def fitswcs_nonlinear(header):
"""
Create a WCS linear transform from a FITS header.
Parameters
----------
header : astropy.io.fits.Header or dict
FITS Header or dict with basic FITS WCS keywords.
"""
if isinstance(header, fits.Header):
wcs_info = read_wcs_from_header(header)
elif isinstance(header, dict):
wcs_info = header
else:
raise TypeError("Expected a FITS Header or a dict.")
transforms = []
projcode = get_projcode(wcs_info)
if projcode is not None:
projection = create_projection_transform(projcode).rename(projcode)
transforms.append(projection)
# Create the sky rotation transform
sky_axes, _, _ = get_axes(wcs_info)
if sky_axes:
phip, lonp = [wcs_info['CRVAL'][i] for i in sky_axes]
# TODO: write "def compute_lonpole(projcode, l)"
# Set a defaul tvalue for now
thetap = 180
n2c = astmodels.RotateNative2Celestial(phip, lonp, thetap, name="crval")
transforms.append(n2c)
if transforms:
return functools.reduce(core._model_oper('|'), transforms)
else:
return None
def fitswcs_nonlinear(header):
"""
Create a WCS linear transform from a FITS header.
Parameters
----------
header : astropy.io.fits.Header or dict
FITS Header or dict with basic FITS WCS keywords.
"""
if isinstance(header, fits.Header):
wcs_info = read_wcs_from_header(header)
elif isinstance(header, dict):
wcs_info = header
else:
raise TypeError("Expected a FITS Header or a dict.")
transforms = []
projcode = get_projcode(wcs_info)
if projcode is not None:
projection = create_projection_transform(projcode).rename(projcode)
transforms.append(projection)
# Create the sky rotation transform
sky_axes, _, _ = get_axes(wcs_info)
if sky_axes:
phip, lonp = [wcs_info['CRVAL'][i] for i in sky_axes]
# TODO: write "def compute_lonpole(projcode, l)"
# Set a defaul tvalue for now
thetap = 180
n2c = astmodels.RotateNative2Celestial(phip, lonp, thetap, name="crval")
transforms.append(n2c)
if transforms:
return functools.reduce(core._model_oper('|'), transforms)
return None
def make_fitswcs_transform(header):
"""
Create a basic FITS WCS transform.
It does not include distortions.
Parameters
----------
header : astropy.io.fits.Header or dict
FITS Header (or dict) with basic WCS information
"""
if isinstance(header, fits.Header):
wcs_info = read_wcs_from_header(header)
elif isinstance(header, dict):
wcs_info = header
else:
raise TypeError("Expected a FITS Header or a dict.")
wcs_linear = fitswcs_linear(wcs_info)
projcode = get_projcode(wcs_info['CTYPE'])
projection = create_projection_transform(projcode)
projection = projection.rename(projcode)
# Create the sky rotation transform
phip, lonp = wcs_info['CRVAL']
# TODO: write "def compute_lonpole(projcode, l)"
# Set a defaul tvalue for now
thetap = 180
n2c = astmodels.RotateNative2Celestial(phip, lonp, thetap, name="crval")
return functools.reduce(core._model_oper('|'), [wcs_linear, projection, n2c])
def make_fitswcs_transform(header):
"""
Create a basic FITS WCS transform.
It does not include distortions.
Parameters
----------
header : `astropy.io.fits.Header` or dict
FITS Header (or dict) with basic WCS information
"""
if isinstance(header, fits.Header):
wcs_info = read_wcs_from_header(header)
elif isinstance(header, dict):
wcs_info = header
else:
raise TypeError("Expected a FITS Header or a dict.")
# If a pixel axis maps directly to an output axis, we want to have that
# model completely self-contained, so don't put all the CRPIXj shifts
# in a single CompoundModel at the beginning
transforms = []
# The tricky stuff!
sky_model = fitswcs_image(wcs_info)
linear_models = fitswcs_linear(wcs_info)
all_models = linear_models
if sky_model:
all_models.append(sky_model)
# Now arrange the models so the inputs and outputs are in the right places
all_models.sort(key=lambda m: m.meta['output_axes'][0])
input_axes = [ax for m in all_models for ax in m.meta['input_axes']]
output_axes = [ax for m in all_models for ax in m.meta['output_axes']]
if input_axes != list(range(len(input_axes))):
input_mapping = models.Mapping(input_axes)
transforms.append(input_mapping)
transforms.append(functools.reduce(core._model_oper('&'), all_models))
if output_axes != list(range(len(output_axes))):
output_mapping = models.Mapping(output_axes)
transforms.append(output_mapping)
return functools.reduce(core._model_oper('|'), transforms)
def make_fitswcs_transform(header):
"""
Create a basic FITS WCS transform.
