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def __eq__(self, other):
'Returns true if both objects are equal'
if (not isinstance(other, ErrorDetails)):
return False
return (self.__dict__ == other.__dict__) | 7,013,632,968,773,976,000 | Returns true if both objects are equal | asposewordscloud/models/error_details.py | __eq__ | rizwanniazigroupdocs/aspose-words-cloud-python | python | def __eq__(self, other):
if (not isinstance(other, ErrorDetails)):
return False
return (self.__dict__ == other.__dict__) |
def __ne__(self, other):
'Returns true if both objects are not equal'
return (not (self == other)) | 7,764,124,047,908,058,000 | Returns true if both objects are not equal | asposewordscloud/models/error_details.py | __ne__ | rizwanniazigroupdocs/aspose-words-cloud-python | python | def __ne__(self, other):
return (not (self == other)) |
@profile
@login_required
def post(self):
'\n Called when saving data from the annotator client\n '
data = request.get_json(force=True)
image = data.get('image')
dataset = data.get('dataset')
image_id = image.get('id')
image_model = ImageModel.objects(id=image_id).first()
if (image_model is None):
return ({'success': False, 'message': 'Image does not exist'}, 400)
db_dataset = current_user.datasets.filter(id=image_model.dataset_id).first()
if (dataset is None):
return {'success': False, 'message': 'Could not find associated dataset'}
db_dataset.update(annotate_url=dataset.get('annotate_url', ''))
categories = CategoryModel.objects.all()
annotations = AnnotationModel.objects(image_id=image_id)
current_user.update(preferences=data.get('user', {}))
annotated = False
for category in data.get('categories', []):
category_id = category.get('id')
db_category = categories.filter(id=category_id).first()
if (db_category is None):
continue
category_update = {'color': category.get('color')}
if current_user.can_edit(db_category):
category_update['keypoint_edges'] = category.get('keypoint_edges', [])
category_update['keypoint_labels'] = category.get('keypoint_labels', [])
db_category.update(**category_update)
for annotation in category.get('annotations', []):
annotation_id = annotation.get('id')
db_annotation = annotations.filter(id=annotation_id).first()
if (db_annotation is None):
continue
sessions = []
total_time = 0
for session in annotation.get('sessions', []):
date = datetime.datetime.fromtimestamp((int(session.get('start')) / 1000.0))
model = SessionEvent(user=current_user.username, created_at=date, milliseconds=session.get('milliseconds'), tools_used=session.get('tools'))
total_time += session.get('milliseconds')
sessions.append(model)
db_annotation.update(add_to_set__events=sessions, inc__milliseconds=total_time, set__isbbox=annotation.get('isbbox', False), set__keypoints=annotation.get('keypoints', []), set__metadata=annotation.get('metadata'), set__color=annotation.get('color'))
paperjs_object = annotation.get('compoundPath', [])
if (len(paperjs_object) == 2):
width = db_annotation.width
height = db_annotation.height
(segmentation, area, bbox) = coco_util.paperjs_to_coco(width, height, paperjs_object)
db_annotation.update(set__segmentation=segmentation, set__area=area, set__isbbox=annotation.get('isbbox', False), set__bbox=bbox, set__paper_object=paperjs_object)
if (area > 0):
annotated = True
image_model.update(set__metadata=image.get('metadata', {}), set__annotated=annotated, set__category_ids=image.get('category_ids', []), set__regenerate_thumbnail=True, set__num_annotations=annotations.filter(deleted=False, area__gt=0).count())
return {'success': True} | 223,986,864,701,691,170 | Called when saving data from the annotator client | coco-annotator/backend/webserver/api/annotator.py | post | Cheol-H-Jeong/Deep-POC-2019 | python | @profile
@login_required
def post(self):
'\n \n '
data = request.get_json(force=True)
image = data.get('image')
dataset = data.get('dataset')
image_id = image.get('id')
image_model = ImageModel.objects(id=image_id).first()
if (image_model is None):
return ({'success': False, 'message': 'Image does not exist'}, 400)
db_dataset = current_user.datasets.filter(id=image_model.dataset_id).first()
if (dataset is None):
return {'success': False, 'message': 'Could not find associated dataset'}
db_dataset.update(annotate_url=dataset.get('annotate_url', ))
categories = CategoryModel.objects.all()
annotations = AnnotationModel.objects(image_id=image_id)
current_user.update(preferences=data.get('user', {}))
annotated = False
for category in data.get('categories', []):
category_id = category.get('id')
db_category = categories.filter(id=category_id).first()
if (db_category is None):
continue
category_update = {'color': category.get('color')}
if current_user.can_edit(db_category):
category_update['keypoint_edges'] = category.get('keypoint_edges', [])
category_update['keypoint_labels'] = category.get('keypoint_labels', [])
db_category.update(**category_update)
for annotation in category.get('annotations', []):
annotation_id = annotation.get('id')
db_annotation = annotations.filter(id=annotation_id).first()
if (db_annotation is None):
continue
sessions = []
total_time = 0
for session in annotation.get('sessions', []):
date = datetime.datetime.fromtimestamp((int(session.get('start')) / 1000.0))
model = SessionEvent(user=current_user.username, created_at=date, milliseconds=session.get('milliseconds'), tools_used=session.get('tools'))
total_time += session.get('milliseconds')
sessions.append(model)
db_annotation.update(add_to_set__events=sessions, inc__milliseconds=total_time, set__isbbox=annotation.get('isbbox', False), set__keypoints=annotation.get('keypoints', []), set__metadata=annotation.get('metadata'), set__color=annotation.get('color'))
paperjs_object = annotation.get('compoundPath', [])
if (len(paperjs_object) == 2):
width = db_annotation.width
height = db_annotation.height
(segmentation, area, bbox) = coco_util.paperjs_to_coco(width, height, paperjs_object)
db_annotation.update(set__segmentation=segmentation, set__area=area, set__isbbox=annotation.get('isbbox', False), set__bbox=bbox, set__paper_object=paperjs_object)
if (area > 0):
annotated = True
image_model.update(set__metadata=image.get('metadata', {}), set__annotated=annotated, set__category_ids=image.get('category_ids', []), set__regenerate_thumbnail=True, set__num_annotations=annotations.filter(deleted=False, area__gt=0).count())
return {'success': True} |
@profile
@login_required
def get(self, image_id):
' Called when loading from the annotator client '
image = ImageModel.objects(id=image_id).exclude('events').first()
if (image is None):
return ({'success': False, 'message': 'Could not load image'}, 400)
dataset = current_user.datasets.filter(id=image.dataset_id).first()
if (dataset is None):
return ({'success': False, 'message': 'Could not find associated dataset'}, 400)
categories = CategoryModel.objects(deleted=False).in_bulk(dataset.categories).items()
images = ImageModel.objects(dataset_id=dataset.id, deleted=False)
pre = images.filter(file_name__lt=image.file_name).order_by('-file_name').first()
nex = images.filter(file_name__gt=image.file_name).order_by('file_name').first()
preferences = {}
if (not Config.LOGIN_DISABLED):
preferences = current_user.preferences
data = {'image': query_util.fix_ids(image), 'categories': [], 'dataset': query_util.fix_ids(dataset), 'preferences': preferences, 'permissions': {'dataset': dataset.permissions(current_user), 'image': image.permissions(current_user)}}
data['image']['previous'] = (pre.id if pre else None)
data['image']['next'] = (nex.id if nex else None)
for category in categories:
category = query_util.fix_ids(category[1])
category_id = category.get('id')
annotations = AnnotationModel.objects(image_id=image_id, category_id=category_id, deleted=False).exclude('events').all()
category['show'] = True
category['visualize'] = False
category['annotations'] = ([] if (annotations is None) else query_util.fix_ids(annotations))
data.get('categories').append(category)
return data | 4,471,360,595,673,237,500 | Called when loading from the annotator client | coco-annotator/backend/webserver/api/annotator.py | get | Cheol-H-Jeong/Deep-POC-2019 | python | @profile
@login_required
def get(self, image_id):
' '
image = ImageModel.objects(id=image_id).exclude('events').first()
if (image is None):
return ({'success': False, 'message': 'Could not load image'}, 400)
dataset = current_user.datasets.filter(id=image.dataset_id).first()
if (dataset is None):
return ({'success': False, 'message': 'Could not find associated dataset'}, 400)
categories = CategoryModel.objects(deleted=False).in_bulk(dataset.categories).items()
images = ImageModel.objects(dataset_id=dataset.id, deleted=False)
pre = images.filter(file_name__lt=image.file_name).order_by('-file_name').first()
nex = images.filter(file_name__gt=image.file_name).order_by('file_name').first()
preferences = {}
if (not Config.LOGIN_DISABLED):
preferences = current_user.preferences
data = {'image': query_util.fix_ids(image), 'categories': [], 'dataset': query_util.fix_ids(dataset), 'preferences': preferences, 'permissions': {'dataset': dataset.permissions(current_user), 'image': image.permissions(current_user)}}
data['image']['previous'] = (pre.id if pre else None)
data['image']['next'] = (nex.id if nex else None)
for category in categories:
category = query_util.fix_ids(category[1])
category_id = category.get('id')
annotations = AnnotationModel.objects(image_id=image_id, category_id=category_id, deleted=False).exclude('events').all()
category['show'] = True
category['visualize'] = False
category['annotations'] = ([] if (annotations is None) else query_util.fix_ids(annotations))
data.get('categories').append(category)
return data |
def __init__(self, **kwargs):
'\n Initializes a new UpdateConnectionFromAmazonS3 object with values from keyword arguments. The default value of the :py:attr:`~oci.data_integration.models.UpdateConnectionFromAmazonS3.model_type` attribute\n of this class is ``AMAZON_S3_CONNECTION`` and it should not be changed.\n The following keyword arguments are supported (corresponding to the getters/setters of this class):\n\n :param model_type:\n The value to assign to the model_type property of this UpdateConnectionFromAmazonS3.\n Allowed values for this property are: "ORACLE_ADWC_CONNECTION", "ORACLE_ATP_CONNECTION", "ORACLE_OBJECT_STORAGE_CONNECTION", "ORACLEDB_CONNECTION", "MYSQL_CONNECTION", "GENERIC_JDBC_CONNECTION", "BICC_CONNECTION", "AMAZON_S3_CONNECTION", "BIP_CONNECTION"\n :type model_type: str\n\n :param key:\n The value to assign to the key property of this UpdateConnectionFromAmazonS3.\n :type key: str\n\n :param model_version:\n The value to assign to the model_version property of this UpdateConnectionFromAmazonS3.\n :type model_version: str\n\n :param parent_ref:\n The value to assign to the parent_ref property of this UpdateConnectionFromAmazonS3.\n :type parent_ref: oci.data_integration.models.ParentReference\n\n :param name:\n The value to assign to the name property of this UpdateConnectionFromAmazonS3.\n :type name: str\n\n :param description:\n The value to assign to the description property of this UpdateConnectionFromAmazonS3.\n :type description: str\n\n :param object_status:\n The value to assign to the object_status property of this UpdateConnectionFromAmazonS3.\n :type object_status: int\n\n :param object_version:\n The value to assign to the object_version property of this UpdateConnectionFromAmazonS3.\n :type object_version: int\n\n :param identifier:\n The value to assign to the identifier property of this UpdateConnectionFromAmazonS3.\n :type identifier: str\n\n :param connection_properties:\n The value to assign to the connection_properties property of this UpdateConnectionFromAmazonS3.\n :type connection_properties: list[oci.data_integration.models.ConnectionProperty]\n\n :param registry_metadata:\n The value to assign to the registry_metadata property of this UpdateConnectionFromAmazonS3.\n :type registry_metadata: oci.data_integration.models.RegistryMetadata\n\n :param access_key:\n The value to assign to the access_key property of this UpdateConnectionFromAmazonS3.\n :type access_key: oci.data_integration.models.SensitiveAttribute\n\n :param secret_key:\n The value to assign to the secret_key property of this UpdateConnectionFromAmazonS3.\n :type secret_key: oci.data_integration.models.SensitiveAttribute\n\n '
self.swagger_types = {'model_type': 'str', 'key': 'str', 'model_version': 'str', 'parent_ref': 'ParentReference', 'name': 'str', 'description': 'str', 'object_status': 'int', 'object_version': 'int', 'identifier': 'str', 'connection_properties': 'list[ConnectionProperty]', 'registry_metadata': 'RegistryMetadata', 'access_key': 'SensitiveAttribute', 'secret_key': 'SensitiveAttribute'}
self.attribute_map = {'model_type': 'modelType', 'key': 'key', 'model_version': 'modelVersion', 'parent_ref': 'parentRef', 'name': 'name', 'description': 'description', 'object_status': 'objectStatus', 'object_version': 'objectVersion', 'identifier': 'identifier', 'connection_properties': 'connectionProperties', 'registry_metadata': 'registryMetadata', 'access_key': 'accessKey', 'secret_key': 'secretKey'}
self._model_type = None
self._key = None
self._model_version = None
self._parent_ref = None
self._name = None
self._description = None
self._object_status = None
self._object_version = None
self._identifier = None
self._connection_properties = None
self._registry_metadata = None
self._access_key = None
self._secret_key = None
self._model_type = 'AMAZON_S3_CONNECTION' | 2,299,845,921,030,368,500 | Initializes a new UpdateConnectionFromAmazonS3 object with values from keyword arguments. The default value of the :py:attr:`~oci.data_integration.models.UpdateConnectionFromAmazonS3.model_type` attribute
of this class is ``AMAZON_S3_CONNECTION`` and it should not be changed.
The following keyword arguments are supported (corresponding to the getters/setters of this class):
:param model_type:
The value to assign to the model_type property of this UpdateConnectionFromAmazonS3.
Allowed values for this property are: "ORACLE_ADWC_CONNECTION", "ORACLE_ATP_CONNECTION", "ORACLE_OBJECT_STORAGE_CONNECTION", "ORACLEDB_CONNECTION", "MYSQL_CONNECTION", "GENERIC_JDBC_CONNECTION", "BICC_CONNECTION", "AMAZON_S3_CONNECTION", "BIP_CONNECTION"
:type model_type: str
:param key:
The value to assign to the key property of this UpdateConnectionFromAmazonS3.
:type key: str
:param model_version:
The value to assign to the model_version property of this UpdateConnectionFromAmazonS3.
:type model_version: str
:param parent_ref:
The value to assign to the parent_ref property of this UpdateConnectionFromAmazonS3.
:type parent_ref: oci.data_integration.models.ParentReference
:param name:
The value to assign to the name property of this UpdateConnectionFromAmazonS3.
:type name: str
:param description:
The value to assign to the description property of this UpdateConnectionFromAmazonS3.
:type description: str
:param object_status:
The value to assign to the object_status property of this UpdateConnectionFromAmazonS3.
:type object_status: int
:param object_version:
The value to assign to the object_version property of this UpdateConnectionFromAmazonS3.
:type object_version: int
:param identifier:
The value to assign to the identifier property of this UpdateConnectionFromAmazonS3.
:type identifier: str
:param connection_properties:
The value to assign to the connection_properties property of this UpdateConnectionFromAmazonS3.
:type connection_properties: list[oci.data_integration.models.ConnectionProperty]
:param registry_metadata:
The value to assign to the registry_metadata property of this UpdateConnectionFromAmazonS3.
:type registry_metadata: oci.data_integration.models.RegistryMetadata
:param access_key:
The value to assign to the access_key property of this UpdateConnectionFromAmazonS3.
:type access_key: oci.data_integration.models.SensitiveAttribute
:param secret_key:
The value to assign to the secret_key property of this UpdateConnectionFromAmazonS3.
:type secret_key: oci.data_integration.models.SensitiveAttribute | src/oci/data_integration/models/update_connection_from_amazon_s3.py | __init__ | pabs3/oci-python-sdk | python | def __init__(self, **kwargs):
'\n Initializes a new UpdateConnectionFromAmazonS3 object with values from keyword arguments. The default value of the :py:attr:`~oci.data_integration.models.UpdateConnectionFromAmazonS3.model_type` attribute\n of this class is ``AMAZON_S3_CONNECTION`` and it should not be changed.\n The following keyword arguments are supported (corresponding to the getters/setters of this class):\n\n :param model_type:\n The value to assign to the model_type property of this UpdateConnectionFromAmazonS3.\n Allowed values for this property are: "ORACLE_ADWC_CONNECTION", "ORACLE_ATP_CONNECTION", "ORACLE_OBJECT_STORAGE_CONNECTION", "ORACLEDB_CONNECTION", "MYSQL_CONNECTION", "GENERIC_JDBC_CONNECTION", "BICC_CONNECTION", "AMAZON_S3_CONNECTION", "BIP_CONNECTION"\n :type model_type: str\n\n :param key:\n The value to assign to the key property of this UpdateConnectionFromAmazonS3.\n :type key: str\n\n :param model_version:\n The value to assign to the model_version property of this UpdateConnectionFromAmazonS3.\n :type model_version: str\n\n :param parent_ref:\n The value to assign to the parent_ref property of this UpdateConnectionFromAmazonS3.\n :type parent_ref: oci.data_integration.models.ParentReference\n\n :param name:\n The value to assign to the name property of this UpdateConnectionFromAmazonS3.\n :type name: str\n\n :param description:\n The value to assign to the description property of this UpdateConnectionFromAmazonS3.\n :type description: str\n\n :param object_status:\n The value to assign to the object_status property of this UpdateConnectionFromAmazonS3.\n :type object_status: int\n\n :param object_version:\n The value to assign to the object_version property of this UpdateConnectionFromAmazonS3.\n :type object_version: int\n\n :param identifier:\n The value to assign to the identifier property of this UpdateConnectionFromAmazonS3.\n :type identifier: str\n\n :param connection_properties:\n The value to assign to the connection_properties property of this UpdateConnectionFromAmazonS3.\n :type connection_properties: list[oci.data_integration.models.ConnectionProperty]\n\n :param registry_metadata:\n The value to assign to the registry_metadata property of this UpdateConnectionFromAmazonS3.\n :type registry_metadata: oci.data_integration.models.RegistryMetadata\n\n :param access_key:\n The value to assign to the access_key property of this UpdateConnectionFromAmazonS3.\n :type access_key: oci.data_integration.models.SensitiveAttribute\n\n :param secret_key:\n The value to assign to the secret_key property of this UpdateConnectionFromAmazonS3.\n :type secret_key: oci.data_integration.models.SensitiveAttribute\n\n '
self.swagger_types = {'model_type': 'str', 'key': 'str', 'model_version': 'str', 'parent_ref': 'ParentReference', 'name': 'str', 'description': 'str', 'object_status': 'int', 'object_version': 'int', 'identifier': 'str', 'connection_properties': 'list[ConnectionProperty]', 'registry_metadata': 'RegistryMetadata', 'access_key': 'SensitiveAttribute', 'secret_key': 'SensitiveAttribute'}
self.attribute_map = {'model_type': 'modelType', 'key': 'key', 'model_version': 'modelVersion', 'parent_ref': 'parentRef', 'name': 'name', 'description': 'description', 'object_status': 'objectStatus', 'object_version': 'objectVersion', 'identifier': 'identifier', 'connection_properties': 'connectionProperties', 'registry_metadata': 'registryMetadata', 'access_key': 'accessKey', 'secret_key': 'secretKey'}
self._model_type = None
self._key = None
self._model_version = None
self._parent_ref = None
self._name = None
self._description = None
self._object_status = None
self._object_version = None
self._identifier = None
self._connection_properties = None
self._registry_metadata = None
self._access_key = None
self._secret_key = None
self._model_type = 'AMAZON_S3_CONNECTION' |
@property
def access_key(self):
'\n Gets the access_key of this UpdateConnectionFromAmazonS3.\n\n :return: The access_key of this UpdateConnectionFromAmazonS3.\n :rtype: oci.data_integration.models.SensitiveAttribute\n '
return self._access_key | -34,331,669,729,738,812 | Gets the access_key of this UpdateConnectionFromAmazonS3.
:return: The access_key of this UpdateConnectionFromAmazonS3.
:rtype: oci.data_integration.models.SensitiveAttribute | src/oci/data_integration/models/update_connection_from_amazon_s3.py | access_key | pabs3/oci-python-sdk | python | @property
def access_key(self):
'\n Gets the access_key of this UpdateConnectionFromAmazonS3.\n\n :return: The access_key of this UpdateConnectionFromAmazonS3.\n :rtype: oci.data_integration.models.SensitiveAttribute\n '
return self._access_key |
@access_key.setter
def access_key(self, access_key):
'\n Sets the access_key of this UpdateConnectionFromAmazonS3.\n\n :param access_key: The access_key of this UpdateConnectionFromAmazonS3.\n :type: oci.data_integration.models.SensitiveAttribute\n '
self._access_key = access_key | -474,915,086,494,389,300 | Sets the access_key of this UpdateConnectionFromAmazonS3.
:param access_key: The access_key of this UpdateConnectionFromAmazonS3.
:type: oci.data_integration.models.SensitiveAttribute | src/oci/data_integration/models/update_connection_from_amazon_s3.py | access_key | pabs3/oci-python-sdk | python | @access_key.setter
def access_key(self, access_key):
'\n Sets the access_key of this UpdateConnectionFromAmazonS3.\n\n :param access_key: The access_key of this UpdateConnectionFromAmazonS3.\n :type: oci.data_integration.models.SensitiveAttribute\n '
self._access_key = access_key |
@property
def secret_key(self):
'\n Gets the secret_key of this UpdateConnectionFromAmazonS3.\n\n :return: The secret_key of this UpdateConnectionFromAmazonS3.\n :rtype: oci.data_integration.models.SensitiveAttribute\n '
return self._secret_key | 7,734,419,076,322,159,000 | Gets the secret_key of this UpdateConnectionFromAmazonS3.
:return: The secret_key of this UpdateConnectionFromAmazonS3.
:rtype: oci.data_integration.models.SensitiveAttribute | src/oci/data_integration/models/update_connection_from_amazon_s3.py | secret_key | pabs3/oci-python-sdk | python | @property
def secret_key(self):
'\n Gets the secret_key of this UpdateConnectionFromAmazonS3.\n\n :return: The secret_key of this UpdateConnectionFromAmazonS3.\n :rtype: oci.data_integration.models.SensitiveAttribute\n '
return self._secret_key |
@secret_key.setter
def secret_key(self, secret_key):
'\n Sets the secret_key of this UpdateConnectionFromAmazonS3.\n\n :param secret_key: The secret_key of this UpdateConnectionFromAmazonS3.\n :type: oci.data_integration.models.SensitiveAttribute\n '
self._secret_key = secret_key | -7,769,865,444,699,896,000 | Sets the secret_key of this UpdateConnectionFromAmazonS3.
:param secret_key: The secret_key of this UpdateConnectionFromAmazonS3.
:type: oci.data_integration.models.SensitiveAttribute | src/oci/data_integration/models/update_connection_from_amazon_s3.py | secret_key | pabs3/oci-python-sdk | python | @secret_key.setter
def secret_key(self, secret_key):
'\n Sets the secret_key of this UpdateConnectionFromAmazonS3.\n\n :param secret_key: The secret_key of this UpdateConnectionFromAmazonS3.\n :type: oci.data_integration.models.SensitiveAttribute\n '
self._secret_key = secret_key |
def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id, lang_adapter_names, task_name, lang2id=None):
'Train the model.'
if (args.local_rank in [(- 1), 0]):
tb_writer = SummaryWriter()
args.train_batch_size = (args.per_gpu_train_batch_size * max(1, args.n_gpu))
print(f'Local Rank = {args.local_rank}')
print(len(train_dataset))
train_sampler = (RandomSampler(train_dataset) if (args.local_rank == (- 1)) else DistributedSampler(train_dataset))
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
if (args.max_steps > 0):
t_total = args.max_steps
args.num_train_epochs = ((args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps)) + 1)
else:
t_total = ((len(train_dataloader) // args.gradient_accumulation_steps) * args.num_train_epochs)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{'params': [p for (n, p) in model.named_parameters() if (not any(((nd in n) for nd in no_decay)))], 'weight_decay': args.weight_decay}, {'params': [p for (n, p) in model.named_parameters() if any(((nd in n) for nd in no_decay))], 'weight_decay': 0.0}]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
logging.info([n for (n, p) in model.named_parameters() if p.requires_grad])
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError('Please install apex from https://www.github.com/nvidia/apex to use fp16 training.')
