def test_init(self):
"""Cleanup test files if they exist.
"""
pheader={"DATE-OBS":'2018-11-30T12:42:10.442593-05:00',"DOSVER":'SIM'}
header={"DETECTOR":'SIM',"CAMERA":'b0 ',"FEEVER":'SIM'}
cfinder = CalibFinder([pheader,header])
print(cfinder.value("DETECTOR"))
if cfinder.haskey("BIAS") :
print(cfinder.findfile("BIAS"))
def test_init(self):
"""Cleanup test files if they exist.
"""
pheader = {
"DATE-OBS": '2018-11-30T12:42:10.442593-05:00',
"DOSVER": 'SIM'
}
header = {"DETECTOR": 'SIM', "CAMERA": 'b0 ', "FEEVER": 'SIM'}
cfinder = CalibFinder([pheader, header])
print(cfinder.value("DETECTOR"))
if cfinder.haskey("BIAS"):
print(cfinder.findfile("BIAS"))
def subtract_overscan(fx,camera):
import desispec.preproc
from desispec.calibfinder import parse_date_obs, CalibFinder
rawimage = fx[camera.upper()].data
rawimage = rawimage.astype(np.float64)
header = fx[camera.upper()].header
hdu=0
primary_header= fx[hdu].header
ccd_calibration_filename=None
log=desispec.preproc.get_logger()
cfinder = CalibFinder([header, primary_header], yaml_file=ccd_calibration_filename)
amp_ids = desispec.preproc.get_amp_ids(header)
#######################
use_overscan_row = False
overscan_per_row=False
#######################
# Subtract overscan
for amp in amp_ids:
# Grab the sections
ov_col = desispec.preproc.parse_sec_keyword(header['BIASSEC'+amp])
if 'ORSEC'+amp in header.keys():
ov_row = desispec.preproc.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
# 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 = desispec.preproc._overscan(raw_overscan_col)
overscan_col += o
rdnoise += r
#- subtract overscan from data region and apply gain
jj = desispec.preproc.parse_sec_keyword(header['DATASEC'+amp])
kk = desispec.preproc.parse_sec_keyword(header['CCDSEC'+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
rawimage[jj]=data
return rawimage
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
if args.gradient :
tmp = sub[:,1:] - sub[:,:-1]
sub[:,1:] = tmp
sub[:,0] = 0
rms_scale = 1./np.sqrt(2.)
i=0
x=np.zeros(3+4*6).astype(float)
x[i] = expid ; i += 1
x[i] = mjdobs ; i += 1
x[i] = dateobs ; i += 1
for a,amp in enumerate(['A','B','C','D']) :
gain = 1.
if cfinder is not None and cfinder.haskey("GAIN"+amp) :
gain=cfinder.value("GAIN"+amp)
x[i] = mean(img[_parse_sec_keyword(header["BIASSEC"+amp])],gain=gain); i+=1
x[i] = rms_scale*rms(sub[_parse_sec_keyword(header["BIASSEC"+amp])],gain=gain); i+=1
if "ORSEC"+amp in header :
x[i] = mean(img[_parse_sec_keyword(header["ORSEC"+amp])],gain=gain); i+=1
x[i] = rms_scale*rms(sub[_parse_sec_keyword(header["ORSEC"+amp])],gain=gain); i+=1
else :
i += 2
x[i] = mean(img[_parse_sec_keyword(header["DATASEC"+amp])],gain=gain); i+=1
x[i] = rms_scale*rms(sub[_parse_sec_keyword(header["DATASEC"+amp])],gain=gain); i+=1
print("assuming gain= {:3.2f} for amp {}, ccd rms= {:3.2f}".format(gain,amp,x[i-1]))
sys.stdout.flush()
xx.append(x)
if args.outfile is not None :
xx=np.vstack(xx)
def main(args=None, comm=None):
if args is None:
args = parse()
# elif isinstance(args, (list, tuple)):
# args = parse(args)
log = get_logger()
start_mpi_connect = time.time()
if comm is not None:
#- Use the provided comm to determine rank and size
rank = comm.rank
size = comm.size
else:
#- Check MPI flags and determine the comm, rank, and size given the arguments
comm, rank, size = assign_mpi(do_mpi=args.mpi,
do_batch=args.batch,
log=log)
stop_mpi_connect = time.time()
#- Start timer; only print log messages from rank 0 (others are silent)
timer = desiutil.timer.Timer(silent=(rank > 0))
#- Fill in timing information for steps before we had the timer created
if args.starttime is not None:
timer.start('startup', starttime=args.starttime)
timer.stop('startup', stoptime=start_imports)
timer.start('imports', starttime=start_imports)
timer.stop('imports', stoptime=stop_imports)
timer.start('mpi_connect', starttime=start_mpi_connect)
timer.stop('mpi_connect', stoptime=stop_mpi_connect)
#- Freeze IERS after parsing args so that it doesn't bother if only --help
timer.start('freeze_iers')
desiutil.iers.freeze_iers()
timer.stop('freeze_iers')
#- Preflight checks
timer.start('preflight')
if rank > 0:
#- Let rank 0 fetch these, and then broadcast
args, hdr, camhdr = None, None, None
else:
args, hdr, camhdr = update_args_with_headers(args)
## Make sure badamps is formatted properly
if comm is not None and rank == 0 and args.badamps is not None:
args.badamps = validate_badamps(args.badamps)
if comm is not None:
args = comm.bcast(args, root=0)
hdr = comm.bcast(hdr, root=0)
camhdr = comm.bcast(camhdr, root=0)
known_obstype = [
'SCIENCE', 'ARC', 'FLAT', 'ZERO', 'DARK', 'TESTARC', 'TESTFLAT',
'PIXFLAT', 'SKY', 'TWILIGHT', 'OTHER'
]
if args.obstype not in known_obstype:
raise RuntimeError('obstype {} not in {}'.format(
args.obstype, known_obstype))
timer.stop('preflight')
#-------------------------------------------------------------------------
#- Create and submit a batch job if requested
if args.batch:
#exp_str = '{:08d}'.format(args.expid)
jobdesc = args.obstype.lower()
if args.obstype == 'SCIENCE':
# if not doing pre-stdstar fitting or stdstar fitting and if there is
# no flag stopping flux calibration, set job to poststdstar
if args.noprestdstarfit and args.nostdstarfit and (
not args.nofluxcalib):
jobdesc = 'poststdstar'
# elif told not to do std or post stdstar but the flag for prestdstar isn't set,
# then perform prestdstar
elif (not args.noprestdstarfit
) and args.nostdstarfit and args.nofluxcalib:
jobdesc = 'prestdstar'
#elif (not args.noprestdstarfit) and (not args.nostdstarfit) and (not args.nofluxcalib):
# jobdesc = 'science'
scriptfile = create_desi_proc_batch_script(night=args.night, exp=args.expid, cameras=args.cameras,\
jobdesc=jobdesc, queue=args.queue, runtime=args.runtime,\
batch_opts=args.batch_opts, timingfile=args.timingfile,
system_name=args.system_name)
err = 0
if not args.nosubmit:
err = subprocess.call(['sbatch', scriptfile])
sys.exit(err)
#-------------------------------------------------------------------------
#- Proceeding with running
#- What are we going to do?
