def plot_psf_vs_time(outdir,cam,exps,fiber,dicLines,xaxis):
dic = copy.deepcopy(exps)
for k,v in dic.items():
dic[k][cam] = {}
idx = str(k).zfill(8)
p = '{}/psf-{}-{}.fits'.format(outdir,cam,idx)
try:
psf = read_xytraceset(p)
except:
print('INFO: did not find: ',p)
continue
dic[k][cam]['SIGMAX'] = psf.xsig_vs_wave(fiber,dicLines[cam[0]]['LINE'])
dic[k][cam]['SIGMAY'] = psf.ysig_vs_wave(fiber,dicLines[cam[0]]['LINE'])
dic[k][cam]['X'] = psf.x_vs_wave(fiber,dicLines[cam[0]]['LINE'])
dic[k][cam]['Y'] = psf.y_vs_wave(fiber,dicLines[cam[0]]['LINE'])
###
for exptime in sorted(set([ dic[tk]['EXPTIME'] for tk in dic.keys() ])):
f, ax = plt.subplots(nrows=4, ncols=1, figsize=(10,10))
plt.subplots_adjust(top=0.95,hspace=0.,wspace=0.)
plt.suptitle(r'$\mathrm{SP'+str(cam[-1])+', cam = '+cam+',\, exptime='+str(exptime)+'}$',fontsize=20)
for i,k in enumerate(['SIGMAX','SIGMAY','X','Y']):
y = sp.array([ dic[tk][cam][k] for tk in dic.keys() if dic[tk]['EXPTIME']==exptime and k in dic[tk][cam].keys() ])
if y.size==0: continue
if xaxis=='DATE_OBS':
x = sp.array([ dic[tk]['DATE_OBS'] for tk in dic.keys() if dic[tk]['EXPTIME']==exptime and k in dic[tk][cam].keys() ])
w = sp.argsort(x)
x = x[w]
y = y[w]
x *= 24.*3600.
x -= x[0]
linemarker = 'o-'
else:
x = sp.array([ dic[tk]['CAM'][cam][xaxis] for tk in dic.keys() if dic[tk]['EXPTIME']==exptime and k in dic[tk][cam].keys() ])
linemarker = 'o'
y -= y[0]
ax[i].plot(x,y,linemarker)
if i==len(['SIGMAX','SIGMAY','X','Y'])-1:
ax[i].set_xlabel(r'$\mathrm{'+xaxis.replace('_','')+'}$')
else:
ax[i].set_xticklabels([])
ax[i].grid()
ax[i].set_ylabel(r'$\mathrm{'+k+'} \, [\mathrm{pix}]$')
#plt.show()
plt.savefig('{}/psf-vs-{}-cam-{}-exptime-{}.png'.format(outdir,xaxis,cam,exptime))
plt.clf()
return
def __init__(self, filename):
print("desispec.psf is DEPRECATED, PLEASE USE desispec.xytraceset")
self.traceset = read_xytraceset(filename)
# all in traceset now.
# psf kept to ease transition
self.npix_y = self.traceset.npix_y
self.xcoeff = self.traceset.x_vs_wave_traceset._coeff # in traceset
self.ycoeff = self.traceset.y_vs_wave_traceset._coeff # in traceset
self.wmin = self.traceset.wavemin # in traceset
self.wmax = self.traceset.wavemax # in traceset
self.nspec = self.traceset.nspec # in traceset
self.ncoeff = self.traceset.x_vs_wave_traceset._coeff.shape[1] #
self.traceset.wave_vs_y(
0, 100.
) # call wave_vs_y for creation of wave_vs_y_traceset and consistent inversion
self.icoeff = self.traceset.wave_vs_y_traceset._coeff # in traceset
self.ymin = self.traceset.wave_vs_y_traceset._xmin # in traceset
self.ymax = self.traceset.wave_vs_y_traceset._xmax # in traceset
def fit_trace_shifts(image, args):
global psfs
log = get_logger()
log.info("starting")
tset = read_xytraceset(args.psf)
wavemin = tset.wavemin
wavemax = tset.wavemax
xcoef = tset.x_vs_wave_traceset._coeff
ycoef = tset.y_vs_wave_traceset._coeff
nfibers = xcoef.shape[0]
log.info(
"read PSF trace with xcoef.shape = {} , ycoef.shape = {} , and wavelength range {}:{}"
.format(xcoef.shape, ycoef.shape, int(wavemin), int(wavemax)))
lines = None
if args.lines is not None:
log.info("We will fit the image using the psf model and lines")
# read lines
lines = np.loadtxt(args.lines, usecols=[0])
ok = (lines > wavemin) & (lines < wavemax)
log.info(
"read {} lines in {}, with {} of them in traces wavelength range".
format(len(lines), args.lines, np.sum(ok)))
lines = lines[ok]
else:
log.info(
"We will do an internal calibration of trace coordinates without using the psf shape in a first step"
)
internal_wavelength_calib = (not args.continuum)
if args.auto:
log.debug("read flavor of input image {}".format(args.image))
hdus = pyfits.open(args.image)
if "FLAVOR" not in hdus[0].header:
log.error(
"no FLAVOR keyword in image header, cannot run with --auto option"
)
raise KeyError(
"no FLAVOR keyword in image header, cannot run with --auto option"
)
flavor = hdus[0].header["FLAVOR"].strip().lower()
hdus.close()
log.info("Input is a '{}' image".format(flavor))
if flavor == "flat":
internal_wavelength_calib = False
elif flavor == "arc":
internal_wavelength_calib = True
args.arc_lamps = True
else:
internal_wavelength_calib = True
args.sky = True
log.info("wavelength calib, internal={}, sky={} , arc_lamps={}".format(
internal_wavelength_calib, args.sky, args.arc_lamps))
spectrum_filename = args.spectrum
if args.sky:
srch_file = "data/spec-sky.dat"
if not resource_exists('desispec', srch_file):
log.error("Cannot find sky spectrum file {:s}".format(srch_file))
raise RuntimeError(
"Cannot find sky spectrum file {:s}".format(srch_file))
spectrum_filename = resource_filename('desispec', srch_file)
elif args.arc_lamps:
srch_file = "data/spec-arc-lamps.dat"
if not resource_exists('desispec', srch_file):
log.error(
"Cannot find arc lamps spectrum file {:s}".format(srch_file))
raise RuntimeError(
"Cannot find arc lamps spectrum file {:s}".format(srch_file))
spectrum_filename = resource_filename('desispec', srch_file)
if spectrum_filename is not None:
log.info(
"Use external calibration from cross-correlation with {}".format(
spectrum_filename))
if args.nfibers is not None:
nfibers = args.nfibers # FOR DEBUGGING
fibers = np.arange(nfibers)
if lines is not None:
# use a forward modeling of the image
# it's slower and works only for individual lines
# it's in principle more accurate
# but gives systematic residuals for complex spectra like the sky
psf = read_specter_psf(args.psf)
x, y, dx, ex, dy, ey, fiber_xy, wave_xy = compute_dx_dy_using_psf(
psf, image, fibers, lines)
x_for_dx = x
y_for_dx = y
fiber_for_dx = fiber_xy
wave_for_dx = wave_xy
x_for_dy = x
y_for_dy = y
fiber_for_dy = fiber_xy
