Datasets:

xlangai

/

the-stack-vault

like 0

73dd760eb7eeee35ecd7836a780605903f466cd6

py

Python

astwro/exttools/__init__.py

majkelx/astwro

4a9bbe3e4757c4076ad7c0d90cf08e38dab4e794

2017-06-15T20:34:51.000Z

2020-04-15T14:21:43.000Z

astwro/exttools/__init__.py

majkelx/astwro

4a9bbe3e4757c4076ad7c0d90cf08e38dab4e794

2017-08-15T20:53:55.000Z

2020-10-05T23:40:34.000Z

astwro/exttools/__init__.py

majkelx/astwro

4a9bbe3e4757c4076ad7c0d90cf08e38dab4e794

2017-11-06T15:33:53.000Z

2020-10-02T21:06:05.000Z

# coding=utf-8 from .Runner import Runner

73ddbbe445acccbb58d676dedfdc5ce96db4c141

py

Python

setup.py

Wykleph/Swarm

c0b10c673a555213fdf46dd0648a2c729dd33494

2018-10-04T01:15:03.000Z

2018-11-03T01:51:28.000Z

setup.py

Wykleph/Swarm

c0b10c673a555213fdf46dd0648a2c729dd33494

2018-10-04T05:47:58.000Z

2018-10-04T15:48:49.000Z

setup.py

Wykleph/Swarm

c0b10c673a555213fdf46dd0648a2c729dd33494

2018-10-04T03:57:46.000Z

2018-10-04T03:57:46.000Z

import setuptools with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name="Swarm", version="0.0.3", author="py-am-i", author_email="duckpuncherirl@gmail.com", description="Swarm is a strategy rouge-like space simulator game written with `pygame/python3`.", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/Wykleph/Swarm", packages=setuptools.find_packages(), install_requires=[ 'pygame' ], classifiers=[ "Programming Language :: Python :: 3.5", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], )

73ddbcbad82a5d4cb55abfeca11847ac41c7e831

py

Python

tests/integration/test_submission.py

ohsu-comp-bio/gen3-etl-lite

c91093e6c7b269db5baca41619a93356a68c4e97

tests/integration/test_submission.py

ohsu-comp-bio/gen3-etl-lite

c91093e6c7b269db5baca41619a93356a68c4e97

2021-03-25T22:26:08.000Z

2021-03-25T22:26:08.000Z

tests/integration/test_submission.py

ohsu-comp-bio/gen3-etl-lite

c91093e6c7b269db5baca41619a93356a68c4e97

import uuid import json import pytest import time import requests SLEEP_TIME=15 try: from types import SimpleNamespace as SN except ImportError as error: class SN (object): def __init__ (self, **kwargs): self.__dict__.update(kwargs) def __repr__ (self): keys = sorted(self.__dict__) items = ("{}={!r}".format(k, self.__dict__[k]) for k in keys) return "{}({})".format(type(self).__name__, ", ".join(items)) def __eq__ (self, other): return self.__dict__ == other.__dict__ @pytest.fixture(scope="session") def program_name(): return 'smmart' @pytest.fixture(scope="session") def project_name(): return 'atac' @pytest.fixture(scope="session") def case_name(): return 'case-1.1' @pytest.fixture(scope="session") def sample_name(): return 'sample-1' @pytest.fixture(scope="session") def aliquot_name(): return 'aliquot-1' @pytest.fixture(scope="session") def submitted_methylation_name(): return 'submitted_methylation' sample_name = 'sample-1' aliquot_name = 'aliquot-1' submitted_methylation_name = 'submitted_methylation-1' def test_submission(submission_client): assert submission_client, 'should have a configured submission_client' def test_list_projects(submission_client): q = '{ project { id, type, code } }' graph = submission_client.query(q) assert graph, 'should have a response to graphQL query' assert graph['data'], 'should have a data node {}'.format(graph) assert graph['data']['project'], 'should have a project(s) node {}'.format(graph) projects = list(map(lambda x: SN(**x), graph['data']['project'])) assert len(projects), 'should have at least one project' assert projects[0].type == 'project', 'first element should be a project' print(projects) def create_program(submission_client, program_name): program = SN(name=program_name, dbgap_accession_number=program_name, type='program').__dict__ response = json.loads(submission_client.create_program(program)) assert 'id' in response, 'could not create program {}'.format(response['message']) return response def create_project(submission_client, program_name, project_name): project = SN(name=project_name, state="open", availability_type="Open", dbgap_accession_number=project_name, code=project_name, type='project').__dict__ response = json.loads(submission_client.create_project(program_name, project)) assert response['code']==200 , 'could not create project {}'.format(response['message']) return response def create_node(submission_client, program_name, project_code, node): response = json.loads(submission_client.submit_node(program_name, project_code, node)) assert response['code']==200 , 'could not create {} {}'.format(node['type'], response['message']) print('created {} {}'.format(response['entities'][0]['type'], response['entities'][0]['id'])) return response def create_experiment(submission_client, program_name, project_code, submitter_id): experiment = { '*projects': {'code': project_code}, '*submitter_id': submitter_id, 'type': 'experiment' } return create_node(submission_client, program_name, project_code, experiment) def create_case(submission_client, program_name, project_code, submitter_id): case = { '*experiments': {'submitter_id': project_code}, '*submitter_id': submitter_id, 'type': 'case' } return create_node(submission_client, program_name, project_code, case) def create_sample(submission_client, program_name, project_code, case_name, submitter_id): sample = { '*cases': {'submitter_id': case_name}, '*submitter_id': submitter_id, 'type': 'sample' } return create_node(submission_client, program_name, project_code, sample) def create_aliquot(submission_client, program_name, project_code, sample_name, submitter_id): aliquot = { '*samples': {'submitter_id': sample_name}, '*submitter_id': submitter_id, 'type': 'aliquot' } return create_node(submission_client, program_name, project_code, aliquot) def create_submitted_methylation(submission_client, program_name, project_code, aliquot_name, submitter_id): submitted_methylation = { "*data_category": 'Methylation Data', "*data_format": 'IDAT', "type": "submitted_methylation", "*submitter_id": submitter_id, "*data_type": 'Methylation Intensity Values', "*md5sum": '12345678901234567890123456789012', "*file_size": 1000, "aliquots": { "submitter_id": aliquot_name }, 'urls': 'foo', "*file_name": 'my-file-name', } return create_node(submission_client, program_name, project_code, submitted_methylation) def delete_all(submission_client, program_name, project_name): types = ['submitted_methylation', 'aliquot', 'sample', 'case', 'experiment'] for t in types: response = submission_client.export_node_all_type("smmart", "atac", t) if 'data' not in response: print('no data?', response) else: for n in response['data']: delete_response = json.loads(submission_client.delete_node(program_name, project_name, n['node_id'])) assert delete_response['code'] == 200, delete_response print('deleted {} {}'.format(t, n['node_id'])) submission_client.delete_project(program_name, project_name) def create_all(submission_client, program_name, project_name, case_name, sample_name, aliquot_name, submitted_methylation_name): program = create_program(submission_client, program_name) print('created program {}'.format(program_name)) project = create_project(submission_client, program_name, project_name) print('created project {}'.format(project_name)) experiment = create_experiment(submission_client, program_name, project_name, submitter_id=project_name) case = create_case(submission_client, program_name, project_name, submitter_id=case_name) sample = create_sample(submission_client, program_name, project_name, case_name, submitter_id=sample_name) aliquot = create_aliquot(submission_client, program_name, project_name, sample_name, submitter_id=aliquot_name) submitted_methylation = create_submitted_methylation(submission_client, program_name, project_name, aliquot_name, submitter_id=submitted_methylation_name) def test_delete_all(submission_client, program_name, project_name, elastic_host): delete_all(submission_client, program_name, project_name) print('waiting {} secs to check replication'.format(SLEEP_TIME)) time.sleep(SLEEP_TIME) url = '{}/submitted_methylation/_search'.format(elastic_host) response = requests.get(url) assert response.status_code in [200, 404] , '{} should return 200 or 404 status'.format(url) # assert response.json()['hits']['total'] == 0, 'should have 0 record {}'.format(url) url = '{}/aliquot/_search'.format(elastic_host) response = requests.get(url) assert response.status_code in [200, 404] , '{} should return 200 or 404 status'.format(url) # assert response.json()['hits']['total'] == 0, 'should have 0 record {}'.format(url) print('delete_all OK') def test_create_program_project(submission_client, program_name, project_name, case_name, sample_name, aliquot_name, submitted_methylation_name, elastic_host): create_all(submission_client, program_name, project_name, case_name, sample_name, aliquot_name, submitted_methylation_name) print('waiting {} secs to check replication'.format(SLEEP_TIME)) time.sleep(SLEEP_TIME) url = '{}/submitted_methylation/_search'.format(elastic_host) response = requests.get(url) assert response.status_code == 200, '{} should return 200 status'.format(url) assert response.json()['hits']['total'] == 1, 'should have 1 record {}'.format(url) url = '{}/aliquot/_search'.format(elastic_host) response = requests.get(url) assert response.status_code == 200, '{} should return 200 status'.format(url) assert response.json()['hits']['total'] == 1, 'should have 1 record {}'.format(url) print('replication OK')

73ddc3664c541f75e13cc636fe83f0a3c8181948

py

Python

dali/python/nvidia/dali/plugin/paddle.py

truthiswill/DALI

1c96cb62018138585b616888d4616646135cedad

2019-12-25T01:36:32.000Z

2019-12-25T01:36:32.000Z

dali/python/nvidia/dali/plugin/paddle.py

CZZLEGEND/DALI

efd1f39b32b893c320ad580e7e84557df8f73983

dali/python/nvidia/dali/plugin/paddle.py

CZZLEGEND/DALI

efd1f39b32b893c320ad580e7e84557df8f73983

# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import ctypes import logging import math import numpy as np from nvidia.dali import types from nvidia.dali.backend import TensorListCPU, TensorGPU, TensorListGPU from paddle import fluid dtype_map = { "=?": fluid.core.VarDesc.VarType.BOOL, "=e": fluid.core.VarDesc.VarType.FP16, "=f": fluid.core.VarDesc.VarType.FP32, "=d": fluid.core.VarDesc.VarType.FP64, "=B": fluid.core.VarDesc.VarType.UINT8, "=b": fluid.core.VarDesc.VarType.INT8, "=h": fluid.core.VarDesc.VarType.INT16, "=i": fluid.core.VarDesc.VarType.INT32, "=q": fluid.core.VarDesc.VarType.INT64, "=l": fluid.core.VarDesc.VarType.INT64 } def to_paddle_type(tensor): r""" Get paddle dtype for given tensor or tensor list Args: tensor: tensor or tensor list Returns: fluid.core.VarDesc.VarType """ if isinstance(tensor, (TensorListCPU, TensorListGPU)): tensor = tensor.at(0) dtype = tensor.dtype if callable(dtype): dtype = dtype() else: dtype = '=' + dtype.char return dtype_map[dtype] def feed_ndarray(dali_tensor, ptr): """ Copy contents of DALI tensor to Paddle's Tensor. Parameters ---------- `dali_tensor` : dali.backend.TensorCPU or dali.backend.TensorGPU Tensor from which to copy `ptr` : LoDTensor data pointer Destination of the copy """ c_type_pointer = ctypes.c_void_p(ptr) dali_tensor.copy_to_external(c_type_pointer) return ptr def recursive_length(tensor, lod_level): def _recurse(data, result, level): if level > 0: if isinstance(data, (TensorListCPU, TensorListGPU)): # handle tensor list length = len(data) result[0].append(length) for i in range(length): _recurse(data.at(i), result[1:], level - 1) elif hasattr(data, 'shape'): # handle dense GPU tensors and numpy.ndarray shape = data.shape if callable(shape): shape = shape() length = shape[0] result[0].append(length) for i in range(length): _recurse(shape[1:], result[1:], level - 1) else: # handle shape length = data[0] result[0].append(length) for i in range(length): _recurse(data[1:], result[1:], level - 1) seq_len = [[] for _ in range(lod_level)] _recurse(tensor, seq_len, lod_level) return seq_len def lod_tensor_clip(lod_tensor, size): output = fluid.core.LoDTensor() ndarray = np.array(lod_tensor) seq_len = lod_tensor.recursive_sequence_lengths() if not seq_len: output.set(ndarray[0:size], fluid.CPUPlace()) else: last_len = size out_seq_len = [] for lengths in seq_len: lengths = lengths[0:last_len] out_seq_len.append(lengths) last_len = sum(lengths) output.set(ndarray[0:sum(out_seq_len[-1])], fluid.CPUPlace()) output.set_recursive_sequence_lengths(out_seq_len) return output class DALIGenericIterator(object): """ General DALI iterator for Paddle. It can return any number of outputs from the DALI pipeline in the form of Paddle's Tensors. Please keep in mind that Tensors returned by the iterator are still owned by DALI. They are valid till the next iterator call. If the content needs to be preserved please copy it to another tensor. Parameters ---------- pipelines : list of nvidia.dali.pipeline.Pipeline List of pipelines to use output_map : list of str or pair of type (str, int) The strings maps consecutive outputs of DALI pipelines to user specified name. Outputs will be returned from iterator as dictionary of those names. Each name should be distinct. Item can also be a pair of (str, int), where the int value specifies the LoD level of the resulting LoDTensor. size : int Number of samples in the epoch (Usually the size of the dataset). auto_reset : bool, optional, default = False Whether the iterator resets itself for the next epoch or it requires reset() to be called separately. fill_last_batch : bool, optional, default = True Whether to return a fraction of a full batch of data such that the total entries returned by the iterator == 'size'. Setting this flag to False will cause the iterator to return the first integer multiple of self._num_gpus * self.batch_size which exceeds 'size'. dynamic_shape: bool, optional, default = False Whether the shape of the output of the DALI pipeline can change during execution. If True, the LoDTensor will be resized accordingly if the shape of DALI returned tensors changes during execution. If False, the iterator will fail in case of change. last_batch_padded : bool, optional, default = False Whether the last batch provided by DALI is padded with the last sample or it just wraps up. In the conjunction with `fill_last_batch` it tells if the iterator returning last batch with data only partially filled with data from the current epoch is dropping padding samples or samples from the next epoch. If set to False next epoch will end sooner as data from it was consumed but dropped. If set to True next epoch would be the same length as the first one. For this happen, the option `pad_last_batch` in the reader need to be set to `True` as well. Example ------- With the data set [1,2,3,4,5,6,7] and the batch size 2: fill_last_batch = False, last_batch_padded = True -> last batch = [7], next iteration will return [1, 2] fill_last_batch = False, last_batch_padded = False -> last batch = [7], next iteration will return [2, 3] fill_last_batch = True, last_batch_padded = True -> last batch = [7, 7], next iteration will return [1, 2] fill_last_batch = True, last_batch_padded = False -> last batch = [7, 1], next iteration will return [2, 3] """ def __init__(self, pipelines, output_map, size, auto_reset=False, fill_last_batch=True, dynamic_shape=False, last_batch_padded=False): if not isinstance(pipelines, list): pipelines = [pipelines] self._num_gpus = len(pipelines) assert pipelines is not None, \ "Number of provided pipelines has to be at least 1" self.batch_size = pipelines[0].batch_size self._size = int(size) self._auto_reset = auto_reset self._dynamic_shape = dynamic_shape self._fill_last_batch = fill_last_batch self._last_batch_padded = last_batch_padded self._pipes = pipelines # Build all pipelines for p in self._pipes: with p._check_api_type_scope(types.PipelineAPIType.ITERATOR): p.build() # Use double-buffering of data batches self._data_batches = [None for i in range(self._num_gpus)] self._counter = 0 normalized_map = {} for v in output_map: if isinstance(v, str): normalized_map[v] = 0 else: normalized_map[v[0]] = v[1] self.normalized_map = normalized_map output_map = [isinstance(v, str) and v or v[0] for v in output_map] assert len(set(output_map)) == len(output_map), \ "output_map names should be distinct" self.output_map = output_map # We need data about the batches (like shape information), # so we need to run a single batch as part of setup to get that info for p in self._pipes: with p._check_api_type_scope(types.PipelineAPIType.ITERATOR): p.schedule_run() self._first_batch = None self._first_batch = self.next() def __next__(self): if self._first_batch is not None: batch = self._first_batch self._first_batch = None return batch if self._counter >= self._size: if self._auto_reset: self.reset() raise StopIteration # Gather outputs outputs = [] for p in self._pipes: with p._check_api_type_scope(types.PipelineAPIType.ITERATOR): outputs.append(p.share_outputs()) for i in range(self._num_gpus): dev_id = self._pipes[i].device_id # Initialize dict for all output categories category_outputs = dict() # Segregate outputs into categories for j, out in enumerate(outputs[i]): category_outputs[self.output_map[j]] = out pd_gpu_place = fluid.CUDAPlace(dev_id) pd_cpu_place = fluid.CPUPlace() category_pd_type = dict() category_place = dict() category_tensors = dict() category_shapes = dict() category_lengths = dict() for cat, out in category_outputs.items(): lod = self.normalized_map[cat] assert out.is_dense_tensor() or lod > 0, \ "non-dense tensor lists must have LoD > 0" if lod > 0: # +1 for batch dim seq_len = recursive_length(out, lod + 1)[1:] shape = out.at(0).shape if callable(shape): shape = shape() shape = [sum(seq_len[-1])] + list(shape[lod:]) category_shapes[cat] = shape category_lengths[cat] = seq_len else: out = out.as_tensor() category_shapes[cat] = out.shape() category_lengths[cat] = [] category_tensors[cat] = out category_pd_type[cat] = to_paddle_type(out) if isinstance(out, (TensorGPU, TensorListGPU)): category_place[cat] = pd_gpu_place else: category_place[cat] = pd_cpu_place if self._data_batches[i] is None: pd_tensors = {} for cat, lod in self.normalized_map.items(): lod_tensor = fluid.core.LoDTensor() lod_tensor._set_dims(category_shapes[cat]) pd_tensors[cat] = lod_tensor self._data_batches[i] = pd_tensors else: pd_tensors = self._data_batches[i] # Copy data from DALI Tensors to LoDTensors for cat, tensor in category_tensors.items(): if hasattr(tensor, 'shape'): # could be tensor list assert self._dynamic_shape or \ tensor.shape() == pd_tensors[cat].shape(), \ ("Shapes do not match: DALI tensor has size {0}, " "but LoDTensor has size {1}".format( tensor.shape(), pd_tensors[cat].shape())) lod_tensor = pd_tensors[cat] lod_tensor._set_dims(category_shapes[cat]) seq_len = category_lengths[cat] lod_tensor.set_recursive_sequence_lengths(seq_len) ptr = lod_tensor._mutable_data(category_place[cat], category_pd_type[cat]) feed_ndarray(tensor, ptr) for p in self._pipes: with p._check_api_type_scope(types.PipelineAPIType.ITERATOR): p.release_outputs() p.schedule_run() self._counter += self._num_gpus * self.batch_size if (not self._fill_last_batch) and (self._counter > self._size): # First calculate how much data is required to # return exactly self._size entries. diff = self._num_gpus * self.batch_size - (self._counter - self._size) # Figure out how many GPUs to grab from. num_gpus_to_grab = int(math.ceil(diff / self.batch_size)) # Figure out how many results to grab from the last GPU # (as a fractional GPU batch may be required to bring us # right up to self._size). mod_diff = diff % self.batch_size data_from_last_gpu = mod_diff if mod_diff else self.batch_size # Grab the relevant data. # 1) Grab everything from the relevant GPUs. # 2) Grab the right data from the last GPU. # 3) Append data together correctly and return. output = self._data_batches[0:num_gpus_to_grab] output[-1] = output[-1].copy() for cat in self.output_map: lod_tensor = output[-1][cat] output[-1][cat] = lod_tensor_clip(lod_tensor, data_from_last_gpu) return output return self._data_batches def next(self): """ Returns the next batch of data. """ return self.__next__() def __iter__(self): return self def reset(self): """ Resets the iterator after the full epoch. DALI iterators do not support resetting before the end of the epoch and will ignore such request. """ if self._counter >= self._size: if self._fill_last_batch and not self._last_batch_padded: self._counter = self._counter % self._size else: self._counter = 0 for p in self._pipes: p.reset() if p.empty(): with p._check_api_type_scope(types.PipelineAPIType.ITERATOR): p.schedule_run() else: logging.warning("DALI iterator does not support resetting while epoch is not finished. Ignoring...") class DALIClassificationIterator(DALIGenericIterator): """ DALI iterator for classification tasks for Paddle. It returns 2 outputs (data and label) in the form of LoDTensor. Calling .. code-block:: python DALIClassificationIterator(pipelines, size) is equivalent to calling .. code-block:: python DALIGenericIterator(pipelines, ["data", "label"], size) Please keep in mind that Tensors returned by the iterator are still owned by DALI. They are valid till the next iterator call. If the content needs to be preserved please copy it to another tensor. Parameters ---------- pipelines : list of nvidia.dali.pipeline.Pipeline List of pipelines to use size : int Number of samples in the epoch (Usually the size of the dataset). auto_reset : bool, optional, default = False Whether the iterator resets itself for the next epoch or it requires reset() to be called separately. fill_last_batch : bool, optional, default = True Whether to return a fraction of a full batch of data such that the total entries returned by the iterator == 'size'. Setting this flag to False will cause the iterator to return the first integer multiple dynamic_shape: bool, optional, default = False Whether the shape of the output of the DALI pipeline can change during execution. If True, the LoDtensor will be resized accordingly if the shape of DALI returned tensors changes during execution. If False, the iterator will fail in case of change. last_batch_padded : bool, optional, default = False Whether the last batch provided by DALI is padded with the last sample or it just wraps up. In the conjunction with `fill_last_batch` it tells if the iterator returning last batch with data only partially filled with data from the current epoch is dropping padding samples or samples from the next epoch. If set to False next epoch will end sooner as data from it was consumed but dropped. If set to True next epoch would be the same length as the first one. Example ------- With the data set [1,2,3,4,5,6,7] and the batch size 2: fill_last_batch = False, last_batch_padded = True -> last batch = [7], next iteration will return [1, 2] fill_last_batch = False, last_batch_padded = False -> last batch = [7], next iteration will return [2, 3] fill_last_batch = True, last_batch_padded = True -> last batch = [7, 7], next iteration will return [1, 2] fill_last_batch = True, last_batch_padded = False -> last batch = [7, 1], next iteration will return [2, 3] """ def __init__(self, pipelines, size, auto_reset=False, fill_last_batch=True, dynamic_shape=False, last_batch_padded=False): super(DALIClassificationIterator, self).__init__( pipelines, ["data", "label"], size, auto_reset=auto_reset, fill_last_batch=fill_last_batch, dynamic_shape=dynamic_shape, last_batch_padded=last_batch_padded)

73ddf5ea7fc34a59fb06937bd5e05dc5452b67d7

py

Python

core/apps/home/urls.py

yavuzbektas/StudentAnalayze

794f433f650633f646bee5cbe08d04afdbd75e84

core/apps/home/urls.py

yavuzbektas/StudentAnalayze

794f433f650633f646bee5cbe08d04afdbd75e84

core/apps/home/urls.py

yavuzbektas/StudentAnalayze

794f433f650633f646bee5cbe08d04afdbd75e84

# -*- encoding: utf-8 -*- """ Copyright (c) 2019 - present AppSeed.us """ from django.urls import path, re_path from apps.home import views def repath_func(request): from django.http import HttpResponseRedirect return HttpResponseRedirect(request.path_info) urlpatterns = [ # The home page path('', views.index, name='home'), # Matches any html file path('profil.html', views.profilUpdate, name='users-edit'), path('view/<int:pk>', views.profilView, name='users-view'), path('usr-ogretmenler.html', views.profilShowList,name='kullanicilar'), path('delete/<int:pk>',views.profilDelete, name='delete'), path('yetki/<int:pk>',views.issuperUser, name='yetki'), #re_path(r'^.*\.*', views.pages, name='pages'), ]

73de01103f3993ddf915e121cfc1b5e897750f3d

py

Python

Implement_google_AdaptiveFL/ServerAndClient/FlowerEC10/server.py

kuihao/KuihaoFL

69c9161497f2a82ab8ef2d785a0c7e2e6975a328

2021-12-13T06:03:35.000Z

2022-03-22T05:12:41.000Z

Implement_google_AdaptiveFL/ServerAndClient/FlowerEC10/server.py

kuihao/KuihaoFL

69c9161497f2a82ab8ef2d785a0c7e2e6975a328

Implement_google_AdaptiveFL/ServerAndClient/FlowerEC10/server.py

kuihao/KuihaoFL

69c9161497f2a82ab8ef2d785a0c7e2e6975a328

from enum import Flag import os os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" from typing import Any, Callable, Dict, List, Optional, Tuple import numpy as np import flwr as fl from flwr.server.strategy import FedAvg, FedYogi, FedAdam, FedAdagrad from mypkg import ( ServerArg, ModelNameGenerator, secure_mkdir, MyFedAdagrad, MyFedYogi, MyFedAdam ) # -------- # [Welcome prompt] Make model name # -------- args = ServerArg() model_name = ModelNameGenerator(args.name) print(f"*** This model name: {model_name} ***\n") # -------- # [Hardware setting] CPU only or limit the GPU usage # -------- if args.cpu: os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = "" else: #from mypkg.TF import setGPU #setGPU(mode=1) if args.gpu is not None: os.environ["CUDA_VISIBLE_DEVICES"]= str(args.gpu) from mypkg.TF import setGPU setGPU(mode=3, device_num=args.gpu) else: from mypkg.TF import setGPU setGPU(mode=1) # Dataset size 會影響 GPU memory 需求 import tensorflow as tf from mypkg.TF import CNN_Model, myResNet # -------- # [Hyperparemeter] # -------- SEED = 2021 '''fix random seed''' np.random.seed(SEED) tf.random.set_seed(SEED) model_input_shape = (32,32,3) model_class_number = 100 # This is LABEL SAVE = True '''(bool) save log or not''' HyperSet_Model = myResNet().ResNet18(model_input_shape,model_class_number) #CNN_Model(model_input_shape,model_class_number) #myResNet().ResNet18(model_input_shape,model_class_number) HyperSet_Aggregation = FedAvg #MyFedAdagrad HyperSet_Agg_eta = 1e-3 #1e-3 HyperSet_Agg_tau = 1e-2 HyperSet_client_number = 10 HyperSet_round = 10 # -------- # [Global varables] # -------- Training_result_distributed = {'loss':[],'accuracy':[],'top_k_categorical_accuracy':[]} '''Clients Training 的聚合結果''' Testing_result_distributed = {'loss':[],'accuracy':[],'top_k_categorical_accuracy':[]} '''[未使用] Clients Testing 的聚合結果''' Training_result_centralized = {'loss':[],'accuracy':[],'top_k_categorical_accuracy':[]} '''[未使用] Server Training 的結果''' Testing_result_centralized = {'loss':[],'accuracy':[],'top_k_categorical_accuracy':[]} '''Server Testing 的結果''' # -------- # [Main] # -------- def main() -> None: """Create Global model, set FL strategy, start Server, save FL results""" # Step 1. Build Global Model (建立全域模型) model = HyperSet_Model optimizer = tf.keras.optimizers.SGD(momentum=0.9) model.compile(optimizer, tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), # `from_logits=True` means use softmax as activaction function metrics=["accuracy", 'top_k_categorical_accuracy']) # Step 2. Make the strategy (制定聯合學習策略) strategy = MyAggregation( fraction_fit=1.0, # 每一輪參與Training的Client比例 #fraction_eval=1.0, # 每一輪參與Evaluating的Client比例 min_fit_clients=HyperSet_client_number, # 每一輪參與Training的最少Client連線數量 (與比例衝突時,以此為準) #min_eval_clients=3, # 每一輪參與Evaluating的最少Client連線數量 (與比例衝突時,以此為準) min_available_clients=HyperSet_client_number, # 啟動聯合學習之前，Client連線的最小數量 on_fit_config_fn=fit_config, # 設定 Client-side Training Hyperparameter on_evaluate_config_fn=None, #evaluate_config, # 設定 Client-side Evaluating Hyperparameter eval_fn=get_eval_fn(model), # 設定 Server-side Evaluating Hyperparameter (用Global Dataset進行評估) initial_parameters=fl.common.weights_to_parameters(model.get_weights()), # Global Model 初始參數設定 ) # Step 3. Run the server with the strategy #fl.server.start_server("localhost:8080", config={"num_rounds": 3}, strategy=strategy) #windows fl.server.start_server("[::]:8080", config={"num_rounds": HyperSet_round}, strategy=strategy) #linux # [Kuihao addition] Save FL results to numpy-zip global Training_result_distributed, Testing_result_centralized if SAVE: FL_Results_folder = secure_mkdir("FL_Results"+"/"+model_name) np.savez(f"{FL_Results_folder}/Training_result_distributed.npz", Training_result_distributed) np.savez(f"{FL_Results_folder}/Testing_result_centralized.npz", Testing_result_centralized) # -------- # [Customized Aggregation Strategy] # -------- class MyAggregation(HyperSet_Aggregation): ''' (1) 制定聚合演算法 (FedAvg MyFedAdagrad MyFedAdam MyFedYogi)\n (2) 保存Clients的Training結果\n (3) 儲存Global model weights ''' def aggregate_fit( self, rnd: int, results: List[Tuple[fl.server.client_proxy.ClientProxy, fl.common.FitRes]], failures: List[BaseException], ) -> Optional[fl.common.Weights]: '''Override''' # Call Parent-class's aggregate_fit() aggregated_weights = super().aggregate_fit(rnd=rnd, #K_defining_eta=HyperSet_Agg_eta, # default: 1e-1 #K_defining_tau=HyperSet_Agg_tau, # default: 1e-9 results=results, failures=failures) # Aggregate clients' training results (loss, acc., top-k-acc.) examples = [r.num_examples for _, r in results] accuracies = [r.metrics["accuracy"] * r.num_examples for _, r in results] accuracy_aggregated = sum(accuracies) / sum(examples) losses = [r.metrics["loss"] * r.num_examples for _, r in results] loss_aggregated = sum(losses) / sum(examples) topK_accuracies = [r.metrics["top_k_categorical_accuracy"] * r.num_examples for _, r in results] topK_accuracies_aggregated = sum(topK_accuracies) / sum(examples) # [Kuihao addition] 暫存Client-side訓練結果 global Training_result_distributed Training_result_distributed["loss"].append(loss_aggregated) Training_result_distributed["accuracy"].append(accuracy_aggregated) Training_result_distributed["top_k_categorical_accuracy"].append(topK_accuracies_aggregated) print(f"\n****\nRound {rnd}, train results from clients after aggregation:\n"\ f"Loss:{loss_aggregated} Acc.:{accuracy_aggregated} TopK Acc.:{topK_accuracies_aggregated}"\ f"\n****") # [Kuihao addition] 保存每一輪 Global Model Weights if SAVE and rnd==HyperSet_round: checkpoint_folder = secure_mkdir("ckpoint"+"/"+model_name) if aggregated_weights is not None: # Save aggregated_weights print(f"****Saving round {rnd} aggregated_weights...****") np.savez(f"{checkpoint_folder}/round-{rnd}-weights.npz", *aggregated_weights) return aggregated_weights # -------- # [Server-side function] config and evaluate # -------- def fit_config(rnd: int): """ [Client-side, training hyperparameter] * 設定Client Training 的 Hyperparameter: 包含batch_size、epochs、 learning-rate...皆可設定。 * 甚至可以設定不同 FL round 給予 client 不同的 Hyperparameter * Return training configuration dict for each round. Keep batch size fixed at 128, perform two rounds of training with one local epoch, increase to two local epochs afterwards. """ config = { "batch_size": 16, "local_epochs": 1, "rnd":rnd, } return config def evaluate_config(rnd: int): """ [Client-side, evaluating hyperparameter] * 設定Client Testing 的 Hyperparameter: 包含epochs、steps(Total number of steps, 也就是 batche個數 (batches of samples))。 * 可以設定不同 FL round 給予 client 不同的 Hyperparameter * Return evaluation configuration dict for each round. Perform five local evaluation steps on each client (i.e., use five batches) during rounds one to three, then increase to ten local evaluation steps. """ val_steps = 1 # not use return {"val_steps": val_steps} def get_eval_fn(model): ''' [Server-side, evaluating hyperparameter] * Return an evaluation function for server-side evaluation. * 用 Global Dataset 評估 Global model (不含訓練) ''' # Load data and model here to avoid the overhead of doing it in `evaluate` itself #_ ,(x_test,y_test) = tf.keras.datasets.cifar10.load_data() tfds_test_server = tf.data.experimental.load(f'dataset/cifar100_test_server/content/zip/cifar100_client/test_server/federated_test_data_all') # Data preprocessing #x_test = x_test / 255.0 #y_test = tf.squeeze(y_test,axis=1) # cifar10 version # The `evaluate` function will be called after every round def evaluate( weights: fl.common.Weights, ) -> Optional[Tuple[float, Dict[str, fl.common.Scalar]]]: model.set_weights(weights) # Update model with the latest parameters loss, accuracy,top_k_categorical_accuracy = model.evaluate(tfds_test_server) #model.evaluate(x_test, y_test) # [Kuihao addition] 暫存Server-side評估結果 global Testing_result_centralized Testing_result_centralized["loss"].append(loss) Testing_result_centralized["accuracy"].append(accuracy) Testing_result_centralized["top_k_categorical_accuracy"].append(top_k_categorical_accuracy) return loss, {"accuracy": accuracy, 'top_k_categorical_accuracy':top_k_categorical_accuracy} return evaluate # -------- # [Main Exe.] # -------- if __name__ == "__main__": main()

73de1a01211701cb3610f9942677e6795fc4c8ed

py

Python

core/argo/core/optimizers/ExtendedNesterovNonconst.py

szokejokepu/natural-rws

bb1ad4ca3ec714e6bf071d2136593dc853492b68

2020-12-07T19:13:13.000Z

2022-01-30T18:52:18.000Z

core/argo/core/optimizers/ExtendedNesterovNonconst.py

szokejokepu/natural-rws

bb1ad4ca3ec714e6bf071d2136593dc853492b68

2020-09-25T22:41:28.000Z

2022-02-09T23:46:34.000Z

core/argo/core/optimizers/ExtendedNesterovNonconst.py

szokejokepu/natural-rws

bb1ad4ca3ec714e6bf071d2136593dc853492b68

2021-03-02T18:31:04.000Z

2021-03-02T21:56:43.000Z

''' DOCUMENTATION: Nesterov method with constant momentum factor is given in the work of Defazio: https://arxiv.org/abs/1812.04634 See Table 1, page 3 - 'Modern Momentum' (here: beta is the momentum factor) The Extended Nesterov method (without prox argument), is given in the following articles: https://arxiv.org/pdf/1908.02574.pdf Here we have 2 non-constant momentum-type sequences, 'alpha_n' and 'beta_n' that depend on 3 coefficients: 'alpha > 0', 'beta in R' and 'gamma >= 0'. We impose, as in the continuous version, the following conditions: 'alpha > 3', 'beta in R' and 'gamma > 0'. Some notes on default values and preliminary computational simulations: 1) I have put the default value of 'alpha' is 4, since 'alpha > 3' 2) For 'beta = 3' or similar high values, it does not work. I have put the default value to 'beta = 0.1' 3) A moderate ~ small value of 'gamma = 0.5' seems goo enough ''' # Loading modules from tensorflow.python.training import optimizer # Here we have the 'Optimizer' class from tensorflow.python.framework import ops # From here we need the function that converts to 'Tensor' object from tensorflow.python.ops import math_ops # From here we need mathematical operations for 'Tensor' objects from tensorflow.python.ops import state_ops # From here we need 'Operations' on 'Tensor' objects from tensorflow.python.ops import control_flow_ops # From here we need the function 'group' # The subclass of Optimizer class, containing Nesterov method with constant momentum coefficients, namely 'alpha' and 'beta' class ExtendedNesterovNonconst(optimizer.Optimizer): # The constructor of the class def __init__(self, model, learning_rate = 1e-2, alpha = 4, beta = 0.1, gamma = 0.5, use_locking = False, name = 'ExtendedNesterovNonconst'): # Call the constructor of the 'Optimizer' superclass using the parameters 'use_locking' and 'name' super(ExtendedNesterovNonconst, self).__init__(use_locking, name) # Initialize the private Python variables of the current subclass self._lr = learning_rate self._alpha = alpha self._beta = beta self._gamma = gamma self._model = model # Initialize the private 'Tensor' objects of the current subclass self._lr_t = None self._alpha_t = None self._beta_t = None self._gamma_t = None # We construct all the 'Tensor' objects before we apply the gradients # Except the learning rate, all the coefficients are part of the momentum-type terms # Private function def _prepare(self): self._lr_t = ops.convert_to_tensor(self._lr, name = 'learning_rate') self._alpha_t = ops.convert_to_tensor(self._alpha, name = 'alpha') self._beta_t = ops.convert_to_tensor(self._beta, name = 'beta') self._gamma_t = ops.convert_to_tensor(self._gamma, name = 'gamma') # We create the slots for the variables. A 'Slot' is an additional variable associated with the variables to train # We allocate and manage these auxiliary variables # Private function def _create_slots(self, var_list): for v in var_list: # The accumulator variable is 'p^{k+1}' in the work of Defazio self._zeros_slot(v, "old_accum", self._name) self._zeros_slot(v, "accum", self._name) self._zeros_slot(v, "curr_it", self._name) # The actual Extended Nesterov implementation for the general case when we have dense 'Tensor' objects # All of the operations are applied to 'Tensor' variables # 'apply_gradients', 'compute_gradients' and 'minimize' are public functions of 'Optimizer' class # Order of functions: # minimize(loss, global_step, var_list) # => grads_and_vars = compute_gradients(loss, var_list) # => grads_and_vars = list(zip(grads, var_list)) # => grads = gradients.gradients(loss, var_refs) # var_list = (variables.trainable_variables() + ops.get_collection(ops.GraphKeys.TRAINABLE_RESOURCE_VARIABLES)) # apply_gradients(grads_and_vars, global_step) # => for g, v in grads_and vars: p = _get_processor(v) # => _TensorProcessor(v), _DenseResourceVariableProcessor(v), _DenseResourceVariableProcessor(v), _RefVariableProcessor(v) # => for grad, var, processor in converted_grads_and_vars: processor.update_op(grad) # => update_op(self, optimizer, g) # => return update_op = optimizer._apply_dense(g, self._v) def _apply_dense(self, grad, var): # 1st step: we convert our 'Tensor' objects to have the type of the training variables lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) alpha_t = math_ops.cast(self._alpha_t, var.dtype.base_dtype) beta_t = math_ops.cast(self._beta_t, var.dtype.base_dtype) gamma_t = math_ops.cast(self._gamma_t, var.dtype.base_dtype) # 2nd step: we define the gradient accumulations, using the identifier 'accum' from '_create_slots()' # We also memorize the old accumulator, since we will update the 'accum' variable. Here, 'old_accum' is 'p^{k+1}' old_accum = self.get_slot(var, "old_accum") accum = self.get_slot(var, "accum") # 3rd step: define the current iteration needed for the momentum inertial sequence # It must be converted to the same type as the trainable variables # We have here the inertial sequences 'alpha_n' and 'beta_n' curr_it = self.get_slot(var, "curr_it") n = curr_it + 1 alpha_iteration = n / (n + alpha_t) beta_iteration = (n * gamma_t + beta_t) / (n + alpha_t) beta_iteration_plus_1 = ((n+1) * gamma_t + beta_t) / (n + alpha_t + 1) # 4th step: we have the Extended Nesterov formula 'accum_t <- accum_t * alpha_t + grad', i.e. 'p^{k+1}' from Defazio # We update 'accum' by assigning the value 'momentum_t * accum + grad' to it. Furthermore, the new value is return in the 'Tensor' object 'accum_t' old_accum_t = state_ops.assign(old_accum, accum) with ops.control_dependencies([old_accum_t]): accum_t = state_ops.assign(accum, alpha_iteration * accum + grad, use_locking = False) # 5th step: variables updates by using 'var_update <- var - ( lr_t * grad + lr_t * beta_t * accum_t + (alpha_t-beta_t) * old_accum )', i.e. 'x^{k+1}' from Defazio # Here, 'accum_t' is 'p^{k+1}' because was already updated before with ops.control_dependencies([old_accum, accum_t]): var_update = state_ops.assign_sub(var, lr_t * grad + lr_t * beta_iteration_plus_1 * accum_t + (alpha_iteration - beta_iteration) * old_accum_t) # 6th step: return the updates, i.e. we return the Graph 'Operation' that will group multiple 'Tensor' ops. # For more complex algorithms, the 'control_flow_ops.group' is used in the '_finish()' function, after '_apply_dense()' return control_flow_ops.group(*[var_update, old_accum_t, accum_t, n]) # I did not implemented the algorithm for the case of 'Sparse Tensor' variables def _apply_sparse(self, grad, var): raise NotImplementedError("Sparse gradient updates are not supported.")

73de1bffb46de3df866150585652ad860bb192ae

py

Python

bcherry-web/playground/main.py

bcherry/bcherry

5d2f1144dbdbf35d6284018fa2c9e24ec5cecec6

2016-11-13T09:06:41.000Z

2021-09-11T23:36:19.000Z

bcherry-web/playground/main.py

bcherry/bcherry

5d2f1144dbdbf35d6284018fa2c9e24ec5cecec6

bcherry-web/playground/main.py

bcherry/bcherry

5d2f1144dbdbf35d6284018fa2c9e24ec5cecec6

2017-04-04T10:03:18.000Z

2021-09-11T23:36:26.000Z

#!/usr/bin/env python import os import wsgiref.handlers from google.appengine.ext import webapp from google.appengine.ext.webapp import template import time class MainHandler(webapp.RequestHandler): def get(self): path = os.path.join(os.path.dirname(__file__), 'index.html') self.response.out.write(template.render(path, {})) class SetTimeoutHandler(webapp.RequestHandler): def get(self): path = os.path.join(os.path.dirname(__file__), 'settimeout.html') self.response.out.write(template.render(path, {})) class ComparingComparisonsHandler(webapp.RequestHandler): def get(self): path = os.path.join(os.path.dirname(__file__), 'comparisonperformance.html') self.response.out.write(template.render(path, {})) class DefaultValuesHandler(webapp.RequestHandler): def get(self): path = os.path.join(os.path.dirname(__file__), 'defaultvalues.html') self.response.out.write(template.render(path, {})) class ChainingHandler(webapp.RequestHandler): def get(self): path = os.path.join(os.path.dirname(__file__), 'chaining.html') self.response.out.write(template.render(path, {})) class SpyConstructorsHandler(webapp.RequestHandler): def get(self): path = os.path.join(os.path.dirname(__file__), 'spying-constructors.html') self.response.out.write(template.render(path, {})) class KeyboardShortcutsHandler(webapp.RequestHandler): def get(self): path = os.path.join(os.path.dirname(__file__), 'keyboardshortcuts.html') self.response.out.write(template.render(path, {})) class SaneHTML5HistoryHandler(webapp.RequestHandler): def get(self, *args): path = os.path.join(os.path.dirname(__file__), 'html5history.html') self.response.out.write(template.render(path, {})) class PushStateImgHandler(webapp.RequestHandler): def get(self): time.sleep(1) self.response.headers["Content-Type"] = "image/jpeg" self.response.out.write('') class AnyHandler(webapp.RequestHandler): def get(self, path): file_path = os.path.join(os.path.dirname(__file__), path + '.html') self.response.out.write(open(file_path).read()) def main(): application = webapp.WSGIApplication([ ('/playground', MainHandler), ('/playground/settimeout', SetTimeoutHandler), ('/playground/comparisons', ComparingComparisonsHandler), ('/playground/defaultvalues', DefaultValuesHandler), ('/playground/spying-constructors', SpyConstructorsHandler), ('/playground/keyboard-shortcuts', KeyboardShortcutsHandler), ('/playground/pushstate.jpg', PushStateImgHandler), ('/playground/sanerhtml5history(/(.+)?)?', SaneHTML5HistoryHandler), ('/playground/(.*)', AnyHandler), ], debug=True) wsgiref.handlers.CGIHandler().run(application) if __name__ == '__main__': main()

73de2f8ecea34a0ee1bced8502ba1bd0cdcc0760

py

Python

python/sdk/setup.py

Omrisnyk/merlin

cc2dbeabe52ac6e413db7f7647ed54c7edb7695f

2020-10-15T08:03:56.000Z

2022-03-31T22:30:59.000Z

python/sdk/setup.py

babywyrm/merlin

29f669ab613d6808d0186067b948496b508caa96

2020-10-26T03:15:27.000Z

2022-03-31T10:19:55.000Z

python/sdk/setup.py

babywyrm/merlin

29f669ab613d6808d0186067b948496b508caa96

2020-10-21T03:53:36.000Z

2022-03-16T06:43:15.000Z

#!/usr/bin/env python # Copyright 2020 The Merlin Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import imp import os from setuptools import setup, find_packages version = imp.load_source( 'merlin.version', os.path.join('merlin', 'version.py')).VERSION REQUIRES = [ "certifi>=2017.4.17", "python-dateutil>=2.1", "six>=1.10", "mlflow>=1.2.0", "google-cloud-storage <=1.28.1", "boto3>=1.9.84", "urllib3>=1.23", "PyPrind>=2.11.2", "google-auth>=1.11.0,<2.0dev", 'Click>=7.0', "cloudpickle==1.2.2", "cookiecutter>=1.7.2", "docker>=4.2.1", "google-api-core<=1.17.0", # https://github.com/googleapis/python-pubsub/issues/115 "grpcio<=1.27.2", "google-cloud-core==1.3.0", "PyYAML>=5.3.1" ] TEST_REQUIRES = [ "pytest", "pytest-dependency", "pytest-cov", "pytest-xdist", "urllib3-mock>=0.3.3", "requests", "xgboost==0.82", "scikit-learn==0.20.3", "joblib>=0.13.0", "mypy==0.812", "google-cloud-bigquery==1.22.0", "google-cloud-bigquery-storage==0.7.0", "grpcio==1.22.0" ] setup( name="merlin-sdk", version=version, description="Python SDK for Merlin", url="https://github.com/gojek/merlin", author="Merlin", packages=find_packages(), package_data={"merlin": [ "docker/pyfunc.Dockerfile", "docker/standard.Dockerfile"]}, zip_safe=True, install_requires=REQUIRES, setup_requires=["setuptools_scm"], tests_require=TEST_REQUIRES, extras_require={'test': TEST_REQUIRES}, python_requires='>=3.7', entry_points=''' [console_scripts] merlin=merlin.merlin:cli ''' )

73de4d0bb16a37bc57add7f17f9a5babc091adfa

py

Python

scripts/converter_v3.py

ATLAS-Titan/allocation-modeling

b315aa7ac0cf613ed02c59188ff19e9738f36aca

scripts/converter_v3.py

ATLAS-Titan/allocation-modeling

b315aa7ac0cf613ed02c59188ff19e9738f36aca

scripts/converter_v3.py

ATLAS-Titan/allocation-modeling

b315aa7ac0cf613ed02c59188ff19e9738f36aca

# # Copyright European Organization for Nuclear Research (CERN) # National Research Centre "Kurchatov Institute" # Rutgers University # # 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 # # Author(s): # - Mikhail Titov, <mikhail.titov@cern.ch>, 2018 # IS_ANALYSIS_FORMAT = False if IS_ANALYSIS_FORMAT: OUTPUT_FORMAT = 'analysis' else: OUTPUT_FORMAT = 'stream' INPUT_FILE_NAMES = [] OUTPUT_FILE_NAME = 'titan-logs-v3-{0}-format.csv'.format(OUTPUT_FORMAT) FTR = [3600, 60, 1] def converter(input_files, output_file): """ Converter. @param input_files: File names with input data (from TITAN support team). @type input_files: list @param output_file: File name for derived data (of simulator format). @type output_file: str """ output = [] for input_file_name in input_files: with open(input_file_name, 'r') as f_in: for line in f_in: params = line.split('\r\n')[0].split(' ') output_line = [None, None, None, None, None, None, None] for p in params: try: if 'user=' in p: output_line[5] = p.split('user=')[1] elif p.startswith('account='): output_line[6] = p.split('=')[1] elif p.startswith('qtime='): output_line[0] = float(p.split('=')[1]) elif p.startswith('start='): output_line[1] = float(p.split('=')[1]) elif p.startswith('end='): output_line[2] = float(p.split('=')[1]) elif 'Resource_List.walltime' in p: output_line[3] = float( sum([a * b for a, b in zip(FTR, map( int, p.split('=')[1].split(':')))])) elif 'Resource_List.nodes' in p: output_line[4] = int(p.split('=')[1].split(':')[0]) # dependency -> 'Resource_List.depend' except Exception, e: print 'Portion: {0} | Line: {1}'.format(p, line) raise Exception(e) if None in output_line: continue output.append((output_line[0], output_line[1], ','.join([str(output_line[3]), str(output_line[2] - output_line[1]), str(output_line[4]), output_line[5], output_line[6]]))) output.sort() if IS_ANALYSIS_FORMAT: with open(output_file, 'a') as f_out: for x in output: f_out.write(','.join([str(x[0]), str(x[1]), x[2]]) + '\n') else: init_time = output[0][0] - 1 with open(output_file, 'a') as f_out: for x in output: f_out.write(','.join([str(x[0] - init_time), x[2]]) + '\n') if __name__ == '__main__': converter(input_files=INPUT_FILE_NAMES, output_file=OUTPUT_FILE_NAME)

73de68566a7a5cadd2256d48bf99e427ce678fe8

py

Python

src/fidesops/service/processors/post_processor_strategy/post_processor_strategy_filter.py

mohan-pogala/fidesops

5c686362d4fb3b85253dd7e2898be1131a5071ab

src/fidesops/service/processors/post_processor_strategy/post_processor_strategy_filter.py

mohan-pogala/fidesops

5c686362d4fb3b85253dd7e2898be1131a5071ab

src/fidesops/service/processors/post_processor_strategy/post_processor_strategy_filter.py

mohan-pogala/fidesops

5c686362d4fb3b85253dd7e2898be1131a5071ab

import logging from typing import Any, Optional, Dict from fidesops.schemas.saas.strategy_configuration import ( FilterPostProcessorConfiguration, StrategyConfiguration, IdentityParamRef, ) from fidesops.service.processors.post_processor_strategy.post_processor_strategy import ( PostProcessorStrategy, ) STRATEGY_NAME = "filter" logger = logging.getLogger(__name__) class FilterPostProcessorStrategy(PostProcessorStrategy): """ Filters object or array given field name and value Value can be reference a dynamic identity passed in through the request OR hard-coded value. E.g. data = [ { "id": 1397429347 "email_contact": somebody@email.com "name": Somebody Awesome }, { "id": 238475234 "email_contact": somebody-else@email.com "name": Somebody Cool } ] field: email_contact value: {"identity": email}, where email == somebody@email.com result = { id: 1397429347 email_contact: somebody@email.com name: Somebody Awesome } """ def __init__(self, configuration: FilterPostProcessorConfiguration): self.field = configuration.field self.value = configuration.value def get_strategy_name(self) -> str: return STRATEGY_NAME def process(self, data: Any, identity_data: Dict[str, Any] = None) -> Optional[Any]: """ :param data: A list or an object :param identity_data: Dict of cached identity data :return: filtered object or None """ if not data: return None filter_value = self.value if isinstance(self.value, IdentityParamRef): if ( identity_data is None or identity_data.get(self.value.identity, None) is None ): logger.warning( f"Could not retrieve identity reference '{self.value.identity}' due to missing identity data for the following post processing strategy: {self.get_strategy_name()}" ) return None filter_value = identity_data.get(self.value.identity) try: if isinstance(data, list): filtered = [item for item in data if item[self.field] == filter_value] return filtered if filtered else None return data if data[self.field] == filter_value else None except KeyError: logger.warning( f"{self.field} could not be found on data for the following post processing strategy: {self.get_strategy_name()}" ) return None @staticmethod def get_configuration_model() -> StrategyConfiguration: return FilterPostProcessorConfiguration

73de72a27148de0df4d07890ab3151d6fae7be4b

py

Python

models/learned_loss_old.py

allenai/interactron

2be41b34adf6917348cb2440cded9975ec4f5d0d

2022-03-30T06:07:32.000Z

2022-03-31T02:05:18.000Z

models/learned_loss.py

allenai/interactron

2be41b34adf6917348cb2440cded9975ec4f5d0d

models/learned_loss.py

allenai/interactron

2be41b34adf6917348cb2440cded9975ec4f5d0d

2022-03-30T10:05:42.000Z

2022-03-31T02:05:23.000Z

import torch import torch.nn as nn import torch.nn.functional as F import torchvision import matplotlib.pyplot as plt import cv2 from models.detectron2_detector import Detectron2Detector from models.gpt import GPT from models.components import LinearBlock from utils.constants import tlvis_classes from utils.model_utils import merge_batch_seq, unmerge_batch_seq from utils.detection_utils import iou from utils.time_utils import Timer from utils.viz_utils import draw_box class LinearBlock(nn.Module): def __init__(self, in_dim, out_dim, bias=False): super().__init__() self.model = nn.Sequential( nn.Linear(in_features=in_dim, out_features=out_dim, bias=bias), nn.LayerNorm(out_dim), nn.GELU(), ) def forward(self, x): og_shape = x.shape x = self.model(x.view(-1, og_shape[-1])) return x.view(*og_shape[:-1], -1) class LearnedLossModel(nn.Module): def __init__(self, cfg): super().__init__() self.model = GPT(cfg.TRANSFORMER) self.proposal_encoder = LinearBlock(2264, cfg.TRANSFORMER.EMBEDDING_DIM, bias=False) self.img_feature_encoder = LinearBlock(2048, cfg.TRANSFORMER.EMBEDDING_DIM, bias=False) self.box_decoder = nn.Linear(in_features=1024, out_features=4, bias=False) self.category_decoder = nn.Linear(in_features=1024, out_features=1236, bias=False) self.cfg = cfg self.is_train = True self.timer = Timer() self.logger = None self.mode = 'train' if cfg.TRANSFORMER.PREDICT_ACTIONS: self.policy_tokens = nn.Parameter(1, 5, cfg.TRANSFORMER.EMBEDDING_DIM) def forward(self, predictions, images): seq = self.fold_sequence(predictions) if self.cfg.TRANSFORMER.PREDICT_ACTIONS: seq = torch.cat((seq, self.policy_tokens), dim=1) out = self.model(seq) pred_embs = out[:, :250] learned_loss = torch.norm(pred_embs, p=2) if self.cfg.TRANSFORMER.PREDICT_ACTIONS: action_predictions = out[:, -5:] return learned_loss, action_predictions return learned_loss def configure_optimizer(self, train_config): optim_groups = self.model.get_optimizer_groups(train_config) assert train_config.OPTIM_TYPE in ["Adam", "AdamW", "SGD"], \ "Invalid optimizer type {}. Please select Adam, AdamW or SGD" if train_config.OPTIM_TYPE == "AdamW": optimizer = torch.optim.AdamW(optim_groups, lr=train_config.LEARNING_RATE, betas=(train_config.BETA1, train_config.BETA2)) elif train_config.OPTIM_TYPE == "Adam": optimizer = torch.optim.Adam(optim_groups, lr=train_config.LEARNING_RATE, betas=(train_config.BETA1, train_config.BETA2)) else: optimizer = torch.optim.SGD(optim_groups, lr=train_config.LEARNING_RATE, momentum=train_config.MOMENTUM) return optimizer def eval(self): return self.train(False) def train(self, mode=True): self.mode = 'train' if mode else 'test' self.model.train(mode) self.is_train = mode return self def fold_sequence(self, predictions): img_features = predictions.get_image_features() box_features = predictions.get_box_features() boxes = predictions.get_boxes() logits = predictions.get_logits() detections = torch.cat((box_features, boxes, logits), dim=-1) b, t = img_features.shape[:2] img_features = img_features.permute(0, 1, 3, 4, 2) seq_img_features = self.img_feature_encoder(img_features.reshape(b, -1, img_features.shape[-1])) det_image_features = self.proposal_encoder(detections.reshape(b, -1, detections.shape[-1])) return torch.cat((det_image_features, seq_img_features), dim=1) def set_logger(self, logger): assert self.logger is None, "This model already has a logger!" self.logger = logger

73de7b41da5d0cb9fcaa2203fc40713383b71f3a

py

Python

components/amp-utility/python/uhashlib.py

ekmixon/AliOS-Things

00334295af8aa474d818724149726ca93da4645d

2017-10-20T05:19:03.000Z

2022-03-30T02:29:30.000Z

components/amp-utility/python/uhashlib.py

ekmixon/AliOS-Things

00334295af8aa474d818724149726ca93da4645d

2017-10-21T07:57:22.000Z

2022-03-31T08:15:49.000Z

components/amp-utility/python/uhashlib.py

willianchanlovegithub/AliOS-Things

637c0802cab667b872d3b97a121e18c66f256eab

2017-10-20T05:22:35.000Z

2022-03-27T10:54:14.000Z

# * coding: UTF8 * """ 该模块实现相应 CPython 模块的子集，如下所示 ， 模块实现二进制数据的散列算法。目前实现了SHA256算法。SHA256是深思熟虑之选，这是一种现代的加密安全算法。 这意味着单个算法既可覆盖任何散列算法的用例，也可覆盖与安全相关的使用，从而省略了诸如MD5或SHA1之类的遗留算法以节省时间。 SHA256 - 最新一代，现代散列算法 类 ------------------------------ """ class sha256(object): """ 创建一个hasher对象，并选择性地将数据输入其中 """ def __init__(self,data): pass def update(self,data): """ 将更多二进制数据输入hash """ pass def digest(self): """ 返回用于所有通过散列传递的所有数据的散列 """ pass

