__author__ = "Andrea Tramacere"
import numpy as np
from jetset.analytical_model import AnalyticalParameter
from jetset.model_parameters import ModelParameterArray
from jetset.base_model import Model
from jetset.spectral_shapes import SED
[docs]
class RadioSpectrum(Model):
"""Simple analytical radio-spectrum component with SSA turnover.
Notes
-----
Implements a smooth synchrotron-like radio shape controlled by spectral
slope, self-absorption turnover, high-frequency cutoff, and normalization.
"""
def __init__(self,nu_size=100,cosmo=None,**keywords):
"""Create a new `RadioSpectrum` instance.
Parameters
----------
nu_size : int, optional
Number of points for frequency grids.
cosmo : object, optional
Cosmology helper used for frame/luminosity conversions.
**keywords : dict
Additional keyword arguments.
"""
super(RadioSpectrum,self).__init__(
nu_size=nu_size,
cosmo=cosmo,
nu_min=1E6,
nu_max=1E12,
**keywords)
self.name='radio_spectrum'
self.model_type='radio_spectrum'
self.SED = SED(name=self.model_type)
self.parameters = ModelParameterArray()
self.parameters.add_par(AnalyticalParameter(self,name='alpha_radio',par_type='spectral-slope',val=0.0,val_min=-10.,val_max=10.,units=''))
self.parameters.add_par(AnalyticalParameter(self,name='nu_ssa',par_type='turn-over freq',val=1E9,val_min=1E6,val_max=1E12,units='Hz'))
self.parameters.add_par(AnalyticalParameter(self,name='nu_cut',par_type='',val=1E11,val_min=1E6,val_max=1E13,units='Hz'))
self.parameters.add_par(AnalyticalParameter(self,name='nuFnu_p',par_type='flux-const',val=1E-13,val_min=1E-30,val_max=1E-5,units='erg cm2 s-1'))
def _func(self,nu,nu_ssa,s2,nu_cut):
return np.power(nu,2.5)*(1.+(nu/nu_ssa))**(-(s2+2.5))*np.exp(-(nu/nu_cut))*nu
[docs]
def lin_func(self,nu):
"""Lin func.
Parameters
----------
nu : object
Frequency values in Hz.
Returns
-------
object
Computed result.
"""
s2=self.parameters.get_par_by_name('alpha_radio').val
nu_ssa=self.parameters.get_par_by_name('nu_ssa').val
nuFnu_p=self.parameters.get_par_by_name('nuFnu_p').val
nu_cut=self.parameters.get_par_by_name('nu_cut').val
res= self._func(nu,nu_ssa,s2,nu_cut)
_Norm=nuFnu_p/self._func(nu_cut,nu_ssa,s2,nu_cut)
return res*_Norm