wave_for_dy = wave_xy
else:
# internal calibration method that does not use the psf
# nor a prior set of lines. this method is much faster
# measure x shifts
x_for_dx, y_for_dx, dx, ex, fiber_for_dx, wave_for_dx = compute_dx_from_cross_dispersion_profiles(
xcoef,
ycoef,
wavemin,
wavemax,
image=image,
fibers=fibers,
width=args.width,
deg=args.degxy,
image_rebin=args.ccd_rows_rebin)
if internal_wavelength_calib:
# measure y shifts
x_for_dy, y_for_dy, dy, ey, fiber_for_dy, wave_for_dy = compute_dy_using_boxcar_extraction(
tset, image=image, fibers=fibers, width=args.width)
mdy = np.median(dy)
log.info("Subtract median(dy)={}".format(mdy))
dy -= mdy # remove median, because this is an internal calibration
else:
# duplicate dx results with zero shift to avoid write special case code below
x_for_dy = x_for_dx.copy()
y_for_dy = y_for_dx.copy()
dy = np.zeros(dx.shape)
ey = 1.e-6 * np.ones(ex.shape)
fiber_for_dy = fiber_for_dx.copy()
wave_for_dy = wave_for_dx.copy()
degxx = args.degxx
degxy = args.degxy
degyx = args.degyx
degyy = args.degyy
while (True): # loop because polynomial degrees could be reduced
log.info(
"polynomial fit of measured offsets with degx=(%d,%d) degy=(%d,%d)"
% (degxx, degxy, degyx, degyy))
try:
dx_coeff, dx_coeff_covariance, dx_errorfloor, dx_mod, dx_mask = polynomial_fit(
z=dx, ez=ex, xx=x_for_dx, yy=y_for_dx, degx=degxx, degy=degxy)
dy_coeff, dy_coeff_covariance, dy_errorfloor, dy_mod, dy_mask = polynomial_fit(
z=dy, ez=ey, xx=x_for_dy, yy=y_for_dy, degx=degyx, degy=degyy)
log.info("dx dy error floor = %4.3f %4.3f pixels" %
(dx_errorfloor, dy_errorfloor))
log.info("check fit uncertainties are ok on edge of CCD")
merr = 0.
for fiber in [0, nfibers - 1]:
for rw in [-1, 1]:
tx = legval(rw, xcoef[fiber])
ty = legval(rw, ycoef[fiber])
m = monomials(tx, ty, degxx, degxy)
tdx = np.inner(dx_coeff, m)
tsx = np.sqrt(np.inner(m, dx_coeff_covariance.dot(m)))
m = monomials(tx, ty, degyx, degyy)
tdy = np.inner(dy_coeff, m)
tsy = np.sqrt(np.inner(m, dy_coeff_covariance.dot(m)))
merr = max(merr, tsx)
merr = max(merr, tsy)
log.info("max edge shift error = %4.3f pixels" % merr)
if degxx == 0 and degxy == 0 and degyx == 0 and degyy == 0:
break
except (LinAlgError, ValueError):
log.warning(
"polynomial fit failed with degx=(%d,%d) degy=(%d,%d)" %
(degxx, degxy, degyx, degyy))
if degxx == 0 and degxy == 0 and degyx == 0 and degyy == 0:
log.error(
"polynomial degrees are already 0. we can fit the offsets")
raise RuntimeError(
"polynomial degrees are already 0. we can fit the offsets")
merr = 100000. # this will lower the pol. degree.
if merr > args.max_error:
if merr != 100000.:
log.warning(
"max edge shift error = %4.3f pixels is too large, reducing degrees"
% merr)
if degxy > 0 and degyy > 0 and degxy > degxx and degyy > degyx: # first along wavelength
if degxy > 0: degxy -= 1
if degyy > 0: degyy -= 1
else: # then along fiber
if degxx > 0: degxx -= 1
if degyx > 0: degyx -= 1
else:
# error is ok, so we quit the loop
break
# write this for debugging
if args.outoffsets:
file = open(args.outoffsets, "w")
file.write(
"# axis wave fiber x y delta error polval (axis 0=y axis1=x)\n")
for e in range(dy.size):
file.write("0 %f %d %f %f %f %f %f\n" %
(wave_for_dy[e], fiber_for_dy[e], x_for_dy[e],
y_for_dy[e], dy[e], ey[e], dy_mod[e]))
for e in range(dx.size):
file.write("1 %f %d %f %f %f %f %f\n" %
(wave_for_dx[e], fiber_for_dx[e], x_for_dx[e],
y_for_dx[e], dx[e], ex[e], dx_mod[e]))
file.close()
log.info("wrote offsets in ASCII file %s" % args.outoffsets)
# print central shift
mx = np.median(x_for_dx)
my = np.median(y_for_dx)
m = monomials(mx, my, degxx, degxy)
mdx = np.inner(dx_coeff, m)
mex = np.sqrt(np.inner(m, dx_coeff_covariance.dot(m)))
mx = np.median(x_for_dy)
my = np.median(y_for_dy)
m = monomials(mx, my, degyx, degyy)
mdy = np.inner(dy_coeff, m)
mey = np.sqrt(np.inner(m, dy_coeff_covariance.dot(m)))
log.info("central shifts dx = %4.3f +- %4.3f dy = %4.3f +- %4.3f " %
(mdx, mex, mdy, mey))
# for each fiber, apply offsets and recompute legendre polynomial
log.info("for each fiber, apply offsets and recompute legendre polynomial")
# compute x y to record max deviations
wave = np.linspace(tset.wavemin, tset.wavemax, 5)
x0 = np.zeros((tset.nspec, wave.size))
y0 = np.zeros((tset.nspec, wave.size))
for s in range(tset.nspec):
x0[s] = tset.x_vs_wave(s, wave)
y0[s] = tset.y_vs_wave(s, wave)
tset.x_vs_wave_traceset._coeff, tset.y_vs_wave_traceset._coeff = recompute_legendre_coefficients(
xcoef=tset.x_vs_wave_traceset._coeff,
ycoef=tset.y_vs_wave_traceset._coeff,
wavemin=tset.wavemin,
wavemax=tset.wavemax,
degxx=degxx,
degxy=degxy,
degyx=degyx,
degyy=degyy,
dx_coeff=dx_coeff,
dy_coeff=dy_coeff)
# use an input spectrum as an external calibration of wavelength
if spectrum_filename is not None:
# the psf is used only to convolve the input spectrum
# the traceset of the psf is not used here
psf = read_specter_psf(args.psf)
tset.y_vs_wave_traceset._coeff = shift_ycoef_using_external_spectrum(
psf=psf,
xytraceset=tset,
image=image,
fibers=fibers,
spectrum_filename=spectrum_filename,
degyy=args.degyy,
width=7)
x = np.zeros(x0.shape)
y = np.zeros(x0.shape)
for s in range(tset.nspec):
x[s] = tset.x_vs_wave(s, wave)
y[s] = tset.y_vs_wave(s, wave)
dx = x - x0
dy = y - y0
if tset.meta is None: tset.meta = dict()
tset.meta["MEANDX"] = np.mean(dx)
tset.meta["MINDX"] = np.min(dx)
tset.meta["MAXDX"] = np.max(dx)
tset.meta["MEANDY"] = np.mean(dy)
tset.meta["MINDY"] = np.min(dy)
tset.meta["MAXDY"] = np.max(dy)
return tset
h = fitsio.FITS(dic[cam]['PATH'])
head = h['IMAGE'].read_header()
camName = head['CAMERA'].strip()
d = h['IMAGE'].read()
w = h['MASK'].read()==0.
