def test_rejection_ala_sdss(self):
"""
Very basic test of reject_cosmic_rays_ala_sdss
"""
#- Does it reject a diagonal cosmic ray?
image = Image(self.pix, self.ivar, camera="r0")
rejected = reject_cosmic_rays_ala_sdss(image, dilate=False)
diff = np.sum(np.abs((self.pix > 0).astype(int) -
rejected.astype(int)))
self.assertTrue(diff == 0)
#- Does it not find a PSF-like object?
image = Image(self.psfpix, self.ivar, camera="r0")
rejected = reject_cosmic_rays_ala_sdss(image, dilate=False)
diff = np.sum(np.abs((self.pix > 0).astype(int) -
rejected.astype(int)))
self.assertTrue(np.all(self.psfpix * (rejected == 0) == self.psfpix))
#- Can it find one and not the other?
image = Image(self.pix + self.psfpix, self.ivar, camera="r0")
rejected = reject_cosmic_rays_ala_sdss(image, dilate=False)
diff = np.sum(np.abs((self.pix > 0).astype(int) -
rejected.astype(int)))
self.assertTrue(np.all(self.psfpix * (rejected == 0) == self.psfpix))
diff = np.sum(np.abs((self.pix > 0).astype(int) -
rejected.astype(int)))
self.assertTrue(diff == 0)
def test_bootcalib(self):
from desispec.bootcalib import bootcalib
from desispec.image import Image
arc = fits.getdata(self.testarc)
flat = fits.getdata(self.testflat)
arcimage = Image(arc, np.ones_like(arc), camera='b0')
flatimage = Image(flat, np.ones_like(flat), camera='b0')
results = bootcalib(3, flatimage, arcimage)
def test_assertions(self):
with self.assertRaises(ValueError):
Image(self.pix[0], self.ivar[0]) #- pix not 2D
with self.assertRaises(ValueError):
Image(self.pix, self.ivar[0]) #- pix.shape != ivar.shape
with self.assertRaises(ValueError):
Image(self.pix, self.ivar,
self.mask[:, 0:1]) #- pix.shape != mask.shape
def test_readnoise(self):
image = Image(self.pix, self.ivar)
self.assertEqual(image.readnoise, 0.0)
image = Image(self.pix, self.ivar, readnoise=1.0)
self.assertEqual(image.readnoise, 1.0)
readnoise = np.random.uniform(size=self.pix.shape)
image = Image(self.pix, self.ivar, readnoise=readnoise)
self.assertTrue(np.all(image.readnoise == readnoise))
def test_mask(self):
image = Image(self.pix, self.ivar)
self.assertTrue(np.all(image.mask == (self.ivar == 0) * ccdmask.BAD))
self.assertEqual(image.mask.dtype, np.uint32)
image = Image(self.pix, self.ivar, self.mask)
self.assertTrue(np.all(image.mask == self.mask))
self.assertEqual(image.mask.dtype, np.uint32)
image = Image(self.pix, self.ivar, self.mask.astype(np.int16))
self.assertEqual(image.mask.dtype, np.uint32)
def test_different_cameras(self):
'''test a PSF-like spot with a masked column going through it'''
for camera in ('b0', 'r1', 'z2'):
image = Image(self.pix, self.ivar, mask=self.badmask, camera=camera)
rejected = reject_cosmic_rays_ala_sdss(image,dilate=False)
cosmic = image.pix > 0
self.assertTrue(np.all(rejected[cosmic]))
#- camera must be valid
with self.assertRaises(KeyError):
image = Image(self.pix, self.ivar, mask=self.badmask, camera='a0')
rejected = reject_cosmic_rays_ala_sdss(image,dilate=False)
def read_image(filename):
"""
Returns desispec.image.Image object from input file
"""
log = get_logger()
t0 = time.time()
with fits.open(filename, uint=True, memmap=False) as fx:
image = native_endian(fx['IMAGE'].data).astype(np.float64)
ivar = native_endian(fx['IVAR'].data).astype(np.float64)
mask = native_endian(fx['MASK'].data).astype(np.uint16)
camera = fx['IMAGE'].header['CAMERA'].lower()
meta = fx['IMAGE'].header
if 'READNOISE' in fx:
readnoise = native_endian(fx['READNOISE'].data).astype(np.float64)
else:
readnoise = fx['IMAGE'].header['RDNOISE']
duration = time.time() - t0
log.info(iotime.format('read', filename, duration))
return Image(image,
ivar,
mask=mask,
readnoise=readnoise,
camera=camera,
meta=meta)
def test_image_rw(self):
shape = (5, 5)
pix = np.random.uniform(size=shape)
ivar = np.random.uniform(size=shape)
mask = np.random.randint(0, 3, size=shape)
img1 = Image(pix, ivar, mask, readnoise=1.0, camera='b0')
desispec.io.write_image(self.testfile, img1)
img2 = desispec.io.read_image(self.testfile)
#- Check output datatypes
self.assertEqual(img2.pix.dtype, np.float64)
self.assertEqual(img2.ivar.dtype, np.float64)
self.assertEqual(img2.mask.dtype, np.uint32)
#- Rounding from keeping np.float32 on disk means they aren't equal
self.assertFalse(np.all(img1.pix == img2.pix))
self.assertFalse(np.all(img1.ivar == img2.ivar))
#- But they should be close, and identical after float64->float32
self.assertTrue(np.allclose(img1.pix, img2.pix))
self.assertTrue(np.all(img1.pix.astype(np.float32) == img2.pix))
self.assertTrue(np.allclose(img1.ivar, img2.ivar))
self.assertTrue(np.all(img1.ivar.astype(np.float32) == img2.ivar))
#- masks should agree
self.assertTrue(np.all(img1.mask == img2.mask))
self.assertEqual(img1.readnoise, img2.readnoise)
self.assertEqual(img1.camera, img2.camera)
self.assertEqual(img2.mask.dtype, np.uint32)
#- should work with various kinds of metadata header input
meta = dict(BLAT='foo', BAR='quat', BIZ=1.0)
img1 = Image(pix, ivar, mask, readnoise=1.0, camera='b0', meta=meta)
desispec.io.write_image(self.testfile, img1)
img2 = desispec.io.read_image(self.testfile)
for key in meta:
self.assertEqual(meta[key], img2.meta[key],
'meta[{}] not propagated'.format(key))
#- img2 has meta as a FITS header instead of a dictionary;
#- confirm that works too
desispec.io.write_image(self.testfile, img2)
img3 = desispec.io.read_image(self.testfile)
for key in meta:
self.assertEqual(meta[key], img3.meta[key],
'meta[{}] not propagated'.format(key))
def test_reject_cosmics(self):
"""
Test that the generic cosmics interface updates the mask
"""
image = Image(self.pix, self.ivar, camera="r0")
reject_cosmic_rays(image)
cosmic = (image.pix > 0)
self.assertTrue(np.all(image.mask[cosmic] & ccdmask.COSMIC))
def read_image(filename):
"""
Returns desispec.image.Image object from input file
"""
fx = fits.open(filename, uint=True, memmap=False)
image = native_endian(fx['IMAGE'].data).astype(np.float64)
ivar = native_endian(fx['IVAR'].data).astype(np.float64)
