def run_ell(file_in, file_tab, file_dat, mag, **ellipse_par):
# prochaine etape : mettre en argument sous une certaine forme les parametres associes epar des taches 1) strict minimum sous forme de dico fixe 2) tout pouvoir changer sous forme de dictionnaire et tester les cles ...
# import iraf packages
iraf.stsdas()
iraf.analysis()
iraf.isophote()
p = Popen("ds9", shell = True)
time.sleep(5)
iraf.display(file_in, 1)
set_iraf_param(ellipse_par)
# iraf.lpar(iraf.geompar)
iraf.ellipse(file_in, file_tab, mag0 = str(mag), verbose = "no")
# iraf.lpar(iraf.isoimap)
iraf.isoimap(file_in, file_tab)
iraf.tproject(file_tab, "temp.fits", "SMA,INTENS,INT_ERR,ELLIP,ELLIP_ERR, PA, PA_ERR, X0, Y0, TFLUX_C, TMAG_E,NPIX_E")
iraf.tprint("temp.fits", pwidth = 200, Stdout = file_dat)
iraf.imdel("temp.fits")
p.kill()
def run_elli_full(cutimage, output, xcntr, ycntr, eg, pa, sma,
sky): #,radd,background):
"""The function responsible for fit ellipse"""
EllGal = c.outdir + 'GalEllFit.fits'
fEl = pyfits.open(os.path.join(c.datadir, cutimage))
GaLaXy = fEl[0].data
fEl.close()
GaLaXy = GaLaXy - sky
hdu = pyfits.PrimaryHDU(GaLaXy.astype(n.float32))
hdu.writeto(EllGal)
try:
iraf.stsdas(_doprint=0)
iraf.tables(_doprint=0)
iraf.stsdas.analysis(_doprint=0)
iraf.stsdas.analysis.isophote(_doprint=0)
except:
iraf.stsdas()
iraf.tables()
iraf.stsdas.analysis()
iraf.stsdas.analysis.isophote()
image_exist = 1
iraf.unlearn('geompar')
iraf.geompar.x0 = xcntr
iraf.geompar.y0 = ycntr
iraf.geompar.ellip0 = eg
iraf.geompar.pa0 = pa
iraf.geompar.sma0 = 6.0
iraf.geompar.minsma = 0.1
iraf.geompar.maxsma = sma * 5.0
iraf.geompar.step = 0.1
iraf.geompar.recente = "no"
iraf.geompar.xylearn = "no"
iraf.unlearn('controlpar')
iraf.controlpar.conver = 0.05
iraf.controlpar.minit = 10
iraf.controlpar.maxit = 50
iraf.controlpar.hcenter = "no"
iraf.controlpar.hellip = "no"
iraf.controlpar.hpa = "no"
iraf.controlpar.wander = "INDEF"
iraf.controlpar.maxgerr = 0.5
iraf.controlpar.olthres = 1
iraf.controlpar.soft = "no"
iraf.samplepar.integrm = "bi-linear"
iraf.samplepar.usclip = 3
iraf.samplepar.lsclip = 3
iraf.samplepar.nclip = 0
iraf.samplepar.fflag = 0.5
iraf.unlearn('ellipse')
iraf.ellipse("".join(EllGal), output="test", interac="no",Stdout="ellip", \
Stderr="err")
iraf.tprint("test.tab", prparam="no", prdata="yes", pwidth=80, plength=0, \
showrow="no", orig_row="no", showhdr="no", showunits="no", \
columns="SMA, INTENS, INT_ERR, ELLIP, ELLIP_ERR, PA, PA_ERR, \
MAG, MAG_LERR, MAG_UERR, TMAG_E, TMAG_C, A3, \
A3_ERR, B3, B3_ERR, A4, A4_ERR, B4_ERR" , rows="-", \
option="plain", align="yes", sp_col="", lgroup=0, Stdout=output)
for myfile in ['ellip', 'err', 'test.tab', EllGal]:
if os.access(c.outdir + myfile, os.F_OK):
os.remove(c.outdir + myfile)
def run_elli(input, xcntr, ycntr, eg, pa, sma):#,radd,background):
iraf.stsdas(_doprint=0)
iraf.tables(_doprint=0)
iraf.stsdas.analysis(_doprint=0)
iraf.stsdas.analysis.isophote(_doprint=0)
image_exist = 1
if(str(input)[:3] == 'ima'):
output = 'elli_' + input[6:-4] + 'txt'
if(str(input)[:3] == 'out'):
output = 'out_elli_' + str(input)[4:-7] + 'txt'
#unlearn geompar controlpar samplepar magpar ellipse
iraf.geompar(x0=xcntr, y0=ycntr, ellip0=eg, pa0=pa, sma0=10, minsma=0.1, \
maxsma=sma*5.0, step=0.1,recente="yes")
