def alpha_beta2XanYan(input_model, reference_files):
"""
Create the transform from detector to Xan, Yan frame.
forward transform:
RegionsSelector
label_mapper is LabelMapperDict()
{channel_wave_range (): channel_number}
selector is {channel_number: ab2Xan & ab2Yan}
bacward_transform
RegionsSelector
label_mapper is LabelMapperDict()
{channel_wave_range (): channel_number}
selector is {channel_number: Xan2ab & Yan2ab}
"""
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]
f = AsdfFile.open(reference_files['v2v3'])
v23 = f.tree['model']
f.close()
f = AsdfFile.open(reference_files['wavelengthrange'])
# the following should go in the asdf reader
wave_range = f.tree['wavelengthrange'].copy()
wave_channels = f.tree['channels']
wr = {}
for ch, r in zip(wave_channels, wave_range):
wr[ch] = r
f.close()
dict_mapper = {}
sel = {}
for c in channels:
ch = int(c[0])
dict_mapper[tuple(wr[c])] = models.Mapping((2,), name="mapping_lam") | \
models.Const1D(ch, name="channel #")
map1 = models.Mapping((1, 0, 1, 0), name='map2poly')
map1._outputs = ('alpha', 'beta', 'alpha', 'beta')
map1._inputs = ('alpha', 'beta')
map1.inverse = models.Mapping((0, 1))
ident1 = models.Identity(1, name='identity_lam')
ident1._inputs = ('lam',)
chan_v23 = v23[c]
v23chan_backward = chan_v23.inverse
del chan_v23.inverse
v23_spatial = map1 | chan_v23
v23_spatial.inverse = map1 | v23chan_backward
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()
ab2xyan = selector.RegionsSelector(('alpha', 'beta', 'lam'), ('v2', 'v3', 'lam'),
label_mapper=wave_range_mapper,
selector=sel)
return ab2xyan
def detector_to_gwa(reference_files, detector, disperser):
"""
Transform from DETECTOR frame to GWA frame.
Parameters
----------
reference_files: dict
Dictionary with reference files returned by CRDS.
detector : str
The detector keyword.
disperser : dict
A corrected disperser ASDF object.
Returns
-------
model : `~astropy.modeling.core.Model` model.
Transform from DETECTOR frame to GWA frame.
"""
with AsdfFile.open(reference_files['fpa']) as f:
fpa = f.tree[detector].copy()
with AsdfFile.open(reference_files['camera']) as f:
camera = f.tree['model'].copy()
angles = [
disperser['theta_x'], disperser['theta_y'], disperser['theta_z'],
disperser['tilt_y']
]
rotation = Rotation3DToGWA(angles, axes_order="xyzy", name='rotaton')
u2dircos = Unitless2DirCos(name='unitless2directional_cosines')
model = (fpa | camera | u2dircos | rotation)
return model
def alpha_beta2XanYan(input_model, reference_files):
"""
Create the transform from detector to Xan, Yan frame.
forward transform:
RegionsSelector
label_mapper is LabelMapperDict()
{channel_wave_range (): channel_number}
selector is {channel_number: ab2Xan & ab2Yan}
bacward_transform
RegionsSelector
label_mapper is LabelMapperDict()
{channel_wave_range (): channel_number}
selector is {channel_number: Xan2ab & Yan2ab}
"""
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]
f = AsdfFile.open(reference_files['v2v3'])
v23 = f.tree['model']
f.close()
f = AsdfFile.open(reference_files['wavelengthrange'])
# the following should go in the asdf reader
wave_range = f.tree['wavelengthrange'].copy()
wave_channels = f.tree['channels']
wr = {}
for ch, r in zip(wave_channels, wave_range):
wr[ch] = r
f.close()
dict_mapper = {}
sel = {}
for c in channels:
ch = int(c[0])
dict_mapper[tuple(wr[c])] = models.Mapping((2,), name="mapping_lam") | \
models.Const1D(ch, name="channel #")
map1 = models.Mapping((1, 0, 1, 0), name='map2poly')
map1._outputs = ('alpha', 'beta', 'alpha', 'beta')
map1._inputs = ('alpha', 'beta')
map1.inverse = models.Mapping((0, 1))
ident1 = models.Identity(1, name='identity_lam')
ident1._inputs = ('lam',)
chan_v23 = v23[c]
v23chan_backward = chan_v23.inverse
del chan_v23.inverse
v23_spatial = map1 | chan_v23
v23_spatial.inverse = map1 | v23chan_backward
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()
ab2xyan = selector.RegionsSelector(('alpha', 'beta', 'lam'), ('v2', 'v3', 'lam'),
label_mapper=wave_range_mapper,
selector=sel)
return ab2xyan
def imaging_distortion(input_model, reference_files):
"""
Create pixe2sky and sky2pixel transformation for the MIRI imager.
Parameters
----------
model : jwst_lib.models.ImagingModel
input model
reference_files : dict
reference files from CRDS
using CDP 3 Reference distortion file
MIRI_FM_MIRIMAGE_F1000W_PSF_03.01.00.fits
reference files/corrections needed (pixel to sky):
1. Filter dependent shift in (x,y) (!with an oposite sign to that delievred by the IT)
2. Apply MI
3. Apply Ai and BI matrices
4. Apply the TI matrix (this gives V2/V3 coordinates)
5. Apply V2/V3 to sky transformation
ref_file: filter_offset.asdf - (1)
ref_file: distortion.asdf -(2,3,4)
"""
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
filter_offset = AsdfFile.open(reference_files['filteroffset']).tree[input_model.meta.instrument.filter]
full_distortion = models.Shift(filter_offset['row_offset']) & models.Shift(
filter_offset['column_offset']) | distortion
full_distortion = full_distortion.rename('distortion')
return full_distortion
def calc_cube(numb, fried_parameter = 4, time_between = 0.7):
filepath = os.getcwd().split("vApp_reduction",1)[0]+"vApp_reduction/data/psf_cube_cache/"
filepath += "psf_cube_"+str(float(fried_parameter))+"_"+str(float(time_between))+"_"+str(int(numb))+".asdf"
#expand to only demand numb =< numb on disk
if os.path.exists(filepath):
tree = AsdfFile.open(filepath, copy_arrays=True).tree
tree_keys = tree.keys()
psf_params = sorted(list(filter(lambda key: isinstance(key, float), tree_keys)))
psf_cube = list(map(lambda param: np.copy(tree[param]), psf_params))
return psf_cube, psf_params
else:
path = os.getcwd()+"/code/psf/generate_vAPP_cube.py"
params = [str(fried_parameter), str(time_between), str(numb)]
cmd = [sys.executable, path, *params]
print("please run: ")
print(' '.join(cmd))
input("Press Enter to continue...")
tree = AsdfFile.open(filepath).tree
tree_keys = tree.keys()
psf_params = sorted(list(filter(lambda key: isinstance(key, float), tree_keys)))
psf_cube = list(map(lambda param: tree[param], psf_params))
return psf_cube, psf_params
def test_backwards_compat_gcrs():
obsgeoloc = (
3.0856775814671916e+16,
9.257032744401574e+16,
6.1713551629343834e+19
)
obsgeovel = (2.0, 1.0, 8.0)
old_frame_yaml = """
frames:
- !wcs/celestial_frame-1.0.0
axes_names: [lon, lat]
name: CelestialFrame
reference_frame:
type: GCRS
obsgeoloc:
- [%f, %f, %f]
- !unit/unit-1.0.0 m
obsgeovel:
- [%f, %f, %f]
- !unit/unit-1.0.0 m s-1
obstime: !time/time-1.0.0 2010-01-01 00:00:00.000
unit: [!unit/unit-1.0.0 deg, !unit/unit-1.0.0 deg]
""" % (obsgeovel + obsgeoloc)
new_frame_yaml = """
frames:
- !wcs/celestial_frame-1.1.0
axes_names: [lon, lat]
name: CelestialFrame
reference_frame:
type: GCRS
obsgeoloc:
- !unit/quantity-1.1.0 {unit: !unit/unit-1.0.0 m, value: %f}
- !unit/quantity-1.1.0 {unit: !unit/unit-1.0.0 m, value: %f}
- !unit/quantity-1.1.0 {unit: !unit/unit-1.0.0 m, value: %f}
obsgeovel:
- !unit/quantity-1.1.0 {unit: !unit/unit-1.0.0 m s-1, value: %f}
- !unit/quantity-1.1.0 {unit: !unit/unit-1.0.0 m s-1, value: %f}
- !unit/quantity-1.1.0 {unit: !unit/unit-1.0.0 m s-1, value: %f}
obstime: !time/time-1.1.0 2010-01-01 00:00:00.000
unit: [!unit/unit-1.0.0 deg, !unit/unit-1.0.0 deg]
""" % (obsgeovel + obsgeoloc)
old_buff = helpers.yaml_to_asdf(old_frame_yaml)
old_asdf = AsdfFile.open(old_buff)
old_frame = old_asdf.tree['frames'][0]
old_loc = old_frame.reference_frame.obsgeoloc
old_vel = old_frame.reference_frame.obsgeovel
new_buff = helpers.yaml_to_asdf(new_frame_yaml)
new_asdf = AsdfFile.open(new_buff)
new_frame = new_asdf.tree['frames'][0]
new_loc = new_frame.reference_frame.obsgeoloc
new_vel = new_frame.reference_frame.obsgeovel
assert (old_loc.x == new_loc.x and old_loc.y == new_loc.y and
old_loc.z == new_loc.z)
assert (old_vel.x == new_vel.x and old_vel.y == new_vel.y and
old_vel.z == new_vel.z)
def detector_to_gwa(reference_files, detector, disperser):
"""
Transform from DETECTOR frame to GWA frame.
Parameters
----------
reference_files: dict
Dictionary with reference files returned by CRDS.
detector : str
The detector keyword.
disperser : dict
A corrected disperser ASDF object.
Returns
-------
model : `~astropy.modeling.core.Model` model.
Transform from DETECTOR frame to GWA frame.
"""
with AsdfFile.open(reference_files['fpa']) as f:
fpa = f.tree[detector].copy()
with AsdfFile.open(reference_files['camera']) as f:
camera = f.tree['model'].copy()
angles = [disperser['theta_x'], disperser['theta_y'],
disperser['theta_z'], disperser['tilt_y']]
rotation = Rotation3DToGWA(angles, axes_order="xyzy", name='rotaton')
u2dircos = Unitless2DirCos(name='unitless2directional_cosines')
model = (models.Shift(-1) & models.Shift(-1) | fpa | camera | u2dircos | rotation)
return model
def ifuslit_to_msa(slits, reference_files):
"""
The transform from slit_frame to msa_frame.
Parameters
----------
slits_id : list
A list of slit IDs for all open shutters/slitlets.
msafile : str
The name of the msa reference file.
Returns
-------
model : `~jwst_lib.pipeline_models.Slit2Msa` model.
Transform from slit_frame to msa_frame.
