Notebook

Tutorial 3 for JetSeT¶

In [1]:

from IPython.core.display import display, HTML
display(HTML("<style>.container { width:95% !important; }</style>"))

import matplotlib

from matplotlib import pyplot as plt

import matplotlib.colors as mcolors

font = {'family' : 'sans-serif',
        'weight' : 'normal',
        'size'   : 18}

matplotlib.rc('font', **font)
matplotlib.pyplot.rc('font', **font)

colors=list(mcolors.BASE_COLORS)

import warnings
warnings.filterwarnings('ignore')
import numpy as np

EC model

In [2]:

from jetset.jet_model import Jet
from jetset.plot_sedfit import PlotSED

In [3]:

from jetset.jet_model import Jet

my_jet=Jet(name='BLR example',electron_distribution='bkn',beaming_expr='bulk_theta')
my_jet.add_EC_component(['EC_BLR','EC_Disk'])
my_jet.show_model()
my_jet.set_par('disk_type',val='MultiBB')
    

--------------------------------------------------------------------------------
jet model description
--------------------------------------------------------------------------------
name: BLRexample  

electrons distribution:
 type: bkn  
 gamma energy grid size:  201
 gmin grid : 2.000000e+00
 gmax grid : 1.000000e+06
 normalization  True
 log-values  False

accretion disk:
 disk Type: BB
 L disk: 1.000000e+45 (erg/s)
 T disk: 1.000000e+05 (K)
 nu peak disk: 8.171810e+15 (Hz)

radiative fields:
 seed photons grid size:  100
 IC emission grid size:  100
 source emissivity lower bound :  1.000000e-120
 spectral components:
   name:Sum, state: on
   name:Sync, state: self-abs
   name:SSC, state: on
   name:EC_BLR, state: on
   name:Disk, state: on
   name:EC_Disk, state: on
external fields transformation method: blob

SED info:
 nu grid size jetkernel: 1000
 nu size: 500
 nu mix (Hz): 1.000000e+06
 nu max (Hz): 1.000000e+30

flux plot lower bound   :  1.000000e-30

--------------------------------------------------------------------------------

Table length=17

model name	name	par type	units	val	phys. bound. min	phys. bound. max	log	frozen
BLRexample	R	region_size	cm	5.000000e+15	1.000000e+03	1.000000e+30	False	False
BLRexample	R_H	region_position	cm	1.000000e+17	0.000000e+00	--	False	True
BLRexample	B	magnetic_field	gauss	1.000000e-01	0.000000e+00	--	False	False
BLRexample	theta	jet-viewing-angle	deg	1.000000e-01	0.000000e+00	--	False	False
BLRexample	BulkFactor	jet-bulk-factor	lorentz-factor*	1.000000e+01	1.000000e+00	--	False	False
BLRexample	z_cosm	redshift		1.000000e-01	0.000000e+00	--	False	False
BLRexample	gmin	low-energy-cut-off	lorentz-factor*	2.000000e+00	1.000000e+00	1.000000e+09	False	False
BLRexample	gmax	high-energy-cut-off	lorentz-factor*	1.000000e+06	1.000000e+00	1.000000e+15	False	False
BLRexample	N	emitters_density	1 / cm3	1.000000e+02	0.000000e+00	--	False	False
BLRexample	gamma_break	turn-over-energy	lorentz-factor*	1.000000e+04	1.000000e+00	1.000000e+09	False	False
BLRexample	p	LE_spectral_slope		2.500000e+00	-1.000000e+01	1.000000e+01	False	False
BLRexample	p_1	HE_spectral_slope		3.500000e+00	-1.000000e+01	1.000000e+01	False	False
BLRexample	tau_BLR	BLR		1.000000e-01	0.000000e+00	1.000000e+00	False	False
BLRexample	R_BLR_in	BLR	cm	1.000000e+18	0.000000e+00	--	False	True
BLRexample	R_BLR_out	BLR	cm	2.000000e+18	0.000000e+00	--	False	True
BLRexample	L_Disk	Disk	erg / s	1.000000e+45	0.000000e+00	--	False	False
BLRexample	T_Disk	Disk	K	1.000000e+05	0.000000e+00	--	False	False

