def abl_to_v2v3l(input_model, reference_files):
"""
Create the transform from (alpha,beta,lambda) to (V2,V3,lambda) frame.
Parameters
----------
input_model : `jwst.datamodels.ImagingModel`
Data model.
reference_files : dict
Dictionary {reftype: reference file name}.
forward transform:
RegionsSelector
label_mapper is LabelMapperDict()
{channel_wave_range (): channel_number}
selector is {channel_number: ab2v2 & ab2v3}
bacward_transform
RegionsSelector
label_mapper is LabelMapperDict()
{channel_wave_range (): channel_number}
selector is {channel_number: v22ab & v32ab}
"""
band = input_model.meta.instrument.band
channel = input_model.meta.instrument.channel
# used to read the wavelength range
channels = [c + band for c in channel]
with DistortionMRSModel(reference_files['distortion']) as dist:
v23 = dict(zip(dist.abv2v3_model.channel_band,
dist.abv2v3_model.model))
with WavelengthrangeModel(reference_files['wavelengthrange']) as f:
wr = dict(zip(f.waverange_selector, f.wavelengthrange))
dict_mapper = {}
sel = {}
# Since there are two channels in each reference file we need to loop over them
for c in channels:
ch = int(c[0])
dict_mapper[tuple(wr[c])] = models.Mapping((2,), name="mapping_lam") | \
models.Const1D(ch, name="channel #")
ident1 = models.Identity(1, name='identity_lam')
ident1._inputs = ('lam', )
chan_v23 = v23[c]
v23chan_backward = chan_v23.inverse
del chan_v23.inverse
# This is the spatial part of the transform; tack on additional conversion to degrees
# Remove this degrees conversion once pipeline can handle v2,v3 in arcsec
v23_spatial = chan_v23 | models.Scale(1 / 3600) & models.Scale(
1 / 3600)
v23_spatial.inverse = models.Scale(3600) & models.Scale(
3600) | v23chan_backward
# Tack on passing the third wavelength component
v23c = v23_spatial & ident1
sel[ch] = v23c
wave_range_mapper = selector.LabelMapperRange(
('alpha', 'beta', 'lam'),
dict_mapper,
inputs_mapping=models.Mapping([
2,
]))
wave_range_mapper.inverse = wave_range_mapper.copy()
abl2v2v3l = selector.RegionsSelector(('alpha', 'beta', 'lam'),
('v2', 'v3', 'lam'),
label_mapper=wave_range_mapper,
selector=sel)
return abl2v2v3l
def lrs(input_model, reference_files):
"""
The LRS-FIXEDSLIT and LRS-SLITLESS WCS pipeline.
It has two coordinate frames: "detecor" and "world".
Uses the "specwcs" and "distortion" reference files.
"""
# Setup the frames.
detector = cf.Frame2D(name='detector',
axes_order=(0, 1),
unit=(u.pix, u.pix))
spec = cf.SpectralFrame(name='wavelength',
axes_order=(2, ),
unit=(u.micron, ),
axes_names=('lambda', ))
sky = cf.CelestialFrame(reference_frame=coord.ICRS(), name='sky')
v2v3_spatial = cf.Frame2D(name='v2v3_spatial',
axes_order=(0, 1),
unit=(u.arcsec, u.arcsec))
v2v3 = cf.CompositeFrame(name="v2v3", frames=[v2v3_spatial, spec])
world = cf.CompositeFrame(name="world", frames=[sky, spec])
# Determine the distortion model.
subarray2full = subarray_transform(input_model)
with DistortionModel(reference_files['distortion']) as dist:
distortion = dist.model
if subarray2full is not None:
distortion = subarray2full | distortion
# Incorporate the small rotation
angle = np.arctan(0.00421924)
rotation = models.Rotation2D(angle)
distortion = distortion | rotation
# Load and process the reference data.
with fits.open(reference_files['specwcs']) as ref:
lrsdata = np.array([l for l in ref[1].data])
