def fit_wave_soln(fnlist, doprint=False):
"""Fits a wavelength solution to rings in a set of images. Appends this
wavelength solution to the image headers as the keywords:
'fpwave0' and 'fpcalf'
Each object in fnlist must have a corresponding "median.fits" in its
image directory, or this routine will not work.
ARC ring images are fitted by adjusting the center, while the center is
held fixed for night sky rings. A combination of fits to both sets of
rings is used to determine a wavelength solution for the whole set of
images.
If the ARC rings disagree substantially with the night sky rings, it is
recommended that users delete the ARC rings from the fit and use only the
night sky rings.
It is also known that the wavelength solution can sometimes be piecewise in
time when a large jump in 'z' happens between two images; i.e. different
wavelength solutions exist before and after the jump. The routine allows
the user to make a piecewise solution for this reason, but this ability
should be used sparingly.
This routine contains one of the few hard-coded numbers in the pipeline,
Fguess=5600. Currently F values are not written to the fits image headers,
and this is a reasonable guess.
Inputs:
fnlist -> List of strings, each the path to a fits image. These images
should all have been taken with the same order filter. If not, the routine
will crash.
"""
# 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
arclist = []
objlist = []
images = [FPImage(fn) for fn in fnlist]
# Separate ARCs and Object images and median-subtract Object images
isarclist = [image.object == "ARC" for image in images]
for i in range(len(isarclist)):
if isarclist[i]:
arclist.append(images[i])
else:
if not isfile(join(split(fnlist[i])[0], "median.fits")):
exit("Error! No 'median.fits' file found.")
medimage = fits.open(join(split(fnlist[i])[0], "median.fits"))
images[i].inty -= medimage[0].data
images[i].inty -= np.median(images[i].inty[images[i].badp != 1])
medimage.close()
objlist.append(images[i])
filts = [image.filter for image in images]
arclibs = []
nightlibs = []
for i in range(len(fnlist)):
if isarclist[i]:
arclibs.append(get_libraries(filts[i])[0])
else:
nightlibs.append(get_libraries(filts[i])[1])
if get_libraries(filts[i])[0] is None:
exit("Error! Filter "+filts[i]+" not in the wavelength library!")
# This next bit fits all of the rings that the user marks
# Fit rings in the object images
radlists = []
for i in range(len(objlist)):
radlists.append([])
i = 0
while True:
xcen = objlist[i].xcen
ycen = objlist[i].ycen
axcen = objlist[i].axcen
aycen = objlist[i].aycen
arad = objlist[i].arad
rgrid = objlist[i].rarray(axcen, aycen)
# Create radius bins
rbins = np.arange(arad-np.int(max(abs(axcen-xcen), abs(aycen-ycen))))+1
intbins = np.empty_like(rbins)
# Get the median intensity within each radius bin
for j in range(len(rbins)):
binmask = np.logical_and(rgrid > rbins[j]-1,
rgrid < rbins[j])
goodbinmask = np.logical_and(binmask,
objlist[i].badp == 0)
if np.sum(goodbinmask) != 0:
intbins[j] = np.median(objlist[i].inty[goodbinmask])
else:
intbins[j] = 0
# Shift/scale the radius and intensity for the purposes of plotting
plotxcen = xcen-axcen+arad
plotycen = ycen-aycen+arad
plotrbins = rbins+plotxcen
plotintbins = intbins*arad/np.percentile(np.abs(intbins), 98)+plotycen
# Plot the data interactively
ringplot = PlotRingProfile(objlist[i].inty[aycen-arad:aycen+arad,
axcen-arad:axcen+arad],
plotrbins, plotintbins,
plotxcen, plotycen,
radlists[i],
repr(i+1)+"/"+repr(len(objlist)))
# Changing images and loop breakout conditions
if ringplot.key == "d":
i += 1
if ringplot.key == "a":