73de89ee3a6f19f38f86e246786fc059f9542160

py

Python

features/steps/calculating_tubing_descriptors.py

tonyroberts/project-tetra-display

ada1169d3884e61c06e90fe50a9886b50564c7dd

2020-07-29T09:18:16.000Z

2021-05-19T22:31:23.000Z

features/steps/calculating_tubing_descriptors.py

tonyroberts/project-tetra-display

ada1169d3884e61c06e90fe50a9886b50564c7dd

2020-07-29T09:12:07.000Z

2021-07-04T01:50:57.000Z

features/steps/calculating_tubing_descriptors.py

tonyroberts/project-tetra-display

ada1169d3884e61c06e90fe50a9886b50564c7dd

2020-07-31T20:02:47.000Z

2021-05-14T08:48:38.000Z

from behave import given, when, then import server from tetra_constants import DESCRIPTORS, NUMBER_OF_PATIENTS @given("there are sensors connected") def step_impl(context): context.sensors = server.Sensors() @given("there is a calculator to parse sensor data") def step_impl(context): context.calculator = server.Calculator() @when("data is requested from the sensors") def step_impl(context): context.sensor_data = context.sensors.poll() @then("the sensors yield all of the descriptors") def step_impl(context): context.calculator.add_datum(context.sensor_data) assert all(descriptor in context.calculator.get_datum()[f"patient-{patient_number}"] for descriptor in DESCRIPTORS for patient_number in range(NUMBER_OF_PATIENTS))

73de95734ae8fc0d79749d49185671ba98ba9b5f

py

Python

src/test/tests/operators/lcs.py

visit-dav/vis

c08bc6e538ecd7d30ddc6399ec3022b9e062127e

2018-12-29T01:13:49.000Z

2022-03-30T19:16:31.000Z

src/test/tests/operators/lcs.py

visit-dav/vis

c08bc6e538ecd7d30ddc6399ec3022b9e062127e

2019-01-14T18:19:25.000Z

2022-03-31T23:08:36.000Z

src/test/tests/operators/lcs.py

visit-dav/vis

c08bc6e538ecd7d30ddc6399ec3022b9e062127e

2019-01-24T17:41:50.000Z

2022-03-10T10:01:46.000Z

# ---------------------------------------------------------------------------- # CLASSES: nightly # # Test Case: LCS.py # # Tests: operator - LCS # # Programmer: Allen Sanderson # Date: August 25, 2015 # # Modifications: # Kathleen Biagas, Thur Sep 17, 2020 # Shorten name used for 'Testing database' TestSection. Turn of warnings. # ---------------------------------------------------------------------------- RequiredDatabasePlugin("PICS_Tester") #-vargs="-debug 5" # For testing using the CLI #def data_path( db_name ): # db="/Projects/VisIt/trunk/build/data/%s" %(db_name) # return db #def Test(case_name): # swatts = SaveWindowAttributes() # swatts.family = 0 # swatts.fileName = "/Projects/tmp/lcs/ser/%s" %(case_name) # SetSaveWindowAttributes(swatts) # SaveWindow() # return #def TestSection(tmpstr): # return #def Exit(): # return # Open the database here and add a plot as for some reason it fails # within a loop. It only happens with all-in-one plots with an operator # such as with "Pseudocolor" and "operators/LCS/velocity" db=data_path("pics_test_data/ftle_double_gyre_1_domain.pics") OpenDatabase(db) AddPlot("Pseudocolor", "operators/LCS/velocity") LCSAtts = LCSAttributes() LCSAtts.Resolution = (101, 51, 1) LCSAtts.integrationDirection = LCSAtts.Forward LCSAtts.auxiliaryGridSpacing = 0.005 LCSAtts.maxSteps = 1000000 LCSAtts.operationType = LCSAtts.Lyapunov LCSAtts.cauchyGreenTensor = LCSAtts.Right LCSAtts.eigenComponent = LCSAtts.Largest LCSAtts.operatorType = LCSAtts.BaseValue LCSAtts.terminationType = LCSAtts.Time LCSAtts.terminateByTime = 1 LCSAtts.termTime = 4 LCSAtts.maxStepLength = 0.001 LCSAtts.integrationType = LCSAtts.AdamsBashforth LCSAtts.parallelizationAlgorithmType = LCSAtts.ParallelStaticDomains LCSAtts.pathlines = 1 LCSAtts.pathlinesCMFE = LCSAtts.CONN_CMFE LCSAtts.issueAdvectionWarnings = 0 LCSAtts.issueBoundaryWarnings = 0 LCSAtts.issueTerminationWarnings = 0 LCSAtts.issueStepsizeWarnings = 0 LCSAtts.issueStiffnessWarnings = 0 LCSAtts.issueCriticalPointsWarnings = 0 SetOperatorOptions(LCSAtts, 0) databases=["ftle_double_gyre_1_domain", "ftle_double_gyre_2_domains"] src_type=[LCSAtts.RegularGrid, LCSAtts.NativeMesh] src_type_str=["RegularGrid", "NativeMesh"] aux_grid=[LCSAtts.NONE, LCSAtts.TwoDim] aux_grid_str=["NoAuxGrid", "2DAuxGrid"] TestSection("Basic FTLE function") for i in range(len(databases)): db=data_path("pics_test_data/%s.pics") %(databases[i]) tmpstr="Testing database = %s.pics" %(databases[i]) TestSection(tmpstr) OpenDatabase(db) # Replace the database from before with this one as a new plot can # not be opened within the loop when using runtest. This issue is a # bug. ReplaceDatabase(db) # DeleteAllPlots() # AddPlot("Pseudocolor", "operators/LCS/velocity") for j in range(len(src_type)): tmpstr="Testing sample source = %s" %(src_type_str[j]) TestSection(tmpstr) LCSAtts.sourceType = src_type[j] # NativeMesh, RegularGrid for k in range(len(aux_grid)): tmpstr="Testing auxiliary grid = %s" %(aux_grid_str[k]) TestSection(tmpstr) LCSAtts.auxiliaryGrid = aux_grid[k] # None, TwoDim SetOperatorOptions(LCSAtts, 0) DrawPlots() tmpstr="lcs_%s_%s_%s" %(databases[i], src_type_str[j], aux_grid_str[k]) Test(tmpstr) # FTLE with smallest exponent LCSAtts.eigenComponent = LCSAtts.Smallest # Smallest, Intermediate, Largest, PosShearVector, NegShearVector, PosLambdaShearVector, NegLambdaShearVector SetOperatorOptions(LCSAtts, 0) DrawPlots() tmpstr="lcs_%s_%s_%s_Smallest" %(databases[i], src_type_str[j], aux_grid_str[k]) Test(tmpstr) # FTLE with left Cauchy Green Tensor LCSAtts.cauchyGreenTensor = LCSAtts.Left # Left, Right LCSAtts.eigenComponent = LCSAtts.Largest # Smallest, Intermediate, Largest, PosShearVector, NegShearVector, PosLambdaShearVector, NegLambdaShearVector SetOperatorOptions(LCSAtts, 0) DrawPlots() tmpstr="lcs_%s_%s_%s_Left" %(databases[i], src_type_str[j], aux_grid_str[k]) Test(tmpstr) # FTLE with eigen value LCSAtts.operationType = LCSAtts.EigenValue # IntegrationTime, ArcLength, AverageDistanceFromSeed, EigenValue, EigenVector, Lyapunov LCSAtts.cauchyGreenTensor = LCSAtts.Right # Left, Right LCSAtts.eigenComponent = LCSAtts.Largest # Smallest, Intermediate, Largest, PosShearVector, NegShearVector, PosLambdaShearVector, NegLambdaShearVector SetOperatorOptions(LCSAtts, 0) DrawPlots() tmpstr="lcs_%s_%s_%s_EigenValue" %(databases[i], src_type_str[j], aux_grid_str[k]) Test(tmpstr) Exit() # 1 processor: #wo/aux grid # Native 1 -0.04343 - 1.066 190 zeros # Match # Rect 1 -0.04343 - 1.066 190 zeros # # Native 2 -0.04343 - 1.066 193 zeros # Match # Rect 2 -0.04343 - 1.233 190 zeros # Match with ghost cells but for a few cells. # Errors in the domain boundary gradients #w/aux grid # Native 1 0.004539 - 1.396 304 exited / 680 zeros # Match # Rect 1 0.004539 - 1.396 304 exited / 680 zeros # # Native 2 0.004539 - 1.396 308 exited / 690 zeros # Match # Rect 2 0.004539 - 1.396 304 exited / 680 zeros # # 4 processors: #wo/aux grid # Native 1 -0.04343 - 1.066 190 zeros # Match # Rect 1 -0.04343 - 1.066 190 zeros # # Native 2 -0.04343 - 1.066 193 zeros # Match # Rect 2 -0.04343 - 1.233 190 zeros # Match with ghost cells but for a few cells. #w/aux grid # Native 1 0.004539 - 1.396 304 exited / 680 zeros # Match # Rect 1 0.004539 - 1.396 304 exited / 680 zeros # # Native 2 0.004539 - 1.396 308 exited / 690 zeros # Match # Rect 2 0.004539 - 1.396 304 exited / 680 zeros #

73dec6057c60d6406f21eb32b61317c770f12e4c

py

Python

src/external/jemalloc.py

cychan-lbnl/devastator

a23bb21f9da5841fa2036edc68f3a89a2241831b

2022-03-16T08:11:02.000Z

2022-03-16T08:11:02.000Z

src/external/jemalloc.py

cychan-lbnl/devastator

a23bb21f9da5841fa2036edc68f3a89a2241831b

src/external/jemalloc.py

cychan-lbnl/devastator

a23bb21f9da5841fa2036edc68f3a89a2241831b

import shlex @brutal.rule(caching='file') def jemalloc_source(): url = "https://github.com/jemalloc/jemalloc/releases/download/5.2.1/jemalloc-5.2.1.tar.bz2" tgz = brutal.download(url) return brutal.untar(tgz) @brutal.rule(caching='file') @brutal.coroutine def jemalloc_context(): src_dir = yield jemalloc_source() build_dir = brutal.mkpath('jemalloc-build') brutal.os.mkdir(build_dir) install_dir = brutal.mkpath('jemalloc-install') cc = c_compiler() cxx = cxx_compiler() cg_flags = base_cg_flags() configure = brutal.os.path.join(src_dir, 'configure') configure = [configure, '--prefix', install_dir, '--with-jemalloc-prefix=je_', '--enable-cxx', '--disable-fill', '--disable-shared'] yield brutal.process(configure, cwd=build_dir, env_add={'CC':cc, 'CFLAGS':cg_flags, 'CXX':cxx, 'CXXFLAGS': cxx14_flags() + cg_flags}) yield brutal.process(['make','install'], cwd=build_dir) jemalloc_config = brutal.os.path.join(install_dir, 'bin', 'jemalloc-config') pp_misc = yield brutal.process([jemalloc_config, '--cppflags']) pp_misc = shlex.split(pp_misc) lib_misc = yield brutal.process([jemalloc_config, '--libs']) lib_misc = shlex.split(lib_misc) yield CodeContext( compiler = cxx, pp_misc = pp_misc, #lib_misc = ['-L'+brutal.os.path.join(install_dir, 'lib'), '-ljemalloc'] + lib_misc lib_misc = [brutal.os.path.join(install_dir, 'lib', 'libjemalloc.a')] + lib_misc )

73dec81bad9814ee479aeb16c7b10c65c3486981

py

Python

setup.py

jdidion/htsget-server

cd00c4165aadb66b21be4a0e5dbaaaddfa97ee64

2019-08-22T16:51:23.000Z

2019-08-22T16:51:23.000Z

setup.py

jdidion/htsget-server

cd00c4165aadb66b21be4a0e5dbaaaddfa97ee64

setup.py

jdidion/htsget-server

cd00c4165aadb66b21be4a0e5dbaaaddfa97ee64

""" Build atropos. Cython is run when * no pre-generated C sources are found, * or the pre-generated C sources are out of date, * or when --cython is given on the command line. """ import sys from setuptools import setup, find_packages # Define install and test requirements based on python version version_info = sys.version_info if sys.version_info < (3, 6): sys.stdout.write("At least Python 3.6 is required.\n") sys.exit(1) with open('README.md') as f: README = f.read() with open('LICENSE') as f: LICENSE = f.read() setup( name='atropos', version='0.1.0', author='John Didion', author_email='github@didion.net', url='https://github.com/jdidion/htsget-server', description='Reference implementation of an htsget server.', long_description=README, license=LICENSE, packages=find_packages(), install_requires=[ 'ngsindex' ], tests_requires=['pytest'], entry_points={ 'console_scripts': [ 'htsget-server=htsgetserver.__main__:main' ] }, classifiers=[ "Development Status :: 5 - Production/Stable", "Environment :: Console", "Intended Audience :: Science/Research", "Topic :: Scientific/Engineering :: Bio-Informatics", "License :: OSI Approved :: MIT License", "License :: Public Domain", "Natural Language :: English", "Programming Language :: Cython", "Programming Language :: Python :: 3.6" ] )

73dee999b8fe71d0515f80076913941dcabb76d2

py

Python

tests/calendar_cli/model/test_event.py

mogproject/calendar-cli

095fe4cfe76bc1890742d22accef48a5f9f6ca45

2015-10-14T09:05:48.000Z

2021-06-27T11:32:48.000Z

tests/calendar_cli/model/test_event.py

mogproject/calendar-cli

095fe4cfe76bc1890742d22accef48a5f9f6ca45

2015-10-14T06:20:26.000Z

2017-01-23T23:33:01.000Z

tests/calendar_cli/model/test_event.py

mogproject/calendar-cli

095fe4cfe76bc1890742d22accef48a5f9f6ca45

2016-10-12T18:35:24.000Z

2019-12-31T17:13:02.000Z

# encoding: utf-8 from __future__ import division, print_function, absolute_import, unicode_literals from datetime import datetime import pytz from mog_commons import unittest from calendar_cli.model import EventTime, Event from calendar_cli.i18n import MSG_ALL_DAY, MSG_WEEK_DAY class TestEventTime(unittest.TestCase): tz_tokyo = pytz.timezone('Asia/Tokyo') d0 = datetime(2015, 10, 17, 0, 0, 0, 0, pytz.timezone('Asia/Tokyo')) d1 = datetime(2015, 10, 18, 0, 0, 0, 0, pytz.timezone('Asia/Tokyo')) d2 = datetime(2015, 10, 17, 0, 0, 0, 0, pytz.utc) d3 = datetime(2016, 10, 17, 0, 0, 0, 0, pytz.utc) d4 = datetime(2015, 10, 17, 12, 34, 56, 0, pytz.timezone('Asia/Tokyo')) d5 = datetime(2015, 10, 18, 12, 34, 56, 0, pytz.timezone('Asia/Tokyo')) d6 = datetime(2015, 10, 17, 3, 34, 56, 0, pytz.utc) d7 = datetime(2015, 10, 17, 3, 34, 57, 0, pytz.utc) def test_init(self): t = EventTime(False, self.d1) self.assertFalse(t.has_time) self.assertEqual(t.datetime_tz, self.d1) t = EventTime(True, self.d5) self.assertTrue(t.has_time) self.assertEqual(t.datetime_tz, self.d5) def test_init_error(self): self.assertRaisesRegexp(AssertionError, 'datetime_tz must be timezone-aware', EventTime, True, datetime(2015, 10, 17, 12, 34, 56)) def test_cmp(self): ts1 = [EventTime(False, t) for t in [self.d0, self.d1, self.d2, self.d3]] ts2 = [EventTime(True, t) for t in [self.d4, self.d5, self.d6, self.d7]] ts = ts1 + ts2 self.assertEqual(sorted(ts), [ts[0], ts[2], ts[1], ts[3], ts[6], ts[4], ts[7], ts[5]]) def test_to_short_summary(self): self.assertEqual(EventTime(False, self.d0).to_short_summary(), None) self.assertEqual(EventTime(False, self.d2).to_short_summary(), None) self.assertEqual(EventTime(True, self.d4).to_short_summary(), '12:34') self.assertEqual(EventTime(True, self.d6).to_short_summary(), '03:34') def test_to_long_summary(self): self.assertEqual(EventTime(False, self.d0).to_long_summary(), '2015-10-17 %s' % MSG_WEEK_DAY[5]) self.assertEqual(EventTime(False, self.d2).to_long_summary(), '2015-10-17 %s' % MSG_WEEK_DAY[5]) self.assertEqual(EventTime(True, self.d4).to_long_summary(), '2015-10-17 %s' % MSG_WEEK_DAY[5]) self.assertEqual(EventTime(True, self.d6).to_long_summary(), '2015-10-17 %s' % MSG_WEEK_DAY[5]) def test_to_dict(self): self.assertEqual(EventTime(False, self.d0).to_dict(), {'date': '2015-10-17', 'timeZone': 'Asia/Tokyo'}) self.assertEqual(EventTime(False, self.d2).to_dict(), {'date': '2015-10-17', 'timeZone': 'UTC'}) self.assertEqual(EventTime(True, self.d4).to_dict(), {'dateTime': '2015-10-17T12:34:56+09:00', 'timeZone': 'Asia/Tokyo'}) self.assertEqual(EventTime(True, self.d6).to_dict(), {'dateTime': '2015-10-17T03:34:56+00:00', 'timeZone': 'UTC'}) def test_parse_dict(self): self.assertEqual(EventTime.parse_dict({'date': '2015-10-17'}, 'Asia/Tokyo'), EventTime(False, datetime(2015, 10, 17, 0, 0, 0, 0, self.tz_tokyo))) self.assertEqual(EventTime.parse_dict({'dateTime': '2015-10-01T10:30:00+09:00'}, 'Asia/Tokyo'), EventTime(True, datetime(2015, 10, 1, 10, 30, 0, 0, self.tz_tokyo))) self.assertEqual(EventTime.parse_dict({'dateTime': '2015-10-01T10:30:00+09:00', 'timeZone': 'Asia/Tokyo'}, 'Asia/Tokyo'), EventTime(True, datetime(2015, 10, 1, 10, 30, 0, 0, self.tz_tokyo))) self.assertEqual(EventTime.parse_dict({'dateTime': '2015-10-01T10:30:00Z'}, 'Asia/Tokyo'), EventTime(True, datetime(2015, 10, 1, 19, 30, 0, 0, self.tz_tokyo))) self.assertEqual(EventTime.parse_dict({'dateTime': '2015-10-01T10:30:00-04:00'}, 'Asia/Tokyo'), EventTime(True, datetime(2015, 10, 1, 23, 30, 0, 0, self.tz_tokyo))) # When the timezone doesn't match the timeZone field, ignore timeZone. self.assertEqual(EventTime.parse_dict({'dateTime': '2015-10-01T10:30:00-04:00'}, 'UTC'), EventTime(True, datetime(2015, 10, 1, 14, 30, 0, 0, pytz.utc))) # When the timezone is missing in dateTime field, use timeZone filed to parse. self.assertEqual(EventTime.parse_dict({'dateTime': '2015-10-01T10:30:00', 'timeZone': 'Asia/Tokyo'}, 'UTC'), EventTime(True, datetime(2015, 10, 1, 1, 30, 0, 0, pytz.utc))) class TestEvent(unittest.TestCase): tz_la = pytz.timezone('America/Los_Angeles') tz_tokyo = pytz.timezone('Asia/Tokyo') t0 = EventTime(True, tz_la.localize(datetime(2015, 5, 28, 9, 0, 0, 0))) t1 = EventTime(True, tz_la.localize(datetime(2015, 5, 28, 17, 0, 0, 0))) t2 = EventTime(False, tz_tokyo.localize(datetime(2015, 10, 17, 0, 0, 0, 0))) t3 = EventTime(False, tz_tokyo.localize(datetime(2015, 10, 18, 0, 0, 0, 0))) t4 = EventTime(False, datetime(2015, 10, 18, 0, 0, 0, 0, pytz.utc)) e0 = Event(t0, t1, 'Google I/O 2015', 'Foo Bar', 'foo@example.com') e1 = Event(t2, t3, 'あいうえお', None, None) e2 = Event(t2, t3, 'あいうえお', None, 'foo@example.com') e3 = Event(t0, t1, 'Google I/O 2016', 'Foo Bar', 'foo@example.com', 'Mountain View') def test_init(self): self.assertEqual(self.e0.start_time, self.t0) self.assertEqual(self.e0.end_time, self.t1) self.assertEqual(self.e0.summary, 'Google I/O 2015') self.assertEqual(self.e0.creator_name, 'Foo Bar') self.assertEqual(self.e0.creator_email, 'foo@example.com') self.assertEqual(self.e1.start_time, self.t2) self.assertEqual(self.e1.end_time, self.t3) self.assertEqual(self.e1.summary, 'あいうえお') self.assertEqual(self.e1.creator_name, None) self.assertEqual(self.e1.creator_email, None) def test_init_error(self): """todo""" def test_to_format(self): self.assertEqual(self.e0.to_format('[%T] %S%L%C'), '[09:00-17:00] Google I/O 2015 (Foo Bar)') self.assertEqual(self.e1.to_format('[%T] %S%L%C'), '[%s] あいうえお' % MSG_ALL_DAY) self.assertEqual(self.e2.to_format('[%T] %S%L%C'), '[%s] あいうえお (foo@example.com)' % MSG_ALL_DAY) self.assertEqual(self.e3.to_format('[%T] %S%L%C'), '[09:00-17:00] Google I/O 2016 @Mountain View (Foo Bar)') def test_to_long_summary(self): self.assertEqual(self.e0.to_long_summary(), '2015-05-28 %s [09:00-17:00] Google I/O 2015' % MSG_WEEK_DAY[3]) self.assertEqual(self.e1.to_long_summary(), '2015-10-17 %s [%s] あいうえお' % (MSG_WEEK_DAY[5], MSG_ALL_DAY)) self.assertEqual(self.e2.to_long_summary(), '2015-10-17 %s [%s] あいうえお' % (MSG_WEEK_DAY[5], MSG_ALL_DAY)) def test_to_dict(self): self.assertEqual(self.e0.to_dict(), { 'summary': 'Google I/O 2015', 'start': {'dateTime': '2015-05-28T09:00:00-07:00', 'timeZone': 'America/Los_Angeles'}, 'end': {'dateTime': '2015-05-28T17:00:00-07:00', 'timeZone': 'America/Los_Angeles'}, 'creator': {'displayName': 'Foo Bar', 'email': 'foo@example.com', }, }) self.assertEqual(self.e1.to_dict(), { 'summary': 'あいうえお', 'start': {'date': '2015-10-17', 'timeZone': 'Asia/Tokyo'}, 'end': {'date': '2015-10-18', 'timeZone': 'Asia/Tokyo'}, }) self.assertEqual(self.e2.to_dict(), { 'summary': 'あいうえお', 'start': {'date': '2015-10-17', 'timeZone': 'Asia/Tokyo'}, 'end': {'date': '2015-10-18', 'timeZone': 'Asia/Tokyo'}, 'creator': {'email': 'foo@example.com'}, }) def test_parse_dict(self): self.assertEqual(Event.parse_dict({ 'summary': 'Google I/O 2015', 'start': {'dateTime': '2015-05-28T09:00:00-07:00', 'timeZone': 'America/Los_Angeles'}, 'end': {'dateTime': '2015-05-28T17:00:00-07:00', 'timeZone': 'America/Los_Angeles'}, 'creator': {'displayName': 'Foo Bar', 'email': 'foo@example.com', }, }, 'America/Los_Angeles'), self.e0) self.assertEqual(Event.parse_dict({ 'summary': 'あいうえお', 'start': {'date': '2015-10-17'}, 'end': {'date': '2015-10-18'}, }, 'Asia/Tokyo'), self.e1) self.assertEqual(Event.parse_dict({ 'summary': 'あいうえお', 'start': {'date': '2015-10-17'}, 'end': {'date': '2015-10-18'}, 'creator': {'email': 'foo@example.com'}, }, 'Asia/Tokyo'), self.e2)

73defb0da4dc8164f81c8b2c90d2ee6f8689cc24

py

Python

geokey_wegovnow/management/commands/__init__.py

ExCiteS/geokey-wegonow

66df7e17fa3eb2d8da2e56e39236b019f98a2a08

geokey_wegovnow/management/commands/__init__.py

ExCiteS/geokey-wegonow

66df7e17fa3eb2d8da2e56e39236b019f98a2a08

2017-02-22T13:20:29.000Z

2018-11-07T16:39:33.000Z

geokey_wegovnow/management/commands/__init__.py

ExCiteS/geokey-wegonow

66df7e17fa3eb2d8da2e56e39236b019f98a2a08

"""All commands for the WeGovNow extension."""

73defe04b20cb398fb6e5d985e57b4f8a7e89e50

py

Python

sdks/python/appcenter_sdk/models/Symbols.py

Brantone/appcenter-sdks

eeb063ecf79908b6e341fb00196d2cd9dc8f3262

sdks/python/appcenter_sdk/models/Symbols.py

Brantone/appcenter-sdks

eeb063ecf79908b6e341fb00196d2cd9dc8f3262

2019-10-23T06:38:53.000Z

2022-01-22T07:57:58.000Z

sdks/python/appcenter_sdk/models/Symbols.py

Brantone/appcenter-sdks

eeb063ecf79908b6e341fb00196d2cd9dc8f3262

2019-10-23T06:31:05.000Z

2021-08-21T17:32:47.000Z

# coding: utf-8 """ App Center Client Microsoft Visual Studio App Center API # noqa: E501 OpenAPI spec version: preview Contact: benedetto.abbenanti@gmail.com Project Repository: https://github.com/b3nab/appcenter-sdks """ import pprint import re # noqa: F401 import six class Symbols(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ allowed enum values """ available = "available" ignored = "ignored" """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { } attribute_map = { } def __init__(self): # noqa: E501 """Symbols - a model defined in Swagger""" # noqa: E501 self.discriminator = None def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value 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, Symbols): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

73df0f98ab8f4b7beafc278117fb89ae875e0922

py

Python

python/pathilico/pygletelm/message.py

OtaYuji/pathilico

b28cb0d3db043de1f8a70723f8a8142b73b8c8bd

python/pathilico/pygletelm/message.py

OtaYuji/pathilico

b28cb0d3db043de1f8a70723f8a8142b73b8c8bd

python/pathilico/pygletelm/message.py

OtaYuji/pathilico

b28cb0d3db043de1f8a70723f8a8142b73b8c8bd

# Copyright # 2019 Department of Dermatology, School of Medicine, Tohoku University # # 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. class Message(object): # Mock class def __init__(self, *args): self.args = args def __call__(self, *args, **kwargs): return self class MetaEq(type): def __eq__(cls, other): return getattr(cls, "identity", 0) == other class MessageBase(object, metaclass=MetaEq): identity = 0 __slots__ = tuple() def __init__(self, *args, **kwargs): d = dict(zip(self.__slots__, args), **kwargs) for k in self.__slots__: setattr(self, k, d[k]) def __eq__(self, other): return self.identity == other def __str__(self): msg = "<[ {0} : {1} ]>".format( getattr(self, "__class__"), str(["{}: {}".format(k, getattr(self, k)) for k in self.__slots__]) ) return msg class MetaMessage(type): # Meta class id_count = 100 def __new__(meta, name, bases, class_dict): new_cls_dict = dict() for key, value in class_dict.items(): if isinstance(value, Message): new_cls_dict[key] = type( key, (MessageBase, ), {"__slots__": value.args, "identity": meta.id_count} ) meta.id_count += 1 cls = type.__new__(meta, name, bases, new_cls_dict) return cls class UnionMessage(object, metaclass=MetaMessage): Ok = Message("value") Err = Message("error") WindowResized = Message("width", "height") WindowApiChangeCursorIcon = Message("cursor") Ok = UnionMessage.Ok Err = UnionMessage.Err WindowResized = UnionMessage.WindowResized WindowApiChangeCursor = UnionMessage.WindowApiChangeCursorIcon

73df28ddff17914b28a2ac223a604cdc97e21b05

py

Python

apps/Analyzer_Datasets/time_series/lib_utils_time.py

c-hydro/hat

6342f87237ae9e71f05d54264bce97299ab4eb61

2022-03-21T03:30:43.000Z

2022-03-21T03:30:43.000Z

apps/Analyzer_Datasets/time_series/lib_utils_time.py

c-hydro/hat

6342f87237ae9e71f05d54264bce97299ab4eb61

2021-05-06T15:22:37.000Z

2021-05-06T16:31:05.000Z

apps/Analyzer_Datasets/time_series/lib_utils_time.py

c-hydro/hat

6342f87237ae9e71f05d54264bce97299ab4eb61

# ------------------------------------------------------------------------------------- # Libraries import logging import pandas as pd from datetime import date from lib_info_args import logger_name # Logging log_stream = logging.getLogger(logger_name) # ------------------------------------------------------------------------------------- # ------------------------------------------------------------------------------------- # Method to set time run def set_time(time_run_args=None, time_run_file=None, time_format='%Y-%m-%d %H:$M', time_run_file_start=None, time_run_file_end=None, time_period=1, time_frequency='H', time_rounding='H', time_reverse=True): log_stream.info(' ----> Set time period ... ') if (time_run_file_start is None) and (time_run_file_end is None): log_stream.info(' -----> Time info defined by "time_run" argument ... ') if time_run_args is not None: time_run = time_run_args log_stream.info(' ------> Time ' + time_run + ' set by argument') elif (time_run_args is None) and (time_run_file is not None): time_run = time_run_file log_stream.info(' ------> Time ' + time_run + ' set by user') elif (time_run_args is None) and (time_run_file is None): time_now = date.today() time_run = time_now.strftime(time_format) logging.info(' ------> Time ' + time_run + ' set by system') else: log_stream.info(' ----> Set time period ... FAILED') log_stream.error(' ===> Argument "time_run" is not correctly set') raise IOError('Time type or format is wrong') time_tmp = pd.Timestamp(time_run) time_run = time_tmp.floor(time_rounding) if time_period > 0: time_range = pd.date_range(end=time_run, periods=time_period, freq=time_frequency) else: log_stream.warning(' ===> TimePeriod must be greater then 0. TimePeriod is set automatically to 1') time_range = pd.DatetimeIndex([time_now], freq=time_frequency) log_stream.info(' -----> Time info defined by "time_run" argument ... DONE') elif (time_run_file_start is not None) and (time_run_file_end is not None): log_stream.info(' -----> Time info defined by "time_start" and "time_end" arguments ... ') time_run_file_start = pd.Timestamp(time_run_file_start) time_run_file_start = time_run_file_start.floor(time_rounding) time_run_file_end = pd.Timestamp(time_run_file_end) time_run_file_end = time_run_file_end.floor(time_rounding) time_now = date.today() time_run = time_now.strftime(time_format) time_run = pd.Timestamp(time_run) time_run = time_run.floor(time_rounding) time_range = pd.date_range(start=time_run_file_start, end=time_run_file_end, freq=time_frequency) log_stream.info(' -----> Time info defined by "time_start" and "time_end" arguments ... DONE') else: log_stream.info(' ----> Set time period ... FAILED') log_stream.error(' ===> Arguments "time_start" and/or "time_end" is/are not correctly set') raise IOError('Time type or format is wrong') if time_reverse: time_range = time_range[::-1] time_chunks = set_chunks(time_range) log_stream.info(' ----> Set time period ... DONE') return time_run, time_range, time_chunks # ------------------------------------------------------------------------------------- # ------------------------------------------------------------------------------------- # Method to set chunks def set_chunks(time_range, time_period='D'): time_groups = time_range.to_period(time_period) time_chunks = time_range.groupby(time_groups) return time_chunks # -------------------------------------------------------------------------------------

73df3c0eb8fec2b7771ceae6cfb845c8b3b75d43

py

Python

platon/beacon/main.py

shinnng/platon.py

3197fac3839896290210da04dd0d45f0bdc731ce

platon/beacon/main.py

shinnng/platon.py

3197fac3839896290210da04dd0d45f0bdc731ce

platon/beacon/main.py

shinnng/platon.py

3197fac3839896290210da04dd0d45f0bdc731ce

from typing import ( Any, Dict, ) import requests from platon.module import ( Module, ) class Beacon(Module): def __getattribute__(self): raise ModuleNotFoundError('This module is not available') # def __init__( # self, # base_url: str, # session: requests.Session = requests.Session(), # ) -> None: # self.base_url = base_url # self.session = session # # def _make_get_request(self, endpoint: str) -> Dict[str, Any]: # url = self.base_url + endpoint # response = self.session.get(url) # response.raise_for_status() # return response.json() # # # [ BEACON endpoints ] # # def get_genesis(self) -> Dict[str, Any]: # endpoint = "/platon/v1/beacon/genesis" # return self._make_get_request(endpoint) # # def get_hash_root(self, state_id: str = "head") -> Dict[str, Any]: # endpoint = f"/platon/v1/beacon/states/{state_id}/root" # return self._make_get_request(endpoint) # # def get_fork_data(self, state_id: str = "head") -> Dict[str, Any]: # endpoint = f"/platon/v1/beacon/states/{state_id}/fork" # return self._make_get_request(endpoint) # # def get_finality_checkpoint(self, state_id: str = "head") -> Dict[str, Any]: # endpoint = f"/platon/v1/beacon/states/{state_id}/finality_checkpoints" # return self._make_get_request(endpoint) # # def get_validators(self, state_id: str = "head") -> Dict[str, Any]: # endpoint = f"/platon/v1/beacon/states/{state_id}/validators" # return self._make_get_request(endpoint) # # def get_validator( # self, validator_id: str, state_id: str = "head" # ) -> Dict[str, Any]: # endpoint = f"/platon/v1/beacon/states/{state_id}/validators/{validator_id}" # return self._make_get_request(endpoint) # # def get_validator_balances(self, state_id: str = "head") -> Dict[str, Any]: # endpoint = f"/platon/v1/beacon/states/{state_id}/validator_balances" # return self._make_get_request(endpoint) # # def get_epoch_committees(self, state_id: str = "head") -> Dict[str, Any]: # endpoint = f"/platon/v1/beacon/states/{state_id}/committees" # return self._make_get_request(endpoint) # # def get_block_headers(self) -> Dict[str, Any]: # endpoint = "/platon/v1/beacon/headers" # return self._make_get_request(endpoint) # # def get_block_header(self, block_id: str) -> Dict[str, Any]: # endpoint = f"/platon/v1/beacon/headers/{block_id}" # return self._make_get_request(endpoint) # # def get_block(self, block_id: str) -> Dict[str, Any]: # endpoint = f"/platon/v1/beacon/blocks/{block_id}" # return self._make_get_request(endpoint) # # def get_block_root(self, block_id: str) -> Dict[str, Any]: # endpoint = f"/platon/v1/beacon/blocks/{block_id}/root" # return self._make_get_request(endpoint) # # def get_block_attestations(self, block_id: str) -> Dict[str, Any]: # endpoint = f"/platon/v1/beacon/blocks/{block_id}/attestations" # return self._make_get_request(endpoint) # # def get_attestations(self) -> Dict[str, Any]: # endpoint = "/platon/v1/beacon/pool/attestations" # return self._make_get_request(endpoint) # # def get_attester_slashings(self) -> Dict[str, Any]: # endpoint = "/platon/v1/beacon/pool/attester_slashings" # return self._make_get_request(endpoint) # # def get_proposer_slashings(self) -> Dict[str, Any]: # endpoint = "/platon/v1/beacon/pool/proposer_slashings" # return self._make_get_request(endpoint) # # def get_voluntary_exits(self) -> Dict[str, Any]: # endpoint = "/platon/v1/beacon/pool/voluntary_exits" # return self._make_get_request(endpoint) # # # [ CONFIG endpoints ] # # def get_fork_schedule(self) -> Dict[str, Any]: # endpoint = "/platon/v1/config/fork_schedule" # return self._make_get_request(endpoint) # # def get_spec(self) -> Dict[str, Any]: # endpoint = "/platon/v1/config/spec" # return self._make_get_request(endpoint) # # def get_deposit_contract(self) -> Dict[str, Any]: # endpoint = "/platon/v1/config/deposit_contract" # return self._make_get_request(endpoint) # # # [ DEBUG endpoints ] # # def get_beacon_state(self, state_id: str = "head") -> Dict[str, Any]: # endpoint = f"/platon/v1/debug/beacon/states/{state_id}" # return self._make_get_request(endpoint) # # def get_beacon_heads(self) -> Dict[str, Any]: # endpoint = "/platon/v1/debug/beacon/heads" # return self._make_get_request(endpoint) # # # [ NODE endpoints ] # # def get_node_identity(self) -> Dict[str, Any]: # endpoint = "/platon/v1/node/identity" # return self._make_get_request(endpoint) # # def get_peers(self) -> Dict[str, Any]: # endpoint = "/platon/v1/node/peers" # return self._make_get_request(endpoint) # # def get_peer(self, peer_id: str) -> Dict[str, Any]: # endpoint = f"/platon/v1/node/peers/{peer_id}" # return self._make_get_request(endpoint) # # def get_health(self) -> int: # endpoint = "/platon/v1/node/health" # url = self.base_url + endpoint # response = self.session.get(url) # return response.status_code # # def get_version(self) -> Dict[str, Any]: # endpoint = "/platon/v1/node/version" # return self._make_get_request(endpoint) # # def get_syncing(self) -> Dict[str, Any]: # endpoint = "/platon/v1/node/syncing" # return self._make_get_request(endpoint)

73df615a6efac0b92b33237a3a04b2320bdb4875

py

Python

ui/app_list/PRESUBMIT.py

Wzzzx/chromium-crosswalk

768dde8efa71169f1c1113ca6ef322f1e8c9e7de

2016-04-27T01:02:03.000Z

2021-12-13T08:53:19.000Z

ui/app_list/PRESUBMIT.py

maidiHaitai/haitaibrowser

a232a56bcfb177913a14210e7733e0ea83a6b18d

2017-03-09T09:00:50.000Z

2017-09-21T15:48:20.000Z

ui/app_list/PRESUBMIT.py

maidiHaitai/haitaibrowser

a232a56bcfb177913a14210e7733e0ea83a6b18d

2016-04-27T02:06:39.000Z

2019-12-18T08:07:00.000Z

# Copyright 2014 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. """Presubmit script for app_list. See http://dev.chromium.org/developers/how-tos/depottools/presubmit-scripts for more details about the presubmit API built into depot_tools. """ INCLUDE_CPP_FILES_ONLY = ( r'.*\.(cc|h)$', ) EXCLUDE = ( # Objective C confuses everything. r'.*/cocoa/.*', ) def CheckChangeLintsClean(input_api, output_api): """Makes sure that the ui/app_list/ code is cpplint clean.""" black_list = input_api.DEFAULT_BLACK_LIST + EXCLUDE sources = lambda x: input_api.FilterSourceFile( x, white_list = INCLUDE_CPP_FILES_ONLY, black_list = black_list) return input_api.canned_checks.CheckChangeLintsClean( input_api, output_api, sources, lint_filters=[], verbose_level=1) def CheckChangeOnUpload(input_api, output_api): results = [] results += CheckChangeLintsClean(input_api, output_api) results += input_api.canned_checks.CheckPatchFormatted(input_api, output_api) return results

73df7ab71f5006a9b92cb7673900fa39c40718f8

py

Python

tests/curve_dags/test_data.py

kevin0120/airflow

fa263cbf0ac002bdb26239ce36d5dc2a1b6251fd

2021-03-03T07:00:02.000Z

2021-03-03T07:00:02.000Z

tests/curve_dags/test_data.py

kevin0120/airflow

fa263cbf0ac002bdb26239ce36d5dc2a1b6251fd

2021-11-26T00:08:49.000Z

2021-11-26T00:09:33.000Z

tests/curve_dags/test_data.py

kevin0120/airflow

fa263cbf0ac002bdb26239ce36d5dc2a1b6251fd

2020-06-30T02:38:17.000Z

2022-01-19T06:14:08.000Z

curve_param = { "torque_low_limit": 4.0, "torque_up_limit_min": 26.0, "torque_up_limit_max": 34.0, "threshold": 0.30000001192092896, "slope_threshold": 16.5905818939209, "torque_threshold": 2.5, "angle_threshold": 0.07500000298023224, "angle_up_limit_min": 20.0, "angle_up_limit_max": 114.0, "sampling_time": 0.004999999888241291 } curve = { "cur_m": [ 0.015539206012, 0.031082325196000002, 0.031082325196000002, 0.015539206012, 0.015539206012, 0.015539206012, 0.015539206012, 0.015539206012, 0.015539206012, 0.015539206012, 0.023310765604, 0.023310765604, 0.015539206012, 0.023310765604, 0.015539206012, 0.023310765604, 0.023310765604, 0.015539206012, 0.015539206012, 0.015539206012, 0.023310765604, 0.015539206012, 0.023310765604, 0.015539206012, 0.015539206012, 0.015539206012, 0.015539206012, 0.023310765604, 0.023310765604, 0.015539206012, 0.023310765604, 0.023310765604, 0.023310765604, 0.023310765604, 0.023310765604, 0.031082325196000002, 0.031082325196000002, 0.023310765604, 0.031082325196000002, 0.031082325196000002, 0.023310765604, 0.031082325196000002, 0.031082325196000002, 0.023310765604, 0.031082325196000002, 0.031082325196000002, 0.031082325196000002, 0.031082325196000002, 0.031082325196000002, 0.031082325196000002, 0.038853884788000004, 0.031082325196000002, 0.038853884788000004, 0.038853884788000004, 0.038853884788000004, 0.038853884788000004, 0.038853884788000004, 0.04662544438, 0.04662544438, 0.04662544438, 0.04662544438, 0.04662544438, 0.04662544438, 0.04662544438, 0.04662544438, 0.04662544438, 0.054397003972, 0.04662544438, 0.054397003972, 0.04662544438, 0.04662544438, 0.054397003972, 0.062168563564, 0.062168563564, 0.062168563564, 0.062168563564, 0.062168563564, 0.069940123156, 0.062168563564, 0.062168563564, 0.062168563564, 0.069940123156, 0.069940123156, 0.069940123156, 0.069940123156, 0.069940123156, 0.069940123156, 0.077711682748, 0.077711682748, 0.077711682748, 0.077711682748, 0.077711682748, 0.077711682748, 0.077711682748, 0.077711682748, 0.08548324234, 0.09325088876, 0.077711682748, 0.08548324234, 0.08548324234, 0.09325088876, 0.09325088876, 0.09325088876, 0.09325088876, 0.09325088876, 0.09325088876, 0.101022448352, 0.101022448352, 0.101022448352, 0.101022448352, 0.101022448352, 0.108794007944, 0.101022448352, 0.108794007944, 0.108794007944, 0.108794007944, 0.116565567536, 0.116565567536, 0.116565567536, 0.116565567536, 0.116565567536, 0.124337127128, 0.116565567536, 0.124337127128, 0.116565567536, 0.124337127128, 0.124337127128, 0.13210868672, 0.13210868672, 0.13210868672, 0.139880246312, 0.139880246312, 0.139880246312, 0.139880246312, 0.147651805904, 0.147651805904, 0.147651805904, 0.147651805904, 0.155423365496, 0.155423365496, 0.155423365496, 0.155423365496, 0.16319492508800001, 0.16319492508800001, 0.16319492508800001, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.155423365496, 0.155423365496, 0.147651805904, 0.147651805904, 0.147651805904, 0.155423365496, 0.155423365496, 0.147651805904, 0.147651805904, 0.147651805904, 0.139880246312, 0.139880246312, 0.139880246312, 0.147651805904, 0.147651805904, 0.155423365496, 0.147651805904, 0.155423365496, 0.155423365496, 0.155423365496, 0.155423365496, 0.155423365496, 0.155423365496, 0.155423365496, 0.155423365496, 0.155423365496, 0.155423365496, 0.155423365496, 0.155423365496, 0.16319492508800001, 0.155423365496, 0.155423365496, 0.16319492508800001, 0.17096648468, 0.16319492508800001, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.16319492508800001, 0.16319492508800001, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.16319492508800001, 0.155423365496, 0.17096648468, 0.17096648468, 0.17096648468, 0.16319492508800001, 0.16319492508800001, 0.155423365496, 0.17096648468, 0.17096648468, 0.16319492508800001, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.1787341311, 0.17096648468, 0.1787341311, 0.17096648468, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.17096648468, 0.17096648468, 0.17096648468, 0.17096648468, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.194277250284, 0.18650569069200001, 0.18650569069200001, 0.1787341311, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.18650569069200001, 0.1787341311, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.194277250284, 0.194277250284, 0.194277250284, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.1787341311, 0.1787341311, 0.1787341311, 0.1787341311, 0.18650569069200001, 0.1787341311, 0.18650569069200001, 0.1787341311, 0.18650569069200001, 0.18650569069200001, 0.18650569069200001, 0.194277250284, 0.194277250284, 0.20204880987600002, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.18650569069200001, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.20204880987600002, 0.20204880987600002, 0.20204880987600002, 0.194277250284, 0.20204880987600002, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.20204880987600002, 0.194277250284, 0.20204880987600002, 0.194277250284, 0.20204880987600002, 0.20204880987600002, 0.194277250284, 0.194277250284, 0.20204880987600002, 0.20204880987600002, 0.20204880987600002, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.20204880987600002, 0.194277250284, 0.194277250284, 0.194277250284, 0.194277250284, 0.20204880987600002, 0.20204880987600002, 0.194277250284, 0.20204880987600002, 0.20204880987600002, 0.20204880987600002, 0.20204880987600002, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.20204880987600002, 0.20204880987600002, 0.209820369468, 0.209820369468, 0.20204880987600002, 0.209820369468, 0.20204880987600002, 0.20204880987600002, 0.20204880987600002, 0.209820369468, 0.209820369468, 0.20204880987600002, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.21759192906, 0.21759192906, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.20204880987600002, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.21759192906, 0.209820369468, 0.209820369468, 0.21759192906, 0.21759192906, 0.21759192906, 0.209820369468, 0.21759192906, 0.21759192906, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.209820369468, 0.21759192906, 0.21759192906, 0.225363488652, 0.21759192906, 0.233135048244, 0.21759192906, 0.225363488652, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.209820369468, 0.21759192906, 0.209820369468, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.225363488652, 0.21759192906, 0.225363488652, 0.225363488652, 0.225363488652, 0.225363488652, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.233135048244, 0.233135048244, 0.225363488652, 0.225363488652, 0.225363488652, 0.225363488652, 0.21759192906, 0.225363488652, 0.225363488652, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.233135048244, 0.225363488652, 0.233135048244, 0.225363488652, 0.233135048244, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.225363488652, 0.21759192906, 0.21759192906, 0.225363488652, 0.225363488652, 0.21759192906, 0.225363488652, 0.225363488652, 0.225363488652, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906, 0.225363488652, 0.225363488652, 0.21759192906, 0.225363488652, 0.225363488652, 0.225363488652, 0.233135048244, 0.233135048244, 0.225363488652, 0.233135048244, 0.225363488652, 0.225363488652, 0.225363488652, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.225363488652, 0.233135048244, 0.233135048244, 0.24090660783600001, 0.233135048244, 0.233135048244, 0.225363488652, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.225363488652, 0.233135048244, 0.24090660783600001, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.24090660783600001, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.24090660783600001, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.24090660783600001, 0.233135048244, 0.233135048244, 0.24090660783600001, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.248678167428, 0.248678167428, 0.248678167428, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.233135048244, 0.233135048244, 0.233135048244, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.248678167428, 0.24090660783600001, 0.24090660783600001, 0.233135048244, 0.233135048244, 0.233135048244, 0.233135048244, 0.24090660783600001, 0.248678167428, 0.248678167428, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.233135048244, 0.24090660783600001, 0.233135048244, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.248678167428, 0.24090660783600001, 0.248678167428, 0.248678167428, 0.248678167428, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.24090660783600001, 0.248678167428, 0.24090660783600001, 0.24090660783600001, 0.248678167428, 0.248678167428, 0.248678167428, 0.256445813848, 0.248678167428, 0.24090660783600001, 0.24090660783600001, 0.248678167428, 0.248678167428, 0.248678167428, 0.248678167428, 0.248678167428, 0.248678167428, 0.248678167428, 0.24090660783600001, 0.248678167428, 0.248678167428, 0.248678167428, 0.248678167428, 0.24090660783600001, 0.24090660783600001, 0.248678167428, 0.248678167428, 0.256445813848, 0.248678167428, 0.248678167428, 0.248678167428, 0.21759192906, 0.20204880987600002, 0.209820369468, 0.21759192906, 0.21759192906, 0.225363488652, 0.225363488652, 0.21759192906, 0.225363488652, 0.21759192906, 0.225363488652, 0.21759192906, 0.21759192906, 0.21759192906, 0.21759192906 ], "cur_w": [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.30000000000000004, 0.30000000000000004, 0.30000000000000004, 0.30000000000000004, 0.4, 0.4, 0.5, 0.5, 0.5, 0.5, 0.6000000000000001, 0.6000000000000001, 0.7000000000000001, 0.7000000000000001, 0.8, 0.8, 0.8, 0.8, 0.9, 0.9, 1, 1, 1.1, 1.1, 1.2000000000000002, 1.2000000000000002, 1.2000000000000002, 1.2000000000000002, 1.3, 1.3, 1.4000000000000001, 1.4000000000000001, 1.5, 1.5, 1.6, 1.6, 1.7000000000000002, 1.7000000000000002, 1.9000000000000001, 1.9000000000000001, 2, 2, 2, 2, 2.2, 2.2, 2.3000000000000003, 2.3000000000000003, 2.4000000000000004, 2.4000000000000004, 2.5, 2.5, 2.7, 2.7, 2.8000000000000003, 2.8000000000000003, 2.9000000000000004, 2.9000000000000004, 3.1, 3.1, 3.2, 3.2, 3.4000000000000004, 3.4000000000000004, 3.5, 3.5, 3.6, 3.6, 3.8000000000000003, 3.8000000000000003, 4, 4, 4.1000000000000005, 4.1000000000000005, 4.3, 4.3, 4.4, 4.4, 4.6000000000000005, 4.6000000000000005, 4.7, 4.7, 4.9, 4.9, 5.1000000000000005, 5.1000000000000005, 5.2, 5.2, 5.4, 5.4, 5.6000000000000005, 5.6000000000000005, 5.800000000000001, 5.800000000000001, 6, 6, 6.2, 6.2, 6.300000000000001, 6.300000000000001, 6.5, 6.5, 6.7, 6.7, 6.9, 6.9, 7.1000000000000005, 7.1000000000000005, 7.300000000000001, 7.300000000000001, 7.5, 7.5, 7.7, 7.7, 7.9, 7.9, 8.200000000000001, 8.200000000000001, 8.4, 8.4, 8.6, 8.6, 8.8, 8.8, 9, 9, 9.3, 9.3, 9.5, 9.5, 9.8, 9.8, 10, 10, 10.200000000000001, 10.200000000000001, 10.5, 10.5, 10.8, 10.8, 11, 11, 11.200000000000001, 11.200000000000001, 11.5, 11.5, 11.700000000000001, 11.700000000000001, 12, 12, 12.200000000000001, 12.200000000000001, 12.5, 12.5, 12.700000000000001, 12.700000000000001, 13, 13, 13.3, 13.3, 13.600000000000001, 13.600000000000001, 13.9, 13.9, 14.200000000000001, 14.200000000000001, 14.4, 14.4, 14.700000000000001, 14.700000000000001, 15, 15, 15.3, 15.3, 15.600000000000001, 15.600000000000001, 15.9, 15.9, 16.2, 16.2, 16.400000000000002, 16.400000000000002, 16.8, 16.8, 17.1, 17.1, 17.400000000000002, 17.400000000000002, 17.7, 17.7, 18, 18, 18.3, 18.3, 18.7, 18.7, 19, 19, 19.3, 19.3, 19.6, 19.6, 20, 20, 20.3, 20.3, 20.6, 20.6, 21, 21, 21.3, 21.3, 21.6, 21.6, 22, 22, 22.3, 22.3, 22.700000000000003, 22.700000000000003, 23, 23, 23.400000000000002, 23.400000000000002, 23.8, 23.8, 24.200000000000003, 24.200000000000003, 24.5, 24.5, 24.900000000000002, 24.900000000000002, 25.3, 25.3, 25.700000000000003, 25.700000000000003, 26, 26, 26.400000000000002, 26.400000000000002, 26.700000000000003, 26.700000000000003, 27.1, 27.1, 27.5, 27.5, 27.900000000000002, 27.900000000000002, 28.3, 28.3, 28.8, 28.8, 29.200000000000003, 29.200000000000003, 29.6, 29.6, 29.900000000000002, 29.900000000000002, 30.3, 30.3, 30.700000000000003, 30.700000000000003, 31.1, 31.1, 31.5, 31.5, 32, 32, 32.4, 32.4, 32.800000000000004, 32.800000000000004, 33.300000000000004, 33.300000000000004, 33.7, 33.7, 34.1, 34.1, 34.6, 34.6, 35, 35, 35.4, 35.4, 35.9, 35.9, 36.300000000000004, 36.300000000000004, 36.7, 36.7, 37.2, 37.2, 37.6, 37.6, 38.1, 38.1, 38.6, 38.6, 39.1, 39.1, 39.6, 39.6, 40, 40, 40.400000000000006, 40.400000000000006, 40.900000000000006, 40.900000000000006, 41.300000000000004, 41.300000000000004, 41.800000000000004, 41.800000000000004, 42.300000000000004, 42.300000000000004, 42.800000000000004, 42.800000000000004, 43.300000000000004, 43.300000000000004, 43.900000000000006, 43.900000000000006, 44.300000000000004, 44.300000000000004, 44.800000000000004, 44.800000000000004, 45.2, 45.2, 45.7, 45.7, 46.2, 46.2, 46.800000000000004, 46.800000000000004, 47.300000000000004, 47.300000000000004, 47.800000000000004, 47.800000000000004, 48.300000000000004, 48.300000000000004, 48.800000000000004, 48.800000000000004, 49.400000000000006, 49.400000000000006, 49.900000000000006, 49.900000000000006, 50.400000000000006, 50.400000000000006, 50.900000000000006, 50.900000000000006, 51.400000000000006, 51.400000000000006, 51.900000000000006, 51.900000000000006, 52.5, 52.5, 53, 53, 53.6, 53.6, 54.2, 54.2, 54.7, 54.7, 55.2, 55.2, 55.7, 55.7, 56.300000000000004, 56.300000000000004, 56.800000000000004, 56.800000000000004, 57.400000000000006, 57.400000000000006, 58, 58, 58.6, 58.6, 59.1, 59.1, 59.7, 59.7, 60.2, 60.2, 60.800000000000004, 60.800000000000004, 61.400000000000006, 61.400000000000006, 62, 62, 62.6, 62.6, 63.2, 63.2, 63.800000000000004, 63.800000000000004, 64.3, 64.3, 64.9, 64.9, 65.5, 65.5, 66.10000000000001, 66.10000000000001, 66.7, 66.7, 67.3, 67.3, 67.9, 67.9, 68.60000000000001, 68.60000000000001, 69.2, 69.2, 69.8, 69.8, 70.4, 70.4, 71, 71, 71.60000000000001, 71.60000000000001, 72.3, 72.3, 73, 73, 73.60000000000001, 73.60000000000001, 74.10000000000001, 74.10000000000001, 74.8, 74.8, 75.4, 75.4, 76, 76, 76.7, 76.7, 77.4, 77.4, 78, 78, 78.7, 78.7, 79.30000000000001, 79.30000000000001, 79.9, 79.9, 80.60000000000001, 80.60000000000001, 81.30000000000001, 81.30000000000001, 81.9, 81.9, 82.7, 82.7, 83.4, 83.4, 84, 84, 84.60000000000001, 84.60000000000001, 85.30000000000001, 85.30000000000001, 86, 86, 86.7, 86.7, 87.5, 87.5, 88.10000000000001, 88.10000000000001, 88.80000000000001, 88.80000000000001, 89.4, 89.4, 90.2, 90.2, 90.9, 90.9, 91.60000000000001, 91.60000000000001, 92.30000000000001, 92.30000000000001, 93, 93, 93.7, 93.7, 94.4, 94.4, 95.10000000000001, 95.10000000000001, 95.80000000000001, 95.80000000000001, 96.60000000000001, 96.60000000000001, 97.4, 97.4, 98.10000000000001, 98.10000000000001, 98.80000000000001, 98.80000000000001, 99.5, 99.5, 100.2, 100.2, 101, 101, 101.80000000000001, 101.80000000000001, 102.5, 102.5, 103.2, 103.2, 103.9, 103.9, 104.7, 104.7, 105.5, 105.5, 106.2, 106.2, 107, 107, 107.80000000000001, 107.80000000000001, 108.5, 108.5, 109.30000000000001, 109.30000000000001, 110, 110, 110.80000000000001, 110.80000000000001, 111.60000000000001, 111.60000000000001, 112.4, 112.4, 113.2, 113.2, 113.9, 113.9, 114.7, 114.7, 115.60000000000001, 115.60000000000001, 116.4, 116.4, 117.2, 117.2, 117.9, 117.9, 118.7, 118.7, 119.5, 119.5, 120.30000000000001, 120.30000000000001, 121.2, 121.2, 122, 122, 122.80000000000001, 122.80000000000001, 123.60000000000001, 123.60000000000001, 124.30000000000001, 124.30000000000001, 125.2, 125.2, 126.10000000000001, 126.10000000000001, 126.9, 126.9, 127.7, 127.7, 128.5, 128.5, 129.4, 129.4, 130.3, 130.3, 131.1, 131.1, 131.9, 131.9, 132.70000000000002, 132.70000000000002, 133.6, 133.6, 134.5, 134.5, 135.3, 135.3, 136.20000000000002, 136.20000000000002, 137, 137, 137.9, 137.9, 138.70000000000002, 138.70000000000002, 139.6, 139.6, 140.5, 140.5, 141.4, 141.4, 142.3, 142.3, 143.1, 143.1, 144, 144, 145, 145, 145.8, 145.8, 146.70000000000002, 146.70000000000002, 147.6, 147.6, 148.5, 148.5, 149.4, 149.4, 150.3, 150.3, 151.1, 151.1, 151.6, 151.6, 151.8, 151.8, 151.9, 151.9, 152, 152, 152, 152, 152, 152, 152, 152 ], "cur_t": [ 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, 10.5, 11, 11.5, 12, 12.5, 13, 13.5, 14, 14.5, 15, 15.5, 16, 16.5, 17, 17.5, 18, 18.5, 19, 19.5, 20, 20.5, 21, 21.5, 22, 22.5, 23, 23.5, 24, 24.5, 25, 25.5, 26, 26.5, 27, 27.5, 28, 28.5, 29, 29.5, 30, 30.5, 31, 31.5, 32, 32.5, 33, 33.5, 34, 34.5, 35, 35.5, 36, 36.5, 37, 37.5, 38, 38.5, 39, 39.5, 40, 40.5, 41, 41.5, 42, 42.5, 43, 43.5, 44, 44.5, 45, 45.5, 46, 46.5, 47, 47.5, 48, 48.5, 49, 49.5, 50, 50.5, 51, 51.5, 52, 52.5, 53, 53.5, 54, 54.5, 55, 55.5, 56, 56.5, 57, 57.5, 58, 58.5, 59, 59.5, 60, 60.5, 61, 61.5, 62, 62.5, 63, 63.5, 64, 64.5, 65, 65.5, 66, 66.5, 67, 67.5, 68, 68.5, 69, 69.5, 70, 70.5, 71, 71.5, 72, 72.5, 73, 73.5, 74, 74.5, 75, 75.5, 76, 76.5, 77, 77.5, 78, 78.5, 79, 79.5, 80, 80.5, 81, 81.5, 82, 82.5, 83, 83.5, 84, 84.5, 85, 85.5, 86, 86.5, 87, 87.5, 88, 88.5, 89, 89.5, 90, 90.5, 91, 91.5, 92, 92.5, 93, 93.5, 94, 94.5, 95, 95.5, 96, 96.5, 97, 97.5, 98, 98.5, 99, 99.5, 100, 100.5, 101, 101.5, 102, 102.5, 103, 103.5, 104, 104.5, 105, 105.5, 106, 106.5, 107, 107.5, 108, 108.5, 109, 109.5, 110, 110.5, 111, 111.5, 112, 112.5, 113, 113.5, 114, 114.5, 115, 115.5, 116, 116.5, 117, 117.5, 118, 118.5, 119, 119.5, 120, 120.5, 121, 121.5, 122, 122.5, 123, 123.5, 124, 124.5, 125, 125.5, 126, 126.5, 127, 127.5, 128, 128.5, 129, 129.5, 130, 130.5, 131, 131.5, 132, 132.5, 133, 133.5, 134, 134.5, 135, 135.5, 136, 136.5, 137, 137.5, 138, 138.5, 139, 139.5, 140, 140.5, 141, 141.5, 142, 142.5, 143, 143.5, 144, 144.5, 145, 145.5, 146, 146.5, 147, 147.5, 148, 148.5, 149, 149.5, 150, 150.5, 151, 151.5, 152, 152.5, 153, 153.5, 154, 154.5, 155, 155.5, 156, 156.5, 157, 157.5, 158, 158.5, 159, 159.5, 160, 160.5, 161, 161.5, 162, 162.5, 163, 163.5, 164, 164.5, 165, 165.5, 166, 166.5, 167, 167.5, 168, 168.5, 169, 169.5, 170, 170.5, 171, 171.5, 172, 172.5, 173, 173.5, 174, 174.5, 175, 175.5, 176, 176.5, 177, 177.5, 178, 178.5, 179, 179.5, 180, 180.5, 181, 181.5, 182, 182.5, 183, 183.5, 184, 184.5, 185, 185.5, 186, 186.5, 187, 187.5, 188, 188.5, 189, 189.5, 190, 190.5, 191, 191.5, 192, 192.5, 193, 193.5, 194, 194.5, 195, 195.5, 196, 196.5, 197, 197.5, 198, 198.5, 199, 199.5, 200, 200.5, 201, 201.5, 202, 202.5, 203, 203.5, 204, 204.5, 205, 205.5, 206, 206.5, 207, 207.5, 208, 208.5, 209, 209.5, 210, 210.5, 211, 211.5, 212, 212.5, 213, 213.5, 214, 214.5, 215, 215.5, 216, 216.5, 217, 217.5, 218, 218.5, 219, 219.5, 220, 220.5, 221, 221.5, 222, 222.5, 223, 223.5, 224, 224.5, 225, 225.5, 226, 226.5, 227, 227.5, 228, 228.5, 229, 229.5, 230, 230.5, 231, 231.5, 232, 232.5, 233, 233.5, 234, 234.5, 235, 235.5, 236, 236.5, 237, 237.5, 238, 238.5, 239, 239.5, 240, 240.5, 241, 241.5, 242, 242.5, 243, 243.5, 244, 244.5, 245, 245.5, 246, 246.5, 247, 247.5, 248, 248.5, 249, 249.5, 250, 250.5, 251, 251.5, 252, 252.5, 253, 253.5, 254, 254.5, 255, 255.5, 256, 256.5, 257, 257.5, 258, 258.5, 259, 259.5, 260, 260.5, 261, 261.5, 262, 262.5, 263, 263.5, 264, 264.5, 265, 265.5, 266, 266.5, 267, 267.5, 268, 268.5, 269, 269.5, 270, 270.5, 271, 271.5, 272, 272.5, 273, 273.5, 274, 274.5, 275, 275.5, 276, 276.5, 277, 277.5, 278, 278.5, 279, 279.5, 280, 280.5, 281, 281.5, 282, 282.5, 283, 283.5, 284, 284.5, 285, 285.5, 286, 286.5, 287, 287.5, 288, 288.5, 289, 289.5, 290, 290.5, 291, 291.5, 292, 292.5, 293, 293.5, 294, 294.5, 295, 295.5, 296, 296.5, 297, 297.5, 298, 298.5, 299, 299.5, 300, 300.5, 301, 301.5, 302, 302.5, 303, 303.5, 304, 304.5, 305, 305.5, 306, 306.5, 307, 307.5, 308, 308.5, 309, 309.5, 310, 310.5, 311, 311.5, 312, 312.5, 313, 313.5, 314, 314.5, 315, 315.5, 316, 316.5, 317, 317.5, 318, 318.5, 319, 319.5, 320, 320.5, 321, 321.5, 322, 322.5, 323, 323.5, 324, 324.5, 325, 325.5, 326, 326.5, 327, 327.5, 328, 328.5, 329, 329.5, 330, 330.5, 331, 331.5, 332, 332.5, 333, 333.5, 334, 334.5, 335, 335.5, 336, 336.5, 337, 337.5, 338, 338.5, 339, 339.5, 340, 340.5, 341, 341.5, 342, 342.5, 343, 343.5, 344 ] } cluster_data = { "algorithm_version": 1, "curve_template_groups_k": 1, "curve_template_group_array": [ { "template_centroid_index": 0, "template_data_array": [ { "template_angle": [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.019819820299744606, 0.04144144058227539, 0.061261262744665146, 0.08288288116455078, 0.10450451076030731, 0.12612612545490265, 0.14774774014949799, 0.16756756603717804, 0.18918919563293457, 0.2108108103275299, 0.23063063621520996, 0.2522522509098053, 0.27387386560440063, 0.29549548029899597, 0.315315306186676, 0.33693695068359375, 0.3567567467689514, 0.37837839126586914, 0.3981982171535492, 0.41981980204582214, 0.44144144654273987, 0.4630630910396576, 0.48288285732269287, 0.5045045018196106, 0.5261261463165283, 0.5477477312088013, 0.569369375705719, 0.5891892313957214, 0.6108108162879944, 0.6324324011802673, 0.6540540456771851, 0.6738739013671875, 0.6954954862594604, 0.7153153419494629, 0.7369369864463806, 0.7567567825317383, 0.7783783674240112, 0.800000011920929, 0.8216215968132019, 0.8432432413101196, 0.8630630970001221, 0.884684681892395, 0.906306266784668, 0.9279279112815857, 0.9495495557785034, 0.9693693518638611, 0.9981982111930847, 1.0 ], "template_torque": [ 4.795041561126709, 6.183536529541016, 7.269715785980225, 7.903450012207031, 8.448033332824707, 8.843896865844727, 9.172185897827148, 9.437216758728027, 9.691280364990234, 9.950499534606934, 10.18355655670166, 10.387411117553711, 10.586793899536133, 10.82284164428711, 11.091927528381348, 11.359953880310059, 11.659576416015625, 11.960306167602539, 12.280977249145508, 12.595656394958496, 12.9249267578125, 13.279316902160645, 13.665048599243164, 14.09547233581543, 14.5075044631958, 14.879796028137207, 15.208659172058105, 15.522689819335938, 15.811670303344727, 16.069271087646484, 16.33078956604004, 16.603214263916016, 16.912200927734375, 17.238882064819336, 17.58001708984375, 17.919179916381836, 18.27083969116211, 18.658002853393555, 19.094768524169922, 19.555355072021484, 20.05722427368164, 20.53191566467285, 20.939228057861328, 21.25922966003418, 21.541889190673828, 21.82962417602539, 22.084102630615234, 22.314346313476562, 22.549890518188477, 22.820247650146484, 23.118629455566406, 23.422975540161133, 23.735721588134766, 24.05453872680664, 24.3846492767334, 24.74663734436035, 25.165857315063477, 25.60770034790039, 26.075511932373047, 26.52484130859375, 26.927270889282227, 27.2296199798584, 27.49077796936035, 27.742076873779297, 27.989971160888672, 28.173410415649414, 28.33307456970215, 28.513696670532227, 28.720355987548828, 28.97340965270996, 29.275951385498047, 29.591073989868164, 29.831607818603516, 26.774738311767578 ], "start_point": 6 } ] } ] }

