I would like to compute a raster layer with for each raster cell the mahalanobis distance to the centre of the environmental space formed by all reference data points (raster cells). In R this can be done as explained here [1].
. I would like to do this using python only (no dependency on R). I came up with the following, which works, but is very slow. I guess this is because it loops over every raster cell to compute the mahalanobis distance? Any idea how this can be done faster (I am very new to python, so bear with me if I am making stupid mistakes)
ref = [‘bio1@clim’,‘bio2@clim’,‘bio3@clim’]
import numpy as np
from scipy.spatial import distance
import scipy.linalg as linalg
import grass.script as grass
import grass.script.array as garray
Create covariance table (could do this in python instead)
text_file = open(“tmpfile”, “w”)
text_file.write(grass.read_command(“r.covar”, quiet=True, map=ref))
text_file.close()
covar = np.loadtxt(tmpcov, skiprows=1)
os.remove(tmpcov)
Import data
dat_ref = None
stat_mean = None
layer = garray.array()
for i, map in enumerate(ref):
layer.read(map, null=np.nan)
s = len(ref)
r, c = layer.shape
if dat_ref is None:
dat_ref = np.empty((s, r, c), dtype=np.double)
if stat_mean is None:
stat_mean = np.empty((s), dtype=np.double)
dat_ref[i,:,:] = layer
stat_mean[i] = np.nanmean(layer)
del layer
Compute mahalanobis distance
r, c = dat_ref[1,:,:].shape
stat_mah = garray.array()
for i in xrange(r):
for j in xrange(c):
cell_ref = dat_ref[…,i,j]
stat_mah[i,j] = distance.mahalanobis(cell_ref, stat_mean
, linalg.inv(covar))
stat_mah.write(“mahalanobisMap”)
