def xyideal_to_v2v3(xi, yi, **kwargs):
if ('siaf1A' in kwargs):
siaf1A = kwargs['siaf1A']
else:
# Import locally to this function so that pysiaf isn't required for everything in mrs_tools
import miricoord.mrs.makesiaf.makesiaf_mrs as makesiaf
siaf1A = makesiaf.create_siaf_oneband('1A')
v2ref, v3ref = siaf1A['inscr_v2ref'], siaf1A['inscr_v3ref']
v2 = -(xi - v2ref)
v3 = yi + v3ref
return v2, v3
def v2v3_to_xyideal(v2, v3, **kwargs):
if ('siaf1A' in kwargs):
siaf1A = kwargs['siaf1A']
else:
# Import locally to this function so that pysiaf isn't required for everything in mrs_tools
import miricoord.mrs.makesiaf.makesiaf_mrs as makesiaf
siaf1A = makesiaf.create_siaf_oneband('1A')
v2ref, v3ref = siaf1A['inscr_v2ref'], siaf1A['inscr_v3ref']
xidl = -(v2 - v2ref)
yidl = v3 - v3ref
return xidl, yidl
def main(detband, dithers, psftot, extval, scan=False, writearea=False):
# Set the distortion solution to use
mt.set_toolversion('cdp8b')
# Define the bands to use
left, right = 'N/A', 'N/A'
if ((detband == '1A') or (detband == '12A')):
left = '1A'
if ((detband == '2A') or (detband == '12A')):
right = '2A'
if ((detband == '1B') or (detband == '12B')):
left = '1B'
if ((detband == '2B') or (detband == '12B')):
right = '2B'
if ((detband == '1C') or (detband == '12C')):
left = '1C'
if ((detband == '2C') or (detband == '12C')):
right = '2C'
if ((detband == '3A') or (detband == '34A')):
right = '3A'
if ((detband == '4A') or (detband == '34A')):
left = '4A'
if ((detband == '3B') or (detband == '34B')):
right = '3B'
if ((detband == '4B') or (detband == '34B')):
left = '4B'
if ((detband == '3C') or (detband == '34C')):
right = '3C'
if ((detband == '4C') or (detband == '34C')):
left = '4C'
#########################################################
print('Setting up the dithers')
# Normal CDP-8b distortion solution dithers from PRDOPSSOC-M-026
if (scan == False):
dxidl = -np.array([
0., 1.094458, -1.012049, 0.988069, -1.117844, 0.102213, -0.127945,
0.008080, -0.034015
])
dyidl = -np.array([
0., -0.385616, 0.296642, -0.311605, 0.371771, -0.485776, 0.467512,
-0.499923, 0.481275
])
# Select desired combination of dither positions
# Warning, this will fail if we have bad input!
dxidl = dxidl[dithers]
dyidl = dyidl[dithers]
nexp = len(dxidl)
# If the 'scan' option was True, then override the setup to create a scanning grid to sample
# the field for a given channel. Note that this also will only populate a single detector at a time!
if (scan == True):
if ((detband == '12A') or (detband == '12B') or (detband == '12C')
or (detband == '34A') or (detband == '34B')
or (detband == '34C')):
print('Cannot use scan with selected band!')
# What is the field for this channel?
chinfo = mrssiaf.create_siaf_oneband(detband)
minalpha, maxalpha = np.min(chinfo['inscr_alpha_corners']), np.max(
chinfo['inscr_alpha_corners'])
minbeta, maxbeta = np.min(chinfo['inscr_beta_corners']), np.max(
chinfo['inscr_beta_corners'])
# And the slice width
sw = mt.slicewidth(detband)
# Number of slices
nslice = ((maxbeta - minbeta) / sw).astype(int)
# PSF FWHM for spacing from detector edges for sim points
fwhm = 2 * rough_fwhm(detband)
# First seven points are the center, corners, and sides
alpha1 = np.array([
0, minalpha + fwhm, maxalpha - fwhm, minalpha + fwhm,
maxalpha - fwhm, minalpha + fwhm, maxalpha - fwhm
])
beta1 = np.array([
0, maxbeta - fwhm, maxbeta - fwhm, 0, 0, minbeta + fwhm,
minbeta + fwhm
])
# Next set of points is a scan up alpha=0 for every slice
alpha2 = np.zeros(nslice)
beta2 = np.arange(nslice) * sw + minbeta + sw / 2.
