Example to use the Sherpa plugin with the sherpa interface#

import jetset
print('tested with ',jetset.__version__)

tested with  1.4.0rc0

In this tutorial we show how to import a jetset model into Sherpa, and finally we perform a model fitting with Sherpa. To run this plugin you have to install Sherpa: https://sherpa.readthedocs.io/en/latest/install.html

import warnings
warnings.filterwarnings('ignore')

import matplotlib.pylab as plt
import jetset
from jetset.test_data_helper import  test_SEDs
from jetset.data_loader import ObsData,Data
from jetset.plot_sedfit import PlotSED
from jetset.test_data_helper import  test_SEDs

from jetset.test_data_helper import test_SEDs

test_SEDs

['/Users/orion/miniforge3/envs/jetset/lib/python3.12/site-packages/jetset/test_data/SEDs_data/SED_3C345.ecsv',
 '/Users/orion/miniforge3/envs/jetset/lib/python3.12/site-packages/jetset/test_data/SEDs_data/SED_MW_Mrk421_EBL_DEABS.ecsv',
 '/Users/orion/miniforge3/envs/jetset/lib/python3.12/site-packages/jetset/test_data/SEDs_data/SED_MW_Mrk501_EBL_ABS.ecsv',
 '/Users/orion/miniforge3/envs/jetset/lib/python3.12/site-packages/jetset/test_data/SEDs_data/SED_MW_Mrk501_EBL_DEABS.ecsv']

Loading data#

see the Data format and SED data user guide for further information about loading data

print(test_SEDs[2])
data=Data.from_file(test_SEDs[2])

/Users/orion/miniforge3/envs/jetset/lib/python3.12/site-packages/jetset/test_data/SEDs_data/SED_MW_Mrk501_EBL_ABS.ecsv

%matplotlib inline
sed_data=ObsData(data_table=data)
sed_data.group_data(bin_width=0.2)

sed_data.add_systematics(0.1,[10.**6,10.**29])
p=sed_data.plot_sed()

================================================================================

*  binning data  *
---> N bins= 88
---> bin_width= 0.2
================================================================================

../../../../_images/sherpa-plugin-sherpa-interface_10_1.png

sed_data.save('Mrk_501.pkl')

phenomenological model constraining#

see the Phenomenological model constraining: application user guide for further information about phenomenological constraining

spectral indices#

from jetset.sed_shaper import  SEDShape
my_shape=SEDShape(sed_data)
my_shape.eval_indices(minimizer='lsb',silent=True)
p=my_shape.plot_indices()
p.setlim(y_min=1E-15,y_max=1E-6)

================================================================================

* evaluating spectral indices for data *
================================================================================

../../../../_images/sherpa-plugin-sherpa-interface_15_1.png

sed shaper#

mm,best_fit=my_shape.sync_fit(check_host_gal_template=True,
                  Ep_start=None,
                  minimizer='lsb',
                  silent=True,
                  fit_range=[10.,21.])

================================================================================

* Log-Polynomial fitting of the synchrotron component *
---> first blind fit run,  fit range: [10.0, 21.0]
---> class:  HSP

---> class:  HSP

Table length=6

model name	name	val	bestfit val	err +	err -	start val	fit range min	fit range max	frozen
LogCubic	b	-5.589204e-02	-5.589204e-02	6.234447e-03	--	-5.237376e-02	-1.000000e+01	0.000000e+00	False
LogCubic	c	-3.292704e-04	-3.292704e-04	8.967926e-04	--	7.639964e-03	-1.000000e+01	1.000000e+01	False
LogCubic	Ep	1.698160e+01	1.698160e+01	8.582826e-02	--	1.556163e+01	0.000000e+00	3.000000e+01	False
LogCubic	Sp	-1.030620e+01	-1.030620e+01	1.607947e-02	--	-1.019011e+01	-3.000000e+01	0.000000e+00	False
host_galaxy	nuFnu_p_host	-9.972939e+00	-9.972939e+00	3.177493e-02	--	-1.019011e+01	-1.219011e+01	-8.190114e+00	False
host_galaxy	nu_scale	-2.290459e-03	-2.290459e-03	1.811351e-03	--	0.000000e+00	-2.908697e-03	2.908697e-03	False

