fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

f723b4692781630404d7488f4a6f1b33fb840d4b

2,190

py

Python

newssimilarity/model/article.py

imackerracher/NewsSimilarity

2e6a85dc9e95ef94bec2339987950f4e88f5d909

[ "Apache-2.0" ]

null

newssimilarity/model/article.py

imackerracher/NewsSimilarity

2e6a85dc9e95ef94bec2339987950f4e88f5d909

[ "Apache-2.0" ]

null

newssimilarity/model/article.py

imackerracher/NewsSimilarity

2e6a85dc9e95ef94bec2339987950f4e88f5d909

[ "Apache-2.0" ]

null

""" Article object, read in from json """ class Article(object): def __init__(self, head, lead, body, date, time, writers, publisher, source_outlet, additional_information, annotation_list, feature_list, raw_article): """ :param head: Headline of the article :param lead: Lead paragraph of the article :param body: Rest of the article :param date: Publishing date of the article. If article was modified, the latest date :param time: Publishing time of the article. If article was modified, the latest time :param writers: The writers that contributed to the article :param publisher: The news outlet that published the article :param source_outlet: If the publisher took the article from a different source, it will be contained in this variable :param additional_information: E.g.: "...contributed to this report" :param annotation_list: A list containing all the annotations as annotation objects :param feature_list: A list containing all the features, as feature objects :param raw_article: The raw article without any processing, etc. (contains the annotations as well) """ self.head = head self.lead = lead self.body = body self.date = date self.time = time self.writers = writers self.publisher = publisher self.source_outlet = source_outlet self.additional_information = additional_information self.annotation_list = annotation_list self.feature_list = feature_list self.raw_article = raw_article def get_instances(self, instance_type): """ Utility function to return a list of instances :param instance_type: Type of instance (e.g. part of speech) :return: List with instances """ instances = [instance for feature in self.feature_list if feature.feature_name == instance_type for instance in feature.feature_instance_list] return instances

40.555556

117

0.637443

class Article(object): def __init__(self, head, lead, body, date, time, writers, publisher, source_outlet, additional_information, annotation_list, feature_list, raw_article): self.head = head self.lead = lead self.body = body self.date = date self.time = time self.writers = writers self.publisher = publisher self.source_outlet = source_outlet self.additional_information = additional_information self.annotation_list = annotation_list self.feature_list = feature_list self.raw_article = raw_article def get_instances(self, instance_type): instances = [instance for feature in self.feature_list if feature.feature_name == instance_type for instance in feature.feature_instance_list] return instances

true

f723b46aad1421a2e114cd58b3973ff37b96a76a

3,323

py

Python

plugins/mustGatherAccessor.py

rvanderp3/mg-helper

349ac88e52bb3895044cb3e1f30046522cad9b0c

[ "Apache-2.0" ]

null

plugins/mustGatherAccessor.py

rvanderp3/mg-helper

349ac88e52bb3895044cb3e1f30046522cad9b0c

[ "Apache-2.0" ]

null

plugins/mustGatherAccessor.py

rvanderp3/mg-helper

349ac88e52bb3895044cb3e1f30046522cad9b0c

[ "Apache-2.0" ]

null

import yaml import os.path from os import path import tarfile class MustGatherAccessor: tarcache = None tar = None name = "must-gather accessor" def __init__ (self,filename): self.filename = filename def readfile(self): if path.isdir(self.filename): pass else: try: # attempt to load a tar.gz file self.tar = tarfile.open(self.filename,"r:gz") except ValueError: try: self.tar = tarfile.open(self.filename,"r") except ValueError: raise self.buildTarCache(self.tar) def buildTarCache(self,tar): self.tarcache = {} for member in tar.getmembers(): if member.isfile() != True: continue # sanitize the paths - this will make a life a little easier for our plugins splits = member.name.split("/") if splits[0].startswith("must-gather"): splits.remove(splits[0]) first=True member.name="" for part in splits: if first: first = False else: member.name+="/" member.name+=part self.tarcache[member.name] = member def getValueFromObj(self,obj,*props): if obj != None: for prop in props: if prop in obj: obj = obj[prop] else: obj = None break return obj def getFileContent(self,thepath): content = None if self.tarcache == None: if path.exists(thepath): f = open(thepath,"r") content = f.read() else: f=self.tar.extractfile(thepath) content = f.read() return content def getEntriesFromPath(self,path): entries = [] if self.tarcache != None: for key in self.tarcache: if path in key: entries.append(key) else: fullPath = os.path.join(self.filename,path) if os.path.exists(fullPath): pathsEntries = os.listdir(fullPath) for entry in pathsEntries: foundPath = os.path.join(fullPath,entry) if(os.path.isfile(foundPath)): entries.append(foundPath) return entries def getDirsFromPath(self,path): entries = [] if self.tarcache != None: for key in self.tarcache: if path in key: entries.append(key) else: fullPath = os.path.join(self.filename,path) if os.path.exists(fullPath): pathsEntries = os.listdir(fullPath) for entry in pathsEntries: foundPath = os.path.join(fullPath,entry) if(os.path.isdir(foundPath)): entries.append(foundPath) return entries def parseYaml(self, content): return yaml.safe_load(content)

31.647619

88

0.476076

import yaml import os.path from os import path import tarfile class MustGatherAccessor: tarcache = None tar = None name = "must-gather accessor" def __init__ (self,filename): self.filename = filename def readfile(self): if path.isdir(self.filename): pass else: try: self.tar = tarfile.open(self.filename,"r:gz") except ValueError: try: self.tar = tarfile.open(self.filename,"r") except ValueError: raise self.buildTarCache(self.tar) def buildTarCache(self,tar): self.tarcache = {} for member in tar.getmembers(): if member.isfile() != True: continue splits = member.name.split("/") if splits[0].startswith("must-gather"): splits.remove(splits[0]) first=True member.name="" for part in splits: if first: first = False else: member.name+="/" member.name+=part self.tarcache[member.name] = member def getValueFromObj(self,obj,*props): if obj != None: for prop in props: if prop in obj: obj = obj[prop] else: obj = None break return obj def getFileContent(self,thepath): content = None if self.tarcache == None: if path.exists(thepath): f = open(thepath,"r") content = f.read() else: f=self.tar.extractfile(thepath) content = f.read() return content def getEntriesFromPath(self,path): entries = [] if self.tarcache != None: for key in self.tarcache: if path in key: entries.append(key) else: fullPath = os.path.join(self.filename,path) if os.path.exists(fullPath): pathsEntries = os.listdir(fullPath) for entry in pathsEntries: foundPath = os.path.join(fullPath,entry) if(os.path.isfile(foundPath)): entries.append(foundPath) return entries def getDirsFromPath(self,path): entries = [] if self.tarcache != None: for key in self.tarcache: if path in key: entries.append(key) else: fullPath = os.path.join(self.filename,path) if os.path.exists(fullPath): pathsEntries = os.listdir(fullPath) for entry in pathsEntries: foundPath = os.path.join(fullPath,entry) if(os.path.isdir(foundPath)): entries.append(foundPath) return entries def parseYaml(self, content): return yaml.safe_load(content)

true

f723b4994184ba630e2af0efd757f41de6e1bb07

5,242

py

Python

vision/tests/unit/gapic/v1/test_image_annotator_client_v1.py

nielm/google-cloud-python

fd126fdea34206109eb00d675374ff7dc4dcc5ef

[ "Apache-2.0" ]

1

2019-01-23T21:54:51.000Z

vision/tests/unit/gapic/v1/test_image_annotator_client_v1.py

nielm/google-cloud-python

fd126fdea34206109eb00d675374ff7dc4dcc5ef

[ "Apache-2.0" ]

1

2018-04-06T19:51:23.000Z

vision/tests/unit/gapic/v1/test_image_annotator_client_v1.py

nielm/google-cloud-python

fd126fdea34206109eb00d675374ff7dc4dcc5ef

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Unit tests.""" import mock import pytest from google.rpc import status_pb2 from google.cloud import vision_v1 from google.cloud.vision_v1.proto import image_annotator_pb2 from google.longrunning import operations_pb2 class MultiCallableStub(object): """Stub for the grpc.UnaryUnaryMultiCallable interface.""" def __init__(self, method, channel_stub): self.method = method self.channel_stub = channel_stub def __call__(self, request, timeout=None, metadata=None, credentials=None): self.channel_stub.requests.append((self.method, request)) response = None if self.channel_stub.responses: response = self.channel_stub.responses.pop() if isinstance(response, Exception): raise response if response: return response class ChannelStub(object): """Stub for the grpc.Channel interface.""" def __init__(self, responses=[]): self.responses = responses self.requests = [] def unary_unary(self, method, request_serializer=None, response_deserializer=None): return MultiCallableStub(method, self) class CustomException(Exception): pass class TestImageAnnotatorClient(object): def test_batch_annotate_images(self): # Setup Expected Response expected_response = {} expected_response = image_annotator_pb2.BatchAnnotateImagesResponse( **expected_response ) # Mock the API response channel = ChannelStub(responses=[expected_response]) patch = mock.patch("google.api_core.grpc_helpers.create_channel") with patch as create_channel: create_channel.return_value = channel client = vision_v1.ImageAnnotatorClient() # Setup Request requests = [] response = client.batch_annotate_images(requests) assert expected_response == response assert len(channel.requests) == 1 expected_request = image_annotator_pb2.BatchAnnotateImagesRequest( requests=requests ) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_batch_annotate_images_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) patch = mock.patch("google.api_core.grpc_helpers.create_channel") with patch as create_channel: create_channel.return_value = channel client = vision_v1.ImageAnnotatorClient() # Setup request requests = [] with pytest.raises(CustomException): client.batch_annotate_images(requests) def test_async_batch_annotate_files(self): # Setup Expected Response expected_response = {} expected_response = image_annotator_pb2.AsyncBatchAnnotateFilesResponse( **expected_response ) operation = operations_pb2.Operation( name="operations/test_async_batch_annotate_files", done=True ) operation.response.Pack(expected_response) # Mock the API response channel = ChannelStub(responses=[operation]) patch = mock.patch("google.api_core.grpc_helpers.create_channel") with patch as create_channel: create_channel.return_value = channel client = vision_v1.ImageAnnotatorClient() # Setup Request requests = [] response = client.async_batch_annotate_files(requests) result = response.result() assert expected_response == result assert len(channel.requests) == 1 expected_request = image_annotator_pb2.AsyncBatchAnnotateFilesRequest( requests=requests ) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_async_batch_annotate_files_exception(self): # Setup Response error = status_pb2.Status() operation = operations_pb2.Operation( name="operations/test_async_batch_annotate_files_exception", done=True ) operation.error.CopyFrom(error) # Mock the API response channel = ChannelStub(responses=[operation]) patch = mock.patch("google.api_core.grpc_helpers.create_channel") with patch as create_channel: create_channel.return_value = channel client = vision_v1.ImageAnnotatorClient() # Setup Request requests = [] response = client.async_batch_annotate_files(requests) exception = response.exception() assert exception.errors[0] == error

32.968553

87

0.681801

import mock import pytest from google.rpc import status_pb2 from google.cloud import vision_v1 from google.cloud.vision_v1.proto import image_annotator_pb2 from google.longrunning import operations_pb2 class MultiCallableStub(object): def __init__(self, method, channel_stub): self.method = method self.channel_stub = channel_stub def __call__(self, request, timeout=None, metadata=None, credentials=None): self.channel_stub.requests.append((self.method, request)) response = None if self.channel_stub.responses: response = self.channel_stub.responses.pop() if isinstance(response, Exception): raise response if response: return response class ChannelStub(object): def __init__(self, responses=[]): self.responses = responses self.requests = [] def unary_unary(self, method, request_serializer=None, response_deserializer=None): return MultiCallableStub(method, self) class CustomException(Exception): pass class TestImageAnnotatorClient(object): def test_batch_annotate_images(self): expected_response = {} expected_response = image_annotator_pb2.BatchAnnotateImagesResponse( **expected_response ) channel = ChannelStub(responses=[expected_response]) patch = mock.patch("google.api_core.grpc_helpers.create_channel") with patch as create_channel: create_channel.return_value = channel client = vision_v1.ImageAnnotatorClient() requests = [] response = client.batch_annotate_images(requests) assert expected_response == response assert len(channel.requests) == 1 expected_request = image_annotator_pb2.BatchAnnotateImagesRequest( requests=requests ) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_batch_annotate_images_exception(self): channel = ChannelStub(responses=[CustomException()]) patch = mock.patch("google.api_core.grpc_helpers.create_channel") with patch as create_channel: create_channel.return_value = channel client = vision_v1.ImageAnnotatorClient() requests = [] with pytest.raises(CustomException): client.batch_annotate_images(requests) def test_async_batch_annotate_files(self): expected_response = {} expected_response = image_annotator_pb2.AsyncBatchAnnotateFilesResponse( **expected_response ) operation = operations_pb2.Operation( name="operations/test_async_batch_annotate_files", done=True ) operation.response.Pack(expected_response) channel = ChannelStub(responses=[operation]) patch = mock.patch("google.api_core.grpc_helpers.create_channel") with patch as create_channel: create_channel.return_value = channel client = vision_v1.ImageAnnotatorClient() requests = [] response = client.async_batch_annotate_files(requests) result = response.result() assert expected_response == result assert len(channel.requests) == 1 expected_request = image_annotator_pb2.AsyncBatchAnnotateFilesRequest( requests=requests ) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_async_batch_annotate_files_exception(self): error = status_pb2.Status() operation = operations_pb2.Operation( name="operations/test_async_batch_annotate_files_exception", done=True ) operation.error.CopyFrom(error) channel = ChannelStub(responses=[operation]) patch = mock.patch("google.api_core.grpc_helpers.create_channel") with patch as create_channel: create_channel.return_value = channel client = vision_v1.ImageAnnotatorClient() requests = [] response = client.async_batch_annotate_files(requests) exception = response.exception() assert exception.errors[0] == error

true

f723b4aa1f30b8829647c7182eab4c6610d10e07

845

py

Python

lib/jayrboltonTest/contig_filter_util.py

jayrbolton/kbase_tutorial_contig_filter

66b5f50db8f584e13a0923bda6f0008c12438d88

[ "MIT" ]

null

lib/jayrboltonTest/contig_filter_util.py

jayrbolton/kbase_tutorial_contig_filter

66b5f50db8f584e13a0923bda6f0008c12438d88

[ "MIT" ]

null

lib/jayrboltonTest/contig_filter_util.py

jayrbolton/kbase_tutorial_contig_filter

66b5f50db8f584e13a0923bda6f0008c12438d88

[ "MIT" ]

null

from Bio import SeqIO def contig_filter(input_path, filtered_path, min_length): # Inside {username}ContigFilterImpl#run_{username}ContigFilter_max, after you have fetched the fasta file: # Parse the downloaded file in FASTA format parsed_assembly = SeqIO.parse(input_path, 'fasta') min_length = min_length # Keep a list of contigs greater than min_length good_contigs = [] # total contigs regardless of length n_total = 0 # total contigs over the min_length n_remaining = 0 for record in list(parsed_assembly): n_total += 1 if len(record.seq) >= min_length: good_contigs.append(record) n_remaining += 1 output = { 'n_total': n_total, 'n_remaining': n_remaining } SeqIO.write(good_contigs, filtered_path, 'fasta') return output

32.5

110

0.678107

from Bio import SeqIO def contig_filter(input_path, filtered_path, min_length): ngth = min_length good_contigs = [] n_total = 0 n_remaining = 0 for record in list(parsed_assembly): n_total += 1 if len(record.seq) >= min_length: good_contigs.append(record) n_remaining += 1 output = { 'n_total': n_total, 'n_remaining': n_remaining } SeqIO.write(good_contigs, filtered_path, 'fasta') return output

true

f723b5aababa8476e324ce06e1a59d0b7db72c76

1,881

py

Python

okapi/urls.py

jbbqqf/okapi

3db29ef1e15685fae304190bd176f75c4e367d03

[ "BSD-3-Clause" ]

null

okapi/urls.py

jbbqqf/okapi

3db29ef1e15685fae304190bd176f75c4e367d03

[ "BSD-3-Clause" ]

null

okapi/urls.py

jbbqqf/okapi

3db29ef1e15685fae304190bd176f75c4e367d03

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """okapi URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.8/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Add an import: from blog import urls as blog_urls 2. Add a URL to urlpatterns: url(r'^blog/', include(blog_urls)) """ from django.conf import settings from django.conf.urls import include, url from django.contrib import admin from django.contrib.staticfiles.urls import staticfiles_urlpatterns from django.conf.urls.static import static urlpatterns = [ url(r'^admin/', include(admin.site.urls)), url(r'^okauth/', include('okauth.urls')), url(r'^users/', include('profiles.urls')), url(r'^users/', include('groups.urls')), url(r'^users/', include('online.urls')), url(r'^chat/', include('chat.urls')), url(r'^share/', include('fileshare.urls')), url(r'^prefs/', include('preferences.urls')), url(r'^news/', include('news.urls')), url(r'^grades/', include('grades.urls')), url(r'^library/', include('library.urls')), url(r'^score/', include('score.urls')), url(r'^button/', include('button.urls')), url(r'^{}(.*)$'.format(settings.PRIVATE_MEDIA_URL.lstrip('/')), 'common.private_media.serve_private_media'), url(r'^docs/', include('rest_framework_swagger.urls')), ] if settings.DEBUG: # admin webinterface urlpatterns += staticfiles_urlpatterns() # serving media files urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

35.490566

77

0.66773

from django.conf import settings from django.conf.urls import include, url from django.contrib import admin from django.contrib.staticfiles.urls import staticfiles_urlpatterns from django.conf.urls.static import static urlpatterns = [ url(r'^admin/', include(admin.site.urls)), url(r'^okauth/', include('okauth.urls')), url(r'^users/', include('profiles.urls')), url(r'^users/', include('groups.urls')), url(r'^users/', include('online.urls')), url(r'^chat/', include('chat.urls')), url(r'^share/', include('fileshare.urls')), url(r'^prefs/', include('preferences.urls')), url(r'^news/', include('news.urls')), url(r'^grades/', include('grades.urls')), url(r'^library/', include('library.urls')), url(r'^score/', include('score.urls')), url(r'^button/', include('button.urls')), url(r'^{}(.*)$'.format(settings.PRIVATE_MEDIA_URL.lstrip('/')), 'common.private_media.serve_private_media'), url(r'^docs/', include('rest_framework_swagger.urls')), ] if settings.DEBUG: urlpatterns += staticfiles_urlpatterns() urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

true

f723b70c324fc4c0f3106b87f660b2c3aa92b9e8

6,885

py

Python

thingsboard_gateway/tb_utility/tb_utility.py

meuron-io/thingsboard-gateway

26e7299047507c74613aeea1f19dcdba2e1e5644

[ "Apache-2.0" ]

1,123

2017-02-07T13:09:40.000Z

2022-03-30T10:40:48.000Z

thingsboard_gateway/tb_utility/tb_utility.py

meuron-io/thingsboard-gateway

26e7299047507c74613aeea1f19dcdba2e1e5644

[ "Apache-2.0" ]

655

2017-03-07T17:25:55.000Z

2022-03-31T07:59:53.000Z

thingsboard_gateway/tb_utility/tb_utility.py

meuron-io/thingsboard-gateway

26e7299047507c74613aeea1f19dcdba2e1e5644

[ "Apache-2.0" ]

648

2017-02-07T13:32:30.000Z

2022-03-31T05:17:55.000Z

# Copyright 2021. ThingsBoard # # 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 logging import getLogger from re import search, findall from jsonpath_rw import parse from simplejson import JSONDecodeError, dumps, loads log = getLogger("service") class TBUtility: @staticmethod def decode(message): try: if isinstance(message.payload, bytes): content = loads(message.payload.decode("utf-8", "ignore")) else: content = loads(message.payload) except JSONDecodeError: try: content = message.payload.decode("utf-8", "ignore") except JSONDecodeError: content = message.payload return content @staticmethod def validate_converted_data(data): error = None if error is None and not data.get("deviceName"): error = 'deviceName is empty in data: ' if error is None and not data.get("deviceType"): error = 'deviceType is empty in data: ' if error is None: got_attributes = False got_telemetry = False if data.get("attributes") is not None and len(data.get("attributes")) > 0: got_attributes = True if data.get("telemetry") is not None: for entry in data.get("telemetry"): if (entry.get("ts") is not None and len(entry.get("values")) > 0) or entry.get("ts") is None: got_telemetry = True break if got_attributes is False and got_telemetry is False: error = 'No telemetry and attributes in data: ' if error is not None: json_data = dumps(data) if isinstance(json_data, bytes): log.error(error + json_data.decode("UTF-8")) else: log.error(error + json_data) return False return True @staticmethod def topic_to_regex(topic): return topic.replace("+", "[^/]+").replace("#", ".+") @staticmethod def regex_to_topic(regex): return regex.replace("[^/]+", "+").replace(".+", "#") @staticmethod def get_value(expression, body=None, value_type="string", get_tag=False, expression_instead_none=False): if isinstance(body, str): body = loads(body) if not expression: return '' positions = search(r'\${(?:(.*))}', expression) if positions is not None: p1 = positions.regs[-1][0] p2 = positions.regs[-1][1] else: p1 = 0 p2 = len(expression) target_str = str(expression[p1:p2]) if get_tag: return target_str full_value = None try: if isinstance(body, dict) and target_str.split()[0] in body: if value_type.lower() == "string": full_value = str(expression[0: max(p1 - 2, 0)]) + str(body[target_str.split()[0]]) + str(expression[ p2 + 1:len( expression)]) else: full_value = body.get(target_str.split()[0]) elif isinstance(body, (dict, list)): try: jsonpath_expression = parse(target_str) jsonpath_match = jsonpath_expression.find(body) if jsonpath_match: full_value = jsonpath_match[0].value except Exception as e: log.debug(e) elif isinstance(body, (str, bytes)): search_result = search(expression, body) if search_result.groups(): full_value = search_result.group(0) if expression_instead_none and full_value is None: full_value = expression except Exception as e: log.exception(e) return full_value @staticmethod def get_values(expression, body=None, value_type="string", get_tag=False, expression_instead_none=False): expression_arr = findall(r'\$\{[${A-Za-z0-9.^\]\[*_]*\}', expression) values = [TBUtility.get_value(exp, body, value_type=value_type, get_tag=get_tag, expression_instead_none=expression_instead_none) for exp in expression_arr] if '${' not in expression: values.append(expression) return values @staticmethod def install_package(package, version="upgrade"): from sys import executable from subprocess import check_call, CalledProcessError result = False if version.lower() == "upgrade": try: result = check_call([executable, "-m", "pip", "install", package, "--upgrade", "--user"]) except CalledProcessError: result = check_call([executable, "-m", "pip", "install", package, "--upgrade"]) else: from pkg_resources import get_distribution current_package_version = None try: current_package_version = get_distribution(package) except Exception: pass if current_package_version is None or current_package_version != version: installation_sign = "==" if ">=" not in version else "" try: result = check_call( [executable, "-m", "pip", "install", package + installation_sign + version, "--user"]) except CalledProcessError: result = check_call([executable, "-m", "pip", "install", package + installation_sign + version]) return result @staticmethod def replace_params_tags(text, data): if '${' in text: for item in text.split('/'): if '${' in item: tag = '${' + TBUtility.get_value(item, data['data'], 'params', get_tag=True) + '}' value = TBUtility.get_value(item, data['data'], 'params', expression_instead_none=True) text = text.replace(tag, str(value)) return text

40.263158

126

0.548729

from logging import getLogger from re import search, findall from jsonpath_rw import parse from simplejson import JSONDecodeError, dumps, loads log = getLogger("service") class TBUtility: @staticmethod def decode(message): try: if isinstance(message.payload, bytes): content = loads(message.payload.decode("utf-8", "ignore")) else: content = loads(message.payload) except JSONDecodeError: try: content = message.payload.decode("utf-8", "ignore") except JSONDecodeError: content = message.payload return content @staticmethod def validate_converted_data(data): error = None if error is None and not data.get("deviceName"): error = 'deviceName is empty in data: ' if error is None and not data.get("deviceType"): error = 'deviceType is empty in data: ' if error is None: got_attributes = False got_telemetry = False if data.get("attributes") is not None and len(data.get("attributes")) > 0: got_attributes = True if data.get("telemetry") is not None: for entry in data.get("telemetry"): if (entry.get("ts") is not None and len(entry.get("values")) > 0) or entry.get("ts") is None: got_telemetry = True break if got_attributes is False and got_telemetry is False: error = 'No telemetry and attributes in data: ' if error is not None: json_data = dumps(data) if isinstance(json_data, bytes): log.error(error + json_data.decode("UTF-8")) else: log.error(error + json_data) return False return True @staticmethod def topic_to_regex(topic): return topic.replace("+", "[^/]+").replace("#", ".+") @staticmethod def regex_to_topic(regex): return regex.replace("[^/]+", "+").replace(".+", "#") @staticmethod def get_value(expression, body=None, value_type="string", get_tag=False, expression_instead_none=False): if isinstance(body, str): body = loads(body) if not expression: return '' positions = search(r'\${(?:(.*))}', expression) if positions is not None: p1 = positions.regs[-1][0] p2 = positions.regs[-1][1] else: p1 = 0 p2 = len(expression) target_str = str(expression[p1:p2]) if get_tag: return target_str full_value = None try: if isinstance(body, dict) and target_str.split()[0] in body: if value_type.lower() == "string": full_value = str(expression[0: max(p1 - 2, 0)]) + str(body[target_str.split()[0]]) + str(expression[ p2 + 1:len( expression)]) else: full_value = body.get(target_str.split()[0]) elif isinstance(body, (dict, list)): try: jsonpath_expression = parse(target_str) jsonpath_match = jsonpath_expression.find(body) if jsonpath_match: full_value = jsonpath_match[0].value except Exception as e: log.debug(e) elif isinstance(body, (str, bytes)): search_result = search(expression, body) if search_result.groups(): full_value = search_result.group(0) if expression_instead_none and full_value is None: full_value = expression except Exception as e: log.exception(e) return full_value @staticmethod def get_values(expression, body=None, value_type="string", get_tag=False, expression_instead_none=False): expression_arr = findall(r'\$\{[${A-Za-z0-9.^\]\[*_]*\}', expression) values = [TBUtility.get_value(exp, body, value_type=value_type, get_tag=get_tag, expression_instead_none=expression_instead_none) for exp in expression_arr] if '${' not in expression: values.append(expression) return values @staticmethod def install_package(package, version="upgrade"): from sys import executable from subprocess import check_call, CalledProcessError result = False if version.lower() == "upgrade": try: result = check_call([executable, "-m", "pip", "install", package, "--upgrade", "--user"]) except CalledProcessError: result = check_call([executable, "-m", "pip", "install", package, "--upgrade"]) else: from pkg_resources import get_distribution current_package_version = None try: current_package_version = get_distribution(package) except Exception: pass if current_package_version is None or current_package_version != version: installation_sign = "==" if ">=" not in version else "" try: result = check_call( [executable, "-m", "pip", "install", package + installation_sign + version, "--user"]) except CalledProcessError: result = check_call([executable, "-m", "pip", "install", package + installation_sign + version]) return result @staticmethod def replace_params_tags(text, data): if '${' in text: for item in text.split('/'): if '${' in item: tag = '${' + TBUtility.get_value(item, data['data'], 'params', get_tag=True) + '}' value = TBUtility.get_value(item, data['data'], 'params', expression_instead_none=True) text = text.replace(tag, str(value)) return text

true

f723b735cf5e93033b633872fb57a7d0a690f5b6

37,513

py

Python

taxcalc/tests/test_calculate.py

ClarePan/Tax-Calculator

d2d6cb4b551f34017db7166d91d982b5c4670816

[ "CC0-1.0" ]

1

2021-02-23T21:03:43.000Z

taxcalc/tests/test_calculate.py

ClarePan/Tax-Calculator

d2d6cb4b551f34017db7166d91d982b5c4670816

[ "CC0-1.0" ]

null

taxcalc/tests/test_calculate.py

ClarePan/Tax-Calculator

d2d6cb4b551f34017db7166d91d982b5c4670816

[ "CC0-1.0" ]

null

# CODING-STYLE CHECKS: # pycodestyle test_calculate.py import os import json from io import StringIO import tempfile import copy import six import pytest import numpy as np import pandas as pd from taxcalc import Policy, Records, Calculator, Behavior, Consumption RAWINPUTFILE_FUNITS = 4 RAWINPUTFILE_YEAR = 2015 RAWINPUTFILE_CONTENTS = ( 'RECID,MARS\n' '1,2\n' '2,1\n' '3,4\n' '4,3\n' ) @pytest.fixture(scope='module', name='rawinputfile') def fixture_rawinputfile(): """ Temporary input file that contains the minimum required input varaibles. """ ifile = tempfile.NamedTemporaryFile(mode='a', delete=False) ifile.write(RAWINPUTFILE_CONTENTS) ifile.close() # must close and then yield for Windows platform yield ifile if os.path.isfile(ifile.name): try: os.remove(ifile.name) except OSError: pass # sometimes we can't remove a generated temporary file @pytest.fixture(scope='module', name='policyfile') def fixture_policyfile(): txt = """{"_almdep": {"value": [7150, 7250, 7400]}, "_almsep": {"value": [40400, 41050]}, "_rt5": {"value": [0.33 ]}, "_rt7": {"value": [0.396]}}""" f = tempfile.NamedTemporaryFile(mode="a", delete=False) f.write(txt + "\n") f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) def test_make_calculator(cps_subsample): syr = 2014 pol = Policy(start_year=syr, num_years=9) assert pol.current_year == syr rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) consump = Consumption() consump.update_consumption({syr: {'_MPC_e20400': [0.05]}}) assert consump.current_year == Consumption.JSON_START_YEAR calc = Calculator(policy=pol, records=rec, consumption=consump, behavior=Behavior()) assert calc.current_year == syr assert calc.records_current_year() == syr # test incorrect Calculator instantiation: with pytest.raises(ValueError): Calculator(policy=None, records=rec) with pytest.raises(ValueError): Calculator(policy=pol, records=None) with pytest.raises(ValueError): Calculator(policy=pol, records=rec, behavior=list()) with pytest.raises(ValueError): Calculator(policy=pol, records=rec, consumption=list()) def test_make_calculator_deepcopy(cps_subsample): pol = Policy() rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc1 = Calculator(policy=pol, records=rec) calc2 = copy.deepcopy(calc1) assert isinstance(calc2, Calculator) def test_make_calculator_with_policy_reform(cps_subsample): rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) year = rec.current_year # create a Policy object and apply a policy reform pol = Policy() reform = {2013: {'_II_em': [4000], '_II_em_cpi': False, '_STD_Aged': [[1600, 1300, 1300, 1600, 1600]], '_STD_Aged_cpi': False}} pol.implement_reform(reform) # create a Calculator object using this policy reform calc = Calculator(policy=pol, records=rec) # check that Policy object embedded in Calculator object is correct assert calc.current_year == year assert calc.policy_param('II_em') == 4000 assert np.allclose(calc.policy_param('_II_em'), np.array([4000] * Policy.DEFAULT_NUM_YEARS)) exp_STD_Aged = [[1600, 1300, 1300, 1600, 1600]] * Policy.DEFAULT_NUM_YEARS assert np.allclose(calc.policy_param('_STD_Aged'), np.array(exp_STD_Aged)) assert np.allclose(calc.policy_param('STD_Aged'), np.array([1600, 1300, 1300, 1600, 1600])) def test_make_calculator_with_multiyear_reform(cps_subsample): rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) year = rec.current_year # create a Policy object and apply a policy reform pol = Policy() reform = {2015: {}, 2016: {}} reform[2015]['_II_em'] = [5000, 6000] # reform values for 2015 and 2016 reform[2015]['_II_em_cpi'] = False reform[2016]['_STD_Aged'] = [[1600, 1300, 1600, 1300, 1600]] pol.implement_reform(reform) # create a Calculator object using this policy-reform calc = Calculator(policy=pol, records=rec) # check that Policy object embedded in Calculator object is correct assert pol.num_years == Policy.DEFAULT_NUM_YEARS assert calc.current_year == year assert calc.policy_param('II_em') == 3950 exp_II_em = [3900, 3950, 5000] + [6000] * (Policy.DEFAULT_NUM_YEARS - 3) assert np.allclose(calc.policy_param('_II_em'), np.array(exp_II_em)) calc.increment_year() calc.increment_year() assert calc.current_year == 2016 assert np.allclose(calc.policy_param('STD_Aged'), np.array([1600, 1300, 1600, 1300, 1600])) def test_calculator_advance_to_year(cps_subsample): rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) pol = Policy() calc = Calculator(policy=pol, records=rec) calc.advance_to_year(2016) assert calc.current_year == 2016 with pytest.raises(ValueError): calc.advance_to_year(2015) def test_make_calculator_raises_on_no_policy(cps_subsample): rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) with pytest.raises(ValueError): Calculator(records=rec) def test_calculator_mtr(cps_subsample): rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) calcx = Calculator(policy=Policy(), records=rec) calcx.calc_all() combinedx = calcx.array('combined') c00100x = calcx.array('c00100') calc = Calculator(policy=Policy(), records=rec) recs_pre_e00200p = copy.deepcopy(calc.array('e00200p')) (mtr_ptx, mtr_itx, mtr_cmb) = calc.mtr(variable_str='e00200p', zero_out_calculated_vars=True) recs_post_e00200p = calc.array('e00200p') assert np.allclose(recs_post_e00200p, recs_pre_e00200p) assert np.allclose(calc.array('combined'), combinedx) assert np.allclose(calc.array('c00100'), c00100x) assert np.array_equal(mtr_cmb, mtr_ptx) is False assert np.array_equal(mtr_ptx, mtr_itx) is False with pytest.raises(ValueError): calc.mtr(variable_str='bad_income_type') (_, _, mtr_combined) = calc.mtr(variable_str='e00200s', calc_all_already_called=True) assert isinstance(mtr_combined, np.ndarray) (_, _, mtr_combined) = calc.mtr(variable_str='e00650', negative_finite_diff=True, calc_all_already_called=True) assert isinstance(mtr_combined, np.ndarray) (_, _, mtr_combined) = calc.mtr(variable_str='e00900p', calc_all_already_called=True) assert isinstance(mtr_combined, np.ndarray) (_, _, mtr_combined) = calc.mtr(variable_str='e01700', calc_all_already_called=True) assert isinstance(mtr_combined, np.ndarray) (_, _, mtr_combined) = calc.mtr(variable_str='e26270', calc_all_already_called=True) assert isinstance(mtr_combined, np.ndarray) (_, _, mtr_combined) = calc.mtr(variable_str='e00200p', calc_all_already_called=True) assert np.allclose(mtr_combined, mtr_cmb) assert np.allclose(calc.array('combined'), combinedx) assert np.allclose(calc.array('c00100'), c00100x) def test_calculator_mtr_when_PT_rates_differ(): reform = {2013: {'_II_rt1': [0.40], '_II_rt2': [0.40], '_II_rt3': [0.40], '_II_rt4': [0.40], '_II_rt5': [0.40], '_II_rt6': [0.40], '_II_rt7': [0.40], '_PT_rt1': [0.30], '_PT_rt2': [0.30], '_PT_rt3': [0.30], '_PT_rt4': [0.30], '_PT_rt5': [0.30], '_PT_rt6': [0.30], '_PT_rt7': [0.30]}} funit = ( u'RECID,MARS,FLPDYR,e00200,e00200p,e00900,e00900p,extraneous\n' u'1, 1, 2009, 200000,200000, 100000,100000, 9999999999\n' ) rec = Records(pd.read_csv(StringIO(funit))) pol = Policy() calc1 = Calculator(policy=pol, records=rec) (_, mtr1, _) = calc1.mtr(variable_str='p23250') pol.implement_reform(reform) calc2 = Calculator(policy=pol, records=rec) (_, mtr2, _) = calc2.mtr(variable_str='p23250') assert np.allclose(mtr1, mtr2, rtol=0.0, atol=1e-06) def test_make_calculator_increment_years_first(cps_subsample): # create Policy object with policy reform syr = 2013 pol = Policy(start_year=syr) reform = {2015: {}, 2016: {}} std5 = 2000 reform[2015]['_STD_Aged'] = [[std5, std5, std5, std5, std5]] reform[2015]['_II_em'] = [5000] reform[2016]['_II_em'] = [6000] reform[2016]['_II_em_cpi'] = False pol.implement_reform(reform) # create Calculator object with Policy object as modified by reform rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc = Calculator(policy=pol, records=rec) # compare expected policy parameter values with those embedded in calc irates = pol.inflation_rates() irate2015 = irates[2015 - syr] irate2016 = irates[2016 - syr] std6 = std5 * (1.0 + irate2015) std7 = std6 * (1.0 + irate2016) exp_STD_Aged = np.array([[1500, 1200, 1200, 1500, 1500], [1550, 1200, 1200, 1550, 1550], [std5, std5, std5, std5, std5], [std6, std6, std6, std6, std6], [std7, std7, std7, std7, std7]]) act_STD_Aged = calc.policy_param('_STD_Aged') assert np.allclose(act_STD_Aged[:5], exp_STD_Aged) exp_II_em = np.array([3900, 3950, 5000, 6000, 6000]) act_II_em = calc.policy_param('_II_em') assert np.allclose(act_II_em[:5], exp_II_em) def test_ID_HC_vs_BS(cps_subsample): """ Test that complete haircut of itemized deductions produces same results as a 100% benefit surtax with no benefit deduction. """ recs = Records.cps_constructor(data=cps_subsample, no_benefits=True) # specify complete-haircut reform policy and Calculator object hc_reform = {2013: {'_ID_Medical_hc': [1.0], '_ID_StateLocalTax_hc': [1.0], '_ID_RealEstate_hc': [1.0], '_ID_Casualty_hc': [1.0], '_ID_Miscellaneous_hc': [1.0], '_ID_InterestPaid_hc': [1.0], '_ID_Charity_hc': [1.0]}} hc_policy = Policy() hc_policy.implement_reform(hc_reform) hc_calc = Calculator(policy=hc_policy, records=recs) hc_calc.calc_all() hc_taxes = hc_calc.dataframe(['iitax', 'payrolltax']) del hc_calc # specify benefit-surtax reform policy and Calculator object bs_reform = {2013: {'_ID_BenefitSurtax_crt': [0.0], '_ID_BenefitSurtax_trt': [1.0]}} bs_policy = Policy() bs_policy.implement_reform(bs_reform) bs_calc = Calculator(policy=bs_policy, records=recs) bs_calc.calc_all() bs_taxes = bs_calc.dataframe(['iitax', 'payrolltax']) del bs_calc # compare calculated taxes generated by the two reforms assert np.allclose(hc_taxes['payrolltax'], bs_taxes['payrolltax']) assert np.allclose(hc_taxes['iitax'], bs_taxes['iitax']) def test_ID_StateLocal_HC_vs_CRT(cps_subsample): """ Test that a cap on state/local income and sales tax deductions at 0 percent of AGI is equivalent to a complete haircut on the same state/local tax deductions. """ rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) # specify state/local complete haircut reform policy and Calculator object hc_reform = {2013: {'_ID_StateLocalTax_hc': [1.0]}} hc_policy = Policy() hc_policy.implement_reform(hc_reform) hc_calc = Calculator(policy=hc_policy, records=rec) hc_calc.calc_all() # specify AGI cap reform policy and Calculator object crt_reform = {2013: {'_ID_StateLocalTax_crt': [0.0]}} crt_policy = Policy() crt_policy.implement_reform(crt_reform) crt_calc = Calculator(policy=crt_policy, records=rec) crt_calc.calc_all() # compare calculated tax results generated by the two reforms assert np.allclose(hc_calc.array('payrolltax'), crt_calc.array('payrolltax')) assert np.allclose(hc_calc.array('iitax'), crt_calc.array('iitax')) def test_ID_RealEstate_HC_vs_CRT(cps_subsample): """ Test that a cap on all state, local, and foreign real estate tax deductions at 0 percent of AGI is equivalent to a complete haircut on the same real estate tax deductions. """ rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) # specify real estate complete haircut reform policy and Calculator object hc_reform = {2013: {'_ID_RealEstate_hc': [1.0]}} hc_policy = Policy() hc_policy.implement_reform(hc_reform) hc_calc = Calculator(policy=hc_policy, records=rec) hc_calc.calc_all() # specify AGI cap reform policy and Calculator object crt_reform = {2013: {'_ID_RealEstate_crt': [0.0]}} crt_policy = Policy() crt_policy.implement_reform(crt_reform) crt_calc = Calculator(policy=crt_policy, records=rec) crt_calc.calc_all() # compare calculated tax results generated by the two reforms assert np.allclose(hc_calc.array('payrolltax'), crt_calc.array('payrolltax')) assert np.allclose(hc_calc.array('iitax'), crt_calc.array('iitax')) def test_calculator_using_nonstd_input(rawinputfile): # check Calculator handling of raw, non-standard input data with no aging pol = Policy() pol.set_year(RAWINPUTFILE_YEAR) # set policy params to input data year nonstd = Records(data=rawinputfile.name, gfactors=None, # keeps raw data unchanged weights=None, start_year=RAWINPUTFILE_YEAR) # set raw input data year assert nonstd.array_length == RAWINPUTFILE_FUNITS calc = Calculator(policy=pol, records=nonstd, sync_years=False) # keeps raw data unchanged assert calc.current_year == RAWINPUTFILE_YEAR calc.calc_all() assert calc.weighted_total('e00200') == 0 assert calc.total_weight() == 0 varlist = ['RECID', 'MARS'] pdf = calc.dataframe(varlist) assert isinstance(pdf, pd.DataFrame) assert pdf.shape == (RAWINPUTFILE_FUNITS, len(varlist)) mars = calc.array('MARS') assert isinstance(mars, np.ndarray) assert mars.shape == (RAWINPUTFILE_FUNITS,) exp_iitax = np.zeros((nonstd.array_length,)) assert np.allclose(calc.array('iitax'), exp_iitax) mtr_ptax, _, _ = calc.mtr(wrt_full_compensation=False) exp_mtr_ptax = np.zeros((nonstd.array_length,)) exp_mtr_ptax.fill(0.153) assert np.allclose(mtr_ptax, exp_mtr_ptax) REFORM_CONTENTS = """ // Example of a reform file suitable for read_json_param_objects(). // This JSON file can contain any number of trailing //-style comments, which // will be removed before the contents are converted from JSON to a dictionary. // Within each "policy" object, the primary keys are parameters and // the secondary keys are years. // Both the primary and secondary key values must be enclosed in quotes ("). // Boolean variables are specified as true or false (no quotes; all lowercase). // Parameter code in the policy object is enclosed inside a pair of double // pipe characters (||). { "policy": { "_AMT_brk1": // top of first AMT tax bracket {"2015": [200000], "2017": [300000] }, "_EITC_c": // maximum EITC amount by number of qualifying kids (0,1,2,3+) {"2016": [[ 900, 5000, 8000, 9000]], "2019": [[1200, 7000, 10000, 12000]] }, "_II_em": // personal exemption amount (see indexing changes below) {"2016": [6000], "2018": [7500], "2020": [9000] }, "_II_em_cpi": // personal exemption amount indexing status {"2016": false, // values in future years are same as this year value "2018": true // values in future years indexed with this year as base }, "_SS_Earnings_c": // social security (OASDI) maximum taxable earnings {"2016": [300000], "2018": [500000], "2020": [700000] }, "_AMT_em_cpi": // AMT exemption amount indexing status {"2017": false, // values in future years are same as this year value "2020": true // values in future years indexed with this year as base } } } """ @pytest.fixture(scope='module', name='reform_file') def fixture_reform_file(): """ Temporary reform file for read_json_param_objects() function. """ rfile = tempfile.NamedTemporaryFile(mode='a', delete=False) rfile.write(REFORM_CONTENTS) rfile.close() # must close and then yield for Windows platform yield rfile if os.path.isfile(rfile.name): try: os.remove(rfile.name) except OSError: pass # sometimes we can't remove a generated temporary file ASSUMP_CONTENTS = """ // Example of assump file suitable for the read_json_param_objects(). // This JSON file can contain any number of trailing //-style comments, which // will be removed before the contents are converted from JSON to a dictionary. // Within each "behavior", "consumption" and "growth" object, the // primary keys are parameters and the secondary keys are years. // Both the primary and secondary key values must be enclosed in quotes ("). // Boolean variables are specified as true or false (no quotes; all lowercase). { "consumption": { "_MPC_e18400": {"2018": [0.05]} }, "behavior": {}, "growdiff_baseline": {}, "growdiff_response": {}, "growmodel": {} } """ @pytest.fixture(scope='module', name='assump_file') def fixture_assump_file(): """ Temporary assumption file for read_json_params_files() function. """ afile = tempfile.NamedTemporaryFile(mode='a', delete=False) afile.write(ASSUMP_CONTENTS) afile.close() # must close and then yield for Windows platform yield afile if os.path.isfile(afile.name): try: os.remove(afile.name) except OSError: pass # sometimes we can't remove a generated temporary file @pytest.mark.parametrize("set_year", [False, True]) def test_read_json_reform_file_and_implement_reform(reform_file, assump_file, set_year): """ Test reading and translation of reform file into a reform dictionary that is then used to call implement_reform method and Calculate.calc_all() NOTE: implement_reform called when policy.current_year == policy.start_year """ pol = Policy() if set_year: pol.set_year(2015) param_dict = Calculator.read_json_param_objects(reform_file.name, assump_file.name) pol.implement_reform(param_dict['policy']) syr = pol.start_year amt_brk1 = pol._AMT_brk1 assert amt_brk1[2015 - syr] == 200000 assert amt_brk1[2016 - syr] > 200000 assert amt_brk1[2017 - syr] == 300000 assert amt_brk1[2018 - syr] > 300000 ii_em = pol._II_em assert ii_em[2016 - syr] == 6000 assert ii_em[2017 - syr] == 6000 assert ii_em[2018 - syr] == 7500 assert ii_em[2019 - syr] > 7500 assert ii_em[2020 - syr] == 9000 assert ii_em[2021 - syr] > 9000 amt_em = pol._AMT_em assert amt_em[2016 - syr, 0] > amt_em[2015 - syr, 0] assert amt_em[2017 - syr, 0] > amt_em[2016 - syr, 0] assert amt_em[2018 - syr, 0] == amt_em[2017 - syr, 0] assert amt_em[2019 - syr, 0] == amt_em[2017 - syr, 0] assert amt_em[2020 - syr, 0] == amt_em[2017 - syr, 0] assert amt_em[2021 - syr, 0] > amt_em[2020 - syr, 0] assert amt_em[2022 - syr, 0] > amt_em[2021 - syr, 0] add4aged = pol._ID_Medical_frt_add4aged assert add4aged[2015 - syr] == -0.025 assert add4aged[2016 - syr] == -0.025 assert add4aged[2017 - syr] == 0.0 assert add4aged[2022 - syr] == 0.0 @pytest.fixture(scope='module', name='bad1reformfile') def fixture_bad1reformfile(): # specify JSON text for reform txt = """ { "policy": { // example of incorrect JSON because 'x' must be "x" 'x': {"2014": [4000]} } } """ f = tempfile.NamedTemporaryFile(mode='a', delete=False) f.write(txt + '\n') f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) @pytest.fixture(scope='module', name='bad2reformfile') def fixture_bad2reformfile(): # specify JSON text for reform txt = """ { "title": "", "policyx": { // example of reform file not containing "policy" key "_SS_Earnings_c": {"2018": [9e99]} } } """ f = tempfile.NamedTemporaryFile(mode='a', delete=False) f.write(txt + '\n') f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) @pytest.fixture(scope='module', name='bad3reformfile') def fixture_bad3reformfile(): # specify JSON text for reform txt = """ { "title": "", "policy": { "_SS_Earnings_c": {"2018": [9e99]} }, "behavior": { // example of misplaced "behavior" key } } """ f = tempfile.NamedTemporaryFile(mode='a', delete=False) f.write(txt + '\n') f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) def test_read_bad_json_reform_file(bad1reformfile, bad2reformfile, bad3reformfile): with pytest.raises(ValueError): Calculator.read_json_param_objects(bad1reformfile.name, None) with pytest.raises(ValueError): Calculator.read_json_param_objects(bad2reformfile.name, None) with pytest.raises(ValueError): Calculator.read_json_param_objects(bad3reformfile.name, None) with pytest.raises(ValueError): Calculator.read_json_param_objects(list(), None) with pytest.raises(ValueError): Calculator.read_json_param_objects(None, 'unknown_file_name') with pytest.raises(ValueError): Calculator.read_json_param_objects(None, list()) @pytest.fixture(scope='module', name='bad1assumpfile') def fixture_bad1assumpfile(): # specify JSON text for assumptions txt = """ { "consumption": {}, "behavior": { // example of incorrect JSON because 'x' must be "x" 'x': {"2014": [0.25]} }, "growdiff_baseline": {}, "growdiff_response": {}, "growmodel": {} } """ f = tempfile.NamedTemporaryFile(mode='a', delete=False) f.write(txt + '\n') f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) @pytest.fixture(scope='module', name='bad2assumpfile') def fixture_bad2assumpfile(): # specify JSON text for assumptions txt = """ { "consumption": {}, "behaviorx": {}, // example of assump file not containing "behavior" key "growdiff_baseline": {}, "growdiff_response": {}, "growmodel": {} } """ f = tempfile.NamedTemporaryFile(mode='a', delete=False) f.write(txt + '\n') f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) @pytest.fixture(scope='module', name='bad3assumpfile') def fixture_bad3assumpfile(): # specify JSON text for assump txt = """ { "consumption": {}, "behavior": {}, "growdiff_baseline": {}, "growdiff_response": {}, "policy": { // example of misplaced policy key "_SS_Earnings_c": {"2018": [9e99]} }, "growmodel": {} } """ f = tempfile.NamedTemporaryFile(mode='a', delete=False) f.write(txt + '\n') f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) def test_read_bad_json_assump_file(bad1assumpfile, bad2assumpfile, bad3assumpfile): with pytest.raises(ValueError): Calculator.read_json_param_objects(None, bad1assumpfile.name) with pytest.raises(ValueError): Calculator.read_json_param_objects(None, bad2assumpfile.name) with pytest.raises(ValueError): Calculator.read_json_param_objects(None, bad3assumpfile.name) with pytest.raises(ValueError): Calculator.read_json_param_objects(None, 'unknown_file_name') with pytest.raises(ValueError): Calculator.read_json_param_objects(None, list()) def test_convert_parameter_dict(): with pytest.raises(ValueError): Calculator._convert_parameter_dict({2013: {'2013': [40000]}}) with pytest.raises(ValueError): Calculator._convert_parameter_dict({'_II_em': {2013: [40000]}}) with pytest.raises(ValueError): Calculator._convert_parameter_dict({4567: {2013: [40000]}}) with pytest.raises(ValueError): Calculator._convert_parameter_dict({'_II_em': 40000}) rdict = Calculator._convert_parameter_dict({'_II_em': {'2013': [40000]}}) assert isinstance(rdict, dict) def test_calc_all(reform_file, rawinputfile): cyr = 2016 pol = Policy() param_dict = Calculator.read_json_param_objects(reform_file.name, None) pol.implement_reform(param_dict['policy']) pol.set_year(cyr) nonstd = Records(data=rawinputfile.name, gfactors=None, weights=None, start_year=cyr) assert nonstd.array_length == RAWINPUTFILE_FUNITS calc = Calculator(policy=pol, records=nonstd, sync_years=False) # keeps raw data unchanged assert calc.current_year == cyr assert calc.reform_warnings == '' def test_translate_json_reform_suffixes_mars_non_indexed(): # test read_json_param_objects() # using MARS-indexed parameter suffixes json1 = """{"policy": { "_II_em": {"2020": [20000], "2015": [15000]}, "_AMEDT_ec_joint": {"2018": [400000], "2016": [300000]}, "_AMEDT_ec_separate": {"2017": [150000], "2019": [200000]} }}""" pdict1 = Calculator.read_json_param_objects(reform=json1, assump=None) rdict1 = pdict1['policy'] json2 = """{"policy": { "_AMEDT_ec": {"2016": [[200000, 300000, 125000, 200000, 200000]], "2017": [[200000, 300000, 150000, 200000, 200000]], "2018": [[200000, 400000, 150000, 200000, 200000]], "2019": [[200000, 400000, 200000, 200000, 200000]]}, "_II_em": {"2015": [15000], "2020": [20000]} }}""" pdict2 = Calculator.read_json_param_objects(reform=json2, assump=None) rdict2 = pdict2['policy'] assert len(rdict2) == len(rdict1) for year in rdict2.keys(): if '_II_em' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_II_em'], rdict2[year]['_II_em'], atol=0.01, rtol=0.0) if '_AMEDT_ec' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_AMEDT_ec'], rdict2[year]['_AMEDT_ec'], atol=0.01, rtol=0.0) def test_translate_json_reform_suffixes_eic(): # test read_json_param_objects(...) # using EIC-indexed parameter suffixes json1 = """{"policy": { "_II_em": {"2020": [20000], "2015": [15000]}, "_EITC_c_0kids": {"2018": [510], "2019": [510]}, "_EITC_c_1kid": {"2019": [3400], "2018": [3400]}, "_EITC_c_2kids": {"2018": [5616], "2019": [5616]}, "_EITC_c_3+kids": {"2019": [6318], "2018": [6318]} }}""" pdict1 = Calculator.read_json_param_objects(reform=json1, assump=None) rdict1 = pdict1['policy'] json2 = """{"policy": { "_EITC_c": {"2019": [[510, 3400, 5616, 6318]], "2018": [[510, 3400, 5616, 6318]]}, "_II_em": {"2020": [20000], "2015": [15000]} }}""" pdict2 = Calculator.read_json_param_objects(reform=json2, assump=None) rdict2 = pdict2['policy'] assert len(rdict2) == len(rdict1) for year in rdict2.keys(): if '_II_em' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_II_em'], rdict2[year]['_II_em'], atol=0.01, rtol=0.0) if '_EITC_c' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_EITC_c'], rdict2[year]['_EITC_c'], atol=0.01, rtol=0.0) def test_translate_json_reform_suffixes_idedtype(): # test read_json_param_objects(...) # using idedtype-indexed parameter suffixes json1 = """{"policy": { "_ID_BenefitCap_rt": {"2019": [0.2]}, "_ID_BenefitCap_Switch_medical": {"2019": [false]}, "_ID_BenefitCap_Switch_casualty": {"2019": [false]}, "_ID_BenefitCap_Switch_misc": {"2019": [false]}, "_ID_BenefitCap_Switch_interest": {"2019": [false]}, "_ID_BenefitCap_Switch_charity": {"2019": [false]}, "_II_em": {"2020": [20000], "2015": [15000]} }}""" pdict1 = Calculator.read_json_param_objects(reform=json1, assump=None) rdict1 = pdict1['policy'] json2 = """{"policy": { "_II_em": {"2020": [20000], "2015": [15000]}, "_ID_BenefitCap_Switch": { "2019": [[false, true, true, false, false, false, false]] }, "_ID_BenefitCap_rt": {"2019": [0.2]} }}""" pdict2 = Calculator.read_json_param_objects(reform=json2, assump=None) rdict2 = pdict2['policy'] assert len(rdict2) == len(rdict1) for year in rdict2.keys(): if '_II_em' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_II_em'], rdict2[year]['_II_em'], atol=0.01, rtol=0.0) if '_ID_BenefitCap_rt' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_ID_BenefitCap_rt'], rdict2[year]['_ID_BenefitCap_rt'], atol=0.01, rtol=0.0) if '_ID_BenefitCap_Switch' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_ID_BenefitCap_Switch'], rdict2[year]['_ID_BenefitCap_Switch'], atol=0.01, rtol=0.0) def test_read_json_param_with_suffixes_and_errors(): # test interaction of policy parameter suffixes and reform errors # (fails without 0.10.2 bug fix as reported by Hank Doupe in PB PR#641) reform = { u'policy': { u'_II_brk4_separate': {u'2017': [5000.0]}, u'_STD_separate': {u'2017': [8000.0]}, u'_STD_single': {u'2018': [1000.0]}, u'_II_brk2_headhousehold': {u'2017': [1000.0]}, u'_II_brk4_single': {u'2017': [500.0]}, u'_STD_joint': {u'2017': [10000.0], u'2020': [150.0]}, u'_II_brk2_separate': {u'2017': [1000.0]}, u'_II_brk2_single': {u'2017': [1000.0]}, u'_II_brk2_joint': {u'2017': [1000.0]}, u'_FICA_ss_trt': {u'2017': [-1.0], u'2019': [0.1]}, u'_II_brk4_headhousehold': {u'2017': [500.0]}, u'_STD_headhousehold': {u'2017': [10000.0], u'2020': [150.0]}, u'_II_brk4_joint': {u'2017': [500.0]}, u'_ID_BenefitSurtax_Switch_medical': {u'2017': [True]} } } json_reform = json.dumps(reform) params = Calculator.read_json_param_objects(json_reform, None) assert isinstance(params, dict) pol = Policy() pol.ignore_reform_errors() pol.implement_reform(params['policy'], print_warnings=False, raise_errors=False) assert len(pol.parameter_errors) > 0 assert len(pol.parameter_warnings) > 0 def test_noreform_documentation(): reform_json = """ { "policy": {} } """ assump_json = """ { "consumption": {}, "behavior": {}, "growdiff_baseline": {}, "growdiff_response": {}, "growmodel": {} } """ params = Calculator.read_json_param_objects(reform_json, assump_json) assert isinstance(params, dict) actual_doc = Calculator.reform_documentation(params) expected_doc = ( 'REFORM DOCUMENTATION\n' 'Baseline Growth-Difference Assumption Values by Year:\n' 'none: using default baseline growth assumptions\n' 'Policy Reform Parameter Values by Year:\n' 'none: using current-law policy parameters\n' ) assert actual_doc == expected_doc def test_reform_documentation(): reform_json = """ { "policy": { "_II_em_cpi": {"2016": false, "2018": true}, "_II_em": {"2016": [5000], "2018": [6000], "2020": [7000]}, "_EITC_indiv": {"2017": [true]}, "_STD_Aged_cpi": {"2016": false}, "_STD_Aged": {"2016": [[1600, 1300, 1300, 1600, 1600]], "2020": [[2000, 2000, 2000, 2000, 2000]]}, "_ID_BenefitCap_Switch_medical": {"2020": [false]}, "_ID_BenefitCap_Switch_casualty": {"2020": [false]}, "_ID_BenefitCap_Switch_misc": {"2020": [false]}, "_ID_BenefitCap_Switch_interest": {"2020": [false]}, "_ID_BenefitCap_Switch_charity": {"2020": [false]} } } """ assump_json = """ { "consumption": {}, "behavior": {}, // increase baseline inflation rate by one percentage point in 2014+ // (has no effect on known policy parameter values) "growdiff_baseline": {"_ACPIU": {"2014": [0.01]}}, "growdiff_response": {}, "growmodel": {} } """ params = Calculator.read_json_param_objects(reform_json, assump_json) assert isinstance(params, dict) doc = Calculator.reform_documentation(params) assert isinstance(doc, six.string_types) dump = False # set to True to print documentation and force test failure if dump: print(doc) assert 1 == 2 def test_distribution_tables(cps_subsample): pol = Policy() recs = Records.cps_constructor(data=cps_subsample) calc1 = Calculator(policy=pol, records=recs) assert calc1.current_year == 2014 calc1.calc_all() dt1, dt2 = calc1.distribution_tables(None, 'weighted_deciles') assert isinstance(dt1, pd.DataFrame) assert dt2 is None dt1, dt2 = calc1.distribution_tables(calc1, 'weighted_deciles') assert isinstance(dt1, pd.DataFrame) assert isinstance(dt2, pd.DataFrame) reform = {2014: {'_UBI_u18': [1000], '_UBI_1820': [1000], '_UBI_21': [1000]}} pol.implement_reform(reform) assert not pol.parameter_errors calc2 = Calculator(policy=pol, records=recs) calc2.calc_all() dt1, dt2 = calc1.distribution_tables(calc2, 'weighted_deciles') assert isinstance(dt1, pd.DataFrame) assert isinstance(dt2, pd.DataFrame) def test_difference_table(cps_subsample): cyr = 2014 pol = Policy() recs = Records.cps_constructor(data=cps_subsample) calc1 = Calculator(policy=pol, records=recs) assert calc1.current_year == cyr reform = {cyr: {'_SS_Earnings_c': [9e99]}} pol.implement_reform(reform) calc2 = Calculator(policy=pol, records=recs) assert calc2.current_year == cyr calc1.calc_all() calc2.calc_all() diff = calc1.difference_table(calc2, 'weighted_deciles', 'iitax') assert isinstance(diff, pd.DataFrame) def test_diagnostic_table(cps_subsample): recs = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc = Calculator(policy=Policy(), records=recs) adt = calc.diagnostic_table(3) assert isinstance(adt, pd.DataFrame) def test_mtr_graph(cps_subsample): recs = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc = Calculator(policy=Policy(), records=recs) fig = calc.mtr_graph(calc, mars=2, income_measure='wages', mtr_measure='ptax') assert fig fig = calc.mtr_graph(calc, income_measure='agi', mtr_measure='itax') assert fig def test_atr_graph(cps_subsample): recs = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc = Calculator(policy=Policy(), records=recs) fig = calc.atr_graph(calc, mars=2, atr_measure='itax') assert fig fig = calc.atr_graph(calc, atr_measure='ptax') assert fig def test_privacy_of_embedded_objects(cps_subsample): recs = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc = Calculator(policy=Policy(), records=recs) with pytest.raises(AttributeError): cyr = calc.__policy.current_year with pytest.raises(AttributeError): wgh = calc.__records.s006 with pytest.raises(AttributeError): cyr = calc.__consumption.current_year with pytest.raises(AttributeError): cyr = calc.__behavior.current_year def test_n65(cps_subsample): recs = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc = Calculator(policy=Policy(), records=recs) assert calc.n65().sum() > 1500

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import os import json from io import StringIO import tempfile import copy import six import pytest import numpy as np import pandas as pd from taxcalc import Policy, Records, Calculator, Behavior, Consumption RAWINPUTFILE_FUNITS = 4 RAWINPUTFILE_YEAR = 2015 RAWINPUTFILE_CONTENTS = ( 'RECID,MARS\n' '1,2\n' '2,1\n' '3,4\n' '4,3\n' ) @pytest.fixture(scope='module', name='rawinputfile') def fixture_rawinputfile(): ifile = tempfile.NamedTemporaryFile(mode='a', delete=False) ifile.write(RAWINPUTFILE_CONTENTS) ifile.close() yield ifile if os.path.isfile(ifile.name): try: os.remove(ifile.name) except OSError: pass @pytest.fixture(scope='module', name='policyfile') def fixture_policyfile(): txt = """{"_almdep": {"value": [7150, 7250, 7400]}, "_almsep": {"value": [40400, 41050]}, "_rt5": {"value": [0.33 ]}, "_rt7": {"value": [0.396]}}""" f = tempfile.NamedTemporaryFile(mode="a", delete=False) f.write(txt + "\n") f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) def test_make_calculator(cps_subsample): syr = 2014 pol = Policy(start_year=syr, num_years=9) assert pol.current_year == syr rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) consump = Consumption() consump.update_consumption({syr: {'_MPC_e20400': [0.05]}}) assert consump.current_year == Consumption.JSON_START_YEAR calc = Calculator(policy=pol, records=rec, consumption=consump, behavior=Behavior()) assert calc.current_year == syr assert calc.records_current_year() == syr # test incorrect Calculator instantiation: with pytest.raises(ValueError): Calculator(policy=None, records=rec) with pytest.raises(ValueError): Calculator(policy=pol, records=None) with pytest.raises(ValueError): Calculator(policy=pol, records=rec, behavior=list()) with pytest.raises(ValueError): Calculator(policy=pol, records=rec, consumption=list()) def test_make_calculator_deepcopy(cps_subsample): pol = Policy() rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc1 = Calculator(policy=pol, records=rec) calc2 = copy.deepcopy(calc1) assert isinstance(calc2, Calculator) def test_make_calculator_with_policy_reform(cps_subsample): rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) year = rec.current_year # create a Policy object and apply a policy reform pol = Policy() reform = {2013: {'_II_em': [4000], '_II_em_cpi': False, '_STD_Aged': [[1600, 1300, 1300, 1600, 1600]], '_STD_Aged_cpi': False}} pol.implement_reform(reform) # create a Calculator object using this policy reform calc = Calculator(policy=pol, records=rec) # check that Policy object embedded in Calculator object is correct assert calc.current_year == year assert calc.policy_param('II_em') == 4000 assert np.allclose(calc.policy_param('_II_em'), np.array([4000] * Policy.DEFAULT_NUM_YEARS)) exp_STD_Aged = [[1600, 1300, 1300, 1600, 1600]] * Policy.DEFAULT_NUM_YEARS assert np.allclose(calc.policy_param('_STD_Aged'), np.array(exp_STD_Aged)) assert np.allclose(calc.policy_param('STD_Aged'), np.array([1600, 1300, 1300, 1600, 1600])) def test_make_calculator_with_multiyear_reform(cps_subsample): rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) year = rec.current_year # create a Policy object and apply a policy reform pol = Policy() reform = {2015: {}, 2016: {}} reform[2015]['_II_em'] = [5000, 6000] # reform values for 2015 and 2016 reform[2015]['_II_em_cpi'] = False reform[2016]['_STD_Aged'] = [[1600, 1300, 1600, 1300, 1600]] pol.implement_reform(reform) # create a Calculator object using this policy-reform calc = Calculator(policy=pol, records=rec) # check that Policy object embedded in Calculator object is correct assert pol.num_years == Policy.DEFAULT_NUM_YEARS assert calc.current_year == year assert calc.policy_param('II_em') == 3950 exp_II_em = [3900, 3950, 5000] + [6000] * (Policy.DEFAULT_NUM_YEARS - 3) assert np.allclose(calc.policy_param('_II_em'), np.array(exp_II_em)) calc.increment_year() calc.increment_year() assert calc.current_year == 2016 assert np.allclose(calc.policy_param('STD_Aged'), np.array([1600, 1300, 1600, 1300, 1600])) def test_calculator_advance_to_year(cps_subsample): rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) pol = Policy() calc = Calculator(policy=pol, records=rec) calc.advance_to_year(2016) assert calc.current_year == 2016 with pytest.raises(ValueError): calc.advance_to_year(2015) def test_make_calculator_raises_on_no_policy(cps_subsample): rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) with pytest.raises(ValueError): Calculator(records=rec) def test_calculator_mtr(cps_subsample): rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) calcx = Calculator(policy=Policy(), records=rec) calcx.calc_all() combinedx = calcx.array('combined') c00100x = calcx.array('c00100') calc = Calculator(policy=Policy(), records=rec) recs_pre_e00200p = copy.deepcopy(calc.array('e00200p')) (mtr_ptx, mtr_itx, mtr_cmb) = calc.mtr(variable_str='e00200p', zero_out_calculated_vars=True) recs_post_e00200p = calc.array('e00200p') assert np.allclose(recs_post_e00200p, recs_pre_e00200p) assert np.allclose(calc.array('combined'), combinedx) assert np.allclose(calc.array('c00100'), c00100x) assert np.array_equal(mtr_cmb, mtr_ptx) is False assert np.array_equal(mtr_ptx, mtr_itx) is False with pytest.raises(ValueError): calc.mtr(variable_str='bad_income_type') (_, _, mtr_combined) = calc.mtr(variable_str='e00200s', calc_all_already_called=True) assert isinstance(mtr_combined, np.ndarray) (_, _, mtr_combined) = calc.mtr(variable_str='e00650', negative_finite_diff=True, calc_all_already_called=True) assert isinstance(mtr_combined, np.ndarray) (_, _, mtr_combined) = calc.mtr(variable_str='e00900p', calc_all_already_called=True) assert isinstance(mtr_combined, np.ndarray) (_, _, mtr_combined) = calc.mtr(variable_str='e01700', calc_all_already_called=True) assert isinstance(mtr_combined, np.ndarray) (_, _, mtr_combined) = calc.mtr(variable_str='e26270', calc_all_already_called=True) assert isinstance(mtr_combined, np.ndarray) (_, _, mtr_combined) = calc.mtr(variable_str='e00200p', calc_all_already_called=True) assert np.allclose(mtr_combined, mtr_cmb) assert np.allclose(calc.array('combined'), combinedx) assert np.allclose(calc.array('c00100'), c00100x) def test_calculator_mtr_when_PT_rates_differ(): reform = {2013: {'_II_rt1': [0.40], '_II_rt2': [0.40], '_II_rt3': [0.40], '_II_rt4': [0.40], '_II_rt5': [0.40], '_II_rt6': [0.40], '_II_rt7': [0.40], '_PT_rt1': [0.30], '_PT_rt2': [0.30], '_PT_rt3': [0.30], '_PT_rt4': [0.30], '_PT_rt5': [0.30], '_PT_rt6': [0.30], '_PT_rt7': [0.30]}} funit = ( u'RECID,MARS,FLPDYR,e00200,e00200p,e00900,e00900p,extraneous\n' u'1, 1, 2009, 200000,200000, 100000,100000, 9999999999\n' ) rec = Records(pd.read_csv(StringIO(funit))) pol = Policy() calc1 = Calculator(policy=pol, records=rec) (_, mtr1, _) = calc1.mtr(variable_str='p23250') pol.implement_reform(reform) calc2 = Calculator(policy=pol, records=rec) (_, mtr2, _) = calc2.mtr(variable_str='p23250') assert np.allclose(mtr1, mtr2, rtol=0.0, atol=1e-06) def test_make_calculator_increment_years_first(cps_subsample): # create Policy object with policy reform syr = 2013 pol = Policy(start_year=syr) reform = {2015: {}, 2016: {}} std5 = 2000 reform[2015]['_STD_Aged'] = [[std5, std5, std5, std5, std5]] reform[2015]['_II_em'] = [5000] reform[2016]['_II_em'] = [6000] reform[2016]['_II_em_cpi'] = False pol.implement_reform(reform) # create Calculator object with Policy object as modified by reform rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc = Calculator(policy=pol, records=rec) # compare expected policy parameter values with those embedded in calc irates = pol.inflation_rates() irate2015 = irates[2015 - syr] irate2016 = irates[2016 - syr] std6 = std5 * (1.0 + irate2015) std7 = std6 * (1.0 + irate2016) exp_STD_Aged = np.array([[1500, 1200, 1200, 1500, 1500], [1550, 1200, 1200, 1550, 1550], [std5, std5, std5, std5, std5], [std6, std6, std6, std6, std6], [std7, std7, std7, std7, std7]]) act_STD_Aged = calc.policy_param('_STD_Aged') assert np.allclose(act_STD_Aged[:5], exp_STD_Aged) exp_II_em = np.array([3900, 3950, 5000, 6000, 6000]) act_II_em = calc.policy_param('_II_em') assert np.allclose(act_II_em[:5], exp_II_em) def test_ID_HC_vs_BS(cps_subsample): recs = Records.cps_constructor(data=cps_subsample, no_benefits=True) # specify complete-haircut reform policy and Calculator object hc_reform = {2013: {'_ID_Medical_hc': [1.0], '_ID_StateLocalTax_hc': [1.0], '_ID_RealEstate_hc': [1.0], '_ID_Casualty_hc': [1.0], '_ID_Miscellaneous_hc': [1.0], '_ID_InterestPaid_hc': [1.0], '_ID_Charity_hc': [1.0]}} hc_policy = Policy() hc_policy.implement_reform(hc_reform) hc_calc = Calculator(policy=hc_policy, records=recs) hc_calc.calc_all() hc_taxes = hc_calc.dataframe(['iitax', 'payrolltax']) del hc_calc # specify benefit-surtax reform policy and Calculator object bs_reform = {2013: {'_ID_BenefitSurtax_crt': [0.0], '_ID_BenefitSurtax_trt': [1.0]}} bs_policy = Policy() bs_policy.implement_reform(bs_reform) bs_calc = Calculator(policy=bs_policy, records=recs) bs_calc.calc_all() bs_taxes = bs_calc.dataframe(['iitax', 'payrolltax']) del bs_calc # compare calculated taxes generated by the two reforms assert np.allclose(hc_taxes['payrolltax'], bs_taxes['payrolltax']) assert np.allclose(hc_taxes['iitax'], bs_taxes['iitax']) def test_ID_StateLocal_HC_vs_CRT(cps_subsample): rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) # specify state/local complete haircut reform policy and Calculator object hc_reform = {2013: {'_ID_StateLocalTax_hc': [1.0]}} hc_policy = Policy() hc_policy.implement_reform(hc_reform) hc_calc = Calculator(policy=hc_policy, records=rec) hc_calc.calc_all() # specify AGI cap reform policy and Calculator object crt_reform = {2013: {'_ID_StateLocalTax_crt': [0.0]}} crt_policy = Policy() crt_policy.implement_reform(crt_reform) crt_calc = Calculator(policy=crt_policy, records=rec) crt_calc.calc_all() # compare calculated tax results generated by the two reforms assert np.allclose(hc_calc.array('payrolltax'), crt_calc.array('payrolltax')) assert np.allclose(hc_calc.array('iitax'), crt_calc.array('iitax')) def test_ID_RealEstate_HC_vs_CRT(cps_subsample): rec = Records.cps_constructor(data=cps_subsample, no_benefits=True) # specify real estate complete haircut reform policy and Calculator object hc_reform = {2013: {'_ID_RealEstate_hc': [1.0]}} hc_policy = Policy() hc_policy.implement_reform(hc_reform) hc_calc = Calculator(policy=hc_policy, records=rec) hc_calc.calc_all() # specify AGI cap reform policy and Calculator object crt_reform = {2013: {'_ID_RealEstate_crt': [0.0]}} crt_policy = Policy() crt_policy.implement_reform(crt_reform) crt_calc = Calculator(policy=crt_policy, records=rec) crt_calc.calc_all() # compare calculated tax results generated by the two reforms assert np.allclose(hc_calc.array('payrolltax'), crt_calc.array('payrolltax')) assert np.allclose(hc_calc.array('iitax'), crt_calc.array('iitax')) def test_calculator_using_nonstd_input(rawinputfile): # check Calculator handling of raw, non-standard input data with no aging pol = Policy() pol.set_year(RAWINPUTFILE_YEAR) # set policy params to input data year nonstd = Records(data=rawinputfile.name, gfactors=None, # keeps raw data unchanged weights=None, start_year=RAWINPUTFILE_YEAR) # set raw input data year assert nonstd.array_length == RAWINPUTFILE_FUNITS calc = Calculator(policy=pol, records=nonstd, sync_years=False) # keeps raw data unchanged assert calc.current_year == RAWINPUTFILE_YEAR calc.calc_all() assert calc.weighted_total('e00200') == 0 assert calc.total_weight() == 0 varlist = ['RECID', 'MARS'] pdf = calc.dataframe(varlist) assert isinstance(pdf, pd.DataFrame) assert pdf.shape == (RAWINPUTFILE_FUNITS, len(varlist)) mars = calc.array('MARS') assert isinstance(mars, np.ndarray) assert mars.shape == (RAWINPUTFILE_FUNITS,) exp_iitax = np.zeros((nonstd.array_length,)) assert np.allclose(calc.array('iitax'), exp_iitax) mtr_ptax, _, _ = calc.mtr(wrt_full_compensation=False) exp_mtr_ptax = np.zeros((nonstd.array_length,)) exp_mtr_ptax.fill(0.153) assert np.allclose(mtr_ptax, exp_mtr_ptax) REFORM_CONTENTS = """ // Example of a reform file suitable for read_json_param_objects(). // This JSON file can contain any number of trailing //-style comments, which // will be removed before the contents are converted from JSON to a dictionary. // Within each "policy" object, the primary keys are parameters and // the secondary keys are years. // Both the primary and secondary key values must be enclosed in quotes ("). // Boolean variables are specified as true or false (no quotes; all lowercase). // Parameter code in the policy object is enclosed inside a pair of double // pipe characters (||). { "policy": { "_AMT_brk1": // top of first AMT tax bracket {"2015": [200000], "2017": [300000] }, "_EITC_c": // maximum EITC amount by number of qualifying kids (0,1,2,3+) {"2016": [[ 900, 5000, 8000, 9000]], "2019": [[1200, 7000, 10000, 12000]] }, "_II_em": // personal exemption amount (see indexing changes below) {"2016": [6000], "2018": [7500], "2020": [9000] }, "_II_em_cpi": // personal exemption amount indexing status {"2016": false, // values in future years are same as this year value "2018": true // values in future years indexed with this year as base }, "_SS_Earnings_c": // social security (OASDI) maximum taxable earnings {"2016": [300000], "2018": [500000], "2020": [700000] }, "_AMT_em_cpi": // AMT exemption amount indexing status {"2017": false, // values in future years are same as this year value "2020": true // values in future years indexed with this year as base } } } """ @pytest.fixture(scope='module', name='reform_file') def fixture_reform_file(): rfile = tempfile.NamedTemporaryFile(mode='a', delete=False) rfile.write(REFORM_CONTENTS) rfile.close() # must close and then yield for Windows platform yield rfile if os.path.isfile(rfile.name): try: os.remove(rfile.name) except OSError: pass # sometimes we can't remove a generated temporary file ASSUMP_CONTENTS = """ // Example of assump file suitable for the read_json_param_objects(). // This JSON file can contain any number of trailing //-style comments, which // will be removed before the contents are converted from JSON to a dictionary. // Within each "behavior", "consumption" and "growth" object, the // primary keys are parameters and the secondary keys are years. // Both the primary and secondary key values must be enclosed in quotes ("). // Boolean variables are specified as true or false (no quotes; all lowercase). { "consumption": { "_MPC_e18400": {"2018": [0.05]} }, "behavior": {}, "growdiff_baseline": {}, "growdiff_response": {}, "growmodel": {} } """ @pytest.fixture(scope='module', name='assump_file') def fixture_assump_file(): afile = tempfile.NamedTemporaryFile(mode='a', delete=False) afile.write(ASSUMP_CONTENTS) afile.close() yield afile if os.path.isfile(afile.name): try: os.remove(afile.name) except OSError: pass @pytest.mark.parametrize("set_year", [False, True]) def test_read_json_reform_file_and_implement_reform(reform_file, assump_file, set_year): pol = Policy() if set_year: pol.set_year(2015) param_dict = Calculator.read_json_param_objects(reform_file.name, assump_file.name) pol.implement_reform(param_dict['policy']) syr = pol.start_year amt_brk1 = pol._AMT_brk1 assert amt_brk1[2015 - syr] == 200000 assert amt_brk1[2016 - syr] > 200000 assert amt_brk1[2017 - syr] == 300000 assert amt_brk1[2018 - syr] > 300000 ii_em = pol._II_em assert ii_em[2016 - syr] == 6000 assert ii_em[2017 - syr] == 6000 assert ii_em[2018 - syr] == 7500 assert ii_em[2019 - syr] > 7500 assert ii_em[2020 - syr] == 9000 assert ii_em[2021 - syr] > 9000 amt_em = pol._AMT_em assert amt_em[2016 - syr, 0] > amt_em[2015 - syr, 0] assert amt_em[2017 - syr, 0] > amt_em[2016 - syr, 0] assert amt_em[2018 - syr, 0] == amt_em[2017 - syr, 0] assert amt_em[2019 - syr, 0] == amt_em[2017 - syr, 0] assert amt_em[2020 - syr, 0] == amt_em[2017 - syr, 0] assert amt_em[2021 - syr, 0] > amt_em[2020 - syr, 0] assert amt_em[2022 - syr, 0] > amt_em[2021 - syr, 0] add4aged = pol._ID_Medical_frt_add4aged assert add4aged[2015 - syr] == -0.025 assert add4aged[2016 - syr] == -0.025 assert add4aged[2017 - syr] == 0.0 assert add4aged[2022 - syr] == 0.0 @pytest.fixture(scope='module', name='bad1reformfile') def fixture_bad1reformfile(): # specify JSON text for reform txt = """ { "policy": { // example of incorrect JSON because 'x' must be "x" 'x': {"2014": [4000]} } } """ f = tempfile.NamedTemporaryFile(mode='a', delete=False) f.write(txt + '\n') f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) @pytest.fixture(scope='module', name='bad2reformfile') def fixture_bad2reformfile(): # specify JSON text for reform txt = """ { "title": "", "policyx": { // example of reform file not containing "policy" key "_SS_Earnings_c": {"2018": [9e99]} } } """ f = tempfile.NamedTemporaryFile(mode='a', delete=False) f.write(txt + '\n') f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) @pytest.fixture(scope='module', name='bad3reformfile') def fixture_bad3reformfile(): # specify JSON text for reform txt = """ { "title": "", "policy": { "_SS_Earnings_c": {"2018": [9e99]} }, "behavior": { // example of misplaced "behavior" key } } """ f = tempfile.NamedTemporaryFile(mode='a', delete=False) f.write(txt + '\n') f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) def test_read_bad_json_reform_file(bad1reformfile, bad2reformfile, bad3reformfile): with pytest.raises(ValueError): Calculator.read_json_param_objects(bad1reformfile.name, None) with pytest.raises(ValueError): Calculator.read_json_param_objects(bad2reformfile.name, None) with pytest.raises(ValueError): Calculator.read_json_param_objects(bad3reformfile.name, None) with pytest.raises(ValueError): Calculator.read_json_param_objects(list(), None) with pytest.raises(ValueError): Calculator.read_json_param_objects(None, 'unknown_file_name') with pytest.raises(ValueError): Calculator.read_json_param_objects(None, list()) @pytest.fixture(scope='module', name='bad1assumpfile') def fixture_bad1assumpfile(): # specify JSON text for assumptions txt = """ { "consumption": {}, "behavior": { // example of incorrect JSON because 'x' must be "x" 'x': {"2014": [0.25]} }, "growdiff_baseline": {}, "growdiff_response": {}, "growmodel": {} } """ f = tempfile.NamedTemporaryFile(mode='a', delete=False) f.write(txt + '\n') f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) @pytest.fixture(scope='module', name='bad2assumpfile') def fixture_bad2assumpfile(): # specify JSON text for assumptions txt = """ { "consumption": {}, "behaviorx": {}, // example of assump file not containing "behavior" key "growdiff_baseline": {}, "growdiff_response": {}, "growmodel": {} } """ f = tempfile.NamedTemporaryFile(mode='a', delete=False) f.write(txt + '\n') f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) @pytest.fixture(scope='module', name='bad3assumpfile') def fixture_bad3assumpfile(): # specify JSON text for assump txt = """ { "consumption": {}, "behavior": {}, "growdiff_baseline": {}, "growdiff_response": {}, "policy": { // example of misplaced policy key "_SS_Earnings_c": {"2018": [9e99]} }, "growmodel": {} } """ f = tempfile.NamedTemporaryFile(mode='a', delete=False) f.write(txt + '\n') f.close() # Must close and then yield for Windows platform yield f os.remove(f.name) def test_read_bad_json_assump_file(bad1assumpfile, bad2assumpfile, bad3assumpfile): with pytest.raises(ValueError): Calculator.read_json_param_objects(None, bad1assumpfile.name) with pytest.raises(ValueError): Calculator.read_json_param_objects(None, bad2assumpfile.name) with pytest.raises(ValueError): Calculator.read_json_param_objects(None, bad3assumpfile.name) with pytest.raises(ValueError): Calculator.read_json_param_objects(None, 'unknown_file_name') with pytest.raises(ValueError): Calculator.read_json_param_objects(None, list()) def test_convert_parameter_dict(): with pytest.raises(ValueError): Calculator._convert_parameter_dict({2013: {'2013': [40000]}}) with pytest.raises(ValueError): Calculator._convert_parameter_dict({'_II_em': {2013: [40000]}}) with pytest.raises(ValueError): Calculator._convert_parameter_dict({4567: {2013: [40000]}}) with pytest.raises(ValueError): Calculator._convert_parameter_dict({'_II_em': 40000}) rdict = Calculator._convert_parameter_dict({'_II_em': {'2013': [40000]}}) assert isinstance(rdict, dict) def test_calc_all(reform_file, rawinputfile): cyr = 2016 pol = Policy() param_dict = Calculator.read_json_param_objects(reform_file.name, None) pol.implement_reform(param_dict['policy']) pol.set_year(cyr) nonstd = Records(data=rawinputfile.name, gfactors=None, weights=None, start_year=cyr) assert nonstd.array_length == RAWINPUTFILE_FUNITS calc = Calculator(policy=pol, records=nonstd, sync_years=False) # keeps raw data unchanged assert calc.current_year == cyr assert calc.reform_warnings == '' def test_translate_json_reform_suffixes_mars_non_indexed(): # test read_json_param_objects() # using MARS-indexed parameter suffixes json1 = """{"policy": { "_II_em": {"2020": [20000], "2015": [15000]}, "_AMEDT_ec_joint": {"2018": [400000], "2016": [300000]}, "_AMEDT_ec_separate": {"2017": [150000], "2019": [200000]} }}""" pdict1 = Calculator.read_json_param_objects(reform=json1, assump=None) rdict1 = pdict1['policy'] json2 = """{"policy": { "_AMEDT_ec": {"2016": [[200000, 300000, 125000, 200000, 200000]], "2017": [[200000, 300000, 150000, 200000, 200000]], "2018": [[200000, 400000, 150000, 200000, 200000]], "2019": [[200000, 400000, 200000, 200000, 200000]]}, "_II_em": {"2015": [15000], "2020": [20000]} }}""" pdict2 = Calculator.read_json_param_objects(reform=json2, assump=None) rdict2 = pdict2['policy'] assert len(rdict2) == len(rdict1) for year in rdict2.keys(): if '_II_em' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_II_em'], rdict2[year]['_II_em'], atol=0.01, rtol=0.0) if '_AMEDT_ec' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_AMEDT_ec'], rdict2[year]['_AMEDT_ec'], atol=0.01, rtol=0.0) def test_translate_json_reform_suffixes_eic(): # test read_json_param_objects(...) # using EIC-indexed parameter suffixes json1 = """{"policy": { "_II_em": {"2020": [20000], "2015": [15000]}, "_EITC_c_0kids": {"2018": [510], "2019": [510]}, "_EITC_c_1kid": {"2019": [3400], "2018": [3400]}, "_EITC_c_2kids": {"2018": [5616], "2019": [5616]}, "_EITC_c_3+kids": {"2019": [6318], "2018": [6318]} }}""" pdict1 = Calculator.read_json_param_objects(reform=json1, assump=None) rdict1 = pdict1['policy'] json2 = """{"policy": { "_EITC_c": {"2019": [[510, 3400, 5616, 6318]], "2018": [[510, 3400, 5616, 6318]]}, "_II_em": {"2020": [20000], "2015": [15000]} }}""" pdict2 = Calculator.read_json_param_objects(reform=json2, assump=None) rdict2 = pdict2['policy'] assert len(rdict2) == len(rdict1) for year in rdict2.keys(): if '_II_em' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_II_em'], rdict2[year]['_II_em'], atol=0.01, rtol=0.0) if '_EITC_c' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_EITC_c'], rdict2[year]['_EITC_c'], atol=0.01, rtol=0.0) def test_translate_json_reform_suffixes_idedtype(): # test read_json_param_objects(...) # using idedtype-indexed parameter suffixes json1 = """{"policy": { "_ID_BenefitCap_rt": {"2019": [0.2]}, "_ID_BenefitCap_Switch_medical": {"2019": [false]}, "_ID_BenefitCap_Switch_casualty": {"2019": [false]}, "_ID_BenefitCap_Switch_misc": {"2019": [false]}, "_ID_BenefitCap_Switch_interest": {"2019": [false]}, "_ID_BenefitCap_Switch_charity": {"2019": [false]}, "_II_em": {"2020": [20000], "2015": [15000]} }}""" pdict1 = Calculator.read_json_param_objects(reform=json1, assump=None) rdict1 = pdict1['policy'] json2 = """{"policy": { "_II_em": {"2020": [20000], "2015": [15000]}, "_ID_BenefitCap_Switch": { "2019": [[false, true, true, false, false, false, false]] }, "_ID_BenefitCap_rt": {"2019": [0.2]} }}""" pdict2 = Calculator.read_json_param_objects(reform=json2, assump=None) rdict2 = pdict2['policy'] assert len(rdict2) == len(rdict1) for year in rdict2.keys(): if '_II_em' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_II_em'], rdict2[year]['_II_em'], atol=0.01, rtol=0.0) if '_ID_BenefitCap_rt' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_ID_BenefitCap_rt'], rdict2[year]['_ID_BenefitCap_rt'], atol=0.01, rtol=0.0) if '_ID_BenefitCap_Switch' in rdict2[year].keys(): assert np.allclose(rdict1[year]['_ID_BenefitCap_Switch'], rdict2[year]['_ID_BenefitCap_Switch'], atol=0.01, rtol=0.0) def test_read_json_param_with_suffixes_and_errors(): # test interaction of policy parameter suffixes and reform errors # (fails without 0.10.2 bug fix as reported by Hank Doupe in PB PR#641) reform = { u'policy': { u'_II_brk4_separate': {u'2017': [5000.0]}, u'_STD_separate': {u'2017': [8000.0]}, u'_STD_single': {u'2018': [1000.0]}, u'_II_brk2_headhousehold': {u'2017': [1000.0]}, u'_II_brk4_single': {u'2017': [500.0]}, u'_STD_joint': {u'2017': [10000.0], u'2020': [150.0]}, u'_II_brk2_separate': {u'2017': [1000.0]}, u'_II_brk2_single': {u'2017': [1000.0]}, u'_II_brk2_joint': {u'2017': [1000.0]}, u'_FICA_ss_trt': {u'2017': [-1.0], u'2019': [0.1]}, u'_II_brk4_headhousehold': {u'2017': [500.0]}, u'_STD_headhousehold': {u'2017': [10000.0], u'2020': [150.0]}, u'_II_brk4_joint': {u'2017': [500.0]}, u'_ID_BenefitSurtax_Switch_medical': {u'2017': [True]} } } json_reform = json.dumps(reform) params = Calculator.read_json_param_objects(json_reform, None) assert isinstance(params, dict) pol = Policy() pol.ignore_reform_errors() pol.implement_reform(params['policy'], print_warnings=False, raise_errors=False) assert len(pol.parameter_errors) > 0 assert len(pol.parameter_warnings) > 0 def test_noreform_documentation(): reform_json = """ { "policy": {} } """ assump_json = """ { "consumption": {}, "behavior": {}, "growdiff_baseline": {}, "growdiff_response": {}, "growmodel": {} } """ params = Calculator.read_json_param_objects(reform_json, assump_json) assert isinstance(params, dict) actual_doc = Calculator.reform_documentation(params) expected_doc = ( 'REFORM DOCUMENTATION\n' 'Baseline Growth-Difference Assumption Values by Year:\n' 'none: using default baseline growth assumptions\n' 'Policy Reform Parameter Values by Year:\n' 'none: using current-law policy parameters\n' ) assert actual_doc == expected_doc def test_reform_documentation(): reform_json = """ { "policy": { "_II_em_cpi": {"2016": false, "2018": true}, "_II_em": {"2016": [5000], "2018": [6000], "2020": [7000]}, "_EITC_indiv": {"2017": [true]}, "_STD_Aged_cpi": {"2016": false}, "_STD_Aged": {"2016": [[1600, 1300, 1300, 1600, 1600]], "2020": [[2000, 2000, 2000, 2000, 2000]]}, "_ID_BenefitCap_Switch_medical": {"2020": [false]}, "_ID_BenefitCap_Switch_casualty": {"2020": [false]}, "_ID_BenefitCap_Switch_misc": {"2020": [false]}, "_ID_BenefitCap_Switch_interest": {"2020": [false]}, "_ID_BenefitCap_Switch_charity": {"2020": [false]} } } """ assump_json = """ { "consumption": {}, "behavior": {}, // increase baseline inflation rate by one percentage point in 2014+ // (has no effect on known policy parameter values) "growdiff_baseline": {"_ACPIU": {"2014": [0.01]}}, "growdiff_response": {}, "growmodel": {} } """ params = Calculator.read_json_param_objects(reform_json, assump_json) assert isinstance(params, dict) doc = Calculator.reform_documentation(params) assert isinstance(doc, six.string_types) dump = False # set to True to print documentation and force test failure if dump: print(doc) assert 1 == 2 def test_distribution_tables(cps_subsample): pol = Policy() recs = Records.cps_constructor(data=cps_subsample) calc1 = Calculator(policy=pol, records=recs) assert calc1.current_year == 2014 calc1.calc_all() dt1, dt2 = calc1.distribution_tables(None, 'weighted_deciles') assert isinstance(dt1, pd.DataFrame) assert dt2 is None dt1, dt2 = calc1.distribution_tables(calc1, 'weighted_deciles') assert isinstance(dt1, pd.DataFrame) assert isinstance(dt2, pd.DataFrame) reform = {2014: {'_UBI_u18': [1000], '_UBI_1820': [1000], '_UBI_21': [1000]}} pol.implement_reform(reform) assert not pol.parameter_errors calc2 = Calculator(policy=pol, records=recs) calc2.calc_all() dt1, dt2 = calc1.distribution_tables(calc2, 'weighted_deciles') assert isinstance(dt1, pd.DataFrame) assert isinstance(dt2, pd.DataFrame) def test_difference_table(cps_subsample): cyr = 2014 pol = Policy() recs = Records.cps_constructor(data=cps_subsample) calc1 = Calculator(policy=pol, records=recs) assert calc1.current_year == cyr reform = {cyr: {'_SS_Earnings_c': [9e99]}} pol.implement_reform(reform) calc2 = Calculator(policy=pol, records=recs) assert calc2.current_year == cyr calc1.calc_all() calc2.calc_all() diff = calc1.difference_table(calc2, 'weighted_deciles', 'iitax') assert isinstance(diff, pd.DataFrame) def test_diagnostic_table(cps_subsample): recs = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc = Calculator(policy=Policy(), records=recs) adt = calc.diagnostic_table(3) assert isinstance(adt, pd.DataFrame) def test_mtr_graph(cps_subsample): recs = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc = Calculator(policy=Policy(), records=recs) fig = calc.mtr_graph(calc, mars=2, income_measure='wages', mtr_measure='ptax') assert fig fig = calc.mtr_graph(calc, income_measure='agi', mtr_measure='itax') assert fig def test_atr_graph(cps_subsample): recs = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc = Calculator(policy=Policy(), records=recs) fig = calc.atr_graph(calc, mars=2, atr_measure='itax') assert fig fig = calc.atr_graph(calc, atr_measure='ptax') assert fig def test_privacy_of_embedded_objects(cps_subsample): recs = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc = Calculator(policy=Policy(), records=recs) with pytest.raises(AttributeError): cyr = calc.__policy.current_year with pytest.raises(AttributeError): wgh = calc.__records.s006 with pytest.raises(AttributeError): cyr = calc.__consumption.current_year with pytest.raises(AttributeError): cyr = calc.__behavior.current_year def test_n65(cps_subsample): recs = Records.cps_constructor(data=cps_subsample, no_benefits=True) calc = Calculator(policy=Policy(), records=recs) assert calc.n65().sum() > 1500

true

f723b7721b96101a76c0e12c365a4083110cae7a

3,147

py

Python

drafthub/core/templatetags/drafthub_extras.py

felipelincoln/drafthub

e8a6055205646d45c26419938c3277f544931925

[ "MIT" ]

3

2020-05-13T09:54:27.000Z

2021-01-06T17:46:53.000Z

drafthub/core/templatetags/drafthub_extras.py

felipelincoln/drafthub

e8a6055205646d45c26419938c3277f544931925

[ "MIT" ]

3

2020-05-20T17:27:10.000Z

2020-05-20T20:41:37.000Z

drafthub/core/templatetags/drafthub_extras.py

felipelincoln/drafthub

e8a6055205646d45c26419938c3277f544931925

[ "MIT" ]

null

import requests import re from django import template from django.utils.safestring import mark_safe import markdown as _markdown import bleach from pymdownx import emoji from drafthub.draft.utils import get_data_from_url markdown_kwargs = { 'extensions':[ 'pymdownx.superfences', 'markdown.extensions.tables', 'pymdownx.betterem', 'pymdownx.tilde', 'pymdownx.emoji', 'pymdownx.tasklist', 'pymdownx.magiclink', 'pymdownx.arithmatex', ], 'extension_configs':{ 'pymdownx.tilde': { 'subscript': False }, 'pymdownx.emoji':{ 'emoji_index': emoji.gemoji, 'emoji_generator': emoji.to_png, 'alt': 'short', 'options': { 'attributes': { 'align': 'absmiddle', 'height': '20px', 'width': '20px' }, } }, 'pymdownx.arithmatex':{ 'generic': True, } } } bleach_kwargs = { 'tags': [ 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'b', 'i', 'strong', 'em', 'tt', 'del', 'p', 'br', 'span', 'div', 'blockquote', 'code', 'hr', 'pre', 'ul', 'ol', 'li', 'dd', 'dt', 'dl', 'img', 'a', 'sub', 'sup', 'table', 'thead','td', 'tr', 'th', 'tbody', 'input', # allow only type, checked and disabled ], 'attributes':{ '*': lambda *_: 1, } } register = template.Library() @register.filter @mark_safe def markdown(github_url): url = github_url data = get_data_from_url(url) raw = data['raw'] login = data['login'] repo = data['repo'] parent = data['parent'] markdown_kwargs['extension_configs']['pymdownx.magiclink'] = { 'repo_url_shortener': True, 'repo_url_shorthand': True, 'social_url_shorthand': True, 'provider': 'github', 'user': login, 'repo': repo, } url_response = requests.get(raw) unsafe_content = url_response.text re_links = '\[(.*)\]$(?!https?:\/\/|#)(.+)$' match_links = re.compile(re_links) content_transform = match_links.sub( r'[\1](' + parent + r'\2)', unsafe_content) markdown_content = _markdown.markdown(content_transform, **markdown_kwargs) sanitized_content = bleach.clean(markdown_content, **bleach_kwargs) return sanitized_content @register.filter @mark_safe def plaintext_markdown(text): markdown_content = _markdown.markdown(text, **markdown_kwargs) sanitized_content = bleach.clean(markdown_content, **bleach_kwargs) return sanitized_content @register.filter def count_range(n): return range(1,n+1) @register.filter def in_queryset(blog, queryset): return blog in queryset @register.filter def get_model_name(queryset): return queryset[0]._meta.model_name @register.filter def timesince_format(value): value_str = value.split(',')[0] if value_str: value_str = value_str + ' ago' return value_str @register.filter def js_bool(value): return str(bool(value)).lower()

24.395349

79

0.5796

import requests import re from django import template from django.utils.safestring import mark_safe import markdown as _markdown import bleach from pymdownx import emoji from drafthub.draft.utils import get_data_from_url markdown_kwargs = { 'extensions':[ 'pymdownx.superfences', 'markdown.extensions.tables', 'pymdownx.betterem', 'pymdownx.tilde', 'pymdownx.emoji', 'pymdownx.tasklist', 'pymdownx.magiclink', 'pymdownx.arithmatex', ], 'extension_configs':{ 'pymdownx.tilde': { 'subscript': False }, 'pymdownx.emoji':{ 'emoji_index': emoji.gemoji, 'emoji_generator': emoji.to_png, 'alt': 'short', 'options': { 'attributes': { 'align': 'absmiddle', 'height': '20px', 'width': '20px' }, } }, 'pymdownx.arithmatex':{ 'generic': True, } } } bleach_kwargs = { 'tags': [ 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'b', 'i', 'strong', 'em', 'tt', 'del', 'p', 'br', 'span', 'div', 'blockquote', 'code', 'hr', 'pre', 'ul', 'ol', 'li', 'dd', 'dt', 'dl', 'img', 'a', 'sub', 'sup', 'table', 'thead','td', 'tr', 'th', 'tbody', 'input', ], 'attributes':{ '*': lambda *_: 1, } } register = template.Library() @register.filter @mark_safe def markdown(github_url): url = github_url data = get_data_from_url(url) raw = data['raw'] login = data['login'] repo = data['repo'] parent = data['parent'] markdown_kwargs['extension_configs']['pymdownx.magiclink'] = { 'repo_url_shortener': True, 'repo_url_shorthand': True, 'social_url_shorthand': True, 'provider': 'github', 'user': login, 'repo': repo, } url_response = requests.get(raw) unsafe_content = url_response.text re_links = '\[(.*)\]$(?!https?:\/\/|#)(.+)$' match_links = re.compile(re_links) content_transform = match_links.sub( r'[\1](' + parent + r'\2)', unsafe_content) markdown_content = _markdown.markdown(content_transform, **markdown_kwargs) sanitized_content = bleach.clean(markdown_content, **bleach_kwargs) return sanitized_content @register.filter @mark_safe def plaintext_markdown(text): markdown_content = _markdown.markdown(text, **markdown_kwargs) sanitized_content = bleach.clean(markdown_content, **bleach_kwargs) return sanitized_content @register.filter def count_range(n): return range(1,n+1) @register.filter def in_queryset(blog, queryset): return blog in queryset @register.filter def get_model_name(queryset): return queryset[0]._meta.model_name @register.filter def timesince_format(value): value_str = value.split(',')[0] if value_str: value_str = value_str + ' ago' return value_str @register.filter def js_bool(value): return str(bool(value)).lower()

true

f723b7b58e53b5843835f93d00c72899fbaaa3ce

16,086

py

Python

models/model/seq2seq_im_mask_cnn_finetune.py

shivgarg/alfred_transformers

3eab07d3a218eb9b809dec8b7120b92ebd00c890

[ "MIT" ]

null

models/model/seq2seq_im_mask_cnn_finetune.py

shivgarg/alfred_transformers

3eab07d3a218eb9b809dec8b7120b92ebd00c890

[ "MIT" ]

null

models/model/seq2seq_im_mask_cnn_finetune.py

shivgarg/alfred_transformers

3eab07d3a218eb9b809dec8b7120b92ebd00c890

[ "MIT" ]

null

import os import torch import numpy as np import nn.vnn as vnn import collections from torch import nn from torch.nn import functional as F from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence from model.seq2seq import Module as Base from models.utils.metric import compute_f1, compute_exact from gen.utils.image_util import decompress_mask from torchvision import transforms from PIL import Image class Module(Base): def __init__(self, args, vocab): ''' Seq2Seq agent ''' super().__init__(args, vocab) # encoder and self-attention self.enc = nn.LSTM(args.demb, args.dhid, bidirectional=True, batch_first=True) self.enc_att = vnn.SelfAttn(args.dhid*2) # subgoal monitoring self.subgoal_monitoring = (self.args.pm_aux_loss_wt > 0 or self.args.subgoal_aux_loss_wt > 0) # frame mask decoder decoder = vnn.ConvFrameMaskDecoderProgressMonitorFinetune if self.subgoal_monitoring else vnn.ConvFrameMaskDecoder self.dec = decoder(self.emb_action_low, args.dframe, 2*args.dhid, pframe=args.pframe, attn_dropout=args.attn_dropout, hstate_dropout=args.hstate_dropout, actor_dropout=args.actor_dropout, input_dropout=args.input_dropout, teacher_forcing=args.dec_teacher_forcing) # dropouts self.vis_dropout = nn.Dropout(args.vis_dropout) self.lang_dropout = nn.Dropout(args.lang_dropout, inplace=True) self.input_dropout = nn.Dropout(args.input_dropout) # internal states self.state_t = None self.e_t = None self.test_mode = False # bce reconstruction loss self.bce_with_logits = torch.nn.BCEWithLogitsLoss(reduction='none') self.mse_loss = torch.nn.MSELoss(reduction='none') # paths self.root_path = os.getcwd() self.feat_pt = 'feat_conv.pt' # params self.max_subgoals = 25 self.max_episode_len = args.max_episode_len # reset model self.reset() def featurize(self, batch, load_mask=True, load_frames=True): ''' tensorize and pad batch input ''' device = torch.device('cuda') if self.args.gpu else torch.device('cpu') feat = collections.defaultdict(list) for ex in batch: ########### # auxillary ########### if not self.test_mode: # subgoal completion supervision if self.args.subgoal_aux_loss_wt > 0: feat['subgoals_completed'].append(np.array(ex['num']['low_to_high_idx']) / self.max_subgoals) # progress monitor supervision if self.args.pm_aux_loss_wt > 0: num_actions = len([a for sg in ex['num']['action_low'] for a in sg]) subgoal_progress = [(i+1)/float(num_actions) for i in range(num_actions)] feat['subgoal_progress'].append(subgoal_progress) ######### # inputs ######### # serialize segments self.serialize_lang_action(ex) # goal and instr language lang_goal, lang_instr = ex['num']['lang_goal'], ex['num']['lang_instr'] # zero inputs if specified lang_goal = self.zero_input(lang_goal) if self.args.zero_goal else lang_goal lang_instr = self.zero_input(lang_instr) if self.args.zero_instr else lang_instr # append goal + instr lang_goal_instr = lang_goal + lang_instr feat['lang_goal_instr'].append(lang_goal_instr) episode_len = 0 # load Resnet features from disk if load_frames and not self.test_mode: root = self.get_task_root(ex) #im = torch.load(os.path.join(root, self.feat_pt)) im = [] path = "{}/{}".format(root,'raw_images') imgs = sorted(os.listdir(path)) tfms = transforms.Compose([transforms.Resize(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),]) for img in imgs: im.append(tfms(Image.open("{}/{}".format(path,img)))) im = torch.stack(im) num_low_actions = len(ex['plan']['low_actions']) num_feat_frames = im.shape[0] if num_low_actions != num_feat_frames: keep = [None] * len(ex['plan']['low_actions']) for i, d in enumerate(ex['images']): # only add frames linked with low-level actions (i.e. skip filler frames like smooth rotations and dish washing) if keep[d['low_idx']] is None: keep[d['low_idx']] = im[i] keep.append(keep[-1]) # stop frame episode_len = min(self.max_episode_len, len(keep)) keep = keep[:episode_len] feat['frames'].append(torch.stack(keep, dim=0)) else: episode_len = min(self.max_episode_len, len(im)) im = im[:episode_len] feat['frames'].append(torch.cat([im, im[-1].unsqueeze(0)], dim=0)) # add stop frame ######### # outputs ######### if self.args.subgoal_aux_loss_wt > 0: feat['subgoals_completed'][-1] = feat['subgoals_completed'][-1][:episode_len] if self.args.pm_aux_loss_wt > 0: feat['subgoal_progress'][-1] = feat['subgoal_progress'][-1][:episode_len] if not self.test_mode: # low-level action feat['action_low'].append([a['action'] for a in ex['num']['action_low']][:episode_len]) # low-level action mask if load_mask: feat['action_low_mask'].append([self.decompress_mask(a['mask']) for i,a in enumerate(ex['num']['action_low']) if a['mask'] is not None and i<episode_len]) # low-level valid interact feat['action_low_valid_interact'].append([a['valid_interact'] for a in ex['num']['action_low']][:episode_len]) # tensorization and padding for k, v in feat.items(): if k in {'lang_goal_instr'}: # language embedding and padding seqs = [torch.tensor(vv, device=device) for vv in v] pad_seq = pad_sequence(seqs, batch_first=True, padding_value=self.pad) seq_lengths = np.array(list(map(len, v))) embed_seq = self.emb_word(pad_seq) packed_input = pack_padded_sequence(embed_seq, seq_lengths, batch_first=True, enforce_sorted=False) feat[k] = packed_input elif k in {'action_low_mask'}: # mask padding seqs = [torch.tensor(vv, device=device, dtype=torch.float) for vv in v] feat[k] = seqs elif k in {'subgoal_progress', 'subgoals_completed'}: # auxillary padding seqs = [torch.tensor(vv, device=device, dtype=torch.float) for vv in v] pad_seq = pad_sequence(seqs, batch_first=True, padding_value=self.pad) feat[k] = pad_seq else: # default: tensorize and pad sequence seqs = [torch.tensor(vv, device=device, dtype=torch.float if ('frames' in k) else torch.long) for vv in v] pad_seq = pad_sequence(seqs, batch_first=True, padding_value=self.pad) feat[k] = pad_seq return feat def serialize_lang_action(self, feat): ''' append segmented instr language and low-level actions into single sequences ''' is_serialized = not isinstance(feat['num']['lang_instr'][0], list) if not is_serialized: feat['num']['lang_instr'] = [word for desc in feat['num']['lang_instr'] for word in desc] if not self.test_mode: feat['num']['action_low'] = [a for a_group in feat['num']['action_low'] for a in a_group] def decompress_mask(self, compressed_mask): ''' decompress mask from json files ''' mask = np.array(decompress_mask(compressed_mask)) mask = np.expand_dims(mask, axis=0) return mask def forward(self, feat, max_decode=300): cont_lang, enc_lang = self.encode_lang(feat) state_0 = cont_lang, torch.zeros_like(cont_lang) frames = self.vis_dropout(feat['frames']) res = self.dec(enc_lang, frames, max_decode=self.max_episode_len, gold=feat['action_low'], state_0=state_0) feat.update(res) return feat def encode_lang(self, feat): ''' encode goal+instr language ''' emb_lang_goal_instr = feat['lang_goal_instr'] self.lang_dropout(emb_lang_goal_instr.data) enc_lang_goal_instr, _ = self.enc(emb_lang_goal_instr) enc_lang_goal_instr, _ = pad_packed_sequence(enc_lang_goal_instr, batch_first=True) self.lang_dropout(enc_lang_goal_instr) cont_lang_goal_instr = self.enc_att(enc_lang_goal_instr) return cont_lang_goal_instr, enc_lang_goal_instr def reset(self): ''' reset internal states (used for real-time execution during eval) ''' self.r_state = { 'state_t': None, 'e_t': None, 'cont_lang': None, 'enc_lang': None } def step(self, feat, prev_action=None): ''' forward the model for a single time-step (used for real-time execution during eval) ''' # encode language features if self.r_state['cont_lang'] is None and self.r_state['enc_lang'] is None: self.r_state['cont_lang'], self.r_state['enc_lang'] = self.encode_lang(feat) # initialize embedding and hidden states if self.r_state['e_t'] is None and self.r_state['state_t'] is None: self.r_state['e_t'] = self.dec.go.repeat(self.r_state['enc_lang'].size(0), 1) self.r_state['state_t'] = self.r_state['cont_lang'], torch.zeros_like(self.r_state['cont_lang']) # previous action embedding e_t = self.embed_action(prev_action) if prev_action is not None else self.r_state['e_t'] # decode and save embedding and hidden states out_action_low, out_action_low_mask, state_t, *_ = self.dec.step(self.r_state['enc_lang'], feat['frames'][:, 0], e_t=e_t, state_tm1=self.r_state['state_t']) # save states self.r_state['state_t'] = state_t self.r_state['e_t'] = self.dec.emb(out_action_low.max(1)[1]) # output formatting feat['out_action_low'] = out_action_low.unsqueeze(0) feat['out_action_low_mask'] = out_action_low_mask.unsqueeze(0) return feat def extract_preds(self, out, batch, feat, clean_special_tokens=True): ''' output processing ''' pred = {} for ex, alow, alow_mask in zip(batch, feat['out_action_low'].max(2)[1].tolist(), feat['out_action_low_mask']): # remove padding tokens if self.pad in alow: pad_start_idx = alow.index(self.pad) alow = alow[:pad_start_idx] alow_mask = alow_mask[:pad_start_idx] if clean_special_tokens: # remove <<stop>> tokens if self.stop_token in alow: stop_start_idx = alow.index(self.stop_token) alow = alow[:stop_start_idx] alow_mask = alow_mask[:stop_start_idx] # index to API actions words = self.vocab['action_low'].index2word(alow) # sigmoid preds to binary mask alow_mask = F.sigmoid(alow_mask) p_mask = [(alow_mask[t] > 0.5).cpu().numpy() for t in range(alow_mask.shape[0])] task_id_ann = self.get_task_and_ann_id(ex) pred[task_id_ann] = { 'action_low': ' '.join(words), 'action_low_mask': p_mask, } return pred def embed_action(self, action): ''' embed low-level action ''' device = torch.device('cuda') if self.args.gpu else torch.device('cpu') action_num = torch.tensor(self.vocab['action_low'].word2index(action), device=device) action_emb = self.dec.emb(action_num).unsqueeze(0) return action_emb def compute_loss(self, out, batch, feat): ''' loss function for Seq2Seq agent ''' losses = dict() # GT and predictions p_alow = out['out_action_low'].view(-1, len(self.vocab['action_low'])) l_alow = feat['action_low'].view(-1) p_alow_mask = out['out_action_low_mask'] valid = feat['action_low_valid_interact'] # action loss pad_valid = (l_alow != self.pad) alow_loss = F.cross_entropy(p_alow, l_alow, reduction='none') alow_loss *= pad_valid.float() alow_loss = alow_loss.mean() losses['action_low'] = alow_loss * self.args.action_loss_wt # mask loss valid_idxs = valid.view(-1).nonzero().view(-1) flat_p_alow_mask = p_alow_mask.view(p_alow_mask.shape[0]*p_alow_mask.shape[1], *p_alow_mask.shape[2:])[valid_idxs] if flat_p_alow_mask.shape[0]!=0: flat_alow_mask = torch.cat(feat['action_low_mask'], dim=0) alow_mask_loss = self.weighted_mask_loss(flat_p_alow_mask, flat_alow_mask) losses['action_low_mask'] = alow_mask_loss * self.args.mask_loss_wt # subgoal completion loss if self.args.subgoal_aux_loss_wt > 0: p_subgoal = feat['out_subgoal'].squeeze(2) l_subgoal = feat['subgoals_completed'] sg_loss = self.mse_loss(p_subgoal, l_subgoal) sg_loss = sg_loss.view(-1) * pad_valid.float() subgoal_loss = sg_loss.mean() losses['subgoal_aux'] = self.args.subgoal_aux_loss_wt * subgoal_loss # progress monitoring loss if self.args.pm_aux_loss_wt > 0: p_progress = feat['out_progress'].squeeze(2) l_progress = feat['subgoal_progress'] pg_loss = self.mse_loss(p_progress, l_progress) pg_loss = pg_loss.view(-1) * pad_valid.float() progress_loss = pg_loss.mean() losses['progress_aux'] = self.args.pm_aux_loss_wt * progress_loss return losses def weighted_mask_loss(self, pred_masks, gt_masks): ''' mask loss that accounts for weight-imbalance between 0 and 1 pixels ''' bce = self.bce_with_logits(pred_masks, gt_masks) flipped_mask = self.flip_tensor(gt_masks) inside = (bce * gt_masks).sum() / (gt_masks).sum() outside = (bce * flipped_mask).sum() / (flipped_mask).sum() return inside + outside def flip_tensor(self, tensor, on_zero=1, on_non_zero=0): ''' flip 0 and 1 values in tensor ''' res = tensor.clone() res[tensor == 0] = on_zero res[tensor != 0] = on_non_zero return res def compute_metric(self, preds, data): ''' compute f1 and extract match scores for output ''' m = collections.defaultdict(list) for task in data: ex = self.load_task_json(task) i = self.get_task_and_ann_id(ex) label = ' '.join([a['discrete_action']['action'] for a in ex['plan']['low_actions']]) m['action_low_f1'].append(compute_f1(label.lower(), preds[i]['action_low'].lower())) m['action_low_em'].append(compute_exact(label.lower(), preds[i]['action_low'].lower())) return {k: sum(v)/len(v) for k, v in m.items()}

40.724051

174

0.586597

import os import torch import numpy as np import nn.vnn as vnn import collections from torch import nn from torch.nn import functional as F from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence from model.seq2seq import Module as Base from models.utils.metric import compute_f1, compute_exact from gen.utils.image_util import decompress_mask from torchvision import transforms from PIL import Image class Module(Base): def __init__(self, args, vocab): super().__init__(args, vocab) self.enc = nn.LSTM(args.demb, args.dhid, bidirectional=True, batch_first=True) self.enc_att = vnn.SelfAttn(args.dhid*2) self.subgoal_monitoring = (self.args.pm_aux_loss_wt > 0 or self.args.subgoal_aux_loss_wt > 0) decoder = vnn.ConvFrameMaskDecoderProgressMonitorFinetune if self.subgoal_monitoring else vnn.ConvFrameMaskDecoder self.dec = decoder(self.emb_action_low, args.dframe, 2*args.dhid, pframe=args.pframe, attn_dropout=args.attn_dropout, hstate_dropout=args.hstate_dropout, actor_dropout=args.actor_dropout, input_dropout=args.input_dropout, teacher_forcing=args.dec_teacher_forcing) self.vis_dropout = nn.Dropout(args.vis_dropout) self.lang_dropout = nn.Dropout(args.lang_dropout, inplace=True) self.input_dropout = nn.Dropout(args.input_dropout) self.state_t = None self.e_t = None self.test_mode = False self.bce_with_logits = torch.nn.BCEWithLogitsLoss(reduction='none') self.mse_loss = torch.nn.MSELoss(reduction='none') self.root_path = os.getcwd() self.feat_pt = 'feat_conv.pt' self.max_subgoals = 25 self.max_episode_len = args.max_episode_len self.reset() def featurize(self, batch, load_mask=True, load_frames=True): device = torch.device('cuda') if self.args.gpu else torch.device('cpu') feat = collections.defaultdict(list) for ex in batch: ubgoal_aux_loss_wt > 0: feat['subgoals_completed'].append(np.array(ex['num']['low_to_high_idx']) / self.max_subgoals) if self.args.pm_aux_loss_wt > 0: num_actions = len([a for sg in ex['num']['action_low'] for a in sg]) subgoal_progress = [(i+1)/float(num_actions) for i in range(num_actions)] feat['subgoal_progress'].append(subgoal_progress) _action(ex) lang_goal, lang_instr = ex['num']['lang_goal'], ex['num']['lang_instr'] lang_goal = self.zero_input(lang_goal) if self.args.zero_goal else lang_goal lang_instr = self.zero_input(lang_instr) if self.args.zero_instr else lang_instr lang_goal_instr = lang_goal + lang_instr feat['lang_goal_instr'].append(lang_goal_instr) episode_len = 0 if load_frames and not self.test_mode: root = self.get_task_root(ex) im = [] path = "{}/{}".format(root,'raw_images') imgs = sorted(os.listdir(path)) tfms = transforms.Compose([transforms.Resize(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),]) for img in imgs: im.append(tfms(Image.open("{}/{}".format(path,img)))) im = torch.stack(im) num_low_actions = len(ex['plan']['low_actions']) num_feat_frames = im.shape[0] if num_low_actions != num_feat_frames: keep = [None] * len(ex['plan']['low_actions']) for i, d in enumerate(ex['images']): if keep[d['low_idx']] is None: keep[d['low_idx']] = im[i] keep.append(keep[-1]) episode_len = min(self.max_episode_len, len(keep)) keep = keep[:episode_len] feat['frames'].append(torch.stack(keep, dim=0)) else: episode_len = min(self.max_episode_len, len(im)) im = im[:episode_len] feat['frames'].append(torch.cat([im, im[-1].unsqueeze(0)], dim=0)) > 0: feat['subgoals_completed'][-1] = feat['subgoals_completed'][-1][:episode_len] if self.args.pm_aux_loss_wt > 0: feat['subgoal_progress'][-1] = feat['subgoal_progress'][-1][:episode_len] if not self.test_mode: feat['action_low'].append([a['action'] for a in ex['num']['action_low']][:episode_len]) if load_mask: feat['action_low_mask'].append([self.decompress_mask(a['mask']) for i,a in enumerate(ex['num']['action_low']) if a['mask'] is not None and i<episode_len]) feat['action_low_valid_interact'].append([a['valid_interact'] for a in ex['num']['action_low']][:episode_len]) for k, v in feat.items(): if k in {'lang_goal_instr'}: seqs = [torch.tensor(vv, device=device) for vv in v] pad_seq = pad_sequence(seqs, batch_first=True, padding_value=self.pad) seq_lengths = np.array(list(map(len, v))) embed_seq = self.emb_word(pad_seq) packed_input = pack_padded_sequence(embed_seq, seq_lengths, batch_first=True, enforce_sorted=False) feat[k] = packed_input elif k in {'action_low_mask'}: seqs = [torch.tensor(vv, device=device, dtype=torch.float) for vv in v] feat[k] = seqs elif k in {'subgoal_progress', 'subgoals_completed'}: seqs = [torch.tensor(vv, device=device, dtype=torch.float) for vv in v] pad_seq = pad_sequence(seqs, batch_first=True, padding_value=self.pad) feat[k] = pad_seq else: seqs = [torch.tensor(vv, device=device, dtype=torch.float if ('frames' in k) else torch.long) for vv in v] pad_seq = pad_sequence(seqs, batch_first=True, padding_value=self.pad) feat[k] = pad_seq return feat def serialize_lang_action(self, feat): is_serialized = not isinstance(feat['num']['lang_instr'][0], list) if not is_serialized: feat['num']['lang_instr'] = [word for desc in feat['num']['lang_instr'] for word in desc] if not self.test_mode: feat['num']['action_low'] = [a for a_group in feat['num']['action_low'] for a in a_group] def decompress_mask(self, compressed_mask): mask = np.array(decompress_mask(compressed_mask)) mask = np.expand_dims(mask, axis=0) return mask def forward(self, feat, max_decode=300): cont_lang, enc_lang = self.encode_lang(feat) state_0 = cont_lang, torch.zeros_like(cont_lang) frames = self.vis_dropout(feat['frames']) res = self.dec(enc_lang, frames, max_decode=self.max_episode_len, gold=feat['action_low'], state_0=state_0) feat.update(res) return feat def encode_lang(self, feat): emb_lang_goal_instr = feat['lang_goal_instr'] self.lang_dropout(emb_lang_goal_instr.data) enc_lang_goal_instr, _ = self.enc(emb_lang_goal_instr) enc_lang_goal_instr, _ = pad_packed_sequence(enc_lang_goal_instr, batch_first=True) self.lang_dropout(enc_lang_goal_instr) cont_lang_goal_instr = self.enc_att(enc_lang_goal_instr) return cont_lang_goal_instr, enc_lang_goal_instr def reset(self): self.r_state = { 'state_t': None, 'e_t': None, 'cont_lang': None, 'enc_lang': None } def step(self, feat, prev_action=None): if self.r_state['cont_lang'] is None and self.r_state['enc_lang'] is None: self.r_state['cont_lang'], self.r_state['enc_lang'] = self.encode_lang(feat) if self.r_state['e_t'] is None and self.r_state['state_t'] is None: self.r_state['e_t'] = self.dec.go.repeat(self.r_state['enc_lang'].size(0), 1) self.r_state['state_t'] = self.r_state['cont_lang'], torch.zeros_like(self.r_state['cont_lang']) e_t = self.embed_action(prev_action) if prev_action is not None else self.r_state['e_t'] out_action_low, out_action_low_mask, state_t, *_ = self.dec.step(self.r_state['enc_lang'], feat['frames'][:, 0], e_t=e_t, state_tm1=self.r_state['state_t']) self.r_state['state_t'] = state_t self.r_state['e_t'] = self.dec.emb(out_action_low.max(1)[1]) feat['out_action_low'] = out_action_low.unsqueeze(0) feat['out_action_low_mask'] = out_action_low_mask.unsqueeze(0) return feat def extract_preds(self, out, batch, feat, clean_special_tokens=True): pred = {} for ex, alow, alow_mask in zip(batch, feat['out_action_low'].max(2)[1].tolist(), feat['out_action_low_mask']): if self.pad in alow: pad_start_idx = alow.index(self.pad) alow = alow[:pad_start_idx] alow_mask = alow_mask[:pad_start_idx] if clean_special_tokens: if self.stop_token in alow: stop_start_idx = alow.index(self.stop_token) alow = alow[:stop_start_idx] alow_mask = alow_mask[:stop_start_idx] words = self.vocab['action_low'].index2word(alow) alow_mask = F.sigmoid(alow_mask) p_mask = [(alow_mask[t] > 0.5).cpu().numpy() for t in range(alow_mask.shape[0])] task_id_ann = self.get_task_and_ann_id(ex) pred[task_id_ann] = { 'action_low': ' '.join(words), 'action_low_mask': p_mask, } return pred def embed_action(self, action): device = torch.device('cuda') if self.args.gpu else torch.device('cpu') action_num = torch.tensor(self.vocab['action_low'].word2index(action), device=device) action_emb = self.dec.emb(action_num).unsqueeze(0) return action_emb def compute_loss(self, out, batch, feat): losses = dict() p_alow = out['out_action_low'].view(-1, len(self.vocab['action_low'])) l_alow = feat['action_low'].view(-1) p_alow_mask = out['out_action_low_mask'] valid = feat['action_low_valid_interact'] pad_valid = (l_alow != self.pad) alow_loss = F.cross_entropy(p_alow, l_alow, reduction='none') alow_loss *= pad_valid.float() alow_loss = alow_loss.mean() losses['action_low'] = alow_loss * self.args.action_loss_wt valid_idxs = valid.view(-1).nonzero().view(-1) flat_p_alow_mask = p_alow_mask.view(p_alow_mask.shape[0]*p_alow_mask.shape[1], *p_alow_mask.shape[2:])[valid_idxs] if flat_p_alow_mask.shape[0]!=0: flat_alow_mask = torch.cat(feat['action_low_mask'], dim=0) alow_mask_loss = self.weighted_mask_loss(flat_p_alow_mask, flat_alow_mask) losses['action_low_mask'] = alow_mask_loss * self.args.mask_loss_wt if self.args.subgoal_aux_loss_wt > 0: p_subgoal = feat['out_subgoal'].squeeze(2) l_subgoal = feat['subgoals_completed'] sg_loss = self.mse_loss(p_subgoal, l_subgoal) sg_loss = sg_loss.view(-1) * pad_valid.float() subgoal_loss = sg_loss.mean() losses['subgoal_aux'] = self.args.subgoal_aux_loss_wt * subgoal_loss if self.args.pm_aux_loss_wt > 0: p_progress = feat['out_progress'].squeeze(2) l_progress = feat['subgoal_progress'] pg_loss = self.mse_loss(p_progress, l_progress) pg_loss = pg_loss.view(-1) * pad_valid.float() progress_loss = pg_loss.mean() losses['progress_aux'] = self.args.pm_aux_loss_wt * progress_loss return losses def weighted_mask_loss(self, pred_masks, gt_masks): bce = self.bce_with_logits(pred_masks, gt_masks) flipped_mask = self.flip_tensor(gt_masks) inside = (bce * gt_masks).sum() / (gt_masks).sum() outside = (bce * flipped_mask).sum() / (flipped_mask).sum() return inside + outside def flip_tensor(self, tensor, on_zero=1, on_non_zero=0): res = tensor.clone() res[tensor == 0] = on_zero res[tensor != 0] = on_non_zero return res def compute_metric(self, preds, data): m = collections.defaultdict(list) for task in data: ex = self.load_task_json(task) i = self.get_task_and_ann_id(ex) label = ' '.join([a['discrete_action']['action'] for a in ex['plan']['low_actions']]) m['action_low_f1'].append(compute_f1(label.lower(), preds[i]['action_low'].lower())) m['action_low_em'].append(compute_exact(label.lower(), preds[i]['action_low'].lower())) return {k: sum(v)/len(v) for k, v in m.items()}

true

f723b7ecde46f054272f9893ebd0e63cffbd1cd6

6,773

py

Python

tests/gamestonk_terminal/etf/discovery/test_disc_controller.py

GarnixJu2015/GamestonkTerminal

ec400e46ddce4ac934af836b863528f14a13d865

[ "MIT" ]

null

tests/gamestonk_terminal/etf/discovery/test_disc_controller.py

GarnixJu2015/GamestonkTerminal

ec400e46ddce4ac934af836b863528f14a13d865

[ "MIT" ]

null

tests/gamestonk_terminal/etf/discovery/test_disc_controller.py

GarnixJu2015/GamestonkTerminal

ec400e46ddce4ac934af836b863528f14a13d865

[ "MIT" ]

null

# IMPORTATION STANDARD import os # IMPORTATION THIRDPARTY import pandas as pd import pytest # IMPORTATION INTERNAL from gamestonk_terminal.etf.discovery import disc_controller # pylint: disable=E1101 # pylint: disable=W0603 # pylint: disable=E1111 EMPTY_DF = pd.DataFrame() @pytest.mark.vcr(record_mode="none") @pytest.mark.parametrize( "queue, expected", [ (["load", "help"], []), (["quit", "help"], ["help"]), ], ) def test_menu_with_queue(expected, mocker, queue): path_controller = "gamestonk_terminal.etf.discovery.disc_controller" # MOCK SWITCH mocker.patch( target=f"{path_controller}.DiscoveryController.switch", return_value=["quit"], ) result_menu = disc_controller.DiscoveryController(queue=queue).menu() assert result_menu == expected @pytest.mark.vcr(record_mode="none") def test_menu_without_queue_completion(mocker): path_controller = "gamestonk_terminal.etf.discovery.disc_controller" # ENABLE AUTO-COMPLETION : HELPER_FUNCS.MENU mocker.patch( target="gamestonk_terminal.feature_flags.USE_PROMPT_TOOLKIT", new=True, ) mocker.patch( target="gamestonk_terminal.parent_classes.session", ) mocker.patch( target="gamestonk_terminal.parent_classes.session.prompt", return_value="quit", ) mocker.patch( target="gamestonk_terminal.etf.financedatabase_model.get_etfs_categories", return_value=["Bank Loan"], ) # DISABLE AUTO-COMPLETION : CONTROLLER.COMPLETER mocker.patch.object( target=disc_controller.gtff, attribute="USE_PROMPT_TOOLKIT", new=True, ) mocker.patch( target=f"{path_controller}.session", ) mocker.patch( target=f"{path_controller}.session.prompt", return_value="quit", ) result_menu = disc_controller.DiscoveryController(queue=None).menu() assert result_menu == [] @pytest.mark.vcr(record_mode="none") @pytest.mark.parametrize( "mock_input", ["help", "homee help", "home help", "mock"], ) def test_menu_without_queue_sys_exit(mock_input, mocker): path_controller = "gamestonk_terminal.etf.discovery.disc_controller" # DISABLE AUTO-COMPLETION mocker.patch.object( target=disc_controller.gtff, attribute="USE_PROMPT_TOOLKIT", new=False, ) mocker.patch( target=f"{path_controller}.session", return_value=None, ) # MOCK USER INPUT mocker.patch("builtins.input", return_value=mock_input) # MOCK SWITCH class SystemExitSideEffect: def __init__(self): self.first_call = True def __call__(self, *args, **kwargs): if self.first_call: self.first_call = False raise SystemExit() return ["quit"] mock_switch = mocker.Mock(side_effect=SystemExitSideEffect()) mocker.patch( target=f"{path_controller}.DiscoveryController.switch", new=mock_switch, ) result_menu = disc_controller.DiscoveryController(queue=None).menu() assert result_menu == [] @pytest.mark.vcr(record_mode="none") @pytest.mark.record_stdout def test_print_help(): controller = disc_controller.DiscoveryController(queue=None) controller.print_help() @pytest.mark.vcr(record_mode="none") @pytest.mark.parametrize( "an_input, expected_queue", [ ("", []), ("/help", ["quit", "quit", "help"]), ("help/help", ["help"]), ("q", ["quit"]), ("h", []), ( "r", [ "quit", "quit", "reset", "etf", "disc", ], ), ], ) def test_switch(an_input, expected_queue): controller = disc_controller.DiscoveryController(queue=None) queue = controller.switch(an_input=an_input) assert queue == expected_queue @pytest.mark.vcr(record_mode="none") def test_call_cls(mocker): mocker.patch("os.system") controller = disc_controller.DiscoveryController(queue=None) controller.call_cls([]) assert controller.queue == [] os.system.assert_called_once_with("cls||clear") @pytest.mark.vcr(record_mode="none") @pytest.mark.parametrize( "func, queue, expected_queue", [ ( "call_exit", [], ["quit", "quit", "quit"], ), ("call_exit", ["help"], ["quit", "quit", "quit", "help"]), ("call_home", [], ["quit", "quit"]), ("call_help", [], []), ("call_quit", [], ["quit"]), ("call_quit", ["help"], ["quit", "help"]), ( "call_reset", [], [ "quit", "quit", "reset", "etf", "disc", ], ), ( "call_reset", ["help"], [ "quit", "quit", "reset", "etf", "disc", "help", ], ), ], ) def test_call_func_expect_queue(expected_queue, func, queue): controller = disc_controller.DiscoveryController(queue=queue) result = getattr(controller, func)([]) assert result is None assert controller.queue == expected_queue @pytest.mark.vcr(record_mode="none") @pytest.mark.parametrize( "tested_func, other_args, mocked_func, called_args, called_kwargs", [ ( "call_gainers", ["-l=10"], "wsj_view.show_top_mover", ["gainers", 10, ""], dict(), ), ( "call_decliners", ["-l=10"], "wsj_view.show_top_mover", ["decliners", 10, ""], dict(), ), ( "call_active", ["-l=10"], "wsj_view.show_top_mover", ["active", 10, ""], dict(), ), ], ) def test_call_func_test( tested_func, mocked_func, other_args, called_args, called_kwargs, mocker ): path_controller = "gamestonk_terminal.etf.discovery.disc_controller" if mocked_func: mock = mocker.Mock() mocker.patch( target=f"{path_controller}.{mocked_func}", new=mock, ) controller = disc_controller.DiscoveryController(queue=None) getattr(controller, tested_func)(other_args) if called_args or called_kwargs: mock.assert_called_once_with(*called_args, **called_kwargs) else: mock.assert_called_once() else: controller = disc_controller.DiscoveryController(queue=None) getattr(controller, tested_func)(other_args)

25.272388

82

0.579655

import os import pandas as pd import pytest from gamestonk_terminal.etf.discovery import disc_controller EMPTY_DF = pd.DataFrame() @pytest.mark.vcr(record_mode="none") @pytest.mark.parametrize( "queue, expected", [ (["load", "help"], []), (["quit", "help"], ["help"]), ], ) def test_menu_with_queue(expected, mocker, queue): path_controller = "gamestonk_terminal.etf.discovery.disc_controller" mocker.patch( target=f"{path_controller}.DiscoveryController.switch", return_value=["quit"], ) result_menu = disc_controller.DiscoveryController(queue=queue).menu() assert result_menu == expected @pytest.mark.vcr(record_mode="none") def test_menu_without_queue_completion(mocker): path_controller = "gamestonk_terminal.etf.discovery.disc_controller" mocker.patch( target="gamestonk_terminal.feature_flags.USE_PROMPT_TOOLKIT", new=True, ) mocker.patch( target="gamestonk_terminal.parent_classes.session", ) mocker.patch( target="gamestonk_terminal.parent_classes.session.prompt", return_value="quit", ) mocker.patch( target="gamestonk_terminal.etf.financedatabase_model.get_etfs_categories", return_value=["Bank Loan"], ) mocker.patch.object( target=disc_controller.gtff, attribute="USE_PROMPT_TOOLKIT", new=True, ) mocker.patch( target=f"{path_controller}.session", ) mocker.patch( target=f"{path_controller}.session.prompt", return_value="quit", ) result_menu = disc_controller.DiscoveryController(queue=None).menu() assert result_menu == [] @pytest.mark.vcr(record_mode="none") @pytest.mark.parametrize( "mock_input", ["help", "homee help", "home help", "mock"], ) def test_menu_without_queue_sys_exit(mock_input, mocker): path_controller = "gamestonk_terminal.etf.discovery.disc_controller" mocker.patch.object( target=disc_controller.gtff, attribute="USE_PROMPT_TOOLKIT", new=False, ) mocker.patch( target=f"{path_controller}.session", return_value=None, ) mocker.patch("builtins.input", return_value=mock_input) class SystemExitSideEffect: def __init__(self): self.first_call = True def __call__(self, *args, **kwargs): if self.first_call: self.first_call = False raise SystemExit() return ["quit"] mock_switch = mocker.Mock(side_effect=SystemExitSideEffect()) mocker.patch( target=f"{path_controller}.DiscoveryController.switch", new=mock_switch, ) result_menu = disc_controller.DiscoveryController(queue=None).menu() assert result_menu == [] @pytest.mark.vcr(record_mode="none") @pytest.mark.record_stdout def test_print_help(): controller = disc_controller.DiscoveryController(queue=None) controller.print_help() @pytest.mark.vcr(record_mode="none") @pytest.mark.parametrize( "an_input, expected_queue", [ ("", []), ("/help", ["quit", "quit", "help"]), ("help/help", ["help"]), ("q", ["quit"]), ("h", []), ( "r", [ "quit", "quit", "reset", "etf", "disc", ], ), ], ) def test_switch(an_input, expected_queue): controller = disc_controller.DiscoveryController(queue=None) queue = controller.switch(an_input=an_input) assert queue == expected_queue @pytest.mark.vcr(record_mode="none") def test_call_cls(mocker): mocker.patch("os.system") controller = disc_controller.DiscoveryController(queue=None) controller.call_cls([]) assert controller.queue == [] os.system.assert_called_once_with("cls||clear") @pytest.mark.vcr(record_mode="none") @pytest.mark.parametrize( "func, queue, expected_queue", [ ( "call_exit", [], ["quit", "quit", "quit"], ), ("call_exit", ["help"], ["quit", "quit", "quit", "help"]), ("call_home", [], ["quit", "quit"]), ("call_help", [], []), ("call_quit", [], ["quit"]), ("call_quit", ["help"], ["quit", "help"]), ( "call_reset", [], [ "quit", "quit", "reset", "etf", "disc", ], ), ( "call_reset", ["help"], [ "quit", "quit", "reset", "etf", "disc", "help", ], ), ], ) def test_call_func_expect_queue(expected_queue, func, queue): controller = disc_controller.DiscoveryController(queue=queue) result = getattr(controller, func)([]) assert result is None assert controller.queue == expected_queue @pytest.mark.vcr(record_mode="none") @pytest.mark.parametrize( "tested_func, other_args, mocked_func, called_args, called_kwargs", [ ( "call_gainers", ["-l=10"], "wsj_view.show_top_mover", ["gainers", 10, ""], dict(), ), ( "call_decliners", ["-l=10"], "wsj_view.show_top_mover", ["decliners", 10, ""], dict(), ), ( "call_active", ["-l=10"], "wsj_view.show_top_mover", ["active", 10, ""], dict(), ), ], ) def test_call_func_test( tested_func, mocked_func, other_args, called_args, called_kwargs, mocker ): path_controller = "gamestonk_terminal.etf.discovery.disc_controller" if mocked_func: mock = mocker.Mock() mocker.patch( target=f"{path_controller}.{mocked_func}", new=mock, ) controller = disc_controller.DiscoveryController(queue=None) getattr(controller, tested_func)(other_args) if called_args or called_kwargs: mock.assert_called_once_with(*called_args, **called_kwargs) else: mock.assert_called_once() else: controller = disc_controller.DiscoveryController(queue=None) getattr(controller, tested_func)(other_args)

true

f723b9c35cf5df83c827a8fdfdc3fba5d26b81e7

728

py

Python

Python/FromUniversity/PYQT5/testgame.py

programmer-666/Codes

fdffe38a789ba3636dff7ceaa9f1b4113ae17c2b

[ "MIT" ]

null

Python/FromUniversity/PYQT5/testgame.py

programmer-666/Codes

fdffe38a789ba3636dff7ceaa9f1b4113ae17c2b

[ "MIT" ]

null

Python/FromUniversity/PYQT5/testgame.py

programmer-666/Codes

fdffe38a789ba3636dff7ceaa9f1b4113ae17c2b

[ "MIT" ]

1

2021-09-16T14:24:29.000Z

import sys from PyQt5 import QtWidgets class game(QtWidgets.QMainWindow): def __init__(self): self.ap = QtWidgets.QApplication(sys.argv) super(game, self).__init__() self.setGeometry(100,100,200,200) self.conts() self.x = 50 self.y = 50 def moving(self): self.x+=10 self.obj1.move(self.x, self.y) def conts(self): self.obj1 = QtWidgets.QLabel(self) self.obj1.setText("0") self.obj1.move(50,50) # object self.rb = QtWidgets.QPushButton(self) self.rb.clicked.connect(self.moving) # movement def showtime(self): self.show() sys.exit(self.ap.exec_()) test = game() test.showtime()

28

50

0.590659

import sys from PyQt5 import QtWidgets class game(QtWidgets.QMainWindow): def __init__(self): self.ap = QtWidgets.QApplication(sys.argv) super(game, self).__init__() self.setGeometry(100,100,200,200) self.conts() self.x = 50 self.y = 50 def moving(self): self.x+=10 self.obj1.move(self.x, self.y) def conts(self): self.obj1 = QtWidgets.QLabel(self) self.obj1.setText("0") self.obj1.move(50,50) self.rb = QtWidgets.QPushButton(self) self.rb.clicked.connect(self.moving) def showtime(self): self.show() sys.exit(self.ap.exec_()) test = game() test.showtime()

true

f723bb3a1ed4cb91e25d960a0f850160a5553547

112,619

py

Python

pygsti/models/modelconstruction.py

maij/pyGSTi

70e83e05fa689f53550feb3914c4fac40ca4a943

[ "Apache-2.0" ]

73

2016-01-28T05:02:05.000Z

2022-03-30T07:46:33.000Z

pygsti/models/modelconstruction.py

00mjk/pyGSTi

4f8bf5337b01b7afcb7b0580b717b5d1fe281be4

[ "Apache-2.0" ]

113

2016-02-25T15:32:18.000Z

2022-03-31T13:18:13.000Z

pygsti/models/modelconstruction.py

00mjk/pyGSTi

4f8bf5337b01b7afcb7b0580b717b5d1fe281be4

[ "Apache-2.0" ]