iraf.controlpar(conver=0.05, minit=10, maxit=50, hcenter="no", hellip="no",\
hpa="no", wander="", maxgerr=0.5, olthres=1,soft="no")
iraf.samplepar(integrm="bi-linear", usclip=3,lsclip=3, nclip=0, fflag=0.5)
iraf.magpar(mag0=0, refer=1, zerolev=0)
iraf.ellipse("".join(input), output="test", interac="no",Stdout="ellip", \
Stderr="err")
iraf.tprint("test.tab", prparam="no", prdata="yes", pwidth=80, plength=0, \
showrow="no", orig_row="no", showhdr="no", showunits="no", \
columns="SMA, INTENS, INT_ERR, MAG, MAG_LERR, MAG_UERR",\
rows="-", \
option="plain", align="yes", sp_col="", lgroup=0, Stdout=output)
for myfile in ['ellip','err','test.tab']:
if os.access(myfile,os.F_OK):
os.remove(myfile)
def run_elli_full(input, output, xcntr, ycntr, eg, pa, sma, sky):#,radd,background):
"""The function responsible for fit ellipse"""
EllGal = 'GalEllFit.fits'
fEl = pyfits.open(input)
GaLaXy = fEl[0].data
fEl.close()
GaLaXy = GaLaXy - sky
hdu = pyfits.PrimaryHDU(GaLaXy.astype(n.float32))
hdu.writeto(EllGal)
try:
iraf.stsdas(_doprint=0)
iraf.tables(_doprint=0)
iraf.stsdas.analysis(_doprint=0)
iraf.stsdas.analysis.isophote(_doprint=0)
except:
iraf.stsdas()
iraf.tables()
iraf.stsdas.analysis()
iraf.stsdas.analysis.isophote()
image_exist = 1
iraf.unlearn('geompar')
iraf.geompar.x0=xcntr
iraf.geompar.y0=ycntr
iraf.geompar.ellip0=eg
iraf.geompar.pa0=pa
iraf.geompar.sma0=6.0
iraf.geompar.minsma=0.1
iraf.geompar.maxsma=sma*5.0
iraf.geompar.step=0.1
iraf.geompar.recente="no"
iraf.geompar.xylearn="no"
iraf.unlearn('controlpar')
iraf.controlpar.conver=0.05
iraf.controlpar.minit=10
iraf.controlpar.maxit=50
iraf.controlpar.hcenter="no"
iraf.controlpar.hellip="no"
iraf.controlpar.hpa="no"
iraf.controlpar.wander="INDEF"
iraf.controlpar.maxgerr=0.5
iraf.controlpar.olthres=1
iraf.controlpar.soft="no"
iraf.samplepar.integrm="bi-linear"
iraf.samplepar.usclip=3
iraf.samplepar.lsclip=3
iraf.samplepar.nclip=0
iraf.samplepar.fflag=0.5
iraf.unlearn('ellipse')
iraf.ellipse("".join(EllGal), output="test", interac="no",Stdout="ellip", \
Stderr="err")
iraf.tprint("test.tab", prparam="no", prdata="yes", pwidth=80, plength=0, \
showrow="no", orig_row="no", showhdr="no", showunits="no", \
columns="SMA, INTENS, INT_ERR, ELLIP, ELLIP_ERR, PA, PA_ERR, \
MAG, MAG_LERR, MAG_UERR, TMAG_E, TMAG_C, A3, \
A3_ERR, B3, B3_ERR, A4, A4_ERR, B4_ERR", rows="-", \
option="plain", align="yes", sp_col="", lgroup=0, Stdout=output)
for myfile in ['ellip','err','test.tab', EllGal]:
if os.access(myfile,os.F_OK):
os.remove(myfile)
def run_elli(input, output, xcntr, ycntr, eg, pa, sma):#,radd,background):
"""The function resposible for fit ellipse"""
iraf.stsdas(_doprint=0)
iraf.tables(_doprint=0)
iraf.stsdas.analysis(_doprint=0)
iraf.stsdas.analysis.isophote(_doprint=0)
image_exist = 1
# if(str(input)[:3] == 'ima'):
# output = 'elli_' + input[6:-4] + 'txt'
# if(str(input)[:3] == 'out'):
# output = 'out_elli_' + str(input)[4:-7] + 'txt'
# else:
# output = 'elli_' + input[:-5] + '.txt'
#unlearn geompar controlpar samplepar magpar ellipse
iraf.unlearn('geompar')
iraf.geompar.x0=xcntr
iraf.geompar.y0=ycntr
iraf.geompar.ellip0=eg
iraf.geompar.pa0=pa
iraf.geompar.sma0=10
iraf.geompar.minsma=0.1
iraf.geompar.maxsma=sma*5.0
iraf.geompar.step=0.1
iraf.geompar.recente="no"
iraf.geompar.xylearn="no"
iraf.unlearn('controlpar')
