def ifu_slicer2asdf(ifuslicer, outname):
"""
Create an asdf reference file with the MSA description.
ifu_slicer2asdf("IFU_slicer.sgd", "ifu_slicer.asdf")
Parameters
----------
ifuslicer : str
A fits file with the IFU slicer description
outname : str
Name of output ASDF file.
"""
ref_kw = common_reference_file_keywords("IFUSLICER", "NIRSPEC IFU SLICER description - CDP4")
f = fits.open(ifuslicer)
tree = ref_kw.copy()
data = f[1].data
header = f[1].header
shiftx = models.Shift(header['XREF'], name='ifu_slicer_xref')
shifty = models.Shift(header['YREF'], name='ifu_slicer_yref')
rot = models.Rotation2D(header['ROT'], name='ifu_slicer_rot')
model = rot | shiftx & shifty
tree['model'] = model
tree['data'] = f[1].data
f.close()
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.write_to(outname)
return fasdf
def wavelength_range(spectral_conf, outname, ref_kw):
"""
Parameters
----------
spectral_conf : str
reference file: spectralconfigurations.txt
outname : str
output file name
"""
with open(spectral_conf) as f:
lines = f.readlines()
lines = [l.strip() for l in lines][13:]
lines = [l.split() for l in lines]
tree = ref_kw.copy()
filter_grating = {}
for l in lines:
f_g = l[0] + '_' + l[1]
filter_grating[f_g] = {
'order': int(l[2]),
'range': [float(l[3]), float(l[4])]
}
tree['filter_grating'] = filter_grating
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.write_to(outname)
return fasdf
def ifu_slicer2asdf(ifuslicer, outname, ref_kw):
"""
Create an asdf reference file with the MSA description.
ifu_slicer2asdf("IFU_slicer.sgd", "ifu_slicer.asdf")
Parameters
----------
ifuslicer : str
A fits file with the IFU slicer description
outname : str
Name of output ASDF file.
"""
f = fits.open(ifuslicer)
tree = ref_kw.copy()
data = f[1].data
header = f[1].header
shiftx = models.Shift(header['XREF'], name='ifu_slicer_xref')
shifty = models.Shift(header['YREF'], name='ifu_slicer_yref')
rot = models.Rotation2D(header['ROT'], name='ifu_slicer_rot')
model = rot | shiftx & shifty
tree['model'] = model
tree['data'] = f[1].data
f.close()
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.add_history_entry("Build 6")
fasdf.write_to(outname)
return fasdf
def prism2asdf(prifile, tiltyfile, tiltxfile, outname):
"""Create a NIRSPEC prism disperser reference file in ASDF format.
Combine information stored in disperser_G?.dis and disperser_G?_TiltY.gtp
files delievred by the IDT.
Parameters
----------
prifile : list or str
File with primary information for the PRSIM
tiltyfile : str
File with tilt_Y data, e.g. disperser_PRISM_TiltY.gtp.
tiltxfile: str
File with tilt_x data, e.g. disperser_PRISM_TiltX.gtp.
outname : str
Name of output ASDF file.
Returns
-------
fasdf : asdf.AsdfFile
"""
params = common_reference_file_keywords("PRISM", "NIRSPEC PRISM Model")
flist = [prifile, tiltyfile, tiltxfile]
# translate the files
for fname in flist:
try:
refparams = dict_from_file(fname)
except:
print("Disperser file was not converted.")
raise
pdict = {}
coeffs = {}
parts = fname.lower().split(".")[0]
ref = str("_".join(parts.split("_")[1:]))
if "pri" not in fname:
try:
for i, c in enumerate(refparams['CoeffsTemperature00']):
coeffs['c' + str(i)] = c
pdict['tilt_model'] = models.Polynomial1D(len(coeffs)-1, **coeffs)
del refparams['CoeffsTemperature00']
except KeyError:
print("Missing CoeffsTemperature in {0}".format(fname))
raise
# store the rest of the keys
for k, v in refparams.items():
pdict[k] = v
print(pdict)
params[ref] = pdict
fasdf = AsdfFile()
fasdf.tree = params
fasdf.write_to(outname)
return fasdf
def ifu_slicer2asdf(ifuslicer, outname, ref_kw):
"""
Create an asdf reference file with the MSA description.
ifu_slicer2asdf("IFU_slicer.sgd", "ifu_slicer.asdf")
Parameters
----------
ifuslicer : str
A fits file with the IFU slicer description
outname : str
Name of output ASDF file.
"""
f = fits.open(ifuslicer)
tree = ref_kw.copy()
data = f[1].data
header = f[1].header
shiftx = models.Shift(header['XREF'], name='ifu_slicer_xref')
shifty = models.Shift(header['YREF'], name='ifu_slicer_yref')
rot = models.Rotation2D(header['ROT'], name='ifu_slicer_rot')
model = rot | shiftx & shifty
tree['model'] = model
tree['data'] = f[1].data
f.close()
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.add_history_entry("Build 6")
fasdf.write_to(outname)
return fasdf
def ifupost2asdf(ifupost_files, outname):
"""
Create a reference file of type ``ifupost`` .
Combines all IDT ``IFU-POST`` reference files in one ASDF file.
forward direction : MSA to Collimator
backward_direction: Collimator to MSA
Parameters
----------
ifupost_files : list
Names of all ``IFU-POST`` IDT reference files
outname : str
Name of output ``ASDF`` file
"""
ref_kw = common_reference_file_keywords("IFUPOST", "NIRSPEC IFU-POST transforms - CDP4")
fa = AsdfFile()
fa.tree = ref_kw
for fifu in ifupost_files:
n = int((fifu.split('IFU-POST_')[1]).split('.pcf')[0])
fa.tree[n] = {}
with open(fifu) as f:
lines = [l.strip() for l in f.readlines()]
factors = lines[lines.index('*Factor 2') + 1].split()
rotation_angle = float(lines[lines.index('*Rotation') + 1])
input_rot_center = lines[lines.index('*InputRotationCentre 2') + 1].split()
output_rot_center = lines[lines.index('*OutputRotationCentre 2') + 1].split()
linear_sky2det = homothetic_sky2det(input_rot_center, rotation_angle, factors, output_rot_center)
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1: xcoeff_index + 22]
xcoeff_forward = coeffs_from_pcf(degree, xlines)
x_poly_forward = models.Polynomial2D(degree, name='x_poly_forward', **xcoeff_forward)
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ycoeff_forward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_forward = models.Polynomial2D(degree, name='y_poly_forward', **ycoeff_forward)
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xcoeff_backward = coeffs_from_pcf(degree, lines[xcoeff_index + 1: xcoeff_index + 22])
x_poly_backward = models.Polynomial2D(degree, name='x_poly_backward', **xcoeff_backward)
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ycoeff_backward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_backward = models.Polynomial2D(degree, name='y_poly_backward', **ycoeff_backward)
output2poly_mapping = Identity(2, name='output_mapping')
output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (x_poly_forward & y_poly_forward) | output2poly_mapping
model = linear_sky2det | model_poly
fa.tree[n]['model'] = model
asdffile = fa.write_to(outname)
return asdffile
def pcf_forward(pcffile, outname):
"""
Create the **IDT** forward transform from collimator to gwa.
"""
with open(pcffile) as f:
lines = [l.strip() for l in f.readlines()]
factors = lines[lines.index('*Factor 2') + 1].split()
# factor==1/factor in backward msa2ote direction and factor==factor in sky2detector direction
scale = models.Scale(float(factors[0]), name="x_scale") & \
models.Scale(float(factors[1]), name="y_scale")
rotation_angle = lines[lines.index('*Rotation') + 1]
# The minius sign here is because astropy.modeling has the opposite direction of rotation than the idl implementation
rotation = models.Rotation2D(-float(rotation_angle), name='rotation')
# Here the model is called "output_shift" but in the team version it is the "input_shift".
input_rot_center = lines[lines.index('*InputRotationCentre 2') + 1].split()
input_rot_shift = models.Shift(-float(input_rot_center[0]), name='input_x_shift') & \
models.Shift(-float(input_rot_center[1]), name='input_y_shift')
# Here the model is called "input_shift" but in the team version it is the "output_shift".
output_rot_center = lines[lines.index('*OutputRotationCentre 2') + 1].split()
output_rot_shift = models.Shift(float(output_rot_center[0]), name='output_x_shift') & \
models.Shift(float(output_rot_center[1]), name='output_y_shift')
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1: xcoeff_index + 22]
xcoeff_forward = coeffs_from_pcf(degree, xlines)
x_poly_forward = models.Polynomial2D(degree, name='x_poly_forward', **xcoeff_forward)
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ycoeff_forward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_forward = models.Polynomial2D(degree, name='y_poly_forward', **ycoeff_forward)
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xcoeff_backward = coeffs_from_pcf(degree, lines[xcoeff_index + 1: xcoeff_index + 22])
x_poly_backward = models.Polynomial2D(degree, name='x_poly_backward', **xcoeff_backward)
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ycoeff_backward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_backward = models.Polynomial2D(degree, name='y_poly_backward', **ycoeff_backward)
x_poly_forward.inverse = x_poly_backward
y_poly_forward.inverse = y_poly_backward
poly_mapping1 = Mapping((0, 1, 0, 1))
poly_mapping1.inverse = Identity(2)
poly_mapping2 = Identity(2)
poly_mapping2.inverse = Mapping((0, 1, 0, 1))
model = input_rot_shift | rotation | scale | output_rot_shift | \
poly_mapping1 | x_poly_forward & y_poly_forward | poly_mapping2
f = AsdfFile()
f.tree = {'model': model}
f.write_to(outname)
def pcf_forward(pcffile, outname):
"""
Create the **IDT** forward transform from collimator to gwa.
"""
with open(pcffile) as f:
lines = [l.strip() for l in f.readlines()]
factors = lines[lines.index('*Factor 2') + 1].split()
# factor==1/factor in backward msa2ote direction and factor==factor in sky2detector direction
scale = models.Scale(float(factors[0]), name="x_scale") & \
models.Scale(float(factors[1]), name="y_scale")
rotation_angle = lines[lines.index('*Rotation') + 1]
# The minius sign here is because astropy.modeling has the opposite direction of rotation than the idl implementation
rotation = models.Rotation2D(-float(rotation_angle), name='rotation')
# Here the model is called "output_shift" but in the team version it is the "input_shift".
input_rot_center = lines[lines.index('*InputRotationCentre 2') + 1].split()
input_rot_shift = models.Shift(-float(input_rot_center[0]), name='input_x_shift') & \
models.Shift(-float(input_rot_center[1]), name='input_y_shift')
# Here the model is called "input_shift" but in the team version it is the "output_shift".
output_rot_center = lines[lines.index('*OutputRotationCentre 2') + 1].split()
output_rot_shift = models.Shift(float(output_rot_center[0]), name='output_x_shift') & \
models.Shift(float(output_rot_center[1]), name='output_y_shift')
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1: xcoeff_index + 22]
xcoeff_forward = coeffs_from_pcf(degree, xlines)
x_poly_forward = models.Polynomial2D(degree, name='x_poly_forward', **xcoeff_forward)
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ycoeff_forward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_forward = models.Polynomial2D(degree, name='y_poly_forward', **ycoeff_forward)
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xcoeff_backward = coeffs_from_pcf(degree, lines[xcoeff_index + 1: xcoeff_index + 22])
x_poly_backward = models.Polynomial2D(degree, name='x_poly_backward', **xcoeff_backward)
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ycoeff_backward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_backward = models.Polynomial2D(degree, name='y_poly_backward', **ycoeff_backward)
x_poly_forward.inverse = x_poly_backward
y_poly_forward.inverse = y_poly_backward
poly_mapping1 = Mapping((0, 1, 0, 1))
poly_mapping1.inverse = Identity(2)
poly_mapping2 = Identity(2)
poly_mapping2.inverse = Mapping((0, 1, 0, 1))
model = input_rot_shift | rotation | scale | output_rot_shift | \
poly_mapping1 | x_poly_forward & y_poly_forward | poly_mapping2
f = AsdfFile()
f.tree = {'model': model}
f.write_to(outname)
def ote2asdf(otepcf, outname, ref_kw):
"""
ref_kw = common_reference_file_keywords('OTE', 'NIRSPEC OTE transform - CDP4')
ote2asdf('Model/Ref_Files/CoordTransform/OTE.pcf', 'jwst_nirspec_ote_0001.asdf', ref_kw)
"""
with open(otepcf) as f:
lines = [l.strip() for l in f.readlines()]
factors = lines[lines.index('*Factor 2 1') + 1].split()
# this corresponds to modeling Rotation direction as is
rotation_angle = float(lines[lines.index('*Rotation') + 1])
input_rot_center = lines[lines.index('*InputRotationCentre 2 1') + 1].split()
output_rot_center = lines[lines.index('*OutputRotationCentre 2 1') + 1].split()
mlinear = homothetic_det2sky(input_rot_center, rotation_angle, factors, output_rot_center)
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1].split('\t')
xcoeff_backward = coeffs_from_pcf(degree, xlines)
x_poly_forward = models.Polynomial2D(degree, name='x_poly_forward', **xcoeff_backward)
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1].split('\t')
xcoeff_forward = coeffs_from_pcf(degree, xlines)
x_poly_backward = models.Polynomial2D(degree, name='x_poly_backward', **xcoeff_forward)
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ylines = lines[ycoeff_index + 1].split('\t')
ycoeff_backward = coeffs_from_pcf(degree, ylines)
y_poly_forward = models.Polynomial2D(degree, name='y_poly_forward', **ycoeff_backward)
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ylines = lines[ycoeff_index + 1].split('\t')
ycoeff_forward = coeffs_from_pcf(degree, ylines)
y_poly_backward = models.Polynomial2D(degree, name='y_poly_backward', **ycoeff_forward)
x_poly_forward.inverse = x_poly_backward
y_poly_forward.inverse = y_poly_backward
output2poly_mapping = Identity(2, name='output_mapping')
output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (x_poly_forward & y_poly_forward) | output2poly_mapping
model = model_poly | mlinear
f = AsdfFile()
f.tree = ref_kw.copy()
f.tree['model'] = model
f.add_history_entry("Build 6")
f.write_to(outname)
return model_poly, mlinear
def create_regions_file(slices, detector, band, channel, name, author,
useafter, description, outformat):
tree = create_reffile_header("REGIONS", detector, band, channel, author,
useafter, description)
tree['filename'] = name
f = AsdfFile()
tree['regions'] = slices
f.tree = tree
# f.add_history_entry("DOCUMENT: MIRI-TN-00001-ETH; SOFTWARE: polyd2c_CDP5.pro; DATA USED: Data set of: - FM Test Campaign relevant to MRS-OPT-01, MRS-OPT-02, MRS-OPT-04, MRS-OPT-08; - CV1 Test Campaign relevant to MRS-OPT-02; - CV2 Test Campaign relevant to MRS-OPT-02; - Laboratory measurement of SPO; ============ DIFFERENCES: - New file structure: Change of Extention names and Table Column Headers.; - Replaced V2/V3 with XAN/YAN;")
f.write_to(name) #,all_array_storage=outformat)
def create_v23(reftype, detector, band, channels, data, name):
"""
Create the transform from MIRI Local to telescope V2/V3 system for all channels.
"""
channel = "".join([ch[0] for ch in channels])
tree = {"detector": detector,
"instrument" : "MIRI",
"band": band,
"channel": channel,
"exp_type": "MIR_MRS",
"pedigree": "GROUND",
"title": "MIRI IFU model - based on CDP-4",
"reftype": reftype,
"author": "N. Dencheva"
}
ab_v23 = data[0]
v23_ab = data[1]
m = {}
c0_0, c0_1, c1_0, c1_1 = ab_v23[0][1:]
ch1_v2 = models.Polynomial2D(2, c0_0=c0_0, c1_0=c1_0, c0_1=c0_1, c1_1=c1_1,
name="ab_v23")
c0_0, c0_1, c1_0, c1_1 = v23_ab[0][1:]
ch1_a = models.Polynomial2D(2, c0_0=c0_0, c1_0=c1_0, c0_1=c0_1, c1_1=c1_1,
name="v23_ab")
c0_0, c0_1, c1_0, c1_1 = ab_v23[1][1:]
ch1_v3 = models.Polynomial2D(2, c0_0=c0_0, c1_0=c1_0, c0_1=c0_1, c1_1=c1_1,
name="ab_v23")
c0_0, c0_1, c1_0, c1_1 = v23_ab[1][1:]
ch1_b = models.Polynomial2D(2, c0_0=c0_0, c1_0=c1_0, c0_1=c0_1, c1_1=c1_1,
name="v23_ab")
c0_0, c0_1, c1_0, c1_1 = ab_v23[2][1:]
ch2_v2 = models.Polynomial2D(2, c0_0=c0_0, c1_0=c1_0, c0_1=c0_1, c1_1=c1_1,
name="ab_v23")
c0_0, c0_1, c1_0, c1_1 = v23_ab[2][1:]
ch2_a = models.Polynomial2D(2, c0_0=c0_0, c1_0=c1_0, c0_1=c0_1, c1_1=c1_1,
name="v23_ab")
c0_0, c0_1, c1_0, c1_1 = ab_v23[3][1:]
ch2_v3 = models.Polynomial2D(2, c0_0=c0_0, c1_0=c1_0, c0_1=c0_1, c1_1=c1_1,
name="ab_v23")
c0_0, c0_1, c1_0, c1_1 = v23_ab[3][1:]
ch2_b = models.Polynomial2D(2, c0_0=c0_0, c1_0=c1_0, c0_1=c0_1, c1_1=c1_1,
name="v23_ab")
ch1_for = ch1_v2 & ch1_v3
ch2_for = ch2_v2 & ch2_v3
ch1_for.inverse = ch1_a & ch1_b
ch2_for.inverse = ch2_a & ch2_b
m[channels[0]] = ch1_for
m[channels[1]] = ch2_for
tree['model'] = m
f = AsdfFile()
f.tree = tree
f.write_to(name)
def fpa2asdf(fpafile, outname, ref_kw):
"""
Create an asdf reference file with the FPA description.