"""
with AsdfFile.open(reference_files['ifufore']) as f:
ifufore = f.tree['model']
ifuslicer = AsdfFile.open(reference_files['ifuslicer'])
models = {}
ifuslicer_model = (ifuslicer.tree['model']).rename('ifuslicer_model')
for slit in slits:
slitdata = ifuslicer.tree['data'][slit]
slitdata_model = (get_slit_location_model(slitdata)).rename('slitdata_model')
msa_transform = slitdata_model | ifuslicer_model
models[slit] = msa_transform
ifuslicer.close()
return Slit2Msa(models)
def test_backwards_compat_gcrs():
obsgeoloc = (
3.0856775814671916e+16,
9.257032744401574e+16,
6.1713551629343834e+19
)
obsgeovel = (2.0, 1.0, 8.0)
old_frame_yaml = """
frames:
- !wcs/celestial_frame-1.0.0
axes_names: [lon, lat]
name: CelestialFrame
reference_frame:
type: GCRS
obsgeoloc:
- [%f, %f, %f]
- !unit/unit-1.0.0 m
obsgeovel:
- [%f, %f, %f]
- !unit/unit-1.0.0 m s-1
obstime: !time/time-1.0.0 2010-01-01 00:00:00.000
unit: [!unit/unit-1.0.0 deg, !unit/unit-1.0.0 deg]
""" % (obsgeovel + obsgeoloc)
new_frame_yaml = """
frames:
- !wcs/celestial_frame-1.1.0
axes_names: [lon, lat]
name: CelestialFrame
reference_frame:
type: GCRS
obsgeoloc:
- !unit/quantity-1.1.0 {unit: !unit/unit-1.0.0 m, value: %f}
- !unit/quantity-1.1.0 {unit: !unit/unit-1.0.0 m, value: %f}
- !unit/quantity-1.1.0 {unit: !unit/unit-1.0.0 m, value: %f}
obsgeovel:
- !unit/quantity-1.1.0 {unit: !unit/unit-1.0.0 m s-1, value: %f}
- !unit/quantity-1.1.0 {unit: !unit/unit-1.0.0 m s-1, value: %f}
- !unit/quantity-1.1.0 {unit: !unit/unit-1.0.0 m s-1, value: %f}
obstime: !time/time-1.1.0 2010-01-01 00:00:00.000
unit: [!unit/unit-1.0.0 deg, !unit/unit-1.0.0 deg]
""" % (obsgeovel + obsgeoloc)
old_buff = helpers.yaml_to_asdf(old_frame_yaml)
old_asdf = AsdfFile.open(old_buff)
old_frame = old_asdf.tree['frames'][0]
old_loc = old_frame.reference_frame.obsgeoloc
old_vel = old_frame.reference_frame.obsgeovel
new_buff = helpers.yaml_to_asdf(new_frame_yaml)
new_asdf = AsdfFile.open(new_buff)
new_frame = new_asdf.tree['frames'][0]
new_loc = new_frame.reference_frame.obsgeoloc
new_vel = new_frame.reference_frame.obsgeovel
assert (old_loc.x == new_loc.x and old_loc.y == new_loc.y and
old_loc.z == new_loc.z)
assert (old_vel.x == new_vel.x and old_vel.y == new_vel.y and
old_vel.z == new_vel.z)
def ifu_msa_to_oteip(reference_files):
"""
Transform from the MSA frame to the OTEIP frame.
Parameters
----------
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~astropy.modeling.core.Model` model.
Transform from MSA to OTEIP.
"""
with AsdfFile.open(reference_files['fore']) as f:
fore = f.tree['model'].copy()
with AsdfFile.open(reference_files['ifufore']) as f:
ifufore = f.tree['model'].copy()
msa2fore_mapping = Mapping((0, 1, 2, 2))
msa2fore_mapping.inverse = Identity(3)
ifu_fore_transform = ifufore & Identity(1)
ifu_fore_transform.inverse = Mapping(
(0, 1, 2, 2)) | ifufore.inverse & Identity(1)
fore_transform = msa2fore_mapping | fore & Identity(1)
return msa2fore_mapping | ifu_fore_transform | fore_transform
def ifuslit_to_msa(slits, reference_files):
"""
The transform from slit_frame to msa_frame.
Parameters
----------
slits_id : list
A list of slit IDs for all open shutters/slitlets.
msafile : str
The name of the msa reference file.
Returns
-------
model : `~jwst_lib.pipeline_models.Slit2Msa` model.
Transform from slit_frame to msa_frame.
"""
with AsdfFile.open(reference_files['ifufore']) as f:
ifufore = f.tree['model']
ifuslicer = AsdfFile.open(reference_files['ifuslicer'])
models = {}
ifuslicer_model = (ifuslicer.tree['model']).rename('ifuslicer_model')
for slit in slits:
slitdata = ifuslicer.tree['data'][slit]
slitdata_model = (
get_slit_location_model(slitdata)).rename('slitdata_model')
msa_transform = slitdata_model | ifuslicer_model
models[slit] = msa_transform
ifuslicer.close()
return Slit2Msa(models)
def ifu_msa_to_oteip(reference_files):
"""
Transform from the MSA frame to the OTEIP frame.
Parameters
----------
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~astropy.modeling.core.Model` model.
Transform from MSA to OTEIP.
"""
with AsdfFile.open(reference_files['fore']) as f:
fore = f.tree['model'].copy()
with AsdfFile.open(reference_files['ifufore']) as f:
ifufore = f.tree['model'].copy()
msa2fore_mapping = Mapping((0, 1, 2, 2))
msa2fore_mapping.inverse = Identity(3)
ifu_fore_transform = ifufore & Identity(1)
ifu_fore_transform.inverse = Mapping((0, 1, 2, 2)) | ifufore.inverse & Identity(1)
fore_transform = msa2fore_mapping | fore & Identity(1)
return msa2fore_mapping | ifu_fore_transform | fore_transform
def imaging_distortion(input_model, reference_files):
"""
Create pixe2sky and sky2pixel transformation for the MIRI imager.
Parameters
----------
model : jwst.datamodels.ImagingModel
input model
reference_files : dict
reference files from CRDS
using CDP 3 Reference distortion file
MIRI_FM_MIRIMAGE_F1000W_PSF_03.01.00.fits
reference files/corrections needed (pixel to sky):
1. Filter dependent shift in (x,y) (!with an oposite sign to that delievred by the IT)
2. Apply MI
3. Apply Ai and BI matrices
4. Apply the TI matrix (this gives V2/V3 coordinates)
5. Apply V2/V3 to sky transformation
ref_file: filter_offset.asdf - (1)
ref_file: distortion.asdf -(2,3,4)
"""
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
filter_offset = AsdfFile.open(reference_files['filteroffset']).tree[input_model.meta.instrument.filter]
full_distortion = models.Shift(filter_offset['row_offset']) & models.Shift(
filter_offset['column_offset']) | distortion
full_distortion = full_distortion.rename('distortion')
return full_distortion
def gwa_to_ifuslit(slits, disperser, wrange, order, reference_files):
"""
GWA to SLIT transform.
Parameters
----------
slits : list
A list of slit IDs for all IFU slits 0-29.
disperser : dict
A corrected disperser ASDF object.
filter : str
The filter used.
grating : str
The grating used in the observation.
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~jwst_lib.pipeline_models.Gwa2Slit` model.
Transform from GWA frame to SLIT frame.
"""
agreq = AngleFromGratingEquation(disperser['groove_density'],
order,
name='alpha_from_greq')
lgreq = WavelengthFromGratingEquation(disperser['groove_density'],
order,
name='lambda_from_greq')
collimator2gwa = collimator_to_gwa(reference_files, disperser)
lam = (wrange[1] - wrange[0]) / 2 + wrange[0]
ifuslicer = AsdfFile.open(reference_files['ifuslicer'])
ifupost = AsdfFile.open(reference_files['ifupost'])
slit_models = {}
ifuslicer_model = ifuslicer.tree['model']
for slit in slits:
slitdata = ifuslicer.tree['data'][slit]
slitdata_model = get_slit_location_model(slitdata)
ifuslicer_transform = (slitdata_model | ifuslicer_model)
ifupost_transform = ifupost.tree[slit]['model']
msa2gwa = ifuslicer_transform | ifupost_transform | collimator2gwa
gwa2msa = gwa_to_ymsa(msa2gwa) # TODO: Use model sets here
bgwa2msa = Mapping((0, 1, 0, 1), n_inputs=3) | \
Const1D(0) * Identity(1) & Const1D(-1) * Identity(1) & Identity(2) | \
Identity(1) & gwa2msa & Identity(2) | \
Mapping((0, 1, 0, 1, 2, 3)) | Identity(2) & msa2gwa & Identity(2) | \
Mapping((0, 1, 2, 5), n_inputs=7)| Identity(2) & lgreq
# msa to before_gwa
#msa2bgwa = Mapping((0, 1, 2, 2)) | msa2gwa & Identity(1) | Mapping((3, 0, 1, 2)) | agreq
msa2bgwa = msa2gwa & Identity(1) | Mapping((3, 0, 1, 2)) | agreq
bgwa2msa.inverse = msa2bgwa
slit_models[slit] = bgwa2msa
ifuslicer.close()
ifupost.close()
return Gwa2Slit(slit_models)
def imaging_distortion(input_model, reference_files):
"""
Create pixe2sky and sky2pixel transformation for the MIRI imager.
Parameters
----------
model : jwst.datamodels.ImagingModel
input model
reference_files : dict
reference files from CRDS
using CDP 3 Reference distortion file
Old one: ~~MIRI_FM_MIRIMAGE_F1000W_PSF_03.01.00.fits~~
Current one: MIRI_FM_MIRIMAGE_DISTORTION_06.03.00.fits
reference files/corrections needed (pixel to sky):
1. Filter dependent shift in (x,y) (!with an oposite sign to that delievred by the IT)
2. Apply MI
3. Apply Ai and BI matrices
4. Apply the TI matrix (this gives V2/V3 coordinates)
5. Apply V2/V3 to sky transformation
ref_file: filter_offset.asdf - (1)
ref_file: distortion.asdf -(2,3,4)
"""