# Get the zero point from the reference data.
# The zero_point is X, Y (which should be COLUMN, ROW)
# TODO: Are imx, imy 0- or 1-indexed? We are treating them here as
# 0-indexed. Since they are FITS, they are probably 1-indexed.
if input_model.meta.exposure.type.lower() == 'mir_lrs-fixedslit':
zero_point = ref[1].header['imx'], ref[1].header['imy']
elif input_model.meta.exposure.type.lower() == 'mir_lrs-slitless':
zero_point = ref[1].header['imxsltl'], ref[1].header['imysltl']
#zero_point = [35, 442] # [35, 763] # account for subarray
# Create the bounding_box
x0 = lrsdata[:, 3]
y0 = lrsdata[:, 4]
x1 = lrsdata[:, 5]
bb = ((x0.min() - 0.5 + zero_point[0], x1.max() + 0.5 + zero_point[0]),
(y0.min() - 0.5 + zero_point[1], y0.max() + 0.5 + zero_point[1]))
# Compute the v2v3 to sky.
tel2sky = pointing.v23tosky(input_model)
# To compute the spatial detector to V2V3 transform:
# Take a row centered on zero_point_y and convert it to v2, v3.
# The forward transform uses constant ``y`` values for each ``x``.
# The inverse transform uses constant ``v3`` values for each ``v2``.
zero_point_v2v3 = distortion(*zero_point)
spatial_forward = models.Identity(1) & models.Const1D(
zero_point[1]) | distortion
spatial_forward.inverse = (
models.Identity(1) & models.Const1D(zero_point_v2v3[1])
| distortion.inverse)
# Create the spectral transforms.
lrs_wav_model = jwmodels.LRSWavelength(lrsdata, zero_point)
try:
velosys = input_model.meta.wcsinfo.velosys
except AttributeError:
pass
else:
if velosys is not None:
velocity_corr = velocity_correction(
input_model.meta.wcsinfo.velosys)
lrs_wav_model = lrs_wav_model | velocity_corr
log.info("Applied Barycentric velocity correction : {}".format(
velocity_corr[1].amplitude.value))
det_to_v2v3 = models.Mapping(
(0, 1, 0, 1)) | spatial_forward & lrs_wav_model
det_to_v2v3.bounding_box = bb[::-1]
v23_to_world = tel2sky & models.Identity(1)
# Now the actual pipeline.
pipeline = [(detector, det_to_v2v3), (v2v3, v23_to_world), (world, None)]
return pipeline
def align_catalogs(xin,
yin,
xref,
yref,
model_guess=None,
translation=None,
translation_range=None,
rotation=None,
rotation_range=None,
magnification=None,
magnification_range=None,
tolerance=0.1,
center_of_field=None):
"""
Generic interface for a 2D catalog match. Either an initial model guess
is provided, or a model will be created using a combination of
translation, rotation, and magnification, as requested. Only those
transformations for which a *range* is specified will be used. In order
to keep the translation close to zero, the rotation and magnification
are performed around the centre of the field, which can either be provided
-- as (x,y) in 1-based pixels -- or will be determined from the mid-range
of the x and y input coordinates.
Parameters
----------
xin, yin: float arrays
input coordinates
xref, yref: float arrays
reference coordinates to map and match to
model_guess: Model
initial model guess (overrides the next parameters)
translation: 2-tuple of floats
initial translation guess
translation_range: None, value, 2-tuple or 2x2-tuple
None => fixed
value => search range from initial guess (same for x and y)
2-tuple => search limits (same for x and y)
2x2-tuple => search limits for x and y
rotation: float
initial rotation guess (degrees)
rotation_range: None, float, or 2-tuple
extent of search space for rotation
magnification: float
initial magnification factor
magnification_range: None, float, or 2-tuple
extent of search space for magnification
tolerance: float
accuracy required for final result
center_of_field: 2-tuple
rotation and magnification have no effect at this location
(if None, uses middle of xin,yin ranges)
Returns
-------
Model: a model that maps (xin,yin) to (xref,yref)
"""
def _get_value_and_range(value, range):
"""Converts inputs to a central value and a range tuple"""
try:
r1, r2 = range
except TypeError:
r1, r2 = range, None
except ValueError:
r1, r2 = None, None
if value is not None:
if r1 is not None and r2 is not None:
if r1 <= value <= r2:
return value, (r1, r2)
else:
extent = 0.5 * abs(r2 - r1)
return value, (value - extent, value + extent)
