def sub_sky_rings(fnlist, medfilelist):
"""Fits for night sky rings in a series of images, then subtracts them off.
If uncertainty images exist, updates them with better uncertainties.
The rings are fitted in an interactive way by the user.
After the ring subtraction, the header keyword "fpdering" is created and
set to "True."
Note: A 'median.fits' image must exist for the object or this routine will
not work.
Inputs:
fnlist -> List of strings, each the path to a fits image.
medfilelist -> List of paths to median images for each image to subtract
off (probably all the same, but could be different)
"""
# This bit takes care of the 's' to save shortcut in matplotlib.
oldsavekey = plt.rcParams["keymap.save"]
plt.rcParams["keymap.save"] = ""
oldfullscreenkey = plt.rcParams['keymap.fullscreen']
plt.rcParams['keymap.fullscreen'] = ""
# Open all of the images, median-subtract them, make wavelength arrays
# make skywavelibs for each, make spectra for each, trim images
images = [FPImage(fn) for fn in fnlist]
wavearraylist = []
skywavelibs = []
skywavelibs_extended = []
spectrum_wave = []
spectrum_inty = []
minwavelist = []
maxwavelist = []
xcenlist = []
ycenlist = []
Flist = []
wave0list = []
has_been_saved = np.zeros(len(fnlist), dtype="bool")
for i in range(len(fnlist)):
# Get a bunch of relevant header values
medimage = fits.open(medfilelist[i])
Flist.append(images[i].calf)
wave0list.append(images[i].wave0)
xcen = images[i].xcen
ycen = images[i].ycen
arad = images[i].arad
axcen = images[i].axcen
aycen = images[i].aycen
# Median subtract it
images[i].inty -= medimage[0].data
images[i].inty -= np.median(images[i].inty[images[i].badp != 1])
# Make wavelength array
r2grid = images[i].rarray(xcen, ycen)**2
wavearraylist.append(wave0list[i]/np.sqrt(1+r2grid/Flist[i]**2))
# Get the sky wave library
minwavelist.append(np.min(wavearraylist[i][r2grid < arad**2]))
maxwavelist.append(np.max(wavearraylist[i][r2grid < arad**2]))
skywaves = get_libraries(images[i].filter)[1]
skywavelibs.append(skywaves[np.logical_and(skywaves > minwavelist[i],
skywaves < maxwavelist[i])])
# Make an "extended" sky wave library
lowermask = np.logical_and(skywaves < minwavelist[i],
skywaves > minwavelist[i]-5)
uppermask = np.logical_and(skywaves > maxwavelist[i],
skywaves < maxwavelist[i]+5)
skywavelibs_extended.append(skywaves[np.logical_or(uppermask,
lowermask)])
# Make the spectrum
minwav = np.min(wavearraylist[i][r2grid < arad**2])
maxwav = np.max(wavearraylist[i][r2grid < arad**2])
wavstep = 0.25 # Quarter-angstrom spacing
spectrum_wave.append(np.linspace(minwav, maxwav,
np.int(((maxwav-minwav)/wavstep))))
spectrum_inty.append(np.zeros_like(spectrum_wave[i][1:]))
for j in range(len(spectrum_wave[i])-1):
uppermask = wavearraylist[i] < spectrum_wave[i][j+1]
lowermask = wavearraylist[i] > spectrum_wave[i][j]
mask = np.logical_and(uppermask, lowermask)
goodmask = np.logical_and(mask, images[i].badp == 0)
spectrum_inty[i][j] = np.median(images[i].inty[goodmask])
spectrum_wave[i] = 0.5*(spectrum_wave[i][:-1]+spectrum_wave[i][1:])
# Trim the images and arrays to the aperture size
images[i].inty = images[i].inty[aycen-arad:aycen+arad,
axcen-arad:axcen+arad]