mask = native_endian(fx['MASK'].data).astype(np.uint16)
camera = fx['IMAGE'].header['CAMERA'].lower()
meta = fx['IMAGE'].header
if 'READNOISE' in fx:
readnoise = native_endian(fx['READNOISE'].data).astype(np.float64)
else:
readnoise = fx['IMAGE'].header['RDNOISE']
return Image(image, ivar, mask=mask, readnoise=readnoise,
camera=camera, meta=meta)
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=0.8,
ccd_calibration_filename=None,
nocrosstalk=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()
calibration_data = None
if ccd_calibration_filename is None:
srch_file = "data/ccd/ccd_calibration.yaml"
if not resource_exists('desispec', srch_file):
log.error(
"Cannot find CCD calibration file {:s}".format(srch_file))
else:
ccd_calibration_filename = resource_filename('desispec', srch_file)
if ccd_calibration_filename is not None and ccd_calibration_filename is not False:
calibration_data = read_ccd_calibration(header, primary_header,
ccd_calibration_filename)
#- Get path to calibration data
if "DESI_CCD_CALIBRATION_DATA" in os.environ:
calibration_data_path = os.environ["DESI_CCD_CALIBRATION_DATA"]
else:
calibration_data_path = None
#- 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(calibration_data, calibration_data_path,
"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 calibration_data and "AMPLIFIERS" in calibration_data:
amp_ids = list(calibration_data["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(calibration_data, calibration_data_path,
"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(calibration_data, calibration_data_path,
"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 calibration_data and "EXPTIMEKEY" in calibration_data:
exptime_key = calibration_data["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])
#- 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 calibration_data and 'GAIN' + amp in calibration_data:
gain = float(calibration_data['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 calibration_data and 'SATURLEV' + amp in calibration_data:
saturlev = float(calibration_data['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 calibration_data and 'OVERSCAN' + amp in calibration_data and calibration_data[
"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
header['OBSRDN' + amp] = median_rdnoise
#- 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 calibration_data is None: continue
if not "CROSSTALK%s%s" % (amp1, amp2) in calibration_data:
continue
crosstalk = calibration_data["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(calibration_data, calibration_data_path,
"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
def simulate(night,
expid,
camera,
nspec=None,
verbose=False,
ncpu=None,
trimxy=False,
cosmics=None):
"""
Run pixel-level simulation of input spectra
Args:
night (string) : YEARMMDD
expid (integer) : exposure id
camera (str) : e.g. b0, r1, z9
nspec (int) : number of spectra to simulate
verbose (boolean) : if True, print status messages
ncpu (int) : number of CPU cores to use in parallel
trimxy (boolean) : trim image to just pixels with input signal
cosmics (str) : filename with dark images with cosmics to add
Reads:
$DESI_SPECTRO_SIM/$PIXPROD/{night}/simspec-{expid}.fits
Writes:
$DESI_SPECTRO_SIM/$PIXPROD/{night}/simpix-{camera}-{expid}.fits
$DESI_SPECTRO_SIM/$PIXPROD/{night}/pix-{camera}-{expid}.fits
"""
if verbose:
print "Reading input files"
channel = camera[0].lower()
ispec = int(camera[1])
assert channel in 'brz'
assert 0 <= ispec < 10
#- Load DESI parameters
params = desimodel.io.load_desiparams()
nfibers = params['spectro']['nfibers']
#- Load simspec file
simfile = io.findfile('simspec', night=night, expid=expid)
simspec = io.read_simspec(simfile)
wave = simspec.wave[channel]
if simspec.skyphot is not None:
phot = simspec.phot[channel] + simspec.skyphot[channel]
else:
phot = simspec.phot[channel]
if ispec * nfibers >= simspec.nspec:
print "ERROR: camera {} not in the {} spectra in {}/{}".format(
camera, simspec.nspec, night, os.path.basename(simfile))
return
#- Load PSF
psf = desimodel.io.load_psf(channel)
#- Trim to just the spectra for this spectrograph
if nspec is None:
ii = slice(nfibers * ispec, nfibers * (ispec + 1))
else:
ii = slice(nfibers * ispec, nfibers * ispec + nspec)
phot = phot[ii]
#- check if simulation has less than 500 input spectra
if phot.shape[0] < nspec:
nspec = phot.shape[0]
#- Project to image and append that to file
if verbose:
print "Projecting photons onto CCD"
img = parallel_project(psf, wave, phot, ncpu=ncpu)
if trimxy:
xmin, xmax, ymin, ymax = psf.xyrange((0, nspec), wave)
img = img[0:ymax, 0:xmax]
# img = img[ymin:ymax, xmin:xmax]
# hdr['CRVAL1'] = xmin+1
# hdr['CRVAL2'] = ymin+1
#- Prepare header
hdr = simspec.header
tmp = '/'.join(simfile.split('/')[-3:]) #- last 3 elements of path
hdr['SIMFILE'] = (tmp, 'Input simulation file')
#- Strip unnecessary keywords
for key in ('EXTNAME', 'LOGLAM', 'AIRORVAC', 'CRVAL1', 'CDELT1'):
if key in hdr:
del hdr[key]
#- Write noiseless output
simpixfile = io.findfile('simpix', night=night, expid=expid, camera=camera)
io.write_simpix(simpixfile, img, meta=hdr)
#- Add cosmics from library of dark images
#- in this case, don't add readnoise since the dark image already has it
if cosmics is not None:
cosmics = io.read_cosmics(cosmics, expid, shape=img.shape)
pix = np.random.poisson(img) + cosmics.pix
readnoise = cosmics.meta['RDNOISE']
ivar = 1.0 / (pix.clip(0) + readnoise**2)
mask = cosmics.mask
#- Or just add noise
else:
params = desimodel.io.load_desiparams()
channel = camera[0].lower()
readnoise = params['ccd'][channel]['readnoise']
pix = np.random.poisson(img) + np.random.normal(scale=readnoise,
size=img.shape)
ivar = 1.0 / (pix.clip(0) + readnoise**2)
mask = np.zeros(img.shape, dtype=np.uint16)
#- Metadata to be included in pix file header is in the fibermap header
#- TODO: this is fragile; consider updating fibermap to use astropy Table
#- that includes the header rather than directly assuming FITS as the
#- underlying format.
