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)> Size: 192B
0.3468 0.4532 0.5054 0.876 0.6265 0.7342 ... 0.4382 0.5668 0.7103 0.2494 0.3485
Coordinates:
  * chain    (chain) int64 32B 0 1 2 3
  * team     (team) <U1 24B 'a' 'b' 'c' 'd' 'e' 'f'

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)
  • chain
    (chain)
    int64
    0 1 2 3

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

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

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

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/82/lib/python3.12/site-packages/scipy/stats/_stats_py.py:3396, in median_abs_deviation(x, axis, center, scale, nan_policy)
   3393         med = np.expand_dims(center(x, axis=axis), axis)
   3394         mad = np.median(np.abs(x - med), axis=axis)
-> 3396 return mad / scale

File ~/checkouts/readthedocs.org/user_builds/xarray-einstats/envs/82/lib/python3.12/site-packages/xarray/computation/arithmetic.py:84, in SupportsArithmetic.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
     75     raise NotImplementedError(
     76         "xarray objects are not yet supported in the `out` argument "
     77         "for ufuncs. As an alternative, consider explicitly "
     78         "converting xarray objects to NumPy arrays (e.g., with "
     79         "`.values`)."
     80     )
     82 join = dataset_join = OPTIONS["arithmetic_join"]
---> 84 return apply_ufunc(
     85     ufunc,
     86     *inputs,
     87     input_core_dims=((),) * ufunc.nin,
     88     output_core_dims=((),) * ufunc.nout,
     89     join=join,
     90     dataset_join=dataset_join,
     91     dataset_fill_value=np.nan,
     92     kwargs=kwargs,
     93     dask="allowed",
     94     keep_attrs=_get_keep_attrs(default=True),
     95 )

File ~/checkouts/readthedocs.org/user_builds/xarray-einstats/envs/82/lib/python3.12/site-packages/xarray/computation/apply_ufunc.py:1267, 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, on_missing_core_dim, *args)
   1265 # feed DataArray apply_variable_ufunc through apply_dataarray_vfunc
   1266 elif any(isinstance(a, DataArray) for a in args):
-> 1267     return apply_dataarray_vfunc(
   1268         variables_vfunc,
   1269         *args,
   1270         signature=signature,
   1271         join=join,
   1272         exclude_dims=exclude_dims,
   1273         keep_attrs=keep_attrs,
   1274     )
   1275 # feed Variables directly through apply_variable_ufunc
   1276 elif any(isinstance(a, Variable) for a in args):

File ~/checkouts/readthedocs.org/user_builds/xarray-einstats/envs/82/lib/python3.12/site-packages/xarray/computation/apply_ufunc.py:310, in apply_dataarray_vfunc(func, signature, join, exclude_dims, keep_attrs, *args)
    305 result_coords, result_indexes = build_output_coords_and_indexes(
    306     args, signature, exclude_dims, combine_attrs=keep_attrs
    307 )
    309 data_vars = [getattr(a, "variable", a) for a in args]
--> 310 result_var = func(*data_vars)
    312 out: tuple[DataArray, ...] | DataArray
    313 if signature.num_outputs > 1:

File ~/checkouts/readthedocs.org/user_builds/xarray-einstats/envs/82/lib/python3.12/site-packages/xarray/computation/apply_ufunc.py:818, in apply_variable_ufunc(func, signature, exclude_dims, dask, output_dtypes, vectorize, keep_attrs, dask_gufunc_kwargs, *args)
    813 elif vectorize:
    814     func = _vectorize(
    815         func, signature, output_dtypes=output_dtypes, exclude_dims=exclude_dims
    816     )
--> 818 result_data = func(*input_data)
    820 if signature.num_outputs == 1:
    821     result_data = (result_data,)

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