Use of xarray

From release 0.2+, RASCIL has moved to use the Xarray library instead of numpy in the data classes. RASCIL data classes are now all derived from xarray.Dataset. This change is motivated by the large range of capababilities available from xarray. These include:

• Named dimensions and coordinates, allowing access via quantities such as time. frequency, polarisation, receptor

• Indexing, selection, iteration, and conditions

• Support of split-apply-recombine operations

• Interpolation in coordinates, including missing values

• Automatic invocation of Dask for array operations

• Arbitrary meta data as attributes

We have chosen to make the RASCIL data classes derive from xarray.Dataset. Instead of adding class methods to the RASCIL data class, which would introduce some interface fragility as xarray changes over time, we have used data accessors to control access to methods specfic to the class. This design is suggested in the xarray documentation on extending xarray. Examples:

# Flagged visibility
vis.visibility_acc.flagged_vis

# UVW in wavelengths
vis.visibility_acc.uvw_lambda

# DataArray sizes
vis.visibility_acc.datasizes

# Phasecentre as an astropy.SkyCoord
im.image_acc.phasecentre

# Image RA, Dec grid
im.image_acc.ra_dec_mesh

# Gaintable number of receptors
gt.gaintable_acc.nrec

For examples of the capabilities afforded by xarray see the jupyter notebooks below:

• Demonstrate visibility xarray format

Here is a simple example of how the capabilities of xarray can be used:

vis = create_visibility_from_ms(ms)[0]

# Don't squeeze out the unit dimensions because we will want
# them for the concat
chan_vis = [v[1] for v in vis.groupby_bins(dim, bins=2)]

# Predict visibility from a model.
chan_vis = [predict_ng(vis, model) in chan_vis]

# Now concatenate
newvis = xarray.concat(chan_vis, dim=dim, data_vars="minimal")

Conversion from previous data classes

The steps required are:

• For Image, GridData, and ConvolutionFunction, the name of the data variable is pixels so for example:

  # The previous numpy format
  im.data
  # as an Xarray becomes
  im["pixels"]
  # as an numpy array or Dask array becomes
  im["pixels"].data
  # as an numpy array becomes
  im["pixels"].values
  # The properties now require using the accessor class. For example:
  im.nchan
  # becomes
  im.image_acc.nchan
  # or directly to the attributes of the xarray.Dataset
  im.attrs["nchan"]
  

• For Visibility, the various columns become data variables:

  # The numpy format
  bvis.data["vis"]
  # becomes
  bvis["vis"]
  # as an numpy array or Dask array becomes
  bvis["vis"].data
  # as an numpy array becomes
  bvis["vis"].values
  # The properties now require using the accessor class. For example:
  bvis.nchan
  # becomes
  bvis.visibility_acc.nchan
  # or directly to the attributes of the xarray.Dataset
  bvis.attrs["nchan"]
  # The convenience methods for handling flags also require the accessor:
  bvis.flagged_vis
  # becomes
  bvis.visibility_acc.flagged_vis