73dfa520d48157846e90bf0a83321dc34ebe7176

py

Python

webapp/admin.py

saintlyzero/Django-API-Assignment

3e582f3388536df05c9c8e2ea2b6ab2fd6349a2d

2019-11-20T10:00:56.000Z

2019-11-20T10:00:56.000Z

webapp/admin.py

saintlyzero/Django-API-Assignment

3e582f3388536df05c9c8e2ea2b6ab2fd6349a2d

webapp/admin.py

saintlyzero/Django-API-Assignment

3e582f3388536df05c9c8e2ea2b6ab2fd6349a2d

from django.contrib import admin from .models import Employees # Register your models here. admin.site.register(Employees)

73dfb28d8fc1858b1963cfe4cbb25f4e3947caaf

py

Python

func_tests/tests/projects/testsmsquestionnaire/project_test_sms_questionnaire_data.py

ICT4H/dcs-web

fb0f53fad4401cfac1c1789ff28b9d5bda40c975

2015-11-02T09:11:12.000Z

2015-11-02T09:11:12.000Z

func_tests/tests/projects/testsmsquestionnaire/project_test_sms_questionnaire_data.py

ICT4H/dcs-web

fb0f53fad4401cfac1c1789ff28b9d5bda40c975

func_tests/tests/projects/testsmsquestionnaire/project_test_sms_questionnaire_data.py

ICT4H/dcs-web

fb0f53fad4401cfac1c1789ff28b9d5bda40c975

from tests.logintests.login_data import USERNAME, PASSWORD VALID_CREDENTIALS = {USERNAME: "quotareached@mailinator.com", PASSWORD: "test123"} PROJECT_NAME = "clinic3 test project" UPGRADE_INSTRUCTION_MSG = u'You have reached your limit of 1000 free Submissions. Subscribe to a monthly subscription to continue submitting data for your questionnaires.' MY_DATASENDERS_PAGE_TITLE = "Questionnaires - Data Senders" MY_SUBJECTS_PAGE_TITLE = "Questionnaires - Identification Numbers" REMINDERS_PAGE_TITLE = "Questionnaire - Reminders" REVIEW_N_TEST_PAGE_TITLE = "Review & Test" DATA_ANALYSIS_PAGE_TITLE = "Data Analysis" SEND_MESSAGE_PAGE_TITLE = "Questionnaire - Messages" SUBMISSION_LOG_PAGE_TITLE = "Submission Log" WEB_SUBMISSION_PAGE_TITLE = "Web Submission"

73dfb2f89834f89c63524cd67d09c68a6f584df0

py

Python

HackerRank/DynamicProg_MaxArraySum.py

RafayAK/CodingPrep

718eccb439db0f6e727806964766a40e8234c8a9

2019-09-07T17:31:17.000Z

2022-03-05T09:59:46.000Z

HackerRank/DynamicProg_MaxArraySum.py

RafayAK/CodingPrep

718eccb439db0f6e727806964766a40e8234c8a9

HackerRank/DynamicProg_MaxArraySum.py

RafayAK/CodingPrep

718eccb439db0f6e727806964766a40e8234c8a9

2019-09-07T17:31:24.000Z

2019-10-28T16:10:52.000Z

'''Given an array of integers, find the subset of non-adjacent elements with the maximum sum. Calculate the sum of that subset. For example, given an array arr= [-2, 1, 3, -4, 5] we have the following possible subsets: Subset Sum [-2, 3, 5] 6 [-2, 3] 1 [-2, -4] -6 [-2, 5] 3 [1, -4] -3 [1, 5] 6 [3, 5] 8 Our maximum subset sum is 8. Function Description Complete the maxSubsetSum function in the editor below. It should return an integer representing the maximum subset sum for the given array. maxSubsetSum has the following parameter(s): arr: an array of integers Return the maximum sum described in the statement. Sample Input 0 5 3 7 4 6 5 Sample Output 0 13 Explanation 0 Our possible subsets are and . The largest subset sum is from subset Sample Input 1 5 2 1 5 8 4 Sample Output 1 11 Explanation 1 Our subsets are and . The maximum subset sum is from the first subset listed. Sample Input 2 5 3 5 -7 8 10 Sample Output 2 15 Explanation 2 Our subsets are and . The maximum subset sum is from the fifth subset listed. ''' # !/bin/python3 import math import os import random import re import sys # Complete the maxSubsetSum function below. ''' Intro: Since we know it's DP, we can solve the problem by solving subproblems of smaller size. Because of the condition. No two adjacent elements can be picked. Therefore we can either take one and then skip one, or skip one and run the subroutine. we can solve this problem in linear time and constant space ;) Idea: store solutions for problem of size i-2 and i-1, where i is the size of the subproblem. The solution for problem of size i is either: 1- solution for problem i-1, or 2- solution for problem i-2, or 3- solution of problem i-2 + arr[i], 4- or ar[i]. Iterate for every i. Start with 0, 0 for problems of size - 2 and -1 ''' def maxSubsetSum(arr): a = float('-inf') # store solution for set of size i-2 b = float('-inf') # store solution for set of size i-1 for value in arr: a,b = b, max(a, a+value, b, value) return b if __name__ == '__main__': #fptr = open(os.environ['OUTPUT_PATH'], 'w') n = int(input()) arr = list(map(int, input().rstrip().split())) res = maxSubsetSum(arr) print(res) # fptr.write(str(res) + '\n') # # fptr.close()

73dff2c940155d5cc3d594399f925533d8ecf12b

py

Python

doc/conf.py

glotzerlab/signac

a03a6d05d0f994ac9c4d5353533883e49cf6b386

2019-01-31T01:37:20.000Z

2022-03-29T10:35:34.000Z

doc/conf.py

glotzerlab/signac

a03a6d05d0f994ac9c4d5353533883e49cf6b386

2019-01-31T14:08:17.000Z

2022-03-31T21:51:48.000Z

doc/conf.py

glotzerlab/signac

a03a6d05d0f994ac9c4d5353533883e49cf6b386

2019-01-31T14:36:50.000Z

2022-03-14T03:48:32.000Z

"""signac documentation build configuration file. This file is executed with the current directory set to its containing dir. Note that not all possible configuration values are present in this autogenerated file. All configuration values have a default; values that are commented out serve to show the default. """ import sys from unittest.mock import MagicMock import sphinx_rtd_theme class Mock(MagicMock): """Mocks modules and their contained objects.""" @classmethod def __getattr__(cls, name): if name == "_mock_methods": return [] if name == "version_tuple": return (3, 0) return Mock() MOCK_MODULES = ["pymongo", "gridfs", "mpi4py"] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "IPython.sphinxext.ipython_console_highlighting", "nbsphinx", "sphinx.ext.autodoc", "sphinx.ext.autosummary", "sphinx.ext.intersphinx", "sphinx.ext.napoleon", "sphinxcontrib.programoutput", ] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # source_suffix = ['.rst', '.md'] source_suffix = ".rst" # The encoding of source files. # source_encoding = 'utf-8-sig' # The master toctree document. master_doc = "index" # General information about the project. project = "signac" copyright = "The Regents of the University of Michigan" author = ( "Carl S. Adorf, Vyas Ramasubramani, Bradley D. Dice, " "Michael M. Henry, Brandon Butler, Paul M. Dodd, Sharon C. Glotzer" ) # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = "1.7.0" # The full version, including alpha/beta/rc tags. release = "1.7.0" # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # today = '' # Else, today_fmt is used as the format for a strftime call. # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ["_build"] # The reST default role (used for this markup: `text`) to use for all # documents. # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. # keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = "sphinx_rtd_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { # Show the [+] icon to expand headings in the sidebar. Default is True. "collapse_navigation": False, } # Add any paths that contain custom themes here, relative to this directory. html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". # html_title = None # A shorter title for the navigation bar. Default is the same as html_title. # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. html_logo = "images/logo.png" # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = "images/favicon.ico" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # These paths are either relative to html_static_path # or fully qualified paths (eg. https://...) html_css_files = [ "css/signac-theme.css", ] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. # html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # html_additional_pages = {} # If false, no module index is generated. # html_domain_indices = True # If false, no index is generated. # html_use_index = True # If true, the index is split into individual pages for each letter. # html_split_index = False # If true, links to the reST sources are added to the pages. # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' # html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # Now only 'ja' uses this config value # html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. # html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = "signacdoc" # -- Options for LaTeX output --------------------------------------------- latex_elements: dict = { # The paper size ('letterpaper' or 'a4paper'). # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # 'preamble': '', # Latex figure (float) alignment # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, "signac.tex", "signac Documentation", "Carl Simon Adorf", "manual"), ] # The name of an image file (relative to this directory) to place at the top of # the title page. # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # latex_use_parts = False # If true, show page references after internal links. # latex_show_pagerefs = False # If true, show URL addresses after external links. # latex_show_urls = False # Documents to append as an appendix to all manuals. # latex_appendices = [] # If false, no module index is generated. # latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, "signac", "signac Documentation", [author], 1)] # If true, show URL addresses after external links. # man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( master_doc, "signac", "signac Documentation", author, "signac", "A simple data management framework.", "Miscellaneous", ), ] # Documents to append as an appendix to all manuals. # texinfo_appendices = [] # If false, no module index is generated. # texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. # texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. # texinfo_no_detailmenu = False intersphinx_mapping = { "python": ("https://docs.python.org/3", None), "pymongo": ("https://pymongo.readthedocs.io/en/stable/", None), "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), "h5py": ("https://docs.h5py.org/en/stable/", None), "zarr": ("https://zarr.readthedocs.io/en/stable", None), "redis": ("https://redis-py.readthedocs.io/en/stable/", None), "numcodecs": ("https://numcodecs.readthedocs.io/en/stable/", None), }

73dff4f34405910632f481fc2e5a3b3700c44449

py

Python

PythonProjects/99-CapstoneProject-202020-DavidAndVIbha/libs/rosebot.py

much2mutch/csse120-public

4f862a6deb7a5373fb5723fb2a23e4042e4d4157

PythonProjects/99-CapstoneProject-202020-DavidAndVIbha/libs/rosebot.py

much2mutch/csse120-public

4f862a6deb7a5373fb5723fb2a23e4042e4d4157

PythonProjects/99-CapstoneProject-202020-DavidAndVIbha/libs/rosebot.py

much2mutch/csse120-public

4f862a6deb7a5373fb5723fb2a23e4042e4d4157

""" Capstone Team Project. Code to run on a ROBOT (NOT a laptop). This code defines the RoseBot class (the top-level class for a robot). In the code that you write for making a robot do things, you should construct a RoseBot object and then use it as in this example: ------------------------------------------------ import libs.rosebot as rb def main(): robot = rb.RoseBot() robot.drive_system.go(100, -40) robot.touch_sensor.wait_until_pressed() etc ------------------------------------------------ Authors: Your professors (for the framework). Winter term, 2019-2020. """ ############################################################################### # STUDENTS: *** READ this file. Understanding it is CRITICAL. *** # *** But do NOT change ANYTHING in this module. *** ############################################################################### # ----------------------------------------------------------------------------- # Note below how to write an IMPORT statement # that imports a module that is in the LIBS sub-folder. # ----------------------------------------------------------------------------- import libs.rosebot_drive_system as drive import libs.rosebot_touch_sensor as touch import libs.rosebot_arm_and_claw as arm import libs.rosebot_leds as leds import libs.rosebot_brick_buttons as bb import libs.rosebot_remote_control as rc import libs.rosebot_color_sensor as color import libs.rosebot_infrared_proximity_sensor as proximity import libs.rosebot_camera_sensor as camera import libs.rosebot_sound as sound import libs.rosebot_beacon_sensor as b_sensor # ----------------------------------------------------------------------------- # The following import OPTIONAL modules, which you will implement or not # as you choose. They contain "stubs" so they will not break any code as is. # ----------------------------------------------------------------------------- import libs.rosebot_beacon_seeker as b_seeker import libs.rosebot_line_follower as follower import libs.rosebot_camera_tracker as tracker ############################################################################### # RoseBot. ############################################################################### class RoseBot(object): """ The top-level class for making a robot do things. """ def __init__(self): """ Constructs instances of each of the sub-systems of a Snatch3r robot and sets instance variables to them. """ self.drive_system = drive.DriveSystem("B", "C") self.touch_sensor = touch.TouchSensor(1) self.arm_and_claw = arm.ArmAndClaw(self.touch_sensor) self.leds = leds.Leds() self.brick_buttons = bb.BrickButtons() self.remote_control = rc.RemoteControl() self.color_sensor = color.ColorSensor(3) self.infrared_proximity_sensor = proximity.InfraredProximitySensor(4) self.camera = camera.CameraSensor(2) self.sound = sound.Sound() self.beacon_sensor = b_sensor.BeaconSensor(4, 1) # --------------------------------------------------------------------- # The following are OPTIONAL sub-systems, which you will implement # or not as you choose. If you do not implement them, # then of course these sub-systems will not work. # --------------------------------------------------------------------- self.beacon_seeker = b_seeker.BeaconSeeker(self.beacon_sensor, self.drive_system) self.line_follower = follower.LineFollower(self.color_sensor, self.drive_system) self.camera_tracker = tracker.CameraTracker(self.camera, self.drive_system)

73dffd013f513141da172fae9acad35f1d938d13

py

Python

djstripe/migrations/0006_2_3.py

ExtraE113/dj-stripe

1b50be13fc99b624388a005b8aa1e26c57392203

2017-06-04T18:44:20.000Z

2022-03-27T07:28:32.000Z

djstripe/migrations/0006_2_3.py

ExtraE113/dj-stripe

1b50be13fc99b624388a005b8aa1e26c57392203

2017-06-05T01:57:20.000Z

2022-03-31T23:42:54.000Z

djstripe/migrations/0006_2_3.py

ExtraE113/dj-stripe

1b50be13fc99b624388a005b8aa1e26c57392203

2017-06-12T03:18:10.000Z

2022-03-29T17:05:18.000Z

# Generated by Django 3.0.5 on 2020-04-10 02:30 import django.db.models.deletion from django.conf import settings from django.db import migrations import djstripe.enums import djstripe.fields class Migration(migrations.Migration): dependencies = [("djstripe", "0001_initial")] operations = [ migrations.RemoveField( model_name="invoice", name="closed", ), migrations.RemoveField( model_name="invoice", name="forgiven", ), migrations.RemoveField( model_name="upcominginvoice", name="closed", ), migrations.RemoveField( model_name="upcominginvoice", name="forgiven", ), migrations.AddField( model_name="invoice", name="status", field=djstripe.fields.StripeEnumField( blank=True, default="", enum=djstripe.enums.InvoiceStatus, help_text="The status of the invoice, one of draft, open, paid, uncollectible, or void.", max_length=13, ), ), migrations.AddField( model_name="upcominginvoice", name="status", field=djstripe.fields.StripeEnumField( blank=True, default="", enum=djstripe.enums.InvoiceStatus, help_text="The status of the invoice, one of draft, open, paid, uncollectible, or void.", max_length=13, ), ), migrations.RenameField( model_name="subscription", old_name="billing", new_name="collection_method", ), migrations.AddField( model_name="invoice", name="discount", field=djstripe.fields.JSONField( blank=True, help_text="Describes the current discount applied to this subscription, if there is one. When billing, a discount applied to a subscription overrides a discount applied on a customer-wide basis.", null=True, ), ), migrations.AddField( model_name="subscription", name="default_payment_method", field=djstripe.fields.StripeForeignKey( blank=True, help_text="The default payment method for the subscription. It must belong to the customer associated with the subscription. If not set, invoices will use the default payment method in the customer's invoice settings.", null=True, on_delete=django.db.models.deletion.SET_NULL, related_name="+", to="djstripe.PaymentMethod", to_field=settings.DJSTRIPE_FOREIGN_KEY_TO_FIELD, ), ), migrations.AddField( model_name="subscription", name="discount", field=djstripe.fields.JSONField(blank=True, null=True), ), migrations.AddField( model_name="subscription", name="next_pending_invoice_item_invoice", field=djstripe.fields.StripeDateTimeField( blank=True, help_text="Specifies the approximate timestamp on which any pending invoice items will be billed according to the schedule provided at pending_invoice_item_interval.", null=True, ), ), migrations.AddField( model_name="subscription", name="pending_invoice_item_interval", field=djstripe.fields.JSONField( blank=True, help_text="Specifies an interval for how often to bill for any pending invoice items. It is analogous to calling Create an invoice for the given subscription at the specified interval.", null=True, ), ), migrations.AddField( model_name="subscription", name="pending_update", field=djstripe.fields.JSONField( blank=True, help_text="If specified, pending updates that will be applied to the subscription once the latest_invoice has been paid.", null=True, ), ), migrations.AddField( model_name="subscription", name="start_date", field=djstripe.fields.StripeDateTimeField( blank=True, help_text="Date when the subscription was first created. The date might differ from the created date due to backdating.", null=True, ), ), migrations.AddField( model_name="upcominginvoice", name="default_source", field=djstripe.fields.PaymentMethodForeignKey( help_text="The default payment source for the invoice. It must belong to the customer associated with the invoice and be in a chargeable state. If not set, defaults to the subscription's default source, if any, or to the customer's default source.", null=True, on_delete=django.db.models.deletion.SET_NULL, related_name="upcoming_invoices", to="djstripe.DjstripePaymentMethod", ), ), migrations.AddField( model_name="upcominginvoice", name="discount", field=djstripe.fields.JSONField( blank=True, help_text="Describes the current discount applied to this subscription, if there is one. When billing, a discount applied to a subscription overrides a discount applied on a customer-wide basis.", null=True, ), ), migrations.AddField( model_name="invoice", name="default_source", field=djstripe.fields.PaymentMethodForeignKey( blank=True, help_text="The default payment source for the invoice. It must belong to the customer associated with the invoice and be in a chargeable state. If not set, defaults to the subscription's default source, if any, or to the customer's default source.", null=True, on_delete=django.db.models.deletion.SET_NULL, related_name="invoices", to="djstripe.DjstripePaymentMethod", ), ), migrations.AddField( model_name="subscription", name="default_source", field=djstripe.fields.PaymentMethodForeignKey( blank=True, help_text="The default payment source for the subscription. It must belong to the customer associated with the subscription and be in a chargeable state. If not set, defaults to the customer's default source.", null=True, on_delete=django.db.models.deletion.SET_NULL, related_name="subscriptions", to="djstripe.DjstripePaymentMethod", ), ), ]

73dfffedbdf487042aae7f145e1657c9691e0c82

py

Python

easyml/helpers/model_builder.py

evancasey1/EasyML

69f0c246cb7e1d6f7167eb504c30693088e703fd

easyml/helpers/model_builder.py

evancasey1/EasyML

69f0c246cb7e1d6f7167eb504c30693088e703fd

easyml/helpers/model_builder.py

evancasey1/EasyML

69f0c246cb7e1d6f7167eb504c30693088e703fd

2020-10-25T08:14:33.000Z

2020-10-25T08:14:33.000Z

import json from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.linear_model import LogisticRegression, LinearRegression from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor from sklearn.neighbors.nearest_centroid import NearestCentroid from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor from sklearn.naive_bayes import GaussianNB from sklearn import svm from .constants import COLUMN_TYPE, ALGORITHM, ALGORITHM_NAME_MAP, ALGORITHM_TYPES from mainsite.models import CsvFile, CsvFileData, MLModel from .util import get_dataframe from .util import get_match_acc def create_model(algorithm_type_num, file_id, parameters): file_data = CsvFileData.objects.filter(parent_file_id=file_id)\ .exclude(type=COLUMN_TYPE.IGNORE).order_by('column_num') if file_data.count() == 0: print("Error: No data for file {}".format(file_id)) return input_data = file_data.filter(type=COLUMN_TYPE.INPUT).order_by('column_num') target_data = file_data.filter(type=COLUMN_TYPE.TARGET) alg_type = ALGORITHM_NAME_MAP[algorithm_type_num] input_df = get_dataframe(input_data) target_df = get_dataframe(target_data) target_df = target_df.values.ravel() algorithm_type_nums = [algorithm_type_num] if algorithm_type_num == ALGORITHM.AUTOMATIC: alg_method = parameters['auto_alg_type'] if alg_method == 'auto_classification': algorithm_type_nums = ALGORITHM_TYPES.CLASSIFICATION else: algorithm_type_nums = ALGORITHM_TYPES.REGRESSION best_acc = None best_acc_type = None best_model = None temp_model = None best_alg_type = alg_type for alg_type_num in algorithm_type_nums: if alg_type_num == ALGORITHM.LINEAR_REGRESSION: temp_model = create_linear_regression_model(input_df, target_df, parameters) elif alg_type_num == ALGORITHM.K_NEAREST_NEIGHBORS_CLASSIFIER: temp_model = create_k_nearest_neighbors_classifier(input_df, target_df, parameters) elif alg_type_num == ALGORITHM.K_NEAREST_NEIGHBORS_REGRESSOR: temp_model = create_k_nearest_neighbors_regressor(input_df, target_df, parameters) elif alg_type_num == ALGORITHM.LOGISTIC_REGRESSION: temp_model = create_logistic_regression_model(input_df, target_df, parameters) elif alg_type_num == ALGORITHM.NEAREST_CENTROID: temp_model = create_nearest_centroid(input_df, target_df, parameters) elif alg_type_num == ALGORITHM.LINEAR_DISCRIMINANT_ANALYSIS: temp_model = create_linear_discriminant_analysis(input_df, target_df, parameters) elif alg_type_num == ALGORITHM.DECISION_TREE_REGRESSOR: temp_model = create_decision_tree_regressor(input_df, target_df, parameters) elif alg_type_num == ALGORITHM.GAUSSIAN_NAIVE_BAYES: temp_model = create_gaussian_naive_bayes(input_df, target_df, parameters) elif alg_type_num == ALGORITHM.RANDOM_FOREST_CLASSIFIER: temp_model = create_random_forest_classifier(input_df, target_df, parameters) elif alg_type_num == ALGORITHM.RANDOM_FOREST_REGRESSOR: temp_model = create_random_forest_regressor(input_df, target_df, parameters) elif alg_type_num == ALGORITHM.SUPPORT_VECTOR_MACHINE_CLASSIFIER: temp_model = create_support_vector_machine_classifier(input_df, target_df, parameters) elif alg_type_num == ALGORITHM.SUPPORT_VECTOR_MACHINE_REGRESSOR: temp_model = create_support_vector_machine_regressor(input_df, target_df, parameters) if not best_acc or parameters['accuracy'] > best_acc: best_acc = parameters['accuracy'] best_acc_type = parameters['accuracy_type'] best_model = temp_model if algorithm_type_num == ALGORITHM.AUTOMATIC: best_alg_type = 'Automatic_' + ALGORITHM_NAME_MAP[alg_type_num] else: best_alg_type = ALGORITHM_NAME_MAP[alg_type_num] if best_model: save_model(best_model, best_alg_type, algorithm_type_num, file_id, parameters, best_acc, best_acc_type) def save_model(model, alg_type, algorithm_type_num, file_id, parameters, best_acc, best_acc_type): parent_file = CsvFile.objects.get(id=file_id) display_name = "{}_{}".format(parent_file.display_name, alg_type) same_name_count = MLModel.objects.filter(name=parent_file.display_name, type=alg_type).count() if same_name_count > 0: display_name += ' ({})'.format(same_name_count) display_name = display_name.replace(' ', '_') model_obj = MLModel() model_obj.type = alg_type model_obj.type_num = algorithm_type_num model_obj.data = model model_obj.name = parent_file.display_name model_obj.display_name = display_name model_obj.parameters = json.dumps(parameters) model_obj.parent_file = CsvFile.objects.get(id=file_id) model_obj.accuracy = best_acc model_obj.accuracy_type = best_acc_type model_obj.save() def create_linear_regression_model(input_df, target_df, parameters): fit_intercept = bool(parameters.get('linreg_fit_intercept', False)) normalize = bool(parameters.get('linreg_normalize', False)) x_train, x_test, y_train, y_test = train_test_split(input_df, target_df) lin_reg = LinearRegression(fit_intercept=fit_intercept, normalize=normalize) lin_reg_test = lin_reg.fit(x_train, y_train) score = round(lin_reg_test.score(x_test, y_test), 4) parameters['accuracy'] = score parameters['accuracy_type'] = 'R^2' lin_reg = lin_reg.fit(input_df, target_df) return lin_reg def create_logistic_regression_model(input_df, target_df, parameters): logreg_penalty = parameters.get('logreg_penalty', 'l2') logreg_c_select = parameters.get('logreg_C_select', 'custom') logreg_fit_intercept = bool(parameters.get('logreg_fit_intercept', False)) if logreg_penalty == 'l1': solver = 'liblinear' else: solver = 'lbfgs' if logreg_c_select == 'custom': logreg_c = int(parameters.get('logreg_C', 1.0)) logreg = LogisticRegression(C=logreg_c, penalty=logreg_penalty, fit_intercept=logreg_fit_intercept, solver=solver) else: steps = [('std_scaler', StandardScaler())] steps += [('log_regression', LogisticRegression(penalty=logreg_penalty, multi_class='auto', fit_intercept=logreg_fit_intercept, solver=solver))] pipe = Pipeline(steps) param_grid = {'log_regression__C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000]} logreg = GridSearchCV(estimator=pipe, param_grid=param_grid, cv=5) x_train, x_test, y_train, y_test = train_test_split(input_df, target_df) clf_test = logreg.fit(x_train, y_train) acc = get_match_acc(clf_test.predict(x_test), y_test) parameters['accuracy'] = acc parameters['accuracy_type'] = 'Accuracy [%]' clf = logreg.fit(input_df, target_df) return clf def create_linear_discriminant_analysis(input_df, target_df, parameters): solver = parameters.get('lda_solver', 'svd') clf = LinearDiscriminantAnalysis(solver=solver) x_train, x_test, y_train, y_test = train_test_split(input_df, target_df) clf_test = clf.fit(x_train, y_train) acc = get_match_acc(clf_test.predict(x_test), y_test) parameters['accuracy'] = acc parameters['accuracy_type'] = 'Accuracy [%]' clf.fit(input_df, target_df) return clf def create_decision_tree_regressor(input_df, target_df, parameters): criterion = parameters.get('dtr_criterion', 'mse') presort = bool(parameters.get('dtr_presort', False)) max_depth_choice = parameters.get('dtr_max_depth', 'none') x_train, x_test, y_train, y_test = train_test_split(input_df, target_df) if max_depth_choice == 'none': best_depth = None elif max_depth_choice == 'custom': best_depth = parameters.get('dtr_custom_depth', None) else: r2_lst = [] depth_iter = 5 depth_start = 10 depth_lst = [] for i in range(depth_iter): depth_lst.append(depth_start**i) # Select model with best r^2 and least depth for depth in depth_lst: dt_regr = DecisionTreeRegressor(max_depth=depth, presort=presort, criterion=criterion) dt_regr.fit(x_train, y_train) r2_lst.append(dt_regr.score(x_test, y_test)) depth_index, r2 = min(enumerate(r2_lst), key=lambda x: abs(x[1] - 1)) best_depth = depth_lst[depth_index] dt_regr = DecisionTreeRegressor(max_depth=best_depth, presort=presort, criterion=criterion) regr_test = dt_regr.fit(x_train, y_train) score = round(regr_test.score(x_test, y_test), 4) parameters['accuracy'] = score parameters['accuracy_type'] = 'R^2' dt_regr.fit(input_df, target_df) return dt_regr def create_gaussian_naive_bayes(input_df, target_df, parameters): gnb = GaussianNB() x_train, x_test, y_train, y_test = train_test_split(input_df, target_df) clf_test = gnb.fit(x_train, y_train) acc = get_match_acc(clf_test.predict(x_test), y_test) parameters['accuracy'] = acc parameters['accuracy_type'] = 'Accuracy [%]' gnb.fit(input_df, target_df) return gnb def create_random_forest_classifier(input_df, target_df, parameters): criterion = parameters.get('rfc_criterion', 'gini') n_estimators = int(parameters.get('rfc_n_estimators', 100)) depth_select = parameters.get('rfc_max_depth', 'none') x_train, x_test, y_train, y_test = train_test_split(input_df, target_df) if depth_select == 'none': best_depth = None elif depth_select == 'custom': best_depth = parameters.get('rfc_custom_depth', None) else: r2_lst = [] depth_iter = 5 depth_start = 10 depth_lst = [] for i in range(depth_iter): depth_lst.append(depth_start**i) # Select model with best r^2 and least depth for depth in depth_lst: rf_clf = RandomForestClassifier(n_estimators=n_estimators, max_depth=depth, criterion=criterion, oob_score=True) rf_clf.fit(x_train, y_train) r2_lst.append(rf_clf.oob_score_) depth_index, r2 = min(enumerate(r2_lst), key=lambda x: abs(x[1] - 1)) best_depth = depth_lst[depth_index] rf_clf = RandomForestClassifier(n_estimators=n_estimators, max_depth=best_depth, criterion=criterion) clf_test = rf_clf.fit(x_train, y_train) acc = get_match_acc(clf_test.predict(x_test), y_test) parameters['accuracy'] = acc parameters['accuracy_type'] = 'Accuracy [%]' rf_clf.fit(input_df, target_df) return rf_clf def create_random_forest_regressor(input_df, target_df, parameters): criterion = parameters.get('rfc_criterion', 'mse') n_estimators = int(parameters.get('rfc_n_estimators', 100)) depth_select = parameters.get('rfc_max_depth', 'none') x_train, x_test, y_train, y_test = train_test_split(input_df, target_df) if depth_select == 'none': best_depth = None elif depth_select == 'custom': best_depth = parameters.get('rfc_custom_depth', None) else: r2_lst = [] depth_iter = 5 depth_start = 10 depth_lst = [] for i in range(depth_iter): depth_lst.append(depth_start**i) # Select model with best r^2 and least depth for depth in depth_lst: rf_regr_test = RandomForestRegressor(n_estimators=n_estimators, max_depth=depth, criterion=criterion, oob_score=True) rf_regr_test.fit(x_train, y_train) r2_lst.append(rf_regr_test.oob_score_) depth_index, r2 = min(enumerate(r2_lst), key=lambda x: abs(x[1] - 1)) best_depth = depth_lst[depth_index] rf_regr = RandomForestRegressor(n_estimators=n_estimators, max_depth=best_depth, criterion=criterion) regr_test = rf_regr.fit(x_train, y_train) score = round(regr_test.score(x_test, y_test), 4) parameters['accuracy'] = score parameters['accuracy_type'] = 'R^2' rf_regr.fit(input_df, target_df) return rf_regr def create_k_nearest_neighbors_classifier(input_df, target_df, parameters): n_neighbors = int(parameters.get('nnc_k', 5)) weights = parameters.get('weights', 'uniform') algorithm = parameters.get('algorithm', 'auto') p = int(parameters.get('nnc_p', 2)) neighbors = KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=algorithm, weights=weights, p=p) x_train, x_test, y_train, y_test = train_test_split(input_df, target_df) clf_test = neighbors.fit(x_train, y_train) acc = get_match_acc(clf_test.predict(x_test), y_test) parameters['accuracy'] = acc parameters['accuracy_type'] = 'Accuracy [%]' neighbors.fit(input_df, target_df) return neighbors def create_k_nearest_neighbors_regressor(input_df, target_df, parameters): n_neighbors = int(parameters.get('nnc_k', 5)) weights = parameters.get('weights', 'uniform') algorithm = parameters.get('algorithm', 'auto') p = int(parameters.get('nnc_p', 2)) x_train, x_test, y_train, y_test = train_test_split(input_df, target_df) neighbors = KNeighborsRegressor(n_neighbors=n_neighbors, algorithm=algorithm, weights=weights, p=p) regr_test = neighbors.fit(x_train, y_train) score = round(regr_test.score(x_test, y_test), 4) parameters['accuracy'] = score parameters['accuracy_type'] = 'R^2' neighbors.fit(input_df, target_df) return neighbors def create_nearest_centroid(input_df, target_df, parameters): clf = NearestCentroid() x_train, x_test, y_train, y_test = train_test_split(input_df, target_df) clf_test = clf.fit(x_train, y_train) acc = get_match_acc(clf_test.predict(x_test), y_test) parameters['accuracy'] = acc parameters['accuracy_type'] = 'Accuracy [%]' clf.fit(input_df, target_df) return clf def create_support_vector_machine_classifier(input_df, target_df, parameters): kernel = parameters.get('svc_kernel', 'rbf') degree = int(parameters.get('svc_degree', 3)) c = parameters.get('svc_C', 1.0) clf = svm.SVC(kernel=kernel, degree=degree, C=c) x_train, x_test, y_train, y_test = train_test_split(input_df, target_df) clf_test = clf.fit(x_train, y_train) acc = get_match_acc(clf_test.predict(x_test), y_test) parameters['accuracy'] = acc parameters['accuracy_type'] = 'Accuracy [%]' clf.fit(input_df, target_df) return clf def create_support_vector_machine_regressor(input_df, target_df, parameters): kernel = parameters.get('svr_kernel', 'rbf') degree = int(parameters.get('svr_degree', 3)) svm_reg = svm.SVR(kernel=kernel, degree=degree) x_train, x_test, y_train, y_test = train_test_split(input_df, target_df) regr_test = svm_reg.fit(x_train, y_train) score = round(regr_test.score(x_test, y_test), 4) parameters['accuracy'] = score parameters['accuracy_type'] = 'R^2' svm_reg.fit(input_df, target_df) return svm_reg

73e016bee0bbdbd050f1186b02c0f18ced34620a

py

Python

sdk/appservice/azure-mgmt-web/azure/mgmt/web/v2019_08_01/operations/_app_service_certificate_orders_operations.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

2021-09-07T18:39:05.000Z

2021-09-07T18:39:05.000Z

sdk/appservice/azure-mgmt-web/azure/mgmt/web/v2019_08_01/operations/_app_service_certificate_orders_operations.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

sdk/appservice/azure-mgmt-web/azure/mgmt/web/v2019_08_01/operations/_app_service_certificate_orders_operations.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

2022-03-04T06:21:56.000Z

2022-03-04T06:21:56.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- import functools from typing import Any, Callable, Dict, Generic, Iterable, List, Optional, TypeVar, Union import warnings from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.paging import ItemPaged from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpResponse from azure.core.polling import LROPoller, NoPolling, PollingMethod from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.arm_polling import ARMPolling from msrest import Serializer from .. import models as _models from .._vendor import _convert_request, _format_url_section T = TypeVar('T') JSONType = Any ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] _SERIALIZER = Serializer() _SERIALIZER.client_side_validation = False def build_list_request( subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/providers/Microsoft.CertificateRegistration/certificateOrders') path_format_arguments = { "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_validate_purchase_information_request( subscription_id: str, *, json: JSONType = None, content: Any = None, **kwargs: Any ) -> HttpRequest: content_type = kwargs.pop('content_type', None) # type: Optional[str] api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/providers/Microsoft.CertificateRegistration/validateCertificateRegistrationInformation') path_format_arguments = { "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="POST", url=url, params=query_parameters, headers=header_parameters, json=json, content=content, **kwargs ) def build_list_by_resource_group_request( resource_group_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_get_request( resource_group_name: str, certificate_order_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_create_or_update_request_initial( resource_group_name: str, certificate_order_name: str, subscription_id: str, *, json: JSONType = None, content: Any = None, **kwargs: Any ) -> HttpRequest: content_type = kwargs.pop('content_type', None) # type: Optional[str] api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="PUT", url=url, params=query_parameters, headers=header_parameters, json=json, content=content, **kwargs ) def build_delete_request( resource_group_name: str, certificate_order_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="DELETE", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_update_request( resource_group_name: str, certificate_order_name: str, subscription_id: str, *, json: JSONType = None, content: Any = None, **kwargs: Any ) -> HttpRequest: content_type = kwargs.pop('content_type', None) # type: Optional[str] api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="PATCH", url=url, params=query_parameters, headers=header_parameters, json=json, content=content, **kwargs ) def build_list_certificates_request( resource_group_name: str, certificate_order_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/certificates') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_get_certificate_request( resource_group_name: str, certificate_order_name: str, name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/certificates/{name}') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "name": _SERIALIZER.url("name", name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_create_or_update_certificate_request_initial( resource_group_name: str, certificate_order_name: str, name: str, subscription_id: str, *, json: JSONType = None, content: Any = None, **kwargs: Any ) -> HttpRequest: content_type = kwargs.pop('content_type', None) # type: Optional[str] api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/certificates/{name}') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "name": _SERIALIZER.url("name", name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="PUT", url=url, params=query_parameters, headers=header_parameters, json=json, content=content, **kwargs ) def build_delete_certificate_request( resource_group_name: str, certificate_order_name: str, name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/certificates/{name}') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "name": _SERIALIZER.url("name", name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="DELETE", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_update_certificate_request( resource_group_name: str, certificate_order_name: str, name: str, subscription_id: str, *, json: JSONType = None, content: Any = None, **kwargs: Any ) -> HttpRequest: content_type = kwargs.pop('content_type', None) # type: Optional[str] api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/certificates/{name}') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "name": _SERIALIZER.url("name", name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="PATCH", url=url, params=query_parameters, headers=header_parameters, json=json, content=content, **kwargs ) def build_reissue_request( resource_group_name: str, certificate_order_name: str, subscription_id: str, *, json: JSONType = None, content: Any = None, **kwargs: Any ) -> HttpRequest: content_type = kwargs.pop('content_type', None) # type: Optional[str] api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/reissue') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="POST", url=url, params=query_parameters, headers=header_parameters, json=json, content=content, **kwargs ) def build_renew_request( resource_group_name: str, certificate_order_name: str, subscription_id: str, *, json: JSONType = None, content: Any = None, **kwargs: Any ) -> HttpRequest: content_type = kwargs.pop('content_type', None) # type: Optional[str] api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/renew') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="POST", url=url, params=query_parameters, headers=header_parameters, json=json, content=content, **kwargs ) def build_resend_email_request( resource_group_name: str, certificate_order_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/resendEmail') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="POST", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_resend_request_emails_request( resource_group_name: str, certificate_order_name: str, subscription_id: str, *, json: JSONType = None, content: Any = None, **kwargs: Any ) -> HttpRequest: content_type = kwargs.pop('content_type', None) # type: Optional[str] api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/resendRequestEmails') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="POST", url=url, params=query_parameters, headers=header_parameters, json=json, content=content, **kwargs ) def build_retrieve_site_seal_request( resource_group_name: str, certificate_order_name: str, subscription_id: str, *, json: JSONType = None, content: Any = None, **kwargs: Any ) -> HttpRequest: content_type = kwargs.pop('content_type', None) # type: Optional[str] api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/retrieveSiteSeal') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="POST", url=url, params=query_parameters, headers=header_parameters, json=json, content=content, **kwargs ) def build_verify_domain_ownership_request( resource_group_name: str, certificate_order_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/verifyDomainOwnership') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "certificateOrderName": _SERIALIZER.url("certificate_order_name", certificate_order_name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="POST", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_retrieve_certificate_actions_request( resource_group_name: str, name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{name}/retrieveCertificateActions') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "name": _SERIALIZER.url("name", name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="POST", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_retrieve_certificate_email_history_request( resource_group_name: str, name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: api_version = "2019-08-01" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{name}/retrieveEmailHistory') path_format_arguments = { "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\w\._\(\)]+[^\.]$'), "name": _SERIALIZER.url("name", name, 'str'), "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="POST", url=url, params=query_parameters, headers=header_parameters, **kwargs ) class AppServiceCertificateOrdersOperations(object): """AppServiceCertificateOrdersOperations operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.web.v2019_08_01.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config @distributed_trace def list( self, **kwargs: Any ) -> Iterable["_models.AppServiceCertificateOrderCollection"]: """List all certificate orders in a subscription. Description for List all certificate orders in a subscription. :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either AppServiceCertificateOrderCollection or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.web.v2019_08_01.models.AppServiceCertificateOrderCollection] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.AppServiceCertificateOrderCollection"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) def prepare_request(next_link=None): if not next_link: request = build_list_request( subscription_id=self._config.subscription_id, template_url=self.list.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) else: request = build_list_request( subscription_id=self._config.subscription_id, template_url=next_link, ) request = _convert_request(request) request.url = self._client.format_url(request.url) request.method = "GET" return request def extract_data(pipeline_response): deserialized = self._deserialize("AppServiceCertificateOrderCollection", pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/providers/Microsoft.CertificateRegistration/certificateOrders'} # type: ignore @distributed_trace def validate_purchase_information( self, app_service_certificate_order: "_models.AppServiceCertificateOrder", **kwargs: Any ) -> None: """Validate information for a certificate order. Description for Validate information for a certificate order. :param app_service_certificate_order: Information for a certificate order. :type app_service_certificate_order: ~azure.mgmt.web.v2019_08_01.models.AppServiceCertificateOrder :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] _json = self._serialize.body(app_service_certificate_order, 'AppServiceCertificateOrder') request = build_validate_purchase_information_request( subscription_id=self._config.subscription_id, content_type=content_type, json=_json, template_url=self.validate_purchase_information.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) validate_purchase_information.metadata = {'url': '/subscriptions/{subscriptionId}/providers/Microsoft.CertificateRegistration/validateCertificateRegistrationInformation'} # type: ignore @distributed_trace def list_by_resource_group( self, resource_group_name: str, **kwargs: Any ) -> Iterable["_models.AppServiceCertificateOrderCollection"]: """Get certificate orders in a resource group. Description for Get certificate orders in a resource group. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either AppServiceCertificateOrderCollection or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.web.v2019_08_01.models.AppServiceCertificateOrderCollection] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.AppServiceCertificateOrderCollection"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) def prepare_request(next_link=None): if not next_link: request = build_list_by_resource_group_request( resource_group_name=resource_group_name, subscription_id=self._config.subscription_id, template_url=self.list_by_resource_group.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) else: request = build_list_by_resource_group_request( resource_group_name=resource_group_name, subscription_id=self._config.subscription_id, template_url=next_link, ) request = _convert_request(request) request.url = self._client.format_url(request.url) request.method = "GET" return request def extract_data(pipeline_response): deserialized = self._deserialize("AppServiceCertificateOrderCollection", pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list_by_resource_group.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders'} # type: ignore @distributed_trace def get( self, resource_group_name: str, certificate_order_name: str, **kwargs: Any ) -> "_models.AppServiceCertificateOrder": """Get a certificate order. Description for Get a certificate order. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order.. :type certificate_order_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: AppServiceCertificateOrder, or the result of cls(response) :rtype: ~azure.mgmt.web.v2019_08_01.models.AppServiceCertificateOrder :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.AppServiceCertificateOrder"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_get_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, subscription_id=self._config.subscription_id, template_url=self.get.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('AppServiceCertificateOrder', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}'} # type: ignore def _create_or_update_initial( self, resource_group_name: str, certificate_order_name: str, certificate_distinguished_name: "_models.AppServiceCertificateOrder", **kwargs: Any ) -> "_models.AppServiceCertificateOrder": cls = kwargs.pop('cls', None) # type: ClsType["_models.AppServiceCertificateOrder"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] _json = self._serialize.body(certificate_distinguished_name, 'AppServiceCertificateOrder') request = build_create_or_update_request_initial( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, subscription_id=self._config.subscription_id, content_type=content_type, json=_json, template_url=self._create_or_update_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('AppServiceCertificateOrder', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('AppServiceCertificateOrder', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}'} # type: ignore @distributed_trace def begin_create_or_update( self, resource_group_name: str, certificate_order_name: str, certificate_distinguished_name: "_models.AppServiceCertificateOrder", **kwargs: Any ) -> LROPoller["_models.AppServiceCertificateOrder"]: """Create or update a certificate purchase order. Description for Create or update a certificate purchase order. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :param certificate_distinguished_name: Distinguished name to use for the certificate order. :type certificate_distinguished_name: ~azure.mgmt.web.v2019_08_01.models.AppServiceCertificateOrder :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.PollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of LROPoller that returns either AppServiceCertificateOrder or the result of cls(response) :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.web.v2019_08_01.models.AppServiceCertificateOrder] :raises: ~azure.core.exceptions.HttpResponseError """ content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.PollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.AppServiceCertificateOrder"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = self._create_or_update_initial( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, certificate_distinguished_name=certificate_distinguished_name, content_type=content_type, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('AppServiceCertificateOrder', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = ARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}'} # type: ignore @distributed_trace def delete( self, resource_group_name: str, certificate_order_name: str, **kwargs: Any ) -> None: """Delete an existing certificate order. Description for Delete an existing certificate order. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_delete_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, subscription_id=self._config.subscription_id, template_url=self.delete.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}'} # type: ignore @distributed_trace def update( self, resource_group_name: str, certificate_order_name: str, certificate_distinguished_name: "_models.AppServiceCertificateOrderPatchResource", **kwargs: Any ) -> "_models.AppServiceCertificateOrder": """Create or update a certificate purchase order. Description for Create or update a certificate purchase order. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :param certificate_distinguished_name: Distinguished name to use for the certificate order. :type certificate_distinguished_name: ~azure.mgmt.web.v2019_08_01.models.AppServiceCertificateOrderPatchResource :keyword callable cls: A custom type or function that will be passed the direct response :return: AppServiceCertificateOrder, or the result of cls(response) :rtype: ~azure.mgmt.web.v2019_08_01.models.AppServiceCertificateOrder :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.AppServiceCertificateOrder"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] _json = self._serialize.body(certificate_distinguished_name, 'AppServiceCertificateOrderPatchResource') request = build_update_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, subscription_id=self._config.subscription_id, content_type=content_type, json=_json, template_url=self.update.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 201]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('AppServiceCertificateOrder', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('AppServiceCertificateOrder', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}'} # type: ignore @distributed_trace def list_certificates( self, resource_group_name: str, certificate_order_name: str, **kwargs: Any ) -> Iterable["_models.AppServiceCertificateCollection"]: """List all certificates associated with a certificate order. Description for List all certificates associated with a certificate order. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either AppServiceCertificateCollection or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.web.v2019_08_01.models.AppServiceCertificateCollection] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.AppServiceCertificateCollection"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) def prepare_request(next_link=None): if not next_link: request = build_list_certificates_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, subscription_id=self._config.subscription_id, template_url=self.list_certificates.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) else: request = build_list_certificates_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, subscription_id=self._config.subscription_id, template_url=next_link, ) request = _convert_request(request) request.url = self._client.format_url(request.url) request.method = "GET" return request def extract_data(pipeline_response): deserialized = self._deserialize("AppServiceCertificateCollection", pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list_certificates.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/certificates'} # type: ignore @distributed_trace def get_certificate( self, resource_group_name: str, certificate_order_name: str, name: str, **kwargs: Any ) -> "_models.AppServiceCertificateResource": """Get the certificate associated with a certificate order. Description for Get the certificate associated with a certificate order. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :param name: Name of the certificate. :type name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: AppServiceCertificateResource, or the result of cls(response) :rtype: ~azure.mgmt.web.v2019_08_01.models.AppServiceCertificateResource :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.AppServiceCertificateResource"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_get_certificate_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, name=name, subscription_id=self._config.subscription_id, template_url=self.get_certificate.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('AppServiceCertificateResource', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_certificate.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/certificates/{name}'} # type: ignore def _create_or_update_certificate_initial( self, resource_group_name: str, certificate_order_name: str, name: str, key_vault_certificate: "_models.AppServiceCertificateResource", **kwargs: Any ) -> "_models.AppServiceCertificateResource": cls = kwargs.pop('cls', None) # type: ClsType["_models.AppServiceCertificateResource"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] _json = self._serialize.body(key_vault_certificate, 'AppServiceCertificateResource') request = build_create_or_update_certificate_request_initial( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, name=name, subscription_id=self._config.subscription_id, content_type=content_type, json=_json, template_url=self._create_or_update_certificate_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('AppServiceCertificateResource', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('AppServiceCertificateResource', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _create_or_update_certificate_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/certificates/{name}'} # type: ignore @distributed_trace def begin_create_or_update_certificate( self, resource_group_name: str, certificate_order_name: str, name: str, key_vault_certificate: "_models.AppServiceCertificateResource", **kwargs: Any ) -> LROPoller["_models.AppServiceCertificateResource"]: """Creates or updates a certificate and associates with key vault secret. Description for Creates or updates a certificate and associates with key vault secret. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :param name: Name of the certificate. :type name: str :param key_vault_certificate: Key vault certificate resource Id. :type key_vault_certificate: ~azure.mgmt.web.v2019_08_01.models.AppServiceCertificateResource :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.PollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of LROPoller that returns either AppServiceCertificateResource or the result of cls(response) :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.web.v2019_08_01.models.AppServiceCertificateResource] :raises: ~azure.core.exceptions.HttpResponseError """ content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.PollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.AppServiceCertificateResource"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = self._create_or_update_certificate_initial( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, name=name, key_vault_certificate=key_vault_certificate, content_type=content_type, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('AppServiceCertificateResource', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = ARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_create_or_update_certificate.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/certificates/{name}'} # type: ignore @distributed_trace def delete_certificate( self, resource_group_name: str, certificate_order_name: str, name: str, **kwargs: Any ) -> None: """Delete the certificate associated with a certificate order. Description for Delete the certificate associated with a certificate order. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :param name: Name of the certificate. :type name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_delete_certificate_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, name=name, subscription_id=self._config.subscription_id, template_url=self.delete_certificate.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) delete_certificate.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/certificates/{name}'} # type: ignore @distributed_trace def update_certificate( self, resource_group_name: str, certificate_order_name: str, name: str, key_vault_certificate: "_models.AppServiceCertificatePatchResource", **kwargs: Any ) -> "_models.AppServiceCertificateResource": """Creates or updates a certificate and associates with key vault secret. Description for Creates or updates a certificate and associates with key vault secret. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :param name: Name of the certificate. :type name: str :param key_vault_certificate: Key vault certificate resource Id. :type key_vault_certificate: ~azure.mgmt.web.v2019_08_01.models.AppServiceCertificatePatchResource :keyword callable cls: A custom type or function that will be passed the direct response :return: AppServiceCertificateResource, or the result of cls(response) :rtype: ~azure.mgmt.web.v2019_08_01.models.AppServiceCertificateResource :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.AppServiceCertificateResource"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] _json = self._serialize.body(key_vault_certificate, 'AppServiceCertificatePatchResource') request = build_update_certificate_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, name=name, subscription_id=self._config.subscription_id, content_type=content_type, json=_json, template_url=self.update_certificate.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 201]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('AppServiceCertificateResource', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('AppServiceCertificateResource', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized update_certificate.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/certificates/{name}'} # type: ignore @distributed_trace def reissue( self, resource_group_name: str, certificate_order_name: str, reissue_certificate_order_request: "_models.ReissueCertificateOrderRequest", **kwargs: Any ) -> None: """Reissue an existing certificate order. Description for Reissue an existing certificate order. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :param reissue_certificate_order_request: Parameters for the reissue. :type reissue_certificate_order_request: ~azure.mgmt.web.v2019_08_01.models.ReissueCertificateOrderRequest :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] _json = self._serialize.body(reissue_certificate_order_request, 'ReissueCertificateOrderRequest') request = build_reissue_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, subscription_id=self._config.subscription_id, content_type=content_type, json=_json, template_url=self.reissue.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) reissue.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/reissue'} # type: ignore @distributed_trace def renew( self, resource_group_name: str, certificate_order_name: str, renew_certificate_order_request: "_models.RenewCertificateOrderRequest", **kwargs: Any ) -> None: """Renew an existing certificate order. Description for Renew an existing certificate order. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :param renew_certificate_order_request: Renew parameters. :type renew_certificate_order_request: ~azure.mgmt.web.v2019_08_01.models.RenewCertificateOrderRequest :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] _json = self._serialize.body(renew_certificate_order_request, 'RenewCertificateOrderRequest') request = build_renew_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, subscription_id=self._config.subscription_id, content_type=content_type, json=_json, template_url=self.renew.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) renew.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/renew'} # type: ignore @distributed_trace def resend_email( self, resource_group_name: str, certificate_order_name: str, **kwargs: Any ) -> None: """Resend certificate email. Description for Resend certificate email. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_resend_email_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, subscription_id=self._config.subscription_id, template_url=self.resend_email.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) resend_email.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/resendEmail'} # type: ignore @distributed_trace def resend_request_emails( self, resource_group_name: str, certificate_order_name: str, name_identifier: "_models.NameIdentifier", **kwargs: Any ) -> None: """Verify domain ownership for this certificate order. Description for Verify domain ownership for this certificate order. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :param name_identifier: Email address. :type name_identifier: ~azure.mgmt.web.v2019_08_01.models.NameIdentifier :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] _json = self._serialize.body(name_identifier, 'NameIdentifier') request = build_resend_request_emails_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, subscription_id=self._config.subscription_id, content_type=content_type, json=_json, template_url=self.resend_request_emails.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) resend_request_emails.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/resendRequestEmails'} # type: ignore @distributed_trace def retrieve_site_seal( self, resource_group_name: str, certificate_order_name: str, site_seal_request: "_models.SiteSealRequest", **kwargs: Any ) -> "_models.SiteSeal": """Verify domain ownership for this certificate order. Description for Verify domain ownership for this certificate order. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :param site_seal_request: Site seal request. :type site_seal_request: ~azure.mgmt.web.v2019_08_01.models.SiteSealRequest :keyword callable cls: A custom type or function that will be passed the direct response :return: SiteSeal, or the result of cls(response) :rtype: ~azure.mgmt.web.v2019_08_01.models.SiteSeal :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.SiteSeal"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] _json = self._serialize.body(site_seal_request, 'SiteSealRequest') request = build_retrieve_site_seal_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, subscription_id=self._config.subscription_id, content_type=content_type, json=_json, template_url=self.retrieve_site_seal.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('SiteSeal', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized retrieve_site_seal.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/retrieveSiteSeal'} # type: ignore @distributed_trace def verify_domain_ownership( self, resource_group_name: str, certificate_order_name: str, **kwargs: Any ) -> None: """Verify domain ownership for this certificate order. Description for Verify domain ownership for this certificate order. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param certificate_order_name: Name of the certificate order. :type certificate_order_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_verify_domain_ownership_request( resource_group_name=resource_group_name, certificate_order_name=certificate_order_name, subscription_id=self._config.subscription_id, template_url=self.verify_domain_ownership.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) verify_domain_ownership.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{certificateOrderName}/verifyDomainOwnership'} # type: ignore @distributed_trace def retrieve_certificate_actions( self, resource_group_name: str, name: str, **kwargs: Any ) -> List["_models.CertificateOrderAction"]: """Retrieve the list of certificate actions. Description for Retrieve the list of certificate actions. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param name: Name of the certificate order. :type name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: list of CertificateOrderAction, or the result of cls(response) :rtype: list[~azure.mgmt.web.v2019_08_01.models.CertificateOrderAction] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[List["_models.CertificateOrderAction"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_retrieve_certificate_actions_request( resource_group_name=resource_group_name, name=name, subscription_id=self._config.subscription_id, template_url=self.retrieve_certificate_actions.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('[CertificateOrderAction]', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized retrieve_certificate_actions.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{name}/retrieveCertificateActions'} # type: ignore @distributed_trace def retrieve_certificate_email_history( self, resource_group_name: str, name: str, **kwargs: Any ) -> List["_models.CertificateEmail"]: """Retrieve email history. Description for Retrieve email history. :param resource_group_name: Name of the resource group to which the resource belongs. :type resource_group_name: str :param name: Name of the certificate order. :type name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: list of CertificateEmail, or the result of cls(response) :rtype: list[~azure.mgmt.web.v2019_08_01.models.CertificateEmail] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[List["_models.CertificateEmail"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_retrieve_certificate_email_history_request( resource_group_name=resource_group_name, name=name, subscription_id=self._config.subscription_id, template_url=self.retrieve_certificate_email_history.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('[CertificateEmail]', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized retrieve_certificate_email_history.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CertificateRegistration/certificateOrders/{name}/retrieveEmailHistory'} # type: ignore

