def test_distortion_schema_bad_valueerror(distortion_model):
"""Check that ValueError is raised for ReferenceFile missing items"""
dist = DistortionModel(distortion_model, strict_validation=True)
dist.meta.author = None
with pytest.raises(ValueError):
dist.validate()
def test_distortion_schema_bad_assertionerror(distortion_model):
"""Check that AssertionError is raised for distortion-specific missing items"""
dist = DistortionModel(distortion_model, strict_validation=True)
dist.meta.instrument.channel = None
with pytest.raises(AssertionError):
dist.validate()
def distortion_model():
"""Create a distortion model that should pass all validation"""
m = models.Shift(1) & models.Shift(2)
dist = DistortionModel(model=m, input_units=u.pixel, output_units=u.arcsec)
dist.meta.reftype = "distortion"
dist.meta.instrument.name = "NIRCAM"
dist.meta.instrument.detector = "NRCA1"
dist.meta.instrument.p_pupil = "F162M|F164N|CLEAR|"
dist.meta.instrument.pupil = "F162M"
dist.meta.exposure.p_exptype = "NRC_IMAGE|NRC_TSIMAGE|NRC_FLAT|NRC_LED|NRC_WFSC|"
dist.meta.exposure.type = "NRC_IMAGE"
dist.meta.psubarray = "FULL|SUB64P|SUB160)|SUB160P|SUB320|SUB400P|SUB640|"
dist.meta.subarray.name = "FULL"
# Populate the following so that no validation warnings or errors happen
dist.meta.instrument.module = "A"
dist.meta.instrument.channel = "SHORT"
dist.meta.input_units = u.degree
dist.meta.output_units = u.degree
dist.meta.description = "NIRCam distortion reference file"
dist.meta.author = "Hank the Septopus"
dist.meta.pedigree = "Cleveland"
dist.meta.useafter = "2000-01-01T00:00:00"
return dist
def test_distortion_schema(tmpdir):
"""Make sure DistortionModel roundtrips"""
m = models.Shift(1) & models.Shift(2)
dist = DistortionModel(model=m, input_units=u.pixel, output_units=u.arcsec)
dist.meta.instrument.name = "NIRCAM"
dist.meta.instrument.detector = "NRCA1"
dist.meta.instrument.p_pupil = "F162M|F164N|CLEAR|"
dist.meta.instrument.pupil = "F162M"
dist.meta.exposure.p_exptype = "NRC_IMAGE|NRC_TSIMAGE|NRC_FLAT|NRC_LED|NRC_WFSC|"
dist.meta.exposure.type = "NRC_IMAGE"
dist.meta.psubarray = "FULL|SUB64P|SUB160)|SUB160P|SUB320|SUB400P|SUB640|"
dist.meta.subarray.name = "FULL"
path = str(tmpdir.join("test_dist.asdf"))
dist.save(path)
with DistortionModel(path) as dist1:
assert dist1.meta.instrument.p_pupil == dist.meta.instrument.p_pupil
assert dist1.meta.instrument.pupil == dist.meta.instrument.pupil
assert dist1.meta.exposure.p_exptype == dist.meta.exposure.p_exptype
assert dist1.meta.exposure.type == dist.meta.exposure.type
assert dist1.meta.psubarray == dist.meta.psubarray
assert dist1.meta.subarray.name == dist.meta.subarray.name
def test_open_asdf_readonly(tmpdir):
tmpfile = str(tmpdir.join('readonly.asdf'))
with DistortionModel() as model:
model.meta.telescope = 'JWST'
model.meta.instrument.name = 'NIRCAM'