iraf.controlpar.conver=0.05
iraf.controlpar.minit=10
iraf.controlpar.maxit=50
iraf.controlpar.hcenter="no"
iraf.controlpar.hellip="no"
iraf.controlpar.hpa="no"
iraf.controlpar.wander="INDEF"
iraf.controlpar.maxgerr=0.5
iraf.controlpar.olthres=1
iraf.controlpar.soft="no"
iraf.samplepar.integrm="bi-linear"
iraf.samplepar.usclip=3
iraf.samplepar.lsclip=3
iraf.samplepar.nclip=0
iraf.samplepar.fflag=0.5
iraf.magpar.mag0=c.mag_zero
iraf.magpar.refer=1
iraf.magpar.zerolev=0
iraf.ellipse("".join(input), output="test", interac="no",Stdout="ellip", \
Stderr="err")
iraf.tprint("test.tab", prparam="no", prdata="yes", pwidth=80, plength=0, \
showrow="no", orig_row="no", showhdr="no", showunits="no", \
columns="SMA, INTENS, INT_ERR, MAG, MAG_LERR, MAG_UERR, \
TFLUX_E", rows="-", \
option="plain", align="yes", sp_col="", lgroup=0, Stdout=output)
for myfile in ['ellip','err','test.tab']:
if os.access(myfile,os.F_OK):
os.remove(myfile)
def run(args):
"""Generate the residual image using Ellipse run."""
"""Call the STSDAS.ANALYSIS.ISOPHOTE package"""
if args.isophote is None:
if args.verbose:
print '\n' + SEP
print "## Call STSDAS.ANALYSIS.ISOPHOTE() "
print SEP
iraf.stsdas()
iraf.analysis()
iraf.isophote()
iraf.ttools()
def run(args):
"""Generate the residual image using Ellipse run."""
"""Call the STSDAS.ANALYSIS.ISOPHOTE package"""
if args.isophote is None:
if args.verbose:
print '\n' + SEP
print "## Call STSDAS.ANALYSIS.ISOPHOTE() "
print SEP
iraf.stsdas()
iraf.analysis()
iraf.isophote()
iraf.ttools()
def _iraf_pset_init():
"""Initialize common IRAF tasks for pset tests
"""
if not HAS_IRAF:
return
# imports & package loading
iraf.stsdas(_doprint=0)
iraf.imgtools(_doprint=0)
iraf.mstools(_doprint=0)
# reset PSET egstp's values
_unlearn_egstp(iraf.egstp)
def _iraf_dqbits_init(_data):
"""Initialize common IRAF tasks for dqbits tests
"""
if not HAS_IRAF:
return
# imports & package loading
iraf.stsdas(_doprint=0)
iraf.imgtools(_doprint=0)
iraf.artdata(_doprint=0)
iraf.mstools(_doprint=0)
# create two data files as input (dont care if appropriate to mscombine)
iraf.imcopy('dev$pix', _data['dqbits']['input1'])
iraf.imcopy('dev$pix', _data['dqbits']['input2'])
def __init__(self,
shortparlists,
parlists,
FitsDir,
logfile,
verbose=1,
clean_up=1,
skyKey='ALIGNSKY',
hdrGain=0,
crlower=None,
imNsci=1):
self.modName = string.split(string.split(str(self))[0], '.')[0][1:]
self.shortparlists = shortparlists
self.parlists = parlists
self.Fits = FitsDir
self.verbose = verbose
self.crmasks = {} # cosmic ray masks names
self.removeList = []
self.clean_up = clean_up
self.skyKey = skyKey
self.hdrGain = hdrGain
self.crlower = crlower
if imNsci < 1:
raise ValueError, 'Error: pyblot got imNsci = ' + imNsci
self.imNsci = imNsci
self.logfile = logfile
print self.modName, 'version', __version__
self.logfile.write('Instantiating ' + self.modName + ' version ' +
__version__)
# make sure these packages are loaded
iraf.stsdas()
iraf.toolbox()
iraf.imgtool()
iraf.fourier()
iraf.fitting()
iraf.ttools()
iraf.analysis()
iraf.dither()
# flush the cash! twice!
iraf.flpr()
iraf.flpr()
iraf.reset(imtype='fits') # seems to make deriv task a bit happier
iraf.set(tmp='./')
def define_lacosmic(path_to_lacos_im=''):
"""Moves to ``STSDAS`` package and defines ``LACOS_IM`` as an
``iraf`` task.
