.. code:: ipython3 import warnings warnings.filterwarnings('ignore') .. code:: ipython3 import numpy as np .. code:: ipython3 import jetset print('tested on jetset',jetset.__version__) .. parsed-literal:: tested on jetset 1.2.2 .. _depending_parameters: Depending parameters ==================== In the following we show how to link parameters in the same model or among different models, and how to make a paramter depending on other parameters according to a mathematical expression. Example: linked paramters for EBL --------------------------------- .. code:: ipython3 from jetset.jet_model import Jet from jetset.template_2Dmodel import EBLAbsorptionTemplate from jetset.model_manager import FitModel my_jet = Jet(electron_distribution='lppl', name='jet_flaring') my_jet.parameters.z_cosm.val = 0.01 ebl_franceschini = EBLAbsorptionTemplate.from_name('Franceschini_2008') composite_model = FitModel(nu_size=500, name='EBL corrected') composite_model.add_component(my_jet) composite_model.add_component(ebl_franceschini) composite_model.show_pars() composite_model.link_par(par_name='z_cosm', from_model='Franceschini_2008', to_model='jet_flaring') v=0.03001 my_jet.parameters.z_cosm.val = v assert (composite_model.Franceschini_2008.parameters.z_cosm.val==v) assert (composite_model.Franceschini_2008.parameters.z_cosm.linked==True) composite_model.composite_expr = '%s*%s'%(my_jet.name,ebl_franceschini.name) composite_model.eval() #if plot is True: # composite_model.plot_model() composite_model.save_model('ebl_jet.pkl') new_composite_model=FitModel.load_model('ebl_jet.pkl') new_composite_model.show_pars() v=2.0 new_composite_model.jet_flaring.parameters.z_cosm.val=v print('new_composite_model.Franceschini_2008.parameters.z_cosm.val',new_composite_model.Franceschini_2008.parameters.z_cosm.val,'v',v) assert (new_composite_model.Franceschini_2008.parameters.z_cosm.val == v) assert (new_composite_model.Franceschini_2008.parameters.z_cosm.linked == True) .. raw:: html Table length=13
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_flaringRregion_sizecm5.000000e+151.000000e+031.000000e+30FalseFalse
jet_flaringR_Hregion_positioncm1.000000e+170.000000e+00--FalseTrue
jet_flaringBmagnetic_fieldgauss1.000000e-011.000000e-101.000000e+10FalseFalse
jet_flaringNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_flaringbeam_objbeaminglorentz-factor*1.000000e+011.000000e-041.000000e+04FalseFalse
jet_flaringz_cosmredshift1.000000e-020.000000e+00--FalseFalse
jet_flaringgminlow-energy-cut-offlorentz-factor*2.000000e+001.000000e+001.000000e+09FalseFalse
jet_flaringgmaxhigh-energy-cut-offlorentz-factor*1.000000e+061.000000e+001.000000e+15FalseFalse
jet_flaringNemitters_density1 / cm31.000000e+020.000000e+00--FalseFalse
jet_flaringgamma0_log_parabturn-over-energylorentz-factor*1.000000e+041.000000e+001.000000e+09FalseFalse
jet_flaringsLE_spectral_slope2.000000e+00-1.000000e+011.000000e+01FalseFalse
jet_flaringrspectral_curvature4.000000e-01-1.500000e+011.500000e+01FalseFalse
Franceschini_2008z_cosmredshift1.000000e+000.000000e+00--FalseTrue
.. parsed-literal:: ==> par: z_cosm from model: Franceschini_2008 linked to same parameter in model jet_flaring ==> par: z_cosm from model: Franceschini_2008 linked to same parameter in model jet_flaring .. raw:: html Table length=13
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_flaringgminlow-energy-cut-offlorentz-factor*2.000000e+001.000000e+001.000000e+09FalseFalse
jet_flaringgmaxhigh-energy-cut-offlorentz-factor*1.000000e+061.000000e+001.000000e+15FalseFalse
jet_flaringNemitters_density1 / cm31.000000e+020.000000e+00--FalseFalse
jet_flaringgamma0_log_parabturn-over-energylorentz-factor*1.000000e+041.000000e+001.000000e+09FalseFalse
jet_flaringsLE_spectral_slope2.000000e+00-1.000000e+011.000000e+01FalseFalse
jet_flaringrspectral_curvature4.000000e-01-1.500000e+011.500000e+01FalseFalse
jet_flaringRregion_sizecm5.000000e+151.000000e+031.000000e+30FalseFalse
jet_flaringR_Hregion_positioncm1.000000e+170.000000e+00--FalseTrue
jet_flaringBmagnetic_fieldgauss1.000000e-011.000000e-101.000000e+10FalseFalse
jet_flaringNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_flaringbeam_objbeaminglorentz-factor*1.000000e+011.000000e-041.000000e+04FalseFalse
jet_flaringz_cosm(M)redshift3.001000e-020.000000e+00--FalseFalse
Franceschini_2008z_cosm(L,jet_flaring)redshift------FalseTrue
.. parsed-literal:: new_composite_model.Franceschini_2008.parameters.z_cosm.val 2.0 v 2.0 Example: depending pars for bkn power-law emitters -------------------------------------------------- here we create a custom ``bkn`` distribution where we impose a functional dependence among the low and high-energy spectral index. .. code:: ipython3 from jetset.jet_emitters import EmittersDistribution import numpy as np from jetset.jet_model import Jet j = Jet(emitters_distribution='bkn') j.parameters .. raw:: html Table length=12