(model, optimizer) = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
if (args.n_gpu > 1):
model = torch.nn.DataParallel(model)
if (args.local_rank != (- 1)):
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True)
logger.info('***** Running training *****')
logger.info(' Num examples = %d', len(train_dataset))
logger.info(' Num Epochs = %d', args.num_train_epochs)
logger.info(' Instantaneous batch size per GPU = %d', args.per_gpu_train_batch_size)
logger.info(' Total train batch size (w. parallel, distributed & accumulation) = %d', ((args.train_batch_size * args.gradient_accumulation_steps) * (torch.distributed.get_world_size() if (args.local_rank != (- 1)) else 1)))
logger.info(' Gradient Accumulation steps = %d', args.gradient_accumulation_steps)
logger.info(' Total optimization steps = %d', t_total)
best_score = 0.0
best_checkpoint = None
patience = 0
global_step = 0
(tr_loss, logging_loss) = (0.0, 0.0)
model.zero_grad()
train_iterator = trange(int(args.num_train_epochs), desc='Epoch', disable=(args.local_rank not in [(- 1), 0]))
set_seed(args)
cur_epoch = 0
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc='Iteration', disable=(args.local_rank not in [(- 1), 0]))
cur_epoch += 1
for (step, batch) in enumerate(epoch_iterator):
batch = tuple((t.to(args.device) for t in batch if (t is not None)))
inputs = {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if (args.model_type != 'distilbert'):
inputs['token_type_ids'] = (batch[2] if (args.model_type in ['bert', 'xlnet']) else None)
if (args.model_type == 'xlm'):
inputs['langs'] = batch[4]
outputs = model(**inputs)
loss = outputs[0]
if (args.n_gpu > 1):
loss = loss.mean()
if (args.gradient_accumulation_steps > 1):
loss = (loss / args.gradient_accumulation_steps)
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
if (((step + 1) % args.gradient_accumulation_steps) == 0):
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
scheduler.step()
optimizer.step()
model.zero_grad()
global_step += 1
if ((args.local_rank in [(- 1), 0]) and (args.logging_steps > 0) and ((global_step % args.logging_steps) == 0)):
if ((args.local_rank == (- 1)) and args.evaluate_during_training):
(results, _) = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode='dev', lang=args.train_langs, lang2id=lang2id, lang_adapter_names=lang_adapter_names, task_name=task_name)
for (key, value) in results.items():
tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
tb_writer.add_scalar('loss', ((tr_loss - logging_loss) / args.logging_steps), global_step)
logging_loss = tr_loss
if ((args.local_rank in [(- 1), 0]) and (args.save_steps > 0) and ((global_step % args.save_steps) == 0)):
if args.save_only_best_checkpoint:
(result, _) = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode='dev', prefix=global_step, lang=args.train_langs, lang2id=lang2id, lang_adapter_names=lang_adapter_names, task_name=task_name)
if (result['f1'] > best_score):
logger.info("result['f1']={} > best_score={}".format(result['f1'], best_score))
best_score = result['f1']
output_dir = os.path.join(args.output_dir, 'checkpoint-best')
best_checkpoint = output_dir
if (not os.path.exists(output_dir)):
os.makedirs(output_dir)
model_to_save = (model.module if hasattr(model, 'module') else model)
if args.do_save_adapters:
model_to_save.save_all_adapters(output_dir)
if args.do_save_adapter_fusions:
model_to_save.save_all_adapter_fusions(output_dir)
if args.do_save_full_model:
model_to_save.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, 'training_args.bin'))
logger.info('Saving the best model checkpoint to %s', output_dir)
logger.info('Reset patience to 0')
patience = 0
else:
patience += 1
logger.info('Hit patience={}'.format(patience))
if ((args.eval_patience > 0) and (patience > args.eval_patience)):
logger.info('early stop! patience={}'.format(patience))
epoch_iterator.close()
train_iterator.close()
if (args.local_rank in [(- 1), 0]):
tb_writer.close()
return (global_step, (tr_loss / global_step))
else:
output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
if (not os.path.exists(output_dir)):
os.makedirs(output_dir)
model_to_save = (model.module if hasattr(model, 'module') else model)
if args.do_save_adapters:
model_to_save.save_all_adapters(output_dir)
if args.do_save_adapter_fusions:
model_to_save.save_all_adapter_fusions(output_dir)
if args.do_save_full_model:
model_to_save.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, 'training_args.bin'))
logger.info('Saving model checkpoint to %s', output_dir)
if ((args.max_steps > 0) and (global_step > args.max_steps)):
epoch_iterator.close()
break
if ((args.max_steps > 0) and (global_step > args.max_steps)):
train_iterator.close()
break
if (args.local_rank in [(- 1), 0]):
tb_writer.close()
return (global_step, (tr_loss / global_step)) | 8,833,718,236,150,706,000 | Train the model. | third_party/ridayesh_run_tag.py | train | rohanshah13/cloud-emea-copy | python | def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id, lang_adapter_names, task_name, lang2id=None):
if (args.local_rank in [(- 1), 0]):
tb_writer = SummaryWriter()
args.train_batch_size = (args.per_gpu_train_batch_size * max(1, args.n_gpu))
print(f'Local Rank = {args.local_rank}')
print(len(train_dataset))
train_sampler = (RandomSampler(train_dataset) if (args.local_rank == (- 1)) else DistributedSampler(train_dataset))
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
if (args.max_steps > 0):
t_total = args.max_steps
args.num_train_epochs = ((args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps)) + 1)
else:
t_total = ((len(train_dataloader) // args.gradient_accumulation_steps) * args.num_train_epochs)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{'params': [p for (n, p) in model.named_parameters() if (not any(((nd in n) for nd in no_decay)))], 'weight_decay': args.weight_decay}, {'params': [p for (n, p) in model.named_parameters() if any(((nd in n) for nd in no_decay))], 'weight_decay': 0.0}]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
logging.info([n for (n, p) in model.named_parameters() if p.requires_grad])
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError('Please install apex from https://www.github.com/nvidia/apex to use fp16 training.')
(model, optimizer) = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
if (args.n_gpu > 1):
model = torch.nn.DataParallel(model)
if (args.local_rank != (- 1)):
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True)
logger.info('***** Running training *****')
logger.info(' Num examples = %d', len(train_dataset))
logger.info(' Num Epochs = %d', args.num_train_epochs)
logger.info(' Instantaneous batch size per GPU = %d', args.per_gpu_train_batch_size)
logger.info(' Total train batch size (w. parallel, distributed & accumulation) = %d', ((args.train_batch_size * args.gradient_accumulation_steps) * (torch.distributed.get_world_size() if (args.local_rank != (- 1)) else 1)))
logger.info(' Gradient Accumulation steps = %d', args.gradient_accumulation_steps)
logger.info(' Total optimization steps = %d', t_total)
best_score = 0.0
best_checkpoint = None
patience = 0
global_step = 0
(tr_loss, logging_loss) = (0.0, 0.0)
model.zero_grad()
train_iterator = trange(int(args.num_train_epochs), desc='Epoch', disable=(args.local_rank not in [(- 1), 0]))
set_seed(args)
cur_epoch = 0
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc='Iteration', disable=(args.local_rank not in [(- 1), 0]))
cur_epoch += 1
for (step, batch) in enumerate(epoch_iterator):
batch = tuple((t.to(args.device) for t in batch if (t is not None)))
inputs = {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if (args.model_type != 'distilbert'):
inputs['token_type_ids'] = (batch[2] if (args.model_type in ['bert', 'xlnet']) else None)
if (args.model_type == 'xlm'):
inputs['langs'] = batch[4]
outputs = model(**inputs)
loss = outputs[0]
if (args.n_gpu > 1):
loss = loss.mean()
if (args.gradient_accumulation_steps > 1):
loss = (loss / args.gradient_accumulation_steps)
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
if (((step + 1) % args.gradient_accumulation_steps) == 0):
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
scheduler.step()
optimizer.step()
model.zero_grad()
global_step += 1
if ((args.local_rank in [(- 1), 0]) and (args.logging_steps > 0) and ((global_step % args.logging_steps) == 0)):
if ((args.local_rank == (- 1)) and args.evaluate_during_training):
(results, _) = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode='dev', lang=args.train_langs, lang2id=lang2id, lang_adapter_names=lang_adapter_names, task_name=task_name)
for (key, value) in results.items():
tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
tb_writer.add_scalar('loss', ((tr_loss - logging_loss) / args.logging_steps), global_step)
logging_loss = tr_loss
if ((args.local_rank in [(- 1), 0]) and (args.save_steps > 0) and ((global_step % args.save_steps) == 0)):
if args.save_only_best_checkpoint:
(result, _) = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode='dev', prefix=global_step, lang=args.train_langs, lang2id=lang2id, lang_adapter_names=lang_adapter_names, task_name=task_name)
if (result['f1'] > best_score):
logger.info("result['f1']={} > best_score={}".format(result['f1'], best_score))
best_score = result['f1']
output_dir = os.path.join(args.output_dir, 'checkpoint-best')
best_checkpoint = output_dir
if (not os.path.exists(output_dir)):
os.makedirs(output_dir)
model_to_save = (model.module if hasattr(model, 'module') else model)
if args.do_save_adapters:
model_to_save.save_all_adapters(output_dir)
if args.do_save_adapter_fusions:
model_to_save.save_all_adapter_fusions(output_dir)
if args.do_save_full_model:
model_to_save.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, 'training_args.bin'))
logger.info('Saving the best model checkpoint to %s', output_dir)
logger.info('Reset patience to 0')
patience = 0
else:
patience += 1
logger.info('Hit patience={}'.format(patience))
if ((args.eval_patience > 0) and (patience > args.eval_patience)):
logger.info('early stop! patience={}'.format(patience))
epoch_iterator.close()
train_iterator.close()
if (args.local_rank in [(- 1), 0]):
tb_writer.close()
return (global_step, (tr_loss / global_step))
else:
output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
if (not os.path.exists(output_dir)):
os.makedirs(output_dir)
model_to_save = (model.module if hasattr(model, 'module') else model)
if args.do_save_adapters:
model_to_save.save_all_adapters(output_dir)
if args.do_save_adapter_fusions:
model_to_save.save_all_adapter_fusions(output_dir)
if args.do_save_full_model:
model_to_save.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, 'training_args.bin'))
logger.info('Saving model checkpoint to %s', output_dir)
if ((args.max_steps > 0) and (global_step > args.max_steps)):
epoch_iterator.close()
break
if ((args.max_steps > 0) and (global_step > args.max_steps)):
train_iterator.close()
break
if (args.local_rank in [(- 1), 0]):
tb_writer.close()
return (global_step, (tr_loss / global_step)) |
def _find_all_hints_in_graph_def(session):
'Look at the current default graph and return a list of LiteFuncCall objs.\n\n Args:\n session: A TensorFlow session that contains the graph to convert.\n Returns:\n a list of `LifeFuncCall` objects in the form\n\n '
func_calls = _collections.defaultdict(_LiteFuncCall)
seen_ops = set()
for op in session.graph.get_operations():
for operand in _itertools.chain(op.inputs, op.outputs):
if (operand in seen_ops):
continue
seen_ops.add(operand)
attr = operand.op.node_def.attr
uuid = attr[OpHint.FUNCTION_UUID_ATTR].s
if (OpHint.FUNCTION_UUID_ATTR not in attr):
continue
call_def = func_calls[uuid]
call_def.uuid = uuid
if (OpHint.FUNCTION_UUID_ATTR in attr):
call_def.function_name = attr[OpHint.FUNCTION_NAME_ATTR].s
if (OpHint.FUNCTION_INPUT_INDEX_ATTR in attr):
call_def.inputs[attr[OpHint.FUNCTION_INPUT_INDEX_ATTR].i] = operand
if (OpHint.FUNCTION_OUTPUT_INDEX_ATTR in attr):
call_def.outputs[attr[OpHint.FUNCTION_OUTPUT_INDEX_ATTR].i] = operand
for a in attr:
if a.startswith('_tflite_attr_'):
call_def.params[a.replace('_tflite_attr_,', '')] = attr[a].tensor
return func_calls | 7,412,164,229,717,128,000 | Look at the current default graph and return a list of LiteFuncCall objs.
Args:
session: A TensorFlow session that contains the graph to convert.
Returns:
a list of `LifeFuncCall` objects in the form | tensorflow/contrib/lite/python/op_hint.py | _find_all_hints_in_graph_def | 188080501/tensorflow | python | def _find_all_hints_in_graph_def(session):
'Look at the current default graph and return a list of LiteFuncCall objs.\n\n Args:\n session: A TensorFlow session that contains the graph to convert.\n Returns:\n a list of `LifeFuncCall` objects in the form\n\n '
func_calls = _collections.defaultdict(_LiteFuncCall)
seen_ops = set()
for op in session.graph.get_operations():
for operand in _itertools.chain(op.inputs, op.outputs):
if (operand in seen_ops):
continue
seen_ops.add(operand)
attr = operand.op.node_def.attr
uuid = attr[OpHint.FUNCTION_UUID_ATTR].s
if (OpHint.FUNCTION_UUID_ATTR not in attr):
continue
call_def = func_calls[uuid]
call_def.uuid = uuid
if (OpHint.FUNCTION_UUID_ATTR in attr):
call_def.function_name = attr[OpHint.FUNCTION_NAME_ATTR].s
if (OpHint.FUNCTION_INPUT_INDEX_ATTR in attr):
call_def.inputs[attr[OpHint.FUNCTION_INPUT_INDEX_ATTR].i] = operand
if (OpHint.FUNCTION_OUTPUT_INDEX_ATTR in attr):
call_def.outputs[attr[OpHint.FUNCTION_OUTPUT_INDEX_ATTR].i] = operand
for a in attr:
if a.startswith('_tflite_attr_'):
call_def.params[a.replace('_tflite_attr_,', )] = attr[a].tensor
return func_calls |
def _tensor_name_base(full_tensor_name):
'Removes the device assignment code from a tensor.\n\n e.g. _tensor_name_base("foo:3") => "foo"\n\n Args:\n full_tensor_name: A tensor name that is annotated with a device placement\n (this is what tensor flow introspection gives).\n Returns:\n A name without any device assignment.\n '
return full_tensor_name.name.split(':')[0] | -9,004,534,146,274,701,000 | Removes the device assignment code from a tensor.
e.g. _tensor_name_base("foo:3") => "foo"
Args:
full_tensor_name: A tensor name that is annotated with a device placement
(this is what tensor flow introspection gives).
Returns:
A name without any device assignment. | tensorflow/contrib/lite/python/op_hint.py | _tensor_name_base | 188080501/tensorflow | python | def _tensor_name_base(full_tensor_name):
'Removes the device assignment code from a tensor.\n\n e.g. _tensor_name_base("foo:3") => "foo"\n\n Args:\n full_tensor_name: A tensor name that is annotated with a device placement\n (this is what tensor flow introspection gives).\n Returns:\n A name without any device assignment.\n '
return full_tensor_name.name.split(':')[0] |
def convert_op_hints_to_stubs(session):
'Converts a graphdef with LiteOp hints into stub operations.\n\n This is used to prepare for toco conversion of complex intrinsic usages.\n\n Args:\n session: A TensorFlow session that contains the graph to convert.\n Returns:\n A new graphdef with all ops contained in OpHints being replaced by\n a single op call with the right parameters.\n '
hints = _find_all_hints_in_graph_def(session)
current_graph_def = session.graph_def
for call in hints.values():
input_names = ([None] * len(call.inputs))
output_names = ([None] * len(call.outputs))
output_dtypes = ([None] * len(call.outputs))
output_quantized = False
for (input_index, tensor) in call.inputs.items():
input_names[input_index] = _tensor_name_base(tensor)
for (output_index, tensor) in call.outputs.items():
output_names[output_index] = _tensor_name_base(tensor)
output_dtypes[output_index] = tensor.dtype.as_datatype_enum
current_graph_def = _framework.fuse_op(current_graph_def, input_names, output_names, output_dtypes, output_quantized, call.uuid, call.function_name)
for node in current_graph_def.node:
if (node.name == call.uuid):
for (param, tensor) in call.params.items():
node.attr[param].tensor.CopyFrom(tensor)
return current_graph_def | 545,267,334,812,460,350 | Converts a graphdef with LiteOp hints into stub operations.
This is used to prepare for toco conversion of complex intrinsic usages.
Args:
session: A TensorFlow session that contains the graph to convert.
Returns:
A new graphdef with all ops contained in OpHints being replaced by
a single op call with the right parameters. | tensorflow/contrib/lite/python/op_hint.py | convert_op_hints_to_stubs | 188080501/tensorflow | python | def convert_op_hints_to_stubs(session):
'Converts a graphdef with LiteOp hints into stub operations.\n\n This is used to prepare for toco conversion of complex intrinsic usages.\n\n Args:\n session: A TensorFlow session that contains the graph to convert.\n Returns:\n A new graphdef with all ops contained in OpHints being replaced by\n a single op call with the right parameters.\n '
hints = _find_all_hints_in_graph_def(session)
current_graph_def = session.graph_def
for call in hints.values():
input_names = ([None] * len(call.inputs))
output_names = ([None] * len(call.outputs))
output_dtypes = ([None] * len(call.outputs))
output_quantized = False
for (input_index, tensor) in call.inputs.items():
input_names[input_index] = _tensor_name_base(tensor)
for (output_index, tensor) in call.outputs.items():
output_names[output_index] = _tensor_name_base(tensor)
output_dtypes[output_index] = tensor.dtype.as_datatype_enum
current_graph_def = _framework.fuse_op(current_graph_def, input_names, output_names, output_dtypes, output_quantized, call.uuid, call.function_name)
for node in current_graph_def.node:
if (node.name == call.uuid):
for (param, tensor) in call.params.items():
node.attr[param].tensor.CopyFrom(tensor)
return current_graph_def |
def __init__(self, function_name, **kwargs):
'Create a OpHint.\n\n Args:\n function_name: Name of the function (the custom op name in tflite)\n **kwargs: Keyword arguments of any constant attributes for the function.\n '
self._function_name = function_name
self._unique_function_id = _uuid.uuid1().hex
self._curr_input_index = 0
self._curr_output_index = 0
self._attrs_to_store_later = kwargs
self._stored_attrs = False | 2,070,700,012,877,376,300 | Create a OpHint.
Args:
function_name: Name of the function (the custom op name in tflite)
**kwargs: Keyword arguments of any constant attributes for the function. | tensorflow/contrib/lite/python/op_hint.py | __init__ | 188080501/tensorflow | python | def __init__(self, function_name, **kwargs):
'Create a OpHint.\n\n Args:\n function_name: Name of the function (the custom op name in tflite)\n **kwargs: Keyword arguments of any constant attributes for the function.\n '
self._function_name = function_name
self._unique_function_id = _uuid.uuid1().hex
self._curr_input_index = 0
self._curr_output_index = 0
self._attrs_to_store_later = kwargs
self._stored_attrs = False |
def add_inputs(self, *args):
"Add a sequence of inputs to the function invocation.\n\n Args:\n *args: List of inputs to be converted (should be Tf.Tensor).\n Returns:\n Wrapped inputs (identity standins that have additional metadata). These\n are also are also tf.Tensor's.\n "
def augmented_identity(arg):
identity_op = _array_ops.identity(arg)
identity_op.op._set_attr(OpHint.FUNCTION_NAME_ATTR, _attr_value_pb2.AttrValue(s=self._function_name))
identity_op.op._set_attr(OpHint.FUNCTION_UUID_ATTR, _attr_value_pb2.AttrValue(s=self._unique_function_id))
identity_op.op._set_attr(OpHint.FUNCTION_INPUT_INDEX_ATTR, _attr_value_pb2.AttrValue(i=self._curr_input_index))
self._curr_input_index += 1
return identity_op
return [augmented_identity(arg) for arg in args] | -2,426,469,873,050,694,700 | Add a sequence of inputs to the function invocation.
Args:
*args: List of inputs to be converted (should be Tf.Tensor).
Returns:
Wrapped inputs (identity standins that have additional metadata). These
are also are also tf.Tensor's. | tensorflow/contrib/lite/python/op_hint.py | add_inputs | 188080501/tensorflow | python | def add_inputs(self, *args):
"Add a sequence of inputs to the function invocation.\n\n Args:\n *args: List of inputs to be converted (should be Tf.Tensor).\n Returns:\n Wrapped inputs (identity standins that have additional metadata). These\n are also are also tf.Tensor's.\n "
def augmented_identity(arg):
identity_op = _array_ops.identity(arg)
identity_op.op._set_attr(OpHint.FUNCTION_NAME_ATTR, _attr_value_pb2.AttrValue(s=self._function_name))
identity_op.op._set_attr(OpHint.FUNCTION_UUID_ATTR, _attr_value_pb2.AttrValue(s=self._unique_function_id))
identity_op.op._set_attr(OpHint.FUNCTION_INPUT_INDEX_ATTR, _attr_value_pb2.AttrValue(i=self._curr_input_index))
self._curr_input_index += 1
return identity_op
return [augmented_identity(arg) for arg in args] |
def add_outputs(self, *args):
"Add a sequence of outputs to the function invocation.\n\n Args:\n *args: List of outputs to be converted (should be tf.Tensor).\n Returns:\n Wrapped outputs (identity standins that have additional metadata). These\n are also tf.Tensor's.\n "
def augmented_identity(arg):
identity_op = _array_ops.identity(arg)
identity_op.op._set_attr(OpHint.FUNCTION_NAME_ATTR, _attr_value_pb2.AttrValue(s=self._function_name))
identity_op.op._set_attr(OpHint.FUNCTION_UUID_ATTR, _attr_value_pb2.AttrValue(s=self._unique_function_id))
identity_op.op._set_attr(OpHint.FUNCTION_OUTPUT_INDEX_ATTR, _attr_value_pb2.AttrValue(i=self._curr_output_index))
self._curr_output_index += 1
return identity_op
wrapped_outputs = [augmented_identity(arg) for arg in args]
if (not self._stored_attrs):
for (key, value) in self._attrs_to_store_later.iteritems():
self._setattr(wrapped_outputs[0], ('_tflite_attr_' + key), value)
self._stored_attrs = True
return wrapped_outputs | -7,205,941,043,342,234,000 | Add a sequence of outputs to the function invocation.
Args:
*args: List of outputs to be converted (should be tf.Tensor).
Returns:
Wrapped outputs (identity standins that have additional metadata). These
are also tf.Tensor's. | tensorflow/contrib/lite/python/op_hint.py | add_outputs | 188080501/tensorflow | python | def add_outputs(self, *args):
"Add a sequence of outputs to the function invocation.\n\n Args:\n *args: List of outputs to be converted (should be tf.Tensor).\n Returns:\n Wrapped outputs (identity standins that have additional metadata). These\n are also tf.Tensor's.\n "
def augmented_identity(arg):
identity_op = _array_ops.identity(arg)
identity_op.op._set_attr(OpHint.FUNCTION_NAME_ATTR, _attr_value_pb2.AttrValue(s=self._function_name))
identity_op.op._set_attr(OpHint.FUNCTION_UUID_ATTR, _attr_value_pb2.AttrValue(s=self._unique_function_id))
identity_op.op._set_attr(OpHint.FUNCTION_OUTPUT_INDEX_ATTR, _attr_value_pb2.AttrValue(i=self._curr_output_index))
self._curr_output_index += 1
return identity_op
wrapped_outputs = [augmented_identity(arg) for arg in args]
if (not self._stored_attrs):
for (key, value) in self._attrs_to_store_later.iteritems():
self._setattr(wrapped_outputs[0], ('_tflite_attr_' + key), value)
self._stored_attrs = True
return wrapped_outputs |
def extract(infile):
'\n Merges bioindex.tsv with the infile (balanced data),\n finds the volsplit.zip location for each bio file and \n extracts the files into secure_volume/holding_folder.\n '
bioindex = pd.read_csv('/media/secure_volume/index/bioindex.tsv', sep='\t')
balanced_bioindex = pd.read_table(infile)
for suffix in balanced_bioindex.filesuffix.unique():
volsplit_file = (('volsplit' + str(suffix)) + '.zip')
volsplit_df = balanced_bioindex.loc[(balanced_bioindex.filesuffix == suffix), :]
try:
with zipfile.ZipFile(('/media/secure_volume/' + volsplit_file), 'r') as myzip:
for (idx, row) in volsplit_df.iterrows():
filename = (row['mainid'] + '.zip')
myzip.extract(filename, '/media/secure_volume/holding_folder')
except Exception as e:
print('ERROR:', filename, 'not found in', volsplit_file, '!', e) | -1,507,047,250,928,302,000 | Merges bioindex.tsv with the infile (balanced data),
finds the volsplit.zip location for each bio file and
extracts the files into secure_volume/holding_folder. | code/extract_balanced.py | extract | afcarl/biographies | python | def extract(infile):
'\n Merges bioindex.tsv with the infile (balanced data),\n finds the volsplit.zip location for each bio file and \n extracts the files into secure_volume/holding_folder.\n '
bioindex = pd.read_csv('/media/secure_volume/index/bioindex.tsv', sep='\t')
balanced_bioindex = pd.read_table(infile)
for suffix in balanced_bioindex.filesuffix.unique():
volsplit_file = (('volsplit' + str(suffix)) + '.zip')
volsplit_df = balanced_bioindex.loc[(balanced_bioindex.filesuffix == suffix), :]
try:
with zipfile.ZipFile(('/media/secure_volume/' + volsplit_file), 'r') as myzip:
for (idx, row) in volsplit_df.iterrows():
filename = (row['mainid'] + '.zip')
myzip.extract(filename, '/media/secure_volume/holding_folder')
except Exception as e:
print('ERROR:', filename, 'not found in', volsplit_file, '!', e) |
@task
@with_validation
def generate(directory=None):
'\n Generate configuration files.\n '
for conffiles in iter_conffiles(directory):
status("Generating templates for '{environment}' and '{role}'", environment=conffiles.environment, role=conffiles.role)
conffiles.generate() | -2,122,800,150,191,893,500 | Generate configuration files. | confab/generate.py | generate | locationlabs/confab | python | @task
@with_validation
def generate(directory=None):
'\n \n '
for conffiles in iter_conffiles(directory):
status("Generating templates for '{environment}' and '{role}'", environment=conffiles.environment, role=conffiles.role)
conffiles.generate() |
def test_overdue_habit(datasett):
"\n please note the 'double tt' for datasett. This stands to differentiate\n the functional test data from the data used for unit tests.\n habit 1 is the overdue habit since its added first in the func/conftest\n module.\n :param datasett: from func/conftest\n :return:\n "
session = datasett
complete(1, session)
result = session.query(HabitHistory.broken_count).filter((HabitHistory.habitid == 1)).all()
assert (result == [(1,)]) | 1,522,588,135,354,832,000 | please note the 'double tt' for datasett. This stands to differentiate
the functional test data from the data used for unit tests.
habit 1 is the overdue habit since its added first in the func/conftest
module.
:param datasett: from func/conftest
:return: | tests/func/test_complete_habit.py | test_overdue_habit | takavarasha-desire/habittracker1_1 | python | def test_overdue_habit(datasett):
"\n please note the 'double tt' for datasett. This stands to differentiate\n the functional test data from the data used for unit tests.\n habit 1 is the overdue habit since its added first in the func/conftest\n module.\n :param datasett: from func/conftest\n :return:\n "
session = datasett
complete(1, session)
result = session.query(HabitHistory.broken_count).filter((HabitHistory.habitid == 1)).all()
assert (result == [(1,)]) |
def test_a_habit_due_for_completion(datasett):
'\n habit 2 is the due habit since its added second in the func/conftest\n module.\n :param datasett: from func/conftest\n :return:\n '
session = datasett
complete(2, session)
result = session.query(HabitHistory.streak).filter((HabitHistory.habitid == 2)).all()
assert (result == [(1,)]) | 3,921,509,153,605,490,000 | habit 2 is the due habit since its added second in the func/conftest
module.
