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def get_all(self):
'\n Retrieve all Products\n\n :rtype: iter[datacube.model.DatasetType]\n '
return (self._make(record) for record in self._db.get_all_dataset_types()) | -7,092,304,018,140,173,000 | Retrieve all Products
:rtype: iter[datacube.model.DatasetType] | datacube/index/_datasets.py | get_all | cronosnull/agdc-v2 | python | def get_all(self):
'\n Retrieve all Products\n\n :rtype: iter[datacube.model.DatasetType]\n '
return (self._make(record) for record in self._db.get_all_dataset_types()) |
def _make(self, query_row):
'\n :rtype datacube.model.DatasetType\n '
return DatasetType(definition=query_row['definition'], metadata_type=self.metadata_type_resource.get(query_row['metadata_type_ref']), id_=query_row['id']) | -5,606,596,896,980,163,000 | :rtype datacube.model.DatasetType | datacube/index/_datasets.py | _make | cronosnull/agdc-v2 | python | def _make(self, query_row):
'\n \n '
return DatasetType(definition=query_row['definition'], metadata_type=self.metadata_type_resource.get(query_row['metadata_type_ref']), id_=query_row['id']) |
def __init__(self, db, dataset_type_resource):
'\n :type db: datacube.index.postgres._api.PostgresDb\n :type dataset_type_resource: datacube.index._datasets.DatasetTypeResource\n '
self._db = db
self.types = dataset_type_resource | 5,608,404,397,755,472,000 | :type db: datacube.index.postgres._api.PostgresDb
:type dataset_type_resource: datacube.index._datasets.DatasetTypeResource | datacube/index/_datasets.py | __init__ | cronosnull/agdc-v2 | python | def __init__(self, db, dataset_type_resource):
'\n :type db: datacube.index.postgres._api.PostgresDb\n :type dataset_type_resource: datacube.index._datasets.DatasetTypeResource\n '
self._db = db
self.types = dataset_type_resource |
def get(self, id_, include_sources=False):
'\n Get dataset by id\n\n :param uuid id_: id of the dataset to retrieve\n :param bool include_sources: get the full provenance graph?\n :rtype: datacube.model.Dataset\n '
if (not include_sources):
return self._make(self._db.get_dataset(id_), full_info=True)
datasets = {result['id']: (self._make(result, full_info=True), result) for result in self._db.get_dataset_sources(id_)}
for (dataset, result) in datasets.values():
dataset.metadata_doc['lineage']['source_datasets'] = {classifier: datasets[str(source)][0].metadata_doc for (source, classifier) in zip(result['sources'], result['classes']) if source}
dataset.sources = {classifier: datasets[str(source)][0] for (source, classifier) in zip(result['sources'], result['classes']) if source}
return datasets[id_][0] | 6,263,144,244,050,827,000 | Get dataset by id
:param uuid id_: id of the dataset to retrieve
:param bool include_sources: get the full provenance graph?
:rtype: datacube.model.Dataset | datacube/index/_datasets.py | get | cronosnull/agdc-v2 | python | def get(self, id_, include_sources=False):
'\n Get dataset by id\n\n :param uuid id_: id of the dataset to retrieve\n :param bool include_sources: get the full provenance graph?\n :rtype: datacube.model.Dataset\n '
if (not include_sources):
return self._make(self._db.get_dataset(id_), full_info=True)
datasets = {result['id']: (self._make(result, full_info=True), result) for result in self._db.get_dataset_sources(id_)}
for (dataset, result) in datasets.values():
dataset.metadata_doc['lineage']['source_datasets'] = {classifier: datasets[str(source)][0].metadata_doc for (source, classifier) in zip(result['sources'], result['classes']) if source}
dataset.sources = {classifier: datasets[str(source)][0] for (source, classifier) in zip(result['sources'], result['classes']) if source}
return datasets[id_][0] |
def get_derived(self, id_):
'\n Get drived datasets\n\n :param uuid id_: dataset id\n :rtype: list[datacube.model.Dataset]\n '
return [self._make(result) for result in self._db.get_derived_datasets(id_)] | -7,787,538,984,594,398,000 | Get drived datasets
:param uuid id_: dataset id
:rtype: list[datacube.model.Dataset] | datacube/index/_datasets.py | get_derived | cronosnull/agdc-v2 | python | def get_derived(self, id_):
'\n Get drived datasets\n\n :param uuid id_: dataset id\n :rtype: list[datacube.model.Dataset]\n '
return [self._make(result) for result in self._db.get_derived_datasets(id_)] |
def has(self, dataset):
'\n Have we already indexed this dataset?\n\n :param datacube.model.Dataset dataset: dataset to check\n :rtype: bool\n '
return self._db.contains_dataset(dataset.id) | 7,644,583,408,200,586,000 | Have we already indexed this dataset?
:param datacube.model.Dataset dataset: dataset to check
:rtype: bool | datacube/index/_datasets.py | has | cronosnull/agdc-v2 | python | def has(self, dataset):
'\n Have we already indexed this dataset?\n\n :param datacube.model.Dataset dataset: dataset to check\n :rtype: bool\n '
return self._db.contains_dataset(dataset.id) |
def add(self, dataset, skip_sources=False):
"\n Ensure a dataset is in the index. Add it if not present.\n\n :param datacube.model.Dataset dataset: dataset to add\n :param bool skip_sources: don't attempt to index source (use when sources are already indexed)\n :rtype: datacube.model.Dataset\n "
if (not skip_sources):
for source in dataset.sources.values():
self.add(source)
was_inserted = False
sources_tmp = dataset.type.dataset_reader(dataset.metadata_doc).sources
dataset.type.dataset_reader(dataset.metadata_doc).sources = {}
try:
_LOG.info('Indexing %s', dataset.id)
with self._db.begin() as transaction:
try:
was_inserted = transaction.insert_dataset(dataset.metadata_doc, dataset.id, dataset.type.id)
for (classifier, source_dataset) in dataset.sources.items():
transaction.insert_dataset_source(classifier, dataset.id, source_dataset.id)
if dataset.local_uri:
transaction.ensure_dataset_location(dataset.id, dataset.local_uri)
except DuplicateRecordError as e:
_LOG.warning(str(e))
if (not was_inserted):
existing = self.get(dataset.id)
if existing:
check_doc_unchanged(existing.metadata_doc, jsonify_document(dataset.metadata_doc), 'Dataset {}'.format(dataset.id))
if dataset.local_uri:
try:
self._db.ensure_dataset_location(dataset.id, dataset.local_uri)
except DuplicateRecordError as e:
_LOG.warning(str(e))
finally:
dataset.type.dataset_reader(dataset.metadata_doc).sources = sources_tmp
return dataset | 5,261,391,490,470,212,000 | Ensure a dataset is in the index. Add it if not present.
:param datacube.model.Dataset dataset: dataset to add
:param bool skip_sources: don't attempt to index source (use when sources are already indexed)
:rtype: datacube.model.Dataset | datacube/index/_datasets.py | add | cronosnull/agdc-v2 | python | def add(self, dataset, skip_sources=False):
"\n Ensure a dataset is in the index. Add it if not present.\n\n :param datacube.model.Dataset dataset: dataset to add\n :param bool skip_sources: don't attempt to index source (use when sources are already indexed)\n :rtype: datacube.model.Dataset\n "
if (not skip_sources):
for source in dataset.sources.values():
self.add(source)
was_inserted = False
sources_tmp = dataset.type.dataset_reader(dataset.metadata_doc).sources
dataset.type.dataset_reader(dataset.metadata_doc).sources = {}
try:
_LOG.info('Indexing %s', dataset.id)
with self._db.begin() as transaction:
try:
was_inserted = transaction.insert_dataset(dataset.metadata_doc, dataset.id, dataset.type.id)
for (classifier, source_dataset) in dataset.sources.items():
transaction.insert_dataset_source(classifier, dataset.id, source_dataset.id)
if dataset.local_uri:
transaction.ensure_dataset_location(dataset.id, dataset.local_uri)
except DuplicateRecordError as e:
_LOG.warning(str(e))
if (not was_inserted):
existing = self.get(dataset.id)
if existing:
check_doc_unchanged(existing.metadata_doc, jsonify_document(dataset.metadata_doc), 'Dataset {}'.format(dataset.id))
if dataset.local_uri:
try:
self._db.ensure_dataset_location(dataset.id, dataset.local_uri)
except DuplicateRecordError as e:
_LOG.warning(str(e))
finally:
dataset.type.dataset_reader(dataset.metadata_doc).sources = sources_tmp
return dataset |
def archive(self, ids):
'\n Mark datasets as archived\n\n :param list[uuid] ids: list of dataset ids to archive\n '
with self._db.begin() as transaction:
for id_ in ids:
transaction.archive_dataset(id_) | 3,311,493,167,582,657,500 | Mark datasets as archived
:param list[uuid] ids: list of dataset ids to archive | datacube/index/_datasets.py | archive | cronosnull/agdc-v2 | python | def archive(self, ids):
'\n Mark datasets as archived\n\n :param list[uuid] ids: list of dataset ids to archive\n '
with self._db.begin() as transaction:
for id_ in ids:
transaction.archive_dataset(id_) |
def restore(self, ids):
'\n Mark datasets as not archived\n\n :param list[uuid] ids: list of dataset ids to restore\n '
with self._db.begin() as transaction:
for id_ in ids:
transaction.restore_dataset(id_) | 7,723,542,168,741,103,000 | Mark datasets as not archived
:param list[uuid] ids: list of dataset ids to restore | datacube/index/_datasets.py | restore | cronosnull/agdc-v2 | python | def restore(self, ids):
'\n Mark datasets as not archived\n\n :param list[uuid] ids: list of dataset ids to restore\n '
with self._db.begin() as transaction:
for id_ in ids:
transaction.restore_dataset(id_) |
def get_field_names(self, type_name=None):
'\n :param str type_name:\n :rtype: __generator[str]\n '
if (type_name is None):
types = self.types.get_all()
else:
types = [self.types.get_by_name(type_name)]
for type_ in types:
for name in type_.metadata_type.dataset_fields:
(yield name) | -9,014,035,561,054,024,000 | :param str type_name:
:rtype: __generator[str] | datacube/index/_datasets.py | get_field_names | cronosnull/agdc-v2 | python | def get_field_names(self, type_name=None):
'\n :param str type_name:\n :rtype: __generator[str]\n '
if (type_name is None):
types = self.types.get_all()
else:
types = [self.types.get_by_name(type_name)]
for type_ in types:
for name in type_.metadata_type.dataset_fields:
(yield name) |
def get_locations(self, dataset):
'\n :param datacube.model.Dataset dataset: dataset\n :rtype: list[str]\n '
return self._db.get_locations(dataset.id) | -8,915,735,042,510,128,000 | :param datacube.model.Dataset dataset: dataset
:rtype: list[str] | datacube/index/_datasets.py | get_locations | cronosnull/agdc-v2 | python | def get_locations(self, dataset):
'\n :param datacube.model.Dataset dataset: dataset\n :rtype: list[str]\n '
return self._db.get_locations(dataset.id) |
def _make(self, dataset_res, full_info=False):
'\n :rtype datacube.model.Dataset\n\n :param bool full_info: Include all available fields\n '
return Dataset(self.types.get(dataset_res.dataset_type_ref), dataset_res.metadata, dataset_res.local_uri, indexed_by=(dataset_res.added_by if full_info else None), indexed_time=(dataset_res.added if full_info else None)) | -5,285,444,197,718,200,000 | :rtype datacube.model.Dataset
:param bool full_info: Include all available fields | datacube/index/_datasets.py | _make | cronosnull/agdc-v2 | python | def _make(self, dataset_res, full_info=False):
'\n :rtype datacube.model.Dataset\n\n :param bool full_info: Include all available fields\n '
return Dataset(self.types.get(dataset_res.dataset_type_ref), dataset_res.metadata, dataset_res.local_uri, indexed_by=(dataset_res.added_by if full_info else None), indexed_time=(dataset_res.added if full_info else None)) |
def _make_many(self, query_result):
'\n :rtype list[datacube.model.Dataset]\n '
return (self._make(dataset) for dataset in query_result) | -1,847,021,940,425,939,000 | :rtype list[datacube.model.Dataset] | datacube/index/_datasets.py | _make_many | cronosnull/agdc-v2 | python | def _make_many(self, query_result):
'\n \n '
return (self._make(dataset) for dataset in query_result) |
def search_by_metadata(self, metadata):
'\n Perform a search using arbitrary metadata, returning results as Dataset objects.\n\n Caution – slow! This will usually not use indexes.\n\n :param dict metadata:\n :rtype: list[datacube.model.Dataset]\n '
return self._make_many(self._db.search_datasets_by_metadata(metadata)) | -5,142,680,656,802,113,000 | Perform a search using arbitrary metadata, returning results as Dataset objects.
