xrspatial.focal.apply#
- xrspatial.focal.apply(raster, kernel, func=CPUDispatcher(<function _calc_mean>), name='focal_apply')[source]#
Returns custom function applied array using a user-created window.
- Parameters
raster (xarray.DataArray) – 2D array of input values to be filtered. Can be a NumPy backed, or Dask with NumPy backed DataArray.
kernel (numpy.ndarray) – 2D array where values of 1 indicate the kernel.
func (callable, default=xrspatial.focal._calc_mean) – Function which takes an input array and returns an array.
- Returns
agg – 2D aggregate array of filtered values.
- Return type
xarray.DataArray of same type as raster
Examples
Focal apply works with NumPy backed xarray DataArray .. sourcecode:: python
>>> import numpy as np >>> import xarray as xr >>> from xrspatial.convolution import circle_kernel >>> from xrspatial.focal import apply >>> data = np.arange(20, dtype=np.float64).reshape(4, 5) >>> raster = xr.DataArray(data, dims=['y', 'x'], name='raster') >>> print(raster) <xarray.DataArray 'raster' (y: 4, x: 5)> array([[ 0., 1., 2., 3., 4.], [ 5., 6., 7., 8., 9.], [10., 11., 12., 13., 14.], [15., 16., 17., 18., 19.]]) Dimensions without coordinates: y, x >>> kernel = circle_kernel(2, 2, 3) >>> kernel array([[0., 1., 0.], [1., 1., 1.], [0., 1., 0.]]) >>> # apply kernel mean by default >>> apply_mean_agg = apply(raster, kernel) >>> apply_mean_agg <xarray.DataArray 'focal_apply' (y: 4, x: 5)> array([[ 2. , 2.25 , 3.25 , 4.25 , 5.33333333], [ 5.25 , 6. , 7. , 8. , 8.75 ], [10.25 , 11. , 12. , 13. , 13.75 ], [13.66666667, 14.75 , 15.75 , 16.75 , 17. ]]) Dimensions without coordinates: y, x
Focal apply works with Dask with NumPy backed xarray DataArray. Note that if input raster is a numpy or dask with numpy backed data array, the applied function must be decorated with
numba.jit
xrspatial already providesngjit
decorator, where:ngjit = numba.jit(nopython=True, nogil=True)
>>> from xrspatial.utils import ngjit >>> from xrspatial.convolution import custom_kernel >>> kernel = custom_kernel(np.array([ [0, 1, 0], [0, 1, 1], [0, 1, 0], ])) >>> weight = np.array([ [0, 0.5, 0], [0, 1, 0.5], [0, 0.5, 0], ]) >>> @ngjit >>> def func(kernel_data): ... weight = np.array([ ... [0, 0.5, 0], ... [0, 1, 0.5], ... [0, 0.5, 0], ... ]) ... return np.nansum(kernel_data * weight)
>>> import dask.array as da >>> data_da = da.from_array(np.ones((6, 4), dtype=np.float64), chunks=(3, 2)) >>> raster_da = xr.DataArray(data_da, dims=['y', 'x'], name='raster_da') >>> print(raster_da) <xarray.DataArray 'raster_da' (y: 6, x: 4)> dask.array<array, shape=(6, 4), dtype=float64, chunksize=(3, 2), chunktype=numpy.ndarray> # noqa Dimensions without coordinates: y, x >>> apply_func_agg = apply(raster_da, kernel, func) >>> print(apply_func_agg) <xarray.DataArray 'focal_apply' (y: 6, x: 4)> dask.array<_trim, shape=(6, 4), dtype=float64, chunksize=(3, 2), chunktype=numpy.ndarray> # noqa Dimensions without coordinates: y, x >>> print(apply_func_agg.compute()) <xarray.DataArray 'focal_apply' (y: 6, x: 4)> array([[2. , 2. , 2. , 1.5], [2.5, 2.5, 2.5, 2. ], [2.5, 2.5, 2.5, 2. ], [2.5, 2.5, 2.5, 2. ], [2.5, 2.5, 2.5, 2. ], [2. , 2. , 2. , 1.5]]) Dimensions without coordinates: y, x