if rank == 0:
log.info('----------')
log.info('Input {}'.format(args.input))
log.info('Night {} expid {}'.format(args.night, args.expid))
log.info('Obstype {}'.format(args.obstype))
log.info('Cameras {}'.format(args.cameras))
log.info('Output root {}'.format(desispec.io.specprod_root()))
log.info('----------')
#- Create output directories if needed
if rank == 0:
preprocdir = os.path.dirname(
findfile('preproc', args.night, args.expid, 'b0'))
expdir = os.path.dirname(
findfile('frame', args.night, args.expid, 'b0'))
os.makedirs(preprocdir, exist_ok=True)
os.makedirs(expdir, exist_ok=True)
#- Wait for rank 0 to make directories before proceeding
if comm is not None:
comm.barrier()
#-------------------------------------------------------------------------
#- Preproc
#- All obstypes get preprocessed
timer.start('fibermap')
#- Assemble fibermap for science exposures
fibermap = None
fibermap_ok = None
if rank == 0 and args.obstype == 'SCIENCE':
fibermap = findfile('fibermap', args.night, args.expid)
if not os.path.exists(fibermap):
tmp = findfile('preproc', args.night, args.expid, 'b0')
preprocdir = os.path.dirname(tmp)
fibermap = os.path.join(preprocdir, os.path.basename(fibermap))
log.info('Creating fibermap {}'.format(fibermap))
cmd = 'assemble_fibermap -n {} -e {} -o {}'.format(
args.night, args.expid, fibermap)
if args.badamps is not None:
cmd += ' --badamps={}'.format(args.badamps)
runcmd(cmd, inputs=[], outputs=[fibermap])
fibermap_ok = os.path.exists(fibermap)
#- Some commissioning files didn't have coords* files that caused assemble_fibermap to fail
#- these are well known failures with no other solution, so for those, just force creation
#- of a fibermap with null coordinate information
if not fibermap_ok and int(args.night) < 20200310:
log.info(
"Since night is before 20200310, trying to force fibermap creation without coords file"
)
cmd += ' --force'
runcmd(cmd, inputs=[], outputs=[fibermap])
fibermap_ok = os.path.exists(fibermap)
#- If assemble_fibermap failed and obstype is SCIENCE, exit now
if comm is not None:
fibermap_ok = comm.bcast(fibermap_ok, root=0)
if args.obstype == 'SCIENCE' and not fibermap_ok:
sys.stdout.flush()
if rank == 0:
log.critical(
'assemble_fibermap failed for science exposure; exiting now')
sys.exit(13)
#- Wait for rank 0 to make fibermap if needed
if comm is not None:
fibermap = comm.bcast(fibermap, root=0)
timer.stop('fibermap')
if not (args.obstype in ['SCIENCE'] and args.noprestdstarfit):
timer.start('preproc')
for i in range(rank, len(args.cameras), size):
camera = args.cameras[i]
outfile = findfile('preproc', args.night, args.expid, camera)
outdir = os.path.dirname(outfile)
cmd = "desi_preproc -i {} -o {} --outdir {} --cameras {}".format(
args.input, outfile, outdir, camera)
if args.scattered_light:
cmd += " --scattered-light"
if fibermap is not None:
cmd += " --fibermap {}".format(fibermap)
if not args.obstype in ['ARC']: # never model variance for arcs
if not args.no_model_pixel_variance:
cmd += " --model-variance"
runcmd(cmd, inputs=[args.input], outputs=[outfile])
timer.stop('preproc')
if comm is not None:
comm.barrier()
#-------------------------------------------------------------------------
#- Get input PSFs
timer.start('findpsf')
input_psf = dict()
if rank == 0:
for camera in args.cameras:
if args.psf is not None:
input_psf[camera] = args.psf
elif args.calibnight is not None:
# look for a psfnight psf for this calib night
psfnightfile = findfile('psfnight', args.calibnight,
args.expid, camera)
if not os.path.isfile(psfnightfile):
log.error("no {}".format(psfnightfile))
raise IOError("no {}".format(psfnightfile))
input_psf[camera] = psfnightfile
else:
# look for a psfnight psf
psfnightfile = findfile('psfnight', args.night, args.expid,
camera)
if os.path.isfile(psfnightfile):
input_psf[camera] = psfnightfile
elif args.most_recent_calib:
nightfile = find_most_recent(args.night,
file_type='psfnight')
if nightfile is None:
input_psf[camera] = findcalibfile(
[hdr, camhdr[camera]], 'PSF')
else:
input_psf[camera] = nightfile
else:
input_psf[camera] = findcalibfile([hdr, camhdr[camera]],
'PSF')
log.info("Will use input PSF : {}".format(input_psf[camera]))
if comm is not None:
input_psf = comm.bcast(input_psf, root=0)
timer.stop('findpsf')
#-------------------------------------------------------------------------
#- Traceshift
if ( args.obstype in ['FLAT', 'TESTFLAT', 'SKY', 'TWILIGHT'] ) or \
( args.obstype in ['SCIENCE'] and (not args.noprestdstarfit) ):
timer.start('traceshift')
if rank == 0 and args.traceshift:
log.info('Starting traceshift at {}'.format(time.asctime()))
for i in range(rank, len(args.cameras), size):
camera = args.cameras[i]
preprocfile = findfile('preproc', args.night, args.expid, camera)
inpsf = input_psf[camera]
outpsf = findfile('psf', args.night, args.expid, camera)
if not os.path.isfile(outpsf):
if args.traceshift:
cmd = "desi_compute_trace_shifts"
cmd += " -i {}".format(preprocfile)
cmd += " --psf {}".format(inpsf)
cmd += " --outpsf {}".format(outpsf)
cmd += " --degxx 2 --degxy 0"
if args.obstype in ['FLAT', 'TESTFLAT', 'TWILIGHT']:
cmd += " --continuum"
else:
cmd += " --degyx 2 --degyy 0"
if args.obstype in ['SCIENCE', 'SKY']:
cmd += ' --sky'
else:
cmd = "ln -s {} {}".format(inpsf, outpsf)
runcmd(cmd, inputs=[preprocfile, inpsf], outputs=[outpsf])
else:
log.info("PSF {} exists".format(outpsf))
timer.stop('traceshift')
if comm is not None:
comm.barrier()