wavearraylist[i] = wavearraylist[i][aycen-arad:aycen+arad,
axcen-arad:axcen+arad]
xcenlist.append(arad+xcen-axcen)
ycenlist.append(arad+ycen-aycen)
# Convert skywavelibs to lists
skywavelibs[i] = list(skywavelibs[i])
skywavelibs_extended[i] = list(skywavelibs_extended[i])
# Close median image
medimage.close()
# Try to fix the NaNs
mask = np.logical_not(np.isnan(spectrum_inty[i]))
spectrum_wave[i] = spectrum_wave[i][mask]
spectrum_inty[i] = spectrum_inty[i][mask]
# Interactive plotting of ring profiles
addedwaves = []
final_fitted_waves = []
final_fitted_intys = []
final_fitted_sigs = []
for i in range(len(fnlist)):
addedwaves.append([])
final_fitted_waves.append([])
final_fitted_intys.append([])
final_fitted_sigs.append([])
i = 0
while True:
# Determine which wavelengths we want to fit
waves_to_fit = skywavelibs[i]+addedwaves[i]
# Generate a fitting function from those wavelengths
func_to_fit = make_sum_gaussians(waves_to_fit)
# Come up with reasonable guesses for the fitting function parameters
contguess = [0]
intyguesses = []
sigguesses = []
for j in range(len(waves_to_fit)):
uppermask = spectrum_wave[i] < waves_to_fit[j]+4
lowermask = spectrum_wave[i] > waves_to_fit[j]-4
mask = np.logical_and(uppermask, lowermask)
# Guess at line intensity via integral
intyguesses.append(np.sum(spectrum_inty[i][mask] *
(spectrum_wave[i][1] -
spectrum_wave[i][0])))
# Guess sigma is 2 angstroms
sigguesses.append(2)
guess = contguess+intyguesses+sigguesses
# Fit the spectrum
fitsuccess = True
try:
fit = curve_fit(func_to_fit, spectrum_wave[i],
spectrum_inty[i], p0=guess)[0]
except RuntimeError:
print ("Warning: Fit did not converge. " +
"Maybe remove some erroneous lines from the fit?")
fit = guess
fitsuccess = False
fitcont = fit[0]
fitintys = np.array(fit[1:len(waves_to_fit)+1])
fitsigs = np.array(fit[len(waves_to_fit)+1:2*len(waves_to_fit)+1])
# Flip signs of negative sigma fits (sign degeneracy)
fitintys[fitsigs < 0] = -1*fitintys[fitsigs < 0]
fitsigs[fitsigs < 0] = -1*fitsigs[fitsigs < 0]
# Make the subtracted plot array
subarray = images[i].inty.copy()
for j in range(len(waves_to_fit)):
subarray -= fitintys[j]*nGauss(wavearraylist[i]-waves_to_fit[j],
fitsigs[j])
# Figure out which radius each wavelength is at
radiilist = []
for j in range(len(waves_to_fit)):
radiilist.append(Flist[i] *
np.sqrt((wave0list[i]/waves_to_fit[j])**2-1))
# Create the subtracted spectrum
sub_spec_inty = spectrum_inty[i] - fitcont
for j in range(len(waves_to_fit)):
sub_spec_inty -= (fitintys[j] *
nGauss(spectrum_wave[i]-waves_to_fit[j],
fitsigs[j]))
# Create the spectrum fit plot
fit_X = np.linspace(minwavelist[i], maxwavelist[i], 500)
fit_Y = np.ones_like(fit_X)*fitcont
for j in range(len(waves_to_fit)):
fit_Y += fitintys[j]*nGauss(fit_X-waves_to_fit[j], fitsigs[j])
# Plot the image, spectrum, and fit
profile_plot = SkyRingPlot(images[i].inty, subarray,
xcenlist[i], ycenlist[i], radiilist,
spectrum_wave[i], spectrum_inty[i],
sub_spec_inty, fit_X, fit_Y,
skywavelibs[i], addedwaves[i],
skywavelibs_extended[i],
repr(i+1)+"/"+repr(len(fnlist)),
has_been_saved[i], fitsuccess)
# Shifting images and breakout condition
if profile_plot.key == "a":
i += -1
if profile_plot.key == "d":
i += 1
quitloop = False