Caution – slow! This will usually not use indexes.
:param dict metadata:
:rtype: list[datacube.model.Dataset] | datacube/index/_datasets.py | search_by_metadata | cronosnull/agdc-v2 | python | def search_by_metadata(self, metadata):
'\n Perform a search using arbitrary metadata, returning results as Dataset objects.\n\n Caution – slow! This will usually not use indexes.\n\n :param dict metadata:\n :rtype: list[datacube.model.Dataset]\n '
return self._make_many(self._db.search_datasets_by_metadata(metadata)) |
def search(self, **query):
'\n Perform a search, returning results as Dataset objects.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :rtype: __generator[datacube.model.Dataset]\n '
for (dataset_type, datasets) in self._do_search_by_product(query):
for dataset in self._make_many(datasets):
(yield dataset) | -5,254,226,837,874,405,000 | Perform a search, returning results as Dataset objects.
:param dict[str,str|float|datacube.model.Range] query:
:rtype: __generator[datacube.model.Dataset] | datacube/index/_datasets.py | search | cronosnull/agdc-v2 | python | def search(self, **query):
'\n Perform a search, returning results as Dataset objects.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :rtype: __generator[datacube.model.Dataset]\n '
for (dataset_type, datasets) in self._do_search_by_product(query):
for dataset in self._make_many(datasets):
(yield dataset) |
def search_by_product(self, **query):
'\n Perform a search, returning datasets grouped by product type.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :rtype: __generator[(datacube.model.DatasetType, __generator[datacube.model.Dataset])]]\n '
for (dataset_type, datasets) in self._do_search_by_product(query):
(yield (dataset_type, self._make_many(datasets))) | -4,136,558,941,424,093,000 | Perform a search, returning datasets grouped by product type.
:param dict[str,str|float|datacube.model.Range] query:
:rtype: __generator[(datacube.model.DatasetType, __generator[datacube.model.Dataset])]] | datacube/index/_datasets.py | search_by_product | cronosnull/agdc-v2 | python | def search_by_product(self, **query):
'\n Perform a search, returning datasets grouped by product type.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :rtype: __generator[(datacube.model.DatasetType, __generator[datacube.model.Dataset])]]\n '
for (dataset_type, datasets) in self._do_search_by_product(query):
(yield (dataset_type, self._make_many(datasets))) |
def count(self, **query):
'\n Perform a search, returning count of results.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :rtype: int\n '
result = 0
for (product_type, count) in self._do_count_by_product(query):
result += count
return result | 2,938,830,149,098,601,500 | Perform a search, returning count of results.
:param dict[str,str|float|datacube.model.Range] query:
:rtype: int | datacube/index/_datasets.py | count | cronosnull/agdc-v2 | python | def count(self, **query):
'\n Perform a search, returning count of results.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :rtype: int\n '
result = 0
for (product_type, count) in self._do_count_by_product(query):
result += count
return result |
def count_by_product(self, **query):
'\n Perform a search, returning a count of for each matching product type.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :returns: Sequence of (product, count)\n :rtype: __generator[(datacube.model.DatasetType, int)]]\n '
return self._do_count_by_product(query) | -1,485,780,272,995,107,000 | Perform a search, returning a count of for each matching product type.
:param dict[str,str|float|datacube.model.Range] query:
:returns: Sequence of (product, count)
:rtype: __generator[(datacube.model.DatasetType, int)]] | datacube/index/_datasets.py | count_by_product | cronosnull/agdc-v2 | python | def count_by_product(self, **query):
'\n Perform a search, returning a count of for each matching product type.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :returns: Sequence of (product, count)\n :rtype: __generator[(datacube.model.DatasetType, int)]]\n '
return self._do_count_by_product(query) |
def count_by_product_through_time(self, period, **query):
"\n Perform a search, returning counts for each product grouped in time slices\n of the given period.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :param str period: Time range for each slice: '1 month', '1 day' etc.\n :returns: For each matching product type, a list of time ranges and their count.\n :rtype: __generator[(datacube.model.DatasetType, list[(datetime.datetime, datetime.datetime), int)]]\n "
return self._do_time_count(period, query) | -1,946,640,937,008,705,300 | Perform a search, returning counts for each product grouped in time slices
of the given period.
:param dict[str,str|float|datacube.model.Range] query:
:param str period: Time range for each slice: '1 month', '1 day' etc.
:returns: For each matching product type, a list of time ranges and their count.
:rtype: __generator[(datacube.model.DatasetType, list[(datetime.datetime, datetime.datetime), int)]] | datacube/index/_datasets.py | count_by_product_through_time | cronosnull/agdc-v2 | python | def count_by_product_through_time(self, period, **query):
"\n Perform a search, returning counts for each product grouped in time slices\n of the given period.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :param str period: Time range for each slice: '1 month', '1 day' etc.\n :returns: For each matching product type, a list of time ranges and their count.\n :rtype: __generator[(datacube.model.DatasetType, list[(datetime.datetime, datetime.datetime), int)]]\n "
return self._do_time_count(period, query) |
def count_product_through_time(self, period, **query):
"\n Perform a search, returning counts for a single product grouped in time slices\n of the given period.\n\n Will raise an error if the search terms match more than one product.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :param str period: Time range for each slice: '1 month', '1 day' etc.\n :returns: For each matching product type, a list of time ranges and their count.\n :rtype: list[(str, list[(datetime.datetime, datetime.datetime), int)]]\n "
return next(self._do_time_count(period, query, ensure_single=True))[1] | 681,976,642,990,149,500 | Perform a search, returning counts for a single product grouped in time slices
of the given period.
Will raise an error if the search terms match more than one product.
:param dict[str,str|float|datacube.model.Range] query:
:param str period: Time range for each slice: '1 month', '1 day' etc.
:returns: For each matching product type, a list of time ranges and their count.
:rtype: list[(str, list[(datetime.datetime, datetime.datetime), int)]] | datacube/index/_datasets.py | count_product_through_time | cronosnull/agdc-v2 | python | def count_product_through_time(self, period, **query):
"\n Perform a search, returning counts for a single product grouped in time slices\n of the given period.\n\n Will raise an error if the search terms match more than one product.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :param str period: Time range for each slice: '1 month', '1 day' etc.\n :returns: For each matching product type, a list of time ranges and their count.\n :rtype: list[(str, list[(datetime.datetime, datetime.datetime), int)]]\n "
return next(self._do_time_count(period, query, ensure_single=True))[1] |
def search_summaries(self, **query):
'\n Perform a search, returning just the search fields of each dataset.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :rtype: dict\n '
for (dataset_type, results) in self._do_search_by_product(query, return_fields=True):
for columns in results:
(yield dict(columns)) | -2,494,574,499,400,412,700 | Perform a search, returning just the search fields of each dataset.
:param dict[str,str|float|datacube.model.Range] query:
:rtype: dict | datacube/index/_datasets.py | search_summaries | cronosnull/agdc-v2 | python | def search_summaries(self, **query):
'\n Perform a search, returning just the search fields of each dataset.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :rtype: dict\n '
for (dataset_type, results) in self._do_search_by_product(query, return_fields=True):
for columns in results:
(yield dict(columns)) |
def search_eager(self, **query):
'\n Perform a search, returning results as Dataset objects.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :rtype: list[datacube.model.Dataset]\n '
return list(self.search(**query)) | 2,361,861,580,404,206,000 | Perform a search, returning results as Dataset objects.
:param dict[str,str|float|datacube.model.Range] query:
:rtype: list[datacube.model.Dataset] | datacube/index/_datasets.py | search_eager | cronosnull/agdc-v2 | python | def search_eager(self, **query):
'\n Perform a search, returning results as Dataset objects.\n\n :param dict[str,str|float|datacube.model.Range] query:\n :rtype: list[datacube.model.Dataset]\n '
return list(self.search(**query)) |
def ultimate_replace(app, docname, source):
'Replaces variables in docs, including code blocks.\n\n From: https://github.com/sphinx-doc/sphinx/issues/4054#issuecomment-329097229\n '
result = source[0]
for key in app.config.ultimate_replacements:
result = result.replace(key, app.config.ultimate_replacements[key])
source[0] = result | 4,424,882,896,295,911,400 | Replaces variables in docs, including code blocks.
From: https://github.com/sphinx-doc/sphinx/issues/4054#issuecomment-329097229 | docs/source/conf.py | ultimate_replace | Aeolun/sqlfluff | python | def ultimate_replace(app, docname, source):
'Replaces variables in docs, including code blocks.\n\n From: https://github.com/sphinx-doc/sphinx/issues/4054#issuecomment-329097229\n '
result = source[0]
for key in app.config.ultimate_replacements:
result = result.replace(key, app.config.ultimate_replacements[key])
source[0] = result |
def setup(app):
'Configures the documentation app.'
app.add_config_value('ultimate_replacements', {}, True)
app.connect('source-read', ultimate_replace) | 8,226,218,855,073,592,000 | Configures the documentation app. | docs/source/conf.py | setup | Aeolun/sqlfluff | python | def setup(app):
app.add_config_value('ultimate_replacements', {}, True)
app.connect('source-read', ultimate_replace) |
@infer_dtype(np.hypot)
def hypot(x1, x2, out=None, where=None, **kwargs):
'\n Given the "legs" of a right triangle, return its hypotenuse.\n\n Equivalent to ``sqrt(x1**2 + x2**2)``, element-wise. If `x1` or\n `x2` is scalar_like (i.e., unambiguously cast-able to a scalar type),\n it is broadcast for use with each element of the other argument.\n (See Examples)\n\n Parameters\n ----------\n x1, x2 : array_like\n Leg of the triangle(s).\n out : Tensor, None, or tuple of Tensor and None, optional\n A location into which the result is stored. If provided, it must have\n a shape that the inputs broadcast to. If not provided or `None`,\n a freshly-allocated array is returned. A tuple (possible only as a\n keyword argument) must have length equal to the number of outputs.\n where : array_like, optional\n Values of True indicate to calculate the ufunc at that position, values\n of False indicate to leave the value in the output alone.\n **kwargs\n\n Returns\n -------\n z : Tensor\n The hypotenuse of the triangle(s).\n\n Examples\n --------\n >>> import mars.tensor as mt\n\n >>> mt.hypot(3*mt.ones((3, 3)), 4*mt.ones((3, 3))).execute()\n array([[ 5., 5., 5.],\n [ 5., 5., 5.],\n [ 5., 5., 5.]])\n\n Example showing broadcast of scalar_like argument:\n\n >>> mt.hypot(3*mt.ones((3, 3)), [4]).execute()\n array([[ 5., 5., 5.],\n [ 5., 5., 5.],\n [ 5., 5., 5.]])\n '
op = TensorHypot(**kwargs)
return op(x1, x2, out=out, where=where) | 3,499,966,831,407,212,000 | Given the "legs" of a right triangle, return its hypotenuse.
Equivalent to ``sqrt(x1**2 + x2**2)``, element-wise. If `x1` or
`x2` is scalar_like (i.e., unambiguously cast-able to a scalar type),
it is broadcast for use with each element of the other argument.
(See Examples)
Parameters
----------
x1, x2 : array_like
Leg of the triangle(s).
out : Tensor, None, or tuple of Tensor and None, optional
A location into which the result is stored. If provided, it must have
a shape that the inputs broadcast to. If not provided or `None`,
a freshly-allocated array is returned. A tuple (possible only as a
keyword argument) must have length equal to the number of outputs.
where : array_like, optional
Values of True indicate to calculate the ufunc at that position, values
of False indicate to leave the value in the output alone.