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicRregion_sizecm5.000000e+151.000000e+031.000000e+30FalseFalse
jet_leptonicR_Hregion_positioncm1.000000e+170.000000e+00--FalseTrue
jet_leptonicBmagnetic_fieldgauss1.000000e-011.000000e-101.000000e+10FalseFalse
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*1.000000e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift1.000000e-010.000000e+00--FalseFalse
jet_leptonicgminlow-energy-cut-offlorentz-factor*2.000000e+001.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*1.000000e+061.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm31.000000e+020.000000e+00--FalseFalse
jet_leptonicgamma_breakturn-over-energylorentz-factor*1.000000e+041.000000e+001.000000e+09FalseFalse
jet_leptonicpLE_spectral_slope2.500000e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonicp_1HE_spectral_slope3.500000e+00-1.000000e+011.000000e+01FalseFalse
.. parsed-literal:: None the functional dependence can be provided by a python function, where the argument (``p`` in this case) is the same name as the parameter: .. code:: ipython3 def f_p(p): return p+1 j.make_dependent_par(par='p_1',depends_on=['p'],par_expr=f_p) j.parameters.p.val=2 np.testing.assert_allclose(j.parameters.p_1.val, j.parameters.p.val + 1) j.parameters .. parsed-literal:: ==> par p_1 is now depending on ['p'] according to expr:p_1 = def f_p(p): return p+1 .. raw:: html Table length=12
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicRregion_sizecm5.000000e+151.000000e+031.000000e+30FalseFalse
jet_leptonicR_Hregion_positioncm1.000000e+170.000000e+00--FalseTrue
jet_leptonicBmagnetic_fieldgauss1.000000e-011.000000e-101.000000e+10FalseFalse
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*1.000000e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift1.000000e-010.000000e+00--FalseFalse
jet_leptonicgminlow-energy-cut-offlorentz-factor*2.000000e+001.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*1.000000e+061.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm31.000000e+020.000000e+00--FalseFalse
jet_leptonicgamma_breakturn-over-energylorentz-factor*1.000000e+041.000000e+001.000000e+09FalseFalse
jet_leptonicp(M)LE_spectral_slope2.000000e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonic*p_1(D,p)HE_spectral_slope3.000000e+00-1.000000e+011.000000e+01FalseTrue
.. parsed-literal:: None as you can notice, now a message is shown describing the dependence of the parameters It is also possible to set the dependence function as a string that can be evaluated .. code:: ipython3 j.make_dependent_par(par='p_1',depends_on=['p'],par_expr='p+1') j.parameters.p.val=2 np.testing.assert_allclose(j.parameters.p_1.val, j.parameters.p.val + 1) j.parameters .. parsed-literal:: ==> par p_1 is now depending on ['p'] according to expr:p_1 = p+1 .. raw:: html Table length=12
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicRregion_sizecm5.000000e+151.000000e+031.000000e+30FalseFalse
jet_leptonicR_Hregion_positioncm1.000000e+170.000000e+00--FalseTrue
jet_leptonicBmagnetic_fieldgauss1.000000e-011.000000e-101.000000e+10FalseFalse
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*1.000000e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift1.000000e-010.000000e+00--FalseFalse
jet_leptonicgminlow-energy-cut-offlorentz-factor*2.000000e+001.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*1.000000e+061.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm31.000000e+020.000000e+00--FalseFalse
jet_leptonicgamma_breakturn-over-energylorentz-factor*1.000000e+041.000000e+001.000000e+09FalseFalse
jet_leptonicp(M)LE_spectral_slope2.000000e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonic*p_1(D,p)HE_spectral_slope3.000000e+00-1.000000e+011.000000e+01FalseTrue
.. parsed-literal:: None In principle, you can use strings for short expressions, and functions for more complicated formulas. You can print the actual expression/function for the depending parameter using the ``print_par_expr`` method: .. code:: ipython3 j.parameters.p_1.print_par_expr .. parsed-literal:: ==> par p_1 is depending on ['p'] according to expr: p_1 = p+1 .. code:: ipython3 j.save_model('jet.pkl') new_jet=Jet.load_model('jet.pkl') .. parsed-literal:: ==> par p_1 is now depending on ['p'] according to expr:p_1 = p+1 .. raw:: html Table length=12