Parameters
-----------
path_to_lacos_im : string
Your path to ``lacos_im.cl``.
If left blank, assume Current Working Directory.
#By default, reads from lacos_setup
Notes
-----
The IRAF way is
task lacos_im = /path/lacos_im.cl
"""
iraf.stsdas()
iraf.task(lacos_im=path_to_lacos_im + 'lacos_im.cl')
# Try iqpkg
try:
import iqpkg
import iqutils
except:
print "No iqpkg, iqutils. Can't run lmi_stats."
# Necessary packages
iraf.images()
iraf.immatch()
# iraf.imfilter()
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.stsdas()
iraf.hst_calib()
iraf.nicmos()
iraf.imutil()
yes = iraf.yes
no = iraf.no
INDEF = iraf.INDEF
globclob = yes
globver = yes
LMIFILTS = ["U", "B", "V", "R", "I", "SDSS-G", "SDSS-R", "SDSS-I", "SDSS-Z", "SDSS-U"]
LMIPIXSCALE = 0.240
ASTROMCMD = "/Users/scenko/python/astrometry/vlt_autoastrometry.py"
FDICT = {"SDSS-U": "UMAG", "SDSS-G": "GMAG", "SDSS-R": "RMAG", "SDSS-I": "IMAG", "SDSS-Z": "ZMAG"}
SEEDICT = {"SDSS-U": "co", "SDSS-G": "go", "SDSS-R": "ro", "SDSS-I": "bo", "SDSS-Z": "ko"}
def imshiftcomb(inlist,
outimg,
fitgeom='shift',
inpref='',
objmask='none',
combine='average',
reject='none',
fscale=False,
fbase=100,
fhead='F1',
second=False,
first_pref='sdfr',
second_pref='sdf2r',
indep=False,
sigmap='none',
expmap='none',
whtmap='none',
gain=5.6,
ffpref=''):
# check output image
if os.access(outimg, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % outimg
return 1
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check input image list
if sigmap != 'none':
ffimg_arr = check_input2(inlist, ffpref)
if isinstance(ffimg_arr, int):
return 1
# check optional output image
if sigmap != 'none':
if os.access(sigmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % sigmap
return 1
if expmap != 'none':
if os.access(expmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % expmap
return 1
if whtmap != 'none':
if os.access(whtmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % whtmap
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 os.access(dbs, os.R_OK):
print >> sys.stderr, 'database file (%s) is already exist' % (dbs)
return 1
if os.access(gmp2, os.R_OK):
print >> sys.stderr, 'modified geomap file (%s) is already 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
#print 'debug'
#print dx, max(dx), min(dx)
#print dy, max(dy), min(dy)
# check object mask
if objmask.lower() == 'none':
objmask = ''
else:
objmask_arr = check_inpref(objmask, inimg_arr)
if isinstance(objmask_arr, int):
return 1
# independent run flag
if indep:
second = True
# prepare for temporary file
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# calculate image median for zero shift
iraf.unlearn('imstat')
iraf.unlearn('mimstat')
bgmed = []
for i in range(len(inimg_arr)):
if objmask == '':
ret = iraf.imstat(inimg_arr[i],
format='no',
fields='midpt',
nclip=50,
lsigma=3.,
usigma=3.,
Stdout=1)
else:
if not second:
ret = iraf.mimstat(inimg_arr[i],
imasks=objmask_arr[i] + '[pl]',
format='no',
fields='midpt',
nclip=50,
lsigma=3.,
usigma=3.,
Stdout=1)
else:
ret = iraf.mimstat(inimg_arr[i],
imasks=objmask_arr[i],
format='no',
fields='midpt',
nclip=50,
lsigma=3.,
usigma=3.,
Stdout=1)
if len(ret) == 1:
bgmed.append(-1.0 * float(ret[0]))
else:
fout.close()
remove_temp_all(tmp_prefix)
print >> sys.stderr, 'failed to calculate median of the background in %s' % inimg_arr[
i]
return 1
# 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)
#print 'debug'
#print x_size, y_size, xcmin, xcmax, ycmin, ycmax
#print xmin, xmax, ymin, ymax
# copy image to larger format and shift image #
iraf.unlearn('geomap')
iraf.unlearn('geotran')
obj_list = tmp_prefix + '_obj.lst'
if os.access(obj_list, os.R_OK):
os.remove(obj_list)
fobj = open(obj_list, 'w')
# for exposure time weight
expweight = tmp_prefix + '_exp.lst'
if os.access(expweight, os.R_OK):
os.remove(expweight)