41

2016-03-15T19:32:07.000Z

2022-02-16T10:22:05.000Z

""" Functions for the construction of new models. """ #*************************************************************************************************** # Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS). # Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights # in this software. # 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 or in the LICENSE file in the root pyGSTi directory. #*************************************************************************************************** import collections as _collections import itertools as _itertools from os import stat from pygsti.modelmembers.instruments.instrument import Instrument import numpy as _np import scipy as _scipy import scipy.linalg as _spl from pygsti.evotypes import Evotype as _Evotype from pygsti.modelmembers import operations as _op from pygsti.modelmembers import povms as _povm from pygsti.modelmembers import states as _state from pygsti.modelmembers import instruments as _instrument from pygsti.modelmembers.operations import opfactory as _opfactory from pygsti.models import stencillabel as _stencil from pygsti.models.modelnoise import OpModelNoise as _OpModelNoise from pygsti.models.modelnoise import OpModelPerOpNoise as _OpModelPerOpNoise from pygsti.models.modelnoise import ComposedOpModelNoise as _ComposedOpModelNoise from pygsti.models.modelnoise import LindbladNoise as _LindbladNoise from pygsti.models.modelnoise import StochasticNoise as _StochasticNoise from pygsti.models.modelnoise import DepolarizationNoise as _DepolarizationNoise from pygsti.models import explicitmodel as _emdl from pygsti.models import gaugegroup as _gg from pygsti.models.localnoisemodel import LocalNoiseModel as _LocalNoiseModel from pygsti.models.cloudnoisemodel import CloudNoiseModel as _CloudNoiseModel from pygsti.baseobjs import label as _label from pygsti.baseobjs import statespace as _statespace from pygsti.baseobjs.basis import Basis as _Basis from pygsti.baseobjs.basis import ExplicitBasis as _ExplicitBasis from pygsti.baseobjs.basis import DirectSumBasis as _DirectSumBasis from pygsti.baseobjs.qubitgraph import QubitGraph as _QubitGraph from pygsti.tools import basistools as _bt from pygsti.tools import internalgates as _itgs from pygsti.tools import optools as _ot from pygsti.tools import listtools as _lt from pygsti.baseobjs.basisconstructors import sqrt2, id2x2, sigmax, sigmay, sigmaz from pygsti.baseobjs.verbosityprinter import VerbosityPrinter as _VerbosityPrinter from pygsti.tools.legacytools import deprecate as _deprecated_fn ############################################# # Build gates based on "standard" gate names ############################################ def create_spam_vector(vec_expr, state_space, basis): """ Build a rho or E vector from an expression. Parameters ---------- vec_expr : string the expression which determines which vector to build. Currenlty, only integers are allowed, which specify a the vector for the pure state of that index. For example, "1" means return vectorize(``|1><1|``). The index labels the absolute index of the state within the entire state space, and is independent of the direct-sum decomposition of density matrix space. state_space : StateSpace The state space that the created operation should act upon. basis : str or Basis The basis of the returned vector. Allowed values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp), and Qutrit (qt) (or a custom basis object). Returns ------- numpy array The vector specified by vec_expr in the desired basis. """ #So far just allow integer prep_expressions that give the index of state (within the state space) that we #prep/measure try: index = int(vec_expr) except: raise ValueError("Expression must be the index of a state (as a string)") state_space = _statespace.StateSpace.cast(state_space) if isinstance(basis, str): basis = _Basis.cast(basis, state_space) assert (state_space.dim == basis.dim), \ "State space labels dim (%s) != basis dim (%s)" % (state_space.dim, basis.dim) #standard basis that has the same direct-sum structure as `basis`: std_basis = basis.create_equivalent('std') vecInSimpleStdBasis = _np.zeros(std_basis.elshape, 'd') # a matrix, but flattened it is our spamvec vecInSimpleStdBasis[index, index] = 1.0 # now a matrix with just a single 1 on the diag vecInReducedStdBasis = _np.dot(std_basis.from_elementstd_transform_matrix, vecInSimpleStdBasis.flatten()) # translates the density matrix / state vector to the std basis with our desired block structure vec = _bt.change_basis(vecInReducedStdBasis, std_basis, basis) return vec.reshape(-1, 1) def create_identity_vec(basis): """ Build a the identity vector for a given space and basis. Parameters ---------- basis : Basis object The basis of the returned vector. Allowed values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp), and Qutrit (qt) (or a custom basis object). Returns ------- numpy array The identity vector in the desired basis. """ opDim = basis.dim if isinstance(basis, _DirectSumBasis): blockDims = [c.dim for c in basis.component_bases] else: blockDims = [opDim] # assume index given as vec_expr refers to a Hilbert-space state index, so "reduced-std" basis vecInReducedStdBasis = _np.zeros((opDim, 1), 'd') #set all diagonal elements of density matrix to 1.0 (end result = identity density mx) start = 0; vecIndex = 0 for blockVecDim in blockDims: blockDim = int(_np.sqrt(blockVecDim)) # vec -> matrix dim for i in range(start, start + blockDim): for j in range(start, start + blockDim): if i == j: vecInReducedStdBasis[vecIndex, 0] = 1.0 # set diagonal element of density matrix vecIndex += 1 start += blockDim return _bt.change_basis(vecInReducedStdBasis, "std", basis) def create_operation(op_expr, state_space, basis="pp", parameterization="full", evotype='default'): """ Build an operation object from an expression. Parameters ---------- op_expr : string expression for the gate to build. String is first split into parts delimited by the colon (:) character, which are composed together to create the final gate. Each part takes on of the allowed forms: - I(ssl_0, ...) = identity operation on one or more state space labels (ssl_i) - X(theta, ssl) = x-rotation by theta radians of qubit labeled by ssl - Y(theta, ssl) = y-rotation by theta radians of qubit labeled by ssl - Z(theta, ssl) = z-rotation by theta radians of qubit labeled by ssl - CX(theta, ssl0, ssl1) = controlled x-rotation by theta radians. Acts on qubit labeled by ssl1 with ssl0 being the control. - CY(theta, ssl0, ssl1) = controlled y-rotation by theta radians. Acts on qubit labeled by ssl1 with ssl0 being the control. - CZ(theta, ssl0, ssl1) = controlled z-rotation by theta radians. Acts on qubit labeled by ssl1 with ssl0 being the control. - CNOT(ssl0, ssl1) = standard controlled-not gate. Acts on qubit labeled by ssl1 with ssl0 being the control. - CPHASE(ssl0, ssl1) = standard controlled-phase gate. Acts on qubit labeled by ssl1 with ssl0 being the control. - LX(theta, i0, i1) = leakage between states i0 and i1. Implemented as an x-rotation between states with integer indices i0 and i1 followed by complete decoherence between the states. state_space : StateSpace The state space that the created operation should act upon. basis : str or Basis The basis the returned operation should be represented in. parameterization : {"full","TP","static"}, optional How to parameterize the resulting gate. - "full" = return a FullArbitraryOp. - "TP" = return a FullTPOp. - "static" = return a StaticArbitraryOp. evotype : Evotype or str, optional The evolution type of this operation, describing how states are represented. The special value `"default"` is equivalent to specifying the value of `pygsti.evotypes.Evotype.default_evotype`. Returns ------- LinearOperator A gate object representing the gate given by op_expr in the desired basis. """ # op_expr can contain single qubit ops: X(theta) ,Y(theta) ,Z(theta) # two qubit ops: CNOT # clevel qubit ops: Leak # two clevel opts: Flip # each of which is given additional parameters specifying which indices it acts upon #Working with a StateSpaceLabels object gives us access to all the info we'll need later state_space = _statespace.StateSpace.cast(state_space) if isinstance(basis, str): basis = _Basis.cast(basis, state_space) assert(state_space.dim == basis.dim), \ "State space labels dim (%s) != basis dim (%s)" % (state_space.dim, basis.dim) # ------------------------------------------------------------------------------------------------------------------ # -- Helper Functions ---------------------------------------------------------------------------------------------- # ------------------------------------------------------------------------------------------------------------------ def to_label(lbl): """ Convert integer-strings to integers in state space label """ try: return int(lbl) except: return lbl.strip() def to_labels(lbls): """ Convert integer-strings to integers in state space labels """ return [to_label(lbl) for lbl in lbls] # ------------------------------------------------------------------------------------------------------------------ # -- End Helper Functions ------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------------------------------ #FUTURE?: type_preferences = ('static standard', 'static clifford', 'static unitary') build_evotype = 'default' superop_mxs_in_basis = [] exprTerms = op_expr.split(':') for exprTerm in exprTerms: l = exprTerm.index('('); r = exprTerm.rindex(')') opName = exprTerm[0:l] argsStr = exprTerm[l + 1:r] args = argsStr.split(',') if opName == "I": # qubit labels (TODO: what about 'L' labels? -- not sure if they work with this...) labels = to_labels(args) stateSpaceUDim = int(_np.product([state_space.label_udimension(l) for l in labels])) # a complex 2x2 mx unitary for the identity in Pauli-product basis Uop = _op.StaticUnitaryOp(_np.identity(stateSpaceUDim, 'complex'), 'pp', build_evotype) #FUTURE?: # stdname = 'Gi' if (stateSpaceUDim == 2) else None # Uop = _op.create_from_unitary_mx(_np.identity(stateSpaceUDim, complex), type_preferences, 'pp', # stdname=stdname, evotype=evotype) # a complex 2*num_qubits x 2*num_qubits mx unitary on full space in Pauli-product basis Uop_embed = _op.EmbeddedOp(state_space, labels, Uop) # a real 4*num_qubits x 4*num_qubits mx superoperator in final basis superop_mx_pp = Uop_embed.to_dense(on_space='HilbertSchmidt') # a real 4*num_qubits x 4*num_qubits mx superoperator in final basis superop_mx_in_basis = _bt.change_basis(superop_mx_pp, 'pp', basis) elif opName == "D": # like 'I', but only parameterize the diagonal elements - so can be a depolarization-type map raise NotImplementedError("Removed temporarily - need to update using embedded gates") # # qubit labels (TODO: what about 'L' labels? -- not sure if they work with this...) # labels = to_labels(args) # stateSpaceDim = sslbls.product_dim(labels) # if parameterization not in ("linear","linearTP"): # raise ValueError("'D' gate only makes sense to use when and parameterization == 'linear'") # if defaultI2P == "TP": # # parameterize only the diagonals els after the first # indicesToParameterize = [ (i,i) for i in range(1,stateSpaceDim**2) ] # else: # # parameterize only the diagonals els # indicesToParameterize = [ (i,i) for i in range(0,stateSpaceDim**2) ] # # *real* 4x4 mx in Pauli-product basis -- still just the identity! # pp_opMx = _np.identity(stateSpaceDim**2, 'd') # # pp_opMx assumed to be in the Pauli-product basis # opTermInFinalBasis = embed_operation(pp_opMx, tuple(labels), indicesToParameterize) elif opName in ('X', 'Y', 'Z'): # single-qubit gate names assert(len(args) == 2) # theta, qubit-index theta = eval(args[0], {"__builtins__": None}, {'pi': _np.pi}) label = to_label(args[1]) assert(state_space.label_dimension(label) == 4), "%s gate must act on qubits!" % opName if opName == 'X': ex = -1j * theta * sigmax / 2 elif opName == 'Y': ex = -1j * theta * sigmay / 2 elif opName == 'Z': ex = -1j * theta * sigmaz / 2 # complex 2x2 unitary matrix operating on single qubit in Pauli-product basis Uop = _op.StaticUnitaryOp(_spl.expm(ex), 'pp', build_evotype) #FUTURE?: #stdname = None #if _np.isclose(theta, _np.pi): stdname = 'G%spi' % opName.lower() #elif _np.isclose(theta, _np.pi/2): stdname = 'G%spi2' % opName.lower() # Uop = _op.create_from_unitary_mx(_spl.expm(ex), type_preferences, 'pp', stdname=stdname, evotype=evotype) # a complex 2*num_qubits x 2*num_qubits mx unitary on full space in Pauli-product basis Uop_embed = _op.EmbeddedOp(state_space, (label,), Uop) # a real 4*num_qubits x 4*num_qubits mx superoperator in Pauli-product basis superop_mx_pp = Uop_embed.to_dense(on_space='HilbertSchmidt') # a real 4*num_qubits x 4*num_qubits mx superoperator in final basis superop_mx_in_basis = _bt.change_basis(superop_mx_pp, 'pp', basis) elif opName == 'N': # more general single-qubit gate assert(len(args) == 5) # theta, sigmaX-coeff, sigmaY-coeff, sigmaZ-coeff, qubit-index theta = eval(args[0], {"__builtins__": None}, {'pi': _np.pi, 'sqrt': _np.sqrt}) sxCoeff = eval(args[1], {"__builtins__": None}, {'pi': _np.pi, 'sqrt': _np.sqrt}) syCoeff = eval(args[2], {"__builtins__": None}, {'pi': _np.pi, 'sqrt': _np.sqrt}) szCoeff = eval(args[3], {"__builtins__": None}, {'pi': _np.pi, 'sqrt': _np.sqrt}) label = to_label(args[4]) assert(state_space.label_dimension(label) == 4), "%s gate must act on qubits!" % opName ex = -1j * theta * (sxCoeff * sigmax / 2. + syCoeff * sigmay / 2. + szCoeff * sigmaz / 2.) # complex 2x2 unitary matrix operating on single qubit in Pauli-product basis Uop = _op.StaticUnitaryOp(_spl.expm(ex), 'pp', evotype=build_evotype) #FUTURE?: Uop = _op.create_from_unitary_mx(_spl.expm(ex), type_preferences, 'pp', evotype=evotype) # a complex 2*num_qubits x 2*num_qubits mx unitary on full space in Pauli-product basis Uop_embed = _op.EmbeddedOp(state_space, (label,), Uop) # a real 4*num_qubits x 4*num_qubits mx superoperator in Pauli-product basis superop_mx_pp = Uop_embed.to_dense(on_space='HilbertSchmidt') # a real 4*num_qubits x 4*num_qubits mx superoperator in final basis superop_mx_in_basis = _bt.change_basis(superop_mx_pp, 'pp', basis) elif opName in ('CX', 'CY', 'CZ', 'CNOT', 'CPHASE'): # two-qubit gate names if opName in ('CX', 'CY', 'CZ'): assert(len(args) == 3) # theta, qubit-label1, qubit-label2 theta = eval(args[0], {"__builtins__": None}, {'pi': _np.pi}) label1 = to_label(args[1]); label2 = to_label(args[2]) if opName == 'CX': ex = -1j * theta * sigmax / 2 elif opName == 'CY': ex = -1j * theta * sigmay / 2 elif opName == 'CZ': ex = -1j * theta * sigmaz / 2 Utarget = _spl.expm(ex) # 2x2 unitary matrix operating on target qubit else: # opName in ('CNOT','CPHASE') assert(len(args) == 2) # qubit-label1, qubit-label2 label1 = to_label(args[0]); label2 = to_label(args[1]) if opName == 'CNOT': Utarget = _np.array([[0, 1], [1, 0]], 'd') elif opName == 'CPHASE': Utarget = _np.array([[1, 0], [0, -1]], 'd') # 4x4 unitary matrix operating on isolated two-qubit space U = _np.identity(4, 'complex'); U[2:, 2:] = Utarget assert(state_space.label_dimension(label1) == 4 and state_space.label_dimension(label2) == 4), \ "%s gate must act on qubits!" % opName # complex 4x4 unitary matrix operating on two-qubit in Pauli-product basis Uop = _op.StaticUnitaryOp(U, 'pp', build_evotype) #FUTURE?: # if opName == "CNOT": stdname = "Gcnot" # elif opName == "CPHASE": stdname = "Gcphase" # else: stdname = None # Uop = _op.create_from_unitary_mx(U, type_preferences, 'pp', stdname=stdname, evotype=evotype) # a complex 2*num_qubits x 2*num_qubits mx unitary on full space Uop_embed = _op.EmbeddedOp(state_space, [label1, label2], Uop) # a real 4*num_qubits x 4*num_qubits mx superoperator in Pauli-product basis superop_mx_pp = Uop_embed.to_dense(on_space='HilbertSchmidt') # a real 4*num_qubits x 4*num_qubits mx superoperator in final basis superop_mx_in_basis = _bt.change_basis(superop_mx_pp, 'pp', basis) elif opName == "LX": # TODO - better way to describe leakage? assert(len(args) == 3) # theta, dmIndex1, dmIndex2 - X rotation between any two density matrix basis states theta = eval(args[0], {"__builtins__": None}, {'pi': _np.pi}) i1 = int(args[1]) # row/column index of a single *state* within the density matrix i2 = int(args[2]) # row/column index of a single *state* within the density matrix ex = -1j * theta * sigmax / 2 Uop = _spl.expm(ex) # 2x2 unitary matrix operating on the i1-th and i2-th states of the state space basis opDim = basis.dim dmDim = int(_np.sqrt(basis.elsize)) # matrix dim of the "embedding space" if isinstance(basis, _DirectSumBasis): blockDims = [c.dim for c in basis.component_bases] else: blockDims = [opDim] Utot = _np.identity(dmDim, 'complex') Utot[i1, i1] = Uop[0, 0] Utot[i1, i2] = Uop[0, 1] Utot[i2, i1] = Uop[1, 0] Utot[i2, i2] = Uop[1, 1] # dmDim^2 x dmDim^2 mx operating on vectorized total densty matrix opTermInStdBasis = _ot.unitary_to_process_mx(Utot) # contract [3] to [2, 1] embedded_std_basis = _Basis.cast('std', 9) # [2] std_basis = _Basis.cast('std', blockDims) # std basis w/blockdim structure, i.e. [4,1] opTermInReducedStdBasis = _bt.resize_std_mx(opTermInStdBasis, 'contract', embedded_std_basis, std_basis) superop_mx_in_basis = _bt.change_basis(opTermInReducedStdBasis, std_basis, basis) else: raise ValueError("Invalid gate name: %s" % opName) superop_mxs_in_basis.append(superop_mx_in_basis) #Note: expressions are listed in "matrix composition order" final_superop_mx = superop_mxs_in_basis[0] for mx in superop_mxs_in_basis[1:]: final_superop_mx = _np.dot(final_superop_mx, mx) if basis.real: assert(_np.linalg.norm(final_superop_mx.imag) < 1e-6), "Operation matrix should be real but isn't!" final_superop_mx = _np.real(final_superop_mx) return _op.create_from_superop_mx(final_superop_mx, parameterization, basis, evotype=evotype, state_space=state_space) def _create_explicit_model_from_expressions(state_space, basis, op_labels, op_expressions, prep_labels=('rho0',), prep_expressions=('0',), effect_labels='standard', effect_expressions='standard', povm_labels='Mdefault', gate_type="full", prep_type="auto", povm_type="auto", instrument_type="auto", evotype='default'): """ Build a new Model given lists of operation labels and expressions. Parameters ---------- state_space : StateSpace The state space for this model. basis : Basis object The source and destination basis, respectively. Allowed values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp), and Qutrit (qt) (or a custom basis object). op_labels : list of strings A list of labels for each created gate in the final model. To conform with text file parsing conventions these names should begin with a capital G and can be followed by any number of lowercase characters, numbers, or the underscore character. op_expressions : list of strings A list of gate expressions, each corresponding to a operation label in op_labels, which determine what operation each gate performs (see documentation for :meth:`create_operation`). prep_labels : list of string, optional A list of labels for each created state preparation in the final model. To conform with conventions these labels should begin with "rho". prep_expressions : list of strings, optional A list of vector expressions for each state preparation vector (see documentation for :meth:`_create_spam_vector`). effect_labels : list, optional If `povm_labels` is a string, then this is just a list of the effect (outcome) labels for the single POVM. If `povm_labels` is a tuple, then `effect_labels` must be a list of lists of effect labels, each list corresponding to a POVM. If set to the special string `"standard"` then the length-n binary strings are used when the state space consists of n qubits (e.g. `"000"`, `"001"`, ... `"111"` for 3 qubits) and the labels `"0"`, `"1"`, ... `"<dim>"` are used, where `<dim>` is the dimension of the state space, in all non-qubit cases. effect_expressions : list, optional A list or list-of-lists of (string) vector expressions for each POVM effect vector (see documentation for :meth:`_create_spam_vector`). Expressions correspond to labels in `effect_labels`. If set to the special string `"standard"`, then the expressions `"0"`, `"1"`, ... `"<dim>"` are used, where `<dim>` is the dimension of the state space. povm_labels : list or string, optional A list of POVM labels, or a single (string) label. In the latter case, only a single POVM is created and the format of `effect_labels` and `effect_expressions` is simplified (see above). parameterization : {"full","TP","static"}, optional How to parameterize the gates of the resulting Model (see documentation for :meth:`create_operation`). evotype : Evotype or str, optional The evolution type of this model, describing how states are represented. The special value `"default"` is equivalent to specifying the value of `pygsti.evotypes.Evotype.default_evotype`. Returns ------- Model The created model. """ #defP = "TP" if (parameterization in ("TP","linearTP")) else "full" state_space = _statespace.StateSpace.cast(state_space) ret = _emdl.ExplicitOpModel(state_space, basis.copy(), default_gate_type=gate_type, default_prep_type=prep_type, default_povm_type=povm_type, default_instrument_type=instrument_type, evotype=evotype) #prep_prefix="rho", effect_prefix="E", gate_prefix="G") if prep_type == "auto": prep_type = _state.state_type_from_op_type(gate_type) if povm_type == "auto": povm_type = _povm.povm_type_from_op_type(gate_type) if instrument_type == "auto": instrument_type = _instrument.instrument_type_from_op_type(gate_type) for label, rhoExpr in zip(prep_labels, prep_expressions): vec = create_spam_vector(rhoExpr, state_space, basis) ret.preps[label] = _state.create_from_dmvec(vec, prep_type, basis, evotype, state_space) if isinstance(povm_labels, str): povm_labels = [povm_labels] effect_labels = [effect_labels] effect_expressions = [effect_expressions] dmDim = int(_np.sqrt(basis.dim)) # "densitymx" evotype assumed... FIX? for povmLbl, ELbls, EExprs in zip(povm_labels, effect_labels, effect_expressions): effect_vecs = {} if ELbls == "standard": qubit_dim = 4 if state_space.num_tensor_product_blocks == 1 and \ all([ldim == qubit_dim for ldim in state_space.tensor_product_block_dimensions(0)]): # a single tensor product block comprised of qubits: '000', '001', etc. nQubits = len(state_space.tensor_product_block_dimensions(0)) ELbls = [''.join(t) for t in _itertools.product(('0', '1'), repeat=nQubits)] else: ELbls = list(map(str, range(dmDim))) # standard = 0,1,...,dmDim if EExprs == "standard": EExprs = list(map(str, range(dmDim))) # standard = 0,1,...,dmDim effect_vecs = {label: create_spam_vector(expr, state_space, basis) for label, expr in zip(ELbls, EExprs)} if len(effect_vecs) > 0: # don't add POVMs with 0 effects ret.povms[povmLbl] = _povm.create_from_dmvecs(effect_vecs, povm_type, basis, evotype, state_space) for (opLabel, opExpr) in zip(op_labels, op_expressions): ret.operations[opLabel] = create_operation(opExpr, state_space, basis, gate_type, evotype) if gate_type == "full": ret.default_gauge_group = _gg.FullGaugeGroup(ret.state_space, evotype) elif gate_type == "full TP": ret.default_gauge_group = _gg.TPGaugeGroup(ret.state_space, evotype) elif gate_type == 'CPTP': ret.default_gauge_group = _gg.UnitaryGaugeGroup(ret.state_space, basis, evotype) else: ret.default_gauge_group = _gg.TrivialGaugeGroup(ret.state_space) ret._clean_paramvec() return ret def create_explicit_model_from_expressions(state_space, op_labels, op_expressions, prep_labels=('rho0',), prep_expressions=('0',), effect_labels='standard', effect_expressions='standard', povm_labels='Mdefault', basis="auto", gate_type="full", prep_type="auto", povm_type="auto", instrument_type="auto", evotype='default'): """ Build a new :class:`ExplicitOpModel` given lists of labels and expressions. Parameters ---------- state_space : StateSpace the state space for the model. op_labels : list of strings A list of labels for each created gate in the final model. To conform with text file parsing conventions these names should begin with a capital G and can be followed by any number of lowercase characters, numbers, or the underscore character. op_expressions : list of strings A list of gate expressions, each corresponding to a operation label in op_labels, which determine what operation each gate performs (see documentation for :meth:`create_operation`). prep_labels : list of string A list of labels for each created state preparation in the final model. To conform with conventions these labels should begin with "rho". prep_expressions : list of strings A list of vector expressions for each state preparation vector (see documentation for :meth:`_create_spam_vector`). effect_labels : list, optional If `povm_labels` is a string, then this is just a list of the effect (outcome) labels for the single POVM. If `povm_labels` is a tuple, then `effect_labels` must be a list of lists of effect labels, each list corresponding to a POVM. If set to the special string `"standard"` then the length-n binary strings are used when the state space consists of n qubits (e.g. `"000"`, `"001"`, ... `"111"` for 3 qubits) and the labels `"0"`, `"1"`, ... `"<dim>"` are used, where `<dim>` is the dimension of the state space, in all non-qubit cases. effect_expressions : list, optional A list or list-of-lists of (string) vector expressions for each POVM effect vector (see documentation for :meth:`_create_spam_vector`). Expressions correspond to labels in `effect_labels`. If set to the special string `"standard"`, then the expressions `"0"`, `"1"`, ... `"<dim>"` are used, where `<dim>` is the dimension of the state space. povm_labels : list or string, optional A list of POVM labels, or a single (string) label. In the latter case, only a single POVM is created and the format of `effect_labels` and `effect_expressions` is simplified (see above). basis : {'gm','pp','std','qt','auto'}, optional the basis of the matrices in the returned Model - "std" = operation matrix operates on density mx expressed as sum of matrix units - "gm" = operation matrix operates on dentity mx expressed as sum of normalized Gell-Mann matrices - "pp" = operation matrix operates on density mx expresses as sum of tensor-product of Pauli matrices - "qt" = operation matrix operates on density mx expressed as sum of Qutrit basis matrices - "auto" = "pp" if possible (integer num of qubits), "qt" if density matrix dim == 3, and "gm" otherwise. parameterization : {"full","TP"}, optional How to parameterize the gates of the resulting Model (see documentation for :meth:`create_operation`). evotype : Evotype or str, optional The evolution type of this model, describing how states are represented. The special value `"default"` is equivalent to specifying the value of `pygsti.evotypes.Evotype.default_evotype`. Returns ------- ExplicitOpModel The created model. """ #Note: so far, all allowed `parameterization` values => densitymx evotype state_space = _statespace.StateSpace.cast(state_space) stateSpaceDim = state_space.dim # Note: what about state_space_labels.tpb_dims? if basis == "auto": if _np.isclose(_np.log2(stateSpaceDim) / 2, round(_np.log2(stateSpaceDim) / 2)): basis = "pp" elif stateSpaceDim == 9: basis = "qt" else: basis = "gm" return _create_explicit_model_from_expressions(state_space, _Basis.cast(basis, state_space), op_labels, op_expressions, prep_labels, prep_expressions, effect_labels, effect_expressions, povm_labels, gate_type=gate_type, prep_type=prep_type, povm_type=povm_type, instrument_type=instrument_type, evotype=evotype) def create_explicit_alias_model(mdl_primitives, alias_dict): """ Creates a model by applying aliases to an existing model. The new model is created by composing the gates of an existing `Model`, `mdl_primitives`, according to a dictionary of `Circuit`s, `alias_dict`. The keys of `alias_dict` are the operation labels of the returned `Model`. state preparations and POVMs are unaltered, and simply copied from `mdl_primitives`. Parameters ---------- mdl_primitives : Model A Model containing the "primitive" gates (those used to compose the gates of the returned model). alias_dict : dictionary A dictionary whose keys are strings and values are Circuit objects specifying sequences of primitive gates. Each key,value pair specifies the composition rule for a creating a gate in the returned model. Returns ------- Model A model whose gates are compositions of primitive gates and whose spam operations are the same as those of `mdl_primitives`. """ mdl_new = mdl_primitives.copy() for gl in mdl_primitives.operations.keys(): del mdl_new.operations[gl] # remove all gates from mdl_new for gl, opstr in alias_dict.items(): mdl_new.operations[gl] = mdl_primitives.sim.product(opstr) #Creates fully parameterized gates by default... mdl_new._clean_paramvec() return mdl_new def create_explicit_model(processor_spec, custom_gates=None, depolarization_strengths=None, stochastic_error_probs=None, lindblad_error_coeffs=None, depolarization_parameterization='depolarize', stochastic_parameterization='stochastic', lindblad_parameterization='auto', evotype="default", simulator="auto", ideal_gate_type='auto', ideal_spam_type='computational', embed_gates=False, basis='pp'): modelnoise = _build_modelnoise_from_args(depolarization_strengths, stochastic_error_probs, lindblad_error_coeffs, depolarization_parameterization, stochastic_parameterization, lindblad_parameterization, allow_nonlocal=True) return _create_explicit_model(processor_spec, modelnoise, custom_gates, evotype, simulator, ideal_gate_type, ideal_spam_type, ideal_spam_type, embed_gates, basis) def _create_explicit_model(processor_spec, modelnoise, custom_gates=None, evotype="default", simulator="auto", ideal_gate_type='auto', ideal_prep_type='auto', ideal_povm_type='auto', embed_gates=False, basis='pp'): qubit_labels = processor_spec.qubit_labels state_space = _statespace.QubitSpace(qubit_labels) evotype = _Evotype.cast(evotype) modelnoise = _OpModelNoise.cast(modelnoise) modelnoise.reset_access_counters() if custom_gates is None: custom_gates = {} if ideal_gate_type == "auto": ideal_gate_type = ('static standard', 'static clifford', 'static unitary') if ideal_prep_type == "auto": ideal_prep_type = _state.state_type_from_op_type(ideal_gate_type) if ideal_povm_type == "auto": ideal_povm_type = _povm.povm_type_from_op_type(ideal_gate_type) def _embed_unitary(statespace, target_labels, unitary): dummyop = _op.EmbeddedOp(statespace, target_labels, _op.StaticUnitaryOp(unitary, basis='pp', evotype="statevec_slow")) # basis hardcode? return dummyop.to_dense("Hilbert") local_gates = _setup_local_gates(processor_spec, evotype, None, {}, ideal_gate_type) # no custom *local* gates ret = _emdl.ExplicitOpModel(state_space, basis, default_gate_type=ideal_gate_type, evotype=evotype, simulator=simulator) # Special rule: when initializng an explicit model, if the processor spec has an implied global idle # gate (e.g. "(idle)", then the created model instead has a empty-tuple Label as the key for this op. global_idle_name = processor_spec.global_idle_gate_name if (global_idle_name is not None) and global_idle_name.startswith('(') and global_idle_name.endswith(')'): gn_to_make_emptytup = global_idle_name else: gn_to_make_emptytup = None for gn, gate_unitary in processor_spec.gate_unitaries.items(): gate_is_factory = callable(gate_unitary) resolved_avail = processor_spec.resolved_availability(gn) if callable(resolved_avail) or resolved_avail == '*': assert (embed_gates), "Cannot create factories with `embed_gates=False` yet!" key = _label.Label(gn) if (gn != gn_to_make_emptytup) else _label.Label(()) allowed_sslbls_fn = resolved_avail if callable(resolved_avail) else None gate_nQubits = processor_spec.gate_num_qubits(gn) ideal_factory = _opfactory.EmbeddingOpFactory( state_space, local_gates[gn], num_target_labels=gate_nQubits, allowed_sslbls_fn=allowed_sslbls_fn) noiseop = modelnoise.create_errormap(key, evotype, state_space) # No target indices... just local errs? factory = ideal_factory if (noiseop is None) else _op.ComposedOpFactory([ideal_factory, noiseop]) ret.factories[key] = factory else: # resolved_avail is a list/tuple of available sslbls for the current gate/factory for inds in resolved_avail: # inds are target qubit labels key = _label.Label(()) if (inds is None and gn == gn_to_make_emptytup) else _label.Label(gn, inds) if key in custom_gates: # allow custom_gates to specify gate elements directly if isinstance(custom_gates[key], _opfactory.OpFactory): ret.factories[key] = custom_gates[key] elif isinstance(custom_gates[key], _op.LinearOperator): ret.operations[key] = custom_gates[key] else: # presumably a numpy array or something like it. ret.operations[key] = _op.StaticArbitraryOp(custom_gates[key], evotype, state_space) # static gates by default continue if gate_is_factory: assert(embed_gates), "Cannot create factories with `embed_gates=False` yet!" # TODO: check for modelnoise on *local* factory, i.e. create_errormap(gn, ...)?? if inds is None or inds == tuple(qubit_labels): # then no need to embed ideal_factory = local_gates[gn] else: ideal_factory = _opfactory.EmbeddedOpFactory(state_space, inds, local_gates[gn]) noiseop = modelnoise.create_errormap(key, evotype, state_space, target_labels=inds) factory = ideal_factory if (noiseop is None) else _op.ComposedOpFactory([ideal_factory, noiseop]) ret.factories[key] = factory else: if inds is None or inds == tuple(qubit_labels): # then no need to embed if isinstance(gate_unitary, (int, _np.int64)): # interpret gate_unitary as identity assert(gate_unitary == len(qubit_labels)), \ "Idle unitary as int should be on all qubits for %s" % (str(gn)) ideal_gate = _op.ComposedOp([], evotype, state_space) # (identity gate on *all* qubits) else: ideal_gate = _op.create_from_unitary_mx(gate_unitary, ideal_gate_type, 'pp', None, evotype, state_space) else: if embed_gates: ideal_gate = local_gates[gn] ideal_gate = _op.EmbeddedOp(state_space, inds, ideal_gate) else: if isinstance(gate_unitary, (int, _np.int64)): # interpret gate_unitary as identity gate_unitary = _np.identity(2**gate_unitary, 'd') # turn into explicit identity op if gate_unitary.shape[0] == state_space.udim: # no need to embed! embedded_unitary = gate_unitary else: embedded_unitary = _embed_unitary(state_space, inds, gate_unitary) ideal_gate = _op.create_from_unitary_mx(embedded_unitary, ideal_gate_type, 'pp', None, evotype, state_space) #TODO: check for modelnoise on *local* gate, i.e. create_errormap(gn, ...)?? noiseop = modelnoise.create_errormap(key, evotype, state_space, target_labels=inds) layer = _op.ComposedOp([ideal_gate, noiseop]) if (noiseop is not None) else ideal_gate ret.operations[key] = layer # SPAM: local_noise = False; independent_gates = True; independent_spam = True prep_layers, povm_layers = _create_spam_layers(processor_spec, modelnoise, local_noise, ideal_prep_type, ideal_povm_type, evotype, state_space, independent_gates, independent_spam) for k, v in prep_layers.items(): ret.preps[k] = v for k, v in povm_layers.items(): ret.povms[k] = v modelnoise.warn_about_zero_counters() ret._clean_paramvec() return ret def _create_spam_layers(processor_spec, modelnoise, local_noise, ideal_prep_type, ideal_povm_type, evotype, state_space, independent_gates, independent_spam): """ local_noise=True creates lindblad ops that are embedded & composed 1Q ops, and assumes that modelnoise specifies 1Q noise. local_noise=False assumes modelnoise specifies n-qubit noise""" qubit_labels = processor_spec.qubit_labels num_qubits = processor_spec.num_qubits singleQ_state_space = _statespace.default_space_for_udim(2) # single qubit state space # Step 1 -- get the ideal prep and POVM, created as the types we want # Step 2 -- add noise, by composing ideal with a noise operation (if desired) prep_layers = {} povm_layers = {} def _add_prep_noise(prep_ops): """ Adds one or more noise ops to prep_ops lists (to compose later) """ if local_noise: # then assume modelnoise specifies 1Q errors prep_noiseop1Q = modelnoise.create_errormap('prep', evotype, singleQ_state_space, target_labels=None) if prep_noiseop1Q is not None: err_gates = [prep_noiseop1Q.copy() for i in range(num_qubits)] \ if independent_gates else [prep_noiseop1Q] * num_qubits prep_ops.extend([_op.EmbeddedOp(state_space, [qubit_labels[i]], err_gates[i]) for i in range(num_qubits)]) else: # use modelnoise to construct n-qubit noise prepNoiseMap = modelnoise.create_errormap('prep', evotype, state_space, target_labels=None, qubit_graph=processor_spec.qubit_graph) if prepNoiseMap is not None: prep_ops.append(prepNoiseMap) def _add_povm_noise(povm_ops): """ Adds one or more noise ops to prep_ops lists (to compose later) """ if local_noise: # then assume modelnoise specifies 1Q errors povm_noiseop1Q = modelnoise.create_errormap('povm', evotype, singleQ_state_space, target_labels=None) if povm_noiseop1Q is not None: err_gates = [povm_noiseop1Q.copy() for i in range(num_qubits)] \ if independent_gates else [povm_noiseop1Q] * num_qubits povm_ops.extend([_op.EmbeddedOp(state_space, [qubit_labels[i]], err_gates[i]) for i in range(num_qubits)]) else: # use modelnoise to construct n-qubit noise povmNoiseMap = modelnoise.create_errormap('povm', evotype, state_space, target_labels=None, qubit_graph=processor_spec.qubit_graph) if povmNoiseMap is not None: povm_ops.append(povmNoiseMap) def _add_to_prep_layers(ideal_prep, prep_ops): """ Adds noise elements to prep_layers """ if len(prep_ops_to_compose) == 0: prep_layers['rho0'] = ideal_prep elif len(prep_ops_to_compose) == 1: prep_layers['rho0'] = _state.ComposedState(ideal_prep, prep_ops[0]) else: prep_layers['rho0'] = _state.ComposedState(ideal_prep, _op.ComposedOp(prep_ops)) def _add_to_povm_layers(ideal_povm, povm_ops): """ Adds noise elements to povm_layers """ if len(povm_ops_to_compose) == 0: povm_layers['Mdefault'] = ideal_povm elif len(povm_ops_to_compose) == 1: povm_layers['Mdefault'] = _povm.ComposedPOVM(povm_ops[0], ideal_povm, 'pp') else: povm_layers['Mdefault'] = _povm.ComposedPOVM(_op.ComposedOp(povm_ops), ideal_povm, 'pp') def _create_nq_noise(lndtype): if local_noise: # create a 1-qubit exp(errorgen) that is applied to each qubit independently errgen_1Q = _op.LindbladErrorgen.from_error_generator(singleQ_state_space.dim, lndtype, 'pp', 'pp', truncate=True, evotype=evotype, state_space=None) err_gateNQ = _op.ComposedOp([_op.EmbeddedOp(state_space, [qubit_labels[i]], _op.ExpErrorgenOp(errgen_1Q.copy())) for i in range(num_qubits)], evotype, state_space) else: # create an n-qubit exp(errorgen) errgen_NQ = _op.LindbladErrorgen.from_error_generator(state_space.dim, lndtype, 'pp', 'pp', truncate=True, evotype=evotype, state_space=state_space) err_gateNQ = _op.ExpErrorgenOp(errgen_NQ) return err_gateNQ # Here's where the actual logic starts. The above functions avoid repeated blocks within the different # cases below. # Prep logic if isinstance(ideal_prep_type, (tuple, list)): ideal_prep_type = ideal_prep_type[0] # HACK to support multiple vals if ideal_prep_type == 'computational' or ideal_prep_type.startswith('lindblad '): ideal_prep = _state.ComputationalBasisState([0] * num_qubits, 'pp', evotype, state_space) prep_ops_to_compose = [] if ideal_prep_type.startswith('lindblad '): # then add a composed exp(errorgen) to computational SPAM lndtype = ideal_prep_type[len('lindblad '):] err_gateNQ = _create_nq_noise(lndtype) prep_ops_to_compose.append(err_gateNQ) # Add noise _add_prep_noise(prep_ops_to_compose) #Add final ops to returned dictionaries (Note: None -> ComputationPOVM within ComposedPOVM) _add_to_prep_layers(ideal_prep, prep_ops_to_compose) elif ideal_prep_type.startswith('tensor product '): #Note: with "tensor product <X>" types, e.g. "tensor product static", we assume modelnoise specifies just # a 1Q noise operation, even when `local_noise=False` vectype = ideal_prep_type[len('tensor product '):] v0, v1 = _np.array([1, 0], 'd'), _np.array([0, 1], 'd') ideal_prep1Q = _state.create_from_pure_vector(v0, vectype, 'pp', evotype, state_space=None) prep_factors = [ideal_prep1Q.copy() for i in range(num_qubits)] # Add noise prep_noiseop1Q = modelnoise.create_errormap('prep', evotype, singleQ_state_space, target_labels=None) if prep_noiseop1Q is not None: prep_factors = [_state.ComposedState( factor, (prep_noiseop1Q.copy() if independent_spam else prep_noiseop1Q)) for factor in prep_factors] prep_layers['rho0'] = _state.TensorProductState(prep_factors, state_space) else: # assume ideal_spam_type is a valid 'vectype' for creating n-qubit state vectors & POVMs vectype = ideal_prep_type vecs = [] # all the basis vectors for num_qubits for i in range(2**num_qubits): v = _np.zeros(2**num_qubits, 'd'); v[i] = 1.0 vecs.append(v) ideal_prep = _state.create_from_pure_vector(vecs[0], vectype, 'pp', evotype, state_space=state_space) # Add noise prep_ops_to_compose = [] _add_prep_noise(prep_ops_to_compose) # Add final ops to returned dictionaries _add_to_prep_layers(ideal_prep, prep_ops_to_compose) # Povm logic if isinstance(ideal_povm_type, (tuple, list)): ideal_povm_type = ideal_povm_type[0] # HACK to support multiple vals if ideal_povm_type == 'computational' or ideal_povm_type.startswith('lindblad '): ideal_povm = _povm.ComputationalBasisPOVM(num_qubits, evotype, state_space=state_space) povm_ops_to_compose = [] if ideal_povm_type.startswith('lindblad '): # then add a composed exp(errorgen) to computational SPAM lndtype = ideal_povm_type[len('lindblad '):] err_gateNQ = _create_nq_noise(lndtype) povm_ops_to_compose.append(err_gateNQ.copy()) # .copy() => POVM errors independent # Add noise _add_povm_noise(povm_ops_to_compose) #Add final ops to returned dictionaries (Note: None -> ComputationPOVM within ComposedPOVM) effective_ideal_povm = None if len(povm_ops_to_compose) > 0 else ideal_povm _add_to_povm_layers(effective_ideal_povm, povm_ops_to_compose) elif ideal_povm_type.startswith('tensor product '): #Note: with "tensor product <X>" types, e.g. "tensor product static", we assume modelnoise specifies just # a 1Q noise operation, even when `local_noise=False` vectype = ideal_povm_type[len('tensor product '):] v0, v1 = _np.array([1, 0], 'd'), _np.array([0, 1], 'd') ideal_povm1Q = _povm.create_from_pure_vectors([('0', v0), ('1', v1)], vectype, 'pp', evotype, state_space=None) povm_factors = [ideal_povm1Q.copy() for i in range(num_qubits)] # Add noise povm_noiseop1Q = modelnoise.create_errormap('povm', evotype, singleQ_state_space, target_labels=None) if povm_noiseop1Q is not None: povm_factors = [_povm.ComposedPOVM( (povm_noiseop1Q.copy() if independent_spam else povm_noiseop1Q), factor, 'pp') for factor in povm_factors] povm_layers['Mdefault'] = _povm.TensorProductPOVM(povm_factors, evotype, state_space) else: # assume ideal_spam_type is a valid 'vectype' for creating n-qubit state vectors & POVMs vectype = ideal_povm_type vecs = [] # all the basis vectors for num_qubits for i in range(2**num_qubits): v = _np.zeros(2**num_qubits, 'd'); v[i] = 1.0 vecs.append(v) ideal_povm = _povm.create_from_pure_vectors( [(format(i, 'b').zfill(num_qubits), v) for i, v in enumerate(vecs)], vectype, 'pp', evotype, state_space=state_space) # Add noise povm_ops_to_compose = [] _add_povm_noise(povm_ops_to_compose) # Add final ops to returned dictionaries _add_to_povm_layers(ideal_povm, povm_ops_to_compose) return prep_layers, povm_layers def _setup_local_gates(processor_spec, evotype, modelnoise=None, custom_gates=None, ideal_gate_type=('static standard', 'static clifford', 'static unitary')): """ Construct a dictionary of potentially noisy gates that act only on their target qubits. These gates are "local" because they act only on their intended target qubits. The gates consist of an ideal gate (obviously local, and crosstalk free) of the type given by `ideal_gate_type` composed with a noise operation given by `modelnoise`, if one exists. The returned dictionary contains keys for all the gate names in `processor_spec`. Custom gate objects can be given by `custom_gates`, which override the normal gate construction. Parameters ---------- processor_spec : ProcessorSpec The processor to create gate operations for. This object specifies the gate names and unitaries for the processor, among other things. evotype : Evotype Create gate objects with this evolution type. modelnoise : ModelNoise, optional Noise that should be applied after the ideal gates. This noise must be *local* to each gate (i.e. acting on its target qubits). See the :class:`ModelNoise` object documentation for details regarding how to specify different types of noise. If `None`, then no noise is added . custom_gates : dict, optional A dictionary of gate objects that should be placed in the returned dictionary in lieu of objects that would normally be constructed. Keys are gate names and values are gates. ideal_gate_type : str or tuple, optional A gate type or tuple of gate types (listed in order of priority) which is used to construct the ideal gates. A gate type usually specifies the Python class that will be created, which determines 1) the parameterization of the gate and 2) the class/category of the gate (e.g. a :class:`StaticClifford` operation has no parameters and is a Clifford operation). Returns ------- gatedict : dict A dictionary mapping gate names to local gate operations. """ std_gate_unitaries = _itgs.standard_gatename_unitaries() if custom_gates is None: custom_gates = {} if modelnoise is None: modelnoise = _OpModelPerOpNoise({}) # All possible entries into the upcoming gate dictionary # Not just gatenames as it is possible to override in qubit-specific operations all_keys = _lt.remove_duplicates(list(processor_spec.gate_names) + list(custom_gates.keys()) + list(modelnoise.keys())) # Cache ideal ops to ensure only one copy for each name ideal_gates = {} ideal_factories = {} gatedict = _collections.OrderedDict() for key in all_keys: # Use custom gate directly as error gate if key in custom_gates: gatedict[key] = custom_gates[key] continue # Skip prep, and povm here, just do gates if key in ['prep', 'povm']: continue # If key has qubits, get base name for lookup label = _label.Label(key) name = label.name U = processor_spec.gate_unitaries[name] # all gate names must be in the processorspec if ((name not in processor_spec.nonstd_gate_unitaries) or (not callable(processor_spec.nonstd_gate_unitaries[name]) and (name in std_gate_unitaries) and processor_spec.nonstd_gate_unitaries[name].shape == std_gate_unitaries[name].shape and _np.allclose(processor_spec.nonstd_gate_unitaries[name], std_gate_unitaries[name]))): stdname = name # setting `stdname` != None means we can try to create a StaticStandardOp below else: stdname = None if isinstance(U, (int, _np.int64)): # signals that the gate is an identity on `U` qubits ideal_gate_state_space = _statespace.default_space_for_num_qubits(U) noiseop = modelnoise.create_errormap(key, evotype, ideal_gate_state_space, target_labels=None) if noiseop is not None: gatedict[key] = noiseop else: gatedict[key] = _op.ComposedOp([], evotype, ideal_gate_state_space) # (identity gate on N qubits) elif not callable(U): # normal operation (not a factory) ideal_gate = ideal_gates.get(name, None) if ideal_gate is None: ideal_gate = _op.create_from_unitary_mx(U, ideal_gate_type, 'pp', stdname, evotype, state_space=None) ideal_gates[name] = ideal_gate noiseop = modelnoise.create_errormap(key, evotype, ideal_gate.state_space, target_labels=None) # Note: above line creates a *local* noise op, working entirely in the ideal gate's target space. # This means it will fail to create error maps with a given (non-local/stencil) set of sslbls, as desired if noiseop is None: gatedict[key] = ideal_gate else: if isinstance(noiseop, _op.ComposedOp): # avoid additional nested ComposedOp if we already have one noiseop.insert(0, ideal_gate) gatedict[key] = noiseop else: gatedict[key] = _op.ComposedOp([ideal_gate, noiseop]) else: # a factory, given by the unitary-valued function U: args -> unitary ideal_factory = ideal_factories.get(name, None) if ideal_factory is None: local_state_space = _statespace.default_space_for_udim(U.shape[0]) # factory *function* SHAPE ideal_factory = _opfactory.UnitaryOpFactory(U, local_state_space, 'pp', evotype) ideal_factories[name] = ideal_factory noiseop = modelnoise.create_errormap(key, evotype, ideal_factory.state_space, target_labels=None) gatedict[key] = _opfactory.ComposedOpFactory([ideal_factory, noiseop]) \ if (noiseop is not None) else ideal_factory return gatedict def create_crosstalk_free_model(processor_spec, custom_gates=None, depolarization_strengths=None, stochastic_error_probs=None, lindblad_error_coeffs=None, depolarization_parameterization='depolarize', stochastic_parameterization='stochastic', lindblad_parameterization='auto', evotype="default", simulator="auto", on_construction_error='raise', independent_gates=False, independent_spam=True, ensure_composed_gates=False, ideal_gate_type='auto', ideal_spam_type='computational', implicit_idle_mode='none'): """ Create a n-qubit "crosstalk-free" model. By virtue of being crosstalk-free, this model's operations only act nontrivially on their target qubits. Gates consist of an ideal gate operation possibly followed by an error operation. Errors can be specified using any combination of the 4 error rate/coeff arguments, but each gate name must be provided exclusively to one type of specification. Each specification results in a different type of operation, depending on the parameterization: - `depolarization_strengths` -> DepolarizeOp, StochasticNoiseOp, or exp(LindbladErrorgen) - `stochastic_error_probs` -> StochasticNoiseOp or exp(LindbladErrorgen) - `lindblad_error_coeffs` -> exp(LindbladErrorgen) In addition to the gate names, the special values `"prep"` and `"povm"` may be used as keys to specify the error on the state preparation, measurement, respectively. Parameters ---------- processor_spec : ProcessorSpec The processor specification to create a model for. This object specifies the gate names and unitaries for the processor, and their availability on the processor. custom_gates : dict, optional A dictionary that associates with gate labels :class:`LinearOperator`, :class:`OpFactory`, or `numpy.ndarray` objects. These objects override any other behavior for constructing their designated operations. Keys of this dictionary may be string-type gate *names* or labels that include target qubits. depolarization_strengths : dict, optional A dictionary whose keys are gate names (e.g. `"Gx"`) and whose values are floats that specify the strength of uniform depolarization. stochastic_error_probs : dict, optional A dictionary whose keys are gate names (e.g. `"Gx"`) and whose values are tuples that specify Pauli-stochastic rates for each of the non-trivial Paulis (so a 3-tuple would be expected for a 1Q gate and a 15-tuple for a 2Q gate). lindblad_error_coeffs : dict, optional A dictionary whose keys are gate names (e.g. `"Gx"`) and whose values are dictionaries corresponding to the `lindblad_term_dict` kwarg taken by `LindbladErrorgen`. Keys are `(termType, basisLabel1, <basisLabel2>)` tuples, where `termType` can be `"H"` (Hamiltonian), `"S"` (Stochastic), or `"A"` (Affine). Hamiltonian and Affine terms always have a single basis label (so key is a 2-tuple) whereas Stochastic tuples with 1 basis label indicate a *diagonal* term, and are the only types of terms allowed when `nonham_mode != "all"`. Otherwise, Stochastic term tuples can include 2 basis labels to specify "off-diagonal" non-Hamiltonian Lindblad terms. Basis labels can be strings or integers. Values are complex coefficients. depolarization_parameterization : str of {"depolarize", "stochastic", or "lindblad"} Determines whether a DepolarizeOp, StochasticNoiseOp, or LindbladErrorgen is used to parameterize the depolarization noise, respectively. When "depolarize" (the default), a DepolarizeOp is created with the strength given in `depolarization_strengths`. When "stochastic", the depolarization strength is split evenly among the stochastic channels of a StochasticOp. When "lindblad", the depolarization strength is split evenly among the coefficients of the stochastic error generators (which are exponentiated to form a LindbladErrorgen with the "depol" parameterization). stochastic_parameterization : str of {"stochastic", or "lindblad"} Determines whether a StochasticNoiseOp or LindbladErrorgen is used to parameterize the stochastic noise, respectively. When "stochastic", elements of `stochastic_error_probs` are used as coefficients in a linear combination of stochastic channels (the default). When "lindblad", the elements of `stochastic_error_probs` are coefficients of stochastic error generators (which are exponentiated to form a LindbladErrorgen with the "cptp" parameterization). lindblad_parameterization : "auto" or a LindbladErrorgen paramtype Determines the parameterization of the LindbladErrorgen. When "auto" (the default), the parameterization is inferred from the types of error generators specified in the `lindblad_error_coeffs` dictionaries. When not "auto", the parameterization type is passed through to the LindbladErrorgen. evotype : Evotype or str, optional The evolution type. The special value `"default"` is equivalent to specifying the value of `pygsti.evotypes.Evotype.default_evotype`. simulator : ForwardSimulator or {"auto", "matrix", "map"} The simulator used to compute predicted probabilities for the resulting :class:`Model`. Using `"auto"` selects `"matrix"` when there are 2 qubits or less, and otherwise selects `"map"`. on_construction_error : {'raise','warn',ignore'} What to do when the creation of a gate with the given `parameterization` fails. Usually you'll want to `"raise"` the error. In some cases, for example when converting as many gates as you can into `parameterization="clifford"` gates, `"warn"` or even `"ignore"` may be useful. independent_gates : bool, optional Whether gates are allowed independent local noise or not. If False, then all gates with the same name (e.g. "Gx") will have the *same* (local) noise (e.g. an overrotation by 1 degree), and the `operation_bks['gates']` dictionary contains a single key per gate name. If True, then gates with the same name acting on different qubits may have different local noise, and so the `operation_bks['gates']` dictionary contains a key for each gate available gate placement. ensure_composed_gates : bool, optional If True then the elements of the `operation_bks['gates']` will always be :class:`ComposedOp` objects. The purpose of this is to facilitate modifying the gate operations after the model is created. If False, then the appropriately parameterized gate objects (often dense gates) are used directly. ideal_gate_type : str or tuple, optional A gate type or tuple of gate types (listed in order of priority) which is used to construct the ideal gates. A gate type usually specifies the Python class that will be created, which determines 1) the parameterization of the gate and 2) the class/category of the gate (e.g. a :class:`StaticClifford` operation has no parameters and is a Clifford operation). ideal_spam_type : str or tuple, optional Similar to `ideal_gate_type` but for SPAM elements (state preparations and POVMs). implicit_idle_mode : {'none', 'add_global'} The way idel operations are added implicitly within the created model. `"none"` doesn't add any "extra" idle operations when there is a layer that contains some gates but not gates on all the qubits. `"add_global"` adds the global idle operation, i.e., the operation for a global idle layer (zero gates - a completely empty layer), to every layer that is simulated, using the global idle as a background idle that always occurs regardless of the operation. Returns ------- LocalNoiseModel A model with `"rho0"` prep, `"Mdefault"` POVM, and gates labeled by the gate names and qubit labels (as specified by `processor_spec`). For instance, the operation label for the `"Gx"` gate on the second qubit might be `Label("Gx",1)`. """ modelnoise = _build_modelnoise_from_args(depolarization_strengths, stochastic_error_probs, lindblad_error_coeffs, depolarization_parameterization, stochastic_parameterization, lindblad_parameterization, allow_nonlocal=False) return _create_crosstalk_free_model(processor_spec, modelnoise, custom_gates, evotype, simulator, on_construction_error, independent_gates, independent_spam, ensure_composed_gates, ideal_gate_type, ideal_spam_type, ideal_spam_type, implicit_idle_mode) def _create_crosstalk_free_model(processor_spec, modelnoise, custom_gates=None, evotype="default", simulator="auto", on_construction_error='raise', independent_gates=False, independent_spam=True, ensure_composed_gates=False, ideal_gate_type='auto', ideal_prep_type='auto', ideal_povm_type='auto', implicit_idle_mode='none'): """ Create a n-qubit "crosstalk-free" model. Similar to :method:`create_crosstalk_free_model` but the noise is input more generally, as a :class:`ModelNoise` object. Arguments are the same as this function except that `modelnoise` is given instead of several more specific noise-describing arguments. Returns ------- LocalNoiseModel """ qubit_labels = processor_spec.qubit_labels state_space = _statespace.QubitSpace(qubit_labels) evotype = _Evotype.cast(evotype) modelnoise = _OpModelNoise.cast(modelnoise) modelnoise.reset_access_counters() if ideal_gate_type == "auto": ideal_gate_type = ('static standard', 'static clifford', 'static unitary') if ideal_prep_type == "auto": ideal_prep_type = _state.state_type_from_op_type(ideal_gate_type) if ideal_povm_type == "auto": ideal_povm_type = _povm.povm_type_from_op_type(ideal_gate_type) gatedict = _setup_local_gates(processor_spec, evotype, modelnoise, custom_gates, ideal_gate_type) # (Note: global idle is now handled through processor-spec processing) # SPAM: local_noise = True prep_layers, povm_layers = _create_spam_layers(processor_spec, modelnoise, local_noise, ideal_prep_type, ideal_povm_type, evotype, state_space, independent_gates, independent_spam) modelnoise.warn_about_zero_counters() return _LocalNoiseModel(processor_spec, gatedict, prep_layers, povm_layers, evotype, simulator, on_construction_error, independent_gates, ensure_composed_gates, implicit_idle_mode) def create_cloud_crosstalk_model(processor_spec, custom_gates=None, depolarization_strengths=None, stochastic_error_probs=None, lindblad_error_coeffs=None, depolarization_parameterization='depolarize', stochastic_parameterization='stochastic', lindblad_parameterization='auto', evotype="default", simulator="auto", independent_gates=False, independent_spam=True, errcomp_type="gates", implicit_idle_mode="none", verbosity=0): """ Create a n-qubit "cloud-crosstalk" model. In a cloud crosstalk model, gates consist of a (local) ideal gates followed by an error operation that can act nontrivially on *any* of the processor's qubits (not just a gate's target qubits). Typically a gate's errors are specified relative to the gate's target qubits, forming a "cloud" of errors around the target qubits using some notion of locality (that may not be spatial, e.g. local in frequency). Currently, the "ideal" portion of each gate can only be created as a *static* (parameterless) object -- all gate parameters come from the error operation. Errors can be specified using any combination of the 4 error rate/coeff arguments, but each gate name must be provided exclusively to one type of specification. Each specification results in a different type of operation, depending on the parameterization: - `depolarization_strengths` -> DepolarizeOp, StochasticNoiseOp, or exp(LindbladErrorgen) - `stochastic_error_probs` -> StochasticNoiseOp or exp(LindbladErrorgen) - `lindblad_error_coeffs` -> exp(LindbladErrorgen) In addition to the gate names, the special values `"prep"` and `"povm"` may be used as keys to specify the error on the state preparation, measurement, respectively. Parameters ---------- processor_spec : ProcessorSpec The processor specification to create a model for. This object specifies the gate names and unitaries for the processor, and their availability on the processor. custom_gates : dict, optional A dictionary that associates with gate labels :class:`LinearOperator`, :class:`OpFactory`, or `numpy.ndarray` objects. These objects override any other behavior for constructing their designated operations. Keys of this dictionary may be string-type gate *names* or labels that include target qubits. depolarization_strengths : dict, optional A dictionary whose keys are gate names (e.g. `"Gx"`) and whose values are floats that specify the strength of uniform depolarization. stochastic_error_probs : dict, optional A dictionary whose keys are gate names (e.g. `"Gx"`) and whose values are tuples that specify Pauli-stochastic rates for each of the non-trivial Paulis (so a 3-tuple would be expected for a 1Q gate and a 15-tuple for a 2Q gate). lindblad_error_coeffs : dict, optional A dictionary whose keys are gate names (e.g. `"Gx"`) and whose values are dictionaries corresponding to the `lindblad_term_dict` kwarg taken by `LindbladErrorgen`. Keys are `(termType, basisLabel1, <basisLabel2>)` tuples, where `termType` can be `"H"` (Hamiltonian), `"S"` (Stochastic), or `"A"` (Affine). Hamiltonian and Affine terms always have a single basis label (so key is a 2-tuple) whereas Stochastic tuples with 1 basis label indicate a *diagonal* term, and are the only types of terms allowed when `nonham_mode != "all"`. Otherwise, Stochastic term tuples can include 2 basis labels to specify "off-diagonal" non-Hamiltonian Lindblad terms. Basis labels can be strings or integers. Values are complex coefficients. depolarization_parameterization : str of {"depolarize", "stochastic", or "lindblad"} Determines whether a DepolarizeOp, StochasticNoiseOp, or LindbladErrorgen is used to parameterize the depolarization noise, respectively. When "depolarize" (the default), a DepolarizeOp is created with the strength given in `depolarization_strengths`. When "stochastic", the depolarization strength is split evenly among the stochastic channels of a StochasticOp. When "lindblad", the depolarization strength is split evenly among the coefficients of the stochastic error generators (which are exponentiated to form a LindbladErrorgen with the "depol" parameterization). stochastic_parameterization : str of {"stochastic", or "lindblad"} Determines whether a StochasticNoiseOp or LindbladErrorgen is used to parameterize the stochastic noise, respectively. When "stochastic", elements of `stochastic_error_probs` are used as coefficients in a linear combination of stochastic channels (the default). When "lindblad", the elements of `stochastic_error_probs` are coefficients of stochastic error generators (which are exponentiated to form a LindbladErrorgen with the "cptp" parameterization). lindblad_parameterization : "auto" or a LindbladErrorgen paramtype Determines the parameterization of the LindbladErrorgen. When "auto" (the default), the parameterization is inferred from the types of error generators specified in the `lindblad_error_coeffs` dictionaries. When not "auto", the parameterization type is passed through to the LindbladErrorgen. evotype : Evotype or str, optional The evolution type. The special value `"default"` is equivalent to specifying the value of `pygsti.evotypes.Evotype.default_evotype`. simulator : ForwardSimulator or {"auto", "matrix", "map"} The simulator used to compute predicted probabilities for the resulting :class:`Model`. Using `"auto"` selects `"matrix"` when there are 2 qubits or less, and otherwise selects `"map"`. independent_gates : bool, optional Whether gates are allowed independent noise or not. If False, then all gates with the same name (e.g. "Gx") will have the *same* noise (e.g. an overrotation by 1 degree), and the `operation_bks['cloudnoise']` dictionary will contains a single key per gate name. If True, then gates with the same name acting on different qubits may have different local noise, and so the `operation_bks['cloudnoise']` dictionary contains a key for each gate available gate placement. independent_spam : bool, optional Similar to `indepenent_gates` but for SPAM operations. errcomp_type : {'gates', 'errorgens'} Whether errors should be combined by composing error maps (`gates`) or by exponentiating the sum of error generators (composing the error generators, `errorgens`). The latter is only an option when the noise is given solely in terms of Lindblad error coefficients. implicit_idle_mode : {'none', 'add_global'} The way idel operations are added implicitly within the created model. `"none"` doesn't add any "extra" idle operations when there is a layer that contains some gates but not gates on all the qubits. `"add_global"` adds the global idle operation, i.e., the operation for a global idle layer (zero gates - a completely empty layer), to every layer that is simulated, using the global idle as a background idle that always occurs regardless of the operation. verbosity : int or VerbosityPrinter, optional Amount of detail to print to stdout. Returns ------- CloudNoiseModel """ modelnoise = _build_modelnoise_from_args(depolarization_strengths, stochastic_error_probs, lindblad_error_coeffs, depolarization_parameterization, stochastic_parameterization, lindblad_parameterization, allow_nonlocal=True) return _create_cloud_crosstalk_model(processor_spec, modelnoise, custom_gates, evotype, simulator, independent_gates, independent_spam, errcomp_type, implicit_idle_mode, verbosity) def _create_cloud_crosstalk_model(processor_spec, modelnoise, custom_gates=None, evotype="default", simulator="auto", independent_gates=False, independent_spam=True, errcomp_type="errorgens", implicit_idle_mode="none", verbosity=0): """ Create a n-qubit "cloud-crosstalk" model. Similar to :method:`create_cloud_crosstalk_model` but the noise is input more generally, as a :class:`ModelNoise` object. Arguments are the same as this function except that `modelnoise` is given instead of several more specific noise-describing arguments. Returns ------- CloudNoiseModel """ qubit_labels = processor_spec.qubit_labels state_space = _statespace.QubitSpace(qubit_labels) # FUTURE: allow other types of state spaces somehow? evotype = _Evotype.cast(evotype) modelnoise = _OpModelNoise.cast(modelnoise) modelnoise.reset_access_counters() printer = _VerbosityPrinter.create_printer(verbosity) #Create static ideal gates without any noise (we use `modelnoise` further down) gatedict = _setup_local_gates(processor_spec, evotype, None, custom_gates, ideal_gate_type=('static standard', 'static clifford', 'static unitary')) stencils = _collections.OrderedDict() # (Note: global idle is now processed with other processorspec gates) # SPAM local_noise = False prep_layers, povm_layers = _create_spam_layers(processor_spec, modelnoise, local_noise, 'computational', 'computational', evotype, state_space, independent_gates, independent_spam) if errcomp_type == 'gates': create_stencil_fn = modelnoise.create_errormap_stencil apply_stencil_fn = modelnoise.apply_errormap_stencil elif errcomp_type == 'errorgens': create_stencil_fn = modelnoise.create_errorgen_stencil apply_stencil_fn = modelnoise.apply_errorgen_stencil else: raise ValueError("Invalid `errcomp_type` value: %s" % str(errcomp_type)) def build_cloudnoise_fn(lbl): # lbl will be for a particular gate and target qubits. If we have error rates for this specific gate # and target qubits (i.e this primitive layer op) then we should build it directly (and independently, # regardless of the value of `independent_gates`) using these rates. Otherwise, if we have a stencil # for this gate, then we should use it to construct the output, using a copy when gates are independent # and a reference to the *same* stencil operations when `independent_gates==False`. num_sslbls = len(lbl.sslbls) if (lbl.sslbls is not None) else None if lbl in modelnoise: stencil = create_stencil_fn(lbl, evotype, state_space, num_target_labels=num_sslbls) elif lbl.name in stencils: stencil = stencils[lbl.name] elif lbl.name in modelnoise: stencils[lbl.name] = create_stencil_fn(lbl.name, evotype, state_space, num_target_labels=num_sslbls) stencil = stencils[lbl.name] else: return None # no cloudnoise error for this label return apply_stencil_fn(stencil, evotype, state_space, target_labels=lbl.sslbls, qubit_graph=processor_spec.qubit_graph, copy=independent_gates and (lbl not in modelnoise)) # no need to copy if first case def build_cloudkey_fn(lbl): num_sslbls = len(lbl.sslbls) if (lbl.sslbls is not None) else None if lbl in modelnoise: stencil = create_stencil_fn(lbl, evotype, state_space, num_target_labels=num_sslbls) elif lbl.name in stencils: stencil = stencils[lbl.name] elif lbl.name in modelnoise: stencils[lbl.name] = create_stencil_fn(lbl.name, evotype, state_space, num_target_labels=num_sslbls) stencil = stencils[lbl.name] else: # simple cloud-key when there is no cloud noise return tuple(lbl.sslbls) if (lbl.sslbls is not None) else qubit_labels #Otherwise, process stencil to get a list of all the qubit labels `lbl`'s cloudnoise error # touches and form this into a key cloud_sslbls = modelnoise.compute_stencil_absolute_sslbls(stencil, state_space, lbl.sslbls, processor_spec.qubit_graph) hashable_sslbls = tuple(lbl.sslbls) if (lbl.sslbls is not None) else qubit_labels cloud_key = (hashable_sslbls, tuple(sorted(cloud_sslbls))) # (sets are unhashable) return cloud_key ret = _CloudNoiseModel(processor_spec, gatedict, prep_layers, povm_layers, build_cloudnoise_fn, build_cloudkey_fn, simulator, evotype, errcomp_type, implicit_idle_mode, printer) modelnoise.warn_about_zero_counters() # must do this after model creation so build_ fns have been run return ret def create_cloud_crosstalk_model_from_hops_and_weights( processor_spec, custom_gates=None, max_idle_weight=1, max_spam_weight=1, maxhops=0, extra_weight_1_hops=0, extra_gate_weight=0, simulator="auto", evotype='default', gate_type="H+S", spam_type="H+S", implicit_idle_mode="none", errcomp_type="gates", independent_gates=True, independent_spam=True, connected_highweight_errors=True, verbosity=0): """ Create a "cloud crosstalk" model based on maximum error weights and hops along the processor's qubit graph. This function provides a convenient way to construct cloud crosstalk models whose gate errors consist of Pauli elementary error generators (i.e. that correspond to Lindblad error coefficients) that are limited in weight (number of non-identity Paulis) and support (which qubits have non-trivial Paulis on them). Errors are taken to be approximately local, meaning they are concentrated near the target qubits of a gate, with the notion of locality taken from the processor specification's qubit graph. The caller provides maximum-weight, maximum-hop (a "hop" is the movement along a single graph edge), and gate type arguments to specify the set of possible errors on a gate. - The global idle gate (corresponding to an empty circuit layer) has errors that are limited only by a maximum weight, `max_idle_weight`. - State preparation and POVM errors are constructed similarly, with a global-idle-like error following or preceding the preparation or measurement, respectively. - Gate errors are placed on all the qubits that can be reached with at most `maxhops` hops from (any of) the gate's target qubits. Elementary error generators up to weight `W`, where `W` equals the number of target qubits (e.g., 2 for a CNOT gate) plus `extra_gate_weight` are allowed. Weight-1 terms are a special case, and the `extra_weight_1_hops` argument adds to the usual `maxhops` in this case to allow weight-1 errors on a possibly larger region of qubits around the target qubits. Parameters ---------- processor_spec : ProcessorSpec The processor specification to create a model for. This object specifies the gate names and unitaries for the processor, and their availability on the processor. custom_gates : dict A dictionary that associates with gate labels :class:`LinearOperator`, :class:`OpFactory`, or `numpy.ndarray` objects. These objects describe the full action of the gate or primitive-layer they're labeled by (so if the model represents states by density matrices these objects are superoperators, not unitaries), and override any standard construction based on builtin gate names or `nonstd_gate_unitaries`. Keys of this dictionary must be string-type gate *names* -- they cannot include state space labels -- and they must be *static* (have zero parameters) because they represent only the ideal behavior of each gate -- the cloudnoise operations represent the parameterized noise. To fine-tune how this noise is parameterized, call the :class:`CloudNoiseModel` constructor directly. max_idle_weight : int, optional The maximum-weight for errors on the global idle gate. max_spam_weight : int, optional The maximum-weight for state preparation and measurement (SPAM) errors. maxhops : int The locality constraint: for a gate, errors (of weight up to the maximum weight for the gate) are allowed to occur on the gate's target qubits and those reachable by hopping at most `maxhops` times from a target qubit along nearest-neighbor links (defined by the `geometry`). extra_weight_1_hops : int, optional Additional hops (adds to `maxhops`) for weight-1 errors. A value > 0 can be useful for allowing just weight-1 errors (of which there are relatively few) to be dispersed farther from a gate's target qubits. For example, a crosstalk-detecting model might use this. extra_gate_weight : int, optional Addtional weight, beyond the number of target qubits (taken as a "base weight" - i.e. weight 2 for a 2Q gate), allowed for gate errors. If this equals 1, for instance, then 1-qubit gates can have up to weight-2 errors and 2-qubit gates can have up to weight-3 errors. simulator : ForwardSimulator or {"auto", "matrix", "map"} The circuit simulator used to compute any requested probabilities, e.g. from :method:`probs` or :method:`bulk_probs`. Using `"auto"` selects `"matrix"` when there are 2 qubits or less, and otherwise selects `"map"`. evotype : Evotype or str, optional The evolution type of this model, describing how states are represented. The special value `"default"` is equivalent to specifying the value of `pygsti.evotypes.Evotype.default_evotype`. gate_type : str, optional The Lindblad-error parameterization type used for gate operations. This may be expanded in the future, but currently the gate errors *must* be of the Lindblad error-generator coefficients type, and this argument specifies what elementary error-generator coefficients are initially allowed (and linked to model parameters), before maximum-weight and locality constraints are imposed. In addition to the usual Lindblad error types, (e.g. `"H"`, `"H+S"`) the special values `"none"` is allowed to indicate that there should be no errors on the gates (useful if you only want errors on the SPAM, for instance). spam_type : str, optional Similar to `gate_type` but for SPAM elements (state preparations and POVMs). This specifies the Lindblad-error parameterization for the state prepearation and POVM. implicit_idle_mode : {'none', 'add_global'} The way idel operations are added implicitly within the created model. `"nonw"` doesn't add any "extra" idle operations when there is a layer that contains some gates but not gates on all the qubits. `"add_global"` adds the global idle operation, i.e., the operation for a global idle layer (zero gates - a completely empty layer), to every layer that is simulated, using the global idle as a background idle that always occurs regardless of the operation. errcomp_type : {"gates","errorgens"} How errors are composed when creating layer operations in the created model. `"gates"` means that the errors on multiple gates in a single layer are composed as separate and subsequent processes. Specifically, the layer operation has the form `Composed(target,idleErr,cloudErr)` where `target` is a composition of all the ideal gate operations in the layer, `idleErr` is the global idle error if `implicit_idle_mode == 'add_global'`, and `cloudErr` is the composition (ordered as layer-label) of cloud- noise contributions, i.e. a map that acts as the product of exponentiated error-generator matrices. `"errorgens"` means that layer operations have the form `Composed(target, error)` where `target` is as above and `error` results from composing (summing) the idle and cloud-noise error *generators*, i.e. a map that acts as the exponentiated sum of error generators (ordering is irrelevant in this case). independent_gates : bool, optional Whether the noise added to a gate when it acts on one set of target qubits is independent of its noise on a different set of target qubits. If False, then all gates with the same name (e.g. "Gx") will be constrained to having the *same* noise on the cloud around the target qubits (even though the target qubits and cloud are different). If True, then gate noise operations for different sets of target qubits are independent. independent_spam : bool, optional Similar to `independent_gates` but for state preparation and measurement operations. When `False`, the noise applied to each set (individual or pair or triple etc.) of qubits must be the same, e.g., if the state preparation is a perfect preparation followed by a single-qubit rotation then this rotation must be by the *same* angle on all of the qubits. connected_highweight_errors : bool, optional An additional constraint regarding high-weight errors. When `True`, only high weight (weight 2+) elementary error generators whose non-trivial Paulis occupy a *connected* portion of the qubit graph are allowed. For example, if the qubit graph is a 1D chain of 4 qubits, 1-2-3-4, and weight-2 errors are allowed on a single-qubit gate with target = qubit-2, then weight-2 errors on 1-2 and 2-3 would be allowed, but errors on 1-3 would be forbidden. When `False`, no constraint is imposed. verbosity : int or VerbosityPrinter, optional An integer >= 0 dictating how must output to send to stdout. Returns ------- CloudNoiseModel """ # construct noise specifications for the cloudnoise model modelnoise = {} all_qubit_labels = processor_spec.qubit_labels conn = connected_highweight_errors # shorthand: whether high-weight errors must be connected on the graph global_idle_name = processor_spec.global_idle_gate_name # Global Idle if max_idle_weight > 0: assert(global_idle_name is not None), \ "`max_idle_weight` must equal 0 for processor specs without a global idle gate!" #printer.log("Creating Idle:") wt_maxhop_tuples = [(i, None) for i in range(1, max_idle_weight + 1)] modelnoise[global_idle_name] = _build_weight_maxhops_modelnoise(all_qubit_labels, wt_maxhop_tuples, gate_type, conn) # SPAM if max_spam_weight > 0: wt_maxhop_tuples = [(i, None) for i in range(1, max_spam_weight + 1)] modelnoise['prep'] = _build_weight_maxhops_modelnoise(all_qubit_labels, wt_maxhop_tuples, spam_type, conn) modelnoise['povm'] = _build_weight_maxhops_modelnoise(all_qubit_labels, wt_maxhop_tuples, spam_type, conn) # Gates weight_maxhops_tuples_1Q = [(1, maxhops + extra_weight_1_hops)] + \ [(1 + x, maxhops) for x in range(1, extra_gate_weight + 1)] weight_maxhops_tuples_2Q = [(1, maxhops + extra_weight_1_hops), (2, maxhops)] + \ [(2 + x, maxhops) for x in range(1, extra_gate_weight + 1)] for gatenm, gate_unitary in processor_spec.gate_unitaries.items(): if gatenm == global_idle_name: continue # processed above gate_nQubits = int(gate_unitary) if isinstance(gate_unitary, (int, _np.int64)) \ else int(round(_np.log2(gate_unitary.shape[0]))) # NOTE: integer gate_unitary => idle on n qubits if gate_nQubits not in (1, 2): raise ValueError("Only 1- and 2-qubit gates are supported. %s acts on %d qubits!" % (str(gatenm), gate_nQubits)) weight_maxhops_tuples = weight_maxhops_tuples_1Q if gate_nQubits == 1 else weight_maxhops_tuples_2Q target_sslbls = ('@0',) if gate_nQubits == 1 else ('@0', '@1') modelnoise[gatenm] = _build_weight_maxhops_modelnoise(target_sslbls, weight_maxhops_tuples, gate_type, conn) return _create_cloud_crosstalk_model(processor_spec, modelnoise, custom_gates, evotype, simulator, independent_gates, independent_spam, errcomp_type, implicit_idle_mode, verbosity) def _iter_basis_inds(weight): """ Iterate over product of `weight` non-identity Pauli 1Q basis indices """ basisIndList = [[1, 2, 3]] * weight # assume pauli 1Q basis, and only iterate over non-identity els for basisInds in _itertools.product(*basisIndList): yield basisInds def _pauli_product_matrix(sigma_inds): """ Construct the Pauli product matrix from the given `sigma_inds` Parameters ---------- sigma_inds : iterable A sequence of integers in the range [0,3] corresponding to the I, X, Y, Z Pauli basis matrices. Returns ------- numpy.ndarray or scipy.sparse.csr_matrix """ sigmaVec = (id2x2 / sqrt2, sigmax / sqrt2, sigmay / sqrt2, sigmaz / sqrt2) M = _np.identity(1, 'complex') for i in sigma_inds: M = _np.kron(M, sigmaVec[i]) return M def _construct_restricted_weight_pauli_basis(wt, sparse=False): basisEl_Id = _pauli_product_matrix(_np.zeros(wt, _np.int64)) errbasis = [basisEl_Id] errbasis_lbls = ['I'] for err_basis_inds in _iter_basis_inds(wt): error = _np.array(err_basis_inds, _np.int64) # length == wt basisEl = _pauli_product_matrix(error) errbasis.append(basisEl) errbasis_lbls.append(''.join(["IXYZ"[i] for i in err_basis_inds])) #printer.log("Error on qubits %s -> error basis of length %d" % (err_qubit_inds, len(errbasis)), 3) return _ExplicitBasis(errbasis, errbasis_lbls, real=True, sparse=sparse) def _build_weight_maxhops_modelnoise(target_sslbls, weight_maxhops_tuples, lnd_parameterization, connected=True): # This function: # loop over all size-`wt` *connected* combinations, `err_qubit_inds`, of the qubit indices in # `possible_err_qubit_inds` # - construct a local weight-`wt` Pauli basis & corresponding LindbladErrorgen on `wt` qubits # => replace with: opnoise.create_errorgen(evotype, state_space=None) where opnoise is for a wt-qubit op # - embed this constructed local error onto `err_qubit_inds` # - append embedded error onto running list # # Noise object structure: # OpModelPerOpNoise( { op_key/'idle': { sslbls : opnoise } } ) # where sslbls can be absolute labels or stencil labels # -- could have a fn that spreads a single opnoise onto all the sslbls # given by size-`wt` connected combos of `possible_err_qubit_inds` - this would work for independent clouds # -- have LindbladNoiseDict and another LindbladPauliAtWeight (?) noise objects, # since we want to specify a lindblad noise by giving a weight and an initial basis (Pauli here) # To build a cloudnoise model from hops & weights: modelnoise_dict = {} if lnd_parameterization == 'none' or lnd_parameterization is None: return {} # special case when we don't want any error parameterization for wt, max_hops in weight_maxhops_tuples: if max_hops is None or max_hops == 0: # Note: maxHops not used in this case stencil_lbl = _stencil.StencilLabelAllCombos(target_sslbls, wt, connected) else: stencil_lbl = _stencil.StencilLabelRadiusCombos(target_sslbls, max_hops, wt, connected) local_state_space = _statespace.default_space_for_num_qubits(wt) modelnoise_dict[stencil_lbl] = _LindbladNoise.from_basis_coefficients( lnd_parameterization, _construct_restricted_weight_pauli_basis(wt), local_state_space) return modelnoise_dict def _build_modelnoise_from_args(depolarization_strengths, stochastic_error_probs, lindblad_error_coeffs, depolarization_parameterization, stochastic_parameterization, lindblad_parameterization, allow_nonlocal): modelnoises = [] if depolarization_strengths is not None: noise_dict = {} for lbl, val in depolarization_strengths.items(): if isinstance(val, dict): # then value is actually a dictionary of sslbls -> noise specifications if not allow_nonlocal: raise ValueError("Nonlocal depolarization strengths not allowed!") noise_dict[lbl] = {k: _DepolarizationNoise(v, depolarization_parameterization) for k, v in val.items()} else: noise_dict[lbl] = _DepolarizationNoise(val, depolarization_parameterization) modelnoises.append(_OpModelPerOpNoise(noise_dict)) if stochastic_error_probs is not None: noise_dict = {} for lbl, val in stochastic_error_probs.items(): if isinstance(val, dict): # then value is actually a dictionary of sslbls -> noise specifications if not allow_nonlocal: raise ValueError("Nonlocal stochastic error probs not allowed!") noise_dict[lbl] = {k: _StochasticNoise(v, stochastic_parameterization) for k, v in val.items()} else: noise_dict[lbl] = _StochasticNoise(val, stochastic_parameterization) modelnoises.append(_OpModelPerOpNoise(noise_dict)) if lindblad_error_coeffs is not None: if not allow_nonlocal: # the easy case modelnoises.append(_OpModelPerOpNoise({lbl: _LindbladNoise(val, lindblad_parameterization) for lbl, val in lindblad_error_coeffs.items()})) else: # then need to process labels like ('H', 'XX:0,1') or 'HXX:0,1' def process_stencil_labels(flat_lindblad_errs): nonlocal_errors = _collections.OrderedDict() local_errors = _collections.OrderedDict() for nm, val in flat_lindblad_errs.items(): if isinstance(nm, str): nm = (nm[0], nm[1:]) # e.g. "HXX" => ('H','XX') err_typ, basisEls = nm[0], nm[1:] sslbls = None local_nm = [err_typ] for bel in basisEls: # e.g. bel could be "X:Q0" or "XX:Q0,Q1" # OR "X:<n>" where n indexes a target qubit or "X:<dir>" where dir indicates # a graph *direction*, e.g. "up" if ':' in bel: bel_name, bel_sslbls = bel.split(':') # should have form <name>:<comma-separated-sslbls> bel_sslbls = bel_sslbls.split(',') # e.g. ('Q0','Q1') integerized_sslbls = [] for ssl in bel_sslbls: try: integerized_sslbls.append(int(ssl)) except: integerized_sslbls.append(ssl) bel_sslbls = tuple(integerized_sslbls) else: bel_name = bel bel_sslbls = None if sslbls is None: sslbls = bel_sslbls else: #Note: sslbls should always be the same if there are multiple basisEls, # i.e for nm == ('S',bel1,bel2) assert(sslbls is bel_sslbls or sslbls == bel_sslbls), \ "All basis elements of the same error term must operate on the *same* state!" local_nm.append(bel_name) # drop the state space labels, e.g. "XY:Q0,Q1" => "XY" # keep track of errors by the qubits they act on, as only each such # set will have it's own LindbladErrorgen local_nm = tuple(local_nm) # so it's hashable if sslbls is not None: sslbls = tuple(sorted(sslbls)) if sslbls not in nonlocal_errors: nonlocal_errors[sslbls] = _collections.OrderedDict() if local_nm in nonlocal_errors[sslbls]: nonlocal_errors[sslbls][local_nm] += val else: nonlocal_errors[sslbls][local_nm] = val else: if local_nm in local_errors: local_errors[local_nm] += val else: local_errors[local_nm] = val if len(nonlocal_errors) == 0: return _LindbladNoise(local_errors, lindblad_parameterization) else: all_errors = [] if len(local_errors) > 0: all_errors.append((None, _LindbladNoise(local_errors, lindblad_parameterization))) for sslbls, errdict in nonlocal_errors.items(): all_errors.append((sslbls, _LindbladNoise(errdict, lindblad_parameterization))) return _collections.OrderedDict(all_errors) modelnoises.append(_OpModelPerOpNoise({lbl: process_stencil_labels(val) for lbl, val in lindblad_error_coeffs.items()})) return _ComposedOpModelNoise(modelnoises) @_deprecated_fn("This function is overly specific and will be removed soon.") def _nparams_xycnot_cloudnoise_model(num_qubits, geometry="line", max_idle_weight=1, maxhops=0, extra_weight_1_hops=0, extra_gate_weight=0, require_connected=False, independent_1q_gates=True, zz_only=False, bidirectional_cnots=True, verbosity=0): """ Compute the number of parameters in a particular :class:`CloudNoiseModel`. Returns the number of parameters in the :class:`CloudNoiseModel` containing X(pi/2), Y(pi/2) and CNOT gates using the specified arguments without actually constructing the model (useful for considering parameter-count scaling). Parameters ---------- num_qubits : int The total number of qubits. geometry : {"line","ring","grid","torus"} or QubitGraph The type of connectivity among the qubits, specifying a graph used to define neighbor relationships. Alternatively, a :class:`QubitGraph` object may be passed directly. max_idle_weight : int, optional The maximum-weight for errors on the global idle gate. maxhops : int The locality constraint: for a gate, errors (of weight up to the maximum weight for the gate) are allowed to occur on the gate's target qubits and those reachable by hopping at most `maxhops` times from a target qubit along nearest-neighbor links (defined by the `geometry`). extra_weight_1_hops : int, optional Additional hops (adds to `maxhops`) for weight-1 errors. A value > 0 can be useful for allowing just weight-1 errors (of which there are relatively few) to be dispersed farther from a gate's target qubits. For example, a crosstalk-detecting model might use this. extra_gate_weight : int, optional Addtional weight, beyond the number of target qubits (taken as a "base weight" - i.e. weight 2 for a 2Q gate), allowed for gate errors. If this equals 1, for instance, then 1-qubit gates can have up to weight-2 errors and 2-qubit gates can have up to weight-3 errors. require_connected : bool, optional If True, then high-weight errors only occur on connected (via `geometry`) qubits. For example in a line of qubits there would not be weight-2 errors on qubits 1 and 3. independent_1q_gates : bool, optional If True, 1Q gates on different qubits have separate (distinct) parameters. If False, the 1Q gates of each type (e.g. an pi/2 X gate) for different qubits share the same set of parameters. zz_only : bool, optional If True, the only high-weight errors allowed are of "Z^n" type. bidirectional_cnots : bool Whether CNOT gates can be performed in either direction (and each direction should be treated as an indepedent gate) verbosity : int, optional An integer >= 0 dictating how much output to send to stdout. Returns ------- int """ # noise can be either a seed or a random array that is long enough to use printer = _VerbosityPrinter.create_printer(verbosity) printer.log("Computing parameters for a %d-qubit %s model" % (num_qubits, geometry)) qubitGraph = _QubitGraph.common_graph(num_qubits, geometry, directed=True, all_directions=True) #printer.log("Created qubit graph:\n"+str(qubitGraph)) def idle_count_nparams(max_weight): """Parameter count of a `build_nqn_global_idle`-constructed gate""" ret = 0 possible_err_qubit_inds = _np.arange(num_qubits) for wt in range(1, max_weight + 1): nErrTargetLocations = qubitGraph.connected_combos(possible_err_qubit_inds, wt) if zz_only and wt > 1: basisSizeWoutId = 1**wt # ( == 1) else: basisSizeWoutId = 3**wt # (X,Y,Z)^wt nErrParams = 2 * basisSizeWoutId # H+S terms ret += nErrTargetLocations * nErrParams return ret def op_count_nparams(target_qubit_inds, weight_maxhops_tuples, debug=False): """Parameter count of a `build_nqn_composed_gate`-constructed gate""" ret = 0 #Note: no contrib from idle noise (already parameterized) for wt, maxHops in weight_maxhops_tuples: possible_err_qubit_inds = _np.array(qubitGraph.radius(target_qubit_inds, maxHops), _np.int64) if require_connected: nErrTargetLocations = qubitGraph.connected_combos(possible_err_qubit_inds, wt) else: nErrTargetLocations = _scipy.special.comb(len(possible_err_qubit_inds), wt) if zz_only and wt > 1: basisSizeWoutId = 1**wt # ( == 1) else: basisSizeWoutId = 3**wt # (X,Y,Z)^wt nErrParams = 2 * basisSizeWoutId # H+S terms if debug: print(" -- wt%d, hops%d: inds=%s locs = %d, eparams=%d, total contrib = %d" % (wt, maxHops, str(possible_err_qubit_inds), nErrTargetLocations, nErrParams, nErrTargetLocations * nErrParams)) ret += nErrTargetLocations * nErrParams return ret nParams = _collections.OrderedDict() printer.log("Creating Idle:") nParams[_label.Label('Gi')] = idle_count_nparams(max_idle_weight) #1Q gates: X(pi/2) & Y(pi/2) on each qubit weight_maxhops_tuples_1Q = [(1, maxhops + extra_weight_1_hops)] + \ [(1 + x, maxhops) for x in range(1, extra_gate_weight + 1)] if independent_1q_gates: for i in range(num_qubits): printer.log("Creating 1Q X(pi/2) and Y(pi/2) gates on qubit %d!!" % i) nParams[_label.Label("Gx", i)] = op_count_nparams((i,), weight_maxhops_tuples_1Q) nParams[_label.Label("Gy", i)] = op_count_nparams((i,), weight_maxhops_tuples_1Q) else: printer.log("Creating common 1Q X(pi/2) and Y(pi/2) gates") rep = int(num_qubits / 2) nParams[_label.Label("Gxrep")] = op_count_nparams((rep,), weight_maxhops_tuples_1Q) nParams[_label.Label("Gyrep")] = op_count_nparams((rep,), weight_maxhops_tuples_1Q) #2Q gates: CNOT gates along each graph edge weight_maxhops_tuples_2Q = [(1, maxhops + extra_weight_1_hops), (2, maxhops)] + \ [(2 + x, maxhops) for x in range(1, extra_gate_weight + 1)] seen_pairs = set() for i, j in qubitGraph.edges(): # note: all edges have i<j so "control" of CNOT is always lower index (arbitrary) if bidirectional_cnots is False: ordered_tup = (i, j) if i <= j else (j, i) if ordered_tup in seen_pairs: continue else: seen_pairs.add(ordered_tup) printer.log("Creating CNOT gate between qubits %d and %d!!" % (i, j)) nParams[_label.Label("Gcnot", (i, j))] = op_count_nparams((i, j), weight_maxhops_tuples_2Q) #SPAM nPOVM_1Q = 4 # params for a single 1Q POVM nParams[_label.Label('rho0')] = 3 * num_qubits # 3 b/c each component is TP nParams[_label.Label('Mdefault')] = nPOVM_1Q * num_qubits # num_qubits 1Q-POVMs return nParams, sum(nParams.values())

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import collections as _collections import itertools as _itertools from os import stat from pygsti.modelmembers.instruments.instrument import Instrument import numpy as _np import scipy as _scipy import scipy.linalg as _spl from pygsti.evotypes import Evotype as _Evotype from pygsti.modelmembers import operations as _op from pygsti.modelmembers import povms as _povm from pygsti.modelmembers import states as _state from pygsti.modelmembers import instruments as _instrument from pygsti.modelmembers.operations import opfactory as _opfactory from pygsti.models import stencillabel as _stencil from pygsti.models.modelnoise import OpModelNoise as _OpModelNoise from pygsti.models.modelnoise import OpModelPerOpNoise as _OpModelPerOpNoise from pygsti.models.modelnoise import ComposedOpModelNoise as _ComposedOpModelNoise from pygsti.models.modelnoise import LindbladNoise as _LindbladNoise from pygsti.models.modelnoise import StochasticNoise as _StochasticNoise from pygsti.models.modelnoise import DepolarizationNoise as _DepolarizationNoise from pygsti.models import explicitmodel as _emdl from pygsti.models import gaugegroup as _gg from pygsti.models.localnoisemodel import LocalNoiseModel as _LocalNoiseModel from pygsti.models.cloudnoisemodel import CloudNoiseModel as _CloudNoiseModel from pygsti.baseobjs import label as _label from pygsti.baseobjs import statespace as _statespace from pygsti.baseobjs.basis import Basis as _Basis from pygsti.baseobjs.basis import ExplicitBasis as _ExplicitBasis from pygsti.baseobjs.basis import DirectSumBasis as _DirectSumBasis from pygsti.baseobjs.qubitgraph import QubitGraph as _QubitGraph from pygsti.tools import basistools as _bt from pygsti.tools import internalgates as _itgs from pygsti.tools import optools as _ot from pygsti.tools import listtools as _lt from pygsti.baseobjs.basisconstructors import sqrt2, id2x2, sigmax, sigmay, sigmaz from pygsti.baseobjs.verbosityprinter import VerbosityPrinter as _VerbosityPrinter from pygsti.tools.legacytools import deprecate as _deprecated_fn --------------------------------------------------------------------------------------- # -- Helper Functions ---------------------------------------------------------------------------------------------- # ------------------------------------------------------------------------------------------------------------------ def to_label(lbl): try: return int(lbl) except: return lbl.strip() def to_labels(lbls): return [to_label(lbl) for lbl in lbls] # ------------------------------------------------------------------------------------------------------------------ # -- End Helper Functions ------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------------------------------ #FUTURE?: type_preferences = ('static standard', 'static clifford', 'static unitary') build_evotype = 'default' superop_mxs_in_basis = [] exprTerms = op_expr.split(':') for exprTerm in exprTerms: l = exprTerm.index('('); r = exprTerm.rindex(')') opName = exprTerm[0:l] argsStr = exprTerm[l + 1:r] args = argsStr.split(',') if opName == "I": # qubit labels (TODO: what about 'L' labels? -- not sure if they work with this...) labels = to_labels(args) stateSpaceUDim = int(_np.product([state_space.label_udimension(l) for l in labels])) # a complex 2x2 mx unitary for the identity in Pauli-product basis Uop = _op.StaticUnitaryOp(_np.identity(stateSpaceUDim, 'complex'), 'pp', build_evotype) #FUTURE?: # stdname = 'Gi' if (stateSpaceUDim == 2) else None # Uop = _op.create_from_unitary_mx(_np.identity(stateSpaceUDim, complex), type_preferences, 'pp', # stdname=stdname, evotype=evotype) # a complex 2*num_qubits x 2*num_qubits mx unitary on full space in Pauli-product basis Uop_embed = _op.EmbeddedOp(state_space, labels, Uop) # a real 4*num_qubits x 4*num_qubits mx superoperator in final basis superop_mx_pp = Uop_embed.to_dense(on_space='HilbertSchmidt') # a real 4*num_qubits x 4*num_qubits mx superoperator in final basis superop_mx_in_basis = _bt.change_basis(superop_mx_pp, 'pp', basis) elif opName == "D": # like 'I', but only parameterize the diagonal elements - so can be a depolarization-type map raise NotImplementedError("Removed temporarily - need to update using embedded gates") # # qubit labels (TODO: what about 'L' labels? -- not sure if they work with this...) # labels = to_labels(args) # stateSpaceDim = sslbls.product_dim(labels) # if parameterization not in ("linear","linearTP"): # raise ValueError("'D' gate only makes sense to use when and parameterization == 'linear'") # if defaultI2P == "TP": # # parameterize only the diagonals els after the first # indicesToParameterize = [ (i,i) for i in range(1,stateSpaceDim**2) ] # else: # # parameterize only the diagonals els # indicesToParameterize = [ (i,i) for i in range(0,stateSpaceDim**2) ] # # *real* 4x4 mx in Pauli-product basis -- still just the identity! # pp_opMx = _np.identity(stateSpaceDim**2, 'd') # # pp_opMx assumed to be in the Pauli-product basis # opTermInFinalBasis = embed_operation(pp_opMx, tuple(labels), indicesToParameterize) elif opName in ('X', 'Y', 'Z'): # single-qubit gate names assert(len(args) == 2) # theta, qubit-index theta = eval(args[0], {"__builtins__": None}, {'pi': _np.pi}) label = to_label(args[1]) assert(state_space.label_dimension(label) == 4), "%s gate must act on qubits!" % opName if opName == 'X': ex = -1j * theta * sigmax / 2 elif opName == 'Y': ex = -1j * theta * sigmay / 2 elif opName == 'Z': ex = -1j * theta * sigmaz / 2 # complex 2x2 unitary matrix operating on single qubit in Pauli-product basis Uop = _op.StaticUnitaryOp(_spl.expm(ex), 'pp', build_evotype) #FUTURE?: #stdname = None #if _np.isclose(theta, _np.pi): stdname = 'G%spi' % opName.lower() #elif _np.isclose(theta, _np.pi/2): stdname = 'G%spi2' % opName.lower() # Uop = _op.create_from_unitary_mx(_spl.expm(ex), type_preferences, 'pp', stdname=stdname, evotype=evotype) # a complex 2*num_qubits x 2*num_qubits mx unitary on full space in Pauli-product basis Uop_embed = _op.EmbeddedOp(state_space, (label,), Uop) # a real 4*num_qubits x 4*num_qubits mx superoperator in Pauli-product basis superop_mx_pp = Uop_embed.to_dense(on_space='HilbertSchmidt') # a real 4*num_qubits x 4*num_qubits mx superoperator in final basis superop_mx_in_basis = _bt.change_basis(superop_mx_pp, 'pp', basis) elif opName == 'N': # more general single-qubit gate assert(len(args) == 5) # theta, sigmaX-coeff, sigmaY-coeff, sigmaZ-coeff, qubit-index theta = eval(args[0], {"__builtins__": None}, {'pi': _np.pi, 'sqrt': _np.sqrt}) sxCoeff = eval(args[1], {"__builtins__": None}, {'pi': _np.pi, 'sqrt': _np.sqrt}) syCoeff = eval(args[2], {"__builtins__": None}, {'pi': _np.pi, 'sqrt': _np.sqrt}) szCoeff = eval(args[3], {"__builtins__": None}, {'pi': _np.pi, 'sqrt': _np.sqrt}) label = to_label(args[4]) assert(state_space.label_dimension(label) == 4), "%s gate must act on qubits!" % opName ex = -1j * theta * (sxCoeff * sigmax / 2. + syCoeff * sigmay / 2. + szCoeff * sigmaz / 2.) # complex 2x2 unitary matrix operating on single qubit in Pauli-product basis Uop = _op.StaticUnitaryOp(_spl.expm(ex), 'pp', evotype=build_evotype) #FUTURE?: Uop = _op.create_from_unitary_mx(_spl.expm(ex), type_preferences, 'pp', evotype=evotype) # a complex 2*num_qubits x 2*num_qubits mx unitary on full space in Pauli-product basis Uop_embed = _op.EmbeddedOp(state_space, (label,), Uop) # a real 4*num_qubits x 4*num_qubits mx superoperator in Pauli-product basis superop_mx_pp = Uop_embed.to_dense(on_space='HilbertSchmidt') # a real 4*num_qubits x 4*num_qubits mx superoperator in final basis superop_mx_in_basis = _bt.change_basis(superop_mx_pp, 'pp', basis) elif opName in ('CX', 'CY', 'CZ', 'CNOT', 'CPHASE'): # two-qubit gate names if opName in ('CX', 'CY', 'CZ'): assert(len(args) == 3) # theta, qubit-label1, qubit-label2 theta = eval(args[0], {"__builtins__": None}, {'pi': _np.pi}) label1 = to_label(args[1]); label2 = to_label(args[2]) if opName == 'CX': ex = -1j * theta * sigmax / 2 elif opName == 'CY': ex = -1j * theta * sigmay / 2 elif opName == 'CZ': ex = -1j * theta * sigmaz / 2 Utarget = _spl.expm(ex) # 2x2 unitary matrix operating on target qubit else: # opName in ('CNOT','CPHASE') assert(len(args) == 2) # qubit-label1, qubit-label2 label1 = to_label(args[0]); label2 = to_label(args[1]) if opName == 'CNOT': Utarget = _np.array([[0, 1], [1, 0]], 'd') elif opName == 'CPHASE': Utarget = _np.array([[1, 0], [0, -1]], 'd') # 4x4 unitary matrix operating on isolated two-qubit space U = _np.identity(4, 'complex'); U[2:, 2:] = Utarget assert(state_space.label_dimension(label1) == 4 and state_space.label_dimension(label2) == 4), \ "%s gate must act on qubits!" % opName # complex 4x4 unitary matrix operating on two-qubit in Pauli-product basis Uop = _op.StaticUnitaryOp(U, 'pp', build_evotype) #FUTURE?: # if opName == "CNOT": stdname = "Gcnot" # elif opName == "CPHASE": stdname = "Gcphase" # else: stdname = None # Uop = _op.create_from_unitary_mx(U, type_preferences, 'pp', stdname=stdname, evotype=evotype) # a complex 2*num_qubits x 2*num_qubits mx unitary on full space Uop_embed = _op.EmbeddedOp(state_space, [label1, label2], Uop) # a real 4*num_qubits x 4*num_qubits mx superoperator in Pauli-product basis superop_mx_pp = Uop_embed.to_dense(on_space='HilbertSchmidt') # a real 4*num_qubits x 4*num_qubits mx superoperator in final basis superop_mx_in_basis = _bt.change_basis(superop_mx_pp, 'pp', basis) elif opName == "LX": # TODO - better way to describe leakage? assert(len(args) == 3) # theta, dmIndex1, dmIndex2 - X rotation between any two density matrix basis states theta = eval(args[0], {"__builtins__": None}, {'pi': _np.pi}) i1 = int(args[1]) # row/column index of a single *state* within the density matrix i2 = int(args[2]) # row/column index of a single *state* within the density matrix ex = -1j * theta * sigmax / 2 Uop = _spl.expm(ex) # 2x2 unitary matrix operating on the i1-th and i2-th states of the state space basis opDim = basis.dim dmDim = int(_np.sqrt(basis.elsize)) # matrix dim of the "embedding space" if isinstance(basis, _DirectSumBasis): blockDims = [c.dim for c in basis.component_bases] else: blockDims = [opDim] Utot = _np.identity(dmDim, 'complex') Utot[i1, i1] = Uop[0, 0] Utot[i1, i2] = Uop[0, 1] Utot[i2, i1] = Uop[1, 0] Utot[i2, i2] = Uop[1, 1] # dmDim^2 x dmDim^2 mx operating on vectorized total densty matrix opTermInStdBasis = _ot.unitary_to_process_mx(Utot) # contract [3] to [2, 1] embedded_std_basis = _Basis.cast('std', 9) # [2] std_basis = _Basis.cast('std', blockDims) # std basis w/blockdim structure, i.e. [4,1] opTermInReducedStdBasis = _bt.resize_std_mx(opTermInStdBasis, 'contract', embedded_std_basis, std_basis) superop_mx_in_basis = _bt.change_basis(opTermInReducedStdBasis, std_basis, basis) else: raise ValueError("Invalid gate name: %s" % opName) superop_mxs_in_basis.append(superop_mx_in_basis) #Note: expressions are listed in "matrix composition order" final_superop_mx = superop_mxs_in_basis[0] for mx in superop_mxs_in_basis[1:]: final_superop_mx = _np.dot(final_superop_mx, mx) if basis.real: assert(_np.linalg.norm(final_superop_mx.imag) < 1e-6), "Operation matrix should be real but isn't!" final_superop_mx = _np.real(final_superop_mx) return _op.create_from_superop_mx(final_superop_mx, parameterization, basis, evotype=evotype, state_space=state_space) def _create_explicit_model_from_expressions(state_space, basis, op_labels, op_expressions, prep_labels=('rho0',), prep_expressions=('0',), effect_labels='standard', effect_expressions='standard', povm_labels='Mdefault', gate_type="full", prep_type="auto", povm_type="auto", instrument_type="auto", evotype='default'): state_space = _statespace.StateSpace.cast(state_space) ret = _emdl.ExplicitOpModel(state_space, basis.copy(), default_gate_type=gate_type, default_prep_type=prep_type, default_povm_type=povm_type, default_instrument_type=instrument_type, evotype=evotype) if prep_type == "auto": prep_type = _state.state_type_from_op_type(gate_type) if povm_type == "auto": povm_type = _povm.povm_type_from_op_type(gate_type) if instrument_type == "auto": instrument_type = _instrument.instrument_type_from_op_type(gate_type) for label, rhoExpr in zip(prep_labels, prep_expressions): vec = create_spam_vector(rhoExpr, state_space, basis) ret.preps[label] = _state.create_from_dmvec(vec, prep_type, basis, evotype, state_space) if isinstance(povm_labels, str): povm_labels = [povm_labels] effect_labels = [effect_labels] effect_expressions = [effect_expressions] dmDim = int(_np.sqrt(basis.dim)) for povmLbl, ELbls, EExprs in zip(povm_labels, effect_labels, effect_expressions): effect_vecs = {} if ELbls == "standard": qubit_dim = 4 if state_space.num_tensor_product_blocks == 1 and \ all([ldim == qubit_dim for ldim in state_space.tensor_product_block_dimensions(0)]): nQubits = len(state_space.tensor_product_block_dimensions(0)) ELbls = [''.join(t) for t in _itertools.product(('0', '1'), repeat=nQubits)] else: ELbls = list(map(str, range(dmDim))) if EExprs == "standard": EExprs = list(map(str, range(dmDim))) effect_vecs = {label: create_spam_vector(expr, state_space, basis) for label, expr in zip(ELbls, EExprs)} if len(effect_vecs) > 0: ret.povms[povmLbl] = _povm.create_from_dmvecs(effect_vecs, povm_type, basis, evotype, state_space) for (opLabel, opExpr) in zip(op_labels, op_expressions): ret.operations[opLabel] = create_operation(opExpr, state_space, basis, gate_type, evotype) if gate_type == "full": ret.default_gauge_group = _gg.FullGaugeGroup(ret.state_space, evotype) elif gate_type == "full TP": ret.default_gauge_group = _gg.TPGaugeGroup(ret.state_space, evotype) elif gate_type == 'CPTP': ret.default_gauge_group = _gg.UnitaryGaugeGroup(ret.state_space, basis, evotype) else: ret.default_gauge_group = _gg.TrivialGaugeGroup(ret.state_space) ret._clean_paramvec() return ret def create_explicit_model_from_expressions(state_space, op_labels, op_expressions, prep_labels=('rho0',), prep_expressions=('0',), effect_labels='standard', effect_expressions='standard', povm_labels='Mdefault', basis="auto", gate_type="full", prep_type="auto", povm_type="auto", instrument_type="auto", evotype='default'): #Note: so far, all allowed `parameterization` values => densitymx evotype state_space = _statespace.StateSpace.cast(state_space) stateSpaceDim = state_space.dim # Note: what about state_space_labels.tpb_dims? if basis == "auto": if _np.isclose(_np.log2(stateSpaceDim) / 2, round(_np.log2(stateSpaceDim) / 2)): basis = "pp" elif stateSpaceDim == 9: basis = "qt" else: basis = "gm" return _create_explicit_model_from_expressions(state_space, _Basis.cast(basis, state_space), op_labels, op_expressions, prep_labels, prep_expressions, effect_labels, effect_expressions, povm_labels, gate_type=gate_type, prep_type=prep_type, povm_type=povm_type, instrument_type=instrument_type, evotype=evotype) def create_explicit_alias_model(mdl_primitives, alias_dict): mdl_new = mdl_primitives.copy() for gl in mdl_primitives.operations.keys(): del mdl_new.operations[gl] # remove all gates from mdl_new for gl, opstr in alias_dict.items(): mdl_new.operations[gl] = mdl_primitives.sim.product(opstr) #Creates fully parameterized gates by default... mdl_new._clean_paramvec() return mdl_new def create_explicit_model(processor_spec, custom_gates=None, depolarization_strengths=None, stochastic_error_probs=None, lindblad_error_coeffs=None, depolarization_parameterization='depolarize', stochastic_parameterization='stochastic', lindblad_parameterization='auto', evotype="default", simulator="auto", ideal_gate_type='auto', ideal_spam_type='computational', embed_gates=False, basis='pp'): modelnoise = _build_modelnoise_from_args(depolarization_strengths, stochastic_error_probs, lindblad_error_coeffs, depolarization_parameterization, stochastic_parameterization, lindblad_parameterization, allow_nonlocal=True) return _create_explicit_model(processor_spec, modelnoise, custom_gates, evotype, simulator, ideal_gate_type, ideal_spam_type, ideal_spam_type, embed_gates, basis) def _create_explicit_model(processor_spec, modelnoise, custom_gates=None, evotype="default", simulator="auto", ideal_gate_type='auto', ideal_prep_type='auto', ideal_povm_type='auto', embed_gates=False, basis='pp'): qubit_labels = processor_spec.qubit_labels state_space = _statespace.QubitSpace(qubit_labels) evotype = _Evotype.cast(evotype) modelnoise = _OpModelNoise.cast(modelnoise) modelnoise.reset_access_counters() if custom_gates is None: custom_gates = {} if ideal_gate_type == "auto": ideal_gate_type = ('static standard', 'static clifford', 'static unitary') if ideal_prep_type == "auto": ideal_prep_type = _state.state_type_from_op_type(ideal_gate_type) if ideal_povm_type == "auto": ideal_povm_type = _povm.povm_type_from_op_type(ideal_gate_type) def _embed_unitary(statespace, target_labels, unitary): dummyop = _op.EmbeddedOp(statespace, target_labels, _op.StaticUnitaryOp(unitary, basis='pp', evotype="statevec_slow")) # basis hardcode? return dummyop.to_dense("Hilbert") local_gates = _setup_local_gates(processor_spec, evotype, None, {}, ideal_gate_type) # no custom *local* gates ret = _emdl.ExplicitOpModel(state_space, basis, default_gate_type=ideal_gate_type, evotype=evotype, simulator=simulator) # Special rule: when initializng an explicit model, if the processor spec has an implied global idle # gate (e.g. "(idle)", then the created model instead has a empty-tuple Label as the key for this op. global_idle_name = processor_spec.global_idle_gate_name if (global_idle_name is not None) and global_idle_name.startswith('(') and global_idle_name.endswith(')'): gn_to_make_emptytup = global_idle_name else: gn_to_make_emptytup = None for gn, gate_unitary in processor_spec.gate_unitaries.items(): gate_is_factory = callable(gate_unitary) resolved_avail = processor_spec.resolved_availability(gn) if callable(resolved_avail) or resolved_avail == '*': assert (embed_gates), "Cannot create factories with `embed_gates=False` yet!" key = _label.Label(gn) if (gn != gn_to_make_emptytup) else _label.Label(()) allowed_sslbls_fn = resolved_avail if callable(resolved_avail) else None gate_nQubits = processor_spec.gate_num_qubits(gn) ideal_factory = _opfactory.EmbeddingOpFactory( state_space, local_gates[gn], num_target_labels=gate_nQubits, allowed_sslbls_fn=allowed_sslbls_fn) noiseop = modelnoise.create_errormap(key, evotype, state_space) # No target indices... just local errs? factory = ideal_factory if (noiseop is None) else _op.ComposedOpFactory([ideal_factory, noiseop]) ret.factories[key] = factory else: # resolved_avail is a list/tuple of available sslbls for the current gate/factory for inds in resolved_avail: # inds are target qubit labels key = _label.Label(()) if (inds is None and gn == gn_to_make_emptytup) else _label.Label(gn, inds) if key in custom_gates: # allow custom_gates to specify gate elements directly if isinstance(custom_gates[key], _opfactory.OpFactory): ret.factories[key] = custom_gates[key] elif isinstance(custom_gates[key], _op.LinearOperator): ret.operations[key] = custom_gates[key] else: # presumably a numpy array or something like it. ret.operations[key] = _op.StaticArbitraryOp(custom_gates[key], evotype, state_space) # static gates by default continue if gate_is_factory: assert(embed_gates), "Cannot create factories with `embed_gates=False` yet!" # TODO: check for modelnoise on *local* factory, i.e. create_errormap(gn, ...)?? if inds is None or inds == tuple(qubit_labels): # then no need to embed ideal_factory = local_gates[gn] else: ideal_factory = _opfactory.EmbeddedOpFactory(state_space, inds, local_gates[gn]) noiseop = modelnoise.create_errormap(key, evotype, state_space, target_labels=inds) factory = ideal_factory if (noiseop is None) else _op.ComposedOpFactory([ideal_factory, noiseop]) ret.factories[key] = factory else: if inds is None or inds == tuple(qubit_labels): # then no need to embed if isinstance(gate_unitary, (int, _np.int64)): # interpret gate_unitary as identity assert(gate_unitary == len(qubit_labels)), \ "Idle unitary as int should be on all qubits for %s" % (str(gn)) ideal_gate = _op.ComposedOp([], evotype, state_space) # (identity gate on *all* qubits) else: ideal_gate = _op.create_from_unitary_mx(gate_unitary, ideal_gate_type, 'pp', None, evotype, state_space) else: if embed_gates: ideal_gate = local_gates[gn] ideal_gate = _op.EmbeddedOp(state_space, inds, ideal_gate) else: if isinstance(gate_unitary, (int, _np.int64)): # interpret gate_unitary as identity gate_unitary = _np.identity(2**gate_unitary, 'd') # turn into explicit identity op if gate_unitary.shape[0] == state_space.udim: # no need to embed! embedded_unitary = gate_unitary else: embedded_unitary = _embed_unitary(state_space, inds, gate_unitary) ideal_gate = _op.create_from_unitary_mx(embedded_unitary, ideal_gate_type, 'pp', None, evotype, state_space) #TODO: check for modelnoise on *local* gate, i.e. create_errormap(gn, ...)?? noiseop = modelnoise.create_errormap(key, evotype, state_space, target_labels=inds) layer = _op.ComposedOp([ideal_gate, noiseop]) if (noiseop is not None) else ideal_gate ret.operations[key] = layer # SPAM: local_noise = False; independent_gates = True; independent_spam = True prep_layers, povm_layers = _create_spam_layers(processor_spec, modelnoise, local_noise, ideal_prep_type, ideal_povm_type, evotype, state_space, independent_gates, independent_spam) for k, v in prep_layers.items(): ret.preps[k] = v for k, v in povm_layers.items(): ret.povms[k] = v modelnoise.warn_about_zero_counters() ret._clean_paramvec() return ret def _create_spam_layers(processor_spec, modelnoise, local_noise, ideal_prep_type, ideal_povm_type, evotype, state_space, independent_gates, independent_spam): qubit_labels = processor_spec.qubit_labels num_qubits = processor_spec.num_qubits singleQ_state_space = _statespace.default_space_for_udim(2) # single qubit state space # Step 1 -- get the ideal prep and POVM, created as the types we want # Step 2 -- add noise, by composing ideal with a noise operation (if desired) prep_layers = {} povm_layers = {} def _add_prep_noise(prep_ops): if local_noise: # then assume modelnoise specifies 1Q errors prep_noiseop1Q = modelnoise.create_errormap('prep', evotype, singleQ_state_space, target_labels=None) if prep_noiseop1Q is not None: err_gates = [prep_noiseop1Q.copy() for i in range(num_qubits)] \ if independent_gates else [prep_noiseop1Q] * num_qubits prep_ops.extend([_op.EmbeddedOp(state_space, [qubit_labels[i]], err_gates[i]) for i in range(num_qubits)]) else: # use modelnoise to construct n-qubit noise prepNoiseMap = modelnoise.create_errormap('prep', evotype, state_space, target_labels=None, qubit_graph=processor_spec.qubit_graph) if prepNoiseMap is not None: prep_ops.append(prepNoiseMap) def _add_povm_noise(povm_ops): if local_noise: # then assume modelnoise specifies 1Q errors povm_noiseop1Q = modelnoise.create_errormap('povm', evotype, singleQ_state_space, target_labels=None) if povm_noiseop1Q is not None: err_gates = [povm_noiseop1Q.copy() for i in range(num_qubits)] \ if independent_gates else [povm_noiseop1Q] * num_qubits povm_ops.extend([_op.EmbeddedOp(state_space, [qubit_labels[i]], err_gates[i]) for i in range(num_qubits)]) else: # use modelnoise to construct n-qubit noise povmNoiseMap = modelnoise.create_errormap('povm', evotype, state_space, target_labels=None, qubit_graph=processor_spec.qubit_graph) if povmNoiseMap is not None: povm_ops.append(povmNoiseMap) def _add_to_prep_layers(ideal_prep, prep_ops): if len(prep_ops_to_compose) == 0: prep_layers['rho0'] = ideal_prep elif len(prep_ops_to_compose) == 1: prep_layers['rho0'] = _state.ComposedState(ideal_prep, prep_ops[0]) else: prep_layers['rho0'] = _state.ComposedState(ideal_prep, _op.ComposedOp(prep_ops)) def _add_to_povm_layers(ideal_povm, povm_ops): if len(povm_ops_to_compose) == 0: povm_layers['Mdefault'] = ideal_povm elif len(povm_ops_to_compose) == 1: povm_layers['Mdefault'] = _povm.ComposedPOVM(povm_ops[0], ideal_povm, 'pp') else: povm_layers['Mdefault'] = _povm.ComposedPOVM(_op.ComposedOp(povm_ops), ideal_povm, 'pp') def _create_nq_noise(lndtype): if local_noise: # create a 1-qubit exp(errorgen) that is applied to each qubit independently errgen_1Q = _op.LindbladErrorgen.from_error_generator(singleQ_state_space.dim, lndtype, 'pp', 'pp', truncate=True, evotype=evotype, state_space=None) err_gateNQ = _op.ComposedOp([_op.EmbeddedOp(state_space, [qubit_labels[i]], _op.ExpErrorgenOp(errgen_1Q.copy())) for i in range(num_qubits)], evotype, state_space) else: # create an n-qubit exp(errorgen) errgen_NQ = _op.LindbladErrorgen.from_error_generator(state_space.dim, lndtype, 'pp', 'pp', truncate=True, evotype=evotype, state_space=state_space) err_gateNQ = _op.ExpErrorgenOp(errgen_NQ) return err_gateNQ # Here's where the actual logic starts. The above functions avoid repeated blocks within the different if isinstance(ideal_prep_type, (tuple, list)): ideal_prep_type = ideal_prep_type[0] if ideal_prep_type == 'computational' or ideal_prep_type.startswith('lindblad '): ideal_prep = _state.ComputationalBasisState([0] * num_qubits, 'pp', evotype, state_space) prep_ops_to_compose = [] if ideal_prep_type.startswith('lindblad '): lndtype = ideal_prep_type[len('lindblad '):] err_gateNQ = _create_nq_noise(lndtype) prep_ops_to_compose.append(err_gateNQ) _add_prep_noise(prep_ops_to_compose) _add_to_prep_layers(ideal_prep, prep_ops_to_compose) elif ideal_prep_type.startswith('tensor product '): vectype = ideal_prep_type[len('tensor product '):] v0, v1 = _np.array([1, 0], 'd'), _np.array([0, 1], 'd') ideal_prep1Q = _state.create_from_pure_vector(v0, vectype, 'pp', evotype, state_space=None) prep_factors = [ideal_prep1Q.copy() for i in range(num_qubits)] prep_noiseop1Q = modelnoise.create_errormap('prep', evotype, singleQ_state_space, target_labels=None) if prep_noiseop1Q is not None: prep_factors = [_state.ComposedState( factor, (prep_noiseop1Q.copy() if independent_spam else prep_noiseop1Q)) for factor in prep_factors] prep_layers['rho0'] = _state.TensorProductState(prep_factors, state_space) else: vectype = ideal_prep_type vecs = [] for i in range(2**num_qubits): v = _np.zeros(2**num_qubits, 'd'); v[i] = 1.0 vecs.append(v) ideal_prep = _state.create_from_pure_vector(vecs[0], vectype, 'pp', evotype, state_space=state_space) prep_ops_to_compose = [] _add_prep_noise(prep_ops_to_compose) _add_to_prep_layers(ideal_prep, prep_ops_to_compose) if isinstance(ideal_povm_type, (tuple, list)): ideal_povm_type = ideal_povm_type[0] if ideal_povm_type == 'computational' or ideal_povm_type.startswith('lindblad '): ideal_povm = _povm.ComputationalBasisPOVM(num_qubits, evotype, state_space=state_space) povm_ops_to_compose = [] if ideal_povm_type.startswith('lindblad '): lndtype = ideal_povm_type[len('lindblad '):] err_gateNQ = _create_nq_noise(lndtype) povm_ops_to_compose.append(err_gateNQ.copy()) _add_povm_noise(povm_ops_to_compose) effective_ideal_povm = None if len(povm_ops_to_compose) > 0 else ideal_povm _add_to_povm_layers(effective_ideal_povm, povm_ops_to_compose) elif ideal_povm_type.startswith('tensor product '): vectype = ideal_povm_type[len('tensor product '):] v0, v1 = _np.array([1, 0], 'd'), _np.array([0, 1], 'd') ideal_povm1Q = _povm.create_from_pure_vectors([('0', v0), ('1', v1)], vectype, 'pp', evotype, state_space=None) povm_factors = [ideal_povm1Q.copy() for i in range(num_qubits)] povm_noiseop1Q = modelnoise.create_errormap('povm', evotype, singleQ_state_space, target_labels=None) if povm_noiseop1Q is not None: povm_factors = [_povm.ComposedPOVM( (povm_noiseop1Q.copy() if independent_spam else povm_noiseop1Q), factor, 'pp') for factor in povm_factors] povm_layers['Mdefault'] = _povm.TensorProductPOVM(povm_factors, evotype, state_space) else: vectype = ideal_povm_type vecs = [] for i in range(2**num_qubits): v = _np.zeros(2**num_qubits, 'd'); v[i] = 1.0 vecs.append(v) ideal_povm = _povm.create_from_pure_vectors( [(format(i, 'b').zfill(num_qubits), v) for i, v in enumerate(vecs)], vectype, 'pp', evotype, state_space=state_space) povm_ops_to_compose = [] _add_povm_noise(povm_ops_to_compose) _add_to_povm_layers(ideal_povm, povm_ops_to_compose) return prep_layers, povm_layers def _setup_local_gates(processor_spec, evotype, modelnoise=None, custom_gates=None, ideal_gate_type=('static standard', 'static clifford', 'static unitary')): std_gate_unitaries = _itgs.standard_gatename_unitaries() if custom_gates is None: custom_gates = {} if modelnoise is None: modelnoise = _OpModelPerOpNoise({}) all_keys = _lt.remove_duplicates(list(processor_spec.gate_names) + list(custom_gates.keys()) + list(modelnoise.keys())) ideal_gates = {} ideal_factories = {} gatedict = _collections.OrderedDict() for key in all_keys: if key in custom_gates: gatedict[key] = custom_gates[key] continue if key in ['prep', 'povm']: continue label = _label.Label(key) name = label.name U = processor_spec.gate_unitaries[name] if ((name not in processor_spec.nonstd_gate_unitaries) or (not callable(processor_spec.nonstd_gate_unitaries[name]) and (name in std_gate_unitaries) and processor_spec.nonstd_gate_unitaries[name].shape == std_gate_unitaries[name].shape and _np.allclose(processor_spec.nonstd_gate_unitaries[name], std_gate_unitaries[name]))): stdname = name else: stdname = None if isinstance(U, (int, _np.int64)): ideal_gate_state_space = _statespace.default_space_for_num_qubits(U) noiseop = modelnoise.create_errormap(key, evotype, ideal_gate_state_space, target_labels=None) if noiseop is not None: gatedict[key] = noiseop else: gatedict[key] = _op.ComposedOp([], evotype, ideal_gate_state_space) elif not callable(U): ideal_gate = ideal_gates.get(name, None) if ideal_gate is None: ideal_gate = _op.create_from_unitary_mx(U, ideal_gate_type, 'pp', stdname, evotype, state_space=None) ideal_gates[name] = ideal_gate noiseop = modelnoise.create_errormap(key, evotype, ideal_gate.state_space, target_labels=None) # This means it will fail to create error maps with a given (non-local/stencil) set of sslbls, as desired if noiseop is None: gatedict[key] = ideal_gate else: if isinstance(noiseop, _op.ComposedOp): # avoid additional nested ComposedOp if we already have one noiseop.insert(0, ideal_gate) gatedict[key] = noiseop else: gatedict[key] = _op.ComposedOp([ideal_gate, noiseop]) else: # a factory, given by the unitary-valued function U: args -> unitary ideal_factory = ideal_factories.get(name, None) if ideal_factory is None: local_state_space = _statespace.default_space_for_udim(U.shape[0]) # factory *function* SHAPE ideal_factory = _opfactory.UnitaryOpFactory(U, local_state_space, 'pp', evotype) ideal_factories[name] = ideal_factory noiseop = modelnoise.create_errormap(key, evotype, ideal_factory.state_space, target_labels=None) gatedict[key] = _opfactory.ComposedOpFactory([ideal_factory, noiseop]) \ if (noiseop is not None) else ideal_factory return gatedict def create_crosstalk_free_model(processor_spec, custom_gates=None, depolarization_strengths=None, stochastic_error_probs=None, lindblad_error_coeffs=None, depolarization_parameterization='depolarize', stochastic_parameterization='stochastic', lindblad_parameterization='auto', evotype="default", simulator="auto", on_construction_error='raise', independent_gates=False, independent_spam=True, ensure_composed_gates=False, ideal_gate_type='auto', ideal_spam_type='computational', implicit_idle_mode='none'): modelnoise = _build_modelnoise_from_args(depolarization_strengths, stochastic_error_probs, lindblad_error_coeffs, depolarization_parameterization, stochastic_parameterization, lindblad_parameterization, allow_nonlocal=False) return _create_crosstalk_free_model(processor_spec, modelnoise, custom_gates, evotype, simulator, on_construction_error, independent_gates, independent_spam, ensure_composed_gates, ideal_gate_type, ideal_spam_type, ideal_spam_type, implicit_idle_mode) def _create_crosstalk_free_model(processor_spec, modelnoise, custom_gates=None, evotype="default", simulator="auto", on_construction_error='raise', independent_gates=False, independent_spam=True, ensure_composed_gates=False, ideal_gate_type='auto', ideal_prep_type='auto', ideal_povm_type='auto', implicit_idle_mode='none'): qubit_labels = processor_spec.qubit_labels state_space = _statespace.QubitSpace(qubit_labels) evotype = _Evotype.cast(evotype) modelnoise = _OpModelNoise.cast(modelnoise) modelnoise.reset_access_counters() if ideal_gate_type == "auto": ideal_gate_type = ('static standard', 'static clifford', 'static unitary') if ideal_prep_type == "auto": ideal_prep_type = _state.state_type_from_op_type(ideal_gate_type) if ideal_povm_type == "auto": ideal_povm_type = _povm.povm_type_from_op_type(ideal_gate_type) gatedict = _setup_local_gates(processor_spec, evotype, modelnoise, custom_gates, ideal_gate_type) # (Note: global idle is now handled through processor-spec processing) # SPAM: local_noise = True prep_layers, povm_layers = _create_spam_layers(processor_spec, modelnoise, local_noise, ideal_prep_type, ideal_povm_type, evotype, state_space, independent_gates, independent_spam) modelnoise.warn_about_zero_counters() return _LocalNoiseModel(processor_spec, gatedict, prep_layers, povm_layers, evotype, simulator, on_construction_error, independent_gates, ensure_composed_gates, implicit_idle_mode) def create_cloud_crosstalk_model(processor_spec, custom_gates=None, depolarization_strengths=None, stochastic_error_probs=None, lindblad_error_coeffs=None, depolarization_parameterization='depolarize', stochastic_parameterization='stochastic', lindblad_parameterization='auto', evotype="default", simulator="auto", independent_gates=False, independent_spam=True, errcomp_type="gates", implicit_idle_mode="none", verbosity=0): modelnoise = _build_modelnoise_from_args(depolarization_strengths, stochastic_error_probs, lindblad_error_coeffs, depolarization_parameterization, stochastic_parameterization, lindblad_parameterization, allow_nonlocal=True) return _create_cloud_crosstalk_model(processor_spec, modelnoise, custom_gates, evotype, simulator, independent_gates, independent_spam, errcomp_type, implicit_idle_mode, verbosity) def _create_cloud_crosstalk_model(processor_spec, modelnoise, custom_gates=None, evotype="default", simulator="auto", independent_gates=False, independent_spam=True, errcomp_type="errorgens", implicit_idle_mode="none", verbosity=0): qubit_labels = processor_spec.qubit_labels state_space = _statespace.QubitSpace(qubit_labels) # FUTURE: allow other types of state spaces somehow? evotype = _Evotype.cast(evotype) modelnoise = _OpModelNoise.cast(modelnoise) modelnoise.reset_access_counters() printer = _VerbosityPrinter.create_printer(verbosity) #Create static ideal gates without any noise (we use `modelnoise` further down) gatedict = _setup_local_gates(processor_spec, evotype, None, custom_gates, ideal_gate_type=('static standard', 'static clifford', 'static unitary')) stencils = _collections.OrderedDict() # (Note: global idle is now processed with other processorspec gates) # SPAM local_noise = False prep_layers, povm_layers = _create_spam_layers(processor_spec, modelnoise, local_noise, 'computational', 'computational', evotype, state_space, independent_gates, independent_spam) if errcomp_type == 'gates': create_stencil_fn = modelnoise.create_errormap_stencil apply_stencil_fn = modelnoise.apply_errormap_stencil elif errcomp_type == 'errorgens': create_stencil_fn = modelnoise.create_errorgen_stencil apply_stencil_fn = modelnoise.apply_errorgen_stencil else: raise ValueError("Invalid `errcomp_type` value: %s" % str(errcomp_type)) def build_cloudnoise_fn(lbl): # lbl will be for a particular gate and target qubits. If we have error rates for this specific gate # and target qubits (i.e this primitive layer op) then we should build it directly (and independently, # regardless of the value of `independent_gates`) using these rates. Otherwise, if we have a stencil # for this gate, then we should use it to construct the output, using a copy when gates are independent # and a reference to the *same* stencil operations when `independent_gates==False`. num_sslbls = len(lbl.sslbls) if (lbl.sslbls is not None) else None if lbl in modelnoise: stencil = create_stencil_fn(lbl, evotype, state_space, num_target_labels=num_sslbls) elif lbl.name in stencils: stencil = stencils[lbl.name] elif lbl.name in modelnoise: stencils[lbl.name] = create_stencil_fn(lbl.name, evotype, state_space, num_target_labels=num_sslbls) stencil = stencils[lbl.name] else: return None # no cloudnoise error for this label return apply_stencil_fn(stencil, evotype, state_space, target_labels=lbl.sslbls, qubit_graph=processor_spec.qubit_graph, copy=independent_gates and (lbl not in modelnoise)) # no need to copy if first case def build_cloudkey_fn(lbl): num_sslbls = len(lbl.sslbls) if (lbl.sslbls is not None) else None if lbl in modelnoise: stencil = create_stencil_fn(lbl, evotype, state_space, num_target_labels=num_sslbls) elif lbl.name in stencils: stencil = stencils[lbl.name] elif lbl.name in modelnoise: stencils[lbl.name] = create_stencil_fn(lbl.name, evotype, state_space, num_target_labels=num_sslbls) stencil = stencils[lbl.name] else: # simple cloud-key when there is no cloud noise return tuple(lbl.sslbls) if (lbl.sslbls is not None) else qubit_labels #Otherwise, process stencil to get a list of all the qubit labels `lbl`'s cloudnoise error cloud_sslbls = modelnoise.compute_stencil_absolute_sslbls(stencil, state_space, lbl.sslbls, processor_spec.qubit_graph) hashable_sslbls = tuple(lbl.sslbls) if (lbl.sslbls is not None) else qubit_labels cloud_key = (hashable_sslbls, tuple(sorted(cloud_sslbls))) return cloud_key ret = _CloudNoiseModel(processor_spec, gatedict, prep_layers, povm_layers, build_cloudnoise_fn, build_cloudkey_fn, simulator, evotype, errcomp_type, implicit_idle_mode, printer) modelnoise.warn_about_zero_counters() return ret def create_cloud_crosstalk_model_from_hops_and_weights( processor_spec, custom_gates=None, max_idle_weight=1, max_spam_weight=1, maxhops=0, extra_weight_1_hops=0, extra_gate_weight=0, simulator="auto", evotype='default', gate_type="H+S", spam_type="H+S", implicit_idle_mode="none", errcomp_type="gates", independent_gates=True, independent_spam=True, connected_highweight_errors=True, verbosity=0): modelnoise = {} all_qubit_labels = processor_spec.qubit_labels conn = connected_highweight_errors global_idle_name = processor_spec.global_idle_gate_name if max_idle_weight > 0: assert(global_idle_name is not None), \ "`max_idle_weight` must equal 0 for processor specs without a global idle gate!" wt_maxhop_tuples = [(i, None) for i in range(1, max_idle_weight + 1)] modelnoise[global_idle_name] = _build_weight_maxhops_modelnoise(all_qubit_labels, wt_maxhop_tuples, gate_type, conn) if max_spam_weight > 0: wt_maxhop_tuples = [(i, None) for i in range(1, max_spam_weight + 1)] modelnoise['prep'] = _build_weight_maxhops_modelnoise(all_qubit_labels, wt_maxhop_tuples, spam_type, conn) modelnoise['povm'] = _build_weight_maxhops_modelnoise(all_qubit_labels, wt_maxhop_tuples, spam_type, conn) weight_maxhops_tuples_1Q = [(1, maxhops + extra_weight_1_hops)] + \ [(1 + x, maxhops) for x in range(1, extra_gate_weight + 1)] weight_maxhops_tuples_2Q = [(1, maxhops + extra_weight_1_hops), (2, maxhops)] + \ [(2 + x, maxhops) for x in range(1, extra_gate_weight + 1)] for gatenm, gate_unitary in processor_spec.gate_unitaries.items(): if gatenm == global_idle_name: continue gate_nQubits = int(gate_unitary) if isinstance(gate_unitary, (int, _np.int64)) \ else int(round(_np.log2(gate_unitary.shape[0]))) if gate_nQubits not in (1, 2): raise ValueError("Only 1- and 2-qubit gates are supported. %s acts on %d qubits!" % (str(gatenm), gate_nQubits)) weight_maxhops_tuples = weight_maxhops_tuples_1Q if gate_nQubits == 1 else weight_maxhops_tuples_2Q target_sslbls = ('@0',) if gate_nQubits == 1 else ('@0', '@1') modelnoise[gatenm] = _build_weight_maxhops_modelnoise(target_sslbls, weight_maxhops_tuples, gate_type, conn) return _create_cloud_crosstalk_model(processor_spec, modelnoise, custom_gates, evotype, simulator, independent_gates, independent_spam, errcomp_type, implicit_idle_mode, verbosity) def _iter_basis_inds(weight): basisIndList = [[1, 2, 3]] * weight for basisInds in _itertools.product(*basisIndList): yield basisInds def _pauli_product_matrix(sigma_inds): sigmaVec = (id2x2 / sqrt2, sigmax / sqrt2, sigmay / sqrt2, sigmaz / sqrt2) M = _np.identity(1, 'complex') for i in sigma_inds: M = _np.kron(M, sigmaVec[i]) return M def _construct_restricted_weight_pauli_basis(wt, sparse=False): basisEl_Id = _pauli_product_matrix(_np.zeros(wt, _np.int64)) errbasis = [basisEl_Id] errbasis_lbls = ['I'] for err_basis_inds in _iter_basis_inds(wt): error = _np.array(err_basis_inds, _np.int64) basisEl = _pauli_product_matrix(error) errbasis.append(basisEl) errbasis_lbls.append(''.join(["IXYZ"[i] for i in err_basis_inds])) return _ExplicitBasis(errbasis, errbasis_lbls, real=True, sparse=sparse) def _build_weight_maxhops_modelnoise(target_sslbls, weight_maxhops_tuples, lnd_parameterization, connected=True): modelnoise_dict = {} if lnd_parameterization == 'none' or lnd_parameterization is None: return {} for wt, max_hops in weight_maxhops_tuples: if max_hops is None or max_hops == 0: # Note: maxHops not used in this case stencil_lbl = _stencil.StencilLabelAllCombos(target_sslbls, wt, connected) else: stencil_lbl = _stencil.StencilLabelRadiusCombos(target_sslbls, max_hops, wt, connected) local_state_space = _statespace.default_space_for_num_qubits(wt) modelnoise_dict[stencil_lbl] = _LindbladNoise.from_basis_coefficients( lnd_parameterization, _construct_restricted_weight_pauli_basis(wt), local_state_space) return modelnoise_dict def _build_modelnoise_from_args(depolarization_strengths, stochastic_error_probs, lindblad_error_coeffs, depolarization_parameterization, stochastic_parameterization, lindblad_parameterization, allow_nonlocal): modelnoises = [] if depolarization_strengths is not None: noise_dict = {} for lbl, val in depolarization_strengths.items(): if isinstance(val, dict): # then value is actually a dictionary of sslbls -> noise specifications if not allow_nonlocal: raise ValueError("Nonlocal depolarization strengths not allowed!") noise_dict[lbl] = {k: _DepolarizationNoise(v, depolarization_parameterization) for k, v in val.items()} else: noise_dict[lbl] = _DepolarizationNoise(val, depolarization_parameterization) modelnoises.append(_OpModelPerOpNoise(noise_dict)) if stochastic_error_probs is not None: noise_dict = {} for lbl, val in stochastic_error_probs.items(): if isinstance(val, dict): # then value is actually a dictionary of sslbls -> noise specifications if not allow_nonlocal: raise ValueError("Nonlocal stochastic error probs not allowed!") noise_dict[lbl] = {k: _StochasticNoise(v, stochastic_parameterization) for k, v in val.items()} else: noise_dict[lbl] = _StochasticNoise(val, stochastic_parameterization) modelnoises.append(_OpModelPerOpNoise(noise_dict)) if lindblad_error_coeffs is not None: if not allow_nonlocal: # the easy case modelnoises.append(_OpModelPerOpNoise({lbl: _LindbladNoise(val, lindblad_parameterization) for lbl, val in lindblad_error_coeffs.items()})) else: # then need to process labels like ('H', 'XX:0,1') or 'HXX:0,1' def process_stencil_labels(flat_lindblad_errs): nonlocal_errors = _collections.OrderedDict() local_errors = _collections.OrderedDict() for nm, val in flat_lindblad_errs.items(): if isinstance(nm, str): nm = (nm[0], nm[1:]) # e.g. "HXX" => ('H','XX') err_typ, basisEls = nm[0], nm[1:] sslbls = None local_nm = [err_typ] for bel in basisEls: # e.g. bel could be "X:Q0" or "XX:Q0,Q1" # OR "X:<n>" where n indexes a target qubit or "X:<dir>" where dir indicates # a graph *direction*, e.g. "up" if ':' in bel: bel_name, bel_sslbls = bel.split(':') # should have form <name>:<comma-separated-sslbls> bel_sslbls = bel_sslbls.split(',') # e.g. ('Q0','Q1') integerized_sslbls = [] for ssl in bel_sslbls: try: integerized_sslbls.append(int(ssl)) except: integerized_sslbls.append(ssl) bel_sslbls = tuple(integerized_sslbls) else: bel_name = bel bel_sslbls = None if sslbls is None: sslbls = bel_sslbls else: #Note: sslbls should always be the same if there are multiple basisEls, # i.e for nm == ('S',bel1,bel2) assert(sslbls is bel_sslbls or sslbls == bel_sslbls), \ "All basis elements of the same error term must operate on the *same* state!" local_nm.append(bel_name) # drop the state space labels, e.g. "XY:Q0,Q1" => "XY" # keep track of errors by the qubits they act on, as only each such # set will have it's own LindbladErrorgen local_nm = tuple(local_nm) if sslbls is not None: sslbls = tuple(sorted(sslbls)) if sslbls not in nonlocal_errors: nonlocal_errors[sslbls] = _collections.OrderedDict() if local_nm in nonlocal_errors[sslbls]: nonlocal_errors[sslbls][local_nm] += val else: nonlocal_errors[sslbls][local_nm] = val else: if local_nm in local_errors: local_errors[local_nm] += val else: local_errors[local_nm] = val if len(nonlocal_errors) == 0: return _LindbladNoise(local_errors, lindblad_parameterization) else: all_errors = [] if len(local_errors) > 0: all_errors.append((None, _LindbladNoise(local_errors, lindblad_parameterization))) for sslbls, errdict in nonlocal_errors.items(): all_errors.append((sslbls, _LindbladNoise(errdict, lindblad_parameterization))) return _collections.OrderedDict(all_errors) modelnoises.append(_OpModelPerOpNoise({lbl: process_stencil_labels(val) for lbl, val in lindblad_error_coeffs.items()})) return _ComposedOpModelNoise(modelnoises) @_deprecated_fn("This function is overly specific and will be removed soon.") def _nparams_xycnot_cloudnoise_model(num_qubits, geometry="line", max_idle_weight=1, maxhops=0, extra_weight_1_hops=0, extra_gate_weight=0, require_connected=False, independent_1q_gates=True, zz_only=False, bidirectional_cnots=True, verbosity=0): # noise can be either a seed or a random array that is long enough to use printer = _VerbosityPrinter.create_printer(verbosity) printer.log("Computing parameters for a %d-qubit %s model" % (num_qubits, geometry)) qubitGraph = _QubitGraph.common_graph(num_qubits, geometry, directed=True, all_directions=True) #printer.log("Created qubit graph:\n"+str(qubitGraph)) def idle_count_nparams(max_weight): ret = 0 possible_err_qubit_inds = _np.arange(num_qubits) for wt in range(1, max_weight + 1): nErrTargetLocations = qubitGraph.connected_combos(possible_err_qubit_inds, wt) if zz_only and wt > 1: basisSizeWoutId = 1**wt # ( == 1) else: basisSizeWoutId = 3**wt # (X,Y,Z)^wt nErrParams = 2 * basisSizeWoutId # H+S terms ret += nErrTargetLocations * nErrParams return ret def op_count_nparams(target_qubit_inds, weight_maxhops_tuples, debug=False): ret = 0 #Note: no contrib from idle noise (already parameterized) for wt, maxHops in weight_maxhops_tuples: possible_err_qubit_inds = _np.array(qubitGraph.radius(target_qubit_inds, maxHops), _np.int64) if require_connected: nErrTargetLocations = qubitGraph.connected_combos(possible_err_qubit_inds, wt) else: nErrTargetLocations = _scipy.special.comb(len(possible_err_qubit_inds), wt) if zz_only and wt > 1: basisSizeWoutId = 1**wt # ( == 1) else: basisSizeWoutId = 3**wt # (X,Y,Z)^wt nErrParams = 2 * basisSizeWoutId # H+S terms if debug: print(" -- wt%d, hops%d: inds=%s locs = %d, eparams=%d, total contrib = %d" % (wt, maxHops, str(possible_err_qubit_inds), nErrTargetLocations, nErrParams, nErrTargetLocations * nErrParams)) ret += nErrTargetLocations * nErrParams return ret nParams = _collections.OrderedDict() printer.log("Creating Idle:") nParams[_label.Label('Gi')] = idle_count_nparams(max_idle_weight) #1Q gates: X(pi/2) & Y(pi/2) on each qubit weight_maxhops_tuples_1Q = [(1, maxhops + extra_weight_1_hops)] + \ [(1 + x, maxhops) for x in range(1, extra_gate_weight + 1)] if independent_1q_gates: for i in range(num_qubits): printer.log("Creating 1Q X(pi/2) and Y(pi/2) gates on qubit %d!!" % i) nParams[_label.Label("Gx", i)] = op_count_nparams((i,), weight_maxhops_tuples_1Q) nParams[_label.Label("Gy", i)] = op_count_nparams((i,), weight_maxhops_tuples_1Q) else: printer.log("Creating common 1Q X(pi/2) and Y(pi/2) gates") rep = int(num_qubits / 2) nParams[_label.Label("Gxrep")] = op_count_nparams((rep,), weight_maxhops_tuples_1Q) nParams[_label.Label("Gyrep")] = op_count_nparams((rep,), weight_maxhops_tuples_1Q) #2Q gates: CNOT gates along each graph edge weight_maxhops_tuples_2Q = [(1, maxhops + extra_weight_1_hops), (2, maxhops)] + \ [(2 + x, maxhops) for x in range(1, extra_gate_weight + 1)] seen_pairs = set() for i, j in qubitGraph.edges(): # note: all edges have i<j so "control" of CNOT is always lower index (arbitrary) if bidirectional_cnots is False: ordered_tup = (i, j) if i <= j else (j, i) if ordered_tup in seen_pairs: continue else: seen_pairs.add(ordered_tup) printer.log("Creating CNOT gate between qubits %d and %d!!" % (i, j)) nParams[_label.Label("Gcnot", (i, j))] = op_count_nparams((i, j), weight_maxhops_tuples_2Q) #SPAM nPOVM_1Q = 4 # params for a single 1Q POVM nParams[_label.Label('rho0')] = 3 * num_qubits # 3 b/c each component is TP nParams[_label.Label('Mdefault')] = nPOVM_1Q * num_qubits # num_qubits 1Q-POVMs return nParams, sum(nParams.values())

true

f723bc9754ddb86c41340d88ffa3f486b80a42f3

258

py

Python

weight_converter/utils.py

adamriaz/weight-converter

1ac82ef2935e76ec6c78f322e995fbce6454b6c8

[ "MIT" ]

null

weight_converter/utils.py

adamriaz/weight-converter

1ac82ef2935e76ec6c78f322e995fbce6454b6c8

[ "MIT" ]

null

weight_converter/utils.py

adamriaz/weight-converter

1ac82ef2935e76ec6c78f322e995fbce6454b6c8

[ "MIT" ]

null

def divide_by_zero_check(func): """ Decorator for checking division by zero from user input """ def inner(value): if value.value == 0: raise ValueError('Cannot divide by zero!') return func(value) return inner

25.8

59

0.616279

def divide_by_zero_check(func): def inner(value): if value.value == 0: raise ValueError('Cannot divide by zero!') return func(value) return inner

true

f723be6ac409c60b2a0d167791d7a99d3f39abbb

2,575

py

Python

external_libs/spglib-1.9.9/python/test/test_hall_number_from_symmetry.py

shunsuke-sato/octopus

dcf68a185cdb13708395546b1557ca46aed969f6

[ "Apache-2.0" ]

4

2016-11-17T09:03:11.000Z

2019-10-17T06:31:08.000Z

external_libs/spglib-1.9.9/python/test/test_hall_number_from_symmetry.py

shunsuke-sato/octopus

dcf68a185cdb13708395546b1557ca46aed969f6

[ "Apache-2.0" ]

1

2020-08-11T19:14:06.000Z

external_libs/spglib-1.9.9/python/test/test_hall_number_from_symmetry.py

shunsuke-sato/octopus

dcf68a185cdb13708395546b1557ca46aed969f6

[ "Apache-2.0" ]

5

2016-11-22T20:30:46.000Z

2020-05-29T23:24:51.000Z

import unittest import numpy as np from spglib import get_symmetry_dataset, get_hall_number_from_symmetry from vasp import read_vasp from os import listdir dirnames = ('cubic', 'hexagonal', 'monoclinic', 'orthorhombic', 'tetragonal', 'triclinic', 'trigonal', 'distorted', 'virtual_structure') class TestGetHallNumberFromSymmetry(unittest.TestCase): def setUp(self): self._filenames = [] for d in dirnames: self._filenames += ["%s/%s" % (d, fname) for fname in listdir("./data/%s" % d)] def tearDown(self): pass def test_get_hall_number_from_symmetry(self): for fname in self._filenames: spgnum = int(fname.split('-')[1]) cell = read_vasp("./data/%s" % fname) if 'distorted' in fname: dataset = get_symmetry_dataset(cell, symprec=1e-1) hall_number = get_hall_number_from_symmetry( dataset['rotations'], dataset['translations'], symprec=1e-1) if hall_number != dataset['hall_number']: print("%d != %d in %s" % (hall_number, dataset['hall_number'], fname)) ref_cell = (dataset['std_lattice'], dataset['std_positions'], dataset['std_types']) dataset = get_symmetry_dataset(ref_cell, symprec=1e-5) hall_number = get_hall_number_from_symmetry( dataset['rotations'], dataset['translations'], symprec=1e-5) print("Using refinced cell: %d, %d in %s" % (hall_number, dataset['hall_number'], fname)) else: dataset = get_symmetry_dataset(cell, symprec=1e-5) hall_number = get_hall_number_from_symmetry( dataset['rotations'], dataset['translations'], symprec=1e-5) self.assertEqual(hall_number, dataset['hall_number'], msg=("%d != %d in %s" % (hall_number, dataset['hall_number'], fname))) if __name__ == '__main__': suite = unittest.TestLoader().loadTestsFromTestCase( TestGetHallNumberFromSymmetry) unittest.TextTestRunner(verbosity=2).run(suite) # unittest.main()

39.015152

80

0.514951

import unittest import numpy as np from spglib import get_symmetry_dataset, get_hall_number_from_symmetry from vasp import read_vasp from os import listdir dirnames = ('cubic', 'hexagonal', 'monoclinic', 'orthorhombic', 'tetragonal', 'triclinic', 'trigonal', 'distorted', 'virtual_structure') class TestGetHallNumberFromSymmetry(unittest.TestCase): def setUp(self): self._filenames = [] for d in dirnames: self._filenames += ["%s/%s" % (d, fname) for fname in listdir("./data/%s" % d)] def tearDown(self): pass def test_get_hall_number_from_symmetry(self): for fname in self._filenames: spgnum = int(fname.split('-')[1]) cell = read_vasp("./data/%s" % fname) if 'distorted' in fname: dataset = get_symmetry_dataset(cell, symprec=1e-1) hall_number = get_hall_number_from_symmetry( dataset['rotations'], dataset['translations'], symprec=1e-1) if hall_number != dataset['hall_number']: print("%d != %d in %s" % (hall_number, dataset['hall_number'], fname)) ref_cell = (dataset['std_lattice'], dataset['std_positions'], dataset['std_types']) dataset = get_symmetry_dataset(ref_cell, symprec=1e-5) hall_number = get_hall_number_from_symmetry( dataset['rotations'], dataset['translations'], symprec=1e-5) print("Using refinced cell: %d, %d in %s" % (hall_number, dataset['hall_number'], fname)) else: dataset = get_symmetry_dataset(cell, symprec=1e-5) hall_number = get_hall_number_from_symmetry( dataset['rotations'], dataset['translations'], symprec=1e-5) self.assertEqual(hall_number, dataset['hall_number'], msg=("%d != %d in %s" % (hall_number, dataset['hall_number'], fname))) if __name__ == '__main__': suite = unittest.TestLoader().loadTestsFromTestCase( TestGetHallNumberFromSymmetry) unittest.TextTestRunner(verbosity=2).run(suite)

true

f723be8c97c6be2454140fb16008806dc90a7f1d

2,740

py

Python

data/get_data.py

OmarJabri7/SAIA

f45f1d8073d4b56f5bed6f378f791102b067317c

[ "MIT" ]