i += -1
if i == -1 or i == len(objlist):
while True:
yn = raw_input("Finished marking sky rings? (y/n) ")
if "n" in yn or "N" in yn:
if i == -1:
i = 0
if i == len(objlist):
i = len(objlist)-1
break
elif "y" in yn or "Y" in yn:
break
if i == -1 or i == len(objlist):
break
# Force-marking a ring
if ringplot.key == "e" and ringplot.xcoo is not None:
radlists[i].append(ringplot.xcoo-arad-(xcen-axcen))
# Deleting a ring
if (ringplot.key == "s" and ringplot.xcoo is not None and
len(radlists[i]) > 0):
distarray = np.abs((np.array(radlists[i]) -
np.sqrt((ringplot.xcoo-arad-(xcen-axcen))**2 +
(ringplot.ycoo-arad-(ycen-aycen))**2)))
radlists[i].pop(np.argmin(distarray))
# Fitting a ring profile
if ringplot.key == "w" and ringplot.xcoo is not None:
lower_index = max(ringplot.xcoo-plotxcen-50, 0)
upper_index = min(ringplot.xcoo-plotxcen+50, len(rbins))
x = rbins[lower_index:upper_index]**2
y = intbins[lower_index:upper_index]
fit = GaussFit(x, y)
fitplot = PlotRingFit(x, y, fit)
if fitplot.key == "w":
radlists[i].append(np.sqrt(fit[2]))
zo = []
to = []
ro = []
lib_o = []
for i in range(len(objlist)):
for j in range(len(radlists[i])):
zo.append(objlist[i].z)
to.append(objlist[i].jd)
ro.append(radlists[i][j])
lib_o.append(nightlibs[i])
if doprint:
print objlist[i].z, objlist[i].jd, radlists[i][j]
# Fit rings in the ARC images if there are any
xcen = objlist[0].xcen
ycen = objlist[0].ycen
radlists = []
for i in range(len(arclist)):
radlists.append([])
i = 0
while len(arclist) > 0:
axcen = arclist[i].axcen
aycen = arclist[i].aycen
arad = arclist[i].arad
rgrid = arclist[i].rarray(axcen, aycen)
# Create radius bins
rbins = np.arange(arad-np.int(max(abs(axcen-xcen), abs(aycen-ycen))))+1
intbins = np.empty_like(rbins)
# Get the median intensity in each radius bin
for j in range(len(rbins)):
binmask = np.logical_and(rgrid > rbins[j]-1,
rgrid < rbins[j])
goodbinmask = np.logical_and(binmask,
arclist[i].badp == 0)
intbins[j] = np.median(arclist[i].inty[goodbinmask])
# Shift/scale the radius and intensity for the purposes of plotting
plotxcen = xcen-axcen+arad
plotycen = ycen-aycen+arad
plotrbins = rbins+plotxcen
plotintbins = intbins*arad/np.percentile(np.abs(intbins), 98)+plotycen
# Plot the data interactively
ringplot = PlotRingProfile(arclist[i].inty[aycen-arad:aycen+arad,
axcen-arad:axcen+arad],
plotrbins, plotintbins,
plotxcen, plotycen,
radlists[i],
repr(i+1)+"/"+repr(len(arclist)))
# Changing images and loop breakout conditions
if ringplot.key == "d":
i += 1
if ringplot.key == "a":
i += -1
if i == -1 or i == len(arclist):
while True:
yn = raw_input("Finished marking ARC rings? (y/n) ")
if "n" in yn or "N" in yn:
if i == -1:
i = 0
if i == len(arclist):
i = len(arclist)-1
break
elif "y" in yn or "Y" in yn:
break
if i == -1 or i == len(arclist):
break
# Force-marking a ring
if ringplot.key == "e" and ringplot.xcoo is not None:
radlists[i].append(ringplot.xcoo-arad-(xcen-axcen))
# Deleting a ring
if (ringplot.key == "s" and ringplot.xcoo is not None and
len(radlists[i]) > 0):
distarray = np.abs((np.array(radlists[i]) -
np.sqrt((ringplot.xcoo-arad-(xcen-axcen))**2 +
(ringplot.ycoo-arad-(ycen-aycen))**2)))
radlists[i].pop(np.argmin(distarray))
# Fitting a ring profile
if ringplot.key == "w" and ringplot.xcoo is not None:
lower_index = max(ringplot.xcoo-plotxcen-50, 0)
upper_index = min(ringplot.xcoo-plotxcen+50, len(rbins))
x = rbins[lower_index:upper_index]**2
y = intbins[lower_index:upper_index]
fit = GaussFit(x, y)
fitplot = PlotRingFit(x, y, fit)
if fitplot.key == "w":
radlists[i].append(np.sqrt(fit[2]))
za = []
ta = []
ra = []
lib_a = []
for i in range(len(arclist)):
