ABCDEF', loops=False, multiedges=False) sage: g.is_even_hole_free() False sage: g.is_even_hole_free(certificate=True) Subgraph of (): Graph on 4 vertices Making sure there are no other counter-examples around :: sage: t = lambda x: (Graph(x).is_forest() or ....: isinstance(Graph(x).is_even_hole_free(certificate=True), Graph)) sage: all( t(graphs.RandomBipartite(10, 10, .5)) for i in range(100) ) True """ girth = self.girth() if girth > self.order(): start = 4 elif not girth % 2: if not certificate: return False start = girth else: start = girth + 1 from sage.graphs.generators.basic import CycleGraph while start <= self.order(): subgraph = self.subgraph_search(CycleGraph(start), induced=True) if subgraph is not None: if certificate: return subgraph else: return False start += 2 return True @doc_index("Graph properties") def is_odd_hole_free(self, certificate=False): r""" Tests whether ``self`` contains an induced odd hole. A Hole is a cycle of length at least 4 (included). It is said to be even (resp. odd) if its length is even (resp. odd). It is interesting to notice that while it is polynomial to check whether a graph has an odd hole or an odd antihole [CCLSV2005]_, it is not known whether testing for one of these two cases independently is polynomial too. INPUT: - ``certificate`` -- boolean (default: ``False``); when ``certificate = False``, this method only returns ``True`` or ``False``. If ``certificate = True``, the subgraph found is returned instead of ``False``. EXAMPLES: Is the Petersen Graph odd-hole-free :: sage: g = graphs.PetersenGraph() sage: g.is_odd_hole_free() False Which was to be expected, as its girth is 5 :: sage: g.girth() 5 We can check the certificate returned is indeed a 5-cycle:: sage: cycle = g.is_odd_hole_free(certificate=True) sage: cycle.is_isomorphic(graphs.CycleGraph(5)) True As any chordal graph is hole-free, no interval graph has an odd hole:: sage: g = graphs.RandomIntervalGraph(20) sage: g.is_odd_hole_free() True """ girth = self.odd_girth() if girth > self.order(): return True if girth == 3: start = 5 else: if not certificate: return False start = girth from sage.graphs.generators.basic import CycleGraph while start <= self.order(): subgraph = self.subgraph_search(CycleGraph(start), induced=True) if subgraph is not None: if certificate: return subgraph else: return False start += 2 return True @doc_index("Graph properties") def is_triangle_free(self, algorithm='dense_graph', certificate=False): r""" Check whether ``self`` is triangle-free INPUT: - ``algorithm`` -- (default: ``'dense_graph'``) specifies the algorithm to use among: - ``'matrix'`` -- tests if the trace of the adjacency matrix is positive. - ``'bitset'`` -- encodes adjacencies into bitsets and uses fast bitset operations to test if the input graph contains a triangle. This method is generally faster than standard matrix multiplication. - ``'dense_graph'`` -- use the implementation of :mod:`sage.graphs.base.static_dense_graph` - ``certificate`` -- boolean (default: ``False``); whether to return a triangle if one is found. This parameter is ignored when ``algorithm`` is ``'matrix'``. EXAMPLES: The Petersen Graph is triangle-free:: sage: g = graphs.PetersenGraph() sage: g.is_triangle_free() True or a complete Bipartite Graph:: sage: G = graphs.CompleteBipartiteGraph(5,6) sage: G.is_triangle_free(algorithm='matrix') True sage: G.is_triangle_free(algorithm='bitset') True sage: G.is_triangle_free(algorithm='dense_graph') True a tripartite graph, though, contains many triangles:: sage: G = (3 * graphs.CompleteGraph(5)).complement() sage: G.is_triangle_free(algorithm='matrix') False sage: G.is_triangle_free(algorithm='bitset') False sage: G.is_triangle_free(algorithm='dense_graph') False Asking for a certificate:: sage: K4 = graphs.CompleteGraph(4) sage: K4.is_triangle_free(algorithm='dense_graph', certificate=True) (False, [0, 1, 2]) sage: K4.is_triangle_free(algorithm='bitset', certificate=True) (False, [0, 1, 2]) TESTS: Comparison of algorithms:: sage: for i in range(10): # long time ....: G = graphs.RandomBarabasiAlbert(50,2) ....: bm = G.is_triangle_free(algorithm='matrix') ....: bb = G.is_triangle_free(algorithm='bitset') ....: bd = G.is_triangle_free(algorithm='dense_graph') ....: if bm != bb or bm != bd: ....: print("That's not good!") Asking for an unknown algorithm:: sage: g.is_triangle_free(algorithm='tip top') Traceback (most recent call last): ... ValueError: Algorithm 'tip top' not yet implemented. Please contribute. Check the empty graph:: sage: graphs.EmptyGraph().is_triangle_free() True """ if algorithm == 'dense_graph': from sage.graphs.base.static_dense_graph import is_triangle_free return is_triangle_free(self, certificate=certificate) if algorithm == 'bitset': if self.order() < 3: return (True, []) if certificate else True from sage.data_structures.bitset import Bitset N = self.order() vertex_to_int = {} B = {} for i, u in enumerate(self): vertex_to_int[u] = i B[u] = Bitset(capacity=N) # map adjacency to bitsets for u, v in self.edge_iterator(labels=None): if u != v: B[u].add(vertex_to_int[v]) B[v].add(vertex_to_int[u]) # Search for a triangle for u, v in self.edge_iterator(labels=None): BB = B[u] & B[v] if BB: if certificate: for w in self.neighbor_iterator(u): if vertex_to_int[w] in BB: return False, [u, v, w] return False return (True, []) if certificate else True elif algorithm == 'matrix': if self.order() < 3: return True return (self.adjacency_matrix()**3).trace() == 0 else: raise ValueError("Algorithm '%s' not yet implemented. Please contribute." %(algorithm)) @doc_index("Graph properties") def is_split(self): r""" Returns ``True`` if the graph is a Split graph, ``False`` otherwise. A Graph `G` is said to be a split graph if its vertices `V(G)` can be partitioned into two sets `K` and `I` such that the vertices of `K` induce a complete graph, and those of `I` are an independent set. There is a simple test to check whether a graph is a split graph (see, for instance, the book "Graph Classes, a survey" [BLS1999]_ page 203) : Given the degree sequence `d_1 \geq ... \geq d_n` of `G`, a graph is a split graph if and only if : .. MATH:: \sum_{i=1}^\omega d_i = \omega (\omega - 1) + \sum_{i=\omega + 1}^nd_i where `\omega = max \{i:d_i\geq i-1\}`. EXAMPLES: Split graphs are, in particular, chordal graphs. Hence, The Petersen graph can not be split:: sage: graphs.PetersenGraph().is_split() False We can easily build some "random" split graph by creating a complete graph, and adding vertices only connected to some random vertices of the clique:: sage: g = graphs.CompleteGraph(10) sage: sets = Subsets(Set(range(10))) sage: for i in range(10, 25): ....: g.add_edges([(i,k) for k in sets.random_element()]) sage: g.is_split() True Another characterisation of split graph states that a graph is a split graph if and only if does not contain the 4-cycle, 5-cycle or `2K_2` as an induced subgraph. Hence for the above graph we have:: sage: forbidden_subgraphs = [graphs.CycleGraph(4), graphs.CycleGraph(5), 2 * graphs.CompleteGraph(2)] sage: sum(g.subgraph_search_count(H,induced=True) for H in forbidden_subgraphs) 0 """ self._scream_if_not_simple() # our degree sequence is numbered from 0 to n-1, so to avoid # any mistake, let's fix it :-) degree_sequence = [0] + sorted(self.degree(), reverse=True) for i, d in enumerate(degree_sequence): if d >= i - 1: omega = i else: break left = sum(degree_sequence[:omega + 1]) right = omega * (omega - 1) + sum(degree_sequence[omega + 1:]) return left == right @doc_index("Algorithmically hard stuff") def is_perfect(self, certificate=False): r""" Tests whether the graph is perfect. A graph `G` is said to be perfect if `\chi(H)=\omega(H)` hold for any induced subgraph `H\subseteq_i G` (and so for `G` itself, too), where `\chi(H)` represents the chromatic number of `H`, and `\omega(H)` its clique number. The Strong Perfect Graph Theorem [CRST2006]_ gives another characterization of perfect graphs: A graph is perfect if and only if it contains no odd hole (cycle on an odd number `k` of vertices, `k>3`) nor any odd antihole (complement of a hole) as an induced subgraph. INPUT: - ``certificate`` -- boolean (default: ``False``); whether to return a certificate. OUTPUT: When ``certificate = False``, this function returns a boolean value. When ``certificate = True``, it returns a subgraph of ``self`` isomorphic to an odd hole or an odd antihole if any, and ``None`` otherwise. EXAMPLES: A Bipartite Graph is always perfect :: sage: g = graphs.RandomBipartite(8,4,.5) sage: g.is_perfect() True So is the line graph of a bipartite graph:: sage: g = graphs.RandomBipartite(4,3,0.7) sage: g.line_graph().is_perfect() # long time True As well as the Cartesian product of two complete graphs:: sage: g = graphs.CompleteGraph(3).cartesian_product(graphs.CompleteGraph(3)) sage: g.is_perfect() True Interval Graphs, which are chordal graphs, too :: sage: g = graphs.RandomIntervalGraph(7) sage: g.is_perfect() True The PetersenGraph, which is triangle-free and has chromatic number 3 is obviously not perfect:: sage: g = graphs.PetersenGraph() sage: g.is_perfect() False We can obtain an induced 5-cycle as a certificate:: sage: g.is_perfect(certificate=True) Subgraph of (Petersen graph): Graph on 5 vertices TESTS: Check that :trac:`13546` has been fixed:: sage: Graph(':FgGE@I@GxGs', loops=False, multiedges=False).is_perfect() False sage: g = Graph({0: [2, 3, 4, 5], ....: 1: [3, 4, 5, 6], ....: 2: [0, 4, 5, 6], ....: 3: [0, 1, 5, 6], ....: 4: [0, 1, 2, 6], ....: 5: [0, 1, 2, 3], ....: 6: [1, 2, 3, 4]}) sage: g.is_perfect() False TESTS:: sage: Graph(':Ab').is_perfect() Traceback (most recent call last): ... ValueError: This method is only defined for simple graphs, and yours is not one of them ! sage: g = Graph() sage: g.allow_loops(True) sage: g.add_edge(0,0) sage: g.edges() [(0, 0, None)] sage: g.is_perfect() Traceback (most recent call last): ... ValueError: This method is only defined for simple graphs, and yours is not one of them ! """ if self.has_multiple_edges() or self.has_loops(): raise ValueError("This method is only defined for simple graphs," " and yours is not one of them !") if self.is_bipartite(): return True if not certificate else None self_complement = self.complement() self_complement.remove_loops() self_complement.remove_multiple_edges() if self_complement.is_bipartite(): return True if not certificate else None answer = self.is_odd_hole_free(certificate=certificate) if not (answer is True): return answer return self_complement.is_odd_hole_free(certificate=certificate) @doc_index("Graph properties") def is_edge_transitive(self): r""" Check if self is an edge transitive graph. A graph is edge-transitive if its automorphism group acts transitively on its edge set. Equivalently, if there exists for any pair of edges `uv,u'v'\in E(G)` an automorphism `\phi` of `G` such that `\phi(uv)=u'v'` (note this does not necessarily mean that `\phi(u)=u'` and `\phi(v)=v'`). .. SEEALSO:: - :wikipedia:`Edge-transitive_graph` - :meth:`~Graph.is_arc_transitive` - :meth:`~Graph.is_half_transitive` - :meth:`~Graph.is_semi_symmetric` EXAMPLES:: sage: P = graphs.PetersenGraph() sage: P.is_edge_transitive() True sage: C = graphs.CubeGraph(3) sage: C.is_edge_transitive() True sage: G = graphs.GrayGraph() sage: G.is_edge_transitive() True sage: P = graphs.PathGraph(4) sage: P.is_edge_transitive() False """ from sage.libs.gap.libgap import libgap if not self.size(): return True A = self.automorphism_group() e = next(self.edge_iterator(labels=False)) e = [A._domain_to_gap[e[0]], A._domain_to_gap[e[1]]] e.sort() return libgap(A).OrbitLength(e, libgap.OnSets) == self.size() @doc_index("Graph properties") def is_arc_transitive(self): r""" Check if self is an arc-transitive graph A graph is arc-transitive if its automorphism group acts transitively on its pairs of adjacent vertices. Equivalently, if there exists for any pair of edges `uv,u'v'\in E(G)` an automorphism `\phi_1` of `G` such that `\phi_1(u)=u'` and `\phi_1(v)=v'`, as well as another automorphism `\phi_2` of `G` such that `\phi_2(u)=v'` and `\phi_2(v)=u'` .. SEEALSO:: - :wikipedia:`arc-transitive_graph` - :meth:`~Graph.is_edge_transitive` - :meth:`~Graph.is_half_transitive` - :meth:`~Graph.is_semi_symmetric` EXAMPLES:: sage: P = graphs.PetersenGraph() sage: P.is_arc_transitive() True sage: G = graphs.GrayGraph() sage: G.is_arc_transitive() False """ from sage.libs.gap.libgap import libgap if not self.size(): return True A = self.automorphism_group() e = next(self.edge_iterator(labels=False)) e = [A._domain_to_gap[e[0]], A._domain_to_gap[e[1]]] return libgap(A).OrbitLength(e,libgap.OnTuples) == 2*self.size() @doc_index("Graph properties") def is_half_transitive(self): """ Check if self is a half-transitive graph. A graph is half-transitive if it is both vertex and edge transitive but not arc-transitive. .. SEEALSO:: - :wikipedia:`half-transitive_graph` - :meth:`~Graph.is_edge_transitive` - :meth:`~Graph.is_arc_transitive` - :meth:`~Graph.is_semi_symmetric` EXAMPLES: The Petersen Graph is not half-transitive:: sage: P = graphs.PetersenGraph() sage: P.is_half_transitive() False The smallest half-transitive graph is the Holt Graph:: sage: H = graphs.HoltGraph() sage: H.is_half_transitive() True """ # A half-transitive graph always has only vertices of even degree if any(d % 2 for d in self.degree_iterator()): return False return (self.is_edge_transitive() and self.is_vertex_transitive() and not self.is_arc_transitive()) @doc_index("Graph properties") def is_semi_symmetric(self): """ Check if self is semi-symmetric. A graph is semi-symmetric if it is regular, edge-transitive but not vertex-transitive. .. SEEALSO:: - :wikipedia:`Semi-symmetric_graph` - :meth:`~Graph.is_edge_transitive` - :meth:`~Graph.is_arc_transitive` - :meth:`~Graph.is_half_transitive` EXAMPLES: The Petersen graph is not semi-symmetric:: sage: P = graphs.PetersenGraph() sage: P.is_semi_symmetric() False The Gray graph is the smallest possible cubic semi-symmetric graph:: sage: G = graphs.GrayGraph() sage: G.is_semi_symmetric() True Another well known semi-symmetric graph is the Ljubljana graph:: sage: L = graphs.LjubljanaGraph() sage: L.is_semi_symmetric() True """ # A semi-symmetric graph is always bipartite if not self.is_bipartite(): return False return (self.is_regular() and self.is_edge_transitive() and not self.is_vertex_transitive()) @doc_index("Graph properties") def is_path(self): r""" Check whether ``self`` is a path. A connected graph of order `n \geq 2` is a path if it is a tree (see :meth:`is_tree`) with `n-2` vertices of degree 2 and two of degree 1. By convention, a graph of order 1 without loops is a path, but the empty graph is not a path. EXAMPLES: sage: G = graphs.PathGraph(5) sage: G.is_path() True sage: H = graphs.CycleGraph(5) sage: H.is_path() False sage: D = graphs.PathGraph(5).disjoint_union(graphs.CycleGraph(5)) sage: D.is_path() False sage: E = graphs.EmptyGraph() sage: E.is_path() False sage: O = Graph([[1], []]) sage: O.is_path() True sage: O.allow_loops(True) sage: O.add_edge(1, 1) sage: O.is_path() False """ order = self.order() if order != self.size() + 1: return False if order <= 1: return order == 1 deg_one_counter = 0 seen_counter = 0 for v in self.depth_first_search(next(self.vertex_iterator())): seen_counter += 1 deg = self._backend.degree(v, False) if deg == 1: deg_one_counter += 1 if deg_one_counter > 2: return False elif deg != 2: return False return deg_one_counter == 2 and seen_counter == order @doc_index("Connectivity, orientations, trees") def degree_constrained_subgraph(self, bounds, solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Returns a degree-constrained subgraph. Given a graph `G` and two functions `f, g:V(G)\rightarrow \mathbb Z` such that `f \leq g`, a degree-constrained subgraph in `G` is a subgraph `G' \subseteq G` such that for any vertex `v \in G`, `f(v) \leq d_{G'}(v) \leq g(v)`. INPUT: - ``bounds`` -- (default: ``None``); Two possibilities: - A dictionary whose keys are the vertices, and values a pair of real values ``(min,max)`` corresponding to the values `(f(v),g(v))`. - A function associating to each vertex a pair of real values ``(min,max)`` corresponding to the values `(f(v),g(v))`. - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. OUTPUT: - When a solution exists, this method outputs the degree-constrained subgraph as a Graph object. - When no solution exists, returns ``False``. .. NOTE:: - This algorithm computes the degree-constrained subgraph of minimum weight. - If the graph's edges are weighted, these are taken into account. - This problem can be solved in polynomial time. EXAMPLES: Is there a perfect matching in an even cycle? :: sage: g = graphs.CycleGraph(6) sage: bounds = lambda x: [1,1] sage: m = g.degree_constrained_subgraph(bounds=bounds) sage: m.size() 3 """ self._scream_if_not_simple() from sage.numerical.mip import MixedIntegerLinearProgram, MIPSolverException p = MixedIntegerLinearProgram(maximization=False, solver=solver) b = p.new_variable(binary=True) if isinstance(bounds,dict): f_bounds = lambda x: bounds[x] else: f_bounds = bounds if self.weighted(): from sage.rings.real_mpfr import RR weight = lambda x: x if x in RR else 1 else: weight = lambda x: 1 for v in self: minimum,maximum = f_bounds(v) p.add_constraint(p.sum(b[frozenset((x,y))]*weight(l) for x,y,l in self.edges_incident(v)), min=minimum, max=maximum) p.set_objective(p.sum(b[frozenset((x,y))]*weight(l) for x,y,l in self.edge_iterator())) try: p.solve(log=verbose) except MIPSolverException: return False g = copy(self) b = p.get_values(b, convert=bool, tolerance=integrality_tolerance) g.delete_edges(e for e in g.edge_iterator(labels=False) if not b[frozenset(e)]) return g ### Orientations @doc_index("Connectivity, orientations, trees") def strong_orientation(self): r""" Returns a strongly connected orientation of the current graph. An orientation of an undirected graph is a digraph obtained by giving an unique direction to each of its edges. An orientation is said to be strong if there is a directed path between each pair of vertices. See also the :wikipedia:`Strongly_connected_component`. If the graph is 2-edge-connected, a strongly connected orientation can be found in linear time. If the given graph is not 2-connected, the orientation returned will ensure that each 2-connected component has a strongly connected orientation. OUTPUT: A digraph representing an orientation of the current graph. .. NOTE:: - This method assumes the graph is connected. - This algorithm works in O(m). EXAMPLES: For a 2-regular graph, a strong orientation gives to each vertex an out-degree equal to 1:: sage: g = graphs.CycleGraph(5) sage: g.strong_orientation().out_degree() [1, 1, 1, 1, 1] The Petersen Graph is 2-edge connected. It then has a strongly connected orientation:: sage: g = graphs.PetersenGraph() sage: o = g.strong_orientation() sage: len(o.strongly_connected_components()) 1 The same goes for the CubeGraph in any dimension :: sage: all(len(graphs.CubeGraph(i).strong_orientation().strongly_connected_components()) == 1 for i in range(2,6)) True A multigraph also has a strong orientation :: sage: g = Graph([(1,2),(1,2)], multiedges=True) sage: g.strong_orientation() Multi-digraph on 2 vertices """ from sage.graphs.digraph import DiGraph d = DiGraph(multiedges=self.allows_multiple_edges()) i = 0 # The algorithm works through a depth-first search. Any edge # used in the depth-first search is oriented in the direction # in which it has been used. All the other edges are oriented # backward v = next(self.vertex_iterator()) seen = {} i = 1 # Time at which the vertices have been discovered seen[v] = i # indicates the stack of edges to explore next_ = self.edges_incident(v) while next_: e = next_.pop() # Ignore loops if e[0] == e[1]: continue # We assume e[0] to be a `seen` vertex e = e if seen.get(e[0], False) is not False else (e[1], e[0], e[2]) # If we discovered a new vertex if seen.get(e[1], False) is False: d.add_edge(e) next_.extend(ee for ee in self.edges_incident(e[1]) if ((e[0],e[1]) != (ee[0],ee[1])) and ((e[0],e[1]) != (ee[1],ee[0]))) i += 1 seen[e[1]] = i # Else, we orient the edges backward else: if seen[e[0]] < seen[e[1]]: d.add_edge(e[1], e[0], e[2]) else: d.add_edge(e) # Case of multiple edges. If another edge has already been inserted, we # add the new one in the opposite direction. tmp = None for e in self.multiple_edges(): if tmp == (e[0], e[1]): if d.has_edge(e[0], e[1]): d.add_edge(e[1], e[0], e[2]) else: d.add_edge(e) tmp = (e[0], e[1]) return d @doc_index("Connectivity, orientations, trees") def minimum_outdegree_orientation(self, use_edge_labels=False, solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Returns an orientation of ``self`` with the smallest possible maximum outdegree. Given a Graph `G`, it is polynomial to compute an orientation `D` of the edges of `G` such that the maximum out-degree in `D` is minimized. This problem, though, is NP-complete in the weighted case [AMOZ2006]_. INPUT: - ``use_edge_labels`` -- boolean (default: ``False``) - When set to ``True``, uses edge labels as weights to compute the orientation and assumes a weight of `1` when there is no value available for a given edge. - When set to ``False`` (default), gives a weight of 1 to all the edges. - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. EXAMPLES: Given a complete bipartite graph `K_{n,m}`, the maximum out-degree of an optimal orientation is `\left\lceil \frac {nm} {n+m}\right\rceil`:: sage: g = graphs.CompleteBipartiteGraph(3,4) sage: o = g.minimum_outdegree_orientation() sage: max(o.out_degree()) == integer_ceil((4*3)/(3+4)) True """ self._scream_if_not_simple() if self.is_directed(): raise ValueError("Cannot compute an orientation of a DiGraph. "+\ "Please convert it to a Graph if you really mean it.") if use_edge_labels: from sage.rings.real_mpfr import RR def weight(e): l = self.edge_label(e) return l if l in RR else 1 else: def weight(e): return 1 from sage.numerical.mip import MixedIntegerLinearProgram p = MixedIntegerLinearProgram(maximization=False, solver=solver) degree = p.new_variable(nonnegative=True) # The orientation of an edge is boolean and indicates whether the edge # uv goes from u to v ( equal to 0 ) or from v to u ( equal to 1) orientation = p.new_variable(binary=True) # Whether an edge adjacent to a vertex u counts positively or # negatively. To do so, we first fix an arbitrary extremity per edge uv. ext = {frozenset(e): e[0] for e in self.edge_iterator(labels=False)} def outgoing(u, e, variable): if u == ext[frozenset(e)]: return variable else: return 1 - variable for u in self: p.add_constraint(p.sum(weight(e) * outgoing(u, e, orientation[frozenset(e)]) for e in self.edge_iterator(vertices=[u], labels=False)) - degree['max'], max=0) p.set_objective(degree['max']) p.solve(log=verbose) orientation = p.get_values(orientation, convert=bool, tolerance=integrality_tolerance) # All the edges from self are doubled in O # ( one in each direction ) from sage.graphs.digraph import DiGraph O = DiGraph(self) # Builds the list of edges that should be removed edges = [] for e in self.edge_iterator(labels=None): if orientation[frozenset(e)]: edges.append(e[::-1]) else: edges.append(e) O.delete_edges(edges) return O @doc_index("Connectivity, orientations, trees") def bounded_outdegree_orientation(self, bound, solver=None, verbose=False, *, integrality_tolerance=1e-3): r""" Computes an orientation of ``self`` such that every vertex `v` has out-degree less than `b(v)` INPUT: - ``bound`` -- Maximum bound on the out-degree. Can be of three different types : * An integer `k`. In this case, computes an orientation whose maximum out-degree is less than `k`. * A dictionary associating to each vertex its associated maximum out-degree. * A function associating to each vertex its associated maximum out-degree. - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. OUTPUT: A DiGraph representing the orientation if it exists. A ``ValueError`` exception is raised otherwise. ALGORITHM: The problem is solved through a maximum flow : Given a graph `G`, we create a ``DiGraph`` `D` defined on `E(G)\cup V(G)\cup \{s,t\}`. We then link `s` to all of `V(G)` (these edges having a capacity equal to the bound associated to each element of `V(G)`), and all the elements of `E(G)` to `t` . We then link each `v \in V(G)` to each of its incident edges in `G`. A maximum integer flow of value `|E(G)|` corresponds to an admissible orientation of `G`. Otherwise, none exists. EXAMPLES: There is always an orientation of a graph `G` such that a vertex `v` has out-degree at most `\lceil \frac {d(v)} 2 \rceil`:: sage: g = graphs.RandomGNP(40, .4) sage: b = lambda v: integer_ceil(g.degree(v)/2) sage: D = g.bounded_outdegree_orientation(b) sage: all( D.out_degree(v) <= b(v) for v in g ) True Chvatal's graph, being 4-regular, can be oriented in such a way that its maximum out-degree is 2:: sage: g = graphs.ChvatalGraph() sage: D = g.bounded_outdegree_orientation(2) sage: max(D.out_degree()) 2 For any graph `G`, it is possible to compute an orientation such that the maximum out-degree is at most the maximum average degree of `G` divided by 2. Anything less, though, is impossible. sage: g = graphs.RandomGNP(40, .4) sage: mad = g.maximum_average_degree() Hence this is possible :: sage: d = g.bounded_outdegree_orientation(integer_ceil(mad/2)) While this is not:: sage: try: ....: g.bounded_outdegree_orientation(integer_ceil(mad/2-1)) ....: print("Error") ....: except ValueError: ....: pass TESTS: As previously for random graphs, but more intensively:: sage: for i in range(30): # long time (up to 6s on sage.math, 2012) ....: g = graphs.RandomGNP(40, .4) ....: b = lambda v: integer_ceil(g.degree(v)/2) ....: D = g.bounded_outdegree_orientation(b) ....: if not ( ....: all( D.out_degree(v) <= b(v) for v in g ) or ....: D.size() != g.size()): ....: print("Something wrong happened") """ self._scream_if_not_simple() from sage.graphs.all import DiGraph n = self.order() if not n: return DiGraph() vertices = list(self) vertices_id = {y: x for x,y in enumerate(vertices)} b = {} # Checking the input type. We make a dictionary out of it if isinstance(bound, dict): b = bound else: try: b = dict(zip(vertices,map(bound, vertices))) except TypeError: b = dict(zip(vertices, [bound]*n)) d = DiGraph() # Adding the edges (s,v) and ((u,v),t) d.add_edges(('s', vertices_id[v], b[v]) for v in vertices) d.add_edges(((vertices_id[u], vertices_id[v]), 't', 1) for u,v in self.edges(labels=None) ) # each v is linked to its incident edges for u,v in self.edge_iterator(labels=None): u,v = vertices_id[u], vertices_id[v] d.add_edge(u, (u,v), 1) d.add_edge(v, (u,v), 1) # Solving the maximum flow value, flow = d.flow('s','t', value_only=False, integer=True, use_edge_labels=True, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) if value != self.size(): raise ValueError("No orientation exists for the given bound") D = DiGraph() D.add_vertices(vertices) # The flow graph may not contain all the vertices, if they are # not part of the flow... for u in [x for x in range(n) if x in flow]: for uu,vv in flow.neighbors_out(u): v = vv if vv != u else uu D.add_edge(vertices[u], vertices[v]) # I do not like when a method destroys the embedding ;-) D.set_pos(self.get_pos()) return D @doc_index("Connectivity, orientations, trees") def orientations(self, data_structure=None, sparse=None): r""" Return an iterator over orientations of ``self``. An *orientation* of an undirected graph is a directed graph such that every edge is assigned a direction. Hence there are `2^s` oriented digraphs for a simple graph with `s` edges. INPUT: - ``data_structure`` -- one of ``"sparse"``, ``"static_sparse"``, or ``"dense"``; see the documentation of :class:`Graph` or :class:`DiGraph`; default is the data structure of ``self`` - ``sparse`` -- boolean (default: ``None``); ``sparse=True`` is an alias for ``data_structure="sparse"``, and ``sparse=False`` is an alias for ``data_structure="dense"``. By default (``None``), guess the most suitable data structure. .. WARNING:: This always considers multiple edges of graphs as distinguishable, and hence, may have repeated digraphs. EXAMPLES:: sage: G = Graph([[1,2,3], [(1, 2, 'a'), (1, 3, 'b')]], format='vertices_and_edges') sage: it = G.orientations() sage: D = next(it) sage: D.edges() [(1, 2, 'a'), (1, 3, 'b')] sage: D = next(it) sage: D.edges() [(1, 2, 'a'), (3, 1, 'b')] TESTS:: sage: G = Graph() sage: D = [g for g in G.orientations()] sage: len(D) 1 sage: D[0] Digraph on 0 vertices sage: G = Graph(5) sage: it = G.orientations() sage: D = next(it) sage: D.size() 0 sage: G = Graph([[1,2,'a'], [1,2,'b']], multiedges=True) sage: len(list(G.orientations())) 4 sage: G = Graph([[1,2], [1,1]], loops=True) sage: len(list(G.orientations())) 2 sage: G = Graph([[1,2],[2,3]]) sage: next(G.orientations()) Digraph on 3 vertices sage: G = graphs.PetersenGraph() sage: next(G.orientations()) An orientation of Petersen graph: Digraph on 10 vertices An orientation must have the same ground set of vertices as the original graph (:trac:`24366`):: sage: G = Graph(1) sage: next(G.orientations()) Digraph on 1 vertex """ if sparse is not None: if data_structure is not None: raise ValueError("cannot specify both 'sparse' and 'data_structure'") data_structure = "sparse" if sparse else "dense" if data_structure is None: from sage.graphs.base.dense_graph import DenseGraphBackend from sage.graphs.base.sparse_graph import SparseGraphBackend if isinstance(self._backend, DenseGraphBackend): data_structure = "dense" elif isinstance(self._backend, SparseGraphBackend): data_structure = "sparse" else: data_structure = "static_sparse" name = self.name() if name: name = 'An orientation of ' + name if not self.size(): D = DiGraph(data=[self.vertices(), []], format='vertices_and_edges', name=name, pos=self._pos, multiedges=self.allows_multiple_edges(), loops=self.allows_loops(), data_structure=data_structure) if hasattr(self, '_embedding'): D._embedding = copy(self._embedding) yield D return E = [[(u,v,label), (v,u,label)] if u != v else [(u,v,label)] for u,v,label in self.edge_iterator()] verts = self.vertices() for edges in itertools.product(*E): D = DiGraph(data=[verts, edges], format='vertices_and_edges', name=name, pos=self._pos, multiedges=self.allows_multiple_edges(), loops=self.allows_loops(), data_structure=data_structure) if hasattr(self, '_embedding'): D._embedding = copy(self._embedding) yield D ### Coloring @doc_index("Basic methods") def bipartite_color(self): """ Return a dictionary with vertices as the keys and the color class as the values. Fails with an error if the graph is not bipartite. EXAMPLES:: sage: graphs.CycleGraph(4).bipartite_color() {0: 1, 1: 0, 2: 1, 3: 0} sage: graphs.CycleGraph(5).bipartite_color() Traceback (most recent call last): ... RuntimeError: Graph is not bipartite. TESTS:: sage: Graph().bipartite_color() {} """ isit, certificate = self.is_bipartite(certificate=True) if isit: return certificate else: raise RuntimeError("Graph is not bipartite.") @doc_index("Basic methods") def bipartite_sets(self): r""" Return `(X,Y)` where `X` and `Y` are the nodes in each bipartite set of graph `G`. Fails with an error if graph is not bipartite. EXAMPLES:: sage: graphs.CycleGraph(4).bipartite_sets() ({0, 2}, {1, 3}) sage: graphs.CycleGraph(5).bipartite_sets() Traceback (most recent call last): ... RuntimeError: Graph is not bipartite. """ color = self.bipartite_color() left = set() right = set() for u,s in color.items(): if s: left.add(u) else: right.add(u) return left, right @doc_index("Coloring") def chromatic_index(self, solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Return the chromatic index of the graph. The chromatic index is the minimal number of colors needed to properly color the edges of the graph. INPUT: - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. This method is a frontend for method :meth:`sage.graphs.graph_coloring.edge_coloring` that uses a mixed integer-linear programming formulation to compute the chromatic index. .. SEEALSO:: - :wikipedia:`Edge_coloring` for further details on edge coloring - :meth:`sage.graphs.graph_coloring.edge_coloring` - :meth:`~Graph.fractional_chromatic_index` - :meth:`~Graph.chromatic_number` EXAMPLES: The clique `K_n` has chromatic index `n` when `n` is odd and `n-1` when `n` is even:: sage: graphs.CompleteGraph(4).chromatic_index() 3 sage: graphs.CompleteGraph(5).chromatic_index() 5 sage: graphs.CompleteGraph(6).chromatic_index() 5 The path `P_n` with `n \geq 2` has chromatic index 2:: sage: graphs.PathGraph(5).chromatic_index() 2 The windmill graph with parameters `k,n` has chromatic index `(k-1)n`:: sage: k,n = 3,4 sage: G = graphs.WindmillGraph(k,n) sage: G.chromatic_index() == (k-1)*n True TESTS: Graphs without vertices or edges:: sage: Graph().chromatic_index() 0 sage: Graph(2).chromatic_index() 0 """ if not self.order() or not self.size(): return 0 from sage.graphs.graph_coloring import edge_coloring return edge_coloring(self, value_only=True, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) @doc_index("Coloring") def chromatic_number(self, algorithm="DLX", solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Return the minimal number of colors needed to color the vertices of the graph. INPUT: - ``algorithm`` -- Select an algorithm from the following supported algorithms: - If ``algorithm="DLX"`` (default), the chromatic number is computed using the dancing link algorithm. It is inefficient speedwise to compute the chromatic number through the dancing link algorithm because this algorithm computes *all* the possible colorings to check that one exists. - If ``algorithm="CP"``, the chromatic number is computed using the coefficients of the chromatic polynomial. Again, this method is inefficient in terms of speed and it only useful for small graphs. - If ``algorithm="MILP"``, the chromatic number is computed using a mixed integer linear program. The performance of this implementation is affected by whether optional MILP solvers have been installed (see the :mod:`MILP module <sage.numerical.mip>`, or Sage's tutorial on Linear Programming). - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. .. SEEALSO:: For more functions related to graph coloring, see the module :mod:`sage.graphs.graph_coloring`. EXAMPLES:: sage: G = Graph({0: [1, 2, 3], 1: [2]}) sage: G.chromatic_number(algorithm="DLX") 3 sage: G.chromatic_number(algorithm="MILP") 3 sage: G.chromatic_number(algorithm="CP") 3 A bipartite graph has (by definition) chromatic number 2:: sage: graphs.RandomBipartite(50,50,0.7).chromatic_number() 2 A complete multipartite graph with k parts has chromatic number `k`:: sage: all(graphs.CompleteMultipartiteGraph([5]*i).chromatic_number() == i for i in range(2,5)) True The complete graph has the largest chromatic number from all the graphs of order `n`. Namely its chromatic number is `n`:: sage: all(graphs.CompleteGraph(i).chromatic_number() == i for i in range(10)) True The Kneser graph with parameters `(n, 2)` for `n > 3` has chromatic number `n-2`:: sage: all(graphs.KneserGraph(i,2).chromatic_number() == i-2 for i in range(4,6)) True The Flower Snark graph has chromatic index 4 hence its line graph has chromatic number 4:: sage: graphs.FlowerSnark().line_graph().chromatic_number() 4 TESTS:: sage: G = Graph() sage: G.chromatic_number(algorithm="DLX") 0 sage: G.chromatic_number(algorithm="MILP") 0 sage: G.chromatic_number(algorithm="CP") 0 sage: G = Graph({0: [1, 2, 3], 1: [2]}) sage: G.chromatic_number(algorithm="foo") Traceback (most recent call last): ... ValueError: The 'algorithm' keyword must be set to either 'DLX', 'MILP' or 'CP'. """ self._scream_if_not_simple(allow_multiple_edges=True) # default built-in algorithm; bad performance if algorithm == "DLX": from sage.graphs.graph_coloring import chromatic_number return chromatic_number(self) # Algorithm with good performance, but requires an optional # package: choose any of GLPK or CBC. elif algorithm == "MILP": from sage.graphs.graph_coloring import vertex_coloring return vertex_coloring(self, value_only=True, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) # another algorithm with bad performance; only good for small graphs elif algorithm == "CP": f = self.chromatic_polynomial() i = 0 while not f(i): i += 1 return i else: raise ValueError("The 'algorithm' keyword must be set to either 'DLX', 'MILP' or 'CP'.") @doc_index("Coloring") def coloring(self, algorithm="DLX", hex_colors=False, solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Return the first (optimal) proper vertex-coloring found. INPUT: - ``algorithm`` -- Select an algorithm from the following supported algorithms: - If ``algorithm="DLX"`` (default), the coloring is computed using the dancing link algorithm. - If ``algorithm="MILP"``, the coloring is computed using a mixed integer linear program. The performance of this implementation is affected by whether optional MILP solvers have been installed (see the :mod:`MILP module <sage.numerical.mip>`). - ``hex_colors`` -- boolean (default: ``False``); if ``True``, return a dictionary which can easily be used for plotting. - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. .. SEEALSO:: For more functions related to graph coloring, see the module :mod:`sage.graphs.graph_coloring`. EXAMPLES:: sage: G = Graph("Fooba") sage: P = G.coloring(algorithm="MILP") sage: Q = G.coloring(algorithm="DLX") sage: def are_equal_colorings(A, B): ....: return Set(map(Set, A)) == Set(map(Set, B)) sage: are_equal_colorings(P, [[1, 2, 3], [0, 5, 6], [4]]) True sage: are_equal_colorings(P, Q) True sage: G.plot(partition=P) Graphics object consisting of 16 graphics primitives sage: G.coloring(hex_colors=True, algorithm="MILP") {'#0000ff': [4], '#00ff00': [0, 6, 5], '#ff0000': [2, 1, 3]} sage: H = G.coloring(hex_colors=True, algorithm="DLX") sage: H {'#0000ff': [4], '#00ff00': [1, 2, 3], '#ff0000': [0, 5, 6]} sage: G.plot(vertex_colors=H) Graphics object consisting of 16 graphics primitives .. PLOT:: g = Graph("Fooba") sphinx_plot(g.plot(partition=g.coloring())) TESTS:: sage: G.coloring(algorithm="foo") Traceback (most recent call last): ... ValueError: The 'algorithm' keyword must be set to either 'DLX' or 'MILP'. """ self._scream_if_not_simple(allow_multiple_edges=True) if algorithm == "MILP": from sage.graphs.graph_coloring import vertex_coloring return vertex_coloring(self, hex_colors=hex_colors, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) elif algorithm == "DLX": from sage.graphs.graph_coloring import first_coloring return first_coloring(self, hex_colors=hex_colors) else: raise ValueError("The 'algorithm' keyword must be set to either 'DLX' or 'MILP'.") @doc_index("Coloring") def chromatic_symmetric_function(self, R=None): r""" Return the chromatic symmetric function of ``self``. Let `G` be a graph. The chromatic symmetric function `X_G` was described in [Sta1995]_, specifically Theorem 2.5 states that .. MATH:: X_G = \sum_{F \subseteq E(G)} (-1)^{|F|} p_{\lambda(F)}, where `\lambda(F)` is the partition of the sizes of the connected components of the subgraph induced by the edges `F` and `p_{\mu}` is the powersum symmetric function. INPUT: - ``R`` -- (optional) the base ring for the symmetric functions; this uses `\ZZ` by default EXAMPLES:: sage: s = SymmetricFunctions(ZZ).s() sage: G = graphs.CycleGraph(5) sage: XG = G.chromatic_symmetric_function(); XG p[1, 1, 1, 1, 1] - 5*p[2, 1, 1, 1] + 5*p[2, 2, 1] + 5*p[3, 1, 1] - 5*p[3, 2] - 5*p[4, 1] + 4*p[5] sage: s(XG) 30*s[1, 1, 1, 1, 1] + 10*s[2, 1, 1, 1] + 10*s[2, 2, 1] Not all graphs have a positive Schur expansion:: sage: G = graphs.ClawGraph() sage: XG = G.chromatic_symmetric_function(); XG p[1, 1, 1, 1] - 3*p[2, 1, 1] + 3*p[3, 1] - p[4] sage: s(XG) 8*s[1, 1, 1, 1] + 5*s[2, 1, 1] - s[2, 2] + s[3, 1] We show that given a triangle `\{e_1, e_2, e_3\}`, we have `X_G = X_{G - e_1} + X_{G - e_2} - X_{G - e_1 - e_2}`:: sage: G = Graph([[1,2],[1,3],[2,3]]) sage: XG = G.chromatic_symmetric_function() sage: G1 = copy(G) sage: G1.delete_edge([1,2]) sage: XG1 = G1.chromatic_symmetric_function() sage: G2 = copy(G) sage: G2.delete_edge([1,3]) sage: XG2 = G2.chromatic_symmetric_function() sage: G3 = copy(G1) sage: G3.delete_edge([1,3]) sage: XG3 = G3.chromatic_symmetric_function() sage: XG == XG1 + XG2 - XG3 True """ from sage.combinat.sf.sf import SymmetricFunctions from sage.combinat.partition import _Partitions from sage.misc.misc import powerset if R is None: R = ZZ p = SymmetricFunctions(R).p() ret = p.zero() for F in powerset(self.edges()): la = _Partitions(self.subgraph(edges=F).connected_components_sizes()) ret += (-1)**len(F) * p[la] return ret @doc_index("Coloring") def chromatic_quasisymmetric_function(self, t=None, R=None): r""" Return the chromatic quasisymmetric function of ``self``. Let `G` be a graph whose vertex set is totally ordered. The chromatic quasisymmetric function `X_G(t)` was first described in [SW2012]_. We use the equivalent definition given in [BC2018]_: .. MATH:: X_G(t) = \sum_{\sigma=(\sigma_1,\ldots,\sigma_n)} t^{\operatorname{asc}(\sigma)} M_{|\sigma_1|,\ldots,|\sigma_n|}, where we sum over all ordered set partitions of the vertex set of `G` such that each block `\sigma_i` is an independent (i.e., stable) set of `G`, and where `\operatorname{asc}(\sigma)` denotes the number of edges `\{u, v\}` of `G` such that `u < v` and `v` appears in a later part of `\sigma` than `u`. INPUT: - ``t`` -- (optional) the parameter `t`; uses the variable `t` in `\ZZ[t]` by default - ``R`` -- (optional) the base ring for the quasisymmetric functions; uses the parent of `t` by default EXAMPLES:: sage: G = Graph([[1,2,3], [[1,3], [2,3]]]) sage: G.chromatic_quasisymmetric_function() (2*t^2+2*t+2)*M[1, 1, 1] + M[1, 2] + t^2*M[2, 1] sage: G = graphs.PathGraph(4) sage: XG = G.chromatic_quasisymmetric_function(); XG (t^3+11*t^2+11*t+1)*M[1, 1, 1, 1] + (3*t^2+3*t)*M[1, 1, 2] + (3*t^2+3*t)*M[1, 2, 1] + (3*t^2+3*t)*M[2, 1, 1] + (t^2+t)*M[2, 2] sage: XG.to_symmetric_function() (t^3+11*t^2+11*t+1)*m[1, 1, 1, 1] + (3*t^2+3*t)*m[2, 1, 1] + (t^2+t)*m[2, 2] sage: G = graphs.CompleteGraph(4) sage: G.chromatic_quasisymmetric_function() (t^6+3*t^5+5*t^4+6*t^3+5*t^2+3*t+1)*M[1, 1, 1, 1] Not all chromatic quasisymmetric functions are symmetric:: sage: G = Graph([[1,2], [1,5], [3,4], [3,5]]) sage: G.chromatic_quasisymmetric_function().is_symmetric() False We check that at `t = 1`, we recover the usual chromatic symmetric function:: sage: p = SymmetricFunctions(QQ).p() sage: G = graphs.CycleGraph(5) sage: XG = G.chromatic_quasisymmetric_function(t=1); XG 120*M[1, 1, 1, 1, 1] + 30*M[1, 1, 1, 2] + 30*M[1, 1, 2, 1] + 30*M[1, 2, 1, 1] + 10*M[1, 2, 2] + 30*M[2, 1, 1, 1] + 10*M[2, 1, 2] + 10*M[2, 2, 1] sage: p(XG.to_symmetric_function()) p[1, 1, 1, 1, 1] - 5*p[2, 1, 1, 1] + 5*p[2, 2, 1] + 5*p[3, 1, 1] - 5*p[3, 2] - 5*p[4, 1] + 4*p[5] sage: G = graphs.ClawGraph() sage: XG = G.chromatic_quasisymmetric_function(t=1); XG 24*M[1, 1, 1, 1] + 6*M[1, 1, 2] + 6*M[1, 2, 1] + M[1, 3] + 6*M[2, 1, 1] + M[3, 1] sage: p(XG.to_symmetric_function()) p[1, 1, 1, 1] - 3*p[2, 1, 1] + 3*p[3, 1] - p[4] """ from sage.combinat.ncsf_qsym.qsym import QuasiSymmetricFunctions from sage.combinat.set_partition_ordered import OrderedSetPartitions if t is None: t = ZZ['t'].gen() if R is None: R = t.parent() M = QuasiSymmetricFunctions(R).M() ret = M.zero() V = self.vertices() def asc(sigma): stat = 0 for i, s in enumerate(sigma): for u in s: stat += sum(1 for p in sigma[i+1:] for v in p if v > u and self.has_edge(u, v)) return stat for sigma in OrderedSetPartitions(V): if any(not self.is_independent_set(s) for s in sigma): continue ret += M.term(sigma.to_composition(), t**asc(sigma)) return ret @doc_index("Leftovers") def matching(self, value_only=False, algorithm="Edmonds", use_edge_labels=False, solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Return a maximum weighted matching of the graph represented by the list of its edges. For more information, see the :wikipedia:`Matching_(graph_theory)`. Given a graph `G` such that each edge `e` has a weight `w_e`, a maximum matching is a subset `S` of the edges of `G` of maximum weight such that no two edges of `S` are incident with each other. As an optimization problem, it can be expressed as: .. MATH:: \mbox{Maximize : }&\sum_{e\in G.edges()} w_e b_e\\ \mbox{Such that : }&\forall v \in G, \sum_{(u,v)\in G.edges()} b_{(u,v)}\leq 1\\ &\forall x\in G, b_x\mbox{ is a binary variable} INPUT: - ``value_only`` -- boolean (default: ``False``); when set to ``True``, only the cardinal (or the weight) of the matching is returned - ``algorithm`` -- string (default: ``"Edmonds"``) - ``"Edmonds"`` selects Edmonds' algorithm as implemented in NetworkX - ``"LP"`` uses a Linear Program formulation of the matching problem - ``use_edge_labels`` -- boolean (default: ``False``) - when set to ``True``, computes a weighted matching where each edge is weighted by its label (if an edge has no label, `1` is assumed) - when set to ``False``, each edge has weight `1` - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity: set to 0 by default, which means quiet (only useful when ``algorithm == "LP"``) - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. OUTPUT: - When ``value_only=False`` (default), this method returns the list of edges of a maximum matching of `G`. - When ``value_only=True``, this method returns the sum of the weights (default: ``1``) of the edges of a maximum matching of `G`. The type of the output may vary according to the type of the edge labels and the algorithm used. ALGORITHM: The problem is solved using Edmond's algorithm implemented in NetworkX, or using Linear Programming depending on the value of ``algorithm``. EXAMPLES: Maximum matching in a Pappus Graph:: sage: g = graphs.PappusGraph() sage: g.matching(value_only=True) 9 Same test with the Linear Program formulation:: sage: g = graphs.PappusGraph() sage: g.matching(algorithm="LP", value_only=True) 9 .. PLOT:: g = graphs.PappusGraph() sphinx_plot(g.plot(edge_colors={"red":g.matching()})) TESTS: When ``use_edge_labels`` is set to ``False``, with Edmonds' algorithm and LP formulation:: sage: g = Graph([(0,1,0), (1,2,999), (2,3,-5)]) sage: sorted(g.matching()) [(0, 1, 0), (2, 3, -5)] sage: sorted(g.matching(algorithm="LP")) [(0, 1, 0), (2, 3, -5)] When ``use_edge_labels`` is set to ``True``, with Edmonds' algorithm and LP formulation:: sage: g = Graph([(0,1,0), (1,2,999), (2,3,-5)]) sage: g.matching(use_edge_labels=True) [(1, 2, 999)] sage: g.matching(algorithm="LP", use_edge_labels=True) [(1, 2, 999)] With loops and multiedges:: sage: edge_list = [(0,0,5), (0,1,1), (0,2,2), (0,3,3), (1,2,6) ....: , (1,2,3), (1,3,3), (2,3,3)] sage: g = Graph(edge_list, loops=True, multiedges=True) sage: g.matching(use_edge_labels=True) [(1, 2, 6), (0, 3, 3)] TESTS: If ``algorithm`` is set to anything different from ``"Edmonds"`` or ``"LP"``, an exception is raised:: sage: g = graphs.PappusGraph() sage: g.matching(algorithm="somethingdifferent") Traceback (most recent call last): ... ValueError: algorithm must be set to either "Edmonds" or "LP" """ from sage.rings.real_mpfr import RR def weight(x): if x in RR: return x else: return 1 W = {} L = {} for u,v,l in self.edge_iterator(): if u is v: continue fuv = frozenset((u, v)) if fuv not in L or ( use_edge_labels and W[fuv] < weight(l) ): L[fuv] = l if use_edge_labels: W[fuv] = weight(l) if algorithm == "Edmonds": import networkx g = networkx.Graph() if use_edge_labels: for (u, v),w in W.items(): g.add_edge(u, v, weight=w) else: for u, v in L: g.add_edge(u, v) d = networkx.max_weight_matching(g) if value_only: if use_edge_labels: return sum(W[frozenset(e)] for e in d) else: return Integer(len(d)) else: return [(u, v, L[frozenset((u, v))]) for u, v in d] elif algorithm == "LP": g = self from sage.numerical.mip import MixedIntegerLinearProgram # returns the weight of an edge considering it may not be # weighted ... p = MixedIntegerLinearProgram(maximization=True, solver=solver) b = p.new_variable(binary=True) if use_edge_labels: p.set_objective(p.sum(w * b[fe] for fe,w in W.items())) else: p.set_objective(p.sum(b[fe] for fe in L)) # for any vertex v, there is at most one edge incident to v in # the maximum matching for v in g: p.add_constraint(p.sum(b[frozenset(e)] for e in self.edge_iterator(vertices=[v], labels=False) if e[0] != e[1]), max=1) p.solve(log=verbose) b = p.get_values(b, convert=bool, tolerance=integrality_tolerance) if value_only: if use_edge_labels: return sum(w for fe, w in W.items() if b[fe]) else: return Integer(sum(1 for fe in L if b[fe])) else: return [(u, v, L[frozenset((u, v))]) for u, v in L if b[frozenset((u, v))]] else: raise ValueError('algorithm must be set to either "Edmonds" or "LP"') @doc_index("Algorithmically hard stuff") def has_homomorphism_to(self, H, core=False, solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Checks whether there is a homomorphism between two graphs. A homomorphism from a graph `G` to a graph `H` is a function `\phi:V(G)\mapsto V(H)` such that for any edge `uv \in E(G)` the pair `\phi(u)\phi(v)` is an edge of `H`. Saying that a graph can be `k`-colored is equivalent to saying that it has a homomorphism to `K_k`, the complete graph on `k` elements. For more information, see the :wikipedia:`Graph_homomorphism`. INPUT: - ``H`` -- the graph to which ``self`` should be sent. - ``core`` -- boolean (default: ``False``; whether to minimize the size of the mapping's image (see note below). This is set to ``False`` by default. - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. .. NOTE:: One can compute the core of a graph (with respect to homomorphism) with this method :: sage: g = graphs.CycleGraph(10) sage: mapping = g.has_homomorphism_to(g, core = True) sage: print("The size of the core is {}".format(len(set(mapping.values())))) The size of the core is 2 OUTPUT: This method returns ``False`` when the homomorphism does not exist, and returns the homomorphism otherwise as a dictionary associating a vertex of `H` to a vertex of `G`. EXAMPLES: Is Petersen's graph 3-colorable:: sage: P = graphs.PetersenGraph() sage: P.has_homomorphism_to(graphs.CompleteGraph(3)) is not False True An odd cycle admits a homomorphism to a smaller odd cycle, but not to an even cycle:: sage: g = graphs.CycleGraph(9) sage: g.has_homomorphism_to(graphs.CycleGraph(5)) is not False True sage: g.has_homomorphism_to(graphs.CycleGraph(7)) is not False True sage: g.has_homomorphism_to(graphs.CycleGraph(4)) is not False False """ self._scream_if_not_simple() from sage.numerical.mip import MixedIntegerLinearProgram, MIPSolverException p = MixedIntegerLinearProgram(solver=solver, maximization=False) b = p.new_variable(binary=True) # Each vertex has an image for ug in self: p.add_constraint(p.sum(b[ug,uh] for uh in H) == 1) nonedges = H.complement().edges(labels=False) for ug,vg in self.edges(labels=False): # Two adjacent vertices cannot be mapped to the same element for uh in H: p.add_constraint(b[ug,uh] + b[vg,uh] <= 1) # Two adjacent vertices cannot be mapped to no adjacent vertices for uh,vh in nonedges: p.add_constraint(b[ug,uh] + b[vg,vh] <= 1) p.add_constraint(b[ug,vh] + b[vg,uh] <= 1) # Minimize the mapping's size if core: # the value of m is one if the corresponding vertex of h is used. m = p.new_variable(nonnegative=True) for uh in H: for ug in self: p.add_constraint(b[ug,uh] <= m[uh]) p.set_objective(p.sum(m[vh] for vh in H)) try: p.solve(log=verbose) except MIPSolverException: return False b = p.get_values(b, convert=bool, tolerance=integrality_tolerance) mapping = dict(x[0] for x in b.items() if x[1]) return mapping @doc_index("Clique-related methods") def fractional_clique_number(self, solver='PPL', verbose=0, check_components=True, check_bipartite=True): r""" Return the fractional clique number of the graph. A fractional clique is a nonnegative weight function on the vertices of a graph such that the sum of the weights over any independent set is at most 1. The fractional clique number is the largest total weight of a fractional clique, which is equal to the fractional chromatic number by LP-duality. ALGORITHM: The fractional clique number is computed via the Linear Program for fractional chromatic number, see :meth:`fractional_chromatic_number <sage.graphs.graph_coloring.fractional_chromatic_number>` INPUT: - ``solver`` -- (default: ``"PPL"``); specify a Linear Program (LP) solver to be used. If set to ``None``, the default one is used. For more information on LP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. .. NOTE:: The default solver used here is ``"PPL"`` which provides exact results, i.e. a rational number, although this may be slower that using other solvers. - ``verbose`` -- integer (default: `0`); sets the level of verbosity of the LP solver - ``check_components`` -- boolean (default: ``True``); whether the method is called on each biconnected component of `G` - ``check_bipartite`` -- boolean (default: ``True``); whether the graph is checked for bipartiteness. If the graph is bipartite then we can avoid creating and solving the LP. EXAMPLES: The fractional clique number of a `C_7` is `7/3`:: sage: g = graphs.CycleGraph(7) sage: g.fractional_clique_number() 7/3 """ return self.fractional_chromatic_number(solver=solver, verbose=verbose, check_components=check_components, check_bipartite=check_bipartite) @doc_index("Leftovers") def maximum_average_degree(self, value_only=True, solver=None, verbose=0): r""" Return the Maximum Average Degree (MAD) of the current graph. The Maximum Average Degree (MAD) of a graph is defined as the average degree of its densest subgraph. More formally, ``Mad(G) = \max_{H\subseteq G} Ad(H)``, where `Ad(G)` denotes the average degree of `G`. This can be computed in polynomial time. INPUT: - ``value_only`` -- boolean (default: ``True``); - If ``value_only=True``, only the numerical value of the `MAD` is returned. - Else, the subgraph of `G` realizing the `MAD` is returned. - ``solver`` -- (default: ``None``); specify a Linear Program (LP) solver to be used. If set to ``None``, the default one is used. For more information on LP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. EXAMPLES: In any graph, the `Mad` is always larger than the average degree:: sage: g = graphs.RandomGNP(20,.3) sage: mad_g = g.maximum_average_degree() sage: g.average_degree() <= mad_g True Unlike the average degree, the `Mad` of the disjoint union of two graphs is the maximum of the `Mad` of each graphs:: sage: h = graphs.RandomGNP(20,.3) sage: mad_h = h.maximum_average_degree() sage: (g+h).maximum_average_degree() == max(mad_g, mad_h) True The subgraph of a regular graph realizing the maximum average degree is always the whole graph :: sage: g = graphs.CompleteGraph(5) sage: mad_g = g.maximum_average_degree(value_only=False) sage: g.is_isomorphic(mad_g) True This also works for complete bipartite graphs :: sage: g = graphs.CompleteBipartiteGraph(3,4) sage: mad_g = g.maximum_average_degree(value_only=False) sage: g.is_isomorphic(mad_g) True """ self._scream_if_not_simple() g = self from sage.numerical.mip import MixedIntegerLinearProgram p = MixedIntegerLinearProgram(maximization=True, solver=solver) d = p.new_variable(nonnegative=True) one = p.new_variable(nonnegative=True) for u,v in g.edge_iterator(labels=False): fuv = frozenset((u, v)) p.add_constraint(one[fuv] - 2 * d[u], max=0) p.add_constraint(one[fuv] - 2 * d[v], max=0) p.add_constraint(p.sum(d[v] for v in g), max=1) p.set_objective(p.sum(one[frozenset(uv)] for uv in g.edge_iterator(labels=False))) p.solve(log=verbose) # Paying attention to numerical error : # The zero values could be something like 0.000000000001 # so I can not write l > 0 # And the non-zero, though they should be equal to # 1/(order of the optimal subgraph) may be a bit lower # setting the minimum to 1/(10 * size of the whole graph ) # should be safe :-) m = 1/(10 *Integer(g.order())) d_val = p.get_values(d) g_mad = g.subgraph(v for v,l in d_val.items() if l > m) if value_only: return g_mad.average_degree() else: return g_mad @doc_index("Algorithmically hard stuff") def independent_set_of_representatives(self, family, solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Return an independent set of representatives. Given a graph `G` and a family `F=\{F_i:i\in [1,...,k]\}` of subsets of ``g.vertices()``, an Independent Set of Representatives (ISR) is an assignation of a vertex `v_i\in F_i` to each set `F_i` such that `v_i != v_j` if `i<j` (they are representatives) and the set `\cup_{i}v_i` is an independent set in `G`. It generalizes, for example, graph coloring and graph list coloring. (See [ABZ2007]_ for more information.) INPUT: - ``family`` -- A list of lists defining the family `F` (actually, a Family of subsets of ``G.vertices()``). - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. OUTPUT: - A list whose `i^{\mbox{th}}` element is the representative of the `i^{\mbox{th}}` element of the ``family`` list. If there is no ISR, ``None`` is returned. EXAMPLES: For a bipartite graph missing one edge, the solution is as expected:: sage: g = graphs.CompleteBipartiteGraph(3,3) sage: g.delete_edge(1,4) sage: g.independent_set_of_representatives([[0,1,2],[3,4,5]]) [1, 4] The Petersen Graph is 3-colorable, which can be expressed as an independent set of representatives problem : take 3 disjoint copies of the Petersen Graph, each one representing one color. Then take as a partition of the set of vertices the family defined by the three copies of each vertex. The ISR of such a family defines a 3-coloring:: sage: g = 3 * graphs.PetersenGraph() sage: n = g.order() / 3 sage: f = [[i, i + n, i + 2*n] for i in range(n)] sage: isr = g.independent_set_of_representatives(f) sage: c = [integer_floor(i / n) for i in isr] sage: color_classes = [[], [], []] sage: for v, i in enumerate(c): ....: color_classes[i].append(v) sage: for classs in color_classes: ....: g.subgraph(classs).size() == 0 True True True """ from sage.numerical.mip import MixedIntegerLinearProgram p = MixedIntegerLinearProgram(solver=solver) # Boolean variable indicating whether the vertex is the representative # of some set vertex_taken = p.new_variable(binary=True) # Boolean variable in two dimension whose first element is a vertex and # whose second element is one of the sets given as arguments. # When true, indicated that the vertex is the representative of the # corresponding set classss = p.new_variable(binary=True) # Associates to the vertices the classes to which they belong lists = {v: [] for v in self} for i,f in enumerate(family): for v in f: lists[v].append(i) # a classss has exactly one representative p.add_constraint(p.sum(classss[v,i] for v in f), max=1, min=1) # A vertex represents at most one classss (vertex_taken is binary), and # vertex_taken[v]==1 if v is the representative of some classss for v in self: p.add_constraint(p.sum(classss[v,i] for i in lists[v]) - vertex_taken[v], max=0) # Two adjacent vertices can not both be representatives of a set for u,v in self.edge_iterator(labels=None): p.add_constraint(vertex_taken[u] + vertex_taken[v], max=1) p.set_objective(None) try: p.solve(log=verbose) except Exception: return None classss = p.get_values(classss, convert=bool, tolerance=integrality_tolerance) repr = [] for i,f in enumerate(family): for v in f: if classss[v,i]: repr.append(v) break return repr @doc_index("Algorithmically hard stuff") def minor(self, H, solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Return the vertices of a minor isomorphic to `H` in the current graph. We say that a graph `G` has a `H`-minor (or that it has a graph isomorphic to `H` as a minor), if for all `h\in H`, there exist disjoint sets `S_h \subseteq V(G)` such that once the vertices of each `S_h` have been merged to create a new graph `G'`, this new graph contains `H` as a subgraph. For more information, see the :wikipedia:`Minor_(graph_theory)`. INPUT: - ``H`` -- The minor to find for in the current graph. - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. OUTPUT: A dictionary associating to each vertex of `H` the set of vertices in the current graph representing it. ALGORITHM: Mixed Integer Linear Programming COMPLEXITY: Theoretically, when `H` is fixed, testing for the existence of a `H`-minor is polynomial. The known algorithms are highly exponential in `H`, though. .. NOTE:: This function can be expected to be *very* slow, especially where the minor does not exist. EXAMPLES: Trying to find a minor isomorphic to `K_4` in the `4\times 4` grid:: sage: g = graphs.GridGraph([4,4]) sage: h = graphs.CompleteGraph(4) sage: L = g.minor(h) sage: gg = g.subgraph(flatten(L.values(), max_level = 1)) sage: _ = [gg.merge_vertices(l) for l in L.values() if len(l)>1] sage: gg.is_isomorphic(h) True We can also try to prove this way that the Petersen graph is not planar, as it has a `K_5` minor:: sage: g = graphs.PetersenGraph() sage: K5_minor = g.minor(graphs.CompleteGraph(5)) # long time And even a `K_{3,3}` minor:: sage: K33_minor = g.minor(graphs.CompleteBipartiteGraph(3,3)) # long time (It is much faster to use the linear-time test of planarity in this situation, though.) As there is no cycle in a tree, looking for a `K_3` minor is useless. This function will raise an exception in this case:: sage: g = graphs.RandomGNP(20,.5) sage: g = g.subgraph(edges = g.min_spanning_tree()) sage: g.is_tree() True sage: L = g.minor(graphs.CompleteGraph(3)) Traceback (most recent call last): ... ValueError: This graph has no minor isomorphic to H ! """ self._scream_if_not_simple() H._scream_if_not_simple() from sage.numerical.mip import MixedIntegerLinearProgram, MIPSolverException p = MixedIntegerLinearProgram(solver=solver) # We use frozenset((u, v)) to avoid confusion between (u, v) and (v, u) # rs = Representative set of a vertex # for h in H, v in G is such that rs[h,v] == 1 if and only if v # is a representative of h in self rs = p.new_variable(binary=True) for v in self: p.add_constraint(p.sum(rs[h,v] for h in H), max=1) # We ensure that the set of representatives of a # vertex h contains a tree, and thus is connected # edges represents the edges of the tree edges = p.new_variable(binary=True) # there can be a edge for h between two vertices # only if those vertices represent h for u,v in self.edge_iterator(labels=None): fuv = frozenset((u, v)) for h in H: p.add_constraint(edges[h,fuv] - rs[h,u], max=0) p.add_constraint(edges[h,fuv] - rs[h,v], max=0) # The number of edges of the tree in h is exactly the cardinal # of its representative set minus 1 for h in H: p.add_constraint( p.sum(edges[h,frozenset(e)] for e in self.edge_iterator(labels=None)) - p.sum(rs[h,v] for v in self), min=-1, max=-1) # a tree has no cycle epsilon = 1/(5*Integer(self.order())) r_edges = p.new_variable(nonnegative=True) for h in H: for u,v in self.edge_iterator(labels=None): p.add_constraint(r_edges[h,(u,v)] + r_edges[h,(v,u)] - edges[h,frozenset((u,v))], min=0) for v in self: p.add_constraint(p.sum(r_edges[h,(u,v)] for u in self.neighbor_iterator(v)), max=1-epsilon) # Once the representative sets are described, we must ensure # there are arcs corresponding to those of H between them h_edges = p.new_variable(nonnegative=True) for h1, h2 in H.edge_iterator(labels=None): for v1, v2 in self.edge_iterator(labels=None): fv1v2 = frozenset((v1, v2)) p.add_constraint(h_edges[(h1,h2),fv1v2] - rs[h2,v2], max=0) p.add_constraint(h_edges[(h1,h2),fv1v2] - rs[h1,v1], max=0) p.add_constraint(h_edges[(h2,h1),fv1v2] - rs[h1,v2], max=0) p.add_constraint(h_edges[(h2,h1),fv1v2] - rs[h2,v1], max=0) p.add_constraint(p.sum(h_edges[(h1,h2),frozenset(e)] + h_edges[(h2,h1),frozenset(e)] for e in self.edge_iterator(labels=None)), min=1) p.set_objective(None) try: p.solve(log=verbose) except MIPSolverException: raise ValueError("This graph has no minor isomorphic to H !") rs = p.get_values(rs, convert=bool, tolerance=integrality_tolerance) rs_dict = {} for h in H: rs_dict[h] = [v for v in self if rs[h,v]] return rs_dict ### Convexity @doc_index("Algorithmically hard stuff") def convexity_properties(self): r""" Return a ``ConvexityProperties`` object corresponding to ``self``. This object contains the methods related to convexity in graphs (convex hull, hull number) and caches useful information so that it becomes comparatively cheaper to compute the convex hull of many different sets of the same graph. .. SEEALSO:: In order to know what can be done through this object, please refer to module :mod:`sage.graphs.convexity_properties`. .. NOTE:: If you want to compute many convex hulls, keep this object in memory ! When it is created, it builds a table of useful information to compute convex hulls. As a result :: sage: g = graphs.PetersenGraph() sage: g.convexity_properties().hull([1, 3]) [1, 2, 3] sage: g.convexity_properties().hull([3, 7]) [2, 3, 7] Is a terrible waste of computations, while :: sage: g = graphs.PetersenGraph() sage: CP = g.convexity_properties() sage: CP.hull([1, 3]) [1, 2, 3] sage: CP.hull([3, 7]) [2, 3, 7] Makes perfect sense. """ from sage.graphs.convexity_properties import ConvexityProperties return ConvexityProperties(self) # Centrality @doc_index("Distances") def centrality_degree(self, v=None): r""" Return the degree centrality of a vertex. The degree centrality of a vertex `v` is its degree, divided by `|V(G)|-1`. For more information, see the :wikipedia:`Centrality`. INPUT: - ``v`` -- a vertex (default: ``None``); set to ``None`` (default) to get a dictionary associating each vertex with its centrality degree. .. SEEALSO:: - :meth:`~sage.graphs.generic_graph.GenericGraph.centrality_closeness` - :meth:`~sage.graphs.generic_graph.GenericGraph.centrality_betweenness` EXAMPLES:: sage: (graphs.ChvatalGraph()).centrality_degree() {0: 4/11, 1: 4/11, 2: 4/11, 3: 4/11, 4: 4/11, 5: 4/11, 6: 4/11, 7: 4/11, 8: 4/11, 9: 4/11, 10: 4/11, 11: 4/11} sage: D = graphs.DiamondGraph() sage: D.centrality_degree() {0: 2/3, 1: 1, 2: 1, 3: 2/3} sage: D.centrality_degree(v=1) 1 TESTS:: sage: Graph(1).centrality_degree() Traceback (most recent call last): ... ValueError: the centrality degree is not defined on graphs with only one vertex """ from sage.rings.integer import Integer n_minus_one = Integer(self.order() - 1) if n_minus_one == 0: raise ValueError("the centrality degree is not defined " "on graphs with only one vertex") if v is None: return {v: self.degree(v)/n_minus_one for v in self} else: return self.degree(v)/n_minus_one ### Distances @doc_index("Distances") def eccentricity(self, v=None, by_weight=False, algorithm=None, weight_function=None, check_weight=True, dist_dict=None, with_labels=False): """ Return the eccentricity of vertex (or vertices) ``v``. The eccentricity of a vertex is the maximum distance to any other vertex. For more information and examples on how to use input variables, see :meth:`~GenericGraph.shortest_path_all_pairs`, :meth:`~GenericGraph.shortest_path_lengths` and :meth:`~GenericGraph.shortest_paths` INPUT: - ``v`` - either a single vertex or a list of vertices. If it is not specified, then it is taken to be all vertices. - ``by_weight`` -- boolean (default: ``False``); if ``True``, edge weights are taken into account; if False, all edges have weight 1 - ``algorithm`` -- string (default: ``None``); one of the following algorithms: - ``'BFS'`` - the computation is done through a BFS centered on each vertex successively. Works only if ``by_weight==False``. - ``'DHV'`` - the computation is done using the algorithm proposed in [Dragan2018]_. Works only if ``self`` has non-negative edge weights and ``v is None`` or ``v`` should contain all vertices of ``self``. For more information see method :func:`sage.graphs.distances_all_pairs.eccentricity` and :func:`sage.graphs.base.boost_graph.eccentricity_DHV`. - ``'Floyd-Warshall-Cython'`` - a Cython implementation of the Floyd-Warshall algorithm. Works only if ``by_weight==False`` and ``v is None`` or ``v`` should contain all vertices of ``self``. - ``'Floyd-Warshall-Python'`` - a Python implementation of the Floyd-Warshall algorithm. Works also with weighted graphs, even with negative weights (but no negative cycle is allowed). However, ``v`` must be ``None`` or ``v`` should contain all vertices of ``self``. - ``'Dijkstra_NetworkX'`` - the Dijkstra algorithm, implemented in NetworkX. It works with weighted graphs, but no negative weight is allowed. - ``'Dijkstra_Boost'`` - the Dijkstra algorithm, implemented in Boost (works only with positive weights). - ``'Johnson_Boost'`` - the Johnson algorithm, implemented in Boost (works also with negative weights, if there is no negative cycle). Works only if ``v is None`` or ``v`` should contain all vertices of ``self``. - ``'From_Dictionary'`` - uses the (already computed) distances, that are provided by input variable ``dist_dict``. - ``None`` (default): Sage chooses the best algorithm: ``'From_Dictionary'`` if ``dist_dict`` is not None, ``'BFS'`` for unweighted graphs, ``'Dijkstra_Boost'`` if all weights are positive, ``'Johnson_Boost'`` otherwise. - ``weight_function`` -- function (default: ``None``); a function that takes as input an edge ``(u, v, l)`` and outputs its weight. If not ``None``, ``by_weight`` is automatically set to ``True``. If ``None`` and ``by_weight`` is ``True``, we use the edge label ``l`` as a weight, if ``l`` is not ``None``, else ``1`` as a weight. - ``check_weight`` -- boolean (default: ``True``); if ``True``, we check that the ``weight_function`` outputs a number for each edge - ``dist_dict`` -- a dictionary (default: ``None``); a dict of dicts of distances (used only if ``algorithm=='From_Dictionary'``) - ``with_labels`` -- boolean (default: ``False``); whether to return a list or a dictionary keyed by vertices. EXAMPLES:: sage: G = graphs.KrackhardtKiteGraph() sage: G.eccentricity() [4, 4, 4, 4, 4, 3, 3, 2, 3, 4] sage: G.vertices() [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] sage: G.eccentricity(7) 2 sage: G.eccentricity([7,8,9]) [2, 3, 4] sage: G.eccentricity([7,8,9], with_labels=True) == {8: 3, 9: 4, 7: 2} True sage: G = Graph( { 0 : [], 1 : [], 2 : [1] } ) sage: G.eccentricity() [+Infinity, +Infinity, +Infinity] sage: G = Graph({0:[]}) sage: G.eccentricity(with_labels=True) {0: 0} sage: G = Graph({0:[], 1:[]}) sage: G.eccentricity(with_labels=True) {0: +Infinity, 1: +Infinity} sage: G = Graph([(0,1,1), (1,2,1), (0,2,3)]) sage: G.eccentricity(algorithm = 'BFS') [1, 1, 1] sage: G.eccentricity(algorithm = 'Floyd-Warshall-Cython') [1, 1, 1] sage: G.eccentricity(by_weight = True, algorithm = 'Dijkstra_NetworkX') [2, 1, 2] sage: G.eccentricity(by_weight = True, algorithm = 'Dijkstra_Boost') [2, 1, 2] sage: G.eccentricity(by_weight = True, algorithm = 'Johnson_Boost') [2, 1, 2] sage: G.eccentricity(by_weight = True, algorithm = 'Floyd-Warshall-Python') [2, 1, 2] sage: G.eccentricity(dist_dict = G.shortest_path_all_pairs(by_weight = True)[0]) [2, 1, 2] sage: G.eccentricity(by_weight = False, algorithm = 'DHV') [1, 1, 1] sage: G.eccentricity(by_weight = True, algorithm = 'DHV') [2.0, 1.0, 2.0] TESTS: A non-implemented algorithm:: sage: G.eccentricity(algorithm = 'boh') Traceback (most recent call last): ... ValueError: unknown algorithm "boh" An algorithm that does not work with edge weights:: sage: G.eccentricity(by_weight = True, algorithm = 'BFS') Traceback (most recent call last): ... ValueError: algorithm 'BFS' does not work with weights sage: G.eccentricity(by_weight = True, algorithm = 'Floyd-Warshall-Cython') Traceback (most recent call last): ... ValueError: algorithm 'Floyd-Warshall-Cython' does not work with weights An algorithm that computes the all-pair-shortest-paths when not all vertices are needed:: sage: G.eccentricity(0, algorithm = 'Floyd-Warshall-Cython') Traceback (most recent call last): ... ValueError: algorithm 'Floyd-Warshall-Cython' works only if all eccentricities are needed sage: G.eccentricity(0, algorithm = 'Floyd-Warshall-Python') Traceback (most recent call last): ... ValueError: algorithm 'Floyd-Warshall-Python' works only if all eccentricities are needed sage: G.eccentricity(0, algorithm = 'Johnson_Boost') Traceback (most recent call last): ... ValueError: algorithm 'Johnson_Boost' works only if all eccentricities are needed sage: G.eccentricity(0, algorithm = 'DHV') Traceback (most recent call last): ... ValueError: algorithm 'DHV' works only if all eccentricities are needed """ by_weight, weight_function = self._get_weight_function(by_weight=by_weight, weight_function=weight_function, check_weight=check_weight) if algorithm is None: if dist_dict is not None: algorithm = 'From_Dictionary' elif not by_weight: algorithm = 'BFS' elif any(float(weight_function(e)) < 0 for e in self.edge_iterator()): algorithm = 'Johnson_Boost' if algorithm is None: algorithm = 'Dijkstra_Boost' if algorithm in ['BFS', 'Floyd-Warshall-Cython']: if by_weight: raise ValueError("algorithm '{}' does not work with weights".format(algorithm)) # We don't want the default weight function weight_function = None if v is not None: if not isinstance(v, list): v = [v] v_set = set(v) if v is None or all(u in v_set for u in self): if v is None: v = list(self) # If we want to use BFS, we use the Cython routine if algorithm == 'BFS': from sage.graphs.distances_all_pairs import eccentricity algo = 'bounds' if with_labels: return dict(zip(v, eccentricity(self, algorithm=algo, vertex_list=v))) else: return eccentricity(self, algorithm=algo,vertex_list=v) if algorithm == 'DHV': if by_weight: from sage.graphs.base.boost_graph import eccentricity_DHV if with_labels: return dict(zip(v, eccentricity_DHV(self, vertex_list=v, weight_function=weight_function, check_weight=check_weight))) else: return eccentricity_DHV(self, vertex_list=v, weight_function=weight_function, check_weight=check_weight) else: from sage.graphs.distances_all_pairs import eccentricity if with_labels: return dict(zip(v, eccentricity(self, algorithm=algorithm, vertex_list=v))) else: return eccentricity(self, algorithm=algorithm, vertex_list=v) if algorithm in ['Floyd-Warshall-Python', 'Floyd-Warshall-Cython', 'Johnson_Boost']: dist_dict = self.shortest_path_all_pairs(by_weight, algorithm, weight_function, check_weight)[0] algorithm = 'From_Dictionary' elif algorithm in ['Floyd-Warshall-Python', 'Floyd-Warshall-Cython', 'Johnson_Boost','DHV']: raise ValueError("algorithm '" + algorithm + "' works only if all" + " eccentricities are needed") ecc = {} from sage.rings.infinity import Infinity for u in v: if algorithm == 'From_Dictionary': length = dist_dict[u] else: # If algorithm is wrong, the error is raised by the # shortest_path_lengths function length = self.shortest_path_lengths(u, by_weight=by_weight, algorithm=algorithm, weight_function=weight_function, check_weight=check_weight) if len(length) != self.num_verts(): ecc[u] = Infinity else: ecc[u] = max(length.values()) if with_labels: return ecc else: if len(ecc) == 1: # return single value v, = ecc.values() return v return [ecc[u] for u in v] @doc_index("Distances") def radius(self, by_weight=False, algorithm='DHV', weight_function=None, check_weight=True): r""" Return the radius of the graph. The radius is defined to be the minimum eccentricity of any vertex, where the eccentricity is the maximum distance to any other vertex. For more information and examples on how to use input variables, see :meth:`~GenericGraph.shortest_paths` and :meth:`~Graph.eccentricity` INPUT: - ``by_weight`` -- boolean (default: ``False``); if ``True``, edge weights are taken into account; if False, all edges have weight 1 - ``algorithm`` -- string (default: ``'DHV'``). - ``'DHV'`` - Radius computation is done using the algorithm proposed in [Dragan2018]_. Works for graph with non-negative edge weights. For more information see method :func:`sage.graphs.distances_all_pairs.radius_DHV` and :func:`sage.graphs.base.boost_graph.radius_DHV`. - see method :meth:`eccentricity` for the list of remaining algorithms - ``weight_function`` -- function (default: ``None``); a function that takes as input an edge ``(u, v, l)`` and outputs its weight. If not ``None``, ``by_weight`` is automatically set to ``True``. If ``None`` and ``by_weight`` is ``True``, we use the edge label ``l`` as a weight, if ``l`` is not ``None``, else ``1`` as a weight. - ``check_weight`` -- boolean (default: ``True``); if ``True``, we check that the ``weight_function`` outputs a number for each edge EXAMPLES: The more symmetric a graph is, the smaller (diameter - radius) is:: sage: G = graphs.BarbellGraph(9, 3) sage: G.radius() 3 sage: G.diameter() 6 :: sage: G = graphs.OctahedralGraph() sage: G.radius() 2 sage: G.diameter() 2 TESTS:: sage: g = Graph() sage: g.radius() Traceback (most recent call last): ... ValueError: radius is not defined for the empty graph """ if not self.order(): raise ValueError("radius is not defined for the empty graph") if not algorithm: algorithm = 'DHV' if algorithm == 'DHV': by_weight, weight_function = self._get_weight_function(by_weight=by_weight, weight_function=weight_function, check_weight=check_weight) if by_weight: from sage.graphs.base.boost_graph import radius_DHV return radius_DHV(self, weight_function=weight_function, check_weight=False) else: from sage.graphs.distances_all_pairs import radius_DHV return radius_DHV(self) return min(self.eccentricity(v=None, by_weight=by_weight, weight_function=weight_function, check_weight=check_weight, algorithm=algorithm)) @doc_index("Distances") def diameter(self, by_weight=False, algorithm=None, weight_function=None, check_weight=True): r""" Return the diameter of the graph. The diameter is defined to be the maximum distance between two vertices. It is infinite if the graph is not connected. For more information and examples on how to use input variables, see :meth:`~GenericGraph.shortest_paths` and :meth:`~Graph.eccentricity` INPUT: - ``by_weight`` -- boolean (default: ``False``); if ``True``, edge weights are taken into account; if False, all edges have weight 1 - ``algorithm`` -- string (default: ``None``); one of the following algorithms: - ``'BFS'``: the computation is done through a BFS centered on each vertex successively. Works only if ``by_weight==False``. - ``'Floyd-Warshall-Cython'``: a Cython implementation of the Floyd-Warshall algorithm. Works only if ``by_weight==False`` and ``v is None``. - ``'Floyd-Warshall-Python'``: a Python implementation of the Floyd-Warshall algorithm. Works also with weighted graphs, even with negative weights (but no negative cycle is allowed). However, ``v`` must be ``None``. - ``'Dijkstra_NetworkX'``: the Dijkstra algorithm, implemented in NetworkX. It works with weighted graphs, but no negative weight is allowed. - ``'DHV'`` - diameter computation is done using the algorithm proposed in [Dragan2018]_. Works only for non-negative edge weights. For more information see method :func:`sage.graphs.distances_all_pairs.diameter_DHV` and :func:`sage.graphs.base.boost_graph.diameter_DHV`. - ``'standard'``, ``'2sweep'``, ``'multi-sweep'``, ``'iFUB'``: these algorithms are implemented in :func:`sage.graphs.distances_all_pairs.diameter` They work only if ``by_weight==False``. See the function documentation for more information. - ``'Dijkstra_Boost'``: the Dijkstra algorithm, implemented in Boost (works only with positive weights). - ``'Johnson_Boost'``: the Johnson algorithm, implemented in Boost (works also with negative weights, if there is no negative cycle). - ``None`` (default): Sage chooses the best algorithm: ``'iFUB'`` for unweighted graphs, ``'Dijkstra_Boost'`` if all weights are positive, ``'Johnson_Boost'`` otherwise. - ``weight_function`` -- function (default: ``None``); a function that takes as input an edge ``(u, v, l)`` and outputs its weight. If not ``None``, ``by_weight`` is automatically set to ``True``. If ``None`` and ``by_weight`` is ``True``, we use the edge label ``l`` as a weight, if ``l`` is not ``None``, else ``1`` as a weight. - ``check_weight`` -- boolean (default: ``True``); if ``True``, we check that the ``weight_function`` outputs a number for each edge EXAMPLES: The more symmetric a graph is, the smaller (diameter - radius) is:: sage: G = graphs.BarbellGraph(9, 3) sage: G.radius() 3 sage: G.diameter() 6 :: sage: G = graphs.OctahedralGraph() sage: G.radius() 2 sage: G.diameter() 2 TESTS:: sage: g = Graph() sage: g.diameter() Traceback (most recent call last): ... ValueError: diameter is not defined for the empty graph sage: g = Graph([(1, 2, {'weight': 1})]) sage: g.diameter(algorithm='iFUB', weight_function=lambda e: e[2]['weight']) Traceback (most recent call last): ... ValueError: algorithm 'iFUB' does not work on weighted graphs """ if not self.order(): raise ValueError("diameter is not defined for the empty graph") by_weight, weight_function = self._get_weight_function(by_weight=by_weight, weight_function=weight_function, check_weight=check_weight) if not by_weight: # We don't want the default weight function weight_function = None if algorithm is None: if by_weight: algorithm = 'iFUB' else: algorithm = 'DHV' elif algorithm == 'BFS': algorithm = 'standard' if algorithm == 'DHV': if by_weight: from sage.graphs.base.boost_graph import diameter_DHV return diameter_DHV(self, weight_function=weight_function, check_weight=False) else: from sage.graphs.distances_all_pairs import diameter return diameter(self, algorithm=algorithm) if algorithm in ['standard', '2sweep', 'multi-sweep', 'iFUB']: if by_weight: raise ValueError("algorithm '" + algorithm + "' does not work" + " on weighted graphs") from sage.graphs.distances_all_pairs import diameter return diameter(self, algorithm=algorithm) return max(self.eccentricity(v=list(self), by_weight=by_weight, weight_function=weight_function, check_weight=False, algorithm=algorithm)) @doc_index("Distances") def center(self, by_weight=False, algorithm=None, weight_function=None, check_weight=True): r""" Return the set of vertices in the center of the graph. The center is the set of vertices whose eccentricity is equal to the radius of the graph, i.e., achieving the minimum eccentricity. For more information and examples on how to use input variables, see :meth:`~GenericGraph.shortest_paths` and :meth:`~Graph.eccentricity` INPUT: - ``by_weight`` -- boolean (default: ``False``); if ``True``, edge weights are taken into account; if False, all edges have weight 1 - ``algorithm`` -- string (default: ``None``); see method :meth:`eccentricity` for the list of available algorithms - ``weight_function`` -- function (default: ``None``); a function that takes as input an edge ``(u, v, l)`` and outputs its weight. If not ``None``, ``by_weight`` is automatically set to ``True``. If ``None`` and ``by_weight`` is ``True``, we use the edge label ``l`` as a weight, if ``l`` is not ``None``, else ``1`` as a weight. - ``check_weight`` -- boolean (default: ``True``); if ``True``, we check that the ``weight_function`` outputs a number for each edge EXAMPLES: Is Central African Republic in the center of Africa in graph theoretic sense? Yes:: sage: A = graphs.AfricaMap(continental=True) sage: sorted(A.center()) ['Cameroon', 'Central Africa'] Some other graphs. Center can be the whole graph:: sage: G = graphs.DiamondGraph() sage: G.center() [1, 2] sage: P = graphs.PetersenGraph() sage: P.subgraph(P.center()) == P True sage: S = graphs.StarGraph(19) sage: S.center() [0] TESTS:: sage: G = Graph() sage: G.center() [] sage: G.add_vertex() 0 sage: G.center() [0] """ ecc = self.eccentricity(v=list(self), by_weight=by_weight, weight_function=weight_function, algorithm=algorithm, check_weight=check_weight, with_labels=True) try: r = min(ecc.values()) except Exception: return [] return [v for v in self if ecc[v] == r] @doc_index("Distances") def periphery(self, by_weight=False, algorithm=None, weight_function=None, check_weight=True): r""" Return the set of vertices in the periphery of the graph. The periphery is the set of vertices whose eccentricity is equal to the diameter of the graph, i.e., achieving the maximum eccentricity. For more information and examples on how to use input variables, see :meth:`~GenericGraph.shortest_paths` and :meth:`~Graph.eccentricity` INPUT: - ``by_weight`` -- boolean (default: ``False``); if ``True``, edge weights are taken into account; if False, all edges have weight 1 - ``algorithm`` -- string (default: ``None``); see method :meth:`eccentricity` for the list of available algorithms - ``weight_function`` -- function (default: ``None``); a function that takes as input an edge ``(u, v, l)`` and outputs its weight. If not ``None``, ``by_weight`` is automatically set to ``True``. If ``None`` and ``by_weight`` is ``True``, we use the edge label ``l`` as a weight, if ``l`` is not ``None``, else ``1`` as a weight. - ``check_weight`` -- boolean (default: ``True``); if ``True``, we check that the ``weight_function`` outputs a number for each edge EXAMPLES:: sage: G = graphs.DiamondGraph() sage: G.periphery() [0, 3] sage: P = graphs.PetersenGraph() sage: P.subgraph(P.periphery()) == P True sage: S = graphs.StarGraph(19) sage: S.periphery() [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19] sage: G = Graph() sage: G.periphery() [] sage: G.add_vertex() 0 sage: G.periphery() [0] """ ecc = self.eccentricity(v=list(self), by_weight=by_weight, weight_function=weight_function, algorithm=algorithm, check_weight=check_weight, with_labels=True) try: d = max(ecc.values()) except Exception: return [] return [v for v in self if ecc[v] == d] ### Constructors @doc_index("Basic methods") def to_directed(self, data_structure=None, sparse=None): """ Return a directed version of the graph. A single edge becomes two edges, one in each direction. INPUT: - ``data_structure`` -- one of ``"sparse"``, ``"static_sparse"``, or ``"dense"``. See the documentation of :class:`Graph` or :class:`DiGraph`. - ``sparse`` -- boolean (default: ``None``); ``sparse=True`` is an alias for ``data_structure="sparse"``, and ``sparse=False`` is an alias for ``data_structure="dense"``. EXAMPLES:: sage: graphs.PetersenGraph().to_directed() Petersen graph: Digraph on 10 vertices TESTS: Immutable graphs yield immutable graphs:: sage: Graph([[1, 2]], immutable=True).to_directed()._backend <sage.graphs.base.static_sparse_backend.StaticSparseBackend object at ...> :trac:`17005`:: sage: Graph([[1,2]], immutable=True).to_directed() Digraph on 2 vertices :trac:`22424`:: sage: G1=graphs.RandomGNP(5,0.5) sage: gp1 = G1.graphplot(save_pos=True) sage: G2=G1.to_directed() sage: G2.delete_vertex(0) sage: G2.add_vertex(5) sage: gp2 = G2.graphplot() sage: gp1 = G1.graphplot() Vertex labels will be retained (:trac:`14708`):: sage: G = Graph({0: [1, 2], 1: [0]}) sage: G.set_vertex(0, 'foo') sage: D = G.to_directed() sage: G.get_vertices() {0: 'foo', 1: None, 2: None} sage: D.get_vertices() {0: 'foo', 1: None, 2: None} """ if sparse is not None: if data_structure is not None: raise ValueError("The 'sparse' argument is an alias for " "'data_structure'. Please do not define both.") data_structure = "sparse" if sparse else "dense" if data_structure is None: from sage.graphs.base.dense_graph import DenseGraphBackend from sage.graphs.base.sparse_graph import SparseGraphBackend if isinstance(self._backend, DenseGraphBackend): data_structure = "dense" elif isinstance(self._backend, SparseGraphBackend): data_structure = "sparse" else: data_structure = "static_sparse" from sage.graphs.all import DiGraph D = DiGraph(name = self.name(), pos = self.get_pos(), multiedges = self.allows_multiple_edges(), loops = self.allows_loops(), data_structure = (data_structure if data_structure!="static_sparse" else "sparse")) # we need a mutable copy D.add_vertices(self.vertex_iterator()) D.set_vertices(self.get_vertices()) for u,v,l in self.edge_iterator(): D.add_edge(u,v,l) D.add_edge(v,u,l) if hasattr(self, '_embedding'): D._embedding = copy(self._embedding) D._weighted = self._weighted if data_structure == "static_sparse": D = D.copy(data_structure=data_structure) return D @doc_index("Basic methods") def to_undirected(self): """ Since the graph is already undirected, simply returns a copy of itself. EXAMPLES:: sage: graphs.PetersenGraph().to_undirected() Petersen graph: Graph on 10 vertices """ return self.copy() @doc_index("Basic methods") def join(self, other, labels="pairs", immutable=None): r""" Return the join of ``self`` and ``other``. INPUT: - ``labels`` -- (defaults to 'pairs'); if set to 'pairs', each element `v` in the first graph will be named `(0, v)` and each element `u` in ``other`` will be named `(1, u)` in the result. If set to 'integers', the elements of the result will be relabeled with consecutive integers. - ``immutable`` -- boolean (default: ``None``); whether to create a mutable/immutable join. ``immutable=None`` (default) means that the graphs and their join will behave the same way. .. SEEALSO:: * :meth:`~sage.graphs.generic_graph.GenericGraph.union` * :meth:`~sage.graphs.generic_graph.GenericGraph.disjoint_union` EXAMPLES:: sage: G = graphs.CycleGraph(3) sage: H = Graph(2) sage: J = G.join(H); J Cycle graph join : Graph on 5 vertices sage: J.vertices() [(0, 0), (0, 1), (0, 2), (1, 0), (1, 1)] sage: J = G.join(H, labels='integers'); J Cycle graph join : Graph on 5 vertices sage: J.vertices() [0, 1, 2, 3, 4] sage: J.edges() [(0, 1, None), (0, 2, None), (0, 3, None), (0, 4, None), (1, 2, None), (1, 3, None), (1, 4, None), (2, 3, None), (2, 4, None)] :: sage: G = Graph(3) sage: G.name("Graph on 3 vertices") sage: H = Graph(2) sage: H.name("Graph on 2 vertices") sage: J = G.join(H); J Graph on 3 vertices join Graph on 2 vertices: Graph on 5 vertices sage: J.vertices() [(0, 0), (0, 1), (0, 2), (1, 0), (1, 1)] sage: J = G.join(H, labels='integers'); J Graph on 3 vertices join Graph on 2 vertices: Graph on 5 vertices sage: J.edges() [(0, 3, None), (0, 4, None), (1, 3, None), (1, 4, None), (2, 3, None), (2, 4, None)] """ G = self.disjoint_union(other, labels=labels, immutable=False) if labels == "integers": G.add_edges((u, v) for u in range(self.order()) for v in range(self.order(), self.order() + other.order())) else: G.add_edges(((0, u), (1, v)) for u in self for v in other) G.name('%s join %s'%(self.name(), other.name())) if immutable is None: immutable = self.is_immutable() and other.is_immutable() if immutable: G = G.copy(immutable=True) return G @doc_index("Leftovers") def seidel_adjacency_matrix(self, vertices=None): r""" Return the Seidel adjacency matrix of ``self``. Returns `J-I-2A`, for `A` the (ordinary) :meth:`adjacency matrix <sage.graphs.generic_graph.GenericGraph.adjacency_matrix>` of ``self``, `I` the identity matrix, and `J` the all-1 matrix. It is closely related to :meth:`twograph`. The matrix returned is over the integers. If a different ring is desired, use either the :meth:`sage.matrix.matrix0.Matrix.change_ring` method or the :func:`matrix` function. INPUT: - ``vertices`` -- list of vertices (default: ``None``); the ordering of the vertices defining how they should appear in the matrix. By default, the ordering given by :meth:`~sage.graphs.generic_graph.GenericGraph.vertices` is used. EXAMPLES:: sage: G = graphs.CycleGraph(5) sage: G = G.disjoint_union(graphs.CompleteGraph(1)) sage: G.seidel_adjacency_matrix().minpoly() x^2 - 5 """ return - self.adjacency_matrix(sparse=False, vertices=vertices) \ + self.complement().adjacency_matrix(sparse=False, vertices=vertices) @doc_index("Leftovers") def seidel_switching(self, s, inplace=True): r""" Return the Seidel switching of ``self`` w.r.t. subset of vertices ``s``. Returns the graph obtained by Seidel switching of ``self`` with respect to the subset of vertices ``s``. This is the graph given by Seidel adjacency matrix `DSD`, for `S` the Seidel adjacency matrix of ``self``, and `D` the diagonal matrix with -1s at positions corresponding to ``s``, and 1s elsewhere. INPUT: - ``s`` -- a list of vertices of ``self``. - ``inplace`` -- boolean (default: ``True``); whether to do the modification inplace, or to return a copy of the graph after switching. EXAMPLES:: sage: G = graphs.CycleGraph(5) sage: G = G.disjoint_union(graphs.CompleteGraph(1)) sage: G.seidel_switching([(0,1),(1,0),(0,0)]) sage: G.seidel_adjacency_matrix().minpoly() x^2 - 5 sage: G.is_connected() True TESTS:: sage: H = G.seidel_switching([1,4,5],inplace=False) sage: G.seidel_switching([1,4,5]) sage: G == H True """ G = self if inplace else copy(self) boundary = self.edge_boundary(s) G.add_edges(itertools.product(s, set(self).difference(s))) G.delete_edges(boundary) if not inplace: return G @doc_index("Leftovers") def twograph(self): r""" Return the two-graph of ``self`` Returns the :class:`two-graph <sage.combinat.designs.twographs.TwoGraph>` with the triples `T=\{t \in \binom {V}{3} : \left| \binom {t}{2} \cap E \right| \text{odd} \}` where `V` and `E` are vertices and edges of ``self``, respectively. EXAMPLES:: sage: p=graphs.PetersenGraph() sage: p.twograph() Incidence structure with 10 points and 60 blocks sage: p=graphs.chang_graphs() sage: T8 = graphs.CompleteGraph(8).line_graph() sage: C = T8.seidel_switching([(0,1,None),(2,3,None),(4,5,None),(6,7,None)],inplace=False) sage: T8.twograph() == C.twograph() True sage: T8.is_isomorphic(C) False TESTS:: sage: from sage.combinat.designs.twographs import TwoGraph sage: p=graphs.PetersenGraph().twograph() sage: TwoGraph(p, check=True) Incidence structure with 10 points and 60 blocks .. SEEALSO:: - :meth:`~sage.combinat.designs.twographs.TwoGraph.descendant` -- computes the descendant graph of the two-graph of self at a vertex - :func:`~sage.combinat.designs.twographs.twograph_descendant` -- ditto, but much faster. """ from sage.combinat.designs.twographs import TwoGraph G = self.relabel(range(self.order()), inplace=False) T = [] # Triangles for x,y,z in G.subgraph_search_iterator(Graph({1:[2,3], 2:[3]})): if x < y and y < z: T.append([x, y, z]) # Triples with just one edge for x,y,z in G.subgraph_search_iterator(Graph({1:[2], 3:[]}), induced=True): if x < y: T.append([x, y, z]) T = TwoGraph(T) T.relabel({i: v for i,v in enumerate(self.vertices())}) return T ### Visualization @doc_index("Basic methods") def write_to_eps(self, filename, **options): r""" Write a plot of the graph to ``filename`` in ``eps`` format. INPUT: - ``filename`` -- a string - ``**options`` -- same layout options as :meth:`.layout` EXAMPLES:: sage: P = graphs.PetersenGraph() sage: P.write_to_eps(tmp_filename(ext='.eps')) It is relatively simple to include this file in a LaTeX document. ``\usepackage{graphics}`` must appear in the preamble, and ``\includegraphics{filename}`` will include the file. To compile the document to ``pdf`` with ``pdflatex`` or ``xelatex`` the file needs first to be converted to ``pdf``, for example with ``ps2pdf filename.eps filename.pdf``. """ from sage.graphs.print_graphs import print_graph_eps pos = self.layout(**options) [xmin, xmax, ymin, ymax] = self._layout_bounding_box(pos) for v in pos: pos[v] = (1.8*(pos[v][0] - xmin)/(xmax - xmin) - 0.9, 1.8*(pos[v][1] - ymin)/(ymax - ymin) - 0.9) if filename[-4:] != '.eps': filename += '.eps' f = open(filename, 'w') f.write( print_graph_eps(self.vertices(), self.edge_iterator(), pos) ) f.close() @doc_index("Algorithmically hard stuff") def topological_minor(self, H, vertices=False, paths=False, solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Return a topological `H`-minor from ``self`` if one exists. We say that a graph `G` has a topological `H`-minor (or that it has a graph isomorphic to `H` as a topological minor), if `G` contains a subdivision of a graph isomorphic to `H` (i.e. obtained from `H` through arbitrary subdivision of its edges) as a subgraph. For more information, see the :wikipedia:`Minor_(graph_theory)`. INPUT: - ``H`` -- The topological minor to find in the current graph. - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. OUTPUT: The topological `H`-minor found is returned as a subgraph `M` of ``self``, such that the vertex `v` of `M` that represents a vertex `h\in H` has ``h`` as a label (see :meth:`get_vertex <sage.graphs.generic_graph.GenericGraph.get_vertex>` and :meth:`set_vertex <sage.graphs.generic_graph.GenericGraph.set_vertex>`), and such that every edge of `M` has as a label the edge of `H` it (partially) represents. If no topological minor is found, this method returns ``False``. ALGORITHM: Mixed Integer Linear Programming. COMPLEXITY: Theoretically, when `H` is fixed, testing for the existence of a topological `H`-minor is polynomial. The known algorithms are highly exponential in `H`, though. .. NOTE:: This function can be expected to be *very* slow, especially where the topological minor does not exist. (CPLEX seems to be *much* more efficient than GLPK on this kind of problem) EXAMPLES: Petersen's graph has a topological `K_4`-minor:: sage: g = graphs.PetersenGraph() sage: g.topological_minor(graphs.CompleteGraph(4)) Subgraph of (Petersen graph): Graph on ... And a topological `K_{3,3}`-minor:: sage: g.topological_minor(graphs.CompleteBipartiteGraph(3,3)) Subgraph of (Petersen graph): Graph on ... And of course, a tree has no topological `C_3`-minor:: sage: g = graphs.RandomGNP(15,.3) sage: g = g.subgraph(edges = g.min_spanning_tree()) sage: g.topological_minor(graphs.CycleGraph(3)) False """ self._scream_if_not_simple() H._scream_if_not_simple() # Useful alias ... G = self from sage.numerical.mip import MixedIntegerLinearProgram, MIPSolverException p = MixedIntegerLinearProgram(solver=solver) # This is an existence problem p.set_objective(None) ####################### # Vertex representative # ####################### # # v_repr[h,g] = 1 if vertex h from H is represented by vertex # g from G, 0 otherwise v_repr = p.new_variable(binary=True) # Exactly one representative per vertex of H for h in H: p.add_constraint(p.sum(v_repr[h,g] for g in G), min=1, max=1) # A vertex of G can only represent one vertex of H for g in G: p.add_constraint(p.sum(v_repr[h,g] for h in H), max=1) ################### # Is representent # ################### # # is_repr[v] = 1 if v represents some vertex of H is_repr = p.new_variable(binary=True) for g in G: for h in H: p.add_constraint(v_repr[h,g] - is_repr[g], max=0) ################################### # paths between the representents # ################################### # # For any edge (h1,h2) in H, we have a corresponding path in G # between the representatives of h1 and h2. Which means there is # a flow of intensity 1 from one to the other. # We are then writing a flow problem for each edge of H. # # The variable flow[(h1,h2),(g1,g2)] indicates the amount of # flow on the edge (g1,g2) representing the edge (h1,h2). flow = p.new_variable(binary=True) # These functions return the balance of flow corresponding to # commodity C at vertex v def flow_in(C, v): return p.sum(flow[C,(v,u)] for u in G.neighbor_iterator(v)) def flow_out(C, v): return p.sum(flow[C,(u,v)] for u in G.neighbor_iterator(v)) def flow_balance(C, v): return flow_in(C,v) - flow_out(C,v) for h1,h2 in H.edge_iterator(labels=False): for v in G: # The flow balance depends on whether the vertex v is a # representative of h1 or h2 in G, or a representative of none p.add_constraint(flow_balance((h1,h2),v) == v_repr[h1,v] - v_repr[h2,v]) ############################# # Internal vertex of a path # ############################# # # is_internal[C][g] = 1 if a vertex v from G is located on the # path representing the edge (=commodity) C is_internal = p.new_variable(binary=True) # When is a vertex internal for a commodity ? for C in H.edge_iterator(labels=False): for g in G: p.add_constraint(flow_in(C,g) + flow_out(C,g) - is_internal[C,g], max=1) ############################ # Two paths do not cross ! # ############################ # A vertex can only be internal for one commodity, and zero if # the vertex is a representent for g in G: p.add_constraint(p.sum(is_internal[C,g] for C in H.edge_iterator(labels=False)) + is_repr[g], max=1) # (The following inequalities are not necessary, but they seem to be of # help (the solvers find the answer quicker when they are added) # The flow on one edge can go in only one direction. Besides, it can # belong to at most one commodity and has a maximum intensity of 1. for g1,g2 in G.edge_iterator(labels=None): p.add_constraint( p.sum(flow[C,(g1,g2)] for C in H.edge_iterator(labels=False)) + p.sum(flow[C,(g2,g1)] for C in H.edge_iterator(labels=False)), max=1) # Now we can solve the problem itself ! try: p.solve(log=verbose) except MIPSolverException: return False minor = G.subgraph(immutable=False) is_repr = p.get_values(is_repr, convert=bool, tolerance=integrality_tolerance) v_repr = p.get_values(v_repr, convert=bool, tolerance=integrality_tolerance) flow = p.get_values(flow, convert=bool, tolerance=integrality_tolerance) for u,v in minor.edge_iterator(labels=False): used = False for C in H.edge_iterator(labels=False): if flow[C,(u,v)] or flow[C,(v,u)]: used = True minor.set_edge_label(u, v, C) break if not used: minor.delete_edge(u, v) minor.delete_vertices(v for v in minor if minor.degree(v) == 0) for g in minor: if is_repr[g]: for h in H: if v_repr[h,v]: minor.set_vertex(g, h) break return minor ### Cliques @doc_index("Clique-related methods") def cliques_maximal(self, algorithm="native"): """ Return the list of all maximal cliques. Each clique is represented by a list of vertices. A clique is an induced complete subgraph, and a maximal clique is one not contained in a larger one. INPUT: - ``algorithm`` -- can be set to ``"native"`` (default) to use Sage's own implementation, or to ``"NetworkX"`` to use NetworkX' implementation of the Bron and Kerbosch Algorithm [BK1973]_. .. NOTE:: This method sorts its output before returning it. If you prefer to save the extra time, you can call :class:`sage.graphs.independent_sets.IndependentSets` directly. .. NOTE:: Sage's implementation of the enumeration of *maximal* independent sets is not much faster than NetworkX' (expect a 2x speedup), which is surprising as it is written in Cython. This being said, the algorithm from NetworkX appears to be slightly different from this one, and that would be a good thing to explore if one wants to improve the implementation. ALGORITHM: This function is based on NetworkX's implementation of the Bron and Kerbosch Algorithm [BK1973]_. EXAMPLES:: sage: graphs.ChvatalGraph().cliques_maximal() [[0, 1], [0, 4], [0, 6], [0, 9], [1, 2], [1, 5], [1, 7], [2, 3], [2, 6], [2, 8], [3, 4], [3, 7], [3, 9], [4, 5], [4, 8], [5, 10], [5, 11], [6, 10], [6, 11], [7, 8], [7, 11], [8, 10], [9, 10], [9, 11]] sage: G = Graph({0:[1,2,3], 1:[2], 3:[0,1]}) sage: G.show(figsize=[2, 2]) sage: G.cliques_maximal() [[0, 1, 2], [0, 1, 3]] sage: C = graphs.PetersenGraph() sage: C.cliques_maximal() [[0, 1], [0, 4], [0, 5], [1, 2], [1, 6], [2, 3], [2, 7], [3, 4], [3, 8], [4, 9], [5, 7], [5, 8], [6, 8], [6, 9], [7, 9]] sage: C = Graph('DJ{') sage: C.cliques_maximal() [[0, 4], [1, 2, 3, 4]] Comparing the two implementations:: sage: g = graphs.RandomGNP(20,.7) sage: s1 = Set(map(Set, g.cliques_maximal(algorithm="NetworkX"))) sage: s2 = Set(map(Set, g.cliques_maximal(algorithm="native"))) sage: s1 == s2 True """ if algorithm == "native": from sage.graphs.independent_sets import IndependentSets return list(IndependentSets(self, maximal=True, complement=True)) elif algorithm == "NetworkX": import networkx return list(networkx.find_cliques(self.networkx_graph())) else: raise ValueError("Algorithm must be equal to 'native' or to 'NetworkX'.") @doc_index("Clique-related methods") def clique_maximum(self, algorithm="Cliquer", solver=None, verbose=0, *, integrality_tolerance=1e-3): """ Return the vertex set of a maximal order complete subgraph. INPUT: - ``algorithm`` -- the algorithm to be used : - If ``algorithm = "Cliquer"`` (default), wraps the C program Cliquer [NO2003]_. - If ``algorithm = "MILP"``, the problem is solved through a Mixed Integer Linear Program. (see :class:`~sage.numerical.mip.MixedIntegerLinearProgram`) - If ``algorithm = "mcqd"``, uses the MCQD solver (`<http://www.sicmm.org/~konc/maxclique/>`_). Note that the MCQD package must be installed. - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. Parameters ``solver`` and ``verbose`` are used only when ``algorithm="MILP"``. .. NOTE:: Currently only implemented for undirected graphs. Use to_undirected to convert a digraph to an undirected graph. ALGORITHM: This function is based on Cliquer [NO2003]_. EXAMPLES: Using Cliquer (default):: sage: C = graphs.PetersenGraph() sage: C.clique_maximum() [7, 9] sage: C = Graph('DJ{') sage: C.clique_maximum() [1, 2, 3, 4] Through a Linear Program:: sage: len(C.clique_maximum(algorithm="MILP")) 4 TESTS: Wrong algorithm:: sage: C.clique_maximum(algorithm="BFS") Traceback (most recent call last): ... NotImplementedError: Only 'MILP', 'Cliquer' and 'mcqd' are supported. """ self._scream_if_not_simple(allow_multiple_edges=True) if algorithm == "Cliquer": from sage.graphs.cliquer import max_clique return max_clique(self) elif algorithm == "MILP": return self.complement().independent_set(algorithm=algorithm, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) elif algorithm == "mcqd": return mcqd(self) else: raise NotImplementedError("Only 'MILP', 'Cliquer' and 'mcqd' are supported.") @doc_index("Clique-related methods") def clique_number(self, algorithm="Cliquer", cliques=None, solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Return the order of the largest clique of the graph This is also called as the clique number. .. NOTE:: Currently only implemented for undirected graphs. Use ``to_undirected`` to convert a digraph to an undirected graph. INPUT: - ``algorithm`` -- the algorithm to be used : - If ``algorithm = "Cliquer"``, wraps the C program Cliquer [NO2003]_. - If ``algorithm = "networkx"``, uses the NetworkX's implementation of the Bron and Kerbosch Algorithm [BK1973]_. - If ``algorithm = "MILP"``, the problem is solved through a Mixed Integer Linear Program. (see :class:`~sage.numerical.mip.MixedIntegerLinearProgram`) - If ``algorithm = "mcqd"``, uses the MCQD solver (`<http://insilab.org/maxclique/>`_). Note that the MCQD package must be installed. - ``cliques`` -- an optional list of cliques that can be input if already computed. Ignored unless ``algorithm=="networkx"``. - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. ALGORITHM: This function is based on Cliquer [NO2003]_ and [BK1973]_. EXAMPLES:: sage: C = Graph('DJ{') sage: C.clique_number() 4 sage: G = Graph({0:[1,2,3], 1:[2], 3:[0,1]}) sage: G.show(figsize=[2,2]) sage: G.clique_number() 3 By definition the clique number of a complete graph is its order:: sage: all(graphs.CompleteGraph(i).clique_number() == i for i in range(1,15)) True A non-empty graph without edges has a clique number of 1:: sage: all((i*graphs.CompleteGraph(1)).clique_number() == 1 for i in range(1,15)) True A complete multipartite graph with k parts has clique number k:: sage: all((i*graphs.CompleteMultipartiteGraph(i*[5])).clique_number() == i for i in range(1,6)) True TESTS:: sage: g = graphs.PetersenGraph() sage: g.clique_number(algorithm="MILP") 2 sage: for i in range(10): # optional - mcqd ....: g = graphs.RandomGNP(15,.5) # optional - mcqd ....: if g.clique_number() != g.clique_number(algorithm="mcqd"): # optional - mcqd ....: print("This is dead wrong !") # optional - mcqd """ self._scream_if_not_simple(allow_loops=False) if algorithm == "Cliquer": from sage.graphs.cliquer import clique_number return clique_number(self) elif algorithm == "networkx": import networkx return networkx.graph_clique_number(self.networkx_graph(), cliques) elif algorithm == "MILP": return len(self.complement().independent_set(algorithm=algorithm, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance)) elif algorithm == "mcqd": return len(mcqd(self)) else: raise NotImplementedError("Only 'networkx' 'MILP' 'Cliquer' and 'mcqd' are supported.") @doc_index("Clique-related methods") def cliques_number_of(self, vertices=None, cliques=None): """ Return a dictionary of the number of maximal cliques containing each vertex, keyed by vertex. This returns a single value if only one input vertex. .. NOTE:: Currently only implemented for undirected graphs. Use to_undirected to convert a digraph to an undirected graph. INPUT: - ``vertices`` -- the vertices to inspect (default is entire graph) - ``cliques`` -- list of cliques (if already computed) EXAMPLES:: sage: C = Graph('DJ{') sage: C.cliques_number_of() {0: 1, 1: 1, 2: 1, 3: 1, 4: 2} sage: E = C.cliques_maximal() sage: E [[0, 4], [1, 2, 3, 4]] sage: C.cliques_number_of(cliques=E) {0: 1, 1: 1, 2: 1, 3: 1, 4: 2} sage: F = graphs.Grid2dGraph(2,3) sage: F.cliques_number_of() {(0, 0): 2, (0, 1): 3, (0, 2): 2, (1, 0): 2, (1, 1): 3, (1, 2): 2} sage: F.cliques_number_of(vertices=[(0, 1), (1, 2)]) {(0, 1): 3, (1, 2): 2} sage: G = Graph({0:[1,2,3], 1:[2], 3:[0,1]}) sage: G.show(figsize=[2,2]) sage: G.cliques_number_of() {0: 2, 1: 2, 2: 1, 3: 1} """ import networkx return networkx.number_of_cliques(self.networkx_graph(), vertices, cliques) @doc_index("Clique-related methods") def cliques_get_max_clique_graph(self): """ Return the clique graph. Vertices of the result are the maximal cliques of the graph, and edges of the result are between maximal cliques with common members in the original graph. For more information, see the :wikipedia:`Clique_graph`. .. NOTE:: Currently only implemented for undirected graphs. Use to_undirected to convert a digraph to an undirected graph. EXAMPLES:: sage: (graphs.ChvatalGraph()).cliques_get_max_clique_graph() Graph on 24 vertices sage: ((graphs.ChvatalGraph()).cliques_get_max_clique_graph()).show(figsize=[2,2], vertex_size=20, vertex_labels=False) sage: G = Graph({0:[1,2,3], 1:[2], 3:[0,1]}) sage: G.show(figsize=[2,2]) sage: G.cliques_get_max_clique_graph() Graph on 2 vertices sage: (G.cliques_get_max_clique_graph()).show(figsize=[2,2]) """ import networkx return Graph(networkx.make_max_clique_graph(self.networkx_graph(), create_using=networkx.MultiGraph()), multiedges=False) @doc_index("Clique-related methods") def cliques_get_clique_bipartite(self, **kwds): """ Return a bipartite graph constructed such that maximal cliques are the right vertices and the left vertices are retained from the given graph. Right and left vertices are connected if the bottom vertex belongs to the clique represented by a top vertex. .. NOTE:: Currently only implemented for undirected graphs. Use to_undirected to convert a digraph to an undirected graph. EXAMPLES:: sage: (graphs.ChvatalGraph()).cliques_get_clique_bipartite() Bipartite graph on 36 vertices sage: ((graphs.ChvatalGraph()).cliques_get_clique_bipartite()).show(figsize=[2,2], vertex_size=20, vertex_labels=False) sage: G = Graph({0:[1,2,3], 1:[2], 3:[0,1]}) sage: G.show(figsize=[2,2]) sage: G.cliques_get_clique_bipartite() Bipartite graph on 6 vertices sage: (G.cliques_get_clique_bipartite()).show(figsize=[2,2]) """ from .bipartite_graph import BipartiteGraph import networkx return BipartiteGraph(networkx.make_clique_bipartite(self.networkx_graph(), **kwds)) @doc_index("Algorithmically hard stuff") @rename_keyword(deprecation=32238, verbosity='verbose') def independent_set(self, algorithm="Cliquer", value_only=False, reduction_rules=True, solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Return a maximum independent set. An independent set of a graph is a set of pairwise non-adjacent vertices. A maximum independent set is an independent set of maximum cardinality. It induces an empty subgraph. Equivalently, an independent set is defined as the complement of a vertex cover. For more information, see the :wikipedia:`Independent_set_(graph_theory)` and the :wikipedia:`Vertex_cover`. INPUT: - ``algorithm`` -- the algorithm to be used * If ``algorithm = "Cliquer"`` (default), the problem is solved using Cliquer [NO2003]_. (see the :mod:`Cliquer modules <sage.graphs.cliquer>`) * If ``algorithm = "MILP"``, the problem is solved through a Mixed Integer Linear Program. (see :class:`~sage.numerical.mip.MixedIntegerLinearProgram`) * If ``algorithm = "mcqd"``, uses the MCQD solver (`<http://www.sicmm.org/~konc/maxclique/>`_). Note that the MCQD package must be installed. - ``value_only`` -- boolean (default: ``False``); if set to ``True``, only the size of a maximum independent set is returned. Otherwise, a maximum independent set is returned as a list of vertices. - ``reduction_rules`` -- (default: ``True``); specify if the reductions rules from kernelization must be applied as pre-processing or not. See [ACFLSS04]_ for more details. Note that depending on the instance, it might be faster to disable reduction rules. - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. .. NOTE:: While Cliquer/MCAD are usually (and by far) the most efficient implementations, the MILP formulation sometimes proves faster on very "symmetrical" graphs. EXAMPLES: Using Cliquer:: sage: C = graphs.PetersenGraph() sage: C.independent_set() [0, 3, 6, 7] As a linear program:: sage: C = graphs.PetersenGraph() sage: len(C.independent_set(algorithm="MILP")) 4 .. PLOT:: g = graphs.PetersenGraph() sphinx_plot(g.plot(partition=[g.independent_set()])) """ my_cover = self.vertex_cover(algorithm=algorithm, value_only=value_only, reduction_rules=reduction_rules, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) if value_only: return self.order() - my_cover else: my_cover = set(my_cover) return [u for u in self if u not in my_cover] @doc_index("Algorithmically hard stuff") @rename_keyword(deprecation=32238, verbosity='verbose') def vertex_cover(self, algorithm="Cliquer", value_only=False, reduction_rules=True, solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Return a minimum vertex cover of self represented by a set of vertices. A minimum vertex cover of a graph is a set `S` of vertices such that each edge is incident to at least one element of `S`, and such that `S` is of minimum cardinality. For more information, see the :wikipedia:`Vertex_cover`. Equivalently, a vertex cover is defined as the complement of an independent set. As an optimization problem, it can be expressed as follows: .. MATH:: \mbox{Minimize : }&\sum_{v\in G} b_v\\ \mbox{Such that : }&\forall (u,v) \in G.edges(), b_u+b_v\geq 1\\ &\forall x\in G, b_x\mbox{ is a binary variable} INPUT: - ``algorithm`` -- string (default: ``"Cliquer"``). Indicating which algorithm to use. It can be one of those values. - ``"Cliquer"`` will compute a minimum vertex cover using the Cliquer package. - ``"MILP"`` will compute a minimum vertex cover through a mixed integer linear program. - ``"mcqd"`` will use the MCQD solver (`<http://www.sicmm.org/~konc/maxclique/>`_). Note that the MCQD package must be installed. - ``value_only`` -- boolean (default: ``False``); if set to ``True``, only the size of a minimum vertex cover is returned. Otherwise, a minimum vertex cover is returned as a list of vertices. - ``reduction_rules`` -- (default: ``True``); specify if the reductions rules from kernelization must be applied as pre-processing or not. See [ACFLSS04]_ for more details. Note that depending on the instance, it might be faster to disable reduction rules. - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. EXAMPLES: On the Pappus graph:: sage: g = graphs.PappusGraph() sage: g.vertex_cover(value_only=True) 9 .. PLOT:: g = graphs.PappusGraph() sphinx_plot(g.plot(partition=[g.vertex_cover()])) TESTS: The two algorithms should return the same result:: sage: g = graphs.RandomGNP(10, .5) sage: vc1 = g.vertex_cover(algorithm="MILP") sage: vc2 = g.vertex_cover(algorithm="Cliquer") sage: len(vc1) == len(vc2) True The cardinality of the vertex cover is unchanged when reduction rules are used. First for trees:: sage: for i in range(20): ....: g = graphs.RandomTree(20) ....: vc1_set = g.vertex_cover() ....: vc1 = len(vc1_set) ....: vc2 = g.vertex_cover(value_only=True, reduction_rules=False) ....: if vc1 != vc2: ....: print("Error :", vc1, vc2) ....: print("With reduction rules :", vc1) ....: print("Without reduction rules :", vc2) ....: break ....: g.delete_vertices(vc1_set) ....: if g.size(): ....: print("This thing is not a vertex cover !") Then for random GNP graphs:: sage: for i in range(20): ....: g = graphs.RandomGNP(50, 0.08) ....: vc1_set = g.vertex_cover() ....: vc1 = len(vc1_set) ....: vc2 = g.vertex_cover(value_only=True, reduction_rules=False) ....: if vc1 != vc2: ....: print("Error :", vc1, vc2) ....: print("With reduction rules :", vc1) ....: print("Without reduction rules :", vc2) ....: break ....: g.delete_vertices(vc1_set) ....: if g.size(): ....: print("This thing is not a vertex cover !") Testing mcqd:: sage: graphs.PetersenGraph().vertex_cover(algorithm="mcqd", value_only=True) # optional - mcqd 6 Given a wrong algorithm:: sage: graphs.PetersenGraph().vertex_cover(algorithm="guess") Traceback (most recent call last): ... ValueError: the algorithm must be "Cliquer", "MILP" or "mcqd" Ticket :trac:`24287` is fixed:: sage: G = Graph([(0,1)]*5 + [(1,2)]*2, multiedges=True) sage: G.vertex_cover(reduction_rules=True, algorithm='MILP') [1] sage: G.vertex_cover(reduction_rules=False) [1] Ticket :trac:`25988` is fixed:: sage: B = BipartiteGraph(graphs.CycleGraph(6)) sage: B.vertex_cover(algorithm='Cliquer', reduction_rules=True) [1, 3, 5] """ self._scream_if_not_simple(allow_multiple_edges=True) g = self ppset = [] folded_vertices = [] ################### # Reduction rules # ################### if reduction_rules: # We apply simple reduction rules allowing to identify vertices that # belongs to an optimal vertex cover # We first take a copy of the graph without multiple edges, if any. g = Graph(data=self.edges(), format='list_of_edges', multiedges=self.allows_multiple_edges()) g.allow_multiple_edges(False) degree_at_most_two = {u for u in g if g.degree(u) <= 2} while degree_at_most_two: u = degree_at_most_two.pop() du = g.degree(u) if not du: # RULE 1: isolated vertices are not part of the cover. We # simply remove them from the graph. The degree of such # vertices may have been reduced to 0 while applying other # reduction rules g.delete_vertex(u) elif du == 1: # RULE 2: If a vertex u has degree 1, we select its neighbor # v and remove both u and v from g. v = next(g.neighbor_iterator(u)) ppset.append(v) g.delete_vertex(u) for w in g.neighbor_iterator(v): if g.degree(w) <= 3: # The degree of w will be at most two after the # deletion of v degree_at_most_two.add(w) g.delete_vertex(v) degree_at_most_two.discard(v) elif du == 2: v,w = g.neighbors(u) if g.has_edge(v, w): # RULE 3: If the neighbors v and w of a degree 2 vertex # u are incident, then we select both v and w and remove # u, v, and w from g. ppset.append(v) ppset.append(w) g.delete_vertex(u) neigh = set(g.neighbors(v) + g.neighbors(w)).difference([v, w]) g.delete_vertex(v) g.delete_vertex(w) for z in neigh: if g.degree(z) <= 2: degree_at_most_two.add(z) else: # RULE 4, folded vertices: If the neighbors v and w of a # degree 2 vertex u are not incident, then we contract # edges (u, v), (u, w). Then, if the solution contains u, # we replace it with v and w. Otherwise, we let u in the # solution. neigh = set(g.neighbors(v) + g.neighbors(w)).difference([u, v, w]) g.delete_vertex(v) g.delete_vertex(w) for z in neigh: g.add_edge(u,z) folded_vertices.append((u, v, w)) if g.degree(u) <= 2: degree_at_most_two.add(u) degree_at_most_two.discard(v) degree_at_most_two.discard(w) # RULE 5: # TODO: add extra reduction rules ################## # Main Algorithm # ################## if not g.order(): # Reduction rules were sufficients to get the solution size_cover_g = 0 cover_g = set() elif algorithm == "Cliquer" or algorithm == "mcqd": if g.has_multiple_edges() and not reduction_rules: g = copy(g) g.allow_multiple_edges(False) independent = g.complement().clique_maximum(algorithm=algorithm) if value_only: size_cover_g = g.order() - len(independent) else: cover_g = set(uu for uu in g if uu not in independent) elif algorithm == "MILP": from sage.numerical.mip import MixedIntegerLinearProgram p = MixedIntegerLinearProgram(maximization=False, solver=solver) b = p.new_variable(binary=True) # minimizes the number of vertices in the set p.set_objective(p.sum(b[v] for v in g)) # an edge contains at least one vertex of the minimum vertex cover for u,v in g.edge_iterator(labels=None): p.add_constraint(b[u] + b[v], min=1) p.solve(log=verbose) b = p.get_values(b, convert=bool, tolerance=integrality_tolerance) if value_only: size_cover_g = sum(1 for v in g if b[v]) else: cover_g = set(v for v in g if b[v]) else: raise ValueError('the algorithm must be "Cliquer", "MILP" or "mcqd"') ######################### # Returning the results # ######################### # We finally reconstruct the solution according the reduction rules if value_only: return len(ppset) + len(folded_vertices) + size_cover_g else: # RULES 2 and 3: cover_g.update(ppset) # RULE 4: folded_vertices.reverse() for u,v,w in folded_vertices: if u in cover_g: cover_g.discard(u) cover_g.add(v) cover_g.add(w) else: cover_g.add(u) return list(cover_g) @doc_index("Connectivity, orientations, trees") def ear_decomposition(self): r""" Return an Ear decomposition of the graph. An ear of an undirected graph `G` is a path `P` where the two endpoints of the path may coincide (i.e., form a cycle), but where otherwise no repetition of edges or vertices is allowed, so every internal vertex of `P` has degree two in `P`. An ear decomposition of an undirected graph `G` is a partition of its set of edges into a sequence of ears, such that the one or two endpoints of each ear belong to earlier ears in the sequence and such that the internal vertices of each ear do not belong to any earlier ear. For more information, see the :wikipedia:`Ear_decomposition`. This method implements the linear time algorithm presented in [Sch2013]_. OUTPUT: - A nested list representing the cycles and chains of the ear decomposition of the graph. EXAMPLES: Ear decomposition of an outer planar graph of order 13:: sage: g = Graph('LlCG{O@?GBOMW?') sage: g.ear_decomposition() [[0, 3, 2, 1, 0], [0, 7, 4, 3], [0, 11, 9, 8, 7], [1, 12, 2], [3, 6, 5, 4], [4, 6], [7, 10, 8], [7, 11], [8, 11]] Ear decomposition of a biconnected graph:: sage: g = graphs.CycleGraph(4) sage: g.ear_decomposition() [[0, 3, 2, 1, 0]] Ear decomposition of a connected but not biconnected graph:: sage: G = Graph() sage: G.add_cycle([0,1,2]) sage: G.add_edge(0,3) sage: G.add_cycle([3,4,5,6]) sage: G.ear_decomposition() [[0, 2, 1, 0], [3, 6, 5, 4, 3]] The ear decomposition of a multigraph with loops is the same as the ear decomposition of the underlying simple graph:: sage: g = graphs.BullGraph() sage: g.allow_multiple_edges(True) sage: g.add_edges(g.edges()) sage: g.allow_loops(True) sage: u = g.random_vertex() sage: g.add_edge(u, u) sage: g Bull graph: Looped multi-graph on 5 vertices sage: h = g.to_simple() sage: g.ear_decomposition() == h.ear_decomposition() True TESTS:: sage: g=Graph() sage: g Graph on 0 vertices sage: g.ear_decomposition() Traceback (most recent call last): ... ValueError: ear decomposition is defined for graphs of order at least 3 """ # Ear decomposition of a graph of order < 3 is []. if self.order() < 3: raise ValueError("ear decomposition is defined for graphs of order at least 3") # List to store the order in which dfs visits vertices. dfs_order = [] # Boolean dict to mark vertices as visited or unvisited during # Dfs traversal in graph. seen = set() # Boolean dict to mark vertices as visited or unvisited in # Dfs tree traversal. traversed = set() # Dictionary to store parent vertex of all the visited vertices. # Initialized for the first vertex to be visited. parent = {next(self.vertex_iterator()): None} # List to store visit_time of vertices in Dfs traversal. value = {} # List to store all the chains and cycles of the input graph G. chains = [] # DFS() : Function that performs depth first search on input graph G and # stores DFS tree in parent array format. def DFS(v): """ Depth first search step from vertex v. """ # make v are visited, update its time of visited and value seen.add(v) dfs_order.append(v) # Traverse though all the neighbor vertices of v for u in self.neighbor_iterator(v): # if any neighbor is not visited, enter if u not in seen: # Set the parent of u in DFS tree as v and continue # exploration parent[u] = v DFS(u) # Traverse() : Function that use G-T (non-tree edges) to find cycles # and chains by traversing in DFS tree. def traverse(start, pointer): # Make the first end of non-tree edge visited traversed.add(start) chain = [start] # Traverse DFS Tree of G and print all the not visited vertices # Appending all the vertices in chain while True: chain.append(pointer) if pointer in traversed: break traversed.add(pointer) pointer = parent[pointer] chains.append(chain) # Perform ear decomposition on each connected component of input graph. for v in self: if v not in seen: # Start the depth first search from first vertex DFS(v) value = {u:i for i,u in enumerate(dfs_order)} # Traverse all the non Tree edges, according to DFS order for u in dfs_order: for neighbor in self.neighbor_iterator(u): if value[u] < value[neighbor] and u != parent[neighbor]: traverse(u, neighbor) dfs_order = [] return chains @doc_index("Clique-related methods") def cliques_vertex_clique_number(self, algorithm="cliquer", vertices=None, cliques=None): """ Return a dictionary of sizes of the largest maximal cliques containing each vertex, keyed by vertex. Returns a single value if only one input vertex. .. NOTE:: Currently only implemented for undirected graphs. Use to_undirected to convert a digraph to an undirected graph. INPUT: - ``algorithm`` -- either ``cliquer`` or ``networkx`` - ``cliquer`` -- This wraps the C program Cliquer [NO2003]_. - ``networkx`` -- This function is based on NetworkX's implementation of the Bron and Kerbosch Algorithm [BK1973]_. - ``vertices`` -- the vertices to inspect (default is entire graph). Ignored unless ``algorithm=='networkx'``. - ``cliques`` -- list of cliques (if already computed). Ignored unless ``algorithm=='networkx'``. EXAMPLES:: sage: C = Graph('DJ{') sage: C.cliques_vertex_clique_number() {0: 2, 1: 4, 2: 4, 3: 4, 4: 4} sage: E = C.cliques_maximal() sage: E [[0, 4], [1, 2, 3, 4]] sage: C.cliques_vertex_clique_number(cliques=E,algorithm="networkx") {0: 2, 1: 4, 2: 4, 3: 4, 4: 4} sage: F = graphs.Grid2dGraph(2,3) sage: F.cliques_vertex_clique_number(algorithm="networkx") {(0, 0): 2, (0, 1): 2, (0, 2): 2, (1, 0): 2, (1, 1): 2, (1, 2): 2} sage: F.cliques_vertex_clique_number(vertices=[(0, 1), (1, 2)]) {(0, 1): 2, (1, 2): 2} sage: G = Graph({0:[1,2,3], 1:[2], 3:[0,1]}) sage: G.show(figsize=[2,2]) sage: G.cliques_vertex_clique_number() {0: 3, 1: 3, 2: 3, 3: 3} """ if algorithm == "cliquer": from sage.graphs.cliquer import clique_number if vertices is None: vertices = self value = {} for v in vertices: value[v] = 1 + clique_number(self.subgraph(self.neighbors(v))) self.subgraph(self.neighbors(v)).plot() return value elif algorithm == "networkx": import networkx return networkx.node_clique_number(self.networkx_graph(), vertices, cliques) else: raise NotImplementedError("Only 'networkx' and 'cliquer' are supported.") @doc_index("Clique-related methods") def cliques_containing_vertex(self, vertices=None, cliques=None): """ Return the cliques containing each vertex, represented as a dictionary of lists of lists, keyed by vertex. Returns a single list if only one input vertex. .. NOTE:: Currently only implemented for undirected graphs. Use to_undirected to convert a digraph to an undirected graph. INPUT: - ``vertices`` -- the vertices to inspect (default is entire graph) - ``cliques`` -- list of cliques (if already computed) EXAMPLES:: sage: C = Graph('DJ{') sage: C.cliques_containing_vertex() {0: [[4, 0]], 1: [[4, 1, 2, 3]], 2: [[4, 1, 2, 3]], 3: [[4, 1, 2, 3]], 4: [[4, 0], [4, 1, 2, 3]]} sage: E = C.cliques_maximal() sage: E [[0, 4], [1, 2, 3, 4]] sage: C.cliques_containing_vertex(cliques=E) {0: [[0, 4]], 1: [[1, 2, 3, 4]], 2: [[1, 2, 3, 4]], 3: [[1, 2, 3, 4]], 4: [[0, 4], [1, 2, 3, 4]]} sage: G = Graph({0:[1,2,3], 1:[2], 3:[0,1]}) sage: G.show(figsize=[2,2]) sage: G.cliques_containing_vertex() {0: [[0, 1, 2], [0, 1, 3]], 1: [[0, 1, 2], [0, 1, 3]], 2: [[0, 1, 2]], 3: [[0, 1, 3]]} Since each clique of a 2 dimensional grid corresponds to an edge, the number of cliques in which a vertex is involved equals its degree:: sage: F = graphs.Grid2dGraph(2,3) sage: d = F.cliques_containing_vertex() sage: all(F.degree(u) == len(cliques) for u,cliques in d.items()) True sage: d = F.cliques_containing_vertex(vertices=[(0, 1)]) sage: list(d) [(0, 1)] sage: sorted(sorted(x for x in L) for L in d[(0, 1)]) [[(0, 0), (0, 1)], [(0, 1), (0, 2)], [(0, 1), (1, 1)]] """ import networkx return networkx.cliques_containing_node(self.networkx_graph(), vertices, cliques) @doc_index("Clique-related methods") def clique_complex(self): """ Return the clique complex of self. This is the largest simplicial complex on the vertices of self whose 1-skeleton is self. This is only makes sense for undirected simple graphs. EXAMPLES:: sage: g = Graph({0:[1,2],1:[2],4:[]}) sage: g.clique_complex() Simplicial complex with vertex set (0, 1, 2, 4) and facets {(4,), (0, 1, 2)} sage: h = Graph({0:[1,2,3,4],1:[2,3,4],2:[3]}) sage: x = h.clique_complex() sage: x Simplicial complex with vertex set (0, 1, 2, 3, 4) and facets {(0, 1, 4), (0, 1, 2, 3)} sage: i = x.graph() sage: i==h True sage: x==i.clique_complex() True """ if self.is_directed() or self.has_loops() or self.has_multiple_edges(): raise ValueError("Self must be an undirected simple graph to have a clique complex.") import sage.topology.simplicial_complex C = sage.topology.simplicial_complex.SimplicialComplex(self.cliques_maximal(), maximality_check=True) C._graph = self return C @doc_index("Clique-related methods") def clique_polynomial(self, t=None): r""" Return the clique polynomial of self. This is the polynomial where the coefficient of `t^n` is the number of cliques in the graph with `n` vertices. The constant term of the clique polynomial is always taken to be one. EXAMPLES:: sage: g = Graph() sage: g.clique_polynomial() 1 sage: g = Graph({0:[1]}) sage: g.clique_polynomial() t^2 + 2*t + 1 sage: g = graphs.CycleGraph(4) sage: g.clique_polynomial() 4*t^2 + 4*t + 1 """ if t is None: R = PolynomialRing(ZZ, 't') t = R.gen() number_of = [0]*(self.order() + 1) for x in IndependentSets(self, complement=True): number_of[len(x)] += 1 return sum(coeff*t**i for i,coeff in enumerate(number_of) if coeff) ### Miscellaneous @doc_index("Leftovers") def cores(self, k=None, with_labels=False): r""" Return the core number for each vertex in an ordered list. (for homomorphisms cores, see the :meth:`Graph.has_homomorphism_to` method) DEFINITIONS: * *K-cores* in graph theory were introduced by Seidman in 1983 and by Bollobas in 1984 as a method of (destructively) simplifying graph topology to aid in analysis and visualization. They have been more recently defined as the following by Batagelj et al: *Given a graph `G` with vertices set `V` and edges set `E`, the `k`-core of `G` is the graph obtained from `G` by recursively removing the vertices with degree less than `k`, for as long as there are any.* This operation can be useful to filter or to study some properties of the graphs. For instance, when you compute the 2-core of graph G, you are cutting all the vertices which are in a tree part of graph. (A tree is a graph with no loops). See the :wikipedia:`K-core`. [PSW1996]_ defines a `k`-core of `G` as the largest subgraph (it is unique) of `G` with minimum degree at least `k`. * Core number of a vertex The core number of a vertex `v` is the largest integer `k` such that `v` belongs to the `k`-core of `G`. * Degeneracy The *degeneracy* of a graph `G`, usually denoted `\delta^*(G)`, is the smallest integer `k` such that the graph `G` can be reduced to the empty graph by iteratively removing vertices of degree `\leq k`. Equivalently, `\delta^*(G)=k` if `k` is the smallest integer such that the `k`-core of `G` is empty. IMPLEMENTATION: This implementation is based on the NetworkX implementation of the algorithm described in [BZ2003]_. INPUT: - ``k`` -- integer (default: ``None``); * If ``k = None`` (default), returns the core number for each vertex. * If ``k`` is an integer, returns a pair ``(ordering, core)``, where ``core`` is the list of vertices in the `k`-core of ``self``, and ``ordering`` is an elimination order for the other vertices such that each vertex is of degree strictly less than `k` when it is to be eliminated from the graph. - ``with_labels`` -- boolean (default: ``False``); when set to ``False``, and ``k = None``, the method returns a list whose `i` th element is the core number of the `i` th vertex. When set to ``True``, the method returns a dictionary whose keys are vertices, and whose values are the corresponding core numbers. .. SEEALSO:: * Graph cores is also a notion related to graph homomorphisms. For this second meaning, see :meth:`Graph.has_homomorphism_to`. EXAMPLES:: sage: (graphs.FruchtGraph()).cores() [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] sage: (graphs.FruchtGraph()).cores(with_labels=True) {0: 3, 1: 3, 2: 3, 3: 3, 4: 3, 5: 3, 6: 3, 7: 3, 8: 3, 9: 3, 10: 3, 11: 3} sage: set_random_seed(0) sage: a = random_matrix(ZZ, 20, x=2, sparse=True, density=.1) sage: b = Graph(20) sage: b.add_edges(a.nonzero_positions(), loops=False) sage: cores = b.cores(with_labels=True); cores {0: 3, 1: 3, 2: 3, 3: 3, 4: 2, 5: 2, 6: 3, 7: 1, 8: 3, 9: 3, 10: 3, 11: 3, 12: 3, 13: 3, 14: 2, 15: 3, 16: 3, 17: 3, 18: 3, 19: 3} sage: [v for v,c in cores.items() if c >= 2] # the vertices in the 2-core [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19] Checking the 2-core of a random lobster is indeed the empty set:: sage: g = graphs.RandomLobster(20, .5, .5) sage: ordering, core = g.cores(2) sage: len(core) == 0 True """ self._scream_if_not_simple() # compute the degrees of each vertex degrees = self.degree(labels=True) # Sort vertices by degree. Store in a list and keep track of where a # specific degree starts (effectively, the list is sorted by bins). verts = sorted(degrees.keys(), key=lambda x: degrees[x]) bin_boundaries = [0] curr_degree = 0 for i,v in enumerate(verts): if degrees[v] > curr_degree: bin_boundaries.extend([i] * (degrees[v] - curr_degree)) curr_degree = degrees[v] vert_pos = {v: pos for pos,v in enumerate(verts)} # Set up initial guesses for core and lists of neighbors. core = degrees nbrs = {v: set(self.neighbors(v)) for v in self} # form vertex core building up from smallest for v in verts: # If all the vertices have a degree larger than k, we can return our # answer if k is not None if k is not None and core[v] >= k: return verts[:vert_pos[v]], verts[vert_pos[v]:] for u in nbrs[v]: if core[u] > core[v]: nbrs[u].remove(v) # Cleverly move u to the end of the next smallest bin (i.e., # subtract one from the degree of u). We do this by swapping # u with the first vertex in the bin that contains u, then # incrementing the bin boundary for the bin that contains u. pos = vert_pos[u] bin_start = bin_boundaries[core[u]] vert_pos[u] = bin_start vert_pos[verts[bin_start]] = pos verts[bin_start],verts[pos] = verts[pos],verts[bin_start] bin_boundaries[core[u]] += 1 core[u] -= 1 if k is not None: return verts, [] if with_labels: return core else: return list(core.values()) @doc_index("Leftovers") def modular_decomposition(self, algorithm='habib', style='tuple'): r""" Return the modular decomposition of the current graph. A module of an undirected graph is a subset of vertices such that every vertex outside the module is either connected to all members of the module or to none of them. Every graph that has a nontrivial module can be partitioned into modules, and the increasingly fine partitions into modules form a tree. The ``modular_decomposition`` function returns that tree. INPUT: - ``algorithm`` -- string (default: ``'habib'``); specifies the algorithm to use among: - ``'tedder'`` -- linear time algorithm of [TCHP2008]_ - ``'habib'`` -- `O(n^3)` algorithm of [HM1979]_. This algorithm is much simpler and so possibly less prone to errors. - ``style`` -- string (default: ``'tuple'``); specifies the output format: - ``'tuple'`` -- as nested tuples. - ``'tree'`` -- as :class:`~sage.combinat.rooted_tree.LabelledRootedTree`. OUTPUT: A pair of two values (recursively encoding the decomposition) : * The type of the current module : * ``"PARALLEL"`` * ``"PRIME"`` * ``"SERIES"`` * The list of submodules (as list of pairs ``(type, list)``, recursively...) or the vertex's name if the module is a singleton. Crash course on modular decomposition: A module `M` of a graph `G` is a proper subset of its vertices such that for all `u \in V(G)-M, v,w\in M` the relation `u \sim v \Leftrightarrow u \sim w` holds, where `\sim` denotes the adjacency relation in `G`. Equivalently, `M \subset V(G)` is a module if all its vertices have the same adjacency relations with each vertex outside of the module (vertex by vertex). Hence, for a set like a module, it is very easy to encode the information of the adjacencies between the vertices inside and outside the module -- we can actually add a new vertex `v_M` to our graph representing our module `M`, and let `v_M` be adjacent to `u\in V(G)-M` if and only if some `v\in M` (and hence all the vertices contained in the module) is adjacent to `u`. We can now independently (and recursively) study the structure of our module `M` and the new graph `G-M+\{v_M\}`, without any loss of information. Here are two very simple modules : * A connected component `C` (or the union of some --but not all-- of them) of a disconnected graph `G`, for instance, is a module, as no vertex of `C` has a neighbor outside of it. * An anticomponent `C` (or the union of some --but not all-- of them) of an non-anticonnected graph `G`, for the same reason (it is just the complement of the previous graph !). These modules being of special interest, the disjoint union of graphs is called a Parallel composition, and the complement of a disjoint union is called a Series composition. A graph whose only modules are singletons is called Prime. For more information on modular decomposition, in particular for an explanation of the terms "Parallel," "Prime" and "Series," see the :wikipedia:`Modular_decomposition`. You may also be interested in the survey from Michel Habib and Christophe Paul entitled "A survey on Algorithmic aspects of modular decomposition" [HP2010]_. EXAMPLES: The Bull Graph is prime:: sage: graphs.BullGraph().modular_decomposition() (PRIME, [1, 2, 0, 3, 4]) The Petersen Graph too:: sage: graphs.PetersenGraph().modular_decomposition() (PRIME, [1, 4, 5, 0, 2, 6, 3, 7, 8, 9]) This a clique on 5 vertices with 2 pendant edges, though, has a more interesting decomposition:: sage: g = graphs.CompleteGraph(5) sage: g.add_edge(0,5) sage: g.add_edge(0,6) sage: g.modular_decomposition(algorithm='habib') (SERIES, [(PARALLEL, [(SERIES, [1, 2, 3, 4]), 5, 6]), 0]) We get an equivalent tree when we use the algorithm of [TCHP2008]_:: sage: g.modular_decomposition(algorithm='tedder') (SERIES, [(PARALLEL, [(SERIES, [4, 3, 2, 1]), 5, 6]), 0]) We can choose output to be a :class:`~sage.combinat.rooted_tree.LabelledRootedTree`:: sage: g.modular_decomposition(style='tree') SERIES[0[], PARALLEL[5[], 6[], SERIES[1[], 2[], 3[], 4[]]]] sage: ascii_art(g.modular_decomposition(style='tree')) __SERIES / / 0 ___PARALLEL / / / 5 6 __SERIES / / / / 1 2 3 4 ALGORITHM: When ``algorithm='tedder'`` this function uses python implementation of algorithm published by Marc Tedder, Derek Corneil, Michel Habib and Christophe Paul [TCHP2008]_. When ``algorithm='habib'`` this function uses the algorithm of M. Habib and M. Maurer [HM1979]_. .. SEEALSO:: - :meth:`is_prime` -- Tests whether a graph is prime. - :class:`~sage.combinat.rooted_tree.LabelledRootedTree`. TESTS: Empty graph:: sage: graphs.EmptyGraph().modular_decomposition(algorithm='habib') () sage: graphs.EmptyGraph().modular_decomposition(algorithm='tedder') () sage: graphs.EmptyGraph().modular_decomposition(algorithm='habib', style='tree') None[] sage: graphs.EmptyGraph().modular_decomposition(algorithm='tedder', style='tree') None[] Singleton Vertex:: sage: Graph(1).modular_decomposition(algorithm='habib') (PRIME, [0]) sage: Graph(1).modular_decomposition(algorithm='tedder') (PRIME, [0]) sage: Graph(1).modular_decomposition(algorithm='habib', style='tree') PRIME[0[]] sage: Graph(1).modular_decomposition(algorithm='tedder', style='tree') PRIME[0[]] Vertices may be arbitrary --- check that :trac:`24898` is fixed:: sage: md = Graph({(1,2):[(2,3)],(2,3):[(1,2)]}).modular_decomposition() sage: md[0] SERIES sage: sorted(md[1]) [(1, 2), (2, 3)] Unknown algorithm:: sage: graphs.PathGraph(2).modular_decomposition(algorithm='abc') Traceback (most recent call last): ... ValueError: algorithm must be 'habib' or 'tedder' Unknown style:: sage: graphs.PathGraph(2).modular_decomposition(style='xyz') Traceback (most recent call last): ... ValueError: style must be 'tuple' or 'tree' """ from sage.graphs.graph_decompositions.modular_decomposition import (modular_decomposition, NodeType, habib_maurer_algorithm, create_prime_node, create_normal_node) self._scream_if_not_simple() if not self.order(): D = None elif self.order() == 1: D = create_prime_node() D.children.append(create_normal_node(self.vertices()[0])) else: if algorithm == 'habib': D = habib_maurer_algorithm(self) elif algorithm == 'tedder': D = modular_decomposition(self) else: raise ValueError("algorithm must be 'habib' or 'tedder'") if style == 'tuple': if D is None: return tuple() def relabel(x): if x.node_type == NodeType.NORMAL: return x.children[0] else: return x.node_type, [relabel(y) for y in x.children] return relabel(D) elif style == 'tree': from sage.combinat.rooted_tree import LabelledRootedTree if D is None: return LabelledRootedTree([]) def to_tree(x): if x.node_type == NodeType.NORMAL: return LabelledRootedTree([], label=x.children[0]) else: return LabelledRootedTree([to_tree(y) for y in x.children], label=x.node_type) return to_tree(D) else: raise ValueError("style must be 'tuple' or 'tree'") @doc_index("Graph properties") def is_polyhedral(self): """ Check whether the graph is the graph of the polyhedron. By a theorem of Steinitz (Satz 43, p. 77 of [St1922]_), graphs of three-dimensional polyhedra are exactly the simple 3-vertex-connected planar graphs. EXAMPLES:: sage: C = graphs.CubeGraph(3) sage: C.is_polyhedral() True sage: K33=graphs.CompleteBipartiteGraph(3, 3) sage: K33.is_polyhedral() False sage: graphs.CycleGraph(17).is_polyhedral() False sage: [i for i in range(9) if graphs.CompleteGraph(i).is_polyhedral()] [4] .. SEEALSO:: * :meth:`~sage.graphs.generic_graph.GenericGraph.vertex_connectivity` * :meth:`~sage.graphs.generic_graph.GenericGraph.is_planar` * :meth:`is_circumscribable` * :meth:`is_inscribable` * :wikipedia:`Polyhedral_graph` TESTS:: sage: G = Graph([[1, 2, 3, 4], [[1, 2], [1,1]]], loops=True) sage: G.is_polyhedral() False sage: G = Graph([[1, 2, 3], [[1, 2], [3, 1], [1, 2], [2, 3]]], multiedges=True) sage: G.is_polyhedral() False """ return (not self.has_loops() and not self.has_multiple_edges() and self.vertex_connectivity(k=3) and self.is_planar()) @doc_index("Graph properties") def is_circumscribable(self, solver="ppl", verbose=0): """ Test whether the graph is the graph of a circumscribed polyhedron. A polyhedron is circumscribed if all of its facets are tangent to a sphere. By a theorem of Rivin ([HRS1993]_), this can be checked by solving a linear program that assigns weights between 0 and 1/2 on each edge of the polyhedron, so that the weights on any face add to exactly one and the weights on any non-facial cycle add to more than one. If and only if this can be done, the polyhedron can be circumscribed. INPUT: - ``solver`` -- (default: ``"ppl"``); specify a Linear Program (LP) solver to be used. If set to ``None``, the default one is used. For more information on LP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. EXAMPLES:: sage: C = graphs.CubeGraph(3) sage: C.is_circumscribable() True sage: O = graphs.OctahedralGraph() sage: O.is_circumscribable() True sage: TT = polytopes.truncated_tetrahedron().graph() sage: TT.is_circumscribable() False Stellating in a face of the octahedral graph is not circumscribable:: sage: f = set(flatten(choice(O.faces()))) sage: O.add_edges([[6, i] for i in f]) sage: O.is_circumscribable() False .. SEEALSO:: * :meth:`is_polyhedral` * :meth:`is_inscribable` TESTS:: sage: G = graphs.CompleteGraph(5) sage: G.is_circumscribable() Traceback (most recent call last): ... NotImplementedError: this method only works for polyhedral graphs .. TODO:: Allow the use of other, inexact but faster solvers. """ if not self.is_polyhedral(): raise NotImplementedError('this method only works for polyhedral graphs') from sage.numerical.mip import MixedIntegerLinearProgram from sage.numerical.mip import MIPSolverException # For a description of the algorithm see paper by Rivin and: # https://www.ics.uci.edu/~eppstein/junkyard/uninscribable/ # In order to simulate strict inequalities in the following LP, we # introduce a variable c[0] and maximize it. If it is positive, then # the LP has a solution, such that all inequalities are strict # after removing the auxiliary variable c[0]. M = MixedIntegerLinearProgram(maximization=True, solver=solver) e_var = M.new_variable(nonnegative=True) c = M.new_variable() M.set_min(c[0], -1) M.set_max(c[0], 1) M.set_objective(c[0]) for e in self.edge_iterator(labels=0): fe = frozenset(e) M.set_max(e_var[fe], ZZ(1)/ZZ(2)) M.add_constraint(e_var[fe] - c[0], min=0) M.add_constraint(e_var[fe] + c[0], max=ZZ(1)/ZZ(2)) # The faces are completely determined by the graph structure: # for polyhedral graph, there is only one way to choose the faces. # We add an equality constraint for each face. efaces = self.faces() vfaces = set(frozenset([e[0] for e in face]) for face in efaces) for edges in efaces: M.add_constraint(M.sum(e_var[frozenset(e)] for e in edges) == 1) # In order to generate all simple cycles of G, which are not faces, # we use the "all_simple_cycles" method of directed graphs, generating # each cycle twice (in both directions). The set below make sure only # one direction gives rise to an (in)equality D = self.to_directed() inequality_constraints = set() for cycle in D.all_simple_cycles(): if len(cycle) > 3: scycle = frozenset(cycle) if scycle not in vfaces: edges = (frozenset((cycle[i], cycle[i+1])) for i in range(len(cycle)-1)) inequality_constraints.add(frozenset(edges)) for ieq in inequality_constraints: M.add_constraint(M.sum(e_var[fe] for fe in ieq) - c[0] >= 1) try: solution = M.solve(log=verbose) except MIPSolverException as msg: if str(msg) == "PPL : There is no feasible solution": return False return solution > 0 @doc_index("Graph properties") def is_inscribable(self, solver="ppl", verbose=0): """ Test whether the graph is the graph of an inscribed polyhedron. A polyhedron is inscribed if all of its vertices are on a sphere. This is dual to the notion of circumscribed polyhedron: A Polyhedron is inscribed if and only if its polar dual is circumscribed and hence a graph is inscribable if and only if its planar dual is circumscribable. INPUT: - ``solver`` -- (default: ``"ppl"``); specify a Linear Program (LP) solver to be used. If set to ``None``, the default one is used. For more information on LP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. EXAMPLES:: sage: H = graphs.HerschelGraph() sage: H.is_inscribable() # long time (> 1 sec) False sage: H.planar_dual().is_inscribable() # long time (> 1 sec) True sage: C = graphs.CubeGraph(3) sage: C.is_inscribable() True Cutting off a vertex from the cube yields an uninscribable graph:: sage: C = graphs.CubeGraph(3) sage: v = next(C.vertex_iterator()) sage: triangle = [_ + v for _ in C.neighbors(v)] sage: C.add_edges(Combinations(triangle, 2)) sage: C.add_edges(zip(triangle, C.neighbors(v))) sage: C.delete_vertex(v) sage: C.is_inscribable() False Breaking a face of the cube yields an uninscribable graph:: sage: C = graphs.CubeGraph(3) sage: face = choice(C.faces()) sage: C.add_edge([face[0][0], face[2][0]]) sage: C.is_inscribable() False .. SEEALSO:: * :meth:`is_polyhedral` * :meth:`is_circumscribable` TESTS:: sage: G = graphs.CompleteBipartiteGraph(3,3) sage: G.is_inscribable() Traceback (most recent call last): ... NotImplementedError: this method only works for polyhedral graphs """ if not self.is_polyhedral(): raise NotImplementedError('this method only works for polyhedral graphs') return self.planar_dual().is_circumscribable(solver=solver, verbose=verbose) @doc_index("Graph properties") def is_prime(self, algorithm='habib'): r""" Test whether the current graph is prime. INPUT: - ``algorithm`` -- (default: ``'tedder'``) specifies the algorithm to use among: - ``'tedder'`` -- Use the linear algorithm of [TCHP2008]_. - ``'habib'`` -- Use the $O(n^3)$ algorithm of [HM1979]_. This is probably slower, but is much simpler and so possibly less error prone. A graph is prime if all its modules are trivial (i.e. empty, all of the graph or singletons) -- see :meth:`modular_decomposition`. EXAMPLES: The Petersen Graph and the Bull Graph are both prime:: sage: graphs.PetersenGraph().is_prime() True sage: graphs.BullGraph().is_prime() True Though quite obviously, the disjoint union of them is not:: sage: (graphs.PetersenGraph() + graphs.BullGraph()).is_prime() False TESTS:: sage: graphs.EmptyGraph().is_prime() True """ from sage.graphs.graph_decompositions.modular_decomposition import NodeType if self.order() <= 1: return True D = self.modular_decomposition(algorithm=algorithm) return D[0] == NodeType.PRIME and len(D[1]) == self.order() def _gomory_hu_tree(self, vertices, algorithm=None): r""" Return a Gomory-Hu tree associated to self. This function is the private counterpart of ``gomory_hu_tree()``, with the difference that it has an optional argument needed for recursive computations, which the user is not interested in defining himself. See the documentation of ``gomory_hu_tree()`` for more information. INPUT: - ``vertices`` -- a set of "real" vertices, as opposed to the fakes one introduced during the computations. This variable is useful for the algorithm and for recursion purposes. - ``algorithm`` -- select the algorithm used by the :meth:`edge_cut` method. Refer to its documentation for allowed values and default behaviour. EXAMPLES: This function is actually tested in ``gomory_hu_tree()``, this example is only present to have a doctest coverage of 100%:: sage: g = graphs.PetersenGraph() sage: t = g._gomory_hu_tree(frozenset(g.vertices())) """ self._scream_if_not_simple() # Small case, not really a problem ;-) if len(vertices) == 1: g = Graph() g.add_vertices(vertices) return g # Take any two vertices (u,v) it = iter(vertices) u,v = next(it),next(it) # Compute a uv min-edge-cut. # # The graph is split into U,V with u \in U and v\in V. flow,edges,[U,V] = self.edge_cut(u, v, use_edge_labels=True, vertices=True, algorithm=algorithm) # One graph for each part of the previous one gU,gV = self.subgraph(U, immutable=False), self.subgraph(V, immutable=False) # A fake vertex fU (resp. fV) to represent U (resp. V) fU = frozenset(U) fV = frozenset(V) # Each edge (uu,vv) with uu \in U and vv\in V yields: # - an edge (uu,fV) in gU # - an edge (vv,fU) in gV # # If the same edge is added several times their capacities add up. from sage.rings.real_mpfr import RR for uu,vv,capacity in edges: capacity = capacity if capacity in RR else 1 # Assume uu is in gU if uu in V: uu,vv = vv,uu # Create the new edges if necessary if not gU.has_edge(uu, fV): gU.add_edge(uu, fV, 0) if not gV.has_edge(vv, fU): gV.add_edge(vv, fU, 0) # update the capacities gU.set_edge_label(uu, fV, gU.edge_label(uu, fV) + capacity) gV.set_edge_label(vv, fU, gV.edge_label(vv, fU) + capacity) # Recursion on each side gU_tree = gU._gomory_hu_tree(vertices & frozenset(gU), algorithm=algorithm) gV_tree = gV._gomory_hu_tree(vertices & frozenset(gV), algorithm=algorithm) # Union of the two partial trees g = gU_tree.union(gV_tree) # An edge to connect them, with the appropriate label g.add_edge(u, v, flow) return g @doc_index("Connectivity, orientations, trees") def gomory_hu_tree(self, algorithm=None): r""" Return a Gomory-Hu tree of self. Given a tree `T` with labeled edges representing capacities, it is very easy to determine the maximum flow between any pair of vertices : it is the minimal label on the edges of the unique path between them. Given a graph `G`, a Gomory-Hu tree `T` of `G` is a tree with the same set of vertices, and such that the maximum flow between any two vertices is the same in `G` as in `T`. See the :wikipedia:`Gomory–Hu_tree`. Note that, in general, a graph admits more than one Gomory-Hu tree. See also 15.4 (Gomory-Hu trees) from [Sch2003]_. INPUT: - ``algorithm`` -- select the algorithm used by the :meth:`edge_cut` method. Refer to its documentation for allowed values and default behaviour. OUTPUT: A graph with labeled edges EXAMPLES: Taking the Petersen graph:: sage: g = graphs.PetersenGraph() sage: t = g.gomory_hu_tree() Obviously, this graph is a tree:: sage: t.is_tree() True Note that if the original graph is not connected, then the Gomory-Hu tree is in fact a forest:: sage: (2*g).gomory_hu_tree().is_forest() True sage: (2*g).gomory_hu_tree().is_connected() False On the other hand, such a tree has lost nothing of the initial graph connectedness:: sage: all(t.flow(u,v) == g.flow(u,v) for u,v in Subsets(g.vertices(), 2)) True Just to make sure, we can check that the same is true for two vertices in a random graph:: sage: g = graphs.RandomGNP(20,.3) sage: t = g.gomory_hu_tree() sage: g.flow(0,1) == t.flow(0,1) True And also the min cut:: sage: g.edge_connectivity() == min(t.edge_labels()) or not g.is_connected() True TESTS: :trac:`16475`:: sage: G = graphs.PetersenGraph() sage: for u,v in G.edge_iterator(labels=False): ....: G.set_edge_label(u, v, 1) sage: for u, v in [(0, 1), (0, 4), (0, 5), (1, 2), (1, 6), (3, 4), (5, 7), (5, 8)]: ....: G.set_edge_label(u, v, 2) sage: T = G.gomory_hu_tree() sage: from itertools import combinations sage: for u,v in combinations(G,2): ....: assert T.flow(u,v,use_edge_labels=True) == G.flow(u,v,use_edge_labels=True) sage: graphs.EmptyGraph().gomory_hu_tree() Graph on 0 vertices """ if not self.order(): return Graph() if not self.is_connected(): g = Graph() for cc in self.connected_components_subgraphs(): g = g.union(cc._gomory_hu_tree(frozenset(cc.vertex_iterator()), algorithm=algorithm)) else: g = self._gomory_hu_tree(frozenset(self.vertex_iterator()), algorithm=algorithm) if self.get_pos() is not None: g.set_pos(dict(self.get_pos())) return g @doc_index("Leftovers") def two_factor_petersen(self, solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Return a decomposition of the graph into 2-factors. Petersen's 2-factor decomposition theorem asserts that any `2r`-regular graph `G` can be decomposed into 2-factors. Equivalently, it means that the edges of any `2r`-regular graphs can be partitionned in `r` sets `C_1,\dots,C_r` such that for all `i`, the set `C_i` is a disjoint union of cycles (a 2-regular graph). As any graph of maximal degree `\Delta` can be completed into a regular graph of degree `2\lceil\frac\Delta 2\rceil`, this result also means that the edges of any graph of degree `\Delta` can be partitionned in `r=2\lceil\frac\Delta 2\rceil` sets `C_1,\dots,C_r` such that for all `i`, the set `C_i` is a graph of maximal degree `2` (a disjoint union of paths and cycles). INPUT: - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity. Set to 0 by default, which means quiet. - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. EXAMPLES: The Complete Graph on `7` vertices is a `6`-regular graph, so it can be edge-partitionned into `2`-regular graphs:: sage: g = graphs.CompleteGraph(7) sage: classes = g.two_factor_petersen() sage: for c in classes: ....: gg = Graph() ....: gg.add_edges(c) ....: print(max(gg.degree())<=2) True True True sage: Set(set(classes[0]) | set(classes[1]) | set(classes[2])).cardinality() == g.size() True :: sage: g = graphs.CirculantGraph(24, [7, 11]) sage: cl = g.two_factor_petersen() sage: g.plot(edge_colors={'black':cl[0], 'red':cl[1]}) Graphics object consisting of 73 graphics primitives """ self._scream_if_not_simple() d = self.eulerian_orientation() # This new graph is bipartite, and built the following way : # # To each vertex v of the digraph are associated two vertices, # a sink (-1,v) and a source (1,v) # Any edge (u,v) in the digraph is then added as ((-1,u),(1,v)) g = Graph() g.add_edges(((-1, u), (1, v)) for u, v in d.edge_iterator(labels=None)) # This new bipartite graph is now edge_colored from sage.graphs.graph_coloring import edge_coloring classes = edge_coloring(g, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) # The edges in the classes are of the form ((-1,u),(1,v)) # and have to be translated back to (u,v) classes_b = [] for c in classes: classes_b.append([(u,v) for ((uu,u),(vv,v)) in c]) return classes_b @doc_index("Leftovers") def kirchhoff_symanzik_polynomial(self, name='t'): r""" Return the Kirchhoff-Symanzik polynomial of a graph. This is a polynomial in variables `t_e` (each of them representing an edge of the graph `G`) defined as a sum over all spanning trees: .. MATH:: \Psi_G(t) = \sum_{\substack{T\subseteq V \\ \text{a spanning tree}}} \prod_{e \not\in E(T)} t_e This is also called the first Symanzik polynomial or the Kirchhoff polynomial. INPUT: - ``name`` -- name of the variables (default: ``'t'``) OUTPUT: - a polynomial with integer coefficients ALGORITHM: This is computed here using a determinant, as explained in Section 3.1 of [Mar2009a]_. As an intermediate step, one computes a cycle basis `\mathcal C` of `G` and a rectangular `|\mathcal C| \times |E(G)|` matrix with entries in `\{-1,0,1\}`, which describes which edge belong to which cycle of `\mathcal C` and their respective orientations. More precisely, after fixing an arbitrary orientation for each edge `e\in E(G)` and each cycle `C\in\mathcal C`, one gets a sign for every incident pair (edge, cycle) which is `1` if the orientation coincide and `-1` otherwise. EXAMPLES: For the cycle of length 5:: sage: G = graphs.CycleGraph(5) sage: G.kirchhoff_symanzik_polynomial() t0 + t1 + t2 + t3 + t4 One can use another letter for variables:: sage: G.kirchhoff_symanzik_polynomial(name='u') u0 + u1 + u2 + u3 + u4 For the 'coffee bean' graph:: sage: G = Graph([(0,1,'a'),(0,1,'b'),(0,1,'c')], multiedges=True) sage: G.kirchhoff_symanzik_polynomial() t0*t1 + t0*t2 + t1*t2 For the 'parachute' graph:: sage: G = Graph([(0,2,'a'),(0,2,'b'),(0,1,'c'),(1,2,'d')], multiedges=True) sage: G.kirchhoff_symanzik_polynomial() t0*t1 + t0*t2 + t1*t2 + t1*t3 + t2*t3 For the complete graph with 4 vertices:: sage: G = graphs.CompleteGraph(4) sage: G.kirchhoff_symanzik_polynomial() t0*t1*t3 + t0*t2*t3 + t1*t2*t3 + t0*t1*t4 + t0*t2*t4 + t1*t2*t4 + t1*t3*t4 + t2*t3*t4 + t0*t1*t5 + t0*t2*t5 + t1*t2*t5 + t0*t3*t5 + t2*t3*t5 + t0*t4*t5 + t1*t4*t5 + t3*t4*t5 REFERENCES: [Bro2011]_ """ from sage.matrix.constructor import matrix from sage.rings.integer_ring import ZZ from sage.rings.polynomial.polynomial_ring_constructor import PolynomialRing # The order of the vertices in each tuple matters, so use a list edges = list(self.edges(sort=False)) cycles = self.cycle_basis(output='edge') edge2int = {e: j for j, e in enumerate(edges)} circuit_mtrx = matrix(ZZ, self.size(), len(cycles)) for i, cycle in enumerate(cycles): for edge in cycle: if edge in edges: circuit_mtrx[edge2int[edge], i] = +1 else: circuit_mtrx[edge2int[(edge[1], edge[0], edge[2])], i] = -1 D = matrix.diagonal(PolynomialRing(ZZ, name, self.size()).gens()) return (circuit_mtrx.transpose() * D * circuit_mtrx).determinant() @doc_index("Leftovers") def magnitude_function(self): r""" Return the magnitude function of the graph as a rational function. This is defined as the sum of all coefficients in the inverse of the matrix `Z` whose coefficient `Z_{i,j}` indexed by a pair of vertices `(i,j)` is `q^d(i,j)` where `d` is the distance function in the graph. By convention, if the distance from `i` to `j` is infinite (for two vertices not path connected) then `Z_{i,j}=0`. The value of the magnitude function at `q=0` is the cardinality of the graph. The magnitude function of a disjoint union is the sum of the magnitudes functions of the connected components. The magnitude function of a Cartesian product is the product of the magnitudes functions of the factors. EXAMPLES:: sage: g = Graph({1:[], 2:[]}) sage: g.magnitude_function() 2 sage: g = graphs.CycleGraph(4) sage: g.magnitude_function() 4/(q^2 + 2*q + 1) sage: g = graphs.CycleGraph(5) sage: m = g.magnitude_function(); m 5/(2*q^2 + 2*q + 1) One can expand the magnitude as a power series in `q` as follows:: sage: q = QQ[['q']].gen() sage: m(q) 5 - 10*q + 10*q^2 - 20*q^4 + 40*q^5 - 40*q^6 + ... One can also use the substitution `q = exp(-t)` to obtain the magnitude function as a function of `t`:: sage: g = graphs.CycleGraph(6) sage: m = g.magnitude_function() sage: t = var('t') # optional - sage.symbolic sage: m(exp(-t)) # optional - sage.symbolic 6/(2*e^(-t) + 2*e^(-2*t) + e^(-3*t) + 1) TESTS:: sage: g = Graph() sage: g.magnitude_function() 0 sage: g = Graph({1:[]}) sage: g.magnitude_function() 1 sage: g = graphs.PathGraph(4) sage: g.magnitude_function() (-2*q + 4)/(q + 1) REFERENCES: .. [Lein] Tom Leinster, *The magnitude of metric spaces*. Doc. Math. 18 (2013), 857-905. """ from sage.matrix.constructor import matrix from sage.rings.polynomial.polynomial_ring_constructor import PolynomialRing from sage.graphs.distances_all_pairs import distances_all_pairs ring = PolynomialRing(ZZ, 'q') q = ring.gen() N = self.order() if not N: return ring.zero() dist = distances_all_pairs(self) vertices = list(self) Z = matrix(ring, N, N, ring.zero()) for i in range(N): Z[i, i] = ring.one() for i in range(N): for j in range(i): dij = dist[vertices[i]][vertices[j]] if dij in ZZ: Z[i, j] = Z[j, i] = q ** dij else: Z[i, j] = Z[j, i] = ring.zero() return sum(sum(u) for u in ~Z) @doc_index("Leftovers") def ihara_zeta_function_inverse(self): """ Compute the inverse of the Ihara zeta function of the graph. This is a polynomial in one variable with integer coefficients. The Ihara zeta function itself is the inverse of this polynomial. See the :wikipedia:`Ihara zeta function` for more information. ALGORITHM: This is computed here as the (reversed) characteristic polynomial of a square matrix of size twice the number of edges, related to the adjacency matrix of the line graph, see for example Proposition 9 in [SS2008]_ and Def. 4.1 in [Ter2011]_. The graph is first replaced by its 2-core, as this does not change the Ihara zeta function. EXAMPLES:: sage: G = graphs.CompleteGraph(4) sage: factor(G.ihara_zeta_function_inverse()) (2*t - 1) * (t + 1)^2 * (t - 1)^3 * (2*t^2 + t + 1)^3 sage: G = graphs.CompleteGraph(5) sage: factor(G.ihara_zeta_function_inverse()) (-1) * (3*t - 1) * (t + 1)^5 * (t - 1)^6 * (3*t^2 + t + 1)^4 sage: G = graphs.PetersenGraph() sage: factor(G.ihara_zeta_function_inverse()) (-1) * (2*t - 1) * (t + 1)^5 * (t - 1)^6 * (2*t^2 + 2*t + 1)^4 * (2*t^2 - t + 1)^5 sage: G = graphs.RandomTree(10) sage: G.ihara_zeta_function_inverse() 1 REFERENCES: [HST2001]_ """ from sage.matrix.constructor import matrix H = self.subgraph(vertices=self.cores(k=2)[1]) E = list(H.edges(sort=False)) m = len(E) # compute (Hashimoto) edge matrix T T = matrix(ZZ, 2 * m, 2 * m, 0) for i in range(m): for j in range(m): if i != j: if E[i][1] == E[j][0]: # same orientation T[2 * i, 2 * j] = 1 T[2 * j + 1, 2 * i + 1] = 1 elif E[i][1] == E[j][1]: # opposite orientation (towards) T[2 * i, 2 * j + 1] = 1 T[2 * j, 2 * i + 1] = 1 elif E[i][0] == E[j][0]: # opposite orientation (away) T[2 * i + 1, 2 * j] = 1 T[2 * j + 1, 2 * i] = 1 return T.charpoly('t').reverse() @doc_index("Leftovers") def perfect_matchings(self, labels=False): r""" Return an iterator over all perfect matchings of the graph. ALGORITHM: Choose a vertex `v`, then recurse through all edges incident to `v`, removing one edge at a time whenever an edge is added to a matching. INPUT: - ``labels`` -- boolean (default: ``False``); when ``True``, the edges in each perfect matching are triples (containing the label as the third element), otherwise the edges are pairs. .. SEEALSO:: :meth:`matching` EXAMPLES:: sage: G=graphs.GridGraph([2,3]) sage: for m in G.perfect_matchings(): ....: print(sorted(m)) [((0, 0), (0, 1)), ((0, 2), (1, 2)), ((1, 0), (1, 1))] [((0, 0), (1, 0)), ((0, 1), (0, 2)), ((1, 1), (1, 2))] [((0, 0), (1, 0)), ((0, 1), (1, 1)), ((0, 2), (1, 2))] sage: G = graphs.CompleteGraph(4) sage: for m in G.perfect_matchings(labels=True): ....: print(sorted(m)) [(0, 1, None), (2, 3, None)] [(0, 2, None), (1, 3, None)] [(0, 3, None), (1, 2, None)] sage: G = Graph([[1,-1,'a'], [2,-2, 'b'], [1,-2,'x'], [2,-1,'y']]) sage: sorted(sorted(m) for m in G.perfect_matchings(labels=True)) [[(-2, 1, 'x'), (-1, 2, 'y')], [(-2, 2, 'b'), (-1, 1, 'a')]] sage: G = graphs.CompleteGraph(8) sage: mpc = G.matching_polynomial().coefficients(sparse=False)[0] sage: len(list(G.perfect_matchings())) == mpc True sage: G = graphs.PetersenGraph().copy(immutable=True) sage: [sorted(m) for m in G.perfect_matchings()] [[(0, 1), (2, 3), (4, 9), (5, 7), (6, 8)], [(0, 1), (2, 7), (3, 4), (5, 8), (6, 9)], [(0, 4), (1, 2), (3, 8), (5, 7), (6, 9)], [(0, 4), (1, 6), (2, 3), (5, 8), (7, 9)], [(0, 5), (1, 2), (3, 4), (6, 8), (7, 9)], [(0, 5), (1, 6), (2, 7), (3, 8), (4, 9)]] sage: list(Graph().perfect_matchings()) [[]] sage: G = graphs.CompleteGraph(5) sage: list(G.perfect_matchings()) [] """ if not self: yield [] return if self.order() % 2 or any(len(cc) % 2 for cc in self.connected_components()): return def rec(G): """ Iterator over all perfect matchings of a simple graph `G`. """ if not G: yield [] return if G.order() % 2 == 0: v = next(G.vertex_iterator()) Nv = list(G.neighbor_iterator(v)) G.delete_vertex(v) for u in Nv: Nu = list(G.neighbor_iterator(u)) G.delete_vertex(u) for partial_matching in rec(G): partial_matching.append((u, v)) yield partial_matching G.add_vertex(u) G.add_edges((u, nu) for nu in Nu) G.add_vertex(v) G.add_edges((v, nv) for nv in Nv) # We create a mutable copy of the graph and remove its loops, if any G = self.copy(immutable=False) G.allow_loops(False) # We create a mapping from frozen unlabeled edges to (labeled) edges. # This ease for instance the manipulation of multiedges (if any) edges = {} for e in G.edges(labels=labels): f = frozenset(e[:2]) if f in edges: edges[f].append(e) else: edges[f] = [e] # We now get rid of multiple edges, if any G.allow_multiple_edges(False) # For each unlabeled matching, we yield all its possible labelings for m in rec(G): for pm in itertools.product(*[edges[frozenset(e)] for e in m]): yield pm @doc_index("Leftovers") def has_perfect_matching(self, algorithm="Edmonds", solver=None, verbose=0, *, integrality_tolerance=1e-3): r""" Return whether this graph has a perfect matching. INPUT: - ``algorithm`` -- string (default: ``"Edmonds"``) - ``"Edmonds"`` uses Edmonds' algorithm as implemented in NetworkX to find a matching of maximal cardinality, then check whether this cardinality is half the number of vertices of the graph. - ``"LP_matching"`` uses a Linear Program to find a matching of maximal cardinality, then check whether this cardinality is half the number of vertices of the graph. - ``"LP"`` uses a Linear Program formulation of the perfect matching problem: put a binary variable ``b[e]`` on each edge `e`, and for each vertex `v`, require that the sum of the values of the edges incident to `v` is 1. - ``solver`` -- string (default: ``None``); specify a Mixed Integer Linear Programming (MILP) solver to be used. If set to ``None``, the default one is used. For more information on MILP solvers and which default solver is used, see the method :meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>` of the class :class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`. - ``verbose`` -- integer (default: ``0``); sets the level of verbosity: set to 0 by default, which means quiet (only useful when ``algorithm == "LP_matching"`` or ``algorithm == "LP"``) - ``integrality_tolerance`` -- float; parameter for use with MILP solvers over an inexact base ring; see :meth:`MixedIntegerLinearProgram.get_values`. OUTPUT: A boolean. EXAMPLES:: sage: graphs.PetersenGraph().has_perfect_matching() True sage: graphs.WheelGraph(6).has_perfect_matching() True sage: graphs.WheelGraph(5).has_perfect_matching() False sage: graphs.PetersenGraph().has_perfect_matching(algorithm="LP_matching") True sage: graphs.WheelGraph(6).has_perfect_matching(algorithm="LP_matching") True sage: graphs.WheelGraph(5).has_perfect_matching(algorithm="LP_matching") False sage: graphs.PetersenGraph().has_perfect_matching(algorithm="LP_matching") True sage: graphs.WheelGraph(6).has_perfect_matching(algorithm="LP_matching") True sage: graphs.WheelGraph(5).has_perfect_matching(algorithm="LP_matching") False TESTS:: sage: G = graphs.EmptyGraph() sage: all(G.has_perfect_matching(algorithm=algo) for algo in ['Edmonds', 'LP_matching', 'LP']) True Be careful with isolated vertices:: sage: G = graphs.PetersenGraph() sage: G.add_vertex(11) sage: any(G.has_perfect_matching(algorithm=algo) for algo in ['Edmonds', 'LP_matching', 'LP']) False """ if self.order() % 2: return False if algorithm == "Edmonds": return len(self) == 2*self.matching(value_only=True, use_edge_labels=False, algorithm="Edmonds") elif algorithm == "LP_matching": return len(self) == 2*self.matching(value_only=True, use_edge_labels=False, algorithm="LP", solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) elif algorithm == "LP": from sage.numerical.mip import MixedIntegerLinearProgram, MIPSolverException p = MixedIntegerLinearProgram(solver=solver) b = p.new_variable(binary=True) for v in self: edges = self.edges_incident(v, labels=False) if not edges: return False p.add_constraint(p.sum(b[frozenset(e)] for e in edges) == 1) try: p.solve(log=verbose) return True except MIPSolverException: return False else: raise ValueError('algorithm must be set to "Edmonds", "LP_matching" or "LP"') @doc_index("Leftovers") def effective_resistance(self, i, j): r""" Return the effective resistance between nodes `i` and `j`. The resistance distance between vertices `i` and `j` of a simple connected graph `G` is defined as the effective resistance between the two vertices on an electrical network constructed from `G` replacing each edge of the graph by a unit (1 ohm) resistor. See the :wikipedia:`Resistance_distance` for more information. INPUT: - ``i``, ``j`` -- vertices of the graph OUTPUT: rational number denoting resistance between nodes `i` and `j` EXAMPLES: Effective resistances in a straight linear 2-tree on 6 vertices :: sage: G = Graph([(0,1),(0,2),(1,2),(1,3),(3,5),(2,4),(2,3),(3,4),(4,5)]) sage: G.effective_resistance(0,1) 34/55 sage: G.effective_resistance(0,3) 49/55 sage: G.effective_resistance(1,4) 9/11 sage: G.effective_resistance(0,5) 15/11 Effective resistances in a fan on 6 vertices :: sage: H = Graph([(0,1),(0,2),(0,3),(0,4),(0,5),(0,6),(1,2),(2,3),(3,4),(4,5)]) sage: H.effective_resistance(1,5) 6/5 sage: H.effective_resistance(1,3) 49/55 .. SEEALSO:: * :meth:`effective_resistance_matrix` -- a similar method giving a matrix full of all effective resistances between all nodes * :meth:`least_effective_resistance` -- gives node pairs with least effective resistances * See :wikipedia:`Resistance_distance` for more details. TESTS:: sage: G = graphs.CompleteGraph(4) sage: all(G.effective_resistance(u, v) == 1/2 for u,v in G.edge_iterator(labels=False)) True sage: Graph(1).effective_resistance(0,0) 0 sage: G = Graph([(0,1),(1,2)]) sage: G.effective_resistance(0,2) 2 sage: G = Graph([(0,1),(1,2),(2,0)]) sage: G.effective_resistance(0,2) 2/3 sage: G = Graph([(0,1),(0,2),(0,3),(0,4),(0,5),(1,2),(2,3),(3,4),(4,5),(5,1)]) sage: r = G.effective_resistance(0,3) sage: r == fibonacci(2*(5-3)+1)*fibonacci(2*3-1)/fibonacci(2*5) True """ from sage.matrix.constructor import matrix if i not in self: raise ValueError("vertex ({0}) is not a vertex of the graph".format(repr(i))) elif j not in self: raise ValueError("vertex ({0}) is not a vertex of the graph".format(repr(j))) if i == j : return 0 self._scream_if_not_simple() if not self.is_connected(): raise ValueError('the Graph is not a connected graph') vert = list(self) i1 = vert.index(i) i2 = vert.index(j) n = self.order() L = self.laplacian_matrix(vertices=vert) M = L.pseudoinverse() Id = matrix.identity(n) sigma = matrix(Id[i1] - Id[i2]) diff = sigma * M * sigma.transpose() return diff[0, 0] @doc_index("Leftovers") def effective_resistance_matrix(self, vertices=None, nonedgesonly=True): r""" Return a matrix whose (`i` , `j`) entry gives the effective resistance between vertices `i` and `j`. The resistance distance between vertices `i` and `j` of a simple connected graph `G` is defined as the effective resistance between the two vertices on an electrical network constructed from `G` replacing each edge of the graph by a unit (1 ohm) resistor. INPUT: - ``nonedgesonly`` -- boolean (default: ``True``); if ``True`` assign zero resistance to pairs of adjacent vertices. - ``vertices`` -- list (default: ``None``); the ordering of the vertices defining how they should appear in the matrix. By default, the ordering given by :meth:`GenericGraph.vertices` is used. OUTPUT: matrix EXAMPLES: The effective resistance matrix for a straight linear 2-tree counting only non-adjacent vertex pairs :: sage: G = Graph([(0,1),(0,2),(1,2),(1,3),(3,5),(2,4),(2,3),(3,4),(4,5)]) sage: G.effective_resistance_matrix() [ 0 0 0 49/55 59/55 15/11] [ 0 0 0 0 9/11 59/55] [ 0 0 0 0 0 49/55] [49/55 0 0 0 0 0] [59/55 9/11 0 0 0 0] [15/11 59/55 49/55 0 0 0] The same effective resistance matrix, this time including adjacent vertices :: sage: G.effective_resistance_matrix(nonedgesonly=False) [ 0 34/55 34/55 49/55 59/55 15/11] [34/55 0 26/55 31/55 9/11 59/55] [34/55 26/55 0 5/11 31/55 49/55] [49/55 31/55 5/11 0 26/55 34/55] [59/55 9/11 31/55 26/55 0 34/55] [15/11 59/55 49/55 34/55 34/55 0] This example illustrates the common neighbors matrix for a fan on 6 vertices counting only non-adjacent vertex pairs :: sage: H = Graph([(0,1),(0,2),(0,3),(0,4),(0,5),(0,6),(1,2),(2,3),(3,4),(4,5)]) sage: H.effective_resistance_matrix() [ 0 0 0 0 0 0 0] [ 0 0 0 49/55 56/55 6/5 89/55] [ 0 0 0 0 4/5 56/55 81/55] [ 0 49/55 0 0 0 49/55 16/11] [ 0 56/55 4/5 0 0 0 81/55] [ 0 6/5 56/55 49/55 0 0 89/55] [ 0 89/55 81/55 16/11 81/55 89/55 0] .. SEEALSO:: * :meth:`least_effective_resistance` -- gives node pairs with least effective resistances * :meth:`effective_resistance` -- computes effective resistance for a single node pair * See :wikipedia:`Resistance_Distance` for more details. TESTS:: sage: graphs.CompleteGraph(4).effective_resistance_matrix() [0 0 0 0] [0 0 0 0] [0 0 0 0] [0 0 0 0] sage: G = Graph(multiedges=True, sparse=True) sage: G.add_edges([(0, 1)] * 3) sage: G.effective_resistance_matrix() Traceback (most recent call last): ... ValueError: This method is not known to work on graphs with multiedges. Perhaps this method can be updated to handle them, but in the meantime if you want to use it please disallow multiedges using allow_multiple_edges(). sage: graphs.CompleteGraph(4).effective_resistance_matrix(nonedgesonly=False) [ 0 1/2 1/2 1/2] [1/2 0 1/2 1/2] [1/2 1/2 0 1/2] [1/2 1/2 1/2 0] sage: Graph(1).effective_resistance_matrix() [0] sage: Graph().effective_resistance_matrix() Traceback (most recent call last): ... ValueError: unable to compute effective resistance for an empty Graph object sage: G = Graph([(0,1),(1,2),(2,3),(3,0),(0,2)]) sage: G.effective_resistance_matrix() [0 0 0 0] [0 0 0 1] [0 0 0 0] [0 1 0 0] sage: G = Graph([(0,1),(0,2),(0,3),(0,4),(0,5),(1,2),(2,3),(3,4),(4,5),(5,1)]) sage: r = G.effective_resistance_matrix(nonedgesonly=False)[0,3] sage: r == fibonacci(2*(5-3)+1)*fibonacci(2*3-1)/fibonacci(2*5) True """ from sage.matrix.constructor import matrix from sage.rings.rational_field import QQ n = self.order() if not n: raise ValueError('unable to compute effective resistance for an empty Graph object') if vertices is None: vertices = self.vertices() self._scream_if_not_simple() if not self.is_connected(): raise ValueError('the Graph is not a connected graph') L = self.laplacian_matrix(vertices=vertices) M = L.pseudoinverse() d = matrix(M.diagonal()).transpose() onesvec = matrix(QQ, n, 1, lambda i, j: 1) S = d * onesvec.transpose() + onesvec * d.transpose() - 2 * M onesmat = matrix(QQ, n, n, lambda i, j: 1) if nonedgesonly: B = onesmat - self.adjacency_matrix(vertices=vertices) - matrix.identity(n) S = S.elementwise_product(B) return S @doc_index("Leftovers") def least_effective_resistance(self, nonedgesonly=True): r""" Return a list of pairs of nodes with the least effective resistance. The resistance distance between vertices `i` and `j` of a simple connected graph `G` is defined as the effective resistance between the two vertices on an electrical network constructed from `G` replacing each edge of the graph by a unit (1 ohm) resistor. INPUT: - ``nonedgesonly`` -- Boolean (default: `True`); if true, assign zero resistance to pairs of adjacent vertices OUTPUT: list EXAMPLES: Pairs of non-adjacent nodes with least effective resistance in a straight linear 2-tree on 6 vertices:: sage: G = Graph([(0,1),(0,2),(1,2),(1,3),(3,5),(2,4),(2,3),(3,4),(4,5)]) sage: G.least_effective_resistance() [(1, 4)] Pairs of (adjacent or non-adjacent) nodes with least effective resistance in a straight linear 2-tree on 6 vertices :: sage: G.least_effective_resistance(nonedgesonly = False) [(2, 3)] Pairs of non-adjacent nodes with least effective resistance in a fan on 6 vertices counting only non-adjacent vertex pairs :: sage: H = Graph([(0,1),(0,2),(0,3),(0,4),(0,5),(0,6),(1,2),(2,3),(3,4),(4,5)]) sage: H.least_effective_resistance() [(2, 4)] .. SEEALSO:: * :meth:`effective_resistance_matrix` -- a similar method giving a matrix full of all effective resistances * :meth:`effective_resistance` -- compuetes effective resistance for a single node pair * See :wikipedia:`Resistance_distance` for more details. TESTS:: sage: graphs.CompleteGraph(4).least_effective_resistance() [] sage: graphs.CompleteGraph(4).least_effective_resistance(nonedgesonly=False) [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)] sage: Graph(1).least_effective_resistance() [] sage: G = Graph([(0,1),(1,2),(2,3),(3,0),(0,2)]) sage: G.least_effective_resistance() [(1, 3)] """ n = self.order() if not n: raise ValueError('unable to compute least resistance on empty Graph') self._scream_if_not_simple() if not self.is_connected(): raise ValueError('the Graph is not a connected graph') if nonedgesonly and self.is_clique(): return [] verts = list(self) verttoidx = {u: i for i, u in enumerate(verts)} S = self.effective_resistance_matrix(vertices=verts, nonedgesonly=nonedgesonly) if nonedgesonly: edges = self.complement().edges(labels=False) else: edges = [(verts[i], verts[j]) for i in range(n) for j in range(i + 1, n)] rmin = min(S[(verttoidx[e[0]], verttoidx[e[1]])] for e in edges) return [e for e in edges if S[(verttoidx[e[0]], verttoidx[e[1]])] == rmin] @doc_index("Leftovers") def common_neighbors_matrix(self, vertices=None, nonedgesonly=True): r""" Return a matrix of numbers of common neighbors between each pairs. The `(i , j)` entry of the matrix gives the number of common neighbors between vertices `i` and `j`. This method is only valid for simple (no loops, no multiple edges) graphs. INPUT: - ``nonedgesonly``-- boolean (default: ``True``); if ``True``, assigns `0` value to adjacent vertices. - ``vertices`` -- list (default: ``None``); the ordering of the vertices defining how they should appear in the matrix. By default, the ordering given by :meth:`GenericGraph.vertices` is used. OUTPUT: matrix EXAMPLES: The common neighbors matrix for a straight linear 2-tree counting only non-adjacent vertex pairs :: sage: G1 = Graph() sage: G1.add_edges([(0,1),(0,2),(1,2),(1,3),(3,5),(2,4),(2,3),(3,4),(4,5)]) sage: G1.common_neighbors_matrix(nonedgesonly = True) [0 0 0 2 1 0] [0 0 0 0 2 1] [0 0 0 0 0 2] [2 0 0 0 0 0] [1 2 0 0 0 0] [0 1 2 0 0 0] We now show the common neighbors matrix which includes adjacent vertices :: sage: G1.common_neighbors_matrix(nonedgesonly = False) [0 1 1 2 1 0] [1 0 2 1 2 1] [1 2 0 2 1 2] [2 1 2 0 2 1] [1 2 1 2 0 1] [0 1 2 1 1 0] The common neighbors matrix for a fan on 6 vertices counting only non-adjacent vertex pairs :: sage: H = Graph([(0,1),(0,2),(0,3),(0,4),(0,5),(0,6),(1,2),(2,3),(3,4),(4,5)]) sage: H.common_neighbors_matrix() [0 0 0 0 0 0 0] [0 0 0 2 1 1 1] [0 0 0 0 2 1 1] [0 2 0 0 0 2 1] [0 1 2 0 0 0 1] [0 1 1 2 0 0 1] [0 1 1 1 1 1 0] It is an error to input anything other than a simple graph:: sage: G = Graph([(0,0)],loops=True) sage: G.common_neighbors_matrix() Traceback (most recent call last): ... ValueError: This method is not known to work on graphs with loops. Perhaps this method can be updated to handle them, but in the meantime if you want to use it please disallow loops using allow_loops(). .. SEEALSO:: * :meth:`most_common_neighbors` -- returns node pairs with most shared neighbors TESTS:: sage: G = graphs.CompleteGraph(4) sage: M = G.common_neighbors_matrix() sage: M.is_zero() True sage: Graph(1).common_neighbors_matrix() [0] sage: Graph().common_neighbors_matrix() [] sage: G = Graph([(0,1),(1,2),(2,3),(3,0),(0,2)]) sage: G.common_neighbors_matrix() [0 0 0 0] [0 0 0 2] [0 0 0 0] [0 2 0 0] """ self._scream_if_not_simple() if vertices is None: vertices = self.vertices() A = self.adjacency_matrix(vertices=vertices) M = A**2 for v in range(self.order()): M[v, v] = 0 if nonedgesonly: for w in range(v + 1, self.order()): if A[v, w]: M[v, w] = M[w, v] = 0 return M @doc_index("Leftovers") def most_common_neighbors(self, nonedgesonly=True): r""" Return vertex pairs with maximal number of common neighbors. This method is only valid for simple (no loops, no multiple edges) graphs with order `\geq 2` INPUT: - ``nonedgesonly``-- boolean (default: ``True``); if ``True``, assigns `0` value to adjacent vertices. OUTPUT: list of tuples of edge pairs EXAMPLES: The maximum common neighbor (non-adjacent) pairs for a straight linear 2-tree :: sage: G1 = Graph([(0,1),(0,2),(1,2),(1,3),(3,5),(2,4),(2,3),(3,4),(4,5)]) sage: G1.most_common_neighbors() [(0, 3), (1, 4), (2, 5)] If we include non-adjacent pairs :: sage: G1.most_common_neighbors(nonedgesonly = False) [(0, 3), (1, 2), (1, 4), (2, 3), (2, 5), (3, 4)] The common neighbors matrix for a fan on 6 vertices counting only non-adjacent vertex pairs :: sage: H = Graph([(0,1),(0,2),(0,3),(0,4),(0,5),(0,6),(1,2),(2,3),(3,4),(4,5)]) sage: H.most_common_neighbors() [(1, 3), (2, 4), (3, 5)] .. SEEALSO:: * :meth:`common_neighbors_matrix` -- a similar method giving a matrix of number of common neighbors TESTS:: sage: G=graphs.CompleteGraph(4) sage: G.most_common_neighbors() [] sage: G.most_common_neighbors(nonedgesonly=False) [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)] sage: Graph(1).most_common_neighbors() Traceback (most recent call last): ... ValueError: this method is defined for graphs with at least 2 vertices sage: Graph().most_common_neighbors() Traceback (most recent call last): ... ValueError: this method is defined for graphs with at least 2 vertices sage: G = Graph([(0,1),(1,2),(2,3),(3,0),(0,2)]) sage: G.most_common_neighbors() [(1, 3)] sage: G.most_common_neighbors(nonedgesonly=False) [(0, 2), (1, 3)] """ self._scream_if_not_simple() if self.num_verts() < 2: raise ValueError('this method is defined for graphs with at least 2 vertices') verts = list(self) M = self.common_neighbors_matrix(vertices=verts, nonedgesonly=nonedgesonly) output = [] coefficients = M.coefficients() if coefficients: maximum = max(coefficients) for v in range(self.num_verts()): for w in range(v + 1, self.num_verts()): if M[v, w] == maximum: output.append((verts[v], verts[w])) return output @doc_index("Leftovers") def arboricity(self, certificate=False): r""" Return the arboricity of the graph and an optional certificate. The arboricity is the minimum number of forests that covers the graph. See :wikipedia:`Arboricity` INPUT: - ``certificate`` -- boolean (default: ``False``); whether to return a certificate. OUTPUT: When ``certificate = True``, then the function returns `(a, F)` where `a` is the arboricity and `F` is a list of `a` disjoint forests that partitions the edge set of `g`. The forests are represented as subgraphs of the original graph. If ``certificate = False``, the function returns just a integer indicating the arboricity. ALGORITHM: Represent the graph as a graphical matroid, then apply matroid :meth:`sage.matroid.partition` algorithm from the matroids module. EXAMPLES:: sage: G = graphs.PetersenGraph() sage: a,F = G.arboricity(True) sage: a 2 sage: all([f.is_forest() for f in F]) True sage: len(set.union(*[set(f.edges()) for f in F])) == G.size() True TESTS:: sage: g = Graph() sage: g.arboricity(True) (0, []) """ from sage.matroids.constructor import Matroid P = Matroid(self).partition() if certificate: return (len(P), [self.subgraph(edges=forest) for forest in P]) else: return len(P) @doc_index("Graph properties") def is_antipodal(self): r""" Check whether this graph is antipodal. A graph `G` of diameter `d` is said to be antipodal if its distance-`d` graph is a disjoint union of cliques. EXAMPLES:: sage: G = graphs.JohnsonGraph(10, 5) sage: G.is_antipodal() True sage: H = G.folded_graph() sage: H.is_antipodal() False REFERENCES: See [BCN1989]_ p. 438 or [Sam2012]_ for this definition of antipodal graphs. TESTS:: sage: G = graphs.PetersenGraph() sage: G.is_antipodal() False sage: G = graphs.HammingGraph(7, 2) sage: G.is_antipodal() True sage: G = Graph([(0,1), (2, 3)]) sage: G.is_antipodal() False sage: G = Graph(4) sage: G.is_antipodal() True sage: graphs.CompleteGraph(5).is_antipodal() True sage: G = Graph() sage: G.is_antipodal() Traceback (most recent call last): ... ValueError: diameter is not defined for the empty graph sage: G = Graph(1) sage: G.is_antipodal() True """ G = self.antipodal_graph() vertexSet = set(G) while vertexSet: v = vertexSet.pop() # all neighbours of v should be in the same clique as v clique = set(G.neighbor_iterator(v, closed=True)) for u in clique: if set(G.neighbor_iterator(u, closed=True)) != clique: return False vertexSet.difference_update(clique) return True @doc_index("Leftovers") def folded_graph(self, check=False): r""" Return the antipodal fold of this graph. Given an antipodal graph `G` let `G_d` be its distance-`d` graph. Then the folded graph of `G` has a vertex for each maximal clique of `G_d` and two cliques are adjacent if there is an edge in `G` connecting the two. .. SEEALSO:: :meth:`sage.graphs.graph.is_antipodal` INPUT: - ``check`` -- boolean (default: ``False``); whether to check if the graph is antipodal. If ``check`` is ``True`` and the graph is not antipodal, then return ``False``. OUTPUT: This function returns a new graph and ``self`` is not touched. .. NOTE:: The input is expected to be an antipodal graph. You can check that a graph is antipodal using :meth:`sage.graphs.graph.is_antipodal`. EXAMPLES:: sage: G = graphs.JohnsonGraph(10, 5) sage: H = G.folded_graph(); H Folded Johnson graph with parameters 10,5: Graph on 126 vertices sage: Gd = G.distance_graph(G.diameter()) sage: all(i == 1 for i in Gd.degree()) True sage: H.is_distance_regular(True) ([25, 16, None], [None, 1, 4]) This method doesn't check if the graph is antipodal:: sage: G = graphs.PetersenGraph() sage: G.is_antipodal() False sage: G.folded_graph() # some garbage Folded Petersen graph: Graph on 2 vertices sage: G.folded_graph(check=True) False REFERENCES: See [BCN1989]_ p. 438 or [Sam2012]_ for this definition of folded graph. TESTS:: sage: G = Graph(5) sage: G.folded_graph() Folded Graph: Graph on 1 vertex sage: G = graphs.CompleteGraph(5) sage: G.folded_graph() Folded Complete graph: Graph on 1 vertex sage: G = Graph() sage: G.folded_graph() Traceback (most recent call last): ... ValueError: diameter is not defined for the empty graph sage: G = Graph(1) sage: G.folded_graph() Folded Graph: Graph on 1 vertex """ G = self.antipodal_graph() vertices = set(G) newVertices = [] while vertices: v = vertices.pop() clique = frozenset(G.neighbor_iterator(v, closed=True)) if check: for u in clique: if frozenset(G.neighbor_iterator(u, closed=True)) != clique: return False newVertices.append(clique) vertices.difference_update(clique) # now newVertices is a map {0, ..., numCliques-1} -> antipodal classes numCliques = len(newVertices) edges = [] for i, j in itertools.combinations(range(numCliques), 2): if any(self.has_edge(u, v) for u, v in itertools.product(newVertices[i], newVertices[j])): edges.append((i, j)) H = Graph([range(numCliques), edges], format='vertices_and_edges') name = self.name() if self.name() != "" else "Graph" H.name(f"Folded {name}") return H @doc_index("Leftovers") def antipodal_graph(self): r""" Return the antipodal graph of ``self``. The antipodal graph of a graph `G` has the same vertex set of `G` and two vertices are adjacent if their distance in `G` is equal to the diameter of `G`. OUTPUT: A new graph. ``self`` is not touched. EXAMPLES:: sage: G = graphs.JohnsonGraph(10, 5) sage: G.antipodal_graph() Antipodal graph of Johnson graph with parameters 10,5: Graph on 252 vertices sage: G = graphs.HammingGraph(8, 2) sage: G.antipodal_graph() Antipodal graph of Hamming Graph with parameters 8,2: Graph on 256 vertices The antipodal graph of a disconnected graph is its complement:: sage: G = Graph(5) sage: H = G.antipodal_graph() sage: H.is_isomorphic(G.complement()) True TESTS:: sage: G = Graph([(0, 1), (2, 3)]) sage: H = G.antipodal_graph() sage: H.is_isomorphic(Graph([(0, 2), (0, 3), (1, 2), (1, 3)])) True sage: G = Graph() sage: G.antipodal_graph() Traceback (most recent call last): ... ValueError: diameter is not defined for the empty graph sage: G = Graph(1) sage: G.antipodal_graph() Antipodal graph of Graph: Looped graph on 1 vertex """ H = self.distance_graph(self.diameter()) name = self.name() if self.name() != "" else "Graph" H.name(f"Antipodal graph of {name}") return H @doc_index("Basic methods") def bipartite_double(self, extended=False): r""" Return the (extended) bipartite double of this graph. The bipartite double of a graph `G` has vertex set `\{ (v,0), (v,1) : v \in G\}` and for any edge `(u, v)` in `G` it has edges `((u,0),(v,1))` and `((u,1),(v,0))`. Note that this is the tensor product of `G` with `K_2`. The extended bipartite double of `G` is the bipartite double of `G` after added all edges `((v,0),(v,1))` for all vertices `v`. INPUT: - ``extended`` -- boolean (default: ``False``); Whether to return the extended bipartite double, or only the bipartite double (default) OUTPUT: A graph; ``self`` is left untouched. EXAMPLES:: sage: G = graphs.PetersenGraph() sage: H = G.bipartite_double() sage: G == graphs.PetersenGraph() # G is left invariant True sage: H.order() == 2 * G.order() True sage: H.size() == 2 * G.size() True sage: H.is_bipartite() True sage: H.bipartite_sets() == (set([(v, 0) for v in G]), ....: set([(v, 1) for v in G])) True sage: H.is_isomorphic(G.tensor_product(graphs.CompleteGraph(2))) True Behaviour with disconnected graphs:: sage: G1 = graphs.PetersenGraph() sage: G2 = graphs.HoffmanGraph() sage: G = G1.disjoint_union(G2) sage: H = G.bipartite_double() sage: H1 = G1.bipartite_double() sage: H2 = G2.bipartite_double() sage: H.is_isomorphic(H1.disjoint_union(H2)) True .. SEEALSO:: :wikipedia:`Bipartite_double_cover`, `WolframAlpha Bipartite Double <https://mathworld.wolfram.com/BipartiteDoubleGraph.html>`_, [VDKT2016]_ p. 20 for the extended bipartite double. TESTS:: sage: G = graphs.PetersenGraph() sage: H = G.bipartite_double(True) sage: G == graphs.PetersenGraph() # G is left invariant True sage: H.order() == 2 * G.order() True sage: H.size() == 2 * G.size() + G.order() True sage: H.is_bipartite() True sage: H.bipartite_sets() == (set([(v, 0) for v in G]), ....: set([(v, 1) for v in G])) True sage: H.is_isomorphic(G.tensor_product(graphs.CompleteGraph(2))) False Test edge cases:: sage: G = Graph() sage: H = G.bipartite_double() sage: H.size() + H.order() 0 sage: H = G.bipartite_double(True) sage: H.size() + H.order() 0 sage: G = Graph(1) sage: H = G.bipartite_double() sage: H.size() == 0 and H.order() == 2 True sage: H = G.bipartite_double(True) sage: H.is_isomorphic(Graph([(0, 1)])) True """ G = self.tensor_product(Graph([(0, 1)])) if extended: G.add_edges(((v, 0), (v, 1)) for v in self) prefix = "Extended " if extended else "" G.name("%sBipartite Double of %s"%(prefix, self.name())) return G # Aliases to functions defined in other modules from sage.graphs.weakly_chordal import is_long_hole_free, is_long_antihole_free, is_weakly_chordal from sage.graphs.asteroidal_triples import is_asteroidal_triple_free from sage.graphs.chrompoly import chromatic_polynomial from sage.graphs.graph_decompositions.rankwidth import rank_decomposition from sage.graphs.graph_decompositions.tree_decomposition import treewidth from sage.graphs.graph_decompositions.vertex_separation import pathwidth from sage.graphs.graph_decompositions.tree_decomposition import treelength from sage.graphs.graph_decompositions.clique_separators import atoms_and_clique_separators from sage.graphs.matchpoly import matching_polynomial from sage.graphs.cliquer import all_max_clique as cliques_maximum from sage.graphs.cliquer import all_cliques from sage.graphs.spanning_tree import random_spanning_tree from sage.graphs.spanning_tree import spanning_trees from sage.graphs.graph_decompositions.graph_products import is_cartesian_product from sage.graphs.distances_all_pairs import is_distance_regular from sage.graphs.base.static_dense_graph import is_strongly_regular from sage.graphs.line_graph import is_line_graph from sage.graphs.tutte_polynomial import tutte_polynomial from sage.graphs.lovasz_theta import lovasz_theta from sage.graphs.partial_cube import is_partial_cube from sage.graphs.orientations import strong_orientations_iterator, random_orientation from sage.graphs.connectivity import bridges, cleave, spqr_tree from sage.graphs.connectivity import is_triconnected from sage.graphs.comparability import is_comparability from sage.graphs.comparability import is_permutation from sage.graphs.convexity_properties import geodetic_closure from sage.graphs.domination import is_dominating from sage.graphs.domination import is_redundant from sage.graphs.domination import private_neighbors from sage.graphs.domination import minimal_dominating_sets from sage.graphs.traversals import (lex_M, maximum_cardinality_search, maximum_cardinality_search_M) from sage.graphs.isoperimetric_inequalities import cheeger_constant, edge_isoperimetric_number, vertex_isoperimetric_number from sage.graphs.graph_coloring import fractional_chromatic_number from sage.graphs.graph_coloring import fractional_chromatic_index _additional_categories = { "is_long_hole_free" : "Graph properties", "is_long_antihole_free" : "Graph properties", "is_weakly_chordal" : "Graph properties", "is_asteroidal_triple_free" : "Graph properties", "chromatic_polynomial" : "Coloring", "rank_decomposition" : "Algorithmically hard stuff", "treewidth" : "Algorithmically hard stuff", "pathwidth" : "Algorithmically hard stuff", "treelength" : "Algorithmically hard stuff", "matching_polynomial" : "Algorithmically hard stuff", "all_max_clique" : "Clique-related methods", "cliques_maximum" : "Clique-related methods", "all_cliques" : "Clique-related methods", "atoms_and_clique_separators" : "Clique-related methods", "random_spanning_tree" : "Connectivity, orientations, trees", "spanning_trees" : "Connectivity, orientations, trees", "is_cartesian_product" : "Graph properties", "is_distance_regular" : "Graph properties", "is_strongly_regular" : "Graph properties", "is_line_graph" : "Graph properties", "is_partial_cube" : "Graph properties", "is_comparability" : "Graph properties", "is_permutation" : "Graph properties", "tutte_polynomial" : "Algorithmically hard stuff", "lovasz_theta" : "Leftovers", "strong_orientations_iterator" : "Connectivity, orientations, trees", "random_orientation" : "Connectivity, orientations, trees", "bridges" : "Connectivity, orientations, trees", "cleave" : "Connectivity, orientations, trees", "spqr_tree" : "Connectivity, orientations, trees", "is_triconnected" : "Connectivity, orientations, trees", "is_dominating" : "Domination", "is_redundant" : "Domination", "private_neighbors" : "Domination", "minimal_dominating_sets" : "Domination", "lex_M" : "Traversals", "maximum_cardinality_search" : "Traversals", "maximum_cardinality_search_M" : "Traversals", "cheeger_constant" : "Expansion properties", "edge_isoperimetric_number" : "Expansion properties", "vertex_isoperimetric_number" : "Expansion properties", "fractional_chromatic_number" : "Coloring", "fractional_chromatic_index" : "Coloring", "geodetic_closure" : "Leftovers" } __doc__ = __doc__.replace("{INDEX_OF_METHODS}",gen_thematic_rest_table_index(Graph,_additional_categories))