td = d.copy()
td[~w] = sp.nan
h.close()
if getattr(args,'{}_psf_path'.format(cam)) is None:
cfinder = CalibFinder([head])
p = cfinder.findfile('PSF')
else:
p = getattr(args,'{}_psf_path'.format(cam))
dic[cam]['PSF'] = read_xytraceset(p)
### image of the PSF
xmin = min( dic[cam]['PSF'].x_vs_wave(fmin,dic[cam]['LINE']['LINE']), dic[cam]['PSF'].x_vs_wave(fmax,dic[cam]['LINE']['LINE']) )
xmax = max( dic[cam]['PSF'].x_vs_wave(fmin,dic[cam]['LINE']['LINE']), dic[cam]['PSF'].x_vs_wave(fmax,dic[cam]['LINE']['LINE']) )
ymin = min( dic[cam]['PSF'].y_vs_wave(fmin,dic[cam]['LINE']['LINE']), dic[cam]['PSF'].y_vs_wave(fmax,dic[cam]['LINE']['LINE']) )
ymax = max( dic[cam]['PSF'].y_vs_wave(fmin,dic[cam]['LINE']['LINE']), dic[cam]['PSF'].y_vs_wave(fmax,dic[cam]['LINE']['LINE']) )
ax[2*i].imshow(td,interpolation='nearest',origin='lower',cmap='hot')
ax[2*i].set_xlim(sp.floor(xmin-offset),sp.floor(xmax+offset))
ax[2*i].set_ylim(sp.floor(ymin-offset),sp.floor(ymax+offset))
ax[2*i].set_ylabel(r'$\mathrm{y-axis}$')
x = sp.array([ dic[cam]['PSF'].x_vs_wave(f,dic[cam]['LINE']['LINE']) for f in range(fmin-1,fmax+2) ])
y = sp.array([ dic[cam]['PSF'].y_vs_wave(f,dic[cam]['LINE']['LINE']) for f in range(fmin-1,fmax+2) ])
fxy = sp.interpolate.interp1d(x,y)
ax[2*i].plot(x,y,color='white',linestyle='--',linewidth=1)
def preproc(rawimage,
header,
primary_header,
bias=True,
dark=True,
pixflat=True,
mask=True,
bkgsub=False,
nocosmic=False,
cosmics_nsig=6,
cosmics_cfudge=3.,
cosmics_c2fudge=0.5,
ccd_calibration_filename=None,
nocrosstalk=False,
nogain=False,
overscan_per_row=False,
use_overscan_row=False,
use_savgol=None,
nodarktrail=False,
remove_scattered_light=False,
psf_filename=None,
bias_img=None):
'''
preprocess image using metadata in header
image = ((rawimage-bias-overscan)*gain)/pixflat
Args:
rawimage : 2D numpy array directly from raw data file
header : dict-like metadata, e.g. from FITS header, with keywords
CAMERA, BIASSECx, DATASECx, CCDSECx
where x = A, B, C, D for each of the 4 amplifiers
(also supports old naming convention 1, 2, 3, 4).
primary_header: dict-like metadata fit keywords EXPTIME, DOSVER
DATE-OBS is also required if bias, pixflat, or mask=True
Optional bias, pixflat, and mask can each be:
False: don't apply that step
True: use default calibration data for that night
ndarray: use that array
filename (str or unicode): read HDU 0 and use that
Optional overscan features:
overscan_per_row : bool, Subtract the overscan_col values
row by row from the data.
use_overscan_row : bool, Subtract off the overscan_row
from the data (default: False). Requires ORSEC in
the Header
use_savgol : bool, Specify whether to use Savitsky-Golay filter for
the overscan. (default: False). Requires use_overscan_row=True
to have any effect.
Optional background subtraction with median filtering if bkgsub=True
Optional disabling of cosmic ray rejection if nocosmic=True
Optional disabling of dark trail correction if nodarktrail=True
Optional bias image (testing only) may be provided by bias_img=
Optional tuning of cosmic ray rejection parameters:
cosmics_nsig: number of sigma above background required
cosmics_cfudge: number of sigma inconsistent with PSF required
cosmics_c2fudge: fudge factor applied to PSF
Optional fit and subtraction of scattered light
Returns Image object with member variables:
pix : 2D preprocessed image in units of electrons per pixel
ivar : 2D inverse variance of image
mask : 2D mask of image (0=good)
readnoise : 2D per-pixel readnoise of image
meta : metadata dictionary
TODO: define what keywords are included
preprocessing includes the following steps:
- bias image subtraction
- overscan subtraction (from BIASSEC* keyword defined regions)
- readnoise estimation (from BIASSEC* keyword defined regions)
- gain correction (from GAIN* keywords)
- pixel flat correction
- cosmic ray masking
- propagation of input known bad pixel mask
- inverse variance estimation
Notes:
The bias image is subtracted before any other calculation to remove any
non-uniformities in the overscan regions prior to calculating overscan
levels and readnoise.
The readnoise is an image not just one number per amp, because the pixflat
image also affects the interpreted readnoise.
The inverse variance is estimated from the readnoise and the image itself,
and thus is biased.