---> sync       nu_p=+1.698160e+01 (err=+8.582826e-02)  nuFnu_p=-1.030620e+01 (err=+1.607947e-02) curv.=-5.589204e-02 (err=+6.234447e-03)
================================================================================

my_shape.IC_fit(fit_range=[23.,29.],minimizer='minuit',silent=True)
p=my_shape.plot_shape_fit()
p.setlim(y_min=1E-15)

================================================================================

* Log-Polynomial fitting of the IC component *
---> fit range: [23.0, 29.0]
---> LogCubic fit

Table length=4

model name	name	val	bestfit val	err +	err -	start val	fit range min	fit range max	frozen
LogCubic	b	-1.627265e-01	-1.627265e-01	2.483991e-02	--	-1.000000e+00	-1.000000e+01	0.000000e+00	False
LogCubic	c	-4.569970e-02	-4.569970e-02	1.984551e-02	--	-1.000000e+00	-1.000000e+01	1.000000e+01	False
LogCubic	Ep	2.526444e+01	2.526444e+01	1.736457e-01	--	2.531190e+01	0.000000e+00	3.000000e+01	False
LogCubic	Sp	-1.057790e+01	-1.057790e+01	4.914805e-02	--	-1.000000e+01	-3.000000e+01	0.000000e+00	False

---> IC         nu_p=+2.526444e+01 (err=+1.736457e-01)  nuFnu_p=-1.057790e+01 (err=+4.914805e-02) curv.=-1.627265e-01 (err=+2.483991e-02)
================================================================================

../../../../_images/sherpa-plugin-sherpa-interface_18_3.png

Model constraining#

In this step we are not fitting the model, we are just obtaining the phenomenological pre_fit model, that will be fitted in using minuit ore least-square bound, as shown below

from jetset.obs_constrain import ObsConstrain
from jetset.model_manager import  FitModel
sed_obspar=ObsConstrain(beaming=25,
                        B_range=[0.001,0.1],
                        distr_e='lppl',
                        t_var_sec=3*86400,
                        nu_cut_IR=1E12,
                        SEDShape=my_shape)


prefit_jet=sed_obspar.constrain_SSC_model(electron_distribution_log_values=False,silent=True)
prefit_jet.save_model('prefit_jet.pkl')

================================================================================

*  constrains parameters from observable *

/Users/orion/miniforge3/envs/jetset/lib/python3.12/site-packages/jetset/obs_constrain.py:1514: RankWarning: Polyfit may be poorly conditioned
  p=polyfit(nu_p_IC_model_log,B_grid_log,2)

Table length=12

model name	name	par type	units	val	phys. bound. min	phys. bound. max	log	frozen
jet_leptonic	R	region_size	cm	1.216622e+16	1.000000e+03	1.000000e+30	False	False
jet_leptonic	R_H	region_position	cm	1.000000e+17	0.000000e+00	--	False	True
jet_leptonic	B	magnetic_field	gauss	5.050000e-02	0.000000e+00	--	False	False
jet_leptonic	NH_cold_to_rel_e	cold_p_to_rel_e_ratio		1.000000e+00	0.000000e+00	--	False	True
jet_leptonic	beam_obj	beaming		2.500000e+01	1.000000e-04	--	False	False
jet_leptonic	z_cosm	redshift		3.360000e-02	0.000000e+00	--	False	False
jet_leptonic	gmin	low-energy-cut-off	lorentz-factor*	4.703917e+02	1.000000e+00	1.000000e+09	False	False
jet_leptonic	gmax	high-energy-cut-off	lorentz-factor*	2.121873e+06	1.000000e+00	1.000000e+15	False	False
jet_leptonic	N	emitters_density	1 / cm3	5.583988e+00	0.000000e+00	--	False	False
jet_leptonic	gamma0_log_parab	turn-over-energy	lorentz-factor*	6.673207e+03	1.000000e+00	1.000000e+09	False	False
jet_leptonic	s	LE_spectral_slope		2.251647e+00	-1.000000e+01	1.000000e+01	False	False
jet_leptonic	r	spectral_curvature		2.794602e-01	-1.500000e+01	1.500000e+01	False	False