#-------------------------------------------------------------------------
#- PSF
#- MPI parallelize this step
if args.obstype in ['ARC', 'TESTARC']:
timer.start('arc_traceshift')
if rank == 0:
log.info('Starting traceshift before specex PSF fit at {}'.format(
time.asctime()))
for i in range(rank, len(args.cameras), size):
camera = args.cameras[i]
preprocfile = findfile('preproc', args.night, args.expid, camera)
inpsf = input_psf[camera]
outpsf = findfile('psf', args.night, args.expid, camera)
outpsf = replace_prefix(outpsf, "psf", "shifted-input-psf")
if not os.path.isfile(outpsf):
cmd = "desi_compute_trace_shifts"
cmd += " -i {}".format(preprocfile)
cmd += " --psf {}".format(inpsf)
cmd += " --outpsf {}".format(outpsf)
cmd += " --degxx 0 --degxy 0 --degyx 0 --degyy 0"
cmd += ' --arc-lamps'
runcmd(cmd, inputs=[preprocfile, inpsf], outputs=[outpsf])
else:
log.info("PSF {} exists".format(outpsf))
timer.stop('arc_traceshift')
if comm is not None:
comm.barrier()
timer.start('psf')
if rank == 0:
log.info('Starting specex PSF fitting at {}'.format(
time.asctime()))
if rank > 0:
cmds = inputs = outputs = None
else:
cmds = dict()
inputs = dict()
outputs = dict()
for camera in args.cameras:
preprocfile = findfile('preproc', args.night, args.expid,
camera)
tmpname = findfile('psf', args.night, args.expid, camera)
inpsf = replace_prefix(tmpname, "psf", "shifted-input-psf")
outpsf = replace_prefix(tmpname, "psf", "fit-psf")
log.info("now run specex psf fit")
cmd = 'desi_compute_psf'
cmd += ' --input-image {}'.format(preprocfile)
cmd += ' --input-psf {}'.format(inpsf)
cmd += ' --output-psf {}'.format(outpsf)
# look for fiber blacklist
cfinder = CalibFinder([hdr, camhdr[camera]])
blacklistkey = "FIBERBLACKLIST"
if not cfinder.haskey(blacklistkey) and cfinder.haskey(
"BROKENFIBERS"):
log.warning(
"BROKENFIBERS yaml keyword deprecated, please use FIBERBLACKLIST"
)
blacklistkey = "BROKENFIBERS"
if cfinder.haskey(blacklistkey):
blacklist = cfinder.value(blacklistkey)
cmd += ' --broken-fibers {}'.format(blacklist)
if rank == 0:
log.warning('broken fibers: {}'.format(blacklist))
if not os.path.exists(outpsf):
cmds[camera] = cmd
inputs[camera] = [preprocfile, inpsf]
outputs[camera] = [
outpsf,
]
if comm is not None:
cmds = comm.bcast(cmds, root=0)
inputs = comm.bcast(inputs, root=0)
outputs = comm.bcast(outputs, root=0)
#- split communicator by 20 (number of bundles)
group_size = 20
if (rank == 0) and (size % group_size != 0):
log.warning(
'MPI size={} should be evenly divisible by {}'.format(
size, group_size))
group = rank // group_size
num_groups = (size + group_size - 1) // group_size
comm_group = comm.Split(color=group)
if rank == 0:
log.info(
f'Fitting PSFs with {num_groups} sub-communicators of size {group_size}'
)
for i in range(group, len(args.cameras), num_groups):
camera = args.cameras[i]
if camera in cmds:
cmdargs = cmds[camera].split()[1:]
cmdargs = desispec.scripts.specex.parse(cmdargs)
if comm_group.rank == 0:
print('RUNNING: {}'.format(cmds[camera]))
t0 = time.time()
timestamp = time.asctime()
log.info(
f'MPI group {group} ranks {rank}-{rank+group_size-1} fitting PSF for {camera} at {timestamp}'
)
try:
desispec.scripts.specex.main(cmdargs, comm=comm_group)
except Exception as e:
if comm_group.rank == 0:
log.error(
f'FAILED: MPI group {group} ranks {rank}-{rank+group_size-1} camera {camera}'
)
log.error('FAILED: {}'.format(cmds[camera]))
log.error(e)
if comm_group.rank == 0:
specex_time = time.time() - t0
log.info(
f'specex fit for {camera} took {specex_time:.1f} seconds'
)
comm.barrier()
else:
log.warning(
'fitting PSFs without MPI parallelism; this will be SLOW')
for camera in args.cameras:
if camera in cmds:
runcmd(cmds[camera],
inputs=inputs[camera],
outputs=outputs[camera])
if comm is not None:
comm.barrier()
# loop on all cameras and interpolate bad fibers
for camera in args.cameras[rank::size]:
t0 = time.time()
log.info(f'Rank {rank} interpolating {camera} PSF over bad fibers')
# look for fiber blacklist
cfinder = CalibFinder([hdr, camhdr[camera]])
blacklistkey = "FIBERBLACKLIST"
if not cfinder.haskey(blacklistkey) and cfinder.haskey(
"BROKENFIBERS"):
log.warning(
"BROKENFIBERS yaml keyword deprecated, please use FIBERBLACKLIST"
)
blacklistkey = "BROKENFIBERS"
if cfinder.haskey(blacklistkey):
fiberblacklist = cfinder.value(blacklistkey)
tmpname = findfile('psf', args.night, args.expid, camera)
inpsf = replace_prefix(tmpname, "psf", "fit-psf")
outpsf = replace_prefix(tmpname, "psf",
"fit-psf-fixed-blacklisted")
if os.path.isfile(inpsf) and not os.path.isfile(outpsf):
cmd = 'desi_interpolate_fiber_psf'
cmd += ' --infile {}'.format(inpsf)
cmd += ' --outfile {}'.format(outpsf)
cmd += ' --fibers {}'.format(fiberblacklist)
log.info(
'For camera {} interpolating PSF for broken fibers: {}'
.format(camera, fiberblacklist))
runcmd(cmd, inputs=[inpsf], outputs=[outpsf])
if os.path.isfile(outpsf):
os.rename(
inpsf,
inpsf.replace("fit-psf",
"fit-psf-before-blacklisted-fix"))
subprocess.call('cp {} {}'.format(outpsf, inpsf),
shell=True)
dt = time.time() - t0
log.info(
f'Rank {rank} {camera} PSF interpolation took {dt:.1f} sec')
timer.stop('psf')
#-------------------------------------------------------------------------
#- Merge PSF of night if applicable
#if args.obstype in ['ARC']:
if False:
if rank == 0:
for camera in args.cameras:
psfnightfile = findfile('psfnight', args.night, args.expid,
camera)
if not os.path.isfile(
psfnightfile
): # we still don't have a psf night, see if we can compute it ...
