def fit_fringes_single_integration(args):
self = args["object"]
slc = args["slc"] # indexes each slice of 3D stack of images
id_tag = args["slc"]
nrm = NRM_Model(mask=self.instrument_data.mask,
pixscale=self.instrument_data.pscale_rad,
holeshape=self.instrument_data.holeshape,
affine2d=self.instrument_data.affine2d,
over=self.oversample)
# for image data from single filter, this is the filter bandpass.
# otherwise it's the IFU wavelength slice.
nrm.bandpass = self.instrument_data.wls[slc]
if self.npix == 'default':
self.npix = self.scidata[slc, :, :].shape[0]
# New or modified in LG++
# center the image on its peak pixel:
# AS subtract 1 from "r" below for testing >1/2 pixel offsets
# AG 03-2019 -- is above comment still relevant?
# Where appropriate, the slice under consideration is centered, and processed
if self.instrument_data.arrname == "jwst_g7s6c":
# get the cropped image and identically-cropped bad pixel data:
self.ctrd, self.dqslice = utils.center_imagepeak(
self.scidata[slc, :, :], dqm=self.dqmask[slc, :, :])
else:
self.ctrd = utils.center_imagepeak(self.scidata[slc, :, :])
# store the 2D cropped image centered on the brightest pixel,
# bad pixels smoothed over
if self.psf_offset_ff is None:
# returned values have offsets x-y flipped:
# Finding centroids the Fourier way assumes no bad pixels case
# - Fourier domain mean slope
# offsets from brightest pixel ctr
centroid = utils.find_centroid(self.ctrd,
self.instrument_data.threshold)
# use flipped centroids to update centroid of image for JWST
# pixel coordinates: - note the flip of [0] and [1] to match DS9 view
image_center = utils.centerpoint(self.ctrd.shape) + \
np.array((centroid[1], centroid[0])) # info only, unused
nrm.xpos = centroid[1] # flip 0 and 1 to convert
nrm.ypos = centroid[0] # flip 0 and 1
nrm.psf_offset = nrm.xpos, nrm.ypos # renamed .bestcenter to .psf_offset
if self.debug:
print(
"nrm.core.fit_fringes_single_integration: utils.find_centroid() -> nrm.psf_offset"
)
else:
# user-provided psf_offset python-style offsets from array center are here.
nrm.psf_offset = self.psf_offset_ff
nrm.make_model(fov=self.ctrd.shape[0],
bandpass=nrm.bandpass,
over=self.oversample,
psf_offset=nrm.psf_offset,
pixscale=nrm.pixel)
# again, fit just one slice...
if self.instrument_data.arrname == "jwst_g7s6c":
nrm.fit_image(self.ctrd,
modelin=nrm.model,
psf_offset=nrm.psf_offset,
dqm=self.dqslice,
weighted=self.weighted)
else:
nrm.fit_image(self.ctrd,
modelin=nrm.model,
psf_offset=nrm.psf_offset,
weighted=self.weighted)
"""
Attributes now stored in nrm object:
-----------------------------------------------------------------------------
soln --- resulting sin/cos coefficients from least squares fitting
fringephase --- baseline phases in radians
fringeamp --- baseline amplitudes (flux normalized)
redundant_cps --- closure phases in radians
redundant_cas --- closure amplitudes
residual --- fit residuals [data - model solution]
cond --- matrix condition for inversion
fringepistons --- zero-mean piston opd in radians on each hole (eigenphases)
-----------------------------------------------------------------------------
For jwst_g7s6 cropped-to-match-data bad pixel array 'ctrb' is also stored
"""
self.save_output(slc, nrm) # Please elucidate what this is for
self.nrm = nrm # store extracted values here
return None
def setUp(self):
# setup parameters for simulation
verbose = 1
overwrite = 1
monochromatic_wavelength_m = np.array([(1.0, 4.3e-6),])
mask = 'MASK_NRM'
filter = 'F430M'
pixelscale_arcsec = 0.0656
filter_name = 'Monochromatic '+np.str(monochromatic_wavelength_m)
self.filter = filter
self.filter_name = filter_name
self.monochromatic_wavelength_m = monochromatic_wavelength_m
# directory containing the test data
data_dir = os.path.join(os.path.dirname(__file__),'test_data/fringe_fitting')
if not os.path.exists(data_dir):
os.makedirs(data_dir)
out_dir = data_dir
name_seed = 'PSF_NIRISS_%s_%s'%(mask,filter)
psf_image_name = name_seed + '_reference.fits'
psf_image = os.path.join(data_dir,psf_image_name)
psf_image_without_oversampling = os.path.join(data_dir,psf_image_name.replace('.fits','_without_oversampling.fits'))
