def make_reference(files, reg, params, reference_image='ref.fits'):
seeing = {}
sky = {}
ref_seeing = 1000
#
# Have we specified the files to make the reference with?
#
if params.ref_include_file:
ref_list = []
for line in open(params.ref_include_file, 'r'):
for f in files:
if f.name == line.split()[0]:
ref_list.append(f)
print f.name, f.fw, f.signal
if f.fw < ref_seeing:
ref_sky = f.sky
ref_seeing = f.fw
best_seeing_ref = f
else:
#
# We try to choose the best images
#
reference_exclude = []
if params.ref_exclude_file:
for line in open(params.ref_exclude_file, 'r'):
reference_exclude.append(line.split()[0])
sig = []
for f in files:
sig.append(f.signal)
sig = np.asarray(sig)
sigcut = np.mean(sig) - 2.0 * np.std(sig)
print 'signal: mean, std, cut = ', np.mean(sig), np.std(sig), sigcut
print 'Searching for lowest-background image'
ref_sky = 1.e6
for f in files:
print f.name, f.fw, f.sky, f.signal
if (f.sky < ref_sky) and (f.fw > params.reference_min_seeing) and \
(f.roundness < params.reference_max_roundness) and (f.signal > sigcut) and not(f.name in reference_exclude):
ref_sky = f.sky
ref_sky = np.max([ref_sky, params.pixel_max / 1.e5])
print 'Searching for best-seeing image'
ref_seeing = 100.0
for f in files:
print f.name, f.fw, f.sky, f.signal
if (f.fw < ref_seeing) and (f.fw > params.reference_min_seeing) and \
(f.roundness < params.reference_max_roundness) and (f.signal > sigcut) and not(f.name in reference_exclude):
ref_seeing = f.fw
best_seeing_ref = f
ref_list = []
while len(ref_list) < params.min_ref_images:
ref_list = []
print 'Reference FWHM = ', ref_seeing
print 'Cutoff FWHM for reference = ', params.reference_seeing_factor * ref_seeing
print 'Cutoff background for reference = ', params.reference_sky_factor * ref_sky
print 'Combining for reference:'
for f in files:
if (f.fw < params.reference_seeing_factor*ref_seeing) and \
(f.roundness < params.reference_max_roundness) and (f.sky < params.reference_sky_factor*ref_sky) and \
(f.fw > params.reference_min_seeing) and (f.signal > sigcut) and not(f.name in reference_exclude):
ref_list.append(f)
print f.name, f.fw, f.sky, f.signal
params.reference_seeing_factor *= 1.02
params.reference_sky_factor *= 1.02
if len(ref_list) > params.max_ref_images:
ref_list = ref_list[:params.max_ref_images]
sig = []
for f in ref_list:
sig.append(f.signal)
sig = np.asarray(sig)
sigcut = np.mean(sig) - 2 * np.std(sig)
print 'signal: mean, std, cut = ', np.mean(sig), np.std(sig), sigcut
ref_seeing = 1000
ref_roundness = 2.0
for f in ref_list:
if (f.fw < ref_seeing) and (f.signal > sigcut):
ref_sky = f.sky
ref_seeing = f.fw
ref_roundness = f.roundness
best_seeing_ref = f
#
# Which ref image has the worst seeing?
#
worst_seeing = 0.0
for f in ref_list:
if f.fw > worst_seeing:
worst_seeing = f.fw
worst_seeing_ref = f
if params.ref_image_list:
with open(params.loc_output + os.path.sep + params.ref_image_list,
'w') as fid:
for f in ref_list:
fid.write(f.name + ' ' + str(f.fw) + ' ' + str(f.sky) +
' ' + str(f.signal) + '\n')
#
# Find the locations of the brightest stars to use as stamp positions
# if required
#
stamp_positions = None
if params.use_stamps:
stars = PH.choose_stamps(best_seeing_ref, params)
stamp_positions = stars[:, 0:2]