elif r1 is not None:
return value, (value - r1, value + r1)
else:
return value, None
elif r1 is not None:
if r2 is None:
return 0.0, (-r1, r1)
else:
return 0.5 * (r1 + r2), (r1, r2)
else:
return None, None
log = logutils.get_logger(__name__)
if model_guess is None:
# Some useful numbers for later
x1, x2 = np.min(xin), np.max(xin)
y1, y2 = np.min(yin), np.max(yin)
pixel_range = 0.5 * max(x2 - x1, y2 - y1)
# Set up translation part of the model
if hasattr(translation, '__len__'):
xoff, yoff = translation
else:
xoff, yoff = translation, translation
trange = np.array(translation_range)
if len(trange.shape) == 2:
xvalue, xrange = _get_value_and_range(xoff, trange[0])
yvalue, yrange = _get_value_and_range(yoff, trange[1])
else:
xvalue, xrange = _get_value_and_range(xoff, translation_range)
yvalue, yrange = _get_value_and_range(yoff, translation_range)
if xvalue is None or yvalue is None:
trans_model = None
else:
trans_model = Shift2D(xvalue, yvalue)
if xrange is None:
trans_model.x_offset.fixed = True
else:
trans_model.x_offset.bounds = xrange
if yrange is None:
trans_model.y_offset.fixed = True
else:
trans_model.y_offset.bounds = yrange
# Set up rotation part of the model
rvalue, rrange = _get_value_and_range(rotation, rotation_range)
if rvalue is None:
rot_model = None
else:
# Getting the rotation wrong by da (degrees) will cause a shift of
# da/57.3*pixel_range at the edge of the data, so we want
# da=tolerance*57.3/pixel_range
rot_scaling = pixel_range / 57.3
rot_model = Rotate2D(rvalue * rot_scaling, angle_scale=rot_scaling)
if rrange is None:
rot_model.angle.fixed = True
else:
rot_model.angle.bounds = tuple(x * rot_scaling for x in rrange)
# Set up magnification part of the model
mvalue, mrange = _get_value_and_range(magnification,
magnification_range)
if mvalue is None:
mag_model = None
else:
# Getting the magnification wrong by dm will cause a shift of
# dm*pixel_range at the edge of the data, so we want
# dm=tolerance/pixel_range
mag_scaling = pixel_range
mag_model = Scale2D(mvalue * mag_scaling, factor_scale=mag_scaling)
if mrange is None:
mag_model.factor.fixed = True
else:
mag_model.factor.bounds = tuple(x * mag_scaling
for x in mrange)
# Make the compound model
if rot_model is None and mag_model is None:
if trans_model is None:
return models.Identity(2) # Nothing to do
else:
init_model = trans_model # Don't need center of field
else:
if center_of_field is None:
center_of_field = (0.5 * (x1 + x2), 0.5 * (y1 + y2))
log.debug('No center of field given, using x={:.2f} '
'y={:.2f}'.format(*center_of_field))
restore = Shift2D(*center_of_field).rename('Centering')
restore.x_offset.fixed = True
restore.y_offset.fixed = True
init_model = restore.inverse
if trans_model is not None:
init_model |= trans_model
if rot_model is not None:
init_model |= rot_model
if mag_model is not None:
init_model |= mag_model
init_model |= restore
elif model_guess.fittable:
init_model = model_guess
else:
log.warning('The transformation is not fittable!')
return models.Identity(2)
final_model = fit_brute_then_simplex(init_model, (xin, yin), (xref, yref),
sigma=10.0,
tolerance=tolerance)
return final_model
def lrs(input_model, reference_files):
"""
Create the WCS pipeline for a MIRI fixed slit observation.
Parameters
----------
input_model : `jwst.datamodels.ImagingModel`
Data model.
reference_files : dict
Dictionary {reftype: reference file name}.
reference_files = {
"specwcs": 'MIRI_FM_MIRIMAGE_P750L_DISTORTION_04.02.00.fits'
}
"""
# Setup the frames.
detector = cf.Frame2D(name='detector',
axes_order=(0, 1),
unit=(u.pix, u.pix))
spec = cf.SpectralFrame(name='wavelength',
axes_order=(2, ),
unit=(u.micron, ),
axes_names=('lambda', ))
sky = cf.CelestialFrame(reference_frame=coord.ICRS(), name='sky')
world = cf.CompositeFrame(name="world", frames=[sky, spec])
# Determine the distortion model.
subarray2full = subarray_transform(input_model)
with DistortionModel(reference_files['distortion']) as dist:
distortion = dist.model
# Distortion is in arcsec. Convert to degrees
full_distortion = subarray2full | distortion | models.Scale(
1 / 3600.) & models.Scale(1 / 3600.)