The CDP2 delivery includes a fits file - "FPA.fpa" which is the
input to this function. This file is converted to asdf and is a
reference file of type "FPA".
nirspec_fs_ref_tools.fpa2asdf('Ref_Files/CoordTransform/Description/FPA.fpa', 'fpa.asdf')
Parameters
----------
fpafile : str
A fits file with FPA description (FPA.fpa)
outname : str
Name of output ASDF file.
"""
with open(fpafile) as f:
lines = [l.strip() for l in f.readlines()]
# NRS1
ind = lines.index("*SCA491_PitchX")
scalex_nrs1 = models.Scale(1 / float(lines[ind + 1]), name='fpa_scale_x')
ind = lines.index("*SCA491_PitchY")
scaley_nrs1 = models.Scale(1 / float(lines[ind + 1]), name='fpa_scale_y')
ind = lines.index("*SCA491_RotAngle")
rot_nrs1 = models.Rotation2D(np.rad2deg(-float(lines[ind + 1])),
name='fpa_rotation')
ind = lines.index("*SCA491_PosX")
shiftx_nrs1 = models.Shift(-float(lines[ind + 1]), name='fpa_shift_x')
ind = lines.index("*SCA491_PosY")
shifty_nrs1 = models.Shift(-float(lines[ind + 1]), name='fpa_shift_y')
# NRS2
ind = lines.index("*SCA492_PitchX")
scalex_nrs2 = models.Scale(1 / float(lines[ind + 1]), name='fpa_scale_x')
ind = lines.index("*SCA492_PitchY")
scaley_nrs2 = models.Scale(1 / float(lines[ind + 1]), name='fpa_scale_y')
ind = lines.index("*SCA492_RotAngle")
rot_nrs2 = models.Rotation2D(np.rad2deg(float(lines[ind + 1])),
name='fpa_rotation')
ind = lines.index("*SCA492_PosX")
shiftx_nrs2 = models.Shift(-float(lines[ind + 1]), name='fpa_shift_x')
ind = lines.index("*SCA492_PosY")
shifty_nrs2 = models.Shift(-float(lines[ind + 1]), name='fpa_shift_y')
tree = ref_kw.copy()
tree['NRS1'] = (shiftx_nrs1 & shifty_nrs1) | rot_nrs1 | (scalex_nrs1
& scaley_nrs1)
tree['NRS2'] = (shiftx_nrs2 & shifty_nrs2) | rot_nrs2 | (scalex_nrs2
& scaley_nrs2)
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.write_to(outname)
return fasdf
def create_specwcs_file(reftype, detector, band, channel, lmodel, name, author,
useafter, description):
tree = create_reffile_header(reftype, detector, band, channel, author,
useafter, description)
tree['subarray'] = "N/A"
tree['filename'] = name
tree['model'] = lmodel
f = AsdfFile()
f.tree = tree
f.add_history_entry(
"DOCUMENT: MIRI-TN-00001-ETH; SOFTWARE: polyd2c_CDP5.pro; DATA USED: Data set of: - FM Test Campaign relevant to MRS-OPT-01, MRS-OPT-02, MRS-OPT-04, MRS-OPT-08; - CV1 Test Campaign relevant to MRS-OPT-02; - CV2 Test Campaign relevant to MRS-OPT-02; - Laboratory measurement of SPO; ============ DIFFERENCES: - New file structure: Change of Extention names and Table Column Headers.; - Replaced V2/V3 with XAN/YAN;"
)
f.write_to(name)
def create_distortion_file(reftype, detector, band, channel, data, name):
tree = create_reffile_header(reftype, detector, band, channel)
adata, bdata, xdata, ydata, sdata1, sdata2 = data
tree['alpha_model'] = adata
tree['beta_model'] = bdata
tree['x_model'] = xdata
tree['y_model'] = ydata
tree['slice_model'] = {str(channel[0])+band: sdata1, str(channel[1])+band: sdata2}
f = AsdfFile()
f.tree = tree
f.write_to(name)
def create_wavelengthrange_file(name):
f = AsdfFile()
#wavelengthrange = {'1SHORT': (4.88, 5.77),
#'1MEDIUM': (5.64, 6.67),
#'1LONG': (6.50, 7.70),
#'2SHORT': (7.47, 8.83),
#'2MEDIUM': (8.63, 10.19),
#'2LONG': (9.96, 11.77),
#'3SHORT': (11.49, 13.55),
#'3MEDIUM': (13.28, 15.66),
#'3LONG': (15.34, 18.09),
#'4SHORT': (17.60, 21.00),
#'4MEDIUM': (20.51, 24.48),
#'4LONG': (23.92, 28.55)
#}
# Relaxing the range to match the distortion. The table above
# comes from the report and is "as designed".
wavelengthrange = {
'1SHORT': (4.68, 5.97),
'1MEDIUM': (5.24, 6.87),
'1LONG': (6.2, 7.90),
'2SHORT': (7.27, 9.03),
'2MEDIUM': (8.43, 10.39),
'2LONG': (9.76, 11.97),
'3SHORT': (11.29, 13.75),
'3MEDIUM': (13.08, 15.86),
'3LONG': (15.14, 18.29),
'4SHORT': (17.40, 21.20),
'4MEDIUM': (20.31, 24.68),
'4LONG': (23.72, 28.75)
}
channels = [
'1SHORT', '1MEDIUM', '1LONG', '2SHORT', '2MEDIUM', '2LONG', '3SHORT',
'3MEDIUM', '3LONG', '4SHORT', '4MEDIUM', '4LONG'
]
tree = {
"instrument": "MIRI",
"exp_type": "MIR_MRS",
"pedigree": "GROUND",
"title": "MIRI IFU model - based on CDP-4",
"reftype": "WAVELENGTHRANGE",
"author": "N. Dencheva"
}
tree['channels'] = channels
f.tree = tree
vr = np.empty((12, 2), dtype=np.float)
for i, ch in enumerate(channels):
vr[i] = wavelengthrange[ch]
f.tree['wavelengthrange'] = vr
f.write_to(name)
def fpa2asdf(fpafile, outname, ref_kw):
"""
Create an asdf reference file with the FPA description.
The CDP2 delivery includes a fits file - "FPA.fpa" which is the
input to this function. This file is converted to asdf and is a
reference file of type "FPA".
nirspec_fs_ref_tools.fpa2asdf('Ref_Files/CoordTransform/Description/FPA.fpa', 'fpa.asdf')
Parameters
----------
fpafile : str
A fits file with FPA description (FPA.fpa)
outname : str
Name of output ASDF file.
"""
with open(fpafile) as f:
lines = [l.strip() for l in f.readlines()]
# NRS1
ind = lines.index("*SCA491_PitchX")
scalex_nrs1 = models.Scale(1/float(lines[ind+1]), name='fpa_scale_x')
ind = lines.index("*SCA491_PitchY")
scaley_nrs1 = models.Scale(1/float(lines[ind+1]), name='fpa_scale_y')
ind = lines.index("*SCA491_RotAngle")
rot_nrs1 = models.Rotation2D(np.rad2deg(-float(lines[ind+1])), name='fpa_rotation')
ind = lines.index("*SCA491_PosX")
shiftx_nrs1 = models.Shift(-float(lines[ind+1]), name='fpa_shift_x')
ind = lines.index("*SCA491_PosY")
shifty_nrs1 = models.Shift(-float(lines[ind+1]), name='fpa_shift_y')
# NRS2
ind = lines.index("*SCA492_PitchX")
scalex_nrs2 = models.Scale(1/float(lines[ind+1]), name='fpa_scale_x')
ind = lines.index("*SCA492_PitchY")
scaley_nrs2 = models.Scale(1/float(lines[ind+1]), name='fpa_scale_y')
ind = lines.index("*SCA492_RotAngle")
rot_nrs2 = models.Rotation2D(np.rad2deg(float(lines[ind+1])), name='fpa_rotation')
ind = lines.index("*SCA492_PosX")
shiftx_nrs2 = models.Shift(-float(lines[ind+1]), name='fpa_shift_x')
ind = lines.index("*SCA492_PosY")
shifty_nrs2 = models.Shift(-float(lines[ind+1]), name='fpa_shift_y')
tree = ref_kw.copy()
tree['NRS1'] = (shiftx_nrs1 & shifty_nrs1) | rot_nrs1 | (scalex_nrs1 & scaley_nrs1)
tree['NRS2'] = (shiftx_nrs2 & shifty_nrs2) | rot_nrs2 | (scalex_nrs2 & scaley_nrs2)
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.write_to(outname)
return fasdf
def wavelength_range(spectral_conf, outname, ref_kw):
"""
Parameters
----------
spectral_conf : str
reference file: spectralconfigurations.txt
outname : str
output file name
"""
with open(spectral_conf) as f:
lines = f.readlines()
lines = [l.strip() for l in lines][13:]
lines = [l.split() for l in lines]
tree = ref_kw.copy()
filter_grating = {}
for l in lines:
f_g = l[0] + '_' + l[1]
filter_grating[f_g] = {
'order': int(l[2]),
'range': [float(l[3]), float(l[4])]
}
tree['filter_grating'] = filter_grating
# values in lamp_grating come from private communication with the INS team
#lamp_grating = {}
filter_grating['FLAT1_G140M'] = {'order': -1, 'range': [1e-6, 1.8e-6]}
filter_grating['LINE1_G140M'] = {'order': -1, 'range': [1e-6, 1.8e-6]}
filter_grating['FLAT1_G140H'] = {'order': -1, 'range': [1e-6, 1.8e-6]}
filter_grating['LINE1_G140H'] = {'order': -1, 'range': [1e-6, 1.8e-6]}
filter_grating['FLAT2_G235M'] = {'order': -1, 'range': [1.7e-6, 3.1e-6]}
filter_grating['LINE2_G235M'] = {'order': -1, 'range': [1.7e-6, 3.1e-6]}
filter_grating['FLAT2_G235H'] = {'order': -1, 'range': [1.7e-6, 3.1e-6]}
filter_grating['LINE2_G235H'] = {'order': -1, 'range': [1.7e-6, 3.1e-6]}
filter_grating['FLAT3_G395M'] = {'order': -1, 'range': [2.9e-6, 5.3e-6]}
filter_grating['LINE3_G395M'] = {'order': -1, 'range': [2.9e-6, 5.3e-6]}
filter_grating['FLAT3_G395H'] = {'order': -1, 'range': [2.9e-6, 5.3e-6]}
filter_grating['LINE3_G395H'] = {'order': -1, 'range': [2.9e-6, 5.3e-6]}
filter_grating['REF_G140M'] = {'order': -1, 'range': [1.3e-6, 1.7e-6]}
filter_grating['REF_G140H'] = {'order': -1, 'range': [1.3e-6, 1.7e-6]}
filter_grating['TEST_MIRROR'] = {'order': -1, 'range': [0.6e-6, 5.3e-6]}
#for grating in ["G140H", "G140M", "G235H", "G235M", "G395H", "G395M", "MIRROR"]:
#lamp_grating['FLAT4_{0}'.format(grating)] = {'order': -1, 'range': [0.7e-6, 1.2e-6]}
#for grating in ["G140H", "G140M", "G235H", "G235M", "G395H", "G395M", "MIRROR"]:
#lamp_grating['LINE4_{0}'.format(grating)] = {'order': -1, 'range': [0.6e-6, 5.3e-6]}
#tree['lamp_grating'] = lamp_grating
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.add_history_entry("Build 6")
fasdf.write_to(outname)
return fasdf
def create_distortion_file(reftype, detector, band, channel, data, name):
tree = create_reffile_header(reftype, detector, band, channel)
adata, bdata, xdata, ydata, sdata1, sdata2 = data
tree['alpha_model'] = adata
tree['beta_model'] = bdata
tree['x_model'] = xdata
tree['y_model'] = ydata
tree['slice_model'] = {
str(channel[0]) + band: sdata1,
str(channel[1]) + band: sdata2
}
f = AsdfFile()
f.tree = tree
f.write_to(name)
def wavelength_range(spectral_conf, outname, ref_kw):
"""
Parameters
----------
spectral_conf : str
reference file: spectralconfigurations.txt
outname : str
output file name
"""
with open(spectral_conf) as f:
lines = f.readlines()
lines = [l.strip() for l in lines][13 :]
lines = [l.split() for l in lines]
tree = ref_kw.copy()
filter_grating = {}
for l in lines:
f_g = l[0] + '_' + l[1]
filter_grating[f_g] = {'order': int(l[2]), 'range': [float(l[3]), float(l[4])]}
tree['filter_grating'] = filter_grating
# values in lamp_grating come from private communication with the INS team
#lamp_grating = {}
filter_grating['FLAT1_G140M'] = {'order': -1, 'range': [1e-6, 1.8e-6]}
filter_grating['LINE1_G140M'] = {'order': -1, 'range': [1e-6, 1.8e-6]}
filter_grating['FLAT1_G140H'] = {'order': -1, 'range': [1e-6, 1.8e-6]}
filter_grating['LINE1_G140H'] = {'order': -1, 'range': [1e-6, 1.8e-6]}
filter_grating['FLAT2_G235M'] = {'order': -1, 'range': [1.7e-6, 3.1e-6]}
filter_grating['LINE2_G235M'] = {'order': -1, 'range': [1.7e-6, 3.1e-6]}
filter_grating['FLAT2_G235H'] = {'order': -1, 'range': [1.7e-6, 3.1e-6]}
filter_grating['LINE2_G235H'] = {'order': -1, 'range': [1.7e-6, 3.1e-6]}
filter_grating['FLAT3_G395M'] = {'order': -1, 'range': [2.9e-6, 5.3e-6]}
filter_grating['LINE3_G395M'] = {'order': -1, 'range': [2.9e-6, 5.3e-6]}
filter_grating['FLAT3_G395H'] = {'order': -1, 'range': [2.9e-6, 5.3e-6]}
filter_grating['LINE3_G395H'] = {'order': -1, 'range': [2.9e-6, 5.3e-6]}
filter_grating['REF_G140M'] = {'order': -1, 'range': [1.3e-6, 1.7e-6]}
filter_grating['REF_G140H'] = {'order': -1, 'range': [1.3e-6, 1.7e-6]}
filter_grating['TEST_MIRROR'] = {'order': -1, 'range': [0.6e-6, 5.3e-6]}
#for grating in ["G140H", "G140M", "G235H", "G235M", "G395H", "G395M", "MIRROR"]:
#lamp_grating['FLAT4_{0}'.format(grating)] = {'order': -1, 'range': [0.7e-6, 1.2e-6]}
#for grating in ["G140H", "G140M", "G235H", "G235M", "G395H", "G395M", "MIRROR"]:
#lamp_grating['LINE4_{0}'.format(grating)] = {'order': -1, 'range': [0.6e-6, 5.3e-6]}
#tree['lamp_grating'] = lamp_grating
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.add_history_entry("Build 6")
fasdf.write_to(outname)
return fasdf
def create_wavelengthrange_file(name):
f = AsdfFile()
#wavelengthrange = {'1SHORT': (4.88, 5.77),
#'1MEDIUM': (5.64, 6.67),
#'1LONG': (6.50, 7.70),
#'2SHORT': (7.47, 8.83),
#'2MEDIUM': (8.63, 10.19),
#'2LONG': (9.96, 11.77),
#'3SHORT': (11.49, 13.55),
#'3MEDIUM': (13.28, 15.66),
#'3LONG': (15.34, 18.09),
#'4SHORT': (17.60, 21.00),
#'4MEDIUM': (20.51, 24.48),
#'4LONG': (23.92, 28.55)
#}
# Relaxing the range to match the distortion. The table above
# comes from the report and is "as designed".
wavelengthrange = {'1SHORT': (4.68, 5.97),
'1MEDIUM': (5.24, 6.87),
'1LONG': (6.2, 7.90),
'2SHORT': (7.27, 9.03),
'2MEDIUM': (8.43, 10.39),
'2LONG': (9.76, 11.97),
'3SHORT': (11.29, 13.75),
'3MEDIUM': (13.08, 15.86),
'3LONG': (15.14, 18.29),
'4SHORT': (17.40, 21.20),
'4MEDIUM': (20.31, 24.68),
'4LONG': (23.72, 28.75)
}
channels = ['1SHORT', '1MEDIUM', '1LONG', '2SHORT', '2MEDIUM', '2LONG',
'3SHORT', '3MEDIUM', '3LONG', '4SHORT', '4MEDIUM', '4LONG']
tree = {
"instrument": "MIRI",
"exp_type": "MIR_MRS",
"pedigree": "GROUND",
"title": "MIRI IFU model - based on CDP-4",
"reftype": "WAVELENGTHRANGE",
"author": "N. Dencheva"
}
tree['channels'] = channels
f.tree = tree
vr = np.empty((12, 2), dtype=np.float)
for i, ch in enumerate(channels):
vr[i] = wavelengthrange[ch]
f.tree['wavelengthrange'] = vr
f.write_to(name)
def create_miri_imager_filter_offset(distfile, outname):
"""
Create an asdf reference file with the filter offsets for the MIRI imager.
