def loadStdSpectrum(self, objectName="G158-100"):
#import the known spectrum of the calibrator and rebin to the histogram parameters given
#must be imported into array with dtype float so division later does not have error
std = MKIDStd.MKIDStd()
a = std.load(objectName)
a = std.countsToErgs(a) #convert std spectrum to ergs/s/Angs/cm^2 for BB fitting and cleaning
self.stdWvls = np.array(a[:,0])
self.stdFlux = np.array(a[:,1]) #std object spectrum in ergs/s/Angs/cm^2
if self.plots:
#create figure for plotting standard spectrum modifications
self.stdFig = plt.figure()
self.stdAx = self.stdFig.add_subplot(111)
plt.xlim(4000,11000)
plt.plot(self.stdWvls,self.stdFlux*1E15,linewidth=1,color='grey',alpha=0.75)
convX_rev,convY_rev = self.cleanSpectrum(self.stdWvls,self.stdFlux)
convX = convX_rev[::-1] #convolved spectrum comes back sorted backwards, from long wvls to low which screws up rebinning
convY = convY_rev[::-1]
#rebin cleaned spectrum to flat cal's wvlBinEdges
newa = rebin(convX,convY,self.wvlBinEdges)
rebinnedWvl = np.array(newa[:,0])
rebinnedFlux = np.array(newa[:,1])
if self.plots:
#plot final resampled spectrum
plt.plot(convX,convY*1E15,color='blue')
plt.step(rebinnedWvl,rebinnedFlux*1E15,color = 'black',where='mid')
plt.legend(['%s Spectrum'%self.objectName,'Blackbody Fit','Gaussian Convolved Spectrum','Rebinned Spectrum'],'upper right', numpoints=1)
plt.xlabel(ur"Wavelength (\r{A})")
plt.ylabel(ur"Flux (10$^{-15}$ ergs s$^{-1}$ cm$^{-2}$ \r{A}$^{-1}$)")
plt.ylim(1,5)
plt.savefig(self.plotSavePath+'FluxCal_StdSpectrum_%s.eps'%self.objectName,format='eps')
#convert standard spectrum back into counts/s/angstrom/cm^2
newa = std.ergsToCounts(newa)
self.binnedSpectrum = np.array(newa[:,1])
def cleanSpectrum_old(self,x,y,objectName):
'''
function to take high resolution spectrum of standard star, extend IR coverage with
an exponential tail, then rebin down to ARCONS resolution. This function has since been
deprecated with the current cleanSpectrum which uses a BB fit to extend IR coverage,
and does the rebinning using a gaussian convolution. This is left in for reference.
'''
#locations and widths of absorption features in Angstroms
#features = [3890,3970,4099,4340,4860,6564,6883,7619]
#widths = [50,50,50,50,50,50,50,50]
#for i in xrange(len(features)):
# #check for absorption feature in std spectrum
# ind = np.where((x<(features[i]+15)) & (x>(features[i]-15)))[0]
# if len(ind)!=0:
# ind = ind[len(ind)/2]
# #if feature is found (flux is higher on both sides of the specified wavelength where the feature should be)
# if y[ind]<y[ind+1] and y[ind]<y[ind-1]:
# #cut out width[i] around feature[i]
# inds = np.where((x >= features[i]+widths[i]) | (x <= features[i]-widths[i]))
# x = x[inds]
# y = y[inds]
#fit a tail to the end of the spectrum to interpolate out to desired wavelength in angstroms
fraction = 3.0/4.0
newx = np.arange(int(x[fraction*len(x)]),20000)
slopeguess = (np.log(y[-1])-np.log(y[fraction*len(x)]))/(x[-1]-x[fraction*len(x)])
print "Guess at exponential slope is %f"%(slopeguess)
guess_a, guess_b, guess_c = float(y[fraction*len(x)]), x[fraction*len(x)], slopeguess
guess = [guess_a, guess_b, guess_c]
fitx = x[fraction*len(x):]
fity = y[fraction*len(x):]
exp_decay = lambda fx, A, x0, t: A * np.exp((fx-x0) * t)
params, cov = curve_fit(exp_decay, fitx, fity, p0=guess, maxfev=2000)
A, x0, t= params
print "A = %s\nx0 = %s\nt = %s\n"%(A, x0, t)
best_fit = lambda fx: A * np.exp((fx-x0)*t)
calcx = np.array(newx,dtype=float)
newy = best_fit(calcx)
#func = interpolate.splrep(x[fration*len(x):],y[fraction*len(x):],s=smooth)
#newx = np.arange(int(x[fraction*len(x)]),self.wvlBinEdges[-1])
#newy = interpolate.splev(newx,func)
wl = np.concatenate((x,newx[newx>max(x)]))
