def main():
"""File modification I/O"""
parser = argparse.ArgumentParser(description=("Given an image"
" in tangent-projected sky coordinates and"
" an ASC-REGION-FITS list of point source exclusions in"
" celestial RA/dec coordinates,"
" fill in the image gaps by drawing counts from a"
" Poisson distribution with mean count rate derived from"
" an annulus surrounding each point source"))
parser.add_argument('image', metavar='FITS',
help=("FITS image in tangent-projected sky coords"))
parser.add_argument('exposure', metavar='FITS',
help=("FITS exposure image in tangent-projected sky coords"))
parser.add_argument('--annulus-width', metavar='pixels', type=float,
help=("Width (pixels) of sampling annulus around each"
" point source."))
parser.add_argument('--mask', metavar='FITS',
help=("ASC-REGION-FITS sources in un-projected RA/DEC,"
" radii in arcmin."))
parser.add_argument('--debug', action='store_true',
help=("Create debugging output w/ sources and annuli"
" highlighted by artificially high/low values"))
parser.add_argument('--clobber', action='store_true',
help=("Clobber any existing output file"))
parser.add_argument('--out', metavar='output.fits',
help=("Output filename, image w/ filled holes"))
args = parser.parse_args()
F_INPUT = args.image
F_EXPOSURE = args.exposure
F_MASK = args.mask
F_OUTPUT = args.out
ANNULUS_WIDTH = args.annulus_width
OPT_DEBUG = args.debug
OPT_CLOBBER = args.clobber
R_EPSILON = 0.5 # Radius error (pixels) for each point source
unit_tests()
assert F_INPUT != F_OUTPUT
fits_mask = fits.open(F_MASK)
mask = fits_mask[1]
assert mask.header['HDUCLASS'] == 'ASC'
assert mask.header['HDUCLAS1'] == 'REGION'
assert mask.header['HDUCLAS2'] == 'STANDARD'
assert mask.header['MTYPE1'] == 'pos'
assert mask.header['MFORM1'] == 'RA,DEC'
assert all(map(lambda x: x == '!CIRCLE', mask.data['SHAPE']))
fits_image = fits.open(F_INPUT)
phdu = fits_image[0]
assert phdu.is_image
assert phdu.header['CTYPE1'] == 'RA---TAN'
assert phdu.header['CUNIT1'] == 'deg'
assert phdu.header['CTYPE2'] == 'DEC--TAN'
assert phdu.header['CUNIT2'] == 'deg'
assert phdu.header['CDELT1'] == -1 * phdu.header['CDELT2']
assert phdu.header['CDELT2'] > 0
# XMM ESAS image convention: X scales inversely w/ RA, Y increases w/ dec.
# X,Y pixel sizes are equal in deg.; the projection transformation accounts
# for RA "compression" at high declination
dat = phdu.data
x0 = phdu.header['CRPIX1']
y0 = phdu.header['CRPIX2']
ra0 = phdu.header['CRVAL1']
dec0 = phdu.header['CRVAL2']
scale = phdu.header['CDELT2'] # pixel size == deg./pixel
fits_exposure = fits.open(F_EXPOSURE)
exposure = fits_exposure[0]
for key in ['CTYPE1', 'CUNIT1', 'CTYPE2', 'CUNIT2', 'CDELT1', 'CDELT2']:
assert exposure.header[key] == phdu.header[key]
dat_exp = exposure.data
# Convert point sources to current image projection
x_srcs, y_srcs = radec2tanproj(mask.data['RA'][:,0], mask.data['DEC'][:,0],
ra0, dec0, x0, y0, 1/scale)
r_srcs = mask.data['R'][:,0] / (abs(scale) * 60) # scale*60 = arcmin/px
n_overlaps = any_sources_overlap(x_srcs, y_srcs, r_srcs)
if n_overlaps:
print "\nWARNING: {} pairwise overlap(s), source filling may be inconsistent!\n".format(n_overlaps)
dat_filled = np.copy(dat)
max_abs_dat = np.max(np.abs(dat))