73e0278697b45741896112b5a9a1676cd7d48dba

py

Python

scripts/Bluetooth/BLEClient.py

arsakhar/FitViz

49bcfa74db6a023e2032c3662dc92ab27b554746

2020-11-24T14:13:52.000Z

2020-11-24T14:13:52.000Z

scripts/Bluetooth/BLEClient.py

arsakhar/FitViz

49bcfa74db6a023e2032c3662dc92ab27b554746

scripts/Bluetooth/BLEClient.py

arsakhar/FitViz

49bcfa74db6a023e2032c3662dc92ab27b554746

import asyncio from bleak import BleakClient """ BLE Client Class. Inherits from BleakClient. """ class BLEClient(BleakClient): def __init__(self, address, loop): super().__init__(address, loop) """ Connect to a specified device. **kwargs specifies timeout period """ def connect(self, **kwargs): return super().connect(**kwargs) """ Check if connection is active. """ def is_connected(self): return super().is_connected() """ Retrieve GATT Services (https://www.bluetooth.com/specifications/gatt/services/) """ def get_services(self): return super().get_services()

73e03e0047e6a3346b25645ca5934ce1fac17921

py

Python

active_learning/acquisition_functions.py

a-r-j/FlowMO

8109be8e2e095e4122873bc4e3d9e290c1986bfa

active_learning/acquisition_functions.py

a-r-j/FlowMO

8109be8e2e095e4122873bc4e3d9e290c1986bfa

active_learning/acquisition_functions.py

a-r-j/FlowMO

8109be8e2e095e4122873bc4e3d9e290c1986bfa

""" Module containing acquisition functions for active learning """ import numpy as np from scipy.stats import norm import gpflow def gp_var(X: np.ndarray, model) -> np.ndarray: """ Suggests samples by maximising the predicted uncertainty (no exploitation, full exploration) :param X: input data :param model: GPFlow model :return: y_var """ _, y_var = model.predict_f(X) return y_var.numpy().flatten() def gp_ei( X_test: np.ndarray, y_train: np.ndarray, model, xi: float = 0.01 ) -> np.ndarray: """ Suggests samples by maximising the predicted expected improvement Balance exploitation & exploration via parameter xi :param X_test: input features of points to be sampled :param y_train: target values from observed samples :param model: GPFlow model :param xi: Controls balance of explore/exploit :return: EI scores """ y_pred, y_var = model.predict_y(X_test) y_best = np.amax(y_train) # Best sample so far y_std = np.sqrt(y_var) with np.errstate(divide="warn"): imp = y_pred - y_best - xi Z = imp / y_std ei = imp * norm.cdf(Z) + y_std * norm.pdf(Z) # ei[y_var<1e-8] = 0.0 return ei.flatten() def gp_greed(X: np.ndarray, model) -> np.ndarray: """ Suggests samples by maximising the predicted y (full exploitation, no exploration) :param X: input data :param model: GPFlow model :return: X_samples """ y_pred, _ = model.predict_y(X) return y_pred.numpy().flatten()

73e06726e327812647777baf08e220f3fc49f2f0

py

Python

td_gen_layout_event_handles.py

sandeep-gh/PySimpleGui_UI_Generator

602daef1035e0ea7173c8f3dd124ee806e6eb2bd

td_gen_layout_event_handles.py

sandeep-gh/PySimpleGui_UI_Generator

602daef1035e0ea7173c8f3dd124ee806e6eb2bd

2021-01-18T00:47:26.000Z

2021-01-18T16:45:10.000Z

td_gen_layout_event_handles.py

sandeep-gh/PySimpleGui_UI_generator

602daef1035e0ea7173c8f3dd124ee806e6eb2bd

import PySimpleGUI as sg import time from layout_directive_definitions import Gelem, BlockLD from layout_directive_definitions import TreeNodeLD import random import event_codegen as ecm import layout_generator_step_by_step as lg from everything_bagel_dictionary import everything_bagel import event_codegen as ecm bld1 = BlockLD([ Gelem("button1", "button 1", sg.Button, sty={"auto_size_button": 'False', "size": (8, 4) }, ex_toggle_attrs=[('button_color', ( ('white', 'green'), ('blue', 'black')))]), Gelem("button2", "button 2", sg.Button, sty={"auto_size_button": 'False', "size": (8, 4) }) ], stacked='H', framed=True ) bld2 = BlockLD([ Gelem("text1", "text 1", sg.Text, sty={"auto_size_text": 'False', "size": (8, 4) }), Gelem("text2", "text 2", sg.Text, sty={"auto_size_text": 'False', "size": (8, 4) }) ], stacked='H', framed=False ) tnld = TreeNodeLD(bld1, bld2, stacked='H', framed=True) for bld in ecm.walk_tld(tnld): print(bld) lg.set_bld_layout_generator(tnld) ecm.gen_event_actions(tnld, ['A', 'B']) lgen = lg.get_layout_generator_tnld(tnld) the_layout = lg.build_layout_set( lgen, ['A', 'B'], stacked='H', framed=False) print(the_layout) layout = [] exit_button_row = [[ sg.Button('Exit') ] ] layout = layout + [[the_layout]] + exit_button_row window = sg.Window('PGAppAnalytics', layout) while True: event, values = window.read() print("event pressed = ", event) if event == 'Exit': break everything_bagel(window, event) window.close()

73e06dbb955d7a5aeebada93f18e58d6eadb1094

py

Python

mwe_discov_eval/databases/NgramDb.py

nicolaCirillo/mwe_discov_eval

36f544a802f68d7e274627d57a65531ff23c4c5e

mwe_discov_eval/databases/NgramDb.py

nicolaCirillo/mwe_discov_eval

36f544a802f68d7e274627d57a65531ff23c4c5e

mwe_discov_eval/databases/NgramDb.py

nicolaCirillo/mwe_discov_eval

36f544a802f68d7e274627d57a65531ff23c4c5e

# -*- coding: utf-8 -*- """ Created on Fri Jan 1 20:58:38 2021 @author: nicol """ from shutil import copyfile from .SqlDatabase import SqlDatabase from . import sql_commands as sql from mwe_discov_eval import messages from mwe_discov_eval.utils import utils COMMIT_EACH = 1000 class NgramDb(SqlDatabase): """An interface to an SQL database that stores n-gram statistics. An interface to an SQLite database containing n-gram statistics. This class is passed as input to the functions that compute n-gram measures to compute that measure and store the output in the SQL database. Use 'from_NgramCounter' to create a new instance and 'load' to load an NgramDb from file. Parameters ---------- fileroot: str Path to the file. Examples ---------- To create a new NgramDb: >>> from mwe_discov_eval.databases import NgramCounter, NgramDb >>> counter = NgramCounter.load('sample_counter') >>> ngram_db = NgramDb.from_NgramCounter(counter, 'sample_ngram_db') To compute an n-gram measure: >>> from mwe_discov_eval.databases import NgramCounter >>> from mwe_discov_eval.measures.am_extended import compute_measure, pmi >>> ngram_db = NgramDb.load('sample_ngram_db') >>> compute_measure(ngam_db, pmi) """ def __init__(self, fileroot: str): self.fileroot = fileroot db_file = fileroot + '.db' self.info_file = fileroot + '.info.json' super().__init__(db_file, new=False) self._query = None @classmethod def from_NgramCounter(cls, ngram_counter, fileroot: str): """Creates a new NgramDb from an NgramCounter. Parameters ---------- ngram_counter: NgramCounter The NgramCounter from which to create the NgramDb. fileroot: str Path to the file of the new NgramCounter. Returns ------- NgramDb """ messages.new_display() copyfile(ngram_counter.db, fileroot + '.db') new_cls = cls(fileroot) new_cls.connect() new_cls._gen_info() new_cls.disconnect() new_cls._load_info() return new_cls @classmethod def load(cls, fileroot: str): """Loads an NgramDb from file Parameters ---------- fileroot: str Path to the file. Returns ------- NgramDb """ new_cls = cls(fileroot) new_cls._load_info() return new_cls def _load_info(self): info = utils.load_json(self.info_file) self.n_max = info['n_max'] self.num_rows = info['num_rows'] def _gen_info(self): messages.msg("Generating info file...") num_rows = dict() tables = self.get_tables() if tables: n_max = self.query(sql.MAX_LEN.format(table=tables[0]))[0][0] for t in tables: for n in range(1, n_max+1): n_rows = self.query(sql.ROW_COUNT.format(table=t), n) num_rows.setdefault(t, dict())[n] = n_rows[0][0] else: n_max, num_rows = 0, None info = {'n_max': n_max, 'num_rows': num_rows} utils.save_json(info, self.info_file) messages.done() def upd_info(self, table: str): """Updates the .info.json file with regards to a table. Parameters ---------- table: str The name of the table of which to update the info """ for n in range(1, self.n_max+1): n_rows = self.query(sql.ROW_COUNT.format(table=table), n) self.num_rows.setdefault(table, dict())[n] = n_rows[0][0] info = {'n_max': self.n_max, 'num_rows': self.num_rows} utils.save_json(info, self.info_file) messages.done() def _load_ngrams(self, n): query = sql.format_query(self._query_table, self.query_fields, cond=True, limit=self._limit, offset=self._offset) self.execute(query, n) def __getitem__(self, n): self._load_ngrams(n) num = self.num_rows[self._query_table][str(n)] return self.cur, num def set_query(self, table: str, fields=['*'], limit=None, offset=None): self._limit = limit self._offset = offset self._query_table = table self.query_fields = fields def frequency_threshold(self, table, threshold): messages.msg("Filtering ngrams...") delete = sql.DELETE.format(table=table) self.execute(delete, threshold) self.commit() messages.done() self.upd_info(table)

73e07aaedeb89a21636a8561748cf0fbc8085ab8

py

Python

dAAMs/__init__.py

yuxiang-zhou/DensePoseModel

382d9cc2ccee629c64ec873110c3653bcc3a30fe

2019-05-07T15:01:16.000Z

2019-05-07T15:01:16.000Z

dAAMs/__init__.py

yuxiang-zhou/DenseHumanPose

382d9cc2ccee629c64ec873110c3653bcc3a30fe

dAAMs/__init__.py

yuxiang-zhou/DenseHumanPose

382d9cc2ccee629c64ec873110c3653bcc3a30fe

from .svs import SVS from .base import dAAMs from .fitter import LucasKanadeDAAMFitter from .results import DAAMFitterResult

73e08ce7d5969de2ae54e2505fa7b449bfaf631a

py

Python

tensorflow/python/debug/wrappers/framework_test.py

tianyapiaozi/tensorflow

fb3ce0467766a8e91f1da0ad7ada7c24fde7a73a

2016-06-08T02:15:50.000Z

2022-03-02T05:30:36.000Z

tensorflow/python/debug/wrappers/framework_test.py

shrikunjsarda/tensorflow

7e8927e7af0c51ac20a63bd4eab6ff83df1a39ae

2017-04-26T16:49:49.000Z

2019-10-15T11:39:26.000Z

tensorflow/python/debug/wrappers/framework_test.py

shrikunjsarda/tensorflow

7e8927e7af0c51ac20a63bd4eab6ff83df1a39ae

2016-06-16T15:34:05.000Z

2022-03-12T13:23:11.000Z

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Framework of debug-wrapped sessions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import shutil import tempfile import threading import numpy as np from tensorflow.core.protobuf import config_pb2 from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.client import session from tensorflow.python.debug.lib import debug_data from tensorflow.python.debug.wrappers import framework from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops # Import resource_variable_ops for the variables-to-tensor implicit conversion. from tensorflow.python.ops import resource_variable_ops # pylint: disable=unused-import from tensorflow.python.ops import variables from tensorflow.python.platform import googletest from tensorflow.python.training import monitored_session from tensorflow.python.util import tf_inspect class TestDebugWrapperSession(framework.BaseDebugWrapperSession): """A concrete implementation of BaseDebugWrapperSession for test.""" def __init__(self, sess, dump_root, observer, thread_name_filter=None): # Supply dump root. self._dump_root = dump_root # Supply observer. self._obs = observer # Invoke superclass constructor. framework.BaseDebugWrapperSession.__init__( self, sess, thread_name_filter=thread_name_filter) def on_session_init(self, request): """Override abstract on-session-init callback method.""" self._obs["sess_init_count"] += 1 self._obs["request_sess"] = request.session return framework.OnSessionInitResponse( framework.OnSessionInitAction.PROCEED) def on_run_start(self, request): """Override abstract on-run-start callback method.""" self._obs["on_run_start_count"] += 1 self._obs["run_fetches"] = request.fetches self._obs["run_feed_dict"] = request.feed_dict return framework.OnRunStartResponse( framework.OnRunStartAction.DEBUG_RUN, ["file://" + self._dump_root]) def on_run_end(self, request): """Override abstract on-run-end callback method.""" self._obs["on_run_end_count"] += 1 self._obs["performed_action"] = request.performed_action self._obs["tf_error"] = request.tf_error return framework.OnRunEndResponse() class TestDebugWrapperSessionBadAction(framework.BaseDebugWrapperSession): """A concrete implementation of BaseDebugWrapperSession for test. This class intentionally puts a bad action value in OnSessionInitResponse and/or in OnRunStartAction to test the handling of such invalid cases. """ def __init__( self, sess, bad_init_action=None, bad_run_start_action=None, bad_debug_urls=None): """Constructor. Args: sess: The TensorFlow Session object to be wrapped. bad_init_action: (str) bad action value to be returned during the on-session-init callback. bad_run_start_action: (str) bad action value to be returned during the the on-run-start callback. bad_debug_urls: Bad URL values to be returned during the on-run-start callback. """ self._bad_init_action = bad_init_action self._bad_run_start_action = bad_run_start_action self._bad_debug_urls = bad_debug_urls # Invoke superclass constructor. framework.BaseDebugWrapperSession.__init__(self, sess) def on_session_init(self, request): if self._bad_init_action: return framework.OnSessionInitResponse(self._bad_init_action) else: return framework.OnSessionInitResponse( framework.OnSessionInitAction.PROCEED) def on_run_start(self, request): debug_urls = self._bad_debug_urls or [] if self._bad_run_start_action: return framework.OnRunStartResponse( self._bad_run_start_action, debug_urls) else: return framework.OnRunStartResponse( framework.OnRunStartAction.DEBUG_RUN, debug_urls) def on_run_end(self, request): return framework.OnRunEndResponse() class DebugWrapperSessionTest(test_util.TensorFlowTestCase): def _no_rewrite_session_config(self): rewriter_config = rewriter_config_pb2.RewriterConfig( disable_model_pruning=True) graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config) return config_pb2.ConfigProto(graph_options=graph_options) def setUp(self): self._observer = { "sess_init_count": 0, "request_sess": None, "on_run_start_count": 0, "run_fetches": None, "run_feed_dict": None, "on_run_end_count": 0, "performed_action": None, "tf_error": None, } self._dump_root = tempfile.mkdtemp() self._sess = session.Session(config=self._no_rewrite_session_config()) self._a_init_val = np.array([[5.0, 3.0], [-1.0, 0.0]]) self._b_init_val = np.array([[2.0], [-1.0]]) self._c_val = np.array([[-4.0], [6.0]]) self._a_init = constant_op.constant( self._a_init_val, shape=[2, 2], name="a_init") self._b_init = constant_op.constant( self._b_init_val, shape=[2, 1], name="b_init") self._ph = array_ops.placeholder(dtype=dtypes.float64, name="ph") self._a = variables.Variable(self._a_init, name="a1") self._b = variables.Variable(self._b_init, name="b") self._c = constant_op.constant(self._c_val, shape=[2, 1], name="c") # Matrix product of a and b. self._p = math_ops.matmul(self._a, self._b, name="p1") # Matrix product of a and ph. self._q = math_ops.matmul(self._a, self._ph, name="q") # Sum of two vectors. self._s = math_ops.add(self._p, self._c, name="s") # Initialize the variables. self._sess.run(self._a.initializer) self._sess.run(self._b.initializer) def tearDown(self): # Tear down temporary dump directory. if os.path.isdir(self._dump_root): shutil.rmtree(self._dump_root) ops.reset_default_graph() def testSessionInit(self): self.assertEqual(0, self._observer["sess_init_count"]) wrapper_sess = TestDebugWrapperSession(self._sess, self._dump_root, self._observer) # Assert that on-session-init callback is invoked. self.assertEqual(1, self._observer["sess_init_count"]) # Assert that the request to the on-session-init callback carries the # correct session object. self.assertEqual(self._sess, self._observer["request_sess"]) # Verify that the wrapper session implements the session.SessionInterface. self.assertTrue(isinstance(wrapper_sess, session.SessionInterface)) self.assertEqual(self._sess.sess_str, wrapper_sess.sess_str) self.assertEqual(self._sess.graph, wrapper_sess.graph) self.assertEqual(self._sess.graph_def, wrapper_sess.graph_def) # Check that the partial_run_setup and partial_run are not implemented for # the debug wrapper session. with self.assertRaises(NotImplementedError): wrapper_sess.partial_run_setup(self._p) def testInteractiveSessionInit(self): """The wrapper should work also on other subclasses of session.Session.""" TestDebugWrapperSession( session.InteractiveSession(), self._dump_root, self._observer) def testSessionRun(self): wrapper = TestDebugWrapperSession( self._sess, self._dump_root, self._observer) # Check initial state of the observer. self.assertEqual(0, self._observer["on_run_start_count"]) self.assertEqual(0, self._observer["on_run_end_count"]) s = wrapper.run(self._s) # Assert the run return value is correct. self.assertAllClose(np.array([[3.0], [4.0]]), s) # Assert the on-run-start method is invoked. self.assertEqual(1, self._observer["on_run_start_count"]) # Assert the on-run-start request reflects the correct fetch. self.assertEqual(self._s, self._observer["run_fetches"]) # Assert the on-run-start request reflects the correct feed_dict. self.assertIsNone(self._observer["run_feed_dict"]) # Assert the file debug URL has led to dump on the filesystem. dump = debug_data.DebugDumpDir(self._dump_root) self.assertEqual(7, len(dump.dumped_tensor_data)) # Assert the on-run-end method is invoked. self.assertEqual(1, self._observer["on_run_end_count"]) # Assert the performed action field in the on-run-end callback request is # correct. self.assertEqual( framework.OnRunStartAction.DEBUG_RUN, self._observer["performed_action"]) # No TensorFlow runtime error should have happened. self.assertIsNone(self._observer["tf_error"]) def testSessionInitInvalidSessionType(self): """Attempt to wrap a non-Session-type object should cause an exception.""" wrapper = TestDebugWrapperSessionBadAction(self._sess) with self.assertRaisesRegexp(TypeError, "Expected type .*; got type .*"): TestDebugWrapperSessionBadAction(wrapper) def testSessionInitBadActionValue(self): with self.assertRaisesRegexp( ValueError, "Invalid OnSessionInitAction value: nonsense_action"): TestDebugWrapperSessionBadAction( self._sess, bad_init_action="nonsense_action") def testRunStartBadActionValue(self): wrapper = TestDebugWrapperSessionBadAction( self._sess, bad_run_start_action="nonsense_action") with self.assertRaisesRegexp( ValueError, "Invalid OnRunStartAction value: nonsense_action"): wrapper.run(self._s) def testRunStartBadURLs(self): # debug_urls ought to be a list of str, not a str. So an exception should # be raised during a run() call. wrapper = TestDebugWrapperSessionBadAction( self._sess, bad_debug_urls="file://foo") with self.assertRaisesRegexp(TypeError, "Expected type .*; got type .*"): wrapper.run(self._s) def testErrorDuringRun(self): wrapper = TestDebugWrapperSession(self._sess, self._dump_root, self._observer) # No matrix size mismatch. self.assertAllClose( np.array([[11.0], [-1.0]]), wrapper.run(self._q, feed_dict={self._ph: np.array([[1.0], [2.0]])})) self.assertEqual(1, self._observer["on_run_end_count"]) self.assertIsNone(self._observer["tf_error"]) # Now there should be a matrix size mismatch error. wrapper.run(self._q, feed_dict={self._ph: np.array([[1.0], [2.0], [3.0]])}) self.assertEqual(2, self._observer["on_run_end_count"]) self.assertTrue( isinstance(self._observer["tf_error"], errors.InvalidArgumentError)) def testUsingWrappedSessionShouldWorkAsContextManager(self): wrapper = TestDebugWrapperSession(self._sess, self._dump_root, self._observer) with wrapper as sess: self.assertAllClose([[3.0], [4.0]], self._s.eval()) self.assertEqual(1, self._observer["on_run_start_count"]) self.assertEqual(self._s, self._observer["run_fetches"]) self.assertEqual(1, self._observer["on_run_end_count"]) self.assertAllClose( [[11.0], [-1.0]], sess.run(self._q, feed_dict={self._ph: np.array([[1.0], [2.0]])})) self.assertEqual(2, self._observer["on_run_start_count"]) self.assertEqual(self._q, self._observer["run_fetches"]) self.assertEqual(2, self._observer["on_run_end_count"]) def testUsingWrappedSessionShouldSupportEvalWithAsDefault(self): wrapper = TestDebugWrapperSession(self._sess, self._dump_root, self._observer) with wrapper.as_default(): foo = constant_op.constant(42, name="foo") self.assertEqual(42, foo.eval()) self.assertEqual(foo, self._observer["run_fetches"]) def testWrapperShouldSupportSessionClose(self): wrapper = TestDebugWrapperSession(self._sess, self._dump_root, self._observer) wrapper.close() def testWrapperThreadNameFilterMainThread(self): wrapper = TestDebugWrapperSession( self._sess, self._dump_root, self._observer, thread_name_filter="MainThread") child_run_output = [] def child_thread_job(): child_run_output.append(wrapper.run(self._b_init)) thread = threading.Thread(name="ChildThread", target=child_thread_job) thread.start() self.assertAllClose(self._a_init_val, wrapper.run(self._a_init)) thread.join() self.assertAllClose([self._b_init_val], child_run_output) dump = debug_data.DebugDumpDir(self._dump_root) self.assertEqual(1, dump.size) self.assertEqual("a_init", dump.dumped_tensor_data[0].node_name) def testWrapperThreadNameFilterChildThread(self): wrapper = TestDebugWrapperSession( self._sess, self._dump_root, self._observer, thread_name_filter=r"Child.*") child_run_output = [] def child_thread_job(): child_run_output.append(wrapper.run(self._b_init)) thread = threading.Thread(name="ChildThread", target=child_thread_job) thread.start() self.assertAllClose(self._a_init_val, wrapper.run(self._a_init)) thread.join() self.assertAllClose([self._b_init_val], child_run_output) dump = debug_data.DebugDumpDir(self._dump_root) self.assertEqual(1, dump.size) self.assertEqual("b_init", dump.dumped_tensor_data[0].node_name) def testWrapperThreadNameFilterBothThreads(self): wrapper = TestDebugWrapperSession( self._sess, self._dump_root, self._observer, thread_name_filter=None) child_run_output = [] def child_thread_job(): child_run_output.append(wrapper.run(self._b_init)) thread = threading.Thread(name="ChildThread", target=child_thread_job) thread.start() self.assertAllClose(self._a_init_val, wrapper.run(self._a_init)) thread.join() self.assertAllClose([self._b_init_val], child_run_output) dump = debug_data.DebugDumpDir(self._dump_root, validate=False) self.assertEqual(2, dump.size) self.assertItemsEqual( ["a_init", "b_init"], [datum.node_name for datum in dump.dumped_tensor_data]) def _is_public_method_name(method_name): return (method_name.startswith("__") and method_name.endswith("__") or not method_name.startswith("_")) class SessionWrapperPublicMethodParityTest(test_util.TensorFlowTestCase): def testWrapperHasAllPublicMethodsOfSession(self): session_public_methods = [ method_tuple[0] for method_tuple in tf_inspect.getmembers(session.Session, predicate=tf_inspect.ismethod) if _is_public_method_name(method_tuple[0])] wrapper_public_methods = [ method_tuple[0] for method_tuple in tf_inspect.getmembers( framework.BaseDebugWrapperSession, predicate=tf_inspect.ismethod) if _is_public_method_name(method_tuple[0])] missing_public_methods = [ method for method in session_public_methods if method not in wrapper_public_methods] self.assertFalse(missing_public_methods) def testWrapperHasAllPublicMethodsOfMonitoredSession(self): session_public_methods = [ method_tuple[0] for method_tuple in tf_inspect.getmembers(monitored_session.MonitoredSession, predicate=tf_inspect.ismethod) if _is_public_method_name(method_tuple[0])] wrapper_public_methods = [ method_tuple[0] for method_tuple in tf_inspect.getmembers( framework.BaseDebugWrapperSession, predicate=tf_inspect.ismethod) if _is_public_method_name(method_tuple[0])] missing_public_methods = [ method for method in session_public_methods if method not in wrapper_public_methods] self.assertFalse(missing_public_methods) if __name__ == "__main__": googletest.main()

73e0a59ba2647a2bcd63d30ed890d89cba61ee37

py

Python

pytorch/torch/optim/adagrad.py

zhou3968322/dl-code-read

aca204a986dabe2755becff0f42de1082299d791

pytorch/torch/optim/adagrad.py

zhou3968322/dl-code-read

aca204a986dabe2755becff0f42de1082299d791

pytorch/torch/optim/adagrad.py

zhou3968322/dl-code-read

aca204a986dabe2755becff0f42de1082299d791

import torch from .optimizer import Optimizer class Adagrad(Optimizer): """Implements Adagrad algorithm. It has been proposed in `Adaptive Subgradient Methods for Online Learning and Stochastic Optimization`_. Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-2) lr_decay (float, optional): learning rate decay (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-10) .. _Adaptive Subgradient Methods for Online Learning and Stochastic Optimization: http://jmlr.org/papers/v12/duchi11a.html """ def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10): if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 <= lr_decay: raise ValueError("Invalid lr_decay value: {}".format(lr_decay)) if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) if not 0.0 <= initial_accumulator_value: raise ValueError("Invalid initial_accumulator_value value: {}".format(initial_accumulator_value)) if not 0.0 <= eps: raise ValueError("Invalid epsilon value: {}".format(eps)) defaults = dict(lr=lr, lr_decay=lr_decay, eps=eps, weight_decay=weight_decay, initial_accumulator_value=initial_accumulator_value) super(Adagrad, self).__init__(params, defaults) for group in self.param_groups: for p in group['params']: state = self.state[p] state['step'] = 0 state['sum'] = torch.full_like(p, initial_accumulator_value, memory_format=torch.preserve_format) def share_memory(self): for group in self.param_groups: for p in group['params']: state = self.state[p] state['sum'].share_memory_() @torch.no_grad() def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: for p in group['params']: if p.grad is None: continue grad = p.grad state = self.state[p] state['step'] += 1 if group['weight_decay'] != 0: if p.grad.is_sparse: raise RuntimeError("weight_decay option is not compatible with sparse gradients") grad = grad.add(p, alpha=group['weight_decay']) clr = group['lr'] / (1 + (state['step'] - 1) * group['lr_decay']) # >>> i = [[1, 1]] # >>> v = [3, 4] # >>> s=torch.sparse_coo_tensor(i, v, (3,)) # >>> s # tensor(indices=tensor([[1, 1]]), # values=tensor( [3, 4]), # size=(3,), nnz=2, layout=torch.sparse_coo) # sparse tensor的layout就是torch.sparse_coo if grad.is_sparse: # the coalescing process will accumulate the multi-valued elements into a single value using summation: # >>> s.coalesce() # tensor(indices=tensor([[1]]), # values=tensor([7]), # size=(3,), nnz=1, layout=torch.sparse_coo) grad = grad.coalesce() # the update is non-linear so indices must be unique grad_indices = grad._indices() grad_values = grad._values() size = grad.size() def make_sparse(values): constructor = grad.new if grad_indices.dim() == 0 or values.dim() == 0: return constructor().resize_as_(grad) return constructor(grad_indices, values, size) # state中获取梯度的和 state['sum'].add_(make_sparse(grad_values.pow(2))) std = state['sum'].sparse_mask(grad) # 来获取std的sqrt std_values = std._values().sqrt_().add_(group['eps']) p.add_(make_sparse(grad_values / std_values), alpha=-clr) else: state['sum'].addcmul_(grad, grad, value=1) std = state['sum'].sqrt().add_(group['eps']) # 也就是对应的公式 p.addcdiv_(grad, std, value=-clr) return loss

73e0a9dc80cd46ff5074a070812f55801da17266

py

Python

chapter_7_design/wake_transcribe.py

fancyerii/voicebook

def82da8577086d0361643a05fec2463006533a9

2020-03-05T01:19:17.000Z

2020-03-05T01:19:17.000Z

chapter_7_design/wake_transcribe.py

fancyerii/voicebook

def82da8577086d0361643a05fec2463006533a9

chapter_7_design/wake_transcribe.py

fancyerii/voicebook

def82da8577086d0361643a05fec2463006533a9

''' ================================================ ## VOICEBOOK REPOSITORY ## ================================================ repository name: voicebook repository version: 1.0 repository link: https://github.com/jim-schwoebel/voicebook author: Jim Schwoebel author contact: js@neurolex.co description: a book and repo to get you started programming voice applications in Python - 10 chapters and 200+ scripts. license category: opensource license: Apache 2.0 license organization name: NeuroLex Laboratories, Inc. location: Seattle, WA website: https://neurolex.ai release date: 2018-09-28 This code (voicebook) is hereby released under a Apache 2.0 license license. For more information, check out the license terms below. ================================================ ## LICENSE TERMS ## ================================================ Copyright 2018 NeuroLex Laboratories, 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. ================================================ ## SERVICE STATEMENT ## ================================================ If you are using the code written for a larger project, we are happy to consult with you and help you with deployment. Our team has >10 world experts in Kafka distributed architectures, microservices built on top of Node.js / Python / Docker, and applying machine learning to model speech and text data. We have helped a wide variety of enterprises - small businesses, researchers, enterprises, and/or independent developers. If you would like to work with us let us know @ js@neurolex.co. ================================================ ## WAKE_TRANSCRIBE.PY ## ================================================ Use asynchronous transcription as a wakeword detector. ''' import soundfile as sf import sounddevice as sd import speech_recognition as sr_audio import pyttsx3 import os, time # transcribe with pocketsphinx (open-source) def speak(): engine = pyttsx3.init() engine.say("hello!!") engine.runAndWait() def find_wake(transcript, hotwords): for i in range(len(hotwords)): ## print(transcript) ## print(transcript.lower().find(hotwords[i])) if transcript.lower().find(hotwords[i])>=0: print('%s wakeword found!!'%(hotwords[i].upper())) speak() break def transcribe_sphinx(file): try: r=sr_audio.Recognizer() with sr_audio.AudioFile(file) as source: audio = r.record(source) transcript=r.recognize_sphinx(audio) print('sphinx transcript: '+transcript) except: transcript='' print(transcript) return transcript def async_record(hotwords, filename, filename2, duration, fs, channels): print('recording') myrecording = sd.rec(int(duration * fs), samplerate=fs, channels=channels) transcript=transcribe_sphinx(filename2) find_wake(transcript, hotwords) sd.wait() sf.write(filename, myrecording, fs) print('done recording') # initial parameters hotwords=['test', 'testing'] i=0 t=1 filename2='n/a' # create infinite loop while t>0: # record a mono file asynchronous, transcribe, and fine wakeword filename=str(i+1)+'.wav' async_record(hotwords, filename, filename2, 3, 16000, 1) filename2=filename i=i+1 try: os.remove(str(i-2)+'.wav') except: pass

73e0ae8db048d9e437287582bf92c241a88d16b4

py

Python

brian2/monitors/ratemonitor.py

achilleas-k/brian2

906563b6b1321585b082f79f74f1b4ab386347ec

brian2/monitors/ratemonitor.py

achilleas-k/brian2

906563b6b1321585b082f79f74f1b4ab386347ec

brian2/monitors/ratemonitor.py

achilleas-k/brian2

906563b6b1321585b082f79f74f1b4ab386347ec

import numpy as np from brian2.core.variables import Variables from brian2.units.allunits import second, hertz from brian2.units.fundamentalunits import Unit, Quantity from brian2.groups.group import CodeRunner, Group __all__ = ['PopulationRateMonitor'] class PopulationRateMonitor(Group, CodeRunner): ''' Record instantaneous firing rates, averaged across neurons from a `NeuronGroup` or other spike source. Parameters ---------- source : (`NeuronGroup`, `SpikeSource`) The source of spikes to record. name : str, optional A unique name for the object, otherwise will use ``source.name+'_ratemonitor_0'``, etc. codeobj_class : class, optional The `CodeObject` class to run code with. Notes ----- Currently, this monitor can only monitor the instantaneous firing rates at each time step of the source clock. Any binning/smoothing of the firing rates has to be done manually afterwards. ''' invalidates_magic_network = False add_to_magic_network = True def __init__(self, source, name='ratemonitor*', codeobj_class=None): #: The group we are recording from self.source = source self.codeobj_class = codeobj_class CodeRunner.__init__(self, group=self, code='', template='ratemonitor', clock=source.clock, when='end', order=0, name=name) self.add_dependency(source) self.variables = Variables(self) # Handle subgroups correctly start = getattr(source, 'start', 0) stop = getattr(source, 'stop', len(source)) self.variables.add_constant('_source_start', Unit(1), start) self.variables.add_constant('_source_stop', Unit(1), stop) self.variables.add_reference('_spikespace', source) self.variables.add_dynamic_array('rate', size=0, unit=hertz, constant_size=False) self.variables.add_dynamic_array('t', size=0, unit=second, constant_size=False) self.variables.add_reference('_num_source_neurons', source, 'N') self.variables.add_attribute_variable('N', unit=Unit(1), obj=self, attribute='_N', dtype=np.int32) self.variables.create_clock_variables(self._clock, prefix='_clock_') self._enable_group_attributes() @property def _N(self): return len(self.variables['t'].get_value()) def resize(self, new_size): self.variables['rate'].resize(new_size) self.variables['t'].resize(new_size) def __len__(self): return self._N def reinit(self): ''' Clears all recorded rates ''' raise NotImplementedError() def __repr__(self): description = '<{classname}, recording {source}>' return description.format(classname=self.__class__.__name__, source=self.source.name)

73e0d1840d2263197b0fc5f8c337460e5faa6272

py

Python

askci/apps/api/admin.py

hpsee/askci

ef1e2e75481b71db7fbe774cb81938055aa596d0

2019-11-21T09:04:36.000Z

2019-11-23T13:29:43.000Z

askci/apps/api/admin.py

hpsee/askci

ef1e2e75481b71db7fbe774cb81938055aa596d0

2019-11-21T20:28:23.000Z

2019-11-26T19:34:22.000Z

askci/apps/api/admin.py

hpsee/askci

ef1e2e75481b71db7fbe774cb81938055aa596d0

""" Copyright (C) 2019-2020 Vanessa Sochat. This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. """ from django.contrib import admin from askci.apps.api.models import Webhook class WebhookAdmin(admin.ModelAdmin): list_display = ("name", "secret", "app_from", "created", "modified") admin.site.register(Webhook, WebhookAdmin)

73e0e6413e54202a25babe46c933b015592be710

py

Python

synapseutils/monitor.py

zimingd/synapsePythonClient

6bddd4d9de2f06f6f79d8ce814ac57970d6bebee

synapseutils/monitor.py

zimingd/synapsePythonClient

6bddd4d9de2f06f6f79d8ce814ac57970d6bebee

synapseutils/monitor.py

zimingd/synapsePythonClient

6bddd4d9de2f06f6f79d8ce814ac57970d6bebee

from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import functools import traceback import sys from multiprocessing import Value, Lock from synapseclient.utils import printTransferProgress def notifyMe(syn, messageSubject='', retries=0): """Function decorator that notifies you via email whenever an function completes running or there is a failure. :param syn: A synapse object as obtained with syn = synapseclient.login() :param messageSubject: A string with subject line for sent out messages. :param retries: Number of retries to attempt on failure (default=0) Example:: # to decorate a function that you define from synapseutils import notifyMe import synapseclient syn = synapseclient.login() @notifyMe(syn, 'Long running function', retries=2) def my_function(x): doing_something() return long_runtime_func(x) my_function(123) ############################# # to wrap a function that already exists from synapseutils import notifyMe import synapseclient syn = synapseclient.login() notify_decorator = notifyMe(syn, 'Long running query', retries=2) my_query = notify_decorator(syn.tableQuery) results = my_query("select id from syn1223") ############################# """ def notify_decorator(func): @functools.wraps(func) def with_retry_and_messaging(*args, **kwargs): attempt = 0 destination = syn.getUserProfile()['ownerId'] while attempt<=retries: try: output = func(*args, **kwargs) syn.sendMessage([destination], messageSubject, messageBody='Call to %s completed successfully!' %func.__name__) return output except Exception as e: sys.stderr.write(traceback.format_exc()) syn.sendMessage([destination], messageSubject, messageBody = ('Encountered a temporary Failure during upload. ' 'Will retry %i more times. \n\n Error message was:\n%s\n\n%s' %(retries-attempt, e, traceback.format_exc()))) attempt +=1 return with_retry_and_messaging return notify_decorator def with_progress_bar(func, totalCalls, prefix = '', postfix='', isBytes=False): """Wraps a function to add a progress bar based on the number of calls to that function. :param func: Function being wrapped with progress Bar :param totalCalls: total number of items/bytes when completed :param prefix: String printed before progress bar :param prefix: String printed after progress bar :param isBytes: A boolean indicating weather to convert bytes to kB, MB, GB etc. :return: a wrapped function that contains a progress bar """ completed = Value('d', 0) lock = Lock() def progress(*args, **kwargs): with lock: completed.value +=1 printTransferProgress(completed.value, totalCalls, prefix, postfix, isBytes) return func(*args, **kwargs) return progress

73e0ff5c2f299cb000f037297a6e449078332f9b

py

Python

cotk/dataloader/context.py

ishine/cotk

2242c16523830254b0ac509f7739b4cbcb03dea4

2019-03-14T15:06:42.000Z

2022-02-28T07:06:34.000Z

cotk/dataloader/context.py

ishine/cotk

2242c16523830254b0ac509f7739b4cbcb03dea4

2019-03-12T09:43:21.000Z

2020-08-24T02:56:34.000Z

cotk/dataloader/context.py

ishine/cotk

2242c16523830254b0ac509f7739b4cbcb03dea4

2019-03-14T15:12:48.000Z

2021-08-09T03:37:48.000Z

from typing import List, Any, Dict, Union, Optional from .._utils.metaclass import DocStringInheritor # For type checking if False: #pylint: disable=using-constant-test #pylint: disable=unused-import from .vocab import Vocab from .tokenizer import Tokenizer from collections import OrderedDict class _UNDEFINED(): pass class Context(metaclass=DocStringInheritor): '''An abstract base class for context manager. This class is used for setting default parameters for :class:`Field` or :class:`Vocab`, without directly passing parameters to ``__init__`` of the object. See :ref:`examples<dataloader_context_ref>` for how to use context manager. Arguments: parameter_dict (Dict[str, Any]): Key-value dict for changed parameters. weak (bool, optional): When ``False``, overwrite existing parameters. Default: ``False``. none_as_ignored (bool, optional): When ``True``, ``None`` values in ``parameter_dict`` are ignored. Otherwise, the corresponding key will be set to ``None``. Default: ``True``. ''' context_dict: Dict[str, Any] = {} corrupted = False UNDEFINED = _UNDEFINED() def __init__(self, parameter_dict: Dict[str, Any], weak=False, none_as_ignored=True): if self.__class__.corrupted: raise RuntimeError("A context object do not close before becoming invalid. Use ``with`` statement, " \ "or make sure of calling close.") self._old_parameters = self._set_parameters(parameter_dict, weak=weak, none_as_ignored=none_as_ignored) self._closed = False @classmethod def _set_parameters(cls, parameter_dict: Dict[str, Any], weak=False, none_as_ignored=True) -> Dict[str, Any]: old_parameters: Dict[str, Any] = {} for key, value in parameter_dict.items(): old_parameters[key] = cls.set(key, value, weak=weak, none_as_ignored=none_as_ignored) return old_parameters @classmethod def get(cls, key: str, default: Any = None, no_default=False) -> Any: '''Get the value of parameter named ``key`` stored in this class. Arguments: key (str): name of the parameter default (Any, optional): Default value if ``key`` is not set. Defaults: ``None``. no_default (bool, optional): When ``True``, Raise ``KeyError`` if ``key`` is not set. Defaults: ``False``. ''' if key in cls.context_dict: return cls.context_dict[key] else: if no_default: raise KeyError("Must specify %s in Context." % key) else: return default WEAK_ARGS = r''' weak (bool, optional): When ``False``, overwrite existing parameters. Defaults: ``False``. ''' NONE_AS_IGNORED_ARGS = r''' none_as_ignored (bool, optional): When ``True``, ``None`` values in ``parameter_dict`` are ignored. Otherwise, the corresponding value will be set to ``None``. Default: ``True``. ''' @classmethod def set(cls, key: str, value: Any, weak=False, none_as_ignored=True) -> Any: '''Set the parameter named ``key`` to ``value``, stored in this class. If weak is ``True``, do not overwrite if ``key`` is already set. Return the old value. Arguments: key (str): The name of the changed parameter. value (Any): The new value of changed parameter. If want to delete the key, use ``Context.UNDEFINED``. {WEAK_ARGS} {NONE_AS_IGNORED_ARGS} ''' if key not in cls.context_dict: old = Context.UNDEFINED if value or not none_as_ignored: cls.context_dict[key] = value return old old = cls.context_dict[key] if not weak: if value is Context.UNDEFINED: del cls.context_dict[key] elif value is not None or not none_as_ignored: cls.context_dict[key] = value return old def __enter__(self): '''Enter a context''' return self @classmethod def _restore(cls, old_parameters): for name, param in old_parameters.items(): if name not in cls.context_dict: continue if param is Context.UNDEFINED: del cls.context_dict[name] else: cls.context_dict[name] = param def __exit__(self, exc_type, exc_val, exc_tb): '''Exit the context and restore the old parameter.''' self.close() def close(self): '''Restore the old parameter.''' self._restore(self._old_parameters) self._closed = True def __del__(self): if hasattr(self, "_closed") and not self._closed: self.__class__.corrupted = True raise RuntimeError("A context object do not close before becoming invalid. Use ``with`` statement, " \ "or make sure of calling close.") class FieldContext(Context): '''Bases: :class:`.dataloader.Context` A context class for setting default parameters for :class:`.Field`. ''' context_dict: Dict[str, Any] = {} corrupted = False UNDEFINED = Context.UNDEFINED NONE_AS_IGNORED_ARGS = Context.NONE_AS_IGNORED_ARGS.replace("``parameter_dict``", "``kwargs``") # pylint: disable=unused-argument @classmethod def set_parameters(cls, *, weak=False, none_as_ignored=True, **kwargs) -> "FieldContext": '''Set a context for initialization of :class:`Field`. See :ref:`examples<dataloader_context_ref>` for how to use context manager. Arguments: {WEAK_ARGS} {NONE_AS_IGNORED_ARGS} \*\*kwargs: Any parameters to be set. Set ``key`` to ``FieldContext.UNDEFINED`` to delete a parameter. ''' return FieldContext(kwargs, weak=weak, none_as_ignored=none_as_ignored) class VocabContext(Context): '''Bases: :class:`.dataloader.Context` A context class for setting default parameters for :class:`.Vocab`. ''' context_dict: Dict[str, Any] = {} corrupted = False UNDEFINED = Context.UNDEFINED NONE_AS_IGNORED_ARGS = Context.NONE_AS_IGNORED_ARGS.replace("``parameter_dict``", "``kwargs``") # pylint: disable=unused-argument @classmethod def set_parameters(cls, *, weak=False, none_as_ignored=True, **kwargs) -> "VocabContext": '''Set a context for initialization of :class:`Vocab`. See :ref:`examples<dataloader_context_ref>` for how to use context manager. Arguments: {WEAK_ARGS} {NONE_AS_IGNORED_ARGS} \*\*kwargs: Any parameters to be set. Set ``key`` to ``VocabContext.UNDEFINED`` to delete a parameter. ''' return VocabContext(kwargs, weak=weak, none_as_ignored=none_as_ignored)

73e1257004bfd59befcb10a0f4cedad704ba387d

py

Python

analysis/computation/ambitus.py

msampaio/FlautaSolo

407253513712462e3521c06bcc35a9b95da0fe19

analysis/computation/ambitus.py

msampaio/FlautaSolo

407253513712462e3521c06bcc35a9b95da0fe19

2019-03-18T20:11:30.000Z

2019-03-18T20:11:59.000Z

analysis/computation/ambitus.py

msampaio/FlautaSolo

407253513712462e3521c06bcc35a9b95da0fe19

from collections import Counter from analysis.computation import utils def frequency(ambitus_list): freq = Counter(ambitus_list) r = [['Ambitus', 'Pieces']] for k, v in sorted(freq.items()): r.append([k, v]) return r def frequency_pie(ambitus_list): r = utils.aux_pie_chart(Counter(ambitus_list)) r.insert(0, ['Ambitus', 'Amount']) return r def analysis(compositions): ambitus_list = utils.get_music_data_attrib(compositions, 'ambitus', 'append') if ambitus_list: basic_stats = utils.aux_basic_stats(ambitus_list, 'Pieces number', False) dist_value = utils.distribution(ambitus_list, basic_stats, False) args = { 'basic_stats': basic_stats, 'frequency': frequency(ambitus_list), 'histogram': utils.histogram(ambitus_list, 10, ['Ambitus', 'Pieces'], False, True), 'distribution_value': dist_value, 'distribution_amount': utils.distribution(ambitus_list, basic_stats, True), 'frequency_pie': frequency_pie(ambitus_list), 'boxplot': utils.boxplot(basic_stats), } return args else: return {}

73e152dd67f965304d1b73b9f581bc57f6f82e12

py

Python

setup.py

textX/EasyColorLang

b7b660777ef04422a9a9df5b59b15bb80df0e3b9

setup.py

textX/EasyColorLang

b7b660777ef04422a9a9df5b59b15bb80df0e3b9

setup.py

textX/EasyColorLang

b7b660777ef04422a9a9df5b59b15bb80df0e3b9

import codecs import os from setuptools import find_packages, setup PACKAGE_NAME = "EasyColorLang" VERSION = "0.1" AUTHOR = "Igor Majic" AUTHOR_EMAIL = "majic753@gmail.com" DESCRIPTION = "A syntax highlighting generator for any textX language" KEYWORDS = "textX DSL python all languages highlighting coloring" LICENSE = "MIT" URL = "https://github.com/IgorMaj/SyntaxColoring" packages = find_packages() print("packages:", packages) README = codecs.open( os.path.join(os.path.dirname(__file__), "README.md"), "r", encoding="utf-8").read() setup( name=PACKAGE_NAME, version=VERSION, description=DESCRIPTION, long_description=README, long_description_content_type="text/markdown", url=URL, author=AUTHOR, author_email=AUTHOR_EMAIL, keywords=KEYWORDS, license=LICENSE, packages=packages, include_package_data=True, install_requires=["click", "jinja2", "textx"], entry_points={ "textx_generators": ["easy_gen_coloring = gen_coloring.coloring_entry_point:textmate_gen_coloring"], "textx_languages": ["easy_coloring_lang = gen_coloring.coloring_entry_point:easy_coloring_lang"] }, classifiers=[ "Development Status :: 2 - Pre-Alpha", "Intended Audience :: Developers", "Intended Audience :: Information Technology", "Topic :: Software Development :: Libraries :: Python Modules", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3 :: Only", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", ], )

73e1fd387d797e62b65c558c725eb9486b1e4469

py

Python

Battleships/model/rules.py

ruslanabdulin1985/TheTask

99e53bd283273a90233b29c8aa7aa4380211263b

Battleships/model/rules.py

ruslanabdulin1985/TheTask

99e53bd283273a90233b29c8aa7aa4380211263b

Battleships/model/rules.py

ruslanabdulin1985/TheTask

99e53bd283273a90233b29c8aa7aa4380211263b

class Rules: def __init__(self, mode, opponent): if mode == 'standard': self.ship_stack = [4,3,3,2,2,2,1,1,1,1]; # each element is a ship. e.g [4,3,3] say that there is 1 of type 4 and 2 self.opponent = opponent # human or computer

