def rouletteWheelSelect(population, SCORES, scorefunc, scoreparams, N):
""" Select N unique individuals from the population based on roulette wheel selection.
If there are not at least N individuals in the population, ValueError is raised """
if len(population) < N: raise ValueError("Cannot Select %d individuals from a population of %d" %(N, len(population)))
# print 'getting wheel' ##
wheel = getRouletteWheel(population, SCORES, scorefunc, scoreparams)
# print 'got wheel' ##
answer = []
selection = set([])
while N:
# print 'while', N ##
try:
# print 'trying' ##
r = randfloat(-1,0)
slot = (s for s in wheel if s[1]<=r<=s[0]).next()
answer.append(wheel[slot])
selection.add(slot)
N -= 1
except:
# print 'excepting' ##
print r
for slot in wheel: print slot
raise
crash()
return answer
clobber=True)
#Get final lists for the velocity map fitting for each object
final_inty_lists = []
final_wave_lists = []
final_unc_lists = []
for i in range(len(list_of_objs)):
final_inty_lists.append([])
final_wave_lists.append([])
final_unc_lists.append([])
finalimagelist = sorted(listdir(list_of_objs[i].replace(" ", "")+"_cube"))
for j in range(len(finalimagelist)):
if "_wave.fits" in finalimagelist[j]: final_wave_lists[i].append(join(list_of_objs[i].replace(" ", "")+"_cube",finalimagelist[j]))
elif "_unc.fits" in finalimagelist[j]: final_unc_lists[i].append(join(list_of_objs[i].replace(" ", "")+"_cube",finalimagelist[j]))
elif "_inty.fits" in finalimagelist[j]: final_inty_lists[i].append(join(list_of_objs[i].replace(" ", "")+"_cube",finalimagelist[j]))
if len(final_wave_lists) != len(final_unc_lists) or len(final_wave_lists) != len(final_inty_lists): crash("Error! Mismatched images in data cube directory!")
#Shift to solar velocity frame
for i in range(len(list_of_objs)):
firstimage = openfits(final_wave_lists[i][0])
velshift = firstimage[0].header.get("fpsolar")
firstimage.close()
if velshift is None:
print "Performing solar velocity shift for object "+list_of_objs[i]+"..."
solar_velocity_shift(final_wave_lists[i], rest_wave)
else: print "Solar velocity shift for object "+list_of_objs[i]+" already done."
#Velocity map fitting
for i in range(len(list_of_objs)):
if (isfile(join(list_of_objs[i].replace(" ", "")+"_cube","velocity.fits"))):
while True:
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 = []
def align_norm(fnlist, tolerance=5, thresh=3.5):
"""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.
tolerance -> How close two objects can be and still be considered the same
object. Default is 3 pixels.
thresh -> Optional. Level above sky background variation to look for objs.
Default is 3.5 (times SkySigma). Decrease if center positions
aren't being found accurately. Increase for crowded fields to
decrease computation time.
"""
# Get image FWHMs
fwhm = np.empty(len(fnlist))
firstimage = FPImage(fnlist[0])
toggle = firstimage.fwhm
axcen = firstimage.axcen
aycen = firstimage.aycen
arad = firstimage.arad
firstimage.close()
if axcen is None:
print "Error! Images have not yet been aperture-masked! Do this first!"
crash()
if toggle is None:
print "Warning! FWHMs have not been measured!"
print "Assuming 5 pixel FWHM for all images."
for i in range(len(fnlist)):
fwhm[i] = 5
else:
for i in range(len(fnlist)):
image = FPImage(fnlist[i])
fwhm[i] = image.fwhm
image.close()
# Get sky background levels
skyavg = np.empty(len(fnlist))
skysig = np.empty(len(fnlist))
for i in range(len(fnlist)):
image = FPImage(fnlist[i])
skyavg[i], skysig[i], _skyvar = image.skybackground()
image.close()
# Identify the stars in each image
xlists = []
ylists = []
print "Identifying stars in each image..."