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicgminlow-energy-cut-offlorentz-factor*2.000000e+001.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*1.000000e+061.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm31.000000e+020.000000e+00--FalseFalse
jet_leptonicgamma_breakturn-over-energylorentz-factor*1.000000e+041.000000e+001.000000e+09FalseFalse
jet_leptonicp(M)LE_spectral_slope2.000000e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonic*p_1(D,p)HE_spectral_slope3.000000e+00-1.000000e+011.000000e+01FalseTrue
jet_leptonicRregion_sizecm5.000000e+151.000000e+031.000000e+30FalseFalse
jet_leptonicR_Hregion_positioncm1.000000e+170.000000e+00--FalseTrue
jet_leptonicBmagnetic_fieldgauss1.000000e-011.000000e-101.000000e+10FalseFalse
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*1.000000e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift1.000000e-010.000000e+00--FalseFalse
.. code:: ipython3 new_jet.parameters.p.val=2.5 np.testing.assert_allclose(new_jet.parameters.p_1.val, new_jet.parameters.p.val + 1) new_jet.parameters .. raw:: html Table length=12
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicgminlow-energy-cut-offlorentz-factor*2.000000e+001.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*1.000000e+061.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm31.000000e+020.000000e+00--FalseFalse
jet_leptonicgamma_breakturn-over-energylorentz-factor*1.000000e+041.000000e+001.000000e+09FalseFalse
jet_leptonicp(M)LE_spectral_slope2.500000e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonic*p_1(D,p)HE_spectral_slope3.500000e+00-1.000000e+011.000000e+01FalseTrue
jet_leptonicRregion_sizecm5.000000e+151.000000e+031.000000e+30FalseFalse
jet_leptonicR_Hregion_positioncm1.000000e+170.000000e+00--FalseTrue
jet_leptonicBmagnetic_fieldgauss1.000000e-011.000000e-101.000000e+10FalseFalse
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*1.000000e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift1.000000e-010.000000e+00--FalseFalse
.. parsed-literal:: None Example depending par: Building a Jet model with B function of R_H and R_0 -------------------------------------------------------------------------- In this example we create a fuctional dependence among the paramters ``B``, ``R_H`` introducing user custom pararameters. Wewant that the value of the mangentic field in the jet is a function or ``R_H``, and of the initial value of ``B=B0`` at ``R=R_H0``, according to the expression: :math:`B=B_0(R_0/R_H)^{1.1}` .. code:: ipython3 jet=Jet(emitters_distribution='plc') fit_model_lsb=FitModel( jet=jet, name='SSC-best-fit-lsb',template=None) fit_model_lsb.jet_leptonic.parameters.beam_obj.fit_range = [5, 50] fit_model_lsb.jet_leptonic.parameters.R_H.val=5E17 fit_model_lsb.jet_leptonic.parameters.R_H.frozen=False fit_model_lsb.jet_leptonic.parameters.R_H.fit_range = [1E15, 1E19] fit_model_lsb.jet_leptonic.parameters.R.fit_range = [10 ** 15.5, 10 ** 17.5] fit_model_lsb.jet_leptonic.add_user_par(name='B0',units='G',val=1E3,val_min=0,val_max=None) fit_model_lsb.jet_leptonic.add_user_par(name='R0', units='cm', val=5E13, val_min=0, val_max=None) fit_model_lsb.jet_leptonic.add_user_par(name='m_B', val=1, val_min=1, val_max=2) fit_model_lsb.jet_leptonic.parameters.R0.frozen=True fit_model_lsb.jet_leptonic.parameters.B0.frozen=True def par_func(R0,B0,R_H,m_B): return B0*np.power((R0/R_H),m_B) fit_model_lsb.jet_leptonic.make_dependent_par(par='B', depends_on=['B0', 'R0', 'R_H','m_B'], par_expr=par_func) B0=fit_model_lsb.jet_leptonic.parameters.B0.val R0 = fit_model_lsb.jet_leptonic.parameters.R0.val R_H = fit_model_lsb.jet_leptonic.parameters.R_H.val m_B= fit_model_lsb.jet_leptonic.parameters.m_B.val np.testing.assert_allclose(fit_model_lsb.jet_leptonic.parameters.B.val, par_func(R0,B0,R_H,m_B)) .. parsed-literal:: ==> par B is now depending on ['B0', 'R0', 'R_H', 'm_B'] according to expr:B = def par_func(R0,B0,R_H,m_B): return B0*np.power((R0/R_H),m_B) .. code:: ipython3 fit_model_lsb.jet_leptonic.parameters .. raw:: html Table length=14
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicRregion_sizecm5.000000e+151.000000e+031.000000e+30FalseFalse
jet_leptonicR_H(M)region_positioncm5.000000e+170.000000e+00--FalseFalse
jet_leptonic*B(D,m_B)magnetic_fieldgauss1.000000e-011.000000e-101.000000e+10FalseTrue
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*1.000000e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift1.000000e-010.000000e+00--FalseFalse
jet_leptonicgminlow-energy-cut-offlorentz-factor*2.000000e+001.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*1.000000e+061.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm31.000000e+020.000000e+00--FalseFalse