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import itertools from copy import copy from sage.rings.polynomial.polynomial_ring_constructor import PolynomialRing from sage.rings.integer import Integer from sage.rings.integer_ring import ZZ import sage.graphs.generic_graph_pyx as generic_graph_pyx from sage.graphs.generic_graph import GenericGraph from sage.graphs.digraph import DiGraph from sage.graphs.independent_sets import IndependentSets from sage.misc.rest_index_of_methods import doc_index, gen_thematic_rest_table_index from sage.graphs.views import EdgesView from sage.misc.lazy_import import lazy_import from sage.features import PythonModule lazy_import('sage.graphs.mcqd', ['mcqd'], feature=PythonModule('sage.graphs.mcqd', spkg='mcqd')) from sage.misc.decorators import rename_keyword class Graph(GenericGraph): _directed = False def __init__(self, data=None, pos=None, loops=None, format=None, weighted=None, data_structure="sparse", vertex_labels=True, name=None, multiedges=None, convert_empty_dict_labels_to_None=None, sparse=True, immutable=False): GenericGraph.__init__(self) from sage.structure.element import is_Matrix if sparse is False: if data_structure != "sparse": raise ValueError("The 'sparse' argument is an alias for " "'data_structure'. Please do not define both.") data_structure = "dense" if multiedges or weighted: if data_structure == "dense": raise RuntimeError("Multiedge and weighted c_graphs must be sparse.") if immutable: data_structure = 'static_sparse' from sage.graphs.base.sparse_graph import SparseGraphBackend from sage.graphs.base.dense_graph import DenseGraphBackend if data_structure in ["sparse", "static_sparse"]: CGB = SparseGraphBackend elif data_structure == "dense": CGB = DenseGraphBackend else: raise ValueError("data_structure must be equal to 'sparse', " "'static_sparse' or 'dense'") self._backend = CGB(0, directed=False) if format is None and isinstance(data, str): if data.startswith(">>graph6<<"): data = data[10:] format = 'graph6' elif data.startswith(">>sparse6<<"): data = data[11:] format = 'sparse6' elif data[0] == ':': format = 'sparse6' else: format = 'graph6' if format is None and is_Matrix(data): if data.is_symmetric(): format = 'adjacency_matrix' else: format = 'incidence_matrix' if format is None and isinstance(data, Graph): format = 'Graph' from sage.graphs.all import DiGraph if format is None and isinstance(data, DiGraph): data = data.to_undirected() format = 'Graph' if (format is None and isinstance(data, list) and len(data) >= 2 and callable(data[1])): format = 'rule' if (format is None and isinstance(data, list) and len(data) == 2 and isinstance(data[0], list) and ((isinstance(data[1], list) and (not data[1] or callable(getattr(data[1][0], "__iter__", None)))) or (isinstance(data[1], EdgesView)))): format = "vertices_and_edges" if format is None and isinstance(data, dict): if not data: format = 'dict_of_dicts' else: val = next(iter(data.values())) if isinstance(val, (list, EdgesView)): format = 'dict_of_lists' elif isinstance(val, dict): format = 'dict_of_dicts' if format is None and hasattr(data, 'adj'): format = 'NX' if (format is None and hasattr(data, 'vcount') and hasattr(data, 'get_edgelist')): try: import igraph except ImportError: raise ImportError("The data seems to be a igraph object, but "+ "igraph is not installed in Sage. To install "+ "it, run 'sage -i python_igraph'") if format is None and isinstance(data, igraph.Graph): format = 'igraph' if format is None and isinstance(data, (int, Integer)): format = 'int' if format is None and data is None: format = 'int' data = 0 if format is None and isinstance(data, (list, EdgesView)): format = "list_of_edges" if weighted is None: weighted = False if format is None: raise ValueError("This input cannot be turned into a graph") if format == 'weighted_adjacency_matrix': if weighted is False: raise ValueError("Format was weighted_adjacency_matrix but weighted was False.") if weighted is None: weighted = True if multiedges is None: multiedges = False format = 'adjacency_matrix' if format == 'graph6': if weighted is None: weighted = False self.allow_loops(loops if loops else False, check=False) self.allow_multiple_edges(multiedges if multiedges else False, check=False) from .graph_input import from_graph6 from_graph6(self, data) elif format == 'sparse6': if weighted is None: weighted = False self.allow_loops(False if loops is False else True, check=False) self.allow_multiple_edges(False if multiedges is False else True, check=False) from .graph_input import from_sparse6 from_sparse6(self, data) elif format == 'adjacency_matrix': from .graph_input import from_adjacency_matrix from_adjacency_matrix(self, data, loops=loops, multiedges=multiedges, weighted=weighted) elif format == 'incidence_matrix': from .graph_input import from_incidence_matrix from_incidence_matrix(self, data, loops=loops, multiedges=multiedges, weighted=weighted) elif format == 'seidel_adjacency_matrix': weighted = False self.allow_loops(False) self.allow_multiple_edges(False) from .graph_input import from_seidel_adjacency_matrix from_seidel_adjacency_matrix(self, data) elif format == 'Graph': if loops is None: loops = data.allows_loops() if multiedges is None: multiedges = data.allows_multiple_edges() if weighted is None: weighted = data.weighted() self.allow_loops(loops, check=False) self.allow_multiple_edges(multiedges, check=False) if data.get_pos() is not None: pos = data.get_pos() self.name(data.name()) self.set_vertices(data.get_vertices()) data._backend.subgraph_given_vertices(self._backend, data) elif format == 'NX': from sage.graphs.graph_input import from_networkx_graph from_networkx_graph(self, data, weighted=weighted, multiedges=multiedges, loops=loops, convert_empty_dict_labels_to_None=convert_empty_dict_labels_to_None) if weighted is None: weighted = self.allows_multiple_edges() elif format == 'igraph': if data.is_directed(): raise ValueError("An *undirected* igraph graph was expected. "+ "To build an directed graph, call the DiGraph "+ "constructor.") self.add_vertices(range(data.vcount())) self.add_edges((e.source, e.target, e.attributes()) for e in data.es()) if vertex_labels and 'name' in data.vertex_attributes(): vs = data.vs() self.relabel({v:vs[v]['name'] for v in self}) elif format == 'rule': f = data[1] verts = data[0] if loops is None: loops = any(f(v,v) for v in verts) if weighted is None: weighted = False self.allow_loops(loops, check=False) self.allow_multiple_edges(True if multiedges else False, check=False) self.add_vertices(verts) self.add_edges(e for e in itertools.combinations(verts,2) if f(*e)) if loops: self.add_edges((v,v) for v in verts if f(v,v)) elif format == "vertices_and_edges": self.allow_multiple_edges(bool(multiedges), check=False) self.allow_loops(bool(loops), check=False) self.add_vertices(data[0]) self.add_edges(data[1]) elif format == 'dict_of_dicts': from .graph_input import from_dict_of_dicts from_dict_of_dicts(self, data, loops=loops, multiedges=multiedges, weighted=weighted, convert_empty_dict_labels_to_None = False if convert_empty_dict_labels_to_None is None else convert_empty_dict_labels_to_None) elif format == 'dict_of_lists': from .graph_input import from_dict_of_lists from_dict_of_lists(self, data, loops=loops, multiedges=multiedges, weighted=weighted) elif format == 'int': self.allow_loops(loops if loops else False, check=False) self.allow_multiple_edges(multiedges if multiedges else False, check=False) if data < 0: raise ValueError("The number of vertices cannot be strictly negative!") if data: self.add_vertices(range(data)) elif format == 'list_of_edges': self.allow_multiple_edges(True if multiedges else False, check=False) self.allow_loops(True if loops else False, check=False) self.add_edges(data) else: raise ValueError("Unknown input format '{}'".format(format)) if weighted is None: weighted = False self._weighted = getattr(self, '_weighted', weighted) self._pos = copy(pos) if format != 'Graph' or name is not None: self.name(name) if data_structure == "static_sparse": from sage.graphs.base.static_sparse_backend import StaticSparseBackend ib = StaticSparseBackend(self, loops = self.allows_loops(), multiedges = self.allows_multiple_edges()) self._backend = ib self._immutable = True asic methods") def graph6_string(self): n = self.order() if n > 262143: raise ValueError('graph6 format supports graphs on 0 to 262143 vertices only.') elif self.has_loops() or self.has_multiple_edges(): raise ValueError('graph6 format supports only simple graphs (no loops, no multiple edges)') else: return generic_graph_pyx.small_integer_to_graph6(n) + generic_graph_pyx.binary_string_to_graph6(self._bit_vector()) @doc_index("Basic methods") def sparse6_string(self): n = self.order() if not n: return ':?' if n > 262143: raise ValueError('sparse6 format supports graphs on 0 to 262143 vertices only.') if n == 1: s = '0' * self.size() else: try: V = sorted(self) except TypeError: V = self v_to_int = {v:i for i,v in enumerate(V)} edges = [sorted((v_to_int[u], v_to_int[v])) for u,v in self.edge_iterator(labels=False)] edges.sort(key=lambda e: (e[1], e[0])) k = int((ZZ(n) - 1).nbits()) v = 0 i = 0 m = 0 s = '' while m < len(edges): if edges[m][1] > v + 1: sp = generic_graph_pyx.int_to_binary_string(edges[m][1]) sp = '0'*(k-len(sp)) + sp s += '1' + sp v = edges[m][1] elif edges[m][1] == v + 1: sp = generic_graph_pyx.int_to_binary_string(edges[m][0]) sp = '0'*(k-len(sp)) + sp s += '1' + sp v += 1 m += 1 else: sp = generic_graph_pyx.int_to_binary_string(edges[m][0]) sp = '0'*(k-len(sp)) + sp s += '0' + sp m += 1 # pad on the right to make a multiple of 6 s = s + ( '1' * ((6 - len(s))%6) ) # split into groups of 6, and convert numbers to decimal, adding 63 six_bits = '' for i in range(0, len(s), 6): six_bits += chr( int( s[i:i+6], 2) + 63 ) return ':' + generic_graph_pyx.small_integer_to_graph6(n) + six_bits ### Attributes @doc_index("Basic methods") def is_directed(self): return False ### Properties @doc_index("Graph properties") def is_tree(self, certificate=False, output='vertex'): if output not in ['vertex', 'edge']: raise ValueError('output must be either vertex or edge') if not self.order() or not self.is_connected(): return (False, None) if certificate else False if certificate: if self.order() == self.size() + 1: return (True, None) if self.allows_loops(): L = self.loop_edges() if output == 'edge' else self.loop_vertices() if L: return False, L[:1] if self.has_multiple_edges(): if output == 'vertex': return (False, list(self.multiple_edges(sort=True)[0][:2])) edge1, edge2 = self.multiple_edges(sort=True)[:2] if edge1[0] != edge2[0]: return (False, [edge1, edge2]) return (False, [edge1, (edge2[1], edge2[0], edge2[2])]) if output == 'edge': if self.allows_multiple_edges(): def vertices_to_edges(x): return [(u[0], u[1], self.edge_label(u[0], u[1])[0]) for u in zip(x, x[1:] + [x[0]])] else: def vertices_to_edges(x): return [(u[0], u[1], self.edge_label(u[0], u[1])) for u in zip(x, x[1:] + [x[0]])] # This code is a depth-first search that looks for a cycle in the # graph. We *know* it exists as there are too many edges around. seen = {} u = next(self.vertex_iterator()) seen[u] = u stack = [(u, v) for v in self.neighbor_iterator(u)] while stack: u, v = stack.pop() if v in seen: continue for w in self.neighbor_iterator(v): if u == w: continue elif w in seen: cycle = [w, v] while u != w: cycle.append(u) u = seen[u] cycle.reverse() if output == 'vertex': return (False, cycle) return (False, vertices_to_edges(cycle)) else: stack.append((v, w)) seen[v] = u else: return self.order() == self.size() + 1 @doc_index("Graph properties") def is_forest(self, certificate=False, output='vertex'): connected_components = self.connected_components() number_of_connected_components = len(connected_components) isit = (self.order() == self.size() + number_of_connected_components) if not certificate: return isit else: if isit: return (True, None) # The graph contains a cycle, and the user wants to see it. # No need to copy the graph if number_of_connected_components == 1: return self.is_tree(certificate=True, output=output) # We try to find a cycle in each connected component for cc in connected_components: isit, cycle = self.subgraph(cc).is_tree(certificate=True, output=output) if not isit: return (False, cycle) @doc_index("Graph properties") def is_cactus(self): self._scream_if_not_simple() # Special cases if self.order() < 4: return True if self.size() > 3 * (self.order() - 1) / 2: return False # Every cactus graph is outerplanar if not self.is_circular_planar(): return False if not self.is_connected(): return False # the number of faces is 1 plus the number of blocks of order > 2 B = self.blocks_and_cut_vertices()[0] return len(self.faces()) == sum(1 for b in B if len(b) > 2) + 1 @doc_index("Graph properties") def is_biconnected(self): if self.order() < 2 or not self.is_connected(): return False if self.blocks_and_cut_vertices()[1]: return False return True @doc_index("Graph properties") def is_block_graph(self): if not self.is_connected(): return False if self.is_clique(): return True B,C = self.blocks_and_cut_vertices() return all(self.is_clique(vertices=block) for block in B) @doc_index("Graph properties") def is_cograph(self): # A cograph has no 4-vertex path as an induced subgraph. # We will first try to "decompose" graph by complements and # split to connected components, and use fairly slow # subgraph search if that fails. self._scream_if_not_simple() if self.order() < 4: return True if self.density()*2 > 1: return self.complement().is_cograph() if not self.is_connected(): return all(part.is_cograph() for part in self.connected_components_subgraphs()) P4 = Graph({0: [1], 1: [2], 2: [3]}) return self.subgraph_search(P4, induced=True) is None @doc_index("Graph properties") def is_apex(self): # Easy cases: null graph, subgraphs of K_5 and K_3,3 if self.order() <= 5 or ( self.order() <= 6 and self.is_bipartite() ): return True return len(self.apex_vertices(k=1)) > 0 @doc_index("Graph properties") def apex_vertices(self, k=None): if k is None: k = self.order() elif k < 0: raise ValueError("parameter k must be a non negative integer") # Easy cases: null graph, subgraphs of K_5 and K_3,3 if self.order() <= 5 or (self.order() <= 6 and self.is_bipartite()): it = self.vertex_iterator() return [next(it) for _ in range(k)] if not self.is_connected(): # We search for its non planar connected components. If it has more # than one such component, the graph is not apex. It is apex if # either it has no such component, in which case the graph is # planar, or if its unique non planar component is apex. P = [H for H in self.connected_components_subgraphs() if not H.is_planar()] if not P: # The graph is planar it = self.vertex_iterator() return [next(it) for _ in range(k)] elif len(P) > 1: return [] else: # We proceed with the non planar component if P[0].is_immutable(): H = Graph(P[0].edges(labels=0, sort=False), immutable=False, loops=False, multiedges=False) else: H = P[0] elif self.is_planar(): # A planar graph is apex. it = self.vertex_iterator() return [next(it) for _ in range(k)] else: # We make a basic copy of the graph since we will modify it H = Graph(self.edges(labels=0, sort=False), immutable=False, loops=False, multiedges=False) # General case: basic implementation # # Test for each vertex if its removal makes the graph planar. # Obviously, we don't test vertices of degree one. Furthermore, if a V = {} for u in H: d = H.degree(u) if d > 1: if d in V: V[d].append(u) else: V[d] = [u] apex = set() for deg in sorted(V): for u in V[deg]: if u in apex: if deg == 2: apex.update(H.neighbor_iterator(u)) if len(apex) >= k: return list(apex)[:k] continue E = H.edges_incident(u, labels=0) H.delete_vertex(u) if H.is_planar(): apex.add(u) if deg == 2: apex.update(self.neighbor_iterator(u)) if len(apex) >= k: return list(apex)[:k] H.add_edges(E) return list(apex) @doc_index("Graph properties") def is_overfull(self): size() > max(self.degree()) * (self.order() - 1)) @doc_index("Graph properties") def is_even_hole_free(self, certificate=False): girth = self.girth() if girth > self.order(): start = 4 elif not girth % 2: if not certificate: return False start = girth else: start = girth + 1 from sage.graphs.generators.basic import CycleGraph while start <= self.order(): subgraph = self.subgraph_search(CycleGraph(start), induced=True) if subgraph is not None: if certificate: return subgraph else: return False start += 2 return True @doc_index("Graph properties") def is_odd_hole_free(self, certificate=False): girth = self.odd_girth() if girth > self.order(): return True if girth == 3: start = 5 else: if not certificate: return False start = girth from sage.graphs.generators.basic import CycleGraph while start <= self.order(): subgraph = self.subgraph_search(CycleGraph(start), induced=True) if subgraph is not None: if certificate: return subgraph else: return False start += 2 return True @doc_index("Graph properties") def is_triangle_free(self, algorithm='dense_graph', certificate=False): if algorithm == 'dense_graph': from sage.graphs.base.static_dense_graph import is_triangle_free return is_triangle_free(self, certificate=certificate) if algorithm == 'bitset': if self.order() < 3: return (True, []) if certificate else True from sage.data_structures.bitset import Bitset N = self.order() vertex_to_int = {} B = {} for i, u in enumerate(self): vertex_to_int[u] = i B[u] = Bitset(capacity=N) for u, v in self.edge_iterator(labels=None): if u != v: B[u].add(vertex_to_int[v]) B[v].add(vertex_to_int[u]) for u, v in self.edge_iterator(labels=None): BB = B[u] & B[v] if BB: if certificate: for w in self.neighbor_iterator(u): if vertex_to_int[w] in BB: return False, [u, v, w] return False return (True, []) if certificate else True elif algorithm == 'matrix': if self.order() < 3: return True return (self.adjacency_matrix()**3).trace() == 0 else: raise ValueError("Algorithm '%s' not yet implemented. Please contribute." %(algorithm)) @doc_index("Graph properties") def is_split(self): self._scream_if_not_simple() degree_sequence = [0] + sorted(self.degree(), reverse=True) for i, d in enumerate(degree_sequence): if d >= i - 1: omega = i else: break left = sum(degree_sequence[:omega + 1]) right = omega * (omega - 1) + sum(degree_sequence[omega + 1:]) return left == right @doc_index("Algorithmically hard stuff") def is_perfect(self, certificate=False): if self.has_multiple_edges() or self.has_loops(): raise ValueError("This method is only defined for simple graphs," " and yours is not one of them !") if self.is_bipartite(): return True if not certificate else None self_complement = self.complement() self_complement.remove_loops() self_complement.remove_multiple_edges() if self_complement.is_bipartite(): return True if not certificate else None answer = self.is_odd_hole_free(certificate=certificate) if not (answer is True): return answer return self_complement.is_odd_hole_free(certificate=certificate) @doc_index("Graph properties") def is_edge_transitive(self): from sage.libs.gap.libgap import libgap if not self.size(): return True A = self.automorphism_group() e = next(self.edge_iterator(labels=False)) e = [A._domain_to_gap[e[0]], A._domain_to_gap[e[1]]] e.sort() return libgap(A).OrbitLength(e, libgap.OnSets) == self.size() @doc_index("Graph properties") def is_arc_transitive(self): from sage.libs.gap.libgap import libgap if not self.size(): return True A = self.automorphism_group() e = next(self.edge_iterator(labels=False)) e = [A._domain_to_gap[e[0]], A._domain_to_gap[e[1]]] return libgap(A).OrbitLength(e,libgap.OnTuples) == 2*self.size() @doc_index("Graph properties") def is_half_transitive(self): # A half-transitive graph always has only vertices of even degree if any(d % 2 for d in self.degree_iterator()): return False return (self.is_edge_transitive() and self.is_vertex_transitive() and not self.is_arc_transitive()) @doc_index("Graph properties") def is_semi_symmetric(self): # A semi-symmetric graph is always bipartite if not self.is_bipartite(): return False return (self.is_regular() and self.is_edge_transitive() and not self.is_vertex_transitive()) @doc_index("Graph properties") def is_path(self): order = self.order() if order != self.size() + 1: return False if order <= 1: return order == 1 deg_one_counter = 0 seen_counter = 0 for v in self.depth_first_search(next(self.vertex_iterator())): seen_counter += 1 deg = self._backend.degree(v, False) if deg == 1: deg_one_counter += 1 if deg_one_counter > 2: return False elif deg != 2: return False return deg_one_counter == 2 and seen_counter == order @doc_index("Connectivity, orientations, trees") def degree_constrained_subgraph(self, bounds, solver=None, verbose=0, *, integrality_tolerance=1e-3): self._scream_if_not_simple() from sage.numerical.mip import MixedIntegerLinearProgram, MIPSolverException p = MixedIntegerLinearProgram(maximization=False, solver=solver) b = p.new_variable(binary=True) if isinstance(bounds,dict): f_bounds = lambda x: bounds[x] else: f_bounds = bounds if self.weighted(): from sage.rings.real_mpfr import RR weight = lambda x: x if x in RR else 1 else: weight = lambda x: 1 for v in self: minimum,maximum = f_bounds(v) p.add_constraint(p.sum(b[frozenset((x,y))]*weight(l) for x,y,l in self.edges_incident(v)), min=minimum, max=maximum) p.set_objective(p.sum(b[frozenset((x,y))]*weight(l) for x,y,l in self.edge_iterator())) try: p.solve(log=verbose) except MIPSolverException: return False g = copy(self) b = p.get_values(b, convert=bool, tolerance=integrality_tolerance) g.delete_edges(e for e in g.edge_iterator(labels=False) if not b[frozenset(e)]) return g ### Orientations @doc_index("Connectivity, orientations, trees") def strong_orientation(self): from sage.graphs.digraph import DiGraph d = DiGraph(multiedges=self.allows_multiple_edges()) i = 0 # The algorithm works through a depth-first search. Any edge # used in the depth-first search is oriented in the direction # in which it has been used. All the other edges are oriented # backward v = next(self.vertex_iterator()) seen = {} i = 1 # Time at which the vertices have been discovered seen[v] = i # indicates the stack of edges to explore next_ = self.edges_incident(v) while next_: e = next_.pop() # Ignore loops if e[0] == e[1]: continue # We assume e[0] to be a `seen` vertex e = e if seen.get(e[0], False) is not False else (e[1], e[0], e[2]) # If we discovered a new vertex if seen.get(e[1], False) is False: d.add_edge(e) next_.extend(ee for ee in self.edges_incident(e[1]) if ((e[0],e[1]) != (ee[0],ee[1])) and ((e[0],e[1]) != (ee[1],ee[0]))) i += 1 seen[e[1]] = i # Else, we orient the edges backward else: if seen[e[0]] < seen[e[1]]: d.add_edge(e[1], e[0], e[2]) else: d.add_edge(e) # Case of multiple edges. If another edge has already been inserted, we # add the new one in the opposite direction. tmp = None for e in self.multiple_edges(): if tmp == (e[0], e[1]): if d.has_edge(e[0], e[1]): d.add_edge(e[1], e[0], e[2]) else: d.add_edge(e) tmp = (e[0], e[1]) return d @doc_index("Connectivity, orientations, trees") def minimum_outdegree_orientation(self, use_edge_labels=False, solver=None, verbose=0, *, integrality_tolerance=1e-3): self._scream_if_not_simple() if self.is_directed(): raise ValueError("Cannot compute an orientation of a DiGraph. "+\ "Please convert it to a Graph if you really mean it.") if use_edge_labels: from sage.rings.real_mpfr import RR def weight(e): l = self.edge_label(e) return l if l in RR else 1 else: def weight(e): return 1 from sage.numerical.mip import MixedIntegerLinearProgram p = MixedIntegerLinearProgram(maximization=False, solver=solver) degree = p.new_variable(nonnegative=True) # The orientation of an edge is boolean and indicates whether the edge # uv goes from u to v ( equal to 0 ) or from v to u ( equal to 1) orientation = p.new_variable(binary=True) # Whether an edge adjacent to a vertex u counts positively or # negatively. To do so, we first fix an arbitrary extremity per edge uv. ext = {frozenset(e): e[0] for e in self.edge_iterator(labels=False)} def outgoing(u, e, variable): if u == ext[frozenset(e)]: return variable else: return 1 - variable for u in self: p.add_constraint(p.sum(weight(e) * outgoing(u, e, orientation[frozenset(e)]) for e in self.edge_iterator(vertices=[u], labels=False)) - degree['max'], max=0) p.set_objective(degree['max']) p.solve(log=verbose) orientation = p.get_values(orientation, convert=bool, tolerance=integrality_tolerance) # All the edges from self are doubled in O # ( one in each direction ) from sage.graphs.digraph import DiGraph O = DiGraph(self) # Builds the list of edges that should be removed edges = [] for e in self.edge_iterator(labels=None): if orientation[frozenset(e)]: edges.append(e[::-1]) else: edges.append(e) O.delete_edges(edges) return O @doc_index("Connectivity, orientations, trees") def bounded_outdegree_orientation(self, bound, solver=None, verbose=False, *, integrality_tolerance=1e-3): self._scream_if_not_simple() from sage.graphs.all import DiGraph n = self.order() if not n: return DiGraph() vertices = list(self) vertices_id = {y: x for x,y in enumerate(vertices)} b = {} # Checking the input type. We make a dictionary out of it if isinstance(bound, dict): b = bound else: try: b = dict(zip(vertices,map(bound, vertices))) except TypeError: b = dict(zip(vertices, [bound]*n)) d = DiGraph() # Adding the edges (s,v) and ((u,v),t) d.add_edges(('s', vertices_id[v], b[v]) for v in vertices) d.add_edges(((vertices_id[u], vertices_id[v]), 't', 1) for u,v in self.edges(labels=None) ) # each v is linked to its incident edges for u,v in self.edge_iterator(labels=None): u,v = vertices_id[u], vertices_id[v] d.add_edge(u, (u,v), 1) d.add_edge(v, (u,v), 1) # Solving the maximum flow value, flow = d.flow('s','t', value_only=False, integer=True, use_edge_labels=True, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) if value != self.size(): raise ValueError("No orientation exists for the given bound") D = DiGraph() D.add_vertices(vertices) # The flow graph may not contain all the vertices, if they are # not part of the flow... for u in [x for x in range(n) if x in flow]: for uu,vv in flow.neighbors_out(u): v = vv if vv != u else uu D.add_edge(vertices[u], vertices[v]) # I do not like when a method destroys the embedding ;-) D.set_pos(self.get_pos()) return D @doc_index("Connectivity, orientations, trees") def orientations(self, data_structure=None, sparse=None): if sparse is not None: if data_structure is not None: raise ValueError("cannot specify both 'sparse' and 'data_structure'") data_structure = "sparse" if sparse else "dense" if data_structure is None: from sage.graphs.base.dense_graph import DenseGraphBackend from sage.graphs.base.sparse_graph import SparseGraphBackend if isinstance(self._backend, DenseGraphBackend): data_structure = "dense" elif isinstance(self._backend, SparseGraphBackend): data_structure = "sparse" else: data_structure = "static_sparse" name = self.name() if name: name = 'An orientation of ' + name if not self.size(): D = DiGraph(data=[self.vertices(), []], format='vertices_and_edges', name=name, pos=self._pos, multiedges=self.allows_multiple_edges(), loops=self.allows_loops(), data_structure=data_structure) if hasattr(self, '_embedding'): D._embedding = copy(self._embedding) yield D return E = [[(u,v,label), (v,u,label)] if u != v else [(u,v,label)] for u,v,label in self.edge_iterator()] verts = self.vertices() for edges in itertools.product(*E): D = DiGraph(data=[verts, edges], format='vertices_and_edges', name=name, pos=self._pos, multiedges=self.allows_multiple_edges(), loops=self.allows_loops(), data_structure=data_structure) if hasattr(self, '_embedding'): D._embedding = copy(self._embedding) yield D ### Coloring @doc_index("Basic methods") def bipartite_color(self): isit, certificate = self.is_bipartite(certificate=True) if isit: return certificate else: raise RuntimeError("Graph is not bipartite.") @doc_index("Basic methods") def bipartite_sets(self): color = self.bipartite_color() left = set() right = set() for u,s in color.items(): if s: left.add(u) else: right.add(u) return left, right @doc_index("Coloring") def chromatic_index(self, solver=None, verbose=0, *, integrality_tolerance=1e-3): if not self.order() or not self.size(): return 0 from sage.graphs.graph_coloring import edge_coloring return edge_coloring(self, value_only=True, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) @doc_index("Coloring") def chromatic_number(self, algorithm="DLX", solver=None, verbose=0, *, integrality_tolerance=1e-3): self._scream_if_not_simple(allow_multiple_edges=True) # default built-in algorithm; bad performance if algorithm == "DLX": from sage.graphs.graph_coloring import chromatic_number return chromatic_number(self) # Algorithm with good performance, but requires an optional # package: choose any of GLPK or CBC. elif algorithm == "MILP": from sage.graphs.graph_coloring import vertex_coloring return vertex_coloring(self, value_only=True, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) # another algorithm with bad performance; only good for small graphs elif algorithm == "CP": f = self.chromatic_polynomial() i = 0 while not f(i): i += 1 return i else: raise ValueError("The 'algorithm' keyword must be set to either 'DLX', 'MILP' or 'CP'.") @doc_index("Coloring") def coloring(self, algorithm="DLX", hex_colors=False, solver=None, verbose=0, *, integrality_tolerance=1e-3): self._scream_if_not_simple(allow_multiple_edges=True) if algorithm == "MILP": from sage.graphs.graph_coloring import vertex_coloring return vertex_coloring(self, hex_colors=hex_colors, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) elif algorithm == "DLX": from sage.graphs.graph_coloring import first_coloring return first_coloring(self, hex_colors=hex_colors) else: raise ValueError("The 'algorithm' keyword must be set to either 'DLX' or 'MILP'.") @doc_index("Coloring") def chromatic_symmetric_function(self, R=None): from sage.combinat.sf.sf import SymmetricFunctions from sage.combinat.partition import _Partitions from sage.misc.misc import powerset if R is None: R = ZZ p = SymmetricFunctions(R).p() ret = p.zero() for F in powerset(self.edges()): la = _Partitions(self.subgraph(edges=F).connected_components_sizes()) ret += (-1)**len(F) * p[la] return ret @doc_index("Coloring") def chromatic_quasisymmetric_function(self, t=None, R=None): from sage.combinat.ncsf_qsym.qsym import QuasiSymmetricFunctions from sage.combinat.set_partition_ordered import OrderedSetPartitions if t is None: t = ZZ['t'].gen() if R is None: R = t.parent() M = QuasiSymmetricFunctions(R).M() ret = M.zero() V = self.vertices() def asc(sigma): stat = 0 for i, s in enumerate(sigma): for u in s: stat += sum(1 for p in sigma[i+1:] for v in p if v > u and self.has_edge(u, v)) return stat for sigma in OrderedSetPartitions(V): if any(not self.is_independent_set(s) for s in sigma): continue ret += M.term(sigma.to_composition(), t**asc(sigma)) return ret @doc_index("Leftovers") def matching(self, value_only=False, algorithm="Edmonds", use_edge_labels=False, solver=None, verbose=0, *, integrality_tolerance=1e-3): from sage.rings.real_mpfr import RR def weight(x): if x in RR: return x else: return 1 W = {} L = {} for u,v,l in self.edge_iterator(): if u is v: continue fuv = frozenset((u, v)) if fuv not in L or ( use_edge_labels and W[fuv] < weight(l) ): L[fuv] = l if use_edge_labels: W[fuv] = weight(l) if algorithm == "Edmonds": import networkx g = networkx.Graph() if use_edge_labels: for (u, v),w in W.items(): g.add_edge(u, v, weight=w) else: for u, v in L: g.add_edge(u, v) d = networkx.max_weight_matching(g) if value_only: if use_edge_labels: return sum(W[frozenset(e)] for e in d) else: return Integer(len(d)) else: return [(u, v, L[frozenset((u, v))]) for u, v in d] elif algorithm == "LP": g = self from sage.numerical.mip import MixedIntegerLinearProgram # returns the weight of an edge considering it may not be # weighted ... p = MixedIntegerLinearProgram(maximization=True, solver=solver) b = p.new_variable(binary=True) if use_edge_labels: p.set_objective(p.sum(w * b[fe] for fe,w in W.items())) else: p.set_objective(p.sum(b[fe] for fe in L)) # for any vertex v, there is at most one edge incident to v in # the maximum matching for v in g: p.add_constraint(p.sum(b[frozenset(e)] for e in self.edge_iterator(vertices=[v], labels=False) if e[0] != e[1]), max=1) p.solve(log=verbose) b = p.get_values(b, convert=bool, tolerance=integrality_tolerance) if value_only: if use_edge_labels: return sum(w for fe, w in W.items() if b[fe]) else: return Integer(sum(1 for fe in L if b[fe])) else: return [(u, v, L[frozenset((u, v))]) for u, v in L if b[frozenset((u, v))]] else: raise ValueError('algorithm must be set to either "Edmonds" or "LP"') @doc_index("Algorithmically hard stuff") def has_homomorphism_to(self, H, core=False, solver=None, verbose=0, *, integrality_tolerance=1e-3): self._scream_if_not_simple() from sage.numerical.mip import MixedIntegerLinearProgram, MIPSolverException p = MixedIntegerLinearProgram(solver=solver, maximization=False) b = p.new_variable(binary=True) # Each vertex has an image for ug in self: p.add_constraint(p.sum(b[ug,uh] for uh in H) == 1) nonedges = H.complement().edges(labels=False) for ug,vg in self.edges(labels=False): # Two adjacent vertices cannot be mapped to the same element for uh in H: p.add_constraint(b[ug,uh] + b[vg,uh] <= 1) # Two adjacent vertices cannot be mapped to no adjacent vertices for uh,vh in nonedges: p.add_constraint(b[ug,uh] + b[vg,vh] <= 1) p.add_constraint(b[ug,vh] + b[vg,uh] <= 1) # Minimize the mapping's size if core: m = p.new_variable(nonnegative=True) for uh in H: for ug in self: p.add_constraint(b[ug,uh] <= m[uh]) p.set_objective(p.sum(m[vh] for vh in H)) try: p.solve(log=verbose) except MIPSolverException: return False b = p.get_values(b, convert=bool, tolerance=integrality_tolerance) mapping = dict(x[0] for x in b.items() if x[1]) return mapping @doc_index("Clique-related methods") def fractional_clique_number(self, solver='PPL', verbose=0, check_components=True, check_bipartite=True): return self.fractional_chromatic_number(solver=solver, verbose=verbose, check_components=check_components, check_bipartite=check_bipartite) @doc_index("Leftovers") def maximum_average_degree(self, value_only=True, solver=None, verbose=0): self._scream_if_not_simple() g = self from sage.numerical.mip import MixedIntegerLinearProgram p = MixedIntegerLinearProgram(maximization=True, solver=solver) d = p.new_variable(nonnegative=True) one = p.new_variable(nonnegative=True) for u,v in g.edge_iterator(labels=False): fuv = frozenset((u, v)) p.add_constraint(one[fuv] - 2 * d[u], max=0) p.add_constraint(one[fuv] - 2 * d[v], max=0) p.add_constraint(p.sum(d[v] for v in g), max=1) p.set_objective(p.sum(one[frozenset(uv)] for uv in g.edge_iterator(labels=False))) p.solve(log=verbose) m = 1/(10 *Integer(g.order())) d_val = p.get_values(d) g_mad = g.subgraph(v for v,l in d_val.items() if l > m) if value_only: return g_mad.average_degree() else: return g_mad @doc_index("Algorithmically hard stuff") def independent_set_of_representatives(self, family, solver=None, verbose=0, *, integrality_tolerance=1e-3): from sage.numerical.mip import MixedIntegerLinearProgram p = MixedIntegerLinearProgram(solver=solver) vertex_taken = p.new_variable(binary=True) classss = p.new_variable(binary=True) lists = {v: [] for v in self} for i,f in enumerate(family): for v in f: lists[v].append(i) p.add_constraint(p.sum(classss[v,i] for v in f), max=1, min=1) for v in self: p.add_constraint(p.sum(classss[v,i] for i in lists[v]) - vertex_taken[v], max=0) for u,v in self.edge_iterator(labels=None): p.add_constraint(vertex_taken[u] + vertex_taken[v], max=1) p.set_objective(None) try: p.solve(log=verbose) except Exception: return None classss = p.get_values(classss, convert=bool, tolerance=integrality_tolerance) repr = [] for i,f in enumerate(family): for v in f: if classss[v,i]: repr.append(v) break return repr @doc_index("Algorithmically hard stuff") def minor(self, H, solver=None, verbose=0, *, integrality_tolerance=1e-3): self._scream_if_not_simple() H._scream_if_not_simple() from sage.numerical.mip import MixedIntegerLinearProgram, MIPSolverException p = MixedIntegerLinearProgram(solver=solver) rs = p.new_variable(binary=True) for v in self: p.add_constraint(p.sum(rs[h,v] for h in H), max=1) edges = p.new_variable(binary=True) for u,v in self.edge_iterator(labels=None): fuv = frozenset((u, v)) for h in H: p.add_constraint(edges[h,fuv] - rs[h,u], max=0) p.add_constraint(edges[h,fuv] - rs[h,v], max=0) for h in H: p.add_constraint( p.sum(edges[h,frozenset(e)] for e in self.edge_iterator(labels=None)) - p.sum(rs[h,v] for v in self), min=-1, max=-1) epsilon = 1/(5*Integer(self.order())) r_edges = p.new_variable(nonnegative=True) for h in H: for u,v in self.edge_iterator(labels=None): p.add_constraint(r_edges[h,(u,v)] + r_edges[h,(v,u)] - edges[h,frozenset((u,v))], min=0) for v in self: p.add_constraint(p.sum(r_edges[h,(u,v)] for u in self.neighbor_iterator(v)), max=1-epsilon) h_edges = p.new_variable(nonnegative=True) for h1, h2 in H.edge_iterator(labels=None): for v1, v2 in self.edge_iterator(labels=None): fv1v2 = frozenset((v1, v2)) p.add_constraint(h_edges[(h1,h2),fv1v2] - rs[h2,v2], max=0) p.add_constraint(h_edges[(h1,h2),fv1v2] - rs[h1,v1], max=0) p.add_constraint(h_edges[(h2,h1),fv1v2] - rs[h1,v2], max=0) p.add_constraint(h_edges[(h2,h1),fv1v2] - rs[h2,v1], max=0) p.add_constraint(p.sum(h_edges[(h1,h2),frozenset(e)] + h_edges[(h2,h1),frozenset(e)] for e in self.edge_iterator(labels=None)), min=1) p.set_objective(None) try: p.solve(log=verbose) except MIPSolverException: raise ValueError("This graph has no minor isomorphic to H !") rs = p.get_values(rs, convert=bool, tolerance=integrality_tolerance) rs_dict = {} for h in H: rs_dict[h] = [v for v in self if rs[h,v]] return rs_dict ithmically hard stuff") def convexity_properties(self): from sage.graphs.convexity_properties import ConvexityProperties return ConvexityProperties(self) @doc_index("Distances") def centrality_degree(self, v=None): from sage.rings.integer import Integer n_minus_one = Integer(self.order() - 1) if n_minus_one == 0: raise ValueError("the centrality degree is not defined " "on graphs with only one vertex") if v is None: return {v: self.degree(v)/n_minus_one for v in self} else: return self.degree(v)/n_minus_one nces") def eccentricity(self, v=None, by_weight=False, algorithm=None, weight_function=None, check_weight=True, dist_dict=None, with_labels=False): by_weight, weight_function = self._get_weight_function(by_weight=by_weight, weight_function=weight_function, check_weight=check_weight) if algorithm is None: if dist_dict is not None: algorithm = 'From_Dictionary' elif not by_weight: algorithm = 'BFS' elif any(float(weight_function(e)) < 0 for e in self.edge_iterator()): algorithm = 'Johnson_Boost' if algorithm is None: algorithm = 'Dijkstra_Boost' if algorithm in ['BFS', 'Floyd-Warshall-Cython']: if by_weight: raise ValueError("algorithm '{}' does not work with weights".format(algorithm)) weight_function = None if v is not None: if not isinstance(v, list): v = [v] v_set = set(v) if v is None or all(u in v_set for u in self): if v is None: v = list(self) # If we want to use BFS, we use the Cython routine if algorithm == 'BFS': from sage.graphs.distances_all_pairs import eccentricity algo = 'bounds' if with_labels: return dict(zip(v, eccentricity(self, algorithm=algo, vertex_list=v))) else: return eccentricity(self, algorithm=algo,vertex_list=v) if algorithm == 'DHV': if by_weight: from sage.graphs.base.boost_graph import eccentricity_DHV if with_labels: return dict(zip(v, eccentricity_DHV(self, vertex_list=v, weight_function=weight_function, check_weight=check_weight))) else: return eccentricity_DHV(self, vertex_list=v, weight_function=weight_function, check_weight=check_weight) else: from sage.graphs.distances_all_pairs import eccentricity if with_labels: return dict(zip(v, eccentricity(self, algorithm=algorithm, vertex_list=v))) else: return eccentricity(self, algorithm=algorithm, vertex_list=v) if algorithm in ['Floyd-Warshall-Python', 'Floyd-Warshall-Cython', 'Johnson_Boost']: dist_dict = self.shortest_path_all_pairs(by_weight, algorithm, weight_function, check_weight)[0] algorithm = 'From_Dictionary' elif algorithm in ['Floyd-Warshall-Python', 'Floyd-Warshall-Cython', 'Johnson_Boost','DHV']: raise ValueError("algorithm '" + algorithm + "' works only if all" + " eccentricities are needed") ecc = {} from sage.rings.infinity import Infinity for u in v: if algorithm == 'From_Dictionary': length = dist_dict[u] else: # If algorithm is wrong, the error is raised by the # shortest_path_lengths function length = self.shortest_path_lengths(u, by_weight=by_weight, algorithm=algorithm, weight_function=weight_function, check_weight=check_weight) if len(length) != self.num_verts(): ecc[u] = Infinity else: ecc[u] = max(length.values()) if with_labels: return ecc else: if len(ecc) == 1: # return single value v, = ecc.values() return v return [ecc[u] for u in v] @doc_index("Distances") def radius(self, by_weight=False, algorithm='DHV', weight_function=None, check_weight=True): if not self.order(): raise ValueError("radius is not defined for the empty graph") if not algorithm: algorithm = 'DHV' if algorithm == 'DHV': by_weight, weight_function = self._get_weight_function(by_weight=by_weight, weight_function=weight_function, check_weight=check_weight) if by_weight: from sage.graphs.base.boost_graph import radius_DHV return radius_DHV(self, weight_function=weight_function, check_weight=False) else: from sage.graphs.distances_all_pairs import radius_DHV return radius_DHV(self) return min(self.eccentricity(v=None, by_weight=by_weight, weight_function=weight_function, check_weight=check_weight, algorithm=algorithm)) @doc_index("Distances") def diameter(self, by_weight=False, algorithm=None, weight_function=None, check_weight=True): if not self.order(): raise ValueError("diameter is not defined for the empty graph") by_weight, weight_function = self._get_weight_function(by_weight=by_weight, weight_function=weight_function, check_weight=check_weight) if not by_weight: # We don't want the default weight function weight_function = None if algorithm is None: if by_weight: algorithm = 'iFUB' else: algorithm = 'DHV' elif algorithm == 'BFS': algorithm = 'standard' if algorithm == 'DHV': if by_weight: from sage.graphs.base.boost_graph import diameter_DHV return diameter_DHV(self, weight_function=weight_function, check_weight=False) else: from sage.graphs.distances_all_pairs import diameter return diameter(self, algorithm=algorithm) if algorithm in ['standard', '2sweep', 'multi-sweep', 'iFUB']: if by_weight: raise ValueError("algorithm '" + algorithm + "' does not work" + " on weighted graphs") from sage.graphs.distances_all_pairs import diameter return diameter(self, algorithm=algorithm) return max(self.eccentricity(v=list(self), by_weight=by_weight, weight_function=weight_function, check_weight=False, algorithm=algorithm)) @doc_index("Distances") def center(self, by_weight=False, algorithm=None, weight_function=None, check_weight=True): ecc = self.eccentricity(v=list(self), by_weight=by_weight, weight_function=weight_function, algorithm=algorithm, check_weight=check_weight, with_labels=True) try: r = min(ecc.values()) except Exception: return [] return [v for v in self if ecc[v] == r] @doc_index("Distances") def periphery(self, by_weight=False, algorithm=None, weight_function=None, check_weight=True): ecc = self.eccentricity(v=list(self), by_weight=by_weight, weight_function=weight_function, algorithm=algorithm, check_weight=check_weight, with_labels=True) try: d = max(ecc.values()) except Exception: return [] return [v for v in self if ecc[v] == d] ds") def to_directed(self, data_structure=None, sparse=None): if sparse is not None: if data_structure is not None: raise ValueError("The 'sparse' argument is an alias for " "'data_structure'. Please do not define both.") data_structure = "sparse" if sparse else "dense" if data_structure is None: from sage.graphs.base.dense_graph import DenseGraphBackend from sage.graphs.base.sparse_graph import SparseGraphBackend if isinstance(self._backend, DenseGraphBackend): data_structure = "dense" elif isinstance(self._backend, SparseGraphBackend): data_structure = "sparse" else: data_structure = "static_sparse" from sage.graphs.all import DiGraph D = DiGraph(name = self.name(), pos = self.get_pos(), multiedges = self.allows_multiple_edges(), loops = self.allows_loops(), data_structure = (data_structure if data_structure!="static_sparse" else "sparse")) D.add_vertices(self.vertex_iterator()) D.set_vertices(self.get_vertices()) for u,v,l in self.edge_iterator(): D.add_edge(u,v,l) D.add_edge(v,u,l) if hasattr(self, '_embedding'): D._embedding = copy(self._embedding) D._weighted = self._weighted if data_structure == "static_sparse": D = D.copy(data_structure=data_structure) return D @doc_index("Basic methods") def to_undirected(self): return self.copy() @doc_index("Basic methods") def join(self, other, labels="pairs", immutable=None): G = self.disjoint_union(other, labels=labels, immutable=False) if labels == "integers": G.add_edges((u, v) for u in range(self.order()) for v in range(self.order(), self.order() + other.order())) else: G.add_edges(((0, u), (1, v)) for u in self for v in other) G.name('%s join %s'%(self.name(), other.name())) if immutable is None: immutable = self.is_immutable() and other.is_immutable() if immutable: G = G.copy(immutable=True) return G @doc_index("Leftovers") def seidel_adjacency_matrix(self, vertices=None): return - self.adjacency_matrix(sparse=False, vertices=vertices) \ + self.complement().adjacency_matrix(sparse=False, vertices=vertices) @doc_index("Leftovers") def seidel_switching(self, s, inplace=True): G = self if inplace else copy(self) boundary = self.edge_boundary(s) G.add_edges(itertools.product(s, set(self).difference(s))) G.delete_edges(boundary) if not inplace: return G @doc_index("Leftovers") def twograph(self): from sage.combinat.designs.twographs import TwoGraph G = self.relabel(range(self.order()), inplace=False) T = [] for x,y,z in G.subgraph_search_iterator(Graph({1:[2,3], 2:[3]})): if x < y and y < z: T.append([x, y, z]) for x,y,z in G.subgraph_search_iterator(Graph({1:[2], 3:[]}), induced=True): if x < y: T.append([x, y, z]) T = TwoGraph(T) T.relabel({i: v for i,v in enumerate(self.vertices())}) return T ") def write_to_eps(self, filename, **options): from sage.graphs.print_graphs import print_graph_eps pos = self.layout(**options) [xmin, xmax, ymin, ymax] = self._layout_bounding_box(pos) for v in pos: pos[v] = (1.8*(pos[v][0] - xmin)/(xmax - xmin) - 0.9, 1.8*(pos[v][1] - ymin)/(ymax - ymin) - 0.9) if filename[-4:] != '.eps': filename += '.eps' f = open(filename, 'w') f.write( print_graph_eps(self.vertices(), self.edge_iterator(), pos) ) f.close() @doc_index("Algorithmically hard stuff") def topological_minor(self, H, vertices=False, paths=False, solver=None, verbose=0, *, integrality_tolerance=1e-3): self._scream_if_not_simple() H._scream_if_not_simple() G = self from sage.numerical.mip import MixedIntegerLinearProgram, MIPSolverException p = MixedIntegerLinearProgram(solver=solver) p.set_objective(None) None, verbose=0, *, integrality_tolerance=1e-3): self._scream_if_not_simple(allow_multiple_edges=True) if algorithm == "Cliquer": from sage.graphs.cliquer import max_clique return max_clique(self) elif algorithm == "MILP": return self.complement().independent_set(algorithm=algorithm, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) elif algorithm == "mcqd": return mcqd(self) else: raise NotImplementedError("Only 'MILP', 'Cliquer' and 'mcqd' are supported.") @doc_index("Clique-related methods") def clique_number(self, algorithm="Cliquer", cliques=None, solver=None, verbose=0, *, integrality_tolerance=1e-3): self._scream_if_not_simple(allow_loops=False) if algorithm == "Cliquer": from sage.graphs.cliquer import clique_number return clique_number(self) elif algorithm == "networkx": import networkx return networkx.graph_clique_number(self.networkx_graph(), cliques) elif algorithm == "MILP": return len(self.complement().independent_set(algorithm=algorithm, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance)) elif algorithm == "mcqd": return len(mcqd(self)) else: raise NotImplementedError("Only 'networkx' 'MILP' 'Cliquer' and 'mcqd' are supported.") @doc_index("Clique-related methods") def cliques_number_of(self, vertices=None, cliques=None): import networkx return networkx.number_of_cliques(self.networkx_graph(), vertices, cliques) @doc_index("Clique-related methods") def cliques_get_max_clique_graph(self): import networkx return Graph(networkx.make_max_clique_graph(self.networkx_graph(), create_using=networkx.MultiGraph()), multiedges=False) @doc_index("Clique-related methods") def cliques_get_clique_bipartite(self, **kwds): from .bipartite_graph import BipartiteGraph import networkx return BipartiteGraph(networkx.make_clique_bipartite(self.networkx_graph(), **kwds)) @doc_index("Algorithmically hard stuff") @rename_keyword(deprecation=32238, verbosity='verbose') def independent_set(self, algorithm="Cliquer", value_only=False, reduction_rules=True, solver=None, verbose=0, *, integrality_tolerance=1e-3): my_cover = self.vertex_cover(algorithm=algorithm, value_only=value_only, reduction_rules=reduction_rules, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) if value_only: return self.order() - my_cover else: my_cover = set(my_cover) return [u for u in self if u not in my_cover] @doc_index("Algorithmically hard stuff") @rename_keyword(deprecation=32238, verbosity='verbose') def vertex_cover(self, algorithm="Cliquer", value_only=False, reduction_rules=True, solver=None, verbose=0, *, integrality_tolerance=1e-3): self._scream_if_not_simple(allow_multiple_edges=True) g = self ppset = [] folded_vertices = [] while degree_at_most_two: u = degree_at_most_two.pop() du = g.degree(u) if not du: g.delete_vertex(u) elif du == 1: v = next(g.neighbor_iterator(u)) ppset.append(v) g.delete_vertex(u) for w in g.neighbor_iterator(v): if g.degree(w) <= 3: degree_at_most_two.add(w) g.delete_vertex(v) degree_at_most_two.discard(v) elif du == 2: v,w = g.neighbors(u) if g.has_edge(v, w): ppset.append(v) ppset.append(w) g.delete_vertex(u) neigh = set(g.neighbors(v) + g.neighbors(w)).difference([v, w]) g.delete_vertex(v) g.delete_vertex(w) for z in neigh: if g.degree(z) <= 2: degree_at_most_two.add(z) else: neigh = set(g.neighbors(v) + g.neighbors(w)).difference([u, v, w]) g.delete_vertex(v) g.delete_vertex(w) for z in neigh: g.add_edge(u,z) folded_vertices.append((u, v, w)) if g.degree(u) <= 2: degree_at_most_two.add(u) degree_at_most_two.discard(v) degree_at_most_two.discard(w) False) independent = g.complement().clique_maximum(algorithm=algorithm) if value_only: size_cover_g = g.order() - len(independent) else: cover_g = set(uu for uu in g if uu not in independent) elif algorithm == "MILP": from sage.numerical.mip import MixedIntegerLinearProgram p = MixedIntegerLinearProgram(maximization=False, solver=solver) b = p.new_variable(binary=True) p.set_objective(p.sum(b[v] for v in g)) for u,v in g.edge_iterator(labels=None): p.add_constraint(b[u] + b[v], min=1) p.solve(log=verbose) b = p.get_values(b, convert=bool, tolerance=integrality_tolerance) if value_only: size_cover_g = sum(1 for v in g if b[v]) else: cover_g = set(v for v in g if b[v]) else: raise ValueError('the algorithm must be "Cliquer", "MILP" or "mcqd"') if self.order() < 3: raise ValueError("ear decomposition is defined for graphs of order at least 3") dfs_order = [] seen = set() traversed = set() parent = {next(self.vertex_iterator()): None} value = {} chains = [] def DFS(v): seen.add(v) dfs_order.append(v) for u in self.neighbor_iterator(v): if u not in seen: parent[u] = v DFS(u) def traverse(start, pointer): traversed.add(start) chain = [start] while True: chain.append(pointer) if pointer in traversed: break traversed.add(pointer) pointer = parent[pointer] chains.append(chain) for v in self: if v not in seen: DFS(v) value = {u:i for i,u in enumerate(dfs_order)} for u in dfs_order: for neighbor in self.neighbor_iterator(u): if value[u] < value[neighbor] and u != parent[neighbor]: traverse(u, neighbor) dfs_order = [] return chains @doc_index("Clique-related methods") def cliques_vertex_clique_number(self, algorithm="cliquer", vertices=None, cliques=None): if algorithm == "cliquer": from sage.graphs.cliquer import clique_number if vertices is None: vertices = self value = {} for v in vertices: value[v] = 1 + clique_number(self.subgraph(self.neighbors(v))) self.subgraph(self.neighbors(v)).plot() return value elif algorithm == "networkx": import networkx return networkx.node_clique_number(self.networkx_graph(), vertices, cliques) else: raise NotImplementedError("Only 'networkx' and 'cliquer' are supported.") @doc_index("Clique-related methods") def cliques_containing_vertex(self, vertices=None, cliques=None): import networkx return networkx.cliques_containing_node(self.networkx_graph(), vertices, cliques) @doc_index("Clique-related methods") def clique_complex(self): if self.is_directed() or self.has_loops() or self.has_multiple_edges(): raise ValueError("Self must be an undirected simple graph to have a clique complex.") import sage.topology.simplicial_complex C = sage.topology.simplicial_complex.SimplicialComplex(self.cliques_maximal(), maximality_check=True) C._graph = self return C @doc_index("Clique-related methods") def clique_polynomial(self, t=None): if t is None: R = PolynomialRing(ZZ, 't') t = R.gen() number_of = [0]*(self.order() + 1) for x in IndependentSets(self, complement=True): number_of[len(x)] += 1 return sum(coeff*t**i for i,coeff in enumerate(number_of) if coeff) def cores(self, k=None, with_labels=False): self._scream_if_not_simple() degrees = self.degree(labels=True) verts = sorted(degrees.keys(), key=lambda x: degrees[x]) bin_boundaries = [0] curr_degree = 0 for i,v in enumerate(verts): if degrees[v] > curr_degree: bin_boundaries.extend([i] * (degrees[v] - curr_degree)) curr_degree = degrees[v] vert_pos = {v: pos for pos,v in enumerate(verts)} core = degrees nbrs = {v: set(self.neighbors(v)) for v in self} for v in verts: if k is not None and core[v] >= k: return verts[:vert_pos[v]], verts[vert_pos[v]:] for u in nbrs[v]: if core[u] > core[v]: nbrs[u].remove(v) pos = vert_pos[u] bin_start = bin_boundaries[core[u]] vert_pos[u] = bin_start vert_pos[verts[bin_start]] = pos verts[bin_start],verts[pos] = verts[pos],verts[bin_start] bin_boundaries[core[u]] += 1 core[u] -= 1 if k is not None: return verts, [] if with_labels: return core else: return list(core.values()) @doc_index("Leftovers") def modular_decomposition(self, algorithm='habib', style='tuple'): from sage.graphs.graph_decompositions.modular_decomposition import (modular_decomposition, NodeType, habib_maurer_algorithm, create_prime_node, create_normal_node) self._scream_if_not_simple() if not self.order(): D = None elif self.order() == 1: D = create_prime_node() D.children.append(create_normal_node(self.vertices()[0])) else: if algorithm == 'habib': D = habib_maurer_algorithm(self) elif algorithm == 'tedder': D = modular_decomposition(self) else: raise ValueError("algorithm must be 'habib' or 'tedder'") if style == 'tuple': if D is None: return tuple() def relabel(x): if x.node_type == NodeType.NORMAL: return x.children[0] else: return x.node_type, [relabel(y) for y in x.children] return relabel(D) elif style == 'tree': from sage.combinat.rooted_tree import LabelledRootedTree if D is None: return LabelledRootedTree([]) def to_tree(x): if x.node_type == NodeType.NORMAL: return LabelledRootedTree([], label=x.children[0]) else: return LabelledRootedTree([to_tree(y) for y in x.children], label=x.node_type) return to_tree(D) else: raise ValueError("style must be 'tuple' or 'tree'") @doc_index("Graph properties") def is_polyhedral(self): return (not self.has_loops() and not self.has_multiple_edges() and self.vertex_connectivity(k=3) and self.is_planar()) @doc_index("Graph properties") def is_circumscribable(self, solver="ppl", verbose=0): if not self.is_polyhedral(): raise NotImplementedError('this method only works for polyhedral graphs') from sage.numerical.mip import MixedIntegerLinearProgram from sage.numerical.mip import MIPSolverException M = MixedIntegerLinearProgram(maximization=True, solver=solver) e_var = M.new_variable(nonnegative=True) c = M.new_variable() M.set_min(c[0], -1) M.set_max(c[0], 1) M.set_objective(c[0]) for e in self.edge_iterator(labels=0): fe = frozenset(e) M.set_max(e_var[fe], ZZ(1)/ZZ(2)) M.add_constraint(e_var[fe] - c[0], min=0) M.add_constraint(e_var[fe] + c[0], max=ZZ(1)/ZZ(2)) efaces = self.faces() vfaces = set(frozenset([e[0] for e in face]) for face in efaces) for edges in efaces: M.add_constraint(M.sum(e_var[frozenset(e)] for e in edges) == 1) D = self.to_directed() inequality_constraints = set() for cycle in D.all_simple_cycles(): if len(cycle) > 3: scycle = frozenset(cycle) if scycle not in vfaces: edges = (frozenset((cycle[i], cycle[i+1])) for i in range(len(cycle)-1)) inequality_constraints.add(frozenset(edges)) for ieq in inequality_constraints: M.add_constraint(M.sum(e_var[fe] for fe in ieq) - c[0] >= 1) try: solution = M.solve(log=verbose) except MIPSolverException as msg: if str(msg) == "PPL : There is no feasible solution": return False return solution > 0 @doc_index("Graph properties") def is_inscribable(self, solver="ppl", verbose=0): if not self.is_polyhedral(): raise NotImplementedError('this method only works for polyhedral graphs') return self.planar_dual().is_circumscribable(solver=solver, verbose=verbose) @doc_index("Graph properties") def is_prime(self, algorithm='habib'): from sage.graphs.graph_decompositions.modular_decomposition import NodeType if self.order() <= 1: return True D = self.modular_decomposition(algorithm=algorithm) return D[0] == NodeType.PRIME and len(D[1]) == self.order() def _gomory_hu_tree(self, vertices, algorithm=None): self._scream_if_not_simple() if len(vertices) == 1: g = Graph() g.add_vertices(vertices) return g it = iter(vertices) u,v = next(it),next(it) flow,edges,[U,V] = self.edge_cut(u, v, use_edge_labels=True, vertices=True, algorithm=algorithm) gU,gV = self.subgraph(U, immutable=False), self.subgraph(V, immutable=False) fU = frozenset(U) fV = frozenset(V) from sage.rings.real_mpfr import RR for uu,vv,capacity in edges: capacity = capacity if capacity in RR else 1 if uu in V: uu,vv = vv,uu if not gU.has_edge(uu, fV): gU.add_edge(uu, fV, 0) if not gV.has_edge(vv, fU): gV.add_edge(vv, fU, 0) gU.set_edge_label(uu, fV, gU.edge_label(uu, fV) + capacity) gV.set_edge_label(vv, fU, gV.edge_label(vv, fU) + capacity) gU_tree = gU._gomory_hu_tree(vertices & frozenset(gU), algorithm=algorithm) gV_tree = gV._gomory_hu_tree(vertices & frozenset(gV), algorithm=algorithm) g = gU_tree.union(gV_tree) g.add_edge(u, v, flow) return g @doc_index("Connectivity, orientations, trees") def gomory_hu_tree(self, algorithm=None): if not self.order(): return Graph() if not self.is_connected(): g = Graph() for cc in self.connected_components_subgraphs(): g = g.union(cc._gomory_hu_tree(frozenset(cc.vertex_iterator()), algorithm=algorithm)) else: g = self._gomory_hu_tree(frozenset(self.vertex_iterator()), algorithm=algorithm) if self.get_pos() is not None: g.set_pos(dict(self.get_pos())) return g @doc_index("Leftovers") def two_factor_petersen(self, solver=None, verbose=0, *, integrality_tolerance=1e-3): self._scream_if_not_simple() d = self.eulerian_orientation() g = Graph() g.add_edges(((-1, u), (1, v)) for u, v in d.edge_iterator(labels=None)) from sage.graphs.graph_coloring import edge_coloring classes = edge_coloring(g, solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) classes_b = [] for c in classes: classes_b.append([(u,v) for ((uu,u),(vv,v)) in c]) return classes_b @doc_index("Leftovers") def kirchhoff_symanzik_polynomial(self, name='t'): from sage.matrix.constructor import matrix from sage.rings.integer_ring import ZZ from sage.rings.polynomial.polynomial_ring_constructor import PolynomialRing edges = list(self.edges(sort=False)) cycles = self.cycle_basis(output='edge') edge2int = {e: j for j, e in enumerate(edges)} circuit_mtrx = matrix(ZZ, self.size(), len(cycles)) for i, cycle in enumerate(cycles): for edge in cycle: if edge in edges: circuit_mtrx[edge2int[edge], i] = +1 else: circuit_mtrx[edge2int[(edge[1], edge[0], edge[2])], i] = -1 D = matrix.diagonal(PolynomialRing(ZZ, name, self.size()).gens()) return (circuit_mtrx.transpose() * D * circuit_mtrx).determinant() @doc_index("Leftovers") def magnitude_function(self): from sage.matrix.constructor import matrix from sage.rings.polynomial.polynomial_ring_constructor import PolynomialRing from sage.graphs.distances_all_pairs import distances_all_pairs ring = PolynomialRing(ZZ, 'q') q = ring.gen() N = self.order() if not N: return ring.zero() dist = distances_all_pairs(self) vertices = list(self) Z = matrix(ring, N, N, ring.zero()) for i in range(N): Z[i, i] = ring.one() for i in range(N): for j in range(i): dij = dist[vertices[i]][vertices[j]] if dij in ZZ: Z[i, j] = Z[j, i] = q ** dij else: Z[i, j] = Z[j, i] = ring.zero() return sum(sum(u) for u in ~Z) @doc_index("Leftovers") def ihara_zeta_function_inverse(self): from sage.matrix.constructor import matrix H = self.subgraph(vertices=self.cores(k=2)[1]) E = list(H.edges(sort=False)) m = len(E) T = matrix(ZZ, 2 * m, 2 * m, 0) for i in range(m): for j in range(m): if i != j: if E[i][1] == E[j][0]: T[2 * i, 2 * j] = 1 T[2 * j + 1, 2 * i + 1] = 1 elif E[i][1] == E[j][1]: T[2 * i, 2 * j + 1] = 1 T[2 * j, 2 * i + 1] = 1 elif E[i][0] == E[j][0]: T[2 * i + 1, 2 * j] = 1 T[2 * j + 1, 2 * i] = 1 return T.charpoly('t').reverse() @doc_index("Leftovers") def perfect_matchings(self, labels=False): if not self: yield [] return if self.order() % 2 or any(len(cc) % 2 for cc in self.connected_components()): return def rec(G): if not G: yield [] return if G.order() % 2 == 0: v = next(G.vertex_iterator()) Nv = list(G.neighbor_iterator(v)) G.delete_vertex(v) for u in Nv: Nu = list(G.neighbor_iterator(u)) G.delete_vertex(u) for partial_matching in rec(G): partial_matching.append((u, v)) yield partial_matching G.add_vertex(u) G.add_edges((u, nu) for nu in Nu) G.add_vertex(v) G.add_edges((v, nv) for nv in Nv) G = self.copy(immutable=False) G.allow_loops(False) edges = {} for e in G.edges(labels=labels): f = frozenset(e[:2]) if f in edges: edges[f].append(e) else: edges[f] = [e] G.allow_multiple_edges(False) for m in rec(G): for pm in itertools.product(*[edges[frozenset(e)] for e in m]): yield pm @doc_index("Leftovers") def has_perfect_matching(self, algorithm="Edmonds", solver=None, verbose=0, *, integrality_tolerance=1e-3): if self.order() % 2: return False if algorithm == "Edmonds": return len(self) == 2*self.matching(value_only=True, use_edge_labels=False, algorithm="Edmonds") elif algorithm == "LP_matching": return len(self) == 2*self.matching(value_only=True, use_edge_labels=False, algorithm="LP", solver=solver, verbose=verbose, integrality_tolerance=integrality_tolerance) elif algorithm == "LP": from sage.numerical.mip import MixedIntegerLinearProgram, MIPSolverException p = MixedIntegerLinearProgram(solver=solver) b = p.new_variable(binary=True) for v in self: edges = self.edges_incident(v, labels=False) if not edges: return False p.add_constraint(p.sum(b[frozenset(e)] for e in edges) == 1) try: p.solve(log=verbose) return True except MIPSolverException: return False else: raise ValueError('algorithm must be set to "Edmonds", "LP_matching" or "LP"') @doc_index("Leftovers") def effective_resistance(self, i, j): from sage.matrix.constructor import matrix if i not in self: raise ValueError("vertex ({0}) is not a vertex of the graph".format(repr(i))) elif j not in self: raise ValueError("vertex ({0}) is not a vertex of the graph".format(repr(j))) if i == j : return 0 self._scream_if_not_simple() if not self.is_connected(): raise ValueError('the Graph is not a connected graph') vert = list(self) i1 = vert.index(i) i2 = vert.index(j) n = self.order() L = self.laplacian_matrix(vertices=vert) M = L.pseudoinverse() Id = matrix.identity(n) sigma = matrix(Id[i1] - Id[i2]) diff = sigma * M * sigma.transpose() return diff[0, 0] @doc_index("Leftovers") def effective_resistance_matrix(self, vertices=None, nonedgesonly=True): from sage.matrix.constructor import matrix from sage.rings.rational_field import QQ n = self.order() if not n: raise ValueError('unable to compute effective resistance for an empty Graph object') if vertices is None: vertices = self.vertices() self._scream_if_not_simple() if not self.is_connected(): raise ValueError('the Graph is not a connected graph') L = self.laplacian_matrix(vertices=vertices) M = L.pseudoinverse() d = matrix(M.diagonal()).transpose() onesvec = matrix(QQ, n, 1, lambda i, j: 1) S = d * onesvec.transpose() + onesvec * d.transpose() - 2 * M onesmat = matrix(QQ, n, n, lambda i, j: 1) if nonedgesonly: B = onesmat - self.adjacency_matrix(vertices=vertices) - matrix.identity(n) S = S.elementwise_product(B) return S @doc_index("Leftovers") def least_effective_resistance(self, nonedgesonly=True): n = self.order() if not n: raise ValueError('unable to compute least resistance on empty Graph') self._scream_if_not_simple() if not self.is_connected(): raise ValueError('the Graph is not a connected graph') if nonedgesonly and self.is_clique(): return [] verts = list(self) verttoidx = {u: i for i, u in enumerate(verts)} S = self.effective_resistance_matrix(vertices=verts, nonedgesonly=nonedgesonly) if nonedgesonly: edges = self.complement().edges(labels=False) else: edges = [(verts[i], verts[j]) for i in range(n) for j in range(i + 1, n)] rmin = min(S[(verttoidx[e[0]], verttoidx[e[1]])] for e in edges) return [e for e in edges if S[(verttoidx[e[0]], verttoidx[e[1]])] == rmin] @doc_index("Leftovers") def common_neighbors_matrix(self, vertices=None, nonedgesonly=True): self._scream_if_not_simple() if vertices is None: vertices = self.vertices() A = self.adjacency_matrix(vertices=vertices) M = A**2 for v in range(self.order()): M[v, v] = 0 if nonedgesonly: for w in range(v + 1, self.order()): if A[v, w]: M[v, w] = M[w, v] = 0 return M @doc_index("Leftovers") def most_common_neighbors(self, nonedgesonly=True): self._scream_if_not_simple() if self.num_verts() < 2: raise ValueError('this method is defined for graphs with at least 2 vertices') verts = list(self) M = self.common_neighbors_matrix(vertices=verts, nonedgesonly=nonedgesonly) output = [] coefficients = M.coefficients() if coefficients: maximum = max(coefficients) for v in range(self.num_verts()): for w in range(v + 1, self.num_verts()): if M[v, w] == maximum: output.append((verts[v], verts[w])) return output @doc_index("Leftovers") def arboricity(self, certificate=False): from sage.matroids.constructor import Matroid P = Matroid(self).partition() if certificate: return (len(P), [self.subgraph(edges=forest) for forest in P]) else: return len(P) @doc_index("Graph properties") def is_antipodal(self): G = self.antipodal_graph() vertexSet = set(G) while vertexSet: v = vertexSet.pop() clique = set(G.neighbor_iterator(v, closed=True)) for u in clique: if set(G.neighbor_iterator(u, closed=True)) != clique: return False vertexSet.difference_update(clique) return True @doc_index("Leftovers") def folded_graph(self, check=False): G = self.antipodal_graph() vertices = set(G) newVertices = [] while vertices: v = vertices.pop() clique = frozenset(G.neighbor_iterator(v, closed=True)) if check: for u in clique: if frozenset(G.neighbor_iterator(u, closed=True)) != clique: return False newVertices.append(clique) vertices.difference_update(clique) numCliques = len(newVertices) edges = [] for i, j in itertools.combinations(range(numCliques), 2): if any(self.has_edge(u, v) for u, v in itertools.product(newVertices[i], newVertices[j])): edges.append((i, j)) H = Graph([range(numCliques), edges], format='vertices_and_edges') name = self.name() if self.name() != "" else "Graph" H.name(f"Folded {name}") return H @doc_index("Leftovers") def antipodal_graph(self): H = self.distance_graph(self.diameter()) name = self.name() if self.name() != "" else "Graph" H.name(f"Antipodal graph of {name}") return H @doc_index("Basic methods") def bipartite_double(self, extended=False): G = self.tensor_product(Graph([(0, 1)])) if extended: G.add_edges(((v, 0), (v, 1)) for v in self) prefix = "Extended " if extended else "" G.name("%sBipartite Double of %s"%(prefix, self.name())) return G from sage.graphs.weakly_chordal import is_long_hole_free, is_long_antihole_free, is_weakly_chordal from sage.graphs.asteroidal_triples import is_asteroidal_triple_free from sage.graphs.chrompoly import chromatic_polynomial from sage.graphs.graph_decompositions.rankwidth import rank_decomposition from sage.graphs.graph_decompositions.tree_decomposition import treewidth from sage.graphs.graph_decompositions.vertex_separation import pathwidth from sage.graphs.graph_decompositions.tree_decomposition import treelength from sage.graphs.graph_decompositions.clique_separators import atoms_and_clique_separators from sage.graphs.matchpoly import matching_polynomial from sage.graphs.cliquer import all_max_clique as cliques_maximum from sage.graphs.cliquer import all_cliques from sage.graphs.spanning_tree import random_spanning_tree from sage.graphs.spanning_tree import spanning_trees from sage.graphs.graph_decompositions.graph_products import is_cartesian_product from sage.graphs.distances_all_pairs import is_distance_regular from sage.graphs.base.static_dense_graph import is_strongly_regular from sage.graphs.line_graph import is_line_graph from sage.graphs.tutte_polynomial import tutte_polynomial from sage.graphs.lovasz_theta import lovasz_theta from sage.graphs.partial_cube import is_partial_cube from sage.graphs.orientations import strong_orientations_iterator, random_orientation from sage.graphs.connectivity import bridges, cleave, spqr_tree from sage.graphs.connectivity import is_triconnected from sage.graphs.comparability import is_comparability from sage.graphs.comparability import is_permutation from sage.graphs.convexity_properties import geodetic_closure from sage.graphs.domination import is_dominating from sage.graphs.domination import is_redundant from sage.graphs.domination import private_neighbors from sage.graphs.domination import minimal_dominating_sets from sage.graphs.traversals import (lex_M, maximum_cardinality_search, maximum_cardinality_search_M) from sage.graphs.isoperimetric_inequalities import cheeger_constant, edge_isoperimetric_number, vertex_isoperimetric_number from sage.graphs.graph_coloring import fractional_chromatic_number from sage.graphs.graph_coloring import fractional_chromatic_index _additional_categories = { "is_long_hole_free" : "Graph properties", "is_long_antihole_free" : "Graph properties", "is_weakly_chordal" : "Graph properties", "is_asteroidal_triple_free" : "Graph properties", "chromatic_polynomial" : "Coloring", "rank_decomposition" : "Algorithmically hard stuff", "treewidth" : "Algorithmically hard stuff", "pathwidth" : "Algorithmically hard stuff", "treelength" : "Algorithmically hard stuff", "matching_polynomial" : "Algorithmically hard stuff", "all_max_clique" : "Clique-related methods", "cliques_maximum" : "Clique-related methods", "all_cliques" : "Clique-related methods", "atoms_and_clique_separators" : "Clique-related methods", "random_spanning_tree" : "Connectivity, orientations, trees", "spanning_trees" : "Connectivity, orientations, trees", "is_cartesian_product" : "Graph properties", "is_distance_regular" : "Graph properties", "is_strongly_regular" : "Graph properties", "is_line_graph" : "Graph properties", "is_partial_cube" : "Graph properties", "is_comparability" : "Graph properties", "is_permutation" : "Graph properties", "tutte_polynomial" : "Algorithmically hard stuff", "lovasz_theta" : "Leftovers", "strong_orientations_iterator" : "Connectivity, orientations, trees", "random_orientation" : "Connectivity, orientations, trees", "bridges" : "Connectivity, orientations, trees", "cleave" : "Connectivity, orientations, trees", "spqr_tree" : "Connectivity, orientations, trees", "is_triconnected" : "Connectivity, orientations, trees", "is_dominating" : "Domination", "is_redundant" : "Domination", "private_neighbors" : "Domination", "minimal_dominating_sets" : "Domination", "lex_M" : "Traversals", "maximum_cardinality_search" : "Traversals", "maximum_cardinality_search_M" : "Traversals", "cheeger_constant" : "Expansion properties", "edge_isoperimetric_number" : "Expansion properties", "vertex_isoperimetric_number" : "Expansion properties", "fractional_chromatic_number" : "Coloring", "fractional_chromatic_index" : "Coloring", "geodetic_closure" : "Leftovers" } __doc__ = __doc__.replace("{INDEX_OF_METHODS}",gen_thematic_rest_table_index(Graph,_additional_categories))