:param datasett: from func/conftest
:return: | tests/func/test_complete_habit.py | test_a_habit_due_for_completion | takavarasha-desire/habittracker1_1 | python | def test_a_habit_due_for_completion(datasett):
'\n habit 2 is the due habit since its added second in the func/conftest\n module.\n :param datasett: from func/conftest\n :return:\n '
session = datasett
complete(2, session)
result = session.query(HabitHistory.streak).filter((HabitHistory.habitid == 2)).all()
assert (result == [(1,)]) |
def __init__(self, *, hass, logger, domain, platform_name, platform, scan_interval, entity_namespace, async_entities_added_callback):
'Initialize the entity platform.\n\n hass: HomeAssistant\n logger: Logger\n domain: str\n platform_name: str\n scan_interval: timedelta\n entity_namespace: str\n async_entities_added_callback: @callback method\n '
self.hass = hass
self.logger = logger
self.domain = domain
self.platform_name = platform_name
self.platform = platform
self.scan_interval = scan_interval
self.entity_namespace = entity_namespace
self.async_entities_added_callback = async_entities_added_callback
self.config_entry = None
self.entities = {}
self._tasks = []
self._async_unsub_polling = None
self._async_cancel_retry_setup = None
self._process_updates = asyncio.Lock()
if (platform is None):
self.parallel_updates = None
self.parallel_updates_semaphore = None
return
self.parallel_updates = getattr(platform, 'PARALLEL_UPDATES', None)
self.parallel_updates_semaphore = None | -3,546,419,058,523,400,000 | Initialize the entity platform.
hass: HomeAssistant
logger: Logger
domain: str
platform_name: str
scan_interval: timedelta
entity_namespace: str
async_entities_added_callback: @callback method | homeassistant/helpers/entity_platform.py | __init__ | crazyfish1111/home-assistant | python | def __init__(self, *, hass, logger, domain, platform_name, platform, scan_interval, entity_namespace, async_entities_added_callback):
'Initialize the entity platform.\n\n hass: HomeAssistant\n logger: Logger\n domain: str\n platform_name: str\n scan_interval: timedelta\n entity_namespace: str\n async_entities_added_callback: @callback method\n '
self.hass = hass
self.logger = logger
self.domain = domain
self.platform_name = platform_name
self.platform = platform
self.scan_interval = scan_interval
self.entity_namespace = entity_namespace
self.async_entities_added_callback = async_entities_added_callback
self.config_entry = None
self.entities = {}
self._tasks = []
self._async_unsub_polling = None
self._async_cancel_retry_setup = None
self._process_updates = asyncio.Lock()
if (platform is None):
self.parallel_updates = None
self.parallel_updates_semaphore = None
return
self.parallel_updates = getattr(platform, 'PARALLEL_UPDATES', None)
self.parallel_updates_semaphore = None |
def _get_parallel_updates_semaphore(self):
'Get or create a semaphore for parallel updates.'
if (self.parallel_updates_semaphore is None):
self.parallel_updates_semaphore = asyncio.Semaphore((self.parallel_updates if self.parallel_updates else 1), loop=self.hass.loop)
return self.parallel_updates_semaphore | 2,508,172,302,676,324,400 | Get or create a semaphore for parallel updates. | homeassistant/helpers/entity_platform.py | _get_parallel_updates_semaphore | crazyfish1111/home-assistant | python | def _get_parallel_updates_semaphore(self):
if (self.parallel_updates_semaphore is None):
self.parallel_updates_semaphore = asyncio.Semaphore((self.parallel_updates if self.parallel_updates else 1), loop=self.hass.loop)
return self.parallel_updates_semaphore |
async def async_setup(self, platform_config, discovery_info=None):
'Set up the platform from a config file.'
platform = self.platform
hass = self.hass
@callback
def async_create_setup_task():
'Get task to set up platform.'
if getattr(platform, 'async_setup_platform', None):
return platform.async_setup_platform(hass, platform_config, self._async_schedule_add_entities, discovery_info)
return hass.loop.run_in_executor(None, platform.setup_platform, hass, platform_config, self._schedule_add_entities, discovery_info)
(await self._async_setup_platform(async_create_setup_task)) | 6,370,612,533,691,341,000 | Set up the platform from a config file. | homeassistant/helpers/entity_platform.py | async_setup | crazyfish1111/home-assistant | python | async def async_setup(self, platform_config, discovery_info=None):
platform = self.platform
hass = self.hass
@callback
def async_create_setup_task():
'Get task to set up platform.'
if getattr(platform, 'async_setup_platform', None):
return platform.async_setup_platform(hass, platform_config, self._async_schedule_add_entities, discovery_info)
return hass.loop.run_in_executor(None, platform.setup_platform, hass, platform_config, self._schedule_add_entities, discovery_info)
(await self._async_setup_platform(async_create_setup_task)) |
async def async_setup_entry(self, config_entry):
'Set up the platform from a config entry.'
self.config_entry = config_entry
platform = self.platform
@callback
def async_create_setup_task():
'Get task to set up platform.'
return platform.async_setup_entry(self.hass, config_entry, self._async_schedule_add_entities)
return (await self._async_setup_platform(async_create_setup_task)) | 75,007,586,245,701,980 | Set up the platform from a config entry. | homeassistant/helpers/entity_platform.py | async_setup_entry | crazyfish1111/home-assistant | python | async def async_setup_entry(self, config_entry):
self.config_entry = config_entry
platform = self.platform
@callback
def async_create_setup_task():
'Get task to set up platform.'
return platform.async_setup_entry(self.hass, config_entry, self._async_schedule_add_entities)
return (await self._async_setup_platform(async_create_setup_task)) |
async def _async_setup_platform(self, async_create_setup_task, tries=0):
'Set up a platform via config file or config entry.\n\n async_create_setup_task creates a coroutine that sets up platform.\n '
logger = self.logger
hass = self.hass
full_name = '{}.{}'.format(self.domain, self.platform_name)
logger.info('Setting up %s', full_name)
warn_task = hass.loop.call_later(SLOW_SETUP_WARNING, logger.warning, 'Setup of platform %s is taking over %s seconds.', self.platform_name, SLOW_SETUP_WARNING)
try:
task = async_create_setup_task()
(await asyncio.wait_for(asyncio.shield(task), SLOW_SETUP_MAX_WAIT))
if self._tasks:
pending = [task for task in self._tasks if (not task.done())]
self._tasks.clear()
if pending:
(await asyncio.wait(pending))
hass.config.components.add(full_name)
return True
except PlatformNotReady:
tries += 1
wait_time = (min(tries, 6) * 30)
logger.warning('Platform %s not ready yet. Retrying in %d seconds.', self.platform_name, wait_time)
async def setup_again(now):
'Run setup again.'
self._async_cancel_retry_setup = None
(await self._async_setup_platform(async_create_setup_task, tries))
self._async_cancel_retry_setup = async_call_later(hass, wait_time, setup_again)
return False
except asyncio.TimeoutError:
logger.error('Setup of platform %s is taking longer than %s seconds. Startup will proceed without waiting any longer.', self.platform_name, SLOW_SETUP_MAX_WAIT)
return False
except Exception:
logger.exception('Error while setting up platform %s', self.platform_name)
return False
finally:
warn_task.cancel() | -8,883,834,158,884,943,000 | Set up a platform via config file or config entry.
async_create_setup_task creates a coroutine that sets up platform. | homeassistant/helpers/entity_platform.py | _async_setup_platform | crazyfish1111/home-assistant | python | async def _async_setup_platform(self, async_create_setup_task, tries=0):
'Set up a platform via config file or config entry.\n\n async_create_setup_task creates a coroutine that sets up platform.\n '
logger = self.logger
hass = self.hass
full_name = '{}.{}'.format(self.domain, self.platform_name)
logger.info('Setting up %s', full_name)
warn_task = hass.loop.call_later(SLOW_SETUP_WARNING, logger.warning, 'Setup of platform %s is taking over %s seconds.', self.platform_name, SLOW_SETUP_WARNING)
try:
task = async_create_setup_task()
(await asyncio.wait_for(asyncio.shield(task), SLOW_SETUP_MAX_WAIT))
if self._tasks:
pending = [task for task in self._tasks if (not task.done())]
self._tasks.clear()
if pending:
(await asyncio.wait(pending))
hass.config.components.add(full_name)
return True
except PlatformNotReady:
tries += 1
wait_time = (min(tries, 6) * 30)
logger.warning('Platform %s not ready yet. Retrying in %d seconds.', self.platform_name, wait_time)
async def setup_again(now):
'Run setup again.'
self._async_cancel_retry_setup = None
(await self._async_setup_platform(async_create_setup_task, tries))
self._async_cancel_retry_setup = async_call_later(hass, wait_time, setup_again)
return False
except asyncio.TimeoutError:
logger.error('Setup of platform %s is taking longer than %s seconds. Startup will proceed without waiting any longer.', self.platform_name, SLOW_SETUP_MAX_WAIT)
return False
except Exception:
logger.exception('Error while setting up platform %s', self.platform_name)
return False
finally:
warn_task.cancel() |
def _schedule_add_entities(self, new_entities, update_before_add=False):
'Schedule adding entities for a single platform, synchronously.'
run_callback_threadsafe(self.hass.loop, self._async_schedule_add_entities, list(new_entities), update_before_add).result() | 7,908,124,374,192,280,000 | Schedule adding entities for a single platform, synchronously. | homeassistant/helpers/entity_platform.py | _schedule_add_entities | crazyfish1111/home-assistant | python | def _schedule_add_entities(self, new_entities, update_before_add=False):
run_callback_threadsafe(self.hass.loop, self._async_schedule_add_entities, list(new_entities), update_before_add).result() |
@callback
def _async_schedule_add_entities(self, new_entities, update_before_add=False):
'Schedule adding entities for a single platform async.'
self._tasks.append(self.hass.async_add_job(self.async_add_entities(new_entities, update_before_add=update_before_add))) | 6,827,352,441,585,063,000 | Schedule adding entities for a single platform async. | homeassistant/helpers/entity_platform.py | _async_schedule_add_entities | crazyfish1111/home-assistant | python | @callback
def _async_schedule_add_entities(self, new_entities, update_before_add=False):
self._tasks.append(self.hass.async_add_job(self.async_add_entities(new_entities, update_before_add=update_before_add))) |
def add_entities(self, new_entities, update_before_add=False):
'Add entities for a single platform.'
if update_before_add:
self.logger.warning("Call 'add_entities' with update_before_add=True only inside tests or you can run into a deadlock!")
run_coroutine_threadsafe(self.async_add_entities(list(new_entities), update_before_add), self.hass.loop).result() | -443,141,501,391,420,860 | Add entities for a single platform. | homeassistant/helpers/entity_platform.py | add_entities | crazyfish1111/home-assistant | python | def add_entities(self, new_entities, update_before_add=False):
if update_before_add:
self.logger.warning("Call 'add_entities' with update_before_add=True only inside tests or you can run into a deadlock!")
run_coroutine_threadsafe(self.async_add_entities(list(new_entities), update_before_add), self.hass.loop).result() |
async def async_add_entities(self, new_entities, update_before_add=False):
'Add entities for a single platform async.\n\n This method must be run in the event loop.\n '
if (not new_entities):
return
hass = self.hass
device_registry = (await hass.helpers.device_registry.async_get_registry())
entity_registry = (await hass.helpers.entity_registry.async_get_registry())
tasks = [self._async_add_entity(entity, update_before_add, entity_registry, device_registry) for entity in new_entities]
if (not tasks):
return
(await asyncio.wait(tasks))
self.async_entities_added_callback()
if ((self._async_unsub_polling is not None) or (not any((entity.should_poll for entity in self.entities.values())))):
return
self._async_unsub_polling = async_track_time_interval(self.hass, self._update_entity_states, self.scan_interval) | -4,472,886,937,978,459,600 | Add entities for a single platform async.
This method must be run in the event loop. | homeassistant/helpers/entity_platform.py | async_add_entities | crazyfish1111/home-assistant | python | async def async_add_entities(self, new_entities, update_before_add=False):
'Add entities for a single platform async.\n\n This method must be run in the event loop.\n '
if (not new_entities):
return
hass = self.hass
device_registry = (await hass.helpers.device_registry.async_get_registry())
entity_registry = (await hass.helpers.entity_registry.async_get_registry())
tasks = [self._async_add_entity(entity, update_before_add, entity_registry, device_registry) for entity in new_entities]
if (not tasks):
return
(await asyncio.wait(tasks))
self.async_entities_added_callback()
if ((self._async_unsub_polling is not None) or (not any((entity.should_poll for entity in self.entities.values())))):
return
self._async_unsub_polling = async_track_time_interval(self.hass, self._update_entity_states, self.scan_interval) |
async def _async_add_entity(self, entity, update_before_add, entity_registry, device_registry):
'Add an entity to the platform.'
if (entity is None):
raise ValueError('Entity cannot be None')
entity.hass = self.hass
entity.platform = self
if (hasattr(entity, 'async_update') and (not self.parallel_updates)):
entity.parallel_updates = None
elif ((not hasattr(entity, 'async_update')) and (self.parallel_updates == 0)):
entity.parallel_updates = None
else:
entity.parallel_updates = self._get_parallel_updates_semaphore()
if update_before_add:
try:
(await entity.async_device_update(warning=False))
except Exception:
self.logger.exception('%s: Error on device update!', self.platform_name)
return
suggested_object_id = None
if (entity.unique_id is not None):
if (entity.entity_id is not None):
suggested_object_id = split_entity_id(entity.entity_id)[1]
else:
suggested_object_id = entity.name
if (self.entity_namespace is not None):
suggested_object_id = '{} {}'.format(self.entity_namespace, suggested_object_id)
if (self.config_entry is not None):
config_entry_id = self.config_entry.entry_id
else:
config_entry_id = None
device_info = entity.device_info
device_id = None
if ((config_entry_id is not None) and (device_info is not None)):
processed_dev_info = {'config_entry_id': config_entry_id}
for key in ('connections', 'identifiers', 'manufacturer', 'model', 'name', 'sw_version', 'via_hub'):
if (key in device_info):
processed_dev_info[key] = device_info[key]
device = device_registry.async_get_or_create(**processed_dev_info)
if device:
device_id = device.id
entry = entity_registry.async_get_or_create(self.domain, self.platform_name, entity.unique_id, suggested_object_id=suggested_object_id, config_entry_id=config_entry_id, device_id=device_id, known_object_ids=self.entities.keys())
if entry.disabled:
self.logger.info("Not adding entity %s because it's disabled", (entry.name or entity.name or '"{} {}"'.format(self.platform_name, entity.unique_id)))
return
entity.entity_id = entry.entity_id
entity.registry_name = entry.name
entity.async_on_remove(entry.add_update_listener(entity))
elif ((entity.entity_id is not None) and entity_registry.async_is_registered(entity.entity_id)):
suggested_object_id = split_entity_id(entity.entity_id)[1]
entity.entity_id = None
if (entity.entity_id is None):
suggested_object_id = (suggested_object_id or entity.name or DEVICE_DEFAULT_NAME)
if (self.entity_namespace is not None):
suggested_object_id = '{} {}'.format(self.entity_namespace, suggested_object_id)
entity.entity_id = entity_registry.async_generate_entity_id(self.domain, suggested_object_id, self.entities.keys())
if (not valid_entity_id(entity.entity_id)):
raise HomeAssistantError('Invalid entity id: {}'.format(entity.entity_id))
if ((entity.entity_id in self.entities) or (entity.entity_id in self.hass.states.async_entity_ids(self.domain))):
msg = 'Entity id already exists: {}'.format(entity.entity_id)
if (entity.unique_id is not None):
msg += '. Platform {} does not generate unique IDs'.format(self.platform_name)
raise HomeAssistantError(msg)
entity_id = entity.entity_id
self.entities[entity_id] = entity
entity.async_on_remove((lambda : self.entities.pop(entity_id)))
(await entity.async_added_to_hass())
(await entity.async_update_ha_state()) | 530,176,300,249,078,340 | Add an entity to the platform. | homeassistant/helpers/entity_platform.py | _async_add_entity | crazyfish1111/home-assistant | python | async def _async_add_entity(self, entity, update_before_add, entity_registry, device_registry):
if (entity is None):
raise ValueError('Entity cannot be None')
entity.hass = self.hass
entity.platform = self
if (hasattr(entity, 'async_update') and (not self.parallel_updates)):
entity.parallel_updates = None
elif ((not hasattr(entity, 'async_update')) and (self.parallel_updates == 0)):
entity.parallel_updates = None
else:
entity.parallel_updates = self._get_parallel_updates_semaphore()
if update_before_add:
try:
(await entity.async_device_update(warning=False))
except Exception:
self.logger.exception('%s: Error on device update!', self.platform_name)
return
suggested_object_id = None
if (entity.unique_id is not None):
if (entity.entity_id is not None):
suggested_object_id = split_entity_id(entity.entity_id)[1]
else:
suggested_object_id = entity.name
if (self.entity_namespace is not None):
suggested_object_id = '{} {}'.format(self.entity_namespace, suggested_object_id)
if (self.config_entry is not None):
config_entry_id = self.config_entry.entry_id
else:
config_entry_id = None
device_info = entity.device_info
device_id = None
if ((config_entry_id is not None) and (device_info is not None)):
processed_dev_info = {'config_entry_id': config_entry_id}
for key in ('connections', 'identifiers', 'manufacturer', 'model', 'name', 'sw_version', 'via_hub'):
if (key in device_info):
processed_dev_info[key] = device_info[key]
device = device_registry.async_get_or_create(**processed_dev_info)
if device:
device_id = device.id
entry = entity_registry.async_get_or_create(self.domain, self.platform_name, entity.unique_id, suggested_object_id=suggested_object_id, config_entry_id=config_entry_id, device_id=device_id, known_object_ids=self.entities.keys())
if entry.disabled:
self.logger.info("Not adding entity %s because it's disabled", (entry.name or entity.name or '"{} {}"'.format(self.platform_name, entity.unique_id)))
return
entity.entity_id = entry.entity_id
entity.registry_name = entry.name
entity.async_on_remove(entry.add_update_listener(entity))
elif ((entity.entity_id is not None) and entity_registry.async_is_registered(entity.entity_id)):
suggested_object_id = split_entity_id(entity.entity_id)[1]
entity.entity_id = None
if (entity.entity_id is None):
suggested_object_id = (suggested_object_id or entity.name or DEVICE_DEFAULT_NAME)
if (self.entity_namespace is not None):
suggested_object_id = '{} {}'.format(self.entity_namespace, suggested_object_id)
entity.entity_id = entity_registry.async_generate_entity_id(self.domain, suggested_object_id, self.entities.keys())
if (not valid_entity_id(entity.entity_id)):
raise HomeAssistantError('Invalid entity id: {}'.format(entity.entity_id))
if ((entity.entity_id in self.entities) or (entity.entity_id in self.hass.states.async_entity_ids(self.domain))):
msg = 'Entity id already exists: {}'.format(entity.entity_id)
if (entity.unique_id is not None):
msg += '. Platform {} does not generate unique IDs'.format(self.platform_name)
raise HomeAssistantError(msg)
entity_id = entity.entity_id
self.entities[entity_id] = entity
entity.async_on_remove((lambda : self.entities.pop(entity_id)))
(await entity.async_added_to_hass())
(await entity.async_update_ha_state()) |
async def async_reset(self):
'Remove all entities and reset data.\n\n This method must be run in the event loop.\n '
if (self._async_cancel_retry_setup is not None):
self._async_cancel_retry_setup()
self._async_cancel_retry_setup = None
if (not self.entities):
return
tasks = [self.async_remove_entity(entity_id) for entity_id in self.entities]
(await asyncio.wait(tasks))
if (self._async_unsub_polling is not None):
self._async_unsub_polling()
self._async_unsub_polling = None | -510,075,945,936,083,100 | Remove all entities and reset data.
This method must be run in the event loop. | homeassistant/helpers/entity_platform.py | async_reset | crazyfish1111/home-assistant | python | async def async_reset(self):
'Remove all entities and reset data.\n\n This method must be run in the event loop.\n '
if (self._async_cancel_retry_setup is not None):
self._async_cancel_retry_setup()
self._async_cancel_retry_setup = None
if (not self.entities):
return
tasks = [self.async_remove_entity(entity_id) for entity_id in self.entities]
(await asyncio.wait(tasks))
if (self._async_unsub_polling is not None):
self._async_unsub_polling()
self._async_unsub_polling = None |
async def async_remove_entity(self, entity_id):
'Remove entity id from platform.'
(await self.entities[entity_id].async_remove())
if ((self._async_unsub_polling is not None) and (not any((entity.should_poll for entity in self.entities.values())))):
self._async_unsub_polling()
self._async_unsub_polling = None | -7,593,386,608,796,709,000 | Remove entity id from platform. | homeassistant/helpers/entity_platform.py | async_remove_entity | crazyfish1111/home-assistant | python | async def async_remove_entity(self, entity_id):
(await self.entities[entity_id].async_remove())
if ((self._async_unsub_polling is not None) and (not any((entity.should_poll for entity in self.entities.values())))):
self._async_unsub_polling()
self._async_unsub_polling = None |
async def _update_entity_states(self, now):
'Update the states of all the polling entities.\n\n To protect from flooding the executor, we will update async entities\n in parallel and other entities sequential.\n\n This method must be run in the event loop.\n '
if self._process_updates.locked():
self.logger.warning('Updating %s %s took longer than the scheduled update interval %s', self.platform_name, self.domain, self.scan_interval)
return
async with self._process_updates:
tasks = []
for entity in self.entities.values():
if (not entity.should_poll):
continue
tasks.append(entity.async_update_ha_state(True))
if tasks:
(await asyncio.wait(tasks)) | 7,350,641,399,040,290,000 | Update the states of all the polling entities.
To protect from flooding the executor, we will update async entities
in parallel and other entities sequential.
This method must be run in the event loop. | homeassistant/helpers/entity_platform.py | _update_entity_states | crazyfish1111/home-assistant | python | async def _update_entity_states(self, now):
'Update the states of all the polling entities.\n\n To protect from flooding the executor, we will update async entities\n in parallel and other entities sequential.\n\n This method must be run in the event loop.\n '
if self._process_updates.locked():
self.logger.warning('Updating %s %s took longer than the scheduled update interval %s', self.platform_name, self.domain, self.scan_interval)
return
async with self._process_updates:
tasks = []
for entity in self.entities.values():
if (not entity.should_poll):
continue
tasks.append(entity.async_update_ha_state(True))
if tasks:
(await asyncio.wait(tasks)) |
@callback
def async_create_setup_task():
'Get task to set up platform.'
if getattr(platform, 'async_setup_platform', None):
return platform.async_setup_platform(hass, platform_config, self._async_schedule_add_entities, discovery_info)
return hass.loop.run_in_executor(None, platform.setup_platform, hass, platform_config, self._schedule_add_entities, discovery_info) | 9,092,128,761,817,666,000 | Get task to set up platform. | homeassistant/helpers/entity_platform.py | async_create_setup_task | crazyfish1111/home-assistant | python | @callback
def async_create_setup_task():
if getattr(platform, 'async_setup_platform', None):
return platform.async_setup_platform(hass, platform_config, self._async_schedule_add_entities, discovery_info)
return hass.loop.run_in_executor(None, platform.setup_platform, hass, platform_config, self._schedule_add_entities, discovery_info) |
@callback
def async_create_setup_task():
'Get task to set up platform.'
return platform.async_setup_entry(self.hass, config_entry, self._async_schedule_add_entities) | -284,641,014,274,873,100 | Get task to set up platform. | homeassistant/helpers/entity_platform.py | async_create_setup_task | crazyfish1111/home-assistant | python | @callback
def async_create_setup_task():
return platform.async_setup_entry(self.hass, config_entry, self._async_schedule_add_entities) |
async def setup_again(now):
'Run setup again.'
self._async_cancel_retry_setup = None
(await self._async_setup_platform(async_create_setup_task, tries)) | -514,513,532,165,713,860 | Run setup again. | homeassistant/helpers/entity_platform.py | setup_again | crazyfish1111/home-assistant | python | async def setup_again(now):
self._async_cancel_retry_setup = None
(await self._async_setup_platform(async_create_setup_task, tries)) |
@with_cupy_rmm
def fit(self, X):
'\n Fit a multi-node multi-GPU KMeans model\n\n Parameters\n ----------\n X : Dask cuDF DataFrame or CuPy backed Dask Array\n Training data to cluster.\n\n '
data = DistributedDataHandler.create(X, client=self.client)
self.datatype = data.datatype
comms = CommsContext(comms_p2p=False)
comms.init(workers=data.workers)
kmeans_fit = [self.client.submit(KMeans._func_fit, comms.sessionId, wf[1], self.datatype, **self.kwargs, workers=[wf[0]], pure=False) for (idx, wf) in enumerate(data.worker_to_parts.items())]
wait(kmeans_fit)
raise_exception_from_futures(kmeans_fit)
comms.destroy()
self.local_model = kmeans_fit[0].result()
self.cluster_centers_ = self.local_model.cluster_centers_
return self | 7,721,958,996,140,420,000 | Fit a multi-node multi-GPU KMeans model
Parameters
----------
X : Dask cuDF DataFrame or CuPy backed Dask Array
Training data to cluster. | python/cuml/dask/cluster/kmeans.py | fit | Chetank99/cuml | python | @with_cupy_rmm
def fit(self, X):
'\n Fit a multi-node multi-GPU KMeans model\n\n Parameters\n ----------\n X : Dask cuDF DataFrame or CuPy backed Dask Array\n Training data to cluster.\n\n '
data = DistributedDataHandler.create(X, client=self.client)
self.datatype = data.datatype
comms = CommsContext(comms_p2p=False)
comms.init(workers=data.workers)
kmeans_fit = [self.client.submit(KMeans._func_fit, comms.sessionId, wf[1], self.datatype, **self.kwargs, workers=[wf[0]], pure=False) for (idx, wf) in enumerate(data.worker_to_parts.items())]
wait(kmeans_fit)
raise_exception_from_futures(kmeans_fit)
comms.destroy()
self.local_model = kmeans_fit[0].result()
self.cluster_centers_ = self.local_model.cluster_centers_
return self |
def fit_predict(self, X, delayed=True):
'\n Compute cluster centers and predict cluster index for each sample.\n\n Parameters\n ----------\n X : Dask cuDF DataFrame or CuPy backed Dask Array\n Data to predict\n\n Returns\n -------\n result: Dask cuDF DataFrame or CuPy backed Dask Array\n Distributed object containing predictions\n\n '
return self.fit(X).predict(X, delayed=delayed) | 6,022,462,453,244,419,000 | Compute cluster centers and predict cluster index for each sample.
