[Help]: Problem opening stack of interferograms

[anaferreira97]

Hi everyone,

I'm trying to open a stack of interferograms (ds_sbas = sbas.open_stack("intf_mlook") but without sucess. I'm having this error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[38], line 3
      1 #ds_sbas = sbas.sync_cube(xr.merge([intf, corr]), 'intf_sbas')
----> 3 ds_sbas = sbas.open_stack("intf_mlook")
      4 ds_sbas = ds_sbas.where(landmask_ra.interp_like(ds_sbas))
      5 intf_sbas = ds_sbas.phase

File ~/.local/lib/python3.11/site-packages/pygmtsar/IO.py:652, in IO.open_stack(self, name, stack)
    649     chunksize = self.chunksize
    650 del data0
--> 652 data = xr.open_mfdataset(
    653     filenames,
    654     engine=self.netcdf_engine,
    655     chunks=chunksize,
    656     parallel=True,
    657     concat_dim='stackvar',
    658     combine='nested'
    659 )
    661 if 'stack' in data.dims:
    662     if 'y' in data.coords and 'x' in data.coords:

File /opt/conda/lib/python3.11/site-packages/xarray/backends/api.py:1040, in open_mfdataset(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)
   1036 try:
   1037     if combine == "nested":
   1038         # Combined nested list by successive concat and merge operations
   1039         # along each dimension, using structure given by "ids"
-> 1040         combined = _nested_combine(
   1041             datasets,
   1042             concat_dims=concat_dim,
   1043             compat=compat,
   1044             data_vars=data_vars,
   1045             coords=coords,
   1046             ids=ids,
   1047             join=join,
   1048             combine_attrs=combine_attrs,
   1049         )
   1050     elif combine == "by_coords":
   1051         # Redo ordering from coordinates, ignoring how they were ordered
   1052         # previously
   1053         combined = combine_by_coords(
   1054             datasets,
   1055             compat=compat,
   (...)
   1059             combine_attrs=combine_attrs,
   1060         )

File /opt/conda/lib/python3.11/site-packages/xarray/core/combine.py:356, in _nested_combine(datasets, concat_dims, compat, data_vars, coords, ids, fill_value, join, combine_attrs)
    353 _check_shape_tile_ids(combined_ids)
    355 # Apply series of concatenate or merge operations along each dimension
--> 356 combined = _combine_nd(
    357     combined_ids,
    358     concat_dims,
    359     compat=compat,
    360     data_vars=data_vars,
    361     coords=coords,
    362     fill_value=fill_value,
    363     join=join,
    364     combine_attrs=combine_attrs,
    365 )
    366 return combined

File /opt/conda/lib/python3.11/site-packages/xarray/core/combine.py:232, in _combine_nd(combined_ids, concat_dims, data_vars, coords, compat, fill_value, join, combine_attrs)
    228 # Each iteration of this loop reduces the length of the tile_ids tuples
    229 # by one. It always combines along the first dimension, removing the first
    230 # element of the tuple
    231 for concat_dim in concat_dims:
--> 232     combined_ids = _combine_all_along_first_dim(
    233         combined_ids,
    234         dim=concat_dim,
    235         data_vars=data_vars,
    236         coords=coords,
    237         compat=compat,
    238         fill_value=fill_value,
    239         join=join,
    240         combine_attrs=combine_attrs,
    241     )
    242 (combined_ds,) = combined_ids.values()
    243 return combined_ds

File /opt/conda/lib/python3.11/site-packages/xarray/core/combine.py:267, in _combine_all_along_first_dim(combined_ids, dim, data_vars, coords, compat, fill_value, join, combine_attrs)
    265     combined_ids = dict(sorted(group))
    266     datasets = combined_ids.values()
--> 267     new_combined_ids[new_id] = _combine_1d(
    268         datasets, dim, compat, data_vars, coords, fill_value, join, combine_attrs
    269     )
    270 return new_combined_ids

File /opt/conda/lib/python3.11/site-packages/xarray/core/combine.py:290, in _combine_1d(datasets, concat_dim, compat, data_vars, coords, fill_value, join, combine_attrs)
    288 if concat_dim is not None:
    289     try:
--> 290         combined = concat(
    291             datasets,
    292             dim=concat_dim,
    293             data_vars=data_vars,
    294             coords=coords,
    295             compat=compat,
    296             fill_value=fill_value,
    297             join=join,
    298             combine_attrs=combine_attrs,
    299         )
    300     except ValueError as err:
    301         if "encountered unexpected variable" in str(err):

File /opt/conda/lib/python3.11/site-packages/xarray/core/concat.py:252, in concat(objs, dim, data_vars, coords, compat, positions, fill_value, join, combine_attrs)
    240     return _dataarray_concat(
    241         objs,
    242         dim=dim,
   (...)
    249         combine_attrs=combine_attrs,
    250     )
    251 elif isinstance(first_obj, Dataset):
--> 252     return _dataset_concat(
    253         objs,
    254         dim=dim,
    255         data_vars=data_vars,
    256         coords=coords,
    257         compat=compat,
    258         positions=positions,
    259         fill_value=fill_value,
    260         join=join,
    261         combine_attrs=combine_attrs,
    262     )
    263 else:
    264     raise TypeError(
    265         "can only concatenate xarray Dataset and DataArray "
    266         f"objects, got {type(first_obj)}"
    267     )

File /opt/conda/lib/python3.11/site-packages/xarray/core/concat.py:487, in _dataset_concat(datasets, dim, data_vars, coords, compat, positions, fill_value, join, combine_attrs)
    484 # Make sure we're working on a copy (we'll be loading variables)
    485 datasets = [ds.copy() for ds in datasets]
    486 datasets = list(
--> 487     align(*datasets, join=join, copy=False, exclude=[dim], fill_value=fill_value)
    488 )
    490 dim_coords, dims_sizes, coord_names, data_names, vars_order = _parse_datasets(
    491     datasets
    492 )
    493 dim_names = set(dim_coords)

File /opt/conda/lib/python3.11/site-packages/xarray/core/alignment.py:888, in align(join, copy, indexes, exclude, fill_value, *objects)
    692 """
    693 Given any number of Dataset and/or DataArray objects, returns new
    694 objects with aligned indexes and dimension sizes.
   (...)
    878 
    879 """
    880 aligner = Aligner(
    881     objects,
    882     join=join,
   (...)
    886     fill_value=fill_value,
    887 )
--> 888 aligner.align()
    889 return aligner.results

File /opt/conda/lib/python3.11/site-packages/xarray/core/alignment.py:575, in Aligner.align(self)
    573 self.assert_no_index_conflict()
    574 self.align_indexes()
--> 575 self.assert_unindexed_dim_sizes_equal()
    577 if self.join == "override":
    578     self.override_indexes()

File /opt/conda/lib/python3.11/site-packages/xarray/core/alignment.py:476, in Aligner.assert_unindexed_dim_sizes_equal(self)
    474     add_err_msg = ""
    475 if len(sizes) > 1:
--> 476     raise ValueError(
    477         f"cannot reindex or align along dimension {dim!r} "
    478         f"because of conflicting dimension sizes: {sizes!r}" + add_err_msg
    479     )

ValueError: cannot reindex or align along dimension 'x' because of conflicting dimension sizes: {5968, 6080, 5967}

Anyone with the same problem?

[AlexeyPechnikov]

It seems your intf_mlook stack is inconsistent, with rasters for different dates saved in varying sizes. Did you use a single call to sbas.compute_interferogram_multilook() to produce the stack? You can delete the stack using sbas.delete_stack('intf_mlook') and recreate it to ensure all rasters are aligned to the same grid.