# Get all pixels in annulus of radii (r_pt + 0.5, r_pt + 0.5 + ANN_WIDTH)
# centered on each point source.
for x_pt, y_pt, r_pt in zip(x_srcs, y_srcs, r_srcs):
ann_r1 = r_pt + R_EPSILON # Excise extra edge pixels
ann_r2 = r_pt + R_EPSILON + ANNULUS_WIDTH
# Inspect all pixels in a box around source + annulus
# Bounds for x, y search: either annulus edge or image boundary
search_x = (max(1, np.floor(x_pt - ann_r2)),
min(dat.shape[0], np.ceil(x_pt + ann_r2)))
search_y = (max(1, np.floor(y_pt - ann_r2)),
min(dat.shape[1], np.ceil(y_pt + ann_r2)))
search_x = map(int, search_x)
search_y = map(int, search_y)
ann_count_rate = 0
ann_px = 0
mean_exp = 0
for x in range(search_x[0], search_x[1] + 1):
for y in range(search_y[0], search_y[1] + 1):
# Order of conditionals matters for short-circuiting:
# distance check is O(1)
# vs. overlapping source check is O(n_sources)
d = distance((x, y), (x_pt, y_pt))
if d > ann_r1 and d < ann_r2 and (not point_overlaps_sources(x, y, x_srcs, y_srcs, r_srcs + R_EPSILON)):
# Prevent division by zero. Counts should be zero in these pixels anyways.
if dat_exp[y-1, x-1] <= 0:
continue
ann_count_rate += (dat[y-1, x-1] / dat_exp[y-1, x-1]) # shift 1- to 0-based indices
ann_px += 1
mean_exp += dat_exp[y-1, x-1]
mean_exp = mean_exp / ann_px # Average exposure for a given pixel
print "Source at ({:g},{:g}), r = {:g}. Search X in {}, Y in {}.".format(
x_pt, y_pt, r_pt, search_x, search_y)
print " Annulus count rate: {:g} cts/s".format(ann_count_rate)
print " usable pixels: {:g} px".format(ann_px)
print " count flux: {:g} cts/px/s".format(ann_count_rate/ann_px)
print " Total cts mean exp: {:g}".format(ann_count_rate * mean_exp)
if ann_px < 100:
print "\n ***WARNING: <100 pixels sampled; adjust --annulus-width***\n"
if ann_count_rate * mean_exp < 1:
print "\n ***WARNING: <1 count in annulus for mean exposure***\n"
flux = ann_count_rate/ann_px # counts / sec / pixel
if flux < 0:
print "\n ***WARNING: negative count rate, forcing to zero!***\n"
flux = 0
# Insert new values into image
for x in range(search_x[0], search_x[1] + 1):
for y in range(search_y[0], search_y[1] + 1):
d = distance((x, y), (x_pt, y_pt))
if OPT_DEBUG:
if d <= ann_r1:
dat_filled[y-1, x-1] = 10 * max_abs_dat
elif d > ann_r1 and d < ann_r2:
dat_filled[y-1, x-1] = -10 * max_abs_dat
else:
if d <= ann_r1:
# Scale flux to counts/px for current pixel's exposure
dat_filled[y-1, x-1] = np.random.poisson(flux * dat_exp[y-1, x-1])
phdu.data = dat_filled
fits_image.writeto(F_OUTPUT, clobber=OPT_CLOBBER)
print "\nWrote output to {:s}. Check image for reasonable annuli".format(F_OUTPUT)
print "(e.g., overlapping sources OK; annuli not sampling beyond sky FOV)."
def main():
"""File conversion I/O"""
parser = argparse.ArgumentParser(description=("Convert an"
" ASC-REGION-FITS point source exclusion (!CIRCLE) file"
" from RA/dec celestial coordinates with radii in arcmin."
" to tangent-projected sky coordinates (xy)"))
parser.add_argument('input', metavar='input-radec.fits',
help=("ASC-REGION-FITS sources in un-projected RA/dec,"
" radii in arcmin."))
