def test_pixel_to_pixel_correlated():
wcs_in = WCS(naxis=2)
wcs_in.wcs.ctype = 'DEC--TAN', 'RA---TAN'
wcs_in.wcs.set()
wcs_out = WCS(naxis=2)
wcs_out.wcs.ctype = 'GLON-CAR', 'GLAT-CAR'
wcs_out.wcs.set()
# First try with scalars
x, y = pixel_to_pixel(wcs_in, wcs_out, 1, 2)
assert x.shape == ()
assert y.shape == ()
# Now try with broadcasted arrays
x = np.linspace(10, 20, 10)
y = np.linspace(10, 20, 20)
Y1, X1 = np.meshgrid(y, x, indexing='ij', copy=False)
Y2, X2 = pixel_to_pixel(wcs_in, wcs_out, X1, Y1)
# The final arrays should have the correct shape
assert X2.shape == (20, 10)
assert Y2.shape == (20, 10)
# and there are no efficiency gains here since the celestial axes are correlated
assert unbroadcast(X2).shape == (20, 10)
def test_pixel_to_pixel_1d():
# Simple test to make sure that when WCS only returns one world coordinate
# this still works correctly (since this requires special treatment behind
# the scenes).
wcs_in = WCS(naxis=1)
wcs_in.wcs.ctype = 'COORD1',
wcs_in.wcs.cunit = 'nm',
wcs_in.wcs.set()
wcs_out = WCS(naxis=1)
wcs_out.wcs.ctype = 'COORD2',
wcs_out.wcs.cunit = 'cm',
wcs_out.wcs.set()
# First try with a scalar
x = pixel_to_pixel(wcs_in, wcs_out, 1)
assert x.shape == ()
# Next with a regular array
x = np.linspace(10, 20, 10)
x = pixel_to_pixel(wcs_in, wcs_out, x)
assert x.shape == (10, )
# And now try with a broadcasted array
x = np.broadcast_to(np.linspace(10, 20, 10), (4, 10))
x = pixel_to_pixel(wcs_in, wcs_out, x)
assert x.shape == (4, 10)
# The broadcasting of the input should be retained
assert unbroadcast(x).shape == (10, )
def test_pixel_to_pixel():
wcs_in = WCS(naxis=3)
wcs_in.wcs.ctype = 'DEC--TAN', 'FREQ', 'RA---TAN'
wcs_in.wcs.set()
wcs_out = WCS(naxis=3)
wcs_out.wcs.ctype = 'GLON-CAR', 'GLAT-CAR', 'FREQ'
wcs_out.wcs.set()
# First try with scalars
with pytest.warns(AstropyUserWarning, match='No observer defined on WCS'):
x, y, z = pixel_to_pixel(wcs_in, wcs_out, 1, 2, 3)
assert x.shape == ()
assert y.shape == ()
assert z.shape == ()
# Now try with broadcasted arrays
x = np.linspace(10, 20, 10)
y = np.linspace(10, 20, 20)
z = np.linspace(10, 20, 30)
Z1, Y1, X1 = np.meshgrid(z, y, x, indexing='ij', copy=False)
with pytest.warns(AstropyUserWarning, match='No observer defined on WCS'):
X2, Y2, Z2 = pixel_to_pixel(wcs_in, wcs_out, X1, Y1, Z1)
# The final arrays should have the correct shape
assert X2.shape == (30, 20, 10)
assert Y2.shape == (30, 20, 10)
assert Z2.shape == (30, 20, 10)
# But behind the scenes should also be broadcasted
assert unbroadcast(X2).shape == (30, 1, 10)
assert unbroadcast(Y2).shape == (30, 1, 10)
assert unbroadcast(Z2).shape == (20, 1)
# We can put the values back through the function to ensure round-tripping
with pytest.warns(AstropyUserWarning, match='No observer defined on WCS'):
X3, Y3, Z3 = pixel_to_pixel(wcs_out, wcs_in, X2, Y2, Z2)
# The final arrays should have the correct shape
assert X2.shape == (30, 20, 10)
assert Y2.shape == (30, 20, 10)
assert Z2.shape == (30, 20, 10)
# But behind the scenes should also be broadcasted
assert unbroadcast(X3).shape == (30, 1, 10)
assert unbroadcast(Y3).shape == (20, 1)
assert unbroadcast(Z3).shape == (30, 1, 10)
# And these arrays should match the input
assert_allclose(X1, X3)
assert_allclose(Y1, Y3)
assert_allclose(Z1, Z3)
def _pixel_to_pixel(pixels, wcs):
"""
Convenience function that wraps astropy.wcs.utils.pixel_to_pixel and maps
SLW cube pixels to nearest SSW cube pixels and vice versa.
