FileSpectrum in pyLikelihood

create_file_spectrum.py

import FuncFactory

# Create the text file with the energies and the fluxes (which can be whatever at this stage)

source_name = 'a'

tempName = "__%s.txt" % source_name

with open(tempName, "w+") as f:
    
    for e, v in zip(energies, fluxes):
        # Gtlike needs energies in MeV and fluxes in ph/MeV/cm2)

        f.write("%s %s\n" % (e, v))

# Now generate the XML source wrapper
# This is convoluted, but that's the ST way of doing things...
# The final product is a class with a writeXml method

src = '\n'.join((('<source name= "%s" ' % source_name) + 'type="PointSource">',
                 '   <spectrum type="PowerLaw2"/>',
                 '   <!-- point source units are cm^-2 s^-1 MeV^-1 -->',
                 '   <spatialModel type="SkyDirFunction"/>',
                 '</source>\n'))
src = FuncFactory.minidom.parseString(src).getElementsByTagName('source')[0]
src = FuncFactory.Source(src)

src.spectrum = FuncFactory.FileFunction()
src.spectrum.file = tempName
src.spectrum.parameters['Normalization'].value = 1.0
src.spectrum.parameters['Normalization'].fix()
src.spectrum.setAttributes()
src.deleteChildElements('spectrum')
src.node.appendChild(src.spectrum.node)

src.spatialModel = FuncFactory.SkyDirFunction()
src.deleteChildElements('spatialModel')
src.node.appendChild(src.spatialModel.node)

ra, dec = 120.6, -12.3 # whatever you need

src.spatialModel.RA.value = ra
src.spatialModel.DEC.value = dec
src.spatialModel.setAttributes()
src.setAttributes()