def __init__(self, datatype=None):
self.datatype=datatype
self.fn_pattern=re.compile(".*")
iraf.images()
iraf.proto()
self.statarea=StatArea
return
def ApplyBadPixelMask(images, cal_path, outbase = '_bpm'):
"""
#####################################################################################
# Fix a list of images with a bad pixel mask
#
# PARAMETERS:
# images - list of images to fix
# cal_path - path to calibration data
# outbase - addition to the name of the image after mask has been applied
#
# RETURNS:
# list of fixed images
#####################################################################################
"""
print '########################################'
print '# Applying Bad Pixel Masks'
print '########################################'
# finds the bad pixel mask
badPixelMask = [FindPixelMask(i, cal_path) for i in images]
# Build the list of output image names
out = [os.path.splitext(i) for i in images] # Split off the extension
out = [i[0] + outbase + '.fit' for i in out] # Paste the outbase at the end of the filename
# and put the extension back on
# Set up to call the task
iraf.set(clobber='yes')
iraf.proto()
iraf.fixpix.unlearn() # Forget everything
iraf.fixpix.mode = 'h' # Don't ask for user input
for (i,o,m) in zip(images, out, badPixelMask):
# gets image data and copies it to the corresponding output file name
shutil.copy(i,o)
#runs fixpix on the output file (fixpix overwrites file data)
if not os.path.isfile(i):
print i + ": Input file does not exist."
elif not os.path.isfile(m):
print i + ": No pixel mask applied."
else:
try:
#set parameters
iraf.fixpix.images = o
iraf.fixpix.masks = m
print i + ": applying mask at " + m
iraf.fixpix()
except iraf.IrafError, e:
print "fixpix failed"
print "error #" + str(e.errno)
print "Msg: " + e.errmsg
print "Task: " + e.errtask
raise e
def FixBadPixels(PC,images,nightdir):
""" This will run iraf task proto.fixpix to interpolate badpixels """
PixelMask = os.path.join(nightdir,PC.BadPixelMaskName)
if not os.path.isfile(PixelMask):
print("No Bad Pixel Mask file found by the name "+ PixelMask)
print("Hence skipping Bad pixel interpolation")
return
iraf.proto(_doprint=0)
iraf.fixpix.unlearn()
iraf.fixpix(images=images,masks=PixelMask)
def marksn2(img,fitstab,frame=1,fitstab2='',verbose=False):
from pyraf import iraf
from numpy import array #,log10
import lsc
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.images(_doprint=0)
iraf.imcoords(_doprint=0)
iraf.proto(_doprint=0)
iraf.set(stdimage='imt1024')
hdr=lsc.util.readhdr(fitstab)
_filter=lsc.util.readkey3(hdr,'filter')
column=lsc.lscabsphotdef.makecatalogue([fitstab])[_filter][fitstab]
rasex=array(column['ra0'],float)
decsex=array(column['dec0'],float)
if fitstab2:
hdr=lsc.util.readhdr(fitstab2)
_filter=lsc.util.readkey3(hdr,'filter')
_exptime=lsc.util.readkey3(hdr,'exptime')
column=lsc.lscabsphotdef.makecatalogue([fitstab2])[_filter][fitstab2]
rasex2=array(column['ra0'],float)
decsex2=array(column['dec0'],float)
iraf.set(stdimage='imt1024')
iraf.display(img + '[0]',frame,fill=True,Stdout=1)
vector=[]
for i in range(0,len(rasex)):
vector.append(str(rasex[i])+' '+str(decsex[i]))
xy = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector,Stdout=1,image=img+'[0]',inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(frame,'STDIN',Stdin=list(xy),mark="circle",number='yes',label='no',radii=10,nxoffse=5,nyoffse=5,color=207,txsize=2)
if verbose:
# print 2.5*log10(_exptime)
for i in range(0,len(column['ra0'])):
print xy[i],column['ra0'][i],column['dec0'][i],column['magp3'][i],column['magp4'][i],column['smagf'][i],column['magp2'][i]
if fitstab2:
vector2=[]
for i in range(0,len(rasex2)):
vector2.append(str(rasex2[i])+' '+str(decsex2[i]))
xy1 = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector2,Stdout=1,image=img+'[0]',inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(frame,'STDIN',Stdin=list(xy1),mark="cross",number='yes',label='no',radii=10,nxoffse=5,nyoffse=5,color=205,txsize=2)
def skysub(inlist,
inpref='',
outpref='s',
mskpref='none',
ndthpos=9,
iternum=50,
nsigma=3,
second=False):
# load IRAF package
iraf.proto()
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check output
outimg_arr = check_outpref(outpref, inimg_arr)
if isinstance(outimg_arr, int):
return 1
# check mask images
if mskpref.lower() == 'none' or mskpref == '':
mask = 0
else:
mask = 1
mskimg_arr = check_inpref(mskpref, inimg_arr)
if isinstance(mskimg_arr, int):
return 1
# check dithering width
if ndthpos != 5 and ndthpos != 9:
print >> sys.stderr, 'number of dithering position should be 5 or 9'
if iternum <= 0:
print >> sys.stderr, 'invalid iternum value (%d)' % iternum
return 1
if nsigma <= 0:
print >> sys.stderr, 'invalid nsigma value (%f)' % nsigma
return 1
# check input, output, and mask files and record mask frame into header
pre_pos = 100
in_array = []
out_array = []
mask_array = []
iraf.unlearn('hedit')
for i in range(len(inimg_arr)):
if mask == 1:
if mskpref[0] != ':':
if not second:
iraf.hedit(inimg_arr[i],
'objmask',
mskimg_arr[i] + '[pl]',
add='yes',
verify='no')
else:
iraf.hedit(inimg_arr[i],
'objmask',
mskimg_arr[i],
add='yes',
verify='no')
# get dithering position from header
im = pyfits.open(inimg_arr[i])
pos = int(im[0].header['I_DTHPOS'])
im.close()
# record each dithering set into subset
if pos < pre_pos:
if i != 0:
in_array.append(in_subset)
out_array.append(out_subset)
if mask == 1:
mask_array.append(mask_subset)
in_subset = []
out_subset = []
in_subset.append(inimg_arr[i])
out_subset.append(outimg_arr[i])
if mask == 1:
mask_subset = []
mask_subset.append(mskimg_arr[i])
pre_pos = pos
else:
in_subset.append(inimg_arr[i])
out_subset.append(outimg_arr[i])
if mask == 1:
mask_subset.append(mskimg_arr[i])
pre_pos = pos
# record rest of frames
in_array.append(in_subset)
out_array.append(out_subset)
if mask == 1:
mask_array.append(mask_subset)
# number of subsets
n_subset = len(in_array)
# sky subtraction
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
for i in range(n_subset):
tmp_sky = '%s_sky_%03d.fits' % (tmp_prefix, int(i))
tmp_subset = '%s_subset_%03d.lst' % (tmp_prefix, int(i))
# make subset list
f = open(tmp_subset, 'w')
for j in range(len(in_array[i])):
f.write(in_array[i][j] + '\n')
f.close()
# combine subset
if mask == 1:
ret = zcombine(tmp_subset,
tmp_sky,
combine='median',
reject='sigclip',
masktype='!OBJMASK')
else:
ret = zcombine(tmp_subset,
tmp_sky,
combine='median',
reject='sigclip',
masktype='none')
if ret != 0:
remove_temp_all(tmp_prefix)
return 1
os.remove(tmp_subset)
# calculate median of combined sky background
iraf.unlearn('imstat')
ret = iraf.imstat(tmp_sky,
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(ret) == 1:
skymed = float(ret[0])
else:
remove_temp_all(tmp_prefix)
print >> sys.stderr, 'failed to calculate median of the combined sky background'
return 1
# save sky data into memory
sky = pyfits.open(tmp_sky)
sky_data = sky[0].data
sky.close()
os.remove(tmp_sky)
# subtract sky
for j in range(len(in_array[i])):
if mask == 1:
iraf.unlearn('mimstat')
if not second:
ret = iraf.mimstat(in_array[i][j],
imasks=mask_array[i][j] + '[pl]',
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
else:
ret = iraf.mimstat(in_array[i][j],
imasks=mask_array[i][j],
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
else:
iraf.unlearn('imstat')
ret = iraf.imstat(in_array[i][j],
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(ret) == 1:
objmed = float(ret[0])
else:
remove_temp_all(tmp_prefix)
print >> sys.stderr, 'failed to calculate median of the object background'
return 1
# record input fits data into memory
im = pyfits.open(in_array[i][j])
im_hdr = im[0].header
im_data = im[0].data
im.close()
# subtract sky in memory
out_data = im_data - (sky_data * objmed / skymed)
# save output fits file
hdu = pyfits.PrimaryHDU(out_data)
imgout = pyfits.HDUList([hdu])
imgout[0].header = im_hdr
imgout.writeto(out_array[i][j])
imgout.close()
remove_temp_all(tmp_prefix)
return 0
def skysub_mod(inlist,
inpref='',
outpref='s',
mskpref='none',
ndthpos=9,
iternum=50,
nsigma=3,
second=False,
step=0.05):
# load IRAF package
iraf.proto()
# check step size
if step >= 0.5:
print >> sys.stderr, 'step size should be smaller than 0.5'
return 1
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check output
outimg_arr = check_outpref(outpref, inimg_arr)
if isinstance(outimg_arr, int):
return 1
# check mask images
if mskpref.lower() == 'none' or mskpref == '':
mask = 0
else:
mask = 1
mskimg_arr = check_inpref(mskpref, inimg_arr)
if isinstance(mskimg_arr, int):
return 1
# check dithering width
if ndthpos != 5 and ndthpos != 9:
print >> sys.stderr, 'number of dithering position should be 5 or 9'
if iternum <= 0:
print >> sys.stderr, 'invalid iternum value (%d)' % iternum
return 1
if nsigma <= 0:
print >> sys.stderr, 'invalid nsigma value (%f)' % nsigma
return 1
# check input, output, and mask files and record mask frame into header
pre_pos = 100
in_array = []
out_array = []
mask_array = []
iraf.unlearn('hedit')
for i in range(len(inimg_arr)):
if mask == 1:
if mskpref[0] != ':':
if not second:
iraf.hedit(inimg_arr[i],
'objmask',
mskimg_arr[i] + '[pl]',
add='yes',
verify='no')
else:
iraf.hedit(inimg_arr[i],
'objmask',
mskimg_arr[i],
add='yes',
verify='no')
# get dithering position from header
im = pyfits.open(inimg_arr[i])
pos = int(im[0].header['I_DTHPOS'])
im.close()
# record each dithering set into subset
if pos < pre_pos:
if i != 0:
in_array.append(in_subset)
out_array.append(out_subset)
if mask == 1:
mask_array.append(mask_subset)
in_subset = []
out_subset = []
in_subset.append(inimg_arr[i])
out_subset.append(outimg_arr[i])
if mask == 1:
mask_subset = []
mask_subset.append(mskimg_arr[i])
pre_pos = pos
else:
in_subset.append(inimg_arr[i])
out_subset.append(outimg_arr[i])
if mask == 1:
mask_subset.append(mskimg_arr[i])
pre_pos = pos
# record rest of frames
in_array.append(in_subset)
out_array.append(out_subset)
if mask == 1:
mask_array.append(mask_subset)
# number of subsets
n_subset = len(in_array)
# sky subtraction
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
for i in range(n_subset):
tmp_sky = '%s_sky_%03d.fits' % (tmp_prefix, int(i))
tmp_subset = '%s_subset_%03d.lst' % (tmp_prefix, int(i))
# make subset list
f = open(tmp_subset, 'w')
for j in range(len(in_array[i])):
f.write(in_array[i][j] + '\n')
f.close()
# combine subset
if mask == 1:
ret = zcombine(tmp_subset,
tmp_sky,
combine='median',
reject='sigclip',
masktype='!OBJMASK')
else:
ret = zcombine(tmp_subset,
tmp_sky,
combine='median',
reject='sigclip',
masktype='none')
if ret != 0:
remove_temp_all(tmp_prefix)
return 1
os.remove(tmp_subset)
# calculate median of combined sky background
iraf.unlearn('imstat')
ret = iraf.imstat(tmp_sky,
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(ret) == 1:
skymed = float(ret[0])
else:
remove_temp_all(tmp_prefix)
print >> sys.stderr, 'failed to calculate median of the combined sky background'
return 1
# save sky data into memory
sky = pyfits.open(tmp_sky)
sky_data = sky[0].data
sky_arr = np.reshape(np.array(sky[0].data - skymed), sky[0].data.size)
sky.close()
os.remove(tmp_sky)
# subtract sky
for j in range(len(in_array[i])):
if mask == 1:
iraf.unlearn('mimstat')
if not second:
ret = iraf.mimstat(in_array[i][j],
imasks=mask_array[i][j] + '[pl]',
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
else:
ret = iraf.mimstat(in_array[i][j],
imasks=mask_array[i][j],
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
else:
iraf.unlearn('imstat')
ret = iraf.imstat(in_array[i][j],
format='no',
fields='midpt',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(ret) == 1:
objmed = float(ret[0])
else:
remove_temp_all(tmp_prefix)
print >> sys.stderr, 'failed to calculate median of the object background'
return 1
# record input fits data into memory
im = pyfits.open(in_array[i][j])
im_hdr = im[0].header
im_data = im[0].data
im_arr = np.reshape(np.array(im[0].data - objmed), im[0].data.size)
im.close()
# check if minimum sigma is obtained at scale_factor = 1.0
m1, s1 = meanclip(im_arr - sky_arr)
m2, s2 = meanclip(im_arr - (1.0 + step) * sky_arr)
m3, s3 = meanclip(im_arr - (1.0 - step) * sky_arr)
if s1 < s2 and s1 < s3:
print 'scale factor = 1.0'
# subtract sky in memory
out_data = (im_data - objmed) - (sky_data - skymed)
else:
# find minimum sigma with coarse sampling
n = 1
min_i = 0
while min_i - 1 < 0 or min_i + 1 >= len(s_list):
s = -1.0 * n
ds = 0.5
sigma_list = []
s_list = []
while s <= 1.0 * n:
sub = im_arr - (s * sky_arr)
mean, sigma = meanclip(sub)
sigma_list.append(sigma)
s_list.append(s)
s += ds
min_i, min_sigma = min(enumerate(sigma_list),
key=itemgetter(1))
n += 1
# find minimum sigma with fine sampling
s = s_list[min_i - 1]
ds = step
s2_list = []
sigma2_list = []
while s <= s_list[min_i + 1]:
sub = im_arr - (s * sky_arr)
mean, sigma = meanclip(sub)
sigma2_list.append(sigma)
s2_list.append(s)
s += ds
min_i, min_sigma = min(enumerate(sigma2_list),
key=itemgetter(1))
print 'scale factor = %f' % s2_list[min_i]
# subtract sky in memory
out_data = (im_data -
objmed) - (sky_data - skymed) * s2_list[min_i]
# save output fits file
hdu = pyfits.PrimaryHDU(out_data)
imgout = pyfits.HDUList([hdu])
imgout[0].header = im_hdr
imgout.writeto(out_array[i][j])
imgout.close()
remove_temp_all(tmp_prefix)
return 0
def mksky(inlist, outfits, inpref='', mskpref='none', second=False):
# load IRAF package
iraf.proto()
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check output fits
if os.access(outfits, os.R_OK):
print >> sys.stderr, 'operation would overwrite exisiting image (%s)' % outfits
return 1
# check mask images
if mskpref.lower() == 'none' or mskpref == '':
mask = 0
else:
mask = 1
mskimg_arr = check_inpref(mskpref, inimg_arr)
if isinstance(mskimg_arr, int):
return 1
# check input, output, and mask files and record mask frame into header
in_subset = []
if mask == 1:
mask_subset = []
iraf.unlearn('hedit')
for i in range(len(inimg_arr)):
if mask == 1:
if mskpref[0] != ':':
if not second:
iraf.hedit(inimg_arr[i],
'objmask',
mskimg_arr[i] + '[pl]',
add='yes',
verify='no')
else:
iraf.hedit(inimg_arr[i],
'objmask',
mskimg_arr[i],
add='yes',
verify='no')
in_subset.append(inimg_arr[i])
# sky subtraction
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
tmp_subset = '%s_subset_%03d.lst' % (tmp_prefix, int(i))
# make subset list
f = open(tmp_subset, 'w')
for j in range(len(in_subset)):
f.write(in_subset[j] + '\n')
f.close()
# combine subset
if mask == 1:
ret = zcombine(tmp_subset,
outfits,
combine='median',
reject='sigclip',
masktype='!OBJMASK')
else:
ret = zcombine(tmp_subset,
outfits,
combine='median',
reject='sigclip',
masktype='none')
if ret != 0:
remove_temp_all(tmp_prefix)
return 1
remove_temp_all(tmp_prefix)
return 0
def efoscreduction(imglist, _interactive, _doflat, _dobias, listflat, listbias, _dobadpixel, badpixelmask,
fringingmask, _archive, typefile, filenameobjects, _system, _cosmic, _verbose=False, method='iraf'):
# print "LOGX:: Entering `efoscreduction` method/function in %(__file__)s"
# % globals()
import ntt
from ntt.efoscphotredudef import searchbias
from ntt.util import delete, readhdr, readkey3, display_image, searchflat, rangedata, correctcard
from numpy import argmin, min, abs, sqrt
import string
import os
import re
import math
import sys
from pyraf import iraf
# ## Call and set parameters for useful iraf tasks
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.proto(_doprint=0)
toforget = ['ccdproc', 'zerocombine',
'flatcombine', 'imreplace', 'proto.fixpix']
for t in toforget:
iraf.unlearn(t)
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.overscan = 'no'
iraf.ccdproc.ccdtype = ''
iraf.ccdproc.biassec = ''
iraf.ccdred.instrument = "/dev/null"
if _verbose:
iraf.ccdred.verbose = 'yes'
else:
iraf.ccdred.verbose = 'no'
import datetime
import time
# starttime=time.time()
now = datetime.datetime.now()
datenow = now.strftime('20%y%m%d%H%M')
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
outputfile = []
reduceddata = rangedata(imglist)
img = re.sub('\n', '', imglist[0])
hdr = readhdr(img)
_gain = readkey3(hdr, 'gain')
_rdnoise = readkey3(hdr, 'ron')
_instrume = readkey3(hdr, 'instrume')
_trimsec = '[3:1010,1:1015]'
biaslist = {}
flatlist1 = {}
flatlist2 = {}
objectlist = {}
filterlist1 = []
filterlist2 = []
for img in imglist:
_type = ''
img = re.sub('\n', '', img)
hdr = readhdr(img)
_naxis1 = readkey3(hdr, 'NAXIS1')
_naxis2 = readkey3(hdr, 'NAXIS2')
if _naxis1 != 1030 or _naxis2 != 1030:
ntt.util.writeinthelog(
'image ' + str(img) + ' different dimension =\n', './logNTT.txt')
_type = 'not good'
if not _type and readkey3(hdr, 'speed') != 'fastL':
_type = 'not good'
if not _type and readkey3(hdr, 'instrume') != 'efosc':
_type = 'not good'
_imagetype = readkey3(hdr, 'tech')
if not _type and _imagetype == 'SPECTRUM':
_type = 'spectroscopic data'
if not _type:
_exptime = readkey3(hdr, 'exptime')
_date = readkey3(hdr, 'date-night')
_filter = readkey3(hdr, 'filter')
if float(_exptime) == 0.0:
if _date not in biaslist:
biaslist[_date] = []
biaslist[_date].append(img)
_type = 'bias'
if not _type:
_object = readkey3(hdr, 'object')
if _filter.lower() in ['g782', 'r784', 'z623', 'u640', 'b639', 'v641', 'r642',
'i705'] and _imagetype == 'IMAGE':
if 'sky,flat' in _object.lower():
_type = 'flat'
elif 'dome' in _object.lower() or 'flat' in _object.lower():
_type = 'flat dome'
if _type == 'flat':
if _filter not in filterlist1:
filterlist1.append(_filter)
flatlist1[_filter] = []
flatlist1[_filter].append(img)
if _type == 'flat dome':
if _filter not in filterlist2:
filterlist2.append(_filter)
flatlist2[_filter] = []
flatlist2[_filter].append(img)
if not _type:
_catg = readkey3(hdr, 'catg')
if 'science' in _catg.lower() or 'acquisition' in _catg.lower():
_type = 'object'
if _filter not in objectlist:
objectlist[_filter] = []
objectlist[_filter].append(img)
if 'acquisition' in _catg.lower():
try:
correctcard(img)
_ra1, _dec1, _name = ntt.util.correctobject(
img, 'standard_efosc_mab.txt')
_ra1, _dec1, _name = ntt.util.correctobject(
img, filenameobjects)
except:
pass
elif 'focus' in _object.lower():
_type = 'not good'
if not _type:
print '\n### warning: object not recognized '
_object = readkey3(hdr, 'object')
print img, _object, _imagetype
answ = raw_input(
'what is it: bias [1], flat [3], object[4], test [5] ? [5] ')
if not answ:
answ = '5'
if answ == '1':
if _date not in biaslist:
biaslist[_date] = ()
biaslist[_date].append(img)
elif answ == '4':
if _filter not in objectlist:
objectlist[_filter] = []
objectlist[_filter].append(img)
elif answ == '3':
tt = raw_input('dome or sky [d/[s]] ? ')
if tt == 's':
_type = 'flat'
_filter = readkey3(hdr, 'filter')
if _filter not in filterlist1:
filterlist1.append(_filter)
flatlist1[_filter] = []
flatlist1[_filter].append(img)
elif tt == 'd':
_type = 'flat dome'
_filter = readkey3(hdr, 'filter')
if _filter not in filterlist2:
filterlist2.append(_filter)
flatlist2[_filter] = []
flatlist2[_filter].append(img)
elif answ == '5':
_type = 'not good'
filterlist = list(set(filterlist1 + filterlist2))
if _verbose:
print filterlist1
print filterlist2
print flatlist1
print flatlist2
flatlist = {}
for _filt in filterlist:
if _filt not in flatlist1.keys():
if _filt in flatlist2.keys():
if len(flatlist2[_filt]) >= 3:
flatlist[_filt] = flatlist2[_filt]
elif len(flatlist1[_filt]) < 3:
if _filt in flatlist2.keys():
if len(flatlist2[_filt]) >= 3:
flatlist[_filt] = flatlist2[_filt]
elif _filt in flatlist1.keys():
if len(flatlist1[_filt]) >= 3:
flatlist[_filt] = flatlist1[_filt]
listaout = []
if _verbose:
print '\n### flat ', str(flatlist), '\n'
print '\n### bias ', str(biaslist), '\n'
print '\n### object ', str(objectlist), '\n'
###### masterbias #################
if _dobias:
if not _archive:
if listbias:
masterbiaslist = listbias
else:
masterbiaslist = []
if biaslist:
for _date in biaslist:
print '\n do bias ' + str(_date) + '\n'
biaslist[_date] = rejectbias(
biaslist[_date], False, 10)
if len(biaslist[_date]) >= 3:
masterbiasfile = 'bias_' + \
str(_date) + '_' + str(MJDtoday) + '.fits'
delete(masterbiasfile)
f = open('biaslist', 'w')
h = open('obiaslist', 'w')
for img in biaslist[_date]:
f.write(img + '\n')
h.write('o' + img + '\n')
delete('o' + img)
f.close()
h.close()
try:
print 'processing bias .....'
iraf.ccdproc('@biaslist', output='@obiaslist', overscan="no", trim="yes", zerocor='no',
fixpix='no', ccdtype='', flatcor='no', darkcor='no', biassec='',
trimsec=str(_trimsec), readaxi='column', Stdout=1)
iraf.zerocombine('@obiaslist', output=masterbiasfile, combine='median',
reject='ccdclip', ccdtype='', process='no',
rdnoise=_rdnoise, gain=_gain, Stdout=1)
correctcard(masterbiasfile)
num = 0
for img in biaslist[_date]:
num = num + 1
ntt.util.updateheader(masterbiasfile, 0, {
'PROV' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file']})
ntt.util.updateheader(masterbiasfile, 0, {
'TRACE' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file']})
delete('o' + img)
ntt.util.updateheader(masterbiasfile, 0,
{'M_EPOCH': [False, 'TRUE if resulting from multiple epochs']})
ntt.util.updateheader(masterbiasfile, 0,
{'SINGLEXP': [False, 'TRUE if resulting from single exposure']})
ntt.util.updateheader(masterbiasfile, 0, {
'FILETYPE': [11201, 'bias']})
masterbiaslist.append(masterbiasfile)
if masterbiasfile not in outputfile:
outputfile.append(masterbiasfile)
except:
ntt.util.writeinthelog(
'Warning ' +
str(biaslist[_date]) +
' problem with this list of bias \n',
'./logNTT.txt')
if masterbiasfile and _interactive:
aa, bb, cc = display_image(
masterbiasfile, 1, '', '', False)
answ = raw_input(
'is the masterbias ok [[y]/n] ?')
if not answ:
answ = 'y'
if answ in ['n', 'no']:
sys.exit(
'remove bad bias from input list and restart')
else:
masterbiaslist = []
########## masterflat #########################
if _doflat:
if not _archive:
if listflat:
masterflatlist = listflat
else:
masterflatlist = []
if flatlist:
for _filter in flatlist:
print '\n do flat ' + str(_filter) + '\n'
flatlist[_filter] = rejectflat(
flatlist[_filter], False)
if len(flatlist[_filter]) >= 3:
_date = readkey3(
readhdr(flatlist[_filter][0]), 'date-night')
masterflat = 'flat_' + \
str(_date) + '_' + str(_filter) + \
'_' + str(MJDtoday) + '.fits'
listaflat = 'flatlist_' + \
str(_date) + '_' + str(_filter)
_bias = ''
if masterbiaslist:
_bias = searchbias(flatlist[_filter][
0], masterbiaslist)[0]
if not _bias:
_bias = searchbias(flatlist[_filter][0], '')[0]
if _bias:
if _bias[0] == '/':
os.system('cp ' + _bias + ' .')
