def backgroundAnalysis():
output = open('/home/kingrice/ARCONS-pipeline/examples/crabN/CrabStack_phaseWvl.pkl', 'rb')
results = pickle.load(output)
vim = results['vim'] #pulls out the dictionary containing all of the relevant information for virtual image object
#this is the info included in the dictionary:
ob = RADecImage()
ob.phaseEdges = vim['phase']
ob.wvlEdges = vim['wvl']
ob.gridDec = vim['Dec']
ob.gridRA = vim['RA']
ob.image = vim['Im']
ob.effIntTimes = vim['effIntT']
ob.totExpTime = vim['totExpT']
ob.expTimeWeights = vim['timeWeights']
ob.vExpTimesStack = vim['vTimeStacks']
ob.imageIsLoaded = vim['loaded']
output.close()
ob.display(DDArr = True) #sums across the wavlength and phase dimension to get a 2D image for the display.
mpl.show()
'''
Here is the fir4st step in the analysis. We want to obtain a sky subtracted spectrum for lets say 5 different scenarios where
the same aperture is used but six different anuli are used in addition to the original annulus. This should be no problem.
'''
spectrumOG, skySubtractionOG, innerApOG, skyMaskOG, wvlBinEdgesOG, apAreaOG, anAreaOG = ob.getApertureSpectrum(cenRA=83.6330495, cenDec=22.0144933, nPixRA=250, nPixDec=250, radius1=.000530, radius2 = .000770, degrees=True) #OG
spectrumT1, skySubtractionT1, innerApT1, skyMaskT1, wvlBinEdgesT1, apAreaT1, anAreaT1 = ob.getApertureSpectrum(cenRA=83.6330495, cenDec=22.0144933, nPixRA=250, nPixDec=250, radius1=.000530, radius2 = .000530, degrees=True, offSet=True, radRA=83.6301176, radDec=22.0143848, radius3=.000770) #T1
spectrumT2, skySubtractionT2, innerApT2, skyMaskT2, wvlBinEdgesT2, apAreaT2, anAreaT2 = ob.getApertureSpectrum(cenRA=83.6330495, cenDec=22.0144933, nPixRA=250, nPixDec=250, radius1=.000530, radius2 = .000530, degrees=True, offSet=True, radRA=83.6317452, radDec=22.0112381, radius3=.000770) #T2
spectrumT3, skySubtractionT3, innerApT3, skyMaskT3, wvlBinEdgesT3, apAreaT3, anAreaT3 = ob.getApertureSpectrum(cenRA=83.6330495, cenDec=22.0144933, nPixRA=250, nPixDec=250, radius1=.000530, radius2 = .000530, degrees=True, offSet=True, radRA=83.6338792, radDec=22.0119614, radius3=.000770) #T3
spectrumT4, skySubtractionT4, innerApT4, skyMaskT4, wvlBinEdgesT4, apAreaT4, anAreaT4 = ob.getApertureSpectrum(cenRA=83.6330495, cenDec=22.0144933, nPixRA=250, nPixDec=250, radius1=.000530, radius2 = .000530, degrees=True, offSet=True, radRA=83.6350004, radDec=22.0135529, radius3=.000770) #T4
spectrumT5, skySubtractionT5, innerApT5, skyMaskT5, wvlBinEdgesT5, apAreaT5, anAreaT5 = ob.getApertureSpectrum(cenRA=83.6330495, cenDec=22.0144933, nPixRA=250, nPixDec=250, radius1=.000530, radius2 = .000530, degrees=True, offSet=True, radRA=83.6341686, radDec=22.0179293, radius3=.000770) #T5
spectrumT6, skySubtractionT6, innerApT6, skyMaskT6, wvlBinEdgesT6, apAreaT6, anAreaT6 = ob.getApertureSpectrum(cenRA=83.6330495, cenDec=22.0144933, nPixRA=250, nPixDec=250, radius1=.000530, radius2 = .000530, degrees=True, offSet=True, radRA=83.6350004, radDec=22.0161571, radius3=.000770) #T6
print 'apArea = ', apAreaOG, ' (arcseconds^2)'
print 'anArea = ', anAreaOG, ' (arcseconds^2)'
print '---determining wavelengthBin centers---'
nWvlBins = len(wvlBinEdgesOG) - 1
wvls = np.empty((nWvlBins), dtype = float)
for n in xrange(nWvlBins):
binsize = wvlBinEdgesOG[n+1] - wvlBinEdgesOG[n]
wvls[n] = (wvlBinEdgesOG[n] + (binsize/2.0))
print '---gathering conversion factors---'
c=2.998E18 #Angs/s
h=6.626E-27 #erg*s
diam = 510.55 #5 meter telescope in centimeters.
