def add_dataset(ctx, dataset):
"""
Add a single dataset. Return the new dataset with a dataset ID.
.. code-block:: python
(Dataset){
value = 123,
unit = 'm^3',
dimension = 'Volume',
name = 'Storage Capacity',
type = 'scalar', #(others are 'descriptor', 'array' and 'timeseries')
metadata = "{'measured_by':'John Doe'}", #Json encoded python dictionary
}
Args:
dataset (Dataset): The dataset complex model (see above)
Returns:
Dataset: The new dataset object, complete with ID
"""
value = dataset.parse_value()
metadata = dataset.get_metadata_as_dict(user_id=ctx.in_header.user_id)
dataset_i = data.add_dataset(dataset.type,
value,
dataset.unit,
dataset.dimension,
metadata,
dataset.name,
ctx.in_header.user_id,
flush=True)
return Dataset(dataset_i)
def get_collection_datasets(ctx, collection_id):
"""
Get all the datasets from the collection with the specified name
Args:
collection_id (int): The collection whose dastasets we want to retrieve
Returns:
List(Dataset): A list of dastaset complex models, all of them in the collection specified
"""
collection_datasets = data.get_collection_datasets(
collection_id, **ctx.in_header.__dict__)
ret_data = [Dataset(d) for d in collection_datasets]
return ret_data
def get_datasets(ctx, dataset_ids):
"""
Get a list of datasets, by ID
Args:
dataset_ids (List(int)): A list of dataset IDs
Returns:
List(Dataset): The corresponding list of datasets. A subset will be returned if not all datasets are available.
Raises:
ResourceNotFoundError: If none of the requested datasets were found.
"""
datasets = data.get_datasets(dataset_ids, **ctx.in_header.__dict__)
ret_datasets = [Dataset(d) for d in datasets]
return ret_datasets
def get_dataset(ctx, dataset_id):
"""
Get a single dataset, by ID
Args:
dataset_id (int): THe ID of the requested dataset
Returns:
Dataset: The dataset complex model
Raises:
ResourceNotFoundError: If the dataset does not exist.
"""
dataset_i = data.get_dataset(dataset_id, **ctx.in_header.__dict__)
return Dataset(dataset_i)
def clone_dataset(ctx, dataset_id):
"""
Clone a single dataset, by ID
Args:
dataset_id (int): THe ID of the dataset to be cloned
Returns:
Dataset: The newly cloned dataset complex model
Raises:
ResourceNotFoundError: If the dataset does not exist.
"""
dataset_i = data.clone_dataset(dataset_id, **ctx.in_header.__dict__)
return Dataset(dataset_i)
def search_datasets(ctx, dataset_id, name, collection_name, data_type,
dimension, unit, scenario_id, metadata_name,
metadata_val, attr_id, type_id, unconnected,
inc_metadata, inc_val, page_start, page_size):
"""
Search for datadets that satisfy the criteria specified.
By default, returns a max of 2000 datasets. To return datasets from 2001 onwards,
set page_start to 2001.
Args:
dataset_id (int) : The ID of the dataset
name (string) : The name of the dataset
collection_name (string) : Search for datsets in a collection with this name
data_type (string) : 'scalar', 'descriptor', 'array', 'timeseries'
dimension (string) : Datasets with this dimension
unit (string) : Datasets with this unit.
scenario_id (int) : Datasets in this scenraio
metadata_name (string) : Datasets that have this metadata
metadata_val (string) : Datasets that have this metadata value
attr_id (int) : Datasts that are associated with this attribute via resource scenario & resource attribute
type_id (int) : Datasets that are associated with this type via resource scenario -> resource attribute -> attribute -> type
unconnected (char) : Datasets that are not in any scenarios
inc_metadata (char) (default 'N') : Return metadata with retrieved datasets. 'Y' gives a performance hit.
inc_val (char) (default 'N') : Include the value with the dataset. 'Y' gives a performance hit
page_start (int) : Return datasets from this point (ex: from index 2001 of 10,000)
page_size (int) : Return this number of datasets in one go. default is 2000.
Returns:
List(Dataset): The datasets matching all the specified criteria.
"""
datasets = data.search_datasets(
dataset_id, name, collection_name, data_type, dimension, unit,
scenario_id, metadata_name, metadata_val, attr_id, type_id,
unconnected, inc_metadata, inc_val, page_start, page_size,
**ctx.in_header.__dict__)
cm_datasets = []
for d in datasets:
cm_datasets.append(Dataset(d))
return cm_datasets
def update_dataset(ctx, dataset):
"""
Update a piece of data directly, rather than through a resource
scenario.
Args:
dataset (Dataset): A complex model representing an existing dataset which is to be updsated (must have an id)
Returns:
Dataset: The updated dataset
"""
val = dataset.parse_value()
metadata = dataset.get_metadata_as_dict()
updated_dataset = data.update_dataset(dataset.id, dataset.name,
dataset.type, val, dataset.unit,
dataset.dimension, metadata,
**ctx.in_header.__dict__)
return Dataset(updated_dataset)
def get_scenario_data(ctx, scenario_id):
scenario_data = scenario.get_scenario_data(scenario_id,
**ctx.in_header.__dict__)
data_cm = [Dataset(d) for d in scenario_data]
return data_cm