Note: The IDT supplied distortion file lists sky to pixel as the
forward transform. Since "forward" in the JWST pipeline is from
pixel to sky, the offsets are taken with the opposite sign.
Parameters
----------
distfile : str
MIRI imager DISTORTION file provided by the IDT team.
outname : str
Name of reference file to be wriiten to disk.
Returns
-------
fasdf : AsdfFile
AsdfFile object
Examples
-------
>>> create_miri_imager_filer_offset('MIRI_FM_MIRIMAGE_DISTORTION_03.02.00.fits',
'jwst_miri_filter_offset_0001.asdf')
"""
with fits.open(distfile) as f:
data = f[9].data
d = dict.fromkeys(data.field('FILTER'))
for i in data:
d[i[0]] = {'column_offset': -i[1], 'row_offset': -i[2]}
tree = {
"title": "MIRI imager filter offset - CDP4",
"reftype": "FILTEROFFSET",
"instrument": "MIRI",
"detector": "MIRIMAGE",
"pedigree": "GROUND",
"author": "N. Dencheva",
"exp_type": "MIR_IMAGE"
}
tree.update(d)
f = AsdfFile()
f.tree = tree
f.write_to(outname)
def create_wavelengthrange_file(name, detector, author, useafter, description):
f = AsdfFile()
# Relaxing the range to match the distortion. The table above
# comes from the report and is "as designed".
wavelengthrange = {
'1SHORT': (4.68, 5.97),
'1MEDIUM': (5.24, 6.87),
'1LONG': (6.2, 7.90),
'2SHORT': (7.27, 9.03),
'2MEDIUM': (8.43, 10.39),
'2LONG': (9.76, 11.97),
'3SHORT': (11.29, 13.75),
'3MEDIUM': (13.08, 15.86),
'3LONG': (15.14, 18.29),
'4SHORT': (17.40, 21.20),
'4MEDIUM': (20.31, 24.68),
'4LONG': (23.72, 28.75)
}
channels = [
'1SHORT', '1MEDIUM', '1LONG', '2SHORT', '2MEDIUM', '2LONG', '3SHORT',
'3MEDIUM', '3LONG', '4SHORT', '4MEDIUM', '4LONG'
]
tree = create_reffile_header("WAVELENGTHRANGE",
detector,
band="N/A",
channel="N/A",
author=author,
useafter=useafter,
description=description)
tree['filename'] = name
tree['author'] = 'Nadia Dencheva'
tree['detector'] = "N/A"
tree['channels'] = channels
f.tree = tree
vr = np.empty((12, 2), dtype=np.float)
for i, ch in enumerate(channels):
vr[i] = wavelengthrange[ch]
f.tree['wavelengthrange'] = vr
f.add_history_entry(
"DOCUMENT: MIRI-TN-00001-ETH; SOFTWARE: polyd2c_CDP5.pro; DATA USED: Data set of: - FM Test Campaign relevant to MRS-OPT-01, MRS-OPT-02, MRS-OPT-04, MRS-OPT-08; - CV1 Test Campaign relevant to MRS-OPT-02; - CV2 Test Campaign relevant to MRS-OPT-02; - Laboratory measurement of SPO; ============ DIFFERENCES: - New file structure: Change of Extention names and Table Column Headers.; - Replaced V2/V3 with XAN/YAN;"
)
f.write_to(name)
def create_miri_imager_filter_offset(distfile, outname):
"""
Create an asdf reference file with the filter offsets for the MIRI imager.
Note: The IDT supplied distortion file lists sky to pixel as the
forward transform. Since "forward" in the JWST pipeline is from
pixel to sky, the offsets are taken with the opposite sign.
Parameters
----------
distfile : str
MIRI imager DISTORTION file provided by the IDT team.
outname : str
Name of reference file to be wriiten to disk.
Returns
-------
fasdf : AsdfFile
AsdfFile object
Examples
-------
>>> create_miri_imager_filer_offset('MIRI_FM_MIRIMAGE_DISTORTION_03.02.00.fits',
'jwst_miri_filter_offset_0001.asdf')
"""
with fits.open(distfile) as f:
data = f[9].data
d = dict.fromkeys(data.field('FILTER'))
for i in data:
d[i[0]] = {'column_offset': -i[1], 'row_offset': -i[2]}
tree = {"title": "MIRI imager filter offset - CDP4",
"reftype": "FILTEROFFSET",
"instrument": "MIRI",
"detector": "MIRIMAGE",
"pedigree": "GROUND",
"author": "N. Dencheva",
"exp_type": "MIR_IMAGE"
}
tree.update(d)
f = AsdfFile()
f.tree = tree
f.write_to(outname)
def create_distortion_file(reftype, detector, band, channel, data, name,
author, useafter, description):
description = 'MIRI MRS Distortion Maps'
tree = create_reffile_header(reftype, detector, band, channel, author,
useafter, description)
tree['filename'] = name
adata, bdata, xdata, ydata, bzero, bdel = data
tree['alpha_model'] = adata
tree['beta_model'] = bdata
tree['x_model'] = xdata
tree['y_model'] = ydata
tree['bzero'] = bzero
tree['bdel'] = bdel
f = AsdfFile()
f.tree = tree
f.add_history_entry(
"DOCUMENT: MIRI-TN-00001-ETH; SOFTWARE: polyd2c_CDP5.pro; DATA USED: Data set of: - FM Test Campaign relevant to MRS-OPT-01, MRS-OPT-02, MRS-OPT-04, MRS-OPT-08; - CV1 Test Campaign relevant to MRS-OPT-02; - CV2 Test Campaign relevant to MRS-OPT-02; - Laboratory measurement of SPO; ============ DIFFERENCES: - New file structure: Change of Extention names and Table Column Headers.; - Replaced V2/V3 with XAN/YAN;"
)
f.write_to(name)
def msa2asdf(msafile, outname, ref_kw):
"""
Create an asdf reference file with the MSA description.
The CDP2 delivery includes a fits file - "MSA.msa".
This file is converted to asdf and serves as a reference file of type "REGIONS".
mas2asfdf("MSA.msa", "msa.asdf") --> creates an 85MB file
Parameters
----------
msafile : str
A fits file with MSA description (MSA.msa)
outname : str
Name of output ASDF file.
"""
f = fits.open(msafile)
tree = ref_kw.copy()
data = f[5].data # SLITS and IFU
header = f[5].header
shiftx = models.Shift(header['SLITXREF'], name='slit_xref')
shifty = models.Shift(header['SLITYREF'], name='slit_yref')
slitrot = models.Rotation2D(header['SLITROT'], name='slit_rot')
for j, slit in enumerate(
['S200A1', 'S200A2', 'S400A1', 'S1600A1', 'S200B1', 'IFU']):
slitdata = data[j]
t = {}
for i, s in enumerate(['xcenter', 'ycenter', 'xsize', 'ysize']):
t[s] = slitdata[i + 1]
model = models.Scale(t['xsize']) & models.Scale(t['ysize']) | \
models.Shift(t['xcenter']) & models.Shift(t['ycenter']) | \
slitrot | shiftx & shifty
t['model'] = model
tree[slit] = t
f.close()
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.write_to(outname)
return fasdf
def msa2asdf(msafile, outname, ref_kw):
"""
Create an asdf reference file with the MSA description.
The CDP2 delivery includes a fits file - "MSA.msa".
This file is converted to asdf and serves as a reference file of type "REGIONS".
mas2asfdf("MSA.msa", "msa.asdf") --> creates an 85MB file
Parameters
----------
msafile : str
A fits file with MSA description (MSA.msa)
outname : str
Name of output ASDF file.
"""
f = fits.open(msafile)
tree = ref_kw.copy()
data = f[5].data # SLITS and IFU
header = f[5].header
shiftx = models.Shift(header['SLITXREF'], name='slit_xref')
shifty = models.Shift(header['SLITYREF'], name='slit_yref')
slitrot = models.Rotation2D(header['SLITROT'], name='slit_rot')
for j, slit in enumerate(['S200A1', 'S200A2', 'S400A1', 'S1600A1', 'S200B1', 'IFU']):
slitdata = data[j]
t = {}
for i, s in enumerate(['xcenter', 'ycenter', 'xsize', 'ysize']):
t[s] = slitdata[i+1]
model = models.Scale(t['xsize']) & models.Scale(t['ysize']) | \
models.Shift(t['xcenter']) & models.Shift(t['ycenter']) | \
slitrot | shiftx & shifty
t['model'] = model
tree[slit] = t
f.close()
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.write_to(outname)
return fasdf
def msa2asdf(msafile, outname, ref_kw):
"""
Create an asdf reference file with the MSA description.
mas2asfdf("MSA.msa", "msa.asdf")
Parameters
----------
msafile : str
A fits file with MSA description (MSA.msa)
outname : str
Name of output ASDF file.
"""
f = fits.open(msafile)
tree = ref_kw.copy()
data = f[5].data # SLITS and IFU
header = f[5].header
shiftx = models.Shift(header['SLITXREF'], name='slit_xref')
shifty = models.Shift(header['SLITYREF'], name='slit_yref')
slitrot = models.Rotation2D(header['SLITROT'], name='slit_rot')
tree[5] = {}
tree[5]['model'] = slitrot | shiftx & shifty
tree[5]['data'] = f[5].data
for i in range(1, 5):
header = f[i].header
shiftx = models.Shift(header['QUADXREF'], name='msa_xref')
shifty = models.Shift(header['QUADYREF'], name='msa_yref')
slitrot = models.Rotation2D(header['QUADROT'], name='msa_rot')
tree[i] = {}
tree[i]['model'] = slitrot | shiftx & shifty
tree[i]['data'] = f[i].data
f.close()
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.add_history_entry("Build 6")
fasdf.write_to(outname)
return fasdf
def msa2asdf(msafile, outname, ref_kw):
"""
Create an asdf reference file with the MSA description.
mas2asfdf("MSA.msa", "msa.asdf")
Parameters
----------
msafile : str
A fits file with MSA description (MSA.msa)
outname : str
Name of output ASDF file.
"""
f = fits.open(msafile)
tree = ref_kw.copy()
data = f[5].data # SLITS and IFU
header = f[5].header
shiftx = models.Shift(header['SLITXREF'], name='slit_xref')
shifty = models.Shift(header['SLITYREF'], name='slit_yref')
slitrot = models.Rotation2D(header['SLITROT'], name='slit_rot')
tree[5] = {}
tree[5]['model'] = slitrot | shiftx & shifty
tree[5]['data'] = f[5].data
for i in range(1, 5):
header = f[i].header
shiftx = models.Shift(header['QUADXREF'], name='msa_xref')
shifty = models.Shift(header['QUADYREF'], name='msa_yref')
slitrot = models.Rotation2D(header['QUADROT'], name='msa_rot')
tree[i] = {}
tree[i]['model'] = slitrot | shiftx & shifty
tree[i]['data'] = f[i].data
f.close()
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.add_history_entry("Build 6")
fasdf.write_to(outname)
return fasdf
def wavelength_range(spectral_conf, outname, ref_kw):
"""
Parameters
----------
spectral_conf : str
reference file: spectralconfigurations.txt
outname : str
output file name
"""
with open(spectral_conf) as f:
lines = f.readlines()
lines = [l.strip() for l in lines][13 :]
lines = [l.split() for l in lines]
tree = ref_kw.copy()
filter_grating = {}
for l in lines:
f_g = l[0] + '_' + l[1]
filter_grating[f_g] = {'order': int(l[2]), 'range': [float(l[3]), float(l[4])]}
tree['filter_grating'] = filter_grating
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.write_to(outname)
return fasdf
def create_distortion_file(reftype, detector, band, channel, channels, data,
name, author, useafter, description, outformat):
description = 'MIRI MRS Distortion Maps'
tree = create_reffile_header(reftype, detector, band, channel, author,
useafter, description)
"""
tree = {"detector": detector,
"instrument" : "MIRI",
"band": band,
"channel": channel,
"exp_type": "MIR_MRS",
"pedigree": "GROUND",
"title": "MIRI IFU model - based on CDP-6",
"reftype": reftype,
"author": author,
"useafter": useafter,
"description": description
}
"""
tree['filename'] = name
# Split the provided data vector into its pieces
adata, bdata, xdata, ydata, bzero, bdel, ab_v23, v23_ab = data
"""
Construct the xy -> alpha,beta model
"""
tree['alpha_model'] = adata
tree['beta_model'] = bdata
tree['x_model'] = xdata
tree['y_model'] = ydata
tree['bzero'] = bzero
tree['bdel'] = bdel
"""
Create the transform from MIRI Local to telescope V2/V3 system for all channels.
"""
channel = "".join([ch[0] for ch in channels])
m = {}
# Read in coefficients from the tables. Note that we'll flip the coefficient
# ordering since they were set up for column-major indexing (IDL) but we're working in
# python (row-major)
# ab -> v2 transform for first channel
c0_0, c1_0, c0_1, c1_1 = ab_v23[0][1:]
ch1_v2 = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="ab_v23")
# v2,v3 -> a transform for first channel
c0_0, c1_0, c0_1, c1_1 = v23_ab[0][1:]
ch1_a = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="v23_ab")
# ab -> v3 transform for first channel
c0_0, c1_0, c0_1, c1_1 = ab_v23[1][1:]
ch1_v3 = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="ab_v23")
# v2,v3 -> b transform for first channel
c0_0, c1_0, c0_1, c1_1 = v23_ab[1][1:]
ch1_b = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="v23_ab")
# ab -> v2 transform for second channel
c0_0, c1_0, c0_1, c1_1 = ab_v23[2][1:]
ch2_v2 = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="ab_v23")
# v2,v3 -> a transform for second channel
c0_0, c1_0, c0_1, c1_1 = v23_ab[2][1:]
ch2_a = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="v23_ab")
# ab -> v3 transform for second channel
c0_0, c1_0, c0_1, c1_1 = ab_v23[3][1:]
ch2_v3 = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="ab_v23")
# v2,v3 -> b transform for second channel
c0_0, c1_0, c0_1, c1_1 = v23_ab[3][1:]
ch2_b = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="v23_ab")
# Transforms from the CDP only went to XAN,YAN, now need a transform to V2,V3
# Mapping to transform from XAN,YAN in arcmin to V2,V3 in arcsec
xanyan_to_v2v3 = models.Identity(1) & (models.Scale(-1) | models.Shift(
-7.8)) | models.Scale(60.) & models.Scale(60.)
# Since the matrix transforms need a 4-element input we need a mapping
# to go from (0,1) to (0,1,0,1)
map = models.Mapping((0, 1, 0, 1), n_inputs=2)
# Put the mappings all together
ch1 = map | ch1_v2 & ch1_v3 | xanyan_to_v2v3
ch2 = map | ch2_v2 & ch2_v3 | xanyan_to_v2v3
# And make the inverse mapping
ch1.inverse = xanyan_to_v2v3.inverse | map | ch1_a & ch1_b
ch2.inverse = xanyan_to_v2v3.inverse | map | ch2_a & ch2_b
#pdb.set_trace()
m[channels[0]] = ch1
m[channels[1]] = ch2
tree['abv2v3_model'] = m
f = AsdfFile()
f.tree = tree
# f.add_history_entry("DOCUMENT: MIRI-TN-00001-ETH; SOFTWARE: polyd2c_CDP5.pro; DATA USED: Data set of: - FM Test Campaign relevant to MRS-OPT-01, MRS-OPT-02, MRS-OPT-04, MRS-OPT-08; - CV1 Test Campaign relevant to MRS-OPT-02; - CV2 Test Campaign relevant to MRS-OPT-02; - Laboratory measurement of SPO; ============ DIFFERENCES: - New file structure: Change of Extention names and Table Column Headers.; - Replaced V2/V3 with XAN/YAN;")
f.write_to(name) #,all_array_storage=outformat)
def create_nircam_distortion(channel, module, detector, outname):
"""
Create an asdf reference file with all distortion components for the MIRI imager.