_bias = string.split(_bias, '/')[-1]
_zerocor = 'yes'
else:
_zerocor = 'yes'
else:
_zerocor = 'no'
answ0 = 'n'
while answ0 != 'y':
f = open(listaflat, 'w')
h = open('o' + listaflat, 'w')
for img in flatlist[_filter]:
f.write(img + '\n')
h.write('o' + img + '\n')
delete('o' + img)
f.close()
h.close()
try:
print 'processing flat .....'
iraf.ccdproc('@' + listaflat, output='@o' + listaflat, overscan='no', trim='yes',
darkcor='no', fixpix='no',
zerocor=_zerocor, flatcor='no', trimsec=str(_trimsec), biassec='',
zero=_bias, readaxi='column', ccdtype='', Stdout=1)
delete(masterflat)
iraf.flatcombine('@o' + listaflat, output=masterflat, combine='average',
reject='avsigclip', ccdtype='', process='no',
rdnoise=_rdnoise, gain=_gain, statsec='[100:800,100:800]',
lsigma=3, hsigma=2, Stdout=1)
masterflatlist.append(masterflat)
correctcard(masterflat)
num = 0
for img in flatlist[_filter]:
num = num + 1
ntt.util.updateheader(masterflat, 0, {
'PROV' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file']})
ntt.util.updateheader(masterflat, 0, {
'TRACE' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file']})
delete('o' + img)
ntt.util.updateheader(
masterflat, 0, {'ZEROCOR': [_bias, '']})
ntt.util.updateheader(masterflat, 0, {
'M_EPOCH': [False, 'TRUE if resulting from multiple epochs']})
ntt.util.updateheader(masterflat, 0, {
'SINGLEXP': [False, 'TRUE if resulting from single exposure']})
ntt.util.updateheader(
masterflat, 0, {'FILETYPE': [11202, 'flat field']})
if masterflat not in outputfile:
outputfile.append(masterflat)
except:
ntt.util.writeinthelog(
'Warning ' +
str(flatlist[
_filter]) + ' problem with this list of flat \n',
'./logNTT.txt')
aa, bb, cc = display_image(
masterflat, 1, '', '', False)
if masterflat and _interactive:
answ = raw_input(
'is the masterflat ok [[y]/n] ?')
if not answ:
answ = 'y'
if answ.lower() in ['n', 'no']:
answ1 = raw_input(
'try again [[y]/n] ?')
if not answ1:
answ1 = 'y'
if answ1.lower() in ['y', 'yes']:
flatlist[_filter] = ntt.efoscphotredudef.rejectflat(
flatlist[_filter], True)
else:
sys.exit(
'error: problem with flat .... exit')
else:
answ0 = 'y'
else:
answ0 = 'y'
else:
masterflatlist = []
##########################################################################
if len(masterbiaslist) == 0:
masterbiaslist = ''
if len(masterflatlist) == 0:
masterflatlist = ''
######################################
if _verbose:
print ''
print '#############################'
print masterflatlist
print masterbiaslist
print '#############################'
print ''
if masterflatlist:
listaout = listaout + masterflatlist
if masterbiaslist:
listaout = listaout + masterbiaslist
if typefile == 'calib':
objectlist = {}
for _filter in objectlist:
for img in objectlist[_filter]:
hdr = readhdr(img)
print '\n#####################################################################\n'
_object = readkey3(hdr, 'object')
_object = re.sub(' ', '', _object)
_object = re.sub('/', '_', _object)
_object = re.sub('\n', '', _object)
_exptime = readkey3(hdr, 'exptime')
_date = readkey3(hdr, 'date-night')
nameout = ntt.util.name_duplicate(img, str(_object) + '_' + str(_date) + '_' + str(_filter) + '_' + str(
MJDtoday), '')
_bias = ''
if _dobias:
if masterbiaslist:
_bias = searchbias(img, masterbiaslist)[0]
if not _bias:
_bias = searchbias(img, '')[0]
_flat = ''
if _doflat:
if masterflatlist:
_flat = searchflat(img, masterflatlist)[0]
if not _flat:
_flat = searchflat(img, '')[0]
if _bias: # bias ###
if _bias[0] == '/':
os.system('cp ' + _bias + ' .')
_bias = string.split(_bias, '/')[-1]
_zerocor = 'yes'
else:
_zerocor = 'no'
else:
_zerocor = 'no'
if _flat: # flat ###
if _flat[0] == '/':
os.system('cp ' + _flat + ' .')
_flat = string.split(_flat, '/')[-1]
_flatcor = 'yes'
else:
_flatcor = 'no'
sss = str(_object) + '_' + str(_date) + '_' + str(_filter)
print '### input', img, sss
print '### bias ', _zerocor, _bias
print '### flat ', _flatcor, _flat
print '### name ', nameout
delete(nameout)
try:
iraf.ccdproc(img, output=nameout, overscan="no", trim="yes", zerocor=_zerocor, flatcor='no',
darkcor='no', trimsec=str(_trimsec), zero=_bias, biassec='', readaxi='column', Stdout=1)
try:
iraf.ccdproc(nameout, output='', overscan="no", trim="no", zerocor='no', flatcor=_flatcor,
darkcor='no', flat=_flat, readaxi='column', ccdtype='', Stdout=1)
except:
iraf.imrepla(images=_flat, value=0.01,
lower='INDEF', upper=0.01, radius=0)
iraf.ccdproc(nameout, output='', overscan="no", trim="no", zerocor='no', flatcor=_flatcor,
darkcor='no', flat=_flat, readaxi='column', ccdtype='', Stdout=1)
correctcard(nameout)
ntt.util.updateheader(nameout, 0, {'FILTER': [readkey3(readhdr(nameout), 'filter'), 'Filter name'],
'SINGLEXP': [True, 'TRUE if resulting from single exposure'],
'M_EPOCH': [False, 'TRUE if resulting from multiple epochs'],
'FLATCOR': [_flat, ''],
'ZEROCOR': [_bias, ''], 'FILETYPE': [12204, 'pre-reduced image'],
'PROV1': [readkey3(readhdr(nameout), 'ARCFILE'), 'Originating file'],
'NCOMBINE': [1, 'Number of raw science data'],
'TRACE1': [readkey3(readhdr(nameout), 'ARCFILE'),
'Originating file']})
ntt.util.airmass(nameout) # phase 3 definitions
ntt.util.writeinthelog('\n', './logNTT.txt')
ntt.util.writeinthelog(
'image= ' + str(img) + ' output= ' + str(nameout) + '\n', './logNTT.txt')
ntt.util.writeinthelog(
'bias= ' + str(_bias) + ', flat= ' + str(_flat) + '\n', './logNTT.txt')
ntt.util.writeinthelog('\n', './logNTT.txt')
if nameout not in outputfile:
outputfile.append(nameout)
except:
ntt.util.writeinthelog(
'image ' + str(img) + ' probably corrupted\n', './logNTT.txt')
if _dobadpixel:
if not badpixelmask:
badpixelmask = 'bad_pixel_mask.fits'
delete(badpixelmask)
os.system('cp ' + ntt.__path__[0] + '/archive/' + str(
_instrume) + '/badpixels/badpixel.fits ' + badpixelmask)
iraf.proto.fixpix(images=nameout, masks=badpixelmask,
linterp='INDEF', cinterp='INDEF', verbose='no')
ntt.util.updateheader(
nameout, 0, {'FIXPIX': [badpixelmask, '']})
ntt.util.writeinthelog('image ' + str(nameout) + ' bad pixel corrected with ' + badpixelmask + '\n',
'./logNTT.txt')
print '\n### bad pixel mask correction ..... done'
else:
ntt.util.writeinthelog(
'image ' + str(nameout) + ' bad pixel NOT corrected\n', './logNTT.txt')
if _cosmic:
try:
print '\n### cosmic ..... '
ntt.cosmics.lacos_im(nameout, _output='', gain=_gain, readn=_rdnoise, xorder=9, yorder=9,
sigclip=4.5, sigfrac=0.5, objlim=1, skyval=0, niter=0, verbose=True,
interactive=False)
ntt.util.updateheader(nameout, 0, {
'LACOSMIC': [True, 'TRUE if Laplacian cosmic ray rejection has been applied to the image']})
print '\n### cosmic ..... removed '
except Exception, e:
print e
else:
ntt.util.updateheader(nameout, 0, {
'LACOSMIC': [False, 'TRUE if Laplacian cosmic ray rejection has been applied to the image']})
try:
##########################
sexvec = ntt.efoscastrodef.sextractor(nameout)
for cat in ['2mass', 'usnoa2', 'usnob1']:
rmsx3, rmsy3, num3, fwhmgess, ellgess, ccc, rasys3, decsys3, magsat3 = ntt.efoscastrodef.efoscastroloop(
[nameout], cat, False, 40, 40, 100, 'rxyscale', 100, 30, sexvec, True, 10, method)
if rmsx3 <= 2 and rmsy3 <= 2:
break
if rmsx3 > 2 and rmsy3 > 2:
for cat in ['2mass', 'usnoa2', 'usnob1']:
rmsx3, rmsy3, num3, fwhmgess, ellgess, ccc, rasys3, decsys3, magsat3 = ntt.efoscastrodef.efoscastroloop(
[nameout], cat, False, 20, int(20), int(50), 'rxyscale', 100, 30, sexvec, True, 5, method)
if rmsx3 <= 2 and rmsy3 <= 2:
break
if rmsx3 > 2 and rmsy3 > 2:
for cat in ['2mass', 'usnoa2', 'usnob1']:
rmsx3, rmsy3, num3, fwhmgess, ellgess, ccc, rasys3, decsys3, magsat3 = ntt.efoscastrodef.efoscastroloop(
[nameout], cat, False, int(10), int(10),
int(25), 'rxyscale', 100, 30, sexvec, True, int(3), method)
##########################
astrostring = str(rmsx3) + ' ' + str(rmsy3) + ' ' + str(num3)
ntt.util.updateheader(
nameout, 0, {'ASTROMET': [astrostring, 'rmsx rmsy nstars']})
print '\n### check astrometry: fine \n### rmsx rmsy nstars: ' + astrostring
except Exception, e:
print e
rmsx3, rmsy3, num3, fwhmgess, ellgess, ccc, rasys3, decsys3, magsat3 = '', '', '', '', '', '', '', '', ''
print '\n### problem with astrometry, do you have network ? '
if fwhmgess and fwhmgess < 99:
ntt.util.updateheader(nameout, 0, {'PSF_FWHM': [fwhmgess, 'Spatial resolution (arcsec)'],
'ELLIPTIC': [ellgess, 'Average ellipticity of point sources'],
'CRDER1': [(1 / sqrt(2.)) * float(rmsx3) * (1. / 3600.),
'Random error (degree)'],
'CRDER2': [(1 / sqrt(2.)) * float(rmsy3) * (1. / 3600.),
'Random error (degree)'],
'CUNIT1': ['deg', 'unit of the coord. trans.'],
'CUNIT2': ['deg', 'unit of the coord. trans.'],
'CSYER1': [rasys3, 'Systematic error (RA_m - Ra_ref)'],
'CSYER2': [decsys3, 'Systematic error (DEC_m - DEC_ref)']})
else:
ntt.util.updateheader(nameout, 0, {'PSF_FWHM': [9999., 'FHWM (arcsec) - computed with sectractor'],
'ELLIPTIC': [9999., 'ellipticity of point sources (1-b/a)'],