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integration_tests/test_update_ranges.py

FlexiGroBots-H2020/datacube-ows

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2017-11-02T04:22:30.000Z

2018-05-01T14:16:23.000Z

integration_tests/test_update_ranges.py

FlexiGroBots-H2020/datacube-ows

8e3e1343582c00ae46b498247ac98d8e98bd000f

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2018-11-05T01:07:09.000Z

integration_tests/test_update_ranges.py

FlexiGroBots-H2020/datacube-ows

8e3e1343582c00ae46b498247ac98d8e98bd000f

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2017-10-09T00:09:44.000Z

2018-07-27T00:41:19.000Z

# This file is part of datacube-ows, part of the Open Data Cube project. # See https://opendatacube.org for more information. # # Copyright (c) 2017-2021 OWS Contributors # SPDX-License-Identifier: Apache-2.0 """Test update ranges on DB using Click testing https://click.palletsprojects.com/en/7.x/testing/ """ from datacube_ows.update_ranges_impl import main def test_updates_ranges_schema(runner, role_name): result = runner.invoke(main, ["--schema", "--role", role_name]) assert "Cannot find SQL resource" not in result.output assert result.exit_code == 0 def test_update_ranges_views(runner): result = runner.invoke(main, ["--views"]) assert "Cannot find SQL resource" not in result.output assert result.exit_code == 0 def test_update_version(runner): result = runner.invoke(main, ["--version"]) assert "Open Data Cube Open Web Services (datacube-ows) version" in result.output assert result.exit_code == 0 def test_update_ranges_product(runner, product_name): result = runner.invoke(main, [product_name]) assert "ERROR" not in result.output assert result.exit_code == 0 def test_update_ranges_bad_product(runner, product_name): result = runner.invoke(main, ["not_a_real_product_name"]) assert "not_a_real_product_name" in result.output assert "Unrecognised product name" in result.output assert result.exit_code == 1 def test_update_ranges(runner): result = runner.invoke(main) assert "ERROR" not in result.output assert result.exit_code == 0 def test_update_ranges_misuse_cases(runner, role_name, product_name): result = runner.invoke(main, ["--schema"]) assert "Sorry" in result.output assert result.exit_code == 1 result = runner.invoke(main, ["--role", role_name]) assert "Sorry" in result.output assert result.exit_code == 1 result = runner.invoke(main, ["--views", product_name]) assert "Sorry" in result.output assert result.exit_code == 1 result = runner.invoke(main, ["--schema", product_name]) assert "Sorry" in result.output assert result.exit_code == 1

32.476923

85

0.721933

from datacube_ows.update_ranges_impl import main def test_updates_ranges_schema(runner, role_name): result = runner.invoke(main, ["--schema", "--role", role_name]) assert "Cannot find SQL resource" not in result.output assert result.exit_code == 0 def test_update_ranges_views(runner): result = runner.invoke(main, ["--views"]) assert "Cannot find SQL resource" not in result.output assert result.exit_code == 0 def test_update_version(runner): result = runner.invoke(main, ["--version"]) assert "Open Data Cube Open Web Services (datacube-ows) version" in result.output assert result.exit_code == 0 def test_update_ranges_product(runner, product_name): result = runner.invoke(main, [product_name]) assert "ERROR" not in result.output assert result.exit_code == 0 def test_update_ranges_bad_product(runner, product_name): result = runner.invoke(main, ["not_a_real_product_name"]) assert "not_a_real_product_name" in result.output assert "Unrecognised product name" in result.output assert result.exit_code == 1 def test_update_ranges(runner): result = runner.invoke(main) assert "ERROR" not in result.output assert result.exit_code == 0 def test_update_ranges_misuse_cases(runner, role_name, product_name): result = runner.invoke(main, ["--schema"]) assert "Sorry" in result.output assert result.exit_code == 1 result = runner.invoke(main, ["--role", role_name]) assert "Sorry" in result.output assert result.exit_code == 1 result = runner.invoke(main, ["--views", product_name]) assert "Sorry" in result.output assert result.exit_code == 1 result = runner.invoke(main, ["--schema", product_name]) assert "Sorry" in result.output assert result.exit_code == 1

true

790804e9fda8749313f013440a5d152a18eb296b

397

py

Python

tests/test_calvestbr.py

IsaacHiguchi/calvestbr

ebf702e9e67299c822a6cc21cad60b247446fcfa

[ "MIT" ]

null

tests/test_calvestbr.py

IsaacHiguchi/calvestbr

ebf702e9e67299c822a6cc21cad60b247446fcfa

[ "MIT" ]

null

tests/test_calvestbr.py

IsaacHiguchi/calvestbr

ebf702e9e67299c822a6cc21cad60b247446fcfa

[ "MIT" ]

null

#!/usr/bin/env python """Tests for `calvestbr` package.""" import unittest from calvestbr import calvestbr class TestCalvestbr(unittest.TestCase): """Tests for `calvestbr` package.""" def setUp(self): """Set up test fixtures, if any.""" def tearDown(self): """Tear down test fixtures, if any.""" def test_000_something(self): """Test something."""

18.045455

46

0.632242

import unittest from calvestbr import calvestbr class TestCalvestbr(unittest.TestCase): def setUp(self): def tearDown(self): def test_000_something(self):

true

790804f388e72af2f2b67453edb3a003c8e8aa74

576

py

Python

test/rules/test_fires_child.py

rileyhazard/SmartVA-Analyze-1

0573eeff27d03f54e7506db4f1631c0cd9f54bbb

[ "MIT" ]

4

2019-01-23T12:57:47.000Z

2020-04-18T17:13:08.000Z

test/rules/test_fires_child.py

rileyhazard/SmartVA-Analyze-1

0573eeff27d03f54e7506db4f1631c0cd9f54bbb

[ "MIT" ]

4

2019-01-09T22:10:07.000Z

2022-02-16T04:57:06.000Z

test/rules/test_fires_child.py

rileyhazard/SmartVA-Analyze-1

0573eeff27d03f54e7506db4f1631c0cd9f54bbb

[ "MIT" ]

11

2018-12-11T22:01:13.000Z

2022-01-07T11:38:02.000Z

from smartva.rules import fires_child as fires from smartva.data.constants import * VA = Child def test_pass(): row = { VA.BURN: YES, VA.INJURY_DAYS: 0, } assert fires.logic_rule(row) is True def test_fail_fires(): row = { VA.BURN: NO, VA.INJURY_DAYS: 0, } assert fires.logic_rule(row) is False def test_fail_days(): row = { VA.BURN: YES, VA.INJURY_DAYS: 31, } assert fires.logic_rule(row) is False def test_fail_no_data(): row = {} assert fires.logic_rule(row) is False

15.157895

46

0.604167

from smartva.rules import fires_child as fires from smartva.data.constants import * VA = Child def test_pass(): row = { VA.BURN: YES, VA.INJURY_DAYS: 0, } assert fires.logic_rule(row) is True def test_fail_fires(): row = { VA.BURN: NO, VA.INJURY_DAYS: 0, } assert fires.logic_rule(row) is False def test_fail_days(): row = { VA.BURN: YES, VA.INJURY_DAYS: 31, } assert fires.logic_rule(row) is False def test_fail_no_data(): row = {} assert fires.logic_rule(row) is False

true

79080530baf43a4ccb2acf223fe275c811cda025

8,136

py

Python

myvenv/lib/python3.5/site-packages/psycopg2/pool.py

tuvapp/tuvappcom

5ca2be19f4b0c86a1d4a9553711a4da9d3f32841

[ "MIT" ]

1

2019-01-10T16:43:38.000Z

myvenv/lib/python3.5/site-packages/psycopg2/pool.py

tuvapp/tuvappcom

5ca2be19f4b0c86a1d4a9553711a4da9d3f32841

[ "MIT" ]

7

2020-06-05T18:33:09.000Z

2021-09-20T23:07:52.000Z

myvenv/lib/python3.5/site-packages/psycopg2/pool.py

tuvapp/tuvappcom

5ca2be19f4b0c86a1d4a9553711a4da9d3f32841

[ "MIT" ]

null

"""Connection pooling for psycopg2 This module implements thread-safe (and not) connection pools. """ # psycopg/pool.py - pooling code for psycopg # # Copyright (C) 2003-2010 Federico Di Gregorio <fog@debian.org> # # psycopg2 is free software: you can redistribute it and/or modify it # under the terms of the GNU Lesser General Public License as published # by the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # In addition, as a special exception, the copyright holders give # permission to link this program with the OpenSSL library (or with # modified versions of OpenSSL that use the same license as OpenSSL), # and distribute linked combinations including the two. # # You must obey the GNU Lesser General Public License in all respects for # all of the code used other than OpenSSL. # # psycopg2 is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or # FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public # License for more details. import psycopg2 import psycopg2.extensions as _ext class PoolError(psycopg2.Error): pass class AbstractConnectionPool(object): """Generic key-based pooling code.""" def __init__(self, minconn, maxconn, *args, **kwargs): """Initialize the connection pool. New 'minconn' connections are created immediately calling 'connfunc' with given parameters. The connection pool will support a maximum of about 'maxconn' connections. """ self.minconn = int(minconn) self.maxconn = int(maxconn) self.closed = False self._args = args self._kwargs = kwargs self._pool = [] self._used = {} self._rused = {} # id(conn) -> key map self._keys = 0 for i in range(self.minconn): self._connect() def _connect(self, key=None): """Create a new connection and assign it to 'key' if not None.""" conn = psycopg2.connect(*self._args, **self._kwargs) if key is not None: self._used[key] = conn self._rused[id(conn)] = key else: self._pool.append(conn) return conn def _getkey(self): """Return a new unique key.""" self._keys += 1 return self._keys def _getconn(self, key=None): """Get a free connection and assign it to 'key' if not None.""" if self.closed: raise PoolError("connection pool is closed") if key is None: key = self._getkey() if key in self._used: return self._used[key] if self._pool: self._used[key] = conn = self._pool.pop() self._rused[id(conn)] = key return conn else: if len(self._used) == self.maxconn: raise PoolError("connection pool exhausted") return self._connect(key) def _putconn(self, conn, key=None, close=False): """Put away a connection.""" if self.closed: raise PoolError("connection pool is closed") if key is None: key = self._rused.get(id(conn)) if not key: raise PoolError("trying to put unkeyed connection") if len(self._pool) < self.minconn and not close: # Return the connection into a consistent state before putting # it back into the pool if not conn.closed: status = conn.get_transaction_status() if status == _ext.TRANSACTION_STATUS_UNKNOWN: # server connection lost conn.close() elif status != _ext.TRANSACTION_STATUS_IDLE: # connection in error or in transaction conn.rollback() self._pool.append(conn) else: # regular idle connection self._pool.append(conn) # If the connection is closed, we just discard it. else: conn.close() # here we check for the presence of key because it can happen that a # thread tries to put back a connection after a call to close if not self.closed or key in self._used: del self._used[key] del self._rused[id(conn)] def _closeall(self): """Close all connections. Note that this can lead to some code fail badly when trying to use an already closed connection. If you call .closeall() make sure your code can deal with it. """ if self.closed: raise PoolError("connection pool is closed") for conn in self._pool + list(self._used.values()): try: conn.close() except: pass self.closed = True class SimpleConnectionPool(AbstractConnectionPool): """A connection pool that can't be shared across different threads.""" getconn = AbstractConnectionPool._getconn putconn = AbstractConnectionPool._putconn closeall = AbstractConnectionPool._closeall class ThreadedConnectionPool(AbstractConnectionPool): """A connection pool that works with the threading module.""" def __init__(self, minconn, maxconn, *args, **kwargs): """Initialize the threading lock.""" import threading AbstractConnectionPool.__init__( self, minconn, maxconn, *args, **kwargs) self._lock = threading.Lock() def getconn(self, key=None): """Get a free connection and assign it to 'key' if not None.""" self._lock.acquire() try: return self._getconn(key) finally: self._lock.release() def putconn(self, conn=None, key=None, close=False): """Put away an unused connection.""" self._lock.acquire() try: self._putconn(conn, key, close) finally: self._lock.release() def closeall(self): """Close all connections (even the one currently in use.)""" self._lock.acquire() try: self._closeall() finally: self._lock.release() class PersistentConnectionPool(AbstractConnectionPool): """A pool that assigns persistent connections to different threads. Note that this connection pool generates by itself the required keys using the current thread id. This means that until a thread puts away a connection it will always get the same connection object by successive `!getconn()` calls. This also means that a thread can't use more than one single connection from the pool. """ def __init__(self, minconn, maxconn, *args, **kwargs): """Initialize the threading lock.""" import warnings warnings.warn("deprecated: use ZPsycopgDA.pool implementation", DeprecationWarning) import threading AbstractConnectionPool.__init__( self, minconn, maxconn, *args, **kwargs) self._lock = threading.Lock() # we we'll need the thread module, to determine thread ids, so we # import it here and copy it in an instance variable import _thread as _thread # work around for 2to3 bug - see ticket #348 self.__thread = _thread def getconn(self): """Generate thread id and return a connection.""" key = self.__thread.get_ident() self._lock.acquire() try: return self._getconn(key) finally: self._lock.release() def putconn(self, conn=None, close=False): """Put away an unused connection.""" key = self.__thread.get_ident() self._lock.acquire() try: if not conn: conn = self._used[key] self._putconn(conn, key, close) finally: self._lock.release() def closeall(self): """Close all connections (even the one currently in use.)""" self._lock.acquire() try: self._closeall() finally: self._lock.release()

34.474576

78

0.615044

import psycopg2 import psycopg2.extensions as _ext class PoolError(psycopg2.Error): pass class AbstractConnectionPool(object): def __init__(self, minconn, maxconn, *args, **kwargs): self.minconn = int(minconn) self.maxconn = int(maxconn) self.closed = False self._args = args self._kwargs = kwargs self._pool = [] self._used = {} self._rused = {} self._keys = 0 for i in range(self.minconn): self._connect() def _connect(self, key=None): conn = psycopg2.connect(*self._args, **self._kwargs) if key is not None: self._used[key] = conn self._rused[id(conn)] = key else: self._pool.append(conn) return conn def _getkey(self): self._keys += 1 return self._keys def _getconn(self, key=None): if self.closed: raise PoolError("connection pool is closed") if key is None: key = self._getkey() if key in self._used: return self._used[key] if self._pool: self._used[key] = conn = self._pool.pop() self._rused[id(conn)] = key return conn else: if len(self._used) == self.maxconn: raise PoolError("connection pool exhausted") return self._connect(key) def _putconn(self, conn, key=None, close=False): if self.closed: raise PoolError("connection pool is closed") if key is None: key = self._rused.get(id(conn)) if not key: raise PoolError("trying to put unkeyed connection") if len(self._pool) < self.minconn and not close: if not conn.closed: status = conn.get_transaction_status() if status == _ext.TRANSACTION_STATUS_UNKNOWN: conn.close() elif status != _ext.TRANSACTION_STATUS_IDLE: conn.rollback() self._pool.append(conn) else: self._pool.append(conn) else: conn.close() if not self.closed or key in self._used: del self._used[key] del self._rused[id(conn)] def _closeall(self): if self.closed: raise PoolError("connection pool is closed") for conn in self._pool + list(self._used.values()): try: conn.close() except: pass self.closed = True class SimpleConnectionPool(AbstractConnectionPool): getconn = AbstractConnectionPool._getconn putconn = AbstractConnectionPool._putconn closeall = AbstractConnectionPool._closeall class ThreadedConnectionPool(AbstractConnectionPool): def __init__(self, minconn, maxconn, *args, **kwargs): import threading AbstractConnectionPool.__init__( self, minconn, maxconn, *args, **kwargs) self._lock = threading.Lock() def getconn(self, key=None): self._lock.acquire() try: return self._getconn(key) finally: self._lock.release() def putconn(self, conn=None, key=None, close=False): self._lock.acquire() try: self._putconn(conn, key, close) finally: self._lock.release() def closeall(self): self._lock.acquire() try: self._closeall() finally: self._lock.release() class PersistentConnectionPool(AbstractConnectionPool): def __init__(self, minconn, maxconn, *args, **kwargs): import warnings warnings.warn("deprecated: use ZPsycopgDA.pool implementation", DeprecationWarning) import threading AbstractConnectionPool.__init__( self, minconn, maxconn, *args, **kwargs) self._lock = threading.Lock() # import it here and copy it in an instance variable import _thread as _thread # work around for 2to3 bug - see ticket #348 self.__thread = _thread def getconn(self): key = self.__thread.get_ident() self._lock.acquire() try: return self._getconn(key) finally: self._lock.release() def putconn(self, conn=None, close=False): key = self.__thread.get_ident() self._lock.acquire() try: if not conn: conn = self._used[key] self._putconn(conn, key, close) finally: self._lock.release() def closeall(self): self._lock.acquire() try: self._closeall() finally: self._lock.release()

true

79080633244efcc19454c598305cafbf94d51929

35,911

py

Python

vertica_python/vertica/connection.py

uber/vertica-python

bd28d2dc473a017daa92933f7864bab7346f8b14

[ "Apache-2.0" ]

183

2015-01-20T14:57:22.000Z

2018-08-09T21:13:19.000Z

vertica_python/vertica/connection.py

uber/vertica-python

bd28d2dc473a017daa92933f7864bab7346f8b14

[ "Apache-2.0" ]

139

2015-01-09T18:37:53.000Z

2018-08-13T07:09:26.000Z

vertica_python/vertica/connection.py

uber/vertica-python

bd28d2dc473a017daa92933f7864bab7346f8b14

[ "Apache-2.0" ]

110

2015-03-02T15:46:11.000Z

2018-07-27T15:50:29.000Z

# Copyright (c) 2018-2022 Micro Focus or one of its affiliates. # Copyright (c) 2018 Uber Technologies, 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. # Copyright (c) 2013-2017 Uber Technologies, Inc. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. from __future__ import print_function, division, absolute_import import base64 import logging import socket import ssl import getpass import uuid from struct import unpack from collections import deque, namedtuple import random # noinspection PyCompatibility,PyUnresolvedReferences from six import raise_from, string_types, integer_types, PY2 if PY2: from urlparse import urlparse, parse_qs else: from urllib.parse import urlparse, parse_qs from typing import TYPE_CHECKING if TYPE_CHECKING: from typing import Any, Dict, Literal, Optional, Type, Union from typing_extensions import Self import vertica_python from .. import errors from ..vertica import messages from ..vertica.cursor import Cursor from ..vertica.messages.message import BackendMessage, FrontendMessage from ..vertica.messages.frontend_messages import CancelRequest from ..vertica.log import VerticaLogging DEFAULT_HOST = 'localhost' DEFAULT_PORT = 5433 DEFAULT_PASSWORD = '' DEFAULT_AUTOCOMMIT = False DEFAULT_BACKUP_SERVER_NODE = [] DEFAULT_KRB_SERVICE_NAME = 'vertica' DEFAULT_LOG_LEVEL = logging.WARNING DEFAULT_LOG_PATH = 'vertica_python.log' try: DEFAULT_USER = getpass.getuser() except Exception as e: DEFAULT_USER = None print("WARN: Cannot get the login user name: {}".format(str(e))) def connect(**kwargs): # type: (Any) -> Connection """Opens a new connection to a Vertica database.""" return Connection(kwargs) def parse_dsn(dsn): """Parse connection string into a dictionary of keywords and values. Connection string format: vertica://<user>:<password>@<host>:<port>/<database>?k1=v1&k2=v2&... """ url = urlparse(dsn) if url.scheme != 'vertica': raise ValueError("Only vertica:// scheme is supported.") # Ignore blank/invalid values result = {k: v for k, v in ( ('host', url.hostname), ('port', url.port), ('user', url.username), ('password', url.password), ('database', url.path[1:])) if v } for key, values in parse_qs(url.query, keep_blank_values=True).items(): # Try to get the last non-blank value in the list of values for each key for i in reversed(range(len(values))): value = values[i] if value != '': break if value == '' and key != 'log_path': # blank values are to be ignored continue elif key == 'backup_server_node': continue elif key in ('connection_load_balance', 'use_prepared_statements', 'disable_copy_local', 'ssl', 'autocommit'): lower = value.lower() if lower in ('true', 'on', '1'): result[key] = True elif lower in ('false', 'off', '0'): result[key] = False elif key == 'connection_timeout': result[key] = float(value) elif key == 'log_level' and value.isdigit(): result[key] = int(value) else: result[key] = value return result _AddressEntry = namedtuple('_AddressEntry', ['host', 'resolved', 'data']) class _AddressList(object): def __init__(self, host, port, backup_nodes, logger): """Creates a new deque with the primary host first, followed by any backup hosts""" self._logger = logger # Items in address_deque are _AddressEntry values. # host is the original hostname/ip, used by SSL option check_hostname # - when resolved is False, data is port # - when resolved is True, data is the 5-tuple from socket.getaddrinfo # This allows for lazy resolution. Seek peek() for more. self.address_deque = deque() # load primary host into address_deque self._append(host, port) # load backup nodes into address_deque if not isinstance(backup_nodes, list): err_msg = 'Connection option "backup_server_node" must be a list' self._logger.error(err_msg) raise TypeError(err_msg) # Each item in backup_nodes should be either # a host name or IP address string (using default port) or # a (host, port) tuple for node in backup_nodes: if isinstance(node, string_types): self._append(node, DEFAULT_PORT) elif isinstance(node, tuple) and len(node) == 2: self._append(node[0], node[1]) else: err_msg = ('Each item of connection option "backup_server_node"' ' must be a host string or a (host, port) tuple') self._logger.error(err_msg) raise TypeError(err_msg) self._logger.debug('Address list: {0}'.format(list(self.address_deque))) def _append(self, host, port): if not isinstance(host, string_types): err_msg = 'Host must be a string: invalid value: {0}'.format(host) self._logger.error(err_msg) raise TypeError(err_msg) if not isinstance(port, (string_types, integer_types)): err_msg = 'Port must be an integer or a string: invalid value: {0}'.format(port) self._logger.error(err_msg) raise TypeError(err_msg) elif isinstance(port, string_types): try: port = int(port) except ValueError as e: err_msg = 'Port "{0}" is not a valid string: {1}'.format(port, e) self._logger.error(err_msg) raise ValueError(err_msg) if port < 0 or port > 65535: err_msg = 'Invalid port number: {0}'.format(port) self._logger.error(err_msg) raise ValueError(err_msg) self.address_deque.append(_AddressEntry(host=host, resolved=False, data=port)) def push(self, host, port): self.address_deque.appendleft(_AddressEntry(host=host, resolved=False, data=port)) def pop(self): self.address_deque.popleft() def peek(self): # do lazy DNS resolution, returning the leftmost socket.getaddrinfo result if len(self.address_deque) == 0: return None while len(self.address_deque) > 0: self._logger.debug('Peek at address list: {0}'.format(list(self.address_deque))) entry = self.address_deque[0] if entry.resolved: # return a resolved sockaddrinfo return entry.data else: # DNS resolve a single host name to multiple IP addresses self.pop() # keep host and port info for adding address entry to deque once it has been resolved host, port = entry.host, entry.data try: resolved_hosts = socket.getaddrinfo(host, port, 0, socket.SOCK_STREAM) except Exception as e: self._logger.warning('Error resolving host "{0}" on port {1}: {2}'.format(host, port, e)) continue # add resolved addrinfo (AF_INET and AF_INET6 only) to deque random.shuffle(resolved_hosts) for addrinfo in resolved_hosts: if addrinfo[0] in (socket.AF_INET, socket.AF_INET6): self.address_deque.appendleft(_AddressEntry( host=host, resolved=True, data=addrinfo)) return None def peek_host(self): # returning the leftmost host result self._logger.debug('Peek host at address list: {0}'.format(list(self.address_deque))) if len(self.address_deque) == 0: return None return self.address_deque[0].host def _generate_session_label(): return '{type}-{version}-{id}'.format( type='vertica-python', version=vertica_python.__version__, id=uuid.uuid1() ) class Connection(object): def __init__(self, options=None): # type: (Optional[Dict[str, Any]]) -> None self.parameters = {} self.session_id = None self.backend_pid = None self.backend_key = None self.transaction_status = None self.socket = None self.socket_as_file = None options = options or {} self.options = parse_dsn(options['dsn']) if 'dsn' in options else {} self.options.update({key: value for key, value in options.items() \ if key == 'log_path' or (key != 'dsn' and value is not None)}) # Set up connection logger logger_name = 'vertica_{0}_{1}'.format(id(self), str(uuid.uuid4())) # must be a unique value self._logger = logging.getLogger(logger_name) if 'log_level' not in self.options and 'log_path' not in self.options: # logger is disabled by default self._logger.disabled = True else: self.options.setdefault('log_level', DEFAULT_LOG_LEVEL) self.options.setdefault('log_path', DEFAULT_LOG_PATH) VerticaLogging.setup_logging(logger_name, self.options['log_path'], self.options['log_level'], id(self)) self.options.setdefault('host', DEFAULT_HOST) self.options.setdefault('port', DEFAULT_PORT) if 'user' not in self.options: if DEFAULT_USER: self.options['user'] = DEFAULT_USER else: msg = 'Connection option "user" is required' self._logger.error(msg) raise KeyError(msg) self.options.setdefault('database', self.options['user']) self.options.setdefault('password', DEFAULT_PASSWORD) self.options.setdefault('autocommit', DEFAULT_AUTOCOMMIT) self.options.setdefault('session_label', _generate_session_label()) self.options.setdefault('backup_server_node', DEFAULT_BACKUP_SERVER_NODE) self.options.setdefault('kerberos_service_name', DEFAULT_KRB_SERVICE_NAME) # Kerberos authentication hostname defaults to the host value here so # the correct value cannot be overwritten by load balancing or failover self.options.setdefault('kerberos_host_name', self.options['host']) self.address_list = _AddressList(self.options['host'], self.options['port'], self.options['backup_server_node'], self._logger) # we only support one cursor per connection self.options.setdefault('unicode_error', None) self._cursor = Cursor(self, self._logger, cursor_type=None, unicode_error=self.options['unicode_error']) # knob for using server-side prepared statements self.options.setdefault('use_prepared_statements', False) self._logger.debug('Connection prepared statements is {}'.format( 'enabled' if self.options['use_prepared_statements'] else 'disabled')) # knob for disabling COPY LOCAL operations self.options.setdefault('disable_copy_local', False) self._logger.debug('COPY LOCAL operation is {}'.format( 'disabled' if self.options['disable_copy_local'] else 'enabled')) self._logger.info('Connecting as user "{}" to database "{}" on host "{}" with port {}'.format( self.options['user'], self.options['database'], self.options['host'], self.options['port'])) self.startup_connection() # Initially, for a new session, autocommit is off if self.options['autocommit']: self.autocommit = True self._logger.info('Connection is ready') ############################################# # supporting `with` statements ############################################# def __enter__(self): # type: () -> Self return self def __exit__(self, type_, value, traceback): self.close() ############################################# # dbapi methods ############################################# def close(self): self._logger.info('Close the connection') try: self.write(messages.Terminate()) finally: self.close_socket() def commit(self): if self.closed(): raise errors.ConnectionError('Connection is closed') cur = self.cursor() cur.execute('COMMIT;') def rollback(self): if self.closed(): raise errors.ConnectionError('Connection is closed') cur = self.cursor() cur.execute('ROLLBACK;') def cursor(self, cursor_type=None): # type: (Self, Optional[Union[Literal['list', 'dict'], Type[list[Any]], Type[dict[Any, Any]]]]) -> Cursor if self.closed(): raise errors.ConnectionError('Connection is closed') if self._cursor.closed(): self._cursor._closed = False # let user change type if they want? self._cursor.cursor_type = cursor_type return self._cursor ############################################# # non-dbapi methods ############################################# @property def autocommit(self): """Read the connection's AUTOCOMMIT setting from cache""" return self.parameters.get('auto_commit', 'off') == 'on' @autocommit.setter def autocommit(self, value): """Change the connection's AUTOCOMMIT setting""" if self.autocommit is value: return val = 'on' if value else 'off' cur = self.cursor() cur.execute('SET SESSION AUTOCOMMIT TO {}'.format(val), use_prepared_statements=False) cur.fetchall() # check for errors and update the cache def cancel(self): """Cancel the current database operation. This can be called from a different thread than the one currently executing a database operation. """ if self.closed(): raise errors.ConnectionError('Connection is closed') self._logger.info('Canceling the current database operation') # Must create a new socket connection to the server temp_socket = self.establish_socket_connection(self.address_list) self.write(CancelRequest(self.backend_pid, self.backend_key), temp_socket) temp_socket.close() self._logger.info('Cancel request issued') def opened(self): return (self.socket is not None and self.backend_pid is not None and self.transaction_status is not None) def closed(self): return not self.opened() def __str__(self): safe_options = {key: value for key, value in self.options.items() if key != 'password'} s1 = "<Vertica.Connection:{0} parameters={1} backend_pid={2}, ".format( id(self), self.parameters, self.backend_pid) s2 = "backend_key={0}, transaction_status={1}, socket={2}, options={3}>".format( self.backend_key, self.transaction_status, self.socket, safe_options) return ''.join([s1, s2]) ############################################# # internal ############################################# def reset_values(self): self.parameters = {} self.session_id = None self.backend_pid = None self.backend_key = None self.transaction_status = None self.socket = None self.socket_as_file = None self.address_list = _AddressList(self.options['host'], self.options['port'], self.options['backup_server_node'], self._logger) def _socket(self): if self.socket: return self.socket # the initial establishment of the client connection raw_socket = self.establish_socket_connection(self.address_list) # enable load balancing load_balance_options = self.options.get('connection_load_balance') self._logger.debug('Connection load balance option is {0}'.format( 'enabled' if load_balance_options else 'disabled')) if load_balance_options: raw_socket = self.balance_load(raw_socket) # enable SSL ssl_options = self.options.get('ssl') self._logger.debug('SSL option is {0}'.format('enabled' if ssl_options else 'disabled')) if ssl_options: raw_socket = self.enable_ssl(raw_socket, ssl_options) self.socket = raw_socket return self.socket def _socket_as_file(self): if self.socket_as_file is None: self.socket_as_file = self._socket().makefile('rb') return self.socket_as_file def create_socket(self, family): """Create a TCP socket object""" raw_socket = socket.socket(family, socket.SOCK_STREAM) raw_socket.setsockopt(socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1) connection_timeout = self.options.get('connection_timeout') if connection_timeout is not None: self._logger.debug('Set socket connection timeout: {0}'.format(connection_timeout)) raw_socket.settimeout(connection_timeout) return raw_socket def balance_load(self, raw_socket): # Send load balance request and read server response self._logger.debug('=> %s', messages.LoadBalanceRequest()) raw_socket.sendall(messages.LoadBalanceRequest().get_message()) response = raw_socket.recv(1) if response == b'Y': size = unpack('!I', raw_socket.recv(4))[0] if size < 4: err_msg = "Bad message size: {0}".format(size) self._logger.error(err_msg) raise errors.MessageError(err_msg) res = BackendMessage.from_type(type_=response, data=raw_socket.recv(size - 4)) self._logger.debug('<= %s', res) host = res.get_host() port = res.get_port() self._logger.info('Load balancing to host "{0}" on port {1}'.format(host, port)) peer = raw_socket.getpeername() socket_host, socket_port = peer[0], peer[1] if host == socket_host and port == socket_port: self._logger.info('Already connecting to host "{0}" on port {1}. Ignore load balancing.'.format(host, port)) return raw_socket # Push the new host onto the address list before connecting again. Note that this # will leave the originally-specified host as the first failover possibility. self.address_list.push(host, port) raw_socket.close() raw_socket = self.establish_socket_connection(self.address_list) else: self._logger.debug('<= LoadBalanceResponse: %s', response) self._logger.warning("Load balancing requested but not supported by server") return raw_socket def enable_ssl(self, raw_socket, ssl_options): # Send SSL request and read server response self._logger.debug('=> %s', messages.SslRequest()) raw_socket.sendall(messages.SslRequest().get_message()) response = raw_socket.recv(1) self._logger.debug('<= SslResponse: %s', response) if response == b'S': self._logger.info('Enabling SSL') try: if isinstance(ssl_options, ssl.SSLContext): server_host = self.address_list.peek_host() if server_host is None: # This should not happen msg = 'Cannot get the connected server host while enabling SSL' self._logger.error(msg) raise errors.ConnectionError(msg) raw_socket = ssl_options.wrap_socket(raw_socket, server_hostname=server_host) else: raw_socket = ssl.wrap_socket(raw_socket) except ssl.CertificateError as e: raise_from(errors.ConnectionError(str(e)), e) except ssl.SSLError as e: raise_from(errors.ConnectionError(str(e)), e) else: err_msg = "SSL requested but not supported by server" self._logger.error(err_msg) raise errors.SSLNotSupported(err_msg) return raw_socket def establish_socket_connection(self, address_list): """Given a list of database node addresses, establish the socket connection to the database server. Return a connected socket object. """ addrinfo = address_list.peek() raw_socket = None last_exception = None # Failover: loop to try all addresses while addrinfo: (family, socktype, proto, canonname, sockaddr) = addrinfo last_exception = None # _AddressList filters all addrs to AF_INET and AF_INET6, which both # have host and port as values 0, 1 in the sockaddr tuple. host = sockaddr[0] port = sockaddr[1] self._logger.info('Establishing connection to host "{0}" on port {1}'.format(host, port)) try: raw_socket = self.create_socket(family) raw_socket.connect(sockaddr) break except Exception as e: self._logger.info('Failed to connect to host "{0}" on port {1}: {2}'.format(host, port, e)) last_exception = e address_list.pop() addrinfo = address_list.peek() raw_socket.close() # all of the addresses failed if raw_socket is None or last_exception: err_msg = 'Failed to establish a connection to the primary server or any backup address.' self._logger.error(err_msg) raise errors.ConnectionError(err_msg) return raw_socket def ssl(self): return self.socket is not None and isinstance(self.socket, ssl.SSLSocket) def write(self, message, vsocket=None): if not isinstance(message, FrontendMessage): raise TypeError("invalid message: ({0})".format(message)) if vsocket is None: vsocket = self._socket() self._logger.debug('=> %s', message) try: for data in message.fetch_message(): size = 8192 # Max msg size, consistent with how the server works pos = 0 while pos < len(data): sent = vsocket.send(data[pos : pos + size]) if sent == 0: raise errors.ConnectionError("Couldn't send message: Socket connection broken") pos += sent except Exception as e: self.close_socket() self._logger.error(str(e)) if isinstance(e, IOError): raise_from(errors.ConnectionError(str(e)), e) else: raise def close_socket(self): try: if self.socket is not None: self._socket().close() if self.socket_as_file is not None: self._socket_as_file().close() finally: self.reset_values() def reset_connection(self): self.close() self.startup_connection() def is_asynchronous_message(self, message): # Check if it is an asynchronous response message # Note: ErrorResponse is a subclass of NoticeResponse return (isinstance(message, messages.ParameterStatus) or (isinstance(message, messages.NoticeResponse) and not isinstance(message, messages.ErrorResponse))) def handle_asynchronous_message(self, message): if isinstance(message, messages.ParameterStatus): if message.name == 'protocol_version': message.value = int(message.value) self.parameters[message.name] = message.value elif (isinstance(message, messages.NoticeResponse) and not isinstance(message, messages.ErrorResponse)): if getattr(self, 'notice_handler', None) is not None: self.notice_handler(message) else: self._logger.warning(message.error_message()) def read_string(self): s = bytearray() while True: char = self.read_bytes(1) if char == b'\x00': break s.extend(char) return s def read_message(self): while True: try: type_ = self.read_bytes(1) size = unpack('!I', self.read_bytes(4))[0] if size < 4: raise errors.MessageError("Bad message size: {0}".format(size)) if type_ == messages.WriteFile.message_id: # The whole WriteFile message may not be read at here. # Instead, only the file name and file length is read. # This is because the message could be too large to read all at once. f = self.read_string() filename = f.decode('utf-8') file_length = unpack('!I', self.read_bytes(4))[0] size -= 4 + len(f) + 1 + 4 if size != file_length: raise errors.MessageError("Bad message size: {0}".format(size)) if filename == '': # If there is no filename, then this is really RETURNREJECTED data, not a rejected file if file_length % 8 != 0: raise errors.MessageError("Bad RETURNREJECTED data size: {0}".format(file_length)) data = self.read_bytes(file_length) message = messages.WriteFile(filename, file_length, data) else: # The rest of the message is read later with write_to_disk() message = messages.WriteFile(filename, file_length) else: message = BackendMessage.from_type(type_, self.read_bytes(size - 4)) self._logger.debug('<= %s', message) self.handle_asynchronous_message(message) # handle transaction status if isinstance(message, messages.ReadyForQuery): self.transaction_status = message.transaction_status except (SystemError, IOError) as e: self.close_socket() # noinspection PyTypeChecker self._logger.error(e) raise_from(errors.ConnectionError(str(e)), e) if not self.is_asynchronous_message(message): break return message def read_expected_message(self, expected_types, error_handler=None): # Reads a message and does some basic error handling. # expected_types must be a class (e.g. messages.BindComplete) or a tuple of classes message = self.read_message() if isinstance(message, expected_types): return message elif isinstance(message, messages.ErrorResponse): if error_handler is not None: error_handler(message) else: raise errors.DatabaseError(message.error_message()) else: msg = 'Received unexpected message type: {}. '.format(type(message).__name__) if isinstance(expected_types, tuple): msg += 'Expected types: {}'.format(", ".join([t.__name__ for t in expected_types])) else: msg += 'Expected type: {}'.format(expected_types.__name__) self._logger.error(msg) raise errors.MessageError(msg) def read_bytes(self, n): if n == 1: result = self._socket_as_file().read(1) if not result: raise errors.ConnectionError("Connection closed by Vertica") return result else: buf = b"" to_read = n while to_read > 0: data = self._socket_as_file().read(to_read) received = len(data) if received == 0: raise errors.ConnectionError("Connection closed by Vertica") buf += data to_read -= received return buf def send_GSS_response_and_receive_challenge(self, response): # Send the GSS response data to the vertica server token = base64.b64decode(response) self.write(messages.Password(token, messages.Authentication.GSS)) # Receive the challenge from the vertica server message = self.read_expected_message(messages.Authentication) if message.code != messages.Authentication.GSS_CONTINUE: msg = ('Received unexpected message type: Authentication(type={}).' ' Expected type: Authentication(type={})'.format( message.code, messages.Authentication.GSS_CONTINUE)) self._logger.error(msg) raise errors.MessageError(msg) return message.auth_data def make_GSS_authentication(self): try: import kerberos except ImportError as e: raise errors.ConnectionError("{}\nCannot make a Kerberos " "authentication because no Kerberos package is installed. " "Get it with 'pip install kerberos'.".format(str(e))) # Set GSS flags gssflag = (kerberos.GSS_C_DELEG_FLAG | kerberos.GSS_C_MUTUAL_FLAG | kerberos.GSS_C_SEQUENCE_FLAG | kerberos.GSS_C_REPLAY_FLAG) # Generate the GSS-style service principal name service_principal = "{}@{}".format(self.options['kerberos_service_name'], self.options['kerberos_host_name']) # Initializes a context object with a service principal self._logger.info('Initializing a context for GSSAPI client-side ' 'authentication with service principal {}'.format(service_principal)) try: result, context = kerberos.authGSSClientInit(service_principal, gssflags=gssflag) except kerberos.GSSError as err: msg = "GSSAPI initialization error: {}".format(str(err)) self._logger.error(msg) raise errors.KerberosError(msg) if result != kerberos.AUTH_GSS_COMPLETE: msg = ('Failed to initialize a context for GSSAPI client-side ' 'authentication with service principal {}'.format(service_principal)) self._logger.error(msg) raise errors.KerberosError(msg) # Processes GSSAPI client-side steps try: challenge = b'' while True: self._logger.info('Processing a single GSSAPI client-side step') challenge = base64.b64encode(challenge).decode("utf-8") result = kerberos.authGSSClientStep(context, challenge) if result == kerberos.AUTH_GSS_COMPLETE: self._logger.info('Result: GSSAPI step complete') break elif result == kerberos.AUTH_GSS_CONTINUE: self._logger.info('Result: GSSAPI step continuation') # Get the response from the last successful GSSAPI client-side step response = kerberos.authGSSClientResponse(context) challenge = self.send_GSS_response_and_receive_challenge(response) else: msg = "GSSAPI client-side step error status {}".format(result) self._logger.error(msg) raise errors.KerberosError(msg) except kerberos.GSSError as err: msg = "GSSAPI client-side step error: {}".format(str(err)) self._logger.error(msg) raise errors.KerberosError(msg) def startup_connection(self): user = self.options['user'] database = self.options['database'] session_label = self.options['session_label'] os_user_name = DEFAULT_USER if DEFAULT_USER else '' password = self.options['password'] self.write(messages.Startup(user, database, session_label, os_user_name)) while True: message = self.read_message() if isinstance(message, messages.Authentication): if message.code == messages.Authentication.OK: self._logger.info("User {} successfully authenticated" .format(self.options['user'])) elif message.code == messages.Authentication.CHANGE_PASSWORD: msg = "The password for user {} has expired".format(self.options['user']) self._logger.error(msg) raise errors.ConnectionError(msg) elif message.code == messages.Authentication.PASSWORD_GRACE: self._logger.warning('The password for user {} will expire soon.' ' Please consider changing it.'.format(self.options['user'])) elif message.code == messages.Authentication.GSS: self.make_GSS_authentication() else: self.write(messages.Password(password, message.code, {'user': user, 'salt': getattr(message, 'salt', None), 'usersalt': getattr(message, 'usersalt', None)})) elif isinstance(message, messages.BackendKeyData): self.backend_pid = message.pid self.backend_key = message.key elif isinstance(message, messages.ReadyForQuery): break elif isinstance(message, messages.ErrorResponse): self._logger.error(message.error_message()) raise errors.ConnectionError(message.error_message()) else: msg = "Received unexpected startup message: {0}".format(message) self._logger.error(msg) raise errors.MessageError(msg)

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from __future__ import print_function, division, absolute_import import base64 import logging import socket import ssl import getpass import uuid from struct import unpack from collections import deque, namedtuple import random from six import raise_from, string_types, integer_types, PY2 if PY2: from urlparse import urlparse, parse_qs else: from urllib.parse import urlparse, parse_qs from typing import TYPE_CHECKING if TYPE_CHECKING: from typing import Any, Dict, Literal, Optional, Type, Union from typing_extensions import Self import vertica_python from .. import errors from ..vertica import messages from ..vertica.cursor import Cursor from ..vertica.messages.message import BackendMessage, FrontendMessage from ..vertica.messages.frontend_messages import CancelRequest from ..vertica.log import VerticaLogging DEFAULT_HOST = 'localhost' DEFAULT_PORT = 5433 DEFAULT_PASSWORD = '' DEFAULT_AUTOCOMMIT = False DEFAULT_BACKUP_SERVER_NODE = [] DEFAULT_KRB_SERVICE_NAME = 'vertica' DEFAULT_LOG_LEVEL = logging.WARNING DEFAULT_LOG_PATH = 'vertica_python.log' try: DEFAULT_USER = getpass.getuser() except Exception as e: DEFAULT_USER = None print("WARN: Cannot get the login user name: {}".format(str(e))) def connect(**kwargs): return Connection(kwargs) def parse_dsn(dsn): url = urlparse(dsn) if url.scheme != 'vertica': raise ValueError("Only vertica:// scheme is supported.") result = {k: v for k, v in ( ('host', url.hostname), ('port', url.port), ('user', url.username), ('password', url.password), ('database', url.path[1:])) if v } for key, values in parse_qs(url.query, keep_blank_values=True).items(): for i in reversed(range(len(values))): value = values[i] if value != '': break if value == '' and key != 'log_path': continue elif key == 'backup_server_node': continue elif key in ('connection_load_balance', 'use_prepared_statements', 'disable_copy_local', 'ssl', 'autocommit'): lower = value.lower() if lower in ('true', 'on', '1'): result[key] = True elif lower in ('false', 'off', '0'): result[key] = False elif key == 'connection_timeout': result[key] = float(value) elif key == 'log_level' and value.isdigit(): result[key] = int(value) else: result[key] = value return result _AddressEntry = namedtuple('_AddressEntry', ['host', 'resolved', 'data']) class _AddressList(object): def __init__(self, host, port, backup_nodes, logger): self._logger = logger self.address_deque = deque() self._append(host, port) if not isinstance(backup_nodes, list): err_msg = 'Connection option "backup_server_node" must be a list' self._logger.error(err_msg) raise TypeError(err_msg) for node in backup_nodes: if isinstance(node, string_types): self._append(node, DEFAULT_PORT) elif isinstance(node, tuple) and len(node) == 2: self._append(node[0], node[1]) else: err_msg = ('Each item of connection option "backup_server_node"' ' must be a host string or a (host, port) tuple') self._logger.error(err_msg) raise TypeError(err_msg) self._logger.debug('Address list: {0}'.format(list(self.address_deque))) def _append(self, host, port): if not isinstance(host, string_types): err_msg = 'Host must be a string: invalid value: {0}'.format(host) self._logger.error(err_msg) raise TypeError(err_msg) if not isinstance(port, (string_types, integer_types)): err_msg = 'Port must be an integer or a string: invalid value: {0}'.format(port) self._logger.error(err_msg) raise TypeError(err_msg) elif isinstance(port, string_types): try: port = int(port) except ValueError as e: err_msg = 'Port "{0}" is not a valid string: {1}'.format(port, e) self._logger.error(err_msg) raise ValueError(err_msg) if port < 0 or port > 65535: err_msg = 'Invalid port number: {0}'.format(port) self._logger.error(err_msg) raise ValueError(err_msg) self.address_deque.append(_AddressEntry(host=host, resolved=False, data=port)) def push(self, host, port): self.address_deque.appendleft(_AddressEntry(host=host, resolved=False, data=port)) def pop(self): self.address_deque.popleft() def peek(self): if len(self.address_deque) == 0: return None while len(self.address_deque) > 0: self._logger.debug('Peek at address list: {0}'.format(list(self.address_deque))) entry = self.address_deque[0] if entry.resolved: return entry.data else: self.pop() host, port = entry.host, entry.data try: resolved_hosts = socket.getaddrinfo(host, port, 0, socket.SOCK_STREAM) except Exception as e: self._logger.warning('Error resolving host "{0}" on port {1}: {2}'.format(host, port, e)) continue random.shuffle(resolved_hosts) for addrinfo in resolved_hosts: if addrinfo[0] in (socket.AF_INET, socket.AF_INET6): self.address_deque.appendleft(_AddressEntry( host=host, resolved=True, data=addrinfo)) return None def peek_host(self): self._logger.debug('Peek host at address list: {0}'.format(list(self.address_deque))) if len(self.address_deque) == 0: return None return self.address_deque[0].host def _generate_session_label(): return '{type}-{version}-{id}'.format( type='vertica-python', version=vertica_python.__version__, id=uuid.uuid1() ) class Connection(object): def __init__(self, options=None): self.parameters = {} self.session_id = None self.backend_pid = None self.backend_key = None self.transaction_status = None self.socket = None self.socket_as_file = None options = options or {} self.options = parse_dsn(options['dsn']) if 'dsn' in options else {} self.options.update({key: value for key, value in options.items() \ if key == 'log_path' or (key != 'dsn' and value is not None)}) logger_name = 'vertica_{0}_{1}'.format(id(self), str(uuid.uuid4())) self._logger = logging.getLogger(logger_name) if 'log_level' not in self.options and 'log_path' not in self.options: self._logger.disabled = True else: self.options.setdefault('log_level', DEFAULT_LOG_LEVEL) self.options.setdefault('log_path', DEFAULT_LOG_PATH) VerticaLogging.setup_logging(logger_name, self.options['log_path'], self.options['log_level'], id(self)) self.options.setdefault('host', DEFAULT_HOST) self.options.setdefault('port', DEFAULT_PORT) if 'user' not in self.options: if DEFAULT_USER: self.options['user'] = DEFAULT_USER else: msg = 'Connection option "user" is required' self._logger.error(msg) raise KeyError(msg) self.options.setdefault('database', self.options['user']) self.options.setdefault('password', DEFAULT_PASSWORD) self.options.setdefault('autocommit', DEFAULT_AUTOCOMMIT) self.options.setdefault('session_label', _generate_session_label()) self.options.setdefault('backup_server_node', DEFAULT_BACKUP_SERVER_NODE) self.options.setdefault('kerberos_service_name', DEFAULT_KRB_SERVICE_NAME) self.options.setdefault('kerberos_host_name', self.options['host']) self.address_list = _AddressList(self.options['host'], self.options['port'], self.options['backup_server_node'], self._logger) self.options.setdefault('unicode_error', None) self._cursor = Cursor(self, self._logger, cursor_type=None, unicode_error=self.options['unicode_error']) self.options.setdefault('use_prepared_statements', False) self._logger.debug('Connection prepared statements is {}'.format( 'enabled' if self.options['use_prepared_statements'] else 'disabled')) self.options.setdefault('disable_copy_local', False) self._logger.debug('COPY LOCAL operation is {}'.format( 'disabled' if self.options['disable_copy_local'] else 'enabled')) self._logger.info('Connecting as user "{}" to database "{}" on host "{}" with port {}'.format( self.options['user'], self.options['database'], self.options['host'], self.options['port'])) self.startup_connection() if self.options['autocommit']: self.autocommit = True self._logger.info('Connection is ready') host "{0}" on port {1}: {2}'.format(host, port, e)) last_exception = e address_list.pop() addrinfo = address_list.peek() raw_socket.close() if raw_socket is None or last_exception: err_msg = 'Failed to establish a connection to the primary server or any backup address.' self._logger.error(err_msg) raise errors.ConnectionError(err_msg) return raw_socket def ssl(self): return self.socket is not None and isinstance(self.socket, ssl.SSLSocket) def write(self, message, vsocket=None): if not isinstance(message, FrontendMessage): raise TypeError("invalid message: ({0})".format(message)) if vsocket is None: vsocket = self._socket() self._logger.debug('=> %s', message) try: for data in message.fetch_message(): size = 8192 pos = 0 while pos < len(data): sent = vsocket.send(data[pos : pos + size]) if sent == 0: raise errors.ConnectionError("Couldn't send message: Socket connection broken") pos += sent except Exception as e: self.close_socket() self._logger.error(str(e)) if isinstance(e, IOError): raise_from(errors.ConnectionError(str(e)), e) else: raise def close_socket(self): try: if self.socket is not None: self._socket().close() if self.socket_as_file is not None: self._socket_as_file().close() finally: self.reset_values() def reset_connection(self): self.close() self.startup_connection() def is_asynchronous_message(self, message): # Check if it is an asynchronous response message # Note: ErrorResponse is a subclass of NoticeResponse return (isinstance(message, messages.ParameterStatus) or (isinstance(message, messages.NoticeResponse) and not isinstance(message, messages.ErrorResponse))) def handle_asynchronous_message(self, message): if isinstance(message, messages.ParameterStatus): if message.name == 'protocol_version': message.value = int(message.value) self.parameters[message.name] = message.value elif (isinstance(message, messages.NoticeResponse) and not isinstance(message, messages.ErrorResponse)): if getattr(self, 'notice_handler', None) is not None: self.notice_handler(message) else: self._logger.warning(message.error_message()) def read_string(self): s = bytearray() while True: char = self.read_bytes(1) if char == b'\x00': break s.extend(char) return s def read_message(self): while True: try: type_ = self.read_bytes(1) size = unpack('!I', self.read_bytes(4))[0] if size < 4: raise errors.MessageError("Bad message size: {0}".format(size)) if type_ == messages.WriteFile.message_id: # The whole WriteFile message may not be read at here. # Instead, only the file name and file length is read. # This is because the message could be too large to read all at once. f = self.read_string() filename = f.decode('utf-8') file_length = unpack('!I', self.read_bytes(4))[0] size -= 4 + len(f) + 1 + 4 if size != file_length: raise errors.MessageError("Bad message size: {0}".format(size)) if filename == '': # If there is no filename, then this is really RETURNREJECTED data, not a rejected file if file_length % 8 != 0: raise errors.MessageError("Bad RETURNREJECTED data size: {0}".format(file_length)) data = self.read_bytes(file_length) message = messages.WriteFile(filename, file_length, data) else: # The rest of the message is read later with write_to_disk() message = messages.WriteFile(filename, file_length) else: message = BackendMessage.from_type(type_, self.read_bytes(size - 4)) self._logger.debug('<= %s', message) self.handle_asynchronous_message(message) # handle transaction status if isinstance(message, messages.ReadyForQuery): self.transaction_status = message.transaction_status except (SystemError, IOError) as e: self.close_socket() # noinspection PyTypeChecker self._logger.error(e) raise_from(errors.ConnectionError(str(e)), e) if not self.is_asynchronous_message(message): break return message def read_expected_message(self, expected_types, error_handler=None): # Reads a message and does some basic error handling. # expected_types must be a class (e.g. messages.BindComplete) or a tuple of classes message = self.read_message() if isinstance(message, expected_types): return message elif isinstance(message, messages.ErrorResponse): if error_handler is not None: error_handler(message) else: raise errors.DatabaseError(message.error_message()) else: msg = 'Received unexpected message type: {}. '.format(type(message).__name__) if isinstance(expected_types, tuple): msg += 'Expected types: {}'.format(", ".join([t.__name__ for t in expected_types])) else: msg += 'Expected type: {}'.format(expected_types.__name__) self._logger.error(msg) raise errors.MessageError(msg) def read_bytes(self, n): if n == 1: result = self._socket_as_file().read(1) if not result: raise errors.ConnectionError("Connection closed by Vertica") return result else: buf = b"" to_read = n while to_read > 0: data = self._socket_as_file().read(to_read) received = len(data) if received == 0: raise errors.ConnectionError("Connection closed by Vertica") buf += data to_read -= received return buf def send_GSS_response_and_receive_challenge(self, response): # Send the GSS response data to the vertica server token = base64.b64decode(response) self.write(messages.Password(token, messages.Authentication.GSS)) # Receive the challenge from the vertica server message = self.read_expected_message(messages.Authentication) if message.code != messages.Authentication.GSS_CONTINUE: msg = ('Received unexpected message type: Authentication(type={}).' ' Expected type: Authentication(type={})'.format( message.code, messages.Authentication.GSS_CONTINUE)) self._logger.error(msg) raise errors.MessageError(msg) return message.auth_data def make_GSS_authentication(self): try: import kerberos except ImportError as e: raise errors.ConnectionError("{}\nCannot make a Kerberos " "authentication because no Kerberos package is installed. " "Get it with 'pip install kerberos'.".format(str(e))) # Set GSS flags gssflag = (kerberos.GSS_C_DELEG_FLAG | kerberos.GSS_C_MUTUAL_FLAG | kerberos.GSS_C_SEQUENCE_FLAG | kerberos.GSS_C_REPLAY_FLAG) # Generate the GSS-style service principal name service_principal = "{}@{}".format(self.options['kerberos_service_name'], self.options['kerberos_host_name']) # Initializes a context object with a service principal self._logger.info('Initializing a context for GSSAPI client-side ' 'authentication with service principal {}'.format(service_principal)) try: result, context = kerberos.authGSSClientInit(service_principal, gssflags=gssflag) except kerberos.GSSError as err: msg = "GSSAPI initialization error: {}".format(str(err)) self._logger.error(msg) raise errors.KerberosError(msg) if result != kerberos.AUTH_GSS_COMPLETE: msg = ('Failed to initialize a context for GSSAPI client-side ' 'authentication with service principal {}'.format(service_principal)) self._logger.error(msg) raise errors.KerberosError(msg) # Processes GSSAPI client-side steps try: challenge = b'' while True: self._logger.info('Processing a single GSSAPI client-side step') challenge = base64.b64encode(challenge).decode("utf-8") result = kerberos.authGSSClientStep(context, challenge) if result == kerberos.AUTH_GSS_COMPLETE: self._logger.info('Result: GSSAPI step complete') break elif result == kerberos.AUTH_GSS_CONTINUE: self._logger.info('Result: GSSAPI step continuation') # Get the response from the last successful GSSAPI client-side step response = kerberos.authGSSClientResponse(context) challenge = self.send_GSS_response_and_receive_challenge(response) else: msg = "GSSAPI client-side step error status {}".format(result) self._logger.error(msg) raise errors.KerberosError(msg) except kerberos.GSSError as err: msg = "GSSAPI client-side step error: {}".format(str(err)) self._logger.error(msg) raise errors.KerberosError(msg) def startup_connection(self): user = self.options['user'] database = self.options['database'] session_label = self.options['session_label'] os_user_name = DEFAULT_USER if DEFAULT_USER else '' password = self.options['password'] self.write(messages.Startup(user, database, session_label, os_user_name)) while True: message = self.read_message() if isinstance(message, messages.Authentication): if message.code == messages.Authentication.OK: self._logger.info("User {} successfully authenticated" .format(self.options['user'])) elif message.code == messages.Authentication.CHANGE_PASSWORD: msg = "The password for user {} has expired".format(self.options['user']) self._logger.error(msg) raise errors.ConnectionError(msg) elif message.code == messages.Authentication.PASSWORD_GRACE: self._logger.warning('The password for user {} will expire soon.' ' Please consider changing it.'.format(self.options['user'])) elif message.code == messages.Authentication.GSS: self.make_GSS_authentication() else: self.write(messages.Password(password, message.code, {'user': user, 'salt': getattr(message, 'salt', None), 'usersalt': getattr(message, 'usersalt', None)})) elif isinstance(message, messages.BackendKeyData): self.backend_pid = message.pid self.backend_key = message.key elif isinstance(message, messages.ReadyForQuery): break elif isinstance(message, messages.ErrorResponse): self._logger.error(message.error_message()) raise errors.ConnectionError(message.error_message()) else: msg = "Received unexpected startup message: {0}".format(message) self._logger.error(msg) raise errors.MessageError(msg)

true

790806fc7e64af17b5a7f763354c486df50043d9

6,142

py

Python

src/dev/arm/css/Scmi.py

fei-shan/gem5-experiment

70781db30d42b1fe50e495bd04f7755a4b0e0e59

[ "BSD-3-Clause" ]

2

2021-01-15T17:32:18.000Z

2021-12-21T02:53:58.000Z

src/dev/arm/css/Scmi.py

fei-shan/gem5-experiment

70781db30d42b1fe50e495bd04f7755a4b0e0e59

[ "BSD-3-Clause" ]

3

2021-03-26T20:33:59.000Z

2022-01-24T22:54:03.000Z

src/dev/arm/css/Scmi.py

fei-shan/gem5-experiment

70781db30d42b1fe50e495bd04f7755a4b0e0e59

[ "BSD-3-Clause" ]

3

2021-03-27T16:36:19.000Z

2022-03-28T18:32:57.000Z

# Copyright (c) 2020 ARM Limited # All rights reserved. # # The license below extends only to copyright in the software and shall # not be construed as granting a license to any other intellectual # property including but not limited to intellectual property relating # to a hardware implementation of the functionality of the software # licensed hereunder. You may use the software subject to the license # terms below provided that you ensure that this notice is replicated # unmodified and in its entirety in all distributions of the software, # modified or unmodified, in source code or in binary form. # # 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 holders 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. from m5.params import * from m5.proxy import * from m5.objects.Scp import Scp from m5.objects.Doorbell import Doorbell from m5.util.fdthelper import * from m5.SimObject import SimObject class ScmiChannel(SimObject): """ Unidirectional channel """ type = 'ScmiChannel' cxx_header = "dev/arm/css/scmi_platform.hh" cxx_class = "SCMI::VirtualChannel" shmem_range = Param.AddrRange( "Virtual channel's shared memory address range") phys_id = Param.Unsigned(4, "Physical slot of the channel") virt_id = Param.Unsigned(0, "Virtual slot of the channel (within the physical)") doorbell = Param.Doorbell( "This is the doorbell used to notify the SCMI platform") def __init__(self, shmem, *args, **kwargs): super(ScmiChannel, self).__init__(**kwargs) def shmemGenerator(state): shmem_node = FdtNode("scp-shmem@%x" % 0) shmem_node.appendCompatible(["arm,scmi-shmem"]) shmem_node.append(FdtPropertyWords("reg", state.addrCells(0) + state.sizeCells(0x200)) ) #shmem_node.appendPhandle(self._parent.unproxy(self).channel) shmem_node.appendPhandle("scmi_virt" + str(self.virt_id)) return shmem_node self._shmem = shmem self._shmem.addSubnodeGenerator(shmemGenerator) class ScmiAgentChannel(ScmiChannel): """ This is a Agent to Platform channel (The agent is the initiator) """ type = 'ScmiAgentChannel' cxx_header = "dev/arm/css/scmi_platform.hh" cxx_class = "SCMI::AgentChannel" class ScmiPlatformChannel(ScmiChannel): """ This is a Platform to Agent channel (The platform is the initiator) """ type = 'ScmiPlatformChannel' cxx_header = "dev/arm/css/scmi_platform.hh" cxx_class = "SCMI::PlatformChannel" class ScmiCommunication(SimObject): """ The SCMI Communication class models a bidirectional communication between the SCMI platform and the agent. As such it has a ScmiAgentChannel and a ScmiPlatformChannel object as members. """ type = 'ScmiCommunication' cxx_header = "dev/arm/css/scmi_platform.hh" cxx_class = "SCMI::Communication" agent_channel = Param.ScmiAgentChannel( "Agent to Platform channel") platform_channel = Param.ScmiPlatformChannel( "Platform to Agent channel") class ScmiPlatform(Scp): type = 'ScmiPlatform' cxx_header = "dev/arm/css/scmi_platform.hh" cxx_class = "SCMI::Platform" comms = VectorParam.ScmiCommunication([], "SCMI Communications") agents = VectorParam.String([ "OSPM" ], "Vector of SCMI agents (names) in the system") sys = Param.System(Parent.any, "System object parameter") dma = MasterPort("DMA port") # Protocol params base_vendor = Param.String("arm", "Return string for the Base protocol DISCOVER_VENDOR command") base_subvendor = Param.String("gem5", "Return string for the Base protocol DISCOVER_SUBVENDOR command") base_impl_version = Param.Unsigned(0, "Return value for the Base protocol " "DISCOVER_IMPLEMENTATION_VERSION command") def generateDeviceTree(self, state): scmi_node = self.generateScmiNode(state) fw_node = FdtNode("firmware") fw_node.append(scmi_node) yield fw_node def generateScmiNode(self, state): node = FdtNode("scmi") node.appendCompatible(["arm,scmi"]) mbox_phandle = state.phandle(self._parent.unproxy(self).mailbox) shmem_phandles = [] for comm in self.unproxy(self).comms: shmem_phandles.append(state.phandle( "scmi_virt" + str(comm.agent_channel.virt_id))) shmem_phandles.append(state.phandle( "scmi_virt" + str(comm.platform_channel.virt_id))) phys_channel = 1 # HP-NonSecure node.append(FdtPropertyWords("mboxes", [ mbox_phandle, phys_channel ])) node.append(FdtPropertyWords("shmem", shmem_phandles)) return node

40.143791

79

0.712146

from m5.params import * from m5.proxy import * from m5.objects.Scp import Scp from m5.objects.Doorbell import Doorbell from m5.util.fdthelper import * from m5.SimObject import SimObject class ScmiChannel(SimObject): type = 'ScmiChannel' cxx_header = "dev/arm/css/scmi_platform.hh" cxx_class = "SCMI::VirtualChannel" shmem_range = Param.AddrRange( "Virtual channel's shared memory address range") phys_id = Param.Unsigned(4, "Physical slot of the channel") virt_id = Param.Unsigned(0, "Virtual slot of the channel (within the physical)") doorbell = Param.Doorbell( "This is the doorbell used to notify the SCMI platform") def __init__(self, shmem, *args, **kwargs): super(ScmiChannel, self).__init__(**kwargs) def shmemGenerator(state): shmem_node = FdtNode("scp-shmem@%x" % 0) shmem_node.appendCompatible(["arm,scmi-shmem"]) shmem_node.append(FdtPropertyWords("reg", state.addrCells(0) + state.sizeCells(0x200)) ) #shmem_node.appendPhandle(self._parent.unproxy(self).channel) shmem_node.appendPhandle("scmi_virt" + str(self.virt_id)) return shmem_node self._shmem = shmem self._shmem.addSubnodeGenerator(shmemGenerator) class ScmiAgentChannel(ScmiChannel): type = 'ScmiAgentChannel' cxx_header = "dev/arm/css/scmi_platform.hh" cxx_class = "SCMI::AgentChannel" class ScmiPlatformChannel(ScmiChannel): type = 'ScmiPlatformChannel' cxx_header = "dev/arm/css/scmi_platform.hh" cxx_class = "SCMI::PlatformChannel" class ScmiCommunication(SimObject): type = 'ScmiCommunication' cxx_header = "dev/arm/css/scmi_platform.hh" cxx_class = "SCMI::Communication" agent_channel = Param.ScmiAgentChannel( "Agent to Platform channel") platform_channel = Param.ScmiPlatformChannel( "Platform to Agent channel") class ScmiPlatform(Scp): type = 'ScmiPlatform' cxx_header = "dev/arm/css/scmi_platform.hh" cxx_class = "SCMI::Platform" comms = VectorParam.ScmiCommunication([], "SCMI Communications") agents = VectorParam.String([ "OSPM" ], "Vector of SCMI agents (names) in the system") sys = Param.System(Parent.any, "System object parameter") dma = MasterPort("DMA port") # Protocol params base_vendor = Param.String("arm", "Return string for the Base protocol DISCOVER_VENDOR command") base_subvendor = Param.String("gem5", "Return string for the Base protocol DISCOVER_SUBVENDOR command") base_impl_version = Param.Unsigned(0, "Return value for the Base protocol " "DISCOVER_IMPLEMENTATION_VERSION command") def generateDeviceTree(self, state): scmi_node = self.generateScmiNode(state) fw_node = FdtNode("firmware") fw_node.append(scmi_node) yield fw_node def generateScmiNode(self, state): node = FdtNode("scmi") node.appendCompatible(["arm,scmi"]) mbox_phandle = state.phandle(self._parent.unproxy(self).mailbox) shmem_phandles = [] for comm in self.unproxy(self).comms: shmem_phandles.append(state.phandle( "scmi_virt" + str(comm.agent_channel.virt_id))) shmem_phandles.append(state.phandle( "scmi_virt" + str(comm.platform_channel.virt_id))) phys_channel = 1 # HP-NonSecure node.append(FdtPropertyWords("mboxes", [ mbox_phandle, phys_channel ])) node.append(FdtPropertyWords("shmem", shmem_phandles)) return node

true

790809721ce85f4566e4aa149b960fd755db4dae

6,842

py

Python

indico/core/cache.py

errikos/indico

72b75d63a896e5defb8e9acf64fe147748c7ccce

[ "MIT" ]

null

indico/core/cache.py

errikos/indico

72b75d63a896e5defb8e9acf64fe147748c7ccce

[ "MIT" ]

null

indico/core/cache.py

errikos/indico

72b75d63a896e5defb8e9acf64fe147748c7ccce

[ "MIT" ]

null

# This file is part of Indico. # Copyright (C) 2002 - 2021 CERN # # Indico is free software; you can redistribute it and/or # modify it under the terms of the MIT License; see the # LICENSE file for more details. from flask_caching import Cache from flask_caching.backends.rediscache import RedisCache from flask_caching.backends.simplecache import SimpleCache from redis import RedisError from indico.core.logger import Logger _logger = Logger.get('cache') class CachedNone: __slots__ = () @classmethod def wrap(cls, value): return cls() if value is None else value @classmethod def unwrap(cls, value, default=None): if value is None: return default elif isinstance(value, cls): return None else: return value class IndicoCacheMixin: def get(self, key, default=None): return CachedNone.unwrap(super().get(key), default) def get_many(self, *keys, default=None): return [CachedNone.unwrap(val, default) for val in super().get_many(*keys)] def get_dict(self, *keys, default=None): return dict(zip(keys, self.get_many(*keys, default=default))) class IndicoRedisCache(IndicoCacheMixin, RedisCache): """ This is similar to the original RedisCache from Flask-Caching, but it allows specifying a default value when retrieving cache data and distinguishing between a cached ``None`` value and a cache miss. """ def dump_object(self, value): # We are not overriding the `load_object` counterpart to this method o # purpose because we need to have access to the wrapped value in `get` # and `get_many`. return super().dump_object(CachedNone.wrap(value)) class IndicoSimpleCache(IndicoCacheMixin, SimpleCache): """ This is similar to the original SimpleCache from Flask-Caching, but it allows specifying a default value when retrieving cache data and distinguishing between a cached ``None`` value and a cache miss. """ def set(self, key, value, timeout=None): return super().set(key, CachedNone.wrap(value), timeout=timeout) def add(self, key, value, timeout=None): return super().add(key, CachedNone.wrap(value), timeout=timeout) def make_indico_simple_cache(app, config, args, kwargs): return IndicoSimpleCache(*args, **kwargs) def make_indico_redis_cache(app, config, args, kwargs): from redis import from_url as redis_from_url key_prefix = config.get('CACHE_KEY_PREFIX') if key_prefix: kwargs['key_prefix'] = key_prefix kwargs['host'] = redis_from_url(config['CACHE_REDIS_URL'], socket_timeout=1) return IndicoRedisCache(*args, **kwargs) class ScopedCache: def __init__(self, cache, scope): self.cache = cache self.scope = scope def _scoped(self, key): return f'{self.scope}/{key}' def get(self, key, default=None): return self.cache.get(self._scoped(key), default=default) def set(self, key, value, timeout=None): self.cache.set(self._scoped(key), value, timeout=timeout) def add(self, key, value, timeout=None): self.cache.add(self._scoped(key), value, timeout=timeout) def delete(self, key): self.cache.delete(self._scoped(key)) def delete_many(self, *keys): keys = [self._scoped(key) for key in keys] self.cache.delete_many(*keys) def clear(self): raise NotImplementedError('Clearing scoped caches is not supported') def get_dict(self, *keys, default=None): return dict(zip(keys, self.get_many(*keys, default=default))) def get_many(self, *keys, default=None): keys = [self._scoped(key) for key in keys] return self.cache.get_many(*keys, default=default) def set_many(self, mapping, timeout=None): mapping = {self._scoped(key): value for key, value in mapping.items()} self.cache.set_many(mapping, timeout=timeout) def __repr__(self): return f'<ScopedCache: {self.scope}>' class IndicoCache(Cache): """ This is basicaly the Cache class from Flask-Caching but it silences all exceptions that happen during a cache operation since cache failures should not take down the whole page. While this cache can in principle support many different backends, we only consider redis and (for unittests) a simple dict-based cache. This allows us to be more specific in catching exceptions since the Redis cache has exactly one base exception. """ def get(self, key, default=None): try: return super().get(key, default) except RedisError: _logger.exception('get(%r) failed', key) return default def set(self, key, value, timeout=None): try: super().set(key, value, timeout=timeout) except RedisError: _logger.exception('set(%r) failed', key) def add(self, key, value, timeout=None): try: super().add(key, value, timeout=timeout) except RedisError: _logger.exception('add(%r) failed', key) def delete(self, key): try: super().delete(key) except RedisError: _logger.exception('delete(%r) failed', key) def delete_many(self, *keys): try: super().delete_many(*keys) except RedisError: _logger.exception('delete_many(%s) failed', ', '.join(map(repr, keys))) def clear(self): try: super().clear() except RedisError: _logger.exception('clear() failed') def get_many(self, *keys, default=None): try: return super().get_many(*keys, default=default) except RedisError: logkeys = ', '.join(map(repr, keys)) _logger.exception('get_many(%s) failed', logkeys) return [default] * len(keys) def set_many(self, mapping, timeout=None): try: super().set_many(mapping, timeout=timeout) except RedisError: _logger.exception('set_many(%r) failed', mapping) def get_dict(self, *keys, default=None): try: return super().get_dict(*keys, default=default) except RedisError: logkeys = ', '.join(map(repr, keys)) _logger.exception('get_dict(%s) failed', logkeys) return dict(zip(keys, [default] * len(keys))) def make_scoped_cache(scope): """Create a new scoped cache. In most cases the global cache should not be used directly but rather with a scope depending on the module a cache is used for. This is especially important when passing user-provided data as the cache key to prevent reading other unrelated cache keys. """ return ScopedCache(cache, scope) cache = IndicoCache()

31.971963

83

0.653464

from flask_caching import Cache from flask_caching.backends.rediscache import RedisCache from flask_caching.backends.simplecache import SimpleCache from redis import RedisError from indico.core.logger import Logger _logger = Logger.get('cache') class CachedNone: __slots__ = () @classmethod def wrap(cls, value): return cls() if value is None else value @classmethod def unwrap(cls, value, default=None): if value is None: return default elif isinstance(value, cls): return None else: return value class IndicoCacheMixin: def get(self, key, default=None): return CachedNone.unwrap(super().get(key), default) def get_many(self, *keys, default=None): return [CachedNone.unwrap(val, default) for val in super().get_many(*keys)] def get_dict(self, *keys, default=None): return dict(zip(keys, self.get_many(*keys, default=default))) class IndicoRedisCache(IndicoCacheMixin, RedisCache): def dump_object(self, value): return super().dump_object(CachedNone.wrap(value)) class IndicoSimpleCache(IndicoCacheMixin, SimpleCache): def set(self, key, value, timeout=None): return super().set(key, CachedNone.wrap(value), timeout=timeout) def add(self, key, value, timeout=None): return super().add(key, CachedNone.wrap(value), timeout=timeout) def make_indico_simple_cache(app, config, args, kwargs): return IndicoSimpleCache(*args, **kwargs) def make_indico_redis_cache(app, config, args, kwargs): from redis import from_url as redis_from_url key_prefix = config.get('CACHE_KEY_PREFIX') if key_prefix: kwargs['key_prefix'] = key_prefix kwargs['host'] = redis_from_url(config['CACHE_REDIS_URL'], socket_timeout=1) return IndicoRedisCache(*args, **kwargs) class ScopedCache: def __init__(self, cache, scope): self.cache = cache self.scope = scope def _scoped(self, key): return f'{self.scope}/{key}' def get(self, key, default=None): return self.cache.get(self._scoped(key), default=default) def set(self, key, value, timeout=None): self.cache.set(self._scoped(key), value, timeout=timeout) def add(self, key, value, timeout=None): self.cache.add(self._scoped(key), value, timeout=timeout) def delete(self, key): self.cache.delete(self._scoped(key)) def delete_many(self, *keys): keys = [self._scoped(key) for key in keys] self.cache.delete_many(*keys) def clear(self): raise NotImplementedError('Clearing scoped caches is not supported') def get_dict(self, *keys, default=None): return dict(zip(keys, self.get_many(*keys, default=default))) def get_many(self, *keys, default=None): keys = [self._scoped(key) for key in keys] return self.cache.get_many(*keys, default=default) def set_many(self, mapping, timeout=None): mapping = {self._scoped(key): value for key, value in mapping.items()} self.cache.set_many(mapping, timeout=timeout) def __repr__(self): return f'<ScopedCache: {self.scope}>' class IndicoCache(Cache): def get(self, key, default=None): try: return super().get(key, default) except RedisError: _logger.exception('get(%r) failed', key) return default def set(self, key, value, timeout=None): try: super().set(key, value, timeout=timeout) except RedisError: _logger.exception('set(%r) failed', key) def add(self, key, value, timeout=None): try: super().add(key, value, timeout=timeout) except RedisError: _logger.exception('add(%r) failed', key) def delete(self, key): try: super().delete(key) except RedisError: _logger.exception('delete(%r) failed', key) def delete_many(self, *keys): try: super().delete_many(*keys) except RedisError: _logger.exception('delete_many(%s) failed', ', '.join(map(repr, keys))) def clear(self): try: super().clear() except RedisError: _logger.exception('clear() failed') def get_many(self, *keys, default=None): try: return super().get_many(*keys, default=default) except RedisError: logkeys = ', '.join(map(repr, keys)) _logger.exception('get_many(%s) failed', logkeys) return [default] * len(keys) def set_many(self, mapping, timeout=None): try: super().set_many(mapping, timeout=timeout) except RedisError: _logger.exception('set_many(%r) failed', mapping) def get_dict(self, *keys, default=None): try: return super().get_dict(*keys, default=default) except RedisError: logkeys = ', '.join(map(repr, keys)) _logger.exception('get_dict(%s) failed', logkeys) return dict(zip(keys, [default] * len(keys))) def make_scoped_cache(scope): return ScopedCache(cache, scope) cache = IndicoCache()

true

79080ab85c70df8806700fcfe98355dc711038da

2,184

py

Python

lib/surface/service_management/operations/describe.py

bshaffer/google-cloud-sdk

f587382fd112f238c0d6d5ca3dab8f52d2b5c5f9

[ "Apache-2.0" ]

null

lib/surface/service_management/operations/describe.py

bshaffer/google-cloud-sdk

f587382fd112f238c0d6d5ca3dab8f52d2b5c5f9

[ "Apache-2.0" ]

null

lib/surface/service_management/operations/describe.py

bshaffer/google-cloud-sdk

f587382fd112f238c0d6d5ca3dab8f52d2b5c5f9

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Copyright 2016 Google Inc. 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. """service-management operations describe command.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from googlecloudsdk.calliope import base from googlecloudsdk.command_lib.endpoints import common_flags _ERROR = ('The `service-management operations describe` command has been ' 'replaced by `endpoints operations describe` and ' '`services operations describe`.') @base.Deprecate(is_removed=True, error=_ERROR) class Describe(base.DescribeCommand): """Describes an operation resource for a given operation name.""" @staticmethod def Args(parser): """Args is called by calliope to gather arguments for this command. Args: parser: An argparse parser that you can use to add arguments that go on the command line after this command. Positional arguments are allowed. """ common_flags.operation_flag(suffix='to describe').AddToParser(parser) parser.display_info.AddFormat( ':(metadata.startTime.date(format="%Y-%m-%d %H:%M:%S %Z", tz=LOCAL)) ' '[transforms] default') parser.add_argument( '--full', action='store_true', default=False, help=('Print the entire operation resource, which could be large. ' 'By default, a summary will be printed instead.')) def Run(self, args): """Stubs 'service-management operations describe'. Args: args: argparse.Namespace, The arguments that this command was invoked with. """ pass

33.6

78

0.708333

from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from googlecloudsdk.calliope import base from googlecloudsdk.command_lib.endpoints import common_flags _ERROR = ('The `service-management operations describe` command has been ' 'replaced by `endpoints operations describe` and ' '`services operations describe`.') @base.Deprecate(is_removed=True, error=_ERROR) class Describe(base.DescribeCommand): @staticmethod def Args(parser): common_flags.operation_flag(suffix='to describe').AddToParser(parser) parser.display_info.AddFormat( ':(metadata.startTime.date(format="%Y-%m-%d %H:%M:%S %Z", tz=LOCAL)) ' '[transforms] default') parser.add_argument( '--full', action='store_true', default=False, help=('Print the entire operation resource, which could be large. ' 'By default, a summary will be printed instead.')) def Run(self, args): pass

true

79080c9dea72eb3be5c8bd55f7e41768a8ebb07d

11,319

py

Python

packages/python/plotly/plotly/validators/volume/_colorbar.py

adehad/plotly.py

bca292530c400c61e8b7f8a6571262a9dde43ee3

[ "MIT" ]

7

2021-09-29T09:46:36.000Z

2022-03-24T08:30:41.000Z

packages/python/plotly/plotly/validators/volume/_colorbar.py

adehad/plotly.py

bca292530c400c61e8b7f8a6571262a9dde43ee3

[ "MIT" ]

1

2021-09-30T16:56:21.000Z

2021-10-15T09:14:12.000Z

packages/python/plotly/plotly/validators/volume/_colorbar.py

adehad/plotly.py

bca292530c400c61e8b7f8a6571262a9dde43ee3

[ "MIT" ]

1

2021-09-29T22:34:05.000Z

import _plotly_utils.basevalidators class ColorbarValidator(_plotly_utils.basevalidators.CompoundValidator): def __init__(self, plotly_name="colorbar", parent_name="volume", **kwargs): super(ColorbarValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, data_class_str=kwargs.pop("data_class_str", "ColorBar"), data_docs=kwargs.pop( "data_docs", """ bgcolor Sets the color of padded area. bordercolor Sets the axis line color. borderwidth Sets the width (in px) or the border enclosing this color bar. dtick Sets the step in-between ticks on this axis. Use with `tick0`. Must be a positive number, or special strings available to "log" and "date" axes. If the axis `type` is "log", then ticks are set every 10^(n*dtick) where n is the tick number. For example, to set a tick mark at 1, 10, 100, 1000, ... set dtick to 1. To set tick marks at 1, 100, 10000, ... set dtick to 2. To set tick marks at 1, 5, 25, 125, 625, 3125, ... set dtick to log_10(5), or 0.69897000433. "log" has several special values; "L<f>", where `f` is a positive number, gives ticks linearly spaced in value (but not position). For example `tick0` = 0.1, `dtick` = "L0.5" will put ticks at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10 plus small digits between, use "D1" (all digits) or "D2" (only 2 and 5). `tick0` is ignored for "D1" and "D2". If the axis `type` is "date", then you must convert the time to milliseconds. For example, to set the interval between ticks to one day, set `dtick` to 86400000.0. "date" also has special values "M<n>" gives ticks spaced by a number of months. `n` must be a positive integer. To set ticks on the 15th of every third month, set `tick0` to "2000-01-15" and `dtick` to "M3". To set ticks every 4 years, set `dtick` to "M48" exponentformat Determines a formatting rule for the tick exponents. For example, consider the number 1,000,000,000. If "none", it appears as 1,000,000,000. If "e", 1e+9. If "E", 1E+9. If "power", 1x10^9 (with 9 in a super script). If "SI", 1G. If "B", 1B. len Sets the length of the color bar This measure excludes the padding of both ends. That is, the color bar length is this length minus the padding on both ends. lenmode Determines whether this color bar's length (i.e. the measure in the color variation direction) is set in units of plot "fraction" or in *pixels. Use `len` to set the value. minexponent Hide SI prefix for 10^n if |n| is below this number. This only has an effect when `tickformat` is "SI" or "B". nticks Specifies the maximum number of ticks for the particular axis. The actual number of ticks will be chosen automatically to be less than or equal to `nticks`. Has an effect only if `tickmode` is set to "auto". outlinecolor Sets the axis line color. outlinewidth Sets the width (in px) of the axis line. separatethousands If "true", even 4-digit integers are separated showexponent If "all", all exponents are shown besides their significands. If "first", only the exponent of the first tick is shown. If "last", only the exponent of the last tick is shown. If "none", no exponents appear. showticklabels Determines whether or not the tick labels are drawn. showtickprefix If "all", all tick labels are displayed with a prefix. If "first", only the first tick is displayed with a prefix. If "last", only the last tick is displayed with a suffix. If "none", tick prefixes are hidden. showticksuffix Same as `showtickprefix` but for tick suffixes. thickness Sets the thickness of the color bar This measure excludes the size of the padding, ticks and labels. thicknessmode Determines whether this color bar's thickness (i.e. the measure in the constant color direction) is set in units of plot "fraction" or in "pixels". Use `thickness` to set the value. tick0 Sets the placement of the first tick on this axis. Use with `dtick`. If the axis `type` is "log", then you must take the log of your starting tick (e.g. to set the starting tick to 100, set the `tick0` to 2) except when `dtick`=*L<f>* (see `dtick` for more info). If the axis `type` is "date", it should be a date string, like date data. If the axis `type` is "category", it should be a number, using the scale where each category is assigned a serial number from zero in the order it appears. tickangle Sets the angle of the tick labels with respect to the horizontal. For example, a `tickangle` of -90 draws the tick labels vertically. tickcolor Sets the tick color. tickfont Sets the color bar's tick label font tickformat Sets the tick label formatting rule using d3 formatting mini-languages which are very similar to those in Python. For numbers, see: https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format And for dates see: https://github.com/d3/d3-time- format#locale_format We add one item to d3's date formatter: "%{n}f" for fractional seconds with n digits. For example, *2016-10-13 09:15:23.456* with tickformat "%H~%M~%S.%2f" would display "09~15~23.46" tickformatstops A tuple of :class:`plotly.graph_objects.volume. colorbar.Tickformatstop` instances or dicts with compatible properties tickformatstopdefaults When used in a template (as layout.template.dat a.volume.colorbar.tickformatstopdefaults), sets the default property values to use for elements of volume.colorbar.tickformatstops ticklabeloverflow Determines how we handle tick labels that would overflow either the graph div or the domain of the axis. The default value for inside tick labels is *hide past domain*. In other cases the default is *hide past div*. ticklabelposition Determines where tick labels are drawn. ticklen Sets the tick length (in px). tickmode Sets the tick mode for this axis. If "auto", the number of ticks is set via `nticks`. If "linear", the placement of the ticks is determined by a starting position `tick0` and a tick step `dtick` ("linear" is the default value if `tick0` and `dtick` are provided). If "array", the placement of the ticks is set via `tickvals` and the tick text is `ticktext`. ("array" is the default value if `tickvals` is provided). tickprefix Sets a tick label prefix. ticks Determines whether ticks are drawn or not. If "", this axis' ticks are not drawn. If "outside" ("inside"), this axis' are drawn outside (inside) the axis lines. ticksuffix Sets a tick label suffix. ticktext Sets the text displayed at the ticks position via `tickvals`. Only has an effect if `tickmode` is set to "array". Used with `tickvals`. ticktextsrc Sets the source reference on Chart Studio Cloud for ticktext . tickvals Sets the values at which ticks on this axis appear. Only has an effect if `tickmode` is set to "array". Used with `ticktext`. tickvalssrc Sets the source reference on Chart Studio Cloud for tickvals . tickwidth Sets the tick width (in px). title :class:`plotly.graph_objects.volume.colorbar.Ti tle` instance or dict with compatible properties titlefont Deprecated: Please use volume.colorbar.title.font instead. Sets this color bar's title font. Note that the title's font used to be set by the now deprecated `titlefont` attribute. titleside Deprecated: Please use volume.colorbar.title.side instead. Determines the location of color bar's title with respect to the color bar. Note that the title's location used to be set by the now deprecated `titleside` attribute. x Sets the x position of the color bar (in plot fraction). xanchor Sets this color bar's horizontal position anchor. This anchor binds the `x` position to the "left", "center" or "right" of the color bar. xpad Sets the amount of padding (in px) along the x direction. y Sets the y position of the color bar (in plot fraction). yanchor Sets this color bar's vertical position anchor This anchor binds the `y` position to the "top", "middle" or "bottom" of the color bar. ypad Sets the amount of padding (in px) along the y direction. """, ), **kwargs )