fibermapfile = desispec.io.findfile('fibermap', night=night, expid=expid)
fm, fmhdr = desispec.io.read_fibermap(fibermapfile, header=True)
meta = dict()
try:
meta['TELRA'] = simspec.header['TELRA']
meta['TELDEC'] = simspec.header['TELDEC']
except KeyError: #- temporary backwards compatibilty
meta['TELRA'] = fmhdr['TELERA']
meta['TELDEC'] = fmhdr['TELEDEC']
meta['TILEID'] = simspec.header['TILEID']
meta['DATE-OBS'] = simspec.header['DATE-OBS']
meta['FLAVOR'] = simspec.header['FLAVOR']
meta['EXPTIME'] = simspec.header['EXPTIME']
meta['AIRMASS'] = simspec.header['AIRMASS']
image = Image(pix,
ivar,
mask,
readnoise=readnoise,
camera=camera,
meta=meta)
pixfile = desispec.io.findfile('pix',
night=night,
camera=camera,
expid=expid)
desispec.io.write_image(pixfile, image)
if verbose:
print "Wrote " + pixfile
def simulate(camera, simspec, psf, nspec=None, ncpu=None,
cosmics=None, wavemin=None, wavemax=None, preproc=True, comm=None):
"""Run pixel-level simulation of input spectra
Args:
camera (string) : b0, r1, .. z9
simspec : desispec.io.SimSpec object from desispec.io.read_simspec()
psf : subclass of specter.psf.psf.PSF, e.g. from desimodel.io.load_psf()
Options:
nspec (int): number of spectra to simulate
ncpu (int): number of CPU cores to use in parallel
cosmics (desispec.image.Image): e.g. from desisim.io.read_cosmics()
wavemin (float): minimum wavelength range to simulate
wavemax (float): maximum wavelength range to simulate
preproc (boolean, optional) : also preprocess raw data (default True)
Returns:
(image, rawpix, truepix) tuple, where image is the preproc Image object
(only header is meaningful if preproc=False), rawpix is a 2D
ndarray of unprocessed raw pixel data, and truepix is a 2D ndarray
of truth for image.pix
"""
if (comm is None) or (comm.rank == 0):
log.info('Starting pixsim.simulate camera {} at {}'.format(camera,
asctime()))
#- parse camera name into channel and spectrograph number
channel = camera[0].lower()
ispec = int(camera[1])
assert channel in 'brz', \
'unrecognized channel {} camera {}'.format(channel, camera)
assert 0 <= ispec < 10, \
'unrecognized spectrograph {} camera {}'.format(ispec, camera)
assert len(camera) == 2, \
'unrecognized camera {}'.format(camera)
#- Load DESI parameters
params = desimodel.io.load_desiparams()
#- this is not necessarily true, the truth in is the fibermap
nfibers = params['spectro']['nfibers']
phot = simspec.cameras[camera].phot
if simspec.cameras[camera].skyphot is not None:
phot += simspec.cameras[camera].skyphot
if nspec is not None:
phot = phot[0:nspec]
else:
nspec = phot.shape[0]
#- Trim wavelengths if needed
wave = simspec.cameras[camera].wave
if wavemin is not None:
ii = (wave >= wavemin)
phot = phot[:, ii]
wave = wave[ii]
if wavemax is not None:
ii = (wave <= wavemax)
phot = phot[:, ii]
wave = wave[ii]
#- Project to image and append that to file
if (comm is None) or (comm.rank == 0):
log.info('Starting {} projection at {}'.format(camera, asctime()))
# The returned true pixel values will only exist on rank 0 in the
# MPI case. Otherwise it will be None.
truepix = parallel_project(psf, wave, phot, ncpu=ncpu, comm=comm)
if (comm is None) or (comm.rank == 0):
log.info('Finished {} projection at {}'.format(camera,
asctime()))
image = None
rawpix = None
if (comm is None) or (comm.rank == 0):
#- Start metadata header
header = simspec.header.copy()
header['CAMERA'] = camera
header['DOSVER'] = 'SIM'
header['FEEVER'] = 'SIM'
header['DETECTOR'] = 'SIM'
#- Add cosmics from library of dark images
ny = truepix.shape[0] // 2
nx = truepix.shape[1] // 2
if cosmics is not None:
# set to zeros values with mask bit 0 (= dead column or hot pixels)
cosmics_pix = cosmics.pix*((cosmics.mask&1)==0)
pix = np.random.poisson(truepix) + cosmics_pix
try: #- cosmics templates >= v0.3
rdnoiseA = cosmics.meta['OBSRDNA']
rdnoiseB = cosmics.meta['OBSRDNB']
rdnoiseC = cosmics.meta['OBSRDNC']
rdnoiseD = cosmics.meta['OBSRDND']
except KeyError: #- cosmics templates <= v0.2
print(cosmic.meta)
rdnoiseA = cosmics.meta['RDNOISE0']
rdnoiseB = cosmics.meta['RDNOISE1']
rdnoiseC = cosmics.meta['RDNOISE2']
rdnoiseD = cosmics.meta['RDNOISE3']
else:
pix = truepix
readnoise = params['ccd'][channel]['readnoise']
rdnoiseA = rdnoiseB = rdnoiseC = rdnoiseD = readnoise
#- data already has noise if cosmics were added
noisydata = (cosmics is not None)
#- Split by amplifier and expand into raw data
nprescan = params['ccd'][channel]['prescanpixels']
if 'overscanpixels' in params['ccd'][channel]:
noverscan = params['ccd'][channel]['overscanpixels']
else:
noverscan = 50
#- Reproducibly random overscan bias level offsets across diff exp
assert channel in 'brz'
if channel == 'b':
irand = ispec
elif channel == 'r':
irand = 10 + ispec
elif channel == 'z':
irand = 20 + ispec
seeds = np.random.RandomState(0).randint(2**32-1, size=30)
rand = np.random.RandomState(seeds[irand])
nyraw = ny
nxraw = nx + nprescan + noverscan
rawpix = np.empty( (nyraw*2, nxraw*2), dtype=np.int32 )
gain = params['ccd'][channel]['gain']
#- Amp A/1 Lower Left
rawpix[0:nyraw, 0:nxraw] = \
photpix2raw(pix[0:ny, 0:nx], gain, rdnoiseA,
readorder='lr', nprescan=nprescan, noverscan=noverscan,