73e242f22a9d8a0fc453ee5907bc2cb97f50d328

py

Python

src/calc/serial/calc_B.py

paytonrodman/athena-analysis

f635338122e15c318dfd754d06cc3dbaa42273d2

src/calc/serial/calc_B.py

paytonrodman/athena-analysis

f635338122e15c318dfd754d06cc3dbaa42273d2

src/calc/serial/calc_B.py

paytonrodman/athena-analysis

f635338122e15c318dfd754d06cc3dbaa42273d2

#!/usr/bin/env python3 # # calc_B.py # # A program to calculate the effective magnetic field derived from the magnetic flux at the inner # simulation edge, and compare to the average field in the disk. # # Usage: python calc_B.py [options] # import numpy as np import os import sys #sys.path.insert(0, '/home/per29/rds/rds-accretion-zyNhkonJSR8/athena-analysis/dependencies') sys.path.insert(0, '/Users/paytonrodman/athena-sim/athena-analysis/dependencies') import athena_read import AAT import glob import re import csv import argparse def main(**kwargs): problem = args.prob_id root_dir = "/Users/paytonrodman/athena-sim/" #root_dir = '/home/per29/rds/rds-accretion-zyNhkonJSR8/' prob_dir = root_dir + problem + '/' data_dir = prob_dir + 'data/' runfile_dir = prob_dir + 'runfiles/' os.chdir(data_dir) csv_time = np.empty(0) csv_array = [] with open(prob_dir + 'flux_with_time.csv', 'r', newline='') as f: csv_reader = csv.reader(f, delimiter='\t') next(csv_reader, None) # skip header for row in csv_reader: csv_time = np.append(csv_time, float(row[0])) array_entry = np.array([float(row[0]),float(row[2]),float(row[3])]) # [time, mf_u, mf_l] csv_array.append(array_entry) files = glob.glob('./*.athdf') times = np.empty(0) for f in files: time_sec = re.findall(r'\b\d+\b', f) if args.update: if float(time_sec[0]) not in times and float(time_sec[0]) in csv_time: times = np.append(times, float(time_sec[0])) else: if float(time_sec[0]) in csv_time: times = np.append(times, float(time_sec[0])) if len(times)==0: sys.exit('No new timesteps to analyse in the given directory. Exiting.') #times = [0.,5000.,10000.,15000.,20000.,25000.,30000.] #times = [25000.] data_input = athena_read.athinput(runfile_dir + 'athinput.' + problem) x1min = data_input['mesh']['x1min'] x1max = data_input['refinement3']['x1max'] scale_height = data_input['problem']['h_r'] angles = [0.,1.*scale_height,2.*scale_height,3.*scale_height] data_init = athena_read.athdf(problem + '.cons.00000.athdf', quantities=['x2v']) x2v = data_init['x2v'] th_l_id = [] th_u_id = [] for val in angles: th_u_id.append(AAT.find_nearest(x2v, np.pi/2. + val)) th_l_id.append(AAT.find_nearest(x2v, np.pi/2. - val)) Bav = [] Bcc1_av_0 = [] Bcc1_av_1 = [] Bcc1_av_2 = [] Bcc1_av_3 = [] sim_time = [] for t in times: str_t = str(int(t)).zfill(5) data_cons = athena_read.athdf(problem + '.cons.' + str_t + '.athdf', quantities=['Bcc1','Bcc2','Bcc3']) for item in csv_array: if (item[0] == data_cons['Time']): mf_u = item[1] mf_l = item[2] continue Bav_l = mf_l / (2.*np.pi*(x1min**2.)) Bav_u = mf_u / (2.*np.pi*(x1min**2.)) Bav.append([Bav_l,Bav_u]) Bcc1 = data_cons['Bcc1'] Bcc2 = data_cons['Bcc2'] Bcc3 = data_cons['Bcc3'] B = np.sqrt(Bcc1**2. + Bcc2**2. + Bcc3**2.) Bcc1_av_0.append(np.average(B[:x1max,th_l_id[0],:])) Bcc1_av_1.append([np.average(B[:x1max,th_l_id[1],:]),np.average(B[:x1max,th_u_id[1],:])]) Bcc1_av_2.append([np.average(B[:x1max,th_l_id[2],:]),np.average(B[:x1max,th_u_id[2],:])]) Bcc1_av_3.append([np.average(B[:x1max,th_l_id[3],:]),np.average(B[:x1max,th_u_id[3],:])]) sim_time.append(data_cons['Time']) sim_time,Bav,Bcc1_av_0,Bcc1_av_1,Bcc1_av_2,Bcc1_av_3 = (list(t) for t in zip(*sorted(zip(sim_time,Bav,Bcc1_av_0,Bcc1_av_1,Bcc1_av_2,Bcc1_av_3)))) os.chdir(prob_dir) if args.update: with open('B_strength_with_time.csv', 'a', newline='') as f: writer = csv.writer(f, delimiter='\t') writer.writerows(zip(sim_time, Bav, Bcc1_av_0, Bcc1_av_1, Bcc1_av_2, Bcc1_av_3)) else: with open('B_strength_with_time.csv', 'w', newline='') as f: writer = csv.writer(f, delimiter='\t') writer.writerow(["sim_time", "Bav", "Bcc1_0sh", "B_1sh", "B_2sh", "B_3sh"]) writer.writerows(zip(sim_time, Bav, Bcc1_av_0, Bcc1_av_1, Bcc1_av_2, Bcc1_av_3)) # Execute main function if __name__ == '__main__': parser = argparse.ArgumentParser(description='Calculate various quality factors from raw simulation data.') parser.add_argument('prob_id', help='base name of the data being analysed, e.g. inflow_var or disk_base') parser.add_argument('-u', '--update', action="store_true", help='specify whether the results being analysed are from a restart') args = parser.parse_args() main(**vars(args))

73e248c16f95da21f08d549ef951aa4d3f852761

gyp

Python

extensions/extensions.gyp

Wzzzx/chromium-crosswalk

768dde8efa71169f1c1113ca6ef322f1e8c9e7de

2019-01-28T08:09:58.000Z

2021-11-15T15:32:10.000Z

extensions/extensions.gyp

Wzzzx/chromium-crosswalk

768dde8efa71169f1c1113ca6ef322f1e8c9e7de

extensions/extensions.gyp

Wzzzx/chromium-crosswalk

768dde8efa71169f1c1113ca6ef322f1e8c9e7de

2020-09-23T08:56:12.000Z

2021-11-18T03:40:49.000Z

# Copyright 2013 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. { 'includes': [ 'extensions.gypi', ], 'variables': { 'chromium_code': 1, }, 'targets': [ { # GN version: //extensions/common 'target_name': 'extensions_common_constants', 'type': 'static_library', 'include_dirs': [ '..', '<(INTERMEDIATE_DIR)', ], 'sources': [ '<@(extensions_common_constants_sources)', ], # Disable c4267 warnings until we fix size_t to int truncations. 'msvs_disabled_warnings': [ 4267, ], }, { # GN version: //extensions/common:mojo 'target_name': 'extensions_common_mojo', # The type of this target must be none. This is so that resources can # depend upon this target for generating the js bindings files. Any # generated cpp files must be listed explicitly in extensions_common 'type': 'none', 'includes': [ '../mojo/mojom_bindings_generator.gypi', ], 'sources': [ '<@(extensions_common_mojo_sources)', ], 'conditions': [ ['proprietary_codecs==1 and enable_wifi_display==1', { 'sources': [ '<@(extensions_common_mojo_sources_wifi_display)', ], }], ], }, { # GN version: //extensions/common 'target_name': 'extensions_common', 'type': 'static_library', 'dependencies': [ '../components/components.gyp:crx_file', '../components/components.gyp:url_matcher', '../content/content.gyp:content_common', '../crypto/crypto.gyp:crypto', '../device/bluetooth/bluetooth.gyp:device_bluetooth', # For Mojo generated headers for generated_api.cc. '../device/serial/serial.gyp:device_serial_mojo', '../device/power_save_blocker/power_save_blocker.gyp:device_power_save_blocker', '../device/usb/usb.gyp:device_usb', '../ipc/ipc.gyp:ipc', '../net/net.gyp:net', '../third_party/icu/icu.gyp:icuuc', '../third_party/re2/re2.gyp:re2', '../ui/base/ui_base.gyp:ui_base', '../ui/gfx/gfx.gyp:gfx_geometry', '../ui/gfx/ipc/geometry/gfx_ipc_geometry.gyp:gfx_ipc_geometry', '../ui/gfx/ipc/gfx_ipc.gyp:gfx_ipc', '../ui/gfx/ipc/skia/gfx_ipc_skia.gyp:gfx_ipc_skia', '../url/url.gyp:url_lib', '../third_party/boringssl/boringssl.gyp:boringssl', '../third_party/libxml/libxml.gyp:libxml', '../url/ipc/url_ipc.gyp:url_ipc', 'common/api/api.gyp:extensions_api', 'extensions_resources.gyp:extensions_resources', 'extensions_strings.gyp:extensions_strings', 'extensions_common_constants', 'extensions_common_mojo', ], 'include_dirs': [ '..', '<(INTERMEDIATE_DIR)', ], 'sources': [ '<@(extensions_common_sources)', # Mojom generated files should not be included in the common gypi source # list because GN has proper mojom dependency support. '<(SHARED_INTERMEDIATE_DIR)/extensions/common/mojo/keep_alive.mojom.cc', '<(SHARED_INTERMEDIATE_DIR)/extensions/common/mojo/keep_alive.mojom.h', '<(SHARED_INTERMEDIATE_DIR)/extensions/common/mojo/stash.mojom.cc', '<(SHARED_INTERMEDIATE_DIR)/extensions/common/mojo/stash.mojom.h', ], # Disable c4267 warnings until we fix size_t to int truncations. 'msvs_disabled_warnings': [ 4267, ], 'conditions': [ ['disable_nacl==0', { # NaClModulesHandler does not use any code in NaCl, so no dependency # on nacl_common. 'sources': [ '<@(extensions_common_sources_nacl)', ], }], ], }, { # GN version: //extensions/browser 'target_name': 'extensions_browser', 'type': 'static_library', 'dependencies': [ '../base/base.gyp:base', '../base/base.gyp:base_i18n', '../components/components.gyp:browsing_data', '../components/components.gyp:cast_certificate', '../components/components.gyp:device_event_log_component', '../components/components.gyp:guest_view_browser', '../components/components.gyp:keyed_service_content', '../components/components.gyp:keyed_service_core', '../components/components.gyp:onc_component', '../components/components.gyp:pref_registry', '../components/components.gyp:sessions_content', '../components/components.gyp:storage_monitor', '../components/components.gyp:update_client', '../components/components.gyp:variations', '../components/components.gyp:version_info', '../components/components.gyp:web_cache_browser', '../components/components.gyp:web_modal', '../components/components.gyp:zoom', '../components/prefs/prefs.gyp:prefs', '../content/content.gyp:content_browser', '../device/bluetooth/bluetooth.gyp:device_bluetooth', '../device/serial/serial.gyp:device_serial', '../google_apis/google_apis.gyp:google_apis', '../skia/skia.gyp:skia', '../third_party/leveldatabase/leveldatabase.gyp:leveldatabase', '../third_party/re2/re2.gyp:re2', 'browser/api/api_registration.gyp:extensions_api_registration', 'common/api/api.gyp:cast_channel_proto', 'common/api/api.gyp:extensions_api', 'extensions_common', 'extensions_resources.gyp:extensions_resources', 'extensions_strings.gyp:extensions_strings', ], 'include_dirs': [ '..', '<(INTERMEDIATE_DIR)', # Needed to access generated API headers. '<(SHARED_INTERMEDIATE_DIR)', ], 'sources': [ '<@(extensions_browser_sources)', ], 'conditions': [ # This condition exists only because the extensions_common_constants # target is always built and thus this file gets evaluated by GYP. # This does not need to be replicated into extensions/browser/BUILD.gn. ['OS == "ios" or OS == "android"', { 'dependencies!': [ '../components/components.gyp:storage_monitor', ], }], ['chromeos == 1', { 'dependencies': [ '../chromeos/chromeos.gyp:chromeos', ], 'sources': [ '<@(extensions_browser_sources_chromeos)', ], }, { # chromeos==0 'sources': [ '<@(extensions_browser_sources_nonchromeos)', ], }], ['OS == "win" or OS == "mac"', { 'dependencies': [ '../components/components.gyp:wifi_component', ], 'sources': [ '<@(extensions_browser_sources_win_or_mac)', ], }], ['OS == "linux" and chromeos == 0', { 'dependencies': [ '../build/linux/system.gyp:dbus', '../dbus/dbus.gyp:dbus', ], 'sources': [ '<@(extensions_browser_sources_linux_nonchromeos)', ], }], ['proprietary_codecs==1 and enable_wifi_display == 1', { 'sources': [ '<@(extensions_browser_sources_wifi_display)', '<(SHARED_INTERMEDIATE_DIR)/extensions/common/mojo/wifi_display_session_service.mojom.cc', '<(SHARED_INTERMEDIATE_DIR)/extensions/common/mojo/wifi_display_session_service.mojom.h', ], }], ], # Disable c4267 warnings until we fix size_t to int truncations. 'msvs_disabled_warnings': [ 4267, ], }, { # GN version: //extensions/renderer 'target_name': 'extensions_renderer', 'type': 'static_library', 'dependencies': [ 'extensions_resources.gyp:extensions_resources', '../components/components.gyp:guest_view_renderer', '../content/content.gyp:content_common', '../content/content.gyp:content_resources', '../gin/gin.gyp:gin', '../mojo/mojo_public.gyp:mojo_js_bindings', '../third_party/WebKit/public/blink.gyp:blink', ], 'include_dirs': [ '..', ], 'sources': [ '<@(extensions_renderer_sources)', ], # Disable c4267 warnings until we fix size_t to int truncations. 'msvs_disabled_warnings': [ 4267, ], 'conditions': [ ['proprietary_codecs==1 and enable_wifi_display==1', { 'dependencies': [ '../third_party/openh264/openh264.gyp:openh264_encoder', '../third_party/wds/wds.gyp:libwds', ], 'sources': [ '<@(extensions_renderer_sources_wifi_display)', ], }], ], }, { # GN version: //extensions/utility 'target_name': 'extensions_utility', 'type': 'static_library', 'dependencies': [ '../content/content.gyp:content_common', '../content/content.gyp:content_utility', '../skia/skia.gyp:skia', '../third_party/zlib/google/zip.gyp:zip', 'extensions_common', 'extensions_strings.gyp:extensions_strings', ], 'include_dirs': [ '..', ], 'sources': [ '<@(extensions_utility_sources)', ], }, { # GN version: //extensions:test_support 'target_name': 'extensions_test_support', 'type': 'static_library', 'dependencies': [ '../base/base.gyp:base', '../components/components.gyp:pref_registry_test_support', '../components/components.gyp:user_prefs', '../components/prefs/prefs.gyp:prefs_test_support', '../content/content.gyp:content_browser', '../content/content.gyp:content_common', '../content/content_shell_and_tests.gyp:test_support_content', '../net/net.gyp:net_test_support', '../testing/gtest.gyp:gtest', '../third_party/cld_2/cld_2.gyp:cld_2', 'browser/api/api_registration.gyp:extensions_api_registration', 'common/api/api.gyp:cast_channel_proto', 'common/api/api.gyp:extensions_api', 'extensions_browser', 'extensions_common', 'extensions_resources.gyp:extensions_resources', ], 'include_dirs': [ '..', '<(SHARED_INTERMEDIATE_DIR)', ], 'sources': [ '<@(extensions_test_support_sources)', ], # Disable c4267 warnings until we fix size_t to int truncations. 'msvs_disabled_warnings': [ 4267, ], }, { # The pak file generated by this target is intended to be shared by # both shell and test targets. It was combined because it might help a # little bit with build time by avoiding a repack step (one instead of # two). 'target_name': 'extensions_shell_and_test_pak', 'type': 'none', 'dependencies': [ '../content/browser/devtools/devtools_resources.gyp:devtools_resources', '../content/content.gyp:content_resources', '../content/content_shell_and_tests.gyp:content_shell_resources', '../third_party/WebKit/public/blink_resources.gyp:blink_resources', '../ui/resources/ui_resources.gyp:ui_resources', '../ui/strings/ui_strings.gyp:ui_strings', 'extensions_resources.gyp:extensions_resources', 'extensions_strings.gyp:extensions_strings', 'shell/app_shell_resources.gyp:app_shell_resources', ], 'actions': [ { 'action_name': 'repack_extensions_shell_and_test_pak', 'variables': { 'pak_inputs': [ '<(SHARED_INTERMEDIATE_DIR)/blink/public/resources/blink_image_resources_100_percent.pak', '<(SHARED_INTERMEDIATE_DIR)/blink/public/resources/blink_resources.pak', '<(SHARED_INTERMEDIATE_DIR)/content/app/strings/content_strings_en-US.pak', '<(SHARED_INTERMEDIATE_DIR)/content/content_resources.pak', '<(SHARED_INTERMEDIATE_DIR)/content/shell_resources.pak', '<(SHARED_INTERMEDIATE_DIR)/extensions/extensions_browser_resources_100_percent.pak', '<(SHARED_INTERMEDIATE_DIR)/extensions/extensions_renderer_resources.pak', '<(SHARED_INTERMEDIATE_DIR)/extensions/extensions_resources.pak', '<(SHARED_INTERMEDIATE_DIR)/extensions/shell/app_shell_resources.pak', '<(SHARED_INTERMEDIATE_DIR)/extensions/strings/extensions_strings_en-US.pak', '<(SHARED_INTERMEDIATE_DIR)/ui/resources/ui_resources_100_percent.pak', '<(SHARED_INTERMEDIATE_DIR)/ui/strings/app_locale_settings_en-US.pak', '<(SHARED_INTERMEDIATE_DIR)/ui/strings/ui_strings_en-US.pak', '<(SHARED_INTERMEDIATE_DIR)/blink/devtools_resources.pak', ], 'pak_output': '<(PRODUCT_DIR)/extensions_shell_and_test.pak', }, 'includes': [ '../build/repack_action.gypi' ], }, ], }, ] }

73e24c193dfdeaaf093d4484761adb8ae1a8f670

py

Python

Python_files/OD_matrix_estimation_GLS_Jan_weekday_PM_ext.py

jingzbu/InverseVITraffic

c0d33d91bdd3c014147d58866c1a2b99fb8a9608

Python_files/OD_matrix_estimation_GLS_Jan_weekday_PM_ext.py

jingzbu/InverseVITraffic

c0d33d91bdd3c014147d58866c1a2b99fb8a9608

Python_files/OD_matrix_estimation_GLS_Jan_weekday_PM_ext.py

jingzbu/InverseVITraffic

c0d33d91bdd3c014147d58866c1a2b99fb8a9608

#!/usr/bin/env python __author__ = "Jing Zhang" __email__ = "jingzbu@gmail.com" __status__ = "Development" from util import * import numpy as np from numpy.linalg import inv import json # load logit_route_choice_probability_matrix P = zload('../temp_files/logit_route_choice_probability_matrix_ext.pkz') P = np.matrix(P) # print(np.size(P,0), np.size(P,1)) # load path-link incidence matrix A = zload('../temp_files/path-link_incidence_matrix_ext.pkz') # load link counts data with open('../temp_files/link_day_minute_Jan_dict_ext_JSON_insert_links_adjusted.json', 'r') as json_file: link_day_minute_Jan_dict_ext_JSON = json.load(json_file) # week_day_Jan_list = [2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 16, 17, 18, 19, 20, 23, 24, 25, 26, 27, 30, 31] week_day_Jan_list = [25, 26, 27, 30, 31] link_day_minute_Jan_list = [] for link_idx in range(74): for day in week_day_Jan_list: for minute_idx in range(120): key = 'link_' + str(link_idx) + '_' + str(day) link_day_minute_Jan_list.append(link_day_minute_Jan_dict_ext_JSON[key] ['PM_flow_minute'][minute_idx]) # print(len(link_day_minute_Jan_list)) x = np.matrix(link_day_minute_Jan_list) x = np.matrix.reshape(x, 74, 600) x = np.nan_to_num(x) y = np.array(np.transpose(x)) y = y[np.all(y != 0, axis=1)] x = np.transpose(y) x = np.matrix(x) # print(np.size(x,0), np.size(x,1)) # print(x[:,:2]) # print(np.size(A,0), np.size(A,1)) L = 22 * (22 - 1) # dimension of lam lam_list = GLS(x, A, P, L) # write estimation result to file n = 22 # number of nodes with open('../temp_files/OD_demand_matrix_Jan_weekday_PM_ext.txt', 'w') as the_file: idx = 0 for i in range(n + 1)[1:]: for j in range(n + 1)[1:]: if i != j: the_file.write("%d,%d,%f\n" %(i, j, lam_list[idx])) idx += 1

73e252228f2622299f80336e10321317810bb8f5

py

Python

cockroach/test_client.py

abhishekgahlot/cockroach-python

72903f583da182530e208cf41644d74e1a7ef505

2015-11-05T19:20:27.000Z

2015-11-05T19:20:27.000Z

cockroach/test_client.py

abhishekgahlot/cockroach-python

72903f583da182530e208cf41644d74e1a7ef505

cockroach/test_client.py

abhishekgahlot/cockroach-python

72903f583da182530e208cf41644d74e1a7ef505

import logging import os import threading import unittest from cockroach import errors from cockroach.interface import KVSender, TransactionOptions from cockroach.kv import KV from cockroach.http_sender import HTTPSender from cockroach.methods import Methods from cockroach.proto import api_pb2, config_pb2, data_pb2 class NotifyingSender(KVSender): """NotifyingSender wraps a KVSender to provide notifications when an RPC is sent.""" def __init__(self, sender): self.wrapped = sender self.callback = None def reset(self, callback): """Schedules callback to be run after the next send. It is undefined to call reset while a previous callback is pending. """ self.callback = callback def send(self, call): self.wrapped.send(call) if self.callback is not None: self.callback() self.callback = None def close(self): self.wrapped.close() @unittest.skipIf('COCKROACH_PORT' not in os.environ, "not running under docker-compose") class ClientTest(unittest.TestCase): def setUp(self): self.assertTrue(os.environ['COCKROACH_PORT'].startswith('tcp://')) addr = os.environ['COCKROACH_PORT'][len('tcp://'):] self.notifying_sender = NotifyingSender(HTTPSender(addr)) self.client = KV(self.notifying_sender, user="root") # This import is delayed because the module is not present by default # on python 2. We only run this test with python 3, but it needs to be # importable on py2. from concurrent.futures import ThreadPoolExecutor self.executor = ThreadPoolExecutor(2) def tearDown(self): self.client.close() self.executor.shutdown() # Verify that we can make a simple RPC to the server. def test_basic(self): reply = self.client.call( Methods.Increment, api_pb2.IncrementRequest( header=api_pb2.RequestHeader(key=b"a"), increment=3, )) self.assertEqual(reply.new_value, 3) # Verify that non-transactional client will succeed despite write/write and read/write # conflicts. In the case where the non-transactional put can push the txn, # we expect the transaction's value to be written after all retries are complete. def test_retry_non_txn(self): # method, isolation, can push, expected attempts test_cases = [ # Write/write conflicts. (Methods.Put, data_pb2.SNAPSHOT, True, 2), (Methods.Put, data_pb2.SERIALIZABLE, True, 2), # Some of the can't-push test cases take 15 seconds each, # and are currently failing when uncommented. # TODO: why? because the go version uses Store.SetRangeRetryOptions? #(Methods.Put, data_pb2.SNAPSHOT, False, 1), #(Methods.Put, data_pb2.SERIALIZABLE, False, 1), # Read/write conflicts. (Methods.Get, data_pb2.SNAPSHOT, True, 1), (Methods.Get, data_pb2.SERIALIZABLE, True, 2), (Methods.Get, data_pb2.SNAPSHOT, False, 1), #(Methods.Get, data_pb2.SERIALIZABLE, False, 1), ] # Lay down a write intent using a txn and attempt to write to same # key. Try this twice--once with priorities which will allow the # intent to be pushed and once with priorities which will not. for i, test_case in enumerate(test_cases): method, isolation, can_push, exp_attempts = test_case logging.info("starting test case %d", i) key = ("key-%d" % i).encode('ascii') txn_pri = -1 client_pri = -1 if can_push: client_pri = -2 else: txn_pri = -2 self.client.user_priority = client_pri done_call = threading.Event() count = [0] def callback(txn): txn.user_priority = txn_pri count[0] += 1 # Lay down the intent. txn.call(Methods.Put, api_pb2.PutRequest( header=api_pb2.RequestHeader(key=key), value=data_pb2.Value(bytes=b"txn-value"))) # On the first attempt, send the non-txn put or get. if count[0] == 1: event = threading.Event() # We use a "notifying" sender here, which allows us to know exactly # when the call has been processed; otherwise, we'd be dependent on # timing. self.notifying_sender.reset(event.set) def non_txn_op(): if method is Methods.Put: args = api_pb2.PutRequest() args.value.bytes = b"value" elif method is Methods.Get: args = api_pb2.GetRequest() else: raise Exception("unexpected method %s" % method) args.header.key = key while True: try: self.client.call(method, args) except errors.WriteIntentError: continue except Exception: # Run until we either succed or get a non-write-intent error. pass break done_call.set() self.executor.submit(non_txn_op) event.wait() self.client.run_transaction(TransactionOptions(isolation=isolation), callback) # Make sure non-txn put or get has finished. done_call.wait() # Get the current value to verify whether the txn happened first. get_reply = self.client.call( Methods.Get, api_pb2.GetRequest(header=api_pb2.RequestHeader(key=key))) if can_push or method is Methods.Get: self.assertEqual(get_reply.value.bytes, b"txn-value") else: self.assertEqual(get_reply.value.bytes, b"value") self.assertEqual(count[0], exp_attempts) def test_run_transaction(self): for commit in [True, False]: value = b"value" key = ("key-%s" % commit).encode("ascii") def callback(txn): # Put transactional value. txn.call(Methods.Put, api_pb2.PutRequest(header=api_pb2.RequestHeader(key=key), value=data_pb2.Value(bytes=value))) # Attempt to read outside of txn. gr = self.client.call( Methods.Get, api_pb2.GetRequest(header=api_pb2.RequestHeader(key=key))) self.assertFalse(gr.HasField('value')) # Read within the transaction. gr = txn.call( Methods.Get, api_pb2.GetRequest(header=api_pb2.RequestHeader(key=key))) self.assertEqual(gr.value.bytes, value) if not commit: raise ValueError("purposefully failing transaction") # Use snapshot isolation so non-transactional read can always push. try: self.client.run_transaction( TransactionOptions(isolation=data_pb2.SNAPSHOT), callback) except ValueError as e: self.assertTrue((not commit) and str(e) == "purposefully failing transaction") else: self.assertTrue(commit) # Verify the value is now visible on commit==True, and not visible otherwise. gr = self.client.call( Methods.Get, api_pb2.GetRequest(header=api_pb2.RequestHeader(key=key))) if commit: self.assertEqual(gr.value.bytes, value) else: self.assertFalse(gr.HasField('value')) # Verify gets and puts of protobufs using the kv client's convenience methods. def test_get_and_put_proto(self): zone_config = config_pb2.ZoneConfig( replica_attrs=[config_pb2.Attributes(attrs=["dc1", "mem"]), config_pb2.Attributes(attrs=["dc2", "mem"])], range_min_bytes=1<<10, # 1k range_max_bytes=1<<18, # 256k ) key = b"zone-config" self.client.put_proto(key, zone_config) read_zone_config = config_pb2.ZoneConfig() ok, ts = self.client.get_proto(key, read_zone_config) self.assertTrue(ok) self.assertNotEqual(ts.wall_time, 0) self.assertEqual(read_zone_config, zone_config) # Verify that empty values are preserved for both empty bytes and integer=0. def test_empty_values(self): self.client.put_bytes(b"a", b"") self.client.call(Methods.Put, api_pb2.PutRequest( header=api_pb2.RequestHeader(key=b"b"), value=data_pb2.Value(integer=0))) get_resp = self.client.call(Methods.Get, api_pb2.GetRequest( header=api_pb2.RequestHeader(key=b"a"))) self.assertTrue(get_resp.value.HasField('bytes')) self.assertFalse(get_resp.value.HasField('integer')) self.assertEqual(get_resp.value.bytes, b"") get_resp = self.client.call(Methods.Get, api_pb2.GetRequest( header=api_pb2.RequestHeader(key=b"b"))) self.assertFalse(get_resp.value.HasField('bytes')) self.assertTrue(get_resp.value.HasField('integer')) self.assertEqual(get_resp.value.integer, 0) # Prepare a sequence of increment calls then flush them and verify the results. def test_prepare_and_flush(self): keys = [] replies = [] for i in range(10): key = ("key %02d" % i).encode('ascii') keys.append(key) reply = api_pb2.IncrementResponse() replies.append(reply) self.client.prepare( Methods.Increment, api_pb2.IncrementRequest(header=api_pb2.RequestHeader(key=key), increment=i), reply) self.client.flush() for i, reply in enumerate(replies): self.assertEqual(i, reply.new_value) # Now try 2 scans. scan1 = api_pb2.ScanResponse() scan2 = api_pb2.ScanResponse() self.client.prepare( Methods.Scan, api_pb2.ScanRequest(header=api_pb2.RequestHeader( key=b"key 00", end_key=b"key 05")), scan1) self.client.prepare( Methods.Scan, api_pb2.ScanRequest(header=api_pb2.RequestHeader( key=b"key 05", end_key=b"key 10")), scan2) self.client.flush() self.assertEqual(len(scan1.rows), 5) self.assertEqual(len(scan2.rows), 5) for i in range(5): self.assertEqual(scan1.rows[i].key, keys[i]) self.assertEqual(scan1.rows[i].value.integer, i) self.assertEqual(scan2.rows[i].key, keys[i+5]) self.assertEqual(scan2.rows[i].value.integer, i+5) # This is an example for using the call() method to Put and then get a value for a # given key. # TODO: In the go version this is an example test; when we have docs consider making it # a doctest. def test_example_call(self): key = b"a" value = b"asdf" # Store test value. self.client.call(Methods.Put, api_pb2.PutRequest(header=api_pb2.RequestHeader(key=key), value=data_pb2.Value(bytes=value))) # Retrieve test value using the same key. get_resp = self.client.call(Methods.Get, api_pb2.GetRequest(header=api_pb2.RequestHeader(key=key))) self.assertTrue(get_resp.HasField('value')) self.assertEqual(get_resp.value.bytes, value) # This is an example for using the prepare() method to submit multiple key value # API operations to be run in parallel. Flush() is then used to begin execution of all # the prepared operations. # TODO: In the go version this is an example test; when we have docs consider making # it a doctest. def test_example_prepare(self): batch_size = 12 keys = [] values = [] for i in range(batch_size): keys.append(("key-%03d" % i).encode('ascii')) values.append(("value-%0d3" % i).encode('ascii')) self.client.prepare( Methods.Put, api_pb2.PutRequest(header=api_pb2.RequestHeader( key=keys[i]), value=data_pb2.Value(bytes=values[i])), api_pb2.PutResponse()) # Flush all puts for parallel execution. self.client.flush() # Scan for the newly inserted rows in parallel. num_scans = 3 rows_per_scan = batch_size // num_scans scan_responses = [] for i in range(num_scans): first_key = keys[i*rows_per_scan] last_key = keys[((i+1)*rows_per_scan)-1] reply = api_pb2.ScanResponse() scan_responses.append(reply) self.client.prepare( Methods.Scan, api_pb2.ScanRequest( header=api_pb2.RequestHeader(key=first_key, end_key=last_key+b"\x00"), max_results=rows_per_scan), reply) # Flush all scans for parallel execution. self.client.flush() # Check results. for i in range(num_scans): for j in range(rows_per_scan): row = scan_responses[i].rows[j] self.assertEqual(row.key, keys[i*rows_per_scan+j]) self.assertEqual(row.value.bytes, values[i*rows_per_scan+j]) # This is an example for using the run_transaction method to submit multiple key value # API operations inside a transaction. # TODO: In the go version this is an example test; when we have docs consider making # it a doctest. def test_example_run_transaction(self): # Create test data. num_pairs = 10 keys = [] values = [] for i in range(num_pairs): keys.append(('testkey-%0d3' % i).encode('ascii')) values.append(('testvalue-%03d' % i).encode('ascii')) # Insert all KV pairs inside a transaction. def callback(txn): for i in range(num_pairs): txn.prepare(Methods.Put, api_pb2.PutRequest(header=api_pb2.RequestHeader(key=keys[i]), value=data_pb2.Value(bytes=values[i])), api_pb2.PutResponse()) # Note that the KV client is flushed automatically on transaction commit. # Invoking flush after individual API methods is only required if the result # needs to be received to take conditional action. put_opts = TransactionOptions(name="example put") self.client.run_transaction(put_opts, callback) # Read back KV pairs inside a transaction. get_responses = [] def callback(txn): for i in range(num_pairs): get_responses.append(api_pb2.GetResponse()) txn.prepare(Methods.Get, api_pb2.GetRequest(header=api_pb2.RequestHeader(key=keys[i])), get_responses[-1]) get_opts = TransactionOptions(name="example get") self.client.run_transaction(get_opts, callback) # Check results. for i, get_resp in enumerate(get_responses): self.assertTrue(get_resp.HasField('value')) self.assertEqual(get_resp.value.bytes, values[i]) def concurrent_increments(self): """Start two threads in parallel, both of which read the integers stored at the other's key and add it onto their own. It is checked that the outcome is serializable, i.e. exactly one of the two threads (the later write) sees the previous write by the other. """ start = threading.Barrier(2) end = threading.Barrier(2+1) for i in range(2): def func(i): try: # Read the other key, write key i. read_key = ("value-%d" % ((i+1) % 2)).encode('ascii') write_key = ("value-%d" % i).encode('ascii') # Wait until the other threads are running. start.wait() def callback(txn): # Retrieve the other key. gr = txn.call(Methods.Get, api_pb2.GetRequest( header=api_pb2.RequestHeader(key=read_key))) other_value = gr.value.integer txn.call(Methods.Increment, api_pb2.IncrementRequest( header=api_pb2.RequestHeader(key=write_key), increment=1+other_value)) txn_opts = TransactionOptions(name='test-%d' % i) self.client.run_transaction(txn_opts, callback) finally: end.wait() self.executor.submit(func, i) # Wait for the threads to finish. end.wait() # Verify that both keys contain something and, more importantly, that one key # actually contains the value of the first writer and not only its own. total = 0 results = [] for i in range(2): read_key = ('value-%d' % i).encode('ascii') gr = self.client.call( Methods.Get, api_pb2.GetRequest(header=api_pb2.RequestHeader(key=read_key))) self.assertTrue(gr.HasField('value')) self.assertTrue(gr.value.HasField('integer')) total += gr.value.integer results.append(gr.value.integer) # First writer should have 1, second one 2. self.assertEqual(total, 3, "got unserializable values %r" % results) # test_concurrent_increments is a simple explicit test for serializability # for the concrete situation described in: # https://groups.google.com/forum/#!topic/cockroach-db/LdrC5_T0VNw def test_concurrent_increments(self): # Convenience loop: Crank up this number for testing this # more often. It'll increase test duration though. for i in range(5): self.client.call(Methods.DeleteRange, api_pb2.DeleteRangeRequest(header=api_pb2.RequestHeader( key=b"value-0", end_key=b"value-1x"))) self.concurrent_increments()

73e2554d4ceae16e3f4c0c8a20fa218a5d49a165

py

Python

tests/configs/test_zoo_urls.py

kmarathe10/mmf

93cd2a7f333ad14e88fa6c2453a0f54b64b36210

2020-06-03T07:47:36.000Z

2020-06-03T07:47:36.000Z

tests/configs/test_zoo_urls.py

kmarathe10/mmf

93cd2a7f333ad14e88fa6c2453a0f54b64b36210

tests/configs/test_zoo_urls.py

kmarathe10/mmf

93cd2a7f333ad14e88fa6c2453a0f54b64b36210

# Copyright (c) Facebook, Inc. and its affiliates. import unittest from omegaconf import OmegaConf from mmf.utils.configuration import load_yaml from mmf.utils.download import DownloadableFile, check_header from tests.test_utils import skip_if_no_network class TestConfigsForKeys(unittest.TestCase): def _test_zoo_for_keys(self, path): zoo_config = load_yaml(path) self._recurse_on_config(zoo_config) def _recurse_on_config(self, config): if OmegaConf.is_list(config) and len(config) > 0 and "url" in config[0]: # Found the urls, let's test them for item in config: # First try making the DownloadableFile class to make sure # everything is fine download = DownloadableFile(**item) # Now check the actual header check_header(download._url, from_google=download._from_google) elif OmegaConf.is_dict(config): # Both version and resources should be present if "version" in config: self.assertIn("resources", config) if "resources" in config: self.assertIn("version", config) # Let's continue recursing for item in config: self._recurse_on_config(config[item]) @skip_if_no_network def test_zoos(self): self._test_zoo_for_keys("configs/zoo/datasets.yaml") self._test_zoo_for_keys("configs/zoo/models.yaml")

73e25eacd8f566ad7c9b269120b9ab0e32ecc589

py

Python

tests/_root_dir.py

sebastian-sz/tfdata-image-loader

7ba03a37086a4bc318961d39b72db76ed1cd85cf

2020-05-12T05:33:17.000Z

2020-05-12T05:33:17.000Z

tests/_root_dir.py

sebastian-sz/tfdata-image-loader

7ba03a37086a4bc318961d39b72db76ed1cd85cf

tests/_root_dir.py

sebastian-sz/tfdata-image-loader

7ba03a37086a4bc318961d39b72db76ed1cd85cf

"""Module for handling data paths.""" import os ROOT_DIR = os.path.dirname(os.path.abspath(__file__))

73e268ced93d4575771ba66687b9f2c9b34dbb5a

py

Python

sdk/cdn/azure-mgmt-cdn/azure/mgmt/cdn/aio/operations/_policies_operations.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

2022-02-01T18:50:12.000Z

2022-02-01T18:50:12.000Z

sdk/cdn/azure-mgmt-cdn/azure/mgmt/cdn/aio/operations/_policies_operations.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

sdk/cdn/azure-mgmt-cdn/azure/mgmt/cdn/aio/operations/_policies_operations.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- import functools from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union import warnings from azure.core.async_paging import AsyncItemPaged, AsyncList from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from azure.core.tracing.decorator_async import distributed_trace_async from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling from ... import models as _models from ..._vendor import _convert_request from ...operations._policies_operations import build_create_or_update_request_initial, build_delete_request, build_get_request, build_list_request, build_update_request_initial T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class PoliciesOperations: """PoliciesOperations async operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.cdn.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config @distributed_trace def list( self, resource_group_name: str, **kwargs: Any ) -> AsyncIterable["_models.CdnWebApplicationFirewallPolicyList"]: """Lists all of the protection policies within a resource group. :param resource_group_name: Name of the Resource group within the Azure subscription. :type resource_group_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either CdnWebApplicationFirewallPolicyList or the result of cls(response) :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.cdn.models.CdnWebApplicationFirewallPolicyList] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.CdnWebApplicationFirewallPolicyList"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) def prepare_request(next_link=None): if not next_link: request = build_list_request( resource_group_name=resource_group_name, subscription_id=self._config.subscription_id, template_url=self.list.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) else: request = build_list_request( resource_group_name=resource_group_name, subscription_id=self._config.subscription_id, template_url=next_link, ) request = _convert_request(request) request.url = self._client.format_url(request.url) request.method = "GET" return request async def extract_data(pipeline_response): deserialized = self._deserialize("CdnWebApplicationFirewallPolicyList", pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, AsyncList(list_of_elem) async def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) return pipeline_response return AsyncItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Cdn/cdnWebApplicationFirewallPolicies'} # type: ignore @distributed_trace_async async def get( self, resource_group_name: str, policy_name: str, **kwargs: Any ) -> "_models.CdnWebApplicationFirewallPolicy": """Retrieve protection policy with specified name within a resource group. :param resource_group_name: Name of the Resource group within the Azure subscription. :type resource_group_name: str :param policy_name: The name of the CdnWebApplicationFirewallPolicy. :type policy_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: CdnWebApplicationFirewallPolicy, or the result of cls(response) :rtype: ~azure.mgmt.cdn.models.CdnWebApplicationFirewallPolicy :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.CdnWebApplicationFirewallPolicy"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_get_request( resource_group_name=resource_group_name, policy_name=policy_name, subscription_id=self._config.subscription_id, template_url=self.get.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = self._deserialize('CdnWebApplicationFirewallPolicy', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Cdn/cdnWebApplicationFirewallPolicies/{policyName}'} # type: ignore async def _create_or_update_initial( self, resource_group_name: str, policy_name: str, cdn_web_application_firewall_policy: "_models.CdnWebApplicationFirewallPolicy", **kwargs: Any ) -> "_models.CdnWebApplicationFirewallPolicy": cls = kwargs.pop('cls', None) # type: ClsType["_models.CdnWebApplicationFirewallPolicy"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] _json = self._serialize.body(cdn_web_application_firewall_policy, 'CdnWebApplicationFirewallPolicy') request = build_create_or_update_request_initial( resource_group_name=resource_group_name, policy_name=policy_name, subscription_id=self._config.subscription_id, content_type=content_type, json=_json, template_url=self._create_or_update_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 201, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('CdnWebApplicationFirewallPolicy', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('CdnWebApplicationFirewallPolicy', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('CdnWebApplicationFirewallPolicy', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Cdn/cdnWebApplicationFirewallPolicies/{policyName}'} # type: ignore @distributed_trace_async async def begin_create_or_update( self, resource_group_name: str, policy_name: str, cdn_web_application_firewall_policy: "_models.CdnWebApplicationFirewallPolicy", **kwargs: Any ) -> AsyncLROPoller["_models.CdnWebApplicationFirewallPolicy"]: """Create or update policy with specified rule set name within a resource group. :param resource_group_name: Name of the Resource group within the Azure subscription. :type resource_group_name: str :param policy_name: The name of the CdnWebApplicationFirewallPolicy. :type policy_name: str :param cdn_web_application_firewall_policy: Policy to be created. :type cdn_web_application_firewall_policy: ~azure.mgmt.cdn.models.CdnWebApplicationFirewallPolicy :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either CdnWebApplicationFirewallPolicy or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.cdn.models.CdnWebApplicationFirewallPolicy] :raises: ~azure.core.exceptions.HttpResponseError """ content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.CdnWebApplicationFirewallPolicy"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._create_or_update_initial( resource_group_name=resource_group_name, policy_name=policy_name, cdn_web_application_firewall_policy=cdn_web_application_firewall_policy, content_type=content_type, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('CdnWebApplicationFirewallPolicy', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Cdn/cdnWebApplicationFirewallPolicies/{policyName}'} # type: ignore async def _update_initial( self, resource_group_name: str, policy_name: str, cdn_web_application_firewall_policy_patch_parameters: "_models.CdnWebApplicationFirewallPolicyPatchParameters", **kwargs: Any ) -> "_models.CdnWebApplicationFirewallPolicy": cls = kwargs.pop('cls', None) # type: ClsType["_models.CdnWebApplicationFirewallPolicy"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] _json = self._serialize.body(cdn_web_application_firewall_policy_patch_parameters, 'CdnWebApplicationFirewallPolicyPatchParameters') request = build_update_request_initial( resource_group_name=resource_group_name, policy_name=policy_name, subscription_id=self._config.subscription_id, content_type=content_type, json=_json, template_url=self._update_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('CdnWebApplicationFirewallPolicy', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('CdnWebApplicationFirewallPolicy', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Cdn/cdnWebApplicationFirewallPolicies/{policyName}'} # type: ignore @distributed_trace_async async def begin_update( self, resource_group_name: str, policy_name: str, cdn_web_application_firewall_policy_patch_parameters: "_models.CdnWebApplicationFirewallPolicyPatchParameters", **kwargs: Any ) -> AsyncLROPoller["_models.CdnWebApplicationFirewallPolicy"]: """Update an existing CdnWebApplicationFirewallPolicy with the specified policy name under the specified subscription and resource group. :param resource_group_name: Name of the Resource group within the Azure subscription. :type resource_group_name: str :param policy_name: The name of the CdnWebApplicationFirewallPolicy. :type policy_name: str :param cdn_web_application_firewall_policy_patch_parameters: CdnWebApplicationFirewallPolicy parameters to be patched. :type cdn_web_application_firewall_policy_patch_parameters: ~azure.mgmt.cdn.models.CdnWebApplicationFirewallPolicyPatchParameters :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either CdnWebApplicationFirewallPolicy or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.cdn.models.CdnWebApplicationFirewallPolicy] :raises: ~azure.core.exceptions.HttpResponseError """ content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.CdnWebApplicationFirewallPolicy"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._update_initial( resource_group_name=resource_group_name, policy_name=policy_name, cdn_web_application_firewall_policy_patch_parameters=cdn_web_application_firewall_policy_patch_parameters, content_type=content_type, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('CdnWebApplicationFirewallPolicy', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Cdn/cdnWebApplicationFirewallPolicies/{policyName}'} # type: ignore @distributed_trace_async async def delete( self, resource_group_name: str, policy_name: str, **kwargs: Any ) -> None: """Deletes Policy. :param resource_group_name: Name of the Resource group within the Azure subscription. :type resource_group_name: str :param policy_name: The name of the CdnWebApplicationFirewallPolicy. :type policy_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_delete_request( resource_group_name=resource_group_name, policy_name=policy_name, subscription_id=self._config.subscription_id, template_url=self.delete.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Cdn/cdnWebApplicationFirewallPolicies/{policyName}'} # type: ignore

73e26c42d03be84833d32be1b861e6d28d243796

py

Python

deepreg/log.py

Zhiyuan-w/DeepReg

3e372d1835fdc9468c026db3767dcf9e8d4a4b0e

2020-07-18T22:00:53.000Z

2022-03-31T05:17:29.000Z

deepreg/log.py

Zhiyuan-w/DeepReg

3e372d1835fdc9468c026db3767dcf9e8d4a4b0e

2020-07-18T08:55:48.000Z

2022-03-29T02:24:54.000Z

deepreg/log.py

Zhiyuan-w/DeepReg

3e372d1835fdc9468c026db3767dcf9e8d4a4b0e

2020-07-26T05:00:23.000Z

2022-02-22T21:58:19.000Z

"""Module for logger.""" import logging import os import sys def get(name: str) -> logging.Logger: """ Configure the logger with formatter and handlers. The logger should be used as: .. code-block:: python from deepreg import log logger = log.get(__name__) The log level depends on the environment variable `DEEPREG_LOG_LEVEL`. - 0: NOTSET, will be set to DEBUG - 1: DEBUG - 2: INFO (default) - 3: WARNING - 4: ERROR - 5: CRITICAL https://docs.python.org/3/library/logging.html#levels :param name: module name. :return: configured logger. """ logger = logging.getLogger(name=name) logger.propagate = False log_level = os.environ.get("DEEPREG_LOG_LEVEL", "2") log_level_int = max(int(log_level) * 10, 10) logger.setLevel(log_level_int) formatter = logging.Formatter( fmt="%(asctime)s | %(levelname)-8s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S" ) stdout_handler = logging.StreamHandler(stream=sys.stdout) stdout_handler.setFormatter(formatter) stdout_handler.setLevel(log_level_int) logger.addHandler(stdout_handler) return logger

73e2703337e31d071623cfa6b64d721f4de6f113

py

Python

setup.py

JHay0112/jmath

3bccf7353c07152ff49ec6aa56785918c9f710ed

2021-06-25T12:57:08.000Z

2021-12-29T21:53:30.000Z

setup.py

JHay0112/jmath

3bccf7353c07152ff49ec6aa56785918c9f710ed

2021-08-11T02:16:12.000Z

2021-08-12T06:05:21.000Z

setup.py

JHay0112/jmath

3bccf7353c07152ff49ec6aa56785918c9f710ed

2021-09-20T21:25:55.000Z

2021-09-20T21:25:55.000Z

''' setup.py Author: Jordan Hay Date: 2021-06-17 ''' from setuptools import setup, find_packages # Open readme with open("README.md", "r", encoding="utf-8") as fh: long_description = fh.read() # Open version with open("version.txt", "r", encoding="utf-8") as fh: version = fh.readline() setup( name = 'jmath', packages = find_packages(include = [ "jmath", "jmath.*" ]), version = version, description = "Mathematics Tools", long_description = long_description, long_description_content_type = "text/markdown", author = "Jordan Hay", license = "MIT", url = "https://github.com/JHay0112/jmath", project_urls={ "Bug Tracker": "https://github.com/JHay0112/jmath/issues", }, classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], install_requires=[], python_requires=">=3.6" )

73e27040e83af8d285bcc929f8ab591bf957b9d5

py

Python

alipay/aop/api/request/AlipayOpenAppAppcontentItemDeleteRequest.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

2018-08-27T16:49:32.000Z

2021-12-29T04:34:12.000Z

alipay/aop/api/request/AlipayOpenAppAppcontentItemDeleteRequest.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

2018-09-29T06:43:00.000Z

2021-09-02T03:27:32.000Z

alipay/aop/api/request/AlipayOpenAppAppcontentItemDeleteRequest.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

2018-08-27T16:59:26.000Z

2022-03-25T10:08:15.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.FileItem import FileItem from alipay.aop.api.constant.ParamConstants import * from alipay.aop.api.domain.AlipayOpenAppAppcontentItemDeleteModel import AlipayOpenAppAppcontentItemDeleteModel class AlipayOpenAppAppcontentItemDeleteRequest(object): def __init__(self, biz_model=None): self._biz_model = biz_model self._biz_content = None self._version = "1.0" self._terminal_type = None self._terminal_info = None self._prod_code = None self._notify_url = None self._return_url = None self._udf_params = None self._need_encrypt = False @property def biz_model(self): return self._biz_model @biz_model.setter def biz_model(self, value): self._biz_model = value @property def biz_content(self): return self._biz_content @biz_content.setter def biz_content(self, value): if isinstance(value, AlipayOpenAppAppcontentItemDeleteModel): self._biz_content = value else: self._biz_content = AlipayOpenAppAppcontentItemDeleteModel.from_alipay_dict(value) @property def version(self): return self._version @version.setter def version(self, value): self._version = value @property def terminal_type(self): return self._terminal_type @terminal_type.setter def terminal_type(self, value): self._terminal_type = value @property def terminal_info(self): return self._terminal_info @terminal_info.setter def terminal_info(self, value): self._terminal_info = value @property def prod_code(self): return self._prod_code @prod_code.setter def prod_code(self, value): self._prod_code = value @property def notify_url(self): return self._notify_url @notify_url.setter def notify_url(self, value): self._notify_url = value @property def return_url(self): return self._return_url @return_url.setter def return_url(self, value): self._return_url = value @property def udf_params(self): return self._udf_params @udf_params.setter def udf_params(self, value): if not isinstance(value, dict): return self._udf_params = value @property def need_encrypt(self): return self._need_encrypt @need_encrypt.setter def need_encrypt(self, value): self._need_encrypt = value def add_other_text_param(self, key, value): if not self.udf_params: self.udf_params = dict() self.udf_params[key] = value def get_params(self): params = dict() params[P_METHOD] = 'alipay.open.app.appcontent.item.delete' params[P_VERSION] = self.version if self.biz_model: params[P_BIZ_CONTENT] = json.dumps(obj=self.biz_model.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':')) if self.biz_content: if hasattr(self.biz_content, 'to_alipay_dict'): params['biz_content'] = json.dumps(obj=self.biz_content.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['biz_content'] = self.biz_content if self.terminal_type: params['terminal_type'] = self.terminal_type if self.terminal_info: params['terminal_info'] = self.terminal_info if self.prod_code: params['prod_code'] = self.prod_code if self.notify_url: params['notify_url'] = self.notify_url if self.return_url: params['return_url'] = self.return_url if self.udf_params: params.update(self.udf_params) return params def get_multipart_params(self): multipart_params = dict() return multipart_params