# Load and process the reference data.
with fits.open(reference_files['specwcs']) as ref:
lrsdata = np.array([l for l in ref[1].data])
# Get the zero point from the reference data.
# The zero_point is X, Y (which should be COLUMN, ROW)
# TODO: Are imx, imy 0- or 1-indexed? We are treating them here as
# 0-indexed. Since they are FITS, they are probably 1-indexed.
if input_model.meta.exposure.type.lower() == 'mir_lrs-fixedslit':
zero_point = ref[1].header['imx'], ref[1].header['imy']
elif input_model.meta.exposure.type.lower() == 'mir_lrs-slitless':
#zero_point = ref[1].header['imxsltl'], ref[1].header['imysltl']
zero_point = [35, 442] # [35, 763] # account for subarray
# Create the bounding_box
x0 = lrsdata[:, 3]
y0 = lrsdata[:, 4]
x1 = lrsdata[:, 5]
bb = ((x0.min() - 0.5 + zero_point[0], x1.max() + 0.5 + zero_point[0]),
(y0.min() - 0.5 + zero_point[1], y0.max() + 0.5 + zero_point[1]))
# Find the ROW of the zero point which should be the [1] of zero_point
row_zero_point = zero_point[1]
# Compute the v2v3 to sky.
tel2sky = pointing.v23tosky(input_model)
# Compute the V2/V3 for each pixel in this row
# x.shape will be something like (1, 388)
y, x = np.mgrid[row_zero_point:row_zero_point + 1,
0:input_model.data.shape[1]]
spatial_transform = full_distortion | tel2sky
radec = np.array(spatial_transform(x, y))[:, 0, :]
ra_full = np.matlib.repmat(radec[0],
_toindex(bb[1][1]) + 1 - _toindex(bb[1][0]), 1)
dec_full = np.matlib.repmat(radec[1],
_toindex(bb[1][1]) + 1 - _toindex(bb[1][0]), 1)
ra_t2d = models.Tabular2D(lookup_table=ra_full,
name='xtable',
bounds_error=False,
fill_value=np.nan)
dec_t2d = models.Tabular2D(lookup_table=dec_full,
name='ytable',
bounds_error=False,
fill_value=np.nan)
# Create the model transforms.
lrs_wav_model = jwmodels.LRSWavelength(lrsdata, zero_point)
# Incorporate the small rotation
angle = np.arctan(0.00421924)
rot = models.Rotation2D(angle)
radec_t2d = ra_t2d & dec_t2d | rot
# Account for the subarray when computing spatial coordinates.
xshift = -bb[0][0]
yshift = -bb[1][0]
det2world = models.Mapping((1, 0, 1, 0, 0, 1)) | models.Shift(yshift, name='yshift1') & \
models.Shift(xshift, name='xshift1') & \
models.Shift(yshift, name='yshift2') & models.Shift(xshift, name='xshift2') & \
models.Identity(2) | radec_t2d & lrs_wav_model
det2world.bounding_box = bb[::-1]
