def make_final_image(input_image, output_image, output_wave_image,
desired_fwhm,
input_uncert_image=None, output_uncert_image=None,
clobber=False):
"""This routine makes the 'final' images for a data cube. At least the paths
to the input image, output image, and output wavelength image are necessary
for this. Beyond that, the user may also have the routine create uncertainty
images as well.
Images are convolved to the resolution 'desired_fwhm'. If the current fwhm
is already higher than that, the routine will throw an error.
A number of fits header keywords are necessary for this program to function
properly. Any of these missing will throw an error.
The output images are intensity-weighted, i.e. the wavelength image will be
created such that the wavelengths at each pixel are the 'most likely'
wavelength for the intensity at that pixel, etc.
Inputs:
input_image -> Path to the input image.
output_image -> Path to the output image.
output_wave_image -> Path to the output wavelength image.
desired_fwhm -> Desired FWHM for the resultant image to have.
Optional Inputs:
input_uncert_image -> Path to the input uncertainty image, if it exists.
output_uncert_image -> Path to the output uncertainty image, if it exists.
clobber -> Overwrite output images if they already exist. Default is False.
"""
print "Making final data cube images for image "+input_image
#Measure the sky background level in the input image
skyavg, skysig = fit_sky_level([input_image])
#Open the input image and get various header keywords, crash if necessary
intyimage = openfits(input_image)
intygrid = intyimage[0].data
fwhm = intyimage[0].header.get("fpfwhm")
wave0 = intyimage[0].header.get("fpwave0")
calf = intyimage[0].header.get("fpcalf")
xcen = intyimage[0].header.get("fpxcen")
ycen = intyimage[0].header.get("fpycen")
if fwhm == None: crash("Error! FWHM not measured for image "+input_image+".")
if wave0 == None or calf == None: crash("Error! Wavelength solution does "+
"not exist for image "+input_image+".")
if xcen == None or ycen == None: crash("Error! Center values not measured "+
"image "+input_image+".")
if fwhm>desired_fwhm: crash("Error! Desired FWHM too low for image "+
input_image+".")
#Subtract the sky background from the image
intygrid[intygrid!=0] -= skyavg[0]
#Calculate the necessary FWHM for convolution and make the gaussian kernel
fwhm_conv = np.sqrt(desired_fwhm**2-fwhm**2)
sig = fwhm_conv/2.3548+0.0001
ksize = np.ceil(4*sig) #Generate the kernel to 4-sigma
kxgrid, kygrid = np.meshgrid(np.linspace(-ksize,ksize,2*ksize+1),np.linspace(-ksize,ksize,2*ksize+1))
kern = np.exp(-(kxgrid**2+kygrid**2)/(2*sig**2)) #Gaussian (unnormalized because sig can be 0)
kern = kern/np.sum(kern) #Normalize the kernel
#Open and convolve the uncertainty image if one exists. Save the output.
if input_uncert_image != None:
uncertimage = openfits(input_uncert_image)
uncertgrid = uncertimage[0].data
#Add the sky background uncertainty to the uncertainty grid
uncertgrid[intygrid!=0] = np.sqrt(uncertgrid[intygrid!=0]**2+skysig[0]**2)
#Convolve the uncertainties appropriately
new_uncert_grid = convolve_uncert(uncertgrid, intygrid, kern)
#Write to output file
writefits(output_uncert_image,new_uncert_grid,header=uncertimage[0].header,clobber=clobber)
uncertimage.close()
#Create and convolve the wavelength image. Save the output.
xgrid, ygrid = np.meshgrid(np.arange(intyimage[0].data.shape[1]),
np.arange(intyimage[0].data.shape[0]))
r2grid = (xgrid-xcen)**2 + (ygrid-ycen)**2
wavegrid = wave0 / np.sqrt(1+r2grid/calf**2)
newwavegrid = convolve_wave(wavegrid, intygrid, kern)
writefits(output_wave_image,newwavegrid,header=intyimage[0].header,clobber=clobber)
#Convolve the intensity image. Save the output
newintygrid = convolve_inty(intygrid, kern)
intyimage[0].header["fpfwhm"] = desired_fwhm #Update header FWHM keyword
writefits(output_image,newintygrid,header=intyimage[0].header,clobber=clobber)
#Close images
intyimage.close()
return
def align_norm(fnlist,uncertlist=None):
"""Aligns a set of images to each other, as well as normalizing the images
to the same average brightness.
