Regridding nemo data to lat/lon grid

[Kazu1886]

I am trying to regrid nemo data on a tri-polar grid so that I can make zonally averaged plots. I have followed a thread on #5172, but have been getting errors trying these. I have tried using esmf_regrid.schemes.ESMFAreaWeighted, however I get an assertion error:

File ~/miniconda3/envs/work/lib/python3.12/site-packages/esmf_regrid/schemes.py:185, in _cube_to_GridInfo(cube, center, resolution, mask)
    183 assert cube.coord_dims(lon) == cube.coord_dims(lat)
    184 if not np.any(mask):
--> 185     assert lon.is_contiguous()
    186     assert lat.is_contiguous()
    187     lon_bound_array = lon.contiguous_bounds()

AssertionError:

I also tried converting to a mesh and regridding: converted the cube to a mesh grid esmf_regrid.experimental.unstructured_scheme.MeshToGridESMFRegridder

This worked, although I could not work out to how to generalise when there are other dimensions, eg time, depth. I then used esmf_regrid.experimental.unstructured_scheme.MeshToGridESMFRegridder to regrid onto the lat-lon grid:

regridder = MeshToGridESMFRegridder(meshcube, grid_cube)
which returns the error:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[65], line 1
----> 1 regridder = MeshToGridESMFRegridder(meshcube, grid_cube)

File ~/miniconda3/envs/work/lib/python3.12/site-packages/esmf_regrid/experimental/unstructured_scheme.py:169, in MeshToGridESMFRegridder.__init__(self, src, tgt, mdtol, method, precomputed_weights, tgt_resolution, use_src_mask, use_tgt_mask)
    167 if src.mesh is None:
    168     raise ValueError("src has no mesh.")
--> 169 super().__init__(
    170     src,
    171     tgt,
    172     method,
    173     mdtol=mdtol,
    174     precomputed_weights=precomputed_weights,
    175     tgt_resolution=tgt_resolution,
    176     use_src_mask=use_src_mask,
    177     use_tgt_mask=use_tgt_mask,
    178 )
    179 self.resolution = tgt_resolution
    180 self.mesh, self.location = self._src

File ~/miniconda3/envs/work/lib/python3.12/site-packages/esmf_regrid/schemes.py:1344, in _ESMFRegridder.__init__(self, src, tgt, method, mdtol, use_src_mask, use_tgt_mask, tgt_location, **kwargs)
   1342     else:
   1343         prepare_func = _regrid_rectilinear_to_rectilinear__prepare
-> 1344 regrid_info = prepare_func(src, tgt, method, **kwargs)
   1346 # Store regrid info.
   1347 self._tgt = regrid_info.target

File ~/miniconda3/envs/work/lib/python3.12/site-packages/esmf_regrid/schemes.py:624, in _regrid_unstructured_to_rectilinear__prepare(src_mesh_cube, target_grid_cube, method, precomputed_weights, tgt_resolution, src_mask, tgt_mask)
    621 meshinfo = _make_meshinfo(src_mesh_cube, method, src_mask, "source")
    622 gridinfo = _make_gridinfo(target_grid_cube, method, tgt_resolution, tgt_mask)
--> 624 regridder = Regridder(
    625     meshinfo, gridinfo, method=method, precomputed_weights=precomputed_weights
    626 )
    628 regrid_info = RegridInfo(
    629     dims=[mesh_dim],
    630     target=GridRecord(grid_x, grid_y),
    631     regridder=regridder,
    632 )
    634 return regrid_info

File ~/miniconda3/envs/work/lib/python3.12/site-packages/esmf_regrid/esmf_regridder.py:108, in Regridder.__init__(self, src, tgt, method, precomputed_weights)
    105 if precomputed_weights is None:
    106     self.esmf_version = esmpy.__version__
    107     weights_dict = _get_regrid_weights_dict(
--> 108         src.make_esmf_field(),
    109         tgt.make_esmf_field(),
    110         regrid_method=esmf_regrid_method,
    111     )
    112     self.weight_matrix = _weights_dict_to_sparse_array(
    113         weights_dict,
    114         (self.tgt._refined_size, self.src._refined_size),
    115         (self.tgt.index_offset, self.src.index_offset),
    116     )
    117     if isinstance(tgt, RefinedGridInfo):
    118         # At this point, the weight matrix represents more target points than
    119         # tgt respresents. In order to collapse these points, we collapse the
    120         # weights matrix by the appropriate matrix multiplication.

File ~/miniconda3/envs/work/lib/python3.12/site-packages/esmf_regrid/_esmf_sdo.py:32, in SDO.make_esmf_field(self)
     30 def make_esmf_field(self):
     31     """Return an ESMF field representing the spatial discretisation object."""
---> 32     sdo = self._make_esmf_sdo()
     33     field = esmpy.Field(sdo, **self._field_kwargs)
     34     return field

File ~/miniconda3/envs/work/lib/python3.12/site-packages/esmf_regrid/experimental/unstructured_regrid.py:124, in MeshInfo._make_esmf_sdo(self)
    123 def _make_esmf_sdo(self):
--> 124     info = self._as_esmf_info()
    125     (
    126         num_node,
    127         num_elem,
   (...)
    136         elemCoord,
    137     ) = info
    138     # ESMF can handle other dimensionalities, but we are unlikely
    139     # to make such a use of ESMF

File ~/miniconda3/envs/work/lib/python3.12/site-packages/esmf_regrid/experimental/unstructured_regrid.py:104, in MeshInfo._as_esmf_info(self)
    102 nodeOwner = np.zeros([num_node])  # regridding currently serial
    103 elemId = np.array(range(self.esi, self.esi + num_elem))
--> 104 elemType = self.fnc.count(axis=1)
    105 # Experiments seem to indicate that ESMF is using 0 indexing here
    106 elemConn = self.fnc.compressed() - self.nsi

AttributeError: 'numpy.ndarray' object has no attribute 'count'

Any help here would be greatly appreciated!

[trexfeathers]

Hi @Kazu1886

is_contiguous() relies on coordinates having bounds that make cells without gaps between them. There are sophisticated operations to check this even for curvilinear coordinates. Without contiguous bounds, iris-esmf-regrid cannot work.

In your workaround: when creating your face-node connectivity, make sure you use a masked NumPy array rather than a straight NumPy array, since that is what the internals of iris-esmf-regrid need (ref)