It does not include distortions.
Parameters
----------
header : astropy.io.fits.Header or dict
FITS Header (or dict) with basic WCS information
"""
if isinstance(header, fits.Header):
wcs_info = read_wcs_from_header(header)
elif isinstance(header, dict):
wcs_info = header
else:
raise TypeError("Expected a FITS Header or a dict.")
wcs_linear = fitswcs_linear(wcs_info)
wcs_nonlinear = fitswcs_nonlinear(wcs_info)
return functools.reduce(core._model_oper('|'), [wcs_linear, wcs_nonlinear])
def make_fitswcs_transform(header):
"""
Create a basic FITS WCS transform.
It does not include distortions.
Parameters
----------
header : astropy.io.fits.Header or dict
FITS Header (or dict) with basic WCS information
"""
if isinstance(header, fits.Header):
wcs_info = read_wcs_from_header(header)
elif isinstance(header, dict):
wcs_info = header
else:
raise TypeError("Expected a FITS Header or a dict.")
transforms = []
wcs_linear = fitswcs_linear(wcs_info)
transforms.append(wcs_linear)
wcs_nonlinear = fitswcs_nonlinear(wcs_info)
if wcs_nonlinear is not None:
transforms.append(wcs_nonlinear)
return functools.reduce(core._model_oper('|'), transforms)
"""
This is a temporary home for a bunch of code for removing stuff from CompoundModels
I would imagine this will end up in gwcs or maybe even astropy at somepoint.
"""
from collections import defaultdict
from astropy.modeling import separable
from astropy.modeling.core import BINARY_OPERATORS, _model_oper
OPERATORS = dict((oper, _model_oper(oper)) for oper in BINARY_OPERATORS)
def tree_is_separable(tree):
"""
Given a tree, convert it to a `CompoundModel` and then return the
separability of the inputs.
"""
return separable.is_separable(tree.evaluate(OPERATORS))
def make_tree_input_map(tree):
"""
Construct a mapping of tree to input names.
This function iterates over all the inputs of a model and determines which
side of the tree (left or right) the input corresponds to. It returns a
mapping of tree to set of all inputs for that side of the tree (which is
also a tree).
def fitswcs_linear(header):
"""
Create a WCS linear transform from a FITS header.
Parameters
----------
header : astropy.io.fits.Header or dict
FITS Header or dict with basic FITS WCS keywords.
"""
if isinstance(header, fits.Header):
wcs_info = read_wcs_from_header(header)
elif isinstance(header, dict):
wcs_info = header
else:
raise TypeError("Expected a FITS Header or a dict.")
wcsaxes = wcs_info['WCSAXES']
pc = wcs_info['PC']
# get the part of the PC matrix corresponding to the imaging axes
sky_axes = None
if pc.shape != (2, 2):
sky_axes, _ = get_axes(wcs_info)
i, j = sky_axes
sky_pc = np.zeros((2,2))
sky_pc[0, 0] = pc[i, i]
sky_pc[0, 1] = pc[i, j]
sky_pc[1, 0] = pc[j, i]
sky_pc[1, 1] = pc[j, j]
pc = sky_pc.copy()
if sky_axes is not None:
crpix = []
cdelt = []
for i in sky_axes:
crpix.append(wcs_info['CRPIX'][i])
cdelt.append(wcs_info['CDELT'][i])
else:
cdelt = wcs_info['CDELT']
crpix = wcs_info['CRPIX']
if wcsaxes == 2:
rotation = astmodels.AffineTransformation2D(matrix=pc, name='pc_matrix')
#elif wcsaxes == 3 :
#rotation = AffineTransformation3D(matrix=matrix)
#else:
#raise DimensionsError("WCSLinearTransform supports only 2 or 3 dimensions, "
#"{0} given".format(wcsaxes))
translation_models = [astmodels.Shift(-shift, name='crpix' + str(i + 1)) \
for i, shift in enumerate(crpix)]
translation = functools.reduce(core._model_oper('&'), translation_models)
if not wcs_info['has_cd']:
# Do not compute scaling since CDELT* = 1 if CD is present.
scaling_models = [astmodels.Scale(scale, name='cdelt' + str(i + 1)) \
for i, scale in enumerate(cdelt)]
scaling = functools.reduce(core._model_oper('&'), scaling_models)
wcs_linear = translation | rotation | scaling
else:
wcs_linear = translation | rotation
return wcs_linear