Parameters
----------
pixels : dictionary
Dictionary containing the SLW and SSW pixels to be mapped. Format
should be {'SLW': [[cols], [rows]], 'SSW': [[cols], [rows]]} for
multiple pixels, or {'SLW': [col, row], 'SSW': [col, row]} for
a single pixel. Both SPIRE bands are not required.
wcs : dictionary of WCS
Dictionary of WCS for SLW and SSW bands. see `_make_wcs`.
Returns
-------
output : dictionary
Dictionary of mapped pixels. Format matches `pixels` with reciprocal
SPIRE band keys.
"""
swap = {'SLW': 'SSW', 'SSW': 'SLW'}
output = dict()
for ary, pix in pixels.items():
pix = np.array(pix)
output[swap[ary]] = np.round(
pixel_to_pixel(wcs[ary], wcs[swap[ary]], *pix),
0).astype(int).tolist()
return output
def identify_invariant_axes(source_wcs,
target_wcs,
input_shape,
atol=1e-6,
rtol=1e-6):
"""
Performs a pixel to pixel transformation to identify if there are any invariant axes
between the given source and target WCS objects.
Parameters
----------
source_wcs: `astropy.wcs.wcsapi.BaseHighLevelWCS` or `astropy.wcs.wcsapi.BaseLowLevelWCS`
target_wcs: `astropy.wcs.wcsapi.BaseHighLevelWCS` or `astropy.wcs.wcsapi.BaseLowLevelWCS`
input_shape: `tuple`
The array shape of the data.
atol: `float`
The absolute tolerance parameter for comparison.
rtol: `float`
The relative tolerance parameter for comparison.
Returns
-------
result: `list`
A list of booleans denoting whether the axis is invariant or not.
Follows the WCS ordering.
"""
input_pixel_coords = np.meshgrid(*[np.arange(n) for n in input_shape])
output_pixel_coords = pixel_to_pixel(source_wcs, target_wcs,
*input_pixel_coords)
return [
np.allclose(input_coord, output_coord, atol=atol,
rtol=rtol) for input_coord, output_coord in zip(
input_pixel_coords, output_pixel_coords)
]
def backwards(*pixel_input):
return pixel_to_pixel(wcs2, wcs1, *pixel_input)
def forwards(*pixel_input):
return pixel_to_pixel(wcs1, wcs2, *pixel_input)
def add_single_spectra_to_map(
spectra_map,
*,
header,
data,
spec_info=None,
wcs_info=None,
units_info=None,
purpose_prefix=None,
all_standard_units,
all_keywords,
valid_wcs,
index=None,
):
spec_wcs_info = {}
spec_units_info = {}
if wcs_info is not None:
spec_wcs_info.update(wcs_info)
if units_info is not None:
spec_units_info.update(units_info)
if spec_info is not None:
spec_wcs_info.update(spec_info.get("wcs", {}))
spec_units_info.update(spec_info.get("units", {}))
purpose = spec_info.get("purpose")
else:
purpose = None
purpose = get_purpose(
header,
purpose=purpose,
purpose_prefix=purpose_prefix,
all_keywords=all_keywords,
index=index,
)
if purpose == Purpose.SKIP:
return None
if valid_wcs or not spec_wcs_info:
wcs = WCS(header)
else:
wcs = compute_wcs_from_keys_and_values(header, **spec_wcs_info)
if all_standard_units:
spec_units_info = {"flux_unit_keyword": "BUNIT"}
flux_unit = get_flux_units_from_keys_and_values(header, **spec_units_info)
flux = data * flux_unit
meta = {"header": header, "purpose": PURPOSE_SPECTRA_MAP[purpose]}
if purpose in CREATE_SPECTRA:
spectrum = Spectrum1D(wcs=wcs, flux=flux, meta=meta)
spectra_map[PURPOSE_SPECTRA_MAP[purpose]].append(spectrum)
elif purpose in ERROR_PURPOSES:
try:
spectrum = spectra_map[PURPOSE_SPECTRA_MAP[purpose]][-1]
except IndexError:
raise ValueError(f"No spectra to associate with {purpose}")
aligned_flux = pixel_to_pixel(wcs, spectrum.wcs, flux)
spectrum.uncertainty = UNCERTAINTY_MAP[purpose](aligned_flux)
spectrum.meta["uncertainty_header"] = header
# We never actually want to return something, this just flags it to pylint
# that we know we're breaking out of the function when skip is selected
return None