psfs = glob.glob(
findfile('psf', args.night, args.expid,
camera).replace("psf", "fit-psf").replace(
str(args.expid), "*"))
log.info(
"Number of PSF for night={} camera={} = {}".format(
args.night, camera, len(psfs)))
if len(psfs) > 4: # lets do it!
log.info("Computing psfnight ...")
dirname = os.path.dirname(psfnightfile)
if not os.path.isdir(dirname):
os.makedirs(dirname)
desispec.scripts.specex.mean_psf(psfs, psfnightfile)
if os.path.isfile(psfnightfile): # now use this one
input_psf[camera] = psfnightfile
#-------------------------------------------------------------------------
#- Extract
#- This is MPI parallel so handle a bit differently
# maybe add ARC and TESTARC too
if ( args.obstype in ['FLAT', 'TESTFLAT', 'SKY', 'TWILIGHT'] ) or \
( args.obstype in ['SCIENCE'] and (not args.noprestdstarfit) ):
timer.start('extract')
if rank == 0:
log.info('Starting extractions at {}'.format(time.asctime()))
if rank > 0:
cmds = inputs = outputs = None
else:
cmds = dict()
inputs = dict()
outputs = dict()
for camera in args.cameras:
cmd = 'desi_extract_spectra'
#- Based on data from SM1-SM8, looking at central and edge fibers
#- with in mind overlapping arc lamps lines
if camera.startswith('b'):
cmd += ' -w 3600.0,5800.0,0.8'
elif camera.startswith('r'):
cmd += ' -w 5760.0,7620.0,0.8'
elif camera.startswith('z'):
cmd += ' -w 7520.0,9824.0,0.8'
preprocfile = findfile('preproc', args.night, args.expid,
camera)
psffile = findfile('psf', args.night, args.expid, camera)
framefile = findfile('frame', args.night, args.expid, camera)
cmd += ' -i {}'.format(preprocfile)
cmd += ' -p {}'.format(psffile)
cmd += ' -o {}'.format(framefile)
cmd += ' --psferr 0.1'
if args.obstype == 'SCIENCE' or args.obstype == 'SKY':
if rank == 0:
log.info('Include barycentric correction')
cmd += ' --barycentric-correction'
if not os.path.exists(framefile):
cmds[camera] = cmd
inputs[camera] = [preprocfile, psffile]
outputs[camera] = [
framefile,
]
#- TODO: refactor/combine this with PSF comm splitting logic
if comm is not None:
cmds = comm.bcast(cmds, root=0)
inputs = comm.bcast(inputs, root=0)
outputs = comm.bcast(outputs, root=0)
#- split communicator by 20 (number of bundles)
extract_size = 20
if (rank == 0) and (size % extract_size != 0):
log.warning(
'MPI size={} should be evenly divisible by {}'.format(
size, extract_size))
extract_group = rank // extract_size
num_extract_groups = (size + extract_size - 1) // extract_size
comm_extract = comm.Split(color=extract_group)
for i in range(extract_group, len(args.cameras),
num_extract_groups):
camera = args.cameras[i]
if camera in cmds:
cmdargs = cmds[camera].split()[1:]
extract_args = desispec.scripts.extract.parse(cmdargs)
if comm_extract.rank == 0:
print('RUNNING: {}'.format(cmds[camera]))
desispec.scripts.extract.main_mpi(extract_args,
comm=comm_extract)
comm.barrier()
else:
log.warning(
'running extractions without MPI parallelism; this will be SLOW'
)
for camera in args.cameras:
if camera in cmds:
runcmd(cmds[camera],
inputs=inputs[camera],
outputs=outputs[camera])
timer.stop('extract')
if comm is not None:
comm.barrier()
#-------------------------------------------------------------------------
#- Fiberflat
if args.obstype in ['FLAT', 'TESTFLAT']:
timer.start('fiberflat')
if rank == 0:
log.info('Starting fiberflats at {}'.format(time.asctime()))
for i in range(rank, len(args.cameras), size):
camera = args.cameras[i]
framefile = findfile('frame', args.night, args.expid, camera)
fiberflatfile = findfile('fiberflat', args.night, args.expid,
camera)
cmd = "desi_compute_fiberflat"
cmd += " -i {}".format(framefile)
cmd += " -o {}".format(fiberflatfile)
runcmd(cmd, inputs=[
framefile,
], outputs=[
fiberflatfile,
])
timer.stop('fiberflat')
if comm is not None:
comm.barrier()
#-------------------------------------------------------------------------
#- Average and auto-calib fiberflats of night if applicable
#if args.obstype in ['FLAT']:
if False:
if rank == 0:
fiberflatnightfile = findfile('fiberflatnight', args.night,
args.expid, args.cameras[0])
fiberflatdirname = os.path.dirname(fiberflatnightfile)
if not os.path.isfile(fiberflatnightfile) and len(
args.cameras
) >= 6: # we still don't have them, see if we can compute them, but need at least 2 spectros ...
