def testnd():
shape = (5, 5)
hdr = fits.Header({'CRPIX1': 1, 'CRPIX2': 2})
nd = NDAstroData(data=np.arange(np.prod(shape)).reshape(shape),
uncertainty=ADVarianceUncertainty(np.ones(shape) + 0.5),
mask=np.zeros(shape, dtype=bool),
wcs=WCS(header=hdr),
unit='ct')
nd.mask[3, 4] = True
return nd
def test_wcs_slicing():
nd = NDAstroData(np.zeros((50, 50)))
in_frame = Frame2D(name="in_frame")
out_frame = Frame2D(name="out_frame")
nd.wcs = gWCS([(in_frame, models.Identity(2)), (out_frame, None)])
assert nd.wcs(10, 10) == (10, 10)
assert nd[10:].wcs(10, 10) == (10, 20)
assert nd[..., 10:].wcs(10, 10) == (20, 10)
assert nd[:, 5].wcs(10) == (5, 10)
assert nd[20, -10:].wcs(0) == (40, 20)
def stack(arrays):
arrays = [x for x in arrays]
data = np.array([arr.data for arr in arrays]).sum(axis=0)
unc = np.array([arr.uncertainty.array for arr in arrays]).sum(axis=0)
mask = np.array([arr.mask for arr in arrays]).sum(axis=0)
return NDAstroData(data=data,
uncertainty=ADVarianceUncertainty(unc),
mask=mask)
def test_windowedOp(testnd):
def stack(arrays):
arrays = [x for x in arrays]
data = np.array([arr.data for arr in arrays]).sum(axis=0)
unc = np.array([arr.uncertainty.array for arr in arrays]).sum(axis=0)
mask = np.array([arr.mask for arr in arrays]).sum(axis=0)
return NDAstroData(data=data, variance=unc, mask=mask)
result = windowedOp(stack, [testnd, testnd],
kernel=(3, 3),
with_uncertainty=True,
with_mask=True)
assert_array_equal(result.data, testnd.data * 2)
assert_array_equal(result.uncertainty.array, testnd.uncertainty.array * 2)
assert result.mask[3, 4] == 2
nd2 = NDAstroData(data=np.zeros((4, 4)))
with pytest.raises(ValueError, match=r"Can't calculate final shape.*"):
result = windowedOp(stack, [testnd, nd2], kernel=(3, 3))
with pytest.raises(AssertionError, match=r"Incompatible shape.*"):
result = windowedOp(stack, [testnd, testnd], kernel=[3], shape=(5, 5))
def test_var(testnd):
data = np.zeros(5)
var = np.array([1.2, 2, 1.5, 1, 1.3])
nd1 = NDAstroData(data=data, uncertainty=ADVarianceUncertainty(var))
nd2 = NDAstroData(data=data, variance=var)
assert_array_equal(nd1.variance, nd2.variance)