flux = np.concatenate((y,newy[newx>max(x)]))
#new method, rebin data to grid of wavelengths generated from a grid of evenly spaced energy bins
#R=7.0 at 4500
#R=E/dE -> dE = R/E
dE = 0.3936 #eV
start = 1000 #Angs
stop = 20000 #Angs
enBins = ObsFile.makeWvlBins(dE,start,stop)
rebinned = rebin(wl,flux,enBins)
re_wl = rebinned[:,0]
re_flux = rebinned[:,1]
#plt.plot(re_wl,re_flux,color='r')
re_wl = re_wl[np.isnan(re_flux)==False]
re_flux = re_flux[np.isnan(re_flux)==False]
start1 = self.wvlBinEdges[0]
stop1 = self.wvlBinEdges[-1]
#regrid downsampled data
new_wl = np.arange(start1,stop1)
#print re_wl
#print re_flux
#print new_wl
#weight=1.0/(re_flux)**(2/1.00)
print len(re_flux)
weight = np.ones(len(re_flux))
#decrease weights near peak
ind = np.where(re_flux == max(re_flux))[0]
weight[ind] = 0.3
for p in [1,2,3]:
if p==1:
wt = 0.3
elif p==2:
wt = 0.6
elif p==3:
wt = 0.7
try:
weight[ind+p] = wt
except IndexError:
pass
try:
if ind-p >= 0:
weight[ind-p] = wt
except IndexError:
pass
weight[-4:] = 1.0
#weight = [0.7,1,0.3,0.3,0.5,0.7,1,1,1]
#print len(weight)
#weight = re_flux/min(re_flux)
#weight = 1.0/weight
#weight = weight/max(weight)
#print weight
f = interpolate.splrep(re_wl,re_flux,w=weight,k=3,s=max(re_flux)**1.71)
new_flux = interpolate.splev(new_wl,f,der=0)
return new_wl, new_flux
def cleanSpectrum(x,y,objectName, wvlBinEdges):
#locations and widths of absorption features in Angstroms
#features = [3890,3970,4099,4340,4860,6564,6883,7619]
#widths = [50,50,50,50,50,50,50,50]
#for i in xrange(len(features)):
# #check for absorption feature in std spectrum
# ind = np.where((x<(features[i]+15)) & (x>(features[i]-15)))[0]
# if len(ind)!=0:
# ind = ind[len(ind)/2]
# #if feature is found (flux is higher on both sides of the specified wavelength where the feature should be)
# if y[ind]<y[ind+1] and y[ind]<y[ind-1]:
# #cut out width[i] around feature[i]
# inds = np.where((x >= features[i]+widths[i]) | (x <= features[i]-widths[i]))
# x = x[inds]
# y = y[inds]
#fit a tail to the end of the spectrum to interpolate out to desired wavelength in angstroms
fraction = 0 #4.0/5.0
newx = np.arange(int(x[fraction*len(x)]),20000)
#slopeguess = (np.log(y[-1])-np.log(y[fraction*len(x)]))/(x[-1]-x[fraction*len(x)])
#print "Guess at exponential slope is %f"%(slopeguess)
#guess_a, guess_b, guess_c = float(y[fraction*len(x)]), x[fraction*len(x)], slopeguess
#guess = [guess_a, guess_b, guess_c]
fitx = x[fraction*len(x)::]
fity = y[fraction*len(x)::]
#exp_decay = lambda fx, A, x0, t: A * np.exp((fx-x0) * t)
#params, cov = curve_fit(exp_decay, fitx, fity, p0=guess, maxfev=2000)
#A, x0, t= params
#print "A = %s\nx0 = %s\nt = %s\n"%(A, x0, t)
#best_fit = lambda fx: A * np.exp((fx-x0)*t)
#calcx = np.array(newx,dtype=float)
#newy = best_fit(calcx)
#normalizing
norm = fity.max()
fity/=norm
guess_a, guess_b = 1/(2*h*c**2/1e-9), 5600 #Constant, Temp
guess = [guess_a, guess_b]
blackbody = lambda fx, N, T: N * 2*h*c**2 / (fx)**5 * (exp(h*c/(k*T*(fx))) - 1)**-1 # Planck Law
#blackbody = lambda fx, N, T: N*2*c*k*T/(fx)**4 #Rayleigh Jeans tail
#blackbody = lambda fx, N, T: N*2*h*c**2/(fx**5) * exp(-h*c/(k*T*fx)) #Wein Approx
params, cov = curve_fit(blackbody, fitx*1.0e-8, fity, p0=guess, maxfev=2000)
N, T= params
print "N = %s\nT = %s\n"%(N, T)
best_fit = lambda fx: N * 2*h*c**2 / (fx)**5 * (exp(h*c/(k*T*(fx))) - 1)**-1 #Planck Law
#best_fit = lambda fx: N*2*c*k*T/(fx)**4 # Rayleigh Jeans Tail
#best_fit = lambda fx: N*2*h*c**2/(fx**5) * exp(-h*c/(k*T*fx)) #Wein Approx
calcx = np.array(newx,dtype=float)
bbfit = best_fit(calcx*1.0E-8)
calcx = np.array(newx,dtype=float)
newy = best_fit(calcx*1.0E-8)
fity*=norm
newy*=norm
plt.plot(calcx[3.0*len(fitx)/4.0::],newy[3.0*len(fitx)/4.0::]*1E15,linestyle='--',linewidth=2, color="black",alpha=0.5) #plot fit