# Load the distortion and filter from the reference files.
# Load in the distortion file.
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
filter_offset = AsdfFile.open(reference_files['filteroffset']).tree[
input_model.meta.instrument.filter]
# Now apply each of the models. The Scale(60) converts from arc-minutes to deg.
full_distortion = models.Shift(
filter_offset['column_offset']) & models.Shift(
filter_offset['row_offset']) | distortion | models.Scale(
1 / 60) & models.Scale(1 / 60)
# ToDo: This will likely have to change in the future, but the "filteroffset" file we have
# ToDo: currently does not contain that key.
filter_offset = None
if input_model.meta.instrument.filter in AsdfFile.open(
reference_files['filteroffset']).tree:
filter_offset = AsdfFile.open(reference_files['filteroffset']).tree[
input_model.meta.instrument.filter]
full_distortion = models.Shift(
filter_offset['row_offset']) & models.Shift(
filter_offset['column_offset']) | distortion
else:
full_distortion = distortion
full_distortion = full_distortion.rename('distortion')
return full_distortion
def imaging(input_model, reference_files):
"""
Imaging pipeline
frames : detector, gwa, msa, sky
"""
# Get the corrected disperser model
disperser = get_disperser(input_model, reference_files['disperser'])
# DETECTOR to GWA transform
det2gwa = detector_to_gwa(reference_files,
input_model.meta.instrument.detector, disperser)
gwa_through = Const1D(-1) * Identity(1) & Const1D(-1) * Identity(
1) & Identity(1)
angles = [
disperser['theta_x'], disperser['theta_y'], disperser['theta_z'],
disperser['tilt_y']
]
rotation = Rotation3DToGWA(angles, axes_order="xyzy",
name='rotaton').inverse
dircos2unitless = DirCos2Unitless(name='directional_cosines2unitless')
col = AsdfFile.open(reference_files['collimator']).tree['model']
# Get the default spectral order and wavelength range and record them in the model.
sporder, wrange = get_spectral_order_wrange(
input_model, reference_files['wavelengthrange'])
input_model.meta.wcsinfo.waverange_start = wrange[0]
input_model.meta.wcsinfo.waverange_end = wrange[1]
input_model.meta.wcsinfo.spectral_order = sporder
lam = wrange[0] + (wrange[1] - wrange[0]) * .5
lam_model = Mapping((0, 1, 1)) | Identity(2) & Const1D(lam)
gwa2msa = gwa_through | rotation | dircos2unitless | col | lam_model
gwa2msa.inverse = col.inverse | dircos2unitless.inverse | rotation.inverse | gwa_through
# MSA to OTEIP transform
msa2ote = msa_to_oteip(reference_files)
msa2oteip = msa2ote | Mapping((0, 1), n_inputs=3)
msa2oteip.inverse = Mapping((0, 1, 0, 1)) | msa2ote.inverse | Mapping(
(0, 1), n_inputs=3)
# OTEIP to V2,V3 transform
with AsdfFile.open(reference_files['ote']) as f:
oteip2v23 = f.tree['model'].copy()
# Create coordinate frames in the NIRSPEC WCS pipeline
# "detector", "gwa", "msa", "oteip", "v2v3", "world"
det, gwa, msa_frame, oteip, v2v3 = create_imaging_frames()
imaging_pipeline = [(det, det2gwa), (gwa, gwa2msa), (msa_frame, msa2oteip),
(oteip, oteip2v23), (v2v3, None)]
return imaging_pipeline
def gwa_to_ifuslit(slits, disperser, wrange, order, reference_files):
"""
GWA to SLIT transform.
Parameters
----------
slits : list
A list of slit IDs for all IFU slits 0-29.
disperser : dict
A corrected disperser ASDF object.
filter : str
The filter used.
grating : str
The grating used in the observation.
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~jwst.transforms.Gwa2Slit` model.
Transform from GWA frame to SLIT frame.
"""
ymin = -.55
ymax = .55
agreq = AngleFromGratingEquation(disperser['groove_density'], order, name='alpha_from_greq')
lgreq = WavelengthFromGratingEquation(disperser['groove_density'], order, name='lambda_from_greq')
collimator2gwa = collimator_to_gwa(reference_files, disperser)
mask = mask_slit(ymin, ymax)
ifuslicer = AsdfFile.open(reference_files['ifuslicer'])
ifupost = AsdfFile.open(reference_files['ifupost'])
slit_models = {}
ifuslicer_model = ifuslicer.tree['model']
for slit in slits:
slitdata = ifuslicer.tree['data'][slit]
slitdata_model = get_slit_location_model(slitdata)
ifuslicer_transform = (slitdata_model | ifuslicer_model)
ifupost_transform = ifupost.tree[slit]['model']
msa2gwa = ifuslicer_transform | ifupost_transform | collimator2gwa
gwa2msa = gwa_to_ymsa(msa2gwa)# TODO: Use model sets here
bgwa2msa = Mapping((0, 1, 0, 1), n_inputs=3) | \
Const1D(0) * Identity(1) & Const1D(-1) * Identity(1) & Identity(2) | \
Identity(1) & gwa2msa & Identity(2) | \
Mapping((0, 1, 0, 1, 2, 3)) | Identity(2) & msa2gwa & Identity(2) | \
Mapping((0, 1, 2, 5), n_inputs=7) | Identity(2) & lgreq | mask
# msa to before_gwa
#msa2bgwa = Mapping((0, 1, 2, 2)) | msa2gwa & Identity(1) | Mapping((3, 0, 1, 2)) | agreq
msa2bgwa = msa2gwa & Identity(1) | Mapping((3, 0, 1, 2)) | agreq
bgwa2msa.inverse = msa2bgwa
slit_models[slit] = bgwa2msa
ifuslicer.close()
ifupost.close()
return Gwa2Slit(slit_models)
def imaging(input_model, reference_files):
"""
Imaging pipeline
frames : detector, gwa, msa, sky
"""
# Get the corrected disperser model
disperser = get_disperser(input_model, reference_files['disperser'])
# DETECTOR to GWA transform
det2gwa = detector_to_gwa(reference_files, input_model.meta.instrument.detector, disperser)
gwa_through = Const1D(-1) * Identity(1) & Const1D(-1) * Identity(1) & Identity(1)
angles = [ disperser['theta_x'], disperser['theta_y'],
disperser['theta_z'], disperser['tilt_y']]
rotation = Rotation3DToGWA(angles, axes_order="xyzy", name='rotaton').inverse
dircos2unitless = DirCos2Unitless(name='directional_cosines2unitless')
col = AsdfFile.open(reference_files['collimator']).tree['model']
# Get the default spectral order and wavelength range and record them in the model.
sporder, wrange = get_spectral_order_wrange(input_model, reference_files['wavelengthrange'])
input_model.meta.wcsinfo.waverange_start = wrange[0]
input_model.meta.wcsinfo.waverange_end = wrange[1]
input_model.meta.wcsinfo.spectral_order = sporder
lam = wrange[0] + (wrange[1] - wrange[0]) * .5
lam_model = Mapping((0, 1, 1)) | Identity(2) & Const1D(lam)
gwa2msa = gwa_through | rotation | dircos2unitless | col | lam_model
gwa2msa.inverse = col.inverse | dircos2unitless.inverse | rotation.inverse | gwa_through
# MSA to OTEIP transform
msa2ote = msa_to_oteip(reference_files)
msa2oteip = msa2ote | Mapping((0, 1), n_inputs=3)
msa2oteip.inverse = Mapping((0, 1, 0, 1)) | msa2ote.inverse | Mapping((0, 1), n_inputs=3)
# OTEIP to V2,V3 transform
with AsdfFile.open(reference_files['ote']) as f:
oteip2v23 = f.tree['model'].copy()
# Create coordinate frames in the NIRSPEC WCS pipeline
# "detector", "gwa", "msa", "oteip", "v2v3", "world"
det, gwa, msa_frame, oteip, v2v3 = create_imaging_frames()
imaging_pipeline = [(det, det2gwa),
(gwa, gwa2msa),
(msa_frame, msa2oteip),
(oteip, oteip2v23),
(v2v3, None)]
return imaging_pipeline
def imaging_distortion(input_model, reference_files):
"""
Create pixe2sky and sky2pixel transformation for the MIRI imager.
Parameters
----------
model : jwst.datamodels.ImagingModel
input model
reference_files : dict
reference files from CRDS
using CDP 3 Reference distortion file
Old one: ~~MIRI_FM_MIRIMAGE_F1000W_PSF_03.01.00.fits~~
Current one: MIRI_FM_MIRIMAGE_DISTORTION_06.03.00.fits
reference files/corrections needed (pixel to sky):
1. Filter dependent shift in (x,y) (!with an oposite sign to that delievred by the IT)
2. Apply MI
3. Apply Ai and BI matrices
4. Apply the TI matrix (this gives V2/V3 coordinates)
5. Apply V2/V3 to sky transformation
ref_file: filter_offset.asdf - (1)
ref_file: distortion.asdf -(2,3,4)
"""
# Load the distortion and filter from the reference files.
# Load in the distortion file.
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
filter_offset = AsdfFile.open(reference_files['filteroffset']).tree[input_model.meta.instrument.filter]
# Now apply each of the models. The Scale(60) converts from arc-minutes to deg.
full_distortion = models.Shift(filter_offset['column_offset']) & models.Shift(
filter_offset['row_offset']) | distortion | models.Scale(1/60) & models.Scale(1/60)
# ToDo: This will likely have to change in the future, but the "filteroffset" file we have
# ToDo: currently does not contain that key.
filter_offset = None
if input_model.meta.instrument.filter in AsdfFile.open(reference_files['filteroffset']).tree:
filter_offset = AsdfFile.open(reference_files['filteroffset']).tree[input_model.meta.instrument.filter]
full_distortion = models.Shift(filter_offset['row_offset']) & models.Shift(
filter_offset['column_offset']) | distortion
else:
full_distortion = distortion
full_distortion = full_distortion.rename('distortion')
return full_distortion
def slit_to_msa(slits_id, msafile):
"""
The transform from slit_frame to msa_frame.
Parameters
----------
slits_id : list
A list of slit IDs for all open shutters/slitlets.
msafile : str
The name of the msa reference file.
Returns
-------
model : `~jwst_lib.pipeline_models.Slit2Msa` model.
Transform from slit_frame to msa_frame.
"""
msa = AsdfFile.open(msafile)
models = {}
for i in range(1, 6):
slit_names = slits_id[slits_id[:, 0] == i]
if slit_names.any():
msa_model = msa.tree[i]['model']
for slit in slit_names:
index = slit[1] - 1
slitdata = msa.tree[slit[0]]['data'][index]
slitdata_model = get_slit_location_model(slitdata)
msa_transform = slitdata_model | msa_model
s = slitid_to_slit(np.array([slit]))[0]
models[s] = msa_transform
msa.close()
return Slit2Msa(models)
def get_coord_transform_model(self, filename):
#read in asdf distortion transformation reference file
#Read in the CRDS-format distortion reference file
with AsdfFile.open(filename) as dist_file:
coord_transform = dist_file.tree['model']
return coord_transform
def get_spectral_order_wrange(input_model, wavelengthrange_file):
"""
Read the spectral order and wavelength range from the reference file.
Parameters
----------
filter : str
The filter used.
grating : str
The grating used in the observation.
wavelength_range_file : str
Reference file of type "wavelengthrange".
"""
full_range = [.6e-6, 5.3e-6]
filter = input_model.meta.instrument.filter
lamp = input_model.meta.instrument.lamp_state
grating = input_model.meta.instrument.grating
wave_range = AsdfFile.open(wavelengthrange_file)
if filter == "OPAQUE":
keyword = lamp + '_' + grating
else:
keyword = filter + '_' + grating
try:
order = wave_range.tree['filter_grating'][keyword]['order']
wrange = wave_range.tree['filter_grating'][keyword]['range']
except KeyError:
order = -1
wrange = full_range
log.warning(
"Combination {0} missing in wavelengthrange file, setting order to -1 and range to {1}."
.format(keyword, full_range))
wave_range.close()
return order, wrange
def test_msa(self):
for slit in self.slits:
msa = AsdfFile.open(
self.model.meta.ref_file.msa.name).tree[slit]['model']
name, xmsa, ymsa, xsizemsa, ysizemsa, xsky, ysky, xsizesky, yskysize, \
xsca1, ysca1, xsca2, ysca2, xsizesca, ysizesca = self._get_reference_data(slit)
assert_allclose(msa(0, 0), (xmsa, ymsa))
def oteip_to_v23(reference_files):
"""
Transform from the OTEIP frame to the V2V3 frame.
Parameters
----------
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~astropy.modeling.core.Model` model.
Transform from OTEIP to V2V3.