if (i == -1 or i == len(fnlist) or profile_plot.key == "q"):
if (np.sum(np.logical_not(has_been_saved)) == 0):
# while True:
# yn = raw_input("Finished fitting ring profiles? (y/n) ")
# if "n" in yn or "N" in yn:
# break
# if "y" in yn or "Y" in yn:
# quitloop = True
# break
quitloop = True
else:
print ("Error: Ring fits have not yet been " +
"saved for the following images:")
imagenumbers = np.arange(len(fnlist))+1
print imagenumbers[np.logical_not(has_been_saved)]
if quitloop:
break
if i == -1:
i = 0
if i == len(fnlist):
i = len(fnlist)-1
# Delete ring option
if profile_plot.key == "s":
nearest_wave = None
if profile_plot.axis in [1, 3] and len(waves_to_fit) != 0:
# The keypress was made in an image plot
clicked_radius = np.sqrt((profile_plot.xcoo - xcenlist[i])**2 +
(profile_plot.ycoo - ycenlist[i])**2)
near_index = np.argmin(np.abs((np.array(radiilist) -
clicked_radius)))
nearest_wave = waves_to_fit[near_index]
elif profile_plot.axis in [2, 4] and len(waves_to_fit) != 0:
# The keypress was in a spectrum plot
clicked_wave = profile_plot.xcoo
near_index = np.argmin(np.abs(np.array(waves_to_fit) -
clicked_wave))
nearest_wave = waves_to_fit[near_index]
if nearest_wave is not None:
# Remove the nearest wavelength from the sky_wave_lib or
# added waves, add it to extra waves
if nearest_wave in skywavelibs[i]:
skywavelibs[i].remove(nearest_wave)
skywavelibs_extended[i].append(nearest_wave)
if nearest_wave in addedwaves[i]:
addedwaves[i].remove(nearest_wave)
# Add ring option
if profile_plot.key == "w":
clicked_wave = None
if profile_plot.axis in [1, 3]:
# The keypress was made in an image plot
clicked_radius = np.sqrt((profile_plot.xcoo - xcenlist[i])**2 +
(profile_plot.ycoo - ycenlist[i])**2)
# Convert radius to wavelength
clicked_wave = (wave0list[i] /
(1+clicked_radius**2/Flist[i]**2)**0.5)
elif profile_plot.axis in [2, 4]:
# The keypress was in a spectrum plot
clicked_wave = profile_plot.xcoo
if clicked_wave is not None:
# Find the nearest wavelength in the extended list
if len(np.array(skywavelibs_extended[i])-clicked_wave) > 0:
near = np.argmin(np.abs(np.array(skywavelibs_extended[i]) -
clicked_wave))
wave_to_add = skywavelibs_extended[i][near]
# Add that wave to skywavelibs, remove it from extended
skywavelibs[i].append(wave_to_add)
skywavelibs_extended[i].remove(wave_to_add)
# Force add ring option
if profile_plot.key == "e":
clicked_wave = None
if profile_plot.axis in [1, 3]:
# The click was made in an image plot
clicked_radius = np.sqrt((profile_plot.xcoo - xcenlist[i])**2 +
(profile_plot.ycoo - ycenlist[i])**2)
# Convert radius to wavelength
clicked_wave = (wave0list[i] /
(1+clicked_radius**2/Flist[i]**2)**0.5)
elif profile_plot.axis in [2, 4]:
# The click was in a spectrum plot
clicked_wave = profile_plot.xcoo
if clicked_wave is not None:
# Add this wavelength to the added array
addedwaves[i].append(clicked_wave)
# Save option
if profile_plot.key == "r":
final_fitted_waves[i] = waves_to_fit[:]
final_fitted_intys[i] = fitintys[:]
final_fitted_sigs[i] = fitsigs[:]
has_been_saved[i] = True
# Manual Mode Option
if profile_plot.key == "x":
# Initialize the manual mode lists
man_waves = list(waves_to_fit)
man_intys = list(fitintys)