flats = glob.glob(
findfile('fiberflat', args.night, args.expid,
"b0").replace(str(args.expid),
"*").replace("b0", "*"))
log.info("Number of fiberflat for night {} = {}".format(
args.night, len(flats)))
if len(flats) >= 3 * 4 * len(
args.cameras
): # lets do it! (3 exposures x 4 lamps x N cameras)
log.info(
"Computing fiberflatnight per lamp and camera ...")
tmpdir = os.path.join(fiberflatdirname, "tmp")
if not os.path.isdir(tmpdir):
os.makedirs(tmpdir)
log.info(
"First average measurements per camera and per lamp")
average_flats = dict()
for camera in args.cameras:
# list of flats for this camera
flats_for_this_camera = []
for flat in flats:
if flat.find(camera) >= 0:
flats_for_this_camera.append(flat)
#log.info("For camera {} , flats = {}".format(camera,flats_for_this_camera))
#sys.exit(12)
# average per lamp (and camera)
average_flats[camera] = list()
for lampbox in range(4):
ofile = os.path.join(
tmpdir,
"fiberflatnight-camera-{}-lamp-{}.fits".format(
camera, lampbox))
if not os.path.isfile(ofile):
log.info(
"Average flat for camera {} and lamp box #{}"
.format(camera, lampbox))
pg = "CALIB DESI-CALIB-0{} LEDs only".format(
lampbox)
cmd = "desi_average_fiberflat --program '{}' --outfile {} -i ".format(
pg, ofile)
for flat in flats_for_this_camera:
cmd += " {} ".format(flat)
runcmd(cmd,
inputs=flats_for_this_camera,
outputs=[
ofile,
])
if os.path.isfile(ofile):
average_flats[camera].append(ofile)
else:
log.info("Will use existing {}".format(ofile))
average_flats[camera].append(ofile)
log.info(
"Auto-calibration across lamps and spectro per camera arm (b,r,z)"
)
for camera_arm in ["b", "r", "z"]:
cameras_for_this_arm = []
flats_for_this_arm = []
for camera in args.cameras:
if camera[0].lower() == camera_arm:
cameras_for_this_arm.append(camera)
if camera in average_flats:
for flat in average_flats[camera]:
flats_for_this_arm.append(flat)
cmd = "desi_autocalib_fiberflat --night {} --arm {} -i ".format(
args.night, camera_arm)
for flat in flats_for_this_arm:
cmd += " {} ".format(flat)
runcmd(cmd, inputs=flats_for_this_arm, outputs=[])
log.info("Done with fiber flats per night")
if comm is not None:
comm.barrier()
#-------------------------------------------------------------------------
#- Get input fiberflat
if args.obstype in ['SCIENCE', 'SKY'] and (not args.nofiberflat):
timer.start('find_fiberflat')
input_fiberflat = dict()
if rank == 0:
for camera in args.cameras:
if args.fiberflat is not None:
input_fiberflat[camera] = args.fiberflat
elif args.calibnight is not None:
# look for a fiberflatnight for this calib night
fiberflatnightfile = findfile('fiberflatnight',
args.calibnight, args.expid,
camera)
if not os.path.isfile(fiberflatnightfile):
log.error("no {}".format(fiberflatnightfile))
raise IOError("no {}".format(fiberflatnightfile))
input_fiberflat[camera] = fiberflatnightfile
else:
# look for a fiberflatnight fiberflat
fiberflatnightfile = findfile('fiberflatnight', args.night,
args.expid, camera)
if os.path.isfile(fiberflatnightfile):
input_fiberflat[camera] = fiberflatnightfile
elif args.most_recent_calib:
nightfile = find_most_recent(
args.night, file_type='fiberflatnight')
if nightfile is None:
input_fiberflat[camera] = findcalibfile(
[hdr, camhdr[camera]], 'FIBERFLAT')
else:
input_fiberflat[camera] = nightfile
else:
input_fiberflat[camera] = findcalibfile(
[hdr, camhdr[camera]], 'FIBERFLAT')
log.info("Will use input FIBERFLAT: {}".format(
input_fiberflat[camera]))
if comm is not None:
input_fiberflat = comm.bcast(input_fiberflat, root=0)
timer.stop('find_fiberflat')
#-------------------------------------------------------------------------
#- Apply fiberflat and write fframe file
if args.obstype in ['SCIENCE', 'SKY'] and args.fframe and \
( not args.nofiberflat ) and (not args.noprestdstarfit):
timer.start('apply_fiberflat')
if rank == 0:
log.info('Applying fiberflat at {}'.format(time.asctime()))
for i in range(rank, len(args.cameras), size):
camera = args.cameras[i]
fframefile = findfile('fframe', args.night, args.expid, camera)
if not os.path.exists(fframefile):
framefile = findfile('frame', args.night, args.expid, camera)
fr = desispec.io.read_frame(framefile)
flatfilename = input_fiberflat[camera]
if flatfilename is not None:
ff = desispec.io.read_fiberflat(flatfilename)
fr.meta['FIBERFLT'] = desispec.io.shorten_filename(
flatfilename)
apply_fiberflat(fr, ff)
fframefile = findfile('fframe', args.night, args.expid,
camera)
desispec.io.write_frame(fframefile, fr)
else:
log.warning(
"Missing fiberflat for camera {}".format(camera))
timer.stop('apply_fiberflat')
if comm is not None:
comm.barrier()
#-------------------------------------------------------------------------
#- Select random sky fibers (inplace update of frame file)
#- TODO: move this to a function somewhere
#- TODO: this assigns different sky fibers to each frame of same spectrograph
if (args.obstype in [
'SKY', 'SCIENCE'
]) and (not args.noskysub) and (not args.noprestdstarfit):
timer.start('picksky')
if rank == 0:
log.info('Picking sky fibers at {}'.format(time.asctime()))
for i in range(rank, len(args.cameras), size):
camera = args.cameras[i]
framefile = findfile('frame', args.night, args.expid, camera)
orig_frame = desispec.io.read_frame(framefile)
#- Make a copy so that we can apply fiberflat
fr = deepcopy(orig_frame)
if np.any(fr.fibermap['OBJTYPE'] == 'SKY'):
log.info('{} sky fibers already set; skipping'.format(
os.path.basename(framefile)))
continue