# Concatenate arrays
alpha = np.concatenate((alpha1, alpha2))
beta = np.concatenate((beta1, beta2))
# Convert to v2,v3
v2, v3 = mt.abtov2v3(alpha, beta, detband)
# Now convert to Ideal coordinate offsets relative to Ch1a reference point
dxidl, dyidl = mt.v2v3_to_xyideal(v2, v3)
# Now flip them, because we're moving the source not the telescope
dxidl, dyidl = -dxidl, -dyidl
nexp = len(dxidl)
# Print the points to a file for reference
pointfile = 'simpoints' + detband + '.txt'
print('# alpha beta', file=open(pointfile, "w"))
for ii in range(0, nexp):
print(alpha[ii], beta[ii], file=open(pointfile, "a"))
# Plot where the points were for reference
plotname = 'qaplot' + detband + '.png'
plot_qascan(chinfo, detband, v2, v3, filename=plotname)
#########################################################
print('Ndither = ', nexp)
# MRS reference location is DEFINED for 1A regardless of band in use
v2ref, v3ref = mt.abtov2v3(0., 0., '1A')
# Define source coordinates (decl=0 makes life easier)
raobj = 45.0
decobj = 0.0
# Make life easier by assuming that telescope roll exactly places
# slices along R.A. for Ch1A (will not be quite as good for other channels)
# Compute what that roll is
a1, b1 = 0., 0.
a2, b2 = 2., 0. # A location along alpha axis
v2_1, v3_1 = mt.abtov2v3(a1, b1, '1A')
v2_2, v3_2 = mt.abtov2v3(a2, b2, '1A')
ra_1, dec_1, _ = tt.jwst_v2v3toradec([v2_1], [v3_1],
v2ref=v2ref,
v3ref=v3ref,
raref=raobj,
decref=decobj,
rollref=0.)
ra_2, dec_2, _ = tt.jwst_v2v3toradec([v2_2], [v3_2],
v2ref=v2ref,
v3ref=v3ref,
raref=raobj,
decref=decobj,
rollref=0.)
dra = (ra_2 - ra_1) * 3600.
ddec = (dec_2 - dec_1) * 3600.
roll = -(np.arctan2(dra, ddec) * 180. / np.pi - 90.0)
# Compute the corresponding raref, decref, rollref of the dither positions.
raref = np.zeros(nexp)
decref = np.zeros(nexp)
rollref = np.zeros(nexp)
for ii in range(0, nexp):
temp1, temp2, temp3 = tt.jwst_v2v3toradec([v2ref] - dxidl[ii],
[v3ref] + dyidl[ii],
v2ref=v2ref,
v3ref=v3ref,
raref=raobj,
decref=decobj,
rollref=roll)
raref[ii] = temp1
decref[ii] = temp2
rollref[ii] = temp3
# Values for each exposure
allexp = np.zeros([nexp, 1024, 1032])
allarea = np.zeros([nexp, 1024, 1032])
# Do left half of detector
print('Working on left half of detector')
roi = rough_fwhm(left) * 3
if (left != 'N/A'):
allexp, allarea = setvalues(allexp, allarea, left, roi, raobj, decobj,
raref, decref, rollref, dxidl, dyidl,
psftot, extval)
# Do right half of detector
print('Working on right half of detector')
roi = rough_fwhm(right) * 3
if (right != 'N/A'):
allexp, allarea = setvalues(allexp, allarea, right, roi, raobj, decobj,
raref, decref, rollref, dxidl, dyidl,
psftot, extval)
# Write the exposures to disk
print('Writing files')
basefile = get_template(detband)
for ii in range(0, nexp):
thisexp = allexp[ii, :, :]
thisarea = allarea[ii, :, :]
# Ensure two-digit sim format code
strii = str(ii)
if (ii <= 9):
strii = '0' + str(ii)
newfile = 'mock' + detband + '-' + strii + '.fits'
newareafile = 'mockarea' + detband + '-' + strii + '.fits'
hdu = fits.open(basefile)
# Hack header WCS
primheader = hdu[0].header
primheader['TARG_RA'] = raobj
primheader['TARG_DEC'] = decobj
header = hdu['SCI'].header
header['V2_REF'] = v2ref
header['V3_REF'] = v3ref
header['RA_REF'] = raref[ii]
header['DEC_REF'] = decref[ii]
header['ROLL_REF'] = rollref[ii]
hdu['SCI'].header = header
hdu['SCI'].data = thisexp
# Overwrite any old DQ problems
hdu['DQ'].data[:] = 0
hdu.writeto(newfile, overwrite=True)
if (writearea == True):
hdu['SCI'].data = thisarea
hdu.writeto(newareafile, overwrite=True)
print('Done!')