offset=rand.uniform(100, 200),
noisydata=noisydata)
#- Amp B/2 Lower Right
rawpix[0:nyraw, nxraw:nxraw+nxraw] = \
photpix2raw(pix[0:ny, nx:nx+nx], gain, rdnoiseB,
readorder='rl', nprescan=nprescan, noverscan=noverscan,
offset=rand.uniform(100, 200),
noisydata=noisydata)
#- Amp C/3 Upper Left
rawpix[nyraw:nyraw+nyraw, 0:nxraw] = \
photpix2raw(pix[ny:ny+ny, 0:nx], gain, rdnoiseC,
readorder='lr', nprescan=nprescan, noverscan=noverscan,
offset=rand.uniform(100, 200),
noisydata=noisydata)
#- Amp D/4 Upper Right
rawpix[nyraw:nyraw+nyraw, nxraw:nxraw+nxraw] = \
photpix2raw(pix[ny:ny+ny, nx:nx+nx], gain, rdnoiseD,
readorder='rl', nprescan=nprescan, noverscan=noverscan,
offset=rand.uniform(100, 200),
noisydata=noisydata)
def xyslice2header(xyslice):
'''
convert 2D slice into IRAF style [a:b,c:d] header value
e.g. xyslice2header(np.s_[0:10, 5:20]) -> '[6:20,1:10]'
'''
yy, xx = xyslice
value = '[{}:{},{}:{}]'.format(xx.start+1, xx.stop,
yy.start+1, yy.stop)
return value
#- Amp order from DESI-1964 (previously 1-4 instead of A-D)
#- C D
#- A B
xoffset = nprescan+nx+noverscan
header['PRESECA'] = xyslice2header(np.s_[0:nyraw, 0:0+nprescan])
header['DATASECA'] = xyslice2header(np.s_[0:nyraw, nprescan:nprescan+nx])
header['BIASSECA'] = xyslice2header(np.s_[0:nyraw, nprescan+nx:nprescan+nx+noverscan])
header['CCDSECA'] = xyslice2header(np.s_[0:ny, 0:nx])
header['PRESECB'] = xyslice2header(np.s_[0:nyraw, xoffset+noverscan+nx:xoffset+noverscan+nx+nprescan])
header['DATASECB'] = xyslice2header(np.s_[0:nyraw, xoffset+noverscan:xoffset+noverscan+nx])
header['BIASSECB'] = xyslice2header(np.s_[0:nyraw, xoffset:xoffset+noverscan])
header['CCDSECB'] = xyslice2header(np.s_[0:ny, nx:2*nx])
header['PRESECC'] = xyslice2header(np.s_[nyraw:2*nyraw, 0:0+nprescan])
header['DATASECC'] = xyslice2header(np.s_[nyraw:2*nyraw, nprescan:nprescan+nx])
header['BIASSECC'] = xyslice2header(np.s_[nyraw:2*nyraw, nprescan+nx:nprescan+nx+noverscan])
header['CCDSECC'] = xyslice2header(np.s_[ny:2*ny, 0:nx])
header['PRESECD'] = xyslice2header(np.s_[nyraw:2*nyraw, xoffset+noverscan+nx:xoffset+noverscan+nx+nprescan])
header['DATASECD'] = xyslice2header(np.s_[nyraw:2*nyraw, xoffset+noverscan:xoffset+noverscan+nx])
header['BIASSECD'] = xyslice2header(np.s_[nyraw:2*nyraw, xoffset:xoffset+noverscan])
header['CCDSECD'] = xyslice2header(np.s_[ny:2*ny, nx:2*nx])
#- Add additional keywords to mimic real raw data
header['INSTRUME'] = 'DESI'
header['PROCTYPE'] = 'RAW'
header['PRODTYPE'] = 'image'
header['EXPFRAME'] = 0
header['REQTIME'] = simspec.header['EXPTIME']
header['TIMESYS'] = 'UTC'
#- DATE-OBS format YEAR-MM-DDThh:mm:ss.sss -> OBSID kpnoYEARMMDDthhmmss
header['OBSID']='kp4m'+header['DATE-OBS'][0:19].replace('-','').replace(':','').lower()
header['TIME-OBS'] = header['DATE-OBS'].split('T')[1]
header['DELTARA'] = 0.0
header['DELTADEC'] = 0.0
header['SPECGRPH'] = ispec
header['CCDNAME'] = 'CCDS' + str(ispec) + str(channel).upper()
header['CCDPREP'] = 'purge,clear'
header['CCDSIZE'] = str(rawpix.shape)
header['CCDTEMP'] = 850.0
header['CPUTEMP'] = 63.7
header['CASETEMP'] = 62.8
header['CCDTMING'] = 'sim_timing.txt'
header['CCDCFG'] = 'sim.cfg'
header['SETTINGS'] = 'sim_detectors.json'
header['VESSEL'] = 7 #- I don't know what this is
header['FEEBOX'] = 'sim097'
header['PGAGAIN'] = 5
header['OCSVER'] = 'SIM'
header['CONSTVER'] = 'SIM'
header['BLDTIME'] = 0.35
header['DIGITIME'] = 61.9
#- Remove some spurious header keywords from upstream
if 'BUNIT' in header and header['BUNIT'] == 'Angstrom':
del header['BUNIT']
if 'MJD' in header and 'MJD-OBS' not in header:
header['MJD-OBS'] = header['MJD']
del header['MJD']
for key in ['RA', 'DEC']:
if key in header:
del header[key]
#- Drive MJD-OBS from DATE-OBS if needed
if 'MJD-OBS' not in header:
header['MJD-OBS'] = Time(header['DATE-OBS']).mjd
#- from http://www-kpno.kpno.noao.edu/kpno-misc/mayall_params.html
kpno_longitude = -(111. + 35/60. + 59.6/3600) * u.deg
#- Convert DATE-OBS to sexigesimal (sigh) Local Sidereal Time
#- Use mean ST as close enough for sims to avoid nutation calc
t = Time(header['DATE-OBS'])
st = t.sidereal_time('mean', kpno_longitude).to('deg').value
hour = st/15
minute = (hour % 1)*60
second = (minute % 1)*60
header['ST'] = '{:02d}:{:02d}:{:0.3f}'.format(
int(hour), int(minute), second)
if preproc:
log.debug('Running preprocessing at {}'.format(asctime()))
image = desispec.preproc.preproc(rawpix, header, primary_header=simspec.header)
else:
log.debug('Skipping preprocessing')
image = Image(np.zeros(truepix.shape), np.zeros(truepix.shape), meta=header)
if (comm is None) or (comm.rank == 0):
log.info('Finished pixsim.simulate for camera {} at {}'.format(camera,
asctime()))
return image, rawpix, truepix
def test_camera(self):
image = Image(self.pix, self.ivar)
self.assertEqual(image.camera, 'unknown')
image = Image(self.pix, self.ivar, camera='b0')
self.assertEqual(image.camera, 'b0')
def test_init(self):
image = Image(self.pix, self.ivar)
self.assertTrue(np.all(image.pix == self.pix))
self.assertTrue(np.all(image.ivar == self.ivar))
def read_cosmics(filename, expid=1, shape=None, jitter=True):
"""
Reads a dark image with cosmics from the input filename.
