argopy.data_fetchers.erddap_data.Fetch_wmo

class Fetch_wmo(ds: str = '', cache: bool = False, cachedir: str = '', parallel: bool = False, progress: bool = False, chunks: str = 'auto', chunks_maxsize: dict = {}, api_timeout: int = 0, params: str | list = 'all', measured: str | list = None, **kwargs)

Manage access to Argo data through Ifremer ERDDAP for: a list of WMOs

This class is instantiated when a call is made to these facade access points:

• ArgoDataFetcher(src=’erddap’).float(**)

• ArgoDataFetcher(src=’erddap’).profile(**)

__init__(ds: str = '', cache: bool = False, cachedir: str = '', parallel: bool = False, progress: bool = False, chunks: str = 'auto', chunks_maxsize: dict = {}, api_timeout: int = 0, params: str | list = 'all', measured: str | list = None, **kwargs)

Instantiate an ERDDAP Argo data fetcher

Parameters:

• ds (str, default = OPTIONS['ds']) – Dataset to load: ‘phy’ or ‘ref’ or ‘bgc-s’

• cache (bool (optional)) – Cache data or not (default: False)

• cachedir (str (optional)) – Path to cache folder

• parallel (bool, str, distributed.Client, default: False) –

  Set whether to use parallelization or not, and possibly which method to use.

  Possible values:

  • False: no parallelization is used

  • True: use default method specified by the parallel_default_method option

  • any other values accepted by the parallel_default_method option

• progress (bool (optional)) – Show a progress bar or not when parallel is set to True.

• chunks ('auto' or dict of integers (optional)) – Dictionary with request access point as keys and number of chunks to create as values. Eg: {‘wmo’: 10} will create a maximum of 10 chunks along WMOs when used with Fetch_wmo.

• chunks_maxsize (dict (optional)) – Dictionary with request access point as keys and chunk size as values (used as maximum values in ‘auto’ chunking). Eg: {‘wmo’: 5} will create chunks with as many as 5 WMOs each.

• api_timeout (int (optional)) – Erddap request time out in seconds. Set to OPTIONS[‘api_timeout’] by default.

• params (Union[str, list] (optional, default='all')) – List of BGC essential variables to retrieve, i.e. that will be in the output xr.DataSet`. By default, this is set to all, i.e. any variable found in at least of the profile in the data selection will be included in the output.

• measured (Union[str, list] (optional, default=None)) – List of BGC essential variables that can’t be NaN. If set to ‘all’, this is an easy way to reduce the size of the xr.DataSet` to points where all variables have been measured. Otherwise, provide a simple list of variables.

• server (str, default = OPTIONS['erddap']) – URL to erddap server

• mode (str, default = OPTIONS['mode'])

Methods

__init__([ds, cache, cachedir, parallel, ...])	Instantiate an ERDDAP Argo data fetcher
clear_cache()	Remove cache files and entries from resources opened with this fetcher
cname()	Return a unique string defining the constraints
dashboard(**kw)	Return 3rd party dashboard for the access point
define_constraints()	Define erddap constraints
filter_data_mode(ds, **kwargs)	Apply xarray argo accessor filter_data_mode method
filter_measured(ds)	Re-enforce the 'measured' criteria for BGC requests
filter_qc(ds, **kwargs)	Apply xarray argo accessor filter_qc method
filter_researchmode(ds, *args, **kwargs)	Filter dataset for research user mode
filter_variables(ds, *args, **kwargs)	Filter variables according to dataset and user mode
get_url()	Return the URL to download requested data
init([WMO, CYC])	Create Argo data loader for WMOs
pre_process(this_ds, *args, **kwargs)
to_xarray([errors, add_dm, concat, max_workers])	Load Argo data and return a xarray.DataSet
transform_data_mode(ds, **kwargs)	Apply xarray argo accessor transform_data_mode method

Attributes

N_POINTS	Number of measurements expected to be returned by a request
cachepath	Return path to cached file(s) for this request
data_source
server	URL of the Erddap server
sha	Returns a unique SHA for a specific cname / fetcher implementation
uri	List of URLs to load for a request