73e293d9729126dcbc6df04ad609fd3212e9bcf9

py

Python

tests/test_tools.py

MichaelTrumbull/regolith

881f543cfa7bbf80b38ed2b357209c5bc009a6af

tests/test_tools.py

MichaelTrumbull/regolith

881f543cfa7bbf80b38ed2b357209c5bc009a6af

tests/test_tools.py

MichaelTrumbull/regolith

881f543cfa7bbf80b38ed2b357209c5bc009a6af

from copy import copy import habanero import pytest import datetime as dt from regolith.tools import ( filter_publications, filter_presentations, fuzzy_retrieval, fragment_retrieval, number_suffix, latex_safe, update_schemas, group, is_fully_appointed, group_member_ids, group_member_employment_start_end, merge_collections_all, merge_collections_intersect, merge_collections_superior, month_and_year, remove_duplicate_docs, awards_grants_honors, get_id_from_name, get_person_contact, date_to_rfc822, key_value_pair_filter, collection_str, search_collection, collect_appts, grant_burn, validate_meeting, get_formatted_crossref_reference, compound_dict, compound_list, filter_employment_for_advisees, get_tags, dereference_institution ) PEOPLE_COLL = [ {"_id": "m1", "name": "member1", "education": [{ "group": "bg", "institution": "columbiau", "degree": "PhD", "department": "apam", "begin_year": 2016 }], "employment": [{ "begin_year": 2020, "begin_month": 1, "organization": "columbiau", "position": "Undergraduate Researcher", "advisor": "sbillinge", "status": "undergrad" }] }, { "_id": "nm1", "name": "non-member1", "education": [{ "institution": "columbiau", "degree": "PhD", "department": "apam", "begin_year": 2016 }], "employment": [{ "begin_year": 2020, "begin_month": 1, "organization": "columbiau", "position": "Undergraduate Researcher", "advisor": "sbillinge", "status": "undergrad" }] }, { "_id": "m2", "name": "member2", "education": [{ "institution": "columbiau", "degree": "PhD", "department": "apam", "begin_year": 2016 }], "employment": [{ "begin_year": 2020, "begin_month": 1, "group": "bg", "organization": "columbiau", "position": "Undergraduate Researcher", "advisor": "sbillinge", "status": "undergrad" }] }, ] CONTACTS_COLL = [ {"_id": "c1", "name": "contact1", "institution": "columbiau" }] @pytest.mark.parametrize( "input, expected", [ (["m1", PEOPLE_COLL, CONTACTS_COLL], {"_id": "m1", "name": "member1", "education": [{ "group": "bg", "institution": "columbiau", "degree": "PhD", "department": "apam", "begin_year": 2016 }], "employment": [{ "begin_year": 2020, "begin_month": 1, "organization": "columbiau", "position": "Undergraduate Researcher", "advisor": "sbillinge", "status": "undergrad" }] }), (["c1", PEOPLE_COLL, CONTACTS_COLL], {"_id": "c1", "name": "contact1", "institution": "columbiau" }), (["bad1", PEOPLE_COLL, CONTACTS_COLL], None), ]) def test_get_person_contact(input, expected): print(input) actual = get_person_contact(input[0],input[1],input[2]) assert actual == expected CITATIONS = [{"_id": "paper", "author": ["m1","cleese"], "ackno": "thanks", "grant": "fwp, dmref", "month": "apr", "year": "2021"}, {"_id": "paper2", "author": ["m1","palin"], "ackno": "thanks", "grant": "fwp2", "year": "2020"}, {"_id": "paper3", "author": ["m1", "jones"], "ackno": "thanks", "grant": "fwp2", "month": "jun", "year": "2020"} ] @pytest.mark.parametrize( "args, kwargs, expected", [ ([CITATIONS, set(["m1"])], {}, [{"_id": "paper3", "author": ["\\textbf{m1}", "jones"], "ackno": "thanks", "grant": "fwp2", "month": "jun", "year": "2020"}, {"_id": "paper2", "author": ["\\textbf{m1}", "palin"], "ackno": "thanks", "grant": "fwp2", "year": "2020"}, {'_id': 'paper', 'ackno': 'thanks', 'author': ['\\textbf{m1}', 'cleese'], 'grant': 'fwp, dmref', 'month': 'apr', 'year': '2021'} ] ), ([CITATIONS, set(["m1"])], {"bold": False, "ackno": True}, [{"_id": "paper3", "author": ["m1", "jones"], "ackno": "thanks", "grant": "fwp2", "month": "jun", "note": "\\newline\\newline\\noindent Acknowledgement:\\newline\\noindent thanks\\newline\\newline\\noindent ", "year": "2020"}, {"_id": "paper2", "author": ["m1", "palin"], "ackno": "thanks", "grant": "fwp2", "note": "\\newline\\newline\\noindent Acknowledgement:\\newline\\noindent thanks\\newline\\newline\\noindent ", "year": "2020"}, {'_id': 'paper', 'ackno': 'thanks', 'author': ['m1', 'cleese'], 'grant': 'fwp, dmref', 'month': 'apr', "note": "\\newline\\newline\\noindent Acknowledgement:\\newline\\noindent thanks\\newline\\newline\\noindent ", 'year': '2021'} ] ), ([CITATIONS, set(["m1"])], {"bold":False}, [{"_id": "paper3", "author": ["m1", "jones"], "ackno": "thanks", "grant": "fwp2", "month": "jun", "year": "2020"}, {"_id": "paper2", "author": ["m1", "palin"], "ackno": "thanks", "grant": "fwp2", "year": "2020"}, {'_id': 'paper', 'ackno': 'thanks', 'author': ['m1', 'cleese'], 'grant': 'fwp, dmref', 'month': 'apr', 'year': '2021'} ] ), ([CITATIONS, set(["m1"])], {"bold": False, "grants": "fwp2"}, [{"_id": "paper3", "author": ["m1", "jones"], "ackno": "thanks", "grant": "fwp2", "month": "jun", "year": "2020"}, {"_id": "paper2", "author": ["m1", "palin"], "ackno": "thanks", "grant": "fwp2", "year": "2020"} ] ), ([CITATIONS, set(["m1"])], {"bold": False, "grants": ["fwp2", "dmref"]}, [{"_id": "paper3", "author": ["m1", "jones"], "ackno": "thanks", "grant": "fwp2", "month": "jun", "year": "2020"}, {"_id": "paper2", "author": ["m1", "palin"], "ackno": "thanks", "grant": "fwp2", "year": "2020"}, {'_id': 'paper', 'ackno': 'thanks', 'author': ['m1', 'cleese'], 'grant': 'fwp, dmref', 'month': 'apr', 'year': '2021'} ] ), ([CITATIONS, set(["m1"])], {"bold": False, "since": dt.date(2021,1,1)}, [{'_id': 'paper', 'ackno': 'thanks', 'author': ['m1', 'cleese'], 'grant': 'fwp, dmref', 'month': 'apr', 'year': '2021'} ] ), ([CITATIONS, set(["m1"])], {"bold": False, "since": dt.date(2020, 5, 1), "before": dt.date(2021,1,1)}, [{"_id": "paper3", "author": ["m1", "jones"], "ackno": "thanks", "grant": "fwp2", "month": "jun", "year": "2020"}, {"_id": "paper2", "author": ["m1", "palin"], "ackno": "thanks", "grant": "fwp2", "year": "2020"}, ] ), ] ) def test_filter_publications(args, kwargs, expected): actual = filter_publications(*args, **kwargs) assert actual == expected def test_author_publications(): citations = [{"author": ["CJ", "SJLB"]}, {"editor": "SJLB"}] filter_publications(citations, {"SJLB"}) def test_fuzzy_retrieval(): person = { "_id": "scopatz", "aka": [ "Scopatz", "Scopatz, A", "Scopatz, A.", "Scopatz, A M", "Anthony Michael Scopatz", ], "name": "Anthony Scopatz", } assert fuzzy_retrieval([person], ["aka", "name", "_id"], "scopatz") == person assert fuzzy_retrieval([person], ["aka", "name", "_id"], "scopatz, a") is None assert ( fuzzy_retrieval( [person], ["aka", "name", "_id"], "scopatz, a", case_sensitive=False, ) == person ) def test_get_formatted_crossref_reference(monkeypatch): def mockreturn(*args, **kwargs): mock_article = {'message': {'author': [{"given": "SJL", "family": "Billinge"}], "short-container-title": ["J. Great Results"], "volume": 10, "title": ["Whamo"], "page": "231-233", "issued": {"date-parts": [[1971,8,20]]}} } return mock_article monkeypatch.setattr(habanero.Crossref, "works", mockreturn) expected = ("Whamo, SJL Billinge, J. Great Results, v. 10, pp. 231-233, (1971).", dt.date(1971, 8, 20)) actual = get_formatted_crossref_reference("test") assert actual == expected @pytest.mark.parametrize( "input,expected", [ (0, "th"), (1, "st"), (2, "nd"), (3, "rd"), (4, "th"), (10, "th"), (13, "th"), (33, "rd"), (None, ""), ("0", ""), ], ) def test_number_suffix(input, expected): assert number_suffix(input) == expected @pytest.mark.parametrize( "input,expected", [ ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", } ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", } ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", }, { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}], ), [ { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", "amount": "50 mph", }, { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"} ], ), [ { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", "amount": "50 mph", }, { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}, { "_id": "grant2", "title": "African swallow", "author": "king arthur", }, ], ), [ { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, { "_id": "grant2", "title": "African swallow", "author": "king arthur", }, ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", "amount": "50 mph", }, { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}, { "_id": "grant2", "title": "African swallow", "author": "king arthur", "linked_to": "proposal2" }, ], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, { "_id": "grant2", "title": "African swallow", "author": "king arthur", "linked_to": "proposal2", }, ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", "amount": "50 mph", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}, { "_id": "grant2", "title": "African swallow", "author": "king arthur", }, ], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, { "_id": "grant2", "title": "African swallow", "author": "king arthur", }, ], ), ], ) def test_merge_collections_all(input, expected): a = input[0] b = input[1] target_id = "linked_to" assert merge_collections_all(a, b, target_id) == expected @pytest.mark.parametrize( "input,expected", [ ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", } ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", } ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", }, { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", "amount": "50 mph", }, { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"} ], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", "amount": "50 mph", }, { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}, { "_id": "grant2", "title": "African swallow", "author": "king arthur", }, ], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, { "_id": "grant2", "title": "African swallow", "author": "king arthur", }, ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", "amount": "50 mph", }, { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}, { "_id": "grant2", "title": "African swallow", "author": "king arthur", "linked_to": "proposal2" }, ], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, { "_id": "grant2", "title": "African swallow", "author": "king arthur", "linked_to": "proposal2", }, ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", "amount": "50 mph", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}, { "_id": "grant2", "title": "African swallow", "author": "king arthur", }, ], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, { "_id": "grant2", "title": "African swallow", "author": "king arthur", }, ], ), ], ) def test_merge_collections_superior(input, expected): a = input[0] b = input[1] target_id = "linked_to" assert merge_collections_superior(a, b, target_id) == expected @pytest.mark.parametrize( "input,expected", [ ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", } ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", } ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", }, { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", "amount": "50 mph", }, { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"} ], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", "amount": "50 mph", }, { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}, ], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", "amount": "50 mph", }, { "_id": "proposal2", "title": "African swallow", "author": "king arthur", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}, { "_id": "grant2", "title": "African swallow", "author": "king arthur", "linked_to": "proposal2" }, ], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, { "_id": "grant2", "title": "African swallow", "author": "king arthur", "linked_to": "proposal2", }, ], ), ( ( [ { "_id": "proposal1", "title": "European swallow", "author": "king arthur", "amount": "50 mph", }, ], [{"_id": "grant1", "linked_to": "proposal1", "amount": "100 mph"}, { "_id": "grant2", "title": "African swallow", "author": "king arthur", }, ], ), [ { "_id": "grant1", "title": "European swallow", "author": "king arthur", "linked_to": "proposal1", "amount": "100 mph", }, ], ), ], ) def test_merge_intersection(input, expected): a = input[0] b = input[1] target_id = "linked_to" assert merge_collections_intersect(a, b, target_id) == expected @pytest.mark.parametrize( "input,expected,kwargs", [ ("$hi", r"\$hi", {}), ( r"Website: https://github.com/CJ-Wright/" r"Masters_Thesis/raw/master/thesis.pdf hi", r"Website: \url{https://github.com/CJ-Wright/" r"Masters_Thesis/raw/master/thesis.pdf} hi", {}, ), ( r"Website: https://github.com/CJ-Wright/" r"Masters_Thesis/raw/master/thesis.pdf hi", r"Website: \href{https://github.com/CJ-Wright/" r"Masters_Thesis/raw/master/thesis.pdf} hi", {"wrapper": "href"}, ), ( r"Website: https://github.com/CJ-Wright/" r"Masters_Thesis/raw/master/thesis.pdf hi", r"Website: https://github.com/CJ-Wright/" r"Masters\_Thesis/raw/master/thesis.pdf hi", {"url_check": False}, ), ], ) def test_latex_safe(input, expected, kwargs): output = latex_safe(input, **kwargs) assert output == expected DEFAULT_SCHEMA = { "expenses": { "itemized_expenses": { "type": "list", "schema": { "type": "dict", "schema": { "day": { "description": "Expense day", "required": True, "type": "integer", }, }, }, }, }, } USER_SCHEMA0 = { "expenses": { "itemized_expenses": { "type": "list", "schema": {"type": "dict", "schema": {"day": {"required": False, }, }, }, }, }, } EXPECTED_SCHEMA0 = { "expenses": { "itemized_expenses": { "type": "list", "schema": { "type": "dict", "schema": { "day": { "description": "Expense day", "required": False, "type": "integer", }, }, }, }, }, } USER_SCHEMA1 = { "expenses": { "itemized_expenses": { "type": "list", "schema": {"type": "dict", "schema": {"day": {"type": "string", }, }, }, }, }, } EXPECTED_SCHEMA1 = { "expenses": { "itemized_expenses": { "type": "list", "schema": { "type": "dict", "schema": { "day": { "description": "Expense day", "required": True, "type": "string", }, }, }, }, }, } USER_SCHEMA2 = { "expenses": { "begin_day": { "description": "The first day of expense", "required": True, "type": "string", } }, } EXPECTED_SCHEMA2 = { "expenses": { "itemized_expenses": { "type": "list", "schema": { "type": "dict", "schema": { "day": { "description": "Expense day", "required": True, "type": "integer", }, }, }, }, "begin_day": { "description": "The first day of expense", "required": True, "type": "string", }, }, } USER_SCHEMA3 = { "expenses": { "itemized_expenses": { "type": "list", "schema": { "type": "dict", "schema": {"day": {"description": "The date on the receipt"}, }, }, }, }, } EXPECTED_SCHEMA3 = { "expenses": { "itemized_expenses": { "type": "list", "schema": { "type": "dict", "schema": { "day": { "description": "The date on the receipt", "required": True, "type": "integer", }, }, }, }, }, } USER_SCHEMA4 = { "expenses": { "itemized_expenses": { "schema": { "schema": { "prepaid_expense": { "description": "Expense paid by the direct billing", "required": True, "type": "float", }, }, }, }, }, } EXPECTED_SCHEMA4 = { "expenses": { "itemized_expenses": { "type": "list", "schema": { "type": "dict", "schema": { "day": { "description": "Expense day", "required": True, "type": "integer", }, "prepaid_expense": { "description": "Expense paid by the direct billing", "required": True, "type": "float", }, }, }, }, }, } USER_SCHEMA5 = {} EXPECTED_SCHEMA5 = { "expenses": { "itemized_expenses": { "type": "list", "schema": { "type": "dict", "schema": { "day": { "description": "Expense day", "required": True, "type": "integer", }, }, }, }, }, } USER_SCHEMA6 = {"expenses": {}} EXPECTED_SCHEMA6 = { "expenses": { "itemized_expenses": { "type": "list", "schema": { "type": "dict", "schema": { "day": { "description": "Expense day", "required": True, "type": "integer", }, }, }, }, }, } @pytest.mark.parametrize( "default_schema, user_schema, expected_schema", [ (DEFAULT_SCHEMA, USER_SCHEMA0, EXPECTED_SCHEMA0), (DEFAULT_SCHEMA, USER_SCHEMA1, EXPECTED_SCHEMA1), (DEFAULT_SCHEMA, USER_SCHEMA2, EXPECTED_SCHEMA2), (DEFAULT_SCHEMA, USER_SCHEMA3, EXPECTED_SCHEMA3), (DEFAULT_SCHEMA, USER_SCHEMA4, EXPECTED_SCHEMA4), (DEFAULT_SCHEMA, USER_SCHEMA5, EXPECTED_SCHEMA5), (DEFAULT_SCHEMA, USER_SCHEMA6, EXPECTED_SCHEMA6), ], ) def test_update_schemas(default_schema, user_schema, expected_schema): updated_schema = update_schemas(default_schema, user_schema) assert updated_schema == expected_schema def test_group(): doc0 = {"k0": "v00", "k1": "v01"} doc1 = {"k0": "v10", "k1": "v11"} doc2 = {"k1": "v21"} doc3 = {"k0": "v00", "k1": "v31"} db = (doc for doc in (doc0, doc1, doc2, doc3)) by = "k0" expect = {"v00": [doc0, doc3], "v10": [doc1]} assert group(db, by) == expect ppl_coll = [ { "_id": "m1", "name": "member1", "education": [{ "group": "bg", "institution": "columbiau", "degree": "PhD", "department": "apam", "begin_year": 2016 }], "employment": [{ "begin_year": 2020, "begin_month": 1, "organization": "columbiau", "position": "Undergraduate Researcher", "advisor": "sbillinge", "status": "undergrad" }] }, { "_id": "nm1", "name": "non-member1", "education": [{ "institution": "columbiau", "degree": "PhD", "department": "apam", "begin_year": 2016 }], "employment": [{ "begin_year": 2020, "begin_month": 1, "organization": "columbiau", "position": "Undergraduate Researcher", "advisor": "sbillinge", "status": "undergrad" }] }, { "_id": "m2", "name": "member2", "education": [{ "institution": "columbiau", "degree": "PhD", "department": "apam", "begin_year": 2016 }], "employment": [{ "begin_year": 2020, "begin_month": 1, "group": "bg", "organization": "columbiau", "position": "Undergraduate Researcher", "advisor": "sbillinge", "status": "undergrad" }] }, ] @pytest.mark.parametrize( "input,expected", [ (ppl_coll, set(["m1", "m2"])), ], ) def test_group_member_ids(input, expected): actual = group_member_ids(input, "bg") assert actual == expected d1 = { 'name': 'John', 'experience': [ {'company': 'Google', 'role': 'product manager'}, {'company': 'Amazon', 'role': 'QA'} ], 'school': { 'name': 'Columbia', 'location': 'NYC', 'year': 'senior' } } d2 = { 'name': 'Sarah', 'experience': [ {'company': 'Verizon', 'role': 'sales'}, {'company': 'AT&T', 'role': 'software engineer'} ], 'school': { 'name': 'Columbia', 'location': 'NYC', 'year': 'junior' }, 'hobbies': ['swimming', 'hiking'], 'info': { 'stats': { 'code': 'a76', 'location': 'California' }, 'software': 'CAD' } } d3 = { '_id': 'abc', 'name': 'Example Lab', 'Members': [ { 'Name': 'Lisa', 'Experience': [ { 'company': 'Google', 'location': {'state': 'CA', 'zip code': '94043'} }, { 'company': 'Amazon', 'location': {'state': 'VA', 'zip code': '20189'} } ] }, { 'Name': 'Stephen', 'Experience': [ { 'company': 'Goldman Sachs', 'location': {'state': 'NY', 'zip code': '10282'} } ] } ] } @pytest.mark.parametrize( "input,expected", [ (d1, ['John', 'Google', 'product manager', 'Amazon', 'QA', 'Columbia', 'NYC', 'senior']), (d2, ['Sarah', 'Verizon', 'sales', 'AT&T', 'software engineer', 'Columbia', 'NYC', 'junior', 'swimming', 'hiking', 'a76', 'California', 'CAD']), (d3, ['abc', 'Example Lab', 'Lisa', 'Google', 'CA', '94043', 'Amazon', 'VA', '20189', 'Stephen', 'Goldman Sachs', 'NY', '10282']) ] ) def test_compound_dict(input, expected): assert(compound_dict(input, []) == expected) l1 = [ 'hello', {'name': 'Fred', 'status': 'active'}, {'name': 'Derf', 'status': 'inactive'}, 'bye' ] l2 = [ ['a', 'b', 'c'], {'name': 'Anthony', 'Status': 'active'}, [{'product': 'phone'}, {'product': 'laptop'}], "end" ] @pytest.mark.parametrize( "input,expected", [ (l1, ['hello', 'Fred', 'active', 'Derf', 'inactive', 'bye']), (l2, ['a', 'b', 'c', 'Anthony', 'active', 'phone', 'laptop', 'end']), ] ) def test_compound_list(input, expected): assert(compound_list(input, []) == expected) p1 = { "_id": "scopatz", "aka": [ "Scopatz", "Scopatz, A", "Scopatz, A.", "Scopatz, A M", "Anthony Michael Scopatz", ], "name": "Anthony Scopatz", } p2 = { "_id": "abc", "aka": [ "A. BC", "BC, A", "Anthony BC", ], "name": "Anthony Bill Chris", } p3 = { "_id": "Leonan", "name": "Leonan Garea", "company": { "name": "Amazon", "role": "Product Manager" }, "projects": [ {'title': 'GUI Application', 'description': 'Make a GUI for the group'}, ] } p4 = { "_id": "cba", "name": "Jackie", "company": {}, "projects": [ {'title': 'PDF Maker', 'description': 'New PDF function'}, {'title': 'Write Paper', 'description': 'Draft the new paper'} ] } p5 = { '_id': 'ghi', 'name': 'Carl', 'experience': [ { 'name': 'Google', 'roles': [ { 'position': 'software engineer', 'location': {'state': 'CA', 'zip code': '92551'} }, { 'position': 'manager', 'location': {'state': 'VA', 'zip code': '20189'} } ] }, { 'name': 'Goldman Sachs', 'Experience': [ { 'position': 'junior associate', 'location': {'state': 'NY', 'zip code': '10282'} } ] } ] } @pytest.mark.parametrize( "input, expected", [ (([p1, p2], ["aka", "name", "_id"], "Anth", False),[p1,p2]), (([p1, p2], ["aka", "name", "_id"], "scopatz, a", True),[]), (([p1, p2], ["aka", "name", "_id"], "scopatz, a", False),[p1]), (([p1, p2], ["aka", "name", "_id"], "ill", False),[p2]), (([p3], ["company"], "Amazon", False), [p3]), (([p3, p4], ["projects"], "PDF", False), [p4]), (([p5], ['experience'], '20189', False), [p5]), (([p5], ['experience'], 'hello', False), []) ], ) def test_fragment_retrieval(input, expected): assert(fragment_retrieval(input[0],input[1],input[2],case_sensitive = input[3]) == expected) @pytest.mark.parametrize( "input, expected", [ ((None, None), "present"), ((None, 2002), "2002"), ((5,2002), "May 2002"), ], ) def test_month_and_year(input,expected): assert(month_and_year(input[0],input[1]) == expected) @pytest.mark.parametrize( "appts,start,end,expected", [ ({"name": "Kurt Godel", "_id": "kgodel", "appointments": { "A": {"begin_year": 2017, "begin_month": 6, "begin_day": 1, "end_year": 2017, "end_month": 6, "end_day": 30, "grant": "grant1", "loading": 1.0, "type": "pd",}}}, "2017-06-01", "2017-07-01", False), ({"name": "MC Escher", "_id": "mcescher", "appointments": { "A": {"begin_date": '2017-06-01', "end_date": '2017-06-30', "grant": "grant1", "loading": 0.5, "type": "pd",}, "B": {"begin_date": '2017-06-01', "end_date": '2017-06-30', "grant": "grant2", "loading": 0.5, "type": "pd", }}},"2017-06-01", "2017-06-30", True), ({"name": "Johann Sebastian Bach", "_id": "jsbach", "appointments":{ "A": {"begin_date": '2017-06-01', "end_date": '2017-06-30', "grant": "grant1", "loading": 0.5, "type": "pd",}, "B": {"begin_date": '2017-06-02', "end_date": '2017-06-29', "grant": "grant2", "loading": 0.5, "type": "pd", }}}, "2017-06-01", "2017-06-30", False), ({"name": "Evariste Galois", "_id": "egalois", "appointments": { "A": {"begin_date": '2017-06-01', "end_date": '2017-06-15', "grant": "grant1", "loading": 1.0, "type": "pd",}, "B": {"begin_date": '2017-06-16', "end_date": '2017-06-30', "grant": "grant2", "loading": 1.0, "type": "pd", }}},"2017-06-01", "2017-06-30", True), ({"name": "Ludwig Wittgenstein", "_id": "lwittgenstein", "appointments": { "A": {"begin_date": '2017-06-01', "end_date": '2017-06-15', "grant": "grant1", "loading": 1.0, "type": "pd",}, "B": {"begin_date": '2017-06-17', "end_date": '2017-06-30', "grant": "grant2", "loading": 1.0, "type": "pd",}, "C": {"begin_date": '2017-07-01', "end_date": '2017-07-30', "grant": "grant3", "loading": 1.0, "type": "pd", }}}, "2017-06-01", "2017-06-30", False), ({"name": "Buckminster Fuller", "_id": "bfuller", "appointments":{ "A": {"begin_date": '2017-06-01', "end_date": '2017-06-30', "grant": "grant1", "loading": 1.0, "type": "pd",}, "B": {"begin_date": '2017-06-17', "end_date": '2017-06-30', "grant": "grant2", "loading": 1.0, "type": "pd", }}}, "2017-06-01", "2017-06-30", False), ({"name": "Lorem Ipsum", "_id": "lipsum", "appointments":{ "A": {"begin_date": '2017-06-01', "end_date": '2017-06-30', "grant": "grant1", "loading": 1.0,"type": "pd",}, "B": {"begin_date": '2017-06-17', "end_date": '2017-06-30', "grant": "grant2", "loading": 1.0, "type": "pd",} }}, "2017-06-01", "2017-06-30", False), ], ) def test_is_fully_appointed(appts, start, end, expected): actual = is_fully_appointed(appts, start, end) assert actual == expected @pytest.mark.parametrize( "input, expected", [ ({'funding':[ {"name": "Omega Laser User's Group Travel Award", "value": 1100, "year": 2013}, {"name": "NIF User's Group Travel Award", "value": 1150, "year": 2013}]}, [{'description': "Omega Laser User's Group Travel Award (\\$1,100)", 'year': 2013, '_key': 2013.0}, {'description':"NIF User's Group Travel Award (\\$1,150)", 'year': 2013, '_key': 2013.0}]), ({'funding':[ {"name": "Omega Laser User's Group Travel Award", "value": 1100, "year": 2013}], "service":[{"name": "International Steering Committee", "role": "chair", "type": "profession", "year": 2020, "month": 3, "notes": ["something"]}]}, [{"description":"International Steering Committee", "year":2020, "_key":2020.03}, {'description': "Omega Laser User's Group Travel Award (\\$1,100)", 'year': 2013, '_key': 2013.0}] ) ], ) def test_get_id_from_name(input,expected): assert(awards_grants_honors(input) == expected) @pytest.mark.parametrize( "input, expected", [ (([{'_id':'afriend','aka':['AB Friend','Tony Friend'], 'name': 'Anthony B Friend'}], 'Simon'), None), (([{'_id':'afriend','aka':['AB Friend','Tony Friend'], 'name': 'Anthony B Friend'}], 'Anthony B Friend'), 'afriend'), (([{'_id':'afriend','aka':['AB Friend','Tony Friend'], 'name': 'Anthony B Friend'}, {'_id':'aeinstein','aka':['Einstein'], 'name': 'Albert Einstein'}], 'Albert Einstein'), 'aeinstein') ], ) def test_get_id_from_name(input,expected): assert(get_id_from_name(input[0],input[1]) == expected) @pytest.mark.parametrize( "input, expected", [ ((2012, 'Jan', 18), 'Wed, 18 Jan 2012 00:00:00 -0000'), ((2020, 6, 22), 'Mon, 22 Jun 2020 00:00:00 -0000'), ], ) def test_date_to_rfc822(input,expected): assert(date_to_rfc822(input[0], input[1], input[2]) == expected) person1 = { "_id": "scopatz", "aka": [ "Scopatz", "Scopatz, A", "Scopatz, A.", "Scopatz, A M", "Anthony Michael Scopatz", ], "name": "Anthony Scopatz", "position": "Professor" } person2 = { "_id": "abc", "aka": [ "A. BC", "BC, A", "Anthony BC", ], "name": "Anthony Bill Chris", "position": "Professor" } person3 = { "_id": "jdoe", "aka": [ "A. BC", "BC, A", "Anthony BC", ], "name": "John Doe", } people = [person1, person2, person3] @pytest.mark.parametrize( "input, expected", [ ((people, ['name', 'Doe']), [person3]), ((people, ['name', 'Jerry']), []), ((people, ['position', 'Prof']), [person1, person2]), ((people, ['position', 'Prof', 'name', 'Chris']), [person2]), ], ) def test_key_value_pair_filter(input, expected): assert(key_value_pair_filter(input[0], input[1]) == expected) @pytest.mark.parametrize( "input, expected", [ (([person3], None), "jdoe \n"), (([], None), ''), (([person1, person2], ['position']), "scopatz position: Professor \nabc position: Professor \n"), (([person2], ['position']), "abc position: Professor \n"), ], ) def test_collection_str(input, expected): assert(collection_str(input[0], input[1]) == expected) @pytest.mark.parametrize( "input, expected", [ ((people, ['name', 'Doe'], None), "jdoe \n"), ((people, ['name', 'Jerry'], None), ""), ((people, ['position', 'Prof', 'name', 'Chris'], None), "abc \n"), ((people, ['position', 'prof', 'name', 'Chris'], ['position']), "abc position: Professor \n"), ], ) def test_search_collection(input, expected): assert(search_collection(input[0], input[1], input[2]) == expected) appointed_people = [ {'name': 'Kurt Godel', '_id': 'kgodel', 'appointments': { "A": {"begin_date": '2019-09-01', "end_date": '2019-09-10', 'grant': 'grant1', 'loading': 0.5, 'type': 'gra'}, "B": {'_id': 'B', "begin_date": '2019-09-01', "end_date": '2019-09-10', 'grant': 'grant1', 'loading': 0.25, 'type': 'gra'}, "C": {'_id': 'C', "begin_date": '2019-09-01', "end_date": '2019-09-10', 'grant': 'grant1', 'loading': 0.25, 'type': 'gra'}}, "employment": [ {'group': 'permutation', 'begin_date': '2014-06-01', 'end_date': '2015-06-01', 'status': 'phd'}, {'group': 'matrix', 'begin_year': '2020', 'end_day': '5', 'end_month': '12', 'end_year': '2020'}, {'group': 'permutation', 'begin_day': 4, 'begin_month': 9, 'begin_year': 2012, 'end_day': 5, 'end_month': 9, 'end_year': 2012, 'permanent': 'true'} ]}, {'name': 'MC Escher', '_id': 'mcescher', 'appointments':{ "A": {"begin_date": '2019-10-01', "end_date": '2019-10-31', 'grant': 'grant1', 'loading': 1.0, 'type': 'ss'}, "B": {"begin_date": '2019-11-01', "end_date": '2019-11-30', 'grant': 'grant2', 'loading': 0.5, 'type': 'ss'}, "C": {"begin_date": '2019-11-01', "end_date": '2019-11-30', 'grant': 'grant3', 'loading': 0.5, 'type': 'ss'},}, 'employment': [ {'group': 'transformation', 'begin_date': '2018-07-24', 'end_date': dt.date(2020, 8, 1), 'status': 'postdoc'}, {'group': 'abstract', 'begin_year': 2010, 'end_day': 5, 'end_month': 12, 'end_year': 2020}, {'group': 'abstract', 'begin_date': '2012-06-30', 'end_date': '2012-09-05'} ]}, {'name': 'Johann Sebastian Bach', '_id': 'jsbach', 'appointments': { "A": {"begin_date": '2019-12-01', "end_date": '2020-12-15', 'grant': 'grant1', 'loading': 0.9, 'type': 'pd'}, "B": {"begin_date": '2019-12-16', "end_date": '2020-12-31', 'grant': 'grant2', 'loading': 0.9, 'type': 'pd'}, "C": {"begin_date": '2019-12-01', "end_date": '2020-12-31', 'grant': 'grant3', 'loading': 0.1, 'type': 'pd'}}, 'employment': [ {'group': 'bg', 'begin_date': '2019-02-03'} ]}, {'name': 'Ludwig Wittgenstein', '_id': 'lwittgenstein', 'appointments': { "A": {'begin_date': '2019-12-10', 'end_date': '2019-12-20', 'grant': 'grant2', 'loading': 1.0, 'type': 'ss'}}}, {'name': 'Karl Popper', '_id': 'kpopper', 'appointments': { "A": {'begin_date': '2019-12-25', 'end_date': '2019-12-31', 'grant': 'grant2', 'loading': 1.0, 'type': 'ss'}}}, {'name': 'GEM Anscombe', '_id': 'ganscombe', 'appointments': {}}, {'name': 'Sophie Germain', '_id': 'sgermain', 'appointments': { "A": {'begin_date': '2019-09-02', 'end_date': '2019-09-06', 'grant': 'grant4', 'loading': 1.0, 'type': 'ss'}}}, ] @pytest.mark.parametrize( "people,key,value,start,end,expected", [(appointed_people, 'grant', 'grant1', None, None, [{'person': 'kgodel', '_id': 'A', "begin_date": '2019-09-01', "end_date": '2019-09-10', 'grant': 'grant1', 'loading': 0.5, 'type': 'gra'}, {'person': 'kgodel', '_id': 'B', "begin_date": '2019-09-01', "end_date": '2019-09-10', 'grant': 'grant1', 'loading': 0.25, 'type': 'gra'}, {'person': 'kgodel', '_id': 'C', "begin_date": '2019-09-01', "end_date": '2019-09-10', 'grant': 'grant1', 'loading': 0.25, 'type': 'gra'}, {'person': 'mcescher', '_id': 'A', "begin_date": '2019-10-01', "end_date": '2019-10-31', 'grant': 'grant1', 'loading': 1.0, 'type': 'ss'}, {'person': 'jsbach', '_id': 'A', "begin_date": '2019-12-01', "end_date": '2020-12-15', 'grant': 'grant1', 'loading': 0.9, 'type': 'pd'}, ]), (appointed_people, None, None, '2019-09-01', '2019-09-30', [{'person': 'kgodel', '_id': 'A', "begin_date": '2019-09-01', "end_date": '2019-09-10', 'grant': 'grant1', 'loading': 0.5, 'type': 'gra'}, {'person': 'kgodel', '_id': 'B', "begin_date": '2019-09-01', "end_date": '2019-09-10', 'grant': 'grant1', 'loading': 0.25, 'type': 'gra'}, {'person': 'kgodel', '_id': 'C', "begin_date": '2019-09-01', "end_date": '2019-09-10', 'grant': 'grant1', 'loading': 0.25, 'type': 'gra'}, {'person': 'sgermain', '_id': 'A', 'begin_date': '2019-09-02', 'end_date': '2019-09-06', 'grant': 'grant4', 'loading': 1.0, 'type': 'ss'} , ]), (appointed_people, ['loading', 'type'], [1.0, 'ss'], '2019-12-15', '2019-12-25', [{'person': 'lwittgenstein', '_id': 'A', 'begin_date': '2019-12-10', 'end_date': '2019-12-20', 'grant': 'grant2', 'loading': 1.0, 'type': 'ss'}, {'person': 'kpopper', '_id': 'A', 'begin_date': '2019-12-25', 'end_date': '2019-12-31', 'grant': 'grant2', 'loading': 1.0, 'type': 'ss'} ]), (appointed_people, ['loading', 'type', 'grant'], [0.9, 'pd', 'grant3'], None, None, []), (appointed_people, None, None, None, None, [{'person': 'kgodel', '_id': 'A', "begin_date": '2019-09-01', "end_date": '2019-09-10', 'grant': 'grant1', 'loading': 0.5, 'type': 'gra'}, {'person': 'kgodel', '_id': 'B', "begin_date": '2019-09-01', "end_date": '2019-09-10', 'grant': 'grant1', 'loading': 0.25, 'type': 'gra'}, {'person': 'kgodel', '_id': 'C', "begin_date": '2019-09-01', "end_date": '2019-09-10', 'grant': 'grant1', 'loading': 0.25, 'type': 'gra'}, {'person': 'mcescher', '_id': 'A', "begin_date": '2019-10-01', "end_date": '2019-10-31', 'grant': 'grant1', 'loading': 1.0, 'type': 'ss'}, {'person': 'mcescher', '_id' :'B', "begin_date": '2019-11-01', "end_date": '2019-11-30', 'grant': 'grant2', 'loading': 0.5, 'type': 'ss'}, {'person': 'mcescher', '_id': 'C', "begin_date": '2019-11-01', "end_date": '2019-11-30', 'grant': 'grant3', 'loading': 0.5, 'type': 'ss'}, {'person': 'jsbach', '_id': 'A', "begin_date": '2019-12-01', "end_date": '2020-12-15', 'grant': 'grant1', 'loading': 0.9, 'type': 'pd'}, {'person': 'jsbach', '_id': 'B', "begin_date": '2019-12-16', "end_date": '2020-12-31', 'grant': 'grant2', 'loading': 0.9, 'type': 'pd'}, {'person': 'jsbach', '_id': 'C', "begin_date": '2019-12-01', "end_date": '2020-12-31', 'grant': 'grant3', 'loading': 0.1, 'type': 'pd'}, {'person': 'lwittgenstein', '_id': 'A', 'begin_date': '2019-12-10', 'end_date': '2019-12-20', 'grant': 'grant2', 'loading': 1.0, 'type': 'ss'}, {'person': 'kpopper', '_id': 'A', 'begin_date': '2019-12-25', 'end_date': '2019-12-31', 'grant': 'grant2', 'loading': 1.0, 'type': 'ss'}, {'person': 'sgermain', '_id': 'A', 'begin_date': '2019-09-02', 'end_date': '2019-09-06', 'grant': 'grant4', 'loading': 1.0, 'type': 'ss'}, ]), (appointed_people, 'type', 'ss', '2019-10-21', '2019-09-01', 'begin date is after end date'), (appointed_people, ['type', 'loading'], None, None, None, 'number of filter keys and filter values do not match'), (appointed_people, 'type', 'pd', '2019-12-10', None, 'please enter both begin date and end date or neither'), ([{'name': 'Magical Person', '_id': 'mperson', 'appointments': {"A": {'begin_date': '2019-09-01', 'end_date': '2019-09-05', 'loading': 1.0, 'grant': 'grant1', 'type': 'imaginary'}}}], None, None, None, None, 'invalid type imaginary for appointment A of mperson' ), ] ) def test_collect_appts(people, key, value, start, end, expected): try: actual = collect_appts(people, filter_key=key, filter_value=value, begin_date=start, end_date=end) assert actual == expected except ValueError: with pytest.raises(ValueError) as excinfo: actual = collect_appts(people, filter_key=key, filter_value=value, begin_date=start, end_date=end) assert str(excinfo.value) == expected except RuntimeError: with pytest.raises(RuntimeError) as excinfo: actual = collect_appts(people, filter_key=key, filter_value=value, begin_date=start, end_date=end) assert str(excinfo.value) == expected appts = collect_appts(appointed_people) grant1 = {'_id': 'grant1', 'alias': 'grant_one', 'budget': [ {'begin_date': '2019-09-01', 'end_date': '2019-09-03', 'student_months': 1, 'postdoc_months': 0.5, 'ss_months': 0}, {'begin_date': '2019-09-04', 'end_date': '2019-09-07', 'student_months': 1.5, 'postdoc_months': 0, 'ss_months': 0}, {'begin_date': '2019-09-08', 'end_date': '2019-09-10', 'student_months': 2, 'postdoc_months': 1.5, 'ss_months': 0}, ]} grant2 = {'_id': 'grant2', 'alias': 'grant_two', 'budget': [ {'begin_date': '2019-09-01', 'end_date': '2019-12-31', 'student_months': 4, 'postdoc_months': 2.5, 'ss_months': 1} ]} grant3 = {'_id': 'grant3', 'budget': [ {'begin_date': '2019-09-01', 'end_date': '2019-10-31', 'student_months': 0, 'postdoc_months': 1, 'ss_months': 2}, {'begin_date': '2019-11-01', 'end_date': '2019-12-31', 'student_months': 2, 'postdoc_months': 0.5, 'ss_months': 0} ]} grant4 = {'_id': 'grant4', 'alias': 'grant_four', 'budget': [ {'begin_date': '2019-09-01', 'end_date': '2019-09-07', 'student_months': 1, 'postdoc_months': 1, 'ss_months': 1}]} @pytest.mark.parametrize( "grant,appointments,start,end,expected", [ (grant1, appts, None, None, {dt.date(2019, 9, 1): {'postdoc_days': 15.25, 'ss_days': 0.0, 'student_days': 29.5}, dt.date(2019, 9, 2): {'postdoc_days': 15.25, 'ss_days': 0.0, 'student_days': 28.5}, dt.date(2019, 9, 3): {'postdoc_days': 15.25, 'ss_days': 0.0, 'student_days': 27.5}, dt.date(2019, 9, 4): {'postdoc_days': 15.25, 'ss_days': 0.0, 'student_days': 72.25}, dt.date(2019, 9, 5): {'postdoc_days': 15.25, 'ss_days': 0.0, 'student_days': 71.25}, dt.date(2019, 9, 6): {'postdoc_days': 15.25, 'ss_days': 0.0, 'student_days': 70.25}, dt.date(2019, 9, 7): {'postdoc_days': 15.25, 'ss_days': 0.0, 'student_days': 69.25}, dt.date(2019, 9, 8): {'postdoc_days': 61.0, 'ss_days': 0.0, 'student_days': 129.25}, dt.date(2019, 9, 9): {'postdoc_days': 61.0, 'ss_days': 0.0, 'student_days': 128.25}, dt.date(2019, 9, 10): {'postdoc_days': 61.0, 'ss_days': 0.0, 'student_days': 127.25}} ), (grant2, appts, '2019-12-15', '2019-12-31', {dt.date(2019, 12, 15): {'postdoc_days': 76.25, 'ss_days': 9.5, 'student_days': 122.0}, dt.date(2019, 12, 16): {'postdoc_days': 75.35, 'ss_days': 8.5, 'student_days': 122.0}, dt.date(2019, 12, 17): {'postdoc_days': 74.45, 'ss_days': 7.5, 'student_days': 122.0}, dt.date(2019, 12, 18): {'postdoc_days': 73.55, 'ss_days': 6.5, 'student_days': 122.0}, dt.date(2019, 12, 19): {'postdoc_days': 72.65, 'ss_days': 5.5, 'student_days': 122.0}, dt.date(2019, 12, 20): {'postdoc_days': 71.75, 'ss_days': 4.5, 'student_days': 122.0}, dt.date(2019, 12, 21): {'postdoc_days': 70.85, 'ss_days': 4.5, 'student_days': 122.0}, dt.date(2019, 12, 22): {'postdoc_days': 69.95, 'ss_days': 4.5, 'student_days': 122.0}, dt.date(2019, 12, 23): {'postdoc_days': 69.05, 'ss_days': 4.5, 'student_days': 122.0}, dt.date(2019, 12, 24): {'postdoc_days': 68.15, 'ss_days': 4.5, 'student_days': 122.0}, dt.date(2019, 12, 25): {'postdoc_days': 67.25, 'ss_days': 3.5, 'student_days': 122.0}, dt.date(2019, 12, 26): {'postdoc_days': 66.35, 'ss_days': 2.5, 'student_days': 122.0}, dt.date(2019, 12, 27): {'postdoc_days': 65.45, 'ss_days': 1.5, 'student_days': 122.0}, dt.date(2019, 12, 28): {'postdoc_days': 64.55, 'ss_days': 0.5, 'student_days': 122.0}, dt.date(2019, 12, 29): {'postdoc_days': 63.65, 'ss_days': -0.5, 'student_days': 122.0}, dt.date(2019, 12, 30): {'postdoc_days': 62.75, 'ss_days': -1.5, 'student_days': 122.0}, dt.date(2019, 12, 31): {'postdoc_days': 61.85, 'ss_days': -2.5, 'student_days': 122.0}} ), (grant3, appts, '2019-12-31', '2019-12-31', {dt.date(2019, 12, 31): {'postdoc_days': 42.65, 'ss_days': 46.0, 'student_days': 61.0}} ), (grant4, appts, None, None, {dt.date(2019, 9, 1): {'postdoc_days': 30.5, 'ss_days': 30.5, 'student_days': 30.5}, dt.date(2019, 9, 2): {'postdoc_days': 30.5, 'ss_days': 29.5, 'student_days': 30.5}, dt.date(2019, 9, 3): {'postdoc_days': 30.5, 'ss_days': 28.5, 'student_days': 30.5}, dt.date(2019, 9, 4): {'postdoc_days': 30.5, 'ss_days': 27.5, 'student_days': 30.5}, dt.date(2019, 9, 5): {'postdoc_days': 30.5, 'ss_days': 26.5, 'student_days': 30.5}, dt.date(2019, 9, 6): {'postdoc_days': 30.5, 'ss_days': 25.5, 'student_days': 30.5}, dt.date(2019, 9, 7): {'postdoc_days': 30.5, 'ss_days': 25.5, 'student_days': 30.5}} ), ({'_id': 'magical_grant', 'alias': 'very_magical_grant'}, appts, '2012-12-23', '2013-01-24', 'magical_grant has no specified budget' ), (grant4, appointed_people[0].get('appointments'), None, None, {dt.date(2019, 9, 1): {'postdoc_days': 30.5, 'ss_days': 30.5, 'student_days': 30.5}, dt.date(2019, 9, 2): {'postdoc_days': 30.5, 'ss_days': 30.5, 'student_days': 30.5}, dt.date(2019, 9, 3): {'postdoc_days': 30.5, 'ss_days': 30.5, 'student_days': 30.5}, dt.date(2019, 9, 4): {'postdoc_days': 30.5, 'ss_days': 30.5, 'student_days': 30.5}, dt.date(2019, 9, 5): {'postdoc_days': 30.5, 'ss_days': 30.5, 'student_days': 30.5}, dt.date(2019, 9, 6): {'postdoc_days': 30.5, 'ss_days': 30.5, 'student_days': 30.5}, dt.date(2019, 9, 7): {'postdoc_days': 30.5, 'ss_days': 30.5, 'student_days': 30.5}} ) ] ) def test_grant_burn(grant, appointments, start, end, expected): try: actual = grant_burn(grant, appointments, begin_date=start, end_date=end) assert actual == expected except ValueError: with pytest.raises(ValueError) as excinfo: actual = grant_burn(grant, appointments, begin_date=start, end_date=end) assert str(excinfo.value) == expected meeting1 = {'_id': 'grp2020-06-15', 'journal_club': {'doi': 'TBD'}} meeting2 = {'_id': 'grp2020-06-22', 'presentation': {'link': 'TBD'}} meeting3 = {'_id': 'grp2020-06-29', 'presentation': {'link': '2002ak_grmtg_presnetation', 'title': 'tbd'}} @pytest.mark.parametrize( "meeting,date,expected", [ (meeting1, dt.date(2020, 8, 15), 'grp2020-06-15 does not have a journal club doi'), (meeting1, dt.date(2020, 5, 15), None), (meeting2, dt.date(2020, 8, 15), 'grp2020-06-22 does not have a presentation link'), (meeting2, dt.date(2020, 5, 15), None), (meeting3, dt.date(2020, 8, 15), 'grp2020-06-29 does not have a presentation title'), (meeting3, dt.date(2020, 5, 15), None), ] ) def test_validate_meeting(meeting, date, expected): try: actual = validate_meeting(meeting, date) assert actual == expected except ValueError: with pytest.raises(ValueError) as excinfo: actual = validate_meeting(meeting, date) assert str(excinfo.value) == expected @pytest.mark.parametrize( "person,grpname,expected", [ (appointed_people[0], 'permutation', [{'_id': 'kgodel', 'begin_date': dt.date(2014, 6, 1), 'end_date': dt.date(2015, 6, 1), 'status': 'phd', 'permanent': None}, {'_id': 'kgodel', 'begin_date': dt.date(2012, 9, 4), 'end_date': dt.date(2012, 9, 5), 'status': None, 'permanent': 'true'}] ), (appointed_people[1], 'transformation', [{'_id': 'mcescher', 'begin_date': dt.date(2018, 7, 24), 'end_date': dt.date(2020, 8, 1), 'status': 'postdoc', 'permanent': None}] ), (appointed_people[2], 'bg', "WARNING: jsbach has no end date in employment for bg starting 2019-02-03" ), (appointed_people[3], 'abstract', []) ] ) def test_group_member_employment_start_end(person, grpname, expected): try: actual = group_member_employment_start_end(person, grpname) assert actual == expected except: with pytest.raises(RuntimeError) as excinfo: actual = group_member_employment_start_end(person, grpname) assert str(excinfo.value) == expected @pytest.mark.parametrize( "inp,expected", [ ([{"dupe_key": 1, "nond": 2}], [{"dupe_key": 1, "nond": 2}]), ([{"dupe_key": 1, "nond": 2}, {"dupe_key": 1, "nond": 3}], [{"dupe_key": 1, "nond": 2}]), ([{"no_dupe_key": 1, "nond": 2}], "ERROR: Target key, dupe_key not found in {'no_dupe_key': 1, 'nond': 2}"), ] ) def test_remove_duplicate_docs(inp, expected): try: actual = remove_duplicate_docs(inp, "dupe_key") assert actual == expected except: with pytest.raises(RuntimeError) as excinfo: actual = remove_duplicate_docs(inp, "dupe_key") assert str(excinfo.value) == expected @pytest.mark.parametrize( "inp,expected", [ ([ [{"_id": "student", "name": "Lancelot", "employment": [ {"status": "ms", "begin_date": "2020-05-05", "end_date": "2020-10-10", "advisor": "awesome", "position": "masters researcher" }]}], "2022-01-01" ], [] ), ([ [{"_id": "student", "name": "Lancelot", "employment": [ {"status": "ms", "begin_date": "2020-05-05", "end_date": "2020-10-10", "advisor": "awesome", "position": "masters researcher" }]}], "2019-01-01" ], [{"_id": "student", "name": "Lancelot", "employment": [ {"status": "ms", "begin_date": "2020-05-05", "end_date": "2020-10-10", "advisor": "awesome", "position": "masters researcher" }], "role": "masters researcher", "begin_year": 2020, "end_year": 2020, 'end_date': dt.date(2020, 10, 10), "status": "ms", "position": "masters researcher" }] ), ([ [{"_id": "student", "name": "Lancelot", "employment": [ {"status": "ms", "advisor": "awesome", "begin_date": "2020-05-05", "position": "masters researcher" }]}], "2019-01-01" ], [{"_id": "student", "name": "Lancelot", "employment": [ {"status": "ms", "advisor": "awesome", "begin_date": "2020-05-05", "position": "masters researcher" }], "role": "masters researcher", "begin_year": 2020, "end_year": "present", 'end_date': dt.date(2021, 6, 3), "status": "ms", "position": "masters researcher" }] ) ]) def test_filter_employment_for_advisees(inp, expected): actual = filter_employment_for_advisees(inp[0], inp[1], "ms", "awesome", dt.date(2021,6,3)) assert actual == expected person1 = {"_id":"tstark", "aka":"iron man", "name":"tony stark"} person2 = {"_id":"nromanov", "aka":"black widow", "name":"natasha romanov"} PEOPLE = [person1, person2] presentation1 = {"_id":"abc", "authors":"tstark", "date":"2018-01-01", "department":"apam", "institution":"columbiau", "status":"accepted", "type":"award"} presentation2 = {"_id":"ghi", "authors":["tstark","nromanov"], "begin_date":"2019-01-02", "end_date":"2019-01-08", "department":"physics", "institution":"rutgersu", "status":"cancelled", "type":"poster"} presentation3 = {"_id":"jkl", "authors":["nromanov"], "begin_year":2020, "begin_month":2, "begin_day":2, "end_year":2020, "end_month":12, "end_day":12, "department":"math", "institution":"rutgersu", "status":"declined", "type":"webinar"} PRESENTATIONS = [presentation1, presentation2, presentation3] institution1 = {"_id":"columbiau", "city":"New York", "country":"USA", "name":"Columbia University", "state":"NY"} institution2 = {"_id":"rutgersu", "city":"New Brunswick", "country":"USA", "name":"Rutgers University", "state":"NJ"} institution3 = {"_id":"barnardc", "city":"New York", "country":"USA", "name":"Barnard College", "state":"NY", "departments": {"physics": {"name": "Department of Physics", "aka": "Phys"}}} institution4 = {"_id":"nyu", "city":"New York", "country":"USA", "name":"New York University", "state":"NY", "street": "23rd", "zip": "10001", "aka": "purple"} institution_overseas = {"_id":"overseasu", "city":"Toronto", "country":"Canada", "name":"Overseas University"} organization1 = {"_id":"3m", "city":"Minneapolis", "country":"USA", "name":"3M", "state":"MN"} INSTITUTIONS = [institution1, institution2, institution3, institution4, institution_overseas, organization1] expected1 = {"_id": "abc", "authors": "tony stark", "begin_day_suffix": "st", "begin_year": 2018, "begin_month": 1, "begin_day": 1, "date": dt.date(2018, 1, 1), "day_suffix": "st", "department": {'name': 'apam'}, "institution": {'city': 'New York', 'country': 'USA', 'name': 'Columbia University', 'state': 'NY'}, "status": "accepted", "type": "award"} expected2 = {"_id": "ghi", "authors": "tony stark, natasha romanov", "begin_date": "2019-01-02", "begin_day_suffix": "nd", "begin_year": 2019, "begin_month": 1, "begin_day": 2, "date": dt.date(2019, 1, 2), "day_suffix": "nd", "end_day": 8, "end_day_suffix": "th", "department": {'name': 'physics'}, "end_date": "2019-01-08", "institution": {'city': 'New Brunswick', 'country': 'USA', 'name': 'Rutgers University', 'state': 'NJ'}, "status": "cancelled", "type": "poster"} expected3 = {'_id': 'jkl', "authors": "natasha romanov", "begin_day": 2, "begin_day_suffix": "nd", "begin_month": 2, "begin_year": 2020, "date": dt.date(2020, 2, 2), "day_suffix": 'nd', "department": {'name': 'math'}, "end_day": 12, "end_day_suffix": 'th', "end_month": 12, "end_year": 2020, "institution": {'city': 'New Brunswick', 'country': 'USA', 'name': 'Rutgers University', 'state': 'NJ'}, "status": "declined", "type": "webinar"} @pytest.mark.parametrize( "input, expected, sysout", [ ({"institution": "columbiau"}, {'department': 'unknown', "location": "New York, NY", "city": "New York", "country":"USA", "institution":"Columbia University", "organization":"Columbia University", "state":"NY"}, ""), ({"institution": "nyu"}, {'department': 'unknown',"location": "New York, NY", "city": "New York", "country":"USA", "institution":"New York University", "organization":"New York University", "state":"NY", "street": "23rd", "zip": "10001", "aka": "purple"}, ""), ({"institution": "barnardc", "department": "physics"}, {"location": "New York, NY", "city": "New York", "country":"USA", "institution":"Barnard College", "organization":"Barnard College", "state":"NY", "department": "Department of Physics"}, ""), ({"institution": "columbiau", "department": "physics"}, {"location": "New York, NY", "city": "New York", "country":"USA", "institution":"Columbia University", "organization":"Columbia University", "state":"NY", "department": "physics"}, "WARNING: no departments in columbiau. physics sought\n"), ({"organization": "3m"}, {'department': 'unknown',"location": "Minneapolis, MN", "city": "Minneapolis", "country":"USA", "institution":"3M", "organization":"3M", "state":"MN"}, ""), ({"institution": "notindbu"}, {"location": "unknown, unknown", "city": "unknown", "country": "unknown", "institution": "notindbu", 'department': 'unknown', "organization": "notindbu", "state": "unknown"}, "WARNING: notindbu not found in institutions\n"), ({"institution": "notindbu", "location": "Near, BY"}, {"location": "Near, BY", "city": "unknown", "country": "unknown", "institution": "notindbu", 'department': 'unknown', "organization": "notindbu", "state": "unknown"}, "WARNING: notindbu not found in institutions\n"), ({"institution": "notindbu", "city": "Near", "state": "BY"}, {"location": "Near, BY", "city": "Near", "country": "unknown", "institution": "notindbu", 'department': 'unknown', "organization": "notindbu", "state": "BY"}, "WARNING: notindbu not found in institutions\n"), ({"institution": "overseasu"}, {"location": "Toronto, Canada", "city": "Toronto", "country": "Canada", 'department': 'unknown', "institution": "Overseas University", "organization": "Overseas University"}, ""), ({"degree": "phd"}, {"degree": "phd"}, "WARNING: no institution or organization in entry: {'degree': 'phd'}\n"), ] ) def test_dereference_institution(input, expected, sysout, capsys): dereference_institution(input, INSTITUTIONS, verbose=True) assert expected == input out, err = capsys.readouterr() assert sysout == out @pytest.mark.parametrize( "args, kwargs, expected",[ #this tests no kwargs ([PEOPLE, PRESENTATIONS, INSTITUTIONS, "tstark"], {}, [expected1]), #this tests 'statuses' kwarg ([PEOPLE, PRESENTATIONS, INSTITUTIONS, "tstark"], {"statuses" : ["all"]}, [expected2, expected1]), #this tests 'statuses' and 'types' kwargs together ([PEOPLE, PRESENTATIONS, INSTITUTIONS, "tstark"], {"statuses" : ["all"], "types" : ["poster"]}, [expected2]), #this tests 'statuses' and 'since' kwargs together ([PEOPLE, PRESENTATIONS, INSTITUTIONS, "nromanov"], {"statuses" : ["all"], "since" : dt.date(2019, 1, 1)}, [expected3, expected2]), #this tests the 'statuses' and 'before' kwargs together ([PEOPLE, PRESENTATIONS, INSTITUTIONS, "tstark"], {"statuses" : ["all"], "before" : dt.date(2018, 1, 2)}, [expected1]) ] ) def test_filter_presentations(args, kwargs, expected): actual = filter_presentations(*args, **kwargs) assert actual == expected @pytest.mark.parametrize( "coll, expected", [ ([{"_id": "id", "name": "test"}], []), ([{"_id": "id", "tags": ""}], []), ([{"_id": "id", "tags": "thing1"}], ["thing1"]), ([{"_id": "id", "tags": "thing2,thing1"}], ["thing1", "thing2"]), ([{"_id": "id", "tags": "thing2 thing1"}], ["thing1", "thing2"]), ([{"_id": "id", "tags": "thing2,thing1 thing3"}], ["thing1", "thing2", "thing3"]), ] ) def test_get_tags(coll, expected): actual = get_tags(coll) assert actual == expected def test_get_tags_invalid(): coll = [{"_id": "id", "tags": ["test"]}] with pytest.raises(TypeError) as e_info: get_tags(coll) assert e_info == 'ERROR: valid tags are comma or space separated strings of tag names'

73e2a262a92dde10c0a4c402338d30236552f731

py

Python

src/sardana/macroserver/basetypes.py

schooft/sardana

76287b416650f40da79871ee3849340d0ff31f1d

src/sardana/macroserver/basetypes.py

schooft/sardana

76287b416650f40da79871ee3849340d0ff31f1d

src/sardana/macroserver/basetypes.py

schooft/sardana

76287b416650f40da79871ee3849340d0ff31f1d

#!/usr/bin/env python ############################################################################## ## # This file is part of Sardana ## # http://www.sardana-controls.org/ ## # Copyright 2011 CELLS / ALBA Synchrotron, Bellaterra, Spain ## # Sardana 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. ## # Sardana 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. ## # You should have received a copy of the GNU Lesser General Public License # along with Sardana. If not, see <http://www.gnu.org/licenses/>. ## ############################################################################## """This module contains the definition of the macroserver base types for macros""" __all__ = ["Integer", "Float", "Boolean", "String", "User", "Filename", "File", "Macro", "MacroLibrary", "Env", "Motor", "MotorParam", "MotorGroup", "ExpChannel", "MeasurementGroup", "ComChannel", "IORegister", "Controller", "Instrument", "ControllerClass", "TriggerGate"] __docformat__ = 'restructuredtext' from sardana import INTERFACES from sardana.macroserver.msparameter import ParamType, AttrParamType, \ ElementParamInterface # Basic types class Any(ParamType): type_class = lambda p: p class Integer(ParamType): type_class = int class Float(ParamType): type_class = float class Boolean(ParamType): type_class = bool def getObj(self, str_repr): str_repr = str_repr.lower() if str_repr in ['true', '1']: value = True elif str_repr in ['false', '0']: value = False else: raise ValueError('{0} is not a boolean'.format(str_repr)) return value class String(ParamType): type_class = str class User(ParamType): type_class = str class Filename(ParamType): type_class = str class File(ParamType): type_class = str def __init__(self, macro_server, name): ParamType.__init__(self, macro_server, name) self.filename = None # self.data is supposed to be an array.array object self.data = None def set(self, filename, data): self.filename = filename self.data = data class JSON(ParamType): def getObj(self, str_repr): import json return json.loads(str_repr) class Env(ParamType): type_class = str class TangoDevice(ParamType): def getObj(self, str_repr): import PyTango return PyTango.DeviceProxy(str_repr) class Device(ParamType): def getObj(self, str_repr): import taurus return taurus.Device(str_repr) # Hardware types class MotorParam(AttrParamType): """Class designed to represent a motor parameter name. Usual values are acceleration,deceleration,velocity,backlash,steps_per_unit,etc""" def __init__(self, macro_server, name): AttrParamType.__init__(self, macro_server, name) self.attr_item_list = ["Acceleration", "Backlash", "Base_rate", "Step_per_unit", "Deceleration", "Velocity", "Offset"] self.non_attr_item_list = ["Controller"] def getItemList(self): return self.non_attr_item_list + self.attr_item_list def getAttrItemList(self): return self.attr_item_list def getNonAttrItemList(self): return self.non_attr_item_list def __build_base_types(): for sardana_type, info in INTERFACES.items(): _, doc = info class _I(ElementParamInterface): __doc__ = doc __name__ = sardana_type globals()[sardana_type] = _I __build_base_types()