jet_leptonicgamma_cutturn-over-energylorentz-factor*1.000000e+041.000000e+001.000000e+09FalseFalse
jet_leptonicpLE_spectral_slope2.000000e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonicB0(M)user_definedG1.000000e+030.000000e+00--FalseTrue
jet_leptonicR0(M)user_definedcm5.000000e+130.000000e+00--FalseTrue
jet_leptonicm_B(M)user_defined1.000000e+001.000000e+002.000000e+00FalseFalse
.. parsed-literal:: None .. code:: ipython3 fit_model_lsb.jet_leptonic.parameters .. raw:: html Table length=14
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicRregion_sizecm5.000000e+151.000000e+031.000000e+30FalseFalse
jet_leptonicR_H(M)region_positioncm5.000000e+170.000000e+00--FalseFalse
jet_leptonic*B(D,m_B)magnetic_fieldgauss1.000000e-011.000000e-101.000000e+10FalseTrue
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*1.000000e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift1.000000e-010.000000e+00--FalseFalse
jet_leptonicgminlow-energy-cut-offlorentz-factor*2.000000e+001.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*1.000000e+061.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm31.000000e+020.000000e+00--FalseFalse
jet_leptonicgamma_cutturn-over-energylorentz-factor*1.000000e+041.000000e+001.000000e+09FalseFalse
jet_leptonicpLE_spectral_slope2.000000e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonicB0(M)user_definedG1.000000e+030.000000e+00--FalseTrue
jet_leptonicR0(M)user_definedcm5.000000e+130.000000e+00--FalseTrue
jet_leptonicm_B(M)user_defined1.000000e+001.000000e+002.000000e+00FalseFalse
.. parsed-literal:: None .. code:: ipython3 fit_model_lsb.save_model('test.pkl') .. code:: ipython3 fit_model_lsb=FitModel.load_model('test.pkl') .. parsed-literal:: ==> par B is now depending on ['B0', 'R0', 'R_H', 'm_B'] according to expr:B = def par_func(R0,B0,R_H,m_B): return B0*np.power((R0/R_H),m_B) .. code:: ipython3 B0=fit_model_lsb.jet_leptonic.parameters.B0.val R0 = fit_model_lsb.jet_leptonic.parameters.R0.val R_H = fit_model_lsb.jet_leptonic.parameters.R_H.val m_B= fit_model_lsb.jet_leptonic.parameters.m_B.val np.testing.assert_allclose(fit_model_lsb.jet_leptonic.parameters.B.val, par_func(R0,B0,R_H,m_B)) Example depending par: fitting with a Jet model with depending pars ------------------------------------------------------------------- In this example we show how to use the previous model during a Fit .. code:: ipython3 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 .. code:: ipython3 data=Data.from_file(test_SEDs[1]) .. code:: ipython3 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() .. parsed-literal:: ================================================================================ *** binning data *** ---> N bins= 89 ---> bin_widht= 0.2 ================================================================================ .. image:: depending_pars_files/depending_pars_34_1.png .. code:: ipython3 from jetset.sed_shaper import SEDShape my_shape=SEDShape(sed_data) my_shape.eval_indices(minimizer='lsb',silent=True) p=my_shape.plot_indices() .. parsed-literal:: ================================================================================ *** evaluating spectral indices for data *** ================================================================================ .. image:: depending_pars_files/depending_pars_35_1.png .. code:: ipython3 mm,best_fit=my_shape.sync_fit(check_host_gal_template=False, Ep_start=None, minimizer='lsb', silent=True, fit_range=[10.,21.]) .. parsed-literal:: ================================================================================ *** Log-Polynomial fitting of the synchrotron component *** ---> first blind fit run, fit range: [10.0, 21.0] ---> class: HSP .. raw:: html Table length=4
model namenamevalbestfit valerr +err -start valfit range minfit range maxfrozen
LogCubicb-1.545300e-01-1.545300e-019.534795e-03---1.000000e+00-1.000000e+010.000000e+00False
LogCubicc-1.023245e-02-1.023245e-021.433073e-03---1.000000e+00-1.000000e+011.000000e+01False
LogCubicEp1.672267e+011.672267e+014.139942e-02--1.667039e+010.000000e+003.000000e+01False
LogCubicSp-9.491659e+00-9.491659e+002.515285e-02---1.000000e+01-3.000000e+010.000000e+00False
.. parsed-literal:: ---> sync nu_p=+1.672267e+01 (err=+4.139942e-02) nuFnu_p=-9.491659e+00 (err=+2.515285e-02) curv.=-1.545300e-01 (err=+9.534795e-03) ================================================================================ .. code:: ipython3 my_shape.IC_fit(fit_range=[23.,29.],minimizer='minuit',silent=True) p=my_shape.plot_shape_fit() p.setlim(y_min=1E-15) .. parsed-literal:: ================================================================================ *** Log-Polynomial fitting of the IC component *** ---> fit range: [23.0, 29.0] ---> LogCubic fit .. raw:: html Table length=4