"""
with AsdfFile.open(reference_files['ote']) as f:
ote = f.tree['model'].copy()
fore2ote_mapping = Identity(3, name='fore2ote_mapping')
fore2ote_mapping.inverse = Mapping((0, 1, 2, 2))
# Create the transform to v2/v3/lambda. The wavelength units up to this point are
# meters as required by the pipeline but the desired output wavelength units is microns.
# So we are going to Scale the spectral units by 1e6 (meters -> microns)
# The spatial units are currently in deg. Convertin to arcsec.
oteip_to_xyan = fore2ote_mapping | (ote & Scale(1e6))
# Add a shift for the aperture.
oteip2v23 = oteip_to_xyan | Identity(1) & (Shift(468 / 3600) | Scale(-1)) & Identity(1)
return oteip2v23
def slit_to_msa(open_slits, msafile):
"""
The transform from slit_frame to msa_frame.
Parameters
----------
open_slits : list
A list of slit IDs for all open shutters/slitlets.
msafile : str
The name of the msa reference file.
Returns
-------
model : `~jwst.transforms.Slit2Msa` model.
Transform from slit_frame to msa_frame.
"""
msa = AsdfFile.open(msafile)
models = []
for quadrant in range(1, 6):
slits_in_quadrant = [s for s in open_slits if s.quadrant==quadrant]
if any(slits_in_quadrant):
msa_data = msa.tree[quadrant]['data']
msa_model = msa.tree[quadrant]['model']
for slit in slits_in_quadrant:
slit_id = slit.shutter_id
slitdata = msa_data[slit_id]
slitdata_model = get_slit_location_model(slitdata)
msa_transform = slitdata_model | msa_model
models.append(msa_transform)
msa.close()
return Slit2Msa(open_slits, models)
def get_wavelength_range(input_model, path=None):
"""
Return the wavelength range used for computing the WCS.
Needs access to the reference file used to construct the WCS object.
Parameters
----------
input_model : `jwst.datamodels.ImagingModel`
Data model after assign_wcs has been run.
path : str
Directory where the reference file is. (optional)
"""
fname = input_model.meta.ref_file.wavelengthrange.name.split('/')[-1]
if path is None and not os.path.exists(fname):
raise IOError(
"Reference file {0} not found. Please specify a path.".format(
fname))
else:
fname = os.path.join(path, fname)
f = AsdfFile.open(fname)
wave_range = f.tree['wavelengthrange'].copy()
wave_channels = f.tree['channels']
f.close()
wr = dict(zip(wave_channels, wave_range))
channel = input_model.meta.instrument.channel
band = input_model.meta.instrument.band
return dict([(ch + band, wr[ch + band]) for ch in channel])
def get_wavelength_range(input_model, path=None):
"""
Return the wavelength range used for computing the WCS.
Needs access to the reference file used to construct the WCS object.
Parameters
----------
input_model : `jwst.datamodels.ImagingModel`
Data model after assign_wcs has been run.
path : str
Directory where the reference file is. (optional)
"""
fname = input_model.meta.ref_file.wavelengthrange.name.split('/')[-1]
if path is None and not os.path.exists(fname):
raise IOError("Reference file {0} not found. Please specify a path.".format(fname))
else:
fname = os.path.join(path, fname)
f = AsdfFile.open(fname)
wave_range = f.tree['wavelengthrange'].copy()
wave_channels = f.tree['channels']
f.close()
wr = dict(zip(wave_channels, wave_range))
channel = input_model.meta.instrument.channel
band = input_model.meta.instrument.band
return dict([(ch+band, wr[ch+band]) for ch in channel ])
def slit_to_msa(slits_id, msafile):
"""
The transform from slit_frame to msa_frame.
Parameters
----------
slits_id : list
A list of slit IDs for all open shutters/slitlets.
msafile : str
The name of the msa reference file.
Returns
-------
model : `~jwst.transforms.Slit2Msa` model.
Transform from slit_frame to msa_frame.
"""
msa = AsdfFile.open(msafile)
models = {}
for i in range(1, 6):
slit_names = slits_id[slits_id[:, 0] == i]
if slit_names.any():
msa_model = msa.tree[i]['model']
for slit in slit_names:
index = slit[1] - 1
slitdata = msa.tree[slit[0]]['data'][index]
slitdata_model = get_slit_location_model(slitdata)
msa_transform = slitdata_model | msa_model
s = slitid_to_slit(np.array([slit]))[0]
models[s] = msa_transform
msa.close()
return Slit2Msa(models)
def collimator_to_gwa(reference_files, disperser):
"""
Transform from COLLIMATOR to GWA frame.
Parameters
----------
reference_files: dict
Dictionary with reference files returned by CRDS.
disperser : dict
A corrected disperser ASDF object.
Returns
-------
model : `~astropy.modeling.core.Model` model.
Transform from COLLIMATOR to GWA frame.
"""
with AsdfFile.open(reference_files['collimator']) as f:
collimator = f.tree['model'].copy()
angles = [disperser['theta_x'], disperser['theta_y'],
disperser['theta_z'], disperser['tilt_y']]
rotation = Rotation3DToGWA(angles, axes_order="xyzy", name='rotaton')
u2dircos = Unitless2DirCos(name='unitless2directional_cosines')
return collimator.inverse | u2dircos | rotation
def get_spectral_order_wrange(input_model, wavelengthrange_file):
"""
Read the spectral order and wavelength range from the reference file.
Parameters
----------
filter : str
The filter used.
grating : str
The grating used in the observation.
wavelength_range_file : str
Reference file of type "wavelengthrange".
"""
full_range = [.6e-6, 5.3e-6]
filter = input_model.meta.instrument.filter
lamp = input_model.meta.instrument.lamp_state
grating = input_model.meta.instrument.grating
wave_range = AsdfFile.open(wavelengthrange_file)
if filter == "OPAQUE":
keyword = lamp + '_' + grating
else:
keyword = filter + '_' + grating
try:
order = wave_range.tree['filter_grating'][keyword]['order']
wrange = wave_range.tree['filter_grating'][keyword]['range']
except KeyError:
order = -1
wrange = full_range
log.warning("Combination {0} missing in wavelengthrange file, setting order to -1 and range to {1}.".format(keyword, full_range))
wave_range.close()
return order, wrange
def collimator_to_gwa(reference_files, disperser):
"""
Transform from COLLIMATOR to GWA frame.
Parameters
----------
reference_files: dict
Dictionary with reference files returned by CRDS.
disperser : dict
A corrected disperser ASDF object.
Returns
-------
model : `~astropy.modeling.core.Model` model.
Transform from COLLIMATOR to GWA frame.
"""
with AsdfFile.open(reference_files['collimator']) as f:
collimator = f.tree['model'].copy()
angles = [
disperser['theta_x'], disperser['theta_y'], disperser['theta_z'],
disperser['tilt_y']
]
rotation = Rotation3DToGWA(angles, axes_order="xyzy", name='rotaton')
u2dircos = Unitless2DirCos(name='unitless2directional_cosines')
return collimator.inverse | u2dircos | rotation
def test_transform(asdf_file):
"""
Parameters
----------
asdf_file: str
reference file with distortion
xy, v2, v3 values are from technical report with CDP-3 delivery
"""
v2 = np.array([-8, -7.5, -7, -6.5, -8, -7.5, -7, -6.5, -8, -7.5, -7, -6.5],
dtype=np.float)
v3 = np.array([-2, -2, -2, -2, -1.5, -1.5, -1.5, -1.5, -1, -1, -1, -1],
dtype=np.float)
xy = np.array([[945.80, 728.45], [676.57, 748.63], [408.29, 768.69],
[138.02, 789.09], [924.30, 456.59], [655.18, 477.89],
[387.05, 498.99], [116.92, 519.96], [904.31, 185.02],
[635.09, 207.37], [366.53, 229.45], [95.58, 250.95]],
dtype=np.float)
f = AsdfFile.open(asdf_file)
transform = f.tree['model']
x, y = transform.inverse(v2, v3)
assert_allclose(x, xy[:, 0], atol=.05)
assert_allclose(y, xy[:, 1], atol=.05)
s1, s2 = transform(xy[:, 0], xy[:, 1])
assert_allclose(s1, v2, atol=0.05)
assert_allclose(s2, v3, atol=.05)
def gwa_to_slit(slits_id, disperser, wrange, order, reference_files):
"""
GWA to SLIT transform.
Parameters
----------
slits_id : list
A list of slit IDs for all open shutters/slitlets.
disperser : dict
A corrected disperser ASDF object.
filter : str
The filter used.
grating : str
The grating used in the observation.
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~jwst_lib.pipeline_models.Gwa2Slit` model.
Transform from GWA frame to SLIT frame.
"""
agreq = AngleFromGratingEquation(disperser['groove_density'],
order,
name='alpha_from_greq')
lgreq = WavelengthFromGratingEquation(disperser['groove_density'],
order,
name='lambda_from_greq')
collimator2gwa = collimator_to_gwa(reference_files, disperser)
msa = AsdfFile.open(reference_files['msa'])
slit_models = {}
for i in range(1, 6):
slit_names = slits_id[slits_id[:, 0] == i]
if slit_names.any():
msa_model = msa.tree[i]['model']
for slit in slit_names:
index = slit[1] - 1
slitdata = msa.tree[slit[0]]['data'][index]
slitdata_model = get_slit_location_model(slitdata)
msa_transform = slitdata_model | msa_model
msa2gwa = (msa_transform | collimator2gwa)
gwa2msa = gwa_to_ymsa(msa2gwa) # TODO: Use model sets here
bgwa2msa = Mapping((0, 1, 0, 1), n_inputs=3) | \
Const1D(0) * Identity(1) & Const1D(-1) * Identity(1) & Identity(2) | \
Identity(1) & gwa2msa & Identity(2) | \
Mapping((0, 1, 0, 1, 2, 3)) | Identity(2) & msa2gwa & Identity(2) | \
Mapping((0, 1, 2, 5), n_inputs=7)| Identity(2) & lgreq
# msa to before_gwa
msa2bgwa = msa2gwa & Identity(1) | Mapping(
(3, 0, 1, 2)) | agreq
bgwa2msa.inverse = msa2bgwa
s = slitid_to_slit(np.array([slit]))[0]
slit_models[s] = bgwa2msa
msa.close()
return Gwa2Slit(slit_models)
def gwa_to_slit(slits_id, disperser, wrange, order, reference_files):
"""
GWA to SLIT transform.
Parameters
----------
slits_id : list
A list of slit IDs for all open shutters/slitlets.
disperser : dict
A corrected disperser ASDF object.
filter : str
The filter used.
grating : str
The grating used in the observation.
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~jwst.transforms.Gwa2Slit` model.
Transform from GWA frame to SLIT frame.
