libpysal.weights.da2WSP

libpysal.weights.da2WSP(da, criterion='queen', z_value=None, coords_labels={}, k=1, include_nodata=False, n_jobs=1)[source]

Create a WSP object from xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex.

Parameters:
daxarray.DataArray

Input 2D or 3D DataArray with shape=(z, y, x)

criterion{“rook”, “queen”}

Type of contiguity. Default is queen.

z_valueint/string/float

Select the z_value of 3D DataArray with multiple layers.

coords_labelsdictionary

Pass dimension labels for coordinates and layers if they do not belong to default dimensions, which are (band/time, y/lat, x/lon) e.g. coords_labels = {“y_label”: “latitude”, “x_label”: “longitude”, “z_label”: “year”} Default is {} empty dictionary.

kint

Order of contiguity, this will select all neighbors upto kth order. Default is 1.

include_nodatabool

If True, missing values will be assumed as non-missing when selecting higher_order neighbors, Default is False

n_jobsint

Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1.

Returns:
wsplibpysal.weights.WSP

instance of spatial weights class WSP with an index attribute

See also

libpysal.weights.weights.WSP

Notes

  1. Lower order contiguities are also selected.

  2. Returned object contains index attribute that includes a Pandas.MultiIndex object from the DataArray.

Examples

>>> from libpysal.weights.raster import da2WSP, testDataArray
>>> da = testDataArray().rename(
        {'band': 'layer', 'x': 'longitude', 'y': 'latitude'})
>>> da.dims
('layer', 'latitude', 'longitude')
>>> da.shape
(3, 4, 4)
>>> da.coords
Coordinates:
    * layer      (layer) int64 1 2 3
    * latitude   (latitude) float64 90.0 30.0 -30.0 -90.0
    * longitude  (longitude) float64 -180.0 -60.0 60.0 180.0
>>> da.attrs
{'nodatavals': (-32768.0,)}
>>> coords_labels = {
    "z_label": "layer",
    "y_label": "latitude",
    "x_label": "longitude"
}
>>> wsp = da2WSP(da, z_value=2, coords_labels=coords_labels)
>>> wsp.n
10
>>> pct_sp = wsp.sparse.nnz *1. / wsp.n**2
>>> "%.3f"%pct_sp
'0.300'
>>> print(wsp.sparse[4].todense())
[[0 0 1 0 0 1 1 1 0 0]]
>>> wsp.index[:2]
MultiIndex([(2, 90.0,  60.0),
            (2, 90.0, 180.0)],
           names=['layer', 'latitude', 'longitude'])