for j in range(len(radlists[i])):
za.append(arclist[i].z)
ta.append(arclist[i].jd)
ra.append(radlists[i][j])
lib_a.append(arclibs[i])
if doprint:
print arclist[i].z, arclist[i].jd, radlists[i][j]
# Now we try to get a good guess at the wavelengths
# Get a good guess at which wavelengths are which
Bguess = objlist[0].b
Fguess = objlist[0].f
if Fguess is None:
Fguess = 5600
# Figure out A by matching rings to the wavelength libraries
master_r = np.array(ro+ra)
master_z = np.array(zo+za)
wavematch = np.zeros_like(master_r)
oldrms = 10000 # Really high initial RMS for comparisons
master_lib = lib_o+lib_a
for i in range(len(master_r)):
lib = master_lib[i]
for j in range(len(lib)):
# Assume the i'th ring is the j'th line
Aguess = (lib[j]*np.sqrt(1+master_r[i]**2/Fguess**2) -
Bguess*master_z[i])
# What are all of the other rings, given this A?
waveguess = ((Aguess+Bguess*master_z) /
np.sqrt(1+master_r**2/Fguess**2))
for k in range(len(master_r)):
wherematch = np.argmin(np.abs(master_lib[k]-waveguess[k]))
wavematch[k] = master_lib[k][wherematch]
rms = np.sqrt(np.average((waveguess-wavematch)**2))
if rms < oldrms:
# This is the new best solution. Keep it!
oldrms = rms
bestA = Aguess
master_wave = wavematch.copy()
# Make more master arrays for the plotting
master_t = np.array(to+ta)
t0 = np.min(master_t)
master_t += -t0
master_t *= 24*60 # Convert to minutes
master_color = np.array(len(ro)*["blue"]+len(ra)*["red"])
toggle = np.ones(len(master_r), dtype="bool")
dotime = False
time_dividers = []
# Do the interactive plotting
while True:
rplot = master_r[toggle]
zplot = master_z[toggle]
tplot = master_t[toggle]
colorplot = master_color[toggle]
wplot = master_wave[toggle]
fitplot = np.zeros(len(wplot))
xs = np.zeros((3, len(rplot)))
xs[0] = rplot
xs[1] = zplot
xs[2] = tplot
fit = [0]*(len(time_dividers)+1)
time_dividers = sorted(time_dividers)
if len(time_dividers) > 1:
print ("Warning: Too many time divisions is likely unphysical." +
"Be careful!")
for i in range(len(time_dividers)+1):
# Create a slice for all of the wavelengths before this time
# divider but after the one before it
if len(time_dividers) == 0:
tslice = tplot == tplot
elif i == 0:
tslice = tplot < time_dividers[i]
elif i == len(time_dividers):
tslice = tplot > time_dividers[i-1]
else:
tslice = np.logical_and(tplot < time_dividers[i],
tplot > time_dividers[i-1])
# Case for fitting time dependence
if dotime:
fit[i] = curve_fit(fpfunc_for_curve_fit_with_t,
xs[:, tslice], wplot[tslice],
p0=(bestA, Bguess, 0, Fguess))[0]
fitplot[tslice] = fpfunc_for_curve_fit_with_t(xs[:, tslice],
fit[i][0],
fit[i][1],
fit[i][2],
fit[i][3])
# Case without time dependence
else:
fit[i] = curve_fit(fpfunc_for_curve_fit,
xs[:, tslice], wplot[tslice],
p0=(bestA, Bguess, Fguess))[0]
fitplot[tslice] = fpfunc_for_curve_fit(xs[:, tslice],
fit[i][0],
fit[i][1],
fit[i][2])
# Calculate residuals to the fit
resid = wplot - fitplot
# Interactively plot the residuals
solnplot = WaveSolnPlot(rplot, zplot, tplot, wplot,
resid, colorplot, time_dividers)
# Breakout case
if solnplot.key == "a":
while True:
for i in range(len(time_dividers)+1):
if dotime:
solnstring = ("Solution "+repr(i+1) +
": A = "+str(fit[i][0]) +
", B = "+str(fit[i][1]) +
", E = "+str(fit[i][2]) +
", F = "+str(fit[i][3]))
else:
solnstring = ("Solution "+repr(i+1) +
": A = "+str(fit[i][0]) +
", B = "+str(fit[i][1]) +
", F = "+str(fit[i][2]))
print solnstring
rms = np.sqrt(np.average(resid**2))
print ("Residual rms="+str(rms) +
" for "+repr(len(time_dividers)+1) +
" independent "+repr(3+dotime) +
"-parameter fits to "+repr(len(rplot))+" rings.")