if (not os.path.isfile(psf_image_without_oversampling)) | (overwrite):
from nrm_analysis.fringefitting.LG_Model import NRM_Model
jw = NRM_Model(mask='jwst',holeshape="hex")
jw.set_pixelscale(pixelscale_arcsec*arcsec2rad)
jw.simulate(fov=n_image,
bandpass=monochromatic_wavelength_m,
over=oversample)
print("FringeFittingTestCase.SetUp: simulation oversampling is", oversample)
# optional writing of oversampled image
if 0:
fits.writeto(psf_image,jw.psf_over, clobber=True)
header = fits.getheader(psf_image)
header['PIXELSCL'] = pixelscale_arcsec/oversample
header['FILTER'] = filter_name
header['PUPIL'] = mask
fits.update(psf_image,jw.psf_over/10000./28., header=header)
# PSF without oversampling
fits.writeto(psf_image_without_oversampling,jw.psf, clobber=True)
header = fits.getheader(psf_image_without_oversampling)
header['PIXELSCL'] = pixelscale_arcsec
header['FILTER'] = filter_name
header['PUPIL'] = mask
fits.update(psf_image_without_oversampling,jw.psf, header=header)
# list of files produced for target
file_list = glob.glob(os.path.join(data_dir,'*%s*.fits' % 'without_oversampling' ));
self.simulated_image = file_list[0]
self.psf_image_without_oversampling = psf_image_without_oversampling
class Affine2dTestCase(unittest.TestCase):
def setUp(self):
# directory containing the test data
data_dir = os.path.join(os.path.dirname(__file__),'test_data/affine2d_rot_psf')
self.data_dir = data_dir
print(data_dir)
if not os.path.exists(data_dir):
os.makedirs(data_dir)
self.fnfmt = data_dir+'/psf_nrm_{2:.1f}_{0}_{1}_{3:.0f}.fits' # expects strings of %(imsize,hole)
self.fmt = " ({0:+.3f}, {1:+.3f}) -> ({2:+.3f}, {3:+.3f})"
self.pixel = 0.0656 * u.arcsec.to(u.rad)
self.npix = 87
self.wave = np.array([(1.0, 4.3e-6),]) # m
self.over = 1
mx, my = 1.0, 1.0
sx, sy= 0.0, 0.0
xo, yo= 0.0, 0.0
affine_ideal = Affine2d(mx=mx,my=my,
sx=sx,sy=sy,
xo=xo,yo=yo, name="Ideal")
for rot in (0,5,10,15,20,25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95):
"""
print(" rot degrees pre", rot, end='')
diagnostic = rot/15.0 - int(rot/15.0)
print(" diagnostic", diagnostic, end='')
rot = avoidhexsingularity(rot) # in utils
print(" rot degrees post", rot)
"""
rot = avoidhexsingularity(rot) # in utils
affine_rot = Affine2d(rotradccw=np.pi*rot/180.0, name="{0:.0f}".format(rot)) # in utils
aff = affine_rot
# hexonly g7s6 jwst
self.jw = NRM_Model(mask='jwst', holeshape="hexonly", affine2d=aff)
self.jw.set_pixelscale(self.pixel*arcsec2rad)
self.jw.simulate(fov=self.npix, bandpass=self.wave, over=self.over)
psffn = self.fnfmt.format(self.npix, 'hexonly', self.wave[:,1][0]/um, rot)
fits.writeto(psffn, self.jw.psf, overwrite=True)
header = fits.getheader(psffn)
header = affinepars2header(header, aff)
fits.update(psffn, self.jw.psf, header=header)
del self.jw
del aff
del affine_rot
def test_psf(self):
""" Read in PSFs with 0, 90 degree affines, 5 and 95 degree affines,
Rotate one set and subtract from the smaller rot PSF - should be zero if
everything is correctly calculated. If we nudge the PSF centers to avoid the
line singularity that hextransformEE will encounter if the psf is centrally
placed in a pixel.
The file names are hand-edited to reflect the oversampling and rotations,
so this is more a sanity check and code development tool than a routine test.
"""
psf0 = fits.getdata(self.fnfmt.format(self.npix, 'hexonly', self.wave[:,1][0]/um, 0))
psf90 = fits.getdata(self.fnfmt.format(self.npix, 'hexonly', self.wave[:,1][0]/um, 90))
psf90r0 = np.rot90(psf90)
psfdiff = psf0 - psf90r0
fits.PrimaryHDU(data=psfdiff).writeto(self.data_dir+"/diff_90_0_off08_ov1.fits", overwrite=True)
psf0 = fits.getdata(self.fnfmt.format(self.npix, 'hexonly', self.wave[:,1][0]/um, 5))
psf90 = fits.getdata(self.fnfmt.format(self.npix, 'hexonly', self.wave[:,1][0]/um, 95))
psf90r0 = np.rot90(psf90)
psfdiff = psf0 - psf90r0
fits.PrimaryHDU(data=psfdiff).writeto(self.data_dir+"/diff_95_5_off08_ov1.fits", overwrite=True)
self.assertTrue(0.0 < 1e-15, 'error: test_affine2d failed')
def verify_pistons(arg):
"""
Create simulated data with pistons,
1. analyze the data by calling fit_image
Do not use fringefitter since the data is perfecrly centered (the if loop).