# Now the actual pipeline.
pipeline = [(detector, det2world), (world, None)]
return pipeline
def test_ifu_bbox():
bbox = {0: ((122.0908542999878, 1586.2584665188083),
(773.5411133037417, 825.1150258966278)),
1: ((140.3793485788431, 1606.8904629423566),
(1190.353197027459, 1243.0853605832503)),
2: ((120.0139534379125, 1583.9271768905855),
(724.3249534782219, 775.8104288584977)),
3: ((142.50252648927454, 1609.3106221382388),
(1239.4122720740888, 1292.288713688988)),
4: ((117.88884113088403, 1581.5517394150106),
(674.9787657901347, 726.3752061973377)),
5: ((144.57465414462143, 1611.688447569682),
(1288.4808318659427, 1341.5035313084197)),
6: ((115.8602297714846, 1579.27471654949),
(625.7982466386104, 677.1147840452901)),
7: ((146.7944728147906, 1614.2161842198498),
(1337.531525654835, 1390.7050687363856)),
8: ((113.86384530944383, 1577.0293086386203),
(576.5344359685643, 627.777022204828)),
9: ((149.0259581360621, 1616.7687282225652),
(1386.5118806905086, 1439.843598490326)),
10: ((111.91564190274217, 1574.8351095461135),
(527.229828693075, 578.402894851317)),
11: ((151.3053466801954, 1619.3720722471498),
(1435.423685040875, 1488.917203728964)),
12: ((109.8957204607345, 1572.570246400894),
(477.9699083444277, 529.0782087498488)),
13: ((153.5023503173659, 1621.9005029476564),
(1484.38405923062, 1538.0443479389924)),
14: ((107.98320121613297, 1570.411787034636),
(428.6704834494425, 479.7217241891257)),
15: ((155.77991404913857, 1624.5184927460925),
(1533.169633314481, 1586.9984359105376)),
16: ((106.10212081215678, 1568.286103827344),
(379.3860245240618, 430.3780648366697)),
17: ((158.23149941845386, 1627.305849064835),
(1582.0496119714928, 1636.0513450787032)),
18: ((104.09366374413436, 1566.030231370944),
(330.0822744105267, 381.01974582564395)),
19: ((160.4511021152353, 1629.888830991371),
(1630.7797743277185, 1684.9592727079018)),
20: ((102.25220592881234, 1563.9475099032868),
(280.7233309522168, 331.6093009077988)),
21: ((162.72784286205734, 1632.5257403739463),
(1679.6815760587567, 1734.03692957156)),
22: ((100.40115742738622, 1561.8476640376036),
(231.35443588323855, 282.19575854747006)),
23: ((165.05939163941662, 1635.2270773628682),
(1728.511467615387, 1783.0485841263735)),
24: ((98.45723949658425, 1559.6499479349648),
(182.0417295679079, 232.83530870639865)),
25: ((167.44628840053574, 1637.9923229870349),
(1777.2512197664128, 1831.971115503598)),
26: ((96.56508092457855, 1557.5079027818058),
(132.5285162704088, 183.27350269292484)),
27: ((169.8529496136358, 1640.778485168005),
(1826.028691168028, 1880.9336718824313)),
28: ((94.71390837793813, 1555.4048050512263),
(82.94691422559131, 133.63901517357235)),
29: ((172.3681094850081, 1643.685604697228),
(1874.8184744639657, 1929.9072657798927))}
hdul = create_nirspec_ifu_file("F290LP", "G140M")
im = datamodels.IFUImageModel(hdul)
im.meta.filename = "test_ifu.fits"
refs = create_reference_files(im)
pipe = nirspec.create_pipeline(im, refs, slit_y_range=[-.5, .5])
w = wcs.WCS(pipe)
im.meta.wcs = w
_, wrange = nirspec.spectral_order_wrange_from_model(im)
pipe = im.meta.wcs.pipeline
g2s = pipe[2].transform
transforms = [pipe[0].transform]
transforms.append(pipe[1].transform[1:])
transforms.append(astmodels.Identity(1))
transforms.append(astmodels.Identity(1))
transforms.extend([step.transform for step in pipe[4:-1]])
for sl in range(30):
transforms[2] = g2s.get_model(sl)
m = functools.reduce(lambda x, y: x | y, [tr.inverse for tr in transforms[:3][::-1]])
bbox_sl = nirspec.compute_bounding_box(m, wrange)
assert_allclose(bbox[sl], bbox_sl)
def prepare_psf_model(psfmodel, xname=None, yname=None, fluxname=None,
renormalize_psf=True):
"""
Convert a 2D PSF model to one suitable for use with
`BasicPSFPhotometry` or its subclasses.
The resulting model may be a composite model, but should have only
the x, y, and flux related parameters un-fixed.