The input might have multiple dark images; use the `expid%n` image where
`n` is the number of images in the input cosmics file.
Args:
filename : FITS filename with EXTNAME=IMAGE-*, IVAR-*, MASK-* HDUs
expid : integer, use `expid % n` image where `n` is number of images
shape : (ny, nx, optional) tuple for output image shape
jitter (bool, optional): If True (default), apply random flips and rolls so you
don't get the exact same cosmics every time
Returns:
`desisim.image.Image` object with attributes pix, ivar, mask
"""
fx = fits.open(filename)
imagekeys = list()
for i in range(len(fx)):
if fx[i].name.startswith('IMAGE-'):
imagekeys.append(fx[i].name.split('-', 1)[1])
assert len(imagekeys) > 0, 'No IMAGE-* extensions found in ' + filename
i = expid % len(imagekeys)
pix = native_endian(fx['IMAGE-' + imagekeys[i]].data.astype(np.float64))
ivar = native_endian(fx['IVAR-' + imagekeys[i]].data.astype(np.float64))
mask = native_endian(fx['MASK-' + imagekeys[i]].data)
meta = fx['IMAGE-' + imagekeys[i]].header
meta['CRIMAGE'] = (imagekeys[i], 'input cosmic ray image')
#- De-trend each amplifier
nx = pix.shape[1] // 2
ny = pix.shape[0] // 2
kernel_size = min(201, ny // 3, nx // 3)
pix[0:ny, 0:nx] -= spline_medfilt2d(pix[0:ny, 0:nx], kernel_size)
pix[0:ny, nx:2 * nx] -= spline_medfilt2d(pix[0:ny, nx:2 * nx], kernel_size)
pix[ny:2 * ny, 0:nx] -= spline_medfilt2d(pix[ny:2 * ny, 0:nx], kernel_size)
pix[ny:2 * ny, nx:2 * nx] -= spline_medfilt2d(pix[ny:2 * ny, nx:2 * nx],
kernel_size)
if shape is not None:
if len(shape) != 2: raise ValueError('Invalid shape {}'.format(shape))
pix = _resize(pix, shape)
ivar = _resize(ivar, shape)
mask = _resize(mask, shape)
if jitter:
#- Randomly flip left-right and/or up-down
if np.random.uniform(0, 1) > 0.5:
pix = np.fliplr(pix)
ivar = np.fliplr(ivar)
mask = np.fliplr(mask)
meta['CRFLIPLR'] = (True, 'Input cosmics image flipped Left/Right')
else:
meta['CRFLIPLR'] = (False,
'Input cosmics image NOT flipped Left/Right')
if np.random.uniform(0, 1) > 0.5:
pix = np.flipud(pix)
ivar = np.flipud(ivar)
mask = np.flipud(mask)
meta['CRFLIPUD'] = (True, 'Input cosmics image flipped Up/Down')
else:
meta['CRFLIPUD'] = (False,
'Input cosmics image NOT flipped Up/Down')
#- Randomly roll image a bit
nx, ny = np.random.randint(-100, 100, size=2)
pix = np.roll(np.roll(pix, ny, axis=0), nx, axis=1)
ivar = np.roll(np.roll(ivar, ny, axis=0), nx, axis=1)
mask = np.roll(np.roll(mask, ny, axis=0), nx, axis=1)
meta['CRSHIFTX'] = (nx, 'Input cosmics image shift in x')
meta['CRSHIFTY'] = (nx, 'Input cosmics image shift in y')
else:
meta['CRFLIPLR'] = (False,
'Input cosmics image NOT flipped Left/Right')
meta['CRFLIPUD'] = (False, 'Input cosmics image NOT flipped Up/Down')
meta['CRSHIFTX'] = (0, 'Input cosmics image shift in x')
meta['CRSHIFTY'] = (0, 'Input cosmics image shift in y')
del meta['RDNOISE0']
#- Amp 1 lower left
nx = pix.shape[1] // 2
ny = pix.shape[0] // 2
iixy = np.s_[0:ny, 0:nx]
cx = pix[iixy][mask[iixy] == 0]
mean, median, std = sigma_clipped_stats(cx, sigma=3, iters=5)
meta['RDNOISE1'] = std
#- Amp 2 lower right
iixy = np.s_[0:ny, nx:2 * nx]
cx = pix[iixy][mask[iixy] == 0]
mean, median, std = sigma_clipped_stats(cx, sigma=3, iters=5)
meta['RDNOISE2'] = std
#- Amp 3 upper left
iixy = np.s_[ny:2 * ny, 0:nx]
mean, median, std = sigma_clipped_stats(pix[iixy], sigma=3, iters=5)
meta['RDNOISE3'] = std
#- Amp 4 upper right
iixy = np.s_[ny:2 * ny, nx:2 * nx]
mean, median, std = sigma_clipped_stats(pix[iixy], sigma=3, iters=5)
meta['RDNOISE4'] = std
fx.close()
return Image(pix, ivar, mask, meta=meta)
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 test_slicing(self):
meta = dict(blat='foo',
NAXIS1=self.pix.shape[1],
NAXIS2=self.pix.shape[0])
img1 = Image(self.pix, self.ivar, meta=meta)
nx, ny = 2, 3
img2 = img1[0:ny, 0:nx]
self.assertEqual(img2.pix.shape[0], ny)
self.assertEqual(img2.pix.shape[1], nx)
self.assertTrue(img2.pix.shape == img2.ivar.shape)
self.assertTrue(img2.pix.shape == img2.mask.shape)
self.assertEqual(img1.camera, img2.camera)
self.assertEqual(img1.readnoise, img2.readnoise)
for key in img1.meta:
