def test_combiner_sigmaclip_low():
ccd_list = [CCDData(np.zeros((10, 10)), unit=u.adu),
CCDData(np.zeros((10, 10)) - 10, unit=u.adu),
CCDData(np.zeros((10, 10)) + 10, unit=u.adu),
CCDData(np.zeros((10, 10)) - 10, unit=u.adu),
CCDData(np.zeros((10, 10)) + 10, unit=u.adu),
CCDData(np.zeros((10, 10)) - 1000, unit=u.adu)]
c = Combiner(ccd_list)
# using mad for more robust statistics vs. std
c.sigma_clipping(high_thresh=None, low_thresh=3, func=np.ma.median,
dev_func=mad)
assert c.data_arr[5].mask.all()
def test_combiner_sigmaclip_single_pix():
ccd_list = [CCDData(np.zeros((10, 10)), unit=u.adu),
CCDData(np.zeros((10, 10)) - 10, unit=u.adu),
CCDData(np.zeros((10, 10)) + 10, unit=u.adu),
CCDData(np.zeros((10, 10)) - 10, unit=u.adu),
CCDData(np.zeros((10, 10)) + 10, unit=u.adu),
CCDData(np.zeros((10, 10)) - 10, unit=u.adu)]
c = Combiner(ccd_list)
# add a single pixel in another array to check that
# that one gets rejected
c.data_arr[0, 5, 5] = 0
c.data_arr[1, 5, 5] = -5
c.data_arr[2, 5, 5] = 5
c.data_arr[3, 5, 5] = -5
c.data_arr[4, 5, 5] = 25
c.sigma_clipping(high_thresh=3, low_thresh=None, func=np.ma.median,
dev_func=mad)
assert c.data_arr.mask[4, 5, 5]