Parameters
----------
psfmodel : a 2D model
The model to assume as representative of the PSF.
xname : str or None
The name of the ``psfmodel`` parameter that corresponds to the
x-axis center of the PSF. If None, the model will be assumed to
be centered at x=0, and a new parameter will be added for the
offset.
yname : str or None
The name of the ``psfmodel`` parameter that corresponds to the
y-axis center of the PSF. If None, the model will be assumed to
be centered at x=0, and a new parameter will be added for the
offset.
fluxname : str or None
The name of the ``psfmodel`` parameter that corresponds to the
total flux of the star. If None, a scaling factor will be added
to the model.
renormalize_psf : bool
If True, the model will be integrated from -inf to inf and
re-scaled so that the total integrates to 1. Note that this
renormalization only occurs *once*, so if the total flux of
``psfmodel`` depends on position, this will *not* be correct.
Returns
-------
outmod : a model
A new model ready to be passed into `BasicPSFPhotometry` or its
subclasses.
"""
if xname is None:
xinmod = models.Shift(0, name='x_offset')
xname = 'offset_0'
else:
xinmod = models.Identity(1)
xname = xname + '_2'
xinmod.fittable = True
if yname is None:
yinmod = models.Shift(0, name='y_offset')
yname = 'offset_1'
else:
yinmod = models.Identity(1)
yname = yname + '_2'
yinmod.fittable = True
outmod = (xinmod & yinmod) | psfmodel
if fluxname is None:
outmod = outmod * models.Const2D(1, name='flux_scaling')
fluxname = 'amplitude_3'
else:
fluxname = fluxname + '_2'
if renormalize_psf:
# we do the import here because other machinery works w/o scipy
from scipy import integrate
integrand = integrate.dblquad(psfmodel, -np.inf, np.inf,
lambda x: -np.inf, lambda x: np.inf)[0]
normmod = models.Const2D(1./integrand, name='renormalize_scaling')
outmod = outmod * normmod
# final setup of the output model - fix all the non-offset/scale
# parameters
for pnm in outmod.param_names:
outmod.fixed[pnm] = pnm not in (xname, yname, fluxname)
# and set the names so that BasicPSFPhotometry knows what to do
outmod.xname = xname
outmod.yname = yname
outmod.fluxname = fluxname
# now some convenience aliases if reasonable
outmod.psfmodel = outmod[2]
if 'x_0' not in outmod.param_names and 'y_0' not in outmod.param_names:
outmod.x_0 = getattr(outmod, xname)
outmod.y_0 = getattr(outmod, yname)
if 'flux' not in outmod.param_names:
outmod.flux = getattr(outmod, fluxname)
return outmod
def fitswcs_image(header):
"""
Make a complete transform from CRPIX-shifted pixels to
sky coordinates from FITS WCS keywords. A Mapping is inserted
at the beginning, which may be removed later
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.")
crpix = wcs_info['CRPIX']
cd = wcs_info['CD']
# get the part of the PC matrix corresponding to the imaging axes
sky_axes, spec_axes, unknown = get_axes(wcs_info)
if not sky_axes:
if len(unknown) == 2:
sky_axes = unknown
else: # No sky here
return
pixel_axes = _get_contributing_axes(wcs_info, sky_axes)
if len(pixel_axes) > 2:
raise ValueError(
"More than 2 pixel axes contribute to the sky coordinates")
translation_models = [
models.Shift(-(crpix[i] - 1), name='crpix' + str(i + 1))
for i in pixel_axes
]
translation = functools.reduce(lambda x, y: x & y, translation_models)
transforms = [translation]
# If only one axis is contributing to the sky (e.g., slit spectrum)
# then it must be that there's an extra axis in the CD matrix, so we
# create a "ghost" orthogonal axis here so an inverse can be defined
# Modify the CD matrix in case we have to use a backup Matrix Model later
if len(pixel_axes) == 1:
cd[sky_axes[0], -1] = -cd[sky_axes[1], pixel_axes[0]]
cd[sky_axes[1], -1] = cd[sky_axes[0], pixel_axes[0]]
sky_cd = cd[np.ix_(sky_axes, pixel_axes + [-1])]
affine = models.AffineTransformation2D(matrix=sky_cd, name='cd_matrix')
# TODO: replace when PR#10362 is in astropy
#rotation = models.fix_inputs(affine, {'y': 0})
rotation = models.Mapping(
(0, 0)) | models.Identity(1) & models.Const1D(0) | affine
rotation.inverse = affine.inverse | models.Mapping((0, ), n_inputs=2)
else:
sky_cd = cd[np.ix_(sky_axes, pixel_axes)]
rotation = models.AffineTransformation2D(matrix=sky_cd,
name='cd_matrix')
transforms.append(rotation)
projection = gwutils.fitswcs_nonlinear(wcs_info)
if projection:
transforms.append(projection)
sky_model = functools.reduce(lambda x, y: x | y, transforms)
sky_model.name = 'SKY'
sky_model.meta.update({'input_axes': pixel_axes, 'output_axes': sky_axes})
return sky_model
def gwcs_to_fits(ndd, hdr=None):
"""
Convert a gWCS object to a collection of FITS WCS keyword/value pairs,
if possible. If the FITS WCS is only approximate, this should be indicated
with a dict entry {'FITS-WCS': 'APPROXIMATE'}. If there is no suitable
FITS representation, then a ValueError or NotImplementedError can be
raised.