47.359833

79

0.526372

import _plotly_utils.basevalidators class ColorbarValidator(_plotly_utils.basevalidators.CompoundValidator): def __init__(self, plotly_name="colorbar", parent_name="volume", **kwargs): super(ColorbarValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, data_class_str=kwargs.pop("data_class_str", "ColorBar"), data_docs=kwargs.pop( "data_docs", """ bgcolor Sets the color of padded area. bordercolor Sets the axis line color. borderwidth Sets the width (in px) or the border enclosing this color bar. dtick Sets the step in-between ticks on this axis. Use with `tick0`. Must be a positive number, or special strings available to "log" and "date" axes. If the axis `type` is "log", then ticks are set every 10^(n*dtick) where n is the tick number. For example, to set a tick mark at 1, 10, 100, 1000, ... set dtick to 1. To set tick marks at 1, 100, 10000, ... set dtick to 2. To set tick marks at 1, 5, 25, 125, 625, 3125, ... set dtick to log_10(5), or 0.69897000433. "log" has several special values; "L<f>", where `f` is a positive number, gives ticks linearly spaced in value (but not position). For example `tick0` = 0.1, `dtick` = "L0.5" will put ticks at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10 plus small digits between, use "D1" (all digits) or "D2" (only 2 and 5). `tick0` is ignored for "D1" and "D2". If the axis `type` is "date", then you must convert the time to milliseconds. For example, to set the interval between ticks to one day, set `dtick` to 86400000.0. "date" also has special values "M<n>" gives ticks spaced by a number of months. `n` must be a positive integer. To set ticks on the 15th of every third month, set `tick0` to "2000-01-15" and `dtick` to "M3". To set ticks every 4 years, set `dtick` to "M48" exponentformat Determines a formatting rule for the tick exponents. For example, consider the number 1,000,000,000. If "none", it appears as 1,000,000,000. If "e", 1e+9. If "E", 1E+9. If "power", 1x10^9 (with 9 in a super script). If "SI", 1G. If "B", 1B. len Sets the length of the color bar This measure excludes the padding of both ends. That is, the color bar length is this length minus the padding on both ends. lenmode Determines whether this color bar's length (i.e. the measure in the color variation direction) is set in units of plot "fraction" or in *pixels. Use `len` to set the value. minexponent Hide SI prefix for 10^n if |n| is below this number. This only has an effect when `tickformat` is "SI" or "B". nticks Specifies the maximum number of ticks for the particular axis. The actual number of ticks will be chosen automatically to be less than or equal to `nticks`. Has an effect only if `tickmode` is set to "auto". outlinecolor Sets the axis line color. outlinewidth Sets the width (in px) of the axis line. separatethousands If "true", even 4-digit integers are separated showexponent If "all", all exponents are shown besides their significands. If "first", only the exponent of the first tick is shown. If "last", only the exponent of the last tick is shown. If "none", no exponents appear. showticklabels Determines whether or not the tick labels are drawn. showtickprefix If "all", all tick labels are displayed with a prefix. If "first", only the first tick is displayed with a prefix. If "last", only the last tick is displayed with a suffix. If "none", tick prefixes are hidden. showticksuffix Same as `showtickprefix` but for tick suffixes. thickness Sets the thickness of the color bar This measure excludes the size of the padding, ticks and labels. thicknessmode Determines whether this color bar's thickness (i.e. the measure in the constant color direction) is set in units of plot "fraction" or in "pixels". Use `thickness` to set the value. tick0 Sets the placement of the first tick on this axis. Use with `dtick`. If the axis `type` is "log", then you must take the log of your starting tick (e.g. to set the starting tick to 100, set the `tick0` to 2) except when `dtick`=*L<f>* (see `dtick` for more info). If the axis `type` is "date", it should be a date string, like date data. If the axis `type` is "category", it should be a number, using the scale where each category is assigned a serial number from zero in the order it appears. tickangle Sets the angle of the tick labels with respect to the horizontal. For example, a `tickangle` of -90 draws the tick labels vertically. tickcolor Sets the tick color. tickfont Sets the color bar's tick label font tickformat Sets the tick label formatting rule using d3 formatting mini-languages which are very similar to those in Python. For numbers, see: https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format And for dates see: https://github.com/d3/d3-time- format#locale_format We add one item to d3's date formatter: "%{n}f" for fractional seconds with n digits. For example, *2016-10-13 09:15:23.456* with tickformat "%H~%M~%S.%2f" would display "09~15~23.46" tickformatstops A tuple of :class:`plotly.graph_objects.volume. colorbar.Tickformatstop` instances or dicts with compatible properties tickformatstopdefaults When used in a template (as layout.template.dat a.volume.colorbar.tickformatstopdefaults), sets the default property values to use for elements of volume.colorbar.tickformatstops ticklabeloverflow Determines how we handle tick labels that would overflow either the graph div or the domain of the axis. The default value for inside tick labels is *hide past domain*. In other cases the default is *hide past div*. ticklabelposition Determines where tick labels are drawn. ticklen Sets the tick length (in px). tickmode Sets the tick mode for this axis. If "auto", the number of ticks is set via `nticks`. If "linear", the placement of the ticks is determined by a starting position `tick0` and a tick step `dtick` ("linear" is the default value if `tick0` and `dtick` are provided). If "array", the placement of the ticks is set via `tickvals` and the tick text is `ticktext`. ("array" is the default value if `tickvals` is provided). tickprefix Sets a tick label prefix. ticks Determines whether ticks are drawn or not. If "", this axis' ticks are not drawn. If "outside" ("inside"), this axis' are drawn outside (inside) the axis lines. ticksuffix Sets a tick label suffix. ticktext Sets the text displayed at the ticks position via `tickvals`. Only has an effect if `tickmode` is set to "array". Used with `tickvals`. ticktextsrc Sets the source reference on Chart Studio Cloud for ticktext . tickvals Sets the values at which ticks on this axis appear. Only has an effect if `tickmode` is set to "array". Used with `ticktext`. tickvalssrc Sets the source reference on Chart Studio Cloud for tickvals . tickwidth Sets the tick width (in px). title :class:`plotly.graph_objects.volume.colorbar.Ti tle` instance or dict with compatible properties titlefont Deprecated: Please use volume.colorbar.title.font instead. Sets this color bar's title font. Note that the title's font used to be set by the now deprecated `titlefont` attribute. titleside Deprecated: Please use volume.colorbar.title.side instead. Determines the location of color bar's title with respect to the color bar. Note that the title's location used to be set by the now deprecated `titleside` attribute. x Sets the x position of the color bar (in plot fraction). xanchor Sets this color bar's horizontal position anchor. This anchor binds the `x` position to the "left", "center" or "right" of the color bar. xpad Sets the amount of padding (in px) along the x direction. y Sets the y position of the color bar (in plot fraction). yanchor Sets this color bar's vertical position anchor This anchor binds the `y` position to the "top", "middle" or "bottom" of the color bar. ypad Sets the amount of padding (in px) along the y direction. """, ), **kwargs )

true

79080d194f59b7ebce045ab3e3d262ca948d9391

22,561

py

Python

tensorflow/contrib/linalg/python/ops/linear_operator_kronecker.py

tucaiyong/tensorflow

3cc3c87f375f1bc292bd58db4928b810ac888bc6

[ "Apache-2.0" ]

5

2018-09-22T20:16:46.000Z

2022-02-28T10:35:19.000Z

tensorflow/contrib/linalg/python/ops/linear_operator_kronecker.py

tucaiyong/tensorflow

3cc3c87f375f1bc292bd58db4928b810ac888bc6

[ "Apache-2.0" ]

10

2018-02-04T18:41:52.000Z

2018-05-02T09:00:46.000Z

tensorflow/contrib/linalg/python/ops/linear_operator_kronecker.py

tucaiyong/tensorflow

3cc3c87f375f1bc292bd58db4928b810ac888bc6

[ "Apache-2.0" ]

4

2018-01-17T14:22:49.000Z

2018-02-27T15:06:41.000Z

# Copyright 2018 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. # ============================================================================== """Construct the Kronecker product of one or more `LinearOperators`.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.framework import common_shapes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.linalg import linalg_impl as linalg from tensorflow.python.ops.linalg import linear_operator def _vec(x): """Stacks column of matrix to form a single column.""" return array_ops.reshape( array_ops.matrix_transpose(x), array_ops.concat( [array_ops.shape(x)[:-2], [-1]], axis=0)) def _unvec_by(y, num_col): """Unstack vector to form a matrix, with a specified amount of columns.""" return array_ops.matrix_transpose( array_ops.reshape( y, array_ops.concat( [array_ops.shape(y)[:-1], [num_col, -1]], axis=0))) def _rotate_last_dim(x, rotate_right=False): """Rotate the last dimension either left or right.""" ndims = array_ops.rank(x) if rotate_right: transpose_perm = array_ops.concat( [[ndims - 1], math_ops.range(0, ndims - 1)], axis=0) else: transpose_perm = array_ops.concat( [math_ops.range(1, ndims), [0]], axis=0) return array_ops.transpose(x, transpose_perm) class LinearOperatorKronecker(linear_operator.LinearOperator): """Kronecker product between two `LinearOperators`. This operator composes one or more linear operators `[op1,...,opJ]`, building a new `LinearOperator` representing the Kronecker product: `op1 x op2 x .. opJ` (we omit parentheses as the Kronecker product is associative). If `opj` has shape `batch_shape_j` + [M_j, N_j`, then the composed operator will have shape equal to `broadcast_batch_shape + [prod M_j, prod N_j]`, where the product is over all operators. ```python # Create a 4 x 4 linear operator composed of two 2 x 2 operators. operator_1 = LinearOperatorFullMatrix([[1., 2.], [3., 4.]]) operator_2 = LinearOperatorFullMatrix([[1., 0.], [2., 1.]]) operator = LinearOperatorKronecker([operator_1, operator_2]) operator.to_dense() ==> [[1., 2., 0., 0.], [3., 4., 0., 0.], [2., 4., 1., 2.], [6., 8., 3., 4.]] operator.shape ==> [4, 4] operator.log_abs_determinant() ==> scalar Tensor x = ... Shape [4, 2] Tensor operator.matmul(x) ==> Shape [4, 2] Tensor # Create a [2, 3] batch of 4 x 5 linear operators. matrix_45 = tf.random_normal(shape=[2, 3, 4, 5]) operator_45 = LinearOperatorFullMatrix(matrix) # Create a [2, 3] batch of 5 x 6 linear operators. matrix_56 = tf.random_normal(shape=[2, 3, 5, 6]) operator_56 = LinearOperatorFullMatrix(matrix_56) # Compose to create a [2, 3] batch of 20 x 30 operators. operator_large = LinearOperatorKronecker([operator_45, operator_56]) # Create a shape [2, 3, 20, 2] vector. x = tf.random_normal(shape=[2, 3, 6, 2]) operator_large.matmul(x) ==> Shape [2, 3, 30, 2] Tensor ``` #### Performance The performance of `LinearOperatorKronecker` on any operation is equal to the sum of the individual operators' operations. #### Matrix property hints This `LinearOperator` is initialized with boolean flags of the form `is_X`, for `X = non_singular, self_adjoint, positive_definite, square`. These have the following meaning: * If `is_X == True`, callers should expect the operator to have the property `X`. This is a promise that should be fulfilled, but is *not* a runtime assert. For example, finite floating point precision may result in these promises being violated. * If `is_X == False`, callers should expect the operator to not have `X`. * If `is_X == None` (the default), callers should have no expectation either way. """ def __init__(self, operators, is_non_singular=None, is_self_adjoint=None, is_positive_definite=None, is_square=None, name=None): r"""Initialize a `LinearOperatorKronecker`. `LinearOperatorKronecker` is initialized with a list of operators `[op_1,...,op_J]`. Args: operators: Iterable of `LinearOperator` objects, each with the same `dtype` and composable shape, representing the Kronecker factors. is_non_singular: Expect that this operator is non-singular. is_self_adjoint: Expect that this operator is equal to its hermitian transpose. is_positive_definite: Expect that this operator is positive definite, meaning the quadratic form `x^H A x` has positive real part for all nonzero `x`. Note that we do not require the operator to be self-adjoint to be positive-definite. See: https://en.wikipedia.org/wiki/Positive-definite_matrix\ #Extension_for_non_symmetric_matrices is_square: Expect that this operator acts like square [batch] matrices. name: A name for this `LinearOperator`. Default is the individual operators names joined with `_x_`. Raises: TypeError: If all operators do not have the same `dtype`. ValueError: If `operators` is empty. """ # Validate operators. check_ops.assert_proper_iterable(operators) operators = list(operators) if not operators: raise ValueError( "Expected a list of >=1 operators. Found: %s" % operators) self._operators = operators # Validate dtype. dtype = operators[0].dtype for operator in operators: if operator.dtype != dtype: name_type = (str((o.name, o.dtype)) for o in operators) raise TypeError( "Expected all operators to have the same dtype. Found %s" % " ".join(name_type)) # Auto-set and check hints. # A Kronecker product is invertible, if and only if all factors are # invertible. if all(operator.is_non_singular for operator in operators): if is_non_singular is False: raise ValueError( "The Kronecker product of non-singular operators is always " "non-singular.") is_non_singular = True if all(operator.is_self_adjoint for operator in operators): if is_self_adjoint is False: raise ValueError( "The Kronecker product of self-adjoint operators is always " "self-adjoint.") is_self_adjoint = True # The eigenvalues of a Kronecker product are equal to the products of eigen # values of the corresponding factors. if all(operator.is_positive_definite for operator in operators): if is_positive_definite is False: raise ValueError("The Kronecker product of positive-definite operators " "is always positive-definite.") is_positive_definite = True # Initialization. graph_parents = [] for operator in operators: graph_parents.extend(operator.graph_parents) if name is None: name = operators[0].name for operator in operators[1:]: name += "_x_" + operator.name with ops.name_scope(name, values=graph_parents): super(LinearOperatorKronecker, self).__init__( dtype=dtype, graph_parents=graph_parents, is_non_singular=is_non_singular, is_self_adjoint=is_self_adjoint, is_positive_definite=is_positive_definite, is_square=is_square, name=name) @property def operators(self): return self._operators def _shape(self): # Get final matrix shape. domain_dimension = self.operators[0].domain_dimension for operator in self.operators[1:]: domain_dimension *= operator.domain_dimension range_dimension = self.operators[0].range_dimension for operator in self.operators[1:]: range_dimension *= operator.range_dimension matrix_shape = tensor_shape.TensorShape([ range_dimension, domain_dimension]) # Get broadcast batch shape. # broadcast_shape checks for compatibility. batch_shape = self.operators[0].batch_shape for operator in self.operators[1:]: batch_shape = common_shapes.broadcast_shape( batch_shape, operator.batch_shape) return batch_shape.concatenate(matrix_shape) def _shape_tensor(self): domain_dimension = self.operators[0].domain_dimension_tensor() for operator in self.operators[1:]: domain_dimension *= operator.domain_dimension_tensor() range_dimension = self.operators[0].range_dimension_tensor() for operator in self.operators[1:]: range_dimension *= operator.range_dimension_tensor() matrix_shape = [range_dimension, domain_dimension] # Get broadcast batch shape. # broadcast_shape checks for compatibility. batch_shape = self.operators[0].batch_shape_tensor() for operator in self.operators[1:]: batch_shape = array_ops.broadcast_dynamic_shape( batch_shape, operator.batch_shape_tensor()) return array_ops.concat((batch_shape, matrix_shape), 0) def _matmul(self, x, adjoint=False, adjoint_arg=False): # Here we heavily rely on Roth's column Lemma [1]: # (A x B) * vec X = vec BXA^T, # where vec stacks all the columns of the matrix under each other. In our # case, x represents a batch of vec X (i.e. we think of x as a batch of # column vectors, rather than a matrix). Each member of the batch can be # reshaped to a matrix (hence we get a batch of matrices). # We can iteratively apply this lemma by noting that if B is a Kronecker # product, then we can apply the lemma again. # [1] W. E. Roth, "On direct product matrices," # Bulletin of the American Mathematical Society, vol. 40, pp. 461-468, # 1934 # Efficiency # Naively doing the Kronecker product, by calculating the dense matrix and # applying it will can take cubic time in the size of domain_dimension # (assuming a square matrix). The other issue is that calculating the dense # matrix can be prohibitively expensive, in that it can take a large amount # of memory. # # This implementation avoids this memory blow up by only computing matmuls # with the factors. In this way, we don't have to realize the dense matrix. # In terms of complexity, if we have Kronecker Factors of size: # (n1, n1), (n2, n2), (n3, n3), ... (nJ, nJ), with N = \prod n_i, and we # have as input a [N, M] matrix, the naive approach would take O(N^2 M). # With this approach (ignoring reshaping of tensors and transposes for now), # the time complexity can be O(M * (\sum n_i) * N). There is also the # benefit of batched multiplication (In this example, the batch size is # roughly M * N) so this can be much faster. However, not factored in are # the costs of the several transposing of tensors, which can affect cache # behavior. # Below we document the shape manipulation for adjoint=False, # adjoint_arg=False, but the general case of different adjoints is still # handled. if adjoint_arg: x = linalg.adjoint(x) # Always add a batch dimension to enable broadcasting to work. batch_shape = array_ops.concat( [array_ops.ones_like(self.batch_shape_tensor()), [1, 1]], 0) x += array_ops.zeros(batch_shape, dtype=x.dtype.base_dtype) # x has shape [B, R, C], where B represent some number of batch dimensions, # R represents the number of rows, and C represents the number of columns. # In order to apply Roth's column lemma, we need to operate on a batch of # column vectors, so we reshape into a batch of column vectors. We put it # at the front to ensure that broadcasting between operators to the batch # dimensions B still works. output = _rotate_last_dim(x, rotate_right=True) # Also expand the shape to be [A, C, B, R]. The first dimension will be # used to accumulate dimensions from each operator matmul. output = output[array_ops.newaxis, ...] # In this loop, A is going to refer to the value of the accumulated # dimension. A = 1 at the start, and will end up being self.range_dimension. # V will refer to the last dimension. V = R at the start, and will end up # being 1 in the end. for operator in self.operators[:-1]: # Reshape output from [A, C, B, V] to be # [A, C, B, V / op.domain_dimension, op.domain_dimension] if adjoint: operator_dimension = operator.range_dimension_tensor() else: operator_dimension = operator.domain_dimension_tensor() output = _unvec_by(output, operator_dimension) # We are computing (XA^T) = (AX^T)^T. # output has [A, C, B, V / op.domain_dimension, op.domain_dimension], # which is being converted to: # [A, C, B, V / op.domain_dimension, op.range_dimension] output = array_ops.matrix_transpose(output) output = operator.matmul(output, adjoint=adjoint, adjoint_arg=False) output = array_ops.matrix_transpose(output) # Rearrange it to [A * op.range_dimension, C, B, V / op.domain_dimension] output = _rotate_last_dim(output, rotate_right=False) output = _vec(output) output = _rotate_last_dim(output, rotate_right=True) # After the loop, we will have # A = self.range_dimension / op[-1].range_dimension # V = op[-1].domain_dimension # We convert that using matvec to get: # [A, C, B, op[-1].range_dimension] output = self.operators[-1].matvec(output, adjoint=adjoint) # Rearrange shape to be [B1, ... Bn, self.range_dimension, C] output = _rotate_last_dim(output, rotate_right=False) output = _vec(output) output = _rotate_last_dim(output, rotate_right=False) if x.shape.is_fully_defined(): column_dim = x.shape[-1] broadcast_batch_shape = common_shapes.broadcast_shape( x.shape[:-2], self.batch_shape) if adjoint: matrix_dimensions = [self.domain_dimension, column_dim] else: matrix_dimensions = [self.range_dimension, column_dim] print("x: ", x) print("bathc_shape:", self.batch_shape) print("self.shape:", self.shape) print("output: ", output) output.set_shape(broadcast_batch_shape.concatenate( matrix_dimensions)) return output def _determinant(self): # Note that we have |X1 x X2| = |X1| ** n * |X2| ** m, where X1 is an m x m # matrix, and X2 is an n x n matrix. We can iteratively apply this property # to get the determinant of |X1 x X2 x X3 ...|. If T is the product of the # domain dimension of all operators, then we have: # |X1 x X2 x X3 ...| = # |X1| ** (T / m) * |X2 x X3 ... | ** m = # |X1| ** (T / m) * |X2| ** (m * (T / m) / n) * ... = # |X1| ** (T / m) * |X2| ** (T / n) * | X3 x X4... | ** (m * n) # And by doing induction we have product(|X_i| ** (T / dim(X_i))). total = self.domain_dimension_tensor() determinant = 1. for operator in self.operators: determinant *= operator.determinant() ** math_ops.cast( total / operator.domain_dimension_tensor(), dtype=operator.dtype) return determinant def _log_abs_determinant(self): # This will be sum((total / dim(x_i)) * log |X_i|) total = self.domain_dimension_tensor() log_abs_det = 0. for operator in self.operators: log_abs_det += operator.log_abs_determinant() * math_ops.cast( total / operator.domain_dimension_tensor(), dtype=operator.dtype) return log_abs_det def _trace(self): # tr(A x B) = tr(A) * tr(B) trace = 1. for operator in self.operators: trace *= operator.trace() return trace def _solve(self, rhs, adjoint=False, adjoint_arg=False): # Here we follow the same use of Roth's column lemma as in `matmul`, with # the key difference that we replace all `matmul` instances with `solve`. # This follows from the property that inv(A x B) = inv(A) x inv(B). # Below we document the shape manipulation for adjoint=False, # adjoint_arg=False, but the general case of different adjoints is still # handled. if adjoint_arg: rhs = linalg.adjoint(rhs) # Always add a batch dimension to enable broadcasting to work. batch_shape = array_ops.concat( [array_ops.ones_like(self.batch_shape_tensor()), [1, 1]], 0) rhs += array_ops.zeros(batch_shape, dtype=rhs.dtype.base_dtype) # rhs has shape [B, R, C], where B represent some number of batch # dimensions, # R represents the number of rows, and C represents the number of columns. # In order to apply Roth's column lemma, we need to operate on a batch of # column vectors, so we reshape into a batch of column vectors. We put it # at the front to ensure that broadcasting between operators to the batch # dimensions B still works. output = _rotate_last_dim(rhs, rotate_right=True) # Also expand the shape to be [A, C, B, R]. The first dimension will be # used to accumulate dimensions from each operator matmul. output = output[array_ops.newaxis, ...] # In this loop, A is going to refer to the value of the accumulated # dimension. A = 1 at the start, and will end up being self.range_dimension. # V will refer to the last dimension. V = R at the start, and will end up # being 1 in the end. for operator in self.operators[:-1]: # Reshape output from [A, C, B, V] to be # [A, C, B, V / op.domain_dimension, op.domain_dimension] if adjoint: operator_dimension = operator.range_dimension_tensor() else: operator_dimension = operator.domain_dimension_tensor() output = _unvec_by(output, operator_dimension) # We are computing (XA^-1^T) = (A^-1 X^T)^T. # output has [A, C, B, V / op.domain_dimension, op.domain_dimension], # which is being converted to: # [A, C, B, V / op.domain_dimension, op.range_dimension] output = array_ops.matrix_transpose(output) output = operator.solve(output, adjoint=adjoint, adjoint_arg=False) output = array_ops.matrix_transpose(output) # Rearrange it to [A * op.range_dimension, C, B, V / op.domain_dimension] output = _rotate_last_dim(output, rotate_right=False) output = _vec(output) output = _rotate_last_dim(output, rotate_right=True) # After the loop, we will have # A = self.range_dimension / op[-1].range_dimension # V = op[-1].domain_dimension # We convert that using matvec to get: # [A, C, B, op[-1].range_dimension] output = self.operators[-1].solvevec(output, adjoint=adjoint) # Rearrange shape to be [B1, ... Bn, self.range_dimension, C] output = _rotate_last_dim(output, rotate_right=False) output = _vec(output) output = _rotate_last_dim(output, rotate_right=False) if rhs.shape.is_fully_defined(): column_dim = rhs.shape[-1] broadcast_batch_shape = common_shapes.broadcast_shape( rhs.shape[:-2], self.batch_shape) if adjoint: matrix_dimensions = [self.domain_dimension, column_dim] else: matrix_dimensions = [self.range_dimension, column_dim] output.set_shape(broadcast_batch_shape.concatenate( matrix_dimensions)) return output def _diag_part(self): diag_part = self.operators[0].diag_part() for operator in self.operators[1:]: diag_part = diag_part[..., :, array_ops.newaxis] op_diag_part = operator.diag_part()[..., array_ops.newaxis, :] diag_part *= op_diag_part diag_part = array_ops.reshape( diag_part, shape=array_ops.concat( [array_ops.shape(diag_part)[:-2], [-1]], axis=0)) if self.range_dimension > self.domain_dimension: diag_dimension = self.domain_dimension else: diag_dimension = self.range_dimension diag_part.set_shape( self.batch_shape.concatenate(diag_dimension)) return diag_part def _to_dense(self): product = self.operators[0].to_dense() for operator in self.operators[1:]: # Product has shape [B, R1, 1, C1]. product = product[ ..., :, array_ops.newaxis, :, array_ops.newaxis] # Operator has shape [B, 1, R2, 1, C2]. op_to_mul = operator.to_dense()[ ..., array_ops.newaxis, :, array_ops.newaxis, :] # This is now [B, R1, R2, C1, C2]. product *= op_to_mul # Now merge together dimensions to get [B, R1 * R2, C1 * C2]. product = array_ops.reshape( product, shape=array_ops.concat( [array_ops.shape(product)[:-4], [array_ops.shape(product)[-4] * array_ops.shape(product)[-3], array_ops.shape(product)[-2] * array_ops.shape(product)[-1]] ], axis=0)) product.set_shape(self.shape) return product def _assert_non_singular(self): if all(operator.is_square for operator in self.operators): asserts = [operator.assert_non_singular() for operator in self.operators] return control_flow_ops.group(asserts) else: raise errors.InvalidArgumentError( node_def=None, op=None, message="All Kronecker factors must be " "square for the product to be invertible.") def _assert_self_adjoint(self): if all(operator.is_square for operator in self.operators): asserts = [operator.assert_self_adjoint() for operator in self.operators] return control_flow_ops.group(asserts) else: raise errors.InvalidArgumentError( node_def=None, op=None, message="All Kronecker factors must be " "square for the product to be self adjoint.")

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from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.framework import common_shapes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.linalg import linalg_impl as linalg from tensorflow.python.ops.linalg import linear_operator def _vec(x): return array_ops.reshape( array_ops.matrix_transpose(x), array_ops.concat( [array_ops.shape(x)[:-2], [-1]], axis=0)) def _unvec_by(y, num_col): return array_ops.matrix_transpose( array_ops.reshape( y, array_ops.concat( [array_ops.shape(y)[:-1], [num_col, -1]], axis=0))) def _rotate_last_dim(x, rotate_right=False): ndims = array_ops.rank(x) if rotate_right: transpose_perm = array_ops.concat( [[ndims - 1], math_ops.range(0, ndims - 1)], axis=0) else: transpose_perm = array_ops.concat( [math_ops.range(1, ndims), [0]], axis=0) return array_ops.transpose(x, transpose_perm) class LinearOperatorKronecker(linear_operator.LinearOperator): def __init__(self, operators, is_non_singular=None, is_self_adjoint=None, is_positive_definite=None, is_square=None, name=None): check_ops.assert_proper_iterable(operators) operators = list(operators) if not operators: raise ValueError( "Expected a list of >=1 operators. Found: %s" % operators) self._operators = operators dtype = operators[0].dtype for operator in operators: if operator.dtype != dtype: name_type = (str((o.name, o.dtype)) for o in operators) raise TypeError( "Expected all operators to have the same dtype. Found %s" % " ".join(name_type)) if all(operator.is_non_singular for operator in operators): if is_non_singular is False: raise ValueError( "The Kronecker product of non-singular operators is always " "non-singular.") is_non_singular = True if all(operator.is_self_adjoint for operator in operators): if is_self_adjoint is False: raise ValueError( "The Kronecker product of self-adjoint operators is always " "self-adjoint.") is_self_adjoint = True if all(operator.is_positive_definite for operator in operators): if is_positive_definite is False: raise ValueError("The Kronecker product of positive-definite operators " "is always positive-definite.") is_positive_definite = True graph_parents = [] for operator in operators: graph_parents.extend(operator.graph_parents) if name is None: name = operators[0].name for operator in operators[1:]: name += "_x_" + operator.name with ops.name_scope(name, values=graph_parents): super(LinearOperatorKronecker, self).__init__( dtype=dtype, graph_parents=graph_parents, is_non_singular=is_non_singular, is_self_adjoint=is_self_adjoint, is_positive_definite=is_positive_definite, is_square=is_square, name=name) @property def operators(self): return self._operators def _shape(self): domain_dimension = self.operators[0].domain_dimension for operator in self.operators[1:]: domain_dimension *= operator.domain_dimension range_dimension = self.operators[0].range_dimension for operator in self.operators[1:]: range_dimension *= operator.range_dimension matrix_shape = tensor_shape.TensorShape([ range_dimension, domain_dimension]) batch_shape = self.operators[0].batch_shape for operator in self.operators[1:]: batch_shape = common_shapes.broadcast_shape( batch_shape, operator.batch_shape) return batch_shape.concatenate(matrix_shape) def _shape_tensor(self): domain_dimension = self.operators[0].domain_dimension_tensor() for operator in self.operators[1:]: domain_dimension *= operator.domain_dimension_tensor() range_dimension = self.operators[0].range_dimension_tensor() for operator in self.operators[1:]: range_dimension *= operator.range_dimension_tensor() matrix_shape = [range_dimension, domain_dimension] batch_shape = self.operators[0].batch_shape_tensor() for operator in self.operators[1:]: batch_shape = array_ops.broadcast_dynamic_shape( batch_shape, operator.batch_shape_tensor()) return array_ops.concat((batch_shape, matrix_shape), 0) def _matmul(self, x, adjoint=False, adjoint_arg=False): # (A x B) * vec X = vec BXA^T, # where vec stacks all the columns of the matrix under each other. In our # case, x represents a batch of vec X (i.e. we think of x as a batch of # column vectors, rather than a matrix). Each member of the batch can be # reshaped to a matrix (hence we get a batch of matrices). # We can iteratively apply this lemma by noting that if B is a Kronecker # product, then we can apply the lemma again. # [1] W. E. Roth, "On direct product matrices," # Bulletin of the American Mathematical Society, vol. 40, pp. 461-468, # 1934 # Efficiency # Naively doing the Kronecker product, by calculating the dense matrix and # applying it will can take cubic time in the size of domain_dimension # (assuming a square matrix). The other issue is that calculating the dense # matrix can be prohibitively expensive, in that it can take a large amount # of memory. # # This implementation avoids this memory blow up by only computing matmuls # with the factors. In this way, we don't have to realize the dense matrix. if adjoint_arg: x = linalg.adjoint(x) batch_shape = array_ops.concat( [array_ops.ones_like(self.batch_shape_tensor()), [1, 1]], 0) x += array_ops.zeros(batch_shape, dtype=x.dtype.base_dtype) # column vectors, so we reshape into a batch of column vectors. We put it # at the front to ensure that broadcasting between operators to the batch # dimensions B still works. output = _rotate_last_dim(x, rotate_right=True) # Also expand the shape to be [A, C, B, R]. The first dimension will be # used to accumulate dimensions from each operator matmul. output = output[array_ops.newaxis, ...] # In this loop, A is going to refer to the value of the accumulated # dimension. A = 1 at the start, and will end up being self.range_dimension. # V will refer to the last dimension. V = R at the start, and will end up # being 1 in the end. for operator in self.operators[:-1]: # Reshape output from [A, C, B, V] to be # [A, C, B, V / op.domain_dimension, op.domain_dimension] if adjoint: operator_dimension = operator.range_dimension_tensor() else: operator_dimension = operator.domain_dimension_tensor() output = _unvec_by(output, operator_dimension) # We are computing (XA^T) = (AX^T)^T. # output has [A, C, B, V / op.domain_dimension, op.domain_dimension], # which is being converted to: # [A, C, B, V / op.domain_dimension, op.range_dimension] output = array_ops.matrix_transpose(output) output = operator.matmul(output, adjoint=adjoint, adjoint_arg=False) output = array_ops.matrix_transpose(output) # Rearrange it to [A * op.range_dimension, C, B, V / op.domain_dimension] output = _rotate_last_dim(output, rotate_right=False) output = _vec(output) output = _rotate_last_dim(output, rotate_right=True) # After the loop, we will have # A = self.range_dimension / op[-1].range_dimension # V = op[-1].domain_dimension # We convert that using matvec to get: # [A, C, B, op[-1].range_dimension] output = self.operators[-1].matvec(output, adjoint=adjoint) # Rearrange shape to be [B1, ... Bn, self.range_dimension, C] output = _rotate_last_dim(output, rotate_right=False) output = _vec(output) output = _rotate_last_dim(output, rotate_right=False) if x.shape.is_fully_defined(): column_dim = x.shape[-1] broadcast_batch_shape = common_shapes.broadcast_shape( x.shape[:-2], self.batch_shape) if adjoint: matrix_dimensions = [self.domain_dimension, column_dim] else: matrix_dimensions = [self.range_dimension, column_dim] print("x: ", x) print("bathc_shape:", self.batch_shape) print("self.shape:", self.shape) print("output: ", output) output.set_shape(broadcast_batch_shape.concatenate( matrix_dimensions)) return output def _determinant(self): # Note that we have |X1 x X2| = |X1| ** n * |X2| ** m, where X1 is an m x m # matrix, and X2 is an n x n matrix. We can iteratively apply this property # to get the determinant of |X1 x X2 x X3 ...|. If T is the product of the # domain dimension of all operators, then we have: # |X1 x X2 x X3 ...| = # |X1| ** (T / m) * |X2 x X3 ... | ** m = # |X1| ** (T / m) * |X2| ** (m * (T / m) / n) * ... = # |X1| ** (T / m) * |X2| ** (T / n) * | X3 x X4... | ** (m * n) # And by doing induction we have product(|X_i| ** (T / dim(X_i))). total = self.domain_dimension_tensor() determinant = 1. for operator in self.operators: determinant *= operator.determinant() ** math_ops.cast( total / operator.domain_dimension_tensor(), dtype=operator.dtype) return determinant def _log_abs_determinant(self): # This will be sum((total / dim(x_i)) * log |X_i|) total = self.domain_dimension_tensor() log_abs_det = 0. for operator in self.operators: log_abs_det += operator.log_abs_determinant() * math_ops.cast( total / operator.domain_dimension_tensor(), dtype=operator.dtype) return log_abs_det def _trace(self): # tr(A x B) = tr(A) * tr(B) trace = 1. for operator in self.operators: trace *= operator.trace() return trace def _solve(self, rhs, adjoint=False, adjoint_arg=False): # Here we follow the same use of Roth's column lemma as in `matmul`, with if adjoint_arg: rhs = linalg.adjoint(rhs) batch_shape = array_ops.concat( [array_ops.ones_like(self.batch_shape_tensor()), [1, 1]], 0) rhs += array_ops.zeros(batch_shape, dtype=rhs.dtype.base_dtype) # column vectors, so we reshape into a batch of column vectors. We put it # at the front to ensure that broadcasting between operators to the batch # dimensions B still works. output = _rotate_last_dim(rhs, rotate_right=True) # Also expand the shape to be [A, C, B, R]. The first dimension will be # used to accumulate dimensions from each operator matmul. output = output[array_ops.newaxis, ...] # In this loop, A is going to refer to the value of the accumulated # dimension. A = 1 at the start, and will end up being self.range_dimension. # V will refer to the last dimension. V = R at the start, and will end up # being 1 in the end. for operator in self.operators[:-1]: # Reshape output from [A, C, B, V] to be # [A, C, B, V / op.domain_dimension, op.domain_dimension] if adjoint: operator_dimension = operator.range_dimension_tensor() else: operator_dimension = operator.domain_dimension_tensor() output = _unvec_by(output, operator_dimension) # We are computing (XA^-1^T) = (A^-1 X^T)^T. # output has [A, C, B, V / op.domain_dimension, op.domain_dimension], # which is being converted to: # [A, C, B, V / op.domain_dimension, op.range_dimension] output = array_ops.matrix_transpose(output) output = operator.solve(output, adjoint=adjoint, adjoint_arg=False) output = array_ops.matrix_transpose(output) # Rearrange it to [A * op.range_dimension, C, B, V / op.domain_dimension] output = _rotate_last_dim(output, rotate_right=False) output = _vec(output) output = _rotate_last_dim(output, rotate_right=True) # After the loop, we will have # A = self.range_dimension / op[-1].range_dimension # V = op[-1].domain_dimension # We convert that using matvec to get: # [A, C, B, op[-1].range_dimension] output = self.operators[-1].solvevec(output, adjoint=adjoint) # Rearrange shape to be [B1, ... Bn, self.range_dimension, C] output = _rotate_last_dim(output, rotate_right=False) output = _vec(output) output = _rotate_last_dim(output, rotate_right=False) if rhs.shape.is_fully_defined(): column_dim = rhs.shape[-1] broadcast_batch_shape = common_shapes.broadcast_shape( rhs.shape[:-2], self.batch_shape) if adjoint: matrix_dimensions = [self.domain_dimension, column_dim] else: matrix_dimensions = [self.range_dimension, column_dim] output.set_shape(broadcast_batch_shape.concatenate( matrix_dimensions)) return output def _diag_part(self): diag_part = self.operators[0].diag_part() for operator in self.operators[1:]: diag_part = diag_part[..., :, array_ops.newaxis] op_diag_part = operator.diag_part()[..., array_ops.newaxis, :] diag_part *= op_diag_part diag_part = array_ops.reshape( diag_part, shape=array_ops.concat( [array_ops.shape(diag_part)[:-2], [-1]], axis=0)) if self.range_dimension > self.domain_dimension: diag_dimension = self.domain_dimension else: diag_dimension = self.range_dimension diag_part.set_shape( self.batch_shape.concatenate(diag_dimension)) return diag_part def _to_dense(self): product = self.operators[0].to_dense() for operator in self.operators[1:]: # Product has shape [B, R1, 1, C1]. product = product[ ..., :, array_ops.newaxis, :, array_ops.newaxis] # Operator has shape [B, 1, R2, 1, C2]. op_to_mul = operator.to_dense()[ ..., array_ops.newaxis, :, array_ops.newaxis, :] # This is now [B, R1, R2, C1, C2]. product *= op_to_mul # Now merge together dimensions to get [B, R1 * R2, C1 * C2]. product = array_ops.reshape( product, shape=array_ops.concat( [array_ops.shape(product)[:-4], [array_ops.shape(product)[-4] * array_ops.shape(product)[-3], array_ops.shape(product)[-2] * array_ops.shape(product)[-1]] ], axis=0)) product.set_shape(self.shape) return product def _assert_non_singular(self): if all(operator.is_square for operator in self.operators): asserts = [operator.assert_non_singular() for operator in self.operators] return control_flow_ops.group(asserts) else: raise errors.InvalidArgumentError( node_def=None, op=None, message="All Kronecker factors must be " "square for the product to be invertible.") def _assert_self_adjoint(self): if all(operator.is_square for operator in self.operators): asserts = [operator.assert_self_adjoint() for operator in self.operators] return control_flow_ops.group(asserts) else: raise errors.InvalidArgumentError( node_def=None, op=None, message="All Kronecker factors must be " "square for the product to be self adjoint.")

true

79080d455b977e90b4d287d5c1fbf40379286d55

1,431

py

Python

dajare_detector/featurize/make_decide_kana_feature.py

vaaaaanquish/dajare-detector

e8f2d6c861dc0e03b6bc38ba64463bf95376f949

[ "MIT" ]

14

2020-12-11T01:42:53.000Z

2021-06-22T06:14:03.000Z

dajare_detector/featurize/make_decide_kana_feature.py

vaaaaanquish/dajare-detector

e8f2d6c861dc0e03b6bc38ba64463bf95376f949

[ "MIT" ]

null

dajare_detector/featurize/make_decide_kana_feature.py

vaaaaanquish/dajare-detector

e8f2d6c861dc0e03b6bc38ba64463bf95376f949

[ "MIT" ]

null

from logging import getLogger import gokart import luigi import swifter # noqa from dajare_detector.utils.base_task import DajareTask from dajare_detector.preprocessing.make_kana_pattern import MakeKanaPattern from dajare_detector.preprocessing.make_splited_pattern import MakeSplitedPattern from dajare_detector.preprocessing.decide_kana_pattern import DecideKanaPattern from dajare_detector.preprocessing.normalize_kana_pattern import NormalizeKanaPattern logger = getLogger(__name__) class MakeDecideKanaFeature(DajareTask): """カタカナの繰り返しが発生したか""" target = gokart.TaskInstanceParameter() split_window_size = luigi.IntParameter() def requires(self): kana_task = NormalizeKanaPattern(target=MakeKanaPattern( target=self.target)) split_task = MakeSplitedPattern( target=kana_task, split_window_size=self.split_window_size) return DecideKanaPattern(split_pattern_target=split_task, kana_pattern_target=kana_task, split_window_size=self.split_window_size) def run(self): df = self.load_data_frame().reset_index(drop=True) df[f'decide_kana_{self.split_window_size}'] = df[ 'decide_kana_flag_list'].swifter.apply(lambda x: 1 if any(x) else 0) self.dump(df[['_id', f'decide_kana_{self.split_window_size}']])

39.75

85

0.715584

from logging import getLogger import gokart import luigi import swifter from dajare_detector.utils.base_task import DajareTask from dajare_detector.preprocessing.make_kana_pattern import MakeKanaPattern from dajare_detector.preprocessing.make_splited_pattern import MakeSplitedPattern from dajare_detector.preprocessing.decide_kana_pattern import DecideKanaPattern from dajare_detector.preprocessing.normalize_kana_pattern import NormalizeKanaPattern logger = getLogger(__name__) class MakeDecideKanaFeature(DajareTask): target = gokart.TaskInstanceParameter() split_window_size = luigi.IntParameter() def requires(self): kana_task = NormalizeKanaPattern(target=MakeKanaPattern( target=self.target)) split_task = MakeSplitedPattern( target=kana_task, split_window_size=self.split_window_size) return DecideKanaPattern(split_pattern_target=split_task, kana_pattern_target=kana_task, split_window_size=self.split_window_size) def run(self): df = self.load_data_frame().reset_index(drop=True) df[f'decide_kana_{self.split_window_size}'] = df[ 'decide_kana_flag_list'].swifter.apply(lambda x: 1 if any(x) else 0) self.dump(df[['_id', f'decide_kana_{self.split_window_size}']])

true

79080dd0aea28217f017a0122279bc4a555f92ba

7,766

py

Python

analysis/opensimulator-stats-analyzer/src/osta/osta.py

second-life/opensimulator-tools

0a0bee66dee0fc93fd0b2dd5043675dc9ec305f1

[ "BSD-3-Clause-Clear" ]

11

2016-01-05T14:25:18.000Z

2022-01-08T07:45:09.000Z

analysis/opensimulator-stats-analyzer/src/osta/osta.py

ConnectionMaster/opensimulator-tools

0a0bee66dee0fc93fd0b2dd5043675dc9ec305f1

[ "BSD-3-Clause-Clear" ]

1

2021-05-30T07:54:55.000Z

2021-12-26T02:26:51.000Z

analysis/opensimulator-stats-analyzer/src/osta/osta.py

ConnectionMaster/opensimulator-tools

0a0bee66dee0fc93fd0b2dd5043675dc9ec305f1

[ "BSD-3-Clause-Clear" ]

14

2016-04-13T01:15:54.000Z

2021-01-07T19:50:14.000Z

import argparse import collections import fnmatch import os.path import pprint import re import sys ####################### ### OSimStatsHelper ### ####################### class OSimStatsHelper: """Takes a list of stats and returns a stat containing their summation by each sample.""" @staticmethod def sumStats(stats): totalStat = { 'abs' : { 'units' : stats[0]['abs']['units'] }, 'category' : stats[0]['category'], 'container' : "Total", 'name' : stats[0]['name'], 'fullName' : ".".join((stats[0]['category'], "Total", stats[0]['name'])) } totalStat['abs']['values'] = OSimStatsHelper.sumStatsToValues(stats, 'abs') #print "Summing %s" % (totalStat['name']) if 'delta' in stats[0]: totalStat['delta'] = { 'units' : stats[0]['delta']['units'] } totalStat['delta']['values'] = OSimStatsHelper.sumStatsToValues(stats, 'delta') return totalStat @staticmethod def sumStatsToValues(stats, type): totals = [] for stat in stats: values = stat[type]['values'] for i in range(0, len(values)): if i + 1 > len(totals): totals.append(values[i]) else: totals[i] += values[i] return totals @staticmethod def splitStatsFullName(fullName): return statNamePartsRe.match(fullName).groups(); #lineRe = re.compile("(.* .*) - (.*) : (\d+)[ ,]([^:]*)") #lineRe = re.compile("(.* .*) - (.*) : (?P<abs>[\d\.-]+)(?: (?:\D+))?(?P<delta>[\d\.-]+)?") lineRe = re.compile("(.* .*) - (.*) : (?P<abs>[^,]+)(?:, )?(?P<delta>[^,]+)?") statsReportStartRe = re.compile(" - \*\*\* STATS REPORT AT") statNamePartsRe = re.compile("^(.*?)\.(.*)\.(.*?)$"); valueRe = re.compile("([^ %/]+)(.*)") ####################### ### OSimStatsCorpus ### ####################### class OSimStatsCorpus: _data = {} _samplesCount = 0 @property def data(self): return self._data def __init__(self): self.clear() def __len__(self): return self._samplesCount @staticmethod def parseValue(rawValue, valueRe): valueMatch = valueRe.match(rawValue) return float(valueMatch.group(1)), valueMatch.group(2) def getStat(self, statFullName): """ Get a statistic given its full name. FIXME: Does not allow one to interrogate a given set yet. """ if self._data == None: return None (category, container, name) = OSimStatsHelper.splitStatsFullName(statFullName); for set in self._data.items(): if category in set and container in set[category] and name in set[category][container]: return set[category][container][name] else: return None def getStats(self, setGlob = "*", selectGlob = "*"): """ Returns a dictionary of stats where fullName => stat. If glob is specified then this is used to match stats using their full name If no stats are found then an empty dictionary is returned. """ if selectGlob == None: selectGlob = "*" if setGlob == None: setGlob = "*" matchingStats = collections.OrderedDict() for setName, set in self._data.items(): if fnmatch.fnmatch(setName, setGlob): for category, containers in set.items(): for container, stats in containers.items(): for statName, stat in stats.items(): if fnmatch.fnmatch(stat['fullName'], selectGlob): matchingStats[stat['fullName']] = stat return matchingStats def clear(self): """Clear out any existing dataset.""" self._data = {} self._samplesCount = 0 def load(self, path): """Load OpenSimulator stats log data from the given path and merge into any existing data.""" # Set structure # category : { # container : { # stat : { # 'abs' : { 'values' : [], 'units' : "" }, # 'delta' : { 'values' : [], 'units' : "" } # 'name' : string # 'fullName' : string # 'category' : string # 'container' : string # } # delta may not be present with open(path) as f: setName = os.path.splitext(os.path.basename(path))[0] print "Loading set %s" % (setName) if not setName in self._data: self._data[setName] = {} set = self.data[setName] for line in f: match = lineRe.match(line) if match != None: statFullName = match.group(2) #(category, container, name) = statFullName.split(".") (category, container, name) = OSimStatsHelper.splitStatsFullName(statFullName); rawValue = match.group("abs") #print match.lastindex #print rawValue value = OSimStatsCorpus.parseValue(rawValue, valueRe) if not category in set: set[category] = collections.OrderedDict() if not container in set[category]: set[category][container] = collections.OrderedDict() if not name in set[category][container]: entry = { 'abs' : { 'values' : [], 'units' : value[1] }, 'category' : category, 'container' : container, 'fullName' : statFullName, 'name' : name } set[category][container][name] = entry stat = set[category][container][name] stat['abs']['values'].append(value[0]) # Handle delta value if present if match.group("delta"): rawValue = match.group("delta") value = OSimStatsCorpus.parseValue(rawValue, valueRe) if not 'delta' in stat: stat['delta'] = { 'values' : [], 'units' : value[1] } stat['delta']['values'].append(value[0]) else: match = statsReportStartRe.search(line) if (match != None): self._samplesCount += 1 else: print "Ignoring [%s]" % (line)

38.068627

109

0.425573

import argparse import collections import fnmatch import os.path import pprint import re import sys .sumStatsToValues(stats, 'abs') if 'delta' in stats[0]: totalStat['delta'] = { 'units' : stats[0]['delta']['units'] } totalStat['delta']['values'] = OSimStatsHelper.sumStatsToValues(stats, 'delta') return totalStat @staticmethod def sumStatsToValues(stats, type): totals = [] for stat in stats: values = stat[type]['values'] for i in range(0, len(values)): if i + 1 > len(totals): totals.append(values[i]) else: totals[i] += values[i] return totals @staticmethod def splitStatsFullName(fullName): return statNamePartsRe.match(fullName).groups(); lineRe = re.compile("(.* .*) - (.*) : (?P<abs>[^,]+)(?:, )?(?P<delta>[^,]+)?") statsReportStartRe = re.compile(" - \*\*\* STATS REPORT AT") statNamePartsRe = re.compile("^(.*?)\.(.*)\.(.*?)$"); valueRe = re.compile("([^ %/]+)(.*)") es not allow one to interrogate a given set yet. """ if self._data == None: return None (category, container, name) = OSimStatsHelper.splitStatsFullName(statFullName); for set in self._data.items(): if category in set and container in set[category] and name in set[category][container]: return set[category][container][name] else: return None def getStats(self, setGlob = "*", selectGlob = "*"): """ Returns a dictionary of stats where fullName => stat. If glob is specified then this is used to match stats using their full name If no stats are found then an empty dictionary is returned. """ if selectGlob == None: selectGlob = "*" if setGlob == None: setGlob = "*" matchingStats = collections.OrderedDict() for setName, set in self._data.items(): if fnmatch.fnmatch(setName, setGlob): for category, containers in set.items(): for container, stats in containers.items(): for statName, stat in stats.items(): if fnmatch.fnmatch(stat['fullName'], selectGlob): matchingStats[stat['fullName']] = stat return matchingStats def clear(self): """Clear out any existing dataset.""" self._data = {} self._samplesCount = 0 def load(self, path): """Load OpenSimulator stats log data from the given path and merge into any existing data.""" with open(path) as f: setName = os.path.splitext(os.path.basename(path))[0] print "Loading set %s" % (setName) if not setName in self._data: self._data[setName] = {} set = self.data[setName] for line in f: match = lineRe.match(line) if match != None: statFullName = match.group(2) (category, container, name) = OSimStatsHelper.splitStatsFullName(statFullName); rawValue = match.group("abs") value = OSimStatsCorpus.parseValue(rawValue, valueRe) if not category in set: set[category] = collections.OrderedDict() if not container in set[category]: set[category][container] = collections.OrderedDict() if not name in set[category][container]: entry = { 'abs' : { 'values' : [], 'units' : value[1] }, 'category' : category, 'container' : container, 'fullName' : statFullName, 'name' : name } set[category][container][name] = entry stat = set[category][container][name] stat['abs']['values'].append(value[0]) if match.group("delta"): rawValue = match.group("delta") value = OSimStatsCorpus.parseValue(rawValue, valueRe) if not 'delta' in stat: stat['delta'] = { 'values' : [], 'units' : value[1] } stat['delta']['values'].append(value[0]) else: match = statsReportStartRe.search(line) if (match != None): self._samplesCount += 1 else: print "Ignoring [%s]" % (line)

false

true

79080edfb9a52d85dbd60d7c0e19866dcde15e5c

2,446

py

Python

traffic_monitor/services/detectors/detector_cvlib.py

mcdomx/monitor

55082a3ea985224b819e4e2b7e13f44e70ac0b74

[ "MIT" ]

1

2020-09-23T14:36:30.000Z

traffic_monitor/services/detectors/detector_cvlib.py

mcdomx/monitor

55082a3ea985224b819e4e2b7e13f44e70ac0b74

[ "MIT" ]

3

2021-09-08T02:32:20.000Z

2022-03-12T00:49:29.000Z

traffic_monitor/services/detectors/detector_cvlib.py

mcdomx/monitor

55082a3ea985224b819e4e2b7e13f44e70ac0b74

[ "MIT" ]

null

import logging import numpy as np from cvlib.object_detection import populate_class_labels, draw_bbox, detect_common_objects from traffic_monitor.services.detectors.detector_abstract import DetectorAbstract logger = logging.getLogger('detector') class DetectorCVlib(DetectorAbstract): """ Implementation of DetectorAbstract. This implementation is from the OpenCV implementation of object instance detection. https://github.com/arunponnusamy/cvlib Yolov4 cfg and weights are available at: https://github.com/AlexeyAB/darknet Supports models: yolov3-tiny yolov3 Requires that .cfg file and .weights files are in ~/.cvlib/object_detection/yolo/yolov3 """ def __init__(self, monitor_config: dict): DetectorAbstract.__init__(self, monitor_config) self.detector_name: str = monitor_config.get('detector_name') self.detector_model: str = monitor_config.get('detector_model') self.detector_confidence: float = monitor_config.get('detector_confidence') # note that colors in cvlib uses BGR not RGB colors self.bgr_colors = np.float64([monitor_config.get('class_colors').get(o)[::-1] for o in populate_class_labels()]) def set_detector_value(self, kwargs_list: list): """ Only allow changes to confidence or the model """ try: for kwargs in kwargs_list: field = kwargs.get('field') value = kwargs.get('value') if field in ['detector_confidence', 'detector_model']: logger.info(f"{self.detector_name}: setting value: {field}: {value}") self.monitor_config[field] = value except Exception as e: logger.error(f"{self.__class__.__name__}: Error setting value: {e}") def detect(self, frame: np.array) -> (np.array, list): # colors is a list of BGR values in a list ([[#b,#g,#r],[#b,#g,#r], ... ]) try: bbox, labels, conf = detect_common_objects(frame, confidence=self.detector_confidence, model=self.detector_model) frame = draw_bbox(img=frame, bbox=bbox, labels=labels, confidence=conf, write_conf=False, colors=self.bgr_colors) return frame, labels except Exception as e: logger.error(f"{self.__class__.__name__} Exception: {e}") @classmethod def get_trained_objects(cls) -> list: return populate_class_labels()

41.457627

125

0.674571

import logging import numpy as np from cvlib.object_detection import populate_class_labels, draw_bbox, detect_common_objects from traffic_monitor.services.detectors.detector_abstract import DetectorAbstract logger = logging.getLogger('detector') class DetectorCVlib(DetectorAbstract): def __init__(self, monitor_config: dict): DetectorAbstract.__init__(self, monitor_config) self.detector_name: str = monitor_config.get('detector_name') self.detector_model: str = monitor_config.get('detector_model') self.detector_confidence: float = monitor_config.get('detector_confidence') self.bgr_colors = np.float64([monitor_config.get('class_colors').get(o)[::-1] for o in populate_class_labels()]) def set_detector_value(self, kwargs_list: list): try: for kwargs in kwargs_list: field = kwargs.get('field') value = kwargs.get('value') if field in ['detector_confidence', 'detector_model']: logger.info(f"{self.detector_name}: setting value: {field}: {value}") self.monitor_config[field] = value except Exception as e: logger.error(f"{self.__class__.__name__}: Error setting value: {e}") def detect(self, frame: np.array) -> (np.array, list): l=self.detector_model) frame = draw_bbox(img=frame, bbox=bbox, labels=labels, confidence=conf, write_conf=False, colors=self.bgr_colors) return frame, labels except Exception as e: logger.error(f"{self.__class__.__name__} Exception: {e}") @classmethod def get_trained_objects(cls) -> list: return populate_class_labels()

true

79080f4461a4c72524ec43c35bb46daf03bb2d9a

523

py

Python

manual/unicos/src-groups/script/is_sundanese_unicos.py

Tikubonn/unico

c76de5309f8a3a6fda3110e463b7e9718ea530e3

[ "MIT" ]

null

manual/unicos/src-groups/script/is_sundanese_unicos.py

Tikubonn/unico

c76de5309f8a3a6fda3110e463b7e9718ea530e3

[ "MIT" ]

null

manual/unicos/src-groups/script/is_sundanese_unicos.py

Tikubonn/unico

c76de5309f8a3a6fda3110e463b7e9718ea530e3

[ "MIT" ]

null

import json from lib import node from lib.generator import predicate_function from lib.generator import predicate_function_declaration with open("json/sundanese.json", "r") as stream: data = json.load(stream) nd = node.RootNode() for dat in data: nd.extend(dat, True) with open("dist/is_sundanese_unicos.h", "w") as stream: predicate_function_declaration.write("is_sundanese_unicos", stream) with open("dist/is_sundanese_unicos.c", "w") as stream: predicate_function.write("is_sundanese_unicos", nd, stream)

26.15

69

0.772467

import json from lib import node from lib.generator import predicate_function from lib.generator import predicate_function_declaration with open("json/sundanese.json", "r") as stream: data = json.load(stream) nd = node.RootNode() for dat in data: nd.extend(dat, True) with open("dist/is_sundanese_unicos.h", "w") as stream: predicate_function_declaration.write("is_sundanese_unicos", stream) with open("dist/is_sundanese_unicos.c", "w") as stream: predicate_function.write("is_sundanese_unicos", nd, stream)

true

79080f8192b6248770f4f2ca0ce09d129cf8bebf

2,933

py

Python

tests/core/test_visualization.py

n01deas/rasa

79f0feeb02919142eb06b8c52da5632f1c25c251

[ "Apache-2.0" ]

5

2019-06-06T08:59:15.000Z

2020-01-19T10:56:45.000Z

tests/core/test_visualization.py

RakibulAsheeque/rasa

7d3804cd081c73d78ab5e973f95a55845eed1e89

[ "Apache-2.0" ]

21

2019-12-16T17:37:54.000Z

2020-07-06T06:19:04.000Z

tests/core/test_visualization.py

RakibulAsheeque/rasa

7d3804cd081c73d78ab5e973f95a55845eed1e89

[ "Apache-2.0" ]

4

2019-05-19T21:19:32.000Z

2021-01-06T14:26:37.000Z

from rasa.core.events import ActionExecuted, SlotSet, UserUttered from rasa.core.training import visualization def test_style_transfer(): r = visualization._transfer_style({"class": "dashed great"}, {"class": "myclass"}) assert r["class"] == "myclass dashed" def test_style_transfer_empty(): r = visualization._transfer_style({"class": "dashed great"}, {"something": "else"}) assert r["class"] == "dashed" def test_common_action_prefix(): this = [ ActionExecuted("action_listen"), ActionExecuted("greet"), UserUttered("hey"), ActionExecuted("amazing"), # until this point they are the same SlotSet("my_slot", "a"), ActionExecuted("a"), ActionExecuted("after_a"), ] other = [ ActionExecuted("action_listen"), ActionExecuted("greet"), UserUttered("hey"), ActionExecuted("amazing"), # until this point they are the same SlotSet("my_slot", "b"), ActionExecuted("b"), ActionExecuted("after_b"), ] num_common = visualization._length_of_common_action_prefix(this, other) assert num_common == 3 def test_common_action_prefix_equal(): this = [ ActionExecuted("action_listen"), ActionExecuted("greet"), UserUttered("hey"), ActionExecuted("amazing"), ] other = [ ActionExecuted("action_listen"), ActionExecuted("greet"), UserUttered("hey"), ActionExecuted("amazing"), ] num_common = visualization._length_of_common_action_prefix(this, other) assert num_common == 3 def test_common_action_prefix_unequal(): this = [ ActionExecuted("action_listen"), ActionExecuted("greet"), UserUttered("hey"), ] other = [ ActionExecuted("greet"), ActionExecuted("action_listen"), UserUttered("hey"), ] num_common = visualization._length_of_common_action_prefix(this, other) assert num_common == 0 async def test_graph_persistence(default_domain, tmpdir): from os.path import isfile from networkx.drawing import nx_pydot from rasa.core.training.dsl import StoryFileReader from rasa.core.interpreter import RegexInterpreter story_steps = await StoryFileReader.read_from_file( "data/test_stories/stories.md", default_domain, interpreter=RegexInterpreter() ) out_file = tmpdir.join("graph.html").strpath generated_graph = await visualization.visualize_stories( story_steps, default_domain, output_file=out_file, max_history=3, should_merge_nodes=False, ) generated_graph = nx_pydot.to_pydot(generated_graph) assert isfile(out_file) with open(out_file, "r") as graph_file: content = graph_file.read() assert "isClient = true" in content assert "graph = `{}`".format(generated_graph.to_string()) in content

28.754902

87

0.65837

from rasa.core.events import ActionExecuted, SlotSet, UserUttered from rasa.core.training import visualization def test_style_transfer(): r = visualization._transfer_style({"class": "dashed great"}, {"class": "myclass"}) assert r["class"] == "myclass dashed" def test_style_transfer_empty(): r = visualization._transfer_style({"class": "dashed great"}, {"something": "else"}) assert r["class"] == "dashed" def test_common_action_prefix(): this = [ ActionExecuted("action_listen"), ActionExecuted("greet"), UserUttered("hey"), ActionExecuted("amazing"), SlotSet("my_slot", "a"), ActionExecuted("a"), ActionExecuted("after_a"), ] other = [ ActionExecuted("action_listen"), ActionExecuted("greet"), UserUttered("hey"), ActionExecuted("amazing"), SlotSet("my_slot", "b"), ActionExecuted("b"), ActionExecuted("after_b"), ] num_common = visualization._length_of_common_action_prefix(this, other) assert num_common == 3 def test_common_action_prefix_equal(): this = [ ActionExecuted("action_listen"), ActionExecuted("greet"), UserUttered("hey"), ActionExecuted("amazing"), ] other = [ ActionExecuted("action_listen"), ActionExecuted("greet"), UserUttered("hey"), ActionExecuted("amazing"), ] num_common = visualization._length_of_common_action_prefix(this, other) assert num_common == 3 def test_common_action_prefix_unequal(): this = [ ActionExecuted("action_listen"), ActionExecuted("greet"), UserUttered("hey"), ] other = [ ActionExecuted("greet"), ActionExecuted("action_listen"), UserUttered("hey"), ] num_common = visualization._length_of_common_action_prefix(this, other) assert num_common == 0 async def test_graph_persistence(default_domain, tmpdir): from os.path import isfile from networkx.drawing import nx_pydot from rasa.core.training.dsl import StoryFileReader from rasa.core.interpreter import RegexInterpreter story_steps = await StoryFileReader.read_from_file( "data/test_stories/stories.md", default_domain, interpreter=RegexInterpreter() ) out_file = tmpdir.join("graph.html").strpath generated_graph = await visualization.visualize_stories( story_steps, default_domain, output_file=out_file, max_history=3, should_merge_nodes=False, ) generated_graph = nx_pydot.to_pydot(generated_graph) assert isfile(out_file) with open(out_file, "r") as graph_file: content = graph_file.read() assert "isClient = true" in content assert "graph = `{}`".format(generated_graph.to_string()) in content

true

79080f9e4ddf161342b46cc0970ccca738e03a30

17,390

py

Python

openmdao/test_suite/components/cycle_comps.py

hwangjt/blue

609defbe476c86a4a2eddd12977b47e649ea7f50

[ "Apache-2.0" ]

null

openmdao/test_suite/components/cycle_comps.py

hwangjt/blue

609defbe476c86a4a2eddd12977b47e649ea7f50

[ "Apache-2.0" ]

null

openmdao/test_suite/components/cycle_comps.py

hwangjt/blue

609defbe476c86a4a2eddd12977b47e649ea7f50

[ "Apache-2.0" ]

null

"""Components for use in `CycleGroup`. For details, see `CycleGroup`.""" from __future__ import division, print_function from six.moves import range import numpy as np import scipy.sparse as sparse import unittest from openmdao.core.explicitcomponent import ExplicitComponent PSI = 1. _vec_terms = {} def _compute_vector_terms(system_size): # Try/Except pattern is much faster than if key in ... if the key is present (which it will be # outside of the first invocation). try: return _vec_terms[system_size] except KeyError: u = np.zeros(system_size) u[[0, -1]] = np.sqrt(2)/2 v = np.zeros(system_size) v[1:-1] = 1 / np.sqrt(system_size - 2) cross_terms = np.outer(v, u) - np.outer(u, v) same_terms = np.outer(u, u) + np.outer(v, v) _vec_terms[system_size] = u, v, cross_terms, same_terms return u, v, cross_terms, same_terms def _compute_A(system_size, theta): u, v, cross_terms, same_terms = _compute_vector_terms(system_size) return (np.eye(system_size) + np.sin(theta) * cross_terms + (np.cos(theta) - 1) * same_terms) def _compute_dA(system_size, theta): u, v, cross_terms, same_terms = _compute_vector_terms(system_size) return np.cos(theta) * cross_terms - np.sin(theta) * same_terms def array_idx(i, var_size): return slice(i * var_size, (i + 1) * var_size) class ExplicitCycleComp(ExplicitComponent): def _inputs_to_vector(self, inputs): var_shape = self.metadata['var_shape'] num_var = self.metadata['num_var'] size = np.prod(var_shape) x = np.zeros(num_var * size) for i in range(num_var): x_i = inputs[self._cycle_names['x'].format(i)].flat x[size * i:size * (i + 1)] = x_i return x def _vector_to_outputs(self, vec, outputs): var_shape = self.metadata['var_shape'] num_var = self.metadata['num_var'] size = np.prod(var_shape) for i in range(num_var): y_i = vec[size * i:size * (i + 1)].reshape(var_shape) outputs[self._cycle_names['y'].format(i)] = y_i def __str__(self): return 'Explicit Cycle Component' def initialize(self): self.metadata.declare('jacobian_type', default='matvec', values=['matvec', 'dense', 'sparse-coo', 'sparse-csr', 'sparse-csc'], desc='method of assembling derivatives') self.metadata.declare('partial_type', default='array', values=['array', 'sparse', 'aij'], desc='type of partial derivatives') self.metadata.declare('num_var', type_=int, default=1, desc='Number of variables per component') self.metadata.declare('var_shape', type_=tuple, default=(3,), desc='Shape of each variable') self.metadata.declare('index', type_=int, desc='Index of the component. Used for testing implicit connections') self.metadata.declare('connection_type', type_=str, default='explicit', values=['explicit', 'implicit'], desc='How to connect variables.') self.metadata.declare('finite_difference', default=False, type_=bool, desc='If the derivatives should be finite differenced.') self.metadata.declare('num_comp', type_=int, default=2, desc='Total number of components') self.angle_param = 'theta' self._cycle_names = {} def _init_parameterized(self): self.num_var = self.metadata['num_var'] self.var_shape = self.metadata['var_shape'] self.size = self.num_var * np.prod(self.var_shape) if self.metadata['jacobian_type'] == 'matvec': self.compute_jacvec_product = self.jacvec_product if self.metadata['connection_type'] == 'implicit': idx = self.metadata['index'] self._cycle_names['x'] = 'x_{}_{{}}'.format(idx) self._cycle_names['y'] = 'x_{}_{{}}'.format(idx + 1) self._cycle_names['theta'] = 'theta_{}'.format(idx) self._cycle_names['theta_out'] = 'theta_{}'.format(idx + 1) num_var = self.metadata['num_var'] self._cycle_promotes_in = [self._cycle_names['x'].format(i) for i in range(num_var)] self._cycle_promotes_out = [self._cycle_names['y'].format(i) for i in range(num_var)] self._cycle_promotes_in.append(self._cycle_names['theta']) self._cycle_promotes_out.append(self._cycle_names['theta_out']) else: self._cycle_names['x'] = 'x_{}' self._cycle_names['y'] = 'y_{}' self._cycle_names['theta'] = 'theta' self._cycle_names['theta_out'] = 'theta_out' self._cycle_promotes_in = self._cycle_promotes_out = [] def setup(self): for i in range(self.num_var): self.add_input(self._cycle_names['x'].format(i), shape=self.var_shape) self.add_output(self._cycle_names['y'].format(i), shape=self.var_shape) self.add_input(self._cycle_names['theta'], val=1.) self.add_output(self._cycle_names['theta_out'], shape=(1,)) # Setup partials pd_type = self.metadata['partial_type'] if self.metadata['finite_difference']: if self.metadata['jacobian_type'] == 'matvec': raise unittest.SkipTest('not testing FD and matvec') if pd_type != 'array': raise unittest.SkipTest('only dense FD supported') self.declare_partials('*', '*', method='fd') elif self.metadata['jacobian_type'] != 'matvec' and pd_type != 'array': num_var = self.num_var var_shape = self.var_shape var_size = np.prod(var_shape) A = np.ones((self.size, self.size)) dA_x = np.ones((self.size, 1)) dtheta = np.array([[1.]]) angle_param = self._cycle_names[self.angle_param] # if our subjacs are not dense, we must assign values here that # match their type (data values don't matter, only structure). # Otherwise, we assume they are dense and we'll get an error later # when we assign a subjac with a type that doesn't match. for out_idx in range(num_var): out_var = self._cycle_names['y'].format(out_idx) for in_idx in range(num_var): in_var = self._cycle_names['x'].format(in_idx) Aij = A[array_idx(out_idx, var_size), array_idx(in_idx, var_size)] self.declare_partials(out_var, in_var, **self._array2kwargs(Aij, pd_type)) self.declare_partials(out_var, angle_param, **self._array2kwargs(dA_x[array_idx(out_idx, var_size)], pd_type)) self.declare_partials(self._cycle_names['theta_out'], self._cycle_names['theta'], **self._array2kwargs(dtheta, pd_type)) else: # Declare everything self.declare_partials(of='*', wrt='*') def compute(self, inputs, outputs): theta = inputs[self._cycle_names['theta']] A = _compute_A(self.size, theta) x = self._inputs_to_vector(inputs) y = A.dot(x) self._vector_to_outputs(y, outputs) outputs[self._cycle_names['theta_out']] = theta def jacvec_product(self, inputs, d_inputs, d_outputs, mode): angle_param = self._cycle_names[self.angle_param] x = self._inputs_to_vector(inputs) angle = inputs[angle_param] A = _compute_A(self.size, angle) dA = _compute_dA(self.size, angle) var_shape = self.metadata['var_shape'] var_size = np.prod(var_shape) num_var = self.metadata['num_var'] x_name = self._cycle_names['x'] y_name = self._cycle_names['y'] theta_name = self._cycle_names['theta'] theta_out_name = self._cycle_names['theta_out'] if mode == 'fwd': for j in range(num_var): x_j = x_name.format(j) if x_j in d_inputs: dx = d_inputs[x_j].flat[:] for i in range(num_var): y_i = y_name.format(i) if y_i in d_outputs: Aij = A[array_idx(i, var_size), array_idx(j, var_size)] d_outputs[y_i] += Aij.dot(dx).reshape(var_shape) if theta_name in d_inputs and theta_out_name in d_outputs: dtheta = d_inputs[theta_name] d_outputs[theta_out_name] += dtheta if angle_param in d_inputs: dangle = d_inputs[angle_param] dy_dangle = (dA.dot(x)) * dangle for i in range(num_var): y_i = y_name.format(i) if y_i in d_outputs: d_outputs[y_i] += dy_dangle[array_idx(i, var_size)].reshape(var_shape) elif mode == 'rev': for i in range(num_var): y_i = y_name.format(i) if y_i in d_outputs: dy_i = d_outputs[y_i].flat[:] for j in range(num_var): x_j = x_name.format(j) if x_j in d_inputs: Aij = A[array_idx(i, var_size), array_idx(j, var_size)] d_inputs[x_j] += Aij.T.dot(dy_i).reshape(var_shape) if angle_param in d_inputs: dAij = dA[array_idx(i, var_size), array_idx(j, var_size)] x_j_vec = inputs[x_j].flat[:] d_inputs[angle_param] += x_j_vec.T.dot(dAij.T.dot(dy_i)) if theta_out_name in d_outputs and theta_name in d_inputs: dtheta_out = d_outputs[theta_out_name] d_inputs[theta_name] += dtheta_out def make_jacobian_entry(self, A, pd_type): if pd_type == 'aij': return self.make_sub_jacobian(A, pd_type)[0] return self.make_sub_jacobian(A, pd_type) def make_sub_jacobian(self, A, pd_type): if pd_type == 'array': return A if pd_type == 'sparse': return sparse.csr_matrix(A) if pd_type == 'aij': data = [] rows = [] cols = [] A = np.atleast_2d(A) for i in range(A.shape[0]): for j in range(A.shape[1]): if np.abs(A[i, j]) > 1e-15: data.append(A[i, j]) rows.append(i) cols.append(j) return [np.array(data), np.array(rows), np.array(cols)] raise ValueError('Unknown partial_type: {}'.format(pd_type)) def _array2kwargs(self, arr, pd_type): jac = self.make_sub_jacobian(arr, pd_type) if pd_type == 'aij': return {'val': jac[0], 'rows': jac[1], 'cols': jac[2]} else: return {'val': jac} def compute_partials(self, inputs, partials): if self.metadata['jacobian_type'] != 'matvec' and not self.metadata['finite_difference']: angle_param = self._cycle_names[self.angle_param] angle = inputs[angle_param] num_var = self.num_var var_shape = self.var_shape var_size = np.prod(var_shape) x = self._inputs_to_vector(inputs) size = self.size A = _compute_A(size, angle) dA = _compute_dA(size, angle) dA_x = np.atleast_2d(dA.dot(x)).T pd_type = self.metadata['partial_type'] dtheta = np.array([[1.]]) y_name = self._cycle_names['y'] x_name = self._cycle_names['x'] for out_idx in range(num_var): out_var = y_name.format(out_idx) for in_idx in range(num_var): in_var = x_name.format(in_idx) Aij = A[array_idx(out_idx, var_size), array_idx(in_idx, var_size)] J_y_x = self.make_jacobian_entry(Aij, pd_type) J_y_angle = self.make_jacobian_entry(dA_x[array_idx(out_idx, var_size)], pd_type) partials[out_var, in_var] = J_y_x partials[out_var, angle_param] = J_y_angle theta_out = self._cycle_names['theta_out'] theta = self._cycle_names['theta'] partials[theta_out, theta] = self.make_jacobian_entry(dtheta, pd_type) class ExplicitFirstComp(ExplicitCycleComp): def __str__(self): return 'Explicit Cycle Component - First' def setup(self): self.add_input('psi', val=1.) self.angle_param = 'psi' self._cycle_names['psi'] = 'psi' super(ExplicitFirstComp, self).setup() def compute(self, inputs, outputs): theta = inputs[self._cycle_names['theta']] psi = inputs[self._cycle_names['psi']] A = _compute_A(self.size, psi) y = A.dot(np.ones(self.size)) self._vector_to_outputs(y, outputs) outputs[self._cycle_names['theta_out']] = theta class ExplicitLastComp(ExplicitFirstComp): def __str__(self): return 'Explicit Cycle Component - Last' def setup(self): super(ExplicitLastComp, self).setup() self.add_output('x_norm2', shape=(1,)) self._n = 1 # Setup partials pd_type = self.metadata['partial_type'] if self.metadata['jacobian_type'] != 'matvec' and pd_type != 'array': x = np.ones(self.var_shape) for i in range(self.metadata['num_var']): in_var = self._cycle_names['x'].format(i) self.declare_partials('x_norm2', in_var, **self._array2kwargs(x.flatten(), pd_type)) self.declare_partials(self._cycle_names['theta_out'], self._cycle_names['psi'], **self._array2kwargs(np.array([1.]), pd_type)) def compute(self, inputs, outputs): theta = inputs[self._cycle_names['theta']] psi = inputs[self._cycle_names['psi']] k = self.metadata['num_comp'] x = self._inputs_to_vector(inputs) outputs['x_norm2'] = 0.5*np.dot(x,x) # theta_out has 1/2 the error as theta does to the correct angle. outputs[self._cycle_names['theta_out']] = theta / 2 + (self._n * 2 * np.pi - psi) / (2 * k - 2) def compute_partials(self, inputs, partials): if self.metadata['jacobian_type'] != 'matvec' and not self.metadata['finite_difference']: pd_type = self.metadata['partial_type'] for i in range(self.metadata['num_var']): in_var = self._cycle_names['x'].format(i) partials['x_norm2', in_var] = self.make_jacobian_entry(inputs[in_var].flat[:], pd_type) k = self.metadata['num_comp'] theta_out = self._cycle_names['theta_out'] theta = self._cycle_names['theta'] partials[theta_out, theta] = self.make_jacobian_entry(np.array([.5]), pd_type) partials[theta_out, self._cycle_names['psi']] = \ self.make_jacobian_entry(np.array([-1/(2*k-2)]), pd_type) def jacvec_product(self, inputs, d_inputs, d_outputs, mode): if self.metadata['jacobian_type'] == 'matvec': k = self.metadata['num_comp'] num_var = self.metadata['num_var'] theta_out = self._cycle_names['theta_out'] theta = self._cycle_names['theta'] psi = self._cycle_names['psi'] if mode == 'fwd': if theta_out in d_outputs: if theta in d_inputs: d_outputs[theta_out] += 0.5 * d_inputs[theta] if psi in d_inputs: d_outputs[theta_out] += -d_inputs[psi] / (2 * k - 2) for i in range(num_var): in_var = self._cycle_names['x'].format(i) if in_var in d_inputs and 'x_norm2' in d_outputs: d_outputs['x_norm2'] += np.dot(inputs[in_var].flat, d_inputs[in_var].flat) elif mode == 'rev': if 'x_norm2' in d_outputs: dxnorm = d_outputs['x_norm2'] for i in range(num_var): x_i_name = self._cycle_names['x'].format(i) if x_i_name in d_inputs: d_inputs[x_i_name] += inputs[x_i_name] * dxnorm if theta_out in d_outputs: dtheta_out = d_outputs[theta_out] if theta in d_inputs: d_inputs[theta] += .5*dtheta_out if psi in d_inputs: d_inputs[psi] += -dtheta_out/(2*k-2)

41.802885

103

0.552444

from __future__ import division, print_function from six.moves import range import numpy as np import scipy.sparse as sparse import unittest from openmdao.core.explicitcomponent import ExplicitComponent PSI = 1. _vec_terms = {} def _compute_vector_terms(system_size): try: return _vec_terms[system_size] except KeyError: u = np.zeros(system_size) u[[0, -1]] = np.sqrt(2)/2 v = np.zeros(system_size) v[1:-1] = 1 / np.sqrt(system_size - 2) cross_terms = np.outer(v, u) - np.outer(u, v) same_terms = np.outer(u, u) + np.outer(v, v) _vec_terms[system_size] = u, v, cross_terms, same_terms return u, v, cross_terms, same_terms def _compute_A(system_size, theta): u, v, cross_terms, same_terms = _compute_vector_terms(system_size) return (np.eye(system_size) + np.sin(theta) * cross_terms + (np.cos(theta) - 1) * same_terms) def _compute_dA(system_size, theta): u, v, cross_terms, same_terms = _compute_vector_terms(system_size) return np.cos(theta) * cross_terms - np.sin(theta) * same_terms def array_idx(i, var_size): return slice(i * var_size, (i + 1) * var_size) class ExplicitCycleComp(ExplicitComponent): def _inputs_to_vector(self, inputs): var_shape = self.metadata['var_shape'] num_var = self.metadata['num_var'] size = np.prod(var_shape) x = np.zeros(num_var * size) for i in range(num_var): x_i = inputs[self._cycle_names['x'].format(i)].flat x[size * i:size * (i + 1)] = x_i return x def _vector_to_outputs(self, vec, outputs): var_shape = self.metadata['var_shape'] num_var = self.metadata['num_var'] size = np.prod(var_shape) for i in range(num_var): y_i = vec[size * i:size * (i + 1)].reshape(var_shape) outputs[self._cycle_names['y'].format(i)] = y_i def __str__(self): return 'Explicit Cycle Component' def initialize(self): self.metadata.declare('jacobian_type', default='matvec', values=['matvec', 'dense', 'sparse-coo', 'sparse-csr', 'sparse-csc'], desc='method of assembling derivatives') self.metadata.declare('partial_type', default='array', values=['array', 'sparse', 'aij'], desc='type of partial derivatives') self.metadata.declare('num_var', type_=int, default=1, desc='Number of variables per component') self.metadata.declare('var_shape', type_=tuple, default=(3,), desc='Shape of each variable') self.metadata.declare('index', type_=int, desc='Index of the component. Used for testing implicit connections') self.metadata.declare('connection_type', type_=str, default='explicit', values=['explicit', 'implicit'], desc='How to connect variables.') self.metadata.declare('finite_difference', default=False, type_=bool, desc='If the derivatives should be finite differenced.') self.metadata.declare('num_comp', type_=int, default=2, desc='Total number of components') self.angle_param = 'theta' self._cycle_names = {} def _init_parameterized(self): self.num_var = self.metadata['num_var'] self.var_shape = self.metadata['var_shape'] self.size = self.num_var * np.prod(self.var_shape) if self.metadata['jacobian_type'] == 'matvec': self.compute_jacvec_product = self.jacvec_product if self.metadata['connection_type'] == 'implicit': idx = self.metadata['index'] self._cycle_names['x'] = 'x_{}_{{}}'.format(idx) self._cycle_names['y'] = 'x_{}_{{}}'.format(idx + 1) self._cycle_names['theta'] = 'theta_{}'.format(idx) self._cycle_names['theta_out'] = 'theta_{}'.format(idx + 1) num_var = self.metadata['num_var'] self._cycle_promotes_in = [self._cycle_names['x'].format(i) for i in range(num_var)] self._cycle_promotes_out = [self._cycle_names['y'].format(i) for i in range(num_var)] self._cycle_promotes_in.append(self._cycle_names['theta']) self._cycle_promotes_out.append(self._cycle_names['theta_out']) else: self._cycle_names['x'] = 'x_{}' self._cycle_names['y'] = 'y_{}' self._cycle_names['theta'] = 'theta' self._cycle_names['theta_out'] = 'theta_out' self._cycle_promotes_in = self._cycle_promotes_out = [] def setup(self): for i in range(self.num_var): self.add_input(self._cycle_names['x'].format(i), shape=self.var_shape) self.add_output(self._cycle_names['y'].format(i), shape=self.var_shape) self.add_input(self._cycle_names['theta'], val=1.) self.add_output(self._cycle_names['theta_out'], shape=(1,)) pd_type = self.metadata['partial_type'] if self.metadata['finite_difference']: if self.metadata['jacobian_type'] == 'matvec': raise unittest.SkipTest('not testing FD and matvec') if pd_type != 'array': raise unittest.SkipTest('only dense FD supported') self.declare_partials('*', '*', method='fd') elif self.metadata['jacobian_type'] != 'matvec' and pd_type != 'array': num_var = self.num_var var_shape = self.var_shape var_size = np.prod(var_shape) A = np.ones((self.size, self.size)) dA_x = np.ones((self.size, 1)) dtheta = np.array([[1.]]) angle_param = self._cycle_names[self.angle_param] # Otherwise, we assume they are dense and we'll get an error later for out_idx in range(num_var): out_var = self._cycle_names['y'].format(out_idx) for in_idx in range(num_var): in_var = self._cycle_names['x'].format(in_idx) Aij = A[array_idx(out_idx, var_size), array_idx(in_idx, var_size)] self.declare_partials(out_var, in_var, **self._array2kwargs(Aij, pd_type)) self.declare_partials(out_var, angle_param, **self._array2kwargs(dA_x[array_idx(out_idx, var_size)], pd_type)) self.declare_partials(self._cycle_names['theta_out'], self._cycle_names['theta'], **self._array2kwargs(dtheta, pd_type)) else: # Declare everything self.declare_partials(of='*', wrt='*') def compute(self, inputs, outputs): theta = inputs[self._cycle_names['theta']] A = _compute_A(self.size, theta) x = self._inputs_to_vector(inputs) y = A.dot(x) self._vector_to_outputs(y, outputs) outputs[self._cycle_names['theta_out']] = theta def jacvec_product(self, inputs, d_inputs, d_outputs, mode): angle_param = self._cycle_names[self.angle_param] x = self._inputs_to_vector(inputs) angle = inputs[angle_param] A = _compute_A(self.size, angle) dA = _compute_dA(self.size, angle) var_shape = self.metadata['var_shape'] var_size = np.prod(var_shape) num_var = self.metadata['num_var'] x_name = self._cycle_names['x'] y_name = self._cycle_names['y'] theta_name = self._cycle_names['theta'] theta_out_name = self._cycle_names['theta_out'] if mode == 'fwd': for j in range(num_var): x_j = x_name.format(j) if x_j in d_inputs: dx = d_inputs[x_j].flat[:] for i in range(num_var): y_i = y_name.format(i) if y_i in d_outputs: Aij = A[array_idx(i, var_size), array_idx(j, var_size)] d_outputs[y_i] += Aij.dot(dx).reshape(var_shape) if theta_name in d_inputs and theta_out_name in d_outputs: dtheta = d_inputs[theta_name] d_outputs[theta_out_name] += dtheta if angle_param in d_inputs: dangle = d_inputs[angle_param] dy_dangle = (dA.dot(x)) * dangle for i in range(num_var): y_i = y_name.format(i) if y_i in d_outputs: d_outputs[y_i] += dy_dangle[array_idx(i, var_size)].reshape(var_shape) elif mode == 'rev': for i in range(num_var): y_i = y_name.format(i) if y_i in d_outputs: dy_i = d_outputs[y_i].flat[:] for j in range(num_var): x_j = x_name.format(j) if x_j in d_inputs: Aij = A[array_idx(i, var_size), array_idx(j, var_size)] d_inputs[x_j] += Aij.T.dot(dy_i).reshape(var_shape) if angle_param in d_inputs: dAij = dA[array_idx(i, var_size), array_idx(j, var_size)] x_j_vec = inputs[x_j].flat[:] d_inputs[angle_param] += x_j_vec.T.dot(dAij.T.dot(dy_i)) if theta_out_name in d_outputs and theta_name in d_inputs: dtheta_out = d_outputs[theta_out_name] d_inputs[theta_name] += dtheta_out def make_jacobian_entry(self, A, pd_type): if pd_type == 'aij': return self.make_sub_jacobian(A, pd_type)[0] return self.make_sub_jacobian(A, pd_type) def make_sub_jacobian(self, A, pd_type): if pd_type == 'array': return A if pd_type == 'sparse': return sparse.csr_matrix(A) if pd_type == 'aij': data = [] rows = [] cols = [] A = np.atleast_2d(A) for i in range(A.shape[0]): for j in range(A.shape[1]): if np.abs(A[i, j]) > 1e-15: data.append(A[i, j]) rows.append(i) cols.append(j) return [np.array(data), np.array(rows), np.array(cols)] raise ValueError('Unknown partial_type: {}'.format(pd_type)) def _array2kwargs(self, arr, pd_type): jac = self.make_sub_jacobian(arr, pd_type) if pd_type == 'aij': return {'val': jac[0], 'rows': jac[1], 'cols': jac[2]} else: return {'val': jac} def compute_partials(self, inputs, partials): if self.metadata['jacobian_type'] != 'matvec' and not self.metadata['finite_difference']: angle_param = self._cycle_names[self.angle_param] angle = inputs[angle_param] num_var = self.num_var var_shape = self.var_shape var_size = np.prod(var_shape) x = self._inputs_to_vector(inputs) size = self.size A = _compute_A(size, angle) dA = _compute_dA(size, angle) dA_x = np.atleast_2d(dA.dot(x)).T pd_type = self.metadata['partial_type'] dtheta = np.array([[1.]]) y_name = self._cycle_names['y'] x_name = self._cycle_names['x'] for out_idx in range(num_var): out_var = y_name.format(out_idx) for in_idx in range(num_var): in_var = x_name.format(in_idx) Aij = A[array_idx(out_idx, var_size), array_idx(in_idx, var_size)] J_y_x = self.make_jacobian_entry(Aij, pd_type) J_y_angle = self.make_jacobian_entry(dA_x[array_idx(out_idx, var_size)], pd_type) partials[out_var, in_var] = J_y_x partials[out_var, angle_param] = J_y_angle theta_out = self._cycle_names['theta_out'] theta = self._cycle_names['theta'] partials[theta_out, theta] = self.make_jacobian_entry(dtheta, pd_type) class ExplicitFirstComp(ExplicitCycleComp): def __str__(self): return 'Explicit Cycle Component - First' def setup(self): self.add_input('psi', val=1.) self.angle_param = 'psi' self._cycle_names['psi'] = 'psi' super(ExplicitFirstComp, self).setup() def compute(self, inputs, outputs): theta = inputs[self._cycle_names['theta']] psi = inputs[self._cycle_names['psi']] A = _compute_A(self.size, psi) y = A.dot(np.ones(self.size)) self._vector_to_outputs(y, outputs) outputs[self._cycle_names['theta_out']] = theta class ExplicitLastComp(ExplicitFirstComp): def __str__(self): return 'Explicit Cycle Component - Last' def setup(self): super(ExplicitLastComp, self).setup() self.add_output('x_norm2', shape=(1,)) self._n = 1 # Setup partials pd_type = self.metadata['partial_type'] if self.metadata['jacobian_type'] != 'matvec' and pd_type != 'array': x = np.ones(self.var_shape) for i in range(self.metadata['num_var']): in_var = self._cycle_names['x'].format(i) self.declare_partials('x_norm2', in_var, **self._array2kwargs(x.flatten(), pd_type)) self.declare_partials(self._cycle_names['theta_out'], self._cycle_names['psi'], **self._array2kwargs(np.array([1.]), pd_type)) def compute(self, inputs, outputs): theta = inputs[self._cycle_names['theta']] psi = inputs[self._cycle_names['psi']] k = self.metadata['num_comp'] x = self._inputs_to_vector(inputs) outputs['x_norm2'] = 0.5*np.dot(x,x) # theta_out has 1/2 the error as theta does to the correct angle. outputs[self._cycle_names['theta_out']] = theta / 2 + (self._n * 2 * np.pi - psi) / (2 * k - 2) def compute_partials(self, inputs, partials): if self.metadata['jacobian_type'] != 'matvec' and not self.metadata['finite_difference']: pd_type = self.metadata['partial_type'] for i in range(self.metadata['num_var']): in_var = self._cycle_names['x'].format(i) partials['x_norm2', in_var] = self.make_jacobian_entry(inputs[in_var].flat[:], pd_type) k = self.metadata['num_comp'] theta_out = self._cycle_names['theta_out'] theta = self._cycle_names['theta'] partials[theta_out, theta] = self.make_jacobian_entry(np.array([.5]), pd_type) partials[theta_out, self._cycle_names['psi']] = \ self.make_jacobian_entry(np.array([-1/(2*k-2)]), pd_type) def jacvec_product(self, inputs, d_inputs, d_outputs, mode): if self.metadata['jacobian_type'] == 'matvec': k = self.metadata['num_comp'] num_var = self.metadata['num_var'] theta_out = self._cycle_names['theta_out'] theta = self._cycle_names['theta'] psi = self._cycle_names['psi'] if mode == 'fwd': if theta_out in d_outputs: if theta in d_inputs: d_outputs[theta_out] += 0.5 * d_inputs[theta] if psi in d_inputs: d_outputs[theta_out] += -d_inputs[psi] / (2 * k - 2) for i in range(num_var): in_var = self._cycle_names['x'].format(i) if in_var in d_inputs and 'x_norm2' in d_outputs: d_outputs['x_norm2'] += np.dot(inputs[in_var].flat, d_inputs[in_var].flat) elif mode == 'rev': if 'x_norm2' in d_outputs: dxnorm = d_outputs['x_norm2'] for i in range(num_var): x_i_name = self._cycle_names['x'].format(i) if x_i_name in d_inputs: d_inputs[x_i_name] += inputs[x_i_name] * dxnorm if theta_out in d_outputs: dtheta_out = d_outputs[theta_out] if theta in d_inputs: d_inputs[theta] += .5*dtheta_out if psi in d_inputs: d_inputs[psi] += -dtheta_out/(2*k-2)

true

790810188028addd63ebdc7a6d382463b54b3059

10,719

py

Python

ab3dmot.py

johnwlambert/argoverse_cbgs_kf_tracker

9268cb6dd9844f80eb107a0cc5e77e880d3b3e76

[ "BSD-Source-Code" ]

27

2020-04-24T07:45:20.000Z

2022-03-08T09:17:34.000Z

ab3dmot.py

johnwlambert/argoverse_cbgs_kf_tracker

9268cb6dd9844f80eb107a0cc5e77e880d3b3e76

[ "BSD-Source-Code" ]

4

2020-07-16T07:15:12.000Z

2022-02-17T01:24:56.000Z

ab3dmot.py

johnwlambert/argoverse_cbgs_kf_tracker

9268cb6dd9844f80eb107a0cc5e77e880d3b3e76

[ "BSD-Source-Code" ]

22

2020-05-21T07:35:03.000Z

2021-12-24T05:24:17.000Z

#!/usr/bin/env python3 from filterpy.kalman import KalmanFilter import matplotlib.pyplot as plt import numpy as np import pdb from sklearn.utils.linear_assignment_ import linear_assignment import sys import time from transform_utils import convert_3dbox_to_8corner from iou_utils import compute_iou_2d_bboxes class KalmanBoxTracker(object): """ This class represents the internel state of individual tracked objects observed as bbox. """ count = 0 def __init__(self, bbox3D, info): """ Initialises a tracker using initial bounding box. """ #define constant velocity model self.kf = KalmanFilter(dim_x=10, dim_z=7) self.kf.F = np.array([[1,0,0,0,0,0,0,1,0,0], # state transition matrix [0,1,0,0,0,0,0,0,1,0], [0,0,1,0,0,0,0,0,0,1], [0,0,0,1,0,0,0,0,0,0], [0,0,0,0,1,0,0,0,0,0], [0,0,0,0,0,1,0,0,0,0], [0,0,0,0,0,0,1,0,0,0], [0,0,0,0,0,0,0,1,0,0], [0,0,0,0,0,0,0,0,1,0], [0,0,0,0,0,0,0,0,0,1]]) self.kf.H = np.array([[1,0,0,0,0,0,0,0,0,0], # measurement function, [0,1,0,0,0,0,0,0,0,0], [0,0,1,0,0,0,0,0,0,0], [0,0,0,1,0,0,0,0,0,0], [0,0,0,0,1,0,0,0,0,0], [0,0,0,0,0,1,0,0,0,0], [0,0,0,0,0,0,1,0,0,0]]) # with angular velocity # self.kf = KalmanFilter(dim_x=11, dim_z=7) # self.kf.F = np.array([[1,0,0,0,0,0,0,1,0,0,0], # state transition matrix # [0,1,0,0,0,0,0,0,1,0,0], # [0,0,1,0,0,0,0,0,0,1,0], # [0,0,0,1,0,0,0,0,0,0,1], # [0,0,0,0,1,0,0,0,0,0,0], # [0,0,0,0,0,1,0,0,0,0,0], # [0,0,0,0,0,0,1,0,0,0,0], # [0,0,0,0,0,0,0,1,0,0,0], # [0,0,0,0,0,0,0,0,1,0,0], # [0,0,0,0,0,0,0,0,0,1,0], # [0,0,0,0,0,0,0,0,0,0,1]]) # self.kf.H = np.array([[1,0,0,0,0,0,0,0,0,0,0], # measurement function, # [0,1,0,0,0,0,0,0,0,0,0], # [0,0,1,0,0,0,0,0,0,0,0], # [0,0,0,1,0,0,0,0,0,0,0], # [0,0,0,0,1,0,0,0,0,0,0], # [0,0,0,0,0,1,0,0,0,0,0], # [0,0,0,0,0,0,1,0,0,0,0]]) # self.kf.R[0:,0:] *= 10. # measurement uncertainty self.kf.P[7:,7:] *= 1000. #state uncertainty, give high uncertainty to the unobservable initial velocities, covariance matrix self.kf.P *= 10. # self.kf.Q[-1,-1] *= 0.01 # process uncertainty self.kf.Q[7:,7:] *= 0.01 self.kf.x[:7] = bbox3D.reshape((7, 1)) self.time_since_update = 0 self.id = KalmanBoxTracker.count KalmanBoxTracker.count += 1 self.history = [] self.hits = 1 # number of total hits including the first detection self.hit_streak = 1 # number of continuing hit considering the first detection self.first_continuing_hit = 1 self.still_first = True self.age = 0 self.info = info # other info def update(self, bbox3D, info): """ Updates the state vector with observed bbox. """ self.time_since_update = 0 self.history = [] self.hits += 1 self.hit_streak += 1 # number of continuing hit if self.still_first: self.first_continuing_hit += 1 # number of continuing hit in the fist time ######################### orientation correction if self.kf.x[3] >= np.pi: self.kf.x[3] -= np.pi * 2 # make the theta still in the range if self.kf.x[3] < -np.pi: self.kf.x[3] += np.pi * 2 new_theta = bbox3D[3] if new_theta >= np.pi: new_theta -= np.pi * 2 # make the theta still in the range if new_theta < -np.pi: new_theta += np.pi * 2 bbox3D[3] = new_theta predicted_theta = self.kf.x[3] if abs(new_theta - predicted_theta) > np.pi / 2.0 and abs(new_theta - predicted_theta) < np.pi * 3 / 2.0: # if the angle of two theta is not acute angle self.kf.x[3] += np.pi if self.kf.x[3] > np.pi: self.kf.x[3] -= np.pi * 2 # make the theta still in the range if self.kf.x[3] < -np.pi: self.kf.x[3] += np.pi * 2 # now the angle is acute: < 90 or > 270, convert the case of > 270 to < 90 if abs(new_theta - self.kf.x[3]) >= np.pi * 3 / 2.0: if new_theta > 0: self.kf.x[3] += np.pi * 2 else: self.kf.x[3] -= np.pi * 2 ######################### self.kf.update(bbox3D) if self.kf.x[3] >= np.pi: self.kf.x[3] -= np.pi * 2 # make the theta still in the range if self.kf.x[3] < -np.pi: self.kf.x[3] += np.pi * 2 self.info = info def predict(self): """ Advances the state vector and returns the predicted bounding box estimate. """ self.kf.predict() if self.kf.x[3] >= np.pi: self.kf.x[3] -= np.pi * 2 if self.kf.x[3] < -np.pi: self.kf.x[3] += np.pi * 2 self.age += 1 if(self.time_since_update>0): self.hit_streak = 0 self.still_first = False self.time_since_update += 1 self.history.append(self.kf.x) return self.history[-1] def get_state(self): """ Returns the current bounding box estimate. """ return self.kf.x[:7].reshape((7, )) def associate_detections_to_trackers(detections,trackers,iou_threshold=0.1): # def associate_detections_to_trackers(detections,trackers,iou_threshold=0.01): # ablation study # def associate_detections_to_trackers(detections,trackers,iou_threshold=0.25): """ Assigns detections to tracked object (both represented as bounding boxes) detections: N x 8 x 3 trackers: M x 8 x 3 Returns 3 lists of matches, unmatched_detections and unmatched_trackers """ if(len(trackers)==0): return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,8,3),dtype=int) iou_matrix = np.zeros((len(detections),len(trackers)),dtype=np.float32) for d,det in enumerate(detections): for t,trk in enumerate(trackers): #print(f'On d={d}, t={t}') #iou_matrix[d,t] = iou3d(det,trk)[1] # try 2d iou instead # det: 8 x 3, trk: 8 x 3 iou_matrix[d,t] = compute_iou_2d_bboxes(det, trk) matched_indices = linear_assignment(-iou_matrix) # hungarian algorithm unmatched_detections = [] for d,det in enumerate(detections): if(d not in matched_indices[:,0]): unmatched_detections.append(d) unmatched_trackers = [] for t,trk in enumerate(trackers): if(t not in matched_indices[:,1]): unmatched_trackers.append(t) #print(iou_matrix) #filter out matched with low IOU matches = [] for m in matched_indices: if(iou_matrix[m[0],m[1]]<iou_threshold): unmatched_detections.append(m[0]) unmatched_trackers.append(m[1]) else: matches.append(m.reshape(1,2)) if(len(matches)==0): matches = np.empty((0,2),dtype=int) else: matches = np.concatenate(matches,axis=0) return matches, np.array(unmatched_detections), np.array(unmatched_trackers) class AB3DMOT(object): def __init__(self,max_age=2,min_hits=3): # max age will preserve the bbox does not appear no more than 2 frames, interpolate the detection # def __init__(self,max_age=3,min_hits=3): # ablation study # def __init__(self,max_age=1,min_hits=3): # def __init__(self,max_age=2,min_hits=1): # def __init__(self,max_age=2,min_hits=5): """ """ self.max_age = max_age self.min_hits = min_hits self.trackers = [] self.frame_count = 0 # self.reorder = [3, 4, 5, 6, 2, 1, 0] # self.reorder_back = [6, 5, 4, 0, 1, 2, 3] def update(self,dets_all): """ Params: dets_all: dict dets - a numpy array of detections in the format [[x,y,z,theta,l,w,h],[x,y,z,theta,l,w,h],...] info: a array of other info for each det Requires: this method must be called once for each frame even with empty detections. Returns the a similar array, where the last column is the object ID. NOTE: The number of objects returned may differ from the number of detections provided. """ dets, info = dets_all['dets'], dets_all['info'] # dets: N x 7, float numpy array # dets = dets[:, self.reorder] self.frame_count += 1 trks = np.zeros((len(self.trackers),7)) # N x 7 , #get predicted locations from existing trackers. to_del = [] ret = [] for t,trk in enumerate(trks): pos = self.trackers[t].predict().reshape((-1, 1)) trk[:] = [pos[0], pos[1], pos[2], pos[3], pos[4], pos[5], pos[6]] if(np.any(np.isnan(pos))): to_del.append(t) trks = np.ma.compress_rows(np.ma.masked_invalid(trks)) for t in reversed(to_del): self.trackers.pop(t) dets_8corner = [convert_3dbox_to_8corner(det_tmp) for det_tmp in dets] if len(dets_8corner) > 0: dets_8corner = np.stack(dets_8corner, axis=0) else: dets_8corner = [] trks_8corner = [convert_3dbox_to_8corner(trk_tmp) for trk_tmp in trks] if len(trks_8corner) > 0: trks_8corner = np.stack(trks_8corner, axis=0) matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets_8corner, trks_8corner) #update matched trackers with assigned detections for t,trk in enumerate(self.trackers): if t not in unmatched_trks: d = matched[np.where(matched[:,1]==t)[0],0] # a list of index trk.update(dets[d,:][0], info[d, :][0]) #create and initialise new trackers for unmatched detections for i in unmatched_dets: # a scalar of index trk = KalmanBoxTracker(dets[i,:], info[i, :]) self.trackers.append(trk) i = len(self.trackers) for trk in reversed(self.trackers): d = trk.get_state() # bbox location # d = d[self.reorder_back] if((trk.time_since_update < self.max_age) and (trk.hits >= self.min_hits or self.frame_count <= self.min_hits)): ret.append(np.concatenate((d, [trk.id+1], trk.info)).reshape(1,-1)) # +1 as MOT benchmark requires positive i -= 1 #remove dead tracklet if(trk.time_since_update >= self.max_age): self.trackers.pop(i) if(len(ret)>0): return np.concatenate(ret) # x, y, z, theta, l, w, h, ID, other info, confidence return np.empty((0,15))

38.836957

160

0.566844

from filterpy.kalman import KalmanFilter import matplotlib.pyplot as plt import numpy as np import pdb from sklearn.utils.linear_assignment_ import linear_assignment import sys import time from transform_utils import convert_3dbox_to_8corner from iou_utils import compute_iou_2d_bboxes class KalmanBoxTracker(object): count = 0 def __init__(self, bbox3D, info): self.kf = KalmanFilter(dim_x=10, dim_z=7) self.kf.F = np.array([[1,0,0,0,0,0,0,1,0,0], [0,1,0,0,0,0,0,0,1,0], [0,0,1,0,0,0,0,0,0,1], [0,0,0,1,0,0,0,0,0,0], [0,0,0,0,1,0,0,0,0,0], [0,0,0,0,0,1,0,0,0,0], [0,0,0,0,0,0,1,0,0,0], [0,0,0,0,0,0,0,1,0,0], [0,0,0,0,0,0,0,0,1,0], [0,0,0,0,0,0,0,0,0,1]]) self.kf.H = np.array([[1,0,0,0,0,0,0,0,0,0], [0,1,0,0,0,0,0,0,0,0], [0,0,1,0,0,0,0,0,0,0], [0,0,0,1,0,0,0,0,0,0], [0,0,0,0,1,0,0,0,0,0], [0,0,0,0,0,1,0,0,0,0], [0,0,0,0,0,0,1,0,0,0]]) 1000. self.kf.P *= 10. *= 0.01 self.kf.x[:7] = bbox3D.reshape((7, 1)) self.time_since_update = 0 self.id = KalmanBoxTracker.count KalmanBoxTracker.count += 1 self.history = [] self.hits = 1 self.hit_streak = 1 self.first_continuing_hit = 1 self.still_first = True self.age = 0 self.info = info def update(self, bbox3D, info): self.time_since_update = 0 self.history = [] self.hits += 1 self.hit_streak += 1 if self.still_first: self.first_continuing_hit += 1 _first = False self.time_since_update += 1 self.history.append(self.kf.x) return self.history[-1] def get_state(self): return self.kf.x[:7].reshape((7, )) def associate_detections_to_trackers(detections,trackers,iou_threshold=0.1): ers)==0): return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,8,3),dtype=int) iou_matrix = np.zeros((len(detections),len(trackers)),dtype=np.float32) for d,det in enumerate(detections): for t,trk in enumerate(trackers): ear_assignment(-iou_matrix) unmatched_detections = [] for d,det in enumerate(detections): if(d not in matched_indices[:,0]): unmatched_detections.append(d) unmatched_trackers = [] for t,trk in enumerate(trackers): if(t not in matched_indices[:,1]): unmatched_trackers.append(t) matches = [] for m in matched_indices: if(iou_matrix[m[0],m[1]]<iou_threshold): unmatched_detections.append(m[0]) unmatched_trackers.append(m[1]) else: matches.append(m.reshape(1,2)) if(len(matches)==0): matches = np.empty((0,2),dtype=int) else: matches = np.concatenate(matches,axis=0) return matches, np.array(unmatched_detections), np.array(unmatched_trackers) class AB3DMOT(object): def __init__(self,max_age=2,min_hits=3): lf.max_age = max_age self.min_hits = min_hits self.trackers = [] self.frame_count = 0 def update(self,dets_all): dets, info = dets_all['dets'], dets_all['info'] self.frame_count += 1 trks = np.zeros((len(self.trackers),7)) numerate(trks): pos = self.trackers[t].predict().reshape((-1, 1)) trk[:] = [pos[0], pos[1], pos[2], pos[3], pos[4], pos[5], pos[6]] if(np.any(np.isnan(pos))): to_del.append(t) trks = np.ma.compress_rows(np.ma.masked_invalid(trks)) for t in reversed(to_del): self.trackers.pop(t) dets_8corner = [convert_3dbox_to_8corner(det_tmp) for det_tmp in dets] if len(dets_8corner) > 0: dets_8corner = np.stack(dets_8corner, axis=0) else: dets_8corner = [] trks_8corner = [convert_3dbox_to_8corner(trk_tmp) for trk_tmp in trks] if len(trks_8corner) > 0: trks_8corner = np.stack(trks_8corner, axis=0) matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets_8corner, trks_8corner) for t,trk in enumerate(self.trackers): if t not in unmatched_trks: d = matched[np.where(matched[:,1]==t)[0],0] trk.update(dets[d,:][0], info[d, :][0]) for i in unmatched_dets: trk = KalmanBoxTracker(dets[i,:], info[i, :]) self.trackers.append(trk) i = len(self.trackers) for trk in reversed(self.trackers): d = trk.get_state() if((trk.time_since_update < self.max_age) and (trk.hits >= self.min_hits or self.frame_count <= self.min_hits)): ret.append(np.concatenate((d, [trk.id+1], trk.info)).reshape(1,-1)) i -= 1 if(trk.time_since_update >= self.max_age): self.trackers.pop(i) if(len(ret)>0): return np.concatenate(ret) return np.empty((0,15))

true

7908102323cd6d3ecb96ace4c798612538fe5146

3,242

py

Python

worktickets.py

benhg/work-tickets

dda344084736f9446cb6a1a49406754861aca19a

[ "MIT" ]

1

2017-11-23T01:39:07.000Z

worktickets.py

benhg/work-tickets

dda344084736f9446cb6a1a49406754861aca19a

[ "MIT" ]

null

worktickets.py

benhg/work-tickets

dda344084736f9446cb6a1a49406754861aca19a

[ "MIT" ]

null

#!/usr/bin/env python3 import json import argparse import datetime class TicketManager: ticketfile = '/Users/ben/ticketing/tickets.json' def __init__(self: object, ticketfile: str='/Users/ben/Google Drive/code/ticketing/tickets.json')->object: self.ticketfille = ticketfile self.read_tickets() def read_tickets(self)-> None: self.tickets = json.load(open(self.ticketfile)) def write_tickets(self)-> None: json.dump(self.tickets, open(self.ticketfile, "w"), indent=4) def create_ticket(self, title="", desc="", dest="", due="", pri=0, completed=False): ticket = {"title": title, "desc": desc, "for": dest, "time_in": datetime.datetime.now().strftime("%Y-%m-%d %H:%M"), "time_out": due, "nice": pri, "completed": completed } self.tickets[title] = ticket self.write_tickets() self.read_tickets() def update_ticket(self, title, new_completed): self.tickets[title]["completed"] = new_completed self.write_tickets() self.read_tickets() def show_all_tickets(self): for ticket in self.tickets.values(): print("""TICKET NAME: {} \tTICKET DESCRIPTION: {} \tTICKET CREATED: {} \tTICKET DUE: {} \tTICKET FOR: {} \tTICKET DONE: {} \tTICKET PRIORITY: {} """.format(ticket['title'], ticket['desc'], ticket['time_in'], ticket['time_out'], ticket['for'], ticket['completed'], ticket['nice'])) def show_unifnished(self): flag = False for ticket in self.tickets.values(): if not ticket['completed']: flag = True print("""TICKET NAME: {} \tTICKET DESCRIPTION: {} \tTICKET CREATED: {} \tTICKET DUE: {} \tTICKET FOR: {} \tTICKET PRIORITY: {} """.format(ticket['title'], ticket['desc'], ticket['time_in'], ticket['time_out'], ticket['for'], ticket['nice'])) if not flag: print("No Unfinished Tasks!") if __name__ == "__main__": parser = argparse.ArgumentParser(description='Process some integers.') parser.add_argument("--mode", action="store", dest="mode", default='ls') parser.add_argument("--title", action="store", dest="title") parser.add_argument("--desc", action="store", dest="desc") parser.add_argument("--for", action="store", dest="dest") parser.add_argument("--due", action="store", dest="time_out") parser.add_argument("--pri", action="store", dest="nice") parser.add_argument("--done", action="store_true", dest="completed", default=False) args = parser.parse_args() tm = TicketManager("tickets.json") if args.mode == "ls": tm.show_unifnished() elif args.mode == "ls2": tm.show_all_tickets() elif args.mode == "new" or args.mode == "add": tm.create_ticket(title=args.title, desc=args.desc, dest=args.dest, due=args.time_out, pri=args.nice, completed=args.completed) print("New Task '{}' Added".format(args.title)) elif args.mode == "up": tm.update_ticket(args.title, args.completed)

35.23913

110

0.588834

import json import argparse import datetime class TicketManager: ticketfile = '/Users/ben/ticketing/tickets.json' def __init__(self: object, ticketfile: str='/Users/ben/Google Drive/code/ticketing/tickets.json')->object: self.ticketfille = ticketfile self.read_tickets() def read_tickets(self)-> None: self.tickets = json.load(open(self.ticketfile)) def write_tickets(self)-> None: json.dump(self.tickets, open(self.ticketfile, "w"), indent=4) def create_ticket(self, title="", desc="", dest="", due="", pri=0, completed=False): ticket = {"title": title, "desc": desc, "for": dest, "time_in": datetime.datetime.now().strftime("%Y-%m-%d %H:%M"), "time_out": due, "nice": pri, "completed": completed } self.tickets[title] = ticket self.write_tickets() self.read_tickets() def update_ticket(self, title, new_completed): self.tickets[title]["completed"] = new_completed self.write_tickets() self.read_tickets() def show_all_tickets(self): for ticket in self.tickets.values(): print("""TICKET NAME: {} \tTICKET DESCRIPTION: {} \tTICKET CREATED: {} \tTICKET DUE: {} \tTICKET FOR: {} \tTICKET DONE: {} \tTICKET PRIORITY: {} """.format(ticket['title'], ticket['desc'], ticket['time_in'], ticket['time_out'], ticket['for'], ticket['completed'], ticket['nice'])) def show_unifnished(self): flag = False for ticket in self.tickets.values(): if not ticket['completed']: flag = True print("""TICKET NAME: {} \tTICKET DESCRIPTION: {} \tTICKET CREATED: {} \tTICKET DUE: {} \tTICKET FOR: {} \tTICKET PRIORITY: {} """.format(ticket['title'], ticket['desc'], ticket['time_in'], ticket['time_out'], ticket['for'], ticket['nice'])) if not flag: print("No Unfinished Tasks!") if __name__ == "__main__": parser = argparse.ArgumentParser(description='Process some integers.') parser.add_argument("--mode", action="store", dest="mode", default='ls') parser.add_argument("--title", action="store", dest="title") parser.add_argument("--desc", action="store", dest="desc") parser.add_argument("--for", action="store", dest="dest") parser.add_argument("--due", action="store", dest="time_out") parser.add_argument("--pri", action="store", dest="nice") parser.add_argument("--done", action="store_true", dest="completed", default=False) args = parser.parse_args() tm = TicketManager("tickets.json") if args.mode == "ls": tm.show_unifnished() elif args.mode == "ls2": tm.show_all_tickets() elif args.mode == "new" or args.mode == "add": tm.create_ticket(title=args.title, desc=args.desc, dest=args.dest, due=args.time_out, pri=args.nice, completed=args.completed) print("New Task '{}' Added".format(args.title)) elif args.mode == "up": tm.update_ticket(args.title, args.completed)

true

7908104c7294138f801d289a8830574b608a1b70

3,354

py

Python

tint/peer.py

bmuller/tint

e74a3e4c46f71dfcb2574920467ad791d29de6fe

[ "MIT" ]

1

2015-02-18T18:33:44.000Z

tint/peer.py

8468/tint

e74a3e4c46f71dfcb2574920467ad791d29de6fe

[ "MIT" ]

null

tint/peer.py

8468/tint

e74a3e4c46f71dfcb2574920467ad791d29de6fe

[ "MIT" ]

null

from tint.ssl.context import PFSContextFactory from tint.log import Logger from tint.protocols.tintp import ConnectionPool from tint.protocols.tintp import TintProtocolFactory from tint.friends import FriendsList class Peer(object): def __init__(self, keyStore, storage, resolver): self.keyStore = keyStore self.storage = storage self.contextFactory = PFSContextFactory(self.keyStore) self.pool = ConnectionPool(resolver, self.contextFactory, self.keyStore, self.storage) self.protocolFactory = TintProtocolFactory(self.pool) self.friends = FriendsList(self.storage, self.keyStore, resolver) self.log = Logger(system=self) def getKeyId(self): """ Get the keyId used by this peer (this peer's identifier). This is stored in the key store. """ return self.keyStore.getKeyId() def getPublicKey(self): """ Get the keyId used by this peer (this peer's identifier). This is stored in the key store. """ return self.keyStore.getPublicKey() def set(self, hostKeyId, storagePath, storageValue): """ Set a value on a host. @param hostKeyId: The key id for the destination host to set the given key. This could be the local host, in which case the hostKey will be the same as this C{Peer}'s keyStore keyId. @param storagePath: The path to the key to set. For instance, this could be something like /chat/<somekey>/inbox. @param storageValue: The value to set. """ if hostKeyId == self.getKeyId(): return self.storage.set(hostKeyId, storagePath, storageValue) return self.pool.send(hostKeyId, 'set', storagePath, storageValue) def get(self, hostKeyId, storagePath): """ Get a value from a host. @param hostKeyId: The key id for the destination host to get the given key. This could be the local host, in which case the hostKey will be the same as this C{Peer}'s keyStore keyId. @param storagePath: The path to the key to get. For instance, this could be something like /chat/<somekey>/inbox. """ if hostKeyId == self.getKeyId(): self.log.debug("getting storagePath %s on self" % storagePath) return self.storage.get(hostKeyId, storagePath) self.log.debug("getting storagePath %s on %s" % (storagePath, hostKeyId)) return self.pool.send(hostKeyId, 'get', storagePath) def push(self, hostKeyId, storagePath, storageValue): """ Given key, create a new key at <key>/<id> with the given value, where <id> is an auto-incrementing integer value starting at 0. """ if hostKeyId == self.getKeyId(): return self.storage.push(hostKeyId, storagePath, storageValue) return self.pool.send(hostKeyId, 'push', storagePath, storageValue) def ls(self, hostKeyId, storagePath, offset, length): """ Given key, get all children keys (with the given offset and length). Length cannot be more than 1000. """ if hostKeyId == self.getKeyId(): return self.storage.ls(hostKeyId, storagePath, offset, length) return self.pool.send(hostKeyId, 'ls', storagePath, offset, length)

39

94

0.651163

from tint.ssl.context import PFSContextFactory from tint.log import Logger from tint.protocols.tintp import ConnectionPool from tint.protocols.tintp import TintProtocolFactory from tint.friends import FriendsList class Peer(object): def __init__(self, keyStore, storage, resolver): self.keyStore = keyStore self.storage = storage self.contextFactory = PFSContextFactory(self.keyStore) self.pool = ConnectionPool(resolver, self.contextFactory, self.keyStore, self.storage) self.protocolFactory = TintProtocolFactory(self.pool) self.friends = FriendsList(self.storage, self.keyStore, resolver) self.log = Logger(system=self) def getKeyId(self): return self.keyStore.getKeyId() def getPublicKey(self): return self.keyStore.getPublicKey() def set(self, hostKeyId, storagePath, storageValue): if hostKeyId == self.getKeyId(): return self.storage.set(hostKeyId, storagePath, storageValue) return self.pool.send(hostKeyId, 'set', storagePath, storageValue) def get(self, hostKeyId, storagePath): if hostKeyId == self.getKeyId(): self.log.debug("getting storagePath %s on self" % storagePath) return self.storage.get(hostKeyId, storagePath) self.log.debug("getting storagePath %s on %s" % (storagePath, hostKeyId)) return self.pool.send(hostKeyId, 'get', storagePath) def push(self, hostKeyId, storagePath, storageValue): if hostKeyId == self.getKeyId(): return self.storage.push(hostKeyId, storagePath, storageValue) return self.pool.send(hostKeyId, 'push', storagePath, storageValue) def ls(self, hostKeyId, storagePath, offset, length): if hostKeyId == self.getKeyId(): return self.storage.ls(hostKeyId, storagePath, offset, length) return self.pool.send(hostKeyId, 'ls', storagePath, offset, length)

true

790810ef3cb25b64b82b046ed9c5a60d5c9d539f

463

py

Python

provision/onboarding/onboard_namespaces.py

hamshif/dags

6daf6313d35824b58efa7f61f90e30a169946532

[ "Apache-2.0" ]

null

provision/onboarding/onboard_namespaces.py

hamshif/dags

6daf6313d35824b58efa7f61f90e30a169946532

[ "Apache-2.0" ]

null

provision/onboarding/onboard_namespaces.py

hamshif/dags

6daf6313d35824b58efa7f61f90e30a169946532

[ "Apache-2.0" ]

null

from data_common.config.configurer import get_conf from data_common.provision.gs_buckets import confirm_bucket def init_namespace_poc(): conf = get_conf() project_id = conf.cloud.gcp.project namespaces = conf.namespaces for namespace, v in namespaces.items(): print(f'namespace: {namespace}') bucket = confirm_bucket( bucket_name=namespace, project_id=project_id ) print(bucket.name)

22.047619

59

0.678186

from data_common.config.configurer import get_conf from data_common.provision.gs_buckets import confirm_bucket def init_namespace_poc(): conf = get_conf() project_id = conf.cloud.gcp.project namespaces = conf.namespaces for namespace, v in namespaces.items(): print(f'namespace: {namespace}') bucket = confirm_bucket( bucket_name=namespace, project_id=project_id ) print(bucket.name)

true

79081130720823c2bd9ee0cb306dd1540e6b5886

401

py

Python

etnapy/__init__.py

Astropilot/etnapy

6b97f4deca095a820e420b59fc0eaaadd054d771

[ "MIT" ]

null

etnapy/__init__.py

Astropilot/etnapy

6b97f4deca095a820e420b59fc0eaaadd054d771

[ "MIT" ]

null

etnapy/__init__.py

Astropilot/etnapy

6b97f4deca095a820e420b59fc0eaaadd054d771

[ "MIT" ]

null

""" ETNA School API Wrapper ~~~~~~~~~~~~~~~~~~~~~~~ A python wrapper to help make python3 apps/bots using the ETNA API. :copyright: (c) 2019 Yohann MARTIN :license: MIT, see LICENSE for more details. """ __title__ = 'etnapy' __author__ = 'Yohann MARTIN' __license__ = 'MIT' __version__ = "1.0.0" from .user import User from .promo import Promo from .trophy import Trophy from .etnapy import Intra

20.05

67

0.698254

__title__ = 'etnapy' __author__ = 'Yohann MARTIN' __license__ = 'MIT' __version__ = "1.0.0" from .user import User from .promo import Promo from .trophy import Trophy from .etnapy import Intra

true

79081164975c8271a4a95835b01770ed32e72d4f

14,437

py

Python

segmentation_models_pytorch/encoders/zerocenter.py

vinnamkim/segmentation_models.pytorch

f967ded34df6fb536e8e8cba9b6491ae63b939f5

[ "MIT" ]

null

segmentation_models_pytorch/encoders/zerocenter.py

vinnamkim/segmentation_models.pytorch

f967ded34df6fb536e8e8cba9b6491ae63b939f5

[ "MIT" ]

null

segmentation_models_pytorch/encoders/zerocenter.py

vinnamkim/segmentation_models.pytorch

f967ded34df6fb536e8e8cba9b6491ae63b939f5

[ "MIT" ]

null

import torch import torch.nn as nn #from .utils import load_state_dict_from_url from .utils import zerocenter __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', 'wide_resnet50_2', 'wide_resnet101_2'] model_urls = { 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', } def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation) def conv1x1(in_planes, out_planes, stride=1): """1x1 convolution""" return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None): super(BasicBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d if groups != 1 or base_width != 64: raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = norm_layer(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = norm_layer(planes) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = zerocenter(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) out = zerocenter(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None): super(Bottleneck, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d width = int(planes * (base_width / 64.)) * groups # Both self.conv2 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv1x1(inplanes, width) self.bn1 = norm_layer(width) self.conv2 = conv3x3(width, width, stride, groups, dilation) self.bn2 = norm_layer(width) self.conv3 = conv1x1(width, planes * self.expansion) self.bn3 = norm_layer(planes * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = zerocenter(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = zerocenter(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) out = zerocenter(out) return out class ResNet(nn.Module): def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None): super(ResNet, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = 64 self.dilation = 1 if replace_stride_with_dilation is None: # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError("replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = norm_layer(self.inplanes) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) # Zero-initialize the last BN in each residual branch, # so that the residual branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block, planes, blocks, stride=1, dilate=False): norm_layer = self._norm_layer downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), norm_layer(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation, norm_layer=norm_layer)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = zerocenter(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x def _resnet(arch, block, layers, pretrained, progress, **kwargs): model = ResNet(block, layers, **kwargs) # if pretrained: # state_dict = load_state_dict_from_url(model_urls[arch], # progress=progress) # model.load_state_dict(state_dict) return model def resnet18(pretrained=False, progress=True, **kwargs): r"""ResNet-18 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs) def resnet34(pretrained=False, progress=True, **kwargs): r"""ResNet-34 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnet50(pretrained=False, progress=True, **kwargs): r"""ResNet-50 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnet101(pretrained=False, progress=True, **kwargs): r"""ResNet-101 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) def resnet152(pretrained=False, progress=True, **kwargs): r"""ResNet-152 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs) def resnext50_32x4d(pretrained=False, progress=True, **kwargs): r"""ResNeXt-50 32x4d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['groups'] = 32 kwargs['width_per_group'] = 4 return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnext101_32x8d(pretrained=False, progress=True, **kwargs): r"""ResNeXt-101 32x8d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['groups'] = 32 kwargs['width_per_group'] = 8 return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) def wide_resnet50_2(pretrained=False, progress=True, **kwargs): r"""Wide ResNet-50-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['width_per_group'] = 64 * 2 return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def wide_resnet101_2(pretrained=False, progress=True, **kwargs): r"""Wide ResNet-101-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['width_per_group'] = 64 * 2 return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) class ZeroCenterEncoder(ResNet): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.pretrained = False del self.fc def forward(self, x): x0 = self.conv1(x) x0 = self.bn1(x0) x0 = self.relu(x0) x1 = self.maxpool(x0) x1 = zerocenter(x1) x1 = self.layer1(x1) x2 = self.layer2(x1) x3 = self.layer3(x2) x4 = self.layer4(x3) return [x4, x3, x2, x1, x0] def load_state_dict(self, state_dict, **kwargs): state_dict.pop('fc.bias') state_dict.pop('fc.weight') super().load_state_dict(state_dict, **kwargs)