73e2afe12920cb00a999e054608caceb109c692e

py

Python

nmgr/orm/base_model.py

mesalu/Viv2.NodeManager

9c616e7c42cb5d543030c00f5e99b6aa9439d108

nmgr/orm/base_model.py

mesalu/Viv2.NodeManager

9c616e7c42cb5d543030c00f5e99b6aa9439d108

nmgr/orm/base_model.py

mesalu/Viv2.NodeManager

9c616e7c42cb5d543030c00f5e99b6aa9439d108

""" """ import enum def column_info(*, name=None, relation=None, autogenerate=False): """ Associates the given method, property, or attribute as column info within the table representing a collection of entities. The name can optionally be remapped by providing a name parameter as a string. E.g. a property may be 'timestamp', where the column in the relation has the name 'datetime_capture'. Supplying the latter to `name` will cause the correct mapping to occur. :param str name: the actual column name in the database that corresponds to this column. :param Relation relation: relation information about this column. :param bool autogenerate: When inserting a new instance into the database, and this column is None, should the database use the default value for this column? """ def decorator(attrib): if relation and not isinstance(relation, Relation): raise TypeError("relation parameter must be Relation instance.") info = { "name": name, "relation": relation, "autogenerate": autogenerate } return _ColInfo(attrib, **info) return decorator class Relation(object): """ Describes a relationship between entities via columns (e.g. foreign keys) and a load-behavior """ class LoadBehavior(enum.Enum): EAGER = enum.auto() # load when instantiated, if possible. LAZY = enum.auto() # load once on first prop call EXTRA_LAZY = enum.auto() # don't load until prop-call. def __init__(self, behavior: LoadBehavior = LoadBehavior.LAZY, *, parent_cls = None, ent_col = None, parent_col = None): """ parent_cls: entity class associated to relation ent_col: local column parent_col: column in referenced entity. """ self._behavior = behavior self._parent_cls = parent_cls self._ent_col = ent_col self._parent_col = parent_col if not issubclass(parent_cls, Entity): raise TypeError("Parent Entity must be a subclass of Entity") if behavior not in Relation.LoadBehavior: raise ValueError("Load behavior must be among Relation.LoadBehavior") class _ColInfo(object): """ Some objects that need to be decorated by `column_info` can't have attributes set on them (properties). This class is used to temporarily wrap decorated items and then is stripped off by the MetaEntity class initializer. """ def __init__(self, attrib, *, name=None, relation=None, autogenerate=False): self._col_name = name self._relation = relation self._autogen = autogenerate self._attr = attrib def __getattr__(self, attr): # there is some interim time between class setup and # when the metaclass can come in and 'shuck' the _ColInfo # instance back out, during which time the _ColInfo object # needs to quack like a duck: try: return getattr(self, attr) except Exception: return getattr(self._attr, attr) class MetaEntity(type): def __init__(cls, name, bases, dct): cls._cols = {} # Discover values annotated with `column_info` in dct. for key, value in dct.items(): if isinstance(value, _ColInfo): cls._cols[key] = value # while we're here fill in the # presumed column name if necessary if value._col_name is None: value._col_name = key for key, value in cls._cols.items(): dct[key] = value._attr setattr(cls, key, value._attr) if not '_table' in dct: cls._table = name type.__init__(cls, name, bases, dct) class Entity(object, metaclass=MetaEntity): """ Common base class for all ORM entity objects. """

73e2cdf9ab6475e3b5fa52b9c77da0577ca7110a

py

Python

openstackclient/compute/v2/host.py

redhat-openstack/python-openstackclient

7dc2e1dc08b0692a3accb343c62451fb3d83f4cd

openstackclient/compute/v2/host.py

redhat-openstack/python-openstackclient

7dc2e1dc08b0692a3accb343c62451fb3d83f4cd

openstackclient/compute/v2/host.py

redhat-openstack/python-openstackclient

7dc2e1dc08b0692a3accb343c62451fb3d83f4cd

# Copyright 2013 OpenStack, 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. # """Host action implementations""" from openstackclient.common import command from openstackclient.common import utils from openstackclient.i18n import _ class ListHost(command.Lister): """List host command""" def get_parser(self, prog_name): parser = super(ListHost, self).get_parser(prog_name) parser.add_argument( "--zone", metavar="<zone>", help=_("Only return hosts in the availability zone") ) return parser def take_action(self, parsed_args): compute_client = self.app.client_manager.compute columns = ( "Host Name", "Service", "Zone" ) data = compute_client.hosts.list_all(parsed_args.zone) return (columns, (utils.get_item_properties( s, columns, ) for s in data)) class SetHost(command.Command): """Set host properties""" def get_parser(self, prog_name): parser = super(SetHost, self).get_parser(prog_name) parser.add_argument( "host", metavar="<host>", help=_("The host to modify (name or ID)") ) status = parser.add_mutually_exclusive_group() status.add_argument( '--enable', action='store_true', help=_("Enable the host") ) status.add_argument( '--disable', action='store_true', help=_("Disable the host") ) maintenance = parser.add_mutually_exclusive_group() maintenance.add_argument( '--enable-maintenance', action='store_true', help=_("Enable maintenance mode for the host") ) maintenance.add_argument( '--disable-maintenance', action='store_true', help=_("Disable maintenance mode for the host") ) return parser def take_action(self, parsed_args): kwargs = {} if parsed_args.enable: kwargs['status'] = True if parsed_args.disable: kwargs['status'] = False if parsed_args.enable_maintenance: kwargs['maintenance_mode'] = True if parsed_args.disable_maintenance: kwargs['maintenance_mode'] = False compute_client = self.app.client_manager.compute foundhost = utils.find_resource( compute_client.hosts, parsed_args.host ) compute_client.hosts.update( foundhost.id, kwargs ) class ShowHost(command.Lister): """Show host command""" def get_parser(self, prog_name): parser = super(ShowHost, self).get_parser(prog_name) parser.add_argument( "host", metavar="<host>", help=_("Name of host") ) return parser def take_action(self, parsed_args): compute_client = self.app.client_manager.compute columns = ( "Host", "Project", "CPU", "Memory MB", "Disk GB" ) data = compute_client.hosts.get(parsed_args.host) return (columns, (utils.get_item_properties( s, columns, ) for s in data))

73e313d795a934476f76f2f3879d5418d5a01d3e

py

Python

API/src/main/resources/Lib/robot/model/tags.py

TagExpress/SikuliX1

de9da11794dd94b3821eddc5c01b534d3f2fe828

API/src/main/resources/Lib/robot/model/tags.py

TagExpress/SikuliX1

de9da11794dd94b3821eddc5c01b534d3f2fe828

API/src/main/resources/Lib/robot/model/tags.py

TagExpress/SikuliX1

de9da11794dd94b3821eddc5c01b534d3f2fe828

# Copyright (c) 2010-2020, sikuli.org, sikulix.com - MIT license # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from robot.utils import (Matcher, NormalizedDict, is_string, py2to3, setter, unic) @py2to3 class Tags(object): def __init__(self, tags=None): self._tags = tags @setter def _tags(self, tags): if not tags: return () if is_string(tags): tags = (tags,) return self._normalize(tags) def _normalize(self, tags): normalized = NormalizedDict(((unic(t), 1) for t in tags), ignore='_') for removed in '', 'NONE': if removed in normalized: normalized.pop(removed) return tuple(normalized) def add(self, tags): self._tags = tuple(self) + tuple(Tags(tags)) def remove(self, tags): tags = TagPatterns(tags) self._tags = [t for t in self if not tags.match(t)] def match(self, tags): return TagPatterns(tags).match(self) def __contains__(self, tags): return self.match(tags) def __len__(self): return len(self._tags) def __iter__(self): return iter(self._tags) def __unicode__(self): return u'[%s]' % ', '.join(self) def __repr__(self): return repr(list(self)) def __getitem__(self, index): item = self._tags[index] return item if not isinstance(index, slice) else Tags(item) def __add__(self, other): return Tags(tuple(self) + tuple(Tags(other))) @py2to3 class TagPatterns(object): def __init__(self, patterns): self._patterns = tuple(TagPattern(p) for p in Tags(patterns)) def match(self, tags): tags = tags if isinstance(tags, Tags) else Tags(tags) return any(p.match(tags) for p in self._patterns) def __contains__(self, tag): return self.match(tag) def __len__(self): return len(self._patterns) def __iter__(self): return iter(self._patterns) def __getitem__(self, index): return self._patterns[index] def __unicode__(self): return u'[%s]' % u', '.join(pattern.__unicode__() for pattern in self) def TagPattern(pattern): pattern = pattern.replace(' ', '') if 'NOT' in pattern: return NotTagPattern(*pattern.split('NOT')) if 'OR' in pattern: return OrTagPattern(pattern.split('OR')) if 'AND' in pattern or '&' in pattern: return AndTagPattern(pattern.replace('&', 'AND').split('AND')) return SingleTagPattern(pattern) @py2to3 class SingleTagPattern(object): def __init__(self, pattern): self._matcher = Matcher(pattern, ignore='_') def match(self, tags): return self._matcher.match_any(tags) def __iter__(self): yield self def __unicode__(self): return self._matcher.pattern def __nonzero__(self): return bool(self._matcher) @py2to3 class AndTagPattern(object): def __init__(self, patterns): self._patterns = tuple(TagPattern(p) for p in patterns) def match(self, tags): return all(p.match(tags) for p in self._patterns) def __iter__(self): return iter(self._patterns) def __unicode__(self): return ' AND '.join(pattern.__unicode__() for pattern in self) @py2to3 class OrTagPattern(object): def __init__(self, patterns): self._patterns = tuple(TagPattern(p) for p in patterns) def match(self, tags): return any(p.match(tags) for p in self._patterns) def __iter__(self): return iter(self._patterns) def __unicode__(self): return ' OR '.join(pattern.__unicode__() for pattern in self) @py2to3 class NotTagPattern(object): def __init__(self, must_match, *must_not_match): self._first = TagPattern(must_match) self._rest = OrTagPattern(must_not_match) def match(self, tags): if not self._first: return not self._rest.match(tags) return self._first.match(tags) and not self._rest.match(tags) def __iter__(self): yield self._first for pattern in self._rest: yield pattern def __unicode__(self): return ' NOT '.join(pattern.__unicode__() for pattern in self).lstrip()

73e348da3b419db4a042e1e3c57b42b2c474ecc2

py

Python

gender_extractor/__init__.py

wwydmanski/gender-extractor

6e328ba4ed1b08e7a874c36d68248540c2d8debc

gender_extractor/__init__.py

wwydmanski/gender-extractor

6e328ba4ed1b08e7a874c36d68248540c2d8debc

gender_extractor/__init__.py

wwydmanski/gender-extractor

6e328ba4ed1b08e7a874c36d68248540c2d8debc

from .extractor import GenderExtractor

73e36b27b76b7c046d43b723895723dd84ac9303

py

Python

modspectra/cube.py

Deech08/modspectra

4af177418f9ac3e1ff30bf99968251ac143a96bc

2020-06-04T13:09:50.000Z

2020-06-04T13:10:03.000Z

modspectra/cube.py

Deech08/modspectra

4af177418f9ac3e1ff30bf99968251ac143a96bc

2020-10-29T19:55:57.000Z

2020-10-29T19:55:57.000Z

modspectra/cube.py

Deech08/modspectra

4af177418f9ac3e1ff30bf99968251ac143a96bc

import numpy as np import os from astropy import units as u from spectral_cube import SpectralCube import numexpr as ne import dask.array as da from dask import delayed from dask.diagnostics import ProgressBar import logging from astropy.coordinates.representation import CylindricalRepresentation, CartesianRepresentation, CartesianDifferential import astropy.coordinates as coord from astropy.coordinates import frame_transform_graph from astropy import wcs from astropy.io import fits import scipy.interpolate import scipy.integrate as integrate import multiprocessing from functools import partial from .cubeMixin import EmissionCubeMixin import datetime # Helper Functions def find_nearest_idx(array,value): idx = (np.abs(array-value)).argmin() return [idx] def find_nannearest_idx(array,value): idx = np.nanargmin(np.abs(array-value)) return [idx] # Defie TiltedDisk Coordinate Frame class TiltedDisk(coord.BaseCoordinateFrame): """ A cartesian coordinate system in the frame of the tilted elliptical disk Requires three attributes - currently defaults to version of Krishnarao, Benjamin, Haffner (2019) Three angles describing geometry of the structure Attributes ---------- alpha: `~astropy.units.Quantity`, optional, must be keyword Tilt Angle beta: `~astropy.units.Quantity`, optional, must be keyword 90*u.deg - inclination theta: `~astropy.units.Quantity`, optional, must be keyword Major Axis Angle Parameters ---------- representation : `BaseRepresentation` or None A representation object or None to have no data (or use the other keywords) x : `~astropy.units.Quantity`, optional, must be keyword The x coordinate in the tilted disk coordinate system y : `~astropy.units.Quantity`, optional, must be keyword The y cooridnate in the titled disk coordinate system z : `~astropy.units.Quantity`, optional, must be keyword The z coordinate in the tilted disk coordinate system v_x : :class:`~astropy.units.Quantity`, optional, must be keyword The x component of the velocity v_y : :class:`~astropy.units.Quantity`, optional, must be keyword The y component of the velocity v_z : :class:`~astropy.units.Quantity`, optional, must be keyword The z component of the velocity """ default_representation = coord.CartesianRepresentation default_differential = coord.CartesianDifferential frame_specific_representation_info = { coord.representation.CartesianDifferential: [ coord.RepresentationMapping('d_x', 'v_x', u.km/u.s), coord.RepresentationMapping('d_y', 'v_y', u.km/u.s), coord.RepresentationMapping('d_z', 'v_z', u.km/u.s), ], } # Specify frame attributes required to fully specify the frame # Rotation angles alpha = coord.QuantityAttribute(default=13.5*u.deg, unit = u.rad) beta = coord.QuantityAttribute(default=20.*u.deg, unit = u.rad) theta = coord.QuantityAttribute(default=48.5*u.deg, unit = u.rad) def get_transformation_matrix(tilteddisk_frame, inverse = False): """ Create coordinate transformation matrix for converting between the TiltedDisk frame and Galactocentric frame Parameters ---------- tilteddisk_frame: TiltedDisk class Coordinate frame inverse: 'bool', optional, must be keyword if True, return the transposed matrix for converting from the Galactocentric frame to the TiltedDisk frame """ alpha = tilteddisk_frame.alpha.value beta = tilteddisk_frame.beta.value theta = tilteddisk_frame.theta.value # Generate rotation matrix for coordinate transformation into coord.Galactocentric R_matrix = np.array([np.cos(beta)*np.cos(theta), np.cos(beta)*np.sin(theta), -np.sin(beta), -np.cos(theta)*np.sin(alpha)*-np.sin(beta) - np.cos(alpha)*np.sin(theta), np.cos(alpha)*np.cos(theta) + np.sin(alpha)*np.sin(beta)*np.sin(theta), np.cos(beta)*np.sin(alpha), np.cos(alpha)*np.cos(theta)*np.sin(beta) + np.sin(alpha)*np.sin(theta), -np.cos(theta)*np.sin(alpha) + np.cos(alpha)*np.sin(beta)*np.sin(theta), np.cos(alpha)*np.cos(beta)]).reshape(3,3) if inverse: return R_matrix.transpose() else: return R_matrix @frame_transform_graph.transform(coord.DynamicMatrixTransform, TiltedDisk, coord.Galactocentric) def td_to_galactocentric(tilteddisk_coord, galactocentric_frame): """ Compute the transformation matrix from the Tilted Disk coordinates to Galactocentric coordinates. """ return get_transformation_matrix(tilteddisk_coord) @frame_transform_graph.transform(coord.DynamicMatrixTransform, coord.Galactocentric, TiltedDisk) def galactocentric_to_td(galactocentric_coord, tilteddisk_frame): """ Compute the transformation matrix from Galactocentric coordinates to Tilted Disk coordinates. """ return get_transformation_matrix(tilteddisk_frame, inverse = True) # Define some helper functions for the Elliptical Shape def ellipse_equation(bd, el_constant1, el_constant2, bd_max, x_coord, y_coord): """ Equation for an ellipse in the form from Burton & Liszt (1978) Function serves to be used in scipy.optimize.brenth to solve for bd Parameters ---------- bd: 'number' semi-minor axis of ellipse el_constant1: 'number' First parameter for defining ellipse el_constant2: 'number' second parameter for defining ellipse bd_max: 'number' Maximum semi-minor axis allowed within defined elliptical disk x_coord: 'number, ndarray' x-coordinate in ellipse y_coord: 'number, ndarray' y-coordinate in ellipse """ a = bd *el_constant1 + el_constant2 * bd**2 / bd_max result = x_coord**2 / a**2 + y_coord**2 / bd**2 - 1. return result # the semi-minor axis parameter, bd, can not be analytically solved def bd_solver(ell, xyz, z_sigma_lim, Hz, bd_max, el_constant1, el_constant2): """ Function to solve for the ellipse equation to fit into form of ellipse_equation Chooses only to solve the equation numerically when necessary, avoiding the special cases. Funciton written in form to use with multiprocessing.pool and functools.partial The defining characteristic of the Ellipse is set by the relation between the semi-major and semi-minor axis with the following equation from Liszt & Burton (1982): ad / bd = el_constant1 + el_constant2 * bd / bd_max You can trick the "Ellipse" shape into a "Circle" by setting el_constant1 = 1., el_constant2 = 0. Parameters ---------- ell: 'int' element number to iterate over xyz: 'ndarray with shape (3,N)' xyz-coordinates z_sigma_lim: 'number' sigma cuttoff to stop solving Ellipse equation for for z above a specified scale height threshold Hz: 'number' Scale height along z axis bd_max: 'number' Maximum semi-minor axis allowed within defined elliptical disk el_constant1: 'number' First parameter for defining ellipse el_constant2: 'number' second parameter for defining ellipse """ x_coord = xyz[0,ell] y_coord = xyz[1,ell] z_coord = xyz[2,ell] if z_coord > z_sigma_lim*Hz: res = bd_max+1. elif np.abs(y_coord) > bd_max: res = bd_max+1. elif np.abs(x_coord) > bd_max * (el_constant1 + el_constant2): res = bd_max+1. elif x_coord == 0.: res = y_coord elif y_coord == 0.: # y_coord = ad - > can directly solve for bd res = (np.sqrt(bd_max) * np.sqrt(4.* np.abs(x_coord) * el_constant2 + bd_max * el_constant1**2) - bd_max * el_constant1) / (2.* el_constant2) else: res = scipy.optimize.brenth(ellipse_equation, 0.000001, 1., args = (el_constant1, el_constant2,bd_max, x_coord, y_coord)) if res<0: print(el_constant1,el_constant2,bd_max, x_coord, y_coord ) return res def EllipticalLBD(resolution, bd_max, Hz, z_sigma_lim, dens0, velocity_factor, vel_0, el_constant1, el_constant2, alpha, beta, theta, L_range, B_range, D_range, species = 'hi', LSR_options={}, galcen_options = {}, visualize = False, flaring = False, flaring_radial = False, min_bd = None, memmap = False, da_chunks_xyz = 50, return_all = False, **kwargs): """ Creates kinematic disk following Elliptical Orbits of the from from Burton & Liszt (1982) or Krishnarao, Benjamin, Haffner (2019) Numerically solves for ellipse equation for every point within the disk space output is used directly to create a Longitude-Latitude-Velocity SpectralCube object using 'modspectra.cube.EllipticalLBD' Uses numexpr package to evaluate math Uses multiprocessing to solve Ellipse Equation Uses Dask for memory mapping Parameters ---------- resolution: 'tuple, list' Resolution to create grid bd_max: 'number' Maximum semi-minor axis allowed within defined elliptical disk Hz: 'number' Scale height along z axis z_sigma_lim: 'number' sigma cuttoff to stop solving Ellipse equation for for z above a specified scale height threshold dens0: 'number' Density at midplane of Elliptical Disk velocity_factor: 'number' Constant used to define velocity field in Burton & Liszt model vel_0: 'number' Max velocity of Elliptical orbit Corresponds to velocity of outermost orbit on semi-minor axis el_constant1: 'number' First parameter for defining ellipse el_constant2: 'number' second parameter for defining ellipse alpha: 'number' Tilt angle alpha - see :class:'TiltedDisk' beta: 'number' Tilt angle Beta - see :class:'TiltedDisk' 90 - beta is the inclination theta: 'number' Tilt angle of major axis of Ellipse - see :class:'TiltedDisk' L_range: :list:'number' Range of Longtiude to create grid over B_range: :list:'number' Range of Latitude to create grid over D_range: :list:'number' Range of Distances to create grid over species: 'str', optional, must be keyword Can be 'hi' or 'ha' to set whether disk will be for neutral or ionized hydrogen (HI 21cm or H-Alpha) Default is 'hi' LSR_options: 'dictionary', optional, must be keyword Dictionary of **kwargs to pass into :class:'~astropy.coordinates.GalacticLSR' galcen_options: 'dictionary', optional, must be keyword set galcen_options to be passed to coordinate frames visualize: 'bool', optional, must be keyword if using dask, returns dask visualization map flaring: 'bool, number', optional, must be keyword if False, disk has a constant scale height if a number, then sets the flaring parameter, F_z, as described in Krishnarao, Benjamin, Haffner (2019) flaring_radial: 'bool', optional, must be keyword if flaring is present then if False (default), flaring of scale height is a function of bd, the semi minor axis if True, flaring of scale height is a function of r, the cyclindrical radius coordinate in the TiltedDisk Frame min_bd: 'number', optional, must be keyword sets the minimum value of the semi minor axis to allow Used to make a ring, rather than a disk structure memmap: 'bool', optional, must be keyword if True, use dask for memory mapping when creating disk structure useful for higher resolution computes da_chunks_xyz = 'number', optional, must be keyword if memmap is True, sets the dask chunk size Default to 50, likely too small for efficiency return_all: 'bool', optional, must be keyword if True, will return all output components used for diagnostic purposes **kwargs: currenlty not implemented Returns ------- lbd_coords_withvel: :class:'~astropy.coordinates.GalacticLSR' astropy.coord array containing all coordinates corresponding to fabricated grid of points dens_grid: 'numpy.ndarray' ndarray with shape (resolution) containing density of points in Longitude-Latitude-Distance grid axes order swapped to be ready for SpectralCube creation (Distance, Latitude, Longitude) cdelt: 'numpy.ndarray' ndarray with shape (3) containing the step size for Longitude, Latitude, and Distance used in the grid Used for WCS object creation in later instances disk_coordinates: :class:'TiltedDisk', optional, only if return_all == True TiltedDisk coordinate class containing grid coordinates in original tilted disk space galcen_coords_withvel: :class:'~astropy.coordinates.Galactocentric' TiltedDisk class transformed to Galactocentric frame bd_grid: 'numpy.ndarray' ndarray with shape (resolution) containing solved values of bd from Ellipse Equation solutions axes order swapped to match dens_grid vel_magnitude_grid: 'numpy.ndarray' ndarray with shape (resolution) contianing velocity vector magnitude at corresponding grid position """ # Extract resolution information nx, ny, nz = resolution if memmap: # Populate a uniform grid in Longitude-Latitude-Distance space lbd_grid = delayed(np.mgrid)[L_range[0]:L_range[1]:nx*1j, B_range[0]:B_range[1]:ny*1j, D_range[0]:D_range[1]:nz*1j] # Transform grid into a 3 x N array for Longitude, Latitude, Distance axes lbd = lbd_grid.T.reshape(-1,3, order = "F").transpose() # Initiate astropy coordinates.Galactic object lbd_coords = delayed(coord.Galactic)(l = lbd[0,:]*u.deg, b = lbd[1,:]*u.deg, distance = lbd[2,:]*u.kpc) galcen_coords = lbd_coords.transform_to(coord.Galactocentric(**galcen_options)) disk_coords = galcen_coords.transform_to(TiltedDisk(alpha = alpha*u.deg, beta = beta*u.deg, theta = theta*u.deg)) #Delayed # Create standard numpy ndarray of disk_coords and reshape to grid, matching original lbd_grid object disk_coords_arr = delayed(np.array)([disk_coords.x.value, disk_coords.y.value, disk_coords.z.value]) xyz_grid = delayed(da.from_array)(disk_coords_arr.reshape(-1,nx,ny,nz), chunks = (-1,da_chunks_xyz,da_chunks_xyz,da_chunks_xyz)) # initiate partial object to solve for Ellipse Equation partial_bd_solver = delayed(partial)(bd_solver, xyz=disk_coords_arr, z_sigma_lim = z_sigma_lim, Hz = Hz, bd_max = bd_max, el_constant1 = el_constant1, el_constant2 = el_constant2) # Solve for bd values #print("Starting bd solver:") #with ProgressBar(): pool = multiprocessing.Pool() bd_vals = delayed(pool.map)(partial_bd_solver, range(nx*ny*nz)) # Create grid of bd values solved from Ellipse Equation, matching original lbd_grid object #print("Computing semi_minor axis parameter, bd:") #if visualize: #bd_vals.visualize(filename = 'bdValsGraph.svg') #with ProgressBar(): bd_vals_arr = delayed(da.from_array)(bd_vals, chunks = int(da_chunks_xyz * da_chunks_xyz * 0.125 * da_chunks_xyz)) #print("start bd_grid Calculation") bd_grid = delayed(da.from_array)(bd_vals_arr.reshape(nx,ny,nz), chunks = da_chunks_xyz) # Create grid of ad values (semi-major axis) derived from bd values ad_grid = delayed(bd_grid * (el_constant1 + el_constant2 * bd_grid / bd_max)) # Create grid of density values for the Elliptical Disk, mathcing original lbd_grid object #print("Computing grid z-coordinates in Disk Frame:") #with ProgressBar(): z_coor = xyz_grid[2,:,:,:].compute() #Delayed Dask Array of z coordinate values if (flaring == False) & (flaring_radial == False): def define_density_grid(z,z_sigma, bd, bdmax, H, density0, min_bd = min_bd): #density[(np.abs(z)<(z_sigma * H)) & (bd<=bdmax)] = density0 * \ #np.exp(-0.5 * (z[(np.abs(z)<(z_sigma * H)) & (bd<=bdmax)] / H)**2) outside = (z > z_sigma*H) | (bd > bdmax) if min_bd != None: outside |= bd < min_bd density = density0 * np.exp(-0.5 * (z / H)**2) density[outside] = 0.0 return density dens_grid = delayed(define_density_grid)(z_coor, z_sigma_lim, bd_grid, bd_max, Hz, dens0) elif flaring_radial == False: def define_density_grid(z, z_sigma, bd, bdmax, H, density0, min_bd = min_bd): outside = (bd > bdmax) if min_bd != None: outside |= bd < min_bd slope = H * (flaring - 1.) / bdmax H_val = slope * bd + H density = density0 * np.exp(-0.5 * (z / H_val)**2) / (H_val / H) density[outside] = 0.0 return density dens_grid = delayed(define_density_grid)(z_coor, z_sigma_lim, bd_grid, bd_max, Hz, dens0) else: print('Computing Disk r-coordinate') with ProgressBar(): r_coor = delayed(np.sqrt)(xyz_grid[0,:,:,:]**2 + xyz_grid[1,:,:,:]**2).compute() def define_density_grid(z, z_sigma, bd, bdmax, H, density0, radial_coordinate, min_bd = min_bd): outside = (bd > bdmax) if min_bd != None: outside |= bd < min_bd admax = bdmax * (el_constant1 + el_constant2) slope = H * (flaring - 1.) / admax H_val = slope * r_coor + H density = density0 * np.exp(-0.5 * (z / H_val)**2) / (H_val / H) density[outside] = 0.0 return density dens_grid = delayed(define_density_grid)(z_coor, z_sigma_lim, bd_grid, bd_max, Hz, dens0, r_coor) r_x = xyz_grid[0,:,:,:] #Delayed dask array r_y = xyz_grid[1,:,:,:] #Delayed dask array normalizer = 1. / delayed(da.sqrt)((r_x / ad_grid**2)**2 + (r_y / bd_grid**2)**2) #Delayed dask array xtangent = r_y / bd_grid**2 * normalizer #Delayed dask array ytangent = -r_x / ad_grid**2 * normalizer #Delayed dask array # Angular momentum along the disk minor axis Lz_minor_axis = 0. - bd_grid * vel_0 * (1. - delayed(da.exp)(-bd_grid / velocity_factor)) vel_magnitude_grid = delayed(da.fabs)(Lz_minor_axis / (r_x * ytangent - r_y * xtangent)) # Greate grid containing velocity vectors for orbits vel_xyz = da.from_array(np.zeros(3*nx*ny*nz).reshape(3,nx,ny,nz), chunks = (-1,da_chunks_xyz,da_chunks_xyz,da_chunks_xyz)) vx = xtangent * vel_magnitude_grid vy = ytangent * vel_magnitude_grid def assign_velocity(velocity_grid, velx, vely): velocity_grid[0,:,:,:] = velx velocity_grid[1,:,:,:] = vely return velocity_grid #Assign Velocities to array vel_xyz = delayed(assign_velocity)(vel_xyz, vx, vy) # vel_xyz[0,:,:,:] = vx.compute() # vel_xyz[1,:,:,:] = vy.compute() velocity_xyz = delayed(da.nan_to_num)(vel_xyz.T.reshape(-1,3, order = "F").transpose() * u.km/ u.s) disk_coordinates = delayed(TiltedDisk)(x = disk_coords.x, y = disk_coords.y, z = disk_coords.z, v_x = velocity_xyz[0,:], v_y = velocity_xyz[1,:], v_z = velocity_xyz[2,:], alpha = alpha*u.deg, beta = beta*u.deg, theta = theta*u.deg) # Transform to GalacticLSR frame if return_all: galcen_coords_withvel = disk_coordinates.transform_to(coord.Galactocentric(**galcen_options)) lbd_coords_withvel = galcen_coords_withvel.transform_to(coord.GalacticLSR(**LSR_options)) else: lbd_coords_withvel = disk_coordinates.transform_to(coord.Galactocentric(**galcen_options)).transform_to(coord.GalacticLSR(**LSR_options)) # save Grid creation information for use in creating accurate WCS object associated with SpectralCube Object in future dD = lbd_grid[2,0,0,1] - lbd_grid[2,0,0,0] dB = lbd_grid[1,0,1,1] - lbd_grid[1,0,0,0] dL = lbd_grid[0,1,0,0] - lbd_grid[0,0,0,0] cdelt = np.array([dL.compute(), dB.compute(), dD.compute()]) print("Computing Coordinates with Disk Velocity information in GalacticLSR Frame:") if visualize: lbd_coords_withvel.visualize(filename = 'LBDCoordsWithVelGraph.svg') with ProgressBar(): lbd_coords_withvel = lbd_coords_withvel.compute() print("Computing Disk Density Grid:") if visualize: dens_grid.visualize(filename = 'DensGridGraph.svg') with ProgressBar(): dens_grid = dens_grid.compute() print("Computing Ellipse Parameters") bd_grid = bd_grid.compute() # dens_grid[(bd_grid > bd_max) | (np.abs(z_coor) >= z_sigma_lim * Hz)] = 0.0 pool.close() if return_all: return lbd_coords_withvel, np.swapaxes(dens_grid,0,2), cdelt, disk_coordinates, \ galcen_coords_withvel, np.swapaxes(bd_grid,0,2), vel_magnitude_grid.swapaxes(0,2) #coordframe,dask array, np array, delayed, delayed, delayed dask array, delayed dask array else: return lbd_coords_withvel, np.swapaxes(dens_grid,0,2), cdelt else: # Populate a uniform grid in Longitude-Latitude-Distance space lbd_grid = np.mgrid[L_range[0]:L_range[1]:nx*1j, B_range[0]:B_range[1]:ny*1j, D_range[0]:D_range[1]:nz*1j] # Transform grid into a 3 x N array for Longitude, Latitude, Distance axes lbd = lbd_grid.T.reshape(-1,3, order = "F").transpose() # Initiate astropy coordinates.Galactic object lbd_coords = coord.Galactic(l = lbd[0,:]*u.deg, b = lbd[1,:]*u.deg, distance = lbd[2,:]*u.kpc) if return_all: # Convert regularized grid points into Galactocentric frame galcen_coords = lbd_coords.transform_to(coord.Galactocentric(**galcen_options)) disk_coords = galcen_coords.transform_to(TiltedDisk(alpha = alpha*u.deg, beta = beta*u.deg, theta = theta*u.deg)) else: disk_coords = lbd_coords.transform_to(coord.Galactocentric(**galcen_options)).transform_to(TiltedDisk(alpha = alpha*u.deg, beta = beta*u.deg, theta = theta*u.deg)) # Create standard numpy ndarray of disk_coords and reshape to grid, matching original lbd_grid object disk_coords_arr = np.array([disk_coords.x.value, disk_coords.y.value, disk_coords.z.value]) xyz_grid = disk_coords_arr.reshape(-1,nx,ny,nz) # initiate partial object to solve for Ellipse Equation partial_bd_solver = partial(bd_solver, xyz=disk_coords_arr, z_sigma_lim = z_sigma_lim, Hz = Hz, bd_max = bd_max, el_constant1 = el_constant1, el_constant2 = el_constant2) # Solve Ellipse Equation across multiple threads pool = multiprocessing.Pool() bd_vals = pool.map(partial_bd_solver, range(len(disk_coords.x.value))) pool.close() # Create grid of bd values solved from Ellipse Equation, matching original lbd_grid object bd_grid = np.array(bd_vals).reshape(nx,ny,nz) # Create grid of ad values (semi-major axis) derived from bd values ad_grid = ne.evaluate("bd_grid * (el_constant1 + el_constant2 * bd_grid / bd_max)") z_coor = xyz_grid[2,:,:,:] # Create grid of density values for the Elliptical Disk, mathcing original lbd_grid object if (flaring == False) & (flaring_radial == False): def define_density_grid(z,z_sigma, bd, bdmax, H, density0, min_bd = min_bd): #density[(np.abs(z)<(z_sigma * H)) & (bd<=bdmax)] = density0 * \ #np.exp(-0.5 * (z[(np.abs(z)<(z_sigma * H)) & (bd<=bdmax)] / H)**2) outside = (z > z_sigma*H) | (bd > bdmax) if min_bd != None: outside |= bd < min_bd density = density0 * np.exp(-0.5 * (z / H)**2) density[outside] = 0.0 return density dens_grid = define_density_grid(z_coor, z_sigma_lim, bd_grid, bd_max, Hz, dens0) elif flaring_radial == False: def define_density_grid(z, z_sigma, bd, bdmax, H, density0, min_bd = min_bd): outside = (bd > bdmax) if min_bd != None: outside |= bd < min_bd slope = H * (flaring - 1.) / bdmax H_val = slope * bd + H density = density0 * np.exp(-0.5 * (z / H_val)**2) / (H_val / H) density[outside] = 0.0 return density dens_grid = define_density_grid(z_coor, z_sigma_lim, bd_grid, bd_max, Hz, dens0) else: r_coor = delayed(np.sqrt)(xyz_grid[0,:,:,:]**2 + xyz_grid[1,:,:,:]**2).compute() def define_density_grid(z, z_sigma, bd, bdmax, H, density0, radial_coordinate, min_bd = min_bd): outside = (bd > bdmax) if min_bd != None: outside |= bd < min_bd admax = bdmax * (el_constant1 + el_constant2) slope = H * (flaring - 1.) / admax H_val = slope * r_coor + H density = density0 * np.exp(-0.5 * (z / H_val)**2) / (H_val / H) density[outside] = 0.0 return density dens_grid = define_density_grid(z_coor, z_sigma_lim, bd_grid, bd_max, Hz, dens0, r_coor) # if flaring == False: # def define_density_grid(z,z_sigma, bd, bdmax, H, density0): # #density[(np.abs(z)<(z_sigma * H)) & (bd<=bdmax)] = density0 * \ # #np.exp(-0.5 * (z[(np.abs(z)<(z_sigma * H)) & (bd<=bdmax)] / H)**2) # outside = (z > z_sigma) | (bd > bdmax) # density = ne.evaluate("density0 * exp(-0.5 * (z / H)**2)") # density[outside] = 0.0 # return density # else: # def define_density_grid(z, z_sigma, bd, bdmax, H, density0): # outside = (z > z_sigma) | (bd > bdmax) # H_val = H + H * (bd/bdmax * flaring) # density = ne.evaluate("density0 * exp(-0.5 * (z / H_val)**2)") # density[outside] = 0.0 # return density # dens_grid = define_density_grid(z_coor, z_sigma_lim, bd_grid, bd_max, Hz, dens0) # dens_grid[(np.abs(z_coor)<(z_sigma_lim * Hz)) & (bd_grid<=bd_max)] = dens0 * \ # np.exp(-0.5 * (z_coor[(np.abs(z_coor)<(z_sigma_lim * Hz)) & (bd_grid<=bd_max)] / Hz)**2) # Solve for velocity magnitude using Angular Momentum and Velcotiy field from Burton & Liszt r_x = xyz_grid[0,:,:,:] r_y = xyz_grid[1,:,:,:] normalizer = ne.evaluate("1 / sqrt((r_x / ad_grid**2)**2 + (r_y / bd_grid**2)**2)") xtangent = ne.evaluate("r_y / bd_grid**2 * normalizer") ytangent = ne.evaluate("-r_x / ad_grid**2 * normalizer") # Angular momentum along the disk minor axis Lz_minor_axis = ne.evaluate("0. - bd_grid * vel_0 * (1. - exp(-bd_grid / velocity_factor))") #r x v vel_magnitude_grid = ne.evaluate("abs(Lz_minor_axis / (r_x * ytangent - r_y * xtangent))") # Greate grid containing velocity vectors for orbits vel_xyz = np.zeros_like(xyz_grid) vel_xyz[0,:,:,:] = ne.evaluate("xtangent * vel_magnitude_grid") vel_xyz[1,:,:,:] = ne.evaluate("ytangent * vel_magnitude_grid") np.nan_to_num(vel_xyz) velocity_xyz = vel_xyz.T.reshape(-1,3, order = "F").transpose() * u.km/ u.s vel_cartesian = CartesianRepresentation(velocity_xyz) # Create new TiltedDisk object containing all newly calculated velocities disk_coordinates = TiltedDisk(x = disk_coords.x, y = disk_coords.y, z = disk_coords.z, v_x = vel_cartesian.x, v_y = vel_cartesian.y, v_z = vel_cartesian.z, alpha = alpha*u.deg, beta = beta*u.deg, theta = theta*u.deg) # Transform to GalacticLSR frame if return_all: galcen_coords_withvel = disk_coordinates.transform_to(coord.Galactocentric(**galcen_options)) lbd_coords_withvel = galcen_coords_withvel.transform_to(coord.GalacticLSR(**LSR_options)) else: lbd_coords_withvel = disk_coordinates.transform_to(coord.Galactocentric(**galcen_options)).transform_to(coord.GalacticLSR(**LSR_options)) # save Grid creation information for use in creating accurate WCS object associated with SpectralCube Object in future dD = lbd_grid[2,0,0,1] - lbd_grid[2,0,0,0] dB = lbd_grid[1,0,1,1] - lbd_grid[1,0,0,0] dL = lbd_grid[0,1,0,0] - lbd_grid[0,0,0,0] cdelt = np.array([dL, dB, dD]) if return_all: return lbd_coords_withvel, np.swapaxes(dens_grid,0,2), cdelt, disk_coordinates, \ galcen_coords_withvel, np.swapaxes(bd_grid,0,2), np.swapaxes(vel_magnitude_grid,0,2) else: return lbd_coords_withvel, np.swapaxes(dens_grid,0,2), cdelt def EmissionLBV(lbd_coords_withvel, density_gridin, cdelt, vel_disp, vmin, vmax, vel_resolution, L_range, B_range, species = 'hi', visualize = False, T_gas = 120. *u.K, memmap = False, da_chunks_xyz = 50, redden = None, case = 'B'): """ Creates a Longitude-Latitude-Velocity SpectralCube object of neutral (HI 21cm) or ionized (H-Alpha) gas emission Uses output calculated from 'modspectra.cube.EllipticalLBD' Uses numexpr package to evaluate math Parameters ---------- lbd_coords_withvel: :class:'~astropy.coordinates.GalacticLSR' astropy.coord array containing all coordinates corresponding to fabricated grid of points dens_grid: 'numpy.ndarray' ndarray with shape (resolution) containing density of points in Longitude-Latitude-Distance grid axes order swapped to be ready for SpectralCube creation cdelt: 'numpy.ndarray' ndarray with shape (3) containing the step size for Longitude, Latitude, and Distance used in the grid Used for WCS object creation in later instances vel_disp: 'number, Quantity Velocity dispersion of the gas in units of km/s (if not Quantity) vmin: 'number, Quantity' Min Velocity to create in grid in units of km/s (if not Quantity) vmax: 'number, Quantity' Max Velocity to create in grid in units of km/s (if not Quantity) vel_resolution: 'int' Resolution to Create along velocity axis L_range: :list:'number' Range of Longtiude to create grid over B_range: :list:'number' Range of Latitude to create grid over species: 'str', optional, must be keyword of either 'hi' or 'ha' Specifies whether emission cube will be neutral (HI 21-cm) gas or ionized (H-Alpha) gas emission Defaults to HI netural gas visualize: 'bool', optional, must be keyword if using dask, returns dask visualization map T_gas: 'number, Quantity', optional, must be keyword Temperature of neutral HI 21-cm emitting gas in Kelvin Defaults to 120 K memmap: 'bool', optional, must be keyword if True, use dask for memory mapping when creating disk structure useful for higher resolution computes da_chunks_xyz: 'number', optional, must be keyword if memmap is True, sets the dask chunk size Default to 50, likely too small for efficiency redden: 'bool', optional, must be keyword if True, apply extinction corrections to emission using 3D dustmaps of Marshall et al. (2006) implemented via the dustmaps and extinction python packages case: 'str', optional, must be keyword if species is 'ha', then sets the recombination case to use Defaults to case B recombination Returns ------- emission_cube: :class:'numpy.ndarray' Emission values in DBL cube DBL_wcs: '~astropy.wcs.WCS' WCS information associated with emission_cube """ # Check for units if not isinstance(vel_disp, u.Quantity): vel_disp = u.Quantity(vel_disp, unit = u.km / u.s) logging.warning("No units specified for Velocity Dispersion, vel_disp, assuming" "{}".format(vel_disp.unit)) elif not vel_disp.unit == u.km/u.s: vel_disp = vel_disp.to(u.km / u.s) if not isinstance(vmin, u.Quantity): vmin = u.Quantity(vmin, unit = u.km / u.s) logging.warning("No units specified for Min Velocity, vmin, assuming" "{}".format(vmin.unit)) elif not vmin.unit == u.km/u.s: vmin = vmin.to(u.km / u.s) if not isinstance(vmax, u.Quantity): vmax = u.Quantity(vmax, unit = u.km / u.s) logging.warning("No units specified for Max Velocity, vmax, assuming" "{}".format(vmax.unit)) elif not vmax.unit == u.km/u.s: vmax = vmax.to(u.km / u.s) if not isinstance(T_gas, u.Quantity): T_gas = u.Quantity(T_gas, unit = u.K) logging.warning("No units specified for T_gas, assuming" "{}".format(T_gas.unit)) if redden: from extinction import fm07 as extinction_law from dustmaps.marshall import MarshallQuery try: marshall = MarshallQuery() except OSError: print("Local Copy of Marhsall et al (2006) dustmap is unavailable") from dustmaps.config import config print("Downloading local copy to {}".format(config['data_dir'])) from dustmaps.marshall import fetch as getMarshall getMarshall() finally: marshall = MarshallQuery() # Define the lookup table of values of Sigma # First create my "lookup table" for the Gaussian evaluated at an array of sigma (5 sigma) # Extract resolution information nz, ny, nx = density_gridin.shape # Define the velocity channels VR, dv = np.linspace(vmin.value,vmax.value,vel_resolution, retstep=True) vr_grid = np.swapaxes(lbd_coords_withvel.radial_velocity.value.reshape(nx,ny,nz),0,2) # Calculate my sigma values vr_grid_plus = vr_grid[:,:,:,None] if memmap: gaussian_cells = da.exp(-1/2. * ((da.from_array(vr_grid_plus, chunks = (da_chunks_xyz,da_chunks_xyz,da_chunks_xyz,1)) - da.from_array(VR, chunks = -1)) / vel_disp.value)**2) else: gaussian_cells = ne.evaluate("exp(-1/2. * ((vr_grid_plus - VR) / vel_disp)**2)") # Calculate emission in each grid cell in Longitude-Latitude-Velocity space # Sums over Distance space dist = cdelt[2] if memmap: if species == 'hi': density_grid = density_gridin * 33.52 / (T_gas.value * vel_disp.value) * dist * 1000. / 50. optical_depth = da.einsum('jkli, jkl->ijkl', gaussian_cells, density_grid).sum(axis = 1) result_cube = T_gas * (1 - da.exp(-1.* optical_depth)) print("Computing Resulting Emission from Density Structure:") if visualize: result_cube.visualize(filename = 'EmissionCubeGraph.svg') with ProgressBar(): emission_cube = delayed(result_cube.compute()) if species == 'ha': if case == 'A': #Recombination case A b_constant = -1.009 a_0_constant = 0.0938 * u.R / u.km * u.s else: #Recombination case B b_constant = -0.942 - 0.031 * np.log(T_gas.value/10.**4) a_0_constant = 0.1442 * u.R / u.km * u.s EM = da.from_array(density_gridin *density_gridin * dist * 1000., chunks = da_chunks_xyz) if redden: # from extinction import fm07 as extinction_law # from dustmaps.marshall import MarshallQuery # try: # marshall = MarshallQuery() # except OSError: # print("Local Copy of Marhsall et al (2006) dustmap is unavailable") # from dustmaps.config import config # print("Downloading local copy to {}".format(config['data_dir'])) # from dustmaps.marshall import fetch as getMarshall # getMarshall() # finally: # marshall = MarshallQuery() wave_Ks = 2.17 *u.micron A_KS_to_A_v = 1. / extinction_law(np.array([wave_Ks.to(u.AA).value]), 1.) wave_ha = np.array([6562.8]) A_V_to_A_ha = extinction_law(wave_ha, 1.) marshall_AK = da.from_array(marshall(coord.SkyCoord(lbd_coords_withvel)), chunks = da_chunks_xyz*100) trans_ha = 10**(-0.4 * A_V_to_A_ha * A_KS_to_A_v * marshall_AK) # print('Computed Av') trans_grid = da.swapaxes(trans_ha.reshape(nx,ny,nz), 0,2) EM = delayed(EM * trans_grid) EM = delayed(EM * a_0_constant / vel_disp.value * (T_gas.value/10.**4)**(b_constant)) result_cube = delayed(da.einsum)('jkli, jkl->ijkl', gaussian_cells, EM) result_cube = delayed(result_cube.sum)(axis=1) print("Computing Resulting Emission from Density Structure:") if visualize: result_cube.visualize(filename = 'EmissionCubeGraph.svg') with ProgressBar(): emission_cube = result_cube.compute() else: if species == 'hi': density_grid = ne.evaluate("density_gridin *33.52 / (T_gas * vel_disp)* dist *1000. / 50.") optical_depth = np.einsum('jkli, jkl->ijkl', gaussian_cells, density_grid).sum(axis = 1) emission_cube = ne.evaluate("T_gas * (1 - exp(-1.* optical_depth))") * u.K if species =='ha': if case == 'A': #Recombination case A b_constant = -1.009 a_0_constant = 0.0938 * u.R / u.km * u.s else: #Recombination case B b_constant = -0.942 - 0.031 * np.log(T_gas.value/10.**4) a_0_constant = 0.1442 * u.R / u.km * u.s EM = ne.evaluate("density_gridin**2 * dist * 1000.") if redden: # from extinction import fm07 as extinction_law # from dustmaps.marshall import MarshallQuery # try: # marshall = MarshallQuery() # except OSError: # print("Local Copy of Marhsall et al (2006) dustmap is unavailable") # from dustmaps.config import config # print("Downloading local copy to {}".format(config['data_dir'])) # from dustmaps.marshall import fetch as getMarshall # getMarshall() # finally: # marshall = MarshallQuery() wave_Ks = 2.17 *u.micron A_KS_to_A_v = 1. / extinction_law(np.array([wave_Ks.to(u.AA).value]), 1.) wave_ha = np.array([6562.8]) A_V_to_A_ha = extinction_law(wave_ha, 1.) marshall_AK = marshall(coord.SkyCoord(lbd_coords_withvel)) trans_ha = 10**(-0.4 * A_V_to_A_ha * A_KS_to_A_v * marshall_AK) # print('Computed Av') trans_grid = np.swapaxes(trans_ha.reshape(nx,ny,nz), 0,2) EM *= trans_grid emission_cube = np.einsum('jkli, jkl->ijkl', gaussian_cells, a_0_constant * EM/ vel_disp.value * (T_gas.value/10.**4)**(b_constant)).sum(axis = 1) # Create WCS Axes DBL_wcs = wcs.WCS(naxis = 3) DBL_wcs.wcs.crpix=[int(nx/2),int(ny/2),int(vel_resolution/2)] DBL_wcs.wcs.crval=[np.sum(L_range)/2, np.sum(B_range)/2, (vmax.value+vmin.value)/2] DBL_wcs.wcs.ctype=["GLON-CAR", "GLAT-CAR", "VRAD"] DBL_wcs.wcs.cunit=["deg", "deg", "km/s"] DBL_wcs.wcs.cdelt=np.array([cdelt[0], cdelt[1], dv]) # Return Emission cube and WCS info return emission_cube, DBL_wcs class EmissionCube(EmissionCubeMixin, SpectralCube): """ Synthetic Emission cube container Parameters ---------- data: 'ndarray', optional, if set, skips cube creation Data cube values wcs: 'astropy.wcs.WCS', optional, if set, skips cube creation WCS info meta: 'dict', optional, if set, skips cube creation Metadata mask: 'ndarray', optional, must be keyword Masking info fill_value: 'number', optional, must be keyword Values to fill for missing data header: 'dict' FITS header allow_huge_operations: 'bool' from SpectralCube beam: '' from SpectralCube wcs_tolerance: 'number' from SpectralCube resolution: 'tuple or list' Defines LBD Resolution of grid, must be shape (3) vel_resolution: 'int' Velocity axis resolution L_range: 'list, tuple, Quantity' Range of longitude in degrees to create grid [low, high] B_range: 'list, tuple, Quantity' Range of latitude in degrees to create grid [low, high] D_range: 'list,tuple,Quantity' Range of Distances in kpc to create grid [near, far] alpha: 'number, Quantity' First Tilt Angle of Disk beta: 'number, Quantity' Second Tilt Angle of Disk 90 degrees - beta is the inclination theta: 'number, Quantity' Third tilt Angle of Disk for allignment of major axis with line of sight bd_max: 'number, Quantity' Max size of Ellipse along minor axis Default in units of kpc Hz: 'number, Quantity' Vertical Scale height of Disk Default in units of kpc z_sigma_lim: 'number' sigma cuttoff to stop solving Ellipse equation for for z above a specified scale height threshold dens0: 'number, Quantity' Density at midplane of Elliptical Disk Default units of cm^-3 velocity_factor: 'number' Constant used to define velocity field in Burton & Liszt model vel_0: 'number' Max velocity of Elliptical orbit Corresponds to velocity of outermost orbit on semi-minor axis Default unit of km/s el_constant1: 'number' First parameter for defining ellipse el_constant2: 'number' second parameter for defining ellipse vel_disp: 'number, Quantity' Velocity dispersion of the gas in units of km/s (if not Quantity) vmin: 'number, Quantity' Min Velocity to create in grid in units of km/s (if not Quantity) vmax: 'number, Quantity' Max Velocity to create in grid in units of km/s (if not Quantity) visualize: 'bool', optional, must be keyword if using dask, returns dask visualization map redden: 'bool', optional, must be keyword if True, apply extinction corrections to emission using 3D dustmaps of Marshall et al. (2006) implemented via the dustmaps and extinction python packages flaring: 'bool, number', optional, must be keyword if False, disk has a constant scale height if a number, then sets the flaring parameter, F_z, as described in Krishnarao, Benjamin, Haffner (2019) flaring_radial: 'bool', optional, must be keyword if flaring is present then if False (default), flaring of scale height is a function of bd, the semi minor axis if True, flaring of scale height is a function of r, the cyclindrical radius coordinate in the TiltedDisk Frame min_bd: 'number', optional, must be keyword sets the minimum value of the semi minor axis to allow Used to make a ring, rather than a disk structure species: 'str', optional, must be keyword of either 'hi' or 'ha' Specifies whether emission cube will be neutral (HI 21-cm) gas or ionized (H-Alpha) gas emission Defaults to HI netural gas T_gas: 'number, Quantity', optional, must be keyword Temperature of neutral HI 21-cm emitting gas in Kelvin Defaults to 120 K LSR_options: 'dictionary', optional, must be keyword Dictionary of **kwargs to pass into :class:'~astropy.coordinates.GalacticLSR' galcen_options: 'dictionary', optional, must be keyword set galcen_options to be passed to coordinate frames return_all: 'bool', optional, must be keyword if True, will return all output components used for diagnosing issues most information can be determined / converted from initial 3 output elements only other useful bit is bd_grid - may incorporate into default output in future LB80: 'bool', optional, must be keyword if True, will create a default cube with the exact parameters of Liszt & Burton (1982) if any parameters are already set, they will be used instead defaults: 'bool' if True, will use detaulf resolution and other information to create object create: 'bool' if True, will create cube using provided parameters ***Important*** must be True to create a cube, otherwise will assume it is loading/initiating cube from provided data memmap: 'bool' if true, will create cube using memory mapping via dask Note: Currently does not work properly with species = 'ha' da_chunks_xyz: 'number', optional, must be keyword if memmap, this sets the default chunksize to be used for dask arrays LBD_output_in: 'Dictionary', optional, must be keyword provide LBD output information to save in EmissionCube class only used if EmissionCube is from a model created by this package LBD_output_keys_in: 'list, str', optional, must be keyword provide keys tot he entries of LBD_output_in only used if EmissionCube is from a model created by this package model_header: 'fits.header' Provide header to gather model parameters and information only used if EmissionCube is from a model created by this package DK20: 'bool' if true, will will create default H-Alpha cube with parameters of (Krishnarao et al. 2019) if any parameters are already set, they will be used instead Returns ------- SpectralCube BaseClass with EmissionCube container Class """ def __init__(self, data = None, wcs = None, meta = None, mask = None, fill_value=np.nan, header=None, allow_huge_operations=False, beam=None, wcs_tolerance=0.0, resolution = None, vel_resolution = None, L_range = None, B_range = None, D_range = None, alpha = None, beta = None, theta = None, bd_max = None, Hz = None, z_sigma_lim = None, dens0 = None, velocity_factor = None, vel_0 = None, el_constant1 = None, el_constant2 = None, vel_disp = None, vmin = None, vmax = None, visualize = False, redden = None, flaring = None, flaring_radial = None, min_bd = None, species = None, T_gas = None, LSR_options = {}, galcen_options = {}, return_all = False, LB80 = False, defaults = False, create = False, memmap = False, da_chunks_xyz = None, LBD_output_in = None, LBD_output_keys_in = None, model_header = None, DK20 = False, case = None, **kwargs): if not meta: meta = {} # Define Burton & Liszt 1982 parameters if needed if LB80: galcen_distance_factor = 8.127 / 10. if el_constant1 == None: el_constant1 = 1.6 if el_constant2 == None: el_constant2 = 1.5 if T_gas == None: T_gas = 120. * u.K if bd_max == None: bd_max = 0.6 * u.kpc * galcen_distance_factor if Hz == None: Hz = 0.1 * u.kpc * galcen_distance_factor if z_sigma_lim == None: z_sigma_lim = 3 if dens0 == None: dens0 = 0.33 * 1/u.cm/u.cm/u.cm if vel_0 == None: vel_0 = 360.*u.km/u.s if velocity_factor == None: velocity_factor = 0.1 if vel_disp == None: vel_disp = 9 * u.km/u.s if alpha == None: alpha = 13.5 * u.deg if beta == None: beta = 20. * u.deg if theta == None: theta = 48.5 * u.deg if species == None: species = 'hi' if flaring == None: flaring = False if flaring_radial == None: flaring_radial = False if DK20: galcen_distance_factor = 8.127 / 10. if el_constant1 == None: el_constant1 = 1.6 if el_constant2 == None: el_constant2 = 1.5 if T_gas == None: T_gas = 8000. * u.K if not bd_max: bd_max = 0.6 * u.kpc * galcen_distance_factor if Hz == None: Hz = 0.26 * u.kpc if z_sigma_lim == None: z_sigma_lim = 3 if dens0 == None: dens0 = 0.39 * 1/u.cm/u.cm/u.cm if vel_0 == None: vel_0 = 360.*u.km/u.s if velocity_factor == None: velocity_factor = 0.1 if vel_disp == None: vel_disp = 12. * u.km/u.s if alpha == None: alpha = 13.5 * u.deg if beta == None: beta = 20. * u.deg if theta == None: theta = 48.5 * u.deg if species == None: species = 'ha' if redden == None: redden = True if not case == 'A': case = 'B' if flaring == None: flaring = 2.05 if flaring_radial == None: flaring_radial = True # Define default parameters if needed if defaults: if resolution == None: resolution = (128,128,128) if vel_resolution == None: vel_resolution = 550 if L_range == None: L_range = [-10,10]*u.deg if B_range == None: B_range = [-8,8] * u.deg if D_range == None: D_range = [5,13] * u.kpc if species == None: species = 'hi' if vmin == None: vmin = -325. * u.km/u.s if vmax == None: vmax = 325. * u.km/u.s if create: # Check units if not isinstance(L_range, u.Quantity): L_range = u.Quantity(L_range, unit = u.deg) logging.warning("No units specified for Longitude Range, assuming " "{}".format(L_range.unit)) elif not L_range.unit == u.deg: L_range = L_range.to(u.deg) if not isinstance(B_range, u.Quantity): B_range = u.Quantity(B_range, unit = u.deg) logging.warning("No units specified for Latitude Range, assuming " "{}".format(B_range.unit)) elif not B_range.unit == u.deg: B_range = B_range.to(u.deg) if not isinstance(D_range, u.Quantity): D_range = u.Quantity(D_range, unit = u.kpc) logging.warning("No units specified for Distance Range, assuming " "{}".format(D_range.unit)) elif not D_range.unit == u.kpc: D_range = D_range.to(u.kpc) if not isinstance(alpha, u.Quantity): alpha = u.Quantity(alpha, unit = u.deg) logging.warning("No units specified for Alpha, assuming " "{}".format(alpha.unit)) elif not alpha.unit == u.deg: alpha = alpha.to(u.deg) if not isinstance(beta, u.Quantity): beta = u.Quantity(beta, unit = u.deg) logging.warning("No units specified for Beta, assuming " "{}".format(beta.unit)) elif not beta.unit == u.deg: beta = beta.to(u.deg) if not isinstance(theta, u.Quantity): theta = u.Quantity(theta, unit = u.deg) logging.warning("No units specified for Theta, assuming " "{}".format(theta.unit)) elif not theta.unit == u.deg: theta = theta.to(u.deg) if not isinstance(bd_max, u.Quantity): bd_max = u.Quantity(bd_max, unit = u.kpc) logging.warning("No units specified for Max Semi-minor axis, bd_max, assuming " "{}".format(bd_max.unit)) elif not bd_max.unit == u.kpc: bd_max = bd_max.to(u.kpc) if not isinstance(Hz, u.Quantity): Hz = u.Quantity(Hz, unit = u.kpc) logging.warning("No units specified for Vertical Scale Height, Hz, assuming " "{}".format(Hz.unit)) elif not Hz.unit == u.kpc: Hz = Hz.to(u.kpc) if not isinstance(dens0, u.Quantity): dens0 = u.Quantity(dens0, unit = 1 / u.cm / u.cm / u.cm) logging.warning("No units specified for Midplane Density, dens0, assuming " "{}".format(dens0.unit)) elif not dens0.unit == 1 / u.cm / u.cm / u.cm: dens0 = dens0.to(1 / u.cm / u.cm / u.cm) if not isinstance(vel_0, u.Quantity): vel_0 = u.Quantity(vel_0, unit = u.km / u.s) logging.warning("No units specified for Max Velocity, vel_0, assuming " "{}".format(vel_0.unit)) elif not vel_0.unit == u.km/u.s: vel_0 = vel_0.to(u.km / u.s) if not isinstance(vel_disp, u.Quantity): vel_disp = u.Quantity(vel_disp, unit = u.km / u.s) logging.warning("No units specified for Velocity Dispersion, vel_disp, assuming " "{}".format(vel_disp.unit)) elif not vel_disp.unit == u.km/u.s: vel_disp = vel_disp.to(u.km / u.s) if not isinstance(vmin, u.Quantity): vmin = u.Quantity(vmin, unit = u.km / u.s) logging.warning("No units specified for Min Velocity, vmin, assuming " "{}".format(vmin.unit)) elif not vmin.unit == u.km/u.s: vmin = vmin.to(u.km / u.s) if not isinstance(vmax, u.Quantity): vmax = u.Quantity(vmax, unit = u.km / u.s) logging.warning("No units specified for Max Velocity, vmax, assuming " "{}".format(vmax.unit)) elif not vmax.unit == u.km/u.s: vmax = vmax.to(u.km / u.s) if (not da_chunks_xyz) and (memmap): da_chunks_xyz = 50 logging.warning("Using a default chunksize of 50 per axis for memory mapping via dask") # Assign attributes self.bd_max = bd_max self.Hz = Hz self.z_sigma_lim = z_sigma_lim self.dens0 = dens0 self.vel_0 = vel_0 self.velocity_factor = velocity_factor self.vel_disp = vel_disp self.el_constant1 = el_constant1 self.el_constant2 = el_constant2 self.T_gas = T_gas self.alpha = alpha self.beta = beta self.theta = theta self.resolution = resolution self.vel_resolution = vel_resolution self.L_range = L_range self.B_range = B_range self.D_range = D_range self.species = species self.vmin = vmin self.vmax = vmax # Get LBD Grid Created self.LBD_output = EllipticalLBD(resolution, bd_max.value, Hz.value, z_sigma_lim, dens0.value, velocity_factor, vel_0.value, el_constant1, el_constant2, alpha.value, beta.value, theta.value, L_range.value, B_range.value, D_range.value, memmap = memmap, da_chunks_xyz = da_chunks_xyz, visualize = visualize, LSR_options = LSR_options, galcen_options = galcen_options, flaring = flaring, flaring_radial = flaring_radial, min_bd = min_bd, return_all = return_all, species = species, **kwargs) if return_all: self.LBD_output_keys = ['lbd_coords', 'disk_density', 'cdelt', 'disk_coordinate_frame', 'galcen_coords', 'bd_grid', 'vel_mag_grid'] else: self.LBD_output_keys = ['lbd_coords', 'disk_density', 'cdelt'] # Create LBV Cube data, wcs = EmissionLBV(self.LBD_output[0], self.LBD_output[1], self.LBD_output[2], vel_disp, vmin, vmax, vel_resolution, L_range.value, B_range.value, visualize = visualize, redden = redden, species = species, T_gas = T_gas, memmap = memmap, da_chunks_xyz = da_chunks_xyz, case = case) if memmap: print('Setting up Data Cube') with ProgressBar(): data = data.compute() # Metadata with unit info if not isinstance(data, u.Quantity): if species == 'hi': data = data * u.K elif species == 'ha': data = data * u.R / u.km * u.s meta['BUNIT'] = '{}'.format(data.unit) # Add placeholder masks for v0.4.4 if mask is None: from spectral_cube import BooleanArrayMask mask_array = np.ones_like(data.value, dtype = bool) mask = BooleanArrayMask(mask=mask_array, wcs=wcs) # Initialize Spectral Cube Object super().__init__(data = data, wcs = wcs, mask=mask, meta=meta, fill_value=fill_value, header=header, allow_huge_operations=allow_huge_operations, beam=beam, wcs_tolerance=wcs_tolerance, **kwargs) if LBD_output_in: self.LBD_output = LBD_output_in if LBD_output_keys_in: self.LBD_output_keys = LBD_output_keys_in if model_header: self.bd_max = model_header['BD_MAX'] * u.Unit(model_header['BD_MAX_U']) self.Hz = model_header['HZ'] * u.Unit(model_header['HZ_U']) self.z_sigma_lim = model_header['Z_SIGMA'] self.dens0 = model_header['DENS0'] * u.Unit(model_header['DENS0_U']) self.vel_0 = model_header['VEL_0'] * u.Unit(model_header['VEL_0_U']) self.velocity_factor = model_header['VEL_FACT'] self.vel_disp = model_header['VEL_DISP'] * u.Unit(model_header['VELDISPU']) self.el_constant1 = model_header['EL_CONS1'] self.el_constant2 = model_header['EL_CONS2'] self.T_gas = model_header['T_GAS'] * u.Unit(model_header['T_GAS_U']) self.alpha = model_header['ALPHA'] * u.Unit(model_header['ALPHA_U']) self.alpha = self.alpha.decompose().scale * u.rad self.alpha = self.alpha.to(u.deg) self.beta = model_header['BETA'] * u.Unit(model_header['BETA_U']) self.beta = self.beta.decompose().scale * u.rad self.beta = self.beta.to(u.deg) self.theta = model_header['THETA'] * u.Unit(model_header['THETA_U']) self.theta = self.theta.decompose().scale * u.rad self.theta = self.theta.to(u.deg) self.resolution = model_header['RESO'] self.vel_resolution = model_header['VEL_RESO'] self.L_range = [model_header['L_RMIN'],model_header['L_RMAX']] * u.Unit(model_header['L_RANGEU']) self.B_range = [model_header['B_RMIN'],model_header['B_RMAX']] * u.Unit(model_header['B_RANGEU']) self.D_range = [model_header['D_RMIN'],model_header['D_RMAX']] * u.Unit(model_header['D_RANGEU']) self.species = model_header['SPECIES'] self.vmin = model_header['VMIN'] * u.Unit(model_header['VMIN_U']) self.vmax = model_header['VMAX'] * u.Unit(model_header['VMAX_U']) @classmethod def read(cls, file, model = False): if model: hdulist = fits.open(file) v_bary_in = CartesianDifferential(hdulist[1].data["v_bary"] * u.km/u.s) LBD_output_in = [coord.GalacticLSR(l = hdulist[1].data["lbd"][0,:]*u.deg, b = hdulist[1].data["lbd"][1,:]*u.deg, distance = hdulist[1].data["lbd"][2,:]*u.kpc, pm_l_cosb = hdulist[1].data["v"][0,:]*u.mas/u.yr, pm_b = hdulist[1].data["v"][1,:]*u.mas/u.yr, radial_velocity = hdulist[1].data["v"][2,:]*u.km/u.s, v_bary = v_bary_in), hdulist[1].data["disk_density_grid"], hdulist[1].data["LBD_cdelt"]] LBD_output_keys_in = ['lbd_coords', 'disk_density', 'cdelt'] model_header = hdulist[1].header hdulist.close() else: model_header = None LBD_output_keys_in = None LBD_output_in = None cube = super().read(file) data = cube.unmasked_data[:] wcs = cube.wcs header = cube.header meta = {} if 'BUNIT' in header: meta['BUNIT'] = header['BUNIT'] return cls(data = data, wcs = wcs, meta = meta, header = header, LBD_output_in = LBD_output_in, LBD_output_keys_in = LBD_output_keys_in, model_header = model_header) def write(self, filename, overwrite = False, format = None, model = False): if model: hdulist = self.hdulist if (hasattr(self, 'LBD_output') & len(hdulist) == 1): length = self.resolution[0]*self.resolution[1]*self.resolution[2] c1 = fits.Column(name='lbd', unit='deg,deg,kpc', format = '{}D'.format(length)) c2 = fits.Column(name='v', unit='mas/yr,mas/yr,km/s', format = '{}D'.format(length)) c3 = fits.Column(name='disk_density_grid', unit='cm-3', format = '{}D'.format(length), dim = '({},{},{})'.format(self.resolution[0], self.resolution[1], self.resolution[2])) c4 = fits.Column(name='LBD_cdelt', array=self.LBD_output[2], unit='deg,deg,kpc', format = 'D') c5 = fits.Column(name='v_bary', array=[self.LBD_output[0].v_bary.d_x.value, self.LBD_output[0].v_bary.d_y.value, self.LBD_output[0].v_bary.d_z.value], format = 'D', unit = '{}'.format(self.LBD_output[0].v_bary.d_z.unit)) table = fits.BinTableHDU.from_columns([c1,c2,c3,c4,c5]) table.data["lbd"][0,:] = self.LBD_output[0].l table.data["lbd"][1,:] = self.LBD_output[0].b table.data["lbd"][2,:] = self.LBD_output[0].distance table.data["v"][0,:] = self.LBD_output[0].pm_l_cosb table.data["v"][1,:] = self.LBD_output[0].pm_b table.data["v"][2,:] = self.LBD_output[0].radial_velocity table.data["disk_density_grid"] = self.LBD_output[1] hdulist.append(table) hdulist[1].header['BD_MAX'] = '{}'.format(self.bd_max.value) hdulist[1].header['BD_MAX_U'] = '{}'.format(self.bd_max.unit) hdulist[1].header['HZ'] = '{}'.format(self.Hz.value) hdulist[1].header['HZ_U'] = '{}'.format(self.Hz.unit) hdulist[1].header['Z_SIGMA'] = '{}'.format(self.z_sigma_lim) hdulist[1].header['DENS0'] = '{}'.format(self.dens0.value) hdulist[1].header['DENS0_U'] = '{}'.format(self.dens0.unit) hdulist[1].header['VEL_0'] = '{}'.format(self.vel_0.value) hdulist[1].header['VEL_0_U'] = '{}'.format(self.vel_0.unit) hdulist[1].header['VEL_FACT'] = '{}'.format(self.velocity_factor) hdulist[1].header['VEL_DISP'] = '{}'.format(self.vel_disp.value) hdulist[1].header['VELDISPU'] = '{}'.format(self.vel_disp.unit) hdulist[1].header['EL_CONS1'] = '{}'.format(self.el_constant1) hdulist[1].header['EL_CONS2'] = '{}'.format(self.el_constant2) hdulist[1].header['T_GAS'] = '{}'.format(self.T_gas.value) hdulist[1].header['T_GAS_U'] = '{}'.format(self.T_gas.unit) hdulist[1].header['ALPHA'] = '{}'.format(self.alpha.value) hdulist[1].header['ALPHA_U'] = '{}'.format(self.alpha.unit) hdulist[1].header['BETA'] = '{}'.format(self.beta.value) hdulist[1].header['BETA_U'] = '{}'.format(self.beta.unit) hdulist[1].header['THETA'] = '{}'.format(self.theta.value) hdulist[1].header['THETA_U'] = '{}'.format(self.theta.unit) hdulist[1].header['RESO'] = '{}'.format(self.resolution) hdulist[1].header['VEL_RESO'] = '{}'.format(self.vel_resolution) hdulist[1].header['L_RMIN'] = self.L_range[0].value hdulist[1].header['L_RMAX'] = self.L_range[1].value hdulist[1].header['L_RANGEU'] = '{}'.format(self.L_range.unit) hdulist[1].header['B_RMIN'] = self.B_range[0].value hdulist[1].header['B_RMAX'] = self.B_range[1].value hdulist[1].header['B_RANGEU'] = '{}'.format(self.B_range.unit) hdulist[1].header['D_RMIN'] = self.L_range[0].value hdulist[1].header['D_RMAX'] = self.L_range[1].value hdulist[1].header['D_RANGEU'] = '{}'.format(self.D_range.unit) hdulist[1].header['SPECIES'] = '{}'.format(self.species) hdulist[1].header['VMIN'] = self.vmin.value hdulist[1].header['VMIN_U'] = '{}'.format(self.vmin.unit) hdulist[1].header['VMAX'] = self.vmax.value hdulist[1].header['VMAX_U'] = '{}'.format(self.vmax.unit) now = datetime.datetime.strftime(datetime.datetime.now(), "%Y/%m/%d-%H:%M:%S") hdulist[0].header.add_history("Written by modspectra on " "{date}".format(date=now)) try: hdulist.writeto(filename, overwrite=overwrite) except TypeError: hdulist.writeto(filename, clobber=overwrite) else: super().write(filename, overwrite = overwrite, format = format) # Convenience functions for quickly making DK20 Disk def create_DK20(**kwargs): """ Quick Create a DK20 H-Alpha emisison cube as described in Krishnarao, Benjamin, Haffner (2019) Parameters ---------- **kwargs: passed to EmissionCube.__init """ return EmissionCube(create = True, DK20 = True, defaults = True, **kwargs) # Convenience functions for quickly making LB80 Disk def create_LB80(**kwargs): """ Quick Create a DK20 H-Alpha emisison cube as described in Krishnarao, Benjamin, Haffner (2019) Parameters ---------- **kwargs: passed to EmissionCube.__init """ return EmissionCube(create = True, LB80 = True, defaults = True, **kwargs) def create_DK20_spectrum(coordinate, radius, l_resolution = 10, b_resolution = 10, distance_resolution = 200, **kwargs): """ Quick Create a DK20 H-Alpha emisison spectrum for a given SkyCoord direction and beam radius Parameters ---------- coordinate: '`astropy.coordinates.SkyCoord' central coordinate to compute spectrum radius: 'Quantity or number' Beam radius to compute spectrum over assumes u.deg if number l_resolution: 'number', optional, must be keyword resolution across longitude dimension b_resolution: 'number', optional, must be keyword resolution across latitude dimension distance_resolution: 'number', optional, must be keyword resolution across distance dimension """ if not isinstance(coordinate, coord.SkyCoord): raise TypeError print("Input coordinate must be a SkyCoord object") elif not isinstance(coordinate.galcen_distance, u.Quantity): coordinate.galcen_distance = 8.127 * u.kpc logging.warning("No galcen_distance attribute specified for SkyCoord, assuming galcen_distance = " "{0}{1}".format(coordinate.galcen_distance.value, coordinate.galcen_distance.unit)) if not isinstance(radius, u.Quantity): radius *= u.deg logging.warning("No units specified for radius, assuming" "{}".format(radius.unit)) resolution = (l_resolution, b_resolution, distance_resolution) c_gal = coordinate.transform_to('galactic') L_range = [c_gal.l.to(u.deg) - radius.to(u.deg)*1.2, c_gal.l.to(u.deg) + radius.to(u.deg)*1.2] B_range = [c_gal.b.to(u.deg) - radius.to(u.deg)*1.2, c_gal.b.to(u.deg) + radius.to(u.deg)*1.2] cube = EmissionCube.create_DK20(resolution = resolution, L_range = L_range, B_range = B_range, **kwargs) print(cube) return cube.extract_beam(coordinate = c_gal, radius = radius, reduce_cube = False)

