Storage

Data features are stored into files in a wide range of ways, formats or structures. We standardize the access to any data format through specific interface classes, called mappers.

A mapper class behaves similarly to the netCDF API for instance, providing introspection functions for a file content, with a little bit more intelligence : it also provides some functions describing the structure of the data layed out in the file for an easy mapping to a datamodel class.

This means the relationship between the file variables, metadata, or dimensions, and some feature’s properties must be fully understood and explicited, following the abstract interface defined in the package’s parent class AbstractMapper.

cerbere comes with a set of predefined mapper classes but any new format (or formatting specificities) must be handled through a new mapper class to be written by the user.

This section describes how to understand and write a new mapper class.

Heritage

Any new mapper class must inherit from AbstractMapper class. This abstract class provides the list of methods of the mapper interface that need to be implemented.

mapper classes already exist for some standard formats such netCDF, HDF4. These formats are very generic but do not standardize the expression of feature information (geolocation information,...) unless some convention is used (such as CF for netCDF). It may therefore be necessary to override these classes in a new more specialized inherited class.

Standardization methods

A datamodel class (for instance Grid) is expecting some information on the data structure (for instance the name and size of each dimension, the time and spatial coordinates). These information must be returned by a mapper class in a standard way.

Some internal methods (never called directly by a user and only used by datamodel classes) help realising the matching between the expected information by a datamodel class to instantiate and the information specified in the proprietary file format (following the data provider convention which is not standard).

get_geolocation_field() returns the internal name (in the proprietary file format) of any spatial and temporal coordinate field (time, lat or lon):

f.get_geolocation_field('time')
f.get_geolocation_field('lat')
f.get_geolocation_field('lon')

Note

in self described format (netCDF, HDF) this will generally consist in providing the equivalent field in the proprietary file format. If the field is not existing (for instance time information is sometimes splitted into several fields for day, milliseconds, etc) or if the file format is not self-described (pure binary file), virtual names are returned. They should be equivalent to the standard names.

get_matching_dimname() returns the internal name (in the proprietary file format) of any standard geolocation dimension:

f.get_matching_dimname('row')
f.get_matching_dimname('cell')

Note

Refer to datamodel section for the list of standard geolocation dimension for each feature.