def create(fn,p0):
# Construct a parameter set from inspection of the input function
npar = len(inspect.getargspec(fn)[0])
pset = ParameterSet()
for i in range(npar): pset.createParameter(p0[i])
return ParamFn(fn,pset)
def create(norm,alpha,ecut,eb):
pset = ParameterSet()
pset.createParameter(np.log10(norm),'norm')
pset.createParameter(alpha,'alpha')
pset.createParameter(np.log10(ecut),'ecut')
pset.createParameter(np.log10(eb),'eb')
return PowerLawExp(pset)
def create(norder,coeff=None,offset=0):
pset = ParameterSet()
if coeff is None: coeff = np.zeros(norder)
for i in range(norder):
pset.addParameter(Parameter(offset+i,coeff[i],'a%i'%i))
return PolarPolyFn(pset)
def create(norm,alpha,beta,eb):
pset = ParameterSet()
pset.createParameter(np.log10(norm),'norm')
pset.createParameter(alpha,'alpha')
pset.createParameter(beta,'beta')
pset.createParameter(np.log10(eb),'eb')
return LogParabola(pset)
def create(norder,coeff=None,pset=None,name=None,prefix=''):
if pset is None: pset = ParameterSet()
if coeff is None: coeff = np.zeros(norder)
pars = []
for i in range(norder):
p = pset.createParameter(coeff[i],prefix+'a%i'%i)
pars.append(p)
return PolyFn(ParameterSet(pars),name)
def create(norm,gamma,eb,pset=None,name=None,prefix=''):
if pset is None: pset = ParameterSet()
p0 = pset.createParameter(np.log10(norm),prefix+'norm')
p1 = pset.createParameter(gamma,prefix+'gamma')
p2 = pset.createParameter(np.log10(eb),prefix+'eb')
return PowerLaw(ParameterSet([p0,p1,p2]),name)
def create(norm,mu,sigma,pset=None):
if pset is None: pset = ParameterSet()
p0 = pset.createParameter(norm,'norm')
p1 = pset.createParameter(mu,'mu')
p2 = pset.createParameter(sigma,'sigma')
return GaussFn(ParameterSet([p0,p1,p2]))
def create(norm,mux,muy,sigma,pset=None):
if pset is None: pset = ParameterSet()
p0 = pset.createParameter(norm,'norm')
p1 = pset.createParameter(mux,'mux')
p2 = pset.createParameter(muy,'muy')
p3 = pset.createParameter(sigma,'sigma')
return Gauss2DFn(ParameterSet([p0,p1,p2,p3]))
class ParamFnBase(object):
"""Base class for a parameterized function."""
def __init__(self, param = None, name = None):
if param is None: self._param = ParameterSet()
else: self._param = param
self._name = name
@staticmethod
def create(npar):
fn = ParamFn()
for i in range(npar):
pass
def setName(self,name):
"""Set the name of the function."""
self._name = name
def name(self):
"""Return the name of the function."""
return self._name
def npar(self):
return self._param.npar()
def param(self,make_copy=False):
"""Get the parameter set of this function. If the optional input
argument set is defined then return a copy of the model
parameter set with values updated from this set."""
if make_copy: return copy.deepcopy(self._param)
else: return self._param
def update(self,pset):
"""Update the parameters of this function."""
self._param.update(pset)
def create(norm,sigma,pset=None):
if pset is None: pset = ParameterSet()
p0 = pset.createParameter(norm,'norm')
p1 = pset.createParameter(sigma,'sigma')
return Gauss2DProjFn(ParameterSet([p0,p1]))
def __init__(self, param = None, name = None):
if param is None: self._param = ParameterSet()
else: self._param = param
self._name = name
def create(h,norm=1.0,pset=None,name=None,prefix=''):
if pset is None: pset = ParameterSet()
p0 = pset.createParameter(norm,prefix + 'norm')
return ScaledHistogramModel(h,ParameterSet([p0]),name)