model namenamevalbestfit valerr +err -start valfit range minfit range maxfrozen
LogCubicb-2.098186e-01-2.098186e-013.133032e-02---1.000000e+00-1.000000e+010.000000e+00False
LogCubicc-4.661868e-02-4.661868e-022.178352e-02---1.000000e+00-1.000000e+011.000000e+01False
LogCubicEp2.524926e+012.524926e+011.147759e-01--2.529412e+010.000000e+003.000000e+01False
LogCubicSp-1.011085e+01-1.011085e+013.498963e-02---1.000000e+01-3.000000e+010.000000e+00False
.. parsed-literal:: ---> IC nu_p=+2.524926e+01 (err=+1.147759e-01) nuFnu_p=-1.011085e+01 (err=+3.498963e-02) curv.=-2.098186e-01 (err=+3.133032e-02) ================================================================================ .. image:: depending_pars_files/depending_pars_37_3.png .. code:: ipython3 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') .. parsed-literal:: ================================================================================ *** constrains parameters from observable *** .. raw:: html Table length=12
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicRregion_sizecm3.112712e+161.000000e+031.000000e+30FalseFalse
jet_leptonicR_Hregion_positioncm1.000000e+170.000000e+00--FalseTrue
jet_leptonicBmagnetic_fieldgauss5.050000e-021.000000e-101.000000e+10FalseFalse
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*2.500000e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift3.080000e-020.000000e+00--FalseFalse
jet_leptonicgminlow-energy-cut-offlorentz-factor*4.697542e+021.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*1.373160e+061.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm39.060842e-010.000000e+00--FalseFalse
jet_leptonicgamma0_log_parabturn-over-energylorentz-factor*3.188500e+041.000000e+001.000000e+09FalseFalse
jet_leptonicsLE_spectral_slope2.181578e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonicrspectral_curvature7.726502e-01-1.500000e+011.500000e+01FalseFalse
.. parsed-literal:: ================================================================================ .. code:: ipython3 from jetset.minimizer import fit_SED,ModelMinimizer from jetset.model_manager import FitModel from jetset.jet_model import Jet prefit_jet=Jet.load_model('prefit_jet.pkl') .. raw:: html Table length=12
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicgminlow-energy-cut-offlorentz-factor*4.697542e+021.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*1.373160e+061.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm39.060842e-010.000000e+00--FalseFalse
jet_leptonicgamma0_log_parabturn-over-energylorentz-factor*3.188500e+041.000000e+001.000000e+09FalseFalse
jet_leptonicsLE_spectral_slope2.181578e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonicrspectral_curvature7.726502e-01-1.500000e+011.500000e+01FalseFalse
jet_leptonicRregion_sizecm3.112712e+161.000000e+031.000000e+30FalseFalse
jet_leptonicR_Hregion_positioncm1.000000e+170.000000e+00--FalseTrue
jet_leptonicBmagnetic_fieldgauss5.050000e-021.000000e-101.000000e+10FalseFalse
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*2.500000e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift3.080000e-020.000000e+00--FalseFalse
.. code:: ipython3 fit_model=FitModel( jet=prefit_jet, name='SSC-best-fit-lsb',template=None) fit_model.parameters .. raw:: html Table length=12
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicgminlow-energy-cut-offlorentz-factor*4.697542e+021.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*1.373160e+061.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm39.060842e-010.000000e+00--FalseFalse
jet_leptonicgamma0_log_parabturn-over-energylorentz-factor*3.188500e+041.000000e+001.000000e+09FalseFalse
jet_leptonicsLE_spectral_slope2.181578e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonicrspectral_curvature7.726502e-01-1.500000e+011.500000e+01FalseFalse
jet_leptonicRregion_sizecm3.112712e+161.000000e+031.000000e+30FalseFalse
jet_leptonicR_Hregion_positioncm1.000000e+170.000000e+00--FalseTrue
jet_leptonicBmagnetic_fieldgauss5.050000e-021.000000e-101.000000e+10FalseFalse
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*2.500000e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift3.080000e-020.000000e+00--FalseFalse
