def test_slicing_all_npndarray_1d():
data = np.arange(10)
mask = data > 3
uncertainty = StdDevUncertainty(np.linspace(10, 20, 10))
naxis = 1
wcs = nd_testing._create_wcs_simple(naxis=naxis,
ctype=["deg"] * naxis,
crpix=[3] * naxis,
crval=[10] * naxis,
cdelt=[1] * naxis)
# Just to have them too
unit = u.s
meta = {'observer': 'Brian'}
nd = NDDataSliceable(data,
mask=mask,
uncertainty=uncertainty,
wcs=wcs,
unit=unit,
meta=meta)
nd2 = nd[2:5]
assert_array_equal(data[2:5], nd2.data)
assert_array_equal(mask[2:5], nd2.mask)
assert_array_equal(uncertainty[2:5].array, nd2.uncertainty.array)
assert nd2.wcs.pixel_to_world(1) == nd.wcs.pixel_to_world(3)
assert unit is nd2.unit
assert meta == nd.meta
def test_nddata_wcs_setter_error_cases():
ndd = NDData(np.ones((5, 5)))
# Setting with a non-WCS should raise an error
with pytest.raises(TypeError):
ndd.wcs = "I am not a WCS"
naxis = 2
# This should succeed since the WCS is currently None
ndd.wcs = nd_testing._create_wcs_simple(naxis=naxis,
ctype=['deg'] * naxis,
crpix=[0] * naxis,
crval=[10] * naxis,
cdelt=[1] * naxis)
with pytest.raises(ValueError):
# This should fail since the WCS is not None
ndd.wcs = nd_testing._create_wcs_simple(naxis=naxis,
ctype=['deg'] * naxis,
crpix=[0] * naxis,
crval=[10] * naxis,
cdelt=[1] * naxis)
def test_slicing_all_npndarray_shape_diff():
data = np.arange(10)
mask = (data > 3)[0:9]
uncertainty = np.linspace(10, 20, 15)
naxis = 1
wcs = nd_testing._create_wcs_simple(naxis=naxis,
ctype=["deg"] * naxis,
crpix=[3] * naxis,
crval=[10] * naxis,
cdelt=[1] * naxis)
nd = NDDataSliceable(data, mask=mask, uncertainty=uncertainty, wcs=wcs)
nd2 = nd[2:5]
assert_array_equal(data[2:5], nd2.data)
# All are sliced even if the shapes differ (no Info)
assert_array_equal(mask[2:5], nd2.mask)
assert_array_equal(uncertainty[2:5], nd2.uncertainty.array)
assert nd2.wcs.pixel_to_world(1) == nd.wcs.pixel_to_world(3)
def test_slicing_all_something_wrong():
data = np.arange(10)
mask = [False] * 10
uncertainty = {'rdnoise': 2.9, 'gain': 1.4}
naxis = 1
wcs = nd_testing._create_wcs_simple(naxis=naxis,
ctype=["deg"] * naxis,
crpix=[3] * naxis,
crval=[10] * naxis,
cdelt=[1] * naxis)
nd = NDDataSliceable(data, mask=mask, uncertainty=uncertainty, wcs=wcs)
nd2 = nd[2:5]
# Sliced properties:
assert_array_equal(data[2:5], nd2.data)
assert_array_equal(mask[2:5], nd2.mask)
# Not sliced attributes (they will raise a Info nevertheless)
uncertainty is nd2.uncertainty
assert nd2.wcs.pixel_to_world(1) == nd.wcs.pixel_to_world(3)
def test_boolean_slicing():
data = np.arange(10)
mask = data.copy()
uncertainty = StdDevUncertainty(data.copy())
naxis = 1
wcs = nd_testing._create_wcs_simple(naxis=naxis,
ctype=["deg"] * naxis,
crpix=[3] * naxis,
crval=[10] * naxis,
cdelt=[1] * naxis)
nd = NDDataSliceable(data, mask=mask, uncertainty=uncertainty, wcs=wcs)
with pytest.raises(ValueError):
nd2 = nd[(nd.data >= 3) & (nd.data < 8)]
nd.wcs = None
nd2 = nd[(nd.data >= 3) & (nd.data < 8)]
assert_array_equal(data[3:8], nd2.data)
assert_array_equal(mask[3:8], nd2.mask)
def test_slicing_all_npndarray_nd():
# See what happens for multidimensional properties
data = np.arange(1000).reshape(10, 10, 10)
mask = data > 3
uncertainty = np.linspace(10, 20, 1000).reshape(10, 10, 10)
naxis = 3
wcs = nd_testing._create_wcs_simple(naxis=naxis,
ctype=["deg"] * naxis,
crpix=[3] * naxis,
crval=[10] * naxis,
cdelt=[1] * naxis)
nd = NDDataSliceable(data, mask=mask, uncertainty=uncertainty, wcs=wcs)
# Slice only 1D
nd2 = nd[2:5]
assert_array_equal(data[2:5], nd2.data)
assert_array_equal(mask[2:5], nd2.mask)
assert_array_equal(uncertainty[2:5], nd2.uncertainty.array)
# Slice 3D
nd2 = nd[2:5, :, 4:7]
assert_array_equal(data[2:5, :, 4:7], nd2.data)
assert_array_equal(mask[2:5, :, 4:7], nd2.mask)
assert_array_equal(uncertainty[2:5, :, 4:7], nd2.uncertainty.array)
assert nd2.wcs.pixel_to_world(1, 5, 1) == nd.wcs.pixel_to_world(5, 5, 3)