Parameters
----------
X : Dask cuDF DataFrame or CuPy backed Dask Array
Data to predict
Returns
-------
result: Dask cuDF DataFrame or CuPy backed Dask Array
Distributed object containing predictions | python/cuml/dask/cluster/kmeans.py | fit_predict | Chetank99/cuml | python | def fit_predict(self, X, delayed=True):
'\n Compute cluster centers and predict cluster index for each sample.\n\n Parameters\n ----------\n X : Dask cuDF DataFrame or CuPy backed Dask Array\n Data to predict\n\n Returns\n -------\n result: Dask cuDF DataFrame or CuPy backed Dask Array\n Distributed object containing predictions\n\n '
return self.fit(X).predict(X, delayed=delayed) |
def predict(self, X, delayed=True):
'\n Predict labels for the input\n\n Parameters\n ----------\n X : Dask cuDF DataFrame or CuPy backed Dask Array\n Data to predict\n\n delayed : bool (default = True)\n Whether to do a lazy prediction (and return Delayed objects) or an\n eagerly executed one.\n\n Returns\n -------\n result: Dask cuDF DataFrame or CuPy backed Dask Array\n Distributed object containing predictions\n '
return self._predict(X, delayed=delayed) | -6,130,491,462,909,309,000 | Predict labels for the input
Parameters
----------
X : Dask cuDF DataFrame or CuPy backed Dask Array
Data to predict
delayed : bool (default = True)
Whether to do a lazy prediction (and return Delayed objects) or an
eagerly executed one.
Returns
-------
result: Dask cuDF DataFrame or CuPy backed Dask Array
Distributed object containing predictions | python/cuml/dask/cluster/kmeans.py | predict | Chetank99/cuml | python | def predict(self, X, delayed=True):
'\n Predict labels for the input\n\n Parameters\n ----------\n X : Dask cuDF DataFrame or CuPy backed Dask Array\n Data to predict\n\n delayed : bool (default = True)\n Whether to do a lazy prediction (and return Delayed objects) or an\n eagerly executed one.\n\n Returns\n -------\n result: Dask cuDF DataFrame or CuPy backed Dask Array\n Distributed object containing predictions\n '
return self._predict(X, delayed=delayed) |
def fit_transform(self, X, delayed=True):
'\n Calls fit followed by transform using a distributed KMeans model\n\n Parameters\n ----------\n X : Dask cuDF DataFrame or CuPy backed Dask Array\n Data to predict\n\n delayed : bool (default = True)\n Whether to execute as a delayed task or eager.\n\n Returns\n -------\n result: Dask cuDF DataFrame or CuPy backed Dask Array\n Distributed object containing the transformed data\n '
return self.fit(X).transform(X, delayed=delayed) | 2,970,504,870,052,390,000 | Calls fit followed by transform using a distributed KMeans model
Parameters
----------
X : Dask cuDF DataFrame or CuPy backed Dask Array
Data to predict
delayed : bool (default = True)
Whether to execute as a delayed task or eager.
Returns
-------
result: Dask cuDF DataFrame or CuPy backed Dask Array
Distributed object containing the transformed data | python/cuml/dask/cluster/kmeans.py | fit_transform | Chetank99/cuml | python | def fit_transform(self, X, delayed=True):
'\n Calls fit followed by transform using a distributed KMeans model\n\n Parameters\n ----------\n X : Dask cuDF DataFrame or CuPy backed Dask Array\n Data to predict\n\n delayed : bool (default = True)\n Whether to execute as a delayed task or eager.\n\n Returns\n -------\n result: Dask cuDF DataFrame or CuPy backed Dask Array\n Distributed object containing the transformed data\n '
return self.fit(X).transform(X, delayed=delayed) |
def transform(self, X, delayed=True):
'\n Transforms the input into the learned centroid space\n\n Parameters\n ----------\n X : Dask cuDF DataFrame or CuPy backed Dask Array\n Data to predict\n\n delayed : bool (default = True)\n Whether to execute as a delayed task or eager.\n\n Returns\n -------\n result: Dask cuDF DataFrame or CuPy backed Dask Array\n Distributed object containing the transformed data\n '
return self._transform(X, n_dims=2, delayed=delayed) | -7,165,475,942,176,801,000 | Transforms the input into the learned centroid space
Parameters
----------
X : Dask cuDF DataFrame or CuPy backed Dask Array
Data to predict
delayed : bool (default = True)
Whether to execute as a delayed task or eager.
Returns
-------
result: Dask cuDF DataFrame or CuPy backed Dask Array
Distributed object containing the transformed data | python/cuml/dask/cluster/kmeans.py | transform | Chetank99/cuml | python | def transform(self, X, delayed=True):
'\n Transforms the input into the learned centroid space\n\n Parameters\n ----------\n X : Dask cuDF DataFrame or CuPy backed Dask Array\n Data to predict\n\n delayed : bool (default = True)\n Whether to execute as a delayed task or eager.\n\n Returns\n -------\n result: Dask cuDF DataFrame or CuPy backed Dask Array\n Distributed object containing the transformed data\n '
return self._transform(X, n_dims=2, delayed=delayed) |
@with_cupy_rmm
def score(self, X):
'\n Computes the inertia score for the trained KMeans centroids.\n\n Parameters\n ----------\n X : dask_cudf.Dataframe\n Dataframe to compute score\n\n Returns\n -------\n\n Inertial score\n '
scores = self._run_parallel_func(KMeans._score, X, n_dims=1, delayed=False, output_futures=True)
return ((- 1) * cp.sum((cp.asarray(self.client.compute(scores, sync=True)) * (- 1.0)))) | 5,906,948,693,175,010,000 | Computes the inertia score for the trained KMeans centroids.
Parameters
----------
X : dask_cudf.Dataframe
Dataframe to compute score
Returns
-------
Inertial score | python/cuml/dask/cluster/kmeans.py | score | Chetank99/cuml | python | @with_cupy_rmm
def score(self, X):
'\n Computes the inertia score for the trained KMeans centroids.\n\n Parameters\n ----------\n X : dask_cudf.Dataframe\n Dataframe to compute score\n\n Returns\n -------\n\n Inertial score\n '
scores = self._run_parallel_func(KMeans._score, X, n_dims=1, delayed=False, output_futures=True)
return ((- 1) * cp.sum((cp.asarray(self.client.compute(scores, sync=True)) * (- 1.0)))) |
def parse_rec(filename):
' Parse a PASCAL VOC xml file '
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = obj.find('name').text
obj_struct['pose'] = obj.find('pose').text
obj_struct['truncated'] = int(obj.find('truncated').text)
obj_struct['difficult'] = int(obj.find('difficult').text)
bbox = obj.find('bndbox')
obj_struct['bbox'] = [(int(bbox.find('xmin').text) - 1), (int(bbox.find('ymin').text) - 1), (int(bbox.find('xmax').text) - 1), (int(bbox.find('ymax').text) - 1)]
objects.append(obj_struct)
return objects | -1,181,628,649,275,111,700 | Parse a PASCAL VOC xml file | eval.py | parse_rec | FLyingLSJ/ssd.pytorch | python | def parse_rec(filename):
' '
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = obj.find('name').text
obj_struct['pose'] = obj.find('pose').text
obj_struct['truncated'] = int(obj.find('truncated').text)
obj_struct['difficult'] = int(obj.find('difficult').text)
bbox = obj.find('bndbox')
obj_struct['bbox'] = [(int(bbox.find('xmin').text) - 1), (int(bbox.find('ymin').text) - 1), (int(bbox.find('xmax').text) - 1), (int(bbox.find('ymax').text) - 1)]
objects.append(obj_struct)
return objects |
def get_output_dir(name, phase):
'Return the directory where experimental artifacts are placed.\n If the directory does not exist, it is created.\n A canonical path is built using the name from an imdb and a network\n (if not None).\n '
filedir = os.path.join(name, phase)
if (not os.path.exists(filedir)):
os.makedirs(filedir)
return filedir | -4,561,549,611,072,020,500 | Return the directory where experimental artifacts are placed.
If the directory does not exist, it is created.
A canonical path is built using the name from an imdb and a network
(if not None). | eval.py | get_output_dir | FLyingLSJ/ssd.pytorch | python | def get_output_dir(name, phase):
'Return the directory where experimental artifacts are placed.\n If the directory does not exist, it is created.\n A canonical path is built using the name from an imdb and a network\n (if not None).\n '
filedir = os.path.join(name, phase)
if (not os.path.exists(filedir)):
os.makedirs(filedir)
return filedir |
def voc_ap(rec, prec, use_07_metric=True):
' ap = voc_ap(rec, prec, [use_07_metric])\n Compute VOC AP given precision and recall.\n If use_07_metric is true, uses the\n VOC 07 11 point method (default:True).\n '
if use_07_metric:
ap = 0.0
for t in np.arange(0.0, 1.1, 0.1):
if (np.sum((rec >= t)) == 0):
p = 0
else:
p = np.max(prec[(rec >= t)])
ap = (ap + (p / 11.0))
else:
mrec = np.concatenate(([0.0], rec, [1.0]))
mpre = np.concatenate(([0.0], prec, [0.0]))
for i in range((mpre.size - 1), 0, (- 1)):
mpre[(i - 1)] = np.maximum(mpre[(i - 1)], mpre[i])
i = np.where((mrec[1:] != mrec[:(- 1)]))[0]
ap = np.sum(((mrec[(i + 1)] - mrec[i]) * mpre[(i + 1)]))
return ap | -5,061,982,948,125,241,000 | ap = voc_ap(rec, prec, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:True). | eval.py | voc_ap | FLyingLSJ/ssd.pytorch | python | def voc_ap(rec, prec, use_07_metric=True):
' ap = voc_ap(rec, prec, [use_07_metric])\n Compute VOC AP given precision and recall.\n If use_07_metric is true, uses the\n VOC 07 11 point method (default:True).\n '
if use_07_metric:
ap = 0.0
for t in np.arange(0.0, 1.1, 0.1):
if (np.sum((rec >= t)) == 0):
p = 0
else:
p = np.max(prec[(rec >= t)])
ap = (ap + (p / 11.0))
else:
mrec = np.concatenate(([0.0], rec, [1.0]))
mpre = np.concatenate(([0.0], prec, [0.0]))
for i in range((mpre.size - 1), 0, (- 1)):
mpre[(i - 1)] = np.maximum(mpre[(i - 1)], mpre[i])
i = np.where((mrec[1:] != mrec[:(- 1)]))[0]
ap = np.sum(((mrec[(i + 1)] - mrec[i]) * mpre[(i + 1)]))
return ap |
def voc_eval(detpath, annopath, imagesetfile, classname, cachedir, ovthresh=0.5, use_07_metric=True):
"rec, prec, ap = voc_eval(detpath,\n annopath,\n imagesetfile,\n classname,\n [ovthresh],\n [use_07_metric])\nTop level function that does the PASCAL VOC evaluation.\ndetpath: Path to detections\n detpath.format(classname) should produce the detection results file.\nannopath: Path to annotations\n annopath.format(imagename) should be the xml annotations file.\nimagesetfile: Text file containing the list of images, one image per line.\nclassname: Category name (duh)\ncachedir: Directory for caching the annotations\n[ovthresh]: Overlap threshold (default = 0.5)\n[use_07_metric]: Whether to use VOC07's 11 point AP computation\n (default True)\n"
if (not os.path.isdir(cachedir)):
os.mkdir(cachedir)
cachefile = os.path.join(cachedir, 'annots.pkl')
with open(imagesetfile, 'r') as f:
lines = f.readlines()
imagenames = [x.strip() for x in lines]
if (not os.path.isfile(cachefile)):
recs = {}
for (i, imagename) in enumerate(imagenames):
recs[imagename] = parse_rec((annopath % imagename))
if ((i % 100) == 0):
print('Reading annotation for {:d}/{:d}'.format((i + 1), len(imagenames)))
print('Saving cached annotations to {:s}'.format(cachefile))
with open(cachefile, 'wb') as f:
pickle.dump(recs, f)
else:
with open(cachefile, 'rb') as f:
recs = pickle.load(f)
class_recs = {}
npos = 0
for imagename in imagenames:
R = [obj for obj in recs[imagename] if (obj['name'] == classname)]
bbox = np.array([x['bbox'] for x in R])
difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
det = ([False] * len(R))
npos = (npos + sum((~ difficult)))
class_recs[imagename] = {'bbox': bbox, 'difficult': difficult, 'det': det}
detfile = detpath.format(classname)
with open(detfile, 'r') as f:
lines = f.readlines()
if (any(lines) == 1):
splitlines = [x.strip().split(' ') for x in lines]
image_ids = [x[0] for x in splitlines]
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
sorted_ind = np.argsort((- confidence))
sorted_scores = np.sort((- confidence))
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
nd = len(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
for d in range(nd):
R = class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
ovmax = (- np.inf)
BBGT = R['bbox'].astype(float)
if (BBGT.size > 0):
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum((ixmax - ixmin), 0.0)
ih = np.maximum((iymax - iymin), 0.0)
inters = (iw * ih)
uni = ((((bb[2] - bb[0]) * (bb[3] - bb[1])) + ((BBGT[:, 2] - BBGT[:, 0]) * (BBGT[:, 3] - BBGT[:, 1]))) - inters)
overlaps = (inters / uni)
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if (ovmax > ovthresh):
if (not R['difficult'][jmax]):
if (not R['det'][jmax]):
tp[d] = 1.0
R['det'][jmax] = 1
else:
fp[d] = 1.0
else:
fp[d] = 1.0
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = (tp / float(npos))
prec = (tp / np.maximum((tp + fp), np.finfo(np.float64).eps))
ap = voc_ap(rec, prec, use_07_metric)
else:
rec = (- 1.0)
prec = (- 1.0)
ap = (- 1.0)
return (rec, prec, ap) | 562,733,316,720,542,660 | rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
classname,
[ovthresh],
[use_07_metric])
Top level function that does the PASCAL VOC evaluation.
detpath: Path to detections
detpath.format(classname) should produce the detection results file.
annopath: Path to annotations
annopath.format(imagename) should be the xml annotations file.
imagesetfile: Text file containing the list of images, one image per line.
classname: Category name (duh)
cachedir: Directory for caching the annotations
[ovthresh]: Overlap threshold (default = 0.5)
[use_07_metric]: Whether to use VOC07's 11 point AP computation
(default True) | eval.py | voc_eval | FLyingLSJ/ssd.pytorch | python | def voc_eval(detpath, annopath, imagesetfile, classname, cachedir, ovthresh=0.5, use_07_metric=True):
"rec, prec, ap = voc_eval(detpath,\n annopath,\n imagesetfile,\n classname,\n [ovthresh],\n [use_07_metric])\nTop level function that does the PASCAL VOC evaluation.\ndetpath: Path to detections\n detpath.format(classname) should produce the detection results file.\nannopath: Path to annotations\n annopath.format(imagename) should be the xml annotations file.\nimagesetfile: Text file containing the list of images, one image per line.\nclassname: Category name (duh)\ncachedir: Directory for caching the annotations\n[ovthresh]: Overlap threshold (default = 0.5)\n[use_07_metric]: Whether to use VOC07's 11 point AP computation\n (default True)\n"
if (not os.path.isdir(cachedir)):
os.mkdir(cachedir)
cachefile = os.path.join(cachedir, 'annots.pkl')
with open(imagesetfile, 'r') as f:
lines = f.readlines()
imagenames = [x.strip() for x in lines]
if (not os.path.isfile(cachefile)):
recs = {}
for (i, imagename) in enumerate(imagenames):
recs[imagename] = parse_rec((annopath % imagename))
if ((i % 100) == 0):
print('Reading annotation for {:d}/{:d}'.format((i + 1), len(imagenames)))
print('Saving cached annotations to {:s}'.format(cachefile))
with open(cachefile, 'wb') as f:
pickle.dump(recs, f)
else:
with open(cachefile, 'rb') as f:
recs = pickle.load(f)
class_recs = {}
npos = 0
for imagename in imagenames:
R = [obj for obj in recs[imagename] if (obj['name'] == classname)]
bbox = np.array([x['bbox'] for x in R])
difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
det = ([False] * len(R))
npos = (npos + sum((~ difficult)))
class_recs[imagename] = {'bbox': bbox, 'difficult': difficult, 'det': det}
detfile = detpath.format(classname)
with open(detfile, 'r') as f:
lines = f.readlines()
if (any(lines) == 1):
splitlines = [x.strip().split(' ') for x in lines]
image_ids = [x[0] for x in splitlines]
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
sorted_ind = np.argsort((- confidence))
sorted_scores = np.sort((- confidence))
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
nd = len(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
for d in range(nd):
R = class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
ovmax = (- np.inf)
BBGT = R['bbox'].astype(float)
if (BBGT.size > 0):
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum((ixmax - ixmin), 0.0)
ih = np.maximum((iymax - iymin), 0.0)
inters = (iw * ih)
uni = ((((bb[2] - bb[0]) * (bb[3] - bb[1])) + ((BBGT[:, 2] - BBGT[:, 0]) * (BBGT[:, 3] - BBGT[:, 1]))) - inters)
overlaps = (inters / uni)
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if (ovmax > ovthresh):
if (not R['difficult'][jmax]):
if (not R['det'][jmax]):
tp[d] = 1.0
R['det'][jmax] = 1
else:
fp[d] = 1.0
else:
fp[d] = 1.0
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = (tp / float(npos))
prec = (tp / np.maximum((tp + fp), np.finfo(np.float64).eps))
ap = voc_ap(rec, prec, use_07_metric)
else:
rec = (- 1.0)
prec = (- 1.0)
ap = (- 1.0)
return (rec, prec, ap) |
def __init__(self, minconn, maxconn, *args, **kwargs):
"Initialize the connection pool.\n\n New 'minconn' connections are created immediately calling 'connfunc'\n with given parameters. The connection pool will support a maximum of\n about 'maxconn' connections. \n "
self.minconn = int(minconn)
self.maxconn = int(maxconn)
self.closed = False
self._args = args
self._kwargs = kwargs
self._pool = []
self._used = {}
self._rused = {}
self._keys = 0
for i in range(self.minconn):
self._connect() | 1,293,587,767,893,814,000 | Initialize the connection pool.
New 'minconn' connections are created immediately calling 'connfunc'
with given parameters. The connection pool will support a maximum of
about 'maxconn' connections. | lexis/Lib/site-packages/psycopg2/pool.py | __init__ | ALEXIS2ES/sherom-Serve | python | def __init__(self, minconn, maxconn, *args, **kwargs):
"Initialize the connection pool.\n\n New 'minconn' connections are created immediately calling 'connfunc'\n with given parameters. The connection pool will support a maximum of\n about 'maxconn' connections. \n "
self.minconn = int(minconn)
self.maxconn = int(maxconn)
self.closed = False
self._args = args
self._kwargs = kwargs
self._pool = []
self._used = {}
self._rused = {}
self._keys = 0
for i in range(self.minconn):
self._connect() |
def _connect(self, key=None):
"Create a new connection and assign it to 'key' if not None."
conn = psycopg2.connect(*self._args, **self._kwargs)
if (key is not None):
self._used[key] = conn
self._rused[id(conn)] = key
else:
self._pool.append(conn)
return conn | 5,585,987,887,297,364,000 | Create a new connection and assign it to 'key' if not None. | lexis/Lib/site-packages/psycopg2/pool.py | _connect | ALEXIS2ES/sherom-Serve | python | def _connect(self, key=None):
conn = psycopg2.connect(*self._args, **self._kwargs)
if (key is not None):
self._used[key] = conn
self._rused[id(conn)] = key
else:
self._pool.append(conn)
return conn |
def _getkey(self):
'Return a new unique key.'
self._keys += 1
return self._keys | -2,913,718,119,693,489,700 | Return a new unique key. | lexis/Lib/site-packages/psycopg2/pool.py | _getkey | ALEXIS2ES/sherom-Serve | python | def _getkey(self):
self._keys += 1
return self._keys |
def _getconn(self, key=None):
"Get a free connection and assign it to 'key' if not None."
if self.closed:
raise PoolError('connection pool is closed')
if (key is None):
key = self._getkey()
if (key in self._used):
return self._used[key]
if self._pool:
self._used[key] = conn = self._pool.pop()
self._rused[id(conn)] = key
return conn
else:
if (len(self._used) == self.maxconn):
raise PoolError('connection pool exhausted')
return self._connect(key) | -1,052,344,869,246,796,800 | Get a free connection and assign it to 'key' if not None. | lexis/Lib/site-packages/psycopg2/pool.py | _getconn | ALEXIS2ES/sherom-Serve | python | def _getconn(self, key=None):
if self.closed:
raise PoolError('connection pool is closed')
if (key is None):
key = self._getkey()
if (key in self._used):
return self._used[key]
if self._pool:
self._used[key] = conn = self._pool.pop()
self._rused[id(conn)] = key
return conn
else:
if (len(self._used) == self.maxconn):
raise PoolError('connection pool exhausted')
return self._connect(key) |
def _putconn(self, conn, key=None, close=False):
'Put away a connection.'
if self.closed:
raise PoolError('connection pool is closed')
if (key is None):
key = self._rused.get(id(conn))
if (not key):
raise PoolError('trying to put unkeyed connection')
if ((len(self._pool) < self.minconn) and (not close)):
if (not conn.closed):
status = conn.get_transaction_status()
if (status == _ext.TRANSACTION_STATUS_UNKNOWN):
conn.close()
elif (status != _ext.TRANSACTION_STATUS_IDLE):
conn.rollback()
self._pool.append(conn)
else:
self._pool.append(conn)
else:
conn.close()
if ((not self.closed) or (key in self._used)):
del self._used[key]
del self._rused[id(conn)] | 1,155,863,612,707,922,400 | Put away a connection. | lexis/Lib/site-packages/psycopg2/pool.py | _putconn | ALEXIS2ES/sherom-Serve | python | def _putconn(self, conn, key=None, close=False):
if self.closed:
raise PoolError('connection pool is closed')
if (key is None):
key = self._rused.get(id(conn))
if (not key):
raise PoolError('trying to put unkeyed connection')
if ((len(self._pool) < self.minconn) and (not close)):
if (not conn.closed):
status = conn.get_transaction_status()
if (status == _ext.TRANSACTION_STATUS_UNKNOWN):
conn.close()
elif (status != _ext.TRANSACTION_STATUS_IDLE):
conn.rollback()
self._pool.append(conn)
else:
self._pool.append(conn)
else:
conn.close()
if ((not self.closed) or (key in self._used)):
del self._used[key]
del self._rused[id(conn)] |
def _closeall(self):
'Close all connections.\n\n Note that this can lead to some code fail badly when trying to use\n an already closed connection. If you call .closeall() make sure\n your code can deal with it.\n '
if self.closed:
raise PoolError('connection pool is closed')
for conn in (self._pool + list(self._used.values())):
try:
conn.close()
except:
pass
self.closed = True | 433,966,829,568,226,200 | Close all connections.