model.meta.instrument.detector = 'NRCA4'
model.meta.instrument.channel = 'SHORT'
model.save(tmpfile)
os.chmod(tmpfile, 0o440)
assert os.access(tmpfile, os.W_OK) == False
with datamodels.open(tmpfile) as model:
assert model.meta.telescope == 'JWST'
def test_distortion_schema(distortion_model, tmpdir):
"""Make sure DistortionModel roundtrips"""
path = str(tmpdir.join("test_dist.asdf"))
dist = distortion_model
dist.save(path)
with pytest.warns(None) as report:
with DistortionModel(path) as dist1:
assert dist1.meta.instrument.p_pupil == dist.meta.instrument.p_pupil
assert dist1.meta.instrument.pupil == dist.meta.instrument.pupil
assert dist1.meta.exposure.p_exptype == dist.meta.exposure.p_exptype
assert dist1.meta.exposure.type == dist.meta.exposure.type
assert dist1.meta.psubarray == dist.meta.psubarray
assert dist1.meta.subarray.name == dist.meta.subarray.name
assert len(report) == 0
def create_wfc3_distortion(detector, outname, sci_pupil,
sci_subarr, sci_exptype, history_entry, filter):
"""
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
--------
"""
# Download WFC3 Image Distortion File
from astropy.utils.data import download_file
fn = download_file('https://hst-crds.stsci.edu/unchecked_get/references/hst/w3m18525i_idc.fits', cache=True)
wfc3_distortion_file = fits.open(fn)
wfc3_filter_info = wfc3_distortion_file[1].data[list(wfc3_distortion_file[1].data['FILTER']).index(filter)]
degree = 4 # WFC3 Distortion is fourth degree
# From Bryan Hilbert:
# The parity term is just an indicator of the relationship between the detector y axis and the “science” y axis.
# A parity of -1 means that the y axes of the two systems run in opposite directions... A value of 1 indicates no flip.
# From Colin Cox:
# ... for WFC3 it is always -1 so maybe people gave up mentioning it.
parity = -1
#full_aperture = detector + '_' + aperture
# Get Siaf instance for detector/aperture
#inst_siaf = pysiaf.Siaf('nircam')
#siaf = inst_siaf[full_aperture]
# *****************************************************
# "Forward' transformations. science --> ideal --> V2V3
xcoeffs, ycoeffs = get_distortion_coeffs(degree, wfc3_filter_info)
sci2idlx = Polynomial2D(degree, **xcoeffs)
sci2idly = Polynomial2D(degree, **ycoeffs)
# Get info for ideal -> v2v3 or v2v3 -> ideal model
idl2v2v3x, idl2v2v3y = v2v3_model('ideal', 'v2v3', parity, np.radians(wfc3_distortion_file[1].data[wfc3_distortion_file[1].data['FILTER'] == filter]['THETA'][0]))
'''
# *****************************************************
# 'Reverse' transformations. V2V3 --> ideal --> science
xcoeffs, ycoeffs = get_distortion_coeffs('Idl2Sci', siaf)
idl2scix = Polynomial2D(degree, **xcoeffs)
idl2sciy = Polynomial2D(degree, **ycoeffs)
# Get info for ideal -> v2v3 or v2v3 -> ideal model
v2v32idlx, v2v32idly = v2v3_model('v2v3', 'ideal', parity, np.radians(wfc3_distortion_file['THETA']))
'''