def make_x1d_fromdict(now, cdp_dir, outplot):
meta = {}
meta['telescope'] = 'JWST'
meta['pedigree'] = 'GROUND'
meta['description'] = 'Default MIRI MRS Extract1d parameters'
meta['date'] = now.value
meta['reftype'] = 'EXTRACT1D'
meta['exposure'] = {'type': 'MIR_MRS'}
meta['useafter'] = '2000-01-01T00:00:00'
meta['version'] = int(now.mjd)
meta['author'] = 'D. Law'
meta['origin'] = 'STSCI'
meta['model_type'] = 'Extract1dIFUModel'
meta['history'] = '1D Extraction defaults'
meta['history'] += ' DOCUMENT: TBD'
meta[
'history'] += ' SOFTWARE: https://github.com/STScI-MIRI/miri3d/tree/master/miri3d/x1d/make_x1d.py'
meta['history'] += ' DATA USED: CDP-7'
meta['history'] += ' Updated 4/26/21 to decrease background annulus size'
meta['instrument'] = {'name': 'MIRI'}
meta['region_type'] = 'target'
meta['subtract_background'] = True
meta['method'] = 'subpixel'
meta['subpixels'] = 10
print('Figuring out wavelength ranges')
wmin1A, _ = mc.waveminmax('1A')
_, wmax4C = mc.waveminmax('4C')
print('Building tables')
# Set up placeholder vectors
waves = np.arange(wmin1A, wmax4C, 0.01, dtype='float32')
nwave = len(waves)
radius = np.ones(nwave, dtype='float32')
inbkg = np.zeros(nwave, dtype='float32')
outbkg = np.zeros(nwave, dtype='float32')
axratio = np.ones(nwave, dtype='float32')
axangle = np.zeros(nwave, dtype='float32')
# Populate real values
# Read in the CDP files
files = [
'MIRI_FM_MIRIFUSHORT_1SHORT_APERCORR_07.00.00.fits',
'MIRI_FM_MIRIFUSHORT_1MEDIUM_APERCORR_07.00.00.fits',
'MIRI_FM_MIRIFUSHORT_1LONG_APERCORR_07.00.00.fits',
'MIRI_FM_MIRIFUSHORT_2SHORT_APERCORR_07.00.00.fits',
'MIRI_FM_MIRIFUSHORT_2MEDIUM_APERCORR_07.00.00.fits',
'MIRI_FM_MIRIFUSHORT_2LONG_APERCORR_07.00.00.fits',
'MIRI_FM_MIRIFULONG_3SHORT_APERCORR_07.00.00.fits',
'MIRI_FM_MIRIFULONG_3MEDIUM_APERCORR_07.00.00.fits',
'MIRI_FM_MIRIFULONG_3LONG_APERCORR_07.00.00.fits',
'MIRI_FM_MIRIFULONG_4SHORT_APERCORR_07.00.00.fits',
'MIRI_FM_MIRIFULONG_4MEDIUM_APERCORR_07.00.00.fits',
'MIRI_FM_MIRIFULONG_4LONG_APERCORR_07.00.00.fits'
]
inwave = []
inap = []
for file in files:
fullfile = os.path.join(cdp_dir, file)
hdu = fits.open(fullfile)
data = hdu[1].data
inwave.append(data['wavelength'])
inap.append(data['a_aperture'])
# Compile into big vectors
# Simple polynomial fit to the aperture
thefit = np.polyfit(np.array(inwave).ravel(), np.array(inap).ravel(), 1)
poly = np.poly1d(thefit)
radius = poly(waves)