Parameters
----------
ndd : `astropy.nddata.NDData`
The NDData whose wcs attribute we want converted
hdr : `astropy.io.fits.Header`
A Header object that may contain some useful keywords
Returns
-------
dict
values to insert into the FITS header to express this WCS
"""
if hdr is None:
hdr = {}
wcs = ndd.wcs
transform = wcs.forward_transform
world_axes = list(wcs.output_frame.axes_names)
nworld_axes = len(world_axes)
wcs_dict = {'WCSAXES': nworld_axes, 'WCSDIM': nworld_axes}
wcs_dict.update({
f'CD{i+1}_{j+1}': 0.
for j in range(nworld_axes) for i in range(nworld_axes)
})
pix_center = [0.5 * (length - 1) for length in ndd.shape[::-1]]
wcs_center = transform(*pix_center)
# Find and process the sky projection first
if {'lon', 'lat'}.issubset(world_axes):
if isinstance(wcs.output_frame, cf.CelestialFrame):
cel_frame = wcs.output_frame
elif isinstance(wcs.output_frame, cf.CompositeFrame):
for frame in wcs.output_frame.frames:
if isinstance(frame, cf.CelestialFrame):
cel_frame = frame
# TODO: Non-ecliptic coordinate frames
cel_ref_frame = cel_frame.reference_frame
if not isinstance(cel_ref_frame, coord.builtin_frames.BaseRADecFrame):
raise NotImplementedError("Cannot write non-ecliptic frames yet")
wcs_dict['RADESYS'] = cel_ref_frame.name.upper()
for m in transform:
if isinstance(m, models.RotateNative2Celestial):
nat2cel = m
if isinstance(m, models.Pix2SkyProjection):
m.name = 'pix2sky'
# Determine which sort of projection this is
for projcode in projections.projcodes:
if isinstance(m, getattr(models, f'Pix2Sky_{projcode}')):
break
else:
raise ValueError("Unknown projection class: {}".format(
m.__class__.__name__))
lon_axis = world_axes.index('lon')
lat_axis = world_axes.index('lat')
world_axes[lon_axis] = f'RA---{projcode}'
world_axes[lat_axis] = f'DEC--{projcode}'
wcs_dict[f'CRVAL{lon_axis+1}'] = nat2cel.lon.value
wcs_dict[f'CRVAL{lat_axis+1}'] = nat2cel.lat.value
# Remove projection parts so we can calculate the CD matrix
if projcode:
nat2cel.name = 'nat2cel'
transform = transform.replace_submodel('pix2sky',
models.Identity(2))
transform = transform.replace_submodel('nat2cel',
models.Identity(2))
# Deal with other axes
# TODO: AD should refactor to allow the descriptor to be used here
for i, axis_type in enumerate(wcs.output_frame.axes_type, start=1):
if f'CRVAL{i}' in wcs_dict:
continue
if axis_type == "SPECTRAL":
wcs_dict[f'CRVAL{i}'] = hdr.get(
'CENTWAVE',
wcs_center[i - 1] if nworld_axes > 1 else wcs_center)
wcs_dict[f'CTYPE{i}'] = wcs.output_frame.axes_names[i -
1] # AWAV/WAVE
else: # Just something
wcs_dict[f'CRVAL{i}'] = 0
# Flag if we can't construct a perfect CD matrix
if not model_is_affine(transform):
wcs_dict['FITS-WCS'] = ('APPROXIMATE', 'FITS WCS is approximate')
affine = calculate_affine_matrices(transform, ndd.shape)
# Convert to x-first order
affine_matrix = affine.matrix[::-1, ::-1]
# Require an inverse to write out
if np.linalg.det(affine_matrix) == 0:
affine_matrix[-1, -1] = 1.
