def _fill_params(tab):
print("here")
tab['param_values'] = np.nan * np.ones((len(tab), 10))
# PowerLaw
# Prefactor, Index, Scale
m = tab['SpectrumType'] == 'PowerLaw'
idxs = {k: i for i, k in enumerate(get_function_par_names('PowerLaw'))}
tab['param_values'][m, idxs['Prefactor']] = tab['PL_Flux_Density'][m]
tab['param_values'][m, idxs['Index']] = -1.0 * tab['PL_Index'][m]
tab['param_values'][m, idxs['Scale']] = tab['Pivot_Energy'][m]
# PLSuperExpCutoff2 or PLSuperExpCutoff
# Prefactor, Index1, Scale, Expfactor, Index2
m = tab['SpectrumType'] == 'PLSuperExpCutoff2'
idxs = {
k: i
for i, k in enumerate(get_function_par_names('PLSuperExpCutoff2'))
}
tab['param_values'][m, idxs['Prefactor']] = (
tab['PLEC_Flux_Density'][m] *
np.exp(tab['PLEC_Expfactor'][m] *
tab['Pivot_Energy'][m]**tab['PLEC_Exp_Index'][m]))
tab['param_values'][m, idxs['Index1']] = -1.0 * tab['PLEC_Index'][m]
tab['param_values'][m, idxs['Scale']] = tab['Pivot_Energy'][m]
tab['param_values'][m, idxs['Expfactor']] = tab['PLEC_Expfactor'][m]
tab['param_values'][m, idxs['Index2']] = tab['PLEC_Exp_Index'][m]
# LogParabola
# norm, alpha, beta, Eb
m = tab['SpectrumType'] == 'LogParabola'
idxs = {
k: i
for i, k in enumerate(get_function_par_names('LogParabola'))
}
tab['param_values'][m, idxs['norm']] = tab['LP_Flux_Density'][m]
tab['param_values'][m, idxs['alpha']] = tab['LP_Index'][m]
tab['param_values'][m, idxs['beta']] = tab['LP_beta'][m]
tab['param_values'][m, idxs['Eb']] = tab['Pivot_Energy'][m]
def _fill_params(tab):
tab['param_values'] = np.nan * np.ones((len(tab), 10))
# PowerLaw
# Prefactor, Index, Scale
m = tab['SpectrumType'] == 'PowerLaw'
idxs = {k: i for i, k in
enumerate(get_function_par_names('PowerLaw'))}
tab['param_values'][m, idxs['Prefactor']] = tab['Flux_Density'][m]
tab['param_values'][m, idxs['Index']] = -1.0 * tab['PL_Index'][m]
tab['param_values'][m, idxs['Scale']] = tab['Pivot_Energy'][m]
# PLSuperExpCutoff2
# Prefactor, Index1, Scale, Expfactor, Index2
m = tab['SpectrumType'] == 'PLSuperExpCutoff2'
idxs = {k: i for i, k in
enumerate(get_function_par_names('PLSuperExpCutoff2'))}
tab['param_values'][m, idxs['Prefactor']] = (tab['Flux_Density'][m] *
np.exp(tab['PLEC_Expfactor'][m] *
tab['Pivot_Energy'][m] **
tab['PLEC_Exp_Index'][m]))
tab['param_values'][m, idxs['Index1']] = -1.0 * tab['PLEC_Index'][m]
tab['param_values'][m, idxs['Scale']] = tab['Pivot_Energy'][m]
tab['param_values'][m, idxs['Expfactor']] = tab['PLEC_Expfactor'][m]
tab['param_values'][m, idxs['Index2']] = tab['PLEC_Exp_Index'][m]
# LogParabola
# norm, alpha, beta, Eb
m = tab['SpectrumType'] == 'LogParabola'
idxs = {k: i for i, k in
enumerate(get_function_par_names('LogParabola'))}
tab['param_values'][m, idxs['norm']] = tab['Flux_Density'][m]
tab['param_values'][m, idxs['alpha']] = tab['LP_Index'][m]
tab['param_values'][m, idxs['beta']] = tab['LP_beta'][m]
tab['param_values'][m, idxs['Eb']] = tab['Pivot_Energy'][m]