if key != 'NAXIS1' and key != 'NAXIS2':
self.assertEqual(img1.meta[key], img2.meta[key])
self.assertFalse(img1.meta is img2.meta)
self.assertEqual(img2.meta['NAXIS1'], nx)
self.assertEqual(img2.meta['NAXIS2'], ny)
#- also works for non-None mask and meta=None
img1 = Image(self.pix, self.ivar, mask=(self.ivar == 0))
nx, ny = 2, 3
img2 = img1[0:ny, 0:nx]
self.assertEqual(img2.pix.shape[0], ny)
self.assertEqual(img2.pix.shape[1], nx)
self.assertTrue(img2.pix.shape == img2.ivar.shape)
self.assertTrue(img2.pix.shape == img2.mask.shape)
#- Slice and dice multiple ways, getting meta NAXIS1/NAXIS2 correct
img1 = Image(self.pix, self.ivar, meta=meta)
img2 = img1[0:ny]
self.assertEqual(img2.pix.shape[0], ny)
self.assertEqual(img2.pix.shape[1], img1.pix.shape[1])
self.assertEqual(img2.pix.shape[0], img2.meta['NAXIS2'])
self.assertEqual(img2.pix.shape[1], img2.meta['NAXIS1'])
img2 = img1[0:ny, :]
self.assertEqual(img2.pix.shape[0], ny)
self.assertEqual(img2.pix.shape[1], img1.pix.shape[1])
self.assertEqual(img2.pix.shape[0], img2.meta['NAXIS2'])
self.assertEqual(img2.pix.shape[1], img2.meta['NAXIS1'])
img2 = img1[:, 0:nx]
self.assertEqual(img2.pix.shape[0], img1.pix.shape[0])
self.assertEqual(img2.pix.shape[1], nx)
self.assertEqual(img2.pix.shape[0], img2.meta['NAXIS2'])
self.assertEqual(img2.pix.shape[1], img2.meta['NAXIS1'])
#- test slicing readnoise image
readnoise = np.random.uniform(size=self.pix.shape)
img1 = Image(self.pix, self.ivar, meta=meta, readnoise=readnoise)
xy = np.s_[1:1 + ny, 2:2 + nx]
img2 = img1[xy]
self.assertTrue(np.all(img1.pix[xy] == img2.pix))
self.assertTrue(np.all(img1.ivar[xy] == img2.ivar))
self.assertTrue(np.all(img1.mask[xy] == img2.mask))
self.assertTrue(np.all(img1.readnoise[xy] == img2.readnoise))
#- Test bad slicing
with self.assertRaises(ValueError):
img1['blat']
with self.assertRaises(ValueError):
img1[1:2, 3:4, 5:6]
with self.assertRaises(ValueError):
img1[1:2, 'blat']
with self.assertRaises(ValueError):
img1[None, 1:2]
def simulate(camera,
simspec,
psf,
nspec=None,
ncpu=None,
cosmics=None,
wavemin=None,
wavemax=None,
preproc=True,
comm=None):
"""Run pixel-level simulation of input spectra
Args:
camera (string) : b0, r1, .. z9
simspec : desispec.io.SimSpec object from desispec.io.read_simspec()
psf : subclass of specter.psf.psf.PSF, e.g. from desimodel.io.load_psf()
Options:
nspec (int): number of spectra to simulate
ncpu (int): number of CPU cores to use in parallel
cosmics (desispec.image.Image): e.g. from desisim.io.read_cosmics()
wavemin (float): minimum wavelength range to simulate
wavemax (float): maximum wavelength range to simulate
preproc (boolean, optional) : also preprocess raw data (default True)
Returns:
(image, rawpix, truepix) tuple, where image is the preproc Image object
(only header is meaningful if preproc=False), rawpix is a 2D
ndarray of unprocessed raw pixel data, and truepix is a 2D ndarray
of truth for image.pix
"""
if (comm is None) or (comm.rank == 0):
log.info('Starting pixsim.simulate camera {} at {}'.format(
camera, asctime()))
#- parse camera name into channel and spectrograph number
channel = camera[0].lower()
ispec = int(camera[1])
assert channel in 'brz', \
'unrecognized channel {} camera {}'.format(channel, camera)
assert 0 <= ispec < 10, \
'unrecognized spectrograph {} camera {}'.format(ispec, camera)
assert len(camera) == 2, \
'unrecognized camera {}'.format(camera)
#- Load DESI parameters
params = desimodel.io.load_desiparams()
#- this is not necessarily true, the truth in is the fibermap
nfibers = params['spectro']['nfibers']
phot = simspec.cameras[camera].phot
if simspec.cameras[camera].skyphot is not None:
phot += simspec.cameras[camera].skyphot
if nspec is not None:
phot = phot[0:nspec]
else:
nspec = phot.shape[0]
#- Trim wavelengths if needed
wave = simspec.cameras[camera].wave
if wavemin is not None:
ii = (wave >= wavemin)
phot = phot[:, ii]
wave = wave[ii]
if wavemax is not None:
ii = (wave <= wavemax)
phot = phot[:, ii]
wave = wave[ii]
#- Project to image and append that to file
if (comm is None) or (comm.rank == 0):
log.info('Starting {} projection at {}'.format(camera, asctime()))