73e3a95266788e4ab3dd425d648d0fd3c527d917

py

Python

examples/kernelml-enhanced-ridge-example.py

Freedomtowin/kernel_optimizer

2676044e0f287cd8dda8f9f92a6d3813544965e4

2019-10-03T18:02:29.000Z

2021-08-09T09:30:33.000Z

examples/kernelml-enhanced-ridge-example.py

Freedomtowin/kernel_optimizer

2676044e0f287cd8dda8f9f92a6d3813544965e4

2019-12-11T09:46:09.000Z

2021-06-17T00:45:16.000Z

examples/kernelml-enhanced-ridge-example.py

Freedomtowin/kernel_optimizer

2676044e0f287cd8dda8f9f92a6d3813544965e4

2020-04-18T10:41:56.000Z

2021-06-17T02:06:14.000Z

import kernelml from numba import jit,njit, prange, types # import seaborn import numpy as np import pandas as pd from matplotlib import pyplot as plt from sklearn import linear_model import numpy train=pd.read_csv("DATA/kc_house_train_data.csv",dtype = {'bathrooms':float, 'waterfront':int, 'sqft_above':int, 'sqft_living15':float, 'grade':int, 'yr_renovated':int, 'price':float, 'bedrooms':float, 'zipcode':str, 'long':float, 'sqft_lot15':float, 'sqft_living':float, 'floors':str, 'condition':int, 'lat':float, 'date':str, 'sqft_basement':int, 'yr_built':int, 'id':str, 'sqft_lot':int, 'view':int}) test=pd.read_csv("DATA/kc_house_test_data.csv",dtype = {'bathrooms':float, 'waterfront':int, 'sqft_above':int, 'sqft_living15':float, 'grade':int, 'yr_renovated':int, 'price':float, 'bedrooms':float, 'zipcode':str, 'long':float, 'sqft_lot15':float, 'sqft_living':float, 'floors':str, 'condition':int, 'lat':float, 'date':str, 'sqft_basement':int, 'yr_built':int, 'id':str, 'sqft_lot':int, 'view':int}) def sampler_uniform_distribution(kmldata): random_samples = kmldata.prior_random_samples variances = np.var(kmldata.update_history[:,:],axis=1).flatten() means = kmldata.best_weight_vector.flatten() # means = kmldata.update_history[:,-1].flatten() return np.vstack([np.random.uniform(mu-np.sqrt(sigma*12)/2,mu+np.sqrt(sigma*12)/2,(random_samples)) for sigma,mu in zip(variances,means)]) @jit('float64(float64[:,:], float64[:,:], float64[:,:])',nopython=True) def ridge_least_sqs_loss(x,y,w): alpha,w = w[0][0],w[1:] penalty = 0 value = 1 if alpha<value: penalty = 3*abs(value-alpha) if alpha<0: alpha=0 hypothesis = x.dot(w) loss = hypothesis-y return np.sum(loss**2)/len(y) + alpha*np.sum(w[1:]**2) + penalty*np.sum(w[1:]**2) @njit('float64[:](float64[:,:], float64[:,:], float64[:,:])',parallel=True) def map_losses(X,y,w_list): N = w_list.shape[1] resX = np.zeros(N) for i in prange(N): loss = ridge_least_sqs_loss(X,y,w_list[:,i:i+1]) resX[i] = loss return resX X_train = train[['sqft_living','bedrooms','bathrooms']].values y_train = train[['price']].values X_test = test[['sqft_living','bedrooms','bathrooms']].values y_test = test[['price']].values SST_train = np.sum((y_train-np.mean(y_train))**2) SST_test = np.sum((y_test-np.mean(y_test))**2) X_train = np.column_stack((np.ones(X_train.shape[0]),X_train)) X_test = np.column_stack((np.ones(X_test.shape[0]),X_test)) runs = 5 zscore = .09 simulation_factor = 100 volatility = 10 min_per_change=0.001 cycles = 100 volume = 100 kml = kernelml.KernelML( prior_sampler_fcn=None, posterior_sampler_fcn=sampler_uniform_distribution, intermediate_sampler_fcn=None, mini_batch_sampler_fcn=None, parameter_transform_fcn=None, loss_calculation_fcn=map_losses, batch_size=None) # args_list = [np.array([1,2,3],dtype=np.float64)] args_list = [] kml.optimize(X_train,y_train, args=args_list, number_of_parameters=5, number_of_realizations=runs, number_of_random_simulations = simulation_factor, number_of_cycles=cycles, update_volatility = volatility, update_volume=volume, convergence_z_score=zscore, prior_uniform_low=1, prior_uniform_high=2, print_feedback=True) #Get model performance on validation data w = kml.model.get_best_param() alpha,w = w[0],w[1:].reshape(-1,1) print('alpha:',alpha) print('w:',w) yp_train = X_train.dot(w) SSE_train = np.sum((y_train-yp_train)**2) yp_test = X_test.dot(w) SSE_test = np.sum((y_test-yp_test)**2) #Compare to sklearn.Ridge(alpha=1) model = linear_model.Ridge(alpha=1) model.fit(X_train,y_train) print('kernelml validation r-squared:',1-SSE_test/SST_test) print('scikit-learn validation r-squared:',model.score(X_test,y_test))

73e3ce5423e1dc84cd4febe940a90fee9f6ff775

py

Python

Python/Games/rpcV3.py

erikhayton/Portfolio

ee1e649fe6340911968f27207ad7253d3ddc2cde

Python/Games/rpcV3.py

erikhayton/Portfolio

ee1e649fe6340911968f27207ad7253d3ddc2cde

Python/Games/rpcV3.py

erikhayton/Portfolio

ee1e649fe6340911968f27207ad7253d3ddc2cde

import random # more specified imput # from random import randint human_wins=0 computer_wins=0 winning_score=2 while human_wins < winning_score and computer_wins < winning_score: print(f"Puny Human Score: {human_wins} Computer Score: {computer_wins}") # shorter human = input("Puny human: Input rock, paper, or scissors! ").lower() if human =="quit" or human == "q": break rand_num = random.randint(0,2) # more specified # rand_num = randint(0,2) # computer if rand_num == 0: computer = "rock" elif rand_num == 1: computer = "paper" else: computer = "scissors" print("computer played: " + computer) # human if human == computer: print("Draw") elif human == "rock" and computer == "scissors": print("puny human wins") human_wins += 1 elif human == "paper" and computer == "rock": print("puny human wins") human_wins += 1 elif human == "scissors" and computer == "paper": print("puny human wins") human_wins += 1 else: print("computer wins") computer_wins += 1 print( "*\n" "*\n" "*") print(f"FINAL SCORES: Puny Human: {human_wins} Computer: {computer_wins}") print( "*\n" "*\n" "*") if human_wins > computer_wins: print("Congrats Dumbass! Now you're on Technology's Hit List!") elif human_wins == computer_wins: print("You'll never amount to anything...Quitter!") else: print("You have pleased the Google overlords...Loser!") print( "\n" )

73e3d3f4cc2f3697c5345fccbd0d3d02956e6f1e

py

Python

app/core/migrations/0010_auto_20201203_2152.py

aakash-sheth/django-rest-api

5dcd5eb03c5efccd7011fd6417b50e2ceacff243

app/core/migrations/0010_auto_20201203_2152.py

aakash-sheth/django-rest-api

5dcd5eb03c5efccd7011fd6417b50e2ceacff243

app/core/migrations/0010_auto_20201203_2152.py

aakash-sheth/django-rest-api

5dcd5eb03c5efccd7011fd6417b50e2ceacff243

# Generated by Django 3.1.3 on 2020-12-03 21:52 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('core', '0009_auto_20201202_2144'), ] operations = [ migrations.RenameField( model_name='unemploymentbyindustry', old_name='indutry_id', new_name='industry_id', ), ]

73e41ea9d55c6019ac5f31281c270fec65e9ea79

py

Python

tests/test_validate.py

jeremylinlin/operator-courier

9e53cee85e02e3ab54cfbef5770cfd58b4895c3b

2019-04-09T04:52:16.000Z

2019-04-09T04:52:16.000Z

tests/test_validate.py

jeremylinlin/operator-courier

9e53cee85e02e3ab54cfbef5770cfd58b4895c3b

tests/test_validate.py

jeremylinlin/operator-courier

9e53cee85e02e3ab54cfbef5770cfd58b4895c3b

import yaml import pytest from operatorcourier.validate import ValidateCmd from operatorcourier.format import unformat_bundle from testfixtures import LogCapture @pytest.mark.parametrize('bundle,expected_validation_results_dict', [ ("tests/test_files/bundles/verification/noicon.valid.bundle.yaml", {'errors': [], 'warnings': [ 'csv metadata.annotations.categories not defined', 'csv spec.icon not defined']}), ("tests/test_files/bundles/verification/nocrd.valid.bundle.yaml", {'errors': [], 'warnings': [ 'csv spec.icon not defined', 'csv spec.maturity not defined']}), ]) def test_valid_bundles(bundle, expected_validation_results_dict): valid, validation_results_dict = get_validation_results(bundle) assert valid is True assert validation_results_dict == expected_validation_results_dict @pytest.mark.parametrize('bundle,expected_validation_results_dict', [ ("tests/test_files/bundles/verification/nopkg.invalid.bundle.yaml", {'errors': ['Bundle does not contain any packages.'], 'warnings': [ 'csv metadata.annotations.categories not defined', 'csv spec.icon not defined']}), ("tests/test_files/bundles/verification/no-data-key.bundle.yaml", {'errors': ['Bundle does not contain any clusterServiceVersions.', 'Bundle does not contain any packages.'], 'warnings': []}), ("tests/test_files/bundles/verification/csvinstallspecnotlists.invalid.bundle.yaml", {'errors': ['csv spec.install.spec.deployments should be a list', 'csv spec.install.spec.permissions should be a list', 'csv spec.install.spec.clusterPermissions should be a list'], 'warnings': ['csv spec.icon not defined']}), ("tests/test_files/bundles/verification/" "csvmissinginstallattributes.invalid.bundle.yaml", {'errors': ['csv spec.install.strategy not defined', 'csv spec.install.spec not defined'], 'warnings': ['csv spec.icon not defined']}), ("tests/test_files/bundles/verification/" "csvinstallstrategywrongvalue.invalid.bundle.yaml", {'errors': ["csv spec.install.strategy must be one of ['deployment']"], 'warnings': ['csv spec.icon not defined']}), ]) def test_invalid_bundle(bundle, expected_validation_results_dict): valid, validation_results_dict = get_validation_results(bundle) assert valid is False assert validation_results_dict == expected_validation_results_dict @pytest.mark.parametrize('bundle,expected_validation_results_dict', [ ("tests/test_files/bundles/verification/valid.bundle.yaml", {'errors': [], 'warnings': []}), ]) def test_ui_valid_bundle_io(bundle, expected_validation_results_dict): valid, validation_results_dict = get_ui_validation_results(bundle) assert valid is True assert validation_results_dict == expected_validation_results_dict @pytest.mark.parametrize('bundle,expected_validation_results_dict', [ ("tests/test_files/bundles/verification/ui.invalid.bundle.yaml", {'errors': [ "csv.spec.links must be a list of name & url pairs.", "spec.version invalid is not a valid semver " "(example of a valid semver is: 1.0.12)", "metadata.annotations.capabilities invalid " "is not a valid capabilities level", "spec.icon[0].mediatype image/invalid is not " "a valid mediatype. It must be one of \"image/gif\", " "\"image/jpeg\", \"image/png\", \"image/svg+xml\"", "category invalid is not a valid category", "UI validation failed to verify that required fields " "for operatorhub.io are properly formatted." ], 'warnings': []}), ]) def test_ui_invalid_bundle_io(bundle, expected_validation_results_dict): valid, validation_results_dict = get_ui_validation_results(bundle) assert valid is False assert validation_results_dict == expected_validation_results_dict @pytest.mark.parametrize('file_name,correct_repo_name', [ ("tests/test_files/bundles/verification/valid.bundle.yaml", 'marketplace'), ("tests/test_files/bundles/verification/nocrd.valid.bundle.yaml", 'svcat'), ]) def test_valid_bundles_with_package_name_and_repo_name_match(file_name, correct_repo_name): with open(file_name) as f: bundle = yaml.safe_load(f) bundle = unformat_bundle(bundle) valid, _ = ValidateCmd().validate(bundle, repository=correct_repo_name) assert valid @pytest.mark.parametrize('file_name,wrong_repo_name', [ ("tests/test_files/bundles/verification/valid.bundle.yaml", 'wrong-repo-name'), ("tests/test_files/bundles/verification/nocrd.valid.bundle.yaml", 'wrong-repo-name'), ]) def test_valid_bundles_with_package_name_and_repo_name_mismatch(file_name, wrong_repo_name): with open(file_name) as f: bundle = yaml.safe_load(f) bundle = unformat_bundle(bundle) valid, _ = ValidateCmd().validate(bundle, repository=wrong_repo_name) assert not valid @pytest.mark.parametrize('file_name', [ "tests/test_files/bundles/verification/multiplepkgs.invalid.bundle.yaml", ]) def test_valid_bundles_with_multiple_packages(file_name): with open(file_name) as f: bundle = yaml.safe_load(f) bundle = unformat_bundle(bundle) with LogCapture() as logs: valid, _ = ValidateCmd().validate(bundle) assert not valid logs.check_present(('operatorcourier.validate', 'ERROR', 'Only 1 package is expected to exist per bundle, but got 2.')) def get_ui_validation_results(bundle): return ValidateCmd(ui_validate_io=True).validate(get_bundle(bundle)) def get_validation_results(bundle): return ValidateCmd().validate(get_bundle(bundle)) def get_bundle(bundle): with open(bundle) as f: bundle = yaml.safe_load(f) return unformat_bundle(bundle) @pytest.mark.parametrize('bundleFile,logInfo', [ ('tests/test_files/bundles/verification/nopkg.invalid.bundle.yaml', ('operatorcourier.validate', 'ERROR', 'Bundle does not contain any packages.')), ('tests/test_files/bundles/verification/crdmissingkindfield.invalid.bundle.yaml', ('operatorcourier.validate', 'ERROR', 'crd spec.names.kind not defined.')), ('tests/test_files/bundles/verification/crdmissingpluralfield.invalid.bundle.yaml', ('operatorcourier.validate', 'ERROR', 'crd spec.names.plural not defined.')), ('tests/test_files/bundles/verification/crdmissingversionfield.invalid.bundle.yaml', ('operatorcourier.validate', 'ERROR', 'crd spec.version not defined.')), ('tests/test_files/bundles/verification/csvmissingkindfield.invalid.bundle.yaml', ('operatorcourier.validate', 'ERROR', 'kind not defined for item in spec.customresourcedefinitions.')), ('tests/test_files/bundles/verification/csvmissingnamefield.invalid.bundle.yaml', ('operatorcourier.validate', 'ERROR', 'name not defined for item in spec.customresourcedefinitions.')), ('tests/test_files/bundles/verification/csvmissingversionfield.invalid.bundle.yaml', ('operatorcourier.validate', 'ERROR', 'version not defined for item in spec.customresourcedefinitions.')), ]) def test_invalid_bundle_missing_fields(bundleFile, logInfo): _test_invalid_bundle_with_log(bundleFile, logInfo) @pytest.mark.parametrize('bundleFile,logInfo', [ ('tests/test_files/bundles/verification/csvcrdfieldmismatch1.invalid.bundle.yaml', ('operatorcourier.validate', 'ERROR', 'CRD.spec.names.kind does not match CSV.spec.crd.owned.kind')), ('tests/test_files/bundles/verification/csvcrdfieldmismatch2.invalid.bundle.yaml', ('operatorcourier.validate', 'ERROR', 'CRD.spec.version does not match CSV.spec.crd.owned.version')), ('tests/test_files/bundles/verification/csvcrdfieldmismatch3.invalid.bundle.yaml', ('operatorcourier.validate', 'ERROR', '`CRD.spec.names.plural`.`CRD.spec.group` does not match CSV.spec.crd.owned.name')), ]) def test_invalid_bundle_crd_csv_fields_mismatch(bundleFile, logInfo): _test_invalid_bundle_with_log(bundleFile, logInfo) def _test_invalid_bundle_with_log(bundleFile, logInfo): with open(bundleFile) as f: bundle = yaml.safe_load(f) bundle = unformat_bundle(bundle) module, level, message = logInfo[0], logInfo[1], logInfo[2] with LogCapture() as logs: valid, _ = ValidateCmd().validate(bundle) assert not valid # check if the input log info is present among all logs captured logs.check_present( (module, level, message), )

73e437db070b903661a65cf95b8cb3437f67e6ac

py

Python

pypiserver/__init__.py

Y-Ge/pypiserver

f6dce96ee7c82a5d4db6cb501f0cd5af2a6d858e

2019-03-18T17:47:54.000Z

2019-03-18T17:47:54.000Z

pypiserver/__init__.py

DalavanCloud/pypiserver

537034eb4323f0a84e66cb8d73e3b152c9d08744

pypiserver/__init__.py

DalavanCloud/pypiserver

537034eb4323f0a84e66cb8d73e3b152c9d08744

import os import re as _re import sys version = __version__ = "1.2.7" __version_info__ = tuple(_re.split('[.-]', __version__)) __updated__ = "2019-01-31 18:43:27" __title__ = "pypiserver" __summary__ = "A minimal PyPI server for use with pip/easy_install." __uri__ = "https://github.com/pypiserver/pypiserver" class Configuration(object): """ .. see:: config-options: :func:`pypiserver.configure()` """ def __init__(self, **kwds): vars(self).update(kwds) def __repr__(self, *args, **kwargs): return 'Configuration(**%s)' % vars(self) def __str__(self, *args, **kwargs): return 'Configuration:\n%s' % '\n'.join('%20s = %s' % (k, v) for k, v in sorted(vars(self).items())) def update(self, props): d = props if isinstance(props, dict) else vars(props) vars(self).update(d) DEFAULT_SERVER = "auto" def default_config( root=None, host="0.0.0.0", port=8080, server=DEFAULT_SERVER, redirect_to_fallback=True, fallback_url=None, authenticated=['update'], password_file=None, overwrite=False, hash_algo='md5', verbosity=1, log_file=None, log_frmt="%(asctime)s|%(name)s|%(levelname)s|%(thread)d|%(message)s", log_req_frmt="%(bottle.request)s", log_res_frmt="%(status)s", log_err_frmt="%(body)s: %(exception)s \n%(traceback)s", welcome_file=None, cache_control=None, auther=None, VERSION=__version__): """ Fetch default-opts with overridden kwds, capable of starting-up pypiserver. Does not validate overridden options. Example usage:: kwds = pypiserver.default_config(<override_kwds> ...) ## More modifications on kwds. pypiserver.app(**kwds)``. Kwds correspond to same-named cmd-line opts, with '-' --> '_' substitution. Non standard args are described below: :param return_defaults_only: When `True`, returns defaults, otherwise, configures "runtime" attributes and returns also the "packages" found in the roots. :param root: A list of paths, derived from the packages specified on cmd-line. If `None`, defaults to '~/packages'. :param redirect_to_fallback: see :option:`--disable-fallback` :param authenticated: see :option:`--authenticate` :param password_file: see :option:`--passwords` :param log_file: see :option:`--log-file` Not used, passed here for logging it. :param log_frmt: see :option:`--log-frmt` Not used, passed here for logging it. :param callable auther: An API-only options that if it evaluates to a callable, it is invoked to allow access to protected operations (instead of htpaswd mechanism) like that:: auther(username, password): bool When defined, `password_file` is ignored. :param host: see :option:`--interface` Not used, passed here for logging it. :param port: see :option:`--port` Not used, passed here for logging it. :param server: see :option:`--server` Not used, passed here for logging it. :param verbosity: see :option:`-v` Not used, passed here for logging it. :param VERSION: Not used, passed here for logging it. :return: a dict of defaults """ return locals() def app(**kwds): """ :param dict kwds: Any overrides for defaults, as fetched by :func:`default_config()`. Check the docstring of this function for supported kwds. """ from . import core _app = __import__("_app", globals(), locals(), ["."], 1) sys.modules.pop('pypiserver._app', None) kwds = default_config(**kwds) config, packages = core.configure(**kwds) _app.config = config _app.packages = packages _app.app.module = _app # HACK for testing. return _app.app def str2bool(s, default): if s is not None and s != '': return s.lower() not in ("no", "off", "0", "false") return default def _str_strip(string): """Provide a generic strip method to pass as a callback.""" return string.strip() def paste_app_factory(global_config, **local_conf): """Parse a paste config and return an app.""" def upd_conf_with_bool_item(conf, attr, sdict): conf[attr] = str2bool(sdict.pop(attr, None), conf[attr]) def upd_conf_with_str_item(conf, attr, sdict): value = sdict.pop(attr, None) if value is not None: conf[attr] = value def upd_conf_with_int_item(conf, attr, sdict): value = sdict.pop(attr, None) if value is not None: conf[attr] = int(value) def upd_conf_with_list_item(conf, attr, sdict, sep=' ', parse=_str_strip): values = sdict.pop(attr, None) if values: conf[attr] = list(filter(None, map(parse, values.split(sep)))) def _make_root(root): root = root.strip() if root.startswith("~"): return os.path.expanduser(root) return root c = default_config() upd_conf_with_bool_item(c, 'overwrite', local_conf) upd_conf_with_bool_item(c, 'redirect_to_fallback', local_conf) upd_conf_with_list_item(c, 'authenticated', local_conf, sep=' ') upd_conf_with_list_item(c, 'root', local_conf, sep='\n', parse=_make_root) upd_conf_with_int_item(c, 'verbosity', local_conf) str_items = [ 'fallback_url', 'hash_algo', 'log_err_frmt', 'log_file', 'log_frmt', 'log_req_frmt', 'log_res_frmt', 'password_file', 'welcome_file' ] for str_item in str_items: upd_conf_with_str_item(c, str_item, local_conf) # cache_control is undocumented; don't know what type is expected: # upd_conf_with_str_item(c, 'cache_control', local_conf) return app(**c) def _logwrite(logger, level, msg): if msg: line_endings = ['\r\n', '\n\r', '\n'] for le in line_endings: if msg.endswith(le): msg = msg[:-len(le)] if msg: logger.log(level, msg)

73e459323254e7d423c487948b57b38b79861676

py

Python

code/generation-model/progressive_transformer/vocabulary.py

Merterm/-Modeling-Intensification-for-SLG

800fff3d3c7bacc86c1db8382f7c2e68d2f0c074

2022-03-14T15:52:09.000Z

2022-03-30T11:42:47.000Z

code/generation-model/progressive_transformer/vocabulary.py

Merterm/Modeling-Intensification-for-SLG

800fff3d3c7bacc86c1db8382f7c2e68d2f0c074

code/generation-model/progressive_transformer/vocabulary.py

Merterm/Modeling-Intensification-for-SLG

800fff3d3c7bacc86c1db8382f7c2e68d2f0c074

# coding: utf-8 """ Vocabulary module """ from collections import defaultdict, Counter from typing import List import numpy as np from torchtext.data import Dataset from constants import UNK_TOKEN, DEFAULT_UNK_ID, \ EOS_TOKEN, BOS_TOKEN, PAD_TOKEN class Vocabulary: """ Vocabulary represents mapping between tokens and indices. """ def __init__(self, tokens: List[str] = None, file: str = None) -> None: # don't rename stoi and itos since needed for torchtext # warning: stoi grows with unknown tokens, don't use for saving or size # special symbols self.specials = [UNK_TOKEN, PAD_TOKEN, BOS_TOKEN, EOS_TOKEN] self.stoi = defaultdict(DEFAULT_UNK_ID) self.itos = [] if tokens is not None: self._from_list(tokens) elif file is not None: self._from_file(file) def _from_list(self, tokens: List[str] = None) -> None: """ Make vocabulary from list of tokens. Tokens are assumed to be unique and pre-selected. Special symbols are added if not in list. :param tokens: list of tokens """ self.add_tokens(tokens=self.specials+tokens) assert len(self.stoi) == len(self.itos) def _from_file(self, file: str) -> None: """ Make vocabulary from contents of file. File format: token with index i is in line i. :param file: path to file where the vocabulary is loaded from """ tokens = [] with open(file, "r") as open_file: for line in open_file: tokens.append(line.strip("\n")) self._from_list(tokens) def __str__(self) -> str: return self.stoi.__str__() def to_file(self, file: str) -> None: """ Save the vocabulary to a file, by writing token with index i in line i. :param file: path to file where the vocabulary is written """ with open(file, "w") as open_file: for t in self.itos: open_file.write("{}\n".format(t)) def add_tokens(self, tokens: List[str]) -> None: """ Add list of tokens to vocabulary :param tokens: list of tokens to add to the vocabulary """ for t in tokens: new_index = len(self.itos) # add to vocab if not already there if t not in self.itos: self.itos.append(t) self.stoi[t] = new_index def is_unk(self, token: str) -> bool: """ Check whether a token is covered by the vocabulary :param token: :return: True if covered, False otherwise """ return self.stoi[token] == DEFAULT_UNK_ID() def __len__(self) -> int: return len(self.itos) def array_to_sentence(self, array: np.array, cut_at_eos=True) -> List[str]: """ Converts an array of IDs to a sentence, optionally cutting the result off at the end-of-sequence token. :param array: 1D array containing indices :param cut_at_eos: cut the decoded sentences at the first <eos> :return: list of strings (tokens) """ sentence = [] for i in array: s = self.itos[i] if cut_at_eos and s == EOS_TOKEN: break sentence.append(s) return sentence def arrays_to_sentences(self, arrays: np.array, cut_at_eos=True) \ -> List[List[str]]: """ Convert multiple arrays containing sequences of token IDs to their sentences, optionally cutting them off at the end-of-sequence token. :param arrays: 2D array containing indices :param cut_at_eos: cut the decoded sentences at the first <eos> :return: list of list of strings (tokens) """ sentences = [] for array in arrays: sentences.append( self.array_to_sentence(array=array, cut_at_eos=cut_at_eos)) return sentences def build_vocab(field: str, max_size: int, min_freq: int, dataset: Dataset, vocab_file: str = None) -> Vocabulary: """ Builds vocabulary for a torchtext `field` from given`dataset` or `vocab_file`. :param field: attribute e.g. "src" :param max_size: maximum size of vocabulary :param min_freq: minimum frequency for an item to be included :param dataset: dataset to load data for field from :param vocab_file: file to store the vocabulary, if not None, load vocabulary from here :return: Vocabulary created from either `dataset` or `vocab_file` """ if vocab_file is not None: # load it from file vocab = Vocabulary(file=vocab_file) else: # create newly def filter_min(counter: Counter, min_freq: int): """ Filter counter by min frequency """ filtered_counter = Counter({t: c for t, c in counter.items() if c >= min_freq}) return filtered_counter def sort_and_cut(counter: Counter, limit: int): """ Cut counter to most frequent, sorted numerically and alphabetically""" # sort by frequency, then alphabetically tokens_and_frequencies = sorted(counter.items(), key=lambda tup: tup[0]) tokens_and_frequencies.sort(key=lambda tup: tup[1], reverse=True) vocab_tokens = [i[0] for i in tokens_and_frequencies[:limit]] return vocab_tokens tokens = [] for i in dataset.examples: if field == "src": tokens.extend(i.src) elif field == "trg": tokens.extend(i.trg) counter = Counter(tokens) if min_freq > -1: counter = filter_min(counter, min_freq) vocab_tokens = sort_and_cut(counter, max_size) assert len(vocab_tokens) <= max_size vocab = Vocabulary(tokens=vocab_tokens) assert len(vocab) <= max_size + len(vocab.specials) assert vocab.itos[DEFAULT_UNK_ID()] == UNK_TOKEN # check for all except for UNK token whether they are OOVs for s in vocab.specials[1:]: assert not vocab.is_unk(s) return vocab

73e46541472028aa4391bf5a949dc70d6afdbbb0

py

Python

venv/lib/python3.6/site-packages/kubernetes/__init__.py

DiptoChakrabarty/Kube-Automate

2072d1aadd58eb405c7308ff5cfecbf50300ead3

venv/lib/python3.6/site-packages/kubernetes/__init__.py

DiptoChakrabarty/Kube-Automate

2072d1aadd58eb405c7308ff5cfecbf50300ead3

venv/lib/python3.6/site-packages/kubernetes/__init__.py

DiptoChakrabarty/Kube-Automate

2072d1aadd58eb405c7308ff5cfecbf50300ead3

# Copyright 2016 The Kubernetes Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. __project__ = 'kubernetes' # The version is auto-updated. Please do not edit. __version__ = "11.0.0" import kubernetes.client import kubernetes.config import kubernetes.dynamic import kubernetes.watch import kubernetes.stream import kubernetes.utils

73e483775b429775c6f14af3bf69a26e36441403

py

Python

appcli/configuration/configuration_dir_state.py

david-homley/appcli

08e336082fb5f4b79169b9c2cd7561bf489e28ac

2019-12-06T00:17:07.000Z

2021-07-23T10:52:34.000Z

appcli/configuration/configuration_dir_state.py

david-homley/appcli

08e336082fb5f4b79169b9c2cd7561bf489e28ac

2019-10-10T04:04:31.000Z

2022-03-28T03:37:52.000Z

appcli/configuration/configuration_dir_state.py

david-homley/appcli

08e336082fb5f4b79169b9c2cd7561bf489e28ac

2019-10-04T02:56:35.000Z

2021-05-07T02:10:27.000Z

#!/usr/bin/env python3 # # -*- coding: utf-8 -*- """ States that the configuration directory can be in. ________________________________________________________________________________ Created by brightSPARK Labs www.brightsparklabs.com """ # standard libraries from collections import defaultdict from pathlib import Path from typing import Iterable # vendor libraries import git # local libraries from appcli.commands.appcli_command import AppcliCommand from appcli.functions import error_and_exit from appcli.git_repositories.git_repositories import ( ConfigurationGitRepository, GeneratedConfigurationGitRepository, ) from appcli.logger import logger # ------------------------------------------------------------------------------ # CLASSES # ------------------------------------------------------------------------------ class ConfigurationDirState: """The state of the configuration directory. This encapsulates both the 'conf' and 'generated' git-managed directories. This is the base class from which all the different 'state' classes of the configuration directory will inherit. """ def __init__(self, disallowed_command, disallowed_command_unless_forced) -> None: self.disallowed_command = disallowed_command self.disallowed_command_unless_forced = disallowed_command_unless_forced def verify_command_allowed(self, command: AppcliCommand, force: bool = False): if command in self.disallowed_command: error_and_exit(self.disallowed_command[command]) if command in self.disallowed_command_unless_forced and not force: error_and_exit( f"{self.disallowed_command_unless_forced[command]}" " If this command supports it, use '--force' to ignore error." ) logger.debug( f"Allowed command [{command}] with current configuration state [{self}], where force is [{force}]." ) class ConfigurationDirStateFactory: """Factory class to get the current ConfigurationDirState state class""" def get_state( configuration_dir: Path, generated_configuration_dir: Path, app_version: str, backup_dir: Path, ) -> ConfigurationDirState: if configuration_dir is None: return NoDirectoryProvidedConfigurationDirState() if backup_dir is None: return NoDirectoryProvidedBackupDirState() if not backup_dir.exists(): return BackupDirectoryDoesNotExist() if not ConfigurationDirStateFactory.__is_git_repo(configuration_dir): return UninitialisedConfigurationDirState() config_repo = ConfigurationGitRepository(configuration_dir) conf_version = config_repo.get_repository_version() if conf_version != app_version: error_message = f"Application requires migration. Configuration version [{conf_version}], Application version [{app_version}]." return RequiresMigrationConfigurationDirState(error_message) if not ConfigurationDirStateFactory.__is_git_repo(generated_configuration_dir): return UnappliedConfigurationDirState() gen_config_repo = GeneratedConfigurationGitRepository( generated_configuration_dir ) if config_repo.is_dirty(): if gen_config_repo.is_dirty(): return DirtyConfAndGenConfigurationDirState() return DirtyConfConfigurationDirState() if gen_config_repo.is_dirty(): return DirtyGenConfigurationDirState() if gen_config_repo.get_commit_count() > 1: return InvalidConfigurationDirState( f"Generated repository [{gen_config_repo.get_repo_path()}] has extra untracked git commits." ) return CleanConfigurationDirState() def __is_git_repo(path: Path): """Checks if the directory at the path is a git repository. Args: path (Path): Path to test. Returns: bool: True if the directory exists and is a git repo. Otherwise False. """ try: # If this doesn't raise an Exception, a git repo exists at this path. git.Repo(path) return True except (git.InvalidGitRepositoryError, git.exc.NoSuchPathError): return False class NoDirectoryProvidedConfigurationDirState(ConfigurationDirState): """Represents the configuration dir state where appcli doesn't know the path to configuration dir.""" def __init__(self) -> None: default_error_message = ( "No configuration directory provided to appcli. Run 'install'." ) disallowed_command = get_disallowed_command_from_allowed_commands( [AppcliCommand.INSTALL], default_error_message, ) disallowed_command_unless_forced = {} super().__init__(disallowed_command, disallowed_command_unless_forced) class NoDirectoryProvidedBackupDirState(ConfigurationDirState): """Represents the backup dir state where appcli doesn't know the path to backup dir.""" def __init__(self) -> None: disallowed_command = { AppcliCommand.BACKUP: "Cannot backup due to missing backup directory. Run 'install'.", AppcliCommand.RESTORE: "Cannot restore due to missing backup directory. Run 'install'.", AppcliCommand.VIEW_BACKUPS: "Cannot view backups due to missing backup directory. Run 'install'.", } disallowed_command_unless_forced = {} super().__init__(disallowed_command, disallowed_command_unless_forced) class BackupDirectoryDoesNotExist(ConfigurationDirState): """Represents the backup dir state where the backup directory does not exist.""" def __init__(self) -> None: disallowed_command = { AppcliCommand.RESTORE: "Cannot restore due to missing backup directory. Run 'backup'.", AppcliCommand.VIEW_BACKUPS: "Cannot view backups due to missing backup directory. Run 'backup'.", } disallowed_command_unless_forced = {} super().__init__(disallowed_command, disallowed_command_unless_forced) class UninitialisedConfigurationDirState(ConfigurationDirState): """Represents the configuration dir state where config directory hasn't been initialised.""" def __init__(self) -> None: default_error_message = "Cannot run command against uninitialised application. Run 'configure init'." disallowed_command = get_disallowed_command_from_allowed_commands( [ AppcliCommand.CONFIGURE_INIT, AppcliCommand.LAUNCHER, AppcliCommand.BACKUP, AppcliCommand.RESTORE, AppcliCommand.VIEW_BACKUPS, ], default_error_message, ) disallowed_command_unless_forced = {} super().__init__(disallowed_command, disallowed_command_unless_forced) class UnappliedConfigurationDirState(ConfigurationDirState): """Represents the configuration dir state where configuration hasn't been applied yet, i.e. the generated configuration doesn't exist.""" def __init__(self) -> None: disallowed_command = { AppcliCommand.CONFIGURE_INIT: "Cannot initialise an existing configuration.", AppcliCommand.SERVICE_START: "Cannot start services due to missing generated configuration. Run 'configure apply'.", AppcliCommand.SERVICE_SHUTDOWN: "Cannot stop services due to missing generated configuration. Run 'configure apply'.", AppcliCommand.SERVICE_LOGS: "Cannot get service logs due to missing generated configuration. Run 'configure apply'.", AppcliCommand.TASK_RUN: "Cannot run tasks due to missing generated configuration. Run 'configure apply'.", AppcliCommand.ORCHESTRATOR: "Cannot run orchestrator commands due to missing generated configuration. Run 'configure apply'.", } disallowed_command_unless_forced = {} super().__init__(disallowed_command, disallowed_command_unless_forced) class CleanConfigurationDirState(ConfigurationDirState): """Represents the configuration dir state where config and generated directories both exist and are in a clean state.""" def __init__(self) -> None: disallowed_command = { AppcliCommand.CONFIGURE_INIT: "Cannot initialise an existing configuration.", } disallowed_command_unless_forced = {} super().__init__(disallowed_command, disallowed_command_unless_forced) class DirtyConfConfigurationDirState(ConfigurationDirState): """Represents the configuration dir state where config directory is dirty.""" def __init__(self) -> None: disallowed_command = { AppcliCommand.CONFIGURE_INIT: "Cannot initialise an existing configuration.", AppcliCommand.MIGRATE: "Cannot migrate with a dirty configuration. Run 'configure apply'.", } disallowed_command_unless_forced = { AppcliCommand.SERVICE_START: "Cannot start with dirty configuration. Run 'configure apply'.", AppcliCommand.TASK_RUN: "Cannot run task with dirty configuration. Run 'configure apply'.", AppcliCommand.ORCHESTRATOR: "Cannot run orchestrator tasks with dirty configuration. Run 'configure apply'.", } super().__init__(disallowed_command, disallowed_command_unless_forced) class DirtyGenConfigurationDirState(ConfigurationDirState): """Represents the configuration dir state where generated directory is dirty.""" def __init__(self) -> None: disallowed_command = { AppcliCommand.CONFIGURE_INIT: "Cannot initialise an existing configuration.", AppcliCommand.MIGRATE: "Cannot migrate with a dirty generated configuration. Run 'configure apply'.", } disallowed_command_unless_forced = { AppcliCommand.CONFIGURE_APPLY: "Cannot 'configure apply' over a dirty generated directory as it will overwrite existing modifications.", AppcliCommand.SERVICE_START: "Cannot start service with dirty generated configuration. Run 'configure apply'.", AppcliCommand.TASK_RUN: "Cannot run task with dirty generated configuration. Run 'configure apply'.", AppcliCommand.ORCHESTRATOR: "Cannot run orchestrator tasks with dirty generated configuration. Run 'configure apply'.", } super().__init__(disallowed_command, disallowed_command_unless_forced) class DirtyConfAndGenConfigurationDirState(ConfigurationDirState): """Represents the configuration dir state where both the conf and generated directory are dirty.""" def __init__(self) -> None: disallowed_command = { AppcliCommand.CONFIGURE_INIT: "Cannot initialise an existing configuration.", AppcliCommand.MIGRATE: "Cannot migrate with a dirty generated configuration. Run 'configure apply'.", } disallowed_command_unless_forced = { AppcliCommand.CONFIGURE_APPLY: "Cannot 'configure apply' over a dirty generated directory as it will overwrite existing modifications.", AppcliCommand.SERVICE_START: "Cannot start service with dirty generated configuration. Run 'configure apply'.", AppcliCommand.TASK_RUN: "Cannot run task with dirty generated configuration. Run 'configure apply'.", AppcliCommand.ORCHESTRATOR: "Cannot run orchestrator tasks with dirty generated configuration. Run 'configure apply'.", } super().__init__(disallowed_command, disallowed_command_unless_forced) class RequiresMigrationConfigurationDirState(ConfigurationDirState): """Represents the configuration dir state where configuration and application versions are misaligned.""" def __init__(self, error: str) -> None: disallowed_command = get_disallowed_command_from_allowed_commands( [AppcliCommand.MIGRATE], error ) disallowed_command_unless_forced = {} super().__init__(disallowed_command, disallowed_command_unless_forced) class InvalidConfigurationDirState(ConfigurationDirState): """Represents the configuration dir state where configuration is invalid and incompatible with appcli.""" def __init__(self, error: str) -> None: default_error_message = f"Invalid configuration state, this error must be rectified before continuing. {error}" # Remove the 'VIEW_BACKUPS' and 'RESTORE' commands from the set of 'disallowed' commands so that we can add them # as 'disallowed unless forced' commands disallowed_command = get_disallowed_command_from_allowed_commands( [AppcliCommand.VIEW_BACKUPS, AppcliCommand.RESTORE], default_error_message ) disallowed_command_unless_forced = { AppcliCommand.VIEW_BACKUPS: default_error_message, AppcliCommand.RESTORE: default_error_message, } super().__init__(disallowed_command, disallowed_command_unless_forced) def get_disallowed_command_from_allowed_commands( allowed_commands: Iterable[AppcliCommand], error_message: str ) -> dict: """Given an Iterable of allowed appcli commands, generates the dict of disallowed commands. Args: allowed_commands (Iterable[AppcliCommand]): Allowed commands. error_message (str): Error message for disallowed commands. Returns: dict: [description] """ disallowed_commands = dict(defaultdict.fromkeys(list(AppcliCommand), error_message)) for command in allowed_commands: disallowed_commands.pop(command, None) return disallowed_commands

73e49410c113212cc0d6966e28424954d6c36c5c

py

Python

tests/script_crawl.py

dimichxp/grab

5fedee3009c18c8a1139b0f82736ebe473021b46

2015-01-01T08:47:33.000Z

2022-03-21T05:02:55.000Z

tests/script_crawl.py

dimichxp/grab

5fedee3009c18c8a1139b0f82736ebe473021b46