yn = raw_input("Accept wavelength solution? (y/n) ")
if "n" in yn or "N" in yn:
break
elif "y" in yn or "Y" in yn:
solnplot.key = "QUIT"
break
if solnplot.key == "QUIT":
break
# Restore all points case
if solnplot.key == "r":
toggle = np.ones(len(master_r), dtype="bool")
# Delete nearest point case
if solnplot.key == "d" and solnplot.axis is not None:
# Figure out which plot was clicked in
if solnplot.axis == 1:
# Resid vs. z plot
z_loc = solnplot.xcoo
resid_loc = solnplot.ycoo
dist2 = (((zplot-z_loc)/(np.max(zplot)-np.min(zplot)))**2 +
((resid-resid_loc)/(np.max(resid)-np.min(resid)))**2)
elif solnplot.axis == 2:
# Resid vs. R plot
r_loc = solnplot.xcoo
resid_loc = solnplot.ycoo
dist2 = (((rplot-r_loc)/(np.max(rplot)-np.min(rplot)))**2 +
((resid-resid_loc)/(np.max(resid)-np.min(resid)))**2)
elif solnplot.axis == 3:
# Resit vs. T plot
t_loc = solnplot.xcoo
resid_loc = solnplot.ycoo
dist2 = (((tplot-t_loc)/(np.max(tplot)-np.min(tplot)))**2 +
((resid-resid_loc)/(np.max(resid)-np.min(resid)))**2)
elif solnplot.axis == 4:
# Resid vs. Wave plot
wave_loc = solnplot.xcoo
resid_loc = solnplot.ycoo
dist2 = (((wplot-wave_loc)/(np.max(wplot)-np.min(wplot)))**2 +
((resid-resid_loc)/(np.max(resid)-np.min(resid)))**2)
# Get the radius and time of the worst ring
r_mask = rplot[dist2 == np.min(dist2)][0]
t_mask = tplot[dist2 == np.min(dist2)][0]
toggle[np.logical_and(master_r == r_mask,
master_t == t_mask)] = False
# Toggle time fit case
if solnplot.key == "t":
dotime = not dotime
# Add time break case
if solnplot.key == "w":
timeplot = TimePlot(tplot, resid, colorplot, time_dividers)
if timeplot.xcoo is not None:
time_dividers.append(timeplot.xcoo)
# Remove time breaks case
if solnplot.key == "q":
time_dividers = []
# Close all images
for image in images:
image.close()
# For each image, write the central wavelength and F to the image header
for i in range(len(fnlist)):
image = FPImage(fnlist[i], update=True)
image_t = (image.jd-t0)*24*60
# Figure out which time division it's in
div_index = np.where(np.array(time_dividers) > image_t)[0]
if len(div_index > 0):
div_index = div_index[0]
else:
div_index = len(time_dividers)
image_fit = fit[div_index]
if dotime:
image_wave0 = (image_fit[0] +
image_fit[1]*image.z +
image_fit[2]*image_t)
image_F = image_fit[3]
else:
image_wave0 = (image_fit[0] +
image_fit[1]*image.z)
image_F = image_fit[2]
image.wave0 = image_wave0
image.calf = image_F
image.calrms = rms
image.calnring = repr(len(rplot))
image.calnfits = repr(len(time_dividers)+1)
image.calnpars = repr(3+dotime)
image.close()
# Restore the old keyword shortcut
plt.rcParams["keymap.save"] = oldsavekey
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
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 fit_wave_soln(fnlist):
"""Fits a wavelength solution to rings in a set of images. Appends this
wavelength solution to the image headers as the keywords:
'fpcala', 'fpcalb', ... 'fpcalf'
Each object in fnlist must have a corresponding "median.fits" in its
image directory, or this routine will not work.