'CRDER1': [9999., 'Random error in axis 1'],
'CRDER2': [9999., 'Random error in axis 2'],
'CUNIT1': ['deg', 'unit of the coord. trans.'],
'CUNIT2': ['deg', 'unit of the coord. trans.'],
'CSYER1': [9999., 'Systematic error (RA_m - Ra_ref)'],
'CSYER2': [9999., 'Systematic error (DEC_m - DEC_ref)']})
try:
result = ntt.efoscastrodef.zeropoint(
nameout, _system, method, False, False)
except:
result = ''
if result:
if os.path.isfile(re.sub('.fits', '.ph', nameout)):
if re.sub('.fits', '.ph', nameout) not in outputfile:
outputfile.append(
re.sub('.fits', '.ph', nameout))
print '\n### zeropoint ..... done'
for ll in result:
valore = '%3.3s %6.6s %6.6s' % (
str(ll), str(result[ll][1]), str(result[ll][0]))
print '### ', valore
ntt.util.updateheader(
nameout, 0, {'zp' + ll: [str(valore), '']})
if magsat3:
if readkey3(readhdr(nameout), 'FLUXCAL') == 'ABSOLUTE':
try:
ntt.util.updateheader(nameout, 0, {
'ABMAGSAT': [float(magsat3) + float(readkey3(readhdr(nameout)), 'PHOTZP'),
'Saturation limit for point sources (AB mags)']})
except:
ntt.util.updateheader(nameout, 0, {
'ABMAGSAT': [float(magsat3), 'Saturation limit for point sources (AB mags)']})
else:
ntt.util.updateheader(nameout, 0, {
'ABMAGSAT': [float(magsat3), 'Saturation limit for point sources (AB mags)']})
else:
ntt.util.updateheader(nameout, 0, {'ABMAGSAT': [
9999., 'Saturation limit for point sources (AB mags)']})
maglim = ntt.util.limmag(nameout)
if maglim:
ntt.util.updateheader(nameout, 0,
{'ABMAGLIM': [maglim, '5-sigma limiting AB magnitude for point sources']})
else:
ntt.util.updateheader(nameout, 0,
{'ABMAGLIM': [9999., '5-sigma limiting AB magnitude for point sources']})
if readkey3(readhdr(nameout), 'filter') in ['i705']:
try:
nameout, maskname = ntt.efoscphotredudef.fringing2(
nameout, fringingmask, _interactive, False)
if nameout not in outputfile:
outputfile.append(nameout)
if maskname not in outputfile:
outputfile.append(maskname)
except:
ntt.util.writeinthelog(
'image ' + str(nameout) + ' probably corrupted\n', './logNTT.txt')
print '\n### problem with fringing correction'
def fringe_sub(inlist, inpref='', outpref='s', mskpref='none', iternum=50, nsigma=3, bpm='none', second=False, fr_x0=0.0, fr_y0=0.0, fr_step=5, surfit=False, xsorder=4, ysorder=4):
# load IRAF package
iraf.proto()
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check output
outimg_arr = check_outpref(outpref, inimg_arr)
if isinstance(outimg_arr, int):
return 1
# check mask images
if mskpref.lower() == 'none' or mskpref == '':
mask = 0
else:
mask = 1
mskimg_arr = check_inpref(mskpref, inimg_arr)
if isinstance(mskimg_arr, int):
return 1
# check bad pixel mask
if bpm.lower() != 'none':
bpmex = 1
if os.access(bpm, os.R_OK) == False:
print >> sys.stderr, 'cannot read bad pixel mask (%s)' % bpm
return 1
else:
bpmex = 0
# prefix for temoprary images
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# subtract sky
for i in range(len(inimg_arr)):
if mask == 1:
iraf.unlearn('mimstat')
if not second:
ret = iraf.mimstat(inimg_arr[i], imasks=mskimg_arr[i]+'[pl]', format='no', fields='midpt', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
else:
if bpmex == 1:
tmp_mask = tmp_prefix + os.path.basename(mask_img_arr[i])
iraf.imarith(maskimg_arr[i], '+', bpm, tmp_mask, verbose='no')
convert_maskfits_int(tmp_mask, tmp_mask)
ret = iraf.mimstat(inimg_arr[i], imasks=tmp_mask, format='no', fields='midpt', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
else:
ret = iraf.mimstat(inimg_arr[i], imasks=mskimg_arr[i], format='no', fields='midpt', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
else:
iraf.unlearn('imstat')
ret = iraf.imstat(inimg_arr[i], format='no', fields='midpt', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
if len(ret) == 1:
objmed = float(ret[0])
else :
print >> sys.stderr, 'failed to calculate median of the object background'
return 1
# record input fits data into memory
im = pyfits.open(inimg_arr[i])
im_hdr = im[0].header
im_data = im[0].data
out_data = im_data - objmed
if surfit:
out_data_org = np.copy(out_data)
im.close()
# fringe subtraction for NB imaging
if mask == 1:
if bpmex == 1:
tmp_mask = tmp_prefix + os.path.basename(mask_img_arr[i])
mask_im = pyfits.open(tmp_mask)
else:
mask_im = pyfits.open(mskimg_arr[i])
if not second:
mask_data = mask_im[1].data
else:
mask_data = mask_im[0].data
mask_im.close()
x = np.ones(out_data.shape[0]*out_data.shape[1]).reshape(out_data.shape[0],out_data.shape[1])* np.arange(out_data.shape[1]) + 1
y = (np.ones(out_data.shape[1]*out_data.shape[0]).reshape(out_data.shape[1],out_data.shape[0]) * np.arange(out_data.shape[0]) + 1).T
r = np.sqrt((x-fr_x0)*(x-fr_x0) + (y-fr_y0)*(y-fr_y0))
rmax = np.max(r)
rmin = np.min(r)
r1 = rmin
while r1 <= rmax:
r2 = r1 + fr_step
idx = np.logical_and(r>=r1, r<r2)
out_subset = out_data[idx]
if mask == 1:
mask_subset = mask_data[idx]
idx_sky = np.where(mask_subset == 0)
#if len(out_subset[idx_sky]) != len(out_subset):
# print "masked:",inimg_arr[i],r1,r2,fr_x0,fr_y0,len(out_subset[idx_sky]),"/",len(out_subset),meanclip(out_subset[idx_sky],median=1)[0],meanclip(out_subset, median=1)[0]
med, sigma = meanclip(out_subset[idx_sky],median=1)
else:
med, sigma = meanclip(out_data[idx],median=1)
out_data[idx] = out_data[idx] - med
r1 += fr_step
# save output fits file
hdu = pyfits.PrimaryHDU(out_data)
imgout = pyfits.HDUList([hdu])
imgout[0].header = im_hdr
if surfit:
tmp_fsub = tmp_prefix + "fsub" + os.path.basename(inimg_arr[i])
tmp_fsub_fit = tmp_prefix + "fsub_fit" + os.path.basename(inimg_arr[i])
imgout.writeto(tmp_fsub)
imgout.close()
iraf.imsurfit(tmp_fsub, tmp_fsub_fit, xsorder, ysorder)
imfit = pyfits.open(tmp_fsub_fit)
imfit_data = imfit[0].data
out_data_fs = np.copy(out_data_org - imfit_data)
r1 = rmin
while r1 <= rmax:
r2 = r1 + fr_step
idx = np.logical_and(r>=r1, r<r2)
out_subset = out_data_fs[idx]
if mask == 1:
mask_subset = mask_data[idx]
idx_sky = np.where(mask_subset == 0)
med, sigma = meanclip(out_subset[idx_sky],median=1)
else:
med, sigma = meanclip(out_data_fs[idx],median=1)
out_data_org[idx] = out_data_org[idx] - med
r1 += fr_step
hdu = pyfits.PrimaryHDU(out_data_org)
imgout = pyfits.HDUList([hdu])
imgout[0].header = im_hdr
imgout.writeto(outimg_arr[i])
imgout.close()
remove_temp_all(tmp_prefix)
else:
imgout.writeto(outimg_arr[i])
imgout.close()
if bpmex == 1:
remove_temp_all(tmp_prefix)
return 0
def makefringingmask(listimg, _output, _interactive, _combine='average', _rejection='avsigclip'):
# print "LOGX:: Entering `makefringingmask` method/function in
# %(__file__)s" % globals()
import ntt
from ntt.util import readhdr, readkey3, delete, updateheader
import glob
import os
import sys
import re
import string
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.immatch(_doprint=0)
iraf.imutil(_doprint=0)
iraf.nproto(_doprint=0)
iraf.proto(_doprint=0)
toforget = ['nproto.objmasks', 'proto.fixpix']
for t in toforget:
iraf.unlearn(t)
if _interactive == True:
listimg2 = []
for img in listimg:
_exptime = readkey3(readhdr(img), 'exptime')
if float(_exptime) >= 10:
answ = 'xxx'
while answ.lower() not in ['y', 'n', 's', 'a']:
iraf.display(img, frame=1, fill='yes')
answ = raw_input(
'use this image (yes,no,stop (not more images),all) [[y]/n/s/a] ? ')
if not answ:
answ = 'y'
if answ.lower() == 'y':
listimg2.append(img)
elif answ.lower() == 'a':
listimg2 = listimg[:]
if answ.lower() in ['a', 's']:
break
listimg = listimg2[:]
iraf.nproto.objmasks1.fitxord = 1
iraf.nproto.objmasks1.fityord = 1
hdr0 = readhdr(listimg[0])
_date = readkey3(hdr0, 'date-obs')
_filter = readkey3(hdr0, 'filter')
_exptime = readkey3(hdr0, 'exptime')
_instrume = readkey3(hdr0, 'instrume')
_ron = readkey3(hdr0, 'ron')
_gain = readkey3(hdr0, 'gain')
badpixelmask = 'bad_pixel_mask.pl'
if not os.path.isfile(badpixelmask):
os.system('cp ' + ntt.__path__[0] + '/archive/' + _instrume +
'/badpixels/badpixel_20100210.pl ' + badpixelmask)
ff = open('_listmask', 'w')
hh = open('_listobgz', 'w')
for img in listimg:
_exptime = readkey3(readhdr(img), 'exptime')
hh.write('z_' + img + '\n')
ff.write('mask_' + img + '\n')
delete('mask_' + img)
aaa = iraf.hedit(img, delete='yes', field='OBJMASK',
up='yes', verify='no', Stdout=1)
aaa = iraf.hedit(img, delete='yes', field='BPM',
up='yes', verify='no', Stdout=1)
delete('z_' + img)
iraf.imutil.imexpr(expr='(a - median(a))/' + str(_exptime),
a=img, output='z_' + img, verbose='no')
ntt.util.updateheader('z_' + img, 0, {'EXPTIME': [1, '']})
ff.close()
hh.close()
if not _output:
_output = 'fringing_' + str(_date) + '_' + str(_filter) + '.fits'
delete(_output)
print ' making mask for each frame .......'
ccc = iraf.nproto.objmasks(images='@_listobgz', objmasks='@_listmask', omtype='boolean',
blksize=-16, convolv='block 3 3', hsigma=5, lsigma=3, minpix=10, ngrow=2, agrow=4., Stdout=1)
print 'combining all frames, masking the objects .....'