for i in range(len(fnlist)):
xlists.append([])
ylists.append([])
image = FPImage(fnlist[i])
axcen = image.axcen
aycen = image.aycen
arad = image.arad
sources = daofind(image.inty-skyavg[i],
fwhm=fwhm[i],
threshold=thresh*skysig[i]).as_array()
for j in range(len(sources)):
# If the source is not near the center or edge
centermask = ((sources[j][1]-axcen)**2 +
(sources[j][2]-aycen)**2 > (0.05*arad)**2)
edgemask = ((sources[j][1]-axcen)**2 +
(sources[j][2]-aycen)**2 < (0.95*arad)**2)
if np.logical_and(centermask, edgemask):
xlists[i].append(sources[j][1])
ylists[i].append(sources[j][2])
image.close()
# Match objects between fields
print "Matching objects between images..."
xcoo = []
ycoo = []
for i in range(len(xlists[0])):
# For each object in the first image
accept = True
for j in range(1, len(fnlist)):
# For each other image
dist2 = ((np.array(xlists[j])-xlists[0][i])**2 +
(np.array(ylists[j])-ylists[0][i])**2)
if (min(dist2) > tolerance**2):
accept = False
break
if accept:
# We found an object at that position in every image
xcoo.append(xlists[0][i])
ycoo.append(ylists[0][i])
# Create coordinate arrays for the photometry and shifting
x = np.zeros((len(fnlist), len(xcoo)))
y = np.zeros_like(x)
for i in range(len(xcoo)):
# For every object found in the first image
for j in range(len(fnlist)):
# Find that object in every image
dist2 = ((np.array(xlists[j])-xcoo[i])**2 +
(np.array(ylists[j])-ycoo[i])**2)
index = np.argmin(dist2)
x[j, i] = xlists[j][index]
y[j, i] = ylists[j][index]
# Do aperture photometry on the matched objects
print "Performing photometry on matched stars..."
counts = np.zeros_like(x)
dcounts = np.zeros_like(x)
for i in range(len(fnlist)):
image = FPImage(fnlist[i])
apertures = CircularAperture((x[i], y[i]), r=2*fwhm[i])
annuli = CircularAnnulus((x[i], y[i]), r_in=3*fwhm[i], r_out=4*fwhm[i])
phot_table = aperture_photometry(image.inty,
apertures, error=np.sqrt(image.vari))
sky_phot_table = aperture_photometry(image.inty, annuli,
error=np.sqrt(image.vari))
counts[i] = phot_table["aperture_sum"] / apertures.area()
counts[i] -= sky_phot_table["aperture_sum"] / annuli.area()
counts[i] *= apertures.area()
dcounts[i] = phot_table["aperture_sum_err"] / apertures.area()
image.close()
# Calculate the shifts and normalizations
norm, dnorm = calc_norm(counts, dcounts)
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)
# Normalize the images and put shifts in the image headers
for i in range(len(fnlist)):
image = FPImage(fnlist[i], update=True)
image.phottog = "True"
image.dnorm = dnorm[i]
image.inty /= norm[i]
image.vari = image.vari/norm[i]**2
image.xshift = xshifts[i]
image.yshift = yshifts[i]
image.close()
return
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 deghost(fn,uncfn=None,g=0.04):
"""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.
uncfn (optional) -> String, the path to the uncertainty image.
g -> The multiple to subtract from the original image, default = 4%
"""
#Open the image and check for center coordinates
image = openfits(fn,mode="update")
xcen = image[0].header.get("fpxcen")
ycen = image[0].header.get("fpycen")
if xcen == None:
print "Error! Image "+fn+" doesn't have center coordinates in header!"
crash()
#Deghost the image
print "Deghosting image "+fn
image[0].header["fpghost"] = "True"
#Determine image size
xsize=image[0].data.shape[1]
ysize=image[0].data.shape[0]
#Make a mask for the chip gaps and outside-aperture stuff
mask = image[0].data==0
#Make an array of the flipped data
flip = image[0].data[::-1,::-1].copy()
#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 in the data array.