**kwargs
Returns
-------
z : Tensor
The hypotenuse of the triangle(s).
Examples
--------
>>> import mars.tensor as mt
>>> mt.hypot(3*mt.ones((3, 3)), 4*mt.ones((3, 3))).execute()
array([[ 5., 5., 5.],
[ 5., 5., 5.],
[ 5., 5., 5.]])
Example showing broadcast of scalar_like argument:
>>> mt.hypot(3*mt.ones((3, 3)), [4]).execute()
array([[ 5., 5., 5.],
[ 5., 5., 5.],
[ 5., 5., 5.]]) | mars/tensor/arithmetic/hypot.py | hypot | Alfa-Shashank/mars | python | @infer_dtype(np.hypot)
def hypot(x1, x2, out=None, where=None, **kwargs):
'\n Given the "legs" of a right triangle, return its hypotenuse.\n\n Equivalent to ``sqrt(x1**2 + x2**2)``, element-wise. If `x1` or\n `x2` is scalar_like (i.e., unambiguously cast-able to a scalar type),\n it is broadcast for use with each element of the other argument.\n (See Examples)\n\n Parameters\n ----------\n x1, x2 : array_like\n Leg of the triangle(s).\n out : Tensor, None, or tuple of Tensor and None, optional\n A location into which the result is stored. If provided, it must have\n a shape that the inputs broadcast to. If not provided or `None`,\n a freshly-allocated array is returned. A tuple (possible only as a\n keyword argument) must have length equal to the number of outputs.\n where : array_like, optional\n Values of True indicate to calculate the ufunc at that position, values\n of False indicate to leave the value in the output alone.\n **kwargs\n\n Returns\n -------\n z : Tensor\n The hypotenuse of the triangle(s).\n\n Examples\n --------\n >>> import mars.tensor as mt\n\n >>> mt.hypot(3*mt.ones((3, 3)), 4*mt.ones((3, 3))).execute()\n array([[ 5., 5., 5.],\n [ 5., 5., 5.],\n [ 5., 5., 5.]])\n\n Example showing broadcast of scalar_like argument:\n\n >>> mt.hypot(3*mt.ones((3, 3)), [4]).execute()\n array([[ 5., 5., 5.],\n [ 5., 5., 5.],\n [ 5., 5., 5.]])\n '
op = TensorHypot(**kwargs)
return op(x1, x2, out=out, where=where) |
def sample_analyze_entities(gcs_content_uri):
'\n Analyzing Entities in text file stored in Cloud Storage\n\n Args:\n gcs_content_uri Google Cloud Storage URI where the file content is located.\n e.g. gs://[Your Bucket]/[Path to File]\n '
client = language_v1.LanguageServiceClient()
type_ = enums.Document.Type.PLAIN_TEXT
language = 'en'
document = {'gcs_content_uri': gcs_content_uri, 'type': type_, 'language': language}
encoding_type = enums.EncodingType.UTF8
response = client.analyze_entities(document, encoding_type=encoding_type)
for entity in response.entities:
print(u'Representative name for the entity: {}'.format(entity.name))
print(u'Entity type: {}'.format(enums.Entity.Type(entity.type).name))
print(u'Salience score: {}'.format(entity.salience))
for (metadata_name, metadata_value) in entity.metadata.items():
print(u'{}: {}'.format(metadata_name, metadata_value))
for mention in entity.mentions:
print(u'Mention text: {}'.format(mention.text.content))
print(u'Mention type: {}'.format(enums.EntityMention.Type(mention.type).name))
print(u'Language of the text: {}'.format(response.language)) | -7,760,454,302,406,207,000 | Analyzing Entities in text file stored in Cloud Storage
Args:
gcs_content_uri Google Cloud Storage URI where the file content is located.
e.g. gs://[Your Bucket]/[Path to File] | samples/v1/language_entities_gcs.py | sample_analyze_entities | MShaffar19/python-language | python | def sample_analyze_entities(gcs_content_uri):
'\n Analyzing Entities in text file stored in Cloud Storage\n\n Args:\n gcs_content_uri Google Cloud Storage URI where the file content is located.\n e.g. gs://[Your Bucket]/[Path to File]\n '
client = language_v1.LanguageServiceClient()
type_ = enums.Document.Type.PLAIN_TEXT
language = 'en'
document = {'gcs_content_uri': gcs_content_uri, 'type': type_, 'language': language}
encoding_type = enums.EncodingType.UTF8
response = client.analyze_entities(document, encoding_type=encoding_type)
for entity in response.entities:
print(u'Representative name for the entity: {}'.format(entity.name))
print(u'Entity type: {}'.format(enums.Entity.Type(entity.type).name))
print(u'Salience score: {}'.format(entity.salience))
for (metadata_name, metadata_value) in entity.metadata.items():
print(u'{}: {}'.format(metadata_name, metadata_value))
for mention in entity.mentions:
print(u'Mention text: {}'.format(mention.text.content))
print(u'Mention type: {}'.format(enums.EntityMention.Type(mention.type).name))
print(u'Language of the text: {}'.format(response.language)) |
def np2th(weights, conv=False):
'Possibly convert HWIO to OIHW.'
if conv:
weights = weights.transpose([3, 2, 0, 1])
return torch.from_numpy(weights) | 2,480,537,502,387,815,000 | Possibly convert HWIO to OIHW. | ViT-V-Net/models.py | np2th | junyuchen245/ViT-V-Net_for_3D_Image_Registration | python | def np2th(weights, conv=False):
if conv:
weights = weights.transpose([3, 2, 0, 1])
return torch.from_numpy(weights) |
@cached_property
def additional_properties_type():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n '
return (bool, date, datetime, dict, float, int, list, str, none_type) | 7,810,842,306,960,415,000 | This must be a method because a model may have properties that are
of type self, this must run after the class is loaded | sdks/python/client/openapi_client/model/fc_volume_source.py | additional_properties_type | 2kindsofcs/argo-workflows | python | @cached_property
def additional_properties_type():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n '
return (bool, date, datetime, dict, float, int, list, str, none_type) |
@cached_property
def openapi_types():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n '
return {'fs_type': (str,), 'lun': (int,), 'read_only': (bool,), 'target_wwns': ([str],), 'wwids': ([str],)} | -7,114,574,193,233,615,000 | This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type. | sdks/python/client/openapi_client/model/fc_volume_source.py | openapi_types | 2kindsofcs/argo-workflows | python | @cached_property
def openapi_types():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n '
return {'fs_type': (str,), 'lun': (int,), 'read_only': (bool,), 'target_wwns': ([str],), 'wwids': ([str],)} |
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, *args, **kwargs):
'FCVolumeSource - a model defined in OpenAPI\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n fs_type (str): Filesystem type to mount. Must be a filesystem type supported by the host operating system. Ex. "ext4", "xfs", "ntfs". Implicitly inferred to be "ext4" if unspecified.. [optional] # noqa: E501\n lun (int): Optional: FC target lun number. [optional] # noqa: E501\n read_only (bool): Optional: Defaults to false (read/write). ReadOnly here will force the ReadOnly setting in VolumeMounts.. [optional] # noqa: E501\n target_wwns ([str]): Optional: FC target worldwide names (WWNs). [optional] # noqa: E501\n wwids ([str]): Optional: FC volume world wide identifiers (wwids) Either wwids or combination of targetWWNs and lun must be set, but not both simultaneously.. [optional] # noqa: E501\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
return self | -4,200,118,095,630,329,000 | FCVolumeSource - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
fs_type (str): Filesystem type to mount. Must be a filesystem type supported by the host operating system. Ex. "ext4", "xfs", "ntfs". Implicitly inferred to be "ext4" if unspecified.. [optional] # noqa: E501
lun (int): Optional: FC target lun number. [optional] # noqa: E501
read_only (bool): Optional: Defaults to false (read/write). ReadOnly here will force the ReadOnly setting in VolumeMounts.. [optional] # noqa: E501
target_wwns ([str]): Optional: FC target worldwide names (WWNs). [optional] # noqa: E501
wwids ([str]): Optional: FC volume world wide identifiers (wwids) Either wwids or combination of targetWWNs and lun must be set, but not both simultaneously.. [optional] # noqa: E501 | sdks/python/client/openapi_client/model/fc_volume_source.py | _from_openapi_data | 2kindsofcs/argo-workflows | python | @classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, *args, **kwargs):
'FCVolumeSource - a model defined in OpenAPI\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n fs_type (str): Filesystem type to mount. Must be a filesystem type supported by the host operating system. Ex. "ext4", "xfs", "ntfs". Implicitly inferred to be "ext4" if unspecified.. [optional] # noqa: E501\n lun (int): Optional: FC target lun number. [optional] # noqa: E501\n read_only (bool): Optional: Defaults to false (read/write). ReadOnly here will force the ReadOnly setting in VolumeMounts.. [optional] # noqa: E501\n target_wwns ([str]): Optional: FC target worldwide names (WWNs). [optional] # noqa: E501\n wwids ([str]): Optional: FC volume world wide identifiers (wwids) Either wwids or combination of targetWWNs and lun must be set, but not both simultaneously.. [optional] # noqa: E501\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
return self |
@convert_js_args_to_python_args
def __init__(self, *args, **kwargs):
'FCVolumeSource - a model defined in OpenAPI\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n fs_type (str): Filesystem type to mount. Must be a filesystem type supported by the host operating system. Ex. "ext4", "xfs", "ntfs". Implicitly inferred to be "ext4" if unspecified.. [optional] # noqa: E501\n lun (int): Optional: FC target lun number. [optional] # noqa: E501\n read_only (bool): Optional: Defaults to false (read/write). ReadOnly here will force the ReadOnly setting in VolumeMounts.. [optional] # noqa: E501\n target_wwns ([str]): Optional: FC target worldwide names (WWNs). [optional] # noqa: E501\n wwids ([str]): Optional: FC volume world wide identifiers (wwids) Either wwids or combination of targetWWNs and lun must be set, but not both simultaneously.. [optional] # noqa: E501\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
if (var_name in self.read_only_vars):
raise ApiAttributeError(f'`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate class with read only attributes.') | 6,153,289,572,221,368,000 | FCVolumeSource - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
fs_type (str): Filesystem type to mount. Must be a filesystem type supported by the host operating system. Ex. "ext4", "xfs", "ntfs". Implicitly inferred to be "ext4" if unspecified.. [optional] # noqa: E501
lun (int): Optional: FC target lun number. [optional] # noqa: E501
read_only (bool): Optional: Defaults to false (read/write). ReadOnly here will force the ReadOnly setting in VolumeMounts.. [optional] # noqa: E501
target_wwns ([str]): Optional: FC target worldwide names (WWNs). [optional] # noqa: E501
wwids ([str]): Optional: FC volume world wide identifiers (wwids) Either wwids or combination of targetWWNs and lun must be set, but not both simultaneously.. [optional] # noqa: E501 | sdks/python/client/openapi_client/model/fc_volume_source.py | __init__ | 2kindsofcs/argo-workflows | python | @convert_js_args_to_python_args
def __init__(self, *args, **kwargs):
'FCVolumeSource - a model defined in OpenAPI\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n fs_type (str): Filesystem type to mount. Must be a filesystem type supported by the host operating system. Ex. "ext4", "xfs", "ntfs". Implicitly inferred to be "ext4" if unspecified.. [optional] # noqa: E501\n lun (int): Optional: FC target lun number. [optional] # noqa: E501\n read_only (bool): Optional: Defaults to false (read/write). ReadOnly here will force the ReadOnly setting in VolumeMounts.. [optional] # noqa: E501\n target_wwns ([str]): Optional: FC target worldwide names (WWNs). [optional] # noqa: E501\n wwids ([str]): Optional: FC volume world wide identifiers (wwids) Either wwids or combination of targetWWNs and lun must be set, but not both simultaneously.. [optional] # noqa: E501\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
if (var_name in self.read_only_vars):
raise ApiAttributeError(f'`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate class with read only attributes.') |
def IsLabBlocked(lab_name):
'Check if the lab is blocked.\n\n Args:\n lab_name: lab name\n Returns:\n true if the lab is blocked, otherwise false.\n '
device_blocklists = datastore_entities.DeviceBlocklist.query().filter((datastore_entities.DeviceBlocklist.lab_name == lab_name)).fetch(1)
return bool(device_blocklists) | -7,152,990,764,287,082,000 | Check if the lab is blocked.
