Source code for argopy.stores.argo_index

import importlib


if importlib.util.find_spec("pyarrow") is not None:
    from .argo_index_pa import indexstore_pyarrow as indexstore
else:
    from .argo_index_pd import indexstore_pandas as 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. You can use the exact index file names or keywords: - ``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. Examples -------- 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 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' Search methods: >>> idx.search_wmo(1901393) >>> idx.search_cyc(1) >>> idx.search_wmo_cyc(1901393, [1,12]) >>> idx.search_tim([-60, -55, 40., 45., '2007-08-01', '2007-09-01']) # Take an index BOX definition >>> idx.search_lat_lon([-60, -55, 40., 45., '2007-08-01', '2007-09-01']) # Take an index BOX definition >>> idx.search_lat_lon_tim([-60, -55, 40., 45., '2007-08-01', '2007-09-01']) # Take an index BOX definition >>> idx.search_params(['C1PHASE_DOXY', 'DOWNWELLING_PAR']) # Take a list of strings, only for BGC index ! >>> idx.search_parameter_data_mode({'BBP700': 'D', 'DOXY': ['A', 'D']}) # Take a dict. 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 Misc: >>> idx.convention # What is the expected index format (core vs BGC profile index) >>> idx.cname >>> idx.read_wmo >>> idx.read_params >>> idx.records_per_wmo """ pass