man_sigs = list(fitsigs)
man_cont = fitcont
man_real = []
for wave in man_waves:
if wave in skywavelibs[i] or wave in skywavelibs_extended[i]:
man_real.append(True)
else:
man_real.append(False)
# Initialize refinement level and selected line
selected_line = 0
refine = 1
inty_refine = 0.25*(np.max(spectrum_inty[i]) -
np.min(spectrum_inty[i]))
sig_refine = 0.5
cont_refine = inty_refine
# Loop until completion
while True:
# Make the subtracted plot array
subarray = images[i].inty.copy()
for j in range(len(man_waves)):
subarray -= man_intys[j]*nGauss((wavearraylist[i] -
man_waves[j]),
man_sigs[j])
# Figure out which radius each wavelength is at
radiilist = []
for j in range(len(man_waves)):
radiilist.append(Flist[i] *
np.sqrt((wave0list[i]/man_waves[j])**2-1))
for skywav in skywavelibs_extended[i]:
radiilist.append(Flist[i] *
np.sqrt((wave0list[i] / skywav)**2-1))
# Create the subtracted spectrum
sub_spec_inty = spectrum_inty[i] - man_cont
for j in range(len(man_waves)):
sub_spec_inty -= (man_intys[j] *
nGauss(spectrum_wave[i]-man_waves[j],
man_sigs[j]))
# Create the spectrum fit plot
fit_X = np.linspace(minwavelist[i], maxwavelist[i], 500)
fit_Y = np.ones_like(fit_X)*man_cont
for j in range(len(man_waves)):
fit_Y += man_intys[j]*nGauss(fit_X-man_waves[j],
man_sigs[j])
# What colors are we using?
colors = []
for k in range(len(man_waves)):
if selected_line == k:
colors.append('purple')
elif not man_real[k]:
colors.append('green')
elif man_real[k]:
colors.append('blue')
for _w in skywavelibs_extended[i]:
colors.append('red')
# Plot the spectrum and fit, get user input
man_plot = ManualPlot(images[i].inty, subarray,
xcenlist[i], ycenlist[i],
spectrum_wave[i], spectrum_inty[i],
sub_spec_inty,
fit_X, fit_Y,
has_been_saved[i], (repr(i+1)+"/" +
repr(len(fnlist))),
radiilist,
man_waves+skywavelibs_extended[i],
colors)
# Cycle selected line
if man_plot.key == "z":
selected_line += 1
if selected_line >= len(man_waves):
selected_line = 0
refine = 1
# Save fit
if man_plot.key == "r":
final_fitted_waves[i] = man_waves[:]
final_fitted_intys[i] = man_intys[:]
final_fitted_sigs[i] = man_sigs[:]
has_been_saved[i] = True
break
# Refinement levels
if man_plot.key == "c":
refine *= 0.5
if man_plot.key == "v":
refine *= 2
# Increase/decrease inty
if len(man_waves) != 0:
if man_plot.key == "w":
man_intys[selected_line] += refine*inty_refine
if man_plot.key == "s":
man_intys[selected_line] -= refine*inty_refine
# Increase/decrease line width
if len(man_waves) != 0:
if man_plot.key == "q":
man_sigs[selected_line] += refine*sig_refine
if man_plot.key == "a":
man_sigs[selected_line] -= refine*sig_refine
# Increase/decrease continuum
if man_plot.key == "t":
man_cont += refine*cont_refine
if man_plot.key == "g":
man_cont -= refine*cont_refine
# Add the known line nearest the keypress
if man_plot.key == "e":
clicked_wave = None
if man_plot.axis in [1, 3]:
# The keypress was made in an image plot
clicked_radius = np.sqrt((man_plot.xcoo -
xcenlist[i])**2 +
(man_plot.ycoo -
ycenlist[i])**2)
# Convert radius to wavelength
clicked_wave = (wave0list[i] /
(1+clicked_radius**2/Flist[i]**2)**0.5)
elif man_plot.axis in [2, 4]:
# The keypress was in a spectrum plot
clicked_wave = man_plot.xcoo