# Background annulus
# Note that Ch1 can be much more generous than Ch4; FWHM increases
# by a factor of 5 from Ch1 to Ch4 but FOV only by a factor of 2.
# We also should not apply any sudden steps in the annulus size
# between channels, otherwise that will manifest as a step in the required
# aperture correction between channels, and we're assuming that it can be
# smooth with wavelength so everything interpolates from the same table.
# Therefore, we'll make annuli that shrink linearly (relative to FWHM)
# with wavelength
in1, in2 = np.min(radius) * 2.5, np.max(radius) * 1.02
out1, out2 = np.min(radius) * 3.0, np.max(radius) * 1.5
inbkg = np.float32(
np.interp(waves, np.array([np.min(waves), np.max(waves)]),
np.array([in1, in2])))
outbkg = np.float32(
np.interp(waves, np.array([np.min(waves), np.max(waves)]),
np.array([out1, out2])))
# QA plot that our aperture and annuli look reasonable
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(5, 5), dpi=150)
tband = ['1A', '2A', '3A', '4A'] # Bands to test
twave = [7.5, 11.75, 18, 28] # Wavelengths to test
ax = [ax1, ax2, ax3, ax4]
for ii in range(0, len(tband)):
siaf = mksiaf.create_siaf_oneband(tband[ii])
indx = np.argmin(np.abs(waves - twave[ii]))
ax[ii].plot(siaf['inscr_v2_corners'],
siaf['inscr_v3_corners'],
color='#000000',
linewidth=2)
# Circle showing FWHM
circle = mpl.patches.Circle((siaf['inscr_v2ref'], siaf['inscr_v3ref']),
0.31 * twave[ii] / 8. / 2,
linewidth=1,
edgecolor='black',
facecolor=(0, 0, 0, .0125))
ax[ii].add_artist(circle)
# Circle showing extraction radius
circle = mpl.patches.Circle((siaf['inscr_v2ref'], siaf['inscr_v3ref']),
radius[indx],
linewidth=1,
edgecolor='r',
facecolor=(0, 0, 0, .0125))
ax[ii].add_artist(circle)
# Circles showing annulus
circle = mpl.patches.Circle((siaf['inscr_v2ref'], siaf['inscr_v3ref']),
inbkg[indx],
linewidth=1,
edgecolor='b',
facecolor=(0, 0, 0, .0125))
ax[ii].add_artist(circle)
circle = mpl.patches.Circle((siaf['inscr_v2ref'], siaf['inscr_v3ref']),
outbkg[indx],
linewidth=1,
edgecolor='b',
facecolor=(0, 0, 0, .0125))
ax[ii].add_artist(circle)
ax[ii].set_xlim(-508, -499)
ax[ii].set_ylim(-324, -315)
ax[ii].set_xlabel('V2 (arcsec)')
ax[ii].set_ylabel('V3 (arcsec)')
ax[ii].set_title(tband[ii])
plt.tight_layout()
plt.savefig(str.replace(outplot, '.png', 'FOV.png'))
plt.close()
# QA plot of final values
plt.plot(inwave, inap, '.')
