csp_integrator.py 文件源码

def integrate( self, age ):

        # calculate range to integrate over
        lower_bound = age - self.maxage
        upper_bound = age - self.minage

        # if upper_bound is negative then this is a later region and we are working on early ages
        # If so, return zeros
        if upper_bound < 0: return ( np.zeros( self.ls.size ), 0 )

        # find things in the age range (include a little extra to account for rounding errors)
        inds = np.where( (self.ages >= lower_bound-age*1e-5) & (self.ages <= upper_bound+age*1e-5) )[0]

        # simpsons rule is based on intervals, so include the SED one age lower if it exists
        # otherwise one interval will be missed at every boundary
        if inds[0] > 0 and np.abs( self.ages[inds[0]] - lower_bound ) > 1e-5*age:
            inds = np.append( inds[0]-1, inds )

        weights = self.sfh_func( age-self.ages[inds] )

        # if weights are all zero then there is no star formation in this region and therefore no need to integrate
        if max( weights ) <= 0:
            return ( np.zeros( self.ls.size ), 0 )

        if self.has_dust:
            # integrate weights*sed*dust
            seds = integrate.simps( weights*self.seds[:,inds]*self.dust_func( self.ages[inds], self.ls ), x=self.ages[inds], even='avg' )
        else:
            # integrate weights*sed
            seds = integrate.simps( weights*self.seds[:,inds], x=self.ages[inds], even='avg' )

        # integrate weights*mass
        mass = integrate.simps( weights*self.masses[inds], x=self.ages[inds], even='avg' ) if self.has_masses else 0

        return ( seds, mass )