'''
log = get_logger()
header = header.copy()
cfinder = None
if ccd_calibration_filename is not False:
cfinder = CalibFinder([header, primary_header],
yaml_file=ccd_calibration_filename)
#- TODO: Check for required keywords first
#- Subtract bias image
camera = header['CAMERA'].lower()
#- convert rawimage to float64 : this is the output format of read_image
rawimage = rawimage.astype(np.float64)
# Savgol
if cfinder and cfinder.haskey("USE_ORSEC"):
use_overscan_row = cfinder.value("USE_ORSEC")
if cfinder and cfinder.haskey("SAVGOL"):
use_savgol = cfinder.value("SAVGOL")
# Set bias image, as desired
if bias_img is None:
bias = get_calibration_image(cfinder, "BIAS", bias)
else:
bias = bias_img
if bias is not False: #- it's an array
if bias.shape == rawimage.shape:
log.info("subtracting bias")
rawimage = rawimage - bias
else:
raise ValueError('shape mismatch bias {} != rawimage {}'.format(
bias.shape, rawimage.shape))
#- Check if this file uses amp names 1,2,3,4 (old) or A,B,C,D (new)
amp_ids = get_amp_ids(header)
#- Double check that we have the necessary keywords
missing_keywords = list()
for prefix in ['CCDSEC', 'BIASSEC']:
for amp in amp_ids:
key = prefix + amp
if not key in header:
log.error('No {} keyword in header'.format(key))
missing_keywords.append(key)
if len(missing_keywords) > 0:
raise KeyError("Missing keywords {}".format(
' '.join(missing_keywords)))
#- Output arrays
ny = 0
nx = 0
for amp in amp_ids:
yy, xx = parse_sec_keyword(header['CCDSEC%s' % amp])
ny = max(ny, yy.stop)
nx = max(nx, xx.stop)
image = np.zeros((ny, nx))
readnoise = np.zeros_like(image)
#- Load mask
mask = get_calibration_image(cfinder, "MASK", mask)
if mask is False:
mask = np.zeros(image.shape, dtype=np.int32)
else:
if mask.shape != image.shape:
raise ValueError('shape mismatch mask {} != image {}'.format(
mask.shape, image.shape))
#- Load dark
dark = get_calibration_image(cfinder, "DARK", dark)
if dark is not False:
if dark.shape != image.shape:
log.error('shape mismatch dark {} != image {}'.format(
dark.shape, image.shape))
raise ValueError('shape mismatch dark {} != image {}'.format(
dark.shape, image.shape))
if cfinder and cfinder.haskey("EXPTIMEKEY"):
exptime_key = cfinder.value("EXPTIMEKEY")
log.info("Using exposure time keyword %s for dark normalization" %
exptime_key)
else:
exptime_key = "EXPTIME"
exptime = primary_header[exptime_key]
log.info("Multiplying dark by exptime %f" % (exptime))
dark *= exptime
for amp in amp_ids:
# Grab the sections
ov_col = parse_sec_keyword(header['BIASSEC' + amp])
if 'ORSEC' + amp in header.keys():
ov_row = parse_sec_keyword(header['ORSEC' + amp])
elif use_overscan_row:
log.error('No ORSEC{} keyword; not using overscan_row'.format(amp))
use_overscan_row = False
if nogain:
gain = 1.
else:
#- Initial teststand data may be missing GAIN* keywords; don't crash
if 'GAIN' + amp in header:
gain = header['GAIN' + amp] #- gain = electrons / ADU
else:
if cfinder and cfinder.haskey('GAIN' + amp):
gain = float(cfinder.value('GAIN' + amp))
log.info('Using GAIN{}={} from calibration data'.format(
amp, gain))
else:
gain = 1.0
log.warning(
'Missing keyword GAIN{} in header and nothing in calib data; using {}'
.format(amp, gain))
#- Add saturation level
if 'SATURLEV' + amp in header:
saturlev = header['SATURLEV' + amp] # in electrons
else:
if cfinder and cfinder.haskey('SATURLEV' + amp):
saturlev = float(cfinder.value('SATURLEV' + amp))
log.info('Using SATURLEV{}={} from calibration data'.format(
amp, saturlev))
else:
saturlev = 200000
log.warning(
'Missing keyword SATURLEV{} in header and nothing in calib data; using 200000'
.format(amp, saturlev))
# Generate the overscan images
raw_overscan_col = rawimage[ov_col].copy()
if use_overscan_row:
raw_overscan_row = rawimage[ov_row].copy()
overscan_row = np.zeros_like(raw_overscan_row)
# Remove overscan_col from overscan_row
raw_overscan_squared = rawimage[ov_row[0], ov_col[1]].copy()
for row in range(raw_overscan_row.shape[0]):
o, r = _overscan(raw_overscan_squared[row])
overscan_row[row] = raw_overscan_row[row] - o
# Now remove the overscan_col
nrows = raw_overscan_col.shape[0]
log.info("nrows in overscan=%d" % nrows)
overscan_col = np.zeros(nrows)
rdnoise = np.zeros(nrows)
if (cfinder and cfinder.haskey('OVERSCAN' + amp)
and cfinder.value("OVERSCAN" + amp).upper()
== "PER_ROW") or overscan_per_row:
log.info(
"Subtracting overscan per row for amplifier %s of camera %s" %
(amp, camera))
for j in range(nrows):
if np.isnan(np.sum(overscan_col[j])):
log.warning(
"NaN values in row %d of overscan of amplifier %s of camera %s"
% (j, amp, camera))
continue
o, r = _overscan(raw_overscan_col[j])
#log.info("%d %f %f"%(j,o,r))
overscan_col[j] = o
rdnoise[j] = r
else:
log.info(
"Subtracting average overscan for amplifier %s of camera %s" %
(amp, camera))
o, r = _overscan(raw_overscan_col)
overscan_col += o
rdnoise += r
rdnoise *= gain
median_rdnoise = np.median(rdnoise)
median_overscan = np.median(overscan_col)
log.info("Median rdnoise and overscan= %f %f" %
(median_rdnoise, median_overscan))
kk = parse_sec_keyword(header['CCDSEC' + amp])
for j in range(nrows):
readnoise[kk][j] = rdnoise[j]
header['OVERSCN' + amp] = (median_overscan, 'ADUs (gain not applied)')
if gain != 1:
rdnoise_message = 'electrons (gain is applied)'
gain_message = 'e/ADU (gain applied to image)'
else:
rdnoise_message = 'ADUs (gain not applied)'
gain_message = 'gain not applied to image'
header['OBSRDN' + amp] = (median_rdnoise, rdnoise_message)
header['GAIN' + amp] = (gain, gain_message)
#- Warn/error if measured readnoise is very different from expected if exists
if 'RDNOISE' + amp in header:
expected_readnoise = header['RDNOISE' + amp]
if median_rdnoise < 0.5 * expected_readnoise:
log.error(
'Amp {} measured readnoise {:.2f} < 0.5 * expected readnoise {:.2f}'
.format(amp, median_rdnoise, expected_readnoise))
elif median_rdnoise < 0.9 * expected_readnoise:
log.warning(
'Amp {} measured readnoise {:.2f} < 0.9 * expected readnoise {:.2f}'
.format(amp, median_rdnoise, expected_readnoise))
elif median_rdnoise > 2.0 * expected_readnoise:
log.error(
'Amp {} measured readnoise {:.2f} > 2 * expected readnoise {:.2f}'
.format(amp, median_rdnoise, expected_readnoise))
elif median_rdnoise > 1.2 * expected_readnoise:
log.warning(
'Amp {} measured readnoise {:.2f} > 1.2 * expected readnoise {:.2f}'
.format(amp, median_rdnoise, expected_readnoise))
#else:
# log.warning('Expected readnoise keyword {} missing'.format('RDNOISE'+amp))
log.info("Measured readnoise for AMP %s = %f" % (amp, median_rdnoise))
#- subtract overscan from data region and apply gain
jj = parse_sec_keyword(header['DATASEC' + amp])
data = rawimage[jj].copy()
# Subtract columns
for k in range(nrows):
data[k] -= overscan_col[k]
# And now the rows
if use_overscan_row:
# Savgol?
if use_savgol:
log.info("Using savgol")
collapse_oscan_row = np.zeros(overscan_row.shape[1])
for col in range(overscan_row.shape[1]):
o, _ = _overscan(overscan_row[:, col])
collapse_oscan_row[col] = o
oscan_row = _savgol_clipped(collapse_oscan_row, niter=0)
oimg_row = np.outer(np.ones(data.shape[0]), oscan_row)
data -= oimg_row
else:
o, r = _overscan(overscan_row)
data -= o
#- apply saturlev (defined in ADU), prior to multiplication by gain
saturated = (rawimage[jj] >= saturlev)
mask[kk][saturated] |= ccdmask.SATURATED
#- ADC to electrons
image[kk] = data * gain
if not nocrosstalk:
#- apply cross-talk
# the ccd looks like :
# C D
# A B
# for cross talk, we need a symmetric 4x4 flip_matrix
# of coordinates ABCD giving flip of both axis
# when computing crosstalk of
# A B C D
#
# A AA AB AC AD
# B BA BB BC BD
# C CA CB CC CD
# D DA DB DC BB
# orientation_matrix_defines change of orientation
#
fip_axis_0 = np.array([[1, 1, -1, -1], [1, 1, -1, -1], [-1, -1, 1, 1],
[-1, -1, 1, 1]])
fip_axis_1 = np.array([[1, -1, 1, -1], [-1, 1, -1, 1], [1, -1, 1, -1],
[-1, 1, -1, 1]])
for a1 in range(len(amp_ids)):
amp1 = amp_ids[a1]
ii1 = parse_sec_keyword(header['CCDSEC' + amp1])
a1flux = image[ii1]
#a1mask=mask[ii1]
for a2 in range(len(amp_ids)):
if a1 == a2:
continue
amp2 = amp_ids[a2]
if cfinder is None: continue
if not cfinder.haskey("CROSSTALK%s%s" % (amp1, amp2)): continue
crosstalk = cfinder.value("CROSSTALK%s%s" % (amp1, amp2))
if crosstalk == 0.: continue
log.info("Correct for crosstalk=%f from AMP %s into %s" %
(crosstalk, amp1, amp2))
a12flux = crosstalk * a1flux.copy()
#a12mask=a1mask.copy()
if fip_axis_0[a1, a2] == -1:
a12flux = a12flux[::-1]
#a12mask=a12mask[::-1]
if fip_axis_1[a1, a2] == -1:
a12flux = a12flux[:, ::-1]
#a12mask=a12mask[:,::-1]
ii2 = parse_sec_keyword(header['CCDSEC' + amp2])
image[ii2] -= a12flux
# mask[ii2] |= a12mask (not sure we really need to propagate the mask)
#- Poisson noise variance (prior to dark subtraction and prior to pixel flat field)
#- This is biasing, but that's what we have for now
poisson_var = image.clip(0)
#- subtract dark after multiplication by gain
if dark is not False:
log.info("subtracting dark for amp %s" % amp)
image -= dark
#- Correct for dark trails if any
if not nodarktrail and cfinder is not None:
for amp in amp_ids:
if cfinder.haskey("DARKTRAILAMP%s" % amp):
amplitude = cfinder.value("DARKTRAILAMP%s" % amp)
width = cfinder.value("DARKTRAILWIDTH%s" % amp)
ii = _parse_sec_keyword(header["CCDSEC" + amp])
log.info(
"Removing dark trails for amplifier %s with width=%3.1f and amplitude=%5.4f"
% (amp, width, amplitude))
correct_dark_trail(image,
ii,
left=((amp == "B") | (amp == "D")),
width=width,
amplitude=amplitude)
#- Divide by pixflat image
pixflat = get_calibration_image(cfinder, "PIXFLAT", pixflat)
if pixflat is not False:
if pixflat.shape != image.shape:
raise ValueError('shape mismatch pixflat {} != image {}'.format(
pixflat.shape, image.shape))
almost_zero = 0.001
if np.all(pixflat > almost_zero):
image /= pixflat
readnoise /= pixflat
poisson_var /= pixflat**2
else:
good = (pixflat > almost_zero)
image[good] /= pixflat[good]
readnoise[good] /= pixflat[good]
poisson_var[good] /= pixflat[good]**2
mask[~good] |= ccdmask.PIXFLATZERO
lowpixflat = (0 < pixflat) & (pixflat < 0.1)
if np.any(lowpixflat):
mask[lowpixflat] |= ccdmask.PIXFLATLOW
#- Inverse variance, estimated directly from the data (BEWARE: biased!)
var = poisson_var + readnoise**2
ivar = np.zeros(var.shape)
ivar[var > 0] = 1.0 / var[var > 0]
#- Ridiculously high readnoise is bad
mask[readnoise > 100] |= ccdmask.BADREADNOISE
if bkgsub:
bkg = _background(image, header)
image -= bkg
img = Image(image,
ivar=ivar,
mask=mask,
meta=header,
readnoise=readnoise,
camera=camera)
#- update img.mask to mask cosmic rays
if not nocosmic:
cosmics.reject_cosmic_rays(img,
nsig=cosmics_nsig,
cfudge=cosmics_cfudge,
c2fudge=cosmics_c2fudge)
if remove_scattered_light:
if psf_filename is None:
psf_filename = cfinder.findfile("PSF")
xyset = read_xytraceset(psf_filename)
img.pix -= model_scattered_light(img, xyset)
return img
def main(args=None):
if args is None:
args = parse()
elif isinstance(args, (list, tuple)):
args = parse(args)
t0 = time.time()
log = get_logger()
# guess if it is a preprocessed or a raw image
hdulist = fits.open(args.image)
is_input_preprocessed = ("IMAGE" in hdulist) & ("IVAR" in hdulist)
primary_header = hdulist[0].header
hdulist.close()
if is_input_preprocessed:
image = read_image(args.image)
else:
if args.camera is None:
print(
"ERROR: Need to specify camera to open a raw fits image (with all cameras in different fits HDUs)"
)
print(
"Try adding the option '--camera xx', with xx in {brz}{0-9}, like r7, or type 'desi_qproc --help' for more options"
)
sys.exit(12)
image = read_raw(args.image, args.camera, fill_header=[
1,
])
if args.auto:
log.debug("AUTOMATIC MODE")
try:
night = image.meta['NIGHT']
if not 'EXPID' in image.meta:
if 'EXPNUM' in image.meta:
log.warning('using EXPNUM {} for EXPID'.format(
image.meta['EXPNUM']))
image.meta['EXPID'] = image.meta['EXPNUM']
expid = image.meta['EXPID']
except KeyError as e:
log.error(
"Need at least NIGHT and EXPID (or EXPNUM) to run in auto mode. Retry without the --auto option."
