def _a_blot_segment_image(self, image_to_blot, tempname, x_excess,
y_excess, interp):
"""Blot the segmentation or other nondrizzled image as if it were
assume self.grism_image is always used as the source wcs reference
Thats just a simple wrapper around the task blot in astrodrizzle
Parameters
----------
image_to_blot: str
the input image name, either the grism or direct drizzled image
tempname: str
the name of the output blotted image
Notes
-----
exposure time is hard coded to 1 since it was made from the
drizzled image and we dont want the id numbers rescaled by the
exposure time that was used for blotting
"""
excess_x = 0
excess_y = 0
# the drizzle coeff information for adriz is taken
# from the self.data image
# use the current data as reference image
input_image = (self.data).split("[")[0]
flt_header = fits.getheader(input_image)
flt_wcs = HSTWCS(self.data)
# check to see if this is a simple fits or MEF and grab
# the science information.
ftype = fileutil.isFits(self.grism_image_name)[1]
if (ftype is 'mef'):
grism_wcs = HSTWCS(self.grism_image_name,
ext=(str(self.fcube_info["ext_nam"]),
self.fcube_info["ext_ver"]))
elif (ftype is 'simple'):
grism_wcs = HSTWCS(self.grism_image_name)
else:
return IOError("File type of fits image is not "
"supported {0:s}".format(image_to_blot))
ftype = fileutil.isFits(image_to_blot)[1]
if (ftype is 'mef'):
image_data = fits.getdata(image_to_blot,
ext=(str(self.fcube_info["ext_nam"]),
self.fcube_info["ext_ver"]))
elif (ftype is 'simple'):
image_data = fits.getdata(image_to_blot)
else:
return IOError("Input image is not a supported FITS "
"type: {0:s}".format(image_to_blot))
# edit the wcs header information to add any dim_info shifts that we
# need the segment image needs to be the same sky area cut without
# the added pixels
type(x_excess)
if x_excess > 0:
excess_x = int(flt_wcs.naxis1 + x_excess * 2.)
flt_wcs.naxis1 = excess_x
crpix = flt_wcs.wcs.crpix
newx = int(crpix[0]) + x_excess
flt_wcs.wcs.crpix = np.array([newx, crpix[1]])
flt_wcs.sip.crpix[0] = newx
if y_excess > 0:
excess_y = int(flt_wcs.naxis2 + y_excess * 2.)
flt_wcs.naxis2 = excess_y
crpix = flt_wcs.wcs.crpix
newy = int(crpix[1]) + y_excess
flt_wcs.wcs.crpix = np.array([int(crpix[0]), newy])
flt_wcs.sip.crpix[1] = newy
# returns a numpy.ndarray which is just the data
outimage = astrodrizzle.ablot.do_blot(image_data.astype(np.float32),
grism_wcs,
flt_wcs,
1.,
interp=interp,
sinscl=1.,
coeffs=True,
wcsmap=None,
stepsize=10)
# update the flt_header with the flt_wcs information I created
flt_header['CRPIX1'] = flt_wcs.wcs.crpix[0]
flt_header['CRPIX2'] = flt_wcs.wcs.crpix[1]
# if the input flt was an MEF we need to write an MEF out
try:
newimage = fits.PrimaryHDU()
newimage.data = outimage
newimage.header = flt_header
newimage.header.update(flt_wcs.to_header())
newimage.verify('silentfix')
newimage.writeto(tempname)
except:
raise IOError("Problem writing fits image {0:s}".format(tempname))
def _a_blot_image(self, image_to_blot, tempname, x_excess, y_excess,
interp):
"""
Blot one image.
Thats just a simple wrapper around the task blot in astrodrizzle
Parameters
----------
image_to_blot: str
the input image name, either the grism or direct drizzled image
tempname: str
the name of the output blotted image
"""
# the drizzle coeff information for adriz is taken
# from the self.data image,
excess_x = 0
excess_y = 0
# use the current data as reference image for output
# self.data comes from the header of the input grism or flux image
# and is one of the input images used to make the drizzled
# image_to_blot
input_image = (self.data).split("[")[0]
bunit = fits.getval(image_to_blot, 'BUNIT')
flt_header = fits.getheader(input_image)
flt_wcs = HSTWCS(self.data)
# now look at the image to blot, this is a drizzled image
ftype = fileutil.isFits(image_to_blot)[1]
if (ftype == 'mef'):
blot_wcs = HSTWCS(image_to_blot,
ext=(str(self.fcube_info["ext_nam"]),
str(self.fcube_info["ext_ver"])))
image_data = fits.getdata(image_to_blot,
extname=str(self.fcube_info["ext_nam"]),
extver=int(self.fcube_info["ext_nam"]))
elif (ftype is 'simple'):
blot_wcs = HSTWCS(image_to_blot) # assume simple
image_data = fits.getdata(image_to_blot)
else:
return IOError("File type of fits image is not "
"supported {0:s}".format(image_to_blot))
# edit the wcs header information to add any dim_info shifts that
# we need, expanding the size of the output image
# make sure this gets saved to the output extension header.
# The lambda image will be bigger than the segment image
if x_excess > 0:
excess_x = int(flt_wcs.naxis1 + x_excess * 2.)
flt_wcs.naxis1 = excess_x
crpix = flt_wcs.wcs.crpix
newx = int(crpix[0]) + x_excess
flt_wcs.wcs.crpix = np.array([newx, int(crpix[1])])
flt_wcs.sip.crpix[0] = newx
if y_excess > 0:
excess_y = int(flt_wcs.naxis2 + y_excess * 2.)
flt_wcs.naxis2 = excess_y
crpix = flt_wcs.wcs.crpix
newy = int(crpix[1]) + y_excess
flt_wcs.wcs.crpix = np.array([int(crpix[0]), newy])
flt_wcs.sip.crpix[1] = newy
# outimage is just the data array
outimage = astrodrizzle.ablot.do_blot(image_data.astype(np.float32),
blot_wcs,
flt_wcs,
1.,
interp=interp,
sinscl=1.,
coeffs=True,
wcsmap=None,
stepsize=10)
# update the flt_header with the flt_wcs information I created
flt_header['CRPIX1'] = flt_wcs.wcs.crpix[0]
flt_header['CRPIX2'] = flt_wcs.wcs.crpix[1]
try:
newimage = fits.PrimaryHDU()
newimage.data = outimage
newimage.header = flt_header
newimage.header['BUNIT'] = bunit
newimage.header.update(flt_wcs.to_header())
newimage.verify('silentfix')
newimage.writeto(tempname)
except:
raise IOError("Problem writing fits image {0:s}".format(tempname))