================================================================================

pl=prefit_jet.plot_model(sed_data=sed_data)
pl.add_model_residual_plot(prefit_jet,sed_data)
pl.setlim(y_min=1E-15,x_min=1E7,x_max=1E29)

../../../../_images/sherpa-plugin-sherpa-interface_22_0.png

Model fitting with using a Sherpa model#

we show now, how to import a jetset model into a Sherpa model

from jetset.sherpa_plugin import JetsetSherpaModel
from jetset.sherpa_plugin import sherpa_model_to_table

from jetset.template_2Dmodel import EBLAbsorptionTemplate
ebl_franceschini=EBLAbsorptionTemplate.from_name('Franceschini_2008')

from jetset.jet_model import Jet
prefit_jet=Jet.load_model('prefit_jet.pkl')

We remove the paramter NH_cold_to_rel_e, not used in the fit, because of problem encountered with the IntervalProjection Sherpa method

p=prefit_jet.parameters.get_par_by_name('NH_cold_to_rel_e')
prefit_jet.parameters.del_par(p)

The following instructions create a Sherpa model for each of the existing jetset models.

sherpa_model_jet=JetsetSherpaModel(prefit_jet)
sherpa_model_gal=JetsetSherpaModel(my_shape.host_gal)
sherpa_model_ebl=JetsetSherpaModel(ebl_franceschini)

jetset model name R renamed to  R_sh due to sherpa internal naming convention

sherpa_model=(sherpa_model_jet+sherpa_model_gal)*sherpa_model_ebl

sherpa_model

Model

Expression: (jet_leptonic + host_galaxy) * Franceschini_2008
Component	Parameter	Value	Min	Max	Units
jet_leptonic	gmin	470.39174855643597	1.0	1000000000.0	lorentz-factor*
	gmax	2121872.5221158755	1.0	1000000000000000.0	lorentz-factor*
	N	5.5839884272436855	0.0	MAX	1 / cm3
	gamma0_log_parab	6673.207394465441	1.0	1000000000.0	lorentz-factor*
	s	2.251646579731128	-10.0	10.0
	r	0.27946018220142366	-15.0	15.0
	R_sh	1.2166221781101818e+16	1000.0	1e+30	cm
	R_H	1e+17	0.0	MAX	cm
	B	0.0505	0.0	MAX	gauss
	beam_obj	25.0	0.0001	MAX
	z_cosm	0.0336	0.0	MAX
host_galaxy	nuFnu_p_host	-9.972939096864089	-12.190114433380414	-8.190114433380414	erg / (s cm2)
host_galaxy	nu_scale	-0.0022904594810536465	-0.0029086966071740477	0.0029086966071740477	Hz
Franceschini_2008	scale_factor	1.0	0.0	MAX
Franceschini_2008	z_cosm	1.0	0.0	MAX

sherpa_model_to_table(sherpa_model)

