intake_erddap.TableDAPReader¶

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

Creates a Data Reader for an ERDDAP TableDAP 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.
variables (list of str, optional) – A list of variables to retrieve from the dataset.
constraints (dict, optional) – A mapping of conditions and constraints. Example: {"time>=": "2022-01-02T12:00:00Z", "lon>": -140, "lon<": 0}
metadata (dict, optional) – Additional metadata to include with the reader passed from the catalog.
erddap_client (type, optional) – A class that implements an interface like erdappy’s ERDDAP class. The reader will rely on this client to interface with ERDDAP for most requests.
http_client (module or object, optional) – An object or module that implements an HTTP Client similar to request’s interface. The reader will use this object to make HTTP requests to ERDDAP in some cases.
mask_failed_qartod (bool, False) – WARNING ALPHA FEATURE. If True and *_qc_agg columns associated with data columns are available, data values associated with QARTOD flags other than 1 and 2 will be nan’ed out. Has not been thoroughly tested.
dropna (bool, False.) – WARNING ALPHA FEATURE. If True, rows with data columns of nans will be dropped from data frame. Has not been thoroughly tested.
cache_kwargs (dict, optional) – WARNING ALPHA FEATURE. If you want to have the data you access stored locally in a cache, use this keyword to input a dictionary of keywords. The cache is set up using fsspec’s simple cache. Example configuration is cache_kwargs=dict(cache_storage="/tmp/fnames/", same_names=True).

Examples

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

>>> reader = TableDAPReader("https://erddap.senors.axds.co/erddap",
... "gov_usgs_waterdata_441759103261203")

Getting a pandas DataFrame from the reader:

>>> ds = reader.read()

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

>>> reader.metadata
{'info_url': 'https://erddap.sensors.axds.co/erddap/info/gov_usgs_waterdata_404513098181201...',
'catalog_dir': '',
'variables': {'time': {'_CoordinateAxisType': 'Time',
'actual_range': [1430828100.0, 1668079800.0],
'axis': 'T',
'ioos_category': 'Time',
'long_name': 'Time',
'standard_name': 'time',
'time_origin': '01-JAN-1970 00:00:00',
'units': 'seconds since 1970-01-01T00:00:00Z'},
    ...

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
`data_cols`(df)	Columns that are not axes, coordinates, nor qc_agg columns.
`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
`run_dropna`(df)	Drop nan rows based on the data columns.
`run_mask_failed_qartod`(df)	Nan data values for which corresponding qc_agg columns is not equal to 1 or 2.
`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`()
`data_cols`(df)	Columns that are not axes, coordinates, nor qc_agg columns.
`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
`run_dropna`(df)	Drop nan rows based on the data columns.
`run_mask_failed_qartod`(df)	Nan data values for which corresponding qc_agg columns is not equal to 1 or 2.
`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`