# The returned true pixel values will only exist on rank 0 in the
# MPI case. Otherwise it will be None.
truepix = parallel_project(psf, wave, phot, ncpu=ncpu, comm=comm)
if (comm is None) or (comm.rank == 0):
log.info('Finished {} projection at {}'.format(camera, asctime()))
image = None
rawpix = None
if (comm is None) or (comm.rank == 0):
#- Start metadata header
header = simspec.header.copy()
header['CAMERA'] = camera
header['DOSVER'] = 'SIM'
header['FEEVER'] = 'SIM'
header['DETECTOR'] = 'SIM'
gain = params['ccd'][channel]['gain']
for amp in ('1', '2', '3', '4'):
header['GAIN' + amp] = gain
#- Add cosmics from library of dark images
ny = truepix.shape[0] // 2
nx = truepix.shape[1] // 2
if cosmics is not None:
# set to zeros values with mask bit 0 (= dead column or hot pixels)
cosmics_pix = cosmics.pix * ((cosmics.mask & 1) == 0)
pix = np.random.poisson(truepix) + cosmics_pix
header['RDNOISE1'] = cosmics.meta['RDNOISE1']
header['RDNOISE2'] = cosmics.meta['RDNOISE2']
header['RDNOISE3'] = cosmics.meta['RDNOISE3']
header['RDNOISE4'] = cosmics.meta['RDNOISE4']
else:
pix = truepix
readnoise = params['ccd'][channel]['readnoise']
header['RDNOISE1'] = readnoise
header['RDNOISE2'] = readnoise
header['RDNOISE3'] = readnoise
header['RDNOISE4'] = readnoise
# if (comm is None) or (comm.rank == 0):
# log.info('RDNOISE1 {}'.format(header['RDNOISE1']))
# log.info('RDNOISE2 {}'.format(header['RDNOISE2']))
# log.info('RDNOISE3 {}'.format(header['RDNOISE3']))
# log.info('RDNOISE4 {}'.format(header['RDNOISE4']))
#- data already has noise if cosmics were added
noisydata = (cosmics is not None)
#- Split by amplifier and expand into raw data
nprescan = params['ccd'][channel]['prescanpixels']
if 'overscanpixels' in params['ccd'][channel]:
noverscan = params['ccd'][channel]['overscanpixels']
else:
noverscan = 50
nyraw = ny
nxraw = nx + nprescan + noverscan
rawpix = np.empty((nyraw * 2, nxraw * 2), dtype=np.int32)
#- Amp 0 Lower Left
rawpix[0:nyraw, 0:nxraw] = \
photpix2raw(pix[0:ny, 0:nx], gain, header['RDNOISE1'],
readorder='lr', nprescan=nprescan, noverscan=noverscan,
noisydata=noisydata)
#- Amp 2 Lower Right
rawpix[0:nyraw, nxraw:nxraw+nxraw] = \
photpix2raw(pix[0:ny, nx:nx+nx], gain, header['RDNOISE2'],
readorder='rl', nprescan=nprescan, noverscan=noverscan,
noisydata=noisydata)
#- Amp 3 Upper Left
rawpix[nyraw:nyraw+nyraw, 0:nxraw] = \
photpix2raw(pix[ny:ny+ny, 0:nx], gain, header['RDNOISE3'],
readorder='lr', nprescan=nprescan, noverscan=noverscan,
noisydata=noisydata)
#- Amp 4 Upper Right
rawpix[nyraw:nyraw+nyraw, nxraw:nxraw+nxraw] = \
photpix2raw(pix[ny:ny+ny, nx:nx+nx], gain, header['RDNOISE4'],
readorder='rl', nprescan=nprescan, noverscan=noverscan,
noisydata=noisydata)
def xyslice2header(xyslice):
'''
convert 2D slice into IRAF style [a:b,c:d] header value
e.g. xyslice2header(np.s_[0:10, 5:20]) -> '[6:20,1:10]'
'''
yy, xx = xyslice
value = '[{}:{},{}:{}]'.format(xx.start + 1, xx.stop, yy.start + 1,
yy.stop)
return value
#- Amp order from DESI-1964
#- 3 4
#- 1 2
xoffset = nprescan + nx + noverscan
header['PRESEC1'] = xyslice2header(np.s_[0:nyraw, 0:0 + nprescan])
header['DATASEC1'] = xyslice2header(np.s_[0:nyraw,
nprescan:nprescan + nx])
header['BIASSEC1'] = xyslice2header(
np.s_[0:nyraw, nprescan + nx:nprescan + nx + noverscan])
header['CCDSEC1'] = xyslice2header(np.s_[0:ny, 0:nx])
header['PRESEC2'] = xyslice2header(
np.s_[0:nyraw, xoffset + noverscan + nx:xoffset + noverscan + nx +
nprescan])
header['DATASEC2'] = xyslice2header(
np.s_[0:nyraw, xoffset + noverscan:xoffset + noverscan + nx])
header['BIASSEC2'] = xyslice2header(np.s_[0:nyraw,
xoffset:xoffset + noverscan])
header['CCDSEC2'] = xyslice2header(np.s_[0:ny, nx:2 * nx])
header['PRESEC3'] = xyslice2header(np.s_[nyraw:2 * nyraw,
0:0 + nprescan])
header['DATASEC3'] = xyslice2header(np.s_[nyraw:2 * nyraw,
nprescan:nprescan + nx])
header['BIASSEC3'] = xyslice2header(
np.s_[nyraw:2 * nyraw, nprescan + nx:nprescan + nx + noverscan])
header['CCDSEC3'] = xyslice2header(np.s_[ny:2 * ny, 0:nx])
header['PRESEC4'] = xyslice2header(
np.s_[nyraw:2 * nyraw, xoffset + noverscan + nx:xoffset +
noverscan + nx + nprescan])
header['DATASEC4'] = xyslice2header(
np.s_[nyraw:2 * nyraw,
xoffset + noverscan:xoffset + noverscan + nx])
header['BIASSEC4'] = xyslice2header(np.s_[nyraw:2 * nyraw,
xoffset:xoffset + noverscan])
header['CCDSEC4'] = xyslice2header(np.s_[ny:2 * ny, nx:2 * nx])
if preproc:
log.debug('Running preprocessing at {}'.format(asctime()))
image = desispec.preproc.preproc(rawpix,
header,
primary_header=simspec.header)
else:
log.debug('Skipping preprocessing')
image = Image(np.zeros(rawpix.shape),
np.zeros(rawpix.shape),
meta=header)
if (comm is None) or (comm.rank == 0):
log.info('Finished pixsim.simulate for camera {} at {}'.format(
camera, asctime()))
return image, rawpix, truepix
def test_psf_with_bad_column(self):
'''test a PSF-like spot with a masked column going through it'''
image = Image(self.psfpix, self.ivar, mask=self.badmask, camera="r0")
image.pix[self.badmask>0] = 0
rejected=reject_cosmic_rays_ala_sdss(image,dilate=False)
self.assertTrue(not np.any(rejected))
def read_cosmics(filename, expid=1, shape=None, jitter=True):
"""
Reads a dark image with cosmics from the input filename.
The input might have multiple dark images; use the `expid%n` image where
`n` is the number of images in the input cosmics file.