Check if input and output pistons match.
2. analyze the data by calling fit_image via fringefitter
Fringefitter finds the bestcenter and makes model with bestcenter.
Since the data is perfecrly centered fringefitter should not affect output pistons.
Check if input and output pistons match.
"""
jw = NRM_Model(mask='jwst', holeshape="hex")
pixelscale_as = 0.0656
arcsec2rad = u.arcsec.to(u.rad)
jw.set_pixelscale(pixelscale_as * arcsec2rad)
np.random.seed(100)
#lambda/14 ~ 80% strehl ratio
phi = np.random.normal(0, 1.0, 7) / 14.0 # waves
print("phi", phi, "varphi", phi.var(), "waves")
phi = phi - phi.mean()
print("phi_nb stdev/w", phi.std())
print("phi_nb stdev/r", phi.std() * 2 * np.pi)
print("phi_nb mean/r", phi.mean() * 2 * np.pi)
pistons = phi * 4.3e-6 #OR
print("/=====input pistons/m=======/\n", pistons)
print("/=====input pistons/r=======/\n", pistons * (2 * np.pi) / 4.3e-6)
jw.set_pistons(pistons)
jw.simulate(fov=81, bandpass=4.3e-6, over=oversample)
fits.PrimaryHDU(data=jw.psf).writeto(
"implaneia_tests/perfect_wpistons.fits", overwrite=True)
if arg == ("no_fringefitter"):
jw.make_model(fov=81, bandpass=4.3e-6, over=oversample)
jw.fit_image(jw.psf, modelin=jw.model)
pos = np.get_printoptions()
np.set_printoptions(precision=4,
formatter={'float': lambda x: '{:+.4e}'.format(x)},
linewidth=80)
print("Residual/psfpeak array center:", jw.psf.shape)
print((jw.residual / jw.psf.max())[36:-36, 36:-36])
np.set_printoptions(pos)
fits.PrimaryHDU(data=jw.residual).writeto(
"residual_pistons_no_ff.fits", overwrite=True)
#return jw
#print("output pistons/r",jw.fringepistons)
#print("output pistons/w",jw.fringepistons/(2*np.pi))
#print("output pistons/m",jw.fringepistons*4.3e-6/(2*np.pi))
#print("input pistons/m ",jw.phi)
elif arg == ("use_fringefitter"):
fits.PrimaryHDU(data=jw.psf).writeto(datadir +
"/perfect_wpistons.fits",
overwrite=True)
amisim2mirage(datadir, ("perfect_wpistons", ), mirexample, filt)
niriss = InstrumentData.NIRISS(filt)
ff_t = nrm_core.FringeFitter(niriss,
datadir=datadir,
savedir=datadir,
oversample=oversample,
interactive=False)
print(test_tar)
ff_t.fit_fringes(test_tar)
print("Residual:")
#print(ff_t.nrm.residual)
print("Residual/psfpeak array center:", ff_t.nrm.reference.shape)
pos = np.get_printoptions()
np.set_printoptions(precision=3,
formatter={'float': lambda x: '{:+.3e}'.format(x)},
linewidth=80)
print((ff_t.nrm.residual / jw.psf.max())[36:-36, 36:-36])
np.set_printoptions(pos)
fits.PrimaryHDU(data=ff_t.nrm.residual).writeto(datadir+\
"/residual_pistons_with_ff.fits",overwrite=True)
utils.compare_pistons(jw.phi * 2 * np.pi / 4.3e-6,
ff_t.nrm.fringepistons,
str="ff_t")
def setUp(self):
# directory containing the test data
data_dir = os.path.join(os.path.dirname(__file__),
'test_data/psf_offset')
self.data_dir = data_dir
print(data_dir)
if not os.path.exists(data_dir):
os.makedirs(data_dir)
self.pixel = 0.0656 * u.arcsec.to(u.rad)
self.npix = 87
self.wave = 4.3e-6 # m
self.over = 1
"""
mx, my = 1.0, 1.0
sx, sy= 0.0, 0.0
xo, yo= 0.0, 0.0
affine_ideal = Affine2d(mx=mx,my=my,
sx=sx,sy=sy,
xo=xo,yo=yo, name="Ideal")
"""
invert = False # greyscale inversion (true/false)
holeshape = "hex"