area = np.pi * ((diam/2.0)**2 -(183/2.0)**2) #secondary obstruction diameter 1.83m
print '---dereddening spectra---'
#this is where the dereddening happens
Alam = (-7.51*np.log10(wvls/10000) + 1.15)*0.52 #find the extinction magnitude for each wavelength bin in terms of magnitude
spectrumOG = spectrumOG*10**(Alam/2.5)
spectrumT1 = spectrumT1*10**(Alam/2.5)
spectrumT2 = spectrumT2*10**(Alam/2.5)
spectrumT3 = spectrumT3*10**(Alam/2.5)
spectrumT4 = spectrumT4*10**(Alam/2.5)
spectrumT5 = spectrumT5*10**(Alam/2.5)
spectrumT6 = spectrumT6*10**(Alam/2.5)
skySubtractionOG = skySubtractionOG*10**(Alam/2.5)
skySubtractionT1 = skySubtractionT1*10**(Alam/2.5)
skySubtractionT2 = skySubtractionT2*10**(Alam/2.5)
skySubtractionT3 = skySubtractionT3*10**(Alam/2.5)
skySubtractionT4 = skySubtractionT4*10**(Alam/2.5)
skySubtractionT5 = skySubtractionT5*10**(Alam/2.5)
skySubtractionT6 = skySubtractionT6*10**(Alam/2.5)
print '---converting flux units to erg/s/cm^2/A---'
spectrumOG = spectrumOG*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
spectrumT1 = spectrumT1*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
spectrumT2 = spectrumT2*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
spectrumT3 = spectrumT3*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
spectrumT4 = spectrumT4*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
spectrumT5 = spectrumT5*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
spectrumT6 = spectrumT6*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
skySubtractionOG = skySubtractionOG*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
skySubtractionT1 = skySubtractionT1*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
skySubtractionT2 = skySubtractionT2*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
skySubtractionT3 = skySubtractionT3*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
skySubtractionT4 = skySubtractionT4*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
skySubtractionT5 = skySubtractionT5*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
skySubtractionT6 = skySubtractionT6*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
print '---plotting photometry points for comparison---'
#photPoints = np.array([17.23,16.66,16.17,15.65])
photPoints = np.array([17.22,16.64,16.14,15.61])
photPointsErr = np.array([.02, .02, .01, .01])
photPointsFlux = np.array([4260.,3640.,3080.,2550.]) #these are in janskys
centerWvl = np.array([4400.,5500.,6400.,7900.]) #these are in Angstrom
AlamPP = (-7.51*np.log10(centerWvl/10000.) + 1.15)*0.51
photPointsFlux = photPointsFlux*(10**(AlamPP/2.5)) #dereddened
photPointsErr = photPointsErr*(10**(AlamPP/2.5))
# 1Jy = 10^-23 erg/sec/cm^2/Hz
photPointsFlux = photPointsFlux*(10**-23) #now in units of erg/sec/cm^2/Hz
photPointsErr = photPointsErr*(10**-23)
photPointsFlux = photPointsFlux*(c/(centerWvl**2)) #now in units of erg/s/cm^2/A
photPointsErr = photPointsErr*(c/(centerWvl**2))
ppfFinal = photPointsFlux*(10**(-photPoints/2.5)) #now same units as plot
ppEFinal = photPointsErr*(10**(-photPoints/2.5))
image = (ob.image*ob.expTimeWeights)
image = np.sum(image, axis = 3)
image = np.sum(image, axis = 2)
image[np.where(skyMaskOG==0)] = 0
image[np.where(skyMaskT1==0)] = 0
image[np.where(skyMaskT2==0)] = 0
image[np.where(skyMaskT3==0)] = 0
image[np.where(skyMaskT4==0)] = 0
image[np.where(skyMaskT5==0)] = 0
image[np.where(skyMaskT6==0)] = 0
#image[np.where(innerApOG==0)] = 0
#image[np.where(innerApOG==0)] = 0
#image[np.where(innerApOG==0)] = 0
ob.display(image = image) #sums across the wavlength and phase dimension to get a 2D image for the display.