fexp = open(expweight, 'w')
# for zero offset
zeroshift = tmp_prefix + '_zeroshift.dat'
if os.access(zeroshift, os.R_OK):
os.remove(zeroshift)
fzero = open(zeroshift, 'w')
# save the original fit geometry
fitgeom_org = fitgeom
# preparing for the sigma list and mask
if sigmap == 'none':
tmp_rejmask = ''
else:
tmp_rejmask = tmp_prefix + 'rejmask.fits'
if os.access(tmp_rejmask, os.R_OK):
os.remove(tmp_rejmask)
inverse_var_list = tmp_prefix + '_var.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)):
# restore the original fit geometry
fitgeom = fitgeom_org
# geometry transformation
fgmp = open(gmp_arr[i])
fgmp2 = open(gmp2_arr[i], 'w')
nobj = 0
for line in fgmp:
if not line.startswith('#'):
param = line[:-1].split()
xref = float(param[0]) + xmin - 1
yref = float(param[1]) + ymin - 1
xin = float(param[2]) + xmin - 1
yin = float(param[3]) + ymin - 1
fgmp2.write('%.3f %.3f %.3f %.3f\n' % (xref, yref, xin, yin))
nobj += 1
fgmp.close()
fgmp2.close()
# check number of objects
if i == 0 and nobj == 1 and fitgeom == 'rotate':
print 'Warning: Number of reference objects is not enought to measure the rotation'
print 'Warning: Only shift applied for all images'
fitgeom = 'shift'
fitgeom_org = 'shift'
if nobj == 1 and fitgeom == 'rotate':
print 'Warning: Number of objects in %s is not enought to measure the rotation' % (
inimg_arr[i])
print 'Warning: Only shift applied for %s' % (inimg_arr[i])
fitgeom = 'shift'
# mapping geometry
iraf.geomap(gmp2_arr[i],
dbs_arr[i],
1,
x_size,
1,
y_size,
fitgeom=fitgeom,
interac='no')
# 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
img = pyfits.open(inimg_arr[i])
if sigmap != 'none':
ffimg = pyfits.open(ffimg_arr[i])
# for exposure time weight
try:
t = float(img[0].header['EXP1TIME'])
coadd = float(img[0].header['COADDS'])
expt = 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 = fbase / flux
weight = expt * (1.0 / flux_scale)**2
else:
flux_scale = 1.0
weight = expt
fzero.write('%f\n' % (bgmed[i] * flux_scale))
fexp.write('%f\n' % weight)
# object frame
obj = np.zeros((y_size, x_size))
obj[ymin - 1:ymax, xmin - 1:xmax] = img[0].data * flux_scale
hdu = pyfits.PrimaryHDU(obj)
obj_img = pyfits.HDUList([hdu])
obj_img[0].header = img[0].header
obj_img[0].header['bpm'] = msktr_fits
obj_img[0].header['expmap'] = expt
obj_fits = tmp_prefix + 'obj' + os.path.basename(inimg_arr[i])
objtr_fits = tmp_prefix + 'objtr' + os.path.basename(inimg_arr[i])
if os.access(obj_fits, os.R_OK):
os.remove(obj_fits)
obj_img.writeto(obj_fits)
obj_img.close()
iraf.geotran(obj_fits,
objtr_fits,
dbs_arr[i],
gmp2_arr[i],
geometr='linear',
boundar='constant',
constant=0)
fobj.write('%s\n' % objtr_fits)
img.close()
if sigmap != 'none':
inverse_var = np.zeros((y_size, x_size))
inverse_var[ymin - 1:ymax, xmin -
1:xmax] = (np.sqrt(ffimg[0].data / (gain * expt)) *
flux_scale)**-2
hdu_var = pyfits.PrimaryHDU(inverse_var)
inverse_var_img = pyfits.HDUList([hdu_var])
inverse_var_img[0].header = img[0].header
inverse_var_img[0].header['bpm2'] = '%s[*,*,%d]' % (tmp_rejmask,
i + 1)
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
fobj.close()
fexp.close()
fzero.close()
if sigmap != 'none':
finverse_var.close()
# combine image
comb_img = tmp_prefix + '_comb.fits'
if os.access(comb_img, os.R_OK):
os.remove(comb_img)
if expmap == 'none':
tmp_expmap = ''
else:
tmp_expmap = tmp_prefix + 'expmap.fits'
if os.access(tmp_expmap, os.R_OK):
os.remove(tmp_expmap)
iraf.unlearn('imcombine')
try:
iraf.imcombine('@' + obj_list,
comb_img,
sigma='',
rejmask=tmp_rejmask,
expmasks=tmp_expmap,
combine=combine,
reject=reject,
masktype='!BPM',
maskvalue=0.0,
zero='@' + zeroshift,
weight='@' + expweight,
expname='EXPMAP')
except:
if os.access(comb_img, os.R_OK):
os.remove(comb_img)
if expmap != 'none':
if os.access(tmp_expmap, os.R_OK):
os.remove(tmp_expmap)