Table length=15

model name	name	val	min	max	frozen	units	linked	linked par	linked model
str17	str16	float64	float64	float64	bool	object	bool	str1	str1
jet_leptonic	gmin	470.39174855643597	1.0	1000000000.0	False	lorentz-factor*	False
jet_leptonic	gmax	2121872.5221158755	1.0	1000000000000000.0	False	lorentz-factor*	False
jet_leptonic	N	5.5839884272436855	0.0	3.4028234663852886e+38	False	1 / cm3	False
jet_leptonic	gamma0_log_parab	6673.207394465441	1.0	1000000000.0	False	lorentz-factor*	False
jet_leptonic	s	2.251646579731128	-10.0	10.0	False		False
jet_leptonic	r	0.27946018220142366	-15.0	15.0	False		False
jet_leptonic	R_sh	1.2166221781101818e+16	1000.0	1e+30	False	cm	False
jet_leptonic	R_H	1e+17	0.0	3.4028234663852886e+38	True	cm	False
jet_leptonic	B	0.0505	0.0	3.4028234663852886e+38	False	gauss	False
jet_leptonic	beam_obj	25.0	0.0001	3.4028234663852886e+38	False		False
jet_leptonic	z_cosm	0.0336	0.0	3.4028234663852886e+38	False		False
host_galaxy	nuFnu_p_host	-9.972939096864089	-12.190114433380414	-8.190114433380414	False	erg / (s cm2)	False
host_galaxy	nu_scale	-0.0022904594810536465	-0.0029086966071740477	0.0029086966071740477	False	Hz	False
Franceschini_2008	scale_factor	1.0	0.0	3.4028234663852886e+38	True		False
Franceschini_2008	z_cosm	1.0	0.0	3.4028234663852886e+38	True		False

sherpa_model_ebl.z_cosm  = sherpa_model_jet.z_cosm

sherpa_model

Model

Expression: (jet_leptonic + host_galaxy) * Franceschini_2008
Component	Parameter	Thawed	Value	Min	Max	Units
jet_leptonic	gmin		470.39174855643597	1.0	1000000000.0	lorentz-factor*
	gmax		2121872.5221158755	1.0	1000000000000000.0	lorentz-factor*
	N		5.5839884272436855	0.0	MAX	1 / cm3
	gamma0_log_parab		6673.207394465441	1.0	1000000000.0	lorentz-factor*
	s		2.251646579731128	-10.0	10.0
	r		0.27946018220142366	-15.0	15.0
	R_sh		1.2166221781101818e+16	1000.0	1e+30	cm
	R_H		1e+17	0.0	MAX	cm
	B		0.0505	0.0	MAX	gauss
	beam_obj		25.0	0.0001	MAX
	z_cosm		0.0336	0.0	MAX
host_galaxy	nuFnu_p_host		-9.972939096864089	-12.190114433380414	-8.190114433380414	erg / (s cm2)
host_galaxy	nu_scale		-0.0022904594810536465	-0.0029086966071740477	0.0029086966071740477	Hz
Franceschini_2008	scale_factor		1.0	0.0	MAX
Franceschini_2008	z_cosm	linked	0.0336	⇐ jet_leptonic.z_cosm

sherpa_model_to_table(sherpa_model)

Table length=15

model name	name	val	min	max	frozen	units	linked	linked par	linked model
str17	str16	float64	float64	float64	bool	object	bool	str6	str12
jet_leptonic	gmin	470.39174855643597	1.0	1000000000.0	False	lorentz-factor*	False
jet_leptonic	gmax	2121872.5221158755	1.0	1000000000000000.0	False	lorentz-factor*	False
jet_leptonic	N	5.5839884272436855	0.0	3.4028234663852886e+38	False	1 / cm3	False
jet_leptonic	gamma0_log_parab	6673.207394465441	1.0	1000000000.0	False	lorentz-factor*	False
jet_leptonic	s	2.251646579731128	-10.0	10.0	False		False
jet_leptonic	r	0.27946018220142366	-15.0	15.0	False		False
jet_leptonic	R_sh	1.2166221781101818e+16	1000.0	1e+30	False	cm	False
jet_leptonic	R_H	1e+17	0.0	3.4028234663852886e+38	True	cm	False
jet_leptonic	B	0.0505	0.0	3.4028234663852886e+38	False	gauss	False
jet_leptonic	beam_obj	25.0	0.0001	3.4028234663852886e+38	False		False
jet_leptonic	z_cosm	0.0336	0.0	3.4028234663852886e+38	False		False
host_galaxy	nuFnu_p_host	-9.972939096864089	-12.190114433380414	-8.190114433380414	False	erg / (s cm2)	False
host_galaxy	nu_scale	-0.0022904594810536465	-0.0029086966071740477	0.0029086966071740477	False	Hz	False
Franceschini_2008	scale_factor	1.0	0.0	3.4028234663852886e+38	True		False
Franceschini_2008	z_cosm	0.0336	0.0	3.4028234663852886e+38	True		True	z_cosm	jet_leptonic