if clicked_wave is not None:
# Find the nearest wavelength in the extended list
if len(np.array(skywavelibs_extended[i]) -
clicked_wave) > 0:
sky = np.array(skywavelibs_extended[i])
near = np.argmin(np.abs(sky-clicked_wave))
wave_to_add = skywavelibs_extended[i][near]
# Add that wave to manwaves,
# remove from extended list
man_waves.append(wave_to_add)
man_intys.append(0)
man_sigs.append(1)
man_real.append(True)
skywavelibs_extended[i].remove(wave_to_add)
selected_line = len(man_waves)-1
# Remove line nearest the keypress
if man_plot.key == "d":
nearest_wave = None
if man_plot.axis in [1, 3] and len(man_waves) != 0:
# The keypress was made in an image plot
clicked_radius = np.sqrt((man_plot.xcoo -
xcenlist[i])**2 +
(man_plot.ycoo -
ycenlist[i])**2)
near_index = np.argmin(np.abs((np.array(radiilist) -
clicked_radius)))
nearest_wave = man_waves[near_index]
elif man_plot.axis in [2, 4] and len(man_waves) != 0:
# The keypress was in a spectrum plot
clicked_wave = man_plot.xcoo
near_index = np.argmin(np.abs(np.array(man_waves) -
clicked_wave))
nearest_wave = man_waves[near_index]
if nearest_wave is not None:
# Remove the nearest wavelength
# re-add to extended list
# if it was real
wave_index = np.where(np.array(man_waves) ==
nearest_wave)[0][0]
man_waves.pop(wave_index)
man_intys.pop(wave_index)
man_sigs.pop(wave_index)
if man_real[wave_index]:
skywavelibs_extended[i].append(nearest_wave)
man_real.pop(wave_index)
if wave_index == selected_line:
selected_line = 0
# Force-add
if man_plot.key == "f":
clicked_wave = None
if profile_plot.axis in [1, 3]:
# The click was made in an image plot
clicked_radius = np.sqrt((man_plot.xcoo -
xcenlist[i])**2 +
(man_plot.ycoo -
ycenlist[i])**2)
# Convert radius to wavelength
clicked_wave = (wave0list[i] /
(1+clicked_radius**2 /
Flist[i]**2)**0.5)
elif profile_plot.axis in [2, 4]:
# The click was in a spectrum plot
clicked_wave = man_plot.xcoo
if clicked_wave is not None:
# Add this wavelength to the added array
man_waves.append(clicked_wave)
man_intys.append(0)
man_sigs.append(1)
man_real.append(False)
selected_line = len(man_waves)-1
# Switch back to automatic
if man_plot.key == "x":
break
# Close all of the images
for image in images:
image.close()
# Subtract the ring profiles and update headers
for i in range(len(fnlist)):
image = FPImage(fnlist[i], update=True)
arad = image.arad
axcen = image.axcen
aycen = image.aycen
mask = image.inty == 0 # For re-correcting chip gaps
for j in range(len(final_fitted_waves[i])):
image.header['subwave'+repr(j)] = final_fitted_waves[i][j]
image.header['subinty'+repr(j)] = final_fitted_intys[i][j]
image.header['subsig'+repr(j)] = final_fitted_sigs[i][j]
ringimg = (final_fitted_intys[i][j] *
nGauss(wavearraylist[i]-final_fitted_waves[i][j],
final_fitted_sigs[i][j]))
image.inty[aycen-arad:aycen+arad, axcen-arad:axcen+arad] -= ringimg
image.ringtog = "True"
image.inty[mask] = 0 # For re-correcting chip gaps
image.close()
# Restore the old keyword shortcut
plt.rcParams["keymap.save"] = oldsavekey
plt.rcParams['keymap.fullscreen'] = oldfullscreenkey
return
def sub_sky_rings(fnlist,medfilelist):
"""Fits for night sky rings in a series of images, then subtracts them off.