)
log.error(str(e))
sys.exit(12)
indir = os.path.dirname(args.image)
if args.fibermap is None:
filename = '{}/fibermap-{:08d}.fits'.format(indir, expid)
if os.path.isfile(filename):
log.debug("auto-mode: found a fibermap, {}, using it!".format(
filename))
args.fibermap = filename
if args.output_preproc is None:
if not is_input_preprocessed:
args.output_preproc = '{}/preproc-{}-{:08d}.fits'.format(
args.auto_output_dir, args.camera.lower(), expid)
log.debug("auto-mode: will write preproc in " +
args.output_preproc)
else:
log.debug(
"auto-mode: will not write preproc because input is a preprocessed image"
)
if args.auto_output_dir != '.':
if not os.path.isdir(args.auto_output_dir):
log.debug("auto-mode: creating directory " +
args.auto_output_dir)
os.makedirs(args.auto_output_dir)
if args.output_preproc is not None:
write_image(args.output_preproc, image)
cfinder = None
if args.psf is None:
if cfinder is None:
cfinder = CalibFinder([image.meta, primary_header])
args.psf = cfinder.findfile("PSF")
log.info(" Using PSF {}".format(args.psf))
tset = read_xytraceset(args.psf)
# add fibermap
if args.fibermap:
if os.path.isfile(args.fibermap):
fibermap = read_fibermap(args.fibermap)
else:
log.error("no fibermap file {}".format(args.fibermap))
fibermap = None
else:
fibermap = None
if "OBSTYPE" in image.meta:
obstype = image.meta["OBSTYPE"].upper()
image.meta["OBSTYPE"] = obstype # make sure it's upper case
qframe = None
else:
log.warning("No OBSTYPE keyword, trying to guess ...")
qframe = qproc_boxcar_extraction(tset,
image,
width=args.width,
fibermap=fibermap)
obstype = check_qframe_flavor(
qframe, input_flavor=image.meta["FLAVOR"]).upper()
image.meta["OBSTYPE"] = obstype
log.info("OBSTYPE = '{}'".format(obstype))
if args.auto:
# now set the things to do
if obstype == "SKY" or obstype == "TWILIGHT" or obstype == "SCIENCE":
args.shift_psf = True
args.output_psf = '{}/psf-{}-{:08d}.fits'.format(
args.auto_output_dir, args.camera, expid)
args.output_rawframe = '{}/qframe-{}-{:08d}.fits'.format(
args.auto_output_dir, args.camera, expid)
args.apply_fiberflat = True
args.skysub = True
args.output_skyframe = '{}/qsky-{}-{:08d}.fits'.format(
args.auto_output_dir, args.camera, expid)
args.fluxcalib = True
args.outframe = '{}/qcframe-{}-{:08d}.fits'.format(
args.auto_output_dir, args.camera, expid)
elif obstype == "ARC" or obstype == "TESTARC":
args.shift_psf = True
args.output_psf = '{}/psf-{}-{:08d}.fits'.format(
args.auto_output_dir, args.camera, expid)
args.output_rawframe = '{}/qframe-{}-{:08d}.fits'.format(
args.auto_output_dir, args.camera, expid)
args.compute_lsf_sigma = True
elif obstype == "FLAT" or obstype == "TESTFLAT":
args.shift_psf = True
args.output_psf = '{}/psf-{}-{:08d}.fits'.format(
args.auto_output_dir, args.camera, expid)
args.output_rawframe = '{}/qframe-{}-{:08d}.fits'.format(
args.auto_output_dir, args.camera, expid)
args.compute_fiberflat = '{}/qfiberflat-{}-{:08d}.fits'.format(
args.auto_output_dir, args.camera, expid)
if args.shift_psf:
# using the trace shift script
if args.auto:
options = option_list({
"psf":
args.psf,
"image":
"dummy",
"outpsf":
"dummy",
"continuum": ((obstype == "FLAT") | (obstype == "TESTFLAT")),
"sky": ((obstype == "SCIENCE") | (obstype == "SKY"))
})
else:
options = option_list({
"psf": args.psf,
"image": "dummy",
"outpsf": "dummy"
})
tmp_args = trace_shifts_script.parse(options=options)
tset = trace_shifts_script.fit_trace_shifts(image=image, args=tmp_args)
qframe = qproc_boxcar_extraction(tset,
image,
width=args.width,
fibermap=fibermap)
if tset.meta is not None:
# add traceshift info in the qframe, this will be saved in the qframe header
if qframe.meta is None:
qframe.meta = dict()
for k in tset.meta.keys():
qframe.meta[k] = tset.meta[k]
if args.output_rawframe is not None:
write_qframe(args.output_rawframe, qframe)
log.info("wrote raw extracted frame in {}".format(
args.output_rawframe))
if args.compute_lsf_sigma:
tset = process_arc(qframe, tset, linelist=None, npoly=2, nbins=2)
if args.output_psf is not None:
for k in qframe.meta:
if k not in tset.meta:
tset.meta[k] = qframe.meta[k]
write_xytraceset(args.output_psf, tset)
if args.compute_fiberflat is not None:
fiberflat = qproc_compute_fiberflat(qframe)
#write_qframe(args.compute_fiberflat,qflat)
write_fiberflat(args.compute_fiberflat, fiberflat, header=qframe.meta)
log.info("wrote fiberflat in {}".format(args.compute_fiberflat))
if args.apply_fiberflat or args.input_fiberflat:
if args.input_fiberflat is None:
if cfinder is None:
cfinder = CalibFinder([image.meta, primary_header])
try:
args.input_fiberflat = cfinder.findfile("FIBERFLAT")
except KeyError as e:
log.error("no FIBERFLAT for this spectro config")
sys.exit(12)
log.info("applying fiber flat {}".format(args.input_fiberflat))
flat = read_fiberflat(args.input_fiberflat)
qproc_apply_fiberflat(qframe, flat)
if args.skysub:
log.info("sky subtraction")
if args.output_skyframe is not None:
skyflux = qproc_sky_subtraction(qframe, return_skymodel=True)
sqframe = QFrame(qframe.wave, skyflux, np.ones(skyflux.shape))
write_qframe(args.output_skyframe, sqframe)
log.info("wrote sky model in {}".format(args.output_skyframe))
else:
qproc_sky_subtraction(qframe)
if args.fluxcalib:
if cfinder is None:
cfinder = CalibFinder([image.meta, primary_header])
# check for flux calib
if cfinder.haskey("FLUXCALIB"):
fluxcalib_filename = cfinder.findfile("FLUXCALIB")
fluxcalib = read_average_flux_calibration(fluxcalib_filename)
log.info("read average calib in {}".format(fluxcalib_filename))
seeing = qframe.meta["SEEING"]
airmass = qframe.meta["AIRMASS"]
exptime = qframe.meta["EXPTIME"]
exposure_calib = fluxcalib.value(seeing=seeing, airmass=airmass)
for q in range(qframe.nspec):
fiber_calib = np.interp(qframe.wave[q], fluxcalib.wave,
exposure_calib) * exptime
inv_calib = (fiber_calib > 0) / (fiber_calib +
(fiber_calib == 0))
qframe.flux[q] *= inv_calib
