argopy.data_fetchers.erddap_data.Fetch_box

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

Manage access to Argo data through Ifremer ERDDAP for: an ocean rectangle

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

Instantiate an ERDDAP Argo data fetcher

Parameters:

• ds (str (optional)) – Dataset to load: ‘phy’ or ‘ref’ or ‘bgc’

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

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

• parallel (bool (optional)) – Chunk request to use parallel fetching (default: False)

• parallel_method (str (optional)) – Define the parallelization method: thread, process or a dask.distributed.client.Client.

• 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.

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 request constraints
filter_data_mode(ds, **kwargs)	Apply xarray argo accessor filter_data_mode method
filter_points(ds)	Enforce request criteria
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, mode, *args, **kwargs)	Filter variables according to user mode
get_url()	Return the URL to download requested data
init(box, **kw)	Create Argo data loader
post_process(this_ds[, add_dm, URI])	Post-process a xarray.DataSet created from a netcdf erddap response
to_xarray([errors, add_dm, concat, max_workers])	Load Argo data and return a xarray.DataSet

Attributes

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