def aperture_mask(fnlist, axcen, aycen, arad):
"""Opens a series of images and masks pixels outside a circular aperture.
The images are overwritten with the new masked image. The values of axcen,
aycen, and arad are added to the FITS headers as keywords 'fpaxcen',
'fpaycen', and 'fparad' respectively.
Inputs:
fnlist -> A list containing strings, each the path to a fits image.
axcen -> The aperture x center
aycen -> The aperture y center
arad -> The aperture radius
"""
for i in range(len(fnlist)):
print ("Masking pixels outside aperture for image " +
str(i+1)+" of "+str(len(fnlist))+": "+fnlist[i])
image = FPImage(fnlist[i], update=True)
rgrid = image.rarray(axcen, aycen)
image.inty[rgrid > arad] = 0
image.badp[rgrid > arad] = 1
image.axcen = axcen
image.aycen = aycen
image.arad = arad
image.close()
def make_final_image(input_image, output_image,
desired_fwhm, 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.
desired_fwhm -> Desired FWHM for the resultant image to have.
Optional Inputs:
clobber -> Overwrite output images if they already exist. Default is False.
"""
print "Making final data cube images for image "+input_image
# Open the image
image = FPImage(input_image)
# Measure the sky background level in the input image
skyavg, _truesig, skysig = image.skybackground()
# Get various header keywords, crash if necessary
intygrid = image.inty
fwhm = image.fwhm
wave0 = image.wave0
calf = image.calf
xcen = image.xcen
ycen = image.ycen
if fwhm is None:
exit("Error! FWHM not measured for image "+input_image+".")
if wave0 is None or calf is None:
exit("Error! Wavelength solution does " +
"not exist for image "+input_image+".")
if xcen is None or ycen is None:
exit("Error! Center values not measured " +
"image "+input_image+".")
if fwhm > desired_fwhm:
exit("Error! Desired FWHM too low for image " +
input_image+".")
# Calculate the necessary FWHM for convolution
fwhm_conv = np.sqrt(desired_fwhm**2-fwhm**2)
# Magic number converts sigma to fwhm
sig = fwhm_conv/2.3548
# Extremely conservative "safe" kernel size
ksize = np.ceil(4*sig)
# Generate the gaussian kernel, shifted to shift the image
kxgrid, kygrid = np.meshgrid(np.linspace(-ksize, ksize, 2*ksize+1),
np.linspace(-ksize, ksize, 2*ksize+1))
xshift, yshift = image.xshift, image.yshift
rad2grid = (kxgrid + xshift)**2 + (kygrid + yshift)**2
kern = np.exp(-rad2grid/(2*sig**2))
# Normalize the kernel
kern = kern/np.sum(kern)
# Extract relevant arrays
vargrid = image.vari
badpixgrid = image.badp
# Convolve the variances appropriately
new_vargrid = convolve_variance(vargrid, badpixgrid, kern)
# Add the sky background uncertainty to the uncertainty grid
vargrid = vargrid+skysig**2
# Create and convolve the wavelength array
if image.wave is None:
rgrid = image.rarray(xcen, ycen)
wavegrid = wave0 / np.sqrt(1+rgrid**2/calf**2)
else:
wavegrid = image.wave
new_wavegrid = convolve_wave(wavegrid, intygrid, badpixgrid, kern)
# Convolve the intensity image
new_intygrid = convolve_inty(intygrid, badpixgrid, kern)
new_intygrid[new_intygrid == 0] = intygrid[new_intygrid == 0]
new_intygrid[np.isnan(new_intygrid)] = intygrid[np.isnan(new_intygrid)]
image.fwhm = desired_fwhm # Update header FWHM keyword
# Subtract the sky background from the image
new_intygrid[new_intygrid != 0] -= skyavg
# Create a new fits extension for the wavelength array
if image.wave is None:
waveheader = image.openimage[2].header.copy()
waveheader['EXTVER'] = 4
wavehdu = ImageHDU(data=new_wavegrid,
header=waveheader,
name="WAVE")
image.openimage[1].header.set('WAVEEXT', 4,
comment='Extension for Wavelength Frame')
image.openimage.append(wavehdu)
# Put all of the convolved images in the right extensions
image.inty = new_intygrid
image.vari = new_vargrid
image.wave = new_wavegrid
# Adjust header keywords (even though they're kinda irrelevant now)
image.xcen += image.xshift
image.ycen += image.yshift
image.axcen += image.xshift
image.aycen += image.yshift
# Write the output file
image.writeto(output_image, clobber=clobber)
# Close images
image.close()
return
def deghost(fn):
"""Routine to deghost an image by rotating it about a central point and
subtracting a constant multiple of this rotated image from the original.
My experience has shown that about 0.04 is the right value, but your
mileage may vary.
