Masking and selection#

Selecting variables#

Xarray itself provides powerful ways to explore, index, subset and aggregate datasets. Here again hyoga is only adding a thin layer of functionality. As soon as hyoga (or any submodule) has been imported, this new functionality will be available in a special .hyoga attribute called “dataset accessor”:

import hyoga
ds ='')

One thing to note in particular, is that hyoga never accesses model variables by their “short names”. For instance, while thk, refers to the ice thickness in PISM, it may refer to a different quantity, or to nothing at all, in another ice-sheet model. This is where CF standard names come into play. To access ice thickness by its standard name you may use:

var = ds.hyoga.getvar('land_ice_thickness')

If a particular variable is missing, hyoga will additionally try to reconstruct it from others, such as the sum of bedrock altitude and ice thickness for surface altitude, or the norm of velocity components for its magnitude.


This mechanism can be disabled using infer=False. Because surface altitude is not actually present in the example dataset, the following would raise an exception:

ds.hyoga.getvar('surface_altitude', infer=False)

Because CF standard names for land ice variables are relatively recent, older ice sheet models may not include them in output metadata. For PISM, a mechanism has been implemented to fill (some of) these missing standard names during initialization.


While hyoga has only been tested with PISM so far, I hope it will become compatible with some other glacier and ice sheet models in the future. If you want to make your glacier model compatible with hyoga, please consider implementing CF standard names.

Adding new variables#

New variables can be added using using xarray’s dictionary interface or methods such as xarray.Dataset.assign(). Besides, hyoga provides a dataset method to assign new variables by their standard name.

bedrock = ds.hyoga.getvar('bedrock_altitude')
thickness = ds.hyoga.getvar('land_ice_thickness')
surface = bedrock + thickness
new = ds.hyoga.assign(surface_altitude=surface)

This returns a new dataset including the surface altitude variable. Some control on the variable (short) name can be achieved by preceding the assign call with xarray.DataArray.rename().

surface = surface.rename('surface')
ds = ds.hyoga.assign(surface_altitude=surface)
assert 'surface' in ds

However, this only works if the data does not already contain a variable with the standard name surface_altitude. In that case, that variable’s data is quietly replaced, and the variable is not renamed.

surface = surface.rename('name_to_ignore')
ds = ds.hyoga.assign(surface_altitude=surface)
assert 'name_to_ignore' not in ds

Masking variables#

Hyoga’s plot methods use an ice mask to determine which grid cells are glacierized and which are not. According to CF conventions, this is defined by the standard variable land_ice_area_fraction. There are several ways to affect the ice mask. The easiest way is to use the (currently single) parametre in hyoga.config:


If the land_ice_area_fraction variable is missing from the dataset, hyoga falls back to compute and ice mask from land_ice_thickness, using this parametre as an ice thickness threshold. The default value is 1 (metre). For PISM output files, a non-zero threshold may be advisable in case winter output files contain a thin cover of “seasonal ice” outside the glacier margin, as is the case in the demo files.

with'') as ds:
    for i, value in enumerate([0.1, 1, 500]):
        hyoga.config.glacier_masking_point = value
        ds.hyoga.plot.ice_margin(edgecolor=f'C{i}', linewidths=1)

# restore the default of 1 m
hyoga.config.glacier_masking_point = 1

For more control, on can set the land_ice_area_fraction variable using assign_icemask(). Suppose that we define glaciers as grid cells filled with ice at least a metre thick, and moving at least ten metres per year:

with'') as ds:
    ds = ds.hyoga.assign_icemask(
        (ds.hyoga.getvar('land_ice_thickness') > 1) &
        (ds.hyoga.getvar('magnitude_of_land_ice_surface_velocity') > 10))

Note that the assign_icemask() method edits (or add) a land_ice_area_fraction variable without affecting the rest of the dataset. Such lossless masking is should be enough for internal use within Hyoga. However in some situations, a lossy (destructive) ice mask may be more useful. This includes exporting data to a compressed netCDF file for the web, where having homogeneous values outside the glacier mask can greatly reduce file size. This can be achieved with Dataset.hyoga.where(), where_icemask(), and where_thicker(). These methods behave like xarray.Dataset.where(): they replace data values with np.nan outside the where condition. However, they are meant to only affect “glacier variables” (currently any variable whose standard name does not start with bedrock_altitude).