loader.loadRun(run=92849).unstack('system')

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[187], line 1
----> 1 scan = loader.loadRun(run=92849).unstack('system')
      2 scan

File ~\.conda\envs\20250308_1\Lib\site-packages\xarray\core\dataarray.py:3019, in DataArray.unstack(self, dim, fill_value, sparse)
   2958 def unstack(
   2959     self,
   2960     dim: Dims = None,
   (...)
   2963     sparse: bool = False,
   2964 ) -> Self:
   2965     """
   2966     Unstack existing dimensions corresponding to MultiIndexes into
   2967     multiple new dimensions.
   (...)
   3017     DataArray.stack
   3018     """
-> 3019     ds = self._to_temp_dataset().unstack(dim, fill_value=fill_value, sparse=sparse)
   3020     return self._from_temp_dataset(ds)

File ~\.conda\envs\20250308_1\Lib\site-packages\xarray\core\dataset.py:5833, in Dataset.unstack(self, dim, fill_value, sparse)
   5829         result = result._unstack_full_reindex(
   5830             d, stacked_indexes[d], fill_value, sparse
   5831         )
   5832     else:
-> 5833         result = result._unstack_once(d, stacked_indexes[d], fill_value, sparse)
   5834 return result

File ~\.conda\envs\20250308_1\Lib\site-packages\xarray\core\dataset.py:5653, in Dataset._unstack_once(self, dim, index_and_vars, fill_value, sparse)
   5650 variables: dict[Hashable, Variable] = {}
   5651 indexes = {k: v for k, v in self._indexes.items() if k != dim}
-> 5653 new_indexes, clean_index = index.unstack()
   5654 indexes.update(new_indexes)
   5656 for idx in new_indexes.values():

File ~\.conda\envs\20250308_1\Lib\site-packages\xarray\core\indexes.py:1057, in PandasMultiIndex.unstack(self)
   1054 clean_index = remove_unused_levels_categories(self.index)
   1056 if not clean_index.is_unique:
-> 1057     raise ValueError(
   1058         "Cannot unstack MultiIndex containing duplicates. Make sure entries "
   1059         f"are unique, e.g., by  calling ``.drop_duplicates('{self.dim}')``, "
   1060         "before unstacking."
   1061     )
   1063 new_indexes: dict[Hashable, Index] = {}
   1064 for name, lev in zip(clean_index.names, clean_index.levels, strict=True):

ValueError: Cannot unstack MultiIndex containing duplicates. Make sure entries are unique, e.g., by  calling ``.drop_duplicates('system')``, before unstacking.

try: from wherever import thing
except ImportError: from elsewhere import thing

try:
    from wherever import thing
except ImportError:
    from elsewhere import thing

scan = loader.loadRun(run=93983).unstack('system')

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
Cell In[10], line 1
----> 1 scan = loader.loadRun(run=93983).unstack('system')
      2 scan

File ~\Anaconda3\envs\20250331_1\Lib\site-packages\PyHyperScattering\SST1RSoXSDB.py:740, in SST1RSoXSDB.loadRun(self, run, dims, coords, return_dataset, useMonitorShutterThinning)
    737     dims_to_join.append(val)
    738     dim_names_to_join.append(key)
--> 740 index = pd.MultiIndex.from_arrays(dims_to_join, names=dim_names_to_join)
    741 # handle the edge case of a partly-finished scan
    742 if len(index) != len(data["time"]):

File ~\Anaconda3\envs\20250331_1\Lib\site-packages\pandas\core\indexes\multi.py:533, in MultiIndex.from_arrays(cls, arrays, sortorder, names)
    530     if len(arrays[i]) != len(arrays[i - 1]):
    531         raise ValueError("all arrays must be same length")
--> 533 codes, levels = factorize_from_iterables(arrays)
    534 if names is lib.no_default:
    535     names = [getattr(arr, "name", None) for arr in arrays]

File ~\Anaconda3\envs\20250331_1\Lib\site-packages\pandas\core\arrays\categorical.py:3069, in factorize_from_iterables(iterables)
   3065 if len(iterables) == 0:
   3066     # For consistency, it should return two empty lists.
   3067     return [], []
-> 3069 codes, categories = zip(*(factorize_from_iterable(it) for it in iterables))
   3070 return list(codes), list(categories)

File ~\Anaconda3\envs\20250331_1\Lib\site-packages\pandas\core\arrays\categorical.py:3069, in <genexpr>(.0)
   3065 if len(iterables) == 0:
   3066     # For consistency, it should return two empty lists.
   3067     return [], []
-> 3069 codes, categories = zip(*(factorize_from_iterable(it) for it in iterables))
   3070 return list(codes), list(categories)

File ~\Anaconda3\envs\20250331_1\Lib\site-packages\pandas\core\arrays\categorical.py:3042, in factorize_from_iterable(values)
   3037     codes = values.codes
   3038 else:
   3039     # The value of ordered is irrelevant since we don't use cat as such,
   3040     # but only the resulting categories, the order of which is independent
   3041     # from ordered. Set ordered to False as default. See GH #15457
-> 3042     cat = Categorical(values, ordered=False)
   3043     categories = cat.categories
   3044     codes = cat.codes

File ~\Anaconda3\envs\20250331_1\Lib\site-packages\pandas\core\arrays\categorical.py:425, in Categorical.__init__(self, values, categories, ordered, dtype, fastpath, copy)
    422 elif isinstance(values, np.ndarray):
    423     if values.ndim > 1:
    424         # preempt sanitize_array from raising ValueError
--> 425         raise NotImplementedError(
    426             "> 1 ndim Categorical are not supported at this time"
    427         )
    428     values = sanitize_array(values, None)
    429 else:
    430     # i.e. must be a list

NotImplementedError: > 1 ndim Categorical are not supported at this time