Note

as you can notice the JetSet frozen state of the parameters has been inherited in sherpa, the line below shows how to freeze parameters in the sherpa model once the sherpa model has already been created

sherpa_model_jet.R_H.freeze()
sherpa_model_jet.z_cosm.freeze()
sherpa_model_gal.nu_scale.freeze()
sherpa_model_ebl.scale_factor.freeze()

sherpa_model_jet.beam_obj.min = 5
sherpa_model_jet.beam_obj.max = 50.

sherpa_model_jet.R_sh.min = 10**15.
sherpa_model_jet.R_sh.max = 10**17.5

sherpa_model_jet.gmax.min = 1E5
sherpa_model_jet.gmax.max = 1E7

sherpa_model_jet.gmin.min = 2
sherpa_model_jet.gmin.max = 1E3

sherpa_model_jet.s.min = 1.5
sherpa_model_jet.s.max = 3


sherpa_model_jet.r.min = 0.1
sherpa_model_jet.r.max = 2

sherpa_model

Model

Expression: (jet_leptonic + host_galaxy) * Franceschini_2008
Component	Parameter	Thawed	Value	Min	Max	Units
jet_leptonic	gmin		470.39174855643597	2.0	1000.0	lorentz-factor*
	gmax		2121872.5221158755	100000.0	10000000.0	lorentz-factor*
	N		5.5839884272436855	0.0	MAX	1 / cm3
	gamma0_log_parab		6673.207394465441	1.0	1000000000.0	lorentz-factor*
	s		2.251646579731128	1.5	3.0
	r		0.27946018220142366	0.1	2.0
	R_sh		1.2166221781101818e+16	1000000000000000.0	3.1622776601683795e+17	cm
	R_H		1e+17	0.0	MAX	cm
	B		0.0505	0.0	MAX	gauss
	beam_obj		25.0	5.0	50.0
	z_cosm		0.0336	0.0	MAX
host_galaxy	nuFnu_p_host		-9.972939096864089	-12.190114433380414	-8.190114433380414	erg / (s cm2)
host_galaxy	nu_scale		-0.0022904594810536465	-0.0029086966071740477	0.0029086966071740477	Hz
Franceschini_2008	scale_factor		1.0	0.0	MAX
Franceschini_2008	z_cosm	linked	0.0336	⇐ jet_leptonic.z_cosm

sherpa_model_to_table(sherpa_model)

Table length=15

model name	name	val	min	max	frozen	units	linked	linked par	linked model
str17	str16	float64	float64	float64	bool	object	bool	str6	str12
jet_leptonic	gmin	470.39174855643597	2.0	1000.0	False	lorentz-factor*	False
jet_leptonic	gmax	2121872.5221158755	100000.0	10000000.0	False	lorentz-factor*	False
jet_leptonic	N	5.5839884272436855	0.0	3.4028234663852886e+38	False	1 / cm3	False
jet_leptonic	gamma0_log_parab	6673.207394465441	1.0	1000000000.0	False	lorentz-factor*	False
jet_leptonic	s	2.251646579731128	1.5	3.0	False		False
jet_leptonic	r	0.27946018220142366	0.1	2.0	False		False
jet_leptonic	R_sh	1.2166221781101818e+16	1000000000000000.0	3.1622776601683795e+17	False	cm	False
jet_leptonic	R_H	1e+17	0.0	3.4028234663852886e+38	True	cm	False
jet_leptonic	B	0.0505	0.0	3.4028234663852886e+38	False	gauss	False
jet_leptonic	beam_obj	25.0	5.0	50.0	False		False
jet_leptonic	z_cosm	0.0336	0.0	3.4028234663852886e+38	True		False
host_galaxy	nuFnu_p_host	-9.972939096864089	-12.190114433380414	-8.190114433380414	False	erg / (s cm2)	False
host_galaxy	nu_scale	-0.0022904594810536465	-0.0029086966071740477	0.0029086966071740477	True	Hz	False
Franceschini_2008	scale_factor	1.0	0.0	3.4028234663852886e+38	True		False
Franceschini_2008	z_cosm	0.0336	0.0	3.4028234663852886e+38	True		True	z_cosm	jet_leptonic