1

2022-03-22T19:11:48.000Z

data/get_data.py

OmarJabri7/Disaster-Tweets-Kaggle

54dfee4684dbfd5bf6cb58cc3974abc051022022

[ "MIT" ]

null

data/get_data.py

OmarJabri7/Disaster-Tweets-Kaggle

54dfee4684dbfd5bf6cb58cc3974abc051022022

[ "MIT" ]

null

import pandas as pd import numpy as np from nltk.corpus import words import nltk import re import string from data_processing import DisasterProcessor X = pd.read_csv("emotion_data/tweet_emotions.csv") stop_wrds = nltk.corpus.stopwords.words("english") columns = X.columns columns = ["content"] preprocessor = DisasterProcessor() eng_words = set(words.words()) for column in columns: X[column] = X[column].apply( lambda x: ' '.join([re.sub("[$@&#]","",w) for w in x.lower().split(" ") if w])) table = str.maketrans('', '', string.punctuation) X[column] = X[column].apply( lambda x: ' '.join([w.translate(table) for w in x.split(" ") if w.isalpha()])) X[column] = X[column].apply( lambda x: preprocessor.utils_preprocess_text(x, flg_stemm=False, flg_lemm=True, lst_stopwords=stop_wrds)) X[column] = X[column].apply( lambda x: ' '.join([w for w in x.split(" ") if len(w) >= 2])) X["content"] = X["content"].apply( lambda x: ' '.join(([w for w in x.split(" ") if w in eng_words])) ) unique_words = list(X['content'].str.split(' ', expand=True).stack().unique()) # X.Sentence = X.Sentence.apply(lambda x: x if len(x) > 2 else np.nan) # X["clean_content"] = X["content"].str.replace('[#,@,&,=,[,http://]', '') print(np.unique(X["sentiment"])) X = X.loc[X['sentiment'].isin(['sadness','happiness','love','hate','fun','enthusiasm','relief','fear','anger', 'surprise', 'worry'])] # X = X["sentiment" in ['sadness','happiness','love','hate','fun','enthusiasm','relief','fear','anger']] X = X[['sentiment','content']] # happy = X.loc[X['sentiment'].isin(['happiness','fun','enthusiasm','relief']), 'content'].values happy = X.loc[X['sentiment'].isin(['happiness']), 'content'].values love = X.loc[X['sentiment'].isin(['love']),'content'].values # sadness = X.loc[X['sentiment'].isin(['sadness','worry']), 'content'].values sadness = X.loc[X['sentiment'].isin(['sadness']), 'content'].values # angry = X.loc[X['sentiment'].isin(['hate','anger']), 'content'].values angry = X.loc[X['sentiment'].isin(['anger']), 'content'].values surprise = X.loc[X['sentiment'].isin(['surprise']), 'content'].values fear = X.loc[X['sentiment'].isin(['fear']),'content'].values # emotions = dict(Emotion = ['happy','love','sadness','angry','surprise','fear']) # data = {"Sentence" : [happy, love, sadness, angry, surprise, fear], # "Emotion" : ['joy','love','sadness','anger','surprise','fear'],} # data = {"Sentence" : [sadness, angry, fear], "Emotion" : ['sadness','anger','fear'],} new_df = pd.DataFrame(data) new_df = new_df.explode('Sentence', ignore_index=True) new_df.to_csv('emotion_data/add_data.txt', header=None, index=None, sep=';')

37.027027

113

0.630657

import pandas as pd import numpy as np from nltk.corpus import words import nltk import re import string from data_processing import DisasterProcessor X = pd.read_csv("emotion_data/tweet_emotions.csv") stop_wrds = nltk.corpus.stopwords.words("english") columns = X.columns columns = ["content"] preprocessor = DisasterProcessor() eng_words = set(words.words()) for column in columns: X[column] = X[column].apply( lambda x: ' '.join([re.sub("[$@&#]","",w) for w in x.lower().split(" ") if w])) table = str.maketrans('', '', string.punctuation) X[column] = X[column].apply( lambda x: ' '.join([w.translate(table) for w in x.split(" ") if w.isalpha()])) X[column] = X[column].apply( lambda x: preprocessor.utils_preprocess_text(x, flg_stemm=False, flg_lemm=True, lst_stopwords=stop_wrds)) X[column] = X[column].apply( lambda x: ' '.join([w for w in x.split(" ") if len(w) >= 2])) X["content"] = X["content"].apply( lambda x: ' '.join(([w for w in x.split(" ") if w in eng_words])) ) unique_words = list(X['content'].str.split(' ', expand=True).stack().unique()) print(np.unique(X["sentiment"])) X = X.loc[X['sentiment'].isin(['sadness','happiness','love','hate','fun','enthusiasm','relief','fear','anger', 'surprise', 'worry'])] X = X[['sentiment','content']] happy = X.loc[X['sentiment'].isin(['happiness']), 'content'].values love = X.loc[X['sentiment'].isin(['love']),'content'].values sadness = X.loc[X['sentiment'].isin(['sadness']), 'content'].values angry = X.loc[X['sentiment'].isin(['anger']), 'content'].values surprise = X.loc[X['sentiment'].isin(['surprise']), 'content'].values fear = X.loc[X['sentiment'].isin(['fear']),'content'].values data = {"Sentence" : [sadness, angry, fear], "Emotion" : ['sadness','anger','fear'],} new_df = pd.DataFrame(data) new_df = new_df.explode('Sentence', ignore_index=True) new_df.to_csv('emotion_data/add_data.txt', header=None, index=None, sep=';')

true

f723c10b490155fd4a2bd2ac7ff767f6f0cf72e9

8,494

py

Python

crosshair/opcode_intercept.py

samuelchassot/CrossHair

4eac7a23e470567cc23e6d0916ce6dd6820eacd8

[ "MIT" ]

null

crosshair/opcode_intercept.py

samuelchassot/CrossHair

4eac7a23e470567cc23e6d0916ce6dd6820eacd8

[ "MIT" ]

null

crosshair/opcode_intercept.py

samuelchassot/CrossHair

4eac7a23e470567cc23e6d0916ce6dd6820eacd8

[ "MIT" ]

null

from collections.abc import MutableMapping, Set import dis from types import CodeType from types import FrameType from sys import version_info from crosshair.core import CrossHairValue from crosshair.core import register_opcode_patch from crosshair.libimpl.builtinslib import SymbolicInt from crosshair.libimpl.builtinslib import AnySymbolicStr from crosshair.libimpl.builtinslib import LazyIntSymbolicStr from crosshair.simplestructs import LinearSet from crosshair.simplestructs import ShellMutableSequence from crosshair.simplestructs import ShellMutableSet from crosshair.simplestructs import SimpleDict from crosshair.simplestructs import SliceView from crosshair.tracers import COMPOSITE_TRACER from crosshair.tracers import TracingModule from crosshair.tracers import frame_stack_read from crosshair.tracers import frame_stack_write from crosshair.util import CrosshairInternal BINARY_SUBSCR = dis.opmap["BINARY_SUBSCR"] BUILD_STRING = dis.opmap["BUILD_STRING"] COMPARE_OP = dis.opmap["COMPARE_OP"] CONTAINS_OP = dis.opmap.get("CONTAINS_OP", 118) FORMAT_VALUE = dis.opmap["FORMAT_VALUE"] MAP_ADD = dis.opmap["MAP_ADD"] SET_ADD = dis.opmap["SET_ADD"] def frame_op_arg(frame): return frame.f_code.co_code[frame.f_lasti + 1] class SymbolicSubscriptInterceptor(TracingModule): opcodes_wanted = frozenset([BINARY_SUBSCR]) def trace_op(self, frame, codeobj, codenum): # Note that because this is called from inside a Python trace handler, tracing # is automatically disabled, so there's no need for a `with NoTracing():` guard. key = frame_stack_read(frame, -1) if isinstance(key, (int, float, str)): return # If we got this far, the index is likely symbolic (or perhaps a slice object) container = frame_stack_read(frame, -2) container_type = type(container) if container_type is dict: # SimpleDict won't hash the keys it's given! wrapped_dict = SimpleDict(list(container.items())) frame_stack_write(frame, -2, wrapped_dict) elif container_type is list: if isinstance(key, slice): if key.step not in (1, None): return start, stop = key.start, key.stop if isinstance(start, SymbolicInt) or isinstance(stop, SymbolicInt): view_wrapper = SliceView(container, 0, len(container)) frame_stack_write(frame, -2, ShellMutableSequence(view_wrapper)) else: pass # Nothing useful to do with concrete list and symbolic numeric index. _CONTAINMENT_OP_TYPES = tuple( i for (i, name) in enumerate(dis.cmp_op) if name in ("in", "not in") ) assert len(_CONTAINMENT_OP_TYPES) in (0, 2) class ContainmentInterceptor(TracingModule): opcodes_wanted = frozenset( [ COMPARE_OP, CONTAINS_OP, ] ) def trace_op(self, frame, codeobj, codenum): if codenum == COMPARE_OP: compare_type = frame_op_arg(frame) if compare_type not in _CONTAINMENT_OP_TYPES: return item = frame_stack_read(frame, -2) if not isinstance(item, CrossHairValue): return container = frame_stack_read(frame, -1) containertype = type(container) new_container = None if containertype is str: new_container = LazyIntSymbolicStr([ord(c) for c in container]) elif containertype is set: new_container = ShellMutableSet(LinearSet(container)) if new_container is not None: frame_stack_write(frame, -1, new_container) class BuildStringInterceptor(TracingModule): """ Adds symbolic handling for the BUILD_STRING opcode (used by f-strings). BUILD_STRING concatenates strings from the stack is a fast, but unforgiving way: it requires all the substrings to be real Python strings. We work around this by replacing the substrings with empty strings, computing the concatenation ourselves, and swaping our result in after the opcode completes. """ opcodes_wanted = frozenset([BUILD_STRING]) def trace_op(self, frame, codeobj, codenum): count = frame_op_arg(frame) real_result = "" for offset in range(-(count), 0): substr = frame_stack_read(frame, offset) if not isinstance(substr, (str, AnySymbolicStr)): raise CrosshairInternal # Because we know these are all symbolic or concrete strings, it's ok to # not have tracing on when we do the concatenation here: real_result += substr frame_stack_write(frame, offset, "") def post_op(): frame_stack_write(frame, -1, real_result) COMPOSITE_TRACER.set_postop_callback(codeobj, post_op) class FormatValueInterceptor(TracingModule): """Avoid realization during FORMAT_VALUE (used by f-strings).""" opcodes_wanted = frozenset([FORMAT_VALUE]) def trace_op(self, frame, codeobj, codenum): flags = frame_op_arg(frame) if flags not in (0x00, 0x01): return # formatting spec is present orig_obj = frame_stack_read(frame, -1) if not isinstance(orig_obj, AnySymbolicStr): return # Format a dummy empty string, and swap the original back in: frame_stack_write(frame, -1, "") def post_op(): frame_stack_write(frame, -1, orig_obj) COMPOSITE_TRACER.set_postop_callback(codeobj, post_op) class MapAddInterceptor(TracingModule): """De-optimize MAP_ADD over symbolics (used in dict comprehensions).""" opcodes_wanted = frozenset([MAP_ADD]) def trace_op(self, frame: FrameType, codeobj: CodeType, codenum: int) -> None: dict_offset = -(frame_op_arg(frame) + 2) dict_obj = frame_stack_read(frame, dict_offset) if not isinstance(dict_obj, (dict, MutableMapping)): raise CrosshairInternal top, second = frame_stack_read(frame, -1), frame_stack_read(frame, -2) # Key and value were swapped in Python 3.8 key, value = (second, top) if version_info >= (3, 8) else (top, second) if isinstance(dict_obj, dict): if isinstance(key, CrossHairValue): dict_obj = SimpleDict(list(dict_obj.items())) else: # Key and dict are concrete; continue as normal. return # Have the interpreter do a fake assinment, namely `{}[1] = 1` frame_stack_write(frame, dict_offset, {}) frame_stack_write(frame, -1, 1) frame_stack_write(frame, -2, 1) # And do our own assignment separately: dict_obj[key] = value # Later, overwrite the interpreter's result with ours: def post_op(): frame_stack_write(frame, dict_offset + 2, dict_obj) COMPOSITE_TRACER.set_postop_callback(codeobj, post_op) class SetAddInterceptor(TracingModule): """De-optimize SET_ADD over symbolics (used in set comprehensions).""" opcodes_wanted = frozenset([SET_ADD]) def trace_op(self, frame: FrameType, codeobj: CodeType, codenum: int) -> None: set_offset = -(frame_op_arg(frame) + 1) set_obj = frame_stack_read(frame, set_offset) if not isinstance(set_obj, Set): raise CrosshairInternal(type(set_obj)) item = frame_stack_read(frame, -1) if isinstance(set_obj, set): if isinstance(item, CrossHairValue): set_obj = ShellMutableSet(set_obj) else: # Set and value are concrete; continue as normal. return # Have the interpreter do a fake addition, namely `set().add(1)` frame_stack_write(frame, set_offset, set()) frame_stack_write(frame, -1, 1) # And do our own addition separately: set_obj.add(item) # Later, overwrite the interpreter's result with ours: def post_op(): frame_stack_write(frame, set_offset + 1, set_obj) COMPOSITE_TRACER.set_postop_callback(codeobj, post_op) def make_registrations(): register_opcode_patch(SymbolicSubscriptInterceptor()) register_opcode_patch(ContainmentInterceptor()) register_opcode_patch(BuildStringInterceptor()) register_opcode_patch(FormatValueInterceptor()) register_opcode_patch(MapAddInterceptor()) register_opcode_patch(SetAddInterceptor())

38.089686

88

0.674005

from collections.abc import MutableMapping, Set import dis from types import CodeType from types import FrameType from sys import version_info from crosshair.core import CrossHairValue from crosshair.core import register_opcode_patch from crosshair.libimpl.builtinslib import SymbolicInt from crosshair.libimpl.builtinslib import AnySymbolicStr from crosshair.libimpl.builtinslib import LazyIntSymbolicStr from crosshair.simplestructs import LinearSet from crosshair.simplestructs import ShellMutableSequence from crosshair.simplestructs import ShellMutableSet from crosshair.simplestructs import SimpleDict from crosshair.simplestructs import SliceView from crosshair.tracers import COMPOSITE_TRACER from crosshair.tracers import TracingModule from crosshair.tracers import frame_stack_read from crosshair.tracers import frame_stack_write from crosshair.util import CrosshairInternal BINARY_SUBSCR = dis.opmap["BINARY_SUBSCR"] BUILD_STRING = dis.opmap["BUILD_STRING"] COMPARE_OP = dis.opmap["COMPARE_OP"] CONTAINS_OP = dis.opmap.get("CONTAINS_OP", 118) FORMAT_VALUE = dis.opmap["FORMAT_VALUE"] MAP_ADD = dis.opmap["MAP_ADD"] SET_ADD = dis.opmap["SET_ADD"] def frame_op_arg(frame): return frame.f_code.co_code[frame.f_lasti + 1] class SymbolicSubscriptInterceptor(TracingModule): opcodes_wanted = frozenset([BINARY_SUBSCR]) def trace_op(self, frame, codeobj, codenum): key = frame_stack_read(frame, -1) if isinstance(key, (int, float, str)): return # If we got this far, the index is likely symbolic (or perhaps a slice object) container = frame_stack_read(frame, -2) container_type = type(container) if container_type is dict: # SimpleDict won't hash the keys it's given! wrapped_dict = SimpleDict(list(container.items())) frame_stack_write(frame, -2, wrapped_dict) elif container_type is list: if isinstance(key, slice): if key.step not in (1, None): return start, stop = key.start, key.stop if isinstance(start, SymbolicInt) or isinstance(stop, SymbolicInt): view_wrapper = SliceView(container, 0, len(container)) frame_stack_write(frame, -2, ShellMutableSequence(view_wrapper)) else: pass # Nothing useful to do with concrete list and symbolic numeric index. _CONTAINMENT_OP_TYPES = tuple( i for (i, name) in enumerate(dis.cmp_op) if name in ("in", "not in") ) assert len(_CONTAINMENT_OP_TYPES) in (0, 2) class ContainmentInterceptor(TracingModule): opcodes_wanted = frozenset( [ COMPARE_OP, CONTAINS_OP, ] ) def trace_op(self, frame, codeobj, codenum): if codenum == COMPARE_OP: compare_type = frame_op_arg(frame) if compare_type not in _CONTAINMENT_OP_TYPES: return item = frame_stack_read(frame, -2) if not isinstance(item, CrossHairValue): return container = frame_stack_read(frame, -1) containertype = type(container) new_container = None if containertype is str: new_container = LazyIntSymbolicStr([ord(c) for c in container]) elif containertype is set: new_container = ShellMutableSet(LinearSet(container)) if new_container is not None: frame_stack_write(frame, -1, new_container) class BuildStringInterceptor(TracingModule): opcodes_wanted = frozenset([BUILD_STRING]) def trace_op(self, frame, codeobj, codenum): count = frame_op_arg(frame) real_result = "" for offset in range(-(count), 0): substr = frame_stack_read(frame, offset) if not isinstance(substr, (str, AnySymbolicStr)): raise CrosshairInternal # Because we know these are all symbolic or concrete strings, it's ok to real_result += substr frame_stack_write(frame, offset, "") def post_op(): frame_stack_write(frame, -1, real_result) COMPOSITE_TRACER.set_postop_callback(codeobj, post_op) class FormatValueInterceptor(TracingModule): opcodes_wanted = frozenset([FORMAT_VALUE]) def trace_op(self, frame, codeobj, codenum): flags = frame_op_arg(frame) if flags not in (0x00, 0x01): return orig_obj = frame_stack_read(frame, -1) if not isinstance(orig_obj, AnySymbolicStr): return frame_stack_write(frame, -1, "") def post_op(): frame_stack_write(frame, -1, orig_obj) COMPOSITE_TRACER.set_postop_callback(codeobj, post_op) class MapAddInterceptor(TracingModule): opcodes_wanted = frozenset([MAP_ADD]) def trace_op(self, frame: FrameType, codeobj: CodeType, codenum: int) -> None: dict_offset = -(frame_op_arg(frame) + 2) dict_obj = frame_stack_read(frame, dict_offset) if not isinstance(dict_obj, (dict, MutableMapping)): raise CrosshairInternal top, second = frame_stack_read(frame, -1), frame_stack_read(frame, -2) key, value = (second, top) if version_info >= (3, 8) else (top, second) if isinstance(dict_obj, dict): if isinstance(key, CrossHairValue): dict_obj = SimpleDict(list(dict_obj.items())) else: return frame_stack_write(frame, dict_offset, {}) frame_stack_write(frame, -1, 1) frame_stack_write(frame, -2, 1) dict_obj[key] = value def post_op(): frame_stack_write(frame, dict_offset + 2, dict_obj) COMPOSITE_TRACER.set_postop_callback(codeobj, post_op) class SetAddInterceptor(TracingModule): opcodes_wanted = frozenset([SET_ADD]) def trace_op(self, frame: FrameType, codeobj: CodeType, codenum: int) -> None: set_offset = -(frame_op_arg(frame) + 1) set_obj = frame_stack_read(frame, set_offset) if not isinstance(set_obj, Set): raise CrosshairInternal(type(set_obj)) item = frame_stack_read(frame, -1) if isinstance(set_obj, set): if isinstance(item, CrossHairValue): set_obj = ShellMutableSet(set_obj) else: # Set and value are concrete; continue as normal. return # Have the interpreter do a fake addition, namely `set().add(1)` frame_stack_write(frame, set_offset, set()) frame_stack_write(frame, -1, 1) # And do our own addition separately: set_obj.add(item) # Later, overwrite the interpreter's result with ours: def post_op(): frame_stack_write(frame, set_offset + 1, set_obj) COMPOSITE_TRACER.set_postop_callback(codeobj, post_op) def make_registrations(): register_opcode_patch(SymbolicSubscriptInterceptor()) register_opcode_patch(ContainmentInterceptor()) register_opcode_patch(BuildStringInterceptor()) register_opcode_patch(FormatValueInterceptor()) register_opcode_patch(MapAddInterceptor()) register_opcode_patch(SetAddInterceptor())

true

f723c12709c80e71332f0ad4c801db953c05c2c8

1,652

py

Python

linptech/packet.py

yangguozhanzhao/linptech

92ee1538d11baf473535cd0ed6b879adcee66e70

[ "MIT" ]

1

2020-07-26T05:37:43.000Z

linptech/packet.py

yangguozhanzhao/linptech

92ee1538d11baf473535cd0ed6b879adcee66e70

[ "MIT" ]

null

linptech/packet.py

yangguozhanzhao/linptech

92ee1538d11baf473535cd0ed6b879adcee66e70

[ "MIT" ]

2

2018-04-03T04:17:12.000Z

2018-10-12T09:44:20.000Z

from linptech.crc8 import crc8 import logging class Packet(object): ''' Base class for Packet. Mainly used for for packet generation and Packet.parse_msg(buf) for parsing message. parse_msg() returns subclass, if one is defined for the data type. ''' def __init__(self, data=None, optional="00"*7): if data is None: logging.warning('Packet.data is None') else: self.data = data if optional is None: logging.info('Packet.optional is None.') else: self.optional = optional @staticmethod def check(packet): """ check packet with crc """ if packet.startswith("550") and \ crc8(packet[2:10])==packet[10:12] and \ crc8(packet[12:-2])==packet[-2:]: return True else: return False @staticmethod def parse(packet): """ parse an packet to data and optional for receive """ if Packet.check(packet): try: data_len=int(packet[4:6],16) data=packet[12:12+data_len*2] optional=packet[12+data_len*2:26+data_len*2] return data,optional except Exception as e: logging.error("parse packet wrong:%s",e) return else : logging.error("packet is invalid") return @staticmethod def create(data=None, optional="00"*7): """ Creates an packet ready for sending. Uses data and optional. """ try: data_len = "{0:>02}".format(hex(int(len(data)/2))[2:]) m1 = "00"+data_len+"0701" m2 = data+optional packet = "55"+m1+crc8(m1)+m2+crc8(m2) return packet except Exception as e: logging.error("create packet wrong:%s",e) return if __name__ == "__main__": logging.getLogger().setLevel(logging.INFO) data="1f8000004581020101" Packet.create(data)

23.6

67

0.672518

from linptech.crc8 import crc8 import logging class Packet(object): def __init__(self, data=None, optional="00"*7): if data is None: logging.warning('Packet.data is None') else: self.data = data if optional is None: logging.info('Packet.optional is None.') else: self.optional = optional @staticmethod def check(packet): if packet.startswith("550") and \ crc8(packet[2:10])==packet[10:12] and \ crc8(packet[12:-2])==packet[-2:]: return True else: return False @staticmethod def parse(packet): if Packet.check(packet): try: data_len=int(packet[4:6],16) data=packet[12:12+data_len*2] optional=packet[12+data_len*2:26+data_len*2] return data,optional except Exception as e: logging.error("parse packet wrong:%s",e) return else : logging.error("packet is invalid") return @staticmethod def create(data=None, optional="00"*7): try: data_len = "{0:>02}".format(hex(int(len(data)/2))[2:]) m1 = "00"+data_len+"0701" m2 = data+optional packet = "55"+m1+crc8(m1)+m2+crc8(m2) return packet except Exception as e: logging.error("create packet wrong:%s",e) return if __name__ == "__main__": logging.getLogger().setLevel(logging.INFO) data="1f8000004581020101" Packet.create(data)

true

f723c13ed49b8c87b5f0275078a084b4c8f235a1

16,785

py

Python

test/latency_position_test.py

AustinHellerRepo/GameManager

2eee8e821f551b4683e59ea8cde7e61c26cf8878

[ "MIT" ]

null

test/latency_position_test.py

AustinHellerRepo/GameManager

2eee8e821f551b4683e59ea8cde7e61c26cf8878

[ "MIT" ]

null

test/latency_position_test.py

AustinHellerRepo/GameManager

2eee8e821f551b4683e59ea8cde7e61c26cf8878

[ "MIT" ]

null

from __future__ import annotations import unittest import time import matplotlib.pyplot as plt import numpy as np from typing import List, Tuple, Dict, Set, Callable, Type class Dot(): def __init__(self, position: Tuple[float, float], velocity: Tuple[float, float], acceleration: Tuple[float, float]): self.__position = position self.__velocity = velocity self.__acceleration = acceleration self.__time_index_offset = 0 self.__acceleration_delta = None # type: Tuple[float, float] self.__acceleration_delta_end_time_index = None # type: float self.__acceleration_delta_end_time_index_acceleration = None # type: Tuple[float, float] def set_positiion(self, *, position: Tuple[float, float]): self.__position = position def set_velocity(self, *, velocity: Tuple[float, float]): self.__velocity = velocity def set_acceleration(self, *, acceleration: Tuple[float, float]): self.__acceleration = acceleration def get_position(self, *, time_index: float) -> Tuple[float, float]: calculated_time_index = time_index + self.__time_index_offset position = list(self.__position) for dimension_index in range(len(position)): position[dimension_index] += self.__velocity[dimension_index] * calculated_time_index if self.__acceleration_delta_end_time_index is None: position[dimension_index] += (self.__acceleration[dimension_index] * calculated_time_index ** 2) / 2.0 else: if calculated_time_index < self.__acceleration_delta_end_time_index: position[dimension_index] += (self.__acceleration[dimension_index] * calculated_time_index ** 2) / 2.0 position[dimension_index] += (self.__acceleration_delta[dimension_index] * calculated_time_index ** 3) / 6.0 else: position[dimension_index] += (self.__acceleration[dimension_index] * self.__acceleration_delta_end_time_index ** 2) / 2.0 position[dimension_index] += (self.__acceleration_delta_end_time_index_acceleration[dimension_index] * (calculated_time_index - self.__acceleration_delta_end_time_index) ** 2) / 2.0 position[dimension_index] += (self.__acceleration_delta[dimension_index] * self.__acceleration_delta_end_time_index ** 3) / 6.0 return tuple(position) def get_velocity(self, *, time_index: float) -> Tuple[float, float]: calculated_time_index = time_index + self.__time_index_offset velocity = list(self.__velocity) for dimension_index in range(len(velocity)): if self.__acceleration_delta_end_time_index is None: velocity[dimension_index] += self.__acceleration[dimension_index] * calculated_time_index else: if calculated_time_index < self.__acceleration_delta_end_time_index: velocity[dimension_index] += self.__acceleration[dimension_index] * calculated_time_index velocity[dimension_index] += (self.__acceleration_delta[dimension_index] * calculated_time_index**2) / 2.0 else: velocity[dimension_index] += self.__acceleration[dimension_index] * self.__acceleration_delta_end_time_index velocity[dimension_index] += self.__acceleration_delta_end_time_index_acceleration[dimension_index] * (calculated_time_index - self.__acceleration_delta_end_time_index) velocity[dimension_index] += (self.__acceleration_delta[dimension_index] * self.__acceleration_delta_end_time_index**2) / 2.0 return tuple(velocity) def get_acceleration(self, *, time_index: float) -> Tuple[float, float]: calculated_time_index = time_index + self.__time_index_offset acceleration = [0] * len(self.__position) for dimension_index in range(len(acceleration)): if self.__acceleration_delta_end_time_index is None: acceleration[dimension_index] += self.__acceleration[dimension_index] else: if calculated_time_index < self.__acceleration_delta_end_time_index: acceleration[dimension_index] += self.__acceleration[dimension_index] acceleration[dimension_index] += (self.__acceleration_delta[dimension_index] * calculated_time_index) else: acceleration[dimension_index] += self.__acceleration_delta_end_time_index_acceleration[dimension_index] acceleration[dimension_index] += (self.__acceleration_delta[dimension_index] * self.__acceleration_delta_end_time_index) return tuple(self.__acceleration) def bounce(self, *, time_index: float): bounce_position = self.get_position( time_index=time_index ) bounce_velocity = self.get_velocity( time_index=time_index ) bounce_acceleration = self.get_acceleration( time_index=time_index ) self.__position = bounce_position self.__velocity = (bounce_velocity[0], -bounce_velocity[1]) self.__acceleration = bounce_acceleration calculated_time_index = time_index + self.__time_index_offset if self.__acceleration_delta_end_time_index is not None: self.__acceleration_delta_end_time_index -= calculated_time_index if self.__acceleration_delta_end_time_index <= 0: self.__acceleration_delta = None self.__acceleration_delta_end_time_index = None self.__acceleration_delta_end_time_index_acceleration = None self.__time_index_offset = -time_index def reflect(self, *, time_index: float): reflect_position = self.get_position( time_index=time_index ) reflect_velocity = self.get_velocity( time_index=time_index ) reflect_acceleration = self.get_acceleration( time_index=time_index ) self.__position = reflect_position self.__velocity = (-reflect_velocity[0], reflect_velocity[1]) self.__acceleration = reflect_acceleration calculated_time_index = time_index + self.__time_index_offset if self.__acceleration_delta_end_time_index is not None: self.__acceleration_delta_end_time_index -= calculated_time_index if self.__acceleration_delta_end_time_index <= 0: self.__acceleration_delta = None self.__acceleration_delta_end_time_index = None self.__acceleration_delta_end_time_index_acceleration = None self.__time_index_offset = -time_index def set_state(self, *, position: Tuple[float, float], velocity: Tuple[float, float], acceleration: Tuple[float, float], time_index: float): self.__position = position self.__velocity = velocity self.__acceleration = acceleration calculated_time_index = time_index + self.__time_index_offset if self.__acceleration_delta_end_time_index is not None: self.__acceleration_delta_end_time_index -= calculated_time_index if self.__acceleration_delta_end_time_index <= 0: self.__acceleration_delta = None self.__acceleration_delta_end_time_index = None self.__acceleration_delta_end_time_index_acceleration = None self.__time_index_offset = -time_index def set_acceleration_delta(self, *, time_index: float, acceleration_delta: Tuple[float, float], end_time_index: float): time_index_position = self.get_position( time_index=time_index ) time_index_velocity = self.get_velocity( time_index=time_index ) time_index_acceleration = self.get_acceleration( time_index=time_index ) self.__position = time_index_position self.__velocity = time_index_velocity self.__acceleration = time_index_acceleration self.__time_index_offset = -time_index self.__acceleration_delta = acceleration_delta self.__acceleration_delta_end_time_index = end_time_index self.__acceleration_delta_end_time_index_acceleration = time_index_acceleration def merge(self, *, dot: Dot, current_time_index: float, merge_time_index_offset: float): self_position = self.get_position( time_index=current_time_index ) self_velocity = self.get_velocity( time_index=current_time_index ) destination_position = dot.get_position( time_index=current_time_index + merge_time_index_offset ) destination_velocity = dot.get_velocity( time_index=current_time_index + merge_time_index_offset ) destination_acceleration = dot.get_acceleration( time_index=current_time_index + merge_time_index_offset ) acceleration_delta = [] acceleration = [] for dimension_index in range(len(self.__position)): temp_acceleration_delta = (-12 * destination_position[dimension_index] + 6 * destination_velocity[dimension_index] * merge_time_index_offset + 12 * self_position[dimension_index] + 6 * self_velocity[dimension_index] * merge_time_index_offset) / (merge_time_index_offset**3) temp_acceleration = (destination_velocity[dimension_index] - self_velocity[dimension_index]) / merge_time_index_offset - 0.5 * temp_acceleration_delta * merge_time_index_offset acceleration_delta.append(temp_acceleration_delta) acceleration.append(temp_acceleration) self.__position = self_position self.__velocity = self_velocity self.__acceleration = tuple(acceleration) self.__acceleration_delta = tuple(acceleration_delta) self.__acceleration_delta_end_time_index = merge_time_index_offset self.__acceleration_delta_end_time_index_acceleration = destination_acceleration self.__time_index_offset = -current_time_index class DotPlotter(): def __init__(self, minimum_position: Tuple[float, float], maximum_position: Tuple[float, float]): self.__minimum_position = minimum_position self.__maximum_position = maximum_position self.__dots = [] # type: List[Dot] self.__x = [] self.__y = [] self.__figure = None self.__scatter = None def add_dot(self, *, dot: Dot): self.__dots.append(dot) def __get_scatter(self, *, time_index: float) -> Tuple[List[float], List[float]]: scatter = ([], []) for dot in self.__dots: position = dot.get_position( time_index=time_index ) if position[1] < self.__minimum_position[1]: dot.bounce( time_index=time_index ) if position[0] < self.__minimum_position[0] or position[0] > self.__maximum_position[0]: dot.reflect( time_index=time_index ) scatter[0].append(position[0]) scatter[1].append(position[1]) print(f"position: {position}") return scatter def show(self): plt.ion() self.__figure, ax = plt.subplots() self.__scatter = ax.scatter(self.__x, self.__y, facecolors="none", edgecolors=["black", "red"], s=10) plt.xlim(self.__minimum_position[0], self.__maximum_position[0]) plt.ylim(self.__minimum_position[1], self.__maximum_position[1]) plt.draw() def refresh(self, *, time_index: float): x, y = self.__get_scatter( time_index=time_index ) self.__x.clear() self.__x.extend(x) self.__y.clear() self.__y.extend(y) self.__scatter.set_offsets(np.c_[self.__x, self.__y]) self.__figure.canvas.draw_idle() plt.pause(0.01) class LatencyPositionTest(unittest.TestCase): def test_initialize(self): dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) self.assertIsNotNone(dot_plotter) def test_move_dot_along_path(self): dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) dot = Dot( position=(1, 9), velocity=(1, 0), acceleration=(0, -1) ) dot_plotter.add_dot( dot=dot ) dot_plotter.show() print(f"refreshing") time_index = 0.0 time_index_delta = 0.05 while time_index < 20.0: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta plt.waitforbuttonpress() def test_move_dot_along_path_in_separate_windows(self): dot_plotters_total = 2 dot_plotters = [] for dot_plotter_index in range(dot_plotters_total): dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) dot = Dot( position=(1, 9), velocity=(1, 0), acceleration=(0, -1) ) dot_plotter.add_dot( dot=dot ) dot_plotter.show() dot_plotters.append(dot_plotter) print(f"refreshing") time_index = 0.0 time_index_delta = 0.05 while time_index < 10.0: for dot_plotter in dot_plotters: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta plt.waitforbuttonpress() def test_move_dot_along_path_then_alter_state(self): dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) dot = Dot( position=(1, 9), velocity=(1, 0), acceleration=(0, -1) ) def alter_dot(*, time_index: float): nonlocal dot dot.set_state( position=dot.get_position( time_index=time_index ), velocity=(-1, 1), acceleration=(0, -1), time_index=time_index ) dot_plotter.add_dot( dot=dot ) dot_plotter.show() print(f"refreshing") time_index = 0.0 time_index_delta = 0.05 maximum_time_index = 20.0 is_altered = False while time_index < maximum_time_index: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta if not is_altered and time_index > maximum_time_index / 2.0: alter_dot( time_index=time_index ) is_altered = True plt.waitforbuttonpress() def test_move_dot_along_path_then_set_acceleration_delta(self): dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) dot = Dot( position=(1, 9), velocity=(1, 0), acceleration=(0, -1) ) def alter_dot(*, time_index: float): nonlocal dot dot.set_acceleration_delta( time_index=time_index, acceleration_delta=(0, 0.5), end_time_index=5.0 ) dot_plotter.add_dot( dot=dot ) dot_plotter.show() print(f"refreshing") time_index = 0.0 time_index_delta = 0.05 maximum_time_index = 30.0 alter_time_index = 10.0 is_altered = False while time_index < maximum_time_index: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta if not is_altered and time_index > alter_time_index: alter_dot( time_index=time_index ) is_altered = True plt.waitforbuttonpress() def test_move_two_dots_along_path_in_same_windows(self): dots_total = 2 dots = [] dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) for dot_index in range(dots_total): dot = Dot( position=(1, 9), velocity=(dot_index + 1, 0), acceleration=(0, -1) ) dot_plotter.add_dot( dot=dot ) dots.append(dot) dot_plotter.show() print(f"refreshing") time_index = 0.0 time_index_delta = 0.05 maximum_time_index = 20.0 while time_index < maximum_time_index: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta plt.waitforbuttonpress() def test_move_two_dots_along_path_in_same_windows_but_first_gets_acceleration_delta(self): dots_total = 2 dots = [] dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) for dot_index in range(dots_total): dot = Dot( position=(1, 9), velocity=(1, 0), acceleration=(0, -1) ) dot_plotter.add_dot( dot=dot ) dots.append(dot) dot_plotter.show() def alter_dot(*, time_index: float): nonlocal dots dots[0].set_acceleration_delta( time_index=time_index, acceleration_delta=(0, 0.5), end_time_index=5.0 ) print(f"refreshing") time_index = 0.0 time_index_delta = 0.05 maximum_time_index = 30.0 alter_time_index = 10.0 is_altered = False while time_index < maximum_time_index: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta if not is_altered and time_index > alter_time_index: alter_dot( time_index=time_index ) is_altered = True plt.waitforbuttonpress() def test_move_two_dots_along_path_in_same_windows_second_merges_specific_time_index_after_first_altered(self): dots_total = 2 dots = [] dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) for dot_index in range(dots_total): dot = Dot( position=(1, 9), velocity=(1, 0), acceleration=(0, -1) ) dot_plotter.add_dot( dot=dot ) dots.append(dot) dot_plotter.show() def alter_dot(*, time_index: float): nonlocal dots if False: dots[0].set_acceleration_delta( time_index=time_index, acceleration_delta=(0, 0.5), end_time_index=1.0 ) else: dots[0].set_velocity( velocity=(-1, 1) ) def merge_dot(*, time_index: float): nonlocal dots dots[1].merge( dot=dots[0], current_time_index=time_index, merge_time_index_offset=1.0 ) print(f"refreshing") time_index = 0.0 time_index_delta = 0.01 maximum_time_index = 30.0 alter_time_index = 10.0 merge_time_index = 11.0 is_altered = False is_merged = False while time_index < maximum_time_index: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta if not is_altered and time_index > alter_time_index: alter_dot( time_index=time_index ) is_altered = True if not is_merged and time_index > merge_time_index: merge_dot( time_index=time_index ) is_merged = True plt.waitforbuttonpress()

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from __future__ import annotations import unittest import time import matplotlib.pyplot as plt import numpy as np from typing import List, Tuple, Dict, Set, Callable, Type class Dot(): def __init__(self, position: Tuple[float, float], velocity: Tuple[float, float], acceleration: Tuple[float, float]): self.__position = position self.__velocity = velocity self.__acceleration = acceleration self.__time_index_offset = 0 self.__acceleration_delta = None self.__acceleration_delta_end_time_index = None self.__acceleration_delta_end_time_index_acceleration = None def set_positiion(self, *, position: Tuple[float, float]): self.__position = position def set_velocity(self, *, velocity: Tuple[float, float]): self.__velocity = velocity def set_acceleration(self, *, acceleration: Tuple[float, float]): self.__acceleration = acceleration def get_position(self, *, time_index: float) -> Tuple[float, float]: calculated_time_index = time_index + self.__time_index_offset position = list(self.__position) for dimension_index in range(len(position)): position[dimension_index] += self.__velocity[dimension_index] * calculated_time_index if self.__acceleration_delta_end_time_index is None: position[dimension_index] += (self.__acceleration[dimension_index] * calculated_time_index ** 2) / 2.0 else: if calculated_time_index < self.__acceleration_delta_end_time_index: position[dimension_index] += (self.__acceleration[dimension_index] * calculated_time_index ** 2) / 2.0 position[dimension_index] += (self.__acceleration_delta[dimension_index] * calculated_time_index ** 3) / 6.0 else: position[dimension_index] += (self.__acceleration[dimension_index] * self.__acceleration_delta_end_time_index ** 2) / 2.0 position[dimension_index] += (self.__acceleration_delta_end_time_index_acceleration[dimension_index] * (calculated_time_index - self.__acceleration_delta_end_time_index) ** 2) / 2.0 position[dimension_index] += (self.__acceleration_delta[dimension_index] * self.__acceleration_delta_end_time_index ** 3) / 6.0 return tuple(position) def get_velocity(self, *, time_index: float) -> Tuple[float, float]: calculated_time_index = time_index + self.__time_index_offset velocity = list(self.__velocity) for dimension_index in range(len(velocity)): if self.__acceleration_delta_end_time_index is None: velocity[dimension_index] += self.__acceleration[dimension_index] * calculated_time_index else: if calculated_time_index < self.__acceleration_delta_end_time_index: velocity[dimension_index] += self.__acceleration[dimension_index] * calculated_time_index velocity[dimension_index] += (self.__acceleration_delta[dimension_index] * calculated_time_index**2) / 2.0 else: velocity[dimension_index] += self.__acceleration[dimension_index] * self.__acceleration_delta_end_time_index velocity[dimension_index] += self.__acceleration_delta_end_time_index_acceleration[dimension_index] * (calculated_time_index - self.__acceleration_delta_end_time_index) velocity[dimension_index] += (self.__acceleration_delta[dimension_index] * self.__acceleration_delta_end_time_index**2) / 2.0 return tuple(velocity) def get_acceleration(self, *, time_index: float) -> Tuple[float, float]: calculated_time_index = time_index + self.__time_index_offset acceleration = [0] * len(self.__position) for dimension_index in range(len(acceleration)): if self.__acceleration_delta_end_time_index is None: acceleration[dimension_index] += self.__acceleration[dimension_index] else: if calculated_time_index < self.__acceleration_delta_end_time_index: acceleration[dimension_index] += self.__acceleration[dimension_index] acceleration[dimension_index] += (self.__acceleration_delta[dimension_index] * calculated_time_index) else: acceleration[dimension_index] += self.__acceleration_delta_end_time_index_acceleration[dimension_index] acceleration[dimension_index] += (self.__acceleration_delta[dimension_index] * self.__acceleration_delta_end_time_index) return tuple(self.__acceleration) def bounce(self, *, time_index: float): bounce_position = self.get_position( time_index=time_index ) bounce_velocity = self.get_velocity( time_index=time_index ) bounce_acceleration = self.get_acceleration( time_index=time_index ) self.__position = bounce_position self.__velocity = (bounce_velocity[0], -bounce_velocity[1]) self.__acceleration = bounce_acceleration calculated_time_index = time_index + self.__time_index_offset if self.__acceleration_delta_end_time_index is not None: self.__acceleration_delta_end_time_index -= calculated_time_index if self.__acceleration_delta_end_time_index <= 0: self.__acceleration_delta = None self.__acceleration_delta_end_time_index = None self.__acceleration_delta_end_time_index_acceleration = None self.__time_index_offset = -time_index def reflect(self, *, time_index: float): reflect_position = self.get_position( time_index=time_index ) reflect_velocity = self.get_velocity( time_index=time_index ) reflect_acceleration = self.get_acceleration( time_index=time_index ) self.__position = reflect_position self.__velocity = (-reflect_velocity[0], reflect_velocity[1]) self.__acceleration = reflect_acceleration calculated_time_index = time_index + self.__time_index_offset if self.__acceleration_delta_end_time_index is not None: self.__acceleration_delta_end_time_index -= calculated_time_index if self.__acceleration_delta_end_time_index <= 0: self.__acceleration_delta = None self.__acceleration_delta_end_time_index = None self.__acceleration_delta_end_time_index_acceleration = None self.__time_index_offset = -time_index def set_state(self, *, position: Tuple[float, float], velocity: Tuple[float, float], acceleration: Tuple[float, float], time_index: float): self.__position = position self.__velocity = velocity self.__acceleration = acceleration calculated_time_index = time_index + self.__time_index_offset if self.__acceleration_delta_end_time_index is not None: self.__acceleration_delta_end_time_index -= calculated_time_index if self.__acceleration_delta_end_time_index <= 0: self.__acceleration_delta = None self.__acceleration_delta_end_time_index = None self.__acceleration_delta_end_time_index_acceleration = None self.__time_index_offset = -time_index def set_acceleration_delta(self, *, time_index: float, acceleration_delta: Tuple[float, float], end_time_index: float): time_index_position = self.get_position( time_index=time_index ) time_index_velocity = self.get_velocity( time_index=time_index ) time_index_acceleration = self.get_acceleration( time_index=time_index ) self.__position = time_index_position self.__velocity = time_index_velocity self.__acceleration = time_index_acceleration self.__time_index_offset = -time_index self.__acceleration_delta = acceleration_delta self.__acceleration_delta_end_time_index = end_time_index self.__acceleration_delta_end_time_index_acceleration = time_index_acceleration def merge(self, *, dot: Dot, current_time_index: float, merge_time_index_offset: float): self_position = self.get_position( time_index=current_time_index ) self_velocity = self.get_velocity( time_index=current_time_index ) destination_position = dot.get_position( time_index=current_time_index + merge_time_index_offset ) destination_velocity = dot.get_velocity( time_index=current_time_index + merge_time_index_offset ) destination_acceleration = dot.get_acceleration( time_index=current_time_index + merge_time_index_offset ) acceleration_delta = [] acceleration = [] for dimension_index in range(len(self.__position)): temp_acceleration_delta = (-12 * destination_position[dimension_index] + 6 * destination_velocity[dimension_index] * merge_time_index_offset + 12 * self_position[dimension_index] + 6 * self_velocity[dimension_index] * merge_time_index_offset) / (merge_time_index_offset**3) temp_acceleration = (destination_velocity[dimension_index] - self_velocity[dimension_index]) / merge_time_index_offset - 0.5 * temp_acceleration_delta * merge_time_index_offset acceleration_delta.append(temp_acceleration_delta) acceleration.append(temp_acceleration) self.__position = self_position self.__velocity = self_velocity self.__acceleration = tuple(acceleration) self.__acceleration_delta = tuple(acceleration_delta) self.__acceleration_delta_end_time_index = merge_time_index_offset self.__acceleration_delta_end_time_index_acceleration = destination_acceleration self.__time_index_offset = -current_time_index class DotPlotter(): def __init__(self, minimum_position: Tuple[float, float], maximum_position: Tuple[float, float]): self.__minimum_position = minimum_position self.__maximum_position = maximum_position self.__dots = [] self.__x = [] self.__y = [] self.__figure = None self.__scatter = None def add_dot(self, *, dot: Dot): self.__dots.append(dot) def __get_scatter(self, *, time_index: float) -> Tuple[List[float], List[float]]: scatter = ([], []) for dot in self.__dots: position = dot.get_position( time_index=time_index ) if position[1] < self.__minimum_position[1]: dot.bounce( time_index=time_index ) if position[0] < self.__minimum_position[0] or position[0] > self.__maximum_position[0]: dot.reflect( time_index=time_index ) scatter[0].append(position[0]) scatter[1].append(position[1]) print(f"position: {position}") return scatter def show(self): plt.ion() self.__figure, ax = plt.subplots() self.__scatter = ax.scatter(self.__x, self.__y, facecolors="none", edgecolors=["black", "red"], s=10) plt.xlim(self.__minimum_position[0], self.__maximum_position[0]) plt.ylim(self.__minimum_position[1], self.__maximum_position[1]) plt.draw() def refresh(self, *, time_index: float): x, y = self.__get_scatter( time_index=time_index ) self.__x.clear() self.__x.extend(x) self.__y.clear() self.__y.extend(y) self.__scatter.set_offsets(np.c_[self.__x, self.__y]) self.__figure.canvas.draw_idle() plt.pause(0.01) class LatencyPositionTest(unittest.TestCase): def test_initialize(self): dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) self.assertIsNotNone(dot_plotter) def test_move_dot_along_path(self): dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) dot = Dot( position=(1, 9), velocity=(1, 0), acceleration=(0, -1) ) dot_plotter.add_dot( dot=dot ) dot_plotter.show() print(f"refreshing") time_index = 0.0 time_index_delta = 0.05 while time_index < 20.0: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta plt.waitforbuttonpress() def test_move_dot_along_path_in_separate_windows(self): dot_plotters_total = 2 dot_plotters = [] for dot_plotter_index in range(dot_plotters_total): dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) dot = Dot( position=(1, 9), velocity=(1, 0), acceleration=(0, -1) ) dot_plotter.add_dot( dot=dot ) dot_plotter.show() dot_plotters.append(dot_plotter) print(f"refreshing") time_index = 0.0 time_index_delta = 0.05 while time_index < 10.0: for dot_plotter in dot_plotters: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta plt.waitforbuttonpress() def test_move_dot_along_path_then_alter_state(self): dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) dot = Dot( position=(1, 9), velocity=(1, 0), acceleration=(0, -1) ) def alter_dot(*, time_index: float): nonlocal dot dot.set_state( position=dot.get_position( time_index=time_index ), velocity=(-1, 1), acceleration=(0, -1), time_index=time_index ) dot_plotter.add_dot( dot=dot ) dot_plotter.show() print(f"refreshing") time_index = 0.0 time_index_delta = 0.05 maximum_time_index = 20.0 is_altered = False while time_index < maximum_time_index: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta if not is_altered and time_index > maximum_time_index / 2.0: alter_dot( time_index=time_index ) is_altered = True plt.waitforbuttonpress() def test_move_dot_along_path_then_set_acceleration_delta(self): dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) dot = Dot( position=(1, 9), velocity=(1, 0), acceleration=(0, -1) ) def alter_dot(*, time_index: float): nonlocal dot dot.set_acceleration_delta( time_index=time_index, acceleration_delta=(0, 0.5), end_time_index=5.0 ) dot_plotter.add_dot( dot=dot ) dot_plotter.show() print(f"refreshing") time_index = 0.0 time_index_delta = 0.05 maximum_time_index = 30.0 alter_time_index = 10.0 is_altered = False while time_index < maximum_time_index: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta if not is_altered and time_index > alter_time_index: alter_dot( time_index=time_index ) is_altered = True plt.waitforbuttonpress() def test_move_two_dots_along_path_in_same_windows(self): dots_total = 2 dots = [] dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) for dot_index in range(dots_total): dot = Dot( position=(1, 9), velocity=(dot_index + 1, 0), acceleration=(0, -1) ) dot_plotter.add_dot( dot=dot ) dots.append(dot) dot_plotter.show() print(f"refreshing") time_index = 0.0 time_index_delta = 0.05 maximum_time_index = 20.0 while time_index < maximum_time_index: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta plt.waitforbuttonpress() def test_move_two_dots_along_path_in_same_windows_but_first_gets_acceleration_delta(self): dots_total = 2 dots = [] dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) for dot_index in range(dots_total): dot = Dot( position=(1, 9), velocity=(1, 0), acceleration=(0, -1) ) dot_plotter.add_dot( dot=dot ) dots.append(dot) dot_plotter.show() def alter_dot(*, time_index: float): nonlocal dots dots[0].set_acceleration_delta( time_index=time_index, acceleration_delta=(0, 0.5), end_time_index=5.0 ) print(f"refreshing") time_index = 0.0 time_index_delta = 0.05 maximum_time_index = 30.0 alter_time_index = 10.0 is_altered = False while time_index < maximum_time_index: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta if not is_altered and time_index > alter_time_index: alter_dot( time_index=time_index ) is_altered = True plt.waitforbuttonpress() def test_move_two_dots_along_path_in_same_windows_second_merges_specific_time_index_after_first_altered(self): dots_total = 2 dots = [] dot_plotter = DotPlotter( minimum_position=(0, 0), maximum_position=(10, 10) ) for dot_index in range(dots_total): dot = Dot( position=(1, 9), velocity=(1, 0), acceleration=(0, -1) ) dot_plotter.add_dot( dot=dot ) dots.append(dot) dot_plotter.show() def alter_dot(*, time_index: float): nonlocal dots if False: dots[0].set_acceleration_delta( time_index=time_index, acceleration_delta=(0, 0.5), end_time_index=1.0 ) else: dots[0].set_velocity( velocity=(-1, 1) ) def merge_dot(*, time_index: float): nonlocal dots dots[1].merge( dot=dots[0], current_time_index=time_index, merge_time_index_offset=1.0 ) print(f"refreshing") time_index = 0.0 time_index_delta = 0.01 maximum_time_index = 30.0 alter_time_index = 10.0 merge_time_index = 11.0 is_altered = False is_merged = False while time_index < maximum_time_index: dot_plotter.refresh( time_index=time_index ) time_index += time_index_delta if not is_altered and time_index > alter_time_index: alter_dot( time_index=time_index ) is_altered = True if not is_merged and time_index > merge_time_index: merge_dot( time_index=time_index ) is_merged = True plt.waitforbuttonpress()

true

f723c14378cc4d16c4baa11917ffffcdd73ab43e

190

py

Python

mmdet3d/ops/paconv/__init__.py

maskjp/mmdetection3d

98f332372b1a4c82bc2d57588a5d764f4176c869

[ "Apache-2.0" ]

1

2022-03-04T19:29:42.000Z

mmdet3d/ops/paconv/__init__.py

maskjp/mmdetection3d

98f332372b1a4c82bc2d57588a5d764f4176c869

[ "Apache-2.0" ]

null

mmdet3d/ops/paconv/__init__.py

maskjp/mmdetection3d

98f332372b1a4c82bc2d57588a5d764f4176c869

[ "Apache-2.0" ]

null

31.666667

56

0.784211

from .assign_score import assign_score_withk from .paconv import PAConv, PAConvCUDA __all__ = ['assign_score_withk', 'PAConv', 'PAConvCUDA']

true

f723c28d10fea756d280aba926ac651ddd9a5b0d

354

py

Python

wiser_care_theme/wiser_care_theme/doctype/wiser_website_settings/wiser_website_settings.py

MostafaFekry/wiser_care_theme

9892442803dffeeb5e02136c87c2eb4cc9144b60

[ "MIT" ]

null

wiser_care_theme/wiser_care_theme/doctype/wiser_website_settings/wiser_website_settings.py

MostafaFekry/wiser_care_theme

9892442803dffeeb5e02136c87c2eb4cc9144b60

[ "MIT" ]

null

wiser_care_theme/wiser_care_theme/doctype/wiser_website_settings/wiser_website_settings.py

MostafaFekry/wiser_care_theme

9892442803dffeeb5e02136c87c2eb4cc9144b60

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2019, Systematic and contributors # For license information, please see license.txt from __future__ import unicode_literals import frappe from frappe.model.document import Document class WiserWebsiteSettings(Document): def on_update(self): from frappe.website.render import clear_cache clear_cache("index")

27.230769

49

0.788136

from __future__ import unicode_literals import frappe from frappe.model.document import Document class WiserWebsiteSettings(Document): def on_update(self): from frappe.website.render import clear_cache clear_cache("index")

true

f723c4bdd9779dd352fdad7e5c8f3b6996c4c2ec

982

py

Python

data-pipline.py

helloxms/Disaster-Response-Pipline-Udacity

cd623ec339b766a7e91fe2e35de95062885c07de

[ "MIT" ]

null

data-pipline.py

helloxms/Disaster-Response-Pipline-Udacity

cd623ec339b766a7e91fe2e35de95062885c07de

[ "MIT" ]

null

data-pipline.py

helloxms/Disaster-Response-Pipline-Udacity

cd623ec339b766a7e91fe2e35de95062885c07de

[ "MIT" ]

null

# import packages import sys def load_data(data_file): # read in file # clean data # load to database # define features and label arrays return X, y def build_model(): # text processing and model pipeline # define parameters for GridSearchCV # create gridsearch object and return as final model pipeline return model_pipeline def train(X, y, model): # train test split # fit model # output model test results return model def export_model(model): # Export model as a pickle file def run_pipeline(data_file): X, y = load_data(data_file) # run ETL pipeline model = build_model() # build model pipeline model = train(X, y, model) # train model pipeline export_model(model) # save model if __name__ == '__main__': data_file = sys.argv[1] # get filename of dataset run_pipeline(data_file) # run data pipeline

16.098361

66

0.62831

import sys def load_data(data_file): return X, y def build_model(): return model_pipeline def train(X, y, model): return model def export_model(model): def run_pipeline(data_file): X, y = load_data(data_file) model = build_model() model = train(X, y, model) export_model(model) if __name__ == '__main__': data_file = sys.argv[1] run_pipeline(data_file)

false

true

f723c54aee85d58c016b328753e3fa153669b6d6

2,533

py

Python

voctocore/tests/test_audiomix_multiple_sources.py

0xflotus/voctomix

3156f3546890e6ae8d379df17e5cc718eee14b15

[ "MIT" ]

521

2015-01-07T21:43:30.000Z

2022-03-17T22:07:13.000Z

voctocore/tests/test_audiomix_multiple_sources.py

0xflotus/voctomix

3156f3546890e6ae8d379df17e5cc718eee14b15

[ "MIT" ]

241

2015-05-27T10:11:09.000Z

2022-02-11T03:29:20.000Z

voctocore/tests/test_audiomix_multiple_sources.py

0xflotus/voctomix

3156f3546890e6ae8d379df17e5cc718eee14b15

[ "MIT" ]

111

2015-08-13T20:06:52.000Z

2022-03-11T09:48:46.000Z

import unittest from lib.errors.configuration_error import ConfigurationError from tests.helper.voctomix_test import VoctomixTest from lib.audiomix import AudioMix from lib.config import Config # noinspection PyUnusedLocal class AudiomixMultipleSources(VoctomixTest): def test_no_configured_audiosource_sets_first_to_full(self): audiomixer = AudioMix() self.assertListEqual(audiomixer.names, ["cam1", "cam2", "grabber"]) self.assertListEqual(audiomixer.volumes, [1.0, 0.0, 0.0]) def test_audiosource_sets_source_volume_to_full(self): Config.given("mix", "audiosource", "cam2") audiomixer = AudioMix() self.assertListEqual(audiomixer.names, ["cam1", "cam2", "grabber"]) self.assertListEqual(audiomixer.volumes, [0.0, 1.0, 0.0]) def test_per_source_volumes_set_volumes_to_configured_level(self): Config.given("source.cam1", "volume", "0.23") Config.given("source.cam2", "volume", "0.0") Config.given("source.grabber", "volume", "0.42") audiomixer = AudioMix() self.assertListEqual(audiomixer.names, ["cam1", "cam2", "grabber"]) self.assertListEqual(audiomixer.volumes, [0.23, 0.0, 0.42]) def test_audiosource_together_with_per_source_volumes_for_the_same_source_raises_an_error(self): Config.given("mix", "audiosource", "cam1") Config.given("source.cam1", "volume", "0.23") with self.assertRaises(ConfigurationError): audiomixer = AudioMix() def test_audiosource_together_with_per_source_volumes_for_different_sources_raises_an_error(self): Config.given("mix", "audiosource", "cam2") Config.given("source.cam1", "volume", "0.23") with self.assertRaises(ConfigurationError): audiomixer = AudioMix() def test_invalid_audiosource_raises_an_error(self): Config.given("mix", "audiosource", "camInvalid") with self.assertRaises(ConfigurationError): audiomixer = AudioMix() def test_configuring_audiosource_disables_ui_audio_selector(self): Config.given("mix", "audiosource", "cam1") audiomixer = AudioMix() self.assertEqual(Config.getboolean('audio', 'volumecontrol'), False) def test_configuring_per_source_volumes_disables_ui_audio_selector(self): Config.given("source.cam1", "volume", "1.0") audiomixer = AudioMix() self.assertEqual(Config.getboolean('audio', 'volumecontrol'), False) if __name__ == '__main__': unittest.main()

36.185714

102

0.698776

import unittest from lib.errors.configuration_error import ConfigurationError from tests.helper.voctomix_test import VoctomixTest from lib.audiomix import AudioMix from lib.config import Config class AudiomixMultipleSources(VoctomixTest): def test_no_configured_audiosource_sets_first_to_full(self): audiomixer = AudioMix() self.assertListEqual(audiomixer.names, ["cam1", "cam2", "grabber"]) self.assertListEqual(audiomixer.volumes, [1.0, 0.0, 0.0]) def test_audiosource_sets_source_volume_to_full(self): Config.given("mix", "audiosource", "cam2") audiomixer = AudioMix() self.assertListEqual(audiomixer.names, ["cam1", "cam2", "grabber"]) self.assertListEqual(audiomixer.volumes, [0.0, 1.0, 0.0]) def test_per_source_volumes_set_volumes_to_configured_level(self): Config.given("source.cam1", "volume", "0.23") Config.given("source.cam2", "volume", "0.0") Config.given("source.grabber", "volume", "0.42") audiomixer = AudioMix() self.assertListEqual(audiomixer.names, ["cam1", "cam2", "grabber"]) self.assertListEqual(audiomixer.volumes, [0.23, 0.0, 0.42]) def test_audiosource_together_with_per_source_volumes_for_the_same_source_raises_an_error(self): Config.given("mix", "audiosource", "cam1") Config.given("source.cam1", "volume", "0.23") with self.assertRaises(ConfigurationError): audiomixer = AudioMix() def test_audiosource_together_with_per_source_volumes_for_different_sources_raises_an_error(self): Config.given("mix", "audiosource", "cam2") Config.given("source.cam1", "volume", "0.23") with self.assertRaises(ConfigurationError): audiomixer = AudioMix() def test_invalid_audiosource_raises_an_error(self): Config.given("mix", "audiosource", "camInvalid") with self.assertRaises(ConfigurationError): audiomixer = AudioMix() def test_configuring_audiosource_disables_ui_audio_selector(self): Config.given("mix", "audiosource", "cam1") audiomixer = AudioMix() self.assertEqual(Config.getboolean('audio', 'volumecontrol'), False) def test_configuring_per_source_volumes_disables_ui_audio_selector(self): Config.given("source.cam1", "volume", "1.0") audiomixer = AudioMix() self.assertEqual(Config.getboolean('audio', 'volumecontrol'), False) if __name__ == '__main__': unittest.main()

true

f723c5c9d3b222d7776eada4ac0bace2610445b6

450

py

Python

V2RaycSpider1225/BusinessCentralLayer/middleware/work_io.py

kujie0121/V2RayCloudSpider

32cd65f7811374679fe09cfae2fda805d42fe7ab

[ "MIT" ]

1

2021-02-17T07:51:09.000Z

V2RaycSpider1225/BusinessCentralLayer/middleware/work_io.py

kujie0121/V2RayCloudSpider

32cd65f7811374679fe09cfae2fda805d42fe7ab

[ "MIT" ]

null

V2RaycSpider1225/BusinessCentralLayer/middleware/work_io.py

kujie0121/V2RayCloudSpider

32cd65f7811374679fe09cfae2fda805d42fe7ab

[ "MIT" ]

null

__all__ = ['Middleware'] from gevent.queue import Queue # 工作栈 class Middleware: # cache of redis zeus = Queue() # Trash apollo = Queue() theseus = {} # 共享任务队列 poseidon = Queue() hera = Queue() # FIXME # 不明原因bug 使用dict（zip（））方案生成的同样的变量， # 在经过同一个函数方案后输出竟然不一样 cache_redis_queue = {'ssr': {}, 'v2ray': {}} # cache_redis_queue = dict(zip(CRAWLER_SEQUENCE, [{}] * CRAWLER_SEQUENCE.__len__()))

16.666667

88

0.6

__all__ = ['Middleware'] from gevent.queue import Queue class Middleware: zeus = Queue() apollo = Queue() theseus = {} poseidon = Queue() hera = Queue() cache_redis_queue = {'ssr': {}, 'v2ray': {}}

true

f723c61d42e207f1fe86c447483a8df191033920

2,661

py

Python

tests/test_basics.py

JunyongYao/flask-backend-seed

9d16f56a9f34ebb1ec32eaab800b7ad6b10d0c9d

[ "MIT" ]

9

2017-10-20T09:26:09.000Z

2021-01-28T02:54:43.000Z

tests/test_basics.py

JunyongYao/flask-backend-seed

9d16f56a9f34ebb1ec32eaab800b7ad6b10d0c9d

[ "MIT" ]

2

2018-03-06T06:27:53.000Z

2018-04-19T01:47:38.000Z

tests/test_basics.py

JunyongYao/flask-backend-seed

9d16f56a9f34ebb1ec32eaab800b7ad6b10d0c9d

[ "MIT" ]

2

2019-07-18T22:32:28.000Z

2020-06-15T14:10:29.000Z

# -*- coding: utf-8 -*- import json import random import string import unittest from flask import current_app from config import config from app import create_app, db, redis, add_api_support class BasicsTestCase(unittest.TestCase): def setUp(self): test_app = create_app(config['testing']) test_app = add_api_support(test_app) self.assertTrue(current_app.config['TESTING']) self.app_context = test_app.app_context() self.app_context.push() self.test_client = test_app.test_client() db.drop_all() db.create_all() redis.flushall() def tearDown(self): db.session.remove() self.app_context.pop() @staticmethod def _parse_result(res_data): data = str(res_data, "utf-8") try: ret_data = json.loads(data) except ValueError: ret_data = data return ret_data @staticmethod def generate_random_string(length): return ''.join(random.choice(string.ascii_lowercase + string.digits) for x in range(length)) def create_test_user(self, name, pwd): from app.model.userModel import UserInfo stored_pwd = UserInfo.generate_sha_pwd(pwd) new_user = UserInfo(name=name, sha_pwd=stored_pwd, nickname=self.generate_random_string(5)) db.session.add(new_user) db.session.commit() return new_user def get_request(self, url, data=None, header=None): response = self.test_client.get(url, data=data, headers=header) return response.status_code, self._parse_result(response.data) def put_request(self, url, data=None, header=None): response = self.test_client.put(url, data=data, headers=header) return response.status_code, self._parse_result(response.data) def post_request(self, url, data=None, header=None): response = self.test_client.post(url, data=data, headers=header) return response.status_code, self._parse_result(response.data) def delete_request(self, url, data=None, header=None): response = self.test_client.delete(url, data=data, headers=header) return response.status_code, self._parse_result(response.data) def post_login(self, data): url = "/api/sample/login" return self.post_request(url, data=data) def get_user_info(self, header): url = "/api/sample/user_info" return self.get_request(url, header=header) def put_user_info(self, data, header): url = "/api/sample/user_info" return self.put_request(url, data=data, header=header)

32.45122

100

0.661405

import json import random import string import unittest from flask import current_app from config import config from app import create_app, db, redis, add_api_support class BasicsTestCase(unittest.TestCase): def setUp(self): test_app = create_app(config['testing']) test_app = add_api_support(test_app) self.assertTrue(current_app.config['TESTING']) self.app_context = test_app.app_context() self.app_context.push() self.test_client = test_app.test_client() db.drop_all() db.create_all() redis.flushall() def tearDown(self): db.session.remove() self.app_context.pop() @staticmethod def _parse_result(res_data): data = str(res_data, "utf-8") try: ret_data = json.loads(data) except ValueError: ret_data = data return ret_data @staticmethod def generate_random_string(length): return ''.join(random.choice(string.ascii_lowercase + string.digits) for x in range(length)) def create_test_user(self, name, pwd): from app.model.userModel import UserInfo stored_pwd = UserInfo.generate_sha_pwd(pwd) new_user = UserInfo(name=name, sha_pwd=stored_pwd, nickname=self.generate_random_string(5)) db.session.add(new_user) db.session.commit() return new_user def get_request(self, url, data=None, header=None): response = self.test_client.get(url, data=data, headers=header) return response.status_code, self._parse_result(response.data) def put_request(self, url, data=None, header=None): response = self.test_client.put(url, data=data, headers=header) return response.status_code, self._parse_result(response.data) def post_request(self, url, data=None, header=None): response = self.test_client.post(url, data=data, headers=header) return response.status_code, self._parse_result(response.data) def delete_request(self, url, data=None, header=None): response = self.test_client.delete(url, data=data, headers=header) return response.status_code, self._parse_result(response.data) def post_login(self, data): url = "/api/sample/login" return self.post_request(url, data=data) def get_user_info(self, header): url = "/api/sample/user_info" return self.get_request(url, header=header) def put_user_info(self, data, header): url = "/api/sample/user_info" return self.put_request(url, data=data, header=header)

true

f723c69ef811ef7571c423cdbc18fa2896c2bcf8

4,253

py

Python

datagen/meerkat.py

tilleyd/point-proposal-net

3731984046cd56101238de30a441d610b79bc8b0

[ "MIT" ]

null

datagen/meerkat.py

tilleyd/point-proposal-net

3731984046cd56101238de30a441d610b79bc8b0

[ "MIT" ]

null

datagen/meerkat.py

tilleyd/point-proposal-net

3731984046cd56101238de30a441d610b79bc8b0

[ "MIT" ]

null

#!/usr/bin/python3 IMAGE_SIZE = 1024 NUM_SOURCES = 200 NUM_IMAGES = 1 RA_MIN, RA_MAX = (-50, 50) DEC_MIN, DEC_MAX = (-30, 50) OFFSET_RA = 1.1 # need to fine tune to fit sources into image OFFSET_DEC = 0.7 # # measure bg rms # from astropy.io import fits import numpy as np try: # if there is already a background image, use it bg = np.squeeze(fits.getdata('output/0-nosource-image.fits')) sigma = np.std(bg) except Exception: print('Warning: no background reference found, using default sigma') sigma = 0.000033921602153 # # create sky models # # create an equal number of point sources with flux from 0.33sigma to 10sigma # with bins of size 0.33 bins = np.arange(1.0/3.0, 10.1, 1.0/3.0) sources_per_bin = NUM_SOURCES // len(bins) total_sources = sources_per_bin * len(bins) if NUM_SOURCES != total_sources: print('Warning: the number of sources is not divisible by the bins') print(' images will have %d/%d sources' %(total_sources, NUM_SOURCES)) # # create stimela recipe # import stimela INPUT = 'input' OUTPUT = 'output' MSDIR = 'msdir' MS = 'meerkat.ms' recipe = stimela.Recipe(name='Make noise image populated with sources', ms_dir=MSDIR, JOB_TYPE='udocker') for img in range(0, NUM_IMAGES): # determine field centre centre_ra = np.random.uniform(RA_MIN, RA_MAX) centre_dec = np.random.uniform(DEC_MIN, DEC_MAX) # create sky model with open('input/%d-skymodel.txt' %(img), 'w') as f: f.write('#format: ra_d dec_d i\n') for flux in bins: for i in range(0, sources_per_bin): ra = np.random.uniform(centre_ra-OFFSET_RA, centre_ra+OFFSET_RA) dec = np.random.uniform(centre_dec-OFFSET_DEC, centre_dec+OFFSET_DEC) f.write(' %.2f %.2f %.15f\n' %(ra, dec, flux * sigma)) f.write('~\n') # create empty measurement set (only once per set of images) recipe.add('cab/simms', 'simms', { "msname" : MS, "telescope" : "meerkat", "synthesis" : 1, # exposure time, 1 hour "dtime" : 60*5, # integration time, 5 minutes "freq0" : '1400MHz', # starting frequency "dfreq" : '2.0MHz', # channel width "nchan" : 10, # number of channels "direction" : 'J2000,%.1fdeg,%.1fdeg' %(centre_ra, centre_dec), # telescope pointing target "feed" : 'perfect R L', "pol" : 'RR RL LR LL', }, input=INPUT, output=OUTPUT, label='simms:: Create empty MS') for img in range(0, NUM_IMAGES): # simulate noise into measurement set recipe.add('cab/simulator', 'simsky', { "msname" : MS, "addnoise" : True, "sefd" : 450, "threads" : 16, "column" : 'DATA', }, input=INPUT, output=OUTPUT, label='simsky:: Simulate sky') # image the noise recipe.add('cab/wsclean', 'image', { "msname" : MS, "prefix" : "%d-nosource" %(img), # image prefix "column" : "DATA", "weight" : "briggs 1.5", "cellsize" : 5, # pixel size in arcsec "npix" : IMAGE_SIZE, "trim" : 1000, "niter" : 10000, }, input=INPUT, output=OUTPUT, label='image:: Image data') # add point sources recipe.add('cab/tigger_restore', 'add_sources', { "input-image" : '%d-nosource-image.fits:output' %(img), # the noise image "input-skymodel" : '%d-skymodel.txt' %(img), # the input skymodel "output-image" : '%d-sky.fits' %(img), # output image name "force" : True, }, input=INPUT, output=OUTPUT, label='add_sources%d' %(img)) recipe.run()

30.818841

106

0.517987

IMAGE_SIZE = 1024 NUM_SOURCES = 200 NUM_IMAGES = 1 RA_MIN, RA_MAX = (-50, 50) DEC_MIN, DEC_MAX = (-30, 50) OFFSET_RA = 1.1 OFFSET_DEC = 0.7 from astropy.io import fits import numpy as np try: bg = np.squeeze(fits.getdata('output/0-nosource-image.fits')) sigma = np.std(bg) except Exception: print('Warning: no background reference found, using default sigma') sigma = 0.000033921602153 bins = np.arange(1.0/3.0, 10.1, 1.0/3.0) sources_per_bin = NUM_SOURCES // len(bins) total_sources = sources_per_bin * len(bins) if NUM_SOURCES != total_sources: print('Warning: the number of sources is not divisible by the bins') print(' images will have %d/%d sources' %(total_sources, NUM_SOURCES)) import stimela INPUT = 'input' OUTPUT = 'output' MSDIR = 'msdir' MS = 'meerkat.ms' recipe = stimela.Recipe(name='Make noise image populated with sources', ms_dir=MSDIR, JOB_TYPE='udocker') for img in range(0, NUM_IMAGES): centre_ra = np.random.uniform(RA_MIN, RA_MAX) centre_dec = np.random.uniform(DEC_MIN, DEC_MAX) with open('input/%d-skymodel.txt' %(img), 'w') as f: f.write('#format: ra_d dec_d i\n') for flux in bins: for i in range(0, sources_per_bin): ra = np.random.uniform(centre_ra-OFFSET_RA, centre_ra+OFFSET_RA) dec = np.random.uniform(centre_dec-OFFSET_DEC, centre_dec+OFFSET_DEC) f.write(' %.2f %.2f %.15f\n' %(ra, dec, flux * sigma)) f.write('~\n') recipe.add('cab/simms', 'simms', { "msname" : MS, "telescope" : "meerkat", "synthesis" : 1, "dtime" : 60*5, "freq0" : '1400MHz', "dfreq" : '2.0MHz', "nchan" : 10, "direction" : 'J2000,%.1fdeg,%.1fdeg' %(centre_ra, centre_dec), "feed" : 'perfect R L', "pol" : 'RR RL LR LL', }, input=INPUT, output=OUTPUT, label='simms:: Create empty MS') for img in range(0, NUM_IMAGES): recipe.add('cab/simulator', 'simsky', { "msname" : MS, "addnoise" : True, "sefd" : 450, "threads" : 16, "column" : 'DATA', }, input=INPUT, output=OUTPUT, label='simsky:: Simulate sky') recipe.add('cab/wsclean', 'image', { "msname" : MS, "prefix" : "%d-nosource" %(img), "column" : "DATA", "weight" : "briggs 1.5", "cellsize" : 5, "npix" : IMAGE_SIZE, "trim" : 1000, "niter" : 10000, }, input=INPUT, output=OUTPUT, label='image:: Image data') recipe.add('cab/tigger_restore', 'add_sources', { "input-image" : '%d-nosource-image.fits:output' %(img), "input-skymodel" : '%d-skymodel.txt' %(img), "output-image" : '%d-sky.fits' %(img), "force" : True, }, input=INPUT, output=OUTPUT, label='add_sources%d' %(img)) recipe.run()

false

true

f723c6be07f0c68bbd987b45988d3ea7dc170622

24,126

py

Python

refactorings/utils/utils_listener_fast.py

mossj77/CodART

ac83a49a4aa9310b09da12fb476a84586812310b

[ "MIT" ]

1

2021-10-10T23:56:49.000Z

refactorings/utils/utils_listener_fast.py

pouorix/CodART

84b35a5a14e583d88319d7f6c2de8dc3b3dc83b2

[ "MIT" ]

null

refactorings/utils/utils_listener_fast.py

pouorix/CodART

84b35a5a14e583d88319d7f6c2de8dc3b3dc83b2

[ "MIT" ]

null

import re # regular expressions import antlr4 from antlr4.Token import CommonToken import antlr4.tree from antlr4.CommonTokenStream import CommonTokenStream from typing import List, Optional from gen.java.JavaParser import JavaParser from gen.java.JavaParserListener import JavaParserListener class Program: def __init__(self): self.packages = {} def __str__(self): return str(self.packages) class Package: def __init__(self): self.name = None self.classes = {} self.package_ctx = None def __str__(self): return str(self.name) + " " + str(self.classes) class TokensInfo: """Note that start and stop are inclusive.""" def __init__(self, parser_context=None): if parser_context is not None: self.token_stream: CommonTokenStream = parser_context.parser.getTokenStream() self.start: int = parser_context.start.tokenIndex self.stop: int = parser_context.stop.tokenIndex else: self.token_stream: CommonTokenStream = None self.start: int = None self.stop: int = None def get_token_index(self, tokens: list, start: int, stop: int): return tokens[start:stop] class FileInfo: def __init__(self, filename: str = None, package_name: str = None): self.filename: str = filename self.package_name: str = package_name self.all_imports = [] self.package_imports = [] self.class_imports = [] def has_imported_class(self, package_name: str, class_name: str) -> bool: if self.package_name == package_name: return True return ( any(lambda x: x.package_name == package_name for package_import in self.package_imports) or any(lambda x: x.package_name == package_name and x.class_name == class_name for class_import in self.class_imports) ) def has_imported_package(self, package_name: str): if self.package_name == package_name: return True return ( any(lambda x: x.package_name == package_name for package_import in self.package_imports) ) class SingleFileElement: """The base class for those elements that are extracted from a single file""" def __init__(self, parser_context, filename: str = None, _file_info: FileInfo = None): self.parser_context = parser_context self.filename = filename self.file_info = _file_info def get_token_stream(self) -> CommonTokenStream: return self.parser_context.parser.getTokenStream() def get_tokens_info(self) -> TokensInfo: return TokensInfo( self.parser_context ) def get_first_symbol(self) -> CommonToken: first_terminal = self.parser_context while not isinstance(first_terminal, antlr4.tree.Tree.TerminalNode): first_terminal = first_terminal.getChild(0) return first_terminal.getSymbol() def get_last_symbol(self) -> CommonToken: last_terminal = self.parser_context while not isinstance(last_terminal, antlr4.tree.Tree.TerminalNode): last_terminal = last_terminal.getChild(last_terminal.getChildCount() - 1) return last_terminal.getSymbol() def get_file_position_range(self) -> tuple: return ( self.get_first_symbol().start, self.get_last_symbol().stop ) def get_text_from_file(self, filename=None) -> str: if filename is None: filename = self.filename if filename is None: return None file = open(filename, 'r') text = file.read() file.close() return text[self.get_first_symbol().start:self.get_last_symbol().stop + 1] class ClassImport(SingleFileElement): """import package_name.class_name;""" def __init__(self, package_name: str = None, class_name: str = None, parser_context: JavaParser.ImportDeclarationContext = None, filename: str = None, file_info: FileInfo = None): self.package_name = package_name self.class_name = class_name self.parser_context = parser_context self.filename = filename self.file_info = file_info def __str__(self): return "import " + str(self.package_name) + '.' + str(self.class_name) class PackageImport(SingleFileElement): """import package_name.*;""" def __init__(self, package_name: str = None, parser_context: JavaParser.ImportDeclarationContext = None, filename: str = None, file_info: FileInfo = None): self.package_name = package_name self.parser_context = parser_context self.filename = filename self.file_info = file_info def __str__(self): return "import " + str(self.package_name) + ".*" class Class(SingleFileElement): def __init__(self, name: str = None, super_class_name: str = None, package_name: str = None, parser_context: JavaParser.ClassDeclarationContext = None, filename: str = None, file_info: FileInfo = None): self.modifiers = [] self.modifiers_parser_contexts = [] self.name = name self.superclass_name = None self.superinterface_names = [] self.fields = {} self.methods = {} self.package_name = package_name self.parser_context = parser_context self.filename = filename self.file_info = file_info self.body_context = None def find_methods_with_name(self, name: str) -> list: result = [] for mk in self.methods: m = self.methods[mk] if m.name == name: result.append(m) return result def __str__(self): return str(self.modifiers) + " " + str(self.name) \ + ((" extends " + str(self.superclass_name)) if self.superclass_name is not None else "") \ + ((" implements " + str(self.superinterface_names)) if len(self.superinterface_names) > 0 else "") \ + " " + str(self.fields) \ + " " + str(self.methods) class Field(SingleFileElement): def __init__(self, datatype: str = None, name: str = None, initializer: str = None, package_name: str = None, class_name: str = None, parser_context: JavaParser.FieldDeclarationContext = None, filename: str = None, file_info: FileInfo = None): self.modifiers = [] self.modifiers_parser_contexts = [] self.datatype = datatype self.name = name self.initializer = initializer self.neighbor_names = [] self.all_variable_declarator_contexts = [] self.index_in_variable_declarators: int = None self.package_name = package_name self.class_name = class_name self.parser_context = parser_context self.filename = filename self.file_info = file_info def __str__(self): return str(self.modifiers) + " " + str(self.datatype) + " " + str(self.name) class Method(SingleFileElement): def __init__(self, returntype: str = None, name: str = None, body_text: str = None, package_name: str = None, class_name: str = None, parser_context=None, filename: str = None, file_info: FileInfo = None): self.modifiers = [] self.modifiers_parser_contexts = [] self.returntype = returntype self.name = name self.parameters = [] self.body_text = body_text self.body_method_invocations = {} self.body_local_vars_and_expr_names = [] # Type: either LocalVariable, ExpressionName or MethodInvocation self.package_name = package_name self.class_name = class_name self.parser_context = parser_context self.filename = filename self.file_info = file_info self.formalparam_context = None self.body_method_invocations_without_typename = {} self.method_declaration_context = None self.is_constructor = False def __str__(self): return str(self.modifiers) + " " + str(self.returntype) + " " + str(self.name) \ + str(tuple(self.parameters)) class LocalVariable: def __init__(self, datatype: str = None, identifier: str = None, parser_context: JavaParser.LocalVariableDeclarationContext = None): self.datatype = datatype self.identifier = identifier self.parser_context = parser_context class ExpressionName: def __init__(self, dot_separated_identifiers: list): self.dot_separated_identifiers = dot_separated_identifiers class MethodInvocation: def __init__(self, dot_separated_identifiers: list, parser_context: JavaParser.ExpressionContext = None): self.dot_separated_identifiers = dot_separated_identifiers self.parser_context = parser_context class UtilsListener(JavaParserListener): def __init__(self, filename): self.package = Package() self.last_modifiers = [] self.last_modifiers_contexts = [] self.current_class_identifier = None self.current_class_identifier_temp = None self.nest_count = 0 self.current_method_identifier = None self.current_method = None self.current_local_var_type = None self.current_local_var_ctx = None self.current_field_decl = None self.current_field_ids = None self.current_field_dims = None self.current_field_inits = None self.current_field_var_ctxs = None self.filename = filename self.file_info = FileInfo(filename=filename) self.field_enter_count = 0 def enterPackageDeclaration(self, ctx: JavaParser.PackageDeclarationContext): self.package.name = ctx.qualifiedName().getText() self.file_info.package_name = self.package.name self.package.package_ctx = ctx def enterImportDeclaration(self, ctx: JavaParser.ImportDeclarationContext): if ctx.STATIC() is None: name: str = ctx.qualifiedName().getText() if ctx.getText().endswith(".*;"): # Package import p = name package_import = PackageImport( package_name=p, parser_context=ctx, filename=self.filename, file_info=self.file_info ) self.file_info.all_imports.append(package_import) self.file_info.package_imports.append(package_import) else: # Class import p = None dot_i = name.rfind('.') if dot_i != -1: p = name[:dot_i] c = name[dot_i + 1:] else: c = name class_import = ClassImport( package_name=p, class_name=c, parser_context=ctx, filename=self.filename, file_info=self.file_info ) self.file_info.all_imports.append(class_import) self.file_info.class_imports.append(class_import) def enterTypeDeclaration(self, ctx: JavaParser.TypeDeclarationContext): self.last_modifiers.clear() self.last_modifiers_contexts.clear() for modifier in ctx.getChildren(lambda x: type(x) == JavaParser.ClassOrInterfaceModifierContext): self.last_modifiers.append(modifier.getText()) self.last_modifiers_contexts.append(modifier) def enterClassBodyDeclaration(self, ctx: JavaParser.ClassBodyDeclarationContext): self.last_modifiers.clear() self.last_modifiers_contexts.clear() for modifier in ctx.getChildren(lambda x: type(x) == JavaParser.ModifierContext): self.last_modifiers.append(modifier.getText()) self.last_modifiers_contexts.append(modifier) def enterClassDeclaration(self, ctx: JavaParser.ClassDeclarationContext): if self.current_class_identifier is None and self.nest_count == 0: self.current_class_identifier = ctx.IDENTIFIER().getText() self.current_class_ctx = ctx.IDENTIFIER() current_class = Class( package_name=self.package.name, parser_context=ctx, filename=self.filename, file_info=self.file_info ) current_class.modifiers = self.last_modifiers.copy() current_class.modifiers_parser_contexts = self.last_modifiers_contexts.copy() current_class.name = self.current_class_identifier if ctx.EXTENDS() is not None: current_class.superclass_name = ctx.typeType().getText() if ctx.IMPLEMENTS() is not None: for interface_type in ctx.typeList().getChildren(lambda x: type(x) == JavaParser.TypeTypeContext): current_class.superinterface_names.append(interface_type.getText()) self.package.classes[current_class.name] = current_class else: if self.nest_count == 0: self.current_class_identifier_temp = self.current_class_identifier self.current_class_identifier = None self.nest_count += 1 def enterClassBody(self, ctx: JavaParser.ClassBodyContext): if self.current_class_identifier is not None: self.package.classes[self.current_class_identifier].body_context = ctx def exitClassDeclaration(self, ctx: JavaParser.ClassDeclarationContext): if self.nest_count > 0: self.nest_count -= 1 if self.nest_count == 0: self.current_class_identifier = self.current_class_identifier_temp self.current_class_identifier_temp = None elif self.current_class_identifier is not None: self.current_class_identifier = None def enterFormalParameterList(self, ctx: JavaParser.FormalParameterListContext): if self.current_method is not None: self.current_method.formalparam_context = ctx def enterMethodDeclaration(self, ctx: JavaParser.MethodDeclarationContext): if self.current_class_identifier is not None: # method_header = ctx.methodHeader() self.current_method_identifier = ctx.IDENTIFIER().getText() method = Method( package_name=self.package.name, class_name=self.current_class_identifier, parser_context=ctx.parentCtx.parentCtx, filename=self.filename, file_info=self.file_info ) method.modifiers = self.last_modifiers.copy() method.modifiers_parser_contexts = self.last_modifiers_contexts.copy() method.returntype = ctx.typeTypeOrVoid().getText() method.name = self.current_method_identifier method.is_constructor = False # This is done on exit to collect params too, to support overloading. # self.package.classes[self.current_class_identifier].methods[method.name] = method self.current_method = method def enterFormalParameters(self, ctx: JavaParser.FormalParametersContext): if self.current_method is not None: self.current_method.method_declaration_context = ctx def enterFormalParameter(self, ctx: JavaParser.FormalParameterContext): if self.current_method is not None: self.current_method.parameters.append( (ctx.typeType().getText(), ctx.variableDeclaratorId().IDENTIFIER().getText()) ) def enterMethodBody(self, ctx: JavaParser.MethodBodyContext): if self.current_method is not None: self.current_method.body_text = ctx.getText() pass def general_exit_method_decl(self): if self.current_class_identifier is not None: if self.current_method is not None: method = self.current_method method_key = ("" if method.name is None else method.name) + '(' is_first = True for param in method.parameters: if not is_first: method_key += ',' is_first = False method_key += param[0] # the type method_key += ')' self.package.classes[self.current_class_identifier].methods[method_key] = method self.current_method_identifier = None self.current_method = None def exitMethodDeclaration(self, ctx: JavaParser.MethodDeclarationContext): self.general_exit_method_decl() def enterConstructorDeclaration(self, ctx: JavaParser.ConstructorDeclarationContext): if self.current_class_identifier is not None: self.current_method_identifier = ctx.IDENTIFIER().getText() method = Method( package_name=self.package.name, class_name=self.current_class_identifier, parser_context=ctx.parentCtx.parentCtx, filename=self.filename, file_info=self.file_info ) method.modifiers = self.last_modifiers.copy() method.modifiers_parser_contexts = self.last_modifiers_contexts.copy() method.returntype = None method.name = None # self.current_method_identifier method.body_text = ctx.constructorBody.getText() method.is_constructor = True # This is done on exit to collect params too, to support overloading. # self.package.classes[self.current_class_identifier].methods[method.name] = method self.current_method = method def exitConstructorDeclaration(self, ctx: JavaParser.ConstructorDeclarationContext): self.general_exit_method_decl() def enterMethodCall(self, ctx: JavaParser.MethodCallContext): if self.current_method is not None: if ctx.parentCtx.IDENTIFIER() != None: if ctx.parentCtx.IDENTIFIER() not in self.current_method.body_method_invocations: self.current_method.body_method_invocations[ctx.parentCtx.IDENTIFIER()] = [ ctx.IDENTIFIER().getText()] else: self.current_method.body_method_invocations[ctx.parentCtx.IDENTIFIER()].append( ctx.IDENTIFIER().getText()) else: a = len(ctx.parentCtx.children) if a == 1: if ctx.IDENTIFIER() != None: if self.current_class_ctx not in self.current_method.body_method_invocations_without_typename: self.current_method.body_method_invocations_without_typename[self.current_class_ctx] = [ctx] else: self.current_method.body_method_invocations_without_typename[self.current_class_ctx].append( ctx) # MethodInvocation txt = ctx.getText() ids = txt[:txt.find('(')].split('.') self.current_method.body_local_vars_and_expr_names.append( MethodInvocation(ids, ctx) ) def enterExpression(self, ctx: JavaParser.ExpressionContext): if self.current_method is not None: if ctx.methodCall() is not None: txt = ctx.getText() ids = txt[:txt.find('(')].split('.') self.current_method.body_local_vars_and_expr_names.append( MethodInvocation(ids, ctx) ) else: names = ctx.getText().split('.') should_add = True for name in names: if not re.match("^[A-Za-z0-9_]*$", name): should_add = False if should_add: self.current_method.body_local_vars_and_expr_names.append(ExpressionName(names)) def enterLocalVariableDeclaration(self, ctx: JavaParser.LocalVariableDeclarationContext): if self.current_method is not None: self.current_local_var_type = ctx.typeType().getText() self.current_local_var_ctx = ctx # The rest in: enterVariableDeclarator def exitLocalVariableDeclaration(self, ctx: JavaParser.LocalVariableDeclarationContext): self.current_local_var_type = None def enterFieldDeclaration(self, ctx: JavaParser.FieldDeclarationContext): self.field_enter_count += 1 if self.current_class_identifier is not None and self.field_enter_count == 1: modifiers = self.last_modifiers.copy() modifiers_contexts = self.last_modifiers_contexts.copy() datatype = ctx.typeType().getText() self.current_field_decl = (modifiers, datatype, ctx, modifiers_contexts) self.current_field_ids = [] self.current_field_dims = [] self.current_field_inits = [] self.current_field_var_ctxs = [] def enterVariableDeclarator(self, ctx: JavaParser.VariableDeclaratorContext): dims = "" v_id: str = ctx.variableDeclaratorId().getText() dims_i = v_id.find('[') if dims_i != -1: dims = v_id[dims_i:] if self.current_field_decl is not None: self.current_field_ids.append(ctx.variableDeclaratorId().IDENTIFIER().getText()) self.current_field_dims.append(dims) init = None init_ctx = ctx.variableInitializer() if init_ctx is not None: init = init_ctx.getText() self.current_field_inits.append(init) self.current_field_var_ctxs.append(ctx) if self.current_local_var_type is not None: if self.current_method is not None: self.current_method.body_local_vars_and_expr_names.append( LocalVariable( self.current_local_var_type + dims, ctx.variableDeclaratorId().IDENTIFIER().getText(), self.current_local_var_ctx ) ) def exitFieldDeclaration(self, ctx: JavaParser.FieldDeclarationContext): self.field_enter_count -= 1 if self.current_class_identifier is not None and self.field_enter_count == 0: for i in range(len(self.current_field_ids)): field_id = self.current_field_ids[i] dims = self.current_field_dims[i] field_init = self.current_field_inits[i] var_ctx = self.current_field_var_ctxs[i] field = Field( package_name=self.package.name, class_name=self.current_class_identifier, parser_context=self.current_field_decl[2], filename=self.filename, file_info=self.file_info ) field.modifiers = self.current_field_decl[0] field.modifiers_parser_contexts = self.current_field_decl[3] field.datatype = self.current_field_decl[1] + dims field.name = field_id field.initializer = field_init field.neighbor_names = [x for x in self.current_field_ids if x != field_id] field.all_variable_declarator_contexts = self.current_field_var_ctxs field.index_in_variable_declarators = i self.package.classes[self.current_class_identifier].fields[field.name] = field self.current_field_decl = None

40.822335

116

0.619829

import re import antlr4 from antlr4.Token import CommonToken import antlr4.tree from antlr4.CommonTokenStream import CommonTokenStream from typing import List, Optional from gen.java.JavaParser import JavaParser from gen.java.JavaParserListener import JavaParserListener class Program: def __init__(self): self.packages = {} def __str__(self): return str(self.packages) class Package: def __init__(self): self.name = None self.classes = {} self.package_ctx = None def __str__(self): return str(self.name) + " " + str(self.classes) class TokensInfo: def __init__(self, parser_context=None): if parser_context is not None: self.token_stream: CommonTokenStream = parser_context.parser.getTokenStream() self.start: int = parser_context.start.tokenIndex self.stop: int = parser_context.stop.tokenIndex else: self.token_stream: CommonTokenStream = None self.start: int = None self.stop: int = None def get_token_index(self, tokens: list, start: int, stop: int): return tokens[start:stop] class FileInfo: def __init__(self, filename: str = None, package_name: str = None): self.filename: str = filename self.package_name: str = package_name self.all_imports = [] self.package_imports = [] self.class_imports = [] def has_imported_class(self, package_name: str, class_name: str) -> bool: if self.package_name == package_name: return True return ( any(lambda x: x.package_name == package_name for package_import in self.package_imports) or any(lambda x: x.package_name == package_name and x.class_name == class_name for class_import in self.class_imports) ) def has_imported_package(self, package_name: str): if self.package_name == package_name: return True return ( any(lambda x: x.package_name == package_name for package_import in self.package_imports) ) class SingleFileElement: def __init__(self, parser_context, filename: str = None, _file_info: FileInfo = None): self.parser_context = parser_context self.filename = filename self.file_info = _file_info def get_token_stream(self) -> CommonTokenStream: return self.parser_context.parser.getTokenStream() def get_tokens_info(self) -> TokensInfo: return TokensInfo( self.parser_context ) def get_first_symbol(self) -> CommonToken: first_terminal = self.parser_context while not isinstance(first_terminal, antlr4.tree.Tree.TerminalNode): first_terminal = first_terminal.getChild(0) return first_terminal.getSymbol() def get_last_symbol(self) -> CommonToken: last_terminal = self.parser_context while not isinstance(last_terminal, antlr4.tree.Tree.TerminalNode): last_terminal = last_terminal.getChild(last_terminal.getChildCount() - 1) return last_terminal.getSymbol() def get_file_position_range(self) -> tuple: return ( self.get_first_symbol().start, self.get_last_symbol().stop ) def get_text_from_file(self, filename=None) -> str: if filename is None: filename = self.filename if filename is None: return None file = open(filename, 'r') text = file.read() file.close() return text[self.get_first_symbol().start:self.get_last_symbol().stop + 1] class ClassImport(SingleFileElement): def __init__(self, package_name: str = None, class_name: str = None, parser_context: JavaParser.ImportDeclarationContext = None, filename: str = None, file_info: FileInfo = None): self.package_name = package_name self.class_name = class_name self.parser_context = parser_context self.filename = filename self.file_info = file_info def __str__(self): return "import " + str(self.package_name) + '.' + str(self.class_name) class PackageImport(SingleFileElement): def __init__(self, package_name: str = None, parser_context: JavaParser.ImportDeclarationContext = None, filename: str = None, file_info: FileInfo = None): self.package_name = package_name self.parser_context = parser_context self.filename = filename self.file_info = file_info def __str__(self): return "import " + str(self.package_name) + ".*" class Class(SingleFileElement): def __init__(self, name: str = None, super_class_name: str = None, package_name: str = None, parser_context: JavaParser.ClassDeclarationContext = None, filename: str = None, file_info: FileInfo = None): self.modifiers = [] self.modifiers_parser_contexts = [] self.name = name self.superclass_name = None self.superinterface_names = [] self.fields = {} self.methods = {} self.package_name = package_name self.parser_context = parser_context self.filename = filename self.file_info = file_info self.body_context = None def find_methods_with_name(self, name: str) -> list: result = [] for mk in self.methods: m = self.methods[mk] if m.name == name: result.append(m) return result def __str__(self): return str(self.modifiers) + " " + str(self.name) \ + ((" extends " + str(self.superclass_name)) if self.superclass_name is not None else "") \ + ((" implements " + str(self.superinterface_names)) if len(self.superinterface_names) > 0 else "") \ + " " + str(self.fields) \ + " " + str(self.methods) class Field(SingleFileElement): def __init__(self, datatype: str = None, name: str = None, initializer: str = None, package_name: str = None, class_name: str = None, parser_context: JavaParser.FieldDeclarationContext = None, filename: str = None, file_info: FileInfo = None): self.modifiers = [] self.modifiers_parser_contexts = [] self.datatype = datatype self.name = name self.initializer = initializer self.neighbor_names = [] self.all_variable_declarator_contexts = [] self.index_in_variable_declarators: int = None self.package_name = package_name self.class_name = class_name self.parser_context = parser_context self.filename = filename self.file_info = file_info def __str__(self): return str(self.modifiers) + " " + str(self.datatype) + " " + str(self.name) class Method(SingleFileElement): def __init__(self, returntype: str = None, name: str = None, body_text: str = None, package_name: str = None, class_name: str = None, parser_context=None, filename: str = None, file_info: FileInfo = None): self.modifiers = [] self.modifiers_parser_contexts = [] self.returntype = returntype self.name = name self.parameters = [] self.body_text = body_text self.body_method_invocations = {} self.body_local_vars_and_expr_names = [] self.package_name = package_name self.class_name = class_name self.parser_context = parser_context self.filename = filename self.file_info = file_info self.formalparam_context = None self.body_method_invocations_without_typename = {} self.method_declaration_context = None self.is_constructor = False def __str__(self): return str(self.modifiers) + " " + str(self.returntype) + " " + str(self.name) \ + str(tuple(self.parameters)) class LocalVariable: def __init__(self, datatype: str = None, identifier: str = None, parser_context: JavaParser.LocalVariableDeclarationContext = None): self.datatype = datatype self.identifier = identifier self.parser_context = parser_context class ExpressionName: def __init__(self, dot_separated_identifiers: list): self.dot_separated_identifiers = dot_separated_identifiers class MethodInvocation: def __init__(self, dot_separated_identifiers: list, parser_context: JavaParser.ExpressionContext = None): self.dot_separated_identifiers = dot_separated_identifiers self.parser_context = parser_context class UtilsListener(JavaParserListener): def __init__(self, filename): self.package = Package() self.last_modifiers = [] self.last_modifiers_contexts = [] self.current_class_identifier = None self.current_class_identifier_temp = None self.nest_count = 0 self.current_method_identifier = None self.current_method = None self.current_local_var_type = None self.current_local_var_ctx = None self.current_field_decl = None self.current_field_ids = None self.current_field_dims = None self.current_field_inits = None self.current_field_var_ctxs = None self.filename = filename self.file_info = FileInfo(filename=filename) self.field_enter_count = 0 def enterPackageDeclaration(self, ctx: JavaParser.PackageDeclarationContext): self.package.name = ctx.qualifiedName().getText() self.file_info.package_name = self.package.name self.package.package_ctx = ctx def enterImportDeclaration(self, ctx: JavaParser.ImportDeclarationContext): if ctx.STATIC() is None: name: str = ctx.qualifiedName().getText() if ctx.getText().endswith(".*;"): p = name package_import = PackageImport( package_name=p, parser_context=ctx, filename=self.filename, file_info=self.file_info ) self.file_info.all_imports.append(package_import) self.file_info.package_imports.append(package_import) else: p = None dot_i = name.rfind('.') if dot_i != -1: p = name[:dot_i] c = name[dot_i + 1:] else: c = name class_import = ClassImport( package_name=p, class_name=c, parser_context=ctx, filename=self.filename, file_info=self.file_info ) self.file_info.all_imports.append(class_import) self.file_info.class_imports.append(class_import) def enterTypeDeclaration(self, ctx: JavaParser.TypeDeclarationContext): self.last_modifiers.clear() self.last_modifiers_contexts.clear() for modifier in ctx.getChildren(lambda x: type(x) == JavaParser.ClassOrInterfaceModifierContext): self.last_modifiers.append(modifier.getText()) self.last_modifiers_contexts.append(modifier) def enterClassBodyDeclaration(self, ctx: JavaParser.ClassBodyDeclarationContext): self.last_modifiers.clear() self.last_modifiers_contexts.clear() for modifier in ctx.getChildren(lambda x: type(x) == JavaParser.ModifierContext): self.last_modifiers.append(modifier.getText()) self.last_modifiers_contexts.append(modifier) def enterClassDeclaration(self, ctx: JavaParser.ClassDeclarationContext): if self.current_class_identifier is None and self.nest_count == 0: self.current_class_identifier = ctx.IDENTIFIER().getText() self.current_class_ctx = ctx.IDENTIFIER() current_class = Class( package_name=self.package.name, parser_context=ctx, filename=self.filename, file_info=self.file_info ) current_class.modifiers = self.last_modifiers.copy() current_class.modifiers_parser_contexts = self.last_modifiers_contexts.copy() current_class.name = self.current_class_identifier if ctx.EXTENDS() is not None: current_class.superclass_name = ctx.typeType().getText() if ctx.IMPLEMENTS() is not None: for interface_type in ctx.typeList().getChildren(lambda x: type(x) == JavaParser.TypeTypeContext): current_class.superinterface_names.append(interface_type.getText()) self.package.classes[current_class.name] = current_class else: if self.nest_count == 0: self.current_class_identifier_temp = self.current_class_identifier self.current_class_identifier = None self.nest_count += 1 def enterClassBody(self, ctx: JavaParser.ClassBodyContext): if self.current_class_identifier is not None: self.package.classes[self.current_class_identifier].body_context = ctx def exitClassDeclaration(self, ctx: JavaParser.ClassDeclarationContext): if self.nest_count > 0: self.nest_count -= 1 if self.nest_count == 0: self.current_class_identifier = self.current_class_identifier_temp self.current_class_identifier_temp = None elif self.current_class_identifier is not None: self.current_class_identifier = None def enterFormalParameterList(self, ctx: JavaParser.FormalParameterListContext): if self.current_method is not None: self.current_method.formalparam_context = ctx def enterMethodDeclaration(self, ctx: JavaParser.MethodDeclarationContext): if self.current_class_identifier is not None: self.current_method_identifier = ctx.IDENTIFIER().getText() method = Method( package_name=self.package.name, class_name=self.current_class_identifier, parser_context=ctx.parentCtx.parentCtx, filename=self.filename, file_info=self.file_info ) method.modifiers = self.last_modifiers.copy() method.modifiers_parser_contexts = self.last_modifiers_contexts.copy() method.returntype = ctx.typeTypeOrVoid().getText() method.name = self.current_method_identifier method.is_constructor = False self.current_method = method def enterFormalParameters(self, ctx: JavaParser.FormalParametersContext): if self.current_method is not None: self.current_method.method_declaration_context = ctx def enterFormalParameter(self, ctx: JavaParser.FormalParameterContext): if self.current_method is not None: self.current_method.parameters.append( (ctx.typeType().getText(), ctx.variableDeclaratorId().IDENTIFIER().getText()) ) def enterMethodBody(self, ctx: JavaParser.MethodBodyContext): if self.current_method is not None: self.current_method.body_text = ctx.getText() pass def general_exit_method_decl(self): if self.current_class_identifier is not None: if self.current_method is not None: method = self.current_method method_key = ("" if method.name is None else method.name) + '(' is_first = True for param in method.parameters: if not is_first: method_key += ',' is_first = False method_key += param[0] method_key += ')' self.package.classes[self.current_class_identifier].methods[method_key] = method self.current_method_identifier = None self.current_method = None def exitMethodDeclaration(self, ctx: JavaParser.MethodDeclarationContext): self.general_exit_method_decl() def enterConstructorDeclaration(self, ctx: JavaParser.ConstructorDeclarationContext): if self.current_class_identifier is not None: self.current_method_identifier = ctx.IDENTIFIER().getText() method = Method( package_name=self.package.name, class_name=self.current_class_identifier, parser_context=ctx.parentCtx.parentCtx, filename=self.filename, file_info=self.file_info ) method.modifiers = self.last_modifiers.copy() method.modifiers_parser_contexts = self.last_modifiers_contexts.copy() method.returntype = None method.name = None method.body_text = ctx.constructorBody.getText() method.is_constructor = True self.current_method = method def exitConstructorDeclaration(self, ctx: JavaParser.ConstructorDeclarationContext): self.general_exit_method_decl() def enterMethodCall(self, ctx: JavaParser.MethodCallContext): if self.current_method is not None: if ctx.parentCtx.IDENTIFIER() != None: if ctx.parentCtx.IDENTIFIER() not in self.current_method.body_method_invocations: self.current_method.body_method_invocations[ctx.parentCtx.IDENTIFIER()] = [ ctx.IDENTIFIER().getText()] else: self.current_method.body_method_invocations[ctx.parentCtx.IDENTIFIER()].append( ctx.IDENTIFIER().getText()) else: a = len(ctx.parentCtx.children) if a == 1: if ctx.IDENTIFIER() != None: if self.current_class_ctx not in self.current_method.body_method_invocations_without_typename: self.current_method.body_method_invocations_without_typename[self.current_class_ctx] = [ctx] else: self.current_method.body_method_invocations_without_typename[self.current_class_ctx].append( ctx) txt = ctx.getText() ids = txt[:txt.find('(')].split('.') self.current_method.body_local_vars_and_expr_names.append( MethodInvocation(ids, ctx) ) def enterExpression(self, ctx: JavaParser.ExpressionContext): if self.current_method is not None: if ctx.methodCall() is not None: txt = ctx.getText() ids = txt[:txt.find('(')].split('.') self.current_method.body_local_vars_and_expr_names.append( MethodInvocation(ids, ctx) ) else: names = ctx.getText().split('.') should_add = True for name in names: if not re.match("^[A-Za-z0-9_]*$", name): should_add = False if should_add: self.current_method.body_local_vars_and_expr_names.append(ExpressionName(names)) def enterLocalVariableDeclaration(self, ctx: JavaParser.LocalVariableDeclarationContext): if self.current_method is not None: self.current_local_var_type = ctx.typeType().getText() self.current_local_var_ctx = ctx def exitLocalVariableDeclaration(self, ctx: JavaParser.LocalVariableDeclarationContext): self.current_local_var_type = None def enterFieldDeclaration(self, ctx: JavaParser.FieldDeclarationContext): self.field_enter_count += 1 if self.current_class_identifier is not None and self.field_enter_count == 1: modifiers = self.last_modifiers.copy() modifiers_contexts = self.last_modifiers_contexts.copy() datatype = ctx.typeType().getText() self.current_field_decl = (modifiers, datatype, ctx, modifiers_contexts) self.current_field_ids = [] self.current_field_dims = [] self.current_field_inits = [] self.current_field_var_ctxs = [] def enterVariableDeclarator(self, ctx: JavaParser.VariableDeclaratorContext): dims = "" v_id: str = ctx.variableDeclaratorId().getText() dims_i = v_id.find('[') if dims_i != -1: dims = v_id[dims_i:] if self.current_field_decl is not None: self.current_field_ids.append(ctx.variableDeclaratorId().IDENTIFIER().getText()) self.current_field_dims.append(dims) init = None init_ctx = ctx.variableInitializer() if init_ctx is not None: init = init_ctx.getText() self.current_field_inits.append(init) self.current_field_var_ctxs.append(ctx) if self.current_local_var_type is not None: if self.current_method is not None: self.current_method.body_local_vars_and_expr_names.append( LocalVariable( self.current_local_var_type + dims, ctx.variableDeclaratorId().IDENTIFIER().getText(), self.current_local_var_ctx ) ) def exitFieldDeclaration(self, ctx: JavaParser.FieldDeclarationContext): self.field_enter_count -= 1 if self.current_class_identifier is not None and self.field_enter_count == 0: for i in range(len(self.current_field_ids)): field_id = self.current_field_ids[i] dims = self.current_field_dims[i] field_init = self.current_field_inits[i] var_ctx = self.current_field_var_ctxs[i] field = Field( package_name=self.package.name, class_name=self.current_class_identifier, parser_context=self.current_field_decl[2], filename=self.filename, file_info=self.file_info ) field.modifiers = self.current_field_decl[0] field.modifiers_parser_contexts = self.current_field_decl[3] field.datatype = self.current_field_decl[1] + dims field.name = field_id field.initializer = field_init field.neighbor_names = [x for x in self.current_field_ids if x != field_id] field.all_variable_declarator_contexts = self.current_field_var_ctxs field.index_in_variable_declarators = i self.package.classes[self.current_class_identifier].fields[field.name] = field self.current_field_decl = None

true

f723c6d8b64443d90a1248040568d87fc7bf7641

4,464

py

Python

tensorflow/python/keras/optimizer_v2/utils.py

luisangel86a/tensorflow

77ee5e02721ba797fe01d47019e6017d2bb09ab7

[ "Apache-2.0" ]

1

2020-10-25T00:12:12.000Z

tensorflow/python/keras/optimizer_v2/utils.py

luisangel86a/tensorflow

77ee5e02721ba797fe01d47019e6017d2bb09ab7

[ "Apache-2.0" ]

null

tensorflow/python/keras/optimizer_v2/utils.py

luisangel86a/tensorflow

77ee5e02721ba797fe01d47019e6017d2bb09ab7

[ "Apache-2.0" ]

null

# Copyright 2020 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. # ============================================================================== """Optimizer utilities.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.distribute import central_storage_strategy from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.ops import clip_ops from tensorflow.python.platform import tf_logging as logging def all_reduce_sum_gradients(grads_and_vars): """Returns all-reduced gradients aggregated via summation. Args: grads_and_vars: List of (gradient, variable) pairs. Returns: A list of all-reduced gradients. """ grads_and_vars = list(grads_and_vars) filtered_grads_and_vars = filter_empty_gradients(grads_and_vars) # We switch to a cross-replica context since there is a bug which causes # IndexedSlices to be converted to dense tensors when all-reduced in a # replica context. # TODO(b/150507409): Do not switch to a cross-replica context once the bug # is fixed. if filtered_grads_and_vars: reduced = distribute_ctx.get_replica_context().merge_call( _all_reduce_sum_fn, args=(filtered_grads_and_vars,)) else: reduced = [] # Copy 'reduced' but add None gradients back in reduced_with_nones = [] reduced_pos = 0 for g, _ in grads_and_vars: if g is None: reduced_with_nones.append(None) else: reduced_with_nones.append(reduced[reduced_pos]) reduced_pos += 1 assert reduced_pos == len(reduced), "Failed to add all gradients" return reduced_with_nones def make_gradient_clipnorm_fn(clipnorm): """Creates a gradient transformation function for clipping by norm.""" def gradient_clipnorm_fn(grads_and_vars): if isinstance(distribute_ctx.get_strategy(), central_storage_strategy.CentralStorageStrategy): raise ValueError( "`clipnorm` is not supported with `CenteralStorageStrategy`") clipped_grads_and_vars = [ (clip_ops.clip_by_norm(g, clipnorm), v) for g, v in grads_and_vars ] return clipped_grads_and_vars return gradient_clipnorm_fn def make_gradient_clipvalue_fn(clipvalue): """Creates a gradient transformation function for clipping by value.""" def gradient_clipvalue_fn(grads_and_vars): if isinstance(distribute_ctx.get_strategy(), central_storage_strategy.CentralStorageStrategy): raise ValueError( "`clipvalue` is not supported with `CenteralStorageStrategy`") clipped_grads_and_vars = [(clip_ops.clip_by_value(g, -clipvalue, clipvalue), v) for g, v in grads_and_vars] return clipped_grads_and_vars return gradient_clipvalue_fn def filter_empty_gradients(grads_and_vars): """Filter out `(grad, var)` pairs that have a gradient equal to `None`.""" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise ValueError("No gradients provided for any variable: %s." % ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( ("Gradients do not exist for variables %s when minimizing the loss."), ([v.name for v in vars_with_empty_grads])) return filtered def _all_reduce_sum_fn(distribution, grads_and_vars): return distribution.extended.batch_reduce_to(ds_reduce_util.ReduceOp.SUM, grads_and_vars)

35.428571

88

0.714382

from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.distribute import central_storage_strategy from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.ops import clip_ops from tensorflow.python.platform import tf_logging as logging def all_reduce_sum_gradients(grads_and_vars): grads_and_vars = list(grads_and_vars) filtered_grads_and_vars = filter_empty_gradients(grads_and_vars) if filtered_grads_and_vars: reduced = distribute_ctx.get_replica_context().merge_call( _all_reduce_sum_fn, args=(filtered_grads_and_vars,)) else: reduced = [] reduced_with_nones = [] reduced_pos = 0 for g, _ in grads_and_vars: if g is None: reduced_with_nones.append(None) else: reduced_with_nones.append(reduced[reduced_pos]) reduced_pos += 1 assert reduced_pos == len(reduced), "Failed to add all gradients" return reduced_with_nones def make_gradient_clipnorm_fn(clipnorm): def gradient_clipnorm_fn(grads_and_vars): if isinstance(distribute_ctx.get_strategy(), central_storage_strategy.CentralStorageStrategy): raise ValueError( "`clipnorm` is not supported with `CenteralStorageStrategy`") clipped_grads_and_vars = [ (clip_ops.clip_by_norm(g, clipnorm), v) for g, v in grads_and_vars ] return clipped_grads_and_vars return gradient_clipnorm_fn def make_gradient_clipvalue_fn(clipvalue): def gradient_clipvalue_fn(grads_and_vars): if isinstance(distribute_ctx.get_strategy(), central_storage_strategy.CentralStorageStrategy): raise ValueError( "`clipvalue` is not supported with `CenteralStorageStrategy`") clipped_grads_and_vars = [(clip_ops.clip_by_value(g, -clipvalue, clipvalue), v) for g, v in grads_and_vars] return clipped_grads_and_vars return gradient_clipvalue_fn def filter_empty_gradients(grads_and_vars): grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise ValueError("No gradients provided for any variable: %s." % ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( ("Gradients do not exist for variables %s when minimizing the loss."), ([v.name for v in vars_with_empty_grads])) return filtered def _all_reduce_sum_fn(distribution, grads_and_vars): return distribution.extended.batch_reduce_to(ds_reduce_util.ReduceOp.SUM, grads_and_vars)

true

f723c71f72bc88cff1f65f82dbd10987ac1732ba

9,771

py

Python

stable_baselines3/common/vec_env/subproc_vec_env.py

qgallouedec/stable-baselines3

a6f5049a99a4c21a6f0bcce458ca3306cef310e0

[ "MIT" ]

null

stable_baselines3/common/vec_env/subproc_vec_env.py

qgallouedec/stable-baselines3

a6f5049a99a4c21a6f0bcce458ca3306cef310e0

[ "MIT" ]

null

stable_baselines3/common/vec_env/subproc_vec_env.py

qgallouedec/stable-baselines3

a6f5049a99a4c21a6f0bcce458ca3306cef310e0

[ "MIT" ]

null

import multiprocessing as mp from collections import OrderedDict from typing import Any, Callable, List, Optional, Sequence, Tuple, Type, Union import gym import numpy as np from stable_baselines3.common.vec_env.base_vec_env import ( CloudpickleWrapper, VecEnv, VecEnvIndices, VecEnvObs, VecEnvStepReturn, ) def _worker( remote: mp.connection.Connection, parent_remote: mp.connection.Connection, env_fn_wrapper: CloudpickleWrapper ) -> None: # Import here to avoid a circular import from stable_baselines3.common.env_util import is_wrapped parent_remote.close() env = env_fn_wrapper.var() while True: try: cmd, data = remote.recv() if cmd == "step": observation, reward, done, info = env.step(data) if done: # save final observation where user can get it, then reset info["terminal_observation"] = observation observation = env.reset() remote.send((observation, reward, done, info)) elif cmd == "seed": remote.send(env.seed(data)) elif cmd == "reset": observation = env.reset() remote.send(observation) elif cmd == "render": remote.send(env.render(data)) elif cmd == "close": env.close() remote.close() break elif cmd == "get_spaces": remote.send((env.observation_space, env.action_space)) elif cmd == "env_method": method = getattr(env, data[0]) remote.send(method(*data[1], **data[2])) elif cmd == "get_attr": remote.send(getattr(env, data)) elif cmd == "set_attr": remote.send(setattr(env, data[0], data[1])) elif cmd == "is_wrapped": remote.send(is_wrapped(env, data)) else: raise NotImplementedError(f"`{cmd}` is not implemented in the worker") except EOFError: break class SubprocVecEnv(VecEnv): """ Creates a multiprocess vectorized wrapper for multiple environments, distributing each environment to its own process, allowing significant speed up when the environment is computationally complex. For performance reasons, if your environment is not IO bound, the number of environments should not exceed the number of logical cores on your CPU. .. warning:: Only 'forkserver' and 'spawn' start methods are thread-safe, which is important when TensorFlow sessions or other non thread-safe libraries are used in the parent (see issue #217). However, compared to 'fork' they incur a small start-up cost and have restrictions on global variables. With those methods, users must wrap the code in an ``if __name__ == "__main__":`` block. For more information, see the multiprocessing documentation. :param env_fns: Environments to run in subprocesses :param start_method: method used to start the subprocesses. Must be one of the methods returned by multiprocessing.get_all_start_methods(). Defaults to 'forkserver' on available platforms, and 'spawn' otherwise. """ def __init__(self, env_fns: List[Callable[[], gym.Env]], start_method: Optional[str] = None): self.waiting = False self.closed = False n_envs = len(env_fns) if start_method is None: # Fork is not a thread safe method (see issue #217) # but is more user friendly (does not require to wrap the code in # a `if __name__ == "__main__":`) forkserver_available = "forkserver" in mp.get_all_start_methods() start_method = "forkserver" if forkserver_available else "spawn" ctx = mp.get_context(start_method) self.remotes, self.work_remotes = zip(*[ctx.Pipe() for _ in range(n_envs)]) self.processes = [] for work_remote, remote, env_fn in zip(self.work_remotes, self.remotes, env_fns): args = (work_remote, remote, CloudpickleWrapper(env_fn)) # daemon=True: if the main process crashes, we should not cause things to hang process = ctx.Process(target=_worker, args=args, daemon=True) # pytype:disable=attribute-error process.start() self.processes.append(process) work_remote.close() self.remotes[0].send(("get_spaces", None)) observation_space, action_space = self.remotes[0].recv() VecEnv.__init__(self, len(env_fns), observation_space, action_space) def step_async(self, actions: np.ndarray) -> None: for remote, action in zip(self.remotes, actions): remote.send(("step", action)) self.waiting = True def step_wait(self) -> VecEnvStepReturn: results = [remote.recv() for remote in self.remotes] self.waiting = False obs, rews, dones, infos = zip(*results) return _flatten_obs(obs, self.observation_space), np.stack(rews), np.stack(dones), infos def seed(self, seed: Optional[int] = None) -> List[Union[None, int]]: if seed is None: seed = np.random.randint(0, 2**32 - 1) for idx, remote in enumerate(self.remotes): remote.send(("seed", seed + idx)) return [remote.recv() for remote in self.remotes] def reset(self) -> VecEnvObs: for remote in self.remotes: remote.send(("reset", None)) obs = [remote.recv() for remote in self.remotes] return _flatten_obs(obs, self.observation_space) def close(self) -> None: if self.closed: return if self.waiting: for remote in self.remotes: remote.recv() for remote in self.remotes: remote.send(("close", None)) for process in self.processes: process.join() self.closed = True def get_images(self) -> Sequence[np.ndarray]: for pipe in self.remotes: # gather images from subprocesses # `mode` will be taken into account later pipe.send(("render", "rgb_array")) imgs = [pipe.recv() for pipe in self.remotes] return imgs def get_attr(self, attr_name: str, indices: VecEnvIndices = None) -> List[Any]: """Return attribute from vectorized environment (see base class).""" target_remotes = self._get_target_remotes(indices) for remote in target_remotes: remote.send(("get_attr", attr_name)) return [remote.recv() for remote in target_remotes] def set_attr(self, attr_name: str, value: Any, indices: VecEnvIndices = None) -> None: """Set attribute inside vectorized environments (see base class).""" target_remotes = self._get_target_remotes(indices) for remote in target_remotes: remote.send(("set_attr", (attr_name, value))) for remote in target_remotes: remote.recv() def env_method(self, method_name: str, *method_args, indices: VecEnvIndices = None, **method_kwargs) -> List[Any]: """Call instance methods of vectorized environments.""" target_remotes = self._get_target_remotes(indices) for remote in target_remotes: remote.send(("env_method", (method_name, method_args, method_kwargs))) return [remote.recv() for remote in target_remotes] def env_is_wrapped(self, wrapper_class: Type[gym.Wrapper], indices: VecEnvIndices = None) -> List[bool]: """Check if worker environments are wrapped with a given wrapper""" target_remotes = self._get_target_remotes(indices) for remote in target_remotes: remote.send(("is_wrapped", wrapper_class)) return [remote.recv() for remote in target_remotes] def _get_target_remotes(self, indices: VecEnvIndices) -> List[Any]: """ Get the connection object needed to communicate with the wanted envs that are in subprocesses. :param indices: refers to indices of envs. :return: Connection object to communicate between processes. """ indices = self._get_indices(indices) return [self.remotes[i] for i in indices] def _flatten_obs(obs: Union[List[VecEnvObs], Tuple[VecEnvObs]], space: gym.spaces.Space) -> VecEnvObs: """ Flatten observations, depending on the observation space. :param obs: observations. A list or tuple of observations, one per environment. Each environment observation may be a NumPy array, or a dict or tuple of NumPy arrays. :return: flattened observations. A flattened NumPy array or an OrderedDict or tuple of flattened numpy arrays. Each NumPy array has the environment index as its first axis. """ assert isinstance(obs, (list, tuple)), "expected list or tuple of observations per environment" assert len(obs) > 0, "need observations from at least one environment" if isinstance(space, gym.spaces.Dict): assert isinstance(space.spaces, OrderedDict), "Dict space must have ordered subspaces" assert isinstance(obs[0], dict), "non-dict observation for environment with Dict observation space" return OrderedDict([(k, np.stack([o[k] for o in obs])) for k in space.spaces.keys()]) elif isinstance(space, gym.spaces.Tuple): assert isinstance(obs[0], tuple), "non-tuple observation for environment with Tuple observation space" obs_len = len(space.spaces) return tuple(np.stack([o[i] for o in obs]) for i in range(obs_len)) else: return np.stack(obs)

43.816143

118

0.635861

import multiprocessing as mp from collections import OrderedDict from typing import Any, Callable, List, Optional, Sequence, Tuple, Type, Union import gym import numpy as np from stable_baselines3.common.vec_env.base_vec_env import ( CloudpickleWrapper, VecEnv, VecEnvIndices, VecEnvObs, VecEnvStepReturn, ) def _worker( remote: mp.connection.Connection, parent_remote: mp.connection.Connection, env_fn_wrapper: CloudpickleWrapper ) -> None: from stable_baselines3.common.env_util import is_wrapped parent_remote.close() env = env_fn_wrapper.var() while True: try: cmd, data = remote.recv() if cmd == "step": observation, reward, done, info = env.step(data) if done: info["terminal_observation"] = observation observation = env.reset() remote.send((observation, reward, done, info)) elif cmd == "seed": remote.send(env.seed(data)) elif cmd == "reset": observation = env.reset() remote.send(observation) elif cmd == "render": remote.send(env.render(data)) elif cmd == "close": env.close() remote.close() break elif cmd == "get_spaces": remote.send((env.observation_space, env.action_space)) elif cmd == "env_method": method = getattr(env, data[0]) remote.send(method(*data[1], **data[2])) elif cmd == "get_attr": remote.send(getattr(env, data)) elif cmd == "set_attr": remote.send(setattr(env, data[0], data[1])) elif cmd == "is_wrapped": remote.send(is_wrapped(env, data)) else: raise NotImplementedError(f"`{cmd}` is not implemented in the worker") except EOFError: break class SubprocVecEnv(VecEnv): def __init__(self, env_fns: List[Callable[[], gym.Env]], start_method: Optional[str] = None): self.waiting = False self.closed = False n_envs = len(env_fns) if start_method is None: forkserver_available = "forkserver" in mp.get_all_start_methods() start_method = "forkserver" if forkserver_available else "spawn" ctx = mp.get_context(start_method) self.remotes, self.work_remotes = zip(*[ctx.Pipe() for _ in range(n_envs)]) self.processes = [] for work_remote, remote, env_fn in zip(self.work_remotes, self.remotes, env_fns): args = (work_remote, remote, CloudpickleWrapper(env_fn)) process = ctx.Process(target=_worker, args=args, daemon=True) process.start() self.processes.append(process) work_remote.close() self.remotes[0].send(("get_spaces", None)) observation_space, action_space = self.remotes[0].recv() VecEnv.__init__(self, len(env_fns), observation_space, action_space) def step_async(self, actions: np.ndarray) -> None: for remote, action in zip(self.remotes, actions): remote.send(("step", action)) self.waiting = True def step_wait(self) -> VecEnvStepReturn: results = [remote.recv() for remote in self.remotes] self.waiting = False obs, rews, dones, infos = zip(*results) return _flatten_obs(obs, self.observation_space), np.stack(rews), np.stack(dones), infos def seed(self, seed: Optional[int] = None) -> List[Union[None, int]]: if seed is None: seed = np.random.randint(0, 2**32 - 1) for idx, remote in enumerate(self.remotes): remote.send(("seed", seed + idx)) return [remote.recv() for remote in self.remotes] def reset(self) -> VecEnvObs: for remote in self.remotes: remote.send(("reset", None)) obs = [remote.recv() for remote in self.remotes] return _flatten_obs(obs, self.observation_space) def close(self) -> None: if self.closed: return if self.waiting: for remote in self.remotes: remote.recv() for remote in self.remotes: remote.send(("close", None)) for process in self.processes: process.join() self.closed = True def get_images(self) -> Sequence[np.ndarray]: for pipe in self.remotes: pipe.send(("render", "rgb_array")) imgs = [pipe.recv() for pipe in self.remotes] return imgs def get_attr(self, attr_name: str, indices: VecEnvIndices = None) -> List[Any]: target_remotes = self._get_target_remotes(indices) for remote in target_remotes: remote.send(("get_attr", attr_name)) return [remote.recv() for remote in target_remotes] def set_attr(self, attr_name: str, value: Any, indices: VecEnvIndices = None) -> None: target_remotes = self._get_target_remotes(indices) for remote in target_remotes: remote.send(("set_attr", (attr_name, value))) for remote in target_remotes: remote.recv() def env_method(self, method_name: str, *method_args, indices: VecEnvIndices = None, **method_kwargs) -> List[Any]: target_remotes = self._get_target_remotes(indices) for remote in target_remotes: remote.send(("env_method", (method_name, method_args, method_kwargs))) return [remote.recv() for remote in target_remotes] def env_is_wrapped(self, wrapper_class: Type[gym.Wrapper], indices: VecEnvIndices = None) -> List[bool]: target_remotes = self._get_target_remotes(indices) for remote in target_remotes: remote.send(("is_wrapped", wrapper_class)) return [remote.recv() for remote in target_remotes] def _get_target_remotes(self, indices: VecEnvIndices) -> List[Any]: indices = self._get_indices(indices) return [self.remotes[i] for i in indices] def _flatten_obs(obs: Union[List[VecEnvObs], Tuple[VecEnvObs]], space: gym.spaces.Space) -> VecEnvObs: assert isinstance(obs, (list, tuple)), "expected list or tuple of observations per environment" assert len(obs) > 0, "need observations from at least one environment" if isinstance(space, gym.spaces.Dict): assert isinstance(space.spaces, OrderedDict), "Dict space must have ordered subspaces" assert isinstance(obs[0], dict), "non-dict observation for environment with Dict observation space" return OrderedDict([(k, np.stack([o[k] for o in obs])) for k in space.spaces.keys()]) elif isinstance(space, gym.spaces.Tuple): assert isinstance(obs[0], tuple), "non-tuple observation for environment with Tuple observation space" obs_len = len(space.spaces) return tuple(np.stack([o[i] for o in obs]) for i in range(obs_len)) else: return np.stack(obs)

true

f723c763e4fef4f96747ccb5bded293f8e7b9e66

1,057

py

Python

app/main/tools/test_report.py

BorrowHome/flasky-sandbox

70ef7aa087a0954f7ff4b4845f6599d8481ef0b1

[ "Apache-2.0" ]

1

2021-03-15T02:59:13.000Z

app/main/tools/test_report.py

BorrowHome/flasky-sandbox

70ef7aa087a0954f7ff4b4845f6599d8481ef0b1

[ "Apache-2.0" ]

6

2021-03-19T09:49:44.000Z

2022-03-12T00:10:14.000Z

app/main/tools/test_report.py

BorrowHome/flasky-sandbox

70ef7aa087a0954f7ff4b4845f6599d8481ef0b1

[ "Apache-2.0" ]

2

2020-01-11T13:39:22.000Z

2020-07-02T03:57:43.000Z

import json from flask import request, render_template from app.utils.docx.docx import set_sand_docxtpl from config import Config from app.main import main @main.route('/test_report/', methods=['GET', 'POST']) def test_report(): if request.method == 'POST': file_location = Config.SAVE_DOCUMENT_PATH origin_data = request.get_data() str_data = str(origin_data, encoding='utf-8') dict_data = json.loads(str_data) with open(file_location + 'data.json', 'w') as f: json.dump(dict_data, f) set_sand_docxtpl(dict_data) return "数据" else: return render_template('test_report.html') @main.route('/update_report/', methods=['GET']) def update_report(): file_location = Config.SAVE_DOCUMENT_PATH location=request.args.get('location') with open(file_location + 'data.json', 'r') as f: dict_data = json.load(f) try: set_sand_docxtpl(dict_data,location) return "成功" except Exception as e: print(str(e)) return str(e)

26.425

57

0.655629

import json from flask import request, render_template from app.utils.docx.docx import set_sand_docxtpl from config import Config from app.main import main @main.route('/test_report/', methods=['GET', 'POST']) def test_report(): if request.method == 'POST': file_location = Config.SAVE_DOCUMENT_PATH origin_data = request.get_data() str_data = str(origin_data, encoding='utf-8') dict_data = json.loads(str_data) with open(file_location + 'data.json', 'w') as f: json.dump(dict_data, f) set_sand_docxtpl(dict_data) return "数据" else: return render_template('test_report.html') @main.route('/update_report/', methods=['GET']) def update_report(): file_location = Config.SAVE_DOCUMENT_PATH location=request.args.get('location') with open(file_location + 'data.json', 'r') as f: dict_data = json.load(f) try: set_sand_docxtpl(dict_data,location) return "成功" except Exception as e: print(str(e)) return str(e)

true

f723c8a42621a6c77b949415bc7d8ebb1efd5fee

8,382

py

Python

rstslide/plugins/Matplotlib/XKCDify.py

rartino/httk-rsttools

57c46362899105a72b3b6efc45b50bcda8e574a7

[ "MIT" ]

null

rstslide/plugins/Matplotlib/XKCDify.py

rartino/httk-rsttools

57c46362899105a72b3b6efc45b50bcda8e574a7

[ "MIT" ]

null

rstslide/plugins/Matplotlib/XKCDify.py

rartino/httk-rsttools

57c46362899105a72b3b6efc45b50bcda8e574a7

[ "MIT" ]

null

""" XKCD plot generator ------------------- Author: Jake Vanderplas This is a script that will take any matplotlib line diagram, and convert it to an XKCD-style plot. It will work for plots with line & text elements, including axes labels and titles (but not axes tick labels). The idea for this comes from work by Damon McDougall http://www.mail-archive.com/matplotlib-users@lists.sourceforge.net/msg25499.html """ import os import numpy as np import pylab as pl from scipy import interpolate, signal import matplotlib.font_manager as fm script_path = os.path.dirname(os.path.abspath(__file__)) # We need a special font for the code below. It can be downloaded this way: #import os #import urllib2 #if not os.path.exists('Humor-Sans.ttf'): # print 'Downloading the font Humor-sans.' # fhandle = urllib2.urlopen('http://antiyawn.com/uploads/Humor-Sans.ttf') # open('Humor-Sans.ttf', 'wb').write(fhandle.read()) def xkcd_line(x, y, xlim=None, ylim=None, mag=1.0, f1=30, f2=0.05, f3=15): """ Mimic a hand-drawn line from (x, y) data Parameters ---------- x, y : array_like arrays to be modified xlim, ylim : data range the assumed plot range for the modification. If not specified, they will be guessed from the data mag : float magnitude of distortions f1, f2, f3 : int, float, int filtering parameters. f1 gives the size of the window, f2 gives the high-frequency cutoff, f3 gives the size of the filter Returns ------- x, y : ndarrays The modified lines """ x = np.asarray(x) y = np.asarray(y) # get limits for rescaling if xlim is None: xlim = (x.min(), x.max()) if ylim is None: ylim = (y.min(), y.max()) if xlim[1] == xlim[0]: xlim = ylim if ylim[1] == ylim[0]: ylim = xlim # scale the data x_scaled = (x - xlim[0]) * 1. / (xlim[1] - xlim[0]) y_scaled = (y - ylim[0]) * 1. / (ylim[1] - ylim[0]) # compute the total distance along the path dx = x_scaled[1:] - x_scaled[:-1] dy = y_scaled[1:] - y_scaled[:-1] dist_tot = np.sum(np.sqrt(dx * dx + dy * dy)) # number of interpolated points is proportional to the distance Nu = int(200 * dist_tot) u = np.arange(-1, Nu + 1) * 1. / (Nu - 1) # interpolate curve at sampled points k = min(3, len(x) - 1) res = interpolate.splprep([x_scaled, y_scaled], s=0, k=k) x_int, y_int = interpolate.splev(u, res[0]) # we'll perturb perpendicular to the drawn line dx = x_int[2:] - x_int[:-2] dy = y_int[2:] - y_int[:-2] dist = np.sqrt(dx * dx + dy * dy) # create a filtered perturbation coeffs = mag * np.random.normal(0, 0.01, len(x_int) - 2) b = signal.firwin(f1, f2 * dist_tot, window=('kaiser', f3)) response = signal.lfilter(b, 1, coeffs) x_int[1:-1] += response * dy / dist y_int[1:-1] += response * dx / dist # un-scale data x_int = x_int[1:-1] * (xlim[1] - xlim[0]) + xlim[0] y_int = y_int[1:-1] * (ylim[1] - ylim[0]) + ylim[0] return x_int, y_int def XKCDify(ax, mag=1.0, f1=50, f2=0.01, f3=15, forecolor='k', bgcolor='w', xaxis_loc=None, yaxis_loc=None, xaxis_arrow='+', yaxis_arrow='+', ax_extend=0.1, expand_axes=False): """Make axis look hand-drawn This adjusts all lines, text, legends, and axes in the figure to look like xkcd plots. Other plot elements are not modified. Parameters ---------- ax : Axes instance the axes to be modified. mag : float the magnitude of the distortion f1, f2, f3 : int, float, int filtering parameters. f1 gives the size of the window, f2 gives the high-frequency cutoff, f3 gives the size of the filter xaxis_loc, yaxis_log : float The locations to draw the x and y axes. If not specified, they will be drawn from the bottom left of the plot xaxis_arrow, yaxis_arrow : str where to draw arrows on the x/y axes. Options are '+', '-', '+-', or '' ax_extend : float How far (fractionally) to extend the drawn axes beyond the original axes limits expand_axes : bool if True, then expand axes to fill the figure (useful if there is only a single axes in the figure) """ # Get axes aspect ext = ax.get_window_extent().extents aspect = (ext[3] - ext[1]) / (ext[2] - ext[0]) xlim = ax.get_xlim() ylim = ax.get_ylim() xspan = xlim[1] - xlim[0] yspan = ylim[1] - xlim[0] xax_lim = (xlim[0] - ax_extend * xspan, xlim[1] + ax_extend * xspan) yax_lim = (ylim[0] - ax_extend * yspan, ylim[1] + ax_extend * yspan) if xaxis_loc is None: xaxis_loc = ylim[0] if yaxis_loc is None: yaxis_loc = xlim[0] # Draw axes xaxis = pl.Line2D([xax_lim[0], xax_lim[1]], [xaxis_loc, xaxis_loc], linestyle='-', color=forecolor) yaxis = pl.Line2D([yaxis_loc, yaxis_loc], [yax_lim[0], yax_lim[1]], linestyle='-', color=forecolor) # Label axes3, 0.5, 'hello', fontsize=14) ax.text(xax_lim[1], xaxis_loc - 0.05 * yspan, ax.get_xlabel(), fontsize=14, ha='right', va='top', rotation=5) ax.text(yaxis_loc - 0.05 * xspan, yax_lim[1], ax.get_ylabel(), fontsize=14, ha='right', va='top', rotation=85) ax.set_xlabel('') ax.set_ylabel('') # Add title ax.text(0.5 * (xax_lim[1] + xax_lim[0]), yax_lim[1], ax.get_title(), ha='center', va='bottom', fontsize=16) ax.set_title('') Nlines = len(ax.lines) lines = [xaxis, yaxis] + [ax.lines.pop(0) for i in range(Nlines)] for line in lines: x, y = line.get_data() x_int, y_int = xkcd_line(x, y, xlim, ylim, mag, f1, f2, f3) # create foreground and background line lw = line.get_linewidth() line.set_linewidth(2 * lw) line.set_data(x_int, y_int) # # don't add background line for axes # if (line is not xaxis) and (line is not yaxis): # line_bg = pl.Line2D(x_int, y_int, color=bgcolor, # linewidth=8 * lw) # ax.add_line(line_bg) ax.add_line(line) # Draw arrow-heads at the end of axes lines arr1 = 0.03 * np.array([-1, 0, -1]) arr2 = 0.02 * np.array([-1, 0, 1]) arr1[::2] += np.random.normal(0, 0.005, 2) arr2[::2] += np.random.normal(0, 0.005, 2) x, y = xaxis.get_data() if '+' in str(xaxis_arrow): ax.plot(x[-1] + arr1 * xspan * aspect, y[-1] + arr2 * yspan, color=forecolor, lw=2) if '-' in str(xaxis_arrow): ax.plot(x[0] - arr1 * xspan * aspect, y[0] - arr2 * yspan, color=forecolor, lw=2) x, y = yaxis.get_data() if '+' in str(yaxis_arrow): ax.plot(x[-1] + arr2 * xspan * aspect, y[-1] + arr1 * yspan, color=forecolor, lw=2) if '-' in str(yaxis_arrow): ax.plot(x[0] - arr2 * xspan * aspect, y[0] - arr1 * yspan, color=forecolor, lw=2) # Change all the fonts to humor-sans. prop = fm.FontProperties(fname=os.path.join(script_path, 'fonts', 'Humor-Sans.ttf'), size=16) for text in ax.texts: text.set_fontproperties(prop) # modify legend leg = ax.get_legend() if leg is not None: leg.set_frame_on(False) for child in leg.get_children(): if isinstance(child, pl.Line2D): x, y = child.get_data() child.set_data(xkcd_line(x, y, mag=1., f1=100, f2=0.001)) child.set_linewidth(2 * child.get_linewidth()) if isinstance(child, pl.Text): child.set_fontproperties(prop) # Set the axis limits ax.set_xlim(xax_lim[0] - 0.1 * xspan, xax_lim[1] + 0.1 * xspan) ax.set_ylim(yax_lim[0] - 0.1 * yspan, yax_lim[1] + 0.1 * yspan) # adjust the axes ax.set_xticks([]) ax.set_yticks([]) if expand_axes: ax.figure.set_facecolor(bgcolor) ax.set_axis_off() ax.set_position([0, 0, 1, 1]) return ax

31.393258

97

0.570866

import os import numpy as np import pylab as pl from scipy import interpolate, signal import matplotlib.font_manager as fm script_path = os.path.dirname(os.path.abspath(__file__)) def xkcd_line(x, y, xlim=None, ylim=None, mag=1.0, f1=30, f2=0.05, f3=15): x = np.asarray(x) y = np.asarray(y) if xlim is None: xlim = (x.min(), x.max()) if ylim is None: ylim = (y.min(), y.max()) if xlim[1] == xlim[0]: xlim = ylim if ylim[1] == ylim[0]: ylim = xlim x_scaled = (x - xlim[0]) * 1. / (xlim[1] - xlim[0]) y_scaled = (y - ylim[0]) * 1. / (ylim[1] - ylim[0]) dx = x_scaled[1:] - x_scaled[:-1] dy = y_scaled[1:] - y_scaled[:-1] dist_tot = np.sum(np.sqrt(dx * dx + dy * dy)) Nu = int(200 * dist_tot) u = np.arange(-1, Nu + 1) * 1. / (Nu - 1) k = min(3, len(x) - 1) res = interpolate.splprep([x_scaled, y_scaled], s=0, k=k) x_int, y_int = interpolate.splev(u, res[0]) dx = x_int[2:] - x_int[:-2] dy = y_int[2:] - y_int[:-2] dist = np.sqrt(dx * dx + dy * dy) # create a filtered perturbation coeffs = mag * np.random.normal(0, 0.01, len(x_int) - 2) b = signal.firwin(f1, f2 * dist_tot, window=('kaiser', f3)) response = signal.lfilter(b, 1, coeffs) x_int[1:-1] += response * dy / dist y_int[1:-1] += response * dx / dist # un-scale data x_int = x_int[1:-1] * (xlim[1] - xlim[0]) + xlim[0] y_int = y_int[1:-1] * (ylim[1] - ylim[0]) + ylim[0] return x_int, y_int def XKCDify(ax, mag=1.0, f1=50, f2=0.01, f3=15, forecolor='k', bgcolor='w', xaxis_loc=None, yaxis_loc=None, xaxis_arrow='+', yaxis_arrow='+', ax_extend=0.1, expand_axes=False): # Get axes aspect ext = ax.get_window_extent().extents aspect = (ext[3] - ext[1]) / (ext[2] - ext[0]) xlim = ax.get_xlim() ylim = ax.get_ylim() xspan = xlim[1] - xlim[0] yspan = ylim[1] - xlim[0] xax_lim = (xlim[0] - ax_extend * xspan, xlim[1] + ax_extend * xspan) yax_lim = (ylim[0] - ax_extend * yspan, ylim[1] + ax_extend * yspan) if xaxis_loc is None: xaxis_loc = ylim[0] if yaxis_loc is None: yaxis_loc = xlim[0] # Draw axes xaxis = pl.Line2D([xax_lim[0], xax_lim[1]], [xaxis_loc, xaxis_loc], linestyle='-', color=forecolor) yaxis = pl.Line2D([yaxis_loc, yaxis_loc], [yax_lim[0], yax_lim[1]], linestyle='-', color=forecolor) # Label axes3, 0.5, 'hello', fontsize=14) ax.text(xax_lim[1], xaxis_loc - 0.05 * yspan, ax.get_xlabel(), fontsize=14, ha='right', va='top', rotation=5) ax.text(yaxis_loc - 0.05 * xspan, yax_lim[1], ax.get_ylabel(), fontsize=14, ha='right', va='top', rotation=85) ax.set_xlabel('') ax.set_ylabel('') # Add title ax.text(0.5 * (xax_lim[1] + xax_lim[0]), yax_lim[1], ax.get_title(), ha='center', va='bottom', fontsize=16) ax.set_title('') Nlines = len(ax.lines) lines = [xaxis, yaxis] + [ax.lines.pop(0) for i in range(Nlines)] for line in lines: x, y = line.get_data() x_int, y_int = xkcd_line(x, y, xlim, ylim, mag, f1, f2, f3) # create foreground and background line lw = line.get_linewidth() line.set_linewidth(2 * lw) line.set_data(x_int, y_int) # # don't add background line for axes ax.add_line(line) arr1 = 0.03 * np.array([-1, 0, -1]) arr2 = 0.02 * np.array([-1, 0, 1]) arr1[::2] += np.random.normal(0, 0.005, 2) arr2[::2] += np.random.normal(0, 0.005, 2) x, y = xaxis.get_data() if '+' in str(xaxis_arrow): ax.plot(x[-1] + arr1 * xspan * aspect, y[-1] + arr2 * yspan, color=forecolor, lw=2) if '-' in str(xaxis_arrow): ax.plot(x[0] - arr1 * xspan * aspect, y[0] - arr2 * yspan, color=forecolor, lw=2) x, y = yaxis.get_data() if '+' in str(yaxis_arrow): ax.plot(x[-1] + arr2 * xspan * aspect, y[-1] + arr1 * yspan, color=forecolor, lw=2) if '-' in str(yaxis_arrow): ax.plot(x[0] - arr2 * xspan * aspect, y[0] - arr1 * yspan, color=forecolor, lw=2) prop = fm.FontProperties(fname=os.path.join(script_path, 'fonts', 'Humor-Sans.ttf'), size=16) for text in ax.texts: text.set_fontproperties(prop) leg = ax.get_legend() if leg is not None: leg.set_frame_on(False) for child in leg.get_children(): if isinstance(child, pl.Line2D): x, y = child.get_data() child.set_data(xkcd_line(x, y, mag=1., f1=100, f2=0.001)) child.set_linewidth(2 * child.get_linewidth()) if isinstance(child, pl.Text): child.set_fontproperties(prop) ax.set_xlim(xax_lim[0] - 0.1 * xspan, xax_lim[1] + 0.1 * xspan) ax.set_ylim(yax_lim[0] - 0.1 * yspan, yax_lim[1] + 0.1 * yspan) ax.set_xticks([]) ax.set_yticks([]) if expand_axes: ax.figure.set_facecolor(bgcolor) ax.set_axis_off() ax.set_position([0, 0, 1, 1]) return ax

true

f723c8c94f31ee4136915426bd76d4aad731bdda

5,323

py

Python

utils/utils_fit.py

bubbliiiing/yolox-tf2

0407c77858d436a6b370e591eea7963cc807f3b4

[ "Apache-2.0" ]

49

2021-11-01T06:02:21.000Z

2022-03-29T07:08:22.000Z

utils/utils_fit.py

bubbliiiing/yolox-tf2

0407c77858d436a6b370e591eea7963cc807f3b4

[ "Apache-2.0" ]