"""
ymin = -.55
ymax = .55
agreq = AngleFromGratingEquation(disperser['groove_density'], order, name='alpha_from_greq')
lgreq = WavelengthFromGratingEquation(disperser['groove_density'], order, name='lambda_from_greq')
collimator2gwa = collimator_to_gwa(reference_files, disperser)
mask = mask_slit(ymin, ymax)
msa = AsdfFile.open(reference_files['msa'])
slit_models = {}
for i in range(1, 6):
slit_names = slits_id[slits_id[:, 0] == i]
if slit_names.any():
msa_model = msa.tree[i]['model']
for slit in slit_names:
index = slit[1] - 1
slitdata = msa.tree[slit[0]]['data'][index]
slitdata_model = get_slit_location_model(slitdata)
msa_transform = slitdata_model | msa_model
msa2gwa = (msa_transform | collimator2gwa)
gwa2msa = gwa_to_ymsa(msa2gwa)# TODO: Use model sets here
bgwa2msa = Mapping((0, 1, 0, 1), n_inputs=3) | \
Const1D(0) * Identity(1) & Const1D(-1) * Identity(1) & Identity(2) | \
Identity(1) & gwa2msa & Identity(2) | \
Mapping((0, 1, 0, 1, 2, 3)) | Identity(2) & msa2gwa & Identity(2) | \
Mapping((0, 1, 2, 5), n_inputs=7) | Identity(2) & lgreq | mask
# msa to before_gwa
msa2bgwa = msa2gwa & Identity(1) | Mapping((3, 0, 1, 2)) | agreq
bgwa2msa.inverse = msa2bgwa
s = slitid_to_slit(np.array([slit]))[0]
slit_models[s] = bgwa2msa
msa.close()
return Gwa2Slit(slit_models)
def imaging_distortion(input_model, reference_files):
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
# Convert to deg - output of distortion models is in arcsec.
transform = distortion | Scale(1 / 3600) & Scale(1 / 3600)
try:
bb = transform.bounding_box
except NotImplementedError:
shape = input_model.data.shape
# Note: Since bounding_box is attached to the model here it's in reverse order.
transform.bounding_box = ((-0.5, shape[0] - 0.5), (-0.5,
shape[1] - 0.5))
return transform
def imaging_distortion(input_model, reference_files):
"""
Create pixe2sky and sky2pixel transformation for the MIRI imager.
Parameters
----------
input_model : `jwst.datamodels.ImagingModel`
Data model.
reference_files : dict
Dictionary {reftype: reference file name}.
1. Filter dependent shift in (x,y) (!with an oposite sign to that delivered by the IT)
2. Apply MI
3. Apply Ai and BI matrices
4. Apply the TI matrix (this gives Xan/Yan coordinates)
5. Aply the XanYan --> V2V3 transform
5. Apply V2V3 --> sky transform
ref_file: filter_offset.asdf - (1)
ref_file: distortion.asdf -(2,3,4)
"""
# Read in the distortion.
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
obsfilter = input_model.meta.instrument.filter
# Add an offset for the filter
with AsdfFile.open(reference_files['filteroffset']) as filter_offset:
if obsfilter in filter_offset.tree:
filter_corr = filter_offset.tree[obsfilter]
distortion = models.Shift(
filter_corr['column_offset']) & models.Shift(
filter_corr['row_offset']) | distortion
# Apply XanYan --> V2V3 and scale to degrees
distortion = distortion | models.Identity(1) & (models.Scale(-1) | models.Shift(-7.8)) | \
models.Scale(1/60) & models.Scale(1/60)
return distortion
def imaging_distortion(input_model, reference_files):
"""
Create pixe2sky and sky2pixel transformation for the MIRI imager.
Parameters
----------
model : jwst.datamodels.ImagingModel
input model
reference_files : dict
reference files from CRDS
1. Filter dependent shift in (x,y) (!with an oposite sign to that delivered by the IT)
2. Apply MI
3. Apply Ai and BI matrices
4. Apply the TI matrix (this gives Xan/Yan coordinates)
5. Aply the XanYan --> V2V3 transform
5. Apply V2V3 --> sky transform
ref_file: filter_offset.asdf - (1)
ref_file: distortion.asdf -(2,3,4)
"""
# Read in the distortion.
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
obsfilter = input_model.meta.instrument.filter
# Add an offset for the filter
with AsdfFile.open(reference_files['filteroffset']) as filter_offset:
if obsfilter in filter_offset.tree:
filter_corr = filter_offset.tree[obsfilter]
distortion = models.Shift(filter_corr['column_offset']) & models.Shift(
filter_corr['row_offset']) | distortion
# Apply XanYan --> V2V3 and scale to degrees
distortion = distortion | models.Identity(1) & (models.Scale(-1) | models.Shift(-7.8)) | \
models.Scale(1/60) & models.Scale(1/60)
return distortion
def niriss_soss(input_model, reference_files):
"""
The NIRISS SOSS pipeline includes 3 coordinate frames -
detector, focal plane and sky
reference_files={'specwcs': 'soss_wavelengths_configuration.asdf'}
"""
# Get the target RA and DEC, they will be used for setting the WCS RA and DEC based on a conversation
# with Kevin Volk.
try:
target_ra = float(input_model['meta.target.ra'])
target_dec = float(input_model['meta.target.dec'])
except:
# There was an error getting the target RA and DEC, so we are not going to continue.
raise ValueError('Problem getting the TARG_RA or TARG_DEC from input model {}'.format(input_model))
# Define the frames
detector = cf.Frame2D(name='detector', axes_order=(0, 1), unit=(u.pix, u.pix))
spec = cf.SpectralFrame(name='spectral', axes_order=(2,), unit=(u.micron,),
axes_names=('wavelength',))
sky = cf.CelestialFrame(reference_frame=coord.ICRS(),
axes_names=('ra', 'dec'),
axes_order=(0, 1), unit=(u.deg, u.deg), name='sky')
world = cf.CompositeFrame([sky, spec], name='world')
try:
with AsdfFile.open(reference_files['specwcs']) as wl:
wl1 = wl.tree[1].copy()
wl2 = wl.tree[2].copy()
wl3 = wl.tree[3].copy()
except Exception as e:
raise IOError('Error reading wavelength correction from {}'.format(reference_files['specwcs']))
cm_order1 = (Mapping((0, 1, 0, 1)) | (Const1D(target_ra) & Const1D(target_dec) & wl1)).rename('Order1')
cm_order2 = (Mapping((0, 1, 0, 1)) | (Const1D(target_ra) & Const1D(target_dec) & wl2)).rename('Order2')
cm_order3 = (Mapping((0, 1, 0, 1)) | (Const1D(target_ra) & Const1D(target_dec) & wl3)).rename('Order3')
# Define the transforms, they should accept (x,y) and return (ra, dec, lambda)
soss_model = NirissSOSSModel([1, 2, 3], [cm_order1, cm_order2, cm_order3]).rename('3-order SOSS Model')
# Define the pipeline based on the frames and models above.
pipeline = [(detector, soss_model),
(world, None)
]
return pipeline
def oteip_to_v23(reference_files):
"""
Transform from the OTEIP frame to the V2V3 frame.
Parameters
----------
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~astropy.modeling.core.Model` model.
Transform from OTEIP to V2V3.
"""
with AsdfFile.open(reference_files['ote']) as f:
ote = f.tree['model'].copy()
fore2ote_mapping = Identity(3, name='fore2ote_mapping')
fore2ote_mapping.inverse = Mapping((0, 1, 2, 2))
# Convert the wavelength to microns
return fore2ote_mapping | (ote & Identity(1) / Const1D(1e-6))
def oteip_to_v23(reference_files):
"""
Transform from the OTEIP frame to the V2V3 frame.
Parameters
----------
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~astropy.modeling.core.Model` model.
Transform from OTEIP to V2V3.
"""
with AsdfFile.open(reference_files['ote']) as f:
ote = f.tree['model'].copy()
fore2ote_mapping = Identity(3, name='fore2ote_mapping')
fore2ote_mapping.inverse = Mapping((0, 1, 2, 2))
# Convert the wavelength to microns
return fore2ote_mapping | (ote & Identity(1) / Const1D(1e-6))
def get_disperser(input_model, disperserfile):
"""
Return the disperser information corrected for the uncertainty in the GWA position.
Parameters
----------
input_model : `jwst_lib.models.DataModel`
The input data model - either an ImageModel or a CubeModel.
disperserfile : str
The name of the disperser reference file.
Returns
-------
disperser : dict
The corrected disperser information.
"""
with AsdfFile.open(disperserfile) as f:
disperser = f.tree
xtilt = input_model.meta.instrument.gwa_xtilt
ytilt = input_model.meta.instrument.gwa_ytilt
disperser = correct_tilt(disperser, xtilt, ytilt)
return disperser
def get_disperser(input_model, disperserfile):
"""
Return the disperser information corrected for the uncertainty in the GWA position.
Parameters
----------
input_model : `jwst.datamodels.DataModel`
The input data model - either an ImageModel or a CubeModel.
disperserfile : str
The name of the disperser reference file.
Returns
-------
disperser : dict
The corrected disperser information.
"""
with AsdfFile.open(disperserfile) as f:
disperser = f.tree
xtilt = input_model.meta.instrument.gwa_xtilt
ytilt = input_model.meta.instrument.gwa_ytilt
disperser = correct_tilt(disperser, xtilt, ytilt)
return disperser
def oteip_to_v23(reference_files):
"""
Transform from the OTEIP frame to the V2V3 frame.
Parameters
----------
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~astropy.modeling.core.Model` model.
Transform from OTEIP to V2V3.
"""
with AsdfFile.open(reference_files['ote']) as f:
ote = f.tree['model'].copy()
fore2ote_mapping = Identity(3, name='fore2ote_mapping')
fore2ote_mapping.inverse = Mapping((0, 1, 2, 2))
# Create the transform to v2/v3/lambda. The wavelength units up to this point are
# meters as required by the pipeline but the desired output wavelength units is microns.
# So we are going to Scale the spectral units by 1e6 (meters -> microns)
return fore2ote_mapping | (ote & Scale(1e6))
def test_transform(asdf_file):
"""
Parameters
----------
asdf_file: str
reference file with distortion
xy, v2, v3 values are from technical report with CDP-3 delivery
"""
v2 = np.array([-2, -2, -2, -2, -1.5, -1.5, -1.5, -1.5, -1, -1, -1, -1], dtype=np.float)
v3 = np.array([-8, -7.5, -7, -6.5, -8, -7.5, -7, -6.5, -8, -7.5, -7, -6.5], dtype=np.float)
xy = np.array([[945.80, 728.45], [676.57, 748.63], [408.29, 768.69], [138.02, 789.09],
[924.30, 456.59],[655.18, 477.89], [387.05, 498.99], [116.92, 519.96],
[904.31, 185.02], [635.09, 207.37], [366.53, 229.45], [95.58, 250.95]],
dtype=np.float)
f = AsdfFile.open(asdf_file)
transform = f.tree['distortion']
x, y = transform.inverse(v2, v3)
assert_allclose(x, xy[:,0], atol=.05)
assert_allclose(y, xy[:,1], atol=.05)
s1, s2 = transform (xy[:,0], xy[:,1])
assert_allclose(s1, v2, atol=0.05)
assert_allclose(s2, v3, atol=.05)
def oteip_to_v23(reference_files):
"""
Transform from the OTEIP frame to the V2V3 frame.
Parameters
----------
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~astropy.modeling.core.Model` model.
Transform from OTEIP to V2V3.