iraf.imcombine('@_listobgz', output=_output, masktyp='!OBJMASK', maskval=0, combine=_combine, reject=_rejection,
scale='none', statsec='[100:800,100:800]', rdnoise='', gain='', nlow=1, nhigh=1, logfile='imcombinelog')
ntt.util.phase3header(_output)
ntt.util.updateheader(
_output, 0, {'BUNIT': ['ADU', 'pixel units(ADU,electrons)']})
ntt.util.updateheader(_output, 0, {'FILETYPE': [11231, 'fringing frame']})
return _output
MedianR=np.median(RatioHL)
StdR=np.std(RatioHL)
MadR=np.median(np.abs(RatioHL-MedianR))
print("Median Ratio = %f , Std = %f Mad =%f "%(MedianR,StdR,MadR))
#(XLbp,YLbp)=np.where(RatioHL < MedianR-LsigCut*StdR)
#(XHbp,YHbp)=np.where(RatioHL > MedianR+HsigCut*StdR)
(XLbp,YLbp)=np.where(RatioHL < MedianR-LsigCut*MadR*1.4826)
(XHbp,YHbp)=np.where(RatioHL > MedianR+HsigCut*MadR*1.4826)
OutFILE=open(Outfname,'w')
for X,Y in zip(XLbp,YLbp): OutFILE.write(str(Y+1)+' '+str(X+1)+'\n')
for X,Y in zip(XHbp,YHbp): OutFILE.write(str(Y+1)+' '+str(X+1)+'\n')
OutFILE.close()
print("Text file of list of Bad pixels created in "+Outfname)
if Createpl :
iraf.proto(_doprint=0)
iraf.text2mask.unlearn()
iraf.text2mask(text=Outfname,mask=Outfile,ncols=1024,nlines=1024)
print("Binary .pl file of Bad pixels created in "+Outfile)
print("To view mask in IRAF : display "+ImgH+" 1 overlay="+Outfile)
def makefringingmask(listimg,
_output,
_interactive,
_combine='average',
_rejection='avsigclip'):
# print "LOGX:: Entering `makefringingmask` method/function in
# %(__file__)s" % globals()
import ntt
from ntt.util import readhdr, readkey3, delete, updateheader
import glob
import os
import sys
import re
import string
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.immatch(_doprint=0)
iraf.imutil(_doprint=0)
iraf.nproto(_doprint=0)
iraf.proto(_doprint=0)
toforget = ['nproto.objmasks', 'proto.fixpix']
for t in toforget:
iraf.unlearn(t)
if _interactive == True:
listimg2 = []
for img in listimg:
_exptime = readkey3(readhdr(img), 'exptime')
if float(_exptime) >= 10:
answ = 'xxx'
while answ.lower() not in ['y', 'n', 's', 'a']:
iraf.display(img, frame=1, fill='yes')
answ = raw_input(
'use this image (yes,no,stop (not more images),all) [[y]/n/s/a] ? '
)
if not answ:
answ = 'y'
if answ.lower() == 'y':
listimg2.append(img)
elif answ.lower() == 'a':
listimg2 = listimg[:]
if answ.lower() in ['a', 's']:
break
listimg = listimg2[:]
iraf.nproto.objmasks1.fitxord = 1
iraf.nproto.objmasks1.fityord = 1
hdr0 = readhdr(listimg[0])
_date = readkey3(hdr0, 'date-obs')
_filter = readkey3(hdr0, 'filter')
_exptime = readkey3(hdr0, 'exptime')
_instrume = readkey3(hdr0, 'instrume')
_ron = readkey3(hdr0, 'ron')
_gain = readkey3(hdr0, 'gain')
badpixelmask = 'bad_pixel_mask.pl'
if not os.path.isfile(badpixelmask):
os.system('cp ' + ntt.__path__[0] + '/archive/' + _instrume +
'/badpixels/badpixel_20100210.pl ' + badpixelmask)
ff = open('_listmask', 'w')
hh = open('_listobgz', 'w')
for img in listimg:
_exptime = readkey3(readhdr(img), 'exptime')
hh.write('z_' + img + '\n')
ff.write('mask_' + img + '\n')
delete('mask_' + img)
aaa = iraf.hedit(img,
delete='yes',
field='OBJMASK',
up='yes',
verify='no',
Stdout=1)
aaa = iraf.hedit(img,
delete='yes',
field='BPM',
up='yes',
verify='no',
Stdout=1)
delete('z_' + img)
iraf.imutil.imexpr(expr='(a - median(a))/' + str(_exptime),
a=img,
output='z_' + img,
verbose='no')
ntt.util.updateheader('z_' + img, 0, {'EXPTIME': [1, '']})
ff.close()
hh.close()
if not _output:
_output = 'fringing_' + str(_date) + '_' + str(_filter) + '.fits'
delete(_output)
print ' making mask for each frame .......'
ccc = iraf.nproto.objmasks(images='@_listobgz',
objmasks='@_listmask',
omtype='boolean',
blksize=-16,
convolv='block 3 3',
hsigma=5,
lsigma=3,
minpix=10,
ngrow=2,
agrow=4.,
Stdout=1)
print 'combining all frames, masking the objects .....'
iraf.imcombine('@_listobgz',
output=_output,
masktyp='!OBJMASK',
maskval=0,
combine=_combine,
reject=_rejection,
scale='none',
statsec='[100:800,100:800]',
rdnoise='',
gain='',
nlow=1,
nhigh=1,
logfile='imcombinelog')
ntt.util.phase3header(_output)
ntt.util.updateheader(_output, 0,
{'BUNIT': ['ADU', 'pixel units(ADU,electrons)']})
ntt.util.updateheader(_output, 0, {'FILETYPE': [11231, 'fringing frame']})
return _output
def mkcalflat(calonlst, calofflst, outimg, mask='none'):
# load IRAF package
iraf.proto()
# open cal off list and check if files exist
on_list = check_input(calonlst, '')
if len(on_list) == 1 and on_list[0] == 1:
return 1
# open cal off list and check if files exist
off_list = check_input(calofflst, '')
if len(off_list) == 1 and off_list[0] == 1:
return 1
# check if output file exists
if os.access(outimg, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % outimg
return 1
# prefix for temporary images and files
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# check exptime, coadds, ndr, slwcnt for off frames
tmp_off_list = tmp_prefix + '_off.lst'
fout = open(tmp_off_list, 'w')
err = 0
for i in range(len(off_list)):
im = pyfits.open(off_list[i])
im_exptime = float(im[0].header['EXP1TIME'])
im_coadds = int(im[0].header['COADDS'])
im_ndr = int(im[0].header['NDR'])
im_slwcnt = int(im[0].header['I_SLWCNT'])
if i == 0:
off_exptime = im_exptime
off_coadds = im_coadds
off_ndr = im_ndr
off_slwcnt = im_slwcnt
else:
if im_exptime != off_exptime:
print >> sys.stderr, 'exptime for %s is not consistent with the first frame (%s)' % (
off_list[i], off_list[0])
err += 1
if im_coadds != off_coadds:
print >> sys.stderr, 'coadds for %s is not consistent with the first frame (%s)' % (
off_list[i], off_list[0])
err += 1
if im_ndr != off_ndr:
print >> sys.stderr, 'ndr for %s is not consistent with the first frame (%s)' % (
off_list[i], off_list[0])
err += 1
if im_slwcnt != off_slwcnt:
print >> sys.stderr, 'slwcnt for %s is not consistent with the first frame (%s)' % (
off_list[i], off_list[0])
err += 1
im.close()
fout.write('%s\n' % off_list[i])
fout.close()
# check error
if err != 0:
remove_temp_all(tmp_prefix)
return 1
# check exptime, coadds, ndr, slwcnt for on frames
tmp_on_list = tmp_prefix + '_on.lst'
fout = open(tmp_on_list, 'w')
for i in range(len(on_list)):
im = pyfits.open(on_list[i])
im_exptime = float(im[0].header['EXP1TIME'])
im_coadds = int(im[0].header['COADDS'])
im_ndr = int(im[0].header['NDR'])
im_slwcnt = int(im[0].header['I_SLWCNT'])
if i == 0:
on_exptime = im_exptime
on_coadds = im_coadds
on_ndr = im_ndr
on_slwcnt = im_slwcnt
else:
if im_exptime != on_exptime:
print >> sys.stderr, 'exptime for %s is not consistent with the first frame (%s)' % (
on_list[i], on_list[0])
err += 1
if im_coadds != on_coadds:
print >> sys.stderr, 'coadds for %s is not consistent with the first frame (%s)' % (
on_list[i], on_list[0])
err += 1
if im_ndr != on_ndr:
print >> sys.stderr, 'ndr for %s is not consistent with the first frame (%s)' % (
on_list[i], on_list[0])
err += 1
if im_slwcnt != on_slwcnt:
print >> sys.stderr, 'slwcnt for %s is not consistent with the first frame (%s)' % (
on_list[i], on_list[0])
err += 1
im.close()
fout.write('%s\n' % on_list[i])
fout.close()
# check error
if err != 0:
remove_temp_all(tmp_prefix)
return 1
# check consistency of EXPTIME, COADDS, NDR and SLWCNT between on and off frames
if on_exptime != off_exptime:
print >> sys.stderr, 'exptime for on and off frames are not consistent'
err += 1
if on_coadds != off_coadds:
print >> sys.stderr, 'coadds for on and off frames are not consistent'
err += 1
if on_ndr != off_ndr:
print >> sys.stderr, 'ndr for on and off frames are not consistent'
err += 1
if on_slwcnt != off_slwcnt:
print >> sys.stderr, 'slwcnt for on and off frames are not consistent'
err += 1
# check error
if err != 0:
remove_temp_all(tmp_prefix)
return 1
# combine off frames
tmpoff = tmp_prefix + '_off.fits'
iraf.unlearn('imcombine')
iraf.imcombine.combine = 'median'
iraf.imcombine.reject = 'sigclip'
iraf.imcombine('@' + tmp_off_list, tmpoff)
# combine on frames
tmpon = tmp_prefix + '_on.fits'
tmp.close()
iraf.unlearn('imcombine')
iraf.imcombine.combine = 'median'
iraf.imcombine.reject = 'sigclip'
iraf.imcombine('@' + tmp_on_list, tmpon)
# subtract off from on
iraf.unlearn('imarith')
iraf.imarith(tmpon, "-", tmpoff, outimg, verbose='no')
# delete temporary files
remove_temp_all(tmp_prefix)
# calculate average
if mask == 'none':
iraf.unlearn('imstat')
res = iraf.imstat(outimg,
format='no',
fields='mean',
nclip=50,
Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate avarage of the combined frame'
os.remove(outimg)
return 1
else:
if os.access(mask, os.R_OK):
iraf.unlearn('mimstat')
res = iraf.mimstat(outimg,
imasks=mask,
format='no',
fields='mean',
nclip=50,
Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate masked avarage of the combined frame'
os.remove(outimg)
return 1
else:
print >> sys.stderr, 'mask frame (%s) does not exist' % mask
os.remove(outimg)
return 1
# divide by average count
iraf.unlearn('imarith')
iraf.imarith(outimg, "/", ave, outimg, verbose='no')
return 0
def mkskyflat(inlist, output, inpref='', mskpref='m', bpm='none', iternum=50, nsigma=3, minpix=250, hsigma=3, lsigma=10, conv='block 3 3'):
# load IRAF package
iraf.proto()
iraf.nproto()
# open input list and check if it exists
inimg_arr = check_input(inlist, inpref)
if len(inimg_arr) == 1 and inimg_arr[0] == 1:
return 1
# check if output image is already exist
if os.access(output, os.R_OK) :
print >> sys.stderr, 'operation would overwrite existing file (%s)' % output
return 1
# check optional parameters
if bpm.lower() == 'none' or bpm == '':
bpmex = 0
else:
bpmex = 1
if not os.access(bpm, os.R_OK):
print >> sys.stderr, 'cannot read bad pixel mask (%s)' % bpm
return 1
if iternum <= 0:
print >> sys.stderr, 'invalid iternum value (%d)' % iternum
return 1
if nsigma <= 0:
print >> sys.stderr, 'invalid nsigma value (%f)' % nsigma
return 1
# prefix for temoprary images
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# temporary normalize raw frames by average
tmp_list = tmp_prefix+'.lst'
fout = open(tmp_list, 'w')
for i in range(len(inimg_arr)):
tmpimg = tmp_prefix+os.path.basename(inimg_arr[i])
