`AdministrativeRegionAnalysis`: ValueError: Cannot apply_along_axis when any iteration dimensions are 0

[tommylees112]

## AdministrativeRegionAnalysis
Produces the following error when trying to run the true vs. predicted for regions

Error Trace:

---------------------------------------------------------------------------
StopIteration                             Traceback (most recent call last)
~/miniconda3/envs/crop/lib/python3.7/site-packages/numpy/lib/shape_base.py in apply_along_axis(func1d, axis, arr, *args, **kwargs)
    376     try:
--> 377         ind0 = next(inds)
    378     except StopIteration:

StopIteration:

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-82-fb568627f573> in <module>
     13
     14 admin_analyzer = AdministrativeRegionAnalysis(data_dir)
---> 15 admin_analyzer.analyze()

~/github/ml_drought/src/analysis/region_analysis/administrative_region_analysis.py in analyze(self, compute_global_errors, compute_regional_errors, save_all_df)
    122
    123         for region_data_path in self.region_data_paths:
--> 124             df = self._analyze_single(region_data_path)
    125             all_regions_dfs.append(df)
    126

~/github/ml_drought/src/analysis/region_analysis/administrative_region_analysis.py in _analyze_single(self, region_data_path)
    109         return self._base_analyze_single(
    110             admin_level_name=admin_level_name, region_da=region_da,
--> 111             region_lookup=region_lookup, region_group_name=region_group_name,
    112         )
    113

~/github/ml_drought/src/analysis/region_analysis/base.py in _base_analyze_single(self, admin_level_name, region_da, region_lookup, region_group_name, landcover_das)
    322                     ) = self.compute_mean_statistics(  # type: ignore
    323                         region_da=region_da, true_da=true_da, pred_da=pred_da,
--> 324                         region_lookup=region_lookup, datetime=dt
    325                     )
    326                 else:

~/github/ml_drought/src/analysis/region_analysis/administrative_region_analysis.py in compute_mean_statistics(self, region_da, region_lookup, pred_da, true_da, datetime)
     78             region_names.append(region_lookup[valid_region_id])
     79             predicted_mean_value.append(
---> 80                 pred_da.where(region_da == valid_region_id).mean().values
     81             )
     82             true_mean_value.append(

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/common.py in where(self, cond, other, drop)
    933             cond = cond.isel(**indexers)
    934
--> 935         return ops.where_method(self, cond, other)
    936
    937     def close(self: Any) -> None:

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/ops.py in where_method(self, cond, other)
    170                        dataset_join=join,
    171                        dask='allowed',
--> 172                        keep_attrs=True)
    173
    174

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/computation.py in apply_ufunc(func, input_core_dims, output_core_dims, exclude_dims, vectorize, join, dataset_join, dataset_fill_value, keep_attrs, kwargs, dask, output_dtypes, output_sizes, *args)
    966                                      join=join,
    967                                      exclude_dims=exclude_dims,
--> 968                                      keep_attrs=keep_attrs)
    969     elif any(isinstance(a, Variable) for a in args):
    970         return variables_vfunc(*args)

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/computation.py in apply_dataarray_vfunc(func, signature, join, exclude_dims, keep_attrs, *args)
    215
    216     data_vars = [getattr(a, 'variable', a) for a in args]
--> 217     result_var = func(*data_vars)
    218
    219     if signature.num_outputs > 1:

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/computation.py in apply_variable_ufunc(func, signature, exclude_dims, dask, output_dtypes, output_sizes, keep_attrs, *args)
    539                   if isinstance(arg, Variable)
    540                   else arg
--> 541                   for arg, core_dims in zip(args, signature.input_core_dims)]
    542
    543     if any(isinstance(array, dask_array_type) for array in input_data):

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/computation.py in <listcomp>(.0)
    539                   if isinstance(arg, Variable)
    540                   else arg
--> 541                   for arg, core_dims in zip(args, signature.input_core_dims)]
    542
    543     if any(isinstance(array, dask_array_type) for array in input_data):

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/computation.py in broadcast_compat_data(variable, broadcast_dims, core_dims)
    470 def broadcast_compat_data(variable, broadcast_dims, core_dims):
    471     # type: (Variable, tuple, tuple) -> Any
--> 472     data = variable.data
    473
    474     old_dims = variable.dims

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/variable.py in data(self)
    292             return self._data
    293         else:
--> 294             return self.values
    295
    296     @data.setter

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/variable.py in values(self)
    387     def values(self):
    388         """The variable's data as a numpy.ndarray"""
--> 389         return _as_array_or_item(self._data)
    390
    391     @values.setter

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/variable.py in _as_array_or_item(data)
    208     TODO: remove this (replace with np.asarray) once these issues are fixed
    209     """
--> 210     data = np.asarray(data)
    211     if data.ndim == 0:
    212         if data.dtype.kind == 'M':

~/miniconda3/envs/crop/lib/python3.7/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
    536
    537     """
--> 538     return array(a, dtype, copy=False, order=order)
    539
    540