The fits header must contain values in "fpxcen" and "fpycen" for the center
about which to be rotated.
Optionally also modifies an uncertainty image to account for the effects
of deghosting on the error propagation.
Creates the fits header keyword "fpghost" = "True".
Inputs:
fn -> String, the path to the fits image to be deghosted.
"""
# Open the image and check for center coordinates
image = FPImage(fn, update=True)
if image.xcen is None:
exit("Error! Image "+fn+" doesn't have center coordinates in header!")
xcen = image.xcen+1
ycen = image.ycen+1
bins = int(image.bins.split()[0])
reflection_xcen = image.xcen + reflection_center_x_shift/bins
reflection_ycen = image.ycen + reflection_center_y_shift/bins
r_array = image.rarray(reflection_xcen, reflection_ycen)
g_array = reflection_intercept + reflection_slope*bins*r_array
# Deghost the intensity image
print "Deghosting image "+fn
# Determine image size
xsize, ysize = image.xsize, image.ysize
# Make a mask for the chip gaps and outside-aperture stuff
mask = image.inty == 0
# Make an array of the flipped data
flipinty = image.inty[::-1, ::-1].copy()*1.0
flipvari = image.vari[::-1, ::-1].copy()*1.0
flip_g = g_array[::-1, ::-1].copy()*1.0
# Calculate the difference between the image's geometric center (midpoint)
# and the axis of rotation
xshift = 2*xcen-xsize-1
yshift = 2*ycen-ysize-1
# A given pixel's position, when rotated, will overlap its four
# neighboring pixels. All pixels will have the same four overlapping
# regions because the rotation is 180 degrees. Here we take a weighted
# sum of these four pixels, where the weights are equal to the areas
# of overlap. This weighted sum is subtracted from the original pixel.
for i in range(4):
# This branching is for the four overlapping pixel regions
if i == 0:
miny = max(np.floor(yshift), 0)
maxy = min(ysize+np.floor(yshift), ysize)
minx = max(np.floor(xshift), 0)
maxx = min(xsize+np.floor(xshift), xsize)
flipminy = max(-np.floor(yshift), 0)
flipmaxy = min(ysize, ysize-np.floor(yshift))
flipminx = max(-np.floor(xshift), 0)
flipmaxx = min(xsize, xsize-np.floor(xshift))
frac = abs((np.ceil(yshift)-yshift)*(np.ceil(xshift)-xshift))
if i == 1:
miny = max(np.ceil(yshift), 0)
maxy = min(ysize+np.ceil(yshift), ysize)
minx = max(np.floor(xshift), 0)
maxx = min(xsize+np.floor(xshift), xsize)
flipminy = max(-np.ceil(yshift), 0)
flipmaxy = min(ysize, ysize-np.ceil(yshift))
flipminx = max(-np.floor(xshift), 0)
flipmaxx = min(xsize, xsize-np.floor(xshift))
frac = abs((np.floor(yshift)-yshift)*(np.ceil(xshift)-xshift))
if i == 2:
miny = max(np.floor(yshift), 0)
maxy = min(ysize+np.floor(yshift), ysize)
minx = max(np.ceil(xshift), 0)
maxx = min(xsize+np.ceil(xshift), xsize)
flipminy = max(-np.floor(yshift), 0)
flipmaxy = min(ysize, ysize-np.floor(yshift))
flipminx = max(-np.ceil(xshift), 0)
flipmaxx = min(xsize, xsize-np.ceil(xshift))
frac = abs((np.ceil(yshift)-yshift)*(np.floor(xshift)-xshift))
if i == 3:
miny = max(np.ceil(yshift), 0)
maxy = min(ysize+np.ceil(yshift), ysize)
minx = max(np.ceil(xshift), 0)
maxx = min(xsize+np.ceil(xshift), xsize)
flipminy = max(-np.ceil(yshift), 0)
flipmaxy = min(ysize, ysize-np.ceil(yshift))
flipminx = max(-np.ceil(xshift), 0)
flipmaxx = min(xsize, xsize-np.ceil(xshift))
frac = abs((np.floor(yshift)-yshift)*(np.floor(xshift)-xshift))
g = flip_g[flipminy:flipmaxy,
flipminx:flipmaxx]
# Rotate and subtract the intensity array
image.inty[miny:maxy,
minx:maxx] -= g*frac*flipinty[flipminy:flipmaxy,
flipminx:flipmaxx]
image.vari[miny:maxy,
minx:maxx] += (g*frac)**2*flipvari[flipminy:flipmaxy,
flipminx:flipmaxx]
# Remask the intensity data using the mask we created
image.inty[mask] = 0
# Update the header
image.ghosttog = "True"
# Close the image
image.close()
return
