Most conventions define grids on non-overlapping sets of dimensions. For example, Arakawa C datasets have four different grids, where each grid uses two dimensions, but no two grids share any dimensions. An index is a combination of a grid kind and a set of indices that match the grid dimensions. All current conventions follow this pattern.
A new DimensionConvention subclass has been added that encapsulates this. By formalising this relationship default implementations for .ravel_index, .wind_index, .ravel, .selector_for_index, and .get_grid_kind can be provided
As part of this, the antonyms 'ravel' and 'wind' replaced 'ravel' and 'unravel'. Numpy uses 'ravel' and 'unravel' to mean the inverse operation, but in English 'ravel' and 'unravel' mean the same thing. 'Wind' was chosen instead, leaving 'ravel' to match numpy.ravel.
Most conventions define grids on non-overlapping sets of dimensions. For example, Arakawa C datasets have four different grids, where each grid uses two dimensions, but no two grids share any dimensions. An index is a combination of a grid kind and a set of indices that match the grid dimensions. All current conventions follow this pattern.
A new DimensionConvention subclass has been added that encapsulates this. By formalising this relationship default implementations for
.ravel_index,.wind_index,.ravel,.selector_for_index, and.get_grid_kindcan be providedAs part of this, the antonyms 'ravel' and 'wind' replaced 'ravel' and 'unravel'. Numpy uses 'ravel' and 'unravel' to mean the inverse operation, but in English 'ravel' and 'unravel' mean the same thing. 'Wind' was chosen instead, leaving 'ravel' to match
numpy.ravel.