If uncertainty images exist, updates them with better uncertainties.
The rings are fitted in an interactive way by the user.
After the ring subtraction, the header keyword "fpdering" is created and
set to "True."
Note: A 'median.fits' image must exist for the object or this routine will
not work.
Inputs:
fnlist -> List of strings, each the path to a fits image.
medfilelist -> List of paths to median images for each image to subtract off
(probably all the same, but generally could be different)
"""
#This bit takes care of the 's' to save shortcut in matplotlib.
oldsavekey = plt.rcParams["keymap.save"]
plt.rcParams["keymap.save"] = ""
#Open all of the images, median-subtract them, make wavelength arrays
# make skywavelibs for each, make spectra for each, trim images
imagelist = []
wavearraylist = []
skywavelibs = []
skywavelibs_extended = []
spectrum_wave = []
spectrum_inty = []
minwavelist = []
maxwavelist = []
xcenlist = []
ycenlist = []
Flist = []
wave0list = []
has_been_saved = np.zeros(len(fnlist),dtype="bool")
for i in range(len(fnlist)):
#Open image
imagelist.append(openfits(fnlist[i]))
medimage = openfits(medfilelist[i])
#Get a bunch of relevant header values
Flist.append(imagelist[i][0].header["fpcalf"])
wave0list.append(imagelist[i][0].header["fpwave0"])
xcen = imagelist[i][0].header["fpxcen"]
ycen = imagelist[i][0].header["fpycen"]
arad = imagelist[i][0].header["fparad"]
axcen = imagelist[i][0].header["fpaxcen"]
aycen = imagelist[i][0].header["fpaycen"]
#Median subtract it
imagelist[i][0].data += -medimage[0].data
#Make wavelength array
xgrid, ygrid = np.meshgrid(np.arange(imagelist[i][0].data.shape[1]),np.arange(imagelist[i][0].data.shape[0]))
r2grid = (xgrid-xcen)**2+(ygrid-ycen)**2
wavearraylist.append(wave0list[i]/np.sqrt(1+r2grid/Flist[i]**2))
#Get the sky wave library
minwavelist.append(np.min(wavearraylist[i][r2grid<arad**2]))
maxwavelist.append(np.max(wavearraylist[i][r2grid<arad**2]))
skywaves = get_libraries(imagelist[i][0].header["FILTER"])[1]
skywavelibs.append(skywaves[np.logical_and(skywaves>minwavelist[i],skywaves<maxwavelist[i])])
#Make an "extended" sky wave library
skywavelibs_extended.append(skywaves[np.logical_or(np.logical_and(skywaves<minwavelist[i],skywaves>minwavelist[i]-5),np.logical_and(skywaves>maxwavelist[i],skywaves<maxwavelist[i]+5))])
#Make the spectrum
spectrum_wave.append(np.linspace(np.min(wavearraylist[i][r2grid<arad**2]),np.max(wavearraylist[i][r2grid<arad**2]),np.int(((np.max(wavearraylist[i][r2grid<arad**2])-np.min(wavearraylist[i][r2grid<arad**2]))/0.25))))
spectrum_inty.append(np.zeros_like(spectrum_wave[i][1:]))
for j in range(len(spectrum_wave[i])-1):
spectrum_inty[i][j] = np.median(imagelist[i][0].data[np.logical_and(np.logical_and(imagelist[i][0].data!=0,wavearraylist[i]>spectrum_wave[i][j]),wavearraylist[i]<spectrum_wave[i][j+1])])
spectrum_wave[i] = 0.5*(spectrum_wave[i][:-1]+spectrum_wave[i][1:])
#Trim the images and arrays to the aperture size
imagelist[i][0].data = imagelist[i][0].data[aycen-arad:aycen+arad,axcen-arad:axcen+arad]
wavearraylist[i] = wavearraylist[i][aycen-arad:aycen+arad,axcen-arad:axcen+arad]