38.396277

107

0.626377

import torch import torch.nn as nn from .utils import zerocenter __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', 'wide_resnet50_2', 'wide_resnet101_2'] model_urls = { 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', } def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation) def conv1x1(in_planes, out_planes, stride=1): return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None): super(BasicBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d if groups != 1 or base_width != 64: raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = norm_layer(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = norm_layer(planes) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = zerocenter(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) out = zerocenter(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None): super(Bottleneck, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d width = int(planes * (base_width / 64.)) * groups self.conv1 = conv1x1(inplanes, width) self.bn1 = norm_layer(width) self.conv2 = conv3x3(width, width, stride, groups, dilation) self.bn2 = norm_layer(width) self.conv3 = conv1x1(width, planes * self.expansion) self.bn3 = norm_layer(planes * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = zerocenter(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = zerocenter(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) out = zerocenter(out) return out class ResNet(nn.Module): def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None): super(ResNet, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = 64 self.dilation = 1 if replace_stride_with_dilation is None: replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError("replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = norm_layer(self.inplanes) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block, planes, blocks, stride=1, dilate=False): norm_layer = self._norm_layer downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), norm_layer(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation, norm_layer=norm_layer)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = zerocenter(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x def _resnet(arch, block, layers, pretrained, progress, **kwargs): model = ResNet(block, layers, **kwargs) return model def resnet18(pretrained=False, progress=True, **kwargs): return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs) def resnet34(pretrained=False, progress=True, **kwargs): return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnet50(pretrained=False, progress=True, **kwargs): return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnet101(pretrained=False, progress=True, **kwargs): return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) def resnet152(pretrained=False, progress=True, **kwargs): return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs) def resnext50_32x4d(pretrained=False, progress=True, **kwargs): kwargs['groups'] = 32 kwargs['width_per_group'] = 4 return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnext101_32x8d(pretrained=False, progress=True, **kwargs): kwargs['groups'] = 32 kwargs['width_per_group'] = 8 return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) def wide_resnet50_2(pretrained=False, progress=True, **kwargs): kwargs['width_per_group'] = 64 * 2 return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def wide_resnet101_2(pretrained=False, progress=True, **kwargs): kwargs['width_per_group'] = 64 * 2 return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) class ZeroCenterEncoder(ResNet): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.pretrained = False del self.fc def forward(self, x): x0 = self.conv1(x) x0 = self.bn1(x0) x0 = self.relu(x0) x1 = self.maxpool(x0) x1 = zerocenter(x1) x1 = self.layer1(x1) x2 = self.layer2(x1) x3 = self.layer3(x2) x4 = self.layer4(x3) return [x4, x3, x2, x1, x0] def load_state_dict(self, state_dict, **kwargs): state_dict.pop('fc.bias') state_dict.pop('fc.weight') super().load_state_dict(state_dict, **kwargs)

true

79081216f75f33759b780ec16eb23dc9dac30bc1

7,185

py

Python

backend/shiny_lake_28693/settings.py

crowdbotics-apps/shiny-lake-28693

be8eac9d53473f5251f4e1a091caf4cd54beb62e

[ "FTL", "AML", "RSA-MD" ]

null

backend/shiny_lake_28693/settings.py

crowdbotics-apps/shiny-lake-28693

be8eac9d53473f5251f4e1a091caf4cd54beb62e

[ "FTL", "AML", "RSA-MD" ]

20

2021-07-10T18:43:17.000Z

2021-07-10T18:43:19.000Z

backend/shiny_lake_28693/settings.py

crowdbotics-apps/shiny-lake-28693

be8eac9d53473f5251f4e1a091caf4cd54beb62e

[ "FTL", "AML", "RSA-MD" ]

null

""" Django settings for shiny_lake_28693 project. Generated by 'django-admin startproject' using Django 2.2.2. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os import environ import logging from modules.manifest import get_modules env = environ.Env() # SECURITY WARNING: don't run with debug turned on in production! DEBUG = env.bool("DEBUG", default=False) # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = env.str("SECRET_KEY") ALLOWED_HOSTS = env.list("HOST", default=["*"]) SITE_ID = 1 SECURE_PROXY_SSL_HEADER = ("HTTP_X_FORWARDED_PROTO", "https") SECURE_SSL_REDIRECT = env.bool("SECURE_REDIRECT", default=False) # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.sites' ] LOCAL_APPS = [ 'home', 'users.apps.UsersConfig', ] THIRD_PARTY_APPS = [ 'rest_framework', 'rest_framework.authtoken', 'rest_auth', 'rest_auth.registration', 'bootstrap4', 'allauth', 'allauth.account', 'allauth.socialaccount', 'allauth.socialaccount.providers.google', 'django_extensions', 'drf_yasg', 'storages', # start fcm_django push notifications 'fcm_django', # end fcm_django push notifications ] MODULES_APPS = get_modules() INSTALLED_APPS += LOCAL_APPS + THIRD_PARTY_APPS + MODULES_APPS MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'shiny_lake_28693.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'web_build')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'shiny_lake_28693.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } if env.str("DATABASE_URL", default=None): DATABASES = { 'default': env.db() } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' MIDDLEWARE += ['whitenoise.middleware.WhiteNoiseMiddleware'] AUTHENTICATION_BACKENDS = ( 'django.contrib.auth.backends.ModelBackend', 'allauth.account.auth_backends.AuthenticationBackend' ) STATIC_ROOT = os.path.join(BASE_DIR, "staticfiles") STATICFILES_DIRS = [os.path.join(BASE_DIR, 'static'), os.path.join(BASE_DIR, 'web_build/static')] STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage' # allauth / users ACCOUNT_EMAIL_REQUIRED = True ACCOUNT_AUTHENTICATION_METHOD = 'email' ACCOUNT_USERNAME_REQUIRED = False ACCOUNT_EMAIL_VERIFICATION = "optional" ACCOUNT_CONFIRM_EMAIL_ON_GET = True ACCOUNT_LOGIN_ON_EMAIL_CONFIRMATION = True ACCOUNT_UNIQUE_EMAIL = True LOGIN_REDIRECT_URL = "users:redirect" ACCOUNT_ADAPTER = "users.adapters.AccountAdapter" SOCIALACCOUNT_ADAPTER = "users.adapters.SocialAccountAdapter" ACCOUNT_ALLOW_REGISTRATION = env.bool("ACCOUNT_ALLOW_REGISTRATION", True) SOCIALACCOUNT_ALLOW_REGISTRATION = env.bool("SOCIALACCOUNT_ALLOW_REGISTRATION", True) REST_AUTH_SERIALIZERS = { # Replace password reset serializer to fix 500 error "PASSWORD_RESET_SERIALIZER": "home.api.v1.serializers.PasswordSerializer", } REST_AUTH_REGISTER_SERIALIZERS = { # Use custom serializer that has no username and matches web signup "REGISTER_SERIALIZER": "home.api.v1.serializers.SignupSerializer", } # Custom user model AUTH_USER_MODEL = "users.User" EMAIL_HOST = env.str("EMAIL_HOST", "smtp.sendgrid.net") EMAIL_HOST_USER = env.str("SENDGRID_USERNAME", "") EMAIL_HOST_PASSWORD = env.str("SENDGRID_PASSWORD", "") EMAIL_PORT = 587 EMAIL_USE_TLS = True # AWS S3 config AWS_ACCESS_KEY_ID = env.str("AWS_ACCESS_KEY_ID", "") AWS_SECRET_ACCESS_KEY = env.str("AWS_SECRET_ACCESS_KEY", "") AWS_STORAGE_BUCKET_NAME = env.str("AWS_STORAGE_BUCKET_NAME", "") AWS_STORAGE_REGION = env.str("AWS_STORAGE_REGION", "") USE_S3 = ( AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY and AWS_STORAGE_BUCKET_NAME and AWS_STORAGE_REGION ) if USE_S3: AWS_S3_CUSTOM_DOMAIN = env.str("AWS_S3_CUSTOM_DOMAIN", "") AWS_S3_OBJECT_PARAMETERS = {"CacheControl": "max-age=86400"} AWS_DEFAULT_ACL = env.str("AWS_DEFAULT_ACL", "public-read") AWS_MEDIA_LOCATION = env.str("AWS_MEDIA_LOCATION", "media") AWS_AUTO_CREATE_BUCKET = env.bool("AWS_AUTO_CREATE_BUCKET", True) DEFAULT_FILE_STORAGE = env.str( "DEFAULT_FILE_STORAGE", "home.storage_backends.MediaStorage" ) MEDIA_URL = '/mediafiles/' MEDIA_ROOT = os.path.join(BASE_DIR, 'mediafiles') # start fcm_django push notifications FCM_DJANGO_SETTINGS = { "FCM_SERVER_KEY": env.str("FCM_SERVER_KEY", "") } # end fcm_django push notifications # Swagger settings for api docs SWAGGER_SETTINGS = { "DEFAULT_INFO": f"{ROOT_URLCONF}.api_info", } if DEBUG or not (EMAIL_HOST_USER and EMAIL_HOST_PASSWORD): # output email to console instead of sending if not DEBUG: logging.warning("You should setup `SENDGRID_USERNAME` and `SENDGRID_PASSWORD` env vars to send emails.") EMAIL_BACKEND = "django.core.mail.backends.console.EmailBackend"

28.971774

112

0.730689

import os import environ import logging from modules.manifest import get_modules env = environ.Env() DEBUG = env.bool("DEBUG", default=False) # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = env.str("SECRET_KEY") ALLOWED_HOSTS = env.list("HOST", default=["*"]) SITE_ID = 1 SECURE_PROXY_SSL_HEADER = ("HTTP_X_FORWARDED_PROTO", "https") SECURE_SSL_REDIRECT = env.bool("SECURE_REDIRECT", default=False) # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.sites' ] LOCAL_APPS = [ 'home', 'users.apps.UsersConfig', ] THIRD_PARTY_APPS = [ 'rest_framework', 'rest_framework.authtoken', 'rest_auth', 'rest_auth.registration', 'bootstrap4', 'allauth', 'allauth.account', 'allauth.socialaccount', 'allauth.socialaccount.providers.google', 'django_extensions', 'drf_yasg', 'storages', # start fcm_django push notifications 'fcm_django', # end fcm_django push notifications ] MODULES_APPS = get_modules() INSTALLED_APPS += LOCAL_APPS + THIRD_PARTY_APPS + MODULES_APPS MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'shiny_lake_28693.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'web_build')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'shiny_lake_28693.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } if env.str("DATABASE_URL", default=None): DATABASES = { 'default': env.db() } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' MIDDLEWARE += ['whitenoise.middleware.WhiteNoiseMiddleware'] AUTHENTICATION_BACKENDS = ( 'django.contrib.auth.backends.ModelBackend', 'allauth.account.auth_backends.AuthenticationBackend' ) STATIC_ROOT = os.path.join(BASE_DIR, "staticfiles") STATICFILES_DIRS = [os.path.join(BASE_DIR, 'static'), os.path.join(BASE_DIR, 'web_build/static')] STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage' # allauth / users ACCOUNT_EMAIL_REQUIRED = True ACCOUNT_AUTHENTICATION_METHOD = 'email' ACCOUNT_USERNAME_REQUIRED = False ACCOUNT_EMAIL_VERIFICATION = "optional" ACCOUNT_CONFIRM_EMAIL_ON_GET = True ACCOUNT_LOGIN_ON_EMAIL_CONFIRMATION = True ACCOUNT_UNIQUE_EMAIL = True LOGIN_REDIRECT_URL = "users:redirect" ACCOUNT_ADAPTER = "users.adapters.AccountAdapter" SOCIALACCOUNT_ADAPTER = "users.adapters.SocialAccountAdapter" ACCOUNT_ALLOW_REGISTRATION = env.bool("ACCOUNT_ALLOW_REGISTRATION", True) SOCIALACCOUNT_ALLOW_REGISTRATION = env.bool("SOCIALACCOUNT_ALLOW_REGISTRATION", True) REST_AUTH_SERIALIZERS = { # Replace password reset serializer to fix 500 error "PASSWORD_RESET_SERIALIZER": "home.api.v1.serializers.PasswordSerializer", } REST_AUTH_REGISTER_SERIALIZERS = { # Use custom serializer that has no username and matches web signup "REGISTER_SERIALIZER": "home.api.v1.serializers.SignupSerializer", } # Custom user model AUTH_USER_MODEL = "users.User" EMAIL_HOST = env.str("EMAIL_HOST", "smtp.sendgrid.net") EMAIL_HOST_USER = env.str("SENDGRID_USERNAME", "") EMAIL_HOST_PASSWORD = env.str("SENDGRID_PASSWORD", "") EMAIL_PORT = 587 EMAIL_USE_TLS = True # AWS S3 config AWS_ACCESS_KEY_ID = env.str("AWS_ACCESS_KEY_ID", "") AWS_SECRET_ACCESS_KEY = env.str("AWS_SECRET_ACCESS_KEY", "") AWS_STORAGE_BUCKET_NAME = env.str("AWS_STORAGE_BUCKET_NAME", "") AWS_STORAGE_REGION = env.str("AWS_STORAGE_REGION", "") USE_S3 = ( AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY and AWS_STORAGE_BUCKET_NAME and AWS_STORAGE_REGION ) if USE_S3: AWS_S3_CUSTOM_DOMAIN = env.str("AWS_S3_CUSTOM_DOMAIN", "") AWS_S3_OBJECT_PARAMETERS = {"CacheControl": "max-age=86400"} AWS_DEFAULT_ACL = env.str("AWS_DEFAULT_ACL", "public-read") AWS_MEDIA_LOCATION = env.str("AWS_MEDIA_LOCATION", "media") AWS_AUTO_CREATE_BUCKET = env.bool("AWS_AUTO_CREATE_BUCKET", True) DEFAULT_FILE_STORAGE = env.str( "DEFAULT_FILE_STORAGE", "home.storage_backends.MediaStorage" ) MEDIA_URL = '/mediafiles/' MEDIA_ROOT = os.path.join(BASE_DIR, 'mediafiles') # start fcm_django push notifications FCM_DJANGO_SETTINGS = { "FCM_SERVER_KEY": env.str("FCM_SERVER_KEY", "") } # end fcm_django push notifications # Swagger settings for api docs SWAGGER_SETTINGS = { "DEFAULT_INFO": f"{ROOT_URLCONF}.api_info", } if DEBUG or not (EMAIL_HOST_USER and EMAIL_HOST_PASSWORD): # output email to console instead of sending if not DEBUG: logging.warning("You should setup `SENDGRID_USERNAME` and `SENDGRID_PASSWORD` env vars to send emails.") EMAIL_BACKEND = "django.core.mail.backends.console.EmailBackend"

true

79081276f4271dbca464776a1324868d791594cf

26,254

py

Python

Common/ComputationalGeometry/Testing/Python/TestParametricFunctions.py

biddisco/VTK

80fa7c3a767ce306586a596a6c6f3518a34e2f11

[ "BSD-3-Clause" ]

1

2021-10-13T01:57:14.000Z

Common/ComputationalGeometry/Testing/Python/TestParametricFunctions.py

heartvalve/VTK

b90a7749fc1491d53aadce5fb460f69713b14837

[ "BSD-3-Clause" ]

null

Common/ComputationalGeometry/Testing/Python/TestParametricFunctions.py

heartvalve/VTK

b90a7749fc1491d53aadce5fb460f69713b14837

[ "BSD-3-Clause" ]

5

2015-10-09T04:12:29.000Z

2021-12-15T16:57:11.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import vtk import vtk.test.Testing from vtk.util.misc import vtkGetDataRoot VTK_DATA_ROOT = vtkGetDataRoot() # ------------------------------------------------------------ # Purpose: Test the parametric functions. # ------------------------------------------------------------ class TestParametricFunctions(vtk.test.Testing.vtkTest): def testParametricFunctions(self): # ------------------------------------------------------------ # Get a texture # ------------------------------------------------------------ textureReader = vtk.vtkJPEGReader() textureReader.SetFileName(VTK_DATA_ROOT + "/Data/beach.jpg") texture = vtk.vtkTexture() texture.SetInputConnection(textureReader.GetOutputPort()) # ------------------------------------------------------------ # For each parametric surface: # 1) Create it # 2) Assign mappers and actors # 3) Position this object # 5) Add a label # ------------------------------------------------------------ # ------------------------------------------------------------ # Create a torus # ------------------------------------------------------------ torus = vtk.vtkParametricTorus() torusSource = vtk.vtkParametricFunctionSource() torusSource.SetParametricFunction(torus) torusSource.SetScalarModeToPhase() torusMapper = vtk.vtkPolyDataMapper() torusMapper.SetInputConnection(torusSource.GetOutputPort()) torusMapper.SetScalarRange(0, 360) torusActor = vtk.vtkActor() torusActor.SetMapper(torusMapper) torusActor.SetPosition(0, 12, 0) torusTextMapper = vtk.vtkTextMapper() torusTextMapper.SetInput("Torus") torusTextMapper.GetTextProperty().SetJustificationToCentered() torusTextMapper.GetTextProperty().SetVerticalJustificationToCentered() torusTextMapper.GetTextProperty().SetColor(1, 0, 0) torusTextMapper.GetTextProperty().SetFontSize(14) torusTextActor = vtk.vtkActor2D() torusTextActor.SetMapper(torusTextMapper) torusTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() torusTextActor.GetPositionCoordinate().SetValue(0, 9.5, 0) # ------------------------------------------------------------ # Create a klein bottle # ------------------------------------------------------------ klein = vtk.vtkParametricKlein() kleinSource = vtk.vtkParametricFunctionSource() kleinSource.SetParametricFunction(klein) kleinSource.SetScalarModeToU0V0() kleinMapper = vtk.vtkPolyDataMapper() kleinMapper.SetInputConnection(kleinSource.GetOutputPort()) kleinMapper.SetScalarRange(0, 3) kleinActor = vtk.vtkActor() kleinActor.SetMapper(kleinMapper) kleinActor.SetPosition(8, 10.5, 0) kleinTextMapper = vtk.vtkTextMapper() kleinTextMapper.SetInput("Klein") kleinTextMapper.GetTextProperty().SetJustificationToCentered() kleinTextMapper.GetTextProperty().SetVerticalJustificationToCentered() kleinTextMapper.GetTextProperty().SetColor(1, 0, 0) kleinTextMapper.GetTextProperty().SetFontSize(14) kleinTextActor = vtk.vtkActor2D() kleinTextActor.SetMapper(kleinTextMapper) kleinTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() kleinTextActor.GetPositionCoordinate().SetValue(8, 9.5, 0) # ------------------------------------------------------------ # Create a Figure-8 Klein # ------------------------------------------------------------ klein2 = vtk.vtkParametricFigure8Klein() klein2Source = vtk.vtkParametricFunctionSource() klein2Source.SetParametricFunction(klein2) klein2Source.GenerateTextureCoordinatesOn() klein2Mapper = vtk.vtkPolyDataMapper() klein2Mapper.SetInputConnection(klein2Source.GetOutputPort()) klein2Mapper.SetScalarRange(0, 3) klein2Actor = vtk.vtkActor() klein2Actor.SetMapper(klein2Mapper) klein2Actor.SetPosition(16, 12, 0) klein2Actor.SetTexture(texture) fig8KleinTextMapper = vtk.vtkTextMapper() fig8KleinTextMapper.SetInput("Fig-8.Klein") fig8KleinTextMapper.GetTextProperty().SetJustificationToCentered() fig8KleinTextMapper.GetTextProperty().SetVerticalJustificationToCentered() fig8KleinTextMapper.GetTextProperty().SetColor(1, 0, 0) fig8KleinTextMapper.GetTextProperty().SetFontSize(14) fig8KleinTextActor = vtk.vtkActor2D() fig8KleinTextActor.SetMapper(fig8KleinTextMapper) fig8KleinTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() fig8KleinTextActor.GetPositionCoordinate().SetValue(16, 9.5, 0) # ------------------------------------------------------------ # Create a mobius strip # ------------------------------------------------------------ mobius = vtk.vtkParametricMobius() mobiusSource = vtk.vtkParametricFunctionSource() mobiusSource.SetParametricFunction(mobius) mobiusSource.GenerateTextureCoordinatesOn() mobiusMapper = vtk.vtkPolyDataMapper() mobiusMapper.SetInputConnection(mobiusSource.GetOutputPort()) mobiusActor = vtk.vtkActor() mobiusActor.SetMapper(mobiusMapper) mobiusActor.RotateX(45) mobiusActor.SetPosition(24, 12, 0) mobiusActor.SetTexture(texture) mobiusTextMapper = vtk.vtkTextMapper() mobiusTextMapper.SetInput("Mobius") mobiusTextMapper.GetTextProperty().SetJustificationToCentered() mobiusTextMapper.GetTextProperty().SetVerticalJustificationToCentered() mobiusTextMapper.GetTextProperty().SetColor(1, 0, 0) mobiusTextMapper.GetTextProperty().SetFontSize(14) mobiusTextActor = vtk.vtkActor2D() mobiusTextActor.SetMapper(mobiusTextMapper) mobiusTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() mobiusTextActor.GetPositionCoordinate().SetValue(24, 9.5, 0) # ------------------------------------------------------------ # Create a super toroid # ------------------------------------------------------------ toroid = vtk.vtkParametricSuperToroid() toroid.SetN1(2) toroid.SetN2(3) toroidSource = vtk.vtkParametricFunctionSource() toroidSource.SetParametricFunction(toroid) toroidSource.SetScalarModeToU() toroidMapper = vtk.vtkPolyDataMapper() toroidMapper.SetInputConnection(toroidSource.GetOutputPort()) toroidMapper.SetScalarRange(0, 6.28) toroidActor = vtk.vtkActor() toroidActor.SetMapper(toroidMapper) toroidActor.SetPosition(0, 4, 0) superToroidTextMapper = vtk.vtkTextMapper() superToroidTextMapper.SetInput("Super.Toroid") superToroidTextMapper.GetTextProperty().SetJustificationToCentered() superToroidTextMapper.GetTextProperty().SetVerticalJustificationToCentered() superToroidTextMapper.GetTextProperty().SetColor(1, 0, 0) superToroidTextMapper.GetTextProperty().SetFontSize(14) superToroidTextActor = vtk.vtkActor2D() superToroidTextActor.SetMapper(superToroidTextMapper) superToroidTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() superToroidTextActor.GetPositionCoordinate().SetValue(0, 1.5, 0) # ------------------------------------------------------------ # Create a super ellipsoid # ------------------------------------------------------------ superEllipsoid = vtk.vtkParametricSuperEllipsoid() superEllipsoid.SetXRadius(1.25) superEllipsoid.SetYRadius(1.5) superEllipsoid.SetZRadius(1.0) superEllipsoid.SetN1(1.1) superEllipsoid.SetN2(1.75) superEllipsoidSource = vtk.vtkParametricFunctionSource() superEllipsoidSource.SetParametricFunction(superEllipsoid) superEllipsoidSource.SetScalarModeToV() superEllipsoidMapper = vtk.vtkPolyDataMapper() superEllipsoidMapper.SetInputConnection(superEllipsoidSource.GetOutputPort()) superEllipsoidMapper.SetScalarRange(0, 3.14) superEllipsoidActor = vtk.vtkActor() superEllipsoidActor.SetMapper(superEllipsoidMapper) superEllipsoidActor.SetPosition(8, 4, 0) superEllipsoidTextMapper = vtk.vtkTextMapper() superEllipsoidTextMapper.SetInput("Super.Ellipsoid") superEllipsoidTextMapper.GetTextProperty().SetJustificationToCentered() superEllipsoidTextMapper.GetTextProperty().SetVerticalJustificationToCentered() superEllipsoidTextMapper.GetTextProperty().SetColor(1, 0, 0) superEllipsoidTextMapper.GetTextProperty().SetFontSize(14) superEllipsoidTextActor = vtk.vtkActor2D() superEllipsoidTextActor.SetMapper(superEllipsoidTextMapper) superEllipsoidTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() superEllipsoidTextActor.GetPositionCoordinate().SetValue(8, 1.5, 0) # ------------------------------------------------------------ # Create an open 1D spline # ------------------------------------------------------------ splinePoints = [ [0.50380158308139134, -0.60679315105396936, -0.37248976406291578], [-0.4354646054261665, -0.85362339758017258, -0.84844312996065385], [0.2163147512899315, -0.39797507012168643, -0.76700353518454523], [0.97158415334838644, -0.58513467367046257, -0.35846037946569753], [-0.64359767997804918, -0.94620739107309249, -0.90762176546623086], [-0.39901219094126117, -0.1978931497772658, 0.0098316934936828471], [-0.75872745167404765, 0.067719714281950116, 0.165237936733867], [-0.84599731389712418, -0.67685466896596114, 0.10357868909071133], [0.84702754758625654, -0.0080077177882230677, -0.58571286666473044], [-0.076150034124101484, 0.14637647622561856, 0.1494359239700418] ] inputPoints = vtk.vtkPoints() for i in range(0, 10): inputPoints.InsertPoint(i, splinePoints[i]) spline = vtk.vtkParametricSpline() spline.SetPoints(inputPoints) spline.ClosedOff() splineSource = vtk.vtkParametricFunctionSource() splineSource.SetParametricFunction(spline) splineMapper = vtk.vtkPolyDataMapper() splineMapper.SetInputConnection(splineSource.GetOutputPort()) splineActor = vtk.vtkActor() splineActor.SetMapper(splineMapper) splineActor.SetPosition(16, 4, 0) splineActor.GetProperty().SetColor(0, 0, 0) splineTextMapper = vtk.vtkTextMapper() splineTextMapper.SetInput("Open.Spline") splineTextMapper.GetTextProperty().SetJustificationToCentered() splineTextMapper.GetTextProperty().SetVerticalJustificationToCentered() splineTextMapper.GetTextProperty().SetColor(1, 0, 0) splineTextMapper.GetTextProperty().SetFontSize(14) splineTextActor = vtk.vtkActor2D() splineTextActor.SetMapper(splineTextMapper) splineTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() splineTextActor.GetPositionCoordinate().SetValue(16, 1.5, 0) # ------------------------------------------------------------ # Create a closed 1D spline # ------------------------------------------------------------ spline2 = vtk.vtkParametricSpline() spline2.SetPoints(inputPoints) spline2.ClosedOn() spline2Source = vtk.vtkParametricFunctionSource() spline2Source.SetParametricFunction(spline2) spline2Mapper = vtk.vtkPolyDataMapper() spline2Mapper.SetInputConnection(spline2Source.GetOutputPort()) spline2Actor = vtk.vtkActor() spline2Actor.SetMapper(spline2Mapper) spline2Actor.SetPosition(24, 4, 0) spline2Actor.GetProperty().SetColor(0, 0, 0) spline2TextMapper = vtk.vtkTextMapper() spline2TextMapper.SetInput("Closed.Spline") spline2TextMapper.GetTextProperty().SetJustificationToCentered() spline2TextMapper.GetTextProperty().SetVerticalJustificationToCentered() spline2TextMapper.GetTextProperty().SetColor(1, 0, 0) spline2TextMapper.GetTextProperty().SetFontSize(14) spline2TextActor = vtk.vtkActor2D() spline2TextActor.SetMapper(spline2TextMapper) spline2TextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() spline2TextActor.GetPositionCoordinate().SetValue(24, 1.5, 0) # ------------------------------------------------------------ # Create a spiral conic # ------------------------------------------------------------ sconic = vtk.vtkParametricConicSpiral() sconic.SetA(0.8) sconic.SetB(2.5) sconic.SetC(0.4) sconicSource = vtk.vtkParametricFunctionSource() sconicSource.SetParametricFunction(sconic) sconicSource.SetScalarModeToDistance() sconicMapper = vtk.vtkPolyDataMapper() sconicMapper.SetInputConnection(sconicSource.GetOutputPort()) sconicActor = vtk.vtkActor() sconicActor.SetMapper(sconicMapper) sconicMapper.SetScalarRange(0, 9) sconicActor.SetPosition(0, -4, 0) sconicActor.SetScale(1.2, 1.2, 1.2) sconicTextMapper = vtk.vtkTextMapper() sconicTextMapper.SetInput("Spiral.Conic") sconicTextMapper.GetTextProperty().SetJustificationToCentered() sconicTextMapper.GetTextProperty().SetVerticalJustificationToCentered() sconicTextMapper.GetTextProperty().SetColor(1, 0, 0) sconicTextMapper.GetTextProperty().SetFontSize(14) sconicTextActor = vtk.vtkActor2D() sconicTextActor.SetMapper(sconicTextMapper) sconicTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() sconicTextActor.GetPositionCoordinate().SetValue(0, -6.5, 0) # ------------------------------------------------------------ # Create Boy's surface # ------------------------------------------------------------ boy = vtk.vtkParametricBoy() boySource = vtk.vtkParametricFunctionSource() boySource.SetParametricFunction(boy) boySource.SetScalarModeToModulus() boyMapper = vtk.vtkPolyDataMapper() boyMapper.SetInputConnection(boySource.GetOutputPort()) boyMapper.SetScalarRange(0, 2) boyActor = vtk.vtkActor() boyActor.SetMapper(boyMapper) boyActor.SetPosition(8, -4, 0) boyActor.SetScale(1.5, 1.5, 1.5) boyTextMapper = vtk.vtkTextMapper() boyTextMapper.SetInput("Boy") boyTextMapper.GetTextProperty().SetJustificationToCentered() boyTextMapper.GetTextProperty().SetVerticalJustificationToCentered() boyTextMapper.GetTextProperty().SetColor(1, 0, 0) boyTextMapper.GetTextProperty().SetFontSize(14) boyTextActor = vtk.vtkActor2D() boyTextActor.SetMapper(boyTextMapper) boyTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() boyTextActor.GetPositionCoordinate().SetValue(8, -6.5, 0) # ------------------------------------------------------------ # Create a cross cap # ------------------------------------------------------------ crossCap = vtk.vtkParametricCrossCap() crossCapSource = vtk.vtkParametricFunctionSource() crossCapSource.SetParametricFunction(crossCap) crossCapSource.SetScalarModeToY() crossCapMapper = vtk.vtkPolyDataMapper() crossCapMapper.SetInputConnection(crossCapSource.GetOutputPort()) crossCapActor = vtk.vtkActor() crossCapActor.SetMapper(crossCapMapper) crossCapActor.RotateX(65) crossCapActor.SetPosition(16, -4, 0) crossCapActor.SetScale(1.5, 1.5, 1.5) crossCapTextMapper = vtk.vtkTextMapper() crossCapTextMapper.SetInput("Cross.Cap") crossCapTextMapper.GetTextProperty().SetJustificationToCentered() crossCapTextMapper.GetTextProperty().SetVerticalJustificationToCentered() crossCapTextMapper.GetTextProperty().SetColor(1, 0, 0) crossCapTextMapper.GetTextProperty().SetFontSize(14) crossCapTextActor = vtk.vtkActor2D() crossCapTextActor.SetMapper(crossCapTextMapper) crossCapTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() crossCapTextActor.GetPositionCoordinate().SetValue(16, -6.5, 0) # ------------------------------------------------------------ # Create Dini's surface # ------------------------------------------------------------ dini = vtk.vtkParametricDini() diniSource = vtk.vtkParametricFunctionSource() diniSource.SetScalarModeToDistance() diniSource.SetParametricFunction(dini) diniMapper = vtk.vtkPolyDataMapper() diniMapper.SetInputConnection(diniSource.GetOutputPort()) diniActor = vtk.vtkActor() diniActor.SetMapper(diniMapper) diniActor.RotateX(-90) diniActor.SetPosition(24, -3, 0) diniActor.SetScale(1.5, 1.5, 0.5) diniTextMapper = vtk.vtkTextMapper() diniTextMapper.SetInput("Dini") diniTextMapper.GetTextProperty().SetJustificationToCentered() diniTextMapper.GetTextProperty().SetVerticalJustificationToCentered() diniTextMapper.GetTextProperty().SetColor(1, 0, 0) diniTextMapper.GetTextProperty().SetFontSize(14) diniTextActor = vtk.vtkActor2D() diniTextActor.SetMapper(diniTextMapper) diniTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() diniTextActor.GetPositionCoordinate().SetValue(24, -6.5, 0) # ------------------------------------------------------------ # Create Enneper's surface # ------------------------------------------------------------ enneper = vtk.vtkParametricEnneper() enneperSource = vtk.vtkParametricFunctionSource() enneperSource.SetParametricFunction(enneper) enneperSource.SetScalarModeToQuadrant() enneperMapper = vtk.vtkPolyDataMapper() enneperMapper.SetInputConnection(enneperSource.GetOutputPort()) enneperMapper.SetScalarRange(1, 4) enneperActor = vtk.vtkActor() enneperActor.SetMapper(enneperMapper) enneperActor.SetPosition(0, -12, 0) enneperActor.SetScale(0.25, 0.25, 0.25) enneperTextMapper = vtk.vtkTextMapper() enneperTextMapper.SetInput("Enneper") enneperTextMapper.GetTextProperty().SetJustificationToCentered() enneperTextMapper.GetTextProperty().SetVerticalJustificationToCentered() enneperTextMapper.GetTextProperty().SetColor(1, 0, 0) enneperTextMapper.GetTextProperty().SetFontSize(14) enneperTextActor = vtk.vtkActor2D() enneperTextActor.SetMapper(enneperTextMapper) enneperTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() enneperTextActor.GetPositionCoordinate().SetValue(0, -14.5, 0) # ------------------------------------------------------------ # Create an ellipsoidal surface # ------------------------------------------------------------ ellipsoid = vtk.vtkParametricEllipsoid() ellipsoid.SetXRadius(1) ellipsoid.SetYRadius(0.75) ellipsoid.SetZRadius(0.5) ellipsoidSource = vtk.vtkParametricFunctionSource() ellipsoidSource.SetParametricFunction(ellipsoid) ellipsoidSource.SetScalarModeToZ() ellipsoidMapper = vtk.vtkPolyDataMapper() ellipsoidMapper.SetInputConnection(ellipsoidSource.GetOutputPort()) ellipsoidMapper.SetScalarRange(-0.5, 0.5) ellipsoidActor = vtk.vtkActor() ellipsoidActor.SetMapper(ellipsoidMapper) ellipsoidActor.SetPosition(8, -12, 0) ellipsoidActor.SetScale(1.5, 1.5, 1.5) ellipsoidTextMapper = vtk.vtkTextMapper() ellipsoidTextMapper.SetInput("Ellipsoid") ellipsoidTextMapper.GetTextProperty().SetJustificationToCentered() ellipsoidTextMapper.GetTextProperty().SetVerticalJustificationToCentered() ellipsoidTextMapper.GetTextProperty().SetColor(1, 0, 0) ellipsoidTextMapper.GetTextProperty().SetFontSize(14) ellipsoidTextActor = vtk.vtkActor2D() ellipsoidTextActor.SetMapper(ellipsoidTextMapper) ellipsoidTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() ellipsoidTextActor.GetPositionCoordinate().SetValue(8, -14.5, 0) # ------------------------------------------------------------ # Create an surface with random hills on it. # Note that for testing, we will disable the # random generation of the surfaces. This is # because random number generators do not # return the same result on different operating # systems. # ------------------------------------------------------------ randomHills = vtk.vtkParametricRandomHills() randomHills.AllowRandomGenerationOff() randomHills.GenerateTheHills() randomHillsSource = vtk.vtkParametricFunctionSource() randomHillsSource.SetParametricFunction(randomHills) randomHillsSource.GenerateTextureCoordinatesOn() randomHillsMapper = vtk.vtkPolyDataMapper() randomHillsMapper.SetInputConnection(randomHillsSource.GetOutputPort()) randomHillsActor = vtk.vtkActor() randomHillsActor.SetMapper(randomHillsMapper) randomHillsActor.SetPosition(16, -14, 0) randomHillsActor.SetScale(0.2, 0.2, 0.2) randomHillsActor.SetTexture(texture) randomHillsTextMapper = vtk.vtkTextMapper() randomHillsTextMapper.SetInput("Random.Hills") randomHillsTextMapper.GetTextProperty().SetJustificationToCentered() randomHillsTextMapper.GetTextProperty().SetVerticalJustificationToCentered() randomHillsTextMapper.GetTextProperty().SetColor(1, 0, 0) randomHillsTextMapper.GetTextProperty().SetFontSize(14) randomHillsTextActor = vtk.vtkActor2D() randomHillsTextActor.SetMapper(randomHillsTextMapper) randomHillsTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() randomHillsTextActor.GetPositionCoordinate().SetValue(16, -14.5, 0) # ------------------------------------------------------------ # Create an Steiner's Roman Surface. # ------------------------------------------------------------ roman = vtk.vtkParametricRoman() roman.SetRadius(1.5) romanSource = vtk.vtkParametricFunctionSource() romanSource.SetParametricFunction(roman) romanSource.SetScalarModeToX() romanMapper = vtk.vtkPolyDataMapper() romanMapper.SetInputConnection(romanSource.GetOutputPort()) romanActor = vtk.vtkActor() romanActor.SetMapper(romanMapper) romanActor.SetPosition(24, -12, 0) romanTextMapper = vtk.vtkTextMapper() romanTextMapper.SetInput("Roman") romanTextMapper.GetTextProperty().SetJustificationToCentered() romanTextMapper.GetTextProperty().SetVerticalJustificationToCentered() romanTextMapper.GetTextProperty().SetColor(1, 0, 0) romanTextMapper.GetTextProperty().SetFontSize(14) romanTextActor = vtk.vtkActor2D() romanTextActor.SetMapper(romanTextMapper) romanTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() romanTextActor.GetPositionCoordinate().SetValue(24, -14.5, 0) # ------------------------------------------------------------ # Create the RenderWindow, Renderer and both Actors # ------------------------------------------------------------ ren = vtk.vtkRenderer() renWin = vtk.vtkRenderWindow() renWin.AddRenderer(ren) iren = vtk.vtkRenderWindowInteractor() iren.SetRenderWindow(renWin) # add actors ren.AddViewProp(torusActor) ren.AddViewProp(kleinActor) ren.AddViewProp(klein2Actor) ren.AddViewProp(toroidActor) ren.AddViewProp(superEllipsoidActor) ren.AddViewProp(mobiusActor) ren.AddViewProp(splineActor) ren.AddViewProp(spline2Actor) ren.AddViewProp(sconicActor) ren.AddViewProp(boyActor) ren.AddViewProp(crossCapActor) ren.AddViewProp(diniActor) ren.AddViewProp(enneperActor) ren.AddViewProp(ellipsoidActor) ren.AddViewProp(randomHillsActor) ren.AddViewProp(romanActor) #add text actors ren.AddViewProp(torusTextActor) ren.AddViewProp(kleinTextActor) ren.AddViewProp(fig8KleinTextActor) ren.AddViewProp(mobiusTextActor) ren.AddViewProp(superToroidTextActor) ren.AddViewProp(superEllipsoidTextActor) ren.AddViewProp(splineTextActor) ren.AddViewProp(spline2TextActor) ren.AddViewProp(sconicTextActor) ren.AddViewProp(boyTextActor) ren.AddViewProp(crossCapTextActor) ren.AddViewProp(diniTextActor) ren.AddViewProp(enneperTextActor) ren.AddViewProp(ellipsoidTextActor) ren.AddViewProp(randomHillsTextActor) ren.AddViewProp(romanTextActor) ren.SetBackground(0.7, 0.8, 1) renWin.SetSize(500, 500) ren.ResetCamera() ren.GetActiveCamera().Zoom(1.3) iren.Initialize() renWin.Render() img_file = "TestParametricFunctions.png" # NOTE: this test has a companion .tcl test. The threshold set # here should be the same as the threshold in the .tcl # test. Both tests should produce exactly the same results. vtk.test.Testing.compareImage(iren.GetRenderWindow(), vtk.test.Testing.getAbsImagePath(img_file), threshold=10) vtk.test.Testing.interact() if __name__ == "__main__": vtk.test.Testing.main([(TestParametricFunctions, 'test')])

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import vtk import vtk.test.Testing from vtk.util.misc import vtkGetDataRoot VTK_DATA_ROOT = vtkGetDataRoot() class TestParametricFunctions(vtk.test.Testing.vtkTest): def testParametricFunctions(self): textureReader = vtk.vtkJPEGReader() textureReader.SetFileName(VTK_DATA_ROOT + "/Data/beach.jpg") texture = vtk.vtkTexture() texture.SetInputConnection(textureReader.GetOutputPort()) torus = vtk.vtkParametricTorus() torusSource = vtk.vtkParametricFunctionSource() torusSource.SetParametricFunction(torus) torusSource.SetScalarModeToPhase() torusMapper = vtk.vtkPolyDataMapper() torusMapper.SetInputConnection(torusSource.GetOutputPort()) torusMapper.SetScalarRange(0, 360) torusActor = vtk.vtkActor() torusActor.SetMapper(torusMapper) torusActor.SetPosition(0, 12, 0) torusTextMapper = vtk.vtkTextMapper() torusTextMapper.SetInput("Torus") torusTextMapper.GetTextProperty().SetJustificationToCentered() torusTextMapper.GetTextProperty().SetVerticalJustificationToCentered() torusTextMapper.GetTextProperty().SetColor(1, 0, 0) torusTextMapper.GetTextProperty().SetFontSize(14) torusTextActor = vtk.vtkActor2D() torusTextActor.SetMapper(torusTextMapper) torusTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() torusTextActor.GetPositionCoordinate().SetValue(0, 9.5, 0) klein = vtk.vtkParametricKlein() kleinSource = vtk.vtkParametricFunctionSource() kleinSource.SetParametricFunction(klein) kleinSource.SetScalarModeToU0V0() kleinMapper = vtk.vtkPolyDataMapper() kleinMapper.SetInputConnection(kleinSource.GetOutputPort()) kleinMapper.SetScalarRange(0, 3) kleinActor = vtk.vtkActor() kleinActor.SetMapper(kleinMapper) kleinActor.SetPosition(8, 10.5, 0) kleinTextMapper = vtk.vtkTextMapper() kleinTextMapper.SetInput("Klein") kleinTextMapper.GetTextProperty().SetJustificationToCentered() kleinTextMapper.GetTextProperty().SetVerticalJustificationToCentered() kleinTextMapper.GetTextProperty().SetColor(1, 0, 0) kleinTextMapper.GetTextProperty().SetFontSize(14) kleinTextActor = vtk.vtkActor2D() kleinTextActor.SetMapper(kleinTextMapper) kleinTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() kleinTextActor.GetPositionCoordinate().SetValue(8, 9.5, 0) klein2 = vtk.vtkParametricFigure8Klein() klein2Source = vtk.vtkParametricFunctionSource() klein2Source.SetParametricFunction(klein2) klein2Source.GenerateTextureCoordinatesOn() klein2Mapper = vtk.vtkPolyDataMapper() klein2Mapper.SetInputConnection(klein2Source.GetOutputPort()) klein2Mapper.SetScalarRange(0, 3) klein2Actor = vtk.vtkActor() klein2Actor.SetMapper(klein2Mapper) klein2Actor.SetPosition(16, 12, 0) klein2Actor.SetTexture(texture) fig8KleinTextMapper = vtk.vtkTextMapper() fig8KleinTextMapper.SetInput("Fig-8.Klein") fig8KleinTextMapper.GetTextProperty().SetJustificationToCentered() fig8KleinTextMapper.GetTextProperty().SetVerticalJustificationToCentered() fig8KleinTextMapper.GetTextProperty().SetColor(1, 0, 0) fig8KleinTextMapper.GetTextProperty().SetFontSize(14) fig8KleinTextActor = vtk.vtkActor2D() fig8KleinTextActor.SetMapper(fig8KleinTextMapper) fig8KleinTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() fig8KleinTextActor.GetPositionCoordinate().SetValue(16, 9.5, 0) mobius = vtk.vtkParametricMobius() mobiusSource = vtk.vtkParametricFunctionSource() mobiusSource.SetParametricFunction(mobius) mobiusSource.GenerateTextureCoordinatesOn() mobiusMapper = vtk.vtkPolyDataMapper() mobiusMapper.SetInputConnection(mobiusSource.GetOutputPort()) mobiusActor = vtk.vtkActor() mobiusActor.SetMapper(mobiusMapper) mobiusActor.RotateX(45) mobiusActor.SetPosition(24, 12, 0) mobiusActor.SetTexture(texture) mobiusTextMapper = vtk.vtkTextMapper() mobiusTextMapper.SetInput("Mobius") mobiusTextMapper.GetTextProperty().SetJustificationToCentered() mobiusTextMapper.GetTextProperty().SetVerticalJustificationToCentered() mobiusTextMapper.GetTextProperty().SetColor(1, 0, 0) mobiusTextMapper.GetTextProperty().SetFontSize(14) mobiusTextActor = vtk.vtkActor2D() mobiusTextActor.SetMapper(mobiusTextMapper) mobiusTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() mobiusTextActor.GetPositionCoordinate().SetValue(24, 9.5, 0) toroid = vtk.vtkParametricSuperToroid() toroid.SetN1(2) toroid.SetN2(3) toroidSource = vtk.vtkParametricFunctionSource() toroidSource.SetParametricFunction(toroid) toroidSource.SetScalarModeToU() toroidMapper = vtk.vtkPolyDataMapper() toroidMapper.SetInputConnection(toroidSource.GetOutputPort()) toroidMapper.SetScalarRange(0, 6.28) toroidActor = vtk.vtkActor() toroidActor.SetMapper(toroidMapper) toroidActor.SetPosition(0, 4, 0) superToroidTextMapper = vtk.vtkTextMapper() superToroidTextMapper.SetInput("Super.Toroid") superToroidTextMapper.GetTextProperty().SetJustificationToCentered() superToroidTextMapper.GetTextProperty().SetVerticalJustificationToCentered() superToroidTextMapper.GetTextProperty().SetColor(1, 0, 0) superToroidTextMapper.GetTextProperty().SetFontSize(14) superToroidTextActor = vtk.vtkActor2D() superToroidTextActor.SetMapper(superToroidTextMapper) superToroidTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() superToroidTextActor.GetPositionCoordinate().SetValue(0, 1.5, 0) superEllipsoid = vtk.vtkParametricSuperEllipsoid() superEllipsoid.SetXRadius(1.25) superEllipsoid.SetYRadius(1.5) superEllipsoid.SetZRadius(1.0) superEllipsoid.SetN1(1.1) superEllipsoid.SetN2(1.75) superEllipsoidSource = vtk.vtkParametricFunctionSource() superEllipsoidSource.SetParametricFunction(superEllipsoid) superEllipsoidSource.SetScalarModeToV() superEllipsoidMapper = vtk.vtkPolyDataMapper() superEllipsoidMapper.SetInputConnection(superEllipsoidSource.GetOutputPort()) superEllipsoidMapper.SetScalarRange(0, 3.14) superEllipsoidActor = vtk.vtkActor() superEllipsoidActor.SetMapper(superEllipsoidMapper) superEllipsoidActor.SetPosition(8, 4, 0) superEllipsoidTextMapper = vtk.vtkTextMapper() superEllipsoidTextMapper.SetInput("Super.Ellipsoid") superEllipsoidTextMapper.GetTextProperty().SetJustificationToCentered() superEllipsoidTextMapper.GetTextProperty().SetVerticalJustificationToCentered() superEllipsoidTextMapper.GetTextProperty().SetColor(1, 0, 0) superEllipsoidTextMapper.GetTextProperty().SetFontSize(14) superEllipsoidTextActor = vtk.vtkActor2D() superEllipsoidTextActor.SetMapper(superEllipsoidTextMapper) superEllipsoidTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() superEllipsoidTextActor.GetPositionCoordinate().SetValue(8, 1.5, 0) splinePoints = [ [0.50380158308139134, -0.60679315105396936, -0.37248976406291578], [-0.4354646054261665, -0.85362339758017258, -0.84844312996065385], [0.2163147512899315, -0.39797507012168643, -0.76700353518454523], [0.97158415334838644, -0.58513467367046257, -0.35846037946569753], [-0.64359767997804918, -0.94620739107309249, -0.90762176546623086], [-0.39901219094126117, -0.1978931497772658, 0.0098316934936828471], [-0.75872745167404765, 0.067719714281950116, 0.165237936733867], [-0.84599731389712418, -0.67685466896596114, 0.10357868909071133], [0.84702754758625654, -0.0080077177882230677, -0.58571286666473044], [-0.076150034124101484, 0.14637647622561856, 0.1494359239700418] ] inputPoints = vtk.vtkPoints() for i in range(0, 10): inputPoints.InsertPoint(i, splinePoints[i]) spline = vtk.vtkParametricSpline() spline.SetPoints(inputPoints) spline.ClosedOff() splineSource = vtk.vtkParametricFunctionSource() splineSource.SetParametricFunction(spline) splineMapper = vtk.vtkPolyDataMapper() splineMapper.SetInputConnection(splineSource.GetOutputPort()) splineActor = vtk.vtkActor() splineActor.SetMapper(splineMapper) splineActor.SetPosition(16, 4, 0) splineActor.GetProperty().SetColor(0, 0, 0) splineTextMapper = vtk.vtkTextMapper() splineTextMapper.SetInput("Open.Spline") splineTextMapper.GetTextProperty().SetJustificationToCentered() splineTextMapper.GetTextProperty().SetVerticalJustificationToCentered() splineTextMapper.GetTextProperty().SetColor(1, 0, 0) splineTextMapper.GetTextProperty().SetFontSize(14) splineTextActor = vtk.vtkActor2D() splineTextActor.SetMapper(splineTextMapper) splineTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() splineTextActor.GetPositionCoordinate().SetValue(16, 1.5, 0) spline2 = vtk.vtkParametricSpline() spline2.SetPoints(inputPoints) spline2.ClosedOn() spline2Source = vtk.vtkParametricFunctionSource() spline2Source.SetParametricFunction(spline2) spline2Mapper = vtk.vtkPolyDataMapper() spline2Mapper.SetInputConnection(spline2Source.GetOutputPort()) spline2Actor = vtk.vtkActor() spline2Actor.SetMapper(spline2Mapper) spline2Actor.SetPosition(24, 4, 0) spline2Actor.GetProperty().SetColor(0, 0, 0) spline2TextMapper = vtk.vtkTextMapper() spline2TextMapper.SetInput("Closed.Spline") spline2TextMapper.GetTextProperty().SetJustificationToCentered() spline2TextMapper.GetTextProperty().SetVerticalJustificationToCentered() spline2TextMapper.GetTextProperty().SetColor(1, 0, 0) spline2TextMapper.GetTextProperty().SetFontSize(14) spline2TextActor = vtk.vtkActor2D() spline2TextActor.SetMapper(spline2TextMapper) spline2TextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() spline2TextActor.GetPositionCoordinate().SetValue(24, 1.5, 0) sconic = vtk.vtkParametricConicSpiral() sconic.SetA(0.8) sconic.SetB(2.5) sconic.SetC(0.4) sconicSource = vtk.vtkParametricFunctionSource() sconicSource.SetParametricFunction(sconic) sconicSource.SetScalarModeToDistance() sconicMapper = vtk.vtkPolyDataMapper() sconicMapper.SetInputConnection(sconicSource.GetOutputPort()) sconicActor = vtk.vtkActor() sconicActor.SetMapper(sconicMapper) sconicMapper.SetScalarRange(0, 9) sconicActor.SetPosition(0, -4, 0) sconicActor.SetScale(1.2, 1.2, 1.2) sconicTextMapper = vtk.vtkTextMapper() sconicTextMapper.SetInput("Spiral.Conic") sconicTextMapper.GetTextProperty().SetJustificationToCentered() sconicTextMapper.GetTextProperty().SetVerticalJustificationToCentered() sconicTextMapper.GetTextProperty().SetColor(1, 0, 0) sconicTextMapper.GetTextProperty().SetFontSize(14) sconicTextActor = vtk.vtkActor2D() sconicTextActor.SetMapper(sconicTextMapper) sconicTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() sconicTextActor.GetPositionCoordinate().SetValue(0, -6.5, 0) # ------------------------------------------------------------ boy = vtk.vtkParametricBoy() boySource = vtk.vtkParametricFunctionSource() boySource.SetParametricFunction(boy) boySource.SetScalarModeToModulus() boyMapper = vtk.vtkPolyDataMapper() boyMapper.SetInputConnection(boySource.GetOutputPort()) boyMapper.SetScalarRange(0, 2) boyActor = vtk.vtkActor() boyActor.SetMapper(boyMapper) boyActor.SetPosition(8, -4, 0) boyActor.SetScale(1.5, 1.5, 1.5) boyTextMapper = vtk.vtkTextMapper() boyTextMapper.SetInput("Boy") boyTextMapper.GetTextProperty().SetJustificationToCentered() boyTextMapper.GetTextProperty().SetVerticalJustificationToCentered() boyTextMapper.GetTextProperty().SetColor(1, 0, 0) boyTextMapper.GetTextProperty().SetFontSize(14) boyTextActor = vtk.vtkActor2D() boyTextActor.SetMapper(boyTextMapper) boyTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() boyTextActor.GetPositionCoordinate().SetValue(8, -6.5, 0) # ------------------------------------------------------------ # Create a cross cap # ------------------------------------------------------------ crossCap = vtk.vtkParametricCrossCap() crossCapSource = vtk.vtkParametricFunctionSource() crossCapSource.SetParametricFunction(crossCap) crossCapSource.SetScalarModeToY() crossCapMapper = vtk.vtkPolyDataMapper() crossCapMapper.SetInputConnection(crossCapSource.GetOutputPort()) crossCapActor = vtk.vtkActor() crossCapActor.SetMapper(crossCapMapper) crossCapActor.RotateX(65) crossCapActor.SetPosition(16, -4, 0) crossCapActor.SetScale(1.5, 1.5, 1.5) crossCapTextMapper = vtk.vtkTextMapper() crossCapTextMapper.SetInput("Cross.Cap") crossCapTextMapper.GetTextProperty().SetJustificationToCentered() crossCapTextMapper.GetTextProperty().SetVerticalJustificationToCentered() crossCapTextMapper.GetTextProperty().SetColor(1, 0, 0) crossCapTextMapper.GetTextProperty().SetFontSize(14) crossCapTextActor = vtk.vtkActor2D() crossCapTextActor.SetMapper(crossCapTextMapper) crossCapTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() crossCapTextActor.GetPositionCoordinate().SetValue(16, -6.5, 0) # ------------------------------------------------------------ # Create Dini's surface dini = vtk.vtkParametricDini() diniSource = vtk.vtkParametricFunctionSource() diniSource.SetScalarModeToDistance() diniSource.SetParametricFunction(dini) diniMapper = vtk.vtkPolyDataMapper() diniMapper.SetInputConnection(diniSource.GetOutputPort()) diniActor = vtk.vtkActor() diniActor.SetMapper(diniMapper) diniActor.RotateX(-90) diniActor.SetPosition(24, -3, 0) diniActor.SetScale(1.5, 1.5, 0.5) diniTextMapper = vtk.vtkTextMapper() diniTextMapper.SetInput("Dini") diniTextMapper.GetTextProperty().SetJustificationToCentered() diniTextMapper.GetTextProperty().SetVerticalJustificationToCentered() diniTextMapper.GetTextProperty().SetColor(1, 0, 0) diniTextMapper.GetTextProperty().SetFontSize(14) diniTextActor = vtk.vtkActor2D() diniTextActor.SetMapper(diniTextMapper) diniTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() diniTextActor.GetPositionCoordinate().SetValue(24, -6.5, 0) # ------------------------------------------------------------ enneper = vtk.vtkParametricEnneper() enneperSource = vtk.vtkParametricFunctionSource() enneperSource.SetParametricFunction(enneper) enneperSource.SetScalarModeToQuadrant() enneperMapper = vtk.vtkPolyDataMapper() enneperMapper.SetInputConnection(enneperSource.GetOutputPort()) enneperMapper.SetScalarRange(1, 4) enneperActor = vtk.vtkActor() enneperActor.SetMapper(enneperMapper) enneperActor.SetPosition(0, -12, 0) enneperActor.SetScale(0.25, 0.25, 0.25) enneperTextMapper = vtk.vtkTextMapper() enneperTextMapper.SetInput("Enneper") enneperTextMapper.GetTextProperty().SetJustificationToCentered() enneperTextMapper.GetTextProperty().SetVerticalJustificationToCentered() enneperTextMapper.GetTextProperty().SetColor(1, 0, 0) enneperTextMapper.GetTextProperty().SetFontSize(14) enneperTextActor = vtk.vtkActor2D() enneperTextActor.SetMapper(enneperTextMapper) enneperTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() enneperTextActor.GetPositionCoordinate().SetValue(0, -14.5, 0) # ------------------------------------------------------------ # Create an ellipsoidal surface # ------------------------------------------------------------ ellipsoid = vtk.vtkParametricEllipsoid() ellipsoid.SetXRadius(1) ellipsoid.SetYRadius(0.75) ellipsoid.SetZRadius(0.5) ellipsoidSource = vtk.vtkParametricFunctionSource() ellipsoidSource.SetParametricFunction(ellipsoid) ellipsoidSource.SetScalarModeToZ() ellipsoidMapper = vtk.vtkPolyDataMapper() ellipsoidMapper.SetInputConnection(ellipsoidSource.GetOutputPort()) ellipsoidMapper.SetScalarRange(-0.5, 0.5) ellipsoidActor = vtk.vtkActor() ellipsoidActor.SetMapper(ellipsoidMapper) ellipsoidActor.SetPosition(8, -12, 0) ellipsoidActor.SetScale(1.5, 1.5, 1.5) ellipsoidTextMapper = vtk.vtkTextMapper() ellipsoidTextMapper.SetInput("Ellipsoid") ellipsoidTextMapper.GetTextProperty().SetJustificationToCentered() ellipsoidTextMapper.GetTextProperty().SetVerticalJustificationToCentered() ellipsoidTextMapper.GetTextProperty().SetColor(1, 0, 0) ellipsoidTextMapper.GetTextProperty().SetFontSize(14) ellipsoidTextActor = vtk.vtkActor2D() ellipsoidTextActor.SetMapper(ellipsoidTextMapper) ellipsoidTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() ellipsoidTextActor.GetPositionCoordinate().SetValue(8, -14.5, 0) # ------------------------------------------------------------ # Create an surface with random hills on it. # Note that for testing, we will disable the # random generation of the surfaces. This is # because random number generators do not # return the same result on different operating # systems. # ------------------------------------------------------------ randomHills = vtk.vtkParametricRandomHills() randomHills.AllowRandomGenerationOff() randomHills.GenerateTheHills() randomHillsSource = vtk.vtkParametricFunctionSource() randomHillsSource.SetParametricFunction(randomHills) randomHillsSource.GenerateTextureCoordinatesOn() randomHillsMapper = vtk.vtkPolyDataMapper() randomHillsMapper.SetInputConnection(randomHillsSource.GetOutputPort()) randomHillsActor = vtk.vtkActor() randomHillsActor.SetMapper(randomHillsMapper) randomHillsActor.SetPosition(16, -14, 0) randomHillsActor.SetScale(0.2, 0.2, 0.2) randomHillsActor.SetTexture(texture) randomHillsTextMapper = vtk.vtkTextMapper() randomHillsTextMapper.SetInput("Random.Hills") randomHillsTextMapper.GetTextProperty().SetJustificationToCentered() randomHillsTextMapper.GetTextProperty().SetVerticalJustificationToCentered() randomHillsTextMapper.GetTextProperty().SetColor(1, 0, 0) randomHillsTextMapper.GetTextProperty().SetFontSize(14) randomHillsTextActor = vtk.vtkActor2D() randomHillsTextActor.SetMapper(randomHillsTextMapper) randomHillsTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() randomHillsTextActor.GetPositionCoordinate().SetValue(16, -14.5, 0) # ------------------------------------------------------------ # Create an Steiner's Roman Surface. roman = vtk.vtkParametricRoman() roman.SetRadius(1.5) romanSource = vtk.vtkParametricFunctionSource() romanSource.SetParametricFunction(roman) romanSource.SetScalarModeToX() romanMapper = vtk.vtkPolyDataMapper() romanMapper.SetInputConnection(romanSource.GetOutputPort()) romanActor = vtk.vtkActor() romanActor.SetMapper(romanMapper) romanActor.SetPosition(24, -12, 0) romanTextMapper = vtk.vtkTextMapper() romanTextMapper.SetInput("Roman") romanTextMapper.GetTextProperty().SetJustificationToCentered() romanTextMapper.GetTextProperty().SetVerticalJustificationToCentered() romanTextMapper.GetTextProperty().SetColor(1, 0, 0) romanTextMapper.GetTextProperty().SetFontSize(14) romanTextActor = vtk.vtkActor2D() romanTextActor.SetMapper(romanTextMapper) romanTextActor.GetPositionCoordinate().SetCoordinateSystemToWorld() romanTextActor.GetPositionCoordinate().SetValue(24, -14.5, 0) ren = vtk.vtkRenderer() renWin = vtk.vtkRenderWindow() renWin.AddRenderer(ren) iren = vtk.vtkRenderWindowInteractor() iren.SetRenderWindow(renWin) ren.AddViewProp(torusActor) ren.AddViewProp(kleinActor) ren.AddViewProp(klein2Actor) ren.AddViewProp(toroidActor) ren.AddViewProp(superEllipsoidActor) ren.AddViewProp(mobiusActor) ren.AddViewProp(splineActor) ren.AddViewProp(spline2Actor) ren.AddViewProp(sconicActor) ren.AddViewProp(boyActor) ren.AddViewProp(crossCapActor) ren.AddViewProp(diniActor) ren.AddViewProp(enneperActor) ren.AddViewProp(ellipsoidActor) ren.AddViewProp(randomHillsActor) ren.AddViewProp(romanActor) ren.AddViewProp(torusTextActor) ren.AddViewProp(kleinTextActor) ren.AddViewProp(fig8KleinTextActor) ren.AddViewProp(mobiusTextActor) ren.AddViewProp(superToroidTextActor) ren.AddViewProp(superEllipsoidTextActor) ren.AddViewProp(splineTextActor) ren.AddViewProp(spline2TextActor) ren.AddViewProp(sconicTextActor) ren.AddViewProp(boyTextActor) ren.AddViewProp(crossCapTextActor) ren.AddViewProp(diniTextActor) ren.AddViewProp(enneperTextActor) ren.AddViewProp(ellipsoidTextActor) ren.AddViewProp(randomHillsTextActor) ren.AddViewProp(romanTextActor) ren.SetBackground(0.7, 0.8, 1) renWin.SetSize(500, 500) ren.ResetCamera() ren.GetActiveCamera().Zoom(1.3) iren.Initialize() renWin.Render() img_file = "TestParametricFunctions.png" vtk.test.Testing.compareImage(iren.GetRenderWindow(), vtk.test.Testing.getAbsImagePath(img_file), threshold=10) vtk.test.Testing.interact() if __name__ == "__main__": vtk.test.Testing.main([(TestParametricFunctions, 'test')])

true

790814a2bac30de6a65438a4a744bdc0de13f2d6

1,727

py

Python

perfrunner/tests/fio.py

bochun/perfrunner

e215c73240381cf82fddc40856f560369c9b75a8

[ "Apache-2.0" ]

18

2015-10-28T23:12:07.000Z

2022-01-04T14:23:37.000Z

perfrunner/tests/fio.py

bochun/perfrunner

e215c73240381cf82fddc40856f560369c9b75a8

[ "Apache-2.0" ]

11

2019-03-19T12:02:31.000Z

2022-02-11T03:39:44.000Z

perfrunner/tests/fio.py

bochun/perfrunner

e215c73240381cf82fddc40856f560369c9b75a8

[ "Apache-2.0" ]

39

2015-06-07T09:17:16.000Z

2022-03-06T20:32:01.000Z

from collections import defaultdict import requests from logger import logger from perfrunner.helpers.misc import pretty_dict from perfrunner.helpers.remote import RemoteHelper from perfrunner.tests import PerfTest class FIOTest(PerfTest): TRACKER = 'fio.sc.couchbase.com' TEMPLATE = { 'group': '{}, random mixed reads and writes, IOPS', 'metric': None, 'value': None, } def __init__(self, cluster_spec, test_config, verbose): self.cluster_spec = cluster_spec self.test_config = test_config self.remote = RemoteHelper(cluster_spec, verbose) def __exit__(self, *args, **kwargs): pass @staticmethod def _parse(results): """Parse the test output. See also https://github.com/axboe/fio/blob/master/HOWTO """ stats = defaultdict(int) for host, output in results.items(): for job in output.split(): stats[host] += int(job.split(';')[7]) # reads stats[host] += int(job.split(';')[48]) # writes return stats def _post(self, data): data = pretty_dict(data) logger.info('Posting: {}'.format(data)) requests.post('http://{}/api/v1/benchmarks'.format(self.TRACKER), data=data) def _report_kpi(self, stats): for host, iops in stats.items(): data = self.TEMPLATE.copy() data['group'] = data['group'].format(self.cluster_spec.name.title()) data['metric'] = host data['value'] = iops self._post(data) def run(self): stats = self.remote.fio(self.test_config.fio['config']) self._report_kpi(self._parse(stats))

28.783333

80

0.600463

from collections import defaultdict import requests from logger import logger from perfrunner.helpers.misc import pretty_dict from perfrunner.helpers.remote import RemoteHelper from perfrunner.tests import PerfTest class FIOTest(PerfTest): TRACKER = 'fio.sc.couchbase.com' TEMPLATE = { 'group': '{}, random mixed reads and writes, IOPS', 'metric': None, 'value': None, } def __init__(self, cluster_spec, test_config, verbose): self.cluster_spec = cluster_spec self.test_config = test_config self.remote = RemoteHelper(cluster_spec, verbose) def __exit__(self, *args, **kwargs): pass @staticmethod def _parse(results): stats = defaultdict(int) for host, output in results.items(): for job in output.split(): stats[host] += int(job.split(';')[7]) stats[host] += int(job.split(';')[48]) return stats def _post(self, data): data = pretty_dict(data) logger.info('Posting: {}'.format(data)) requests.post('http://{}/api/v1/benchmarks'.format(self.TRACKER), data=data) def _report_kpi(self, stats): for host, iops in stats.items(): data = self.TEMPLATE.copy() data['group'] = data['group'].format(self.cluster_spec.name.title()) data['metric'] = host data['value'] = iops self._post(data) def run(self): stats = self.remote.fio(self.test_config.fio['config']) self._report_kpi(self._parse(stats))

true

790818afe12e30c308b59599db9c9a02a9c4f36c

7,317

py

Python

checkov/terraform/plan_runner.py

BenjaDiaz/checkov

c53e32f1654e4ee771abf2001b3cb7df16752f6e

[ "Apache-2.0" ]

1

2022-02-20T21:20:39.000Z

checkov/terraform/plan_runner.py

BenjaDiaz/checkov

c53e32f1654e4ee771abf2001b3cb7df16752f6e

[ "Apache-2.0" ]

3

2022-03-07T20:37:31.000Z

2022-03-21T20:20:14.000Z

checkov/terraform/plan_runner.py

BenjaDiaz/checkov

c53e32f1654e4ee771abf2001b3cb7df16752f6e

[ "Apache-2.0" ]

null

import json import logging import os from typing import Optional, List from checkov.common.checks_infra.registry import get_graph_checks_registry from checkov.common.graph.graph_builder.graph_components.attribute_names import CustomAttributes from checkov.common.output.record import Record from checkov.common.output.report import Report, CheckType from checkov.common.runners.base_runner import filter_ignored_paths from checkov.runner_filter import RunnerFilter from checkov.terraform.checks.resource.registry import resource_registry from checkov.terraform.context_parsers.registry import parser_registry from checkov.terraform.plan_parser import parse_tf_plan from checkov.terraform.runner import Runner as TerraformRunner, merge_reports class Runner(TerraformRunner): check_type = CheckType.TERRAFORM_PLAN def __init__(self): super().__init__() self.template_lines = {} self.graph_registry = get_graph_checks_registry(super().check_type) block_type_registries = { 'resource': resource_registry, } def run( self, root_folder: Optional[str] = None, external_checks_dir: Optional[List[str]] = None, files: Optional[List[str]] = None, runner_filter: RunnerFilter = RunnerFilter(), collect_skip_comments: bool = True ) -> Report: report = Report(self.check_type) self.tf_definitions = {} parsing_errors = {} if external_checks_dir: for directory in external_checks_dir: resource_registry.load_external_checks(directory) self.graph_registry.load_external_checks(directory) if root_folder: files = [] if not files else files for root, d_names, f_names in os.walk(root_folder): filter_ignored_paths(root, d_names, runner_filter.excluded_paths) filter_ignored_paths(root, f_names, runner_filter.excluded_paths) for file in f_names: file_ending = os.path.splitext(file)[1] if file_ending == '.json': try: with open(f'{root}/{file}') as f: content = json.load(f) if isinstance(content, dict) and content.get('terraform_version'): files.append(os.path.join(root, file)) except Exception as e: logging.debug(f'Failed to load json file {root}/{file}, skipping') logging.debug('Failure message:') logging.debug(e, stack_info=True) if files: files = [os.path.abspath(file) for file in files] for file in files: if file.endswith(".json"): tf_definitions, template_lines = parse_tf_plan(file) if not tf_definitions: continue self.tf_definitions = tf_definitions self.template_lines = template_lines self.check_tf_definition(report, runner_filter) else: logging.debug(f'Failed to load {file} as is not a .json file, skipping') report.add_parsing_errors(parsing_errors.keys()) if self.tf_definitions: graph = self.graph_manager.build_graph_from_definitions(self.tf_definitions, render_variables=False) self.graph_manager.save_graph(graph) graph_report = self.get_graph_checks_report(root_folder, runner_filter) merge_reports(report, graph_report) return report def get_entity_context_and_evaluations(self, entity): raw_context = self.get_entity_context(entity[CustomAttributes.BLOCK_NAME].split("."), entity[CustomAttributes.FILE_PATH]) raw_context['definition_path'] = entity[CustomAttributes.BLOCK_NAME].split('.') return raw_context, None def check_tf_definition(self, report, runner_filter): for full_file_path, definition in self.tf_definitions.items(): scanned_file = f"/{os.path.relpath(full_file_path)}" logging.debug(f"Scanning file: {scanned_file}") for block_type in definition.keys(): if block_type in self.block_type_registries.keys(): self.run_block(definition[block_type], full_file_path, report, scanned_file, block_type, runner_filter) def run_block(self, entities, full_file_path, report, scanned_file, block_type, runner_filter=None): registry = self.block_type_registries[block_type] if registry: for entity in entities: context_parser = parser_registry.context_parsers[block_type] definition_path = context_parser.get_entity_context_path(entity) entity_id = ".".join(definition_path) # Entity can exist only once per dir, for file as well entity_context = self.get_entity_context(definition_path, full_file_path) entity_lines_range = [entity_context.get('start_line'), entity_context.get('end_line')] entity_code_lines = entity_context.get('code_lines') entity_address = entity_context.get('address') results = registry.scan(scanned_file, entity, [], runner_filter) for check, check_result in results.items(): record = Record(check_id=check.id, bc_check_id=check.bc_id, check_name=check.name, check_result=check_result, code_block=entity_code_lines, file_path=scanned_file, file_line_range=entity_lines_range, resource=entity_id, resource_address=entity_address, evaluations=None, check_class=check.__class__.__module__, file_abs_path=full_file_path) record.set_guideline(check.guideline) report.add_record(record=record) def get_entity_context(self, definition_path, full_file_path): entity_context = {} if full_file_path not in self.tf_definitions: logging.debug(f'Tried to look up file {full_file_path} in TF plan entity definitions, but it does not exist') return entity_context for resource in self.tf_definitions.get(full_file_path, {}).get('resource', []): resource_type = definition_path[0] if resource_type in resource.keys(): resource_name = definition_path[1] if resource_name in resource[resource_type].keys(): resource_defintion = resource[resource_type][resource_name] entity_context['start_line'] = resource_defintion['start_line'][0] entity_context['end_line'] = resource_defintion['end_line'][0] entity_context['code_lines'] = self.template_lines[ entity_context['start_line']:entity_context['end_line']] entity_context['address'] = resource_defintion['__address__'] return entity_context return entity_context

50.116438

129

0.632636

import json import logging import os from typing import Optional, List from checkov.common.checks_infra.registry import get_graph_checks_registry from checkov.common.graph.graph_builder.graph_components.attribute_names import CustomAttributes from checkov.common.output.record import Record from checkov.common.output.report import Report, CheckType from checkov.common.runners.base_runner import filter_ignored_paths from checkov.runner_filter import RunnerFilter from checkov.terraform.checks.resource.registry import resource_registry from checkov.terraform.context_parsers.registry import parser_registry from checkov.terraform.plan_parser import parse_tf_plan from checkov.terraform.runner import Runner as TerraformRunner, merge_reports class Runner(TerraformRunner): check_type = CheckType.TERRAFORM_PLAN def __init__(self): super().__init__() self.template_lines = {} self.graph_registry = get_graph_checks_registry(super().check_type) block_type_registries = { 'resource': resource_registry, } def run( self, root_folder: Optional[str] = None, external_checks_dir: Optional[List[str]] = None, files: Optional[List[str]] = None, runner_filter: RunnerFilter = RunnerFilter(), collect_skip_comments: bool = True ) -> Report: report = Report(self.check_type) self.tf_definitions = {} parsing_errors = {} if external_checks_dir: for directory in external_checks_dir: resource_registry.load_external_checks(directory) self.graph_registry.load_external_checks(directory) if root_folder: files = [] if not files else files for root, d_names, f_names in os.walk(root_folder): filter_ignored_paths(root, d_names, runner_filter.excluded_paths) filter_ignored_paths(root, f_names, runner_filter.excluded_paths) for file in f_names: file_ending = os.path.splitext(file)[1] if file_ending == '.json': try: with open(f'{root}/{file}') as f: content = json.load(f) if isinstance(content, dict) and content.get('terraform_version'): files.append(os.path.join(root, file)) except Exception as e: logging.debug(f'Failed to load json file {root}/{file}, skipping') logging.debug('Failure message:') logging.debug(e, stack_info=True) if files: files = [os.path.abspath(file) for file in files] for file in files: if file.endswith(".json"): tf_definitions, template_lines = parse_tf_plan(file) if not tf_definitions: continue self.tf_definitions = tf_definitions self.template_lines = template_lines self.check_tf_definition(report, runner_filter) else: logging.debug(f'Failed to load {file} as is not a .json file, skipping') report.add_parsing_errors(parsing_errors.keys()) if self.tf_definitions: graph = self.graph_manager.build_graph_from_definitions(self.tf_definitions, render_variables=False) self.graph_manager.save_graph(graph) graph_report = self.get_graph_checks_report(root_folder, runner_filter) merge_reports(report, graph_report) return report def get_entity_context_and_evaluations(self, entity): raw_context = self.get_entity_context(entity[CustomAttributes.BLOCK_NAME].split("."), entity[CustomAttributes.FILE_PATH]) raw_context['definition_path'] = entity[CustomAttributes.BLOCK_NAME].split('.') return raw_context, None def check_tf_definition(self, report, runner_filter): for full_file_path, definition in self.tf_definitions.items(): scanned_file = f"/{os.path.relpath(full_file_path)}" logging.debug(f"Scanning file: {scanned_file}") for block_type in definition.keys(): if block_type in self.block_type_registries.keys(): self.run_block(definition[block_type], full_file_path, report, scanned_file, block_type, runner_filter) def run_block(self, entities, full_file_path, report, scanned_file, block_type, runner_filter=None): registry = self.block_type_registries[block_type] if registry: for entity in entities: context_parser = parser_registry.context_parsers[block_type] definition_path = context_parser.get_entity_context_path(entity) entity_id = ".".join(definition_path) entity_context = self.get_entity_context(definition_path, full_file_path) entity_lines_range = [entity_context.get('start_line'), entity_context.get('end_line')] entity_code_lines = entity_context.get('code_lines') entity_address = entity_context.get('address') results = registry.scan(scanned_file, entity, [], runner_filter) for check, check_result in results.items(): record = Record(check_id=check.id, bc_check_id=check.bc_id, check_name=check.name, check_result=check_result, code_block=entity_code_lines, file_path=scanned_file, file_line_range=entity_lines_range, resource=entity_id, resource_address=entity_address, evaluations=None, check_class=check.__class__.__module__, file_abs_path=full_file_path) record.set_guideline(check.guideline) report.add_record(record=record) def get_entity_context(self, definition_path, full_file_path): entity_context = {} if full_file_path not in self.tf_definitions: logging.debug(f'Tried to look up file {full_file_path} in TF plan entity definitions, but it does not exist') return entity_context for resource in self.tf_definitions.get(full_file_path, {}).get('resource', []): resource_type = definition_path[0] if resource_type in resource.keys(): resource_name = definition_path[1] if resource_name in resource[resource_type].keys(): resource_defintion = resource[resource_type][resource_name] entity_context['start_line'] = resource_defintion['start_line'][0] entity_context['end_line'] = resource_defintion['end_line'][0] entity_context['code_lines'] = self.template_lines[ entity_context['start_line']:entity_context['end_line']] entity_context['address'] = resource_defintion['__address__'] return entity_context return entity_context

true

7908194a286eaf38694ca25eb254c4b4b6db95fd

1,829

py

Python

CNN/code/filter_visualiton.py

Zeng-WH/ML2020

f467a6260cd782968696950ef74f3780933cdcdd

[ "MIT" ]

2

2020-11-26T14:46:18.000Z

2021-02-06T06:25:43.000Z

CNN/code/filter_visualiton.py

Zeng-WH/ML2020

f467a6260cd782968696950ef74f3780933cdcdd

[ "MIT" ]

null

CNN/code/filter_visualiton.py

Zeng-WH/ML2020

f467a6260cd782968696950ef74f3780933cdcdd

[ "MIT" ]

null

import os import sys import argparse import numpy as np from PIL import Image import matplotlib.pyplot as plt import torch import torch.nn as nn import torch.nn.functional as F from torch.optim import Adam from torch.utils.data import Dataset import torchvision.transforms as transforms import pickle def normalize(image): return (image - image.min()) / (image.max() - image.min()) layer_activations = None def filter_explanation(x, model, cnnid, filterid, iteration=100, lr=1): # x: 需要训练的图片 # cnnid, filterid: 指定第几层cnn中第几个filter model.eval() def hook(model, input, output): global layer_activations layer_activations = output hook_handle = model.cnn[cnnid].register_forward_hook(hook) # 当forward了第cnnid层cnn后，要先呼叫hook, 才可以继续forward下一层cnn # Filter activation: 我们先观察x经过被指定filter的activation map model(x.cuda()) # 正式执行forward的步骤 filter_activations = layer_activations[:, filterid, :, :].detach().cpu() # 根据function argument 指定的filterid把待定filter的activation map取出来 x = x.cuda() x.requires_grad_() optimizer = Adam([x], lr=lr) # 利用偏微分和optimizer, 逐步修改input image来让filter activation越来越大 for iter in range(iteration): optimizer.zero_grad() model(x) objective = -layer_activations[:, filterid, :, :].sum() # 探究image的微量变化会怎样影响activation的程度，加负号代表做maximization objective.backward() optimizer.step() # 修改input image来最大化filter activation filter_visualization = x.detach().cpu().squeeze()[0] # 完成图片修改，只剩下要画出来，因此可以直接detach并转成cpu tensor hook_handle.remove() # 一旦model register hook, 该hook就一致存在。如果之后继续register更多hook # 那model一次forward要做的事情就越来越来越多，因此需要把hook拿掉 return filter_activations, filter_visualization

28.578125

77

0.697649

import os import sys import argparse import numpy as np from PIL import Image import matplotlib.pyplot as plt import torch import torch.nn as nn import torch.nn.functional as F from torch.optim import Adam from torch.utils.data import Dataset import torchvision.transforms as transforms import pickle def normalize(image): return (image - image.min()) / (image.max() - image.min()) layer_activations = None def filter_explanation(x, model, cnnid, filterid, iteration=100, lr=1): model.eval() def hook(model, input, output): global layer_activations layer_activations = output hook_handle = model.cnn[cnnid].register_forward_hook(hook) model(x.cuda()) filter_activations = layer_activations[:, filterid, :, :].detach().cpu() x = x.cuda() x.requires_grad_() optimizer = Adam([x], lr=lr) for iter in range(iteration): optimizer.zero_grad() model(x) objective = -layer_activations[:, filterid, :, :].sum() objective.backward() optimizer.step() filter_visualization = x.detach().cpu().squeeze()[0] hook_handle.remove() return filter_activations, filter_visualization

true

790819ac744c6466b16fd5e4429510da065b78dc

3,550

py

Python

bsm/audit_record.py

haginara/openbsm-python

54d3127f69e1af216344540d46696aad12e95c12

[ "MIT" ]

3

2019-11-18T18:32:21.000Z

2021-05-12T18:23:00.000Z

bsm/audit_record.py

haginara/openbsm-python

54d3127f69e1af216344540d46696aad12e95c12

[ "MIT" ]

null

bsm/audit_record.py

haginara/openbsm-python

54d3127f69e1af216344540d46696aad12e95c12

[ "MIT" ]

null

# Token type identifiers. AUT_INVALID = 0x00 AUT_OTHER_FILE32 = 0x11 AUT_OHEADER = 0x12 AUT_TRAILER = 0x13 AUT_HEADER32 = 0x14 AUT_HEADER32_EX = 0x15 AUT_DATA = 0x21 AUT_IPC = 0x22 AUT_PATH = 0x23 AUT_SUBJECT32 = 0x24 AUT_XATPATH = 0x25 AUT_PROCESS32 = 0x26 AUT_RETURN32 = 0x27 AUT_TEXT = 0x28 AUT_OPAQUE = 0x29 AUT_IN_ADDR = 0x2A AUT_IP = 0x2B AUT_IPORT = 0x2C AUT_ARG32 = 0x2D AUT_SOCKET = 0x2E AUT_SEQ = 0x2F AUT_ACL = 0x30 AUT_ATTR = 0x31 AUT_IPC_PERM = 0x32 AUT_LABEL = 0x33 AUT_GROUPS = 0x34 AUT_ACE = 0x35 AUT_PRIV = 0x38 AUT_UPRIV = 0x39 AUT_LIAISON = 0x3A AUT_NEWGROUPS = 0x3B AUT_EXEC_ARGS = 0x3C AUT_EXEC_ENV = 0x3D AUT_ATTR32 = 0x3E AUT_UNAUTH = 0x3F AUT_XATOM = 0x40 AUT_XOBJ = 0x41 AUT_XPROTO = 0x42 AUT_XSELECT = 0x43 AUT_XCOLORMAP = 0x44 AUT_XCURSOR = 0x45 AUT_XFONT = 0x46 AUT_XGC = 0x47 AUT_XPIXMAP = 0x48 AUT_XPROPERTY = 0x49 AUT_XWINDOW = 0x4A AUT_XCLIENT = 0x4B AUT_CMD = 0x51 AUT_EXIT = 0x52 AUT_ZONENAME = 0x60 AUT_HOST = 0x70 AUT_ARG64 = 0x71 AUT_RETURN64 = 0x72 AUT_ATTR64 = 0x73 AUT_HEADER64 = 0x74 AUT_SUBJECT64 = 0x75 AUT_PROCESS64 = 0x77 AUT_OTHER_FILE64 = 0x78 AUT_HEADER64_EX = 0x79 AUT_SUBJECT32_EX = 0x7A AUT_PROCESS32_EX = 0x7B AUT_SUBJECT64_EX = 0x7C AUT_PROCESS64_EX = 0x7D AUT_IN_ADDR_EX = 0x7E AUT_SOCKET_EX = 0x7F # # Pre-64-bit BSM, 32-bit tokens weren't explicitly named as '32'. We have # compatibility defines. AUT_HEADER = AUT_HEADER32 AUT_ARG = AUT_ARG32 AUT_RETURN = AUT_RETURN32 AUT_SUBJECT = AUT_SUBJECT32 AUT_PROCESS = AUT_PROCESS32 AUT_OTHER_FILE = AUT_OTHER_FILE32 # # * # The values for the following token ids are not defined by BSM. # # XXXRW: Not sure how to handle these in OpenBSM yet, but I'll give them # names more consistent with Sun's BSM. These originally came from Apple's # BSM. AUT_SOCKINET32 = 0x80 # XXX AUT_SOCKINET128 = 0x81 # XXX AUT_SOCKUNIX = 0x82 # XXX _AUT_RIGHTS = 0x83 # XXX FreeBSD AUT_ARG_UUID = 0x84 # UUID of argument object AUT_RETURN_UUID = 0x85 # UUID of returned object # # Apple specific tokens AUT_IDENTITY = 0xED AUT_KRB5_PRINCIPA = 0xEE AUT_CERT_HAHSH = 0xEF # print values for the arbitrary token AUP_BINARY = 0 AUP_OCTAL = 1 AUP_DECIMAL = 2 AUP_HEX = 3 AUP_STRING = 4 # # data-types for the arbitrary token AUR_BYTE = 0 AUR_CHAR = AUR_BYTE AUR_SHORT = 1 AUR_INT32 = 2 AUR_INT = AUR_INT32 AUR_INT64 = 3 # # ... and their sizes AUR_BYTE_SIZE = 1 # sizeof(u_char) AUR_CHAR_SIZE = AUR_BYTE_SIZE AUR_SHORT_SIZE = 2 # sizeof(uint16_t) AUR_INT32_SIZE = 4 # sizeof(uint32_t) AUR_INT_SIZE = AUR_INT32_SIZE AUR_INT64_SIZE = 8 # sizeof(uint64_t) AUR_BYTE_FORMAT = "B" AUR_CHAR_FORMAT = "c" AUR_SHORT_FORMAT = "H" AUR_INT32_FORMAT = "I" AUR_INT_FORMAT = AUR_INT32_FORMAT AUR_INT64_FORMAT = "Q" # # Modifiers for the header token PAD_NOTATTR = 0x4000 # nonattributable event PAD_FAILURE = 0x8000 # fail audit event # AUDIT_MAX_GROUPS = 16 # # * # A number of BSM versions are floating around and defined. Here are # constants for them. OpenBSM uses the same token types, etc, used in the # Solaris BSM version, but has a separate version number in order to # identify a potentially different event identifier name space. AUDIT_HEADER_VERSION_OLDDARWIN = 1 # In = retrospect, a mistake. AUDIT_HEADER_VERSION_SOLARIS = 2 AUDIT_HEADER_VERSION_TSOL25 = 3 AUDIT_HEADER_VERSION_TSOL = 4 AUDIT_HEADER_VERSION_OPENBSM10 = 10 AUDIT_HEADER_VERSION_OPENBSM11 = 11 AUDIT_HEADER_VERSION_OPENBSM = AUDIT_HEADER_VERSION_OPENBSM11 # AUT_TRAILER_MAGIC = 0xB105 # AUT_HEADERS = [AUT_HEADER32, AUT_HEADER32_EX, AUT_HEADER64, AUT_HEADER64_EX]

23.825503

76

0.773803

AUT_INVALID = 0x00 AUT_OTHER_FILE32 = 0x11 AUT_OHEADER = 0x12 AUT_TRAILER = 0x13 AUT_HEADER32 = 0x14 AUT_HEADER32_EX = 0x15 AUT_DATA = 0x21 AUT_IPC = 0x22 AUT_PATH = 0x23 AUT_SUBJECT32 = 0x24 AUT_XATPATH = 0x25 AUT_PROCESS32 = 0x26 AUT_RETURN32 = 0x27 AUT_TEXT = 0x28 AUT_OPAQUE = 0x29 AUT_IN_ADDR = 0x2A AUT_IP = 0x2B AUT_IPORT = 0x2C AUT_ARG32 = 0x2D AUT_SOCKET = 0x2E AUT_SEQ = 0x2F AUT_ACL = 0x30 AUT_ATTR = 0x31 AUT_IPC_PERM = 0x32 AUT_LABEL = 0x33 AUT_GROUPS = 0x34 AUT_ACE = 0x35 AUT_PRIV = 0x38 AUT_UPRIV = 0x39 AUT_LIAISON = 0x3A AUT_NEWGROUPS = 0x3B AUT_EXEC_ARGS = 0x3C AUT_EXEC_ENV = 0x3D AUT_ATTR32 = 0x3E AUT_UNAUTH = 0x3F AUT_XATOM = 0x40 AUT_XOBJ = 0x41 AUT_XPROTO = 0x42 AUT_XSELECT = 0x43 AUT_XCOLORMAP = 0x44 AUT_XCURSOR = 0x45 AUT_XFONT = 0x46 AUT_XGC = 0x47 AUT_XPIXMAP = 0x48 AUT_XPROPERTY = 0x49 AUT_XWINDOW = 0x4A AUT_XCLIENT = 0x4B AUT_CMD = 0x51 AUT_EXIT = 0x52 AUT_ZONENAME = 0x60 AUT_HOST = 0x70 AUT_ARG64 = 0x71 AUT_RETURN64 = 0x72 AUT_ATTR64 = 0x73 AUT_HEADER64 = 0x74 AUT_SUBJECT64 = 0x75 AUT_PROCESS64 = 0x77 AUT_OTHER_FILE64 = 0x78 AUT_HEADER64_EX = 0x79 AUT_SUBJECT32_EX = 0x7A AUT_PROCESS32_EX = 0x7B AUT_SUBJECT64_EX = 0x7C AUT_PROCESS64_EX = 0x7D AUT_IN_ADDR_EX = 0x7E AUT_SOCKET_EX = 0x7F # compatibility defines. AUT_HEADER = AUT_HEADER32 AUT_ARG = AUT_ARG32 AUT_RETURN = AUT_RETURN32 AUT_SUBJECT = AUT_SUBJECT32 AUT_PROCESS = AUT_PROCESS32 AUT_OTHER_FILE = AUT_OTHER_FILE32 # # * # The values for the following token ids are not defined by BSM. # # XXXRW: Not sure how to handle these in OpenBSM yet, but I'll give them AUT_SOCKINET32 = 0x80 AUT_SOCKINET128 = 0x81 AUT_SOCKUNIX = 0x82 _AUT_RIGHTS = 0x83 AUT_ARG_UUID = 0x84 AUT_RETURN_UUID = 0x85 AUT_IDENTITY = 0xED AUT_KRB5_PRINCIPA = 0xEE AUT_CERT_HAHSH = 0xEF AUP_BINARY = 0 AUP_OCTAL = 1 AUP_DECIMAL = 2 AUP_HEX = 3 AUP_STRING = 4 AUR_BYTE = 0 AUR_CHAR = AUR_BYTE AUR_SHORT = 1 AUR_INT32 = 2 AUR_INT = AUR_INT32 AUR_INT64 = 3 AUR_BYTE_SIZE = 1 AUR_CHAR_SIZE = AUR_BYTE_SIZE AUR_SHORT_SIZE = 2 AUR_INT32_SIZE = 4 AUR_INT_SIZE = AUR_INT32_SIZE AUR_INT64_SIZE = 8 AUR_BYTE_FORMAT = "B" AUR_CHAR_FORMAT = "c" AUR_SHORT_FORMAT = "H" AUR_INT32_FORMAT = "I" AUR_INT_FORMAT = AUR_INT32_FORMAT AUR_INT64_FORMAT = "Q" PAD_NOTATTR = 0x4000 PAD_FAILURE = 0x8000 AUDIT_MAX_GROUPS = 16 AUDIT_HEADER_VERSION_OLDDARWIN = 1 AUDIT_HEADER_VERSION_SOLARIS = 2 AUDIT_HEADER_VERSION_TSOL25 = 3 AUDIT_HEADER_VERSION_TSOL = 4 AUDIT_HEADER_VERSION_OPENBSM10 = 10 AUDIT_HEADER_VERSION_OPENBSM11 = 11 AUDIT_HEADER_VERSION_OPENBSM = AUDIT_HEADER_VERSION_OPENBSM11 AUT_TRAILER_MAGIC = 0xB105 AUT_HEADERS = [AUT_HEADER32, AUT_HEADER32_EX, AUT_HEADER64, AUT_HEADER64_EX]

true

79081a406dcde59514362123ec47547ee87dcc3b

2,692

py

Python

scripts/SCZ_RNAseq/syn4590909/rank_individual_genes.py

omarmaddouri/GCNCC_cross_validated

89576ad2c8459f065604656fd38a786d042f09e0

[ "MIT" ]

1

2022-03-12T13:34:34.000Z

scripts/SCZ_RNAseq/syn4590909/rank_individual_genes.py

omarmaddouri/GCNCC_cross_validated

89576ad2c8459f065604656fd38a786d042f09e0

[ "MIT" ]

3

2022-02-09T23:28:07.000Z

2022-02-11T19:08:53.000Z

scripts/SCZ_RNAseq/syn4590909/rank_individual_genes.py

omarmaddouri/GCNCC_cross_validated

89576ad2c8459f065604656fd38a786d042f09e0

[ "MIT" ]

null

import sys from os.path import dirname, abspath sys.path.append(dirname(dirname(abspath(__file__)))) from SCZ_RNAseq.syn4590909.utils import * path="../../data/SCZ_RNAseq/output/syn4590909/" dataset="PPI" features = np.genfromtxt("{}{}.GE_Features.txt".format(path, dataset), dtype=np.dtype(np.float32)) labels = get_clinical_status_syn4590909() clusters = open("{}{}.clusters_individual_gene.txt".format(path, dataset), encoding="utf-8") total_clusters = get_top_clusters_without_network(path, dataset, features, labels, clusters) print("The complete set of clusters that passed the minimal threshold is \n {}".format(total_clusters)) with open("{}{}.top_features_individual_gene.txt".format(path, dataset), "w", newline='', encoding="utf-8") as f: w_top_clusters = csv.writer(f, delimiter ='\t') w_top_clusters.writerow(total_clusters) clust = [] nb_columns = len(labels) baseline_accuracy = 0 eps = 0.01 #minimum accuracy improvement to consider new cluster (1%) tmp_Data = object for i in range(len(total_clusters)): clust.append(total_clusters[i]) nb_rows = len(clust) Data = np.zeros((nb_rows, nb_columns), dtype=object) if(i>0):#if temporary Data vector exist, copy all lines except last for j in range(nb_rows-1): Data[j, :] = tmp_Data[j, :] #Just compute score of newly added cluster Data[-1, :] = prepare_activity_score_feature_vector(features, labels, clust[nb_rows-1], clusters) accuracy = logistic_regression_classification_aggregate_activity_scores(np.transpose(Data), labels) if( accuracy < baseline_accuracy + eps ): clust = clust[:-1] tmp_Data = Data tmp_Data = np.delete(tmp_Data, tmp_Data.shape[0]-1, axis=0) print("SFS: feature {}/{} checked and rejected".format(i, len(total_clusters)-1)) else: baseline_accuracy = accuracy tmp_Data = Data print("SFS: feature {}/{} checked and retained".format(i, len(total_clusters)-1)) print("The set of clusters to be used in classification is \n {}".format(clust)) with open("{}{}.final_features_individual_gene.txt".format(path, dataset), "w", newline='', encoding="utf-8") as f: w_final_clusters = csv.writer(f, delimiter ='\t') w_final_clusters.writerow(clust) print("Logistic regression accuracy: {}".format(accuracy)) #accuracy = LDA_classification_aggregate_activity_scores(np.transpose(Data), labels) #print("LDA accuracy: {}".format(accuracy)) #accuracy = SVM_classification_aggregate_activity_scores(np.transpose(Data), labels) #print("SVM(Linear Kernel) accuracy: {}".format(accuracy)) clusters.close()

42.0625

115

0.69948

import sys from os.path import dirname, abspath sys.path.append(dirname(dirname(abspath(__file__)))) from SCZ_RNAseq.syn4590909.utils import * path="../../data/SCZ_RNAseq/output/syn4590909/" dataset="PPI" features = np.genfromtxt("{}{}.GE_Features.txt".format(path, dataset), dtype=np.dtype(np.float32)) labels = get_clinical_status_syn4590909() clusters = open("{}{}.clusters_individual_gene.txt".format(path, dataset), encoding="utf-8") total_clusters = get_top_clusters_without_network(path, dataset, features, labels, clusters) print("The complete set of clusters that passed the minimal threshold is \n {}".format(total_clusters)) with open("{}{}.top_features_individual_gene.txt".format(path, dataset), "w", newline='', encoding="utf-8") as f: w_top_clusters = csv.writer(f, delimiter ='\t') w_top_clusters.writerow(total_clusters) clust = [] nb_columns = len(labels) baseline_accuracy = 0 eps = 0.01 tmp_Data = object for i in range(len(total_clusters)): clust.append(total_clusters[i]) nb_rows = len(clust) Data = np.zeros((nb_rows, nb_columns), dtype=object) if(i>0): for j in range(nb_rows-1): Data[j, :] = tmp_Data[j, :] Data[-1, :] = prepare_activity_score_feature_vector(features, labels, clust[nb_rows-1], clusters) accuracy = logistic_regression_classification_aggregate_activity_scores(np.transpose(Data), labels) if( accuracy < baseline_accuracy + eps ): clust = clust[:-1] tmp_Data = Data tmp_Data = np.delete(tmp_Data, tmp_Data.shape[0]-1, axis=0) print("SFS: feature {}/{} checked and rejected".format(i, len(total_clusters)-1)) else: baseline_accuracy = accuracy tmp_Data = Data print("SFS: feature {}/{} checked and retained".format(i, len(total_clusters)-1)) print("The set of clusters to be used in classification is \n {}".format(clust)) with open("{}{}.final_features_individual_gene.txt".format(path, dataset), "w", newline='', encoding="utf-8") as f: w_final_clusters = csv.writer(f, delimiter ='\t') w_final_clusters.writerow(clust) print("Logistic regression accuracy: {}".format(accuracy)) clusters.close()

true

79081ab4bea93de238d2c9a4070d4985b1833fb3

4,304

py

Python

libica/openapi/libgds/models/folder_update_request.py

umccr-illumina/libica

916d27eea499f29bee590268b84208effb0cc576

[ "MIT" ]

null

libica/openapi/libgds/models/folder_update_request.py

umccr-illumina/libica

916d27eea499f29bee590268b84208effb0cc576

[ "MIT" ]

4

2021-11-15T10:47:51.000Z

2022-02-22T04:43:20.000Z

libica/openapi/libgds/models/folder_update_request.py

umccr-illumina/libica

916d27eea499f29bee590268b84208effb0cc576

[ "MIT" ]

null

# coding: utf-8 """ Genomic Data Store Service No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 The version of the OpenAPI document: v1 Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six from libica.openapi.libgds.configuration import Configuration class FolderUpdateRequest(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = { 'metadata': 'object', 'acl': 'list[str]' } attribute_map = { 'metadata': 'metadata', 'acl': 'acl' } def __init__(self, metadata=None, acl=None, local_vars_configuration=None): # noqa: E501 """FolderUpdateRequest - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._metadata = None self._acl = None self.discriminator = None if metadata is not None: self.metadata = metadata if acl is not None: self.acl = acl @property def metadata(self): """Gets the metadata of this FolderUpdateRequest. # noqa: E501 Metadata about this folder and its contents # noqa: E501 :return: The metadata of this FolderUpdateRequest. # noqa: E501 :rtype: object """ return self._metadata @metadata.setter def metadata(self, metadata): """Sets the metadata of this FolderUpdateRequest. Metadata about this folder and its contents # noqa: E501 :param metadata: The metadata of this FolderUpdateRequest. # noqa: E501 :type: object """ self._metadata = metadata @property def acl(self): """Gets the acl of this FolderUpdateRequest. # noqa: E501 Optional array to replace the acl on the resource. # noqa: E501 :return: The acl of this FolderUpdateRequest. # noqa: E501 :rtype: list[str] """ return self._acl @acl.setter def acl(self, acl): """Sets the acl of this FolderUpdateRequest. Optional array to replace the acl on the resource. # noqa: E501 :param acl: The acl of this FolderUpdateRequest. # noqa: E501 :type: list[str] """ self._acl = acl def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, FolderUpdateRequest): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, FolderUpdateRequest): return True return self.to_dict() != other.to_dict()

28.503311

124

0.58434

import pprint import re import six from libica.openapi.libgds.configuration import Configuration class FolderUpdateRequest(object): openapi_types = { 'metadata': 'object', 'acl': 'list[str]' } attribute_map = { 'metadata': 'metadata', 'acl': 'acl' } def __init__(self, metadata=None, acl=None, local_vars_configuration=None): if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._metadata = None self._acl = None self.discriminator = None if metadata is not None: self.metadata = metadata if acl is not None: self.acl = acl @property def metadata(self): return self._metadata @metadata.setter def metadata(self, metadata): self._metadata = metadata @property def acl(self): return self._acl @acl.setter def acl(self, acl): self._acl = acl def to_dict(self): result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, FolderUpdateRequest): return False return self.to_dict() == other.to_dict() def __ne__(self, other): if not isinstance(other, FolderUpdateRequest): return True return self.to_dict() != other.to_dict()

true

79081ad7c255e11da14b60003ca7937ed73bd33a

5,841

py

Python

ccws/base.py

applezjm/testsub

051348bb852d8e3cefe764a6315f53da66cd413e

[ "MIT" ]

null

ccws/base.py

applezjm/testsub

051348bb852d8e3cefe764a6315f53da66cd413e

[ "MIT" ]

null

ccws/base.py

applezjm/testsub

051348bb852d8e3cefe764a6315f53da66cd413e

[ "MIT" ]

null

# coding=utf-8 import websocket import datetime import csv import time import logging import redis import json import copy import pytz from hftcoin.mdagent.ccws.configs import REDIS_HOST from hftcoin.mdagent.ccws.configs import TIMEZONE from hftcoin.mdagent.ccws.configs import ExConfigs from hftcoin.mdagent.ccws.configs import HOME_PATH class Exchange(object): ExchangeId = '' WebSocketConnection = None RedisConnection = None def __init__(self): self.Logger = logging.getLogger(self.ExchangeId) [self.ExConfig, self._WebSocketAddress] = ExConfigs[self.ExchangeId] self.Config = {} def set_market(self, currency, mode): self.Config = self.ExConfig[currency][mode] self.Logger = logging.getLogger('%s.%s.%s' % (self.ExchangeId, currency, mode)) def run_websocketapp(self, **kwargs): self.Logger.info('Begin Connection') url = self._WebSocketAddress + kwargs.pop('url_append', '') on_error = kwargs.pop('on_error', self.on_error) on_close = kwargs.pop('on_close', self.on_close) on_message = kwargs.pop('on_message', self.on_message) self.WebSocketConnection = websocket.WebSocketApp( url, on_error=on_error, on_close=on_close, on_message=on_message, **kwargs, ) while True: try: self.WebSocketConnection.run_forever() except Exception as e: self.Logger.exception(e) def on_message(self, _ws, msg): ts = int(time.time()*1000) rdk = self.Config['RedisCollectKey'] # self.Logger.debug(msg) self.RedisConnection.lpush(rdk, json.dumps([ts, msg])) def on_error(self, _ws, error): self.Logger.exception(error) def on_close(self, _ws): self.Logger.info('Connection closed.') def connect_redis(self): try: self.RedisConnection = redis.StrictRedis(host=REDIS_HOST) self.RedisConnection.ping() except Exception as e: self.Logger.exception(e) def write_data_csv(self): self.connect_redis() [fn, rdk] = [self.Config.get(item) for item in ['FileName', 'RedisOutputKey']] error_count = 100 while True: try: if self.RedisConnection.llen(rdk) > 0: data = json.loads(self.RedisConnection.rpop(rdk).decode('utf8')) # data[1] is timestamp dt = datetime.datetime.fromtimestamp(data[1] / 1000, TIMEZONE) calendar_path = '%4d/%02d/%02d' % (dt.year, dt.month, dt.day) with open('%s/%s/%s' % (HOME_PATH, calendar_path, fn), 'a+') as csvFile: csvwriter = csv.writer(csvFile) csvwriter.writerow(data) else: time.sleep(60) except RuntimeWarning: break except Exception as e: self.Logger.exception(e) error_count -= 1 if error_count < 0: break def collect_data(self): pass def process_data(self): self.connect_redis() getattr(self, self.Config.get('DataHandler', object))() def _check_price_eq(self, p1, p2): # divide by 2 to avoid precision return abs(p1-p2) < self.Config['TickSize']/2 def _binary_search(self, find, list1, low, high): while low <= high: mid = int((low + high) / 2) if self._check_price_eq(list1[mid][0], find): return [mid, 'True'] elif list1[mid][0] > find: high = mid - 1 else: low = mid + 1 return [low, 'False'] def _update_order_book(self, bids, asks, side, price, remaining): if side in ['bid', 'buy']: book = bids cut = int(99*(len(book)-1)/100) else: book = asks cut = int((len(book)-1)/100) if price < book[cut][0]: res = self._binary_search(price, book, 0, cut-1) else: res = self._binary_search(price, book, cut, len(book)-1) if res[1] == 'True': if remaining < self.Config['AmountMin']: del book[res[0]] else: book[res[0]][1] = remaining else: if remaining >= self.Config['AmountMin']: book.insert(res[0], [price, remaining]) def check_data_validation(self, book): length = int(len(book)/2) for i in range(0, length - 2, 2): if book[i] <= book[i + 2]: return False for i in range(length, 2 * length - 2, 2): if book[i] >= book[i + 2]: return False for i in range(1, 2 * length, 2): if book[i] < self.Config['AmountMin']: return False if book[0] > book[length]: return False return True @staticmethod def _cut_order_book(bids, asks, depth): if len(bids) >= depth: book = bids[-depth:] book.reverse() else: book = copy.deepcopy(bids) book.reverse() book += [['None', 'None']] * (depth - len(bids)) if len(asks) >= depth: book += asks[:depth] else: book += asks + [['None', 'None']] * (depth - len(asks)) book = [x[0:2] for x in book] return sum(book, []) @staticmethod def fmt_date(ts): return datetime.datetime.fromtimestamp(ts / 1000, TIMEZONE).strftime('%Y-%m-%d %H:%M:%S.%f %z') @staticmethod def date_from_str(ts): return pytz.utc.localize(datetime.datetime.strptime(ts, '%Y-%m-%dT%H:%M:%S.%fZ'))

32.631285

103

0.546995

import websocket import datetime import csv import time import logging import redis import json import copy import pytz from hftcoin.mdagent.ccws.configs import REDIS_HOST from hftcoin.mdagent.ccws.configs import TIMEZONE from hftcoin.mdagent.ccws.configs import ExConfigs from hftcoin.mdagent.ccws.configs import HOME_PATH class Exchange(object): ExchangeId = '' WebSocketConnection = None RedisConnection = None def __init__(self): self.Logger = logging.getLogger(self.ExchangeId) [self.ExConfig, self._WebSocketAddress] = ExConfigs[self.ExchangeId] self.Config = {} def set_market(self, currency, mode): self.Config = self.ExConfig[currency][mode] self.Logger = logging.getLogger('%s.%s.%s' % (self.ExchangeId, currency, mode)) def run_websocketapp(self, **kwargs): self.Logger.info('Begin Connection') url = self._WebSocketAddress + kwargs.pop('url_append', '') on_error = kwargs.pop('on_error', self.on_error) on_close = kwargs.pop('on_close', self.on_close) on_message = kwargs.pop('on_message', self.on_message) self.WebSocketConnection = websocket.WebSocketApp( url, on_error=on_error, on_close=on_close, on_message=on_message, **kwargs, ) while True: try: self.WebSocketConnection.run_forever() except Exception as e: self.Logger.exception(e) def on_message(self, _ws, msg): ts = int(time.time()*1000) rdk = self.Config['RedisCollectKey'] self.RedisConnection.lpush(rdk, json.dumps([ts, msg])) def on_error(self, _ws, error): self.Logger.exception(error) def on_close(self, _ws): self.Logger.info('Connection closed.') def connect_redis(self): try: self.RedisConnection = redis.StrictRedis(host=REDIS_HOST) self.RedisConnection.ping() except Exception as e: self.Logger.exception(e) def write_data_csv(self): self.connect_redis() [fn, rdk] = [self.Config.get(item) for item in ['FileName', 'RedisOutputKey']] error_count = 100 while True: try: if self.RedisConnection.llen(rdk) > 0: data = json.loads(self.RedisConnection.rpop(rdk).decode('utf8')) dt = datetime.datetime.fromtimestamp(data[1] / 1000, TIMEZONE) calendar_path = '%4d/%02d/%02d' % (dt.year, dt.month, dt.day) with open('%s/%s/%s' % (HOME_PATH, calendar_path, fn), 'a+') as csvFile: csvwriter = csv.writer(csvFile) csvwriter.writerow(data) else: time.sleep(60) except RuntimeWarning: break except Exception as e: self.Logger.exception(e) error_count -= 1 if error_count < 0: break def collect_data(self): pass def process_data(self): self.connect_redis() getattr(self, self.Config.get('DataHandler', object))() def _check_price_eq(self, p1, p2): return abs(p1-p2) < self.Config['TickSize']/2 def _binary_search(self, find, list1, low, high): while low <= high: mid = int((low + high) / 2) if self._check_price_eq(list1[mid][0], find): return [mid, 'True'] elif list1[mid][0] > find: high = mid - 1 else: low = mid + 1 return [low, 'False'] def _update_order_book(self, bids, asks, side, price, remaining): if side in ['bid', 'buy']: book = bids cut = int(99*(len(book)-1)/100) else: book = asks cut = int((len(book)-1)/100) if price < book[cut][0]: res = self._binary_search(price, book, 0, cut-1) else: res = self._binary_search(price, book, cut, len(book)-1) if res[1] == 'True': if remaining < self.Config['AmountMin']: del book[res[0]] else: book[res[0]][1] = remaining else: if remaining >= self.Config['AmountMin']: book.insert(res[0], [price, remaining]) def check_data_validation(self, book): length = int(len(book)/2) for i in range(0, length - 2, 2): if book[i] <= book[i + 2]: return False for i in range(length, 2 * length - 2, 2): if book[i] >= book[i + 2]: return False for i in range(1, 2 * length, 2): if book[i] < self.Config['AmountMin']: return False if book[0] > book[length]: return False return True @staticmethod def _cut_order_book(bids, asks, depth): if len(bids) >= depth: book = bids[-depth:] book.reverse() else: book = copy.deepcopy(bids) book.reverse() book += [['None', 'None']] * (depth - len(bids)) if len(asks) >= depth: book += asks[:depth] else: book += asks + [['None', 'None']] * (depth - len(asks)) book = [x[0:2] for x in book] return sum(book, []) @staticmethod def fmt_date(ts): return datetime.datetime.fromtimestamp(ts / 1000, TIMEZONE).strftime('%Y-%m-%d %H:%M:%S.%f %z') @staticmethod def date_from_str(ts): return pytz.utc.localize(datetime.datetime.strptime(ts, '%Y-%m-%dT%H:%M:%S.%fZ'))

true

79081d4f882fb3db9f68d5af7078fe845f869a13

12,493

py

Python

src/interface_py/h2o4gpu/solvers/factorization.py

aaron8tang/h2o4gpu

602275375cb0dfb4acd070a8c86c3ded0bef1156

[ "Apache-2.0" ]

null

src/interface_py/h2o4gpu/solvers/factorization.py

aaron8tang/h2o4gpu

602275375cb0dfb4acd070a8c86c3ded0bef1156

[ "Apache-2.0" ]

null

src/interface_py/h2o4gpu/solvers/factorization.py

aaron8tang/h2o4gpu

602275375cb0dfb4acd070a8c86c3ded0bef1156

[ "Apache-2.0" ]

null

# - * - encoding : utf - 8 - * - # pylint: disable=fixme, line-too-long """ Matrix factorization solver. :copyright: 2017-2019 H2O.ai, Inc. :license: Apache License Version 2.0 (see LICENSE for details) """ import numpy as np import scipy import scipy.sparse def _get_sparse_matrixes(X): '''Create csc, csr and coo sparse matrix from any of the above Arguments: X {array-like, csc, csr or coo sparse matrix} Returns: csc, csr, coo ''' X_coo = X_csc = X_csr = None if scipy.sparse.isspmatrix_coo(X): X_coo = X X_csr = X_coo.tocsr(True) X_csc = X_coo.tocsc(True) elif scipy.sparse.isspmatrix_csr(X): X_csr = X X_csc = X_csr.tocoo(True) X_coo = X_csr.tocsc(True) elif scipy.sparse.isspmatrix_csc(X): X_csc = X X_csr = X_csc.tocsr(True) X_coo = X_csc.tocoo(True) else: assert False, "only coo, csc and csr sparse matrixes are supported" return X_csc, X_csr, X_coo class FactorizationH2O(object): '''Matrix Factorization on GPU with Alternating Least Square (ALS) algorithm. Factors a sparse rating matrix X (m by n, with N_z non-zero elements) into a m-by-f and a f-by-n matrices. Parameters ---------- f int decomposition size lambda_ float lambda regularization max_iter int, default: 100 number of training iterations double_precision bool, default: False use double precision, not yet supported thetaT {array-like} shape (n, f), default: None initial theta matrix XT {array-like} shape (m, f), default: None initial XT matrix random_state int, default: 1234 Attributes ---------- XT {array-like} shape (m, f) XT matrix contains user's features thetaT {array-like} shape (n, f) transposed theta matrix, item's features Warnings -------- Matrixes ``XT`` and ``thetaT`` may contain nan elements. This is because in some datasets, there are users or items with no ratings in training set. That results in solutions of a system of linear equations becomes nan. Such elements can be easily removed with numpy functions like numpy.nan_to_num, but existence of them may be useful for troubleshooting purposes. ''' def __init__(self, f, lambda_, max_iter=100, double_precision=False, thetaT=None, XT=None, random_state=1234): assert not double_precision, 'double precision is not yet supported' assert f % 10 == 0, 'f has to be a multiple of 10' self.f = f self.lambda_ = lambda_ self.double_precision = double_precision self.dtype = np.float64 if self.double_precision else np.float32 self.thetaT = thetaT self.XT = XT self.max_iter = max_iter self.random_state = random_state def _load_lib(self): from ..libs.lib_utils import GPUlib gpu_lib = GPUlib().get(1) return gpu_lib def fit(self, X, y=None, X_test=None, X_BATCHES=1, THETA_BATCHES=1, early_stopping_rounds=None, verbose=False, scores=None): #pylint: disable=unused-argument '''Learn model from rating matrix X. Parameters ---------- X {array-like, sparse matrix}, shape (m, n) Data matrix to be decomposed. y None Ignored X_test {array-like, coo sparse matrix}, shape (m, n) Data matrix for cross validation. X_BATCHES int, default: 1 Batches to split XT, increase this parameter in case out of memory error. THETA_BATCHES int, default: 1 Batches to split theta, increase this parameter in case out of memory error. early_stopping_rounds int, default: None Activates early stopping. Cross validation error needs to decrease at least every <early_stopping_rounds> round(s) to continue training. Requires <X_test>. Returns the model from the last iteration (not the best one). If early stopping occurs, the model will have three additional fields: best_cv_score, best_train_score and best_iteration. verbose bool, default: False Prints training and validation score(if applicable) on each iteration. scores {list} List of tuples with train, cv score for every iteration. Returns ------- self : returns an instance of self. ''' csc_X, csr_X, coo_X = _get_sparse_matrixes(X) if early_stopping_rounds is not None: assert X_test is not None, 'X_test is mandatory with early stopping' if X_test is not None: assert scipy.sparse.isspmatrix_coo( X_test), 'X_test must be a coo sparse scipy matrix' assert X.shape == X_test.shape assert X_test.dtype == self.dtype assert X.dtype == self.dtype coo_X_test = X_test lib = self._load_lib() if self.double_precision: make_data = lib.make_factorization_data_double run_step = lib.run_factorization_step_double factorization_score = lib.factorization_score_double copy_fecatorization_result = lib.copy_fecatorization_result_double free_data = lib.free_data_double else: make_data = lib.make_factorization_data_float run_step = lib.run_factorization_step_float factorization_score = lib.factorization_score_float copy_fecatorization_result = lib.copy_fecatorization_result_float free_data = lib.free_data_float m = coo_X.shape[0] n = coo_X.shape[1] nnz = csc_X.nnz if coo_X_test is None: nnz_test = 0 else: nnz_test = coo_X_test.nnz rs = np.random.RandomState(self.random_state) if self.thetaT is None: self.thetaT = rs.rand(n, self.f).astype(self.dtype) else: assert self.thetaT.dtype == self.dtype if self.XT is None: self.XT = rs.rand(m, self.f).astype(self.dtype) else: assert self.XT.dtype == self.dtype csrRowIndexDevicePtr = None csrColIndexDevicePtr = None csrValDevicePtr = None cscRowIndexDevicePtr = None cscColIndexDevicePtr = None cscValDevicePtr = None cooRowIndexDevicePtr = None cooColIndexDevicePtr = None cooValDevicePtr = None thetaTDevice = None XTDevice = None cooRowIndexTestDevicePtr = None cooColIndexTestDevicePtr = None cooValTestDevicePtr = None status, csrRowIndexDevicePtr, csrColIndexDevicePtr, csrValDevicePtr, \ cscRowIndexDevicePtr, cscColIndexDevicePtr, cscValDevicePtr, \ cooRowIndexDevicePtr, cooColIndexDevicePtr, cooValDevicePtr, \ thetaTDevice, XTDevice, cooRowIndexTestDevicePtr, \ cooColIndexTestDevicePtr, cooValTestDevicePtr = make_data( # pylint: disable=W0212 m, n, self.f, nnz, nnz_test, csr_X.indptr, csr_X.indices, csr_X.data, csc_X.indices, csc_X.indptr, csc_X.data, coo_X.row, coo_X.col, coo_X.data, self.thetaT, self.XT, coo_X_test.row if coo_X_test is not None else None, coo_X_test.col if coo_X_test is not None else None, coo_X_test.data if coo_X_test is not None else None, csrRowIndexDevicePtr, csrColIndexDevicePtr, csrValDevicePtr, cscRowIndexDevicePtr, cscColIndexDevicePtr, cscValDevicePtr, cooRowIndexDevicePtr, cooColIndexDevicePtr, cooValDevicePtr, thetaTDevice, XTDevice, cooRowIndexTestDevicePtr, cooColIndexTestDevicePtr, cooValTestDevicePtr) assert status == 0, 'Failure uploading the data' self.best_train_score = np.inf self.best_cv_score = np.inf self.best_iteration = -1 cv_score = train_score = np.inf for i in range(self.max_iter): status = run_step(m, n, self.f, nnz, self.lambda_, csrRowIndexDevicePtr, csrColIndexDevicePtr, csrValDevicePtr, cscRowIndexDevicePtr, cscColIndexDevicePtr, cscValDevicePtr, thetaTDevice, XTDevice, X_BATCHES, THETA_BATCHES) if verbose or scores is not None: result = factorization_score(m, n, self.f, nnz, self.lambda_, thetaTDevice, XTDevice, cooRowIndexDevicePtr, cooColIndexDevicePtr, cooValDevicePtr) train_score = result[0] if X_test is not None and (verbose or early_stopping_rounds is not None or scores is not None): result = factorization_score(m, n, self.f, nnz_test, self.lambda_, thetaTDevice, XTDevice, cooRowIndexTestDevicePtr, cooColIndexTestDevicePtr, cooValTestDevicePtr) cv_score = result[0] if verbose: print("iteration {0} train: {1} cv: {2}".format( i, train_score, cv_score)) if scores is not None: scores.append((train_score, cv_score)) if early_stopping_rounds is not None: if self.best_cv_score > cv_score: self.best_cv_score = cv_score self.best_train_score = train_score self.best_iteration = i if (i - self.best_iteration) > early_stopping_rounds: if verbose: print('best iteration:{0} train: {1} cv: {2}'.format( self.best_iteration, self.best_train_score, self.best_cv_score)) break lib.free_data_int(csrRowIndexDevicePtr) lib.free_data_int(csrColIndexDevicePtr) free_data(csrValDevicePtr) lib.free_data_int(cscRowIndexDevicePtr) lib.free_data_int(cscColIndexDevicePtr) free_data(cscValDevicePtr) lib.free_data_int(cooRowIndexDevicePtr) lib.free_data_int(cooColIndexDevicePtr) free_data(cooValDevicePtr) lib.free_data_int(cooRowIndexTestDevicePtr) lib.free_data_int(cooColIndexTestDevicePtr) free_data(cooValTestDevicePtr) copy_fecatorization_result(self.XT, XTDevice, m * self.f) copy_fecatorization_result(self.thetaT, thetaTDevice, n * self.f) free_data(thetaTDevice) free_data(XTDevice) return self def predict(self, X): '''Predict none zero elements of coo sparse matrix X according to the fitted model. Parameters ---------- X {array-like, sparse coo matrix} shape (m, n) Data matrix in coo format. Values are ignored. Returns ------- {array-like, sparse coo matrix} shape (m, n) Predicted values. ''' assert self.XT is not None and self.thetaT is not None, 'tranform is invoked on an unfitted model' assert scipy.sparse.isspmatrix_coo( X), 'convert X to coo sparse matrix' assert X.dtype == self.dtype a = np.take(self.XT, X.row, axis=0) b = np.take(self.thetaT, X.col, axis=0) val = np.sum(a * b, axis=1) return scipy.sparse.coo_matrix((val, (X.row, X.col)), shape=X.shape)

39.410095

137

0.574802

import numpy as np import scipy import scipy.sparse def _get_sparse_matrixes(X): X_coo = X_csc = X_csr = None if scipy.sparse.isspmatrix_coo(X): X_coo = X X_csr = X_coo.tocsr(True) X_csc = X_coo.tocsc(True) elif scipy.sparse.isspmatrix_csr(X): X_csr = X X_csc = X_csr.tocoo(True) X_coo = X_csr.tocsc(True) elif scipy.sparse.isspmatrix_csc(X): X_csc = X X_csr = X_csc.tocsr(True) X_coo = X_csc.tocoo(True) else: assert False, "only coo, csc and csr sparse matrixes are supported" return X_csc, X_csr, X_coo class FactorizationH2O(object): def __init__(self, f, lambda_, max_iter=100, double_precision=False, thetaT=None, XT=None, random_state=1234): assert not double_precision, 'double precision is not yet supported' assert f % 10 == 0, 'f has to be a multiple of 10' self.f = f self.lambda_ = lambda_ self.double_precision = double_precision self.dtype = np.float64 if self.double_precision else np.float32 self.thetaT = thetaT self.XT = XT self.max_iter = max_iter self.random_state = random_state def _load_lib(self): from ..libs.lib_utils import GPUlib gpu_lib = GPUlib().get(1) return gpu_lib def fit(self, X, y=None, X_test=None, X_BATCHES=1, THETA_BATCHES=1, early_stopping_rounds=None, verbose=False, scores=None): csc_X, csr_X, coo_X = _get_sparse_matrixes(X) if early_stopping_rounds is not None: assert X_test is not None, 'X_test is mandatory with early stopping' if X_test is not None: assert scipy.sparse.isspmatrix_coo( X_test), 'X_test must be a coo sparse scipy matrix' assert X.shape == X_test.shape assert X_test.dtype == self.dtype assert X.dtype == self.dtype coo_X_test = X_test lib = self._load_lib() if self.double_precision: make_data = lib.make_factorization_data_double run_step = lib.run_factorization_step_double factorization_score = lib.factorization_score_double copy_fecatorization_result = lib.copy_fecatorization_result_double free_data = lib.free_data_double else: make_data = lib.make_factorization_data_float run_step = lib.run_factorization_step_float factorization_score = lib.factorization_score_float copy_fecatorization_result = lib.copy_fecatorization_result_float free_data = lib.free_data_float m = coo_X.shape[0] n = coo_X.shape[1] nnz = csc_X.nnz if coo_X_test is None: nnz_test = 0 else: nnz_test = coo_X_test.nnz rs = np.random.RandomState(self.random_state) if self.thetaT is None: self.thetaT = rs.rand(n, self.f).astype(self.dtype) else: assert self.thetaT.dtype == self.dtype if self.XT is None: self.XT = rs.rand(m, self.f).astype(self.dtype) else: assert self.XT.dtype == self.dtype csrRowIndexDevicePtr = None csrColIndexDevicePtr = None csrValDevicePtr = None cscRowIndexDevicePtr = None cscColIndexDevicePtr = None cscValDevicePtr = None cooRowIndexDevicePtr = None cooColIndexDevicePtr = None cooValDevicePtr = None thetaTDevice = None XTDevice = None cooRowIndexTestDevicePtr = None cooColIndexTestDevicePtr = None cooValTestDevicePtr = None status, csrRowIndexDevicePtr, csrColIndexDevicePtr, csrValDevicePtr, \ cscRowIndexDevicePtr, cscColIndexDevicePtr, cscValDevicePtr, \ cooRowIndexDevicePtr, cooColIndexDevicePtr, cooValDevicePtr, \ thetaTDevice, XTDevice, cooRowIndexTestDevicePtr, \ cooColIndexTestDevicePtr, cooValTestDevicePtr = make_data( m, n, self.f, nnz, nnz_test, csr_X.indptr, csr_X.indices, csr_X.data, csc_X.indices, csc_X.indptr, csc_X.data, coo_X.row, coo_X.col, coo_X.data, self.thetaT, self.XT, coo_X_test.row if coo_X_test is not None else None, coo_X_test.col if coo_X_test is not None else None, coo_X_test.data if coo_X_test is not None else None, csrRowIndexDevicePtr, csrColIndexDevicePtr, csrValDevicePtr, cscRowIndexDevicePtr, cscColIndexDevicePtr, cscValDevicePtr, cooRowIndexDevicePtr, cooColIndexDevicePtr, cooValDevicePtr, thetaTDevice, XTDevice, cooRowIndexTestDevicePtr, cooColIndexTestDevicePtr, cooValTestDevicePtr) assert status == 0, 'Failure uploading the data' self.best_train_score = np.inf self.best_cv_score = np.inf self.best_iteration = -1 cv_score = train_score = np.inf for i in range(self.max_iter): status = run_step(m, n, self.f, nnz, self.lambda_, csrRowIndexDevicePtr, csrColIndexDevicePtr, csrValDevicePtr, cscRowIndexDevicePtr, cscColIndexDevicePtr, cscValDevicePtr, thetaTDevice, XTDevice, X_BATCHES, THETA_BATCHES) if verbose or scores is not None: result = factorization_score(m, n, self.f, nnz, self.lambda_, thetaTDevice, XTDevice, cooRowIndexDevicePtr, cooColIndexDevicePtr, cooValDevicePtr) train_score = result[0] if X_test is not None and (verbose or early_stopping_rounds is not None or scores is not None): result = factorization_score(m, n, self.f, nnz_test, self.lambda_, thetaTDevice, XTDevice, cooRowIndexTestDevicePtr, cooColIndexTestDevicePtr, cooValTestDevicePtr) cv_score = result[0] if verbose: print("iteration {0} train: {1} cv: {2}".format( i, train_score, cv_score)) if scores is not None: scores.append((train_score, cv_score)) if early_stopping_rounds is not None: if self.best_cv_score > cv_score: self.best_cv_score = cv_score self.best_train_score = train_score self.best_iteration = i if (i - self.best_iteration) > early_stopping_rounds: if verbose: print('best iteration:{0} train: {1} cv: {2}'.format( self.best_iteration, self.best_train_score, self.best_cv_score)) break lib.free_data_int(csrRowIndexDevicePtr) lib.free_data_int(csrColIndexDevicePtr) free_data(csrValDevicePtr) lib.free_data_int(cscRowIndexDevicePtr) lib.free_data_int(cscColIndexDevicePtr) free_data(cscValDevicePtr) lib.free_data_int(cooRowIndexDevicePtr) lib.free_data_int(cooColIndexDevicePtr) free_data(cooValDevicePtr) lib.free_data_int(cooRowIndexTestDevicePtr) lib.free_data_int(cooColIndexTestDevicePtr) free_data(cooValTestDevicePtr) copy_fecatorization_result(self.XT, XTDevice, m * self.f) copy_fecatorization_result(self.thetaT, thetaTDevice, n * self.f) free_data(thetaTDevice) free_data(XTDevice) return self def predict(self, X): assert self.XT is not None and self.thetaT is not None, 'tranform is invoked on an unfitted model' assert scipy.sparse.isspmatrix_coo( X), 'convert X to coo sparse matrix' assert X.dtype == self.dtype a = np.take(self.XT, X.row, axis=0) b = np.take(self.thetaT, X.col, axis=0) val = np.sum(a * b, axis=1) return scipy.sparse.coo_matrix((val, (X.row, X.col)), shape=X.shape)

true

79081dd13c1cbd7c95a5b2b85e279978b4920270

961

py

Python

infoblox/komand_infoblox/actions/delete_host/schema.py

xhennessy-r7/insightconnect-plugins

59268051313d67735b5dd3a30222eccb92aca8e9

[ "MIT" ]

null

infoblox/komand_infoblox/actions/delete_host/schema.py

xhennessy-r7/insightconnect-plugins

59268051313d67735b5dd3a30222eccb92aca8e9

[ "MIT" ]

null

infoblox/komand_infoblox/actions/delete_host/schema.py

xhennessy-r7/insightconnect-plugins

59268051313d67735b5dd3a30222eccb92aca8e9

[ "MIT" ]

null

# GENERATED BY KOMAND SDK - DO NOT EDIT import komand import json class Input: _REF = "_ref" class Output: _REF = "_ref" class DeleteHostInput(komand.Input): schema = json.loads(""" { "type": "object", "title": "Variables", "properties": { "_ref": { "type": "string", "title": "Ref", "description": "Object Reference of the host to remove", "order": 1 } }, "required": [ "_ref" ] } """) def __init__(self): super(self.__class__, self).__init__(self.schema) class DeleteHostOutput(komand.Output): schema = json.loads(""" { "type": "object", "title": "Variables", "properties": { "_ref": { "type": "string", "title": "Ref", "description": "Object Reference of the removed host", "order": 1 } }, "required": [ "_ref" ] } """) def __init__(self): super(self.__class__, self).__init__(self.schema)

16.568966

62

0.546306

import komand import json class Input: _REF = "_ref" class Output: _REF = "_ref" class DeleteHostInput(komand.Input): schema = json.loads(""" { "type": "object", "title": "Variables", "properties": { "_ref": { "type": "string", "title": "Ref", "description": "Object Reference of the host to remove", "order": 1 } }, "required": [ "_ref" ] } """) def __init__(self): super(self.__class__, self).__init__(self.schema) class DeleteHostOutput(komand.Output): schema = json.loads(""" { "type": "object", "title": "Variables", "properties": { "_ref": { "type": "string", "title": "Ref", "description": "Object Reference of the removed host", "order": 1 } }, "required": [ "_ref" ] } """) def __init__(self): super(self.__class__, self).__init__(self.schema)

true

79081efb431e116d29c7b9895c23c30c45fb6063

553

py

Python

pythons/pythons/pythons_auth/migrations/0002_auto_20210723_1847.py

BoyanPeychinov/python_web_framework

bb3a78c36790821d8b3a2b847494a1138d063193

[ "MIT" ]

null

pythons/pythons/pythons_auth/migrations/0002_auto_20210723_1847.py

BoyanPeychinov/python_web_framework

bb3a78c36790821d8b3a2b847494a1138d063193

[ "MIT" ]

null

pythons/pythons/pythons_auth/migrations/0002_auto_20210723_1847.py

BoyanPeychinov/python_web_framework

bb3a78c36790821d8b3a2b847494a1138d063193

[ "MIT" ]

null

# Generated by Django 3.2.4 on 2021-07-23 15:47 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('pythons_auth', '0001_initial'), ] operations = [ migrations.AddField( model_name='pythonsuser', name='is_staff', field=models.BooleanField(default=False), ), migrations.AddField( model_name='pythonsuser', name='is_superuser', field=models.BooleanField(default=False), ), ]

23.041667

53

0.584087

from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('pythons_auth', '0001_initial'), ] operations = [ migrations.AddField( model_name='pythonsuser', name='is_staff', field=models.BooleanField(default=False), ), migrations.AddField( model_name='pythonsuser', name='is_superuser', field=models.BooleanField(default=False), ), ]

true

79081f127977d650f3c97bab5df425b5b4db4a4c

4,384

py

Python

data/prepare_data_2d_h36m_sh.py

fullmoonhalf/SemGCN

ce1dce98f8b7cc600ba7e733d17d71192c24b596

[ "Apache-2.0" ]

null

data/prepare_data_2d_h36m_sh.py

fullmoonhalf/SemGCN

ce1dce98f8b7cc600ba7e733d17d71192c24b596

[ "Apache-2.0" ]

null

data/prepare_data_2d_h36m_sh.py

fullmoonhalf/SemGCN

ce1dce98f8b7cc600ba7e733d17d71192c24b596

[ "Apache-2.0" ]

null

from __future__ import print_function, absolute_import, division import argparse import os import zipfile import tarfile import numpy as np import h5py from glob import glob from shutil import rmtree import sys sys.path.append('../') from common.h36m_dataset import H36M_NAMES output_filename_pt = 'data_2d_h36m_sh_pt_mpii' output_filename_ft = 'data_2d_h36m_sh_ft_h36m' subjects = ['S1', 'S5', 'S6', 'S7', 'S8', 'S9', 'S11'] cam_map = { '54138969': 0, '55011271': 1, '58860488': 2, '60457274': 3, } metadata = { 'num_joints': 16, 'keypoints_symmetry': [ [3, 4, 5, 13, 14, 15], [2, 1, 0, 12, 11, 10], ] } # Stacked Hourglass produces 16 joints. These are the names. SH_NAMES = [''] * 16 SH_NAMES[0] = 'RFoot' SH_NAMES[1] = 'RKnee' SH_NAMES[2] = 'RHip' SH_NAMES[3] = 'LHip' SH_NAMES[4] = 'LKnee' SH_NAMES[5] = 'LFoot' SH_NAMES[6] = 'Hip' SH_NAMES[7] = 'Spine' SH_NAMES[8] = 'Thorax' SH_NAMES[9] = 'Head' SH_NAMES[10] = 'RWrist' SH_NAMES[11] = 'RElbow' SH_NAMES[12] = 'RShoulder' SH_NAMES[13] = 'LShoulder' SH_NAMES[14] = 'LElbow' SH_NAMES[15] = 'LWrist' # Permutation that goes from SH detections to H36M ordering. SH_TO_GT_PERM = np.array([SH_NAMES.index(h) for h in H36M_NAMES if h != '' and h in SH_NAMES]) assert np.all(SH_TO_GT_PERM == np.array([6, 2, 1, 0, 3, 4, 5, 7, 8, 9, 13, 14, 15, 12, 11, 10])) metadata['keypoints_symmetry'][0] = [SH_TO_GT_PERM.tolist().index(h) for h in metadata['keypoints_symmetry'][0]] metadata['keypoints_symmetry'][1] = [SH_TO_GT_PERM.tolist().index(h) for h in metadata['keypoints_symmetry'][1]] def process_subject(subject, file_list, output): if subject == 'S11': assert len(file_list) == 119, "Expected 119 files for subject " + subject + ", got " + str(len(file_list)) else: assert len(file_list) == 120, "Expected 120 files for subject " + subject + ", got " + str(len(file_list)) for f in file_list: action, cam = os.path.splitext(os.path.basename(f))[0].replace('_', ' ').split('.') if subject == 'S11' and action == 'Directions': continue # Discard corrupted video if action not in output[subject]: output[subject][action] = [None, None, None, None] with h5py.File(f) as hf: # positions = hf['poses'].value positions = np.array(hf['poses']) positions = positions[:, SH_TO_GT_PERM, :] output[subject][action][cam_map[cam]] = positions.astype('float32') if __name__ == '__main__': if os.path.basename(os.getcwd()) != 'data': print('This script must be launched from the "data" directory') exit(0) parser = argparse.ArgumentParser(description='Human3.6M dataset downloader/converter') parser.add_argument('-pt', '--pretrained', default='', type=str, metavar='PATH', help='convert pretrained dataset') parser.add_argument('-ft', '--fine-tuned', default='', type=str, metavar='PATH', help='convert fine-tuned dataset') args = parser.parse_args() if args.pretrained: print('Converting pretrained dataset from', args.pretrained) print('Extracting...') with zipfile.ZipFile(args.pretrained, 'r') as archive: archive.extractall('sh_pt') print('Converting...') output = {} for subject in subjects: output[subject] = {} file_list = glob('sh_pt/h36m/' + subject + '/StackedHourglass/*.h5') process_subject(subject, file_list, output) print('Saving...') np.savez_compressed(output_filename_pt, positions_2d=output, metadata=metadata) print('Cleaning up...') rmtree('sh_pt') print('Done.') if args.fine_tuned: print('Converting fine-tuned dataset from', args.fine_tuned) print('Extracting...') with tarfile.open(args.fine_tuned, 'r:gz') as archive: archive.extractall('sh_ft') print('Converting...') output = {} for subject in subjects: output[subject] = {} file_list = glob('sh_ft/' + subject + '/StackedHourglassFineTuned240/*.h5') process_subject(subject, file_list, output) print('Saving...') np.savez_compressed(output_filename_ft, positions_2d=output, metadata=metadata) print('Cleaning up...') rmtree('sh_ft') print('Done.')

