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sigmap.py
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sigmap.py
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#!/usr/bin/env python
import os,sys
import pyfits
from pyraf import iraf
import tempfile
from zcombine import *
from misc import *
import math
import numpy as np
from optparse import OptionParser
import shutil
def get_large_region(nx, ny, dx, dy):
xpad = []
dx_min = min(dx)
if dx_min > 0:
xpad.append(0.0)
else :
xpad.append(math.fabs(dx_min))
dx_max = max(dx)
if dx_max < 0:
xpad.append(0.0)
else:
xpad.append(dx_max)
xpad_size = int(max(xpad)) + 1
ypad = []
dy_min = min(dy)
if dy_min > 0:
ypad.append(0.0)
else:
ypad.append(math.fabs(dy_min))
dy_max = max(dy)
if dy_max < 0:
ypad.append(0.0)
else:
ypad.append(dy_max)
ypad_size = int(max(ypad))+1
x_size = nx + xpad_size + xpad_size
y_size = ny + ypad_size + ypad_size
xcmin = int((x_size-nx)/2 + 1 + dx_min)
xcmax = int((x_size-nx)/2 + nx + dx_max)
ycmin = int((y_size-ny)/2 + 1 + dy_min)
ycmax = int((y_size-ny)/2 + ny + dy_max)
return x_size, y_size, xcmin, xcmax, ycmin, ycmax
def sigmap(inlist, sigmap, expmap='none', whtmap='none', inpref='', ffpref='', objmask='none', reject='sigclip', fscale=False, fbase=100, fhead='F1', gain=5.6):
# check output image
if os.access(sigmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % sigmap
return 1
if os.access(expmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % expmap
return 1
if os.access(whtmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % whtmap
return 1
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr,int):
return 1
# check input image list
ffimg_arr = check_input2(inlist, ffpref)
if isinstance(ffimg_arr,int):
return 1
# get array size
im = pyfits.open(inimg_arr[0])
nx = im[0].header['NAXIS1']
ny = im[0].header['NAXIS2']
im.close()
# check geomap data
dx = []
dy = []
gmp_arr = []
gmp2_arr = []
dbs_arr = []
for i in range(len(inimg_arr)):
fname,ext = os.path.splitext(inimg_arr[i])
gmp = fname + '.gmp'
gmp2 = fname + '.gmp2'
dbs = fname + '.dbs'
gmp_arr.append(gmp)
gmp2_arr.append(gmp2)
dbs_arr.append(dbs)
if not os.access(gmp, os.R_OK):
print >> sys.stderr, 'geomap file (%s) does not exist' % (gmp)
return 1
if not os.access(dbs, os.R_OK):
print >> sys.stderr, 'database file (%s) does not exist' % (dbs)
return 1
if not os.access(gmp2, os.R_OK):
print >> sys.stderr, 'modified geomap file (%s) does not exist' % (gmp2)
return 1
fgmp = open(gmp)
nl = 1
dx_ave = 0.0
dy_ave = 0.0
for line in fgmp:
if not line.startswith('#'):
param = line[:-1].split()
if len(param) != 4:
print >> sys.stderr, 'Invalid format in line %d of %s: %s' % (nl, gmp, line[:-1])
fgmp.close()
return 1
else:
if isfloat(param[0]) == False or isfloat(param[1]) == False or isfloat(param[2]) == False or isfloat(param[3]) == False:
print >> sys.stderr, 'failed to decode line %d of %s: %s' % (nl, gmp, line[:-1])
fgmp.close()
return 1
else :
dx_ave += float(param[0]) - float(param[2])
dy_ave += float(param[1]) - float(param[3])
nl += 1
#print inimg_arr[i],nl
dx.append(dx_ave / (nl-1))
dy.append(dy_ave / (nl-1))
if len(inimg_arr) != len(dx):
print >> sys.stderr, 'number of input images does not match with that of offsets'
return 1
# check object mask
if objmask.lower() == 'none':
objmask = ''
else:
objmask_arr = check_inpref(objmask, inimg_arr)
if isinstance(objmask_arr, int):
return 1