#- Apply fiberflat then select random fibers below a flux cut
flatfilename = input_fiberflat[camera]
if flatfilename is None:
log.error("No fiberflat for {}".format(camera))
continue
ff = desispec.io.read_fiberflat(flatfilename)
apply_fiberflat(fr, ff)
sumflux = np.sum(fr.flux, axis=1)
fluxcut = np.percentile(sumflux, 30)
iisky = np.where(sumflux < fluxcut)[0]
iisky = np.random.choice(iisky, size=100, replace=False)
#- Update fibermap or original frame and write out
orig_frame.fibermap['OBJTYPE'][iisky] = 'SKY'
orig_frame.fibermap['DESI_TARGET'][iisky] |= desi_mask.SKY
desispec.io.write_frame(framefile, orig_frame)
timer.stop('picksky')
if comm is not None:
comm.barrier()
#-------------------------------------------------------------------------
#- Sky subtraction
if args.obstype in [
'SCIENCE', 'SKY'
] and (not args.noskysub) and (not args.noprestdstarfit):
timer.start('skysub')
if rank == 0:
log.info('Starting sky subtraction at {}'.format(time.asctime()))
for i in range(rank, len(args.cameras), size):
camera = args.cameras[i]
framefile = findfile('frame', args.night, args.expid, camera)
hdr = fitsio.read_header(framefile, 'FLUX')
fiberflatfile = input_fiberflat[camera]
if fiberflatfile is None:
log.error("No fiberflat for {}".format(camera))
continue
skyfile = findfile('sky', args.night, args.expid, camera)
cmd = "desi_compute_sky"
cmd += " -i {}".format(framefile)
cmd += " --fiberflat {}".format(fiberflatfile)
cmd += " --o {}".format(skyfile)
if args.no_extra_variance:
cmd += " --no-extra-variance"
if not args.no_sky_wavelength_adjustment:
cmd += " --adjust-wavelength"
if not args.no_sky_lsf_adjustment: cmd += " --adjust-lsf"
runcmd(cmd, inputs=[framefile, fiberflatfile], outputs=[
skyfile,
])
#- sframe = flatfielded sky-subtracted but not flux calibrated frame
#- Note: this re-reads and re-does steps previously done for picking
#- sky fibers; desi_proc is about human efficiency,
#- not I/O or CPU efficiency...
sframefile = desispec.io.findfile('sframe', args.night, args.expid,
camera)
if not os.path.exists(sframefile):
frame = desispec.io.read_frame(framefile)
fiberflat = desispec.io.read_fiberflat(fiberflatfile)
sky = desispec.io.read_sky(skyfile)
apply_fiberflat(frame, fiberflat)
subtract_sky(frame, sky, apply_throughput_correction=True)
frame.meta['IN_SKY'] = shorten_filename(skyfile)
frame.meta['FIBERFLT'] = shorten_filename(fiberflatfile)
desispec.io.write_frame(sframefile, frame)
timer.stop('skysub')
if comm is not None:
comm.barrier()
#-------------------------------------------------------------------------
#- Standard Star Fitting
if args.obstype in ['SCIENCE',] and \
(not args.noskysub ) and \
(not args.nostdstarfit) :
timer.start('stdstarfit')
if rank == 0:
log.info('Starting flux calibration at {}'.format(time.asctime()))
#- Group inputs by spectrograph
framefiles = dict()
skyfiles = dict()
fiberflatfiles = dict()
night, expid = args.night, args.expid #- shorter
for camera in args.cameras:
sp = int(camera[1])
if sp not in framefiles:
framefiles[sp] = list()
skyfiles[sp] = list()
fiberflatfiles[sp] = list()
framefiles[sp].append(findfile('frame', night, expid, camera))
skyfiles[sp].append(findfile('sky', night, expid, camera))
fiberflatfiles[sp].append(input_fiberflat[camera])
#- Hardcoded stdstar model version
starmodels = os.path.join(os.getenv('DESI_BASIS_TEMPLATES'),
'stdstar_templates_v2.2.fits')
#- Fit stdstars per spectrograph (not per-camera)
spectro_nums = sorted(framefiles.keys())
## for sp in spectro_nums[rank::size]:
for i in range(rank, len(spectro_nums), size):
sp = spectro_nums[i]
stdfile = findfile('stdstars', night, expid, spectrograph=sp)
cmd = "desi_fit_stdstars"
cmd += " --frames {}".format(' '.join(framefiles[sp]))
cmd += " --skymodels {}".format(' '.join(skyfiles[sp]))
cmd += " --fiberflats {}".format(' '.join(fiberflatfiles[sp]))
cmd += " --starmodels {}".format(starmodels)
cmd += " --outfile {}".format(stdfile)
cmd += " --delta-color 0.1"
if args.maxstdstars is not None:
cmd += " --maxstdstars {}".format(args.maxstdstars)
inputs = framefiles[sp] + skyfiles[sp] + fiberflatfiles[sp]
runcmd(cmd, inputs=inputs, outputs=[stdfile])
timer.stop('stdstarfit')
if comm is not None:
comm.barrier()
# -------------------------------------------------------------------------
# - Flux calibration
if args.obstype in ['SCIENCE'] and \
(not args.noskysub) and \
(not args.nofluxcalib):
timer.start('fluxcalib')
night, expid = args.night, args.expid #- shorter
#- Compute flux calibration vectors per camera
for camera in args.cameras[rank::size]:
framefile = findfile('frame', night, expid, camera)
skyfile = findfile('sky', night, expid, camera)
spectrograph = int(camera[1])
stdfile = findfile('stdstars',
night,
expid,
spectrograph=spectrograph)
calibfile = findfile('fluxcalib', night, expid, camera)
fiberflatfile = input_fiberflat[camera]
cmd = "desi_compute_fluxcalibration"
cmd += " --infile {}".format(framefile)
cmd += " --sky {}".format(skyfile)
cmd += " --fiberflat {}".format(fiberflatfile)
cmd += " --models {}".format(stdfile)
cmd += " --outfile {}".format(calibfile)
cmd += " --delta-color-cut 0.1"
inputs = [framefile, skyfile, fiberflatfile, stdfile]
runcmd(cmd, inputs=inputs, outputs=[
calibfile,
])
timer.stop('fluxcalib')
if comm is not None:
comm.barrier()
#-------------------------------------------------------------------------
#- Applying flux calibration
if args.obstype in [
'SCIENCE',
] and (not args.noskysub) and (not args.nofluxcalib):