pixel_as = 0.0656 / 1.3 # slightly finer-than-actual-detector sampling, for clearer images
print(
"\n\t*** Watch out *** if comparing to real data: slightly finer-than-actual-detector sampling used for clearer images\n"
)
fov = 101 # 'detpix' but at finer sampling than actual
over = 1
wav_m = 4.3e-6
filt = 'F430M'
band = 'Monochromatic ' + np.str(wav_m)
psf_offsets = (
(0, 0),
(1.0, 0),
)
psf_offsets = ((0, 0), )
psf_offsets = ((0, 0), (1.0, 0), (0, 1.0), (1.0, 1.0))
print((enumerate(psf_offsets)))
psfs = np.zeros(
(len(psf_offsets), fov, fov)) # for easy to understand movie
print(('psfs.shape', psfs.shape))
fncube3 = data_dir + "/cube.fits"
# Loop over psf_offsets
for noff, psfo in enumerate(psf_offsets):
jw3 = NRM_Model(mask='jwst',
holeshape=holeshape,
pixscale=arcsec2rad * pixel_as,
datapath="",
refdir="./refdir")
#chooseholes=None)
jw3.simulate(fov=fov, bandpass=wav_m, over=over, psf_offset=psfo)
mnem = "imctr_%.1f_%.1f" % psfo
fn3 = data_dir + "/" + mnem + '.fits'
name_seed = mnem
# PSF
fits.writeto(fn3, jw3.psf, overwrite=True)
header = fits.getheader(fn3)
header['SEGNAMES'] = "asbuilt"
header['PIXELSCL'] = pixel_as
header['over'] = over
header['FILTER'] = wav_m
header['hole'] = holeshape
header['PUPIL'] = 'asbuilt'
header['psfoff0'] = (psfo[0], "ds9X / detector pixels")
header['psfoff1'] = (psfo[1], "ds9Y / detector pixels")
header['SRC'] = ("test_psf_offset.py", "generating code")
header['author'] = ("*****@*****.**", "responsible person?")
fits.update(fn3, jw3.psf, header=header)
psfs[noff, :, :] = jw3.psf
""" calls tkinter, needs $DISPLAY environment variable, breaks travis testing
figname = UC.make_standard_image(
fn3,
image_title=name_seed,
save_plot=1,
plot_dir=data_dir,
name_seed=name_seed,
stretch='linear',
x_axis_label=None,
y_axis_label=None,
invert=invert,
add_grid=True)
print(figname + " written to " + data_dir)
"""
del jw3
fits.PrimaryHDU(data=psfs).writeto(fncube3,
overwrite=True) # good for movie
"""
def find_scale(
imagedata,
affine_best, # best current guess at data geometry cf analytical ideal
scales, # scales are near-unity
pixel,
npix,
bandpass,
over,
holeshape,
outdir=None): # for nrm_model
""" Preserve incoming "pixel" value, put the scale correction into the Affine2d object
Is that kosher??? Should we change the pixel scale and leave affine2d the same?
Affine2d can also incorporate unequal x and y scales, shears...
For now place scale corrections into the Affine2d object
Note - placing isotropic scale change into Affine2d is equivalent to changing
the effective image distance in the optical train while insisting that the
mask physical geometry does not change, and the wavelength is perfectly knowm
AS 2018 10 """
affine_best.show("\tfind_scale")
vprint("\tBefore Loop: ", scales)
#Extend this name to include multi-paramater searches?
psffmt = 'psf_nrm_{0:d}_{1:s}_{2:.3f}um_scl{3:.3f}.fits'
# expect (npix, holeshape, bandpass/um, scl)
if hasattr(scales, '__iter__') is False:
scales = (scales, )
affine2d_list = create_afflist_scales(scales, affine_best.mx,
affine_best.my, affine_best.sx,
affine_best.sy, affine_best.xo,
affine_best.yo)
crosscorrs = []
for (scl, aff) in zip(scales, affine2d_list):
vprint(aff.name + "...")