fout.write('%s\n' % tmpimg)
# remove temporary files if it exists
if os.access(tmpimg, os.R_OK):
os.remove(tmpimg)
# calculate average counts in each frame
if bpmex == 0:
iraf.unlearn('imstat')
res = iraf.imstat(inimg_arr[i], format='no', fields='mean', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate avarage from the frame (%s)' % inimg
remove_temp_all(tmp_prefix)
fout.close()
return 1
else:
iraf.unlearn('mimstat')
res = iraf.mimstat(inimg_arr[i], imasks=bpm, format='no', fields='mean', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate masked avarage from the frame (%s)'% inimg
remove_temp_all(tmp_prefix)
fout.close()
return 1
# normalize each frame by average
iraf.unlearn('imarith')
iraf.imarith(inimg_arr[i], '/', ave, tmpimg, verbose='no')
# close output file handler
fout.close()
# make temporary sky flat frame
tmp_flat = tmp_prefix+'_sflat.fits'
ret = zcombine(tmp_list, tmp_flat, combine='median', reject='sigclip')
if ret != 0:
remove_temp_all(tmp_prefix)
return 1
# temporary flat field
iraf.unlearn('imarith')
tmpff_list = tmp_prefix+'_ff.lst'
fout = open(tmpff_list, 'w')
for i in range(len(inimg_arr)):
tmpimg = tmp_prefix+'_ff_'+os.path.basename(inimg_arr[i])
fout.write('%s\n' % tmpimg)
# remove temporary files if it exists
if os.access(tmpimg, os.R_OK):
os.remove(tmpimg)
# divided by temporary flat
iraf.imarith(inimg_arr[i], '/', tmp_flat, tmpimg, verbose='no')
# read image fits header
im = pyfits.open(inimg_arr[i])
exptime = float(im[0].header['EXP1TIME'])
coadds = int(im[0].header['COADDS'])
ndr = int(im[0].header['NDR'])
im.close()
# normalize by coadds, ndr, and exptime values
iraf.imarith(tmpimg, '/', (coadds*ndr*exptime), tmpimg, verbose='no')
# close output file handler
fout.close()
# remove temporary flat
os.remove(tmp_flat)
# calculate standard deviation of the temporary flat divided images
try:
iraf.unlearn('imstat')
stddev = iraf.imstat('@'+tmpff_list, format='no', fields='stddev', nclip=50, Stdout=1)
except:
remove_temp_all(tmp_prefix)
return 1
# calculate mean and sigma of the stddev of the image
y = map(float, stddev)
mean, sigma = meanclip(np.array(y, dtype=float))
#print mean, sigma
# flag bad frames
frame_flag = []
for i in range(len(stddev)):
im = pyfits.open(inimg_arr[i], mode='update')
if math.fabs(y[i] - mean) < nsigma * sigma:
frame_flag.append(0)
im[0].header['PATTERN'] = 0
else:
frame_flag.append(1)
im[0].header['PATTERN'] = 1
im.close()
# make object mask
ret = mkobjmask(tmpff_list, inpref='', outpref=mskpref, bpm=bpm, minpix=minpix, hsigma=hsigma, lsigma=lsigma, conv=conv)
# rename mask file
mskimg_arr = []
for i in range(len(inimg_arr)):
base = os.path.basename(inimg_arr[i])
tmpimg = tmp_prefix+'_ff_'+base
maskimg = mskpref+base
tmpmask = mskpref+os.path.basename(tmpimg)
move(tmpmask, maskimg)
mskimg_arr.append(maskimg)
# delete temporary flat fielded files
remove_temp_all(tmp_prefix)
# check mkobjmask result
if ret != 0:
return 1
# nomalize by object masked average
fout = open(tmp_list, 'w')
for i in range(len(inimg_arr)):
if frame_flag[i] == 0:
tmpimg = tmp_prefix+os.path.basename(inimg_arr[i])
maskimg = mskimg_arr[i]
tmpmask = tmp_prefix+maskimg
fout.write('%s\n' % tmpimg)
if os.access(tmpimg, os.R_OK):
os.remove(tmpimg)
if os.access(tmpmask, os.R_OK):
os.remove(tmpmask)
if bpmex == 1:
iraf.unlearn('imarith')
iraf.imarith(maskimg+'[pl]','+',bpm,tmpmask,verbose='no')
else :
iraf.unlearn('imcopy')
iraf.imcopy(maskimg+'[pl]',tmpmask,verbose='no')
iraf.unlearn('mimstat')
# calculate average of the raw images with mask
res = iraf.mimstat(inimg_arr[i], imasks=tmpmask, format='no', fields='mean', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate masked avarage from the frame (%s)' % inimg_arr[i]
remove_temp_all(tmp_prefix)
return 1
# normalize each frame by average
iraf.unlearn('imarith')
iraf.imarith(inimg_arr[i], '/', ave, tmpimg, verbose='no')
# add mask into header
iraf.unlearn('hedit')
iraf.hedit(tmpimg, 'objmask', maskimg+'[pl]', add='yes', verify='no')
# close output file handler
fout.close()
# combine
ret = zcombine(tmp_list, output, combine='median', reject='sigclip', masktype='!objmask')
if ret != 0:
print >> sys.stderr, 'failed to combine normalized frames'
remove_temp_all(tmp_prefix)
return 1
# remove temoprary files
remove_temp_all(tmp_prefix)
return 0
def marksn2(img, fitstab, frame=1, fitstab2='', verbose=False):
from pyraf import iraf
from numpy import array #,log10
import lsc
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.images(_doprint=0)
iraf.imcoords(_doprint=0)
iraf.proto(_doprint=0)
iraf.set(stdimage='imt1024')
hdr = lsc.util.readhdr(fitstab)
_filter = lsc.util.readkey3(hdr, 'filter')
column = lsc.lscabsphotdef.makecatalogue([fitstab])[_filter][fitstab]
rasex = array(column['ra0'], float)
decsex = array(column['dec0'], float)
if fitstab2:
hdr = lsc.util.readhdr(fitstab2)
_filter = lsc.util.readkey3(hdr, 'filter')
_exptime = lsc.util.readkey3(hdr, 'exptime')
column = lsc.lscabsphotdef.makecatalogue([fitstab2])[_filter][fitstab2]
rasex2 = array(column['ra0'], float)
decsex2 = array(column['dec0'], float)
iraf.set(stdimage='imt1024')
iraf.display(img + '[0]', frame, fill=True, Stdout=1)
vector = []
for i in range(0, len(rasex)):
vector.append(str(rasex[i]) + ' ' + str(decsex[i]))
xy = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector,Stdout=1,image=img+'[0]',inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(frame,
'STDIN',
Stdin=list(xy),
mark="circle",
number='yes',
label='no',
radii=10,
nxoffse=5,
nyoffse=5,
color=207,
txsize=2)
if verbose:
# print 2.5*log10(_exptime)
for i in range(0, len(column['ra0'])):
print xy[i], column['ra0'][i], column['dec0'][i], column['magp3'][
i], column['magp4'][i], column['smagf'][i], column['magp2'][i]
if fitstab2:
vector2 = []
for i in range(0, len(rasex2)):
vector2.append(str(rasex2[i]) + ' ' + str(decsex2[i]))
xy1 = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector2,Stdout=1,image=img+'[0]',inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(frame,
'STDIN',
Stdin=list(xy1),
mark="cross",
number='yes',
label='no',
radii=10,
nxoffse=5,
nyoffse=5,
color=205,
txsize=2)
import numpy
import time
from types import *
# Local packages
import add_wcs
import iqpkg
from iqutils import *
# Necessary IRAF packages
iraf.imred()
iraf.ccdred()
iraf.images()
iraf.imutil()
iraf.imfit()
iraf.proto()
# Shortcuts
yes=iraf.yes
no=iraf.no
INDEF=iraf.INDEF
hedit=iraf.hedit
imgets=iraf.imgets
# Pyraf parameters: Where to find them
pyrafdir="python/pyraf/"
pyrafdir_key='PYRAFPARS'
if os.environ.has_key(pyrafdir_key):
pardir=os.environ[pyrafdir_key]
else:
def mkskyflat_with_mask(inlist,
output,
inpref='',
mskpref='m',
bpm='none',
iternum=50,
nsigma=3):
# load IRAF package
iraf.proto()
iraf.nproto()
# open input list and check if it exists
inimg_arr = check_input(inlist, inpref)
if len(inimg_arr) == 1 and inimg_arr[0] == 1:
return 1
# check if output image is already exist
if os.access(output, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing file (%s)' % output
return 1
# check mask images
if mskpref.lower() == 'none' or mskpref == '':
print >> sys.stderr, 'mask frames are required for this task'
return 1
else:
mskimg_arr = check_inpref(mskpref, inimg_arr)
if isinstance(mskimg_arr, int):
return 1
# check optional parameters
if bpm.lower() == 'none' or bpm == '':
bpmex = 0
else:
bpmex = 1
if not os.access(bpm, os.R_OK):
print >> sys.stderr, 'cannot read bad pixel mask (%s)' % bpm
return 1
if iternum <= 0:
print >> sys.stderr, 'invalid iternum value (%d)' % iternum
return 1
if nsigma <= 0:
print >> sys.stderr, 'invalid nsigma value (%f)' % nsigma
return 1
# prefix for temoprary images
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# nomalize by object masked average
tmp_list = tmp_prefix + '.lst'
fout = open(tmp_list, 'w')
for i in range(len(inimg_arr)):
im = pyfits.open(inimg_arr[i], mode='update')
try:
pattern = im[0].header['PATTERN']
except KeyError:
pattern = 0
im.close()
if pattern == 0:
tmpimg = tmp_prefix + os.path.basename(inimg_arr[i])
maskimg = mskimg_arr[i]
tmpmask = tmp_prefix + maskimg
fout.write('%s\n' % tmpimg)
if os.access(tmpimg, os.R_OK):
os.remove(tmpimg)
if os.access(tmpmask, os.R_OK):
os.remove(tmpmask)
if bpmex == 1:
iraf.unlearn('imarith')
#iraf.imarith(maskimg+'[pl]','+',bpm,tmpmask,verbose='no')
iraf.imarith(maskimg, '+', bpm, tmpmask, verbose='no')
else:
iraf.unlearn('imcopy')
#iraf.imcopy(maskimg+'[pl]',tmpmask,verbose='no')
iraf.imcopy(maskimg, tmpmask, verbose='no')
iraf.unlearn('mimstat')
res = iraf.mimstat(inimg_arr[i],
imasks=tmpmask,
format='no',
fields='mean',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate masked avarage from the frame (%s)' % inimg_arr[
i]
remove_temp_all(tmp_prefix)
return 1
# normalize each frame by average
iraf.unlearn('imarith')
iraf.imarith(inimg_arr[i], '/', ave, tmpimg, verbose='no')
# add mask into header
iraf.unlearn('hedit')
if mskpref[0] != ':':
iraf.hedit(tmpimg, 'objmask', maskimg, add='yes', verify='no')
# close output file handler
fout.close()
# combine
ret = zcombine(tmp_list,
output,
combine='median',
reject='sigclip',
masktype='!objmask')
if ret != 0:
print >> sys.stderr, 'failed to combine normalized frames'
remove_temp_all(tmp_prefix)
return 1
# remove temoprary files
remove_temp_all(tmp_prefix)
return 0
def absphot(img,_field,_catalogue,_fix,_color,rejection,_interactive,_type='fit',redo=False,show=False,cutmag=-1,database='dataredulco',_calib='sloan'):
from astropy.io import fits
import lsc
import math
import sys,re,string,os
from lsc.util import readkey3, readhdr
from numpy import array, compress, zeros, median, std, asarray, isfinite,mean
from pyraf import iraf
if show:
from pylab import ion,plot,draw,clf
import time
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.images(_doprint=0)
iraf.imcoords(_doprint=0)
iraf.proto(_doprint=0)
t = fits.open(img)
tbdata = t[1].data
hdr2=t[1].header
hdr=lsc.util.readhdr(img)
_cat=readkey3(hdr,'catalog')
_telescope=lsc.util.readkey3(hdr,'telescop')
_instrume=lsc.util.readkey3(hdr,'instrume')
