def test_smad_uint16(self):
stat = hdstats.smad_pcm(self.data, self.gm)
intdata = (self.data * 10000).astype(np.uint16)
intdata[1,1,0,:] = 0
intstat = hdstats.smad_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)
def test_bcmad(self):
bcmad = hdstats.smad_pcm(self.data, self.gm)
assert bcmad.shape == (200, 200)
def test_smad_baddata(self):
baddata = self.data[:3,:3,:,:].copy()
baddata[1,1,0,:] = np.nan
smad = hdstats.smad_pcm(baddata, self.gm)
assert np.isnan(smad[1,1])