--------------------------------------------------------------------------------

In [4]:

my_jet.set_par('L_Disk',val=5E46)
my_jet.set_par('gmax',val=1E4)
my_jet.set_par('gmin',val=2.)
my_jet.set_par('R_H',val=3E17)

my_jet.set_par('p',val=1.5)
my_jet.set_par('p_1',val=3.5)
my_jet.set_par('R',val=3E16)
my_jet.set_par('B',val=2.0)
my_jet.set_par('z_cosm',val=0.6)
my_jet.set_par('BulkFactor',val=20)
my_jet.set_par('theta',val=1)
my_jet.set_par('gamma_break',val=2E2)
my_jet.set_N_from_nuLnu(nu_src=3E12,nuLnu_src=1E47)
my_jet.set_IC_nu_size(100)

In [5]:

matplotlib.rc('font', **font)
p=PlotSED(figsize=(12,9),frame='src')
my_jet.eval()
p=my_jet.plot_model(plot_obj=p,frame='src')
p.setlim(x_min=1E10,x_max=1E26,y_max=1E50,y_min=1E42)

In [6]:

p=my_jet.electron_distribution.plot()
p.ax.axvline(np.log10(my_jet.parameters.gamma_break.val),ls='--')

Out[6]:

<matplotlib.lines.Line2D at 0x7fc9c89fb700>

In [7]:

matplotlib.rc('font', **font)
my_jet.add_EC_component('EC_DT')
my_jet.show_model()

--------------------------------------------------------------------------------
jet model description
--------------------------------------------------------------------------------
name: BLRexample  

electrons distribution:
 type: bkn  
 gamma energy grid size:  201
 gmin grid : 2.000000e+00
 gmax grid : 1.000000e+04
 normalization  True
 log-values  False

accretion disk:
 disk Type: BB
 L disk: 5.000000e+46 (erg/s)
 T disk: 7.500322e+04 (K)
 nu peak disk: 6.129121e+15 (Hz)

radiative fields:
 seed photons grid size:  100
 IC emission grid size:  100
 source emissivity lower bound :  1.000000e-120
 spectral components:
   name:Sum, state: on
   name:Sync, state: self-abs
   name:SSC, state: on
   name:EC_BLR, state: on
   name:Disk, state: on
   name:EC_Disk, state: on
   name:EC_DT, state: on
   name:DT, state: on
external fields transformation method: blob

SED info:
 nu grid size jetkernel: 1000
 nu size: 500
 nu mix (Hz): 1.000000e+06
 nu max (Hz): 1.000000e+30

flux plot lower bound   :  1.000000e-30

--------------------------------------------------------------------------------

Table length=20

model name	name	par type	units	val	phys. bound. min	phys. bound. max	log	frozen
BLRexample	R	region_size	cm	3.000000e+16	1.000000e+03	1.000000e+30	False	False
BLRexample	R_H	region_position	cm	3.000000e+17	0.000000e+00	--	False	True
BLRexample	B	magnetic_field	gauss	2.000000e+00	0.000000e+00	--	False	False
BLRexample	theta	jet-viewing-angle	deg	1.000000e+00	0.000000e+00	--	False	False
BLRexample	BulkFactor	jet-bulk-factor	lorentz-factor*	2.000000e+01	1.000000e+00	--	False	False
BLRexample	z_cosm	redshift		6.000000e-01	0.000000e+00	--	False	False
BLRexample	gmin	low-energy-cut-off	lorentz-factor*	2.000000e+00	1.000000e+00	1.000000e+09	False	False
BLRexample	gmax	high-energy-cut-off	lorentz-factor*	1.000000e+04	1.000000e+00	1.000000e+15	False	False
BLRexample	N	emitters_density	1 / cm3	2.584166e+02	0.000000e+00	--	False	False
BLRexample	gamma_break	turn-over-energy	lorentz-factor*	2.000000e+02	1.000000e+00	1.000000e+09	False	False
BLRexample	p	LE_spectral_slope		1.500000e+00	-1.000000e+01	1.000000e+01	False	False
BLRexample	p_1	HE_spectral_slope		3.500000e+00	-1.000000e+01	1.000000e+01	False	False
BLRexample	tau_BLR	BLR		1.000000e-01	0.000000e+00	1.000000e+00	False	False
BLRexample	R_BLR_in	BLR	cm	1.000000e+18	0.000000e+00	--	False	True
BLRexample	R_BLR_out	BLR	cm	2.000000e+18	0.000000e+00	--	False	True
BLRexample	T_Disk	Disk	K	1.000000e+05	0.000000e+00	--	False	False
BLRexample	T_DT	DT	K	1.000000e+02	0.000000e+00	--	False	False
BLRexample	R_DT	DT	cm	5.000000e+18	0.000000e+00	--	False	False
BLRexample	tau_DT	DT		1.000000e-01	0.000000e+00	1.000000e+00	False	False
BLRexample	L_Disk	Disk	erg / s	5.000000e+46	0.000000e+00	--	False	False