NOTE: The IDT has not provided any distortion information. The files are constructed
using ISIM transformations provided/(computed?) by the TEL team which they use to
create the SIAF file.
These reference files should be replaced when/if the IDT provides us with distortion.
Parameters
----------
channel : str
header['channel'] or ImageModel.meta.imstrument.channel
("SHORT" or "LONG")
module : str
header['module'] or ImageModel.meta.imstrument.module
("A" or "B")
detector : str
NRCB1, NRCB2, NRCB3, NRCB4, NRCB5, NRCA1, NRCA2, NRCA3, NRCA4, NRCA5
(NRCA5 and NRCB5 are the LW detectors? or simply NRCA and NRCB)
outname : str
Name of output file.
Examples
--------
"""
if channel == "SHORT":
ch = "SW"
elif channel == "LONG":
ch = "LW"
else:
raise ValueError("Channel {0} not found".format(ch))
numdet = detector[-1]
det = detector[-2]
if numdet == '5':
numdet = "1"
from_system = "NIRCAM" + det + ch + "_" + numdet
to_system = "NIRCAM" + det + ch
amodel, bmodel, startunit, endunit = read_siaf_table.get_siaf_transform(
from_system, to_system, 1, 1)
to_otesky_x, to_otesky_y, startunit, endunit = read_siaf_table.get_siaf_transform(
to_system, "OTESKY", 5, 5)
ote2nrcx, ote2nrcy, startunit, endunit = read_siaf_table.get_siaf_transform(
"OTESKY", to_system, 5, 5)
nrc2detx, nrc2dety, startunit, endunit = read_siaf_table.get_siaf_transform(
to_system, from_system, 1, 1)
model = Mapping([0, 1, 0, 1]) | amodel & bmodel | Mapping([0, 1, 0, 1]) | \
to_otesky_x & to_otesky_y
model_inv = Mapping([0, 1, 0, 1]) | ote2nrcx & ote2nrcy | Mapping((0, 1, 0, 1)) | \
nrc2detx & nrc2dety
model.inverse = model_inv
tree = {"title": "NIRCAM Distortion",
"instrument": "NIRCAM",
"pedigree": "GROUND",
"reftype" : "DISTORTION",
"author": "N. Dencheva",
"detector": detector,
"module": module,
"channel": channel,
"exp_type": "NRC_IMAGE",
"model": model
}
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.write_to(outname)
def create_specwcs_file(reftype, detector, band, channel, lmodel, name):
tree = create_reffile_header(reftype, detector, band, channel)
tree['model'] = lmodel
f = AsdfFile()
f.tree = tree
f.write_to(name)
def ote2asdf(otepcf, outname, ref_kw):
"""
ref_kw = common_reference_file_keywords('OTE', 'NIRSPEC OTE transform - CDP4')
ote2asdf('Model/Ref_Files/CoordTransform/OTE.pcf', 'jwst_nirspec_ote_0001.asdf', ref_kw)
"""
with open(otepcf) as f:
lines = [l.strip() for l in f.readlines()]
factors = lines[lines.index('*Factor 2 1') + 1].split()
# this corresponds to modeling Rotation direction as is
rotation_angle = float(lines[lines.index('*Rotation') + 1])
input_rot_center = lines[lines.index('*InputRotationCentre 2 1') +
1].split()
output_rot_center = lines[lines.index('*OutputRotationCentre 2 1') +
1].split()
mlinear = homothetic_det2sky(input_rot_center, rotation_angle, factors,
output_rot_center)
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1].split('\t')
xcoeff_backward = coeffs_from_pcf(degree, xlines)
x_poly_forward = models.Polynomial2D(degree,
name='x_poly_forward',
**xcoeff_backward)
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1].split('\t')
xcoeff_forward = coeffs_from_pcf(degree, xlines)
x_poly_backward = models.Polynomial2D(degree,
name='x_poly_backward',
**xcoeff_forward)
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ylines = lines[ycoeff_index + 1].split('\t')
ycoeff_backward = coeffs_from_pcf(degree, ylines)
y_poly_forward = models.Polynomial2D(degree,
name='y_poly_forward',
**ycoeff_backward)
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ylines = lines[ycoeff_index + 1].split('\t')
ycoeff_forward = coeffs_from_pcf(degree, ylines)
y_poly_backward = models.Polynomial2D(degree,
name='y_poly_backward',
**ycoeff_forward)
x_poly_forward.inverse = x_poly_backward
y_poly_forward.inverse = y_poly_backward
output2poly_mapping = Identity(2, name='output_mapping')
output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (x_poly_forward
& y_poly_forward) | output2poly_mapping
model = model_poly | mlinear
f = AsdfFile()
f.tree = ref_kw.copy()
f.tree['model'] = model
f.add_history_entry("Build 6")
f.write_to(outname)
return model_poly, mlinear
def pcf2asdf(pcffile, outname, ref_file_kw):
"""
Create an asdf reference file with the transformation coded in a NIRSPEC
Camera.pcf or Collimator*.pcf file.
- forward (team): sky to detector
- Shift inputs to input_rotation_center
- Rotate inputs
- Scale inputs
- Shift inputs to output_rot_center
- Apply polynomial distortion
- backward_team (team definition) detector to sky
- Apply polynomial distortion
- Shift inputs to output_rot_center
- Scale inputs
- Rotate inputs
- Shift inputs to input_rotation_center
WCS implementation
- forward: detector to sky
- equivalent to backward_team
- backward: sky to detector
- equivalent to forward_team
Parameters
----------
pcffile : str
one of the NIRSPEC ".pcf" reference files provided by the IDT team.
"pcf" stands for "polynomial coefficients fit"
outname : str
Name of reference file to be wriiten to disk.
Returns
-------
fasdf : AsdfFile
AsdfFile object
Examples
--------
>>> pcf2asdf("Camera.pcf", "camera.asdf")
"""
linear_det2sky = linear_from_pcf_det2sky(pcffile)
with open(pcffile) as f:
lines = [l.strip() for l in f.readlines()]
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1: xcoeff_index + 22]
xcoeff_forward = coeffs_from_pcf(degree, xlines)
x_poly_backward = models.Polynomial2D(degree, name='x_poly_backward', **xcoeff_forward)
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ycoeff_forward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_backward = models.Polynomial2D(degree, name='y_poly_backward', **ycoeff_forward)
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xcoeff_backward = coeffs_from_pcf(degree, lines[xcoeff_index + 1: xcoeff_index + 22])
x_poly_forward = models.Polynomial2D(degree, name='x_poly_forward', **xcoeff_backward)
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ycoeff_backward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_forward = models.Polynomial2D(degree, name='y_poly_forward', **ycoeff_backward)
x_poly_forward.inverse = x_poly_backward
y_poly_forward.inverse = y_poly_backward
output2poly_mapping = Identity(2, name='output_mapping')
output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (x_poly_forward & y_poly_forward) | output2poly_mapping
model = model_poly | linear_det2sky
f = AsdfFile()
f.tree = ref_file_kw.copy()
f.tree['model'] = model
f.write_to(outname)
def disperser2asdf(disfile, tiltyfile, tiltxfile, outname, ref_kw):
"""
Create a NIRSPEC disperser reference file in ASDF format.
Combine information stored in disperser_G?.dis and disperser_G?_TiltY.gtp
files delievred by the IDT.
disperser2asdf("disperser_G140H.dis", "disperser_G140H_TiltY.gtp", "disperserG140H.asdf")
Parameters
----------
disfile : list or str
A list of .dis files or a wild card string (*.dis).
tiltyfile : str
File with tilt_Y data, e.g. disperser_G395H_TiltY.gtp.
outname : str
Name of output ASDF file.
Returns
-------
fasdf : asdf.AsdfFile
"""
disperser = disfile.split('.dis')[0].split('_')[1]
with open(disfile) as f:
lines = [l.strip() for l in f.readlines()]
try:
ind = lines.index('*TYPE')
disperser_type = (lines[ind + 1]).lower()
except ValueError:
raise ValueError("Unknown disperser type in {0}".format(disfile))
if disperser_type == 'gratingdata':
d = dict.fromkeys(
['groove_density', 'theta_z', 'theta_y', 'theta_x', 'tilt_y'])
elif disperser_type == 'prismdata':
d = dict.fromkeys([
'tref', 'pref', 'angle', 'coefformula', 'thermalcoef', 'wbound'
'theta_z', 'theta_y', 'theta_x', 'tilt_y'
])
d.update(ref_kw)
try:
ind = lines.index('*GRATINGNAME')
grating_name = lines[ind + 1]
except ValueError:
grating_name = 'PRISM'
#for line in lines:
ind = lines.index('*THETAZ')
d['theta_z'] = float(lines[ind + 1]) / 3600. # in degrees
ind = lines.index('*THETAX')
d['theta_x'] = float(lines[ind + 1]) / 3600. # in degrees
ind = lines.index('*THETAY')
d['theta_y'] = float(lines[ind + 1]) / 3600. # in degrees
ind = lines.index('*TILTY')
d['tilt_y'] = float(lines[ind + 1]) # in degrees
try:
ind = lines.index('*TILTX')
d['tilt_x'] = float(lines[ind + 1]) # in degrees
except ValueError:
d['tilt_x'] = 0.0
if disperser_type == 'gratingdata':
ind = lines.index('*GROOVEDENSITY')
d['groove_density'] = float(lines[ind + 1])
elif disperser_type == 'prismdata':
ind = lines.index('*ANGLE')
d['angle'] = float(lines[ind + 1]) # in degrees
ind = lines.index('*TREF')
d['tref'] = float(lines[ind + 1]) # Temperature in K
ind = lines.index('*PREF')
d['pref'] = float(lines[ind + 1]) # Pressure in ATM
ind = lines.index('*COEFFORMULA')
coefs = np.array(lines[ind:ind + int(l[-1])], dtype=np.float)
kcoef = coefs[::2]
lcoef = coefs[1::2]
d['lcoef'] = lcoef
d['kcoef'] = kcoef
# 6 coeffs - D0, D1, D2, E0, E1, lambdak
ind = lines.index('*THERMALCOEF')
coefs = lines[ind:ind + int(l[-1])]
d['tcoef'] = [float(c) for c in coefs]
ind = lines.index('*WBOUND')
coefs = lines[ind:ind + 2]
d['wbound'] = [float(c) for c in coefs]
assert grating_name in tiltyfile
assert grating_name in tiltxfile
tiltyd = disperser_tilt(tiltyfile)
tiltxd = disperser_tilt(tiltxfile)
d['gwa_tiltx'] = tiltyd
d['gwa_tilty'] = tiltxd
fasdf = AsdfFile()
fasdf.tree = d
fasdf.add_history_entry("Build 6")
fasdf.write_to(outname)
return fasdf
def disperser2asdf(disfile, tiltyfile, tiltxfile, outname, ref_kw):
"""
Create a NIRSPEC disperser reference file in ASDF format.
Combine information stored in disperser_G?.dis and disperser_G?_TiltY.gtp
files delievred by the IDT.
disperser2asdf("disperser_G140H.dis", "disperser_G140H_TiltY.gtp", "disperserG140H.asdf")
Parameters
----------
disfile : list or str
A list of .dis files or a wild card string (*.dis).
tiltyfile : str
File with tilt_Y data, e.g. disperser_G395H_TiltY.gtp.
outname : str
Name of output ASDF file.
Returns
-------
fasdf : asdf.AsdfFile
"""
disperser = disfile.split('.dis')[0].split('_')[1]
with open(disfile) as f:
lines=[l.strip() for l in f.readlines()]
d = dict.fromkeys(['groove_density', 'theta_z', 'theta_y', 'theta_x', 'tilt_y'])
d.update(ref_kw)
for line in lines:
ind = lines.index('*GRATINGNAME')
grating_name = lines[ind + 1]
ind = lines.index('*GROOVEDENSITY')
d['groove_density'] = float(lines[ind + 1])
ind = lines.index('*THETAZ')
d['theta_z'] = float(lines[ind + 1]) / 3600. # in degrees
ind = lines.index('*THETAX')
d['theta_x'] = float(lines[ind + 1]) / 3600. # in degrees
ind = lines.index('*THETAY')
d['theta_y'] = float(lines[ind + 1]) / 3600. # in degrees
ind = lines.index('*TILTY')
d['tilt_y'] = float(lines[ind + 1]) # in degrees
try:
ind = lines.index('*TILTX')
d['tilt_x'] = float(lines[ind + 1]) # in degrees
except ValueError:
d['tilt_x'] = 0.0
assert grating_name in tiltyfile
assert grating_name in tiltxfile
with open(tiltyfile) as f:
s = f.read()
tiltyd = {}
vals = s.split('*')
for line in vals[::-1]:
if line.startswith("CoeffsTemperature00"):
l = line.split('\n')
n = int(l[0].split()[1])
coeffs = {}
for i , c in enumerate([float(c) for c in l[1:1+n]]):
coeffs['c' + str(i)] = c
tiltyd['tilt_model'] = models.Polynomial1D(n-1, **coeffs)
elif line.startswith("Temperatures"):
l = line.split('\n')
n = int(l[0].split()[1])
coeffs = l[1:1+n]
tiltyd['temperatures'] = [float(c) for c in coeffs]
elif line.startswith("Zeroreadings"):
l = line.split('\n')
n = int(l[0].split()[1])
coeffs = l[1:1+n]
tiltyd['zeroreadings'] = [float(c) for c in coeffs]
elif line.startswith("Unit"):
tiltyd['unit'] = line.split('\n')[1]
with open(tiltxfile) as f:
s = f.read()
tiltxd = {}
vals = s.split('*')
for line in vals[::-1]:
if line.startswith("CoeffsTemperature00"):
l = line.split('\n')
n = int(l[0].split()[1])
coeffs = {}
for i , c in enumerate([float(c) for c in l[1:1+n]]):
coeffs['c' + str(i)] = c
tiltxd['tilt_model'] = models.Polynomial1D(n-1, **coeffs)
elif line.startswith("Temperatures"):
l = line.split('\n')
n = int(l[0].split()[1])
coeffs = l[1:1+n]
tiltxd['temperatures'] = [float(c) for c in coeffs]
elif line.startswith("Zeroreadings"):
l = line.split('\n')
n = int(l[0].split()[1])
coeffs = l[1:1+n]
tiltxd['zeroreadings'] = [float(c) for c in coeffs]
elif line.startswith("Unit"):
tiltxd['unit'] = line.split('\n')[1]
d['gwa_tiltx'] = tiltyd
d['gwa_tilty'] = tiltxd
fasdf = AsdfFile()
fasdf.tree = d
fasdf.write_to(outname)
return fasdf
def create_miri_imager_distortion(distfile, outname):
"""
Create an asdf reference file with all distortion components for the MIRI imager.
The filter offsets are stored in a sepaate file.
Note: The IDT supplied distortion file lists sky to pixel as the
forward transform. Since "forward" in the JWST pipeline is from
pixel to sky, the meaning of forward and inverse matrices and the order
in which they are applied is switched.
The order of operation from pixel to sky is:
- Apply MI matrix
- Apply Ai and BI matrices
- Apply the TI matrix (this gives V2/V3 coordinates)
Parameters
----------
distfile : str
MIRI imager DISTORTION file provided by the IDT team.
outname : str
Name of reference file to be wriiten to disk.