# Now create a compound model for each with the appropriate inverse
# Inverse polynomials were removed in favor of using GWCS' numerical inverse capabilities
sci2idl = Mapping([0, 1, 0, 1]) | sci2idlx & sci2idly
#sci2idl.inverse = Mapping([0, 1, 0, 1]) | idl2scix & idl2sciy
idl2v2v3 = Mapping([0, 1, 0, 1]) | idl2v2v3x & idl2v2v3y
#idl2v2v3.inverse = Mapping([0, 1, 0, 1]) | v2v32idlx & v2v32idly
# Now string the models together to make a single transformation
# We also need
# to account for the difference of 1 between the SIAF
# coordinate values (indexed to 1) and python (indexed to 0).
# Nadia said that this shift should be present in the
# distortion reference file.
core_model = sci2idl# | idl2v2v3
# Now add in the shifts to create the full model
# including the shift to go from 0-indexed python coords to
# 1-indexed
# Find the distance between (0,0) and the reference location
xshift = Shift(wfc3_filter_info['XREF'])
yshift = Shift(wfc3_filter_info['YREF'])
# Finally, we need to shift by the v2,v3 value of the reference
# location in order to get to absolute v2,v3 coordinates
v2shift = Shift(wfc3_filter_info['V2REF'])
v3shift = Shift(wfc3_filter_info['V3REF'])
# SIAF coords
index_shift = Shift(1)
model = index_shift & index_shift | xshift & yshift | core_model | v2shift & v3shift
# Since the inverse of all model components are now defined,
# the total model inverse is also defined automatically
# Save using the DistortionModel datamodel
d = DistortionModel(model=model, input_units=u.pix,
output_units=u.arcsec)
#Populate metadata
# Keyword values in science data to which this file should
# be applied
p_pupil = ''
for p in sci_pupil:
p_pupil = p_pupil + p + '|'
p_subarr = ''
for p in sci_subarr:
p_subarr = p_subarr + p + '|'
p_exptype = ''
for p in sci_exptype:
p_exptype = p_exptype + p + '|'
d.meta.instrument.p_pupil = p_pupil
d.meta.subarray.p_subarray = p_subarr
d.meta.exposure.p_exptype = p_exptype
# metadata describing the reference file itself
d.meta.title = "WFC3 Distortion"
d.meta.instrument.name = "WFC3"
d.meta.instrument.module = detector[-2]
numdet = detector[-1]
d.meta.instrument.channel = "LONG" if numdet == '5' else "SHORT"
# In the reference file headers, we need to switch NRCA5 to
# NRCALONG, and same for module B.
d.meta.instrument.detector = (detector[0:4] + 'LONG') if numdet == 5 else detector
d.meta.telescope = 'HST'
d.meta.subarray.name = 'FULL'
d.meta.pedigree = 'GROUND'
d.meta.reftype = 'DISTORTION'
d.meta.author = 'D. Nguyen'
d.meta.litref = "https://github.com/spacetelescope/jwreftools"
d.meta.description = "Distortion model from SIAF coefficients in pysiaf version 0.6.1"
#d.meta.exp_type = exp_type
d.meta.useafter = "2014-10-01T00:00:00"
# To be ready for the future where we will have filter-dependent solutions
d.meta.instrument.filter = 'N/A'
# Create initial HISTORY ENTRY
sdict = {'name': 'nircam_distortion_reffiles_from_pysiaf.py',
'author': 'B.Hilbert',
'homepage': 'https://github.com/spacetelescope/jwreftools',
'version': '0.8'}
entry = util.create_history_entry(history_entry, software=sdict)
d.history = [entry]
#Create additional HISTORY entries
#entry2 = util.create_history_entry(history_2)
#d.history.append(entry2)
d.save(outname)
print("Output saved to {}".format(outname))
def make_distortion(distfile, outname):
"""
Create an asdf reference file with all distortion components for the MIRI imager.
The filter offsets are stored in a separate 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
--------
>>> make_distortion("MIRI_FM_MIRIMAGE_DISTORTION_07.04.01.fits", 'test.asdf')
"""
# Transform from 0-indexed Detector frame (used by pipeline) to 0-indexed Science frame (used by CDP)
det_to_sci = models.Shift(-4) & models.Identity(1)
fdist = fits.open(distfile)
mi_matrix = fdist['MI matrix'].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['M matrix'].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.copy()
mi_row.inverse = m_row.copy()
m_transform = mi_col & mi_row
m_transform.inverse = m_col & m_row
# This turns the output of the MI transform into the shape needed for the AI/BI transforms
mapping = models.Mapping([0, 1, 0, 1])
mapping.inverse = models.Identity(2)
ai_matrix = fdist['AI matrix'].data
a_matrix = fdist['A matrix'].data
col_poly = polynomial_from_coeffs_matrix_swap(ai_matrix,
name="A_correction")
col_poly.inverse = polynomial_from_coeffs_matrix(a_matrix)
bi_matrix = fdist['BI matrix'].data
b_matrix = fdist['B matrix'].data
row_poly = polynomial_from_coeffs_matrix_swap(bi_matrix,