mpl.show()
mpl.figure(1)
mpl.step(wvls, spectrumOG*(10**14), color = 'r', where = 'mid')
mpl.step(wvls, spectrumT1*(10**14), color = 'b', where = 'mid')
mpl.step(wvls, spectrumT2*(10**14), color = 'g', where = 'mid')
mpl.step(wvls, spectrumT3*(10**14), color = 'y', where = 'mid')
mpl.step(wvls, spectrumT4*(10**14), color = 'c', where = 'mid')
mpl.step(wvls, spectrumT5*(10**14), color = 'k', where = 'mid')
mpl.step(wvls, spectrumT6*(10**14), color = 'm', where = 'mid')
mpl.plot(centerWvl, ppfFinal*(10**14), 'bo')
mpl.xlim(4000,10000)
mpl.ylim(0,1)
mpl.figure(2)
mpl.step(wvls, skySubtractionOG*(10**16), color = 'r', where = 'mid')
mpl.step(wvls, skySubtractionT1*(10**16), color = 'b', where = 'mid')
mpl.step(wvls, skySubtractionT2*(10**16), color = 'g', where = 'mid')
mpl.step(wvls, skySubtractionT3*(10**16), color = 'y', where = 'mid')
mpl.step(wvls, skySubtractionT4*(10**16), color = 'c', where = 'mid')
mpl.step(wvls, skySubtractionT5*(10**16), color = 'k', where = 'mid')
mpl.step(wvls, skySubtractionT6*(10**16), color = 'm', where = 'mid')
mpl.xlim(4000,10000)
mpl.ylim(0,1)
mpl.show()
###############################################################################################################
'''
This next step is to demonstrate the functionality of the getApertureSpectrum when phase is included
'''
spectrumAlt, phaseSpectrumAlt, skySubtractionAlt, innerAppAlt, skyMaskAlt, wvlBinEdgesAlt, phaseBinEdgesAlt, apAreaAlt, anAreaAlt = ob.getApertureSpectrum(cenRA=83.6330495, cenDec=22.0144933, nPixRA=250, nPixDec=250, radius1=.000530, radius2 = .000770, degrees=True, phase = True)
skySubtractionAlt = np.sum(skySubtractionAlt, axis = 1)
phaseProFileAlt = np.sum(phaseSpectrumAlt, axis = 0)
print '---determining wavelengthBin centers---'
nWvlBins = len(wvlBinEdgesAlt) - 1
wvls = np.empty((nWvlBins), dtype = float)
for n in xrange(nWvlBins):
binsize = wvlBinEdgesAlt[n+1] - wvlBinEdgesAlt[n]
wvls[n] = (wvlBinEdgesAlt[n] + (binsize/2.0))
print '---determining phase bin centers---'
nphaseBins = len(phaseBinEdgesAlt) - 1
phases = np.empty((nphaseBins), dtype = float)
for n in xrange(nphaseBins):
pbinsize = phaseBinEdgesAlt[n+1]-phaseBinEdgesAlt[n]
phases[n] = (phaseBinEdgesAlt[n] + (pbinsize/2.0))
print '---gathering conversion factors---'
c=2.998E18 #Angs/s
h=6.626E-27 #erg*s
diam = 510.55 #5 meter telescope in centimeters.