Returns
-------
fasdf : AsdfFile
AsdfFile object
Examples
--------
>>> create_miri_imager_distortion("MIRI_FM_MIRIMAGE_DISTORTION_03.02.00.fits", 'test.asdf')
"""
fdist = fits.open(distfile)
mi_matrix = fdist[8].data
mi_col = models.Polynomial1D(1,
c0=mi_matrix[0, 2],
c1=mi_matrix[0, 0],
name="M_column_correction")
mi_row = models.Polynomial1D(1,
c0=mi_matrix[1, 2],
c1=mi_matrix[1, 1],
name="M_row_correction")
m_matrix = fdist[4].data
m_col = models.Polynomial1D(1, c0=m_matrix[0, 2], c1=m_matrix[0, 0])
m_row = models.Polynomial1D(1, c0=m_matrix[1, 2], c1=m_matrix[1, 1])
mi_col.inverse = m_col
mi_row.inverse = m_row
m_transform = mi_col & mi_row #mi_row & mi_col
ai_matrix = fdist[6].data
a_matrix = fdist[2].data
col_poly = polynomial_from_coeffs_matrix(ai_matrix, name="A_correction")
col_poly.inverse = polynomial_from_coeffs_matrix(a_matrix)
bi_matrix = fdist[5].data
b_matrix = fdist[1].data
row_poly = polynomial_from_coeffs_matrix(bi_matrix, name="B_correction")
row_poly.inverse = polynomial_from_coeffs_matrix(b_matrix)
poly = col_poly & row_poly
ti_matrix = fdist[7].data
t_matrix = fdist[3].data
ti_col = models.Polynomial2D(1, name='TI_column_correction')
ti_col.parameters = ti_matrix[0][::-1]
ti_row = models.Polynomial2D(1, name='TI_row_correction')
ti_row.parameters = ti_matrix[1][::-1]
t_col = models.Polynomial2D(1, name='T_column_correction')
t_col.parameters = t_matrix[0][::-1]
t_row = models.Polynomial2D(1, name='T_row_correction')
t_row.parameters = t_matrix[1][::-1]
ti_col.inverse = t_col
ti_row.inverse = t_row
t_transform = ti_row & ti_col
mapping = models.Mapping([0, 1, 0, 1])
mapping.inverse = models.Identity(2)
# ident is created here so that mapping can be assigned as inverse
ident = models.Identity(2)
ident.inverse = models.Mapping([0, 1, 0, 1])
poly2t_mapping = models.Mapping([0, 1, 0, 1])
poly2t_mapping.inverse = models.Mapping([0, 1, 0, 1])
distortion_transform = m_transform | mapping | poly | poly2t_mapping | t_transform | ident | models.Mapping(
[1, 0])
fdist.close()
f = AsdfFile()
tree = {
"title": "MIRI imager distortion - CDP4",
"reftype": "DISTORTION",
"instrument": "MIRI",
"detector": "MIRIMAGE",
"exp_type": "MIR_IMAGE",
"pedigree": "GROUND",
"author": "N. Dencheva",
"model": distortion_transform
}
f.tree = tree
fasdf = f.write_to(outname)
def ifupost2asdf(ifupost_files, outname, ref_kw):
"""
Create a reference file of type ``ifupost`` .
Combines all IDT ``IFU-POST`` reference files in one ASDF file.
forward direction : MSA to Collimator
backward_direction: Collimator to MSA
Parameters
----------
ifupost_files : list
Names of all ``IFU-POST`` IDT reference files
outname : str
Name of output ``ASDF`` file
"""
fa = AsdfFile()
fa.tree = ref_kw
for fifu in ifupost_files:
n = int((fifu.split('IFU-POST_')[1]).split('.pcf')[0])
fa.tree[n] = {}
with open(fifu) as f:
lines = [l.strip() for l in f.readlines()]
factors = lines[lines.index('*Factor 2') + 1].split()
rotation_angle = float(lines[lines.index('*Rotation') + 1])
input_rot_center = lines[lines.index('*InputRotationCentre 2') +
1].split()
output_rot_center = lines[lines.index('*OutputRotationCentre 2') +
1].split()
linear_sky2det = homothetic_sky2det(input_rot_center, rotation_angle,
factors, output_rot_center)
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1:xcoeff_index + 22]
xcoeff_forward = coeffs_from_pcf(degree, xlines)
x_poly_forward = models.Polynomial2D(degree,
name='x_poly_forward',
**xcoeff_forward)
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ycoeff_forward = coeffs_from_pcf(
degree, lines[ycoeff_index + 1:ycoeff_index + 22])
y_poly_forward = models.Polynomial2D(degree,
name='y_poly_forward',
**ycoeff_forward)
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xcoeff_backward = coeffs_from_pcf(
degree, lines[xcoeff_index + 1:xcoeff_index + 22])
x_poly_backward = models.Polynomial2D(degree,
name='x_poly_backward',
**xcoeff_backward)
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ycoeff_backward = coeffs_from_pcf(
degree, lines[ycoeff_index + 1:ycoeff_index + 22])
y_poly_backward = models.Polynomial2D(degree,
name='y_poly_backward',
**ycoeff_backward)
output2poly_mapping = Identity(2, name='output_mapping')
output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (
x_poly_forward & y_poly_forward) | output2poly_mapping
model = linear_sky2det | model_poly
fa.tree[n]['model'] = model
fa.add_history_entry("Build 6")
asdffile = fa.write_to(outname)
return asdffile
def fore2asdf(pcffore, outname, ref_kw):
"""
forward direction : msa 2 ote
backward_direction: msa 2 fpa
"""
with open(pcffore) as f:
lines = [l.strip() for l in f.readlines()]
factors = lines[lines.index('*Factor 2') + 1].split()
# factor==1/factor in backward msa2ote direction and == factor in ote2msa direction
scale = models.Scale(1. / float(factors[0]), name='scale_x') & \
models.Scale(1 / float(factors[1]), name='scale_y')
rotation_angle = lines[lines.index('*Rotation') + 1]
rotation = models.Rotation2D(float(rotation_angle), name='rotation')
input_rot_center = lines[lines.index('*InputRotationCentre 2') + 1].split()
output_offset = models.Shift(float(input_rot_center[0]), name="output_x_shift") & \
models.Shift(float(input_rot_center[1]), name="output_y_shift")
output_rot_center = lines[lines.index('*OutputRotationCentre 2') +
1].split()
input_offset = models.Shift(-float(output_rot_center[0]), name="input_x_shift") & \
models.Shift(-float(output_rot_center[1]), name="input_y_shift")
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1:xcoeff_index + 22]
xcoeff_forward = coeffs_from_pcf(degree, xlines)
# Polynomial Correction in x
x_poly_backward = models.Polynomial2D(degree,
name="x_poly_backward",
**xcoeff_forward)
xlines_distortion = lines[xcoeff_index + 22:xcoeff_index + 43]
xcoeff_forward_distortion = coeffs_from_pcf(degree, xlines_distortion)
x_poly_backward_distortion = models.Polynomial2D(
degree, name="x_backward_distortion", **xcoeff_forward_distortion)
# do chromatic correction
# the input is Xote, Yote, lam
model_x_backward = (Mapping((0, 1), n_inputs=3) | x_poly_backward) + \
((Mapping((0,1), n_inputs=3) | x_poly_backward_distortion) * Mapping((2,)))
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ycoeff_forward = coeffs_from_pcf(degree,
lines[ycoeff_index + 1:ycoeff_index + 22])
y_poly_backward = models.Polynomial2D(degree,
name="y_poly_backward",
**ycoeff_forward)
ylines_distortion = lines[ycoeff_index + 22:ycoeff_index + 43]
ycoeff_forward_distortion = coeffs_from_pcf(degree, ylines_distortion)
y_poly_backward_distortion = models.Polynomial2D(
degree, name="y_backward_distortion", **ycoeff_forward_distortion)
# do chromatic correction
# the input is Xote, Yote, lam
model_y_backward = (Mapping((0,1), n_inputs=3) | y_poly_backward) + \
((Mapping((0, 1), n_inputs=3) | y_poly_backward_distortion) * Mapping((2,)))
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xcoeff_backward = coeffs_from_pcf(
degree, lines[xcoeff_index + 1:xcoeff_index + 22])
x_poly_forward = models.Polynomial2D(degree,
name="x_poly_forward",
**xcoeff_backward)
xcoeff_backward_distortion = coeffs_from_pcf(
degree, lines[xcoeff_index + 22:xcoeff_index + 43])
x_poly_forward_distortion = models.Polynomial2D(
degree, name="x_forward_distortion", **xcoeff_backward_distortion)
# the chromatic correction is done here
# the input is Xmsa, Ymsa, lam
model_x_forward = (Mapping((0,1), n_inputs=3) | x_poly_forward) + \
((Mapping((0,1), n_inputs=3) | x_poly_forward_distortion) * Mapping((2,)))
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ycoeff_backward = coeffs_from_pcf(
degree, lines[ycoeff_index + 1:ycoeff_index + 22])
y_poly_forward = models.Polynomial2D(degree,
name="y_poly_forward",
**ycoeff_backward)
ycoeff_backward_distortion = coeffs_from_pcf(
degree, lines[ycoeff_index + 22:ycoeff_index + 43])
y_poly_forward_distortion = models.Polynomial2D(
degree, name="y_forward_distortion", **ycoeff_backward_distortion)
# do chromatic correction
# the input is Xmsa, Ymsa, lam
model_y_forward = (Mapping((0,1), n_inputs=3) | y_poly_forward) + \
((Mapping((0,1), n_inputs=3) | y_poly_forward_distortion) * Mapping((2,)))
#assign inverse transforms
model_x = model_x_forward.copy()
model_y = model_y_forward.copy()
model_x.inverse = model_x_backward
model_y.inverse = model_y_backward
output2poly_mapping = Identity(2, name="output_mapping")
output2poly_mapping.inverse = Mapping([0, 1, 2, 0, 1, 2])
input2poly_mapping = Mapping([0, 1, 2, 0, 1, 2], name="input_mapping")
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (model_x & model_y) | output2poly_mapping
fore_linear = (input_offset | rotation | scale | output_offset)
fore_linear_inverse = fore_linear.inverse
fore_linear.inverse = fore_linear_inverse & Identity(1)
model = model_poly | fore_linear
f = AsdfFile()
f.tree = ref_kw.copy()
f.tree['model'] = model
asdffile = f.write_to(outname)
return asdffile
def disperser2asdf(disfile, tiltyfile, tiltxfile, outname, ref_kw):
"""
Create a NIRSPEC disperser reference file in ASDF format.
Combine information stored in disperser_G?.dis and disperser_G?_TiltY.gtp
files delievred by the IDT.
disperser2asdf("disperser_G140H.dis", "disperser_G140H_TiltY.gtp", "disperserG140H.asdf")
Parameters
----------
disfile : list or str
A list of .dis files or a wild card string (*.dis).
tiltyfile : str
File with tilt_Y data, e.g. disperser_G395H_TiltY.gtp.
outname : str
Name of output ASDF file.
Returns
-------
fasdf : asdf.AsdfFile
"""
#t = {}
disperser = disfile.split('.dis')[0].split('_')[1]
with open(disfile) as f:
lines = [l.strip() for l in f.readlines()]
d = dict.fromkeys(
['groove_density', 'theta_z', 'theta_y', 'theta_x', 'tilt_y'])
d.update(ref_kw)
for line in lines:
ind = lines.index('*GRATINGNAME')
grating_name = lines[ind + 1]
ind = lines.index('*GROOVEDENSITY')
d['groove_density'] = float(lines[ind + 1])
ind = lines.index('*THETAZ')
d['theta_z'] = float(lines[ind + 1]) / 3600. # in degrees
ind = lines.index('*THETAX')
d['theta_x'] = float(lines[ind + 1]) / 3600. # in degrees
ind = lines.index('*THETAY')
d['theta_y'] = float(lines[ind + 1]) / 3600. # in degrees
ind = lines.index('*TILTY')
d['tilt_y'] = float(lines[ind + 1]) # in degrees
try:
ind = lines.index('*TILTX')
d['tilt_x'] = float(lines[ind + 1]) # in degrees
except ValueError:
d['tilt_x'] = 0.0
assert grating_name in tiltyfile
assert grating_name in tiltxfile
with open(tiltyfile) as f:
s = f.read()
tiltyd = {}
vals = s.split('*')
for line in vals[::-1]:
if line.startswith("CoeffsTemperature00"):
l = line.split('\n')
n = int(l[0].split()[1])
coeffs = {}
for i, c in enumerate([float(c) for c in l[1:1 + n]]):
coeffs['c' + str(i)] = c
tiltyd['tilt_model'] = models.Polynomial1D(n - 1, **coeffs)
elif line.startswith("Temperatures"):
l = line.split('\n')
n = int(l[0].split()[1])
coeffs = l[1:1 + n]
tiltyd['temperatures'] = [float(c) for c in coeffs]
elif line.startswith("Zeroreadings"):
l = line.split('\n')
n = int(l[0].split()[1])
coeffs = l[1:1 + n]
tiltyd['zeroreadings'] = [float(c) for c in coeffs]
elif line.startswith("Unit"):
tiltyd['unit'] = line.split('\n')[1]
with open(tiltxfile) as f:
s = f.read()
tiltxd = {}
vals = s.split('*')
for line in vals[::-1]:
if line.startswith("CoeffsTemperature00"):
l = line.split('\n')
n = int(l[0].split()[1])
coeffs = {}
for i, c in enumerate([float(c) for c in l[1:1 + n]]):
coeffs['c' + str(i)] = c
tiltxd['tilt_model'] = models.Polynomial1D(n - 1, **coeffs)
elif line.startswith("Temperatures"):
l = line.split('\n')
n = int(l[0].split()[1])
coeffs = l[1:1 + n]
tiltxd['temperatures'] = [float(c) for c in coeffs]
elif line.startswith("Zeroreadings"):
l = line.split('\n')
n = int(l[0].split()[1])
coeffs = l[1:1 + n]
tiltxd['zeroreadings'] = [float(c) for c in coeffs]
elif line.startswith("Unit"):
tiltxd['unit'] = line.split('\n')[1]
"""
TODO: add prism specific coefficients, get_disperser_info.pro
"""
d['gwa_tiltx'] = tiltyd
d['gwa_tilty'] = tiltxd
fasdf = AsdfFile()
fasdf.tree = d
fasdf.write_to(outname)
return fasdf
def pcf2asdf(pcffile, outname, ref_file_kw):
"""
Create an asdf reference file with the transformation coded in a NIRSPEC
Camera.pcf or Collimator*.pcf file.
- forward (team): sky to detector
- Shift inputs to input_rotation_center
- Rotate inputs
- Scale inputs
- Shift inputs to output_rot_center
- Apply polynomial distortion
- backward_team (team definition) detector to sky
- Apply polynomial distortion
- Shift inputs to output_rot_center
- Scale inputs
- Rotate inputs
- Shift inputs to input_rotation_center
WCS implementation
- forward: detector to sky
- equivalent to backward_team
- backward: sky to detector
- equivalent to forward_team
Parameters
----------
pcffile : str
one of the NIRSPEC ".pcf" reference files provided by the IDT team.
"pcf" stands for "polynomial coefficients fit"
outname : str
Name of reference file to be wriiten to disk.
Returns
-------
fasdf : AsdfFile
AsdfFile object
Examples
--------
>>> pcf2asdf("Camera.pcf", "camera.asdf")
"""
with open(pcffile) as f:
lines = [l.strip() for l in f.readlines()]
factors = lines[lines.index('*Factor 2') + 1].split()
scale = models.Scale(1 / float(factors[0]), name="x_scale") & \
models.Scale(1 / float(factors[1]), name="y_scale")
rotation_angle = lines[lines.index('*Rotation') + 1]
# Backward rotation is in the counter-clockwise direction as in modeling
# Forward is clockwise
backward_rotation = models.Rotation2D(float(rotation_angle),
name='rotation')
rotation = backward_rotation.copy()
# Here the model is called "output_shift" but in the team version it is the "input_shift".
input_rot_center = lines[lines.index('*InputRotationCentre 2') + 1].split()
output_offset = models.Shift(float(input_rot_center[0]), name='output_x_shift') & \
models.Shift(float(input_rot_center[1]), name='output_y_shift')
# Here the model is called "input_shift" but in the team version it is the "output_shift".
output_rot_center = lines[lines.index('*OutputRotationCentre 2') +
1].split()
input_offset = models.Shift(-float(output_rot_center[0]), name='input_x_shift') & \
models.Shift(-float(output_rot_center[1]), name='input_y_shift')
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1:xcoeff_index + 22]
xcoeff_forward = coeffs_from_pcf(degree, xlines)
x_poly_backward = models.Polynomial2D(degree,
name='x_poly_backward',
**xcoeff_forward)
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ycoeff_forward = coeffs_from_pcf(degree,
lines[ycoeff_index + 1:ycoeff_index + 22])
y_poly_backward = models.Polynomial2D(degree,
name='y_poly_backward',
**ycoeff_forward)
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xcoeff_backward = coeffs_from_pcf(
degree, lines[xcoeff_index + 1:xcoeff_index + 22])
x_poly_forward = models.Polynomial2D(degree,
name='x_poly_forward',
**xcoeff_backward)
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ycoeff_backward = coeffs_from_pcf(
degree, lines[ycoeff_index + 1:ycoeff_index + 22])
y_poly_forward = models.Polynomial2D(degree,
name='y_poly_forward',
**ycoeff_backward)
x_poly_forward.inverse = x_poly_backward
y_poly_forward.inverse = y_poly_backward
output2poly_mapping = Identity(2, name='output_mapping')
output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (x_poly_forward
& y_poly_forward) | output2poly_mapping
model = model_poly | input_offset | scale | rotation | output_offset
f = AsdfFile()
f.tree = ref_file_kw.copy()
f.tree['model'] = model
f.write_to(outname)
def ote2asdf(otepcf, outname, ref_kw):
"""
ref_kw = common_reference_file_keywords('OTE', 'NIRSPEC OTE transform - CDP4')
ote2asdf('Model/Ref_Files/CoordTransform/OTE.pcf', 'jwst_nirspec_ote_0001.asdf', ref_kw)
"""
with open(otepcf) as f:
lines = [l.strip() for l in f.readlines()]
factors = lines[lines.index('*Factor 2 1') + 1].split()
scale = models.Scale(1 / float(factors[0]), name="x_scale") & \
models.Scale(1 / float(factors[1]), name="y_scale")
# this corresponds to modeling Rotation direction as is
rotation_angle = lines[lines.index('*Rotation') + 1]
rotation = models.Rotation2D(float(rotation_angle), name='rotation')
input_rot_center = lines[lines.index('*InputRotationCentre 2 1') + 1].split()
output_offset = models.Shift(float(input_rot_center[0]), name='output_x_shift') & \
models.Shift(float(input_rot_center[1]), name='output_y_shift')