ARC ring images are fitted by adjusting the center, while the center is held
fixed for night sky rings. A combination of fits to both sets of rings is
used to determine a wavelength solution for the whole set of images.
If the ARC rings disagree substantially with the night sky rings, it is
recommended that users delete the ARC rings from the fit and use only the
night sky rings.
It is also known that the wavelength solution can sometimes be piecewise in
time when a large jump in 'z' happens between two images; i.e. different
wavelength solutions exist before and after the jump. The routine allows
the user to make a piecewise solution for this reason, but this ability
should be used sparingly.
This routine contains one of the few hard-coded numbers in the pipeline,
Fguess=5600. Currently F values are not written to the fits image headers,
and this is a reasonable guess.
Inputs:
fnlist -> List of strings, each the path to a fits image. These images
should all have been taken with the same order filter. If not, the routine
will crash.
"""
#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
imagelist = []
arclist = []
objlist = []
for i in range(len(fnlist)):
imagelist.append(openfits(fnlist[i]))
if i == 0: filt = imagelist[0][0].header["FILTER"]
if imagelist[i][0].header["FILTER"] != filt:
print "Error! Some of these images are in different filters!"
crash()
if imagelist[i][0].header["OBJECT"]=="ARC": arclist.append(imagelist[i])
else:
if not isfile(join(split(fnlist[i])[0],"median.fits")):
print "Error! No 'median.fits' file found."
crash()
medimage = openfits(join(split(fnlist[i])[0],"median.fits"))
imagelist[i][0].data += -medimage[0].data
medimage.close()
objlist.append(imagelist[i])
#Load wavelength libraries
arclib, nightlib = get_libraries(filt)
if arclib is None:
print "Error! Your filter isn't the wavelength library!"
crash()
#This next bit fits all of the rings that the user marks
#Fit rings in the object images
radlists = []
for i in range(len(objlist)):
radlists.append([])
i=0
while True:
xgrid, ygrid = np.meshgrid(np.arange(objlist[i][0].data.shape[1]), np.arange(objlist[i][0].data.shape[0]))
xcen = objlist[i][0].header["FPXCEN"]
ycen = objlist[i][0].header["FPYCEN"]
axcen = objlist[i][0].header["FPAXCEN"]
aycen = objlist[i][0].header["FPAYCEN"]
arad = objlist[i][0].header["FPARAD"]
rgrid = np.sqrt((xgrid - xcen)**2 + (ygrid - ycen)**2)
rbins = np.arange(arad-np.int(max(abs(axcen-xcen),abs(aycen-ycen))))+1
intbins = np.empty_like(rbins)
for j in range(len(rbins)):
intbins[j] = np.median(objlist[i][0].data[np.logical_and(np.logical_and(objlist[i][0].data!=0,rgrid<rbins[j]),rgrid>rbins[j]-1)])
ringplot = PlotRingProfile(objlist[i][0].data[aycen-arad:aycen+arad,axcen-arad:axcen+arad], #Data to be plotted. Only want stuff inside aperture
rbins+(xcen-axcen)+arad, #Radii bins shifted to image center
intbins*arad/np.percentile(np.abs(intbins),98)+(ycen-aycen)+arad, #Intensity bins, rescaled and shifted by image center