#
# Construct the reference image.
#
ref = np.zeros([1, 1])
sum1 = 0
sum2 = 0
good_ref_list = []
for f in ref_list:
f.blur = IM.boxcar_blur(f.image)
good_ref_list.append(f)
print 'difference_image:', f.name, best_seeing_ref.name
if not (params.use_GPU) and (params.n_parallel > 1):
#
# Use ParallelProcessing to process images in the reference list
#
pool = Pool(params.n_parallel)
results = pool.map(
process_reference_image_helper,
itertools.izip(
ref_list,
itertools.repeat((best_seeing_ref, params, stamp_positions))))
for i, f in enumerate(ref_list):
f.result = results[i]
else:
for f in ref_list:
f.result = process_reference_image(
f, (best_seeing_ref, params, stamp_positions))
#
# Remove bad reference models
#
rlist = [g for g in good_ref_list]
for g in rlist:
if not (isinstance(g.result.diff, np.ndarray)):
print 'removing', g.name
good_ref_list.remove(g)
print 'good reference list:'
for g in good_ref_list:
print g.name
print 'kappa-clipping reference list'
for iterations in range(5):
if len(good_ref_list) < 4:
break
sd = np.zeros(len(good_ref_list))
for i, g in enumerate(good_ref_list):
print g.name, g.result.diff
sd[i] = np.std(g.result.diff)
sds = sd.std()
sdm = sd.mean()
rlist = [g for g in good_ref_list]
for g in rlist:
if np.std(g.result.diff) > (sdm + 2.5 * sds):
print 'removing', g.name
good_ref_list.remove(g)
#
# Combine the good reference models
#
g = good_ref_list[0]
gstack = np.zeros(
[len(good_ref_list), g.result.model.shape[0], g.result.model.shape[1]])
var_ref = np.zeros_like(g.result.model)
mask = np.ones_like(g.result.model)
print 'final reference list'
for i, g in enumerate(good_ref_list):
if isinstance(g.result.model, np.ndarray):
print g.name, np.std(g.result.diff), np.median(g.result.model)
IO.write_image(g.result.model,
params.loc_output + os.path.sep + 'mr_' + g.name)
gstack[i, :, :] = g.result.model
var_ref += g.result.model / params.gain + (params.readnoise /
params.gain)**2
mask *= g.mask
rr = np.median(gstack, axis=0)
IO.write_image(rr, params.loc_output + os.path.sep + reference_image)
IO.write_image(mask,
params.loc_output + os.path.sep + 'mask_' + reference_image)
var_ref /= np.float(len(good_ref_list))
IO.write_image(var_ref,
params.loc_output + os.path.sep + 'var_' + reference_image)
if params.error_image_prefix is not None:
IO.write_image(
np.sqrt(var_ref), params.loc_output + os.path.sep +
params.error_image_prefix + reference_image)
for f in ref_list:
f.result = None
return stamp_positions
def imsub_all_fits(params, reference='ref.fits'):
#
# Create the output directory if it doesn't exist
#
if not (os.path.exists(params.loc_output)):
os.mkdir(params.loc_output)
#
# The degree of spatial shape changes has to be at least as
# high as the degree of spatial photometric scale
#
if (params.sdeg < params.pdeg):
print 'Increasing params.sdeg to ', params.pdeg
params.sdeg = params.pdeg
#
# Print out the parameters for this run.
#
print 'Parameters:'
for par in dir(params):
print par, getattr(params, par)
print
#
# Determine our list of images
#
all_files = os.listdir(params.loc_data)
all_files.sort()
files = []
for f in all_files:
print 'file', f
if fnmatch.fnmatch(f, params.name_pattern):
g = DS.Observation(params.loc_data + os.path.sep + f, params)
del g.data
del g.mask
print 'fw', g.fw
if g.fw > 0.0:
files.append(g)
print g.name, 'accepted'
if len(files) < 3:
print 'Only', len(files), 'files found matching', params.name_pattern
print 'Exiting'
sys.exit(0)