Args:
lab_name: lab name
Returns:
true if the lab is blocked, otherwise false. | tradefed_cluster/device_blocker.py | IsLabBlocked | maksonlee/tradefed_cluster | python | def IsLabBlocked(lab_name):
'Check if the lab is blocked.\n\n Args:\n lab_name: lab name\n Returns:\n true if the lab is blocked, otherwise false.\n '
device_blocklists = datastore_entities.DeviceBlocklist.query().filter((datastore_entities.DeviceBlocklist.lab_name == lab_name)).fetch(1)
return bool(device_blocklists) |
def __init__(self, oracle: QuantumCircuit, state_preparation: Optional[QuantumCircuit]=None, zero_reflection: Optional[Union[(QuantumCircuit, Operator)]]=None, reflection_qubits: Optional[List[int]]=None, insert_barriers: bool=False, mcx_mode: str='noancilla', name: str='Q') -> None:
"\n Args:\n oracle: The phase oracle implementing a reflection about the bad state. Note that this\n is not a bitflip oracle, see the docstring for more information.\n state_preparation: The operator preparing the good and bad state.\n For Grover's algorithm, this is a n-qubit Hadamard gate and for amplitude\n amplification or estimation the operator :math:`\\mathcal{A}`.\n zero_reflection: The reflection about the zero state, :math:`\\mathcal{S}_0`.\n reflection_qubits: Qubits on which the zero reflection acts on.\n insert_barriers: Whether barriers should be inserted between the reflections and A.\n mcx_mode: The mode to use for building the default zero reflection.\n name: The name of the circuit.\n "
super().__init__(name=name)
self._oracle = oracle
self._zero_reflection = zero_reflection
self._reflection_qubits = reflection_qubits
self._state_preparation = state_preparation
self._insert_barriers = insert_barriers
self._mcx_mode = mcx_mode
self._build() | -7,204,777,035,918,854,000 | Args:
oracle: The phase oracle implementing a reflection about the bad state. Note that this
is not a bitflip oracle, see the docstring for more information.
state_preparation: The operator preparing the good and bad state.
For Grover's algorithm, this is a n-qubit Hadamard gate and for amplitude
amplification or estimation the operator :math:`\mathcal{A}`.
zero_reflection: The reflection about the zero state, :math:`\mathcal{S}_0`.
reflection_qubits: Qubits on which the zero reflection acts on.
insert_barriers: Whether barriers should be inserted between the reflections and A.
mcx_mode: The mode to use for building the default zero reflection.
name: The name of the circuit. | qiskit/circuit/library/grover_operator.py | __init__ | SpinQTech/SpinQKit | python | def __init__(self, oracle: QuantumCircuit, state_preparation: Optional[QuantumCircuit]=None, zero_reflection: Optional[Union[(QuantumCircuit, Operator)]]=None, reflection_qubits: Optional[List[int]]=None, insert_barriers: bool=False, mcx_mode: str='noancilla', name: str='Q') -> None:
"\n Args:\n oracle: The phase oracle implementing a reflection about the bad state. Note that this\n is not a bitflip oracle, see the docstring for more information.\n state_preparation: The operator preparing the good and bad state.\n For Grover's algorithm, this is a n-qubit Hadamard gate and for amplitude\n amplification or estimation the operator :math:`\\mathcal{A}`.\n zero_reflection: The reflection about the zero state, :math:`\\mathcal{S}_0`.\n reflection_qubits: Qubits on which the zero reflection acts on.\n insert_barriers: Whether barriers should be inserted between the reflections and A.\n mcx_mode: The mode to use for building the default zero reflection.\n name: The name of the circuit.\n "
super().__init__(name=name)
self._oracle = oracle
self._zero_reflection = zero_reflection
self._reflection_qubits = reflection_qubits
self._state_preparation = state_preparation
self._insert_barriers = insert_barriers
self._mcx_mode = mcx_mode
self._build() |
@property
def reflection_qubits(self):
'Reflection qubits, on which S0 is applied (if S0 is not user-specified).'
if (self._reflection_qubits is not None):
return self._reflection_qubits
num_state_qubits = (self.oracle.num_qubits - self.oracle.num_ancillas)
return list(range(num_state_qubits)) | 7,827,234,141,069,266,000 | Reflection qubits, on which S0 is applied (if S0 is not user-specified). | qiskit/circuit/library/grover_operator.py | reflection_qubits | SpinQTech/SpinQKit | python | @property
def reflection_qubits(self):
if (self._reflection_qubits is not None):
return self._reflection_qubits
num_state_qubits = (self.oracle.num_qubits - self.oracle.num_ancillas)
return list(range(num_state_qubits)) |
@property
def zero_reflection(self) -> QuantumCircuit:
'The subcircuit implementing the reflection about 0.'
if (self._zero_reflection is not None):
return self._zero_reflection
num_state_qubits = (self.oracle.num_qubits - self.oracle.num_ancillas)
return _zero_reflection(num_state_qubits, self.reflection_qubits, self._mcx_mode) | 5,482,765,425,153,560,000 | The subcircuit implementing the reflection about 0. | qiskit/circuit/library/grover_operator.py | zero_reflection | SpinQTech/SpinQKit | python | @property
def zero_reflection(self) -> QuantumCircuit:
if (self._zero_reflection is not None):
return self._zero_reflection
num_state_qubits = (self.oracle.num_qubits - self.oracle.num_ancillas)
return _zero_reflection(num_state_qubits, self.reflection_qubits, self._mcx_mode) |
@property
def state_preparation(self) -> QuantumCircuit:
'The subcircuit implementing the A operator or Hadamards.'
if (self._state_preparation is not None):
return self._state_preparation
num_state_qubits = (self.oracle.num_qubits - self.oracle.num_ancillas)
hadamards = QuantumCircuit(num_state_qubits, name='H')
hadamards.h(self.reflection_qubits)
return hadamards | 2,549,075,357,350,532,000 | The subcircuit implementing the A operator or Hadamards. | qiskit/circuit/library/grover_operator.py | state_preparation | SpinQTech/SpinQKit | python | @property
def state_preparation(self) -> QuantumCircuit:
if (self._state_preparation is not None):
return self._state_preparation
num_state_qubits = (self.oracle.num_qubits - self.oracle.num_ancillas)
hadamards = QuantumCircuit(num_state_qubits, name='H')
hadamards.h(self.reflection_qubits)
return hadamards |
@property
def oracle(self):
'The oracle implementing a reflection about the bad state.'
return self._oracle | -1,036,016,382,031,906,400 | The oracle implementing a reflection about the bad state. | qiskit/circuit/library/grover_operator.py | oracle | SpinQTech/SpinQKit | python | @property
def oracle(self):
return self._oracle |
def __init__(self, path, name):
'\n Initizlize.\n :param path: path to the storage file;\n empty means the current direcory.\n :param name: file name, json file; may include a path.\n '
if path:
os.makedirs(path, exist_ok=True)
self.file = os.path.normpath(os.path.join(path, name))
try:
with open(self.file) as data_file:
self.data = json.load(data_file)
except FileNotFoundError:
self.data = dict()
self.dump() | -9,043,549,866,801,133,000 | Initizlize.
:param path: path to the storage file;
empty means the current direcory.
:param name: file name, json file; may include a path. | netdata/workers/json_storage.py | __init__ | mincode/netdata | python | def __init__(self, path, name):
'\n Initizlize.\n :param path: path to the storage file;\n empty means the current direcory.\n :param name: file name, json file; may include a path.\n '
if path:
os.makedirs(path, exist_ok=True)
self.file = os.path.normpath(os.path.join(path, name))
try:
with open(self.file) as data_file:
self.data = json.load(data_file)
except FileNotFoundError:
self.data = dict()
self.dump() |
def dump(self):
'\n Dump data into storage file.\n '
with open(self.file, 'w') as out_file:
json.dump(self.data, out_file, indent=self.indent) | -7,103,314,947,930,550,000 | Dump data into storage file. | netdata/workers/json_storage.py | dump | mincode/netdata | python | def dump(self):
'\n \n '
with open(self.file, 'w') as out_file:
json.dump(self.data, out_file, indent=self.indent) |
def get(self, item):
'\n Get stored item.\n :param item: name, string, of item to get.\n :return: stored item; raises a KeyError if item does not exist.\n '
return self.data[item] | -6,757,231,109,967,430,000 | Get stored item.
:param item: name, string, of item to get.
:return: stored item; raises a KeyError if item does not exist. | netdata/workers/json_storage.py | get | mincode/netdata | python | def get(self, item):
'\n Get stored item.\n :param item: name, string, of item to get.\n :return: stored item; raises a KeyError if item does not exist.\n '
return self.data[item] |
def set(self, item, value):
"\n Set item's value; causes the data to be dumped into the storage file.\n :param item: name, string of item to set.\n :param value: value to set.\n "
self.data[item] = value
self.dump() | 4,817,724,891,476,354,000 | Set item's value; causes the data to be dumped into the storage file.
:param item: name, string of item to set.
:param value: value to set. | netdata/workers/json_storage.py | set | mincode/netdata | python | def set(self, item, value):
"\n Set item's value; causes the data to be dumped into the storage file.\n :param item: name, string of item to set.\n :param value: value to set.\n "
self.data[item] = value
self.dump() |
def __getattr__(self, item):
'\n Get stored item with .-notation if not defined as a class member.\n :param item: name, string of item compatible\n with Python class member name.\n :return value of item.\n '
if (item in self.data):
return self.data[item]
else:
raise AttributeError | 8,440,963,905,404,084,000 | Get stored item with .-notation if not defined as a class member.
:param item: name, string of item compatible
with Python class member name.
:return value of item. | netdata/workers/json_storage.py | __getattr__ | mincode/netdata | python | def __getattr__(self, item):
'\n Get stored item with .-notation if not defined as a class member.\n :param item: name, string of item compatible\n with Python class member name.\n :return value of item.\n '
if (item in self.data):
return self.data[item]
else:
raise AttributeError |
def _request_locks(self, locks: list[str], id: Hashable, num_locks: int) -> bool:
'Request locks\n\n Parameters\n ----------\n locks: List[str]\n Names of the locks to request.\n id: Hashable\n Identifier of the `MultiLock` instance requesting the locks.\n num_locks: int\n Number of locks in `locks` requesting\n\n Return\n ------\n result: bool\n Whether `num_locks` requested locks are free immediately or not.\n '
assert (id not in self.requests)
self.requests[id] = set(locks)
assert ((len(locks) >= num_locks) and (num_locks > 0))
self.requests_left[id] = num_locks
locks = sorted(locks, key=(lambda x: len(self.locks[x])))
for (i, lock) in enumerate(locks):
self.locks[lock].append(id)
if (len(self.locks[lock]) == 1):
self.requests_left[id] -= 1
if (self.requests_left[id] == 0):
self.requests[id] -= set(locks[(i + 1):])
return True
return False | -8,840,431,474,768,864,000 | Request locks
Parameters
----------
locks: List[str]
Names of the locks to request.
id: Hashable
Identifier of the `MultiLock` instance requesting the locks.