iraf.imcombine('@' + obj_list,
comb_img,
sigma='',
rejmask='',
expmasks=tmp_expmap,
combine=combine,
reject=reject,
masktype='!BPM',
maskvalue=0.0,
zero='@' + zeroshift,
weight='@' + expweight,
expname='EXPMAP')
if sigmap != 'none':
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)
iraf.imcombine('@' + inverse_var_list,
tmp_inverse_var_sum,
combine='sum',
reject='none',
masktype='!BPM',
maskvalue=0.0)
iraf.stsdas()
tmp_sigma = tmp_prefix + 'sigma.fits'
if os.access(tmp_sigma, os.R_OK):
os.remove(tmp_sigma)
iraf.imcalc(tmp_inverse_var_sum,
tmp_sigma,
'sqrt(1.0/im1)',
pixtype='double')
# cut image
iraf.unlearn('imcopy')
cut_img = '%s[%d:%d,%d:%d]' % (comb_img, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_img, outimg)
if expmap != 'none':
cut_exp = '%s[%d:%d,%d:%d]' % (tmp_expmap, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_exp, expmap)
if sigmap != 'none':
cut_sigma = '%s[%d:%d,%d:%d]' % (tmp_sigma, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_sigma, sigmap)
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
remove_temp_all(tmp_prefix + 'obj')
os.remove(obj_list)
os.remove(comb_img)
# record relative offset between input images and combined image and rotation
for i in range(len(inimg_arr)):
im = pyfits.open(inimg_arr[i], mode='update')
if second:
if indep:
# calculate offset
dxc = xcmin - xmin - dx[i]
dyc = ycmin - ymin - dy[i]
# retrieve rotation
rot = 0.0
fdbs = open(dbs_arr[i])
for line in fdbs:
param = line[:-1].split()
if param[0] == 'xrotation':
rot = float(param[1])
if rot > 180.0:
rot = rot - 360.0
im[0].header['dx'] = dx[i]
im[0].header['dy'] = dy[i]
im[0].header['dxc'] = dxc
im[0].header['dyc'] = dyc
im[0].header['rotation'] = rot
else:
# check number of objects in the geomap file
nobj = 0
fgmp = open(gmp_arr[0])
for line in fgmp:
nobj += 1
im1 = pyfits.open(inimg_arr[i].replace(second_pref,
first_pref),
mode='update')
for j in range(nobj):
key = 'XC%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'YC%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'PEAK%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'FWHM%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'DX'
im[0].header[key] = float(im1[0].header[key])
key = 'DY'
im[0].header[key] = float(im1[0].header[key])
key = 'DXC'
im[0].header[key] = float(im1[0].header[key])
key = 'DYC'
im[0].header[key] = float(im1[0].header[key])
key = 'ROTATION'
im[0].header[key] = float(im1[0].header[key])
im1.close()
else:
# calculate offset
dxc = xcmin - xmin - dx[i]
dyc = ycmin - ymin - dy[i]
# retrieve rotation
rot = 0.0
fdbs = open(dbs_arr[i])
for line in fdbs:
param = line[:-1].split()
if param[0] == 'xrotation':
rot = float(param[1])
if rot > 180.0:
rot = rot - 360.0
im[0].header['dx'] = dx[i]
im[0].header['dy'] = dy[i]
im[0].header['dxc'] = dxc
im[0].header['dyc'] = dyc
im[0].header['rotation'] = rot
im.close()
# remove all temporary files
remove_temp_all(tmp_prefix)
return 0
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
def decon(r, f, idx, r1, r2, fiber_size, pixel_pitch):
# x1 = np.where(r >= r1)[0][0]
# x2 = np.where(r >= r2)[0][0]
'in pixel units'
radius = (fiber_size/pixel_pitch)/(2)#*(r2-r1)/(x2-x1))
# idx = np.where(r >= peak)[0][0]
idx += chi(r,f,radius, idx)
size = f.shape[0]
_ = np.seterr(invalid='ignore')
theta = np.arccos(np.abs(r - r[idx])/radius)
pn = radius*np.nan_to_num(np.sin(theta))
zpn = pn[np.where(pn != 0)[0]]
# zpn = pn
# zpn = np.zeros(f.size)
# zpn[0:f.size] = pn[np.where(pn != 0)[0][0]]
ff = np.copy(f)/np.sum(f[np.where(f > 0)[0]])
pnf = np.copy(zpn)/np.sum(zpn)
if debug:
fig1 = plt.figure(fignum)
plt.clf()
sp3 = fig1.add_subplot(223)
sp3.plot(r*pixel_pitch,ff,label='Ring profile')
sp3.plot(r*pixel_pitch,pn/pn.sum(),label='Ideal beam profile')
sp3.legend()
sp3.set_title("Area normalized data and ideal beam profile")
