def params2spec(self,d,params,components=False):
# start with the powerlaw spectrum:
norm = params[0] # powerlaw norm (photons/s/keV/cm^2 at 1 keV)
alpha = params[1] # photon index of powerlaw (dimensionless)
nH = params[2] # in units of 10^22 atoms/cm^2
Espec = zeros(len(d.energies))
# powerlaw spectrum (ph/s/cm^2/keV)
c1 = norm*d.energies**(-alpha)
Espec += c1
# Sc line (adjust flux only):
#Espec += lines.shell(d.energies, 4000, params[3].getValue())
c2 = lines.delta(d.energies, params[3])
#Espec += lines.spitzer(d.energies, params[3].getValue())
Espec += c2
comps = [c1,c2]
# add the nuisance gaussian lines:
npar = len(params)
for i in [npar-6, npar-3]: # hard coded! 2 nuisance lines!
area = params[i] # photons/s/cm^2
center = params[i+1] # keV
sigma = params[i+2] # keV
# ph/s/cm^2/keV :
#Espec += area/sigma/sqrt(2*pi)*exp( -(self.energies-center)**2/2/sigma**2 )
cn = lines.gaussian(d.energies, area, center, sigma)
Espec += cn
comps.append(cn)
# account for binning, which is \int_{E_l}^{E_h} dE S(E)
dE = roll(d.energies,-1) - d.energies
dE[-1] = dE[-2]
# trapezoidal integration:
dSpec = roll(Espec,-1) - Espec
dSpec[-1] = dSpec[-2]
Espec = dE*Espec - dE*dSpec/2 # ph/s/cm^2
Espec *= d.exposure # photons/cm^2
Espec *= self.transmission**nH # dimmer photons/cm^2
# for plotting purposes:
if components:
compsOut = []
for c in comps:
dSpec = roll(c,-1) - c
dSpec[-1] = dSpec[-2]
c = dE*c - dE*dSpec/2 # ph/s/cm^2
c *= d.exposure
c *= self.transmission**nH
compsOut.append(c)
return Espec, compsOut
else:
return Espec
def params2spec(self,params):
# start with the powerlaw spectrum:
norm = params[0].getValue() # powerlaw norm (photons/s/keV/cm^2 at 1 keV)
alpha = params[1].getValue() # photon index of powerlaw (dimensionless)
nH = params[2].getValue() # in units of 10^22 atoms/cm^2
# powerlaw spectrum (ph/s/cm^2/keV)
Espec = norm*self.energies**(-alpha)
# Sc line (adjust flux only):
#Espec += lines.shell(self.energies, 4000, params[3].getValue())
Espec += lines.delta(self.energies, params[3].getValue())
#Espec += lines.spitzer(self.energies, params[3].getValue())
# add the nuisance gaussian lines:
npar = len(params)
for i in [npar-6, npar-3]: # hard coded! 2 nuisance lines!
area = params[i].getValue() # photons/s/cm^2
center = params[i+1].getValue() # keV
sigma = params[i+2].getValue() # keV
# ph/s/cm^2/keV :
#Espec += area/sigma/sqrt(2*pi)*exp( -(self.energies-center)**2/2/sigma**2 )
Espec += lines.gaussian(self.energies, area, center, sigma)
# account for binning, which is \int_{E_l}^{E_h} dE S(E)
dE = np.roll(self.energies,-1) - self.energies
dE[-1] = dE[-2]
# trapezoidal integration:
dSpec = np.roll(Espec,-1) - Espec
dSpec[-1] = dSpec[-2]
Espec = dE*Espec - dE*dSpec/2 # ph/s/cm^2
Espec *= self.exposure # photons/cm^2
Espec *= self.transmission**nH # dimmer photons/cm^2
return Espec
def sourceSpectrum(E, linemode = 2, confused = True): ''' produce a true spectrum (pre-instrument) from parameters: params = [norm, alpha, Sc area, line 1 area, line 2 area] assuming 25 eV natural sigma for all lines; fix them at known energies modes: 0: delta 1: shell 2: broadened 3: spitzer ''' sourceComponents = [] # start with the powerlaw spectrum: norm = params[0] # powerlaw norm (photons/s/keV/cm^2 at 1 keV) alpha = params[1] # photon index of powerlaw (dimensionless) nH = params[2] # in units of 10^22 atoms/cm^2 # powerlaw spectrum (ph/s/cm^2/keV) c1 = norm*E**(-alpha) sourceComponents.append(c1) # Sc line (adjust flux only): if linemode == 0: c2 = lines.delta(E, params[3]) print 'delta function Sc line' elif linemode == 1: c2 = lines.shell(E, 2000, params[3]) print 'thin shell Sc line' elif linemode == 2: c2 = lines.gaussian(E, params[3], 4.09, 0.010) print 'broadened (10 eV sigma) Sc line' elif linemode == 3: c2 = lines.spitzer(E, params[3]) print 'spitzer Sc line' #print 'sc flux: %.2f' % (params[3]*1.7e5*388) sourceComponents.append(c2) # add the nuisance gaussian lines: for i in [4, 7]: # hard coded! 2 nuisance lines! area = params[i] # photons/s/cm^2 center = params[i+1] # keV s = params[i+2] # keV # ph/s/cm^2/keV : #Espec += area/sigma/sqrt(2*pi)*exp( -(self.energies-center)**2/2/sigma**2 ) ci = lines.gaussian(E, area, center, s) sourceComponents.append(ci) # confusing nearby lines: if confused: print 'confused spectrum' sourceComponents.append(lines.gaussian(E, params[3]*1.05, 4.22, 0.010)) sourceComponents.append(lines.gaussian(E, params[3]*0.85, 4.04, 0.012)) sourceComponents.append(lines.gaussian(E, params[3]*0.85, 4.22, 0.008)) sourceComponents.append(lines.gaussian(E, params[3]*1.45, 4.13, 0.015)) sourceComponents.append(lines.gaussian(E, params[3]*0.75, 3.98, 0.010)) for c in sourceComponents: c *= phabs**nH # absorption: dimmer ph/s/cm^2/kev return sourceComponents