from sherpa import data
from sherpa.fit import Fit
from sherpa.stats import Chi2
from sherpa.optmethods import LevMar, NelderMead

sherpa_data=data.Data1D("sed", sed_data.table['nu_data'], sed_data.table['nuFnu_data'], staterror=sed_data.table['dnuFnu_data'])

fitter = Fit(sherpa_data, sherpa_model, stat=Chi2(), method=LevMar())
fit_range=[1e11,1e29]

sherpa_data.notice(fit_range[0], fit_range[1])

results = fitter.fit()

print("fit succeeded", results.succeeded)

fit succeeded True

results

Fit parameters

Parameter	Best-fit value	Approximate error
jet_leptonic.gmin	282.886	± 143.682
jet_leptonic.gmax	1.92947e+06	± 0
jet_leptonic.N	6.22249	± 2.50462
jet_leptonic.gamma0_log_parab	2627.67	± 0
jet_leptonic.s	2.19686	± 0.1099
jet_leptonic.r	0.177731	± 0.034032
jet_leptonic.R_sh	1.6661e+16	± 0
jet_leptonic.B	0.011725	± 0.00308555
jet_leptonic.beam_obj	43.3791	± 6.80526
host_galaxy.nuFnu_p_host	-9.96737	± 0.0308671

Summary (10)

Method

levmar

Statistic

chi2

Final statistic

13.9981

Number of evaluations

391

Reduced statistic

0.666576

Probability (Q-value)

0.869681

Initial statistic

147.739

Δ statistic

133.741

Number of data points

Degrees of freedom

print(results)

datasets       = None
itermethodname = none
methodname     = levmar
statname       = chi2
succeeded      = True
parnames       = ('jet_leptonic.gmin', 'jet_leptonic.gmax', 'jet_leptonic.N', 'jet_leptonic.gamma0_log_parab', 'jet_leptonic.s', 'jet_leptonic.r', 'jet_leptonic.R_sh', 'jet_leptonic.B', 'jet_leptonic.beam_obj', 'host_galaxy.nuFnu_p_host')
parvals        = (282.88625455729, 1929468.7195682812, 6.222485756131704, 2627.670944162405, 2.1968609128619963, 0.17773127895038787, 1.6661000241195552e+16, 0.011725010326451955, 43.37906579397914, -9.967371696830856)
statval        = 13.998098958800451
istatval       = 147.73930624101467
dstatval       = 133.74120728221422
numpoints      = 31
dof            = 21
qval           = 0.8696809139492879
rstat          = 0.6665761408952596
message        = successful termination
nfev           = 391

sherpa_model

Model

Expression: (jet_leptonic + host_galaxy) * Franceschini_2008
Component	Parameter	Thawed	Value	Min	Max	Units
jet_leptonic	gmin		282.88625455729	2.0	1000.0	lorentz-factor*
	gmax		1929468.7195682812	100000.0	10000000.0	lorentz-factor*
	N		6.222485756131704	0.0	MAX	1 / cm3
	gamma0_log_parab		2627.670944162405	1.0	1000000000.0	lorentz-factor*
	s		2.1968609128619963	1.5	3.0
	r		0.17773127895038787	0.1	2.0
	R_sh		1.6661000241195552e+16	1000000000000000.0	3.1622776601683795e+17	cm
	R_H		1e+17	0.0	MAX	cm
	B		0.011725010326451955	0.0	MAX	gauss
	beam_obj		43.37906579397914	5.0	50.0
	z_cosm		0.0336	0.0	MAX
host_galaxy	nuFnu_p_host		-9.967371696830856	-12.190114433380414	-8.190114433380414	erg / (s cm2)
host_galaxy	nu_scale		-0.0022904594810536465	-0.0029086966071740477	0.0029086966071740477	Hz
Franceschini_2008	scale_factor		1.0	0.0	MAX
Franceschini_2008	z_cosm	linked	0.0336	⇐ jet_leptonic.z_cosm

sherpa_model_to_table(sherpa_model)