"""
with AsdfFile.open(reference_files['ote']) as f:
ote = f.tree['model'].copy()
fore2ote_mapping = Identity(3, name='fore2ote_mapping')
fore2ote_mapping.inverse = Mapping((0, 1, 2, 2))
# Create the transform to v2/v3/lambda. The wavelength units up to this point are
# meters as required by the pipeline but the desired output wavelength units is microns.
# So we are going to Scale the spectral units by 1e6 (meters -> microns)
return fore2ote_mapping | (ote & Scale(1e6))
def run(self):
filename = self.arguments[0]
cwd = os.getcwd()
os.chdir(TMPDIR)
parts = []
try:
ff = AsdfFile()
code = AsdfFile._open_impl(ff, filename, _get_yaml_content=True)
code = '{0} {1}\n'.format(ASDF_MAGIC, version_string) + code.strip().decode('utf-8')
literal = nodes.literal_block(code, code)
literal['language'] = 'yaml'
set_source_info(self, literal)
parts.append(literal)
kwargs = dict()
# Use the ignore_unrecognized_tag parameter as a proxy for both options
kwargs['ignore_unrecognized_tag'] = 'ignore_unrecognized_tag' in self.arguments
kwargs['ignore_missing_extensions'] = 'ignore_unrecognized_tag' in self.arguments
with AsdfFile.open(filename, **kwargs) as ff:
for i, block in enumerate(ff.blocks.internal_blocks):
data = codecs.encode(block.data.tostring(), 'hex')
if len(data) > 40:
data = data[:40] + '...'.encode()
allocated = block._allocated
size = block._size
data_size = block._data_size
flags = block._flags
if flags & BLOCK_FLAG_STREAMED:
allocated = size = data_size = 0
lines = []
lines.append('BLOCK {0}:'.format(i))
human_flags = []
for key, val in FLAGS.items():
if flags & key:
human_flags.append(val)
if len(human_flags):
lines.append(' flags: {0}'.format(' | '.join(human_flags)))
if block.input_compression:
lines.append(' compression: {0}'.format(block.input_compression))
lines.append(' allocated_size: {0}'.format(allocated))
lines.append(' used_size: {0}'.format(size))
lines.append(' data_size: {0}'.format(data_size))
lines.append(' data: {0}'.format(data))
code = '\n'.join(lines)
literal = nodes.literal_block(code, code)
literal['language'] = 'yaml'
set_source_info(self, literal)
parts.append(literal)
internal_blocks = list(ff.blocks.internal_blocks)
if (len(internal_blocks) and
internal_blocks[-1].array_storage != 'streamed'):
buff = io.BytesIO()
ff.blocks.write_block_index(buff, ff)
block_index = buff.getvalue().decode('utf-8')
literal = nodes.literal_block(block_index, block_index)
literal['language'] = 'yaml'
set_source_info(self, literal)
parts.append(literal)
finally:
os.chdir(cwd)
result = nodes.literal_block()
textnodes, messages = self.state.inline_text(filename, self.lineno)
title = nodes.title(filename, '', *textnodes)
result += title
result += parts
return [result]
def dist_coeff():
""" Create distortion correction reference file.
Parameters:
___________
coefficients: float
coefficients
The order of the input text files with coefficients is from det2sky and then sky2det.
"""
"Put coefficients into variables by reading in text files"
coeffxr, coeffyr = np.loadtxt(sys.argv[1],
skiprows=6,
usecols=(1, 2),
unpack=True)
print(coeffxr)
print(coeffyr)
coeffxi, coeffyi = np.loadtxt(sys.argv[2],
skiprows=6,
usecols=(1, 2),
unpack=True)
print(coeffxi)
print(coeffyi)
"Transform coefficients to get undistorted, real, sky x."
x0_0r = coeffxr[0]
print('Coefficient for x0_0: ', x0_0r)
x1_0r = coeffxr[1]
print('Coefficient for x1_0: ', x1_0r)
x0_1r = coeffxr[2]
print('Coefficient for x0_1: ', x0_1r)
x2_0r = coeffxr[3]
print('Coefficient for x2_0: ', x2_0r)
x1_1r = coeffxr[4]
print('Coefficient for x1_1: ', x1_1r)
x0_2r = coeffxr[5]
print('Coefficient for x0_2: ', x0_2r)
x3_0r = coeffxr[6]
print('Coefficient for x3_0: ', x3_0r)
x2_1r = coeffxr[7]
print('Coefficient for x2_1: ', x2_1r)
x1_2r = coeffxr[8]
print('Coefficient for x1_2: ', x1_2r)
x0_3r = coeffxr[9]
print('Coefficient for x0_3: ', x0_3r)
x4_0r = coeffxr[10]
print('Coefficient for x4_0: ', x4_0r)
x3_1r = coeffxr[11]
print('Coefficient for x3_1: ', x3_1r)
x2_2r = coeffxr[12]
print('Coefficient for x2_2: ', x2_2r)
x1_3r = coeffxr[13]
print('Coefficient for x1_3: ', x1_3r)
x0_4r = coeffxr[14]
print('Coefficient for x0_4: ', x0_4r)
"Transform coefficients to get undistorted, real, sky y."
y0_0r = coeffyr[0]
print('Coefficient for y0_0: ', y0_0r)
y1_0r = coeffyr[1]
print('Coefficient for y1_0: ', y1_0r)
y0_1r = coeffyr[2]
print('Coefficient for y0_1: ', y0_1r)
y2_0r = coeffyr[3]
print('Coefficient for y2_0: ', y2_0r)
y1_1r = coeffyr[4]
print('Coefficient for y1_1: ', y1_1r)
y0_2r = coeffyr[5]
print('Coefficient for y0_2: ', y0_2r)
y3_0r = coeffyr[6]
print('Coefficient for y3_0: ', y3_0r)
y2_1r = coeffyr[7]
print('Coefficient for y2_1: ', y2_1r)
y1_2r = coeffyr[8]
print('Coefficient for y1_2: ', y1_2r)
y0_3r = coeffyr[9]
print('Coefficient for y0_3: ', y0_3r)
y4_0r = coeffyr[10]
print('Coefficient for y4_0: ', y4_0r)
y3_1r = coeffyr[11]
print('Coefficient for y3_1: ', y3_1r)
y2_2r = coeffyr[12]
print('Coefficient for y2_2: ', y2_2r)
y1_3r = coeffyr[13]
print('Coefficient for y1_3: ', y1_3r)
y0_4r = coeffyr[14]
print('Coefficient for y0_4: ', y0_4r)
"Transform coefficients to get distorted, ideal, detector x."
x0_0i = coeffxi[0]
print('Coefficient for x0_0: ', x0_0i)
x1_0i = coeffxi[1]
print('Coefficient for x1_0: ', x1_0i)
x0_1i = coeffxi[2]
print('Coefficient for x0_1: ', x0_1i)
x2_0i = coeffxi[3]
print('Coefficient for x2_0: ', x2_0i)
x1_1i = coeffxi[4]
print('Coefficient for x1_1: ', x1_1i)
x0_2i = coeffxi[5]
print('Coefficient for x0_2: ', x0_2i)
x3_0i = coeffxi[6]
print('Coefficient for x3_0: ', x3_0i)
x2_1i = coeffxi[7]
print('Coefficient for x2_1: ', x2_1i)
x1_2i = coeffxi[8]
print('Coefficient for x1_2: ', x1_2i)
x0_3i = coeffxi[9]
print('Coefficient for x0_3: ', x0_3i)
x4_0i = coeffxi[10]
print('Coefficient for x4_0: ', x4_0i)
x3_1i = coeffxi[11]
print('Coefficient for x3_1: ', x3_1i)
x2_2i = coeffxi[12]
print('Coefficient for x2_2: ', x2_2i)
x1_3i = coeffxi[13]
print('Coefficient for x1_3: ', x1_3i)
x0_4i = coeffxi[14]
print('Coefficient for x0_4: ', x0_4i)
"Transform coefficients to get distorted, ideal, detector y."
y0_0i = coeffyi[0]
print('Coefficient for y0_0: ', y0_0i)
y1_0i = coeffyi[1]
print('Coefficient for y1_0: ', y1_0i)
y0_1i = coeffyi[2]
print('Coefficient for y0_1: ', y0_1i)
y2_0i = coeffyi[3]
print('Coefficient for y2_0: ', y2_0i)
y1_1i = coeffyi[4]
print('Coefficient for y1_1: ', y1_1i)
y0_2i = coeffyi[5]
print('Coefficient for y0_2: ', y0_2i)
y3_0i = coeffyi[6]
print('Coefficient for y3_0: ', y3_0i)
y2_1i = coeffyi[7]
print('Coefficient for y2_1: ', y2_1i)
y1_2i = coeffyi[8]
print('Coefficient for y1_2: ', y1_2i)
y0_3i = coeffyi[9]
print('Coefficient for y0_3: ', y0_3i)
y4_0i = coeffyi[10]
print('Coefficient for y4_0: ', y4_0i)
y3_1i = coeffyi[11]
print('Coefficient for y3_1: ', y3_1i)
y2_2i = coeffyi[12]
print('Coefficient for y2_2: ', y2_2i)
y1_3i = coeffyi[13]
print('Coefficient for y1_3: ', y1_3i)
y0_4i = coeffyi[14]
print('Coefficient for y0_4: ', y0_4i)
"Generate ideal or detector coordinates."
x_pix = np.arange(0, 2048, 1)
y_pix = np.arange(0, 2048, 1)
x_det_orig = []
y_det_orig = []
for x, y in itertools.product(x_pix, y_pix):
x_det_orig.append(x)
y_det_orig.append(y)
print('Done with first for loop!!!!')
print(len(x_det_orig))
print(len(y_det_orig))
"To go from ideal, distorted to real, undistorted."
x_sky = []
y_sky = []
#-------
for x, y in zip(x_det_orig, y_det_orig):
x_real, y_real = apply_coeff(x, y, coeffxr, coeffyr)
x_sky.append(x_real)
y_sky.append(y_real)
#-------
print(' ')
print('Done with second for loop!!!!')
print(len(x_sky))
print(len(y_sky))
x_sky = np.array(x_sky)
y_sky = np.array(y_sky)
print('Above is for transformation to sky pixels.')
"To go from real, undistorted to ideal, distorted."
x_det = []
y_det = []
for x, y in zip(x_sky, y_sky):
x_ideal, y_ideal = apply_coeff(x, y, coeffxi, coeffyi)
x_det.append(x_ideal)
y_det.append(y_ideal)
print(' ')
print('Done with third for loop!!!!')
print(len(x_det))
print(len(y_det))
print('Above is for transformation to detector pixels.')
