xrspatial.classify.std_mean#

xrspatial.classify.std_mean(agg: DataArray, name: str | None = 'std_mean') → DataArray[source]#

Classify data based on standard deviations from the mean.

Creates bins at mean +/- 1 and 2 standard deviations.

Parameters:

agg (xr.DataArray or xr.Dataset) – 2D NumPy, CuPy, NumPy-backed Dask, or CuPy-backed Dask array of values to be classified.
name (str, default='std_mean') – Name of output aggregate array.

Returns:

std_mean_agg – 2D aggregate array of standard deviation classifications. All other input attributes are preserved. If agg is a Dataset, returns a Dataset with each variable classified independently.

Return type:

xr.DataArray or xr.Dataset

References

PySAL: https://pysal.org/mapclassify/_modules/mapclassify/classifiers.html#StdMean

Examples

>>> import numpy as np
>>> import xarray as xr
>>> from xrspatial.classify import std_mean
>>> elevation = np.array([
    [np.nan,  1.,  2.,  3.,  4.],
    [ 5.,  6.,  7.,  8.,  9.],
    [10., 11., 12., 13., 14.],
    [15., 16., 17., 18., 19.],
    [20., 21., 22., 23., np.inf]
])
>>> agg_numpy = xr.DataArray(elevation, attrs={'res': (10.0, 10.0)})
>>> numpy_std_mean = std_mean(agg_numpy)
>>> print(numpy_std_mean)
<xarray.DataArray 'std_mean' (dim_0: 5, dim_1: 5)>
array([[nan,  1.,  1.,  1.,  1.],
       [ 1.,  2.,  2.,  2.,  2.],
       [ 2.,  2.,  2.,  2.,  2.],
       [ 2.,  2.,  2.,  2.,  3.],
       [ 3.,  3.,  3.,  3., nan]], dtype=float32)
Dimensions without coordinates: dim_0, dim_1
Attributes:
    res:      (10.0, 10.0)