6

2021-11-17T08:35:09.000Z

2022-02-15T12:43:14.000Z

utils/utils_fit.py

bubbliiiing/yolox-tf2

0407c77858d436a6b370e591eea7963cc807f3b4

[ "Apache-2.0" ]

20

2021-11-19T12:03:21.000Z

2022-03-16T01:45:25.000Z

import os import tensorflow as tf from nets.yolo import get_yolo_loss from tqdm import tqdm #------------------------------# # 防止bug #------------------------------# def get_train_step_fn(strategy): @tf.function def train_step(imgs, targets, net, yolo_loss, optimizer): with tf.GradientTape() as tape: #------------------------------# # 计算loss #------------------------------# P5_output, P4_output, P3_output = net(imgs, training=True) args = [P5_output, P4_output, P3_output] + [targets] loss_value = yolo_loss(args) #------------------------------# # 添加上l2正则化参数 #------------------------------# loss_value = tf.reduce_sum(net.losses) + loss_value grads = tape.gradient(loss_value, net.trainable_variables) optimizer.apply_gradients(zip(grads, net.trainable_variables)) return loss_value if strategy == None: return train_step else: #----------------------# # 多gpu训练 #----------------------# @tf.function def distributed_train_step(imgs, targets, net, yolo_loss, optimizer): per_replica_losses = strategy.run(train_step, args=(imgs, targets, net, yolo_loss, optimizer,)) return strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_losses, axis=None) return distributed_train_step #----------------------# # 防止bug #----------------------# def get_val_step_fn(strategy): @tf.function def val_step(imgs, targets, net, yolo_loss, optimizer): #------------------------------# # 计算loss #------------------------------# P5_output, P4_output, P3_output = net(imgs, training=False) args = [P5_output, P4_output, P3_output] + [targets] loss_value = yolo_loss(args) #------------------------------# # 添加上l2正则化参数 #------------------------------# loss_value = tf.reduce_sum(net.losses) + loss_value return loss_value if strategy == None: return val_step else: #----------------------# # 多gpu验证 #----------------------# @tf.function def distributed_val_step(imgs, targets, net, yolo_loss, optimizer): per_replica_losses = strategy.run(val_step, args=(imgs, targets, net, yolo_loss, optimizer,)) return strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_losses, axis=None) return distributed_val_step def fit_one_epoch(net, yolo_loss, loss_history, eval_callback, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, Epoch, input_shape, num_classes, save_period, save_dir, strategy): train_step = get_train_step_fn(strategy) val_step = get_val_step_fn(strategy) loss = 0 val_loss = 0 print('Start Train') with tqdm(total=epoch_step,desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) as pbar: for iteration, batch in enumerate(gen): if iteration >= epoch_step: break images, targets = batch[0], batch[1] loss_value = train_step(images, targets, net, yolo_loss, optimizer) loss = loss + loss_value pbar.set_postfix(**{'total_loss': float(loss) / (iteration + 1), 'lr' : optimizer.lr.numpy()}) pbar.update(1) print('Finish Train') print('Start Validation') with tqdm(total=epoch_step_val, desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) as pbar: for iteration, batch in enumerate(gen_val): if iteration >= epoch_step_val: break images, targets = batch[0], batch[1] loss_value = val_step(images, targets, net, yolo_loss, optimizer) val_loss = val_loss + loss_value pbar.set_postfix(**{'total_loss': float(val_loss) / (iteration + 1)}) pbar.update(1) print('Finish Validation') logs = {'loss': loss.numpy() / epoch_step, 'val_loss': val_loss.numpy() / epoch_step_val} loss_history.on_epoch_end([], logs) eval_callback.on_epoch_end(epoch, logs) print('Epoch:'+ str(epoch + 1) + '/' + str(Epoch)) print('Total Loss: %.3f || Val Loss: %.3f ' % (loss / epoch_step, val_loss / epoch_step_val)) #-----------------------------------------------# # 保存权值 #-----------------------------------------------# if (epoch + 1) % save_period == 0 or epoch + 1 == Epoch: net.save_weights(os.path.join(save_dir, "ep%03d-loss%.3f-val_loss%.3f.h5" % (epoch + 1, loss / epoch_step, val_loss / epoch_step_val))) if len(loss_history.val_loss) <= 1 or (val_loss / epoch_step_val) <= min(loss_history.val_loss): print('Save best model to best_epoch_weights.pth') net.save_weights(os.path.join(save_dir, "best_epoch_weights.h5")) net.save_weights(os.path.join(save_dir, "last_epoch_weights.h5"))

43.276423

144

0.520195

import os import tensorflow as tf from nets.yolo import get_yolo_loss from tqdm import tqdm ef get_train_step_fn(strategy): @tf.function def train_step(imgs, targets, net, yolo_loss, optimizer): with tf.GradientTape() as tape: P5_output, P4_output, P3_output = net(imgs, training=True) args = [P5_output, P4_output, P3_output] + [targets] loss_value = yolo_loss(args) loss_value = tf.reduce_sum(net.losses) + loss_value grads = tape.gradient(loss_value, net.trainable_variables) optimizer.apply_gradients(zip(grads, net.trainable_variables)) return loss_value if strategy == None: return train_step else: @tf.function def distributed_train_step(imgs, targets, net, yolo_loss, optimizer): per_replica_losses = strategy.run(train_step, args=(imgs, targets, net, yolo_loss, optimizer,)) return strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_losses, axis=None) return distributed_train_step ef get_val_step_fn(strategy): @tf.function def val_step(imgs, targets, net, yolo_loss, optimizer): P5_output, P4_output, P3_output = net(imgs, training=False) args = [P5_output, P4_output, P3_output] + [targets] loss_value = yolo_loss(args) loss_value = tf.reduce_sum(net.losses) + loss_value return loss_value if strategy == None: return val_step else: @tf.function def distributed_val_step(imgs, targets, net, yolo_loss, optimizer): per_replica_losses = strategy.run(val_step, args=(imgs, targets, net, yolo_loss, optimizer,)) return strategy.reduce(tf.distribute.ReduceOp.MEAN, per_replica_losses, axis=None) return distributed_val_step def fit_one_epoch(net, yolo_loss, loss_history, eval_callback, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, Epoch, input_shape, num_classes, save_period, save_dir, strategy): train_step = get_train_step_fn(strategy) val_step = get_val_step_fn(strategy) loss = 0 val_loss = 0 print('Start Train') with tqdm(total=epoch_step,desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) as pbar: for iteration, batch in enumerate(gen): if iteration >= epoch_step: break images, targets = batch[0], batch[1] loss_value = train_step(images, targets, net, yolo_loss, optimizer) loss = loss + loss_value pbar.set_postfix(**{'total_loss': float(loss) / (iteration + 1), 'lr' : optimizer.lr.numpy()}) pbar.update(1) print('Finish Train') print('Start Validation') with tqdm(total=epoch_step_val, desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) as pbar: for iteration, batch in enumerate(gen_val): if iteration >= epoch_step_val: break images, targets = batch[0], batch[1] loss_value = val_step(images, targets, net, yolo_loss, optimizer) val_loss = val_loss + loss_value pbar.set_postfix(**{'total_loss': float(val_loss) / (iteration + 1)}) pbar.update(1) print('Finish Validation') logs = {'loss': loss.numpy() / epoch_step, 'val_loss': val_loss.numpy() / epoch_step_val} loss_history.on_epoch_end([], logs) eval_callback.on_epoch_end(epoch, logs) print('Epoch:'+ str(epoch + 1) + '/' + str(Epoch)) print('Total Loss: %.3f || Val Loss: %.3f ' % (loss / epoch_step, val_loss / epoch_step_val)) if (epoch + 1) % save_period == 0 or epoch + 1 == Epoch: net.save_weights(os.path.join(save_dir, "ep%03d-loss%.3f-val_loss%.3f.h5" % (epoch + 1, loss / epoch_step, val_loss / epoch_step_val))) if len(loss_history.val_loss) <= 1 or (val_loss / epoch_step_val) <= min(loss_history.val_loss): print('Save best model to best_epoch_weights.pth') net.save_weights(os.path.join(save_dir, "best_epoch_weights.h5")) net.save_weights(os.path.join(save_dir, "last_epoch_weights.h5"))

true

f723c9484375a916d20b7a139369a7ec0dc8afb7

3,140

py

Python

warrior/WarriorCore/Classes/war_print_class.py

YutakaMizugaki/warriorframework

685761cf044182ec88ce86a942d4be1e150a1256

[ "Apache-2.0" ]

24

2017-06-06T15:48:08.000Z

2021-03-17T07:52:52.000Z

warrior/WarriorCore/Classes/war_print_class.py

YutakaMizugaki/warriorframework

685761cf044182ec88ce86a942d4be1e150a1256

[ "Apache-2.0" ]

272

2017-05-19T20:39:12.000Z

2021-12-13T19:34:51.000Z

warrior/WarriorCore/Classes/war_print_class.py

pavithra-gowda/warrior

19b153310552b986b86b5470fcfea9547a74c3a9

[ "Apache-2.0" ]

37

2017-05-17T21:24:37.000Z

2021-07-24T18:09:22.000Z

''' Copyright 2017, Fujitsu Network Communications, Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ''' import sys import re """ This class will trap stdout and redirects the message to logfile and stdout It takes console_logfile and write_to_stdout ( boolean flag) as arguments. !!! Important!!! DO NOT import any modules from warrior/Framework package that uses warrior/Framework/Utils/print_Utils.py at module level into this module as it will lead to cyclic imports. """ def print_main(message, print_type, color_message=None, *args, **kwargs): """The main print function will be called by other print functions """ if color_message is not None: print_string = print_type + " " + str(color_message) elif color_message is None: print_string = print_type + " " + str(message) if len(args) > 0: print_string = (print_type + " " + str(message) + str(args)) # set logging argument default to True, to write the message in the log file if isinstance(sys.stdout, RedirectPrint): sys.stdout.write((print_string + '\n'), logging=kwargs.get('logging', True)) else: sys.stdout.write(print_string + '\n') sys.stdout.flush() from Framework.Utils.testcase_Utils import TCOBJ if TCOBJ.pnote is False: TCOBJ.p_note_level(message, print_type) return print_string class RedirectPrint(object): """Class that has methods to redirect prints from stdout to correct console log files """ def __init__(self, console_logfile): """Constructor""" self.get_file(console_logfile) # self.write_to_stdout = write_to_stdout self.stdout = sys.stdout def get_file(self, console_logfile): """If the console logfile is not None redirect sys.stdout to console logfile """ self.file = console_logfile if self.file is not None: sys.stdout = self def write(self, data, logging=True): """ - Writes data to the sys.stdout - Writes data to log file only if the logging is True - Removes the ansii escape chars before writing to file """ self.stdout.write(data) ansi_escape = re.compile(r'\x1b[^m]*m') data = ansi_escape.sub('', data) # write to log file if logging is set to True if logging is True: self.file.write(data) self.file.flush() def isatty(self): """Check if sys.stdout is a tty """ # print self.stdout.isatty() return self.stdout.isatty() def flush(self): """flush logfile """ return self.stdout.flush()

35.280899

80

0.670064

import sys import re def print_main(message, print_type, color_message=None, *args, **kwargs): if color_message is not None: print_string = print_type + " " + str(color_message) elif color_message is None: print_string = print_type + " " + str(message) if len(args) > 0: print_string = (print_type + " " + str(message) + str(args)) if isinstance(sys.stdout, RedirectPrint): sys.stdout.write((print_string + '\n'), logging=kwargs.get('logging', True)) else: sys.stdout.write(print_string + '\n') sys.stdout.flush() from Framework.Utils.testcase_Utils import TCOBJ if TCOBJ.pnote is False: TCOBJ.p_note_level(message, print_type) return print_string class RedirectPrint(object): def __init__(self, console_logfile): self.get_file(console_logfile) self.stdout = sys.stdout def get_file(self, console_logfile): self.file = console_logfile if self.file is not None: sys.stdout = self def write(self, data, logging=True): self.stdout.write(data) ansi_escape = re.compile(r'\x1b[^m]*m') data = ansi_escape.sub('', data) if logging is True: self.file.write(data) self.file.flush() def isatty(self): return self.stdout.isatty() def flush(self): return self.stdout.flush()

true

f723c959dc405638e9be66e367589488a0bb7950

74,965

py

Python

doppyo/sugar.py

aaronspring/doppyo

e29e21fbb997f024f39d2e5e67decfc235b0dcca

[ "MIT" ]

null

doppyo/sugar.py

aaronspring/doppyo

e29e21fbb997f024f39d2e5e67decfc235b0dcca

[ "MIT" ]

null

doppyo/sugar.py

aaronspring/doppyo

e29e21fbb997f024f39d2e5e67decfc235b0dcca

[ "MIT" ]

null

""" Collection of old doppyo functions and useful tidbits for internal dcfp use Authors: Dougie Squire and Thomas Moore Date created: 01/10/2018 Python Version: 3.6 """ # =================================================================================================== # Packages # =================================================================================================== import numpy as np import pandas as pd import xarray as xr import cartopy from collections import Sequence from itertools import chain, count import matplotlib import matplotlib.pyplot as plt import matplotlib.ticker as mticker from cartopy.util import add_cyclic_point from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER # Load doppyo packages ----- from doppyo import utils # =================================================================================================== def rank_gufunc(x): ''' Returns ranked data along specified dimension ''' import bottleneck ranks = bottleneck.nanrankdata(x,axis=-1) ranks = ranks[...,0] return ranks def compute_rank(da_1, da_2, over_dim): ''' Feeds forecast and observation data to ufunc that ranks data along specified dimension''' # Add 'ensemble' coord to obs if one does not exist ----- if over_dim not in da_2.coords: da_2_pass = da_2.copy() da_2_pass.coords[over_dim] = -1 da_2_pass = da_2_pass.expand_dims(over_dim) else: da_2_pass = da_2.copy() # Only keep and combine instances that appear in both dataarrays (excluding the ensemble dim) ----- aligned = xr.align(da_2_pass, da_1, join='inner', exclude=over_dim) combined = xr.concat(aligned, dim=over_dim) return xr.apply_ufunc(rank_gufunc, combined, input_core_dims=[[over_dim]], dask='allowed', output_dtypes=[int]).rename('rank') # =================================================================================================== def categorize(da, bin_edges): """ Returns the indices of the bins to which each value in input array belongs Output indices are such that bin_edges[i-1] <= x < bin_edges[i] """ return xr.apply_ufunc(np.digitize, da, bin_edges, input_core_dims=[[],[]], dask='allowed', output_dtypes=[int]).rename('categorized') # =================================================================================================== def unstack_and_count(da, dims): """ Unstacks provided xarray object and returns the total number of elements along dims """ try: unstacked = da.unstack(da.dims[0]) except ValueError: unstacked = da if dims is None: return ((0 * unstacked) + 1) else: return ((0 * unstacked) + 1).sum(dim=dims, skipna=True) def compute_histogram(da, bin_edges, over_dims): """ Returns the histogram of data over the specified dimensions """ # To use groupby_bins, da must have a name ----- da = da.rename('data') hist = da.groupby_bins(da, bins=bin_edges, squeeze=False) \ .apply(unstack_and_count, dims=over_dims) \ .fillna(0) \ .rename({'data_bins' : 'bins'}) hist['bins'] = (bin_edges[0:-1]+bin_edges[1:])/2 # Add nans where data did not fall in any bin ----- return hist.astype(int).where(hist.sum('bins') != 0) # =================================================================================================== def calc_gradient(da, dim, x=None): """ Returns the gradient computed using second order accurate central differences in the interior points and either first order accurate one-sided (forward or backwards) differences at the boundaries See https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.gradient.html """ # Replace dimension values if specified ----- da_n = da.copy() if x is None: x = da_n[dim] centre_chunk = range(len(x[dim])-2) f_hd = da_n.shift(**{dim:-2}) f = da_n.shift(**{dim:-1}) f_hs = da_n hs = x.shift(**{dim:-1}) - x hd = x.shift(**{dim:-2}) - x.shift(**{dim:-1}) c = (hs ** 2 * f_hd + (hd ** 2 - hs ** 2) * f - hd ** 2 * f_hs) / \ (hs * hd * (hd + hs)).isel(**{dim : centre_chunk}) c[dim] = x[dim][1:-1] l = (da_n.shift(**{dim:-1}) - da_n).isel(**{dim : 0}) / \ (x.shift(**{dim:-1}) - x).isel(**{dim : 0}) r = (-da_n.shift(**{dim:1}) + da_n).isel(**{dim : -1}) / \ (-x.shift(**{dim:1}) + x).isel(**{dim : -1}) grad = xr.concat([l, c, r], dim=dim) grad[dim] = da[dim] return grad # =================================================================================================== def bias_correct_ms(da_biased, da_target, da_target_clim=None, init_date_name='init_date', lead_time_name='lead_time'): """ Adjusts, per month and lead time, the mean and standard deviation of da_biased to match that of da_target. Author: Dougie Squire Date: 01/09/2018 Parameters ---------- da_biased : xarray DataArray Array containing values to be corrected. The time information of this array is anticipated in a lead_time/inital_date format da_target : xarray DataArray Array containing values to use for the correction. da_target_clim : xarray DataArray, optional Array containing a climatology of da_target. If da_target_clim is provided, this function returns both the corrected full field and the anomalies. Otherwise, returns only the anomalies init_date_name : str, optional Name of initial date dimension lead_time_name : str, optional Name of lead time dimension Returns ------- corrected : xarray DataArray Bias corrected array Examples -------- >>> biased = xr.DataArray(np.random.normal(size=(48,6)), ... coords=[('init_date', pd.date_range(start='1/1/2018', periods=48, freq='M')), ... ('lead_time', np.arange(6))]) >>> biased['lead_time'].attrs['units'] = 'M' >>> target = xr.DataArray(np.random.normal(size=(48)), ... coords=[('time', pd.date_range(start='1/1/2000', periods=48, freq='M'))]) >>> doppyo.utils.bias_correct_ms(biased, target) <xarray.DataArray (init_date: 48, lead_time: 6)> array([[ 9.336394e-02, 1.133997e-01, -5.851293e-01, -4.908594e-02, 7.952765e-01, 5.325052e-01], [-1.131123e+00, 1.603380e-01, -1.626906e+00, -1.811439e+00, -1.653359e-01, -1.871170e-01], [ 6.515435e-01, -1.064662e+00, 2.249610e+00, 6.881682e-01, -1.831233e-01, -1.159470e+00], ..., [-2.096226e+00, 3.143062e-04, 3.603787e-01, -1.515535e+00, 5.421578e-02, -6.446119e-01], [-8.186274e-01, -9.858171e-01, 1.933307e+00, 5.227265e-02, 5.443201e-01, -7.059492e-01], [ 2.253396e-02, 2.238470e+00, 1.138728e-01, -3.617103e-01, 1.678223e+00, -2.413158e+00]]) Coordinates: * lead_time (lead_time) int64 0 1 2 3 4 5 * init_date (init_date) datetime64[ns] 2018-01-31 2018-02-28 ... 2021-12-31 Notes ----------- Many years of initial dates (in da_biased) and times (in da_target) must exist for the mean and standard deviation to be computed reliably """ def _groupby_lead_and_mean(da, over_dims, init_date_name, lead_time_name): """ Groups provided array by lead time and computes mean """ return da.unstack('stacked_' + init_date_name + '_' + lead_time_name).groupby(lead_time_name).mean(over_dims, skipna=True) def _groupby_lead_and_std(da, over_dims, init_date_name, lead_time_name): """ Groups provided array by lead time and computes standard deviation """ return da.unstack('stacked_' + init_date_name + '_' + lead_time_name).groupby(lead_time_name).std(over_dims, skipna=True) def _unstack_and_shift_per_month(da, shift, init_date_name, lead_time_name): """ Unstacks and adjusts input array by a constant shift as a function of month """ da_us = da.unstack('stacked_' + init_date_name + '_' + lead_time_name) the_month = np.ndarray.flatten(da_us.month.values) the_month = int(np.unique(the_month[~np.isnan(the_month)])) return da_us - shift.sel(month=the_month) def _unstack_and_scale_per_month(da, scale, init_date_name, lead_time_name): """ Unstacks and scales input array by a constant value as a function of month """ da_us = da.unstack('stacked_' + init_date_name + '_' + lead_time_name) the_month = np.ndarray.flatten(da_us.month.values) the_month = int(np.unique(the_month[~np.isnan(the_month)])) return da_us * scale.sel(month=the_month) def _scale_per_month(da, scale): """ Scales input array by a constant value as a function of month """ return da.groupby('time.month') * scale _anomalize = lambda data, clim: datetime_to_leadtime( anomalize( leadtime_to_datetime(data),clim)) _rescale = lambda da, scale : datetime_to_leadtime( _scale_per_month( leadtime_to_datetime(da), scale)) da_biased = da_biased.copy() da_target = da_target.copy() month = (da_biased[init_date_name].dt.month + da_biased[lead_time_name]) % 12 month = month.where(month != 0, 12) # Correct the mean ----- da_biased.coords['month'] = month try: da_biased_mean = da_biased.groupby('month').apply(_groupby_lead_and_mean, over_dims=[init_date_name,'ensemble'], init_date_name=init_date_name, lead_time_name=lead_time_name) except ValueError: da_biased_mean = da_biased.groupby('month').apply(_groupby_lead_and_mean, over_dims=init_date_name, init_date_name=init_date_name, lead_time_name=lead_time_name) if da_target_clim is not None: da_target_mean = da_target.groupby('time.month').mean('time') da_meancorr = da_biased.groupby('month').apply(_unstack_and_shift_per_month, shift=(da_biased_mean - da_target_mean), init_date_name=init_date_name, lead_time_name=lead_time_name) \ .mean('month', skipna=True) da_meancorr[lead_time_name] = da_biased[lead_time_name] da_meancorr.coords['month'] = month # Compute the corrected anomalies ----- da_anom_meancorr = da_meancorr.groupby(init_date_name).apply(_anomalize, clim=da_target_clim) da_anom_meancorr.coords['month'] = month else: da_anom_meancorr = da_biased.groupby('month').apply(_unstack_and_shift_per_month, shift=(da_biased_mean), init_date_name=init_date_name, lead_time_name=lead_time_name) \ .mean('month', skipna=True) da_anom_meancorr[lead_time_name] = da_anom_meancorr[lead_time_name] da_anom_meancorr.coords['month'] = month # Correct the standard deviation ----- try: da_biased_std_tmp = da_anom_meancorr.groupby('month').apply(_groupby_lead_and_std, over_dims=[init_date_name,'ensemble'], init_date_name=init_date_name, lead_time_name=lead_time_name) except ValueError: da_biased_std_tmp = da_anom_meancorr.groupby('month').apply(_groupby_lead_and_std, over_dims=init_date_name, init_date_name=init_date_name, lead_time_name=lead_time_name) try: da_target_std = da_target.sel(lat=da_biased.lat, lon=da_biased.lon).groupby('time.month').std('time') except: da_target_std = da_target.groupby('time.month').std('time') da_anom_stdcorr_tmp = da_anom_meancorr.groupby('month').apply(_unstack_and_scale_per_month, scale=(da_target_std / da_biased_std_tmp), init_date_name=init_date_name, lead_time_name=lead_time_name) \ .mean('month', skipna=True) da_anom_stdcorr_tmp[lead_time_name] = da_biased[lead_time_name] da_anom_stdcorr_tmp.coords['month'] = month # This will "squeeze" each pdf at each lead time appropriately. However, the total variance across all leads for # a given month will now be incorrect. Thus, we now rescale as a function of month only try: da_biased_std = concat_times(da_anom_stdcorr_tmp).groupby('time.month').std(['time','ensemble']) except ValueError: da_biased_std = concat_times(da_anom_stdcorr_tmp).groupby('time.month').std('time') da_anom_stdcorr = da_anom_stdcorr_tmp.groupby(init_date_name).apply(_rescale, scale=(da_target_std / da_biased_std)) if da_target_clim is not None: da_stdcorr = da_anom_stdcorr.groupby(init_date_name).apply(_anomalize, clim=-da_target_clim) return da_stdcorr.drop('month'), da_anom_stdcorr.drop('month') else: return da_anom_stdcorr.drop('month') # =================================================================================================== def bias_correct_m(da_biased, da_target, da_target_clim=None, init_date_name='init_date', lead_time_name='lead_time'): """ Adjusts, per month and lead time, the mean of da_biased to match that of da_target Author: Dougie Squire Date: 01/09/2018 Parameters ---------- da_biased : xarray DataArray Array containing values to be corrected. The time information of this array is anticipated in a lead_time/inital_date format da_target : xarray DataArray Array containing values to use for the correction. da_target_clim : xarray DataArray, optional Array containing a climatology of da_target. If da_target_clim is provided, this function returns both the corrected full field and the anomalies. Otherwise, returns only the anomalies init_date_name : str, optional Name of initial date dimension lead_time_name : str, optional Name of lead time dimension Returns ------- corrected : xarray DataArray Bias corrected array Examples -------- >>> biased = xr.DataArray(np.random.normal(size=(48,6)), ... coords=[('init_date', pd.date_range(start='1/1/2018', periods=48, freq='M')), ... ('lead_time', np.arange(6))]) >>> biased['lead_time'].attrs['units'] = 'M' >>> target = xr.DataArray(np.random.normal(size=(48)), ... coords=[('time', pd.date_range(start='1/1/2000', periods=48, freq='M'))]) >>> doppyo.utils.bias_correct_m(biased, target) <xarray.DataArray (init_date: 48, lead_time: 6)> array([[ 0.541226, 0.693622, -0.367322, 0.820282, 0.111487, 0.078355], [-0.299829, 0.164297, -0.976883, 0.463365, -0.26428 , -0.536119], [ 0.078832, -0.260615, -0.235059, -0.349185, 0.567183, -1.543395], ..., [ 0.335494, -1.121158, 1.313004, 0.604279, 0.135053, 0.031851], [ 0.33103 , 0.876521, -0.980873, 0.640328, 1.053691, 0.166768], [ 1.207329, 0.021916, 0.210883, -0.189922, 0.075786, 0.047616]]) Coordinates: * init_date (init_date) datetime64[ns] 2018-01-31 2018-02-28 ... 2021-12-31 * lead_time (lead_time) int64 0 1 2 3 4 5 Notes ----------- Many years of initial dates (in da_biased) and times (in da_target) must exist for the mean to be computed reliably """ def _groupby_lead_and_mean(da, over_dims, init_date_name, lead_time_name): """ Groups provided array by lead time and computes mean """ return da.unstack('stacked_' + init_date_name + '_' + lead_time_name).groupby(lead_time_name).mean(over_dims, skipna=True) def _unstack_and_shift_per_month(da, shift, init_date_name, lead_time_name): """ Unstacks and adjusts input array by a constant shift as a function of month """ da_us = da.unstack('stacked_' + init_date_name + '_' + lead_time_name) the_month = np.ndarray.flatten(da_us.month.values) the_month = int(np.unique(the_month[~np.isnan(the_month)])) return da_us - shift.sel(month=the_month) _anomalize = lambda data, clim: datetime_to_leadtime( anomalize( leadtime_to_datetime(data),clim)) da_biased = da_biased.copy() da_target = da_target.copy() month = (da_biased[init_date_name].dt.month + da_biased[lead_time_name]) % 12 month = month.where(month != 0, 12) # Correct the mean ----- da_biased.coords['month'] = month try: da_biased_mean = da_biased.groupby('month').apply(_groupby_lead_and_mean, over_dims=[init_date_name,'ensemble'], init_date_name=init_date_name, lead_time_name=lead_time_name) except ValueError: da_biased_mean = da_biased.groupby('month').apply(_groupby_lead_and_mean, over_dims=init_date_name, init_date_name=init_date_name, lead_time_name=lead_time_name) if da_target_clim is not None: da_target_mean = da_target.groupby('time.month').mean('time') da_meancorr = da_biased.groupby('month').apply(_unstack_and_shift_per_month, shift=(da_biased_mean - da_target_mean), init_date_name=init_date_name, lead_time_name=lead_time_name) \ .mean('month', skipna=True) da_meancorr[lead_time_name] = da_biased[lead_time_name] da_meancorr.coords['month'] = month # Compute the corrected anomalies ----- da_anom_meancorr = da_meancorr.groupby(init_date_name).apply(_anomalize, clim=da_target_clim) da_anom_meancorr.coords['month'] = month else: da_anom_meancorr = da_biased.groupby('month').apply(_unstack_and_shift_per_month, shift=(da_biased_mean), init_date_name=init_date_name, lead_time_name=lead_time_name) \ .mean('month', skipna=True) da_anom_meancorr[lead_time_name] = da_anom_meancorr[lead_time_name] da_anom_meancorr.coords['month'] = month if da_target_clim is not None: da_meancorrr = da_anom_meancorr.groupby(init_date_name).apply(_anomalize, clim=-da_target_clim) return da_meancorr.drop('month'), da_anom_meancorr.drop('month') else: return da_anom_meancorr.drop('month') # =================================================================================================== def conditional_bias_correct(da_cmp, da_ref, over_dims): """ Return conditional bias corrected data using the approach of Goddard et al. 2013 """ cc = skill.compute_Pearson_corrcoef(da_cmp.mean('ensemble'), da_ref, over_dims=over_dims, subtract_local_mean=False) correct_cond_bias = (da_ref.std(over_dims) / da_cmp.mean('ensemble').std(over_dims)) * cc return da_cmp * correct_cond_bias # =================================================================================================== def trunc_time(time, freq): """ Truncates values in provided time array to provided frequency. E.g. 2018-01-15T12:00 with freq = 'M' becomes 2018-01-01. """ return time.astype('<M8[' + freq + ']') # =================================================================================================== def month_delta(date_in, delta, trunc_to_start=False): """ Increments provided datetime64 array by delta months """ date_mod = pd.Timestamp(date_in) m, y = (date_mod.month + delta) % 12, date_mod.year + ((date_mod.month) + delta - 1) // 12 if not m: m = 12 d = min(date_mod.day, [31, 29 if y % 4 == 0 and not y % 400 == 0 else 28,31,30,31,30,31,31,30,31,30,31][m - 1]) if trunc_to_start: date_out = trunc_time(np.datetime64(date_mod.replace(day=d,month=m, year=y)),'M') else: date_out = np.datetime64(date_mod.replace(day=d,month=m, year=y)) return np.datetime64(date_out,'ns') # =================================================================================================== def year_delta(date_in, delta, trunc_to_start=False): """ Increments provided datetime64 array by delta years """ date_mod = month_delta(date_in, 12 * delta) if trunc_to_start: date_out = trunc_time(date_mod,'Y') else: date_out = date_mod return date_out # =================================================================================================== def datetime_to_leadtime(data_in): """ Converts time information from single datetime dimension to init_date/lead_time dimension pair """ init_date = data_in.time.values[0] lead_times = range(len(data_in.time)) try: freq = pd.infer_freq(data_in.time.values) # If pandas tries to assign start time to frequency (e.g. QS-OCT), remove this ----- if '-' in freq: freq = freq[:freq.find('-')] # Split frequency into numbers and strings ----- incr_string = ''.join([i for i in freq if i.isdigit()]) freq_incr = [int(incr_string) if incr_string else 1][0] freq_type = ''.join([i for i in freq if not i.isdigit()]) # Specify all lengths great than 1 month in months ----- if 'QS' in freq_type: freq = str(3*freq_incr) + 'MS' elif 'Q' in freq_type: freq = str(3*freq_incr) + 'M' elif ('YS' in freq_type) | ('AS' in freq_type): freq = str(12*freq_incr) + 'MS' elif ('Y' in freq_type) | ('A' in freq_type): freq = str(12*freq_incr) + 'M' except ValueError: dt = (data_in.time.values[1] - data_in.time.values[0]) / np.timedelta64(1, 's') month = data_in.time.dt.month[0] if dt == 60*60*24: freq = 'D' elif ((month == 1) | (month == 3) | (month == 5) | (month == 7) | (month == 8) | (month == 10) | (month == 12)) & (dt == 31*60*60*24): freq = 'MS' elif ((month == 4) | (month == 6) | (month == 9) | (month == 11)) & (dt == 30*60*60*24): freq = 'MS' elif (month == 2) & ((dt == 28*60*60*24) | (dt == 29*60*60*24)): freq = 'MS' elif (dt == 365*60*60*24) | (dt == 366*60*60*24): freq = 'A' else: freq = 'NA' data_out = data_in.rename({'time' : 'lead_time'}) data_out['lead_time'] = lead_times data_out['lead_time'].attrs['units'] = freq data_out.coords['init_date'] = init_date return data_out # =================================================================================================== def leadtime_to_datetime(data_in, init_date_name='init_date', lead_time_name='lead_time'): """ Converts time information from lead time/initial date dimension pair to single datetime dimension """ try: init_date = data_in[init_date_name].values[0] except IndexError: init_date = data_in[init_date_name].values lead_times = list(map(int, data_in[lead_time_name].values)) freq = data_in[lead_time_name].attrs['units'] # # Split frequency into numbers and strings ----- # incr_string = ''.join([i for i in freq if i.isdigit()]) # freq_incr = [int(incr_string) if incr_string else 1][0] # freq_type = ''.join([i for i in freq if not i.isdigit()]) # Deal with special cases of monthly and yearly frequencies ----- # if 'M' in freq_type: # datetimes = np.array([month_delta(init_date, freq_incr * ix) for ix in lead_times]) # elif ('A' in freq_type) | ('Y' in freq_type): # datetimes = np.array([year_delta(init_date, freq_incr * ix) for ix in lead_times]) # else: # datetimes = (pd.date_range(init_date, periods=len(lead_times), freq=freq)).values datetimes = (pd.date_range(init_date, periods=len(lead_times), freq=freq)).values data_out = data_in.drop(init_date_name) data_out = data_out.rename({lead_time_name : 'time'}) data_out['time'] = datetimes return prune(data_out) # =================================================================================================== def get_nearest_point(da, lat, lon): """ Returns the nearest grid point to the specified lat/lon location """ return da.sel(lat=lat,lon=lon,method='nearest') # =================================================================================================== # visualization tools # =================================================================================================== def plot_fields(data, title=None, headings=None, ncol=2, contour=False, vlims=None, clims=None, squeeze_row=1, squeeze_col=1, squeeze_cbar=1, shift_cbar=1, cmap='viridis', fontsize=12, invert=False): """ Plots tiles of figures """ def _depth(seq): for level in count(): if not seq: return level seq = list(chain.from_iterable(s for s in seq if isinstance(s, Sequence))) matplotlib.rc('font', family='sans-serif') matplotlib.rc('font', serif='Helvetica') matplotlib.rc('text', usetex='false') matplotlib.rcParams.update({'font.size': fontsize}) nrow = int(np.ceil(len(data)/ncol)); fig = plt.figure(figsize=(11*squeeze_col, nrow*4*squeeze_row)) if (clims is not None) & (np.shape(vlims) != np.shape(clims)): raise ValueError('The input clims must be equal in size to vlims') # Check if vlims are given per figure or for all figures ----- one_cbar = False if vlims is None: vlims = [[None, None]] * len(data) if _depth(vlims) == 1: one_cbar = True over_count = 1 for idx,dat in enumerate(data): if one_cbar: vmin, vmax = vlims if clims is not None: cmin, cmax = clims else: vmin, vmax = vlims[idx] if clims is not None: cmin, cmax = clims[idx] if ('lat' in dat.dims) and ('lon' in dat.dims): trans = cartopy.crs.PlateCarree() ax = plt.subplot(nrow, ncol, over_count, projection=cartopy.crs.PlateCarree(central_longitude=180)) extent = [dat.lon.min(), dat.lon.max(), dat.lat.min(), dat.lat.max()] if contour is True: if clims is not None: ax.coastlines(color='gray') im = ax.contourf(dat.lon, dat.lat, dat, levels=np.linspace(vmin,vmax,12), origin='lower', transform=trans, vmin=vmin, vmax=vmax, cmap=cmap) ax.contour(dat.lon, dat.lat, dat, levels=np.linspace(cmin,cmax,12), origin='lower', transform=trans, vmin=vmin, vmax=vmax, colors='w', linewidths=2) ax.contour(dat.lon, dat.lat, dat, levels=np.linspace(cmin,cmax,12), origin='lower', transform=trans, vmin=vmin, vmax=vmax, colors='k', linewidths=1) else: ax.coastlines(color='black') im = ax.contourf(dat.lon, dat.lat, dat, origin='lower', transform=trans, vmin=vmin, vmax=vmax, cmap=cmap) else: ax.coastlines(color='black') im = ax.imshow(dat, origin='lower', extent=extent, transform=trans, vmin=vmin, vmax=vmax, cmap=cmap) gl = ax.gridlines(crs=cartopy.crs.PlateCarree(), draw_labels=True) gl.xlines = False gl.ylines = False gl.xlabels_top = False if over_count % ncol == 0: gl.ylabels_left = False elif (over_count+ncol-1) % ncol == 0: gl.ylabels_right = False else: gl.ylabels_left = False gl.ylabels_right = False gl.xlocator = mticker.FixedLocator([-90, 0, 90, 180]) gl.ylocator = mticker.FixedLocator([-90, -60, 0, 60, 90]) gl.xformatter = LONGITUDE_FORMATTER gl.yformatter = LATITUDE_FORMATTER if not one_cbar: cbar = plt.colorbar(im, ax=ax, orientation="horizontal", aspect=30/squeeze_cbar, pad=shift_cbar*0.1) tick_locator = mticker.MaxNLocator(nbins=6) cbar.locator = tick_locator cbar.update_ticks() if headings is not None: cbar.set_label(headings[idx], labelpad=5, fontsize=fontsize); elif headings is not None: ax.set_title(headings[idx], fontsize=fontsize) else: ax = plt.subplot(nrow, ncol, over_count) if 'lat' in dat.dims: x_plt = dat['lat'] y_plt = dat[utils.get_other_dims(dat,'lat')[0]] # if dat.get_axis_num('lat') > 0: # dat = dat.transpose() elif 'lon' in dat.dims: x_plt = dat['lon'] y_plt = dat[utils.get_other_dims(dat,'lon')[0]] # if dat.get_axis_num('lon') > 0: # dat = dat.transpose() else: x_plt = dat[dat.dims[1]] y_plt = dat[dat.dims[0]] extent = [x_plt.min(), x_plt.max(), y_plt.min(), y_plt.max()] if contour is True: if clims is not None: im = ax.contourf(x_plt, y_plt, dat, levels=np.linspace(vmin,vmax,12), vmin=vmin, vmax=vmax, cmap=cmap) ax.contour(x_plt, y_plt, dat, levels=np.linspace(cmin,cmax,12), colors='w', linewidths=2) ax.contour(x_plt, y_plt, dat, levels=np.linspace(cmin,cmax,12), colors='k', linewidths=1) else: im = ax.contourf(x_plt, y_plt, dat, vmin=vmin, vmax=vmax, cmap=cmap) else: im = ax.imshow(dat, origin='lower', extent=extent, vmin=vmin, vmax=vmax, cmap=cmap) if over_count % ncol == 0: ax.yaxis.tick_right() elif (over_count+ncol-1) % ncol == 0: ax.set_ylabel(y_plt.dims[0], fontsize=fontsize) else: ax.set_yticks([]) if idx / ncol >= nrow - 1: ax.set_xlabel(x_plt.dims[0], fontsize=fontsize) if not one_cbar: cbar = plt.colorbar(im, ax=ax, orientation="horizontal", aspect=30/squeeze_cbar, pad=shift_cbar*0.1) tick_locator = mticker.MaxNLocator(nbins=6) cbar.locator = tick_locator cbar.update_ticks() if headings is not None: cbar.set_label(headings[idx], labelpad=5, fontsize=fontsize); elif headings is not None: ax.set_title(headings[idx], fontsize=fontsize) if invert: ax.invert_yaxis() over_count += 1 plt.tight_layout() if one_cbar: vmin, vmax = vlims fig.subplots_adjust(bottom=shift_cbar*0.16) cbar_ax = fig.add_axes([0.15, 0.13, 0.7, squeeze_cbar*0.020]) cbar = fig.colorbar(im, cax=cbar_ax, orientation='horizontal'); cbar_ax.set_xlabel(title, rotation=0, labelpad=15, fontsize=fontsize); cbar.set_ticks(np.linspace(vmin,vmax,5)) elif title is not None: fig.suptitle(title, y=1) # =================================================================================================== def size_GB(xr_object): """ How many GB (or GiB) is your xarray object? // Requires an xarray object // Returns: * equivalent GB (GBytes) - 10^9 conversion * equivalent GiB (GiBytes) - 2^ 30 conversion < Thomas Moore - thomas.moore@csiro.au - 10102018 > """ bytes = xr_object.nbytes Ten2the9 = 10**9 Two2the30 = 2**30 GBytes = bytes / Ten2the9 GiBytes = bytes / Two2the30 #print out results print(xr_object.name, "is", GBytes, "GB", 'which is', GiBytes,"GiB") return GBytes,GiBytes # =================================================================================================== def get_pres_name(da): """ Returns name of pressure dimension in input array Author: Dougie Squire Date: 03/03/2018 Parameters ---------- da : xarray DataArray Array with coordinate corresponding to pressure Returns ------- name : str Name of dimension corresponding to pressure Examples -------- >>> A = xr.DataArray(np.random.normal(size=(2,2,2,2,2)), ... coords=[('lat', np.arange(2)), ('lon', np.arange(2)), ... ('depth', np.arange(2)), ('level', np.arange(2)), ... ('pfull', np.arange(2))]) >>> doppyo.utils.get_pres_name(A) 'pfull' """ if 'pfull' in da.dims: return 'pfull' elif 'phalf' in da.dims: return 'phalf' else: raise KeyError('Unable to determine pressure dimension') pass # =================================================================================================== def did_event(da, event): """ Returns array containing True/False where event occurs/does not occur Notes ----- See http://www.cawcr.gov.au/projects/verification/ """ eval_expr = event.replace(">", "da >").replace("<", "da <").replace("==", "da ==") \ .replace("=", "da ==").replace('&&', '&').replace('||', '|') \ .replace("and", "&").replace("or", "|") eval_expr = '(' + eval_expr + ').rename("event_logical")' return eval(eval_expr) # =================================================================================================== def compute_likelihood(da_logical, dim='ensemble'): """ Returns array of likelihoods computed along dim from logical event data Notes ----- See http://www.cawcr.gov.au/projects/verification/ """ if dim == None: likelihood = da_logical else: likelihood = da_logical.mean(dim=dim).rename('likelihood') return likelihood # =================================================================================================== def atmos_energy_cycle(temp, u, v, omega, gh, terms=None, vgradz=False, spectral=False, n_wavenumbers=20, integrate=True, loop_triple_terms=False, lat_name=None, lon_name=None, plevel_name=None): """ Returns all terms in the Lorenz energy cycle. Follows formulae and notation used in `Marques et al. 2011 Global diagnostic energetics of five state-of-the-art climate models. Climate Dynamics`. Note that this decomposition is in the space domain. A space-time decomposition can also be carried out (though not in Fourier space, but this is not implemented here (see `Oort. 1964 On Estimates of the atmospheric energy cycle. Monthly Weather Review`). Parameters ---------- temp : xarray DataArray Array containing fields of temperature with at least coordinates latitude, longitude and level (following standard naming - see Limitations) u : xarray DataArray Array containing fields of zonal velocity with at least coordinates latitude, longitude and level (following standard naming - see Limitations) v : xarray DataArray Array containing fields of meridional velocity with at least coordinates latitude, longitude and level (following standard naming - see Limitations) omega : xarray DataArray Array containing fields of vertical velocity (pressure coordinates) with at least coordinates latitude, longitude and level (following standard naming - see Limitations) gh : xarray DataArray Array containing fields of geopotential height with at least coordinates latitude, longitude and level (following standard naming - see Limitations) terms : str or sequence of str List of terms to compute. If None, returns all terms. Available options are: Pz; total available potential energy in the zonally averaged temperature distribution Kz; total kinetic energy in zonally averaged motion Pe; total eddy available potential energy [= sum_n Pn (n > 0 only) for spectral=True] (Note that for spectral=True, an additional term, Sn, quantifying the rate of transfer of available potential energy to eddies of wavenumber n from eddies of all other wavenumbers is also returned) Ke; total eddy kinetic energy [= sum_n Kn (n > 0 only) for spectral=True] (Note that for spectral=True, an additional term, Ln, quantifying the rate of transfer of kinetic energy to eddies of wavenumber n from eddies of all other wavenumbers is also returned) Cz; rate of conversion of zonal available potential energy to zonal kinetic energy Ca; rate of transfer of total available potential energy in the zonally averaged temperature distribution (Pz) to total eddy available potential energy (Pe) [= sum_n Rn (n > 0 only) for spectral=True] Ce; rate of transfer of total eddy available potential energy (Pe) to total eddy kinetic energy (Ke) [= sum_n Cn (n > 0 only) for spectral=True] Ck; rate of transfer of total eddy kinetic energy (Ke) to total kinetic energy in zonally averaged motion (Kz) [= sum_n Mn (n > 0 only) for spectral=True] Gz; rate of generation of zonal available potential energy due to the zonally averaged heating (Pz). Note that this term is computed as a residual (Cz + Ca) and cannot be returned in spectral space. If Gz is requested with spectral=True, Gz is returned in real-space only Ge; rate of generation of eddy available potential energy (Pe). Note that this term is computed as a residual (Ce - Ca) and cannot be returned in spectral space. If Ge is requested with spectral=True, Ge is returned in real-space only Dz; rate of viscous dissipation of zonal kinetic energy (Kz). Note that this term is computed as a residual (Cz - Ck) and cannot be returned in spectral space. If Dz is requested with spectral=True, Dz is returned in real-space only De; rate of dissipation of eddy kinetic energy (Ke). Note that this term is computed as a residual (Ce - Ck) and cannot be returned in spectral space. If De is requested with spectral=True, De is returned in real-space only vgradz : bool, optional If True, uses `v-grad-z` approach for computing terms relating to conversion of potential energy to kinetic energy. Otherwise, defaults to using the `omaga-alpha` approach (see reference above for details) spectral : bool, optional If True, computes all terms as a function of wavenumber on longitudinal bands. To use this option, longitudes must be regularly spaced. Note that Ge and De are computed as residuals and cannot be computed in spectral space n_wavenumbers : int, optional Number of wavenumbers to retain either side of wavenumber=0. Obviously only does anything if spectral=True integrate : bool, optional If True, computes and returns the integral of each term over the mass of the atmosphere. Otherwise, only the integrands are returned. Returns ------- atmos_energy_cycle : xarray Dataset Limitations ----------- All input array coordinates must follow standard naming (see doppyo.utils.get_lat_name(), doppyo.utils.get_lon_name(), etc) Pressure levels must be provided in units of hPa Notes ----- The following notation is used below (stackable, e.g. *_ZT indicates the time average of the zonal average): *_A -> area average over an isobaric surface *_a -> departure from area average *_Z -> zonal average *_z -> departure from zonal average *_T -> time average *_t -> departure from time average Additionally, capital variables indicate Fourier transforms: F(u) = U F(v) = V F(omega) = O F(gh) = A F(temp) = B """ def _flip_n(da): """ Flips data along wavenumber coordinate """ daf = da.copy() daf['n'] = -daf['n'] return daf.sortby(daf['n']) def _truncate(F, n_truncate, dim): """ Converts spatial frequency dim to wavenumber, n, and truncates all wavenumbers greater than n_truncate """ F[dim] = 360 * F[dim] F = F.rename({dim : 'n'}) F = F.where(abs(F.n) <= n_truncate, drop=True) return F, _flip_n(F) def _triple_terms(A, B, C): """ Calculate triple term summation of the form \int_{m=-inf}^{inf} A(m) * B(n) * C(n - m) """ # Use rolling operator to build shifted terms ----- Am = A.rename({'n' : 'm'}) Cnm = C.rolling(n=len(C.n), center=True).construct('m', fill_value=0) Cnm['m'] = -C['n'].values # Drop m = 0 and n < 0 ----- Am = Am.where(Am['m'] != 0, drop=True) Cnm = Cnm.where(Cnm['m'] != 0, drop=True) return (B * (Am * Cnm)).sum(dim='m', skipna=False) def _triple_terms_loop(A, B, C): """ Calculate triple term summation of the form \int_{m=-inf}^{inf} A(m) * B(n) * C(n - m) """ # Loop over all m's and perform rolling sum ----- ms = A['n'].where(A['n'] != 0, drop=True).values ABC = A.copy() * 0 for m in ms: Am = A.sel(n=m) Cnm = C.shift(n=int(m)).fillna(0) ABC = ABC + (Am * B * Cnm) return ABC if terms is None: terms = ['Pz', 'Kz', 'Pe', 'Ke', 'Cz', 'Ca', 'Ce', 'Ck', 'Gz', 'Ge', 'Dz', 'De'] if isinstance(terms, str): terms = [terms] # Initialize some things ----- if lat_name is None: lat_name = utils.get_lat_name(temp) if lon_name is None: lon_name = utils.get_lon_name(temp) if plevel_name is None: plevel_name = utils.get_plevel_name(temp) degtorad = utils.constants().pi / 180 tan_lat = xr.ufuncs.tan(temp[lat_name] * degtorad) cos_lat = xr.ufuncs.cos(temp[lat_name] * degtorad) # Determine the stability parameter using Saltzman's approach ----- kappa = utils.constants().R_d / utils.constants().C_pd p_kap = (1000 / temp[plevel_name]) ** kappa theta_A = utils.average(temp * p_kap, [lat_name, lon_name], weights=cos_lat) dtheta_Adp = utils.differentiate_wrt(theta_A, dim=plevel_name, x=(theta_A[plevel_name] * 100)) gamma = - p_kap * (utils.constants().R_d) / ((temp[plevel_name] * 100) * utils.constants().C_pd) / dtheta_Adp # [1/K] energies = gamma.rename('gamma').to_dataset() # Compute zonal terms # ======================== if ('Pz' in terms): # Compute the total available potential energy in the zonally averaged temperature # distribution, Pz [also commonly called Az] ----- temp_A = utils.average(temp, [lat_name, lon_name], weights=cos_lat) temp_Z = temp.mean(dim=lon_name) temp_Za = temp_Z - temp_A Pz_int = gamma * utils.constants().C_pd / 2 * temp_Za ** 2 # [J/kg] energies['Pz_int'] = Pz_int if integrate: Pz = _int_over_atmos(Pz_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) # [J/m^2] energies['Pz'] = Pz if ('Kz' in terms): # Compute the total kinetic energy in zonally averaged motion, Kz [also commonly # called Kz] ----- u_Z = u.mean(dim=lon_name) v_Z = v.mean(dim=lon_name) Kz_int = 0.5 * (u_Z ** 2 + v_Z ** 2) # [J/kg] energies['Kz_int'] = Kz_int if integrate: Kz = _int_over_atmos(Kz_int, lat_name, lon_name, plevel_name, lon_dim=u[lon_name]) # [J/m^2] energies['Kz'] = Kz if ('Cz' in terms): # Compute the rate of conversion of zonal available potential energy (Pz) to zonal kinetic # energy (Kz), Cz [also commonly called Cz] ----- if vgradz: if 'v_Z' not in locals(): v_Z = v.mean(dim=lon_name) gh_Z = gh.mean(dim=lon_name) dghdlat = utils.differentiate_wrt(gh_Z, dim=lat_name, x=(gh_Z[lat_name] * degtorad)) Cz_int = - (utils.constants().g / utils.constants().R_earth) * v_Z * dghdlat # [W/kg] energies['Cz_int'] = Cz_int if integrate: Cz = _int_over_atmos(Cz_int, lat_name, lon_name, plevel_name, lon_dim=gh[lon_name]) # [W/m^2] energies['Cz'] = Cz else: if 'temp_Za' not in locals(): temp_A = utils.average(temp, [lat_name, lon_name], weights=cos_lat) temp_Z = temp.mean(dim=lon_name) temp_Za = temp_Z - temp_A omega_A = utils.average(omega, [lat_name, lon_name], weights=cos_lat) omega_Z = omega.mean(dim=lon_name) omega_Za = omega_Z - omega_A Cz_int = - (utils.constants().R_d / (temp[plevel_name] * 100)) * omega_Za * temp_Za # [W/kg] energies['Cz_int'] = Cz_int if integrate: Cz = _int_over_atmos(Cz_int, lat_name, lon_name, plevel_name, lon_dim=omega[lon_name]) # [W/m^2] energies['Cz'] = Cz # Compute eddy terms in Fourier space if spectral=True # ========================================================== if spectral: if ('Pe' in terms): # Compute the total available potential energy eddies of wavenumber n, Pn ----- Bp, Bn = _truncate(utils.fft(temp, dim=lon_name, nfft=len(temp[lon_name]), twosided=True, shift=True) / len(temp[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) Pn_int = (gamma * utils.constants().C_pd * abs(Bp) ** 2) energies['Pn_int'] = Pn_int if integrate: Pn = _int_over_atmos(Pn_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) # [J/m^2] energies['Pn'] = Pn # Compute the rate of transfer of available potential energy to eddies of # wavenumber n from eddies of all other wavenumbers, Sn ----- Up, Un = _truncate(utils.fft(u, dim=lon_name, nfft=len(u[lon_name]), twosided=True, shift=True) / len(u[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) Vp, Vn = _truncate(utils.fft(v, dim=lon_name, nfft=len(v[lon_name]), twosided=True, shift=True) / len(v[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) Op, On = _truncate(utils.fft(omega, dim=lon_name, nfft=len(omega[lon_name]), twosided=True, shift=True) / len(omega[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) dBpdlat = utils.differentiate_wrt(Bp, dim=lat_name, x=(Bp[lat_name] * degtorad)) dBndlat = utils.differentiate_wrt(Bn, dim=lat_name, x=(Bn[lat_name] * degtorad)) dBpdp = utils.differentiate_wrt(Bp, dim=plevel_name, x=(Bp[plevel_name] * 100)) dBndp = utils.differentiate_wrt(Bn, dim=plevel_name, x=(Bn[plevel_name] * 100)) if loop_triple_terms: BpBnUp = _triple_terms_loop(Bp, Bn, Up) BpBpUn = _triple_terms_loop(Bp, Bp, Un) BpglBnVp = _triple_terms_loop(Bp, dBndlat, Vp) BpglBpVn = _triple_terms_loop(Bp, dBpdlat, Vn) BpgpBnOp = _triple_terms_loop(Bp, dBndp, Op) BpgpBpOn = _triple_terms_loop(Bp, dBpdp, On) BpBnOp = _triple_terms_loop(Bp, Bn, Op) BpBpOn = _triple_terms_loop(Bp, Bp, On) else: BpBnUp = _triple_terms(Bp, Bn, Up) BpBpUn = _triple_terms(Bp, Bp, Un) BpglBnVp = _triple_terms(Bp, dBndlat, Vp) BpglBpVn = _triple_terms(Bp, dBpdlat, Vn) BpgpBnOp = _triple_terms(Bp, dBndp, Op) BpgpBpOn = _triple_terms(Bp, dBpdp, On) BpBnOp = _triple_terms(Bp, Bn, Op) BpBpOn = _triple_terms(Bp, Bp, On) Sn_int = -gamma * utils.constants().C_pd * (1j * Bp['n']) / \ (utils.constants().R_earth * xr.ufuncs.cos(Bp[lat_name] * degtorad)) * \ (BpBnUp + BpBpUn) + \ gamma * utils.constants().C_pd / utils.constants().R_earth * \ (BpglBnVp + BpglBpVn) + \ gamma * utils.constants().C_pd * (BpgpBnOp + BpgpBpOn) + \ gamma * utils.constants().R_d / Bp[plevel_name] * \ (BpBnOp + BpBpOn) energies['Sn_int'] = Sn_int if integrate: Sn = abs(_int_over_atmos(Sn_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name])) # [W/m^2] energies['Sn'] = Sn if ('Ke' in terms): # Compute the total kinetic energy in eddies of wavenumber n, Kn ----- if 'U' not in locals(): Up, Un = _truncate(utils.fft(u, dim=lon_name, nfft=len(u[lon_name]), twosided=True, shift=True) / len(u[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'V' not in locals(): Vp, Vn = _truncate(utils.fft(v, dim=lon_name, nfft=len(v[lon_name]), twosided=True, shift=True) / len(v[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) Kn_int = abs(Up) ** 2 + abs(Vp) ** 2 energies['Kn_int'] = Kn_int if integrate: Kn = _int_over_atmos(Kn_int, lat_name, lon_name, plevel_name, lon_dim=u[lon_name]) # [J/m^2] energies['Kn'] = Kn # Compute the rate of transfer of kinetic energy to eddies of wavenumber n from # eddies of all other wavenumbers, Ln ----- if 'O' not in locals(): Op, On = _truncate(utils.fft(omega, dim=lon_name, nfft=len(omega[lon_name]), twosided=True, shift=True) / len(omega[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) dUpdp = utils.differentiate_wrt(Up, dim=plevel_name, x=(Up[plevel_name] * 100)) dVpdp = utils.differentiate_wrt(Vp, dim=plevel_name, x=(Vp[plevel_name] * 100)) dOpdp = utils.differentiate_wrt(Op, dim=plevel_name, x=(Op[plevel_name] * 100)) dOndp = utils.differentiate_wrt(On, dim=plevel_name, x=(On[plevel_name] * 100)) dVpcdl = utils.differentiate_wrt(Vp * cos_lat, dim=lat_name, x=(Vp[lat_name] * degtorad)) dVncdl = utils.differentiate_wrt(Vn * cos_lat, dim=lat_name, x=(Vn[lat_name] * degtorad)) dUpdl = utils.differentiate_wrt(Up, dim=lat_name, x=(Up[lat_name] * degtorad)) dVpdl = utils.differentiate_wrt(Vp, dim=lat_name, x=(Vp[lat_name] * degtorad)) if loop_triple_terms: UpUnUp = _triple_terms_loop(Up, Un, Up) UpUpUn = _triple_terms_loop(Up, Up, Un) VpVnUp = _triple_terms_loop(Vp, Vn, Up) VpVpUn = _triple_terms_loop(Vp, Vp, Un) VpUnUp = _triple_terms_loop(Vp, Un, Up) VpUpUn = _triple_terms_loop(Vp, Up, Un) UpVnUp = _triple_terms_loop(Up, Vn, Up) UpVpUn = _triple_terms_loop(Up, Vp, Un) gpUpUngpOp = _triple_terms_loop(dUpdp, Un, dOpdp) gpUpUpgpOn = _triple_terms_loop(dUpdp, Up, dOndp) gpVpVngpOp = _triple_terms_loop(dVpdp, Vn, dOpdp) gpVpVpgpOn = _triple_terms_loop(dVpdp, Vp, dOndp) glUpUnglVpc = _triple_terms_loop(dUpdl, Un, dVpcdl) glUpUpglVnc = _triple_terms_loop(dUpdl, Up, dVncdl) glVpVnglVpc = _triple_terms_loop(dVpdl, Vn, dVpcdl) glVpVpglVnc = _triple_terms_loop(dVpdl, Vp, dVncdl) else: UpUnUp = _triple_terms(Up, Un, Up) UpUpUn = _triple_terms(Up, Up, Un) VpVnUp = _triple_terms(Vp, Vn, Up) VpVpUn = _triple_terms(Vp, Vp, Un) VpUnUp = _triple_terms(Vp, Un, Up) VpUpUn = _triple_terms(Vp, Up, Un) UpVnUp = _triple_terms(Up, Vn, Up) UpVpUn = _triple_terms(Up, Vp, Un) gpUpUngpOp = _triple_terms(dUpdp, Un, dOpdp) gpUpUpgpOn = _triple_terms(dUpdp, Up, dOndp) gpVpVngpOp = _triple_terms(dVpdp, Vn, dOpdp) gpVpVpgpOn = _triple_terms(dVpdp, Vp, dOndp) glUpUnglVpc = _triple_terms(dUpdl, Un, dVpcdl) glUpUpglVnc = _triple_terms(dUpdl, Up, dVncdl) glVpVnglVpc = _triple_terms(dVpdl, Vn, dVpcdl) glVpVpglVnc = _triple_terms(dVpdl, Vp, dVncdl) Ln_int = -(1j * Up['n']) / (utils.constants().R_earth * cos_lat) * \ (UpUnUp - UpUpUn) + \ (1j * Vp['n']) / (utils.constants().R_earth * cos_lat) * \ (VpVnUp - VpVpUn) - \ tan_lat / utils.constants().R_earth * \ (VpUnUp + VpUpUn) + \ tan_lat / utils.constants().R_earth * \ (UpVnUp + UpVpUn) + \ (gpUpUngpOp + gpUpUpgpOn) + \ (gpVpVngpOp + gpVpVpgpOn) + \ 1 / (utils.constants().R_earth * cos_lat) * \ (glUpUnglVpc + glUpUpglVnc + glVpVnglVpc + glVpVpglVnc) energies['Ln_int'] = Ln_int if integrate: Ln = abs(_int_over_atmos(Ln_int, lat_name, lon_name, plevel_name, lon_dim=u[lon_name])) # [W/m^2] energies['Ln'] = Ln if ('Ca' in terms): # Compute the rate of transfer of zonal available potential energy to eddy # available potential energy in wavenumber n, Rn ----- if 'temp_Z' not in locals(): temp_Z = temp.mean(dim=lon_name) if 'V' not in locals(): Vp, Vn = _truncate(utils.fft(v, dim=lon_name, nfft=len(v[lon_name]), twosided=True, shift=True) / len(v[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'B' not in locals(): Bp, Bn = _truncate(utils.fft(temp, dim=lon_name, nfft=len(temp[lon_name]), twosided=True, shift=True) / len(temp[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'O' not in locals(): Op, On = _truncate(utils.fft(omega, dim=lon_name, nfft=len(omega[lon_name]), twosided=True, shift=True) / len(omega[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) dtemp_Zdlat = utils.differentiate_wrt(temp_Z, dim=lat_name, x=(temp_Z[lat_name] * degtorad)) theta = temp * p_kap theta_Z = theta.mean(dim=lon_name) theta_Za = theta_Z - theta_A dtheta_Zadp = utils.differentiate_wrt(theta_Za, dim=plevel_name, x=(theta_Za[plevel_name] * 100)) Rn_int = gamma * utils.constants().C_pd * ((dtemp_Zdlat / utils.constants().R_earth) * (Vp * Bn + Vn * Bp) + (p_kap * dtheta_Zadp) * (Op * Bn + On * Bp)) # [W/kg] energies['Rn_int'] = Rn_int if integrate: Rn = abs(_int_over_atmos(Rn_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name])) # [W/m^2] energies['Rn'] = Rn if ('Ce' in terms): # Compute the rate of conversion of available potential energy of wavenumber n # to eddy kinetic energy of wavenumber n, Cn ----- if vgradz: if 'U' not in locals(): Up, Un = _truncate(utils.fft(u, dim=lon_name, nfft=len(u[lon_name]), twosided=True, shift=True) / len(u[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'V' not in locals(): Vp, Vn = _truncate(utils.fft(v, dim=lon_name, nfft=len(v[lon_name]), twosided=True, shift=True) / len(v[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) Ap, An = _truncate(utils.fft(gh, dim=lon_name, nfft=len(gh[lon_name]), twosided=True, shift=True) / len(gh[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) dApdlat = utils.differentiate_wrt(Ap, dim=lat_name, x=(Ap[lat_name] * degtorad)) dAndlat = utils.differentiate_wrt(An, dim=lat_name, x=(An[lat_name] * degtorad)) Cn_int = (((-1j * utils.constants().g * Up['n']) / \ (utils.constants().R_earth * xr.ufuncs.cos(Up[lat_name] * degtorad))) * \ (Ap * Un - An * Up)) - \ ((utils.constants().g / utils.constants().R_earth) * \ (dApdlat * Vn + dAndlat * Vp)) # [W/kg] energies['Cn_int'] = Cn_int if integrate: Cn = abs(_int_over_atmos(Cn_int, lat_name, lon_name, plevel_name, lon_dim=u[lon_name])) # [W/m^2] energies['Cn'] = Cn else: if 'O' not in locals(): Op, On = _truncate(utils.fft(omega, dim=lon_name, nfft=len(omega[lon_name]), twosided=True, shift=True) / len(omega[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'B' not in locals(): Bp, Bn = _truncate(utils.fft(temp, dim=lon_name, nfft=len(temp[lon_name]), twosided=True, shift=True) / len(temp[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) Cn_int = - (utils.constants().R_d / (omega[plevel_name] * 100)) * (Op * Bn + On * Bp) # [W/kg] energies['Cn_int'] = Cn_int if integrate: Cn = abs(_int_over_atmos(Cn_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name])) # [W/m^2] energies['Cn'] = Cn if ('Ck' in terms): # Compute the rate of transfer of kinetic energy to the zonally averaged flow # from eddies of wavenumber n, Mn ----- if 'v_Z' not in locals(): v_Z = v.mean(dim=lon_name) if 'u_Z' not in locals(): u_Z = u.mean(dim=lon_name) if 'U' not in locals(): Up, Un = _truncate(utils.fft(u, dim=lon_name, nfft=len(u[lon_name]), twosided=True, shift=True) / len(u[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'V' not in locals(): Vp, Vn = _truncate(utils.fft(v, dim=lon_name, nfft=len(v[lon_name]), twosided=True, shift=True) / len(v[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'O' not in locals(): Op, On = _truncate(utils.fft(omega, dim=lon_name, nfft=len(omega[lon_name]), twosided=True, shift=True) / len(omega[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) dv_Zdlat = utils.differentiate_wrt(v_Z, dim=lat_name, x=(v[lat_name] * degtorad)) du_Zndlat = utils.differentiate_wrt(u_Z / xr.ufuncs.cos(u[lat_name] * degtorad), dim=lat_name, x=(u[lat_name] * degtorad)) dv_Zdp = utils.differentiate_wrt(v_Z, dim=plevel_name, x=(v[plevel_name] * 100)) du_Zdp = utils.differentiate_wrt(u_Z, dim=plevel_name, x=(u[plevel_name] * 100)) Mn_int = (-2 * Up * Un * v_Z * tan_lat / utils.constants().R_earth) + \ (2 * Vp * Vn * dv_Zdlat / utils.constants().R_earth + (Vp * On + Vn * Op) * dv_Zdp) + \ ((Up * On + Un * Op) * du_Zdp) + \ ((Up * Vn + Un * Vp) * xr.ufuncs.cos(u[lat_name] * degtorad) / \ utils.constants().R_earth * du_Zndlat) # [W/kg] energies['Mn_int'] = Mn_int if integrate: Mn = abs(_int_over_atmos(Mn_int, lat_name, lon_name, plevel_name, lon_dim=u[lon_name])) # [W/m^2] energies['Mn'] = Mn else: if ('Pe' in terms): # Compute the total eddy available potential energy, Pe [also commonly called # Ae] ----- if 'temp_Z' not in locals(): temp_Z = temp.mean(dim=lon_name) temp_z = temp - temp_Z Pe_int = gamma * utils.constants().C_pd / 2 * (temp_z ** 2).mean(dim=lon_name) # [J/kg] energies['Pe_int'] = Pe_int if integrate: Pe = _int_over_atmos(Pe_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) # [J/m^2] energies['Pe'] = Pe if ('Ke' in terms): # Compute the total eddy kinetic energy, Ke ----- if 'u_Z' not in locals(): u_Z = u.mean(dim=lon_name) if 'v_Z' not in locals(): v_Z = v.mean(dim=lon_name) u_z = u - u_Z v_z = v - v_Z Ke_int = 0.5 * (u_z ** 2 + v_z ** 2).mean(dim=lon_name) # [J/kg] energies['Ke_int'] = Ke_int if integrate: Ke = _int_over_atmos(Ke_int, lat_name, lon_name, plevel_name, lon_dim=u[lon_name]) # [J/m^2] energies['Ke'] = Ke if ('Ca' in terms): # Compute the rate of transfer of total available potential energy in the zonally # averaged temperature distribution (Pz) to total eddy available potential energy # (Pe), Ca ----- if 'v_Z' not in locals(): v_Z = v.mean(dim=lon_name) if 'temp_Z' not in locals(): temp_Z = temp.mean(dim=lon_name) if 'omega_Z' not in locals(): omega_Z = omega.mean(dim=lon_name) if 'theta_Z' not in locals(): theta = temp * p_kap theta_Z = theta.mean(dim=lon_name) if 'dtemp_Zdlat' not in locals(): dtemp_Zdlat = utils.differentiate_wrt(temp_Z, dim=lat_name, x=(temp_Z[lat_name] * degtorad)) v_z = v - v_Z temp_z = temp - temp_Z omega_z = omega - omega_Z oT_Z = (omega_z * temp_z).mean(dim=lon_name) oT_A = utils.average(omega_z * temp_z, [lat_name, lon_name], weights=cos_lat) oT_Za = oT_Z - oT_A theta_Za = theta_Z - theta_A dtheta_Zadp = utils.differentiate_wrt(theta_Za, dim=plevel_name, x=(theta_Za[plevel_name] * 100)) Ca_int = - gamma * utils.constants().C_pd * \ (((v_z * temp_z).mean(dim=lon_name) * dtemp_Zdlat / utils.constants().R_earth) + \ (p_kap * oT_Za * dtheta_Zadp)) # [W/kg] energies['Ca_int'] = Ca_int if integrate: Ca = _int_over_atmos(Ca_int, lat_name, lon_name, plevel_name, lon_dim=v[lon_name]) # [W/m^2] energies['Ca'] = Ca if ('Ce' in terms): # Compute the rate of transfer of total eddy available potential energy (Pe) to # total eddy kinetic energy (Ke), Ce ----- if 'temp_Z' not in locals(): temp_Z = temp.mean(dim=lon_name) if 'omega_Z' not in locals(): omega_Z = omega.mean(dim=lon_name) temp_z = temp - temp_Z omega_z = omega - omega_Z Ce_int = - (utils.constants().R_d / (temp[plevel_name] * 100)) * \ (omega_z * temp_z).mean(dim=lon_name) # [W/kg] energies['Ce_int'] = Ce_int if integrate: Ce = _int_over_atmos(Ce_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) # [W/m^2] energies['Ce'] = Ce if ('Ck' in terms): # Compute the rate of transfer of total eddy kinetic energy (Ke) to total kinetic # energy in zonally averaged motion (Kz), Ck ----- if 'u_Z' not in locals(): u_Z = u.mean(dim=lon_name) if 'v_Z' not in locals(): v_Z = v.mean(dim=lon_name) if 'omega_Z' not in locals(): omega_Z = omega.mean(dim=lon_name) u_z = u - u_Z v_z = v - v_Z omega_z = omega - omega_Z du_Zndlat = utils.differentiate_wrt(u_Z / cos_lat, dim=lat_name, x=(u_Z[lat_name] * degtorad)) dv_Zdlat = utils.differentiate_wrt(v_Z, dim=lat_name, x=(v_Z[lat_name] * degtorad)) du_Zdp = utils.differentiate_wrt(u_Z, dim=plevel_name, x=(u_Z[plevel_name] * 100)) dv_Zdp = utils.differentiate_wrt(v_Z, dim=plevel_name, x=(v_Z[plevel_name] * 100)) Ck_int = (u_z * v_z).mean(dim=lon_name) * cos_lat * du_Zndlat / utils.constants().R_earth + \ (u_z * omega_z).mean(dim=lon_name) * du_Zdp + \ (v_z ** 2).mean(dim=lon_name) * dv_Zdlat / utils.constants().R_earth + \ (v_z * omega_z).mean(dim=lon_name) * dv_Zdp - \ (u_z ** 2).mean(dim=lon_name) * v_Z * tan_lat / utils.constants().R_earth energies['Ck_int'] = Ck_int if integrate: Ck = _int_over_atmos(Ck_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) # [W/m^2] energies['Ck'] = Ck if ('Gz' in terms): # Compute the rate of generation of zonal available potential energy due to the zonally # averaged heating, Gz ----- if ('Cz' not in terms) | ('Ca' not in terms): raise ValueError('The rate of generation of zonal available potential energy, Gz, is computed from the sum of Cz and Ca. Please add these to the list, terms=[<terms>].') if spectral: warnings.warn('Rate of generation of zonal available potential energy is computed from the sum of Cz and Ca and cannot be computed in Fourier space. Returning Gz in real-space.') Ca_int = Rn_int.where(Rn_int.n > 0, drop=True).sum(dim='n').real # sum Rn to get Ca Gz_int = Cz_int + Ca_int energies['Gz_int'] = Gz_int if integrate: Gz = _int_over_atmos(Gz_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) # [W/m^2] energies['Gz'] = Gz if ('Ge' in terms): # Compute the rate of generation of eddy available potential energy (Ae), Ge ----- if ('Ce' not in terms) | ('Ca' not in terms): raise ValueError('The rate of generation of eddy available potential energy, Ge, is computed from the residual of Ce and Ca. Please add these to the list, terms=[<terms>].') if spectral: warnings.warn('The rate of generation of eddy available potential energy is computed from the residual of Ce and Ca and cannot be computed in Fourier space. Returning Ge in real-space.') Ce_int = Cn_int.where(Cn_int.n > 0, drop=True).sum(dim='n').real # sum Cn to get Ce if 'Ca_int' not in locals(): Ca_int = Rn_int.where(Rn_int.n > 0, drop=True).sum(dim='n').real # sum Rn to get Ca Ge_int = Ce_int - Ca_int energies['Ge_int'] = Ge_int if integrate: Ge = _int_over_atmos(Ge_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) # [W/m^2] energies['Ge'] = Ge if ('Dz' in terms): # Compute the rate of viscous dissipation of zonal kinetic energy, Dz ----- if ('Cz' not in terms) | ('Ck' not in terms): raise ValueError('The rate of viscous dissipation of zonal kinetic energy, Dz, is computed from the residual of Cz and Ck. Please add these to the list, terms=[<terms>].') if spectral: warnings.warn('The rate of viscous dissipation of zonal kinetic energy, Dz, is computed from the residual of Cz and Ck and cannot be computed in Fourier space. Returning De in real-space.') Ck_int = Mn_int.where(Mn_int.n > 0, drop=True).sum(dim='n').real # sum Mn to get Ck Dz_int = Cz_int - Ck_int energies['Dz_int'] = Dz_int if integrate: Dz = _int_over_atmos(Dz_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) # [W/m^2] energies['Dz'] = Dz if ('De' in terms): # Compute the rate of dissipation of eddy kinetic energy (Ke), De ----- if ('Ce' not in terms) | ('Ck' not in terms): raise ValueError('The rate of viscous dissipation of eddy kinetic energy, De, is computed from the residual of Ce and Ck. Please add these to the list, terms=[<terms>].') if spectral: warnings.warn('The rate of viscous dissipation of eddy kinetic energy, De, is computed from the residual of Ce and Ck and cannot be computed in Fourier space. Returning De in real-space.') if 'Ce_int' not in locals(): Ce_int = Cn_int.where(Cn_int.n > 0, drop=True).sum(dim='n').real # sum Cn to get Ce if 'Ck_int' not in locals(): Ck_int = Mn_int.where(Mn_int.n > 0, drop=True).sum(dim='n').real # sum Mn to get Ck De_int = Ce_int - Ck_int energies['De_int'] = De_int if integrate: De = _int_over_atmos(De_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) # [W/m^2] energies['De'] = De return energies # =================================================================================================== def auto_merge(paths, preprocess=None, parallel=True, **kwargs): """ Automatically merge a split xarray Dataset. This is designed to behave like `xarray.open_mfdataset`, except it supports concatenation along multiple dimensions. Parameters ---------- datasets : str or list of str or list of xarray.Dataset Either a glob expression or list of paths as you would pass to xarray.open_mfdataset, or a list of xarray datasets. If a list of datasets is passed, you should make sure that they are represented as dask arrays to avoid reading the whole dataset into memory. Returns ------- xarray.Dataset The merged dataset. """ if parallel: # wrap the open_dataset, getattr, and preprocess with delayed open_ = dask.delayed(xr.open_dataset) getattr_ = dask.delayed(getattr) if preprocess is not None: preprocess = dask.delayed(preprocess) else: open_ = open_dataset getattr_ = getattr datasets = [open_(p, **kwargs) for p in paths] file_objs = [getattr_(ds, '_file_obj') for ds in datasets] if preprocess is not None: datasets = [preprocess(ds) for ds in datasets] if parallel: # calling compute here will return the datasets/file_objs lists, # the underlying datasets will still be stored as dask arrays datasets, file_objs = dask.compute(datasets, file_objs) def _combine_along_last_dim(datasets): merged = [] # Determine the dimension along which the dataset is split split_dims = [d for d in datasets[0].dims if len(np.unique([ds[d].values[0] for ds in datasets])) > 1] # Concatenate along one of the split dimensions concat_dim = split_dims[-1] # Group along the remaining dimensions and concatenate within each # group. sorted_ds = sorted(datasets, key=lambda ds: tuple(ds[d].values[0] for d in split_dims)) for _, group in itertools.groupby( sorted_ds, key=lambda ds: tuple(ds[d].values[0] for d in split_dims[:-1]) ): merged.append(xr.auto_combine(group, concat_dim=concat_dim)) return merged merged = datasets while len(merged) > 1: merged = _combine_along_last_dim(merged) return merged[0]

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import numpy as np import pandas as pd import xarray as xr import cartopy from collections import Sequence from itertools import chain, count import matplotlib import matplotlib.pyplot as plt import matplotlib.ticker as mticker from cartopy.util import add_cyclic_point from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER from doppyo import utils def rank_gufunc(x): import bottleneck ranks = bottleneck.nanrankdata(x,axis=-1) ranks = ranks[...,0] return ranks def compute_rank(da_1, da_2, over_dim): if over_dim not in da_2.coords: da_2_pass = da_2.copy() da_2_pass.coords[over_dim] = -1 da_2_pass = da_2_pass.expand_dims(over_dim) else: da_2_pass = da_2.copy() aligned = xr.align(da_2_pass, da_1, join='inner', exclude=over_dim) combined = xr.concat(aligned, dim=over_dim) return xr.apply_ufunc(rank_gufunc, combined, input_core_dims=[[over_dim]], dask='allowed', output_dtypes=[int]).rename('rank') def categorize(da, bin_edges): return xr.apply_ufunc(np.digitize, da, bin_edges, input_core_dims=[[],[]], dask='allowed', output_dtypes=[int]).rename('categorized') def unstack_and_count(da, dims): try: unstacked = da.unstack(da.dims[0]) except ValueError: unstacked = da if dims is None: return ((0 * unstacked) + 1) else: return ((0 * unstacked) + 1).sum(dim=dims, skipna=True) def compute_histogram(da, bin_edges, over_dims): da = da.rename('data') hist = da.groupby_bins(da, bins=bin_edges, squeeze=False) \ .apply(unstack_and_count, dims=over_dims) \ .fillna(0) \ .rename({'data_bins' : 'bins'}) hist['bins'] = (bin_edges[0:-1]+bin_edges[1:])/2 return hist.astype(int).where(hist.sum('bins') != 0) def calc_gradient(da, dim, x=None): da_n = da.copy() if x is None: x = da_n[dim] centre_chunk = range(len(x[dim])-2) f_hd = da_n.shift(**{dim:-2}) f = da_n.shift(**{dim:-1}) f_hs = da_n hs = x.shift(**{dim:-1}) - x hd = x.shift(**{dim:-2}) - x.shift(**{dim:-1}) c = (hs ** 2 * f_hd + (hd ** 2 - hs ** 2) * f - hd ** 2 * f_hs) / \ (hs * hd * (hd + hs)).isel(**{dim : centre_chunk}) c[dim] = x[dim][1:-1] l = (da_n.shift(**{dim:-1}) - da_n).isel(**{dim : 0}) / \ (x.shift(**{dim:-1}) - x).isel(**{dim : 0}) r = (-da_n.shift(**{dim:1}) + da_n).isel(**{dim : -1}) / \ (-x.shift(**{dim:1}) + x).isel(**{dim : -1}) grad = xr.concat([l, c, r], dim=dim) grad[dim] = da[dim] return grad def bias_correct_ms(da_biased, da_target, da_target_clim=None, init_date_name='init_date', lead_time_name='lead_time'): def _groupby_lead_and_mean(da, over_dims, init_date_name, lead_time_name): return da.unstack('stacked_' + init_date_name + '_' + lead_time_name).groupby(lead_time_name).mean(over_dims, skipna=True) def _groupby_lead_and_std(da, over_dims, init_date_name, lead_time_name): return da.unstack('stacked_' + init_date_name + '_' + lead_time_name).groupby(lead_time_name).std(over_dims, skipna=True) def _unstack_and_shift_per_month(da, shift, init_date_name, lead_time_name): da_us = da.unstack('stacked_' + init_date_name + '_' + lead_time_name) the_month = np.ndarray.flatten(da_us.month.values) the_month = int(np.unique(the_month[~np.isnan(the_month)])) return da_us - shift.sel(month=the_month) def _unstack_and_scale_per_month(da, scale, init_date_name, lead_time_name): da_us = da.unstack('stacked_' + init_date_name + '_' + lead_time_name) the_month = np.ndarray.flatten(da_us.month.values) the_month = int(np.unique(the_month[~np.isnan(the_month)])) return da_us * scale.sel(month=the_month) def _scale_per_month(da, scale): return da.groupby('time.month') * scale _anomalize = lambda data, clim: datetime_to_leadtime( anomalize( leadtime_to_datetime(data),clim)) _rescale = lambda da, scale : datetime_to_leadtime( _scale_per_month( leadtime_to_datetime(da), scale)) da_biased = da_biased.copy() da_target = da_target.copy() month = (da_biased[init_date_name].dt.month + da_biased[lead_time_name]) % 12 month = month.where(month != 0, 12) da_biased.coords['month'] = month try: da_biased_mean = da_biased.groupby('month').apply(_groupby_lead_and_mean, over_dims=[init_date_name,'ensemble'], init_date_name=init_date_name, lead_time_name=lead_time_name) except ValueError: da_biased_mean = da_biased.groupby('month').apply(_groupby_lead_and_mean, over_dims=init_date_name, init_date_name=init_date_name, lead_time_name=lead_time_name) if da_target_clim is not None: da_target_mean = da_target.groupby('time.month').mean('time') da_meancorr = da_biased.groupby('month').apply(_unstack_and_shift_per_month, shift=(da_biased_mean - da_target_mean), init_date_name=init_date_name, lead_time_name=lead_time_name) \ .mean('month', skipna=True) da_meancorr[lead_time_name] = da_biased[lead_time_name] da_meancorr.coords['month'] = month da_anom_meancorr = da_meancorr.groupby(init_date_name).apply(_anomalize, clim=da_target_clim) da_anom_meancorr.coords['month'] = month else: da_anom_meancorr = da_biased.groupby('month').apply(_unstack_and_shift_per_month, shift=(da_biased_mean), init_date_name=init_date_name, lead_time_name=lead_time_name) \ .mean('month', skipna=True) da_anom_meancorr[lead_time_name] = da_anom_meancorr[lead_time_name] da_anom_meancorr.coords['month'] = month try: da_biased_std_tmp = da_anom_meancorr.groupby('month').apply(_groupby_lead_and_std, over_dims=[init_date_name,'ensemble'], init_date_name=init_date_name, lead_time_name=lead_time_name) except ValueError: da_biased_std_tmp = da_anom_meancorr.groupby('month').apply(_groupby_lead_and_std, over_dims=init_date_name, init_date_name=init_date_name, lead_time_name=lead_time_name) try: da_target_std = da_target.sel(lat=da_biased.lat, lon=da_biased.lon).groupby('time.month').std('time') except: da_target_std = da_target.groupby('time.month').std('time') da_anom_stdcorr_tmp = da_anom_meancorr.groupby('month').apply(_unstack_and_scale_per_month, scale=(da_target_std / da_biased_std_tmp), init_date_name=init_date_name, lead_time_name=lead_time_name) \ .mean('month', skipna=True) da_anom_stdcorr_tmp[lead_time_name] = da_biased[lead_time_name] da_anom_stdcorr_tmp.coords['month'] = month try: da_biased_std = concat_times(da_anom_stdcorr_tmp).groupby('time.month').std(['time','ensemble']) except ValueError: da_biased_std = concat_times(da_anom_stdcorr_tmp).groupby('time.month').std('time') da_anom_stdcorr = da_anom_stdcorr_tmp.groupby(init_date_name).apply(_rescale, scale=(da_target_std / da_biased_std)) if da_target_clim is not None: da_stdcorr = da_anom_stdcorr.groupby(init_date_name).apply(_anomalize, clim=-da_target_clim) return da_stdcorr.drop('month'), da_anom_stdcorr.drop('month') else: return da_anom_stdcorr.drop('month') def bias_correct_m(da_biased, da_target, da_target_clim=None, init_date_name='init_date', lead_time_name='lead_time'): def _groupby_lead_and_mean(da, over_dims, init_date_name, lead_time_name): return da.unstack('stacked_' + init_date_name + '_' + lead_time_name).groupby(lead_time_name).mean(over_dims, skipna=True) def _unstack_and_shift_per_month(da, shift, init_date_name, lead_time_name): da_us = da.unstack('stacked_' + init_date_name + '_' + lead_time_name) the_month = np.ndarray.flatten(da_us.month.values) the_month = int(np.unique(the_month[~np.isnan(the_month)])) return da_us - shift.sel(month=the_month) _anomalize = lambda data, clim: datetime_to_leadtime( anomalize( leadtime_to_datetime(data),clim)) da_biased = da_biased.copy() da_target = da_target.copy() month = (da_biased[init_date_name].dt.month + da_biased[lead_time_name]) % 12 month = month.where(month != 0, 12) da_biased.coords['month'] = month try: da_biased_mean = da_biased.groupby('month').apply(_groupby_lead_and_mean, over_dims=[init_date_name,'ensemble'], init_date_name=init_date_name, lead_time_name=lead_time_name) except ValueError: da_biased_mean = da_biased.groupby('month').apply(_groupby_lead_and_mean, over_dims=init_date_name, init_date_name=init_date_name, lead_time_name=lead_time_name) if da_target_clim is not None: da_target_mean = da_target.groupby('time.month').mean('time') da_meancorr = da_biased.groupby('month').apply(_unstack_and_shift_per_month, shift=(da_biased_mean - da_target_mean), init_date_name=init_date_name, lead_time_name=lead_time_name) \ .mean('month', skipna=True) da_meancorr[lead_time_name] = da_biased[lead_time_name] da_meancorr.coords['month'] = month da_anom_meancorr = da_meancorr.groupby(init_date_name).apply(_anomalize, clim=da_target_clim) da_anom_meancorr.coords['month'] = month else: da_anom_meancorr = da_biased.groupby('month').apply(_unstack_and_shift_per_month, shift=(da_biased_mean), init_date_name=init_date_name, lead_time_name=lead_time_name) \ .mean('month', skipna=True) da_anom_meancorr[lead_time_name] = da_anom_meancorr[lead_time_name] da_anom_meancorr.coords['month'] = month if da_target_clim is not None: da_meancorrr = da_anom_meancorr.groupby(init_date_name).apply(_anomalize, clim=-da_target_clim) return da_meancorr.drop('month'), da_anom_meancorr.drop('month') else: return da_anom_meancorr.drop('month') def conditional_bias_correct(da_cmp, da_ref, over_dims): cc = skill.compute_Pearson_corrcoef(da_cmp.mean('ensemble'), da_ref, over_dims=over_dims, subtract_local_mean=False) correct_cond_bias = (da_ref.std(over_dims) / da_cmp.mean('ensemble').std(over_dims)) * cc return da_cmp * correct_cond_bias def trunc_time(time, freq): return time.astype('<M8[' + freq + ']') def month_delta(date_in, delta, trunc_to_start=False): date_mod = pd.Timestamp(date_in) m, y = (date_mod.month + delta) % 12, date_mod.year + ((date_mod.month) + delta - 1) // 12 if not m: m = 12 d = min(date_mod.day, [31, 29 if y % 4 == 0 and not y % 400 == 0 else 28,31,30,31,30,31,31,30,31,30,31][m - 1]) if trunc_to_start: date_out = trunc_time(np.datetime64(date_mod.replace(day=d,month=m, year=y)),'M') else: date_out = np.datetime64(date_mod.replace(day=d,month=m, year=y)) return np.datetime64(date_out,'ns') def year_delta(date_in, delta, trunc_to_start=False): date_mod = month_delta(date_in, 12 * delta) if trunc_to_start: date_out = trunc_time(date_mod,'Y') else: date_out = date_mod return date_out def datetime_to_leadtime(data_in): init_date = data_in.time.values[0] lead_times = range(len(data_in.time)) try: freq = pd.infer_freq(data_in.time.values) if '-' in freq: freq = freq[:freq.find('-')] incr_string = ''.join([i for i in freq if i.isdigit()]) freq_incr = [int(incr_string) if incr_string else 1][0] freq_type = ''.join([i for i in freq if not i.isdigit()]) if 'QS' in freq_type: freq = str(3*freq_incr) + 'MS' elif 'Q' in freq_type: freq = str(3*freq_incr) + 'M' elif ('YS' in freq_type) | ('AS' in freq_type): freq = str(12*freq_incr) + 'MS' elif ('Y' in freq_type) | ('A' in freq_type): freq = str(12*freq_incr) + 'M' except ValueError: dt = (data_in.time.values[1] - data_in.time.values[0]) / np.timedelta64(1, 's') month = data_in.time.dt.month[0] if dt == 60*60*24: freq = 'D' elif ((month == 1) | (month == 3) | (month == 5) | (month == 7) | (month == 8) | (month == 10) | (month == 12)) & (dt == 31*60*60*24): freq = 'MS' elif ((month == 4) | (month == 6) | (month == 9) | (month == 11)) & (dt == 30*60*60*24): freq = 'MS' elif (month == 2) & ((dt == 28*60*60*24) | (dt == 29*60*60*24)): freq = 'MS' elif (dt == 365*60*60*24) | (dt == 366*60*60*24): freq = 'A' else: freq = 'NA' data_out = data_in.rename({'time' : 'lead_time'}) data_out['lead_time'] = lead_times data_out['lead_time'].attrs['units'] = freq data_out.coords['init_date'] = init_date return data_out def leadtime_to_datetime(data_in, init_date_name='init_date', lead_time_name='lead_time'): try: init_date = data_in[init_date_name].values[0] except IndexError: init_date = data_in[init_date_name].values lead_times = list(map(int, data_in[lead_time_name].values)) freq = data_in[lead_time_name].attrs['units'] datetimes = (pd.date_range(init_date, periods=len(lead_times), freq=freq)).values data_out = data_in.drop(init_date_name) data_out = data_out.rename({lead_time_name : 'time'}) data_out['time'] = datetimes return prune(data_out) def get_nearest_point(da, lat, lon): return da.sel(lat=lat,lon=lon,method='nearest') def plot_fields(data, title=None, headings=None, ncol=2, contour=False, vlims=None, clims=None, squeeze_row=1, squeeze_col=1, squeeze_cbar=1, shift_cbar=1, cmap='viridis', fontsize=12, invert=False): def _depth(seq): for level in count(): if not seq: return level seq = list(chain.from_iterable(s for s in seq if isinstance(s, Sequence))) matplotlib.rc('font', family='sans-serif') matplotlib.rc('font', serif='Helvetica') matplotlib.rc('text', usetex='false') matplotlib.rcParams.update({'font.size': fontsize}) nrow = int(np.ceil(len(data)/ncol)); fig = plt.figure(figsize=(11*squeeze_col, nrow*4*squeeze_row)) if (clims is not None) & (np.shape(vlims) != np.shape(clims)): raise ValueError('The input clims must be equal in size to vlims') one_cbar = False if vlims is None: vlims = [[None, None]] * len(data) if _depth(vlims) == 1: one_cbar = True over_count = 1 for idx,dat in enumerate(data): if one_cbar: vmin, vmax = vlims if clims is not None: cmin, cmax = clims else: vmin, vmax = vlims[idx] if clims is not None: cmin, cmax = clims[idx] if ('lat' in dat.dims) and ('lon' in dat.dims): trans = cartopy.crs.PlateCarree() ax = plt.subplot(nrow, ncol, over_count, projection=cartopy.crs.PlateCarree(central_longitude=180)) extent = [dat.lon.min(), dat.lon.max(), dat.lat.min(), dat.lat.max()] if contour is True: if clims is not None: ax.coastlines(color='gray') im = ax.contourf(dat.lon, dat.lat, dat, levels=np.linspace(vmin,vmax,12), origin='lower', transform=trans, vmin=vmin, vmax=vmax, cmap=cmap) ax.contour(dat.lon, dat.lat, dat, levels=np.linspace(cmin,cmax,12), origin='lower', transform=trans, vmin=vmin, vmax=vmax, colors='w', linewidths=2) ax.contour(dat.lon, dat.lat, dat, levels=np.linspace(cmin,cmax,12), origin='lower', transform=trans, vmin=vmin, vmax=vmax, colors='k', linewidths=1) else: ax.coastlines(color='black') im = ax.contourf(dat.lon, dat.lat, dat, origin='lower', transform=trans, vmin=vmin, vmax=vmax, cmap=cmap) else: ax.coastlines(color='black') im = ax.imshow(dat, origin='lower', extent=extent, transform=trans, vmin=vmin, vmax=vmax, cmap=cmap) gl = ax.gridlines(crs=cartopy.crs.PlateCarree(), draw_labels=True) gl.xlines = False gl.ylines = False gl.xlabels_top = False if over_count % ncol == 0: gl.ylabels_left = False elif (over_count+ncol-1) % ncol == 0: gl.ylabels_right = False else: gl.ylabels_left = False gl.ylabels_right = False gl.xlocator = mticker.FixedLocator([-90, 0, 90, 180]) gl.ylocator = mticker.FixedLocator([-90, -60, 0, 60, 90]) gl.xformatter = LONGITUDE_FORMATTER gl.yformatter = LATITUDE_FORMATTER if not one_cbar: cbar = plt.colorbar(im, ax=ax, orientation="horizontal", aspect=30/squeeze_cbar, pad=shift_cbar*0.1) tick_locator = mticker.MaxNLocator(nbins=6) cbar.locator = tick_locator cbar.update_ticks() if headings is not None: cbar.set_label(headings[idx], labelpad=5, fontsize=fontsize); elif headings is not None: ax.set_title(headings[idx], fontsize=fontsize) else: ax = plt.subplot(nrow, ncol, over_count) if 'lat' in dat.dims: x_plt = dat['lat'] y_plt = dat[utils.get_other_dims(dat,'lat')[0]] elif 'lon' in dat.dims: x_plt = dat['lon'] y_plt = dat[utils.get_other_dims(dat,'lon')[0]] else: x_plt = dat[dat.dims[1]] y_plt = dat[dat.dims[0]] extent = [x_plt.min(), x_plt.max(), y_plt.min(), y_plt.max()] if contour is True: if clims is not None: im = ax.contourf(x_plt, y_plt, dat, levels=np.linspace(vmin,vmax,12), vmin=vmin, vmax=vmax, cmap=cmap) ax.contour(x_plt, y_plt, dat, levels=np.linspace(cmin,cmax,12), colors='w', linewidths=2) ax.contour(x_plt, y_plt, dat, levels=np.linspace(cmin,cmax,12), colors='k', linewidths=1) else: im = ax.contourf(x_plt, y_plt, dat, vmin=vmin, vmax=vmax, cmap=cmap) else: im = ax.imshow(dat, origin='lower', extent=extent, vmin=vmin, vmax=vmax, cmap=cmap) if over_count % ncol == 0: ax.yaxis.tick_right() elif (over_count+ncol-1) % ncol == 0: ax.set_ylabel(y_plt.dims[0], fontsize=fontsize) else: ax.set_yticks([]) if idx / ncol >= nrow - 1: ax.set_xlabel(x_plt.dims[0], fontsize=fontsize) if not one_cbar: cbar = plt.colorbar(im, ax=ax, orientation="horizontal", aspect=30/squeeze_cbar, pad=shift_cbar*0.1) tick_locator = mticker.MaxNLocator(nbins=6) cbar.locator = tick_locator cbar.update_ticks() if headings is not None: cbar.set_label(headings[idx], labelpad=5, fontsize=fontsize); elif headings is not None: ax.set_title(headings[idx], fontsize=fontsize) if invert: ax.invert_yaxis() over_count += 1 plt.tight_layout() if one_cbar: vmin, vmax = vlims fig.subplots_adjust(bottom=shift_cbar*0.16) cbar_ax = fig.add_axes([0.15, 0.13, 0.7, squeeze_cbar*0.020]) cbar = fig.colorbar(im, cax=cbar_ax, orientation='horizontal'); cbar_ax.set_xlabel(title, rotation=0, labelpad=15, fontsize=fontsize); cbar.set_ticks(np.linspace(vmin,vmax,5)) elif title is not None: fig.suptitle(title, y=1) def size_GB(xr_object): bytes = xr_object.nbytes Ten2the9 = 10**9 Two2the30 = 2**30 GBytes = bytes / Ten2the9 GiBytes = bytes / Two2the30 print(xr_object.name, "is", GBytes, "GB", 'which is', GiBytes,"GiB") return GBytes,GiBytes def get_pres_name(da): if 'pfull' in da.dims: return 'pfull' elif 'phalf' in da.dims: return 'phalf' else: raise KeyError('Unable to determine pressure dimension') pass def did_event(da, event): eval_expr = event.replace(">", "da >").replace("<", "da <").replace("==", "da ==") \ .replace("=", "da ==").replace('&&', '&').replace('||', '|') \ .replace("and", "&").replace("or", "|") eval_expr = '(' + eval_expr + ').rename("event_logical")' return eval(eval_expr) def compute_likelihood(da_logical, dim='ensemble'): if dim == None: likelihood = da_logical else: likelihood = da_logical.mean(dim=dim).rename('likelihood') return likelihood def atmos_energy_cycle(temp, u, v, omega, gh, terms=None, vgradz=False, spectral=False, n_wavenumbers=20, integrate=True, loop_triple_terms=False, lat_name=None, lon_name=None, plevel_name=None): def _flip_n(da): daf = da.copy() daf['n'] = -daf['n'] return daf.sortby(daf['n']) def _truncate(F, n_truncate, dim): F[dim] = 360 * F[dim] F = F.rename({dim : 'n'}) F = F.where(abs(F.n) <= n_truncate, drop=True) return F, _flip_n(F) def _triple_terms(A, B, C): Am = A.rename({'n' : 'm'}) Cnm = C.rolling(n=len(C.n), center=True).construct('m', fill_value=0) Cnm['m'] = -C['n'].values Am = Am.where(Am['m'] != 0, drop=True) Cnm = Cnm.where(Cnm['m'] != 0, drop=True) return (B * (Am * Cnm)).sum(dim='m', skipna=False) def _triple_terms_loop(A, B, C): ms = A['n'].where(A['n'] != 0, drop=True).values ABC = A.copy() * 0 for m in ms: Am = A.sel(n=m) Cnm = C.shift(n=int(m)).fillna(0) ABC = ABC + (Am * B * Cnm) return ABC if terms is None: terms = ['Pz', 'Kz', 'Pe', 'Ke', 'Cz', 'Ca', 'Ce', 'Ck', 'Gz', 'Ge', 'Dz', 'De'] if isinstance(terms, str): terms = [terms] # Initialize some things ----- if lat_name is None: lat_name = utils.get_lat_name(temp) if lon_name is None: lon_name = utils.get_lon_name(temp) if plevel_name is None: plevel_name = utils.get_plevel_name(temp) degtorad = utils.constants().pi / 180 tan_lat = xr.ufuncs.tan(temp[lat_name] * degtorad) cos_lat = xr.ufuncs.cos(temp[lat_name] * degtorad) # Determine the stability parameter using Saltzman's approach ----- kappa = utils.constants().R_d / utils.constants().C_pd p_kap = (1000 / temp[plevel_name]) ** kappa theta_A = utils.average(temp * p_kap, [lat_name, lon_name], weights=cos_lat) dtheta_Adp = utils.differentiate_wrt(theta_A, dim=plevel_name, x=(theta_A[plevel_name] * 100)) gamma = - p_kap * (utils.constants().R_d) / ((temp[plevel_name] * 100) * utils.constants().C_pd) / dtheta_Adp energies = gamma.rename('gamma').to_dataset() if ('Pz' in terms): temp_A = utils.average(temp, [lat_name, lon_name], weights=cos_lat) temp_Z = temp.mean(dim=lon_name) temp_Za = temp_Z - temp_A Pz_int = gamma * utils.constants().C_pd / 2 * temp_Za ** 2 energies['Pz_int'] = Pz_int if integrate: Pz = _int_over_atmos(Pz_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) energies['Pz'] = Pz if ('Kz' in terms): u_Z = u.mean(dim=lon_name) v_Z = v.mean(dim=lon_name) Kz_int = 0.5 * (u_Z ** 2 + v_Z ** 2) energies['Kz_int'] = Kz_int if integrate: Kz = _int_over_atmos(Kz_int, lat_name, lon_name, plevel_name, lon_dim=u[lon_name]) energies['Kz'] = Kz if ('Cz' in terms): if vgradz: if 'v_Z' not in locals(): v_Z = v.mean(dim=lon_name) gh_Z = gh.mean(dim=lon_name) dghdlat = utils.differentiate_wrt(gh_Z, dim=lat_name, x=(gh_Z[lat_name] * degtorad)) Cz_int = - (utils.constants().g / utils.constants().R_earth) * v_Z * dghdlat energies['Cz_int'] = Cz_int if integrate: Cz = _int_over_atmos(Cz_int, lat_name, lon_name, plevel_name, lon_dim=gh[lon_name]) energies['Cz'] = Cz else: if 'temp_Za' not in locals(): temp_A = utils.average(temp, [lat_name, lon_name], weights=cos_lat) temp_Z = temp.mean(dim=lon_name) temp_Za = temp_Z - temp_A omega_A = utils.average(omega, [lat_name, lon_name], weights=cos_lat) omega_Z = omega.mean(dim=lon_name) omega_Za = omega_Z - omega_A Cz_int = - (utils.constants().R_d / (temp[plevel_name] * 100)) * omega_Za * temp_Za energies['Cz_int'] = Cz_int if integrate: Cz = _int_over_atmos(Cz_int, lat_name, lon_name, plevel_name, lon_dim=omega[lon_name]) energies['Cz'] = Cz if spectral: if ('Pe' in terms): Bp, Bn = _truncate(utils.fft(temp, dim=lon_name, nfft=len(temp[lon_name]), twosided=True, shift=True) / len(temp[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) Pn_int = (gamma * utils.constants().C_pd * abs(Bp) ** 2) energies['Pn_int'] = Pn_int if integrate: Pn = _int_over_atmos(Pn_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) energies['Pn'] = Pn Up, Un = _truncate(utils.fft(u, dim=lon_name, nfft=len(u[lon_name]), twosided=True, shift=True) / len(u[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) Vp, Vn = _truncate(utils.fft(v, dim=lon_name, nfft=len(v[lon_name]), twosided=True, shift=True) / len(v[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) Op, On = _truncate(utils.fft(omega, dim=lon_name, nfft=len(omega[lon_name]), twosided=True, shift=True) / len(omega[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) dBpdlat = utils.differentiate_wrt(Bp, dim=lat_name, x=(Bp[lat_name] * degtorad)) dBndlat = utils.differentiate_wrt(Bn, dim=lat_name, x=(Bn[lat_name] * degtorad)) dBpdp = utils.differentiate_wrt(Bp, dim=plevel_name, x=(Bp[plevel_name] * 100)) dBndp = utils.differentiate_wrt(Bn, dim=plevel_name, x=(Bn[plevel_name] * 100)) if loop_triple_terms: BpBnUp = _triple_terms_loop(Bp, Bn, Up) BpBpUn = _triple_terms_loop(Bp, Bp, Un) BpglBnVp = _triple_terms_loop(Bp, dBndlat, Vp) BpglBpVn = _triple_terms_loop(Bp, dBpdlat, Vn) BpgpBnOp = _triple_terms_loop(Bp, dBndp, Op) BpgpBpOn = _triple_terms_loop(Bp, dBpdp, On) BpBnOp = _triple_terms_loop(Bp, Bn, Op) BpBpOn = _triple_terms_loop(Bp, Bp, On) else: BpBnUp = _triple_terms(Bp, Bn, Up) BpBpUn = _triple_terms(Bp, Bp, Un) BpglBnVp = _triple_terms(Bp, dBndlat, Vp) BpglBpVn = _triple_terms(Bp, dBpdlat, Vn) BpgpBnOp = _triple_terms(Bp, dBndp, Op) BpgpBpOn = _triple_terms(Bp, dBpdp, On) BpBnOp = _triple_terms(Bp, Bn, Op) BpBpOn = _triple_terms(Bp, Bp, On) Sn_int = -gamma * utils.constants().C_pd * (1j * Bp['n']) / \ (utils.constants().R_earth * xr.ufuncs.cos(Bp[lat_name] * degtorad)) * \ (BpBnUp + BpBpUn) + \ gamma * utils.constants().C_pd / utils.constants().R_earth * \ (BpglBnVp + BpglBpVn) + \ gamma * utils.constants().C_pd * (BpgpBnOp + BpgpBpOn) + \ gamma * utils.constants().R_d / Bp[plevel_name] * \ (BpBnOp + BpBpOn) energies['Sn_int'] = Sn_int if integrate: Sn = abs(_int_over_atmos(Sn_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name])) energies['Sn'] = Sn if ('Ke' in terms): if 'U' not in locals(): Up, Un = _truncate(utils.fft(u, dim=lon_name, nfft=len(u[lon_name]), twosided=True, shift=True) / len(u[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'V' not in locals(): Vp, Vn = _truncate(utils.fft(v, dim=lon_name, nfft=len(v[lon_name]), twosided=True, shift=True) / len(v[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) Kn_int = abs(Up) ** 2 + abs(Vp) ** 2 energies['Kn_int'] = Kn_int if integrate: Kn = _int_over_atmos(Kn_int, lat_name, lon_name, plevel_name, lon_dim=u[lon_name]) energies['Kn'] = Kn if 'O' not in locals(): Op, On = _truncate(utils.fft(omega, dim=lon_name, nfft=len(omega[lon_name]), twosided=True, shift=True) / len(omega[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) dUpdp = utils.differentiate_wrt(Up, dim=plevel_name, x=(Up[plevel_name] * 100)) dVpdp = utils.differentiate_wrt(Vp, dim=plevel_name, x=(Vp[plevel_name] * 100)) dOpdp = utils.differentiate_wrt(Op, dim=plevel_name, x=(Op[plevel_name] * 100)) dOndp = utils.differentiate_wrt(On, dim=plevel_name, x=(On[plevel_name] * 100)) dVpcdl = utils.differentiate_wrt(Vp * cos_lat, dim=lat_name, x=(Vp[lat_name] * degtorad)) dVncdl = utils.differentiate_wrt(Vn * cos_lat, dim=lat_name, x=(Vn[lat_name] * degtorad)) dUpdl = utils.differentiate_wrt(Up, dim=lat_name, x=(Up[lat_name] * degtorad)) dVpdl = utils.differentiate_wrt(Vp, dim=lat_name, x=(Vp[lat_name] * degtorad)) if loop_triple_terms: UpUnUp = _triple_terms_loop(Up, Un, Up) UpUpUn = _triple_terms_loop(Up, Up, Un) VpVnUp = _triple_terms_loop(Vp, Vn, Up) VpVpUn = _triple_terms_loop(Vp, Vp, Un) VpUnUp = _triple_terms_loop(Vp, Un, Up) VpUpUn = _triple_terms_loop(Vp, Up, Un) UpVnUp = _triple_terms_loop(Up, Vn, Up) UpVpUn = _triple_terms_loop(Up, Vp, Un) gpUpUngpOp = _triple_terms_loop(dUpdp, Un, dOpdp) gpUpUpgpOn = _triple_terms_loop(dUpdp, Up, dOndp) gpVpVngpOp = _triple_terms_loop(dVpdp, Vn, dOpdp) gpVpVpgpOn = _triple_terms_loop(dVpdp, Vp, dOndp) glUpUnglVpc = _triple_terms_loop(dUpdl, Un, dVpcdl) glUpUpglVnc = _triple_terms_loop(dUpdl, Up, dVncdl) glVpVnglVpc = _triple_terms_loop(dVpdl, Vn, dVpcdl) glVpVpglVnc = _triple_terms_loop(dVpdl, Vp, dVncdl) else: UpUnUp = _triple_terms(Up, Un, Up) UpUpUn = _triple_terms(Up, Up, Un) VpVnUp = _triple_terms(Vp, Vn, Up) VpVpUn = _triple_terms(Vp, Vp, Un) VpUnUp = _triple_terms(Vp, Un, Up) VpUpUn = _triple_terms(Vp, Up, Un) UpVnUp = _triple_terms(Up, Vn, Up) UpVpUn = _triple_terms(Up, Vp, Un) gpUpUngpOp = _triple_terms(dUpdp, Un, dOpdp) gpUpUpgpOn = _triple_terms(dUpdp, Up, dOndp) gpVpVngpOp = _triple_terms(dVpdp, Vn, dOpdp) gpVpVpgpOn = _triple_terms(dVpdp, Vp, dOndp) glUpUnglVpc = _triple_terms(dUpdl, Un, dVpcdl) glUpUpglVnc = _triple_terms(dUpdl, Up, dVncdl) glVpVnglVpc = _triple_terms(dVpdl, Vn, dVpcdl) glVpVpglVnc = _triple_terms(dVpdl, Vp, dVncdl) Ln_int = -(1j * Up['n']) / (utils.constants().R_earth * cos_lat) * \ (UpUnUp - UpUpUn) + \ (1j * Vp['n']) / (utils.constants().R_earth * cos_lat) * \ (VpVnUp - VpVpUn) - \ tan_lat / utils.constants().R_earth * \ (VpUnUp + VpUpUn) + \ tan_lat / utils.constants().R_earth * \ (UpVnUp + UpVpUn) + \ (gpUpUngpOp + gpUpUpgpOn) + \ (gpVpVngpOp + gpVpVpgpOn) + \ 1 / (utils.constants().R_earth * cos_lat) * \ (glUpUnglVpc + glUpUpglVnc + glVpVnglVpc + glVpVpglVnc) energies['Ln_int'] = Ln_int if integrate: Ln = abs(_int_over_atmos(Ln_int, lat_name, lon_name, plevel_name, lon_dim=u[lon_name])) energies['Ln'] = Ln if ('Ca' in terms): if 'temp_Z' not in locals(): temp_Z = temp.mean(dim=lon_name) if 'V' not in locals(): Vp, Vn = _truncate(utils.fft(v, dim=lon_name, nfft=len(v[lon_name]), twosided=True, shift=True) / len(v[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'B' not in locals(): Bp, Bn = _truncate(utils.fft(temp, dim=lon_name, nfft=len(temp[lon_name]), twosided=True, shift=True) / len(temp[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'O' not in locals(): Op, On = _truncate(utils.fft(omega, dim=lon_name, nfft=len(omega[lon_name]), twosided=True, shift=True) / len(omega[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) dtemp_Zdlat = utils.differentiate_wrt(temp_Z, dim=lat_name, x=(temp_Z[lat_name] * degtorad)) theta = temp * p_kap theta_Z = theta.mean(dim=lon_name) theta_Za = theta_Z - theta_A dtheta_Zadp = utils.differentiate_wrt(theta_Za, dim=plevel_name, x=(theta_Za[plevel_name] * 100)) Rn_int = gamma * utils.constants().C_pd * ((dtemp_Zdlat / utils.constants().R_earth) * (Vp * Bn + Vn * Bp) + (p_kap * dtheta_Zadp) * (Op * Bn + On * Bp)) energies['Rn_int'] = Rn_int if integrate: Rn = abs(_int_over_atmos(Rn_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name])) energies['Rn'] = Rn if ('Ce' in terms): if vgradz: if 'U' not in locals(): Up, Un = _truncate(utils.fft(u, dim=lon_name, nfft=len(u[lon_name]), twosided=True, shift=True) / len(u[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'V' not in locals(): Vp, Vn = _truncate(utils.fft(v, dim=lon_name, nfft=len(v[lon_name]), twosided=True, shift=True) / len(v[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) Ap, An = _truncate(utils.fft(gh, dim=lon_name, nfft=len(gh[lon_name]), twosided=True, shift=True) / len(gh[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) dApdlat = utils.differentiate_wrt(Ap, dim=lat_name, x=(Ap[lat_name] * degtorad)) dAndlat = utils.differentiate_wrt(An, dim=lat_name, x=(An[lat_name] * degtorad)) Cn_int = (((-1j * utils.constants().g * Up['n']) / \ (utils.constants().R_earth * xr.ufuncs.cos(Up[lat_name] * degtorad))) * \ (Ap * Un - An * Up)) - \ ((utils.constants().g / utils.constants().R_earth) * \ (dApdlat * Vn + dAndlat * Vp)) energies['Cn_int'] = Cn_int if integrate: Cn = abs(_int_over_atmos(Cn_int, lat_name, lon_name, plevel_name, lon_dim=u[lon_name])) energies['Cn'] = Cn else: if 'O' not in locals(): Op, On = _truncate(utils.fft(omega, dim=lon_name, nfft=len(omega[lon_name]), twosided=True, shift=True) / len(omega[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'B' not in locals(): Bp, Bn = _truncate(utils.fft(temp, dim=lon_name, nfft=len(temp[lon_name]), twosided=True, shift=True) / len(temp[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) Cn_int = - (utils.constants().R_d / (omega[plevel_name] * 100)) * (Op * Bn + On * Bp) energies['Cn_int'] = Cn_int if integrate: Cn = abs(_int_over_atmos(Cn_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name])) energies['Cn'] = Cn if ('Ck' in terms): if 'v_Z' not in locals(): v_Z = v.mean(dim=lon_name) if 'u_Z' not in locals(): u_Z = u.mean(dim=lon_name) if 'U' not in locals(): Up, Un = _truncate(utils.fft(u, dim=lon_name, nfft=len(u[lon_name]), twosided=True, shift=True) / len(u[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'V' not in locals(): Vp, Vn = _truncate(utils.fft(v, dim=lon_name, nfft=len(v[lon_name]), twosided=True, shift=True) / len(v[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) if 'O' not in locals(): Op, On = _truncate(utils.fft(omega, dim=lon_name, nfft=len(omega[lon_name]), twosided=True, shift=True) / len(omega[lon_name]), n_truncate=n_wavenumbers, dim='f_'+lon_name) dv_Zdlat = utils.differentiate_wrt(v_Z, dim=lat_name, x=(v[lat_name] * degtorad)) du_Zndlat = utils.differentiate_wrt(u_Z / xr.ufuncs.cos(u[lat_name] * degtorad), dim=lat_name, x=(u[lat_name] * degtorad)) dv_Zdp = utils.differentiate_wrt(v_Z, dim=plevel_name, x=(v[plevel_name] * 100)) du_Zdp = utils.differentiate_wrt(u_Z, dim=plevel_name, x=(u[plevel_name] * 100)) Mn_int = (-2 * Up * Un * v_Z * tan_lat / utils.constants().R_earth) + \ (2 * Vp * Vn * dv_Zdlat / utils.constants().R_earth + (Vp * On + Vn * Op) * dv_Zdp) + \ ((Up * On + Un * Op) * du_Zdp) + \ ((Up * Vn + Un * Vp) * xr.ufuncs.cos(u[lat_name] * degtorad) / \ utils.constants().R_earth * du_Zndlat) energies['Mn_int'] = Mn_int if integrate: Mn = abs(_int_over_atmos(Mn_int, lat_name, lon_name, plevel_name, lon_dim=u[lon_name])) energies['Mn'] = Mn else: if ('Pe' in terms): if 'temp_Z' not in locals(): temp_Z = temp.mean(dim=lon_name) temp_z = temp - temp_Z Pe_int = gamma * utils.constants().C_pd / 2 * (temp_z ** 2).mean(dim=lon_name) energies['Pe_int'] = Pe_int if integrate: Pe = _int_over_atmos(Pe_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) energies['Pe'] = Pe if ('Ke' in terms): if 'u_Z' not in locals(): u_Z = u.mean(dim=lon_name) if 'v_Z' not in locals(): v_Z = v.mean(dim=lon_name) u_z = u - u_Z v_z = v - v_Z Ke_int = 0.5 * (u_z ** 2 + v_z ** 2).mean(dim=lon_name) energies['Ke_int'] = Ke_int if integrate: Ke = _int_over_atmos(Ke_int, lat_name, lon_name, plevel_name, lon_dim=u[lon_name]) energies['Ke'] = Ke if ('Ca' in terms): if 'v_Z' not in locals(): v_Z = v.mean(dim=lon_name) if 'temp_Z' not in locals(): temp_Z = temp.mean(dim=lon_name) if 'omega_Z' not in locals(): omega_Z = omega.mean(dim=lon_name) if 'theta_Z' not in locals(): theta = temp * p_kap theta_Z = theta.mean(dim=lon_name) if 'dtemp_Zdlat' not in locals(): dtemp_Zdlat = utils.differentiate_wrt(temp_Z, dim=lat_name, x=(temp_Z[lat_name] * degtorad)) v_z = v - v_Z temp_z = temp - temp_Z omega_z = omega - omega_Z oT_Z = (omega_z * temp_z).mean(dim=lon_name) oT_A = utils.average(omega_z * temp_z, [lat_name, lon_name], weights=cos_lat) oT_Za = oT_Z - oT_A theta_Za = theta_Z - theta_A dtheta_Zadp = utils.differentiate_wrt(theta_Za, dim=plevel_name, x=(theta_Za[plevel_name] * 100)) Ca_int = - gamma * utils.constants().C_pd * \ (((v_z * temp_z).mean(dim=lon_name) * dtemp_Zdlat / utils.constants().R_earth) + \ (p_kap * oT_Za * dtheta_Zadp)) energies['Ca_int'] = Ca_int if integrate: Ca = _int_over_atmos(Ca_int, lat_name, lon_name, plevel_name, lon_dim=v[lon_name]) energies['Ca'] = Ca if ('Ce' in terms): if 'temp_Z' not in locals(): temp_Z = temp.mean(dim=lon_name) if 'omega_Z' not in locals(): omega_Z = omega.mean(dim=lon_name) temp_z = temp - temp_Z omega_z = omega - omega_Z Ce_int = - (utils.constants().R_d / (temp[plevel_name] * 100)) * \ (omega_z * temp_z).mean(dim=lon_name) energies['Ce_int'] = Ce_int if integrate: Ce = _int_over_atmos(Ce_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) energies['Ce'] = Ce if ('Ck' in terms): if 'u_Z' not in locals(): u_Z = u.mean(dim=lon_name) if 'v_Z' not in locals(): v_Z = v.mean(dim=lon_name) if 'omega_Z' not in locals(): omega_Z = omega.mean(dim=lon_name) u_z = u - u_Z v_z = v - v_Z omega_z = omega - omega_Z du_Zndlat = utils.differentiate_wrt(u_Z / cos_lat, dim=lat_name, x=(u_Z[lat_name] * degtorad)) dv_Zdlat = utils.differentiate_wrt(v_Z, dim=lat_name, x=(v_Z[lat_name] * degtorad)) du_Zdp = utils.differentiate_wrt(u_Z, dim=plevel_name, x=(u_Z[plevel_name] * 100)) dv_Zdp = utils.differentiate_wrt(v_Z, dim=plevel_name, x=(v_Z[plevel_name] * 100)) Ck_int = (u_z * v_z).mean(dim=lon_name) * cos_lat * du_Zndlat / utils.constants().R_earth + \ (u_z * omega_z).mean(dim=lon_name) * du_Zdp + \ (v_z ** 2).mean(dim=lon_name) * dv_Zdlat / utils.constants().R_earth + \ (v_z * omega_z).mean(dim=lon_name) * dv_Zdp - \ (u_z ** 2).mean(dim=lon_name) * v_Z * tan_lat / utils.constants().R_earth energies['Ck_int'] = Ck_int if integrate: Ck = _int_over_atmos(Ck_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) energies['Ck'] = Ck if ('Gz' in terms): if ('Cz' not in terms) | ('Ca' not in terms): raise ValueError('The rate of generation of zonal available potential energy, Gz, is computed from the sum of Cz and Ca. Please add these to the list, terms=[<terms>].') if spectral: warnings.warn('Rate of generation of zonal available potential energy is computed from the sum of Cz and Ca and cannot be computed in Fourier space. Returning Gz in real-space.') Ca_int = Rn_int.where(Rn_int.n > 0, drop=True).sum(dim='n').real Gz_int = Cz_int + Ca_int energies['Gz_int'] = Gz_int if integrate: Gz = _int_over_atmos(Gz_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) energies['Gz'] = Gz if ('Ge' in terms): if ('Ce' not in terms) | ('Ca' not in terms): raise ValueError('The rate of generation of eddy available potential energy, Ge, is computed from the residual of Ce and Ca. Please add these to the list, terms=[<terms>].') if spectral: warnings.warn('The rate of generation of eddy available potential energy is computed from the residual of Ce and Ca and cannot be computed in Fourier space. Returning Ge in real-space.') Ce_int = Cn_int.where(Cn_int.n > 0, drop=True).sum(dim='n').real if 'Ca_int' not in locals(): Ca_int = Rn_int.where(Rn_int.n > 0, drop=True).sum(dim='n').real Ge_int = Ce_int - Ca_int energies['Ge_int'] = Ge_int if integrate: Ge = _int_over_atmos(Ge_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) energies['Ge'] = Ge if ('Dz' in terms): if ('Cz' not in terms) | ('Ck' not in terms): raise ValueError('The rate of viscous dissipation of zonal kinetic energy, Dz, is computed from the residual of Cz and Ck. Please add these to the list, terms=[<terms>].') if spectral: warnings.warn('The rate of viscous dissipation of zonal kinetic energy, Dz, is computed from the residual of Cz and Ck and cannot be computed in Fourier space. Returning De in real-space.') Ck_int = Mn_int.where(Mn_int.n > 0, drop=True).sum(dim='n').real Dz_int = Cz_int - Ck_int energies['Dz_int'] = Dz_int if integrate: Dz = _int_over_atmos(Dz_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) energies['Dz'] = Dz if ('De' in terms): if ('Ce' not in terms) | ('Ck' not in terms): raise ValueError('The rate of viscous dissipation of eddy kinetic energy, De, is computed from the residual of Ce and Ck. Please add these to the list, terms=[<terms>].') if spectral: warnings.warn('The rate of viscous dissipation of eddy kinetic energy, De, is computed from the residual of Ce and Ck and cannot be computed in Fourier space. Returning De in real-space.') if 'Ce_int' not in locals(): Ce_int = Cn_int.where(Cn_int.n > 0, drop=True).sum(dim='n').real if 'Ck_int' not in locals(): Ck_int = Mn_int.where(Mn_int.n > 0, drop=True).sum(dim='n').real De_int = Ce_int - Ck_int energies['De_int'] = De_int if integrate: De = _int_over_atmos(De_int, lat_name, lon_name, plevel_name, lon_dim=temp[lon_name]) energies['De'] = De return energies def auto_merge(paths, preprocess=None, parallel=True, **kwargs): if parallel: open_ = dask.delayed(xr.open_dataset) getattr_ = dask.delayed(getattr) if preprocess is not None: preprocess = dask.delayed(preprocess) else: open_ = open_dataset getattr_ = getattr datasets = [open_(p, **kwargs) for p in paths] file_objs = [getattr_(ds, '_file_obj') for ds in datasets] if preprocess is not None: datasets = [preprocess(ds) for ds in datasets] if parallel: datasets, file_objs = dask.compute(datasets, file_objs) def _combine_along_last_dim(datasets): merged = [] split_dims = [d for d in datasets[0].dims if len(np.unique([ds[d].values[0] for ds in datasets])) > 1] concat_dim = split_dims[-1] sorted_ds = sorted(datasets, key=lambda ds: tuple(ds[d].values[0] for d in split_dims)) for _, group in itertools.groupby( sorted_ds, key=lambda ds: tuple(ds[d].values[0] for d in split_dims[:-1]) ): merged.append(xr.auto_combine(group, concat_dim=concat_dim)) return merged merged = datasets while len(merged) > 1: merged = _combine_along_last_dim(merged) return merged[0]

true

f723c95d5658ceeced474744cccb763f8b5be84d

2,382

py

Python

Foundations_of_Private_Computation/Split_Learning/concepts-definitions-code/ite-repo/demos/analytical_values/demo_k_ejt1.py

gonzalo-munillag/Private_AI_OpenMined

c23da9cc1c914d10646a0c0bc1a2497fe2cbaaca

[ "MIT" ]

5

2021-01-06T16:49:22.000Z

2021-02-19T05:34:27.000Z

Foundations_of_Private_Computation/Split_Learning/concepts-definitions-code/ite-repo/demos/analytical_values/demo_k_ejt1.py

gonzalo-munillag/Private_AI_OpenMined

c23da9cc1c914d10646a0c0bc1a2497fe2cbaaca

[ "MIT" ]

null

Foundations_of_Private_Computation/Split_Learning/concepts-definitions-code/ite-repo/demos/analytical_values/demo_k_ejt1.py

gonzalo-munillag/Private_AI_OpenMined

c23da9cc1c914d10646a0c0bc1a2497fe2cbaaca

[ "MIT" ]

null

#!/usr/bin/env python3 """ Demo for exponentiated Jensen-Tsallis kernel-1 estimators. Analytical vs estimated value is illustrated for spherical normal random variables. """ from numpy import eye from numpy.random import rand, multivariate_normal, randn from scipy import arange, zeros, ones import matplotlib.pyplot as plt from ite.cost.x_factory import co_factory from ite.cost.x_analytical_values import analytical_value_k_ejt1 def main(): # parameters: dim = 1 # dimension of the distribution num_of_samples_v = arange(1000, 50*1000+1, 2000) u = 0.8 # >0, parameter of the Jensen-Tsallis kernel cost_name = 'MKExpJT1_HT' # dim >= 1 # initialization: alpha = 2 # fixed; parameter of the Jensen-Tsallis kernel; for alpha = 2 we have # explicit formula for the Tsallis entropy, and hence for the # Jensen-Tsallis kernel(-1). distr = 'normal' # fixed num_of_samples_max = num_of_samples_v[-1] length = len(num_of_samples_v) co = co_factory(cost_name, mult=True, alpha=alpha, u=u) # cost object k_hat_v = zeros(length) # vector of estimated kernel values # distr, dim -> samples (y1,y2), distribution parameters (par1,par2), # analytical value (k): if distr == 'normal': # generate samples (y1,y2); y1~N(m1,s1^2xI), y2~N(m2,s2^2xI): m1, s1 = randn(dim), rand(1) m2, s2 = randn(dim), rand(1) y1 = multivariate_normal(m1, s1**2 * eye(dim), num_of_samples_max) y2 = multivariate_normal(m2, s2**2 * eye(dim), num_of_samples_max) par1 = {"mean": m1, "std": s1} par2 = {"mean": m2, "std": s2} else: raise Exception('Distribution=?') k = analytical_value_k_ejt1(distr, distr, u, par1, par2) # estimation: for (tk, num_of_samples) in enumerate(num_of_samples_v): k_hat_v[tk] = co.estimation(y1[0:num_of_samples], y2[0:num_of_samples]) # broadcast print("tk={0}/{1}".format(tk+1, length)) # plot: plt.plot(num_of_samples_v, k_hat_v, num_of_samples_v, ones(length)*k) plt.xlabel('Number of samples') plt.ylabel('Exponentiated Jensen-Tsallis kernel-1') plt.legend(('estimation', 'analytical value'), loc='best') plt.title("Estimator: " + cost_name) plt.show() if __name__ == "__main__": main()

33.549296

74

0.646096

from numpy import eye from numpy.random import rand, multivariate_normal, randn from scipy import arange, zeros, ones import matplotlib.pyplot as plt from ite.cost.x_factory import co_factory from ite.cost.x_analytical_values import analytical_value_k_ejt1 def main(): dim = 1 num_of_samples_v = arange(1000, 50*1000+1, 2000) u = 0.8 cost_name = 'MKExpJT1_HT' alpha = 2 distr = 'normal' num_of_samples_max = num_of_samples_v[-1] length = len(num_of_samples_v) co = co_factory(cost_name, mult=True, alpha=alpha, u=u) k_hat_v = zeros(length) if distr == 'normal': m1, s1 = randn(dim), rand(1) m2, s2 = randn(dim), rand(1) y1 = multivariate_normal(m1, s1**2 * eye(dim), num_of_samples_max) y2 = multivariate_normal(m2, s2**2 * eye(dim), num_of_samples_max) par1 = {"mean": m1, "std": s1} par2 = {"mean": m2, "std": s2} else: raise Exception('Distribution=?') k = analytical_value_k_ejt1(distr, distr, u, par1, par2) for (tk, num_of_samples) in enumerate(num_of_samples_v): k_hat_v[tk] = co.estimation(y1[0:num_of_samples], y2[0:num_of_samples]) print("tk={0}/{1}".format(tk+1, length)) plt.plot(num_of_samples_v, k_hat_v, num_of_samples_v, ones(length)*k) plt.xlabel('Number of samples') plt.ylabel('Exponentiated Jensen-Tsallis kernel-1') plt.legend(('estimation', 'analytical value'), loc='best') plt.title("Estimator: " + cost_name) plt.show() if __name__ == "__main__": main()

true

f723ca086eef0104ef4640b52705cddbd642cc36

2,063

py

Python

parsifal/reviews/migrations/0046_auto_20190717_2301.py

glauberferreira/parsifal-mec

66f85e0d48a270bddd1170caa2131bc74872462d

[ "MIT" ]

1

2019-06-13T16:09:26.000Z

parsifal/reviews/migrations/0046_auto_20190717_2301.py

glauberferreira/parsifal-mec

66f85e0d48a270bddd1170caa2131bc74872462d

[ "MIT" ]

null

parsifal/reviews/migrations/0046_auto_20190717_2301.py

glauberferreira/parsifal-mec

66f85e0d48a270bddd1170caa2131bc74872462d

[ "MIT" ]

3

2019-10-05T04:16:59.000Z

2021-04-20T05:00:50.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations import django.db.models.deletion from django.conf import settings class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('reviews', '0045_articlefile'), ] operations = [ migrations.CreateModel( name='ArticleEvaluation', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('status', models.CharField(default='U', max_length=1, choices=[('U', 'Unclassified'), ('R', 'Rejected'), ('A', 'Accepted'), ('D', 'Duplicated')])), ('comments', models.TextField(max_length=2000, null=True, blank=True)), ], ), migrations.RemoveField( model_name='article', name='comments', ), migrations.RemoveField( model_name='article', name='selection_criteria', ), migrations.RemoveField( model_name='article', name='status', ), migrations.AddField( model_name='articleevaluation', name='article', field=models.ForeignKey(related_name='evaluation_article', to='reviews.Article'), ), migrations.AddField( model_name='articleevaluation', name='review', field=models.ForeignKey(related_name='evaluation_review', to='reviews.Review'), ), migrations.AddField( model_name='articleevaluation', name='selection_criteria', field=models.ForeignKey(on_delete=django.db.models.deletion.SET_NULL, blank=True, to='reviews.SelectionCriteria', null=True), ), migrations.AddField( model_name='articleevaluation', name='user', field=models.ForeignKey(related_name='evaluation_user', to=settings.AUTH_USER_MODEL), ), ]

35.568966

164

0.596704

from __future__ import unicode_literals from django.db import models, migrations import django.db.models.deletion from django.conf import settings class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('reviews', '0045_articlefile'), ] operations = [ migrations.CreateModel( name='ArticleEvaluation', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('status', models.CharField(default='U', max_length=1, choices=[('U', 'Unclassified'), ('R', 'Rejected'), ('A', 'Accepted'), ('D', 'Duplicated')])), ('comments', models.TextField(max_length=2000, null=True, blank=True)), ], ), migrations.RemoveField( model_name='article', name='comments', ), migrations.RemoveField( model_name='article', name='selection_criteria', ), migrations.RemoveField( model_name='article', name='status', ), migrations.AddField( model_name='articleevaluation', name='article', field=models.ForeignKey(related_name='evaluation_article', to='reviews.Article'), ), migrations.AddField( model_name='articleevaluation', name='review', field=models.ForeignKey(related_name='evaluation_review', to='reviews.Review'), ), migrations.AddField( model_name='articleevaluation', name='selection_criteria', field=models.ForeignKey(on_delete=django.db.models.deletion.SET_NULL, blank=True, to='reviews.SelectionCriteria', null=True), ), migrations.AddField( model_name='articleevaluation', name='user', field=models.ForeignKey(related_name='evaluation_user', to=settings.AUTH_USER_MODEL), ), ]

true

f723ca1a6623fc6b30625bd969dcb19ab9192111

358

py

Python

Lessons/simpleWebBrowser.py

Luderio/Scientific-Computing-with-Python

c7eebcc3b46b68b3d5c08ad25fb802ae9ff42f7f

[ "MIT" ]

null

Lessons/simpleWebBrowser.py

Luderio/Scientific-Computing-with-Python

c7eebcc3b46b68b3d5c08ad25fb802ae9ff42f7f

[ "MIT" ]

null

Lessons/simpleWebBrowser.py

Luderio/Scientific-Computing-with-Python

c7eebcc3b46b68b3d5c08ad25fb802ae9ff42f7f

[ "MIT" ]

null

#Simple We Browser using sockets import socket mysock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) mysock.connect(('data.pr4e.org', 80)) cmd = 'GET http://data.pr4e.org/romeo.txt HTTP/1.0\r\n\r\n'.encode() mysock.send(cmd) while True : data = mysock.recv(512) if len(data) < 1 : break print(data.decode()) mysock.close()

23.866667

68

0.664804

import socket mysock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) mysock.connect(('data.pr4e.org', 80)) cmd = 'GET http://data.pr4e.org/romeo.txt HTTP/1.0\r\n\r\n'.encode() mysock.send(cmd) while True : data = mysock.recv(512) if len(data) < 1 : break print(data.decode()) mysock.close()

true

f723ca2e7cf32da6dc08be2afefe8b8e7395f254

287

py

Python

app.py

IIVIIIII/2020_Weathering

0759f2848ca912c8f1f9875f18c8e0aa604948f5

[ "MIT" ]

null

app.py

IIVIIIII/2020_Weathering

0759f2848ca912c8f1f9875f18c8e0aa604948f5

[ "MIT" ]

null

app.py

IIVIIIII/2020_Weathering

0759f2848ca912c8f1f9875f18c8e0aa604948f5

[ "MIT" ]

null

from flask import Flask, jsonify import data4app app = Flask(__name__) @app.route("/") def home(): return "Lets goooo!!!" @app.route("/<var>") def jsonified(var): data = data4app.get_data(var) return jsonify(data) if __name__ == "__main__": app.run(debug=True)

15.105263

33

0.648084

from flask import Flask, jsonify import data4app app = Flask(__name__) @app.route("/") def home(): return "Lets goooo!!!" @app.route("/<var>") def jsonified(var): data = data4app.get_data(var) return jsonify(data) if __name__ == "__main__": app.run(debug=True)

true

f723ca8574f5c324eb54931b511ea756f5d6462f

18,973

py

Python

sdk/python/tekton_pipeline/models/pod_template.py

jmcshane/experimental

3c47c7e87bcdadc6172941169f3f24fc3f159ae0

[ "Apache-2.0" ]

null

sdk/python/tekton_pipeline/models/pod_template.py

jmcshane/experimental

3c47c7e87bcdadc6172941169f3f24fc3f159ae0

[ "Apache-2.0" ]

null

sdk/python/tekton_pipeline/models/pod_template.py

jmcshane/experimental

3c47c7e87bcdadc6172941169f3f24fc3f159ae0

[ "Apache-2.0" ]

1

2020-07-30T15:55:45.000Z

# Copyright 2020 The Tekton 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. # coding: utf-8 """ Tekton Tekton Pipeline # noqa: E501 The version of the OpenAPI document: v0.17.2 Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six from tekton_pipeline.configuration import Configuration class PodTemplate(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = { 'affinity': 'V1Affinity', 'automount_service_account_token': 'bool', 'dns_config': 'V1PodDNSConfig', 'dns_policy': 'str', 'enable_service_links': 'bool', 'host_network': 'bool', 'image_pull_secrets': 'list[V1LocalObjectReference]', 'node_selector': 'dict(str, str)', 'priority_class_name': 'str', 'runtime_class_name': 'str', 'scheduler_name': 'str', 'security_context': 'V1PodSecurityContext', 'tolerations': 'list[V1Toleration]', 'volumes': 'list[V1Volume]' } attribute_map = { 'affinity': 'affinity', 'automount_service_account_token': 'automountServiceAccountToken', 'dns_config': 'dnsConfig', 'dns_policy': 'dnsPolicy', 'enable_service_links': 'enableServiceLinks', 'host_network': 'hostNetwork', 'image_pull_secrets': 'imagePullSecrets', 'node_selector': 'nodeSelector', 'priority_class_name': 'priorityClassName', 'runtime_class_name': 'runtimeClassName', 'scheduler_name': 'schedulerName', 'security_context': 'securityContext', 'tolerations': 'tolerations', 'volumes': 'volumes' } def __init__(self, affinity=None, automount_service_account_token=None, dns_config=None, dns_policy=None, enable_service_links=None, host_network=None, image_pull_secrets=None, node_selector=None, priority_class_name=None, runtime_class_name=None, scheduler_name=None, security_context=None, tolerations=None, volumes=None, local_vars_configuration=None): # noqa: E501 """PodTemplate - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._affinity = None self._automount_service_account_token = None self._dns_config = None self._dns_policy = None self._enable_service_links = None self._host_network = None self._image_pull_secrets = None self._node_selector = None self._priority_class_name = None self._runtime_class_name = None self._scheduler_name = None self._security_context = None self._tolerations = None self._volumes = None self.discriminator = None if affinity is not None: self.affinity = affinity if automount_service_account_token is not None: self.automount_service_account_token = automount_service_account_token if dns_config is not None: self.dns_config = dns_config if dns_policy is not None: self.dns_policy = dns_policy if enable_service_links is not None: self.enable_service_links = enable_service_links if host_network is not None: self.host_network = host_network if image_pull_secrets is not None: self.image_pull_secrets = image_pull_secrets if node_selector is not None: self.node_selector = node_selector if priority_class_name is not None: self.priority_class_name = priority_class_name if runtime_class_name is not None: self.runtime_class_name = runtime_class_name if scheduler_name is not None: self.scheduler_name = scheduler_name if security_context is not None: self.security_context = security_context if tolerations is not None: self.tolerations = tolerations if volumes is not None: self.volumes = volumes @property def affinity(self): """Gets the affinity of this PodTemplate. # noqa: E501 :return: The affinity of this PodTemplate. # noqa: E501 :rtype: V1Affinity """ return self._affinity @affinity.setter def affinity(self, affinity): """Sets the affinity of this PodTemplate. :param affinity: The affinity of this PodTemplate. # noqa: E501 :type: V1Affinity """ self._affinity = affinity @property def automount_service_account_token(self): """Gets the automount_service_account_token of this PodTemplate. # noqa: E501 AutomountServiceAccountToken indicates whether pods running as this service account should have an API token automatically mounted. # noqa: E501 :return: The automount_service_account_token of this PodTemplate. # noqa: E501 :rtype: bool """ return self._automount_service_account_token @automount_service_account_token.setter def automount_service_account_token(self, automount_service_account_token): """Sets the automount_service_account_token of this PodTemplate. AutomountServiceAccountToken indicates whether pods running as this service account should have an API token automatically mounted. # noqa: E501 :param automount_service_account_token: The automount_service_account_token of this PodTemplate. # noqa: E501 :type: bool """ self._automount_service_account_token = automount_service_account_token @property def dns_config(self): """Gets the dns_config of this PodTemplate. # noqa: E501 :return: The dns_config of this PodTemplate. # noqa: E501 :rtype: V1PodDNSConfig """ return self._dns_config @dns_config.setter def dns_config(self, dns_config): """Sets the dns_config of this PodTemplate. :param dns_config: The dns_config of this PodTemplate. # noqa: E501 :type: V1PodDNSConfig """ self._dns_config = dns_config @property def dns_policy(self): """Gets the dns_policy of this PodTemplate. # noqa: E501 Set DNS policy for the pod. Defaults to \"ClusterFirst\". Valid values are 'ClusterFirst', 'Default' or 'None'. DNS parameters given in DNSConfig will be merged with the policy selected with DNSPolicy. # noqa: E501 :return: The dns_policy of this PodTemplate. # noqa: E501 :rtype: str """ return self._dns_policy @dns_policy.setter def dns_policy(self, dns_policy): """Sets the dns_policy of this PodTemplate. Set DNS policy for the pod. Defaults to \"ClusterFirst\". Valid values are 'ClusterFirst', 'Default' or 'None'. DNS parameters given in DNSConfig will be merged with the policy selected with DNSPolicy. # noqa: E501 :param dns_policy: The dns_policy of this PodTemplate. # noqa: E501 :type: str """ self._dns_policy = dns_policy @property def enable_service_links(self): """Gets the enable_service_links of this PodTemplate. # noqa: E501 EnableServiceLinks indicates whether information about services should be injected into pod's environment variables, matching the syntax of Docker links. Optional: Defaults to true. # noqa: E501 :return: The enable_service_links of this PodTemplate. # noqa: E501 :rtype: bool """ return self._enable_service_links @enable_service_links.setter def enable_service_links(self, enable_service_links): """Sets the enable_service_links of this PodTemplate. EnableServiceLinks indicates whether information about services should be injected into pod's environment variables, matching the syntax of Docker links. Optional: Defaults to true. # noqa: E501 :param enable_service_links: The enable_service_links of this PodTemplate. # noqa: E501 :type: bool """ self._enable_service_links = enable_service_links @property def host_network(self): """Gets the host_network of this PodTemplate. # noqa: E501 HostNetwork specifies whether the pod may use the node network namespace # noqa: E501 :return: The host_network of this PodTemplate. # noqa: E501 :rtype: bool """ return self._host_network @host_network.setter def host_network(self, host_network): """Sets the host_network of this PodTemplate. HostNetwork specifies whether the pod may use the node network namespace # noqa: E501 :param host_network: The host_network of this PodTemplate. # noqa: E501 :type: bool """ self._host_network = host_network @property def image_pull_secrets(self): """Gets the image_pull_secrets of this PodTemplate. # noqa: E501 ImagePullSecrets gives the name of the secret used by the pod to pull the image if specified # noqa: E501 :return: The image_pull_secrets of this PodTemplate. # noqa: E501 :rtype: list[V1LocalObjectReference] """ return self._image_pull_secrets @image_pull_secrets.setter def image_pull_secrets(self, image_pull_secrets): """Sets the image_pull_secrets of this PodTemplate. ImagePullSecrets gives the name of the secret used by the pod to pull the image if specified # noqa: E501 :param image_pull_secrets: The image_pull_secrets of this PodTemplate. # noqa: E501 :type: list[V1LocalObjectReference] """ self._image_pull_secrets = image_pull_secrets @property def node_selector(self): """Gets the node_selector of this PodTemplate. # noqa: E501 NodeSelector is a selector which must be true for the pod to fit on a node. Selector which must match a node's labels for the pod to be scheduled on that node. More info: https://kubernetes.io/docs/concepts/configuration/assign-pod-node/ # noqa: E501 :return: The node_selector of this PodTemplate. # noqa: E501 :rtype: dict(str, str) """ return self._node_selector @node_selector.setter def node_selector(self, node_selector): """Sets the node_selector of this PodTemplate. NodeSelector is a selector which must be true for the pod to fit on a node. Selector which must match a node's labels for the pod to be scheduled on that node. More info: https://kubernetes.io/docs/concepts/configuration/assign-pod-node/ # noqa: E501 :param node_selector: The node_selector of this PodTemplate. # noqa: E501 :type: dict(str, str) """ self._node_selector = node_selector @property def priority_class_name(self): """Gets the priority_class_name of this PodTemplate. # noqa: E501 If specified, indicates the pod's priority. \"system-node-critical\" and \"system-cluster-critical\" are two special keywords which indicate the highest priorities with the former being the highest priority. Any other name must be defined by creating a PriorityClass object with that name. If not specified, the pod priority will be default or zero if there is no default. # noqa: E501 :return: The priority_class_name of this PodTemplate. # noqa: E501 :rtype: str """ return self._priority_class_name @priority_class_name.setter def priority_class_name(self, priority_class_name): """Sets the priority_class_name of this PodTemplate. If specified, indicates the pod's priority. \"system-node-critical\" and \"system-cluster-critical\" are two special keywords which indicate the highest priorities with the former being the highest priority. Any other name must be defined by creating a PriorityClass object with that name. If not specified, the pod priority will be default or zero if there is no default. # noqa: E501 :param priority_class_name: The priority_class_name of this PodTemplate. # noqa: E501 :type: str """ self._priority_class_name = priority_class_name @property def runtime_class_name(self): """Gets the runtime_class_name of this PodTemplate. # noqa: E501 RuntimeClassName refers to a RuntimeClass object in the node.k8s.io group, which should be used to run this pod. If no RuntimeClass resource matches the named class, the pod will not be run. If unset or empty, the \"legacy\" RuntimeClass will be used, which is an implicit class with an empty definition that uses the default runtime handler. More info: https://git.k8s.io/enhancements/keps/sig-node/runtime-class.md This is a beta feature as of Kubernetes v1.14. # noqa: E501 :return: The runtime_class_name of this PodTemplate. # noqa: E501 :rtype: str """ return self._runtime_class_name @runtime_class_name.setter def runtime_class_name(self, runtime_class_name): """Sets the runtime_class_name of this PodTemplate. RuntimeClassName refers to a RuntimeClass object in the node.k8s.io group, which should be used to run this pod. If no RuntimeClass resource matches the named class, the pod will not be run. If unset or empty, the \"legacy\" RuntimeClass will be used, which is an implicit class with an empty definition that uses the default runtime handler. More info: https://git.k8s.io/enhancements/keps/sig-node/runtime-class.md This is a beta feature as of Kubernetes v1.14. # noqa: E501 :param runtime_class_name: The runtime_class_name of this PodTemplate. # noqa: E501 :type: str """ self._runtime_class_name = runtime_class_name @property def scheduler_name(self): """Gets the scheduler_name of this PodTemplate. # noqa: E501 SchedulerName specifies the scheduler to be used to dispatch the Pod # noqa: E501 :return: The scheduler_name of this PodTemplate. # noqa: E501 :rtype: str """ return self._scheduler_name @scheduler_name.setter def scheduler_name(self, scheduler_name): """Sets the scheduler_name of this PodTemplate. SchedulerName specifies the scheduler to be used to dispatch the Pod # noqa: E501 :param scheduler_name: The scheduler_name of this PodTemplate. # noqa: E501 :type: str """ self._scheduler_name = scheduler_name @property def security_context(self): """Gets the security_context of this PodTemplate. # noqa: E501 :return: The security_context of this PodTemplate. # noqa: E501 :rtype: V1PodSecurityContext """ return self._security_context @security_context.setter def security_context(self, security_context): """Sets the security_context of this PodTemplate. :param security_context: The security_context of this PodTemplate. # noqa: E501 :type: V1PodSecurityContext """ self._security_context = security_context @property def tolerations(self): """Gets the tolerations of this PodTemplate. # noqa: E501 If specified, the pod's tolerations. # noqa: E501 :return: The tolerations of this PodTemplate. # noqa: E501 :rtype: list[V1Toleration] """ return self._tolerations @tolerations.setter def tolerations(self, tolerations): """Sets the tolerations of this PodTemplate. If specified, the pod's tolerations. # noqa: E501 :param tolerations: The tolerations of this PodTemplate. # noqa: E501 :type: list[V1Toleration] """ self._tolerations = tolerations @property def volumes(self): """Gets the volumes of this PodTemplate. # noqa: E501 List of volumes that can be mounted by containers belonging to the pod. More info: https://kubernetes.io/docs/concepts/storage/volumes # noqa: E501 :return: The volumes of this PodTemplate. # noqa: E501 :rtype: list[V1Volume] """ return self._volumes @volumes.setter def volumes(self, volumes): """Sets the volumes of this PodTemplate. List of volumes that can be mounted by containers belonging to the pod. More info: https://kubernetes.io/docs/concepts/storage/volumes # noqa: E501 :param volumes: The volumes of this PodTemplate. # noqa: E501 :type: list[V1Volume] """ self._volumes = volumes def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, PodTemplate): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, PodTemplate): return True return self.to_dict() != other.to_dict()