Note that this can lead to some code fail badly when trying to use
an already closed connection. If you call .closeall() make sure
your code can deal with it. | lexis/Lib/site-packages/psycopg2/pool.py | _closeall | ALEXIS2ES/sherom-Serve | python | def _closeall(self):
'Close all connections.\n\n Note that this can lead to some code fail badly when trying to use\n an already closed connection. If you call .closeall() make sure\n your code can deal with it.\n '
if self.closed:
raise PoolError('connection pool is closed')
for conn in (self._pool + list(self._used.values())):
try:
conn.close()
except:
pass
self.closed = True |
def __init__(self, minconn, maxconn, *args, **kwargs):
'Initialize the threading lock.'
import threading
AbstractConnectionPool.__init__(self, minconn, maxconn, *args, **kwargs)
self._lock = threading.Lock() | 8,024,484,810,999,034,000 | Initialize the threading lock. | lexis/Lib/site-packages/psycopg2/pool.py | __init__ | ALEXIS2ES/sherom-Serve | python | def __init__(self, minconn, maxconn, *args, **kwargs):
import threading
AbstractConnectionPool.__init__(self, minconn, maxconn, *args, **kwargs)
self._lock = threading.Lock() |
def getconn(self, key=None):
"Get a free connection and assign it to 'key' if not None."
self._lock.acquire()
try:
return self._getconn(key)
finally:
self._lock.release() | 6,270,094,374,509,713,000 | Get a free connection and assign it to 'key' if not None. | lexis/Lib/site-packages/psycopg2/pool.py | getconn | ALEXIS2ES/sherom-Serve | python | def getconn(self, key=None):
self._lock.acquire()
try:
return self._getconn(key)
finally:
self._lock.release() |
def putconn(self, conn=None, key=None, close=False):
'Put away an unused connection.'
self._lock.acquire()
try:
self._putconn(conn, key, close)
finally:
self._lock.release() | -2,805,035,333,017,517,600 | Put away an unused connection. | lexis/Lib/site-packages/psycopg2/pool.py | putconn | ALEXIS2ES/sherom-Serve | python | def putconn(self, conn=None, key=None, close=False):
self._lock.acquire()
try:
self._putconn(conn, key, close)
finally:
self._lock.release() |
def closeall(self):
'Close all connections (even the one currently in use.)'
self._lock.acquire()
try:
self._closeall()
finally:
self._lock.release() | 8,940,636,885,304,963,000 | Close all connections (even the one currently in use.) | lexis/Lib/site-packages/psycopg2/pool.py | closeall | ALEXIS2ES/sherom-Serve | python | def closeall(self):
self._lock.acquire()
try:
self._closeall()
finally:
self._lock.release() |
def __init__(self, minconn, maxconn, *args, **kwargs):
'Initialize the threading lock.'
import warnings
warnings.warn('deprecated: use ZPsycopgDA.pool implementation', DeprecationWarning)
import threading
AbstractConnectionPool.__init__(self, minconn, maxconn, *args, **kwargs)
self._lock = threading.Lock()
import _thread as _thread
self.__thread = _thread | -4,742,599,862,310,846,000 | Initialize the threading lock. | lexis/Lib/site-packages/psycopg2/pool.py | __init__ | ALEXIS2ES/sherom-Serve | python | def __init__(self, minconn, maxconn, *args, **kwargs):
import warnings
warnings.warn('deprecated: use ZPsycopgDA.pool implementation', DeprecationWarning)
import threading
AbstractConnectionPool.__init__(self, minconn, maxconn, *args, **kwargs)
self._lock = threading.Lock()
import _thread as _thread
self.__thread = _thread |
def getconn(self):
'Generate thread id and return a connection.'
key = self.__thread.get_ident()
self._lock.acquire()
try:
return self._getconn(key)
finally:
self._lock.release() | 7,005,839,141,883,069,000 | Generate thread id and return a connection. | lexis/Lib/site-packages/psycopg2/pool.py | getconn | ALEXIS2ES/sherom-Serve | python | def getconn(self):
key = self.__thread.get_ident()
self._lock.acquire()
try:
return self._getconn(key)
finally:
self._lock.release() |
def putconn(self, conn=None, close=False):
'Put away an unused connection.'
key = self.__thread.get_ident()
self._lock.acquire()
try:
if (not conn):
conn = self._used[key]
self._putconn(conn, key, close)
finally:
self._lock.release() | 2,892,461,049,250,483,700 | Put away an unused connection. | lexis/Lib/site-packages/psycopg2/pool.py | putconn | ALEXIS2ES/sherom-Serve | python | def putconn(self, conn=None, close=False):
key = self.__thread.get_ident()
self._lock.acquire()
try:
if (not conn):
conn = self._used[key]
self._putconn(conn, key, close)
finally:
self._lock.release() |
def closeall(self):
'Close all connections (even the one currently in use.)'
self._lock.acquire()
try:
self._closeall()
finally:
self._lock.release() | 8,940,636,885,304,963,000 | Close all connections (even the one currently in use.) | lexis/Lib/site-packages/psycopg2/pool.py | closeall | ALEXIS2ES/sherom-Serve | python | def closeall(self):
self._lock.acquire()
try:
self._closeall()
finally:
self._lock.release() |
def from_ppc(ppc, f_hz=50, validate_conversion=False, **kwargs):
'\n This function converts pypower case files to pandapower net structure.\n\n INPUT:\n\n **ppc** : The pypower case file.\n\n OPTIONAL:\n\n **f_hz** (float, 50) - The frequency of the network.\n\n **validate_conversion** (bool, False) - If True, validate_from_ppc is run after conversion.\n For running the validation, the ppc must already contain the pypower\n powerflow results or pypower must be importable.\n\n ****kwargs** keyword arguments for validate_from_ppc if validate_conversion is True\n\n OUTPUT:\n\n **net** : pandapower net.\n\n EXAMPLE:\n\n import pandapower.converter as pc\n\n from pypower import case4gs\n\n ppc_net = case4gs.case4gs()\n\n net = pc.from_ppc(ppc_net, f_hz=60)\n\n '
if Series((ppc['bus'][:, BASE_KV] <= 0)).any():
logger.info('There are false baseKV given in the pypower case file.')
baseMVA = ppc['baseMVA']
omega = (pi * f_hz)
MAX_VAL = 99999.0
net = pp.create_empty_network(f_hz=f_hz, sn_mva=baseMVA)
for i in range(len(ppc['bus'])):
pp.create_bus(net, name=int(ppc['bus'][(i, 0)]), vn_kv=ppc['bus'][(i, 9)], type='b', zone=ppc['bus'][(i, 10)], in_service=bool((ppc['bus'][(i, 1)] != 4)), max_vm_pu=ppc['bus'][(i, 11)], min_vm_pu=ppc['bus'][(i, 12)])
if (ppc['bus'][(i, 2)] > 0):
pp.create_load(net, i, p_mw=ppc['bus'][(i, 2)], q_mvar=ppc['bus'][(i, 3)], controllable=False)
elif (ppc['bus'][(i, 2)] < 0):
pp.create_sgen(net, i, p_mw=(- ppc['bus'][(i, 2)]), q_mvar=(- ppc['bus'][(i, 3)]), type='', controllable=False)
elif (ppc['bus'][(i, 3)] != 0):
pp.create_load(net, i, p_mw=ppc['bus'][(i, 2)], q_mvar=ppc['bus'][(i, 3)], controllable=False)
if ((ppc['bus'][(i, 4)] != 0) or (ppc['bus'][(i, 5)] != 0)):
pp.create_shunt(net, i, p_mw=ppc['bus'][(i, 4)], q_mvar=(- ppc['bus'][(i, 5)]))
gen_lookup = DataFrame(nan, columns=['element', 'element_type'], index=range(len(ppc['gen'][:, 0])))
if (len(ppc['gen'].shape) == 1):
ppc['gen'] = array(ppc['gen'], ndmin=2)
for i in range(len(ppc['gen'][:, 0])):
(current_bus_type, current_bus_idx, same_bus_gen_idx, first_same_bus_in_service_gen_idx, last_same_bus_in_service_gen_idx) = _gen_bus_info(ppc, i)
if (current_bus_type == 3):
if (i == first_same_bus_in_service_gen_idx):
gen_lookup.element.loc[i] = pp.create_ext_grid(net, bus=current_bus_idx, vm_pu=ppc['gen'][(last_same_bus_in_service_gen_idx, 5)], va_degree=ppc['bus'][(current_bus_idx, 8)], in_service=bool((ppc['gen'][(i, 7)] > 0)), max_p_mw=ppc['gen'][(i, PMAX)], min_p_mw=ppc['gen'][(i, PMIN)], max_q_mvar=ppc['gen'][(i, QMAX)], min_q_mvar=ppc['gen'][(i, QMIN)])
gen_lookup.element_type.loc[i] = 'ext_grid'
if (ppc['gen'][(i, 4)] > ppc['gen'][(i, 3)]):
logger.info(('min_q_mvar of gen %d must be less than max_q_mvar but is not.' % i))
if ((- ppc['gen'][(i, 9)]) < (- ppc['gen'][(i, 8)])):
logger.info(('max_p_mw of gen %d must be less than min_p_mw but is not.' % i))
else:
current_bus_type = 1
elif (current_bus_type == 2):
if (i == first_same_bus_in_service_gen_idx):
gen_lookup.element.loc[i] = pp.create_gen(net, bus=current_bus_idx, vm_pu=ppc['gen'][(last_same_bus_in_service_gen_idx, 5)], p_mw=ppc['gen'][(i, 1)], in_service=bool((ppc['gen'][(i, 7)] > 0)), controllable=True, max_p_mw=ppc['gen'][(i, PMAX)], min_p_mw=ppc['gen'][(i, PMIN)], max_q_mvar=ppc['gen'][(i, QMAX)], min_q_mvar=ppc['gen'][(i, QMIN)])
gen_lookup.element_type.loc[i] = 'gen'
if (ppc['gen'][(i, 1)] < 0):
logger.info(('p_mw of gen %d must be less than zero but is not.' % i))
if (ppc['gen'][(i, 4)] > ppc['gen'][(i, 3)]):
logger.info(('min_q_mvar of gen %d must be less than max_q_mvar but is not.' % i))
if ((- ppc['gen'][(i, 9)]) < (- ppc['gen'][(i, 8)])):
logger.info(('max_p_mw of gen %d must be less than min_p_mw but is not.' % i))
else:
current_bus_type = 1
if (current_bus_type == 1):
gen_lookup.element.loc[i] = pp.create_sgen(net, bus=current_bus_idx, p_mw=ppc['gen'][(i, 1)], q_mvar=ppc['gen'][(i, 2)], type='', in_service=bool((ppc['gen'][(i, 7)] > 0)), max_p_mw=ppc['gen'][(i, PMAX)], min_p_mw=ppc['gen'][(i, PMIN)], max_q_mvar=ppc['gen'][(i, QMAX)], min_q_mvar=ppc['gen'][(i, QMIN)], controllable=True)
gen_lookup.element_type.loc[i] = 'sgen'
if (ppc['gen'][(i, 1)] < 0):
logger.info(('p_mw of sgen %d must be less than zero but is not.' % i))
if (ppc['gen'][(i, 4)] > ppc['gen'][(i, 3)]):
logger.info(('min_q_mvar of gen %d must be less than max_q_mvar but is not.' % i))
if ((- ppc['gen'][(i, 9)]) < (- ppc['gen'][(i, 8)])):
logger.info(('max_p_mw of gen %d must be less than min_p_mw but is not.' % i))
for i in range(len(ppc['branch'])):
from_bus = pp.get_element_index(net, 'bus', name=int(ppc['branch'][(i, 0)]))
to_bus = pp.get_element_index(net, 'bus', name=int(ppc['branch'][(i, 1)]))
from_vn_kv = ppc['bus'][(from_bus, 9)]
to_vn_kv = ppc['bus'][(to_bus, 9)]
if (((from_vn_kv == to_vn_kv) & ((ppc['branch'][(i, 8)] == 0) | (ppc['branch'][(i, 8)] == 1))) & (ppc['branch'][(i, 9)] == 0)):
Zni = ((ppc['bus'][(to_bus, 9)] ** 2) / baseMVA)
max_i_ka = ((ppc['branch'][(i, 5)] / ppc['bus'][(to_bus, 9)]) / sqrt(3))
if (max_i_ka == 0.0):
max_i_ka = MAX_VAL
logger.debug(('ppc branch rateA is zero -> Using MAX_VAL instead to calculate ' + 'maximum branch flow'))
pp.create_line_from_parameters(net, from_bus=from_bus, to_bus=to_bus, length_km=1, r_ohm_per_km=(ppc['branch'][(i, 2)] * Zni), x_ohm_per_km=(ppc['branch'][(i, 3)] * Zni), c_nf_per_km=((((ppc['branch'][(i, 4)] / Zni) / omega) * 1000000000.0) / 2), max_i_ka=max_i_ka, type='ol', max_loading_percent=100, in_service=bool(ppc['branch'][(i, 10)]))
else:
if (from_vn_kv >= to_vn_kv):
hv_bus = from_bus
vn_hv_kv = from_vn_kv
lv_bus = to_bus
vn_lv_kv = to_vn_kv
tap_side = 'hv'
else:
hv_bus = to_bus
vn_hv_kv = to_vn_kv
lv_bus = from_bus
vn_lv_kv = from_vn_kv
tap_side = 'lv'
if (from_vn_kv == to_vn_kv):
logger.warning('The pypower branch %d (from_bus, to_bus)=(%d, %d) is considered as a transformer because of a ratio != 0 | 1 but it connects the same voltage level', i, ppc['branch'][(i, 0)], ppc['branch'][(i, 1)])
rk = ppc['branch'][(i, 2)]
xk = ppc['branch'][(i, 3)]
zk = (((rk ** 2) + (xk ** 2)) ** 0.5)
sn = ppc['branch'][(i, 5)]
if (sn == 0.0):
sn = MAX_VAL
logger.debug(('ppc branch rateA is zero -> Using MAX_VAL instead to calculate ' + 'apparent power'))
ratio_1 = (0 if (ppc['branch'][(i, 8)] == 0) else ((ppc['branch'][(i, 8)] - 1) * 100))
i0_percent = ((((- ppc['branch'][(i, 4)]) * 100) * baseMVA) / sn)
if (i0_percent < 0):
logger.info('A transformer always behaves inductive consumpting but the susceptance of pypower branch %d (from_bus, to_bus)=(%d, %d) is positive.', i, ppc['branch'][(i, 0)], ppc['branch'][(i, 1)])
pp.create_transformer_from_parameters(net, hv_bus=hv_bus, lv_bus=lv_bus, sn_mva=sn, vn_hv_kv=vn_hv_kv, vn_lv_kv=vn_lv_kv, vk_percent=((((sign(xk) * zk) * sn) * 100) / baseMVA), vkr_percent=(((rk * sn) * 100) / baseMVA), max_loading_percent=100, pfe_kw=0, i0_percent=i0_percent, shift_degree=ppc['branch'][(i, 9)], tap_step_percent=abs(ratio_1), tap_pos=sign(ratio_1), tap_side=tap_side, tap_neutral=0)
if ('gencost' in ppc):
if (len(ppc['gencost'].shape) == 1):
ppc['gencost'] = ppc['gencost'].reshape((1, (- 1)))
if (ppc['gencost'].shape[0] <= gen_lookup.shape[0]):
idx_p = range(ppc['gencost'].shape[0])
idx_q = []
elif (ppc['gencost'].shape[0] > gen_lookup.shape[0]):
idx_p = range(gen_lookup.shape[0])
idx_q = range(gen_lookup.shape[0], ppc['gencost'].shape[0])
if (ppc['gencost'].shape[0] >= (2 * gen_lookup.shape[0])):
idx_p = range(gen_lookup.shape[0])
idx_q = range(gen_lookup.shape[0], (2 * gen_lookup.shape[0]))
for idx in idx_p:
_create_costs(net, ppc, gen_lookup, 'p', idx)
for idx in idx_q:
_create_costs(net, ppc, gen_lookup, 'q', idx)
if validate_conversion:
logger.setLevel(logging.DEBUG)
if (not validate_from_ppc(ppc, net, **kwargs)):
logger.error('Validation failed.')
net._options = {}
net._options['gen_lookup'] = gen_lookup
return net | -607,897,207,075,075,600 | This function converts pypower case files to pandapower net structure.
INPUT:
**ppc** : The pypower case file.
OPTIONAL:
**f_hz** (float, 50) - The frequency of the network.
**validate_conversion** (bool, False) - If True, validate_from_ppc is run after conversion.
For running the validation, the ppc must already contain the pypower
powerflow results or pypower must be importable.
****kwargs** keyword arguments for validate_from_ppc if validate_conversion is True
OUTPUT:
**net** : pandapower net.
EXAMPLE:
import pandapower.converter as pc
from pypower import case4gs
ppc_net = case4gs.case4gs()
net = pc.from_ppc(ppc_net, f_hz=60) | pandapower/converter/pypower/from_ppc.py | from_ppc | BaraaUniKassel/pandapower | python | def from_ppc(ppc, f_hz=50, validate_conversion=False, **kwargs):
'\n This function converts pypower case files to pandapower net structure.\n\n INPUT:\n\n **ppc** : The pypower case file.\n\n OPTIONAL:\n\n **f_hz** (float, 50) - The frequency of the network.\n\n **validate_conversion** (bool, False) - If True, validate_from_ppc is run after conversion.\n For running the validation, the ppc must already contain the pypower\n powerflow results or pypower must be importable.\n\n ****kwargs** keyword arguments for validate_from_ppc if validate_conversion is True\n\n OUTPUT:\n\n **net** : pandapower net.\n\n EXAMPLE:\n\n import pandapower.converter as pc\n\n from pypower import case4gs\n\n ppc_net = case4gs.case4gs()\n\n net = pc.from_ppc(ppc_net, f_hz=60)\n\n '
if Series((ppc['bus'][:, BASE_KV] <= 0)).any():
logger.info('There are false baseKV given in the pypower case file.')
baseMVA = ppc['baseMVA']
omega = (pi * f_hz)
MAX_VAL = 99999.0
net = pp.create_empty_network(f_hz=f_hz, sn_mva=baseMVA)
for i in range(len(ppc['bus'])):
pp.create_bus(net, name=int(ppc['bus'][(i, 0)]), vn_kv=ppc['bus'][(i, 9)], type='b', zone=ppc['bus'][(i, 10)], in_service=bool((ppc['bus'][(i, 1)] != 4)), max_vm_pu=ppc['bus'][(i, 11)], min_vm_pu=ppc['bus'][(i, 12)])
if (ppc['bus'][(i, 2)] > 0):
pp.create_load(net, i, p_mw=ppc['bus'][(i, 2)], q_mvar=ppc['bus'][(i, 3)], controllable=False)
elif (ppc['bus'][(i, 2)] < 0):
pp.create_sgen(net, i, p_mw=(- ppc['bus'][(i, 2)]), q_mvar=(- ppc['bus'][(i, 3)]), type=, controllable=False)
elif (ppc['bus'][(i, 3)] != 0):
pp.create_load(net, i, p_mw=ppc['bus'][(i, 2)], q_mvar=ppc['bus'][(i, 3)], controllable=False)
if ((ppc['bus'][(i, 4)] != 0) or (ppc['bus'][(i, 5)] != 0)):
pp.create_shunt(net, i, p_mw=ppc['bus'][(i, 4)], q_mvar=(- ppc['bus'][(i, 5)]))
gen_lookup = DataFrame(nan, columns=['element', 'element_type'], index=range(len(ppc['gen'][:, 0])))
if (len(ppc['gen'].shape) == 1):
ppc['gen'] = array(ppc['gen'], ndmin=2)
for i in range(len(ppc['gen'][:, 0])):
(current_bus_type, current_bus_idx, same_bus_gen_idx, first_same_bus_in_service_gen_idx, last_same_bus_in_service_gen_idx) = _gen_bus_info(ppc, i)
if (current_bus_type == 3):
if (i == first_same_bus_in_service_gen_idx):
gen_lookup.element.loc[i] = pp.create_ext_grid(net, bus=current_bus_idx, vm_pu=ppc['gen'][(last_same_bus_in_service_gen_idx, 5)], va_degree=ppc['bus'][(current_bus_idx, 8)], in_service=bool((ppc['gen'][(i, 7)] > 0)), max_p_mw=ppc['gen'][(i, PMAX)], min_p_mw=ppc['gen'][(i, PMIN)], max_q_mvar=ppc['gen'][(i, QMAX)], min_q_mvar=ppc['gen'][(i, QMIN)])
gen_lookup.element_type.loc[i] = 'ext_grid'
if (ppc['gen'][(i, 4)] > ppc['gen'][(i, 3)]):
logger.info(('min_q_mvar of gen %d must be less than max_q_mvar but is not.' % i))
if ((- ppc['gen'][(i, 9)]) < (- ppc['gen'][(i, 8)])):
logger.info(('max_p_mw of gen %d must be less than min_p_mw but is not.' % i))
else:
current_bus_type = 1
elif (current_bus_type == 2):
if (i == first_same_bus_in_service_gen_idx):
gen_lookup.element.loc[i] = pp.create_gen(net, bus=current_bus_idx, vm_pu=ppc['gen'][(last_same_bus_in_service_gen_idx, 5)], p_mw=ppc['gen'][(i, 1)], in_service=bool((ppc['gen'][(i, 7)] > 0)), controllable=True, max_p_mw=ppc['gen'][(i, PMAX)], min_p_mw=ppc['gen'][(i, PMIN)], max_q_mvar=ppc['gen'][(i, QMAX)], min_q_mvar=ppc['gen'][(i, QMIN)])
gen_lookup.element_type.loc[i] = 'gen'
if (ppc['gen'][(i, 1)] < 0):
logger.info(('p_mw of gen %d must be less than zero but is not.' % i))
if (ppc['gen'][(i, 4)] > ppc['gen'][(i, 3)]):
logger.info(('min_q_mvar of gen %d must be less than max_q_mvar but is not.' % i))
if ((- ppc['gen'][(i, 9)]) < (- ppc['gen'][(i, 8)])):
logger.info(('max_p_mw of gen %d must be less than min_p_mw but is not.' % i))
else:
current_bus_type = 1
if (current_bus_type == 1):
gen_lookup.element.loc[i] = pp.create_sgen(net, bus=current_bus_idx, p_mw=ppc['gen'][(i, 1)], q_mvar=ppc['gen'][(i, 2)], type=, in_service=bool((ppc['gen'][(i, 7)] > 0)), max_p_mw=ppc['gen'][(i, PMAX)], min_p_mw=ppc['gen'][(i, PMIN)], max_q_mvar=ppc['gen'][(i, QMAX)], min_q_mvar=ppc['gen'][(i, QMIN)], controllable=True)
gen_lookup.element_type.loc[i] = 'sgen'
if (ppc['gen'][(i, 1)] < 0):
logger.info(('p_mw of sgen %d must be less than zero but is not.' % i))
if (ppc['gen'][(i, 4)] > ppc['gen'][(i, 3)]):
logger.info(('min_q_mvar of gen %d must be less than max_q_mvar but is not.' % i))
if ((- ppc['gen'][(i, 9)]) < (- ppc['gen'][(i, 8)])):
logger.info(('max_p_mw of gen %d must be less than min_p_mw but is not.' % i))
for i in range(len(ppc['branch'])):
from_bus = pp.get_element_index(net, 'bus', name=int(ppc['branch'][(i, 0)]))
to_bus = pp.get_element_index(net, 'bus', name=int(ppc['branch'][(i, 1)]))
from_vn_kv = ppc['bus'][(from_bus, 9)]
to_vn_kv = ppc['bus'][(to_bus, 9)]
if (((from_vn_kv == to_vn_kv) & ((ppc['branch'][(i, 8)] == 0) | (ppc['branch'][(i, 8)] == 1))) & (ppc['branch'][(i, 9)] == 0)):
Zni = ((ppc['bus'][(to_bus, 9)] ** 2) / baseMVA)
max_i_ka = ((ppc['branch'][(i, 5)] / ppc['bus'][(to_bus, 9)]) / sqrt(3))
if (max_i_ka == 0.0):
max_i_ka = MAX_VAL
logger.debug(('ppc branch rateA is zero -> Using MAX_VAL instead to calculate ' + 'maximum branch flow'))
pp.create_line_from_parameters(net, from_bus=from_bus, to_bus=to_bus, length_km=1, r_ohm_per_km=(ppc['branch'][(i, 2)] * Zni), x_ohm_per_km=(ppc['branch'][(i, 3)] * Zni), c_nf_per_km=((((ppc['branch'][(i, 4)] / Zni) / omega) * 1000000000.0) / 2), max_i_ka=max_i_ka, type='ol', max_loading_percent=100, in_service=bool(ppc['branch'][(i, 10)]))
else:
if (from_vn_kv >= to_vn_kv):
hv_bus = from_bus
vn_hv_kv = from_vn_kv
lv_bus = to_bus
vn_lv_kv = to_vn_kv
tap_side = 'hv'
else:
hv_bus = to_bus
vn_hv_kv = to_vn_kv
lv_bus = from_bus
vn_lv_kv = from_vn_kv
tap_side = 'lv'
if (from_vn_kv == to_vn_kv):
logger.warning('The pypower branch %d (from_bus, to_bus)=(%d, %d) is considered as a transformer because of a ratio != 0 | 1 but it connects the same voltage level', i, ppc['branch'][(i, 0)], ppc['branch'][(i, 1)])
rk = ppc['branch'][(i, 2)]
xk = ppc['branch'][(i, 3)]
zk = (((rk ** 2) + (xk ** 2)) ** 0.5)
sn = ppc['branch'][(i, 5)]
if (sn == 0.0):
sn = MAX_VAL
logger.debug(('ppc branch rateA is zero -> Using MAX_VAL instead to calculate ' + 'apparent power'))
ratio_1 = (0 if (ppc['branch'][(i, 8)] == 0) else ((ppc['branch'][(i, 8)] - 1) * 100))
i0_percent = ((((- ppc['branch'][(i, 4)]) * 100) * baseMVA) / sn)
if (i0_percent < 0):
logger.info('A transformer always behaves inductive consumpting but the susceptance of pypower branch %d (from_bus, to_bus)=(%d, %d) is positive.', i, ppc['branch'][(i, 0)], ppc['branch'][(i, 1)])
pp.create_transformer_from_parameters(net, hv_bus=hv_bus, lv_bus=lv_bus, sn_mva=sn, vn_hv_kv=vn_hv_kv, vn_lv_kv=vn_lv_kv, vk_percent=((((sign(xk) * zk) * sn) * 100) / baseMVA), vkr_percent=(((rk * sn) * 100) / baseMVA), max_loading_percent=100, pfe_kw=0, i0_percent=i0_percent, shift_degree=ppc['branch'][(i, 9)], tap_step_percent=abs(ratio_1), tap_pos=sign(ratio_1), tap_side=tap_side, tap_neutral=0)
if ('gencost' in ppc):
if (len(ppc['gencost'].shape) == 1):
ppc['gencost'] = ppc['gencost'].reshape((1, (- 1)))
if (ppc['gencost'].shape[0] <= gen_lookup.shape[0]):
idx_p = range(ppc['gencost'].shape[0])
idx_q = []
elif (ppc['gencost'].shape[0] > gen_lookup.shape[0]):
idx_p = range(gen_lookup.shape[0])
idx_q = range(gen_lookup.shape[0], ppc['gencost'].shape[0])
if (ppc['gencost'].shape[0] >= (2 * gen_lookup.shape[0])):
idx_p = range(gen_lookup.shape[0])
idx_q = range(gen_lookup.shape[0], (2 * gen_lookup.shape[0]))
for idx in idx_p:
_create_costs(net, ppc, gen_lookup, 'p', idx)
for idx in idx_q:
_create_costs(net, ppc, gen_lookup, 'q', idx)
if validate_conversion:
logger.setLevel(logging.DEBUG)
if (not validate_from_ppc(ppc, net, **kwargs)):
logger.error('Validation failed.')
net._options = {}
net._options['gen_lookup'] = gen_lookup
return net |
def validate_from_ppc(ppc_net, net, pf_type='runpp', max_diff_values={'bus_vm_pu': 1e-06, 'bus_va_degree': 1e-05, 'branch_p_mw': 1e-06, 'branch_q_mvar': 1e-06, 'gen_p_mw': 1e-06, 'gen_q_mvar': 1e-06}, run=True):
'\n This function validates the pypower case files to pandapower net structure conversion via a comparison of loadflow calculation results. (Hence the opf cost conversion is not validated.)\n\n INPUT:\n\n **ppc_net** - The pypower case file, which must already contain the pypower powerflow\n results or pypower must be importable.\n\n **net** - The pandapower network.\n\n OPTIONAL:\n\n **pf_type** ("runpp", string) - Type of validated power flow. Possible are ("runpp",\n "rundcpp", "runopp", "rundcopp")\n\n **max_diff_values** - Dict of maximal allowed difference values. The keys must be\n \'vm_pu\', \'va_degree\', \'p_branch_mw\', \'q_branch_mvar\', \'p_gen_mw\' and \'q_gen_mvar\' and\n the values floats.\n\n **run** (True, bool or list of two bools) - changing the value to False avoids trying to run\n (optimal) loadflows. Giving a list of two bools addresses first pypower and second\n pandapower.\n\n OUTPUT:\n\n **conversion_success** - conversion_success is returned as False if pypower or pandapower\n cannot calculate a powerflow or if the maximum difference values (max_diff_values )\n cannot be hold.\n\n EXAMPLE:\n\n import pandapower.converter as pc\n\n net = cv.from_ppc(ppc_net, f_hz=50)\n\n conversion_success = cv.validate_from_ppc(ppc_net, net)\n\n NOTE:\n\n The user has to take care that the loadflow results already are included in the provided ppc_net or pypower is importable.\n '
if ('opp' in pf_type):
if (not (len(net.polynomial_cost) | len(net.piecewise_linear_cost))):
if ('gencost' in ppc_net):
if (not len(ppc_net['gencost'])):
logger.debug('ppc and pandapower net do not include cost information.')