jw = NRM_Model(mask='jwst',
holeshape=holeshape,
over=over,
affine2d=aff)
jw.set_pixelscale(pixel)
jw.simulate(fov=npix, bandpass=bandpass, over=over)
psffn = psffmt.format(npix, holeshape, bandpass / um, scl)
if outdir:
fits.PrimaryHDU(data=jw.psf).writeto(outdir + "/" + psffn,
overwrite=True)
fits.writeto(psffn, jw.psf, overwrite=True)
header = fits.getheader(psffn)
utils.affinepars2header(header, aff)
fits.update(psffn, jw.psf, header=header)
crosscorrs.append(utils.rcrosscorrelate(imagedata, jw.psf).max())
del jw
vprint("\t*******************")
vprint("\tDebug: crosscorrelations", crosscorrs)
vprint("\tDebug: scales", scales)
scl_measured, max_cor = utils.findpeak_1d(crosscorrs, scales)
vprint("\tScale factor measured {0:.5f} Max correlation {1:.3e}".format(
scl_measured, max_cor))
vprint("\tpixel pitch from header {0:.3f} mas".format(pixel * rad2mas))
vprint("\tpixel pitch {0:.3f} mas (implemented using affine2d)".format(
scl_measured * pixel * rad2mas))
# return convenient affine2d
return utils.Affine2d(affine_best.mx * scl_measured,
affine_best.my * scl_measured,
affine_best.sx * scl_measured,
affine_best.sy * scl_measured,
affine_best.xo * scl_measured,
affine_best.yo * scl_measured,
name="scale_{0:.4f}".format(scl))
def setUp(self):
# directory containing the test data
data_dir = os.path.join(os.path.dirname(__file__),
'test_data/affine2d_xyscale_psf')
self.data_dir = data_dir
print(data_dir)
if not os.path.exists(data_dir):
os.makedirs(data_dir)
self.fnfmt = data_dir + '/psf_nrm_{0:d}_{1:s}_{2:.3f}um_{3:s}.fits'
# expects e.g. self.fnfmt.format(self.npix, 'hex', self.wave/um, aff.name)
self.pixel = 0.0656 * u.arcsec.to(u.rad)
self.npix = 87
self.wave = 4.3e-6 # m
self.over = 1
################
self.pointsfmt = " ({0:+.3f}, {1:+.3f}) -> ({2:+.3f}, {3:+.3f})"
self.numtestfmt = " min: {0:+.1e}, max: {1:+.1e}, avg: {2:+.1e}, sig: {3:+.1e}" # min max avg stddev
self.testpoints = (np.array((0, 0)), np.array((1, 0)), np.array(
(0, 1)), np.array((1, 1)))
xo, yo = 0.0, 0.0 # always set these for PSFs...
mx, my = 1.0, 1.0
sx, sy = 0.0, 0.0
self.affine_ideal = Affine2d(mx=mx,
my=my,
sx=sx,
sy=sy,
xo=xo,
yo=yo,
name="Ideal")
mx, my = -1.0, 1.0
sx, sy = 0.0, 0.0
self.affine_xrev = Affine2d(mx=mx,
my=my,
sx=sx,
sy=sy,
xo=xo,
yo=yo,
name="Xreverse")
mx, my = 2.0, 1.0
sx, sy = 0.0, 0.0
self.affine_xmag = Affine2d(mx=mx,
my=my,
sx=sx,
sy=sy,
xo=xo,
yo=yo,
name="Xmag")
mx, my = 1.0, 1.0
sx, sy = 0.5, 0.0
self.affine_sigx = Affine2d(mx=mx,
my=my,
sx=sx,
sy=sy,
xo=xo,
yo=yo,
name="Xshear")
for aff in (self.affine_ideal, self.affine_xrev, self.affine_xmag,
self.affine_sigx):
# circ g7s6 jwst created for info only
self.jw = NRM_Model(mask='jwst', holeshape="circ", affine2d=aff)
self.jw.set_pixelscale(self.pixel)
self.jw.simulate(fov=self.npix, bandpass=self.wave, over=self.over)
psffn = self.fnfmt.format(self.npix, 'circ', self.wave / um,
aff.name)
fits.writeto(psffn, self.jw.psf, overwrite=True)
header = fits.getheader(psffn)
header = affinepars2header(header, aff)
fits.update(psffn, self.jw.psf, header=header)
del self.jw
# circonly g7s6 jwst created for info only
self.jw = NRM_Model(mask='jwst',
holeshape="circonly",
affine2d=aff)
self.jw.set_pixelscale(self.pixel)
self.jw.simulate(fov=self.npix, bandpass=self.wave, over=self.over)
psffn = self.fnfmt.format(self.npix, 'circonly', self.wave / um,
aff.name)
fits.writeto(psffn, self.jw.psf, overwrite=True)
header = fits.getheader(psffn)
header = affinepars2header(header, aff)
fits.update(psffn, self.jw.psf, header=header)
del self.jw
# hex g7s6 jwst
self.jw = NRM_Model(mask='jwst', holeshape="hex", affine2d=aff)
