# ------------------------------------------------------------------
# wavelengths and names of reference images used during fit; number of output simulations
waves = ["0.345", "0.475", "0.622", "0.763", "0.905", "1.22", "1.63", "2.19"]
refs = [
"U_NORM.fits", "G_NORM.fits", "R_NORM.fits", "I_NORM.fits", "Z_NORM.fits",
"J_extr.fits", "H_extr.fits", "K_NORM.fits"
]
numSimulations = 88
# ------------------------------------------------------------------
# build a list of reference images
refImages = []
for ref in refs:
im = RGBImage(ref)
im.setrange(0, 8e-5)
im.applycmap("afmhot")
refImages.append(im)
# make all reference images the same shape, and remember the final shape of a single image
for i in refImages:
for j in refImages:
i.enlargecanvas(j)
imageShape = refImages[0].shape
# stack the reference images on top of each other
fullRef = refImages[0]
for i in range(1, len(refImages)):
fullRef.addbelow(refImages[i])
print(waves)
# Define new colormap for residuals
def discrete_cmap(N=8):
# define individual colors as hex values
cpool = [ '#000000', '#00EE00', '#0000EE', '#00EEEE', '#EE0000',
'#FFFF00', '#EE00EE', '#FFFFFF']
cmap_i8 = col.ListedColormap(cpool[0:N], 'i8')
cm.register_cmap(cmap=cmap_i8)
# -----------------------------------------------------------------
# build a list of residual images
resImages = []
for res in res:
im = RGBImage(res)
im.setrange(0,1)
discrete_cmap();
im.applycmap("i8")
resImages.append(im)
# make all residual images the same shape, and remember the final shape of a single image
for i in resImages:
for j in resImages:
i.enlargecanvas(j)
imageShape = resImages[0].shape
# stack the residual images on top of each other
fullRes = resImages[0]
for i in range(1,len(resImages)):
fullRes.addbelow(resImages[i])