wcs_dict.update({
f'CD{i+1}_{j+1}': affine_matrix[i, j]
for j, _ in enumerate(world_axes) for i, _ in enumerate(world_axes)
})
# Don't overwrite CTYPEi keywords we've already created
wcs_dict.update({
f'CTYPE{i}': axis.upper()[:8]
for i, axis in enumerate(world_axes, start=1)
if f'CTYPE{i}' not in wcs_dict
})
crval = [wcs_dict[f'CRVAL{i+1}'] for i, _ in enumerate(world_axes)]
crpix = np.array(wcs.backward_transform(*crval)) + 1
if nworld_axes == 1:
wcs_dict['CRPIX1'] = crpix
else:
# Comply with FITS standard, must define CRPIXj for "extra" axes
wcs_dict.update({
f'CRPIX{j}': cpix
for j, cpix in enumerate(np.concatenate(
[crpix, [0] * (nworld_axes - len(ndd.shape))]),
start=1)
})
for i, unit in enumerate(wcs.output_frame.unit, start=1):
try:
wcs_dict[f'CUNIT{i}'] = unit.name
except AttributeError:
pass
return wcs_dict
def compute_footprint_nrs_ifu(dmodel, mod):
"""
Determine NIRSPEC IFU footprint using the instrument model.
For efficiency this function uses the transforms directly,
instead of the WCS object. The common transforms in the WCS
model chain are referenced and reused; only the slice specific
transforms are computed.
If the transforms change this function should be revised.
Parameters
----------
output_model : `~jwst.datamodels.IFUImageModel`
The output of assign_wcs.
mod : module
The imported ``nirspec`` module.
Returns
-------
footprint : ndarray
The spatial footprint
spectral_region : tuple
The wavelength range for the observation.
"""
ra_total = []
dec_total = []
lam_total = []
_, wrange = mod.spectral_order_wrange_from_model(dmodel)
pipe = dmodel.meta.wcs.pipeline
# Get the GWA to slit_frame transform
g2s = pipe[2].transform
# Construct a list of the transforms between coordinate frames.
# Set a place holder ``Identity`` transform at index 2 and 3.
# Update them with slice specific transforms.
transforms = [pipe[0].transform]
transforms.append(pipe[1].transform[1:])
transforms.append(astmodels.Identity(1))
transforms.append(astmodels.Identity(1))
transforms.extend([step.transform for step in pipe[4:-1]])
for sl in range(30):
transforms[2] = g2s.get_model(sl)
# Create the full transform from ``slit_frame`` to ``detector``.
# It is used to compute the bounding box.
m = functools.reduce(lambda x, y: x | y,
[tr.inverse for tr in transforms[:3][::-1]])
bbox = mod.compute_bounding_box(m, wrange)
# Add the remaining transforms - from ``sli_frame`` to ``world``
transforms[3] = pipe[3].transform.get_model(sl) & astmodels.Identity(1)
mforw = functools.reduce(lambda x, y: x | y, transforms)
x1, y1 = grid_from_bounding_box(bbox)
ra, dec, lam = mforw(x1, y1)
ra_total.extend(np.ravel(ra))
dec_total.extend(np.ravel(dec))
lam_total.extend(np.ravel(lam))
# the wrapped ra values are forced to be on one side of ra-border
# the wrapped ra are used to determine the correct min and max ra
ra_total = wrap_ra(ra_total)
ra_max = np.nanmax(ra_total)
ra_min = np.nanmin(ra_total)
# for the footprint we want ra to be between 0 to 360
if (ra_min < 0):
ra_min = ra_min + 360.0
if (ra_max >= 360.0):
ra_max = ra_max - 360.0
dec_max = np.nanmax(dec_total)
dec_min = np.nanmin(dec_total)
lam_max = np.nanmax(lam_total)
lam_min = np.nanmin(lam_total)
footprint = np.array(
[ra_min, dec_min, ra_max, dec_min, ra_max, dec_max, ra_min, dec_max])
return footprint, (lam_min, lam_max)