"Derive residuals from the complete transformation."
x_det = np.array(x_det)
y_det = np.array(y_det)
x_det_orig = np.array(x_det_orig)
y_det_orig = np.array(y_det_orig)
resid_x = x_det - x_det_orig
resid_y = y_det - y_det_orig
print(' ')
print('Residual in x: ', resid_x)
print(np.min(resid_x), np.max(resid_x))
print(' ')
print('Residual in y: ', resid_y)
print(np.min(resid_y), np.max(resid_y))
'Open asdf reference file and transfrom from det to sky.'
f = AsdfFile.open('niriss_ref_distortion_image.asdf')
det2sky_trans = f.tree['model']
sky_x, sky_y = det2sky_trans(x_det_orig, y_det_orig)
'Transform from sky to det.'
sky2det_trans = det2sky_trans.inverse
inv_x, inv_y = sky2det_trans(sky_x, sky_y)
'Calculate residuals from reference file.'
x_resid_ref = inv_x - x_det_orig
y_resid_ref = inv_y - y_det_orig
'Calculate residuals of residuals.'
x_resid_resid = np.absolute(resid_x - x_resid_ref)
y_resid_resid = np.absolute(resid_y - y_resid_ref)
print(' ')
print(x_resid_resid)
print('The minimum in x_resid_resid is: ', np.min(x_resid_resid))
print('The maximum in x_resid_resid is: ', np.max(x_resid_resid))
print('The mean in x_resid_resid is: ', np.mean(x_resid_resid))
print('The standard deviation in x_resid_resid is: ',
np.std(x_resid_resid))
print('The median in x_resid_resid is: ', np.median(x_resid_resid))
print(' ')
print(y_resid_resid)
print('The minimum in y_resid_resid is: ', np.min(y_resid_resid))
print('The maximum in y_resid_resid is: ', np.max(y_resid_resid))
print('The mean in y_resid_resid is: ', np.mean(y_resid_resid))
print('The standard deviation in y_resid_resid is: ',
np.std(y_resid_resid))
print('The median in y_resid_resid is: ', np.median(y_resid_resid))
print(' ')
print('Done, Done, and Done. Git the bleep along little doggies.')
def detector_to_alpha_beta(input_model, reference_files):
"""
Create the transform from detector to alpha, beta frame.
forward transform:
RegionsSelector
label_mapper is the regions array
selector is {slice_number: alphs_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
channels = [c + band for c in channel]
f = AsdfFile.open(reference_files['distortion'])
# The name of the model indicates the output coordinate
alpha_model = f.tree['alpha_model']
beta_model = f.tree['beta_model']
x_model = f.tree['x_model']
y_model = f.tree['y_model']
slice_model = f.tree['slice_model']
f.close()
f = AsdfFile.open(reference_files['specwcs'])
lambda_model = f.tree['model']
f.close()
f = AsdfFile.open(reference_files['regions'])
regions = f.tree['regions'].copy()
f.close()
label_mapper = selector.LabelMapperArray(regions)
transforms = {}
for sl in alpha_model:
chan = str(sl // 100) + band
forward = models.Mapping([1, 0, 0, 1, 0]) | \
alpha_model[sl] & beta_model[sl] & lambda_model[sl]
inv = models.Mapping([2, 0, 2, 0]) | x_model[sl] & y_model[sl]
forward.inverse = inv
transforms[sl] = forward
f = AsdfFile.open(reference_files['wavelengthrange'])
# the following should go in the asdf reader
wave_range = f.tree['wavelengthrange'].copy()
wave_channels = f.tree['channels']
wr = {}
for ch, r in zip(wave_channels, wave_range):
wr[ch] = r
f.close()
ch_dict = {}
for c in channels:
ch_dict.update({tuple(wr[c]): selector.LabelMapperDict(('alpha', 'beta', 'lam'), slice_model[c],
models.Mapping([1,], n_inputs=3))})
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 slit_to_detector(input_model, slits_id, lam, reference_files):
"""
For each slit computes the transform from the MSA to the detector.
Used to flag stuck open shutters.
Reftypes needed:
reftypes = ['fpa', 'camera', 'disperser', 'collimator', 'msa', 'wavelengthrange']
Parameters
----------
input_model : jwst.datamodels.DataModel
Input data model
slits : list
A list of slit IDs. A slit ID is a tuple of (quadrant, slit_nimber)
lam : float
wavelength, in meters
reference_files : dict
A dictionary with WCS reference_files, {reftype: refname}
Returns
-------
msa2det : dict
A dictionary with the MSA to detector transform for each slit, {slit_id: astropy.modeling.Model}
"""
slits_msa2det = {}
# read models from reference files
with AsdfFile.open(reference_files['fpa']) as f:
fpa = f.tree[input_model.meta.instrument.detector].copy()
with AsdfFile.open(reference_files['camera']) as f:
camera = f.tree['model'].copy()
with AsdfFile.open(reference_files['disperser']) as f:
disperser = f.tree
dircos2u = DirCos2Unitless(name='directional2unitless_cosines')
disperser = correct_tilt(disperser, input_model.meta.instrument.gwa_xtilt,
input_model.meta.instrument.gwa_ytilt)
angles = [disperser['theta_x'], disperser['theta_y'],
disperser['theta_z'], disperser['tilt_y']]
rotation = Rotation3DToGWA(angles, axes_order="xyzy", name='rotaton')
order, wrange = get_spectral_order_wrange(input_model, reference_files['wavelengthrange'])
input_model.meta.wcsinfo.waverange_start = wrange[0]
input_model.meta.wcsinfo.waverange_end = wrange[1]
input_model.meta.wcsinfo.spectral_order = order
agreq = AngleFromGratingEquation(disperser['groove_density'], order, name='alpha_from_greq')
# GWA to detector
gwa2det = rotation.inverse | dircos2u | camera.inverse | fpa.inverse
# collimator to GWA
collimator2gwa = collimator_to_gwa(reference_files, disperser)
msa = AsdfFile.open(reference_files['msa'])
for i in range(1, 5):
slit_names = slits_id[slits_id[:, 0] == i]
if slit_names.any():
msa_model = msa.tree[i]['model']
for slit in slit_names:
index = slit[1] - 1
slitdata = msa.tree[slit[0]]['data'][index]
slitdata_model = get_slit_location_model(slitdata)
# absolute positions of the slit on the MSA
msa_transform = slitdata_model | msa_model
s = slitid_to_slit(np.array([slit]))[0]
msa2gwa = (msa_transform | collimator2gwa) & Identity(1) | Mapping((3, 0, 1, 2)) | agreq
slits_msa2det[s] = msa2gwa | gwa2det
msa.close()
return slits_msa2det
def __init__(self, init=None, schema=None, extensions=None,
pass_invalid_values=False):
"""
Parameters
----------
init : shape tuple, file path, file object, astropy.io.fits.HDUList, numpy array, None
- None: A default data model with no shape
- shape tuple: Initialize with empty data of the given
shape
- file path: Initialize from the given file (FITS or ASDF)
- readable file object: Initialize from the given file
object
- ``astropy.io.fits.HDUList``: Initialize from the given
`~astropy.io.fits.HDUList`.
- A numpy array: Used to initialize the data array
- dict: The object model tree for the data model
schema : tree of objects representing a JSON schema, or string naming a schema, optional
The schema to use to understand the elements on the model.
If not provided, the schema associated with this class
will be used.
extensions: classes extending the standard set of extensions
pass_invalid_values: If True, values that do not validate the schema can
be read and written, but with a warning message
"""
filename = os.path.abspath(inspect.getfile(self.__class__))
base_url = os.path.join(
os.path.dirname(filename), 'schemas', '')
if schema is None:
schema_path = os.path.join(base_url, self.schema_url)
schema = asdf_schema.load_schema(
schema_path, resolve_references=True)
self._schema = mschema.flatten_combiners(schema)
if extensions is not None:
extensions.extend(jwst_extensions)
else:
extensions = jwst_extensions[:]
self._extensions = extensions
if "PASS_INVALID_VALUES" in os.environ:
pass_invalid_values = os.environ["PASS_INVALID_VALUES"]
try:
self._pass_invalid_values = bool(int(pass_invalid_values))
except ValueError:
self._pass_invalid_values = False
else:
self._pass_invalid_values = pass_invalid_values
self._files_to_close = []
is_array = False
is_shape = False
shape = None
if init is None:
asdf = AsdfFile(extensions=extensions)
elif isinstance(init, dict):
asdf = AsdfFile(init, extensions=extensions)
elif isinstance(init, np.ndarray):
asdf = AsdfFile(extensions=extensions)
shape = init.shape
is_array = True
elif isinstance(init, self.__class__):
instance = copy.deepcopy(init._instance)
self._schema = init._schema
self._shape = init._shape
self._asdf = AsdfFile(instance, extensions=self._extensions)
self._instance = instance
self._ctx = self
self.__class__ = init.__class__
return
elif isinstance(init, DataModel):
raise TypeError(
"Passed in {0!r} is not of the expected subclass {1!r}".format(
init.__class__.__name__, self.__class__.__name__))
elif isinstance(init, AsdfFile):
asdf = init
elif isinstance(init, tuple):
for item in init:
if not isinstance(item, int):
raise ValueError("shape must be a tuple of ints")
shape = init
asdf = AsdfFile()
is_shape = True
elif isinstance(init, fits.HDUList):
asdf = fits_support.from_fits(init, self._schema,
extensions=self._extensions,
validate=False,
pass_invalid_values=self._pass_invalid_values)
elif isinstance(init, six.string_types):
if isinstance(init, bytes):
init = init.decode(sys.getfilesystemencoding())
try:
hdulist = fits.open(init)
except IOError:
try:
asdf = AsdfFile.open(init, extensions=self._extensions)
# TODO: Add json support
except ValueError:
raise IOError(
"File does not appear to be a FITS or ASDF file.")
else:
asdf = fits_support.from_fits(hdulist, self._schema,
extensions=self._extensions,
validate=False,
pass_invalid_values=self._pass_invalid_values)
self._files_to_close.append(hdulist)
self._shape = shape
self._instance = asdf.tree
self._asdf = asdf
self._ctx = self
# if the input model doesn't have a date set, use the current date/time
if self.meta.date is None:
self.meta.date = Time(datetime.datetime.now())
self.meta.date.format='isot'
self.meta.date = self.meta.date.value
# if the input is from a file, set the filename attribute
if isinstance(init, six.string_types):
self.meta.filename = os.path.basename(init)
if is_array:
primary_array_name = self.get_primary_array_name()
if primary_array_name is None:
raise TypeError(
"Array passed to model.__init__, but model has no primary "
"array in its schema")
setattr(self, primary_array_name, init)
if is_shape:
getattr(self, self.get_primary_array_name())
def imaging_distortion(input_model, reference_files):
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
# Convert to deg - output of distortion models is in arcsec.
transform = distortion | Scale(1/3600) & Scale(1/3600)
return transform
def gwa_to_slit(open_slits, input_model, disperser, reference_files):
"""
GWA to SLIT transform.
Parameters
----------
open_slits : list
A list of slit IDs for all open shutters/slitlets.
disperser : dict
A corrected disperser ASDF object.
filter : str
The filter used.
grating : str
The grating used in the observation.
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~jwst.transforms.Gwa2Slit` model.
Transform from GWA frame to SLIT frame.