38.329293

485

0.669056

import pprint import re import six from tekton_pipeline.configuration import Configuration class PodTemplate(object): openapi_types = { 'affinity': 'V1Affinity', 'automount_service_account_token': 'bool', 'dns_config': 'V1PodDNSConfig', 'dns_policy': 'str', 'enable_service_links': 'bool', 'host_network': 'bool', 'image_pull_secrets': 'list[V1LocalObjectReference]', 'node_selector': 'dict(str, str)', 'priority_class_name': 'str', 'runtime_class_name': 'str', 'scheduler_name': 'str', 'security_context': 'V1PodSecurityContext', 'tolerations': 'list[V1Toleration]', 'volumes': 'list[V1Volume]' } attribute_map = { 'affinity': 'affinity', 'automount_service_account_token': 'automountServiceAccountToken', 'dns_config': 'dnsConfig', 'dns_policy': 'dnsPolicy', 'enable_service_links': 'enableServiceLinks', 'host_network': 'hostNetwork', 'image_pull_secrets': 'imagePullSecrets', 'node_selector': 'nodeSelector', 'priority_class_name': 'priorityClassName', 'runtime_class_name': 'runtimeClassName', 'scheduler_name': 'schedulerName', 'security_context': 'securityContext', 'tolerations': 'tolerations', 'volumes': 'volumes' } def __init__(self, affinity=None, automount_service_account_token=None, dns_config=None, dns_policy=None, enable_service_links=None, host_network=None, image_pull_secrets=None, node_selector=None, priority_class_name=None, runtime_class_name=None, scheduler_name=None, security_context=None, tolerations=None, volumes=None, local_vars_configuration=None): if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._affinity = None self._automount_service_account_token = None self._dns_config = None self._dns_policy = None self._enable_service_links = None self._host_network = None self._image_pull_secrets = None self._node_selector = None self._priority_class_name = None self._runtime_class_name = None self._scheduler_name = None self._security_context = None self._tolerations = None self._volumes = None self.discriminator = None if affinity is not None: self.affinity = affinity if automount_service_account_token is not None: self.automount_service_account_token = automount_service_account_token if dns_config is not None: self.dns_config = dns_config if dns_policy is not None: self.dns_policy = dns_policy if enable_service_links is not None: self.enable_service_links = enable_service_links if host_network is not None: self.host_network = host_network if image_pull_secrets is not None: self.image_pull_secrets = image_pull_secrets if node_selector is not None: self.node_selector = node_selector if priority_class_name is not None: self.priority_class_name = priority_class_name if runtime_class_name is not None: self.runtime_class_name = runtime_class_name if scheduler_name is not None: self.scheduler_name = scheduler_name if security_context is not None: self.security_context = security_context if tolerations is not None: self.tolerations = tolerations if volumes is not None: self.volumes = volumes @property def affinity(self): return self._affinity @affinity.setter def affinity(self, affinity): self._affinity = affinity @property def automount_service_account_token(self): return self._automount_service_account_token @automount_service_account_token.setter def automount_service_account_token(self, automount_service_account_token): self._automount_service_account_token = automount_service_account_token @property def dns_config(self): return self._dns_config @dns_config.setter def dns_config(self, dns_config): self._dns_config = dns_config @property def dns_policy(self): return self._dns_policy @dns_policy.setter def dns_policy(self, dns_policy): self._dns_policy = dns_policy @property def enable_service_links(self): return self._enable_service_links @enable_service_links.setter def enable_service_links(self, enable_service_links): self._enable_service_links = enable_service_links @property def host_network(self): return self._host_network @host_network.setter def host_network(self, host_network): self._host_network = host_network @property def image_pull_secrets(self): return self._image_pull_secrets @image_pull_secrets.setter def image_pull_secrets(self, image_pull_secrets): self._image_pull_secrets = image_pull_secrets @property def node_selector(self): return self._node_selector @node_selector.setter def node_selector(self, node_selector): self._node_selector = node_selector @property def priority_class_name(self): return self._priority_class_name @priority_class_name.setter def priority_class_name(self, priority_class_name): self._priority_class_name = priority_class_name @property def runtime_class_name(self): return self._runtime_class_name @runtime_class_name.setter def runtime_class_name(self, runtime_class_name): self._runtime_class_name = runtime_class_name @property def scheduler_name(self): return self._scheduler_name @scheduler_name.setter def scheduler_name(self, scheduler_name): self._scheduler_name = scheduler_name @property def security_context(self): return self._security_context @security_context.setter def security_context(self, security_context): self._security_context = security_context @property def tolerations(self): return self._tolerations @tolerations.setter def tolerations(self, tolerations): self._tolerations = tolerations @property def volumes(self): return self._volumes @volumes.setter def volumes(self, volumes): self._volumes = volumes def to_dict(self): result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, PodTemplate): return False return self.to_dict() == other.to_dict() def __ne__(self, other): if not isinstance(other, PodTemplate): return True return self.to_dict() != other.to_dict()

true

f723cb619eaf3108159317787a8063eb46bfbdab

413

py

Python

experiments/fdtd-2d/tmp_files/144.py

LoopTilingBenchmark/benchmark

52a3d2e70216552a498fd91de02a2fa9cb62122c

[ "BSD-2-Clause" ]

null

experiments/fdtd-2d/tmp_files/144.py

LoopTilingBenchmark/benchmark

52a3d2e70216552a498fd91de02a2fa9cb62122c

[ "BSD-2-Clause" ]

null

experiments/fdtd-2d/tmp_files/144.py

LoopTilingBenchmark/benchmark

52a3d2e70216552a498fd91de02a2fa9cb62122c

[ "BSD-2-Clause" ]

null

from chill import * source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/fdtd-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/fdtd-2d/tmp_files/144.c') procedure('kernel_fdtd_2d') loop(0) known(' nx > 1 ') known(' ny > 1 ') tile(1,2,128,2) tile(1,4,32,4) tile(2,2,128,2) tile(2,4,32,4) tile(3,2,128,2) tile(3,4,32,4)

22.944444

116

0.72155

from chill import * source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/fdtd-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/fdtd-2d/tmp_files/144.c') procedure('kernel_fdtd_2d') loop(0) known(' nx > 1 ') known(' ny > 1 ') tile(1,2,128,2) tile(1,4,32,4) tile(2,2,128,2) tile(2,4,32,4) tile(3,2,128,2) tile(3,4,32,4)

true

f723cba44ec9074d909b91e8b3bb8d8e84df91e5

506

py

Python

tests/test_base.py

not-nexus/shelf

ea59703082402ad3b6454482f0487418295fbd19

[ "MIT" ]

4

2016-11-07T13:02:18.000Z

2019-09-03T02:04:05.000Z

tests/test_base.py

not-nexus/shelf

ea59703082402ad3b6454482f0487418295fbd19

[ "MIT" ]

21

2016-11-30T20:44:52.000Z

2017-05-02T15:38:56.000Z

tests/test_base.py

not-nexus/shelf

ea59703082402ad3b6454482f0487418295fbd19

[ "MIT" ]

2

2017-01-24T14:36:04.000Z

2020-01-13T16:10:05.000Z

import pyproctor class TestBase(pyproctor.TestBase): @classmethod def setUpClass(cls): """ This exists to make sure that no matter what, tests will log on stdout. Every call to basicConfig after this point will be a no-op """ # AGI-731 # See jira for more information # # https://github.com/gabrielfalcao/HTTPretty/issues/280 # # logging.basicConfig( # stream=sys.stdout # )

25.3

64

0.561265

import pyproctor class TestBase(pyproctor.TestBase): @classmethod def setUpClass(cls):

true

f723cc233a58a2785fb04f6b38b7dd40a29e256b

97

py

Python

tests/test_import.py

Quiltomics/indexd

95274d40f16de881492c2db70a969eb77c8f5e7c

[ "Apache-2.0" ]

2

2019-06-10T15:30:51.000Z

2020-01-18T23:24:13.000Z

tests/test_import.py

lookcrabs/indexd

646a7f336148496b07462ce3d3f8e930fa08a06c

[ "Apache-2.0" ]

15

2019-03-19T21:57:31.000Z

2021-08-11T21:01:33.000Z

tests/test_import.py

NCI-GDC/indexd

d159a82e7da100c807621bc41f2626dae64b4be9

[ "Apache-2.0" ]

1

2020-11-05T15:03:24.000Z

def test_import_index(): ''' Try to import the indexd package. ''' import indexd

16.166667

37

0.608247

def test_import_index(): import indexd

true

f723ccb1d235fd916b20e25c9c162899696706eb

4,292

py

Python

tests/python/contrib/test_gemm_acc32_vnni.py

jiangzoi/incubator-tvm

144c6f45f7217b9df2f5605e06f0903e470ac11c

[ "Apache-2.0" ]

9

2019-12-17T08:03:54.000Z

2022-01-19T02:34:23.000Z

tests/python/contrib/test_gemm_acc32_vnni.py

jiangzoi/incubator-tvm

144c6f45f7217b9df2f5605e06f0903e470ac11c

[ "Apache-2.0" ]

2

2020-06-18T21:15:42.000Z

2020-06-24T17:38:37.000Z

tests/python/contrib/test_gemm_acc32_vnni.py

jiangzoi/incubator-tvm

144c6f45f7217b9df2f5605e06f0903e470ac11c

[ "Apache-2.0" ]

3

2020-10-04T20:30:18.000Z

2022-01-24T18:03:52.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # pylint: disable=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition import tvm from tvm import te import numpy as np from topi.x86.tensor_intrin import dot_16x1x16_uint8_int8_int32_cascadelake from topi.x86.tensor_intrin import dot_16x1x16_uint8_int8_int32 import pytest @pytest.mark.skip("skip because feature not enabled") def test_fc_int8_acc32(): m = 1024 n = 1024 k = 1024 X = te.placeholder((m, k), name='X', dtype="uint8") W = te.placeholder((n, k), name='W', dtype="int8") peak = 280 print("Peak {} Gops/s".format(peak)) memory_ops = m * k + n * k + 2 * m * n gops_per_mm = 2 * m * n * k # For LLVM < 8.0, it shows "'cascadelake' is not a recognized processor for this target # (ignoring processor)" error with the following setting. After LLVM 8.0 is enabled in the # test, we should use cascadelake setting. def verify(target="llvm -mcpu=cascadelake"): if not tvm.runtime.enabled(target): print("skip because %s is not enabled..." % target) return ctx = tvm.context(target, 0) pc = dot_16x1x16_uint8_int8_int32_cascadelake() ak = te.reduce_axis((0, k), name='k') packedW = te.placeholder( (n // 16, 16 * (k // 4), 4), name='packedW', dtype="int8") t_fc = te.compute((m, n), lambda i, j: te.sum(X[i, ak].astype( "int32") * packedW[j / 16, (ak / 4) * 16 + j % 16, ak % 4].astype("int32"), axis=ak), name="F") t_sch = te.create_schedule(t_fc.op) a_x, a_y = t_fc.op.axis a_k, = t_fc.op.reduce_axis a_yo, a_yi = t_sch[t_fc].split(a_y, factor=16) a_xo, a_xi = t_sch[t_fc].split(a_x, factor=32) a_ko, a_ki = t_sch[t_fc].split(a_k, factor=4) a_koo, a_koi = t_sch[t_fc].split(a_ko, factor=4) t_sch[t_fc].reorder(a_yo, a_xo, a_xi, a_koo, a_koi, a_yi, a_ki) t_sch[t_fc].unroll(a_koi) t_sch[t_fc].tensorize(a_yi, pc) t_func = tvm.build(t_sch, [X, packedW, t_fc], target, name="intrinsic") t_evaluator = t_func.time_evaluator(t_func.entry_name, ctx, number=10) # generate the plain data a_ = np.random.uniform(1, 10, size=(m, k)).astype("uint8") b_ = np.random.uniform(1, 10, size=(n, k)).astype("int8") packW = np.random.uniform(1, 10, size=( n // 16, 16 * (k // 4), 4)).astype("int8") # This occurs in pre_compute stage for r_idx in range(n // 16): for s_idx in range(16 * (k // 4)): for t_idx in range(4): packW[r_idx][s_idx][t_idx] = b_[r_idx * 16 + s_idx % 16][(s_idx // 16) * 4 + t_idx] x = tvm.nd.array(a_, ctx) w = tvm.nd.array(packW, ctx) y = tvm.nd.array(np.zeros((m, n), dtype="int32"), ctx) result = t_evaluator(x, w, y) gops_per_sec = gops_per_mm / result.mean / 1e9 # verify the correctness tvm.testing.assert_allclose(y.asnumpy(), np.dot(a_, b_.T), rtol=0) print('Tensorization: running time: {:.3f} ms, {:.2f} Gops/s, effiency: {:.2f}'.format( result.mean * 1000, gops_per_sec, gops_per_sec / peak)) t_func.export_library("tensorize_acc32.o") verify() if __name__ == "__main__": # The test requires Cascade Lake and newer Intel machines to generate the # correct AVX512 VNNI instruction. So, disabling the test. # test_fc_int8_acc32() pass

40.11215

107

0.627213

import tvm from tvm import te import numpy as np from topi.x86.tensor_intrin import dot_16x1x16_uint8_int8_int32_cascadelake from topi.x86.tensor_intrin import dot_16x1x16_uint8_int8_int32 import pytest @pytest.mark.skip("skip because feature not enabled") def test_fc_int8_acc32(): m = 1024 n = 1024 k = 1024 X = te.placeholder((m, k), name='X', dtype="uint8") W = te.placeholder((n, k), name='W', dtype="int8") peak = 280 print("Peak {} Gops/s".format(peak)) memory_ops = m * k + n * k + 2 * m * n gops_per_mm = 2 * m * n * k # (ignoring processor)" error with the following setting. After LLVM 8.0 is enabled in the def verify(target="llvm -mcpu=cascadelake"): if not tvm.runtime.enabled(target): print("skip because %s is not enabled..." % target) return ctx = tvm.context(target, 0) pc = dot_16x1x16_uint8_int8_int32_cascadelake() ak = te.reduce_axis((0, k), name='k') packedW = te.placeholder( (n // 16, 16 * (k // 4), 4), name='packedW', dtype="int8") t_fc = te.compute((m, n), lambda i, j: te.sum(X[i, ak].astype( "int32") * packedW[j / 16, (ak / 4) * 16 + j % 16, ak % 4].astype("int32"), axis=ak), name="F") t_sch = te.create_schedule(t_fc.op) a_x, a_y = t_fc.op.axis a_k, = t_fc.op.reduce_axis a_yo, a_yi = t_sch[t_fc].split(a_y, factor=16) a_xo, a_xi = t_sch[t_fc].split(a_x, factor=32) a_ko, a_ki = t_sch[t_fc].split(a_k, factor=4) a_koo, a_koi = t_sch[t_fc].split(a_ko, factor=4) t_sch[t_fc].reorder(a_yo, a_xo, a_xi, a_koo, a_koi, a_yi, a_ki) t_sch[t_fc].unroll(a_koi) t_sch[t_fc].tensorize(a_yi, pc) t_func = tvm.build(t_sch, [X, packedW, t_fc], target, name="intrinsic") t_evaluator = t_func.time_evaluator(t_func.entry_name, ctx, number=10) a_ = np.random.uniform(1, 10, size=(m, k)).astype("uint8") b_ = np.random.uniform(1, 10, size=(n, k)).astype("int8") packW = np.random.uniform(1, 10, size=( n // 16, 16 * (k // 4), 4)).astype("int8") for r_idx in range(n // 16): for s_idx in range(16 * (k // 4)): for t_idx in range(4): packW[r_idx][s_idx][t_idx] = b_[r_idx * 16 + s_idx % 16][(s_idx // 16) * 4 + t_idx] x = tvm.nd.array(a_, ctx) w = tvm.nd.array(packW, ctx) y = tvm.nd.array(np.zeros((m, n), dtype="int32"), ctx) result = t_evaluator(x, w, y) gops_per_sec = gops_per_mm / result.mean / 1e9 tvm.testing.assert_allclose(y.asnumpy(), np.dot(a_, b_.T), rtol=0) print('Tensorization: running time: {:.3f} ms, {:.2f} Gops/s, effiency: {:.2f}'.format( result.mean * 1000, gops_per_sec, gops_per_sec / peak)) t_func.export_library("tensorize_acc32.o") verify() if __name__ == "__main__": pass

true

f723ccc97ea70d87a17b104645eede99a1853ac3

2,675

py

Python

wings/api_client.py

KnowledgeCaptureAndDiscovery/wings-client

af1d068f4adc07d9060afa94dc99e0b2565be088

[ "Apache-2.0" ]

null

wings/api_client.py

KnowledgeCaptureAndDiscovery/wings-client

af1d068f4adc07d9060afa94dc99e0b2565be088

[ "Apache-2.0" ]

8

2019-07-28T17:04:38.000Z

2019-08-06T23:57:08.000Z

wings/api_client.py

KnowledgeCaptureAndDiscovery/wings-client

af1d068f4adc07d9060afa94dc99e0b2565be088

[ "Apache-2.0" ]

1

2019-07-29T22:53:41.000Z

import atexit import importlib import logging import requests class ApiClient: def __init__(self, **kwargs): self.kwargs = kwargs self.session = requests.Session() self.libns = self.get_export_url() + "components/library.owl#" self.dcdom = self.get_export_url() + "data/ontology.owl#" self.dclib = self.get_export_url() + "data/library.owl#" self.xsdns = "http://www.w3.org/2001/XMLSchema#" self.topcls = "http://www.wings-workflows.org/ontology/component.owl#Component" if self.login(kwargs["password"]) is False: raise ValueError("Login failed") atexit.register(self.logout) def get_server(self): return self.server def get_username(self): return self.username def login(self, password): self.session.get(self.server + "/sparql") data = {"j_username": self.username, "j_password": password} response = self.session.post(self.server + "/j_security_check", data) if response.status_code == 403 or response.status_code == 200: return True return False def logout(self): self.session.get(self.server + "/jsp/login/logout.jsp") self.session.close() def session(self): return self.session def _initialize(self, name): try: module_ = importlib.import_module(".%s" % name, __package__) try: class_ = getattr(module_, name.title()) return class_(api_client=self) except AttributeError: logging.error("Class does not exist") except ImportError: logging.error("Module does not exist %s", name) def close(self): """ Shutdown sessions across all instantiated services """ self.logout() def __getattr__(self, attr): try: setattr(self, attr, self.kwargs[attr]) return getattr(self, attr) except KeyError: setattr(self, attr, self._initialize(attr)) return getattr(self, attr) def get_request_url(self): return self.server + "/users/" + self.username + "/" + self.domain + "/" def get_export_url(self): return ( self.export_url + "/export/users/" + self.username + "/" + self.domain + "/" ) @staticmethod def check_request(resp): try: resp.raise_for_status() except requests.exceptions.HTTPError: raise requests.exceptions.HTTPError except requests.exceptions.RequestException: raise requests.exceptions.RequestException return resp

31.104651

88

0.603738

import atexit import importlib import logging import requests class ApiClient: def __init__(self, **kwargs): self.kwargs = kwargs self.session = requests.Session() self.libns = self.get_export_url() + "components/library.owl#" self.dcdom = self.get_export_url() + "data/ontology.owl#" self.dclib = self.get_export_url() + "data/library.owl#" self.xsdns = "http://www.w3.org/2001/XMLSchema#" self.topcls = "http://www.wings-workflows.org/ontology/component.owl#Component" if self.login(kwargs["password"]) is False: raise ValueError("Login failed") atexit.register(self.logout) def get_server(self): return self.server def get_username(self): return self.username def login(self, password): self.session.get(self.server + "/sparql") data = {"j_username": self.username, "j_password": password} response = self.session.post(self.server + "/j_security_check", data) if response.status_code == 403 or response.status_code == 200: return True return False def logout(self): self.session.get(self.server + "/jsp/login/logout.jsp") self.session.close() def session(self): return self.session def _initialize(self, name): try: module_ = importlib.import_module(".%s" % name, __package__) try: class_ = getattr(module_, name.title()) return class_(api_client=self) except AttributeError: logging.error("Class does not exist") except ImportError: logging.error("Module does not exist %s", name) def close(self): self.logout() def __getattr__(self, attr): try: setattr(self, attr, self.kwargs[attr]) return getattr(self, attr) except KeyError: setattr(self, attr, self._initialize(attr)) return getattr(self, attr) def get_request_url(self): return self.server + "/users/" + self.username + "/" + self.domain + "/" def get_export_url(self): return ( self.export_url + "/export/users/" + self.username + "/" + self.domain + "/" ) @staticmethod def check_request(resp): try: resp.raise_for_status() except requests.exceptions.HTTPError: raise requests.exceptions.HTTPError except requests.exceptions.RequestException: raise requests.exceptions.RequestException return resp

true

f723ce701eeae1b756748f8b4615052fa8ad1b50

4,318

py

Python

ramp-database/ramp_database/tests/test_testing.py

agramfort/ramp-board

1c2cfe7af486e57ee0d4fb017b5266bb8ad152e3

[ "BSD-3-Clause" ]

null

ramp-database/ramp_database/tests/test_testing.py

agramfort/ramp-board

1c2cfe7af486e57ee0d4fb017b5266bb8ad152e3

[ "BSD-3-Clause" ]

null

ramp-database/ramp_database/tests/test_testing.py

agramfort/ramp-board

1c2cfe7af486e57ee0d4fb017b5266bb8ad152e3

[ "BSD-3-Clause" ]

null

import os import shutil import pytest from ramp_utils import read_config from ramp_utils import generate_ramp_config from ramp_utils.testing import database_config_template from ramp_utils.testing import ramp_config_template from ramp_database.utils import setup_db from ramp_database.utils import session_scope from ramp_database.model import Model from ramp_database.exceptions import NameClashError from ramp_database.tools.user import get_user_by_name from ramp_database.tools.event import get_problem from ramp_database.testing import create_test_db from ramp_database.testing import add_events from ramp_database.testing import add_users from ramp_database.testing import add_problems from ramp_database.testing import ramp_config_boston_housing from ramp_database.testing import ramp_config_iris from ramp_database.testing import setup_ramp_kit_ramp_data from ramp_database.testing import sign_up_teams_to_events from ramp_database.testing import submit_all_starting_kits @pytest.fixture(scope='module') def database_config(): return read_config(database_config_template()) @pytest.fixture(scope='module') def ramp_config(): return ramp_config_template() @pytest.fixture def session_scope_function(database_config, ramp_config): try: deployment_dir = create_test_db(database_config, ramp_config) with session_scope(database_config['sqlalchemy']) as session: yield session finally: shutil.rmtree(deployment_dir, ignore_errors=True) db, _ = setup_db(database_config['sqlalchemy']) Model.metadata.drop_all(db) def test_ramp_kit_ramp_data(session_scope_function, ramp_config): internal_ramp_config = generate_ramp_config(read_config(ramp_config)) setup_ramp_kit_ramp_data(internal_ramp_config, 'iris') msg_err = 'The RAMP kit repository was previously cloned.' with pytest.raises(ValueError, match=msg_err): setup_ramp_kit_ramp_data(internal_ramp_config, 'iris') # retrieve the path to the ramp kit to remove it shutil.rmtree(internal_ramp_config['ramp_kit_dir']) msg_err = 'The RAMP data repository was previously cloned.' with pytest.raises(ValueError, match=msg_err): setup_ramp_kit_ramp_data(internal_ramp_config, 'iris') setup_ramp_kit_ramp_data(internal_ramp_config, 'iris', force=True) def test_add_users(session_scope_function): add_users(session_scope_function) users = get_user_by_name(session_scope_function, None) for user in users: assert user.name in ('test_user', 'test_user_2', 'test_iris_admin') err_msg = 'username is already in use' with pytest.raises(NameClashError, match=err_msg): add_users(session_scope_function) def test_add_problems(session_scope_function): add_problems(session_scope_function) problems = get_problem(session_scope_function, None) for problem in problems: assert problem.name in ('iris', 'boston_housing') # trying to add twice the same problem will raise a git error since the # repositories already exist. msg_err = 'The RAMP kit repository was previously cloned.' with pytest.raises(ValueError, match=msg_err): add_problems(session_scope_function) def test_add_events(session_scope_function): add_problems(session_scope_function) add_events(session_scope_function) with pytest.raises(ValueError): add_events(session_scope_function) def test_sign_up_team_to_events(session_scope_function): add_users(session_scope_function) add_problems(session_scope_function) add_events(session_scope_function) sign_up_teams_to_events(session_scope_function) def test_submit_all_starting_kits(session_scope_function): add_users(session_scope_function) add_problems(session_scope_function) add_events(session_scope_function) sign_up_teams_to_events(session_scope_function) submit_all_starting_kits(session_scope_function) def test_ramp_config_iris(): filename = ramp_config_iris() assert os.path.join('tests', 'data', 'ramp_config_iris.yml') in filename def test_ramp_config_boston_housing(): filename = ramp_config_boston_housing() expected_path = os.path.join('tests', 'data', 'ramp_config_boston_housing.yml') assert expected_path in filename

34.822581

76

0.789717

import os import shutil import pytest from ramp_utils import read_config from ramp_utils import generate_ramp_config from ramp_utils.testing import database_config_template from ramp_utils.testing import ramp_config_template from ramp_database.utils import setup_db from ramp_database.utils import session_scope from ramp_database.model import Model from ramp_database.exceptions import NameClashError from ramp_database.tools.user import get_user_by_name from ramp_database.tools.event import get_problem from ramp_database.testing import create_test_db from ramp_database.testing import add_events from ramp_database.testing import add_users from ramp_database.testing import add_problems from ramp_database.testing import ramp_config_boston_housing from ramp_database.testing import ramp_config_iris from ramp_database.testing import setup_ramp_kit_ramp_data from ramp_database.testing import sign_up_teams_to_events from ramp_database.testing import submit_all_starting_kits @pytest.fixture(scope='module') def database_config(): return read_config(database_config_template()) @pytest.fixture(scope='module') def ramp_config(): return ramp_config_template() @pytest.fixture def session_scope_function(database_config, ramp_config): try: deployment_dir = create_test_db(database_config, ramp_config) with session_scope(database_config['sqlalchemy']) as session: yield session finally: shutil.rmtree(deployment_dir, ignore_errors=True) db, _ = setup_db(database_config['sqlalchemy']) Model.metadata.drop_all(db) def test_ramp_kit_ramp_data(session_scope_function, ramp_config): internal_ramp_config = generate_ramp_config(read_config(ramp_config)) setup_ramp_kit_ramp_data(internal_ramp_config, 'iris') msg_err = 'The RAMP kit repository was previously cloned.' with pytest.raises(ValueError, match=msg_err): setup_ramp_kit_ramp_data(internal_ramp_config, 'iris') shutil.rmtree(internal_ramp_config['ramp_kit_dir']) msg_err = 'The RAMP data repository was previously cloned.' with pytest.raises(ValueError, match=msg_err): setup_ramp_kit_ramp_data(internal_ramp_config, 'iris') setup_ramp_kit_ramp_data(internal_ramp_config, 'iris', force=True) def test_add_users(session_scope_function): add_users(session_scope_function) users = get_user_by_name(session_scope_function, None) for user in users: assert user.name in ('test_user', 'test_user_2', 'test_iris_admin') err_msg = 'username is already in use' with pytest.raises(NameClashError, match=err_msg): add_users(session_scope_function) def test_add_problems(session_scope_function): add_problems(session_scope_function) problems = get_problem(session_scope_function, None) for problem in problems: assert problem.name in ('iris', 'boston_housing') msg_err = 'The RAMP kit repository was previously cloned.' with pytest.raises(ValueError, match=msg_err): add_problems(session_scope_function) def test_add_events(session_scope_function): add_problems(session_scope_function) add_events(session_scope_function) with pytest.raises(ValueError): add_events(session_scope_function) def test_sign_up_team_to_events(session_scope_function): add_users(session_scope_function) add_problems(session_scope_function) add_events(session_scope_function) sign_up_teams_to_events(session_scope_function) def test_submit_all_starting_kits(session_scope_function): add_users(session_scope_function) add_problems(session_scope_function) add_events(session_scope_function) sign_up_teams_to_events(session_scope_function) submit_all_starting_kits(session_scope_function) def test_ramp_config_iris(): filename = ramp_config_iris() assert os.path.join('tests', 'data', 'ramp_config_iris.yml') in filename def test_ramp_config_boston_housing(): filename = ramp_config_boston_housing() expected_path = os.path.join('tests', 'data', 'ramp_config_boston_housing.yml') assert expected_path in filename

true

f723cf6dae464950f3d8b77edb889dcb5492b6ce

4,453

py

Python

python/ingestor/business.py

agahchen/RSBC-DataHub-API

d3742a09851d5753809e8eb8e1f7f6ca10b121ad

[ "Apache-2.0" ]

null

python/ingestor/business.py

agahchen/RSBC-DataHub-API

d3742a09851d5753809e8eb8e1f7f6ca10b121ad

[ "Apache-2.0" ]

null

python/ingestor/business.py

agahchen/RSBC-DataHub-API

d3742a09851d5753809e8eb8e1f7f6ca10b121ad

[ "Apache-2.0" ]

null

import python.common.middleware as middleware import python.common.actions as actions import python.common.rsi_email as rsi_email import python.common.rest as rest def get_available_time_slots() -> list: """ An application is ready for scheduling when all the payment rules are satisfied plus: - the application has been paid - the window to schedule the review has not elapsed """ return [ {"try": middleware.create_correlation_id, "fail": []}, {"try": middleware.determine_current_datetime, "fail": []}, {"try": middleware.clean_prohibition_number, "fail": []}, {"try": middleware.validate_prohibition_number, "fail": []}, {"try": middleware.get_vips_status, "fail": []}, {"try": middleware.prohibition_exists_in_vips, "fail": []}, {"try": middleware.user_submitted_last_name_matches_vips, "fail": []}, {"try": middleware.application_has_been_saved_to_vips, "fail": []}, {"try": middleware.get_payment_status, "fail": []}, {"try": middleware.received_valid_payment_status, "fail": []}, {"try": middleware.paid_not_more_than_24hrs_ago, "fail": []}, {"try": middleware.application_has_been_paid, "fail": []}, {"try": middleware.review_has_not_been_scheduled, "fail": []}, {"try": middleware.get_application_details, "fail": []}, {"try": middleware.valid_application_received_from_vips, "fail": []}, {"try": middleware.get_invoice_details, "fail": []}, {"try": middleware.calculate_schedule_window, "fail": []}, {"try": middleware.query_review_times_available, "fail": []}, {"try": middleware.does_applicant_have_enough_review_options, "fail": [ {"try": middleware.query_for_additional_review_times, "fail": []}, {"try": middleware.does_applicant_have_enough_review_options, "fail": [ {"try": rsi_email.insufficient_reviews_available, "fail": []}, ]} ]}, ] def ingest_form() -> list: return [ {"try": middleware.content_type_is_xml, "fail": [ {"try": rest.failed_validation, "fail": []}, ]}, {"try": middleware.content_length_within_bounds, "fail": [ {"try": rest.failed_validation, "fail": []}, ]}, {"try": middleware.form_name_provided, "fail": [ {"try": rest.failed_validation, "fail": []}, ]}, {"try": middleware.validate_form_name, "fail": [ {"try": rest.failed_validation, "fail": []}, ]}, {"try": middleware.add_encrypt_at_rest_attribute, "fail": []}, {"try": middleware.convert_xml_to_dictionary_object, "fail": [ {"try": rest.server_error, "fail": []}, ]}, {"try": middleware.get_xml_from_request, "fail": []}, {"try": middleware.base_64_encode_xml, "fail": []}, {"try": middleware.create_form_payload, "fail": []}, {"try": middleware.encode_payload, "fail": []}, {"try": middleware.get_queue_name_from_parameters, "fail": []}, {"try": actions.add_to_rabbitmq_queue, "fail": [ {"try": rest.server_error, "fail": []}, ]}, # Useful for debugging: {"try": rsi_email.send_form_xml_to_admin, "fail": []}, {"try": rest.okay, "fail": []} ] def is_okay_to_submit_evidence() -> list: """ Check to determine if an applicant can upload / submit evidence. To submit evidence an applicant must have: - submitted an application, - paid for a review, - scheduled a review date and - the review date must be at 24 hours in the future """ return [ {"try": middleware.create_correlation_id, "fail": []}, {"try": middleware.determine_current_datetime, "fail": []}, {"try": middleware.clean_prohibition_number, "fail": []}, {"try": middleware.validate_prohibition_number, "fail": []}, {"try": middleware.get_vips_status, "fail": []}, {"try": middleware.prohibition_exists_in_vips, "fail": []}, {"try": middleware.user_submitted_last_name_matches_vips, "fail": []}, {"try": middleware.application_has_been_saved_to_vips, "fail": []}, {"try": middleware.application_has_been_paid, "fail": []}, {"try": middleware.review_has_been_scheduled, "fail": []}, {"try": middleware.is_review_more_than_48_hours_in_the_future, "fail": []} ]

46.385417

89

0.614193

import python.common.middleware as middleware import python.common.actions as actions import python.common.rsi_email as rsi_email import python.common.rest as rest def get_available_time_slots() -> list: return [ {"try": middleware.create_correlation_id, "fail": []}, {"try": middleware.determine_current_datetime, "fail": []}, {"try": middleware.clean_prohibition_number, "fail": []}, {"try": middleware.validate_prohibition_number, "fail": []}, {"try": middleware.get_vips_status, "fail": []}, {"try": middleware.prohibition_exists_in_vips, "fail": []}, {"try": middleware.user_submitted_last_name_matches_vips, "fail": []}, {"try": middleware.application_has_been_saved_to_vips, "fail": []}, {"try": middleware.get_payment_status, "fail": []}, {"try": middleware.received_valid_payment_status, "fail": []}, {"try": middleware.paid_not_more_than_24hrs_ago, "fail": []}, {"try": middleware.application_has_been_paid, "fail": []}, {"try": middleware.review_has_not_been_scheduled, "fail": []}, {"try": middleware.get_application_details, "fail": []}, {"try": middleware.valid_application_received_from_vips, "fail": []}, {"try": middleware.get_invoice_details, "fail": []}, {"try": middleware.calculate_schedule_window, "fail": []}, {"try": middleware.query_review_times_available, "fail": []}, {"try": middleware.does_applicant_have_enough_review_options, "fail": [ {"try": middleware.query_for_additional_review_times, "fail": []}, {"try": middleware.does_applicant_have_enough_review_options, "fail": [ {"try": rsi_email.insufficient_reviews_available, "fail": []}, ]} ]}, ] def ingest_form() -> list: return [ {"try": middleware.content_type_is_xml, "fail": [ {"try": rest.failed_validation, "fail": []}, ]}, {"try": middleware.content_length_within_bounds, "fail": [ {"try": rest.failed_validation, "fail": []}, ]}, {"try": middleware.form_name_provided, "fail": [ {"try": rest.failed_validation, "fail": []}, ]}, {"try": middleware.validate_form_name, "fail": [ {"try": rest.failed_validation, "fail": []}, ]}, {"try": middleware.add_encrypt_at_rest_attribute, "fail": []}, {"try": middleware.convert_xml_to_dictionary_object, "fail": [ {"try": rest.server_error, "fail": []}, ]}, {"try": middleware.get_xml_from_request, "fail": []}, {"try": middleware.base_64_encode_xml, "fail": []}, {"try": middleware.create_form_payload, "fail": []}, {"try": middleware.encode_payload, "fail": []}, {"try": middleware.get_queue_name_from_parameters, "fail": []}, {"try": actions.add_to_rabbitmq_queue, "fail": [ {"try": rest.server_error, "fail": []}, ]}, {"try": rest.okay, "fail": []} ] def is_okay_to_submit_evidence() -> list: return [ {"try": middleware.create_correlation_id, "fail": []}, {"try": middleware.determine_current_datetime, "fail": []}, {"try": middleware.clean_prohibition_number, "fail": []}, {"try": middleware.validate_prohibition_number, "fail": []}, {"try": middleware.get_vips_status, "fail": []}, {"try": middleware.prohibition_exists_in_vips, "fail": []}, {"try": middleware.user_submitted_last_name_matches_vips, "fail": []}, {"try": middleware.application_has_been_saved_to_vips, "fail": []}, {"try": middleware.application_has_been_paid, "fail": []}, {"try": middleware.review_has_been_scheduled, "fail": []}, {"try": middleware.is_review_more_than_48_hours_in_the_future, "fail": []} ]

true

f723cf8e92377defaba93082120d82ab6f27e07b

343

py

Python

tests/test_models.py

devopsmakers/dj-prosftpd

fcef6b4a5b8b872c9830eaf0315f0467dd1944ff

[ "MIT" ]

null

tests/test_models.py

devopsmakers/dj-prosftpd

fcef6b4a5b8b872c9830eaf0315f0467dd1944ff

[ "MIT" ]

null

tests/test_models.py

devopsmakers/dj-prosftpd

fcef6b4a5b8b872c9830eaf0315f0467dd1944ff

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ test_dj-prosftpd ------------ Tests for `dj-prosftpd` models module. """ from django.test import TestCase from dj_prosftpd import models class TestDj_prosftpd(TestCase): def setUp(self): pass def test_something(self): pass def tearDown(self): pass

13.192308

38

0.620991

from django.test import TestCase from dj_prosftpd import models class TestDj_prosftpd(TestCase): def setUp(self): pass def test_something(self): pass def tearDown(self): pass

true

f723cfc4543d31b1df927182b8dd2e714298c92e

14,427

py

Python

fermi_blind_search/database.py

giacomov/fermi_blind_search

f8d52cb8b61519223918d197682b4f70c78cce10

[ "BSD-3-Clause" ]

null

fermi_blind_search/database.py

giacomov/fermi_blind_search

f8d52cb8b61519223918d197682b4f70c78cce10

[ "BSD-3-Clause" ]

null

fermi_blind_search/database.py

giacomov/fermi_blind_search

f8d52cb8b61519223918d197682b4f70c78cce10

[ "BSD-3-Clause" ]

1

2017-04-01T10:42:07.000Z

#!/usr/bin/env python from contextlib import contextmanager import argparse import sys import sshtunnel from sqlalchemy import * from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker from fermi_blind_search.configuration import get_config from fermi_blind_search import myLogging _logger = myLogging.log.getLogger("database") # will store the engine that will connect to the database _engine = None # we need this to handle the tables Base = declarative_base() # defines the class that will connect to the database Session = sessionmaker() @contextmanager def database_connection(config): if config.get("SSH db tunnel", "remote_host") != '': """ As of now, we are not using this in the real time search. Instead we are using an autossh connection to facilitate tunneling. However, we are keeping the code here in case an ssh tunnel needs to be established from a python script in the future. """ with sshtunnel.SSHTunnelForwarder(config.get("SSH db tunnel", "remote_host"), ssh_username=config.get("SSH db tunnel", "username"), host_pkey_directories=[ config.get("SSH db tunnel", "key_directory")], remote_bind_address=('127.0.0.1', int(config.get("SSH db tunnel", "tunnel_port"))), local_bind_address=('localhost', int(config.get('Real time', 'db_port'))), ): db_instance = Database(config) try: yield db_instance except: raise finally: db_instance.close() else: db_instance = Database(config) try: yield db_instance except: raise finally: db_instance.close() class Database(object): def __init__(self, config): global Base global Session global _engine # initialize the engine using parameters from the config file if config.get("Real time", "is_sqlite") == "True": engine_url = "sqlite:///" + config.get("Real time", "db_path") else: engine_url = config.get("Real time", "db_dialect") + "://" + config.get("Real time", "db_username") + ":" + \ config.get("Real time", "db_password") + "@" + config.get("Real time", "db_host") + ":" + \ config.get("Real time", "db_port") + "/" + config.get("Real time", "db_path") _logger.debug("Database engine URL: %s" % engine_url) _engine = create_engine(engine_url) # bind the engine to the Base Base.metadata.bind = _engine # bind the engine to the session Session.configure(bind=_engine) self._config = config def create_tables(self): # create the Analysis and Results tables Base.metadata.create_all(_engine) _logger.info("Successfully created database tables") def delete_analysis_table(self): # drop the table from the DB try: Analysis.__table__.drop() except: try: # another way to drop the table Analysis.__table__.drop(_engine) except: _logger.error('ERROR: Could not delete Analysis Table') raise else: _logger.info("Successfully deleted Analysis table") def delete_results_table(self): # drop the table from the DB try: Results.__table__.drop() except: try: # another way to drop the table Results.__table__.drop(_engine) except: _logger.error('ERROR: Could not delete Results Table') raise else: _logger.info("Successfully delete Results table") def add_analysis(self, analysis_vals): # TODO: which check that analysis_vals contains the correct field? # TODO: do we want to add a check that the analysis doesn't already exist? assert (analysis_vals['met_start'] is not None and analysis_vals['duration'] is not None and analysis_vals['counts'] is not None and analysis_vals['directory'] is not None), \ "One of the parameters to enter the analysis into the database is missing. Parameters are met_start, " \ "duration, counts, and directory" assert isinstance(analysis_vals["counts"], int), "Counts is not an integer" try: # set the values of the analysis to be added to the table new_analysis = Analysis(met_start=analysis_vals['met_start'], duration=analysis_vals['duration'], counts=analysis_vals['counts'], directory=analysis_vals['directory']) _logger.info("Adding this Analysis to the database: %s" % new_analysis) except KeyError: _logger.error('ERROR: The analysis you want to add does not have the proper fields!') raise except: raise else: # open a session, add the analysis to the table, close the session session = Session() session.add(new_analysis) try: session.commit() except: raise else: _logger.debug("Successfully added analysis to db") def update_analysis_counts(self, met_start, duration, new_counts): # open a session with the DB session = Session() # get the analysis to be updated results = session.query(Analysis).filter(Analysis.met_start == met_start).filter( Analysis.duration == duration).all() # check that there is only one analysis that matches these parameters assert len(results) != 0, "Cannot update this analysis because it does not exist" assert len(results) == 1, 'More than one analysis exists with these parameters! This should never happen' analysis = results[0] _logger.info("Updating this analysis: %s to have %s counts" % (analysis, new_counts)) # update the counts column of the analysis in question analysis.counts = new_counts try: # commit the change session.commit() except: raise else: _logger.debug("Successfully updated analysis") def add_candidate(self, candidate_vals): # TODO: which check that condidate_vals contains the correct field? # TODO: do we want to add a check that the candidate doesn't already exist? assert (candidate_vals['ra'] is not None and candidate_vals['dec'] is not None and candidate_vals['met_start'] is not None and candidate_vals['interval'] is not None and candidate_vals['email'] is not None), \ "One of the parameters to enter the candidate into the database is missing. Parameters are ra, dec, " \ "met_start, interval, email" try: # set the values of the result to be added to the table new_candidate = Results(ra=candidate_vals['ra'], dec=candidate_vals['dec'], met_start=candidate_vals['met_start'], interval=candidate_vals['interval'], email=candidate_vals['email']) _logger.info("Adding this result to the database %s" % new_candidate) except KeyError: _logger.error('ERROR: The result you want to add does not have the proper fields') raise except: raise else: # open a session, add the result to the table, close the session session = Session() session.add(new_candidate) try: session.commit() except: raise else: _logger.debug("Successfully added result to database") return new_candidate def get_analysis_between_times(self, start, stop): _logger.info("Fetching analyses using data between %s and %s" % (start, stop)) # open a session session = Session() # get all analyses with met_start or met_stop (met_start + duration) times within the range [start,stop] return session.query(Analysis).filter(or_(and_(Analysis.met_start >= start, Analysis.met_start <= stop), and_(Analysis.met_start + Analysis.duration >= start, Analysis.met_start + Analysis.duration <= stop))).all() def get_exact_analysis(self, start, stop): _logger.info("Fetching analysis with met_start = %s and met_start + duration = %s" % (start, stop)) # open a session session = Session() # get all analyses with start time and stop times exactly matching the parameters return session.query(Analysis).filter(and_(Analysis.met_start == start, Analysis.met_start + Analysis.duration == stop)).all() def get_results(self, candidate_vals): # check that candidate vals has the correct fields to perform a search assert (candidate_vals['ra'] is not None and candidate_vals['dec'] is not None and candidate_vals['met_start'] is not None and candidate_vals['interval'] is not None), \ "One of the parameters to enter the candidate into the database is missing. Parameters are ra, dec, " \ "met_start, interval" # open a session session = Session() # get the tolerance ranges for determining if we have a match ra_tol = float(self._config.get("Real time", "ra_tol")) dec_tol = float(self._config.get("Real time", "dec_tol")) start_tol = float(self._config.get("Real time", "start_tol")) int_tol = float(self._config.get("Real time", "int_tol")) _logger.info("Fetching results within %s of ra, %s of dec, %s of met_start, and %s of interval of %s" % (ra_tol, dec_tol, start_tol, int_tol, candidate_vals)) # get all results that match the passed candidate within a certain tolerance return session.query(Results).filter(and_(candidate_vals['ra'] - ra_tol <= Results.ra, Results.ra <= candidate_vals['ra'] + ra_tol, candidate_vals['dec'] - dec_tol <= Results.dec, Results.dec <= candidate_vals['dec'] + dec_tol, candidate_vals['met_start'] - start_tol <= Results.met_start, Results.met_start <= candidate_vals['met_start'] + start_tol, candidate_vals['interval'] - int_tol <= Results.interval, Results.interval <= candidate_vals['interval'] + int_tol)).all() def get_results_to_email(self): _logger.info("Fetching results with email = False (0 in database)") # open a session session = Session() # get all results that have not been emailed yet return session.query(Results).filter(Results.email == 0).all() def update_result_email(self, candidate, email_val=False): _logger.info("Updating result: %s to have email value: %s" % (candidate, email_val)) # open a session session = Session() # update the value of the candidate candidate.email = email_val try: # commit the change session.commit() except: raise else: _logger.debug("Successfully updated result") def close(self): global _logger _logger.info("Closing database") Session.close_all() class Analysis(Base): # give the table a name __tablename__ = 'analysis' # define the columns of the table met_start = Column(Float(32), Sequence('analysis_met_start_seq'), primary_key=True) duration = Column(Float(32), Sequence('analysis_duration_seq'), primary_key=True) counts = Column(Integer) directory = Column(String(250)) def __repr__(self): # formatting string so that printing rows from the table is more readable return "<Analysis(met_start= %s, duration= %s, counts= %s, directory= %s)>" % \ (self.met_start, self.duration, self.counts, self.directory) class Results(Base): # give the table a name __tablename__ = 'results' # define the columns of the table ra = Column(Float(32)) dec = Column(Float(32)) met_start = Column(Float(32), Sequence('results_met_start_seq'), primary_key=True) interval = Column(Float(32), Sequence('results_interval_seq'), primary_key=True) email = Column(Boolean) def __repr__(self): # formatting string so that printing rows from the table is more readable return "<Results(ra= %s, dec= %s, met_start= %s, interval= %s, email=%s)>" % (self.ra, self.dec, self.met_start, self.interval, self.email) if __name__ == "__main__": # Allows you to quickly delete and re-create the database. parser = argparse.ArgumentParser() parser.add_argument('--config', help='Path to config file', type=get_config, required=True) parser.add_argument('--clear', help="If set, delete the database tables, and recreate them", action="store_true") args = parser.parse_args() configuration = args.config # start db connection db = Database(configuration) if args.clear: # delete the tables db.delete_analysis_table() db.delete_results_table() # re-create the tables db.create_tables()

37.27907

121

0.582311

from contextlib import contextmanager import argparse import sys import sshtunnel from sqlalchemy import * from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker from fermi_blind_search.configuration import get_config from fermi_blind_search import myLogging _logger = myLogging.log.getLogger("database") _engine = None Base = declarative_base() Session = sessionmaker() @contextmanager def database_connection(config): if config.get("SSH db tunnel", "remote_host") != '': with sshtunnel.SSHTunnelForwarder(config.get("SSH db tunnel", "remote_host"), ssh_username=config.get("SSH db tunnel", "username"), host_pkey_directories=[ config.get("SSH db tunnel", "key_directory")], remote_bind_address=('127.0.0.1', int(config.get("SSH db tunnel", "tunnel_port"))), local_bind_address=('localhost', int(config.get('Real time', 'db_port'))), ): db_instance = Database(config) try: yield db_instance except: raise finally: db_instance.close() else: db_instance = Database(config) try: yield db_instance except: raise finally: db_instance.close() class Database(object): def __init__(self, config): global Base global Session global _engine if config.get("Real time", "is_sqlite") == "True": engine_url = "sqlite:///" + config.get("Real time", "db_path") else: engine_url = config.get("Real time", "db_dialect") + "://" + config.get("Real time", "db_username") + ":" + \ config.get("Real time", "db_password") + "@" + config.get("Real time", "db_host") + ":" + \ config.get("Real time", "db_port") + "/" + config.get("Real time", "db_path") _logger.debug("Database engine URL: %s" % engine_url) _engine = create_engine(engine_url) Base.metadata.bind = _engine Session.configure(bind=_engine) self._config = config def create_tables(self): Base.metadata.create_all(_engine) _logger.info("Successfully created database tables") def delete_analysis_table(self): try: Analysis.__table__.drop() except: try: Analysis.__table__.drop(_engine) except: _logger.error('ERROR: Could not delete Analysis Table') raise else: _logger.info("Successfully deleted Analysis table") def delete_results_table(self): try: Results.__table__.drop() except: try: Results.__table__.drop(_engine) except: _logger.error('ERROR: Could not delete Results Table') raise else: _logger.info("Successfully delete Results table") def add_analysis(self, analysis_vals): assert (analysis_vals['met_start'] is not None and analysis_vals['duration'] is not None and analysis_vals['counts'] is not None and analysis_vals['directory'] is not None), \ "One of the parameters to enter the analysis into the database is missing. Parameters are met_start, " \ "duration, counts, and directory" assert isinstance(analysis_vals["counts"], int), "Counts is not an integer" try: # set the values of the analysis to be added to the table new_analysis = Analysis(met_start=analysis_vals['met_start'], duration=analysis_vals['duration'], counts=analysis_vals['counts'], directory=analysis_vals['directory']) _logger.info("Adding this Analysis to the database: %s" % new_analysis) except KeyError: _logger.error('ERROR: The analysis you want to add does not have the proper fields!') raise except: raise else: # open a session, add the analysis to the table, close the session session = Session() session.add(new_analysis) try: session.commit() except: raise else: _logger.debug("Successfully added analysis to db") def update_analysis_counts(self, met_start, duration, new_counts): # open a session with the DB session = Session() # get the analysis to be updated results = session.query(Analysis).filter(Analysis.met_start == met_start).filter( Analysis.duration == duration).all() # check that there is only one analysis that matches these parameters assert len(results) != 0, "Cannot update this analysis because it does not exist" assert len(results) == 1, 'More than one analysis exists with these parameters! This should never happen' analysis = results[0] _logger.info("Updating this analysis: %s to have %s counts" % (analysis, new_counts)) # update the counts column of the analysis in question analysis.counts = new_counts try: # commit the change session.commit() except: raise else: _logger.debug("Successfully updated analysis") def add_candidate(self, candidate_vals): # TODO: which check that condidate_vals contains the correct field? # TODO: do we want to add a check that the candidate doesn't already exist? assert (candidate_vals['ra'] is not None and candidate_vals['dec'] is not None and candidate_vals['met_start'] is not None and candidate_vals['interval'] is not None and candidate_vals['email'] is not None), \ "One of the parameters to enter the candidate into the database is missing. Parameters are ra, dec, " \ "met_start, interval, email" try: new_candidate = Results(ra=candidate_vals['ra'], dec=candidate_vals['dec'], met_start=candidate_vals['met_start'], interval=candidate_vals['interval'], email=candidate_vals['email']) _logger.info("Adding this result to the database %s" % new_candidate) except KeyError: _logger.error('ERROR: The result you want to add does not have the proper fields') raise except: raise else: session = Session() session.add(new_candidate) try: session.commit() except: raise else: _logger.debug("Successfully added result to database") return new_candidate def get_analysis_between_times(self, start, stop): _logger.info("Fetching analyses using data between %s and %s" % (start, stop)) session = Session() return session.query(Analysis).filter(or_(and_(Analysis.met_start >= start, Analysis.met_start <= stop), and_(Analysis.met_start + Analysis.duration >= start, Analysis.met_start + Analysis.duration <= stop))).all() def get_exact_analysis(self, start, stop): _logger.info("Fetching analysis with met_start = %s and met_start + duration = %s" % (start, stop)) session = Session() return session.query(Analysis).filter(and_(Analysis.met_start == start, Analysis.met_start + Analysis.duration == stop)).all() def get_results(self, candidate_vals): assert (candidate_vals['ra'] is not None and candidate_vals['dec'] is not None and candidate_vals['met_start'] is not None and candidate_vals['interval'] is not None), \ "One of the parameters to enter the candidate into the database is missing. Parameters are ra, dec, " \ "met_start, interval" session = Session() ra_tol = float(self._config.get("Real time", "ra_tol")) dec_tol = float(self._config.get("Real time", "dec_tol")) start_tol = float(self._config.get("Real time", "start_tol")) int_tol = float(self._config.get("Real time", "int_tol")) _logger.info("Fetching results within %s of ra, %s of dec, %s of met_start, and %s of interval of %s" % (ra_tol, dec_tol, start_tol, int_tol, candidate_vals)) return session.query(Results).filter(and_(candidate_vals['ra'] - ra_tol <= Results.ra, Results.ra <= candidate_vals['ra'] + ra_tol, candidate_vals['dec'] - dec_tol <= Results.dec, Results.dec <= candidate_vals['dec'] + dec_tol, candidate_vals['met_start'] - start_tol <= Results.met_start, Results.met_start <= candidate_vals['met_start'] + start_tol, candidate_vals['interval'] - int_tol <= Results.interval, Results.interval <= candidate_vals['interval'] + int_tol)).all() def get_results_to_email(self): _logger.info("Fetching results with email = False (0 in database)") session = Session() return session.query(Results).filter(Results.email == 0).all() def update_result_email(self, candidate, email_val=False): _logger.info("Updating result: %s to have email value: %s" % (candidate, email_val)) session = Session() candidate.email = email_val try: session.commit() except: raise else: _logger.debug("Successfully updated result") def close(self): global _logger _logger.info("Closing database") Session.close_all() class Analysis(Base): __tablename__ = 'analysis' met_start = Column(Float(32), Sequence('analysis_met_start_seq'), primary_key=True) duration = Column(Float(32), Sequence('analysis_duration_seq'), primary_key=True) counts = Column(Integer) directory = Column(String(250)) def __repr__(self): return "<Analysis(met_start= %s, duration= %s, counts= %s, directory= %s)>" % \ (self.met_start, self.duration, self.counts, self.directory) class Results(Base): __tablename__ = 'results' ra = Column(Float(32)) dec = Column(Float(32)) met_start = Column(Float(32), Sequence('results_met_start_seq'), primary_key=True) interval = Column(Float(32), Sequence('results_interval_seq'), primary_key=True) email = Column(Boolean) def __repr__(self): return "<Results(ra= %s, dec= %s, met_start= %s, interval= %s, email=%s)>" % (self.ra, self.dec, self.met_start, self.interval, self.email) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--config', help='Path to config file', type=get_config, required=True) parser.add_argument('--clear', help="If set, delete the database tables, and recreate them", action="store_true") args = parser.parse_args() configuration = args.config db = Database(configuration) if args.clear: db.delete_analysis_table() db.delete_results_table() db.create_tables()

true

f723cfd084712344629373ab39822ddba59ccffa

2,538

py

Python

irrigator_pro/farms/migrations/0012_copy_field_list_to_field_for_waterhistory_and_probe.py

warnes/irrigatorpro

4838f8832bdbf87f394a0298adc5dabfc26e82e8

[ "MIT" ]

null

irrigator_pro/farms/migrations/0012_copy_field_list_to_field_for_waterhistory_and_probe.py

warnes/irrigatorpro

4838f8832bdbf87f394a0298adc5dabfc26e82e8

[ "MIT" ]

null

irrigator_pro/farms/migrations/0012_copy_field_list_to_field_for_waterhistory_and_probe.py

warnes/irrigatorpro

4838f8832bdbf87f394a0298adc5dabfc26e82e8

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations import django.utils.timezone import sys from django.db import connection def convert_probe_field_list_to_field(apps, schema_editor): """ For each current Probe, copy field_list[0] into field, and create new records for field_list[1:] ** For some reason, probe.field_list.all() doesn't work here, so use direct SQL instead. ** """ Probe = apps.get_model("farms", "Probe") cursor = connection.cursor() cursor.execute("select * from farms_probe_field_list") previous_id = -1 for (id, probe_id, field_id) in cursor.fetchall(): #print "Working on record #%d: Probe #%d, Field #%d" %(id, probe_id, field_id) """ If there are multiple fields assigned just preserve the first one otherwise a unique constraing on the pair (cdop_season_id, radio_id) will be violated. This is necessary only for Alain's test server. """ if probe_id != previous_id: previous_id = probe_id probe = Probe.objects.get(id=probe_id) if probe.field_id is None: probe.field_id = field_id else: probe.id = None probe.pk = None probe.field_id = field_id probe.save() def convert_waterhistory_field_list_to_field(apps, schema_editor): """ For each current WaterHistory object, copy field_list[0] into field, and create new records for field_list[1:] ** For some reason, waterhistory.field_list.all() doesn't work here, so use direct SQL instead. ** """ WaterHistory = apps.get_model("farms", "WaterHistory") cursor = connection.cursor() cursor.execute("select * from farms_waterhistory_field_list") for (id, waterhistory_id, field_id) in cursor.fetchall(): #print "Working on ", (id, waterhistory_id, field_id) waterhistory = WaterHistory.objects.get(id=waterhistory_id) if waterhistory.field_id is None: waterhistory.field_id = field_id else: waterhistory.id = None waterhistory.pk = None waterhistory.field_id = field_id waterhistory.save() class Migration(migrations.Migration): dependencies = [ ('farms', '0011_add_ForeignKey_for_field_to_waterhistory_and_probe'), ] operations = [ migrations.RunPython( convert_probe_field_list_to_field ), migrations.RunPython( convert_waterhistory_field_list_to_field), ]

29.511628

84

0.670607

from __future__ import unicode_literals from django.db import models, migrations import django.utils.timezone import sys from django.db import connection def convert_probe_field_list_to_field(apps, schema_editor): Probe = apps.get_model("farms", "Probe") cursor = connection.cursor() cursor.execute("select * from farms_probe_field_list") previous_id = -1 for (id, probe_id, field_id) in cursor.fetchall(): if probe_id != previous_id: previous_id = probe_id probe = Probe.objects.get(id=probe_id) if probe.field_id is None: probe.field_id = field_id else: probe.id = None probe.pk = None probe.field_id = field_id probe.save() def convert_waterhistory_field_list_to_field(apps, schema_editor): WaterHistory = apps.get_model("farms", "WaterHistory") cursor = connection.cursor() cursor.execute("select * from farms_waterhistory_field_list") for (id, waterhistory_id, field_id) in cursor.fetchall(): waterhistory = WaterHistory.objects.get(id=waterhistory_id) if waterhistory.field_id is None: waterhistory.field_id = field_id else: waterhistory.id = None waterhistory.pk = None waterhistory.field_id = field_id waterhistory.save() class Migration(migrations.Migration): dependencies = [ ('farms', '0011_add_ForeignKey_for_field_to_waterhistory_and_probe'), ] operations = [ migrations.RunPython( convert_probe_field_list_to_field ), migrations.RunPython( convert_waterhistory_field_list_to_field), ]

true

f723d086e096878de8624880928432132d608eb2

7,000

py

Python

rllib/agents/marwil/tests/test_marwil.py

kifarid/ray

43c97c2afb979987be82fa50048674e9b6776d5d

[ "Apache-2.0" ]

3

2021-08-29T20:41:21.000Z

2022-01-31T18:47:51.000Z

rllib/agents/marwil/tests/test_marwil.py

kifarid/ray

43c97c2afb979987be82fa50048674e9b6776d5d

[ "Apache-2.0" ]

61

2021-06-05T07:05:08.000Z

2022-03-19T07:14:56.000Z

rllib/agents/marwil/tests/test_marwil.py

kifarid/ray

43c97c2afb979987be82fa50048674e9b6776d5d

[ "Apache-2.0" ]

null

import numpy as np import os from pathlib import Path import unittest import ray import ray.rllib.agents.marwil as marwil from ray.rllib.evaluation.postprocessing import compute_advantages from ray.rllib.offline import JsonReader from ray.rllib.utils.framework import try_import_tf, try_import_torch from ray.rllib.utils.test_utils import check, check_compute_single_action, \ framework_iterator tf1, tf, tfv = try_import_tf() torch, _ = try_import_torch() class TestMARWIL(unittest.TestCase): @classmethod def setUpClass(cls): ray.init(num_cpus=4) @classmethod def tearDownClass(cls): ray.shutdown() def test_marwil_compilation_and_learning_from_offline_file(self): """Test whether a MARWILTrainer can be built with all frameworks. Learns from a historic-data file. To generate this data, first run: $ ./train.py --run=PPO --env=CartPole-v0 \ --stop='{"timesteps_total": 50000}' \ --config='{"output": "/tmp/out", "batch_mode": "complete_episodes"}' """ rllib_dir = Path(__file__).parent.parent.parent.parent print("rllib dir={}".format(rllib_dir)) data_file = os.path.join(rllib_dir, "tests/data/cartpole/large.json") print("data_file={} exists={}".format(data_file, os.path.isfile(data_file))) config = marwil.DEFAULT_CONFIG.copy() config["num_workers"] = 2 config["evaluation_num_workers"] = 1 config["evaluation_interval"] = 2 # Evaluate on actual environment. config["evaluation_config"] = {"input": "sampler"} # Learn from offline data. config["input"] = [data_file] num_iterations = 350 min_reward = 70.0 # Test for all frameworks. for _ in framework_iterator(config, frameworks=("tf", "torch")): trainer = marwil.MARWILTrainer(config=config, env="CartPole-v0") learnt = False for i in range(num_iterations): eval_results = trainer.train().get("evaluation") if eval_results: print("iter={} R={} ".format( i, eval_results["episode_reward_mean"])) # Learn until some reward is reached on an actual live env. if eval_results["episode_reward_mean"] > min_reward: print("learnt!") learnt = True break if not learnt: raise ValueError( "MARWILTrainer did not reach {} reward from expert " "offline data!".format(min_reward)) check_compute_single_action( trainer, include_prev_action_reward=True) trainer.stop() def test_marwil_loss_function(self): """ To generate the historic data used in this test case, first run: $ ./train.py --run=PPO --env=CartPole-v0 \ --stop='{"timesteps_total": 50000}' \ --config='{"output": "/tmp/out", "batch_mode": "complete_episodes"}' """ rllib_dir = Path(__file__).parent.parent.parent.parent print("rllib dir={}".format(rllib_dir)) data_file = os.path.join(rllib_dir, "tests/data/cartpole/small.json") print("data_file={} exists={}".format(data_file, os.path.isfile(data_file))) config = marwil.DEFAULT_CONFIG.copy() config["num_workers"] = 0 # Run locally. # Learn from offline data. config["input"] = [data_file] for fw, sess in framework_iterator(config, session=True): reader = JsonReader(inputs=[data_file]) batch = reader.next() trainer = marwil.MARWILTrainer(config=config, env="CartPole-v0") policy = trainer.get_policy() model = policy.model # Calculate our own expected values (to then compare against the # agent's loss output). cummulative_rewards = compute_advantages( batch, 0.0, config["gamma"], 1.0, False, False)["advantages"] if fw == "torch": cummulative_rewards = torch.tensor(cummulative_rewards) if fw != "tf": batch = policy._lazy_tensor_dict(batch) model_out, _ = model.from_batch(batch) vf_estimates = model.value_function() if fw == "tf": model_out, vf_estimates = \ policy.get_session().run([model_out, vf_estimates]) adv = cummulative_rewards - vf_estimates if fw == "torch": adv = adv.detach().cpu().numpy() adv_squared = np.mean(np.square(adv)) c_2 = 100.0 + 1e-8 * (adv_squared - 100.0) c = np.sqrt(c_2) exp_advs = np.exp(config["beta"] * (adv / c)) dist = policy.dist_class(model_out, model) logp = dist.logp(batch["actions"]) if fw == "torch": logp = logp.detach().cpu().numpy() elif fw == "tf": logp = sess.run(logp) # Calculate all expected loss components. expected_vf_loss = 0.5 * adv_squared expected_pol_loss = -1.0 * np.mean(exp_advs * logp) expected_loss = \ expected_pol_loss + config["vf_coeff"] * expected_vf_loss # Calculate the algorithm's loss (to check against our own # calculation above). batch.set_get_interceptor(None) postprocessed_batch = policy.postprocess_trajectory(batch) loss_func = marwil.marwil_tf_policy.marwil_loss if fw != "torch" \ else marwil.marwil_torch_policy.marwil_loss if fw != "tf": policy._lazy_tensor_dict(postprocessed_batch) loss_out = loss_func(policy, model, policy.dist_class, postprocessed_batch) else: loss_out, v_loss, p_loss = policy.get_session().run( [policy._loss, policy.loss.v_loss, policy.loss.p_loss], feed_dict=policy._get_loss_inputs_dict( postprocessed_batch, shuffle=False)) # Check all components. if fw == "torch": check(policy.v_loss, expected_vf_loss, decimals=4) check(policy.p_loss, expected_pol_loss, decimals=4) elif fw == "tf": check(v_loss, expected_vf_loss, decimals=4) check(p_loss, expected_pol_loss, decimals=4) else: check(policy.loss.v_loss, expected_vf_loss, decimals=4) check(policy.loss.p_loss, expected_pol_loss, decimals=4) check(loss_out, expected_loss, decimals=3) if __name__ == "__main__": import pytest import sys sys.exit(pytest.main(["-v", __file__]))

41.666667

79

0.576857

import numpy as np import os from pathlib import Path import unittest import ray import ray.rllib.agents.marwil as marwil from ray.rllib.evaluation.postprocessing import compute_advantages from ray.rllib.offline import JsonReader from ray.rllib.utils.framework import try_import_tf, try_import_torch from ray.rllib.utils.test_utils import check, check_compute_single_action, \ framework_iterator tf1, tf, tfv = try_import_tf() torch, _ = try_import_torch() class TestMARWIL(unittest.TestCase): @classmethod def setUpClass(cls): ray.init(num_cpus=4) @classmethod def tearDownClass(cls): ray.shutdown() def test_marwil_compilation_and_learning_from_offline_file(self): rllib_dir = Path(__file__).parent.parent.parent.parent print("rllib dir={}".format(rllib_dir)) data_file = os.path.join(rllib_dir, "tests/data/cartpole/large.json") print("data_file={} exists={}".format(data_file, os.path.isfile(data_file))) config = marwil.DEFAULT_CONFIG.copy() config["num_workers"] = 2 config["evaluation_num_workers"] = 1 config["evaluation_interval"] = 2 config["evaluation_config"] = {"input": "sampler"} config["input"] = [data_file] num_iterations = 350 min_reward = 70.0 for _ in framework_iterator(config, frameworks=("tf", "torch")): trainer = marwil.MARWILTrainer(config=config, env="CartPole-v0") learnt = False for i in range(num_iterations): eval_results = trainer.train().get("evaluation") if eval_results: print("iter={} R={} ".format( i, eval_results["episode_reward_mean"])) if eval_results["episode_reward_mean"] > min_reward: print("learnt!") learnt = True break if not learnt: raise ValueError( "MARWILTrainer did not reach {} reward from expert " "offline data!".format(min_reward)) check_compute_single_action( trainer, include_prev_action_reward=True) trainer.stop() def test_marwil_loss_function(self): rllib_dir = Path(__file__).parent.parent.parent.parent print("rllib dir={}".format(rllib_dir)) data_file = os.path.join(rllib_dir, "tests/data/cartpole/small.json") print("data_file={} exists={}".format(data_file, os.path.isfile(data_file))) config = marwil.DEFAULT_CONFIG.copy() config["num_workers"] = 0 config["input"] = [data_file] for fw, sess in framework_iterator(config, session=True): reader = JsonReader(inputs=[data_file]) batch = reader.next() trainer = marwil.MARWILTrainer(config=config, env="CartPole-v0") policy = trainer.get_policy() model = policy.model cummulative_rewards = compute_advantages( batch, 0.0, config["gamma"], 1.0, False, False)["advantages"] if fw == "torch": cummulative_rewards = torch.tensor(cummulative_rewards) if fw != "tf": batch = policy._lazy_tensor_dict(batch) model_out, _ = model.from_batch(batch) vf_estimates = model.value_function() if fw == "tf": model_out, vf_estimates = \ policy.get_session().run([model_out, vf_estimates]) adv = cummulative_rewards - vf_estimates if fw == "torch": adv = adv.detach().cpu().numpy() adv_squared = np.mean(np.square(adv)) c_2 = 100.0 + 1e-8 * (adv_squared - 100.0) c = np.sqrt(c_2) exp_advs = np.exp(config["beta"] * (adv / c)) dist = policy.dist_class(model_out, model) logp = dist.logp(batch["actions"]) if fw == "torch": logp = logp.detach().cpu().numpy() elif fw == "tf": logp = sess.run(logp) # Calculate all expected loss components. expected_vf_loss = 0.5 * adv_squared expected_pol_loss = -1.0 * np.mean(exp_advs * logp) expected_loss = \ expected_pol_loss + config["vf_coeff"] * expected_vf_loss # Calculate the algorithm's loss (to check against our own batch.set_get_interceptor(None) postprocessed_batch = policy.postprocess_trajectory(batch) loss_func = marwil.marwil_tf_policy.marwil_loss if fw != "torch" \ else marwil.marwil_torch_policy.marwil_loss if fw != "tf": policy._lazy_tensor_dict(postprocessed_batch) loss_out = loss_func(policy, model, policy.dist_class, postprocessed_batch) else: loss_out, v_loss, p_loss = policy.get_session().run( [policy._loss, policy.loss.v_loss, policy.loss.p_loss], feed_dict=policy._get_loss_inputs_dict( postprocessed_batch, shuffle=False)) if fw == "torch": check(policy.v_loss, expected_vf_loss, decimals=4) check(policy.p_loss, expected_pol_loss, decimals=4) elif fw == "tf": check(v_loss, expected_vf_loss, decimals=4) check(p_loss, expected_pol_loss, decimals=4) else: check(policy.loss.v_loss, expected_vf_loss, decimals=4) check(policy.loss.p_loss, expected_pol_loss, decimals=4) check(loss_out, expected_loss, decimals=3) if __name__ == "__main__": import pytest import sys sys.exit(pytest.main(["-v", __file__]))

true

f723d0fdbd1846134fc03f67f0123e5aedd5f7e6

1,938

py

Python

config.py

LucaMalavolta/q2

d4cd62c3ea898c99334ea84e2b41ec75db9558f7

[ "BSD-2-Clause" ]

null

config.py

LucaMalavolta/q2

d4cd62c3ea898c99334ea84e2b41ec75db9558f7

[ "BSD-2-Clause" ]

null

config.py

LucaMalavolta/q2

d4cd62c3ea898c99334ea84e2b41ec75db9558f7

[ "BSD-2-Clause" ]

null

import os import logging import matplotlib.pyplot as plt logger = logging.getLogger(__name__) path = os.path.dirname(os.path.realpath(__file__)) path = os.path.join(path, 'Data') COLORTEFF_PATH = os.path.join(path, 'ColorTeff') MODATM_PATH = os.path.join(path, 'ModelAtmospheres') ISOCHRONES_PATH = os.path.join(path, 'Isochrones') OTHER_PATH = os.path.join(path, 'Other') plt.rc("font", family='serif', serif='Ubuntu', monospace='Ubuntu Mono', \ size=14) plt.rc("axes", labelsize=15, titlesize=12) plt.rc("xtick", top=True, direction='in', labelsize=14) plt.rc("xtick.major", size=8, width=1) plt.rc("ytick", right=True, direction='in', labelsize=14) plt.rc("ytick.major", size=8, width=1) plt.rc("lines", markersize=10, markeredgewidth=2) plt.rc("lines", linewidth=3) def moog_is_available(): """You should be able to run MOOGSILENT from the command line in order to use the MOOG features included in q2. This function checks if MOOG is available on your system. If False, you wont be able to connect q2 to MOOG and many things will fail. """ if os.system('which MOOGSILENT >/dev/null'): logger.warning("MOOGSILENT is not available") return False else: logger.info("MOOGSILENT is available") return True def data_are_available(): """q2 needs data files with model atmosphere and isochrone grids. These files can be downloaded from: http://www.astrochasqui.com/projects/astro/share/q2Data.tar.gz They need to be extracted inside the q2 directory. 'tar xvfz q2Data.tar.gz' will create the Data folder. """ if os.path.exists(path): logger.info("Data folder exists") return True else: logger.warning("Data folder does not exist. See the 'Data' section "\ "at https://github.com/astroChasqui/q2") return False

36.566038

78

0.663055

import os import logging import matplotlib.pyplot as plt logger = logging.getLogger(__name__) path = os.path.dirname(os.path.realpath(__file__)) path = os.path.join(path, 'Data') COLORTEFF_PATH = os.path.join(path, 'ColorTeff') MODATM_PATH = os.path.join(path, 'ModelAtmospheres') ISOCHRONES_PATH = os.path.join(path, 'Isochrones') OTHER_PATH = os.path.join(path, 'Other') plt.rc("font", family='serif', serif='Ubuntu', monospace='Ubuntu Mono', \ size=14) plt.rc("axes", labelsize=15, titlesize=12) plt.rc("xtick", top=True, direction='in', labelsize=14) plt.rc("xtick.major", size=8, width=1) plt.rc("ytick", right=True, direction='in', labelsize=14) plt.rc("ytick.major", size=8, width=1) plt.rc("lines", markersize=10, markeredgewidth=2) plt.rc("lines", linewidth=3) def moog_is_available(): if os.system('which MOOGSILENT >/dev/null'): logger.warning("MOOGSILENT is not available") return False else: logger.info("MOOGSILENT is available") return True def data_are_available(): if os.path.exists(path): logger.info("Data folder exists") return True else: logger.warning("Data folder does not exist. See the 'Data' section "\ "at https://github.com/astroChasqui/q2") return False

true

f723d13c5a9085305d4224504015e2a6d6c0eef1

1,341

py

Python

concepts/recursion/fibonacci_sesies.py

dnootana/Python

2881bafe8bc378fa3cae50a747fcea1a55630c63

[ "MIT" ]

1

2021-02-19T11:00:11.000Z

concepts/recursion/fibonacci_sesies.py

dnootana/Python

2881bafe8bc378fa3cae50a747fcea1a55630c63

[ "MIT" ]

null

concepts/recursion/fibonacci_sesies.py

dnootana/Python

2881bafe8bc378fa3cae50a747fcea1a55630c63

[ "MIT" ]

null

#!/usr/bin/env python3.8 """ given a number N generate N no of fibonacci numbers """ from memoize import memoize number = 25 fib_list = [None] * (number) def fibonacci(num): """ fibonacci series using iteration """ a, b = 0, 1 for i in range(num-1): fib_list[i], fib_list[i+1] = a, b a, b = b, a+b return a fibonacci(number) print("fibonacci series using iteration : ", fib_list) fib_list = [None] * (number) def fibonacci_a(num): fib_list[0:2] = [0,1] for i in range(2,num): fib_list[i] = fib_list[i-2] + fib_list[i-1] fibonacci_a(number) print("fibonacci series using iteration : ", fib_list) fib_list = [None] * (number) def fibonacci1(num): """ fibonacci series using recursion """ global fib_list if fib_list[num] is None: if num == 0 or num == 1: fib_list[num] = num else: fib_list[num] = fibonacci1(num-1) + fibonacci1(num-2) return fib_list[num] fibonacci1(number-1) print("fibonacci series using recursion : ",fib_list) fib_list = [None] * (number) def fibonacci2(num): """ fibonacci series using recursion with memoization """ global fib_list def fib(N): if N==0 or N==1: return N else: return fib(N-1) + fib(N-2) fib = memoize(fib) for i in range(num): fib_list[i] = fib(i) fibonacci2(number) print("fibonacci series using recursion with memoization : ", fib_list)

19.434783

71

0.671141

from memoize import memoize number = 25 fib_list = [None] * (number) def fibonacci(num): a, b = 0, 1 for i in range(num-1): fib_list[i], fib_list[i+1] = a, b a, b = b, a+b return a fibonacci(number) print("fibonacci series using iteration : ", fib_list) fib_list = [None] * (number) def fibonacci_a(num): fib_list[0:2] = [0,1] for i in range(2,num): fib_list[i] = fib_list[i-2] + fib_list[i-1] fibonacci_a(number) print("fibonacci series using iteration : ", fib_list) fib_list = [None] * (number) def fibonacci1(num): global fib_list if fib_list[num] is None: if num == 0 or num == 1: fib_list[num] = num else: fib_list[num] = fibonacci1(num-1) + fibonacci1(num-2) return fib_list[num] fibonacci1(number-1) print("fibonacci series using recursion : ",fib_list) fib_list = [None] * (number) def fibonacci2(num): global fib_list def fib(N): if N==0 or N==1: return N else: return fib(N-1) + fib(N-2) fib = memoize(fib) for i in range(num): fib_list[i] = fib(i) fibonacci2(number) print("fibonacci series using recursion with memoization : ", fib_list)

true

f723d38041e06dcc3fe6b444f4b546e4e28b9d49

754

py

Python

pydatastructs/linear_data_structures/__init__.py

hpnightowl/pydatastructs

ec69ef887fee200390bff41ab6859a4ab0b26fbf

[ "BSD-3-Clause" ]

null

pydatastructs/linear_data_structures/__init__.py

hpnightowl/pydatastructs

ec69ef887fee200390bff41ab6859a4ab0b26fbf

[ "BSD-3-Clause" ]

null

pydatastructs/linear_data_structures/__init__.py

hpnightowl/pydatastructs

ec69ef887fee200390bff41ab6859a4ab0b26fbf

[ "BSD-3-Clause" ]

1

2021-02-05T04:49:55.000Z

__all__ = [] from pydatastructs.linear_data_structures import arrays, linked_lists, algorithms from pydatastructs.linear_data_structures.arrays import OneDimensionalArray, DynamicOneDimensionalArray, \ MultiDimensionalArray from pydatastructs.linear_data_structures.algorithms import merge_sort_parallel, brick_sort, brick_sort_parallel, \ heapsort, matrix_multiply_parallel, counting_sort, bucket_sort, cocktail_shaker_sort, quick_sort, \ longest_common_subsequence from pydatastructs.linear_data_structures.linked_lists import SinglyLinkedList, DoublyLinkedList, \ SinglyCircularLinkedList, DoublyCircularLinkedList, SkipList __all__.extend(arrays.__all__) __all__.extend(linked_lists.__all__) __all__.extend(algorithms.__all__)

37.7

115

0.855438

__all__ = [] from pydatastructs.linear_data_structures import arrays, linked_lists, algorithms from pydatastructs.linear_data_structures.arrays import OneDimensionalArray, DynamicOneDimensionalArray, \ MultiDimensionalArray from pydatastructs.linear_data_structures.algorithms import merge_sort_parallel, brick_sort, brick_sort_parallel, \ heapsort, matrix_multiply_parallel, counting_sort, bucket_sort, cocktail_shaker_sort, quick_sort, \ longest_common_subsequence from pydatastructs.linear_data_structures.linked_lists import SinglyLinkedList, DoublyLinkedList, \ SinglyCircularLinkedList, DoublyCircularLinkedList, SkipList __all__.extend(arrays.__all__) __all__.extend(linked_lists.__all__) __all__.extend(algorithms.__all__)

true

f723d3bddf9b5128923113de82df064942d27440

391

py

Python

Epitome/wsgi.py

pbout/ept

7da64e606c9c163ffc8285cd8c77288807f4f477

[ "RSA-MD" ]

null

Epitome/wsgi.py

pbout/ept

7da64e606c9c163ffc8285cd8c77288807f4f477

[ "RSA-MD" ]

null

Epitome/wsgi.py

pbout/ept

7da64e606c9c163ffc8285cd8c77288807f4f477

[ "RSA-MD" ]

null

""" WSGI config for Epitome project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/1.9/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "Epitome.settings") application = get_wsgi_application()

23

78

0.785166

import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "Epitome.settings") application = get_wsgi_application()

true

f723d4daf4d337355a5c29418d6ad94c37865a78

3,109

py

Python

meiduotest/meiduotest/settings.py

littrell0/meiduo_project_test

3d2f6a6e528e6c45a5ffc1db0ddc5de9b4e52bf8

[ "MIT" ]

null

meiduotest/meiduotest/settings.py

littrell0/meiduo_project_test

3d2f6a6e528e6c45a5ffc1db0ddc5de9b4e52bf8

[ "MIT" ]

null

meiduotest/meiduotest/settings.py

littrell0/meiduo_project_test

3d2f6a6e528e6c45a5ffc1db0ddc5de9b4e52bf8

[ "MIT" ]

null

""" Django settings for meiduotest project. Generated by 'django-admin startproject' using Django 1.11.11. For more information on this file, see https://docs.djangoproject.com/en/1.11/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.11/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '8zj*1u%iy2m+d0hg$@#5d(nrr5_-2u))cqkf^_3wj&f3ayr)o=' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'meiduotest.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'meiduotest.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/'

25.694215

91

0.69733

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = '8zj*1u%iy2m+d0hg$@#5d(nrr5_-2u))cqkf^_3wj&f3ayr)o=' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'meiduotest.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'meiduotest.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/'

true

f723d5a84455b1eb99d7bfc740ea30ad3707a3a0

4,334

py

Python

mmdet/models/seg_heads/panoptic_fpn_head.py

SeHwanJoo/mmdetection_body

1e1cadc6df91926fc99c4afbae383df0ea9cfed3

[ "Apache-2.0" ]

7

2021-08-08T08:34:30.000Z

2022-01-10T18:37:47.000Z

mmdet/models/seg_heads/panoptic_fpn_head.py

SeHwanJoo/mmdetection_body

1e1cadc6df91926fc99c4afbae383df0ea9cfed3

[ "Apache-2.0" ]

null

mmdet/models/seg_heads/panoptic_fpn_head.py

SeHwanJoo/mmdetection_body

1e1cadc6df91926fc99c4afbae383df0ea9cfed3

[ "Apache-2.0" ]

2

2021-08-11T05:57:50.000Z

2022-01-04T11:13:32.000Z

import torch import torch.nn as nn from mmcv.runner import ModuleList from ..builder import HEADS from ..utils import ConvUpsample from .base_semantic_head import BaseSemanticHead @HEADS.register_module() class PanopticFPNHead(BaseSemanticHead): """PanopticFPNHead used in Panoptic FPN. Arg: num_classes (int): Number of classes, including all stuff classes and one thing class. in_channels (int): Number of channels in the input feature map. inner_channels (int): Number of channels in inner features. start_level (int): The start level of the input features used in PanopticFPN. end_level (int): The end level of the used features, the `end_level`-th layer will not be used. fg_range (tuple): Range of the foreground classes. bg_range (tuple): Range of the background classes. conv_cfg (dict): Dictionary to construct and config conv layer. Default: None. norm_cfg (dict): Dictionary to construct and config norm layer. Use ``GN`` by default. init_cfg (dict or list[dict], optional): Initialization config dict. loss_seg (dict): the loss of the semantic head. """ def __init__(self, num_classes, in_channels=256, inner_channels=128, start_level=0, end_level=4, fg_range=(1, 80), bg_range=(81, 133), conv_cfg=None, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), init_cfg=None, loss_seg=dict( type='CrossEntropyLoss', ignore_index=-1, loss_weight=1.0)): super(PanopticFPNHead, self).__init__(num_classes, init_cfg, loss_seg) self.fg_range = fg_range self.bg_range = bg_range self.fg_nums = self.fg_range[1] - self.fg_range[0] + 1 self.bg_nums = self.bg_range[1] - self.bg_range[0] + 1 # Used feature layers are [start_level, end_level) self.start_level = start_level self.end_level = end_level self.num_stages = end_level - start_level self.inner_channels = inner_channels self.conv_upsample_layers = ModuleList() for i in range(start_level, end_level): self.conv_upsample_layers.append( ConvUpsample( in_channels, inner_channels, num_layers=i if i > 0 else 1, num_upsample=i if i > 0 else 0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, )) self.conv_logits = nn.Conv2d(inner_channels, num_classes, 1) def _set_things_to_void(self, gt_semantic_seg): """Merge thing classes to one class.""" gt_semantic_seg = gt_semantic_seg.int() fg_mask = (gt_semantic_seg >= self.fg_range[0]) * ( gt_semantic_seg <= self.fg_range[1]) bg_mask = (gt_semantic_seg >= self.bg_range[0]) * ( gt_semantic_seg <= self.bg_range[1]) new_gt_seg = fg_mask.int() * (self.bg_nums + 1) new_gt_seg = torch.where(bg_mask, gt_semantic_seg - self.fg_nums, new_gt_seg) return new_gt_seg def loss(self, seg_preds, gt_semantic_seg, label_bias=-1): """The loss of PanopticFPN head. Things classes will be merged to one class in PanopticFPN. """ gt_semantic_seg = self._set_things_to_void(gt_semantic_seg) return super().loss(seg_preds, gt_semantic_seg, label_bias) def init_weights(self): super().init_weights() nn.init.normal_(self.conv_logits.weight.data, 0, 0.01) self.conv_logits.bias.data.zero_() def forward(self, x): # the number of subnets must be not more than # the length of features. assert self.num_stages <= len(x) feats = [] for i, layer in enumerate(self.conv_upsample_layers): f = layer(x[self.start_level + i]) feats.append(f) feats = torch.sum(torch.stack(feats, dim=0), dim=0) seg_preds = self.conv_logits(feats) out = dict(seg_preds=seg_preds, feats=feats) return out

38.696429

78

0.601292

import torch import torch.nn as nn from mmcv.runner import ModuleList from ..builder import HEADS from ..utils import ConvUpsample from .base_semantic_head import BaseSemanticHead @HEADS.register_module() class PanopticFPNHead(BaseSemanticHead): def __init__(self, num_classes, in_channels=256, inner_channels=128, start_level=0, end_level=4, fg_range=(1, 80), bg_range=(81, 133), conv_cfg=None, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), init_cfg=None, loss_seg=dict( type='CrossEntropyLoss', ignore_index=-1, loss_weight=1.0)): super(PanopticFPNHead, self).__init__(num_classes, init_cfg, loss_seg) self.fg_range = fg_range self.bg_range = bg_range self.fg_nums = self.fg_range[1] - self.fg_range[0] + 1 self.bg_nums = self.bg_range[1] - self.bg_range[0] + 1 self.start_level = start_level self.end_level = end_level self.num_stages = end_level - start_level self.inner_channels = inner_channels self.conv_upsample_layers = ModuleList() for i in range(start_level, end_level): self.conv_upsample_layers.append( ConvUpsample( in_channels, inner_channels, num_layers=i if i > 0 else 1, num_upsample=i if i > 0 else 0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, )) self.conv_logits = nn.Conv2d(inner_channels, num_classes, 1) def _set_things_to_void(self, gt_semantic_seg): gt_semantic_seg = gt_semantic_seg.int() fg_mask = (gt_semantic_seg >= self.fg_range[0]) * ( gt_semantic_seg <= self.fg_range[1]) bg_mask = (gt_semantic_seg >= self.bg_range[0]) * ( gt_semantic_seg <= self.bg_range[1]) new_gt_seg = fg_mask.int() * (self.bg_nums + 1) new_gt_seg = torch.where(bg_mask, gt_semantic_seg - self.fg_nums, new_gt_seg) return new_gt_seg def loss(self, seg_preds, gt_semantic_seg, label_bias=-1): gt_semantic_seg = self._set_things_to_void(gt_semantic_seg) return super().loss(seg_preds, gt_semantic_seg, label_bias) def init_weights(self): super().init_weights() nn.init.normal_(self.conv_logits.weight.data, 0, 0.01) self.conv_logits.bias.data.zero_() def forward(self, x): assert self.num_stages <= len(x) feats = [] for i, layer in enumerate(self.conv_upsample_layers): f = layer(x[self.start_level + i]) feats.append(f) feats = torch.sum(torch.stack(feats, dim=0), dim=0) seg_preds = self.conv_logits(feats) out = dict(seg_preds=seg_preds, feats=feats) return out

true

f723d8eb5daadf4e81d3c79aabb857dd8a2823c2

276

py

Python

dashboard/apps.py

lynetteoh/COVID19dashboard

61193d35acf004999443c47a30f0b9f9c6220c03

[ "MIT" ]

null

dashboard/apps.py

lynetteoh/COVID19dashboard

61193d35acf004999443c47a30f0b9f9c6220c03

[ "MIT" ]

null

dashboard/apps.py

lynetteoh/COVID19dashboard

61193d35acf004999443c47a30f0b9f9c6220c03

[ "MIT" ]

null

from django.apps import AppConfig import pandas as pd import sys class DashboardConfig(AppConfig): name = 'dashboard' def ready(self): if 'runserver' not in sys.argv: return True from dashboard.models import Case, State, Country

21.230769

57

0.65942

from django.apps import AppConfig import pandas as pd import sys class DashboardConfig(AppConfig): name = 'dashboard' def ready(self): if 'runserver' not in sys.argv: return True from dashboard.models import Case, State, Country

true

f723d9b3d877c48369368b51d3310d0d2b722ba2

372

py

Python

exercicios-turtle/.history/flower_20210624115429.py

Aleff13/poo-ufsc

bc1574df26f840a3c0fd5b1e0c72e5d69f61493d

[ "MIT" ]

1

2021-11-28T18:49:21.000Z

exercicios-turtle/.history/flower_20210624115429.py

Aleff13/poo-ufsc

bc1574df26f840a3c0fd5b1e0c72e5d69f61493d

[ "MIT" ]

null

exercicios-turtle/.history/flower_20210624115429.py

Aleff13/poo-ufsc

bc1574df26f840a3c0fd5b1e0c72e5d69f61493d

[ "MIT" ]

null

import turtle tortuguita = turtle.Turtle() tortuguita.color('blue') tortuguita.speed(100) for i in range (18): tortuguita.circle(200,100) tortuguita.left(110) tortuguita.up() tortuguita.left(35) tortuguita.forward(160) tortuguita.down() tortuguita.dot(70,"black") tortuguita.left(35) tortuguita.up() tortuguita.color('green') tortuguita.forward() turtle.done()

17.714286

30

0.752688

import turtle tortuguita = turtle.Turtle() tortuguita.color('blue') tortuguita.speed(100) for i in range (18): tortuguita.circle(200,100) tortuguita.left(110) tortuguita.up() tortuguita.left(35) tortuguita.forward(160) tortuguita.down() tortuguita.dot(70,"black") tortuguita.left(35) tortuguita.up() tortuguita.color('green') tortuguita.forward() turtle.done()

true

f723d9cabe29840070a01c4f2c8c2878b8b99f27

5,505

py

Python

ccc/elasticsearch.py

mliepold/cc-utils

3f8c4b0d11d6a52d1605026f478371411daab81e

[ "BSD-3-Clause" ]

15

2018-04-18T13:25:30.000Z

2022-03-04T09:25:41.000Z

ccc/elasticsearch.py

mliepold/cc-utils

3f8c4b0d11d6a52d1605026f478371411daab81e

[ "BSD-3-Clause" ]

221

2018-04-12T06:29:43.000Z

2022-03-27T03:01:40.000Z

ccc/elasticsearch.py

mliepold/cc-utils

3f8c4b0d11d6a52d1605026f478371411daab81e

[ "BSD-3-Clause" ]

29

2018-04-11T14:42:23.000Z

2021-11-09T16:26:32.000Z

# Copyright (c) 2019-2020 SAP SE or an SAP affiliate company. All rights reserved. This file is # licensed under the Apache Software License, v. 2 except as noted otherwise in the LICENSE file # # 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 datetime import functools import os import json import elasticsearch import ci.util import concourse.util import model.elasticsearch def default_client_if_available(): if not ci.util._running_on_ci(): return None cfg_factory = ci.util.ctx().cfg_factory() cfg_set = cfg_factory.cfg_set(ci.util.current_config_set_name()) es_config = cfg_set.elasticsearch() return from_cfg(elasticsearch_cfg=es_config) def from_cfg( elasticsearch_cfg:model.elasticsearch.ElasticSearchConfig ): return ElasticSearchClient( elasticsearch=_from_cfg(elasticsearch_cfg=elasticsearch_cfg) ) def _from_cfg( elasticsearch_cfg:model.elasticsearch.ElasticSearchConfig ): credentials = elasticsearch_cfg.credentials() return elasticsearch.Elasticsearch( elasticsearch_cfg.endpoints(), http_auth=(credentials.username(), credentials.passwd()), ) @functools.lru_cache() def _metadata_dict(): # XXX mv to concourse package; deduplicate with notify step if not ci.util._running_on_ci(): return {} build = concourse.util.find_own_running_build() pipeline_metadata = concourse.util.get_pipeline_metadata() config_set = ci.util.ctx().cfg_factory().cfg_set(pipeline_metadata.current_config_set_name) concourse_cfg = config_set.concourse() meta_dict = { 'build-id': build.id(), 'build-name': build.build_number(), 'build-job-name': pipeline_metadata.job_name, 'build-team-name': pipeline_metadata.team_name, 'build-pipeline-name': pipeline_metadata.pipeline_name, 'atc-external-url': concourse_cfg.external_url(), } # XXX deduplicate; mv to concourse package meta_dict['concourse_url'] = ci.util.urljoin( meta_dict['atc-external-url'], 'teams', meta_dict['build-team-name'], 'pipelines', meta_dict['build-pipeline-name'], 'jobs', meta_dict['build-job-name'], 'builds', meta_dict['build-name'], ) # XXX do not hard-code env variables meta_dict['effective_version'] = os.environ.get('EFFECTIVE_VERSION') meta_dict['component_name'] = os.environ.get('COMPONENT_NAME') meta_dict['creation_date'] = datetime.datetime.now().isoformat() return meta_dict class ElasticSearchClient: def __init__( self, elasticsearch: elasticsearch.Elasticsearch, ): self._api = elasticsearch def store_document( self, index: str, body: dict, inject_metadata=True, *args, **kwargs, ): ci.util.check_type(index, str) ci.util.check_type(body, dict) if 'doc_type' in kwargs: raise ValueError( ''' doc_type attribute has been deprecated - see: https://www.elastic.co/guide/en/elasticsearch/reference/6.0/removal-of-types.html ''' ) if inject_metadata and _metadata_dict(): md = _metadata_dict() body['cc_meta'] = md return self._api.index( index=index, doc_type='_doc', body=body, *args, **kwargs, ) def store_documents( self, index: str, body: [dict], inject_metadata=True, *args, **kwargs, ): # Bulk-loading uses a special format: A json specifying index name and doc-type # (always _doc) followed by the actual document json. These pairs (one for each document) # are then converted to newline delimited json # The index json does not change for bulk-loading into a single index. index_json = json.dumps({ 'index': { '_index': index, '_type': '_doc' } }) return self.store_bulk( body='\n'.join([f'{index_json}\n{json.dumps(d)}' for d in body]), inject_metadata=inject_metadata, *args, **kwargs, ) def store_bulk( self, body: str, inject_metadata=True, *args, **kwargs, ): ci.util.check_type(body, str) if inject_metadata and _metadata_dict(): def inject_meta(line): parsed = json.loads(line) if 'index' not in parsed: parsed['cc_meta'] = md return json.dumps(parsed) return line md = _metadata_dict() patched_body = '\n'.join([inject_meta(line) for line in body.splitlines()]) body = patched_body return self._api.bulk( body=body, *args, **kwargs, )

29.438503

97

0.622525

import datetime import functools import os import json import elasticsearch import ci.util import concourse.util import model.elasticsearch def default_client_if_available(): if not ci.util._running_on_ci(): return None cfg_factory = ci.util.ctx().cfg_factory() cfg_set = cfg_factory.cfg_set(ci.util.current_config_set_name()) es_config = cfg_set.elasticsearch() return from_cfg(elasticsearch_cfg=es_config) def from_cfg( elasticsearch_cfg:model.elasticsearch.ElasticSearchConfig ): return ElasticSearchClient( elasticsearch=_from_cfg(elasticsearch_cfg=elasticsearch_cfg) ) def _from_cfg( elasticsearch_cfg:model.elasticsearch.ElasticSearchConfig ): credentials = elasticsearch_cfg.credentials() return elasticsearch.Elasticsearch( elasticsearch_cfg.endpoints(), http_auth=(credentials.username(), credentials.passwd()), ) @functools.lru_cache() def _metadata_dict(): if not ci.util._running_on_ci(): return {} build = concourse.util.find_own_running_build() pipeline_metadata = concourse.util.get_pipeline_metadata() config_set = ci.util.ctx().cfg_factory().cfg_set(pipeline_metadata.current_config_set_name) concourse_cfg = config_set.concourse() meta_dict = { 'build-id': build.id(), 'build-name': build.build_number(), 'build-job-name': pipeline_metadata.job_name, 'build-team-name': pipeline_metadata.team_name, 'build-pipeline-name': pipeline_metadata.pipeline_name, 'atc-external-url': concourse_cfg.external_url(), } meta_dict['concourse_url'] = ci.util.urljoin( meta_dict['atc-external-url'], 'teams', meta_dict['build-team-name'], 'pipelines', meta_dict['build-pipeline-name'], 'jobs', meta_dict['build-job-name'], 'builds', meta_dict['build-name'], ) meta_dict['effective_version'] = os.environ.get('EFFECTIVE_VERSION') meta_dict['component_name'] = os.environ.get('COMPONENT_NAME') meta_dict['creation_date'] = datetime.datetime.now().isoformat() return meta_dict class ElasticSearchClient: def __init__( self, elasticsearch: elasticsearch.Elasticsearch, ): self._api = elasticsearch def store_document( self, index: str, body: dict, inject_metadata=True, *args, **kwargs, ): ci.util.check_type(index, str) ci.util.check_type(body, dict) if 'doc_type' in kwargs: raise ValueError( ''' doc_type attribute has been deprecated - see: https://www.elastic.co/guide/en/elasticsearch/reference/6.0/removal-of-types.html ''' ) if inject_metadata and _metadata_dict(): md = _metadata_dict() body['cc_meta'] = md return self._api.index( index=index, doc_type='_doc', body=body, *args, **kwargs, ) def store_documents( self, index: str, body: [dict], inject_metadata=True, *args, **kwargs, ): index_json = json.dumps({ 'index': { '_index': index, '_type': '_doc' } }) return self.store_bulk( body='\n'.join([f'{index_json}\n{json.dumps(d)}' for d in body]), inject_metadata=inject_metadata, *args, **kwargs, ) def store_bulk( self, body: str, inject_metadata=True, *args, **kwargs, ): ci.util.check_type(body, str) if inject_metadata and _metadata_dict(): def inject_meta(line): parsed = json.loads(line) if 'index' not in parsed: parsed['cc_meta'] = md return json.dumps(parsed) return line md = _metadata_dict() patched_body = '\n'.join([inject_meta(line) for line in body.splitlines()]) body = patched_body return self._api.bulk( body=body, *args, **kwargs, )

true

f723da3d31fc0d1c210b41c7779cc8ee4f4cd08c

6,876

py

Python

src/wallet/wallet_block_store.py

DONG-Jason/chia-blockchain

27b28d62f6b315e45bc00231e007c775f07a414a

[ "Apache-2.0" ]

null

src/wallet/wallet_block_store.py

DONG-Jason/chia-blockchain

27b28d62f6b315e45bc00231e007c775f07a414a

[ "Apache-2.0" ]

null

src/wallet/wallet_block_store.py

DONG-Jason/chia-blockchain

27b28d62f6b315e45bc00231e007c775f07a414a

[ "Apache-2.0" ]

null

from typing import Dict, Optional, Tuple, List import aiosqlite from src.consensus.sub_block_record import SubBlockRecord from src.types.header_block import HeaderBlock from src.util.ints import uint32, uint64 from src.wallet.block_record import HeaderBlockRecord from src.types.sized_bytes import bytes32 class WalletBlockStore: """ This object handles HeaderBlocks and SubBlocks stored in DB used by wallet. """ db: aiosqlite.Connection @classmethod async def create(cls, connection: aiosqlite.Connection): self = cls() self.db = connection await self.db.execute( "CREATE TABLE IF NOT EXISTS header_blocks(header_hash text PRIMARY KEY, sub_height int, height int," " timestamp int, block blob)" ) await self.db.execute("CREATE INDEX IF NOT EXISTS header_hash on header_blocks(header_hash)") await self.db.execute("CREATE INDEX IF NOT EXISTS timestamp on header_blocks(timestamp)") await self.db.execute("CREATE INDEX IF NOT EXISTS sub_height on header_blocks(sub_height)") await self.db.execute("CREATE INDEX IF NOT EXISTS height on header_blocks(height)") # Sub block records await self.db.execute( "CREATE TABLE IF NOT EXISTS sub_block_records(header_hash " "text PRIMARY KEY, prev_hash text, sub_height bigint, height int, weight bigint, total_iters text," "sub_block blob, is_peak tinyint)" ) # Height index so we can look up in order of height for sync purposes await self.db.execute("CREATE INDEX IF NOT EXISTS sub_block_height on sub_block_records(sub_height)") await self.db.execute("CREATE INDEX IF NOT EXISTS height on sub_block_records(height)") await self.db.execute("CREATE INDEX IF NOT EXISTS hh on sub_block_records(header_hash)") await self.db.execute("CREATE INDEX IF NOT EXISTS peak on sub_block_records(is_peak)") await self.db.commit() await self.db.commit() return self async def _clear_database(self): cursor_2 = await self.db.execute("DELETE FROM header_blocks") await cursor_2.close() await self.db.commit() async def rollback_lca_to_block(self, block_index): # TODO pass async def add_block_record(self, block_record: HeaderBlockRecord, sub_block: SubBlockRecord): """ Adds a block record to the database. This block record is assumed to be connected to the chain, but it may or may not be in the LCA path. """ if block_record.header.foliage_block is not None: timestamp = block_record.header.foliage_block.timestamp else: timestamp = uint64(0) cursor = await self.db.execute( "INSERT OR REPLACE INTO header_blocks VALUES(?, ?, ?, ?, ?)", ( block_record.header_hash.hex(), block_record.sub_block_height, sub_block.height, timestamp, bytes(block_record), ), ) await cursor.close() cursor_2 = await self.db.execute( "INSERT OR REPLACE INTO sub_block_records VALUES(?, ?, ?, ?, ?, ?, ?, ?)", ( block_record.header.header_hash.hex(), block_record.header.prev_header_hash.hex(), block_record.header.sub_block_height, block_record.header.height, block_record.header.weight.to_bytes(128 // 8, "big", signed=False).hex(), block_record.header.total_iters.to_bytes(128 // 8, "big", signed=False).hex(), bytes(sub_block), False, ), ) await cursor_2.close() await self.db.commit() async def get_header_block(self, header_hash: bytes32) -> Optional[HeaderBlock]: """Gets a block record from the database, if present""" cursor = await self.db.execute("SELECT * from header_blocks WHERE header_hash=?", (header_hash.hex(),)) row = await cursor.fetchone() await cursor.close() if row is not None: hbr = HeaderBlockRecord.from_bytes(row[4]) return hbr.header else: return None async def get_header_block_at(self, sub_heights: List[uint32]) -> List[HeaderBlock]: if len(sub_heights) == 0: return [] heights_db = tuple(sub_heights) formatted_str = f'SELECT block from header_blocks WHERE sub_height in ({"?," * (len(heights_db) - 1)}?)' cursor = await self.db.execute(formatted_str, heights_db) rows = await cursor.fetchall() await cursor.close() return [HeaderBlock.from_bytes(row[0]) for row in rows] async def get_header_block_record(self, header_hash: bytes32) -> Optional[HeaderBlockRecord]: """Gets a block record from the database, if present""" cursor = await self.db.execute("SELECT * from header_blocks WHERE header_hash=?", (header_hash.hex(),)) row = await cursor.fetchone() await cursor.close() if row is not None: hbr = HeaderBlockRecord.from_bytes(row[4]) return hbr else: return None async def get_sub_block_record(self, header_hash: bytes32) -> Optional[SubBlockRecord]: cursor = await self.db.execute( "SELECT sub_block from sub_block_records WHERE header_hash=?", (header_hash.hex(),), ) row = await cursor.fetchone() await cursor.close() if row is not None: return SubBlockRecord.from_bytes(row[0]) return None async def get_sub_block_records( self, ) -> Tuple[Dict[bytes32, SubBlockRecord], Optional[bytes32]]: """ Returns a dictionary with all sub blocks, as well as the header hash of the peak, if present. """ cursor = await self.db.execute("SELECT * from sub_block_records") rows = await cursor.fetchall() await cursor.close() ret: Dict[bytes32, SubBlockRecord] = {} peak: Optional[bytes32] = None for row in rows: header_hash = bytes.fromhex(row[0]) ret[header_hash] = SubBlockRecord.from_bytes(row[6]) if row[7]: assert peak is None # Sanity check, only one peak peak = header_hash return ret, peak async def set_peak(self, header_hash: bytes32) -> None: cursor_1 = await self.db.execute("UPDATE sub_block_records SET is_peak=0 WHERE is_peak=1") await cursor_1.close() cursor_2 = await self.db.execute( "UPDATE sub_block_records SET is_peak=1 WHERE header_hash=?", (header_hash.hex(),), ) await cursor_2.close() await self.db.commit()

39.745665

112

0.627981

from typing import Dict, Optional, Tuple, List import aiosqlite from src.consensus.sub_block_record import SubBlockRecord from src.types.header_block import HeaderBlock from src.util.ints import uint32, uint64 from src.wallet.block_record import HeaderBlockRecord from src.types.sized_bytes import bytes32 class WalletBlockStore: db: aiosqlite.Connection @classmethod async def create(cls, connection: aiosqlite.Connection): self = cls() self.db = connection await self.db.execute( "CREATE TABLE IF NOT EXISTS header_blocks(header_hash text PRIMARY KEY, sub_height int, height int," " timestamp int, block blob)" ) await self.db.execute("CREATE INDEX IF NOT EXISTS header_hash on header_blocks(header_hash)") await self.db.execute("CREATE INDEX IF NOT EXISTS timestamp on header_blocks(timestamp)") await self.db.execute("CREATE INDEX IF NOT EXISTS sub_height on header_blocks(sub_height)") await self.db.execute("CREATE INDEX IF NOT EXISTS height on header_blocks(height)") await self.db.execute( "CREATE TABLE IF NOT EXISTS sub_block_records(header_hash " "text PRIMARY KEY, prev_hash text, sub_height bigint, height int, weight bigint, total_iters text," "sub_block blob, is_peak tinyint)" ) await self.db.execute("CREATE INDEX IF NOT EXISTS sub_block_height on sub_block_records(sub_height)") await self.db.execute("CREATE INDEX IF NOT EXISTS height on sub_block_records(height)") await self.db.execute("CREATE INDEX IF NOT EXISTS hh on sub_block_records(header_hash)") await self.db.execute("CREATE INDEX IF NOT EXISTS peak on sub_block_records(is_peak)") await self.db.commit() await self.db.commit() return self async def _clear_database(self): cursor_2 = await self.db.execute("DELETE FROM header_blocks") await cursor_2.close() await self.db.commit() async def rollback_lca_to_block(self, block_index): pass async def add_block_record(self, block_record: HeaderBlockRecord, sub_block: SubBlockRecord): if block_record.header.foliage_block is not None: timestamp = block_record.header.foliage_block.timestamp else: timestamp = uint64(0) cursor = await self.db.execute( "INSERT OR REPLACE INTO header_blocks VALUES(?, ?, ?, ?, ?)", ( block_record.header_hash.hex(), block_record.sub_block_height, sub_block.height, timestamp, bytes(block_record), ), ) await cursor.close() cursor_2 = await self.db.execute( "INSERT OR REPLACE INTO sub_block_records VALUES(?, ?, ?, ?, ?, ?, ?, ?)", ( block_record.header.header_hash.hex(), block_record.header.prev_header_hash.hex(), block_record.header.sub_block_height, block_record.header.height, block_record.header.weight.to_bytes(128 // 8, "big", signed=False).hex(), block_record.header.total_iters.to_bytes(128 // 8, "big", signed=False).hex(), bytes(sub_block), False, ), ) await cursor_2.close() await self.db.commit() async def get_header_block(self, header_hash: bytes32) -> Optional[HeaderBlock]: cursor = await self.db.execute("SELECT * from header_blocks WHERE header_hash=?", (header_hash.hex(),)) row = await cursor.fetchone() await cursor.close() if row is not None: hbr = HeaderBlockRecord.from_bytes(row[4]) return hbr.header else: return None async def get_header_block_at(self, sub_heights: List[uint32]) -> List[HeaderBlock]: if len(sub_heights) == 0: return [] heights_db = tuple(sub_heights) formatted_str = f'SELECT block from header_blocks WHERE sub_height in ({"?," * (len(heights_db) - 1)}?)' cursor = await self.db.execute(formatted_str, heights_db) rows = await cursor.fetchall() await cursor.close() return [HeaderBlock.from_bytes(row[0]) for row in rows] async def get_header_block_record(self, header_hash: bytes32) -> Optional[HeaderBlockRecord]: cursor = await self.db.execute("SELECT * from header_blocks WHERE header_hash=?", (header_hash.hex(),)) row = await cursor.fetchone() await cursor.close() if row is not None: hbr = HeaderBlockRecord.from_bytes(row[4]) return hbr else: return None async def get_sub_block_record(self, header_hash: bytes32) -> Optional[SubBlockRecord]: cursor = await self.db.execute( "SELECT sub_block from sub_block_records WHERE header_hash=?", (header_hash.hex(),), ) row = await cursor.fetchone() await cursor.close() if row is not None: return SubBlockRecord.from_bytes(row[0]) return None async def get_sub_block_records( self, ) -> Tuple[Dict[bytes32, SubBlockRecord], Optional[bytes32]]: cursor = await self.db.execute("SELECT * from sub_block_records") rows = await cursor.fetchall() await cursor.close() ret: Dict[bytes32, SubBlockRecord] = {} peak: Optional[bytes32] = None for row in rows: header_hash = bytes.fromhex(row[0]) ret[header_hash] = SubBlockRecord.from_bytes(row[6]) if row[7]: assert peak is None peak = header_hash return ret, peak async def set_peak(self, header_hash: bytes32) -> None: cursor_1 = await self.db.execute("UPDATE sub_block_records SET is_peak=0 WHERE is_peak=1") await cursor_1.close() cursor_2 = await self.db.execute( "UPDATE sub_block_records SET is_peak=1 WHERE header_hash=?", (header_hash.hex(),), ) await cursor_2.close() await self.db.commit()

true

f723db4534fac7bbdbc1fca243f9cc9d68e9c713

56,361

py

Python

plugins/modules/oci_loadbalancer_backend_set.py

LaudateCorpus1/oci-ansible-collection

2b1cd87b4d652a97c1ca752cfc4fdc4bdb37a7e7

[ "Apache-2.0" ]

null

plugins/modules/oci_loadbalancer_backend_set.py

LaudateCorpus1/oci-ansible-collection

2b1cd87b4d652a97c1ca752cfc4fdc4bdb37a7e7

[ "Apache-2.0" ]

null

plugins/modules/oci_loadbalancer_backend_set.py

LaudateCorpus1/oci-ansible-collection

2b1cd87b4d652a97c1ca752cfc4fdc4bdb37a7e7

[ "Apache-2.0" ]

null

#!/usr/bin/python # Copyright (c) 2020, 2022 Oracle and/or its affiliates. # This software is made available to you under the terms of the GPL 3.0 license or the Apache 2.0 license. # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) # Apache License v2.0 # See LICENSE.TXT for details. # GENERATED FILE - DO NOT EDIT - MANUAL CHANGES WILL BE OVERWRITTEN from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_loadbalancer_backend_set short_description: Manage a BackendSet resource in Oracle Cloud Infrastructure description: - This module allows the user to create, update and delete a BackendSet resource in Oracle Cloud Infrastructure - For I(state=present), adds a backend set to a load balancer. version_added: "2.9.0" author: Oracle (@oracle) options: name: description: - A friendly name for the backend set. It must be unique and it cannot be changed. - Valid backend set names include only alphanumeric characters, dashes, and underscores. Backend set names cannot contain spaces. Avoid entering confidential information. - "Example: `example_backend_set`" type: str required: true policy: description: - The load balancer policy for the backend set. To get a list of available policies, use the L(ListPolicies,https://docs.cloud.oracle.com/en-us/iaas/api/#/en/loadbalancer/20170115/LoadBalancerPolicy/ListPolicies) operation. - "Example: `LEAST_CONNECTIONS`" - Required for create using I(state=present), update using I(state=present) with name present. type: str backends: description: - "" - Required for update using I(state=present) with name present. type: list elements: dict suboptions: ip_address: description: - The IP address of the backend server. - "Example: `10.0.0.3`" type: str required: true port: description: - The communication port for the backend server. - "Example: `8080`" type: int required: true weight: description: - The load balancing policy weight assigned to the server. Backend servers with a higher weight receive a larger proportion of incoming traffic. For example, a server weighted '3' receives 3 times the number of new connections as a server weighted '1'. For more information on load balancing policies, see L(How Load Balancing Policies Work,https://docs.cloud.oracle.com/Content/Balance/Reference/lbpolicies.htm). - "Example: `3`" type: int backup: description: - "Whether the load balancer should treat this server as a backup unit. If `true`, the load balancer forwards no ingress traffic to this backend server unless all other backend servers not marked as \\"backup\\" fail the health check policy." - "**Note:** You cannot add a backend server marked as `backup` to a backend set that uses the IP Hash policy." - "Example: `false`" type: bool drain: description: - "Whether the load balancer should drain this server. Servers marked \\"drain\\" receive no new incoming traffic." - "Example: `false`" type: bool offline: description: - Whether the load balancer should treat this server as offline. Offline servers receive no incoming traffic. - "Example: `false`" type: bool health_checker: description: - "" - Required for create using I(state=present), update using I(state=present) with name present. type: dict suboptions: protocol: description: - The protocol the health check must use; either HTTP or TCP. - "Example: `HTTP`" type: str required: true url_path: description: - The path against which to run the health check. - "Example: `/healthcheck`" type: str port: description: - The backend server port against which to run the health check. If the port is not specified, the load balancer uses the port information from the `Backend` object. - "Example: `8080`" type: int return_code: description: - The status code a healthy backend server should return. - "Example: `200`" type: int retries: description: - "The number of retries to attempt before a backend server is considered \\"unhealthy\\". This number also applies when recovering a server to the \\"healthy\\" state." - "Example: `3`" type: int timeout_in_millis: description: - The maximum time, in milliseconds, to wait for a reply to a health check. A health check is successful only if a reply returns within this timeout period. - "Example: `3000`" type: int interval_in_millis: description: - The interval between health checks, in milliseconds. - "Example: `10000`" type: int response_body_regex: description: - A regular expression for parsing the response body from the backend server. - "Example: `^((?!false).|\\\\s)*$`" type: str ssl_configuration: description: - "" - This parameter is updatable. type: dict suboptions: verify_depth: description: - The maximum depth for peer certificate chain verification. - "Example: `3`" type: int verify_peer_certificate: description: - Whether the load balancer listener should verify peer certificates. - "Example: `true`" type: bool trusted_certificate_authority_ids: description: - Ids for OCI certificates service CA or CA bundles for the load balancer to trust. - "Example: `[ocid1.cabundle.oc1.us-ashburn-1.amaaaaaaav3bgsaagl4zzyqdop5i2vuwoqewdvauuw34llqa74otq2jdsfyq]`" type: list elements: str certificate_ids: description: - Ids for OCI certificates service certificates. Currently only a single Id may be passed. - "Example: `[ocid1.certificate.oc1.us-ashburn-1.amaaaaaaav3bgsaa5o2q7rh5nfmkkukfkogasqhk6af2opufhjlqg7m6jqzq]`" type: list elements: str certificate_name: description: - A friendly name for the certificate bundle. It must be unique and it cannot be changed. Valid certificate bundle names include only alphanumeric characters, dashes, and underscores. Certificate bundle names cannot contain spaces. Avoid entering confidential information. - "Example: `example_certificate_bundle`" type: str protocols: description: - A list of SSL protocols the load balancer must support for HTTPS or SSL connections. - The load balancer uses SSL protocols to establish a secure connection between a client and a server. A secure connection ensures that all data passed between the client and the server is private. - "The Load Balancing service supports the following protocols:" - "* TLSv1 * TLSv1.1 * TLSv1.2" - If this field is not specified, TLSv1.2 is the default. - "**Warning:** All SSL listeners created on a given port must use the same set of SSL protocols." - "**Notes:**" - "* The handshake to establish an SSL connection fails if the client supports none of the specified protocols. * You must ensure compatibility between the specified SSL protocols and the ciphers configured in the cipher suite. * For all existing load balancer listeners and backend sets that predate this feature, the `GET` operation displays a list of SSL protocols currently used by those resources." - "example: `[\\"TLSv1.1\\", \\"TLSv1.2\\"]`" type: list elements: str cipher_suite_name: description: - The name of the cipher suite to use for HTTPS or SSL connections. - If this field is not specified, the default is `oci-default-ssl-cipher-suite-v1`. - "**Notes:**" - "* You must ensure compatibility between the specified SSL protocols and the ciphers configured in the cipher suite. Clients cannot perform an SSL handshake if there is an incompatible configuration. * You must ensure compatibility between the ciphers configured in the cipher suite and the configured certificates. For example, RSA-based ciphers require RSA certificates and ECDSA-based ciphers require ECDSA certificates. * If the cipher configuration is not modified after load balancer creation, the `GET` operation returns `oci-default-ssl-cipher-suite-v1` as the value of this field in the SSL configuration for existing listeners that predate this feature. * If the cipher configuration was modified using Oracle operations after load balancer creation, the `GET` operation returns `oci-customized-ssl-cipher-suite` as the value of this field in the SSL configuration for existing listeners that predate this feature. * The `GET` operation returns `oci-wider-compatible-ssl-cipher-suite-v1` as the value of this field in the SSL configuration for existing backend sets that predate this feature. * If the `GET` operation on a listener returns `oci-customized-ssl-cipher-suite` as the value of this field, you must specify an appropriate predefined or custom cipher suite name when updating the resource. * The `oci-customized-ssl-cipher-suite` Oracle reserved cipher suite name is not accepted as valid input for this field." - "example: `example_cipher_suite`" type: str server_order_preference: description: - When this attribute is set to ENABLED, the system gives preference to the server ciphers over the client ciphers. - "**Note:** This configuration is applicable only when the load balancer is acting as an SSL/HTTPS server. This field is ignored when the `SSLConfiguration` object is associated with a backend set." type: str choices: - "ENABLED" - "DISABLED" session_persistence_configuration: description: - "" - This parameter is updatable. type: dict suboptions: cookie_name: description: - "The name of the cookie used to detect a session initiated by the backend server. Use '*' to specify that any cookie set by the backend causes the session to persist." - "Example: `example_cookie`" type: str required: true disable_fallback: description: - Whether the load balancer is prevented from directing traffic from a persistent session client to a different backend server if the original server is unavailable. Defaults to false. - "Example: `false`" type: bool lb_cookie_session_persistence_configuration: description: - "" - This parameter is updatable. type: dict suboptions: cookie_name: description: - "The name of the cookie inserted by the load balancer. If this field is not configured, the cookie name defaults to \\"X-Oracle-BMC-LBS-Route\\"." - "Example: `example_cookie`" - "**Notes:**" - "* Ensure that the cookie name used at the backend application servers is different from the cookie name used at the load balancer. To minimize the chance of name collision, Oracle recommends that you use a prefix such as \\"X-Oracle-OCI-\\" for this field." - "* If a backend server and the load balancer both insert cookies with the same name, the client or browser behavior can vary depending on the domain and path values associated with the cookie. If the name, domain, and path values of the `Set-cookie` generated by a backend server and the `Set-cookie` generated by the load balancer are all the same, the client or browser treats them as one cookie and returns only one of the cookie values in subsequent requests. If both `Set-cookie` names are the same, but the domain and path names are different, the client or browser treats them as two different cookies." type: str disable_fallback: description: - Whether the load balancer is prevented from directing traffic from a persistent session client to a different backend server if the original server is unavailable. Defaults to false. - "Example: `false`" type: bool domain: description: - The domain in which the cookie is valid. The `Set-cookie` header inserted by the load balancer contains a domain attribute with the specified value. - This attribute has no default value. If you do not specify a value, the load balancer does not insert the domain attribute into the `Set-cookie` header. - "**Notes:**" - "* L(RFC 6265 - HTTP State Management Mechanism,https://www.ietf.org/rfc/rfc6265.txt) describes client and browser behavior when the domain attribute is present or not present in the `Set-cookie` header." - If the value of the `Domain` attribute is `example.com` in the `Set-cookie` header, the client includes the same cookie in the `Cookie` header when making HTTP requests to `example.com`, `www.example.com`, and `www.abc.example.com`. If the `Domain` attribute is not present, the client returns the cookie only for the domain to which the original request was made. - "* Ensure that this attribute specifies the correct domain value. If the `Domain` attribute in the `Set-cookie` header does not include the domain to which the original request was made, the client or browser might reject the cookie. As specified in RFC 6265, the client accepts a cookie with the `Domain` attribute value `example.com` or `www.example.com` sent from `www.example.com`. It does not accept a cookie with the `Domain` attribute `abc.example.com` or `www.abc.example.com` sent from `www.example.com`." - "Example: `example.com`" type: str path: description: - The path in which the cookie is valid. The `Set-cookie header` inserted by the load balancer contains a `Path` attribute with the specified value. - Clients include the cookie in an HTTP request only if the path portion of the request-uri matches, or is a subdirectory of, the cookie's `Path` attribute. - The default value is `/`. - "Example: `/example`" type: str max_age_in_seconds: description: - The amount of time the cookie remains valid. The `Set-cookie` header inserted by the load balancer contains a `Max-Age` attribute with the specified value. - The specified value must be at least one second. There is no default value for this attribute. If you do not specify a value, the load balancer does not include the `Max-Age` attribute in the `Set-cookie` header. In most cases, the client or browser retains the cookie until the current session ends, as defined by the client. - "Example: `3600`" type: int is_secure: description: - Whether the `Set-cookie` header should contain the `Secure` attribute. If `true`, the `Set-cookie` header inserted by the load balancer contains the `Secure` attribute, which directs the client or browser to send the cookie only using a secure protocol. - "**Note:** If you set this field to `true`, you cannot associate the corresponding backend set with an HTTP listener." - "Example: `true`" type: bool is_http_only: description: - Whether the `Set-cookie` header should contain the `HttpOnly` attribute. If `true`, the `Set-cookie` header inserted by the load balancer contains the `HttpOnly` attribute, which limits the scope of the cookie to HTTP requests. This attribute directs the client or browser to omit the cookie when providing access to cookies through non-HTTP APIs. For example, it restricts the cookie from JavaScript channels. - "Example: `true`" type: bool load_balancer_id: description: - The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the load balancer on which to add a backend set. type: str aliases: ["id"] required: true state: description: - The state of the BackendSet. - Use I(state=present) to create or update a BackendSet. - Use I(state=absent) to delete a BackendSet. type: str required: false default: 'present' choices: ["present", "absent"] extends_documentation_fragment: [ oracle.oci.oracle, oracle.oci.oracle_creatable_resource, oracle.oci.oracle_wait_options ] """ EXAMPLES = """ - name: Create backend_set oci_loadbalancer_backend_set: # required name: name_example policy: policy_example health_checker: # required protocol: protocol_example # optional url_path: url_path_example port: 56 return_code: 56 retries: 56 timeout_in_millis: 56 interval_in_millis: 56 response_body_regex: response_body_regex_example load_balancer_id: "ocid1.loadbalancer.oc1..xxxxxxEXAMPLExxxxxx" # optional backends: - # required ip_address: ip_address_example port: 56 # optional weight: 56 backup: true drain: true offline: true ssl_configuration: # optional verify_depth: 56 verify_peer_certificate: true trusted_certificate_authority_ids: [ "trusted_certificate_authority_ids_example" ] certificate_ids: [ "certificate_ids_example" ] certificate_name: certificate_name_example protocols: [ "protocols_example" ] cipher_suite_name: cipher_suite_name_example server_order_preference: ENABLED session_persistence_configuration: # required cookie_name: cookie_name_example # optional disable_fallback: true lb_cookie_session_persistence_configuration: # optional cookie_name: cookie_name_example disable_fallback: true domain: domain_example path: path_example max_age_in_seconds: 56 is_secure: true is_http_only: true - name: Update backend_set oci_loadbalancer_backend_set: # required name: name_example policy: policy_example backends: - # required ip_address: ip_address_example port: 56 # optional weight: 56 backup: true drain: true offline: true health_checker: # required protocol: protocol_example # optional url_path: url_path_example port: 56 return_code: 56 retries: 56 timeout_in_millis: 56 interval_in_millis: 56 response_body_regex: response_body_regex_example load_balancer_id: "ocid1.loadbalancer.oc1..xxxxxxEXAMPLExxxxxx" # optional ssl_configuration: # optional verify_depth: 56 verify_peer_certificate: true trusted_certificate_authority_ids: [ "trusted_certificate_authority_ids_example" ] certificate_ids: [ "certificate_ids_example" ] certificate_name: certificate_name_example protocols: [ "protocols_example" ] cipher_suite_name: cipher_suite_name_example server_order_preference: ENABLED session_persistence_configuration: # required cookie_name: cookie_name_example # optional disable_fallback: true lb_cookie_session_persistence_configuration: # optional cookie_name: cookie_name_example disable_fallback: true domain: domain_example path: path_example max_age_in_seconds: 56 is_secure: true is_http_only: true - name: Delete backend_set oci_loadbalancer_backend_set: # required name: name_example load_balancer_id: "ocid1.loadbalancer.oc1..xxxxxxEXAMPLExxxxxx" state: absent """ RETURN = """ backend_set: description: - Details of the BackendSet resource acted upon by the current operation returned: on success type: complex contains: name: description: - A friendly name for the backend set. It must be unique and it cannot be changed. - Valid backend set names include only alphanumeric characters, dashes, and underscores. Backend set names cannot contain spaces. Avoid entering confidential information. - "Example: `example_backend_set`" returned: on success type: str sample: name_example policy: description: - The load balancer policy for the backend set. To get a list of available policies, use the L(ListPolicies,https://docs.cloud.oracle.com/en-us/iaas/api/#/en/loadbalancer/20170115/LoadBalancerPolicy/ListPolicies) operation. - "Example: `LEAST_CONNECTIONS`" returned: on success type: str sample: policy_example backends: description: - "" returned: on success type: complex contains: name: description: - A read-only field showing the IP address and port that uniquely identify this backend server in the backend set. - "Example: `10.0.0.3:8080`" returned: on success type: str sample: name_example ip_address: description: - The IP address of the backend server. - "Example: `10.0.0.3`" returned: on success type: str sample: ip_address_example port: description: - The communication port for the backend server. - "Example: `8080`" returned: on success type: int sample: 56 weight: description: - The load balancing policy weight assigned to the server. Backend servers with a higher weight receive a larger proportion of incoming traffic. For example, a server weighted '3' receives 3 times the number of new connections as a server weighted '1'. For more information on load balancing policies, see L(How Load Balancing Policies Work,https://docs.cloud.oracle.com/Content/Balance/Reference/lbpolicies.htm). - "Example: `3`" returned: on success type: int sample: 56 drain: description: - "Whether the load balancer should drain this server. Servers marked \\"drain\\" receive no new incoming traffic." - "Example: `false`" returned: on success type: bool sample: true backup: description: - "Whether the load balancer should treat this server as a backup unit. If `true`, the load balancer forwards no ingress traffic to this backend server unless all other backend servers not marked as \\"backup\\" fail the health check policy." - "**Note:** You cannot add a backend server marked as `backup` to a backend set that uses the IP Hash policy." - "Example: `false`" returned: on success type: bool sample: true offline: description: - Whether the load balancer should treat this server as offline. Offline servers receive no incoming traffic. - "Example: `false`" returned: on success type: bool sample: true health_checker: description: - "" returned: on success type: complex contains: protocol: description: - The protocol the health check must use; either HTTP or TCP. - "Example: `HTTP`" returned: on success type: str sample: protocol_example url_path: description: - The path against which to run the health check. - "Example: `/healthcheck`" returned: on success type: str sample: url_path_example port: description: - The backend server port against which to run the health check. If the port is not specified, the load balancer uses the port information from the `Backend` object. - "Example: `8080`" returned: on success type: int sample: 56 return_code: description: - "The status code a healthy backend server should return. If you configure the health check policy to use the HTTP protocol, you can use common HTTP status codes such as \\"200\\"." - "Example: `200`" returned: on success type: int sample: 56 retries: description: - "The number of retries to attempt before a backend server is considered \\"unhealthy\\". This number also applies when recovering a server to the \\"healthy\\" state. Defaults to 3." - "Example: `3`" returned: on success type: int sample: 56 timeout_in_millis: description: - The maximum time, in milliseconds, to wait for a reply to a health check. A health check is successful only if a reply returns within this timeout period. Defaults to 3000 (3 seconds). - "Example: `3000`" returned: on success type: int sample: 56 interval_in_millis: description: - The interval between health checks, in milliseconds. The default is 10000 (10 seconds). - "Example: `10000`" returned: on success type: int sample: 56 response_body_regex: description: - A regular expression for parsing the response body from the backend server. - "Example: `^((?!false).|\\\\s)*$`" returned: on success type: str sample: response_body_regex_example ssl_configuration: description: - "" returned: on success type: complex contains: verify_depth: description: - The maximum depth for peer certificate chain verification. - "Example: `3`" returned: on success type: int sample: 56 verify_peer_certificate: description: - Whether the load balancer listener should verify peer certificates. - "Example: `true`" returned: on success type: bool sample: true trusted_certificate_authority_ids: description: - Ids for OCI certificates service CA or CA bundles for the load balancer to trust. - "Example: `[ocid1.cabundle.oc1.us-ashburn-1.amaaaaaaav3bgsaagl4zzyqdop5i2vuwoqewdvauuw34llqa74otq2jdsfyq]`" returned: on success type: list sample: [] certificate_ids: description: - Ids for OCI certificates service certificates. Currently only a single Id may be passed. - "Example: `[ocid1.certificate.oc1.us-ashburn-1.amaaaaaaav3bgsaa5o2q7rh5nfmkkukfkogasqhk6af2opufhjlqg7m6jqzq]`" returned: on success type: list sample: [] certificate_name: description: - A friendly name for the certificate bundle. It must be unique and it cannot be changed. Valid certificate bundle names include only alphanumeric characters, dashes, and underscores. Certificate bundle names cannot contain spaces. Avoid entering confidential information. - "Example: `example_certificate_bundle`" returned: on success type: str sample: certificate_name_example server_order_preference: description: - When this attribute is set to ENABLED, the system gives preference to the server ciphers over the client ciphers. - "**Note:** This configuration is applicable only when the load balancer is acting as an SSL/HTTPS server. This field is ignored when the `SSLConfiguration` object is associated with a backend set." returned: on success type: str sample: ENABLED cipher_suite_name: description: - The name of the cipher suite to use for HTTPS or SSL connections. - If this field is not specified, the default is `oci-default-ssl-cipher-suite-v1`. - "**Notes:**" - "* You must ensure compatibility between the specified SSL protocols and the ciphers configured in the cipher suite. Clients cannot perform an SSL handshake if there is an incompatible configuration. * You must ensure compatibility between the ciphers configured in the cipher suite and the configured certificates. For example, RSA-based ciphers require RSA certificates and ECDSA-based ciphers require ECDSA certificates. * If the cipher configuration is not modified after load balancer creation, the `GET` operation returns `oci-default-ssl-cipher-suite-v1` as the value of this field in the SSL configuration for existing listeners that predate this feature. * If the cipher configuration was modified using Oracle operations after load balancer creation, the `GET` operation returns `oci-customized-ssl-cipher-suite` as the value of this field in the SSL configuration for existing listeners that predate this feature. * The `GET` operation returns `oci-wider-compatible-ssl-cipher-suite-v1` as the value of this field in the SSL configuration for existing backend sets that predate this feature. * If the `GET` operation on a listener returns `oci-customized-ssl-cipher-suite` as the value of this field, you must specify an appropriate predefined or custom cipher suite name when updating the resource. * The `oci-customized-ssl-cipher-suite` Oracle reserved cipher suite name is not accepted as valid input for this field." - "example: `example_cipher_suite`" returned: on success type: str sample: cipher_suite_name_example protocols: description: - A list of SSL protocols the load balancer must support for HTTPS or SSL connections. - The load balancer uses SSL protocols to establish a secure connection between a client and a server. A secure connection ensures that all data passed between the client and the server is private. - "The Load Balancing service supports the following protocols:" - "* TLSv1 * TLSv1.1 * TLSv1.2" - If this field is not specified, TLSv1.2 is the default. - "**Warning:** All SSL listeners created on a given port must use the same set of SSL protocols." - "**Notes:**" - "* The handshake to establish an SSL connection fails if the client supports none of the specified protocols. * You must ensure compatibility between the specified SSL protocols and the ciphers configured in the cipher suite. * For all existing load balancer listeners and backend sets that predate this feature, the `GET` operation displays a list of SSL protocols currently used by those resources." - "example: `[\\"TLSv1.1\\", \\"TLSv1.2\\"]`" returned: on success type: list sample: [] session_persistence_configuration: description: - "" returned: on success type: complex contains: cookie_name: description: - "The name of the cookie used to detect a session initiated by the backend server. Use '*' to specify that any cookie set by the backend causes the session to persist." - "Example: `example_cookie`" returned: on success type: str sample: cookie_name_example disable_fallback: description: - Whether the load balancer is prevented from directing traffic from a persistent session client to a different backend server if the original server is unavailable. Defaults to false. - "Example: `false`" returned: on success type: bool sample: true lb_cookie_session_persistence_configuration: description: - "" returned: on success type: complex contains: cookie_name: description: - "The name of the cookie inserted by the load balancer. If this field is not configured, the cookie name defaults to \\"X-Oracle-BMC-LBS-Route\\"." - "Example: `example_cookie`" - "**Notes:**" - "* Ensure that the cookie name used at the backend application servers is different from the cookie name used at the load balancer. To minimize the chance of name collision, Oracle recommends that you use a prefix such as \\"X-Oracle-OCI-\\" for this field." - "* If a backend server and the load balancer both insert cookies with the same name, the client or browser behavior can vary depending on the domain and path values associated with the cookie. If the name, domain, and path values of the `Set-cookie` generated by a backend server and the `Set-cookie` generated by the load balancer are all the same, the client or browser treats them as one cookie and returns only one of the cookie values in subsequent requests. If both `Set-cookie` names are the same, but the domain and path names are different, the client or browser treats them as two different cookies." returned: on success type: str sample: cookie_name_example disable_fallback: description: - Whether the load balancer is prevented from directing traffic from a persistent session client to a different backend server if the original server is unavailable. Defaults to false. - "Example: `false`" returned: on success type: bool sample: true domain: description: - The domain in which the cookie is valid. The `Set-cookie` header inserted by the load balancer contains a domain attribute with the specified value. - This attribute has no default value. If you do not specify a value, the load balancer does not insert the domain attribute into the `Set-cookie` header. - "**Notes:**" - "* L(RFC 6265 - HTTP State Management Mechanism,https://www.ietf.org/rfc/rfc6265.txt) describes client and browser behavior when the domain attribute is present or not present in the `Set-cookie` header." - If the value of the `Domain` attribute is `example.com` in the `Set-cookie` header, the client includes the same cookie in the `Cookie` header when making HTTP requests to `example.com`, `www.example.com`, and `www.abc.example.com`. If the `Domain` attribute is not present, the client returns the cookie only for the domain to which the original request was made. - "* Ensure that this attribute specifies the correct domain value. If the `Domain` attribute in the `Set-cookie` header does not include the domain to which the original request was made, the client or browser might reject the cookie. As specified in RFC 6265, the client accepts a cookie with the `Domain` attribute value `example.com` or `www.example.com` sent from `www.example.com`. It does not accept a cookie with the `Domain` attribute `abc.example.com` or `www.abc.example.com` sent from `www.example.com`." - "Example: `example.com`" returned: on success type: str sample: domain_example path: description: - The path in which the cookie is valid. The `Set-cookie header` inserted by the load balancer contains a `Path` attribute with the specified value. - Clients include the cookie in an HTTP request only if the path portion of the request-uri matches, or is a subdirectory of, the cookie's `Path` attribute. - The default value is `/`. - "Example: `/example`" returned: on success type: str sample: path_example max_age_in_seconds: description: - The amount of time the cookie remains valid. The `Set-cookie` header inserted by the load balancer contains a `Max-Age` attribute with the specified value. - The specified value must be at least one second. There is no default value for this attribute. If you do not specify a value, the load balancer does not include the `Max-Age` attribute in the `Set-cookie` header. In most cases, the client or browser retains the cookie until the current session ends, as defined by the client. - "Example: `3600`" returned: on success type: int sample: 56 is_secure: description: - Whether the `Set-cookie` header should contain the `Secure` attribute. If `true`, the `Set-cookie` header inserted by the load balancer contains the `Secure` attribute, which directs the client or browser to send the cookie only using a secure protocol. - "**Note:** If you set this field to `true`, you cannot associate the corresponding backend set with an HTTP listener." - "Example: `true`" returned: on success type: bool sample: true is_http_only: description: - Whether the `Set-cookie` header should contain the `HttpOnly` attribute. If `true`, the `Set-cookie` header inserted by the load balancer contains the `HttpOnly` attribute, which limits the scope of the cookie to HTTP requests. This attribute directs the client or browser to omit the cookie when providing access to cookies through non-HTTP APIs. For example, it restricts the cookie from JavaScript channels. - "Example: `true`" returned: on success type: bool sample: true sample: { "name": "name_example", "policy": "policy_example", "backends": [{ "name": "name_example", "ip_address": "ip_address_example", "port": 56, "weight": 56, "drain": true, "backup": true, "offline": true }], "health_checker": { "protocol": "protocol_example", "url_path": "url_path_example", "port": 56, "return_code": 56, "retries": 56, "timeout_in_millis": 56, "interval_in_millis": 56, "response_body_regex": "response_body_regex_example" }, "ssl_configuration": { "verify_depth": 56, "verify_peer_certificate": true, "trusted_certificate_authority_ids": [], "certificate_ids": [], "certificate_name": "certificate_name_example", "server_order_preference": "ENABLED", "cipher_suite_name": "cipher_suite_name_example", "protocols": [] }, "session_persistence_configuration": { "cookie_name": "cookie_name_example", "disable_fallback": true }, "lb_cookie_session_persistence_configuration": { "cookie_name": "cookie_name_example", "disable_fallback": true, "domain": "domain_example", "path": "path_example", "max_age_in_seconds": 56, "is_secure": true, "is_http_only": true } } """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import ( oci_common_utils, oci_wait_utils, ) from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceHelperBase, get_custom_class, ) try: from oci.load_balancer import LoadBalancerClient from oci.load_balancer.models import CreateBackendSetDetails from oci.load_balancer.models import UpdateBackendSetDetails HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class BackendSetHelperGen(OCIResourceHelperBase): """Supported operations: create, update, get, list and delete""" def get_possible_entity_types(self): return super(BackendSetHelperGen, self).get_possible_entity_types() + [ "backendset", "backendsets", "loadBalancerbackendset", "loadBalancerbackendsets", "backendsetresource", "backendsetsresource", "loadbalancer", ] def get_module_resource_id_param(self): return "name" def get_module_resource_id(self): return self.module.params.get("name") def get_get_fn(self): return self.client.get_backend_set def get_resource(self): return oci_common_utils.call_with_backoff( self.client.get_backend_set, load_balancer_id=self.module.params.get("load_balancer_id"), backend_set_name=self.module.params.get("name"), ) def get_required_kwargs_for_list(self): required_list_method_params = [ "load_balancer_id", ] return dict( (param, self.module.params[param]) for param in required_list_method_params ) def get_optional_kwargs_for_list(self): return dict() def list_resources(self): required_kwargs = self.get_required_kwargs_for_list() optional_kwargs = self.get_optional_kwargs_for_list() kwargs = oci_common_utils.merge_dicts(required_kwargs, optional_kwargs) return oci_common_utils.list_all_resources( self.client.list_backend_sets, **kwargs ) def get_create_model_class(self): return CreateBackendSetDetails def is_update(self): if not self.module.params.get("state") == "present": return False return self.does_resource_exist() def is_create(self): if not self.module.params.get("state") == "present": return False return not self.does_resource_exist() def create_resource(self): create_details = self.get_create_model() return oci_wait_utils.call_and_wait( call_fn=self.client.create_backend_set, call_fn_args=(), call_fn_kwargs=dict( create_backend_set_details=create_details, load_balancer_id=self.module.params.get("load_balancer_id"), ), waiter_type=oci_wait_utils.WORK_REQUEST_WAITER_KEY, operation=oci_common_utils.CREATE_OPERATION_KEY, waiter_client=self.get_waiter_client(), resource_helper=self, wait_for_states=oci_common_utils.get_work_request_completed_states(), ) def get_update_model_class(self): return UpdateBackendSetDetails def update_resource(self): update_details = self.get_update_model() return oci_wait_utils.call_and_wait( call_fn=self.client.update_backend_set, call_fn_args=(), call_fn_kwargs=dict( update_backend_set_details=update_details, load_balancer_id=self.module.params.get("load_balancer_id"), backend_set_name=self.module.params.get("name"), ), waiter_type=oci_wait_utils.WORK_REQUEST_WAITER_KEY, operation=oci_common_utils.UPDATE_OPERATION_KEY, waiter_client=self.get_waiter_client(), resource_helper=self, wait_for_states=oci_common_utils.get_work_request_completed_states(), ) def delete_resource(self): return oci_wait_utils.call_and_wait( call_fn=self.client.delete_backend_set, call_fn_args=(), call_fn_kwargs=dict( load_balancer_id=self.module.params.get("load_balancer_id"), backend_set_name=self.module.params.get("name"), ), waiter_type=oci_wait_utils.WORK_REQUEST_WAITER_KEY, operation=oci_common_utils.DELETE_OPERATION_KEY, waiter_client=self.get_waiter_client(), resource_helper=self, wait_for_states=oci_common_utils.get_work_request_completed_states(), ) BackendSetHelperCustom = get_custom_class("BackendSetHelperCustom") class ResourceHelper(BackendSetHelperCustom, BackendSetHelperGen): pass def main(): module_args = oci_common_utils.get_common_arg_spec( supports_create=True, supports_wait=True ) module_args.update( dict( name=dict(type="str", required=True), policy=dict(type="str"), backends=dict( type="list", elements="dict", options=dict( ip_address=dict(type="str", required=True), port=dict(type="int", required=True), weight=dict(type="int"), backup=dict(type="bool"), drain=dict(type="bool"), offline=dict(type="bool"), ), ), health_checker=dict( type="dict", options=dict( protocol=dict(type="str", required=True), url_path=dict(type="str"), port=dict(type="int"), return_code=dict(type="int"), retries=dict(type="int"), timeout_in_millis=dict(type="int"), interval_in_millis=dict(type="int"), response_body_regex=dict(type="str"), ), ), ssl_configuration=dict( type="dict", options=dict( verify_depth=dict(type="int"), verify_peer_certificate=dict(type="bool"), trusted_certificate_authority_ids=dict(type="list", elements="str"), certificate_ids=dict(type="list", elements="str"), certificate_name=dict(type="str"), protocols=dict(type="list", elements="str"), cipher_suite_name=dict(type="str"), server_order_preference=dict( type="str", choices=["ENABLED", "DISABLED"] ), ), ), session_persistence_configuration=dict( type="dict", options=dict( cookie_name=dict(type="str", required=True), disable_fallback=dict(type="bool"), ), ), lb_cookie_session_persistence_configuration=dict( type="dict", options=dict( cookie_name=dict(type="str"), disable_fallback=dict(type="bool"), domain=dict(type="str"), path=dict(type="str"), max_age_in_seconds=dict(type="int"), is_secure=dict(type="bool"), is_http_only=dict(type="bool"), ), ), load_balancer_id=dict(aliases=["id"], type="str", required=True), state=dict(type="str", default="present", choices=["present", "absent"]), ) ) module = AnsibleModule(argument_spec=module_args, supports_check_mode=True) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_helper = ResourceHelper( module=module, resource_type="backend_set", service_client_class=LoadBalancerClient, namespace="load_balancer", ) result = dict(changed=False) if resource_helper.is_delete(): result = resource_helper.delete() elif resource_helper.is_update(): result = resource_helper.update() elif resource_helper.is_create(): result = resource_helper.create() module.exit_json(**result) if __name__ == "__main__": main()

