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)[source]#

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