Table length=15

model name	name	val	min	max	frozen	units	linked	linked par	linked model
str17	str16	float64	float64	float64	bool	object	bool	str6	str12
jet_leptonic	gmin	282.88625455729	2.0	1000.0	False	lorentz-factor*	False
jet_leptonic	gmax	1929468.7195682812	100000.0	10000000.0	False	lorentz-factor*	False
jet_leptonic	N	6.222485756131704	0.0	3.4028234663852886e+38	False	1 / cm3	False
jet_leptonic	gamma0_log_parab	2627.670944162405	1.0	1000000000.0	False	lorentz-factor*	False
jet_leptonic	s	2.1968609128619963	1.5	3.0	False		False
jet_leptonic	r	0.17773127895038787	0.1	2.0	False		False
jet_leptonic	R_sh	1.6661000241195552e+16	1000000000000000.0	3.1622776601683795e+17	False	cm	False
jet_leptonic	R_H	1e+17	0.0	3.4028234663852886e+38	True	cm	False
jet_leptonic	B	0.011725010326451955	0.0	3.4028234663852886e+38	False	gauss	False
jet_leptonic	beam_obj	43.37906579397914	5.0	50.0	False		False
jet_leptonic	z_cosm	0.0336	0.0	3.4028234663852886e+38	True		False
host_galaxy	nuFnu_p_host	-9.967371696830856	-12.190114433380414	-8.190114433380414	False	erg / (s cm2)	False
host_galaxy	nu_scale	-0.0022904594810536465	-0.0029086966071740477	0.0029086966071740477	True	Hz	False
Franceschini_2008	scale_factor	1.0	0.0	3.4028234663852886e+38	True		False
Franceschini_2008	z_cosm	0.0336	0.0	3.4028234663852886e+38	True		True	z_cosm	jet_leptonic

from jetset.sherpa_plugin import plot_sherpa_model

p=plot_sherpa_model(sherpa_model_jet,label='SSC',line_style='--')
p=plot_sherpa_model(sherpa_model_gal,plot_obj=p,label='host gal',line_style='--')
p=plot_sherpa_model(sherpa_model=sherpa_model,plot_obj=p,sed_data=sed_data,fit_range=fit_range,add_res=True,label='(SSC+host gal)*ebl')

../../../../_images/sherpa-plugin-sherpa-interface_53_0.png

You can access all the sherpa fetarues https://sherpa.readthedocs.io/en/latest/fit/index.html

from sherpa.plot import IntervalProjection
iproj = IntervalProjection()
iproj.prepare(fac=5, nloop=15)
iproj.calc(fitter, sherpa_model_jet.s)
iproj.plot()

WARNING: hard minimum hit for parameter jet_leptonic.gmin
WARNING: hard maximum hit for parameter jet_leptonic.gmin
WARNING: hard minimum hit for parameter jet_leptonic.gmax
WARNING: hard maximum hit for parameter jet_leptonic.gmax
WARNING: hard minimum hit for parameter jet_leptonic.B
WARNING: hard maximum hit for parameter jet_leptonic.B

../../../../_images/sherpa-plugin-sherpa-interface_55_1.png

Example to use the Sherpa plugin with the sherpa interface

Contents

Example to use the Sherpa plugin with the sherpa interface#

Loading data#

phenomenological model constraining#

spectral indices#

sed shaper#

Model constraining#

Model fitting with using a Sherpa model#