"""
wrange = (input_model.meta.wcsinfo.waverange_start,
input_model.meta.wcsinfo.waverange_end),
order = input_model.meta.wcsinfo.spectral_order
agreq = angle_from_disperser(disperser, input_model)
collimator2gwa = collimator_to_gwa(reference_files, disperser)
lgreq = wavelength_from_disperser(disperser, input_model)
# The wavelength units up to this point are
# meters as required by the pipeline but the desired output wavelength units is microns.
# So we are going to Scale the spectral units by 1e6 (meters -> microns)
if input_model.meta.instrument.filter == 'OPAQUE':
lgreq = lgreq | Scale(1e6)
msa = AsdfFile.open(reference_files['msa'])
slit_models = []
for quadrant in range(1, 6):
slits_in_quadrant = [s for s in open_slits if s.quadrant==quadrant]
log.info("There are {0} open slits in quadrant {1}".format(len(slits_in_quadrant), quadrant))
if any(slits_in_quadrant):
msa_model = msa.tree[quadrant]['model']
log.info("Getting slits location for quadrant {0}".format(quadrant))
msa_data = msa.tree[quadrant]['data']
for slit in slits_in_quadrant:
mask = mask_slit(slit.ymin, slit.ymax)
slit_id = slit.shutter_id
slitdata = msa_data[slit_id]
slitdata_model = get_slit_location_model(slitdata)
msa_transform = slitdata_model | msa_model
msa2gwa = (msa_transform | collimator2gwa)
gwa2msa = gwa_to_ymsa(msa2gwa)# TODO: Use model sets here
bgwa2msa = Mapping((0, 1, 0, 1), n_inputs=3) | \
Const1D(0) * Identity(1) & Const1D(-1) * Identity(1) & Identity(2) | \
Identity(1) & gwa2msa & Identity(2) | \
Mapping((0, 1, 0, 1, 2, 3)) | Identity(2) & msa2gwa & Identity(2) | \
Mapping((0, 1, 2, 3, 5), n_inputs=7) | Identity(2) & lgreq | mask
#Mapping((0, 1, 2, 5), n_inputs=7) | Identity(2) & lgreq | mask
# and modify lgreq to accept alpha_in, beta_in, alpha_out
# msa to before_gwa
msa2bgwa = msa2gwa & Identity(1) | Mapping((3, 0, 1, 2)) | agreq
bgwa2msa.inverse = msa2bgwa
slit_models.append(bgwa2msa)
msa.close()
return Gwa2Slit(open_slits, slit_models)
def __init__(self,
init=None,
schema=None,
extensions=None,
pass_invalid_values=False):
"""
Parameters
----------
init : shape tuple, file path, file object, astropy.io.fits.HDUList, numpy array, None
- None: A default data model with no shape
- shape tuple: Initialize with empty data of the given
shape
- file path: Initialize from the given file (FITS or ASDF)
- readable file object: Initialize from the given file
object
- ``astropy.io.fits.HDUList``: Initialize from the given
`~astropy.io.fits.HDUList`.
- A numpy array: Used to initialize the data array
- dict: The object model tree for the data model
schema : tree of objects representing a JSON schema, or string naming a schema, optional
The schema to use to understand the elements on the model.
If not provided, the schema associated with this class
will be used.
extensions: classes extending the standard set of extensions, optional.
If an extension is defined, the prefix used should be 'url'.
pass_invalid_values: If true, values that do not validate the schema can
be read and written and only a warning will be generated
"""
# Set the extensions
if extensions is None:
extensions = jwst_extensions[:]
else:
extensions.extend(jwst_extensions)
self._extensions = extensions
# Override value of pass_invalid value if environment value set
if "PASS_INVALID_VALUES" in os.environ:
pass_invalid_values = os.environ["PASS_INVALID_VALUES"]
try:
pass_invalid_values = bool(int(pass_invalid_values))
except ValueError:
pass_invalid_values = False
self._pass_invalid_values = pass_invalid_values
# Construct the path to the schema files
filename = os.path.abspath(inspect.getfile(self.__class__))
base_url = os.path.join(os.path.dirname(filename), 'schemas', '')
# Load the schema files
if schema is None:
schema_path = os.path.join(base_url, self.schema_url)
extension_list = asdf_extension.AsdfExtensionList(self._extensions)
schema = asdf_schema.load_schema(
schema_path,
resolver=extension_list.url_mapping,
resolve_references=True)
self._schema = mschema.flatten_combiners(schema)
# Determine what kind of input we have (init) and execute the
# proper code to intiailize the model
self._files_to_close = []
self._iscopy = False
is_array = False
is_shape = False
shape = None
if init is None:
asdf = AsdfFile(extensions=extensions)
elif isinstance(init, dict):
asdf = AsdfFile(init, extensions=extensions)
elif isinstance(init, np.ndarray):
asdf = AsdfFile(extensions=extensions)
shape = init.shape
is_array = True
elif isinstance(init, self.__class__):
self.clone(self, init)
return
elif isinstance(init, DataModel):
raise TypeError(
"Passed in {0!r} is not of the expected subclass {1!r}".format(
init.__class__.__name__, self.__class__.__name__))
elif isinstance(init, AsdfFile):
asdf = init
elif isinstance(init, tuple):
for item in init:
if not isinstance(item, int):
raise ValueError("shape must be a tuple of ints")
shape = init
asdf = AsdfFile()
is_shape = True
elif isinstance(init, fits.HDUList):
asdf = fits_support.from_fits(init, self._schema, extensions,
pass_invalid_values)
elif isinstance(init, (six.string_types, bytes)):
if isinstance(init, bytes):
init = init.decode(sys.getfilesystemencoding())
file_type = filetype.check(init)
if file_type == "fits":
hdulist = fits.open(init)
asdf = fits_support.from_fits(hdulist, self._schema,
extensions, pass_invalid_values)
self._files_to_close.append(hdulist)
elif file_type == "asdf":
asdf = AsdfFile.open(init, extensions=extensions)
else:
# TODO handle json files as well
raise IOError(
"File does not appear to be a FITS or ASDF file.")
else:
raise ValueError("Can't initialize datamodel using {0}".format(
str(type(init))))
# Initialize object fields as determined fro the code above
self._shape = shape
self._instance = asdf.tree
self._asdf = asdf
self._ctx = self
# if the input is from a file, set the filename attribute
if isinstance(init, six.string_types):
self.meta.filename = os.path.basename(init)
elif isinstance(init, fits.HDUList):
info = init.fileinfo(0)
if info is not None:
filename = info.get('filename')
if filename is not None:
self.meta.filename = os.path.basename(filename)
# if the input model doesn't have a date set, use the current date/time
if self.meta.date is None:
self.meta.date = Time(datetime.datetime.now())
if hasattr(self.meta.date, 'value'):
self.meta.date.format = 'isot'
self.meta.date = str(self.meta.date.value)
# store the data model type, if not already set
if hasattr(self.meta, 'model_type'):
if self.meta.model_type is None:
self.meta.model_type = self.__class__.__name__
else:
self.meta.model_type = None
if is_array:
primary_array_name = self.get_primary_array_name()
if primary_array_name is None:
raise TypeError(
"Array passed to DataModel.__init__, but model has "
"no primary array in its schema")
setattr(self, primary_array_name, init)
# TODO this code looks useless
if is_shape:
getattr(self, self.get_primary_array_name())
def __init__(self,
init=None,
schema=None,
extensions=None,
pass_invalid_values=False,
strict_validation=False):
"""
Parameters
----------
init : shape tuple, file path, file object, astropy.io.fits.HDUList, numpy array, None
- None: A default data model with no shape
- shape tuple: Initialize with empty data of the given
shape
- file path: Initialize from the given file (FITS or ASDF)
- readable file object: Initialize from the given file
object
- ``astropy.io.fits.HDUList``: Initialize from the given
`~astropy.io.fits.HDUList`.
- A numpy array: Used to initialize the data array
- dict: The object model tree for the data model
schema : tree of objects representing a JSON schema, or string naming a schema, optional
The schema to use to understand the elements on the model.
If not provided, the schema associated with this class
will be used.
extensions: classes extending the standard set of extensions, optional.
If an extension is defined, the prefix used should be 'url'.
pass_invalid_values: If true, values that do not validate the schema
will be added to the metadata. If false, they will be set to None
strict_validation: if true, an schema validation errors will generate
an excption. If false, they will generate a warning.
"""
# Set the extensions
self._extensions = extensions
# Override value of validation parameters
# if environment value set
self._pass_invalid_values = self.get_envar("PASS_INVALID_VALUES",
pass_invalid_values)
self._strict_validation = self.get_envar("STRICT_VALIDATION",
strict_validation)
# Construct the path to the schema files
filename = os.path.abspath(inspect.getfile(self.__class__))
base_url = os.path.join(os.path.dirname(filename), 'schemas', '')
# Load the schema files
if schema is None:
schema_path = os.path.join(base_url, self.schema_url)
# Create an AsdfFile so we can use its resolver for loading schemas
asdf_file = AsdfFile(extensions=self._extensions)
if hasattr(asdf_file, 'resolver'):
file_resolver = asdf_file.resolver
else:
file_resolver = self.get_resolver(asdf_file)
schema = asdf_schema.load_schema(schema_path,
resolver=file_resolver,
resolve_references=True)
self._schema = mschema.flatten_combiners(schema)
# Provide the object as context to other classes and functions
self._ctx = self
# Determine what kind of input we have (init) and execute the
# proper code to intiailize the model
self._files_to_close = []
self._iscopy = False
is_array = False
is_shape = False
shape = None
if init is None:
asdf = AsdfFile(extensions=extensions)
elif isinstance(init, dict):
asdf = AsdfFile(init, extensions=self._extensions)
elif isinstance(init, np.ndarray):
asdf = AsdfFile(extensions=self._extensions)
shape = init.shape
is_array = True
elif isinstance(init, tuple):
for item in init:
if not isinstance(item, int):
raise ValueError("shape must be a tuple of ints")
shape = init
asdf = AsdfFile()
is_shape = True
elif isinstance(init, DataModel):
self.clone(self, init)
if not isinstance(init, self.__class__):
self.validate()
return
elif isinstance(init, AsdfFile):
asdf = init
elif isinstance(init, fits.HDUList):
asdf = fits_support.from_fits(init, self._schema, self._extensions,
self._ctx)
elif isinstance(init, (str, bytes)):
if isinstance(init, bytes):
init = init.decode(sys.getfilesystemencoding())
file_type = filetype.check(init)
if file_type == "fits":
hdulist = fits.open(init)
asdf = fits_support.from_fits(hdulist, self._schema,
self._extensions, self._ctx)
self._files_to_close.append(hdulist)
elif file_type == "asdf":
asdf = AsdfFile.open(init, extensions=self._extensions)
else:
# TODO handle json files as well
raise IOError(
"File does not appear to be a FITS or ASDF file.")
else:
raise ValueError("Can't initialize datamodel using {0}".format(
str(type(init))))
# Initialize object fields as determined from the code above
self._shape = shape
self._instance = asdf.tree
self._asdf = asdf
# Initalize class dependent hidden fields
self._no_asdf_extension = False
# Instantiate the primary array of the image
if is_array:
primary_array_name = self.get_primary_array_name()
if not primary_array_name:
raise TypeError(
"Array passed to DataModel.__init__, but model has "
"no primary array in its schema")
setattr(self, primary_array_name, init)
if is_shape:
if not self.get_primary_array_name():
raise TypeError(
"Shape passed to DataModel.__init__, but model has "
"no primary array in its schema")
# if the input is from a file, set the filename attribute
if isinstance(init, str):
self.meta.filename = os.path.basename(init)
elif isinstance(init, fits.HDUList):
info = init.fileinfo(0)
if info is not None:
filename = info.get('filename')
if filename is not None:
self.meta.filename = os.path.basename(filename)
# if the input model doesn't have a date set, use the current date/time
if not self.meta.hasattr('date'):
current_date = Time(datetime.datetime.now())
current_date.format = 'isot'
self.meta.date = current_date.value
# store the data model type, if not already set
klass = self.__class__.__name__
if klass != 'DataModel':
if not self.meta.hasattr('model_type'):
self.meta.model_type = klass
def imaging_distortion(input_model, reference_files):
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
return distortion
def imaging_distortion(input_model, reference_files):
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
return distortion