area = np.pi * ((diam/2.0)**2 -(183/2.0)**2) #secondary obstruction diameter 1.83m
print '---dereddening spectra---'
#this is where the dereddening happens
Alam = (-7.51*np.log10(wvls/10000) + 1.15)*0.52 #find the extinction magnitude for each wavelength bin in terms of magnitude
spectrumAlt = spectrumAlt*10**(Alam/2.5)
skySubtractionAlt = skySubtractionAlt*10**(Alam/2.5)
print '---converting flux units to erg/s/cm^2/A---'
spectrumAlt = spectrumAlt*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
skySubtractionAlt = skySubtractionAlt*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
mpl.figure(3)
mpl.step(wvls, spectrumAlt*(10**14), color = 'r', where = 'mid')
mpl.plot(centerWvl, ppfFinal*(10**14), 'bo')
mpl.xlim(4000,10000)
mpl.ylim(0,1)
mpl.figure(4)
mpl.step(wvls, skySubtractionAlt*(10**16), color = 'r', where = 'mid')
mpl.xlim(4000,10000)
mpl.ylim(0,1)
mpl.figure(5)
mpl.step(phases, phaseProFileAlt, color = 'k', where = 'mid')
mpl.show()
#############################################################################################################################
'''
the previous step was to get an idea of how the sky background changes from place to place in the sky. Now we want to compare the
off pulse flux to the various sky annuli. In adittion we should now like to plot the off pulse region
'''
phaseWvlCount, wvlBinEdges, phaseBinEdges, innerAp, apArea = ob.getAperturePhase(cenRA=83.6330495, cenDec=22.0144933, nPixRA=250, nPixDec=250, radius1=.000530, degrees=True, spectrum = True)
print np.shape(phaseWvlCount)
phaseProfile = np.sum(phaseWvlCount, axis = 1)
phaseProfile = np.sum(phaseProfile, axis = 0)
print np.shape(phaseProfile)
print '---determining phase bin centers---'
nphaseBins = len(phaseBinEdges) - 1
phases = np.empty((nphaseBins), dtype = float)
for n in xrange(nphaseBins):
pbinsize = phaseBinEdges[n+1]-phaseBinEdges[n]
phases[n] = (phaseBinEdges[n] + (pbinsize/2.0))
print np.shape(phases)
print '---grouping spectrum by phases---'
#spectrumAll = ob.getPhaseSpectrum(countArray=phaseWvlCount, lowLim=0., highLim=1.)
#spectrumAll = np.sum(phaseWvlCount, axis = 2)
#spectrumAll = np.sum(spectrumAll, axis = 0)
#for i in range(len(spectrumAll)):
# spectrumAll[i]/=(wvlBinEdges[i+1]-wvlBinEdges[i])
spectrumP = ob.getPhaseSpectrum(countArray=phaseWvlCount, lowLim=.6, highLim=.74)
spectrumIP = ob.getPhaseSpectrum(countArray=phaseWvlCount, lowLim=.02, highLim=.16)
spectrumNeb1 = ob.getPhaseSpectrum(countArray=phaseWvlCount, lowLim=.4, highLim=.54, background = True, med = True)
spectrumNeb2 = ob.getPhaseSpectrum(countArray=phaseWvlCount, lowLim=.4, highLim=.54, background = True)
print '---gathering conversion factors---'
spectrumAll = np.sum(phaseWvlCount, axis = 2)
for i in range(len(spectrumAll)):
for j in range(len(spectrumAll[i])):
spectrumAll[i][j]/=(wvlBinEdges[j+1]-wvlBinEdges[j])
for i in range(len(spectrumNeb1)):
spectrumNeb1[i]/=(wvlBinEdges[i+1]-wvlBinEdges[i])
for i in range(len(spectrumP)):
for j in range(len(spectrumP[i])):
spectrumP[i][j]/=(wvlBinEdges[j+1]-wvlBinEdges[j])
for i in range(len(spectrumIP)):
for j in range(len(spectrumIP[i])):
spectrumIP[i][j]/=(wvlBinEdges[j+1]-wvlBinEdges[j])
for i in range(len(spectrumNeb2)):
for j in range(len(spectrumNeb2[i])):
spectrumNeb2[i][j]/=(wvlBinEdges[j+1]-wvlBinEdges[j])
c=3.00E18 #Angs/s
h=6.626E-27 #erg*s
diam = 510.55 #5 meter telescope in centimeters.