# prepare for temporary file
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# get large array size and combined image size
ret = get_large_region(nx, ny, dx, dy)
if len(ret) != 6:
print >> sys.stderr, 'failed to get large array size'
return 1
x_size = ret[0]
y_size = ret[1]
xcmin = ret[2]
xcmax = ret[3]
ycmin = ret[4]
ycmax = ret[5]
# calculate image region in the large format
xmin = int((x_size - nx)/2)+1
xmax = nx + int((x_size - nx)/2)
ymin = int((y_size - ny)/2)+1
ymax = ny + int((y_size - ny)/2)
# copy image to larger format and shift image #
iraf.unlearn('geomap')
iraf.unlearn('geotran')
# for exposure time weight and flux scaling
expt_arr = []
flux_scale_arr = []
for i in range(len(inimg_arr)):
# load original frame
img = pyfits.open(inimg_arr[i])
# for exposure time weight
try :
t = float(img[0].header['EXP1TIME'])
coadd = float(img[0].header['COADDS'])
expt_arr.append(t * coadd)
except KeyError:
print >> sys.stderr, 'can not read exposure time from the header of %s' % inimg_arr[i]
img.close()
return 1
# for flux scaling and weight
if fscale:
try:
flux = float(img[0].header[fhead])
except KeyError:
print >> sys.stderr, 'can not read flux keyword (%s) from the header of %s' % (fhead, inimg_arr[i])
img.close()
return 1
flux_scale_arr.append(fbase / flux)
else:
flux_scale_arr.append(1.0)
img.close()
# preparing weighted variance map for each image
inverse_var_list = tmp_prefix+'_obj.lst'
if os.access(inverse_var_list,os.R_OK):
os.remove(inverse_var_list)
finverse_var = open(inverse_var_list,'w')
for i in range(len(inimg_arr)):
# mask frame
msk = np.ones((y_size,x_size))
msk[ymin-1:ymax,xmin-1:xmax] = 0
hdu = pyfits.PrimaryHDU(msk)
msk_img = pyfits.HDUList([hdu])
msk_fits = tmp_prefix+'mask'+os.path.basename(inimg_arr[i])
msktr_fits = tmp_prefix+'masktr'+os.path.basename(inimg_arr[i])
if os.access(msk_fits,os.R_OK):
os.remove(msk_fits)
if os.access(msktr_fits,os.R_OK):
os.remove(msktr_fits)
msk_img.writeto(msk_fits)
msk_img.close()
# transform mask geometry
iraf.geotran(msk_fits, msktr_fits, dbs_arr[i], gmp2_arr[i], geometr='linear', boundar='constant', constant=1)
os.remove(msk_fits)
convert_maskfits_int(msktr_fits, msktr_fits)
# load original frame
ffimg = pyfits.open(ffimg_arr[i])
img = pyfits.open(inimg_arr[i])
# object frame
inverse_var = np.zeros((y_size, x_size))
#print np.median(ffimg[0].data), ffimg_arr[i], gain, expt_arr[i], flux_scale_arr[i]
#print np.median(np.sqrt(ffimg[0].data / (gain * expt_arr[i]))), flux_scale_arr[i]
inverse_var[ymin-1:ymax,xmin-1:xmax] = (np.sqrt(ffimg[0].data / (gain * expt_arr[i])) * flux_scale_arr[i])**-2
hdu = pyfits.PrimaryHDU(inverse_var)
inverse_var_img = pyfits.HDUList([hdu])
inverse_var_img[0].header = img[0].header
inverse_var_img[0].header['bpm'] = msktr_fits
inverse_var_img[0].header.update('EXPTIME', expt_arr[i])
#inverse_var_img[0].header.update('MASKSCAL', expt_arr[i])
#inverse_var_img[0].header.update('MASKZERO', expt_arr[i])
#print 'EXPT = %f' % (expt_arr[i])
inverse_var_fits = tmp_prefix+'var'+os.path.basename(inimg_arr[i])
inverse_vartr_fits = tmp_prefix+'vartr'+os.path.basename(inimg_arr[i])
if os.access(inverse_var_fits,os.R_OK):
os.remove(inverse_var_fits)
if os.access(inverse_vartr_fits,os.R_OK):
os.remove(inverse_vartr_fits)
inverse_var_img.writeto(inverse_var_fits)
inverse_var_img.close()
iraf.geotran(inverse_var_fits, inverse_vartr_fits, dbs_arr[i], gmp2_arr[i], geometr='linear', boundar='constant', constant=0)