plt.plot(waves, radius)
plt.plot(waves, inbkg, color='red')
plt.plot(waves, outbkg, color='red')
plt.grid()
plt.xlabel('Wavelength (micron)')
plt.ylabel('Extraction Radius (arcsec)')
plt.savefig(outplot)
plt.close()
data = {
'wavelength': waves,
'wavelength_units': 'micron',
'radius': radius,
'radius_units': 'arcsec',
'inner_bkg': inbkg,
'inner_bkg_units': 'arcsec',
'outer_bkg': outbkg,
'outer_bkg_units': 'arcsec',
'axis_ratio': axratio,
'axis_pa': axangle,
'axis_pa_units': 'degrees',
}
tree = {'meta': meta, 'data': data}
ff = asdf.AsdfFile(tree)
return ff
def assess_dith(rootdir=False,
siafdir=False,
write_result=False,
channel='1A',
dith=False,
wave=False,
da=None,
db=None):
#set root and siaf directories
if rootdir == False:
rootdir = os.getenv('MIRICOORD_DATA_DIR')
if siafdir == False:
siafdir = os.getenv('MIRICOORD_DATA_DIR')
#
if dith == False:
dith = [1, 2]
ndith = len(dith)
#read in list of dithers
dithers = pd.read_csv('mrs_dithers.txt', header=2, sep='\s+', index_col=0)
#convert dithers to ra/dec
dra = dithers.dXIdeal[dith].array / 3600.
ddec = dithers.dYIdeal[dith].array / 3600.
siaf = makesiaf.create_siaf_oneband(channel)
#define shape of channel in alpha/beta
maxalpha = siaf['inscr_alpha_corners'][0] * 2
minalpha = siaf['inscr_alpha_corners'][2] * 2
maxbeta = siaf['inscr_beta_corners'][0] * 2
minbeta = siaf['inscr_beta_corners'][2] * 2
racen = 45.
decen = 0.
# WARNING- this code will fail if DEC != 0 !!!!
dtheta = 0.02
#number of pix in ra/dec dims
nra = int(np.abs(maxalpha - minalpha) / dtheta)
ndec = int(np.abs(maxbeta - minbeta) / dtheta)
#define the wavelength on each pixel
if wave != False:
lam = np.ones((nra * ndec)) * wave
else:
lam = np.ones((nra * ndec)) * -1
if (da != None) and (db != None):
v2, v3 = mmrs.abtov2v3(da, db, channel)
zpv2, zpv3 = mmrs.abtov2v3(0, 0, channel)
zpx, zpy = mmrs.v2v3_to_xyideal(zpv2, zpv3)
tempx, tempy = mmrs.v2v3_to_xyideal(v2, v3)
dra = (tempx - zpx) / 3600.
ddec = (tempy - zpy) / 3600.
#define actual ra/dec range
ra = (np.arange(nra) * dtheta + minalpha) / 3600. + racen
dec = (np.arange(ndec) * dtheta + minbeta) / 3600. + decen
#redefine center
racen = (min(ra) + max(ra)) / 2.
decen = (min(dec) + max(dec)) / 2.
#create a cube of ra/dec coordinates
skyloc = np.zeros((ndith, ndec, nra))
for i in range(0, nra - 1):
skyloc[0, :, i] = ra[i]
for i in range(0, ndec - 1):
skyloc[1, i, :] = dec[i]
rall = skyloc[0, :, :].reshape(nra * ndec)
deall = skyloc[1, :, :].reshape(nra * ndec)
# construct phase images
slice_phase, pixel_phase, wave_phase = make_phase_im(
rall, deall, racen, decen, dra, ddec, nra, ndec, lam, ndith, channel)
temp = np.sum(wave_phase, axis=0)
goodval = np.where(temp > -10)
#construct coverage map
covmap = make_covmap(goodval, wave_phase, pixel_phase)
temp = covmap[0, :, :]
indx = np.where(temp != 0)
nindx = len(indx[0])
print('Coverage area: ' + str(nindx * dtheta * dtheta) + ' arcsec^2')
offsets = np.sqrt((dra - dra[0])**2 + (ddec - ddec[0])**2)
maxoffset = max(offsets) * 3600.
if write_result == True:
output_result(maxoffset, channel, phase_pix, phase_slice, covmap)
return wave_phase, pixel_phase, covmap