--------------------------------------------------------------------------------

In [8]:

matplotlib.rc('font', **font)
p=PlotSED(figsize=(12,9),frame='src')
my_jet.eval()
p=my_jet.plot_model(plot_obj=p,frame='src')
p.setlim(x_min=1E10,x_max=1E26,y_max=1E50,y_min=1E42)

setting the BLR and DT radius as a function of the disk luminosity¶

Using the depending parameters (see :ref:depending_parameters, for more details) we can set the BLR and DT radius, as a function of the disk luminosity

In [9]:

#kaspi+ 2007:https://iopscience.iop.org/article/10.1086/512094/pdf
my_jet.make_dependent_par(par='R_BLR_in', depends_on=['L_Disk'], par_expr='1E17*(L_Disk/1E45)**0.5')

my_jet.make_dependent_par(par='R_BLR_out', depends_on=['R_BLR_in'], par_expr='R_BLR_in*1.1')

#Cleary+ 2007:https://iopscience.iop.org/article/10.1086/511969/pdf
my_jet.make_dependent_par(par='R_DT', depends_on=['L_Disk'], par_expr='2.5E18*(L_Disk/1E45)**0.5')

==> par R_BLR_in is now depending on ['L_Disk'] according to expr:R_BLR_in =
1E17*(L_Disk/1E45)**0.5
==> par R_BLR_out is now depending on ['R_BLR_in'] according to expr:R_BLR_out =
R_BLR_in*1.1
==> par R_DT is now depending on ['L_Disk'] according to expr:R_DT =
2.5E18*(L_Disk/1E45)**0.5

In [10]:

from jetset.poly_fit import do_log_Parab_FIT

def get_gamma_3p(j):
    #j.set_gamma_grid_size(1000)
    j.electron_distribution.update()
    
    x=np.log10(j.electron_distribution.gamma)
    y=np.log10(j.electron_distribution.n_gamma)+3*x
    y_p=y.max()
    x_p=x[np.argmax(y)]
    p,err=do_log_Parab_FIT(x,y,x_p,y_p,-1,x_range=[x_p-0.5,x_p+0.5],dy=np.ones(x.size))
    return p

def get_log_par_peak(x_p,y_p,j,comp):
    c=j.get_spectral_component_by_name(comp)
    x=np.log10(c.SED.nu.value)
    y=np.log10(c.SED.nuFnu.value)
    
    p,err=do_log_Parab_FIT(x,y,x_p,y_p,-0.1,x_range=[x_p,x_p+1],dy=np.ones(x.size))
    p,err=do_log_Parab_FIT(x,y,p[0],p[1],p[2],x_range=[p[0],p[0]+1],dy=np.ones(x.size))
   
    return p,err

In [11]:

matplotlib.rc('font', **font)

my_jet.set_par('R_H',val=1E17)

size=5
p_dt=PlotSED(figsize=(12,9),frame='src')
p_blr=PlotSED(figsize=(12,9),frame='src')
my_jet.set_nu_grid_size(200)
my_jet.set_IC_nu_size(200)
my_jet.set_gamma_grid_size(100)
nu_IC_curv=np.zeros(size)
nu_IC_curv_err=np.zeros(size)
p1_val=np.zeros(size)
for ID,p_1 in enumerate(np.linspace(2.5,3.5,5)):
    p1_val[ID]=p_1
    my_jet.parameters.p_1.val=p_1
    my_jet.set_N_from_nuLnu(nu_src=3E12,nuLnu_src=1E47)

    my_jet.eval()
    
    my_jet.plot_model(plot_obj=p_blr,comp='Sync',label=None,auto_label=False,line_style='--',color=colors[ID],frame='src')
    my_jet.plot_model(plot_obj=p_dt,comp='Sync',label=None,auto_label=False,line_style='--',color=colors[ID],frame='src')
    #my_jet.plot_model(plot_obj=p_blr,comp='Sum',label='p_1=%2.2f'%p_1,color=colors[ID])
    #my_jet.plot_model(plot_obj=p_dt,comp='Sum',label='p_1=%2.2f'%p_1,color=colors[ID])
    my_jet.plot_model(plot_obj=p_blr,comp='EC_BLR',label='EC BLR p_1=%2.2f'%p_1,color=colors[ID],auto_label=False,frame='src')
    x_p,y_p=my_jet.get_component_peak('EC_BLR',log_log=True)
    (_,_,nu_IC_curv[ID]),err=get_log_par_peak(x_p,y_p,my_jet,'EC_BLR')
    nu_IC_curv_err[ID]=err[2]
    my_jet.plot_model(plot_obj=p_dt,comp='EC_DT',label='EC DT p_1=%2.2f'%p_1,color=colors[ID],auto_label=False,frame='src')