return True
else:
logger.error('The pandapower net does not include cost information.')
return False
else:
logger.debug('ppc and pandapower net do not include cost information.')
return True
run = ([run, run] if isinstance(run, bool) else run)
if (pypower_import and run[0]):
try:
if (pf_type == 'runpp'):
ppc_net = runpf.runpf(ppc_net, ppopt)[0]
elif (pf_type == 'rundcpp'):
ppc_net = rundcpf.rundcpf(ppc_net, ppopt)[0]
elif (pf_type == 'runopp'):
ppc_net = runopf.runopf(ppc_net, ppopt)
elif (pf_type == 'rundcopp'):
ppc_net = rundcopf.rundcopf(ppc_net, ppopt)
else:
raise ValueError(('The pf_type %s is unknown' % pf_type))
except:
logger.debug('The pypower run did not work.')
ppc_success = True
if ('success' in ppc_net.keys()):
if (ppc_net['success'] != 1):
ppc_success = False
logger.error(('The given ppc data indicates an unsuccessful pypower powerflow: ' + "'ppc_net['success'] != 1'"))
if (ppc_net['branch'].shape[1] < 17):
ppc_success = False
logger.error('The shape of given ppc data indicates missing pypower powerflow results.')
if run[1]:
if (pf_type == 'runpp'):
try:
pp.runpp(net, init='dc', calculate_voltage_angles=True, trafo_model='pi')
except pp.LoadflowNotConverged:
try:
pp.runpp(net, calculate_voltage_angles=True, init='flat', trafo_model='pi')
except pp.LoadflowNotConverged:
try:
pp.runpp(net, trafo_model='pi', calculate_voltage_angles=False)
if ('bus_va_degree' in max_diff_values.keys()):
max_diff_values['bus_va_degree'] = (100.0 if (max_diff_values['bus_va_degree'] < 100.0) else max_diff_values['bus_va_degree'])
logger.info('voltage_angles could be calculated.')
except pp.LoadflowNotConverged:
logger.error('The pandapower powerflow does not converge.')
elif (pf_type == 'rundcpp'):
try:
pp.rundcpp(net, trafo_model='pi')
except pp.LoadflowNotConverged:
logger.error('The pandapower dc powerflow does not converge.')
elif (pf_type == 'runopp'):
try:
pp.runopp(net, init='flat', calculate_voltage_angles=True)
except pp.OPFNotConverged:
try:
pp.runopp(net, init='pf', calculate_voltage_angles=True)
except (pp.OPFNotConverged, pp.LoadflowNotConverged, KeyError):
try:
pp.runopp(net, init='flat', calculate_voltage_angles=False)
logger.info('voltage_angles could be calculated.')
if ('bus_va_degree' in max_diff_values.keys()):
max_diff_values['bus_va_degree'] = (100.0 if (max_diff_values['bus_va_degree'] < 100.0) else max_diff_values['bus_va_degree'])
except pp.OPFNotConverged:
try:
pp.runopp(net, init='pf', calculate_voltage_angles=False)
if ('bus_va_degree' in max_diff_values.keys()):
max_diff_values['bus_va_degree'] = (100.0 if (max_diff_values['bus_va_degree'] < 100.0) else max_diff_values['bus_va_degree'])
logger.info('voltage_angles could be calculated.')
except (pp.OPFNotConverged, pp.LoadflowNotConverged, KeyError):
logger.error('The pandapower optimal powerflow does not converge.')
elif (pf_type == 'rundcopp'):
try:
pp.rundcopp(net)
except pp.LoadflowNotConverged:
logger.error('The pandapower dc optimal powerflow does not converge.')
else:
raise ValueError(('The pf_type %s is unknown' % pf_type))
if (not ppc_success):
return False
if ('opp' in pf_type):
if (not net.OPF_converged):
return
elif (not net.converged):
return False
ppc_res = dict.fromkeys(ppc_elms)
ppc_res['branch'] = ppc_net['branch'][:, 13:17]
ppc_res['bus'] = ppc_net['bus'][:, 7:9]
ppc_res['gen'] = ppc_net['gen'][:, 1:3]
pp_res = dict.fromkeys(ppc_elms)
pp_res['bus'] = array(net.res_bus.sort_index()[['vm_pu', 'va_degree']])
pp_res['gen'] = zeros([1, 2])
if (len(ppc_net['gen'].shape) == 1):
ppc_net['gen'] = array(ppc_net['gen'], ndmin=2)
GENS = DataFrame(ppc_net['gen'][:, [0]].astype(int))
GEN_uniq = GENS.drop_duplicates()
already_used_gen = Series(zeros(GEN_uniq.shape[0]).astype(int), index=[int(v) for v in GEN_uniq.values])
change_q_compare = []
for (i, j) in GENS.iterrows():
(current_bus_type, current_bus_idx, same_bus_gen_idx, first_same_bus_in_service_gen_idx, last_same_bus_in_service_gen_idx) = _gen_bus_info(ppc_net, i)
if ((current_bus_type == 3) and (i == first_same_bus_in_service_gen_idx)):
pp_res['gen'] = append(pp_res['gen'], array(net.res_ext_grid[(net.ext_grid.bus == current_bus_idx)][['p_mw', 'q_mvar']]).reshape((1, 2)), 0)
elif ((current_bus_type == 2) and (i == first_same_bus_in_service_gen_idx)):
pp_res['gen'] = append(pp_res['gen'], array(net.res_gen[(net.gen.bus == current_bus_idx)][['p_mw', 'q_mvar']]).reshape((1, 2)), 0)
else:
pp_res['gen'] = append(pp_res['gen'], array(net.res_sgen[(net.sgen.bus == current_bus_idx)][['p_mw', 'q_mvar']])[already_used_gen.at[int(j)]].reshape((1, 2)), 0)
already_used_gen.at[int(j)] += 1
change_q_compare += [int(j)]
pp_res['gen'] = pp_res['gen'][1:, :]
pp_res['branch'] = zeros([1, 4])
try:
init1 = concat([net.line.from_bus, net.line.to_bus], axis=1, sort=True).drop_duplicates()
init2 = concat([net.trafo.hv_bus, net.trafo.lv_bus], axis=1, sort=True).drop_duplicates()
except TypeError:
init1 = concat([net.line.from_bus, net.line.to_bus], axis=1).drop_duplicates()
init2 = concat([net.trafo.hv_bus, net.trafo.lv_bus], axis=1).drop_duplicates()
init1['hv_bus'] = nan
init1['lv_bus'] = nan
init2['from_bus'] = nan
init2['to_bus'] = nan
try:
already_used_branches = concat([init1, init2], axis=0, sort=True)
except TypeError:
already_used_branches = concat([init1, init2], axis=0)
already_used_branches['number'] = zeros([already_used_branches.shape[0], 1]).astype(int)
BRANCHES = DataFrame(ppc_net['branch'][:, [0, 1, 8, 9]])
for i in BRANCHES.index:
from_bus = pp.get_element_index(net, 'bus', name=int(ppc_net['branch'][(i, 0)]))
to_bus = pp.get_element_index(net, 'bus', name=int(ppc_net['branch'][(i, 1)]))
from_vn_kv = ppc_net['bus'][(from_bus, 9)]
to_vn_kv = ppc_net['bus'][(to_bus, 9)]
ratio = BRANCHES[2].at[i]
angle = BRANCHES[3].at[i]
if (((from_vn_kv == to_vn_kv) & ((ratio == 0) | (ratio == 1))) & (angle == 0)):
pp_res['branch'] = append(pp_res['branch'], array(net.res_line[((net.line.from_bus == from_bus) & (net.line.to_bus == to_bus))][['p_from_mw', 'q_from_mvar', 'p_to_mw', 'q_to_mvar']])[int(already_used_branches.number.loc[((already_used_branches.from_bus == from_bus) & (already_used_branches.to_bus == to_bus))].values)].reshape(1, 4), 0)
already_used_branches.number.loc[((already_used_branches.from_bus == from_bus) & (already_used_branches.to_bus == to_bus))] += 1
elif (from_vn_kv >= to_vn_kv):
pp_res['branch'] = append(pp_res['branch'], array(net.res_trafo[((net.trafo.hv_bus == from_bus) & (net.trafo.lv_bus == to_bus))][['p_hv_mw', 'q_hv_mvar', 'p_lv_mw', 'q_lv_mvar']])[int(already_used_branches.number.loc[((already_used_branches.hv_bus == from_bus) & (already_used_branches.lv_bus == to_bus))].values)].reshape(1, 4), 0)
already_used_branches.number.loc[((already_used_branches.hv_bus == from_bus) & (already_used_branches.lv_bus == to_bus))] += 1
else:
pp_res['branch'] = append(pp_res['branch'], array(net.res_trafo[((net.trafo.hv_bus == to_bus) & (net.trafo.lv_bus == from_bus))][['p_lv_mw', 'q_lv_mvar', 'p_hv_mw', 'q_hv_mvar']])[int(already_used_branches.number.loc[((already_used_branches.hv_bus == to_bus) & (already_used_branches.lv_bus == from_bus))].values)].reshape(1, 4), 0)
already_used_branches.number.loc[((already_used_branches.hv_bus == to_bus) & (already_used_branches.lv_bus == from_bus))] += 1
pp_res['branch'] = pp_res['branch'][1:, :]
diff_res = dict.fromkeys(ppc_elms)
diff_res['bus'] = (ppc_res['bus'] - pp_res['bus'])
diff_res['bus'][:, 1] -= diff_res['bus'][(0, 1)]
diff_res['branch'] = (ppc_res['branch'] - pp_res['branch'])
diff_res['gen'] = (ppc_res['gen'] - pp_res['gen'])
for i in GEN_uniq.loc[GEN_uniq[0].isin(change_q_compare)].index:
next_is = GEN_uniq.index[(GEN_uniq.index > i)]
if (len(next_is) > 0):
next_i = next_is[0]
else:
next_i = (GENS.index[(- 1)] + 1)
if ((next_i - i) > 1):
diff_res['gen'][i:next_i, 1] = sum(diff_res['gen'][i:next_i, 1])
logger.debug(('Maximum voltage magnitude difference between pypower and pandapower: %.2e pu' % max_(abs(diff_res['bus'][:, 0]))))
logger.debug(('Maximum voltage angle difference between pypower and pandapower: %.2e degree' % max_(abs(diff_res['bus'][:, 1]))))
logger.debug(('Maximum branch flow active power difference between pypower and pandapower: %.2e MW' % max_(abs(diff_res['branch'][:, [0, 2]]))))
logger.debug(('Maximum branch flow reactive power difference between pypower and pandapower: %.2e MVAr' % max_(abs(diff_res['branch'][:, [1, 3]]))))
logger.debug(('Maximum active power generation difference between pypower and pandapower: %.2e MW' % max_(abs(diff_res['gen'][:, 0]))))
logger.debug(('Maximum reactive power generation difference between pypower and pandapower: %.2e MVAr' % max_(abs(diff_res['gen'][:, 1]))))
if (_validate_diff_res(diff_res, {'bus_vm_pu': 0.001, 'bus_va_degree': 0.001, 'branch_p_mw': 1e-06, 'branch_q_mvar': 1e-06}) and (max_(abs(diff_res['gen'])) > 0.1).any()):
logger.debug('The active/reactive power generation difference possibly results because of a pypower error. Please validate the results via pypower loadflow.')
if isinstance(max_diff_values, dict):
return _validate_diff_res(diff_res, max_diff_values)
else:
logger.debug("'max_diff_values' must be a dict.") | -2,964,167,797,680,866,300 | This function validates the pypower case files to pandapower net structure conversion via a comparison of loadflow calculation results. (Hence the opf cost conversion is not validated.)
INPUT:
**ppc_net** - The pypower case file, which must already contain the pypower powerflow
results or pypower must be importable.
**net** - The pandapower network.
OPTIONAL:
**pf_type** ("runpp", string) - Type of validated power flow. Possible are ("runpp",
"rundcpp", "runopp", "rundcopp")
**max_diff_values** - Dict of maximal allowed difference values. The keys must be
'vm_pu', 'va_degree', 'p_branch_mw', 'q_branch_mvar', 'p_gen_mw' and 'q_gen_mvar' and
the values floats.
**run** (True, bool or list of two bools) - changing the value to False avoids trying to run
(optimal) loadflows. Giving a list of two bools addresses first pypower and second
pandapower.
OUTPUT:
**conversion_success** - conversion_success is returned as False if pypower or pandapower
cannot calculate a powerflow or if the maximum difference values (max_diff_values )
cannot be hold.
EXAMPLE:
import pandapower.converter as pc
net = cv.from_ppc(ppc_net, f_hz=50)
conversion_success = cv.validate_from_ppc(ppc_net, net)
NOTE:
The user has to take care that the loadflow results already are included in the provided ppc_net or pypower is importable. | pandapower/converter/pypower/from_ppc.py | validate_from_ppc | BaraaUniKassel/pandapower | python | def validate_from_ppc(ppc_net, net, pf_type='runpp', max_diff_values={'bus_vm_pu': 1e-06, 'bus_va_degree': 1e-05, 'branch_p_mw': 1e-06, 'branch_q_mvar': 1e-06, 'gen_p_mw': 1e-06, 'gen_q_mvar': 1e-06}, run=True):
'\n This function validates the pypower case files to pandapower net structure conversion via a comparison of loadflow calculation results. (Hence the opf cost conversion is not validated.)\n\n INPUT:\n\n **ppc_net** - The pypower case file, which must already contain the pypower powerflow\n results or pypower must be importable.\n\n **net** - The pandapower network.\n\n OPTIONAL:\n\n **pf_type** ("runpp", string) - Type of validated power flow. Possible are ("runpp",\n "rundcpp", "runopp", "rundcopp")\n\n **max_diff_values** - Dict of maximal allowed difference values. The keys must be\n \'vm_pu\', \'va_degree\', \'p_branch_mw\', \'q_branch_mvar\', \'p_gen_mw\' and \'q_gen_mvar\' and\n the values floats.\n\n **run** (True, bool or list of two bools) - changing the value to False avoids trying to run\n (optimal) loadflows. Giving a list of two bools addresses first pypower and second\n pandapower.\n\n OUTPUT:\n\n **conversion_success** - conversion_success is returned as False if pypower or pandapower\n cannot calculate a powerflow or if the maximum difference values (max_diff_values )\n cannot be hold.\n\n EXAMPLE:\n\n import pandapower.converter as pc\n\n net = cv.from_ppc(ppc_net, f_hz=50)\n\n conversion_success = cv.validate_from_ppc(ppc_net, net)\n\n NOTE:\n\n The user has to take care that the loadflow results already are included in the provided ppc_net or pypower is importable.\n '
if ('opp' in pf_type):
if (not (len(net.polynomial_cost) | len(net.piecewise_linear_cost))):
if ('gencost' in ppc_net):
if (not len(ppc_net['gencost'])):
logger.debug('ppc and pandapower net do not include cost information.')
return True
else:
logger.error('The pandapower net does not include cost information.')
return False
else:
logger.debug('ppc and pandapower net do not include cost information.')
return True
run = ([run, run] if isinstance(run, bool) else run)
if (pypower_import and run[0]):
try:
if (pf_type == 'runpp'):
ppc_net = runpf.runpf(ppc_net, ppopt)[0]
elif (pf_type == 'rundcpp'):
ppc_net = rundcpf.rundcpf(ppc_net, ppopt)[0]
elif (pf_type == 'runopp'):
ppc_net = runopf.runopf(ppc_net, ppopt)
elif (pf_type == 'rundcopp'):
ppc_net = rundcopf.rundcopf(ppc_net, ppopt)
else:
raise ValueError(('The pf_type %s is unknown' % pf_type))
except:
logger.debug('The pypower run did not work.')
ppc_success = True
if ('success' in ppc_net.keys()):
if (ppc_net['success'] != 1):
ppc_success = False
logger.error(('The given ppc data indicates an unsuccessful pypower powerflow: ' + "'ppc_net['success'] != 1'"))
if (ppc_net['branch'].shape[1] < 17):
ppc_success = False
logger.error('The shape of given ppc data indicates missing pypower powerflow results.')
if run[1]:
if (pf_type == 'runpp'):
try:
pp.runpp(net, init='dc', calculate_voltage_angles=True, trafo_model='pi')
except pp.LoadflowNotConverged:
try:
pp.runpp(net, calculate_voltage_angles=True, init='flat', trafo_model='pi')
except pp.LoadflowNotConverged:
try:
pp.runpp(net, trafo_model='pi', calculate_voltage_angles=False)
if ('bus_va_degree' in max_diff_values.keys()):
max_diff_values['bus_va_degree'] = (100.0 if (max_diff_values['bus_va_degree'] < 100.0) else max_diff_values['bus_va_degree'])
logger.info('voltage_angles could be calculated.')
except pp.LoadflowNotConverged:
logger.error('The pandapower powerflow does not converge.')
elif (pf_type == 'rundcpp'):
try:
pp.rundcpp(net, trafo_model='pi')
except pp.LoadflowNotConverged:
logger.error('The pandapower dc powerflow does not converge.')
elif (pf_type == 'runopp'):
try:
pp.runopp(net, init='flat', calculate_voltage_angles=True)
except pp.OPFNotConverged:
try:
pp.runopp(net, init='pf', calculate_voltage_angles=True)
except (pp.OPFNotConverged, pp.LoadflowNotConverged, KeyError):
try:
pp.runopp(net, init='flat', calculate_voltage_angles=False)
logger.info('voltage_angles could be calculated.')
if ('bus_va_degree' in max_diff_values.keys()):
max_diff_values['bus_va_degree'] = (100.0 if (max_diff_values['bus_va_degree'] < 100.0) else max_diff_values['bus_va_degree'])
except pp.OPFNotConverged:
try:
pp.runopp(net, init='pf', calculate_voltage_angles=False)
if ('bus_va_degree' in max_diff_values.keys()):
max_diff_values['bus_va_degree'] = (100.0 if (max_diff_values['bus_va_degree'] < 100.0) else max_diff_values['bus_va_degree'])
logger.info('voltage_angles could be calculated.')
except (pp.OPFNotConverged, pp.LoadflowNotConverged, KeyError):
logger.error('The pandapower optimal powerflow does not converge.')
elif (pf_type == 'rundcopp'):
try:
pp.rundcopp(net)
except pp.LoadflowNotConverged:
logger.error('The pandapower dc optimal powerflow does not converge.')
else:
raise ValueError(('The pf_type %s is unknown' % pf_type))
if (not ppc_success):
return False
if ('opp' in pf_type):
if (not net.OPF_converged):
return
elif (not net.converged):
return False
ppc_res = dict.fromkeys(ppc_elms)
ppc_res['branch'] = ppc_net['branch'][:, 13:17]
ppc_res['bus'] = ppc_net['bus'][:, 7:9]
ppc_res['gen'] = ppc_net['gen'][:, 1:3]
pp_res = dict.fromkeys(ppc_elms)
pp_res['bus'] = array(net.res_bus.sort_index()[['vm_pu', 'va_degree']])
pp_res['gen'] = zeros([1, 2])
if (len(ppc_net['gen'].shape) == 1):
ppc_net['gen'] = array(ppc_net['gen'], ndmin=2)
GENS = DataFrame(ppc_net['gen'][:, [0]].astype(int))
GEN_uniq = GENS.drop_duplicates()
already_used_gen = Series(zeros(GEN_uniq.shape[0]).astype(int), index=[int(v) for v in GEN_uniq.values])
change_q_compare = []
for (i, j) in GENS.iterrows():
(current_bus_type, current_bus_idx, same_bus_gen_idx, first_same_bus_in_service_gen_idx, last_same_bus_in_service_gen_idx) = _gen_bus_info(ppc_net, i)
if ((current_bus_type == 3) and (i == first_same_bus_in_service_gen_idx)):
pp_res['gen'] = append(pp_res['gen'], array(net.res_ext_grid[(net.ext_grid.bus == current_bus_idx)][['p_mw', 'q_mvar']]).reshape((1, 2)), 0)
elif ((current_bus_type == 2) and (i == first_same_bus_in_service_gen_idx)):
pp_res['gen'] = append(pp_res['gen'], array(net.res_gen[(net.gen.bus == current_bus_idx)][['p_mw', 'q_mvar']]).reshape((1, 2)), 0)
else:
pp_res['gen'] = append(pp_res['gen'], array(net.res_sgen[(net.sgen.bus == current_bus_idx)][['p_mw', 'q_mvar']])[already_used_gen.at[int(j)]].reshape((1, 2)), 0)
already_used_gen.at[int(j)] += 1
change_q_compare += [int(j)]
pp_res['gen'] = pp_res['gen'][1:, :]
pp_res['branch'] = zeros([1, 4])
try:
init1 = concat([net.line.from_bus, net.line.to_bus], axis=1, sort=True).drop_duplicates()
init2 = concat([net.trafo.hv_bus, net.trafo.lv_bus], axis=1, sort=True).drop_duplicates()
except TypeError:
init1 = concat([net.line.from_bus, net.line.to_bus], axis=1).drop_duplicates()
init2 = concat([net.trafo.hv_bus, net.trafo.lv_bus], axis=1).drop_duplicates()
init1['hv_bus'] = nan
init1['lv_bus'] = nan
init2['from_bus'] = nan
init2['to_bus'] = nan
try:
already_used_branches = concat([init1, init2], axis=0, sort=True)
except TypeError:
already_used_branches = concat([init1, init2], axis=0)
already_used_branches['number'] = zeros([already_used_branches.shape[0], 1]).astype(int)
BRANCHES = DataFrame(ppc_net['branch'][:, [0, 1, 8, 9]])
for i in BRANCHES.index:
from_bus = pp.get_element_index(net, 'bus', name=int(ppc_net['branch'][(i, 0)]))
to_bus = pp.get_element_index(net, 'bus', name=int(ppc_net['branch'][(i, 1)]))
from_vn_kv = ppc_net['bus'][(from_bus, 9)]
to_vn_kv = ppc_net['bus'][(to_bus, 9)]
ratio = BRANCHES[2].at[i]
angle = BRANCHES[3].at[i]
if (((from_vn_kv == to_vn_kv) & ((ratio == 0) | (ratio == 1))) & (angle == 0)):
pp_res['branch'] = append(pp_res['branch'], array(net.res_line[((net.line.from_bus == from_bus) & (net.line.to_bus == to_bus))][['p_from_mw', 'q_from_mvar', 'p_to_mw', 'q_to_mvar']])[int(already_used_branches.number.loc[((already_used_branches.from_bus == from_bus) & (already_used_branches.to_bus == to_bus))].values)].reshape(1, 4), 0)
already_used_branches.number.loc[((already_used_branches.from_bus == from_bus) & (already_used_branches.to_bus == to_bus))] += 1
elif (from_vn_kv >= to_vn_kv):
pp_res['branch'] = append(pp_res['branch'], array(net.res_trafo[((net.trafo.hv_bus == from_bus) & (net.trafo.lv_bus == to_bus))][['p_hv_mw', 'q_hv_mvar', 'p_lv_mw', 'q_lv_mvar']])[int(already_used_branches.number.loc[((already_used_branches.hv_bus == from_bus) & (already_used_branches.lv_bus == to_bus))].values)].reshape(1, 4), 0)
already_used_branches.number.loc[((already_used_branches.hv_bus == from_bus) & (already_used_branches.lv_bus == to_bus))] += 1
else:
pp_res['branch'] = append(pp_res['branch'], array(net.res_trafo[((net.trafo.hv_bus == to_bus) & (net.trafo.lv_bus == from_bus))][['p_lv_mw', 'q_lv_mvar', 'p_hv_mw', 'q_hv_mvar']])[int(already_used_branches.number.loc[((already_used_branches.hv_bus == to_bus) & (already_used_branches.lv_bus == from_bus))].values)].reshape(1, 4), 0)
already_used_branches.number.loc[((already_used_branches.hv_bus == to_bus) & (already_used_branches.lv_bus == from_bus))] += 1
pp_res['branch'] = pp_res['branch'][1:, :]
diff_res = dict.fromkeys(ppc_elms)
diff_res['bus'] = (ppc_res['bus'] - pp_res['bus'])
diff_res['bus'][:, 1] -= diff_res['bus'][(0, 1)]
diff_res['branch'] = (ppc_res['branch'] - pp_res['branch'])
diff_res['gen'] = (ppc_res['gen'] - pp_res['gen'])
for i in GEN_uniq.loc[GEN_uniq[0].isin(change_q_compare)].index:
next_is = GEN_uniq.index[(GEN_uniq.index > i)]
if (len(next_is) > 0):
next_i = next_is[0]
else:
next_i = (GENS.index[(- 1)] + 1)
if ((next_i - i) > 1):
diff_res['gen'][i:next_i, 1] = sum(diff_res['gen'][i:next_i, 1])
logger.debug(('Maximum voltage magnitude difference between pypower and pandapower: %.2e pu' % max_(abs(diff_res['bus'][:, 0]))))
logger.debug(('Maximum voltage angle difference between pypower and pandapower: %.2e degree' % max_(abs(diff_res['bus'][:, 1]))))
logger.debug(('Maximum branch flow active power difference between pypower and pandapower: %.2e MW' % max_(abs(diff_res['branch'][:, [0, 2]]))))
logger.debug(('Maximum branch flow reactive power difference between pypower and pandapower: %.2e MVAr' % max_(abs(diff_res['branch'][:, [1, 3]]))))
logger.debug(('Maximum active power generation difference between pypower and pandapower: %.2e MW' % max_(abs(diff_res['gen'][:, 0]))))
logger.debug(('Maximum reactive power generation difference between pypower and pandapower: %.2e MVAr' % max_(abs(diff_res['gen'][:, 1]))))
if (_validate_diff_res(diff_res, {'bus_vm_pu': 0.001, 'bus_va_degree': 0.001, 'branch_p_mw': 1e-06, 'branch_q_mvar': 1e-06}) and (max_(abs(diff_res['gen'])) > 0.1).any()):
logger.debug('The active/reactive power generation difference possibly results because of a pypower error. Please validate the results via pypower loadflow.')