finverse_var.write('%s\n' % inverse_vartr_fits)
ffimg.close()
# close file handlers
finverse_var.close()
# sum weighted variance images
tmp_inverse_var_sum = tmp_prefix+'inverse_var.fits'
if os.access(tmp_inverse_var_sum, os.R_OK):
os.remove(tmp_inverse_var_sum)
tmp_sigma = tmp_prefix+'sigma.fits'
if os.access(tmp_sigma, os.R_OK):
os.remove(tmp_sigma)
tmp_exp = tmp_prefix+'exp.fits'
if os.access(tmp_exp, os.R_OK):
os.remove(tmp_exp)
if expmap != 'none':
#iraf.hselect('@'+inverse_var_list,"$I,EXPTIME,MASKSCAL,MASKZERO","yes")
iraf.imcombine('@'+inverse_var_list, tmp_inverse_var_sum, expmasks=tmp_exp, combine='sum', reject=reject, masktype='!BPM', maskvalue=0.0, expname='EXPTIME')
else:
iraf.imcombine('@'+inverse_var_list, tmp_inverse_var_sum, combine='sum', reject=reject, masktype='!BPM', maskvalue=0.0)
# calculate sigma
iraf.stsdas()
iraf.imcalc(tmp_inverse_var_sum, tmp_sigma, 'sqrt(1.0/im1)', pixtype='double')
# cut image
iraf.unlearn('imcopy')
cut_sig = '%s[%d:%d,%d:%d]' % (tmp_sigma, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_sig, sigmap)
if expmap != 'none':
cut_exp = '%s[%d:%d,%d:%d]' % (tmp_exp, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_exp, expmap)
# calc weight map
if whtmap != 'none':
cut_wht = '%s[%d:%d,%d:%d]' % (tmp_inverse_var_sum, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_wht, whtmap)
# delete temporary object files
os.remove(inverse_var_list)
# remove all temporary files
remove_temp_all(tmp_prefix)
return 0
if __name__=="__main__":
usage = "usage: %prog input_list sigma_map [options]"
parser = OptionParser(usage)
parser.add_option("--expmap", dest="expmap", type="string", default="none",
help="exposure map file name (default=none)")
parser.add_option("--whtmap", dest="whtmap", type="string", default="none",
help="weight map file name (default=none)")
parser.add_option("--inpref", dest="inpref", type="string", default="",
help="prefix for input frame (default=none)")
parser.add_option("--ffpref", dest="ffpref", type="string", default="",
help="prefix for flat frame (to be replaced from the original prefix in the input list) (default=none)")
parser.add_option("--objmask", dest="objmask", type="string", default="none",
help="prefix for mask image, image list with \'@\', or header keyword with \':\'")
parser.add_option("--reject", dest="reject", type="choice", default="sigclip",
choices=["none", "minmax", "ccdclip", "crreject", "sigclip", "avsigclip", "pclipor"],
help="type of rejection (none|minmax|ccdclip|crreject|sigclip|avsigclip|pclipor, default=sigclip)")
parser.add_option("--fscale", dest="fscale", action="store_true", default=False,
help="Scaling with the reference object flux (default=False)")
parser.add_option("--fbase", dest="fbase", type="float", default=100.0,
help="Baseline flux for the flux scaling (default=100.0)")
parser.add_option("--fhead", dest="fhead", type="string", default="F1",
help="Header keyword for the reference object flux (default=F1)")
parser.add_option("--gain", dest="gain", type="float", default=5.6,
help="Detector gain [e/ADU] (default=5.6)")
(options, args) = parser.parse_args()
if len(args) != 2:
parser.print_help()
sys.exit()
sigmap(args[0], args[1], expmap=options.expmap, whtmap=options.whtmap, inpref=options.inpref, ffpref=options.ffpref, objmask=options.objmask, reject=options.reject, fscale=options.fscale, fbase=options.fbase, fhead=options.fhead, gain=options.gain)