area = np.pi * ((diam/2.0)**2 -(183/2.0)**2) #secondary obstruction diameter 1.83m
print '---dereddening spectra---'
#this is where the dereddening happens
Alam = (-7.51*np.log10(wvls/10000) + 1.15)*0.51 #find the extinction magnitude for each wavelength bin in terms of magnitude
spectrumAll = spectrumAll*10**(Alam/2.5)
spectrumP = spectrumP*10**(Alam/2.5) #this is specifically for the pulse spectrum
spectrumIP = spectrumIP*10**(Alam/2.5) #interpulse spectrum
spectrumNeb1 = spectrumNeb1*10**(Alam/2.5) #deadtime betweeen IP and P
spectrumNeb2 = spectrumNeb2*10**(Alam/2.5) #deadtime betweeen P and IP
print '---converting flux units to erg/s/cm^2/A---'
spectrumAll = spectrumAll*(h*c/wvls)*(1./area)*(1./float(ob.totExpTime))
spectrumP = spectrumP*(h*c/wvls)*(1./area)*(50./7.)*(1./float(ob.totExpTime))
spectrumIP = spectrumIP*(h*c/wvls)*(1./area)*(50./7.)*(1./float(ob.totExpTime))
spectrumNeb1 = spectrumNeb1*(h*c/wvls)*(1./area)*(50./7.)*(1./float(ob.totExpTime))
spectrumNeb2 = spectrumNeb2*(h*c/wvls)*(1./area)*(50./7.)*(1./float(ob.totExpTime))
for i in range(len(spectrumAll)):
spectrumAll[i] = spectrumAll[i] - spectrumNeb1
#spectrumAllCorr = spectrumAll - spectrumNeb1
#spectrumPCorr = spectrumP - spectrumNeb1
#spectrumIPCorr = spectrumIP - spectrumNeb2
for i in range(len(spectrumP)):
spectrumP[i] = spectrumP[i] - spectrumNeb2[i]
for i in range(len(spectrumIP)):
spectrumIP[i] = spectrumIP[i] -spectrumNeb2[i]
spectrumAll = np.sum(spectrumAll, axis = 0)
spectrumIP = np.sum(spectrumIP, axis = 0)
spectrumP = np.sum(spectrumP, axis = 0)
mpl.figure(6)
mpl.step(phases, phaseProfile, color = 'k', where = 'mid')
mpl.figure(7)
mpl.step(wvls, spectrumAll*(10**14), color = 'r', where = 'mid')
mpl.step(wvls, spectrumP*(10**14), color = 'b', where = 'mid')
mpl.step(wvls, spectrumIP*(10**14), color = 'g', where = 'mid')
mpl.plot(centerWvl, ppfFinal*(10**14), 'bo')
mpl.errorbar(centerWvl, ppfFinal*(10**14), yerr = ppEFinal*(10**14), fmt = 'k.')
mpl.xlim(4000,10000)
mpl.ylim(0,2.5)
mpl.figure(8)
mpl.step(wvls, spectrumNeb1*(10**16), color = 'r', where = 'mid')
#mpl.step(wvls, spectrumNeb2*(10**16), color = 'b', where = 'mid')
mpl.xlim(4000,10000)
mpl.ylim(0,1)
mpl.figure(9)
mpl.step(wvls, spectrumNeb2[0]*(10**16), color = 'b', where = 'mid')
mpl.step(wvls, spectrumNeb2[1]*(10**16), color = 'b', where = 'mid')
mpl.step(wvls, spectrumNeb2[2]*(10**16), color = 'b', where = 'mid')
mpl.step(wvls, spectrumNeb2[3]*(10**16), color = 'b', where = 'mid')
mpl.step(wvls, spectrumNeb2[50]*(10**16), color = 'b', where = 'mid')
mpl.step(wvls, spectrumNeb2[100]*(10**16), color = 'b', where = 'mid')
mpl.step(wvls, spectrumNeb2[-1]*(10**16), color = 'b', where = 'mid')
mpl.xlim(4000,10000)
mpl.ylim(0,1)
mpl.show()
'''
Now we apply our Fits
'''
spectrumNeb2 = np.sum(spectrumNeb2, axis = 0)
print '---determining initial gusses for fit parameters---'
x0 = np.array([5.9*10**-15,.2]) #initial guesses for fit paramaters for main pulse
x1 = np.array([1.9*10**-15,.2]) #initial guesses for fit paramaters for interPulse
x2 = np.array([3.4*10**-15,-.4]) #initial guesses for fit paramaters for nebula
#this makes the fit for the various spectra
