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Cellmethod tolerance #5126

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Cellmethod tolerance #5126

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10af0c1
Move cell-method parsing to own sourcefile, as it is not part of netc…
pp-mo Dec 21, 2022
78ad583
Convert existing tests to pytest.
pp-mo Dec 21, 2022
63387d9
Various 'empty' cell-method testcases: failing.
pp-mo Jan 4, 2023
2700ff1
Tolerate and warn various 'empty' cell-methods.
pp-mo Jan 4, 2023
040dc91
Snapshot some additional existing behaviours.
pp-mo Jan 4, 2023
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Tolerate and warn various 'empty' cell-methods.
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@pp-mo
pp-mo committed Jan 4, 2023
commit 2700ff1b82e9b7986cd51c986faef84418043d71
15 changes: 10 additions & 5 deletions lib/iris/fileformats/netcdf/_parse_cell_methods.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,10 +93,8 @@ def _split_cell_methods(nc_cell_methods: str) -> List[re.Match]:
name_start_inds.remove(ind)

# List tuples of indices of starts and ends of the cell methods in the string
method_indices = []
for ii in range(len(name_start_inds) - 1):
method_indices.append((name_start_inds[ii], name_start_inds[ii + 1]))
method_indices.append((name_start_inds[-1], len(nc_cell_methods)))
name_start_inds.append(len(nc_cell_methods))
method_indices = list(zip(name_start_inds[:-1], name_start_inds[1:]))

# Index the string and match against each substring
nc_cell_methods_matches = []
Expand Down Expand Up @@ -137,7 +135,14 @@ def parse_cell_methods(nc_cell_methods):

cell_methods = []
if nc_cell_methods is not None:
for m in _split_cell_methods(nc_cell_methods):
splits = _split_cell_methods(nc_cell_methods)
if not splits:
msg = (
f"NetCDF variable cell_methods of {nc_cell_methods!r} "
"contains no valid cell methods."
)
warnings.warn(msg, UserWarning)
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I think this should be an Error

#5067 (comment)

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Well I disagree, for all the reasons listed there.
I think it's just unhelpful to refuse to load a file, if the problem can be stepped around.

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In order to work with data from ESGF, it is important for ESMValTool that we can load malformed data and correct the iris cube after loading the bits that are fine. The alternatives are less attractive:

  1. Copy the malformed file, fix it and then load that with iris. This is computationally very inefficient because it requires a lot of writing to disk.
  2. Use xarray to load the malformed file, fix the issues, and convert the DataArray to an iris cube. This would be a good alternative if the conversion reliably worked.

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@pp-mo pp-mo Jan 5, 2023 •
edited
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Also recalling this passionate appeal ...
#4506 (comment)
!

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But... we are really serious now about the approach (2.) "Use xarray ... convert"
Relating to #4994, proposals are still working up : see here

Current status : I have functional code, and we have pretty much decided now on a plan...

  • set up a separate scitools repo
  • generic python classes for handling netcdf-data
  • free+fast conversion between these and any of : netcdf file data / iris cubes / xarray datasets

Frankly though, this will take a while to establish properly : I have as yet no home repo, no tests written + plenty of other priorities getting in the way.

for m in splits:
d = m.groupdict()
method = d[_CM_METHOD]
method = method.strip()
Expand Down