.. parsed-literal:: None .. code:: ipython3 fit_model.jet_leptonic.parameters.beam_obj.fit_range = [5, 50] fit_model.jet_leptonic.parameters.R_H.val=5E17 fit_model.jet_leptonic.parameters.R_H.frozen=False fit_model.jet_leptonic.parameters.R_H.fit_range = [1E15, 1E19] fit_model.jet_leptonic.parameters.R.fit_range = [10 ** 15.5, 10 ** 17.5] fit_model.jet_leptonic.parameters.gamma0_log_parab.fit_range = [1E3,1E6] fit_model.jet_leptonic.parameters.gmin.fit_range = [10,1000] fit_model.jet_leptonic.parameters.gmax.fit_range = [1E5,1E8] fit_model.jet_leptonic.add_user_par(name='B0',units='G',val=1E3,val_min=0,val_max=None) fit_model.jet_leptonic.add_user_par(name='R0', units='cm', val=5E13, val_min=0, val_max=None) fit_model.jet_leptonic.add_user_par(name='m_B', val=1, val_min=1, val_max=2) fit_model.jet_leptonic.parameters.R0.frozen=True fit_model.jet_leptonic.parameters.B0.frozen=True def par_func(R0,B0,R_H,m_B): return B0*np.power((R0/R_H),m_B) fit_model.jet_leptonic.make_dependent_par(par='B', depends_on=['B0', 'R0', 'R_H','m_B'], par_expr=par_func) fit_model.parameters .. parsed-literal:: ==> par B is now depending on ['B0', 'R0', 'R_H', 'm_B'] according to expr:B = def par_func(R0,B0,R_H,m_B): return B0*np.power((R0/R_H),m_B) .. raw:: html Table length=15
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicgminlow-energy-cut-offlorentz-factor*4.697542e+021.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*1.373160e+061.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm39.060842e-010.000000e+00--FalseFalse
jet_leptonicgamma0_log_parabturn-over-energylorentz-factor*3.188500e+041.000000e+001.000000e+09FalseFalse
jet_leptonicsLE_spectral_slope2.181578e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonicrspectral_curvature7.726502e-01-1.500000e+011.500000e+01FalseFalse
jet_leptonicRregion_sizecm3.112712e+161.000000e+031.000000e+30FalseFalse
jet_leptonicR_H(M)region_positioncm5.000000e+170.000000e+00--FalseFalse
jet_leptonic*B(D,m_B)magnetic_fieldgauss1.000000e-011.000000e-101.000000e+10FalseTrue
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*2.500000e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift3.080000e-020.000000e+00--FalseFalse
jet_leptonicB0(M)user_definedG1.000000e+030.000000e+00--FalseTrue
jet_leptonicR0(M)user_definedcm5.000000e+130.000000e+00--FalseTrue
jet_leptonicm_B(M)user_defined1.000000e+001.000000e+002.000000e+00FalseFalse
.. parsed-literal:: None .. code:: ipython3 %matplotlib inline import matplotlib.pyplot as plt plt.figure(dpi=150) R_H_array=np.logspace(13,18,100) B_array=np.zeros(R_H_array.shape) for ID,R_H in enumerate(R_H_array): fit_model_lsb.jet_leptonic.parameters.R_H.val=R_H B_array[ID]=fit_model_lsb.jet_leptonic.parameters.B.val plt.loglog(R_H_array,B_array) plt.xlabel('R_H (cm)') plt.ylabel('B (G)') .. parsed-literal:: Text(0, 0.5, 'B (G)') .. image:: depending_pars_files/depending_pars_42_1.png .. code:: ipython3 fit_model.jet_leptonic.parameters.R_H.val=5E17 .. code:: ipython3 fit_model.parameters .. raw:: html Table length=15
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicgminlow-energy-cut-offlorentz-factor*4.697542e+021.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*1.373160e+061.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm39.060842e-010.000000e+00--FalseFalse
jet_leptonicgamma0_log_parabturn-over-energylorentz-factor*3.188500e+041.000000e+001.000000e+09FalseFalse
jet_leptonicsLE_spectral_slope2.181578e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonicrspectral_curvature7.726502e-01-1.500000e+011.500000e+01FalseFalse
jet_leptonicRregion_sizecm3.112712e+161.000000e+031.000000e+30FalseFalse
jet_leptonicR_H(M)region_positioncm5.000000e+170.000000e+00--FalseFalse
jet_leptonic*B(D,m_B)magnetic_fieldgauss1.000000e-011.000000e-101.000000e+10FalseTrue
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*2.500000e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift3.080000e-020.000000e+00--FalseFalse
jet_leptonicB0(M)user_definedG1.000000e+030.000000e+00--FalseTrue
jet_leptonicR0(M)user_definedcm5.000000e+130.000000e+00--FalseTrue
jet_leptonicm_B(M)user_defined1.000000e+001.000000e+002.000000e+00FalseFalse
.. parsed-literal:: None As a resuslt of the best fit modeling, we are able to determine the value of ``R_H``. We now perform the fit with minuit to get a better estimate of the errors .. code:: ipython3 model_minimizer_minuit=ModelMinimizer('minuit') .. code:: ipython3 best_fit_minuit=model_minimizer_minuit.fit(fit_model, sed_data, 1E11, 1E29, fitname='SSC-best-fit-minuit', repeat=3) .. parsed-literal:: filtering data in fit range = [1.000000e+11,1.000000e+29] data length 35 ================================================================================ *** start fit process *** ----- fit run: 0 .. parsed-literal:: 0it [00:00, ?it/s] .. parsed-literal:: - best chisq=1.55264e+01 fit run: 1 - old chisq=1.55264e+01 .. parsed-literal:: 0it [00:00, ?it/s] .. parsed-literal:: - best chisq=1.55263e+01 fit run: 2 - old chisq=1.55263e+01 .. parsed-literal:: 0it [00:00, ?it/s] .. parsed-literal:: - best chisq=1.55263e+01 ------------------------------------------------------------------------- Fit report Model: SSC-best-fit-minuit .. raw:: html Table length=15
