intake_erddap.GridDAPReader¶

class intake_erddap.GridDAPReader(*args, metadata: dict | None = None, output_instance: str | None = None, **kwargs)[source]¶

Creates a Data Reader for an ERDDAP GridDAP Dataset.

Parameters:

server (str) –
URL to the ERDDAP service. Example: "https://coastwatch.pfeg.noaa.gov/erddap"

Note

Do not include a trailing slash.
dataset_id (str) – The dataset identifier from ERDDAP.
constraints (dict, optional) – A mapping of conditions and constraints.
chunks (None or int or dict or str, optional) – If chunks is provided, it is used to load the new dataset into dask arrays. chunks=-1 loads the dataset with dask using a single chunk for all arrays. chunks={} loads the dataset with dask using engine preferred chunks if exposed by the backend, otherwise with a single chunk for all arrays. chunks=’auto’ will use dask auto chunking taking into account the engine preferred chunks. See dask chunking for more details.
xarray_kwargs (dict, optional) – Arguments to be passed to the xarray open_dataset function.

Examples

Readers are normally returned from a catalog object, but a reader can be instantiated directly:

>>> reader = GridDAPReader("https://coastwatch.pfeg.noaa.gov/erddap", "charmForecast1day",
... chunks={"time": 1})

Getting an xarray dataset from the reader object:

>>> ds = reader.read()

Once the dataset object has been instantiated, the dataset’s full metadata is available in the reader.

>>> reader.metadata
{'catalog_dir': '',
'dims': {'time': 1182, 'latitude': 391, 'longitude': 351},
'data_vars': {'pseudo_nitzschia': ['time', 'latitude', 'longitude'],
'particulate_domoic': ['time', 'latitude', 'longitude'],
'cellular_domoic': ['time', 'latitude', 'longitude'],
'chla_filled': ['time', 'latitude', 'longitude'],
'r555_filled': ['time', 'latitude', 'longitude'],
'r488_filled': ['time', 'latitude', 'longitude']},
'coords': ('time', 'latitude', 'longitude'),
'acknowledgement':
    ...

Attributes:

data: The BaseData this reader depends on, if it has one
func_doc
token: Token is computed from all non-_ attributes and then cached.
transform

Methods

`__call__`(args, *kwargs)	New version of this instance with altered arguments
`apply`(func, *args[, output_instance])	Make a pipeline by applying a function to this reader's output
`discover`(**kwargs)	Part of the data
`doc`()	Doc associated with loading function
`from_dict`(data)	Recreate instance from the results of to_dict()
`get_client`(server, protocol, dataset_id, ...)	Return an initialized ERDDAP Client.
`output_doc`()	Doc associated with output type
`pprint`()	Produce nice text formatting of the instance's contents
`qname`()	package.module:class name of this class, makes str for import_name
`read`(args, *kwargs)	Produce data artefact
`to_cat`([name])	Create a Catalog containing on this reader
`to_dict`()	Dictionary representation of the instances contents
`to_entry`()	Create an entry version of this, ready to be inserted into a Catalog
`to_reader`([outtype, reader])	Make a different reader for the data used by this reader

auto_pipeline
check_imports
tab_completion_fixer

__init__(*args, metadata: dict | None = None, output_instance: str | None = None, **kwargs)¶

Methods

`__init__`(*args[, metadata, output_instance])
`apply`(func, *args[, output_instance])	Make a pipeline by applying a function to this reader's output
`auto_pipeline`(outtype[, avoid])
`check_imports`()
`discover`(**kwargs)	Part of the data
`doc`()	Doc associated with loading function
`from_dict`(data)	Recreate instance from the results of to_dict()
`get_client`(server, protocol, dataset_id, ...)	Return an initialized ERDDAP Client.
`output_doc`()	Doc associated with output type
`pprint`()	Produce nice text formatting of the instance's contents
`qname`()	package.module:class name of this class, makes str for import_name
`read`(args, *kwargs)	Produce data artefact
`tab_completion_fixer`(item)
`to_cat`([name])	Create a Catalog containing on this reader
`to_dict`()	Dictionary representation of the instances contents
`to_entry`()	Create an entry version of this, ready to be inserted into a Catalog
`to_reader`([outtype, reader])	Make a different reader for the data used by this reader

Attributes

`data`	The BaseData this reader depends on, if it has one
`func`	function name for loading data
`func_doc`	docstring origin if not from func
`implements`	datatype(s) this applies to
`imports`	top-level packages required to use this
`optional_imports`	packages that might be required by some options
`other_funcs`	function names to recognise when matching user calls
`output_instance`	type the reader produces
`token`	Token is computed from all non-_ attributes and then cached.
`transform`