import os
import xcdat as xc

target_grid = xc.create_uniform_grid(-90, 89, 1.0, 0, 359, 1.0)

input_data = os.path.join(
    "/p/css03/esgf_publish/",
    "CMIP6/CMIP/AWI/AWI-CM-1-1-MR/historical/r1i1p1f1/Amon/psl/gn/v20181218/",
    "psl_Amon_AWI-CM-1-1-MR_historical_r1i1p1f1_gn_201301-201312.nc")

ds = xc.open_dataset(input_data)
ds_regrid = ds.regridder.horizontal("psl", target_grid, tool="regrid2")  # WORKS FINE

ds = xc.open_mfdataset(input_data)
ds_regrid = ds.regridder.horizontal("psl", target_grid, tool="regrid2")  # ERROR

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[54], line 1
----> 1 ds_regrid = ds.regridder.horizontal("psl", target_grid, tool="regrid2")

File ~/.conda/envs/pcmdi_metrics_dev_20240220/lib/python3.10/site-packages/xcdat/regridder/accessor.py:324, in RegridderAccessor.horizontal(self, data_var, output_grid, tool, **options)
    322 input_grid = _get_input_grid(self._ds, data_var, ["X", "Y"])
    323 regridder = regrid_tool(input_grid, output_grid, **options)
--> 324 output_ds = regridder.horizontal(data_var, self._ds)
    326 return output_ds

File ~/.conda/envs/pcmdi_metrics_dev_20240220/lib/python3.10/site-packages/xcdat/regridder/regrid2.py:81, in Regrid2Regridder.horizontal(self, data_var, ds)
     78     # Xarray defaults to masking with np.nan, CDAT masked with _FillValue or missing_value which defaults to 1e20
     79     input_data_var = input_data_var.where(src_mask != 0.0, masked_value)
---> 81 output_data = _regrid(
     82     input_data_var, src_lat_bnds, src_lon_bnds, dst_lat_bnds, dst_lon_bnds
     83 )
     85 output_ds = _build_dataset(
     86     ds,
     87     data_var,
   (...)
     92     self._output_grid,
     93 )
     95 return output_ds

File ~/.conda/envs/pcmdi_metrics_dev_20240220/lib/python3.10/site-packages/xcdat/regridder/regrid2.py:105, in _regrid(input_data_var, src_lat_bnds, src_lon_bnds, dst_lat_bnds, dst_lon_bnds)
     98 def _regrid(
     99     input_data_var: xr.DataArray,
    100     src_lat_bnds: np.ndarray,
   (...)
    103     dst_lon_bnds: np.ndarray,
    104 ) -> np.ndarray:
--> 105     lat_mapping, lat_weights = _map_latitude(src_lat_bnds, dst_lat_bnds)
    106     lon_mapping, lon_weights = _map_longitude(src_lon_bnds, dst_lon_bnds)
    108     # convert to pure numpy

File ~/.conda/envs/pcmdi_metrics_dev_20240220/lib/python3.10/site-packages/xcdat/regridder/regrid2.py:258, in _map_latitude(src, dst)
    252 bounds = [
    253     (np.minimum(dst_north[x], src_north[y]), np.maximum(dst_south[x], src_south[y]))
    254     for x, y in enumerate(mapping)
    255 ]
    257 # convert latitude to cell weight (difference of height above/below equator)
--> 258 weights = [
    259     (np.sin(np.deg2rad(x)) - np.sin(np.deg2rad(y))).reshape((-1, 1))
    260     for x, y in bounds
    261 ]
    263 return mapping, weights

File ~/.conda/envs/pcmdi_metrics_dev_20240220/lib/python3.10/site-packages/xcdat/regridder/regrid2.py:259, in <listcomp>(.0)
    252 bounds = [
    253     (np.minimum(dst_north[x], src_north[y]), np.maximum(dst_south[x], src_south[y]))
    254     for x, y in enumerate(mapping)
    255 ]
    257 # convert latitude to cell weight (difference of height above/below equator)
    258 weights = [
--> 259     (np.sin(np.deg2rad(x)) - np.sin(np.deg2rad(y))).reshape((-1, 1))
    260     for x, y in bounds
    261 ]
    263 return mapping, weights

File ~/.conda/envs/pcmdi_metrics_dev_20240220/lib/python3.10/site-packages/dask/array/core.py:2219, in Array.reshape(self, merge_chunks, limit, *shape)
   2217 if len(shape) == 1 and not isinstance(shape[0], Number):
   2218     shape = shape[0]
-> 2219 return reshape(self, shape, merge_chunks=merge_chunks, limit=limit)

File ~/.conda/envs/pcmdi_metrics_dev_20240220/lib/python3.10/site-packages/dask/array/reshape.py:218, in reshape(x, shape, merge_chunks, limit)
    216     if len(shape) == 1 and x.ndim == 1:
    217         return x
--> 218     missing_size = sanitize_index(x.size / reduce(mul, known_sizes, 1))
    219     shape = tuple(missing_size if s == -1 else s for s in shape)
    221 if np.isnan(sum(x.shape)):

File ~/.conda/envs/pcmdi_metrics_dev_20240220/lib/python3.10/site-packages/dask/array/slicing.py:72, in sanitize_index(ind)
     66     return slice(
     67         _sanitize_index_element(ind.start),
     68         _sanitize_index_element(ind.stop),
     69         _sanitize_index_element(ind.step),
     70     )
     71 elif isinstance(ind, Number):
---> 72     return _sanitize_index_element(ind)
     73 elif is_dask_collection(ind):
     74     return ind

File ~/.conda/envs/pcmdi_metrics_dev_20240220/lib/python3.10/site-packages/dask/array/slicing.py:26, in _sanitize_index_element(ind)
     24 """Sanitize a one-element index."""
     25 if isinstance(ind, Number):
---> 26     ind2 = int(ind)
     27     if ind2 != ind:
     28         raise IndexError("Bad index.  Must be integer-like: %s" % ind)

ValueError: cannot convert float NaN to integer