night, expid = args.night, args.expid #- shorter
timer.start('applycalib')
if rank == 0:
log.info('Starting cframe file creation at {}'.format(
time.asctime()))
for camera in args.cameras[rank::size]:
framefile = findfile('frame', night, expid, camera)
skyfile = findfile('sky', night, expid, camera)
spectrograph = int(camera[1])
stdfile = findfile('stdstars',
night,
expid,
spectrograph=spectrograph)
calibfile = findfile('fluxcalib', night, expid, camera)
cframefile = findfile('cframe', night, expid, camera)
fiberflatfile = input_fiberflat[camera]
cmd = "desi_process_exposure"
cmd += " --infile {}".format(framefile)
cmd += " --fiberflat {}".format(fiberflatfile)
cmd += " --sky {}".format(skyfile)
cmd += " --calib {}".format(calibfile)
cmd += " --outfile {}".format(cframefile)
cmd += " --cosmics-nsig 6"
if args.no_xtalk:
cmd += " --no-xtalk"
inputs = [framefile, fiberflatfile, skyfile, calibfile]
runcmd(cmd, inputs=inputs, outputs=[
cframefile,
])
if comm is not None:
comm.barrier()
timer.stop('applycalib')
#-------------------------------------------------------------------------
#- Wrap up
# if rank == 0:
# report = timer.report()
# log.info('Rank 0 timing report:\n' + report)
if comm is not None:
timers = comm.gather(timer, root=0)
else:
timers = [
timer,
]
if rank == 0:
stats = desiutil.timer.compute_stats(timers)
log.info('Timing summary statistics:\n' + json.dumps(stats, indent=2))
if args.timingfile:
if os.path.exists(args.timingfile):
with open(args.timingfile) as fx:
previous_stats = json.load(fx)
#- augment previous_stats with new entries, but don't overwrite old
for name in stats:
if name not in previous_stats:
previous_stats[name] = stats[name]
stats = previous_stats
tmpfile = args.timingfile + '.tmp'
with open(tmpfile, 'w') as fx:
json.dump(stats, fx, indent=2)
os.rename(tmpfile, args.timingfile)
if rank == 0:
log.info('All done at {}'.format(time.asctime()))
def cal_rdnoise(hdul, camera, ccd_calibration_filename, use_overscan_row,
overscan_per_row, use_active):
if True:
hdul_this = hdul[camera]
header = hdul_this.header
rawimage = hdul_this.data
# works! preproc.preproc(hdul['B0'].data,hdul['B0'].header,h1)
amp_ids = preproc.get_amp_ids(header)
#- 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)
nogain = False
cfinder = None
if ccd_calibration_filename is not False:
cfinder = CalibFinder([header, primary_header],
yaml_file=ccd_calibration_filename)
for amp in amp_ids:
# Grab the sections
ov_col = parse_sec_keyword(header['BIASSEC' + amp])
ov_row = parse_sec_keyword(header['ORSEC' + amp])
if use_active:
if amp == 'A' or amp == 'D':
ov_col2 = np.s_[ov_col[1].start:ov_col[1].stop, 0:100]
else:
ov_col2 = np.s_[ov_col[1].start:ov_col[1].stop, 0:100]
import pdb
pdb.set_trace()
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() # 2064*64
if use_overscan_row:
raw_overscan_row = rawimage[ov_row].copy() # 32*2057
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() # 32*64
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)
return rdnoise, median_rdnoise
def compute_bias_file(rawfiles, outfile, camera, explistfile=None):
"""
Compute a bias file from input ZERO rawfiles
Args:
rawfiles: list of input raw file names
outfile (str): output filename
camera (str): camera, e.g. b0, r1, z9
Options:
explistfile: filename with text list of NIGHT EXPID to use
Notes: explistfile is only used if rawfiles=None; it should have
one NIGHT EXPID entry per line.
"""
log = get_logger()
if explistfile is not None:
if rawfiles is not None:
msg = "specify rawfiles or explistfile, but not both"
log.error(msg)
raise ValueError(msg)
rawfiles = list()
with open(explistfile, 'r') as fx:
for line in fx:
line = line.strip()
if line.startswith('#') or len(line) < 2:
continue
night, expid = map(int, line.split())
filename = io.findfile('raw', night, expid)
if not os.path.exists(filename):
msg = f'Missing {filename}'
log.critical(msg)
raise RuntimeError(msg)
rawfiles.append(filename)
log.info("read images ...")
images = []
shape = None
first_image_header = None
for filename in rawfiles:
log.info("reading %s" % filename)
fitsfile = pyfits.open(filename)
primary_header = fitsfile[0].header
image_header = fitsfile[camera].header
if first_image_header is None:
first_image_header = image_header
flavor = image_header['FLAVOR'].upper()
if flavor != 'ZERO':
message = f'Input {filename} flavor {flavor} != ZERO'
log.error(message)
raise ValueError(message)
# subtract overscan region
cfinder = CalibFinder([image_header, primary_header])
image = fitsfile[camera].data.astype("float64")
if cfinder and cfinder.haskey("AMPLIFIERS"):
amp_ids = list(cfinder.value("AMPLIFIERS"))
else:
amp_ids = ['A', 'B', 'C', 'D']
n0 = image.shape[0] // 2
n1 = image.shape[1] // 2
for a, amp in enumerate(amp_ids):
ii = parse_sec_keyword(image_header['BIASSEC' + amp])
overscan_image = image[ii].copy()
overscan, rdnoise = _overscan(overscan_image)
log.info("amp {} overscan = {}".format(amp, overscan))
if ii[0].start < n0 and ii[1].start < n1:
image[:n0, :n1] -= overscan
elif ii[0].start < n0 and ii[1].start >= n1:
image[:n0, n1:] -= overscan
elif ii[0].start >= n0 and ii[1].start < n1:
image[n0:, :n1] -= overscan
elif ii[0].start >= n0 and ii[1].start >= n1:
image[n0:, n1:] -= overscan
if shape is None:
shape = image.shape
images.append(image.ravel())
fitsfile.close()
images = np.array(images)
print(images.shape)
# compute a mask
log.info("compute median image ...")