print '---determining fits---'
#poptP, pcovP = optimization.curve_fit(func, wvls[15:28], spectrumP[15:28], x0) # main pulse
#poptIP, pcovIP = optimization.curve_fit(func, wvls[15:28], spectrumIP[15:28], x1) #interpulse
#poptNeb1, pcovNeb1 = optimization.curve_fit(func, wvls[15:28], (spectrumNeb1[15:28]/apArea), x2) # main pulse
poptNeb2, pcovNeb2 = optimization.curve_fit(func, wvls[15:28], (spectrumNeb2[15:28]/apArea), x2) #interpulse
#poptNebMed, pcovNebMed = optimization.curve_fit(func, wvls, (nebMedian[15:28]/apArea), x2) #interpulse
poptP, pcovP = optimization.curve_fit(func, wvls[15:28], spectrumP[15:28], x0) # main pulse
poptIP, pcovIP = optimization.curve_fit(func, wvls[15:28], spectrumIP[15:28], x1) #interpulse
print 'fit prameters: '
#print poptP
#print poptIP
#print 'Nebula1/apArea - ', poptNeb1
print 'Nebula2/apArea - ', poptNeb2
#print 'Median Nebula/apArea', poptNebMed
print 'Main Pulse - ', poptP
print 'Interpulse - ', poptIP
#fitP = poptP[0]*(wvls[15:28]/6000)**-(poptP[1]+2) # main pulse
#fitIP = poptIP[0]*(wvls[15:28]/6000)**-(poptIP[1]+2) #interpulse
#fitNeb1 = poptNeb1[0]*(wvls[15:28]/6000)**-(poptNeb1[1]+2) # nebula
fitNeb2 = poptNeb2[0]*(wvls[15:28]/6000)**-(poptNeb2[1]+2) #nebula
#fitNebMed = poptNebMed[0]*(wvls/6000)**-(poptNebMed[1]+2) #nebula
fitP = poptP[0]*(wvls[15:28]/6000)**-(poptP[1]+2) # main pulse
fitIP = poptIP[0]*(wvls[15:28]/6000)**-(poptIP[1]+2) #interpulse
print 'wvls[15:28]', wvls[15:28]
print 'wvls[12:28]', wvls[12:28]
mpl.figure(10)
mpl.step(wvls, np.log10(spectrumP), color = 'b', where = 'mid')
mpl.step(wvls, np.log10(spectrumIP), color = 'g', where = 'mid')
mpl.plot(wvls[15:28], np.log10(fitP), color = 'k')
mpl.plot(wvls[15:28], np.log10(fitIP), color = 'k')
mpl.xlim(4000,10000)
mpl.figure(11)
mpl.step(wvls, np.log10(spectrumNeb2/apArea), color = 'b', where = 'mid')
mpl.plot(wvls[15:28], np.log10(fitNeb2), color = 'k')
mpl.xlim(4000,10000)
mpl.show()
def Image():
#output = open('/home/kingrice/ARCONS-pipeline/examples/crabN/stackedImageCrab.pkl', 'r')
output = open('/home/kingrice/ARCONS-pipeline/examples/crabN/CrabStack_phaseWvl.pkl', 'rb')
results = pickle.load(output)
vim = results['vim'] #pulls out the dictionary containing all of the relevant information for virtual image object
#this is the info from the dictionary:
ob = RADecImage()
ob.phaseEdges = vim['phase']
ob.wvlEdges = vim['wvl']
ob.gridDec = vim['Dec']
ob.gridRA = vim['RA']
ob.image = vim['Im']
ob.effIntTimes = vim['effIntT']
ob.totExpTime = vim['totExpT']
ob.expTimeWeights = vim['timeWeights']
ob.vExpTimesStack = vim['vTimeStacks']
ob.imageIsLoaded = vim['loaded']
output.close()
print 'ob.image', np.shape(ob.image)
#print 'ob.image', ob.image, np.shape(ob.image)
#print 'ob.expTimeWeights', ob.expTimeWeights, np.shape(ob.expTimeWeights)
#image = np.sum(ob.image*ob.expTimeWeights, axis = 2)
image = ob.image/ob.effIntTimes #divides image cube by effective integration times to get a veiwable image. This will be 4D here.
nanspot = np.isnan(image) #sets the NANS of the image = 0 for the display
image[nanspot] = 0
ob.display(DDArr = True) #sums across the wavlength and phase dimension to get a 2D image for the display.