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from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_loadbalancer_backend_set short_description: Manage a BackendSet resource in Oracle Cloud Infrastructure description: - This module allows the user to create, update and delete a BackendSet resource in Oracle Cloud Infrastructure - For I(state=present), adds a backend set to a load balancer. version_added: "2.9.0" author: Oracle (@oracle) options: name: description: - A friendly name for the backend set. It must be unique and it cannot be changed. - Valid backend set names include only alphanumeric characters, dashes, and underscores. Backend set names cannot contain spaces. Avoid entering confidential information. - "Example: `example_backend_set`" type: str required: true policy: description: - The load balancer policy for the backend set. To get a list of available policies, use the L(ListPolicies,https://docs.cloud.oracle.com/en-us/iaas/api/#/en/loadbalancer/20170115/LoadBalancerPolicy/ListPolicies) operation. - "Example: `LEAST_CONNECTIONS`" - Required for create using I(state=present), update using I(state=present) with name present. type: str backends: description: - "" - Required for update using I(state=present) with name present. type: list elements: dict suboptions: ip_address: description: - The IP address of the backend server. - "Example: `10.0.0.3`" type: str required: true port: description: - The communication port for the backend server. - "Example: `8080`" type: int required: true weight: description: - The load balancing policy weight assigned to the server. Backend servers with a higher weight receive a larger proportion of incoming traffic. For example, a server weighted '3' receives 3 times the number of new connections as a server weighted '1'. For more information on load balancing policies, see L(How Load Balancing Policies Work,https://docs.cloud.oracle.com/Content/Balance/Reference/lbpolicies.htm). - "Example: `3`" type: int backup: description: - "Whether the load balancer should treat this server as a backup unit. If `true`, the load balancer forwards no ingress traffic to this backend server unless all other backend servers not marked as \\"backup\\" fail the health check policy." - "**Note:** You cannot add a backend server marked as `backup` to a backend set that uses the IP Hash policy." - "Example: `false`" type: bool drain: description: - "Whether the load balancer should drain this server. Servers marked \\"drain\\" receive no new incoming traffic." - "Example: `false`" type: bool offline: description: - Whether the load balancer should treat this server as offline. Offline servers receive no incoming traffic. - "Example: `false`" type: bool health_checker: description: - "" - Required for create using I(state=present), update using I(state=present) with name present. type: dict suboptions: protocol: description: - The protocol the health check must use; either HTTP or TCP. - "Example: `HTTP`" type: str required: true url_path: description: - The path against which to run the health check. - "Example: `/healthcheck`" type: str port: description: - The backend server port against which to run the health check. If the port is not specified, the load balancer uses the port information from the `Backend` object. - "Example: `8080`" type: int return_code: description: - The status code a healthy backend server should return. - "Example: `200`" type: int retries: description: - "The number of retries to attempt before a backend server is considered \\"unhealthy\\". This number also applies when recovering a server to the \\"healthy\\" state." - "Example: `3`" type: int timeout_in_millis: description: - The maximum time, in milliseconds, to wait for a reply to a health check. A health check is successful only if a reply returns within this timeout period. - "Example: `3000`" type: int interval_in_millis: description: - The interval between health checks, in milliseconds. - "Example: `10000`" type: int response_body_regex: description: - A regular expression for parsing the response body from the backend server. - "Example: `^((?!false).|\\\\s)*$`" type: str ssl_configuration: description: - "" - This parameter is updatable. type: dict suboptions: verify_depth: description: - The maximum depth for peer certificate chain verification. - "Example: `3`" type: int verify_peer_certificate: description: - Whether the load balancer listener should verify peer certificates. - "Example: `true`" type: bool trusted_certificate_authority_ids: description: - Ids for OCI certificates service CA or CA bundles for the load balancer to trust. - "Example: `[ocid1.cabundle.oc1.us-ashburn-1.amaaaaaaav3bgsaagl4zzyqdop5i2vuwoqewdvauuw34llqa74otq2jdsfyq]`" type: list elements: str certificate_ids: description: - Ids for OCI certificates service certificates. Currently only a single Id may be passed. - "Example: `[ocid1.certificate.oc1.us-ashburn-1.amaaaaaaav3bgsaa5o2q7rh5nfmkkukfkogasqhk6af2opufhjlqg7m6jqzq]`" type: list elements: str certificate_name: description: - A friendly name for the certificate bundle. It must be unique and it cannot be changed. Valid certificate bundle names include only alphanumeric characters, dashes, and underscores. Certificate bundle names cannot contain spaces. Avoid entering confidential information. - "Example: `example_certificate_bundle`" type: str protocols: description: - A list of SSL protocols the load balancer must support for HTTPS or SSL connections. - The load balancer uses SSL protocols to establish a secure connection between a client and a server. A secure connection ensures that all data passed between the client and the server is private. - "The Load Balancing service supports the following protocols:" - "* TLSv1 * TLSv1.1 * TLSv1.2" - If this field is not specified, TLSv1.2 is the default. - "**Warning:** All SSL listeners created on a given port must use the same set of SSL protocols." - "**Notes:**" - "* The handshake to establish an SSL connection fails if the client supports none of the specified protocols. * You must ensure compatibility between the specified SSL protocols and the ciphers configured in the cipher suite. * For all existing load balancer listeners and backend sets that predate this feature, the `GET` operation displays a list of SSL protocols currently used by those resources." - "example: `[\\"TLSv1.1\\", \\"TLSv1.2\\"]`" type: list elements: str cipher_suite_name: description: - The name of the cipher suite to use for HTTPS or SSL connections. - If this field is not specified, the default is `oci-default-ssl-cipher-suite-v1`. - "**Notes:**" - "* You must ensure compatibility between the specified SSL protocols and the ciphers configured in the cipher suite. Clients cannot perform an SSL handshake if there is an incompatible configuration. * You must ensure compatibility between the ciphers configured in the cipher suite and the configured certificates. For example, RSA-based ciphers require RSA certificates and ECDSA-based ciphers require ECDSA certificates. * If the cipher configuration is not modified after load balancer creation, the `GET` operation returns `oci-default-ssl-cipher-suite-v1` as the value of this field in the SSL configuration for existing listeners that predate this feature. * If the cipher configuration was modified using Oracle operations after load balancer creation, the `GET` operation returns `oci-customized-ssl-cipher-suite` as the value of this field in the SSL configuration for existing listeners that predate this feature. * The `GET` operation returns `oci-wider-compatible-ssl-cipher-suite-v1` as the value of this field in the SSL configuration for existing backend sets that predate this feature. * If the `GET` operation on a listener returns `oci-customized-ssl-cipher-suite` as the value of this field, you must specify an appropriate predefined or custom cipher suite name when updating the resource. * The `oci-customized-ssl-cipher-suite` Oracle reserved cipher suite name is not accepted as valid input for this field." - "example: `example_cipher_suite`" type: str server_order_preference: description: - When this attribute is set to ENABLED, the system gives preference to the server ciphers over the client ciphers. - "**Note:** This configuration is applicable only when the load balancer is acting as an SSL/HTTPS server. This field is ignored when the `SSLConfiguration` object is associated with a backend set." type: str choices: - "ENABLED" - "DISABLED" session_persistence_configuration: description: - "" - This parameter is updatable. type: dict suboptions: cookie_name: description: - "The name of the cookie used to detect a session initiated by the backend server. Use '*' to specify that any cookie set by the backend causes the session to persist." - "Example: `example_cookie`" type: str required: true disable_fallback: description: - Whether the load balancer is prevented from directing traffic from a persistent session client to a different backend server if the original server is unavailable. Defaults to false. - "Example: `false`" type: bool lb_cookie_session_persistence_configuration: description: - "" - This parameter is updatable. type: dict suboptions: cookie_name: description: - "The name of the cookie inserted by the load balancer. If this field is not configured, the cookie name defaults to \\"X-Oracle-BMC-LBS-Route\\"." - "Example: `example_cookie`" - "**Notes:**" - "* Ensure that the cookie name used at the backend application servers is different from the cookie name used at the load balancer. To minimize the chance of name collision, Oracle recommends that you use a prefix such as \\"X-Oracle-OCI-\\" for this field." - "* If a backend server and the load balancer both insert cookies with the same name, the client or browser behavior can vary depending on the domain and path values associated with the cookie. If the name, domain, and path values of the `Set-cookie` generated by a backend server and the `Set-cookie` generated by the load balancer are all the same, the client or browser treats them as one cookie and returns only one of the cookie values in subsequent requests. If both `Set-cookie` names are the same, but the domain and path names are different, the client or browser treats them as two different cookies." type: str disable_fallback: description: - Whether the load balancer is prevented from directing traffic from a persistent session client to a different backend server if the original server is unavailable. Defaults to false. - "Example: `false`" type: bool domain: description: - The domain in which the cookie is valid. The `Set-cookie` header inserted by the load balancer contains a domain attribute with the specified value. - This attribute has no default value. If you do not specify a value, the load balancer does not insert the domain attribute into the `Set-cookie` header. - "**Notes:**" - "* L(RFC 6265 - HTTP State Management Mechanism,https://www.ietf.org/rfc/rfc6265.txt) describes client and browser behavior when the domain attribute is present or not present in the `Set-cookie` header." - If the value of the `Domain` attribute is `example.com` in the `Set-cookie` header, the client includes the same cookie in the `Cookie` header when making HTTP requests to `example.com`, `www.example.com`, and `www.abc.example.com`. If the `Domain` attribute is not present, the client returns the cookie only for the domain to which the original request was made. - "* Ensure that this attribute specifies the correct domain value. If the `Domain` attribute in the `Set-cookie` header does not include the domain to which the original request was made, the client or browser might reject the cookie. As specified in RFC 6265, the client accepts a cookie with the `Domain` attribute value `example.com` or `www.example.com` sent from `www.example.com`. It does not accept a cookie with the `Domain` attribute `abc.example.com` or `www.abc.example.com` sent from `www.example.com`." - "Example: `example.com`" type: str path: description: - The path in which the cookie is valid. The `Set-cookie header` inserted by the load balancer contains a `Path` attribute with the specified value. - Clients include the cookie in an HTTP request only if the path portion of the request-uri matches, or is a subdirectory of, the cookie's `Path` attribute. - The default value is `/`. - "Example: `/example`" type: str max_age_in_seconds: description: - The amount of time the cookie remains valid. The `Set-cookie` header inserted by the load balancer contains a `Max-Age` attribute with the specified value. - The specified value must be at least one second. There is no default value for this attribute. If you do not specify a value, the load balancer does not include the `Max-Age` attribute in the `Set-cookie` header. In most cases, the client or browser retains the cookie until the current session ends, as defined by the client. - "Example: `3600`" type: int is_secure: description: - Whether the `Set-cookie` header should contain the `Secure` attribute. If `true`, the `Set-cookie` header inserted by the load balancer contains the `Secure` attribute, which directs the client or browser to send the cookie only using a secure protocol. - "**Note:** If you set this field to `true`, you cannot associate the corresponding backend set with an HTTP listener." - "Example: `true`" type: bool is_http_only: description: - Whether the `Set-cookie` header should contain the `HttpOnly` attribute. If `true`, the `Set-cookie` header inserted by the load balancer contains the `HttpOnly` attribute, which limits the scope of the cookie to HTTP requests. This attribute directs the client or browser to omit the cookie when providing access to cookies through non-HTTP APIs. For example, it restricts the cookie from JavaScript channels. - "Example: `true`" type: bool load_balancer_id: description: - The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the load balancer on which to add a backend set. type: str aliases: ["id"] required: true state: description: - The state of the BackendSet. - Use I(state=present) to create or update a BackendSet. - Use I(state=absent) to delete a BackendSet. type: str required: false default: 'present' choices: ["present", "absent"] extends_documentation_fragment: [ oracle.oci.oracle, oracle.oci.oracle_creatable_resource, oracle.oci.oracle_wait_options ] """ EXAMPLES = """ - name: Create backend_set oci_loadbalancer_backend_set: # required name: name_example policy: policy_example health_checker: # required protocol: protocol_example # optional url_path: url_path_example port: 56 return_code: 56 retries: 56 timeout_in_millis: 56 interval_in_millis: 56 response_body_regex: response_body_regex_example load_balancer_id: "ocid1.loadbalancer.oc1..xxxxxxEXAMPLExxxxxx" # optional backends: - # required ip_address: ip_address_example port: 56 # optional weight: 56 backup: true drain: true offline: true ssl_configuration: # optional verify_depth: 56 verify_peer_certificate: true trusted_certificate_authority_ids: [ "trusted_certificate_authority_ids_example" ] certificate_ids: [ "certificate_ids_example" ] certificate_name: certificate_name_example protocols: [ "protocols_example" ] cipher_suite_name: cipher_suite_name_example server_order_preference: ENABLED session_persistence_configuration: # required cookie_name: cookie_name_example # optional disable_fallback: true lb_cookie_session_persistence_configuration: # optional cookie_name: cookie_name_example disable_fallback: true domain: domain_example path: path_example max_age_in_seconds: 56 is_secure: true is_http_only: true - name: Update backend_set oci_loadbalancer_backend_set: # required name: name_example policy: policy_example backends: - # required ip_address: ip_address_example port: 56 # optional weight: 56 backup: true drain: true offline: true health_checker: # required protocol: protocol_example # optional url_path: url_path_example port: 56 return_code: 56 retries: 56 timeout_in_millis: 56 interval_in_millis: 56 response_body_regex: response_body_regex_example load_balancer_id: "ocid1.loadbalancer.oc1..xxxxxxEXAMPLExxxxxx" # optional ssl_configuration: # optional verify_depth: 56 verify_peer_certificate: true trusted_certificate_authority_ids: [ "trusted_certificate_authority_ids_example" ] certificate_ids: [ "certificate_ids_example" ] certificate_name: certificate_name_example protocols: [ "protocols_example" ] cipher_suite_name: cipher_suite_name_example server_order_preference: ENABLED session_persistence_configuration: # required cookie_name: cookie_name_example # optional disable_fallback: true lb_cookie_session_persistence_configuration: # optional cookie_name: cookie_name_example disable_fallback: true domain: domain_example path: path_example max_age_in_seconds: 56 is_secure: true is_http_only: true - name: Delete backend_set oci_loadbalancer_backend_set: # required name: name_example load_balancer_id: "ocid1.loadbalancer.oc1..xxxxxxEXAMPLExxxxxx" state: absent """ RETURN = """ backend_set: description: - Details of the BackendSet resource acted upon by the current operation returned: on success type: complex contains: name: description: - A friendly name for the backend set. It must be unique and it cannot be changed. - Valid backend set names include only alphanumeric characters, dashes, and underscores. Backend set names cannot contain spaces. Avoid entering confidential information. - "Example: `example_backend_set`" returned: on success type: str sample: name_example policy: description: - The load balancer policy for the backend set. To get a list of available policies, use the L(ListPolicies,https://docs.cloud.oracle.com/en-us/iaas/api/#/en/loadbalancer/20170115/LoadBalancerPolicy/ListPolicies) operation. - "Example: `LEAST_CONNECTIONS`" returned: on success type: str sample: policy_example backends: description: - "" returned: on success type: complex contains: name: description: - A read-only field showing the IP address and port that uniquely identify this backend server in the backend set. - "Example: `10.0.0.3:8080`" returned: on success type: str sample: name_example ip_address: description: - The IP address of the backend server. - "Example: `10.0.0.3`" returned: on success type: str sample: ip_address_example port: description: - The communication port for the backend server. - "Example: `8080`" returned: on success type: int sample: 56 weight: description: - The load balancing policy weight assigned to the server. Backend servers with a higher weight receive a larger proportion of incoming traffic. For example, a server weighted '3' receives 3 times the number of new connections as a server weighted '1'. For more information on load balancing policies, see L(How Load Balancing Policies Work,https://docs.cloud.oracle.com/Content/Balance/Reference/lbpolicies.htm). - "Example: `3`" returned: on success type: int sample: 56 drain: description: - "Whether the load balancer should drain this server. Servers marked \\"drain\\" receive no new incoming traffic." - "Example: `false`" returned: on success type: bool sample: true backup: description: - "Whether the load balancer should treat this server as a backup unit. If `true`, the load balancer forwards no ingress traffic to this backend server unless all other backend servers not marked as \\"backup\\" fail the health check policy." - "**Note:** You cannot add a backend server marked as `backup` to a backend set that uses the IP Hash policy." - "Example: `false`" returned: on success type: bool sample: true offline: description: - Whether the load balancer should treat this server as offline. Offline servers receive no incoming traffic. - "Example: `false`" returned: on success type: bool sample: true health_checker: description: - "" returned: on success type: complex contains: protocol: description: - The protocol the health check must use; either HTTP or TCP. - "Example: `HTTP`" returned: on success type: str sample: protocol_example url_path: description: - The path against which to run the health check. - "Example: `/healthcheck`" returned: on success type: str sample: url_path_example port: description: - The backend server port against which to run the health check. If the port is not specified, the load balancer uses the port information from the `Backend` object. - "Example: `8080`" returned: on success type: int sample: 56 return_code: description: - "The status code a healthy backend server should return. If you configure the health check policy to use the HTTP protocol, you can use common HTTP status codes such as \\"200\\"." - "Example: `200`" returned: on success type: int sample: 56 retries: description: - "The number of retries to attempt before a backend server is considered \\"unhealthy\\". This number also applies when recovering a server to the \\"healthy\\" state. Defaults to 3." - "Example: `3`" returned: on success type: int sample: 56 timeout_in_millis: description: - The maximum time, in milliseconds, to wait for a reply to a health check. A health check is successful only if a reply returns within this timeout period. Defaults to 3000 (3 seconds). - "Example: `3000`" returned: on success type: int sample: 56 interval_in_millis: description: - The interval between health checks, in milliseconds. The default is 10000 (10 seconds). - "Example: `10000`" returned: on success type: int sample: 56 response_body_regex: description: - A regular expression for parsing the response body from the backend server. - "Example: `^((?!false).|\\\\s)*$`" returned: on success type: str sample: response_body_regex_example ssl_configuration: description: - "" returned: on success type: complex contains: verify_depth: description: - The maximum depth for peer certificate chain verification. - "Example: `3`" returned: on success type: int sample: 56 verify_peer_certificate: description: - Whether the load balancer listener should verify peer certificates. - "Example: `true`" returned: on success type: bool sample: true trusted_certificate_authority_ids: description: - Ids for OCI certificates service CA or CA bundles for the load balancer to trust. - "Example: `[ocid1.cabundle.oc1.us-ashburn-1.amaaaaaaav3bgsaagl4zzyqdop5i2vuwoqewdvauuw34llqa74otq2jdsfyq]`" returned: on success type: list sample: [] certificate_ids: description: - Ids for OCI certificates service certificates. Currently only a single Id may be passed. - "Example: `[ocid1.certificate.oc1.us-ashburn-1.amaaaaaaav3bgsaa5o2q7rh5nfmkkukfkogasqhk6af2opufhjlqg7m6jqzq]`" returned: on success type: list sample: [] certificate_name: description: - A friendly name for the certificate bundle. It must be unique and it cannot be changed. Valid certificate bundle names include only alphanumeric characters, dashes, and underscores. Certificate bundle names cannot contain spaces. Avoid entering confidential information. - "Example: `example_certificate_bundle`" returned: on success type: str sample: certificate_name_example server_order_preference: description: - When this attribute is set to ENABLED, the system gives preference to the server ciphers over the client ciphers. - "**Note:** This configuration is applicable only when the load balancer is acting as an SSL/HTTPS server. This field is ignored when the `SSLConfiguration` object is associated with a backend set." returned: on success type: str sample: ENABLED cipher_suite_name: description: - The name of the cipher suite to use for HTTPS or SSL connections. - If this field is not specified, the default is `oci-default-ssl-cipher-suite-v1`. - "**Notes:**" - "* You must ensure compatibility between the specified SSL protocols and the ciphers configured in the cipher suite. Clients cannot perform an SSL handshake if there is an incompatible configuration. * You must ensure compatibility between the ciphers configured in the cipher suite and the configured certificates. For example, RSA-based ciphers require RSA certificates and ECDSA-based ciphers require ECDSA certificates. * If the cipher configuration is not modified after load balancer creation, the `GET` operation returns `oci-default-ssl-cipher-suite-v1` as the value of this field in the SSL configuration for existing listeners that predate this feature. * If the cipher configuration was modified using Oracle operations after load balancer creation, the `GET` operation returns `oci-customized-ssl-cipher-suite` as the value of this field in the SSL configuration for existing listeners that predate this feature. * The `GET` operation returns `oci-wider-compatible-ssl-cipher-suite-v1` as the value of this field in the SSL configuration for existing backend sets that predate this feature. * If the `GET` operation on a listener returns `oci-customized-ssl-cipher-suite` as the value of this field, you must specify an appropriate predefined or custom cipher suite name when updating the resource. * The `oci-customized-ssl-cipher-suite` Oracle reserved cipher suite name is not accepted as valid input for this field." - "example: `example_cipher_suite`" returned: on success type: str sample: cipher_suite_name_example protocols: description: - A list of SSL protocols the load balancer must support for HTTPS or SSL connections. - The load balancer uses SSL protocols to establish a secure connection between a client and a server. A secure connection ensures that all data passed between the client and the server is private. - "The Load Balancing service supports the following protocols:" - "* TLSv1 * TLSv1.1 * TLSv1.2" - If this field is not specified, TLSv1.2 is the default. - "**Warning:** All SSL listeners created on a given port must use the same set of SSL protocols." - "**Notes:**" - "* The handshake to establish an SSL connection fails if the client supports none of the specified protocols. * You must ensure compatibility between the specified SSL protocols and the ciphers configured in the cipher suite. * For all existing load balancer listeners and backend sets that predate this feature, the `GET` operation displays a list of SSL protocols currently used by those resources." - "example: `[\\"TLSv1.1\\", \\"TLSv1.2\\"]`" returned: on success type: list sample: [] session_persistence_configuration: description: - "" returned: on success type: complex contains: cookie_name: description: - "The name of the cookie used to detect a session initiated by the backend server. Use '*' to specify that any cookie set by the backend causes the session to persist." - "Example: `example_cookie`" returned: on success type: str sample: cookie_name_example disable_fallback: description: - Whether the load balancer is prevented from directing traffic from a persistent session client to a different backend server if the original server is unavailable. Defaults to false. - "Example: `false`" returned: on success type: bool sample: true lb_cookie_session_persistence_configuration: description: - "" returned: on success type: complex contains: cookie_name: description: - "The name of the cookie inserted by the load balancer. If this field is not configured, the cookie name defaults to \\"X-Oracle-BMC-LBS-Route\\"." - "Example: `example_cookie`" - "**Notes:**" - "* Ensure that the cookie name used at the backend application servers is different from the cookie name used at the load balancer. To minimize the chance of name collision, Oracle recommends that you use a prefix such as \\"X-Oracle-OCI-\\" for this field." - "* If a backend server and the load balancer both insert cookies with the same name, the client or browser behavior can vary depending on the domain and path values associated with the cookie. If the name, domain, and path values of the `Set-cookie` generated by a backend server and the `Set-cookie` generated by the load balancer are all the same, the client or browser treats them as one cookie and returns only one of the cookie values in subsequent requests. If both `Set-cookie` names are the same, but the domain and path names are different, the client or browser treats them as two different cookies." returned: on success type: str sample: cookie_name_example disable_fallback: description: - Whether the load balancer is prevented from directing traffic from a persistent session client to a different backend server if the original server is unavailable. Defaults to false. - "Example: `false`" returned: on success type: bool sample: true domain: description: - The domain in which the cookie is valid. The `Set-cookie` header inserted by the load balancer contains a domain attribute with the specified value. - This attribute has no default value. If you do not specify a value, the load balancer does not insert the domain attribute into the `Set-cookie` header. - "**Notes:**" - "* L(RFC 6265 - HTTP State Management Mechanism,https://www.ietf.org/rfc/rfc6265.txt) describes client and browser behavior when the domain attribute is present or not present in the `Set-cookie` header." - If the value of the `Domain` attribute is `example.com` in the `Set-cookie` header, the client includes the same cookie in the `Cookie` header when making HTTP requests to `example.com`, `www.example.com`, and `www.abc.example.com`. If the `Domain` attribute is not present, the client returns the cookie only for the domain to which the original request was made. - "* Ensure that this attribute specifies the correct domain value. If the `Domain` attribute in the `Set-cookie` header does not include the domain to which the original request was made, the client or browser might reject the cookie. As specified in RFC 6265, the client accepts a cookie with the `Domain` attribute value `example.com` or `www.example.com` sent from `www.example.com`. It does not accept a cookie with the `Domain` attribute `abc.example.com` or `www.abc.example.com` sent from `www.example.com`." - "Example: `example.com`" returned: on success type: str sample: domain_example path: description: - The path in which the cookie is valid. The `Set-cookie header` inserted by the load balancer contains a `Path` attribute with the specified value. - Clients include the cookie in an HTTP request only if the path portion of the request-uri matches, or is a subdirectory of, the cookie's `Path` attribute. - The default value is `/`. - "Example: `/example`" returned: on success type: str sample: path_example max_age_in_seconds: description: - The amount of time the cookie remains valid. The `Set-cookie` header inserted by the load balancer contains a `Max-Age` attribute with the specified value. - The specified value must be at least one second. There is no default value for this attribute. If you do not specify a value, the load balancer does not include the `Max-Age` attribute in the `Set-cookie` header. In most cases, the client or browser retains the cookie until the current session ends, as defined by the client. - "Example: `3600`" returned: on success type: int sample: 56 is_secure: description: - Whether the `Set-cookie` header should contain the `Secure` attribute. If `true`, the `Set-cookie` header inserted by the load balancer contains the `Secure` attribute, which directs the client or browser to send the cookie only using a secure protocol. - "**Note:** If you set this field to `true`, you cannot associate the corresponding backend set with an HTTP listener." - "Example: `true`" returned: on success type: bool sample: true is_http_only: description: - Whether the `Set-cookie` header should contain the `HttpOnly` attribute. If `true`, the `Set-cookie` header inserted by the load balancer contains the `HttpOnly` attribute, which limits the scope of the cookie to HTTP requests. This attribute directs the client or browser to omit the cookie when providing access to cookies through non-HTTP APIs. For example, it restricts the cookie from JavaScript channels. - "Example: `true`" returned: on success type: bool sample: true sample: { "name": "name_example", "policy": "policy_example", "backends": [{ "name": "name_example", "ip_address": "ip_address_example", "port": 56, "weight": 56, "drain": true, "backup": true, "offline": true }], "health_checker": { "protocol": "protocol_example", "url_path": "url_path_example", "port": 56, "return_code": 56, "retries": 56, "timeout_in_millis": 56, "interval_in_millis": 56, "response_body_regex": "response_body_regex_example" }, "ssl_configuration": { "verify_depth": 56, "verify_peer_certificate": true, "trusted_certificate_authority_ids": [], "certificate_ids": [], "certificate_name": "certificate_name_example", "server_order_preference": "ENABLED", "cipher_suite_name": "cipher_suite_name_example", "protocols": [] }, "session_persistence_configuration": { "cookie_name": "cookie_name_example", "disable_fallback": true }, "lb_cookie_session_persistence_configuration": { "cookie_name": "cookie_name_example", "disable_fallback": true, "domain": "domain_example", "path": "path_example", "max_age_in_seconds": 56, "is_secure": true, "is_http_only": true } } """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import ( oci_common_utils, oci_wait_utils, ) from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceHelperBase, get_custom_class, ) try: from oci.load_balancer import LoadBalancerClient from oci.load_balancer.models import CreateBackendSetDetails from oci.load_balancer.models import UpdateBackendSetDetails HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class BackendSetHelperGen(OCIResourceHelperBase): def get_possible_entity_types(self): return super(BackendSetHelperGen, self).get_possible_entity_types() + [ "backendset", "backendsets", "loadBalancerbackendset", "loadBalancerbackendsets", "backendsetresource", "backendsetsresource", "loadbalancer", ] def get_module_resource_id_param(self): return "name" def get_module_resource_id(self): return self.module.params.get("name") def get_get_fn(self): return self.client.get_backend_set def get_resource(self): return oci_common_utils.call_with_backoff( self.client.get_backend_set, load_balancer_id=self.module.params.get("load_balancer_id"), backend_set_name=self.module.params.get("name"), ) def get_required_kwargs_for_list(self): required_list_method_params = [ "load_balancer_id", ] return dict( (param, self.module.params[param]) for param in required_list_method_params ) def get_optional_kwargs_for_list(self): return dict() def list_resources(self): required_kwargs = self.get_required_kwargs_for_list() optional_kwargs = self.get_optional_kwargs_for_list() kwargs = oci_common_utils.merge_dicts(required_kwargs, optional_kwargs) return oci_common_utils.list_all_resources( self.client.list_backend_sets, **kwargs ) def get_create_model_class(self): return CreateBackendSetDetails def is_update(self): if not self.module.params.get("state") == "present": return False return self.does_resource_exist() def is_create(self): if not self.module.params.get("state") == "present": return False return not self.does_resource_exist() def create_resource(self): create_details = self.get_create_model() return oci_wait_utils.call_and_wait( call_fn=self.client.create_backend_set, call_fn_args=(), call_fn_kwargs=dict( create_backend_set_details=create_details, load_balancer_id=self.module.params.get("load_balancer_id"), ), waiter_type=oci_wait_utils.WORK_REQUEST_WAITER_KEY, operation=oci_common_utils.CREATE_OPERATION_KEY, waiter_client=self.get_waiter_client(), resource_helper=self, wait_for_states=oci_common_utils.get_work_request_completed_states(), ) def get_update_model_class(self): return UpdateBackendSetDetails def update_resource(self): update_details = self.get_update_model() return oci_wait_utils.call_and_wait( call_fn=self.client.update_backend_set, call_fn_args=(), call_fn_kwargs=dict( update_backend_set_details=update_details, load_balancer_id=self.module.params.get("load_balancer_id"), backend_set_name=self.module.params.get("name"), ), waiter_type=oci_wait_utils.WORK_REQUEST_WAITER_KEY, operation=oci_common_utils.UPDATE_OPERATION_KEY, waiter_client=self.get_waiter_client(), resource_helper=self, wait_for_states=oci_common_utils.get_work_request_completed_states(), ) def delete_resource(self): return oci_wait_utils.call_and_wait( call_fn=self.client.delete_backend_set, call_fn_args=(), call_fn_kwargs=dict( load_balancer_id=self.module.params.get("load_balancer_id"), backend_set_name=self.module.params.get("name"), ), waiter_type=oci_wait_utils.WORK_REQUEST_WAITER_KEY, operation=oci_common_utils.DELETE_OPERATION_KEY, waiter_client=self.get_waiter_client(), resource_helper=self, wait_for_states=oci_common_utils.get_work_request_completed_states(), ) BackendSetHelperCustom = get_custom_class("BackendSetHelperCustom") class ResourceHelper(BackendSetHelperCustom, BackendSetHelperGen): pass def main(): module_args = oci_common_utils.get_common_arg_spec( supports_create=True, supports_wait=True ) module_args.update( dict( name=dict(type="str", required=True), policy=dict(type="str"), backends=dict( type="list", elements="dict", options=dict( ip_address=dict(type="str", required=True), port=dict(type="int", required=True), weight=dict(type="int"), backup=dict(type="bool"), drain=dict(type="bool"), offline=dict(type="bool"), ), ), health_checker=dict( type="dict", options=dict( protocol=dict(type="str", required=True), url_path=dict(type="str"), port=dict(type="int"), return_code=dict(type="int"), retries=dict(type="int"), timeout_in_millis=dict(type="int"), interval_in_millis=dict(type="int"), response_body_regex=dict(type="str"), ), ), ssl_configuration=dict( type="dict", options=dict( verify_depth=dict(type="int"), verify_peer_certificate=dict(type="bool"), trusted_certificate_authority_ids=dict(type="list", elements="str"), certificate_ids=dict(type="list", elements="str"), certificate_name=dict(type="str"), protocols=dict(type="list", elements="str"), cipher_suite_name=dict(type="str"), server_order_preference=dict( type="str", choices=["ENABLED", "DISABLED"] ), ), ), session_persistence_configuration=dict( type="dict", options=dict( cookie_name=dict(type="str", required=True), disable_fallback=dict(type="bool"), ), ), lb_cookie_session_persistence_configuration=dict( type="dict", options=dict( cookie_name=dict(type="str"), disable_fallback=dict(type="bool"), domain=dict(type="str"), path=dict(type="str"), max_age_in_seconds=dict(type="int"), is_secure=dict(type="bool"), is_http_only=dict(type="bool"), ), ), load_balancer_id=dict(aliases=["id"], type="str", required=True), state=dict(type="str", default="present", choices=["present", "absent"]), ) ) module = AnsibleModule(argument_spec=module_args, supports_check_mode=True) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_helper = ResourceHelper( module=module, resource_type="backend_set", service_client_class=LoadBalancerClient, namespace="load_balancer", ) result = dict(changed=False) if resource_helper.is_delete(): result = resource_helper.delete() elif resource_helper.is_update(): result = resource_helper.update() elif resource_helper.is_create(): result = resource_helper.create() module.exit_json(**result) if __name__ == "__main__": main()

true

f723dcbf78bff84e6aa9186e1ba18550f5791807

4,002

py

Python

CodingEasy/settings.py

Atif0604/CodingEasy

75d7e88dd7ab514ee4fdaa4b1b80175d78c5a91c

[ "MIT" ]

40

2021-12-22T15:16:03.000Z

2022-03-26T08:24:04.000Z

CodingEasy/settings.py

Atif0604/CodingEasy

75d7e88dd7ab514ee4fdaa4b1b80175d78c5a91c

[ "MIT" ]

222

2021-12-14T05:37:10.000Z

2022-03-31T16:38:59.000Z

CodingEasy/settings.py

Atif0604/CodingEasy

75d7e88dd7ab514ee4fdaa4b1b80175d78c5a91c

[ "MIT" ]

89

2021-12-14T05:00:23.000Z

2022-03-29T10:55:25.000Z

""" Django settings for CodingEasy project. Generated by 'django-admin startproject' using Django 4.0.1. For more information on this file, see https://docs.djangoproject.com/en/4.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/4.0/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/4.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'django-insecure-x_1=q*b(j*34f(dg0^2sa)-f$k^!0d(qa=@geze9s@8)-(!hy5' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ["*"] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'home.apps.HomeConfig', 'blog.apps.BlogConfig', 'crispy_forms', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'CodingEasy.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [BASE_DIR / 'templates'], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'CodingEasy.wsgi.application' # Database # https://docs.djangoproject.com/en/4.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/4.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/4.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/4.0/howto/static-files/ STATIC_URL = 'static/' STATICFILES_DIRS = [ BASE_DIR / "static", '/var/www/static/', ] # Make a directory to save user profile images MEDIA_ROOT = BASE_DIR / 'media' MEDIA_URL = '/media/' # Default primary key field type # https://docs.djangoproject.com/en/4.0/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' # Crispy Forms Styling Engine definition CRISPY_TEMPLATE_PACK = 'bootstrap4' # Register the Login Redirect URL LOGIN_REDIRECT_URL = 'home-index' # Restricted pages automatically redirect at login form LOGIN_URL = 'login' EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' EMAIL_HOST = 'smtp.gmail.com' EMAIL_PORT = 587 EMAIL_USE_TLS = True EMAIL_HOST_USER = '##########' # Put your gmail address here EMAIL_HOST_PASSWORD = '##########' # Put your gmail address password here

26.156863

91

0.703648

from pathlib import Path BASE_DIR = Path(__file__).resolve().parent.parent SECRET_KEY = 'django-insecure-x_1=q*b(j*34f(dg0^2sa)-f$k^!0d(qa=@geze9s@8)-(!hy5' DEBUG = True ALLOWED_HOSTS = ["*"] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'home.apps.HomeConfig', 'blog.apps.BlogConfig', 'crispy_forms', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'CodingEasy.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [BASE_DIR / 'templates'], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'CodingEasy.wsgi.application' # Database # https://docs.djangoproject.com/en/4.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/4.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/4.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/4.0/howto/static-files/ STATIC_URL = 'static/' STATICFILES_DIRS = [ BASE_DIR / "static", '/var/www/static/', ] # Make a directory to save user profile images MEDIA_ROOT = BASE_DIR / 'media' MEDIA_URL = '/media/' # Default primary key field type # https://docs.djangoproject.com/en/4.0/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' # Crispy Forms Styling Engine definition CRISPY_TEMPLATE_PACK = 'bootstrap4' # Register the Login Redirect URL LOGIN_REDIRECT_URL = 'home-index' # Restricted pages automatically redirect at login form LOGIN_URL = 'login' EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' EMAIL_HOST = 'smtp.gmail.com' EMAIL_PORT = 587 EMAIL_USE_TLS = True EMAIL_HOST_USER = '

true

f723def610c1a3cf3e0216947eb0d00eb4392e68

9,125

py

Python

tf_object_detection/to_tfrecords.py

AndresGarciaEscalante/bstld

cc37fb3388b7731be9e76fd1c4e2be13b6716afe

[ "MIT" ]

null

tf_object_detection/to_tfrecords.py

AndresGarciaEscalante/bstld

cc37fb3388b7731be9e76fd1c4e2be13b6716afe

[ "MIT" ]

null

tf_object_detection/to_tfrecords.py

AndresGarciaEscalante/bstld

cc37fb3388b7731be9e76fd1c4e2be13b6716afe

[ "MIT" ]

null

#!/usr/bin/env python3 """ Creates full-image tfrecords to use the Bosch Small Traffic Lights Dataset with the Tensorflow Object Detection API. The training set is split into training and validation. Tfrecords are created for a training, validation, and test set. Labels are grouped by their respective colors to simplify training and because the test-set does not contain any arrows. Depending on the training method, you may want to look into creating random crops from the images which can increase training performance due to translated inputs. The tfrecords come without any image augmentation. The created tfrecords will be about 18GB. Usage: In the folder with the extracted traffic lights dataset, run python /path/to/this/file/to_tfrecords.py and it will create the tfrecords there. The path of the annotation files, tfrecords, and dataset folder can be specified. Note that this is a tutorial file. There are only few checks and no logging. """ import argparse from collections import OrderedDict, defaultdict import hashlib import os from random import shuffle import cv2 import tensorflow as tf import tqdm # https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md from object_detection.utils import dataset_util import sys # getting the name of the directory # where the this file is present. current = os.path.dirname(os.path.realpath(__file__)) # Getting the parent directory name # where the current directory is present. parent = os.path.dirname(current) # adding the parent directory to # the sys.path. sys.path.append(parent) from read_label_file import get_all_labels from tf_object_detection import constants def label_id(label_string): """ For detections without classification """ # For object proposals only, you could return 1 return constants.TF_ID_MAP[constants.SIMPLIFIED_CLASSES[label_string]] def modified_label_string(label_string): """ To simplify the problem, training classes are grouped by color """ return constants.SIMPLIFIED_CLASSES[label_string].encode('utf8') def list_of_dicts_to_dict_of_lists(list_of_dicts): """ [{'a': 0, 'b':3}, {'a': 3, 'b':5}] --> {'a': [0, 3], 'b': [3, 5]}""" assert isinstance(list_of_dicts, list) dict_lists = defaultdict(list) for some_dict in list_of_dicts: for key, value in some_dict.items(): dict_lists[key].append(value) return dict_lists def clip(some_value): """ Clip values outside [0, 1]. float -> float """ # Just in case some very eager annotators detected lights outside the image. It happens return max(0, min(some_value, 1)) def create_object_detection_tfrecords(labels, tfrecords_path, dataset_folder, set_name=''): """ Creates a tfrecord dataset specific to tensorflow/models/research/objection_detection params: labels: list of annotations as defined in annotation yamls tfrecords_path: output path to create tfrecords dataset_folder: path to bstld folder, must include rgb directory """ #shuffle(labels) writer = tf.io.TFRecordWriter(tfrecords_path) for label in tqdm.tqdm(labels, desc='Creating {}-set'.format(set_name)): image_path = os.path.join(dataset_folder, label['path']) image = cv2.imread(image_path) if image is None: print('Did you extract the training, validation, and additional images?') raise IOError('Missing: {}'.format(image_path)) height, width, _ = image.shape boxes = list_of_dicts_to_dict_of_lists(label['boxes']) classes = boxes['label'] xmin = list(map(lambda x: clip(x / float(width)), boxes['x_min'])) ymin = list(map(lambda y: clip(y / float(height)), boxes['y_min'])) xmax = list(map(lambda x: clip(x / float(width)), boxes['x_max'])) ymax = list(map(lambda y: clip(y / float(height)), boxes['y_max'])) assert len(xmin) == len(xmax) == len(ymin) assert len(ymax) == len(classes) == len(label['boxes']) if not classes: continue # We don't need empty images, there are enough negatives _, image = cv2.imencode('.png', image) # Assuming that works image = image.tostring() sha256 = hashlib.sha256(image).hexdigest() image_format = 'png' complete_example = tf.train.Example(features=tf.train.Features(feature={ 'image/height': dataset_util.int64_feature(height), 'image/width': dataset_util.int64_feature(width), 'image/filename': dataset_util.bytes_feature(image_path.encode('utf8')), 'image/encoded': tf.train.Feature(bytes_list=tf.train.BytesList(value=[image])), 'image/format': dataset_util.bytes_feature(image_format.encode('utf8')), 'image/source_id': dataset_util.bytes_feature(image_path.encode('utf8')), 'image/key/sha256': dataset_util.bytes_feature(sha256.encode('utf8')), 'image/object/bbox/xmin': dataset_util.float_list_feature(xmin), 'image/object/bbox/xmax': dataset_util.float_list_feature(xmax), 'image/object/bbox/ymin': dataset_util.float_list_feature(ymin), 'image/object/bbox/ymax': dataset_util.float_list_feature(ymax), 'image/object/class/text': dataset_util.bytes_list_feature( list(map(modified_label_string, classes))), 'image/object/class/label': dataset_util.int64_list_feature( list(map(label_id, classes))), })) writer.write(complete_example.SerializeToString()) writer.close() def split_train_labels(train_labels): # one entry for each image in a folder/video to check their sizes later train_videos = [os.path.split(os.path.split(train_label['path'])[0])[1] for train_label in train_labels] # NOTE Because set order is not guaranteed (and we want to support different Python versions) video_dict = OrderedDict().fromkeys(train_videos) video_lengths = [train_videos.count(video) for video in video_dict.keys()] # The first three videos are used for the validation set. # Note that this may not be a completely clean validation set as the sequences # were captured independently but may be on the same day and are taken within # the same general area. This split is for object detection demonstation # purposes only. For clean dataset separation, the sequences would need to be # recorded on separate days and preferably in different areas. # # validation samples: 933, training samples: 4160 (+215 additional) num_valid_samples = sum(video_lengths[:3]) return train_labels[num_valid_samples:], train_labels[:num_valid_samples] def create_datasets(config): """ Splits labels and creates datasets """ train_labels = get_all_labels(config['train_yaml']) test_labels = get_all_labels(config['test_yaml']) if config['additional_yaml']: additional_labels = get_all_labels(config['additional_yaml']) # Split training labels into training and validation for "more correct" validation train_labels, valid_labels = split_train_labels(train_labels) train_labels.extend(additional_labels) # add unappealing images to training set if not os.path.isdir(config['dataset_folder']) or\ not os.path.isdir(os.path.join(config['dataset_folder'], 'rgb')): print('Dataset_folder needs to contain extracted dataset, including the rgb folder') print('{} does not fulfill those requirements'.format(config['dataset_folder'])) create_object_detection_tfrecords( train_labels, config['train_tfrecord'], config['dataset_folder'], 'train') create_object_detection_tfrecords( valid_labels, config['valid_tfrecord'], config['dataset_folder'], 'valid') create_object_detection_tfrecords( test_labels, config['test_tfrecord'], config['dataset_folder'], 'test') print('Done creating tfrecords') def parse_args(): """ Command line args to tfrecords creation config """ parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('--train_yaml', default='train.yaml', help='Path to train.yaml') parser.add_argument('--test_yaml', default='test.yaml', help='Path to test.yaml') parser.add_argument('--additional_yaml', default='additional_train.yaml', help='Path to train_additional.yaml') parser.add_argument('--dataset_folder', default='.', help='Path to dataset folder') parser.add_argument('--train_tfrecord', default='train.tfrecords', help='Path to train.tfrecord') parser.add_argument('--valid_tfrecord', default='valid.tfrecords', help='Path to valid.tfrecord') parser.add_argument('--test_tfrecord', default='test.tfrecords', help='Path to test.tfrecord') args = vars(parser.parse_args()) return args if __name__ == '__main__': config = parse_args() create_datasets(config)

43.452381

98

0.699288

import argparse from collections import OrderedDict, defaultdict import hashlib import os from random import shuffle import cv2 import tensorflow as tf import tqdm from object_detection.utils import dataset_util import sys current = os.path.dirname(os.path.realpath(__file__)) parent = os.path.dirname(current) sys.path.append(parent) from read_label_file import get_all_labels from tf_object_detection import constants def label_id(label_string): return constants.TF_ID_MAP[constants.SIMPLIFIED_CLASSES[label_string]] def modified_label_string(label_string): return constants.SIMPLIFIED_CLASSES[label_string].encode('utf8') def list_of_dicts_to_dict_of_lists(list_of_dicts): assert isinstance(list_of_dicts, list) dict_lists = defaultdict(list) for some_dict in list_of_dicts: for key, value in some_dict.items(): dict_lists[key].append(value) return dict_lists def clip(some_value): return max(0, min(some_value, 1)) def create_object_detection_tfrecords(labels, tfrecords_path, dataset_folder, set_name=''): writer = tf.io.TFRecordWriter(tfrecords_path) for label in tqdm.tqdm(labels, desc='Creating {}-set'.format(set_name)): image_path = os.path.join(dataset_folder, label['path']) image = cv2.imread(image_path) if image is None: print('Did you extract the training, validation, and additional images?') raise IOError('Missing: {}'.format(image_path)) height, width, _ = image.shape boxes = list_of_dicts_to_dict_of_lists(label['boxes']) classes = boxes['label'] xmin = list(map(lambda x: clip(x / float(width)), boxes['x_min'])) ymin = list(map(lambda y: clip(y / float(height)), boxes['y_min'])) xmax = list(map(lambda x: clip(x / float(width)), boxes['x_max'])) ymax = list(map(lambda y: clip(y / float(height)), boxes['y_max'])) assert len(xmin) == len(xmax) == len(ymin) assert len(ymax) == len(classes) == len(label['boxes']) if not classes: continue _, image = cv2.imencode('.png', image) # Assuming that works image = image.tostring() sha256 = hashlib.sha256(image).hexdigest() image_format = 'png' complete_example = tf.train.Example(features=tf.train.Features(feature={ 'image/height': dataset_util.int64_feature(height), 'image/width': dataset_util.int64_feature(width), 'image/filename': dataset_util.bytes_feature(image_path.encode('utf8')), 'image/encoded': tf.train.Feature(bytes_list=tf.train.BytesList(value=[image])), 'image/format': dataset_util.bytes_feature(image_format.encode('utf8')), 'image/source_id': dataset_util.bytes_feature(image_path.encode('utf8')), 'image/key/sha256': dataset_util.bytes_feature(sha256.encode('utf8')), 'image/object/bbox/xmin': dataset_util.float_list_feature(xmin), 'image/object/bbox/xmax': dataset_util.float_list_feature(xmax), 'image/object/bbox/ymin': dataset_util.float_list_feature(ymin), 'image/object/bbox/ymax': dataset_util.float_list_feature(ymax), 'image/object/class/text': dataset_util.bytes_list_feature( list(map(modified_label_string, classes))), 'image/object/class/label': dataset_util.int64_list_feature( list(map(label_id, classes))), })) writer.write(complete_example.SerializeToString()) writer.close() def split_train_labels(train_labels): # one entry for each image in a folder/video to check their sizes later train_videos = [os.path.split(os.path.split(train_label['path'])[0])[1] for train_label in train_labels] # NOTE Because set order is not guaranteed (and we want to support different Python versions) video_dict = OrderedDict().fromkeys(train_videos) video_lengths = [train_videos.count(video) for video in video_dict.keys()] # The first three videos are used for the validation set. # Note that this may not be a completely clean validation set as the sequences # were captured independently but may be on the same day and are taken within # the same general area. This split is for object detection demonstation # purposes only. For clean dataset separation, the sequences would need to be # recorded on separate days and preferably in different areas. # # validation samples: 933, training samples: 4160 (+215 additional) num_valid_samples = sum(video_lengths[:3]) return train_labels[num_valid_samples:], train_labels[:num_valid_samples] def create_datasets(config): train_labels = get_all_labels(config['train_yaml']) test_labels = get_all_labels(config['test_yaml']) if config['additional_yaml']: additional_labels = get_all_labels(config['additional_yaml']) # Split training labels into training and validation for "more correct" validation train_labels, valid_labels = split_train_labels(train_labels) train_labels.extend(additional_labels) # add unappealing images to training set if not os.path.isdir(config['dataset_folder']) or\ not os.path.isdir(os.path.join(config['dataset_folder'], 'rgb')): print('Dataset_folder needs to contain extracted dataset, including the rgb folder') print('{} does not fulfill those requirements'.format(config['dataset_folder'])) create_object_detection_tfrecords( train_labels, config['train_tfrecord'], config['dataset_folder'], 'train') create_object_detection_tfrecords( valid_labels, config['valid_tfrecord'], config['dataset_folder'], 'valid') create_object_detection_tfrecords( test_labels, config['test_tfrecord'], config['dataset_folder'], 'test') print('Done creating tfrecords') def parse_args(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('--train_yaml', default='train.yaml', help='Path to train.yaml') parser.add_argument('--test_yaml', default='test.yaml', help='Path to test.yaml') parser.add_argument('--additional_yaml', default='additional_train.yaml', help='Path to train_additional.yaml') parser.add_argument('--dataset_folder', default='.', help='Path to dataset folder') parser.add_argument('--train_tfrecord', default='train.tfrecords', help='Path to train.tfrecord') parser.add_argument('--valid_tfrecord', default='valid.tfrecords', help='Path to valid.tfrecord') parser.add_argument('--test_tfrecord', default='test.tfrecords', help='Path to test.tfrecord') args = vars(parser.parse_args()) return args if __name__ == '__main__': config = parse_args() create_datasets(config)

true

f723e0a0bf9e03267963a7c69b5889fe7fcdda12

2,069

py

Python

scripts/generate_delta_sysroot_unittest.py

hustwei/chromite

10eb79abeb64e859362546214b7e039096ac9830

[ "BSD-3-Clause" ]

null

scripts/generate_delta_sysroot_unittest.py

hustwei/chromite

10eb79abeb64e859362546214b7e039096ac9830

[ "BSD-3-Clause" ]

null

scripts/generate_delta_sysroot_unittest.py

hustwei/chromite

10eb79abeb64e859362546214b7e039096ac9830

[ "BSD-3-Clause" ]

null

# Copyright (c) 2013 The Chromium OS Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """Unittests for generate_delta_sysroot.""" from __future__ import print_function import os from chromite.lib import cros_build_lib from chromite.lib import cros_test_lib from chromite.scripts import generate_delta_sysroot as gds # pylint: disable=W0212 def _Parse(argv): return gds._ParseCommandLine(argv) class InterfaceTest(cros_test_lib.OutputTestCase, cros_test_lib.TempDirTestCase): """Test the commandline interface of the script""" def testNoBoard(self): """Test no board specified.""" argv = ['--out-dir', '/path/to/nowhere'] self.assertParseError(argv) def testNoOutDir(self): """Test no out dir specified.""" argv = ['--board', 'link'] self.assertParseError(argv) def testCorrectArgv(self): """Test successful parsing""" argv = ['--board', 'link', '--out-dir', self.tempdir] options = _Parse(argv) gds.FinishParsing(options) def testTestsSet(self): """Test successful parsing""" argv = ['--board', 'link', '--out-dir', self.tempdir] options = _Parse(argv) self.assertTrue(options.build_tests) def testNoTestsSet(self): """Test successful parsing""" argv = ['--board', 'link', '--out-dir', self.tempdir, '--skip-tests'] options = _Parse(argv) self.assertFalse(options.build_tests) def assertParseError(self, argv): """Helper to assert parsing error, given argv.""" with self.OutputCapturer(): self.assertRaises2(SystemExit, _Parse, argv) class TestCreateBatchFile(cros_test_lib.TempDirTestCase): """Test the batch file creation.""" def testSourceDirDoesNotExist(self): """Test error is raised if there is no source directory.""" no_source = os.path.join(self.tempdir, 'foo/bar/cow') self.assertRaises2( cros_build_lib.RunCommandError, gds.CreateBatchFile, no_source, self.tempdir, os.path.join(self.tempdir, 'batch'))

29.985507

73

0.697438

from __future__ import print_function import os from chromite.lib import cros_build_lib from chromite.lib import cros_test_lib from chromite.scripts import generate_delta_sysroot as gds def _Parse(argv): return gds._ParseCommandLine(argv) class InterfaceTest(cros_test_lib.OutputTestCase, cros_test_lib.TempDirTestCase): def testNoBoard(self): argv = ['--out-dir', '/path/to/nowhere'] self.assertParseError(argv) def testNoOutDir(self): argv = ['--board', 'link'] self.assertParseError(argv) def testCorrectArgv(self): argv = ['--board', 'link', '--out-dir', self.tempdir] options = _Parse(argv) gds.FinishParsing(options) def testTestsSet(self): argv = ['--board', 'link', '--out-dir', self.tempdir] options = _Parse(argv) self.assertTrue(options.build_tests) def testNoTestsSet(self): argv = ['--board', 'link', '--out-dir', self.tempdir, '--skip-tests'] options = _Parse(argv) self.assertFalse(options.build_tests) def assertParseError(self, argv): with self.OutputCapturer(): self.assertRaises2(SystemExit, _Parse, argv) class TestCreateBatchFile(cros_test_lib.TempDirTestCase): def testSourceDirDoesNotExist(self): no_source = os.path.join(self.tempdir, 'foo/bar/cow') self.assertRaises2( cros_build_lib.RunCommandError, gds.CreateBatchFile, no_source, self.tempdir, os.path.join(self.tempdir, 'batch'))

true

f723e168770bfc02d7b2018b83bc4abe150a4e30

678

py

Python

ex9_1_applications_agumentation.py

soyoung9306/-3-keras

e65f40171aadef3fe0b59c649b55b3f0bd09ca41

[ "MIT" ]

200

2017-10-23T05:05:34.000Z

2022-01-25T00:58:45.000Z

ex9_1_applications_agumentation.py

MyeongHaHwang/keraspp

4090fcc86072cda816d1d6056b5113ace49534ae

[ "MIT" ]

8

2018-02-07T08:33:49.000Z

2020-09-11T20:59:30.000Z

ex9_1_applications_agumentation.py

MyeongHaHwang/keraspp

4090fcc86072cda816d1d6056b5113ace49534ae

[ "MIT" ]

135

2017-12-15T05:41:47.000Z

2021-12-15T12:21:09.000Z

""" CH 9.1 Applications/Image Augmentation """ from sklearn import model_selection from keras import datasets import keras assert keras.backend.image_data_format() == 'channels_last' from keraspp import aigen class Machine(aigen.Machine_Generator): def __init__(self): (x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data() _, X, _, y = model_selection.train_test_split(x_train, y_train, test_size=0.02) X = X.astype(float) gen_param_dict = {'rotation_range': 10} super().__init__(X, y, nb_classes=10, gen_param_dict=gen_param_dict) def main(): m = Machine() m.run() if __name__ == '__main__': main()

23.37931

87

0.690265

from sklearn import model_selection from keras import datasets import keras assert keras.backend.image_data_format() == 'channels_last' from keraspp import aigen class Machine(aigen.Machine_Generator): def __init__(self): (x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data() _, X, _, y = model_selection.train_test_split(x_train, y_train, test_size=0.02) X = X.astype(float) gen_param_dict = {'rotation_range': 10} super().__init__(X, y, nb_classes=10, gen_param_dict=gen_param_dict) def main(): m = Machine() m.run() if __name__ == '__main__': main()

true

f723e1dd1990acf6f94ec2386a12f7b48ddc2589

274

py

Python

core/index_db/index_object.py

AliRezaBeigy/Gitools

b7defd332bb144cb45962a351b1f56e941c8ca4b

[ "MIT" ]

6

2021-01-06T05:18:06.000Z

2022-03-17T06:44:29.000Z

core/index_db/index_object.py

AliRezaBeigy/Gitools

b7defd332bb144cb45962a351b1f56e941c8ca4b

[ "MIT" ]

null

core/index_db/index_object.py

AliRezaBeigy/Gitools

b7defd332bb144cb45962a351b1f56e941c8ca4b

[ "MIT" ]

1

2021-04-15T20:51:26.000Z

class IndexObject: hash: str crc32: int pack_end_offset: int pack_start_offset: int def __init__(self, hash: str, crc32: int, pack_start_offset: int): self.hash = hash self.crc32 = crc32 self.pack_start_offset = pack_start_offset

27.4

70

0.667883

class IndexObject: hash: str crc32: int pack_end_offset: int pack_start_offset: int def __init__(self, hash: str, crc32: int, pack_start_offset: int): self.hash = hash self.crc32 = crc32 self.pack_start_offset = pack_start_offset

true

f723e22ccbf007fd40e2124e7515473ede314c02

1,440

py

Python

setup.py

dharif23/xtermcolors

fb35b9b7a04fbf7a0ea236bb94275240c6322b1a

[ "MIT" ]

null

setup.py

dharif23/xtermcolors

fb35b9b7a04fbf7a0ea236bb94275240c6322b1a

[ "MIT" ]

null

setup.py

dharif23/xtermcolors

fb35b9b7a04fbf7a0ea236bb94275240c6322b1a

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- __author__ = 'Dalwar Hossain' __email__ = 'dalwar.hossain@protonmail.com' from setuptools import setup def readme(): with open('README.md') as f: return f.read() setup(name='pyrainbowterm', version='1.0', description='pyrainbowterm - Smart custom print function with color and log information support', long_description=readme(), classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Topic :: Software Development :: Libraries :: Python Modules'], keywords='terminal colors xterm python colored output', url='https://github.com/dharif23/pyrainbowterm', author='Dalwar Hossain', author_email='dalwar.hossain@protonmail.com', license='MIT', packages=['pyrainbowterm'], include_package_data=True, zip_safe=False, )

32.727273

103

0.603472

__author__ = 'Dalwar Hossain' __email__ = 'dalwar.hossain@protonmail.com' from setuptools import setup def readme(): with open('README.md') as f: return f.read() setup(name='pyrainbowterm', version='1.0', description='pyrainbowterm - Smart custom print function with color and log information support', long_description=readme(), classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Topic :: Software Development :: Libraries :: Python Modules'], keywords='terminal colors xterm python colored output', url='https://github.com/dharif23/pyrainbowterm', author='Dalwar Hossain', author_email='dalwar.hossain@protonmail.com', license='MIT', packages=['pyrainbowterm'], include_package_data=True, zip_safe=False, )

true

f723e3633555478d9d24ed98d7498972bfe2deda

13,424

py

Python

pyabsa/core/tc/prediction/text_classifier.py

yangheng95/PyABSA

f5b46047a58fa8054a0469486be3f1cada933814

[ "MIT" ]

199

2021-06-07T15:07:28.000Z

2022-03-31T11:53:28.000Z

pyabsa/core/tc/prediction/text_classifier.py

yangheng95/PyABSA

f5b46047a58fa8054a0469486be3f1cada933814

[ "MIT" ]

98

2021-06-06T06:01:02.000Z

2022-03-31T15:48:28.000Z

pyabsa/core/tc/prediction/text_classifier.py

yangheng95/PyABSA

f5b46047a58fa8054a0469486be3f1cada933814

[ "MIT" ]

55

2021-06-10T08:52:17.000Z

2022-03-31T11:08:58.000Z

# -*- coding: utf-8 -*- # file: text_classifier.py # author: yangheng <yangheng@m.scnu.edu.cn> # Copyright (C) 2020. All Rights Reserved. import json import os import pickle import random import numpy import torch from findfile import find_file from termcolor import colored from torch.utils.data import DataLoader from transformers import AutoTokenizer, AutoModel from pyabsa.functional.dataset import detect_infer_dataset from ..models import GloVeClassificationModelList, BERTClassificationModelList from ..classic.__glove__.dataset_utils.data_utils_for_inferring import GloVeClassificationDataset from ..classic.__bert__.dataset_utils.data_utils_for_inferring import BERTClassificationDataset from ..classic.__glove__.dataset_utils.data_utils_for_training import LABEL_PADDING, build_embedding_matrix, build_tokenizer from pyabsa.utils.pyabsa_utils import print_args, TransformerConnectionError class TextClassifier: def __init__(self, model_arg=None, label_map=None, eval_batch_size=128): ''' from_train_model: load inferring_tutorials model from trained model ''' self.initializers = { 'xavier_uniform_': torch.nn.init.xavier_uniform_, 'xavier_normal_': torch.nn.init.xavier_normal, 'orthogonal_': torch.nn.init.orthogonal_ } # load from a training if not isinstance(model_arg, str): print('Load text classifier from training') self.model = model_arg[0] self.opt = model_arg[1] self.tokenizer = model_arg[2] else: try: if 'fine-tuned' in model_arg: raise ValueError('Do not support to directly load a fine-tuned model, please load a .state_dict or .model instead!') print('Load text classifier from', model_arg) state_dict_path = find_file(model_arg, '.state_dict', exclude_key=['__MACOSX']) model_path = find_file(model_arg, '.model', exclude_key=['__MACOSX']) tokenizer_path = find_file(model_arg, '.tokenizer', exclude_key=['__MACOSX']) config_path = find_file(model_arg, '.config', exclude_key=['__MACOSX']) print('config: {}'.format(config_path)) print('state_dict: {}'.format(state_dict_path)) print('model: {}'.format(model_path)) print('tokenizer: {}'.format(tokenizer_path)) self.opt = pickle.load(open(config_path, mode='rb')) if state_dict_path or model_path: if not hasattr(GloVeClassificationModelList, self.opt.model.__name__.upper()): if 'pretrained_bert_name' in self.opt.args or 'pretrained_bert' in self.opt.args: if 'pretrained_bert_name' in self.opt.args: self.opt.pretrained_bert = self.opt.pretrained_bert_name if state_dict_path: try: self.bert = AutoModel.from_pretrained(self.opt.pretrained_bert) self.model = self.opt.model(self.bert, self.opt) except ValueError: raise TransformerConnectionError() elif model_path: if model_path: self.model = torch.load(model_path, map_location='cpu') if tokenizer_path: self.tokenizer = pickle.load(open(tokenizer_path, mode='rb')) else: raise ValueError('No .tokenizer found!') else: self.tokenizer = build_tokenizer( dataset_list=self.opt.dataset_file, max_seq_len=self.opt.max_seq_len, dat_fname='{0}_tokenizer.dat'.format(os.path.basename(self.opt.dataset_name)), opt=self.opt ) if model_path: self.model = torch.load(model_path, map_location='cpu') else: self.embedding_matrix = build_embedding_matrix( word2idx=self.tokenizer.word2idx, embed_dim=self.opt.embed_dim, dat_fname='{0}_{1}_embedding_matrix.dat'.format(str(self.opt.embed_dim), os.path.basename(self.opt.dataset_name)), opt=self.opt ) self.model = self.opt.model(self.embedding_matrix, self.opt).to(self.opt.device) self.model.load_state_dict(torch.load(state_dict_path, map_location='cpu')) print('Config used in Training:') print_args(self.opt, mode=1) except Exception as e: raise RuntimeError('Exception: {} Fail to load the model from {}! '.format(e, model_arg)) if not hasattr(GloVeClassificationModelList, self.model.__class__.__name__) \ and not hasattr(BERTClassificationModelList, self.model.__class__.__name__): raise KeyError('The checkpoint you are loading is not from classifier model.') if hasattr(BERTClassificationModelList, self.opt.model.__name__): self.dataset = BERTClassificationDataset(tokenizer=self.tokenizer, opt=self.opt) elif hasattr(GloVeClassificationModelList, self.opt.model.__name__): self.dataset = GloVeClassificationDataset(tokenizer=self.tokenizer, opt=self.opt) self.opt.inputs_cols = self.model.inputs self.infer_dataloader = None self.opt.eval_batch_size = eval_batch_size if self.opt.seed is not None: random.seed(self.opt.seed) numpy.random.seed(self.opt.seed) torch.manual_seed(self.opt.seed) torch.cuda.manual_seed(self.opt.seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False self.opt.initializer = self.opt.initializer self.label_map = None self.set_label_map(label_map) def set_label_map(self, label_map): if label_map: print(colored('Warning: label map is deprecated, please directly set labels within dataset.', 'red')) label_map[LABEL_PADDING] = '' self.label_map = label_map def to(self, device=None): self.opt.device = device self.model.to(device) def cpu(self): self.opt.device = 'cpu' self.model.to('cpu') def cuda(self, device='cuda:0'): self.opt.device = device self.model.to(device) def _log_write_args(self): n_trainable_params, n_nontrainable_params = 0, 0 for p in self.model.parameters(): n_params = torch.prod(torch.tensor(p.shape)) if p.requires_grad: n_trainable_params += n_params else: n_nontrainable_params += n_params print( 'n_trainable_params: {0}, n_nontrainable_params: {1}'.format(n_trainable_params, n_nontrainable_params)) for arg in vars(self.opt): if getattr(self.opt, arg) is not None: print('>>> {0}: {1}'.format(arg, getattr(self.opt, arg))) def batch_infer(self, target_file=None, print_result=True, save_result=False, clear_input_samples=True, ignore_error=True): if clear_input_samples: self.clear_input_samples() save_path = os.path.join(os.getcwd(), 'text_classification.result.json') target_file = detect_infer_dataset(target_file, task='text_classification') if not target_file: raise FileNotFoundError('Can not find inference datasets!') self.dataset.prepare_infer_dataset(target_file, ignore_error=ignore_error) self.infer_dataloader = DataLoader(dataset=self.dataset, batch_size=self.opt.eval_batch_size, pin_memory=True, shuffle=False) return self._infer(save_path=save_path if save_result else None, print_result=print_result) def infer(self, text: str = None, print_result=True, clear_input_samples=True): if clear_input_samples: self.clear_input_samples() if text: self.dataset.prepare_infer_sample(text) else: raise RuntimeError('Please specify your datasets path!') self.infer_dataloader = DataLoader(dataset=self.dataset, batch_size=self.opt.eval_batch_size, shuffle=False) return self._infer(print_result=print_result) def merge_results(self, results): """ merge APC results have the same input text """ final_res = [] for result in results: if final_res and "".join(final_res[-1]['text'].split()) == "".join(result['text'].split()): final_res[-1]['label'].append(result['label']) final_res[-1]['ref_label'].append(result['ref_label']) final_res[-1]['ref_check'].append(result['ref_check']) else: final_res.append( { 'text': result['text'].replace(' ', ' '), 'label': [result['label']], 'ref_label': [result['ref_label']], 'ref_check': [result['ref_check']] } ) return final_res def _infer(self, save_path=None, print_result=True): _params = filter(lambda p: p.requires_grad, self.model.parameters()) correct = {True: 'Correct', False: 'Wrong'} results = [] with torch.no_grad(): self.model.eval() n_correct = 0 n_labeled = 0 n_total = 0 for _, sample in enumerate(self.infer_dataloader): inputs = [sample[col].to(self.opt.device) for col in self.opt.inputs_cols if col != 'label'] self.model.eval() outputs = self.model(inputs) sen_logits = outputs t_probs = torch.softmax(sen_logits, dim=-1).cpu().numpy() for i, i_probs in enumerate(t_probs): if 'index_to_label' in self.opt.args and int(i_probs.argmax(axis=-1)): sent = self.opt.index_to_label[int(i_probs.argmax(axis=-1))] if sample['label'] != -999: real_sent = sample['label'][i] if isinstance(sample['label'][i], str) else self.opt.index_to_label.get(int(sample['label'][i]), 'N.A.') else: real_sent = 'N.A.' if real_sent != -999 and real_sent != '-999': n_labeled += 1 if sent == real_sent: n_correct += 1 else: # for the former versions until 1.2.0 sent = int(i_probs.argmax(axis=-1)) real_sent = int(sample['label'][i]) text_raw = sample['text_raw'][i] results.append({ 'text': text_raw, 'label': sent, 'ref_label': real_sent, 'ref_check': correct[sent == real_sent] if real_sent != '-999' else '', }) n_total += 1 if len(self.infer_dataloader) > 1: print('Total samples:{}'.format(n_total)) print('Labeled samples:{}'.format(n_labeled)) print('Prediction Accuracy:{}%'.format(100 * n_correct / n_labeled if n_labeled else 'N.A.')) try: if print_result: for result in results: text_printing = result['text'] if result['ref_label'] != -999: if result['label'] == result['ref_label']: text_info = colored(' -> {}(ref:{})'.format(result['label'], result['ref_label']), 'green') else: text_info = colored(' -> {}(ref:{})'.format(result['label'], result['ref_label']), 'red') else: text_info = ' -> {}'.format(result['label']) text_printing += text_info print(text_printing) if save_path: fout = open(save_path, 'w', encoding='utf8') json.dump(json.JSONEncoder().encode({'results': results}), fout, ensure_ascii=False) # fout.write('Total samples:{}\n'.format(n_total)) # fout.write('Labeled samples:{}\n'.format(n_labeled)) # fout.write('Prediction Accuracy:{}%\n'.format(100 * n_correct / n_labeled)) if n_labeled else 'N.A.' print('inference result saved in: {}'.format(save_path)) except Exception as e: print('Can not save result: {}, Exception: {}'.format(text_raw, e)) return results def clear_input_samples(self): self.dataset.all_data = []

45.505085

163

0.560489

import json import os import pickle import random import numpy import torch from findfile import find_file from termcolor import colored from torch.utils.data import DataLoader from transformers import AutoTokenizer, AutoModel from pyabsa.functional.dataset import detect_infer_dataset from ..models import GloVeClassificationModelList, BERTClassificationModelList from ..classic.__glove__.dataset_utils.data_utils_for_inferring import GloVeClassificationDataset from ..classic.__bert__.dataset_utils.data_utils_for_inferring import BERTClassificationDataset from ..classic.__glove__.dataset_utils.data_utils_for_training import LABEL_PADDING, build_embedding_matrix, build_tokenizer from pyabsa.utils.pyabsa_utils import print_args, TransformerConnectionError class TextClassifier: def __init__(self, model_arg=None, label_map=None, eval_batch_size=128): self.initializers = { 'xavier_uniform_': torch.nn.init.xavier_uniform_, 'xavier_normal_': torch.nn.init.xavier_normal, 'orthogonal_': torch.nn.init.orthogonal_ } if not isinstance(model_arg, str): print('Load text classifier from training') self.model = model_arg[0] self.opt = model_arg[1] self.tokenizer = model_arg[2] else: try: if 'fine-tuned' in model_arg: raise ValueError('Do not support to directly load a fine-tuned model, please load a .state_dict or .model instead!') print('Load text classifier from', model_arg) state_dict_path = find_file(model_arg, '.state_dict', exclude_key=['__MACOSX']) model_path = find_file(model_arg, '.model', exclude_key=['__MACOSX']) tokenizer_path = find_file(model_arg, '.tokenizer', exclude_key=['__MACOSX']) config_path = find_file(model_arg, '.config', exclude_key=['__MACOSX']) print('config: {}'.format(config_path)) print('state_dict: {}'.format(state_dict_path)) print('model: {}'.format(model_path)) print('tokenizer: {}'.format(tokenizer_path)) self.opt = pickle.load(open(config_path, mode='rb')) if state_dict_path or model_path: if not hasattr(GloVeClassificationModelList, self.opt.model.__name__.upper()): if 'pretrained_bert_name' in self.opt.args or 'pretrained_bert' in self.opt.args: if 'pretrained_bert_name' in self.opt.args: self.opt.pretrained_bert = self.opt.pretrained_bert_name if state_dict_path: try: self.bert = AutoModel.from_pretrained(self.opt.pretrained_bert) self.model = self.opt.model(self.bert, self.opt) except ValueError: raise TransformerConnectionError() elif model_path: if model_path: self.model = torch.load(model_path, map_location='cpu') if tokenizer_path: self.tokenizer = pickle.load(open(tokenizer_path, mode='rb')) else: raise ValueError('No .tokenizer found!') else: self.tokenizer = build_tokenizer( dataset_list=self.opt.dataset_file, max_seq_len=self.opt.max_seq_len, dat_fname='{0}_tokenizer.dat'.format(os.path.basename(self.opt.dataset_name)), opt=self.opt ) if model_path: self.model = torch.load(model_path, map_location='cpu') else: self.embedding_matrix = build_embedding_matrix( word2idx=self.tokenizer.word2idx, embed_dim=self.opt.embed_dim, dat_fname='{0}_{1}_embedding_matrix.dat'.format(str(self.opt.embed_dim), os.path.basename(self.opt.dataset_name)), opt=self.opt ) self.model = self.opt.model(self.embedding_matrix, self.opt).to(self.opt.device) self.model.load_state_dict(torch.load(state_dict_path, map_location='cpu')) print('Config used in Training:') print_args(self.opt, mode=1) except Exception as e: raise RuntimeError('Exception: {} Fail to load the model from {}! '.format(e, model_arg)) if not hasattr(GloVeClassificationModelList, self.model.__class__.__name__) \ and not hasattr(BERTClassificationModelList, self.model.__class__.__name__): raise KeyError('The checkpoint you are loading is not from classifier model.') if hasattr(BERTClassificationModelList, self.opt.model.__name__): self.dataset = BERTClassificationDataset(tokenizer=self.tokenizer, opt=self.opt) elif hasattr(GloVeClassificationModelList, self.opt.model.__name__): self.dataset = GloVeClassificationDataset(tokenizer=self.tokenizer, opt=self.opt) self.opt.inputs_cols = self.model.inputs self.infer_dataloader = None self.opt.eval_batch_size = eval_batch_size if self.opt.seed is not None: random.seed(self.opt.seed) numpy.random.seed(self.opt.seed) torch.manual_seed(self.opt.seed) torch.cuda.manual_seed(self.opt.seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False self.opt.initializer = self.opt.initializer self.label_map = None self.set_label_map(label_map) def set_label_map(self, label_map): if label_map: print(colored('Warning: label map is deprecated, please directly set labels within dataset.', 'red')) label_map[LABEL_PADDING] = '' self.label_map = label_map def to(self, device=None): self.opt.device = device self.model.to(device) def cpu(self): self.opt.device = 'cpu' self.model.to('cpu') def cuda(self, device='cuda:0'): self.opt.device = device self.model.to(device) def _log_write_args(self): n_trainable_params, n_nontrainable_params = 0, 0 for p in self.model.parameters(): n_params = torch.prod(torch.tensor(p.shape)) if p.requires_grad: n_trainable_params += n_params else: n_nontrainable_params += n_params print( 'n_trainable_params: {0}, n_nontrainable_params: {1}'.format(n_trainable_params, n_nontrainable_params)) for arg in vars(self.opt): if getattr(self.opt, arg) is not None: print('>>> {0}: {1}'.format(arg, getattr(self.opt, arg))) def batch_infer(self, target_file=None, print_result=True, save_result=False, clear_input_samples=True, ignore_error=True): if clear_input_samples: self.clear_input_samples() save_path = os.path.join(os.getcwd(), 'text_classification.result.json') target_file = detect_infer_dataset(target_file, task='text_classification') if not target_file: raise FileNotFoundError('Can not find inference datasets!') self.dataset.prepare_infer_dataset(target_file, ignore_error=ignore_error) self.infer_dataloader = DataLoader(dataset=self.dataset, batch_size=self.opt.eval_batch_size, pin_memory=True, shuffle=False) return self._infer(save_path=save_path if save_result else None, print_result=print_result) def infer(self, text: str = None, print_result=True, clear_input_samples=True): if clear_input_samples: self.clear_input_samples() if text: self.dataset.prepare_infer_sample(text) else: raise RuntimeError('Please specify your datasets path!') self.infer_dataloader = DataLoader(dataset=self.dataset, batch_size=self.opt.eval_batch_size, shuffle=False) return self._infer(print_result=print_result) def merge_results(self, results): final_res = [] for result in results: if final_res and "".join(final_res[-1]['text'].split()) == "".join(result['text'].split()): final_res[-1]['label'].append(result['label']) final_res[-1]['ref_label'].append(result['ref_label']) final_res[-1]['ref_check'].append(result['ref_check']) else: final_res.append( { 'text': result['text'].replace(' ', ' '), 'label': [result['label']], 'ref_label': [result['ref_label']], 'ref_check': [result['ref_check']] } ) return final_res def _infer(self, save_path=None, print_result=True): _params = filter(lambda p: p.requires_grad, self.model.parameters()) correct = {True: 'Correct', False: 'Wrong'} results = [] with torch.no_grad(): self.model.eval() n_correct = 0 n_labeled = 0 n_total = 0 for _, sample in enumerate(self.infer_dataloader): inputs = [sample[col].to(self.opt.device) for col in self.opt.inputs_cols if col != 'label'] self.model.eval() outputs = self.model(inputs) sen_logits = outputs t_probs = torch.softmax(sen_logits, dim=-1).cpu().numpy() for i, i_probs in enumerate(t_probs): if 'index_to_label' in self.opt.args and int(i_probs.argmax(axis=-1)): sent = self.opt.index_to_label[int(i_probs.argmax(axis=-1))] if sample['label'] != -999: real_sent = sample['label'][i] if isinstance(sample['label'][i], str) else self.opt.index_to_label.get(int(sample['label'][i]), 'N.A.') else: real_sent = 'N.A.' if real_sent != -999 and real_sent != '-999': n_labeled += 1 if sent == real_sent: n_correct += 1 else: sent = int(i_probs.argmax(axis=-1)) real_sent = int(sample['label'][i]) text_raw = sample['text_raw'][i] results.append({ 'text': text_raw, 'label': sent, 'ref_label': real_sent, 'ref_check': correct[sent == real_sent] if real_sent != '-999' else '', }) n_total += 1 if len(self.infer_dataloader) > 1: print('Total samples:{}'.format(n_total)) print('Labeled samples:{}'.format(n_labeled)) print('Prediction Accuracy:{}%'.format(100 * n_correct / n_labeled if n_labeled else 'N.A.')) try: if print_result: for result in results: text_printing = result['text'] if result['ref_label'] != -999: if result['label'] == result['ref_label']: text_info = colored(' -> {}(ref:{})'.format(result['label'], result['ref_label']), 'green') else: text_info = colored(' -> {}(ref:{})'.format(result['label'], result['ref_label']), 'red') else: text_info = ' -> {}'.format(result['label']) text_printing += text_info print(text_printing) if save_path: fout = open(save_path, 'w', encoding='utf8') json.dump(json.JSONEncoder().encode({'results': results}), fout, ensure_ascii=False) print('inference result saved in: {}'.format(save_path)) except Exception as e: print('Can not save result: {}, Exception: {}'.format(text_raw, e)) return results def clear_input_samples(self): self.dataset.all_data = []

true

f723e39e9c8312aae14d30f2933e41983a1269e0

16,210

py

Python

syfertext/language.py

AlexKer/SyferText

021eea2255d9d8e1fc49c98c7b5f98b9e516ba21

[ "Apache-2.0" ]

null

syfertext/language.py

AlexKer/SyferText

021eea2255d9d8e1fc49c98c7b5f98b9e516ba21

[ "Apache-2.0" ]

null

syfertext/language.py

AlexKer/SyferText

021eea2255d9d8e1fc49c98c7b5f98b9e516ba21

[ "Apache-2.0" ]

null

from .tokenizer import Tokenizer from .vocab import Vocab from .doc import Doc from .pointers.doc_pointer import DocPointer from .pipeline import SubPipeline from syft.generic.object import AbstractObject from syft.workers.base import BaseWorker from syft.generic.string import String from syft.generic.pointers.string_pointer import StringPointer from syft.generic.pointers.object_pointer import ObjectPointer from typing import List, Union, Tuple class BaseDefaults(object): """A class that defines all the defaults of the Language class """ @classmethod def create_vocab(cls, model_name) -> Vocab: """ Creates the Vocab object that holds the vocabulary along with vocabulary meta data Todo: I started by a very simple Vocab class that contains only a variable called 'vectors' of type DICT to hold word vectors vocab.vectors['word'] = float. To be reviewed for more complex functionality. """ # Instantiate the Vocab object vocab = Vocab(model_name) return vocab @classmethod def create_tokenizer(cls, vocab,) -> Tokenizer: """Creates a Tokenizer object that will be used to create the Doc object, which is the main container for annotated tokens. """ # Instantiate the Tokenizer object and return it tokenizer = Tokenizer(vocab,) return tokenizer class Language(AbstractObject): """Inspired by spaCy Language class. Orchestrates the interactions between different components of the pipeline to accomplish core text-processing task. It create the Doc object which is the container into which all text-processing pipeline components feed their results. """ def __init__( self, model_name, id: int = None, owner: BaseWorker = None, tags: List[str] = None, description: str = None, ): # Define the default settings self.Defaults = BaseDefaults # Create the vocabulary self.vocab = self.Defaults.create_vocab(model_name) # Create a dictionary that associates to the name of each text-processing component # of the pipeline, an object that is charged to accomplish the job. self.factories = {"tokenizer": self.Defaults.create_tokenizer(self.vocab)} # Initialize the subpipeline template # It only contains the tokenizer at initialization self.pipeline_template = [{"remote": True, "name": "tokenizer"}] # Intialize the main pipeline self._reset_pipeline() super(Language, self).__init__(id=id, owner=owner, tags=tags, description=description) @property def pipe_names(self) -> List[str]: """Returns a list of component names in the pipeline in order of execution. Returns: (list): List of all pipeline component name in order of execution. """ return [pipe_template["name"] for pipe_template in self.pipeline_template] def _parse_pipeline_template(self): """Parses the `pipeline_template` property to create the `subpipeline_templates` property. """ # Initialize a subpipeline template with the # tokenizer. The tokenizer alway has 'remote' set # to True. subpipeline_template = dict( remote=self.pipeline_template[0]["remote"], names=[self.pipeline_template[0]["name"]], ) # Initialize the subpipeline templates list as a class property self.subpipeline_templates = [subpipeline_template] # Loop through the pipeline template elements for pipe_template in self.pipeline_template[1:]: # compare `remote` properties between templates: # If the pipe template has the same `remote` value, # it is appended to the existing subpipeline template if pipe_template["remote"] == subpipeline_template["remote"]: subpipeline_template["names"].append(pipe_template["name"]) # Otherwise, create a new subpipeline template and add the # pipe template to it else: subpipeline_template = dict( remote=pipe_template["remote"], names=[pipe_template["name"]] ) self.subpipeline_templates.append(subpipeline_template) def _reset_pipeline(self): """Reset the `pipeline` class property. """ # Read the pipeline components from the template and aggregate them into # a list of subpipline templates. # This method will create the instance variable # self.subpipeline_templates self._parse_pipeline_template() # Get the number of subpipelines subpipeline_count = len(self.subpipeline_templates) # Initialize a new empty pipeline with as many # empty dicts as there are subpipelines self.pipeline = [dict() for i in range(subpipeline_count)] def add_pipe( self, component: callable, remote: bool = False, name: str = None, before: str = None, after: str = None, first: bool = False, last: bool = True, ): """Adds a pipe template to a subpipeline tempaltes. A pipe template is a dict of the form `{'remote': remote, 'name': name}`. Few main steps are carried out here: 1- The new pipe name is added at the right position in the pipeline template. Here is an example of how pipeline template list looks like self.pipeline_template = [{'remote': True, 'name': 'tokenizer'}, {'remote': True, 'name': <pipe_1_name>}, {'remote': True, 'name': <pipe_2_name>}, {'remote': False, 'name': <pipe_3_name>}, {'remote': False, 'name': <pipe_4_name>}] 2- The pipeline template is parsed into a list or subpipeline templates. Each subpipeline template is an aggregation of adjacent pipes with the same value for 'remote' Here is an example of how the subpipeline template list for the above pipeline template would look like: self.subpipeline_templates = [{'remote': True, 'names': ['tokenizer', 'pipe_1_name', 'pipe_2_name']}, {'remote': False, 'name': ['pipe_3_name', 'pipe_4_name']} ] 3- The pipeline is initialize by creating a list with as many empty dicts as there are subpipelines: self.pipeline = [dict(), dict()] Args: component (callable): This is a callable that takes a Doc object and modifies it inplace. name (str): The name of the pipeline component to be added. Defaults to None. remote (bool): If True, the pipe component will be sent to the remote worker where the Doc object resides. If False, the pipe will operate locally, either on a Doc object directly, or on a DocPointer returned by the previous component in the pipeline. Defaults to False. before (str): The name of the pipeline component before which the new component is to be added. Defaults to None. after (str): The name of the pipeline component after which the new component is to be added. Defaults to None. first (bool): if set to True, the new pipeline component will be add as the first element of the pipeline (after the tokenizer). Defaults to False. last (bool): if set to True, the new pipeline component will be add as the last element of the pipeline (after the tokenizer). Defaults to True. """ # The component argument must be callable # [TODO] An exception with a custom error message should be thrown assert hasattr(component, "__call__"), "Argument `component` is not a callable." # Make sure the `component` argument is an object that has a `factory()` method assert hasattr( component, "factory" ), "Argument `component` should be an object that has a `factory()` method" # [TODO] The following requirement should be relaxed and a name should be # automatically assigned in case `name` is None. This would be convenient # as done by spaCy assert ( isinstance(name, str) and len(name) >= 1 ), "Argument `name` should be of type `str` with at least one character." # [TODO] Add custom error message assert ( name not in self.pipe_names ), "Pipeline component name '{}' that you have chosen is already used by another pipeline component.".format( name ) # Make sure only one of 'before', 'after', 'first' or 'last' is set # [TODO] Add custom error message assert ( sum([bool(before), bool(after), bool(first), bool(last)]) < 2 ), "Only one among arguments 'before', 'after', 'first' or 'last' should be set." # Add the new pipe component to the list of factories self.factories[name] = component # Create the pipe template that will be added the pipeline # template pipe_template = dict(remote=remote, name=name) # Add the pipe template at the right position if last or not any([before, after, first]): self.pipeline_template.append(pipe_template) elif first: # The index 0 is reserved for the tokenizer self.pipeline_template.insert(index=1, element=pipe_template) elif before in self.pipe_names: self.pipeline_template.insert( index=self.pipe_names.index(before), element=pipe_template ) elif after in self.pipe_names: self.pipeline_template.insert( index=self.pipe_names.index(after) + 1, element=pipe_template ) else: # [TODO] Raise exception with custom error message assert ( False ), "component cannot be added to the pipeline, \ please double check argument values of the `add_pipe` method call." # Reset the pipeline. # The instance variable that will be affected is: # self.pipeline self._reset_pipeline() def remove_pipe(self, name: str) -> Tuple[str, callable]: """Removes the pipeline whose name is 'name' Args: name (str): The name of the pipeline component to remove. Returns: The removed pipe """ # [TODO] Add custom error message assert ( name in self.pipe_names ), "No pipeline component with the specified name '{}' was found".format(name) # Get the index of the pipeline to be removed in the # self.pipeline list pipe_index = self.pipe_names.index(name) # Delete the pipe using its index pipe = self.pipeline_template.pop(pipe_index) # Parse the pipeline template again # to create the subpipeline templates self._parse_pipeline_template() # Reset the pipeline. self._reset_pipeline() return pipe def _run_subpipeline_from_template( self, template_index: int, input=Union[str, String, StringPointer, Doc, DocPointer], ) -> Union[Doc, DocPointer]: """Runs the subpipeline at position `template_index` of self.pipeline on the appropriate worker. The worker on which the subpipeline is run is either the the same worker on which `input` lives, if the `remote` property of the subpipeline template is True. Or, it is the local worker if `remote` is False. If no subpipeline is yet created for the specified worker, one is created using the template, and added to the pipeline. Args: template_index (int): The index of the subpipeline template in `self.subpipelines_templates` input (str, String, StringPointer, Doc, DocPointer): The input on which the subpipeline operates. It can be either the text to tokenize (or a pointer to it) for the subpipeline at index 0, or it could be the Doc (or its pointer) for all subsequent subpipelines. Returns: (Doc or DocPointer): The new or updated Doc object or a pointer to a Doc object. """ # Get the location ID of the worker where the text to be tokenized, # or the Doc to be processed is located if isinstance(input, ObjectPointer): location_id = input.location.id else: location_id = self.owner.id # Create a new SubPipeline object if one doesn't already exist on the # worker where the input is located if location_id not in self.pipeline[template_index]: # Get the subpipeline template subpipeline_template = self.subpipeline_templates[template_index] # Is the pipeline a remote one? remote = subpipeline_template["remote"] # Instantiate a subpipeline and load the subpipeline template subpipeline = SubPipeline() subpipeline.load_template(template=subpipeline_template, factories=self.factories) # Add the subpipeline to the pipeline self.pipeline[template_index][location_id] = subpipeline # Send the subpipeline to the worker where the input is located if ( isinstance(input, ObjectPointer) and input.location != self.owner # Is the input remote? and remote # Is the subpipeline is sendable? ): self.pipeline[template_index][location_id] = self.pipeline[template_index][ location_id ].send(input.location) # Apply the subpipeline and get the doc or the Doc id. # If a Doc ID is obtained, this signifies the ID of the # Doc object on the remote worker. doc_or_id = self.pipeline[template_index][location_id](input) # If the doc is of type str or int, this means that a # DocPointer should be created if isinstance(doc_or_id, int) or isinstance(doc_or_id, str): doc = DocPointer(location=input.location, id_at_location=doc_or_id, owner=self.owner) # This is of type Doc then else: doc = doc_or_id # return the doc return doc def __call__(self, text: Union[str, String, StringPointer]) -> Union[Doc, DocPointer]: """The text is tokenized and pipeline components are called here, and the Doc object is returned. Args: text (str, String or StringPointer): the text to be tokenized and processed by the pipeline components. Returns: (Doc or DocPointer): The Doc object or a pointer to a Doc object. This object provides access to all token data. """ # Runs the first subpipeline. # The first subpipeline is the one that has the tokenizer doc = self._run_subpipeline_from_template(template_index=0, input=text) # Apply the the rest of subpipelines sequentially # Each subpipeline will modify the document `doc` inplace for i, subpipeline in enumerate(self.pipeline[1:], start=1): doc = self._run_subpipeline_from_template(template_index=i, input=doc) # return the Doc object return doc

39.440389

117

0.613017

from .tokenizer import Tokenizer from .vocab import Vocab from .doc import Doc from .pointers.doc_pointer import DocPointer from .pipeline import SubPipeline from syft.generic.object import AbstractObject from syft.workers.base import BaseWorker from syft.generic.string import String from syft.generic.pointers.string_pointer import StringPointer from syft.generic.pointers.object_pointer import ObjectPointer from typing import List, Union, Tuple class BaseDefaults(object): @classmethod def create_vocab(cls, model_name) -> Vocab: vocab = Vocab(model_name) return vocab @classmethod def create_tokenizer(cls, vocab,) -> Tokenizer: tokenizer = Tokenizer(vocab,) return tokenizer class Language(AbstractObject): def __init__( self, model_name, id: int = None, owner: BaseWorker = None, tags: List[str] = None, description: str = None, ): self.Defaults = BaseDefaults self.vocab = self.Defaults.create_vocab(model_name) self.factories = {"tokenizer": self.Defaults.create_tokenizer(self.vocab)} self.pipeline_template = [{"remote": True, "name": "tokenizer"}] self._reset_pipeline() super(Language, self).__init__(id=id, owner=owner, tags=tags, description=description) @property def pipe_names(self) -> List[str]: return [pipe_template["name"] for pipe_template in self.pipeline_template] def _parse_pipeline_template(self): subpipeline_template = dict( remote=self.pipeline_template[0]["remote"], names=[self.pipeline_template[0]["name"]], ) self.subpipeline_templates = [subpipeline_template] for pipe_template in self.pipeline_template[1:]: if pipe_template["remote"] == subpipeline_template["remote"]: subpipeline_template["names"].append(pipe_template["name"]) else: subpipeline_template = dict( remote=pipe_template["remote"], names=[pipe_template["name"]] ) self.subpipeline_templates.append(subpipeline_template) def _reset_pipeline(self): self._parse_pipeline_template() subpipeline_count = len(self.subpipeline_templates) self.pipeline = [dict() for i in range(subpipeline_count)] def add_pipe( self, component: callable, remote: bool = False, name: str = None, before: str = None, after: str = None, first: bool = False, last: bool = True, ): assert hasattr(component, "__call__"), "Argument `component` is not a callable." assert hasattr( component, "factory" ), "Argument `component` should be an object that has a `factory()` method" assert ( isinstance(name, str) and len(name) >= 1 ), "Argument `name` should be of type `str` with at least one character." assert ( name not in self.pipe_names ), "Pipeline component name '{}' that you have chosen is already used by another pipeline component.".format( name ) assert ( sum([bool(before), bool(after), bool(first), bool(last)]) < 2 ), "Only one among arguments 'before', 'after', 'first' or 'last' should be set." self.factories[name] = component pipe_template = dict(remote=remote, name=name) if last or not any([before, after, first]): self.pipeline_template.append(pipe_template) elif first: self.pipeline_template.insert(index=1, element=pipe_template) elif before in self.pipe_names: self.pipeline_template.insert( index=self.pipe_names.index(before), element=pipe_template ) elif after in self.pipe_names: self.pipeline_template.insert( index=self.pipe_names.index(after) + 1, element=pipe_template ) else: assert ( False ), "component cannot be added to the pipeline, \ please double check argument values of the `add_pipe` method call." self._reset_pipeline() def remove_pipe(self, name: str) -> Tuple[str, callable]: assert ( name in self.pipe_names ), "No pipeline component with the specified name '{}' was found".format(name) pipe_index = self.pipe_names.index(name) pipe = self.pipeline_template.pop(pipe_index) self._parse_pipeline_template() self._reset_pipeline() return pipe def _run_subpipeline_from_template( self, template_index: int, input=Union[str, String, StringPointer, Doc, DocPointer], ) -> Union[Doc, DocPointer]: if isinstance(input, ObjectPointer): location_id = input.location.id else: location_id = self.owner.id # worker where the input is located if location_id not in self.pipeline[template_index]: # Get the subpipeline template subpipeline_template = self.subpipeline_templates[template_index] # Is the pipeline a remote one? remote = subpipeline_template["remote"] # Instantiate a subpipeline and load the subpipeline template subpipeline = SubPipeline() subpipeline.load_template(template=subpipeline_template, factories=self.factories) # Add the subpipeline to the pipeline self.pipeline[template_index][location_id] = subpipeline # Send the subpipeline to the worker where the input is located if ( isinstance(input, ObjectPointer) and input.location != self.owner # Is the input remote? and remote # Is the subpipeline is sendable? ): self.pipeline[template_index][location_id] = self.pipeline[template_index][ location_id ].send(input.location) # Apply the subpipeline and get the doc or the Doc id. # If a Doc ID is obtained, this signifies the ID of the # Doc object on the remote worker. doc_or_id = self.pipeline[template_index][location_id](input) # If the doc is of type str or int, this means that a # DocPointer should be created if isinstance(doc_or_id, int) or isinstance(doc_or_id, str): doc = DocPointer(location=input.location, id_at_location=doc_or_id, owner=self.owner) # This is of type Doc then else: doc = doc_or_id # return the doc return doc def __call__(self, text: Union[str, String, StringPointer]) -> Union[Doc, DocPointer]: # Runs the first subpipeline. # The first subpipeline is the one that has the tokenizer doc = self._run_subpipeline_from_template(template_index=0, input=text) # Apply the the rest of subpipelines sequentially # Each subpipeline will modify the document `doc` inplace for i, subpipeline in enumerate(self.pipeline[1:], start=1): doc = self._run_subpipeline_from_template(template_index=i, input=doc) # return the Doc object return doc

true

f723e3b85ffbd830c43dccf93a9dd2bd55bc2e30

31,635

py

Python

sdk/python/pulumi_google_native/apigee/v1/rate_plan.py

AaronFriel/pulumi-google-native

75d1cda425e33d4610348972cd70bddf35f1770d

[ "Apache-2.0" ]

44

2021-04-18T23:00:48.000Z

2022-02-14T17:43:15.000Z

sdk/python/pulumi_google_native/apigee/v1/rate_plan.py

AaronFriel/pulumi-google-native

75d1cda425e33d4610348972cd70bddf35f1770d

[ "Apache-2.0" ]

354

2021-04-16T16:48:39.000Z

2022-03-31T17:16:39.000Z

sdk/python/pulumi_google_native/apigee/v1/rate_plan.py

AaronFriel/pulumi-google-native

75d1cda425e33d4610348972cd70bddf35f1770d

[ "Apache-2.0" ]

8

2021-04-24T17:46:51.000Z

2022-01-05T10:40:21.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs from ._enums import * from ._inputs import * __all__ = ['RatePlanArgs', 'RatePlan'] @pulumi.input_type class RatePlanArgs: def __init__(__self__, *, apiproduct_id: pulumi.Input[str], organization_id: pulumi.Input[str], apiproduct: Optional[pulumi.Input[str]] = None, billing_period: Optional[pulumi.Input['RatePlanBillingPeriod']] = None, consumption_pricing_rates: Optional[pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RateRangeArgs']]]] = None, consumption_pricing_type: Optional[pulumi.Input['RatePlanConsumptionPricingType']] = None, currency_code: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, display_name: Optional[pulumi.Input[str]] = None, end_time: Optional[pulumi.Input[str]] = None, fixed_fee_frequency: Optional[pulumi.Input[int]] = None, fixed_recurring_fee: Optional[pulumi.Input['GoogleTypeMoneyArgs']] = None, revenue_share_rates: Optional[pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RevenueShareRangeArgs']]]] = None, revenue_share_type: Optional[pulumi.Input['RatePlanRevenueShareType']] = None, setup_fee: Optional[pulumi.Input['GoogleTypeMoneyArgs']] = None, start_time: Optional[pulumi.Input[str]] = None, state: Optional[pulumi.Input['RatePlanState']] = None): """ The set of arguments for constructing a RatePlan resource. :param pulumi.Input[str] apiproduct: Name of the API product that the rate plan is associated with. :param pulumi.Input['RatePlanBillingPeriod'] billing_period: Frequency at which the customer will be billed. :param pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RateRangeArgs']]] consumption_pricing_rates: API call volume ranges and the fees charged when the total number of API calls is within a given range. The method used to calculate the final fee depends on the selected pricing model. For example, if the pricing model is `STAIRSTEP` and the ranges are defined as follows: ``` { "start": 1, "end": 100, "fee": 75 }, { "start": 101, "end": 200, "fee": 100 }, } ``` Then the following fees would be charged based on the total number of API calls (assuming the currency selected is `USD`): * 1 call costs $75 * 50 calls cost $75 * 150 calls cost $100 The number of API calls cannot exceed 200. :param pulumi.Input['RatePlanConsumptionPricingType'] consumption_pricing_type: Pricing model used for consumption-based charges. :param pulumi.Input[str] currency_code: Currency to be used for billing. Consists of a three-letter code as defined by the [ISO 4217](https://en.wikipedia.org/wiki/ISO_4217) standard. :param pulumi.Input[str] description: Description of the rate plan. :param pulumi.Input[str] display_name: Display name of the rate plan. :param pulumi.Input[str] end_time: Time when the rate plan will expire in milliseconds since epoch. Set to 0 or `null` to indicate that the rate plan should never expire. :param pulumi.Input[int] fixed_fee_frequency: Frequency at which the fixed fee is charged. :param pulumi.Input['GoogleTypeMoneyArgs'] fixed_recurring_fee: Fixed amount that is charged at a defined interval and billed in advance of use of the API product. The fee will be prorated for the first billing period. :param pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RevenueShareRangeArgs']]] revenue_share_rates: Details of the revenue sharing model. :param pulumi.Input['RatePlanRevenueShareType'] revenue_share_type: Method used to calculate the revenue that is shared with developers. :param pulumi.Input['GoogleTypeMoneyArgs'] setup_fee: Initial, one-time fee paid when purchasing the API product. :param pulumi.Input[str] start_time: Time when the rate plan becomes active in milliseconds since epoch. :param pulumi.Input['RatePlanState'] state: Current state of the rate plan (draft or published). """ pulumi.set(__self__, "apiproduct_id", apiproduct_id) pulumi.set(__self__, "organization_id", organization_id) if apiproduct is not None: pulumi.set(__self__, "apiproduct", apiproduct) if billing_period is not None: pulumi.set(__self__, "billing_period", billing_period) if consumption_pricing_rates is not None: pulumi.set(__self__, "consumption_pricing_rates", consumption_pricing_rates) if consumption_pricing_type is not None: pulumi.set(__self__, "consumption_pricing_type", consumption_pricing_type) if currency_code is not None: pulumi.set(__self__, "currency_code", currency_code) if description is not None: pulumi.set(__self__, "description", description) if display_name is not None: pulumi.set(__self__, "display_name", display_name) if end_time is not None: pulumi.set(__self__, "end_time", end_time) if fixed_fee_frequency is not None: pulumi.set(__self__, "fixed_fee_frequency", fixed_fee_frequency) if fixed_recurring_fee is not None: pulumi.set(__self__, "fixed_recurring_fee", fixed_recurring_fee) if revenue_share_rates is not None: pulumi.set(__self__, "revenue_share_rates", revenue_share_rates) if revenue_share_type is not None: pulumi.set(__self__, "revenue_share_type", revenue_share_type) if setup_fee is not None: pulumi.set(__self__, "setup_fee", setup_fee) if start_time is not None: pulumi.set(__self__, "start_time", start_time) if state is not None: pulumi.set(__self__, "state", state) @property @pulumi.getter(name="apiproductId") def apiproduct_id(self) -> pulumi.Input[str]: return pulumi.get(self, "apiproduct_id") @apiproduct_id.setter def apiproduct_id(self, value: pulumi.Input[str]): pulumi.set(self, "apiproduct_id", value) @property @pulumi.getter(name="organizationId") def organization_id(self) -> pulumi.Input[str]: return pulumi.get(self, "organization_id") @organization_id.setter def organization_id(self, value: pulumi.Input[str]): pulumi.set(self, "organization_id", value) @property @pulumi.getter def apiproduct(self) -> Optional[pulumi.Input[str]]: """ Name of the API product that the rate plan is associated with. """ return pulumi.get(self, "apiproduct") @apiproduct.setter def apiproduct(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "apiproduct", value) @property @pulumi.getter(name="billingPeriod") def billing_period(self) -> Optional[pulumi.Input['RatePlanBillingPeriod']]: """ Frequency at which the customer will be billed. """ return pulumi.get(self, "billing_period") @billing_period.setter def billing_period(self, value: Optional[pulumi.Input['RatePlanBillingPeriod']]): pulumi.set(self, "billing_period", value) @property @pulumi.getter(name="consumptionPricingRates") def consumption_pricing_rates(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RateRangeArgs']]]]: """ API call volume ranges and the fees charged when the total number of API calls is within a given range. The method used to calculate the final fee depends on the selected pricing model. For example, if the pricing model is `STAIRSTEP` and the ranges are defined as follows: ``` { "start": 1, "end": 100, "fee": 75 }, { "start": 101, "end": 200, "fee": 100 }, } ``` Then the following fees would be charged based on the total number of API calls (assuming the currency selected is `USD`): * 1 call costs $75 * 50 calls cost $75 * 150 calls cost $100 The number of API calls cannot exceed 200. """ return pulumi.get(self, "consumption_pricing_rates") @consumption_pricing_rates.setter def consumption_pricing_rates(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RateRangeArgs']]]]): pulumi.set(self, "consumption_pricing_rates", value) @property @pulumi.getter(name="consumptionPricingType") def consumption_pricing_type(self) -> Optional[pulumi.Input['RatePlanConsumptionPricingType']]: """ Pricing model used for consumption-based charges. """ return pulumi.get(self, "consumption_pricing_type") @consumption_pricing_type.setter def consumption_pricing_type(self, value: Optional[pulumi.Input['RatePlanConsumptionPricingType']]): pulumi.set(self, "consumption_pricing_type", value) @property @pulumi.getter(name="currencyCode") def currency_code(self) -> Optional[pulumi.Input[str]]: """ Currency to be used for billing. Consists of a three-letter code as defined by the [ISO 4217](https://en.wikipedia.org/wiki/ISO_4217) standard. """ return pulumi.get(self, "currency_code") @currency_code.setter def currency_code(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "currency_code", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ Description of the rate plan. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter(name="displayName") def display_name(self) -> Optional[pulumi.Input[str]]: """ Display name of the rate plan. """ return pulumi.get(self, "display_name") @display_name.setter def display_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "display_name", value) @property @pulumi.getter(name="endTime") def end_time(self) -> Optional[pulumi.Input[str]]: """ Time when the rate plan will expire in milliseconds since epoch. Set to 0 or `null` to indicate that the rate plan should never expire. """ return pulumi.get(self, "end_time") @end_time.setter def end_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "end_time", value) @property @pulumi.getter(name="fixedFeeFrequency") def fixed_fee_frequency(self) -> Optional[pulumi.Input[int]]: """ Frequency at which the fixed fee is charged. """ return pulumi.get(self, "fixed_fee_frequency") @fixed_fee_frequency.setter def fixed_fee_frequency(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "fixed_fee_frequency", value) @property @pulumi.getter(name="fixedRecurringFee") def fixed_recurring_fee(self) -> Optional[pulumi.Input['GoogleTypeMoneyArgs']]: """ Fixed amount that is charged at a defined interval and billed in advance of use of the API product. The fee will be prorated for the first billing period. """ return pulumi.get(self, "fixed_recurring_fee") @fixed_recurring_fee.setter def fixed_recurring_fee(self, value: Optional[pulumi.Input['GoogleTypeMoneyArgs']]): pulumi.set(self, "fixed_recurring_fee", value) @property @pulumi.getter(name="revenueShareRates") def revenue_share_rates(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RevenueShareRangeArgs']]]]: """ Details of the revenue sharing model. """ return pulumi.get(self, "revenue_share_rates") @revenue_share_rates.setter def revenue_share_rates(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RevenueShareRangeArgs']]]]): pulumi.set(self, "revenue_share_rates", value) @property @pulumi.getter(name="revenueShareType") def revenue_share_type(self) -> Optional[pulumi.Input['RatePlanRevenueShareType']]: """ Method used to calculate the revenue that is shared with developers. """ return pulumi.get(self, "revenue_share_type") @revenue_share_type.setter def revenue_share_type(self, value: Optional[pulumi.Input['RatePlanRevenueShareType']]): pulumi.set(self, "revenue_share_type", value) @property @pulumi.getter(name="setupFee") def setup_fee(self) -> Optional[pulumi.Input['GoogleTypeMoneyArgs']]: """ Initial, one-time fee paid when purchasing the API product. """ return pulumi.get(self, "setup_fee") @setup_fee.setter def setup_fee(self, value: Optional[pulumi.Input['GoogleTypeMoneyArgs']]): pulumi.set(self, "setup_fee", value) @property @pulumi.getter(name="startTime") def start_time(self) -> Optional[pulumi.Input[str]]: """ Time when the rate plan becomes active in milliseconds since epoch. """ return pulumi.get(self, "start_time") @start_time.setter def start_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "start_time", value) @property @pulumi.getter def state(self) -> Optional[pulumi.Input['RatePlanState']]: """ Current state of the rate plan (draft or published). """ return pulumi.get(self, "state") @state.setter def state(self, value: Optional[pulumi.Input['RatePlanState']]): pulumi.set(self, "state", value) class RatePlan(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, apiproduct: Optional[pulumi.Input[str]] = None, apiproduct_id: Optional[pulumi.Input[str]] = None, billing_period: Optional[pulumi.Input['RatePlanBillingPeriod']] = None, consumption_pricing_rates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['GoogleCloudApigeeV1RateRangeArgs']]]]] = None, consumption_pricing_type: Optional[pulumi.Input['RatePlanConsumptionPricingType']] = None, currency_code: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, display_name: Optional[pulumi.Input[str]] = None, end_time: Optional[pulumi.Input[str]] = None, fixed_fee_frequency: Optional[pulumi.Input[int]] = None, fixed_recurring_fee: Optional[pulumi.Input[pulumi.InputType['GoogleTypeMoneyArgs']]] = None, organization_id: Optional[pulumi.Input[str]] = None, revenue_share_rates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['GoogleCloudApigeeV1RevenueShareRangeArgs']]]]] = None, revenue_share_type: Optional[pulumi.Input['RatePlanRevenueShareType']] = None, setup_fee: Optional[pulumi.Input[pulumi.InputType['GoogleTypeMoneyArgs']]] = None, start_time: Optional[pulumi.Input[str]] = None, state: Optional[pulumi.Input['RatePlanState']] = None, __props__=None): """ Create a rate plan that is associated with an API product in an organization. Using rate plans, API product owners can monetize their API products by configuring one or more of the following: - Billing frequency - Initial setup fees for using an API product - Payment funding model (postpaid only) - Fixed recurring or consumption-based charges for using an API product - Revenue sharing with developer partners An API product can have multiple rate plans associated with it but *only one* rate plan can be active at any point of time. **Note: From the developer's perspective, they purchase API products not rate plans. Auto-naming is currently not supported for this resource. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] apiproduct: Name of the API product that the rate plan is associated with. :param pulumi.Input['RatePlanBillingPeriod'] billing_period: Frequency at which the customer will be billed. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['GoogleCloudApigeeV1RateRangeArgs']]]] consumption_pricing_rates: API call volume ranges and the fees charged when the total number of API calls is within a given range. The method used to calculate the final fee depends on the selected pricing model. For example, if the pricing model is `STAIRSTEP` and the ranges are defined as follows: ``` { "start": 1, "end": 100, "fee": 75 }, { "start": 101, "end": 200, "fee": 100 }, } ``` Then the following fees would be charged based on the total number of API calls (assuming the currency selected is `USD`): * 1 call costs $75 * 50 calls cost $75 * 150 calls cost $100 The number of API calls cannot exceed 200. :param pulumi.Input['RatePlanConsumptionPricingType'] consumption_pricing_type: Pricing model used for consumption-based charges. :param pulumi.Input[str] currency_code: Currency to be used for billing. Consists of a three-letter code as defined by the [ISO 4217](https://en.wikipedia.org/wiki/ISO_4217) standard. :param pulumi.Input[str] description: Description of the rate plan. :param pulumi.Input[str] display_name: Display name of the rate plan. :param pulumi.Input[str] end_time: Time when the rate plan will expire in milliseconds since epoch. Set to 0 or `null` to indicate that the rate plan should never expire. :param pulumi.Input[int] fixed_fee_frequency: Frequency at which the fixed fee is charged. :param pulumi.Input[pulumi.InputType['GoogleTypeMoneyArgs']] fixed_recurring_fee: Fixed amount that is charged at a defined interval and billed in advance of use of the API product. The fee will be prorated for the first billing period. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['GoogleCloudApigeeV1RevenueShareRangeArgs']]]] revenue_share_rates: Details of the revenue sharing model. :param pulumi.Input['RatePlanRevenueShareType'] revenue_share_type: Method used to calculate the revenue that is shared with developers. :param pulumi.Input[pulumi.InputType['GoogleTypeMoneyArgs']] setup_fee: Initial, one-time fee paid when purchasing the API product. :param pulumi.Input[str] start_time: Time when the rate plan becomes active in milliseconds since epoch. :param pulumi.Input['RatePlanState'] state: Current state of the rate plan (draft or published). """ ... @overload def __init__(__self__, resource_name: str, args: RatePlanArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Create a rate plan that is associated with an API product in an organization. Using rate plans, API product owners can monetize their API products by configuring one or more of the following: - Billing frequency - Initial setup fees for using an API product - Payment funding model (postpaid only) - Fixed recurring or consumption-based charges for using an API product - Revenue sharing with developer partners An API product can have multiple rate plans associated with it but *only one* rate plan can be active at any point of time. **Note: From the developer's perspective, they purchase API products not rate plans. Auto-naming is currently not supported for this resource. :param str resource_name: The name of the resource. :param RatePlanArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(RatePlanArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, apiproduct: Optional[pulumi.Input[str]] = None, apiproduct_id: Optional[pulumi.Input[str]] = None, billing_period: Optional[pulumi.Input['RatePlanBillingPeriod']] = None, consumption_pricing_rates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['GoogleCloudApigeeV1RateRangeArgs']]]]] = None, consumption_pricing_type: Optional[pulumi.Input['RatePlanConsumptionPricingType']] = None, currency_code: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, display_name: Optional[pulumi.Input[str]] = None, end_time: Optional[pulumi.Input[str]] = None, fixed_fee_frequency: Optional[pulumi.Input[int]] = None, fixed_recurring_fee: Optional[pulumi.Input[pulumi.InputType['GoogleTypeMoneyArgs']]] = None, organization_id: Optional[pulumi.Input[str]] = None, revenue_share_rates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['GoogleCloudApigeeV1RevenueShareRangeArgs']]]]] = None, revenue_share_type: Optional[pulumi.Input['RatePlanRevenueShareType']] = None, setup_fee: Optional[pulumi.Input[pulumi.InputType['GoogleTypeMoneyArgs']]] = None, start_time: Optional[pulumi.Input[str]] = None, state: Optional[pulumi.Input['RatePlanState']] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = RatePlanArgs.__new__(RatePlanArgs) __props__.__dict__["apiproduct"] = apiproduct if apiproduct_id is None and not opts.urn: raise TypeError("Missing required property 'apiproduct_id'") __props__.__dict__["apiproduct_id"] = apiproduct_id __props__.__dict__["billing_period"] = billing_period __props__.__dict__["consumption_pricing_rates"] = consumption_pricing_rates __props__.__dict__["consumption_pricing_type"] = consumption_pricing_type __props__.__dict__["currency_code"] = currency_code __props__.__dict__["description"] = description __props__.__dict__["display_name"] = display_name __props__.__dict__["end_time"] = end_time __props__.__dict__["fixed_fee_frequency"] = fixed_fee_frequency __props__.__dict__["fixed_recurring_fee"] = fixed_recurring_fee if organization_id is None and not opts.urn: raise TypeError("Missing required property 'organization_id'") __props__.__dict__["organization_id"] = organization_id __props__.__dict__["revenue_share_rates"] = revenue_share_rates __props__.__dict__["revenue_share_type"] = revenue_share_type __props__.__dict__["setup_fee"] = setup_fee __props__.__dict__["start_time"] = start_time __props__.__dict__["state"] = state __props__.__dict__["created_at"] = None __props__.__dict__["last_modified_at"] = None __props__.__dict__["name"] = None super(RatePlan, __self__).__init__( 'google-native:apigee/v1:RatePlan', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'RatePlan': """ Get an existing RatePlan resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = RatePlanArgs.__new__(RatePlanArgs) __props__.__dict__["apiproduct"] = None __props__.__dict__["billing_period"] = None __props__.__dict__["consumption_pricing_rates"] = None __props__.__dict__["consumption_pricing_type"] = None __props__.__dict__["created_at"] = None __props__.__dict__["currency_code"] = None __props__.__dict__["description"] = None __props__.__dict__["display_name"] = None __props__.__dict__["end_time"] = None __props__.__dict__["fixed_fee_frequency"] = None __props__.__dict__["fixed_recurring_fee"] = None __props__.__dict__["last_modified_at"] = None __props__.__dict__["name"] = None __props__.__dict__["revenue_share_rates"] = None __props__.__dict__["revenue_share_type"] = None __props__.__dict__["setup_fee"] = None __props__.__dict__["start_time"] = None __props__.__dict__["state"] = None return RatePlan(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def apiproduct(self) -> pulumi.Output[str]: """ Name of the API product that the rate plan is associated with. """ return pulumi.get(self, "apiproduct") @property @pulumi.getter(name="billingPeriod") def billing_period(self) -> pulumi.Output[str]: """ Frequency at which the customer will be billed. """ return pulumi.get(self, "billing_period") @property @pulumi.getter(name="consumptionPricingRates") def consumption_pricing_rates(self) -> pulumi.Output[Sequence['outputs.GoogleCloudApigeeV1RateRangeResponse']]: """ API call volume ranges and the fees charged when the total number of API calls is within a given range. The method used to calculate the final fee depends on the selected pricing model. For example, if the pricing model is `STAIRSTEP` and the ranges are defined as follows: ``` { "start": 1, "end": 100, "fee": 75 }, { "start": 101, "end": 200, "fee": 100 }, } ``` Then the following fees would be charged based on the total number of API calls (assuming the currency selected is `USD`): * 1 call costs $75 * 50 calls cost $75 * 150 calls cost $100 The number of API calls cannot exceed 200. """ return pulumi.get(self, "consumption_pricing_rates") @property @pulumi.getter(name="consumptionPricingType") def consumption_pricing_type(self) -> pulumi.Output[str]: """ Pricing model used for consumption-based charges. """ return pulumi.get(self, "consumption_pricing_type") @property @pulumi.getter(name="createdAt") def created_at(self) -> pulumi.Output[str]: """ Time that the rate plan was created in milliseconds since epoch. """ return pulumi.get(self, "created_at") @property @pulumi.getter(name="currencyCode") def currency_code(self) -> pulumi.Output[str]: """ Currency to be used for billing. Consists of a three-letter code as defined by the [ISO 4217](https://en.wikipedia.org/wiki/ISO_4217) standard. """ return pulumi.get(self, "currency_code") @property @pulumi.getter def description(self) -> pulumi.Output[str]: """ Description of the rate plan. """ return pulumi.get(self, "description") @property @pulumi.getter(name="displayName") def display_name(self) -> pulumi.Output[str]: """ Display name of the rate plan. """ return pulumi.get(self, "display_name") @property @pulumi.getter(name="endTime") def end_time(self) -> pulumi.Output[str]: """ Time when the rate plan will expire in milliseconds since epoch. Set to 0 or `null` to indicate that the rate plan should never expire. """ return pulumi.get(self, "end_time") @property @pulumi.getter(name="fixedFeeFrequency") def fixed_fee_frequency(self) -> pulumi.Output[int]: """ Frequency at which the fixed fee is charged. """ return pulumi.get(self, "fixed_fee_frequency") @property @pulumi.getter(name="fixedRecurringFee") def fixed_recurring_fee(self) -> pulumi.Output['outputs.GoogleTypeMoneyResponse']: """ Fixed amount that is charged at a defined interval and billed in advance of use of the API product. The fee will be prorated for the first billing period. """ return pulumi.get(self, "fixed_recurring_fee") @property @pulumi.getter(name="lastModifiedAt") def last_modified_at(self) -> pulumi.Output[str]: """ Time the rate plan was last modified in milliseconds since epoch. """ return pulumi.get(self, "last_modified_at") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Name of the rate plan. """ return pulumi.get(self, "name") @property @pulumi.getter(name="revenueShareRates") def revenue_share_rates(self) -> pulumi.Output[Sequence['outputs.GoogleCloudApigeeV1RevenueShareRangeResponse']]: """ Details of the revenue sharing model. """ return pulumi.get(self, "revenue_share_rates") @property @pulumi.getter(name="revenueShareType") def revenue_share_type(self) -> pulumi.Output[str]: """ Method used to calculate the revenue that is shared with developers. """ return pulumi.get(self, "revenue_share_type") @property @pulumi.getter(name="setupFee") def setup_fee(self) -> pulumi.Output['outputs.GoogleTypeMoneyResponse']: """ Initial, one-time fee paid when purchasing the API product. """ return pulumi.get(self, "setup_fee") @property @pulumi.getter(name="startTime") def start_time(self) -> pulumi.Output[str]: """ Time when the rate plan becomes active in milliseconds since epoch. """ return pulumi.get(self, "start_time") @property @pulumi.getter def state(self) -> pulumi.Output[str]: """ Current state of the rate plan (draft or published). """ return pulumi.get(self, "state")

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import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs from ._enums import * from ._inputs import * __all__ = ['RatePlanArgs', 'RatePlan'] @pulumi.input_type class RatePlanArgs: def __init__(__self__, *, apiproduct_id: pulumi.Input[str], organization_id: pulumi.Input[str], apiproduct: Optional[pulumi.Input[str]] = None, billing_period: Optional[pulumi.Input['RatePlanBillingPeriod']] = None, consumption_pricing_rates: Optional[pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RateRangeArgs']]]] = None, consumption_pricing_type: Optional[pulumi.Input['RatePlanConsumptionPricingType']] = None, currency_code: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, display_name: Optional[pulumi.Input[str]] = None, end_time: Optional[pulumi.Input[str]] = None, fixed_fee_frequency: Optional[pulumi.Input[int]] = None, fixed_recurring_fee: Optional[pulumi.Input['GoogleTypeMoneyArgs']] = None, revenue_share_rates: Optional[pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RevenueShareRangeArgs']]]] = None, revenue_share_type: Optional[pulumi.Input['RatePlanRevenueShareType']] = None, setup_fee: Optional[pulumi.Input['GoogleTypeMoneyArgs']] = None, start_time: Optional[pulumi.Input[str]] = None, state: Optional[pulumi.Input['RatePlanState']] = None): pulumi.set(__self__, "apiproduct_id", apiproduct_id) pulumi.set(__self__, "organization_id", organization_id) if apiproduct is not None: pulumi.set(__self__, "apiproduct", apiproduct) if billing_period is not None: pulumi.set(__self__, "billing_period", billing_period) if consumption_pricing_rates is not None: pulumi.set(__self__, "consumption_pricing_rates", consumption_pricing_rates) if consumption_pricing_type is not None: pulumi.set(__self__, "consumption_pricing_type", consumption_pricing_type) if currency_code is not None: pulumi.set(__self__, "currency_code", currency_code) if description is not None: pulumi.set(__self__, "description", description) if display_name is not None: pulumi.set(__self__, "display_name", display_name) if end_time is not None: pulumi.set(__self__, "end_time", end_time) if fixed_fee_frequency is not None: pulumi.set(__self__, "fixed_fee_frequency", fixed_fee_frequency) if fixed_recurring_fee is not None: pulumi.set(__self__, "fixed_recurring_fee", fixed_recurring_fee) if revenue_share_rates is not None: pulumi.set(__self__, "revenue_share_rates", revenue_share_rates) if revenue_share_type is not None: pulumi.set(__self__, "revenue_share_type", revenue_share_type) if setup_fee is not None: pulumi.set(__self__, "setup_fee", setup_fee) if start_time is not None: pulumi.set(__self__, "start_time", start_time) if state is not None: pulumi.set(__self__, "state", state) @property @pulumi.getter(name="apiproductId") def apiproduct_id(self) -> pulumi.Input[str]: return pulumi.get(self, "apiproduct_id") @apiproduct_id.setter def apiproduct_id(self, value: pulumi.Input[str]): pulumi.set(self, "apiproduct_id", value) @property @pulumi.getter(name="organizationId") def organization_id(self) -> pulumi.Input[str]: return pulumi.get(self, "organization_id") @organization_id.setter def organization_id(self, value: pulumi.Input[str]): pulumi.set(self, "organization_id", value) @property @pulumi.getter def apiproduct(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "apiproduct") @apiproduct.setter def apiproduct(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "apiproduct", value) @property @pulumi.getter(name="billingPeriod") def billing_period(self) -> Optional[pulumi.Input['RatePlanBillingPeriod']]: return pulumi.get(self, "billing_period") @billing_period.setter def billing_period(self, value: Optional[pulumi.Input['RatePlanBillingPeriod']]): pulumi.set(self, "billing_period", value) @property @pulumi.getter(name="consumptionPricingRates") def consumption_pricing_rates(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RateRangeArgs']]]]: return pulumi.get(self, "consumption_pricing_rates") @consumption_pricing_rates.setter def consumption_pricing_rates(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RateRangeArgs']]]]): pulumi.set(self, "consumption_pricing_rates", value) @property @pulumi.getter(name="consumptionPricingType") def consumption_pricing_type(self) -> Optional[pulumi.Input['RatePlanConsumptionPricingType']]: return pulumi.get(self, "consumption_pricing_type") @consumption_pricing_type.setter def consumption_pricing_type(self, value: Optional[pulumi.Input['RatePlanConsumptionPricingType']]): pulumi.set(self, "consumption_pricing_type", value) @property @pulumi.getter(name="currencyCode") def currency_code(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "currency_code") @currency_code.setter def currency_code(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "currency_code", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter(name="displayName") def display_name(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "display_name") @display_name.setter def display_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "display_name", value) @property @pulumi.getter(name="endTime") def end_time(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "end_time") @end_time.setter def end_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "end_time", value) @property @pulumi.getter(name="fixedFeeFrequency") def fixed_fee_frequency(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "fixed_fee_frequency") @fixed_fee_frequency.setter def fixed_fee_frequency(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "fixed_fee_frequency", value) @property @pulumi.getter(name="fixedRecurringFee") def fixed_recurring_fee(self) -> Optional[pulumi.Input['GoogleTypeMoneyArgs']]: return pulumi.get(self, "fixed_recurring_fee") @fixed_recurring_fee.setter def fixed_recurring_fee(self, value: Optional[pulumi.Input['GoogleTypeMoneyArgs']]): pulumi.set(self, "fixed_recurring_fee", value) @property @pulumi.getter(name="revenueShareRates") def revenue_share_rates(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RevenueShareRangeArgs']]]]: return pulumi.get(self, "revenue_share_rates") @revenue_share_rates.setter def revenue_share_rates(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['GoogleCloudApigeeV1RevenueShareRangeArgs']]]]): pulumi.set(self, "revenue_share_rates", value) @property @pulumi.getter(name="revenueShareType") def revenue_share_type(self) -> Optional[pulumi.Input['RatePlanRevenueShareType']]: return pulumi.get(self, "revenue_share_type") @revenue_share_type.setter def revenue_share_type(self, value: Optional[pulumi.Input['RatePlanRevenueShareType']]): pulumi.set(self, "revenue_share_type", value) @property @pulumi.getter(name="setupFee") def setup_fee(self) -> Optional[pulumi.Input['GoogleTypeMoneyArgs']]: return pulumi.get(self, "setup_fee") @setup_fee.setter def setup_fee(self, value: Optional[pulumi.Input['GoogleTypeMoneyArgs']]): pulumi.set(self, "setup_fee", value) @property @pulumi.getter(name="startTime") def start_time(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "start_time") @start_time.setter def start_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "start_time", value) @property @pulumi.getter def state(self) -> Optional[pulumi.Input['RatePlanState']]: return pulumi.get(self, "state") @state.setter def state(self, value: Optional[pulumi.Input['RatePlanState']]): pulumi.set(self, "state", value) class RatePlan(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, apiproduct: Optional[pulumi.Input[str]] = None, apiproduct_id: Optional[pulumi.Input[str]] = None, billing_period: Optional[pulumi.Input['RatePlanBillingPeriod']] = None, consumption_pricing_rates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['GoogleCloudApigeeV1RateRangeArgs']]]]] = None, consumption_pricing_type: Optional[pulumi.Input['RatePlanConsumptionPricingType']] = None, currency_code: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, display_name: Optional[pulumi.Input[str]] = None, end_time: Optional[pulumi.Input[str]] = None, fixed_fee_frequency: Optional[pulumi.Input[int]] = None, fixed_recurring_fee: Optional[pulumi.Input[pulumi.InputType['GoogleTypeMoneyArgs']]] = None, organization_id: Optional[pulumi.Input[str]] = None, revenue_share_rates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['GoogleCloudApigeeV1RevenueShareRangeArgs']]]]] = None, revenue_share_type: Optional[pulumi.Input['RatePlanRevenueShareType']] = None, setup_fee: Optional[pulumi.Input[pulumi.InputType['GoogleTypeMoneyArgs']]] = None, start_time: Optional[pulumi.Input[str]] = None, state: Optional[pulumi.Input['RatePlanState']] = None, __props__=None): ... @overload def __init__(__self__, resource_name: str, args: RatePlanArgs, opts: Optional[pulumi.ResourceOptions] = None): ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(RatePlanArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, apiproduct: Optional[pulumi.Input[str]] = None, apiproduct_id: Optional[pulumi.Input[str]] = None, billing_period: Optional[pulumi.Input['RatePlanBillingPeriod']] = None, consumption_pricing_rates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['GoogleCloudApigeeV1RateRangeArgs']]]]] = None, consumption_pricing_type: Optional[pulumi.Input['RatePlanConsumptionPricingType']] = None, currency_code: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, display_name: Optional[pulumi.Input[str]] = None, end_time: Optional[pulumi.Input[str]] = None, fixed_fee_frequency: Optional[pulumi.Input[int]] = None, fixed_recurring_fee: Optional[pulumi.Input[pulumi.InputType['GoogleTypeMoneyArgs']]] = None, organization_id: Optional[pulumi.Input[str]] = None, revenue_share_rates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['GoogleCloudApigeeV1RevenueShareRangeArgs']]]]] = None, revenue_share_type: Optional[pulumi.Input['RatePlanRevenueShareType']] = None, setup_fee: Optional[pulumi.Input[pulumi.InputType['GoogleTypeMoneyArgs']]] = None, start_time: Optional[pulumi.Input[str]] = None, state: Optional[pulumi.Input['RatePlanState']] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = RatePlanArgs.__new__(RatePlanArgs) __props__.__dict__["apiproduct"] = apiproduct if apiproduct_id is None and not opts.urn: raise TypeError("Missing required property 'apiproduct_id'") __props__.__dict__["apiproduct_id"] = apiproduct_id __props__.__dict__["billing_period"] = billing_period __props__.__dict__["consumption_pricing_rates"] = consumption_pricing_rates __props__.__dict__["consumption_pricing_type"] = consumption_pricing_type __props__.__dict__["currency_code"] = currency_code __props__.__dict__["description"] = description __props__.__dict__["display_name"] = display_name __props__.__dict__["end_time"] = end_time __props__.__dict__["fixed_fee_frequency"] = fixed_fee_frequency __props__.__dict__["fixed_recurring_fee"] = fixed_recurring_fee if organization_id is None and not opts.urn: raise TypeError("Missing required property 'organization_id'") __props__.__dict__["organization_id"] = organization_id __props__.__dict__["revenue_share_rates"] = revenue_share_rates __props__.__dict__["revenue_share_type"] = revenue_share_type __props__.__dict__["setup_fee"] = setup_fee __props__.__dict__["start_time"] = start_time __props__.__dict__["state"] = state __props__.__dict__["created_at"] = None __props__.__dict__["last_modified_at"] = None __props__.__dict__["name"] = None super(RatePlan, __self__).__init__( 'google-native:apigee/v1:RatePlan', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'RatePlan': opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = RatePlanArgs.__new__(RatePlanArgs) __props__.__dict__["apiproduct"] = None __props__.__dict__["billing_period"] = None __props__.__dict__["consumption_pricing_rates"] = None __props__.__dict__["consumption_pricing_type"] = None __props__.__dict__["created_at"] = None __props__.__dict__["currency_code"] = None __props__.__dict__["description"] = None __props__.__dict__["display_name"] = None __props__.__dict__["end_time"] = None __props__.__dict__["fixed_fee_frequency"] = None __props__.__dict__["fixed_recurring_fee"] = None __props__.__dict__["last_modified_at"] = None __props__.__dict__["name"] = None __props__.__dict__["revenue_share_rates"] = None __props__.__dict__["revenue_share_type"] = None __props__.__dict__["setup_fee"] = None __props__.__dict__["start_time"] = None __props__.__dict__["state"] = None return RatePlan(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def apiproduct(self) -> pulumi.Output[str]: return pulumi.get(self, "apiproduct") @property @pulumi.getter(name="billingPeriod") def billing_period(self) -> pulumi.Output[str]: return pulumi.get(self, "billing_period") @property @pulumi.getter(name="consumptionPricingRates") def consumption_pricing_rates(self) -> pulumi.Output[Sequence['outputs.GoogleCloudApigeeV1RateRangeResponse']]: return pulumi.get(self, "consumption_pricing_rates") @property @pulumi.getter(name="consumptionPricingType") def consumption_pricing_type(self) -> pulumi.Output[str]: return pulumi.get(self, "consumption_pricing_type") @property @pulumi.getter(name="createdAt") def created_at(self) -> pulumi.Output[str]: return pulumi.get(self, "created_at") @property @pulumi.getter(name="currencyCode") def currency_code(self) -> pulumi.Output[str]: return pulumi.get(self, "currency_code") @property @pulumi.getter def description(self) -> pulumi.Output[str]: return pulumi.get(self, "description") @property @pulumi.getter(name="displayName") def display_name(self) -> pulumi.Output[str]: return pulumi.get(self, "display_name") @property @pulumi.getter(name="endTime") def end_time(self) -> pulumi.Output[str]: return pulumi.get(self, "end_time") @property @pulumi.getter(name="fixedFeeFrequency") def fixed_fee_frequency(self) -> pulumi.Output[int]: return pulumi.get(self, "fixed_fee_frequency") @property @pulumi.getter(name="fixedRecurringFee") def fixed_recurring_fee(self) -> pulumi.Output['outputs.GoogleTypeMoneyResponse']: return pulumi.get(self, "fixed_recurring_fee") @property @pulumi.getter(name="lastModifiedAt") def last_modified_at(self) -> pulumi.Output[str]: return pulumi.get(self, "last_modified_at") @property @pulumi.getter def name(self) -> pulumi.Output[str]: return pulumi.get(self, "name") @property @pulumi.getter(name="revenueShareRates") def revenue_share_rates(self) -> pulumi.Output[Sequence['outputs.GoogleCloudApigeeV1RevenueShareRangeResponse']]: return pulumi.get(self, "revenue_share_rates") @property @pulumi.getter(name="revenueShareType") def revenue_share_type(self) -> pulumi.Output[str]: return pulumi.get(self, "revenue_share_type") @property @pulumi.getter(name="setupFee") def setup_fee(self) -> pulumi.Output['outputs.GoogleTypeMoneyResponse']: return pulumi.get(self, "setup_fee") @property @pulumi.getter(name="startTime") def start_time(self) -> pulumi.Output[str]: return pulumi.get(self, "start_time") @property @pulumi.getter def state(self) -> pulumi.Output[str]: return pulumi.get(self, "state")

true

f723e43173495e3c39cb07e034e93d2ac42f8401

2,275

py

Python

frappe/cache_manager.py

karthikeyan5/frappe

d2c652ef3a3cc6997eedcc3925e359e216b8a569

[ "MIT" ]

null

frappe/cache_manager.py

karthikeyan5/frappe

d2c652ef3a3cc6997eedcc3925e359e216b8a569

[ "MIT" ]

null

frappe/cache_manager.py

karthikeyan5/frappe

d2c652ef3a3cc6997eedcc3925e359e216b8a569

[ "MIT" ]

null

# Copyright (c) 2018, Frappe Technologies Pvt. Ltd. and Contributors # MIT License. See license.txt from __future__ import unicode_literals import frappe import frappe.defaults from frappe.desk.notifications import (delete_notification_count_for, clear_notifications) common_default_keys = ["__default", "__global"] def clear_user_cache(user=None): cache = frappe.cache() groups = ("bootinfo", "user_recent", "roles", "user_doc", "lang", "defaults", "user_permissions", "home_page", "linked_with", "desktop_icons", 'portal_menu_items') # this will automatically reload the global cache # so it is important to clear this first clear_notifications(user) if user: for name in groups: cache.hdel(name, user) cache.delete_keys("user:" + user) clear_defaults_cache(user) else: for name in groups: cache.delete_key(name) clear_defaults_cache() clear_global_cache() def clear_global_cache(): from frappe.website.render import clear_cache as clear_website_cache clear_doctype_cache() clear_website_cache() frappe.cache().delete_value(["app_hooks", "installed_apps", "app_modules", "module_app", "notification_config", 'system_settings', 'scheduler_events', 'time_zone', 'webhooks', 'active_domains', 'active_modules']) frappe.setup_module_map() def clear_defaults_cache(user=None): if user: for p in ([user] + common_default_keys): frappe.cache().hdel("defaults", p) elif frappe.flags.in_install!="frappe": frappe.cache().delete_key("defaults") def clear_doctype_cache(doctype=None): cache = frappe.cache() if getattr(frappe.local, 'meta_cache') and (doctype in frappe.local.meta_cache): del frappe.local.meta_cache[doctype] for key in ('is_table', 'doctype_modules'): cache.delete_value(key) groups = ["meta", "form_meta", "table_columns", "last_modified", "linked_doctypes", 'notifications', 'workflow'] def clear_single(dt): for name in groups: cache.hdel(name, dt) if doctype: clear_single(doctype) # clear all parent doctypes for dt in frappe.db.get_all('DocField', 'parent', dict(fieldtype='Table', options=doctype)): clear_single(dt.parent) # clear all notifications delete_notification_count_for(doctype) else: # clear all for name in groups: cache.delete_value(name)

27.083333

94

0.745495

from __future__ import unicode_literals import frappe import frappe.defaults from frappe.desk.notifications import (delete_notification_count_for, clear_notifications) common_default_keys = ["__default", "__global"] def clear_user_cache(user=None): cache = frappe.cache() groups = ("bootinfo", "user_recent", "roles", "user_doc", "lang", "defaults", "user_permissions", "home_page", "linked_with", "desktop_icons", 'portal_menu_items') clear_notifications(user) if user: for name in groups: cache.hdel(name, user) cache.delete_keys("user:" + user) clear_defaults_cache(user) else: for name in groups: cache.delete_key(name) clear_defaults_cache() clear_global_cache() def clear_global_cache(): from frappe.website.render import clear_cache as clear_website_cache clear_doctype_cache() clear_website_cache() frappe.cache().delete_value(["app_hooks", "installed_apps", "app_modules", "module_app", "notification_config", 'system_settings', 'scheduler_events', 'time_zone', 'webhooks', 'active_domains', 'active_modules']) frappe.setup_module_map() def clear_defaults_cache(user=None): if user: for p in ([user] + common_default_keys): frappe.cache().hdel("defaults", p) elif frappe.flags.in_install!="frappe": frappe.cache().delete_key("defaults") def clear_doctype_cache(doctype=None): cache = frappe.cache() if getattr(frappe.local, 'meta_cache') and (doctype in frappe.local.meta_cache): del frappe.local.meta_cache[doctype] for key in ('is_table', 'doctype_modules'): cache.delete_value(key) groups = ["meta", "form_meta", "table_columns", "last_modified", "linked_doctypes", 'notifications', 'workflow'] def clear_single(dt): for name in groups: cache.hdel(name, dt) if doctype: clear_single(doctype) for dt in frappe.db.get_all('DocField', 'parent', dict(fieldtype='Table', options=doctype)): clear_single(dt.parent) delete_notification_count_for(doctype) else: for name in groups: cache.delete_value(name)

true

f723e7c0843a98cbd38780ee04a037d43c171c68

15,765

py

Python

dataset_loader.py

fqnchina/NeuralRouting

333dc95cb2d9a779de88e2349883a0002111d1b3

[ "MIT" ]

58

2021-03-25T19:18:56.000Z

2022-03-30T04:59:32.000Z

dataset_loader.py

fqnchina/NeuralRouting

333dc95cb2d9a779de88e2349883a0002111d1b3

[ "MIT" ]

null

dataset_loader.py

fqnchina/NeuralRouting

333dc95cb2d9a779de88e2349883a0002111d1b3

[ "MIT" ]

6

2021-06-19T03:48:50.000Z

2021-07-02T13:05:04.000Z

import numpy as np import torch from torch.utils.data import Dataset, DataLoader from torchvision import transforms as tfs from PIL import Image import os, cv2, copy, time from config import * # args. image_height, image_width = opt.image_height, opt.image_width intrinsics = opt.intrinsics close_radius, far_radiuses = 0, opt.far_radiuses n_neighb_pts = opt.n_neighb_pts def isSon(son, fa): for i in range(len(fa)): if son[i] != fa[i]: return False return True # todo: to be migrated... def depth2local(depth): # depth: float32, meter. cx, cy, fx, fy = intrinsics[0, 2], intrinsics[1, 2], intrinsics[0, 0], intrinsics[1, 1] u_base = np.tile(np.arange(image_width), (image_height, 1)) v_base = np.tile(np.arange(image_height)[:, np.newaxis], (1, image_width)) X = (u_base - cx) * depth / fx Y = (v_base - cy) * depth / fy coord_local = np.stack((X, Y, depth), axis=2) return coord_local def partial_pts(pts_all_in, p, r_min, r_max): # pts_all_in.shape (#points, #channel) pts_all = copy.deepcopy(pts_all_in) p_mat = p[np.newaxis, 0:3].repeat(pts_all.shape[0], axis=0) norms = np.linalg.norm((p_mat - pts_all[:, 0:3]), axis=1) return pts_all[np.logical_and(norms >= r_min, norms <= r_max)] def sample_pts(pts_in, num): # pts_in.shape (#points, #channel) pts = copy.deepcopy(pts_in) while pts.shape[0] < num: pts = np.concatenate((pts, pts), axis=0) rand_ids = np.arange(pts.shape[0]) np.random.shuffle(rand_ids) return pts[rand_ids[0:num], :] def sample_pts_rc(pts_in, rcs_in, num): # pts_in.shape (#points, #channel) pts = copy.deepcopy(pts_in) rcs = copy.deepcopy(rcs_in) while pts.shape[0] < num: pts = np.concatenate((pts, pts), axis=0) rand_ids = np.arange(pts.shape[0]) np.random.shuffle(rand_ids) return pts[rand_ids[0:num], :], rcs_in[rand_ids[0:num], :] def sample_pts9d_r3d(pts_in, num, radius): # pts_in.shape (#points, #channel) pts = copy.deepcopy(pts_in) thresh = 500 # remove background by 3d radius xyz = pts[:, 0:3] pts = pts[np.linalg.norm(xyz, axis=1) <= radius] # print('pt num after r3d {}'.format(pts.shape[0])) if pts.shape[0] < thresh: # avoid infinite loop. return None while pts.shape[0] < num: pts = np.concatenate((pts, pts), axis=0) rand_ids = np.arange(pts.shape[0]) np.random.shuffle(rand_ids) return pts[rand_ids[0:num], :] def shift_pts(pts_in, cen): # pts_in.shape (#points, #channel) pts = copy.deepcopy(pts_in) cen_mat = cen[np.newaxis, :].repeat(pts.shape[0], axis=0) pts[:, 0:3] = pts[:, 0:3] - cen_mat return pts def shift_pts6d(pts_in, cen): # pts_in.shape (#points, #channel) pts = copy.deepcopy(pts_in) cen_mat = cen[np.newaxis, :].repeat(pts.shape[0], axis=0) pts[:, :] = pts[:, :] - cen_mat return pts def shift_pts9d(pts_in, cen): # pts_in.shape (#points, #channel) cpt = copy.deepcopy(cen) cpt[3:6] = np.zeros(3) # remove shift of normal pts = copy.deepcopy(pts_in) cpt_mat = cpt[np.newaxis, :].repeat(pts.shape[0], axis=0) pts[:, :] = pts[:, :] - cpt_mat return pts def make_ppf(pts9d, cen9d): # (N,9), (9,) # prepare n_pts = pts9d.shape[0] d = pts9d[:, 0:3] n2 = pts9d[:, 3:6] n1 = np.repeat(cen9d[3:6].reshape(1, 3), n_pts, axis=0) # ppf dim1 = np.linalg.norm(d, axis=1).reshape(n_pts, 1) d = d / (dim1.reshape(n_pts, 1)) dim2 = np.sum(n1 * d, axis=1).reshape(n_pts, 1) dim3 = np.sum(n2 * d, axis=1).reshape(n_pts, 1) dim4 = np.sum(n1 * n2, axis=1).reshape(n_pts, 1) ppf = np.concatenate((dim1, dim2, dim3, dim4), axis=1) ppf7d = np.concatenate((ppf, pts9d[:, 6:9]), axis=1) return ppf7d def compute_points_normal(pts): raw_shape = pts.shape normal = np.zeros((raw_shape)) # (r,c,3) t0 = time.time() for r in range(2, raw_shape[0] - 2): for c in range(2, raw_shape[1] - 2): pts_local = pts[r - 2:r + 3, c - 2:c + 3, :] # (5,5,3) pts_local = pts_local.reshape(-1, 3) # (N,3) pts_local = pts_local[np.linalg.norm(pts_local - pts[r, c, :], axis=1) < 0.1] # remove outliers. if pts_local.shape[0] < 4: continue pts_local = pts_local - np.mean(pts_local, axis=0) C = pts_local.T @ pts_local / pts_local.shape[0] e, v = np.linalg.eig(C) d = v[:, np.where(e == np.min(e))[0][0]] n = d / np.linalg.norm(d) if np.dot(n, np.array([0, 0, 1])) > 0: n = -n normal[r, c, :] = n t1 = time.time() print('preprocess data: compute normal cost {:.2f}s'.format(t1 - t0)) return normal # for depth adaptive 2d def partial_pts_2d(pts_rc, cen_rc, list_drdc): result = None r_max, c_max = int(image_height / 4 - 1), int(image_width / 4 - 1) mat_drdc = (np.array(list_drdc) / 4).astype(int) mat_cen_rc = np.array(cen_rc) mat_targ_rc = cen_rc + mat_drdc mat_targ_rc[mat_targ_rc < 0] = 0 targ_r = mat_targ_rc[:, 0] targ_r[targ_r > r_max] = r_max targ_c = mat_targ_rc[:, 1] targ_c[targ_c > c_max] = c_max result = pts_rc[targ_r, targ_c] return copy.deepcopy(result) # for depth adaptive 2d def partial_pts_2d_rc(pts_rc, cen_rc, list_drdc): result = None r_max, c_max = int(image_height / 4 - 1), int(image_width / 4 - 1) mat_drdc = (np.array(list_drdc) / 4).astype(int) mat_cen_rc = np.array(cen_rc) mat_targ_rc = cen_rc + mat_drdc mat_targ_rc[mat_targ_rc < 0] = 0 targ_r = mat_targ_rc[:, 0] targ_r[targ_r > r_max] = r_max targ_c = mat_targ_rc[:, 1] targ_c[targ_c > c_max] = c_max result = pts_rc[targ_r, targ_c] return copy.deepcopy(result), copy.deepcopy( np.concatenate((targ_r.reshape(targ_r.shape[0], 1), targ_c.reshape(targ_c.shape[0], 1)), axis=1)) # for depth adaptive 2d with dynamics label def partial_pts_2d_with_label(pts_rc, cen_rc, list_drdc, mask): # mask: 0 for static pixel, 255 for dynamic pixel. result = None r_max, c_max = int(image_height / 4 - 1), int(image_width / 4 - 1) mat_drdc = (np.array(list_drdc) / 4).astype(int) mat_cen_rc = np.array(cen_rc) mat_targ_rc = cen_rc + mat_drdc mat_targ_rc[mat_targ_rc < 0] = 0 targ_r = mat_targ_rc[:, 0] targ_r[targ_r > r_max] = r_max targ_c = mat_targ_rc[:, 1] targ_c[targ_c > c_max] = c_max m1 = np.zeros((mask.shape[0], mask.shape[1])) m1[mask == 0] = 1 m2 = np.zeros((mask.shape[0], mask.shape[1])) m2[targ_r, targ_c] = 1 m3 = np.logical_and(m1, m2) result = pts_rc[m3] return copy.deepcopy(result) class LevelDataset_PPF(Dataset): def __init__(self, data_dir, the_list, n_pts_per_frame=opt.n_pts_per_frame, neighbor_da2d=None, far_radius=None, enable_color_aug=True, specified_node=None): super().__init__() self.data_dir, self.the_list = data_dir, the_list self.n_pts_per_frame = n_pts_per_frame self.neighbor_da2d = neighbor_da2d # (n_pts, dim_pt). self.far_radius = far_radius # scalar. self.enable_color_aug = enable_color_aug self.specified_node = specified_node def __len__(self): return len(self.the_list) def __getitem__(self, idx): fid, rc_route = self.the_list[idx] # load depth = cv2.imread('{}/{}'.format(self.data_dir, opt.depth_format.format(fid)), cv2.IMREAD_UNCHANGED) / 1000.0 color = cv2.imread('{}/{}'.format(self.data_dir, opt.color_format.format(fid)), cv2.IMREAD_UNCHANGED)[:, :, 0:3] # color jitter if self.enable_color_aug: img = Image.fromarray(color) if np.random.rand() < 0.5: img = tfs.ColorJitter(brightness=1.)(img) if np.random.rand() < 0.5: img = tfs.ColorJitter(contrast=1.)(img) if np.random.rand() < 0.5: img = tfs.ColorJitter(saturation=1.)(img) color = np.array(img) if np.max(color) > 1: color = color / 255. - 0.5 local = depth2local(depth) r_ids, c_ids = list(range(0, image_height, 4)), list(range(0, image_width, 4)) depth, color, local = depth[r_ids, :][:, c_ids], color[r_ids, :, :][:, c_ids, :], local[r_ids, :, :][:, c_ids, :] # normal by 3d neighbor plane fitting. normal_path = '{}/frame-{:06d}.scaled.normal.npy'.format(self.data_dir, fid) if os.path.exists(normal_path): # print('fid {}'.format(fid)) # to debug rio10 scene09 10 # normal = np.load(normal_path) if os.path.getsize(normal_path) > 1: normal = np.load(normal_path, encoding='bytes', allow_pickle=True) else: normal = compute_points_normal(local) np.save(normal_path, normal) else: normal = compute_points_normal(local) np.save(normal_path, normal) lclnmlclr = np.concatenate((np.concatenate((local, normal), axis=2), color), axis=2) # build a patch rand_ids = np.arange(len(rc_route)) np.random.shuffle(rand_ids) selected_ids = rand_ids[0:self.n_pts_per_frame * 2] # more candidates pt_in = torch.zeros((self.n_pts_per_frame, 7, 1)) nb_in = torch.zeros((self.n_pts_per_frame, 7, opt.n_neighb_pts)) route_labs = torch.zeros((self.n_pts_per_frame, opt.tree_height - 1)).fill_(ary) rc_list = [] # da2d+3d neighbor if not self.neighbor_da2d is None: sid = 0 for tmp_idx in range(len(selected_ids)): r, c = rc_route[selected_ids[tmp_idx]][0], rc_route[selected_ids[tmp_idx]][1] if np.isnan(lclnmlclr[r, c, 3]): continue if self.specified_node: if not isSon(rc_route[selected_ids[tmp_idx]][2], self.specified_node): continue route_labs[sid] = torch.Tensor(rc_route[selected_ids[tmp_idx]][2]) rc_list.append([r, c]) pt_in[sid] = torch.Tensor( np.concatenate((np.array([[0.], [0.], [0.], [0.]]), color[r, c, 0:3][:, np.newaxis]), axis=0)) da2d_list = (np.array(self.neighbor_da2d) / depth[r, c]).astype(int) # ppf pts9d = shift_pts9d(sample_pts(partial_pts_2d(lclnmlclr, (r, c), da2d_list), opt.n_neighb_pts), lclnmlclr[r, c, :]) cen9d = copy.deepcopy(lclnmlclr[r, c, :]) cen9d[0:3] = np.zeros(3) ppf7d = make_ppf(pts9d, cen9d) # (N,9), (9,) ppf7d[np.isnan(ppf7d)] = 0.0 nb_in[sid] = torch.Tensor(ppf7d).transpose(1, 0) # remove background by 3d radius xyz = pts9d[:, 0:3] ids_out_of_bound = np.linalg.norm(xyz, axis=1) > self.far_radius nb_in[sid, :, ids_out_of_bound] = 0. # count sid += 1 if sid >= self.n_pts_per_frame: break pt_in = pt_in[:sid] nb_in = nb_in[:sid] route_labs = route_labs[:sid] return pt_in, nb_in, route_labs, fid, torch.Tensor(np.array(rc_list)) class TestDataset_PPF(Dataset): def __init__(self, data_dir, the_list, n_pts_per_frame=opt.n_pts_per_frame, neighbor_da2d=None): super().__init__() self.data_dir, self.the_list = data_dir, the_list self.n_pts_per_frame = n_pts_per_frame self.neighbor_da2d = neighbor_da2d # list of (n_pts, dim_pt) def __len__(self): return len(self.the_list) def __getitem__(self, idx): fid = self.the_list[idx] # load depth = cv2.imread('{}/{}'.format(self.data_dir, opt.depth_format.format(fid)), cv2.IMREAD_UNCHANGED) / 1000.0 color = cv2.imread('{}/{}'.format(self.data_dir, opt.color_format.format(fid)), cv2.IMREAD_UNCHANGED)[:, :, 0:3] if np.max(color) > 1: color = color / 255. - 0.5 local = depth2local(depth) r_ids, c_ids = list(range(0, image_height, 4)), list(range(0, image_width, 4)) depth, color, local = depth[r_ids, :][:, c_ids], color[r_ids, :, :][:, c_ids, :], local[r_ids, :, :][:, c_ids, :] # normal by 3d neighbor plane fitting. normal_path = '{}/frame-{:06d}.scaled.normal.npy'.format(self.data_dir, fid) if os.path.exists(normal_path): # normal = np.load(normal_path) if os.path.getsize(normal_path) > 1: normal = np.load(normal_path, encoding='bytes', allow_pickle=True) else: normal = compute_points_normal(local) np.save(normal_path, normal) else: normal = compute_points_normal(local) np.save(normal_path, normal) lclnmlclr = np.concatenate((np.concatenate((local, normal), axis=2), color), axis=2) # build a patch pt_in = torch.zeros((self.n_pts_per_frame, 7, 1)) nb_ms_in = torch.zeros((self.n_pts_per_frame, opt.tree_height - 1, 7, opt.n_neighb_pts)) route_labs = torch.zeros((self.n_pts_per_frame, opt.tree_height - 1)) r_max, c_max = int(image_height / 4 - 1), int(image_width / 4 - 1) rc_list = [] # da2d+3d neighbor if not self.neighbor_da2d is None: sid, count_crt, count_max = 0, 0, 9999 mask = np.zeros((r_max, c_max)) while len(rc_list) < self.n_pts_per_frame: # avoid infinite loop count_crt += 1 if count_crt > count_max: break r, c = np.random.randint(0, r_max), np.random.randint(0, c_max) if depth[r, c] == 0. or mask[r, c] == 1.: continue if np.isnan(lclnmlclr[r, c, 3]): continue mask[r, c] = 1. rc_list.append([r, c]) pt_in[sid] = torch.Tensor( np.concatenate((np.array([[0.], [0.], [0.], [0.]]), color[r, c, 0:3][:, np.newaxis]), axis=0)) for lid in range(opt.tree_height - 1): da2d_list = (np.array(self.neighbor_da2d[lid]) / depth[r, c]).astype(int) # ppf pts9d = shift_pts9d( sample_pts(partial_pts_2d(lclnmlclr, (r, c), da2d_list), opt.n_neighb_pts), lclnmlclr[r, c, :]) cen9d = copy.deepcopy(lclnmlclr[r, c, :]) cen9d[0:3] = np.zeros(3) ppf7d = make_ppf(pts9d, cen9d) # (N,9), (9,) ppf7d[np.isnan(ppf7d)] = 0.0 nb_ms_in[sid, lid, :, :] = torch.Tensor(ppf7d).transpose(1, 0) # remove background by 3d radius xyz = pts9d[:, 0:3] ids_out_of_bound = np.linalg.norm(xyz, axis=1) > opt.far_radiuses[lid] nb_ms_in[sid, lid, :, ids_out_of_bound] = 0. # count sid += 1 return pt_in, nb_ms_in, -1, fid, torch.Tensor(np.array(rc_list)) # # debug # if __name__ == '__main__': # print('done.')