medimage = np.median(images, axis=0) #.reshape(shape)
log.info("compute mask ...")
ares = np.abs(images - medimage)
nsig = 4.
mask = (ares < nsig * 1.4826 * np.median(ares, axis=0))
# average (not median)
log.info("compute average ...")
meanimage = np.sum(images * mask, axis=0) / np.sum(mask, axis=0)
meanimage = meanimage.reshape(shape)
log.info("write result in %s ..." % outfile)
hdus = pyfits.HDUList([pyfits.PrimaryHDU(meanimage.astype('float32'))])
# copy some keywords
for key in [
"TELESCOP", "INSTRUME", "SPECGRPH", "SPECID", "DETECTOR", "CAMERA",
"CCDNAME", "CCDPREP", "CCDSIZE", "CCDTEMP", "CPUTEMP", "CASETEMP",
"CCDTMING", "CCDCFG", "SETTINGS", "VESSEL", "FEEVER", "FEEBOX",
"PRESECA", "PRRSECA", "DATASECA", "TRIMSECA", "BIASSECA", "ORSECA",
"CCDSECA", "DETSECA", "AMPSECA", "PRESECB", "PRRSECB", "DATASECB",
"TRIMSECB", "BIASSECB", "ORSECB", "CCDSECB", "DETSECB", "AMPSECB",
"PRESECC", "PRRSECC", "DATASECC", "TRIMSECC", "BIASSECC", "ORSECC",
"CCDSECC", "DETSECC", "AMPSECC", "PRESECD", "PRRSECD", "DATASECD",
"TRIMSECD", "BIASSECD", "ORSECD", "CCDSECD", "DETSECD", "AMPSECD",
"DAC0", "DAC1", "DAC2", "DAC3", "DAC4", "DAC5", "DAC6", "DAC7",
"DAC8", "DAC9", "DAC10", "DAC11", "DAC12", "DAC13", "DAC14",
"DAC15", "DAC16", "DAC17", "CLOCK0", "CLOCK1", "CLOCK2", "CLOCK3",
"CLOCK4", "CLOCK5", "CLOCK6", "CLOCK7", "CLOCK8", "CLOCK9",
"CLOCK10", "CLOCK11", "CLOCK12", "CLOCK13", "CLOCK14", "CLOCK15",
"CLOCK16", "CLOCK17", "CLOCK18", "OFFSET0", "OFFSET1", "OFFSET2",
"OFFSET3", "OFFSET4", "OFFSET5", "OFFSET6", "OFFSET7", "DELAYS",
"CDSPARMS", "PGAGAIN", "OCSVER", "DOSVER", "CONSTVER"
]:
if key in first_image_header:
hdus[0].header[key] = (first_image_header[key],
first_image_header.comments[key])
hdus[0].header["BUNIT"] = "adu"
hdus[0].header["EXTNAME"] = "BIAS"
for filename in rawfiles:
hdus[0].header["COMMENT"] = "Inc. {}".format(
os.path.basename(filename))
hdus.writeto(outfile, overwrite="True")
log.info("done")
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):
'''
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 background subtraction with median filtering if bkgsub=True
Optional disabling of cosmic ray rejection if nocosmic=True
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
Returns Image object with member variables:
image : 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)
bias = get_calibration_image(cfinder,"BIAS",bias)
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))
if cfinder and cfinder.haskey("AMPLIFIERS") :
amp_ids=list(cfinder.value("AMPLIFIERS"))
else :
amp_ids=['A','B','C','D']
#- check whether it's indeed CCDSECx with x in ['A','B','C','D']
# or older version with x in ['1','2','3','4']
# we can remove this piece of code at later times
has_valid_keywords = True
for amp in amp_ids :
if not 'CCDSEC%s'%amp in header :
log.warning("No CCDSEC%s keyword in header , will look for alternative naming CCDSEC{1,2,3,4} ..."%amp)
has_valid_keywords = False
break
if not has_valid_keywords :
amp_ids=['1','2','3','4']
for amp in ['1','2','3','4'] :
if not 'CCDSEC%s'%amp in header :
log.error("No CCDSEC%s keyword, exit"%amp)
raise KeyError("No CCDSEC%s keyword"%amp)
#- 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 :
ii = _parse_sec_keyword(header['BIASSEC'+amp])
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))
overscan_image = rawimage[ii].copy()
nrows=overscan_image.shape[0]
log.info("nrows in overscan=%d"%nrows)
overscan = np.zeros(nrows)
rdnoise = np.zeros(nrows)
overscan_per_row = True
if cfinder and cfinder.haskey('OVERSCAN'+amp) and cfinder.value("OVERSCAN"+amp).upper()=="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_image[j])) :
log.warning("NaN values in row %d of overscan of amplifier %s of camera %s"%(j,amp,camera))
continue
o,r = _overscan(overscan_image[j])
#log.info("%d %f %f"%(j,o,r))
overscan[j]=o
rdnoise[j]=r
else :
log.info("Subtracting average overscan for amplifier %s of camera %s"%(amp,camera))
o,r = _overscan(overscan_image)
overscan += o
rdnoise += r
rdnoise *= gain
median_rdnoise = np.median(rdnoise)
median_overscan = np.median(overscan)
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()
for k in range(nrows) :
data[k] -= overscan[k]
#- apply saturlev (defined in ADU), prior to multiplication by gain
saturated = (rawimage[jj]>=saturlev)
mask[kk][saturated] |= ccdmask.SATURATED
#- subtract dark prior to multiplication by gain
if dark is not False :
log.info("subtracting dark for amp %s"%amp)
data -= dark[kk]
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)
#- 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))
if np.all(pixflat != 0.0):
image /= pixflat
readnoise /= pixflat
else:
good = (pixflat != 0.0)
image[good] /= pixflat[good]
readnoise[good] /= pixflat[good]
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 = image.clip(0) + readnoise**2
ivar = np.zeros(var.shape)
ivar[var>0] = 1.0 / var[var>0]
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)
return img