# Here the model is called "input_shift" but in the team version it is the "output_shift".
output_rot_center = lines[lines.index('*OutputRotationCentre 2 1') + 1].split()
input_offset = models.Shift(-float(output_rot_center[0]), name='input_x_shift') & \
models.Shift(-float(output_rot_center[1]), name='input_y_shift')
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1].split('\t')
xcoeff_forward = coeffs_from_pcf(degree, xlines)
x_poly_backward = models.Polynomial2D(degree, name='x_poly_backward', **xcoeff_forward)
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ylines = lines[ycoeff_index + 1].split('\t')
ycoeff_forward = coeffs_from_pcf(degree, ylines)
y_poly_backward = models.Polynomial2D(degree, name='y_poly_backward', **ycoeff_forward)
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1].split('\t')
xcoeff_backward = coeffs_from_pcf(degree, xlines)
x_poly_forward = models.Polynomial2D(degree, name='x_poly_forward', **xcoeff_backward)
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ylines = lines[ycoeff_index + 1].split('\t')
ycoeff_backward = coeffs_from_pcf(degree, ylines)
y_poly_forward = models.Polynomial2D(degree, name='y_poly_forward', **ycoeff_backward)
x_poly_forward.inverse = x_poly_backward
y_poly_forward.inverse = y_poly_backward
mlinear = input_offset | rotation | scale | output_offset
output2poly_mapping = Identity(2, name='output_mapping')
output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (x_poly_forward & y_poly_forward) | output2poly_mapping
model = model_poly | mlinear
f = AsdfFile()
f.tree = ref_kw.copy()
f.tree['model'] = model
f.write_to(outname)
def create_specwcs_file(reftype, detector, band, channel, lmodel, name):
tree = create_reffile_header(reftype, detector, band, channel)
tree['model'] = lmodel
f = AsdfFile()
f.tree = tree
f.write_to(name)
def pcf2asdf(pcffile, outname, ref_file_kw):
"""
Create an asdf reference file with the transformation coded in a NIRSPEC
Camera.pcf or Collimator*.pcf file.
- forward (team): sky to detector
- Shift inputs to input_rotation_center
- Rotate inputs
- Scale inputs
- Shift inputs to output_rot_center
- Apply polynomial distortion
- backward_team (team definition) detector to sky
- Apply polynomial distortion
- Shift inputs to output_rot_center
- Scale inputs
- Rotate inputs
- Shift inputs to input_rotation_center
WCS implementation
- forward: detector to sky
- equivalent to backward_team
- backward: sky to detector
- equivalent to forward_team
Parameters
----------
pcffile : str
one of the NIRSPEC ".pcf" reference files provided by the IDT team.
"pcf" stands for "polynomial coefficients fit"
outname : str
Name of reference file to be wriiten to disk.
Returns
-------
fasdf : AsdfFile
AsdfFile object
Examples
--------
>>> pcf2asdf("Camera.pcf", "camera.asdf")
"""
linear_det2sky = linear_from_pcf_det2sky(pcffile)
with open(pcffile) as f:
lines = [l.strip() for l in f.readlines()]
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1:xcoeff_index + 22]
xcoeff_forward = coeffs_from_pcf(degree, xlines)
x_poly_backward = models.Polynomial2D(degree,
name='x_poly_backward',
**xcoeff_forward)
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ycoeff_forward = coeffs_from_pcf(degree,
lines[ycoeff_index + 1:ycoeff_index + 22])
y_poly_backward = models.Polynomial2D(degree,
name='y_poly_backward',
**ycoeff_forward)
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xcoeff_backward = coeffs_from_pcf(
degree, lines[xcoeff_index + 1:xcoeff_index + 22])
x_poly_forward = models.Polynomial2D(degree,
name='x_poly_forward',
**xcoeff_backward)
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ycoeff_backward = coeffs_from_pcf(
degree, lines[ycoeff_index + 1:ycoeff_index + 22])
y_poly_forward = models.Polynomial2D(degree,
name='y_poly_forward',
**ycoeff_backward)
x_poly_forward.inverse = x_poly_backward
y_poly_forward.inverse = y_poly_backward
output2poly_mapping = Identity(2, name='output_mapping')
output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (x_poly_forward
& y_poly_forward) | output2poly_mapping
model = model_poly | linear_det2sky
f = AsdfFile()
f.tree = ref_file_kw.copy()
f.add_history_entry("Build 6")
f.tree['model'] = model
f.write_to(outname)
def create_miri_imager_distortion(distfile, outname):
"""
Create an asdf reference file with all distortion components for the MIRI imager.
The filter offsets are stored in a sepaate file.
Note: The IDT supplied distortion file lists sky to pixel as the
forward transform. Since "forward" in the JWST pipeline is from
pixel to sky, the meaning of forward and inverse matrices and the order
in which they are applied is switched.
The order of operation from pixel to sky is:
- Apply MI matrix
- Apply Ai and BI matrices
- Apply the TI matrix (this gives V2/V3 coordinates)
Parameters
----------
distfile : str
MIRI imager DISTORTION file provided by the IDT team.
outname : str
Name of reference file to be wriiten to disk.
Returns
-------
fasdf : AsdfFile
AsdfFile object
Examples
--------
>>> create_miri_imager_distortion("MIRI_FM_MIRIMAGE_DISTORTION_03.02.00.fits", 'test.asdf')
"""
fdist = fits.open(distfile)
mi_matrix = fdist[8].data
mi_col = models.Polynomial1D(1, c0=mi_matrix[0, 2], c1=mi_matrix[0,0], name="M_column_correction")
mi_row = models.Polynomial1D(1, c0=mi_matrix[1, 2], c1=mi_matrix[1,1], name="M_row_correction")
m_matrix = fdist[4].data
m_col = models.Polynomial1D(1, c0=m_matrix[0, 2], c1=m_matrix[0,0])
m_row = models.Polynomial1D(1, c0=m_matrix[1, 2], c1=m_matrix[1,1])
mi_col.inverse = m_col
mi_row.inverse = m_row
m_transform = mi_col & mi_row #mi_row & mi_col
ai_matrix = fdist[6].data
a_matrix = fdist[2].data
col_poly = polynomial_from_coeffs_matrix(ai_matrix, name="A_correction")
col_poly.inverse = polynomial_from_coeffs_matrix(a_matrix)
bi_matrix = fdist[5].data
b_matrix = fdist[1].data
row_poly = polynomial_from_coeffs_matrix(bi_matrix, name="B_correction")
row_poly.inverse = polynomial_from_coeffs_matrix(b_matrix)
poly = col_poly & row_poly
ti_matrix = fdist[7].data
t_matrix = fdist[3].data
ti_col = models.Polynomial2D(1, name='TI_column_correction')
ti_col.parameters = ti_matrix[0][::-1]
ti_row = models.Polynomial2D(1, name='TI_row_correction')
ti_row.parameters = ti_matrix[1][::-1]
t_col = models.Polynomial2D(1, name='T_column_correction')
t_col.parameters = t_matrix[0][::-1]
t_row = models.Polynomial2D(1, name='T_row_correction')
t_row.parameters = t_matrix[1][::-1]
ti_col.inverse = t_col
ti_row.inverse = t_row
t_transform = ti_row & ti_col
mapping = models.Mapping([0, 1, 0, 1])
mapping.inverse = models.Identity(2)
# ident is created here so that mapping can be assigned as inverse
ident = models.Identity(2)
ident.inverse = models.Mapping([0,1,0,1])
poly2t_mapping = models.Mapping([0, 1, 0, 1])
poly2t_mapping.inverse = models.Mapping([0, 1, 0, 1])
distortion_transform = m_transform | mapping | poly | poly2t_mapping | t_transform | ident
fdist.close()
f = AsdfFile()
tree = {"title": "MIRI imager distortion - CDP4",
"reftype": "DISTORTION",
"instrument": "MIRI",
"detector": "MIRIMAGE",
"exp_type": "MIR_IMAGE",
"pedigree": "GROUND",
"author": "N. Dencheva",
"model": distortion_transform
}
f.tree = tree
fasdf = f.write_to(outname)
def fore2asdf(pcffore, outname, ref_kw):
"""
forward direction : msa 2 ote
backward_direction: msa 2 fpa
"""
with open(pcffore) as f:
lines = [l.strip() for l in f.readlines()]
fore_det2sky = linear_from_pcf_det2sky(pcffore)
fore_linear = fore_det2sky
fore_linear.inverse = fore_det2sky.inverse & Identity(1)
# compute the polynomial
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1:xcoeff_index + 22]
xcoeff_forward = coeffs_from_pcf(degree, xlines)
# Polynomial Correction in x
x_poly_backward = models.Polynomial2D(degree,
name="x_poly_backward",
**xcoeff_forward)
xlines_distortion = lines[xcoeff_index + 22:xcoeff_index + 43]
xcoeff_forward_distortion = coeffs_from_pcf(degree, xlines_distortion)
x_poly_backward_distortion = models.Polynomial2D(
degree, name="x_backward_distortion", **xcoeff_forward_distortion)
# do chromatic correction
# the input is Xote, Yote, lam
model_x_backward = (Mapping((0, 1), n_inputs=3) | x_poly_backward) + \
((Mapping((0,1), n_inputs=3) | x_poly_backward_distortion) * \
(Mapping((2,)) | Identity(1)))
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ycoeff_forward = coeffs_from_pcf(degree,
lines[ycoeff_index + 1:ycoeff_index + 22])
y_poly_backward = models.Polynomial2D(degree,
name="y_poly_backward",
**ycoeff_forward)
ylines_distortion = lines[ycoeff_index + 22:ycoeff_index + 43]
ycoeff_forward_distortion = coeffs_from_pcf(degree, ylines_distortion)
y_poly_backward_distortion = models.Polynomial2D(
degree, name="y_backward_distortion", **ycoeff_forward_distortion)
# do chromatic correction
# the input is Xote, Yote, lam
model_y_backward = (Mapping((0,1), n_inputs=3) | y_poly_backward) + \
((Mapping((0, 1), n_inputs=3) | y_poly_backward_distortion) * \
(Mapping((2,)) | Identity(1) ))
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xcoeff_backward = coeffs_from_pcf(
degree, lines[xcoeff_index + 1:xcoeff_index + 22])
x_poly_forward = models.Polynomial2D(degree,
name="x_poly_forward",
**xcoeff_backward)
xcoeff_backward_distortion = coeffs_from_pcf(
degree, lines[xcoeff_index + 22:xcoeff_index + 43])
x_poly_forward_distortion = models.Polynomial2D(
degree, name="x_forward_distortion", **xcoeff_backward_distortion)
# the chromatic correction is done here
# the input is Xmsa, Ymsa, lam
model_x_forward = (Mapping((0,1), n_inputs=3) | x_poly_forward) + \
((Mapping((0,1), n_inputs=3) | x_poly_forward_distortion) * \
(Mapping((2,)) | Identity(1)))
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ycoeff_backward = coeffs_from_pcf(
degree, lines[ycoeff_index + 1:ycoeff_index + 22])
y_poly_forward = models.Polynomial2D(degree,
name="y_poly_forward",
**ycoeff_backward)
ycoeff_backward_distortion = coeffs_from_pcf(
degree, lines[ycoeff_index + 22:ycoeff_index + 43])
y_poly_forward_distortion = models.Polynomial2D(
degree, name="y_forward_distortion", **ycoeff_backward_distortion)
# do chromatic correction
# the input is Xmsa, Ymsa, lam
model_y_forward = (Mapping((0,1), n_inputs=3) | y_poly_forward) + \
((Mapping((0,1), n_inputs=3) | y_poly_forward_distortion) * \
(Mapping((2,)) | Identity(1)))
#assign inverse transforms
model_x = model_x_forward.copy()
model_y = model_y_forward.copy()
model_x.inverse = model_x_backward
model_y.inverse = model_y_backward
output2poly_mapping = Identity(2, name="output_mapping")
output2poly_mapping.inverse = Mapping([0, 1, 2, 0, 1, 2])
input2poly_mapping = Mapping([0, 1, 2, 0, 1, 2], name="input_mapping")
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (model_x & model_y) | output2poly_mapping
model = model_poly | fore_linear
f = AsdfFile()
f.tree = ref_kw.copy()
f.tree['model'] = model
f.add_history_entry("Build 6")
asdffile = f.write_to(outname)
return asdffile
def fore2asdf(pcffore, outname, ref_kw):
"""
forward direction : msa 2 ote
backward_direction: msa 2 fpa
"""
with open(pcffore) as f:
lines = [l.strip() for l in f.readlines()]
fore_det2sky = linear_from_pcf_det2sky(pcffore)
fore_linear = fore_det2sky
fore_linear.inverse = fore_det2sky.inverse & Identity(1)
# compute the polynomial
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1: xcoeff_index + 22]
xcoeff_forward = coeffs_from_pcf(degree, xlines)
# Polynomial Correction in x
x_poly_backward = models.Polynomial2D(degree, name="x_poly_backward", **xcoeff_forward)
xlines_distortion = lines[xcoeff_index + 22: xcoeff_index + 43]
xcoeff_forward_distortion = coeffs_from_pcf(degree, xlines_distortion)
x_poly_backward_distortion = models.Polynomial2D(degree, name="x_backward_distortion",
**xcoeff_forward_distortion)
# do chromatic correction
# the input is Xote, Yote, lam
model_x_backward = (Mapping((0, 1), n_inputs=3) | x_poly_backward) + \
((Mapping((0,1), n_inputs=3) | x_poly_backward_distortion) * \
(Mapping((2,)) | Identity(1)))
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ycoeff_forward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_backward = models.Polynomial2D(degree, name="y_poly_backward", **ycoeff_forward)
ylines_distortion = lines[ycoeff_index + 22: ycoeff_index + 43]
ycoeff_forward_distortion = coeffs_from_pcf(degree, ylines_distortion)
y_poly_backward_distortion = models.Polynomial2D(degree, name="y_backward_distortion",
**ycoeff_forward_distortion)
# do chromatic correction
# the input is Xote, Yote, lam
model_y_backward = (Mapping((0,1), n_inputs=3) | y_poly_backward) + \
((Mapping((0, 1), n_inputs=3) | y_poly_backward_distortion) * \
(Mapping((2,)) | Identity(1) ))
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xcoeff_backward = coeffs_from_pcf(degree, lines[xcoeff_index + 1: xcoeff_index + 22])
x_poly_forward = models.Polynomial2D(degree,name="x_poly_forward", **xcoeff_backward)
xcoeff_backward_distortion = coeffs_from_pcf(degree, lines[xcoeff_index + 22: xcoeff_index + 43])
x_poly_forward_distortion = models.Polynomial2D(degree, name="x_forward_distortion", **xcoeff_backward_distortion)
# the chromatic correction is done here
# the input is Xmsa, Ymsa, lam
model_x_forward = (Mapping((0,1), n_inputs=3) | x_poly_forward) + \
((Mapping((0,1), n_inputs=3) | x_poly_forward_distortion) * \
(Mapping((2,)) | Identity(1)))
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ycoeff_backward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_forward = models.Polynomial2D(degree, name="y_poly_forward",**ycoeff_backward)
ycoeff_backward_distortion = coeffs_from_pcf(degree, lines[ycoeff_index + 22: ycoeff_index + 43])
y_poly_forward_distortion = models.Polynomial2D(degree, name="y_forward_distortion",
**ycoeff_backward_distortion)
# do chromatic correction
# the input is Xmsa, Ymsa, lam
model_y_forward = (Mapping((0,1), n_inputs=3) | y_poly_forward) + \
((Mapping((0,1), n_inputs=3) | y_poly_forward_distortion) * \
(Mapping((2,)) | Identity(1)))
#assign inverse transforms
model_x = model_x_forward.copy()
model_y = model_y_forward.copy()
model_x.inverse = model_x_backward
model_y.inverse = model_y_backward
output2poly_mapping = Identity(2, name="output_mapping")
output2poly_mapping.inverse = Mapping([0, 1, 2, 0, 1, 2])
input2poly_mapping = Mapping([0, 1, 2, 0, 1, 2], name="input_mapping")
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (model_x & model_y) | output2poly_mapping
model = model_poly | fore_linear
f = AsdfFile()
f.tree = ref_kw.copy()
f.tree['model'] = model
asdffile = f.write_to(outname)
return asdffile
def fpa2asdf(fpafile, outname, ref_kw):
"""
Create an asdf reference file with the FPA description.
