xrspatial.normalize.standardize#

xrspatial.normalize.standardize(agg, ddof=0, name='standardize')[source]#

Z-score normalization of a raster.

Subtracts the mean and divides by the standard deviation of finite values. NaN and infinite values pass through unchanged. If all finite values are identical (std == 0), the output is filled with 0.0 for those cells.

Parameters:

agg (xr.DataArray or xr.Dataset) – 2D NumPy, CuPy, NumPy-backed Dask, or CuPy-backed Dask array.
ddof (int, default=0) – Delta degrees of freedom for standard deviation. Use 0 for population std, 1 for sample std.
name (str, default='standardize') – Name of the output DataArray.

Returns:

standardized – Z-score normalized raster with the same shape, dims, coords, and attrs. If agg is a Dataset, each variable is standardized independently.

Return type:

xr.DataArray or xr.Dataset

Examples

>>> import numpy as np
>>> import xarray as xr
>>> from xrspatial.normalize import standardize

>>> data = np.array([
        [np.nan, 1., 2.],
        [3.,    4.,  5.],
    ], dtype=np.float64)
>>> agg = xr.DataArray(data)
>>> standardize(agg)
<xarray.DataArray 'standardize' (dim_0: 2, dim_1: 3)>
array([[        nan, -1.41421356, -0.70710678],
       [ 0.        ,  0.70710678,  1.41421356]])
Dimensions without coordinates: dim_0, dim_1