name="B_correction")
row_poly.inverse = polynomial_from_coeffs_matrix(b_matrix)
poly = row_poly & col_poly # DRL: I had to switch the order here
poly.inverse = col_poly.inverse & row_poly.inverse # but not switch here
ti_matrix = fdist['TI matrix'].data
t_matrix = fdist['T matrix'].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]
t_transform = ti_row & ti_col
t_transform.inverse = t_row & t_col
# ident is created here so that mapping can be assigned as inverse
ident = models.Identity(2)
ident.inverse = models.Mapping([0, 1, 0, 1])
# This turns the output of the AI/BI transforms into the shape needed for the TI transform
poly2t_mapping = models.Mapping([0, 1, 0, 1])
poly2t_mapping.inverse = models.Mapping([0, 1, 0, 1])
map_t2_xanyan = models.Mapping((1, 0))
map_t2_xanyan.inverse = models.Mapping((0, 1, 0, 1))
distortion_transform = det_to_sci | m_transform | mapping | poly | poly2t_mapping | t_transform | ident | models.Mapping(
[1, 0])
# Inverse transform created automatically, but if we needed to do it by hand
# it would look like this
#distortion_transform.inverse=models.Mapping([1,0]).inverse | ident.inverse | t_transform.inverse | poly2t_mapping.inverse | poly.inverse | mapping.inverse | m_transform.inverse | det_to_sci.inverse
fdist.close()
dist = DistortionModel()
# Add general metadata
dist = create_reffile_header(dist)
# Add file-specific metadata
dist.model = distortion_transform
dist.meta.input_units = u.pix
dist.meta.output_units = u.arcsec
dist.meta.title = "MIRI imager distortion - CDP7"
dist.meta.description = "CDP7 delivery"
dist.save(outname)
def create_nircam_distortion(detector,
aperture,
outname,
sci_pupil,
sci_subarr,
sci_exptype,
history_entry,
author=None,
descrip=None,
pedigree=None,
useafter=None,
dist_coeffs_file=None,
siaf_xml_file=None):
"""
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.
siaf_xml_file : str
Name of SIAF xml file to use in place of the default SIAF version from pysiaf.
If None, the default version in pysiaf will be used.
sci_pupil : list
Pupil wheel values for which this distortion solution applies
sci_subarr : list
List of subarray/aperture names to which this distortion solution applies
sci_exptype : list
List of exposure types to which this distortion solution applies
history_entry : str
Text to be added as a HISTORY entry in the output reference file
author : str
Value to place in the output file's Author metadata entry
descrip : str
Text to place in the output file's DECRIP header keyword
pedgree : str
Value to place in the output file's PEDIGREE header keyword
useafter : str
Value to place in the output file's USEAFTER header keyword (e.g. "2014-10-01T00:00:01")
dist_coeffs_file : str
Name of ascii file (nominally output by jwst_fpa package) containing distortion
coefficients. If this is provided, the coefficients in this file are used, rather
than those in pysiaf.
Examples
--------
"""
degree = 5 # distotion in pysiaf is a 5th order polynomial
numdet = detector[-1]
module = detector[-2]
channel = 'SHORT'
if numdet == '5':
channel = 'LONG'
full_aperture = detector + '_' + aperture
# Get Siaf instance for detector/aperture
if siaf_xml_file is None:
print('Using default SIAF version in pysiaf.')
inst_siaf = pysiaf.Siaf('nircam')
else:
print(f'SIAF to be loaded from {siaf_xml_file}...')
inst_siaf = pysiaf.Siaf(filename=siaf_xml_file, instrument='nircam')
siaf = inst_siaf[full_aperture]
# Find the distance between (0,0) and the reference location
xshift, yshift = get_refpix(inst_siaf, full_aperture)
# *****************************************************
# If the user provides files containing distortion coefficients
# (as output by the jwst_fpa package), use those rather than
# retrieving coefficients from siaf.
if dist_coeffs_file is not None:
coeff_tab = read_distortion_coeffs_file(dist_coeffs_file)
xcoeffs = convert_distortion_coeffs_table(coeff_tab, 'Sci2IdlX')
ycoeffs = convert_distortion_coeffs_table(coeff_tab, 'Sci2IdlY')
inv_xcoeffs = convert_distortion_coeffs_table(coeff_tab, 'Idl2SciX')
inv_ycoeffs = convert_distortion_coeffs_table(coeff_tab, 'Idl2SciY')
elif dist_coeffs_file is None:
xcoeffs, ycoeffs = get_distortion_coeffs('Sci2Idl', siaf)
inv_xcoeffs, inv_ycoeffs = get_distortion_coeffs('Idl2Sci', siaf)
# V3IdlYAngle and V2Ref, V3Ref should always be taken from the latest version
# of SIAF, rather than the output of jwst_fpa. Separate FGS/NIRISS analyses must
# be done in order to modify these values.
v3_ideal_y_angle = siaf.V3IdlYAngle * np.pi / 180.