The CDP2 delivery includes a fits file - "FPA.fpa" which is the
input to this function. This file is converted to asdf and is a
reference file of type "FPA".
nirspec_fs_ref_tools.fpa2asdf('Ref_Files/CoordTransform/Description/FPA.fpa', 'fpa.asdf')
Parameters
----------
fpafile : str
A fits file with FPA description (FPA.fpa)
outname : str
Name of output ASDF file.
"""
with open(fpafile) as f:
lines = [l.strip() for l in f.readlines()]
# NRS1
ind = lines.index("*SCA491_PitchX")
nrs1_pitchx = float(lines[ind+1])
ind = lines.index("*SCA491_PitchY")
nrs1_pitchy = float(lines[ind+1])
ind = lines.index("*SCA491_RotAngle")
nrs1_angle = float(lines[ind+1])
ind = lines.index("*SCA491_PosX")
nrs1_posx = float(lines[ind+1])
ind = lines.index("*SCA491_PosY")
nrs1_posy = float(lines[ind+1])
# NRS2
ind = lines.index("*SCA492_PitchX")
nrs2_pitchx = float(lines[ind+1])
ind = lines.index("*SCA492_PitchY")
nrs2_pitchy = float(lines[ind+1])
ind = lines.index("*SCA492_RotAngle")
nrs2_angle = float(lines[ind+1])
ind = lines.index("*SCA492_PosX")
nrs2_posx = float(lines[ind+1])
ind = lines.index("*SCA492_PosY")
nrs2_posy = float(lines[ind+1])
tree = ref_kw.copy()
# NRS1 Sky to Detector
scaling = np.array([[1/nrs1_pitchx, 0], [0, 1/nrs1_pitchy]])
rotmat = models.Rotation2D._compute_matrix(-nrs1_angle)
matrix = np.dot(rotmat, scaling)
aff = models.AffineTransformation2D(matrix, name='fpa_affine_sky2detector')
nrs1_sky2det = models.Shift(-nrs1_posx, name='fpa_shift_x') & \
models.Shift(-nrs1_posy, name='fpa_shift_y') | aff
# NRS1 Detector to Sky
rotmat = models.Rotation2D._compute_matrix(-nrs1_angle)
scaling = np.array([[nrs1_pitchx, 0], [0, nrs1_pitchy]])
matrix = np.dot(rotmat, scaling)
aff = models.AffineTransformation2D(matrix, name='fpa_affine_detector2sky')
nrs1_det2sky = aff | models.Shift(nrs1_posx, name='fpa_shift_x_det2sky') & \
models.Shift(nrs1_posy, name='fpa_shift_y_det2sky')
nrs1_det2sky.inverse = nrs1_sky2det
# NRS2 Sky to Detector
scaling = np.array([[-1/nrs2_pitchx, 0], [0, -1/nrs2_pitchy]])
rotmat = models.Rotation2D._compute_matrix(-nrs2_angle)
matrix = np.dot(rotmat, scaling)
aff = models.AffineTransformation2D(matrix, name='fpa_affine_sky2detector')
nrs2_sky2det = models.Shift(-nrs2_posx, name='fpa_shixft_x') & \
models.Shift(-nrs2_posy, name='fpa_shift_y') | aff
# NRS2 Detector to Sky
rotmat = models.Rotation2D._compute_matrix(nrs2_angle)
scaling = np.array([[-nrs2_pitchx, 0], [0, -nrs2_pitchy]])
matrix = np.dot(rotmat, scaling)
aff = models.AffineTransformation2D(matrix, name='fpa_affine_detector2sky')
nrs2_det2sky = aff | models.Shift(nrs2_posx, name='fpa_shift_x_det2sky') & \
models.Shift(nrs2_posy, name='fpa_shift_y_det2sky')
nrs2_det2sky.inverse = nrs2_sky2det
tree['NRS1'] = nrs1_det2sky
tree['NRS2'] = nrs2_det2sky
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.add_history_entry("Build 6")
fasdf.write_to(outname)
return fasdf
def create_regions_file(slices, detector, band, channel, name):
tree = create_reffile_header("REGIONS", detector, band, channel)
f = AsdfFile()
tree['regions'] = slices
f.tree = tree
f.write_to(name)
def fpa2asdf(fpafile, outname, ref_kw):
"""
Create an asdf reference file with the FPA description.
The CDP2 delivery includes a fits file - "FPA.fpa" which is the
input to this function. This file is converted to asdf and is a
reference file of type "FPA".
nirspec_fs_ref_tools.fpa2asdf('Ref_Files/CoordTransform/Description/FPA.fpa', 'fpa.asdf')
Parameters
----------
fpafile : str
A fits file with FPA description (FPA.fpa)
outname : str
Name of output ASDF file.
"""
with open(fpafile) as f:
lines = [l.strip() for l in f.readlines()]
# NRS1
ind = lines.index("*SCA491_PitchX")
nrs1_pitchx = float(lines[ind + 1])
ind = lines.index("*SCA491_PitchY")
nrs1_pitchy = float(lines[ind + 1])
ind = lines.index("*SCA491_RotAngle")
nrs1_angle = float(lines[ind + 1])
ind = lines.index("*SCA491_PosX")
nrs1_posx = float(lines[ind + 1])
ind = lines.index("*SCA491_PosY")
nrs1_posy = float(lines[ind + 1])
# NRS2
ind = lines.index("*SCA492_PitchX")
nrs2_pitchx = float(lines[ind + 1])
ind = lines.index("*SCA492_PitchY")
nrs2_pitchy = float(lines[ind + 1])
ind = lines.index("*SCA492_RotAngle")
nrs2_angle = float(lines[ind + 1])
ind = lines.index("*SCA492_PosX")
nrs2_posx = float(lines[ind + 1])
ind = lines.index("*SCA492_PosY")
nrs2_posy = float(lines[ind + 1])
tree = ref_kw.copy()
# NRS1 Sky to Detector
scaling = np.array([[1 / nrs1_pitchx, 0], [0, 1 / nrs1_pitchy]])
rotmat = models.Rotation2D._compute_matrix(-nrs1_angle)
matrix = np.dot(rotmat, scaling)
aff = models.AffineTransformation2D(matrix, name='fpa_affine_sky2detector')
nrs1_sky2det = models.Shift(-nrs1_posx, name='fpa_shift_x') & \
models.Shift(-nrs1_posy, name='fpa_shift_y') | aff
# NRS1 Detector to Sky
rotmat = models.Rotation2D._compute_matrix(-nrs1_angle)
scaling = np.array([[nrs1_pitchx, 0], [0, nrs1_pitchy]])
matrix = np.dot(rotmat, scaling)
aff = models.AffineTransformation2D(matrix, name='fpa_affine_detector2sky')
nrs1_det2sky = aff | models.Shift(nrs1_posx, name='fpa_shift_x_det2sky') & \
models.Shift(nrs1_posy, name='fpa_shift_y_det2sky')
nrs1_det2sky.inverse = nrs1_sky2det
# NRS2 Sky to Detector
scaling = np.array([[-1 / nrs2_pitchx, 0], [0, -1 / nrs2_pitchy]])
rotmat = models.Rotation2D._compute_matrix(-nrs2_angle)
matrix = np.dot(rotmat, scaling)
aff = models.AffineTransformation2D(matrix, name='fpa_affine_sky2detector')
nrs2_sky2det = models.Shift(-nrs2_posx, name='fpa_shixft_x') & \
models.Shift(-nrs2_posy, name='fpa_shift_y') | aff
# NRS2 Detector to Sky
rotmat = models.Rotation2D._compute_matrix(nrs2_angle)
scaling = np.array([[-nrs2_pitchx, 0], [0, -nrs2_pitchy]])
matrix = np.dot(rotmat, scaling)
aff = models.AffineTransformation2D(matrix, name='fpa_affine_detector2sky')
nrs2_det2sky = aff | models.Shift(nrs2_posx, name='fpa_shift_x_det2sky') & \
models.Shift(nrs2_posy, name='fpa_shift_y_det2sky')
nrs2_det2sky.inverse = nrs2_sky2det
tree['NRS1'] = nrs1_det2sky
tree['NRS2'] = nrs2_det2sky
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.add_history_entry("Build 6")
fasdf.write_to(outname)
return fasdf
def pcf2asdf(pcffile, outname, ref_file_kw):
"""
Create an asdf reference file with the transformation coded in a NIRSPEC
Camera.pcf or Collimator*.pcf file.
- forward (team): sky to detector
- Shift inputs to input_rotation_center
- Rotate inputs
- Scale inputs
- Shift inputs to output_rot_center
- Apply polynomial distortion
- backward_team (team definition) detector to sky
- Apply polynomial distortion
- Shift inputs to output_rot_center
- Scale inputs
- Rotate inputs
- Shift inputs to input_rotation_center
WCS implementation
- forward: detector to sky
- equivalent to backward_team
- backward: sky to detector
- equivalent to forward_team
Parameters
----------
pcffile : str
one of the NIRSPEC ".pcf" reference files provided by the IDT team.
"pcf" stands for "polynomial coefficients fit"
outname : str
Name of reference file to be wriiten to disk.
Returns
-------
fasdf : AsdfFile
AsdfFile object
Examples
--------
>>> pcf2asdf("Camera.pcf", "camera.asdf")
"""
with open(pcffile) as f:
lines = [l.strip() for l in f.readlines()]
factors = lines[lines.index('*Factor 2') + 1].split()
scale = models.Scale(1 / float(factors[0]), name="x_scale") & \
models.Scale(1 / float(factors[1]), name="y_scale")
rotation_angle = lines[lines.index('*Rotation') + 1]
# Backward rotation is in the counter-clockwise direction as in modeling
# Forward is clockwise
backward_rotation = models.Rotation2D(float(rotation_angle), name='rotation')
rotation = backward_rotation.copy()
# Here the model is called "output_shift" but in the team version it is the "input_shift".
input_rot_center = lines[lines.index('*InputRotationCentre 2') + 1].split()
output_offset = models.Shift(float(input_rot_center[0]), name='output_x_shift') & \
models.Shift(float(input_rot_center[1]), name='output_y_shift')
# Here the model is called "input_shift" but in the team version it is the "output_shift".
output_rot_center = lines[lines.index('*OutputRotationCentre 2') + 1].split()
input_offset = models.Shift(-float(output_rot_center[0]), name='input_x_shift') & \
models.Shift(-float(output_rot_center[1]), name='input_y_shift')
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1: xcoeff_index + 22]
xcoeff_forward = coeffs_from_pcf(degree, xlines)
x_poly_backward = models.Polynomial2D(degree, name='x_poly_backward', **xcoeff_forward)
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ycoeff_forward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_backward = models.Polynomial2D(degree, name='y_poly_backward', **ycoeff_forward)
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xcoeff_backward = coeffs_from_pcf(degree, lines[xcoeff_index + 1: xcoeff_index + 22])
x_poly_forward = models.Polynomial2D(degree, name='x_poly_forward', **xcoeff_backward)
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ycoeff_backward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_forward = models.Polynomial2D(degree, name='y_poly_forward', **ycoeff_backward)
x_poly_forward.inverse = x_poly_backward
y_poly_forward.inverse = y_poly_backward
output2poly_mapping = Identity(2, name='output_mapping')
output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (x_poly_forward & y_poly_forward) | output2poly_mapping
model = model_poly | input_offset |scale |rotation |output_offset
f = AsdfFile()
f.tree = ref_file_kw.copy()
f.tree['model'] = model
f.write_to(outname)
def create_v23(reftype, detector, band, channels, data, name, author, useafter,
description):
"""
Create the transform from MIRI Local to telescope V2/V3 system for all channels.
"""
channel = "".join([ch[0] for ch in channels])
tree = create_reffile_header(reftype, detector, band, channel, author,
useafter, description)
"""
tree = {"detector": detector,
"instrument" : "MIRI",
"band": band,
"channel": channel,
"exp_type": "MIR_MRS",
"pedigree": "GROUND",
"title": "MIRI IFU model - based on CDP-4",
"reftype": reftype,
"author": author,
"useafter": useafter,
"description": description
}
"""
tree['filename'] = name
ab_v23 = data[0]
v23_ab = data[1]
m = {}
c0_0, c0_1, c1_0, c1_1 = ab_v23[0][1:]
ch1_v2 = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="ab_v23")
c0_0, c0_1, c1_0, c1_1 = v23_ab[0][1:]
ch1_a = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="v23_ab")
c0_0, c0_1, c1_0, c1_1 = ab_v23[1][1:]
ch1_v3 = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="ab_v23")
c0_0, c0_1, c1_0, c1_1 = v23_ab[1][1:]
ch1_b = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="v23_ab")
c0_0, c0_1, c1_0, c1_1 = ab_v23[2][1:]
ch2_v2 = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="ab_v23")
c0_0, c0_1, c1_0, c1_1 = v23_ab[2][1:]
ch2_a = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="v23_ab")
c0_0, c0_1, c1_0, c1_1 = ab_v23[3][1:]
ch2_v3 = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="ab_v23")
c0_0, c0_1, c1_0, c1_1 = v23_ab[3][1:]
ch2_b = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="v23_ab")
ch1_for = ch1_v2 & ch1_v3
ch2_for = ch2_v2 & ch2_v3
ch1_for.inverse = ch1_a & ch1_b
ch2_for.inverse = ch2_a & ch2_b
m[channels[0]] = ch1_for
m[channels[1]] = ch2_for
tree['model'] = m
f = AsdfFile()
f.tree = tree
f.add_history_entry(
"DOCUMENT: MIRI-TN-00001-ETH; SOFTWARE: polyd2c_CDP5.pro; DATA USED: Data set of: - FM Test Campaign relevant to MRS-OPT-01, MRS-OPT-02, MRS-OPT-04, MRS-OPT-08; - CV1 Test Campaign relevant to MRS-OPT-02; - CV2 Test Campaign relevant to MRS-OPT-02; - Laboratory measurement of SPO; ============ DIFFERENCES: - New file structure: Change of Extention names and Table Column Headers.; - Replaced V2/V3 with XAN/YAN;"
)
f.write_to(name)
def disperser2asdf(disfile, tiltyfile, tiltxfile, outname, ref_kw):
"""
Create a NIRSPEC disperser reference file in ASDF format.
Combine information stored in disperser_G?.dis and disperser_G?_TiltY.gtp
files delievred by the IDT.
disperser2asdf("disperser_G140H.dis", "disperser_G140H_TiltY.gtp", "disperserG140H.asdf")
Parameters
----------
disfile : list or str
A list of .dis files or a wild card string (*.dis).
tiltyfile : str
File with tilt_Y data, e.g. disperser_G395H_TiltY.gtp.
outname : str
Name of output ASDF file.