my_jet.plot_model(plot_obj=p_dt,comp='DT',color=colors[ID+1],frame='src')
my_jet.plot_model(plot_obj=p_blr,comp='Disk',color=colors[ID+1],frame='src')

p_dt.setlim(x_min=1E10,x_max=1E26,y_max=1E50,y_min=1E43)
p_blr.setlim(x_min=1E10,x_max=1E26,y_max=1E50,y_min=1E43)

In [12]:

matplotlib.rc('font', **font)

fig = plt.figure(figsize=(12,8))

ax=fig.add_subplot(111)
ax.errorbar(p1_val,nu_IC_curv,yerr=nu_IC_curv_err,fmt='-o',label='EC BLR fit above the peak')
ax.fill_between(p1_val, nu_IC_curv - nu_IC_curv_err, nu_IC_curv + nu_IC_curv_err,
                 color='gray', alpha=0.2)
ax.set_ylabel(r'curvature')
ax.set_xlabel(r'p_1')
#ax.legend(fontsize='large',loc='lower left')

Out[12]:

Text(0.5, 0, 'p_1')

In [ ]:

In [13]:

matplotlib.rc('font', **font)

my_jet.set_par('R_H',val=1E17)

size=5
p_dt=PlotSED(figsize=(12,9),frame='src')
p_blr=PlotSED(figsize=(12,9),frame='src')
my_jet.set_nu_grid_size(200)
my_jet.set_IC_nu_size(200)
my_jet.set_gamma_grid_size(100)
nu_IC_curv=np.zeros(size)
nu_IC_curv_err=np.zeros(size)
T_DT_val=np.zeros(size)
for ID,T_DT in enumerate(np.logspace(1,3.5,size)):
    p1_val[ID]=p_1
    my_jet.parameters.T_DT.val=T_DT
    my_jet.set_N_from_nuLnu(nu_src=3E12,nuLnu_src=1E47)

    my_jet.eval()
    
    my_jet.plot_model(plot_obj=p_blr,comp='Sync',label=None,auto_label=False,line_style='--',color=colors[ID],frame='src')
    my_jet.plot_model(plot_obj=p_dt,comp='Sync',label=None,auto_label=False,line_style='--',color=colors[ID],frame='src')
    
    my_jet.plot_model(plot_obj=p_dt,comp='EC_DT',label='EC DT   T=%2.2f'%T_DT,color=colors[ID],auto_label=False,frame='src')

    my_jet.plot_model(plot_obj=p_dt,comp='DT',color=colors[ID], label='DT   T=%2.2f'%T_DT,frame='src')

p_dt.setlim(x_min=1E10,x_max=1E26,y_max=1E50,y_min=1e43)

Changing the external field transformation¶

In [17]:

my_jet.set_external_field_transf('blob')

In [18]:

my_jet.set_external_field_transf('disk')

In [19]:

def iso_field_transf(L,R,BulckFactor):
    beta=1.0 - 1/(BulckFactor*BulckFactor)
    return L/(4*np.pi*R*R*3E10)*BulckFactor*BulckFactor*(1+((beta**2)/3))

def external_iso_behind_transf(L,R,BulckFactor):
    beta=1.0 - 1/(BulckFactor*BulckFactor)
    return L/((4*np.pi*R*R*3E10)*(BulckFactor*BulckFactor*(1+beta)**2))

EC seed photon fields, in the Disk rest frame

In [20]:

%matplotlib inline
fig = plt.figure(figsize=(8,6))
ax=fig.subplots(1)
N=50
G=1
R_range=np.logspace(13,25,N)
y=np.zeros((8,N))
my_jet.set_verbosity(0)

for ID,R in enumerate(R_range):
    my_jet.set_par('R_H',val=R)
    my_jet.set_external_fields()
    my_jet.energetic_report(verbose=False)
    
    y[1,ID]=my_jet.energetic_dict['U_BLR_DRF']
    y[0,ID]=my_jet.energetic_dict['U_Disk_DRF']
    y[2,ID]=my_jet.energetic_dict['U_DT_DRF']
    
y[4,:]=iso_field_transf(my_jet._blob.L_Disk_radiative*my_jet.parameters.tau_DT.val,my_jet.parameters.R_DT.val,G)
y[3,:]=iso_field_transf(my_jet._blob.L_Disk_radiative*my_jet.parameters.tau_BLR.val,my_jet.parameters.R_BLR_in.val,G)
y[5,:]=external_iso_behind_transf(my_jet._blob.L_Disk_radiative*my_jet.parameters.tau_BLR.val,R_range,G)
y[6,:]=external_iso_behind_transf(my_jet._blob.L_Disk_radiative*my_jet.parameters.tau_DT.val,R_range,G)
y[7,:]=external_iso_behind_transf(my_jet._blob.L_Disk_radiative,R_range,G)

ax.plot(np.log10(R_range),np.log10(y[0,:]),label='Disk')
ax.plot(np.log10(R_range),np.log10(y[1,:]),'-',label='BLR')
ax.plot(np.log10(R_range),np.log10(y[2,:]),label='DT')
ax.plot(np.log10(R_range),np.log10(y[3,:]),'--',label='BLR uniform')
ax.plot(np.log10(R_range),np.log10(y[4,:]),'--',label='DT uniform')
ax.plot(np.log10(R_range),np.log10(y[5,:]),'--',label='BLR 1/R2')
ax.plot(np.log10(R_range),np.log10(y[6,:]),'--',label='DT 1/R2')
ax.plot(np.log10(R_range),np.log10(y[7,:]),'--',label='Disk 1/R2')
ax.set_xlabel('log(R_H) cm')
ax.set_ylabel('log(Uph) erg cm-3 s-1')

ax.legend()

Out[20]:

<matplotlib.legend.Legend at 0x7fc9ab126a00>

EC seed photon fields, in the blob rest frame

In [21]:

%matplotlib inline

fig = plt.figure(figsize=(8,6))
ax=fig.subplots(1)

L_Disk=1E45
N=50
G=my_jet.parameters.BulkFactor.val
R_range=np.logspace(15,22,N)
y=np.zeros((8,N))
my_jet.set_par('L_Disk',val=L_Disk)
my_jet._blob.theta_n_int=100
my_jet._blob.l_n_int=100
my_jet._blob.theta_n_int=100
my_jet._blob.l_n_int=100
for ID,R in enumerate(R_range):
    my_jet.set_par('R_H',val=R)
    
    my_jet.set_external_fields()
    my_jet.energetic_report(verbose=False)
    
    y[1,ID]=my_jet.energetic_dict['U_BLR']
    y[0,ID]=my_jet.energetic_dict['U_Disk']
    y[2,ID]=my_jet.energetic_dict['U_DT']
    


y[4,:]=iso_field_transf(my_jet._blob.L_Disk_radiative*my_jet.parameters.tau_DT.val,my_jet.parameters.R_DT.val,G)
y[3,:]=iso_field_transf(my_jet._blob.L_Disk_radiative*my_jet.parameters.tau_BLR.val,my_jet.parameters.R_BLR_in.val,G)
y[5,:]=external_iso_behind_transf(my_jet._blob.L_Disk_radiative*my_jet.parameters.tau_BLR.val,R_range,G)
y[6,:]=external_iso_behind_transf(my_jet._blob.L_Disk_radiative*my_jet.parameters.tau_DT.val,R_range,G)
y[7,:]=external_iso_behind_transf(my_jet._blob.L_Disk_radiative,R_range,G)

ax.plot(np.log10(R_range),np.log10(y[0,:]),label='Disk')
ax.plot(np.log10(R_range),np.log10(y[1,:]),'-',label='BLR')
ax.plot(np.log10(R_range),np.log10(y[2,:]),'-',label='DT')
ax.plot(np.log10(R_range),np.log10(y[3,:]),'--',label='BLR uniform')
ax.plot(np.log10(R_range),np.log10(y[4,:]),'--',label='DT uniform')
ax.plot(np.log10(R_range),np.log10(y[5,:]),'--',label='BLR 1/R2')
ax.plot(np.log10(R_range),np.log10(y[6,:]),'--',label='DT 1/R2')
ax.plot(np.log10(R_range),np.log10(y[7,:]),'--',label='Disk 1/R2')
ax.axvline(np.log10( my_jet.parameters.R_DT.val ))
ax.axvline(np.log10( my_jet.parameters.R_BLR_out.val))

ax.set_xlabel('log(R_H) cm')
ax.set_ylabel('log(Uph`) erg cm-3 s-1')

ax.legend()