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import numpy as np import torch from torch.utils.data import Dataset, DataLoader from torchvision import transforms as tfs from PIL import Image import os, cv2, copy, time from config import * image_height, image_width = opt.image_height, opt.image_width intrinsics = opt.intrinsics close_radius, far_radiuses = 0, opt.far_radiuses n_neighb_pts = opt.n_neighb_pts def isSon(son, fa): for i in range(len(fa)): if son[i] != fa[i]: return False return True def depth2local(depth): cx, cy, fx, fy = intrinsics[0, 2], intrinsics[1, 2], intrinsics[0, 0], intrinsics[1, 1] u_base = np.tile(np.arange(image_width), (image_height, 1)) v_base = np.tile(np.arange(image_height)[:, np.newaxis], (1, image_width)) X = (u_base - cx) * depth / fx Y = (v_base - cy) * depth / fy coord_local = np.stack((X, Y, depth), axis=2) return coord_local def partial_pts(pts_all_in, p, r_min, r_max): pts_all_in) p_mat = p[np.newaxis, 0:3].repeat(pts_all.shape[0], axis=0) norms = np.linalg.norm((p_mat - pts_all[:, 0:3]), axis=1) return pts_all[np.logical_and(norms >= r_min, norms <= r_max)] def sample_pts(pts_in, num): in) while pts.shape[0] < num: pts = np.concatenate((pts, pts), axis=0) rand_ids = np.arange(pts.shape[0]) np.random.shuffle(rand_ids) return pts[rand_ids[0:num], :] def sample_pts_rc(pts_in, rcs_in, num): in) rcs = copy.deepcopy(rcs_in) while pts.shape[0] < num: pts = np.concatenate((pts, pts), axis=0) rand_ids = np.arange(pts.shape[0]) np.random.shuffle(rand_ids) return pts[rand_ids[0:num], :], rcs_in[rand_ids[0:num], :] def sample_pts9d_r3d(pts_in, num, radius): in) thresh = 500 xyz = pts[:, 0:3] pts = pts[np.linalg.norm(xyz, axis=1) <= radius] if pts.shape[0] < thresh: return None while pts.shape[0] < num: pts = np.concatenate((pts, pts), axis=0) rand_ids = np.arange(pts.shape[0]) np.random.shuffle(rand_ids) return pts[rand_ids[0:num], :] def shift_pts(pts_in, cen): in) cen_mat = cen[np.newaxis, :].repeat(pts.shape[0], axis=0) pts[:, 0:3] = pts[:, 0:3] - cen_mat return pts def shift_pts6d(pts_in, cen): in) cen_mat = cen[np.newaxis, :].repeat(pts.shape[0], axis=0) pts[:, :] = pts[:, :] - cen_mat return pts def shift_pts9d(pts_in, cen): cpt[3:6] = np.zeros(3) pts = copy.deepcopy(pts_in) cpt_mat = cpt[np.newaxis, :].repeat(pts.shape[0], axis=0) pts[:, :] = pts[:, :] - cpt_mat return pts def make_ppf(pts9d, cen9d): n_pts = pts9d.shape[0] d = pts9d[:, 0:3] n2 = pts9d[:, 3:6] n1 = np.repeat(cen9d[3:6].reshape(1, 3), n_pts, axis=0) dim1 = np.linalg.norm(d, axis=1).reshape(n_pts, 1) d = d / (dim1.reshape(n_pts, 1)) dim2 = np.sum(n1 * d, axis=1).reshape(n_pts, 1) dim3 = np.sum(n2 * d, axis=1).reshape(n_pts, 1) dim4 = np.sum(n1 * n2, axis=1).reshape(n_pts, 1) ppf = np.concatenate((dim1, dim2, dim3, dim4), axis=1) ppf7d = np.concatenate((ppf, pts9d[:, 6:9]), axis=1) return ppf7d def compute_points_normal(pts): raw_shape = pts.shape normal = np.zeros((raw_shape)) t0 = time.time() for r in range(2, raw_shape[0] - 2): for c in range(2, raw_shape[1] - 2): pts_local = pts[r - 2:r + 3, c - 2:c + 3, :] pts_local = pts_local.reshape(-1, 3) pts_local = pts_local[np.linalg.norm(pts_local - pts[r, c, :], axis=1) < 0.1] if pts_local.shape[0] < 4: continue pts_local = pts_local - np.mean(pts_local, axis=0) C = pts_local.T @ pts_local / pts_local.shape[0] e, v = np.linalg.eig(C) d = v[:, np.where(e == np.min(e))[0][0]] n = d / np.linalg.norm(d) if np.dot(n, np.array([0, 0, 1])) > 0: n = -n normal[r, c, :] = n t1 = time.time() print('preprocess data: compute normal cost {:.2f}s'.format(t1 - t0)) return normal def partial_pts_2d(pts_rc, cen_rc, list_drdc): result = None r_max, c_max = int(image_height / 4 - 1), int(image_width / 4 - 1) mat_drdc = (np.array(list_drdc) / 4).astype(int) mat_cen_rc = np.array(cen_rc) mat_targ_rc = cen_rc + mat_drdc mat_targ_rc[mat_targ_rc < 0] = 0 targ_r = mat_targ_rc[:, 0] targ_r[targ_r > r_max] = r_max targ_c = mat_targ_rc[:, 1] targ_c[targ_c > c_max] = c_max result = pts_rc[targ_r, targ_c] return copy.deepcopy(result) def partial_pts_2d_rc(pts_rc, cen_rc, list_drdc): result = None r_max, c_max = int(image_height / 4 - 1), int(image_width / 4 - 1) mat_drdc = (np.array(list_drdc) / 4).astype(int) mat_cen_rc = np.array(cen_rc) mat_targ_rc = cen_rc + mat_drdc mat_targ_rc[mat_targ_rc < 0] = 0 targ_r = mat_targ_rc[:, 0] targ_r[targ_r > r_max] = r_max targ_c = mat_targ_rc[:, 1] targ_c[targ_c > c_max] = c_max result = pts_rc[targ_r, targ_c] return copy.deepcopy(result), copy.deepcopy( np.concatenate((targ_r.reshape(targ_r.shape[0], 1), targ_c.reshape(targ_c.shape[0], 1)), axis=1)) def partial_pts_2d_with_label(pts_rc, cen_rc, list_drdc, mask): result = None r_max, c_max = int(image_height / 4 - 1), int(image_width / 4 - 1) mat_drdc = (np.array(list_drdc) / 4).astype(int) mat_cen_rc = np.array(cen_rc) mat_targ_rc = cen_rc + mat_drdc mat_targ_rc[mat_targ_rc < 0] = 0 targ_r = mat_targ_rc[:, 0] targ_r[targ_r > r_max] = r_max targ_c = mat_targ_rc[:, 1] targ_c[targ_c > c_max] = c_max m1 = np.zeros((mask.shape[0], mask.shape[1])) m1[mask == 0] = 1 m2 = np.zeros((mask.shape[0], mask.shape[1])) m2[targ_r, targ_c] = 1 m3 = np.logical_and(m1, m2) result = pts_rc[m3] return copy.deepcopy(result) class LevelDataset_PPF(Dataset): def __init__(self, data_dir, the_list, n_pts_per_frame=opt.n_pts_per_frame, neighbor_da2d=None, far_radius=None, enable_color_aug=True, specified_node=None): super().__init__() self.data_dir, self.the_list = data_dir, the_list self.n_pts_per_frame = n_pts_per_frame self.neighbor_da2d = neighbor_da2d self.far_radius = far_radius self.enable_color_aug = enable_color_aug self.specified_node = specified_node def __len__(self): return len(self.the_list) def __getitem__(self, idx): fid, rc_route = self.the_list[idx] depth = cv2.imread('{}/{}'.format(self.data_dir, opt.depth_format.format(fid)), cv2.IMREAD_UNCHANGED) / 1000.0 color = cv2.imread('{}/{}'.format(self.data_dir, opt.color_format.format(fid)), cv2.IMREAD_UNCHANGED)[:, :, 0:3] if self.enable_color_aug: img = Image.fromarray(color) if np.random.rand() < 0.5: img = tfs.ColorJitter(brightness=1.)(img) if np.random.rand() < 0.5: img = tfs.ColorJitter(contrast=1.)(img) if np.random.rand() < 0.5: img = tfs.ColorJitter(saturation=1.)(img) color = np.array(img) if np.max(color) > 1: color = color / 255. - 0.5 local = depth2local(depth) r_ids, c_ids = list(range(0, image_height, 4)), list(range(0, image_width, 4)) depth, color, local = depth[r_ids, :][:, c_ids], color[r_ids, :, :][:, c_ids, :], local[r_ids, :, :][:, c_ids, :] normal_path = '{}/frame-{:06d}.scaled.normal.npy'.format(self.data_dir, fid) if os.path.exists(normal_path): os.path.getsize(normal_path) > 1: normal = np.load(normal_path, encoding='bytes', allow_pickle=True) else: normal = compute_points_normal(local) np.save(normal_path, normal) else: normal = compute_points_normal(local) np.save(normal_path, normal) lclnmlclr = np.concatenate((np.concatenate((local, normal), axis=2), color), axis=2) rand_ids = np.arange(len(rc_route)) np.random.shuffle(rand_ids) selected_ids = rand_ids[0:self.n_pts_per_frame * 2] pt_in = torch.zeros((self.n_pts_per_frame, 7, 1)) nb_in = torch.zeros((self.n_pts_per_frame, 7, opt.n_neighb_pts)) route_labs = torch.zeros((self.n_pts_per_frame, opt.tree_height - 1)).fill_(ary) rc_list = [] if not self.neighbor_da2d is None: sid = 0 for tmp_idx in range(len(selected_ids)): r, c = rc_route[selected_ids[tmp_idx]][0], rc_route[selected_ids[tmp_idx]][1] if np.isnan(lclnmlclr[r, c, 3]): continue if self.specified_node: if not isSon(rc_route[selected_ids[tmp_idx]][2], self.specified_node): continue route_labs[sid] = torch.Tensor(rc_route[selected_ids[tmp_idx]][2]) rc_list.append([r, c]) pt_in[sid] = torch.Tensor( np.concatenate((np.array([[0.], [0.], [0.], [0.]]), color[r, c, 0:3][:, np.newaxis]), axis=0)) da2d_list = (np.array(self.neighbor_da2d) / depth[r, c]).astype(int) pts9d = shift_pts9d(sample_pts(partial_pts_2d(lclnmlclr, (r, c), da2d_list), opt.n_neighb_pts), lclnmlclr[r, c, :]) cen9d = copy.deepcopy(lclnmlclr[r, c, :]) cen9d[0:3] = np.zeros(3) ppf7d = make_ppf(pts9d, cen9d) ppf7d[np.isnan(ppf7d)] = 0.0 nb_in[sid] = torch.Tensor(ppf7d).transpose(1, 0) xyz = pts9d[:, 0:3] ids_out_of_bound = np.linalg.norm(xyz, axis=1) > self.far_radius nb_in[sid, :, ids_out_of_bound] = 0. sid += 1 if sid >= self.n_pts_per_frame: break pt_in = pt_in[:sid] nb_in = nb_in[:sid] route_labs = route_labs[:sid] return pt_in, nb_in, route_labs, fid, torch.Tensor(np.array(rc_list)) class TestDataset_PPF(Dataset): def __init__(self, data_dir, the_list, n_pts_per_frame=opt.n_pts_per_frame, neighbor_da2d=None): super().__init__() self.data_dir, self.the_list = data_dir, the_list self.n_pts_per_frame = n_pts_per_frame self.neighbor_da2d = neighbor_da2d def __len__(self): return len(self.the_list) def __getitem__(self, idx): fid = self.the_list[idx] depth = cv2.imread('{}/{}'.format(self.data_dir, opt.depth_format.format(fid)), cv2.IMREAD_UNCHANGED) / 1000.0 color = cv2.imread('{}/{}'.format(self.data_dir, opt.color_format.format(fid)), cv2.IMREAD_UNCHANGED)[:, :, 0:3] if np.max(color) > 1: color = color / 255. - 0.5 local = depth2local(depth) r_ids, c_ids = list(range(0, image_height, 4)), list(range(0, image_width, 4)) depth, color, local = depth[r_ids, :][:, c_ids], color[r_ids, :, :][:, c_ids, :], local[r_ids, :, :][:, c_ids, :] normal_path = '{}/frame-{:06d}.scaled.normal.npy'.format(self.data_dir, fid) if os.path.exists(normal_path): if os.path.getsize(normal_path) > 1: normal = np.load(normal_path, encoding='bytes', allow_pickle=True) else: normal = compute_points_normal(local) np.save(normal_path, normal) else: normal = compute_points_normal(local) np.save(normal_path, normal) lclnmlclr = np.concatenate((np.concatenate((local, normal), axis=2), color), axis=2) pt_in = torch.zeros((self.n_pts_per_frame, 7, 1)) nb_ms_in = torch.zeros((self.n_pts_per_frame, opt.tree_height - 1, 7, opt.n_neighb_pts)) route_labs = torch.zeros((self.n_pts_per_frame, opt.tree_height - 1)) r_max, c_max = int(image_height / 4 - 1), int(image_width / 4 - 1) rc_list = [] if not self.neighbor_da2d is None: sid, count_crt, count_max = 0, 0, 9999 mask = np.zeros((r_max, c_max)) while len(rc_list) < self.n_pts_per_frame: count_crt += 1 if count_crt > count_max: break r, c = np.random.randint(0, r_max), np.random.randint(0, c_max) if depth[r, c] == 0. or mask[r, c] == 1.: continue if np.isnan(lclnmlclr[r, c, 3]): continue mask[r, c] = 1. rc_list.append([r, c]) pt_in[sid] = torch.Tensor( np.concatenate((np.array([[0.], [0.], [0.], [0.]]), color[r, c, 0:3][:, np.newaxis]), axis=0)) for lid in range(opt.tree_height - 1): da2d_list = (np.array(self.neighbor_da2d[lid]) / depth[r, c]).astype(int) pts9d = shift_pts9d( sample_pts(partial_pts_2d(lclnmlclr, (r, c), da2d_list), opt.n_neighb_pts), lclnmlclr[r, c, :]) cen9d = copy.deepcopy(lclnmlclr[r, c, :]) cen9d[0:3] = np.zeros(3) ppf7d = make_ppf(pts9d, cen9d) ppf7d[np.isnan(ppf7d)] = 0.0 nb_ms_in[sid, lid, :, :] = torch.Tensor(ppf7d).transpose(1, 0) xyz = pts9d[:, 0:3] ids_out_of_bound = np.linalg.norm(xyz, axis=1) > opt.far_radiuses[lid] nb_ms_in[sid, lid, :, ids_out_of_bound] = 0. sid += 1 return pt_in, nb_ms_in, -1, fid, torch.Tensor(np.array(rc_list))

true

f723e84d3365845116c25dda9340902ab173b6cd

799

py

Python

core/data/dataloader/__init__.py

HareshKarnan/awesome-semantic-segmentation-pytorch

3c53fc004973abcb88882dcc8be899570c3053cf

[ "Apache-2.0" ]

null

core/data/dataloader/__init__.py

HareshKarnan/awesome-semantic-segmentation-pytorch

3c53fc004973abcb88882dcc8be899570c3053cf

[ "Apache-2.0" ]

null

core/data/dataloader/__init__.py

HareshKarnan/awesome-semantic-segmentation-pytorch

3c53fc004973abcb88882dcc8be899570c3053cf

[ "Apache-2.0" ]

null

""" This module provides data loaders and transformers for popular vision datasets. """ from .mscoco import COCOSegmentation from .cityscapes import CitySegmentation from .ade import ADE20KSegmentation from .pascal_voc import VOCSegmentation from .pascal_aug import VOCAugSegmentation from .sbu_shadow import SBUSegmentation from .ycb import YCBSegmentation from .robocup import RobocupSegmentation datasets = { 'ade20k': ADE20KSegmentation, 'pascal_voc': VOCSegmentation, 'pascal_aug': VOCAugSegmentation, 'coco': COCOSegmentation, 'citys': CitySegmentation, 'sbu': SBUSegmentation, 'ycb': YCBSegmentation, 'robocup': RobocupSegmentation, } def get_segmentation_dataset(name, **kwargs): """Segmentation Datasets""" return datasets[name.lower()](**kwargs)

28.535714

79

0.767209

from .mscoco import COCOSegmentation from .cityscapes import CitySegmentation from .ade import ADE20KSegmentation from .pascal_voc import VOCSegmentation from .pascal_aug import VOCAugSegmentation from .sbu_shadow import SBUSegmentation from .ycb import YCBSegmentation from .robocup import RobocupSegmentation datasets = { 'ade20k': ADE20KSegmentation, 'pascal_voc': VOCSegmentation, 'pascal_aug': VOCAugSegmentation, 'coco': COCOSegmentation, 'citys': CitySegmentation, 'sbu': SBUSegmentation, 'ycb': YCBSegmentation, 'robocup': RobocupSegmentation, } def get_segmentation_dataset(name, **kwargs): return datasets[name.lower()](**kwargs)

true

f723e8b52caef4e5fb333357c7a8f97adccc1b2a

11,436

py

Python

src/roslaunch2/package.py

CodeFinder2/roslaunch2

5c2aa58129671647aa8e5cbc0541caf280accffb

[ "BSD-3-Clause" ]

10

2019-11-19T12:35:30.000Z

2022-01-16T15:59:44.000Z

src/roslaunch2/package.py

CodeFinder2/roslaunch2

5c2aa58129671647aa8e5cbc0541caf280accffb

[ "BSD-3-Clause" ]

1

2022-01-11T09:30:36.000Z

2022-02-07T22:03:36.000Z

src/roslaunch2/package.py

CodeFinder2/roslaunch2

5c2aa58129671647aa8e5cbc0541caf280accffb

[ "BSD-3-Clause" ]

3

2019-08-01T08:50:00.000Z

2021-05-02T01:27:47.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Author: Adrian Böckenkamp # License: BSD (https://opensource.org/licenses/BSD-3-Clause) # Date: 08/06/2020 import rospkg import os import sys import Pyro4 from . import logger class Package: """ Encapsulates a ROS package and its ability to find files in the package directory structure. A caching mechanism is used to speedup `*find()` commands. """ __pkg_cache = {} __dir_cache = {} __find_cache = {} @staticmethod def invalidate_cache(): """ Invalidates the package, directory and file cache for finding packages, enforcing re-lookups. """ Package.__pkg_cache = {} Package.__dir_cache = {} Package.__find_cache = {} @staticmethod def get_paths_to_file(start_dir, file_comp): """ Searches for file_comp in `start_dir` recursively (also using a cache for speedup). :param start_dir: root directory where to start the search :param file_comp: file path component (like some/dir/myfile.xml) name to search for :return: Set of files found (with their full path) """ file_name = os.path.basename(file_comp) dir_comp = os.path.dirname(file_comp) result = [] if start_dir in Package.__dir_cache: # use cached file listing of $start_dir for root, file_set in Package.__dir_cache[start_dir]: for a_file in file_set: if a_file == file_name and root.endswith(dir_comp): result.append(os.path.join(root, a_file)) else: # crawl the file system at $start_dir (and cache for future requests) cache_entry = [] for root, _, file_set in os.walk(start_dir): cache_entry.append((root, file_set)) for a_file in file_set: if a_file == file_name and root.endswith(dir_comp): result.append(os.path.join(root, a_file)) Package.__dir_cache[start_dir] = cache_entry return result @staticmethod def __get_pkg_path_cached(name): """ Tries to find the given package name in the cache. If its not present in the cache, the cache is updated by a (slower) filesystem lookup. :param name: Name of ROS package :return: Path to package """ if name not in Package.__pkg_cache: Package.__pkg_cache[name] = rospkg.RosPack().get_path(name) # may throws rospkg.ResourceNotFound return Package.__pkg_cache[name] def __init__(self, name=None, silent=False): """ Initializes the ROS package given its name. The path to the package will automatically be resolved on construction. :param name: Name of ROS package :param silent: True if no exceptions should be thrown if the package was not found """ try: self.set_name(name) except rospkg.ResourceNotFound: if not silent: raise @Pyro4.expose def get_name(self): """ Returns the package name. :return: ROS package name """ return self.__name @Pyro4.expose def set_name(self, name): """ Updates/sets the package name. :param name: ROS package name """ self.__name = name self.__path = Package.__get_pkg_path_cached(name) name = property(get_name, set_name) @Pyro4.expose def get_path(self): """ Retrieves the package path. :return: ROS package path """ return self.__path def _set_path(self, pkg_path): # not exposed to Pyro! if self.__name: self.__path = pkg_path else: self.__path = None def __nonzero__(self): return bool(self.__path) # for Python 2.x def __bool__(self): return self.__nonzero__() # for Python 3.x path = property(get_path, _set_path) def __str__(self): return self.__name @staticmethod def valid(pkg): """ Tests whether pkg is a valid ROS package on the current system. :param pkg: Name of ROS package (type: str) or a valid package.Package object) :return: Path to pkg if valid or None if not found """ try: if type(pkg) is str: name = pkg elif isinstance(pkg, Package): name = pkg.name else: raise ValueError('Cannot process type {}'.format(str(type(pkg)))) return Package.__get_pkg_path_cached(name) except rospkg.ResourceNotFound: return None def has_node(self, node_name, warn=True): """ Tests if a ROS node actually exists. This method checks whether a ROS node named $node_name exists in the current ROS package. :param node_name: name of ROS node to test :param warn: True if a warning about the missing node should be emitted :return: True if node exists, False otherwise """ pkg = os.path.join(self.__path, '../..') # Just consider files that are executable: if [f for f in Package.get_paths_to_file(pkg, node_name) if os.access(f, os.X_OK)]: # if len(res) > 1: # log.warning("Found {} executable files named {}, assuming existence." # .format(len(res), node_name, res[0])) return True else: if warn: logger.warning("Node '{}' in package '{}' not found.".format(node_name, self.__name)) return False @staticmethod def include(pkg_name, path_comp, **kwargs): """ Like use() but static for convenience. :param pkg_name: Name of ROS package to be used for search of path_comp :param path_comp: (partial) path or file name to launch module (if it does not end with .pyl, this is added automatically) :param kwargs: optional arguments to be passed to the main() function of the launch module :return: GeneratorBase object as returned by the main() function """ assert type(pkg_name) is str return Package(pkg_name).use(path_comp, **kwargs) def use(self, path_comp, **kwargs): """ Imports (aka uses) the content of a launch module located in the current package (self). :param path_comp: (partial) path or file name to launch module (if it does not end with .pyl, this is added automatically) :param kwargs: optional arguments to be passed to the main() function of the launch module :return: GeneratorBase object as returned by the main() function """ if not os.path.splitext(path_comp)[1]: path_comp += '.pyl' mod_path = self.find(path_comp, True) if not mod_path: raise ValueError("Launch module '{:s}' in package '{:s}' not found.".format(path_comp, self.__name)) m = Package.import_launch_module(mod_path) return m.main(**kwargs) @staticmethod def import_launch_module(full_module_path): """ Rather internal helper function for important a Python module (i. e., a roslaunch2 launch module/file). This function handles all various cases related to different versions of Python. :param full_module_path: Full path to module file :return: Handle to imported module (like "foo" in "import bar as foo") """ if sys.version_info < (2, 4): # Python < 2.4 is not supported raise RuntimeError('Must use Python version >= 2.4!') if not os.path.isfile(full_module_path): raise ValueError("Launch module '{:s}' not found.".format(full_module_path)) module_name = os.path.splitext(full_module_path)[0] # Hot-patch PYTHONPATH to find . imports: search_path = os.path.dirname(os.path.abspath(module_name)) if search_path not in sys.path: sys.path.append(search_path) if sys.version_info < (3, 3): # Python 2.x and 3.y where x >= 4 and y < 3 import imp return imp.load_source(module_name, full_module_path) elif sys.version_info < (3, 4): # Python 3.3 and 3.4 import importlib.machinery return importlib.machinery.SourceFileLoader(module_name, full_module_path).load_module() elif sys.version_info >= (3, 5): # Python 3.5+ import importlib.util import importlib.machinery # Allow any extenstions (not only .py and .so, and .pyl in particular): importlib.machinery.SOURCE_SUFFIXES.append('') spec = importlib.util.spec_from_file_location(module_name, full_module_path) m = importlib.util.module_from_spec(spec) spec.loader.exec_module(m) return m @Pyro4.expose def find(self, path_comp, silent=False): """ Searches for a file or directory in the current package (self). :param path_comp: (partial) path or file name :param silent: if True return None when nothing is found, otherwise an IOError is raised in case of failure :return: first found file (full path) or None if silent==True and nothing found """ key = ''.join([self.__name, path_comp]) if key in Package.__find_cache: return Package.__find_cache[key] if not path_comp: return self.__path dir_path = os.path.join(self.__path, path_comp if not path_comp.startswith(os.path.sep) else path_comp[1:]) if os.path.isdir(dir_path): Package.__find_cache[key] = dir_path return dir_path f = Package.get_paths_to_file(self.__path, path_comp) if len(f) > 1: logger.log("Found {} files, unique selection impossible (using first).".format(', '.join(f))) if not f: if not silent: raise IOError("No files like '{}' found in '{}'.".format(path_comp, self.__name)) else: return None Package.__find_cache[key] = f[0] return f[0] @Pyro4.expose def selective_find(self, path_comp_options, path_comp_prefix='', silent=False): """ Searches for a set of files or directories in the current package (self). Tries to find any path from the path_comp_options list starting at the first element. Once a path is found the search for the remaining paths is canceled and the found path is returned. :param path_comp_options: list of (partial) path or file names :param path_comp_prefix: prefix to each element of path_comp_options :param silent: if True return None when nothing is found, otherwise an IOError is raised in case of failure :return: first found file (full path) or None if silent==True and nothing found """ for path_comp in path_comp_options: path = self.find(path_comp=os.path.join(path_comp_prefix, path_comp), silent=True) if path is not None: return path # Nothing found if not silent: raise IOError("None of the queried files found in '{}'.".format(self.__name)) else: return None

39.030717

119

0.612977

import rospkg import os import sys import Pyro4 from . import logger class Package: __pkg_cache = {} __dir_cache = {} __find_cache = {} @staticmethod def invalidate_cache(): Package.__pkg_cache = {} Package.__dir_cache = {} Package.__find_cache = {} @staticmethod def get_paths_to_file(start_dir, file_comp): file_name = os.path.basename(file_comp) dir_comp = os.path.dirname(file_comp) result = [] if start_dir in Package.__dir_cache: for root, file_set in Package.__dir_cache[start_dir]: for a_file in file_set: if a_file == file_name and root.endswith(dir_comp): result.append(os.path.join(root, a_file)) else: cache_entry = [] for root, _, file_set in os.walk(start_dir): cache_entry.append((root, file_set)) for a_file in file_set: if a_file == file_name and root.endswith(dir_comp): result.append(os.path.join(root, a_file)) Package.__dir_cache[start_dir] = cache_entry return result @staticmethod def __get_pkg_path_cached(name): if name not in Package.__pkg_cache: Package.__pkg_cache[name] = rospkg.RosPack().get_path(name) return Package.__pkg_cache[name] def __init__(self, name=None, silent=False): try: self.set_name(name) except rospkg.ResourceNotFound: if not silent: raise @Pyro4.expose def get_name(self): return self.__name @Pyro4.expose def set_name(self, name): self.__name = name self.__path = Package.__get_pkg_path_cached(name) name = property(get_name, set_name) @Pyro4.expose def get_path(self): return self.__path def _set_path(self, pkg_path): if self.__name: self.__path = pkg_path else: self.__path = None def __nonzero__(self): return bool(self.__path) def __bool__(self): return self.__nonzero__() path = property(get_path, _set_path) def __str__(self): return self.__name @staticmethod def valid(pkg): try: if type(pkg) is str: name = pkg elif isinstance(pkg, Package): name = pkg.name else: raise ValueError('Cannot process type {}'.format(str(type(pkg)))) return Package.__get_pkg_path_cached(name) except rospkg.ResourceNotFound: return None def has_node(self, node_name, warn=True): pkg = os.path.join(self.__path, '../..') if [f for f in Package.get_paths_to_file(pkg, node_name) if os.access(f, os.X_OK)]: return True else: if warn: logger.warning("Node '{}' in package '{}' not found.".format(node_name, self.__name)) return False @staticmethod def include(pkg_name, path_comp, **kwargs): assert type(pkg_name) is str return Package(pkg_name).use(path_comp, **kwargs) def use(self, path_comp, **kwargs): if not os.path.splitext(path_comp)[1]: path_comp += '.pyl' mod_path = self.find(path_comp, True) if not mod_path: raise ValueError("Launch module '{:s}' in package '{:s}' not found.".format(path_comp, self.__name)) m = Package.import_launch_module(mod_path) return m.main(**kwargs) @staticmethod def import_launch_module(full_module_path): if sys.version_info < (2, 4): raise RuntimeError('Must use Python version >= 2.4!') if not os.path.isfile(full_module_path): raise ValueError("Launch module '{:s}' not found.".format(full_module_path)) module_name = os.path.splitext(full_module_path)[0] search_path = os.path.dirname(os.path.abspath(module_name)) if search_path not in sys.path: sys.path.append(search_path) if sys.version_info < (3, 3): import imp return imp.load_source(module_name, full_module_path) elif sys.version_info < (3, 4): import importlib.machinery return importlib.machinery.SourceFileLoader(module_name, full_module_path).load_module() elif sys.version_info >= (3, 5): import importlib.util import importlib.machinery importlib.machinery.SOURCE_SUFFIXES.append('') spec = importlib.util.spec_from_file_location(module_name, full_module_path) m = importlib.util.module_from_spec(spec) spec.loader.exec_module(m) return m @Pyro4.expose def find(self, path_comp, silent=False): key = ''.join([self.__name, path_comp]) if key in Package.__find_cache: return Package.__find_cache[key] if not path_comp: return self.__path dir_path = os.path.join(self.__path, path_comp if not path_comp.startswith(os.path.sep) else path_comp[1:]) if os.path.isdir(dir_path): Package.__find_cache[key] = dir_path return dir_path f = Package.get_paths_to_file(self.__path, path_comp) if len(f) > 1: logger.log("Found {} files, unique selection impossible (using first).".format(', '.join(f))) if not f: if not silent: raise IOError("No files like '{}' found in '{}'.".format(path_comp, self.__name)) else: return None Package.__find_cache[key] = f[0] return f[0] @Pyro4.expose def selective_find(self, path_comp_options, path_comp_prefix='', silent=False): for path_comp in path_comp_options: path = self.find(path_comp=os.path.join(path_comp_prefix, path_comp), silent=True) if path is not None: return path if not silent: raise IOError("None of the queried files found in '{}'.".format(self.__name)) else: return None

true

f723e8f7914d47ae6d2455622f8a55aacd1c6ccf

72

py

Python

cibin/__init__.py

betochimasan/AntibodyCocktailEfficiency

67850eb85d502af80b691b49001803f2e3091a0b

[ "BSD-3-Clause" ]

null

cibin/__init__.py

betochimasan/AntibodyCocktailEfficiency

67850eb85d502af80b691b49001803f2e3091a0b

[ "BSD-3-Clause" ]

null

cibin/__init__.py

betochimasan/AntibodyCocktailEfficiency

67850eb85d502af80b691b49001803f2e3091a0b

[ "BSD-3-Clause" ]

null

""" The Cibin package. """ __version__ = "0.0.1" from .cibin import *

9

21

0.611111

__version__ = "0.0.1" from .cibin import *

true

f723e91ac20eac5aead5f13782edc311af79e501

4,293

py

Python

python/cairo/text_align_center.py

jeremiedecock/snippets

4bd4e7f459eee610d5cf19f845299ca942ff4b64

[ "MIT" ]

23

2015-06-08T13:01:00.000Z

2021-12-30T08:20:04.000Z

python/cairo/text_align_center.py

jeremiedecock/snippets

4bd4e7f459eee610d5cf19f845299ca942ff4b64

[ "MIT" ]

1

2020-10-22T02:36:10.000Z

python/cairo/text_align_center.py

jeremiedecock/snippets

4bd4e7f459eee610d5cf19f845299ca942ff4b64

[ "MIT" ]

7

2017-10-31T09:48:14.000Z

2022-01-04T15:59:45.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # Copyright (c) 2014 Jérémie DECOCK (http://www.jdhp.org) # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # SEE: # - http://cairographics.org/samples/ # - http://cairographics.org/documentation/pycairo/3/reference/index.html # - http://cairographics.org/pycairo/tutorial/ # - http://www.tortall.net/mu/wiki/CairoTutorial import cairo import math WIDTH, HEIGHT = 400, 400 def main(): """Main function""" # Image surfaces provide the ability to render to memory buffers either # allocated by cairo or by the calling code. # List of supported surfaces: http://www.cairographics.org/manual/cairo-surfaces.html surface = cairo.SVGSurface("text_align_center.svg", WIDTH, HEIGHT) # cairo.Context is the object that you send your drawing commands to. context = cairo.Context(surface) ### DRAW ### # context.set_source_rgb(0., 0., 0.) # context.set_source_rgba(0., 0., 0., 1.) # Sets the source pattern within context to an opaque color. This opaque color # will then be used for any subsequent drawing operation until a new source # pattern is set. # The color components are floating point numbers in the range 0 to 1. If # the values passed in are outside that range, they will be clamped. # The default source pattern is opaque black, (that is, it is equivalent to # cairo_set_source_rgb(context, 0.0, 0.0, 0.0)). # Using set_source_rgb(r, g, b) is equivalent to using # set_source_rgba(r, g, b, 1.0), and it sets your source color to use # full opacity. # # context.stroke() # The stroke() operation takes a virtual pen along the current path # according to the current line width, line join, line cap, and dash # settings. After cairo_stroke(), the current path will be cleared from # the cairo context. # See http://www.cairographics.org/manual/cairo-cairo-t.html#cairo-stroke # # context.fill() # A drawing operator that fills the current path according to the current # fill rule, (each sub-path is implicitly closed before being filled). # After cairo_fill(), the current path will be cleared from the cairo # context. # See http://www.cairographics.org/manual/cairo-cairo-t.html#cairo-fill context.set_line_width(0.02) context.set_source_rgb(1, 1, 1) context.rectangle(0, 0, WIDTH, HEIGHT) context.fill() # TEXT context.set_source_rgb(0, 0, 0) context.select_font_face("Sans", cairo.FONT_SLANT_NORMAL, cairo.FONT_WEIGHT_NORMAL) context.set_font_size(52.0) (x, y, width, height, dx, dy) = context.text_extents("Hello") context.move_to(WIDTH/2. - width/2., HEIGHT/2. + height/2.) context.show_text("Hello") # DRAW HELPING LINES context.set_source_rgba(1, 0.2, 0.2, 0.6) context.set_line_width(6.0) context.arc(WIDTH/2. - width/2., HEIGHT/2. + height/2., 5, 0, math.radians(360)) context.fill() context.move_to(WIDTH/2., 0) context.line_to(WIDTH/2, HEIGHT) context.stroke() context.move_to(0, HEIGHT/2.) context.line_to(WIDTH, HEIGHT/2.) context.stroke() ### WRITE THE SVG FILE ### surface.finish() if __name__ == '__main__': main()

37.008621

89

0.699278

import cairo import math WIDTH, HEIGHT = 400, 400 def main(): surface = cairo.SVGSurface("text_align_center.svg", WIDTH, HEIGHT) context = cairo.Context(surface) xt.set_line_width(0.02) context.set_source_rgb(1, 1, 1) context.rectangle(0, 0, WIDTH, HEIGHT) context.fill() context.set_source_rgb(0, 0, 0) context.select_font_face("Sans", cairo.FONT_SLANT_NORMAL, cairo.FONT_WEIGHT_NORMAL) context.set_font_size(52.0) (x, y, width, height, dx, dy) = context.text_extents("Hello") context.move_to(WIDTH/2. - width/2., HEIGHT/2. + height/2.) context.show_text("Hello") context.set_source_rgba(1, 0.2, 0.2, 0.6) context.set_line_width(6.0) context.arc(WIDTH/2. - width/2., HEIGHT/2. + height/2., 5, 0, math.radians(360)) context.fill() context.move_to(WIDTH/2., 0) context.line_to(WIDTH/2, HEIGHT) context.stroke() context.move_to(0, HEIGHT/2.) context.line_to(WIDTH, HEIGHT/2.) context.stroke() main()

true

f723e929ff2525653af0e5fc4da9eb8c8a0a5165

532

py

Python

deploy.py

oneofthezombies/cpp-enum-class-string-idl

e1188e6a94d5c7c6a4bc6a7d025b12f4d22c53e1

[ "MIT" ]

1

2021-05-08T08:34:09.000Z

deploy.py

oneofthezombies/argparse-best-practice

c29bffb014bb66d7694e4e27ad2911cc5abb3eef

[ "MIT" ]

null

deploy.py

oneofthezombies/argparse-best-practice

c29bffb014bb66d7694e4e27ad2911cc5abb3eef

[ "MIT" ]

null

from shutil import rmtree from subprocess import run from pathlib import Path from itertools import chain from more_itertools import consume dirnames = ['build', 'dist'] paths = map(lambda path: Path(path), dirnames) outputs = chain(paths, Path().glob('*.egg-info')) exists = filter(lambda path: path.exists(), outputs) deletes = map(lambda path: rmtree(path), exists) consume(deletes) run(['python3', 'setup.py', 'sdist', 'bdist_wheel']).check_returncode() run(['python3', '-m', 'twine', 'upload', 'dist/*']).check_returncode()

31.294118

71

0.721805

from shutil import rmtree from subprocess import run from pathlib import Path from itertools import chain from more_itertools import consume dirnames = ['build', 'dist'] paths = map(lambda path: Path(path), dirnames) outputs = chain(paths, Path().glob('*.egg-info')) exists = filter(lambda path: path.exists(), outputs) deletes = map(lambda path: rmtree(path), exists) consume(deletes) run(['python3', 'setup.py', 'sdist', 'bdist_wheel']).check_returncode() run(['python3', '-m', 'twine', 'upload', 'dist/*']).check_returncode()

true

f723ea0c1c6678a854eaf1b914ce06a246ec9ada

32,902

py

Python

api/registrations/serializers.py

mattclarkcos/osf.io

0e5ee8c0ff2a9bc7449061124ac8ce6d00f775ca

[ "Apache-2.0" ]

null

api/registrations/serializers.py

mattclarkcos/osf.io

0e5ee8c0ff2a9bc7449061124ac8ce6d00f775ca

[ "Apache-2.0" ]

4

2022-02-26T03:28:02.000Z

2022-03-08T23:36:45.000Z

api/registrations/serializers.py

mattclarkcos/osf.io

0e5ee8c0ff2a9bc7449061124ac8ce6d00f775ca

[ "Apache-2.0" ]

null

import pytz import json from unicodedata import normalize from distutils.version import StrictVersion from django.core.exceptions import ValidationError from rest_framework import serializers as ser from rest_framework import exceptions from api.base.exceptions import Conflict, InvalidModelValueError, JSONAPIException from api.base.serializers import is_anonymized from api.base.utils import absolute_reverse, get_user_auth, is_truthy from api.base.versioning import CREATE_REGISTRATION_FIELD_CHANGE_VERSION from website.project.model import NodeUpdateError from api.files.serializers import OsfStorageFileSerializer from api.nodes.serializers import ( NodeSerializer, NodeStorageProviderSerializer, NodeLicenseRelationshipField, NodeLinksSerializer, update_institutions, NodeLicenseSerializer, NodeContributorsSerializer, RegistrationProviderRelationshipField, get_license_details, ) from api.base.serializers import ( IDField, RelationshipField, LinksField, HideIfWithdrawal, FileRelationshipField, NodeFileHyperLinkField, HideIfRegistration, ShowIfVersion, VersionedDateTimeField, ValuesListField, ) from framework.auth.core import Auth from osf.exceptions import ValidationValueError, NodeStateError from osf.models import Node, AbstractNode from osf.utils.registrations import strip_registered_meta_comments from framework.sentry import log_exception class RegistrationSerializer(NodeSerializer): admin_only_editable_fields = [ 'custom_citation', 'is_pending_retraction', 'is_public', 'withdrawal_justification', ] # Remember to add new RegistrationSerializer fields to this list # if you don't need them to be anonymized non_anonymized_fields = NodeSerializer.non_anonymized_fields + [ 'archiving', 'article_doi', 'date_registered', 'date_withdrawn', 'embargo_end_date', 'embargoed', 'pending_embargo_approval', 'pending_embargo_termination_approval', 'pending_registration_approval', 'pending_withdrawal', 'provider', 'registered_by', 'registered_from', 'registered_meta', 'registration_responses', 'registration_schema', 'registration_supplement', 'withdrawal_justification', 'withdrawn', ] reviews_state = ser.CharField(source='moderation_state', read_only=True) title = ser.CharField(read_only=True) description = ser.CharField(required=False, allow_blank=True, allow_null=True) category_choices = NodeSerializer.category_choices category_choices_string = NodeSerializer.category_choices_string category = ser.ChoiceField(required=False, choices=category_choices, help_text='Choices: ' + category_choices_string) date_modified = VersionedDateTimeField(source='last_logged', read_only=True) fork = HideIfWithdrawal(ser.BooleanField(read_only=True, source='is_fork')) collection = HideIfWithdrawal(ser.BooleanField(read_only=True, source='is_collection')) access_requests_enabled = HideIfWithdrawal(ser.BooleanField(read_only=True)) node_license = HideIfWithdrawal(NodeLicenseSerializer(required=False, source='license')) tags = HideIfWithdrawal(ValuesListField(attr_name='name', child=ser.CharField(), required=False)) article_doi = ser.CharField(required=False, allow_null=True) public = HideIfWithdrawal(ser.BooleanField( source='is_public', required=False, help_text='Nodes that are made public will give read-only access ' 'to everyone. Private nodes require explicit read ' 'permission. Write and admin access are the same for ' 'public and private nodes. Administrators on a parent ' 'node have implicit read permissions for all child nodes', )) current_user_permissions = HideIfWithdrawal(ser.SerializerMethodField( help_text='List of strings representing the permissions ' 'for the current user on this node.', )) pending_embargo_approval = HideIfWithdrawal(ser.BooleanField( read_only=True, source='is_pending_embargo', help_text='The associated Embargo is awaiting approval by project admins.', )) pending_embargo_termination_approval = HideIfWithdrawal(ser.BooleanField( read_only=True, source='is_pending_embargo_termination', help_text='The associated Embargo early termination is awaiting approval by project admins', )) embargoed = HideIfWithdrawal(ser.BooleanField(read_only=True, source='is_embargoed')) pending_registration_approval = HideIfWithdrawal(ser.BooleanField( source='is_pending_registration', read_only=True, help_text='The associated RegistrationApproval is awaiting approval by project admins.', )) archiving = HideIfWithdrawal(ser.BooleanField(read_only=True)) pending_withdrawal = HideIfWithdrawal(ser.BooleanField( source='is_pending_retraction', read_only=True, help_text='The registration is awaiting withdrawal approval by project admins.', )) withdrawn = ser.BooleanField( source='is_retracted', read_only=True, help_text='The registration has been withdrawn.', ) has_project = ser.SerializerMethodField() date_registered = VersionedDateTimeField(source='registered_date', read_only=True, help_text='Date time of registration.') date_withdrawn = VersionedDateTimeField(read_only=True, help_text='Date time of when this registration was retracted.') embargo_end_date = HideIfWithdrawal(ser.SerializerMethodField(help_text='When the embargo on this registration will be lifted.')) custom_citation = HideIfWithdrawal(ser.CharField(allow_blank=True, required=False)) withdrawal_justification = ser.CharField(read_only=True) template_from = HideIfWithdrawal(ser.CharField( read_only=True, allow_blank=False, allow_null=False, help_text='Specify a node id for a node you would like to use as a template for the ' 'new node. Templating is like forking, except that you do not copy the ' 'files, only the project structure. Some information is changed on the top ' 'level project by submitting the appropriate fields in the request body, ' 'and some information will not change. By default, the description will ' 'be cleared and the project will be made private.', )) registration_supplement = ser.SerializerMethodField() # Will be deprecated in favor of registration_responses registered_meta = HideIfWithdrawal(ser.SerializerMethodField( help_text='A dictionary with supplemental registration questions and responses.', )) registration_responses = HideIfWithdrawal(ser.SerializerMethodField( help_text='A dictionary with supplemental registration questions and responses.', )) registered_by = HideIfWithdrawal(RelationshipField( related_view='users:user-detail', related_view_kwargs={'user_id': '<registered_user._id>'}, )) registered_from = RelationshipField( related_view='nodes:node-detail', related_view_kwargs={'node_id': '<registered_from._id>'}, ) children = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-children', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_node_count'}, )) comments = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-comments', related_view_kwargs={'node_id': '<_id>'}, related_meta={ 'unread': 'get_unread_comments_count', 'count': 'get_total_comments_count', }, filter={'target': '<_id>'}, )) contributors = RelationshipField( related_view='registrations:registration-contributors', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_contrib_count'}, ) bibliographic_contributors = RelationshipField( related_view='registrations:registration-bibliographic-contributors', related_view_kwargs={'node_id': '<_id>'}, ) implicit_contributors = RelationshipField( related_view='registrations:registration-implicit-contributors', related_view_kwargs={'node_id': '<_id>'}, help_text='This feature is experimental and being tested. It may be deprecated.', ) files = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-storage-providers', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_files_count'}, )) wikis = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-wikis', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_wiki_page_count'}, )) forked_from = HideIfWithdrawal(RelationshipField( related_view=lambda n: 'registrations:registration-detail' if getattr(n, 'is_registration', False) else 'nodes:node-detail', related_view_kwargs={'node_id': '<forked_from_id>'}, )) template_node = HideIfWithdrawal(RelationshipField( related_view='nodes:node-detail', related_view_kwargs={'node_id': '<template_node._id>'}, )) license = HideIfWithdrawal(NodeLicenseRelationshipField( related_view='licenses:license-detail', related_view_kwargs={'license_id': '<license.node_license._id>'}, read_only=False, )) logs = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-logs', related_view_kwargs={'node_id': '<_id>'}, )) forks = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-forks', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_forks_count'}, )) groups = HideIfRegistration(RelationshipField( related_view='nodes:node-groups', related_view_kwargs={'node_id': '<_id>'}, )) node_links = ShowIfVersion( HideIfWithdrawal(RelationshipField( related_view='registrations:registration-pointers', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_pointers_count'}, help_text='This feature is deprecated as of version 2.1. Use linked_nodes instead.', )), min_version='2.0', max_version='2.0', ) linked_by_nodes = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-linked-by-nodes', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_linked_by_nodes_count'}, )) linked_by_registrations = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-linked-by-registrations', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_linked_by_registrations_count'}, )) parent = RelationshipField( related_view='registrations:registration-detail', related_view_kwargs={'node_id': '<parent_node._id>'}, filter_key='parent_node', ) root = RelationshipField( related_view='registrations:registration-detail', related_view_kwargs={'node_id': '<root._id>'}, ) region = HideIfWithdrawal(RelationshipField( related_view='regions:region-detail', related_view_kwargs={'region_id': '<osfstorage_region._id>'}, read_only=True, )) affiliated_institutions = RelationshipField( related_view='registrations:registration-institutions', related_view_kwargs={'node_id': '<_id>'}, self_view='registrations:registration-relationships-institutions', self_view_kwargs={'node_id': '<_id>'}, read_only=False, many=True, required=False, ) registration_schema = RelationshipField( related_view='schemas:registration-schema-detail', related_view_kwargs={'schema_id': '<registered_schema_id>'}, ) settings = HideIfRegistration(RelationshipField( related_view='nodes:node-settings', related_view_kwargs={'node_id': '<_id>'}, )) registrations = HideIfRegistration(RelationshipField( related_view='nodes:node-registrations', related_view_kwargs={'node_id': '<_id>'}, )) draft_registrations = HideIfRegistration(RelationshipField( related_view='nodes:node-draft-registrations', related_view_kwargs={'node_id': '<_id>'}, )) preprints = HideIfWithdrawal(HideIfRegistration(RelationshipField( related_view='nodes:node-preprints', related_view_kwargs={'node_id': '<_id>'}, ))) identifiers = RelationshipField( related_view='registrations:identifier-list', related_view_kwargs={'node_id': '<_id>'}, ) linked_nodes = HideIfWithdrawal(RelationshipField( related_view='registrations:linked-nodes', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_node_links_count'}, self_view='registrations:node-pointer-relationship', self_view_kwargs={'node_id': '<_id>'}, )) linked_registrations = HideIfWithdrawal(RelationshipField( related_view='registrations:linked-registrations', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_registration_links_count'}, self_view='registrations:node-registration-pointer-relationship', self_view_kwargs={'node_id': '<_id>'}, )) view_only_links = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-view-only-links', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_view_only_links_count'}, )) citation = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-citation', related_view_kwargs={'node_id': '<_id>'}, )) provider = RegistrationProviderRelationshipField( related_view='providers:registration-providers:registration-provider-detail', related_view_kwargs={'provider_id': '<provider._id>'}, read_only=True, ) review_actions = RelationshipField( related_view='registrations:registration-actions-list', related_view_kwargs={'node_id': '<_id>'}, ) requests = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-requests-list', related_view_kwargs={'node_id': '<_id>'}, )) @property def subjects_related_view(self): # Overrides TaxonomizableSerializerMixin return 'registrations:registration-subjects' @property def subjects_self_view(self): # Overrides TaxonomizableSerializerMixin return 'registrations:registration-relationships-subjects' links = LinksField({'html': 'get_absolute_html_url'}) def get_has_project(self, obj): return obj.has_project def get_absolute_url(self, obj): return obj.get_absolute_url() def get_registered_meta(self, obj): if obj.registered_meta: meta_values = self.anonymize_registered_meta(obj) try: return json.loads(meta_values) except TypeError: return meta_values except ValueError: return meta_values return None def get_registration_responses(self, obj): if obj.registration_responses: return self.anonymize_registration_responses(obj) return None def get_embargo_end_date(self, obj): if obj.embargo_end_date: return obj.embargo_end_date return None def get_registration_supplement(self, obj): if obj.registered_schema: schema = obj.registered_schema.first() if schema is None: return None return schema.name return None def get_current_user_permissions(self, obj): return NodeSerializer.get_current_user_permissions(self, obj) def get_view_only_links_count(self, obj): return obj.private_links.filter(is_deleted=False).count() def get_total_comments_count(self, obj): return obj.comment_set.filter(page='node', is_deleted=False).count() def get_files_count(self, obj): return obj.files_count or 0 def anonymize_registered_meta(self, obj): """ Looks at every question on every page of the schema, for any titles that have a contributor-input block type. If present, deletes that question's response from meta_values. """ cleaned_registered_meta = strip_registered_meta_comments(list(obj.registered_meta.values())[0]) return self.anonymize_fields(obj, cleaned_registered_meta) def anonymize_registration_responses(self, obj): """ For any questions that have a `contributor-input` block type, delete that question's response from registration_responses. We want to make sure author's names that need to be anonymized aren't surfaced when viewed through an anonymous VOL """ return self.anonymize_fields(obj, obj.registration_responses) def anonymize_fields(self, obj, data): """ Consolidates logic to anonymize fields with contributor information on both registered_meta and registration_responses """ if is_anonymized(self.context['request']): anonymous_registration_response_keys = obj.get_contributor_registration_response_keys() for key in anonymous_registration_response_keys: if key in data: del data[key] return data def check_admin_perms(self, registration, user, validated_data): """ While admin/write users can make both make modifications to registrations, most fields are restricted to admin-only edits. You must be an admin contributor on the registration; you cannot have gotten your admin permissions through group membership. Add fields that need admin perms to admin_only_editable_fields """ user_is_admin = registration.is_admin_contributor(user) for field in validated_data: if field in self.admin_only_editable_fields and not user_is_admin: raise exceptions.PermissionDenied() def update_registration_tags(self, registration, validated_data, auth): new_tags = validated_data.pop('tags', []) try: registration.update_tags(new_tags, auth=auth) except NodeStateError as err: raise Conflict(str(err)) def retract_registration(self, registration, validated_data, user): is_pending_retraction = validated_data.pop('is_pending_retraction', None) withdrawal_justification = validated_data.pop('withdrawal_justification', None) if withdrawal_justification and not is_pending_retraction: raise exceptions.ValidationError( 'You cannot provide a withdrawal_justification without a concurrent withdrawal request.', ) if is_truthy(is_pending_retraction): if registration.is_pending_retraction: raise exceptions.ValidationError('This registration is already pending withdrawal.') try: retraction = registration.retract_registration(user, withdrawal_justification, save=True) except NodeStateError as err: raise exceptions.ValidationError(str(err)) retraction.ask(registration.get_active_contributors_recursive(unique_users=True)) elif is_pending_retraction is not None: raise exceptions.ValidationError('You cannot set is_pending_withdrawal to False.') def update(self, registration, validated_data): user = self.context['request'].user auth = Auth(user) self.check_admin_perms(registration, user, validated_data) validated_data.pop('_id', None) if 'tags' in validated_data: self.update_registration_tags(registration, validated_data, auth) if 'custom_citation' in validated_data: registration.update_custom_citation(validated_data.pop('custom_citation'), auth) if 'license_type' in validated_data or 'license' in validated_data: license_details = get_license_details(registration, validated_data) validated_data['node_license'] = license_details validated_data.pop('license_type', None) validated_data.pop('license', None) if 'affiliated_institutions' in validated_data: institutions_list = validated_data.pop('affiliated_institutions') new_institutions = [{'_id': institution} for institution in institutions_list] update_institutions(registration, new_institutions, user) registration.save() if 'subjects' in validated_data: subjects = validated_data.pop('subjects', None) self.update_subjects(registration, subjects, auth) if 'withdrawal_justification' in validated_data or 'is_pending_retraction' in validated_data: self.retract_registration(registration, validated_data, user) if 'is_public' in validated_data: if validated_data.get('is_public') is False: raise exceptions.ValidationError('Registrations can only be turned from private to public.') try: registration.update(validated_data, auth=auth) except ValidationError as e: raise InvalidModelValueError(detail=e.messages[0]) except NodeUpdateError as err: raise exceptions.ValidationError(err.reason) except NodeStateError as err: raise exceptions.ValidationError(str(err)) return registration class Meta: type_ = 'registrations' class RegistrationCreateSerializer(RegistrationSerializer): """ Overrides RegistrationSerializer to add draft_registration, registration_choice, and lift_embargo fields - """ def expect_cleaner_attributes(self, request): return StrictVersion(getattr(request, 'version', '2.0')) >= StrictVersion(CREATE_REGISTRATION_FIELD_CHANGE_VERSION) def __init__(self, *args, **kwargs): super(RegistrationCreateSerializer, self).__init__(*args, **kwargs) request = kwargs['context']['request'] # required fields defined here for the different versions if self.expect_cleaner_attributes(request): self.fields['draft_registration_id'] = ser.CharField(write_only=True) else: self.fields['draft_registration'] = ser.CharField(write_only=True) # For newer versions embargo_end_date = VersionedDateTimeField(write_only=True, allow_null=True, default=None) included_node_ids = ser.ListField(write_only=True, required=False) # For older versions lift_embargo = VersionedDateTimeField(write_only=True, default=None, input_formats=['%Y-%m-%dT%H:%M:%S']) children = ser.ListField(write_only=True, required=False) registration_choice = ser.ChoiceField(write_only=True, required=False, choices=['immediate', 'embargo']) users = RelationshipField( related_view='users:user-detail', related_view_kwargs={'user_id': '<user._id>'}, always_embed=True, required=False, ) def get_registration_choice_by_version(self, validated_data): """ Old API versions should pass in "immediate" or "embargo" under `registration_choice`. New API versions should pass in an "embargo_end_date" if it should be embargoed, else it will be None """ if self.expect_cleaner_attributes(self.context['request']): if validated_data.get('registration_choice'): raise JSONAPIException( source={'pointer': '/data/attributes/registration_choice'}, detail=f'Deprecated in version {CREATE_REGISTRATION_FIELD_CHANGE_VERSION}. Use embargo_end_date instead.', ) return 'embargo' if validated_data.get('embargo_end_date', None) else 'immediate' return validated_data.get('registration_choice', 'immediate') def get_embargo_end_date_by_version(self, validated_data): """ Old API versions should pass in "lift_embargo". New API versions should pass in "embargo_end_date" """ if self.expect_cleaner_attributes(self.context['request']): if validated_data.get('lift_embargo'): raise JSONAPIException( source={'pointer': '/data/attributes/lift_embargo'}, detail=f'Deprecated in version {CREATE_REGISTRATION_FIELD_CHANGE_VERSION}. Use embargo_end_date instead.', ) return validated_data.get('embargo_end_date', None) return validated_data.get('lift_embargo') def get_children_by_version(self, validated_data): """ Old API versions should pass in 'children' New API versions should pass in 'included_node_ids'. """ if self.expect_cleaner_attributes(self.context['request']): return validated_data.get('included_node_ids', []) return validated_data.get('children', []) def create(self, validated_data): auth = get_user_auth(self.context['request']) draft = validated_data.pop('draft', None) registration_choice = self.get_registration_choice_by_version(validated_data) embargo_lifted = self.get_embargo_end_date_by_version(validated_data) children = self.get_children_by_version(validated_data) if children: # First check that all children are valid child_nodes = Node.objects.filter(guids___id__in=children) if child_nodes.count() != len(children): raise exceptions.ValidationError('Some child nodes could not be found.') # Second check that metadata doesn't have files that are not in the child nodes being registered. registering = children + [draft.branched_from._id] orphan_files = self._find_orphan_files(registering, draft) if orphan_files: orphan_files_names = [file_data['selectedFileName'] for file_data in orphan_files] raise exceptions.ValidationError('All files attached to this form must be registered to complete the process. ' 'The following file(s) are attached, but are not part of a component being' ' registered: {}'.format(', '.join(orphan_files_names))) try: # Still validating metadata, but whether `registration_responses` or `registration_metadata` were populated # on the draft, the other field was built and populated as well. Both should exist. draft.validate_metadata(metadata=draft.registration_metadata, required_fields=True) except ValidationValueError: log_exception() # Probably indicates a bug on our end, so log to sentry # TODO: Raise an error once our JSON schemas are updated try: registration = draft.register(auth, save=True, child_ids=children) except NodeStateError as err: raise exceptions.ValidationError(err) if registration_choice == 'embargo': if not embargo_lifted: raise exceptions.ValidationError('lift_embargo must be specified.') embargo_end_date = embargo_lifted.replace(tzinfo=pytz.utc) try: registration.embargo_registration(auth.user, embargo_end_date) except ValidationError as err: raise exceptions.ValidationError(err.message) else: try: registration.require_approval(auth.user) except NodeStateError as err: raise exceptions.ValidationError(err) registration.save() return registration def _find_orphan_files(self, registering, draft): from website.archiver.utils import find_selected_files files = find_selected_files(draft.registration_schema, draft.registration_metadata) orphan_files = [] for key, value in files.items(): if 'extra' in value: for file_metadata in value['extra']: if not self._is_attached_file_valid(file_metadata, registering): orphan_files.append(file_metadata) return orphan_files def _is_attached_file_valid(self, file_metadata, registering): """ Validation of file information on registration_metadata. Theoretically, the file information on registration_responses does not have to be valid, so we enforce their accuracy here, to ensure file links load properly. Verifying that nodeId in the file_metadata is one of the files we're registering. Verify that selectedFileName is the name of a file on the node. Verify that the sha256 matches a version on that file. :param file_metadata - under "registration_metadata" :param registering - node ids you are registering :return boolean """ node_id = file_metadata.get('nodeId') if node_id not in registering: return False node = AbstractNode.load(node_id) if not node: # node in registration_metadata doesn't exist return False specified_sha = file_metadata.get('sha256', '') file = node.files.filter(name=normalize('NFD', file_metadata.get('selectedFileName', ''))).first() or \ node.files.filter(name=normalize('NFC', file_metadata.get('selectedFileName', ''))).first() if not file: # file with this name does not exist on the node return False match = False for version in file.versions.all(): if specified_sha == version.metadata.get('sha256'): match = True if not match: # Specified sha256 does not match a version on the specified file return False return True class RegistrationDetailSerializer(RegistrationSerializer): """ Overrides RegistrationSerializer make _id required and other fields writeable """ id = IDField(source='_id', required=True) pending_withdrawal = HideIfWithdrawal(ser.BooleanField( source='is_pending_retraction', required=False, help_text='The registration is awaiting withdrawal approval by project admins.', )) withdrawal_justification = ser.CharField(required=False) class RegistrationNodeLinksSerializer(NodeLinksSerializer): def get_absolute_url(self, obj): return absolute_reverse( 'registrations:registration-pointer-detail', kwargs={ 'node_link_id': obj._id, 'node_id': self.context['request'].parser_context['kwargs']['node_id'], 'version': self.context['request'].parser_context['kwargs']['version'], }, ) class RegistrationContributorsSerializer(NodeContributorsSerializer): def get_absolute_url(self, obj): return absolute_reverse( 'registrations:registration-contributor-detail', kwargs={ 'user_id': obj.user._id, 'node_id': self.context['request'].parser_context['kwargs']['node_id'], 'version': self.context['request'].parser_context['kwargs']['version'], }, ) class RegistrationFileSerializer(OsfStorageFileSerializer): files = NodeFileHyperLinkField( related_view='registrations:registration-files', related_view_kwargs={'node_id': '<target._id>', 'path': '<path>', 'provider': '<provider>'}, kind='folder', ) comments = FileRelationshipField( related_view='registrations:registration-comments', related_view_kwargs={'node_id': '<target._id>'}, related_meta={'unread': 'get_unread_comments_count'}, filter={'target': 'get_file_guid'}, ) node = RelationshipField( related_view='registrations:registration-detail', related_view_kwargs={'node_id': '<target._id>'}, help_text='The registration that this file belongs to', ) class RegistrationStorageProviderSerializer(NodeStorageProviderSerializer): """ Overrides NodeStorageProviderSerializer to lead to correct registration file links """ files = NodeFileHyperLinkField( related_view='registrations:registration-files', related_view_kwargs={'node_id': '<target._id>', 'path': '<path>', 'provider': '<provider>'}, kind='folder', never_embed=True, )

42.619171

133

0.687891

import pytz import json from unicodedata import normalize from distutils.version import StrictVersion from django.core.exceptions import ValidationError from rest_framework import serializers as ser from rest_framework import exceptions from api.base.exceptions import Conflict, InvalidModelValueError, JSONAPIException from api.base.serializers import is_anonymized from api.base.utils import absolute_reverse, get_user_auth, is_truthy from api.base.versioning import CREATE_REGISTRATION_FIELD_CHANGE_VERSION from website.project.model import NodeUpdateError from api.files.serializers import OsfStorageFileSerializer from api.nodes.serializers import ( NodeSerializer, NodeStorageProviderSerializer, NodeLicenseRelationshipField, NodeLinksSerializer, update_institutions, NodeLicenseSerializer, NodeContributorsSerializer, RegistrationProviderRelationshipField, get_license_details, ) from api.base.serializers import ( IDField, RelationshipField, LinksField, HideIfWithdrawal, FileRelationshipField, NodeFileHyperLinkField, HideIfRegistration, ShowIfVersion, VersionedDateTimeField, ValuesListField, ) from framework.auth.core import Auth from osf.exceptions import ValidationValueError, NodeStateError from osf.models import Node, AbstractNode from osf.utils.registrations import strip_registered_meta_comments from framework.sentry import log_exception class RegistrationSerializer(NodeSerializer): admin_only_editable_fields = [ 'custom_citation', 'is_pending_retraction', 'is_public', 'withdrawal_justification', ] non_anonymized_fields = NodeSerializer.non_anonymized_fields + [ 'archiving', 'article_doi', 'date_registered', 'date_withdrawn', 'embargo_end_date', 'embargoed', 'pending_embargo_approval', 'pending_embargo_termination_approval', 'pending_registration_approval', 'pending_withdrawal', 'provider', 'registered_by', 'registered_from', 'registered_meta', 'registration_responses', 'registration_schema', 'registration_supplement', 'withdrawal_justification', 'withdrawn', ] reviews_state = ser.CharField(source='moderation_state', read_only=True) title = ser.CharField(read_only=True) description = ser.CharField(required=False, allow_blank=True, allow_null=True) category_choices = NodeSerializer.category_choices category_choices_string = NodeSerializer.category_choices_string category = ser.ChoiceField(required=False, choices=category_choices, help_text='Choices: ' + category_choices_string) date_modified = VersionedDateTimeField(source='last_logged', read_only=True) fork = HideIfWithdrawal(ser.BooleanField(read_only=True, source='is_fork')) collection = HideIfWithdrawal(ser.BooleanField(read_only=True, source='is_collection')) access_requests_enabled = HideIfWithdrawal(ser.BooleanField(read_only=True)) node_license = HideIfWithdrawal(NodeLicenseSerializer(required=False, source='license')) tags = HideIfWithdrawal(ValuesListField(attr_name='name', child=ser.CharField(), required=False)) article_doi = ser.CharField(required=False, allow_null=True) public = HideIfWithdrawal(ser.BooleanField( source='is_public', required=False, help_text='Nodes that are made public will give read-only access ' 'to everyone. Private nodes require explicit read ' 'permission. Write and admin access are the same for ' 'public and private nodes. Administrators on a parent ' 'node have implicit read permissions for all child nodes', )) current_user_permissions = HideIfWithdrawal(ser.SerializerMethodField( help_text='List of strings representing the permissions ' 'for the current user on this node.', )) pending_embargo_approval = HideIfWithdrawal(ser.BooleanField( read_only=True, source='is_pending_embargo', help_text='The associated Embargo is awaiting approval by project admins.', )) pending_embargo_termination_approval = HideIfWithdrawal(ser.BooleanField( read_only=True, source='is_pending_embargo_termination', help_text='The associated Embargo early termination is awaiting approval by project admins', )) embargoed = HideIfWithdrawal(ser.BooleanField(read_only=True, source='is_embargoed')) pending_registration_approval = HideIfWithdrawal(ser.BooleanField( source='is_pending_registration', read_only=True, help_text='The associated RegistrationApproval is awaiting approval by project admins.', )) archiving = HideIfWithdrawal(ser.BooleanField(read_only=True)) pending_withdrawal = HideIfWithdrawal(ser.BooleanField( source='is_pending_retraction', read_only=True, help_text='The registration is awaiting withdrawal approval by project admins.', )) withdrawn = ser.BooleanField( source='is_retracted', read_only=True, help_text='The registration has been withdrawn.', ) has_project = ser.SerializerMethodField() date_registered = VersionedDateTimeField(source='registered_date', read_only=True, help_text='Date time of registration.') date_withdrawn = VersionedDateTimeField(read_only=True, help_text='Date time of when this registration was retracted.') embargo_end_date = HideIfWithdrawal(ser.SerializerMethodField(help_text='When the embargo on this registration will be lifted.')) custom_citation = HideIfWithdrawal(ser.CharField(allow_blank=True, required=False)) withdrawal_justification = ser.CharField(read_only=True) template_from = HideIfWithdrawal(ser.CharField( read_only=True, allow_blank=False, allow_null=False, help_text='Specify a node id for a node you would like to use as a template for the ' 'new node. Templating is like forking, except that you do not copy the ' 'files, only the project structure. Some information is changed on the top ' 'level project by submitting the appropriate fields in the request body, ' 'and some information will not change. By default, the description will ' 'be cleared and the project will be made private.', )) registration_supplement = ser.SerializerMethodField() # Will be deprecated in favor of registration_responses registered_meta = HideIfWithdrawal(ser.SerializerMethodField( help_text='A dictionary with supplemental registration questions and responses.', )) registration_responses = HideIfWithdrawal(ser.SerializerMethodField( help_text='A dictionary with supplemental registration questions and responses.', )) registered_by = HideIfWithdrawal(RelationshipField( related_view='users:user-detail', related_view_kwargs={'user_id': '<registered_user._id>'}, )) registered_from = RelationshipField( related_view='nodes:node-detail', related_view_kwargs={'node_id': '<registered_from._id>'}, ) children = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-children', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_node_count'}, )) comments = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-comments', related_view_kwargs={'node_id': '<_id>'}, related_meta={ 'unread': 'get_unread_comments_count', 'count': 'get_total_comments_count', }, filter={'target': '<_id>'}, )) contributors = RelationshipField( related_view='registrations:registration-contributors', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_contrib_count'}, ) bibliographic_contributors = RelationshipField( related_view='registrations:registration-bibliographic-contributors', related_view_kwargs={'node_id': '<_id>'}, ) implicit_contributors = RelationshipField( related_view='registrations:registration-implicit-contributors', related_view_kwargs={'node_id': '<_id>'}, help_text='This feature is experimental and being tested. It may be deprecated.', ) files = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-storage-providers', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_files_count'}, )) wikis = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-wikis', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_wiki_page_count'}, )) forked_from = HideIfWithdrawal(RelationshipField( related_view=lambda n: 'registrations:registration-detail' if getattr(n, 'is_registration', False) else 'nodes:node-detail', related_view_kwargs={'node_id': '<forked_from_id>'}, )) template_node = HideIfWithdrawal(RelationshipField( related_view='nodes:node-detail', related_view_kwargs={'node_id': '<template_node._id>'}, )) license = HideIfWithdrawal(NodeLicenseRelationshipField( related_view='licenses:license-detail', related_view_kwargs={'license_id': '<license.node_license._id>'}, read_only=False, )) logs = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-logs', related_view_kwargs={'node_id': '<_id>'}, )) forks = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-forks', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_forks_count'}, )) groups = HideIfRegistration(RelationshipField( related_view='nodes:node-groups', related_view_kwargs={'node_id': '<_id>'}, )) node_links = ShowIfVersion( HideIfWithdrawal(RelationshipField( related_view='registrations:registration-pointers', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_pointers_count'}, help_text='This feature is deprecated as of version 2.1. Use linked_nodes instead.', )), min_version='2.0', max_version='2.0', ) linked_by_nodes = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-linked-by-nodes', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_linked_by_nodes_count'}, )) linked_by_registrations = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-linked-by-registrations', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_linked_by_registrations_count'}, )) parent = RelationshipField( related_view='registrations:registration-detail', related_view_kwargs={'node_id': '<parent_node._id>'}, filter_key='parent_node', ) root = RelationshipField( related_view='registrations:registration-detail', related_view_kwargs={'node_id': '<root._id>'}, ) region = HideIfWithdrawal(RelationshipField( related_view='regions:region-detail', related_view_kwargs={'region_id': '<osfstorage_region._id>'}, read_only=True, )) affiliated_institutions = RelationshipField( related_view='registrations:registration-institutions', related_view_kwargs={'node_id': '<_id>'}, self_view='registrations:registration-relationships-institutions', self_view_kwargs={'node_id': '<_id>'}, read_only=False, many=True, required=False, ) registration_schema = RelationshipField( related_view='schemas:registration-schema-detail', related_view_kwargs={'schema_id': '<registered_schema_id>'}, ) settings = HideIfRegistration(RelationshipField( related_view='nodes:node-settings', related_view_kwargs={'node_id': '<_id>'}, )) registrations = HideIfRegistration(RelationshipField( related_view='nodes:node-registrations', related_view_kwargs={'node_id': '<_id>'}, )) draft_registrations = HideIfRegistration(RelationshipField( related_view='nodes:node-draft-registrations', related_view_kwargs={'node_id': '<_id>'}, )) preprints = HideIfWithdrawal(HideIfRegistration(RelationshipField( related_view='nodes:node-preprints', related_view_kwargs={'node_id': '<_id>'}, ))) identifiers = RelationshipField( related_view='registrations:identifier-list', related_view_kwargs={'node_id': '<_id>'}, ) linked_nodes = HideIfWithdrawal(RelationshipField( related_view='registrations:linked-nodes', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_node_links_count'}, self_view='registrations:node-pointer-relationship', self_view_kwargs={'node_id': '<_id>'}, )) linked_registrations = HideIfWithdrawal(RelationshipField( related_view='registrations:linked-registrations', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_registration_links_count'}, self_view='registrations:node-registration-pointer-relationship', self_view_kwargs={'node_id': '<_id>'}, )) view_only_links = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-view-only-links', related_view_kwargs={'node_id': '<_id>'}, related_meta={'count': 'get_view_only_links_count'}, )) citation = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-citation', related_view_kwargs={'node_id': '<_id>'}, )) provider = RegistrationProviderRelationshipField( related_view='providers:registration-providers:registration-provider-detail', related_view_kwargs={'provider_id': '<provider._id>'}, read_only=True, ) review_actions = RelationshipField( related_view='registrations:registration-actions-list', related_view_kwargs={'node_id': '<_id>'}, ) requests = HideIfWithdrawal(RelationshipField( related_view='registrations:registration-requests-list', related_view_kwargs={'node_id': '<_id>'}, )) @property def subjects_related_view(self): # Overrides TaxonomizableSerializerMixin return 'registrations:registration-subjects' @property def subjects_self_view(self): # Overrides TaxonomizableSerializerMixin return 'registrations:registration-relationships-subjects' links = LinksField({'html': 'get_absolute_html_url'}) def get_has_project(self, obj): return obj.has_project def get_absolute_url(self, obj): return obj.get_absolute_url() def get_registered_meta(self, obj): if obj.registered_meta: meta_values = self.anonymize_registered_meta(obj) try: return json.loads(meta_values) except TypeError: return meta_values except ValueError: return meta_values return None def get_registration_responses(self, obj): if obj.registration_responses: return self.anonymize_registration_responses(obj) return None def get_embargo_end_date(self, obj): if obj.embargo_end_date: return obj.embargo_end_date return None def get_registration_supplement(self, obj): if obj.registered_schema: schema = obj.registered_schema.first() if schema is None: return None return schema.name return None def get_current_user_permissions(self, obj): return NodeSerializer.get_current_user_permissions(self, obj) def get_view_only_links_count(self, obj): return obj.private_links.filter(is_deleted=False).count() def get_total_comments_count(self, obj): return obj.comment_set.filter(page='node', is_deleted=False).count() def get_files_count(self, obj): return obj.files_count or 0 def anonymize_registered_meta(self, obj): cleaned_registered_meta = strip_registered_meta_comments(list(obj.registered_meta.values())[0]) return self.anonymize_fields(obj, cleaned_registered_meta) def anonymize_registration_responses(self, obj): return self.anonymize_fields(obj, obj.registration_responses) def anonymize_fields(self, obj, data): if is_anonymized(self.context['request']): anonymous_registration_response_keys = obj.get_contributor_registration_response_keys() for key in anonymous_registration_response_keys: if key in data: del data[key] return data def check_admin_perms(self, registration, user, validated_data): user_is_admin = registration.is_admin_contributor(user) for field in validated_data: if field in self.admin_only_editable_fields and not user_is_admin: raise exceptions.PermissionDenied() def update_registration_tags(self, registration, validated_data, auth): new_tags = validated_data.pop('tags', []) try: registration.update_tags(new_tags, auth=auth) except NodeStateError as err: raise Conflict(str(err)) def retract_registration(self, registration, validated_data, user): is_pending_retraction = validated_data.pop('is_pending_retraction', None) withdrawal_justification = validated_data.pop('withdrawal_justification', None) if withdrawal_justification and not is_pending_retraction: raise exceptions.ValidationError( 'You cannot provide a withdrawal_justification without a concurrent withdrawal request.', ) if is_truthy(is_pending_retraction): if registration.is_pending_retraction: raise exceptions.ValidationError('This registration is already pending withdrawal.') try: retraction = registration.retract_registration(user, withdrawal_justification, save=True) except NodeStateError as err: raise exceptions.ValidationError(str(err)) retraction.ask(registration.get_active_contributors_recursive(unique_users=True)) elif is_pending_retraction is not None: raise exceptions.ValidationError('You cannot set is_pending_withdrawal to False.') def update(self, registration, validated_data): user = self.context['request'].user auth = Auth(user) self.check_admin_perms(registration, user, validated_data) validated_data.pop('_id', None) if 'tags' in validated_data: self.update_registration_tags(registration, validated_data, auth) if 'custom_citation' in validated_data: registration.update_custom_citation(validated_data.pop('custom_citation'), auth) if 'license_type' in validated_data or 'license' in validated_data: license_details = get_license_details(registration, validated_data) validated_data['node_license'] = license_details validated_data.pop('license_type', None) validated_data.pop('license', None) if 'affiliated_institutions' in validated_data: institutions_list = validated_data.pop('affiliated_institutions') new_institutions = [{'_id': institution} for institution in institutions_list] update_institutions(registration, new_institutions, user) registration.save() if 'subjects' in validated_data: subjects = validated_data.pop('subjects', None) self.update_subjects(registration, subjects, auth) if 'withdrawal_justification' in validated_data or 'is_pending_retraction' in validated_data: self.retract_registration(registration, validated_data, user) if 'is_public' in validated_data: if validated_data.get('is_public') is False: raise exceptions.ValidationError('Registrations can only be turned from private to public.') try: registration.update(validated_data, auth=auth) except ValidationError as e: raise InvalidModelValueError(detail=e.messages[0]) except NodeUpdateError as err: raise exceptions.ValidationError(err.reason) except NodeStateError as err: raise exceptions.ValidationError(str(err)) return registration class Meta: type_ = 'registrations' class RegistrationCreateSerializer(RegistrationSerializer): def expect_cleaner_attributes(self, request): return StrictVersion(getattr(request, 'version', '2.0')) >= StrictVersion(CREATE_REGISTRATION_FIELD_CHANGE_VERSION) def __init__(self, *args, **kwargs): super(RegistrationCreateSerializer, self).__init__(*args, **kwargs) request = kwargs['context']['request'] # required fields defined here for the different versions if self.expect_cleaner_attributes(request): self.fields['draft_registration_id'] = ser.CharField(write_only=True) else: self.fields['draft_registration'] = ser.CharField(write_only=True) # For newer versions embargo_end_date = VersionedDateTimeField(write_only=True, allow_null=True, default=None) included_node_ids = ser.ListField(write_only=True, required=False) # For older versions lift_embargo = VersionedDateTimeField(write_only=True, default=None, input_formats=['%Y-%m-%dT%H:%M:%S']) children = ser.ListField(write_only=True, required=False) registration_choice = ser.ChoiceField(write_only=True, required=False, choices=['immediate', 'embargo']) users = RelationshipField( related_view='users:user-detail', related_view_kwargs={'user_id': '<user._id>'}, always_embed=True, required=False, ) def get_registration_choice_by_version(self, validated_data): if self.expect_cleaner_attributes(self.context['request']): if validated_data.get('registration_choice'): raise JSONAPIException( source={'pointer': '/data/attributes/registration_choice'}, detail=f'Deprecated in version {CREATE_REGISTRATION_FIELD_CHANGE_VERSION}. Use embargo_end_date instead.', ) return 'embargo' if validated_data.get('embargo_end_date', None) else 'immediate' return validated_data.get('registration_choice', 'immediate') def get_embargo_end_date_by_version(self, validated_data): if self.expect_cleaner_attributes(self.context['request']): if validated_data.get('lift_embargo'): raise JSONAPIException( source={'pointer': '/data/attributes/lift_embargo'}, detail=f'Deprecated in version {CREATE_REGISTRATION_FIELD_CHANGE_VERSION}. Use embargo_end_date instead.', ) return validated_data.get('embargo_end_date', None) return validated_data.get('lift_embargo') def get_children_by_version(self, validated_data): if self.expect_cleaner_attributes(self.context['request']): return validated_data.get('included_node_ids', []) return validated_data.get('children', []) def create(self, validated_data): auth = get_user_auth(self.context['request']) draft = validated_data.pop('draft', None) registration_choice = self.get_registration_choice_by_version(validated_data) embargo_lifted = self.get_embargo_end_date_by_version(validated_data) children = self.get_children_by_version(validated_data) if children: # First check that all children are valid child_nodes = Node.objects.filter(guids___id__in=children) if child_nodes.count() != len(children): raise exceptions.ValidationError('Some child nodes could not be found.') # Second check that metadata doesn't have files that are not in the child nodes being registered. registering = children + [draft.branched_from._id] orphan_files = self._find_orphan_files(registering, draft) if orphan_files: orphan_files_names = [file_data['selectedFileName'] for file_data in orphan_files] raise exceptions.ValidationError('All files attached to this form must be registered to complete the process. ' 'The following file(s) are attached, but are not part of a component being' ' registered: {}'.format(', '.join(orphan_files_names))) try: draft.validate_metadata(metadata=draft.registration_metadata, required_fields=True) except ValidationValueError: log_exception() try: registration = draft.register(auth, save=True, child_ids=children) except NodeStateError as err: raise exceptions.ValidationError(err) if registration_choice == 'embargo': if not embargo_lifted: raise exceptions.ValidationError('lift_embargo must be specified.') embargo_end_date = embargo_lifted.replace(tzinfo=pytz.utc) try: registration.embargo_registration(auth.user, embargo_end_date) except ValidationError as err: raise exceptions.ValidationError(err.message) else: try: registration.require_approval(auth.user) except NodeStateError as err: raise exceptions.ValidationError(err) registration.save() return registration def _find_orphan_files(self, registering, draft): from website.archiver.utils import find_selected_files files = find_selected_files(draft.registration_schema, draft.registration_metadata) orphan_files = [] for key, value in files.items(): if 'extra' in value: for file_metadata in value['extra']: if not self._is_attached_file_valid(file_metadata, registering): orphan_files.append(file_metadata) return orphan_files def _is_attached_file_valid(self, file_metadata, registering): node_id = file_metadata.get('nodeId') if node_id not in registering: return False node = AbstractNode.load(node_id) if not node: return False specified_sha = file_metadata.get('sha256', '') file = node.files.filter(name=normalize('NFD', file_metadata.get('selectedFileName', ''))).first() or \ node.files.filter(name=normalize('NFC', file_metadata.get('selectedFileName', ''))).first() if not file: # file with this name does not exist on the node return False match = False for version in file.versions.all(): if specified_sha == version.metadata.get('sha256'): match = True if not match: # Specified sha256 does not match a version on the specified file return False return True class RegistrationDetailSerializer(RegistrationSerializer): id = IDField(source='_id', required=True) pending_withdrawal = HideIfWithdrawal(ser.BooleanField( source='is_pending_retraction', required=False, help_text='The registration is awaiting withdrawal approval by project admins.', )) withdrawal_justification = ser.CharField(required=False) class RegistrationNodeLinksSerializer(NodeLinksSerializer): def get_absolute_url(self, obj): return absolute_reverse( 'registrations:registration-pointer-detail', kwargs={ 'node_link_id': obj._id, 'node_id': self.context['request'].parser_context['kwargs']['node_id'], 'version': self.context['request'].parser_context['kwargs']['version'], }, ) class RegistrationContributorsSerializer(NodeContributorsSerializer): def get_absolute_url(self, obj): return absolute_reverse( 'registrations:registration-contributor-detail', kwargs={ 'user_id': obj.user._id, 'node_id': self.context['request'].parser_context['kwargs']['node_id'], 'version': self.context['request'].parser_context['kwargs']['version'], }, ) class RegistrationFileSerializer(OsfStorageFileSerializer): files = NodeFileHyperLinkField( related_view='registrations:registration-files', related_view_kwargs={'node_id': '<target._id>', 'path': '<path>', 'provider': '<provider>'}, kind='folder', ) comments = FileRelationshipField( related_view='registrations:registration-comments', related_view_kwargs={'node_id': '<target._id>'}, related_meta={'unread': 'get_unread_comments_count'}, filter={'target': 'get_file_guid'}, ) node = RelationshipField( related_view='registrations:registration-detail', related_view_kwargs={'node_id': '<target._id>'}, help_text='The registration that this file belongs to', ) class RegistrationStorageProviderSerializer(NodeStorageProviderSerializer): files = NodeFileHyperLinkField( related_view='registrations:registration-files', related_view_kwargs={'node_id': '<target._id>', 'path': '<path>', 'provider': '<provider>'}, kind='folder', never_embed=True, )

true

f723eb375382b38526c20e9d0239da24728a5d3d

698

py

Python

tests/nested_foreign_keys/models.py

Yoann-Vie/esgi-hearthstone

115d03426c7e8e80d89883b78ac72114c29bed12

[ "PSF-2.0", "BSD-3-Clause" ]

null

tests/nested_foreign_keys/models.py

Yoann-Vie/esgi-hearthstone

115d03426c7e8e80d89883b78ac72114c29bed12

[ "PSF-2.0", "BSD-3-Clause" ]

null

tests/nested_foreign_keys/models.py

Yoann-Vie/esgi-hearthstone

115d03426c7e8e80d89883b78ac72114c29bed12

[ "PSF-2.0", "BSD-3-Clause" ]

null

from django.db import models class Person(models.Model): name = models.CharField(max_length=200) class Movie(models.Model): title = models.CharField(max_length=200) director = models.ForeignKey(Person, models.CASCADE) class Event(models.Model): pass class Screening(Event): movie = models.ForeignKey(Movie, models.CASCADE) class ScreeningNullFK(Event): movie = models.ForeignKey(Movie, models.SET_NULL, null=True) class Package(models.Model): screening = models.ForeignKey(Screening, models.SET_NULL, null=True) class PackageNullFK(models.Model): screening = models.ForeignKey(ScreeningNullFK, models.SET_NULL, null=True)

22.516129

79

0.717765

from django.db import models class Person(models.Model): name = models.CharField(max_length=200) class Movie(models.Model): title = models.CharField(max_length=200) director = models.ForeignKey(Person, models.CASCADE) class Event(models.Model): pass class Screening(Event): movie = models.ForeignKey(Movie, models.CASCADE) class ScreeningNullFK(Event): movie = models.ForeignKey(Movie, models.SET_NULL, null=True) class Package(models.Model): screening = models.ForeignKey(Screening, models.SET_NULL, null=True) class PackageNullFK(models.Model): screening = models.ForeignKey(ScreeningNullFK, models.SET_NULL, null=True)

true

f723eb9b8474a649714e9207663dcbb042ef7f19

14,422

py

Python

scipy/optimize/tests/test_linesearch.py

ischrot/scipy_rmt_bsc

1dd8f7f0ee7ac1311ed1735ca6b6025150524418

[ "BSD-3-Clause" ]

null

scipy/optimize/tests/test_linesearch.py

ischrot/scipy_rmt_bsc

1dd8f7f0ee7ac1311ed1735ca6b6025150524418

[ "BSD-3-Clause" ]

null

scipy/optimize/tests/test_linesearch.py

ischrot/scipy_rmt_bsc

1dd8f7f0ee7ac1311ed1735ca6b6025150524418

[ "BSD-3-Clause" ]

null

""" Tests for line search routines """ from numpy.testing import (assert_, assert_equal, assert_array_almost_equal, assert_array_almost_equal_nulp, assert_warns, suppress_warnings) import scipy.optimize.linesearch as ls import scipy.optimize.nonlin as nl #(LS) from scipy.linalg import norm from scipy.optimize.linesearch import LineSearchWarning import numpy as np from copy import deepcopy # (IS) def assert_wolfe(s, phi, derphi, c1=1e-4, c2=0.9, err_msg=""): """ Check that strong Wolfe conditions apply """ phi1 = phi(s) phi0 = phi(0) derphi0 = derphi(0) derphi1 = derphi(s) msg = "s = %s; phi(0) = %s; phi(s) = %s; phi'(0) = %s; phi'(s) = %s; %s" % ( s, phi0, phi1, derphi0, derphi1, err_msg) assert_(phi1 <= phi0 + c1*s*derphi0, "Wolfe 1 failed: " + msg) assert_(abs(derphi1) <= abs(c2*derphi0), "Wolfe 2 failed: " + msg) def assert_armijo(s, phi, c1=1e-4, err_msg=""): """ Check that Armijo condition applies """ phi1 = phi(s) phi0 = phi(0) msg = "s = %s; phi(0) = %s; phi(s) = %s; %s" % (s, phi0, phi1, err_msg) assert_(phi1 <= (1 - c1*s)*phi0, msg) ###(LS)### def assert_rmt(alpha, dx, F0, Fx_new, jacobian, param, c1=1e-4, err_msg=""): """ Check that RMT condition applies """ parameters = ls.prepare_parameters('rmt',param,jacobian,dx) rmt_eta_upper = parameters['rmt_eta_upper'] rmt_eta_lower = parameters['rmt_eta_lower'] amin = parameters['amin'] #Step 1: Eval t_dx_omega dxbar = jacobian.solve( Fx_new ) dx_diff = dxbar + (1 - alpha) * dx # note that dx = - J(x_k)^(-1)F(x_k) nominator = 2 * norm(dx_diff) denominator = alpha * norm(dx) t_dx_omega = nominator / denominator tester = (rmt_eta_lower <= t_dx_omega and t_dx_omega <= rmt_eta_upper) or (rmt_eta_lower > t_dx_omega and alpha == 1.0) msg = "s = %s; phi(0) = %s; phi(s) = %s; %s" % (alpha, F0, Fx_new, err_msg) assert_(tester or (alpha<amin), msg) def assert_bsc(alpha, x, dx, func, old_jacobian, param, err_msg): parameters = ls.prepare_parameters('bsc',param, old_jacobian, dx) H_lower = parameters['H_lower'] H_upper = parameters['H_upper'] amin = parameters['amin'] x_new = x + alpha * dx Fx_new = func(x_new) jacobian = deepcopy(old_jacobian) jacobian.update( x_new.copy(), Fx_new ) dx_next_it = -jacobian.solve( Fx_new ) dx_diff = dx_next_it - dx H_prime = alpha * norm(dx_diff) tester = (H_lower <= H_prime and H_prime <= H_upper) or (H_lower > H_prime and alpha >= 1.0) msg = "s = %s; phi(0) = %s; phi(s) = %s; %s" % (alpha, func(x), Fx_new, err_msg) assert_(tester or (alpha<amin), msg) ###(LS)### def assert_line_wolfe(x, p, s, f, fprime, **kw): assert_wolfe(s, phi=lambda sp: f(x + p*sp), derphi=lambda sp: np.dot(fprime(x + p*sp), p), **kw) def assert_line_armijo(x, p, s, f, **kw): assert_armijo(s, phi=lambda sp: f(x + p*sp), **kw) def assert_fp_equal(x, y, err_msg="", nulp=50): """Assert two arrays are equal, up to some floating-point rounding error""" try: assert_array_almost_equal_nulp(x, y, nulp) except AssertionError as e: raise AssertionError("%s\n%s" % (e, err_msg)) from e class TestLineSearch(object): # -- scalar functions; must have dphi(0.) < 0 def _scalar_func_1(self, s): self.fcount += 1 p = -s - s**3 + s**4 dp = -1 - 3*s**2 + 4*s**3 return p, dp def _scalar_func_2(self, s): self.fcount += 1 p = np.exp(-4*s) + s**2 dp = -4*np.exp(-4*s) + 2*s return p, dp def _scalar_func_3(self, s): self.fcount += 1 p = -np.sin(10*s) dp = -10*np.cos(10*s) return p, dp # -- n-d functions def _line_func_1(self, x): self.fcount += 1 f = np.dot(x, x) df = 2*x return f, df def _line_func_2(self, x): self.fcount += 1 f = np.dot(x, np.dot(self.A, x)) + 1 df = np.dot(self.A + self.A.T, x) return f, df # -- def setup_method(self): self.scalar_funcs = [] self.line_funcs = [] self.N = 20 self.fcount = 0 def bind_index(func, idx): # Remember Python's closure semantics! return lambda *a, **kw: func(*a, **kw)[idx] for name in sorted(dir(self)): if name.startswith('_scalar_func_'): value = getattr(self, name) self.scalar_funcs.append( (name, bind_index(value, 0), bind_index(value, 1))) elif name.startswith('_line_func_'): value = getattr(self, name) self.line_funcs.append( (name, bind_index(value, 0), bind_index(value, 1))) np.random.seed(1234) self.A = np.random.randn(self.N, self.N) def scalar_iter(self): for name, phi, derphi in self.scalar_funcs: for old_phi0 in np.random.randn(3): yield name, phi, derphi, old_phi0 def line_iter(self): for name, f, fprime in self.line_funcs: k = 0 while k < 9: x = np.random.randn(self.N) p = np.random.randn(self.N) if np.dot(p, fprime(x)) >= 0: # always pick a descent direction continue k += 1 old_fv = float(np.random.randn()) yield name, f, fprime, x, p, old_fv # -- Generic scalar searches def test_scalar_search_wolfe1(self): c = 0 for name, phi, derphi, old_phi0 in self.scalar_iter(): c += 1 s, phi1, phi0 = ls.scalar_search_wolfe1(phi, derphi, phi(0), old_phi0, derphi(0)) assert_fp_equal(phi0, phi(0), name) assert_fp_equal(phi1, phi(s), name) assert_wolfe(s, phi, derphi, err_msg=name) assert_(c > 3) # check that the iterator really works... def test_scalar_search_wolfe2(self): for name, phi, derphi, old_phi0 in self.scalar_iter(): s, phi1, phi0, derphi1 = ls.scalar_search_wolfe2( phi, derphi, phi(0), old_phi0, derphi(0)) assert_fp_equal(phi0, phi(0), name) assert_fp_equal(phi1, phi(s), name) if derphi1 is not None: assert_fp_equal(derphi1, derphi(s), name) assert_wolfe(s, phi, derphi, err_msg="%s %g" % (name, old_phi0)) def test_scalar_search_wolfe2_with_low_amax(self): def phi(alpha): return (alpha - 5) ** 2 def derphi(alpha): return 2 * (alpha - 5) s, _, _, _ = assert_warns(LineSearchWarning, ls.scalar_search_wolfe2, phi, derphi, amax=0.001) assert_(s is None) def test_scalar_search_wolfe2_regression(self): # Regression test for gh-12157 # This phi has its minimum at alpha=4/3 ~ 1.333. def phi(alpha): if alpha < 1: return - 3*np.pi/2 * (alpha - 1) else: return np.cos(3*np.pi/2 * alpha - np.pi) def derphi(alpha): if alpha < 1: return - 3*np.pi/2 else: return - 3*np.pi/2 * np.sin(3*np.pi/2 * alpha - np.pi) s, _, _, _ = ls.scalar_search_wolfe2(phi, derphi) # Without the fix in gh-13073, the scalar_search_wolfe2 # returned s=2.0 instead. assert_(s < 1.5) def test_scalar_search_armijo(self): for name, phi, derphi, old_phi0 in self.scalar_iter(): s, phi1 = ls.scalar_search_armijo(phi, phi(0), derphi(0)) assert_fp_equal(phi1, phi(s), name) assert_armijo(s, phi, err_msg="%s %g" % (name, old_phi0)) ###(LS)### ##RMT not usefull for scalar functions, thus no need for test_scalar_search_rmt? def test_line_search_rmt(self): #There is at least 1 function R^20->R to be tested, but this leads to s=None for name, f, fprime, x, p, old_f in self.line_iter(): jac = lambda x: fprime(x) x0 = nl._as_inexact(x) func = lambda z: nl._as_inexact(f(nl._array_like(z, x0))).flatten() x = x0.flatten() jacobian = nl.asjacobian(jac) jacobian.setup(x.copy(), f(x), func) options = {'jacobian': jacobian, 'jac_tol': min(1e-03,1e-03*norm(f(x))), 'amin':1e-8} #print("1: ",f(x),np.shape(fprime(x))) s, dxbar, f_new = ls.scalar_search_rmt(f, x, fprime(x), parameters=options) #print("2: ",p_new, s) assert_fp_equal(f_new, x+s*fprime(x), name) assert_rmt(s, fprime(x), f(x), f_new, jacobian, options, err_msg="%s %g" % name) def test_line_search_bsc(self): #There is at least 1 function R^20->R to be tested, but this leads to s=None for name, f, fprime, x, p, old_f in self.line_iter(): jac = lambda x: fprime(x) x0 = nl._as_inexact(x) func = lambda z: nl._as_inexact(f(nl._array_like(z, x0))).flatten() x = x0.flatten() jacobian = nl.asjacobian(jac) jacobian.setup(x.copy(), f(x), func) options = {'jacobian': jacobian, 'jac_tol': min(1e-03,1e-03*norm(f(x))), 'amin':1e-8} #print("1: ",f(x),np.shape(dp(x))) s, f_new= ls.scalar_search_bsc(func, x, fprime(x), f(x), parameters=options) #print("2: ",p_new, s) assert_fp_equal(f_new, x+s*fprime(x), name) assert_bsc(s, x, fprime(x), func, jacobian, options, err_msg="%s %g" % name) ###(LS)### # -- Generic line searches def test_line_search_wolfe1(self): c = 0 smax = 100 for name, f, fprime, x, p, old_f in self.line_iter(): f0 = f(x) g0 = fprime(x) self.fcount = 0 s, fc, gc, fv, ofv, gv = ls.line_search_wolfe1(f, fprime, x, p, g0, f0, old_f, amax=smax) assert_equal(self.fcount, fc+gc) assert_fp_equal(ofv, f(x)) if s is None: continue assert_fp_equal(fv, f(x + s*p)) assert_array_almost_equal(gv, fprime(x + s*p), decimal=14) if s < smax: c += 1 assert_line_wolfe(x, p, s, f, fprime, err_msg=name) assert_(c > 3) # check that the iterator really works... def test_line_search_wolfe2(self): c = 0 smax = 512 for name, f, fprime, x, p, old_f in self.line_iter(): f0 = f(x) g0 = fprime(x) self.fcount = 0 with suppress_warnings() as sup: sup.filter(LineSearchWarning, "The line search algorithm could not find a solution") sup.filter(LineSearchWarning, "The line search algorithm did not converge") s, fc, gc, fv, ofv, gv = ls.line_search_wolfe2(f, fprime, x, p, g0, f0, old_f, amax=smax) assert_equal(self.fcount, fc+gc) assert_fp_equal(ofv, f(x)) assert_fp_equal(fv, f(x + s*p)) if gv is not None: assert_array_almost_equal(gv, fprime(x + s*p), decimal=14) if s < smax: c += 1 assert_line_wolfe(x, p, s, f, fprime, err_msg=name) assert_(c > 3) # check that the iterator really works... def test_line_search_wolfe2_bounds(self): # See gh-7475 # For this f and p, starting at a point on axis 0, the strong Wolfe # condition 2 is met if and only if the step length s satisfies # |x + s| <= c2 * |x| f = lambda x: np.dot(x, x) fp = lambda x: 2 * x p = np.array([1, 0]) # Smallest s satisfying strong Wolfe conditions for these arguments is 30 x = -60 * p c2 = 0.5 s, _, _, _, _, _ = ls.line_search_wolfe2(f, fp, x, p, amax=30, c2=c2) assert_line_wolfe(x, p, s, f, fp) s, _, _, _, _, _ = assert_warns(LineSearchWarning, ls.line_search_wolfe2, f, fp, x, p, amax=29, c2=c2) assert_(s is None) # s=30 will only be tried on the 6th iteration, so this won't converge assert_warns(LineSearchWarning, ls.line_search_wolfe2, f, fp, x, p, c2=c2, maxiter=5) def test_line_search_armijo(self): c = 0 for name, f, fprime, x, p, old_f in self.line_iter(): f0 = f(x) g0 = fprime(x) self.fcount = 0 s, fc, fv = ls.line_search_armijo(f, x, p, g0, f0) c += 1 assert_equal(self.fcount, fc) assert_fp_equal(fv, f(x + s*p)) assert_line_armijo(x, p, s, f, err_msg=name) assert_(c >= 9) # -- More specific tests def test_armijo_terminate_1(self): # Armijo should evaluate the function only once if the trial step # is already suitable count = [0] def phi(s): count[0] += 1 return -s + 0.01*s**2 s, phi1 = ls.scalar_search_armijo(phi, phi(0), -1, alpha0=1) assert_equal(s, 1) assert_equal(count[0], 2) assert_armijo(s, phi) def test_wolfe_terminate(self): # wolfe1 and wolfe2 should also evaluate the function only a few # times if the trial step is already suitable def phi(s): count[0] += 1 return -s + 0.05*s**2 def derphi(s): count[0] += 1 return -1 + 0.05*2*s for func in [ls.scalar_search_wolfe1, ls.scalar_search_wolfe2]: count = [0] r = func(phi, derphi, phi(0), None, derphi(0)) assert_(r[0] is not None, (r, func)) assert_(count[0] <= 2 + 2, (count, func)) assert_wolfe(r[0], phi, derphi, err_msg=str(func))

35.348039

123

0.533491

from numpy.testing import (assert_, assert_equal, assert_array_almost_equal, assert_array_almost_equal_nulp, assert_warns, suppress_warnings) import scipy.optimize.linesearch as ls import scipy.optimize.nonlin as nl from scipy.linalg import norm from scipy.optimize.linesearch import LineSearchWarning import numpy as np from copy import deepcopy def assert_wolfe(s, phi, derphi, c1=1e-4, c2=0.9, err_msg=""): phi1 = phi(s) phi0 = phi(0) derphi0 = derphi(0) derphi1 = derphi(s) msg = "s = %s; phi(0) = %s; phi(s) = %s; phi'(0) = %s; phi'(s) = %s; %s" % ( s, phi0, phi1, derphi0, derphi1, err_msg) assert_(phi1 <= phi0 + c1*s*derphi0, "Wolfe 1 failed: " + msg) assert_(abs(derphi1) <= abs(c2*derphi0), "Wolfe 2 failed: " + msg) def assert_armijo(s, phi, c1=1e-4, err_msg=""): phi1 = phi(s) phi0 = phi(0) msg = "s = %s; phi(0) = %s; phi(s) = %s; %s" % (s, phi0, phi1, err_msg) assert_(phi1 <= (1 - c1*s)*phi0, msg) dx, F0, Fx_new, jacobian, param, c1=1e-4, err_msg=""): parameters = ls.prepare_parameters('rmt',param,jacobian,dx) rmt_eta_upper = parameters['rmt_eta_upper'] rmt_eta_lower = parameters['rmt_eta_lower'] amin = parameters['amin'] dxbar = jacobian.solve( Fx_new ) dx_diff = dxbar + (1 - alpha) * dx nominator = 2 * norm(dx_diff) denominator = alpha * norm(dx) t_dx_omega = nominator / denominator tester = (rmt_eta_lower <= t_dx_omega and t_dx_omega <= rmt_eta_upper) or (rmt_eta_lower > t_dx_omega and alpha == 1.0) msg = "s = %s; phi(0) = %s; phi(s) = %s; %s" % (alpha, F0, Fx_new, err_msg) assert_(tester or (alpha<amin), msg) def assert_bsc(alpha, x, dx, func, old_jacobian, param, err_msg): parameters = ls.prepare_parameters('bsc',param, old_jacobian, dx) H_lower = parameters['H_lower'] H_upper = parameters['H_upper'] amin = parameters['amin'] x_new = x + alpha * dx Fx_new = func(x_new) jacobian = deepcopy(old_jacobian) jacobian.update( x_new.copy(), Fx_new ) dx_next_it = -jacobian.solve( Fx_new ) dx_diff = dx_next_it - dx H_prime = alpha * norm(dx_diff) tester = (H_lower <= H_prime and H_prime <= H_upper) or (H_lower > H_prime and alpha >= 1.0) msg = "s = %s; phi(0) = %s; phi(s) = %s; %s" % (alpha, func(x), Fx_new, err_msg) assert_(tester or (alpha<amin), msg) (x, p, s, f, fprime, **kw): assert_wolfe(s, phi=lambda sp: f(x + p*sp), derphi=lambda sp: np.dot(fprime(x + p*sp), p), **kw) def assert_line_armijo(x, p, s, f, **kw): assert_armijo(s, phi=lambda sp: f(x + p*sp), **kw) def assert_fp_equal(x, y, err_msg="", nulp=50): try: assert_array_almost_equal_nulp(x, y, nulp) except AssertionError as e: raise AssertionError("%s\n%s" % (e, err_msg)) from e class TestLineSearch(object): def _scalar_func_1(self, s): self.fcount += 1 p = -s - s**3 + s**4 dp = -1 - 3*s**2 + 4*s**3 return p, dp def _scalar_func_2(self, s): self.fcount += 1 p = np.exp(-4*s) + s**2 dp = -4*np.exp(-4*s) + 2*s return p, dp def _scalar_func_3(self, s): self.fcount += 1 p = -np.sin(10*s) dp = -10*np.cos(10*s) return p, dp def _line_func_1(self, x): self.fcount += 1 f = np.dot(x, x) df = 2*x return f, df def _line_func_2(self, x): self.fcount += 1 f = np.dot(x, np.dot(self.A, x)) + 1 df = np.dot(self.A + self.A.T, x) return f, df def setup_method(self): self.scalar_funcs = [] self.line_funcs = [] self.N = 20 self.fcount = 0 def bind_index(func, idx): return lambda *a, **kw: func(*a, **kw)[idx] for name in sorted(dir(self)): if name.startswith('_scalar_func_'): value = getattr(self, name) self.scalar_funcs.append( (name, bind_index(value, 0), bind_index(value, 1))) elif name.startswith('_line_func_'): value = getattr(self, name) self.line_funcs.append( (name, bind_index(value, 0), bind_index(value, 1))) np.random.seed(1234) self.A = np.random.randn(self.N, self.N) def scalar_iter(self): for name, phi, derphi in self.scalar_funcs: for old_phi0 in np.random.randn(3): yield name, phi, derphi, old_phi0 def line_iter(self): for name, f, fprime in self.line_funcs: k = 0 while k < 9: x = np.random.randn(self.N) p = np.random.randn(self.N) if np.dot(p, fprime(x)) >= 0: # always pick a descent direction continue k += 1 old_fv = float(np.random.randn()) yield name, f, fprime, x, p, old_fv # -- Generic scalar searches def test_scalar_search_wolfe1(self): c = 0 for name, phi, derphi, old_phi0 in self.scalar_iter(): c += 1 s, phi1, phi0 = ls.scalar_search_wolfe1(phi, derphi, phi(0), old_phi0, derphi(0)) assert_fp_equal(phi0, phi(0), name) assert_fp_equal(phi1, phi(s), name) assert_wolfe(s, phi, derphi, err_msg=name) assert_(c > 3) # check that the iterator really works... def test_scalar_search_wolfe2(self): for name, phi, derphi, old_phi0 in self.scalar_iter(): s, phi1, phi0, derphi1 = ls.scalar_search_wolfe2( phi, derphi, phi(0), old_phi0, derphi(0)) assert_fp_equal(phi0, phi(0), name) assert_fp_equal(phi1, phi(s), name) if derphi1 is not None: assert_fp_equal(derphi1, derphi(s), name) assert_wolfe(s, phi, derphi, err_msg="%s %g" % (name, old_phi0)) def test_scalar_search_wolfe2_with_low_amax(self): def phi(alpha): return (alpha - 5) ** 2 def derphi(alpha): return 2 * (alpha - 5) s, _, _, _ = assert_warns(LineSearchWarning, ls.scalar_search_wolfe2, phi, derphi, amax=0.001) assert_(s is None) def test_scalar_search_wolfe2_regression(self): # Regression test for gh-12157 # This phi has its minimum at alpha=4/3 ~ 1.333. def phi(alpha): if alpha < 1: return - 3*np.pi/2 * (alpha - 1) else: return np.cos(3*np.pi/2 * alpha - np.pi) def derphi(alpha): if alpha < 1: return - 3*np.pi/2 else: return - 3*np.pi/2 * np.sin(3*np.pi/2 * alpha - np.pi) s, _, _, _ = ls.scalar_search_wolfe2(phi, derphi) # Without the fix in gh-13073, the scalar_search_wolfe2 # returned s=2.0 instead. assert_(s < 1.5) def test_scalar_search_armijo(self): for name, phi, derphi, old_phi0 in self.scalar_iter(): s, phi1 = ls.scalar_search_armijo(phi, phi(0), derphi(0)) assert_fp_equal(phi1, phi(s), name) assert_armijo(s, phi, err_msg="%s %g" % (name, old_phi0)) ###(LS)### ##RMT not usefull for scalar functions, thus no need for test_scalar_search_rmt? def test_line_search_rmt(self): #There is at least 1 function R^20->R to be tested, but this leads to s=None for name, f, fprime, x, p, old_f in self.line_iter(): jac = lambda x: fprime(x) x0 = nl._as_inexact(x) func = lambda z: nl._as_inexact(f(nl._array_like(z, x0))).flatten() x = x0.flatten() jacobian = nl.asjacobian(jac) jacobian.setup(x.copy(), f(x), func) options = {'jacobian': jacobian, 'jac_tol': min(1e-03,1e-03*norm(f(x))), 'amin':1e-8} #print("1: ",f(x),np.shape(fprime(x))) s, dxbar, f_new = ls.scalar_search_rmt(f, x, fprime(x), parameters=options) #print("2: ",p_new, s) assert_fp_equal(f_new, x+s*fprime(x), name) assert_rmt(s, fprime(x), f(x), f_new, jacobian, options, err_msg="%s %g" % name) def test_line_search_bsc(self): #There is at least 1 function R^20->R to be tested, but this leads to s=None for name, f, fprime, x, p, old_f in self.line_iter(): jac = lambda x: fprime(x) x0 = nl._as_inexact(x) func = lambda z: nl._as_inexact(f(nl._array_like(z, x0))).flatten() x = x0.flatten() jacobian = nl.asjacobian(jac) jacobian.setup(x.copy(), f(x), func) options = {'jacobian': jacobian, 'jac_tol': min(1e-03,1e-03*norm(f(x))), 'amin':1e-8} #print("1: ",f(x),np.shape(dp(x))) s, f_new= ls.scalar_search_bsc(func, x, fprime(x), f(x), parameters=options) #print("2: ",p_new, s) assert_fp_equal(f_new, x+s*fprime(x), name) assert_bsc(s, x, fprime(x), func, jacobian, options, err_msg="%s %g" % name) ###(LS)### # -- Generic line searches def test_line_search_wolfe1(self): c = 0 smax = 100 for name, f, fprime, x, p, old_f in self.line_iter(): f0 = f(x) g0 = fprime(x) self.fcount = 0 s, fc, gc, fv, ofv, gv = ls.line_search_wolfe1(f, fprime, x, p, g0, f0, old_f, amax=smax) assert_equal(self.fcount, fc+gc) assert_fp_equal(ofv, f(x)) if s is None: continue assert_fp_equal(fv, f(x + s*p)) assert_array_almost_equal(gv, fprime(x + s*p), decimal=14) if s < smax: c += 1 assert_line_wolfe(x, p, s, f, fprime, err_msg=name) assert_(c > 3) # check that the iterator really works... def test_line_search_wolfe2(self): c = 0 smax = 512 for name, f, fprime, x, p, old_f in self.line_iter(): f0 = f(x) g0 = fprime(x) self.fcount = 0 with suppress_warnings() as sup: sup.filter(LineSearchWarning, "The line search algorithm could not find a solution") sup.filter(LineSearchWarning, "The line search algorithm did not converge") s, fc, gc, fv, ofv, gv = ls.line_search_wolfe2(f, fprime, x, p, g0, f0, old_f, amax=smax) assert_equal(self.fcount, fc+gc) assert_fp_equal(ofv, f(x)) assert_fp_equal(fv, f(x + s*p)) if gv is not None: assert_array_almost_equal(gv, fprime(x + s*p), decimal=14) if s < smax: c += 1 assert_line_wolfe(x, p, s, f, fprime, err_msg=name) assert_(c > 3) # check that the iterator really works... def test_line_search_wolfe2_bounds(self): # See gh-7475 # For this f and p, starting at a point on axis 0, the strong Wolfe # condition 2 is met if and only if the step length s satisfies # |x + s| <= c2 * |x| f = lambda x: np.dot(x, x) fp = lambda x: 2 * x p = np.array([1, 0]) # Smallest s satisfying strong Wolfe conditions for these arguments is 30 x = -60 * p c2 = 0.5 s, _, _, _, _, _ = ls.line_search_wolfe2(f, fp, x, p, amax=30, c2=c2) assert_line_wolfe(x, p, s, f, fp) s, _, _, _, _, _ = assert_warns(LineSearchWarning, ls.line_search_wolfe2, f, fp, x, p, amax=29, c2=c2) assert_(s is None) # s=30 will only be tried on the 6th iteration, so this won't converge assert_warns(LineSearchWarning, ls.line_search_wolfe2, f, fp, x, p, c2=c2, maxiter=5) def test_line_search_armijo(self): c = 0 for name, f, fprime, x, p, old_f in self.line_iter(): f0 = f(x) g0 = fprime(x) self.fcount = 0 s, fc, fv = ls.line_search_armijo(f, x, p, g0, f0) c += 1 assert_equal(self.fcount, fc) assert_fp_equal(fv, f(x + s*p)) assert_line_armijo(x, p, s, f, err_msg=name) assert_(c >= 9) def test_armijo_terminate_1(self): count = [0] def phi(s): count[0] += 1 return -s + 0.01*s**2 s, phi1 = ls.scalar_search_armijo(phi, phi(0), -1, alpha0=1) assert_equal(s, 1) assert_equal(count[0], 2) assert_armijo(s, phi) def test_wolfe_terminate(self): def phi(s): count[0] += 1 return -s + 0.05*s**2 def derphi(s): count[0] += 1 return -1 + 0.05*2*s for func in [ls.scalar_search_wolfe1, ls.scalar_search_wolfe2]: count = [0] r = func(phi, derphi, phi(0), None, derphi(0)) assert_(r[0] is not None, (r, func)) assert_(count[0] <= 2 + 2, (count, func)) assert_wolfe(r[0], phi, derphi, err_msg=str(func))

true

f723ece8e845f677ad57a09ea90a361f54d50c23

12,408

bzl

Python

apple/internal/apple_toolchains.bzl

wendyliga/rules_apple

ac43c1e467564d9df6b3355ff93fcaf224f2c0f9

[ "Apache-2.0" ]

null

apple/internal/apple_toolchains.bzl

wendyliga/rules_apple

ac43c1e467564d9df6b3355ff93fcaf224f2c0f9

[ "Apache-2.0" ]

null

apple/internal/apple_toolchains.bzl

wendyliga/rules_apple

ac43c1e467564d9df6b3355ff93fcaf224f2c0f9

[ "Apache-2.0" ]

null

# Copyright 2021 The Bazel 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. """Shared toolchain required for processing Apple bundling rules.""" AppleMacToolsToolchainInfo = provider( doc = """ Propagates information about an Apple toolchain to internal bundling rules that use the toolchain. This provider exists as an internal detail for the rules to reference common, executable tools and files used as script templates for the purposes of executing Apple actions. Defined by the `apple_mac_tools_toolchain` rule. This toolchain is for the tools (and support files) for actions that *must* run on a Mac. """, fields = { "dsym_info_plist_template": """\ A `File` referencing a plist template for dSYM bundles. """, "process_and_sign_template": """\ A `File` referencing a template for a shell script to process and sign. """, "resolved_alticonstool": """\ A `struct` from `ctx.resolve_tools` referencing a tool to insert alternate icons entries in the app bundle's `Info.plist`. """, "resolved_bundletool_experimental": """\ A `struct` from `ctx.resolve_tools` referencing an experimental tool to create an Apple bundle by combining the bundling, post-processing, and signing steps into a single action that eliminates the archiving step. """, "resolved_clangrttool": """\ A `struct` from `ctx.resolve_tools` referencing a tool to find all Clang runtime libs linked to a binary. """, "resolved_codesigningtool": """\ A `struct` from `ctx.resolve_tools` referencing a tool to select the appropriate signing identity for Apple apps and Apple executable bundles. """, "resolved_dossier_codesigningtool": """\ A `struct` from `ctx.resolve_tools` referencing a tool to generate codesigning dossiers. """, "resolved_environment_plist_tool": """\ A `struct` from `ctx.resolve_tools` referencing a tool for collecting dev environment values. """, "resolved_imported_dynamic_framework_processor": """\ A `struct` from `ctx.resolve_tools` referencing a tool to process an imported dynamic framework such that the given framework only contains the same slices as the app binary, every file belonging to the dynamic framework is copied to a temporary location, and the dynamic framework is codesigned and zipped as a cacheable artifact. """, "resolved_plisttool": """\ A `struct` from `ctx.resolve_tools` referencing a tool to perform plist operations such as variable substitution, merging, and conversion of plist files to binary format. """, "resolved_provisioning_profile_tool": """\ A `struct` from `ctx.resolve_tools` referencing a tool that extracts entitlements from a provisioning profile. """, "resolved_swift_stdlib_tool": """\ A `struct` from `ctx.resolve_tools` referencing a tool that copies and lipos Swift stdlibs required for the target to run. """, "resolved_xctoolrunner": """\ A `struct` from `ctx.resolve_tools` referencing a tool that acts as a wrapper for xcrun actions. """, }, ) AppleXPlatToolsToolchainInfo = provider( doc = """ Propagates information about an Apple toolchain to internal bundling rules that use the toolchain. This provider exists as an internal detail for the rules to reference common, executable tools and files used as script templates for the purposes of executing Apple actions. Defined by the `apple_xplat_tools_toolchain` rule. This toolchain is for the tools (and support files) for actions that can run on any platform, i.e. - they do *not* have to run on a Mac. """, fields = { "resolved_bundletool": """\ A `struct` from `ctx.resolve_tools` referencing a tool to create an Apple bundle by taking a list of files/ZIPs and destinations paths to build the directory structure for those files. """, "resolved_versiontool": """\ A `struct` from `ctx.resolve_tools` referencing a tool that acts as a wrapper for xcrun actions. """, }, ) def _shared_attrs(): """Private attributes on every rule to provide access to bundling tools and other file deps.""" return { "_mac_toolchain": attr.label( default = Label("@build_bazel_rules_apple//apple/internal:mac_tools_toolchain"), providers = [[AppleMacToolsToolchainInfo]], ), "_xplat_toolchain": attr.label( default = Label("@build_bazel_rules_apple//apple/internal:xplat_tools_toolchain"), providers = [[AppleXPlatToolsToolchainInfo]], ), } def _resolve_tools_for_executable(*, rule_ctx, attr_name): """Helper macro to resolve executable runfile dependencies across the rule boundary.""" # TODO(b/111036105) Migrate away from this helper and its outputs once ctx.executable works # across rule boundaries. executable = getattr(rule_ctx.executable, attr_name) target = getattr(rule_ctx.attr, attr_name) inputs, input_manifests = rule_ctx.resolve_tools(tools = [target]) return struct( executable = executable, inputs = inputs, input_manifests = input_manifests, ) def _apple_mac_tools_toolchain_impl(ctx): return [ AppleMacToolsToolchainInfo( dsym_info_plist_template = ctx.file.dsym_info_plist_template, process_and_sign_template = ctx.file.process_and_sign_template, resolved_alticonstool = _resolve_tools_for_executable( attr_name = "alticonstool", rule_ctx = ctx, ), resolved_bundletool_experimental = _resolve_tools_for_executable( attr_name = "bundletool_experimental", rule_ctx = ctx, ), resolved_codesigningtool = _resolve_tools_for_executable( attr_name = "codesigningtool", rule_ctx = ctx, ), resolved_dossier_codesigningtool = _resolve_tools_for_executable( attr_name = "dossier_codesigningtool", rule_ctx = ctx, ), resolved_clangrttool = _resolve_tools_for_executable( attr_name = "clangrttool", rule_ctx = ctx, ), resolved_environment_plist_tool = _resolve_tools_for_executable( attr_name = "environment_plist_tool", rule_ctx = ctx, ), resolved_imported_dynamic_framework_processor = _resolve_tools_for_executable( attr_name = "imported_dynamic_framework_processor", rule_ctx = ctx, ), resolved_plisttool = _resolve_tools_for_executable( attr_name = "plisttool", rule_ctx = ctx, ), resolved_provisioning_profile_tool = _resolve_tools_for_executable( attr_name = "provisioning_profile_tool", rule_ctx = ctx, ), resolved_swift_stdlib_tool = _resolve_tools_for_executable( attr_name = "swift_stdlib_tool", rule_ctx = ctx, ), resolved_xctoolrunner = _resolve_tools_for_executable( attr_name = "xctoolrunner", rule_ctx = ctx, ), ), DefaultInfo(), ] apple_mac_tools_toolchain = rule( attrs = { "alticonstool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool to insert alternate icons entries in the app bundle's `Info.plist`. """, ), "bundletool_experimental": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing an experimental tool to create an Apple bundle by combining the bundling, post-processing, and signing steps into a single action that eliminates the archiving step. """, ), "clangrttool": attr.label( cfg = "exec", executable = True, doc = "A `File` referencing a tool to find all Clang runtime libs linked to a binary.", ), "codesigningtool": attr.label( cfg = "exec", executable = True, doc = "A `File` referencing a tool to assist in signing bundles.", ), "dossier_codesigningtool": attr.label( cfg = "exec", executable = True, doc = "A `File` referencing a tool to assist in generating signing dossiers.", ), "dsym_info_plist_template": attr.label( cfg = "exec", allow_single_file = True, doc = "A `File` referencing a plist template for dSYM bundles.", ), "environment_plist_tool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool to collect data from the development environment to be record into final bundles. """, ), "imported_dynamic_framework_processor": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool to process an imported dynamic framework such that the given framework only contains the same slices as the app binary, every file belonging to the dynamic framework is copied to a temporary location, and the dynamic framework is codesigned and zipped as a cacheable artifact. """, ), "plisttool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool to perform plist operations such as variable substitution, merging, and conversion of plist files to binary format. """, ), "process_and_sign_template": attr.label( allow_single_file = True, doc = "A `File` referencing a template for a shell script to process and sign.", ), "provisioning_profile_tool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool that extracts entitlements from a provisioning profile. """, ), "swift_stdlib_tool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool that copies and lipos Swift stdlibs required for the target to run. """, ), "xctoolrunner": attr.label( cfg = "exec", executable = True, doc = "A `File` referencing a tool that acts as a wrapper for xcrun actions.", ), }, doc = """Represents an Apple support toolchain for tools that must run on a Mac""", implementation = _apple_mac_tools_toolchain_impl, ) def _apple_xplat_tools_toolchain_impl(ctx): return [ AppleXPlatToolsToolchainInfo( resolved_bundletool = _resolve_tools_for_executable( attr_name = "bundletool", rule_ctx = ctx, ), resolved_versiontool = _resolve_tools_for_executable( attr_name = "versiontool", rule_ctx = ctx, ), ), DefaultInfo(), ] apple_xplat_tools_toolchain = rule( attrs = { "bundletool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool to create an Apple bundle by taking a list of files/ZIPs and destination paths to build the directory structure for those files. """, ), "versiontool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool for extracting version info from builds. """, ), }, doc = """Represents an Apple support toolchain for tools that can run on any platform""", implementation = _apple_xplat_tools_toolchain_impl, ) # Define the loadable module that lists the exported symbols in this file. apple_toolchain_utils = struct( shared_attrs = _shared_attrs, )

39.390476

100

0.651273

AppleMacToolsToolchainInfo = provider( doc = """ Propagates information about an Apple toolchain to internal bundling rules that use the toolchain. This provider exists as an internal detail for the rules to reference common, executable tools and files used as script templates for the purposes of executing Apple actions. Defined by the `apple_mac_tools_toolchain` rule. This toolchain is for the tools (and support files) for actions that *must* run on a Mac. """, fields = { "dsym_info_plist_template": """\ A `File` referencing a plist template for dSYM bundles. """, "process_and_sign_template": """\ A `File` referencing a template for a shell script to process and sign. """, "resolved_alticonstool": """\ A `struct` from `ctx.resolve_tools` referencing a tool to insert alternate icons entries in the app bundle's `Info.plist`. """, "resolved_bundletool_experimental": """\ A `struct` from `ctx.resolve_tools` referencing an experimental tool to create an Apple bundle by combining the bundling, post-processing, and signing steps into a single action that eliminates the archiving step. """, "resolved_clangrttool": """\ A `struct` from `ctx.resolve_tools` referencing a tool to find all Clang runtime libs linked to a binary. """, "resolved_codesigningtool": """\ A `struct` from `ctx.resolve_tools` referencing a tool to select the appropriate signing identity for Apple apps and Apple executable bundles. """, "resolved_dossier_codesigningtool": """\ A `struct` from `ctx.resolve_tools` referencing a tool to generate codesigning dossiers. """, "resolved_environment_plist_tool": """\ A `struct` from `ctx.resolve_tools` referencing a tool for collecting dev environment values. """, "resolved_imported_dynamic_framework_processor": """\ A `struct` from `ctx.resolve_tools` referencing a tool to process an imported dynamic framework such that the given framework only contains the same slices as the app binary, every file belonging to the dynamic framework is copied to a temporary location, and the dynamic framework is codesigned and zipped as a cacheable artifact. """, "resolved_plisttool": """\ A `struct` from `ctx.resolve_tools` referencing a tool to perform plist operations such as variable substitution, merging, and conversion of plist files to binary format. """, "resolved_provisioning_profile_tool": """\ A `struct` from `ctx.resolve_tools` referencing a tool that extracts entitlements from a provisioning profile. """, "resolved_swift_stdlib_tool": """\ A `struct` from `ctx.resolve_tools` referencing a tool that copies and lipos Swift stdlibs required for the target to run. """, "resolved_xctoolrunner": """\ A `struct` from `ctx.resolve_tools` referencing a tool that acts as a wrapper for xcrun actions. """, }, ) AppleXPlatToolsToolchainInfo = provider( doc = """ Propagates information about an Apple toolchain to internal bundling rules that use the toolchain. This provider exists as an internal detail for the rules to reference common, executable tools and files used as script templates for the purposes of executing Apple actions. Defined by the `apple_xplat_tools_toolchain` rule. This toolchain is for the tools (and support files) for actions that can run on any platform, i.e. - they do *not* have to run on a Mac. """, fields = { "resolved_bundletool": """\ A `struct` from `ctx.resolve_tools` referencing a tool to create an Apple bundle by taking a list of files/ZIPs and destinations paths to build the directory structure for those files. """, "resolved_versiontool": """\ A `struct` from `ctx.resolve_tools` referencing a tool that acts as a wrapper for xcrun actions. """, }, ) def _shared_attrs(): return { "_mac_toolchain": attr.label( default = Label("@build_bazel_rules_apple//apple/internal:mac_tools_toolchain"), providers = [[AppleMacToolsToolchainInfo]], ), "_xplat_toolchain": attr.label( default = Label("@build_bazel_rules_apple//apple/internal:xplat_tools_toolchain"), providers = [[AppleXPlatToolsToolchainInfo]], ), } def _resolve_tools_for_executable(*, rule_ctx, attr_name): # TODO(b/111036105) Migrate away from this helper and its outputs once ctx.executable works # across rule boundaries. executable = getattr(rule_ctx.executable, attr_name) target = getattr(rule_ctx.attr, attr_name) inputs, input_manifests = rule_ctx.resolve_tools(tools = [target]) return struct( executable = executable, inputs = inputs, input_manifests = input_manifests, ) def _apple_mac_tools_toolchain_impl(ctx): return [ AppleMacToolsToolchainInfo( dsym_info_plist_template = ctx.file.dsym_info_plist_template, process_and_sign_template = ctx.file.process_and_sign_template, resolved_alticonstool = _resolve_tools_for_executable( attr_name = "alticonstool", rule_ctx = ctx, ), resolved_bundletool_experimental = _resolve_tools_for_executable( attr_name = "bundletool_experimental", rule_ctx = ctx, ), resolved_codesigningtool = _resolve_tools_for_executable( attr_name = "codesigningtool", rule_ctx = ctx, ), resolved_dossier_codesigningtool = _resolve_tools_for_executable( attr_name = "dossier_codesigningtool", rule_ctx = ctx, ), resolved_clangrttool = _resolve_tools_for_executable( attr_name = "clangrttool", rule_ctx = ctx, ), resolved_environment_plist_tool = _resolve_tools_for_executable( attr_name = "environment_plist_tool", rule_ctx = ctx, ), resolved_imported_dynamic_framework_processor = _resolve_tools_for_executable( attr_name = "imported_dynamic_framework_processor", rule_ctx = ctx, ), resolved_plisttool = _resolve_tools_for_executable( attr_name = "plisttool", rule_ctx = ctx, ), resolved_provisioning_profile_tool = _resolve_tools_for_executable( attr_name = "provisioning_profile_tool", rule_ctx = ctx, ), resolved_swift_stdlib_tool = _resolve_tools_for_executable( attr_name = "swift_stdlib_tool", rule_ctx = ctx, ), resolved_xctoolrunner = _resolve_tools_for_executable( attr_name = "xctoolrunner", rule_ctx = ctx, ), ), DefaultInfo(), ] apple_mac_tools_toolchain = rule( attrs = { "alticonstool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool to insert alternate icons entries in the app bundle's `Info.plist`. """, ), "bundletool_experimental": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing an experimental tool to create an Apple bundle by combining the bundling, post-processing, and signing steps into a single action that eliminates the archiving step. """, ), "clangrttool": attr.label( cfg = "exec", executable = True, doc = "A `File` referencing a tool to find all Clang runtime libs linked to a binary.", ), "codesigningtool": attr.label( cfg = "exec", executable = True, doc = "A `File` referencing a tool to assist in signing bundles.", ), "dossier_codesigningtool": attr.label( cfg = "exec", executable = True, doc = "A `File` referencing a tool to assist in generating signing dossiers.", ), "dsym_info_plist_template": attr.label( cfg = "exec", allow_single_file = True, doc = "A `File` referencing a plist template for dSYM bundles.", ), "environment_plist_tool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool to collect data from the development environment to be record into final bundles. """, ), "imported_dynamic_framework_processor": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool to process an imported dynamic framework such that the given framework only contains the same slices as the app binary, every file belonging to the dynamic framework is copied to a temporary location, and the dynamic framework is codesigned and zipped as a cacheable artifact. """, ), "plisttool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool to perform plist operations such as variable substitution, merging, and conversion of plist files to binary format. """, ), "process_and_sign_template": attr.label( allow_single_file = True, doc = "A `File` referencing a template for a shell script to process and sign.", ), "provisioning_profile_tool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool that extracts entitlements from a provisioning profile. """, ), "swift_stdlib_tool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool that copies and lipos Swift stdlibs required for the target to run. """, ), "xctoolrunner": attr.label( cfg = "exec", executable = True, doc = "A `File` referencing a tool that acts as a wrapper for xcrun actions.", ), }, doc = """Represents an Apple support toolchain for tools that must run on a Mac""", implementation = _apple_mac_tools_toolchain_impl, ) def _apple_xplat_tools_toolchain_impl(ctx): return [ AppleXPlatToolsToolchainInfo( resolved_bundletool = _resolve_tools_for_executable( attr_name = "bundletool", rule_ctx = ctx, ), resolved_versiontool = _resolve_tools_for_executable( attr_name = "versiontool", rule_ctx = ctx, ), ), DefaultInfo(), ] apple_xplat_tools_toolchain = rule( attrs = { "bundletool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool to create an Apple bundle by taking a list of files/ZIPs and destination paths to build the directory structure for those files. """, ), "versiontool": attr.label( cfg = "exec", executable = True, doc = """ A `File` referencing a tool for extracting version info from builds. """, ), }, doc = """Represents an Apple support toolchain for tools that can run on any platform""", implementation = _apple_xplat_tools_toolchain_impl, ) apple_toolchain_utils = struct( shared_attrs = _shared_attrs, )

true

f723ee4aca2ba51e913883657260206b3974214b

597

py

Python

psaw/decorators.py

LeartS/PSAW

fd0faac7205e10cc6fcb3654de8e2b23a0d79bf2

[ "MIT" ]

null

psaw/decorators.py

LeartS/PSAW

fd0faac7205e10cc6fcb3654de8e2b23a0d79bf2

[ "MIT" ]

null

psaw/decorators.py

LeartS/PSAW

fd0faac7205e10cc6fcb3654de8e2b23a0d79bf2

[ "MIT" ]

null

from .exceptions import PSAWException def requires_private_key(method): def wrapper(self, *args, **kwargs): if not self.private_key: raise PSAWException( 'The {} method requires a private key'.format(method.__name__)) return method(self, *args, **kwargs) return wrapper def requires_api_key(method): def wrapper(self, *args, **kwargs): if not self.api_key: raise PSAWException( 'The {} method requires an API key'.format(method.__name__)) return method(self, *args, **kwargs) return wrapper

33.166667

79

0.633166

from .exceptions import PSAWException def requires_private_key(method): def wrapper(self, *args, **kwargs): if not self.private_key: raise PSAWException( 'The {} method requires a private key'.format(method.__name__)) return method(self, *args, **kwargs) return wrapper def requires_api_key(method): def wrapper(self, *args, **kwargs): if not self.api_key: raise PSAWException( 'The {} method requires an API key'.format(method.__name__)) return method(self, *args, **kwargs) return wrapper

true

f723ee57e5b3ea5abd16c6bfccb377f6f8af7698

532

py

Python

stats/attendance.py

lxchen2019/Python-Baseball

0498830e92c67de8221aac1777651ae141df0ec6

[ "MIT" ]

null

stats/attendance.py

lxchen2019/Python-Baseball

0498830e92c67de8221aac1777651ae141df0ec6

[ "MIT" ]

null

stats/attendance.py

lxchen2019/Python-Baseball

0498830e92c67de8221aac1777651ae141df0ec6

[ "MIT" ]

null

import pandas as pd import matplotlib.pyplot as plt from data import games attendance = games.loc[(games['type'] == 'info') & (games['multi2'] == 'attendance'), ['year', 'multi3']] attendance.columns = ['year', 'attendance'] attendance.loc[:, 'attendance'] = pd.to_numeric(attendance.loc[:, 'attendance']) attendance.plot(x='year', y='attendance', figsize = (15, 7), kind = 'bar') plt.xlabel('Year') plt.ylabel('Attendance') plt.axhline(y=attendance['attendance'].mean(), label='Mean', linestyle='--', color='green') plt.show()

33.25

105

0.680451

import pandas as pd import matplotlib.pyplot as plt from data import games attendance = games.loc[(games['type'] == 'info') & (games['multi2'] == 'attendance'), ['year', 'multi3']] attendance.columns = ['year', 'attendance'] attendance.loc[:, 'attendance'] = pd.to_numeric(attendance.loc[:, 'attendance']) attendance.plot(x='year', y='attendance', figsize = (15, 7), kind = 'bar') plt.xlabel('Year') plt.ylabel('Attendance') plt.axhline(y=attendance['attendance'].mean(), label='Mean', linestyle='--', color='green') plt.show()

true

f723ef0ead92ce0867c0219f60d16635c90e2cd6

2,755

py

Python

ch1/recipe4/load_save_model.py

xinglu/Tensorflow-2.0-Computer-Vision-Cookbook

d02c57d566f9df8b5980d58fc51a1194faef442c

[ "MIT" ]

1

2021-11-27T05:44:01.000Z

ch1/recipe4/load_save_model.py

ArjunVarma39/Tensorflow-2.0-Computer-Vision-Cookbook

92ea6713f664cff9eccaaccea8ac756f808e2066

[ "MIT" ]

null

ch1/recipe4/load_save_model.py

ArjunVarma39/Tensorflow-2.0-Computer-Vision-Cookbook

92ea6713f664cff9eccaaccea8ac756f808e2066

[ "MIT" ]

1

2021-01-21T04:36:33.000Z

import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelBinarizer from tensorflow.keras.datasets import mnist from tensorflow.keras.layers import * from tensorflow.keras.models import * def load_data(): (X_train, y_train), (X_test, y_test) = mnist.load_data() # Normalize data. X_train = X_train.astype('float32') / 255.0 X_test = X_test.astype('float32') / 255.0 # Reshape grayscale to include channel dimension. X_train = np.expand_dims(X_train, axis=3) X_test = np.expand_dims(X_test, axis=3) # Process labels. label_binarizer = LabelBinarizer() y_train = label_binarizer.fit_transform(y_train) y_test = label_binarizer.fit_transform(y_test) return X_train, y_train, X_test, y_test def build_network(): input_layer = Input(shape=(28, 28, 1), name='input_layer') convolution_1 = Conv2D(kernel_size=(2, 2), padding='same', strides=(2, 2), filters=32, name='convolution_1')(input_layer) activation_1 = ReLU(name='activation_1')(convolution_1) batch_normalization_1 = BatchNormalization(name='batch_normalization_1')(activation_1) pooling_1 = MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding='same', name='pooling_1')(batch_normalization_1) dropout = Dropout(rate=0.5, name='dropout')(pooling_1) flatten = Flatten(name='flatten')(dropout) dense_1 = Dense(units=128, name='dense_1')(flatten) activation_2 = ReLU(name='activation_2')(dense_1) dense_2 = Dense(units=10, name='dense_2')(activation_2) output = Softmax(name='output')(dense_2) network = Model(inputs=input_layer, outputs=output, name='my_model') return network def evaluate(model, X_test, y_test): _, accuracy = model.evaluate(X_test, y_test, verbose=0) print(f'Accuracy: {accuracy}') print('Loading and pre-processing data.') X_train, y_train, X_test, y_test = load_data() # Split dataset. X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, train_size=0.8) # Build network. model = build_network() # Compile and train model. print('Training network...') model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(X_train, y_train, validation_data=(X_valid, y_valid), epochs=40, batch_size=1024) print('Saving model and weights as HDF5.') model.save('model_and_weights.hdf5') print('Loading model and weights as HDF5.') loaded_model = load_model('model_and_weights.hdf5') print('Evaluating using loaded model.') evaluate(loaded_model, X_test, y_test)