# *****************************************************
# "Forward' transformations. science --> ideal --> V2V3
#label = 'Sci2Idl'
##from_units = 'distorted pixels'
##to_units = 'arcsec'
#xcoeffs, ycoeffs = get_distortion_coeffs(label, siaf)
sci2idlx = Polynomial2D(degree, **xcoeffs)
sci2idly = Polynomial2D(degree, **ycoeffs)
# Get info for ideal -> v2v3 or v2v3 -> ideal model
parity = siaf.VIdlParity
#v3_ideal_y_angle = siaf.V3IdlYAngle * np.pi / 180.
idl2v2v3x, idl2v2v3y = v2v3_model('ideal', 'v2v3', parity,
v3_ideal_y_angle)
# Finally, we need to shift by the v2,v3 value of the reference
# location in order to get to absolute v2,v3 coordinates
v2shift, v3shift = get_v2v3ref(siaf)
# *****************************************************
# 'Reverse' transformations. V2V3 --> ideal --> science
#label = 'Idl2Sci'
##from_units = 'arcsec'
##to_units = 'distorted pixels'
#xcoeffs, ycoeffs = get_distortion_coeffs(label, siaf)
idl2scix = Polynomial2D(degree, **inv_xcoeffs)
idl2sciy = Polynomial2D(degree, **inv_ycoeffs)
# Get info for ideal -> v2v3 or v2v3 -> ideal model
#parity = siaf.VIdlParity
#v3_ideal_y_angle = siaf.V3IdlYAngle * np.pi / 180.
v2v32idlx, v2v32idly = v2v3_model('v2v3', 'ideal', parity,
v3_ideal_y_angle)
##"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)
# Now create a compound model for each with the appropriate inverse
sci2idl = Mapping([0, 1, 0, 1]) | sci2idlx & sci2idly
sci2idl.inverse = Mapping([0, 1, 0, 1]) | idl2scix & idl2sciy
idl2v2v3 = Mapping([0, 1, 0, 1]) | idl2v2v3x & idl2v2v3y
idl2v2v3.inverse = Mapping([0, 1, 0, 1]) | v2v32idlx & v2v32idly
# Now string the models together to make a single transformation
# We also need
# to account for the difference of 1 between the SIAF
# coordinate values (indexed to 1) and python (indexed to 0).
# Nadia said that this shift should be present in the
# distortion reference file.
core_model = sci2idl | idl2v2v3
# Now add in the shifts to create the full model
# including the shift to go from 0-indexed python coords to
# 1-indexed
# SIAF coords
index_shift = Shift(1)
model = index_shift & index_shift | xshift & yshift | core_model | v2shift & v3shift
# Since the inverse of all model components are now defined,
# the total model inverse is also defined automatically
# 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'
# Save using the DistortionModel datamodel
d = DistortionModel(model=model, input_units=u.pix, output_units=u.arcsec)
#Populate metadata
# Keyword values in science data to which this file should
# be applied
p_pupil = ''
for p in sci_pupil:
p_pupil = p_pupil + p + '|'
p_subarr = ''
for p in sci_subarr:
p_subarr = p_subarr + p + '|'
p_exptype = ''
for p in sci_exptype:
p_exptype = p_exptype + p + '|'
d.meta.instrument.p_pupil = p_pupil
d.meta.subarray.p_subarray = p_subarr
d.meta.exposure.p_exptype = p_exptype
#d.meta.instrument.p_pupil = "CLEAR|F162M|F164N|F323N|F405N|F470N|"
#d.meta.p_subarray = "FULL|SUB64P|SUB160|SUB160P|SUB320|SUB400P|SUB640|SUB32TATS|SUB32TATSGRISM|SUB8FP1A|SUB8FP1B|SUB96DHSPILA|SUB96DHSPILB|SUB64FP1A|SUB64FP1B|"
#d.meta.exposure.p_exptype = "NRC_IMAGE|NRC_TSIMAGE|NRC_FLAT|NRC_LED|NRC_WFSC|"
# metadata describing the reference file itself
d.meta.title = "NIRCam Distortion"
d.meta.instrument.name = "NIRCAM"
d.meta.instrument.module = module
d.meta.instrument.channel = channel
d.meta.instrument.detector = detector
d.meta.telescope = 'JWST'
d.meta.subarray.name = 'FULL'
if pedigree is None:
d.meta.pedigree = 'GROUND'
else:
if pedigree.upper() not in ['DUMMY', 'GROUND', 'FLIGHT']:
raise ValueError("Bad PEDIGREE value.")