qframe.ivar[q] *= fiber_calib**2 * (fiber_calib > 0)
# add keyword in header giving the calibration factor applied at a reference wavelength
band = qframe.meta["CAMERA"].upper()[0]
if band == "B":
refwave = 4500
elif band == "R":
refwave = 6500
else:
refwave = 8500
calvalue = np.interp(refwave, fluxcalib.wave,
exposure_calib) * exptime
qframe.meta["CALWAVE"] = refwave
qframe.meta["CALVALUE"] = calvalue
else:
log.error(
"Cannot calibrate fluxes because no FLUXCALIB keywork in calibration files"
)
fibers = parse_fibers(args.fibers)
if fibers is None:
fibers = qframe.flux.shape[0]
else:
ii = np.arange(qframe.fibers.size)[np.in1d(qframe.fibers, fibers)]
if ii.size == 0:
log.error("no such fibers in frame,")
log.error("fibers are in range [{}:{}]".format(
qframe.fibers[0], qframe.fibers[-1] + 1))
sys.exit(12)
qframe = qframe[ii]
if args.outframe is not None:
write_qframe(args.outframe, qframe)
log.info("wrote {}".format(args.outframe))
t1 = time.time()
log.info("all done in {:3.1f} sec".format(t1 - t0))
if args.plot:
log.info("plotting {} spectra".format(qframe.wave.shape[0]))
import matplotlib.pyplot as plt
fig = plt.figure()
for i in range(qframe.wave.shape[0]):
j = (qframe.ivar[i] > 0)
plt.plot(qframe.wave[i, j], qframe.flux[i, j])
plt.grid()
plt.xlabel("wavelength")
plt.ylabel("flux")
plt.show()
def main(args) :
global psfs
log=get_logger()
log.info("starting")
# read preprocessed image
image=read_image(args.image)
log.info("read image {}".format(args.image))
if image.mask is not None :
image.ivar *= (image.mask==0)
xytraceset = read_xytraceset(args.psf)
wavemin = xytraceset.wavemin
wavemax = xytraceset.wavemax
xcoef = xytraceset.x_vs_wave_traceset._coeff
ycoef = xytraceset.y_vs_wave_traceset._coeff
nfibers=xcoef.shape[0]
log.info("read PSF trace with xcoef.shape = {} , ycoef.shape = {} , and wavelength range {}:{}".format(xcoef.shape,ycoef.shape,int(wavemin),int(wavemax)))
lines=None
if args.lines is not None :
log.info("We will fit the image using the psf model and lines")
# read lines
lines=np.loadtxt(args.lines,usecols=[0])
ok=(lines>wavemin)&(lines<wavemax)
log.info("read {} lines in {}, with {} of them in traces wavelength range".format(len(lines),args.lines,np.sum(ok)))
lines=lines[ok]
else :
log.info("We will do an internal calibration of trace coordinates without using the psf shape in a first step")
internal_wavelength_calib = (not args.continuum)
external_wavelength_calib = args.sky | ( args.spectrum is not None )
if args.auto :
log.debug("read flavor of input image {}".format(args.image))
hdus = pyfits.open(args.image)
if "FLAVOR" not in hdus[0].header :
log.error("no FLAVOR keyword in image header, cannot run with --auto option")
raise KeyError("no FLAVOR keyword in image header, cannot run with --auto option")
flavor = hdus[0].header["FLAVOR"].strip().lower()
hdus.close()
log.info("Input is a '{}' image".format(flavor))
if flavor == "flat" :
internal_wavelength_calib = False
external_wavelength_calib = False
elif flavor == "arc" :
internal_wavelength_calib = True
external_wavelength_calib = False
else :
internal_wavelength_calib = True
external_wavelength_calib = True
log.info("wavelength calib, internal={}, external={}".format(internal_wavelength_calib,external_wavelength_calib))
spectrum_filename = args.spectrum
if external_wavelength_calib and spectrum_filename is None :
srch_file = "data/spec-sky.dat"
if not resource_exists('desispec', srch_file):
log.error("Cannot find sky spectrum file {:s}".format(srch_file))
raise RuntimeError("Cannot find sky spectrum file {:s}".format(srch_file))
else :
spectrum_filename=resource_filename('desispec', srch_file)
log.info("Use external calibration from cross-correlation with {}".format(spectrum_filename))
if args.nfibers is not None :
nfibers = args.nfibers # FOR DEBUGGING
fibers=np.arange(nfibers)
if lines is not None :
# use a forward modeling of the image
# it's slower and works only for individual lines
# it's in principle more accurate
# but gives systematic residuals for complex spectra like the sky
psf = read_specter_psf(args.psf)
x,y,dx,ex,dy,ey,fiber_xy,wave_xy=compute_dx_dy_using_psf(psf,image,fibers,lines)
x_for_dx=x
y_for_dx=y
fiber_for_dx=fiber_xy
wave_for_dx=wave_xy
x_for_dy=x
y_for_dy=y
fiber_for_dy=fiber_xy
wave_for_dy=wave_xy
else :
# internal calibration method that does not use the psf
# nor a prior set of lines. this method is much faster
# measure x shifts
x_for_dx,y_for_dx,dx,ex,fiber_for_dx,wave_for_dx = compute_dx_from_cross_dispersion_profiles(xcoef,ycoef,wavemin,wavemax, image=image, fibers=fibers, width=args.width, deg=args.degxy,image_rebin=args.ccd_rows_rebin)
if internal_wavelength_calib :
# measure y shifts
x_for_dy,y_for_dy,dy,ey,fiber_for_dy,wave_for_dy = compute_dy_using_boxcar_extraction(xytraceset, image=image, fibers=fibers, width=args.width)
mdy = np.median(dy)
log.info("Subtract median(dy)={}".format(mdy))
dy -= mdy # remove median, because this is an internal calibration
else :
# duplicate dx results with zero shift to avoid write special case code below
x_for_dy = x_for_dx.copy()
y_for_dy = y_for_dx.copy()
dy = np.zeros(dx.shape)
ey = 1.e-6*np.ones(ex.shape)
fiber_for_dy = fiber_for_dx.copy()
wave_for_dy = wave_for_dx.copy()
degxx=args.degxx
degxy=args.degxy
degyx=args.degyx
degyy=args.degyy
while(True) : # loop because polynomial degrees could be reduced
log.info("polynomial fit of measured offsets with degx=(%d,%d) degy=(%d,%d)"%(degxx,degxy,degyx,degyy))
try :
dx_coeff,dx_coeff_covariance,dx_errorfloor,dx_mod,dx_mask=polynomial_fit(z=dx,ez=ex,xx=x_for_dx,yy=y_for_dx,degx=degxx,degy=degxy)
dy_coeff,dy_coeff_covariance,dy_errorfloor,dy_mod,dy_mask=polynomial_fit(z=dy,ez=ey,xx=x_for_dy,yy=y_for_dy,degx=degyx,degy=degyy)
log.info("dx dy error floor = %4.3f %4.3f pixels"%(dx_errorfloor,dy_errorfloor))
log.info("check fit uncertainties are ok on edge of CCD")
merr=0.
