def __init__(self, Fit, pars):
self.Fit = Fit
self.Model = Fit[pars.srcname].funcs['Spectrum'].genericName()
self.ptsrc = pyLikelihood.PointSource_cast(Fit[pars.srcname].src)
self.covar = np.array(utils.GetCovar(pars.srcname, self.Fit, False))
self.srcpars = pyLikelihood.StringVector()
Fit[pars.srcname].src.spectrum().getFreeParamNames(self.srcpars)
def calcBowtie(self, srcName, minE, maxE, numBins):
'''This is derived from T. Johnson's likeSED code which was in turn
derived from D. Sanchez's pyUnfoldPlot code which was probably
based on some code developed by J. Chiang. '''
'''make some energy bounds for the fit, same max and min as for the
bands before but with more bins.'''
modEs = qU.log_array(numBins, minE, maxE)
centEs = [0.5 * (e1 + e2) for e1, e2 in zip(modEs[0:-1], modEs[1:])]
'''Get the model.'''
mysrc = pyLike.PointSource_cast(self.MIN[srcName].src)
spec = [
float(1000. * mysrc.spectrum()(pyLike.dArg(x))) for x in centEs
]
if (self.MIN.covariance is None):
print "Whoa, you didn't compute the covariance yet..."
bt = [0]
else:
bt = []
covArray = np.array(self.MIN.covariance)
srcCovArray = []
par_index_map = {}
indx = 0
for src in self.MIN.sourceNames():
parNames = pyLike.StringVector()
self.MIN[src].src.spectrum().getFreeParamNames(parNames)
for par in parNames:
par_index_map['::'.join((src, par))] = indx
indx += 1
srcPars = pyLike.StringVector()
self.MIN[srcName].src.spectrum().getFreeParamNames(srcPars)
pars = ['::'.join((srcName, x)) for x in srcPars]
for xpar in pars:
ix = par_index_map[xpar]
srcCovArray.append(
[covArray[ix][par_index_map[ypar]] for ypar in pars])
cov = np.array(srcCovArray)
''' The whole point here is to get the srcCovArray.'''
for x in centEs:
arg = pyLike.dArg(x)
partials = np.array(
[mysrc.spectrum().derivByParam(arg, y) for y in srcPars])
val = np.sqrt(np.dot(partials, np.dot(cov, partials)))
'''These should come out same as the model so convert to ph/cm^2/s/GeV as well.'''
bt += [float(1000. * val)]
return centEs, bt, spec
def dNde(energy, Fit, name):
'''Compute the dN/dE value at energy E fir the source name'''
import pyLikelihood
ptsrc = pyLikelihood.PointSource_cast(Fit[name].src)
arg = pyLikelihood.dArg(energy)
return ptsrc.spectrum()(arg)