self.jw.set_pixelscale(self.pixel)
self.jw.simulate(fov=self.npix, bandpass=self.wave, over=self.over)
psffn = self.fnfmt.format(self.npix, 'hex', self.wave / um,
aff.name)
fits.writeto(psffn, self.jw.psf, overwrite=True)
header = fits.getheader(psffn)
header = affinepars2header(header, aff)
fits.update(psffn, self.jw.psf, header=header)
del self.jw
# hexonly g7s6 jwst
self.jw = NRM_Model(mask='jwst', holeshape="hexonly", affine2d=aff)
self.jw.set_pixelscale(self.pixel)
self.jw.simulate(fov=self.npix, bandpass=self.wave, over=self.over)
psffn = self.fnfmt.format(self.npix, 'hexonly', self.wave / um,
aff.name)
fits.writeto(psffn, self.jw.psf, overwrite=True)
header = fits.getheader(psffn)
header = affinepars2header(header, aff)
fits.update(psffn, self.jw.psf, header=header)
del self.jw
# fringeonly g7s6 jwst
self.jw = NRM_Model(mask='jwst',
holeshape="fringeonly",
affine2d=aff)
self.jw.set_pixelscale(self.pixel)
self.jw.simulate(fov=self.npix, bandpass=self.wave, over=self.over)
psffn = self.fnfmt.format(self.npix, 'fringeonly', self.wave / um,
aff.name)
fits.writeto(psffn, self.jw.psf, overwrite=True)
header = fits.getheader(psffn)
header = affinepars2header(header, aff)
fits.update(psffn, self.jw.psf, header=header)
del self.jw
del aff
self.hextest = self.eval_psf_hex()
self.fringeonlytest = self.eval_psf_fringeonly()
self.hexonlytest = self.eval_psf_hexonly()
self.evals = [self.hextest, self.fringeonlytest, self.hexonlytest]
self.sigsum = self.hextest + self.fringeonlytest + self.hexonlytest
class Affine2dTestCase(unittest.TestCase):
def setUp(self):
# directory containing the test data
data_dir = os.path.join(os.path.dirname(__file__),
'test_data/affine2d_xyscale_psf')
self.data_dir = data_dir
print(data_dir)
if not os.path.exists(data_dir):
os.makedirs(data_dir)
self.fnfmt = data_dir + '/psf_nrm_{0:d}_{1:s}_{2:.3f}um_{3:s}.fits'
# expects e.g. self.fnfmt.format(self.npix, 'hex', self.wave/um, aff.name)
self.pixel = 0.0656 * u.arcsec.to(u.rad)
self.npix = 87
self.wave = 4.3e-6 # m
self.over = 1
################
self.pointsfmt = " ({0:+.3f}, {1:+.3f}) -> ({2:+.3f}, {3:+.3f})"
self.numtestfmt = " min: {0:+.1e}, max: {1:+.1e}, avg: {2:+.1e}, sig: {3:+.1e}" # min max avg stddev
self.testpoints = (np.array((0, 0)), np.array((1, 0)), np.array(
(0, 1)), np.array((1, 1)))
xo, yo = 0.0, 0.0 # always set these for PSFs...
mx, my = 1.0, 1.0
sx, sy = 0.0, 0.0
self.affine_ideal = Affine2d(mx=mx,
my=my,
sx=sx,
sy=sy,
xo=xo,
yo=yo,
name="Ideal")
mx, my = -1.0, 1.0
sx, sy = 0.0, 0.0
self.affine_xrev = Affine2d(mx=mx,
my=my,
sx=sx,
sy=sy,
xo=xo,
yo=yo,
name="Xreverse")
mx, my = 2.0, 1.0
sx, sy = 0.0, 0.0
self.affine_xmag = Affine2d(mx=mx,
my=my,
sx=sx,
sy=sy,
xo=xo,
yo=yo,
name="Xmag")
mx, my = 1.0, 1.0
sx, sy = 0.5, 0.0
self.affine_sigx = Affine2d(mx=mx,
my=my,
sx=sx,
sy=sy,
xo=xo,
yo=yo,
name="Xshear")
for aff in (self.affine_ideal, self.affine_xrev, self.affine_xmag,
self.affine_sigx):
# circ g7s6 jwst created for info only
self.jw = NRM_Model(mask='jwst', holeshape="circ", affine2d=aff)
self.jw.set_pixelscale(self.pixel)
self.jw.simulate(fov=self.npix, bandpass=self.wave, over=self.over)
psffn = self.fnfmt.format(self.npix, 'circ', self.wave / um,
aff.name)
fits.writeto(psffn, self.jw.psf, overwrite=True)
header = fits.getheader(psffn)
header = affinepars2header(header, aff)
fits.update(psffn, self.jw.psf, header=header)
del self.jw
# circonly g7s6 jwst created for info only
self.jw = NRM_Model(mask='jwst',
holeshape="circonly",
affine2d=aff)
self.jw.set_pixelscale(self.pixel)
self.jw.simulate(fov=self.npix, bandpass=self.wave, over=self.over)
