argopy.reference.vocabulary.ArgoReferenceTable

class ArgoReferenceTable(identifier: str | None = None, *args, **kwargs)

A class to work with an Argo reference table, i.e. a NVS “vocabulary”

An Argo reference table is a NVS “vocabulary”, aka a SKOS “collection”.

For instance, the vocabulary for “Argo sensor models”, corresponds to the Argo reference table 27 (“R27”) and is used to document possible values of the “SENSOR_MODEL” parameter in netcdf files. All possible values for this parameter are instances of ArgoReferenceValue.

Note

# Deprecated API: ArgoNVSReferenceTables.tbl(‘R25’)

# New API, but not backward compatible (new column names): ArgoReferenceTable(‘R25’).to_dataframe()

Examples

Creation and Attributes

from argopy import ArgoReferenceTable

# Use an Argo parameter name, documented by one of the Argo reference tables:
art = ArgoReferenceTable('SENSOR')

# or a reference table identifier:
art = ArgoReferenceTable('R25')

# or a URN:
art = ArgoReferenceTable.from_urn('SDN:R25::CTD_TEMP')

# All possible attributes are listed in:
art.attrs

# Reference Table attributes:
art.parameter   # Name of the netcdf dataset parameter filled with values from this table
art.identifier  # Reference Table ID
art.description # [nvs['@graph']['@type']=='skos:Collection']["dc:description"]
art.uri         # [nvs['@graph']['@type']=='skos:Collection']["@id"]
art.version     # [nvs['@graph']['@type']=='skos:Collection']['owl:versionInfo']
art.date        # [nvs['@graph']['@type']=='skos:Collection']['dc:date']

# Raw NVS json data:
art.nvs

Indexing and values

from argopy import ArgoReferenceTable
art = ArgoReferenceTable('SENSOR')

# Values (or concept) within this reference table:
len(art)     # Number of reference values
art.keys()   # List of reference values name
art.values() # List of :class:`ArgoReferenceValue`

# Check for values:
'CTD_TEMP_CNDC' in art  # Return True

# Index by value key, like a simple dictionary:
art['CTD_TEMP_CNDC']  # Return a :class:`ArgoReferenceValue` instance

# Allows to iterate over all values/concepts:
for concept in art:
        print(concept.name, concept.urn)

Export methods

from argopy import ArgoReferenceTable
art = ArgoReferenceTable('SENSOR')

# Export table attributes to a dictionary (rq: this is not to export values):
art.to_dict()
art.to_dict(keys=['parameter', 'date', 'uri'])  # Select Table attributes to export in dictionary keys

# Export table values to a pd.DataFrame:
art.to_dataframe()
art.to_dataframe(columns=['name', 'deprecated'])  # Select value attributes to export in columns

# Export table values to a dictionary, use pd.DataFrame:
art.to_dataframe(columns=['name', 'deprecated']).to_dict(orient='records')

Search the table

# Search methods (return a list of :class:`ArgoReferenceValue` with match):
# Any of the :class:`ArgoReferenceValue` attribute can be searched
art.search(name='RAMSES')         # Search in values name
art.search(definition='imaging')  # Search in values definition
art.search(long_name='TriOS')     # Search in values long name

# Possible change to output format:
art.search(deprecated=True, output='df')  # To a :class:`pd.DataFrame`

__init__(identifier: str | None = None, *args, **kwargs) → None

Methods

__init__([identifier])
from_urn(urn)
keys()
search(**kwargs)	Search in table list of ArgoReferenceValue attributes
to_dataframe([columns])	Export all reference values attributes to a pd.DataFrame
to_dict([keys])	Export reference table attributes to a dictionary
valid_identifier()
values()

Attributes

identifier
parameter
long_name
description
version
date
uri	Two methods: 1- From the skos:Collection list of members: >>> values = [m['@id'].split("/")[-2] for m in Collection['skos:member']] 2- From skos:Concept in the @graph: >>> values = [c['skos:altLabel'] for c in [item for item in self['@graph'] if item['@type'] == 'skos:Concept']] We stick to Collection for consistency with other attributes gathering
nvs
attrs	Public attributes