#
# Have we specified a registration template?
#
if params.registration_image:
reg = DS.Observation(params.registration_image, params)
else:
reg = DS.EmptyBase()
reg.fw = 999.0
for f in files:
if (f.fw < reg.fw) and (f.fw > params.reference_min_seeing) and (
f.sky < params.registration_max_background):
reg = f
print 'Registration image:', reg.name
#
# Register images
#
print 'Registering images'
files_copy = [f for f in files]
for f in files:
print f.name
if f == reg:
f.image = f.data
rf = params.loc_output + os.path.sep + 'r_' + f.name
IO.write_image(f.image, rf)
else:
if not f.register(reg, params):
files_copy.remove(f)
# delete image arrays to save memory
del f.image
del f.mask
del f.inv_variance
del reg.data
del reg.image
del reg.mask
del reg.inv_variance
files = files_copy
#
# Write image names and dates to a file
#
if params.image_list_file:
try:
with open(params.loc_output + os.path.sep + params.image_list_file,
'w') as fid:
for f in files:
date = None
if params.datekey:
date = IO.get_date(
params.loc_data + os.path.sep + f.name,
key=params.datekey) - 2450000
if date:
fid.write(f.name + ' %10.5f\n' % date)
else:
fid.write(f.name)
except:
raise
#
# Make the photometric reference image if we don't have it.
# Find stamp positions if required.
#
if not (os.path.exists(params.loc_output + os.path.sep + reference)):
print 'Reg = ', reg.name
stamp_positions = make_reference(files,
reg,
params,
reference_image=reference)
ref = DS.Observation(params.loc_output + os.path.sep + reference,
params)
mask, _ = IO.read_fits_file(params.loc_output + os.path.sep + 'mask_' +
reference)
variance, _ = IO.read_fits_file(params.loc_output + os.path.sep +
'var_' + reference)
ref.mask = mask
ref.inv_variance = 1.0 / variance
ref.register(reg, params)
else:
ref = DS.Observation(params.loc_output + os.path.sep + reference,
params)
if os.path.exists(params.loc_output + os.path.sep + 'mask_' +
reference):
mask, _ = IO.read_fits_file(params.loc_output + os.path.sep +
'mask_' + reference)
else:
mask = np.ones_like(ref.data)
ref.mask = mask
ref.register(reg, params)
stamp_positions = None
if params.use_stamps:
stamp_file = params.loc_output + os.path.sep + 'stamp_positions'
if os.path.exists(stamp_file):
stamp_positions = np.genfromtxt(stamp_file)
else:
stars = PF.choose_stamps(ref, params)
stamp_positions = stars[:, 0:2]
np.savetxt(stamp_file, stamp_positions)
pm = params.pixel_max
params.pixel_max *= 0.9
ref.mask *= IM.compute_saturated_pixel_mask(ref.image, params)
params.pixel_max = pm
ref.blur = IM.boxcar_blur(ref.image)
if params.mask_cluster:
ref.mask *= IM.mask_cluster(ref.image, ref.mask, params)
#
# Detect stars and compute the PSF if we are doing photometry
#
star_positions = None
sky = 0.0
if params.do_photometry:
star_file = params.loc_output + os.path.sep + 'star_positions'
psf_file = params.loc_output + os.path.sep + 'psf.fits'
if not (os.path.exists(psf_file)) or not (os.path.exists(star_file)):
stars = PH.compute_psf_image(params, ref, psf_image=psf_file)
star_positions = stars[:, 0:2]
star_sky = stars[:, 4]
if os.path.exists(star_file):
star_positions = np.genfromtxt(star_file)
star_sky = star_positions[:, 0] * 0.0
else:
np.savetxt(star_file, star_positions)
print 'sky =', sky
#
# If we have pre-determined star positions
#
if params.star_file:
stars = np.genfromtxt(params.star_file)
star_positions = stars[:, 1:3]
if params.star_reference_image:
star_ref, h = IO.read_fits_file(params.star_reference_image)
offset, _, _ = register_translation(star_ref, ref.image, 1000)
dy, dx = offset
#dy, dx = IM.positional_shift(ref.image,star_ref)
print 'position shift =', dx, dy
star_positions[:, 0] -= dx
star_positions[:, 1] -= dy
np.savetxt(star_file, star_positions)
#
# If we are using a CPU, group the stars by location
#
print 'Group_check'
print 'params.do_photometry', params.do_photometry