xcen-axcen+arad, ycen-aycen+arad, #Shifted center
radlists[i], #Previously fitted rings
repr(i+1)+"/"+repr(len(objlist))) #numstring
#Changing images and loop breakout conditions
if ringplot.key == "d": i+=1
if ringplot.key == "a": i+=-1
if i == -1 or i == len(objlist):
while True:
yn = raw_input("Finished marking sky rings? (y/n) ")
if "n" in yn or "N" in yn:
if i == -1: i=0
if i == len(objlist): i = len(objlist)-1
break
elif "y" in yn or "Y" in yn:
break
if i == -1 or i == len(objlist): break
#Force-marking a ring
if ringplot.key == "e" and ringplot.xcoo != None: radlists[i].append(ringplot.xcoo-arad-(xcen-axcen))
#Deleting a ring
if ringplot.key == "s" and ringplot.xcoo != None and len(radlists[i])>0:
radlists[i].pop(np.argmin(np.array(radlists[i])-np.sqrt((ringplot.xcoo-arad-(xcen-axcen))**2 + (ringplot.ycoo-arad-(ycen-aycen))**2)))
#Fitting a ring profile
if ringplot.key == "w" and ringplot.xcoo != None:
x = rbins[max(ringplot.xcoo-arad-(xcen-axcen)-50,0):min(ringplot.xcoo-arad-(xcen-axcen)+50,len(rbins))]**2
y = intbins[max(ringplot.xcoo-arad-(xcen-axcen)-50,0):min(ringplot.xcoo-arad-(xcen-axcen)+50,len(rbins))]
fit = GaussFit(x,y)
fitplot = PlotRingFit(x,y,fit)
if fitplot.key == "w": radlists[i].append(np.sqrt(fit[2]))
zo = []
to = []
ro = []
for i in range(len(objlist)):
for j in range(len(radlists[i])):
zo.append(objlist[i][0].header["ET1Z"])
to.append(objlist[i][0].header["JD"])
ro.append(radlists[i][j])
#Fit rings in the ARC images
xcen = objlist[0][0].header["FPXCEN"]
ycen = objlist[0][0].header["FPYCEN"]
radlists = []
for i in range(len(arclist)):
radlists.append([])
i=0
while True:
xgrid, ygrid = np.meshgrid(np.arange(arclist[i][0].data.shape[1]), np.arange(arclist[i][0].data.shape[0]))
axcen = arclist[i][0].header["FPAXCEN"]
aycen = arclist[i][0].header["FPAYCEN"]
arad = arclist[i][0].header["FPARAD"]
rgrid = np.sqrt((xgrid - xcen)**2 + (ygrid - ycen)**2)
rbins = np.arange(arad-np.int(max(abs(axcen-xcen),abs(aycen-ycen))))+1
intbins = np.empty_like(rbins)
for j in range(len(rbins)):
intbins[j] = np.median(arclist[i][0].data[np.logical_and(np.logical_and(arclist[i][0].data!=0,rgrid<rbins[j]),rgrid>rbins[j]-1)])
ringplot = PlotRingProfile(arclist[i][0].data[aycen-arad:aycen+arad,axcen-arad:axcen+arad], #Data to be plotted. Only want stuff inside aperture
rbins+(xcen-axcen)+arad, #Radii bins shifted to image center
intbins*arad/np.percentile(np.abs(intbins),98)+(ycen-aycen)+arad, #Intensity bins, rescaled and shifted by image center
xcen-axcen+arad, ycen-aycen+arad, #Shifted center
radlists[i], #Previously fitted rings
repr(i+1)+"/"+repr(len(arclist))) #numstring
#Changing images and loop breakout conditions
if ringplot.key == "d": i+=1
if ringplot.key == "a": i+=-1
if i == -1 or i == len(arclist):
while True:
yn = raw_input("Finished marking ARC rings? (y/n) ")
if "n" in yn or "N" in yn:
if i == -1: i=0
if i == len(arclist): i = len(arclist)-1
break
elif "y" in yn or "Y" in yn:
break
if i == -1 or i == len(arclist): break
#Force-marking a ring
