def detector_to_abl(input_model, reference_files):
"""
Create the transform from "detector" to "alpha_beta" frame.
Transform description:
forward transform
RegionsSelector
label_mapper is the regions array
selector is {slice_number: alpha_model & beta_model & lambda_model}
backward transform
RegionsSelector
label_mapper is LabelMapperDict
{channel_wave_range (): LabelMapperDict}
{beta: slice_number}
selector is {slice_number: x_transform & y_transform}
"""
band = input_model.meta.instrument.band
channel = input_model.meta.instrument.channel
# used to read the wavelength range
with DistortionMRSModel(reference_files['distortion']) as dist:
alpha_model = dist.alpha_model
beta_model = dist.beta_model
x_model = dist.x_model
y_model = dist.y_model
bzero = dict(zip(dist.bzero.channel_band, dist.bzero.beta_zero))
bdel = dict(zip(dist.bdel.channel_band, dist.bdel.delta_beta))
slices = dist.slices
with SpecwcsModel(reference_files['specwcs']) as f:
lambda_model = f.model
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)
lambda_model = [m | velocity_corr for m in lambda_model]
log.info("Applied Barycentric velocity correction : {}".format(
velocity_corr[1].amplitude.value))
with RegionsModel(reference_files['regions']) as f:
allregions = f.regions.copy()
# Use the 50% throughput slice mask
regions = allregions[4, :, :]
label_mapper = selector.LabelMapperArray(regions)
transforms = {}
for i, sl in enumerate(slices):
forward = models.Mapping([1, 0, 0, 1, 0]) | \
alpha_model[i] & beta_model[i] & lambda_model[i]
inv = models.Mapping([2, 0, 2, 0]) | x_model[i] & y_model[i]
forward.inverse = inv
transforms[sl] = forward
with WavelengthrangeModel(reference_files['wavelengthrange']) as f:
wr = dict(zip(f.waverange_selector, f.wavelengthrange))
ch_dict = {}
for c in channel:
cb = c + band
mapper = jwmodels.MIRI_AB2Slice(bzero[cb], bdel[cb], c)
lm = selector.LabelMapper(inputs=('alpha', 'beta', 'lam'),
mapper=mapper,
inputs_mapping=models.Mapping((1, ),
n_inputs=3))
ch_dict[tuple(wr[cb])] = lm
alpha_beta_mapper = selector.LabelMapperRange(
('alpha', 'beta', 'lam'), ch_dict, models.Mapping((2, )))
label_mapper.inverse = alpha_beta_mapper
det2alpha_beta = selector.RegionsSelector(('x', 'y'),
('alpha', 'beta', 'lam'),
label_mapper=label_mapper,
selector=transforms)
return det2alpha_beta
def detector_to_abl(input_model, reference_files):
"""
Create the transform from "detector" to "alpha_beta" frame.
Transform description:
forward transform
RegionsSelector
label_mapper is the regions array
selector is {slice_number: alpha_model & beta_model & lambda_model}
backward transform
RegionsSelector
label_mapper is LabelMapperDict
{channel_wave_range (): LabelMapperDict}
{beta: slice_number}
selector is {slice_number: x_transform & y_transform}
"""
band = input_model.meta.instrument.band
channel = input_model.meta.instrument.channel
# used to read the wavelength range
with DistortionMRSModel(reference_files['distortion']) as dist:
alpha_model = dist.alpha_model
beta_model = dist.beta_model
x_model = dist.x_model
y_model = dist.y_model
bzero = dict(zip(dist.bzero.channel_band, dist.bzero.beta_zero))
bdel = dict(zip(dist.bdel.channel_band, dist.bdel.delta_beta))
slices = dist.slices
with SpecwcsModel(reference_files['specwcs']) as f:
lambda_model = f.model
with RegionsModel(reference_files['regions']) as f:
regions = f.regions.copy()
label_mapper = selector.LabelMapperArray(regions)
transforms = {}
for i, sl in enumerate(slices):
forward = models.Mapping([1, 0, 0, 1, 0]) | \
alpha_model[i] & beta_model[i] & lambda_model[i]
inv = models.Mapping([2, 0, 2, 0]) | x_model[i] & y_model[i]
forward.inverse = inv
transforms[sl] = forward
with WavelengthrangeModel(reference_files['wavelengthrange']) as f:
wr = dict(zip(f.waverange_selector, f.wavelengthrange))
ch_dict = {}
for c in channel:
cb = c + band
mapper = jwmodels.MIRI_AB2Slice(bzero[cb], bdel[cb], c)
lm = selector.LabelMapper(inputs=('alpha', 'beta', 'lam'),
mapper=mapper,
inputs_mapping=models.Mapping((1, ),
n_inputs=3))
ch_dict[tuple(wr[cb])] = lm
alpha_beta_mapper = selector.LabelMapperRange(
('alpha', 'beta', 'lam'), ch_dict, models.Mapping((2, )))
label_mapper.inverse = alpha_beta_mapper
det2alpha_beta = selector.RegionsSelector(('x', 'y'),
('alpha', 'beta', 'lam'),
label_mapper=label_mapper,
selector=transforms)
return det2alpha_beta
