manipulating non-missing elements in a field

In order to use a scikit regression module, I need to

• Extract the non-missing elements from a 3D field fin(z,y,x) into a numpy array (z,p), where p is a 1D index over all the non-missing (y,x) locations, which are the same for every z.

• Put a 1D array of results r(p) into the non-missing data elements of a 2D field fout(y,x).

I've done it as follows:

findata=fin.data.compressed().to_dask_array()
findata.compute_chunk_sizes()
findata=findata.reshape(fin.shape[0],fin[0,:].data.compressed().size)
# compute r from findata
foutdata=fout.get_data().flatten()
foutdata[~foutdata.mask]=r
fout.set_data(foutdata.reshape(fout.shape))

Are there better ways, or could you provide better ways? The following occur to me:

• It would be handy if compressed() was a method of a CF field, to flatten it into a 1D array containing only the non-missing values.
• I needed three lines to produce findata because of having to compute_chunk_sizes on the dask array. Without that step, I got errors as below.
• Is there a way to avoid having to get the Data out of the field in order to assign to some elements of it using the mask? This was a bit clumsy.

In the Met O IDL lib, both input and output are easier:

findata=fin.data(where(fin(0).data ne fin(0).bmdi))
; compute r from findata
fout.data(where(fout.data ne fout.bmdi))=r

It would be good to have something equally convenient in cf-python. Perhaps there is?

Here's the dask problem:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/users/sws02jmg/.local/lib/python3.11/site-packages/cfdm/decorators.py", line 44, in inplace_wrapper
    processed_copy = operation_method(self, *args, **kwargs)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/users/sws02jmg/.local/lib/python3.11/site-packages/cf/data/data.py", line 10489, in reshape
    dx = dx.reshape(*shape, merge_chunks=merge_chunks, limit=limit)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/software/applications/anaconda/2023.09-0/lib/python3.11/site-packages/dask/array/core.py", line 2220, in reshape
    return reshape(self, shape, merge_chunks=merge_chunks, limit=limit)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/software/applications/anaconda/2023.09-0/lib/python3.11/site-packages/dask/array/reshape.py", line 222, in reshape
    raise ValueError(
ValueError: Array chunk size or shape is unknown. shape: (nan,)

Possible solution with x.compute_chunk_sizes()

Thanks. Jonathan

Start by inspecting the CF field and Data entry points mentioned in the report: compressed(), reshape(), get_data(), and set_data(), including how masked and dask-backed arrays are handled. Define the desired convenience operations for extracting non-missing values and assigning results, then add coverage for the 3D-to-2D workflow and the unknown dask chunk-size case.