argopy.stores.argo_index_pd.indexstore_pandas#

class indexstore_pandas(host: str = 'https://data-argo.ifremer.fr', index_file: str = 'ar_index_global_prof.txt', cache: bool = False, cachedir: str = '', timeout: int = 0)[source]#

Argo GDAC index store using pandas.DataFrame as internal storage format.

With this store, index and search results are saved as pickle files in cache

Examples

An index store is instantiated with the access path (host) and the index file:

>>> idx = indexstore()
>>> idx = indexstore(host="ftp://ftp.ifremer.fr/ifremer/argo")
>>> idx = indexstore(host="https://data-argo.ifremer.fr", index_file="ar_index_global_prof.txt")
>>> idx = indexstore(host="https://data-argo.ifremer.fr", index_file="ar_index_global_prof.txt", cache=True)

Index methods and properties:

>>> idx.load()
>>> idx.load(nrows=12)  # Only load the first N rows 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'
>>> 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

Search methods and properties:

>>> 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.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

Misc:

>>> idx.cname
>>> idx.read_wmo
>>> idx.records_per_wmo

__init__(host: str = 'https://data-argo.ifremer.fr', index_file: str = 'ar_index_global_prof.txt', cache: bool = False, cachedir: str = '', timeout: int = 0)#

Create an Argo index file store

Parameters

host (str, default: https://data-argo.ifremer.fr) – Host is a local or remote ftp/http path to a dac folder (GDAC structure compliant). This takes values like: ftp://ftp.ifremer.fr/ifremer/argo, ftp://usgodae.org/pub/outgoing/argo or a local absolute path.
index_file (str, default: ar_index_global_prof.txt) – Name of the csv-like text file with the index
cache (bool, default: False) – Use cache or not.
cachedir (str, default: OPTIONS['cachedir'])) – Folder where to store cached files

Methods

`__init__`([host, index_file, cache, ...])	Create an Argo index file store
`cachepath`(path)	Return path to a cached file
`clear_cache`()	Clear cache registry and files associated with this store instance.
`load`([nrows, force])	Load an Argo-index file content
`read_wmo`([index])	Return list of unique WMOs in search results
`records_per_wmo`([index])	Return the number of records per unique WMOs in search results
`run`([nrows])	Filter index with search criteria
`search_cyc`(CYCs[, nrows])	Search index for cycle numbers
`search_lat_lon`(BOX[, nrows])	Search index for a rectangular latitude/longitude domain
`search_lat_lon_tim`(BOX[, nrows])	Search index for a rectangular latitude/longitude domain and time range
`search_tim`(BOX[, nrows])	Search index for a time range
`search_wmo`(WMOs[, nrows])	Search index for floats defined by their WMO
`search_wmo_cyc`(WMOs, CYCs[, nrows])	Search index for floats defined by their WMO and specific cycle numbers
`to_dataframe`([nrows, index])	Return index or search results as `pandas.DataFrame`

Attributes

`N_FILES`	Number of rows in search result or index if search not triggered
`N_MATCH`	Number of rows in search result
`N_RECORDS`	Number of rows in the full index
`backend`	Name of store backend
`cname`	Return the search constraint(s) as a pretty formatted string
`ext`	Storage file extension
`search_path`	Path to search result uri
`search_type`	Dictionary with search meta-data
`sha_df`	Returns a unique SHA for a cname/dataframe
`sha_h5`	Returns a unique SHA for a cname/hdf5
`sha_pq`	Returns a unique SHA for a cname/parquet
`shape`	Shape of the index array
`uri`	List of URI from search results
`uri_full_index`	List of URI from index