mpl.show()
#spectrum, spectrumP, spectrumIP, phaseProfile, wvlBinEdges, phaseBinEdges, innerAp = ob.getApertureSpectrum(cenRA=83.6330495, cenDec=22.0144933, nPixRA=250, nPixDec=250, radius1=.0005, radius2 = .0008, degrees=True, error = False, phase = True, crab = True) #this calls the spectrum code
print 'getting phase of crab now'
spectrum, spectrumP, spectrumIP, phaseProfile, wvlBinEdges, phaseBinEdges, innerAp = ob.getAperturePhaseCrab(cenRA=83.6330495, cenDec=22.0144933, nPixRA=250, nPixDec=250, radius1=.0005, degrees=True, phase = True, crab = True)
#summed_arrayIn, wvlBinEdges, errIn, innerAp = ob.getApertureSpectrum(cenRA=83.6335573, cenDec=22.0142943, nPixRA=250, nPixDec=250, radius1=.00015, radius2 = .0002, degrees=True, error = True, offSet = True, radRA = 83.6296510, radDec = 22.0145475, radius3 = .0005) #object spectrum
print 'wavelengths:', wvlBinEdges[16:25] #this is the desired wavlength range for the crab spectrum it may be worth checking.
#test
#summed_arrayIn, wvlBinEdges, errIn, innerAp = ob.getApertureSpectrum(cenRA=83.6308, cenDec=22.0151, nPixRA=250, nPixDec=250, radius1=.0007, radius2 = .0014, degrees=True, error = True)
#errIn = errIn/np.mean(summed_arrayIn)
#summed_arrayIn = summed_arrayIn/np.mean(summed_arrayIn)
c=3.00E18 #Angs/s
h=6.626E-27 #erg*s
#finds the center of the wavelength bins
nWvlBins = len(wvlBinEdges) - 1
wvls = np.empty((nWvlBins), dtype = float)
for n in xrange(nWvlBins):
binsize = wvlBinEdges[n+1] - wvlBinEdges[n]
wvls[n] = (wvlBinEdges[n] + (binsize/2.0))
#finds the center of the phase bins
nphaseBins = len(phaseBinEdges) - 1
phases = np.empty((nphaseBins), dtype = float)
for n in xrange(nphaseBins):
pbinsize = phaseBinEdges[n+1]-phaseBinEdges[n]
phases[n] = (phaseBinEdges[n] + (pbinsize/2.0))
#this is where the dereddening happens
Alam = (-7.51*np.log10(wvls/10000) + 1.15)*0.51 #find the extinction magnitude for each wavelength bin in terms of magnitude
AlamF = spectrum*10**(-Alam/2.5) #converts to flux for entire spectrum of all time
AlamFP = spectrumP*10**(-Alam/2.5) #this is specifically for the pulse spectrum
AlamFIP = spectrumIP*10**(-Alam/2.5) #interpulse spectrum
#AlamFErr = errIn*10**(-Alam/2.5)
spectrum = spectrum - AlamF #subtracts from the flux values we observed
spectrumP = spectrumP - AlamFP #main pulse
spectrumIP = spectrumIP - AlamFIP #interPulse
#errIn = np.sqrt(errIn**2+ AlamFErr**2)
diam = 510.55 #5 meter telescope in centimeters.
area = np.pi * ((diam/2.0)**2 -(183/2.0)**2) #secondary obstruction diameter 1.83m
spectrum = spectrum*(h*c/wvls)*(1/area)*(1./float(ob.totExpTime)) #(count/Angs)*(erg*sec/count)*(Angs/s)/(Angs)/(cm^2)/(s) = erg/s/Angs/cm^2
spectrumP = spectrumP*(h*c/wvls)*(1./area)*(50./6.)*(1./float(ob.totExpTime)) #same units as above only now scaled by number of phase bins used
spectrumIP = spectrumIP*(h*c/wvls)*(1./area)*(50./6.)*(1./float(ob.totExpTime))
#errIn = errIn*(h*c/wvls)
#errIn = errIn*(h*c/wvls)*(1/area)
#print 'errIn', errIn, np.shape(errIn)
print 'spectrum', spectrum, np.shape(spectrum)
print 'spectrumP', spectrumP, np.shape(spectrumP)
print 'spectrumIP', spectrumIP, np.shape(spectrumIP)
#note that in this particular case we are plotting the spectrum on a log scale. In order to convert te errors do the following.