sp3.set_xlabel("Radius (um)")
sp3.set_ylabel("Normalized Counts")
'so we can parallelize'
temp_str = 'temp_'+str(datetime.now().microsecond)
idl_name = temp_str+'_idl.fits'
pyfits.PrimaryHDU(ff).writeto(temp_str+'_ff.fits')
pyfits.PrimaryHDU(pnf).writeto(temp_str+'_pn.fits')
stsdas()
stsdas.analysis()
stsdas.analysis.restore()
stsdas.analysis.restore.lucy\
(temp_str+'_ff.fits',temp_str+'_pn.fits',temp_str+'_frd.fits',1,0)
command = "\"MWRFITS, MRDFITS('"+temp_str+"_frd.fits'),'"+idl_name+"'\""
os.system('idl -quiet -e '+command)
frd = pyfits.open(idl_name)[0].data
global frd_sav
frd_save = frd
(peak, r1, r2) = find_peak(r,frd)
os.system('rm ./'+temp_str+'*.fits')
return ((r2 - r1),frd)
# #
#########################################################################################
'''
import os
from pyraf import iraf
from astropy.io import fits
from shutil import copy2
import rsstools as rt
import numpy as np
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.apextract(dispaxis='1')
iraf.stsdas(_doprint=0)
iraf.analysis(_doprint=0)
iraf.fitting(_doprint=0)
class arc(object):
def __init__(self, name):
self.name = name
self.noext = os.path.splitext(self.name)[0]
self.idname = rt.makesetups(self.name,"arc")
self.idfits = self.idname+'.fits'
self.linelist = self.idname.split('_')[0].lower()+'.dat'
self.header = self.read_header()
def read_header(self):
return fits.getheader(self.name)
def __str__(self):
#!/usr/bin/env python
import numpy as np
import pyfits
from pyraf import iraf
iraf.stsdas()
import subtract_scattered_background as ssb
import rmscheck
import os, sys
import pylab as plt
from stats import gauss
"""
A pipeline that prepares the IRAC mosaics for use by TFIT.
Assume the starting images to be the following:
- a drizzled IRAC mosaic image with pixel scale 0.6 arcsec/pixel, with the
same astrometry as the high-resolution HST mosaics, and has the unit of
MJy/sr
- an IRAC uncertainty image (*_unc.fits), in units of MJy/sr
- usually the cutout of the IRAC mosaic should cover a larger area than the HST
mosaics, so that TFIT doesn't break down when there are high-resolution
templates near (or even beyond) the image border of the IRAC mosaic
"""
exptimes={'ch1':96.8, 'ch2':96.8, 'ch3':96.8, 'ch4':46.8} # exposure time per AOR (used to convert cov map to exp map)
fluxconv = {'ch1':0.1253, 'ch2':0.1469}
subreg_default = {'bullet':(141,260,365,450)}
# the FLUXCONV factor in units of MJy/sr per DN/sec, for the warm mission only
# taken from the webpage
# http://irsa.ipac.caltech.edu/data/SPITZER/docs/irac/warmfeatures/
def irac_pipeline(drz_img, unc_img, channel, rmsmode='unc', pixscale=0.6, gain=3.7,
def ellipse(image, outname, verbose=False, **kwargs):
"""run ellipse task
image : input image name
outname : output filename
Keywords:
*geompar*
x0, y0 -- required
ellip0 : initial ellipticity (default=0.2)
pa0 : initial PA in degrees ccw from +y (default=20)
sma0 : initial semi-major axis (default=10)
minsma : minimum sma (default=1)
maxsma : maximum sma (default='INDEF')
step : sma step size (default=0.1)
linear : linear sampling of sma? (default='no')
*controlpar*
conver :
"""
# verify input
if 'x0' not in kwargs.keys() or 'y0' not in kwargs.keys():
raise KeyError('x0 or y0 is not found')
# load packages
iraf.stsdas(_doprint=0, motd=False)
iraf.analysis(_doprint=0)
iraf.isophote(_doprint=0)
# reset all parameters
iraf.unlearn('ellipse', 'geompar', 'controlpar', 'samplepar')
iraf.ellipse.interactive = False
iraf.ellipse.xylearn = False # if True, it prompts input upon faling to find center
# geompar
iraf.ellipse.x0 = kwargs.pop('x0')
iraf.ellipse.y0 = kwargs.pop('y0')
iraf.ellipse.ellip0 = kwargs.pop('ellip0', 0.2)
iraf.ellipse.pa0 = kwargs.pop('pa0', 20.0)
iraf.ellipse.sma0 = kwargs.pop('sma0', 10.0)
iraf.ellipse.minsma = kwargs.pop('minsma', 1.)