d.meta.pedigree = pedigree.upper()
d.meta.reftype = 'DISTORTION'
if author is None:
author = "B. Hilbert"
d.meta.author = author
d.meta.litref = "https://github.com/spacetelescope/nircam_calib/nircam_calib/reffile_creation/pipeline/distortion/nircam_distortion_reffiles_from_pysiaf.py"
if descrip is None:
d.meta.description = "TEST OF UPDATED CODE"
else:
d.meta.description = descrip
#d.meta.exp_type = exp_type
if useafter is None:
d.meta.useafter = "2014-10-01T00:00:01"
else:
d.meta.useafter = useafter
# To be ready for the future where we will have filter-dependent solutions
d.meta.instrument.filter = 'N/A'
# Create initial HISTORY ENTRY
sdict = {
'name': 'nircam_distortion_reffiles_from_pysiaf.py',
'author': author,
'homepage': 'https://github.com/spacetelescope/nircam_calib',
'version': '0.0'
}
entry = util.create_history_entry(history_entry, software=sdict)
d.history = [entry]
#Create additional HISTORY entries
#entry2 = util.create_history_entry(history_2)
#d.history.append(entry2)
d.save(outname)
print("Output saved to {}".format(outname))
def create_nircam_distortion(detector, aperture, outname, sci_pupil,
sci_subarr, sci_exptype, history_entry):
"""
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
--------
"""
degree = 5 # distotion in pysiaf is a 5th order polynomial
numdet = detector[-1]
module = detector[-2]
channel = 'SHORT'
if numdet == '5':
channel = 'LONG'
full_aperture = detector + '_' + aperture
# Get Siaf instance for detector/aperture
inst_siaf = pysiaf.Siaf('nircam')
siaf = inst_siaf[full_aperture]
# Find the distance between (0,0) and the reference location
xshift, yshift = get_refpix(inst_siaf, full_aperture)
# *****************************************************
# "Forward' transformations. science --> ideal --> V2V3
label = 'Sci2Idl'
#from_units = 'distorted pixels'
#to_units = 'arcsec'
xcoeffs, ycoeffs = get_distortion_coeffs(label, siaf)
sci2idlx = Polynomial2D(degree, **xcoeffs)
sci2idly = Polynomial2D(degree, **ycoeffs)
# Get info for ideal -> v2v3 or v2v3 -> ideal model
parity = siaf.VIdlParity
v3_ideal_y_angle = siaf.V3IdlYAngle * np.pi / 180.
idl2v2v3x, idl2v2v3y = v2v3_model('ideal', 'v2v3', parity, v3_ideal_y_angle)
# Finally, we need to shift by the v2,v3 value of the reference
# location in order to get to absolute v2,v3 coordinates
v2shift, v3shift = get_v2v3ref(siaf)
# *****************************************************
# 'Reverse' transformations. V2V3 --> ideal --> science
label = 'Idl2Sci'
#from_units = 'arcsec'
#to_units = 'distorted pixels'
xcoeffs, ycoeffs = get_distortion_coeffs(label, siaf)
idl2scix = Polynomial2D(degree, **xcoeffs)
idl2sciy = Polynomial2D(degree, **ycoeffs)
# Get info for ideal -> v2v3 or v2v3 -> ideal model
parity = siaf.VIdlParity
v3_ideal_y_angle = siaf.V3IdlYAngle * np.pi / 180.