Args:
filename : FITS filename with EXTNAME=IMAGE-*, IVAR-*, MASK-* HDUs
expid : integer, use `expid % n` image where `n` is number of images
Optional:
shape : (ny, nx) tuple for output image shape
jitter : If True (default), apply random flips and rolls so you
don't get the exact same cosmics every time
Returns:
`desisim.image.Image` object with attributes pix, ivar, mask
"""
fx = fits.open(filename)
imagekeys = list()
for i in range(len(fx)):
if fx[i].name.startswith('IMAGE-'):
imagekeys.append(fx[i].name.split('-', 1)[1])
assert len(imagekeys) > 0, 'No IMAGE-* extensions found in ' + filename
i = expid % len(imagekeys)
pix = native_endian(fx['IMAGE-' + imagekeys[i]].data.astype(np.float64))
ivar = native_endian(fx['IVAR-' + imagekeys[i]].data.astype(np.float64))
mask = native_endian(fx['MASK-' + imagekeys[i]].data)
meta = fx['IMAGE-' + imagekeys[i]].header
meta['CRIMAGE'] = (imagekeys[i], 'input cosmic ray image')
if shape is not None:
if len(shape) != 2: raise ValueError('Invalid shape {}'.format(shape))
newpix = np.empty(shape, dtype=np.float64)
ny = min(shape[0], pix.shape[0])
nx = min(shape[1], pix.shape[1])
newpix[0:ny, 0:nx] = pix[0:ny, 0:nx]
pix = _resize(pix, shape)
ivar = _resize(ivar, shape)
mask = _resize(mask, shape)
if jitter:
#- Randomly flip left-right and/or up-down
if np.random.uniform(0, 1) > 0.5:
pix = np.fliplr(pix)
ivar = np.fliplr(ivar)
mask = np.fliplr(mask)
meta['CRFLIPLR'] = (True, 'Input cosmics image flipped Left/Right')
else:
meta['CRFLIPLR'] = (False,
'Input cosmics image NOT flipped Left/Right')
if np.random.uniform(0, 1) > 0.5:
pix = np.flipud(pix)
ivar = np.flipud(ivar)
mask = np.flipud(mask)
meta['CRFLIPUD'] = (True, 'Input cosmics image flipped Up/Down')
else:
meta['CRFLIPUD'] = (True,
'Input cosmics image NOT flipped Up/Down')
#- Randomly roll image a bit
nx, ny = np.random.randint(-200, 200, size=2)
pix = np.roll(np.roll(pix, ny, axis=0), nx, axis=1)
ivar = np.roll(np.roll(ivar, ny, axis=0), nx, axis=1)
mask = np.roll(np.roll(mask, ny, axis=0), nx, axis=1)
meta['CRSHIFTX'] = (nx, 'Input cosmics image shift in x')
meta['CRSHIFTY'] = (nx, 'Input cosmics image shift in y')
else:
meta['CRFLIPLR'] = (False,
'Input cosmics image NOT flipped Left/Right')
meta['CRFLIPUD'] = (True, 'Input cosmics image NOT flipped Up/Down')
meta['CRSHIFTX'] = (0, 'Input cosmics image shift in x')
meta['CRSHIFTY'] = (0, 'Input cosmics image shift in y')
#- RDNOISEn -> average RDNOISE
if 'RDNOISE' not in meta:
x = meta['RDNOISE0'] + meta['RDNOISE1'] + meta['RDNOISE2'] + meta[
'RDNOISE3']
meta['RDNOISE'] = x / 4.0
return Image(pix, ivar, mask, meta=meta)
def preproc(rawimage, header, bias=False, pixflat=False, mask=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 = 1, 2, 3, 4 for each of the 4 amplifiers.
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
DATE-OBS is required in header if bias, pixflat, or mask=True
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.
'''
#- TODO: Check for required keywords first
#- Subtract bias image
camera = header['CAMERA'].lower()
if bias is not False and bias is not None:
if bias is True:
#- use default bias file for this camera/night
dateobs = header['DATE-OBS']
bias = read_bias(camera=camera, dateobs=dateobs)
elif isinstance(bias, (str, unicode)):
#- treat as filename
bias = read_bias(filename=bias)
if bias.shape == rawimage.shape:
rawimage = rawimage - bias
else:
raise ValueError('shape mismatch bias {} != rawimage {}'.format(
bias.shape, rawimage.shape))
#- Output arrays
yy, xx = _parse_sec_keyword(header['CCDSEC4']) #- 4 = upper right
image = np.zeros((yy.stop, xx.stop))
readnoise = np.zeros_like(image)
for amp in ['1', '2', '3', '4']:
ii = _parse_sec_keyword(header['BIASSEC' + amp])
#- Initial teststand data may be missing GAIN* keywords; don't crash
if 'GAIN' + amp in header:
gain = header['GAIN' + amp] #- gain = electrons / ADU
else:
log.error('Missing keyword GAIN{}; using 1.0'.format(amp))
gain = 1.0
overscan, rdnoise = _overscan(rawimage[ii])
rdnoise *= gain
kk = _parse_sec_keyword(header['CCDSEC' + amp])
readnoise[kk] = rdnoise
header['OVERSCN' + amp] = overscan
header['OBSRDN' + amp] = rdnoise
#- Warn/error if measured readnoise is very different from expected
if 'RDNOISE' + amp in header:
expected_readnoise = header['RDNOISE' + amp]
if rdnoise < 0.5 * expected_readnoise:
log.error(
'Amp {} measured readnoise {:.2f} < 0.5 * expected readnoise {:.2f}'
.format(amp, rdnoise, expected_readnoise))
elif rdnoise < 0.9 * expected_readnoise:
log.warn(
'Amp {} measured readnoise {:.2f} < 0.9 * expected readnoise {:.2f}'
.format(amp, rdnoise, expected_readnoise))
elif rdnoise > 2.0 * expected_readnoise:
log.error(
'Amp {} measured readnoise {:.2f} > 2 * expected readnoise {:.2f}'
.format(amp, rdnoise, expected_readnoise))
elif rdnoise > 1.2 * expected_readnoise:
log.warn(
'Amp {} measured readnoise {:.2f} > 1.2 * expected readnoise {:.2f}'
.format(amp, rdnoise, expected_readnoise))
else:
log.warn('Expected readnoise keyword {} missing'.format('RDNOISE' +
amp))
#- subtract overscan from data region and apply gain
jj = _parse_sec_keyword(header['DATASEC' + amp])
data = rawimage[jj] - overscan
image[kk] = data * gain
#- Load mask
if mask is not False and mask is not None:
if mask is True:
dateobs = header['DATE-OBS']
mask = read_mask(camera=camera, dateobs=dateobs)
elif isinstance(mask, (str, unicode)):
mask = read_mask(filename=mask)
else:
mask = np.zeros(image.shape, dtype=np.int32)
if mask.shape != image.shape:
raise ValueError('shape mismatch mask {} != image {}'.format(
mask.shape, image.shape))
#- Divide by pixflat image
if pixflat is not False and pixflat is not None:
if pixflat is True:
dateobs = header['DATE-OBS']
pixflat = read_pixflat(camera=camera, dateobs=dateobs)
elif isinstance(pixflat, (str, unicode)):
pixflat = read_pixflat(filename=pixflat)
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 = 1.0 / var
img = Image(image,
ivar=ivar,
mask=mask,
meta=header,
readnoise=readnoise,
camera=camera)
#- update img.mask to mask cosmic rays
cosmics.reject_cosmic_rays(img)
return img