iraf.ellipse.maxsma = kwargs.pop('maxsma', 'INDEF')
iraf.ellipse.step = kwargs.pop('step', 0.1)
iraf.ellipse.linear = kwargs.pop('linear', 0)
iraf.ellipse.verbose = verbose
# controlpar
iraf.ellipse.conver = kwargs.pop('conver', 0.05)
iraf.ellipse.minit = kwargs.pop('minit', 10)
iraf.ellipse.maxit = kwargs.pop('maxit', 50)
iraf.ellipse.hcenter = kwargs.pop('hcenter', 'no')
# samplepar
iraf.ellipse.tsample = kwargs.pop('tsample', 'none')
iraf.ellipse.harmonics = kwargs.pop('harmonics', 'none')
if verbose:
iraf.lparam('ellipse')
iraf.lparam('geompar')
iraf.lparam('controlpar')
iraf.lparam('samplepar')
# run ellipse
tempname = shorten_iauname(image) + str(uuid.uuid4()) + '.tab'
try:
iraf.ellipse(image, tempname)
# print ascii table
iraf.tprint(tempname, pwidth='INDEF', Stdout=outname)
os.remove(tempname)
except:
print 'ellipse failed'
from pyraf import iraf
from astropy.io import fits
import numpy as np
import os
import glob
os.putenv('iraf', '/iraf/iraf')
iraf.stsdas(_doprint=1)
objectName = os.getcwd()[-5:].lower()
print objectName
# iraf.stsdas.analysis.gasp.xyeq()
os.putenv('iraf', '/iraf.216/iraf')
iraf.stsdas(_doprint=1)
os.putenv('iraf', '/iraf/iraf')
iraf.stsdas(_doprint=1)
def pyraf_bmodel(binary,
parent,
output=None,
highar=False,
verbose=False,
interp='spline',
backgr=0.0):
"""Wrapper of the `Pyraf` `bmodel` function to build 2-D model.
For details about the `bmodel` task, please see:
http://stsdas.stsci.edu/cgi-bin/gethelp.cgi?bmodel
Parameters
----------
binary: str
Location of the output binary file from `ellipse` task.
parent: str
Location of the input image that `ellipse` run is based on.
output: str, optional
Name of the output FITS image
highar: boolen, optional
Whether to include the higher-order Fourier component. Default: False.
verbose: boolen, optional
Verbose mode. Default: False.
interp: str, optional
The interpolation mode used by the `trebin` task.
Allowed values are: nearest | linear | poly3 | spline. Default: spline.
Returns
-------
model: ndarray
2-D model image of the `ellipse` results.
"""
# Initiate the isophote module in IRAF/STSDAS
try:
from pyraf import iraf
iraf.stsdas()
iraf.analysis()
iraf.isophote()
except ImportError:
raise Exception("# Unfortunately, you need to install pyraf first.")
# Make sure the files are available
if not os.path.isfile(binary):
raise FileNotFoundError(
"# Can not find the binary output file: {}".format(binary))
if not os.path.isfile(parent):
raise FileNotFoundError(
"# Can not find the input image: {}".format(parent))
# Name of the output FITS file
if output is None:
output = os.path.splitext(binary)[0] + '_model.fits'
# Remove the output file if it exists
if os.path.isfile(output):
os.remove(output)
# Check the interpolation method
if interp not in PYRAF_INTERP:
raise Exception("# Wrong interpolation method! Choices are {}".format(
PYRAF_INTERP))
# Higher-order harmonic modes
highar_str = "yes" if highar else "no"
verbose_str = "yes" if verbose else "no"
try:
iraf.bmodel(binary,
output,
parent=parent,
interp=interp,
highar=highar_str,
backgr=backgr,
verbose=verbose_str)
except Exception:
warnings.warn("# Something is wrong with the Bmodel task!")
return None
if not os.path.isfile(output):
raise FileNotFoundError(
"# Cannot find the output file: {}".format(output))
else:
model = fits.open(output)[0].data
return model
from pyraf import iraf
from astropy.io import fits
import numpy as np
import os
import glob
os.putenv('iraf','/iraf/iraf')
iraf.stsdas(_doprint=1)
objectName = os.getcwd()[-5:].lower()
print objectName
# iraf.stsdas.analysis.gasp.xyeq()
os.putenv('iraf','/iraf.216/iraf')
iraf.stsdas(_doprint=1)
os.putenv('iraf','/iraf/iraf')
iraf.stsdas(_doprint=1)