v2v32idlx, v2v32idly = v2v3_model('v2v3', 'ideal', parity, v3_ideal_y_angle)
##"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)
# Now create a compound model for each with the appropriate inverse
sci2idl = Mapping([0, 1, 0, 1]) | sci2idlx & sci2idly
sci2idl.inverse = Mapping([0, 1, 0, 1]) | idl2scix & idl2sciy
idl2v2v3 = Mapping([0, 1, 0, 1]) | idl2v2v3x & idl2v2v3y
idl2v2v3.inverse = Mapping([0, 1, 0, 1]) | v2v32idlx & v2v32idly
# Now string the models together to make a single transformation
# We also need
# to account for the difference of 1 between the SIAF
# coordinate values (indexed to 1) and python (indexed to 0).
# Nadia said that this shift should be present in the
# distortion reference file.
core_model = sci2idl | idl2v2v3
# Now add in the shifts to create the full model
# including the shift to go from 0-indexed python coords to
# 1-indexed
# SIAF coords
index_shift = Shift(1)
model = index_shift & index_shift | xshift & yshift | core_model | v2shift & v3shift
# Since the inverse of all model components are now defined,
# the total model inverse is also defined automatically
# 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'
# Save using the DistortionModel datamodel
d = DistortionModel(model=model, input_units=u.pix,
output_units=u.arcsec)
#Populate metadata
# Keyword values in science data to which this file should
# be applied
p_pupil = ''
for p in sci_pupil:
p_pupil = p_pupil + p + '|'
p_subarr = ''
for p in sci_subarr:
p_subarr = p_subarr + p + '|'
p_exptype = ''
for p in sci_exptype:
p_exptype = p_exptype + p + '|'
d.meta.instrument.p_pupil = p_pupil
d.meta.subarray.p_subarray = p_subarr
d.meta.exposure.p_exptype = p_exptype
#d.meta.instrument.p_pupil = "CLEAR|F162M|F164N|F323N|F405N|F470N|"
#d.meta.p_subarray = "FULL|SUB64P|SUB160|SUB160P|SUB320|SUB400P|SUB640|SUB32TATS|SUB32TATSGRISM|SUB8FP1A|SUB8FP1B|SUB96DHSPILA|SUB96DHSPILB|SUB64FP1A|SUB64FP1B|"
#d.meta.exposure.p_exptype = "NRC_IMAGE|NRC_TSIMAGE|NRC_FLAT|NRC_LED|NRC_WFSC|"
# metadata describing the reference file itself
d.meta.title = "NIRCam Distortion"
d.meta.instrument.name = "NIRCAM"
d.meta.instrument.module = module
d.meta.instrument.channel = channel
d.meta.instrument.detector = detector
d.meta.telescope = 'JWST'
d.meta.subarray.name = 'FULL'
d.meta.pedigree = 'GROUND'
d.meta.reftype = 'DISTORTION'
d.meta.author = 'B. Hilbert'
d.meta.litref = "https://github.com/spacetelescope/jwreftools"
d.meta.description = "Distortion model from SIAF coefficients in pysiaf version 0.6.1"
#d.meta.exp_type = exp_type
d.meta.useafter = "2014-10-01T00:00:00"
# To be ready for the future where we will have filter-dependent solutions
d.meta.instrument.filter = 'N/A'
# Create initial HISTORY ENTRY
sdict = {'name': 'nircam_distortion_reffiles_from_pysiaf.py',
'author': 'B.Hilbert',
'homepage': 'https://github.com/spacetelescope/jwreftools',
'version': '0.8'}
entry = util.create_history_entry(history_entry, software=sdict)
d.history = [entry]
#Create additional HISTORY entries
#entry2 = util.create_history_entry(history_2)
#d.history.append(entry2)
d.save(outname)
print("Output saved to {}".format(outname))