xcenlist.append(arad+xcen-axcen)
ycenlist.append(arad+ycen-aycen)
#Convert skywavelibs to lists
skywavelibs[i] = list(skywavelibs[i])
skywavelibs_extended[i] = list(skywavelibs_extended[i])
#Close median image
medimage.close()
#Interactive plotting of ring profiles
addedwaves = []
final_fitted_waves = []
final_fitted_intys = []
final_fitted_sigs = []
for i in range(len(fnlist)):
addedwaves.append([])
final_fitted_waves.append([])
final_fitted_intys.append([])
final_fitted_sigs.append([])
i = 0
while True:
#Determine which wavelengths we want to fit
waves_to_fit = skywavelibs[i]+addedwaves[i]
#Generate a fitting function from those wavelengths
func_to_fit = make_sum_gaussians(waves_to_fit)
#Come up with reasonable guesses for the fitting function parameters
contguess = [0]
intyguesses = []
sigguesses = []
for j in range(len(waves_to_fit)):
intyguesses.append(np.sum(spectrum_inty[i][np.logical_and(spectrum_wave[i]<waves_to_fit[j]+4,spectrum_wave[i]>waves_to_fit[j]-4)]*(spectrum_wave[i][1]-spectrum_wave[i][0])))
sigguesses.append(2)
guess = contguess+intyguesses+sigguesses
#Fit the spectrum
fitsuccess = True
try:
fit = curve_fit(func_to_fit,spectrum_wave[i],spectrum_inty[i],p0=guess)[0]
except RuntimeError:
print "Warning: Fit did not converge. Maybe remove some erroneous lines from the fit?"
fit = guess
fitsuccess = False
fitcont = fit[0]
fitintys = np.array(fit[1:len(waves_to_fit)+1])
fitsigs = np.array(fit[len(waves_to_fit)+1:2*len(waves_to_fit)+1])
#Make the subtracted plot array
subarray = imagelist[i][0].data.copy()
for j in range(len(waves_to_fit)): subarray += -fitintys[j]*nGauss(wavearraylist[i]-waves_to_fit[j],fitsigs[j])
#Figure out which radius each wavelength is at
radiilist = []
for j in range(len(waves_to_fit)): radiilist.append(Flist[i]*np.sqrt((wave0list[i]/waves_to_fit[j])**2-1))
#Create the subtracted spectrum
sub_spec_inty = spectrum_inty[i] - fitcont
for j in range(len(waves_to_fit)): sub_spec_inty += -fitintys[j]*nGauss(spectrum_wave[i]-waves_to_fit[j],fitsigs[j])
#Create the spectrum fit plot
fit_X = np.linspace(minwavelist[i],maxwavelist[i],500)
fit_Y = np.ones_like(fit_X)*fitcont
for j in range(len(waves_to_fit)): fit_Y += fitintys[j]*nGauss(fit_X-waves_to_fit[j], fitsigs[j])
#Plot the image, spectrum, and fit
profile_plot = SkyRingPlot(imagelist[i][0].data, subarray,
xcenlist[i], ycenlist[i], radiilist,
spectrum_wave[i], spectrum_inty[i], sub_spec_inty,
fit_X, fit_Y,
skywavelibs[i], addedwaves[i], skywavelibs_extended[i],
repr(i+1)+"/"+repr(len(fnlist)), has_been_saved[i], fitsuccess)
#Shifting images and breakout condition
if profile_plot.key == "a": i+=-1
if profile_plot.key == "d": i+=1
quitloop = False
if (i==-1 or i==len(fnlist) or profile_plot.key=="q"):
if (np.sum(np.logical_not(has_been_saved))==0):
while True:
yn = raw_input("Finished fitting ring profiles? (y/n) ")
if "n" in yn or "N" in yn:
break
if "y" in yn or "Y" in yn:
quitloop=True
break
else:
print "Error: Ring fits have not yet been saved for the following images:"
print (np.arange(len(fnlist))+1)[np.logical_not(has_been_saved)]
if quitloop: break
if i == -1: i=0
if i == len(fnlist): i=len(fnlist)-1