if isinstance(max_diff_values, dict):
return _validate_diff_res(diff_res, max_diff_values)
else:
logger.debug("'max_diff_values' must be a dict.") |
def _get_zh_a_page_count() -> int:
'\n 所有股票的总页数\n http://vip.stock.finance.sina.com.cn/mkt/#hs_a\n :return: 需要抓取的股票总页数\n :rtype: int\n '
res = requests.get(zh_sina_a_stock_count_url)
page_count = (int(re.findall(re.compile('\\d+'), res.text)[0]) / 80)
if isinstance(page_count, int):
return page_count
else:
return (int(page_count) + 1) | 5,514,657,700,420,927,000 | 所有股票的总页数
http://vip.stock.finance.sina.com.cn/mkt/#hs_a
:return: 需要抓取的股票总页数
:rtype: int | akshare/stock/zh_stock_a_sina.py | _get_zh_a_page_count | fellowfun/akshare | python | def _get_zh_a_page_count() -> int:
'\n 所有股票的总页数\n http://vip.stock.finance.sina.com.cn/mkt/#hs_a\n :return: 需要抓取的股票总页数\n :rtype: int\n '
res = requests.get(zh_sina_a_stock_count_url)
page_count = (int(re.findall(re.compile('\\d+'), res.text)[0]) / 80)
if isinstance(page_count, int):
return page_count
else:
return (int(page_count) + 1) |
def stock_zh_a_spot() -> pd.DataFrame:
'\n 从新浪财经-A股获取所有A股的实时行情数据, 重复运行本函数会被新浪暂时封 IP\n http://vip.stock.finance.sina.com.cn/mkt/#qbgg_hk\n :return: pandas.DataFrame\n symbol code name trade pricechange changepercent buy 0 sh600000 600000 浦发银行 12.920 -0.030 -0.232 12.920\n 1 sh600004 600004 白云机场 18.110 -0.370 -2.002 18.110\n 2 sh600006 600006 东风汽车 4.410 -0.030 -0.676 4.410\n 3 sh600007 600007 中国国贸 17.240 -0.360 -2.045 17.240\n 4 sh600008 600008 首创股份 3.320 -0.030 -0.896 3.310\n ... ... ... ... ... ... ...\n 3755 sh600096 600096 云天化 5.270 -0.220 -4.007 5.270\n 3756 sh600097 600097 开创国际 10.180 -0.120 -1.165 10.180\n 3757 sh600098 600098 广州发展 6.550 -0.040 -0.607 6.540\n 3758 sh600099 600099 林海股份 6.540 -0.150 -2.242 6.540\n 3759 sh600100 600100 同方股份 8.200 -0.100 -1.205 8.200\n sell settlement open high low volume amount 0 12.930 12.950 12.950 13.100 12.860 46023920 597016896\n 1 18.120 18.480 18.510 18.510 17.880 24175071 437419344\n 2 4.420 4.440 4.490 4.490 4.410 4304900 19130233\n 3 17.280 17.600 17.670 17.670 17.220 684801 11879731\n 4 3.320 3.350 3.360 3.360 3.300 8284294 27579688\n ... ... ... ... ... ... ...\n 3755 5.280 5.490 5.490 5.500 5.220 16964636 90595172\n 3756 10.190 10.300 10.220 10.340 10.090 1001676 10231669\n 3757 6.550 6.590 6.560 6.620 6.500 1996449 13098901\n 3758 6.580 6.690 6.650 6.680 6.530 1866180 12314997\n 3759 8.210 8.300 8.300 8.310 8.120 12087236 99281447\n ticktime per pb mktcap nmc turnoverratio\n 0 15:00:00 6.984 0.790 3.792289e+07 3.631006e+07 0.16376\n 1 15:00:07 32.927 2.365 3.747539e+06 3.747539e+06 1.16826\n 2 15:00:02 15.926 1.207 8.820000e+05 8.820000e+05 0.21525\n 3 15:00:02 22.390 2.367 1.736555e+06 1.736555e+06 0.06798\n 4 15:00:07 22.912 1.730 1.887569e+06 1.600444e+06 0.17185\n ... ... ... ... ... ...\n 3755 15:00:00 56.728 1.566 7.523847e+05 6.963668e+05 1.28386\n 3756 15:00:00 17.552 1.434 2.452734e+05 2.303459e+05 0.44268\n 3757 15:00:00 25.476 1.059 1.785659e+06 1.785659e+06 0.07323\n 3758 15:00:00 540.496 3.023 1.433045e+05 1.433045e+05 0.85167\n 3759 15:00:07 -6.264 1.465 2.430397e+06 2.430397e+06 0.40782\n '
big_df = pd.DataFrame()
page_count = _get_zh_a_page_count()
zh_sina_stock_payload_copy = zh_sina_a_stock_payload.copy()
for page in tqdm(range(1, (page_count + 1)), desc='Please wait for a moment'):
zh_sina_stock_payload_copy.update({'page': page})
r = requests.get(zh_sina_a_stock_url, params=zh_sina_stock_payload_copy)
data_json = demjson.decode(r.text)
big_df = big_df.append(pd.DataFrame(data_json), ignore_index=True)
return big_df | -3,537,146,474,981,795,300 | 从新浪财经-A股获取所有A股的实时行情数据, 重复运行本函数会被新浪暂时封 IP
http://vip.stock.finance.sina.com.cn/mkt/#qbgg_hk
:return: pandas.DataFrame
symbol code name trade pricechange changepercent buy 0 sh600000 600000 浦发银行 12.920 -0.030 -0.232 12.920
1 sh600004 600004 白云机场 18.110 -0.370 -2.002 18.110
2 sh600006 600006 东风汽车 4.410 -0.030 -0.676 4.410
3 sh600007 600007 中国国贸 17.240 -0.360 -2.045 17.240
4 sh600008 600008 首创股份 3.320 -0.030 -0.896 3.310
... ... ... ... ... ... ...
3755 sh600096 600096 云天化 5.270 -0.220 -4.007 5.270
3756 sh600097 600097 开创国际 10.180 -0.120 -1.165 10.180
3757 sh600098 600098 广州发展 6.550 -0.040 -0.607 6.540
3758 sh600099 600099 林海股份 6.540 -0.150 -2.242 6.540
3759 sh600100 600100 同方股份 8.200 -0.100 -1.205 8.200
sell settlement open high low volume amount 0 12.930 12.950 12.950 13.100 12.860 46023920 597016896
1 18.120 18.480 18.510 18.510 17.880 24175071 437419344
2 4.420 4.440 4.490 4.490 4.410 4304900 19130233
3 17.280 17.600 17.670 17.670 17.220 684801 11879731
4 3.320 3.350 3.360 3.360 3.300 8284294 27579688
... ... ... ... ... ... ...
3755 5.280 5.490 5.490 5.500 5.220 16964636 90595172
3756 10.190 10.300 10.220 10.340 10.090 1001676 10231669
3757 6.550 6.590 6.560 6.620 6.500 1996449 13098901
3758 6.580 6.690 6.650 6.680 6.530 1866180 12314997
3759 8.210 8.300 8.300 8.310 8.120 12087236 99281447
ticktime per pb mktcap nmc turnoverratio
0 15:00:00 6.984 0.790 3.792289e+07 3.631006e+07 0.16376
1 15:00:07 32.927 2.365 3.747539e+06 3.747539e+06 1.16826
2 15:00:02 15.926 1.207 8.820000e+05 8.820000e+05 0.21525
3 15:00:02 22.390 2.367 1.736555e+06 1.736555e+06 0.06798
4 15:00:07 22.912 1.730 1.887569e+06 1.600444e+06 0.17185
... ... ... ... ... ...
3755 15:00:00 56.728 1.566 7.523847e+05 6.963668e+05 1.28386
3756 15:00:00 17.552 1.434 2.452734e+05 2.303459e+05 0.44268
3757 15:00:00 25.476 1.059 1.785659e+06 1.785659e+06 0.07323
3758 15:00:00 540.496 3.023 1.433045e+05 1.433045e+05 0.85167
3759 15:00:07 -6.264 1.465 2.430397e+06 2.430397e+06 0.40782 | akshare/stock/zh_stock_a_sina.py | stock_zh_a_spot | fellowfun/akshare | python | def stock_zh_a_spot() -> pd.DataFrame:
'\n 从新浪财经-A股获取所有A股的实时行情数据, 重复运行本函数会被新浪暂时封 IP\n http://vip.stock.finance.sina.com.cn/mkt/#qbgg_hk\n :return: pandas.DataFrame\n symbol code name trade pricechange changepercent buy 0 sh600000 600000 浦发银行 12.920 -0.030 -0.232 12.920\n 1 sh600004 600004 白云机场 18.110 -0.370 -2.002 18.110\n 2 sh600006 600006 东风汽车 4.410 -0.030 -0.676 4.410\n 3 sh600007 600007 中国国贸 17.240 -0.360 -2.045 17.240\n 4 sh600008 600008 首创股份 3.320 -0.030 -0.896 3.310\n ... ... ... ... ... ... ...\n 3755 sh600096 600096 云天化 5.270 -0.220 -4.007 5.270\n 3756 sh600097 600097 开创国际 10.180 -0.120 -1.165 10.180\n 3757 sh600098 600098 广州发展 6.550 -0.040 -0.607 6.540\n 3758 sh600099 600099 林海股份 6.540 -0.150 -2.242 6.540\n 3759 sh600100 600100 同方股份 8.200 -0.100 -1.205 8.200\n sell settlement open high low volume amount 0 12.930 12.950 12.950 13.100 12.860 46023920 597016896\n 1 18.120 18.480 18.510 18.510 17.880 24175071 437419344\n 2 4.420 4.440 4.490 4.490 4.410 4304900 19130233\n 3 17.280 17.600 17.670 17.670 17.220 684801 11879731\n 4 3.320 3.350 3.360 3.360 3.300 8284294 27579688\n ... ... ... ... ... ... ...\n 3755 5.280 5.490 5.490 5.500 5.220 16964636 90595172\n 3756 10.190 10.300 10.220 10.340 10.090 1001676 10231669\n 3757 6.550 6.590 6.560 6.620 6.500 1996449 13098901\n 3758 6.580 6.690 6.650 6.680 6.530 1866180 12314997\n 3759 8.210 8.300 8.300 8.310 8.120 12087236 99281447\n ticktime per pb mktcap nmc turnoverratio\n 0 15:00:00 6.984 0.790 3.792289e+07 3.631006e+07 0.16376\n 1 15:00:07 32.927 2.365 3.747539e+06 3.747539e+06 1.16826\n 2 15:00:02 15.926 1.207 8.820000e+05 8.820000e+05 0.21525\n 3 15:00:02 22.390 2.367 1.736555e+06 1.736555e+06 0.06798\n 4 15:00:07 22.912 1.730 1.887569e+06 1.600444e+06 0.17185\n ... ... ... ... ... ...\n 3755 15:00:00 56.728 1.566 7.523847e+05 6.963668e+05 1.28386\n 3756 15:00:00 17.552 1.434 2.452734e+05 2.303459e+05 0.44268\n 3757 15:00:00 25.476 1.059 1.785659e+06 1.785659e+06 0.07323\n 3758 15:00:00 540.496 3.023 1.433045e+05 1.433045e+05 0.85167\n 3759 15:00:07 -6.264 1.465 2.430397e+06 2.430397e+06 0.40782\n '
big_df = pd.DataFrame()
page_count = _get_zh_a_page_count()
zh_sina_stock_payload_copy = zh_sina_a_stock_payload.copy()
for page in tqdm(range(1, (page_count + 1)), desc='Please wait for a moment'):
zh_sina_stock_payload_copy.update({'page': page})
r = requests.get(zh_sina_a_stock_url, params=zh_sina_stock_payload_copy)
data_json = demjson.decode(r.text)
big_df = big_df.append(pd.DataFrame(data_json), ignore_index=True)
return big_df |
def stock_zh_a_daily(symbol: str='sz000613', adjust: str='qfq') -> pd.DataFrame:
'\n 新浪财经-A股-个股的历史行情数据, 大量抓取容易封IP\n :param symbol: sh600000\n :type symbol: str\n :param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; hfq-factor: 返回后复权因子; hfq-factor: 返回前复权因子\n :type adjust: str\n :return: specific data\n :rtype: pandas.DataFrame\n '
res = requests.get(zh_sina_a_stock_hist_url.format(symbol))
js_code = execjs.compile(hk_js_decode)
dict_list = js_code.call('d', res.text.split('=')[1].split(';')[0].replace('"', ''))
data_df = pd.DataFrame(dict_list)
data_df['date'] = data_df['date'].str.split('T', expand=True).iloc[:, 0]
data_df.index = pd.to_datetime(data_df['date'])
del data_df['date']
data_df = data_df.astype('float')
r = requests.get(zh_sina_a_stock_amount_url.format(symbol, symbol))
amount_data_json = demjson.decode(r.text[r.text.find('['):(r.text.rfind(']') + 1)])
amount_data_df = pd.DataFrame(amount_data_json)
amount_data_df.index = pd.to_datetime(amount_data_df.date)
del amount_data_df['date']
temp_df = pd.merge(data_df, amount_data_df, left_index=True, right_index=True, how='left')
temp_df.fillna(method='ffill', inplace=True)
temp_df = temp_df.astype(float)
temp_df['amount'] = (temp_df['amount'] * 10000)
temp_df['turnover'] = (temp_df['volume'] / temp_df['amount'])
temp_df.columns = ['open', 'high', 'low', 'close', 'volume', 'outstanding_share', 'turnover']
if (adjust == ''):
return temp_df
if (adjust == 'hfq'):
res = requests.get(zh_sina_a_stock_hfq_url.format(symbol))
hfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
hfq_factor_df.columns = ['date', 'hfq_factor']
hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
del hfq_factor_df['date']
temp_df = pd.merge(temp_df, hfq_factor_df, left_index=True, right_index=True, how='left')
temp_df.fillna(method='ffill', inplace=True)
temp_df = temp_df.astype(float)
temp_df['open'] = (temp_df['open'] * temp_df['hfq_factor'])
temp_df['high'] = (temp_df['high'] * temp_df['hfq_factor'])
temp_df['close'] = (temp_df['close'] * temp_df['hfq_factor'])
temp_df['low'] = (temp_df['low'] * temp_df['hfq_factor'])
return temp_df.iloc[:, :(- 1)]
if (adjust == 'qfq'):
res = requests.get(zh_sina_a_stock_qfq_url.format(symbol))
qfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
qfq_factor_df.columns = ['date', 'qfq_factor']
qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
del qfq_factor_df['date']
temp_df = pd.merge(temp_df, qfq_factor_df, left_index=True, right_index=True, how='left')
temp_df.fillna(method='ffill', inplace=True)
temp_df = temp_df.astype(float)
temp_df['open'] = (temp_df['open'] / temp_df['qfq_factor'])
temp_df['high'] = (temp_df['high'] / temp_df['qfq_factor'])
temp_df['close'] = (temp_df['close'] / temp_df['qfq_factor'])
temp_df['low'] = (temp_df['low'] / temp_df['qfq_factor'])
return temp_df.iloc[:, :(- 1)]
if (adjust == 'hfq-factor'):
res = requests.get(zh_sina_a_stock_hfq_url.format(symbol))
hfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
hfq_factor_df.columns = ['date', 'hfq_factor']
hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
del hfq_factor_df['date']
return hfq_factor_df
if (adjust == 'qfq-factor'):
res = requests.get(zh_sina_a_stock_qfq_url.format(symbol))
qfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
qfq_factor_df.columns = ['date', 'qfq_factor']
qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
del qfq_factor_df['date']
return qfq_factor_df | 1,219,581,013,612,928,500 | 新浪财经-A股-个股的历史行情数据, 大量抓取容易封IP
:param symbol: sh600000
:type symbol: str
:param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; hfq-factor: 返回后复权因子; hfq-factor: 返回前复权因子
:type adjust: str
:return: specific data
:rtype: pandas.DataFrame | akshare/stock/zh_stock_a_sina.py | stock_zh_a_daily | fellowfun/akshare | python | def stock_zh_a_daily(symbol: str='sz000613', adjust: str='qfq') -> pd.DataFrame:
'\n 新浪财经-A股-个股的历史行情数据, 大量抓取容易封IP\n :param symbol: sh600000\n :type symbol: str\n :param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; hfq-factor: 返回后复权因子; hfq-factor: 返回前复权因子\n :type adjust: str\n :return: specific data\n :rtype: pandas.DataFrame\n '
res = requests.get(zh_sina_a_stock_hist_url.format(symbol))
js_code = execjs.compile(hk_js_decode)
dict_list = js_code.call('d', res.text.split('=')[1].split(';')[0].replace('"', ))
data_df = pd.DataFrame(dict_list)
data_df['date'] = data_df['date'].str.split('T', expand=True).iloc[:, 0]
data_df.index = pd.to_datetime(data_df['date'])
del data_df['date']
data_df = data_df.astype('float')
r = requests.get(zh_sina_a_stock_amount_url.format(symbol, symbol))
amount_data_json = demjson.decode(r.text[r.text.find('['):(r.text.rfind(']') + 1)])
amount_data_df = pd.DataFrame(amount_data_json)
amount_data_df.index = pd.to_datetime(amount_data_df.date)
del amount_data_df['date']
temp_df = pd.merge(data_df, amount_data_df, left_index=True, right_index=True, how='left')
temp_df.fillna(method='ffill', inplace=True)
temp_df = temp_df.astype(float)
temp_df['amount'] = (temp_df['amount'] * 10000)
temp_df['turnover'] = (temp_df['volume'] / temp_df['amount'])
temp_df.columns = ['open', 'high', 'low', 'close', 'volume', 'outstanding_share', 'turnover']
if (adjust == ):
return temp_df
if (adjust == 'hfq'):
res = requests.get(zh_sina_a_stock_hfq_url.format(symbol))
hfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
hfq_factor_df.columns = ['date', 'hfq_factor']
hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
del hfq_factor_df['date']
temp_df = pd.merge(temp_df, hfq_factor_df, left_index=True, right_index=True, how='left')
temp_df.fillna(method='ffill', inplace=True)
temp_df = temp_df.astype(float)
temp_df['open'] = (temp_df['open'] * temp_df['hfq_factor'])
temp_df['high'] = (temp_df['high'] * temp_df['hfq_factor'])
temp_df['close'] = (temp_df['close'] * temp_df['hfq_factor'])
temp_df['low'] = (temp_df['low'] * temp_df['hfq_factor'])
return temp_df.iloc[:, :(- 1)]
if (adjust == 'qfq'):
res = requests.get(zh_sina_a_stock_qfq_url.format(symbol))
qfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
qfq_factor_df.columns = ['date', 'qfq_factor']
qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
del qfq_factor_df['date']
temp_df = pd.merge(temp_df, qfq_factor_df, left_index=True, right_index=True, how='left')
temp_df.fillna(method='ffill', inplace=True)
temp_df = temp_df.astype(float)
temp_df['open'] = (temp_df['open'] / temp_df['qfq_factor'])
temp_df['high'] = (temp_df['high'] / temp_df['qfq_factor'])
temp_df['close'] = (temp_df['close'] / temp_df['qfq_factor'])
temp_df['low'] = (temp_df['low'] / temp_df['qfq_factor'])
return temp_df.iloc[:, :(- 1)]
if (adjust == 'hfq-factor'):
res = requests.get(zh_sina_a_stock_hfq_url.format(symbol))
hfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
hfq_factor_df.columns = ['date', 'hfq_factor']
hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
del hfq_factor_df['date']
return hfq_factor_df
if (adjust == 'qfq-factor'):
res = requests.get(zh_sina_a_stock_qfq_url.format(symbol))
qfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
qfq_factor_df.columns = ['date', 'qfq_factor']
qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
del qfq_factor_df['date']
return qfq_factor_df |
async def async_setup_platform(hass, config, async_add_entities, discovery_info=None) -> None:
'Old way of setting up HomematicIP Cloud lights.'
pass | -2,221,725,257,671,890,000 | Old way of setting up HomematicIP Cloud lights. | homeassistant/components/homematicip_cloud/light.py | async_setup_platform | 0x00-0xFF/home-assistant | python | async def async_setup_platform(hass, config, async_add_entities, discovery_info=None) -> None:
pass |
async def async_setup_entry(hass: HomeAssistantType, config_entry: ConfigEntry, async_add_entities) -> None:
'Set up the HomematicIP Cloud lights from a config entry.'
hap = hass.data[HMIPC_DOMAIN][config_entry.data[HMIPC_HAPID]]
entities = []
for device in hap.home.devices:
if isinstance(device, AsyncBrandSwitchMeasuring):
entities.append(HomematicipLightMeasuring(hap, device))
elif isinstance(device, AsyncBrandSwitchNotificationLight):
entities.append(HomematicipLight(hap, device))
entities.append(HomematicipNotificationLight(hap, device, device.topLightChannelIndex))
entities.append(HomematicipNotificationLight(hap, device, device.bottomLightChannelIndex))
elif isinstance(device, (AsyncDimmer, AsyncPluggableDimmer, AsyncBrandDimmer, AsyncFullFlushDimmer)):
entities.append(HomematicipDimmer(hap, device))
if entities:
async_add_entities(entities) | 481,496,749,042,861,000 | Set up the HomematicIP Cloud lights from a config entry. | homeassistant/components/homematicip_cloud/light.py | async_setup_entry | 0x00-0xFF/home-assistant | python | async def async_setup_entry(hass: HomeAssistantType, config_entry: ConfigEntry, async_add_entities) -> None:
hap = hass.data[HMIPC_DOMAIN][config_entry.data[HMIPC_HAPID]]
entities = []
for device in hap.home.devices:
if isinstance(device, AsyncBrandSwitchMeasuring):
entities.append(HomematicipLightMeasuring(hap, device))
elif isinstance(device, AsyncBrandSwitchNotificationLight):
entities.append(HomematicipLight(hap, device))
entities.append(HomematicipNotificationLight(hap, device, device.topLightChannelIndex))
entities.append(HomematicipNotificationLight(hap, device, device.bottomLightChannelIndex))
elif isinstance(device, (AsyncDimmer, AsyncPluggableDimmer, AsyncBrandDimmer, AsyncFullFlushDimmer)):
entities.append(HomematicipDimmer(hap, device))
if entities:
async_add_entities(entities) |
def _convert_color(color: tuple) -> RGBColorState:
'\n Convert the given color to the reduced RGBColorState color.\n\n RGBColorStat contains only 8 colors including white and black,\n so a conversion is required.\n '
if (color is None):
return RGBColorState.WHITE
hue = int(color[0])
saturation = int(color[1])
if (saturation < 5):
return RGBColorState.WHITE
if (30 < hue <= 90):
return RGBColorState.YELLOW
if (90 < hue <= 160):
return RGBColorState.GREEN
if (150 < hue <= 210):
return RGBColorState.TURQUOISE
if (210 < hue <= 270):
return RGBColorState.BLUE
if (270 < hue <= 330):
return RGBColorState.PURPLE
return RGBColorState.RED | 3,999,648,746,070,601,000 | Convert the given color to the reduced RGBColorState color.
RGBColorStat contains only 8 colors including white and black,
so a conversion is required. | homeassistant/components/homematicip_cloud/light.py | _convert_color | 0x00-0xFF/home-assistant | python | def _convert_color(color: tuple) -> RGBColorState:
'\n Convert the given color to the reduced RGBColorState color.\n\n RGBColorStat contains only 8 colors including white and black,\n so a conversion is required.\n '
if (color is None):
return RGBColorState.WHITE
hue = int(color[0])
saturation = int(color[1])
if (saturation < 5):
return RGBColorState.WHITE
if (30 < hue <= 90):
return RGBColorState.YELLOW
if (90 < hue <= 160):
return RGBColorState.GREEN
if (150 < hue <= 210):
return RGBColorState.TURQUOISE
if (210 < hue <= 270):
return RGBColorState.BLUE
if (270 < hue <= 330):
return RGBColorState.PURPLE
return RGBColorState.RED |
def __init__(self, hap: HomematicipHAP, device) -> None:
'Initialize the light device.'
super().__init__(hap, device) | 4,148,022,420,929,488,400 | Initialize the light device. | homeassistant/components/homematicip_cloud/light.py | __init__ | 0x00-0xFF/home-assistant | python | def __init__(self, hap: HomematicipHAP, device) -> None:
super().__init__(hap, device) |
@property
def is_on(self) -> bool:
'Return true if device is on.'
return self._device.on | -2,283,132,927,271,933,000 | Return true if device is on. | homeassistant/components/homematicip_cloud/light.py | is_on | 0x00-0xFF/home-assistant | python | @property
def is_on(self) -> bool:
return self._device.on |
async def async_turn_on(self, **kwargs) -> None:
'Turn the device on.'
(await self._device.turn_on()) | 2,166,206,960,677,107,000 | Turn the device on. | homeassistant/components/homematicip_cloud/light.py | async_turn_on | 0x00-0xFF/home-assistant | python | async def async_turn_on(self, **kwargs) -> None:
(await self._device.turn_on()) |
async def async_turn_off(self, **kwargs) -> None:
'Turn the device off.'