for fiber in [0,nfibers-1] :
for rw in [-1,1] :
tx = legval(rw,xcoef[fiber])
ty = legval(rw,ycoef[fiber])
m=monomials(tx,ty,degxx,degxy)
tdx=np.inner(dx_coeff,m)
tsx=np.sqrt(np.inner(m,dx_coeff_covariance.dot(m)))
m=monomials(tx,ty,degyx,degyy)
tdy=np.inner(dy_coeff,m)
tsy=np.sqrt(np.inner(m,dy_coeff_covariance.dot(m)))
merr=max(merr,tsx)
merr=max(merr,tsy)
log.info("max edge shift error = %4.3f pixels"%merr)
if degxx==0 and degxy==0 and degyx==0 and degyy==0 :
break
except ( LinAlgError , ValueError ) :
log.warning("polynomial fit failed with degx=(%d,%d) degy=(%d,%d)"%(degxx,degxy,degyx,degyy))
if degxx==0 and degxy==0 and degyx==0 and degyy==0 :
log.error("polynomial degrees are already 0. we can fit the offsets")
raise RuntimeError("polynomial degrees are already 0. we can fit the offsets")
merr = 100000. # this will lower the pol. degree.
if merr > args.max_error :
if merr != 100000. :
log.warning("max edge shift error = %4.3f pixels is too large, reducing degrees"%merr)
if degxy>0 and degyy>0 and degxy>degxx and degyy>degyx : # first along wavelength
if degxy>0 : degxy-=1
if degyy>0 : degyy-=1
else : # then along fiber
if degxx>0 : degxx-=1
if degyx>0 : degyx-=1
else :
# error is ok, so we quit the loop
break
# write this for debugging
if args.outoffsets :
file=open(args.outoffsets,"w")
file.write("# axis wave fiber x y delta error polval (axis 0=y axis1=x)\n")
for e in range(dy.size) :
file.write("0 %f %d %f %f %f %f %f\n"%(wave_for_dy[e],fiber_for_dy[e],x_for_dy[e],y_for_dy[e],dy[e],ey[e],dy_mod[e]))
for e in range(dx.size) :
file.write("1 %f %d %f %f %f %f %f\n"%(wave_for_dx[e],fiber_for_dx[e],x_for_dx[e],y_for_dx[e],dx[e],ex[e],dx_mod[e]))
file.close()
log.info("wrote offsets in ASCII file %s"%args.outoffsets)
# print central shift
mx=np.median(x_for_dx)
my=np.median(y_for_dx)
m=monomials(mx,my,degxx,degxy)
mdx=np.inner(dx_coeff,m)
mex=np.sqrt(np.inner(m,dx_coeff_covariance.dot(m)))
mx=np.median(x_for_dy)
my=np.median(y_for_dy)
m=monomials(mx,my,degyx,degyy)
mdy=np.inner(dy_coeff,m)
mey=np.sqrt(np.inner(m,dy_coeff_covariance.dot(m)))
log.info("central shifts dx = %4.3f +- %4.3f dy = %4.3f +- %4.3f "%(mdx,mex,mdy,mey))
# for each fiber, apply offsets and recompute legendre polynomial
log.info("for each fiber, apply offsets and recompute legendre polynomial")
# compute x y to record max deviations
u = np.linspace(-1,1,5)
x0 = np.zeros((xcoef.shape[0],u.size))
y0 = np.zeros((ycoef.shape[0],u.size))
for f in range(xcoef.shape[0]) :
x0[f]=legval(u,xcoef[f])
y0[f]=legval(u,ycoef[f])
xcoef,ycoef = recompute_legendre_coefficients(xcoef=xcoef,ycoef=ycoef,wavemin=wavemin,wavemax=wavemax,degxx=degxx,degxy=degxy,degyx=degyx,degyy=degyy,dx_coeff=dx_coeff,dy_coeff=dy_coeff)
# use an input spectrum as an external calibration of wavelength
if spectrum_filename :
log.info("write and reread PSF to be sure predetermined shifts were propagated")
write_traces_in_psf(args.psf,args.outpsf,xcoef,ycoef,wavemin,wavemax)
#psf,xcoef,ycoef,wavemin,wavemax = read_psf_and_traces(args.outpsf)
xytraceset = read_xytraceset(args.outpsf)
wavemin = xytraceset.wavemin
wavemax = xytraceset.wavemax
xcoef = xytraceset.x_vs_wave_traceset._coeff
ycoef = xytraceset.y_vs_wave_traceset._coeff
psf = read_specter_psf(args.outpsf)
ycoef=shift_ycoef_using_external_spectrum(psf=psf,xytraceset=xytraceset,
image=image,fibers=fibers,spectrum_filename=spectrum_filename,degyy=args.degyy,width=7)
x = np.zeros((xcoef.shape[0],u.size))
y = np.zeros((ycoef.shape[0],u.size))
for f in range(xcoef.shape[0]) :
x[f]=legval(u,xcoef[f])
y[f]=legval(u,ycoef[f])
dx = x-x0
dy = y-y0
header_keywords = {}
header_keywords["MEANDX"]=np.mean(dx)
header_keywords["MINDX"]=np.min(dx)
header_keywords["MAXDX"]=np.max(dx)
header_keywords["MEANDY"]=np.mean(dy)
header_keywords["MINDY"]=np.min(dy)
header_keywords["MAXDY"]=np.max(dy)
write_traces_in_psf(args.psf,args.outpsf,xcoef,ycoef,wavemin,wavemax,header_keywords=header_keywords)
log.info("wrote modified PSF in %s"%args.outpsf)
else :
x = np.zeros((xcoef.shape[0],u.size))
y = np.zeros((ycoef.shape[0],u.size))
for f in range(xcoef.shape[0]) :
x[f]=legval(u,xcoef[f])
y[f]=legval(u,ycoef[f])
dx = x-x0
dy = y-y0
header_keywords = {}
header_keywords["MEANDX"]=np.mean(dx)
header_keywords["MINDX"]=np.min(dx)
header_keywords["MAXDX"]=np.max(dx)
header_keywords["MEANDY"]=np.mean(dy)
header_keywords["MINDY"]=np.min(dy)
header_keywords["MAXDY"]=np.max(dy)
write_traces_in_psf(args.psf,args.outpsf,xcoef,ycoef,wavemin,wavemax,header_keywords=header_keywords)
log.info("wrote modified PSF in %s"%args.outpsf)