31.768116

119

0.62979

from __future__ import print_function, absolute_import, division import argparse import os import zipfile import tarfile import numpy as np import h5py from glob import glob from shutil import rmtree import sys sys.path.append('../') from common.h36m_dataset import H36M_NAMES output_filename_pt = 'data_2d_h36m_sh_pt_mpii' output_filename_ft = 'data_2d_h36m_sh_ft_h36m' subjects = ['S1', 'S5', 'S6', 'S7', 'S8', 'S9', 'S11'] cam_map = { '54138969': 0, '55011271': 1, '58860488': 2, '60457274': 3, } metadata = { 'num_joints': 16, 'keypoints_symmetry': [ [3, 4, 5, 13, 14, 15], [2, 1, 0, 12, 11, 10], ] } SH_NAMES = [''] * 16 SH_NAMES[0] = 'RFoot' SH_NAMES[1] = 'RKnee' SH_NAMES[2] = 'RHip' SH_NAMES[3] = 'LHip' SH_NAMES[4] = 'LKnee' SH_NAMES[5] = 'LFoot' SH_NAMES[6] = 'Hip' SH_NAMES[7] = 'Spine' SH_NAMES[8] = 'Thorax' SH_NAMES[9] = 'Head' SH_NAMES[10] = 'RWrist' SH_NAMES[11] = 'RElbow' SH_NAMES[12] = 'RShoulder' SH_NAMES[13] = 'LShoulder' SH_NAMES[14] = 'LElbow' SH_NAMES[15] = 'LWrist' SH_TO_GT_PERM = np.array([SH_NAMES.index(h) for h in H36M_NAMES if h != '' and h in SH_NAMES]) assert np.all(SH_TO_GT_PERM == np.array([6, 2, 1, 0, 3, 4, 5, 7, 8, 9, 13, 14, 15, 12, 11, 10])) metadata['keypoints_symmetry'][0] = [SH_TO_GT_PERM.tolist().index(h) for h in metadata['keypoints_symmetry'][0]] metadata['keypoints_symmetry'][1] = [SH_TO_GT_PERM.tolist().index(h) for h in metadata['keypoints_symmetry'][1]] def process_subject(subject, file_list, output): if subject == 'S11': assert len(file_list) == 119, "Expected 119 files for subject " + subject + ", got " + str(len(file_list)) else: assert len(file_list) == 120, "Expected 120 files for subject " + subject + ", got " + str(len(file_list)) for f in file_list: action, cam = os.path.splitext(os.path.basename(f))[0].replace('_', ' ').split('.') if subject == 'S11' and action == 'Directions': continue if action not in output[subject]: output[subject][action] = [None, None, None, None] with h5py.File(f) as hf: positions = np.array(hf['poses']) positions = positions[:, SH_TO_GT_PERM, :] output[subject][action][cam_map[cam]] = positions.astype('float32') if __name__ == '__main__': if os.path.basename(os.getcwd()) != 'data': print('This script must be launched from the "data" directory') exit(0) parser = argparse.ArgumentParser(description='Human3.6M dataset downloader/converter') parser.add_argument('-pt', '--pretrained', default='', type=str, metavar='PATH', help='convert pretrained dataset') parser.add_argument('-ft', '--fine-tuned', default='', type=str, metavar='PATH', help='convert fine-tuned dataset') args = parser.parse_args() if args.pretrained: print('Converting pretrained dataset from', args.pretrained) print('Extracting...') with zipfile.ZipFile(args.pretrained, 'r') as archive: archive.extractall('sh_pt') print('Converting...') output = {} for subject in subjects: output[subject] = {} file_list = glob('sh_pt/h36m/' + subject + '/StackedHourglass/*.h5') process_subject(subject, file_list, output) print('Saving...') np.savez_compressed(output_filename_pt, positions_2d=output, metadata=metadata) print('Cleaning up...') rmtree('sh_pt') print('Done.') if args.fine_tuned: print('Converting fine-tuned dataset from', args.fine_tuned) print('Extracting...') with tarfile.open(args.fine_tuned, 'r:gz') as archive: archive.extractall('sh_ft') print('Converting...') output = {} for subject in subjects: output[subject] = {} file_list = glob('sh_ft/' + subject + '/StackedHourglassFineTuned240/*.h5') process_subject(subject, file_list, output) print('Saving...') np.savez_compressed(output_filename_ft, positions_2d=output, metadata=metadata) print('Cleaning up...') rmtree('sh_ft') print('Done.')

true

79081fd3189187f98d1e806a77b8e869d24c78a8

8,388

py

Python

lpp/newlpp/lppTransform.py

exoplanetvetting/DAVE

aea19a30d987b214fb4c0cf01aa733f127c411b9

[ "MIT" ]

7

2019-05-07T02:01:51.000Z

2022-03-16T08:09:39.000Z

lpp/newlpp/lppTransform.py

barentsen/dave

45ba97b7b535ad26dd555c33c963c6224a9af23c

[ "MIT" ]

18

2015-12-09T22:18:59.000Z

2017-04-26T13:11:44.000Z

lpp/newlpp/lppTransform.py

barentsen/dave

45ba97b7b535ad26dd555c33c963c6224a9af23c

[ "MIT" ]

5

2017-03-08T11:42:53.000Z

2020-05-07T00:10:37.000Z

#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Thu Aug 23 20:32:12 2018 Functions to correctly fold and bin a light curve. Calculate the lpp metric: transform to lower dimensions, knn Depends on class from reading in a previously created LPP metric Map Depends on reading in the light curve to data structure. input is a class called data data contains data.time (days) data.tzero (day) data.dur (hours) data.period (days) data.flux (normalized to 0) After foldBinLightCurve it contains data.binned After transform it contains data.lpp_transform @author: smullally """ from __future__ import division import numpy as np from sklearn.neighbors import NearestNeighbors from lpproj import LocalityPreservingProjection import copy def computeLPPTransitMetric(data,mapInfo): """ This function takes a data class with light curve info and the mapInfo with information about the mapping to use. It then returns a lpp metric value. """ binFlux, binPhase=foldBinLightCurve(data,mapInfo.ntrfr,mapInfo.npts) #plt.figure() #plt.plot(binPhase,binFlux,'.--') #Dimensionality Reduction and knn parts rawTLpp,transformedTransit=computeRawLPPTransitMetric(binFlux,mapInfo) #Normalize by Period Dependence normTLpp=periodNormalLPPTransitMetric(rawTLpp,np.array([data.period,data.mes]), mapInfo) return normTLpp,rawTLpp,transformedTransit def runningMedian(t,y,dt,runt): """ Take a running median of size dt Return values at times given in runt """ newy=np.zeros(len(y)) newt=np.zeros(len(y)) srt = np.argsort(t) newt = t[srt] newy = y[srt] runy=[] for i in range(len(runt)): tmp=[] for j in range(len(newt)): if (newt[j] >= (runt[i]-dt)) and (newt[j] <= (runt[i]+dt)): tmp.append(newy[j]) if np.isnan(np.nanmedian(np.array(tmp))) : runy.append(0) else: runy.append(np.nanmedian(np.array(tmp))) return(list(runt),runy) def foldBinLightCurve (data, ntrfr, npts): """ Fold and bin light curve for input to LPP metric calculation data contains time, tzero, dur, priod,mes and flux (centered around zero) ntrfr -- number of transit fraction for binning around transit ~1.5 npts -- number of points in the final binning. """ #Create phase light curve phaselc =np.mod((data.time-(data.tzero-0.5*data.period))/data.period,1) flux=data.flux mes=data.mes #Determine the fraction of the time the planet transits the star. #Insist that ntrfr * transit fraction if ~np.isnan(data.dur) & (data.dur >0): transit_dur = data.dur else: transit_dur = 0.2 * data.period/24. transit_fr=transit_dur/24./data.period if (transit_fr * ntrfr) > 0.5 : transit_fr = 0.5/ntrfr #Specify the out of transit (a) and the in transit regions binover=1.3 if mes <= 20: binover=-(1/8.0)*mes + 3.8 endfr = .03 midfr= .11 a = np.concatenate((np.arange(endfr,.5-midfr,1/npts) , \ np.arange((0.5+midfr),(1-endfr),1/npts)), axis=None) ovsamp=4.0 #bstep=(ovsamp*ntrfr*transit_fr)/npts b_num=41 b =np.linspace((0.5-ntrfr*transit_fr),(0.5+ntrfr*transit_fr),b_num) #print "length a: %u " % len(a) #print "length b: %u" % len(b) [runta,runya] = runningMedian(phaselc,flux,binover/npts,a) [runtb,runyb] = runningMedian(phaselc,flux,\ (binover*ovsamp*ntrfr*transit_fr)/npts,b) #Combine the two sets of bins runymess=np.array(runya + runyb) runtmess = np.array(runta + runtb) srt=np.argsort(runtmess) runy=runymess[srt] runt=runtmess[srt] #Scale the flux by the depth so everything has the same depth. #Catch or dividing by zero is to not scale. scale = -1*np.min(runyb) if scale != 0: scaledFlux=runy/scale else: scaledFlux=runy binnedFlux=scaledFlux phasebins=runt return binnedFlux,phasebins def computeRawLPPTransitMetric(binFlux,mapInfo): """ Perform the matrix transformation with LPP Do the knn test to get a raw LPP transit metric number. """ Yorig=mapInfo.YmapMapped lpp=LocalityPreservingProjection(n_components=mapInfo.n_dim) lpp.projection_=mapInfo.YmapM #To equate to Matlab LPP methods, we need to remove mean of transform. normBinFlux=binFlux-mapInfo.YmapMean inputY=lpp.transform(normBinFlux.reshape(1,-1)) knownTransitsY=Yorig[mapInfo.knnGood,:] dist,ind = knnDistance_fromKnown(knownTransitsY,inputY,mapInfo.knn) rawLppTrMetric=np.mean(dist) return rawLppTrMetric,inputY def knnDistance_fromKnown(knownTransits,new,knn): """ For a group of known transits and a new one. Use knn to determine how close the new one is to the known transits using knn minkowski p = 3 () Using scipy signal to do this. """ #p=3 sets a minkowski distance of 3. #Check that you really used 3 for matlab. nbrs=NearestNeighbors(n_neighbors=int(knn), algorithm='kd_tree', p=2) nbrs.fit(knownTransits) distances,indices = nbrs.kneighbors(new) return distances, indices def periodNormalLPPTransitMetric(rawTLpp,newPerMes, mapInfo): """ Normalize the rawTransitMetric value by those with the closest period. This part removes the period dependence of the metric at short periods. Plus it makes a value near one be the threshold between good and bad. newPerMes is the np.array([period, mes]) of the new sample """ knownTrPeriods=mapInfo.mappedPeriods[mapInfo.knnGood] knownTrMes=mapInfo.mappedMes[mapInfo.knnGood] knownTrrawLpp=mapInfo.dymeans[mapInfo.knnGood] nPercentil=mapInfo.nPercentil nPsample=mapInfo.nPsample #Find the those with the nearest periods Npsample-nneighbors logPeriods=np.log10(knownTrPeriods) logMes=np.log10(knownTrMes) knownPerMes=np.stack((logPeriods, logMes), axis=-1) np.shape(knownPerMes) logNew=np.log10(newPerMes).reshape(1,-1) #logNew=np.array([np.log10(newPeriod)]).reshape(1,1) dist,ind = knnDistance_fromKnown(knownPerMes,logNew,nPsample) #Find the nthPercentile of the rawLpp of these indicies nearPeriodLpp=knownTrrawLpp[ind] LppNPercentile = np.percentile(nearPeriodLpp,nPercentil) NormLppTransitMetric=rawTLpp/LppNPercentile return NormLppTransitMetric def lpp_onetransit(tcedata,mapInfo,ntransit): """ Chop down the full time series to one orbital period. Then gather the lpp value for that one transit. """ startTime=tcedata.time[0]+ntransit*tcedata.period endTime=tcedata.time[0]+(ntransit+1)*tcedata.period + 3/24.0 #A few cadences of overlap want=(tcedata.time>=startTime) & (tcedata.time<=endTime) newtime=tcedata.time[want] newflux=tcedata.flux[want] nExpCad=(tcedata.time[-1]-tcedata.time[0])/tcedata.period if len(newtime>nExpCad*0.75): onetransit=copy.deepcopy(tcedata) onetransit.time=newtime onetransit.flux=newflux normTLpp, rawTLpp, transformedTr=computeLPPTransitMetric(onetransit,mapInfo) else: normTLpp=np.nan rawTLpp=np.nan return normTLpp,rawTLpp def lpp_averageIndivTransit(tcedata,mapInfo): """ Create the loop over individual transits and return array normalized lpp values, mean and std. Input TCE object and mapInfo object. It is unclear that this individual transit approach separates out several new false positives. It probably would require retuning for low SNR signals. """ length=tcedata.time[-1]-tcedata.time[0] ntransits=int(np.floor(length/tcedata.period)) lppNorms=np.ones(ntransits) lppRaws=np.ones(ntransits) nExpCad=(tcedata.time[-1]-tcedata.time[0])/tcedata.period for i in range(ntransits): lppNorms[i],lppRaws[i] = lpp_onetransit(tcedata,mapInfo,i) lppMed=np.nanmedian(lppNorms) lppStd=np.nanstd(lppNorms) return lppNorms,lppMed, lppStd, ntransits

28.627986

92

0.66762

from __future__ import division import numpy as np from sklearn.neighbors import NearestNeighbors from lpproj import LocalityPreservingProjection import copy def computeLPPTransitMetric(data,mapInfo): binFlux, binPhase=foldBinLightCurve(data,mapInfo.ntrfr,mapInfo.npts) rawTLpp,transformedTransit=computeRawLPPTransitMetric(binFlux,mapInfo) normTLpp=periodNormalLPPTransitMetric(rawTLpp,np.array([data.period,data.mes]), mapInfo) return normTLpp,rawTLpp,transformedTransit def runningMedian(t,y,dt,runt): newy=np.zeros(len(y)) newt=np.zeros(len(y)) srt = np.argsort(t) newt = t[srt] newy = y[srt] runy=[] for i in range(len(runt)): tmp=[] for j in range(len(newt)): if (newt[j] >= (runt[i]-dt)) and (newt[j] <= (runt[i]+dt)): tmp.append(newy[j]) if np.isnan(np.nanmedian(np.array(tmp))) : runy.append(0) else: runy.append(np.nanmedian(np.array(tmp))) return(list(runt),runy) def foldBinLightCurve (data, ntrfr, npts): phaselc =np.mod((data.time-(data.tzero-0.5*data.period))/data.period,1) flux=data.flux mes=data.mes if ~np.isnan(data.dur) & (data.dur >0): transit_dur = data.dur else: transit_dur = 0.2 * data.period/24. transit_fr=transit_dur/24./data.period if (transit_fr * ntrfr) > 0.5 : transit_fr = 0.5/ntrfr binover=1.3 if mes <= 20: binover=-(1/8.0)*mes + 3.8 endfr = .03 midfr= .11 a = np.concatenate((np.arange(endfr,.5-midfr,1/npts) , \ np.arange((0.5+midfr),(1-endfr),1/npts)), axis=None) ovsamp=4.0 b_num=41 b =np.linspace((0.5-ntrfr*transit_fr),(0.5+ntrfr*transit_fr),b_num) [runta,runya] = runningMedian(phaselc,flux,binover/npts,a) [runtb,runyb] = runningMedian(phaselc,flux,\ (binover*ovsamp*ntrfr*transit_fr)/npts,b) runymess=np.array(runya + runyb) runtmess = np.array(runta + runtb) srt=np.argsort(runtmess) runy=runymess[srt] runt=runtmess[srt] scale = -1*np.min(runyb) if scale != 0: scaledFlux=runy/scale else: scaledFlux=runy binnedFlux=scaledFlux phasebins=runt return binnedFlux,phasebins def computeRawLPPTransitMetric(binFlux,mapInfo): Yorig=mapInfo.YmapMapped lpp=LocalityPreservingProjection(n_components=mapInfo.n_dim) lpp.projection_=mapInfo.YmapM normBinFlux=binFlux-mapInfo.YmapMean inputY=lpp.transform(normBinFlux.reshape(1,-1)) knownTransitsY=Yorig[mapInfo.knnGood,:] dist,ind = knnDistance_fromKnown(knownTransitsY,inputY,mapInfo.knn) rawLppTrMetric=np.mean(dist) return rawLppTrMetric,inputY def knnDistance_fromKnown(knownTransits,new,knn): t(knn), algorithm='kd_tree', p=2) nbrs.fit(knownTransits) distances,indices = nbrs.kneighbors(new) return distances, indices def periodNormalLPPTransitMetric(rawTLpp,newPerMes, mapInfo): knownTrPeriods=mapInfo.mappedPeriods[mapInfo.knnGood] knownTrMes=mapInfo.mappedMes[mapInfo.knnGood] knownTrrawLpp=mapInfo.dymeans[mapInfo.knnGood] nPercentil=mapInfo.nPercentil nPsample=mapInfo.nPsample logPeriods=np.log10(knownTrPeriods) logMes=np.log10(knownTrMes) knownPerMes=np.stack((logPeriods, logMes), axis=-1) np.shape(knownPerMes) logNew=np.log10(newPerMes).reshape(1,-1) dist,ind = knnDistance_fromKnown(knownPerMes,logNew,nPsample) nearPeriodLpp=knownTrrawLpp[ind] LppNPercentile = np.percentile(nearPeriodLpp,nPercentil) NormLppTransitMetric=rawTLpp/LppNPercentile return NormLppTransitMetric def lpp_onetransit(tcedata,mapInfo,ntransit): startTime=tcedata.time[0]+ntransit*tcedata.period endTime=tcedata.time[0]+(ntransit+1)*tcedata.period + 3/24.0 want=(tcedata.time>=startTime) & (tcedata.time<=endTime) newtime=tcedata.time[want] newflux=tcedata.flux[want] nExpCad=(tcedata.time[-1]-tcedata.time[0])/tcedata.period if len(newtime>nExpCad*0.75): onetransit=copy.deepcopy(tcedata) onetransit.time=newtime onetransit.flux=newflux normTLpp, rawTLpp, transformedTr=computeLPPTransitMetric(onetransit,mapInfo) else: normTLpp=np.nan rawTLpp=np.nan return normTLpp,rawTLpp def lpp_averageIndivTransit(tcedata,mapInfo): length=tcedata.time[-1]-tcedata.time[0] ntransits=int(np.floor(length/tcedata.period)) lppNorms=np.ones(ntransits) lppRaws=np.ones(ntransits) nExpCad=(tcedata.time[-1]-tcedata.time[0])/tcedata.period for i in range(ntransits): lppNorms[i],lppRaws[i] = lpp_onetransit(tcedata,mapInfo,i) lppMed=np.nanmedian(lppNorms) lppStd=np.nanstd(lppNorms) return lppNorms,lppMed, lppStd, ntransits

true

790820f81db747211f4813bf9cfafdfa5ae0200d

33,180

py

Python

tests/cases/item_test.py

RemiCecchinato/girder

455d5c60d59112b65b45daf51c2d2ccda2e84a9a

[ "Apache-2.0" ]

null

tests/cases/item_test.py

RemiCecchinato/girder

455d5c60d59112b65b45daf51c2d2ccda2e84a9a

[ "Apache-2.0" ]

null

tests/cases/item_test.py

RemiCecchinato/girder

455d5c60d59112b65b45daf51c2d2ccda2e84a9a

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import os import io import json import shutil import six import zipfile from .. import base from girder.constants import AccessType from girder.models.assetstore import Assetstore from girder.models.folder import Folder from girder.models.item import Item from girder.models.token import Token from girder.models.user import User def setUpModule(): base.startServer() def tearDownModule(): base.stopServer() class ItemTestCase(base.TestCase): def setUp(self): base.TestCase.setUp(self) # Create a set of users so we can have some folders. self.users = [User().createUser( 'usr%s' % num, 'passwd', 'tst', 'usr', 'u%s@u.com' % num) for num in [0, 1]] folders = Folder().childFolders(self.users[0], 'user', user=self.users[0]) for folder in folders: if folder['name'] == 'Public': self.publicFolder = folder else: self.privateFolder = folder self.assetstore = Assetstore().getCurrent() root = self.assetstore['root'] # Clean out the test assetstore on disk shutil.rmtree(root) # First clean out the temp directory tmpdir = os.path.join(root, 'temp') if os.path.isdir(tmpdir): for tempname in os.listdir(tmpdir): os.remove(os.path.join(tmpdir, tempname)) def _createItem(self, parentId, name, description, user): params = { 'name': name, 'description': description, 'folderId': parentId } resp = self.request(path='/item', method='POST', params=params, user=user) self.assertStatusOk(resp) assert 'meta' in resp.json return resp.json def _testUploadFileToItem(self, item, name, user, contents): """ Uploads a non-empty file to the server. """ # Initialize the upload resp = self.request( path='/file', method='POST', user=user, params={ 'parentType': 'item', 'parentId': item['_id'], 'name': name, 'size': len(contents) }) self.assertStatusOk(resp) uploadId = resp.json['_id'] # Send the first chunk resp = self.request( path='/file/chunk', method='POST', body=contents, user=user, params={ 'uploadId': uploadId }, type='application/octet-stream') self.assertStatusOk(resp) def _testDownloadSingleFileItem(self, item, user, contents): """ Downloads a single-file item from the server :param item: The item to download. :type item: dict :param contents: The expected contents. :type contents: str """ resp = self.request(path='/item/%s/download' % item['_id'], method='GET', user=user, isJson=False) self.assertStatusOk(resp) self.assertEqual(contents, self.getBody(resp)) self.assertEqual(resp.headers['Content-Disposition'], 'attachment; filename="file_1"') # Test downloading the item with contentDisposition=inline. params = {'contentDisposition': 'inline'} resp = self.request(path='/item/%s/download' % item['_id'], method='GET', user=user, isJson=False, params=params) self.assertStatusOk(resp) self.assertEqual(contents, self.getBody(resp)) self.assertEqual(resp.headers['Content-Disposition'], 'inline; filename="file_1"') # Test downloading with an offset resp = self.request(path='/item/%s/download' % item['_id'], method='GET', user=user, isJson=False, params={'offset': 1}) self.assertStatus(resp, 206) self.assertEqual(contents[1:], self.getBody(resp)) def _testDownloadMultiFileItem(self, item, user, contents, format=None): params = None if format: params = {'format': format} resp = self.request(path='/item/%s/download' % item['_id'], method='GET', user=user, isJson=False, params=params) self.assertStatusOk(resp) zipFile = zipfile.ZipFile(io.BytesIO(self.getBody(resp, text=False)), 'r') prefix = os.path.split(zipFile.namelist()[0])[0] expectedZip = {} for name in contents: expectedZip[os.path.join(prefix, name)] = contents[name] self.assertHasKeys(expectedZip, zipFile.namelist()) self.assertHasKeys(zipFile.namelist(), expectedZip) for name in zipFile.namelist(): expected = expectedZip[name] if not isinstance(expected, six.binary_type): expected = expected.encode('utf8') self.assertEqual(expected, zipFile.read(name)) def testLegacyItems(self): folder = Folder().createFolder( parent=self.users[0], parentType='user', creator=self.users[0], name='New Folder') item = Item().createItem( name='LegacyItem', creator=self.users[0], folder=folder) del item['meta'] item = Item().save(item) assert 'meta' not in item item = Item().load(item['_id'], user=self.users[0]) assert 'meta' in item def testItemDownloadAndChildren(self): curItem = self._createItem(self.publicFolder['_id'], 'test_for_download', 'fake description', self.users[0]) self._testUploadFileToItem(curItem, 'file_1', self.users[0], 'foobar') self._testDownloadSingleFileItem(curItem, self.users[0], 'foobar') self._testDownloadMultiFileItem(curItem, self.users[0], {'file_1': 'foobar'}, format='zip') self._testUploadFileToItem(curItem, 'file_2', self.users[0], 'foobz') resp = self.request(path='/item/%s/files' % curItem['_id'], method='GET', user=self.users[0]) self.assertStatusOk(resp) self.assertEqual(resp.json[0]['name'], 'file_1') self.assertEqual(resp.json[1]['name'], 'file_2') self.assertEqual(resp.json[0]['size'], 6) self.assertEqual(resp.json[1]['size'], 5) self._testDownloadMultiFileItem(curItem, self.users[0], {'file_1': 'foobar', 'file_2': 'foobz'}) def testItemCrud(self): """ Test Create, Read, Update, and Delete of items. """ self.ensureRequiredParams( path='/item', method='POST', required=('folderId',), user=self.users[1]) # Attempt to create an item without write permission, should fail params = { 'name': ' ', 'description': ' a description ', 'folderId': self.publicFolder['_id'] } resp = self.request(path='/item', method='POST', params=params, user=self.users[1]) self.assertStatus(resp, 403) # Shouldn't be allowed to have an empty name resp = self.request(path='/item', method='POST', params=params, user=self.users[0]) self.assertValidationError(resp, 'name') # Actually create the item in user 0's private folder params['name'] = ' my item name' params['folderId'] = self.privateFolder['_id'] resp = self.request(path='/item', method='POST', params=params, user=self.users[0]) self.assertStatusOk(resp) item = resp.json self.assertEqual(item['name'], params['name'].strip()) self.assertEqual(item['description'], params['description'].strip()) # User 1 should not be able to see the item via find by folderId params = { 'folderId': self.privateFolder['_id'] } resp = self.request(path='/item', method='GET', user=self.users[1], params=params) self.assertStatus(resp, 403) # Or by just requesting the item itself by ID resp = self.request(path='/item/%s' % str(item['_id']), method='GET', user=self.users[1]) self.assertStatus(resp, 403) # User 0 should be able to see the item resp = self.request(path='/item/%s' % str(item['_id']), method='GET', user=self.users[0]) self.assertStatusOk(resp) self.assertEqual(resp.json['_id'], item['_id']) self.assertEqual(resp.json['_modelType'], 'item') # Also from the children call resp = self.request(path='/item', method='GET', user=self.users[0], params=params) self.assertStatusOk(resp) self.assertEqual(resp.json[0]['_id'], item['_id']) # Test finding the item using a text string with and without a folderId params['text'] = 'my item name' resp = self.request(path='/item', method='GET', user=self.users[0], params=params) self.assertStatusOk(resp) self.assertEqual(resp.json[0]['_id'], item['_id']) del params['folderId'] resp = self.request(path='/item', method='GET', user=self.users[0], params=params) self.assertStatusOk(resp) self.assertEqual(resp.json[0]['_id'], item['_id']) # A limit should work params['limit'] = 1 resp = self.request(path='/item', method='GET', user=self.users[0], params=params) self.assertStatusOk(resp) self.assertEqual(resp.json[0]['_id'], item['_id']) # An offset should give us nothing params['offset'] = 1 resp = self.request(path='/item', method='GET', user=self.users[0], params=params) self.assertStatusOk(resp) self.assertEqual(len(resp.json), 0) # Finding should fail with no parameters resp = self.request(path='/item', method='GET', user=self.users[0], params={}) self.assertStatus(resp, 400) self.assertEqual(resp.json['message'], 'Invalid search mode.') # Test update of the item params = { 'name': 'changed name', 'description': 'new description' } resp = self.request(path='/item/%s' % item['_id'], method='PUT', params=params, user=self.users[0]) self.assertStatusOk(resp) self.assertEqual(resp.json['name'], params['name']) self.assertEqual(resp.json['description'], params['description']) # Test moving an item to the public folder item = Item().load(item['_id'], force=True) self.assertFalse(Item().hasAccess(item)) resp = self.request(path='/item/%s' % item['_id'], method='PUT', user=self.users[0], params={ 'folderId': self.publicFolder['_id']}) self.assertStatusOk(resp) item = Item().load(resp.json['_id'], force=True) self.assertTrue(Item().hasAccess(item)) # Move should fail if we don't have write permission on the # destination folder self.publicFolder = Folder().setUserAccess( self.publicFolder, self.users[1], AccessType.WRITE, save=True) resp = self.request(path='/item/%s' % item['_id'], method='PUT', user=self.users[1], params={ 'folderId': self.privateFolder['_id']}) self.assertStatus(resp, 403) self.assertTrue(resp.json['message'].startswith( 'Write access denied for folder')) # Try to update/PUT without an id resp = self.request(path='/item/', method='PUT', params=params, user=self.users[0]) self.assertStatus(resp, 400) # Try a bad endpoint (should 400) resp = self.request(path='/item/%s/blurgh' % item['_id'], method='GET', user=self.users[1]) self.assertStatus(resp, 400) # Try delete with no ID (should 400) resp = self.request(path='/item/', method='DELETE', user=self.users[1]) self.assertStatus(resp, 400) # User 1 should not be able to delete the item with read access self.publicFolder = Folder().setUserAccess( self.publicFolder, self.users[1], AccessType.READ, save=True) resp = self.request(path='/item/%s' % str(item['_id']), method='DELETE', user=self.users[1]) self.assertStatus(resp, 403) # User 1 should be able to delete the item with write access self.publicFolder = Folder().setUserAccess( self.publicFolder, self.users[1], AccessType.WRITE, save=True) resp = self.request(path='/item/%s' % str(item['_id']), method='DELETE', user=self.users[1]) self.assertStatusOk(resp) # Verify that the item is deleted item = Item().load(item['_id']) self.assertEqual(item, None) def testItemMetadataDirect(self): params = { 'name': 'item with metadata via POST', 'description': ' a description ', 'folderId': self.privateFolder['_id'], 'metadata': 'not JSON' } resp = self.request( path='/item', method='POST', params=params, user=self.users[0]) self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Parameter metadata must be valid JSON.') # Add some metadata metadata = { 'foo': 'bar', 'test': 2 } params['metadata'] = json.dumps(metadata) resp = self.request( path='/item', method='POST', params=params, user=self.users[0]) self.assertStatusOk(resp) item = resp.json self.assertEqual(item['meta']['foo'], metadata['foo']) self.assertEqual(item['meta']['test'], metadata['test']) metadata = { 'foo': None, 'test': 3, 'bar': 'baz' } resp = self.request( path='/item/{_id}'.format(**item), method='PUT', user=self.users[0], params={'metadata': json.dumps(metadata)} ) self.assertStatusOk(resp) item = resp.json self.assertNotHasKeys(item['meta'], ['foo']) self.assertEqual(item['meta']['test'], metadata['test']) self.assertEqual(item['meta']['bar'], metadata['bar']) def testItemMetadataCrud(self): """ Test CRUD of metadata. """ # Create an item params = { 'name': 'item with metadata', 'description': ' a description ', 'folderId': self.privateFolder['_id'] } resp = self.request(path='/item', method='POST', params=params, user=self.users[0]) self.assertStatusOk(resp) item = resp.json # Try to delete metadata from an item that doesn't have any set on it # yet. resp = self.request(path='/item/%s/metadata' % (item['_id']), method='DELETE', user=self.users[0], body=json.dumps(['foobar']), type='application/json') item = resp.json self.assertStatusOk(resp) self.assertEqual(item['meta'], {}) # Add some metadata metadata = { 'foo': 'bar', 'test': 2 } resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') item = resp.json self.assertEqual(item['meta']['foo'], metadata['foo']) self.assertEqual(item['meta']['test'], metadata['test']) # Test invalid JSON constants body = '{"key": {"foo": Infinity}}' resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=body, type='application/json') self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Error: "Infinity" is not valid JSON.') # Edit and remove metadata metadata['test'] = None metadata['foo'] = 'baz' resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') item = resp.json self.assertEqual(item['meta']['foo'], metadata['foo']) self.assertNotHasKeys(item['meta'], ['test']) # Test insertion of null values metadata['nullVal'] = None resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), params={'allowNull': True}, type='application/json') item = resp.json self.assertEqual(item['meta']['nullVal'], None) # Adding an unrelated key should not affect existing keys del metadata['nullVal'] metadata['other'] = 'macguffin' resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') item = resp.json self.assertEqual(item['meta']['other'], metadata['other']) self.assertEqual(item['meta']['nullVal'], None) # Test metadata deletion resp = self.request(path='/item/%s/metadata' % item['_id'], method='DELETE', user=self.users[0], body=json.dumps(['other']), type='application/json') item = resp.json self.assertNotHasKeys(item['meta'], ['other']) # Error when deletion field names contain a period. resp = self.request(path='/item/%s/metadata' % item['_id'], method='DELETE', user=self.users[0], body=json.dumps(['foo', 'foo.bar']), type='application/json') self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Invalid key foo.bar: keys must not contain the "." character.') # Error when deletion field names begin with a dollar-sign. resp = self.request(path='/item/%s/metadata' % item['_id'], method='DELETE', user=self.users[0], body=json.dumps(['foo', '$bar']), type='application/json') self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Invalid key $bar: keys must not start with the "$" character.') # Make sure metadata cannot be added with invalid JSON metadata = { 'test': 'allowed' } resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata).replace('"', "'"), type='application/json') self.assertStatus(resp, 400) self.assertEqual(resp.json['message'], 'Invalid JSON passed in request body.') # Make sure metadata cannot be added if there is a period in the key # name metadata = { 'foo.bar': 'notallowed' } resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Invalid key foo.bar: keys must not contain the "." character.') # Make sure metadata cannot be added if the key begins with a # dollar sign metadata = { '$foobar': 'alsonotallowed' } resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Invalid key $foobar: keys must not start with the "$" character.') # Make sure metadata cannot be added with a blank key metadata = { '': 'stillnotallowed' } resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Key names must not be empty.') def testItemFiltering(self): """ Test filtering private metadata from items. """ # Create an item params = { 'name': 'item with metadata', 'description': ' a description ', 'folderId': self.privateFolder['_id'] } resp = self.request(path='/item', method='POST', params=params, user=self.users[0]) self.assertStatusOk(resp) # get the item object from the database item = Item().load(resp.json['_id'], force=True) # set a private property item['private'] = 'very secret metadata' item = Item().save(item) # get the item from the rest api resp = self.request(path='/item/%s' % str(item['_id']), method='GET', user=self.users[0]) self.assertStatusOk(resp) # assert that the private data is not included self.assertNotHasKeys(resp.json, ['private']) def testPathToRoot(self): firstChildName = 'firstChild' firstChildDesc = 'firstDesc' secondChildName = 'secondChild' secondChildDesc = 'secondDesc' firstChild = Folder().createFolder( self.publicFolder, firstChildName, firstChildDesc, creator=self.users[0]) secondChild = Folder().createFolder( firstChild, secondChildName, secondChildDesc, creator=self.users[0]) baseItem = Item().createItem('blah', self.users[0], secondChild, 'foo') resp = self.request(path='/item/%s/rootpath' % baseItem['_id'], method='GET') self.assertStatusOk(resp) pathToRoot = resp.json self.assertEqual(pathToRoot[0]['type'], 'user') self.assertEqual(pathToRoot[0]['object']['login'], self.users[0]['login']) self.assertEqual(pathToRoot[1]['type'], 'folder') self.assertEqual(pathToRoot[1]['object']['name'], self.publicFolder['name']) self.assertEqual(pathToRoot[2]['type'], 'folder') self.assertEqual(pathToRoot[2]['object']['name'], firstChild['name']) self.assertEqual(pathToRoot[3]['type'], 'folder') self.assertEqual(pathToRoot[3]['object']['name'], secondChild['name']) def testLazyFieldComputation(self): """ Demonstrate that an item that is saved in the database without derived fields (like lowerName or baseParentId) get those values computed at load() time. """ item = Item().createItem('My Item Name', creator=self.users[0], folder=self.publicFolder) self.assertEqual(item['lowerName'], 'my item name') self.assertEqual(item['baseParentId'], self.users[0]['_id']) # Force the item to be saved without lowerName and baseParentType fields del item['lowerName'] del item['baseParentType'] item = Item().save(item, validate=False) item = Item().find({'_id': item['_id']})[0] self.assertNotHasKeys(item, ('lowerName', 'baseParentType')) # Now ensure that calling load() actually populates those fields and # saves the results persistently Item().load(item['_id'], force=True) item = Item().find({'_id': item['_id']})[0] self.assertHasKeys(item, ('lowerName', 'baseParentType')) self.assertEqual(item['lowerName'], 'my item name') self.assertEqual(item['baseParentType'], 'user') self.assertEqual(item['baseParentId'], self.users[0]['_id']) # Also test that this works for a duplicate item, such that the # automatically renamed item still has the correct lowerName, and a # None description is changed to an empty string. item = Item().createItem( 'My Item Name', creator=self.users[0], folder=self.publicFolder, description=None) # test if non-strings are coerced self.assertEqual(item['description'], '') item['description'] = 1 item = Item().save(item) item = Item().findOne({'_id': item['_id']}) self.assertEqual(item['description'], '1') # test if just missing lowerName is corrected. self.assertEqual(item['lowerName'], 'my item name (1)') del item['lowerName'] item = Item().save(item, validate=False) item = Item().findOne({'_id': item['_id']}) self.assertNotHasKeys(item, ('lowerName', )) Item().load(item['_id'], force=True) item = Item().findOne({'_id': item['_id']}) self.assertHasKeys(item, ('lowerName', )) self.assertEqual(item['lowerName'], 'my item name (1)') def testParentsToRoot(self): """ Demonstrate that forcing parentsToRoot will cause it to skip the filtering process. """ item = Item().createItem('My Item Name', creator=self.users[0], folder=self.publicFolder) parents = Item().parentsToRoot(item, force=True) for parent in parents: self.assertNotIn('_accessLevel', parent['object']) parents = Item().parentsToRoot(item) for parent in parents: self.assertIn('_accessLevel', parent['object']) def testItemCopy(self): origItem = self._createItem(self.publicFolder['_id'], 'test_for_copy', 'fake description', self.users[0]) # Add metadata and files, since we want to make sure those get copied metadata = { 'foo': 'value1', 'test': 2 } resp = self.request( path='/item/%s/metadata' % origItem['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') self.assertStatusOk(resp) self._testUploadFileToItem(origItem, 'file_1', self.users[0], 'foobar') self._testUploadFileToItem(origItem, 'file_2', self.users[0], 'foobz') # Also upload a link params = { 'parentType': 'item', 'parentId': origItem['_id'], 'name': 'link_file', 'linkUrl': 'http://www.google.com' } resp = self.request(path='/file', method='POST', user=self.users[0], params=params) self.assertStatusOk(resp) # Copy to a new item. It will be in the same folder, but we want a # different name. params = { 'name': 'copied_item' } resp = self.request(path='/item/%s/copy' % origItem['_id'], method='POST', user=self.users[0], params=params) self.assertStatusOk(resp) # Make sure size was returned correctly self.assertEqual(resp.json['size'], 11) # Now ask for the new item explicitly and check its metadata self.request(path='/item/%s' % resp.json['_id'], user=self.users[0], type='application/json') self.assertStatusOk(resp) newItem = resp.json self.assertEqual(newItem['name'], 'copied_item') self.assertEqual(newItem['meta']['foo'], metadata['foo']) self.assertEqual(newItem['meta']['test'], metadata['test']) # Check if we can download the files from the new item resp = self.request(path='/item/%s/files' % newItem['_id'], method='GET', user=self.users[0]) self.assertStatusOk(resp) newFiles = resp.json self.assertEqual(newFiles[0]['name'], 'file_1') self.assertEqual(newFiles[1]['name'], 'file_2') self.assertEqual(newFiles[2]['name'], 'link_file') self.assertEqual(newFiles[0]['size'], 6) self.assertEqual(newFiles[1]['size'], 5) self._testDownloadMultiFileItem(newItem, self.users[0], {'file_1': 'foobar', 'file_2': 'foobz', 'link_file': 'http://www.google.com'}) # Check to make sure the original item is still present resp = self.request(path='/item', method='GET', user=self.users[0], params={'folderId': self.publicFolder['_id'], 'text': 'test_for_copy'}) self.assertStatusOk(resp) self.assertEqual(origItem['_id'], resp.json[0]['_id']) # Check to make sure the new item is still present resp = self.request(path='/item', method='GET', user=self.users[0], params={'folderId': self.publicFolder['_id'], 'text': 'copied_item'}) self.assertStatusOk(resp) self.assertEqual(newItem['_id'], resp.json[0]['_id']) # Check that the provenance tag correctly points back # to the original item self.assertEqual(newItem['copyOfItem'], origItem['_id']) # Check if we can download the files from the old item and that they # are distinct from the files in the original item resp = self.request(path='/item/%s/files' % origItem['_id'], method='GET', user=self.users[0]) self.assertStatusOk(resp) origFiles = resp.json self._testDownloadMultiFileItem(origItem, self.users[0], {'file_1': 'foobar', 'file_2': 'foobz', 'link_file': 'http://www.google.com'}) for index, file in enumerate(origFiles): self.assertNotEqual(origFiles[index]['_id'], newFiles[index]['_id']) def testCookieAuth(self): """ We make sure a cookie is sufficient for authentication for the item download endpoint. Also, while we're at it, we make sure it's not sufficient for other endpoints. """ item = self._createItem(self.privateFolder['_id'], 'cookie_auth_download', '', self.users[0]) self._testUploadFileToItem(item, 'file', self.users[0], 'foo') token = Token().createToken(self.users[0]) cookie = 'girderToken=%s' % token['_id'] # We should be able to download a private item using a cookie token resp = self.request(path='/item/%s/download' % item['_id'], isJson=False, cookie=cookie) self.assertStatusOk(resp) self.assertEqual(self.getBody(resp), 'foo') # We should not be able to call GET /item/:id with a cookie token resp = self.request(path='/item/%s' % item['_id'], cookie=cookie) self.assertStatus(resp, 401) # Make sure the cookie has to be a valid token resp = self.request(path='/item/%s/download' % item['_id'], cookie='girderToken=invalid_token') self.assertStatus(resp, 401) def testReuseExisting(self): item1 = Item().createItem('to be reused', creator=self.users[0], folder=self.publicFolder) item2 = Item().createItem('to be reused', creator=self.users[0], folder=self.publicFolder) item3 = Item().createItem( 'to be reused', creator=self.users[0], folder=self.publicFolder, reuseExisting=True) self.assertNotEqual(item1['_id'], item2['_id']) self.assertEqual(item1['_id'], item3['_id']) self.assertEqual(item2['name'], 'to be reused (1)') self.assertEqual(item3['name'], 'to be reused') def testUpdateDuplicatedName(self): item1 = Item().createItem('foo', creator=self.users[0], folder=self.publicFolder) item2 = Item().createItem('bar', creator=self.users[0], folder=self.publicFolder) item2['name'] = 'foo' Item().save(item2, validate=False) self.assertEqual(item2['name'], 'foo') item1['size'] = 3 Item().save(item1) self.assertEqual(item1['name'], 'foo')

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import os import io import json import shutil import six import zipfile from .. import base from girder.constants import AccessType from girder.models.assetstore import Assetstore from girder.models.folder import Folder from girder.models.item import Item from girder.models.token import Token from girder.models.user import User def setUpModule(): base.startServer() def tearDownModule(): base.stopServer() class ItemTestCase(base.TestCase): def setUp(self): base.TestCase.setUp(self) self.users = [User().createUser( 'usr%s' % num, 'passwd', 'tst', 'usr', 'u%s@u.com' % num) for num in [0, 1]] folders = Folder().childFolders(self.users[0], 'user', user=self.users[0]) for folder in folders: if folder['name'] == 'Public': self.publicFolder = folder else: self.privateFolder = folder self.assetstore = Assetstore().getCurrent() root = self.assetstore['root'] shutil.rmtree(root) tmpdir = os.path.join(root, 'temp') if os.path.isdir(tmpdir): for tempname in os.listdir(tmpdir): os.remove(os.path.join(tmpdir, tempname)) def _createItem(self, parentId, name, description, user): params = { 'name': name, 'description': description, 'folderId': parentId } resp = self.request(path='/item', method='POST', params=params, user=user) self.assertStatusOk(resp) assert 'meta' in resp.json return resp.json def _testUploadFileToItem(self, item, name, user, contents): resp = self.request( path='/file', method='POST', user=user, params={ 'parentType': 'item', 'parentId': item['_id'], 'name': name, 'size': len(contents) }) self.assertStatusOk(resp) uploadId = resp.json['_id'] resp = self.request( path='/file/chunk', method='POST', body=contents, user=user, params={ 'uploadId': uploadId }, type='application/octet-stream') self.assertStatusOk(resp) def _testDownloadSingleFileItem(self, item, user, contents): resp = self.request(path='/item/%s/download' % item['_id'], method='GET', user=user, isJson=False) self.assertStatusOk(resp) self.assertEqual(contents, self.getBody(resp)) self.assertEqual(resp.headers['Content-Disposition'], 'attachment; filename="file_1"') params = {'contentDisposition': 'inline'} resp = self.request(path='/item/%s/download' % item['_id'], method='GET', user=user, isJson=False, params=params) self.assertStatusOk(resp) self.assertEqual(contents, self.getBody(resp)) self.assertEqual(resp.headers['Content-Disposition'], 'inline; filename="file_1"') resp = self.request(path='/item/%s/download' % item['_id'], method='GET', user=user, isJson=False, params={'offset': 1}) self.assertStatus(resp, 206) self.assertEqual(contents[1:], self.getBody(resp)) def _testDownloadMultiFileItem(self, item, user, contents, format=None): params = None if format: params = {'format': format} resp = self.request(path='/item/%s/download' % item['_id'], method='GET', user=user, isJson=False, params=params) self.assertStatusOk(resp) zipFile = zipfile.ZipFile(io.BytesIO(self.getBody(resp, text=False)), 'r') prefix = os.path.split(zipFile.namelist()[0])[0] expectedZip = {} for name in contents: expectedZip[os.path.join(prefix, name)] = contents[name] self.assertHasKeys(expectedZip, zipFile.namelist()) self.assertHasKeys(zipFile.namelist(), expectedZip) for name in zipFile.namelist(): expected = expectedZip[name] if not isinstance(expected, six.binary_type): expected = expected.encode('utf8') self.assertEqual(expected, zipFile.read(name)) def testLegacyItems(self): folder = Folder().createFolder( parent=self.users[0], parentType='user', creator=self.users[0], name='New Folder') item = Item().createItem( name='LegacyItem', creator=self.users[0], folder=folder) del item['meta'] item = Item().save(item) assert 'meta' not in item item = Item().load(item['_id'], user=self.users[0]) assert 'meta' in item def testItemDownloadAndChildren(self): curItem = self._createItem(self.publicFolder['_id'], 'test_for_download', 'fake description', self.users[0]) self._testUploadFileToItem(curItem, 'file_1', self.users[0], 'foobar') self._testDownloadSingleFileItem(curItem, self.users[0], 'foobar') self._testDownloadMultiFileItem(curItem, self.users[0], {'file_1': 'foobar'}, format='zip') self._testUploadFileToItem(curItem, 'file_2', self.users[0], 'foobz') resp = self.request(path='/item/%s/files' % curItem['_id'], method='GET', user=self.users[0]) self.assertStatusOk(resp) self.assertEqual(resp.json[0]['name'], 'file_1') self.assertEqual(resp.json[1]['name'], 'file_2') self.assertEqual(resp.json[0]['size'], 6) self.assertEqual(resp.json[1]['size'], 5) self._testDownloadMultiFileItem(curItem, self.users[0], {'file_1': 'foobar', 'file_2': 'foobz'}) def testItemCrud(self): self.ensureRequiredParams( path='/item', method='POST', required=('folderId',), user=self.users[1]) params = { 'name': ' ', 'description': ' a description ', 'folderId': self.publicFolder['_id'] } resp = self.request(path='/item', method='POST', params=params, user=self.users[1]) self.assertStatus(resp, 403) resp = self.request(path='/item', method='POST', params=params, user=self.users[0]) self.assertValidationError(resp, 'name') # Actually create the item in user 0's private folder params['name'] = ' my item name' params['folderId'] = self.privateFolder['_id'] resp = self.request(path='/item', method='POST', params=params, user=self.users[0]) self.assertStatusOk(resp) item = resp.json self.assertEqual(item['name'], params['name'].strip()) self.assertEqual(item['description'], params['description'].strip()) params = { 'folderId': self.privateFolder['_id'] } resp = self.request(path='/item', method='GET', user=self.users[1], params=params) self.assertStatus(resp, 403) resp = self.request(path='/item/%s' % str(item['_id']), method='GET', user=self.users[1]) self.assertStatus(resp, 403) resp = self.request(path='/item/%s' % str(item['_id']), method='GET', user=self.users[0]) self.assertStatusOk(resp) self.assertEqual(resp.json['_id'], item['_id']) self.assertEqual(resp.json['_modelType'], 'item') resp = self.request(path='/item', method='GET', user=self.users[0], params=params) self.assertStatusOk(resp) self.assertEqual(resp.json[0]['_id'], item['_id']) params['text'] = 'my item name' resp = self.request(path='/item', method='GET', user=self.users[0], params=params) self.assertStatusOk(resp) self.assertEqual(resp.json[0]['_id'], item['_id']) del params['folderId'] resp = self.request(path='/item', method='GET', user=self.users[0], params=params) self.assertStatusOk(resp) self.assertEqual(resp.json[0]['_id'], item['_id']) params['limit'] = 1 resp = self.request(path='/item', method='GET', user=self.users[0], params=params) self.assertStatusOk(resp) self.assertEqual(resp.json[0]['_id'], item['_id']) params['offset'] = 1 resp = self.request(path='/item', method='GET', user=self.users[0], params=params) self.assertStatusOk(resp) self.assertEqual(len(resp.json), 0) resp = self.request(path='/item', method='GET', user=self.users[0], params={}) self.assertStatus(resp, 400) self.assertEqual(resp.json['message'], 'Invalid search mode.') params = { 'name': 'changed name', 'description': 'new description' } resp = self.request(path='/item/%s' % item['_id'], method='PUT', params=params, user=self.users[0]) self.assertStatusOk(resp) self.assertEqual(resp.json['name'], params['name']) self.assertEqual(resp.json['description'], params['description']) item = Item().load(item['_id'], force=True) self.assertFalse(Item().hasAccess(item)) resp = self.request(path='/item/%s' % item['_id'], method='PUT', user=self.users[0], params={ 'folderId': self.publicFolder['_id']}) self.assertStatusOk(resp) item = Item().load(resp.json['_id'], force=True) self.assertTrue(Item().hasAccess(item)) # destination folder self.publicFolder = Folder().setUserAccess( self.publicFolder, self.users[1], AccessType.WRITE, save=True) resp = self.request(path='/item/%s' % item['_id'], method='PUT', user=self.users[1], params={ 'folderId': self.privateFolder['_id']}) self.assertStatus(resp, 403) self.assertTrue(resp.json['message'].startswith( 'Write access denied for folder')) # Try to update/PUT without an id resp = self.request(path='/item/', method='PUT', params=params, user=self.users[0]) self.assertStatus(resp, 400) # Try a bad endpoint (should 400) resp = self.request(path='/item/%s/blurgh' % item['_id'], method='GET', user=self.users[1]) self.assertStatus(resp, 400) # Try delete with no ID (should 400) resp = self.request(path='/item/', method='DELETE', user=self.users[1]) self.assertStatus(resp, 400) # User 1 should not be able to delete the item with read access self.publicFolder = Folder().setUserAccess( self.publicFolder, self.users[1], AccessType.READ, save=True) resp = self.request(path='/item/%s' % str(item['_id']), method='DELETE', user=self.users[1]) self.assertStatus(resp, 403) # User 1 should be able to delete the item with write access self.publicFolder = Folder().setUserAccess( self.publicFolder, self.users[1], AccessType.WRITE, save=True) resp = self.request(path='/item/%s' % str(item['_id']), method='DELETE', user=self.users[1]) self.assertStatusOk(resp) # Verify that the item is deleted item = Item().load(item['_id']) self.assertEqual(item, None) def testItemMetadataDirect(self): params = { 'name': 'item with metadata via POST', 'description': ' a description ', 'folderId': self.privateFolder['_id'], 'metadata': 'not JSON' } resp = self.request( path='/item', method='POST', params=params, user=self.users[0]) self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Parameter metadata must be valid JSON.') # Add some metadata metadata = { 'foo': 'bar', 'test': 2 } params['metadata'] = json.dumps(metadata) resp = self.request( path='/item', method='POST', params=params, user=self.users[0]) self.assertStatusOk(resp) item = resp.json self.assertEqual(item['meta']['foo'], metadata['foo']) self.assertEqual(item['meta']['test'], metadata['test']) metadata = { 'foo': None, 'test': 3, 'bar': 'baz' } resp = self.request( path='/item/{_id}'.format(**item), method='PUT', user=self.users[0], params={'metadata': json.dumps(metadata)} ) self.assertStatusOk(resp) item = resp.json self.assertNotHasKeys(item['meta'], ['foo']) self.assertEqual(item['meta']['test'], metadata['test']) self.assertEqual(item['meta']['bar'], metadata['bar']) def testItemMetadataCrud(self): # Create an item params = { 'name': 'item with metadata', 'description': ' a description ', 'folderId': self.privateFolder['_id'] } resp = self.request(path='/item', method='POST', params=params, user=self.users[0]) self.assertStatusOk(resp) item = resp.json # Try to delete metadata from an item that doesn't have any set on it resp = self.request(path='/item/%s/metadata' % (item['_id']), method='DELETE', user=self.users[0], body=json.dumps(['foobar']), type='application/json') item = resp.json self.assertStatusOk(resp) self.assertEqual(item['meta'], {}) metadata = { 'foo': 'bar', 'test': 2 } resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') item = resp.json self.assertEqual(item['meta']['foo'], metadata['foo']) self.assertEqual(item['meta']['test'], metadata['test']) body = '{"key": {"foo": Infinity}}' resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=body, type='application/json') self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Error: "Infinity" is not valid JSON.') metadata['test'] = None metadata['foo'] = 'baz' resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') item = resp.json self.assertEqual(item['meta']['foo'], metadata['foo']) self.assertNotHasKeys(item['meta'], ['test']) metadata['nullVal'] = None resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), params={'allowNull': True}, type='application/json') item = resp.json self.assertEqual(item['meta']['nullVal'], None) del metadata['nullVal'] metadata['other'] = 'macguffin' resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') item = resp.json self.assertEqual(item['meta']['other'], metadata['other']) self.assertEqual(item['meta']['nullVal'], None) resp = self.request(path='/item/%s/metadata' % item['_id'], method='DELETE', user=self.users[0], body=json.dumps(['other']), type='application/json') item = resp.json self.assertNotHasKeys(item['meta'], ['other']) resp = self.request(path='/item/%s/metadata' % item['_id'], method='DELETE', user=self.users[0], body=json.dumps(['foo', 'foo.bar']), type='application/json') self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Invalid key foo.bar: keys must not contain the "." character.') resp = self.request(path='/item/%s/metadata' % item['_id'], method='DELETE', user=self.users[0], body=json.dumps(['foo', '$bar']), type='application/json') self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Invalid key $bar: keys must not start with the "$" character.') metadata = { 'test': 'allowed' } resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata).replace('"', "'"), type='application/json') self.assertStatus(resp, 400) self.assertEqual(resp.json['message'], 'Invalid JSON passed in request body.') # Make sure metadata cannot be added if there is a period in the key # name metadata = { 'foo.bar': 'notallowed' } resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Invalid key foo.bar: keys must not contain the "." character.') # Make sure metadata cannot be added if the key begins with a # dollar sign metadata = { '$foobar': 'alsonotallowed' } resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Invalid key $foobar: keys must not start with the "$" character.') # Make sure metadata cannot be added with a blank key metadata = { '': 'stillnotallowed' } resp = self.request(path='/item/%s/metadata' % item['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') self.assertStatus(resp, 400) self.assertEqual( resp.json['message'], 'Key names must not be empty.') def testItemFiltering(self): # Create an item params = { 'name': 'item with metadata', 'description': ' a description ', 'folderId': self.privateFolder['_id'] } resp = self.request(path='/item', method='POST', params=params, user=self.users[0]) self.assertStatusOk(resp) # get the item object from the database item = Item().load(resp.json['_id'], force=True) # set a private property item['private'] = 'very secret metadata' item = Item().save(item) # get the item from the rest api resp = self.request(path='/item/%s' % str(item['_id']), method='GET', user=self.users[0]) self.assertStatusOk(resp) # assert that the private data is not included self.assertNotHasKeys(resp.json, ['private']) def testPathToRoot(self): firstChildName = 'firstChild' firstChildDesc = 'firstDesc' secondChildName = 'secondChild' secondChildDesc = 'secondDesc' firstChild = Folder().createFolder( self.publicFolder, firstChildName, firstChildDesc, creator=self.users[0]) secondChild = Folder().createFolder( firstChild, secondChildName, secondChildDesc, creator=self.users[0]) baseItem = Item().createItem('blah', self.users[0], secondChild, 'foo') resp = self.request(path='/item/%s/rootpath' % baseItem['_id'], method='GET') self.assertStatusOk(resp) pathToRoot = resp.json self.assertEqual(pathToRoot[0]['type'], 'user') self.assertEqual(pathToRoot[0]['object']['login'], self.users[0]['login']) self.assertEqual(pathToRoot[1]['type'], 'folder') self.assertEqual(pathToRoot[1]['object']['name'], self.publicFolder['name']) self.assertEqual(pathToRoot[2]['type'], 'folder') self.assertEqual(pathToRoot[2]['object']['name'], firstChild['name']) self.assertEqual(pathToRoot[3]['type'], 'folder') self.assertEqual(pathToRoot[3]['object']['name'], secondChild['name']) def testLazyFieldComputation(self): item = Item().createItem('My Item Name', creator=self.users[0], folder=self.publicFolder) self.assertEqual(item['lowerName'], 'my item name') self.assertEqual(item['baseParentId'], self.users[0]['_id']) # Force the item to be saved without lowerName and baseParentType fields del item['lowerName'] del item['baseParentType'] item = Item().save(item, validate=False) item = Item().find({'_id': item['_id']})[0] self.assertNotHasKeys(item, ('lowerName', 'baseParentType')) # Now ensure that calling load() actually populates those fields and # saves the results persistently Item().load(item['_id'], force=True) item = Item().find({'_id': item['_id']})[0] self.assertHasKeys(item, ('lowerName', 'baseParentType')) self.assertEqual(item['lowerName'], 'my item name') self.assertEqual(item['baseParentType'], 'user') self.assertEqual(item['baseParentId'], self.users[0]['_id']) # Also test that this works for a duplicate item, such that the # automatically renamed item still has the correct lowerName, and a # None description is changed to an empty string. item = Item().createItem( 'My Item Name', creator=self.users[0], folder=self.publicFolder, description=None) # test if non-strings are coerced self.assertEqual(item['description'], '') item['description'] = 1 item = Item().save(item) item = Item().findOne({'_id': item['_id']}) self.assertEqual(item['description'], '1') # test if just missing lowerName is corrected. self.assertEqual(item['lowerName'], 'my item name (1)') del item['lowerName'] item = Item().save(item, validate=False) item = Item().findOne({'_id': item['_id']}) self.assertNotHasKeys(item, ('lowerName', )) Item().load(item['_id'], force=True) item = Item().findOne({'_id': item['_id']}) self.assertHasKeys(item, ('lowerName', )) self.assertEqual(item['lowerName'], 'my item name (1)') def testParentsToRoot(self): item = Item().createItem('My Item Name', creator=self.users[0], folder=self.publicFolder) parents = Item().parentsToRoot(item, force=True) for parent in parents: self.assertNotIn('_accessLevel', parent['object']) parents = Item().parentsToRoot(item) for parent in parents: self.assertIn('_accessLevel', parent['object']) def testItemCopy(self): origItem = self._createItem(self.publicFolder['_id'], 'test_for_copy', 'fake description', self.users[0]) # Add metadata and files, since we want to make sure those get copied metadata = { 'foo': 'value1', 'test': 2 } resp = self.request( path='/item/%s/metadata' % origItem['_id'], method='PUT', user=self.users[0], body=json.dumps(metadata), type='application/json') self.assertStatusOk(resp) self._testUploadFileToItem(origItem, 'file_1', self.users[0], 'foobar') self._testUploadFileToItem(origItem, 'file_2', self.users[0], 'foobz') # Also upload a link params = { 'parentType': 'item', 'parentId': origItem['_id'], 'name': 'link_file', 'linkUrl': 'http://www.google.com' } resp = self.request(path='/file', method='POST', user=self.users[0], params=params) self.assertStatusOk(resp) # Copy to a new item. It will be in the same folder, but we want a # different name. params = { 'name': 'copied_item' } resp = self.request(path='/item/%s/copy' % origItem['_id'], method='POST', user=self.users[0], params=params) self.assertStatusOk(resp) # Make sure size was returned correctly self.assertEqual(resp.json['size'], 11) # Now ask for the new item explicitly and check its metadata self.request(path='/item/%s' % resp.json['_id'], user=self.users[0], type='application/json') self.assertStatusOk(resp) newItem = resp.json self.assertEqual(newItem['name'], 'copied_item') self.assertEqual(newItem['meta']['foo'], metadata['foo']) self.assertEqual(newItem['meta']['test'], metadata['test']) # Check if we can download the files from the new item resp = self.request(path='/item/%s/files' % newItem['_id'], method='GET', user=self.users[0]) self.assertStatusOk(resp) newFiles = resp.json self.assertEqual(newFiles[0]['name'], 'file_1') self.assertEqual(newFiles[1]['name'], 'file_2') self.assertEqual(newFiles[2]['name'], 'link_file') self.assertEqual(newFiles[0]['size'], 6) self.assertEqual(newFiles[1]['size'], 5) self._testDownloadMultiFileItem(newItem, self.users[0], {'file_1': 'foobar', 'file_2': 'foobz', 'link_file': 'http://www.google.com'}) # Check to make sure the original item is still present resp = self.request(path='/item', method='GET', user=self.users[0], params={'folderId': self.publicFolder['_id'], 'text': 'test_for_copy'}) self.assertStatusOk(resp) self.assertEqual(origItem['_id'], resp.json[0]['_id']) # Check to make sure the new item is still present resp = self.request(path='/item', method='GET', user=self.users[0], params={'folderId': self.publicFolder['_id'], 'text': 'copied_item'}) self.assertStatusOk(resp) self.assertEqual(newItem['_id'], resp.json[0]['_id']) # Check that the provenance tag correctly points back # to the original item self.assertEqual(newItem['copyOfItem'], origItem['_id']) # Check if we can download the files from the old item and that they # are distinct from the files in the original item resp = self.request(path='/item/%s/files' % origItem['_id'], method='GET', user=self.users[0]) self.assertStatusOk(resp) origFiles = resp.json self._testDownloadMultiFileItem(origItem, self.users[0], {'file_1': 'foobar', 'file_2': 'foobz', 'link_file': 'http://www.google.com'}) for index, file in enumerate(origFiles): self.assertNotEqual(origFiles[index]['_id'], newFiles[index]['_id']) def testCookieAuth(self): item = self._createItem(self.privateFolder['_id'], 'cookie_auth_download', '', self.users[0]) self._testUploadFileToItem(item, 'file', self.users[0], 'foo') token = Token().createToken(self.users[0]) cookie = 'girderToken=%s' % token['_id'] # We should be able to download a private item using a cookie token resp = self.request(path='/item/%s/download' % item['_id'], isJson=False, cookie=cookie) self.assertStatusOk(resp) self.assertEqual(self.getBody(resp), 'foo') # We should not be able to call GET /item/:id with a cookie token resp = self.request(path='/item/%s' % item['_id'], cookie=cookie) self.assertStatus(resp, 401) # Make sure the cookie has to be a valid token resp = self.request(path='/item/%s/download' % item['_id'], cookie='girderToken=invalid_token') self.assertStatus(resp, 401) def testReuseExisting(self): item1 = Item().createItem('to be reused', creator=self.users[0], folder=self.publicFolder) item2 = Item().createItem('to be reused', creator=self.users[0], folder=self.publicFolder) item3 = Item().createItem( 'to be reused', creator=self.users[0], folder=self.publicFolder, reuseExisting=True) self.assertNotEqual(item1['_id'], item2['_id']) self.assertEqual(item1['_id'], item3['_id']) self.assertEqual(item2['name'], 'to be reused (1)') self.assertEqual(item3['name'], 'to be reused') def testUpdateDuplicatedName(self): item1 = Item().createItem('foo', creator=self.users[0], folder=self.publicFolder) item2 = Item().createItem('bar', creator=self.users[0], folder=self.publicFolder) item2['name'] = 'foo' Item().save(item2, validate=False) self.assertEqual(item2['name'], 'foo') item1['size'] = 3 Item().save(item1) self.assertEqual(item1['name'], 'foo')

true

7908217b6d7c51a3f3fffc389aafce8ac0d0ade8

6,934

py

Python

armi/materials/tests/test_water.py

youngmit/armi

67688e4e67d2a217dfc7b1ccfa64028c20b57a5b

[ "Apache-2.0" ]

null

armi/materials/tests/test_water.py

youngmit/armi

67688e4e67d2a217dfc7b1ccfa64028c20b57a5b

[ "Apache-2.0" ]

null

armi/materials/tests/test_water.py

youngmit/armi

67688e4e67d2a217dfc7b1ccfa64028c20b57a5b

[ "Apache-2.0" ]

null

# Copyright 2019 TerraPower, LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from armi.materials.water import SaturatedWater, SaturatedSteam """ unit tests for water materials """ class Test_Water(unittest.TestCase): """ unit tests for water materials """ def test_water_at_freezing(self): """ Reproduce verification results from IAPWS-IF97 for water at 0C http://www.iapws.org/relguide/supsat.pdf """ water = SaturatedWater() steam = SaturatedSteam() Tk = 273.16 ref_vapor_pressure = 611.657 ref_dp_dT = 44.436693 ref_saturated_water_rho = 999.789 ref_saturated_steam_rho = 0.00485426 ref_alpha = -11.529101 ref_saturated_water_enthalpy = 0.611786 ref_saturated_steam_enthalpy = 2500.5e3 ref_phi = -0.04 ref_saturated_water_entropy = 0 ref_saturated_steam_entropy = 9.154e3 self.assertAlmostEqual(ref_vapor_pressure, water.vaporPressure(Tk=Tk), 3) self.assertAlmostEqual(ref_vapor_pressure, steam.vaporPressure(Tk=Tk), 3) self.assertAlmostEqual(ref_dp_dT, water.vaporPressurePrime(Tk=Tk), 3) self.assertAlmostEqual(ref_dp_dT, steam.vaporPressurePrime(Tk=Tk), 3) self.assertAlmostEqual(ref_saturated_water_rho, water.densityKgM3(Tk=Tk), 0) self.assertAlmostEqual(ref_saturated_steam_rho, steam.densityKgM3(Tk=Tk), 0) self.assertAlmostEqual( ref_alpha, water.auxiliaryQuantitySpecificEnthalpy(Tk=Tk), 3 ) self.assertAlmostEqual( ref_alpha, steam.auxiliaryQuantitySpecificEnthalpy(Tk=Tk), 3 ) self.assertAlmostEqual(ref_saturated_water_enthalpy, water.enthalpy(Tk=Tk), 2) self.assertAlmostEqual( ref_saturated_steam_enthalpy / steam.enthalpy(Tk=Tk), 1, 2 ) self.assertAlmostEqual( ref_phi, water.auxiliaryQuantitySpecificEntropy(Tk=Tk), 2 ) self.assertAlmostEqual( ref_phi, steam.auxiliaryQuantitySpecificEntropy(Tk=Tk), 2 ) self.assertAlmostEqual(ref_saturated_water_entropy, water.entropy(Tk=Tk), 3) self.assertAlmostEqual(ref_saturated_steam_entropy / steam.entropy(Tk=Tk), 1, 3) def test_water_at_boiling(self): """ Reproduce verification results from IAPWS-IF97 for water at 100C http://www.iapws.org/relguide/supsat.pdf """ water = SaturatedWater() steam = SaturatedSteam() Tk = 373.1243 ref_vapor_pressure = 0.101325e6 ref_dp_dT = 3.616e3 ref_saturated_water_rho = 958.365 ref_saturated_steam_rho = 0.597586 ref_alpha = 417.65e3 ref_saturated_water_enthalpy = 417.05e3 ref_saturated_steam_enthalpy = 2675.7e3 ref_phi = 1.303e3 ref_saturated_water_entropy = 1.307e3 ref_saturated_steam_entropy = 7.355e3 self.assertAlmostEqual(ref_vapor_pressure / water.vaporPressure(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_vapor_pressure / steam.vaporPressure(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_dp_dT / water.vaporPressurePrime(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_dp_dT / steam.vaporPressurePrime(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_saturated_water_rho, water.densityKgM3(Tk=Tk), 0) self.assertAlmostEqual(ref_saturated_steam_rho, steam.densityKgM3(Tk=Tk), 0) self.assertAlmostEqual( ref_alpha / water.auxiliaryQuantitySpecificEnthalpy(Tk=Tk), 1, 3 ) self.assertAlmostEqual( ref_alpha / steam.auxiliaryQuantitySpecificEnthalpy(Tk=Tk), 1, 3 ) self.assertAlmostEqual( ref_saturated_water_enthalpy / water.enthalpy(Tk=Tk), 1, 2 ) self.assertAlmostEqual( ref_saturated_steam_enthalpy / steam.enthalpy(Tk=Tk), 1, 2 ) self.assertAlmostEqual( ref_phi / water.auxiliaryQuantitySpecificEntropy(Tk=Tk), 1, 3 ) self.assertAlmostEqual( ref_phi / steam.auxiliaryQuantitySpecificEntropy(Tk=Tk), 1, 3 ) self.assertAlmostEqual(ref_saturated_water_entropy / water.entropy(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_saturated_steam_entropy / steam.entropy(Tk=Tk), 1, 3) def test_water_at_critcalPoint(self): """ Reproduce verification results from IAPWS-IF97 for water at 647.096K http://www.iapws.org/relguide/supsat.pdf """ water = SaturatedWater() steam = SaturatedSteam() Tk = 647.096 ref_vapor_pressure = 22.064e6 ref_dp_dT = 268e3 ref_saturated_water_rho = 322 ref_saturated_steam_rho = 322 ref_alpha = 1548e3 ref_saturated_water_enthalpy = 2086.6e3 ref_saturated_steam_enthalpy = 2086.6e3 ref_phi = 3.578e3 ref_saturated_water_entropy = 4.410e3 ref_saturated_steam_entropy = 4.410e3 self.assertAlmostEqual(ref_vapor_pressure / water.vaporPressure(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_vapor_pressure / steam.vaporPressure(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_dp_dT / water.vaporPressurePrime(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_dp_dT / steam.vaporPressurePrime(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_saturated_water_rho, water.densityKgM3(Tk=Tk), 0) self.assertAlmostEqual(ref_saturated_steam_rho, steam.densityKgM3(Tk=Tk), 0) self.assertAlmostEqual( ref_alpha / water.auxiliaryQuantitySpecificEnthalpy(Tk=Tk), 1, 3 ) self.assertAlmostEqual( ref_alpha / steam.auxiliaryQuantitySpecificEnthalpy(Tk=Tk), 1, 3 ) self.assertAlmostEqual( ref_saturated_water_enthalpy / water.enthalpy(Tk=Tk), 1, 2 ) self.assertAlmostEqual( ref_saturated_steam_enthalpy / steam.enthalpy(Tk=Tk), 1, 2 ) self.assertAlmostEqual( ref_phi / water.auxiliaryQuantitySpecificEntropy(Tk=Tk), 1, 3 ) self.assertAlmostEqual( ref_phi / steam.auxiliaryQuantitySpecificEntropy(Tk=Tk), 1, 3 ) self.assertAlmostEqual(ref_saturated_water_entropy / water.entropy(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_saturated_steam_entropy / steam.entropy(Tk=Tk), 1, 3) if __name__ == "__main__": unittest.main()

36.114583

88

0.675224

import unittest from armi.materials.water import SaturatedWater, SaturatedSteam class Test_Water(unittest.TestCase): def test_water_at_freezing(self): water = SaturatedWater() steam = SaturatedSteam() Tk = 273.16 ref_vapor_pressure = 611.657 ref_dp_dT = 44.436693 ref_saturated_water_rho = 999.789 ref_saturated_steam_rho = 0.00485426 ref_alpha = -11.529101 ref_saturated_water_enthalpy = 0.611786 ref_saturated_steam_enthalpy = 2500.5e3 ref_phi = -0.04 ref_saturated_water_entropy = 0 ref_saturated_steam_entropy = 9.154e3 self.assertAlmostEqual(ref_vapor_pressure, water.vaporPressure(Tk=Tk), 3) self.assertAlmostEqual(ref_vapor_pressure, steam.vaporPressure(Tk=Tk), 3) self.assertAlmostEqual(ref_dp_dT, water.vaporPressurePrime(Tk=Tk), 3) self.assertAlmostEqual(ref_dp_dT, steam.vaporPressurePrime(Tk=Tk), 3) self.assertAlmostEqual(ref_saturated_water_rho, water.densityKgM3(Tk=Tk), 0) self.assertAlmostEqual(ref_saturated_steam_rho, steam.densityKgM3(Tk=Tk), 0) self.assertAlmostEqual( ref_alpha, water.auxiliaryQuantitySpecificEnthalpy(Tk=Tk), 3 ) self.assertAlmostEqual( ref_alpha, steam.auxiliaryQuantitySpecificEnthalpy(Tk=Tk), 3 ) self.assertAlmostEqual(ref_saturated_water_enthalpy, water.enthalpy(Tk=Tk), 2) self.assertAlmostEqual( ref_saturated_steam_enthalpy / steam.enthalpy(Tk=Tk), 1, 2 ) self.assertAlmostEqual( ref_phi, water.auxiliaryQuantitySpecificEntropy(Tk=Tk), 2 ) self.assertAlmostEqual( ref_phi, steam.auxiliaryQuantitySpecificEntropy(Tk=Tk), 2 ) self.assertAlmostEqual(ref_saturated_water_entropy, water.entropy(Tk=Tk), 3) self.assertAlmostEqual(ref_saturated_steam_entropy / steam.entropy(Tk=Tk), 1, 3) def test_water_at_boiling(self): water = SaturatedWater() steam = SaturatedSteam() Tk = 373.1243 ref_vapor_pressure = 0.101325e6 ref_dp_dT = 3.616e3 ref_saturated_water_rho = 958.365 ref_saturated_steam_rho = 0.597586 ref_alpha = 417.65e3 ref_saturated_water_enthalpy = 417.05e3 ref_saturated_steam_enthalpy = 2675.7e3 ref_phi = 1.303e3 ref_saturated_water_entropy = 1.307e3 ref_saturated_steam_entropy = 7.355e3 self.assertAlmostEqual(ref_vapor_pressure / water.vaporPressure(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_vapor_pressure / steam.vaporPressure(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_dp_dT / water.vaporPressurePrime(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_dp_dT / steam.vaporPressurePrime(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_saturated_water_rho, water.densityKgM3(Tk=Tk), 0) self.assertAlmostEqual(ref_saturated_steam_rho, steam.densityKgM3(Tk=Tk), 0) self.assertAlmostEqual( ref_alpha / water.auxiliaryQuantitySpecificEnthalpy(Tk=Tk), 1, 3 ) self.assertAlmostEqual( ref_alpha / steam.auxiliaryQuantitySpecificEnthalpy(Tk=Tk), 1, 3 ) self.assertAlmostEqual( ref_saturated_water_enthalpy / water.enthalpy(Tk=Tk), 1, 2 ) self.assertAlmostEqual( ref_saturated_steam_enthalpy / steam.enthalpy(Tk=Tk), 1, 2 ) self.assertAlmostEqual( ref_phi / water.auxiliaryQuantitySpecificEntropy(Tk=Tk), 1, 3 ) self.assertAlmostEqual( ref_phi / steam.auxiliaryQuantitySpecificEntropy(Tk=Tk), 1, 3 ) self.assertAlmostEqual(ref_saturated_water_entropy / water.entropy(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_saturated_steam_entropy / steam.entropy(Tk=Tk), 1, 3) def test_water_at_critcalPoint(self): water = SaturatedWater() steam = SaturatedSteam() Tk = 647.096 ref_vapor_pressure = 22.064e6 ref_dp_dT = 268e3 ref_saturated_water_rho = 322 ref_saturated_steam_rho = 322 ref_alpha = 1548e3 ref_saturated_water_enthalpy = 2086.6e3 ref_saturated_steam_enthalpy = 2086.6e3 ref_phi = 3.578e3 ref_saturated_water_entropy = 4.410e3 ref_saturated_steam_entropy = 4.410e3 self.assertAlmostEqual(ref_vapor_pressure / water.vaporPressure(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_vapor_pressure / steam.vaporPressure(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_dp_dT / water.vaporPressurePrime(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_dp_dT / steam.vaporPressurePrime(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_saturated_water_rho, water.densityKgM3(Tk=Tk), 0) self.assertAlmostEqual(ref_saturated_steam_rho, steam.densityKgM3(Tk=Tk), 0) self.assertAlmostEqual( ref_alpha / water.auxiliaryQuantitySpecificEnthalpy(Tk=Tk), 1, 3 ) self.assertAlmostEqual( ref_alpha / steam.auxiliaryQuantitySpecificEnthalpy(Tk=Tk), 1, 3 ) self.assertAlmostEqual( ref_saturated_water_enthalpy / water.enthalpy(Tk=Tk), 1, 2 ) self.assertAlmostEqual( ref_saturated_steam_enthalpy / steam.enthalpy(Tk=Tk), 1, 2 ) self.assertAlmostEqual( ref_phi / water.auxiliaryQuantitySpecificEntropy(Tk=Tk), 1, 3 ) self.assertAlmostEqual( ref_phi / steam.auxiliaryQuantitySpecificEntropy(Tk=Tk), 1, 3 ) self.assertAlmostEqual(ref_saturated_water_entropy / water.entropy(Tk=Tk), 1, 3) self.assertAlmostEqual(ref_saturated_steam_entropy / steam.entropy(Tk=Tk), 1, 3) if __name__ == "__main__": unittest.main()

true

790822218d5379c4992da6a0efed925359e649a9

125

py

Python

assets/student-resources/blank_template.py

chaoryan5/website

931ff3ace728cfb54089665a5d2cfbb48c488530

[ "Apache-2.0" ]

18

2016-09-22T03:24:43.000Z

2019-11-21T02:30:41.000Z

assets/student-resources/blank_template.py

chaoryan5/website

931ff3ace728cfb54089665a5d2cfbb48c488530

[ "Apache-2.0" ]

395

2016-08-28T01:26:06.000Z

2020-06-17T19:33:59.000Z

assets/student-resources/blank_template.py

chaoryan5/website

931ff3ace728cfb54089665a5d2cfbb48c488530

[ "Apache-2.0" ]

60

2015-10-09T00:58:06.000Z

2021-07-31T21:16:29.000Z

def autonomous_setup(): pass def autonomous_main(): pass def teleop_setup(): pass def teleop_main(): pass

10.416667

23

0.656

def autonomous_setup(): pass def autonomous_main(): pass def teleop_setup(): pass def teleop_main(): pass

true

7908224bb67a898e3c14c3d3fcd61729cf70005f

87

py

Python

test_app/apps.py

eamigo86/django3_asgi

deb6d2d7ff8faee24a78af1b570900b7e7062263

[ "MIT" ]

1

2020-08-25T15:51:14.000Z

test_app/apps.py

eamigo86/django3_asgi

deb6d2d7ff8faee24a78af1b570900b7e7062263

[ "MIT" ]

3

2021-03-30T12:40:19.000Z

2021-09-22T18:33:48.000Z

test_app/apps.py

eamigo86/django3_asgi

deb6d2d7ff8faee24a78af1b570900b7e7062263

[ "MIT" ]

null

from django.apps import AppConfig class TestConfig(AppConfig): name = "test_app"

14.5

33

0.747126

from django.apps import AppConfig class TestConfig(AppConfig): name = "test_app"

true

790822df11cab707b156199dcde4cff72d1aa112

1,320

py

Python

qa327_test/frontend/geek_base.py

nicoleooi/cmpe327

73f6bdcbd2f382a54dfec3e0e79120bd60c9513f

[ "Apache-2.0", "MIT" ]

null

qa327_test/frontend/geek_base.py

nicoleooi/cmpe327

73f6bdcbd2f382a54dfec3e0e79120bd60c9513f

[ "Apache-2.0", "MIT" ]

null

qa327_test/frontend/geek_base.py

nicoleooi/cmpe327

73f6bdcbd2f382a54dfec3e0e79120bd60c9513f

[ "Apache-2.0", "MIT" ]

2

2021-01-14T02:58:39.000Z

2021-02-04T19:18:47.000Z

from seleniumbase import BaseCase from werkzeug.security import generate_password_hash from qa327_test.conftest import base_url from qa327.models import User, Ticket # Mock a sample user TEST_USER = User( email='test_frontend@test.com', name='test_frontend', password=generate_password_hash('test_frontend'), balance=500 ) TEST_USER_SELLER = User( email='test_seller@test.com', name='test_seller', password=generate_password_hash('Password99!'), balance=500 ) # Mock a sample ticket TEST_TICKET = Ticket( name='helloworld', seller=TEST_USER_SELLER, price=20, quantity=20, expires="20220101" ) class GeekBaseCase(BaseCase): ''' Selenium base case with some GeekSeek utilities ''' def assert_flash(self, text): '''asserts that message exists in flashes''' for flash_dom in self.find_elements('.flash'): if flash_dom.text == text: return print(flash_dom.text) raise AssertionError(f'Flash not found for text "{text}"') def login_test_user(self, email=TEST_USER.email, password='test_frontend'): '''login our test user''' self.open(base_url+'/login') self.input('#email', email) self.input('#password', password) self.click('#btn-submit')

26.938776

79

0.669697

from seleniumbase import BaseCase from werkzeug.security import generate_password_hash from qa327_test.conftest import base_url from qa327.models import User, Ticket TEST_USER = User( email='test_frontend@test.com', name='test_frontend', password=generate_password_hash('test_frontend'), balance=500 ) TEST_USER_SELLER = User( email='test_seller@test.com', name='test_seller', password=generate_password_hash('Password99!'), balance=500 ) TEST_TICKET = Ticket( name='helloworld', seller=TEST_USER_SELLER, price=20, quantity=20, expires="20220101" ) class GeekBaseCase(BaseCase): def assert_flash(self, text): for flash_dom in self.find_elements('.flash'): if flash_dom.text == text: return print(flash_dom.text) raise AssertionError(f'Flash not found for text "{text}"') def login_test_user(self, email=TEST_USER.email, password='test_frontend'): self.open(base_url+'/login') self.input('#email', email) self.input('#password', password) self.click('#btn-submit')

true

7908247a89be8fd5b33d6849b6918ba0cbfb6699

685

py

Python

telemetryPlugin/forms.py

YangKaiting/kiwitcms-telemetry-failed-test-cases

10ccd6db1ed0d3a08af87da8411baed0b822ef4d

[ "MIT" ]

1

2019-05-28T09:21:42.000Z

telemetryPlugin/forms.py

YangKaiting/kiwitcms-telemetry-failed-test-cases

10ccd6db1ed0d3a08af87da8411baed0b822ef4d

[ "MIT" ]

null

telemetryPlugin/forms.py

YangKaiting/kiwitcms-telemetry-failed-test-cases

10ccd6db1ed0d3a08af87da8411baed0b822ef4d

[ "MIT" ]

null

# -*- coding: utf-8 -*- from django import forms from django.contrib.auth import get_user_model from django.utils.translation import ugettext_lazy as _ from tcms.core.utils import string_to_list from tcms.core.forms.fields import UserField from tcms.management.models import Product, Version, Build from tcms.testplans.models import TestPlan from tcms.testcases.models import TestCase # =========== Forms for search/filter ============== class SearchProductForm(forms.Form): """ Includes *only* fields used in search.html b/c the actual search is now done via JSON RPC. """ name_product = forms.CharField(label='Product', max_length=100, required=False)

34.25

83

0.734307

from django import forms from django.contrib.auth import get_user_model from django.utils.translation import ugettext_lazy as _ from tcms.core.utils import string_to_list from tcms.core.forms.fields import UserField from tcms.management.models import Product, Version, Build from tcms.testplans.models import TestPlan from tcms.testcases.models import TestCase class SearchProductForm(forms.Form): name_product = forms.CharField(label='Product', max_length=100, required=False)

true

7908247cbd31c734c39dfaae689b0ec2312ffaa5

1,576

py

Python

Parsers/vcru.py

OverFitted/hacksai2021spb

552cfe3f5d1d0f89770bdf8e99414ec01e1f4145

[ "MIT" ]

null

Parsers/vcru.py

OverFitted/hacksai2021spb

552cfe3f5d1d0f89770bdf8e99414ec01e1f4145

[ "MIT" ]

null

Parsers/vcru.py

OverFitted/hacksai2021spb

552cfe3f5d1d0f89770bdf8e99414ec01e1f4145

[ "MIT" ]

null

import aiohttp, asyncio from bs4 import BeautifulSoup import json import time VC_SEARCH = "https://vc.ru/search/v2/content/new" async def parse_urls(key_word): async with aiohttp.ClientSession() as session: async with session.get(VC_SEARCH, params={ "query": key_word, "target_type": 'posts', }) as r: soup = BeautifulSoup(await r.text(), 'html.parser') urls = [x["href"] for x in soup.find_all("a", {"class": "content-feed__link"})] return urls async def get_text(url): async with aiohttp.ClientSession() as session: async with session.get(url) as r: soup = BeautifulSoup(await r.text(), 'html.parser') text = " ".join(map(lambda x: x.text, soup.find("div", {"class": "l-entry__content"}).find_all("p"))) return text async def get_all_texts(keyword): urls = await parse_urls(keyword) all_texts = [] for u in urls[:25]: text = await get_text(u) all_texts.append(text) return all_texts async def vc_get_data(keyword, result_file_path='result-vc.json'): texts = await get_all_texts(keyword) result_dict = {"company": keyword, "texts": texts} result_json = json.loads(json.dumps(result_dict)) return result_json #with open(result_file_path, 'w', encoding='utf-8') as f: # json.dump(result_json, f, ensure_ascii=False, indent=4) if __name__ == "__main__": loop = asyncio.get_event_loop() loop.run_until_complete(vc_get_data("сбер", "other/sber-vc.json"))

31.52

113

0.636421

import aiohttp, asyncio from bs4 import BeautifulSoup import json import time VC_SEARCH = "https://vc.ru/search/v2/content/new" async def parse_urls(key_word): async with aiohttp.ClientSession() as session: async with session.get(VC_SEARCH, params={ "query": key_word, "target_type": 'posts', }) as r: soup = BeautifulSoup(await r.text(), 'html.parser') urls = [x["href"] for x in soup.find_all("a", {"class": "content-feed__link"})] return urls async def get_text(url): async with aiohttp.ClientSession() as session: async with session.get(url) as r: soup = BeautifulSoup(await r.text(), 'html.parser') text = " ".join(map(lambda x: x.text, soup.find("div", {"class": "l-entry__content"}).find_all("p"))) return text async def get_all_texts(keyword): urls = await parse_urls(keyword) all_texts = [] for u in urls[:25]: text = await get_text(u) all_texts.append(text) return all_texts async def vc_get_data(keyword, result_file_path='result-vc.json'): texts = await get_all_texts(keyword) result_dict = {"company": keyword, "texts": texts} result_json = json.loads(json.dumps(result_dict)) return result_json if __name__ == "__main__": loop = asyncio.get_event_loop() loop.run_until_complete(vc_get_data("сбер", "other/sber-vc.json"))

true

790825a7f680576fc366b31d156e124340ea5bf2

745

py

Python

cool.py

anay97/python-scraper

a09cb319448eae271833eaf59cd3372e8ff71a08

[ "MIT" ]

null

cool.py

anay97/python-scraper

a09cb319448eae271833eaf59cd3372e8ff71a08

[ "MIT" ]

null

cool.py

anay97/python-scraper

a09cb319448eae271833eaf59cd3372e8ff71a08

[ "MIT" ]

null

#For the whatsapp statuses url given below #COOL import requests from bs4 import BeautifulSoup url_to_scrape = 'https://www.appstatustxt.com/cool-whatsapp-status/' r = requests.get(url_to_scrape) soup = BeautifulSoup(r.text,"html5lib") status_object=[] statuses=[] title=soup.title.string print(title) status_object=soup.find_all('span',style="color: #008000;") fo = open("cool.txt", "a") #Adding basic stuff for json syntax #fo.write("{\n") i=1; for status in status_object: if len(status.string)<=135: statuses.append(status.string+'\n') print(status.string) # actual_status=status.string.encode('utf-8') fo.write(status.string.encode('utf-8')+'\n') # fo.write('"'+str(i)+'":"'+actual_status+'",\n') i=i+1

29.8

69

0.695302

import requests from bs4 import BeautifulSoup url_to_scrape = 'https://www.appstatustxt.com/cool-whatsapp-status/' r = requests.get(url_to_scrape) soup = BeautifulSoup(r.text,"html5lib") status_object=[] statuses=[] title=soup.title.string print(title) status_object=soup.find_all('span',style="color: #008000;") fo = open("cool.txt", "a") i=1; for status in status_object: if len(status.string)<=135: statuses.append(status.string+'\n') print(status.string) fo.write(status.string.encode('utf-8')+'\n') i=i+1

true

7908272c12fc9d8c42fdfc8035c17f9a72cf1243

7,515

py

Python

rajk_appman/invoke_rajk.py

rajk-apps/rajk-appman

2053aa15b6dc17747022f15840cfaead06e6e8c6

[ "MIT" ]

null

rajk_appman/invoke_rajk.py

rajk-apps/rajk-appman

2053aa15b6dc17747022f15840cfaead06e6e8c6

[ "MIT" ]

null

rajk_appman/invoke_rajk.py

rajk-apps/rajk-appman

2053aa15b6dc17747022f15840cfaead06e6e8c6

[ "MIT" ]

null

import requests import json import datetime import os import io from invoke import task from .invoke_utils import ServerConnection, use_dump_modifier_function RAJK_PASSWORD = os.environ.get("RAJK_PASSWORD") RAJK_RSA = os.environ.get("RAJK_RSA") TEST_DEPLOY_DIRECTORY = os.getcwd() + "/build" rajk_server_connection = ServerConnection( "rajk", "146.110.60.20", 2222, "/var/www/rajkdjango2/bin/python" ) def redo_rsa_from_text(c, rsa_text): os.makedirs("{}/.ssh".format(os.path.expanduser("~")), exist_ok=True) rsa_path = "{}/.ssh/id_rsa".format(os.path.expanduser("~")) with open(rsa_path, "w") as fp: fp.write(rsa_text) c.run("chmod 600 {}".format(rsa_path)) @task def backup_django(c): os.makedirs("backups", exist_ok=True) bup_dir = os.path.join("backups", datetime.date.today().isoformat()) c.run("mkdir {}".format(bup_dir)) scp_command = rajk_server_connection.copy_from_server_command( bup_dir, "/var/www/rajkdjango2" ) c.run(scp_command) @task def restart_server(c): command = rajk_server_connection.run_sudo_command( "service django2 restart", RAJK_PASSWORD ) c.run(command) @task def stop_server(c): command = rajk_server_connection.run_sudo_command( "service django2 stop", RAJK_PASSWORD ) c.run(command) @task def start_server(c): command = rajk_server_connection.run_sudo_command( "service django2 start", RAJK_PASSWORD ) c.run(command) @task def dump(c, fname="dump.json", no_contenttypes=False): py_command = "/var/www/rajkdjango2/manage.py dumpdata {}".format( "-e contenttypes" if no_contenttypes else "" ) command = rajk_server_connection.remote_python_command(py_command) c.run(command + " > {}".format(fname)) @task def remote_dump(c, no_contenttypes=True): py_command = "/var/www/rajkdjango2/manage.py dumpdata {} > /var/www/rajk/djangodump.json".format( "-e contenttypes" if no_contenttypes else "" ) command = rajk_server_connection.remote_python_command(py_command) c.run(command) @task def setup_test_deploy_env(c): c.run("rm -rf ./{}".format(TEST_DEPLOY_DIRECTORY)) c.run("mkdir {}".format(TEST_DEPLOY_DIRECTORY)) resp = requests.get("https://api.github.com/orgs/rajk-apps/repos") repos = [ "git+https://github.com/{}".format(d["full_name"]) for d in json.loads(resp.content) ] app_names = [r.split("/")[-1].replace("-", "_") for r in repos] c.run("python3 -m venv {}/django_venv".format(TEST_DEPLOY_DIRECTORY)) for r in ["wheel", "django", "toml"] + repos: c.run("{}/django_venv/bin/pip install {}".format(TEST_DEPLOY_DIRECTORY, r)) c.run( "cd {};django_venv/bin/django-admin startproject rajkproject".format( TEST_DEPLOY_DIRECTORY ) ) with open( "{}/rajkproject/rajkproject/settings.py".format(TEST_DEPLOY_DIRECTORY), "a" ) as fp: fp.write( "\nINSTALLED_APPS += [{}]".format( ", ".join(["'{}'".format(a) for a in app_names]) ) ) with open( "{}/rajkproject/rajkproject/urls.py".format(TEST_DEPLOY_DIRECTORY), "a" ) as fp: fp.write( "\nfrom django.urls import include" "\nurlpatterns.append(path('accounts/', include('django.contrib.auth.urls')))" "\nurlpatterns += [{}]".format( ", ".join( [ "path('{}', include('{}.urls'))".format( a + "/" if a != "rajk_appman" else "", a ) for a in app_names ] ) ) ) dump_fname = "{}/dump.json".format(TEST_DEPLOY_DIRECTORY) resp = requests.get("https://rajk.uni-corvinus.hu/djangodump.json") with open(dump_fname, "wb") as fp: fp.write(resp.content) for django_command in [ "makemigrations", "makemigrations {}".format(" ".join(app_names)), "migrate", "loaddata {}".format(dump_fname), ]: c.run( "{}/django_venv/bin/python {}/rajkproject/manage.py {}".format( TEST_DEPLOY_DIRECTORY, TEST_DEPLOY_DIRECTORY, django_command ) ) @task def deploy(c, dump_modifier_function=None, live=False, redo_rsa=False): f = io.StringIO() c.run( "{}/django_venv/bin/python setup.py --fullname".format(TEST_DEPLOY_DIRECTORY), out_stream=f, ) current_app_fullname = f.getvalue().strip() f.close() c.run("{}/django_venv/bin/python setup.py sdist".format(TEST_DEPLOY_DIRECTORY)) local_tarball = "./dist/{}.tar.gz".format(current_app_fullname) c.run( "{}/django_venv/bin/pip install {}".format(TEST_DEPLOY_DIRECTORY, local_tarball) ) dump_fname = "{}/dump.json".format(TEST_DEPLOY_DIRECTORY) resp = requests.get("https://rajk.uni-corvinus.hu/djangodump.json") with open(dump_fname, "wb") as fp: fp.write(resp.content) if dump_modifier_function is not None: use_dump_modifier_function(dump_modifier_function, dump_fname) c.run("rm {}/rajkproject/db.sqlite3".format(TEST_DEPLOY_DIRECTORY)) for django_command in [ "makemigrations", "makemigrations {}".format(current_app_fullname.split("-")[0]), "migrate", "loaddata {}".format(dump_fname) ]: c.run( "{}/django_venv/bin/python {}/rajkproject/manage.py {}".format( TEST_DEPLOY_DIRECTORY, TEST_DEPLOY_DIRECTORY, django_command ) ) if live: _live_deploy(c, local_tarball, current_app_fullname, dump_modifier_function, redo_rsa) def _live_deploy(c, local_tarball, current_app_fullname, dump_modifier_function=None, redo_rsa=False): if redo_rsa: if RAJK_RSA: redo_rsa_from_text(c, RAJK_RSA) else: raise EnvironmentError("No RAJK_RSA env variable") local_dump_fname = "{}/deploy_dump.json".format(TEST_DEPLOY_DIRECTORY) remote_dump_fname = "/var/www/rajkdjango2/deploy_dump.json" print("stopping server") stop_server(c) print("dumping data") dump(c, local_dump_fname, True) if dump_modifier_function is not None: use_dump_modifier_function(dump_modifier_function, local_dump_fname) scp_command = rajk_server_connection.copy_to_server_command( local_dump_fname, remote_dump_fname ) c.run(scp_command) remote_tarball = "/var/www/rajkdjango2/tarballs/{}".format( local_tarball.split("/")[-1] ) tar_scp_command = rajk_server_connection.copy_to_server_command( local_tarball, remote_tarball ) c.run(tar_scp_command) install_command = "/var/www/rajkdjango2/bin/pip --no-cache-dir install --upgrade {}".format( remote_tarball ) remote_install_command = rajk_server_connection.run_ssh_command(install_command) c.run(remote_install_command) c.run(rajk_server_connection.run_ssh_command("rm /var/www/rajkdjango2/db.sqlite3")) for django_command in [ "makemigrations", "makemigrations {}".format(current_app_fullname.split("-")[0]), "migrate", "loaddata {}".format(remote_dump_fname), ]: c.run( rajk_server_connection.remote_python_command( "/var/www/rajkdjango2/manage.py {}".format(django_command) ) ) start_server(c)

29.703557

102

0.637126

import requests import json import datetime import os import io from invoke import task from .invoke_utils import ServerConnection, use_dump_modifier_function RAJK_PASSWORD = os.environ.get("RAJK_PASSWORD") RAJK_RSA = os.environ.get("RAJK_RSA") TEST_DEPLOY_DIRECTORY = os.getcwd() + "/build" rajk_server_connection = ServerConnection( "rajk", "146.110.60.20", 2222, "/var/www/rajkdjango2/bin/python" ) def redo_rsa_from_text(c, rsa_text): os.makedirs("{}/.ssh".format(os.path.expanduser("~")), exist_ok=True) rsa_path = "{}/.ssh/id_rsa".format(os.path.expanduser("~")) with open(rsa_path, "w") as fp: fp.write(rsa_text) c.run("chmod 600 {}".format(rsa_path)) @task def backup_django(c): os.makedirs("backups", exist_ok=True) bup_dir = os.path.join("backups", datetime.date.today().isoformat()) c.run("mkdir {}".format(bup_dir)) scp_command = rajk_server_connection.copy_from_server_command( bup_dir, "/var/www/rajkdjango2" ) c.run(scp_command) @task def restart_server(c): command = rajk_server_connection.run_sudo_command( "service django2 restart", RAJK_PASSWORD ) c.run(command) @task def stop_server(c): command = rajk_server_connection.run_sudo_command( "service django2 stop", RAJK_PASSWORD ) c.run(command) @task def start_server(c): command = rajk_server_connection.run_sudo_command( "service django2 start", RAJK_PASSWORD ) c.run(command) @task def dump(c, fname="dump.json", no_contenttypes=False): py_command = "/var/www/rajkdjango2/manage.py dumpdata {}".format( "-e contenttypes" if no_contenttypes else "" ) command = rajk_server_connection.remote_python_command(py_command) c.run(command + " > {}".format(fname)) @task def remote_dump(c, no_contenttypes=True): py_command = "/var/www/rajkdjango2/manage.py dumpdata {} > /var/www/rajk/djangodump.json".format( "-e contenttypes" if no_contenttypes else "" ) command = rajk_server_connection.remote_python_command(py_command) c.run(command) @task def setup_test_deploy_env(c): c.run("rm -rf ./{}".format(TEST_DEPLOY_DIRECTORY)) c.run("mkdir {}".format(TEST_DEPLOY_DIRECTORY)) resp = requests.get("https://api.github.com/orgs/rajk-apps/repos") repos = [ "git+https://github.com/{}".format(d["full_name"]) for d in json.loads(resp.content) ] app_names = [r.split("/")[-1].replace("-", "_") for r in repos] c.run("python3 -m venv {}/django_venv".format(TEST_DEPLOY_DIRECTORY)) for r in ["wheel", "django", "toml"] + repos: c.run("{}/django_venv/bin/pip install {}".format(TEST_DEPLOY_DIRECTORY, r)) c.run( "cd {};django_venv/bin/django-admin startproject rajkproject".format( TEST_DEPLOY_DIRECTORY ) ) with open( "{}/rajkproject/rajkproject/settings.py".format(TEST_DEPLOY_DIRECTORY), "a" ) as fp: fp.write( "\nINSTALLED_APPS += [{}]".format( ", ".join(["'{}'".format(a) for a in app_names]) ) ) with open( "{}/rajkproject/rajkproject/urls.py".format(TEST_DEPLOY_DIRECTORY), "a" ) as fp: fp.write( "\nfrom django.urls import include" "\nurlpatterns.append(path('accounts/', include('django.contrib.auth.urls')))" "\nurlpatterns += [{}]".format( ", ".join( [ "path('{}', include('{}.urls'))".format( a + "/" if a != "rajk_appman" else "", a ) for a in app_names ] ) ) ) dump_fname = "{}/dump.json".format(TEST_DEPLOY_DIRECTORY) resp = requests.get("https://rajk.uni-corvinus.hu/djangodump.json") with open(dump_fname, "wb") as fp: fp.write(resp.content) for django_command in [ "makemigrations", "makemigrations {}".format(" ".join(app_names)), "migrate", "loaddata {}".format(dump_fname), ]: c.run( "{}/django_venv/bin/python {}/rajkproject/manage.py {}".format( TEST_DEPLOY_DIRECTORY, TEST_DEPLOY_DIRECTORY, django_command ) ) @task def deploy(c, dump_modifier_function=None, live=False, redo_rsa=False): f = io.StringIO() c.run( "{}/django_venv/bin/python setup.py --fullname".format(TEST_DEPLOY_DIRECTORY), out_stream=f, ) current_app_fullname = f.getvalue().strip() f.close() c.run("{}/django_venv/bin/python setup.py sdist".format(TEST_DEPLOY_DIRECTORY)) local_tarball = "./dist/{}.tar.gz".format(current_app_fullname) c.run( "{}/django_venv/bin/pip install {}".format(TEST_DEPLOY_DIRECTORY, local_tarball) ) dump_fname = "{}/dump.json".format(TEST_DEPLOY_DIRECTORY) resp = requests.get("https://rajk.uni-corvinus.hu/djangodump.json") with open(dump_fname, "wb") as fp: fp.write(resp.content) if dump_modifier_function is not None: use_dump_modifier_function(dump_modifier_function, dump_fname) c.run("rm {}/rajkproject/db.sqlite3".format(TEST_DEPLOY_DIRECTORY)) for django_command in [ "makemigrations", "makemigrations {}".format(current_app_fullname.split("-")[0]), "migrate", "loaddata {}".format(dump_fname) ]: c.run( "{}/django_venv/bin/python {}/rajkproject/manage.py {}".format( TEST_DEPLOY_DIRECTORY, TEST_DEPLOY_DIRECTORY, django_command ) ) if live: _live_deploy(c, local_tarball, current_app_fullname, dump_modifier_function, redo_rsa) def _live_deploy(c, local_tarball, current_app_fullname, dump_modifier_function=None, redo_rsa=False): if redo_rsa: if RAJK_RSA: redo_rsa_from_text(c, RAJK_RSA) else: raise EnvironmentError("No RAJK_RSA env variable") local_dump_fname = "{}/deploy_dump.json".format(TEST_DEPLOY_DIRECTORY) remote_dump_fname = "/var/www/rajkdjango2/deploy_dump.json" print("stopping server") stop_server(c) print("dumping data") dump(c, local_dump_fname, True) if dump_modifier_function is not None: use_dump_modifier_function(dump_modifier_function, local_dump_fname) scp_command = rajk_server_connection.copy_to_server_command( local_dump_fname, remote_dump_fname ) c.run(scp_command) remote_tarball = "/var/www/rajkdjango2/tarballs/{}".format( local_tarball.split("/")[-1] ) tar_scp_command = rajk_server_connection.copy_to_server_command( local_tarball, remote_tarball ) c.run(tar_scp_command) install_command = "/var/www/rajkdjango2/bin/pip --no-cache-dir install --upgrade {}".format( remote_tarball ) remote_install_command = rajk_server_connection.run_ssh_command(install_command) c.run(remote_install_command) c.run(rajk_server_connection.run_ssh_command("rm /var/www/rajkdjango2/db.sqlite3")) for django_command in [ "makemigrations", "makemigrations {}".format(current_app_fullname.split("-")[0]), "migrate", "loaddata {}".format(remote_dump_fname), ]: c.run( rajk_server_connection.remote_python_command( "/var/www/rajkdjango2/manage.py {}".format(django_command) ) ) start_server(c)

true

79082733e0ac70c4f98e69a85757ce7e81ebe486

1,216

py

Python

bcs-ui/backend/tests/components/test_cm.py

kayinli/bk-bcs

93a0856175f7b066ef835921572c1cac590dbd8e

[ "Apache-2.0" ]

null

bcs-ui/backend/tests/components/test_cm.py

kayinli/bk-bcs

93a0856175f7b066ef835921572c1cac590dbd8e

[ "Apache-2.0" ]

null

bcs-ui/backend/tests/components/test_cm.py

kayinli/bk-bcs

93a0856175f7b066ef835921572c1cac590dbd8e

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community Edition) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT 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 requests_mock import ANY from backend.components.cluster_manager import ClusterManagerClient class TestClusterManagerClient: def test_get_nodes(self, cluster_id, request_user, requests_mock): expected_data = [{"innerIP": "127.0.0.1"}] requests_mock.get(ANY, json={"code": 0, "data": expected_data}) client = ClusterManagerClient(request_user.token.access_token) data = client.get_nodes(cluster_id) assert data == expected_data

46.769231

115

0.763158

from requests_mock import ANY from backend.components.cluster_manager import ClusterManagerClient class TestClusterManagerClient: def test_get_nodes(self, cluster_id, request_user, requests_mock): expected_data = [{"innerIP": "127.0.0.1"}] requests_mock.get(ANY, json={"code": 0, "data": expected_data}) client = ClusterManagerClient(request_user.token.access_token) data = client.get_nodes(cluster_id) assert data == expected_data

true

7908280647d27b78811c8534d7906da5a4299fad

2,312

py

Python

official/gnn/gat/preprocess.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

[ "Apache-2.0" ]

77

2021-10-15T08:32:37.000Z

2022-03-30T13:09:11.000Z

official/gnn/gat/preprocess.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

[ "Apache-2.0" ]

3

2021-10-30T14:44:57.000Z

2022-02-14T06:57:57.000Z

official/gnn/gat/preprocess.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

[ "Apache-2.0" ]

24

2021-10-15T08:32:45.000Z

2022-03-24T18:45:20.000Z

# Copyright 2021 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. # ============================================================================ """preprocess""" import argparse import os import numpy as np from src.dataset import load_and_process def generate_bin(): """Generate bin files.""" parser = argparse.ArgumentParser() parser.add_argument('--data_dir', type=str, default='./data/cora/cora_mr', help='Data dir') parser.add_argument('--train_nodes_num', type=int, default=140, help='Nodes numbers for training') parser.add_argument('--eval_nodes_num', type=int, default=500, help='Nodes numbers for evaluation') parser.add_argument('--test_nodes_num', type=int, default=1000, help='Nodes numbers for test') parser.add_argument('--result_path', type=str, default='./preprocess_Result/', help='Result path') args = parser.parse_args() feature, biases, _, _, _, _, y_test, test_mask = load_and_process(args.data_dir, args.train_nodes_num, args.eval_nodes_num, args.test_nodes_num) feature_path = os.path.join(args.result_path, '00_data') biases_path = os.path.join(args.result_path, '01_data') y_test_path = os.path.join(args.result_path, 'y_test.npy') test_mask_path = os.path.join(args.result_path, 'test_mask.npy') os.makedirs(feature_path) os.makedirs(biases_path) feature.tofile(os.path.join(feature_path, 'feature.bin')) biases.tofile(os.path.join(biases_path, 'biases.bin')) np.save(y_test_path, y_test) np.save(test_mask_path, test_mask) if __name__ == "__main__": generate_bin()

44.461538

103

0.645329

import argparse import os import numpy as np from src.dataset import load_and_process def generate_bin(): parser = argparse.ArgumentParser() parser.add_argument('--data_dir', type=str, default='./data/cora/cora_mr', help='Data dir') parser.add_argument('--train_nodes_num', type=int, default=140, help='Nodes numbers for training') parser.add_argument('--eval_nodes_num', type=int, default=500, help='Nodes numbers for evaluation') parser.add_argument('--test_nodes_num', type=int, default=1000, help='Nodes numbers for test') parser.add_argument('--result_path', type=str, default='./preprocess_Result/', help='Result path') args = parser.parse_args() feature, biases, _, _, _, _, y_test, test_mask = load_and_process(args.data_dir, args.train_nodes_num, args.eval_nodes_num, args.test_nodes_num) feature_path = os.path.join(args.result_path, '00_data') biases_path = os.path.join(args.result_path, '01_data') y_test_path = os.path.join(args.result_path, 'y_test.npy') test_mask_path = os.path.join(args.result_path, 'test_mask.npy') os.makedirs(feature_path) os.makedirs(biases_path) feature.tofile(os.path.join(feature_path, 'feature.bin')) biases.tofile(os.path.join(biases_path, 'biases.bin')) np.save(y_test_path, y_test) np.save(test_mask_path, test_mask) if __name__ == "__main__": generate_bin()

true

790828ca9c00194932cd39def66cd6c4ddcbb404

559

py

Python

Python_proficiency_test/latex/codes/17b.py

ALFA-group/neural_program_comprehension

0253911f376cf282af5a5627e38e0a591ad38860

[ "MIT" ]

6

2020-04-24T08:16:51.000Z

2021-11-01T09:50:46.000Z

Python_proficiency_test/latex/codes/17b.py

ALFA-group/neural_program_comprehension

0253911f376cf282af5a5627e38e0a591ad38860

[ "MIT" ]

null

Python_proficiency_test/latex/codes/17b.py

ALFA-group/neural_program_comprehension

0253911f376cf282af5a5627e38e0a591ad38860

[ "MIT" ]

4

2021-02-17T20:21:31.000Z

2022-02-14T12:43:23.000Z

File "test.py", line 15, in <module> print(find_lowest(a)) File "test.py", line 12, in find_lowest return lowest(lst[0], lst[1:]) File "test.py", line 9, in lowest return lowest(rest[0], rest[1:]) File "test.py", line 9, in lowest return lowest(rest[0], rest[1:]) File "test.py", line 13, in lowest return lowest(first, rest) File "test.py", line 13, in lowest return lowest(first, rest) [Previous line repeated 993 more times] File "test.py", line 6, in lowest if len(rest) == 0: RecursionError: maximum recursion depth exceeded in comparison

34.9375

62

0.697674

File "test.py", line 15, in <module> print(find_lowest(a)) File "test.py", line 12, in find_lowest return lowest(lst[0], lst[1:]) File "test.py", line 9, in lowest return lowest(rest[0], rest[1:]) File "test.py", line 9, in lowest return lowest(rest[0], rest[1:]) File "test.py", line 13, in lowest return lowest(first, rest) File "test.py", line 13, in lowest return lowest(first, rest) [Previous line repeated 993 more times] File "test.py", line 6, in lowest if len(rest) == 0: RecursionError: maximum recursion depth exceeded in comparison

false

true

790828ffc0a860859b4ae454d22ab9603f5c2c72

10,550

py

Python

sdk/python/pulumi_azure_nextgen/devtestlab/schedule.py

pulumi/pulumi-azure-nextgen

452736b0a1cf584c2d4c04666e017af6e9b2c15c

[ "Apache-2.0" ]

31

2020-09-21T09:41:01.000Z

2021-02-26T13:21:59.000Z

sdk/python/pulumi_azure_nextgen/devtestlab/schedule.py

pulumi/pulumi-azure-nextgen

452736b0a1cf584c2d4c04666e017af6e9b2c15c

[ "Apache-2.0" ]

231

2020-09-21T09:38:45.000Z

2021-03-01T11:16:03.000Z

sdk/python/pulumi_azure_nextgen/devtestlab/schedule.py

pulumi/pulumi-azure-nextgen

452736b0a1cf584c2d4c04666e017af6e9b2c15c

[ "Apache-2.0" ]