psffn = self.fnfmt.format(self.npix, 'circonly', self.wave / um,
aff.name)
fits.writeto(psffn, self.jw.psf, overwrite=True)
header = fits.getheader(psffn)
header = affinepars2header(header, aff)
fits.update(psffn, self.jw.psf, header=header)
del self.jw
# hex g7s6 jwst
self.jw = NRM_Model(mask='jwst', holeshape="hex", affine2d=aff)
self.jw.set_pixelscale(self.pixel)
self.jw.simulate(fov=self.npix, bandpass=self.wave, over=self.over)
psffn = self.fnfmt.format(self.npix, 'hex', self.wave / um,
aff.name)
fits.writeto(psffn, self.jw.psf, overwrite=True)
header = fits.getheader(psffn)
header = affinepars2header(header, aff)
fits.update(psffn, self.jw.psf, header=header)
del self.jw
# hexonly g7s6 jwst
self.jw = NRM_Model(mask='jwst', holeshape="hexonly", affine2d=aff)
self.jw.set_pixelscale(self.pixel)
self.jw.simulate(fov=self.npix, bandpass=self.wave, over=self.over)
psffn = self.fnfmt.format(self.npix, 'hexonly', self.wave / um,
aff.name)
fits.writeto(psffn, self.jw.psf, overwrite=True)
header = fits.getheader(psffn)
header = affinepars2header(header, aff)
fits.update(psffn, self.jw.psf, header=header)
del self.jw
# fringeonly g7s6 jwst
self.jw = NRM_Model(mask='jwst',
holeshape="fringeonly",
affine2d=aff)
self.jw.set_pixelscale(self.pixel)
self.jw.simulate(fov=self.npix, bandpass=self.wave, over=self.over)
psffn = self.fnfmt.format(self.npix, 'fringeonly', self.wave / um,
aff.name)
fits.writeto(psffn, self.jw.psf, overwrite=True)
header = fits.getheader(psffn)
header = affinepars2header(header, aff)
fits.update(psffn, self.jw.psf, header=header)
del self.jw
del aff
self.hextest = self.eval_psf_hex()
self.fringeonlytest = self.eval_psf_fringeonly()
self.hexonlytest = self.eval_psf_hexonly()
self.evals = [self.hextest, self.fringeonlytest, self.hexonlytest]
self.sigsum = self.hextest + self.fringeonlytest + self.hexonlytest
""" Ideal and Xreverse_xflipped should be machine zero or close """
def eval_psf_hex(self):
psfIdeal = fits.getdata(
self.fnfmt.format(self.npix, 'hex', self.wave / um, "Ideal"))
psfXrev = fits.getdata(
self.fnfmt.format(self.npix, 'hex', self.wave / um, "Xreverse"))
psfdiff = psfIdeal - psfXrev[::-1, :]
print(" Numerical Summary: hex psf: " +\
self.numtestfmt.format(psfIdeal.min(), psfIdeal.max(), psfIdeal.mean(), psfIdeal.std()))
print(" Numerical Summary: hex test: " +\
self.numtestfmt.format(psfdiff.min(), psfdiff.max(), psfdiff.mean(), psfdiff.std()))
fits.PrimaryHDU(data=psfdiff).writeto(self.data_dir + "/diff_hex.fits",
overwrite=True)
return psfdiff.std()
def eval_psf_fringeonly(self):
psfIdeal = fits.getdata(
self.fnfmt.format(self.npix, 'fringeonly', self.wave / um,
"Ideal"))
psfXrev = fits.getdata(
self.fnfmt.format(self.npix, 'fringeonly', self.wave / um,
"Xreverse"))
psfdiff = psfIdeal - psfXrev[::-1, :]
print(" Numerical Summary: fringeonly psf: " +\
self.numtestfmt.format(psfIdeal.min(), psfIdeal.max(), psfIdeal.mean(), psfIdeal.std()))
print(" Numerical Summary: fringeonly test: " +\
self.numtestfmt.format(psfdiff.min(), psfdiff.max(), psfdiff.mean(), psfdiff.std()))
fits.PrimaryHDU(data=psfdiff).writeto(self.data_dir +
"/diff_fringeonly.fits",
overwrite=True)
return psfdiff.std()
def eval_psf_hexonly(self):
psfIdeal = fits.getdata(
self.fnfmt.format(self.npix, 'hexonly', self.wave / um, "Ideal"))
psfXrev = fits.getdata(
self.fnfmt.format(self.npix, 'hexonly', self.wave / um,
"Xreverse"))
psfdiff = psfIdeal - psfXrev[::-1, :]
print(" Numerical Summary: hexonly psf: " +\
self.numtestfmt.format(psfIdeal.min(), psfIdeal.max(), psfIdeal.mean(), psfIdeal.std()))
print(" Numerical Summary: hexonly test: " +\
