def _make_const_image(self, value):
"""
Creates a constant image
"""
# unlearn some iraf tasks
iraf.unlearn('mkpattern')
# get a random filename
tmpfile1 = get_random_filename('t', '.fits')
iraf.mkpattern(input=tmpfile1,
output="",
pattern="constant",
option="replace",
v1=value,
v2=0.0,
size=1,
title="",
pixtype="real",
ndim=2,
ncols=self.dimension[1],
nlines=self.dimension[0],
n3=1,
n4=1,
n5=1,
n6=1,
n7=1,
header="")
return tmpfile1
def makeOneImage(self, imgname, nx, ny, metadata, drzmeta=None):
"""
Creates one dummy image
The method creates a dummy image with the given name and dimension.
A list of general metadata and a list of drizzle metadata is added
to the zero extension header of the image.
@param imgname: name of the image to be created
@type imgname: string
@param nx: image dimension in x
@type nx: int
@param ny: image dimension in y
@type ny: int
@param metadata: the list of general image metadata
@type metadata: []
@param drzmeta: the list of drizzle metadata
@type drzmeta: []
"""
from pyraf import iraf
from iraf import noao, artdata
# delete a previous version
if os.path.isfile(imgname):
os.unlink(imgname)
# open a HDU-list
mex_hdu = pyfits.HDUList()
# create a primary HDU,
# append it to the list
hdrpr = pyfits.PrimaryHDU()
mex_hdu.append(hdrpr)
# go the the header and put
# the exposure time
hdr = mex_hdu[0].header
hdr['EXPTIME'] = (1.0, 'dummy exposure time')
if drzmeta != None:
# update the header
for item in drzmeta:
hdr[item[0]] = (item[1], item[2])
# write the image and close it
mex_hdu.writeto(imgname)
mex_hdu.close()
# get a random filename
tmpfile = get_random_filename('t', '.fits')
# create a tmp-image with the right dimension
iraf.mkpattern(input=tmpfile,
output="",
pattern="constant",
option="replace",
v1=0.0,
v2=0.0,
size=1,
title="aXeSIM simulation",
pixtype="real",
ndim=2,
ncols=nx,
nlines=ny,
n3=1,
n4=1,
n5=1,
n6=1,
n7=1,
header="")
# copy the tmp-image to the empty dummy image
# as science extension
iraf.imcopy(input=tmpfile,
output=(imgname + '[SCI,1,append]'),
verbose='YES',
Stdout=1)
# open the dummy image, go to the header
img = pyfits.open(imgname, 'update')
hdr = img[1].header
# update the header
for item in metadata:
hdr[item[0]] = (item[1], item[2])
# write to disk and close
img.flush()
img.close
# delete the tmp-image
if os.path.isfile(tmpfile):
os.unlink(tmpfile)