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicgminlow-energy-cut-offlorentz-factor*5.707170e+021.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*6.628146e+051.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm39.096259e-010.000000e+00--FalseFalse
jet_leptonicgamma0_log_parabturn-over-energylorentz-factor*3.542531e+041.000000e+001.000000e+09FalseFalse
jet_leptonicsLE_spectral_slope2.275366e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonicrspectral_curvature7.219607e-01-1.500000e+011.500000e+01FalseFalse
jet_leptonicRregion_sizecm3.022039e+161.000000e+031.000000e+30FalseFalse
jet_leptonicR_H(M)region_positioncm6.570078e+170.000000e+00--FalseFalse
jet_leptonic*B(D,m_B)magnetic_fieldgauss4.997430e-021.000000e-101.000000e+10FalseTrue
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*4.643401e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift9.556801e-020.000000e+00--FalseFalse
jet_leptonicB0(M)user_definedG1.000000e+030.000000e+00--FalseTrue
jet_leptonicR0(M)user_definedcm5.000000e+130.000000e+00--FalseTrue
jet_leptonicm_B(M)user_defined1.044348e+001.000000e+002.000000e+00FalseFalse
.. parsed-literal:: converged=True calls=187 mesg= .. raw:: html
FCN = 15.53 Nfcn = 187
EDM = 0.000156 (Goal: 0.0002)
Valid Minimum Valid Parameters No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok APPROXIMATE NOT pos. def. FORCED
Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 par_0 571 18 10 1E+03
1 par_1 0.663e6 0.011e6 1E+05 1E+08
2 par_2 0.910 0.029 0
3 par_3 35.4e3 3.3e3 1E+03 1E+06
4 par_4 2.275 0.015 -10 10
5 par_5 0.72 0.04 -15 15
6 par_6 30.2e15 0.6e15 3.16E+15 3.16E+17
7 par_7 0.66e18 0.06e18 1E+15 1E+19
8 par_8 46.4 0.5 5 50
9 par_9 0.0956 0.0020 0
10 par_10 1.044 0.008 1 2
par_0 par_1 par_2 par_3 par_4 par_5 par_6 par_7 par_8 par_9 par_10
par_0 314 -4.13e+04 (-0.207) -0.19 (-0.367) 3.01e+04 (0.510) 0.157 (0.575) 0.0518 (0.081) 3.01e+15 (0.299) 6.09e+17 (0.617) -2.3 (-0.246) 0.00721 (0.204) -0.0841 (-0.573)
par_1 -4.13e+04 (-0.207) 1.27e+08 52.7 (0.160) -7.33e+06 (-0.195) -28.7 (-0.165) -1.24 (-0.003) -1.17e+18 (-0.183) -8.6e+19 (-0.137) 227 (0.038) -1.32 (-0.059) 9.53 (0.102)
par_2 -0.19 (-0.367) 52.7 (0.160) 0.000853 -11.3 (-0.116) -1.06e-05 (-0.024) -1.51e-05 (-0.014) -4.56e+12 (-0.275) -3.1e+14 (-0.191) -0.000306 (-0.020) 3.7e-06 (0.064) 4.78e-05 (0.198)
par_3 3.01e+04 (0.510) -7.33e+06 (-0.195) -11.3 (-0.116) 1.11e+07 42.8 (0.834) 64.2 (0.531) 1.11e+18 (0.586) 1.49e+20 (0.800) -455 (-0.258) 1.44 (0.216) -21.2 (-0.770)
par_4 0.157 (0.575) -28.7 (-0.165) -1.06e-05 (-0.024) 42.8 (0.834) 0.000238 0.00014 (0.250) 5.68e+12 (0.649) 7.08e+14 (0.824) -0.00171 (-0.209) 5.84e-06 (0.190) -0.000101 (-0.790)
par_5 0.0518 (0.081) -1.24 (-0.003) -1.51e-05 (-0.014) 64.2 (0.531) 0.00014 (0.250) 0.00132 2.46e+12 (0.119) 3.9e+14 (0.193) 0.00025 (0.013) 4.33e-06 (0.060) -8.1e-05 (-0.269)
par_6 3.01e+15 (0.299) -1.17e+18 (-0.183) -4.56e+12 (-0.275) 1.11e+18 (0.586) 5.68e+12 (0.649) 2.46e+12 (0.119) 3.23e+29 2.08e+31 (0.657) -9.7e+13 (-0.323) 3.66e+11 (0.323) -2.89e+12 (-0.613)
par_7 6.09e+17 (0.617) -8.6e+19 (-0.137) -3.1e+14 (-0.191) 1.49e+20 (0.800) 7.08e+14 (0.824) 3.9e+14 (0.193) 2.08e+31 (0.657) 3.11e+33 -7.51e+15 (-0.255) 2.32e+13 (0.209) -4.42e+14 (-0.956)
par_8 -2.3 (-0.246) 227 (0.038) -0.000306 (-0.020) -455 (-0.258) -0.00171 (-0.209) 0.00025 (0.013) -9.7e+13 (-0.323) -7.51e+15 (-0.255) 0.279 0.000108 (0.102) 0.0014 (0.321)
par_9 0.00721 (0.204) -1.32 (-0.059) 3.7e-06 (0.064) 1.44 (0.216) 5.84e-06 (0.190) 4.33e-06 (0.060) 3.66e+11 (0.323) 2.32e+13 (0.209) 0.000108 (0.102) 3.98e-06 -4.61e-06 (-0.279)
par_10 -0.0841 (-0.573) 9.53 (0.102) 4.78e-05 (0.198) -21.2 (-0.770) -0.000101 (-0.790) -8.1e-05 (-0.269) -2.89e+12 (-0.613) -4.42e+14 (-0.956) 0.0014 (0.321) -4.61e-06 (-0.279) 6.86e-05
.. parsed-literal:: dof=24 chisq=15.526262, chisq/red=0.646928 null hypothesis sig=0.904412 best fit pars .. raw:: html Table length=15
model namenamevalbestfit valerr +err -start valfit range minfit range maxfrozen
jet_leptonicgmin5.707170e+025.707170e+021.770879e+01--4.697542e+021.000000e+011.000000e+03False
jet_leptonicgmax6.628146e+056.628146e+051.128277e+04--1.373160e+061.000000e+051.000000e+08False
jet_leptonicN9.096259e-019.096259e-012.920374e-02--9.060842e-010.000000e+00--False
jet_leptonicgamma0_log_parab3.542531e+043.542531e+043.330848e+03--3.188500e+041.000000e+031.000000e+06False