num_locks: int
Number of locks in `locks` requesting
Return
------
result: bool
Whether `num_locks` requested locks are free immediately or not. | distributed/multi_lock.py | _request_locks | bryanwweber/distributed | python | def _request_locks(self, locks: list[str], id: Hashable, num_locks: int) -> bool:
'Request locks\n\n Parameters\n ----------\n locks: List[str]\n Names of the locks to request.\n id: Hashable\n Identifier of the `MultiLock` instance requesting the locks.\n num_locks: int\n Number of locks in `locks` requesting\n\n Return\n ------\n result: bool\n Whether `num_locks` requested locks are free immediately or not.\n '
assert (id not in self.requests)
self.requests[id] = set(locks)
assert ((len(locks) >= num_locks) and (num_locks > 0))
self.requests_left[id] = num_locks
locks = sorted(locks, key=(lambda x: len(self.locks[x])))
for (i, lock) in enumerate(locks):
self.locks[lock].append(id)
if (len(self.locks[lock]) == 1):
self.requests_left[id] -= 1
if (self.requests_left[id] == 0):
self.requests[id] -= set(locks[(i + 1):])
return True
return False |
def _refain_locks(self, locks, id):
'Cancel/release previously requested/acquired locks\n\n Parameters\n ----------\n locks: List[str]\n Names of the locks to refain.\n id: Hashable\n Identifier of the `MultiLock` instance refraining the locks.\n '
waiters_ready = set()
for lock in locks:
if (self.locks[lock][0] == id):
self.locks[lock].pop(0)
if self.locks[lock]:
new_first = self.locks[lock][0]
self.requests_left[new_first] -= 1
if (self.requests_left[new_first] <= 0):
self.requests_left[new_first] = 0
waiters_ready.add(new_first)
else:
self.locks[lock].remove(id)
assert (id not in self.locks[lock])
del self.requests[id]
del self.requests_left[id]
for waiter in waiters_ready:
self.scheduler.loop.add_callback(self.events[waiter].set) | 4,364,352,498,279,683,600 | Cancel/release previously requested/acquired locks
Parameters
----------
locks: List[str]
Names of the locks to refain.
id: Hashable
Identifier of the `MultiLock` instance refraining the locks. | distributed/multi_lock.py | _refain_locks | bryanwweber/distributed | python | def _refain_locks(self, locks, id):
'Cancel/release previously requested/acquired locks\n\n Parameters\n ----------\n locks: List[str]\n Names of the locks to refain.\n id: Hashable\n Identifier of the `MultiLock` instance refraining the locks.\n '
waiters_ready = set()
for lock in locks:
if (self.locks[lock][0] == id):
self.locks[lock].pop(0)
if self.locks[lock]:
new_first = self.locks[lock][0]
self.requests_left[new_first] -= 1
if (self.requests_left[new_first] <= 0):
self.requests_left[new_first] = 0
waiters_ready.add(new_first)
else:
self.locks[lock].remove(id)
assert (id not in self.locks[lock])
del self.requests[id]
del self.requests_left[id]
for waiter in waiters_ready:
self.scheduler.loop.add_callback(self.events[waiter].set) |
def acquire(self, blocking=True, timeout=None, num_locks=None):
'Acquire the lock\n\n Parameters\n ----------\n blocking : bool, optional\n If false, don\'t wait on the lock in the scheduler at all.\n timeout : string or number or timedelta, optional\n Seconds to wait on the lock in the scheduler. This does not\n include local coroutine time, network transfer time, etc..\n It is forbidden to specify a timeout when blocking is false.\n Instead of number of seconds, it is also possible to specify\n a timedelta in string format, e.g. "200ms".\n num_locks : int, optional\n Number of locks needed. If None, all locks are needed\n\n Examples\n --------\n >>> lock = MultiLock([\'x\', \'y\']) # doctest: +SKIP\n >>> lock.acquire(timeout="1s") # doctest: +SKIP\n\n Returns\n -------\n True or False whether or not it successfully acquired the lock\n '
timeout = parse_timedelta(timeout)
if (not blocking):
if (timeout is not None):
raise ValueError("can't specify a timeout for a non-blocking call")
timeout = 0
result = self.client.sync(self.client.scheduler.multi_lock_acquire, locks=self.names, id=self.id, timeout=timeout, num_locks=(num_locks or len(self.names)))
self._locked = True
return result | -4,150,933,186,845,028,400 | Acquire the lock
Parameters
----------
blocking : bool, optional
If false, don't wait on the lock in the scheduler at all.
timeout : string or number or timedelta, optional
Seconds to wait on the lock in the scheduler. This does not
include local coroutine time, network transfer time, etc..
It is forbidden to specify a timeout when blocking is false.
Instead of number of seconds, it is also possible to specify
a timedelta in string format, e.g. "200ms".
num_locks : int, optional
Number of locks needed. If None, all locks are needed
Examples
--------
>>> lock = MultiLock(['x', 'y']) # doctest: +SKIP
>>> lock.acquire(timeout="1s") # doctest: +SKIP
Returns
-------
True or False whether or not it successfully acquired the lock | distributed/multi_lock.py | acquire | bryanwweber/distributed | python | def acquire(self, blocking=True, timeout=None, num_locks=None):
'Acquire the lock\n\n Parameters\n ----------\n blocking : bool, optional\n If false, don\'t wait on the lock in the scheduler at all.\n timeout : string or number or timedelta, optional\n Seconds to wait on the lock in the scheduler. This does not\n include local coroutine time, network transfer time, etc..\n It is forbidden to specify a timeout when blocking is false.\n Instead of number of seconds, it is also possible to specify\n a timedelta in string format, e.g. "200ms".\n num_locks : int, optional\n Number of locks needed. If None, all locks are needed\n\n Examples\n --------\n >>> lock = MultiLock([\'x\', \'y\']) # doctest: +SKIP\n >>> lock.acquire(timeout="1s") # doctest: +SKIP\n\n Returns\n -------\n True or False whether or not it successfully acquired the lock\n '
timeout = parse_timedelta(timeout)
if (not blocking):
if (timeout is not None):
raise ValueError("can't specify a timeout for a non-blocking call")
timeout = 0
result = self.client.sync(self.client.scheduler.multi_lock_acquire, locks=self.names, id=self.id, timeout=timeout, num_locks=(num_locks or len(self.names)))
self._locked = True
return result |
def release(self):
'Release the lock if already acquired'
if (not self.locked()):
raise ValueError('Lock is not yet acquired')
ret = self.client.sync(self.client.scheduler.multi_lock_release, id=self.id)
self._locked = False
return ret | 3,468,605,964,698,990,600 | Release the lock if already acquired | distributed/multi_lock.py | release | bryanwweber/distributed | python | def release(self):
if (not self.locked()):
raise ValueError('Lock is not yet acquired')
ret = self.client.sync(self.client.scheduler.multi_lock_release, id=self.id)
self._locked = False
return ret |
def display_and_save_batch(title, batch, data, save=True, display=True):
'Display and save batch of image using plt'
im = torchvision.utils.make_grid(batch, nrow=int((batch.shape[0] ** 0.5)))
plt.title(title)
plt.imshow(np.transpose(im.cpu().numpy(), (1, 2, 0)), cmap='gray')
if save:
plt.savefig(((('results/' + title) + data) + '.png'), transparent=True, bbox_inches='tight')
if display:
plt.show() | -7,719,224,254,690,918,000 | Display and save batch of image using plt | Implementations/Conditional-Variational-Autoencoder/plot_utils.py | display_and_save_batch | jaywonchung/Learning-ML | python | def display_and_save_batch(title, batch, data, save=True, display=True):
im = torchvision.utils.make_grid(batch, nrow=int((batch.shape[0] ** 0.5)))
plt.title(title)
plt.imshow(np.transpose(im.cpu().numpy(), (1, 2, 0)), cmap='gray')
if save:
plt.savefig(((('results/' + title) + data) + '.png'), transparent=True, bbox_inches='tight')
if display:
plt.show() |
def display_and_save_latent(batch, label, data, save=True, display=True):
'Display and save batch of 2-D latent variable using plt'
colors = ['black', 'red', 'green', 'blue', 'yellow', 'cyan', 'magenta', 'pink', 'violet', 'grey']
z = batch.cpu().detach().numpy()
l = label.cpu().numpy()
plt.title('Latent variables')
plt.scatter(z[:, 0], z[:, 1], c=l, cmap=matplotlib.colors.ListedColormap(colors))
plt.xlim((- 3), 3)
plt.ylim((- 3), 3)
if save:
plt.savefig((('results/latent-variable' + data) + '.png'), transparent=True, bbox_inches='tight')
if display:
plt.show() | 7,770,787,316,878,940,000 | Display and save batch of 2-D latent variable using plt | Implementations/Conditional-Variational-Autoencoder/plot_utils.py | display_and_save_latent | jaywonchung/Learning-ML | python | def display_and_save_latent(batch, label, data, save=True, display=True):
colors = ['black', 'red', 'green', 'blue', 'yellow', 'cyan', 'magenta', 'pink', 'violet', 'grey']
z = batch.cpu().detach().numpy()
l = label.cpu().numpy()
plt.title('Latent variables')
plt.scatter(z[:, 0], z[:, 1], c=l, cmap=matplotlib.colors.ListedColormap(colors))
plt.xlim((- 3), 3)
plt.ylim((- 3), 3)
if save:
plt.savefig((('results/latent-variable' + data) + '.png'), transparent=True, bbox_inches='tight')
if display:
plt.show() |
@staticmethod
def Args(parser):
'Args is called by calliope to gather arguments for this command.\n\n Args:\n parser: An argparse parser that you can use to add arguments that go\n on the command line after this command. Positional arguments are\n allowed.\n '
common_flags.operation_flag(suffix='to describe').AddToParser(parser)
parser.display_info.AddFormat(':(metadata.startTime.date(format="%Y-%m-%d %H:%M:%S %Z", tz=LOCAL)) [transforms] default')
parser.add_argument('--full', action='store_true', default=False, help='Print the entire operation resource, which could be large. By default, a summary will be printed instead.') | -2,756,768,806,353,174,500 | Args is called by calliope to gather arguments for this command.
Args:
parser: An argparse parser that you can use to add arguments that go
on the command line after this command. Positional arguments are
allowed. | lib/surface/service_management/operations/describe.py | Args | bshaffer/google-cloud-sdk | python | @staticmethod
def Args(parser):
'Args is called by calliope to gather arguments for this command.\n\n Args:\n parser: An argparse parser that you can use to add arguments that go\n on the command line after this command. Positional arguments are\n allowed.\n '
common_flags.operation_flag(suffix='to describe').AddToParser(parser)
parser.display_info.AddFormat(':(metadata.startTime.date(format="%Y-%m-%d %H:%M:%S %Z", tz=LOCAL)) [transforms] default')
parser.add_argument('--full', action='store_true', default=False, help='Print the entire operation resource, which could be large. By default, a summary will be printed instead.') |
def Run(self, args):
"Stubs 'service-management operations describe'.\n\n Args:\n args: argparse.Namespace, The arguments that this command was invoked\n with.\n "
pass | -7,147,610,344,865,069,000 | Stubs 'service-management operations describe'.
