median_abs_deviation

xarray_einstats.stats.median_abs_deviation(da, dims=None, *, center=None, scale=1, nan_policy=None, **kwargs)

Wrap and extend scipy.stats.median_abs_deviation.

Usage examples available at Intro to the stats module.

All parameters take the same values and types as the scipy counterpart with the exception of scale. Here scale can also take DataArray values in which case, broadcasting is handled by xarray, as shown in the example.

Examples

Use a DataArray as scale.

import xarray as xr
from xarray_einstats import tutorial, stats
ds = tutorial.generate_mcmc_like_dataset(3)
s_da = xr.DataArray([1, 2, 1, 1], coords={"chain": ds.chain})
stats.median_abs_deviation(ds["mu"], dims="draw", scale=s_da)

<xarray.DataArray (chain: 4, team: 6)>
0.3468 0.4532 0.5054 0.876 0.6265 0.7342 ... 0.4382 0.5668 0.7103 0.2494 0.3485
Coordinates:
  * team     (team) <U1 'a' 'b' 'c' 'd' 'e' 'f'
  * chain    (chain) int64 0 1 2 3

xarray.DataArray

• chain: 4
• team: 6

• 0.3468 0.4532 0.5054 0.876 0.6265 ... 0.5668 0.7103 0.2494 0.3485

  array([[0.34676596, 0.45320205, 0.50539165, 0.8760277 , 0.62646553,
          0.73418378],
         [0.19816075, 0.29451242, 0.26859113, 0.35003099, 0.3795313 ,
          0.45268261],
         [0.41984653, 0.33699965, 0.55332021, 0.15398   , 0.3057486 ,
          0.39133525],
         [1.40888282, 0.43822259, 0.5668226 , 0.71029986, 0.24942531,
          0.34849295]])

• Coordinates: (2)
  • team
    (team)
    <U1
    'a' 'b' 'c' 'd' 'e' 'f'

    array(['a', 'b', 'c', 'd', 'e', 'f'], dtype='<U1')

  • chain
    (chain)
    int64
    0 1 2 3

    array([0, 1, 2, 3])

• Indexes: (2)
  • team
    PandasIndex

    PandasIndex(Index(['a', 'b', 'c', 'd', 'e', 'f'], dtype='object', name='team'))

  • chain
    PandasIndex

    PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))

• Attributes: (0)

Note that this doesn’t work with the scipy counterpart because s_da can’t be broadcasted with the output:

from scipy import stats
stats.median_abs_deviation(ds["mu"], axis=1, scale=s_da)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[2], line 2
      1 from scipy import stats
----> 2 stats.median_abs_deviation(ds["mu"], axis=1, scale=s_da)

File ~/checkouts/readthedocs.org/user_builds/xarray-einstats/envs/v0.5.1/lib/python3.10/site-packages/scipy/stats/_stats_py.py:3365, in median_abs_deviation(x, axis, center, scale, nan_policy)
   3362         med = np.expand_dims(center(x, axis=axis), axis)
   3363         mad = np.median(np.abs(x - med), axis=axis)
-> 3365 return mad / scale

File ~/checkouts/readthedocs.org/user_builds/xarray-einstats/envs/v0.5.1/lib/python3.10/site-packages/xarray/core/arithmetic.py:86, in SupportsArithmetic.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
     77     raise NotImplementedError(
     78         "xarray objects are not yet supported in the `out` argument "
     79         "for ufuncs. As an alternative, consider explicitly "
     80         "converting xarray objects to NumPy arrays (e.g., with "
     81         "`.values`)."
     82     )
     84 join = dataset_join = OPTIONS["arithmetic_join"]
---> 86 return apply_ufunc(
     87     ufunc,
     88     *inputs,
     89     input_core_dims=((),) * ufunc.nin,
     90     output_core_dims=((),) * ufunc.nout,
     91     join=join,
     92     dataset_join=dataset_join,
     93     dataset_fill_value=np.nan,
     94     kwargs=kwargs,
     95     dask="allowed",
     96     keep_attrs=_get_keep_attrs(default=True),
     97 )

File ~/checkouts/readthedocs.org/user_builds/xarray-einstats/envs/v0.5.1/lib/python3.10/site-packages/xarray/core/computation.py:1208, 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, meta, dask_gufunc_kwargs, *args)
   1206 # feed DataArray apply_variable_ufunc through apply_dataarray_vfunc
   1207 elif any(isinstance(a, DataArray) for a in args):
-> 1208     return apply_dataarray_vfunc(
   1209         variables_vfunc,
   1210         *args,
   1211         signature=signature,
   1212         join=join,
   1213         exclude_dims=exclude_dims,
   1214         keep_attrs=keep_attrs,
   1215     )
   1216 # feed Variables directly through apply_variable_ufunc
   1217 elif any(isinstance(a, Variable) for a in args):

File ~/checkouts/readthedocs.org/user_builds/xarray-einstats/envs/v0.5.1/lib/python3.10/site-packages/xarray/core/computation.py:315, in apply_dataarray_vfunc(func, signature, join, exclude_dims, keep_attrs, *args)
    310 result_coords, result_indexes = build_output_coords_and_indexes(
    311     args, signature, exclude_dims, combine_attrs=keep_attrs
    312 )
    314 data_vars = [getattr(a, "variable", a) for a in args]
--> 315 result_var = func(*data_vars)
    317 out: tuple[DataArray, ...] | DataArray
    318 if signature.num_outputs > 1:

File ~/checkouts/readthedocs.org/user_builds/xarray-einstats/envs/v0.5.1/lib/python3.10/site-packages/xarray/core/computation.py:771, in apply_variable_ufunc(func, signature, exclude_dims, dask, output_dtypes, vectorize, keep_attrs, dask_gufunc_kwargs, *args)
    766     if vectorize:
    767         func = _vectorize(
    768             func, signature, output_dtypes=output_dtypes, exclude_dims=exclude_dims
    769         )
--> 771 result_data = func(*input_data)
    773 if signature.num_outputs == 1:
    774     result_data = (result_data,)

ValueError: operands could not be broadcast together with shapes (4,6) (4,)