if ringplot.key == "e" and ringplot.xcoo != None: radlists[i].append(ringplot.xcoo-arad-(xcen-axcen))
#Deleting a ring
if ringplot.key == "s" and ringplot.xcoo != None and len(radlists[i])>0:
radlists[i].pop(np.argmin(np.array(radlists[i])-np.sqrt((ringplot.xcoo-arad-(xcen-axcen))**2 + (ringplot.ycoo-arad-(ycen-aycen))**2)))
#Fitting a ring profile
if ringplot.key == "w" and ringplot.xcoo != None:
x = rbins[max(ringplot.xcoo-arad-(xcen-axcen)-50,0):min(ringplot.xcoo-arad-(xcen-axcen)+50,len(rbins))]**2
y = intbins[max(ringplot.xcoo-arad-(xcen-axcen)-50,0):min(ringplot.xcoo-arad-(xcen-axcen)+50,len(rbins))]
fit = GaussFit(x,y)
fitplot = PlotRingFit(x,y,fit)
if fitplot.key == "w": radlists[i].append(np.sqrt(fit[2]))
za = []
ta = []
ra = []
for i in range(len(arclist)):
for j in range(len(radlists[i])):
za.append(arclist[i][0].header["ET1Z"])
ta.append(arclist[i][0].header["JD"])
ra.append(radlists[i][j])
# #Load previous ring fits from a text file - COMMENT THIS OUT LATER
# rr,zz,tt = np.loadtxt("test.out",unpack=True)
# za = list(zz[zz>0])
# ta = list(tt[zz>0])
# ra = list(rr[zz>0])
# zo = list(zz[zz<0])
# to = list(tt[zz<0])
# ro = list(rr[zz<0])
#Now we try to get a good guess at the wavelengths
#Get a good guess at which wavelengths are which
Bguess = objlist[0][0].header["ET1B"]
Fguess = 5600
#Figure out A by matching rings to the wavelength libraries
master_r = np.array(ro+ra)
master_z = np.array(zo+za)
wavematch = np.zeros_like(master_r)
isnight = np.array([True]*len(ro)+[False]*len(ra))
oldrms = 10000 #Really high initial RMS for comparisons
for i in range(len(master_r)):
if isnight[i]: lib = nightlib
else: lib = arclib
for j in range(len(lib)):
#Assume the i'th ring is the j'th line
Aguess = lib[j]*np.sqrt(1+master_r[i]**2/Fguess**2)-Bguess*master_z[i]
#What are all of the other rings, given this A?
waveguess = (Aguess+Bguess*master_z)/np.sqrt(1+master_r**2/Fguess**2)
for k in range(len(master_r)):
if isnight[k]: wavematch[k] = nightlib[np.argmin(np.abs(nightlib-waveguess[k]))]
else: wavematch[k] = arclib[np.argmin(np.abs(arclib-waveguess[k]))]
rms = np.sqrt(np.average((waveguess-wavematch)**2))
if rms < oldrms:
#This is the new best solution. Keep it!
oldrms = rms
bestA = Aguess
master_wave = wavematch.copy()
#Make more master arrays for the plotting
master_t = np.array(to+ta)
t0 = np.min(master_t)
master_t += -t0
master_t *= 24*60 #Convert to minutes
master_color = np.array(len(ro)*["blue"]+len(ra)*["red"]) #Colors for plotting
toggle = np.ones(len(master_r),dtype="bool")
dotime = False
time_dividers = []
#Do the interactive plotting
while True:
rplot = master_r[toggle]
zplot = master_z[toggle]
tplot = master_t[toggle]
colorplot = master_color[toggle]
waveplot = master_wave[toggle]
fitplot = np.zeros(len(waveplot))
xs = np.zeros((3,len(rplot)))
xs[0] = rplot
xs[1] = zplot
xs[2] = tplot
fit = [0]*(len(time_dividers)+1)
time_dividers = sorted(time_dividers)
if len(time_dividers)>1: print "Warning: Too many time divisions is likely unphysical. Be careful!"