image[0].data[max(np.floor(yshift),0):min(ysize+np.floor(yshift),ysize),max(np.floor(xshift),0):min(xsize+np.floor(xshift),xsize)] += ( -g*abs((np.ceil(yshift)-yshift)*(np.ceil(xshift)-xshift))*flip[max(-np.floor(yshift),0):min(ysize,ysize-np.floor(yshift)),max(-np.floor(xshift),0):min(xsize,xsize-np.floor(xshift))])
image[0].data[max(np.ceil(yshift),0):min(ysize+np.ceil(yshift),ysize),max(np.floor(xshift),0):min(xsize+np.floor(xshift),xsize)] += ( -g*abs((np.floor(yshift)-yshift)*(np.ceil(xshift)-xshift))*flip[max(-np.ceil(yshift),0):min(ysize,ysize-np.ceil(yshift)),max(-np.floor(xshift),0):min(xsize,xsize-np.floor(xshift))])
image[0].data[max(np.floor(yshift),0):min(ysize+np.floor(yshift),ysize),max(np.ceil(xshift),0):min(xsize+np.ceil(xshift),xsize)] += ( -g*abs((np.ceil(yshift)-yshift)*(np.floor(xshift)-xshift))*flip[max(-np.floor(yshift),0):min(ysize,ysize-np.floor(yshift)),max(-np.ceil(xshift),0):min(xsize,xsize-np.ceil(xshift))])
image[0].data[max(np.ceil(yshift),0):min(ysize+np.ceil(yshift),ysize),max(np.ceil(xshift),0):min(xsize+np.ceil(xshift),xsize)] += ( -g*abs((np.floor(yshift)-yshift)*(np.floor(xshift)-xshift))*flip[max(-np.ceil(yshift),0):min(ysize,ysize-np.ceil(yshift)),max(-np.ceil(xshift),0):min(xsize,xsize-np.ceil(xshift))])
#Remask the data using the mask
image[0].data[mask] = 0
#Close the image
image.close()
#Modify the uncertainty image if it exists
if not (uncfn is None):
print "Updating uncertainty image "+uncfn
uncimage = openfits(uncfn,mode="update")
#Make an array of the flipped data
flip = uncimage[0].data[::-1,::-1].copy()*1.0
#Uncertainties add in quadrature
uncimage[0].data = np.power(uncimage[0].data,2)
#Add the uncertainty in the deghosted uncertainty image to the original
uncimage[0].data[max(np.floor(yshift),0):min(ysize+np.floor(yshift),ysize),max(np.floor(xshift),0):min(xsize+np.floor(xshift),xsize)] += (g*abs((np.ceil(yshift)-yshift)*(np.ceil(xshift)-xshift))*flip[max(-np.floor(yshift),0):min(ysize,ysize-np.floor(yshift)),max(-np.floor(xshift),0):min(xsize,xsize-np.floor(xshift))])**2
uncimage[0].data[max(np.ceil(yshift),0):min(ysize+np.ceil(yshift),ysize),max(np.floor(xshift),0):min(xsize+np.floor(xshift),xsize)] += ( g*abs((np.floor(yshift)-yshift)*(np.ceil(xshift)-xshift))*flip[max(-np.ceil(yshift),0):min(ysize,ysize-np.ceil(yshift)),max(-np.floor(xshift),0):min(xsize,xsize-np.floor(xshift))])**2
uncimage[0].data[max(np.floor(yshift),0):min(ysize+np.floor(yshift),ysize),max(np.ceil(xshift),0):min(xsize+np.ceil(xshift),xsize)] += ( g*abs((np.ceil(yshift)-yshift)*(np.floor(xshift)-xshift))*flip[max(-np.floor(yshift),0):min(ysize,ysize-np.floor(yshift)),max(-np.ceil(xshift),0):min(xsize,xsize-np.ceil(xshift))])**2
uncimage[0].data[max(np.ceil(yshift),0):min(ysize+np.ceil(yshift),ysize),max(np.ceil(xshift),0):min(xsize+np.ceil(xshift),xsize)] += ( g*abs((np.floor(yshift)-yshift)*(np.floor(xshift)-xshift))*flip[max(-np.ceil(yshift),0):min(ysize,ysize-np.ceil(yshift)),max(-np.ceil(xshift),0):min(xsize,xsize-np.ceil(xshift))])**2
uncimage[0].data = np.sqrt(uncimage[0].data)
#Close the uncertainty image
uncimage.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