#Delete ring option
if profile_plot.key == "s":
nearest_wave = None
if profile_plot.axis in [1,3]:
#The click was made in an image plot
clicked_radius = np.sqrt((profile_plot.xcoo - xcenlist[i])**2 + (profile_plot.ycoo - ycenlist[i])**2)
nearest_wave = waves_to_fit[np.argmin(np.abs((np.array(radiilist)-clicked_radius)))]
elif profile_plot.axis in [2,4]:
#The click was in a spectrum plot
clicked_wave = profile_plot.xcoo
nearest_wave = waves_to_fit[np.argmin(np.abs(np.array(waves_to_fit)-clicked_wave))]
if nearest_wave != None:
#Remove the nearest wavelength from the sky_wave_lib or added waves, add it to extra waves
if nearest_wave in skywavelibs[i]:
skywavelibs[i].remove(nearest_wave)
skywavelibs_extended[i].append(nearest_wave)
if nearest_wave in addedwaves[i]: addedwaves[i].remove(nearest_wave)
#Add ring option
if profile_plot.key == "w":
clicked_wave = None
if profile_plot.axis in [1,3]:
#The click was made in an image plot
clicked_radius = np.sqrt((profile_plot.xcoo - xcenlist[i])**2 + (profile_plot.ycoo - ycenlist[i])**2)
#Convert radius to wavelength
clicked_wave = wave0list[i] / (1+clicked_radius**2/Flist[i]**2)**0.5
elif profile_plot.axis in [2,4]:
#The click was in a spectrum plot
clicked_wave = profile_plot.xcoo
if clicked_wave != None:
#Find the nearest wavelength in the extended list (if there are any)
if len(np.abs(np.array(skywavelibs_extended[i])-clicked_wave))>0:
wave_to_add = skywavelibs_extended[i][np.argmin(np.abs(np.array(skywavelibs_extended[i])-clicked_wave))]
#Add that wave to skywavelibs, remove it from extended
skywavelibs[i].append(wave_to_add)
skywavelibs_extended[i].remove(wave_to_add)
#Force add ring option
if profile_plot.key == "e":
clicked_wave = None
if profile_plot.axis in [1,3]:
#The click was made in an image plot
clicked_radius = np.sqrt((profile_plot.xcoo - xcenlist[i])**2 + (profile_plot.ycoo - ycenlist[i])**2)
#Convert radius to wavelength
clicked_wave = wave0list[i] / (1+clicked_radius**2/Flist[i]**2)**0.5
elif profile_plot.axis in [2,4]:
#The click was in a spectrum plot
clicked_wave = profile_plot.xcoo
if clicked_wave != None:
#Add this wavelength to the added array
addedwaves[i].append(clicked_wave)
#Save option
if profile_plot.key == "r":
final_fitted_waves[i] = waves_to_fit[:]
final_fitted_intys[i] = fitintys[:]
final_fitted_sigs[i] = fitsigs[:]
has_been_saved[i] = True
#Close all of the images
medimage.close()
for i in range(len(fnlist)):
imagelist[i].close()
#Subtract the ring profiles and update headers
for i in range(len(fnlist)):
image = openfits(fnlist[i], mode="update")
arad = image[0].header["fparad"]
axcen = image[0].header["fpaxcen"]
aycen = image[0].header["fpaycen"]
mask = image[0].data == 0 #For re-correcting chip gaps
for j in range(len(final_fitted_waves[i])):
image[0].data[aycen-arad:aycen+arad,axcen-arad:axcen+arad] += -final_fitted_intys[i][j]*nGauss(wavearraylist[i]-final_fitted_waves[i][j], final_fitted_sigs[i][j])
image[0].header["fpdering"] = "True"
image[0].data[mask]=0 #For re-correcting chip gaps
image.close()
#Restore the old keyword shortcut
plt.rcParams["keymap.save"] = oldsavekey
return