for i in range(len(time_dividers)+1):
#Create a slice for all of the wavelengths before this time divider
#but after the one before it
if len(time_dividers)==0: tslice = tplot==tplot
elif i == 0: tslice = tplot<time_dividers[i]
elif i==len(time_dividers): tslice = tplot>time_dividers[i-1]
else: tslice = np.logical_and(tplot<time_dividers[i],tplot>time_dividers[i-1])
if dotime:
fit[i] = curve_fit(fpfunc_for_curve_fit_with_t, xs[:,tslice], waveplot[tslice], p0=(bestA,Bguess,0,Fguess))[0]
fitplot[tslice] = fpfunc_for_curve_fit_with_t(xs[:,tslice], fit[i][0], fit[i][1], fit[i][2], fit[i][3])
else:
fit[i] = curve_fit(fpfunc_for_curve_fit, xs[:,tslice], waveplot[tslice], p0=(bestA,Bguess,Fguess))[0]
fitplot[tslice] = fpfunc_for_curve_fit(xs[:,tslice], fit[i][0], fit[i][1], fit[i][2])
resid = waveplot - fitplot
solnplot = WaveSolnPlot(rplot,zplot,tplot,waveplot,resid,colorplot,time_dividers)
#Breakout case
if solnplot.key == "a":
while True:
for i in range(len(time_dividers)+1):
if dotime: print "Solution 1: A = "+str(fit[i][0])+", B = "+str(fit[i][1])+", E = "+str(fit[i][2])+", F = "+str(fit[i][3])
else: print "Solution 1: A = "+str(fit[i][0])+", B = "+str(fit[i][1])+", F = "+str(fit[i][2])
print "Residual rms="+str(np.sqrt(np.average(resid**2)))+" for "+repr(len(time_dividers)+1)+" independent "+repr(3+dotime)+"-parameter fits to "+repr(len(rplot))+" rings."
yn = raw_input("Accept wavelength solution? (y/n) ")
if "n" in yn or "N" in yn:
break
elif "y" in yn or "Y" in yn:
solnplot.key = "QUIT"
break
if solnplot.key == "QUIT": break
#Restore all points case
if solnplot.key == "r": toggle = np.ones(len(master_r),dtype="bool")
#Delete nearest point case
if solnplot.key == "d" and solnplot.axis != None:
#Figure out which plot was clicked in
if solnplot.axis == 1:
#Resid vs. z plot
z_loc = solnplot.xcoo
resid_loc = solnplot.ycoo
dist2 = ((zplot-z_loc)/(np.max(zplot)-np.min(zplot)))**2 + ((resid-resid_loc)/(np.max(resid)-np.min(resid)))**2
elif solnplot.axis == 2:
#Resid vs. R plot
r_loc = solnplot.xcoo
resid_loc = solnplot.ycoo
dist2 = ((rplot-r_loc)/(np.max(rplot)-np.min(rplot)))**2 + ((resid-resid_loc)/(np.max(resid)-np.min(resid)))**2
elif solnplot.axis == 3:
#Resit vs. T plot
t_loc = solnplot.xcoo
resid_loc = solnplot.ycoo
dist2 = ((tplot-t_loc)/(np.max(tplot)-np.min(tplot)))**2 + ((resid-resid_loc)/(np.max(resid)-np.min(resid)))**2
elif solnplot.axis == 4:
#Resid vs. Wave plot
wave_loc = solnplot.xcoo
resid_loc = solnplot.ycoo
dist2 = ((waveplot-wave_loc)/(np.max(waveplot)-np.min(waveplot)))**2 + ((resid-resid_loc)/(np.max(resid)-np.min(resid)))**2
#Get the radius and time of the worst ring
r_mask = rplot[dist2 == np.min(dist2)][0]
t_mask = tplot[dist2 == np.min(dist2)][0]
toggle[np.logical_and(master_r == r_mask, master_t == t_mask)] = False
#Fit for time case
if solnplot.key == "t": dotime = not dotime
#Add time break
if solnplot.key == "w":
timeplot = TimePlot(tplot,resid,colorplot,time_dividers)
if timeplot.xcoo != None: time_dividers.append(timeplot.xcoo)
#Remove time breaks
if solnplot.key == "q":
time_dividers = []