#func = interpolate.splrep(x[fration*len(x):],y[fraction*len(x):],s=smooth)
#newx = np.arange(int(x[fraction*len(x)]),self.wvlBinEdges[-1])
#newy = interpolate.splev(newx,func)
wl = np.concatenate((x,newx[newx>max(x)]))
flux = np.concatenate((y,newy[newx>max(x)]))
#new method, rebin data to grid of wavelengths generated from a grid of evenly spaced energy bins
#R=7.0 at 4500
#R=E/dE -> dE = E/R
dE = 0.3936 #eV
start = 1000 #Angs
stop = 25000 #Angs
enBins = ObsFile.makeWvlBins(dE,start,stop)
rebinned = rebin(wl,flux,enBins)
re_wl = rebinned[:,0]
re_flux = rebinned[:,1]
plt.plot(re_wl,re_flux*1E15,linestyle="o", marker="o",markersize=10, color="blue") #plot rebinned spectrum with exp tail
re_wl = re_wl[np.isnan(re_flux)==False]
re_flux = re_flux[np.isnan(re_flux)==False]
start1 = wvlBinEdges[0]
stop1 = wvlBinEdges[-1]
#regrid downsampled data
new_wl = np.arange(start1,stop1)
#print re_wl
#print re_flux
#print new_wl
#weight=1.0/(re_flux)**(2/1.00)
print len(re_flux)
weight = np.ones(len(re_flux))
#decrease weights near peak
ind = np.where(re_flux == max(re_flux))[0]
weight[ind] = 0.3
for p in [1,2,3]:
if p==1:
wt = 0.3
elif p==2:
wt = 0.6
elif p==3:
wt = 0.7
try:
weight[ind+p] = wt
except IndexError:
pass
try:
if ind-p >= 0:
weight[ind-p] = wt
except IndexError:
pass
#change weights to set how tightly fit must match data points
#weight[-4:] = 1.0
weight = [0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7]
#print len(weight)
#weight = re_flux/min(re_flux)
#weight = 1.0/weight
#weight = weight/max(weight)
print weight
f = interpolate.splrep(re_wl,re_flux,w=weight,k=2,s=0)#max(re_flux)**300)
new_flux = interpolate.splev(new_wl,f,der=0)
return new_wl, new_flux
a = std.load(objectName)
stdWvls = np.array(a[:, 0], dtype=float) # wavelengths in angstroms
stdFlux = np.array(a[:, 1], dtype=float) # flux in counts/s/cm^2/Angs
stdFlux *= h * c * 1e10 / stdWvls # flux in J/s/cm^2/Angs or W/cm^2/Angs
# cut spectrum arrays to our wavelength region
stdFlux = stdFlux[(stdWvls > 3000) & (stdWvls < 13000)]
stdWvls = stdWvls[(stdWvls > 3000) & (stdWvls < 13000)]
plt.plot(stdWvls[(stdWvls > 3158) & (stdWvls < 11089)], stdFlux[(stdWvls > 3158) & (stdWvls < 11089)])
total = integrate.simps(stdFlux[(stdWvls > 3158) & (stdWvls < 11089)], x=stdWvls[(stdWvls > 3158) & (stdWvls < 11089)])
print "Original total flux = ", total
# rebin test
dE = 0.3936 # eV
start = 3000 # Angs
stop = 13000 # Angs
for n in xrange(10):
n = float(n) + 1.0
enBins = ObsFile.makeWvlBins(dE / n, start, stop)
rebinned = rebin(stdWvls, stdFlux, enBins)
re_wl = rebinned[:, 0]
re_flux = rebinned[:, 1]
print re_wl[0]
print re_wl[-1]
plt.plot(re_wl[(re_wl > 3158) & (re_wl < 11089)], re_flux[(re_wl > 3158) & (re_wl < 11089)])
total = integrate.simps(re_flux[(re_wl > 3158) & (re_wl < 11089)], x=re_wl[(re_wl > 3158) & (re_wl < 11089)])
print "Total flux for dE = ", dE / n, "eV is ", total, "W/cm^2"
plt.show()
y = np.array(fdata[:,1])/scale #convert to flux in Janskys y = y*(3E-5)/(x**2) #flux in ergs/s/cm^2/Angs #y = y/(h*c/x) #flux in counts/s/cm^2/Angs #print y print "Loaded standard spectrum of %s"%(objectName) '''#End manual standard loading block plt.plot(x,y*1E15,linewidth=1,color='grey',alpha=0.75) newwl, newflux = cleanSpectrum(x,y,objectName,wvlBinEdges) print newwl print newflux #plt.plot(newwl,newflux*1E15,color = 'red') #plt.show() newa = rebin(newwl,newflux,wvlBinEdges) x = newa[:,0] y = newa[:,1] plt.step(x,y*1E15,color = 'black',where='mid') #convert ARCONS measured spectrum to ergs/s/cm^2/A curve*=h*(c*1E8)/x #print x #print y #fig = plt.figure() #ax = fig.add_subplot(111) #plt.plot(wvls,curve*1E15) #plot ARCONS measured spectrum