Args:
args: argparse.Namespace, The arguments that this command was invoked
with. | lib/surface/service_management/operations/describe.py | Run | bshaffer/google-cloud-sdk | python | def Run(self, args):
"Stubs 'service-management operations describe'.\n\n Args:\n args: argparse.Namespace, The arguments that this command was invoked\n with.\n "
pass |
def initialize_instances(infile):
'Read the m_trg.csv CSV data into a list of instances.'
instances = []
dat = open(infile, 'r')
reader = csv.reader(dat)
dat.close()
for row in reader:
instance = Instance([float(value) for value in row[:(- 1)]])
if (float(row[(- 1)]) < 0):
instance.setLabel(Instance(0))
else:
instance.setLabel(Instance(1))
instances.append(instance)
return instances | 563,886,251,217,483,300 | Read the m_trg.csv CSV data into a list of instances. | ABAGAIL_execution/flipflop.py | initialize_instances | tirthajyoti/Randomized_Optimization | python | def initialize_instances(infile):
instances = []
dat = open(infile, 'r')
reader = csv.reader(dat)
dat.close()
for row in reader:
instance = Instance([float(value) for value in row[:(- 1)]])
if (float(row[(- 1)]) < 0):
instance.setLabel(Instance(0))
else:
instance.setLabel(Instance(1))
instances.append(instance)
return instances |
def train(oa, network, oaName, training_ints, validation_ints, testing_ints, measure):
'Train a given network on a set of instances.\n '
print('\nError results for {}\n---------------------------'.format(oaName))
times = [0]
for iteration in xrange(TRAINING_ITERATIONS):
start = time.clock()
oa.train()
elapsed = (time.clock() - start)
times.append((times[(- 1)] + elapsed))
if ((iteration % 10) == 0):
(MSE_trg, acc_trg) = errorOnDataSet(network, training_ints, measure)
(MSE_val, acc_val) = errorOnDataSet(network, validation_ints, measure)
(MSE_tst, acc_tst) = errorOnDataSet(network, testing_ints, measure)
txt = '{},{},{},{},{},{},{},{}\n'.format(iteration, MSE_trg, MSE_val, MSE_tst, acc_trg, acc_val, acc_tst, times[(- 1)])
print(txt)
f = open(OUTFILE, 'a+')
f.write(txt)
f.close() | 6,266,635,343,969,500,000 | Train a given network on a set of instances. | ABAGAIL_execution/flipflop.py | train | tirthajyoti/Randomized_Optimization | python | def train(oa, network, oaName, training_ints, validation_ints, testing_ints, measure):
'\n '
print('\nError results for {}\n---------------------------'.format(oaName))
times = [0]
for iteration in xrange(TRAINING_ITERATIONS):
start = time.clock()
oa.train()
elapsed = (time.clock() - start)
times.append((times[(- 1)] + elapsed))
if ((iteration % 10) == 0):
(MSE_trg, acc_trg) = errorOnDataSet(network, training_ints, measure)
(MSE_val, acc_val) = errorOnDataSet(network, validation_ints, measure)
(MSE_tst, acc_tst) = errorOnDataSet(network, testing_ints, measure)
txt = '{},{},{},{},{},{},{},{}\n'.format(iteration, MSE_trg, MSE_val, MSE_tst, acc_trg, acc_val, acc_tst, times[(- 1)])
print(txt)
f = open(OUTFILE, 'a+')
f.write(txt)
f.close() |
def main():
'Run this experiment'
training_ints = initialize_instances('m_trg.csv')
testing_ints = initialize_instances('m_test.csv')
validation_ints = initialize_instances('m_val.csv')
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
relu = RELU()
rule = RPROPUpdateRule()
oa_names = ['Backprop']
classification_network = factory.createClassificationNetwork([INPUT_LAYER, HIDDEN_LAYER1, HIDDEN_LAYER2, HIDDEN_LAYER3, OUTPUT_LAYER], relu)
train(BatchBackPropagationTrainer(data_set, classification_network, measure, rule), classification_network, 'Backprop', training_ints, validation_ints, testing_ints, measure) | -8,651,872,616,011,747,000 | Run this experiment | ABAGAIL_execution/flipflop.py | main | tirthajyoti/Randomized_Optimization | python | def main():
training_ints = initialize_instances('m_trg.csv')
testing_ints = initialize_instances('m_test.csv')
validation_ints = initialize_instances('m_val.csv')
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
relu = RELU()
rule = RPROPUpdateRule()
oa_names = ['Backprop']
classification_network = factory.createClassificationNetwork([INPUT_LAYER, HIDDEN_LAYER1, HIDDEN_LAYER2, HIDDEN_LAYER3, OUTPUT_LAYER], relu)
train(BatchBackPropagationTrainer(data_set, classification_network, measure, rule), classification_network, 'Backprop', training_ints, validation_ints, testing_ints, measure) |
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, compute_name: Optional[pulumi.Input[str]]=None, identity: Optional[pulumi.Input[pulumi.InputType['IdentityArgs']]]=None, location: Optional[pulumi.Input[str]]=None, properties: Optional[pulumi.Input[Union[(pulumi.InputType['AKSArgs'], pulumi.InputType['AmlComputeArgs'], pulumi.InputType['ComputeInstanceArgs'], pulumi.InputType['DataFactoryArgs'], pulumi.InputType['DataLakeAnalyticsArgs'], pulumi.InputType['DatabricksArgs'], pulumi.InputType['HDInsightArgs'], pulumi.InputType['VirtualMachineArgs'])]]]=None, resource_group_name: Optional[pulumi.Input[str]]=None, sku: Optional[pulumi.Input[pulumi.InputType['SkuArgs']]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, workspace_name: Optional[pulumi.Input[str]]=None, __props__=None, __name__=None, __opts__=None):
"\n Machine Learning compute object wrapped into ARM resource envelope.\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] compute_name: Name of the Azure Machine Learning compute.\n :param pulumi.Input[pulumi.InputType['IdentityArgs']] identity: The identity of the resource.\n :param pulumi.Input[str] location: Specifies the location of the resource.\n :param pulumi.Input[Union[pulumi.InputType['AKSArgs'], pulumi.InputType['AmlComputeArgs'], pulumi.InputType['ComputeInstanceArgs'], pulumi.InputType['DataFactoryArgs'], pulumi.InputType['DataLakeAnalyticsArgs'], pulumi.InputType['DatabricksArgs'], pulumi.InputType['HDInsightArgs'], pulumi.InputType['VirtualMachineArgs']]] properties: Compute properties\n :param pulumi.Input[str] resource_group_name: Name of the resource group in which workspace is located.\n :param pulumi.Input[pulumi.InputType['SkuArgs']] sku: The sku of the workspace.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Contains resource tags defined as key/value pairs.\n :param pulumi.Input[str] workspace_name: Name of Azure Machine Learning workspace.\n "
if (__name__ is not None):
warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning)
resource_name = __name__
if (__opts__ is not None):
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if (opts is None):
opts = pulumi.ResourceOptions()
if (not isinstance(opts, pulumi.ResourceOptions)):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if (opts.version is None):
opts.version = _utilities.get_version()
if (opts.id is None):
if (__props__ is not None):
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['compute_name'] = compute_name
__props__['identity'] = identity
__props__['location'] = location
__props__['properties'] = properties
if ((resource_group_name is None) and (not opts.urn)):
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
__props__['sku'] = sku
__props__['tags'] = tags
if ((workspace_name is None) and (not opts.urn)):
raise TypeError("Missing required property 'workspace_name'")
__props__['workspace_name'] = workspace_name
__props__['name'] = None
__props__['system_data'] = None
__props__['type'] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20210101:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/latest:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/latest:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20180301preview:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20180301preview:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20181119:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20181119:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20190501:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20190501:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20190601:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20190601:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20191101:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20191101:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200101:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200101:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200218preview:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200218preview:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200301:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200301:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200401:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200401:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200501preview:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200501preview:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200515preview:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200515preview:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200601:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200601:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200801:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200801:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200901preview:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200901preview:MachineLearningCompute')])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(MachineLearningCompute, __self__).__init__('azure-native:machinelearningservices/v20210101:MachineLearningCompute', resource_name, __props__, opts) | 8,050,948,739,499,512,000 | Machine Learning compute object wrapped into ARM resource envelope.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] compute_name: Name of the Azure Machine Learning compute.
:param pulumi.Input[pulumi.InputType['IdentityArgs']] identity: The identity of the resource.
:param pulumi.Input[str] location: Specifies the location of the resource.
:param pulumi.Input[Union[pulumi.InputType['AKSArgs'], pulumi.InputType['AmlComputeArgs'], pulumi.InputType['ComputeInstanceArgs'], pulumi.InputType['DataFactoryArgs'], pulumi.InputType['DataLakeAnalyticsArgs'], pulumi.InputType['DatabricksArgs'], pulumi.InputType['HDInsightArgs'], pulumi.InputType['VirtualMachineArgs']]] properties: Compute properties
:param pulumi.Input[str] resource_group_name: Name of the resource group in which workspace is located.
:param pulumi.Input[pulumi.InputType['SkuArgs']] sku: The sku of the workspace.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Contains resource tags defined as key/value pairs.
:param pulumi.Input[str] workspace_name: Name of Azure Machine Learning workspace. | sdk/python/pulumi_azure_native/machinelearningservices/v20210101/machine_learning_compute.py | __init__ | pulumi-bot/pulumi-azure-native | python | def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, compute_name: Optional[pulumi.Input[str]]=None, identity: Optional[pulumi.Input[pulumi.InputType['IdentityArgs']]]=None, location: Optional[pulumi.Input[str]]=None, properties: Optional[pulumi.Input[Union[(pulumi.InputType['AKSArgs'], pulumi.InputType['AmlComputeArgs'], pulumi.InputType['ComputeInstanceArgs'], pulumi.InputType['DataFactoryArgs'], pulumi.InputType['DataLakeAnalyticsArgs'], pulumi.InputType['DatabricksArgs'], pulumi.InputType['HDInsightArgs'], pulumi.InputType['VirtualMachineArgs'])]]]=None, resource_group_name: Optional[pulumi.Input[str]]=None, sku: Optional[pulumi.Input[pulumi.InputType['SkuArgs']]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, workspace_name: Optional[pulumi.Input[str]]=None, __props__=None, __name__=None, __opts__=None):
"\n Machine Learning compute object wrapped into ARM resource envelope.\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] compute_name: Name of the Azure Machine Learning compute.\n :param pulumi.Input[pulumi.InputType['IdentityArgs']] identity: The identity of the resource.\n :param pulumi.Input[str] location: Specifies the location of the resource.\n :param pulumi.Input[Union[pulumi.InputType['AKSArgs'], pulumi.InputType['AmlComputeArgs'], pulumi.InputType['ComputeInstanceArgs'], pulumi.InputType['DataFactoryArgs'], pulumi.InputType['DataLakeAnalyticsArgs'], pulumi.InputType['DatabricksArgs'], pulumi.InputType['HDInsightArgs'], pulumi.InputType['VirtualMachineArgs']]] properties: Compute properties\n :param pulumi.Input[str] resource_group_name: Name of the resource group in which workspace is located.\n :param pulumi.Input[pulumi.InputType['SkuArgs']] sku: The sku of the workspace.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Contains resource tags defined as key/value pairs.\n :param pulumi.Input[str] workspace_name: Name of Azure Machine Learning workspace.\n "
if (__name__ is not None):
warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning)
resource_name = __name__
if (__opts__ is not None):
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if (opts is None):
opts = pulumi.ResourceOptions()
if (not isinstance(opts, pulumi.ResourceOptions)):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if (opts.version is None):
opts.version = _utilities.get_version()
if (opts.id is None):
if (__props__ is not None):
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['compute_name'] = compute_name
__props__['identity'] = identity
__props__['location'] = location
__props__['properties'] = properties
if ((resource_group_name is None) and (not opts.urn)):
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
__props__['sku'] = sku
__props__['tags'] = tags
if ((workspace_name is None) and (not opts.urn)):
raise TypeError("Missing required property 'workspace_name'")
__props__['workspace_name'] = workspace_name
__props__['name'] = None
__props__['system_data'] = None
__props__['type'] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20210101:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/latest:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/latest:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20180301preview:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20180301preview:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20181119:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20181119:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20190501:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20190501:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20190601:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20190601:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20191101:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20191101:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200101:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200101:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200218preview:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200218preview:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200301:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200301:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200401:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200401:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200501preview:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200501preview:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200515preview:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200515preview:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200601:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200601:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200801:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200801:MachineLearningCompute'), pulumi.Alias(type_='azure-native:machinelearningservices/v20200901preview:MachineLearningCompute'), pulumi.Alias(type_='azure-nextgen:machinelearningservices/v20200901preview:MachineLearningCompute')])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(MachineLearningCompute, __self__).__init__('azure-native:machinelearningservices/v20210101:MachineLearningCompute', resource_name, __props__, opts) |
@staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'MachineLearningCompute':
"\n Get an existing MachineLearningCompute resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__['identity'] = None
__props__['location'] = None
__props__['name'] = None
__props__['properties'] = None
__props__['sku'] = None
__props__['system_data'] = None
__props__['tags'] = None
__props__['type'] = None
return MachineLearningCompute(resource_name, opts=opts, __props__=__props__) | -3,952,396,233,049,537,500 | Get an existing MachineLearningCompute resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource. | sdk/python/pulumi_azure_native/machinelearningservices/v20210101/machine_learning_compute.py | get | pulumi-bot/pulumi-azure-native | python | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'MachineLearningCompute':
"\n Get an existing MachineLearningCompute resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__['identity'] = None
__props__['location'] = None
__props__['name'] = None
__props__['properties'] = None
__props__['sku'] = None
__props__['system_data'] = None
__props__['tags'] = None
__props__['type'] = None
return MachineLearningCompute(resource_name, opts=opts, __props__=__props__) |
@property
@pulumi.getter
def identity(self) -> pulumi.Output[Optional['outputs.IdentityResponse']]:
'\n The identity of the resource.\n '
return pulumi.get(self, 'identity') | -2,580,811,553,100,511,000 | The identity of the resource. | sdk/python/pulumi_azure_native/machinelearningservices/v20210101/machine_learning_compute.py | identity | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def identity(self) -> pulumi.Output[Optional['outputs.IdentityResponse']]:
'\n \n '
return pulumi.get(self, 'identity') |
@property
@pulumi.getter
def location(self) -> pulumi.Output[Optional[str]]:
'\n Specifies the location of the resource.\n '
return pulumi.get(self, 'location') | 6,302,777,286,934,958,000 | Specifies the location of the resource. | sdk/python/pulumi_azure_native/machinelearningservices/v20210101/machine_learning_compute.py | location | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def location(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'location') |
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n Specifies the name of the resource.\n '
return pulumi.get(self, 'name') | -5,472,184,884,634,436,000 | Specifies the name of the resource. | sdk/python/pulumi_azure_native/machinelearningservices/v20210101/machine_learning_compute.py | name | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'name') |
@property
@pulumi.getter
def properties(self) -> pulumi.Output[Any]:
'\n Compute properties\n '
return pulumi.get(self, 'properties') | -7,218,582,079,494,190,000 | Compute properties | sdk/python/pulumi_azure_native/machinelearningservices/v20210101/machine_learning_compute.py | properties | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def properties(self) -> pulumi.Output[Any]:
'\n \n '
return pulumi.get(self, 'properties') |
@property
@pulumi.getter
def sku(self) -> pulumi.Output[Optional['outputs.SkuResponse']]:
'\n The sku of the workspace.\n '
return pulumi.get(self, 'sku') | -3,322,611,284,534,289,000 | The sku of the workspace. | sdk/python/pulumi_azure_native/machinelearningservices/v20210101/machine_learning_compute.py | sku | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def sku(self) -> pulumi.Output[Optional['outputs.SkuResponse']]:
'\n \n '
return pulumi.get(self, 'sku') |
@property
@pulumi.getter(name='systemData')
def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']:
'\n Read only system data\n '
return pulumi.get(self, 'system_data') | 723,081,282,536,590,700 | Read only system data | sdk/python/pulumi_azure_native/machinelearningservices/v20210101/machine_learning_compute.py | system_data | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter(name='systemData')
def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']:
'\n \n '
return pulumi.get(self, 'system_data') |
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n Contains resource tags defined as key/value pairs.\n '
return pulumi.get(self, 'tags') | -4,864,786,089,036,755,000 | Contains resource tags defined as key/value pairs. | sdk/python/pulumi_azure_native/machinelearningservices/v20210101/machine_learning_compute.py | tags | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n \n '
return pulumi.get(self, 'tags') |
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
'\n Specifies the type of the resource.\n '
return pulumi.get(self, 'type') | 5,546,388,334,793,997,000 | Specifies the type of the resource. | sdk/python/pulumi_azure_native/machinelearningservices/v20210101/machine_learning_compute.py | type | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'type') |
async def async_setup_entry(hass, config_entry, async_add_entities):
'Set up OpenWeatherMap weather entity based on a config entry.'