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/indexing.py in __array__(self, dtype)
    630
    631     def __array__(self, dtype=None):
--> 632         self._ensure_cached()
    633         return np.asarray(self.array, dtype=dtype)
    634

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/indexing.py in _ensure_cached(self)
    627     def _ensure_cached(self):
    628         if not isinstance(self.array, NumpyIndexingAdapter):
--> 629             self.array = NumpyIndexingAdapter(np.asarray(self.array))
    630
    631     def __array__(self, dtype=None):

~/miniconda3/envs/crop/lib/python3.7/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
    536
    537     """
--> 538     return array(a, dtype, copy=False, order=order)
    539
    540

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/indexing.py in __array__(self, dtype)
    608
    609     def __array__(self, dtype=None):
--> 610         return np.asarray(self.array, dtype=dtype)
    611
    612     def __getitem__(self, key):

~/miniconda3/envs/crop/lib/python3.7/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
    536
    537     """
--> 538     return array(a, dtype, copy=False, order=order)
    539
    540

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/indexing.py in __array__(self, dtype)
    514     def __array__(self, dtype=None):
    515         array = as_indexable(self.array)
--> 516         return np.asarray(array[self.key], dtype=None)
    517
    518     def transpose(self, order):

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/backends/netCDF4_.py in __getitem__(self, key)
     62         return indexing.explicit_indexing_adapter(
     63             key, self.shape, indexing.IndexingSupport.OUTER,
---> 64             self._getitem)
     65
     66     def _getitem(self, key):

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/core/indexing.py in explicit_indexing_adapter(key, shape, indexing_support, raw_indexing_method)
    782     """
    783     raw_key, numpy_indices = decompose_indexer(key, shape, indexing_support)
--> 784     result = raw_indexing_method(raw_key.tuple)
    785     if numpy_indices.tuple:
    786         # index the loaded np.ndarray

~/miniconda3/envs/crop/lib/python3.7/site-packages/xarray/backends/netCDF4_.py in _getitem(self, key)
     73             with self.datastore.lock:
     74                 original_array = self.get_array(needs_lock=False)
---> 75                 array = getitem(original_array, key)
     76         except IndexError:
     77             # Catch IndexError in netCDF4 and return a more informative

netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable.__getitem__()

~/miniconda3/envs/crop/lib/python3.7/site-packages/netCDF4/utils.py in _StartCountStride(elem, shape, dimensions, grp, datashape, put, use_get_vars)
    435         #    ITERABLE    #
    436         elif np.iterable(e) and np.array(e).dtype.kind in 'i':  # Sequence of integers
--> 437             start[...,i] = np.apply_along_axis(lambda x: e*x, i, np.ones(sdim[:-1]))
    438             indices[...,i] = np.apply_along_axis(lambda x: np.arange(sdim[i])*x, i, np.ones(sdim[:-1], int))
    439

~/miniconda3/envs/crop/lib/python3.7/site-packages/numpy/lib/shape_base.py in apply_along_axis(func1d, axis, arr, *args, **kwargs)
    377         ind0 = next(inds)
    378     except StopIteration:
--> 379         raise ValueError('Cannot apply_along_axis when any iteration dimensions are 0')
    380     res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))
    381

ValueError: Cannot apply_along_axis when any iteration dimensions are 0

[tommylees112]

This error is caused by the different shapes of the predicted/true xr.DataArray objects at data/models/{experiment}/{model}/*.nc & data/features/{experiment}/train/{timestep}/*.nc

ipdb> pred_da
<xarray.DataArray 'preds' (lat: 224, lon: 176)>
array([[33.623024, 45.5946  , 36.096382, ...,       nan,       nan,       nan],
       [27.531542, 37.75934 , 37.770805, ...,       nan,       nan,       nan],
       [35.528862, 37.54693 , 43.118332, ...,       nan,       nan,       nan],
       ...,
       [49.296875, 55.80316 , 35.041126, ..., 18.268587, 26.510838, 17.255913],
       [57.478218, 37.032967, 49.80915 , ..., 25.295422, 22.875132, 15.904449],
       [43.87786 , 45.465843, 61.007156, ..., 24.108068, 28.35307 , 26.160402]],
      dtype=float32)
Coordinates:
  * lat      (lat) float64 -5.175 -5.125 -5.075 -5.025 ... 5.875 5.925 5.975
  * lon      (lon) float64 33.52 33.57 33.62 33.68 ... 42.12 42.18 42.22 42.27

and the region_da at ``

ipdb> region_da
<xarray.DataArray 'district_l2' (lat: 311, lon: 244)>
array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       ...,
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]])
Coordinates:
  * lat      (lat) float64 -5.17 -5.134 -5.098 -5.062 ... 5.921 5.957 5.993
  * lon      (lon) float64 33.5 33.54 33.57 33.61 ... 42.14 42.18 42.21 42.25

[tommylees112]

Written some assert statements to çapture this error and give some more informative warnings!