parser.add_argument('--template', metavar='reference.fits',
help=("Reference ASC-REGION-FITS file (with desired"
" projection and header keywords"))
parser.add_argument('--out', metavar='output-sky.fits',
help=("Output filename, converted ASC-REGION-FITS in"
" tangent-projected sky coordinates"))
args = parser.parse_args()
F_INPUT = args.input
F_TEMPLATE = args.template
F_OUTPUT = args.out
# Parse and validate input
if not re.match(".*bkg_region-sky.*\.fits", F_TEMPLATE):
warn(("--template {:s} doesn't follow ESAS naming").format(F_TEMPLATE))
if not re.match(".*bkg_region-sky.*\.fits", F_OUTPUT):
warn(("--out {:s} doesn't follow ESAS naming").format(F_OUTPUT))
#shutil.copy(F_TEMPLATE, F_OUT)
fits_input = fits.open(F_INPUT)
fits_template = fits.open(F_TEMPLATE)
t_in = fits_input[1] # first BinTable
t_template = fits_template[1]
for t in [t_in, t_template]:
assert t.header['HDUCLASS'] == 'ASC'
assert t.header['HDUCLAS1'] == 'REGION'
assert t.header['HDUCLAS2'] == 'STANDARD'
assert t_in.header['MTYPE1'] == 'pos'
assert t_in.header['MFORM1'] == 'RA,DEC'
assert all(map(lambda x: x == '!CIRCLE', t_in.data['SHAPE']))
# Set up projection center
assert t_template.header['TCTYP2'] == "RA---TAN"
assert t_template.header['TCUNI2'] == "deg"
x0 = t_template.header['TCRPX2']
ra0 = t_template.header['TCRVL2']
assert t_template.header['TCTYP3'] == "DEC--TAN"
assert t_template.header['TCUNI3'] == "deg"
y0 = t_template.header['TCRPX3']
dec0 = t_template.header['TCRVL3']
# Square coordinates
assert t_template.header['TCDLT2'] == -1 * t_template.header['TCDLT3']
assert t_template.header['TCDLT3'] > 0
scale = t_template.header['TCDLT3']
# Perform the coordinate conversion (vectorized)
x, y = radec2tanproj(t_in.data['RA'][:,0], t_in.data['DEC'][:,0],
ra0, dec0, x0, y0, 1/scale)
# 1/scale needed to get pixel/deg., as FITS sky coords records deg./pixel
radius = t_in.data['R'][:,0] / (abs(scale) * 60) # scale*60 = arcmin/px
# Construct output FITS file in same format as output by SAS task region
# (except use 'E' instead of '4E' for X, Y, R, ROTANG)
x_pad = pad_E_to_4E(x)
y_pad = pad_E_to_4E(y)
radius_pad = pad_E_to_4E(radius)
bhdu = fits.BinTableHDU.from_columns(
[fits.Column(name='SHAPE', format=t_in.columns['SHAPE'].format,
array=t_in.data['SHAPE']),
fits.Column(name='X', format='4E', array=x_pad),
fits.Column(name='Y', format='4E', array=y_pad),
fits.Column(name='R', format='4E', array=radius_pad),
fits.Column(name='ROTANG', format='4E', array=t_in.data['ROTANG']),
fits.Column(name='COMPONENT', format='J', array=t_in.data['COMPONENT'])
]
)
bhdu.name = 'REGION'
# Loosely following ASC-REGION-FITS spec
bhdu.header['HDUVERS'] = '1.2.0'
bhdu.header['HDUCLASS'] = 'ASC'
bhdu.header['HDUCLAS1'] = 'REGION'
bhdu.header['HDUCLAS2'] = 'STANDARD'
bhdu.header['HDUDOC'] = 'ASC-FITS-REGION-1.2: Rots, McDowell'
bhdu.header['MTYPE1'] = 'pos'
bhdu.header['MFORM1'] = 'X,Y'
for kw in ['TCTYP2', 'TCRPX2', 'TCRVL2', 'TCUNI2', 'TCDLT2',
'TCTYP3', 'TCRPX3', 'TCRVL3', 'TCUNI3', 'TCDLT3']:
# Coordinate transformation parameters already validated above
if ("TCRPX" in kw) or ("TCRVL" in kw) or ("TCDLT" in kw):
bhdu.header[kw] = "{:.14e}".format(t_template.header[kw]).upper()
else:
bhdu.header[kw] = t_template.header[kw]
phdu = fits_template[0] # Work off of template header
phdu.header['HISTORY'] = ('Rewritten by {} (atran@cfa)'
' using RA/dec list {} on date {}').format(
os.path.basename(__file__),
os.path.basename(F_INPUT),
datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%Sz'))
# RFC3339/ISO8601 date as used by XMM tools; add 'Z' to indicate timezone
f_out = fits.HDUList([phdu, bhdu])
f_out.writeto(F_OUTPUT, clobber=True)