Both the alignment and normalization are accomplished through stellar
photometry using the IRAF routine 'daophot'. The centroids of a handful
of stars are found and used to run the IRAF routine 'imalign'. The
instrumental magnitudes of the stars are used to determine by how much
each image must be scaled for the photometry to match across images.
The images are simply updated with their rescaled, shifted selves. This
overwrites the previous images and adds the header keyword 'fpphot' to
the images.
A handful of temporary files are created during this process, which should
all be deleted by the routine at the end. But if it is interrupted, they
might not be.
If the uncertainty images exist, this routine also shifts them by the same
amounts as the intensity images, as well as updating the uncertainty values
for both the new normalization and the uncertainties in normalizing the
images.
Inputs:
fnlist -> List of strings, each the path to a fits image.
uncertlist (optional) -> List of paths to uncertainty images.
"""
#Fit for the sky background level
_skyavg, skysig = fit_sky_level(fnlist)
#Get image FWHMs
fwhm = np.empty(len(fnlist))
firstimage = openfits(fnlist[0])
toggle = firstimage[0].header.get("fpfwhm")
axcen = firstimage[0].header.get("fpaxcen")
aycen = firstimage[0].header.get("fpaycen")
arad = firstimage[0].header.get("fparad")
firstimage.close()
if axcen == None:
print "Error! Images have not yet been aperture-masked! Do this first!"
crash()
if toggle == None:
print "Warning: FWHMs have not been measured! Assuming 5 pixel FWHM for all images."
for i in range(len(fnlist)): fwhm[i] = 5
else:
for i in range(len(fnlist)):
image = openfits(fnlist[i])
fwhm[i] = image[0].header["fpfwhm"]
image.close()
#Identify objects in the fields
coolist = identify_objects(fnlist,skysig,fwhm)
#Match objects between fields
coofile = match_objects(coolist)
#Do aperture photometry on the matched objects
photlist = do_phot(fnlist,coofile,fwhm,skysig)
#Read the photometry files
x, y, mag, dmag = read_phot(photlist)
#Calculate the normalizations
norm, dnorm = calc_norm(mag,dmag)
#Normalize the images (and optionally, the uncertainty images)
for i in range(len(fnlist)):
print "Normalizing image "+fnlist[i]
image = openfits(fnlist[i],mode="update")
if not (uncertlist is None):
uncimage = openfits(uncertlist[i],mode="update")
uncimage[0].data = np.sqrt(norm[i]**2*uncimage[0].data**2 + dnorm[i]**2*image[0].data**2)
uncimage.close()
image[0].data *= norm[i]
image.close()
#Calculate the shifts
for i in range(x.shape[1]):
x[:,i] = -(x[:,i] - x[0,i])
y[:,i] = -(y[:,i] - y[0,i])
xshifts = np.average(x,axis=1)
yshifts = np.average(y,axis=1)
#Shift the images (and optionally, the uncertainty images)
iraf.images(_doprint=0)
iraf.immatch(_doprint=0)
for i in range(len(fnlist)):
print "Shifting image "+fnlist[i]
iraf.geotran(input=fnlist[i],
output=fnlist[i],
geometry="linear",
xshift=xshifts[i],
yshift=yshifts[i],
database="",
verbose="no")
if not (uncertlist is None):
iraf.geotran(input=uncertlist[i],
output=uncertlist[i],
geometry="linear",
xshift=xshifts[i],
yshift=yshifts[i],
database="",
verbose="no")
#Update the image headers
for i in range(len(fnlist)):
image = openfits(fnlist[i],mode="update")
image[0].header["fpphot"]="True"
image[0].header["fpxcen"]+=xshifts[i]
image[0].header["fpycen"]+=yshifts[i]
image[0].header["fpaxcen"]+=xshifts[i]
image[0].header["fpaycen"]+=yshifts[i]
image.close()
#Clean up the coordinate file list
clean_files(fnlist)
remove(coofile)
for i in range(len(photlist)):
remove(photlist[i])
return