Out[21]:

<matplotlib.legend.Legend at 0x7fc9abefc820>

Exercise¶

derive the trend for the Compton dominance (CD) as a function of gmin, B, and BulkFactor

hint: use the get_component_peak to extract the peak of the SED for each component

IC against the CMB¶

In [22]:

my_jet=Jet(name='test_equipartition',electron_distribution='lppl',beaming_expr='bulk_theta')
my_jet.set_par('R',val=1E21)
my_jet.set_par('z_cosm',val= 0.651)
my_jet.set_par('B',val=2E-5)
my_jet.set_par('gmin',val=50)
my_jet.set_par('gamma0_log_parab',val=35.0E3)
my_jet.set_par('gmax',val=30E5)
my_jet.set_par('theta',val=12.0)
my_jet.set_par('BulkFactor',val=3.5)
my_jet.set_par('s',val=2.58)
my_jet.set_par('r',val=0.42)
my_jet.set_N_from_nuFnu(5E-15,1E12)
my_jet.add_EC_component('EC_CMB')

In [23]:

from jetset.plot_sedfit import PlotSED
p=PlotSED()

my_jet.set_external_field_transf('blob')
c= ['k', 'g', 'r', 'c']
for ID,theta in enumerate(np.linspace(2,20,4)):
    my_jet.parameters.theta.val=theta
    my_jet.eval()
    my_jet.plot_model(plot_obj=p,comp='Sum',label='blob, theta=%2.2f'%theta,line_style='--',color=c[ID])

my_jet.set_external_field_transf('disk')
for ID,theta in enumerate(np.linspace(2,20,4)):
    my_jet.parameters.theta.val=theta
    my_jet.eval()
    my_jet.plot_model(plot_obj=p,comp='Sum',label='disk, theta=%2.2f'%theta,line_style='',color=c[ID])

p.setlim(y_min=3E-17,y_max=3E-12,x_max=1E28)

Equipartition¶

In [24]:

my_jet.parameters.theta.val=12
B_min,b_grid,U_B,U_e=my_jet.set_B_eq(nuFnu_obs=5E-15,nu_obs=1E12,B_min=1E-9,N_pts=50,plot=True)
my_jet.show_pars()

my_jet.eval()

B grid min  1e-09
B grid max  1.0
grid points 50

setting B to  0.0001389495494373139
setting N to  9.160733610838076e-06

Table length=14

model name	name	par type	units	val	phys. bound. min	phys. bound. max	log	frozen
test_equipartition	R	region_size	cm	1.000000e+21	1.000000e+03	1.000000e+30	False	False
test_equipartition	R_H	region_position	cm	1.000000e+17	0.000000e+00	--	False	True
test_equipartition	B	magnetic_field	gauss	1.389495e-04	0.000000e+00	--	False	False
test_equipartition	theta	jet-viewing-angle	deg	1.200000e+01	0.000000e+00	--	False	False
test_equipartition	BulkFactor	jet-bulk-factor	lorentz-factor*	3.500000e+00	1.000000e+00	--	False	False
test_equipartition	z_cosm	redshift		6.510000e-01	0.000000e+00	--	False	False
test_equipartition	gmin	low-energy-cut-off	lorentz-factor*	5.000000e+01	1.000000e+00	1.000000e+09	False	False
test_equipartition	gmax	high-energy-cut-off	lorentz-factor*	3.000000e+06	1.000000e+00	1.000000e+15	False	False
test_equipartition	N	emitters_density	1 / cm3	9.160734e-06	0.000000e+00	--	False	False
test_equipartition	gamma0_log_parab	turn-over-energy	lorentz-factor*	3.500000e+04	1.000000e+00	1.000000e+09	False	False
test_equipartition	s	LE_spectral_slope		2.580000e+00	-1.000000e+01	1.000000e+01	False	False
test_equipartition	r	spectral_curvature		4.200000e-01	-1.500000e+01	1.500000e+01	False	False
test_equipartition	L_Disk	Disk	erg / s	1.000000e+45	0.000000e+00	--	False	False
test_equipartition	T_Disk	Disk	K	1.000000e+05	0.000000e+00	--	False	False

In [25]:

p=my_jet.plot_model()
p.setlim(y_min=3E16,y_max=3E13,x_max=1E28)

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