def test_bcmad_uint16(self):
stat = hdstats.bcmad_pcm(self.data, self.gm)
intdata = (self.data * 10000).astype(np.uint16)
intdata[1,1,0,:] = 0
intstat = hdstats.bcmad_pcm(intdata, self.gm, nodata=0)
npt.assert_approx_equal(np.nanmean(stat),
np.nanmean(intstat),
significant=4)
def gm_tmad(arr, **kw):
"""
arr: a high dimensional numpy array where the last dimension will be reduced.
returns: a numpy array with one less dimension than input.
"""
gm = hdstats.nangeomedian_pcm(arr, **kw)
nt = kw.pop('num_threads', None)
emad = hdstats.emad_pcm(arr, gm, num_threads=nt)[:,:, np.newaxis]
smad = hdstats.smad_pcm(arr, gm, num_threads=nt)[:,:, np.newaxis]
bcmad = hdstats.bcmad_pcm(arr, gm, num_threads=nt)[:,:, np.newaxis]
return np.concatenate([gm, emad, smad, bcmad], axis=-1)