Source code for argopy.stores.index.argo_index

import importlib


if importlib.util.find_spec("pyarrow") is not None:
    from .implementations.pyarrow.index import indexstore
else:
    from .implementations.pandas.index import indexstore


[docs] class ArgoIndex(indexstore): """Argo GDAC index store If Pyarrow is available, this class will use :class:`pyarrow.Table` as internal storage format; otherwise, a :class:`pandas.DataFrame` will be used. Shortcuts for ``host`` argument: - ``http`` or ``https`` for ``https://data-argo.ifremer.fr`` - ``us-http`` or ``us-https`` for ``https://usgodae.org/pub/outgoing/argo`` - ``ftp`` for ``ftp://ftp.ifremer.fr/ifremer/argo`` - ``s3`` or ``aws`` for ``s3://argo-gdac-sandbox/pub/idx`` Shortcuts for ``index_file`` argument: - ``core`` for the ``ar_index_global_prof.txt`` index file, - ``bgc-b`` for the ``argo_bio-profile_index.txt`` index file, - ``bgc-s`` for the ``argo_synthetic-profile_index.txt`` index file, - ``aux`` for the ``etc/argo-index/argo_aux-profile_index.txt`` index file. - ``meta`` for the ``ar_index_global_meta.txt`` index file. Examples -------- .. code-block:: python :caption: An index store is instantiated with a host (any access path, local, http or ftp) and an index file >>> idx = ArgoIndex() >>> idx = ArgoIndex(host="https://data-argo.ifremer.fr") # Default host >>> idx = ArgoIndex(host="ftp://ftp.ifremer.fr/ifremer/argo", index_file="ar_index_global_prof.txt") # Default index >>> idx = ArgoIndex(index_file="bgc-s") # Use keywords instead of exact file names >>> idx = ArgoIndex(host="https://data-argo.ifremer.fr", index_file="bgc-b", cache=True) # Use cache for performances >>> idx = ArgoIndex(host=".", index_file="dummy_index.txt", convention="core") # Load your own index .. code-block:: python :caption: Full index methods and properties >>> idx.load() >>> idx.load(nrows=12) # Only load the first N rows of the index >>> idx.to_dataframe(index=True) # Convert index to user-friendly :class:`pandas.DataFrame` >>> idx.to_dataframe(index=True, nrows=2) # Only returns the first nrows of the index >>> idx.N_RECORDS # Shortcut for length of 1st dimension of the index array >>> idx.index # internal storage structure of the full index (:class:`pyarrow.Table` or :class:`pandas.DataFrame`) >>> idx.shape # shape of the full index array >>> idx.uri_full_index # List of absolute path to files from the full index table column 'file' .. code-block:: python :caption: Search methods >>> idx.query.wmo(1901393) >>> idx.query.cyc(1) >>> idx.query.wmo_cyc(1901393, [1,12]) >>> idx.query.lat([-60, -55, 40., 45., '2007-08-01', '2007-09-01']) # Take an index BOX definition >>> idx.query.lon([-60, -55, 40., 45., '2007-08-01', '2007-09-01']) # Take an index BOX definition >>> idx.query.date([-60, -55, 40., 45., '2007-08-01', '2007-09-01']) # Take an index BOX definition >>> idx.query.lon_lat([-60, -55, 40., 45., '2007-08-01', '2007-09-01']) # Take an index BOX definition >>> idx.query.box([-60, -55, 40., 45., '2007-08-01', '2007-09-01']) # Take an index BOX definition >>> idx.query.params(['C1PHASE_DOXY', 'DOWNWELLING_PAR']) # Take a list of strings, only for BGC index ! >>> idx.query.parameter_data_mode({'BBP700': 'D', 'DOXY': ['A', 'D']}) # Take a dict. >>> idx.query.profiler_type(845) >>> idx.query.profiler_label('NINJA') .. code-block:: python :caption: Composing search methods >>> idx.query.compose({'box': BOX, 'wmo': WMOs}) >>> idx.query.compose({'box': BOX, 'params': 'DOXY'}) >>> idx.query.compose({'box': BOX, 'params': (['DOXY', 'DOXY2'], {'logical': 'and'})}) >>> idx.query.compose({'params': 'DOXY', 'profiler_label': 'ARVOR'}) .. code-block:: python :caption: Search result properties and methods >>> idx.N_MATCH # Shortcut for length of 1st dimension of the search results array >>> idx.search # Internal table with search results >>> idx.uri # List of absolute path to files from the search results table column 'file' >>> idx.run() # Run the search and save results in cache if necessary >>> idx.to_dataframe() # Convert search results to user-friendly :class:`pandas.DataFrame` >>> idx.to_dataframe(nrows=2) # Only returns the first nrows of the search results >>> idx.to_indexfile("search_index.txt") # Export search results to Argo standard index file .. code-block:: python :caption: List of file properties >>> idx.read_wmo() >>> idx.read_dac_wmo() >>> idx.read_params() >>> idx.read_domain() >>> idx.read_files() >>> idx.records_per_wmo() .. code-block:: python :caption: Misc >>> idx.convention # What is the expected index format (core vs BGC profile index) >>> idx.cname >>> idx.domain # the default read_domain() output, as a property >>> idx.copy(deep=False) .. code-block:: python :caption: Iterate on :class:`argopy.ArgoFloat` instance >>> for a_float in idx.iterfloats(): >>> print(a_float.WMO) >>> ds = a_float.open_dataset('prof') """
[docs] def __init__(self, **kwargs): super().__init__(**kwargs)