- xrspatial.proximity.allocation(raster: xarray.core.dataarray.DataArray, x: str = 'x', y: str = 'y', target_values: list = , max_distance: float = inf, distance_metric: str = 'EUCLIDEAN')¶
Calculates, for all cells in the array, the downward slope direction Calculates, for all pixels in the input raster, the nearest source based on a set of target values and a distance metric.
This function attempts to produce the value of nearest feature of all pixels in the image to a set of pixels in the source image. The following options are used to define the behavior of the function. By default all non-zero pixels in raster.values will be considered as”target”, and all allocation will be computed in pixels.
Allocation supports NumPy backed, and Dask with NumPy backed xarray DataArray. The return values of allocation are of the same type as the input type. If input raster is a NumPy-backed DataArray, the result is NumPy-backed. If input raster is a Dask-backed DataArray, the result is Dask-backed.
allocation uses the same approach as proximity, which is ported from GDAL. A dynamic programming approach is used for identifying nearest target of a pixel from its surrounding neighborhood in a 3x3 window. The implementation for Dask-backed uses dask.map_overlap to compute allocation chunk by chunk by expanding the chunk’s borders to cover the max_distance.
raster (xr.DataArray) – 2D array of target data.
x (str, default='x') – Name of x-coordinates.
y (str, default='y') – Name of y-coordinates.
target_values (list) – Target pixel values to measure the distance from. If this option is not provided, allocation will be computed from non-zero pixel values.
max_distance (float, default=np.inf) –
The maximum distance to search. Proximity distances greater than this value will be set to NaN. Should be given in the same distance unit as input. For example, if input raster is in lat-lon and distances between points within the raster is calculated using Euclidean distance metric, max_distance should also be provided in lat-lon unit. If using Great Circle distance metric, and thus all distances is in km, max_distance should also be provided in kilometer unit.
When scaling with Dask, whether the function scales well depends on the max_distance value. If max_distance is infinite by default, this function only works on a single machine. It should scale well, however, if max_distance is relatively small compared to the maximum possible distance in two arbitrary points in the input raster. Note that if max_distance is equal or larger than the max possible distance between 2 arbitrary points in the input raster, the input data array will be rechunked.
distance_metric (str, default='EUCLIDEAN') – The metric for calculating distance between 2 points. Valid distance metrics are: ‘EUCLIDEAN’, ‘GREAT_CIRCLE’, and ‘MANHATTAN’.
allocation_agg – 2D array of allocation values. All other input attributes are preserved.
- Return type
xr.DataArray of same type as raster
import numpy as np import xarray as xr from xrspatial import allocation data = np.array([ [0., 0., 0., 0., 0.], [0., 1., 0., 2., 0.], [0., 0., 3., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.] ]) n, m = data.shape raster = xr.DataArray(data, dims=['y', 'x'], name='raster') raster['y'] = np.arange(n)[::-1] raster['x'] = np.arange(m) allocation_agg = allocation(raster)
>>> raster <xarray.DataArray 'raster' (y: 5, x: 5)> array([[0., 0., 0., 0., 0.], [0., 1., 0., 2., 0.], [0., 0., 3., 0., 0.], [0., 0., 0., 0., 0.], [1., 0., 0., 0., 0.]]) Coordinates: * y (y) int64 4 3 2 1 0 * x (x) int64 0 1 2 3 4 >>> allocation_agg <xarray.DataArray (y: 5, x: 5)> array([[1., 1., 2., 2., 2.], [1., 1., 1., 2., 2.], [1., 1., 3., 2., 2.], [1., 3., 3., 3., 2.], [3., 3., 3., 3., 3.]]) Coordinates: * y (y) int64 4 3 2 1 0 * x (x) int64 0 1 2 3 4