domain_data = hass.data[DOMAIN][config_entry.entry_id]
name = domain_data[ENTRY_NAME]
weather_coordinator = domain_data[ENTRY_WEATHER_COORDINATOR]
unique_id = f'{config_entry.unique_id}'
owm_weather = OpenWeatherMapWeather(name, unique_id, weather_coordinator)
async_add_entities([owm_weather], False) | -5,971,107,687,580,836,000 | Set up OpenWeatherMap weather entity based on a config entry. | homeassistant/components/openweathermap/weather.py | async_setup_entry | 123dev/core | python | async def async_setup_entry(hass, config_entry, async_add_entities):
domain_data = hass.data[DOMAIN][config_entry.entry_id]
name = domain_data[ENTRY_NAME]
weather_coordinator = domain_data[ENTRY_WEATHER_COORDINATOR]
unique_id = f'{config_entry.unique_id}'
owm_weather = OpenWeatherMapWeather(name, unique_id, weather_coordinator)
async_add_entities([owm_weather], False) |
def __init__(self, name, unique_id, weather_coordinator: WeatherUpdateCoordinator):
'Initialize the sensor.'
self._name = name
self._unique_id = unique_id
self._weather_coordinator = weather_coordinator | 4,389,320,978,801,046,000 | Initialize the sensor. | homeassistant/components/openweathermap/weather.py | __init__ | 123dev/core | python | def __init__(self, name, unique_id, weather_coordinator: WeatherUpdateCoordinator):
self._name = name
self._unique_id = unique_id
self._weather_coordinator = weather_coordinator |
@property
def name(self):
'Return the name of the sensor.'
return self._name | 8,691,954,631,286,512,000 | Return the name of the sensor. | homeassistant/components/openweathermap/weather.py | name | 123dev/core | python | @property
def name(self):
return self._name |
@property
def unique_id(self):
'Return a unique_id for this entity.'
return self._unique_id | 2,237,810,817,326,574,000 | Return a unique_id for this entity. | homeassistant/components/openweathermap/weather.py | unique_id | 123dev/core | python | @property
def unique_id(self):
return self._unique_id |
@property
def should_poll(self):
'Return the polling requirement of the entity.'
return False | 9,164,027,142,541,335,000 | Return the polling requirement of the entity. | homeassistant/components/openweathermap/weather.py | should_poll | 123dev/core | python | @property
def should_poll(self):
return False |
@property
def attribution(self):
'Return the attribution.'
return ATTRIBUTION | -4,777,674,644,330,317,000 | Return the attribution. | homeassistant/components/openweathermap/weather.py | attribution | 123dev/core | python | @property
def attribution(self):
return ATTRIBUTION |
@property
def condition(self):
'Return the current condition.'
return self._weather_coordinator.data[ATTR_API_CONDITION] | 8,621,755,588,087,073,000 | Return the current condition. | homeassistant/components/openweathermap/weather.py | condition | 123dev/core | python | @property
def condition(self):
return self._weather_coordinator.data[ATTR_API_CONDITION] |
@property
def temperature(self):
'Return the temperature.'
return self._weather_coordinator.data[ATTR_API_TEMPERATURE] | 7,313,664,048,823,649,000 | Return the temperature. | homeassistant/components/openweathermap/weather.py | temperature | 123dev/core | python | @property
def temperature(self):
return self._weather_coordinator.data[ATTR_API_TEMPERATURE] |
@property
def temperature_unit(self):
'Return the unit of measurement.'
return TEMP_CELSIUS | 4,571,780,805,438,814,700 | Return the unit of measurement. | homeassistant/components/openweathermap/weather.py | temperature_unit | 123dev/core | python | @property
def temperature_unit(self):
return TEMP_CELSIUS |
@property
def pressure(self):
'Return the pressure.'
return self._weather_coordinator.data[ATTR_API_PRESSURE] | 2,965,828,343,547,977,700 | Return the pressure. | homeassistant/components/openweathermap/weather.py | pressure | 123dev/core | python | @property
def pressure(self):
return self._weather_coordinator.data[ATTR_API_PRESSURE] |
@property
def humidity(self):
'Return the humidity.'
return self._weather_coordinator.data[ATTR_API_HUMIDITY] | 7,101,145,499,980,695,000 | Return the humidity. | homeassistant/components/openweathermap/weather.py | humidity | 123dev/core | python | @property
def humidity(self):
return self._weather_coordinator.data[ATTR_API_HUMIDITY] |
@property
def wind_speed(self):
'Return the wind speed.'
wind_speed = self._weather_coordinator.data[ATTR_API_WIND_SPEED]
if (self.hass.config.units.name == 'imperial'):
return round((wind_speed * 2.24), 2)
return round((wind_speed * 3.6), 2) | 2,837,666,101,896,959,000 | Return the wind speed. | homeassistant/components/openweathermap/weather.py | wind_speed | 123dev/core | python | @property
def wind_speed(self):
wind_speed = self._weather_coordinator.data[ATTR_API_WIND_SPEED]
if (self.hass.config.units.name == 'imperial'):
return round((wind_speed * 2.24), 2)
return round((wind_speed * 3.6), 2) |
@property
def wind_bearing(self):
'Return the wind bearing.'
return self._weather_coordinator.data[ATTR_API_WIND_BEARING] | 5,297,157,121,137,046,000 | Return the wind bearing. | homeassistant/components/openweathermap/weather.py | wind_bearing | 123dev/core | python | @property
def wind_bearing(self):
return self._weather_coordinator.data[ATTR_API_WIND_BEARING] |
@property
def forecast(self):
'Return the forecast array.'
return self._weather_coordinator.data[ATTR_API_FORECAST] | -6,175,109,922,992,382,000 | Return the forecast array. | homeassistant/components/openweathermap/weather.py | forecast | 123dev/core | python | @property
def forecast(self):
return self._weather_coordinator.data[ATTR_API_FORECAST] |
@property
def available(self):
'Return True if entity is available.'
return self._weather_coordinator.last_update_success | -3,304,158,879,303,020,000 | Return True if entity is available. | homeassistant/components/openweathermap/weather.py | available | 123dev/core | python | @property
def available(self):
return self._weather_coordinator.last_update_success |
async def async_added_to_hass(self):
'Connect to dispatcher listening for entity data notifications.'
self.async_on_remove(self._weather_coordinator.async_add_listener(self.async_write_ha_state)) | 7,899,978,953,877,624,000 | Connect to dispatcher listening for entity data notifications. | homeassistant/components/openweathermap/weather.py | async_added_to_hass | 123dev/core | python | async def async_added_to_hass(self):
self.async_on_remove(self._weather_coordinator.async_add_listener(self.async_write_ha_state)) |
async def async_update(self):
'Get the latest data from OWM and updates the states.'
(await self._weather_coordinator.async_request_refresh()) | -2,303,072,366,161,045,800 | Get the latest data from OWM and updates the states. | homeassistant/components/openweathermap/weather.py | async_update | 123dev/core | python | async def async_update(self):
(await self._weather_coordinator.async_request_refresh()) |
def get_widgets(self):
"\n Returns a list of widgets sorted by their 'order'.\n If two or more widgets have the same 'order', sort by label.\n "
return map((lambda x: x['widget']), filter((lambda x: (x['widget'] not in self.removed_widgets)), sorted(self.widgets.values(), key=(lambda x: (x['order'], x['widget'].label))))) | 4,196,100,985,637,145,000 | Returns a list of widgets sorted by their 'order'.