_filter=lsc.util.readkey3(hdr,'filter')
_airmass=lsc.util.readkey3(hdr,'airmass')
_exptime=lsc.util.readkey3(hdr,'exptime')
_date=lsc.util.readkey3(hdr,'date-obs')
_object=lsc.util.readkey3(hdr,'object')
_ra=lsc.util.readkey3(hdr,'RA')
_dec=lsc.util.readkey3(hdr,'DEC')
print _filter
if _telescope in ['lsc','1m0-04','1m0-05','1m0-09']: kk=lsc.sites.extintion('ctio')
elif _telescope in ['elp','1m0-08']: kk=lsc.sites.extintion('mcdonald')
elif _telescope in ['cpt','1m0-12','1m0-10','1m0-13']: kk=lsc.sites.extintion('southafrica')
elif _telescope in ['ftn','Faulkes Telescope North']: kk=lsc.sites.extintion('mauna')
elif _telescope in ['1m0-03','1m0-11','coj','fts','Faulkes Telescope South']: kk=lsc.sites.extintion('siding')
if _calib not in ['sloan','sloanprime','natural','apass','']: colorefisso=lsc.sites.colfix(_instrume)
else: colorefisso=lsc.sites.colfix(_instrume,_calib)
print redo
print _cat
if _cat and not redo:
print 'already calibrated'
else:
try:
lsc.mysqldef.updatevalue(database,'zcat','X',string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
if os.path.isfile(string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1]):
lsc.mysqldef.updatevalue(database,'zcat','X',string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
except: print 'module mysqldef not found'
column=makecatalogue([img])[_filter][img]
rasex=array(column['ra0'],float)
decsex=array(column['dec0'],float)
if _type=='fit':
magsex=array(column['smagf'],float)
magerrsex=array(column['smagerrf'],float)
elif _type=='ph':
magsex=array(column['magp3'],float)
magerrsex=array(column['merrp3'],float)
else: sys.exit(_type+' not valid (ph or fit)')
print len(rasex)
if not cutmag: cutmag=99
#else: cutmag= cutmag-2.5*math.log10(float(_exptime))
if len(compress( array(magsex) < float(cutmag) , magsex)) < 5 : cutmag=99 # not cut if only few object
rasex = compress(array(magsex,float)<=cutmag,rasex)
decsex = compress(array(magsex,float)<=cutmag,decsex)
magerrsex = compress(array(magsex,float)<=cutmag,magerrsex)
magsex = compress(array(magsex,float)<=cutmag,array(magsex))
print len(rasex)
if _interactive:
iraf.set(stdimage='imt1024')
iraf.display(re.sub('.sn2','',img),1,fill=True,Stdout=1)
vector=[]
for i in range(0,len(rasex)):
vector.append(str(rasex[i])+' '+str(decsex[i]))
xy = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector,Stdout=1,image=img,inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(1,'STDIN',Stdin=list(xy),mark="circle",number='yes',label='no',radii=10,nxoffse=5,nyoffse=5,color=207,txsize=2)
# raw_input('here')
if _catalogue:
######## use external catalogue
if _catalogue[0]=='/': stdcooC=lsc.lscastrodef.readtxt(_catalogue)
else: stdcooC=lsc.lscastrodef.readtxt(lsc.__path__[0]+'/standard/cat/'+_catalogue)
rastdC,decstdL=array(stdcooC['ra'],float),array(stdcooC['dec'],float)
lsc.util.delete('tmp.stdL.pix')
colonne=str(stdcooC['rapos'])+' '+str(stdcooC['decpos'])
if _catalogue[0]=='/':
iraf.wcsctran(_catalogue,'tmp.stdL.pix',img,inwcs='world',units='degrees degrees',outwcs='logical',\
columns=colonne,formats='%10.1f %10.1f',verbose='no')
else:
iraf.wcsctran(lsc.__path__[0]+'/standard/cat/'+_catalogue,'tmp.stdL.pix',img,inwcs='world',units='degrees degrees',outwcs='logical',\
columns=colonne,formats='%10.1f %10.1f',verbose='no')
standardpixC=lsc.lscastrodef.readtxt('tmp.stdL.pix')
if _interactive:
iraf.tvmark(1,'tmp.stdL.pix',mark="circle",number='yes',label='no',radii=8,nxoffse=5,nyoffse=5,color=204,txsize=2)
xstdC=standardpixC['ra']
ystdC=standardpixC['dec']
idstdC=standardpixC['id']
xstdC=compress((array(xstdC,float)<readkey3(hdr,'XDIM'))&(array(xstdC,float)>0)&(array(ystdC,float)>0)&(array(ystdC,float)<readkey3(hdr,'YDIM')),xstdC)
xstdL=xstdLL=xstdS=xstdC
standardpixL=standardpixLL=standardpixS=standardpixC
stdcooL=stdcooLL=stdcooS=stdcooC
else:
######## check if it is landolt field
stdcooL=lsc.lscastrodef.readtxt(lsc.__path__[0]+'/standard/cat/landolt.cat')
rastdL,decstdL=array(stdcooL['ra'],float),array(stdcooL['dec'],float)
lsc.util.delete('tmp.stdL.pix')
iraf.wcsctran(lsc.__path__[0]+'/standard/cat/landolt.cat','tmp.stdL.pix',img,inwcs='world',units='degrees degrees',outwcs='logical',\
columns='1 2',formats='%10.1f %10.1f',verbose='no')
standardpixL=lsc.lscastrodef.readtxt('tmp.stdL.pix')
if _interactive:
iraf.tvmark(1,'tmp.stdL.pix',mark="circle",number='yes',label='no',radii=8,nxoffse=5,nyoffse=5,color=204,txsize=2)
print 'yelow circles sextractor'
xstdL=standardpixL['ra']
ystdL=standardpixL['dec']
idstdL=standardpixL['id']
xstdL=compress((array(xstdL,float)<readkey3(hdr,'XDIM'))&(array(xstdL,float)>0)&(array(ystdL,float)>0)&(array(ystdL,float)<readkey3(hdr,'YDIM')),xstdL)
######## check if it is Stetson field
stdcooLL=lsc.lscastrodef.readtxt(lsc.__path__[0]+'/standard/cat/StetsonCat.dat')
ww=asarray([i for i in range(len(stdcooLL['ra'])) if ( abs(float(stdcooLL['ra'][i])-float(_ra))<.2 and abs(float(stdcooLL['dec'][i])-_dec)<.2 )])
if len(ww)>0:
for hh in stdcooLL.keys():
if type(stdcooLL[hh])!=int:
if hh not in ['id','ra','dec']:
stdcooLL[hh]=array(array(stdcooLL[hh])[ww],float)
else:
stdcooLL[hh]=array(stdcooLL[hh])[ww]
lll=[]
for i in range(0,len(stdcooLL['ra'])): lll.append(stdcooLL['ra'][i]+' '+stdcooLL['dec'][i])
rastdLL,decstdLL=array(stdcooLL['ra'],float),array(stdcooLL['dec'],float)
lsc.util.delete('tmp.stdLL.pix')
iraf.wcsctran('STDIN','tmp.stdLL.pix',img,inwcs='world',Stdin=lll,units='degrees degrees',outwcs='logical',\
columns='1 2',formats='%10.1f %10.1f',verbose='no')
if _interactive:
iraf.tvmark(1,'tmp.stdLL.pix',mark="cross",number='yes',label='no',radii=8,nxoffse=5,nyoffse=5,color=204,txsize=2)
print 'red crosses Stetson'
standardpixLL={}
for ii in stdcooLL.keys(): standardpixLL[ii]=stdcooLL[ii]
standardpixLL['ra']=array(iraf.proto.fields('tmp.stdLL.pix',fields='1',Stdout=1),float) #standardpixLL['ra']
standardpixLL['dec']=array(iraf.proto.fields('tmp.stdLL.pix',fields='2',Stdout=1),float) #standardpixLL['dec']
xstdLL=array(iraf.proto.fields('tmp.stdLL.pix',fields='1',Stdout=1),float) #standardpixLL['ra']
ystdLL=array(iraf.proto.fields('tmp.stdLL.pix',fields='2',Stdout=1),float) #standardpixLL['dec']
idstdLL=standardpixLL['id']
xstdLL=compress((array(xstdLL,float)<readkey3(hdr,'XDIM'))&(array(xstdLL,float)>0)&(array(ystdLL,float)>0)&(array(ystdLL,float)<readkey3(hdr,'YDIM')),xstdLL)
######## check if it is sloan field
magsel0,magsel1=12,18
_ids=lsc.lscastrodef.sloan2file(_ra,_dec,20,float(magsel0),float(magsel1),'_tmpsloan.cat')
ascifile='_tmpsloan.cat'
stdcooS=lsc.lscastrodef.readtxt(ascifile)
rastdS,decstdS=array(stdcooS['ra'],float),array(stdcooS['dec'],float)
lsc.util.delete('tmp.stdS.pix')
iraf.wcsctran(ascifile,'tmp.stdS.pix',img,inwcs='world',units='degrees degrees',outwcs='logical',columns='1 2',formats='%10.1f %10.1f',verbose='no')
standardpixS=lsc.lscastrodef.readtxt('tmp.stdS.pix')
if _interactive:
iraf.tvmark(1,'tmp.stdS.pix',mark="cross",number='yes',label='no',radii=8,nxoffse=5,nyoffse=5,color=205,txsize=2)
print 'green cross sloan'
xstdS=standardpixS['ra']
ystdS=standardpixS['dec']
idstdS=standardpixS['id']
xstdS=compress((array(xstdS,float)<readkey3(hdr,'XDIM'))&(array(xstdS,float)>0)&(array(ystdS,float)>0)&(array(ystdS,float)<readkey3(hdr,'YDIM')),xstdS)
##############################################3
if not _catalogue and len(xstdLL)>0:
xstdL=xstdLL
standardpixL=standardpixLL
stdcooL=stdcooLL
if _filter in ['U', 'B', 'V', 'R','I','Bessell-B','Bessell-V','Bessell-R','Bessell-I']:
filters={'U':'U', 'B':'B', 'V':'V', 'R':'R', 'I':'I','Bessell-B':'B','Bessell-V':'V','Bessell-R':'R','Bessell-I':'I'}
if _color:
colors=lsc.myloopdef.chosecolor(_color,False)
if not colors: colors={'U':['UB'],'B':['UB','BV'],'V':['BV','VR'],'R':['VR','RI'],'I':['RI']}
else:
colors={'U':['UB'],'B':['UB','BV'],'V':['BV','VR'],'R':['VR','RI'],'I':['RI']}
if _field=='sloan':
standardpix,stdcoo={'ra':[9999],'dec':[9999],'id':[1]},{'ra':[9999],'dec':[9999]}
print 'filters and field selected do not match'
else:
_field='landolt'
if len(xstdL)>=1:
standardpix=standardpixL
stdcoo=stdcooL
if not _catalogue:
if len(xstdLL)>0: _catalogue='StetsonCat.dat'
else: _catalogue='landolt.dat'
elif len(xstdS)>=1:
if not _catalogue: _catalogue='sdss8'
standardpix=standardpixS
stdcoo=stdcooS
stdcoo=lsc.lscastrodef.transformsloanlandolt(stdcoo)
if not _catalogue: _catalogue='sdss8'
print '\n### transform sloan in landolt'
else:
print 'landolt, but catalogue not found'
standardpix,stdcoo={'ra':[9999],'dec':[9999],'id':[1]},{'ra':[9999],'dec':[9999]}
elif _filter in ['up','gp','rp','ip','zs','SDSS-G','SDSS-R','SDSS-I','Pan-Starrs-Z']:
filters={'up':'u','ip':'i','gp':'g','rp':'r','zs':'z','SDSS-G':'g','SDSS-R':'r','SDSS-I':'i','Pan-Starrs-Z':'z'}
if _color:
colors=lsc.myloopdef.chosecolor(_color,False)
if not colors: colors={'i':['ri','iz'],'r':['gr','ri'],'g':['ug','gr'],'z':['iz'],'u':['ug']}
else:
colors={'i':['ri','iz'],'r':['gr','ri'],'g':['ug','gr'],'z':['iz'],'u':['ug']}
if _field=='landolt':
standardpix,stdcoo={'ra':[9999],'dec':[9999],'id':[1]},{'ra':[9999],'dec':[9999]}
print 'filters and field selected do not match'
else:
_field='sloan'
if len(xstdS)>=1:
if not _catalogue: _catalogue='sdss8'
standardpix=standardpixS
stdcoo=stdcooS
elif len(xstdL)>=1:
standardpix=standardpixL
stdcoo=stdcooL
stdcoo=lsc.lscastrodef.transformlandoltsloan(stdcoo)
if not _catalogue: _catalogue='landolt.dat'
print '\n### transform landolt to sloan'
else:
print 'sloan, but not in the sdss footprint'
standardpix,stdcoo={'ra':[9999],'dec':[9999],'id':[1]},{'ra':[9999],'dec':[9999]}
xstd=standardpix['ra']
ystd=standardpix['dec']
idstd=standardpix['id']
rastd,decstd=array(stdcoo['ra'],float),array(stdcoo['dec'],float)
xstd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)&(array(ystd,float)<readkey3(hdr,'YDIM')),xstd)
if len(xstd0)>1: ######## go only if standard stars are in the field ##########
magstd0={}
airmass0={}
result={}
fileph={}
print '\n### standard field: '+str(_field)
ystd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),ystd)