#errlog = (1/(summed_arrayIn*np.log(10)))*errIn
'''
K = 5.9*10**-15
lam0 = 6000
alph = .2
#func = K*(wvls/lam0)**-(alph+2)
params = [K,lam0,alph]
errs = errIn
quiet = True
parinfo = [{'n':0,'value':params[0],'limits':[1.9*10**-15,10*10**-15],'limited':[True,True],'fixed':False,'parname':"k",'error':0},{'n':1,'value':params[1],'limits':[3000,9000],'limited':[True,True],'fixed':False,'parname':"lam0",'error':0}, {'n':2,'value':params[2],'limits':[-.5,.5],'limited':[True,True],'fixed':False,'parname':"alph",'error':0}]
fa = {'x':wvls,'y':summed_arrayIn,'err':errIn}
m = mpfit.mpfit(func, functkw=fa, parinfo=parinfo, maxiter=1000, quiet=quiet)
if m.status<=0:
print m.status, m.errmsg
mpp = m.params
mpperr = m.perror
for k,p in enumerate(mpp):
parinfo[k]['value'] = p
parinfo[k]['error'] = mpperr[k]
if k==0: K = p
if k==1: lam0 = p
if k==2: alph = p
fit = K*(wvls/lam0)**-(alph+2)
'''
print 'wvls', wvls, np.shape(wvls)
x0 = np.array([5.9*10**-15,.2]) #initial guesses for fit paramaters for main pulse
x1 = np.array([1.9*10**-15,.2]) #initial guesses for fit paramaters for main pulse
#this makes the fit for the various spectra
popt, pcov = optimization.curve_fit(func, wvls[15:28], spectrum[15:28], x0) #all
poptP, pcovP = optimization.curve_fit(func, wvls[15:28], spectrumP[15:28], x0) # main pulse
poptIP, pcovIP = optimization.curve_fit(func, wvls[15:28], spectrumIP[15:28], x1) #interpulse
print popt
print poptP
print poptIP
#calculates the fit bases on the fit parameters
fit = popt[0]*(wvls[15:28]/6000)**-(popt[1]+2)
fitP = poptP[0]*(wvls[15:28]/6000)**-(poptP[1]+2) # main pulse
fitIP = poptIP[0]*(wvls[15:28]/6000)**-(poptIP[1]+2) #interpulse
'''
plotArray(np.sum(image[:,:,0:5],axis=2))
plotArray(np.sum(image[:,:,5:10],axis=2))
plotArray(np.sum(image[:,:,10:15],axis=2))
plotArray(np.sum(image[:,:,15:20],axis=2))
plotArray(np.sum(image[:,:,20:25],axis=2))
plotArray(np.sum(image[:,:,25:31],axis=2))
'''
#plotArray(image)
ob.display(DDArr = True)
mpl.show()
mpl.figure(1) #for all phases
mpl.step(wvls,np.log10(spectrum), color = 'r', where = 'pre')
#mpl.errorbar(wvls,np.log10(summed_arrayIn), yerr = errlog, ecolor = 'b', fmt = None)
mpl.plot(wvls[15:28], np.log10(fit), color = 'b')
mpl.xlim(wvls[15], 9000)
mpl.figure(2) #this plot is the main pulse and interpulse
mpl.step(wvls,np.log10(spectrumP), color = 'r', where = 'pre')
#mpl.errorbar(wvls,np.log10(summed_arrayIn), yerr = errlog, ecolor = 'b', fmt = None)
mpl.plot(wvls[15:28], np.log10(fitP), color = 'b')
mpl.xlim(wvls[15], 9000)
mpl.step(wvls,np.log10(spectrumIP), color = 'g', where = 'pre')
#mpl.errorbar(wvls,np.log10(summed_arrayIn), yerr = errlog, ecolor = 'b', fmt = None)
mpl.plot(wvls[15:28], np.log10(fitIP), color = 'b')
mpl.xlim(wvls[15], 9000)
mpl.figure(3) #this is the phase profile
mpl.step(phases, phaseProfile, color = 'b', where = 'pre')
'''
mpl.plot(wvMid, np.log10(FlamMid), color = 'b')
mpl.plot(wvHigh, np.log10(FlamHigh), color = 'b')
mpl.plot(wvLow, np.log10(FlamLow), color = 'b')
mpl.plot(wvPrev, np.log10(FlamPrev), color = 'g')
#mpl.errorbar(wvlBinEdges[1:],np.log(summed_arrayIn), yerr = errIn, ecolor = 'b', fmt = None)
mpl.xlim(4000, 9000)
mpl.ylim(-14.6, -13.9)
mpl.figure(2)
'''
mpl.show()