self.numtestfmt.format(psfdiff.min(), psfdiff.max(), psfdiff.mean(), psfdiff.std()))
fits.PrimaryHDU(data=psfdiff).writeto(self.data_dir +
"/diff_hexonly.fits",
overwrite=True)
return psfdiff.std()
def test_psfs(self):
print("Standard deviations of all three tests:", self.evals)
self.assertTrue(self.sigsum < 1e-7,
'error: test_affine2d_xyscale_psf failed')
def setUp(self):
# setup parameters for simulation, most are passed on to driver_scene
verbose = 0
overwrite = 1
apply_dither = 0
apply_jitter = 0
include_detection_noise = 0
uniform_flatfield = 1
OVERSAMPLE = 11
monochromatic_wavelength_m = 4.3e-6
create_calibrator = 0
random_seed = 124
flip = False
mask = 'MASK_NRM'
filter = 'F430M'
n_image = 77
filter_name = 'Monochromatic ' + np.str(monochromatic_wavelength_m)
# directory containing the static test data
data_dir = os.path.join(os.path.dirname(__file__), 'test_data')
# define output directory for dynamically generated data
out_dir = os.path.join(os.path.dirname(__file__), 'tmp_data')
if not os.path.exists(out_dir):
os.makedirs(out_dir)
name_seed = 'PSF_NIRISS_%s_%s' % (mask, filter)
point_source_image_name = 'point_source_image.fits'
point_source_image = os.path.join(out_dir, point_source_image_name)
psf_image_name = name_seed + '_reference.fits'
psf_image = os.path.join(data_dir, psf_image_name)
psf_image_without_oversampling = os.path.join(
data_dir,
psf_image_name.replace('.fits', '_without_oversampling.fits'))
# generate delta-function like image to feed as point-source into scene_sim
n_image2 = n_image * OVERSAMPLE
data = np.zeros((n_image2, n_image2))
bright_pixel_index = (np.int(np.floor(n_image2 / 2.)),
np.int(np.floor(n_image2 / 2.)))
data[bright_pixel_index] = 1.
fits.writeto(point_source_image, data, clobber=True)
# following code can be used to regenerate reference PSF file, but it depends on nrm_analysis
if 0:
from nrm_analysis.fringefitting.LG_Model import NRM_Model
jw = NRM_Model(mask='jwst', holeshape="hex", flip=flip)
jw.simulate(fov=n_image,
bandpass=monochromatic_wavelength_m,
over=OVERSAMPLE,
pixel=mas2rad(PIXELSCL_arcsec * 1000))
fits.writeto(psf_image, jw.psf_over, clobber=True)
header = fits.getheader(psf_image)
header['PIXELSCL'] = PIXELSCL_arcsec / OVERSAMPLE
header['FILTER'] = filter_name
header['PUPIL'] = mask
fits.update(psf_image, jw.psf_over / 10000. / 28., header=header)
# PSF without oversampling
fits.writeto(psf_image_without_oversampling, jw.psf, clobber=True)
header = fits.getheader(psf_image_without_oversampling)
header['PIXELSCL'] = PIXELSCL_arcsec
header['FILTER'] = filter_name
header['PUPIL'] = mask
fits.update(psf_image_without_oversampling, jw.psf, header=header)
NGROUP = 1
NINT = 1
COUNTRATE = 5e8
# run driver_scene to generate target and calibrator images
driver_scene.main(['--output_absolute_path','%s'%out_dir,\
'--overwrite','%d'%overwrite,'-utr','0', '-f','%s'%filter,'-v','%d'%verbose,'--apply_dither','%d'%apply_dither,'--apply_jitter','%d'%apply_jitter,\
'-p',psf_image,'-s','%s'%point_source_image,'-os','%d'%OVERSAMPLE,'-cr','%e'%COUNTRATE,'--include_detection_noise','%d'%include_detection_noise,\
'-tag',('%3.2e_NGROUP%d'%(COUNTRATE,NGROUP)).replace('.','p'),'--uniform_flatfield','%d'%uniform_flatfield,'--random_seed','%d'%random_seed,\
'--create_calibrator','%d'%create_calibrator,'--nint','%d'%NINT,'--ngroups','%d'%NGROUP])
# directory where simulated data was written
filter_dir = os.path.join(out_dir, filter)
self.filter_dir = filter_dir
# list of files produced for target
file_list = glob.glob(os.path.join(filter_dir, '%s*.fits' % 't_'))
self.simulated_image = file_list[0]
self.psf_image_without_oversampling = psf_image_without_oversampling