rastd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),rastd)
decstd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),decstd)
idstd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),idstd)
stdcoo0={}
for key in stdcoo.keys():
if key in 'ugrizUBVRI':
stdcoo0[key]=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),stdcoo[key])
###############################################################
# pos0 = standard pos1 = sextractor
distvec,pos0,pos1=lsc.lscastrodef.crossmatch(array(rastd0),array(decstd0),array(rasex),array(decsex),10)
for key in stdcoo0.keys(): stdcoo0[key]=stdcoo0[key][pos0]
rastd0=rastd0[pos0]
decstd0=decstd0[pos0]
idstd0=idstd0[pos0]
rasex=rasex[pos1]
decsex=decsex[pos1]
# after change in may 2013 mag in sn2.fits file are already at 1s
magsex=magsex[pos1]-kk[filters[_filter]]*float(_airmass) # - K x airmass
# magsex=magsex[pos1]+2.5*math.log10(float(_exptime))-kk[filters[_filter]]*float(_airmass) # mag exptime - K x airmass
#################################################################################
if _field=='landolt':
print '\n### landolt system'
for _filtlandolt in 'UBVRI':
if _filtlandolt==filters[_filter]: airmass0[_filtlandolt]= 0 #_airmass
else: airmass0[_filtlandolt]= 0
magstd0[_filtlandolt]=stdcoo0[_filtlandolt]
fileph['mU']=zeros(len(rastd0))+999
fileph['mB']=zeros(len(rastd0))+999
fileph['mV']=zeros(len(rastd0))+999
fileph['mR']=zeros(len(rastd0))+999
fileph['mI']=zeros(len(rastd0))+999
fileph['V']=magstd0['V']
fileph['BV']=array(array(magstd0['B'],float)-array(magstd0['V'],float),str)
fileph['UB']=array(array(magstd0['U'],float)-array(magstd0['B'],float),str)
fileph['VR']=array(array(magstd0['V'],float)-array(magstd0['R'],float),str)
fileph['RI']=array(array(magstd0['R'],float)-array(magstd0['I'],float),str)
elif _field=='sloan':
for _filtsloan in 'ugriz':
if _filtsloan==filters[_filter]: airmass0[_filtsloan]= 0 # _airmass
else: airmass0[_filtsloan]=0
magstd0[_filtsloan]=stdcoo0[_filtsloan]
fileph['mu']=zeros(len(rastd0))+999
fileph['mg']=zeros(len(rastd0))+999
fileph['mr']=zeros(len(rastd0))+999
fileph['mi']=zeros(len(rastd0))+999
fileph['mz']=zeros(len(rastd0))+999
fileph['r']=magstd0['r']
fileph['gr']=array(array(magstd0['g'],float)-array(magstd0['r'],float),str)
fileph['ri']=array(array(magstd0['r'],float)-array(magstd0['i'],float),str)
fileph['ug']=array(array(magstd0['u'],float)-array(magstd0['g'],float),str)
fileph['iz']=array(array(magstd0['i'],float)-array(magstd0['z'],float),str)
########################################################################################
zero=[]
magcor=[]
fil = open(re.sub('.fits','.ph',img),'w')
fil.write(str(_instrume)+' '+str(_date)+'\n')
fil.write('*** '+_object+' '+str(len(magsex))+'\n')
if _field=='landolt':
fil.write('%6.6s\t%6.6s\t%6.6s\t%6.6s\t%6.6s\n' % (str(1),str(1),str(1),str(1),str(1))) # exptime
fil.write('%6.6s\t%6.6s\t%6.6s\t%6.6s\t%6.6s\n' % (str(airmass0['U']),str(airmass0['B']),str(airmass0['V']),str(airmass0['R']),str(airmass0['I'])))
elif _field=='sloan':
fil.write('%6.6s\t%6.6s\t%6.6s\t%6.6s\t%6.6s\n' % (str(1),str(1),str(1),str(1),str(1))) # exptime
fil.write('%6.6s\t%6.6s\t%6.6s\t%6.6s\t%6.6s\n' % (str(airmass0['u']),str(airmass0['g']),str(airmass0['r']),str(airmass0['i']),str(airmass0['z'])))
for i in range(0,len(magsex)):
fileph['m'+filters[_filter]][i]=magsex[i] # instrumental mangitude of std in pos0[i]
if _field=='landolt':
stringastandard='%12.12s\t%7.7s\t%7.7s\t%7.7s\t%7.7s\t%7.7s' % (idstd0[i],fileph['V'][i],fileph['BV'][i],fileph['UB'][i],\
fileph['VR'][i],fileph['RI'][i])
fil.write('%7.7s\t%7.7s\t%7.7s\t%7.7s\t%7.7s\t%60.60s\n' \
% (str(fileph['mU'][i]),str(fileph['mB'][i]),str(fileph['mV'][i]),str(fileph['mR'][i]),str(fileph['mI'][i]),str(stringastandard)))
elif _field=='sloan':
stringastandard='%12.12s\t%7.7s\t%7.7s\t%7.7s\t%7.7s\t%7.7s' % (idstd0[i],fileph['r'][i],fileph['gr'][i],fileph['ug'][i],\
fileph['ri'][i],fileph['iz'][i])
fil.write('%7.7s\t%7.7s\t%7.7s\t%7.7s\t%7.7s\t%60.60s\n' \
% (str(fileph['mu'][i]),str(fileph['mg'][i]),str(fileph['mr'][i]),str(fileph['mi'][i]),str(fileph['mz'][i]),str(stringastandard)))
zero.append(float(float(magstd0[filters[_filter]][i]))-float(magsex[i]))
magcor.append(magsex[i])
fil.close()
if show:
import time
from pylab import ion,plot,draw
ion()
aa=mean(compress(abs(array(zero))<99,zero))
xxx=compress((abs(array(magcor))<99)&(abs(array(zero))<99),magcor)
yyy=compress((abs(array(zero))<99)&(abs(array(magcor))<99),zero)
plot(xxx,yyy,'or')
plot([min(compress(abs(array(magcor))<99,magcor)),max(compress(abs(array(magcor))<99,magcor))],[aa,aa],'-b')
draw()
print std(compress(abs(array(zero))<99,zero))
time.sleep(5)
colorvec=colors[filters[_filter]]
for col in colorvec:
col0=magstd0[col[0]]
col1=magstd0[col[1]]
colstd0=array(col0,float)-array(col1,float)
################## sex ######################
colore=[]
for i in range(0,len(pos1)): colore.append(colstd0[i])
colore1=compress(abs(array(zero))<50,array(colore))
zero1=compress(abs(array(zero))<50,array(zero))
zero2=compress(abs(array(colore1))<2,array(zero1))
colore2=compress(abs(array(colore1))<2,array(colore1))
if _fix: fisso=colorefisso[filters[_filter]+col]
else: fisso=''
if len(colore2)==0:
print 'no calibration, '+_filter+' '+_field
b,a,sa,sb=9999,9999,0,0
else:
if _interactive: a,sa,b,sb=fitcol(colore2,zero2,_filter,col,fisso)
else: a,sa,b,sb=fitcol2(colore2,zero2,_filter,col,fisso,show,rejection)
print a,sa,b,sb
result[filters[_filter]+col]=[a,sa,b,sb]
if result:
print '\n### zeropoint ..... done at airmass 0'
if _catalogue:
lsc.util.updateheader(img,0,{'CATALOG':[str(string.split(_catalogue,'/')[-1]),'catalogue source']})
stringa=''
for ll in result:
for kk in range(0,len(result[ll])):
if not isfinite(result[ll][kk]): result[ll][kk]=0.0
valore='%3.3s %6.6s %6.6s %6.6s %6.6s' % (str(ll),str(result[ll][0]),str(result[ll][2]),str(result[ll][1]),str(result[ll][3]))
print '### ',valore
lsc.util.updateheader(img,0,{'zp'+ll:[str(valore),'a b sa sb in y=a+bx']})
if ll[0]==ll[2]: num=2
elif ll[0]==ll[1]: num=1
else: sys.exit('somthing wrong with color '+ll)
print ll,num
try:
print 'zcol'+str(num),ll[1:],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1]
lsc.mysqldef.updatevalue(database,'zcol'+str(num),ll[1:],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'z'+str(num),result[ll][0],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'c'+str(num),result[ll][2],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'dz'+str(num),result[ll][1],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'dc'+str(num),result[ll][3],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
if os.path.isfile(string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1]):
lsc.mysqldef.updatevalue(database,'zcol'+str(num),ll[1:],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'z'+str(num),result[ll][0],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'c'+str(num),result[ll][2],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'dz'+str(num),result[ll][1],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'dc'+str(num),result[ll][3],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
if result[ll][0]!=9999:
print _catalogue
lsc.mysqldef.updatevalue(database,'zcat',string.split(_catalogue,'/')[-1],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
if os.path.isfile(string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1]):
lsc.mysqldef.updatevalue(database,'zcat',string.split(_catalogue,'/')[-1],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
else:
lsc.mysqldef.updatevalue(database,'zcat','X',string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
if os.path.isfile(string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1]):
lsc.mysqldef.updatevalue(database,'zcat','X',string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
except: print 'module mysqldef not found'
def mkskyflat_quick(inlist,
output,
inpref='',
bpm='none',
iternum=50,
nsigma=3):
# load IRAF package
iraf.proto()
iraf.nproto()
# open input list and check if it exists
inimg_arr = check_input(inlist, inpref)
if len(inimg_arr) == 1 and inimg_arr[0] == 1:
return 1
# check if output image is already exist
if os.access(output, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing file (%s)' % output
return 1
# check optional parameters
if bpm.lower() == 'none' or bpm == '':
bpmex = 0
else:
bpmex = 1
if not os.access(bpm, os.R_OK):
print >> sys.stderr, 'cannot read bad pixel mask (%s)' % bpm
return 1
if iternum <= 0:
print >> sys.stderr, 'invalid iternum value (%d)' % iternum
return 1
if nsigma <= 0:
print >> sys.stderr, 'invalid nsigma value (%f)' % nsigma
return 1
# prefix for temoprary images
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# temporary normalize raw frames by average
tmp_list = tmp_prefix + '.lst'
fout = open(tmp_list, 'w')
for i in range(len(inimg_arr)):
tmpimg = tmp_prefix + os.path.basename(inimg_arr[i])
fout.write('%s\n' % tmpimg)
# remove temporary files if it exists
if os.access(tmpimg, os.R_OK):
os.remove(tmpimg)
# calculate average counts in each frame
if bpmex == 0:
iraf.unlearn('imstat')
res = iraf.imstat(inimg_arr[i],
format='no',
fields='mean',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate avarage from the frame (%s)' % inimg
remove_temp_all(tmp_prefix)
fout.close()
return 1
else:
iraf.unlearn('mimstat')
res = iraf.mimstat(inimg_arr[i],
imasks=bpm,
format='no',
fields='mean',
nclip=iternum,
lsigma=nsigma,
usigma=nsigma,
Stdout=1)
if len(res) == 1:
ave = float(res[0])
else:
print >> sys.stderr, 'failed to calculate masked avarage from the frame (%s)' % inimg
remove_temp_all(tmp_prefix)
fout.close()
return 1
# normalize each frame by average
iraf.unlearn('imarith')
iraf.imarith(inimg_arr[i], '/', ave, tmpimg, verbose='no')
# close output file handler
fout.close()
# make temporary sky flat frame
tmp_flat = tmp_prefix + '_sflat.fits'
ret = zcombine(tmp_list, output, combine='median', reject='sigclip')
if ret != 0:
remove_temp_all(tmp_prefix)
return 1
# remove temoprary files
remove_temp_all(tmp_prefix)
return 0