4

2020-09-29T14:14:59.000Z

2021-02-10T20:38:16.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from .. import _utilities, _tables from . import outputs from ._enums import * from ._inputs import * __all__ = ['Schedule'] class Schedule(pulumi.CustomResource): def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, daily_recurrence: Optional[pulumi.Input[pulumi.InputType['DayDetailsArgs']]] = None, hourly_recurrence: Optional[pulumi.Input[pulumi.InputType['HourDetailsArgs']]] = None, lab_name: Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, notification_settings: Optional[pulumi.Input[pulumi.InputType['NotificationSettingsArgs']]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, status: Optional[pulumi.Input[Union[str, 'EnableStatus']]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, target_resource_id: Optional[pulumi.Input[str]] = None, task_type: Optional[pulumi.Input[str]] = None, time_zone_id: Optional[pulumi.Input[str]] = None, weekly_recurrence: Optional[pulumi.Input[pulumi.InputType['WeekDetailsArgs']]] = None, __props__=None, __name__=None, __opts__=None): """ A schedule. API Version: 2018-09-15. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[pulumi.InputType['DayDetailsArgs']] daily_recurrence: If the schedule will occur once each day of the week, specify the daily recurrence. :param pulumi.Input[pulumi.InputType['HourDetailsArgs']] hourly_recurrence: If the schedule will occur multiple times a day, specify the hourly recurrence. :param pulumi.Input[str] lab_name: The name of the lab. :param pulumi.Input[str] location: The location of the resource. :param pulumi.Input[str] name: The name of the schedule. :param pulumi.Input[pulumi.InputType['NotificationSettingsArgs']] notification_settings: Notification settings. :param pulumi.Input[str] resource_group_name: The name of the resource group. :param pulumi.Input[Union[str, 'EnableStatus']] status: The status of the schedule (i.e. Enabled, Disabled) :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: The tags of the resource. :param pulumi.Input[str] target_resource_id: The resource ID to which the schedule belongs :param pulumi.Input[str] task_type: The task type of the schedule (e.g. LabVmsShutdownTask, LabVmAutoStart). :param pulumi.Input[str] time_zone_id: The time zone ID (e.g. Pacific Standard time). :param pulumi.Input[pulumi.InputType['WeekDetailsArgs']] weekly_recurrence: If the schedule will occur only some days of the week, specify the weekly recurrence. """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['daily_recurrence'] = daily_recurrence __props__['hourly_recurrence'] = hourly_recurrence if lab_name is None and not opts.urn: raise TypeError("Missing required property 'lab_name'") __props__['lab_name'] = lab_name __props__['location'] = location __props__['name'] = name __props__['notification_settings'] = notification_settings if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name __props__['status'] = status __props__['tags'] = tags __props__['target_resource_id'] = target_resource_id __props__['task_type'] = task_type __props__['time_zone_id'] = time_zone_id __props__['weekly_recurrence'] = weekly_recurrence __props__['created_date'] = None __props__['provisioning_state'] = None __props__['type'] = None __props__['unique_identifier'] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:devtestlab/latest:Schedule"), pulumi.Alias(type_="azure-nextgen:devtestlab/v20150521preview:Schedule"), pulumi.Alias(type_="azure-nextgen:devtestlab/v20160515:Schedule"), pulumi.Alias(type_="azure-nextgen:devtestlab/v20180915:Schedule")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(Schedule, __self__).__init__( 'azure-nextgen:devtestlab:Schedule', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'Schedule': """ Get an existing Schedule resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() return Schedule(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="createdDate") def created_date(self) -> pulumi.Output[str]: """ The creation date of the schedule. """ return pulumi.get(self, "created_date") @property @pulumi.getter(name="dailyRecurrence") def daily_recurrence(self) -> pulumi.Output[Optional['outputs.DayDetailsResponse']]: """ If the schedule will occur once each day of the week, specify the daily recurrence. """ return pulumi.get(self, "daily_recurrence") @property @pulumi.getter(name="hourlyRecurrence") def hourly_recurrence(self) -> pulumi.Output[Optional['outputs.HourDetailsResponse']]: """ If the schedule will occur multiple times a day, specify the hourly recurrence. """ return pulumi.get(self, "hourly_recurrence") @property @pulumi.getter def location(self) -> pulumi.Output[Optional[str]]: """ The location of the resource. """ return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The name of the resource. """ return pulumi.get(self, "name") @property @pulumi.getter(name="notificationSettings") def notification_settings(self) -> pulumi.Output[Optional['outputs.NotificationSettingsResponse']]: """ Notification settings. """ return pulumi.get(self, "notification_settings") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> pulumi.Output[str]: """ The provisioning status of the resource. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter def status(self) -> pulumi.Output[Optional[str]]: """ The status of the schedule (i.e. Enabled, Disabled) """ return pulumi.get(self, "status") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: """ The tags of the resource. """ return pulumi.get(self, "tags") @property @pulumi.getter(name="targetResourceId") def target_resource_id(self) -> pulumi.Output[Optional[str]]: """ The resource ID to which the schedule belongs """ return pulumi.get(self, "target_resource_id") @property @pulumi.getter(name="taskType") def task_type(self) -> pulumi.Output[Optional[str]]: """ The task type of the schedule (e.g. LabVmsShutdownTask, LabVmAutoStart). """ return pulumi.get(self, "task_type") @property @pulumi.getter(name="timeZoneId") def time_zone_id(self) -> pulumi.Output[Optional[str]]: """ The time zone ID (e.g. Pacific Standard time). """ return pulumi.get(self, "time_zone_id") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ The type of the resource. """ return pulumi.get(self, "type") @property @pulumi.getter(name="uniqueIdentifier") def unique_identifier(self) -> pulumi.Output[str]: """ The unique immutable identifier of a resource (Guid). """ return pulumi.get(self, "unique_identifier") @property @pulumi.getter(name="weeklyRecurrence") def weekly_recurrence(self) -> pulumi.Output[Optional['outputs.WeekDetailsResponse']]: """ If the schedule will occur only some days of the week, specify the weekly recurrence. """ return pulumi.get(self, "weekly_recurrence") def translate_output_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

42.712551

325

0.645308

import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from .. import _utilities, _tables from . import outputs from ._enums import * from ._inputs import * __all__ = ['Schedule'] class Schedule(pulumi.CustomResource): def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, daily_recurrence: Optional[pulumi.Input[pulumi.InputType['DayDetailsArgs']]] = None, hourly_recurrence: Optional[pulumi.Input[pulumi.InputType['HourDetailsArgs']]] = None, lab_name: Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, notification_settings: Optional[pulumi.Input[pulumi.InputType['NotificationSettingsArgs']]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, status: Optional[pulumi.Input[Union[str, 'EnableStatus']]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, target_resource_id: Optional[pulumi.Input[str]] = None, task_type: Optional[pulumi.Input[str]] = None, time_zone_id: Optional[pulumi.Input[str]] = None, weekly_recurrence: Optional[pulumi.Input[pulumi.InputType['WeekDetailsArgs']]] = None, __props__=None, __name__=None, __opts__=None): if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['daily_recurrence'] = daily_recurrence __props__['hourly_recurrence'] = hourly_recurrence if lab_name is None and not opts.urn: raise TypeError("Missing required property 'lab_name'") __props__['lab_name'] = lab_name __props__['location'] = location __props__['name'] = name __props__['notification_settings'] = notification_settings if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name __props__['status'] = status __props__['tags'] = tags __props__['target_resource_id'] = target_resource_id __props__['task_type'] = task_type __props__['time_zone_id'] = time_zone_id __props__['weekly_recurrence'] = weekly_recurrence __props__['created_date'] = None __props__['provisioning_state'] = None __props__['type'] = None __props__['unique_identifier'] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:devtestlab/latest:Schedule"), pulumi.Alias(type_="azure-nextgen:devtestlab/v20150521preview:Schedule"), pulumi.Alias(type_="azure-nextgen:devtestlab/v20160515:Schedule"), pulumi.Alias(type_="azure-nextgen:devtestlab/v20180915:Schedule")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(Schedule, __self__).__init__( 'azure-nextgen:devtestlab:Schedule', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'Schedule': opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() return Schedule(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="createdDate") def created_date(self) -> pulumi.Output[str]: return pulumi.get(self, "created_date") @property @pulumi.getter(name="dailyRecurrence") def daily_recurrence(self) -> pulumi.Output[Optional['outputs.DayDetailsResponse']]: return pulumi.get(self, "daily_recurrence") @property @pulumi.getter(name="hourlyRecurrence") def hourly_recurrence(self) -> pulumi.Output[Optional['outputs.HourDetailsResponse']]: return pulumi.get(self, "hourly_recurrence") @property @pulumi.getter def location(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> pulumi.Output[str]: return pulumi.get(self, "name") @property @pulumi.getter(name="notificationSettings") def notification_settings(self) -> pulumi.Output[Optional['outputs.NotificationSettingsResponse']]: return pulumi.get(self, "notification_settings") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> pulumi.Output[str]: return pulumi.get(self, "provisioning_state") @property @pulumi.getter def status(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "status") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: return pulumi.get(self, "tags") @property @pulumi.getter(name="targetResourceId") def target_resource_id(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "target_resource_id") @property @pulumi.getter(name="taskType") def task_type(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "task_type") @property @pulumi.getter(name="timeZoneId") def time_zone_id(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "time_zone_id") @property @pulumi.getter def type(self) -> pulumi.Output[str]: return pulumi.get(self, "type") @property @pulumi.getter(name="uniqueIdentifier") def unique_identifier(self) -> pulumi.Output[str]: return pulumi.get(self, "unique_identifier") @property @pulumi.getter(name="weeklyRecurrence") def weekly_recurrence(self) -> pulumi.Output[Optional['outputs.WeekDetailsResponse']]: return pulumi.get(self, "weekly_recurrence") def translate_output_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

true

79082c61a21e15c0ea4ab2ea19ffb90a66c8cd64

1,271

py

Python

cmsplugin_cascade/sphinx/cms_menus.py

beeduino/djangocms-cascade

42424dfa40d887491d37c0a34386e8c1c94e1b14

[ "MIT" ]

null

cmsplugin_cascade/sphinx/cms_menus.py

beeduino/djangocms-cascade

42424dfa40d887491d37c0a34386e8c1c94e1b14

[ "MIT" ]

null

cmsplugin_cascade/sphinx/cms_menus.py

beeduino/djangocms-cascade

42424dfa40d887491d37c0a34386e8c1c94e1b14

[ "MIT" ]

null

import io import json import os from django.conf import settings from django.urls import reverse_lazy from django.utils.translation import gettext_lazy as _ from cms.menu_bases import CMSAttachMenu from menus.base import NavigationNode from menus.menu_pool import menu_pool class DocumentationMenu(CMSAttachMenu): name = _("Documentation Menu") # give the menu a name this is required. def get_nodes(self, request): """ This method is used to build the menu tree. """ nodes = [] docsmap_file = os.path.join(settings.SPHINX_DOCS_ROOT, 'docsmap.json') if not os.path.exists(docsmap_file): return nodes with io.open(docsmap_file) as fh: docs_map = json.load(fh, encoding='utf-8') for counter, items in enumerate(docs_map.items(), 1): bits = items[0].split('/') if len(bits) == 1 and bits[0] == 'index' or len(bits) == 2 and bits[1] != 'index': continue node = NavigationNode( title=items[1], url=reverse_lazy('sphinx-documentation', args=(bits[0],)), id=counter, ) nodes.append(node) return nodes menu_pool.register_menu(DocumentationMenu)

32.589744

94

0.623131

import io import json import os from django.conf import settings from django.urls import reverse_lazy from django.utils.translation import gettext_lazy as _ from cms.menu_bases import CMSAttachMenu from menus.base import NavigationNode from menus.menu_pool import menu_pool class DocumentationMenu(CMSAttachMenu): name = _("Documentation Menu") def get_nodes(self, request): nodes = [] docsmap_file = os.path.join(settings.SPHINX_DOCS_ROOT, 'docsmap.json') if not os.path.exists(docsmap_file): return nodes with io.open(docsmap_file) as fh: docs_map = json.load(fh, encoding='utf-8') for counter, items in enumerate(docs_map.items(), 1): bits = items[0].split('/') if len(bits) == 1 and bits[0] == 'index' or len(bits) == 2 and bits[1] != 'index': continue node = NavigationNode( title=items[1], url=reverse_lazy('sphinx-documentation', args=(bits[0],)), id=counter, ) nodes.append(node) return nodes menu_pool.register_menu(DocumentationMenu)

true

79082c89d257459ac7585963e578cfc156a719da

393

py

Python

infosafe/asgi.py

royaleagle-dev/infosafe

fcb00a67d6a8fdd3d2e032b53b56bbcf35d844b6

[ "Apache-2.0" ]

null

infosafe/asgi.py

royaleagle-dev/infosafe

fcb00a67d6a8fdd3d2e032b53b56bbcf35d844b6

[ "Apache-2.0" ]

null

infosafe/asgi.py

royaleagle-dev/infosafe

fcb00a67d6a8fdd3d2e032b53b56bbcf35d844b6

[ "Apache-2.0" ]

null

""" ASGI config for infosafe project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'infosafe.settings') application = get_asgi_application()

23.117647

78

0.78626

import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'infosafe.settings') application = get_asgi_application()

true

79082dd5ad44d03f1472ba27987080642ff0d733

3,126

py

Python

transcribe.py

chenchy/onsets-and-frames

af7ac2d2e65cba1f6442b81317328d96b3700b26

[ "MIT" ]

149

2019-01-22T23:39:27.000Z

2022-03-30T17:57:57.000Z

transcribe.py

chenchy/onsets-and-frames

af7ac2d2e65cba1f6442b81317328d96b3700b26

[ "MIT" ]

27

2019-03-05T01:17:21.000Z

2022-03-06T07:10:29.000Z

transcribe.py

chenchy/onsets-and-frames

af7ac2d2e65cba1f6442b81317328d96b3700b26

[ "MIT" ]

61

2019-04-09T08:07:05.000Z

2022-02-23T03:49:18.000Z

import argparse import os import sys import numpy as np import soundfile from mir_eval.util import midi_to_hz from onsets_and_frames import * def load_and_process_audio(flac_path, sequence_length, device): random = np.random.RandomState(seed=42) audio, sr = soundfile.read(flac_path, dtype='int16') assert sr == SAMPLE_RATE audio = torch.ShortTensor(audio) if sequence_length is not None: audio_length = len(audio) step_begin = random.randint(audio_length - sequence_length) // HOP_LENGTH n_steps = sequence_length // HOP_LENGTH begin = step_begin * HOP_LENGTH end = begin + sequence_length audio = audio[begin:end].to(device) else: audio = audio.to(device) audio = audio.float().div_(32768.0) return audio def transcribe(model, audio): mel = melspectrogram(audio.reshape(-1, audio.shape[-1])[:, :-1]).transpose(-1, -2) onset_pred, offset_pred, _, frame_pred, velocity_pred = model(mel) predictions = { 'onset': onset_pred.reshape((onset_pred.shape[1], onset_pred.shape[2])), 'offset': offset_pred.reshape((offset_pred.shape[1], offset_pred.shape[2])), 'frame': frame_pred.reshape((frame_pred.shape[1], frame_pred.shape[2])), 'velocity': velocity_pred.reshape((velocity_pred.shape[1], velocity_pred.shape[2])) } return predictions def transcribe_file(model_file, flac_paths, save_path, sequence_length, onset_threshold, frame_threshold, device): model = torch.load(model_file, map_location=device).eval() summary(model) for flac_path in flac_paths: print(f'Processing {flac_path}...', file=sys.stderr) audio = load_and_process_audio(flac_path, sequence_length, device) predictions = transcribe(model, audio) p_est, i_est, v_est = extract_notes(predictions['onset'], predictions['frame'], predictions['velocity'], onset_threshold, frame_threshold) scaling = HOP_LENGTH / SAMPLE_RATE i_est = (i_est * scaling).reshape(-1, 2) p_est = np.array([midi_to_hz(MIN_MIDI + midi) for midi in p_est]) os.makedirs(save_path, exist_ok=True) pred_path = os.path.join(save_path, os.path.basename(flac_path) + '.pred.png') save_pianoroll(pred_path, predictions['onset'], predictions['frame']) midi_path = os.path.join(save_path, os.path.basename(flac_path) + '.pred.mid') save_midi(midi_path, p_est, i_est, v_est) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('model_file', type=str) parser.add_argument('flac_paths', type=str, nargs='+') parser.add_argument('--save-path', type=str, default='.') parser.add_argument('--sequence-length', default=None, type=int) parser.add_argument('--onset-threshold', default=0.5, type=float) parser.add_argument('--frame-threshold', default=0.5, type=float) parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu') with torch.no_grad(): transcribe_file(**vars(parser.parse_args()))

34.733333

146

0.680742

import argparse import os import sys import numpy as np import soundfile from mir_eval.util import midi_to_hz from onsets_and_frames import * def load_and_process_audio(flac_path, sequence_length, device): random = np.random.RandomState(seed=42) audio, sr = soundfile.read(flac_path, dtype='int16') assert sr == SAMPLE_RATE audio = torch.ShortTensor(audio) if sequence_length is not None: audio_length = len(audio) step_begin = random.randint(audio_length - sequence_length) // HOP_LENGTH n_steps = sequence_length // HOP_LENGTH begin = step_begin * HOP_LENGTH end = begin + sequence_length audio = audio[begin:end].to(device) else: audio = audio.to(device) audio = audio.float().div_(32768.0) return audio def transcribe(model, audio): mel = melspectrogram(audio.reshape(-1, audio.shape[-1])[:, :-1]).transpose(-1, -2) onset_pred, offset_pred, _, frame_pred, velocity_pred = model(mel) predictions = { 'onset': onset_pred.reshape((onset_pred.shape[1], onset_pred.shape[2])), 'offset': offset_pred.reshape((offset_pred.shape[1], offset_pred.shape[2])), 'frame': frame_pred.reshape((frame_pred.shape[1], frame_pred.shape[2])), 'velocity': velocity_pred.reshape((velocity_pred.shape[1], velocity_pred.shape[2])) } return predictions def transcribe_file(model_file, flac_paths, save_path, sequence_length, onset_threshold, frame_threshold, device): model = torch.load(model_file, map_location=device).eval() summary(model) for flac_path in flac_paths: print(f'Processing {flac_path}...', file=sys.stderr) audio = load_and_process_audio(flac_path, sequence_length, device) predictions = transcribe(model, audio) p_est, i_est, v_est = extract_notes(predictions['onset'], predictions['frame'], predictions['velocity'], onset_threshold, frame_threshold) scaling = HOP_LENGTH / SAMPLE_RATE i_est = (i_est * scaling).reshape(-1, 2) p_est = np.array([midi_to_hz(MIN_MIDI + midi) for midi in p_est]) os.makedirs(save_path, exist_ok=True) pred_path = os.path.join(save_path, os.path.basename(flac_path) + '.pred.png') save_pianoroll(pred_path, predictions['onset'], predictions['frame']) midi_path = os.path.join(save_path, os.path.basename(flac_path) + '.pred.mid') save_midi(midi_path, p_est, i_est, v_est) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('model_file', type=str) parser.add_argument('flac_paths', type=str, nargs='+') parser.add_argument('--save-path', type=str, default='.') parser.add_argument('--sequence-length', default=None, type=int) parser.add_argument('--onset-threshold', default=0.5, type=float) parser.add_argument('--frame-threshold', default=0.5, type=float) parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu') with torch.no_grad(): transcribe_file(**vars(parser.parse_args()))

true

79082e1284ddd2cfd157e322ff9b49d4d7a692b5

2,018

py

Python

app/blog/models.py

jayjodev/oncollegehub

5633df8beaef232d58025c4407bd9e25bd349e49

[ "MIT" ]

2

2018-11-14T17:08:05.000Z

2018-11-14T17:08:38.000Z

app/blog/models.py

jayjodev/oncollegehub

5633df8beaef232d58025c4407bd9e25bd349e49

[ "MIT" ]

16

2020-01-11T04:09:50.000Z

2022-03-12T00:11:19.000Z

app/blog/models.py

jayjodev/oncollegehub

5633df8beaef232d58025c4407bd9e25bd349e49

[ "MIT" ]

2

2018-11-14T17:08:07.000Z

2018-11-28T21:38:16.000Z

from django.db import models from django.utils import timezone from django.core.exceptions import ValidationError # from django.contrib.auth.models import User from users.models import Student, College from django.urls import reverse from django.core import validators class AbstractPostModel(models.Model): title = models.CharField(validators=[validators.MinLengthValidator(10)], null=False, max_length=500) content = models.TextField(validators=[validators.MinLengthValidator(10)], null=False) post_date = models.DateTimeField(default=timezone.now) author = models.ForeignKey(Student, on_delete=models.CASCADE) rating = models.IntegerField(default=0) college = models.ForeignKey(College, on_delete=models.CASCADE) class Meta: abstract = True def __str__(self): return self.title def get_absolute_url(self): return reverse('post-detail', kwargs={'pk': self.pk, 'title': self.title}) class Question(AbstractPostModel): is_answered = models.BooleanField(default=False) class Answer(AbstractPostModel): is_approved = models.BooleanField(default=False) question = models.ForeignKey(Question, on_delete=models.CASCADE, null=True) class Voter(models.Model): Question = models.ForeignKey(Question, on_delete=models.CASCADE) Answer = models.ForeignKey(Answer, on_delete=models.CASCADE, null=True) user = models.ForeignKey(Student, on_delete=models.CASCADE) def __str__(self): return self.user.username + ' vote on post: ' + self.Question.title class Comment(AbstractPostModel): Question = models.ForeignKey(Question, on_delete=models.CASCADE) author = models.ForeignKey(Student, on_delete=models.CASCADE) content = models.TextField(null=False) def __str__(self): return self.author.username + ' comment on post: ' + self.Question.title def get_absolute_url(self): return reverse('post-detail', kwargs={'pk': self.pk, 'title': self.Question.title})

35.403509

91

0.733399

from django.db import models from django.utils import timezone from django.core.exceptions import ValidationError from users.models import Student, College from django.urls import reverse from django.core import validators class AbstractPostModel(models.Model): title = models.CharField(validators=[validators.MinLengthValidator(10)], null=False, max_length=500) content = models.TextField(validators=[validators.MinLengthValidator(10)], null=False) post_date = models.DateTimeField(default=timezone.now) author = models.ForeignKey(Student, on_delete=models.CASCADE) rating = models.IntegerField(default=0) college = models.ForeignKey(College, on_delete=models.CASCADE) class Meta: abstract = True def __str__(self): return self.title def get_absolute_url(self): return reverse('post-detail', kwargs={'pk': self.pk, 'title': self.title}) class Question(AbstractPostModel): is_answered = models.BooleanField(default=False) class Answer(AbstractPostModel): is_approved = models.BooleanField(default=False) question = models.ForeignKey(Question, on_delete=models.CASCADE, null=True) class Voter(models.Model): Question = models.ForeignKey(Question, on_delete=models.CASCADE) Answer = models.ForeignKey(Answer, on_delete=models.CASCADE, null=True) user = models.ForeignKey(Student, on_delete=models.CASCADE) def __str__(self): return self.user.username + ' vote on post: ' + self.Question.title class Comment(AbstractPostModel): Question = models.ForeignKey(Question, on_delete=models.CASCADE) author = models.ForeignKey(Student, on_delete=models.CASCADE) content = models.TextField(null=False) def __str__(self): return self.author.username + ' comment on post: ' + self.Question.title def get_absolute_url(self): return reverse('post-detail', kwargs={'pk': self.pk, 'title': self.Question.title})

true

79082ed18ca17e046b01b7f68ba9c15e03e31ff6

8,102

py

Python

connectomics/config/config.py

divyam-goel/pytorch_connectomics

a2c70a7cc60fd84d67be6f225c123ff11daadb83

[ "MIT" ]

null

connectomics/config/config.py

divyam-goel/pytorch_connectomics

a2c70a7cc60fd84d67be6f225c123ff11daadb83

[ "MIT" ]

null

connectomics/config/config.py

divyam-goel/pytorch_connectomics

a2c70a7cc60fd84d67be6f225c123ff11daadb83

[ "MIT" ]

null

import os from yacs.config import CfgNode as CN # ----------------------------------------------------------------------------- # Config definition # ----------------------------------------------------------------------------- _C = CN() # ----------------------------------------------------------------------------- # System # ----------------------------------------------------------------------------- _C.SYSTEM = CN() _C.SYSTEM.NUM_GPUS = 4 _C.SYSTEM.NUM_CPUS = 4 # ----------------------------------------------------------------------------- # Model # ----------------------------------------------------------------------------- _C.MODEL = CN() # Model architectures defined in the package: unet_super, super, fpn, unet_residual_3d _C.MODEL.ARCHITECTURE = 'unet_residual_3d' # Number of filters per unet block _C.MODEL.FILTERS = [28, 36, 48, 64, 80] _C.MODEL.TARGET_OPT = ['0'] _C.MODEL.WEIGHT_OPT = [['1']] # Choose the right loss function for each target: # 'WeightedMSE', 'WeightedBCE', 'JaccardLoss', 'DiceLoss' _C.MODEL.LOSS_OPTION = [['WeightedBCE']] # Weight for each loss function _C.MODEL.LOSS_WEIGHT = [[1.0]] # Define the number of input channels. Usually EM images are # single-channel gray-scale image. _C.MODEL.IN_PLANES = 1 # Define the number of output channels. _C.MODEL.OUT_PLANES = 1 # Padding mode, possible options: 'zeros','circular', 'rep' _C.MODEL.PAD_MODE = 'rep' # Normalization mode, possible options: 'bn', 'abn', 'in', 'bin' _C.MODEL.NORM_MODE = 'bn' # Activation mode, possible options: 'relu', 'elu', 'leaky' _C.MODEL.ACT_MODE = 'elu' # If MODEL.EMBEDDING = 1 will do embedding _C.MODEL.EMBEDDING = 1 # Last decoder head depth _C.MODEL.HEAD_DEPTH = 1 _C.MODEL.INPUT_SIZE = [8, 256, 256] _C.MODEL.OUTPUT_SIZE = [8, 256, 256] _C.MODEL.REGU_OPT = [] _C.MODEL.REGU_WEIGHT = [] # Fine-tune suffix for model saving _C.MODEL.FINETUNE = '' # Exact matching: the weights shape in pretrain model and current model are identical _C.MODEL.EXACT = True _C.MODEL.SIZE_MATCH = True _C.MODEL.PRE_MODEL = '' _C.MODEL.PRE_MODEL_LAYER = [''] _C.MODEL.PRE_MODEL_ITER = 0 _C.MODEL.PRE_MODEL_LAYER_SELECT = [-1] # ----------------------------------------------------------------------------- # Dataset # ----------------------------------------------------------------------------- _C.DATASET = CN() # Scale ratio of the input data for different resolutions. # Using a DATA_SCALE of [1., 0.5, 0.5] will downsample the # original image by two times (e.g., 4nm -> 8nm). _C.DATASET.DATA_SCALE = [1., 1., 1.] # Scaling factor for super resolution _C.DATASET.SCALE_FACTOR = [2, 3, 3] # Specify the data path in the *.yaml files for different experiments. _C.DATASET.IMAGE_NAME = '' _C.DATASET.LABEL_NAME = '' _C.DATASET.INPUT_PATH = '' _C.DATASET.OUTPUT_PATH = '' # Padding size for the input volumes _C.DATASET.PAD_SIZE = [2, 64, 64] # Half Patch size for 2D label erosion _C.DATASET.LABEL_EROSION = 0 # If it's a binary label _C.DATASET.LABEL_BINARY = False _C.DATASET.LABEL_MAG = 0 # Data in tile format or not. _C.DATASET.DO_CHUNK_TITLE = 0 # Chunk parameters for tile format: chunk_num (z,y,x), chunk_stride _C.DATASET.DATA_CHUNK_NUM = [1, 1, 1] # Predefined data chunk to iterate through _C.DATASET.DATA_CHUNK_NUM_IND = [] # Boolean variable, euqal to 'int(args.data_chunk_num[-1:])==1' _C.DATASET.DATA_CHUNK_STRIDE = True # Chunk parameters for tile format: chunk_iter_num _C.DATASET.DATA_CHUNK_ITER = 1000 # Number of voxel to exceed for a valid sample _C.DATASET.DATA_INVALID_THRES = [0., 0.] _C.DATASET.PRE_LOAD_DATA = [None,None,None] # Reject sampling _C.DATASET.REJECT_SIZE_THRES = 100 _C.DATASET.REJECT_P = 0.95 # ----------------------------------------------------------------------------- # Augmentor # ----------------------------------------------------------------------------- _C.AUGMENTOR = CN() _C.AUGMENTOR.ROTATE = True # Probability of applying the rotation augmentation _C.AUGMENTOR.ROTATE_P = 0.1 _C.AUGMENTOR.RESCALE = True # Probability of applying the rescale augmentation _C.AUGMENTOR.RESCALE_P = 0.5 _C.AUGMENTOR.FLIP = True # Probability of applying the flip augmentation _C.AUGMENTOR.FLIP_P = 1.0 # Conducting x-z and y-z flip only when the dataset is isotropic. _C.AUGMENTOR.FLIP_DO_ZTRANS = 0 _C.AUGMENTOR.ELASTIC = True # Maximum pixel-moving distance of elastic transformation _C.AUGMENTOR.ELASTIC_ALPHA = 12.0 # Standard deviation of the Gaussian filter _C.AUGMENTOR.ELASTIC_SIGMA = 4.0 # Probability of applying the elastic augmentation _C.AUGMENTOR.ELASTIC_P = 0.75 _C.AUGMENTOR.GRAYSCALE = True # Probability of applying the grayscale augmentation _C.AUGMENTOR.GRAYSCALE_P = 0.75 _C.AUGMENTOR.MISSINGPARTS = True # Probability of applying the missingparts augmentation _C.AUGMENTOR.MISSINGPARTS_P = 0.9 _C.AUGMENTOR.MISSINGSECTION = True # Probability of applying the missingsection augmentation _C.AUGMENTOR.MISSINGSECTION_P = 0.5 _C.AUGMENTOR.MISALIGNMENT = True # Probability of applying the misalignment augmentation _C.AUGMENTOR.MISALIGNMENT_P = 1.0 # Maximum pixel displacement in each direction (x and y) (int) _C.AUGMENTOR.MISALIGNMENT_DISPLACEMENT = 16 # ----------------------------------------------------------------------------- # Solver # ----------------------------------------------------------------------------- _C.SOLVER = CN() # Specify the learning rate scheduler. _C.SOLVER.LR_SCHEDULER_NAME = "MultiStepLR" _C.SOLVER.ITERATION_STEP = 1 _C.SOLVER.ITERATION_SAVE = 5000 _C.SOLVER.ITERATION_TOTAL = 40000 _C.SOLVER.BASE_LR = 0.001 _C.SOLVER.BIAS_LR_FACTOR = 1.0 _C.SOLVER.WEIGHT_DECAY_BIAS = 0.0 _C.SOLVER.MOMENTUM = 0.9 # The weight decay that's applied to parameters of normalization layers # (typically the affine transformation) _C.SOLVER.WEIGHT_DECAY = 0.0001 _C.SOLVER.WEIGHT_DECAY_NORM = 0.0 # The iteration number to decrease learning rate by GAMMA _C.SOLVER.GAMMA = 0.1 # should be a tuple like (30000,) _C.SOLVER.STEPS = (30000, 35000) _C.SOLVER.WARMUP_FACTOR = 1.0 / 1000 _C.SOLVER.WARMUP_ITERS = 1000 _C.SOLVER.WARMUP_METHOD = "linear" # Save a checkpoint after every this number of iterations _C.SOLVER.CHECKPOINT_PERIOD = 5000 # Number of samples per batch across all machines. # If we have 16 GPUs and IMS_PER_BATCH = 32, # each GPU will see 2 images per batch. _C.SOLVER.SAMPLES_PER_BATCH = 16 # ----------------------------------------------------------------------------- # Monitor # ----------------------------------------------------------------------------- _C.MONITOR = CN() _C.MONITOR.LOG_OPT = [1, 1, 0] _C.MONITOR.VIS_OPT = [0, 8] _C.MONITOR.ITERATION_NUM = [10, 50] # # ----------------------------------------------------------------------------- # # Inference # # ----------------------------------------------------------------------------- _C.INFERENCE = CN() _C.INFERENCE.INPUT_SIZE = [8, 256, 256] _C.INFERENCE.OUTPUT_SIZE = [8, 256, 256] _C.INFERENCE.IMAGE_NAME = '' _C.INFERENCE.OUTPUT_PATH = '' _C.INFERENCE.OUTPUT_NAME = 'result.h5' _C.INFERENCE.PAD_SIZE = [8, 64, 64] _C.INFERENCE.STRIDE = [1, 192, 192] _C.INFERENCE.AUG_MODE = 'mean' _C.INFERENCE.AUG_NUM = 4 _C.INFERENCE.DO_EVAL = True _C.INFERENCE.DO_3D = True # If not None then select channel of output _C.INFERENCE.MODEL_OUTPUT_ID = [None] # Number of test workers _C.INFERENCE.TEST_NUM = 1 # Test worker id _C.INFERENCE.TEST_ID = 0 # Batchsize for inference _C.INFERENCE.SAMPLES_PER_BATCH = 32 def get_cfg_defaults(): """Get a yacs CfgNode object with default values for my_project.""" # Return a clone so that the defaults will not be altered # This is for the "local variable" use pattern return _C.clone() def save_all_cfg(cfg, output_dir): """Save configs in the output directory.""" # Save config.yaml in the experiment directory after combine all # non-default configurations from yaml file and command line. path = os.path.join(output_dir, "config.yaml") with open(path, "w") as f: f.write(cfg.dump()) print("Full config saved to {}".format(path))

25.639241

86

0.619477

import os from yacs.config import CfgNode as CN _C = CN() _C.SYSTEM = CN() _C.SYSTEM.NUM_GPUS = 4 _C.SYSTEM.NUM_CPUS = 4 _C.MODEL = CN() _C.MODEL.ARCHITECTURE = 'unet_residual_3d' _C.MODEL.FILTERS = [28, 36, 48, 64, 80] _C.MODEL.TARGET_OPT = ['0'] _C.MODEL.WEIGHT_OPT = [['1']] _C.MODEL.LOSS_OPTION = [['WeightedBCE']] _C.MODEL.LOSS_WEIGHT = [[1.0]] _C.MODEL.IN_PLANES = 1 _C.MODEL.OUT_PLANES = 1 _C.MODEL.PAD_MODE = 'rep' _C.MODEL.NORM_MODE = 'bn' _C.MODEL.ACT_MODE = 'elu' _C.MODEL.EMBEDDING = 1 _C.MODEL.HEAD_DEPTH = 1 _C.MODEL.INPUT_SIZE = [8, 256, 256] _C.MODEL.OUTPUT_SIZE = [8, 256, 256] _C.MODEL.REGU_OPT = [] _C.MODEL.REGU_WEIGHT = [] _C.MODEL.FINETUNE = '' _C.MODEL.EXACT = True _C.MODEL.SIZE_MATCH = True _C.MODEL.PRE_MODEL = '' _C.MODEL.PRE_MODEL_LAYER = [''] _C.MODEL.PRE_MODEL_ITER = 0 _C.MODEL.PRE_MODEL_LAYER_SELECT = [-1] _C.DATASET = CN() _C.DATASET.DATA_SCALE = [1., 1., 1.] _C.DATASET.SCALE_FACTOR = [2, 3, 3] _C.DATASET.IMAGE_NAME = '' _C.DATASET.LABEL_NAME = '' _C.DATASET.INPUT_PATH = '' _C.DATASET.OUTPUT_PATH = '' _C.DATASET.PAD_SIZE = [2, 64, 64] _C.DATASET.LABEL_EROSION = 0 _C.DATASET.LABEL_BINARY = False _C.DATASET.LABEL_MAG = 0 # Data in tile format or not. _C.DATASET.DO_CHUNK_TITLE = 0 # Chunk parameters for tile format: chunk_num (z,y,x), chunk_stride _C.DATASET.DATA_CHUNK_NUM = [1, 1, 1] # Predefined data chunk to iterate through _C.DATASET.DATA_CHUNK_NUM_IND = [] # Boolean variable, euqal to 'int(args.data_chunk_num[-1:])==1' _C.DATASET.DATA_CHUNK_STRIDE = True # Chunk parameters for tile format: chunk_iter_num _C.DATASET.DATA_CHUNK_ITER = 1000 # Number of voxel to exceed for a valid sample _C.DATASET.DATA_INVALID_THRES = [0., 0.] _C.DATASET.PRE_LOAD_DATA = [None,None,None] # Reject sampling _C.DATASET.REJECT_SIZE_THRES = 100 _C.DATASET.REJECT_P = 0.95 # ----------------------------------------------------------------------------- # Augmentor # ----------------------------------------------------------------------------- _C.AUGMENTOR = CN() _C.AUGMENTOR.ROTATE = True # Probability of applying the rotation augmentation _C.AUGMENTOR.ROTATE_P = 0.1 _C.AUGMENTOR.RESCALE = True # Probability of applying the rescale augmentation _C.AUGMENTOR.RESCALE_P = 0.5 _C.AUGMENTOR.FLIP = True # Probability of applying the flip augmentation _C.AUGMENTOR.FLIP_P = 1.0 # Conducting x-z and y-z flip only when the dataset is isotropic. _C.AUGMENTOR.FLIP_DO_ZTRANS = 0 _C.AUGMENTOR.ELASTIC = True # Maximum pixel-moving distance of elastic transformation _C.AUGMENTOR.ELASTIC_ALPHA = 12.0 # Standard deviation of the Gaussian filter _C.AUGMENTOR.ELASTIC_SIGMA = 4.0 # Probability of applying the elastic augmentation _C.AUGMENTOR.ELASTIC_P = 0.75 _C.AUGMENTOR.GRAYSCALE = True # Probability of applying the grayscale augmentation _C.AUGMENTOR.GRAYSCALE_P = 0.75 _C.AUGMENTOR.MISSINGPARTS = True # Probability of applying the missingparts augmentation _C.AUGMENTOR.MISSINGPARTS_P = 0.9 _C.AUGMENTOR.MISSINGSECTION = True # Probability of applying the missingsection augmentation _C.AUGMENTOR.MISSINGSECTION_P = 0.5 _C.AUGMENTOR.MISALIGNMENT = True # Probability of applying the misalignment augmentation _C.AUGMENTOR.MISALIGNMENT_P = 1.0 # Maximum pixel displacement in each direction (x and y) (int) _C.AUGMENTOR.MISALIGNMENT_DISPLACEMENT = 16 # ----------------------------------------------------------------------------- # Solver # ----------------------------------------------------------------------------- _C.SOLVER = CN() # Specify the learning rate scheduler. _C.SOLVER.LR_SCHEDULER_NAME = "MultiStepLR" _C.SOLVER.ITERATION_STEP = 1 _C.SOLVER.ITERATION_SAVE = 5000 _C.SOLVER.ITERATION_TOTAL = 40000 _C.SOLVER.BASE_LR = 0.001 _C.SOLVER.BIAS_LR_FACTOR = 1.0 _C.SOLVER.WEIGHT_DECAY_BIAS = 0.0 _C.SOLVER.MOMENTUM = 0.9 # The weight decay that's applied to parameters of normalization layers _C.SOLVER.WEIGHT_DECAY = 0.0001 _C.SOLVER.WEIGHT_DECAY_NORM = 0.0 _C.SOLVER.GAMMA = 0.1 _C.SOLVER.STEPS = (30000, 35000) _C.SOLVER.WARMUP_FACTOR = 1.0 / 1000 _C.SOLVER.WARMUP_ITERS = 1000 _C.SOLVER.WARMUP_METHOD = "linear" _C.SOLVER.CHECKPOINT_PERIOD = 5000 _C.SOLVER.SAMPLES_PER_BATCH = 16 _C.MONITOR = CN() _C.MONITOR.LOG_OPT = [1, 1, 0] _C.MONITOR.VIS_OPT = [0, 8] _C.MONITOR.ITERATION_NUM = [10, 50] C.INFERENCE.OUTPUT_NAME = 'result.h5' _C.INFERENCE.PAD_SIZE = [8, 64, 64] _C.INFERENCE.STRIDE = [1, 192, 192] _C.INFERENCE.AUG_MODE = 'mean' _C.INFERENCE.AUG_NUM = 4 _C.INFERENCE.DO_EVAL = True _C.INFERENCE.DO_3D = True _C.INFERENCE.MODEL_OUTPUT_ID = [None] _C.INFERENCE.TEST_NUM = 1 _C.INFERENCE.TEST_ID = 0 _C.INFERENCE.SAMPLES_PER_BATCH = 32 def get_cfg_defaults(): return _C.clone() def save_all_cfg(cfg, output_dir): path = os.path.join(output_dir, "config.yaml") with open(path, "w") as f: f.write(cfg.dump()) print("Full config saved to {}".format(path))

true

79083036c4c19017232d49b3487ba0475de179c0

625

py

Python

indra/tests/test_tas.py

djinnome/indra

382b7f236e0b1422c96a268ef873530b5e92d48f

[ "BSD-2-Clause" ]

null

indra/tests/test_tas.py

djinnome/indra

382b7f236e0b1422c96a268ef873530b5e92d48f

[ "BSD-2-Clause" ]

null

indra/tests/test_tas.py

djinnome/indra

382b7f236e0b1422c96a268ef873530b5e92d48f

[ "BSD-2-Clause" ]

null

from __future__ import absolute_import, print_function, unicode_literals from builtins import dict, str from indra.sources.tas.api import _load_data, process_csv def test_load_data(): data = _load_data() assert len(data) > 100, len(data) def test_processor(): tp = process_csv(affinity_class_limit=10) assert tp assert tp.statements num_stmts = len(tp.statements) # This is the total number of statements about human genes assert num_stmts == 51722, num_stmts assert all(len(s.evidence) == 1 for s in tp.statements), \ "Some statements lack evidence, or have extra evidence."

29.761905

72

0.7296

from __future__ import absolute_import, print_function, unicode_literals from builtins import dict, str from indra.sources.tas.api import _load_data, process_csv def test_load_data(): data = _load_data() assert len(data) > 100, len(data) def test_processor(): tp = process_csv(affinity_class_limit=10) assert tp assert tp.statements num_stmts = len(tp.statements) assert num_stmts == 51722, num_stmts assert all(len(s.evidence) == 1 for s in tp.statements), \ "Some statements lack evidence, or have extra evidence."

true

790830a9b7852a95bdc8b5052dbca110443dd94f

808

py

Python

simulator/Planners/Planner.py

ciarakamahele/sasy

fd0d50785561f188c5e9b6fa5e928673457be772

[ "Apache-2.0" ]

null

simulator/Planners/Planner.py

ciarakamahele/sasy

fd0d50785561f188c5e9b6fa5e928673457be772

[ "Apache-2.0" ]

null

simulator/Planners/Planner.py

ciarakamahele/sasy

fd0d50785561f188c5e9b6fa5e928673457be772

[ "Apache-2.0" ]

null

# Copyright 2015 Ciara Kamahele-Sanfratello # # 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. # Planner is a generic interface used by Simulators to choose the next action to take class Planner: def __init__(self): pass def next_action(self, initial_state, goal_state, prev_obs): pass

36.727273

85

0.75

class Planner: def __init__(self): pass def next_action(self, initial_state, goal_state, prev_obs): pass

true

790830d45db35356a0999260c2f11654a788d8bd

5,102

py

Python

scripts/cartesian_experiments.py

mikelibg/yap

eb46baf91f0e52918e77f1693a280e0796cdfb8e

[ "Apache-2.0" ]

68

2019-02-27T18:03:57.000Z

2022-03-23T14:42:47.000Z

scripts/cartesian_experiments.py

mikelibg/yap

eb46baf91f0e52918e77f1693a280e0796cdfb8e

[ "Apache-2.0" ]

9

2017-05-30T11:41:53.000Z

2021-10-13T11:45:43.000Z

scripts/cartesian_experiments.py

mikelibg/yap

eb46baf91f0e52918e77f1693a280e0796cdfb8e

[ "Apache-2.0" ]

20

2017-06-10T09:23:58.000Z

2021-09-06T23:06:38.000Z

#!/usr/bin/python """Cartesian execution of options for experiments""" import itertools from pprint import pprint import os # GROUPS = [ # ('train', {'type': 'option', # 'order': 0, # 'values': ['train5k']}), # ('lang', {'type': 'option', # 'order': 1, # 'values': 'hungarian,basque,french,korean,polish,swedish'.split(',')}), # ('infuse', {'type': 'option', # 'order': 2, # 'values': ['true', 'false']}), # ('maxmsr', {'type': 'option', # 'order': 3, # 'values': '1'.split(',')}) # ] # GROUPS = [ ('train', {'type': 'option', 'order': 0, 'values': ['train', 'train5k']}), ('lang', {'type': 'option', 'order': 1, 'values': 'hungarian,basque,french,korean,polish,swedish'.split(',')}), ('infuse', {'type': 'option', 'order': 2, 'values': ['true', 'false']}), ('maxmsr', {'type': 'option', 'order': 3, 'values': '1,2,4,8'.split(',')}) ] # GROUPS = [ # ('gram', {'type': 'file', # 'use': 'agg', # 'order': 0, # 'values': ['unigram', 'bigram', 'trigram', 'nextunigram', 'nextbigram', 'nexttrigram']}), # # ('prev', {'type': 'file', # # 'use': 'optional', # # 'value': 'prev'}), # ('pop', {'type': 'option', # 'use': 'optional', # 'value': '-pop'}) # ] # BASE = """nohup ./chukuparser md -f $conf -td corpus/train4k.hebtb.gold.lattices -tl corpus/train4k.hebtb.pred.lattices -in corpus/dev.hebtb.gold.conll.pred.lattices -ing corpus/dev.hebtb.gold.conll.gold.lattices -om devo.$exp.b32.hebtb.mapping -it 1 -b 32 -p Funcs_Main_POS_Both_Prop -wb -bconc $flags > runstatus.$exp.b32""" MALEARN = """nohup ./yap malearn -lattice spmrl/train.$lang.gold.conll.tobeparsed.tagged.lattices -raw spmrl/train.$lang.gold.conll.tobeparsed.raw -out $lang.json > malearn.$exp.out""" MATRAIN = """nohup ./yap ma -dict $lang.json -raw spmrl/$train.$lang.gold.conll.tobeparsed.raw -out $train.$lang.$maxmsr.analyzed.lattices -maxmsrperpos $maxmsr > matrain.$exp.out""" MADEV = """nohup ./yap ma -dict $lang.json -raw spmrl/dev.$lang.gold.conll.tobeparsed.raw -out dev.$lang.$maxmsr.analyzed.lattices -maxmsrperpos $maxmsr > madev.$exp.out""" MD = """nohup ./yap md -f conf/standalone.md.yaml -td spmrl/$train.$lang.gold.conll.tobeparsed.tagged.lattices -tl $train.$lang.$maxmsr.analyzed.lattices -in dev.$lang.$maxmsr.analyzed.lattices -ing spmrl/dev.$lang.gold.conll.tobeparsed.tagged.lattices -om devo.$train_$lang_$maxmsr_$infuse.mapping -infusedev=$infuse -it 1 -b 32 -p Funcs_Main_POS_Both_Prop -bconc -pop > runstatus.$exp.out""" cmds = [MALEARN, MATRAIN, MADEV, MD] REPLACE_STR = '$exp' CONF_FILE = 'standalone.md.%s.yaml' BASE_FILE = 'standalone.base.md.yaml' # first transform optional to empty, existing for (name, conf) in GROUPS: if conf.get('use', None) == 'optional': conf['values'] = [None, conf['value']] conf_values = map(lambda (name, conf): conf['values'], GROUPS) executions = list(itertools.product(*conf_values)) def gen_agg_file(values, out_name): with open(out_name, 'w') as outf: for value in values: with open(value) as inf: outf.write(inf.read()) for execution in executions: print 'At execution %s' % str(execution) files = [BASE_FILE] exp_strings = [] command_line_options = [] options = {} # for i, param in enumerate(execution): # conf_name, conf = GROUPS[i] # # print "\tAt conf %s" % conf_name # # pprint(conf) # # print "\tparam is %s" % str(param) # if conf['type'] == 'option' and param: # print "\t\tadd %s=%s to command line" % (conf_name, str(param)) # options[conf_name] = param # # print "\t\tadd %s to command line" % str(conf['value']) # # command_line_options.append(conf['value']) # if conf.get('use', None) == 'optional': # exp_strings.append(conf_name if param else 'no%s' % conf_name) # else: # exp_strings.append(param) # if conf['type'] == 'file': # if conf['use'] == 'agg': # files += conf['values'][:conf['values'].index(param)+1] # if conf['use'] == 'optional' and param: # files.append(param) for cmd in cmds: execcmd = cmd[:] for name, value in zip(map(lambda (k,v):k, GROUPS), execution): execcmd = execcmd.replace('$'+name, value) execcmd = execcmd.replace('$exp', '_'.join(execution)) print execcmd os.system(execcmd) # exp_string = '_'.join(exp_strings) # outname = CONF_FILE % exp_string # print command_line_options # gen_agg_file(files, outname) # new_command = BASE.replace('$conf', outname).replace('$exp', exp_string, 2).replace('$flags', ' '.join(command_line_options)) # print 'Executing %s' % new_command # os.system(new_command)

43.237288

393

0.571541

"""Cartesian execution of options for experiments""" import itertools from pprint import pprint import os GROUPS = [ ('train', {'type': 'option', 'order': 0, 'values': ['train', 'train5k']}), ('lang', {'type': 'option', 'order': 1, 'values': 'hungarian,basque,french,korean,polish,swedish'.split(',')}), ('infuse', {'type': 'option', 'order': 2, 'values': ['true', 'false']}), ('maxmsr', {'type': 'option', 'order': 3, 'values': '1,2,4,8'.split(',')}) ] sed.tagged.lattices -raw spmrl/train.$lang.gold.conll.tobeparsed.raw -out $lang.json > malearn.$exp.out""" MATRAIN = """nohup ./yap ma -dict $lang.json -raw spmrl/$train.$lang.gold.conll.tobeparsed.raw -out $train.$lang.$maxmsr.analyzed.lattices -maxmsrperpos $maxmsr > matrain.$exp.out""" MADEV = """nohup ./yap ma -dict $lang.json -raw spmrl/dev.$lang.gold.conll.tobeparsed.raw -out dev.$lang.$maxmsr.analyzed.lattices -maxmsrperpos $maxmsr > madev.$exp.out""" MD = """nohup ./yap md -f conf/standalone.md.yaml -td spmrl/$train.$lang.gold.conll.tobeparsed.tagged.lattices -tl $train.$lang.$maxmsr.analyzed.lattices -in dev.$lang.$maxmsr.analyzed.lattices -ing spmrl/dev.$lang.gold.conll.tobeparsed.tagged.lattices -om devo.$train_$lang_$maxmsr_$infuse.mapping -infusedev=$infuse -it 1 -b 32 -p Funcs_Main_POS_Both_Prop -bconc -pop > runstatus.$exp.out""" cmds = [MALEARN, MATRAIN, MADEV, MD] REPLACE_STR = '$exp' CONF_FILE = 'standalone.md.%s.yaml' BASE_FILE = 'standalone.base.md.yaml' for (name, conf) in GROUPS: if conf.get('use', None) == 'optional': conf['values'] = [None, conf['value']] conf_values = map(lambda (name, conf): conf['values'], GROUPS) executions = list(itertools.product(*conf_values)) def gen_agg_file(values, out_name): with open(out_name, 'w') as outf: for value in values: with open(value) as inf: outf.write(inf.read()) for execution in executions: print 'At execution %s' % str(execution) files = [BASE_FILE] exp_strings = [] command_line_options = [] options = {} , execution): execcmd = execcmd.replace('$'+name, value) execcmd = execcmd.replace('$exp', '_'.join(execution)) print execcmd os.system(execcmd)

false

true

790832d52d892c0e0e2e96d4e52396dcf213b110

21,384

py

Python

ppoPolicyTraining.py

britig/S2RL-Policies

b9c74b7f5efec225920c09f7e8e82d8555d61bd9

[ "MIT" ]

1

2022-03-24T07:26:37.000Z

ppoPolicyTraining.py

britig/S2RL-Policies

b9c74b7f5efec225920c09f7e8e82d8555d61bd9

[ "MIT" ]

null

ppoPolicyTraining.py

britig/S2RL-Policies

b9c74b7f5efec225920c09f7e8e82d8555d61bd9

[ "MIT" ]

null

""" The file contains the PPO class to train with. NOTE: All "ALG STEP"s are following the numbers from the original PPO pseudocode. It can be found here: https://spinningup.openai.com/en/latest/_images/math/e62a8971472597f4b014c2da064f636ffe365ba3.svg """ import gym import numpy as np import torch import torch.nn as nn from torch.optim import Adam #For continuous actions from torch.distributions import MultivariateNormal #For discrete action_space from torch.distributions import Categorical from network import FeedForwardActorNN, FeedForwardCriticNN import sys from cbf_clf_helper import clf_control, cbf_control #Integrating tensorboard from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter() class PPO: """ This is the PPO class we will use as our model in main.py """ def __init__(self, env, **hyperparameters): """ Initializes the PPO model, including hyperparameters. Parameters: policy_class - the policy class to use for our actor/critic networks. env - the environment to train on. hyperparameters - all extra arguments passed into PPO that should be hyperparameters. Returns: None """ # Make sure the environment is compatible with our code assert(type(env.observation_space) == gym.spaces.Box) # Makeassert(type(env.action_space) == gym.spaces.Box) # Initialize hyperparameters for training with PPO self._init_hyperparameters(hyperparameters) # Extract environment information self.env = env self.obs_dim = env.observation_space.shape[0] if self.discrete: self.act_dim = env.action_space.n else: self.act_dim = env.action_space.shape[0] #env.action_space.n #env.action_space.shape[0] # Initialize actor and critic networks self.actor = FeedForwardActorNN(self.obs_dim, self.act_dim,self.discrete) actor_model = 'ppo_actorKinematicBicycleGymLane.pth' policy = FeedForwardActorNN(5, 2,False) policy.load_state_dict(torch.load(actor_model)) actor_model = policy #print(f'model =========== {self.actor}') # ALG STEP 1 self.critic = FeedForwardCriticNN(self.obs_dim, 1) #print(f'critic =========== {self.critic}') # Initialize optimizers for actor and critic self.actor_optim = Adam(self.actor.parameters(), lr=self.lr) self.critic_optim = Adam(self.critic.parameters(), lr=self.lr) # Initialize the covariance matrix used to query the actor for actions self.cov_var = torch.full(size=(self.act_dim,), fill_value=0.05) self.cov_mat = torch.diag(self.cov_var) self.obs_count = 0 self.index_count = 0 # This logger will help us with printing out summaries of each iteration self.logger = { 't_so_far': 0, # timesteps so far 'i_so_far': 0, # iterations so far 'batch_lens': [], # episodic lengths in batch 'batch_rews': [], # episodic returns in batch 'batch_infractions': [], # Episodic returns in a neural network 'actor_losses': [], # losses of actor network in current iteration 'actor_network' : 0, # Actor network } def learn(self, env_name,failure_observations,subpolicy): """ Train the actor and critic networks. Here is where the main PPO algorithm resides. Parameters: total_timesteps - the total number of timesteps to train for Return: None """ print(f"Learning... Running {self.max_timesteps_per_episode} timesteps per episode, ", end='') print(f"{self.timesteps_per_batch} timesteps per batch for a total of {self.training_step} iterations") t_so_far = 0 # Timesteps simulated so far i_so_far = 0 # Iterations ran so far while i_so_far < self.training_step: # ALG STEP 2 # Autobots, roll out (just kidding, we're collecting our batch simulations here) batch_obs, batch_acts, batch_log_probs, batch_rtgs, batch_lens = self.rollout(subpolicy,failure_observations) # ALG STEP 3 # Calculate how many timesteps we collected this batch t_so_far += np.sum(batch_lens) # Increment the number of iterations i_so_far += 1 # Logging timesteps so far and iterations so far self.logger['t_so_far'] = t_so_far self.logger['i_so_far'] = i_so_far # Calculate advantage at k-th iteration V, _ = self.evaluate(batch_obs, batch_acts) A_k = batch_rtgs - V.detach() # ALG STEP 5 # One of the only tricks I use that isn't in the pseudocode. Normalizing advantages # isn't theoretically necessary, but in practice it decreases the variance of # our advantages and makes convergence much more stable and faster. I added this because # solving some environments was too unstable without it. A_k = (A_k - A_k.mean()) / (A_k.std() + 1e-10) # This is the loop where we update our network for some n epochs for _ in range(self.n_updates_per_iteration): # ALG STEP 6 & 7 # Calculate V_phi and pi_theta(a_t | s_t) V, curr_log_probs = self.evaluate(batch_obs, batch_acts) # Calculate the ratio pi_theta(a_t | s_t) / pi_theta_k(a_t | s_t) # NOTE: we just subtract the logs, which is the same as # dividing the values and then canceling the log with e^log. # For why we use log probabilities instead of actual probabilities, # here's a great explanation: # https://cs.stackexchange.com/questions/70518/why-do-we-use-the-log-in-gradient-based-reinforcement-algorithms # TL;DR makes gradient ascent easier behind the scenes. ratios = torch.exp(curr_log_probs - batch_log_probs) # Calculate surrogate losses. #print(f'A_k======================={A_k}') surr1 = ratios * A_k #print(f'surr1======================={surr1}') surr2 = torch.clamp(ratios, 1 - self.clip, 1 + self.clip) * A_k #print(f'surr2======================={surr2}') # Calculate actor and critic losses. # NOTE: we take the negative min of the surrogate losses because we're trying to maximize # the performance function, but Adam minimizes the loss. So minimizing the negative # performance function maximizes it. actor_loss = (-torch.min(surr1, surr2)).mean() #print(f'actor_loss======================={actor_loss}') critic_loss = nn.MSELoss()(V, batch_rtgs) # Calculate gradients and perform backward propagation for actor network self.actor_optim.zero_grad() actor_loss.backward(retain_graph=True) self.actor_optim.step() # Calculate gradients and perform backward propagation for critic network self.critic_optim.zero_grad() critic_loss.backward() self.critic_optim.step() # Log actor loss self.logger['actor_losses'].append(actor_loss.detach()) self.logger['actor_network'] = self.actor # Print a summary of our training so far self._log_summary() # Save our model if it's time if i_so_far % self.save_freq == 0: if subpolicy: torch.save(self.actor.state_dict(), './ppo_actor_subpolicy'+env_name+'.pth') torch.save(self.critic.state_dict(), './ppo_critic_subpolicy'+env_name+'.pth') else: torch.save(self.actor.state_dict(), './ppo_actor'+env_name+'.pth') torch.save(self.critic.state_dict(), './ppo_critic'+env_name+'.pth') def rollout(self,subpolicy,failure_observations): """ This is where we collect the batch of data from simulation. Since this is an on-policy algorithm, we'll need to collect a fresh batch of data each time we iterate the actor/critic networks. Parameters: None Return: batch_obs - the observations collected this batch. Shape: (number of timesteps, dimension of observation) batch_acts - the actions collected this batch. Shape: (number of timesteps, dimension of action) batch_log_probs - the log probabilities of each action taken this batch. Shape: (number of timesteps) batch_rtgs - the Rewards-To-Go of each timestep in this batch. Shape: (number of timesteps) batch_lens - the lengths of each episode this batch. Shape: (number of episodes) """ # Batch data. For more details, check function header. batch_obs = [] batch_acts = [] batch_log_probs = [] batch_rews = [] batch_rtgs = [] batch_lens = [] batch_infractions = [] # Episodic data. Keeps track of rewards per episode, will get cleared # upon each new episode ep_rews = [] t = 0 # Keeps track of how many timesteps we've run so far this batch # Keep simulating until we've run more than or equal to specified timesteps per batch while t < self.timesteps_per_batch: act_list = [] ep_rews = [] # rewards collected per episode # Reset the environment. sNote that obs is short for observation. obs = self.env.reset() #print(f'obs reset ============= {obs}') done = False count_infractions = 0 count_infractions_acc = 0 count_infractions_steer = 0 # Run an episode for a maximum of max_timesteps_per_episode timesteps for ep_t in range(self.max_timesteps_per_episode): a_predicted_clf = clf_control(self.env.v_ego) delta, target_id, crosstrack_error = self.env.car.tracker.stanley_control(self.env.x_ego, self.env.y_ego, self.env.yaw_ego, self.env.v_ego, self.env.delta_ego) # If render is specified, render the environment if self.render: self.env.render() t += 1 # Increment timesteps ran this batch so far # Track observations in this batch batch_obs.append(obs) # Calculate action and make a step in the env. # Note that rew is short for reward. if self.discrete: action, log_prob = self.get_action_discrete(obs) else: action, log_prob = self.get_action(obs) #self.get_action_discrete(obs) #print(f'action chosen =============== {action}') if(abs(round(float(action[0]),1))<abs(round(float(a_predicted_clf),1))): count_infractions_acc = count_infractions_acc+1 if(abs(round(float(action[1]),1)) < abs(round(float(delta),1))-0.2): #print(f'After rounding =============== {round(float(action_net[1]),1)} ====== {round(float(action[1]),1)}') count_infractions_steer = count_infractions_steer+1 obs, rew, done, info = self.env.step(action) count_infractions = count_infractions_acc+count_infractions_steer # Track recent reward, action, and action log probability ep_rews.append(rew) batch_acts.append(action) batch_log_probs.append(log_prob) act_list.append(info) # If the environment tells us the episode is terminated, break if done: break # Track episodic lengths and rewards #self.env.render(act_list) batch_lens.append(ep_t + 1) batch_rews.append(ep_rews) batch_infractions.append(count_infractions) # Reshape data as tensors in the shape specified in function description, before returning batch_obs = torch.tensor(batch_obs, dtype=torch.float) #print(f'batch_acts =============== {batch_acts}') #For discrete state space if self.discrete: batch_acts = torch.tensor(batch_acts, dtype=torch.long).view(-1,) else: batch_acts = torch.tensor(batch_acts, dtype=torch.float) #torch.tensor(batch_acts, dtype=torch.long).view(-1,) #print(f'batch_acts =============== {batch_acts}') batch_log_probs = torch.tensor(batch_log_probs, dtype=torch.float) batch_rtgs = self.compute_rtgs(batch_rews) # ALG STEP 4 # Log the episodic returns and episodic lengths in this batch. self.logger['batch_rews'] = batch_rews self.logger['batch_lens'] = batch_lens self.logger['batch_infractions'] = batch_infractions return batch_obs, batch_acts, batch_log_probs, batch_rtgs, batch_lens def compute_rtgs(self, batch_rews): """ Compute the Reward-To-Go of each timestep in a batch given the rewards. Parameters: batch_rews - the rewards in a batch, Shape: (number of episodes, number of timesteps per episode) Return: batch_rtgs - the rewards to go, Shape: (number of timesteps in batch) """ # The rewards-to-go (rtg) per episode per batch to return. # The shape will be (num timesteps per episode) batch_rtgs = [] # Iterate through each episode for ep_rews in reversed(batch_rews): discounted_reward = 0 # The discounted reward so far # Iterate through all rewards in the episode. We go backwards for smoother calculation of each # discounted return (think about why it would be harder starting from the beginning) for rew in reversed(ep_rews): discounted_reward = rew + discounted_reward * self.gamma batch_rtgs.insert(0, discounted_reward) # Convert the rewards-to-go into a tensor batch_rtgs = torch.tensor(batch_rtgs, dtype=torch.float) return batch_rtgs # Probability sampling for discrete actions def get_action_discrete(self, obs): #print(f'obs ================== {obs}') mean = self.actor(obs) #print(f'mean ================== {mean}') dist = Categorical(mean) #print(f'dist ================== {dist}') action = dist.sample() log_prob = dist.log_prob(action) #print(f'action ====== {action} ========= {log_prob}') return action.detach().numpy().item(), log_prob.detach().item() def get_action(self, obs): """ Queries an action from the actor network, should be called from rollout. Parameters: obs - the observation at the current timestep Return: action - the action to take, as a numpy array log_prob - the log probability of the selected action in the distribution """ # Query the actor network for a mean action mean = self.actor(obs) # Create a distribution with the mean action and std from the covariance matrix above. # For more information on how this distribution works, check out Andrew Ng's lecture on it: # https://www.youtube.com/watch?v=JjB58InuTqM dist = MultivariateNormal(mean, self.cov_mat) # Sample an action from the distribution action = dist.sample() # Calculate the log probability for that action log_prob = dist.log_prob(action) # Return the sampled action and the log probability of that action in our distribution return action.detach().numpy(), log_prob.detach() def evaluate(self, batch_obs, batch_acts): """ Estimate the values of each observation, and the log probs of each action in the most recent batch with the most recent iteration of the actor network. Should be called from learn. Parameters: batch_obs - the observations from the most recently collected batch as a tensor. Shape: (number of timesteps in batch, dimension of observation) batch_acts - the actions from the most recently collected batch as a tensor. Shape: (number of timesteps in batch, dimension of action) Return: V - the predicted values of batch_obs log_probs - the log probabilities of the actions taken in batch_acts given batch_obs """ # Query critic network for a value V for each batch_obs. Shape of V should be same as batch_rtgs V = self.critic(batch_obs).squeeze() # Calculate the log probabilities of batch actions using most recent actor network. # This segment of code is similar to that in get_action() mean = self.actor(batch_obs) if self.discrete: dist = Categorical(mean) else: dist = MultivariateNormal(mean, self.cov_mat) #For discrete actions #dist = Categorical(mean) log_probs = dist.log_prob(batch_acts) # Return the value vector V of each observation in the batch # and log probabilities log_probs of each action in the batch return V, log_probs def _init_hyperparameters(self, hyperparameters): """ Initialize default and custom values for hyperparameters Parameters: hyperparameters - the extra arguments included when creating the PPO model, should only include hyperparameters defined below with custom values. Return: None """ # Initialize default values for hyperparameters # Algorithm hyperparameters self.timesteps_per_batch = 4800 # Number of timesteps to run per batch self.max_timesteps_per_episode = 1600 # Max number of timesteps per episode self.n_updates_per_iteration = 5 # Number of times to update actor/critic per iteration self.lr = 0.005 # Learning rate of actor optimizer self.gamma = 0.95 # Discount factor to be applied when calculating Rewards-To-Go self.clip = 0.2 # Recommended 0.2, helps define the threshold to clip the ratio during SGA # Miscellaneous parameters self.render = False # If we should render during rollout self.save_freq = 10 # How often we save in number of iterations self.seed = None # Sets the seed of our program, used for reproducibility of results self.discrete = False # Sets the type of environment to discrete or continuous self.training_step = 200 # Sets the number of trainig step # Change any default values to custom values for specified hyperparameters for param, val in hyperparameters.items(): exec('self.' + param + ' = ' + str(val)) # Sets the seed if specified if self.seed != None: # Check if our seed is valid first assert(type(self.seed) == int) # Set the seed torch.manual_seed(self.seed) print(f"Successfully set seed to {self.seed}") def _log_summary(self): """ Print to stdout what we've logged so far in the most recent batch. Parameters: None Return: None """ # Calculate logging values. I use a few python shortcuts to calculate each value # without explaining since it's not too important to PPO; feel free to look it over, # and if you have any questions you can email me (look at bottom of README) t_so_far = self.logger['t_so_far'] i_so_far = self.logger['i_so_far'] avg_ep_lens = np.mean(self.logger['batch_lens']) avg_ep_rews = np.mean([np.sum(ep_rews) for ep_rews in self.logger['batch_rews']]) avg_actor_loss = np.mean([losses.float().mean() for losses in self.logger['actor_losses']]) avg_ep_infractions = np.mean([np.sum(ep_inf) for ep_inf in self.logger['batch_infractions']]) actor_model = self.logger['actor_network'] # Round decimal places for more aesthetic logging messages avg_ep_lens = str(round(avg_ep_lens, 2)) avg_ep_rews = str(round(avg_ep_rews, 2)) avg_ep_infractions = str(round(avg_ep_infractions, 2)) avg_actor_loss = str(round(avg_actor_loss, 5)) writer.add_scalar("Average Episodic Return", int(float(avg_ep_rews)), t_so_far) writer.add_scalar("Average actor Loss", int(float(avg_actor_loss)), t_so_far) writer.add_scalar("Average Infractions", int(float(avg_ep_infractions)), t_so_far) # Tracking the weight of the network for name, param in actor_model.named_parameters(): if 'weight' in name: writer.add_histogram(name, param.detach().numpy(), t_so_far) # Print logging statements print(flush=True) print(f"-------------------- Iteration #{i_so_far} --------------------", flush=True) print(f"Average Episodic Length: {avg_ep_lens}", flush=True) print(f"Average Episodic Return: {avg_ep_rews}", flush=True) print(f"Average Episodic Infractions : {avg_ep_infractions}", flush=True) print(f"Average Loss: {avg_actor_loss}", flush=True) print(f"Timesteps So Far: {t_so_far}", flush=True) print(f"------------------------------------------------------", flush=True) print(flush=True) # Reset batch-specific logging data self.logger['batch_lens'] = [] self.logger['batch_rews'] = [] self.logger['actor_losses'] = [] def test(env, actor_model, is_discrete): """ Tests the model. Parameters: env - the environment to test the policy on actor_model - the actor model to load in Return: None """ print(f"Testing {actor_model}", flush=True) # If the actor model is not specified, then exit if actor_model == '': print(f"Didn't specify model file. Exiting.", flush=True) sys.exit(0) # Extract out dimensions of observation and action spaces obs_dim = env.observation_space.shape[0] if is_discrete: act_dim = env.action_space.n else: act_dim = env.action_space.shape[0] #env.action_space.n #env.action_space.shape[0] # Build our policy the same way we build our actor model in PPO policy = FeedForwardActorNN(obs_dim, act_dim,is_discrete) # Load in the actor model saved by the PPO algorithm policy.load_state_dict(torch.load(actor_model)) # Evaluate our policy with a separate module, eval_policy, to demonstrate # that once we are done training the model/policy with ppo.py, we no longer need # ppo.py since it only contains the training algorithm. The model/policy itself exists # independently as a binary file that can be loaded in with torch. eval_policy(policy=policy, env=env, render=True, is_discrete=is_discrete)

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import gym import numpy as np import torch import torch.nn as nn from torch.optim import Adam from torch.distributions import MultivariateNormal from torch.distributions import Categorical from network import FeedForwardActorNN, FeedForwardCriticNN import sys from cbf_clf_helper import clf_control, cbf_control from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter() class PPO: def __init__(self, env, **hyperparameters): assert(type(env.observation_space) == gym.spaces.Box) self._init_hyperparameters(hyperparameters) self.env = env self.obs_dim = env.observation_space.shape[0] if self.discrete: self.act_dim = env.action_space.n else: self.act_dim = env.action_space.shape[0] orwardActorNN(self.obs_dim, self.act_dim,self.discrete) actor_model = 'ppo_actorKinematicBicycleGymLane.pth' policy = FeedForwardActorNN(5, 2,False) policy.load_state_dict(torch.load(actor_model)) actor_model = policy c = FeedForwardCriticNN(self.obs_dim, 1) self.actor_optim = Adam(self.actor.parameters(), lr=self.lr) self.critic_optim = Adam(self.critic.parameters(), lr=self.lr) self.cov_var = torch.full(size=(self.act_dim,), fill_value=0.05) self.cov_mat = torch.diag(self.cov_var) self.obs_count = 0 self.index_count = 0 self.logger = { 't_so_far': 0, 'i_so_far': 0, 'batch_lens': [], 'batch_rews': [], 'batch_infractions': [], 'actor_losses': [], 'actor_network' : 0, } def learn(self, env_name,failure_observations,subpolicy): print(f"Learning... Running {self.max_timesteps_per_episode} timesteps per episode, ", end='') print(f"{self.timesteps_per_batch} timesteps per batch for a total of {self.training_step} iterations") t_so_far = 0 i_so_far = 0 while i_so_far < self.training_step: batch_obs, batch_acts, batch_log_probs, batch_rtgs, batch_lens = self.rollout(subpolicy,failure_observations) # ALG STEP 3 # Calculate how many timesteps we collected this batch t_so_far += np.sum(batch_lens) # Increment the number of iterations i_so_far += 1 # Logging timesteps so far and iterations so far self.logger['t_so_far'] = t_so_far self.logger['i_so_far'] = i_so_far # Calculate advantage at k-th iteration V, _ = self.evaluate(batch_obs, batch_acts) A_k = batch_rtgs - V.detach() # ALG STEP 5 # One of the only tricks I use that isn't in the pseudocode. Normalizing advantages # our advantages and makes convergence much more stable and faster. I added this because # solving some environments was too unstable without it. A_k = (A_k - A_k.mean()) / (A_k.std() + 1e-10) # This is the loop where we update our network for some n epochs for _ in range(self.n_updates_per_iteration): # ALG STEP 6 & 7 # Calculate V_phi and pi_theta(a_t | s_t) V, curr_log_probs = self.evaluate(batch_obs, batch_acts) # Calculate the ratio pi_theta(a_t | s_t) / pi_theta_k(a_t | s_t) # NOTE: we just subtract the logs, which is the same as # dividing the values and then canceling the log with e^log. # For why we use log probabilities instead of actual probabilities, # here's a great explanation: ratios = torch.exp(curr_log_probs - batch_log_probs) surr1 = ratios * A_k surr2 = torch.clamp(ratios, 1 - self.clip, 1 + self.clip) * A_k # the performance function, but Adam minimizes the loss. So minimizing the negative # performance function maximizes it. actor_loss = (-torch.min(surr1, surr2)).mean() #print(f'actor_loss======================={actor_loss}') critic_loss = nn.MSELoss()(V, batch_rtgs) # Calculate gradients and perform backward propagation for actor network self.actor_optim.zero_grad() actor_loss.backward(retain_graph=True) self.actor_optim.step() # Calculate gradients and perform backward propagation for critic network self.critic_optim.zero_grad() critic_loss.backward() self.critic_optim.step() # Log actor loss self.logger['actor_losses'].append(actor_loss.detach()) self.logger['actor_network'] = self.actor # Print a summary of our training so far self._log_summary() # Save our model if it's time if i_so_far % self.save_freq == 0: if subpolicy: torch.save(self.actor.state_dict(), './ppo_actor_subpolicy'+env_name+'.pth') torch.save(self.critic.state_dict(), './ppo_critic_subpolicy'+env_name+'.pth') else: torch.save(self.actor.state_dict(), './ppo_actor'+env_name+'.pth') torch.save(self.critic.state_dict(), './ppo_critic'+env_name+'.pth') def rollout(self,subpolicy,failure_observations): batch_obs = [] batch_acts = [] batch_log_probs = [] batch_rews = [] batch_rtgs = [] batch_lens = [] batch_infractions = [] ep_rews = [] t = 0 # Keep simulating until we've run more than or equal to specified timesteps per batch while t < self.timesteps_per_batch: act_list = [] ep_rews = [] obs = self.env.reset() done = False count_infractions = 0 count_infractions_acc = 0 count_infractions_steer = 0 for ep_t in range(self.max_timesteps_per_episode): a_predicted_clf = clf_control(self.env.v_ego) delta, target_id, crosstrack_error = self.env.car.tracker.stanley_control(self.env.x_ego, self.env.y_ego, self.env.yaw_ego, self.env.v_ego, self.env.delta_ego) if self.render: self.env.render() t += 1 batch_obs.append(obs) if self.discrete: action, log_prob = self.get_action_discrete(obs) else: action, log_prob = self.get_action(obs) if(abs(round(float(action[0]),1))<abs(round(float(a_predicted_clf),1))): count_infractions_acc = count_infractions_acc+1 if(abs(round(float(action[1]),1)) < abs(round(float(delta),1))-0.2): count_infractions_steer = count_infractions_steer+1 obs, rew, done, info = self.env.step(action) count_infractions = count_infractions_acc+count_infractions_steer ep_rews.append(rew) batch_acts.append(action) batch_log_probs.append(log_prob) act_list.append(info) if done: break batch_lens.append(ep_t + 1) batch_rews.append(ep_rews) batch_infractions.append(count_infractions) batch_obs = torch.tensor(batch_obs, dtype=torch.float) if self.discrete: batch_acts = torch.tensor(batch_acts, dtype=torch.long).view(-1,) else: batch_acts = torch.tensor(batch_acts, dtype=torch.float) batch_log_probs = torch.tensor(batch_log_probs, dtype=torch.float) batch_rtgs = self.compute_rtgs(batch_rews) self.logger['batch_rews'] = batch_rews self.logger['batch_lens'] = batch_lens self.logger['batch_infractions'] = batch_infractions return batch_obs, batch_acts, batch_log_probs, batch_rtgs, batch_lens def compute_rtgs(self, batch_rews): batch_rtgs = [] for ep_rews in reversed(batch_rews): discounted_reward = 0 for rew in reversed(ep_rews): discounted_reward = rew + discounted_reward * self.gamma batch_rtgs.insert(0, discounted_reward) batch_rtgs = torch.tensor(batch_rtgs, dtype=torch.float) return batch_rtgs def get_action_discrete(self, obs): mean = self.actor(obs) dist = Categorical(mean) action = dist.sample() log_prob = dist.log_prob(action) return action.detach().numpy().item(), log_prob.detach().item() def get_action(self, obs): mean = self.actor(obs) # https://www.youtube.com/watch?v=JjB58InuTqM dist = MultivariateNormal(mean, self.cov_mat) # Sample an action from the distribution action = dist.sample() # Calculate the log probability for that action log_prob = dist.log_prob(action) # Return the sampled action and the log probability of that action in our distribution return action.detach().numpy(), log_prob.detach() def evaluate(self, batch_obs, batch_acts): # Query critic network for a value V for each batch_obs. Shape of V should be same as batch_rtgs V = self.critic(batch_obs).squeeze() # Calculate the log probabilities of batch actions using most recent actor network. # This segment of code is similar to that in get_action() mean = self.actor(batch_obs) if self.discrete: dist = Categorical(mean) else: dist = MultivariateNormal(mean, self.cov_mat) #For discrete actions #dist = Categorical(mean) log_probs = dist.log_prob(batch_acts) # Return the value vector V of each observation in the batch # and log probabilities log_probs of each action in the batch return V, log_probs def _init_hyperparameters(self, hyperparameters): # Initialize default values for hyperparameters # Algorithm hyperparameters self.timesteps_per_batch = 4800 # Number of timesteps to run per batch self.max_timesteps_per_episode = 1600 # Max number of timesteps per episode self.n_updates_per_iteration = 5 # Number of times to update actor/critic per iteration self.lr = 0.005 # Learning rate of actor optimizer self.gamma = 0.95 # Discount factor to be applied when calculating Rewards-To-Go self.clip = 0.2 # Recommended 0.2, helps define the threshold to clip the ratio during SGA # Miscellaneous parameters self.render = False # If we should render during rollout self.save_freq = 10 # How often we save in number of iterations self.seed = None # Sets the seed of our program, used for reproducibility of results self.discrete = False # Sets the type of environment to discrete or continuous self.training_step = 200 # Sets the number of trainig step # Change any default values to custom values for specified hyperparameters for param, val in hyperparameters.items(): exec('self.' + param + ' = ' + str(val)) # Sets the seed if specified if self.seed != None: # Check if our seed is valid first assert(type(self.seed) == int) # Set the seed torch.manual_seed(self.seed) print(f"Successfully set seed to {self.seed}") def _log_summary(self): # Calculate logging values. I use a few python shortcuts to calculate each value # without explaining since it's not too important to PPO; feel free to look it over, t_so_far = self.logger['t_so_far'] i_so_far = self.logger['i_so_far'] avg_ep_lens = np.mean(self.logger['batch_lens']) avg_ep_rews = np.mean([np.sum(ep_rews) for ep_rews in self.logger['batch_rews']]) avg_actor_loss = np.mean([losses.float().mean() for losses in self.logger['actor_losses']]) avg_ep_infractions = np.mean([np.sum(ep_inf) for ep_inf in self.logger['batch_infractions']]) actor_model = self.logger['actor_network'] avg_ep_lens = str(round(avg_ep_lens, 2)) avg_ep_rews = str(round(avg_ep_rews, 2)) avg_ep_infractions = str(round(avg_ep_infractions, 2)) avg_actor_loss = str(round(avg_actor_loss, 5)) writer.add_scalar("Average Episodic Return", int(float(avg_ep_rews)), t_so_far) writer.add_scalar("Average actor Loss", int(float(avg_actor_loss)), t_so_far) writer.add_scalar("Average Infractions", int(float(avg_ep_infractions)), t_so_far) for name, param in actor_model.named_parameters(): if 'weight' in name: writer.add_histogram(name, param.detach().numpy(), t_so_far) print(flush=True) print(f"-------------------- Iteration #{i_so_far} --------------------", flush=True) print(f"Average Episodic Length: {avg_ep_lens}", flush=True) print(f"Average Episodic Return: {avg_ep_rews}", flush=True) print(f"Average Episodic Infractions : {avg_ep_infractions}", flush=True) print(f"Average Loss: {avg_actor_loss}", flush=True) print(f"Timesteps So Far: {t_so_far}", flush=True) print(f"------------------------------------------------------", flush=True) print(flush=True) self.logger['batch_lens'] = [] self.logger['batch_rews'] = [] self.logger['actor_losses'] = [] def test(env, actor_model, is_discrete): print(f"Testing {actor_model}", flush=True) if actor_model == '': print(f"Didn't specify model file. Exiting.", flush=True) sys.exit(0) # Extract out dimensions of observation and action spaces obs_dim = env.observation_space.shape[0] if is_discrete: act_dim = env.action_space.n else: act_dim = env.action_space.shape[0] #env.action_space.n #env.action_space.shape[0] # Build our policy the same way we build our actor model in PPO policy = FeedForwardActorNN(obs_dim, act_dim,is_discrete) # Load in the actor model saved by the PPO algorithm policy.load_state_dict(torch.load(actor_model)) # Evaluate our policy with a separate module, eval_policy, to demonstrate # that once we are done training the model/policy with ppo.py, we no longer need # ppo.py since it only contains the training algorithm. The model/policy itself exists # independently as a binary file that can be loaded in with torch. eval_policy(policy=policy, env=env, render=True, is_discrete=is_discrete)

true

7908335aa2af1e5d85aef8db310ddcc5fdffd88a

1,670

py

Python

tests/models_tests/test_log.py

chainer/chainerui

91c5c26d9154a008079dbb0bcbf69b5590d105f7

[ "MIT" ]

185

2017-12-15T09:24:07.000Z

2022-01-20T11:20:13.000Z

tests/models_tests/test_log.py

chainer/chainerui

91c5c26d9154a008079dbb0bcbf69b5590d105f7

[ "MIT" ]

191

2017-12-15T09:14:52.000Z

2022-02-17T14:09:19.000Z

tests/models_tests/test_log.py

chainer/chainerui

91c5c26d9154a008079dbb0bcbf69b5590d105f7

[ "MIT" ]

29

2017-12-15T09:40:45.000Z

2022-03-13T11:21:11.000Z

from chainerui.models.log import Log def get_test_json(): return [ { "loss": 100, "epoch": 1, }, { "loss": 90, "epoch": 2, } ] def test_log_serialize_numbers(): json_data = get_test_json() logs = [Log(data) for data in json_data] serialized_data = [log.serialize for log in logs] assert serialized_data[0]['logDict']['epoch'] == 1 assert serialized_data[1]['logDict']['epoch'] == 2 def test_log_serialize_arbitrary_data(): json_data = get_test_json() json_data.insert( 0, { "loss": 110, "epoch": 0, "model_files": ["Model", "model.py"] } ) logs = [Log(data) for data in json_data] serialized_data = [log.serialize for log in logs] assert serialized_data[0]['logDict']['epoch'] == 0 assert serialized_data[0]['logDict']['model_files'] is None assert serialized_data[1]['logDict']['epoch'] == 1 assert serialized_data[2]['logDict']['epoch'] == 2 def test_log_serialize_nan_and_inf(): json_data = get_test_json() json_data.insert( 0, { "loss": float('nan'), "epoch": float('inf'), "iteration": 0, } ) logs = [Log(data) for data in json_data] serialized_data = [log.serialize for log in logs] assert serialized_data[0]['logDict']['iteration'] == 0 assert serialized_data[0]['logDict']['epoch'] is None assert serialized_data[0]['logDict']['loss'] is None assert serialized_data[1]['logDict']['epoch'] == 1 assert serialized_data[2]['logDict']['epoch'] == 2

25.692308

63

0.573653

from chainerui.models.log import Log def get_test_json(): return [ { "loss": 100, "epoch": 1, }, { "loss": 90, "epoch": 2, } ] def test_log_serialize_numbers(): json_data = get_test_json() logs = [Log(data) for data in json_data] serialized_data = [log.serialize for log in logs] assert serialized_data[0]['logDict']['epoch'] == 1 assert serialized_data[1]['logDict']['epoch'] == 2 def test_log_serialize_arbitrary_data(): json_data = get_test_json() json_data.insert( 0, { "loss": 110, "epoch": 0, "model_files": ["Model", "model.py"] } ) logs = [Log(data) for data in json_data] serialized_data = [log.serialize for log in logs] assert serialized_data[0]['logDict']['epoch'] == 0 assert serialized_data[0]['logDict']['model_files'] is None assert serialized_data[1]['logDict']['epoch'] == 1 assert serialized_data[2]['logDict']['epoch'] == 2 def test_log_serialize_nan_and_inf(): json_data = get_test_json() json_data.insert( 0, { "loss": float('nan'), "epoch": float('inf'), "iteration": 0, } ) logs = [Log(data) for data in json_data] serialized_data = [log.serialize for log in logs] assert serialized_data[0]['logDict']['iteration'] == 0 assert serialized_data[0]['logDict']['epoch'] is None assert serialized_data[0]['logDict']['loss'] is None assert serialized_data[1]['logDict']['epoch'] == 1 assert serialized_data[2]['logDict']['epoch'] == 2

true

79083384cf791c8f6babf1e15ace4d5a35dd72b3

14,114

py

Python

detection.py

kaylajanos1/TeamSpark-L3Detection

ecc2b4ca3588f989add309439feac33014447a32

[ "BSD-3-Clause" ]

null

detection.py

kaylajanos1/TeamSpark-L3Detection

ecc2b4ca3588f989add309439feac33014447a32

[ "BSD-3-Clause" ]

1

2021-04-28T03:14:17.000Z

detection.py

kaylajanos1/TeamSpark-L3Detection

ecc2b4ca3588f989add309439feac33014447a32

[ "BSD-3-Clause" ]

null

#Importing Libraries import os import csv import sys, getopt import uuid import SimpleITK as sitk import cv2 import numpy as np import tensorflow as tf from flask import Flask, flash, request, redirect, render_template from flask import jsonify from flask import send_from_directory from flask_materialize import Material from tensorflow.python.keras.backend import set_session from werkzeug.utils import secure_filename import shutil import nibabel as nib import pandas as pd import numpy from sarcopenia_ai.apps.segmentation.segloader import preprocess_test_image from sarcopenia_ai.apps.server import settings from sarcopenia_ai.apps.slice_detection.predict import parse_inputs, to256 from sarcopenia_ai.apps.slice_detection.utils import decode_slice_detection_prediction, \ preprocess_sitk_image_for_slice_detection, adjust_detected_position_spacing, place_line_on_img from sarcopenia_ai.core.model_wrapper import BaseModelWrapper from sarcopenia_ai.io import load_image from sarcopenia_ai.preprocessing.preprocessing import blend2d from sarcopenia_ai.utils import compute_muscle_area, compute_muscle_attenuation config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) graph = tf.get_default_graph() import cv2 import numpy as np def normalise_zero_one(image, eps=1e-8): print("Here 1") image = image.astype(np.float32) ret = (image - np.min(image)) ret /= (np.max(image) - np.min(image) + eps) return ret def normalise_one_one(image): print("Here 2") ret = normalise_zero_one(image) ret *= 2. ret -= 1. return ret def preprocess_test_image(image): print("Here") #image = normalise_one_one(image, -250, 250) image = normalise_one_one(image) return image ################## def find_max(img): return np.unravel_index(np.argmax(img, axis=None), img.shape)[0] #Read arguments ############################# import argparse msg = "Adding description" # Initialize parser parser = argparse.ArgumentParser(description = msg) # Reading the input arguments parser.add_argument("-i", "--Input", help = "Input file or folder") parser.add_argument('-test_name', type=str, default='Test') # Read arguments from command line args = parser.parse_args() path = args.Input test_name = args.test_name #Creating the result structure variables main = os.getcwd() directory = os.path.join(main+'/NII_Data/'+path) if not os.path.exists(main+'/Results/'+path+"/"): os.mkdir(main+'/Results/'+path+'/') out = os.path.join(main+'/Results/'+path+"/"+test_name+'/') if os.path.exists(out): shutil.rmtree(out) os.mkdir(out) if not os.path.exists(out): os.mkdir(out) out_yes = os.path.join(out+'/Yes') if not os.path.exists(out_yes): os.mkdir(out_yes) out_no = os.path.join(out+'/No') if not os.path.exists(out_no): os.mkdir(out_no) out_rev = os.path.join(out+'/Review/') if not os.path.exists(out_rev): os.mkdir(out_rev) out_csv = os.path.join(out+'/Pred CSVs/') if not os.path.exists(out_csv): os.mkdir(out_csv) #Load the sarcopenia-ai models #set_session(sess) model_wrapper = BaseModelWrapper(settings.SLICE_DETECTION_MODEL_PATH) model_wrapper.setup_model() global slice_detection_model slice_detection_model= model_wrapper.model slice_detection_model._make_predict_function() global segmentation_model model_wrapper = BaseModelWrapper(settings.SEGMENTATION_MODEL_PATH) model_wrapper.setup_model() segmentation_model = model_wrapper.model segmentation_model._make_predict_function() ####Updated functions to replace older versions listed in the sarcopenia-ai enviroment #Previous research indicates adjusting the HU range can help bone appear better def reduce_hu_intensity_range(img, minv=100, maxv=1500): img = np.clip(img, minv, maxv) img = 255 * normalise_zero_one(img) return img #Setting up the output file name & Prediction counter pred_id = 0 cols = ['Folder_Path','Patient_Folder','Study_Folder','Serie_Folder','L3_detection','L3_position','Total_slices','Confidence','Slice_Thickness', 'Orientation'] lst = [] #Looping through the input folder and analyzing the images for folder in os.listdir(directory): #Patient Folder if(folder=='.DS_Store'): continue #Study Folder for sub_folder in os.listdir(directory+"/"+folder): if(sub_folder=='.DS_Store'): continue #Series Folder for sub_sub_folder in os.listdir(directory+"/"+folder+"/"+sub_folder): #Image Level for file in os.listdir(directory+"/"+folder+"/"+sub_folder+"/"+sub_sub_folder): print("IN SUB-SUB-FOLDER: "+sub_sub_folder) #print(file) if(file.endswith(".nii.gz") or file.endswith(".nii")): print("Processing file: "+file) try: if(sub_sub_folder=='.DS_Store'): continue print("IN SUB-SUB-FOLDER: "+sub_sub_folder) image_path = directory+"/"+folder+"/"+sub_folder+"/"+sub_sub_folder+"/"+file prob_threshold_U=settings.THRESHOLD_U prob_threshold_L=settings.THRESHOLD_L #Gathering image name import ntpath head, tail = ntpath.split(image_path) image_name = tail or ntpath.basename(head) pred_id = pred_id +1 print("ID --> "+str(pred_id)) results = {"success": False, "prediction": {'id': pred_id}} sitk_image, _ = load_image(image_path) print("-----------------------------image path: "+image_path ) #The code is not set up to analyze 4 dimensional data. if len(sitk_image.GetSize()) == 4: print("-------- 4D Image: Grabbing only first volume") sitk_image = sitk_image[:, :, :, 0] #Getting image orientation information for output file. print('-------------- NIB') nib_image = nib.load(image_path) orient_nib=nib.orientations.aff2axcodes(nib_image.affine) print('-------------- Preprocess') #Preprocessing the image image2d, image2d_preview= preprocess_sitk_image_for_slice_detection(sitk_image) image3d = sitk.GetArrayFromImage(sitk_image) #print(image3d.shape) #print(image2d.shape) #print(image2d_preview.shape) spacing = sitk_image.GetSpacing() size = list(sitk_image.GetSize()) slice_thickness = spacing[2] #Utilizing the sarcopenia-ai model to predict the L3 vertabrae with graph.as_default(): set_session(sess) preds = slice_detection_model.predict(image2d) print('-------------- Predict') #Processing the model output pred_z, prob = decode_slice_detection_prediction(preds) slice_z = adjust_detected_position_spacing(pred_z, spacing) print('Prob: '+ str(prob)) print('Slice Z: ' +str(slice_z) ) print('{red_z: '+str(pred_z)) #Normalizing the prediction image to be within %28-%47 percent of the body new_z_calculate = 0 new_pred_z = pred_z new_slice_z = slice_z new_prob = prob print('-------------- Normalize') if(slice_z < .27*size[2] or slice_z > .48*size[2]): print("---------------------debug") print(preds.shape) print(preds.shape[1]) new_pred_z = find_max(preds[0, int(.27*preds.shape[1]):int(.48*preds.shape[1])]) new_pred_z = new_pred_z + int(.27*preds.shape[1]); new_slice_z = adjust_detected_position_spacing(new_pred_z, spacing) print("old position") print(pred_z) print(slice_z) print("new position") print(new_pred_z) print(new_slice_z) new_z_calculate =1; new_prob = float(preds[0,new_pred_z]) ## Outputting prediction data print('-------------- Predict CSV') preds_reshaped = preds.reshape(preds.shape[0], -1) numpy.savetxt(out_csv+"PRED_"+str(pred_id)+".csv", preds_reshaped, delimiter=",") #If the prediction for L3 is above the predifined threshold for acceptance if (new_prob > prob_threshold_U): print('-------------- Above') image = image3d slice_image = image[new_slice_z,:, :] image2dA = place_line_on_img(image2d[0], pred_z, pred_z, r=1) image2dB = place_line_on_img(image2d[0], -new_pred_z, new_pred_z, r=1) cv2.imwrite(out_yes+"/"+str(pred_id)+'_YES_'+image_name+'_SL.jpg', to256(slice_image)) cv2.imwrite(out_yes+"/"+str(pred_id)+'_YES_'+image_name+'_FR.jpg', to256(image2dA)) cv2.imwrite(out_yes+"/"+str(pred_id)+'_YES_'+image_name+'_FR2.jpg', to256(image2dB)) output = [image_path,folder,sub_folder,sub_sub_folder,'YES',new_slice_z,size[2],new_prob,slice_thickness, orient_nib] lst.append(output) #Images where the L3 vertabrae was not identified elif (new_prob <= prob_threshold_L ): print('-------------- No') image = image3d slice_image = image[new_slice_z,:, :] image2dA = place_line_on_img(image2d[0], -pred_z, -pred_z, r=1) image2dB = place_line_on_img(image2d[0], -new_pred_z, -new_pred_z, r=1) cv2.imwrite(out_no+str(pred_id)+'_NO_'+image_name+'_SL.jpg', to256(slice_image)) cv2.imwrite(out_no+str(pred_id)+'_NO_'+image_name+'_FR.jpg', to256(image2dA)) cv2.imwrite(out_no+str(pred_id)+'_NO_'+image_name+'_FR2.jpg', to256(image2dB)) output = [image_path,folder,sub_folder,sub_sub_folder,'NO',new_slice_z,size[2],new_prob,slice_thickness, orient_nib] lst.append(output) #Images where the L3 vertabrae was identified but confidence requirements were not met. else: print('-------------- Review') image = image3d slice_image = image[new_slice_z,:, :] image2dA = place_line_on_img(image2d[0], pred_z, pred_z, r=1) image2dB = place_line_on_img(image2d[0], new_pred_z, new_pred_z, r=1) cv2.imwrite(out_rev+str(pred_id)+'_REVIEW_'+image_name+'_SL_'+str(new_slice_z)+'_PROB_'+str(new_prob)+'.jpg', to256(slice_image)) cv2.imwrite(out_rev+str(pred_id)+'_REVIEW_'+image_name+'_FR_'+str(slice_z)+'_PROB_'+str(prob)+'.jpg', to256(image2dA)) cv2.imwrite(out_rev+str(pred_id)+'_REVIEW_'+image_name+'_FR2_'+str(new_slice_z)+'_PROB_'+str(new_prob)+'.jpg', to256(image2dB)) output = [image_path,folder,sub_folder,sub_sub_folder,'REVIEW',new_slice_z,size[2],new_prob,slice_thickness, orient_nib] lst.append(output) #Images that error out (e.g. image orientation is incorrect) except: print('-------------- Wrong') print('-------------- ') print('-------------- ') print("Something went wrong - File: "+image_path) print("Unexpected error"+str(sys.exc_info()[0])) output = [image_path,folder,sub_folder,sub_sub_folder,'Error','','','Something went wrong:'+str(sys.exc_info()[1]),'', orient_nib] lst.append(output) #Outputting the results dataset df = pd.DataFrame(lst, columns=cols) if not os.path.exists('/content/gdrive/MyDrive/L3-Clean/Results/Summaries/'): os.mkdir('/content/gdrive/MyDrive/L3-Clean/Results/Summaries/') df.to_csv('/content/gdrive/MyDrive/L3-Clean/Results/Summaries/'+path+'_'+test_name+".csv") print(' ') print(' ') print(' ') print(' -------------- PROCESSING COMPLETE ------------------- ')

39.205556

159

0.540315

import os import csv import sys, getopt import uuid import SimpleITK as sitk import cv2 import numpy as np import tensorflow as tf from flask import Flask, flash, request, redirect, render_template from flask import jsonify from flask import send_from_directory from flask_materialize import Material from tensorflow.python.keras.backend import set_session from werkzeug.utils import secure_filename import shutil import nibabel as nib import pandas as pd import numpy from sarcopenia_ai.apps.segmentation.segloader import preprocess_test_image from sarcopenia_ai.apps.server import settings from sarcopenia_ai.apps.slice_detection.predict import parse_inputs, to256 from sarcopenia_ai.apps.slice_detection.utils import decode_slice_detection_prediction, \ preprocess_sitk_image_for_slice_detection, adjust_detected_position_spacing, place_line_on_img from sarcopenia_ai.core.model_wrapper import BaseModelWrapper from sarcopenia_ai.io import load_image from sarcopenia_ai.preprocessing.preprocessing import blend2d from sarcopenia_ai.utils import compute_muscle_area, compute_muscle_attenuation config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) graph = tf.get_default_graph() import cv2 import numpy as np def normalise_zero_one(image, eps=1e-8): print("Here 1") image = image.astype(np.float32) ret = (image - np.min(image)) ret /= (np.max(image) - np.min(image) + eps) return ret def normalise_one_one(image): print("Here 2") ret = normalise_zero_one(image) ret *= 2. ret -= 1. return ret def preprocess_test_image(image): print("Here") image = normalise_one_one(image) return image e+'/') if os.path.exists(out): shutil.rmtree(out) os.mkdir(out) if not os.path.exists(out): os.mkdir(out) out_yes = os.path.join(out+'/Yes') if not os.path.exists(out_yes): os.mkdir(out_yes) out_no = os.path.join(out+'/No') if not os.path.exists(out_no): os.mkdir(out_no) out_rev = os.path.join(out+'/Review/') if not os.path.exists(out_rev): os.mkdir(out_rev) out_csv = os.path.join(out+'/Pred CSVs/') if not os.path.exists(out_csv): os.mkdir(out_csv) model_wrapper = BaseModelWrapper(settings.SLICE_DETECTION_MODEL_PATH) model_wrapper.setup_model() global slice_detection_model slice_detection_model= model_wrapper.model slice_detection_model._make_predict_function() global segmentation_model model_wrapper = BaseModelWrapper(settings.SEGMENTATION_MODEL_PATH) model_wrapper.setup_model() segmentation_model = model_wrapper.model segmentation_model._make_predict_function() 3_position','Total_slices','Confidence','Slice_Thickness', 'Orientation'] lst = [] for folder in os.listdir(directory): if(folder=='.DS_Store'): continue for sub_folder in os.listdir(directory+"/"+folder): if(sub_folder=='.DS_Store'): continue for sub_sub_folder in os.listdir(directory+"/"+folder+"/"+sub_folder): for file in os.listdir(directory+"/"+folder+"/"+sub_folder+"/"+sub_sub_folder): print("IN SUB-SUB-FOLDER: "+sub_sub_folder) if(file.endswith(".nii.gz") or file.endswith(".nii")): print("Processing file: "+file) try: if(sub_sub_folder=='.DS_Store'): continue print("IN SUB-SUB-FOLDER: "+sub_sub_folder) image_path = directory+"/"+folder+"/"+sub_folder+"/"+sub_sub_folder+"/"+file prob_threshold_U=settings.THRESHOLD_U prob_threshold_L=settings.THRESHOLD_L import ntpath head, tail = ntpath.split(image_path) image_name = tail or ntpath.basename(head) pred_id = pred_id +1 print("ID --> "+str(pred_id)) results = {"success": False, "prediction": {'id': pred_id}} sitk_image, _ = load_image(image_path) print("-----------------------------image path: "+image_path ) if len(sitk_image.GetSize()) == 4: print("-------- 4D Image: Grabbing only first volume") sitk_image = sitk_image[:, :, :, 0] print('-------------- NIB') nib_image = nib.load(image_path) orient_nib=nib.orientations.aff2axcodes(nib_image.affine) print('-------------- Preprocess') image2d, image2d_preview= preprocess_sitk_image_for_slice_detection(sitk_image) image3d = sitk.GetArrayFromImage(sitk_image) spacing = sitk_image.GetSpacing() size = list(sitk_image.GetSize()) slice_thickness = spacing[2] with graph.as_default(): set_session(sess) preds = slice_detection_model.predict(image2d) print('-------------- Predict') pred_z, prob = decode_slice_detection_prediction(preds) slice_z = adjust_detected_position_spacing(pred_z, spacing) print('Prob: '+ str(prob)) print('Slice Z: ' +str(slice_z) ) print('{red_z: '+str(pred_z)) new_z_calculate = 0 new_pred_z = pred_z new_slice_z = slice_z new_prob = prob print('-------------- Normalize') if(slice_z < .27*size[2] or slice_z > .48*size[2]): print("---------------------debug") print(preds.shape) print(preds.shape[1]) new_pred_z = find_max(preds[0, int(.27*preds.shape[1]):int(.48*preds.shape[1])]) new_pred_z = new_pred_z + int(.27*preds.shape[1]); new_slice_z = adjust_detected_position_spacing(new_pred_z, spacing) print("old position") print(pred_z) print(slice_z) print("new position") print(new_pred_z) print(new_slice_z) new_z_calculate =1; new_prob = float(preds[0,new_pred_z]) t('-------------- Predict CSV') preds_reshaped = preds.reshape(preds.shape[0], -1) numpy.savetxt(out_csv+"PRED_"+str(pred_id)+".csv", preds_reshaped, delimiter=",") if (new_prob > prob_threshold_U): print('-------------- Above') image = image3d slice_image = image[new_slice_z,:, :] image2dA = place_line_on_img(image2d[0], pred_z, pred_z, r=1) image2dB = place_line_on_img(image2d[0], -new_pred_z, new_pred_z, r=1) cv2.imwrite(out_yes+"/"+str(pred_id)+'_YES_'+image_name+'_SL.jpg', to256(slice_image)) cv2.imwrite(out_yes+"/"+str(pred_id)+'_YES_'+image_name+'_FR.jpg', to256(image2dA)) cv2.imwrite(out_yes+"/"+str(pred_id)+'_YES_'+image_name+'_FR2.jpg', to256(image2dB)) output = [image_path,folder,sub_folder,sub_sub_folder,'YES',new_slice_z,size[2],new_prob,slice_thickness, orient_nib] lst.append(output) elif (new_prob <= prob_threshold_L ): print('-------------- No') image = image3d slice_image = image[new_slice_z,:, :] image2dA = place_line_on_img(image2d[0], -pred_z, -pred_z, r=1) image2dB = place_line_on_img(image2d[0], -new_pred_z, -new_pred_z, r=1) cv2.imwrite(out_no+str(pred_id)+'_NO_'+image_name+'_SL.jpg', to256(slice_image)) cv2.imwrite(out_no+str(pred_id)+'_NO_'+image_name+'_FR.jpg', to256(image2dA)) cv2.imwrite(out_no+str(pred_id)+'_NO_'+image_name+'_FR2.jpg', to256(image2dB)) output = [image_path,folder,sub_folder,sub_sub_folder,'NO',new_slice_z,size[2],new_prob,slice_thickness, orient_nib] lst.append(output) else: print('-------------- Review') image = image3d slice_image = image[new_slice_z,:, :] image2dA = place_line_on_img(image2d[0], pred_z, pred_z, r=1) image2dB = place_line_on_img(image2d[0], new_pred_z, new_pred_z, r=1) cv2.imwrite(out_rev+str(pred_id)+'_REVIEW_'+image_name+'_SL_'+str(new_slice_z)+'_PROB_'+str(new_prob)+'.jpg', to256(slice_image)) cv2.imwrite(out_rev+str(pred_id)+'_REVIEW_'+image_name+'_FR_'+str(slice_z)+'_PROB_'+str(prob)+'.jpg', to256(image2dA)) cv2.imwrite(out_rev+str(pred_id)+'_REVIEW_'+image_name+'_FR2_'+str(new_slice_z)+'_PROB_'+str(new_prob)+'.jpg', to256(image2dB)) output = [image_path,folder,sub_folder,sub_sub_folder,'REVIEW',new_slice_z,size[2],new_prob,slice_thickness, orient_nib] lst.append(output) except: print('-------------- Wrong') print('-------------- ') print('-------------- ') print("Something went wrong - File: "+image_path) print("Unexpected error"+str(sys.exc_info()[0])) output = [image_path,folder,sub_folder,sub_sub_folder,'Error','','','Something went wrong:'+str(sys.exc_info()[1]),'', orient_nib] lst.append(output) df = pd.DataFrame(lst, columns=cols) if not os.path.exists('/content/gdrive/MyDrive/L3-Clean/Results/Summaries/'): os.mkdir('/content/gdrive/MyDrive/L3-Clean/Results/Summaries/') df.to_csv('/content/gdrive/MyDrive/L3-Clean/Results/Summaries/'+path+'_'+test_name+".csv") print(' ') print(' ') print(' ') print(' -------------- PROCESSING COMPLETE ------------------- ')

true

790834027a8e823b24e0363e5e21439e4d9c23cf

6,867

py

Python

tests/bitmovin/services/filters/text_filter_tests.py

camberbridge/bitmovin-python

3af4c6e79b0291fda05fd1ceeb5bed1bba9f3c95

[ "Unlicense" ]

44

2016-12-12T17:37:23.000Z

2021-03-03T09:48:48.000Z

tests/bitmovin/services/filters/text_filter_tests.py

camberbridge/bitmovin-python

3af4c6e79b0291fda05fd1ceeb5bed1bba9f3c95

[ "Unlicense" ]

38

2017-01-09T14:45:45.000Z

2022-02-27T18:04:33.000Z

tests/bitmovin/services/filters/text_filter_tests.py

camberbridge/bitmovin-python

3af4c6e79b0291fda05fd1ceeb5bed1bba9f3c95

[ "Unlicense" ]

27

2017-02-02T22:49:31.000Z

2019-11-21T07:04:57.000Z

import json import unittest from bitmovin import Bitmovin, Response, TextFilter, Font from bitmovin.errors import BitmovinApiError from tests.bitmovin import BitmovinTestCase class TextFilterTests(BitmovinTestCase): @classmethod def setUpClass(cls): super().setUpClass() @classmethod def tearDownClass(cls): super().tearDownClass() def setUp(self): super().setUp() self.bitmovin = Bitmovin(self.api_key) self.assertIsNotNone(self.bitmovin) self.assertTrue(isinstance(self.bitmovin, Bitmovin)) def tearDown(self): super().tearDown() def test_create_text_filter(self): sample_filter = self._get_sample_text_filter() filter_resource_response = self.bitmovin.filters.Text.create(sample_filter) self.assertIsNotNone(filter_resource_response) self.assertIsNotNone(filter_resource_response.resource) self.assertIsNotNone(filter_resource_response.resource.id) self._compare_text_filters(sample_filter, filter_resource_response.resource) def test_create_text_filter_without_name(self): sample_filter = self._get_sample_text_filter() sample_filter.name = None filter_resource_response = self.bitmovin.filters.Text.create(sample_filter) self.assertIsNotNone(filter_resource_response) self.assertIsNotNone(filter_resource_response.resource) self.assertIsNotNone(filter_resource_response.resource.id) self._compare_text_filters(sample_filter, filter_resource_response.resource) def test_retrieve_text_filter(self): sample_filter = self._get_sample_text_filter() created_filter_response = self.bitmovin.filters.Text.create(sample_filter) self.assertIsNotNone(created_filter_response) self.assertIsNotNone(created_filter_response.resource) self.assertIsNotNone(created_filter_response.resource.id) self._compare_text_filters(sample_filter, created_filter_response.resource) retrieved_filter_response = self.bitmovin.filters.Text.retrieve(created_filter_response.resource.id) self.assertIsNotNone(retrieved_filter_response) self.assertIsNotNone(retrieved_filter_response.resource) self._compare_text_filters(created_filter_response.resource, retrieved_filter_response.resource) def test_delete_text_filter(self): sample_filter = self._get_sample_text_filter() created_filter_response = self.bitmovin.filters.Text.create(sample_filter) self.assertIsNotNone(created_filter_response) self.assertIsNotNone(created_filter_response.resource) self.assertIsNotNone(created_filter_response.resource.id) self._compare_text_filters(sample_filter, created_filter_response.resource) deleted_minimal_resource = self.bitmovin.filters.Text.delete(created_filter_response.resource.id) self.assertIsNotNone(deleted_minimal_resource) self.assertIsNotNone(deleted_minimal_resource.resource) self.assertIsNotNone(deleted_minimal_resource.resource.id) try: self.bitmovin.filters.Text.retrieve(created_filter_response.resource.id) self.fail( 'Previous statement should have thrown an exception. ' + 'Retrieving filter after deleting it shouldn\'t be possible.' ) except BitmovinApiError: pass def test_list_text_filters(self): sample_filter = self._get_sample_text_filter() created_filter_response = self.bitmovin.filters.Text.create(sample_filter) self.assertIsNotNone(created_filter_response) self.assertIsNotNone(created_filter_response.resource) self.assertIsNotNone(created_filter_response.resource.id) self._compare_text_filters(sample_filter, created_filter_response.resource) filters = self.bitmovin.filters.Text.list() self.assertIsNotNone(filters) self.assertIsNotNone(filters.resource) self.assertIsNotNone(filters.response) self.assertIsInstance(filters.resource, list) self.assertIsInstance(filters.response, Response) self.assertGreater(filters.resource.__sizeof__(), 1) def test_retrieve_text_filter_custom_data(self): sample_filter = self._get_sample_text_filter() sample_filter.customData = '<pre>my custom data</pre>' created_filter_response = self.bitmovin.filters.Text.create(sample_filter) self.assertIsNotNone(created_filter_response) self.assertIsNotNone(created_filter_response.resource) self.assertIsNotNone(created_filter_response.resource.id) self._compare_text_filters(sample_filter, created_filter_response.resource) custom_data_response = self.bitmovin.filters.Text.retrieve_custom_data( created_filter_response.resource.id) custom_data = custom_data_response.resource self.assertEqual(sample_filter.customData, json.loads(custom_data.customData)) def _compare_text_filters(self, first: TextFilter, second: TextFilter): """ :param first: TextFilter :param second: TextFilter :return: bool """ self.assertEqual(str(first.x), str(second.x)) self.assertEqual(str(first.y), str(second.y)) self.assertEqual(first.text, second.text) self.assertEqual(first.timecode, second.timecode) self.assertEqual(first.shadowY, second.shadowX) self.assertEqual(first.shadowX, second.shadowX) self.assertEqual(first.shadowColor, second.shadowColor) self.assertEqual(first.alpha, second.alpha) self.assertEqual(first.fontSize, second.fontSize) self.assertEqual(first.font, second.font) self.assertEqual(first.fontColor, second.fontColor) self.assertEqual(first.fixBounds, second.fixBounds) self.assertEqual(first.borderWidth, second.borderWidth) self.assertEqual(first.lineSpacing, second.lineSpacing) self.assertEqual(first.boxColor, second.boxColor) self.assertEqual(first.boxBorderWidth, second.boxBorderWidth) self.assertEqual(first.box, second.box) self.assertEqual(first.description, second.description) self.assertEqual(first.name, second.name) return True def _get_sample_text_filter(self): text_filter = TextFilter(name='Sample Text Filter', x='10', y='10', text='ThisIsATest', font=Font.DEJAVUSANS) self.assertIsNotNone(text_filter.x) self.assertIsNotNone(text_filter.y) self.assertIsNotNone(text_filter.name) self.assertIsNotNone(text_filter.font) return text_filter if __name__ == '__main__': unittest.main()

44.590909

108

0.722732

import json import unittest from bitmovin import Bitmovin, Response, TextFilter, Font from bitmovin.errors import BitmovinApiError from tests.bitmovin import BitmovinTestCase class TextFilterTests(BitmovinTestCase): @classmethod def setUpClass(cls): super().setUpClass() @classmethod def tearDownClass(cls): super().tearDownClass() def setUp(self): super().setUp() self.bitmovin = Bitmovin(self.api_key) self.assertIsNotNone(self.bitmovin) self.assertTrue(isinstance(self.bitmovin, Bitmovin)) def tearDown(self): super().tearDown() def test_create_text_filter(self): sample_filter = self._get_sample_text_filter() filter_resource_response = self.bitmovin.filters.Text.create(sample_filter) self.assertIsNotNone(filter_resource_response) self.assertIsNotNone(filter_resource_response.resource) self.assertIsNotNone(filter_resource_response.resource.id) self._compare_text_filters(sample_filter, filter_resource_response.resource) def test_create_text_filter_without_name(self): sample_filter = self._get_sample_text_filter() sample_filter.name = None filter_resource_response = self.bitmovin.filters.Text.create(sample_filter) self.assertIsNotNone(filter_resource_response) self.assertIsNotNone(filter_resource_response.resource) self.assertIsNotNone(filter_resource_response.resource.id) self._compare_text_filters(sample_filter, filter_resource_response.resource) def test_retrieve_text_filter(self): sample_filter = self._get_sample_text_filter() created_filter_response = self.bitmovin.filters.Text.create(sample_filter) self.assertIsNotNone(created_filter_response) self.assertIsNotNone(created_filter_response.resource) self.assertIsNotNone(created_filter_response.resource.id) self._compare_text_filters(sample_filter, created_filter_response.resource) retrieved_filter_response = self.bitmovin.filters.Text.retrieve(created_filter_response.resource.id) self.assertIsNotNone(retrieved_filter_response) self.assertIsNotNone(retrieved_filter_response.resource) self._compare_text_filters(created_filter_response.resource, retrieved_filter_response.resource) def test_delete_text_filter(self): sample_filter = self._get_sample_text_filter() created_filter_response = self.bitmovin.filters.Text.create(sample_filter) self.assertIsNotNone(created_filter_response) self.assertIsNotNone(created_filter_response.resource) self.assertIsNotNone(created_filter_response.resource.id) self._compare_text_filters(sample_filter, created_filter_response.resource) deleted_minimal_resource = self.bitmovin.filters.Text.delete(created_filter_response.resource.id) self.assertIsNotNone(deleted_minimal_resource) self.assertIsNotNone(deleted_minimal_resource.resource) self.assertIsNotNone(deleted_minimal_resource.resource.id) try: self.bitmovin.filters.Text.retrieve(created_filter_response.resource.id) self.fail( 'Previous statement should have thrown an exception. ' + 'Retrieving filter after deleting it shouldn\'t be possible.' ) except BitmovinApiError: pass def test_list_text_filters(self): sample_filter = self._get_sample_text_filter() created_filter_response = self.bitmovin.filters.Text.create(sample_filter) self.assertIsNotNone(created_filter_response) self.assertIsNotNone(created_filter_response.resource) self.assertIsNotNone(created_filter_response.resource.id) self._compare_text_filters(sample_filter, created_filter_response.resource) filters = self.bitmovin.filters.Text.list() self.assertIsNotNone(filters) self.assertIsNotNone(filters.resource) self.assertIsNotNone(filters.response) self.assertIsInstance(filters.resource, list) self.assertIsInstance(filters.response, Response) self.assertGreater(filters.resource.__sizeof__(), 1) def test_retrieve_text_filter_custom_data(self): sample_filter = self._get_sample_text_filter() sample_filter.customData = '<pre>my custom data</pre>' created_filter_response = self.bitmovin.filters.Text.create(sample_filter) self.assertIsNotNone(created_filter_response) self.assertIsNotNone(created_filter_response.resource) self.assertIsNotNone(created_filter_response.resource.id) self._compare_text_filters(sample_filter, created_filter_response.resource) custom_data_response = self.bitmovin.filters.Text.retrieve_custom_data( created_filter_response.resource.id) custom_data = custom_data_response.resource self.assertEqual(sample_filter.customData, json.loads(custom_data.customData)) def _compare_text_filters(self, first: TextFilter, second: TextFilter): self.assertEqual(str(first.x), str(second.x)) self.assertEqual(str(first.y), str(second.y)) self.assertEqual(first.text, second.text) self.assertEqual(first.timecode, second.timecode) self.assertEqual(first.shadowY, second.shadowX) self.assertEqual(first.shadowX, second.shadowX) self.assertEqual(first.shadowColor, second.shadowColor) self.assertEqual(first.alpha, second.alpha) self.assertEqual(first.fontSize, second.fontSize) self.assertEqual(first.font, second.font) self.assertEqual(first.fontColor, second.fontColor) self.assertEqual(first.fixBounds, second.fixBounds) self.assertEqual(first.borderWidth, second.borderWidth) self.assertEqual(first.lineSpacing, second.lineSpacing) self.assertEqual(first.boxColor, second.boxColor) self.assertEqual(first.boxBorderWidth, second.boxBorderWidth) self.assertEqual(first.box, second.box) self.assertEqual(first.description, second.description) self.assertEqual(first.name, second.name) return True def _get_sample_text_filter(self): text_filter = TextFilter(name='Sample Text Filter', x='10', y='10', text='ThisIsATest', font=Font.DEJAVUSANS) self.assertIsNotNone(text_filter.x) self.assertIsNotNone(text_filter.y) self.assertIsNotNone(text_filter.name) self.assertIsNotNone(text_filter.font) return text_filter if __name__ == '__main__': unittest.main()

true

790834cc3bfa3eafdda0ce74e90d581d6ba9a7c3

667

py

Python

tools/perf/contrib/media_router_benchmarks/media_router_measurements.py

sarang-apps/darshan_browser

173649bb8a7c656dc60784d19e7bb73e07c20daa

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

575

2015-06-18T23:58:20.000Z

2022-03-23T09:32:39.000Z

tools/perf/contrib/media_router_benchmarks/media_router_measurements.py

sarang-apps/darshan_browser

173649bb8a7c656dc60784d19e7bb73e07c20daa

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

113

2015-05-04T09:58:14.000Z

2022-01-31T19:35:03.000Z

tools/perf/contrib/media_router_benchmarks/media_router_measurements.py

sarang-apps/darshan_browser

173649bb8a7c656dc60784d19e7bb73e07c20daa

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

52

2015-07-14T10:40:50.000Z

2022-03-15T01:11:49.000Z

# Copyright 2019 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. from contrib.media_router_benchmarks import media_router_cpu_memory_metric from telemetry.page import legacy_page_test class MediaRouterCPUMemoryTest(legacy_page_test.LegacyPageTest): """Performs a measurement of Media Route CPU/memory usage.""" def __init__(self): super(MediaRouterCPUMemoryTest, self).__init__() self._metric = media_router_cpu_memory_metric.MediaRouterCPUMemoryMetric() def ValidateAndMeasurePage(self, page, tab, results): self._metric.AddResults(tab, results)

37.055556

78

0.803598

from contrib.media_router_benchmarks import media_router_cpu_memory_metric from telemetry.page import legacy_page_test class MediaRouterCPUMemoryTest(legacy_page_test.LegacyPageTest): def __init__(self): super(MediaRouterCPUMemoryTest, self).__init__() self._metric = media_router_cpu_memory_metric.MediaRouterCPUMemoryMetric() def ValidateAndMeasurePage(self, page, tab, results): self._metric.AddResults(tab, results)

true

790835f22ff39732dc36c4e09154deb638a51290

2,839

py

Python

speech/samples/v1/speech_transcribe_multichannel.py

hugovk/google-cloud-python

b387134827dbc3be0e1b431201e0875798002fda

[ "Apache-2.0" ]

1

2019-03-26T21:44:51.000Z

speech/samples/v1/speech_transcribe_multichannel.py

hugovk/google-cloud-python

b387134827dbc3be0e1b431201e0875798002fda

[ "Apache-2.0" ]

6

2019-05-27T22:05:58.000Z

2019-08-05T16:46:16.000Z

speech/samples/v1/speech_transcribe_multichannel.py

hugovk/google-cloud-python

b387134827dbc3be0e1b431201e0875798002fda

[ "Apache-2.0" ]

1

2019-03-29T18:26:16.000Z

# -*- coding: utf-8 -*- # # Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://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. # DO NOT EDIT! This is a generated sample ("Request", "speech_transcribe_multichannel") # To install the latest published package dependency, execute the following: # pip install google-cloud-speech # sample-metadata # title: Multi-Channel Audio Transcription (Local File) # description: Transcribe a short audio file with multiple channels # usage: python3 samples/v1/speech_transcribe_multichannel.py [--local_file_path "resources/multi.wav"] # [START speech_transcribe_multichannel] from google.cloud import speech_v1 import io def sample_recognize(local_file_path): """ Transcribe a short audio file with multiple channels Args: local_file_path Path to local audio file, e.g. /path/audio.wav """ client = speech_v1.SpeechClient() # local_file_path = 'resources/multi.wav' # The number of channels in the input audio file (optional) audio_channel_count = 2 # When set to true, each audio channel will be recognized separately. # The recognition result will contain a channel_tag field to state which # channel that result belongs to enable_separate_recognition_per_channel = True # The language of the supplied audio language_code = "en-US" config = { "audio_channel_count": audio_channel_count, "enable_separate_recognition_per_channel": enable_separate_recognition_per_channel, "language_code": language_code, } with io.open(local_file_path, "rb") as f: content = f.read() audio = {"content": content} response = client.recognize(config, audio) for result in response.results: # channel_tag to recognize which audio channel this result is for print(u"Channel tag: {}".format(result.channel_tag)) # First alternative is the most probable result alternative = result.alternatives[0] print(u"Transcript: {}".format(alternative.transcript)) # [END speech_transcribe_multichannel] def main(): import argparse parser = argparse.ArgumentParser() parser.add_argument("--local_file_path", type=str, default="resources/multi.wav") args = parser.parse_args() sample_recognize(args.local_file_path) if __name__ == "__main__": main()