34.012346

91

0.684211

import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelBinarizer from tensorflow.keras.datasets import mnist from tensorflow.keras.layers import * from tensorflow.keras.models import * def load_data(): (X_train, y_train), (X_test, y_test) = mnist.load_data() X_train = X_train.astype('float32') / 255.0 X_test = X_test.astype('float32') / 255.0 X_train = np.expand_dims(X_train, axis=3) X_test = np.expand_dims(X_test, axis=3) label_binarizer = LabelBinarizer() y_train = label_binarizer.fit_transform(y_train) y_test = label_binarizer.fit_transform(y_test) return X_train, y_train, X_test, y_test def build_network(): input_layer = Input(shape=(28, 28, 1), name='input_layer') convolution_1 = Conv2D(kernel_size=(2, 2), padding='same', strides=(2, 2), filters=32, name='convolution_1')(input_layer) activation_1 = ReLU(name='activation_1')(convolution_1) batch_normalization_1 = BatchNormalization(name='batch_normalization_1')(activation_1) pooling_1 = MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding='same', name='pooling_1')(batch_normalization_1) dropout = Dropout(rate=0.5, name='dropout')(pooling_1) flatten = Flatten(name='flatten')(dropout) dense_1 = Dense(units=128, name='dense_1')(flatten) activation_2 = ReLU(name='activation_2')(dense_1) dense_2 = Dense(units=10, name='dense_2')(activation_2) output = Softmax(name='output')(dense_2) network = Model(inputs=input_layer, outputs=output, name='my_model') return network def evaluate(model, X_test, y_test): _, accuracy = model.evaluate(X_test, y_test, verbose=0) print(f'Accuracy: {accuracy}') print('Loading and pre-processing data.') X_train, y_train, X_test, y_test = load_data() X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, train_size=0.8) model = build_network() print('Training network...') model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(X_train, y_train, validation_data=(X_valid, y_valid), epochs=40, batch_size=1024) print('Saving model and weights as HDF5.') model.save('model_and_weights.hdf5') print('Loading model and weights as HDF5.') loaded_model = load_model('model_and_weights.hdf5') print('Evaluating using loaded model.') evaluate(loaded_model, X_test, y_test)

true

f723f0b24682ef7fb196a22b5764bea459ee3007

1,103

py

Python

String-Algorithms/String-Algorithms-master/One Edit Away/oneEditAway.py

SrijaniSom/dsa-code-store

148292c8f963214629f271ec8601e73d3d0e145e

[ "MIT" ]

3

2021-02-19T07:09:46.000Z

2021-10-04T10:12:45.000Z

String-Algorithms/String-Algorithms-master/One Edit Away/oneEditAway.py

SrijaniSom/dsa-code-store

148292c8f963214629f271ec8601e73d3d0e145e

[ "MIT" ]

6

2021-02-21T19:35:18.000Z

2021-05-06T11:51:37.000Z

String-Algorithms/String-Algorithms-master/One Edit Away/oneEditAway.py

SrijaniSom/dsa-code-store

148292c8f963214629f271ec8601e73d3d0e145e

[ "MIT" ]

6

2021-02-21T19:28:03.000Z

2021-10-04T03:35:57.000Z

class solution: def oneEditAwayInsert(self,input1,input2): index1 = 0 index2 = 0 while((index2 < len(input2)) and (index1 < len(input1))): if(input1[index1] != input2[index2]): if(index1 != index2): return False index2+=1 else: index1+=1 index2+=1 return True def oneEditAwayReplace(self,input1,input2): flag = False for i in range(len(input1)): if(input2[i]!=input1[i]): if(flag): return False flag = True return True def oneEditAway(self,input1,input2): if(len(input1)==len(input2)): return self.oneEditAwayReplace(input1,input2) elif(len(input1)+1==len(input2)): return self.oneEditAwayInsert(input1,input2) elif(len(input1)-1==len(input2)): return self.oneEditAwayInsert(input2,input1) return False input1 = input() input2 = input() sol = solution() print(sol.oneEditAway(input1,input2))

29.810811

65

0.537625

class solution: def oneEditAwayInsert(self,input1,input2): index1 = 0 index2 = 0 while((index2 < len(input2)) and (index1 < len(input1))): if(input1[index1] != input2[index2]): if(index1 != index2): return False index2+=1 else: index1+=1 index2+=1 return True def oneEditAwayReplace(self,input1,input2): flag = False for i in range(len(input1)): if(input2[i]!=input1[i]): if(flag): return False flag = True return True def oneEditAway(self,input1,input2): if(len(input1)==len(input2)): return self.oneEditAwayReplace(input1,input2) elif(len(input1)+1==len(input2)): return self.oneEditAwayInsert(input1,input2) elif(len(input1)-1==len(input2)): return self.oneEditAwayInsert(input2,input1) return False input1 = input() input2 = input() sol = solution() print(sol.oneEditAway(input1,input2))

true

f723f1da4d9a432f7832da330a3205c3fef79a2c

6,577

py

Python

python/ccxt/async/coinexchange.py

hippylover/ccxt

db304e95b699c1971ad37b9053ae71fcb5dc3b03

[ "MIT" ]

2

2018-02-28T02:51:59.000Z

2018-02-28T03:25:51.000Z

python/ccxt/async/coinexchange.py

hippylover/ccxt

db304e95b699c1971ad37b9053ae71fcb5dc3b03

[ "MIT" ]

null

python/ccxt/async/coinexchange.py

hippylover/ccxt

db304e95b699c1971ad37b9053ae71fcb5dc3b03

[ "MIT" ]

9

2018-02-20T18:24:00.000Z

2019-06-18T14:23:11.000Z

# -*- coding: utf-8 -*- from ccxt.async.base.exchange import Exchange import math from ccxt.base.errors import ExchangeError class coinexchange (Exchange): def describe(self): return self.deep_extend(super(coinexchange, self).describe(), { 'id': 'coinexchange', 'name': 'CoinExchange', 'countries': ['IN', 'JP', 'KR', 'VN', 'US'], 'rateLimit': 1000, # new metainfo interface 'has': { 'privateAPI': False, 'fetchTrades': False, 'fetchCurrencies': True, 'fetchTickers': True, }, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/34842303-29c99fca-f71c-11e7-83c1-09d900cb2334.jpg', 'api': 'https://www.coinexchange.io/api/v1', 'www': 'https://www.coinexchange.io', 'doc': 'https://coinexchangeio.github.io/slate/', 'fees': 'https://www.coinexchange.io/fees', }, 'api': { 'public': { 'get': [ 'getcurrency', 'getcurrencies', 'getmarkets', 'getmarketsummaries', 'getmarketsummary', 'getorderbook', ], }, }, 'fees': { 'trading': { 'maker': 0.0015, 'taker': 0.0015, }, }, 'precision': { 'amount': 8, 'price': 8, }, }) def common_currency_code(self, currency): return currency async def fetch_currencies(self, params={}): currencies = await self.publicGetCurrencies(params) precision = self.precision['amount'] result = {} for i in range(0, len(currencies)): currency = currencies[i] id = currency['CurrencyID'] code = self.common_currency_code(currency['TickerCode']) active = currency['WalletStatus'] == 'online' status = 'ok' if not active: status = 'disabled' result[code] = { 'id': id, 'code': code, 'name': currency['Name'], 'active': active, 'status': status, 'precision': precision, 'limits': { 'amount': { 'min': None, 'max': math.pow(10, precision), }, 'price': { 'min': math.pow(10, -precision), 'max': math.pow(10, precision), }, 'cost': { 'min': None, 'max': None, }, 'withdraw': { 'min': None, 'max': math.pow(10, precision), }, }, 'info': currency, } return result async def fetch_markets(self): markets = await self.publicGetMarkets() result = [] for i in range(0, len(markets)): market = markets[i] id = market['MarketID'] base = self.common_currency_code(market['MarketAssetCode']) quote = self.common_currency_code(market['BaseCurrencyCode']) symbol = base + '/' + quote result.append({ 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'active': True, 'lot': None, 'info': market, }) return result def parse_ticker(self, ticker, market=None): if not market: marketId = ticker['MarketID'] market = self.marketsById[marketId] symbol = None if market: symbol = market['symbol'] timestamp = self.milliseconds() return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': float(ticker['HighPrice']), 'low': float(ticker['LowPrice']), 'bid': float(ticker['BidPrice']), 'ask': float(ticker['AskPrice']), 'vwap': None, 'open': None, 'close': None, 'first': None, 'last': float(ticker['LastPrice']), 'change': float(ticker['Change']), 'percentage': None, 'average': None, 'baseVolume': None, 'quoteVolume': float(ticker['Volume']), 'info': ticker, } async def fetch_ticker(self, symbol, params={}): await self.load_markets() market = self.market(symbol) ticker = await self.publicGetMarketsummary(self.extend({ 'market_id': market['id'], }, params)) return self.parse_ticker(ticker, market) async def fetch_tickers(self, symbols=None, params={}): await self.load_markets() tickers = await self.publicGetMarketsummaries(params) result = {} for i in range(0, len(tickers)): ticker = self.parse_ticker(tickers[i]) symbol = ticker['symbol'] result[symbol] = ticker return result async def fetch_order_book(self, symbol, params={}): await self.load_markets() orderbook = await self.publicGetOrderbook(self.extend({ 'market_id': self.market_id(symbol), }, params)) return self.parse_order_book(orderbook, None, 'BuyOrders', 'SellOrders', 'Price', 'Quantity') def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): url = self.urls['api'] + '/' + path if api == 'public': params = self.urlencode(params) if len(params): url += '?' + params return {'url': url, 'method': method, 'body': body, 'headers': headers} async def request(self, path, api='public', method='GET', params={}, headers=None, body=None): response = await self.fetch2(path, api, method, params, headers, body) success = self.safe_integer(response, 'success') if success != 1: raise ExchangeError(response['message']) return response['result']

35.360215

126

0.466626

from ccxt.async.base.exchange import Exchange import math from ccxt.base.errors import ExchangeError class coinexchange (Exchange): def describe(self): return self.deep_extend(super(coinexchange, self).describe(), { 'id': 'coinexchange', 'name': 'CoinExchange', 'countries': ['IN', 'JP', 'KR', 'VN', 'US'], 'rateLimit': 1000, 'has': { 'privateAPI': False, 'fetchTrades': False, 'fetchCurrencies': True, 'fetchTickers': True, }, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/34842303-29c99fca-f71c-11e7-83c1-09d900cb2334.jpg', 'api': 'https://www.coinexchange.io/api/v1', 'www': 'https://www.coinexchange.io', 'doc': 'https://coinexchangeio.github.io/slate/', 'fees': 'https://www.coinexchange.io/fees', }, 'api': { 'public': { 'get': [ 'getcurrency', 'getcurrencies', 'getmarkets', 'getmarketsummaries', 'getmarketsummary', 'getorderbook', ], }, }, 'fees': { 'trading': { 'maker': 0.0015, 'taker': 0.0015, }, }, 'precision': { 'amount': 8, 'price': 8, }, }) def common_currency_code(self, currency): return currency async def fetch_currencies(self, params={}): currencies = await self.publicGetCurrencies(params) precision = self.precision['amount'] result = {} for i in range(0, len(currencies)): currency = currencies[i] id = currency['CurrencyID'] code = self.common_currency_code(currency['TickerCode']) active = currency['WalletStatus'] == 'online' status = 'ok' if not active: status = 'disabled' result[code] = { 'id': id, 'code': code, 'name': currency['Name'], 'active': active, 'status': status, 'precision': precision, 'limits': { 'amount': { 'min': None, 'max': math.pow(10, precision), }, 'price': { 'min': math.pow(10, -precision), 'max': math.pow(10, precision), }, 'cost': { 'min': None, 'max': None, }, 'withdraw': { 'min': None, 'max': math.pow(10, precision), }, }, 'info': currency, } return result async def fetch_markets(self): markets = await self.publicGetMarkets() result = [] for i in range(0, len(markets)): market = markets[i] id = market['MarketID'] base = self.common_currency_code(market['MarketAssetCode']) quote = self.common_currency_code(market['BaseCurrencyCode']) symbol = base + '/' + quote result.append({ 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'active': True, 'lot': None, 'info': market, }) return result def parse_ticker(self, ticker, market=None): if not market: marketId = ticker['MarketID'] market = self.marketsById[marketId] symbol = None if market: symbol = market['symbol'] timestamp = self.milliseconds() return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': float(ticker['HighPrice']), 'low': float(ticker['LowPrice']), 'bid': float(ticker['BidPrice']), 'ask': float(ticker['AskPrice']), 'vwap': None, 'open': None, 'close': None, 'first': None, 'last': float(ticker['LastPrice']), 'change': float(ticker['Change']), 'percentage': None, 'average': None, 'baseVolume': None, 'quoteVolume': float(ticker['Volume']), 'info': ticker, } async def fetch_ticker(self, symbol, params={}): await self.load_markets() market = self.market(symbol) ticker = await self.publicGetMarketsummary(self.extend({ 'market_id': market['id'], }, params)) return self.parse_ticker(ticker, market) async def fetch_tickers(self, symbols=None, params={}): await self.load_markets() tickers = await self.publicGetMarketsummaries(params) result = {} for i in range(0, len(tickers)): ticker = self.parse_ticker(tickers[i]) symbol = ticker['symbol'] result[symbol] = ticker return result async def fetch_order_book(self, symbol, params={}): await self.load_markets() orderbook = await self.publicGetOrderbook(self.extend({ 'market_id': self.market_id(symbol), }, params)) return self.parse_order_book(orderbook, None, 'BuyOrders', 'SellOrders', 'Price', 'Quantity') def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): url = self.urls['api'] + '/' + path if api == 'public': params = self.urlencode(params) if len(params): url += '?' + params return {'url': url, 'method': method, 'body': body, 'headers': headers} async def request(self, path, api='public', method='GET', params={}, headers=None, body=None): response = await self.fetch2(path, api, method, params, headers, body) success = self.safe_integer(response, 'success') if success != 1: raise ExchangeError(response['message']) return response['result']

false

true

f723f2da7cb69cc97d7d2508483485aef439d3c6

5,384

py

Python

1-50/p13.py

YiWeiShen/Project-Euler-Hints

a79cacab075dd98d393516f083aaa7ffc6115a06

[ "MIT" ]

1

2019-02-25T13:00:31.000Z

1-50/p13.py

YiWeiShen/Project-Euler-Hints

a79cacab075dd98d393516f083aaa7ffc6115a06

[ "MIT" ]

null

1-50/p13.py

YiWeiShen/Project-Euler-Hints

a79cacab075dd98d393516f083aaa7ffc6115a06

[ "MIT" ]

null

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22918802058777319719839450180888072429661980811197\ 77158542502016545090413245809786882778948721859617\ 72107838435069186155435662884062257473692284509516\ 20849603980134001723930671666823555245252804609722\ 53503534226472524250874054075591789781264330331690' if __name__ == '__main__': for x in range(0, 5000, 50): l.append(''.join(list(a)[x:x+50])) sum = 0 for x in l: sum += int(x) print(sum)

47.646018

56

0.944279

l = [] a = '37107287533902102798797998220837590246510135740250\ 46376937677490009712648124896970078050417018260538\ 74324986199524741059474233309513058123726617309629\ 91942213363574161572522430563301811072406154908250\ 23067588207539346171171980310421047513778063246676\ 89261670696623633820136378418383684178734361726757\ 28112879812849979408065481931592621691275889832738\ 44274228917432520321923589422876796487670272189318\ 47451445736001306439091167216856844588711603153276\ 70386486105843025439939619828917593665686757934951\ 62176457141856560629502157223196586755079324193331\ 64906352462741904929101432445813822663347944758178\ 92575867718337217661963751590579239728245598838407\ 58203565325359399008402633568948830189458628227828\ 80181199384826282014278194139940567587151170094390\ 35398664372827112653829987240784473053190104293586\ 86515506006295864861532075273371959191420517255829\ 71693888707715466499115593487603532921714970056938\ 54370070576826684624621495650076471787294438377604\ 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97142617910342598647204516893989422179826088076852\ 87783646182799346313767754307809363333018982642090\ 10848802521674670883215120185883543223812876952786\ 71329612474782464538636993009049310363619763878039\ 62184073572399794223406235393808339651327408011116\ 66627891981488087797941876876144230030984490851411\ 60661826293682836764744779239180335110989069790714\ 85786944089552990653640447425576083659976645795096\ 66024396409905389607120198219976047599490197230297\ 64913982680032973156037120041377903785566085089252\ 16730939319872750275468906903707539413042652315011\ 94809377245048795150954100921645863754710598436791\ 78639167021187492431995700641917969777599028300699\ 15368713711936614952811305876380278410754449733078\ 40789923115535562561142322423255033685442488917353\ 44889911501440648020369068063960672322193204149535\ 41503128880339536053299340368006977710650566631954\ 81234880673210146739058568557934581403627822703280\ 82616570773948327592232845941706525094512325230608\ 22918802058777319719839450180888072429661980811197\ 77158542502016545090413245809786882778948721859617\ 72107838435069186155435662884062257473692284509516\ 20849603980134001723930671666823555245252804609722\ 53503534226472524250874054075591789781264330331690' if __name__ == '__main__': for x in range(0, 5000, 50): l.append(''.join(list(a)[x:x+50])) sum = 0 for x in l: sum += int(x) print(sum)

true

f723f3fb8c08361f204ed3b00ea1c886a4107a59

1,457

py

Python

botutils/searchforlinks.py

yashprakash13/Honeysuckle

d8adb83a63318a8c4994d18aea6fd28116b46f4e

[ "MIT" ]

2

2020-08-11T17:23:05.000Z

2021-02-20T04:02:33.000Z

botutils/searchforlinks.py

yashprakash13/Honeysuckle

d8adb83a63318a8c4994d18aea6fd28116b46f4e

[ "MIT" ]

null

botutils/searchforlinks.py

yashprakash13/Honeysuckle

d8adb83a63318a8c4994d18aea6fd28116b46f4e

[ "MIT" ]

1

2020-08-15T05:29:14.000Z

import re import requests from bs4 import BeautifulSoup from botutils.constants import IS_URL_REGEX def get_ffn_url_from_query(query): ffn_list = [] href = [] url = 'https://www.google.com/search?q=' + \ query+"+fanfiction" page = requests.get(url) soup = BeautifulSoup(page.content, 'html.parser') found = soup.findAll('a') for link in found: href.append(link['href']) for i in range(len(href)): if re.search(r"fanfiction.net/s/", href[i]) is not None: ffn_list.append(href[i]) if not ffn_list: return None ffn_url = re.search(IS_URL_REGEX, ffn_list[0]) return ffn_url.group(0) def get_ao3_url_from_query(query): ao3_list = [] href = [] url = 'https://www.google.com/search?q=' + \ query+"+archiveofourown" page = requests.get(url) soup = BeautifulSoup(page.content, 'html.parser') found = soup.findAll('a') for link in found: href.append(link['href']) for i in range(len(href)): # append /works/ first if re.search(r"\barchiveofourown.org/works/\b", href[i]) is not None: ao3_list.append(href[i]) # append /chapters/ next if re.search(r"\barchiveofourown.org/chapters/\b", href[i]) is not None: ao3_list.append(href[i]) if not ao3_list: return None ao3_url = re.search(IS_URL_REGEX, ao3_list[0]) return ao3_url.group(0)

22.075758

80

0.614276

import re import requests from bs4 import BeautifulSoup from botutils.constants import IS_URL_REGEX def get_ffn_url_from_query(query): ffn_list = [] href = [] url = 'https://www.google.com/search?q=' + \ query+"+fanfiction" page = requests.get(url) soup = BeautifulSoup(page.content, 'html.parser') found = soup.findAll('a') for link in found: href.append(link['href']) for i in range(len(href)): if re.search(r"fanfiction.net/s/", href[i]) is not None: ffn_list.append(href[i]) if not ffn_list: return None ffn_url = re.search(IS_URL_REGEX, ffn_list[0]) return ffn_url.group(0) def get_ao3_url_from_query(query): ao3_list = [] href = [] url = 'https://www.google.com/search?q=' + \ query+"+archiveofourown" page = requests.get(url) soup = BeautifulSoup(page.content, 'html.parser') found = soup.findAll('a') for link in found: href.append(link['href']) for i in range(len(href)): if re.search(r"\barchiveofourown.org/works/\b", href[i]) is not None: ao3_list.append(href[i]) if re.search(r"\barchiveofourown.org/chapters/\b", href[i]) is not None: ao3_list.append(href[i]) if not ao3_list: return None ao3_url = re.search(IS_URL_REGEX, ao3_list[0]) return ao3_url.group(0)

true

f723f47402f819e72e9efeddd056d1ccea3fb2f6

1,107

py

Python

bach/tests/unit/bach/test_series_json.py

objectiv/objectiv-analytics

86ec1508f71c2d61ea7d67479800e4dc417a46e1

[ "Apache-2.0" ]

23

2021-11-10T21:37:42.000Z

2022-03-30T11:46:19.000Z

bach/tests/unit/bach/test_series_json.py

objectiv/objectiv-analytics

86ec1508f71c2d61ea7d67479800e4dc417a46e1

[ "Apache-2.0" ]

163

2021-11-10T10:11:26.000Z

2022-03-31T16:04:27.000Z

bach/tests/unit/bach/test_series_json.py

objectiv/objectiv-analytics

86ec1508f71c2d61ea7d67479800e4dc417a46e1

[ "Apache-2.0" ]

null

""" Copyright 2022 Objectiv B.V. """ import pytest from bach.series.series_json import JsonBigQueryAccessorImpl from tests.unit.bach.util import get_fake_df @pytest.mark.skip_postgres def test_bq_get_slice_partial_expr(dialect): # Here we test the _get_slice_partial_expr function of the BigQuery specific JsonBigQueryAccessor. So # skipping all other dialects df = get_fake_df( dialect=dialect, index_names=['i'], data_names=['a'], dtype='json' ) jbqa = JsonBigQueryAccessorImpl(df.a) assert jbqa._get_slice_partial_expr(None, True).to_sql(dialect) == '0' assert jbqa._get_slice_partial_expr(None, False).to_sql(dialect) == '9223372036854775807' assert jbqa._get_slice_partial_expr(5, False).to_sql(dialect) == '5' assert jbqa._get_slice_partial_expr(5, True).to_sql(dialect) == '5' assert jbqa._get_slice_partial_expr(-5, False).to_sql(dialect) == \ '(ARRAY_LENGTH(JSON_QUERY_ARRAY(`a`)) -5)' assert jbqa._get_slice_partial_expr(-5, True).to_sql(dialect) == \ '(ARRAY_LENGTH(JSON_QUERY_ARRAY(`a`)) -5)'

36.9

105

0.712737

import pytest from bach.series.series_json import JsonBigQueryAccessorImpl from tests.unit.bach.util import get_fake_df @pytest.mark.skip_postgres def test_bq_get_slice_partial_expr(dialect): df = get_fake_df( dialect=dialect, index_names=['i'], data_names=['a'], dtype='json' ) jbqa = JsonBigQueryAccessorImpl(df.a) assert jbqa._get_slice_partial_expr(None, True).to_sql(dialect) == '0' assert jbqa._get_slice_partial_expr(None, False).to_sql(dialect) == '9223372036854775807' assert jbqa._get_slice_partial_expr(5, False).to_sql(dialect) == '5' assert jbqa._get_slice_partial_expr(5, True).to_sql(dialect) == '5' assert jbqa._get_slice_partial_expr(-5, False).to_sql(dialect) == \ '(ARRAY_LENGTH(JSON_QUERY_ARRAY(`a`)) -5)' assert jbqa._get_slice_partial_expr(-5, True).to_sql(dialect) == \ '(ARRAY_LENGTH(JSON_QUERY_ARRAY(`a`)) -5)'

true

f723f53cdb2ac0facc06438330246f32fcc5c7e7

3,101

py

Python

pupil/models/clustering.py

hadi-gharibi/pupil

9d266572cc1ebf659e87206be6e5f1548959d510

[ "Apache-2.0" ]

2

2022-03-31T23:17:14.000Z

2022-03-31T23:24:58.000Z

pupil/models/clustering.py

hadi-gharibi/pupil

9d266572cc1ebf659e87206be6e5f1548959d510

[ "Apache-2.0" ]

null

pupil/models/clustering.py

hadi-gharibi/pupil

9d266572cc1ebf659e87206be6e5f1548959d510

[ "Apache-2.0" ]

null

from abc import ABC, abstractmethod from typing import Dict, Protocol, Tuple import faiss import numpy as np from pupil.types import NDArray2D from sklearn.cluster import AgglomerativeClustering class Clustering(Protocol): n_clusters: int def fit(self, X: NDArray2D): ... def predict(self, X: NDArray2D) -> Tuple[NDArray2D, NDArray2D]: ... def distance_to_cluster_centers(self, X: NDArray2D) -> Tuple[NDArray2D, NDArray2D]: """After having the center of your clusters, you can use this function to see the distance from X and center of all clusters Args: X (NDArray2D): The input to check. Returns: Tuple[NDArray2D, NDArray2D]: Return (Distances, cluster_ids). Shape of each: (#queries, #clusters) """ ... class FaissKMeansClustering: def __init__( self, n_clusters: int, n_init: int = 10, max_iter: int = 100, ) -> None: self.n_clusters = n_clusters self.n_init = n_init self.max_iter = max_iter self.cluster_centers_ = None self.inertia_ = None def fit(self, X: NDArray2D) -> None: self.kmeans = faiss.Kmeans( d=X.shape[1], k=self.n_clusters, niter=self.max_iter, nredo=self.n_init, ) X = X / np.linalg.norm(X) self.kmeans.train(X.astype(np.float32)) self.cluster_centers_ = self.kmeans.centroids self.inertia_ = self.kmeans.obj[-1] def predict(self, X: NDArray2D) -> Tuple[NDArray2D, NDArray2D]: X = X / np.linalg.norm(X) return self.kmeans.index.search(X.astype(np.float32), 1) # type: ignore def distance_to_cluster_centers(self, X: NDArray2D) -> Tuple[NDArray2D, NDArray2D]: X = X / np.linalg.norm(X) D, I = self.kmeans.index.search(X.astype(np.float32), self.n_clusters) # type: ignore return D, I class Splitter(Protocol): def fit(self, X: NDArray2D, clsuter_inds: NDArray2D): ... @property def splits( self, ): ... class Distance1DSplitter: def __init__(self, nsplits=3): self.nsplits = nsplits def fit(self, X: NDArray2D, clsuter_inds: NDArray2D) -> None: self.clsuter_inds = clsuter_inds self.alg = AgglomerativeClustering(n_clusters=self.nsplits) self.alg.fit(X.reshape((-1, 1))) self._tag_to_index_dict = self._tag_to_index() def _tag_to_index(self) -> Dict[str, Tuple[int, int]]: tags = ["priority_" + str(i) for i in range(self.nsplits)] inds = np.argwhere(np.diff(self.alg.labels_) != 0).flatten().tolist() inds.insert(0, -1) inds.append(len(self.alg.labels_)) tag_dict = {} for i, end in enumerate(inds[1:]): start = inds[i] + 1 tag_dict[tags[i]] = (start, end + 1) return tag_dict @property def splits(self): res = {} for k, v in self._tag_to_index_dict.items(): res[k] = self.clsuter_inds[0][v[0] : v[1]] return res

28.981308

132

0.603354

from abc import ABC, abstractmethod from typing import Dict, Protocol, Tuple import faiss import numpy as np from pupil.types import NDArray2D from sklearn.cluster import AgglomerativeClustering class Clustering(Protocol): n_clusters: int def fit(self, X: NDArray2D): ... def predict(self, X: NDArray2D) -> Tuple[NDArray2D, NDArray2D]: ... def distance_to_cluster_centers(self, X: NDArray2D) -> Tuple[NDArray2D, NDArray2D]: ... class FaissKMeansClustering: def __init__( self, n_clusters: int, n_init: int = 10, max_iter: int = 100, ) -> None: self.n_clusters = n_clusters self.n_init = n_init self.max_iter = max_iter self.cluster_centers_ = None self.inertia_ = None def fit(self, X: NDArray2D) -> None: self.kmeans = faiss.Kmeans( d=X.shape[1], k=self.n_clusters, niter=self.max_iter, nredo=self.n_init, ) X = X / np.linalg.norm(X) self.kmeans.train(X.astype(np.float32)) self.cluster_centers_ = self.kmeans.centroids self.inertia_ = self.kmeans.obj[-1] def predict(self, X: NDArray2D) -> Tuple[NDArray2D, NDArray2D]: X = X / np.linalg.norm(X) return self.kmeans.index.search(X.astype(np.float32), 1) def distance_to_cluster_centers(self, X: NDArray2D) -> Tuple[NDArray2D, NDArray2D]: X = X / np.linalg.norm(X) D, I = self.kmeans.index.search(X.astype(np.float32), self.n_clusters) return D, I class Splitter(Protocol): def fit(self, X: NDArray2D, clsuter_inds: NDArray2D): ... @property def splits( self, ): ... class Distance1DSplitter: def __init__(self, nsplits=3): self.nsplits = nsplits def fit(self, X: NDArray2D, clsuter_inds: NDArray2D) -> None: self.clsuter_inds = clsuter_inds self.alg = AgglomerativeClustering(n_clusters=self.nsplits) self.alg.fit(X.reshape((-1, 1))) self._tag_to_index_dict = self._tag_to_index() def _tag_to_index(self) -> Dict[str, Tuple[int, int]]: tags = ["priority_" + str(i) for i in range(self.nsplits)] inds = np.argwhere(np.diff(self.alg.labels_) != 0).flatten().tolist() inds.insert(0, -1) inds.append(len(self.alg.labels_)) tag_dict = {} for i, end in enumerate(inds[1:]): start = inds[i] + 1 tag_dict[tags[i]] = (start, end + 1) return tag_dict @property def splits(self): res = {} for k, v in self._tag_to_index_dict.items(): res[k] = self.clsuter_inds[0][v[0] : v[1]] return res

true

f723f5a286cb6bd6f00ddea9012b0aaff76a5524

6,044

py

Python

sdks/python/apache_beam/testing/benchmarks/nexmark/queries/query3.py

eyal0/beam

9c6922976cc2a5c6a2ef836c1986ff769cda99a5

[ "Apache-2.0" ]

2

2017-12-19T18:34:54.000Z

2019-05-14T21:50:06.000Z

sdks/python/apache_beam/testing/benchmarks/nexmark/queries/query3.py

eyal0/beam

9c6922976cc2a5c6a2ef836c1986ff769cda99a5

[ "Apache-2.0" ]

80

2020-01-16T09:55:09.000Z

2020-10-03T13:43:07.000Z

sdks/python/apache_beam/testing/benchmarks/nexmark/queries/query3.py

eyal0/beam

9c6922976cc2a5c6a2ef836c1986ff769cda99a5

[ "Apache-2.0" ]

1

2020-11-11T18:45:54.000Z

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # """ Query 3, 'Local Item Suggestion'. Who is selling in OR, ID or CA in category 10, and for what auction ids? In CQL syntax:: SELECT Istream(P.name, P.city, P.state, A.id) FROM Auction A [ROWS UNBOUNDED], Person P [ROWS UNBOUNDED] WHERE A.seller = P.id AND (P.state = `OR' OR P.state = `ID' OR P.state = `CA') AND A.category = 10; We'll implement this query to allow 'new auction' events to come before the 'new person' events for the auction seller. Those auctions will be stored until the matching person is seen. Then all subsequent auctions for a person will use the stored person record. """ from __future__ import absolute_import import logging import apache_beam as beam from apache_beam.testing.benchmarks.nexmark.models import nexmark_model from apache_beam.testing.benchmarks.nexmark.queries import nexmark_query_util from apache_beam.testing.benchmarks.nexmark.queries.nexmark_query_util import ResultNames from apache_beam.transforms import trigger from apache_beam.transforms import userstate from apache_beam.transforms import window from apache_beam.transforms.userstate import on_timer def load(events, metadata=None): num_events_in_pane = 30 windowed_events = ( events | beam.WindowInto( window.GlobalWindows(), trigger=trigger.Repeatedly(trigger.AfterCount(num_events_in_pane)), accumulation_mode=trigger.AccumulationMode.DISCARDING)) auction_by_seller_id = ( windowed_events | nexmark_query_util.JustAuctions() | 'query3_filter_category' >> beam.Filter(lambda auc: auc.category == 10) | 'query3_key_by_seller' >> beam.ParDo( nexmark_query_util.AuctionBySellerFn())) person_by_id = ( windowed_events | nexmark_query_util.JustPerson() | 'query3_filter_region' >> beam.Filter(lambda person: person.state in ['OR', 'ID', 'CA']) | 'query3_key_by_person_id' >> beam.ParDo( nexmark_query_util.PersonByIdFn())) return ({ nexmark_query_util.AUCTION_TAG: auction_by_seller_id, nexmark_query_util.PERSON_TAG: person_by_id, } | beam.CoGroupByKey() | 'query3_join' >> beam.ParDo( JoinFn(metadata.get('max_auction_waiting_time'))) | 'query3_output' >> beam.Map( lambda t: { ResultNames.NAME: t[1].name, ResultNames.CITY: t[1].city, ResultNames.STATE: t[1].state, ResultNames.AUCTION_ID: t[0].id })) class JoinFn(beam.DoFn): """ Join auctions and person by person id and emit their product one pair at a time. We know a person may submit any number of auctions. Thus new person event must have the person record stored in persistent state in order to match future auctions by that person. However we know that each auction is associated with at most one person, so only need to store auction records in persistent state until we have seen the corresponding person record. And of course may have already seen that record. """ AUCTIONS = 'auctions_state' PERSON = 'person_state' PERSON_EXPIRING = 'person_state_expiring' auction_spec = userstate.BagStateSpec(AUCTIONS, nexmark_model.Auction.CODER) person_spec = userstate.ReadModifyWriteStateSpec( PERSON, nexmark_model.Person.CODER) person_timer_spec = userstate.TimerSpec( PERSON_EXPIRING, userstate.TimeDomain.WATERMARK) def __init__(self, max_auction_wait_time): self.max_auction_wait_time = max_auction_wait_time def process( self, element, auction_state=beam.DoFn.StateParam(auction_spec), person_state=beam.DoFn.StateParam(person_spec), person_timer=beam.DoFn.TimerParam(person_timer_spec)): # extract group with tags from element tuple _, group = element existing_person = person_state.read() if existing_person: # the person exists in person_state for this person id for auction in group[nexmark_query_util.AUCTION_TAG]: yield auction, existing_person return new_person = None for person in group[nexmark_query_util.PERSON_TAG]: if not new_person: new_person = person else: logging.error( 'two new person wtih same key: %s and %s' % (person, new_person)) continue # read all pending auctions for this person id, output and flush it pending_auctions = auction_state.read() if pending_auctions: for pending_auction in pending_auctions: yield pending_auction, new_person auction_state.clear() # output new auction for this person id for auction in group[nexmark_query_util.AUCTION_TAG]: yield auction, new_person # remember person for max_auction_wait_time seconds for future auctions person_state.write(new_person) person_timer.set(new_person.date_time + self.max_auction_wait_time) # we are done if we have seen a new person if new_person: return # remember auction until we see person for auction in group[nexmark_query_util.AUCTION_TAG]: auction_state.add(auction) @on_timer(person_timer_spec) def expiry(self, person_state=beam.DoFn.StateParam(person_spec)): person_state.clear()

38.012579

89

0.720218

from __future__ import absolute_import import logging import apache_beam as beam from apache_beam.testing.benchmarks.nexmark.models import nexmark_model from apache_beam.testing.benchmarks.nexmark.queries import nexmark_query_util from apache_beam.testing.benchmarks.nexmark.queries.nexmark_query_util import ResultNames from apache_beam.transforms import trigger from apache_beam.transforms import userstate from apache_beam.transforms import window from apache_beam.transforms.userstate import on_timer def load(events, metadata=None): num_events_in_pane = 30 windowed_events = ( events | beam.WindowInto( window.GlobalWindows(), trigger=trigger.Repeatedly(trigger.AfterCount(num_events_in_pane)), accumulation_mode=trigger.AccumulationMode.DISCARDING)) auction_by_seller_id = ( windowed_events | nexmark_query_util.JustAuctions() | 'query3_filter_category' >> beam.Filter(lambda auc: auc.category == 10) | 'query3_key_by_seller' >> beam.ParDo( nexmark_query_util.AuctionBySellerFn())) person_by_id = ( windowed_events | nexmark_query_util.JustPerson() | 'query3_filter_region' >> beam.Filter(lambda person: person.state in ['OR', 'ID', 'CA']) | 'query3_key_by_person_id' >> beam.ParDo( nexmark_query_util.PersonByIdFn())) return ({ nexmark_query_util.AUCTION_TAG: auction_by_seller_id, nexmark_query_util.PERSON_TAG: person_by_id, } | beam.CoGroupByKey() | 'query3_join' >> beam.ParDo( JoinFn(metadata.get('max_auction_waiting_time'))) | 'query3_output' >> beam.Map( lambda t: { ResultNames.NAME: t[1].name, ResultNames.CITY: t[1].city, ResultNames.STATE: t[1].state, ResultNames.AUCTION_ID: t[0].id })) class JoinFn(beam.DoFn): AUCTIONS = 'auctions_state' PERSON = 'person_state' PERSON_EXPIRING = 'person_state_expiring' auction_spec = userstate.BagStateSpec(AUCTIONS, nexmark_model.Auction.CODER) person_spec = userstate.ReadModifyWriteStateSpec( PERSON, nexmark_model.Person.CODER) person_timer_spec = userstate.TimerSpec( PERSON_EXPIRING, userstate.TimeDomain.WATERMARK) def __init__(self, max_auction_wait_time): self.max_auction_wait_time = max_auction_wait_time def process( self, element, auction_state=beam.DoFn.StateParam(auction_spec), person_state=beam.DoFn.StateParam(person_spec), person_timer=beam.DoFn.TimerParam(person_timer_spec)): _, group = element existing_person = person_state.read() if existing_person: for auction in group[nexmark_query_util.AUCTION_TAG]: yield auction, existing_person return new_person = None for person in group[nexmark_query_util.PERSON_TAG]: if not new_person: new_person = person else: logging.error( 'two new person wtih same key: %s and %s' % (person, new_person)) continue pending_auctions = auction_state.read() if pending_auctions: for pending_auction in pending_auctions: yield pending_auction, new_person auction_state.clear() for auction in group[nexmark_query_util.AUCTION_TAG]: yield auction, new_person person_state.write(new_person) person_timer.set(new_person.date_time + self.max_auction_wait_time) if new_person: return for auction in group[nexmark_query_util.AUCTION_TAG]: auction_state.add(auction) @on_timer(person_timer_spec) def expiry(self, person_state=beam.DoFn.StateParam(person_spec)): person_state.clear()

true

f723f5e67a95794ea25c2e636d620a2789ac60ad

48,091

py

Python

core/controllers/admin.py

Ragify/oppia

a530c7e4d5274b646afc7dd7040d13c7ed45b829

[ "Apache-2.0" ]

4

2021-09-16T16:46:53.000Z

2022-02-06T13:00:14.000Z

core/controllers/admin.py

Ragify/oppia

a530c7e4d5274b646afc7dd7040d13c7ed45b829

[ "Apache-2.0" ]

null

core/controllers/admin.py

Ragify/oppia

a530c7e4d5274b646afc7dd7040d13c7ed45b829

[ "Apache-2.0" ]

null

# Copyright 2014 The Oppia 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. """Controllers for the admin view.""" from __future__ import absolute_import from __future__ import unicode_literals import io import logging import random from core import feconf from core import python_utils from core import utils from core.constants import constants from core.controllers import acl_decorators from core.controllers import base from core.controllers import domain_objects_validator as validation_method from core.domain import auth_services from core.domain import blog_services from core.domain import collection_services from core.domain import config_domain from core.domain import config_services from core.domain import email_manager from core.domain import exp_domain from core.domain import exp_fetchers from core.domain import exp_services from core.domain import opportunity_services from core.domain import platform_feature_services as feature_services from core.domain import platform_parameter_domain as parameter_domain from core.domain import question_domain from core.domain import question_services from core.domain import recommendations_services from core.domain import rights_manager from core.domain import role_services from core.domain import search_services from core.domain import skill_domain from core.domain import skill_services from core.domain import state_domain from core.domain import stats_services from core.domain import story_domain from core.domain import story_services from core.domain import subtopic_page_domain from core.domain import subtopic_page_services from core.domain import topic_domain from core.domain import topic_fetchers from core.domain import topic_services from core.domain import user_services from core.domain import wipeout_service class AdminPage(base.BaseHandler): """Admin page shown in the App Engine admin console.""" URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = {'GET': {}} @acl_decorators.can_access_admin_page def get(self): """Handles GET requests.""" self.render_template('admin-page.mainpage.html') class AdminHandler(base.BaseHandler): """Handler for the admin page.""" GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'GET': {}, 'POST': { 'action': { 'schema': { 'type': 'basestring', 'choices': [ 'reload_exploration', 'reload_collection', 'generate_dummy_explorations', 'clear_search_index', 'generate_dummy_new_structures_data', 'generate_dummy_new_skill_data', 'save_config_properties', 'revert_config_property', 'upload_topic_similarities', 'regenerate_topic_related_opportunities', 'update_feature_flag_rules' ] }, # TODO(#13331): Remove default_value when it is confirmed that, # for clearing the search indices of exploration & collection # 'action' field must be provided in the payload. 'default_value': None }, 'exploration_id': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'collection_id': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'num_dummy_exps_to_generate': { 'schema': { 'type': 'int' }, 'default_value': None }, 'num_dummy_exps_to_publish': { 'schema': { 'type': 'int' }, 'default_value': None }, 'new_config_property_values': { 'schema': { 'type': 'object_dict', 'validation_method': ( validation_method.validate_new_config_property_values) }, 'default_value': None }, 'config_property_id': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'data': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'topic_id': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'feature_name': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'commit_message': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'new_rules': { 'schema': { 'type': 'list', 'items': { 'type': 'object_dict', 'object_class': parameter_domain.PlatformParameterRule } }, 'default_value': None } } } @acl_decorators.can_access_admin_page def get(self): """Handles GET requests.""" demo_exploration_ids = list(feconf.DEMO_EXPLORATIONS.keys()) topic_summaries = topic_fetchers.get_all_topic_summaries() topic_summary_dicts = [ summary.to_dict() for summary in topic_summaries] feature_flag_dicts = feature_services.get_all_feature_flag_dicts() config_properties = config_domain.Registry.get_config_property_schemas() # Removes promo-bar related configs as promo-bar is handlded by # release coordinators in /release-coordinator page. del config_properties['promo_bar_enabled'] del config_properties['promo_bar_message'] # Remove blog related configs as they will be handled by 'blog admins' # on blog admin page. del config_properties['max_number_of_tags_assigned_to_blog_post'] del config_properties['list_of_default_tags_for_blog_post'] self.render_json({ 'config_properties': config_properties, 'demo_collections': sorted(feconf.DEMO_COLLECTIONS.items()), 'demo_explorations': sorted(feconf.DEMO_EXPLORATIONS.items()), 'demo_exploration_ids': demo_exploration_ids, 'updatable_roles': role_services.UPDATABLE_ROLES, 'viewable_roles': role_services.VIEWABLE_ROLES, 'human_readable_roles': role_services.HUMAN_READABLE_ROLES, 'role_to_actions': role_services.get_role_actions(), 'topic_summaries': topic_summary_dicts, 'feature_flags': feature_flag_dicts, }) @acl_decorators.can_access_admin_page def post(self): """Handles POST requests.""" action = self.normalized_payload.get('action') try: result = {} if action == 'reload_exploration': exploration_id = self.normalized_payload.get('exploration_id') self._reload_exploration(exploration_id) elif action == 'reload_collection': collection_id = self.normalized_payload.get('collection_id') self._reload_collection(collection_id) elif action == 'generate_dummy_explorations': num_dummy_exps_to_generate = self.normalized_payload.get( 'num_dummy_exps_to_generate') num_dummy_exps_to_publish = self.normalized_payload.get( 'num_dummy_exps_to_publish') if num_dummy_exps_to_generate < num_dummy_exps_to_publish: raise self.InvalidInputException( 'Generate count cannot be less than publish count') else: self._generate_dummy_explorations( num_dummy_exps_to_generate, num_dummy_exps_to_publish) elif action == 'clear_search_index': search_services.clear_collection_search_index() search_services.clear_exploration_search_index() elif action == 'generate_dummy_new_structures_data': self._load_dummy_new_structures_data() elif action == 'generate_dummy_new_skill_data': self._generate_dummy_skill_and_questions() elif action == 'save_config_properties': new_config_property_values = self.normalized_payload.get( 'new_config_property_values') logging.info( '[ADMIN] %s saved config property values: %s' % (self.user_id, new_config_property_values)) for (name, value) in new_config_property_values.items(): config_services.set_property(self.user_id, name, value) elif action == 'revert_config_property': config_property_id = self.normalized_payload.get( 'config_property_id') logging.info( '[ADMIN] %s reverted config property: %s' % (self.user_id, config_property_id)) config_services.revert_property( self.user_id, config_property_id) elif action == 'upload_topic_similarities': data = self.normalized_payload.get('data') recommendations_services.update_topic_similarities(data) elif action == 'regenerate_topic_related_opportunities': topic_id = self.normalized_payload.get('topic_id') opportunities_count = ( opportunity_services .regenerate_opportunities_related_to_topic( topic_id, delete_existing_opportunities=True)) result = { 'opportunities_count': opportunities_count } elif action == 'update_feature_flag_rules': feature_name = self.normalized_payload.get('feature_name') new_rule_dicts = self.normalized_payload.get('new_rules') commit_message = self.normalized_payload.get('commit_message') try: feature_services.update_feature_flag_rules( feature_name, self.user_id, commit_message, new_rule_dicts) except ( utils.ValidationError, feature_services.FeatureFlagNotFoundException) as e: raise self.InvalidInputException(e) logging.info( '[ADMIN] %s updated feature %s with new rules: ' '%s.' % (self.user_id, feature_name, new_rule_dicts)) self.render_json(result) except Exception as e: logging.exception('[ADMIN] %s', e) self.render_json({'error': python_utils.UNICODE(e)}) python_utils.reraise_exception() def _reload_exploration(self, exploration_id): """Reloads the exploration in dev_mode corresponding to the given exploration id. Args: exploration_id: str. The exploration id. Raises: Exception. Cannot reload an exploration in production. """ if constants.DEV_MODE: logging.info( '[ADMIN] %s reloaded exploration %s' % (self.user_id, exploration_id)) exp_services.load_demo(python_utils.UNICODE(exploration_id)) rights_manager.release_ownership_of_exploration( user_services.get_system_user(), python_utils.UNICODE(exploration_id)) else: raise Exception('Cannot reload an exploration in production.') def _create_dummy_question( self, question_id, question_content, linked_skill_ids): """Creates a dummy question object with the given question ID. Args: question_id: str. The ID of the question to be created. question_content: str. The question content. linked_skill_ids: list(str). The IDs of the skills to which the question is linked to. Returns: Question. The dummy question with given values. """ state = state_domain.State.create_default_state( 'ABC', is_initial_state=True) state.update_interaction_id('TextInput') state.update_interaction_customization_args({ 'placeholder': { 'value': { 'content_id': 'ca_placeholder_0', 'unicode_str': '' } }, 'rows': {'value': 1} }) state.update_next_content_id_index(1) state.update_linked_skill_id(None) state.update_content(state_domain.SubtitledHtml('1', question_content)) recorded_voiceovers = state_domain.RecordedVoiceovers({}) written_translations = state_domain.WrittenTranslations({}) recorded_voiceovers.add_content_id_for_voiceover('ca_placeholder_0') recorded_voiceovers.add_content_id_for_voiceover('1') recorded_voiceovers.add_content_id_for_voiceover('default_outcome') written_translations.add_content_id_for_translation('ca_placeholder_0') written_translations.add_content_id_for_translation('1') written_translations.add_content_id_for_translation('default_outcome') state.update_recorded_voiceovers(recorded_voiceovers) state.update_written_translations(written_translations) solution = state_domain.Solution( 'TextInput', False, 'Solution', state_domain.SubtitledHtml( 'solution', 'This is a solution.')) hints_list = [ state_domain.Hint( state_domain.SubtitledHtml('hint_1', 'This is a hint.') ) ] state.update_interaction_solution(solution) state.update_interaction_hints(hints_list) state.update_interaction_default_outcome( state_domain.Outcome( None, state_domain.SubtitledHtml( 'feedback_id', 'Dummy Feedback'), True, [], None, None ) ) question = question_domain.Question( question_id, state, feconf.CURRENT_STATE_SCHEMA_VERSION, constants.DEFAULT_LANGUAGE_CODE, 0, linked_skill_ids, []) return question def _create_dummy_skill(self, skill_id, skill_description, explanation): """Creates a dummy skill object with the given values. Args: skill_id: str. The ID of the skill to be created. skill_description: str. The description of the skill. explanation: str. The review material for the skill. Returns: Skill. The dummy skill with given values. """ rubrics = [ skill_domain.Rubric( constants.SKILL_DIFFICULTIES[0], ['Explanation 1']), skill_domain.Rubric( constants.SKILL_DIFFICULTIES[1], ['Explanation 2']), skill_domain.Rubric( constants.SKILL_DIFFICULTIES[2], ['Explanation 3'])] skill = skill_domain.Skill.create_default_skill( skill_id, skill_description, rubrics) skill.update_explanation(state_domain.SubtitledHtml('1', explanation)) return skill def _load_dummy_new_structures_data(self): """Loads the database with two topics (one of which is empty), a story and three skills in the topic (two of them in a subtopic) and a question attached to each skill. Raises: Exception. Cannot load new structures data in production mode. Exception. User does not have enough rights to generate data. """ if constants.DEV_MODE: if feconf.ROLE_ID_CURRICULUM_ADMIN not in self.user.roles: raise Exception( 'User does not have enough rights to generate data.') topic_id_1 = topic_fetchers.get_new_topic_id() topic_id_2 = topic_fetchers.get_new_topic_id() story_id = story_services.get_new_story_id() skill_id_1 = skill_services.get_new_skill_id() skill_id_2 = skill_services.get_new_skill_id() skill_id_3 = skill_services.get_new_skill_id() question_id_1 = question_services.get_new_question_id() question_id_2 = question_services.get_new_question_id() question_id_3 = question_services.get_new_question_id() skill_1 = self._create_dummy_skill( skill_id_1, 'Dummy Skill 1', 'Dummy Explanation 1') skill_2 = self._create_dummy_skill( skill_id_2, 'Dummy Skill 2', 'Dummy Explanation 2') skill_3 = self._create_dummy_skill( skill_id_3, 'Dummy Skill 3', 'Dummy Explanation 3') question_1 = self._create_dummy_question( question_id_1, 'Question 1', [skill_id_1]) question_2 = self._create_dummy_question( question_id_2, 'Question 2', [skill_id_2]) question_3 = self._create_dummy_question( question_id_3, 'Question 3', [skill_id_3]) question_services.add_question(self.user_id, question_1) question_services.add_question(self.user_id, question_2) question_services.add_question(self.user_id, question_3) question_services.create_new_question_skill_link( self.user_id, question_id_1, skill_id_1, 0.3) question_services.create_new_question_skill_link( self.user_id, question_id_2, skill_id_2, 0.5) question_services.create_new_question_skill_link( self.user_id, question_id_3, skill_id_3, 0.7) topic_1 = topic_domain.Topic.create_default_topic( topic_id_1, 'Dummy Topic 1', 'dummy-topic-one', 'description') topic_2 = topic_domain.Topic.create_default_topic( topic_id_2, 'Empty Topic', 'empty-topic', 'description') topic_1.add_canonical_story(story_id) topic_1.add_uncategorized_skill_id(skill_id_1) topic_1.add_uncategorized_skill_id(skill_id_2) topic_1.add_uncategorized_skill_id(skill_id_3) topic_1.add_subtopic(1, 'Dummy Subtopic Title') topic_1.move_skill_id_to_subtopic(None, 1, skill_id_2) topic_1.move_skill_id_to_subtopic(None, 1, skill_id_3) subtopic_page = ( subtopic_page_domain.SubtopicPage.create_default_subtopic_page( 1, topic_id_1)) # These explorations were chosen since they pass the validations # for published stories. self._reload_exploration('15') self._reload_exploration('25') self._reload_exploration('13') exp_services.update_exploration( self.user_id, '15', [exp_domain.ExplorationChange({ 'cmd': exp_domain.CMD_EDIT_EXPLORATION_PROPERTY, 'property_name': 'correctness_feedback_enabled', 'new_value': True })], 'Changed correctness_feedback_enabled.') exp_services.update_exploration( self.user_id, '25', [exp_domain.ExplorationChange({ 'cmd': exp_domain.CMD_EDIT_EXPLORATION_PROPERTY, 'property_name': 'correctness_feedback_enabled', 'new_value': True })], 'Changed correctness_feedback_enabled.') exp_services.update_exploration( self.user_id, '13', [exp_domain.ExplorationChange({ 'cmd': exp_domain.CMD_EDIT_EXPLORATION_PROPERTY, 'property_name': 'correctness_feedback_enabled', 'new_value': True })], 'Changed correctness_feedback_enabled.') story = story_domain.Story.create_default_story( story_id, 'Help Jaime win the Arcade', 'Description', topic_id_1, 'help-jamie-win-arcade') story_node_dicts = [{ 'exp_id': '15', 'title': 'What are the place values?', 'description': 'Jaime learns the place value of each digit ' + 'in a big number.' }, { 'exp_id': '25', 'title': 'Finding the value of a number', 'description': 'Jaime understands the value of his ' + 'arcade score.' }, { 'exp_id': '13', 'title': 'Comparing Numbers', 'description': 'Jaime learns if a number is smaller or ' + 'greater than another number.' }] def generate_dummy_story_nodes(node_id, exp_id, title, description): """Generates and connects sequential story nodes. Args: node_id: int. The node id. exp_id: str. The exploration id. title: str. The title of the story node. description: str. The description of the story node. """ story.add_node( '%s%d' % (story_domain.NODE_ID_PREFIX, node_id), title) story.update_node_description( '%s%d' % (story_domain.NODE_ID_PREFIX, node_id), description) story.update_node_exploration_id( '%s%d' % (story_domain.NODE_ID_PREFIX, node_id), exp_id) if node_id != len(story_node_dicts): story.update_node_destination_node_ids( '%s%d' % (story_domain.NODE_ID_PREFIX, node_id), ['%s%d' % (story_domain.NODE_ID_PREFIX, node_id + 1)]) exp_services.update_exploration( self.user_id, exp_id, [exp_domain.ExplorationChange({ 'cmd': exp_domain.CMD_EDIT_EXPLORATION_PROPERTY, 'property_name': 'category', 'new_value': 'Astronomy' })], 'Change category') for i, story_node_dict in enumerate(story_node_dicts): generate_dummy_story_nodes(i + 1, **story_node_dict) skill_services.save_new_skill(self.user_id, skill_1) skill_services.save_new_skill(self.user_id, skill_2) skill_services.save_new_skill(self.user_id, skill_3) story_services.save_new_story(self.user_id, story) topic_services.save_new_topic(self.user_id, topic_1) topic_services.save_new_topic(self.user_id, topic_2) subtopic_page_services.save_subtopic_page( self.user_id, subtopic_page, 'Added subtopic', [topic_domain.TopicChange({ 'cmd': topic_domain.CMD_ADD_SUBTOPIC, 'subtopic_id': 1, 'title': 'Dummy Subtopic Title' })] ) # Generates translation opportunities for the Contributor Dashboard. exp_ids_in_story = story.story_contents.get_all_linked_exp_ids() opportunity_services.add_new_exploration_opportunities( story_id, exp_ids_in_story) topic_services.publish_story(topic_id_1, story_id, self.user_id) else: raise Exception('Cannot load new structures data in production.') def _generate_dummy_skill_and_questions(self): """Generate and loads the database with a skill and 15 questions linked to the skill. Raises: Exception. Cannot load new structures data in production mode. Exception. User does not have enough rights to generate data. """ if constants.DEV_MODE: if feconf.ROLE_ID_CURRICULUM_ADMIN not in self.user.roles: raise Exception( 'User does not have enough rights to generate data.') skill_id = skill_services.get_new_skill_id() skill_name = 'Dummy Skill %s' % python_utils.UNICODE( random.getrandbits(32)) skill = self._create_dummy_skill( skill_id, skill_name, 'Dummy Explanation 1') skill_services.save_new_skill(self.user_id, skill) for i in range(15): question_id = question_services.get_new_question_id() question_name = 'Question number %s %s' % ( python_utils.UNICODE(i), skill_name) question = self._create_dummy_question( question_id, question_name, [skill_id]) question_services.add_question(self.user_id, question) question_difficulty = list( constants.SKILL_DIFFICULTY_LABEL_TO_FLOAT.values()) random_difficulty = random.choice(question_difficulty) question_services.create_new_question_skill_link( self.user_id, question_id, skill_id, random_difficulty) else: raise Exception('Cannot generate dummy skills in production.') def _reload_collection(self, collection_id): """Reloads the collection in dev_mode corresponding to the given collection id. Args: collection_id: str. The collection id. Raises: Exception. Cannot reload a collection in production. """ if constants.DEV_MODE: logging.info( '[ADMIN] %s reloaded collection %s' % (self.user_id, collection_id)) collection_services.load_demo(collection_id) rights_manager.release_ownership_of_collection( user_services.get_system_user(), collection_id) else: raise Exception('Cannot reload a collection in production.') def _generate_dummy_explorations( self, num_dummy_exps_to_generate, num_dummy_exps_to_publish): """Generates and publishes the given number of dummy explorations. Args: num_dummy_exps_to_generate: int. Count of dummy explorations to be generated. num_dummy_exps_to_publish: int. Count of explorations to be published. Raises: Exception. Environment is not DEVMODE. """ if constants.DEV_MODE: logging.info( '[ADMIN] %s generated %s number of dummy explorations' % (self.user_id, num_dummy_exps_to_generate)) possible_titles = ['Hulk Neuroscience', 'Quantum Starks', 'Wonder Anatomy', 'Elvish, language of "Lord of the Rings', 'The Science of Superheroes'] exploration_ids_to_publish = [] for i in range(num_dummy_exps_to_generate): title = random.choice(possible_titles) category = random.choice(constants.SEARCH_DROPDOWN_CATEGORIES) new_exploration_id = exp_fetchers.get_new_exploration_id() exploration = exp_domain.Exploration.create_default_exploration( new_exploration_id, title=title, category=category, objective='Dummy Objective') exp_services.save_new_exploration(self.user_id, exploration) if i <= num_dummy_exps_to_publish - 1: exploration_ids_to_publish.append(new_exploration_id) rights_manager.publish_exploration( self.user, new_exploration_id) exp_services.index_explorations_given_ids( exploration_ids_to_publish) else: raise Exception('Cannot generate dummy explorations in production.') class AdminRoleHandler(base.BaseHandler): """Handler for roles tab of admin page. Used to view and update roles.""" GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'GET': { 'filter_criterion': { 'schema': { 'type': 'basestring', 'choices': [ feconf.USER_FILTER_CRITERION_ROLE, feconf.USER_FILTER_CRITERION_USERNAME ] } }, 'role': { 'schema': { 'type': 'basestring', 'choices': role_services.VIEWABLE_ROLES }, 'default_value': None }, 'username': { 'schema': { 'type': 'basestring' }, 'default_value': None } }, 'PUT': { 'role': { 'schema': { 'type': 'basestring', 'choices': feconf.ALLOWED_USER_ROLES } }, 'username': { 'schema': { 'type': 'basestring' } } }, 'DELETE': { 'role': { 'schema': { 'type': 'basestring', 'choices': feconf.ALLOWED_USER_ROLES } }, 'username': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_access_admin_page def get(self): filter_criterion = self.normalized_request.get( 'filter_criterion') if filter_criterion == feconf.USER_FILTER_CRITERION_ROLE: role = self.normalized_request.get( feconf.USER_FILTER_CRITERION_ROLE) role_services.log_role_query( self.user_id, feconf.ROLE_ACTION_VIEW_BY_ROLE, role=role) self.render_json({ 'usernames': user_services.get_usernames_by_role(role) }) elif filter_criterion == feconf.USER_FILTER_CRITERION_USERNAME: username = self.normalized_request.get( feconf.USER_FILTER_CRITERION_USERNAME) user_id = user_services.get_user_id_from_username(username) role_services.log_role_query( self.user_id, feconf.ROLE_ACTION_VIEW_BY_USERNAME, username=username) if user_id is None: raise self.InvalidInputException( 'User with given username does not exist.') user_settings = user_services.get_user_settings(user_id) user_roles = user_settings.roles managed_topic_ids = [] if feconf.ROLE_ID_TOPIC_MANAGER in user_roles: managed_topic_ids = [ rights.id for rights in topic_fetchers.get_topic_rights_with_user(user_id)] user_roles_dict = { 'roles': user_roles, 'managed_topic_ids': managed_topic_ids, 'banned': user_settings.banned } self.render_json(user_roles_dict) @acl_decorators.can_access_admin_page def put(self): username = self.payload.get('username') role = self.payload.get('role') user_settings = user_services.get_user_settings_from_username(username) if user_settings is None: raise self.InvalidInputException( 'User with given username does not exist.') if role == feconf.ROLE_ID_TOPIC_MANAGER: # The Topic manager role assignment is handled via # TopicManagerRoleHandler. raise self.InvalidInputException( 'Unsupported role for this handler.') user_services.add_user_role(user_settings.user_id, role) self.render_json({}) @acl_decorators.can_access_admin_page def delete(self): username = self.request.get('username') role = self.request.get('role') user_id = user_services.get_user_id_from_username(username) if user_id is None: raise self.InvalidInputException( 'User with given username does not exist.') if role == feconf.ROLE_ID_TOPIC_MANAGER: topic_services.deassign_user_from_all_topics(self.user, user_id) user_services.remove_user_role(user_id, role) self.render_json({}) class TopicManagerRoleHandler(base.BaseHandler): """Handler to assign or deassigning manager to a topic.""" GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'PUT': { 'username': { 'schema': { 'type': 'basestring' } }, 'action': { 'schema': { 'type': 'basestring', 'choices': ['assign', 'deassign'] } }, 'topic_id': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_access_admin_page def put(self): username = self.normalized_payload.get('username') action = self.normalized_payload.get('action') topic_id = self.normalized_payload.get('topic_id') user_settings = user_services.get_user_settings_from_username(username) if user_settings is None: raise self.InvalidInputException( 'User with given username does not exist.') user_id = user_settings.user_id if action == 'assign': if not feconf.ROLE_ID_TOPIC_MANAGER in user_settings.roles: user_services.add_user_role( user_id, feconf.ROLE_ID_TOPIC_MANAGER) topic_manager = user_services.get_user_actions_info(user_id) topic_services.assign_role( user_services.get_system_user(), topic_manager, topic_domain.ROLE_MANAGER, topic_id) elif action == 'deassign': topic_services.deassign_manager_role_from_topic( user_services.get_system_user(), user_id, topic_id) if not topic_fetchers.get_topic_rights_with_user(user_id): user_services.remove_user_role( user_id, feconf.ROLE_ID_TOPIC_MANAGER) self.render_json({}) class BannedUsersHandler(base.BaseHandler): """Handler to ban and unban users.""" GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'PUT': { 'username': { 'schema': { 'type': 'basestring' } } }, 'DELETE': { 'username': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_access_admin_page def put(self): username = self.normalized_payload.get('username') user_id = user_services.get_user_id_from_username(username) if user_id is None: raise self.InvalidInputException( 'User with given username does not exist.') topic_services.deassign_user_from_all_topics(self.user, user_id) user_services.mark_user_banned(user_id) self.render_json({}) @acl_decorators.can_access_admin_page def delete(self): username = self.normalized_request.get('username') user_id = user_services.get_user_id_from_username(username) if user_id is None: raise self.InvalidInputException( 'User with given username does not exist.') user_services.unmark_user_banned(user_id) self.render_json({}) class AdminSuperAdminPrivilegesHandler(base.BaseHandler): """Handler for granting a user super admin privileges.""" PUT_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON DELETE_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'PUT': { 'username': { 'schema': { 'type': 'basestring' } } }, 'DELETE': { 'username': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_access_admin_page def put(self): if self.email != feconf.ADMIN_EMAIL_ADDRESS: raise self.UnauthorizedUserException( 'Only the default system admin can manage super admins') username = self.normalized_payload.get('username') user_id = user_services.get_user_id_from_username(username) if user_id is None: raise self.InvalidInputException('No such user exists') auth_services.grant_super_admin_privileges(user_id) self.render_json(self.values) @acl_decorators.can_access_admin_page def delete(self): if self.email != feconf.ADMIN_EMAIL_ADDRESS: raise self.UnauthorizedUserException( 'Only the default system admin can manage super admins') username = self.normalized_request.get('username') user_settings = user_services.get_user_settings_from_username(username) if user_settings is None: raise self.InvalidInputException('No such user exists') if user_settings.email == feconf.ADMIN_EMAIL_ADDRESS: raise self.InvalidInputException( 'Cannot revoke privileges from the default super admin account') auth_services.revoke_super_admin_privileges(user_settings.user_id) self.render_json(self.values) class AdminTopicsCsvFileDownloader(base.BaseHandler): """Retrieves topic similarity data for download.""" GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_DOWNLOADABLE URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = {'GET': {}} @acl_decorators.can_access_admin_page def get(self): topic_similarities = ( recommendations_services.get_topic_similarities_as_csv() ) # Downloadable file accepts only bytes, so we need to encode # topic_similarities to bytes. self.render_downloadable_file( io.BytesIO(topic_similarities.encode('utf-8')), 'topic_similarities.csv', 'text/csv' ) class DataExtractionQueryHandler(base.BaseHandler): """Handler for data extraction query.""" GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'GET': { 'exp_id': { 'schema': { 'type': 'basestring' } }, 'exp_version': { 'schema': { 'type': 'int' } }, 'state_name': { 'schema': { 'type': 'basestring' } }, 'num_answers': { 'schema': { 'type': 'int' } } } } @acl_decorators.can_access_admin_page def get(self): exp_id = self.normalized_request.get('exp_id') exp_version = self.normalized_request.get('exp_version') exploration = exp_fetchers.get_exploration_by_id( exp_id, strict=False, version=exp_version) if exploration is None: raise self.InvalidInputException( 'Entity for exploration with id %s and version %s not found.' % (exp_id, exp_version)) state_name = self.normalized_request.get('state_name') num_answers = self.normalized_request.get('num_answers') if state_name not in exploration.states: raise self.InvalidInputException( 'Exploration \'%s\' does not have \'%s\' state.' % (exp_id, state_name)) state_answers = stats_services.get_state_answers( exp_id, exp_version, state_name) extracted_answers = state_answers.get_submitted_answer_dict_list() if num_answers > 0: extracted_answers = extracted_answers[:num_answers] response = { 'data': extracted_answers } self.render_json(response) class SendDummyMailToAdminHandler(base.BaseHandler): """This function handles sending test emails.""" URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = {'POST': {}} @acl_decorators.can_access_admin_page def post(self): username = self.username if feconf.CAN_SEND_EMAILS: email_manager.send_dummy_mail_to_admin(username) self.render_json({}) else: raise self.InvalidInputException('This app cannot send emails.') class UpdateUsernameHandler(base.BaseHandler): """Handler for renaming usernames.""" URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'PUT': { 'old_username': { 'schema': { 'type': 'basestring' } }, 'new_username': { 'schema': { 'type': 'basestring', 'validators': [{ 'id': 'has_length_at_most', 'max_value': constants.MAX_USERNAME_LENGTH }] } } } } @acl_decorators.can_access_admin_page def put(self): old_username = self.normalized_payload.get('old_username') new_username = self.normalized_payload.get('new_username') user_id = user_services.get_user_id_from_username(old_username) if user_id is None: raise self.InvalidInputException( 'Invalid username: %s' % old_username) if user_services.is_username_taken(new_username): raise self.InvalidInputException('Username already taken.') user_services.set_username(user_id, new_username) user_services.log_username_change( self.user_id, old_username, new_username) self.render_json({}) class NumberOfDeletionRequestsHandler(base.BaseHandler): """Handler for getting the number of pending deletion requests via admin page. """ GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = {'GET': {}} @acl_decorators.can_access_admin_page def get(self): self.render_json({ 'number_of_pending_deletion_models': ( wipeout_service.get_number_of_pending_deletion_requests()) }) class VerifyUserModelsDeletedHandler(base.BaseHandler): """Handler for getting whether any models exist for specific user ID.""" GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'GET': { 'user_id': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_access_admin_page def get(self): user_id = self.normalized_request.get('user_id') user_is_deleted = wipeout_service.verify_user_deleted( user_id, include_delete_at_end_models=True) self.render_json({'related_models_exist': not user_is_deleted}) class DeleteUserHandler(base.BaseHandler): """Handler for deleting a user with specific ID.""" URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'DELETE': { 'user_id': { 'schema': { 'type': 'basestring' } }, 'username': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_delete_any_user def delete(self): user_id = self.normalized_request.get('user_id') username = self.normalized_request.get('username') user_id_from_username = ( user_services.get_user_id_from_username(username)) if user_id_from_username is None: raise self.InvalidInputException( 'The username doesn\'t belong to any user' ) if user_id_from_username != user_id: raise self.InvalidInputException( 'The user ID retrieved from the username and ' 'the user ID provided by admin differ.' ) wipeout_service.pre_delete_user(user_id) self.render_json({'success': True}) class UpdateBlogPostHandler(base.BaseHandler): """Handler for changing author ids and published on date in blog posts.""" URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'PUT': { 'blog_post_id': { 'schema': { 'type': 'basestring' } }, 'author_username': { 'schema': { 'type': 'basestring', 'validators': [{ 'id': 'has_length_at_most', 'max_value': constants.MAX_USERNAME_LENGTH }] } }, 'published_on': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_access_admin_page def put(self): blog_post_id = self.normalized_payload.get('blog_post_id') author_username = self.normalized_payload.get('author_username') published_on = self.normalized_payload.get('published_on') author_id = user_services.get_user_id_from_username(author_username) if author_id is None: raise self.InvalidInputException( 'Invalid username: %s' % author_username) user_actions = user_services.get_user_actions_info(author_id).actions if role_services.ACTION_ACCESS_BLOG_DASHBOARD not in user_actions: raise self.InvalidInputException( 'User does not have enough rights to be blog post author.') blog_post = ( blog_services.get_blog_post_by_id(blog_post_id, strict=False)) if blog_post is None: raise self.PageNotFoundException( Exception( 'The blog post with the given id or url doesn\'t exist.')) blog_services.update_blog_models_author_and_published_on_date( blog_post_id, author_id, published_on) self.render_json({})

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from __future__ import absolute_import from __future__ import unicode_literals import io import logging import random from core import feconf from core import python_utils from core import utils from core.constants import constants from core.controllers import acl_decorators from core.controllers import base from core.controllers import domain_objects_validator as validation_method from core.domain import auth_services from core.domain import blog_services from core.domain import collection_services from core.domain import config_domain from core.domain import config_services from core.domain import email_manager from core.domain import exp_domain from core.domain import exp_fetchers from core.domain import exp_services from core.domain import opportunity_services from core.domain import platform_feature_services as feature_services from core.domain import platform_parameter_domain as parameter_domain from core.domain import question_domain from core.domain import question_services from core.domain import recommendations_services from core.domain import rights_manager from core.domain import role_services from core.domain import search_services from core.domain import skill_domain from core.domain import skill_services from core.domain import state_domain from core.domain import stats_services from core.domain import story_domain from core.domain import story_services from core.domain import subtopic_page_domain from core.domain import subtopic_page_services from core.domain import topic_domain from core.domain import topic_fetchers from core.domain import topic_services from core.domain import user_services from core.domain import wipeout_service class AdminPage(base.BaseHandler): URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = {'GET': {}} @acl_decorators.can_access_admin_page def get(self): self.render_template('admin-page.mainpage.html') class AdminHandler(base.BaseHandler): GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'GET': {}, 'POST': { 'action': { 'schema': { 'type': 'basestring', 'choices': [ 'reload_exploration', 'reload_collection', 'generate_dummy_explorations', 'clear_search_index', 'generate_dummy_new_structures_data', 'generate_dummy_new_skill_data', 'save_config_properties', 'revert_config_property', 'upload_topic_similarities', 'regenerate_topic_related_opportunities', 'update_feature_flag_rules' ] }, ult_value': None }, 'exploration_id': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'collection_id': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'num_dummy_exps_to_generate': { 'schema': { 'type': 'int' }, 'default_value': None }, 'num_dummy_exps_to_publish': { 'schema': { 'type': 'int' }, 'default_value': None }, 'new_config_property_values': { 'schema': { 'type': 'object_dict', 'validation_method': ( validation_method.validate_new_config_property_values) }, 'default_value': None }, 'config_property_id': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'data': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'topic_id': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'feature_name': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'commit_message': { 'schema': { 'type': 'basestring' }, 'default_value': None }, 'new_rules': { 'schema': { 'type': 'list', 'items': { 'type': 'object_dict', 'object_class': parameter_domain.PlatformParameterRule } }, 'default_value': None } } } @acl_decorators.can_access_admin_page def get(self): demo_exploration_ids = list(feconf.DEMO_EXPLORATIONS.keys()) topic_summaries = topic_fetchers.get_all_topic_summaries() topic_summary_dicts = [ summary.to_dict() for summary in topic_summaries] feature_flag_dicts = feature_services.get_all_feature_flag_dicts() config_properties = config_domain.Registry.get_config_property_schemas() del config_properties['promo_bar_enabled'] del config_properties['promo_bar_message'] del config_properties['max_number_of_tags_assigned_to_blog_post'] del config_properties['list_of_default_tags_for_blog_post'] self.render_json({ 'config_properties': config_properties, 'demo_collections': sorted(feconf.DEMO_COLLECTIONS.items()), 'demo_explorations': sorted(feconf.DEMO_EXPLORATIONS.items()), 'demo_exploration_ids': demo_exploration_ids, 'updatable_roles': role_services.UPDATABLE_ROLES, 'viewable_roles': role_services.VIEWABLE_ROLES, 'human_readable_roles': role_services.HUMAN_READABLE_ROLES, 'role_to_actions': role_services.get_role_actions(), 'topic_summaries': topic_summary_dicts, 'feature_flags': feature_flag_dicts, }) @acl_decorators.can_access_admin_page def post(self): action = self.normalized_payload.get('action') try: result = {} if action == 'reload_exploration': exploration_id = self.normalized_payload.get('exploration_id') self._reload_exploration(exploration_id) elif action == 'reload_collection': collection_id = self.normalized_payload.get('collection_id') self._reload_collection(collection_id) elif action == 'generate_dummy_explorations': num_dummy_exps_to_generate = self.normalized_payload.get( 'num_dummy_exps_to_generate') num_dummy_exps_to_publish = self.normalized_payload.get( 'num_dummy_exps_to_publish') if num_dummy_exps_to_generate < num_dummy_exps_to_publish: raise self.InvalidInputException( 'Generate count cannot be less than publish count') else: self._generate_dummy_explorations( num_dummy_exps_to_generate, num_dummy_exps_to_publish) elif action == 'clear_search_index': search_services.clear_collection_search_index() search_services.clear_exploration_search_index() elif action == 'generate_dummy_new_structures_data': self._load_dummy_new_structures_data() elif action == 'generate_dummy_new_skill_data': self._generate_dummy_skill_and_questions() elif action == 'save_config_properties': new_config_property_values = self.normalized_payload.get( 'new_config_property_values') logging.info( '[ADMIN] %s saved config property values: %s' % (self.user_id, new_config_property_values)) for (name, value) in new_config_property_values.items(): config_services.set_property(self.user_id, name, value) elif action == 'revert_config_property': config_property_id = self.normalized_payload.get( 'config_property_id') logging.info( '[ADMIN] %s reverted config property: %s' % (self.user_id, config_property_id)) config_services.revert_property( self.user_id, config_property_id) elif action == 'upload_topic_similarities': data = self.normalized_payload.get('data') recommendations_services.update_topic_similarities(data) elif action == 'regenerate_topic_related_opportunities': topic_id = self.normalized_payload.get('topic_id') opportunities_count = ( opportunity_services .regenerate_opportunities_related_to_topic( topic_id, delete_existing_opportunities=True)) result = { 'opportunities_count': opportunities_count } elif action == 'update_feature_flag_rules': feature_name = self.normalized_payload.get('feature_name') new_rule_dicts = self.normalized_payload.get('new_rules') commit_message = self.normalized_payload.get('commit_message') try: feature_services.update_feature_flag_rules( feature_name, self.user_id, commit_message, new_rule_dicts) except ( utils.ValidationError, feature_services.FeatureFlagNotFoundException) as e: raise self.InvalidInputException(e) logging.info( '[ADMIN] %s updated feature %s with new rules: ' '%s.' % (self.user_id, feature_name, new_rule_dicts)) self.render_json(result) except Exception as e: logging.exception('[ADMIN] %s', e) self.render_json({'error': python_utils.UNICODE(e)}) python_utils.reraise_exception() def _reload_exploration(self, exploration_id): if constants.DEV_MODE: logging.info( '[ADMIN] %s reloaded exploration %s' % (self.user_id, exploration_id)) exp_services.load_demo(python_utils.UNICODE(exploration_id)) rights_manager.release_ownership_of_exploration( user_services.get_system_user(), python_utils.UNICODE(exploration_id)) else: raise Exception('Cannot reload an exploration in production.') def _create_dummy_question( self, question_id, question_content, linked_skill_ids): state = state_domain.State.create_default_state( 'ABC', is_initial_state=True) state.update_interaction_id('TextInput') state.update_interaction_customization_args({ 'placeholder': { 'value': { 'content_id': 'ca_placeholder_0', 'unicode_str': '' } }, 'rows': {'value': 1} }) state.update_next_content_id_index(1) state.update_linked_skill_id(None) state.update_content(state_domain.SubtitledHtml('1', question_content)) recorded_voiceovers = state_domain.RecordedVoiceovers({}) written_translations = state_domain.WrittenTranslations({}) recorded_voiceovers.add_content_id_for_voiceover('ca_placeholder_0') recorded_voiceovers.add_content_id_for_voiceover('1') recorded_voiceovers.add_content_id_for_voiceover('default_outcome') written_translations.add_content_id_for_translation('ca_placeholder_0') written_translations.add_content_id_for_translation('1') written_translations.add_content_id_for_translation('default_outcome') state.update_recorded_voiceovers(recorded_voiceovers) state.update_written_translations(written_translations) solution = state_domain.Solution( 'TextInput', False, 'Solution', state_domain.SubtitledHtml( 'solution', 'This is a solution.')) hints_list = [ state_domain.Hint( state_domain.SubtitledHtml('hint_1', 'This is a hint.') ) ] state.update_interaction_solution(solution) state.update_interaction_hints(hints_list) state.update_interaction_default_outcome( state_domain.Outcome( None, state_domain.SubtitledHtml( 'feedback_id', 'Dummy Feedback'), True, [], None, None ) ) question = question_domain.Question( question_id, state, feconf.CURRENT_STATE_SCHEMA_VERSION, constants.DEFAULT_LANGUAGE_CODE, 0, linked_skill_ids, []) return question def _create_dummy_skill(self, skill_id, skill_description, explanation): rubrics = [ skill_domain.Rubric( constants.SKILL_DIFFICULTIES[0], ['Explanation 1']), skill_domain.Rubric( constants.SKILL_DIFFICULTIES[1], ['Explanation 2']), skill_domain.Rubric( constants.SKILL_DIFFICULTIES[2], ['Explanation 3'])] skill = skill_domain.Skill.create_default_skill( skill_id, skill_description, rubrics) skill.update_explanation(state_domain.SubtitledHtml('1', explanation)) return skill def _load_dummy_new_structures_data(self): if constants.DEV_MODE: if feconf.ROLE_ID_CURRICULUM_ADMIN not in self.user.roles: raise Exception( 'User does not have enough rights to generate data.') topic_id_1 = topic_fetchers.get_new_topic_id() topic_id_2 = topic_fetchers.get_new_topic_id() story_id = story_services.get_new_story_id() skill_id_1 = skill_services.get_new_skill_id() skill_id_2 = skill_services.get_new_skill_id() skill_id_3 = skill_services.get_new_skill_id() question_id_1 = question_services.get_new_question_id() question_id_2 = question_services.get_new_question_id() question_id_3 = question_services.get_new_question_id() skill_1 = self._create_dummy_skill( skill_id_1, 'Dummy Skill 1', 'Dummy Explanation 1') skill_2 = self._create_dummy_skill( skill_id_2, 'Dummy Skill 2', 'Dummy Explanation 2') skill_3 = self._create_dummy_skill( skill_id_3, 'Dummy Skill 3', 'Dummy Explanation 3') question_1 = self._create_dummy_question( question_id_1, 'Question 1', [skill_id_1]) question_2 = self._create_dummy_question( question_id_2, 'Question 2', [skill_id_2]) question_3 = self._create_dummy_question( question_id_3, 'Question 3', [skill_id_3]) question_services.add_question(self.user_id, question_1) question_services.add_question(self.user_id, question_2) question_services.add_question(self.user_id, question_3) question_services.create_new_question_skill_link( self.user_id, question_id_1, skill_id_1, 0.3) question_services.create_new_question_skill_link( self.user_id, question_id_2, skill_id_2, 0.5) question_services.create_new_question_skill_link( self.user_id, question_id_3, skill_id_3, 0.7) topic_1 = topic_domain.Topic.create_default_topic( topic_id_1, 'Dummy Topic 1', 'dummy-topic-one', 'description') topic_2 = topic_domain.Topic.create_default_topic( topic_id_2, 'Empty Topic', 'empty-topic', 'description') topic_1.add_canonical_story(story_id) topic_1.add_uncategorized_skill_id(skill_id_1) topic_1.add_uncategorized_skill_id(skill_id_2) topic_1.add_uncategorized_skill_id(skill_id_3) topic_1.add_subtopic(1, 'Dummy Subtopic Title') topic_1.move_skill_id_to_subtopic(None, 1, skill_id_2) topic_1.move_skill_id_to_subtopic(None, 1, skill_id_3) subtopic_page = ( subtopic_page_domain.SubtopicPage.create_default_subtopic_page( 1, topic_id_1)) self._reload_exploration('15') self._reload_exploration('25') self._reload_exploration('13') exp_services.update_exploration( self.user_id, '15', [exp_domain.ExplorationChange({ 'cmd': exp_domain.CMD_EDIT_EXPLORATION_PROPERTY, 'property_name': 'correctness_feedback_enabled', 'new_value': True })], 'Changed correctness_feedback_enabled.') exp_services.update_exploration( self.user_id, '25', [exp_domain.ExplorationChange({ 'cmd': exp_domain.CMD_EDIT_EXPLORATION_PROPERTY, 'property_name': 'correctness_feedback_enabled', 'new_value': True })], 'Changed correctness_feedback_enabled.') exp_services.update_exploration( self.user_id, '13', [exp_domain.ExplorationChange({ 'cmd': exp_domain.CMD_EDIT_EXPLORATION_PROPERTY, 'property_name': 'correctness_feedback_enabled', 'new_value': True })], 'Changed correctness_feedback_enabled.') story = story_domain.Story.create_default_story( story_id, 'Help Jaime win the Arcade', 'Description', topic_id_1, 'help-jamie-win-arcade') story_node_dicts = [{ 'exp_id': '15', 'title': 'What are the place values?', 'description': 'Jaime learns the place value of each digit ' + 'in a big number.' }, { 'exp_id': '25', 'title': 'Finding the value of a number', 'description': 'Jaime understands the value of his ' + 'arcade score.' }, { 'exp_id': '13', 'title': 'Comparing Numbers', 'description': 'Jaime learns if a number is smaller or ' + 'greater than another number.' }] def generate_dummy_story_nodes(node_id, exp_id, title, description): story.add_node( '%s%d' % (story_domain.NODE_ID_PREFIX, node_id), title) story.update_node_description( '%s%d' % (story_domain.NODE_ID_PREFIX, node_id), description) story.update_node_exploration_id( '%s%d' % (story_domain.NODE_ID_PREFIX, node_id), exp_id) if node_id != len(story_node_dicts): story.update_node_destination_node_ids( '%s%d' % (story_domain.NODE_ID_PREFIX, node_id), ['%s%d' % (story_domain.NODE_ID_PREFIX, node_id + 1)]) exp_services.update_exploration( self.user_id, exp_id, [exp_domain.ExplorationChange({ 'cmd': exp_domain.CMD_EDIT_EXPLORATION_PROPERTY, 'property_name': 'category', 'new_value': 'Astronomy' })], 'Change category') for i, story_node_dict in enumerate(story_node_dicts): generate_dummy_story_nodes(i + 1, **story_node_dict) skill_services.save_new_skill(self.user_id, skill_1) skill_services.save_new_skill(self.user_id, skill_2) skill_services.save_new_skill(self.user_id, skill_3) story_services.save_new_story(self.user_id, story) topic_services.save_new_topic(self.user_id, topic_1) topic_services.save_new_topic(self.user_id, topic_2) subtopic_page_services.save_subtopic_page( self.user_id, subtopic_page, 'Added subtopic', [topic_domain.TopicChange({ 'cmd': topic_domain.CMD_ADD_SUBTOPIC, 'subtopic_id': 1, 'title': 'Dummy Subtopic Title' })] ) exp_ids_in_story = story.story_contents.get_all_linked_exp_ids() opportunity_services.add_new_exploration_opportunities( story_id, exp_ids_in_story) topic_services.publish_story(topic_id_1, story_id, self.user_id) else: raise Exception('Cannot load new structures data in production.') def _generate_dummy_skill_and_questions(self): if constants.DEV_MODE: if feconf.ROLE_ID_CURRICULUM_ADMIN not in self.user.roles: raise Exception( 'User does not have enough rights to generate data.') skill_id = skill_services.get_new_skill_id() skill_name = 'Dummy Skill %s' % python_utils.UNICODE( random.getrandbits(32)) skill = self._create_dummy_skill( skill_id, skill_name, 'Dummy Explanation 1') skill_services.save_new_skill(self.user_id, skill) for i in range(15): question_id = question_services.get_new_question_id() question_name = 'Question number %s %s' % ( python_utils.UNICODE(i), skill_name) question = self._create_dummy_question( question_id, question_name, [skill_id]) question_services.add_question(self.user_id, question) question_difficulty = list( constants.SKILL_DIFFICULTY_LABEL_TO_FLOAT.values()) random_difficulty = random.choice(question_difficulty) question_services.create_new_question_skill_link( self.user_id, question_id, skill_id, random_difficulty) else: raise Exception('Cannot generate dummy skills in production.') def _reload_collection(self, collection_id): if constants.DEV_MODE: logging.info( '[ADMIN] %s reloaded collection %s' % (self.user_id, collection_id)) collection_services.load_demo(collection_id) rights_manager.release_ownership_of_collection( user_services.get_system_user(), collection_id) else: raise Exception('Cannot reload a collection in production.') def _generate_dummy_explorations( self, num_dummy_exps_to_generate, num_dummy_exps_to_publish): if constants.DEV_MODE: logging.info( '[ADMIN] %s generated %s number of dummy explorations' % (self.user_id, num_dummy_exps_to_generate)) possible_titles = ['Hulk Neuroscience', 'Quantum Starks', 'Wonder Anatomy', 'Elvish, language of "Lord of the Rings', 'The Science of Superheroes'] exploration_ids_to_publish = [] for i in range(num_dummy_exps_to_generate): title = random.choice(possible_titles) category = random.choice(constants.SEARCH_DROPDOWN_CATEGORIES) new_exploration_id = exp_fetchers.get_new_exploration_id() exploration = exp_domain.Exploration.create_default_exploration( new_exploration_id, title=title, category=category, objective='Dummy Objective') exp_services.save_new_exploration(self.user_id, exploration) if i <= num_dummy_exps_to_publish - 1: exploration_ids_to_publish.append(new_exploration_id) rights_manager.publish_exploration( self.user, new_exploration_id) exp_services.index_explorations_given_ids( exploration_ids_to_publish) else: raise Exception('Cannot generate dummy explorations in production.') class AdminRoleHandler(base.BaseHandler): GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'GET': { 'filter_criterion': { 'schema': { 'type': 'basestring', 'choices': [ feconf.USER_FILTER_CRITERION_ROLE, feconf.USER_FILTER_CRITERION_USERNAME ] } }, 'role': { 'schema': { 'type': 'basestring', 'choices': role_services.VIEWABLE_ROLES }, 'default_value': None }, 'username': { 'schema': { 'type': 'basestring' }, 'default_value': None } }, 'PUT': { 'role': { 'schema': { 'type': 'basestring', 'choices': feconf.ALLOWED_USER_ROLES } }, 'username': { 'schema': { 'type': 'basestring' } } }, 'DELETE': { 'role': { 'schema': { 'type': 'basestring', 'choices': feconf.ALLOWED_USER_ROLES } }, 'username': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_access_admin_page def get(self): filter_criterion = self.normalized_request.get( 'filter_criterion') if filter_criterion == feconf.USER_FILTER_CRITERION_ROLE: role = self.normalized_request.get( feconf.USER_FILTER_CRITERION_ROLE) role_services.log_role_query( self.user_id, feconf.ROLE_ACTION_VIEW_BY_ROLE, role=role) self.render_json({ 'usernames': user_services.get_usernames_by_role(role) }) elif filter_criterion == feconf.USER_FILTER_CRITERION_USERNAME: username = self.normalized_request.get( feconf.USER_FILTER_CRITERION_USERNAME) user_id = user_services.get_user_id_from_username(username) role_services.log_role_query( self.user_id, feconf.ROLE_ACTION_VIEW_BY_USERNAME, username=username) if user_id is None: raise self.InvalidInputException( 'User with given username does not exist.') user_settings = user_services.get_user_settings(user_id) user_roles = user_settings.roles managed_topic_ids = [] if feconf.ROLE_ID_TOPIC_MANAGER in user_roles: managed_topic_ids = [ rights.id for rights in topic_fetchers.get_topic_rights_with_user(user_id)] user_roles_dict = { 'roles': user_roles, 'managed_topic_ids': managed_topic_ids, 'banned': user_settings.banned } self.render_json(user_roles_dict) @acl_decorators.can_access_admin_page def put(self): username = self.payload.get('username') role = self.payload.get('role') user_settings = user_services.get_user_settings_from_username(username) if user_settings is None: raise self.InvalidInputException( 'User with given username does not exist.') if role == feconf.ROLE_ID_TOPIC_MANAGER: # The Topic manager role assignment is handled via # TopicManagerRoleHandler. raise self.InvalidInputException( 'Unsupported role for this handler.') user_services.add_user_role(user_settings.user_id, role) self.render_json({}) @acl_decorators.can_access_admin_page def delete(self): username = self.request.get('username') role = self.request.get('role') user_id = user_services.get_user_id_from_username(username) if user_id is None: raise self.InvalidInputException( 'User with given username does not exist.') if role == feconf.ROLE_ID_TOPIC_MANAGER: topic_services.deassign_user_from_all_topics(self.user, user_id) user_services.remove_user_role(user_id, role) self.render_json({}) class TopicManagerRoleHandler(base.BaseHandler): GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'PUT': { 'username': { 'schema': { 'type': 'basestring' } }, 'action': { 'schema': { 'type': 'basestring', 'choices': ['assign', 'deassign'] } }, 'topic_id': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_access_admin_page def put(self): username = self.normalized_payload.get('username') action = self.normalized_payload.get('action') topic_id = self.normalized_payload.get('topic_id') user_settings = user_services.get_user_settings_from_username(username) if user_settings is None: raise self.InvalidInputException( 'User with given username does not exist.') user_id = user_settings.user_id if action == 'assign': if not feconf.ROLE_ID_TOPIC_MANAGER in user_settings.roles: user_services.add_user_role( user_id, feconf.ROLE_ID_TOPIC_MANAGER) topic_manager = user_services.get_user_actions_info(user_id) topic_services.assign_role( user_services.get_system_user(), topic_manager, topic_domain.ROLE_MANAGER, topic_id) elif action == 'deassign': topic_services.deassign_manager_role_from_topic( user_services.get_system_user(), user_id, topic_id) if not topic_fetchers.get_topic_rights_with_user(user_id): user_services.remove_user_role( user_id, feconf.ROLE_ID_TOPIC_MANAGER) self.render_json({}) class BannedUsersHandler(base.BaseHandler): GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'PUT': { 'username': { 'schema': { 'type': 'basestring' } } }, 'DELETE': { 'username': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_access_admin_page def put(self): username = self.normalized_payload.get('username') user_id = user_services.get_user_id_from_username(username) if user_id is None: raise self.InvalidInputException( 'User with given username does not exist.') topic_services.deassign_user_from_all_topics(self.user, user_id) user_services.mark_user_banned(user_id) self.render_json({}) @acl_decorators.can_access_admin_page def delete(self): username = self.normalized_request.get('username') user_id = user_services.get_user_id_from_username(username) if user_id is None: raise self.InvalidInputException( 'User with given username does not exist.') user_services.unmark_user_banned(user_id) self.render_json({}) class AdminSuperAdminPrivilegesHandler(base.BaseHandler): PUT_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON DELETE_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'PUT': { 'username': { 'schema': { 'type': 'basestring' } } }, 'DELETE': { 'username': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_access_admin_page def put(self): if self.email != feconf.ADMIN_EMAIL_ADDRESS: raise self.UnauthorizedUserException( 'Only the default system admin can manage super admins') username = self.normalized_payload.get('username') user_id = user_services.get_user_id_from_username(username) if user_id is None: raise self.InvalidInputException('No such user exists') auth_services.grant_super_admin_privileges(user_id) self.render_json(self.values) @acl_decorators.can_access_admin_page def delete(self): if self.email != feconf.ADMIN_EMAIL_ADDRESS: raise self.UnauthorizedUserException( 'Only the default system admin can manage super admins') username = self.normalized_request.get('username') user_settings = user_services.get_user_settings_from_username(username) if user_settings is None: raise self.InvalidInputException('No such user exists') if user_settings.email == feconf.ADMIN_EMAIL_ADDRESS: raise self.InvalidInputException( 'Cannot revoke privileges from the default super admin account') auth_services.revoke_super_admin_privileges(user_settings.user_id) self.render_json(self.values) class AdminTopicsCsvFileDownloader(base.BaseHandler): GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_DOWNLOADABLE URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = {'GET': {}} @acl_decorators.can_access_admin_page def get(self): topic_similarities = ( recommendations_services.get_topic_similarities_as_csv() ) # Downloadable file accepts only bytes, so we need to encode # topic_similarities to bytes. self.render_downloadable_file( io.BytesIO(topic_similarities.encode('utf-8')), 'topic_similarities.csv', 'text/csv' ) class DataExtractionQueryHandler(base.BaseHandler): GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'GET': { 'exp_id': { 'schema': { 'type': 'basestring' } }, 'exp_version': { 'schema': { 'type': 'int' } }, 'state_name': { 'schema': { 'type': 'basestring' } }, 'num_answers': { 'schema': { 'type': 'int' } } } } @acl_decorators.can_access_admin_page def get(self): exp_id = self.normalized_request.get('exp_id') exp_version = self.normalized_request.get('exp_version') exploration = exp_fetchers.get_exploration_by_id( exp_id, strict=False, version=exp_version) if exploration is None: raise self.InvalidInputException( 'Entity for exploration with id %s and version %s not found.' % (exp_id, exp_version)) state_name = self.normalized_request.get('state_name') num_answers = self.normalized_request.get('num_answers') if state_name not in exploration.states: raise self.InvalidInputException( 'Exploration \'%s\' does not have \'%s\' state.' % (exp_id, state_name)) state_answers = stats_services.get_state_answers( exp_id, exp_version, state_name) extracted_answers = state_answers.get_submitted_answer_dict_list() if num_answers > 0: extracted_answers = extracted_answers[:num_answers] response = { 'data': extracted_answers } self.render_json(response) class SendDummyMailToAdminHandler(base.BaseHandler): URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = {'POST': {}} @acl_decorators.can_access_admin_page def post(self): username = self.username if feconf.CAN_SEND_EMAILS: email_manager.send_dummy_mail_to_admin(username) self.render_json({}) else: raise self.InvalidInputException('This app cannot send emails.') class UpdateUsernameHandler(base.BaseHandler): URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'PUT': { 'old_username': { 'schema': { 'type': 'basestring' } }, 'new_username': { 'schema': { 'type': 'basestring', 'validators': [{ 'id': 'has_length_at_most', 'max_value': constants.MAX_USERNAME_LENGTH }] } } } } @acl_decorators.can_access_admin_page def put(self): old_username = self.normalized_payload.get('old_username') new_username = self.normalized_payload.get('new_username') user_id = user_services.get_user_id_from_username(old_username) if user_id is None: raise self.InvalidInputException( 'Invalid username: %s' % old_username) if user_services.is_username_taken(new_username): raise self.InvalidInputException('Username already taken.') user_services.set_username(user_id, new_username) user_services.log_username_change( self.user_id, old_username, new_username) self.render_json({}) class NumberOfDeletionRequestsHandler(base.BaseHandler): GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = {'GET': {}} @acl_decorators.can_access_admin_page def get(self): self.render_json({ 'number_of_pending_deletion_models': ( wipeout_service.get_number_of_pending_deletion_requests()) }) class VerifyUserModelsDeletedHandler(base.BaseHandler): GET_HANDLER_ERROR_RETURN_TYPE = feconf.HANDLER_TYPE_JSON URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'GET': { 'user_id': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_access_admin_page def get(self): user_id = self.normalized_request.get('user_id') user_is_deleted = wipeout_service.verify_user_deleted( user_id, include_delete_at_end_models=True) self.render_json({'related_models_exist': not user_is_deleted}) class DeleteUserHandler(base.BaseHandler): URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'DELETE': { 'user_id': { 'schema': { 'type': 'basestring' } }, 'username': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_delete_any_user def delete(self): user_id = self.normalized_request.get('user_id') username = self.normalized_request.get('username') user_id_from_username = ( user_services.get_user_id_from_username(username)) if user_id_from_username is None: raise self.InvalidInputException( 'The username doesn\'t belong to any user' ) if user_id_from_username != user_id: raise self.InvalidInputException( 'The user ID retrieved from the username and ' 'the user ID provided by admin differ.' ) wipeout_service.pre_delete_user(user_id) self.render_json({'success': True}) class UpdateBlogPostHandler(base.BaseHandler): URL_PATH_ARGS_SCHEMAS = {} HANDLER_ARGS_SCHEMAS = { 'PUT': { 'blog_post_id': { 'schema': { 'type': 'basestring' } }, 'author_username': { 'schema': { 'type': 'basestring', 'validators': [{ 'id': 'has_length_at_most', 'max_value': constants.MAX_USERNAME_LENGTH }] } }, 'published_on': { 'schema': { 'type': 'basestring' } } } } @acl_decorators.can_access_admin_page def put(self): blog_post_id = self.normalized_payload.get('blog_post_id') author_username = self.normalized_payload.get('author_username') published_on = self.normalized_payload.get('published_on') author_id = user_services.get_user_id_from_username(author_username) if author_id is None: raise self.InvalidInputException( 'Invalid username: %s' % author_username) user_actions = user_services.get_user_actions_info(author_id).actions if role_services.ACTION_ACCESS_BLOG_DASHBOARD not in user_actions: raise self.InvalidInputException( 'User does not have enough rights to be blog post author.') blog_post = ( blog_services.get_blog_post_by_id(blog_post_id, strict=False)) if blog_post is None: raise self.PageNotFoundException( Exception( 'The blog post with the given id or url doesn\'t exist.')) blog_services.update_blog_models_author_and_published_on_date( blog_post_id, author_id, published_on) self.render_json({})

true

f723f6aed494f61892583333010aad58dbc25f9a

1,503

py

Python

test/test_maintenance_configuration_api.py

cvent/octopus-deploy-api-client

0e03e842e1beb29b132776aee077df570b88366a

[ "Apache-2.0" ]

null

test/test_maintenance_configuration_api.py

cvent/octopus-deploy-api-client

0e03e842e1beb29b132776aee077df570b88366a

[ "Apache-2.0" ]

null

test/test_maintenance_configuration_api.py

cvent/octopus-deploy-api-client

0e03e842e1beb29b132776aee077df570b88366a

[ "Apache-2.0" ]

null

# coding: utf-8 """ Octopus Server API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: 2019.6.7+Branch.tags-2019.6.7.Sha.aa18dc6809953218c66f57eff7d26481d9b23d6a Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import octopus_deploy_swagger_client from octopus_deploy_client.maintenance_configuration_api import MaintenanceConfigurationApi # noqa: E501 from octopus_deploy_swagger_client.rest import ApiException class TestMaintenanceConfigurationApi(unittest.TestCase): """MaintenanceConfigurationApi unit test stubs""" def setUp(self): self.api = octopus_deploy_client.maintenance_configuration_api.MaintenanceConfigurationApi() # noqa: E501 def tearDown(self): pass def test_custom_action_response_descriptor_octopus_server_web_api_actions_maintenance_configuration_get_action(self): """Test case for custom_action_response_descriptor_octopus_server_web_api_actions_maintenance_configuration_get_action """ pass def test_custom_action_response_descriptor_octopus_server_web_api_actions_maintenance_configuration_update_action(self): """Test case for custom_action_response_descriptor_octopus_server_web_api_actions_maintenance_configuration_update_action """ pass if __name__ == '__main__': unittest.main()

31.978723

129

0.795742

from __future__ import absolute_import import unittest import octopus_deploy_swagger_client from octopus_deploy_client.maintenance_configuration_api import MaintenanceConfigurationApi from octopus_deploy_swagger_client.rest import ApiException class TestMaintenanceConfigurationApi(unittest.TestCase): def setUp(self): self.api = octopus_deploy_client.maintenance_configuration_api.MaintenanceConfigurationApi() def tearDown(self): pass def test_custom_action_response_descriptor_octopus_server_web_api_actions_maintenance_configuration_get_action(self): pass def test_custom_action_response_descriptor_octopus_server_web_api_actions_maintenance_configuration_update_action(self): pass if __name__ == '__main__': unittest.main()

true

f723f6d3146fbf9c9696d5956391430c0797a221

6,843

py

Python

src/azure-cli/setup.py

t-bzhan/azure-cli

d64b25204b661438e9284f261bc5a11f3221c837

[ "MIT" ]

null

src/azure-cli/setup.py

t-bzhan/azure-cli

d64b25204b661438e9284f261bc5a11f3221c837

[ "MIT" ]

null

src/azure-cli/setup.py

t-bzhan/azure-cli

d64b25204b661438e9284f261bc5a11f3221c837

[ "MIT" ]

null

#!/usr/bin/env python # -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- from __future__ import print_function from codecs import open from setuptools import setup, find_packages import sys try: from azure_cli_bdist_wheel import cmdclass except ImportError: from distutils import log as logger logger.warn("Wheel is not available, disabling bdist_wheel hook") cmdclass = {} VERSION = "2.20.0" # If we have source, validate that our version numbers match # This should prevent uploading releases with mismatched versions. try: with open('azure/cli/__main__.py', 'r', encoding='utf-8') as f: content = f.read() except OSError: pass else: import re m = re.search(r'__version__\s*=\s*[\'"](.+?)[\'"]', content) if not m: print('Could not find __version__ in azure/cli/__main__.py') sys.exit(1) if m.group(1) != VERSION: print('Expected __version__ = "{}"; found "{}"'.format(VERSION, m.group(1))) sys.exit(1) CLASSIFIERS = [ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Intended Audience :: System Administrators', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'License :: OSI Approved :: MIT License', ] DEPENDENCIES = [ 'antlr4-python3-runtime~=4.7.2', 'azure-appconfiguration~=1.1.1', 'azure-batch~=10.0.0', 'azure-cli-core=={}'.format(VERSION), 'azure-cosmos~=3.0,>=3.0.2', 'azure-datalake-store~=0.0.49', 'azure-functions-devops-build~=0.0.22', 'azure-graphrbac~=0.60.0', 'azure-keyvault~=1.1.0', 'azure-keyvault-administration==4.0.0b3', 'azure-mgmt-advisor>=2.0.1,<3.0.0', 'azure-mgmt-apimanagement~=0.2.0', 'azure-mgmt-applicationinsights~=0.1.1', 'azure-mgmt-appconfiguration~=1.0.1', 'azure-mgmt-authorization~=0.61.0', 'azure-mgmt-batch~=9.0.0', 'azure-mgmt-batchai~=2.0', 'azure-mgmt-billing==1.0.0', 'azure-mgmt-botservice~=0.3.0', 'azure-mgmt-cdn==7.0.0', 'azure-mgmt-cognitiveservices~=6.3.0', 'azure-mgmt-compute~=19.0.0', 'azure-mgmt-consumption~=2.0', 'azure-mgmt-containerinstance~=1.4', 'azure-mgmt-containerregistry==3.0.0rc17', 'azure-mgmt-cosmosdb~=3.0.0', 'azure-mgmt-containerservice~=9.4.0', 'azure-mgmt-databoxedge~=0.2.0', 'azure-mgmt-datalake-analytics~=0.2.1', 'azure-mgmt-datalake-store~=0.5.0', 'azure-mgmt-datamigration~=4.1.0', 'azure-mgmt-deploymentmanager~=0.2.0', 'azure-mgmt-devtestlabs~=4.0', 'azure-mgmt-dns~=2.1', 'azure-mgmt-eventgrid==3.0.0rc7', 'azure-mgmt-eventhub~=4.1.0', 'azure-mgmt-hdinsight~=2.2.0', 'azure-mgmt-imagebuilder~=0.4.0', 'azure-mgmt-iotcentral~=4.1.0', 'azure-mgmt-iothub~=0.12.0', 'azure-mgmt-iothubprovisioningservices~=0.2.0', 'azure-mgmt-keyvault==8.0.0', 'azure-mgmt-kusto~=0.3.0', 'azure-mgmt-loganalytics~=8.0.0', 'azure-mgmt-managedservices~=1.0', 'azure-mgmt-managementgroups~=0.1', 'azure-mgmt-maps~=0.1.0', 'azure-mgmt-marketplaceordering~=0.1', 'azure-mgmt-media~=3.0', 'azure-mgmt-monitor~=2.0.0', 'azure-mgmt-msi~=0.2', 'azure-mgmt-netapp~=0.16.0', 'azure-mgmt-network~=17.1.0', 'azure-mgmt-policyinsights~=0.5.0', 'azure-mgmt-privatedns~=0.1.0', 'azure-mgmt-rdbms~=3.1.0rc1', 'azure-mgmt-recoveryservices~=0.4.0', 'azure-mgmt-recoveryservicesbackup~=0.11.0', 'azure-mgmt-redhatopenshift==0.1.0', 'azure-mgmt-redis~=7.0.0rc1', 'azure-mgmt-relay~=0.1.0', # 'azure-mgmt-reservations~=0.6.0', 'azure-mgmt-reservations==0.6.0', # TODO: Use requirements.txt instead of '==' #9781 'azure-mgmt-resource==12.0.0', 'azure-mgmt-search~=8.0', 'azure-mgmt-security~=0.6.0', 'azure-mgmt-servicebus~=0.6.0', 'azure-mgmt-servicefabric~=0.5.0', 'azure-mgmt-signalr~=0.4.0', 'azure-mgmt-sql~=0.26.0', 'azure-mgmt-sqlvirtualmachine~=0.5.0', 'azure-mgmt-storage~=17.0.0', 'azure-mgmt-trafficmanager~=0.51.0', 'azure-mgmt-web~=0.48.0', 'azure-mgmt-synapse~=0.6.0', 'azure-multiapi-storage~=0.6.0', 'azure-loganalytics~=0.1.0', 'azure-storage-common~=1.4', 'azure-synapse-accesscontrol~=0.2.0', 'azure-synapse-artifacts~=0.3.0', 'azure-synapse-spark~=0.2.0', 'fabric~=2.4', 'jsmin~=2.2.2', 'pytz==2019.1', 'scp~=0.13.2', 'sshtunnel~=0.1.4', 'urllib3[secure]>=1.25.9,<2.0.0', 'vsts-cd-manager~=1.0.0,>=1.0.2', 'websocket-client~=0.56.0', 'xmltodict~=0.12', 'javaproperties==0.5.1', 'jsondiff==1.2.0', 'semver==2.13.0' ] TESTS_REQUIRE = [ 'mock~=4.0' ] with open('README.rst', 'r', encoding='utf-8') as f: README = f.read() with open('HISTORY.rst', 'r', encoding='utf-8') as f: HISTORY = f.read() setup( name='azure-cli', version=VERSION, description='Microsoft Azure Command-Line Tools', long_description=README + '\n\n' + HISTORY, license='MIT', author='Microsoft Corporation', author_email='azpycli@microsoft.com', url='https://github.com/Azure/azure-cli', zip_safe=False, classifiers=CLASSIFIERS, scripts=[ 'az', 'az.completion.sh', 'az.bat', ], packages=find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests", "azure", "azure.cli"]), install_requires=DEPENDENCIES, python_requires='>=3.6.0', package_data={ 'azure.cli.command_modules.acr': ['*.json'], 'azure.cli.command_modules.botservice': ['*.json', '*.config'], 'azure.cli.command_modules.monitor.operations': ['autoscale-parameters-template.json'], 'azure.cli.command_modules.servicefabric': [ 'template/windows/template.json', 'template/windows/parameter.json', 'template/linux/template.json', 'template/linux/parameter.json', 'template/service/template.json', 'template/service/parameter.json' ], 'azure.cli.command_modules.appservice': [ 'resources/WindowsFunctionsStacks.json', 'resources/LinuxFunctionsStacks.json', 'resources/WebappRuntimeStacks.json', 'resources/GenerateRandomAppNames.json' ], 'azure.cli.command_modules.rdbms': [ 'randomname/adjectives.txt', 'randomname/nouns.txt' ] }, cmdclass=cmdclass )

34.044776

103

0.604998

from __future__ import print_function from codecs import open from setuptools import setup, find_packages import sys try: from azure_cli_bdist_wheel import cmdclass except ImportError: from distutils import log as logger logger.warn("Wheel is not available, disabling bdist_wheel hook") cmdclass = {} VERSION = "2.20.0" try: with open('azure/cli/__main__.py', 'r', encoding='utf-8') as f: content = f.read() except OSError: pass else: import re m = re.search(r'__version__\s*=\s*[\'"](.+?)[\'"]', content) if not m: print('Could not find __version__ in azure/cli/__main__.py') sys.exit(1) if m.group(1) != VERSION: print('Expected __version__ = "{}"; found "{}"'.format(VERSION, m.group(1))) sys.exit(1) CLASSIFIERS = [ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Intended Audience :: System Administrators', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'License :: OSI Approved :: MIT License', ] DEPENDENCIES = [ 'antlr4-python3-runtime~=4.7.2', 'azure-appconfiguration~=1.1.1', 'azure-batch~=10.0.0', 'azure-cli-core=={}'.format(VERSION), 'azure-cosmos~=3.0,>=3.0.2', 'azure-datalake-store~=0.0.49', 'azure-functions-devops-build~=0.0.22', 'azure-graphrbac~=0.60.0', 'azure-keyvault~=1.1.0', 'azure-keyvault-administration==4.0.0b3', 'azure-mgmt-advisor>=2.0.1,<3.0.0', 'azure-mgmt-apimanagement~=0.2.0', 'azure-mgmt-applicationinsights~=0.1.1', 'azure-mgmt-appconfiguration~=1.0.1', 'azure-mgmt-authorization~=0.61.0', 'azure-mgmt-batch~=9.0.0', 'azure-mgmt-batchai~=2.0', 'azure-mgmt-billing==1.0.0', 'azure-mgmt-botservice~=0.3.0', 'azure-mgmt-cdn==7.0.0', 'azure-mgmt-cognitiveservices~=6.3.0', 'azure-mgmt-compute~=19.0.0', 'azure-mgmt-consumption~=2.0', 'azure-mgmt-containerinstance~=1.4', 'azure-mgmt-containerregistry==3.0.0rc17', 'azure-mgmt-cosmosdb~=3.0.0', 'azure-mgmt-containerservice~=9.4.0', 'azure-mgmt-databoxedge~=0.2.0', 'azure-mgmt-datalake-analytics~=0.2.1', 'azure-mgmt-datalake-store~=0.5.0', 'azure-mgmt-datamigration~=4.1.0', 'azure-mgmt-deploymentmanager~=0.2.0', 'azure-mgmt-devtestlabs~=4.0', 'azure-mgmt-dns~=2.1', 'azure-mgmt-eventgrid==3.0.0rc7', 'azure-mgmt-eventhub~=4.1.0', 'azure-mgmt-hdinsight~=2.2.0', 'azure-mgmt-imagebuilder~=0.4.0', 'azure-mgmt-iotcentral~=4.1.0', 'azure-mgmt-iothub~=0.12.0', 'azure-mgmt-iothubprovisioningservices~=0.2.0', 'azure-mgmt-keyvault==8.0.0', 'azure-mgmt-kusto~=0.3.0', 'azure-mgmt-loganalytics~=8.0.0', 'azure-mgmt-managedservices~=1.0', 'azure-mgmt-managementgroups~=0.1', 'azure-mgmt-maps~=0.1.0', 'azure-mgmt-marketplaceordering~=0.1', 'azure-mgmt-media~=3.0', 'azure-mgmt-monitor~=2.0.0', 'azure-mgmt-msi~=0.2', 'azure-mgmt-netapp~=0.16.0', 'azure-mgmt-network~=17.1.0', 'azure-mgmt-policyinsights~=0.5.0', 'azure-mgmt-privatedns~=0.1.0', 'azure-mgmt-rdbms~=3.1.0rc1', 'azure-mgmt-recoveryservices~=0.4.0', 'azure-mgmt-recoveryservicesbackup~=0.11.0', 'azure-mgmt-redhatopenshift==0.1.0', 'azure-mgmt-redis~=7.0.0rc1', 'azure-mgmt-relay~=0.1.0', 'azure-mgmt-reservations==0.6.0', 'azure-mgmt-resource==12.0.0', 'azure-mgmt-search~=8.0', 'azure-mgmt-security~=0.6.0', 'azure-mgmt-servicebus~=0.6.0', 'azure-mgmt-servicefabric~=0.5.0', 'azure-mgmt-signalr~=0.4.0', 'azure-mgmt-sql~=0.26.0', 'azure-mgmt-sqlvirtualmachine~=0.5.0', 'azure-mgmt-storage~=17.0.0', 'azure-mgmt-trafficmanager~=0.51.0', 'azure-mgmt-web~=0.48.0', 'azure-mgmt-synapse~=0.6.0', 'azure-multiapi-storage~=0.6.0', 'azure-loganalytics~=0.1.0', 'azure-storage-common~=1.4', 'azure-synapse-accesscontrol~=0.2.0', 'azure-synapse-artifacts~=0.3.0', 'azure-synapse-spark~=0.2.0', 'fabric~=2.4', 'jsmin~=2.2.2', 'pytz==2019.1', 'scp~=0.13.2', 'sshtunnel~=0.1.4', 'urllib3[secure]>=1.25.9,<2.0.0', 'vsts-cd-manager~=1.0.0,>=1.0.2', 'websocket-client~=0.56.0', 'xmltodict~=0.12', 'javaproperties==0.5.1', 'jsondiff==1.2.0', 'semver==2.13.0' ] TESTS_REQUIRE = [ 'mock~=4.0' ] with open('README.rst', 'r', encoding='utf-8') as f: README = f.read() with open('HISTORY.rst', 'r', encoding='utf-8') as f: HISTORY = f.read() setup( name='azure-cli', version=VERSION, description='Microsoft Azure Command-Line Tools', long_description=README + '\n\n' + HISTORY, license='MIT', author='Microsoft Corporation', author_email='azpycli@microsoft.com', url='https://github.com/Azure/azure-cli', zip_safe=False, classifiers=CLASSIFIERS, scripts=[ 'az', 'az.completion.sh', 'az.bat', ], packages=find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests", "azure", "azure.cli"]), install_requires=DEPENDENCIES, python_requires='>=3.6.0', package_data={ 'azure.cli.command_modules.acr': ['*.json'], 'azure.cli.command_modules.botservice': ['*.json', '*.config'], 'azure.cli.command_modules.monitor.operations': ['autoscale-parameters-template.json'], 'azure.cli.command_modules.servicefabric': [ 'template/windows/template.json', 'template/windows/parameter.json', 'template/linux/template.json', 'template/linux/parameter.json', 'template/service/template.json', 'template/service/parameter.json' ], 'azure.cli.command_modules.appservice': [ 'resources/WindowsFunctionsStacks.json', 'resources/LinuxFunctionsStacks.json', 'resources/WebappRuntimeStacks.json', 'resources/GenerateRandomAppNames.json' ], 'azure.cli.command_modules.rdbms': [ 'randomname/adjectives.txt', 'randomname/nouns.txt' ] }, cmdclass=cmdclass )

true

f723f6f2ec0f62ea9f018768408724d303030146

1,583

py

Python

valid_subsequence.py

GerardCod/algoexpert-python

35d7f635f68e0d28eaead815f653bf749aa275cb

[ "Apache-2.0" ]

null

valid_subsequence.py

GerardCod/algoexpert-python

35d7f635f68e0d28eaead815f653bf749aa275cb

[ "Apache-2.0" ]

null

valid_subsequence.py

GerardCod/algoexpert-python

35d7f635f68e0d28eaead815f653bf749aa275cb

[ "Apache-2.0" ]

null

def validateSubSequence(array, sequence): """ ### Description validateSubSequence -> validates if a sequence of elements is a subsequence of a list. ### Parameters - array: the list where it will validate the subsequence. - sequence: the potential subsequence of elements ### Returns - True when the sequence is a valid subsequence of array. - False when the sequence is not a valid subsequence of array. """ arrIdx = 0 seqIdx = 0 while arrIdx < len(array) and seqIdx < len(sequence): if array[arrIdx] == sequence[seqIdx]: seqIdx += 1 arrIdx += 1 return seqIdx == len(sequence) def validateSubSequenceFor(array, sequence): """ ### Description validateSubSequence -> validates if a sequence of elements is a subsequence of a list. ### Parameters array: the list where it will validate the subsequence. sequence: the potential subsequence of elements ### Returns - True when the sequence is a valid subsequence of array. - False when the sequence is not a valid subsequence of array. """ seqIdx = 0 for element in array: if seqIdx == len(sequence): break if element == sequence[seqIdx]: seqIdx += 1 return seqIdx == len(sequence) if __name__ == "__main__": print(validateSubSequence([5, 1, 22, 25, 6, -1, 8, 10], [1, 6, -1, 10])) print(validateSubSequenceFor([5, 1, 22, 25, 6, -1, 8, 10], [1, 6, -1, 10]))

29.867925

94

0.598863

def validateSubSequence(array, sequence): arrIdx = 0 seqIdx = 0 while arrIdx < len(array) and seqIdx < len(sequence): if array[arrIdx] == sequence[seqIdx]: seqIdx += 1 arrIdx += 1 return seqIdx == len(sequence) def validateSubSequenceFor(array, sequence): seqIdx = 0 for element in array: if seqIdx == len(sequence): break if element == sequence[seqIdx]: seqIdx += 1 return seqIdx == len(sequence) if __name__ == "__main__": print(validateSubSequence([5, 1, 22, 25, 6, -1, 8, 10], [1, 6, -1, 10])) print(validateSubSequenceFor([5, 1, 22, 25, 6, -1, 8, 10], [1, 6, -1, 10]))

true

f723f81e9a3f4e12dffa9c9a8c28e3e333ab9a3c

3,092

py

Python

fhirbug/Fhir/Resources/address.py

VerdantAI/fhirbug

8a8e2555c0edfeee0a7edbc8d67f2fcb2edd3c2d

[ "MIT" ]

8

2019-01-06T18:11:20.000Z

2022-02-24T02:06:55.000Z

fhirbug/Fhir/Resources/address.py

VerdantAI/fhirbug

8a8e2555c0edfeee0a7edbc8d67f2fcb2edd3c2d

[ "MIT" ]

5

2019-01-25T14:15:35.000Z

2021-06-01T23:22:41.000Z

fhirbug/Fhir/Resources/address.py

VerdantAI/fhirbug

8a8e2555c0edfeee0a7edbc8d67f2fcb2edd3c2d

[ "MIT" ]

3

2020-10-14T23:09:29.000Z

2021-08-09T19:27:31.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Generated from FHIR 4.0.0-a53ec6ee1b (http://hl7.org/fhir/StructureDefinition/Address) on 2019-01-25. # 2019, SMART Health IT. ## from . import element class Address(element.Element): """ An address expressed using postal conventions (as opposed to GPS or other location definition formats). An address expressed using postal conventions (as opposed to GPS or other location definition formats). This data type may be used to convey addresses for use in delivering mail as well as for visiting locations which might not be valid for mail delivery. There are a variety of postal address formats defined around the world. """ resource_type = "Address" def __init__(self, jsondict=None, strict=True, **kwargs): """ Initialize all valid properties. :raises: FHIRValidationError on validation errors, unless strict is False :param dict jsondict: A JSON dictionary to use for initialization :param bool strict: If True (the default), invalid variables will raise a TypeError """ self.city = None """ Name of city, town etc.. Type `str`. """ self.country = None """ Country (e.g. can be ISO 3166 2 or 3 letter code). Type `str`. """ self.district = None """ District name (aka county). Type `str`. """ self.line = None """ Street name, number, direction & P.O. Box etc.. List of `str` items. """ self.period = None """ Time period when address was/is in use. Type `Period` (represented as `dict` in JSON). """ self.postalCode = None """ Postal code for area. Type `str`. """ self.state = None """ Sub-unit of country (abbreviations ok). Type `str`. """ self.text = None """ Text representation of the address. Type `str`. """ self.type = None """ postal | physical | both. Type `str`. """ self.use = None """ home | work | temp | old | billing - purpose of this address. Type `str`. """ super(Address, self).__init__(jsondict=jsondict, strict=strict, **kwargs) def elementProperties(self): js = super(Address, self).elementProperties() js.extend([ ("city", "city", str, False, None, False), ("country", "country", str, False, None, False), ("district", "district", str, False, None, False), ("line", "line", str, True, None, False), ("period", "period", period.Period, False, None, False), ("postalCode", "postalCode", str, False, None, False), ("state", "state", str, False, None, False), ("text", "text", str, False, None, False), ("type", "type", str, False, None, False), ("use", "use", str, False, None, False), ]) return js import sys try: from . import period except ImportError: period = sys.modules[__package__ + '.period']

32.547368

104

0.583441

from . import element class Address(element.Element): resource_type = "Address" def __init__(self, jsondict=None, strict=True, **kwargs): self.city = None self.country = None self.district = None self.line = None self.period = None self.postalCode = None self.state = None self.text = None self.type = None self.use = None super(Address, self).__init__(jsondict=jsondict, strict=strict, **kwargs) def elementProperties(self): js = super(Address, self).elementProperties() js.extend([ ("city", "city", str, False, None, False), ("country", "country", str, False, None, False), ("district", "district", str, False, None, False), ("line", "line", str, True, None, False), ("period", "period", period.Period, False, None, False), ("postalCode", "postalCode", str, False, None, False), ("state", "state", str, False, None, False), ("text", "text", str, False, None, False), ("type", "type", str, False, None, False), ("use", "use", str, False, None, False), ]) return js import sys try: from . import period except ImportError: period = sys.modules[__package__ + '.period']

true

f723f8a8d4760f751862af7dd8140ab1cad4f937

755

py

Python

setup.py

salesforce/bite

0619bc6d87b81ec65cf311906da3889043176ead

[ "BSD-3-Clause" ]

6

2020-12-09T01:57:13.000Z

2021-10-09T01:50:21.000Z

setup.py

salesforce/bite

0619bc6d87b81ec65cf311906da3889043176ead

[ "BSD-3-Clause" ]

1

2021-02-16T14:50:09.000Z

2021-02-23T07:29:15.000Z

setup.py

salesforce/bite

0619bc6d87b81ec65cf311906da3889043176ead

[ "BSD-3-Clause" ]

null

from setuptools import setup with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name="bite", version="0.1", author="Samson Tan", author_email="samson.tan@salesforce.com", description="A tokenizer that splits words into bases and inflections.", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/salesforce/bite", package_dir={"": "src"}, packages=setuptools.find_packages("src"), classifiers=[ "Programming Language :: Python :: 3", #"License :: OSI Approved :: BSD License", "Development Status :: 3 - Alpha", "Operating System :: OS Independent", ], python_requires='>=3.6', )

30.2

76

0.65298

from setuptools import setup with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name="bite", version="0.1", author="Samson Tan", author_email="samson.tan@salesforce.com", description="A tokenizer that splits words into bases and inflections.", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/salesforce/bite", package_dir={"": "src"}, packages=setuptools.find_packages("src"), classifiers=[ "Programming Language :: Python :: 3", "Development Status :: 3 - Alpha", "Operating System :: OS Independent", ], python_requires='>=3.6', )

true

f723f8c5703335b1a0fa5a181861df8ecc2a13f8

6,240

py

Python

python/istio_api/mixer/v1/config/client/service_pb2.py

mt-inside/api

3197d4dee332beb55f830899f37091c9899833f9

[ "Apache-2.0" ]

3

2020-11-30T15:35:37.000Z

2022-01-06T14:17:18.000Z

python/istio_api/mixer/v1/config/client/service_pb2.py

mt-inside/api

3197d4dee332beb55f830899f37091c9899833f9

[ "Apache-2.0" ]

54

2020-06-23T17:34:04.000Z

2022-03-31T02:04:06.000Z

python/istio_api/mixer/v1/config/client/service_pb2.py

mt-inside/api

3197d4dee332beb55f830899f37091c9899833f9

[ "Apache-2.0" ]

12

2020-07-14T23:59:57.000Z

2022-03-22T09:59:18.000Z

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: mixer/v1/config/client/service.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from gogoproto import gogo_pb2 as gogoproto_dot_gogo__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='mixer/v1/config/client/service.proto', package='istio.mixer.v1.config.client', syntax='proto3', serialized_options=_b('Z#istio.io/api/mixer/v1/config/client\310\341\036\000\250\342\036\000\360\341\036\000\330\342\036\001'), serialized_pb=_b('\n$mixer/v1/config/client/service.proto\x12\x1cistio.mixer.v1.config.client\x1a\x14gogoproto/gogo.proto\"\xc7\x01\n\x0cIstioService\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x11\n\tnamespace\x18\x02 \x01(\t\x12\x0e\n\x06\x64omain\x18\x03 \x01(\t\x12\x0f\n\x07service\x18\x04 \x01(\t\x12\x46\n\x06labels\x18\x05 \x03(\x0b\x32\x36.istio.mixer.v1.config.client.IstioService.LabelsEntry\x1a-\n\x0bLabelsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\x42\x35Z#istio.io/api/mixer/v1/config/client\xc8\xe1\x1e\x00\xa8\xe2\x1e\x00\xf0\xe1\x1e\x00\xd8\xe2\x1e\x01\x62\x06proto3') , dependencies=[gogoproto_dot_gogo__pb2.DESCRIPTOR,]) _ISTIOSERVICE_LABELSENTRY = _descriptor.Descriptor( name='LabelsEntry', full_name='istio.mixer.v1.config.client.IstioService.LabelsEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='istio.mixer.v1.config.client.IstioService.LabelsEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='istio.mixer.v1.config.client.IstioService.LabelsEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=247, serialized_end=292, ) _ISTIOSERVICE = _descriptor.Descriptor( name='IstioService', full_name='istio.mixer.v1.config.client.IstioService', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='istio.mixer.v1.config.client.IstioService.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='namespace', full_name='istio.mixer.v1.config.client.IstioService.namespace', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='domain', full_name='istio.mixer.v1.config.client.IstioService.domain', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='service', full_name='istio.mixer.v1.config.client.IstioService.service', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='labels', full_name='istio.mixer.v1.config.client.IstioService.labels', index=4, number=5, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_ISTIOSERVICE_LABELSENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=93, serialized_end=292, ) _ISTIOSERVICE_LABELSENTRY.containing_type = _ISTIOSERVICE _ISTIOSERVICE.fields_by_name['labels'].message_type = _ISTIOSERVICE_LABELSENTRY DESCRIPTOR.message_types_by_name['IstioService'] = _ISTIOSERVICE _sym_db.RegisterFileDescriptor(DESCRIPTOR) IstioService = _reflection.GeneratedProtocolMessageType('IstioService', (_message.Message,), { 'LabelsEntry' : _reflection.GeneratedProtocolMessageType('LabelsEntry', (_message.Message,), { 'DESCRIPTOR' : _ISTIOSERVICE_LABELSENTRY, '__module__' : 'mixer.v1.config.client.service_pb2' # @@protoc_insertion_point(class_scope:istio.mixer.v1.config.client.IstioService.LabelsEntry) }) , 'DESCRIPTOR' : _ISTIOSERVICE, '__module__' : 'mixer.v1.config.client.service_pb2' # @@protoc_insertion_point(class_scope:istio.mixer.v1.config.client.IstioService) }) _sym_db.RegisterMessage(IstioService) _sym_db.RegisterMessage(IstioService.LabelsEntry) DESCRIPTOR._options = None _ISTIOSERVICE_LABELSENTRY._options = None # @@protoc_insertion_point(module_scope)

41.6

624

0.747276

import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database _sym_db = _symbol_database.Default() from gogoproto import gogo_pb2 as gogoproto_dot_gogo__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='mixer/v1/config/client/service.proto', package='istio.mixer.v1.config.client', syntax='proto3', serialized_options=_b('Z#istio.io/api/mixer/v1/config/client\310\341\036\000\250\342\036\000\360\341\036\000\330\342\036\001'), serialized_pb=_b('\n$mixer/v1/config/client/service.proto\x12\x1cistio.mixer.v1.config.client\x1a\x14gogoproto/gogo.proto\"\xc7\x01\n\x0cIstioService\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x11\n\tnamespace\x18\x02 \x01(\t\x12\x0e\n\x06\x64omain\x18\x03 \x01(\t\x12\x0f\n\x07service\x18\x04 \x01(\t\x12\x46\n\x06labels\x18\x05 \x03(\x0b\x32\x36.istio.mixer.v1.config.client.IstioService.LabelsEntry\x1a-\n\x0bLabelsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\x42\x35Z#istio.io/api/mixer/v1/config/client\xc8\xe1\x1e\x00\xa8\xe2\x1e\x00\xf0\xe1\x1e\x00\xd8\xe2\x1e\x01\x62\x06proto3') , dependencies=[gogoproto_dot_gogo__pb2.DESCRIPTOR,]) _ISTIOSERVICE_LABELSENTRY = _descriptor.Descriptor( name='LabelsEntry', full_name='istio.mixer.v1.config.client.IstioService.LabelsEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='istio.mixer.v1.config.client.IstioService.LabelsEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='istio.mixer.v1.config.client.IstioService.LabelsEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=247, serialized_end=292, ) _ISTIOSERVICE = _descriptor.Descriptor( name='IstioService', full_name='istio.mixer.v1.config.client.IstioService', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='istio.mixer.v1.config.client.IstioService.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='namespace', full_name='istio.mixer.v1.config.client.IstioService.namespace', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='domain', full_name='istio.mixer.v1.config.client.IstioService.domain', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='service', full_name='istio.mixer.v1.config.client.IstioService.service', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='labels', full_name='istio.mixer.v1.config.client.IstioService.labels', index=4, number=5, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_ISTIOSERVICE_LABELSENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=93, serialized_end=292, ) _ISTIOSERVICE_LABELSENTRY.containing_type = _ISTIOSERVICE _ISTIOSERVICE.fields_by_name['labels'].message_type = _ISTIOSERVICE_LABELSENTRY DESCRIPTOR.message_types_by_name['IstioService'] = _ISTIOSERVICE _sym_db.RegisterFileDescriptor(DESCRIPTOR) IstioService = _reflection.GeneratedProtocolMessageType('IstioService', (_message.Message,), { 'LabelsEntry' : _reflection.GeneratedProtocolMessageType('LabelsEntry', (_message.Message,), { 'DESCRIPTOR' : _ISTIOSERVICE_LABELSENTRY, '__module__' : 'mixer.v1.config.client.service_pb2' # @@protoc_insertion_point(class_scope:istio.mixer.v1.config.client.IstioService.LabelsEntry) }) , 'DESCRIPTOR' : _ISTIOSERVICE, '__module__' : 'mixer.v1.config.client.service_pb2' # @@protoc_insertion_point(class_scope:istio.mixer.v1.config.client.IstioService) }) _sym_db.RegisterMessage(IstioService) _sym_db.RegisterMessage(IstioService.LabelsEntry) DESCRIPTOR._options = None _ISTIOSERVICE_LABELSENTRY._options = None # @@protoc_insertion_point(module_scope)

true

f723f948cdc428c8f98ae5492744213241a82570

3,751

py

Python

nest_csa_fan.py

danhje/neural-network-connection-algorithms-tester

9183d8ba0cccf4bc98284d1c859471d1eba901a3

[ "MIT" ]

null

nest_csa_fan.py

danhje/neural-network-connection-algorithms-tester

9183d8ba0cccf4bc98284d1c859471d1eba901a3

[ "MIT" ]

null

nest_csa_fan.py

danhje/neural-network-connection-algorithms-tester

9183d8ba0cccf4bc98284d1c859471d1eba901a3

[ "MIT" ]

null

''' @author: Daniel Hjertholm Tests for fan-in / -out networks created by the CSA implementation in NEST. ''' import numpy.random as rnd import random import nest import csa from testsuite.fan_test import FanTester class NEST_FanTester(FanTester): ''' Tests for fan-in / -out networks created by the CSA implementation in NEST. ''' def __init__(self, N_s, N_t, C, e_min=10): ''' Construct a test object. Parameters ---------- N_s : Number of nodes in source population. N_t : Number of nodes in target population. C : In-degree (number of connections per target neuron). e_min: Minimum expected number of observations in each bin. ''' nest.set_verbosity('M_FATAL') FanTester.__init__(self, N_s=N_s, N_t=N_t, C=C, e_min=e_min) def _reset(self, seed): ''' Reset the simulator and seed the PRNGs. Parameters ---------- seed: PRNG seed value. ''' nest.ResetKernel() # Set PRNG seed values: if seed == None: seed = rnd.randint(10 ** 10) seed = 4 * seed # Reduces probability of overlapping seed values. random.seed(seed) # CSA uses random. rnd.seed(seed + 1) # _get_expected_distribution uses numpy.random. nest.SetKernelStatus({'grng_seed': seed + 2, 'rng_seeds': [seed + 3]}) def _build(self): '''Create populations.''' self._source_pop = nest.Create('iaf_neuron', self._N_s) self._target_pop = nest.Create('iaf_neuron', self._N_t) def _connect(self): '''Connect populations.''' finite_set = csa.cross(xrange(self._N_s), xrange(self._N_t)) if self._fan == 'in': cs = csa.cset(csa.random(fanIn=self._C) * finite_set) else: cs = csa.cset(csa.random(fanOut=self._C) * finite_set) nest.CGConnect(self._source_pop, self._target_pop, csa.cset(cs)) def _degrees(self): '''Return list of degrees.''' connections = nest.GetConnections(source=self._source_pop) i = 0 if self._fan == 'in' else 1 connections = [conn[i] for conn in connections] return self._counter(connections) class FanInTester(NEST_FanTester): '''Tests for fan-in networks created by the CSA implementation in NEST.''' def __init__(self, N_s, N_t, C, e_min=10): ''' Construct a test object. Parameters ---------- N_s : Number of nodes in source population. N_t : Number of nodes in target population. C : In-degree (number of connections per target neuron). e_min: Minimum expected number of observations in each bin. ''' self._fan = 'in' NEST_FanTester.__init__(self, N_s, N_t, C, e_min=e_min) class FanOutTester(NEST_FanTester): '''Tests for fan-out networks created by the CSA implementation in NEST.''' def __init__(self, N_s, N_t, C, e_min=10): ''' Construct a test object. Parameters ---------- N_s : Number of nodes in source population. N_t : Number of nodes in target population. C : In-degree (number of connections per target neuron). e_min: Minimum expected number of observations in each bin. ''' self._fan = 'out' NEST_FanTester.__init__(self, N_s, N_t, C, e_min=e_min) if __name__ == '__main__': test = FanInTester(N_s=100, N_t=100, C=100) ks, p = test.two_level_test(n_runs=100, start_seed=0) print 'p-value of KS-test of uniformity:', p test.show_CDF() test.show_histogram()

30.008

79

0.596641

''' @author: Daniel Hjertholm Tests for fan-in / -out networks created by the CSA implementation in NEST. ''' import numpy.random as rnd import random import nest import csa from testsuite.fan_test import FanTester class NEST_FanTester(FanTester): ''' Tests for fan-in / -out networks created by the CSA implementation in NEST. ''' def __init__(self, N_s, N_t, C, e_min=10): ''' Construct a test object. Parameters ---------- N_s : Number of nodes in source population. N_t : Number of nodes in target population. C : In-degree (number of connections per target neuron). e_min: Minimum expected number of observations in each bin. ''' nest.set_verbosity('M_FATAL') FanTester.__init__(self, N_s=N_s, N_t=N_t, C=C, e_min=e_min) def _reset(self, seed): ''' Reset the simulator and seed the PRNGs. Parameters ---------- seed: PRNG seed value. ''' nest.ResetKernel() if seed == None: seed = rnd.randint(10 ** 10) seed = 4 * seed random.seed(seed) rnd.seed(seed + 1) nest.SetKernelStatus({'grng_seed': seed + 2, 'rng_seeds': [seed + 3]}) def _build(self): '''Create populations.''' self._source_pop = nest.Create('iaf_neuron', self._N_s) self._target_pop = nest.Create('iaf_neuron', self._N_t) def _connect(self): '''Connect populations.''' finite_set = csa.cross(xrange(self._N_s), xrange(self._N_t)) if self._fan == 'in': cs = csa.cset(csa.random(fanIn=self._C) * finite_set) else: cs = csa.cset(csa.random(fanOut=self._C) * finite_set) nest.CGConnect(self._source_pop, self._target_pop, csa.cset(cs)) def _degrees(self): '''Return list of degrees.''' connections = nest.GetConnections(source=self._source_pop) i = 0 if self._fan == 'in' else 1 connections = [conn[i] for conn in connections] return self._counter(connections) class FanInTester(NEST_FanTester): '''Tests for fan-in networks created by the CSA implementation in NEST.''' def __init__(self, N_s, N_t, C, e_min=10): ''' Construct a test object. Parameters ---------- N_s : Number of nodes in source population. N_t : Number of nodes in target population. C : In-degree (number of connections per target neuron). e_min: Minimum expected number of observations in each bin. ''' self._fan = 'in' NEST_FanTester.__init__(self, N_s, N_t, C, e_min=e_min) class FanOutTester(NEST_FanTester): '''Tests for fan-out networks created by the CSA implementation in NEST.''' def __init__(self, N_s, N_t, C, e_min=10): ''' Construct a test object. Parameters ---------- N_s : Number of nodes in source population. N_t : Number of nodes in target population. C : In-degree (number of connections per target neuron). e_min: Minimum expected number of observations in each bin. ''' self._fan = 'out' NEST_FanTester.__init__(self, N_s, N_t, C, e_min=e_min) if __name__ == '__main__': test = FanInTester(N_s=100, N_t=100, C=100) ks, p = test.two_level_test(n_runs=100, start_seed=0) print 'p-value of KS-test of uniformity:', p test.show_CDF() test.show_histogram()

false

true

f723fa96b4b622359c95f9d0ad4cdd27364d7401

3,267

py

Python

tests/test_paper_collection.py

h1-the-swan/paper_collection

f07ad5cd8c40ddd75df2031b15c49eee60f1d914

[ "MIT" ]

null

tests/test_paper_collection.py

h1-the-swan/paper_collection

f07ad5cd8c40ddd75df2031b15c49eee60f1d914

[ "MIT" ]

2

2020-03-31T11:20:29.000Z

2020-03-31T15:20:21.000Z

tests/test_paper_collection.py

h1-the-swan/paper_collection

f07ad5cd8c40ddd75df2031b15c49eee60f1d914

[ "MIT" ]

null

#!/usr/bin/env python """Tests for `paper_collection` package.""" import unittest from paper_collection import paper_collection import pandas as pd import numpy as np class TestPaper_collection(unittest.TestCase): """Tests for `paper_collection` package.""" def setUp(self): """Set up test fixtures, if any.""" self.df_papers = pd.read_csv('tests/jw_papers_mag2019.tsv', sep='\t') self.df_papers.drop_duplicates(subset=['PaperId'], inplace=True) self.num_papers = len(self.df_papers) self.df_citations = pd.read_csv('tests/jw_citations_mag2019.tsv', sep='\t') self.num_citations = len(self.df_citations) self.df_authors = pd.read_csv('tests/jw_PaperAuthorAffiliations_mag2019.tsv', sep='\t') self.authors_by_paper = self.get_authors_by_paper(self.df_authors) def tearDown(self): """Tear down test fixtures, if any.""" def get_authors_by_paper(self, df_authors): """Get a dictionary mapping paper_id to author data """ author_data = {} for paper_id, group in df_authors.groupby('PaperId'): group = group.sort_values('AuthorSequenceNumber') this_authors = [] for _, row in group.iterrows(): this_authors.append({'name': row.OriginalAuthor, 'author_id': row.AuthorId}) author_data[paper_id] = this_authors return author_data def load_paper(self, prow): paper_id = prow.PaperId authors = self.authors_by_paper[paper_id] return paper_collection.Paper(dataset='mag', dataset_version='mag-2019-11-22', paper_id=paper_id, title=prow.PaperTitle, display_title=prow.OriginalTitle, doi=prow.Doi, pub_date=prow.Date, year=prow.Year, venue=prow.OriginalVenue, authors=authors, node_rank=prow.flow) def test_000_single_paper(self): """Load a single paper""" prow = self.df_papers.iloc[0] p = self.load_paper(prow) assert p.display_title is not None assert len(p.display_title) def test_001_collection(self): """Load a collection""" coll = paper_collection.PaperCollection(description="Paper Collection") for _, prow in self.df_papers.iterrows(): p = self.load_paper(prow) coll.papers.append(p) assert len(coll) == self.num_papers def test_002_graph(self): """Construct graph""" coll = paper_collection.PaperCollection(description="Paper Collection") for _, prow in self.df_papers.iterrows(): p = self.load_paper(prow) coll.papers.append(p) for _, row in self.df_citations.iterrows(): coll.citations.append((row.PaperId, row.PaperReferenceId)) G = coll.construct_graph() assert G.number_of_nodes() == self.num_papers assert G.number_of_edges() == self.num_citations

38.435294

95

0.585859

import unittest from paper_collection import paper_collection import pandas as pd import numpy as np class TestPaper_collection(unittest.TestCase): def setUp(self): self.df_papers = pd.read_csv('tests/jw_papers_mag2019.tsv', sep='\t') self.df_papers.drop_duplicates(subset=['PaperId'], inplace=True) self.num_papers = len(self.df_papers) self.df_citations = pd.read_csv('tests/jw_citations_mag2019.tsv', sep='\t') self.num_citations = len(self.df_citations) self.df_authors = pd.read_csv('tests/jw_PaperAuthorAffiliations_mag2019.tsv', sep='\t') self.authors_by_paper = self.get_authors_by_paper(self.df_authors) def tearDown(self): def get_authors_by_paper(self, df_authors): author_data = {} for paper_id, group in df_authors.groupby('PaperId'): group = group.sort_values('AuthorSequenceNumber') this_authors = [] for _, row in group.iterrows(): this_authors.append({'name': row.OriginalAuthor, 'author_id': row.AuthorId}) author_data[paper_id] = this_authors return author_data def load_paper(self, prow): paper_id = prow.PaperId authors = self.authors_by_paper[paper_id] return paper_collection.Paper(dataset='mag', dataset_version='mag-2019-11-22', paper_id=paper_id, title=prow.PaperTitle, display_title=prow.OriginalTitle, doi=prow.Doi, pub_date=prow.Date, year=prow.Year, venue=prow.OriginalVenue, authors=authors, node_rank=prow.flow) def test_000_single_paper(self): prow = self.df_papers.iloc[0] p = self.load_paper(prow) assert p.display_title is not None assert len(p.display_title) def test_001_collection(self): coll = paper_collection.PaperCollection(description="Paper Collection") for _, prow in self.df_papers.iterrows(): p = self.load_paper(prow) coll.papers.append(p) assert len(coll) == self.num_papers def test_002_graph(self): coll = paper_collection.PaperCollection(description="Paper Collection") for _, prow in self.df_papers.iterrows(): p = self.load_paper(prow) coll.papers.append(p) for _, row in self.df_citations.iterrows(): coll.citations.append((row.PaperId, row.PaperReferenceId)) G = coll.construct_graph() assert G.number_of_nodes() == self.num_papers assert G.number_of_edges() == self.num_citations

true

f723faadd5d49357ab40cc0e11d377652ed79a23

541

py

Python

scripts/idc/sortsymbols.py

camden314/CacaoSDK

57b7e0654595eb7a432ef1faec9b239a3854cf45

[ "MIT" ]

4

2021-01-26T10:00:43.000Z

2021-08-06T21:35:15.000Z

scripts/idc/sortsymbols.py

camden314/CacaoSDK

57b7e0654595eb7a432ef1faec9b239a3854cf45

[ "MIT" ]

2

2021-07-26T01:55:43.000Z

2021-07-26T17:42:20.000Z

scripts/idc/sortsymbols.py

camden314/CacaoSDK

57b7e0654595eb7a432ef1faec9b239a3854cf45

[ "MIT" ]

1

2021-06-01T17:40:01.000Z

import re l = [] with open("functionsthunk.txt", "r") as f: s = f.readlines() for k, m in zip(s[0::2], s[1::2]): if 'non-virtual' not in k: l.append((k, m)) def sfun(a): m = re.search(r"(?:non-virtual thunk to )?(.+?$.*$(?: const)?\n.+\n\n)", a) print(a) return m.group(1) with open("functions.txt", 'w') as f: def sfun(a): m = re.search(r"(?:non-virtual thunk to )?(.+?)$.*$(?: const)?", a[0]) print(a) return m.group(1) l.sort(key=sfun) f.write("".join([k + m for k, m in l]))

24.590909

81

0.502773

import re l = [] with open("functionsthunk.txt", "r") as f: s = f.readlines() for k, m in zip(s[0::2], s[1::2]): if 'non-virtual' not in k: l.append((k, m)) def sfun(a): m = re.search(r"(?:non-virtual thunk to )?(.+?$.*$(?: const)?\n.+\n\n)", a) print(a) return m.group(1) with open("functions.txt", 'w') as f: def sfun(a): m = re.search(r"(?:non-virtual thunk to )?(.+?)$.*$(?: const)?", a[0]) print(a) return m.group(1) l.sort(key=sfun) f.write("".join([k + m for k, m in l]))

true