Returns
-------
fasdf : asdf.AsdfFile
"""
disperser = disfile.split('.dis')[0].split('_')[1]
with open(disfile) as f:
lines=[l.strip() for l in f.readlines()]
try:
ind = lines.index('*TYPE')
disperser_type = (lines[ind + 1]).lower()
except ValueError:
raise ValueError("Unknown disperser type in {0}".format(disfile))
if disperser_type == 'gratingdata':
d = dict.fromkeys(['groove_density', 'theta_z', 'theta_y', 'theta_x', 'tilt_y'])
elif disperser_type == 'prismdata':
d = dict.fromkeys(['tref', 'pref', 'angle', 'coefformula', 'thermalcoef', 'wbound'
'theta_z', 'theta_y', 'theta_x', 'tilt_y'])
d.update(ref_kw)
try:
ind = lines.index('*GRATINGNAME')
grating_name = lines[ind + 1]
except ValueError:
grating_name = 'PRISM'
#for line in lines:
ind = lines.index('*THETAZ')
d['theta_z'] = float(lines[ind + 1]) / 3600. # in degrees
ind = lines.index('*THETAX')
d['theta_x'] = float(lines[ind + 1]) / 3600. # in degrees
ind = lines.index('*THETAY')
d['theta_y'] = float(lines[ind + 1]) / 3600. # in degrees
ind = lines.index('*TILTY')
d['tilt_y'] = float(lines[ind + 1]) # in degrees
try:
ind = lines.index('*TILTX')
d['tilt_x'] = float(lines[ind + 1]) # in degrees
except ValueError:
d['tilt_x'] = 0.0
if disperser_type == 'gratingdata':
ind = lines.index('*GROOVEDENSITY')
d['groove_density'] = float(lines[ind + 1])
elif disperser_type == 'prismdata':
ind = lines.index('*ANGLE')
d['angle'] = float(lines[ind + 1]) # in degrees
ind = lines.index('*TREF')
d['tref'] = float(lines[ind + 1]) # Temperature in K
ind = lines.index('*PREF')
d['pref'] = float(lines[ind + 1]) # Pressure in ATM
ind = lines.index('*COEFFORMULA')
coefs = np.array(lines[ind : ind+int(l[-1])], dtype=np.float)
kcoef = coefs[::2]
lcoef = coefs[1::2]
d['lcoef'] = lcoef
d['kcoef'] = kcoef
# 6 coeffs - D0, D1, D2, E0, E1, lambdak
ind = lines.index('*THERMALCOEF')
coefs = lines[ind : ind+int(l[-1])]
d['tcoef'] = [float(c) for c in coefs]
ind = lines.index('*WBOUND')
coefs = lines[ind : ind + 2]
d['wbound'] = [float(c) for c in coefs]
assert grating_name in tiltyfile
assert grating_name in tiltxfile
tiltyd = disperser_tilt(tiltyfile)
tiltxd = disperser_tilt(tiltxfile)
d['gwa_tiltx'] = tiltyd
d['gwa_tilty'] = tiltxd
fasdf = AsdfFile()
fasdf.tree = d
fasdf.add_history_entry("Build 6")
fasdf.write_to(outname)
return fasdf
def fore2asdf(pcffore, outname, ref_kw):
"""
forward direction : msa 2 ote
backward_direction: msa 2 fpa
"""
with open(pcffore) as f:
lines = [l.strip() for l in f.readlines()]
factors = lines[lines.index('*Factor 2') + 1].split()
# factor==1/factor in backward msa2ote direction and == factor in ote2msa direction
scale = models.Scale(1. / float(factors[0]), name='scale_x') & \
models.Scale(1 / float(factors[1]), name='scale_y')
rotation_angle = lines[lines.index('*Rotation') + 1]
rotation = models.Rotation2D(float(rotation_angle), name='rotation')
input_rot_center = lines[lines.index('*InputRotationCentre 2') + 1].split()
output_offset = models.Shift(float(input_rot_center[0]), name="output_x_shift") & \
models.Shift(float(input_rot_center[1]), name="output_y_shift")
output_rot_center = lines[lines.index('*OutputRotationCentre 2') + 1].split()
input_offset = models.Shift(-float(output_rot_center[0]), name="input_x_shift") & \
models.Shift(-float(output_rot_center[1]), name="input_y_shift")
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1: xcoeff_index + 22]
xcoeff_forward = coeffs_from_pcf(degree, xlines)
# Polynomial Correction in x
x_poly_backward = models.Polynomial2D(degree, name="x_poly_backward", **xcoeff_forward)
xlines_distortion = lines[xcoeff_index + 22: xcoeff_index + 43]
xcoeff_forward_distortion = coeffs_from_pcf(degree, xlines_distortion)
x_poly_backward_distortion = models.Polynomial2D(degree, name="x_backward_distortion",
**xcoeff_forward_distortion)
# do chromatic correction
# the input is Xote, Yote, lam
model_x_backward = (Mapping((0, 1), n_inputs=3) | x_poly_backward) + \
((Mapping((0,1), n_inputs=3) | x_poly_backward_distortion) * Mapping((2,)))
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ycoeff_forward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_backward = models.Polynomial2D(degree, name="y_poly_backward", **ycoeff_forward)
ylines_distortion = lines[ycoeff_index + 22: ycoeff_index + 43]
ycoeff_forward_distortion = coeffs_from_pcf(degree, ylines_distortion)
y_poly_backward_distortion = models.Polynomial2D(degree, name="y_backward_distortion",
**ycoeff_forward_distortion)
# do chromatic correction
# the input is Xote, Yote, lam
model_y_backward = (Mapping((0,1), n_inputs=3) | y_poly_backward) + \
((Mapping((0, 1), n_inputs=3) | y_poly_backward_distortion) * Mapping((2,)))
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xcoeff_backward = coeffs_from_pcf(degree, lines[xcoeff_index + 1: xcoeff_index + 22])
x_poly_forward = models.Polynomial2D(degree,name="x_poly_forward", **xcoeff_backward)
xcoeff_backward_distortion = coeffs_from_pcf(degree, lines[xcoeff_index + 22: xcoeff_index + 43])
x_poly_forward_distortion = models.Polynomial2D(degree, name="x_forward_distortion", **xcoeff_backward_distortion)
# the chromatic correction is done here
# the input is Xmsa, Ymsa, lam
model_x_forward = (Mapping((0,1), n_inputs=3) | x_poly_forward) + \
((Mapping((0,1), n_inputs=3) | x_poly_forward_distortion) * Mapping((2,)))
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ycoeff_backward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_forward = models.Polynomial2D(degree, name="y_poly_forward",**ycoeff_backward)
ycoeff_backward_distortion = coeffs_from_pcf(degree, lines[ycoeff_index + 22: ycoeff_index + 43])
y_poly_forward_distortion = models.Polynomial2D(degree, name="y_forward_distortion",
**ycoeff_backward_distortion)
# do chromatic correction
# the input is Xmsa, Ymsa, lam
model_y_forward = (Mapping((0,1), n_inputs=3) | y_poly_forward) + \
((Mapping((0,1), n_inputs=3) | y_poly_forward_distortion) * Mapping((2,)))
#assign inverse transforms
model_x = model_x_forward.copy()
model_y = model_y_forward.copy()
model_x.inverse = model_x_backward
model_y.inverse = model_y_backward
output2poly_mapping = Identity(2, name="output_mapping")
output2poly_mapping.inverse = Mapping([0, 1, 2, 0, 1, 2])
input2poly_mapping = Mapping([0, 1, 2, 0, 1, 2], name="input_mapping")
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (model_x & model_y) | output2poly_mapping
fore_linear = (input_offset | rotation | scale | output_offset)
fore_linear_inverse = fore_linear.inverse
fore_linear.inverse = fore_linear_inverse & Identity(1)
model = model_poly | fore_linear
f = AsdfFile()
f.tree = ref_kw.copy()
f.tree['model'] = model
asdffile = f.write_to(outname)
return asdffile
def ote2asdf(otepcf, outname, ref_kw):
"""
ref_kw = common_reference_file_keywords('OTE', 'NIRSPEC OTE transform - CDP4')
ote2asdf('Model/Ref_Files/CoordTransform/OTE.pcf', 'jwst_nirspec_ote_0001.asdf', ref_kw)
"""
with open(otepcf) as f:
lines = [l.strip() for l in f.readlines()]
factors = lines[lines.index('*Factor 2 1') + 1].split()
scale = models.Scale(1 / float(factors[0]), name="x_scale") & \
models.Scale(1 / float(factors[1]), name="y_scale")
# this corresponds to modeling Rotation direction as is
rotation_angle = lines[lines.index('*Rotation') + 1]
rotation = models.Rotation2D(float(rotation_angle), name='rotation')
input_rot_center = lines[lines.index('*InputRotationCentre 2 1') +
1].split()
output_offset = models.Shift(float(input_rot_center[0]), name='output_x_shift') & \
models.Shift(float(input_rot_center[1]), name='output_y_shift')
# Here the model is called "input_shift" but in the team version it is the "output_shift".
output_rot_center = lines[lines.index('*OutputRotationCentre 2 1') +
1].split()
input_offset = models.Shift(-float(output_rot_center[0]), name='input_x_shift') & \
models.Shift(-float(output_rot_center[1]), name='input_y_shift')
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1].split('\t')
xcoeff_forward = coeffs_from_pcf(degree, xlines)
x_poly_backward = models.Polynomial2D(degree,
name='x_poly_backward',
**xcoeff_forward)
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ylines = lines[ycoeff_index + 1].split('\t')
ycoeff_forward = coeffs_from_pcf(degree, ylines)
y_poly_backward = models.Polynomial2D(degree,
name='y_poly_backward',
**ycoeff_forward)
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1].split('\t')
xcoeff_backward = coeffs_from_pcf(degree, xlines)
x_poly_forward = models.Polynomial2D(degree,
name='x_poly_forward',
**xcoeff_backward)
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ylines = lines[ycoeff_index + 1].split('\t')
ycoeff_backward = coeffs_from_pcf(degree, ylines)
y_poly_forward = models.Polynomial2D(degree,
name='y_poly_forward',
**ycoeff_backward)
x_poly_forward.inverse = x_poly_backward
y_poly_forward.inverse = y_poly_backward
mlinear = input_offset | rotation | scale | output_offset
output2poly_mapping = Identity(2, name='output_mapping')
output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
input2poly_mapping.inverse = Identity(2)
model_poly = input2poly_mapping | (x_poly_forward
& y_poly_forward) | output2poly_mapping
model = model_poly | mlinear
f = AsdfFile()
f.tree = ref_kw.copy()
f.tree['model'] = model
f.write_to(outname)
def create_regions_file(slices, detector, band, channel, name):
tree = create_reffile_header("REGIONS", detector, band, channel)
f = AsdfFile()
tree['regions'] = slices
f.tree = tree
f.write_to(name)
def fpa2asdf(fpafile, outname, ref_kw):
"""
Create an asdf reference file with the FPA description.
The CDP2 delivery includes a fits file - "FPA.fpa" which is the
input to this function. This file is converted to asdf and is a
reference file of type "FPA".
nirspec_fs_ref_tools.fpa2asdf('Ref_Files/CoordTransform/Description/FPA.fpa', 'fpa.asdf')
Parameters
----------
fpafile : str
A fits file with FPA description (FPA.fpa)
outname : str
Name of output ASDF file.
"""
with open(fpafile) as f:
lines = [l.strip() for l in f.readlines()]
# NRS1
ind = lines.index("*SCA491_PitchX")
nrs1_pitchx = float(lines[ind+1])
ind = lines.index("*SCA491_PitchY")
nrs1_pitchy = float(lines[ind+1])
ind = lines.index("*SCA491_RotAngle")
nrs1_angle = np.rad2deg(float(lines[ind+1]))
ind = lines.index("*SCA491_PosX")
nrs1_posx = float(lines[ind+1])
ind = lines.index("*SCA491_PosY")
nrs1_posy = float(lines[ind+1])
# NRS2
ind = lines.index("*SCA492_PitchX")
nrs2_pitchx = float(lines[ind+1])
ind = lines.index("*SCA492_PitchY")
nrs2_pitchy = float(lines[ind+1])
ind = lines.index("*SCA492_RotAngle")
nrs2_angle = np.rad2deg(float(lines[ind+1]))
ind = lines.index("*SCA492_PosX")
nrs2_posx = float(lines[ind+1])
ind = lines.index("*SCA492_PosY")
nrs2_posy = float(lines[ind+1])
tree = ref_kw.copy()
nrs1_sky2det = models.Shift(-nrs1_posx) & models.Shift(-nrs1_posy) | \
models.Rotation2D(-nrs1_angle) | \
models.Scale(1/nrs1_pitchx) & models.Scale(1/nrs1_pitchy)
nrs1_det2sky = models.Rotation2D(nrs1_angle) | \
models.Scale(nrs1_pitchx) & models.Scale(nrs1_pitchy) | \
models.Shift(nrs1_posx) & models.Shift(nrs1_posy)
nrs1_det2sky.inverse = nrs1_sky2det
nrs2_sky2det = models.Shift(-nrs2_posx) & models.Shift(-nrs2_posy) | \
models.Rotation2D(-nrs2_angle) | \
models.Scale(1/nrs2_pitchx) & models.Scale(1/nrs2_pitchy)
nrs2_det2sky = models.Rotation2D(nrs2_angle) | \
models.Scale(nrs2_pitchx) & models.Scale(nrs2_pitchy) | \
models.Shift(nrs2_posx) & models.Shift(nrs2_posy)
nrs2_det2sky.inverse = nrs1_sky2det
tree['NRS1'] = nrs1_det2sky
tree['NRS2'] = nrs2_sky2det
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.write_to(outname)
return fasdf
def create_nircam_distortion(coefffile, detector, aperture, opgsname, outname):
"""
Create an asdf reference file with all distortion components for the NIRCam imager.
NOTE: The IDT has not provided any distortion information. The files are constructed
using ISIM transformations provided/(computed?) by the TEL team which they use to
create the SIAF file.
These reference files should be replaced when/if the IDT provides us with distortion.
Parameters
----------
detector : str
NRCB1, NRCB2, NRCB3, NRCB4, NRCB5, NRCA1, NRCA2, NRCA3, NRCA4, NRCA5
aperture : str
Name of the aperture/subarray. (e.g. FULL, SUB160, SUB320, SUB640, GRISM_F322W2)
outname : str
Name of output file.
Examples
--------
"""
numdet = detector[-1]
module = detector[-2]
channel = 'SHORT'
if numdet == '5':
channel = 'LONG'
full_aperture = detector + '_' + aperture
#"Forward' transformations. science --> ideal --> V2V3
sci2idlx, sci2idly, sciunit, idlunit = read_siaf_table.get_siaf_transform(coefffile,full_aperture,'science','ideal', 5)
idl2v2v3x, idl2v2v3y = read_siaf_table.get_siaf_v2v3_transform(coefffile,full_aperture,from_system='ideal')
#'Reverse' transformations. V2V3 --> ideal --> science
v2v32idlx, v2v32idly = read_siaf_table.get_siaf_v2v3_transform(coefffile,full_aperture,to_system='ideal')
idl2scix, idl2sciy, idlunit, sciunit = read_siaf_table.get_siaf_transform(coefffile,full_aperture,'ideal','science', 5)
#Map the models together to make a single transformation
model = Mapping([0, 1, 0, 1]) | sci2idlx & sci2idly | Mapping([0, 1, 0, 1]) | idl2v2v3x & idl2v2v3y
model_inv = Mapping([0, 1, 0, 1]) | v2v32idlx & v2v32idly | Mapping([0, 1, 0, 1]) | idl2scix & idl2sciy
model.inverse = model_inv
#In the reference file headers, we need to switch NRCA5 to NRCALONG, and same
#for module B.
if detector[-1] == '5':
detector = detector[0:4] + 'LONG'
tree = {"TITLE": "NIRCAM Distortion",
"TELESCOP": "JWST",
"INSTRUMENT": "NIRCAM",
"PEDIGREE": "GROUND",
"REFTYPE" : "DISTORTION",
"AUTHOR": "B. Hilbert",
"DETECTOR": detector,
"MODULE": module,
"CHANNEL": channel,
"SUBARRAY": opgsname,
"DESCRIP": "Distortion model function created from SIAF coefficients",
"EXP_TYPE": "NRC_IMAGE",
"USEAFTER": "2014-01-01T00:00:00",
"model": model
}
fasdf = AsdfFile()
fasdf.tree = tree
sdict = {'name':'nircam_reftools.py','author':'B.Hilbert','homepage':'https://github.com/spacetelescope/jwreftools','version':'0.7'}
fasdf.add_history_entry("File created from a file of distortion coefficients, NIRCam_SIAF_2016-09-29.csv, provided by Colin Cox in October 2016. Software used: https://github.com/spacetelescope/jwreftools",software=sdict)
fasdf.write_to(outname)
def create_v23(reftype, detector, band, channels, data, name):
"""
Create the transform from MIRI Local to telescope V2/V3 system for all channels.
"""
channel = "".join([ch[0] for ch in channels])
tree = {
"detector": detector,
"instrument": "MIRI",
"band": band,
"channel": channel,
"exp_type": "MIR_MRS",
"pedigree": "GROUND",
"title": "MIRI IFU model - based on CDP-4",
"reftype": reftype,
"author": "N. Dencheva"
}
ab_v23 = data[0]
v23_ab = data[1]
m = {}
c0_0, c0_1, c1_0, c1_1 = ab_v23[0][1:]
ch1_v2 = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="ab_v23")
c0_0, c0_1, c1_0, c1_1 = v23_ab[0][1:]
ch1_a = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="v23_ab")
c0_0, c0_1, c1_0, c1_1 = ab_v23[1][1:]
ch1_v3 = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="ab_v23")
c0_0, c0_1, c1_0, c1_1 = v23_ab[1][1:]
ch1_b = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="v23_ab")
c0_0, c0_1, c1_0, c1_1 = ab_v23[2][1:]
ch2_v2 = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="ab_v23")
c0_0, c0_1, c1_0, c1_1 = v23_ab[2][1:]
ch2_a = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="v23_ab")
c0_0, c0_1, c1_0, c1_1 = ab_v23[3][1:]
ch2_v3 = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="ab_v23")
c0_0, c0_1, c1_0, c1_1 = v23_ab[3][1:]
ch2_b = models.Polynomial2D(2,
c0_0=c0_0,
c1_0=c1_0,
c0_1=c0_1,
c1_1=c1_1,
name="v23_ab")
ch1_for = ch1_v2 & ch1_v3
ch2_for = ch2_v2 & ch2_v3
ch1_for.inverse = ch1_a & ch1_b
ch2_for.inverse = ch2_a & ch2_b
m[channels[0]] = ch1_for
m[channels[1]] = ch2_for
tree['model'] = m
f = AsdfFile()
f.tree = tree
f.write_to(name)