print 'params.use_GPU', params.use_GPU
if params.do_photometry:
star_group_boundaries = None
detector_mean_positions_x = None
detector_mean_positions_y = None
star_unsort_index = None
star_sort_index,star_group_boundaries,detector_mean_positions_x,detector_mean_positions_y = \
PH.group_stars_ccd(params,star_positions,params.loc_output+os.path.sep+reference)
star_positions = star_positions[star_sort_index]
star_sky = star_sky[star_sort_index]
star_unsort_index = np.argsort(star_sort_index)
#
# Do photometry of the reference image
#
if params.do_photometry:
ref_flux_file = params.loc_output + os.path.sep + 'ref.flux'
if not (os.path.exists(ref_flux_file)):
result = difference_image(
ref,
ref,
params,
stamp_positions=stamp_positions,
psf_image=psf_file,
star_positions=star_positions,
star_group_boundaries=star_group_boundaries,
detector_mean_positions_x=detector_mean_positions_x,
detector_mean_positions_y=detector_mean_positions_y,
star_sky=star_sky)
if isinstance(result.flux, np.ndarray):
print 'ungrouping fluxes'
result.flux = result.flux[star_unsort_index].copy()
result.dflux = result.dflux[star_unsort_index].copy()
np.savetxt(ref_flux_file,
np.vstack((result.flux, result.dflux)).T)
#
# Process images
#
if params.make_difference_images:
if not (params.use_GPU) and (params.n_parallel > 1):
pool = Pool(params.n_parallel)
pool.map(
process_image_helper,
itertools.izip(
files,
itertools.repeat(
(ref, params, stamp_positions, star_positions,
star_group_boundaries, star_unsort_index,
detector_mean_positions_x,
detector_mean_positions_y))))
else:
for f in files:
process_image(
f, (ref, params, stamp_positions, star_positions,
star_group_boundaries, star_unsort_index,
detector_mean_positions_x, detector_mean_positions_y))
return files
def make_diff_images(filenamelist, refim, params):
""" make a diff image for each file in filenamelist: file - refim.
filenamelist : list of filenames for 'target' images
refim : reference image, either a filename or a DIA Observation object
params : DIA parameters object
"""
star_group_boundaries = None
detector_mean_positions_x = None
detector_mean_positions_y = None
star_unsort_index = None
star_positions = None
stamp_positions = None
sky = 0.0
if isinstance(refim, str) and os.path.exists(refim):
refim = DS.Observation(refim, params)
# TODO: investigate what is really being done here:
# Apply saturation mask and boxcar blurring to reference image
mask, _ = IO.read_fits_file(
params.loc_output + os.path.sep + 'mask_' + refim.name)
refim.mask = mask
pm = params.pixel_max
params.pixel_max *= 0.9
refim.mask *= IM.compute_saturated_pixel_mask(refim.image, 4, params)
params.pixel_max = pm
refim.blur = IM.boxcar_blur(refim.image)
if params.mask_cluster:
refim.mask *= IM.mask_cluster(refim.image, refim.mask, params)
# For each given filename, get a pyDIA observation object
image_list = get_observation_list(filenamelist, params)
# Register the images, using the ref image as the registration template,
# unless the user has specified otherwise
if not params.registration_image:
params.registration_image = refim.fullname
registered_image_list = register_images(image_list, params)
# make diff images: im - ref
for im in registered_image_list:
result = DIA.difference_image(
refim, im, params,
stamp_positions=stamp_positions,
psf_image=params.loc_output + os.path.sep + 'psf.fits',
star_positions=star_positions,
star_group_boundaries=star_group_boundaries,
detector_mean_positions_x=detector_mean_positions_x,
detector_mean_positions_y=detector_mean_positions_y)
del im.image
del im.mask
del im.inv_variance
hdr = fits.getheader(im.fullname)
# TODO : use astropy fits to propagate header with WCS from parent image
# Save output images to files
if isinstance(result.diff, np.ndarray):
IO.write_image(result.diff,
params.loc_output + os.path.sep + 'd_' + im.name,
header=hdr)