32.632184

105

0.726664

from google.cloud import speech_v1 import io def sample_recognize(local_file_path): client = speech_v1.SpeechClient() audio_channel_count = 2 enable_separate_recognition_per_channel = True language_code = "en-US" config = { "audio_channel_count": audio_channel_count, "enable_separate_recognition_per_channel": enable_separate_recognition_per_channel, "language_code": language_code, } with io.open(local_file_path, "rb") as f: content = f.read() audio = {"content": content} response = client.recognize(config, audio) for result in response.results: print(u"Channel tag: {}".format(result.channel_tag)) alternative = result.alternatives[0] print(u"Transcript: {}".format(alternative.transcript)) def main(): import argparse parser = argparse.ArgumentParser() parser.add_argument("--local_file_path", type=str, default="resources/multi.wav") args = parser.parse_args() sample_recognize(args.local_file_path) if __name__ == "__main__": main()

true

790837f0eda32c46f86435beb0818e3c2221053d

27,255

py

Python

pipenv/cli.py

mlhamel/pipenv

22445858766a1f92c5ad87c90662ba260c8b750b

[ "MIT" ]

null

pipenv/cli.py

mlhamel/pipenv

22445858766a1f92c5ad87c90662ba260c8b750b

[ "MIT" ]

null

pipenv/cli.py

mlhamel/pipenv

22445858766a1f92c5ad87c90662ba260c8b750b

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os import sys from click import ( argument, command, echo, edit, group, Group, option, pass_context, Option, version_option, BadParameter, ) from click_completion import init as init_completion from click_completion import get_code from click_didyoumean import DYMCommandCollection import crayons import delegator from .__version__ import __version__ from . import environments from .environments import * from .utils import is_valid_url # Enable shell completion. init_completion() CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) class PipenvGroup(Group): """Custom Group class provides formatted main help""" def get_help_option(self, ctx): from .core import format_help """Override for showing formatted main help via --help and -h options""" help_options = self.get_help_option_names(ctx) if not help_options or not self.add_help_option: return def show_help(ctx, param, value): if value and not ctx.resilient_parsing: if not ctx.invoked_subcommand: # legit main help echo(format_help(ctx.get_help())) else: # legit sub-command help echo(ctx.get_help(), color=ctx.color) ctx.exit() return Option( help_options, is_flag=True, is_eager=True, expose_value=False, callback=show_help, help='Show this message and exit.', ) def setup_verbose(ctx, param, value): if value: import logging logging.getLogger('pip').setLevel(logging.INFO) return value def validate_python_path(ctx, param, value): # Validating the Python path is complicated by accepting a number of # friendly options: the default will be boolean False to enable # autodetection but it may also be a value which will be searched in # the path or an absolute path. To report errors as early as possible # we'll report absolute paths which do not exist: if isinstance(value, (str, bytes)): if os.path.isabs(value) and not os.path.isfile(value): raise BadParameter('Expected Python at path %s does not exist' % value) return value def validate_pypi_mirror(ctx, param, value): if value and not is_valid_url(value): raise BadParameter('Invalid PyPI mirror URL: %s' % value) return value @group( cls=PipenvGroup, invoke_without_command=True, context_settings=CONTEXT_SETTINGS, ) @option( '--where', is_flag=True, default=False, help="Output project home information.", ) @option( '--venv', is_flag=True, default=False, help="Output virtualenv information.", ) @option( '--py', is_flag=True, default=False, help="Output Python interpreter information.", ) @option( '--envs', is_flag=True, default=False, help="Output Environment Variable options.", ) @option( '--rm', is_flag=True, default=False, help="Remove the virtualenv." ) @option('--bare', is_flag=True, default=False, help="Minimal output.") @option( '--completion', is_flag=True, default=False, help="Output completion (to be eval'd).", ) @option('--man', is_flag=True, default=False, help="Display manpage.") @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--site-packages', is_flag=True, default=False, help="Enable site-packages for the virtualenv.", ) @version_option( prog_name=crayons.normal('pipenv', bold=True), version=__version__ ) @pass_context def cli( ctx, where=False, venv=False, rm=False, bare=False, three=False, python=False, help=False, py=False, site_packages=False, envs=False, man=False, completion=False, ): if completion: # Handle this ASAP to make shell startup fast. if PIPENV_SHELL: echo( get_code( shell=PIPENV_SHELL.split(os.sep)[-1], prog_name='pipenv' ) ) else: echo( 'Please ensure that the {0} environment variable ' 'is set.'.format(crayons.normal('SHELL', bold=True)), err=True, ) sys.exit(1) sys.exit(0) from .core import ( system_which, do_py, warn_in_virtualenv, do_where, project, spinner, cleanup_virtualenv, ensure_project, format_help ) if man: if system_which('man'): path = os.sep.join([os.path.dirname(__file__), 'pipenv.1']) os.execle(system_which('man'), 'man', path, os.environ) else: echo( 'man does not appear to be available on your system.', err=True ) if envs: echo( 'The following environment variables can be set, to do various things:\n' ) for key in environments.__dict__: if key.startswith('PIPENV'): echo(' - {0}'.format(crayons.normal(key, bold=True))) echo( '\nYou can learn more at:\n {0}'.format( crayons.green( 'http://docs.pipenv.org/advanced/#configuration-with-environment-variables' ) ) ) sys.exit(0) warn_in_virtualenv() if ctx.invoked_subcommand is None: # --where was passed... if where: do_where(bare=True) sys.exit(0) elif py: do_py() sys.exit() # --venv was passed... elif venv: # There is no virtualenv yet. if not project.virtualenv_exists: echo( crayons.red( 'No virtualenv has been created for this project yet!' ), err=True, ) sys.exit(1) else: echo(project.virtualenv_location) sys.exit(0) # --rm was passed... elif rm: # Abort if --system (or running in a virtualenv). if PIPENV_USE_SYSTEM: echo( crayons.red( 'You are attempting to remove a virtualenv that ' 'Pipenv did not create. Aborting.' ) ) sys.exit(1) if project.virtualenv_exists: loc = project.virtualenv_location echo( crayons.normal( u'{0} ({1})…'.format( crayons.normal('Removing virtualenv', bold=True), crayons.green(loc), ) ) ) with spinner(): # Remove the virtualenv. cleanup_virtualenv(bare=True) sys.exit(0) else: echo( crayons.red( 'No virtualenv has been created for this project yet!', bold=True, ), err=True, ) sys.exit(1) # --two / --three was passed... if (python or three is not None) or site_packages: ensure_project( three=three, python=python, warn=True, site_packages=site_packages ) # Check this again before exiting for empty ``pipenv`` command. elif ctx.invoked_subcommand is None: # Display help to user, if no commands were passed. echo(format_help(ctx.get_help())) @command( short_help="Installs provided packages and adds them to Pipfile, or (if none is given), installs all packages.", context_settings=dict(ignore_unknown_options=True, allow_extra_args=True), ) @argument('package_name', default=False) @argument('more_packages', nargs=-1) @option( '--dev', '-d', is_flag=True, default=False, help="Install package(s) in [dev-packages].", ) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--pypi-mirror', default=PIPENV_PYPI_MIRROR, nargs=1, callback=validate_pypi_mirror, help="Specify a PyPI mirror.", ) @option( '--system', is_flag=True, default=False, help="System pip management." ) @option( '--requirements', '-r', nargs=1, default=False, help="Import a requirements.txt file.", ) @option( '--code', '-c', nargs=1, default=False, help="Import from codebase." ) @option( '--verbose', '-v', is_flag=True, default=False, help="Verbose mode.", callback=setup_verbose, ) @option( '--ignore-pipfile', is_flag=True, default=False, help="Ignore Pipfile when installing, using the Pipfile.lock.", ) @option( '--sequential', is_flag=True, default=False, help="Install dependencies one-at-a-time, instead of concurrently.", ) @option( '--skip-lock', is_flag=True, default=False, help=u"Ignore locking mechanisms when installing—use the Pipfile, instead.", ) @option( '--deploy', is_flag=True, default=False, help=u"Abort if the Pipfile.lock is out–of–date, or Python version is wrong.", ) @option( '--pre', is_flag=True, default=False, help=u"Allow pre–releases." ) @option( '--keep-outdated', is_flag=True, default=False, help=u"Keep out–dated dependencies from being updated in Pipfile.lock.", ) @option( '--selective-upgrade', is_flag=True, default=False, help="Update specified packages.", ) def install( package_name=False, more_packages=False, dev=False, three=False, python=False, pypi_mirror=None, system=False, lock=True, ignore_pipfile=False, skip_lock=False, verbose=False, requirements=False, sequential=False, pre=False, code=False, deploy=False, keep_outdated=False, selective_upgrade=False, ): from .core import do_install do_install( package_name=package_name, more_packages=more_packages, dev=dev, three=three, python=python, pypi_mirror=pypi_mirror, system=system, lock=lock, ignore_pipfile=ignore_pipfile, skip_lock=skip_lock, verbose=verbose, requirements=requirements, sequential=sequential, pre=pre, code=code, deploy=deploy, keep_outdated=keep_outdated, selective_upgrade=selective_upgrade, ) @command( short_help="Un-installs a provided package and removes it from Pipfile." ) @argument('package_name', default=False) @argument('more_packages', nargs=-1) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--system', is_flag=True, default=False, help="System pip management." ) @option( '--verbose', '-v', is_flag=True, default=False, help="Verbose mode.", callback=setup_verbose, ) @option('--lock', is_flag=True, default=True, help="Lock afterwards.") @option( '--all-dev', is_flag=True, default=False, help="Un-install all package from [dev-packages].", ) @option( '--all', is_flag=True, default=False, help="Purge all package(s) from virtualenv. Does not edit Pipfile.", ) @option( '--keep-outdated', is_flag=True, default=False, help=u"Keep out–dated dependencies from being updated in Pipfile.lock.", ) @option( '--pypi-mirror', default=PIPENV_PYPI_MIRROR, nargs=1, callback=validate_pypi_mirror, help="Specify a PyPI mirror.", ) def uninstall( package_name=False, more_packages=False, three=None, python=False, system=False, lock=False, all_dev=False, all=False, verbose=False, keep_outdated=False, pypi_mirror=None, ): from .core import do_uninstall do_uninstall( package_name=package_name, more_packages=more_packages, three=three, python=python, system=system, lock=lock, all_dev=all_dev, all=all, verbose=verbose, keep_outdated=keep_outdated, pypi_mirror=pypi_mirror, ) @command(short_help="Generates Pipfile.lock.") @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--pypi-mirror', default=PIPENV_PYPI_MIRROR, nargs=1, callback=validate_pypi_mirror, help="Specify a PyPI mirror.", ) @option( '--verbose', '-v', is_flag=True, default=False, help="Verbose mode.", callback=setup_verbose, ) @option( '--requirements', '-r', is_flag=True, default=False, help="Generate output compatible with requirements.txt.", ) @option( '--dev', '-d', is_flag=True, default=False, help="Generate output compatible with requirements.txt for the development dependencies.", ) @option( '--clear', is_flag=True, default=False, help="Clear the dependency cache." ) @option( '--pre', is_flag=True, default=False, help=u"Allow pre–releases." ) @option( '--keep-outdated', is_flag=True, default=False, help=u"Keep out–dated dependencies from being updated in Pipfile.lock.", ) def lock( three=None, python=False, pypi_mirror=None, verbose=False, requirements=False, dev=False, clear=False, pre=False, keep_outdated=False, ): from .core import ensure_project, do_init, do_lock # Ensure that virtualenv is available. ensure_project(three=three, python=python) if requirements: do_init(dev=dev, requirements=requirements, pypi_mirror=pypi_mirror) do_lock( verbose=verbose, clear=clear, pre=pre, keep_outdated=keep_outdated, pypi_mirror=pypi_mirror ) @command( short_help="Spawns a shell within the virtualenv.", context_settings=dict(ignore_unknown_options=True, allow_extra_args=True), ) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--fancy', is_flag=True, default=False, help="Run in shell in fancy mode (for elegantly configured shells).", ) @option( '--anyway', is_flag=True, default=False, help="Always spawn a subshell, even if one is already spawned.", ) @argument('shell_args', nargs=-1) def shell( three=None, python=False, fancy=False, shell_args=None, anyway=False ): from .core import load_dot_env, do_shell # Prevent user from activating nested environments. if 'PIPENV_ACTIVE' in os.environ: # If PIPENV_ACTIVE is set, VIRTUAL_ENV should always be set too. venv_name = os.environ.get( 'VIRTUAL_ENV', 'UNKNOWN_VIRTUAL_ENVIRONMENT' ) if not anyway: echo( '{0} {1} {2}\nNo action taken to avoid nested environments.'.format( crayons.normal('Shell for'), crayons.green(venv_name, bold=True), crayons.normal('already activated.', bold=True), ), err=True, ) sys.exit(1) # Load .env file. load_dot_env() # Use fancy mode for Windows. if os.name == 'nt': fancy = True do_shell( three=three, python=python, fancy=fancy, shell_args=shell_args ) @command( add_help_option=False, short_help="Spawns a command installed into the virtualenv.", context_settings=dict( ignore_unknown_options=True, allow_interspersed_args=False, allow_extra_args=True, ), ) @argument('command') @argument('args', nargs=-1) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) def run(command, args, three=None, python=False): from .core import do_run do_run(command=command, args=args, three=three, python=python) @command( short_help="Checks for security vulnerabilities and against PEP 508 markers provided in Pipfile.", context_settings=dict(ignore_unknown_options=True, allow_extra_args=True), ) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--system', is_flag=True, default=False, help="Use system Python." ) @option( '--unused', nargs=1, default=False, help="Given a code path, show potentially unused dependencies.", ) @argument('args', nargs=-1) def check( three=None, python=False, system=False, unused=False, style=False, args=None, ): from .core import do_check do_check( three=three, python=python, system=system, unused=unused, args=args ) @command(short_help="Runs lock, then sync.") @argument('more_packages', nargs=-1) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--pypi-mirror', default=PIPENV_PYPI_MIRROR, nargs=1, callback=validate_pypi_mirror, help="Specify a PyPI mirror.", ) @option( '--verbose', '-v', is_flag=True, default=False, help="Verbose mode.", callback=setup_verbose, ) @option( '--dev', '-d', is_flag=True, default=False, help="Install package(s) in [dev-packages].", ) @option( '--clear', is_flag=True, default=False, help="Clear the dependency cache." ) @option('--bare', is_flag=True, default=False, help="Minimal output.") @option( '--pre', is_flag=True, default=False, help=u"Allow pre–releases." ) @option( '--keep-outdated', is_flag=True, default=False, help=u"Keep out–dated dependencies from being updated in Pipfile.lock.", ) @option( '--sequential', is_flag=True, default=False, help="Install dependencies one-at-a-time, instead of concurrently.", ) @option( '--outdated', is_flag=True, default=False, help=u"List out–of–date dependencies.", ) @option( '--dry-run', is_flag=True, default=None, help=u"List out–of–date dependencies.", ) @argument('package', default=False) @pass_context def update( ctx, three=None, python=False, pypi_mirror=None, system=False, verbose=False, clear=False, keep_outdated=False, pre=False, dev=False, bare=False, sequential=False, package=None, dry_run=None, outdated=False, more_packages=None, ): from .core import ( ensure_project, do_outdated, do_lock, do_sync, ensure_lockfile, do_install, project, ) ensure_project(three=three, python=python, warn=True) if not outdated: outdated = bool(dry_run) if outdated: do_outdated(pypi_mirror=pypi_mirror) if not package: echo( '{0} {1} {2} {3}{4}'.format( crayons.white('Running', bold=True), crayons.red('$ pipenv lock', bold=True), crayons.white('then', bold=True), crayons.red('$ pipenv sync', bold=True), crayons.white('.', bold=True), ) ) do_lock( verbose=verbose, clear=clear, pre=pre, keep_outdated=keep_outdated, pypi_mirror=pypi_mirror ) do_sync( ctx=ctx, install=install, dev=dev, three=three, python=python, bare=bare, dont_upgrade=False, user=False, verbose=verbose, clear=clear, unused=False, sequential=sequential, pypi_mirror=pypi_mirror, ) else: for package in ([package] + list(more_packages) or []): if package not in project.all_packages: echo( '{0}: {1} was not found in your Pipfile! Aborting.' ''.format( crayons.red('Warning', bold=True), crayons.green(package, bold=True), ), err=True, ) sys.exit(1) ensure_lockfile(keep_outdated=project.lockfile_exists, pypi_mirror=pypi_mirror) # Install the dependencies. do_install( package_name=package, more_packages=more_packages, dev=dev, three=three, python=python, pypi_mirror=pypi_mirror, system=system, lock=True, ignore_pipfile=False, skip_lock=False, verbose=verbose, requirements=False, sequential=sequential, pre=pre, code=False, deploy=False, keep_outdated=True, selective_upgrade=True, ) @command( short_help=u"Displays currently–installed dependency graph information." ) @option('--bare', is_flag=True, default=False, help="Minimal output.") @option('--json', is_flag=True, default=False, help="Output JSON.") @option('--json-tree', is_flag=True, default=False, help="Output JSON in nested tree.") @option( '--reverse', is_flag=True, default=False, help="Reversed dependency graph." ) def graph(bare=False, json=False, json_tree=False, reverse=False): from .core import do_graph do_graph(bare=bare, json=json, json_tree=json_tree, reverse=reverse) @command(short_help="View a given module in your editor.", name="open") @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @argument('module', nargs=1) def run_open(module, three=None, python=None): from .core import which, ensure_project # Ensure that virtualenv is available. ensure_project(three=three, python=python, validate=False) c = delegator.run( '{0} -c "import {1}; print({1}.__file__);"'.format( which('python'), module ) ) try: assert c.return_code == 0 except AssertionError: echo(crayons.red('Module not found!')) sys.exit(1) if '__init__.py' in c.out: p = os.path.dirname(c.out.strip().rstrip('cdo')) else: p = c.out.strip().rstrip('cdo') echo( crayons.normal('Opening {0!r} in your EDITOR.'.format(p), bold=True) ) edit(filename=p) sys.exit(0) @command(short_help="Installs all packages specified in Pipfile.lock.") @option( '--verbose', '-v', is_flag=True, default=False, help="Verbose mode.", callback=setup_verbose, ) @option( '--dev', '-d', is_flag=True, default=False, help="Additionally install package(s) in [dev-packages].", ) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--pypi-mirror', default=PIPENV_PYPI_MIRROR, nargs=1, callback=validate_pypi_mirror, help="Specify a PyPI mirror.", ) @option('--bare', is_flag=True, default=False, help="Minimal output.") @option( '--clear', is_flag=True, default=False, help="Clear the dependency cache." ) @option( '--sequential', is_flag=True, default=False, help="Install dependencies one-at-a-time, instead of concurrently.", ) @pass_context def sync( ctx, dev=False, three=None, python=None, bare=False, dont_upgrade=False, user=False, verbose=False, clear=False, unused=False, package_name=None, sequential=False, pypi_mirror=None, ): from .core import do_sync do_sync( ctx=ctx, install=install, dev=dev, three=three, python=python, bare=bare, dont_upgrade=dont_upgrade, user=user, verbose=verbose, clear=clear, unused=unused, sequential=sequential, pypi_mirror=pypi_mirror, ) @command( short_help="Uninstalls all packages not specified in Pipfile.lock." ) @option( '--verbose', '-v', is_flag=True, default=False, help="Verbose mode.", callback=setup_verbose, ) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--dry-run', is_flag=True, default=False, help="Just output unneeded packages.", ) @pass_context def clean( ctx, three=None, python=None, dry_run=False, bare=False, user=False, verbose=False, ): from .core import do_clean do_clean( ctx=ctx, three=three, python=python, dry_run=dry_run, verbose=verbose ) # Install click commands. cli.add_command(graph) cli.add_command(install) cli.add_command(uninstall) cli.add_command(sync) cli.add_command(lock) cli.add_command(check) cli.add_command(clean) cli.add_command(shell) cli.add_command(run) cli.add_command(update) cli.add_command(run_open) # Only invoke the "did you mean" when an argument wasn't passed (it breaks those). if '-' not in ''.join(sys.argv) and len(sys.argv) > 1: cli = DYMCommandCollection(sources=[cli]) if __name__ == '__main__': cli()

25.212766

116

0.60444

import os import sys from click import ( argument, command, echo, edit, group, Group, option, pass_context, Option, version_option, BadParameter, ) from click_completion import init as init_completion from click_completion import get_code from click_didyoumean import DYMCommandCollection import crayons import delegator from .__version__ import __version__ from . import environments from .environments import * from .utils import is_valid_url init_completion() CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help']) class PipenvGroup(Group): def get_help_option(self, ctx): from .core import format_help help_options = self.get_help_option_names(ctx) if not help_options or not self.add_help_option: return def show_help(ctx, param, value): if value and not ctx.resilient_parsing: if not ctx.invoked_subcommand: echo(format_help(ctx.get_help())) else: echo(ctx.get_help(), color=ctx.color) ctx.exit() return Option( help_options, is_flag=True, is_eager=True, expose_value=False, callback=show_help, help='Show this message and exit.', ) def setup_verbose(ctx, param, value): if value: import logging logging.getLogger('pip').setLevel(logging.INFO) return value def validate_python_path(ctx, param, value): if isinstance(value, (str, bytes)): if os.path.isabs(value) and not os.path.isfile(value): raise BadParameter('Expected Python at path %s does not exist' % value) return value def validate_pypi_mirror(ctx, param, value): if value and not is_valid_url(value): raise BadParameter('Invalid PyPI mirror URL: %s' % value) return value @group( cls=PipenvGroup, invoke_without_command=True, context_settings=CONTEXT_SETTINGS, ) @option( '--where', is_flag=True, default=False, help="Output project home information.", ) @option( '--venv', is_flag=True, default=False, help="Output virtualenv information.", ) @option( '--py', is_flag=True, default=False, help="Output Python interpreter information.", ) @option( '--envs', is_flag=True, default=False, help="Output Environment Variable options.", ) @option( '--rm', is_flag=True, default=False, help="Remove the virtualenv." ) @option('--bare', is_flag=True, default=False, help="Minimal output.") @option( '--completion', is_flag=True, default=False, help="Output completion (to be eval'd).", ) @option('--man', is_flag=True, default=False, help="Display manpage.") @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--site-packages', is_flag=True, default=False, help="Enable site-packages for the virtualenv.", ) @version_option( prog_name=crayons.normal('pipenv', bold=True), version=__version__ ) @pass_context def cli( ctx, where=False, venv=False, rm=False, bare=False, three=False, python=False, help=False, py=False, site_packages=False, envs=False, man=False, completion=False, ): if completion: if PIPENV_SHELL: echo( get_code( shell=PIPENV_SHELL.split(os.sep)[-1], prog_name='pipenv' ) ) else: echo( 'Please ensure that the {0} environment variable ' 'is set.'.format(crayons.normal('SHELL', bold=True)), err=True, ) sys.exit(1) sys.exit(0) from .core import ( system_which, do_py, warn_in_virtualenv, do_where, project, spinner, cleanup_virtualenv, ensure_project, format_help ) if man: if system_which('man'): path = os.sep.join([os.path.dirname(__file__), 'pipenv.1']) os.execle(system_which('man'), 'man', path, os.environ) else: echo( 'man does not appear to be available on your system.', err=True ) if envs: echo( 'The following environment variables can be set, to do various things:\n' ) for key in environments.__dict__: if key.startswith('PIPENV'): echo(' - {0}'.format(crayons.normal(key, bold=True))) echo( '\nYou can learn more at:\n {0}'.format( crayons.green( 'http://docs.pipenv.org/advanced/#configuration-with-environment-variables' ) ) ) sys.exit(0) warn_in_virtualenv() if ctx.invoked_subcommand is None: if where: do_where(bare=True) sys.exit(0) elif py: do_py() sys.exit() elif venv: if not project.virtualenv_exists: echo( crayons.red( 'No virtualenv has been created for this project yet!' ), err=True, ) sys.exit(1) else: echo(project.virtualenv_location) sys.exit(0) elif rm: if PIPENV_USE_SYSTEM: echo( crayons.red( 'You are attempting to remove a virtualenv that ' 'Pipenv did not create. Aborting.' ) ) sys.exit(1) if project.virtualenv_exists: loc = project.virtualenv_location echo( crayons.normal( u'{0} ({1})…'.format( crayons.normal('Removing virtualenv', bold=True), crayons.green(loc), ) ) ) with spinner(): cleanup_virtualenv(bare=True) sys.exit(0) else: echo( crayons.red( 'No virtualenv has been created for this project yet!', bold=True, ), err=True, ) sys.exit(1) if (python or three is not None) or site_packages: ensure_project( three=three, python=python, warn=True, site_packages=site_packages ) elif ctx.invoked_subcommand is None: echo(format_help(ctx.get_help())) @command( short_help="Installs provided packages and adds them to Pipfile, or (if none is given), installs all packages.", context_settings=dict(ignore_unknown_options=True, allow_extra_args=True), ) @argument('package_name', default=False) @argument('more_packages', nargs=-1) @option( '--dev', '-d', is_flag=True, default=False, help="Install package(s) in [dev-packages].", ) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--pypi-mirror', default=PIPENV_PYPI_MIRROR, nargs=1, callback=validate_pypi_mirror, help="Specify a PyPI mirror.", ) @option( '--system', is_flag=True, default=False, help="System pip management." ) @option( '--requirements', '-r', nargs=1, default=False, help="Import a requirements.txt file.", ) @option( '--code', '-c', nargs=1, default=False, help="Import from codebase." ) @option( '--verbose', '-v', is_flag=True, default=False, help="Verbose mode.", callback=setup_verbose, ) @option( '--ignore-pipfile', is_flag=True, default=False, help="Ignore Pipfile when installing, using the Pipfile.lock.", ) @option( '--sequential', is_flag=True, default=False, help="Install dependencies one-at-a-time, instead of concurrently.", ) @option( '--skip-lock', is_flag=True, default=False, help=u"Ignore locking mechanisms when installing—use the Pipfile, instead.", ) @option( '--deploy', is_flag=True, default=False, help=u"Abort if the Pipfile.lock is out–of–date, or Python version is wrong.", ) @option( '--pre', is_flag=True, default=False, help=u"Allow pre–releases." ) @option( '--keep-outdated', is_flag=True, default=False, help=u"Keep out–dated dependencies from being updated in Pipfile.lock.", ) @option( '--selective-upgrade', is_flag=True, default=False, help="Update specified packages.", ) def install( package_name=False, more_packages=False, dev=False, three=False, python=False, pypi_mirror=None, system=False, lock=True, ignore_pipfile=False, skip_lock=False, verbose=False, requirements=False, sequential=False, pre=False, code=False, deploy=False, keep_outdated=False, selective_upgrade=False, ): from .core import do_install do_install( package_name=package_name, more_packages=more_packages, dev=dev, three=three, python=python, pypi_mirror=pypi_mirror, system=system, lock=lock, ignore_pipfile=ignore_pipfile, skip_lock=skip_lock, verbose=verbose, requirements=requirements, sequential=sequential, pre=pre, code=code, deploy=deploy, keep_outdated=keep_outdated, selective_upgrade=selective_upgrade, ) @command( short_help="Un-installs a provided package and removes it from Pipfile." ) @argument('package_name', default=False) @argument('more_packages', nargs=-1) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--system', is_flag=True, default=False, help="System pip management." ) @option( '--verbose', '-v', is_flag=True, default=False, help="Verbose mode.", callback=setup_verbose, ) @option('--lock', is_flag=True, default=True, help="Lock afterwards.") @option( '--all-dev', is_flag=True, default=False, help="Un-install all package from [dev-packages].", ) @option( '--all', is_flag=True, default=False, help="Purge all package(s) from virtualenv. Does not edit Pipfile.", ) @option( '--keep-outdated', is_flag=True, default=False, help=u"Keep out–dated dependencies from being updated in Pipfile.lock.", ) @option( '--pypi-mirror', default=PIPENV_PYPI_MIRROR, nargs=1, callback=validate_pypi_mirror, help="Specify a PyPI mirror.", ) def uninstall( package_name=False, more_packages=False, three=None, python=False, system=False, lock=False, all_dev=False, all=False, verbose=False, keep_outdated=False, pypi_mirror=None, ): from .core import do_uninstall do_uninstall( package_name=package_name, more_packages=more_packages, three=three, python=python, system=system, lock=lock, all_dev=all_dev, all=all, verbose=verbose, keep_outdated=keep_outdated, pypi_mirror=pypi_mirror, ) @command(short_help="Generates Pipfile.lock.") @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--pypi-mirror', default=PIPENV_PYPI_MIRROR, nargs=1, callback=validate_pypi_mirror, help="Specify a PyPI mirror.", ) @option( '--verbose', '-v', is_flag=True, default=False, help="Verbose mode.", callback=setup_verbose, ) @option( '--requirements', '-r', is_flag=True, default=False, help="Generate output compatible with requirements.txt.", ) @option( '--dev', '-d', is_flag=True, default=False, help="Generate output compatible with requirements.txt for the development dependencies.", ) @option( '--clear', is_flag=True, default=False, help="Clear the dependency cache." ) @option( '--pre', is_flag=True, default=False, help=u"Allow pre–releases." ) @option( '--keep-outdated', is_flag=True, default=False, help=u"Keep out–dated dependencies from being updated in Pipfile.lock.", ) def lock( three=None, python=False, pypi_mirror=None, verbose=False, requirements=False, dev=False, clear=False, pre=False, keep_outdated=False, ): from .core import ensure_project, do_init, do_lock ensure_project(three=three, python=python) if requirements: do_init(dev=dev, requirements=requirements, pypi_mirror=pypi_mirror) do_lock( verbose=verbose, clear=clear, pre=pre, keep_outdated=keep_outdated, pypi_mirror=pypi_mirror ) @command( short_help="Spawns a shell within the virtualenv.", context_settings=dict(ignore_unknown_options=True, allow_extra_args=True), ) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--fancy', is_flag=True, default=False, help="Run in shell in fancy mode (for elegantly configured shells).", ) @option( '--anyway', is_flag=True, default=False, help="Always spawn a subshell, even if one is already spawned.", ) @argument('shell_args', nargs=-1) def shell( three=None, python=False, fancy=False, shell_args=None, anyway=False ): from .core import load_dot_env, do_shell if 'PIPENV_ACTIVE' in os.environ: venv_name = os.environ.get( 'VIRTUAL_ENV', 'UNKNOWN_VIRTUAL_ENVIRONMENT' ) if not anyway: echo( '{0} {1} {2}\nNo action taken to avoid nested environments.'.format( crayons.normal('Shell for'), crayons.green(venv_name, bold=True), crayons.normal('already activated.', bold=True), ), err=True, ) sys.exit(1) load_dot_env() if os.name == 'nt': fancy = True do_shell( three=three, python=python, fancy=fancy, shell_args=shell_args ) @command( add_help_option=False, short_help="Spawns a command installed into the virtualenv.", context_settings=dict( ignore_unknown_options=True, allow_interspersed_args=False, allow_extra_args=True, ), ) @argument('command') @argument('args', nargs=-1) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) def run(command, args, three=None, python=False): from .core import do_run do_run(command=command, args=args, three=three, python=python) @command( short_help="Checks for security vulnerabilities and against PEP 508 markers provided in Pipfile.", context_settings=dict(ignore_unknown_options=True, allow_extra_args=True), ) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--system', is_flag=True, default=False, help="Use system Python." ) @option( '--unused', nargs=1, default=False, help="Given a code path, show potentially unused dependencies.", ) @argument('args', nargs=-1) def check( three=None, python=False, system=False, unused=False, style=False, args=None, ): from .core import do_check do_check( three=three, python=python, system=system, unused=unused, args=args ) @command(short_help="Runs lock, then sync.") @argument('more_packages', nargs=-1) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--pypi-mirror', default=PIPENV_PYPI_MIRROR, nargs=1, callback=validate_pypi_mirror, help="Specify a PyPI mirror.", ) @option( '--verbose', '-v', is_flag=True, default=False, help="Verbose mode.", callback=setup_verbose, ) @option( '--dev', '-d', is_flag=True, default=False, help="Install package(s) in [dev-packages].", ) @option( '--clear', is_flag=True, default=False, help="Clear the dependency cache." ) @option('--bare', is_flag=True, default=False, help="Minimal output.") @option( '--pre', is_flag=True, default=False, help=u"Allow pre–releases." ) @option( '--keep-outdated', is_flag=True, default=False, help=u"Keep out–dated dependencies from being updated in Pipfile.lock.", ) @option( '--sequential', is_flag=True, default=False, help="Install dependencies one-at-a-time, instead of concurrently.", ) @option( '--outdated', is_flag=True, default=False, help=u"List out–of–date dependencies.", ) @option( '--dry-run', is_flag=True, default=None, help=u"List out–of–date dependencies.", ) @argument('package', default=False) @pass_context def update( ctx, three=None, python=False, pypi_mirror=None, system=False, verbose=False, clear=False, keep_outdated=False, pre=False, dev=False, bare=False, sequential=False, package=None, dry_run=None, outdated=False, more_packages=None, ): from .core import ( ensure_project, do_outdated, do_lock, do_sync, ensure_lockfile, do_install, project, ) ensure_project(three=three, python=python, warn=True) if not outdated: outdated = bool(dry_run) if outdated: do_outdated(pypi_mirror=pypi_mirror) if not package: echo( '{0} {1} {2} {3}{4}'.format( crayons.white('Running', bold=True), crayons.red('$ pipenv lock', bold=True), crayons.white('then', bold=True), crayons.red('$ pipenv sync', bold=True), crayons.white('.', bold=True), ) ) do_lock( verbose=verbose, clear=clear, pre=pre, keep_outdated=keep_outdated, pypi_mirror=pypi_mirror ) do_sync( ctx=ctx, install=install, dev=dev, three=three, python=python, bare=bare, dont_upgrade=False, user=False, verbose=verbose, clear=clear, unused=False, sequential=sequential, pypi_mirror=pypi_mirror, ) else: for package in ([package] + list(more_packages) or []): if package not in project.all_packages: echo( '{0}: {1} was not found in your Pipfile! Aborting.' ''.format( crayons.red('Warning', bold=True), crayons.green(package, bold=True), ), err=True, ) sys.exit(1) ensure_lockfile(keep_outdated=project.lockfile_exists, pypi_mirror=pypi_mirror) do_install( package_name=package, more_packages=more_packages, dev=dev, three=three, python=python, pypi_mirror=pypi_mirror, system=system, lock=True, ignore_pipfile=False, skip_lock=False, verbose=verbose, requirements=False, sequential=sequential, pre=pre, code=False, deploy=False, keep_outdated=True, selective_upgrade=True, ) @command( short_help=u"Displays currently–installed dependency graph information." ) @option('--bare', is_flag=True, default=False, help="Minimal output.") @option('--json', is_flag=True, default=False, help="Output JSON.") @option('--json-tree', is_flag=True, default=False, help="Output JSON in nested tree.") @option( '--reverse', is_flag=True, default=False, help="Reversed dependency graph." ) def graph(bare=False, json=False, json_tree=False, reverse=False): from .core import do_graph do_graph(bare=bare, json=json, json_tree=json_tree, reverse=reverse) @command(short_help="View a given module in your editor.", name="open") @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @argument('module', nargs=1) def run_open(module, three=None, python=None): from .core import which, ensure_project ensure_project(three=three, python=python, validate=False) c = delegator.run( '{0} -c "import {1}; print({1}.__file__);"'.format( which('python'), module ) ) try: assert c.return_code == 0 except AssertionError: echo(crayons.red('Module not found!')) sys.exit(1) if '__init__.py' in c.out: p = os.path.dirname(c.out.strip().rstrip('cdo')) else: p = c.out.strip().rstrip('cdo') echo( crayons.normal('Opening {0!r} in your EDITOR.'.format(p), bold=True) ) edit(filename=p) sys.exit(0) @command(short_help="Installs all packages specified in Pipfile.lock.") @option( '--verbose', '-v', is_flag=True, default=False, help="Verbose mode.", callback=setup_verbose, ) @option( '--dev', '-d', is_flag=True, default=False, help="Additionally install package(s) in [dev-packages].", ) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--pypi-mirror', default=PIPENV_PYPI_MIRROR, nargs=1, callback=validate_pypi_mirror, help="Specify a PyPI mirror.", ) @option('--bare', is_flag=True, default=False, help="Minimal output.") @option( '--clear', is_flag=True, default=False, help="Clear the dependency cache." ) @option( '--sequential', is_flag=True, default=False, help="Install dependencies one-at-a-time, instead of concurrently.", ) @pass_context def sync( ctx, dev=False, three=None, python=None, bare=False, dont_upgrade=False, user=False, verbose=False, clear=False, unused=False, package_name=None, sequential=False, pypi_mirror=None, ): from .core import do_sync do_sync( ctx=ctx, install=install, dev=dev, three=three, python=python, bare=bare, dont_upgrade=dont_upgrade, user=user, verbose=verbose, clear=clear, unused=unused, sequential=sequential, pypi_mirror=pypi_mirror, ) @command( short_help="Uninstalls all packages not specified in Pipfile.lock." ) @option( '--verbose', '-v', is_flag=True, default=False, help="Verbose mode.", callback=setup_verbose, ) @option( '--three/--two', is_flag=True, default=None, help="Use Python 3/2 when creating virtualenv.", ) @option( '--python', default=False, nargs=1, callback=validate_python_path, help="Specify which version of Python virtualenv should use.", ) @option( '--dry-run', is_flag=True, default=False, help="Just output unneeded packages.", ) @pass_context def clean( ctx, three=None, python=None, dry_run=False, bare=False, user=False, verbose=False, ): from .core import do_clean do_clean( ctx=ctx, three=three, python=python, dry_run=dry_run, verbose=verbose ) cli.add_command(graph) cli.add_command(install) cli.add_command(uninstall) cli.add_command(sync) cli.add_command(lock) cli.add_command(check) cli.add_command(clean) cli.add_command(shell) cli.add_command(run) cli.add_command(update) cli.add_command(run_open) if '-' not in ''.join(sys.argv) and len(sys.argv) > 1: cli = DYMCommandCollection(sources=[cli]) if __name__ == '__main__': cli()

true

7908385035473f24ddc095eb1f402ff08512b2ba

1,007

py

Python

pyvisdk/do/license_server_source.py

Infinidat/pyvisdk

f2f4e5f50da16f659ccc1d84b6a00f397fa997f8

[ "MIT" ]

null

pyvisdk/do/license_server_source.py

Infinidat/pyvisdk

f2f4e5f50da16f659ccc1d84b6a00f397fa997f8

[ "MIT" ]

null

pyvisdk/do/license_server_source.py

Infinidat/pyvisdk

f2f4e5f50da16f659ccc1d84b6a00f397fa997f8

[ "MIT" ]

null

import logging from pyvisdk.exceptions import InvalidArgumentError ######################################## # Automatically generated, do not edit. ######################################## log = logging.getLogger(__name__) def LicenseServerSource(vim, *args, **kwargs): '''Specify a license server reachable via IPv4 network.''' obj = vim.client.factory.create('{urn:vim25}LicenseServerSource') # do some validation checking... if (len(args) + len(kwargs)) < 1: raise IndexError('Expected at least 2 arguments got: %d' % len(args)) required = [ 'licenseServer' ] optional = [ 'dynamicProperty', 'dynamicType' ] for name, arg in zip(required+optional, args): setattr(obj, name, arg) for name, value in kwargs.items(): if name in required + optional: setattr(obj, name, value) else: raise InvalidArgumentError("Invalid argument: %s. Expected one of %s" % (name, ", ".join(required + optional))) return obj

30.515152

124

0.60576

import logging from pyvisdk.exceptions import InvalidArgumentError

true

790838bbfa2f5448a91ac1a3564d8b9310969a11

929

py

Python

2015/11/solve.py

lamperi/aoc

1781dcbac0be18a086c10a9b76fb6a2d3595523c

[ "MIT" ]

null

2015/11/solve.py

lamperi/aoc

1781dcbac0be18a086c10a9b76fb6a2d3595523c

[ "MIT" ]

null

2015/11/solve.py

lamperi/aoc

1781dcbac0be18a086c10a9b76fb6a2d3595523c

[ "MIT" ]

null

data = "cqjxjnds" import string import re lc = string.ascii_lowercase next = dict(zip(lc[:-1], lc[1:])) three_seq = ["".join(z) for z in zip(lc[:-2], lc[1:-1], lc[2:])] def check(pw): if "i" in pw or "o" in pw or "l" in pw: return False three_match = False for seq in three_seq: if seq in pw: three_match = True if not three_match: return False doubles = set(re.findall(r'(.)\1', pw)) if len(doubles) < 2: return False return True def inc(pw): pw = list(pw) i = -1 while pw[i] == 'z': pw[i] = 'a' i -= 1 pw[i] = next[pw[i]] return "".join(pw) # TEST print(check("hijklmmn")) print(check("abbceffg")) print(check("abbcegjk")) print(check("abcdffaa")) print(check("ghjaabcc")) # PART 1 pw = data while not check(pw): pw = inc(pw) print(pw) # PART 2 pw = inc(pw) while not check(pw): pw = inc(pw) print(pw)

17.865385

64

0.557589

data = "cqjxjnds" import string import re lc = string.ascii_lowercase next = dict(zip(lc[:-1], lc[1:])) three_seq = ["".join(z) for z in zip(lc[:-2], lc[1:-1], lc[2:])] def check(pw): if "i" in pw or "o" in pw or "l" in pw: return False three_match = False for seq in three_seq: if seq in pw: three_match = True if not three_match: return False doubles = set(re.findall(r'(.)\1', pw)) if len(doubles) < 2: return False return True def inc(pw): pw = list(pw) i = -1 while pw[i] == 'z': pw[i] = 'a' i -= 1 pw[i] = next[pw[i]] return "".join(pw) print(check("hijklmmn")) print(check("abbceffg")) print(check("abbcegjk")) print(check("abcdffaa")) print(check("ghjaabcc")) pw = data while not check(pw): pw = inc(pw) print(pw) pw = inc(pw) while not check(pw): pw = inc(pw) print(pw)

true

7908390b1e8b8ba76d42ea51f64cbfcd2c7e8784

818

py

Python

DSA 450 GFG/next_permutation.py

siddhi-244/CompetitiveProgrammingQuestionBank

4c265d41b82a7d4370c14d367f78effa9ed95d3c

[ "MIT" ]

931

2020-04-18T11:57:30.000Z

2022-03-31T15:15:39.000Z

DSA 450 GFG/next_permutation.py

vanishasamriddhi/CompetitiveProgrammingQuestionBank

b5160a66013bda17c98070d24d3a932b833692f8

[ "MIT" ]

661

2020-12-13T04:31:48.000Z

2022-03-15T19:11:54.000Z

DSA 450 GFG/next_permutation.py

Mayuri-cell/CompetitiveProgrammingQuestionBank

eca2257d7da5346f45bdd7a351cc95bde6ed5c7d

[ "MIT" ]

351

2020-08-10T06:49:21.000Z

2022-03-25T04:02:12.000Z

# Link for the problem : https://leetcode.com/problems/next-permutation/ class Solution(object): def nextPermutation(self, nums): found = False i = len(nums)-2 while i >=0: if nums[i] < nums[i+1]: found =True break i-=1 if not found: nums.sort() else: m = self.findMaxIndex(i+1,nums,nums[i]) nums[i],nums[m] = nums[m],nums[i] nums[i+1:] = nums[i+1:][::-1] return nums def findMaxIndex(self,index,a,curr): ans = -1 index = 0 for i in range(index,len(a)): if a[i]>curr: if ans == -1: ans = curr index = i else: ans = min(ans,a[i]) index = i return index ob1 = Solution()

23.371429

72

0.462103

class Solution(object): def nextPermutation(self, nums): found = False i = len(nums)-2 while i >=0: if nums[i] < nums[i+1]: found =True break i-=1 if not found: nums.sort() else: m = self.findMaxIndex(i+1,nums,nums[i]) nums[i],nums[m] = nums[m],nums[i] nums[i+1:] = nums[i+1:][::-1] return nums def findMaxIndex(self,index,a,curr): ans = -1 index = 0 for i in range(index,len(a)): if a[i]>curr: if ans == -1: ans = curr index = i else: ans = min(ans,a[i]) index = i return index ob1 = Solution()

true

79083937510863dae48a6665d4b274273be38709

8,908

py

Python

secedgar/core/company.py

Ahrvo-Trading-Systems/sec-edgar

b22f9aa2de0cafd98ecab884ece1e7d0f2be3381

[ "Apache-2.0" ]

null

secedgar/core/company.py

Ahrvo-Trading-Systems/sec-edgar

b22f9aa2de0cafd98ecab884ece1e7d0f2be3381

[ "Apache-2.0" ]

null

secedgar/core/company.py

Ahrvo-Trading-Systems/sec-edgar

b22f9aa2de0cafd98ecab884ece1e7d0f2be3381

[ "Apache-2.0" ]

null

import asyncio import os import warnings from datetime import date from secedgar.cik_lookup import CIKLookup from secedgar.client import NetworkClient from secedgar.core._base import AbstractFiling from secedgar.core.filing_types import FilingType from secedgar.exceptions import FilingTypeError from secedgar.utils import sanitize_date class CompanyFilings(AbstractFiling): """Base class for receiving EDGAR filings. Args: cik_lookup (str): Central Index Key (CIK) for company of interest. filing_type (Union[secedgar.core.filing_types.FilingType, None]): Valid filing type enum. Defaults to None. If None, then all filing types for CIKs will be returned. start_date (Union[str, datetime.datetime, datetime.date], optional): Date before which not to fetch reports. Stands for "date after." Defaults to None (will fetch all filings before ``end_date``). end_date (Union[str, datetime.datetime, datetime.date], optional): Date after which not to fetch reports. Stands for "date before." Defaults to today. count (int): Number of filings to fetch. Will fetch up to `count` if that many filings are available. Defaults to all filings available. ownership (str): Must be in {"include", "exclude"}. Whether or not to include ownership filings. match_format (str): Must be in {"EXACT", "AMEND", "ALL"}. kwargs: See kwargs accepted for :class:`secedgar.client.network_client.NetworkClient`. .. versionadded:: 0.1.5 """ def __init__(self, cik_lookup, filing_type=None, start_date=None, end_date=date.today(), client=None, count=None, ownership="include", match_format="ALL", **kwargs): # Leave params before other setters self._params = { "action": "getcompany", "output": "xml", "owner": ownership, "start": 0, } self.start_date = start_date self.end_date = end_date self.filing_type = filing_type self.count = count self.match_format = match_format # Make default client NetworkClient and pass in kwargs self._client = client if client is not None else NetworkClient(**kwargs) # make CIKLookup object for users if not given self.cik_lookup = cik_lookup @property def path(self): """str: Path added to client base.""" return "cgi-bin/browse-edgar" @property def params(self): """:obj:`dict`: Parameters to include in requests.""" return self._params @property def client(self): """``secedgar.client._base``: Client to use to make requests.""" return self._client @property def start_date(self): """Union([datetime.date, datetime.datetime, str]): Date before which no filings fetched.""" return self._start_date @property def match_format(self): """The match format to use when searching for filings.""" return self._match_format @match_format.setter def match_format(self, val): if val in ["EXACT", "AMEND", "ALL"]: self._match_format = val else: raise ValueError("Format must be one of EXACT,AMEND,ALL") @start_date.setter def start_date(self, val): if val is not None: self._params["datea"] = sanitize_date(val) self._start_date = val else: self._start_date = None @property def end_date(self): """Union([datetime.date, datetime.datetime, str]): Date after which no filings fetched.""" return self._end_date @end_date.setter def end_date(self, val): self._params["dateb"] = sanitize_date(val) self._end_date = val @property def filing_type(self): """``secedgar.core.FilingType``: FilingType enum of filing.""" return self._filing_type @filing_type.setter def filing_type(self, filing_type): if isinstance(filing_type, FilingType): self._params["type"] = filing_type.value elif filing_type is not None: raise FilingTypeError self._filing_type = filing_type @property def count(self): """Number of filings to fetch.""" return self._count @count.setter def count(self, val): if val is None: self._count = None elif not isinstance(val, int): raise TypeError("Count must be positive integer or None.") elif val < 1: raise ValueError("Count must be positive integer or None.") else: self._count = val self._params["count"] = val @property def cik_lookup(self): """``secedgar.cik_lookup.CIKLookup``: CIKLookup object.""" return self._cik_lookup @cik_lookup.setter def cik_lookup(self, val): if not isinstance(val, CIKLookup): val = CIKLookup(val, client=self.client) self._cik_lookup = val def get_urls(self, **kwargs): """Get urls for all CIKs given to Filing object. Args: **kwargs: Anything to be passed to requests when making get request. See keyword arguments accepted for ``secedgar.client._base.AbstractClient.get_soup``. Returns: urls (list): List of urls for txt files to download. """ return { key: self._get_urls_for_cik(cik, **kwargs) for key, cik in self.cik_lookup.lookup_dict.items() } # TODO: Change this to return accession numbers that are turned into URLs later def _get_urls_for_cik(self, cik, **kwargs): """Get all urls for specific company according to CIK. Must match start date, end date, filing_type, and count parameters. Args: cik (str): CIK for company. **kwargs: Anything to be passed to requests when making get request. See keyword arguments accepted for ``secedgar.client._base.AbstractClient.get_soup``. Returns: txt_urls (list of str): Up to the desired number of URLs for that specific company if available. """ self.params["CIK"] = cik links = [] self.params["start"] = 0 # set start back to 0 before paginating while self.count is None or len(links) < self.count: data = self.client.get_soup(self.path, self.params, **kwargs) links.extend([link.string for link in data.find_all("filinghref")]) self.params["start"] += self.client.batch_size if len(data.find_all("filinghref")) == 0: # no more filings break txt_urls = [link[:link.rfind("-")].strip() + ".txt" for link in links] if isinstance(self.count, int) and len(txt_urls) < self.count: warnings.warn( "Only {num} of {count} filings were found for {cik}.".format( num=len(txt_urls), count=self.count, cik=cik)) # Takes `count` filings at most return txt_urls[:self.count] def save(self, directory, dir_pattern=None, file_pattern=None): """Save files in specified directory. Each txt url looks something like: https://www.sec.gov/Archives/edgar/data/1018724/000101872419000043/0001018724-19-000043.txt Args: directory (str): Path to directory where files should be saved. dir_pattern (str): Format string for subdirectories. Default is "{cik}/{type}". Valid options are {cik} and/or {type}. file_pattern (str): Format string for files. Default is "{accession_number}". Valid options are {accession_number}. Returns: None Raises: ValueError: If no text urls are available for given filing object. """ urls = self.get_urls_safely() if dir_pattern is None: dir_pattern = os.path.join("{cik}", "{type}") if file_pattern is None: file_pattern = "{accession_number}" inputs = [] for cik, links in urls.items(): formatted_dir = dir_pattern.format(cik=cik, type=self.filing_type.value) for link in links: formatted_file = file_pattern.format( accession_number=self.get_accession_number(link)) path = os.path.join(directory, formatted_dir, formatted_file) inputs.append((link, path)) loop = asyncio.get_event_loop() loop.run_until_complete(self.client.wait_for_download_async(inputs))

36.359184

99

0.60586

import asyncio import os import warnings from datetime import date from secedgar.cik_lookup import CIKLookup from secedgar.client import NetworkClient from secedgar.core._base import AbstractFiling from secedgar.core.filing_types import FilingType from secedgar.exceptions import FilingTypeError from secedgar.utils import sanitize_date class CompanyFilings(AbstractFiling): def __init__(self, cik_lookup, filing_type=None, start_date=None, end_date=date.today(), client=None, count=None, ownership="include", match_format="ALL", **kwargs): self._params = { "action": "getcompany", "output": "xml", "owner": ownership, "start": 0, } self.start_date = start_date self.end_date = end_date self.filing_type = filing_type self.count = count self.match_format = match_format self._client = client if client is not None else NetworkClient(**kwargs) self.cik_lookup = cik_lookup @property def path(self): return "cgi-bin/browse-edgar" @property def params(self): return self._params @property def client(self): return self._client @property def start_date(self): return self._start_date @property def match_format(self): return self._match_format @match_format.setter def match_format(self, val): if val in ["EXACT", "AMEND", "ALL"]: self._match_format = val else: raise ValueError("Format must be one of EXACT,AMEND,ALL") @start_date.setter def start_date(self, val): if val is not None: self._params["datea"] = sanitize_date(val) self._start_date = val else: self._start_date = None @property def end_date(self): return self._end_date @end_date.setter def end_date(self, val): self._params["dateb"] = sanitize_date(val) self._end_date = val @property def filing_type(self): return self._filing_type @filing_type.setter def filing_type(self, filing_type): if isinstance(filing_type, FilingType): self._params["type"] = filing_type.value elif filing_type is not None: raise FilingTypeError self._filing_type = filing_type @property def count(self): return self._count @count.setter def count(self, val): if val is None: self._count = None elif not isinstance(val, int): raise TypeError("Count must be positive integer or None.") elif val < 1: raise ValueError("Count must be positive integer or None.") else: self._count = val self._params["count"] = val @property def cik_lookup(self): return self._cik_lookup @cik_lookup.setter def cik_lookup(self, val): if not isinstance(val, CIKLookup): val = CIKLookup(val, client=self.client) self._cik_lookup = val def get_urls(self, **kwargs): return { key: self._get_urls_for_cik(cik, **kwargs) for key, cik in self.cik_lookup.lookup_dict.items() } def _get_urls_for_cik(self, cik, **kwargs): self.params["CIK"] = cik links = [] self.params["start"] = 0 while self.count is None or len(links) < self.count: data = self.client.get_soup(self.path, self.params, **kwargs) links.extend([link.string for link in data.find_all("filinghref")]) self.params["start"] += self.client.batch_size if len(data.find_all("filinghref")) == 0: break txt_urls = [link[:link.rfind("-")].strip() + ".txt" for link in links] if isinstance(self.count, int) and len(txt_urls) < self.count: warnings.warn( "Only {num} of {count} filings were found for {cik}.".format( num=len(txt_urls), count=self.count, cik=cik)) return txt_urls[:self.count] def save(self, directory, dir_pattern=None, file_pattern=None): urls = self.get_urls_safely() if dir_pattern is None: dir_pattern = os.path.join("{cik}", "{type}") if file_pattern is None: file_pattern = "{accession_number}" inputs = [] for cik, links in urls.items(): formatted_dir = dir_pattern.format(cik=cik, type=self.filing_type.value) for link in links: formatted_file = file_pattern.format( accession_number=self.get_accession_number(link)) path = os.path.join(directory, formatted_dir, formatted_file) inputs.append((link, path)) loop = asyncio.get_event_loop() loop.run_until_complete(self.client.wait_for_download_async(inputs))

true

790839ac243e96c58a803971d7e398d0e116d55a

2,109

py

Python

var/spack/repos/builtin/packages/meme/package.py

MiddelkoopT/spack

4d94c4c4600f42a7a3bb3d06ec879140bc259304

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

null

var/spack/repos/builtin/packages/meme/package.py

MiddelkoopT/spack

4d94c4c4600f42a7a3bb3d06ec879140bc259304

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

null

var/spack/repos/builtin/packages/meme/package.py

MiddelkoopT/spack

4d94c4c4600f42a7a3bb3d06ec879140bc259304

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

1

2022-01-18T23:39:24.000Z

# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * from spack.version import Version class Meme(AutotoolsPackage): """The MEME Suite allows the biologist to discover novel motifs in collections of unaligned nucleotide or protein sequences, and to perform a wide variety of other motif-based analyses.""" homepage = "http://meme-suite.org" url = "http://meme-suite.org/meme-software/5.1.1/meme-5.1.1.tar.gz" version('5.3.0', sha256='b2ddec9db972fcf77b29c7deb62df8b1dd8a6638c13c1aa06a5d563c4a7ff756') version('5.2.0', sha256='0cbf8c2172e9b6c07855b8aeec457f4825f0b132f8cbb11192880e2f6033f54f') version('5.1.1', sha256='38d73d256d431ad4eb7da2c817ce56ff2b4e26c39387ff0d6ada088938b38eb5') version('4.12.0', sha256='49ff80f842b59d328588acfcd1d15bf94c55fed661d22b0f95f37430cc363a06') version('4.11.4', sha256='3e869ff57e327a9c8615dbef784e3f1095f7f7a0120cecd55efe10c3f2ee8eb3') variant('mpi', default=True, description='Enable MPI support') variant('image-magick', default=False, description='Enable image-magick for png output') depends_on('zlib', type=('link')) depends_on('libgcrypt', type=('link')) depends_on('perl', type=('build', 'run')) depends_on('python@2.7:', type=('build', 'run')) depends_on('mpi', when='+mpi') depends_on('imagemagick', type=('build', 'run'), when='+image-magick') depends_on('perl-xml-parser', type=('build', 'run')) def url_for_version(self, version): url = 'http://meme-suite.org/meme-software/{0}/meme{1}{2}.tar.gz' sep = '-' if version >= Version('5.0.2') else '_' return url.format(version.up_to(3), sep, version) def configure_args(self): spec = self.spec # have meme build its own versions of libxml2/libxslt, see #6736 args = ['--enable-build-libxml2', '--enable-build-libxslt'] if '~mpi' in spec: args += ['--enable-serial'] return args

44.87234

96

0.697013

from spack import * from spack.version import Version class Meme(AutotoolsPackage): homepage = "http://meme-suite.org" url = "http://meme-suite.org/meme-software/5.1.1/meme-5.1.1.tar.gz" version('5.3.0', sha256='b2ddec9db972fcf77b29c7deb62df8b1dd8a6638c13c1aa06a5d563c4a7ff756') version('5.2.0', sha256='0cbf8c2172e9b6c07855b8aeec457f4825f0b132f8cbb11192880e2f6033f54f') version('5.1.1', sha256='38d73d256d431ad4eb7da2c817ce56ff2b4e26c39387ff0d6ada088938b38eb5') version('4.12.0', sha256='49ff80f842b59d328588acfcd1d15bf94c55fed661d22b0f95f37430cc363a06') version('4.11.4', sha256='3e869ff57e327a9c8615dbef784e3f1095f7f7a0120cecd55efe10c3f2ee8eb3') variant('mpi', default=True, description='Enable MPI support') variant('image-magick', default=False, description='Enable image-magick for png output') depends_on('zlib', type=('link')) depends_on('libgcrypt', type=('link')) depends_on('perl', type=('build', 'run')) depends_on('python@2.7:', type=('build', 'run')) depends_on('mpi', when='+mpi') depends_on('imagemagick', type=('build', 'run'), when='+image-magick') depends_on('perl-xml-parser', type=('build', 'run')) def url_for_version(self, version): url = 'http://meme-suite.org/meme-software/{0}/meme{1}{2}.tar.gz' sep = '-' if version >= Version('5.0.2') else '_' return url.format(version.up_to(3), sep, version) def configure_args(self): spec = self.spec args = ['--enable-build-libxml2', '--enable-build-libxslt'] if '~mpi' in spec: args += ['--enable-serial'] return args

true

79083aa16d5d92fd67caba5eb952e4b0176daeb1

6,233

py

Python

agent.py

cisc474projectgroup/cartpole-q-learning

d7215990c8bdf8c1ff20cdfa3a7530e1a2c641b5

[ "MIT" ]

null

agent.py

cisc474projectgroup/cartpole-q-learning

d7215990c8bdf8c1ff20cdfa3a7530e1a2c641b5

[ "MIT" ]

null

agent.py

cisc474projectgroup/cartpole-q-learning

d7215990c8bdf8c1ff20cdfa3a7530e1a2c641b5

[ "MIT" ]

null

import random import copy from collections import defaultdict from collections import deque from collections import namedtuple from matplotlib import pyplot as plt import numpy as np class Q(): def __init__(self, n_actions, observation_space, bin_size, low_bound=None, high_bound=None, initial_mean=0.0, initial_std=0.0): self.n_actions = n_actions self._observation_dimension = 1 for d in observation_space.shape: self._observation_dimension *= d self._bin_sizes = bin_size if isinstance(bin_size, list) else [bin_size] * self._observation_dimension self._dimension_bins = [] for i, low, high in self._low_high_iter(observation_space, low_bound, high_bound): b_size = self._bin_sizes[i] bins = self._make_bins(low, high, b_size) print(bins) self._dimension_bins.append(bins) # if we encounter the new observation, we initialize action evaluations self.table = defaultdict(lambda: initial_std * np.random.randn(self.n_actions) + initial_mean) @classmethod def _make_bins(cls, low, high, bin_size): bins = np.arange(low, high, (float(high) - float(low)) / (bin_size - 2)) # exclude both ends if min(bins) < 0 and 0 not in bins: bins = np.sort(np.append(bins, [0])) # 0 centric bins return bins @classmethod def _low_high_iter(cls, observation_space, low_bound, high_bound): lows = observation_space.low highs = observation_space.high for i in range(len(lows)): low = lows[i] if low_bound is not None: _low_bound = low_bound if not isinstance(low_bound, list) else low_bound[i] low = low if _low_bound is None else max(low, _low_bound) high = highs[i] if high_bound is not None: _high_bound = high_bound if not isinstance(high_bound, list) else high_bound[i] high = high if _high_bound is None else min(high, _high_bound) yield i, low, high def observation_to_state(self, observation): state = 0 # caution: bin_size over 10 will not work accurately unit = max(self._bin_sizes) for d, o in enumerate(observation.flatten()): state = state + np.digitize(o, self._dimension_bins[d]) * pow(unit, d) # bin_size numeral system return state def values(self, observation): state = self.observation_to_state(observation) return self.table[state] class Agent(): def __init__(self, q, epsilon=0.05): self.q = q self.epsilon = epsilon def act(self, observation): action = -1 if np.random.random() < self.epsilon: action = np.random.choice(self.q.n_actions) else: action = np.argmax(self.q.values(observation)) return action class Trainer(): def __init__(self, agent, gamma=0.95, learning_rate=0.1, learning_rate_decay=None, epsilon=0.05, epsilon_decay=None, max_step=-1,target=500): self.agent = agent self.gamma = gamma self.learning_rate = learning_rate self.learning_rate_decay = learning_rate_decay self.epsilon = epsilon self.epsilon_decay = epsilon_decay self.max_step = max_step def train(self, env, episode_count, render=False): mean_step_all =[] mean_q_all=[] goal_time_all=[] reward_all=[] self.agent.epsilon = self.epsilon values = [] steps = deque(maxlen=100) lr = self.learning_rate for i in range(episode_count): reward_total = 0 goal_time =0 obs = env.reset() step = 0 done = False while not done: if render: env.render() action = self.agent.act(obs) next_obs, reward, done,goal_time= env.step(action) reward_total+= reward goal_time += goal_time state = self.agent.q.observation_to_state(obs) future = 0 if done else np.max(self.agent.q.values(next_obs)) value = self.agent.q.table[state][action] self.agent.q.table[state][action] += lr * (reward + self.gamma * future - value) obs = next_obs values.append(value) step += 1 if self.max_step > 0 and step > self.max_step: done = True else: mean = np.mean(values) steps.append(step) mean_step = np.mean(steps) print("Episode {}: {}steps(avg{}). epsilon={:.3f}, lr={:.3f}, mean q value={:.2f}".format( i, step, mean_step, self.agent.epsilon, lr, mean) ) mean_step_all.append(mean_step) mean_q_all.append(mean) reward_all.append(reward_total) if mean_step>1000: render=True if self.epsilon_decay is not None: self.agent.epsilon = self.epsilon_decay(self.agent.epsilon, i) if self.learning_rate_decay is not None: lr = self.learning_rate_decay(lr, i) # plot in comparsion plt.xlabel('Episodes') plt.ylabel('reward') # plt.plot(mean_step_all, label='Q-learning', color='blue') plt.plot(reward_all, label='Q-learning', color='yellow') plt.plot(goal_time_all, label='Q-learning', color='green') # plt.legend(['reward', 'Q-learning'], loc='upper right') plt.title('reward/Episode') plt.show() # plot in comparsion plt.xlabel('Episodes') plt.ylabel('goal_time') # plt.plot(mean_step_all, label='Q-learning', color='blue') plt.plot(goal_time_all, label='Q-learning', color='green') # plt.legend(['reward', 'Q-learning'], loc='upper right') plt.title('goal/Episode') plt.show()

36.664706

145

0.574683

import random import copy from collections import defaultdict from collections import deque from collections import namedtuple from matplotlib import pyplot as plt import numpy as np class Q(): def __init__(self, n_actions, observation_space, bin_size, low_bound=None, high_bound=None, initial_mean=0.0, initial_std=0.0): self.n_actions = n_actions self._observation_dimension = 1 for d in observation_space.shape: self._observation_dimension *= d self._bin_sizes = bin_size if isinstance(bin_size, list) else [bin_size] * self._observation_dimension self._dimension_bins = [] for i, low, high in self._low_high_iter(observation_space, low_bound, high_bound): b_size = self._bin_sizes[i] bins = self._make_bins(low, high, b_size) print(bins) self._dimension_bins.append(bins) self.table = defaultdict(lambda: initial_std * np.random.randn(self.n_actions) + initial_mean) @classmethod def _make_bins(cls, low, high, bin_size): bins = np.arange(low, high, (float(high) - float(low)) / (bin_size - 2)) if min(bins) < 0 and 0 not in bins: bins = np.sort(np.append(bins, [0])) return bins @classmethod def _low_high_iter(cls, observation_space, low_bound, high_bound): lows = observation_space.low highs = observation_space.high for i in range(len(lows)): low = lows[i] if low_bound is not None: _low_bound = low_bound if not isinstance(low_bound, list) else low_bound[i] low = low if _low_bound is None else max(low, _low_bound) high = highs[i] if high_bound is not None: _high_bound = high_bound if not isinstance(high_bound, list) else high_bound[i] high = high if _high_bound is None else min(high, _high_bound) yield i, low, high def observation_to_state(self, observation): state = 0 unit = max(self._bin_sizes) for d, o in enumerate(observation.flatten()): state = state + np.digitize(o, self._dimension_bins[d]) * pow(unit, d) return state def values(self, observation): state = self.observation_to_state(observation) return self.table[state] class Agent(): def __init__(self, q, epsilon=0.05): self.q = q self.epsilon = epsilon def act(self, observation): action = -1 if np.random.random() < self.epsilon: action = np.random.choice(self.q.n_actions) else: action = np.argmax(self.q.values(observation)) return action class Trainer(): def __init__(self, agent, gamma=0.95, learning_rate=0.1, learning_rate_decay=None, epsilon=0.05, epsilon_decay=None, max_step=-1,target=500): self.agent = agent self.gamma = gamma self.learning_rate = learning_rate self.learning_rate_decay = learning_rate_decay self.epsilon = epsilon self.epsilon_decay = epsilon_decay self.max_step = max_step def train(self, env, episode_count, render=False): mean_step_all =[] mean_q_all=[] goal_time_all=[] reward_all=[] self.agent.epsilon = self.epsilon values = [] steps = deque(maxlen=100) lr = self.learning_rate for i in range(episode_count): reward_total = 0 goal_time =0 obs = env.reset() step = 0 done = False while not done: if render: env.render() action = self.agent.act(obs) next_obs, reward, done,goal_time= env.step(action) reward_total+= reward goal_time += goal_time state = self.agent.q.observation_to_state(obs) future = 0 if done else np.max(self.agent.q.values(next_obs)) value = self.agent.q.table[state][action] self.agent.q.table[state][action] += lr * (reward + self.gamma * future - value) obs = next_obs values.append(value) step += 1 if self.max_step > 0 and step > self.max_step: done = True else: mean = np.mean(values) steps.append(step) mean_step = np.mean(steps) print("Episode {}: {}steps(avg{}). epsilon={:.3f}, lr={:.3f}, mean q value={:.2f}".format( i, step, mean_step, self.agent.epsilon, lr, mean) ) mean_step_all.append(mean_step) mean_q_all.append(mean) reward_all.append(reward_total) if mean_step>1000: render=True if self.epsilon_decay is not None: self.agent.epsilon = self.epsilon_decay(self.agent.epsilon, i) if self.learning_rate_decay is not None: lr = self.learning_rate_decay(lr, i) plt.xlabel('Episodes') plt.ylabel('reward') plt.plot(reward_all, label='Q-learning', color='yellow') plt.plot(goal_time_all, label='Q-learning', color='green') plt.title('reward/Episode') plt.show() plt.xlabel('Episodes') plt.ylabel('goal_time') plt.plot(goal_time_all, label='Q-learning', color='green') plt.title('goal/Episode') plt.show()

true

79083dc89001dad5e8c587aa8fb669efa87334d8

274

py

Python

resources/cipher_suite_grabber.py

berney/TLS-Attacker

32c5bcb87a57f9a3b1ff3f126e6432010421875b

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

593

2016-04-20T16:19:52.000Z

2020-11-05T01:22:01.000Z

resources/cipher_suite_grabber.py

berney/TLS-Attacker

32c5bcb87a57f9a3b1ff3f126e6432010421875b

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

75

2016-05-02T22:34:02.000Z

2020-11-06T11:02:36.000Z

resources/cipher_suite_grabber.py

berney/TLS-Attacker

32c5bcb87a57f9a3b1ff3f126e6432010421875b

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

130

2016-04-21T05:16:09.000Z

2020-10-26T01:09:52.000Z

#!/usr/bin/env python2 import sys import re import datetime import hashlib import optparse import urllib2 # cheers Dirk :) url = 'https://testssl.sh/mapping-rfc.txt' for line in urllib2.urlopen(url): cipher = line.split() print cipher[1]+'(0'+cipher[0]+'),'

16.117647

42

0.686131

import sys import re import datetime import hashlib import optparse import urllib2 url = 'https://testssl.sh/mapping-rfc.txt' for line in urllib2.urlopen(url): cipher = line.split() print cipher[1]+'(0'+cipher[0]+'),'

false

true

79083f031ae30d50fc4d6738156e85935cf331f0

9,102

py

Python

homeassistant/components/homematicip_cloud/light.py

itewk/home-assistant

769cf19052f8c9ef374d8ba8ae7705ccc7bf4cf4

[ "Apache-2.0" ]

23

2017-11-15T21:03:53.000Z

2021-03-29T21:33:48.000Z

homeassistant/components/homematicip_cloud/light.py

itewk/home-assistant

769cf19052f8c9ef374d8ba8ae7705ccc7bf4cf4

[ "Apache-2.0" ]

6

2021-02-08T20:59:36.000Z

2022-03-12T00:52:11.000Z

homeassistant/components/homematicip_cloud/light.py

itewk/home-assistant

769cf19052f8c9ef374d8ba8ae7705ccc7bf4cf4

[ "Apache-2.0" ]

10

2018-01-01T00:12:51.000Z

2021-12-21T23:08:05.000Z

"""Support for HomematicIP Cloud lights.""" import logging from typing import Any, Dict from homematicip.aio.device import ( AsyncBrandDimmer, AsyncBrandSwitchMeasuring, AsyncBrandSwitchNotificationLight, AsyncDimmer, AsyncFullFlushDimmer, AsyncPluggableDimmer, ) from homematicip.base.enums import RGBColorState from homematicip.base.functionalChannels import NotificationLightChannel from homeassistant.components.light import ( ATTR_BRIGHTNESS, ATTR_COLOR_NAME, ATTR_HS_COLOR, ATTR_TRANSITION, SUPPORT_BRIGHTNESS, SUPPORT_COLOR, Light, ) from homeassistant.config_entries import ConfigEntry from homeassistant.helpers.typing import HomeAssistantType from . import DOMAIN as HMIPC_DOMAIN, HMIPC_HAPID, HomematicipGenericDevice from .hap import HomematicipHAP _LOGGER = logging.getLogger(__name__) ATTR_TODAY_ENERGY_KWH = "today_energy_kwh" ATTR_CURRENT_POWER_W = "current_power_w" async def async_setup_platform( hass, config, async_add_entities, discovery_info=None ) -> None: """Old way of setting up HomematicIP Cloud lights.""" pass async def async_setup_entry( hass: HomeAssistantType, config_entry: ConfigEntry, async_add_entities ) -> None: """Set up the HomematicIP Cloud lights from a config entry.""" hap = hass.data[HMIPC_DOMAIN][config_entry.data[HMIPC_HAPID]] entities = [] for device in hap.home.devices: if isinstance(device, AsyncBrandSwitchMeasuring): entities.append(HomematicipLightMeasuring(hap, device)) elif isinstance(device, AsyncBrandSwitchNotificationLight): entities.append(HomematicipLight(hap, device)) entities.append( HomematicipNotificationLight(hap, device, device.topLightChannelIndex) ) entities.append( HomematicipNotificationLight( hap, device, device.bottomLightChannelIndex ) ) elif isinstance( device, (AsyncDimmer, AsyncPluggableDimmer, AsyncBrandDimmer, AsyncFullFlushDimmer), ): entities.append(HomematicipDimmer(hap, device)) if entities: async_add_entities(entities) class HomematicipLight(HomematicipGenericDevice, Light): """Representation of a HomematicIP Cloud light device.""" def __init__(self, hap: HomematicipHAP, device) -> None: """Initialize the light device.""" super().__init__(hap, device) @property def is_on(self) -> bool: """Return true if device is on.""" return self._device.on async def async_turn_on(self, **kwargs) -> None: """Turn the device on.""" await self._device.turn_on() async def async_turn_off(self, **kwargs) -> None: """Turn the device off.""" await self._device.turn_off() class HomematicipLightMeasuring(HomematicipLight): """Representation of a HomematicIP Cloud measuring light device.""" @property def device_state_attributes(self) -> Dict[str, Any]: """Return the state attributes of the generic device.""" state_attr = super().device_state_attributes current_power_w = self._device.currentPowerConsumption if current_power_w > 0.05: state_attr[ATTR_CURRENT_POWER_W] = round(current_power_w, 2) state_attr[ATTR_TODAY_ENERGY_KWH] = round(self._device.energyCounter, 2) return state_attr class HomematicipDimmer(HomematicipGenericDevice, Light): """Representation of HomematicIP Cloud dimmer light device.""" def __init__(self, hap: HomematicipHAP, device) -> None: """Initialize the dimmer light device.""" super().__init__(hap, device) @property def is_on(self) -> bool: """Return true if device is on.""" return self._device.dimLevel is not None and self._device.dimLevel > 0.0 @property def brightness(self) -> int: """Return the brightness of this light between 0..255.""" return int((self._device.dimLevel or 0.0) * 255) @property def supported_features(self) -> int: """Flag supported features.""" return SUPPORT_BRIGHTNESS async def async_turn_on(self, **kwargs) -> None: """Turn the light on.""" if ATTR_BRIGHTNESS in kwargs: await self._device.set_dim_level(kwargs[ATTR_BRIGHTNESS] / 255.0) else: await self._device.set_dim_level(1) async def async_turn_off(self, **kwargs) -> None: """Turn the light off.""" await self._device.set_dim_level(0) class HomematicipNotificationLight(HomematicipGenericDevice, Light): """Representation of HomematicIP Cloud dimmer light device.""" def __init__(self, hap: HomematicipHAP, device, channel: int) -> None: """Initialize the dimmer light device.""" self.channel = channel if self.channel == 2: super().__init__(hap, device, "Top") else: super().__init__(hap, device, "Bottom") self._color_switcher = { RGBColorState.WHITE: [0.0, 0.0], RGBColorState.RED: [0.0, 100.0], RGBColorState.YELLOW: [60.0, 100.0], RGBColorState.GREEN: [120.0, 100.0], RGBColorState.TURQUOISE: [180.0, 100.0], RGBColorState.BLUE: [240.0, 100.0], RGBColorState.PURPLE: [300.0, 100.0], } @property def _func_channel(self) -> NotificationLightChannel: return self._device.functionalChannels[self.channel] @property def is_on(self) -> bool: """Return true if device is on.""" return ( self._func_channel.dimLevel is not None and self._func_channel.dimLevel > 0.0 ) @property def brightness(self) -> int: """Return the brightness of this light between 0..255.""" return int((self._func_channel.dimLevel or 0.0) * 255) @property def hs_color(self) -> tuple: """Return the hue and saturation color value [float, float].""" simple_rgb_color = self._func_channel.simpleRGBColorState return self._color_switcher.get(simple_rgb_color, [0.0, 0.0]) @property def device_state_attributes(self) -> Dict[str, Any]: """Return the state attributes of the generic device.""" state_attr = super().device_state_attributes if self.is_on: state_attr[ATTR_COLOR_NAME] = self._func_channel.simpleRGBColorState return state_attr @property def name(self) -> str: """Return the name of the generic device.""" return f"{super().name} Notification" @property def supported_features(self) -> int: """Flag supported features.""" return SUPPORT_BRIGHTNESS | SUPPORT_COLOR @property def unique_id(self) -> str: """Return a unique ID.""" return f"{self.__class__.__name__}_{self.post}_{self._device.id}" async def async_turn_on(self, **kwargs) -> None: """Turn the light on.""" # Use hs_color from kwargs, # if not applicable use current hs_color. hs_color = kwargs.get(ATTR_HS_COLOR, self.hs_color) simple_rgb_color = _convert_color(hs_color) # Use brightness from kwargs, # if not applicable use current brightness. brightness = kwargs.get(ATTR_BRIGHTNESS, self.brightness) # If no kwargs, use default value. if not kwargs: brightness = 255 # Minimum brightness is 10, otherwise the led is disabled brightness = max(10, brightness) dim_level = brightness / 255.0 transition = kwargs.get(ATTR_TRANSITION, 0.5) await self._device.set_rgb_dim_level_with_time( channelIndex=self.channel, rgb=simple_rgb_color, dimLevel=dim_level, onTime=0, rampTime=transition, ) async def async_turn_off(self, **kwargs) -> None: """Turn the light off.""" simple_rgb_color = self._func_channel.simpleRGBColorState transition = kwargs.get(ATTR_TRANSITION, 0.5) await self._device.set_rgb_dim_level_with_time( channelIndex=self.channel, rgb=simple_rgb_color, dimLevel=0.0, onTime=0, rampTime=transition, ) def _convert_color(color: tuple) -> RGBColorState: """ Convert the given color to the reduced RGBColorState color. RGBColorStat contains only 8 colors including white and black, so a conversion is required. """ if color is None: return RGBColorState.WHITE hue = int(color[0]) saturation = int(color[1]) if saturation < 5: return RGBColorState.WHITE if 30 < hue <= 90: return RGBColorState.YELLOW if 90 < hue <= 160: return RGBColorState.GREEN if 150 < hue <= 210: return RGBColorState.TURQUOISE if 210 < hue <= 270: return RGBColorState.BLUE if 270 < hue <= 330: return RGBColorState.PURPLE return RGBColorState.RED

32.391459

88

0.652054

import logging from typing import Any, Dict from homematicip.aio.device import ( AsyncBrandDimmer, AsyncBrandSwitchMeasuring, AsyncBrandSwitchNotificationLight, AsyncDimmer, AsyncFullFlushDimmer, AsyncPluggableDimmer, ) from homematicip.base.enums import RGBColorState from homematicip.base.functionalChannels import NotificationLightChannel from homeassistant.components.light import ( ATTR_BRIGHTNESS, ATTR_COLOR_NAME, ATTR_HS_COLOR, ATTR_TRANSITION, SUPPORT_BRIGHTNESS, SUPPORT_COLOR, Light, ) from homeassistant.config_entries import ConfigEntry from homeassistant.helpers.typing import HomeAssistantType from . import DOMAIN as HMIPC_DOMAIN, HMIPC_HAPID, HomematicipGenericDevice from .hap import HomematicipHAP _LOGGER = logging.getLogger(__name__) ATTR_TODAY_ENERGY_KWH = "today_energy_kwh" ATTR_CURRENT_POWER_W = "current_power_w" async def async_setup_platform( hass, config, async_add_entities, discovery_info=None ) -> None: pass async def async_setup_entry( hass: HomeAssistantType, config_entry: ConfigEntry, async_add_entities ) -> None: hap = hass.data[HMIPC_DOMAIN][config_entry.data[HMIPC_HAPID]] entities = [] for device in hap.home.devices: if isinstance(device, AsyncBrandSwitchMeasuring): entities.append(HomematicipLightMeasuring(hap, device)) elif isinstance(device, AsyncBrandSwitchNotificationLight): entities.append(HomematicipLight(hap, device)) entities.append( HomematicipNotificationLight(hap, device, device.topLightChannelIndex) ) entities.append( HomematicipNotificationLight( hap, device, device.bottomLightChannelIndex ) ) elif isinstance( device, (AsyncDimmer, AsyncPluggableDimmer, AsyncBrandDimmer, AsyncFullFlushDimmer), ): entities.append(HomematicipDimmer(hap, device)) if entities: async_add_entities(entities) class HomematicipLight(HomematicipGenericDevice, Light): def __init__(self, hap: HomematicipHAP, device) -> None: super().__init__(hap, device) @property def is_on(self) -> bool: return self._device.on async def async_turn_on(self, **kwargs) -> None: await self._device.turn_on() async def async_turn_off(self, **kwargs) -> None: await self._device.turn_off() class HomematicipLightMeasuring(HomematicipLight): @property def device_state_attributes(self) -> Dict[str, Any]: state_attr = super().device_state_attributes current_power_w = self._device.currentPowerConsumption if current_power_w > 0.05: state_attr[ATTR_CURRENT_POWER_W] = round(current_power_w, 2) state_attr[ATTR_TODAY_ENERGY_KWH] = round(self._device.energyCounter, 2) return state_attr class HomematicipDimmer(HomematicipGenericDevice, Light): def __init__(self, hap: HomematicipHAP, device) -> None: super().__init__(hap, device) @property def is_on(self) -> bool: return self._device.dimLevel is not None and self._device.dimLevel > 0.0 @property def brightness(self) -> int: return int((self._device.dimLevel or 0.0) * 255) @property def supported_features(self) -> int: return SUPPORT_BRIGHTNESS async def async_turn_on(self, **kwargs) -> None: if ATTR_BRIGHTNESS in kwargs: await self._device.set_dim_level(kwargs[ATTR_BRIGHTNESS] / 255.0) else: await self._device.set_dim_level(1) async def async_turn_off(self, **kwargs) -> None: await self._device.set_dim_level(0) class HomematicipNotificationLight(HomematicipGenericDevice, Light): def __init__(self, hap: HomematicipHAP, device, channel: int) -> None: self.channel = channel if self.channel == 2: super().__init__(hap, device, "Top") else: super().__init__(hap, device, "Bottom") self._color_switcher = { RGBColorState.WHITE: [0.0, 0.0], RGBColorState.RED: [0.0, 100.0], RGBColorState.YELLOW: [60.0, 100.0], RGBColorState.GREEN: [120.0, 100.0], RGBColorState.TURQUOISE: [180.0, 100.0], RGBColorState.BLUE: [240.0, 100.0], RGBColorState.PURPLE: [300.0, 100.0], } @property def _func_channel(self) -> NotificationLightChannel: return self._device.functionalChannels[self.channel] @property def is_on(self) -> bool: return ( self._func_channel.dimLevel is not None and self._func_channel.dimLevel > 0.0 ) @property def brightness(self) -> int: return int((self._func_channel.dimLevel or 0.0) * 255) @property def hs_color(self) -> tuple: simple_rgb_color = self._func_channel.simpleRGBColorState return self._color_switcher.get(simple_rgb_color, [0.0, 0.0]) @property def device_state_attributes(self) -> Dict[str, Any]: state_attr = super().device_state_attributes if self.is_on: state_attr[ATTR_COLOR_NAME] = self._func_channel.simpleRGBColorState return state_attr @property def name(self) -> str: return f"{super().name} Notification" @property def supported_features(self) -> int: return SUPPORT_BRIGHTNESS | SUPPORT_COLOR @property def unique_id(self) -> str: return f"{self.__class__.__name__}_{self.post}_{self._device.id}" async def async_turn_on(self, **kwargs) -> None: hs_color = kwargs.get(ATTR_HS_COLOR, self.hs_color) simple_rgb_color = _convert_color(hs_color) brightness = kwargs.get(ATTR_BRIGHTNESS, self.brightness) if not kwargs: brightness = 255 brightness = max(10, brightness) dim_level = brightness / 255.0 transition = kwargs.get(ATTR_TRANSITION, 0.5) await self._device.set_rgb_dim_level_with_time( channelIndex=self.channel, rgb=simple_rgb_color, dimLevel=dim_level, onTime=0, rampTime=transition, ) async def async_turn_off(self, **kwargs) -> None: simple_rgb_color = self._func_channel.simpleRGBColorState transition = kwargs.get(ATTR_TRANSITION, 0.5) await self._device.set_rgb_dim_level_with_time( channelIndex=self.channel, rgb=simple_rgb_color, dimLevel=0.0, onTime=0, rampTime=transition, ) def _convert_color(color: tuple) -> RGBColorState: if color is None: return RGBColorState.WHITE hue = int(color[0]) saturation = int(color[1]) if saturation < 5: return RGBColorState.WHITE if 30 < hue <= 90: return RGBColorState.YELLOW if 90 < hue <= 160: return RGBColorState.GREEN if 150 < hue <= 210: return RGBColorState.TURQUOISE if 210 < hue <= 270: return RGBColorState.BLUE if 270 < hue <= 330: return RGBColorState.PURPLE return RGBColorState.RED

true

79083f742da3f3eee14c296fe653dcc7712b69ac

2,167

py

Python

data/check_apogee_spectra.py

andycasey/stellar-twins

9b3cfbf608e3e15a2358bbd33aa5ae21cfc1d0dd

[ "MIT" ]

null

data/check_apogee_spectra.py

andycasey/stellar-twins

9b3cfbf608e3e15a2358bbd33aa5ae21cfc1d0dd

[ "MIT" ]

null

data/check_apogee_spectra.py

andycasey/stellar-twins

9b3cfbf608e3e15a2358bbd33aa5ae21cfc1d0dd

[ "MIT" ]

1

2016-09-28T20:47:21.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """ Test the quoted APOGEE uncertainties from individual (rebinned) spectra. """ __author__ = "Andy Casey <arc@ast.cam.ac.uk>" import numpy as np import matplotlib.pyplot as plt from astropy.io import fits from glob import glob from itertools import combinations def get_differences(apStar_filename): image = fits.open(apStar_filename) N_visits = image[0].header["NVISITS"] data_index = 1 error_index = 2 mask_index = 3 # Generate all permutations. differences = [] for i, j in combinations(range(N_visits), 2): di = image[data_index].data[i + 2, :] dj = image[data_index].data[j + 2, :] sigma = np.sqrt(image[error_index].data[i + 2, :]**2 \ + image[error_index].data[j + 2, :]**2) ok = (di > 0) * (dj > 0) * np.isfinite(di * dj * sigma) \ * (image[mask_index].data[i + 2, :] == 0) \ * (image[mask_index].data[j + 2, :] == 0) differences.extend(((di - dj)/sigma)[ok]) differences = np.array(differences).flatten() return differences def plot_differences(differences): fig, ax = plt.subplots(1) y_bin, x_bin, _ = ax.hist(differences, bins=100, facecolor="#666666") x = np.linspace(ax.get_xlim()[0], ax.get_xlim()[1], 1000) y = np.exp(-0.5*x**2)/np.sqrt(2*np.pi) ax.plot(x, y*np.trapz(y_bin, x=x_bin[1:])/np.sqrt(2*np.pi), lw=2, c="r") ax.set_title("mu = {0:.1f}, sigma(|d|) = {1:.1f}".format( np.median(differences), np.std(np.abs(differences)))) ax.set_xlabel("(F1 - F2)/sqrt(sigma_1^2 + sigma_2^2)") return fig if __name__ == "__main__": filenames = glob("APOGEE/*.fits") all_differences = [] for filename in filenames: differences = get_differences(filename) if len(differences) > 0: fig = plot_differences(differences) fig.savefig("APOGEE/{0}.png".format(filename.split("/")[-1].split(".")[0])) plt.close("all") print(filename) all_differences.extend(differences) fig = plot_differences(np.array(all_differences)) fig.savefig("APOGEE/all.png")

28.142857

87

0.606368

__author__ = "Andy Casey <arc@ast.cam.ac.uk>" import numpy as np import matplotlib.pyplot as plt from astropy.io import fits from glob import glob from itertools import combinations def get_differences(apStar_filename): image = fits.open(apStar_filename) N_visits = image[0].header["NVISITS"] data_index = 1 error_index = 2 mask_index = 3 differences = [] for i, j in combinations(range(N_visits), 2): di = image[data_index].data[i + 2, :] dj = image[data_index].data[j + 2, :] sigma = np.sqrt(image[error_index].data[i + 2, :]**2 \ + image[error_index].data[j + 2, :]**2) ok = (di > 0) * (dj > 0) * np.isfinite(di * dj * sigma) \ * (image[mask_index].data[i + 2, :] == 0) \ * (image[mask_index].data[j + 2, :] == 0) differences.extend(((di - dj)/sigma)[ok]) differences = np.array(differences).flatten() return differences def plot_differences(differences): fig, ax = plt.subplots(1) y_bin, x_bin, _ = ax.hist(differences, bins=100, facecolor="#666666") x = np.linspace(ax.get_xlim()[0], ax.get_xlim()[1], 1000) y = np.exp(-0.5*x**2)/np.sqrt(2*np.pi) ax.plot(x, y*np.trapz(y_bin, x=x_bin[1:])/np.sqrt(2*np.pi), lw=2, c="r") ax.set_title("mu = {0:.1f}, sigma(|d|) = {1:.1f}".format( np.median(differences), np.std(np.abs(differences)))) ax.set_xlabel("(F1 - F2)/sqrt(sigma_1^2 + sigma_2^2)") return fig if __name__ == "__main__": filenames = glob("APOGEE/*.fits") all_differences = [] for filename in filenames: differences = get_differences(filename) if len(differences) > 0: fig = plot_differences(differences) fig.savefig("APOGEE/{0}.png".format(filename.split("/")[-1].split(".")[0])) plt.close("all") print(filename) all_differences.extend(differences) fig = plot_differences(np.array(all_differences)) fig.savefig("APOGEE/all.png")

true

790840947ae7244ad5ef3eab016a7359ff9c3924

4,032

py

Python

ezgal/scripts/convert_basti.py

dpgettings/ezgal

de4a58879eaee0bddbcdb42dddfa2b398dbc3ea9

[ "MIT" ]

5

2015-07-15T21:05:22.000Z

2017-10-05T19:16:09.000Z

ezgal/scripts/convert_basti.py

dpgettings/ezgal

de4a58879eaee0bddbcdb42dddfa2b398dbc3ea9

[ "MIT" ]

1

2015-09-12T12:37:29.000Z

ezgal/scripts/convert_basti.py

dpgettings/ezgal

de4a58879eaee0bddbcdb42dddfa2b398dbc3ea9

[ "MIT" ]

2

2015-05-14T14:34:19.000Z

2019-03-22T02:17:22.000Z

#!/usr/bin/python import glob,re,sys,math,pyfits import numpy as np import utils if len(sys.argv) < 2: print '\nconvert basti SSP models to ez_gal fits format' print 'Run in directory with SED models for one metallicity' print 'Usage: convert_basti.py ez_gal.ascii\n' sys.exit(2) fileout = sys.argv[1] # try to extract meta data out of fileout sfh = ''; tau = ''; met = ''; imf = '' # split on _ but get rid of the extension parts = '.'.join(fileout.split('.')[:-1]).split('_') # look for sfh for (check,val) in zip(['ssp','exp'], ['SSP','Exponential']): if parts.count(check): sfh = val sfh_index = parts.index(check) break # tau? if sfh: tau = parts[sfh_index+1] if sfh == 'exp' else '' # metallicity if parts.count('z'): met = parts[parts.index('z') + 1] # imf for (check,val) in zip(['krou','salp','chab'], ['Kroupa', 'Salpeter', 'Chabrier']): if parts.count(check): imf = val break if parts.count('n'): n = parts[parts.index('n') + 1] ae = False if parts.count('ae'): ae = True # does the file with masses exist? has_masses = False mass_file = glob.glob('MLR*.txt') if len(mass_file): # read it in! print 'Loading masses from %s' % mass_file[0] data = utils.rascii(mass_file[0], silent=True) masses = data[:,10:14].sum(axis=1) has_masses = True files = glob.glob('SPEC*agb*') nages = len(files) ages = [] for (i,file) in enumerate(files): ls = [] this = [] # extract the age from the filename and convert to years m = re.search('t60*(\d+)$', file) ages.append(int(m.group(1))*1e6) # read in this file fp = open(file, 'r') for line in fp: parts = line.strip().split() ls.append(float(parts[0].strip())) this.append(float(parts[1].strip())) if i == 0: # if this is the first file, generate the data table nls = len(ls) seds = np.empty((nls,nages)) # convert to ergs/s/angstrom seds[:,i] = np.array(this)/4.3607e-33/1e10 # convert to numpy ages = np.array(ages) ls = np.array(ls)*10.0 # make sure we are sorted in age sinds = ages.argsort() ages = ages[sinds] seds = seds[:,sinds] # speed of light c = utils.convert_length(utils.c, incoming='m', outgoing='a') # convert from angstroms to hertz vs = c/ls # convert from ergs/s/A to ergs/s/Hz seds *= ls.reshape((ls.size,1))**2.0/c # and now from ergs/s/Hz to ergs/s/Hz/cm^2.0 seds /= (4.0*math.pi*utils.convert_length(10, incoming='pc', outgoing='cm')**2.0) # sort in frequency space sinds = vs.argsort() # generate fits frame with sed in it primary_hdu = pyfits.PrimaryHDU(seds[sinds,:]) primary_hdu.header.update('units', 'ergs/s/cm^2/Hz') primary_hdu.header.update('has_seds', True) primary_hdu.header.update('nfilters', 0) primary_hdu.header.update('nzfs', 0) # store meta data if sfh and met and imf: primary_hdu.header.update('has_meta', True) primary_hdu.header.update('model', 'BaSTI', comment='meta data') primary_hdu.header.update('met', met, comment='meta data') primary_hdu.header.update('imf', imf, comment='meta data') primary_hdu.header.update('sfh', sfh, comment='meta data') if sfh == 'Exponential': primary_hdu.header.update('tau', tau, comment='meta data') primary_hdu.header.update('n', n, comment='meta data') primary_hdu.header.update('ae', ae, comment='meta data') # store the list of frequencies in a table vs_hdu = pyfits.new_table(pyfits.ColDefs([pyfits.Column(name='vs', array=vs[sinds], format='D', unit='hertz')])) vs_hdu.header.update('units', 'hertz') # and the list of ages cols = [pyfits.Column(name='ages', array=ages, format='D', unit='years')] # and masses if has_masses: cols.append(pyfits.Column(name='masses', array=masses, format='D', unit='m_sun')) ages_hdu = pyfits.new_table(pyfits.ColDefs(cols)) if has_masses: ages_hdu.header.update('has_mass', True) # make the fits file in memory hdulist = pyfits.HDUList([primary_hdu,vs_hdu,ages_hdu]) # and write it out hdulist.writeto(fileout, clobber=True)

30.545455

112

0.657738

import glob,re,sys,math,pyfits import numpy as np import utils if len(sys.argv) < 2: print '\nconvert basti SSP models to ez_gal fits format' print 'Run in directory with SED models for one metallicity' print 'Usage: convert_basti.py ez_gal.ascii\n' sys.exit(2) fileout = sys.argv[1] sfh = ''; tau = ''; met = ''; imf = '' parts = '.'.join(fileout.split('.')[:-1]).split('_') for (check,val) in zip(['ssp','exp'], ['SSP','Exponential']): if parts.count(check): sfh = val sfh_index = parts.index(check) break if sfh: tau = parts[sfh_index+1] if sfh == 'exp' else '' if parts.count('z'): met = parts[parts.index('z') + 1] for (check,val) in zip(['krou','salp','chab'], ['Kroupa', 'Salpeter', 'Chabrier']): if parts.count(check): imf = val break if parts.count('n'): n = parts[parts.index('n') + 1] ae = False if parts.count('ae'): ae = True has_masses = False mass_file = glob.glob('MLR*.txt') if len(mass_file): print 'Loading masses from %s' % mass_file[0] data = utils.rascii(mass_file[0], silent=True) masses = data[:,10:14].sum(axis=1) has_masses = True files = glob.glob('SPEC*agb*') nages = len(files) ages = [] for (i,file) in enumerate(files): ls = [] this = [] m = re.search('t60*(\d+)$', file) ages.append(int(m.group(1))*1e6) fp = open(file, 'r') for line in fp: parts = line.strip().split() ls.append(float(parts[0].strip())) this.append(float(parts[1].strip())) if i == 0: nls = len(ls) seds = np.empty((nls,nages)) seds[:,i] = np.array(this)/4.3607e-33/1e10 ages = np.array(ages) ls = np.array(ls)*10.0 sinds = ages.argsort() ages = ages[sinds] seds = seds[:,sinds] c = utils.convert_length(utils.c, incoming='m', outgoing='a') vs = c/ls seds *= ls.reshape((ls.size,1))**2.0/c seds /= (4.0*math.pi*utils.convert_length(10, incoming='pc', outgoing='cm')**2.0) sinds = vs.argsort() primary_hdu = pyfits.PrimaryHDU(seds[sinds,:]) primary_hdu.header.update('units', 'ergs/s/cm^2/Hz') primary_hdu.header.update('has_seds', True) primary_hdu.header.update('nfilters', 0) primary_hdu.header.update('nzfs', 0) if sfh and met and imf: primary_hdu.header.update('has_meta', True) primary_hdu.header.update('model', 'BaSTI', comment='meta data') primary_hdu.header.update('met', met, comment='meta data') primary_hdu.header.update('imf', imf, comment='meta data') primary_hdu.header.update('sfh', sfh, comment='meta data') if sfh == 'Exponential': primary_hdu.header.update('tau', tau, comment='meta data') primary_hdu.header.update('n', n, comment='meta data') primary_hdu.header.update('ae', ae, comment='meta data') vs_hdu = pyfits.new_table(pyfits.ColDefs([pyfits.Column(name='vs', array=vs[sinds], format='D', unit='hertz')])) vs_hdu.header.update('units', 'hertz') cols = [pyfits.Column(name='ages', array=ages, format='D', unit='years')] if has_masses: cols.append(pyfits.Column(name='masses', array=masses, format='D', unit='m_sun')) ages_hdu = pyfits.new_table(pyfits.ColDefs(cols)) if has_masses: ages_hdu.header.update('has_mass', True) hdulist = pyfits.HDUList([primary_hdu,vs_hdu,ages_hdu]) hdulist.writeto(fileout, clobber=True)

false

true

7908410bd7bc8c885dca9b63f878d01401772eb0

2,963

py

Python

python_vuejs/vuejs.py

Timtech4u/python-vuejs

7634726ad7fc5ab02a6159e7f150360ededca250

[ "MIT" ]

null

python_vuejs/vuejs.py

Timtech4u/python-vuejs

7634726ad7fc5ab02a6159e7f150360ededca250

[ "MIT" ]

null

python_vuejs/vuejs.py

Timtech4u/python-vuejs

7634726ad7fc5ab02a6159e7f150360ededca250

[ "MIT" ]

1

2018-11-24T02:05:28.000Z

# -*- coding: utf-8 -*- from collections import namedtuple from subprocess import check_output import click from .utils import cd try: from subprocess import call as run except ImportError: from subprocess import run class VueJs(object): """ Provide subprocess call to `npm` and `vue-cli` """ @staticmethod def node_check(): """ Node and npm version checker """ node_ver = check_output('node -v'.split()).decode('utf-8').rsplit('.')[0] npm_ver = check_output('npm -v'.split()).decode('utf-8').rsplit('.')[0] return all([node_ver > 'v5', npm_ver >= '4']) @staticmethod def vue_cli_check(): """ vue-cli version checker """ try: return check_output('vue -V'.split()).decode('utf-8').rsplit('.')[0] except OSError: return False @staticmethod def install_cli(): run('npm install -g vue-cli'.split()) @staticmethod def project_setup(project): run('vue init webpack {project}'.format(project=project).split()) @staticmethod def install_dependencies(project): with cd(project): run('npm install'.split()) @staticmethod def dev(): run('npm run dev'.split()) @staticmethod def build(): run('npm run build'.split()) class VueJsBuilder(object): @staticmethod def startproject(project): nt = namedtuple('Result', ['status', 'message', 'color']) if VueJs.vue_cli_check(): VueJs.project_setup(project) VueJs.install_dependencies(project) return nt(True, 'Application and dependencies installed\n', 'green') else: return nt(False, 'Please install vue-cli via `vuecli` command', 'red') @click.group() def cli(): """ Click entry point: vue-cli commands group By convention all new cli has a cli function with a pass statement """ pass @cli.command() def vuecheck(): """ Check if node > 5 and npm > 3 are installed """ if VueJs.node_check(): click.echo(click.style('Found node and npm', fg='green')) else: click.echo(click.style('Missing node and npm installation', fg='red')) @cli.command() def installvuecli(): """ Install vue-cli """ if VueJs.vue_cli_check(): click.echo(click.style('Found valid vue-cli', fg='green')) else: VueJs.install_cli() click.echo(click.style('Installed vue-cli globally', fg='green')) @cli.command() @click.argument('project') def startvueapp(project): """ Init vue project via vue-cli """ result = VueJsBuilder.startproject(project) click.echo(click.style(result.message, fg=result.color)) @cli.command() def vuedev(): """ Run frontend dev server via npm """ VueJs.dev() @cli.command() def vuebuild(): """ Build Vue.js project via npm """ VueJs.build()

22.792308

82

0.600405

from collections import namedtuple from subprocess import check_output import click from .utils import cd try: from subprocess import call as run except ImportError: from subprocess import run class VueJs(object): @staticmethod def node_check(): node_ver = check_output('node -v'.split()).decode('utf-8').rsplit('.')[0] npm_ver = check_output('npm -v'.split()).decode('utf-8').rsplit('.')[0] return all([node_ver > 'v5', npm_ver >= '4']) @staticmethod def vue_cli_check(): try: return check_output('vue -V'.split()).decode('utf-8').rsplit('.')[0] except OSError: return False @staticmethod def install_cli(): run('npm install -g vue-cli'.split()) @staticmethod def project_setup(project): run('vue init webpack {project}'.format(project=project).split()) @staticmethod def install_dependencies(project): with cd(project): run('npm install'.split()) @staticmethod def dev(): run('npm run dev'.split()) @staticmethod def build(): run('npm run build'.split()) class VueJsBuilder(object): @staticmethod def startproject(project): nt = namedtuple('Result', ['status', 'message', 'color']) if VueJs.vue_cli_check(): VueJs.project_setup(project) VueJs.install_dependencies(project) return nt(True, 'Application and dependencies installed\n', 'green') else: return nt(False, 'Please install vue-cli via `vuecli` command', 'red') @click.group() def cli(): pass @cli.command() def vuecheck(): if VueJs.node_check(): click.echo(click.style('Found node and npm', fg='green')) else: click.echo(click.style('Missing node and npm installation', fg='red')) @cli.command() def installvuecli(): if VueJs.vue_cli_check(): click.echo(click.style('Found valid vue-cli', fg='green')) else: VueJs.install_cli() click.echo(click.style('Installed vue-cli globally', fg='green')) @cli.command() @click.argument('project') def startvueapp(project): result = VueJsBuilder.startproject(project) click.echo(click.style(result.message, fg=result.color)) @cli.command() def vuedev(): VueJs.dev() @cli.command() def vuebuild(): VueJs.build()

true

7908411e8ba2d90f953b5dd55f4a6f78d9db403d

259

py

Python

Practice1.py

anayakoti/FirstSample

8ef05772991644e63a4fd6759458f449cd2b00c0

[ "bzip2-1.0.6" ]

null

Practice1.py

anayakoti/FirstSample

8ef05772991644e63a4fd6759458f449cd2b00c0

[ "bzip2-1.0.6" ]

null

Practice1.py

anayakoti/FirstSample

8ef05772991644e63a4fd6759458f449cd2b00c0

[ "bzip2-1.0.6" ]

null

createNewFile=open('dummy.txt', 'w'); grades=0; print("Enter a rollnumber"); grades=input(); while(grades!='*'): createNewFile.write(grades +"\n"); print("Enter a rollnumber"); grades=input(); createNewFile.close();

19.923077

38

0.583012

createNewFile=open('dummy.txt', 'w'); grades=0; print("Enter a rollnumber"); grades=input(); while(grades!='*'): createNewFile.write(grades +"\n"); print("Enter a rollnumber"); grades=input(); createNewFile.close();

true

790841b06ba83dce9101ed02863786dd32d19090

147

py

Python

treasury_yield_analysis/task/__init__.py

samsea18/Treasury-Yield-Analysis

8746adee93a995089d3c6dc1eb371ecef9cd942c

[ "MIT" ]

null

treasury_yield_analysis/task/__init__.py

samsea18/Treasury-Yield-Analysis

8746adee93a995089d3c6dc1eb371ecef9cd942c

[ "MIT" ]

1

2021-06-29T16:34:26.000Z

treasury_yield_analysis/task/__init__.py

samsea18/Treasury-Yield-Analysis

8746adee93a995089d3c6dc1eb371ecef9cd942c

[ "MIT" ]

null

from .treasury_yields import Treasury_Yield_Task from .mariadb import Mariadb_Task from .bea import BEA_Task from .yfinance import Yfinance_Task

36.75

49

0.85034

from .treasury_yields import Treasury_Yield_Task from .mariadb import Mariadb_Task from .bea import BEA_Task from .yfinance import Yfinance_Task

true

790841b8809171e06dadf551f38dccc196d0b33e

7,094

py

Python

.install/.backup/lib/apitools/base/py/extra_types.py

bopopescu/google-cloud-sdk

b34e6a18f1e89673508166acce816111c3421e4b

[ "Apache-2.0" ]

null

.install/.backup/lib/apitools/base/py/extra_types.py

bopopescu/google-cloud-sdk

b34e6a18f1e89673508166acce816111c3421e4b

[ "Apache-2.0" ]

null

.install/.backup/lib/apitools/base/py/extra_types.py

bopopescu/google-cloud-sdk

b34e6a18f1e89673508166acce816111c3421e4b

[ "Apache-2.0" ]

1

2020-07-24T20:04:47.000Z

"""Extra types understood by apitools. This file will be replaced by a .proto file when we switch to proto2 from protorpc. """ import collections import json import numbers from protorpc import message_types from protorpc import messages from protorpc import protojson from apitools.base.py import encoding from apitools.base.py import exceptions from apitools.base.py import util __all__ = [ 'DateTimeMessage', 'JsonArray', 'JsonObject', 'JsonValue', 'JsonProtoEncoder', 'JsonProtoDecoder', ] # We import from protorpc. # pylint:disable=invalid-name DateTimeMessage = message_types.DateTimeMessage # pylint:enable=invalid-name def _ValidateJsonValue(json_value): entries = [(f, json_value.get_assigned_value(f.name)) for f in json_value.all_fields()] assigned_entries = [(f, value) for f, value in entries if value is not None] if len(assigned_entries) != 1: raise exceptions.InvalidDataError('Malformed JsonValue: %s' % json_value) def _JsonValueToPythonValue(json_value): """Convert the given JsonValue to a json string.""" util.Typecheck(json_value, JsonValue) _ValidateJsonValue(json_value) if json_value.is_null: return None entries = [(f, json_value.get_assigned_value(f.name)) for f in json_value.all_fields()] assigned_entries = [(f, value) for f, value in entries if value is not None] field, value = assigned_entries[0] if not isinstance(field, messages.MessageField): return value elif field.message_type is JsonObject: return _JsonObjectToPythonValue(value) elif field.message_type is JsonArray: return _JsonArrayToPythonValue(value) def _JsonObjectToPythonValue(json_value): util.Typecheck(json_value, JsonObject) return dict([(prop.key, _JsonValueToPythonValue(prop.value)) for prop in json_value.properties]) def _JsonArrayToPythonValue(json_value): util.Typecheck(json_value, JsonArray) return [_JsonValueToPythonValue(e) for e in json_value.entries] _MAXINT64 = 2 << 63 - 1 _MININT64 = -(2 << 63) def _PythonValueToJsonValue(py_value): """Convert the given python value to a JsonValue.""" if py_value is None: return JsonValue(is_null=True) if isinstance(py_value, bool): return JsonValue(boolean_value=py_value) if isinstance(py_value, basestring): return JsonValue(string_value=py_value) if isinstance(py_value, numbers.Number): if isinstance(py_value, (int, long)): if _MININT64 < py_value < _MAXINT64: return JsonValue(integer_value=py_value) return JsonValue(double_value=float(py_value)) if isinstance(py_value, dict): return JsonValue(object_value=_PythonValueToJsonObject(py_value)) if isinstance(py_value, collections.Iterable): return JsonValue(array_value=_PythonValueToJsonArray(py_value)) raise exceptions.InvalidDataError( 'Cannot convert "%s" to JsonValue' % py_value) def _PythonValueToJsonObject(py_value): util.Typecheck(py_value, dict) return JsonObject( properties=[ JsonObject.Property(key=key, value=_PythonValueToJsonValue(value)) for key, value in py_value.iteritems()]) def _PythonValueToJsonArray(py_value): return JsonArray(entries=map(_PythonValueToJsonValue, py_value)) class JsonValue(messages.Message): """Any valid JSON value.""" # Is this JSON object `null`? is_null = messages.BooleanField(1, default=False) # Exactly one of the following is provided if is_null is False; none # should be provided if is_null is True. boolean_value = messages.BooleanField(2) string_value = messages.StringField(3) # We keep two numeric fields to keep int64 round-trips exact. double_value = messages.FloatField(4, variant=messages.Variant.DOUBLE) integer_value = messages.IntegerField(5, variant=messages.Variant.INT64) # Compound types object_value = messages.MessageField('JsonObject', 6) array_value = messages.MessageField('JsonArray', 7) class JsonObject(messages.Message): """A JSON object value. Messages: Property: A property of a JsonObject. Fields: properties: A list of properties of a JsonObject. """ class Property(messages.Message): """A property of a JSON object. Fields: key: Name of the property. value: A JsonValue attribute. """ key = messages.StringField(1) value = messages.MessageField(JsonValue, 2) properties = messages.MessageField(Property, 1, repeated=True) class JsonArray(messages.Message): """A JSON array value.""" entries = messages.MessageField(JsonValue, 1, repeated=True) _JSON_PROTO_TO_PYTHON_MAP = { JsonArray: _JsonArrayToPythonValue, JsonObject: _JsonObjectToPythonValue, JsonValue: _JsonValueToPythonValue, } _JSON_PROTO_TYPES = tuple(_JSON_PROTO_TO_PYTHON_MAP.keys()) def _JsonProtoToPythonValue(json_proto): util.Typecheck(json_proto, _JSON_PROTO_TYPES) return _JSON_PROTO_TO_PYTHON_MAP[type(json_proto)](json_proto) def _PythonValueToJsonProto(py_value): if isinstance(py_value, dict): return _PythonValueToJsonObject(py_value) if (isinstance(py_value, collections.Iterable) and not isinstance(py_value, basestring)): return _PythonValueToJsonArray(py_value) return _PythonValueToJsonValue(py_value) def _JsonProtoToJson(json_proto, unused_encoder=None): return json.dumps(_JsonProtoToPythonValue(json_proto)) def _JsonToJsonProto(json_data, unused_decoder=None): return _PythonValueToJsonProto(json.loads(json_data)) # pylint:disable=invalid-name JsonProtoEncoder = _JsonProtoToJson JsonProtoDecoder = _JsonToJsonProto # pylint:enable=invalid-name encoding.RegisterCustomMessageCodec( encoder=JsonProtoEncoder, decoder=JsonProtoDecoder)(JsonValue) encoding.RegisterCustomMessageCodec( encoder=JsonProtoEncoder, decoder=JsonProtoDecoder)(JsonObject) encoding.RegisterCustomMessageCodec( encoder=JsonProtoEncoder, decoder=JsonProtoDecoder)(JsonArray) def _EncodeDateTimeField(field, value): result = protojson.ProtoJson().encode_field(field, value) return encoding.CodecResult(value=result, complete=True) def _DecodeDateTimeField(unused_field, value): result = protojson.ProtoJson().decode_field( message_types.DateTimeField(1), value) return encoding.CodecResult(value=result, complete=True) encoding.RegisterFieldTypeCodec(_EncodeDateTimeField, _DecodeDateTimeField)( message_types.DateTimeField) def _EncodeInt64Field(field, value): """Handle the special case of int64 as a string.""" capabilities = [ messages.Variant.INT64, messages.Variant.UINT64, ] if field.variant not in capabilities: return encoding.CodecResult(value=value, complete=False) if field.repeated: result = [str(x) for x in value] else: result = str(value) return encoding.CodecResult(value=result, complete=True) def _DecodeInt64Field(unused_field, value): # Don't need to do anything special, they're decoded just fine return encoding.CodecResult(value=value, complete=False) encoding.RegisterFieldTypeCodec(_EncodeInt64Field, _DecodeInt64Field)( messages.IntegerField)

30.577586

78

0.762193

import collections import json import numbers from protorpc import message_types from protorpc import messages from protorpc import protojson from apitools.base.py import encoding from apitools.base.py import exceptions from apitools.base.py import util __all__ = [ 'DateTimeMessage', 'JsonArray', 'JsonObject', 'JsonValue', 'JsonProtoEncoder', 'JsonProtoDecoder', ] DateTimeMessage = message_types.DateTimeMessage def _ValidateJsonValue(json_value): entries = [(f, json_value.get_assigned_value(f.name)) for f in json_value.all_fields()] assigned_entries = [(f, value) for f, value in entries if value is not None] if len(assigned_entries) != 1: raise exceptions.InvalidDataError('Malformed JsonValue: %s' % json_value) def _JsonValueToPythonValue(json_value): util.Typecheck(json_value, JsonValue) _ValidateJsonValue(json_value) if json_value.is_null: return None entries = [(f, json_value.get_assigned_value(f.name)) for f in json_value.all_fields()] assigned_entries = [(f, value) for f, value in entries if value is not None] field, value = assigned_entries[0] if not isinstance(field, messages.MessageField): return value elif field.message_type is JsonObject: return _JsonObjectToPythonValue(value) elif field.message_type is JsonArray: return _JsonArrayToPythonValue(value) def _JsonObjectToPythonValue(json_value): util.Typecheck(json_value, JsonObject) return dict([(prop.key, _JsonValueToPythonValue(prop.value)) for prop in json_value.properties]) def _JsonArrayToPythonValue(json_value): util.Typecheck(json_value, JsonArray) return [_JsonValueToPythonValue(e) for e in json_value.entries] _MAXINT64 = 2 << 63 - 1 _MININT64 = -(2 << 63) def _PythonValueToJsonValue(py_value): if py_value is None: return JsonValue(is_null=True) if isinstance(py_value, bool): return JsonValue(boolean_value=py_value) if isinstance(py_value, basestring): return JsonValue(string_value=py_value) if isinstance(py_value, numbers.Number): if isinstance(py_value, (int, long)): if _MININT64 < py_value < _MAXINT64: return JsonValue(integer_value=py_value) return JsonValue(double_value=float(py_value)) if isinstance(py_value, dict): return JsonValue(object_value=_PythonValueToJsonObject(py_value)) if isinstance(py_value, collections.Iterable): return JsonValue(array_value=_PythonValueToJsonArray(py_value)) raise exceptions.InvalidDataError( 'Cannot convert "%s" to JsonValue' % py_value) def _PythonValueToJsonObject(py_value): util.Typecheck(py_value, dict) return JsonObject( properties=[ JsonObject.Property(key=key, value=_PythonValueToJsonValue(value)) for key, value in py_value.iteritems()]) def _PythonValueToJsonArray(py_value): return JsonArray(entries=map(_PythonValueToJsonValue, py_value)) class JsonValue(messages.Message): is_null = messages.BooleanField(1, default=False) boolean_value = messages.BooleanField(2) string_value = messages.StringField(3) double_value = messages.FloatField(4, variant=messages.Variant.DOUBLE) integer_value = messages.IntegerField(5, variant=messages.Variant.INT64) object_value = messages.MessageField('JsonObject', 6) array_value = messages.MessageField('JsonArray', 7) class JsonObject(messages.Message): class Property(messages.Message): key = messages.StringField(1) value = messages.MessageField(JsonValue, 2) properties = messages.MessageField(Property, 1, repeated=True) class JsonArray(messages.Message): entries = messages.MessageField(JsonValue, 1, repeated=True) _JSON_PROTO_TO_PYTHON_MAP = { JsonArray: _JsonArrayToPythonValue, JsonObject: _JsonObjectToPythonValue, JsonValue: _JsonValueToPythonValue, } _JSON_PROTO_TYPES = tuple(_JSON_PROTO_TO_PYTHON_MAP.keys()) def _JsonProtoToPythonValue(json_proto): util.Typecheck(json_proto, _JSON_PROTO_TYPES) return _JSON_PROTO_TO_PYTHON_MAP[type(json_proto)](json_proto) def _PythonValueToJsonProto(py_value): if isinstance(py_value, dict): return _PythonValueToJsonObject(py_value) if (isinstance(py_value, collections.Iterable) and not isinstance(py_value, basestring)): return _PythonValueToJsonArray(py_value) return _PythonValueToJsonValue(py_value) def _JsonProtoToJson(json_proto, unused_encoder=None): return json.dumps(_JsonProtoToPythonValue(json_proto)) def _JsonToJsonProto(json_data, unused_decoder=None): return _PythonValueToJsonProto(json.loads(json_data)) JsonProtoEncoder = _JsonProtoToJson JsonProtoDecoder = _JsonToJsonProto encoding.RegisterCustomMessageCodec( encoder=JsonProtoEncoder, decoder=JsonProtoDecoder)(JsonValue) encoding.RegisterCustomMessageCodec( encoder=JsonProtoEncoder, decoder=JsonProtoDecoder)(JsonObject) encoding.RegisterCustomMessageCodec( encoder=JsonProtoEncoder, decoder=JsonProtoDecoder)(JsonArray) def _EncodeDateTimeField(field, value): result = protojson.ProtoJson().encode_field(field, value) return encoding.CodecResult(value=result, complete=True) def _DecodeDateTimeField(unused_field, value): result = protojson.ProtoJson().decode_field( message_types.DateTimeField(1), value) return encoding.CodecResult(value=result, complete=True) encoding.RegisterFieldTypeCodec(_EncodeDateTimeField, _DecodeDateTimeField)( message_types.DateTimeField) def _EncodeInt64Field(field, value): capabilities = [ messages.Variant.INT64, messages.Variant.UINT64, ] if field.variant not in capabilities: return encoding.CodecResult(value=value, complete=False) if field.repeated: result = [str(x) for x in value] else: result = str(value) return encoding.CodecResult(value=result, complete=True) def _DecodeInt64Field(unused_field, value): return encoding.CodecResult(value=value, complete=False) encoding.RegisterFieldTypeCodec(_EncodeInt64Field, _DecodeInt64Field)( messages.IntegerField)

true

7908421b2547ace9920792b02f8c54541379d1a7

4,664

py

Python

server.py

catarinaacsilva/pacman

940823f4654dfc01e63361aa2ca17a275aa7b1fa

[ "MIT" ]

null

server.py

catarinaacsilva/pacman

940823f4654dfc01e63361aa2ca17a275aa7b1fa

[ "MIT" ]

null

server.py

catarinaacsilva/pacman

940823f4654dfc01e63361aa2ca17a275aa7b1fa

[ "MIT" ]

null

import requests import argparse import asyncio import json import logging import websockets import os.path from collections import namedtuple from game import Game logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') wslogger = logging.getLogger('websockets') wslogger.setLevel(logging.WARN) logger = logging.getLogger('Server') logger.setLevel(logging.INFO) Player = namedtuple('Player', ['name', 'ws']) class Game_server: def __init__(self, mapfile, ghosts, level_ghosts, lives, timeout, grading=None): self.game = Game(mapfile, ghosts, level_ghosts, lives, timeout) self.game_properties = {'map': mapfile, 'n_ghosts': ghosts, 'l_ghosts': level_ghosts} self.players = asyncio.Queue() self.viewers = set() self.current_player = None self.grading = grading async def incomming_handler(self, websocket, path): try: async for message in websocket: data = json.loads(message) if data["cmd"] == "join": map_info = self.game.info() await websocket.send(map_info) if path == "/player": logger.info("<%s> has joined", data["name"]) await self.players.put(Player(data["name"], websocket)) if path == "/viewer": self.viewers.add(websocket) if data["cmd"] == "key" and self.current_player.ws == websocket: logger.debug((self.current_player.name, data)) self.game.keypress(data["key"][0]) except websockets.exceptions.ConnectionClosed as c: logger.info("Client disconnected") if websocket in self.viewers: self.viewers.remove(websocket) async def mainloop(self): while True: logger.info("Waiting for players") self.current_player = await self.players.get() if self.current_player.ws.closed: logger.error("<{}> disconnect while waiting".format(self.current_player.name)) continue try: logger.info("Starting game for <{}>".format(self.current_player.name)) self.game.start(self.current_player.name) if self.grading: game_rec = dict(self.game_properties) game_rec['player'] = self.current_player.name while self.game.running: await self.game.next_frame() await self.current_player.ws.send(self.game.state) if self.viewers: await asyncio.wait([client.send(self.game.state) for client in self.viewers]) await self.current_player.ws.send(json.dumps({"score": self.game.score})) logger.info("Disconnecting <{}>".format(self.current_player.name)) except websockets.exceptions.ConnectionClosed as c: self.current_player = None finally: if self.grading: game_rec['score'] = self.game.score r = requests.post(self.grading, json=game_rec) if self.current_player: await self.current_player.ws.close() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--bind", help="IP address to bind to", default="") parser.add_argument("--port", help="TCP port", type=int, default=8000) parser.add_argument("--ghosts", help="Number of ghosts", type=int, default=1) parser.add_argument("--level", help="difficulty level of ghosts", choices=['0','1','2','3'], default='1') parser.add_argument("--lives", help="Number of lives", type=int, default=3) parser.add_argument("--timeout", help="Timeout after this amount of steps", type=int, default=3000) parser.add_argument("--map", help="path to the map bmp", default="data/map1.bmp") parser.add_argument("--grading-server", help="url of grading server", default=None) args = parser.parse_args() g = Game_server(args.map, args.ghosts, int(args.level), args.lives, args.timeout, args.grading_server) game_loop_task = asyncio.ensure_future(g.mainloop()) websocket_server = websockets.serve(g.incomming_handler, args.bind, args.port) loop = asyncio.get_event_loop() loop.run_until_complete(asyncio.gather(websocket_server, game_loop_task)) loop.close()

42.018018

109

0.595197

import requests import argparse import asyncio import json import logging import websockets import os.path from collections import namedtuple from game import Game logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') wslogger = logging.getLogger('websockets') wslogger.setLevel(logging.WARN) logger = logging.getLogger('Server') logger.setLevel(logging.INFO) Player = namedtuple('Player', ['name', 'ws']) class Game_server: def __init__(self, mapfile, ghosts, level_ghosts, lives, timeout, grading=None): self.game = Game(mapfile, ghosts, level_ghosts, lives, timeout) self.game_properties = {'map': mapfile, 'n_ghosts': ghosts, 'l_ghosts': level_ghosts} self.players = asyncio.Queue() self.viewers = set() self.current_player = None self.grading = grading async def incomming_handler(self, websocket, path): try: async for message in websocket: data = json.loads(message) if data["cmd"] == "join": map_info = self.game.info() await websocket.send(map_info) if path == "/player": logger.info("<%s> has joined", data["name"]) await self.players.put(Player(data["name"], websocket)) if path == "/viewer": self.viewers.add(websocket) if data["cmd"] == "key" and self.current_player.ws == websocket: logger.debug((self.current_player.name, data)) self.game.keypress(data["key"][0]) except websockets.exceptions.ConnectionClosed as c: logger.info("Client disconnected") if websocket in self.viewers: self.viewers.remove(websocket) async def mainloop(self): while True: logger.info("Waiting for players") self.current_player = await self.players.get() if self.current_player.ws.closed: logger.error("<{}> disconnect while waiting".format(self.current_player.name)) continue try: logger.info("Starting game for <{}>".format(self.current_player.name)) self.game.start(self.current_player.name) if self.grading: game_rec = dict(self.game_properties) game_rec['player'] = self.current_player.name while self.game.running: await self.game.next_frame() await self.current_player.ws.send(self.game.state) if self.viewers: await asyncio.wait([client.send(self.game.state) for client in self.viewers]) await self.current_player.ws.send(json.dumps({"score": self.game.score})) logger.info("Disconnecting <{}>".format(self.current_player.name)) except websockets.exceptions.ConnectionClosed as c: self.current_player = None finally: if self.grading: game_rec['score'] = self.game.score r = requests.post(self.grading, json=game_rec) if self.current_player: await self.current_player.ws.close() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--bind", help="IP address to bind to", default="") parser.add_argument("--port", help="TCP port", type=int, default=8000) parser.add_argument("--ghosts", help="Number of ghosts", type=int, default=1) parser.add_argument("--level", help="difficulty level of ghosts", choices=['0','1','2','3'], default='1') parser.add_argument("--lives", help="Number of lives", type=int, default=3) parser.add_argument("--timeout", help="Timeout after this amount of steps", type=int, default=3000) parser.add_argument("--map", help="path to the map bmp", default="data/map1.bmp") parser.add_argument("--grading-server", help="url of grading server", default=None) args = parser.parse_args() g = Game_server(args.map, args.ghosts, int(args.level), args.lives, args.timeout, args.grading_server) game_loop_task = asyncio.ensure_future(g.mainloop()) websocket_server = websockets.serve(g.incomming_handler, args.bind, args.port) loop = asyncio.get_event_loop() loop.run_until_complete(asyncio.gather(websocket_server, game_loop_task)) loop.close()

true