jet_leptonics2.275366e+002.275366e+001.541557e-02--2.181578e+00-1.000000e+011.000000e+01False
jet_leptonicr7.219607e-017.219607e-013.630592e-02--7.726502e-01-1.500000e+011.500000e+01False
jet_leptonicR3.022039e+163.022039e+165.684398e+14--3.112712e+163.162278e+153.162278e+17False
jet_leptonicR_H(M)6.570078e+176.570078e+175.574426e+16--5.000000e+171.000000e+151.000000e+19False
jet_leptonic*B(D,m_B)4.997430e-02------1.000000e-011.000000e-101.000000e+10True
jet_leptonicNH_cold_to_rel_e1.000000e-01------1.000000e-010.000000e+00--True
jet_leptonicbeam_obj4.643401e+014.643401e+015.282534e-01--2.500000e+015.000000e+005.000000e+01False
jet_leptonicz_cosm9.556801e-029.556801e-021.995728e-03--3.080000e-020.000000e+00--False
jet_leptonicB0(M)1.000000e+03------1.000000e+030.000000e+00--True
jet_leptonicR0(M)5.000000e+13------5.000000e+130.000000e+00--True
jet_leptonicm_B(M)1.044348e+001.044348e+008.281168e-03--1.000000e+001.000000e+002.000000e+00False
.. parsed-literal:: ------------------------------------------------------------------------- ================================================================================ .. code:: ipython3 fit_model.plot_model(sed_data=sed_data) .. parsed-literal:: .. image:: depending_pars_files/depending_pars_48_1.png .. code:: ipython3 %matplotlib inline plt.figure(dpi=150) R_H_array=np.logspace(13,18,100) B_array=np.zeros(R_H_array.shape) for ID,R_H in enumerate(R_H_array): fit_model_lsb.jet_leptonic.parameters.R_H.val=R_H B_array[ID]=fit_model_lsb.jet_leptonic.parameters.B.val plt.loglog(R_H_array,B_array) plt.xlabel('R_H (cm)') plt.ylabel('B (G)') .. parsed-literal:: Text(0, 0.5, 'B (G)') .. image:: depending_pars_files/depending_pars_49_1.png .. code:: ipython3 fit_model.save_model('test.pkl') .. code:: ipython3 from jetset.model_manager import FitModel new_fit_model=FitModel.load_model('test.pkl') .. parsed-literal:: ==> par B is now depending on ['B0', 'R0', 'R_H', 'm_B'] according to expr:B = def par_func(R0,B0,R_H,m_B): return B0*np.power((R0/R_H),m_B) .. code:: ipython3 new_fit_model.parameters .. raw:: html Table length=15
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicgminlow-energy-cut-offlorentz-factor*5.707170e+021.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*6.628146e+051.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm39.096259e-010.000000e+00--FalseFalse
jet_leptonicgamma0_log_parabturn-over-energylorentz-factor*3.542531e+041.000000e+001.000000e+09FalseFalse
jet_leptonicsLE_spectral_slope2.275366e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonicrspectral_curvature7.219607e-01-1.500000e+011.500000e+01FalseFalse
jet_leptonicRregion_sizecm3.022039e+161.000000e+031.000000e+30FalseFalse
jet_leptonicR_H(M)region_positioncm6.570078e+170.000000e+00--FalseFalse
jet_leptonic*B(D,m_B)magnetic_fieldgauss4.997430e-021.000000e-101.000000e+10FalseTrue
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*4.643401e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift9.556801e-020.000000e+00--FalseFalse
jet_leptonicB0(M)user_definedG1.000000e+030.000000e+00--FalseTrue
jet_leptonicR0(M)user_definedcm5.000000e+130.000000e+00--FalseTrue
jet_leptonicm_B(M)user_defined1.044348e+001.000000e+002.000000e+00FalseFalse
.. parsed-literal:: None .. code:: ipython3 new_fit_model.jet_leptonic.parameters.reset_dependencies() .. code:: ipython3 new_fit_model.parameters .. raw:: html Table length=15
model namenamepar typeunitsvalphys. bound. minphys. bound. maxlogfrozen
jet_leptonicgminlow-energy-cut-offlorentz-factor*5.707170e+021.000000e+001.000000e+09FalseFalse
jet_leptonicgmaxhigh-energy-cut-offlorentz-factor*6.628146e+051.000000e+001.000000e+15FalseFalse
jet_leptonicNemitters_density1 / cm39.096259e-010.000000e+00--FalseFalse
jet_leptonicgamma0_log_parabturn-over-energylorentz-factor*3.542531e+041.000000e+001.000000e+09FalseFalse
jet_leptonicsLE_spectral_slope2.275366e+00-1.000000e+011.000000e+01FalseFalse
jet_leptonicrspectral_curvature7.219607e-01-1.500000e+011.500000e+01FalseFalse
jet_leptonicRregion_sizecm3.022039e+161.000000e+031.000000e+30FalseFalse
jet_leptonicR_Hregion_positioncm6.570078e+170.000000e+00--FalseFalse
jet_leptonicBmagnetic_fieldgauss4.997430e-021.000000e-101.000000e+10FalseTrue
jet_leptonicNH_cold_to_rel_ecold_p_to_rel_e_ratio1.000000e-010.000000e+00--FalseTrue
jet_leptonicbeam_objbeaminglorentz-factor*4.643401e+011.000000e-041.000000e+04FalseFalse
jet_leptonicz_cosmredshift9.556801e-020.000000e+00--FalseFalse
jet_leptonicB0user_definedG1.000000e+030.000000e+00--FalseTrue
jet_leptonicR0user_definedcm5.000000e+130.000000e+00--FalseTrue
jet_leptonicm_Buser_defined1.044348e+001.000000e+002.000000e+00FalseFalse
.. parsed-literal:: None