(await self._device.turn_off()) | 155,385,039,799,394,780 | Turn the device off. | homeassistant/components/homematicip_cloud/light.py | async_turn_off | 0x00-0xFF/home-assistant | python | async def async_turn_off(self, **kwargs) -> None:
(await self._device.turn_off()) |
@property
def device_state_attributes(self) -> Dict[(str, Any)]:
'Return the state attributes of the generic device.'
state_attr = super().device_state_attributes
current_power_w = self._device.currentPowerConsumption
if (current_power_w > 0.05):
state_attr[ATTR_CURRENT_POWER_W] = round(current_power_w, 2)
state_attr[ATTR_TODAY_ENERGY_KWH] = round(self._device.energyCounter, 2)
return state_attr | -3,098,059,166,993,918,000 | Return the state attributes of the generic device. | homeassistant/components/homematicip_cloud/light.py | device_state_attributes | 0x00-0xFF/home-assistant | python | @property
def device_state_attributes(self) -> Dict[(str, Any)]:
state_attr = super().device_state_attributes
current_power_w = self._device.currentPowerConsumption
if (current_power_w > 0.05):
state_attr[ATTR_CURRENT_POWER_W] = round(current_power_w, 2)
state_attr[ATTR_TODAY_ENERGY_KWH] = round(self._device.energyCounter, 2)
return state_attr |
def __init__(self, hap: HomematicipHAP, device) -> None:
'Initialize the dimmer light device.'
super().__init__(hap, device) | 4,226,430,284,465,216,000 | Initialize the dimmer light device. | homeassistant/components/homematicip_cloud/light.py | __init__ | 0x00-0xFF/home-assistant | python | def __init__(self, hap: HomematicipHAP, device) -> None:
super().__init__(hap, device) |
@property
def is_on(self) -> bool:
'Return true if device is on.'
return ((self._device.dimLevel is not None) and (self._device.dimLevel > 0.0)) | -6,862,420,167,665,377,000 | Return true if device is on. | homeassistant/components/homematicip_cloud/light.py | is_on | 0x00-0xFF/home-assistant | python | @property
def is_on(self) -> bool:
return ((self._device.dimLevel is not None) and (self._device.dimLevel > 0.0)) |
@property
def brightness(self) -> int:
'Return the brightness of this light between 0..255.'
return int(((self._device.dimLevel or 0.0) * 255)) | 4,879,828,942,923,381,000 | Return the brightness of this light between 0..255. | homeassistant/components/homematicip_cloud/light.py | brightness | 0x00-0xFF/home-assistant | python | @property
def brightness(self) -> int:
return int(((self._device.dimLevel or 0.0) * 255)) |
@property
def supported_features(self) -> int:
'Flag supported features.'
return SUPPORT_BRIGHTNESS | -7,275,260,559,451,487,000 | Flag supported features. | homeassistant/components/homematicip_cloud/light.py | supported_features | 0x00-0xFF/home-assistant | python | @property
def supported_features(self) -> int:
return SUPPORT_BRIGHTNESS |
async def async_turn_on(self, **kwargs) -> None:
'Turn the light on.'
if (ATTR_BRIGHTNESS in kwargs):
(await self._device.set_dim_level((kwargs[ATTR_BRIGHTNESS] / 255.0)))
else:
(await self._device.set_dim_level(1)) | 5,651,431,970,317,736,000 | Turn the light on. | homeassistant/components/homematicip_cloud/light.py | async_turn_on | 0x00-0xFF/home-assistant | python | async def async_turn_on(self, **kwargs) -> None:
if (ATTR_BRIGHTNESS in kwargs):
(await self._device.set_dim_level((kwargs[ATTR_BRIGHTNESS] / 255.0)))
else:
(await self._device.set_dim_level(1)) |
async def async_turn_off(self, **kwargs) -> None:
'Turn the light off.'
(await self._device.set_dim_level(0)) | 904,547,101,540,762,200 | Turn the light off. | homeassistant/components/homematicip_cloud/light.py | async_turn_off | 0x00-0xFF/home-assistant | python | async def async_turn_off(self, **kwargs) -> None:
(await self._device.set_dim_level(0)) |
def __init__(self, hap: HomematicipHAP, device, channel: int) -> None:
'Initialize the dimmer light device.'
self.channel = channel
if (self.channel == 2):
super().__init__(hap, device, 'Top')
else:
super().__init__(hap, device, 'Bottom')
self._color_switcher = {RGBColorState.WHITE: [0.0, 0.0], RGBColorState.RED: [0.0, 100.0], RGBColorState.YELLOW: [60.0, 100.0], RGBColorState.GREEN: [120.0, 100.0], RGBColorState.TURQUOISE: [180.0, 100.0], RGBColorState.BLUE: [240.0, 100.0], RGBColorState.PURPLE: [300.0, 100.0]} | -936,554,559,333,744,100 | Initialize the dimmer light device. | homeassistant/components/homematicip_cloud/light.py | __init__ | 0x00-0xFF/home-assistant | python | def __init__(self, hap: HomematicipHAP, device, channel: int) -> None:
self.channel = channel
if (self.channel == 2):
super().__init__(hap, device, 'Top')
else:
super().__init__(hap, device, 'Bottom')
self._color_switcher = {RGBColorState.WHITE: [0.0, 0.0], RGBColorState.RED: [0.0, 100.0], RGBColorState.YELLOW: [60.0, 100.0], RGBColorState.GREEN: [120.0, 100.0], RGBColorState.TURQUOISE: [180.0, 100.0], RGBColorState.BLUE: [240.0, 100.0], RGBColorState.PURPLE: [300.0, 100.0]} |
@property
def is_on(self) -> bool:
'Return true if device is on.'
return ((self._func_channel.dimLevel is not None) and (self._func_channel.dimLevel > 0.0)) | -6,904,967,177,971,977,000 | Return true if device is on. | homeassistant/components/homematicip_cloud/light.py | is_on | 0x00-0xFF/home-assistant | python | @property
def is_on(self) -> bool:
return ((self._func_channel.dimLevel is not None) and (self._func_channel.dimLevel > 0.0)) |
@property
def brightness(self) -> int:
'Return the brightness of this light between 0..255.'
return int(((self._func_channel.dimLevel or 0.0) * 255)) | -5,342,752,628,957,432,000 | Return the brightness of this light between 0..255. | homeassistant/components/homematicip_cloud/light.py | brightness | 0x00-0xFF/home-assistant | python | @property
def brightness(self) -> int:
return int(((self._func_channel.dimLevel or 0.0) * 255)) |
@property
def hs_color(self) -> tuple:
'Return the hue and saturation color value [float, float].'
simple_rgb_color = self._func_channel.simpleRGBColorState
return self._color_switcher.get(simple_rgb_color, [0.0, 0.0]) | 6,329,802,148,743,832,000 | Return the hue and saturation color value [float, float]. | homeassistant/components/homematicip_cloud/light.py | hs_color | 0x00-0xFF/home-assistant | python | @property
def hs_color(self) -> tuple:
simple_rgb_color = self._func_channel.simpleRGBColorState
return self._color_switcher.get(simple_rgb_color, [0.0, 0.0]) |
@property
def device_state_attributes(self) -> Dict[(str, Any)]:
'Return the state attributes of the generic device.'
state_attr = super().device_state_attributes
if self.is_on:
state_attr[ATTR_COLOR_NAME] = self._func_channel.simpleRGBColorState
return state_attr | -7,103,013,381,797,680,000 | Return the state attributes of the generic device. | homeassistant/components/homematicip_cloud/light.py | device_state_attributes | 0x00-0xFF/home-assistant | python | @property
def device_state_attributes(self) -> Dict[(str, Any)]:
state_attr = super().device_state_attributes
if self.is_on:
state_attr[ATTR_COLOR_NAME] = self._func_channel.simpleRGBColorState
return state_attr |
@property
def name(self) -> str:
'Return the name of the generic device.'
return f'{super().name} Notification' | 9,124,239,975,491,450,000 | Return the name of the generic device. | homeassistant/components/homematicip_cloud/light.py | name | 0x00-0xFF/home-assistant | python | @property
def name(self) -> str:
return f'{super().name} Notification' |
@property
def supported_features(self) -> int:
'Flag supported features.'
return (SUPPORT_BRIGHTNESS | SUPPORT_COLOR) | 8,128,663,612,521,723,000 | Flag supported features. | homeassistant/components/homematicip_cloud/light.py | supported_features | 0x00-0xFF/home-assistant | python | @property
def supported_features(self) -> int:
return (SUPPORT_BRIGHTNESS | SUPPORT_COLOR) |
@property
def unique_id(self) -> str:
'Return a unique ID.'
return f'{self.__class__.__name__}_{self.post}_{self._device.id}' | -2,511,959,092,211,002,000 | Return a unique ID. | homeassistant/components/homematicip_cloud/light.py | unique_id | 0x00-0xFF/home-assistant | python | @property
def unique_id(self) -> str:
return f'{self.__class__.__name__}_{self.post}_{self._device.id}' |
async def async_turn_on(self, **kwargs) -> None:
'Turn the light on.'
hs_color = kwargs.get(ATTR_HS_COLOR, self.hs_color)
simple_rgb_color = _convert_color(hs_color)
brightness = kwargs.get(ATTR_BRIGHTNESS, self.brightness)
if (not kwargs):
brightness = 255
brightness = max(10, brightness)
dim_level = (brightness / 255.0)
transition = kwargs.get(ATTR_TRANSITION, 0.5)
(await self._device.set_rgb_dim_level_with_time(channelIndex=self.channel, rgb=simple_rgb_color, dimLevel=dim_level, onTime=0, rampTime=transition)) | -8,156,840,869,278,348,000 | Turn the light on. | homeassistant/components/homematicip_cloud/light.py | async_turn_on | 0x00-0xFF/home-assistant | python | async def async_turn_on(self, **kwargs) -> None:
hs_color = kwargs.get(ATTR_HS_COLOR, self.hs_color)
simple_rgb_color = _convert_color(hs_color)
brightness = kwargs.get(ATTR_BRIGHTNESS, self.brightness)
if (not kwargs):
brightness = 255
brightness = max(10, brightness)
dim_level = (brightness / 255.0)
transition = kwargs.get(ATTR_TRANSITION, 0.5)
(await self._device.set_rgb_dim_level_with_time(channelIndex=self.channel, rgb=simple_rgb_color, dimLevel=dim_level, onTime=0, rampTime=transition)) |
async def async_turn_off(self, **kwargs) -> None:
'Turn the light off.'
simple_rgb_color = self._func_channel.simpleRGBColorState
transition = kwargs.get(ATTR_TRANSITION, 0.5)
(await self._device.set_rgb_dim_level_with_time(channelIndex=self.channel, rgb=simple_rgb_color, dimLevel=0.0, onTime=0, rampTime=transition)) | -6,279,083,896,082,220,000 | Turn the light off. | homeassistant/components/homematicip_cloud/light.py | async_turn_off | 0x00-0xFF/home-assistant | python | async def async_turn_off(self, **kwargs) -> None:
simple_rgb_color = self._func_channel.simpleRGBColorState
transition = kwargs.get(ATTR_TRANSITION, 0.5)
(await self._device.set_rgb_dim_level_with_time(channelIndex=self.channel, rgb=simple_rgb_color, dimLevel=0.0, onTime=0, rampTime=transition)) |
@property
def exists(self):
'\n checks if the db exist and logs it\n\n Returns\n -------\n bool\n bool if the file exist or not\n '
if os.path.isfile(self.db_loc):
log.info('database at %s, does EXIST', self.db_loc)
return True
else:
log.info('databse at %s does NOT EXIST', self.db_loc)
return False | 1,824,685,546,315,325,000 | checks if the db exist and logs it
Returns
-------
bool
bool if the file exist or not | antipetros_discordbot/utility/gidsql/db_action_base.py | exists | official-antistasi-community/Antipetros_Discord_Bot | python | @property
def exists(self):
'\n checks if the db exist and logs it\n\n Returns\n -------\n bool\n bool if the file exist or not\n '
if os.path.isfile(self.db_loc):
log.info('database at %s, does EXIST', self.db_loc)
return True
else:
log.info('databse at %s does NOT EXIST', self.db_loc)
return False |
@property
def exists(self):
'\n checks if the db exist and logs it\n\n Returns\n -------\n bool\n bool if the file exist or not\n '
if os.path.isfile(self.db_loc):
log.info('database at %s, does EXIST', self.db_loc)
return True
else:
log.info('databse at %s does NOT EXIST', self.db_loc)
return False | 1,824,685,546,315,325,000 | checks if the db exist and logs it
Returns
-------
bool
bool if the file exist or not | antipetros_discordbot/utility/gidsql/db_action_base.py | exists | official-antistasi-community/Antipetros_Discord_Bot | python | @property
def exists(self):
'\n checks if the db exist and logs it\n\n Returns\n -------\n bool\n bool if the file exist or not\n '
if os.path.isfile(self.db_loc):
log.info('database at %s, does EXIST', self.db_loc)
return True
else:
log.info('databse at %s does NOT EXIST', self.db_loc)
return False |
def discounted_reverse_cumsum(data, gamma: float):
'\n Use a linear filter to compute the reverse discounted cumulative sum.\n\n .. note::\n `scipy.signal.lfilter` assumes an initialization with 0 by default.\n\n :param data: input data with samples along the 0 axis (e.g. time series)\n :param gamma: discount factor\n :return: cumulative sums for every step\n '
return signal.lfilter([1], [1, (- gamma)], data[::(- 1)], axis=0)[::(- 1)] | -5,288,915,096,824,507,000 | Use a linear filter to compute the reverse discounted cumulative sum.
.. note::
`scipy.signal.lfilter` assumes an initialization with 0 by default.
:param data: input data with samples along the 0 axis (e.g. time series)
:param gamma: discount factor
:return: cumulative sums for every step | mushroom_rl/core/parallelization_tools/step_sequence.py | discounted_reverse_cumsum | nifunk/GNNMushroomRL | python | def discounted_reverse_cumsum(data, gamma: float):
'\n Use a linear filter to compute the reverse discounted cumulative sum.\n\n .. note::\n `scipy.signal.lfilter` assumes an initialization with 0 by default.\n\n :param data: input data with samples along the 0 axis (e.g. time series)\n :param gamma: discount factor\n :return: cumulative sums for every step\n '
return signal.lfilter([1], [1, (- gamma)], data[::(- 1)], axis=0)[::(- 1)] |
def discounted_value(rollout: StepSequence, gamma: float):
'\n Compute the discounted state values for one rollout.\n\n :param rollout: input data\n :param gamma: temporal discount factor\n :return: state values for every time step in the rollout\n '
rewards = [step.reward for step in rollout]
return discounted_reverse_cumsum(rewards, gamma) | 3,926,704,981,727,231,500 | Compute the discounted state values for one rollout.
:param rollout: input data
:param gamma: temporal discount factor
:return: state values for every time step in the rollout | mushroom_rl/core/parallelization_tools/step_sequence.py | discounted_value | nifunk/GNNMushroomRL | python | def discounted_value(rollout: StepSequence, gamma: float):
'\n Compute the discounted state values for one rollout.\n\n :param rollout: input data\n :param gamma: temporal discount factor\n :return: state values for every time step in the rollout\n '
rewards = [step.reward for step in rollout]
return discounted_reverse_cumsum(rewards, gamma) |
def discounted_values(rollouts: Sequence[StepSequence], gamma: float, data_format: Optional[str]='torch'):
'\n Compute the discounted state values for multiple rollouts.\n\n :param rollouts: input data\n :param gamma: temporal discount factor\n :param data_format: data format of the given\n :return: state values for every time step in the rollouts (concatenated sequence across rollouts)\n '
if (data_format == 'torch'):
return to.cat([to.from_numpy(discounted_value(ro, gamma).copy()).to(to.get_default_dtype()) for ro in rollouts])
elif (data_format == 'numpy'):
raise np.array([discounted_value(ro, gamma) for ro in rollouts])
else:
raise pyrado.ValueErr(given=data_format, eq_constraint="'torch' or 'numpy'") | 645,887,553,901,988,900 | Compute the discounted state values for multiple rollouts.
:param rollouts: input data
:param gamma: temporal discount factor
:param data_format: data format of the given
:return: state values for every time step in the rollouts (concatenated sequence across rollouts) | mushroom_rl/core/parallelization_tools/step_sequence.py | discounted_values | nifunk/GNNMushroomRL | python | def discounted_values(rollouts: Sequence[StepSequence], gamma: float, data_format: Optional[str]='torch'):
'\n Compute the discounted state values for multiple rollouts.\n\n :param rollouts: input data\n :param gamma: temporal discount factor\n :param data_format: data format of the given\n :return: state values for every time step in the rollouts (concatenated sequence across rollouts)\n '
if (data_format == 'torch'):
return to.cat([to.from_numpy(discounted_value(ro, gamma).copy()).to(to.get_default_dtype()) for ro in rollouts])
elif (data_format == 'numpy'):
raise np.array([discounted_value(ro, gamma) for ro in rollouts])
else:
raise pyrado.ValueErr(given=data_format, eq_constraint="'torch' or 'numpy'") |
def gae_returns(rollout: StepSequence, gamma: float=0.99, lamb: float=0.95):
"\n Compute returns using generalized advantage estimation.\n\n .. seealso::\n [1] J. Schulmann, P. Moritz, S. Levine, M. Jordan, P. Abbeel, 'High-Dimensional Continuous Control Using\n Generalized Advantage Estimation', ICLR 2016\n\n :param rollout: sequence of steps\n :param gamma: temporal discount factor\n :param lamb: discount factor\n :return: estimated advantage\n "
def _next_value(step: Step) -> float:
' Helper to return `next_value = 0` for last step '
if step.done:
return 0.0
return step.next_value
deltas = [((step.reward + (gamma * _next_value(step))) - step.value) for step in rollout]
cumsum = discounted_reverse_cumsum(deltas, (gamma * lamb))
return cumsum | 4,842,705,186,051,923,000 | Compute returns using generalized advantage estimation.
.. seealso::
[1] J. Schulmann, P. Moritz, S. Levine, M. Jordan, P. Abbeel, 'High-Dimensional Continuous Control Using
Generalized Advantage Estimation', ICLR 2016
:param rollout: sequence of steps
:param gamma: temporal discount factor
:param lamb: discount factor
:return: estimated advantage | mushroom_rl/core/parallelization_tools/step_sequence.py | gae_returns | nifunk/GNNMushroomRL | python | def gae_returns(rollout: StepSequence, gamma: float=0.99, lamb: float=0.95):
"\n Compute returns using generalized advantage estimation.\n\n .. seealso::\n [1] J. Schulmann, P. Moritz, S. Levine, M. Jordan, P. Abbeel, 'High-Dimensional Continuous Control Using\n Generalized Advantage Estimation', ICLR 2016\n\n :param rollout: sequence of steps\n :param gamma: temporal discount factor\n :param lamb: discount factor\n :return: estimated advantage\n "
def _next_value(step: Step) -> float:
' Helper to return `next_value = 0` for last step '
if step.done:
return 0.0
return step.next_value
deltas = [((step.reward + (gamma * _next_value(step))) - step.value) for step in rollout]
cumsum = discounted_reverse_cumsum(deltas, (gamma * lamb))
return cumsum |
def __init__(self, rollout, index):
'\n Constructor\n\n :param rollout: `StepSequence` object to which this step belongs\n :param index: index of this step in the rollout\n '
super(Step, self).__init__(rollout.__dict__, index)
self._rollout = rollout | -7,175,570,219,185,015,000 | Constructor
:param rollout: `StepSequence` object to which this step belongs
:param index: index of this step in the rollout | mushroom_rl/core/parallelization_tools/step_sequence.py | __init__ | nifunk/GNNMushroomRL | python | def __init__(self, rollout, index):
'\n Constructor\n\n :param rollout: `StepSequence` object to which this step belongs\n :param index: index of this step in the rollout\n '
super(Step, self).__init__(rollout.__dict__, index)
self._rollout = rollout |
def __init__(self, *, complete: Optional[bool]=True, rollout_info=None, data_format: Optional[str]=None, done: Optional[np.ndarray]=None, continuous: Optional[bool]=True, rollout_bounds=None, rewards: Sequence, observations: Sequence, actions: Sequence, **data):
"\n Constructor\n\n :param complete: `False` if the rollout is incomplete, i.e. as part of a mini-batch\n :param rollout_info: data staying constant through the whole episode\n :param data_format: 'torch' to use Tensors, 'numpy' to use ndarrays.\n Will use Tensors if any data argument does, else ndarrays\n :param done: boolean ndarray, specifying for each step whether it led to termination.\n The last step of continuous rollouts, i.e. not mini-batches, is done if `complete` is `True`.\n :param continuous: true if the steps form one continuous sequence.\n :param rewards: sequence of reward values, determines sequence length\n :param observations: sequence of observation values, the length must be `len(rewards) + 1`\n :param actions: sequence of action values, the length must be `len(rewards)`\n :param data: additional data lists, their length must be `len(rewards)` or `len(rewards) + 1`\n "
self.length = len(rewards)
if (self.length == 0):
raise pyrado.ShapeErr(msg='StepSequence cannot be empty!')
self.rollout_info = rollout_info
self.continuous = continuous
if (data_format is None):
for value in data.values():
if (isinstance(value, to.Tensor) or (isinstance(value, list) and isinstance(value[0], to.Tensor))):
data_format = 'torch'
break
else:
data_format = 'numpy'
self._data_format = data_format
missing_fields = (StepSequence.required_fields - data.keys())
if missing_fields:
raise ValueError(f'Missing required data fields: {missing_fields}')
self._data_names = []
self.add_data('rewards', rewards)
self.add_data('observations', observations)
self.add_data('actions', actions)
for (name, value) in data.items():
self.add_data(name, value)
if (done is None):
done = np.zeros(self.length, dtype=np.bool)
if (complete and continuous):
done[(- 1)] = True
else:
done = np.asarray(done, dtype=np.bool)
assert (done.shape[0] == self.length)
self.done = done
if continuous:
if (rollout_bounds is None):
rollout_bounds = [0]
rollout_bounds.extend((np.flatnonzero(done) + 1))
if (not done[(- 1)]):
rollout_bounds.append(self.length)
else:
for i in range((len(rollout_bounds) - 1)):
assert (rollout_bounds[i] < rollout_bounds[(i + 1)])
assert (rollout_bounds[0] == 0)
assert (rollout_bounds[(- 1)] == self.length)
self._rollout_bounds = np.array(rollout_bounds)
else:
self._rollout_bounds = None | -5,813,278,499,522,838,000 | Constructor
:param complete: `False` if the rollout is incomplete, i.e. as part of a mini-batch
:param rollout_info: data staying constant through the whole episode
:param data_format: 'torch' to use Tensors, 'numpy' to use ndarrays.
Will use Tensors if any data argument does, else ndarrays
:param done: boolean ndarray, specifying for each step whether it led to termination.
The last step of continuous rollouts, i.e. not mini-batches, is done if `complete` is `True`.
:param continuous: true if the steps form one continuous sequence.
:param rewards: sequence of reward values, determines sequence length
:param observations: sequence of observation values, the length must be `len(rewards) + 1`
:param actions: sequence of action values, the length must be `len(rewards)`
:param data: additional data lists, their length must be `len(rewards)` or `len(rewards) + 1` | mushroom_rl/core/parallelization_tools/step_sequence.py | __init__ | nifunk/GNNMushroomRL | python | def __init__(self, *, complete: Optional[bool]=True, rollout_info=None, data_format: Optional[str]=None, done: Optional[np.ndarray]=None, continuous: Optional[bool]=True, rollout_bounds=None, rewards: Sequence, observations: Sequence, actions: Sequence, **data):
"\n Constructor\n\n :param complete: `False` if the rollout is incomplete, i.e. as part of a mini-batch\n :param rollout_info: data staying constant through the whole episode\n :param data_format: 'torch' to use Tensors, 'numpy' to use ndarrays.\n Will use Tensors if any data argument does, else ndarrays\n :param done: boolean ndarray, specifying for each step whether it led to termination.\n The last step of continuous rollouts, i.e. not mini-batches, is done if `complete` is `True`.\n :param continuous: true if the steps form one continuous sequence.\n :param rewards: sequence of reward values, determines sequence length\n :param observations: sequence of observation values, the length must be `len(rewards) + 1`\n :param actions: sequence of action values, the length must be `len(rewards)`\n :param data: additional data lists, their length must be `len(rewards)` or `len(rewards) + 1`\n "
self.length = len(rewards)
if (self.length == 0):
raise pyrado.ShapeErr(msg='StepSequence cannot be empty!')
self.rollout_info = rollout_info
self.continuous = continuous
if (data_format is None):
for value in data.values():
if (isinstance(value, to.Tensor) or (isinstance(value, list) and isinstance(value[0], to.Tensor))):
data_format = 'torch'
break
else:
data_format = 'numpy'
self._data_format = data_format
missing_fields = (StepSequence.required_fields - data.keys())
if missing_fields:
raise ValueError(f'Missing required data fields: {missing_fields}')
self._data_names = []
self.add_data('rewards', rewards)
self.add_data('observations', observations)
self.add_data('actions', actions)
for (name, value) in data.items():
self.add_data(name, value)
if (done is None):
done = np.zeros(self.length, dtype=np.bool)
if (complete and continuous):
done[(- 1)] = True
else:
done = np.asarray(done, dtype=np.bool)
assert (done.shape[0] == self.length)
self.done = done
if continuous:
if (rollout_bounds is None):
rollout_bounds = [0]
rollout_bounds.extend((np.flatnonzero(done) + 1))
if (not done[(- 1)]):
rollout_bounds.append(self.length)
else:
for i in range((len(rollout_bounds) - 1)):
assert (rollout_bounds[i] < rollout_bounds[(i + 1)])
assert (rollout_bounds[0] == 0)
assert (rollout_bounds[(- 1)] == self.length)
self._rollout_bounds = np.array(rollout_bounds)
else:
self._rollout_bounds = None |
@property
def data_format(self) -> str:
" Get the name of data format ('torch' or 'numpy'). "
return self._data_format | -3,737,586,975,972,980,700 | Get the name of data format ('torch' or 'numpy'). | mushroom_rl/core/parallelization_tools/step_sequence.py | data_format | nifunk/GNNMushroomRL | python | @property
def data_format(self) -> str:
" "
return self._data_format |
@property
def data_names(self) -> Sequence[str]:
' Get the list of data attribute names. '
return self._data_names | 7,636,364,652,369,576,000 | Get the list of data attribute names. | mushroom_rl/core/parallelization_tools/step_sequence.py | data_names | nifunk/GNNMushroomRL | python | @property
def data_names(self) -> Sequence[str]:
' '
return self._data_names |
@property
def rollout_count(self):
' Count the number of sub-rollouts inside this step sequence. '
if (not self.continuous):
raise pyrado.ValueErr(msg='Sub-rollouts are only supported on continuous data.')
return (len(self._rollout_bounds) - 1) | -8,265,467,451,147,833,000 | Count the number of sub-rollouts inside this step sequence. | mushroom_rl/core/parallelization_tools/step_sequence.py | rollout_count | nifunk/GNNMushroomRL | python | @property
def rollout_count(self):
' '
if (not self.continuous):
raise pyrado.ValueErr(msg='Sub-rollouts are only supported on continuous data.')
return (len(self._rollout_bounds) - 1) |
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