If two or more widgets have the same 'order', sort by label. | mayan/apps/common/classes.py | get_widgets | marumadang/mayan-edms | python | def get_widgets(self):
"\n Returns a list of widgets sorted by their 'order'.\n If two or more widgets have the same 'order', sort by label.\n "
return map((lambda x: x['widget']), filter((lambda x: (x['widget'] not in self.removed_widgets)), sorted(self.widgets.values(), key=(lambda x: (x['order'], x['widget'].label))))) |
def get_result(self, name):
'\n The method that produces the actual result. Must be implemented\n by each subclass.\n '
raise NotImplementedError | 2,257,598,814,406,162,000 | The method that produces the actual result. Must be implemented
by each subclass. | mayan/apps/common/classes.py | get_result | marumadang/mayan-edms | python | def get_result(self, name):
'\n The method that produces the actual result. Must be implemented\n by each subclass.\n '
raise NotImplementedError |
def sample_recognize(local_file_path):
'\n Transcribe a short audio file with multiple channels\n\n Args:\n local_file_path Path to local audio file, e.g. /path/audio.wav\n '
client = speech_v1.SpeechClient()
audio_channel_count = 2
enable_separate_recognition_per_channel = True
language_code = 'en-US'
config = {'audio_channel_count': audio_channel_count, 'enable_separate_recognition_per_channel': enable_separate_recognition_per_channel, 'language_code': language_code}
with io.open(local_file_path, 'rb') as f:
content = f.read()
audio = {'content': content}
response = client.recognize(config, audio)
for result in response.results:
print(u'Channel tag: {}'.format(result.channel_tag))
alternative = result.alternatives[0]
print(u'Transcript: {}'.format(alternative.transcript)) | 6,229,858,521,637,275,000 | Transcribe a short audio file with multiple channels
Args:
local_file_path Path to local audio file, e.g. /path/audio.wav | speech/samples/v1/speech_transcribe_multichannel.py | sample_recognize | AzemaBaptiste/google-cloud-python | python | def sample_recognize(local_file_path):
'\n Transcribe a short audio file with multiple channels\n\n Args:\n local_file_path Path to local audio file, e.g. /path/audio.wav\n '
client = speech_v1.SpeechClient()
audio_channel_count = 2
enable_separate_recognition_per_channel = True
language_code = 'en-US'
config = {'audio_channel_count': audio_channel_count, 'enable_separate_recognition_per_channel': enable_separate_recognition_per_channel, 'language_code': language_code}
with io.open(local_file_path, 'rb') as f:
content = f.read()
audio = {'content': content}
response = client.recognize(config, audio)
for result in response.results:
print(u'Channel tag: {}'.format(result.channel_tag))
alternative = result.alternatives[0]
print(u'Transcript: {}'.format(alternative.transcript)) |
def __init__(self, security_group_id=None):
'ShowSecurityGroupRequest - a model defined in huaweicloud sdk'
self._security_group_id = None
self.discriminator = None
self.security_group_id = security_group_id | 4,764,584,815,604,628,000 | ShowSecurityGroupRequest - a model defined in huaweicloud sdk | huaweicloud-sdk-vpc/huaweicloudsdkvpc/v3/model/show_security_group_request.py | __init__ | huaweicloud/huaweicloud-sdk-python-v3 | python | def __init__(self, security_group_id=None):
self._security_group_id = None
self.discriminator = None
self.security_group_id = security_group_id |
@property
def security_group_id(self):
'Gets the security_group_id of this ShowSecurityGroupRequest.\n\n 安全组资源ID\n\n :return: The security_group_id of this ShowSecurityGroupRequest.\n :rtype: str\n '
return self._security_group_id | 6,141,350,925,777,083,000 | Gets the security_group_id of this ShowSecurityGroupRequest.
安全组资源ID
:return: The security_group_id of this ShowSecurityGroupRequest.
:rtype: str | huaweicloud-sdk-vpc/huaweicloudsdkvpc/v3/model/show_security_group_request.py | security_group_id | huaweicloud/huaweicloud-sdk-python-v3 | python | @property
def security_group_id(self):
'Gets the security_group_id of this ShowSecurityGroupRequest.\n\n 安全组资源ID\n\n :return: The security_group_id of this ShowSecurityGroupRequest.\n :rtype: str\n '
return self._security_group_id |
@security_group_id.setter
def security_group_id(self, security_group_id):
'Sets the security_group_id of this ShowSecurityGroupRequest.\n\n 安全组资源ID\n\n :param security_group_id: The security_group_id of this ShowSecurityGroupRequest.\n :type: str\n '
self._security_group_id = security_group_id | -7,290,699,017,112,613,000 | Sets the security_group_id of this ShowSecurityGroupRequest.
安全组资源ID
:param security_group_id: The security_group_id of this ShowSecurityGroupRequest.
:type: str | huaweicloud-sdk-vpc/huaweicloudsdkvpc/v3/model/show_security_group_request.py | security_group_id | huaweicloud/huaweicloud-sdk-python-v3 | python | @security_group_id.setter
def security_group_id(self, security_group_id):
'Sets the security_group_id of this ShowSecurityGroupRequest.\n\n 安全组资源ID\n\n :param security_group_id: The security_group_id of this ShowSecurityGroupRequest.\n :type: str\n '
self._security_group_id = security_group_id |
def to_dict(self):
'Returns the model properties as a dict'
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
elif (attr in self.sensitive_list):
result[attr] = '****'
else:
result[attr] = value
return result | 2,594,216,033,120,720,000 | Returns the model properties as a dict | huaweicloud-sdk-vpc/huaweicloudsdkvpc/v3/model/show_security_group_request.py | to_dict | huaweicloud/huaweicloud-sdk-python-v3 | python | def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
elif (attr in self.sensitive_list):
result[attr] = '****'
else:
result[attr] = value
return result |
def to_str(self):
'Returns the string representation of the model'
import simplejson as json
if six.PY2:
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
return json.dumps(sanitize_for_serialization(self), ensure_ascii=False) | -6,095,553,759,700,562,000 | Returns the string representation of the model | huaweicloud-sdk-vpc/huaweicloudsdkvpc/v3/model/show_security_group_request.py | to_str | huaweicloud/huaweicloud-sdk-python-v3 | python | def to_str(self):
import simplejson as json
if six.PY2:
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
return json.dumps(sanitize_for_serialization(self), ensure_ascii=False) |
def __repr__(self):
'For `print`'
return self.to_str() | -1,581,176,371,750,213,000 | For `print` | huaweicloud-sdk-vpc/huaweicloudsdkvpc/v3/model/show_security_group_request.py | __repr__ | huaweicloud/huaweicloud-sdk-python-v3 | python | def __repr__(self):
return self.to_str() |
def __eq__(self, other):
'Returns true if both objects are equal'
if (not isinstance(other, ShowSecurityGroupRequest)):
return False
return (self.__dict__ == other.__dict__) | -2,403,763,859,980,322,300 | Returns true if both objects are equal | huaweicloud-sdk-vpc/huaweicloudsdkvpc/v3/model/show_security_group_request.py | __eq__ | huaweicloud/huaweicloud-sdk-python-v3 | python | def __eq__(self, other):
if (not isinstance(other, ShowSecurityGroupRequest)):
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 | huaweicloud-sdk-vpc/huaweicloudsdkvpc/v3/model/show_security_group_request.py | __ne__ | huaweicloud/huaweicloud-sdk-python-v3 | python | def __ne__(self, other):
return (not (self == other)) |
def __init__(self, error_date_time=None, request_id=None):
'ErrorDetails - a model defined in Swagger'
self._error_date_time = None
self._request_id = None
self.discriminator = None
if (error_date_time is not None):
self.error_date_time = error_date_time
if (request_id is not None):
self.request_id = request_id | 180,176,011,986,121,900 | ErrorDetails - a model defined in Swagger | asposewordscloud/models/error_details.py | __init__ | rizwanniazigroupdocs/aspose-words-cloud-python | python | def __init__(self, error_date_time=None, request_id=None):
self._error_date_time = None
self._request_id = None
self.discriminator = None
if (error_date_time is not None):
self.error_date_time = error_date_time
if (request_id is not None):
self.request_id = request_id |
@property
def error_date_time(self):
'Gets the error_date_time of this ErrorDetails. # noqa: E501\n\n Error datetime. # noqa: E501\n\n :return: The error_date_time of this ErrorDetails. # noqa: E501\n :rtype: datetime\n '
return self._error_date_time | -9,134,720,129,385,786,000 | Gets the error_date_time of this ErrorDetails. # noqa: E501
Error datetime. # noqa: E501
:return: The error_date_time of this ErrorDetails. # noqa: E501
:rtype: datetime | asposewordscloud/models/error_details.py | error_date_time | rizwanniazigroupdocs/aspose-words-cloud-python | python | @property
def error_date_time(self):
'Gets the error_date_time of this ErrorDetails. # noqa: E501\n\n Error datetime. # noqa: E501\n\n :return: The error_date_time of this ErrorDetails. # noqa: E501\n :rtype: datetime\n '
return self._error_date_time |
@error_date_time.setter
def error_date_time(self, error_date_time):
'Sets the error_date_time of this ErrorDetails.\n\n Error datetime. # noqa: E501\n\n :param error_date_time: The error_date_time of this ErrorDetails. # noqa: E501\n :type: datetime\n '
self._error_date_time = error_date_time | 2,731,700,064,338,604,000 | Sets the error_date_time of this ErrorDetails.
Error datetime. # noqa: E501
:param error_date_time: The error_date_time of this ErrorDetails. # noqa: E501
:type: datetime | asposewordscloud/models/error_details.py | error_date_time | rizwanniazigroupdocs/aspose-words-cloud-python | python | @error_date_time.setter
def error_date_time(self, error_date_time):
'Sets the error_date_time of this ErrorDetails.\n\n Error datetime. # noqa: E501\n\n :param error_date_time: The error_date_time of this ErrorDetails. # noqa: E501\n :type: datetime\n '
self._error_date_time = error_date_time |
@property
def request_id(self):
'Gets the request_id of this ErrorDetails. # noqa: E501\n\n The request id. # noqa: E501\n\n :return: The request_id of this ErrorDetails. # noqa: E501\n :rtype: str\n '
return self._request_id | -2,747,279,147,444,605,400 | Gets the request_id of this ErrorDetails. # noqa: E501
The request id. # noqa: E501
:return: The request_id of this ErrorDetails. # noqa: E501
:rtype: str | asposewordscloud/models/error_details.py | request_id | rizwanniazigroupdocs/aspose-words-cloud-python | python | @property
def request_id(self):
'Gets the request_id of this ErrorDetails. # noqa: E501\n\n The request id. # noqa: E501\n\n :return: The request_id of this ErrorDetails. # noqa: E501\n :rtype: str\n '
return self._request_id |
@request_id.setter
def request_id(self, request_id):
'Sets the request_id of this ErrorDetails.\n\n The request id. # noqa: E501\n\n :param request_id: The request_id of this ErrorDetails. # noqa: E501\n :type: str\n '
self._request_id = request_id | 4,101,524,972,968,898,600 | Sets the request_id of this ErrorDetails.
The request id. # noqa: E501
:param request_id: The request_id of this ErrorDetails. # noqa: E501
:type: str | asposewordscloud/models/error_details.py | request_id | rizwanniazigroupdocs/aspose-words-cloud-python | python | @request_id.setter
def request_id(self, request_id):
'Sets the request_id of this ErrorDetails.\n\n The request id. # noqa: E501\n\n :param request_id: The request_id of this ErrorDetails. # noqa: E501\n :type: str\n '
self._request_id = request_id |
def to_dict(self):
'Returns the model properties as a dict'
result = {}
for (attr, _) in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
return result | -2,772,352,302,133,010,000 | Returns the model properties as a dict | asposewordscloud/models/error_details.py | to_dict | rizwanniazigroupdocs/aspose-words-cloud-python | python | def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
return result |
def to_json(self):
'Returns the model properties as a dict'
result = {}
for (attr, _) in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[self.attribute_map[attr]] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[self.attribute_map[attr]] = value.to_dict()
elif isinstance(value, dict):
result[self.attribute_map[attr]] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[self.attribute_map[attr]] = value
return json.dumps(result) | -5,130,988,191,037,985,000 | Returns the model properties as a dict | asposewordscloud/models/error_details.py | to_json | rizwanniazigroupdocs/aspose-words-cloud-python | python | def to_json(self):
result = {}
for (attr, _) in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[self.attribute_map[attr]] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[self.attribute_map[attr]] = value.to_dict()
elif isinstance(value, dict):
result[self.attribute_map[attr]] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[self.attribute_map[attr]] = value
return json.dumps(result) |
def to_str(self):
'Returns the string representation of the model'
return pprint.pformat(self.to_dict()) | 5,849,158,643,760,736,000 | Returns the string representation of the model | asposewordscloud/models/error_details.py | to_str | rizwanniazigroupdocs/aspose-words-cloud-python | python | def to_str(self):
return pprint.pformat(self.to_dict()) |
def __repr__(self):
'For `print` and `pprint`'
return self.to_str() | -8,960,031,694,814,905,000 | For `print` and `pprint` | asposewordscloud/models/error_details.py | __repr__ | rizwanniazigroupdocs/aspose-words-cloud-python | python | def __repr__(self):
return self.to_str() |
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