def quadratic_results():
global EM_par_SM_1, EM_par_SM_2 , EM_par_NM_1 , EM_par_NM_2
EM_par_SM_1, EM_par_SM_2 , EM_par_NM_1 , EM_par_NM_2 = EM.EM_results()
global d,e
d,e = read_data()
global td,te
td,te = interpolation()
global e_sym2
e_sym2 = e_sym2_delta()
global esym2_eta
esym2_eta,esym4_eta = e_sym2_eta()
global e_sym2_av,e_sym2_pot_av,e_sym2_pot_eff_av,s
global e_sym2_eta_av,e_sym2_eta_pot_av,e_sym2_eta_pot_eff_av,s_eta
e_sym2_av,e_sym2_pot_av,e_sym2_pot_eff_av,s,\
e_sym2_eta_av,e_sym2_eta_pot_av,e_sym2_eta_pot_eff_av,s_eta = data_preperation()
global f_esym2_c,e_sym2_par,e_sym2_eta_par
f_esym2_c,e_sym2_par,e_sym2_eta_par = Analyse_e_sym2()
return d,e,td,te,e_sym2,esym2_eta,esym4_eta,e_sym2_av,e_sym2_eta_av,f_esym2_c,e_sym2_par,e_sym2_eta_par
def main():
# Import results from Effective Mass module
# EM = Effective Mass; par = Best fit parameter values
# SM = Symmetric matter; NM = Neutron matter
global EM_par_SM_1, EM_par_NM_1
EM_par_SM_1, _, EM_par_NM_1, _ = EM.EM_results()
# Import results from symmetry energy module
# Refer to this module for explanation of variables
global te_SM_av, te_NM_av, f_SM, SM3_par, f_NM, NM3_par
te_SM_av, te_NM_av, f_SM, SM3_par, f_NM, NM3_par = symmetry_energy.e_sym_results(
)
# Import results from quadratic_symmetry energy module
# Refer to this module for explanation of variables
global d, e, td, te, e_sym2, esym2_eta, esym4_eta, e_sym2_av, e_sym2_eta_av, f_esym2_c, e_sym2_par, e_sym2_eta_par
d, e, td, te, e_sym2, esym2_eta, esym4_eta, e_sym2_av, e_sym2_eta_av, f_esym2_c, e_sym2_par, e_sym2_eta_par = quadratic_symmetry_energy.quadratic_results(
)
# Calculate and plot non-quadratic symmetry energies
plot_e_symnq()
# Calculate and plot Final residuals of the fit wrt the data
plot_residues()
# Print best fit value of parameters
print('----------Delta----------')
print("E_sym,nq = ",
NM3_par['E_sat+E_sym'] - SM3_par['E_sat'] - e_sym2_par['E_sym2'])
print("L_sym,nq = ", NM3_par['L_sym'] - e_sym2_par['L_sym2'])
print("K_sym,nq = ",
NM3_par['K_sat+K_sym'] - SM3_par['K_sat'] - e_sym2_par['K_sym2'])
print("Q_sym,nq = ",
NM3_par['Q_sat+Q_sym'] - SM3_par['Q_sat'] - e_sym2_par['Q_sym2'])
print("Z_sym,nq = ",
NM3_par['Z_sat+Z_sym'] - SM3_par['Z_sat'] - e_sym2_par['Z_sym2'])
print('----------Eta----------')
print("E_sym,nq = ",
NM3_par['E_sat+E_sym'] - SM3_par['E_sat'] - e_sym2_eta_par['E_sym2'])
print("L_sym,nq = ", NM3_par['L_sym'] - e_sym2_eta_par['L_sym2'])
print("K_sym,nq = ",
NM3_par['K_sat+K_sym'] - SM3_par['K_sat'] - e_sym2_eta_par['K_sym2'])
print("Q_sym,nq = ",
NM3_par['Q_sat+Q_sym'] - SM3_par['Q_sat'] - e_sym2_eta_par['Q_sym2'])
print("Z_sym,nq = ",
NM3_par['Z_sat+Z_sym'] - SM3_par['Z_sat'] - e_sym2_eta_par['Z_sym2'])
print('----------Fit to E_sym4 without meta-model----------')
e_sym4_eta_par = Fit_e_sym4_eta()
print(e_sym4_eta_par)
print('----------Fit to E_symnq without meta-model----------')
e_symnq_eta_par = Fit_e_symnq_eta()
print(e_symnq_eta_par)
def main():
# Import results from Effective Mass module
# EM = Effective Mass; par = Best fit parameter values
# SM = Symmetric matter; NM = Neutron matter
# 1 = liner fit; 2 = quadratic fit
global EM_par_SM_1, EM_par_SM_2 , EM_par_NM_1 , EM_par_NM_2
EM_par_SM_1, EM_par_SM_2 , EM_par_NM_1 , EM_par_NM_2 = EM.EM_results()
# Read input data of energy/particle
# d =density, e = energy
global d,e
d,e = read_data()
# Perform interpolation to go to uniform grid in density
# td = target density with unifrom grid, te = target energy
global td,te
td,te = interpolation()
# Extract e_sym2 from data using delta expansion
global e_sym2
e_sym2 = e_sym2_delta()
# Extract e_sym2 from data using eta expansion
global esym2_eta
esym2_eta,_ = e_sym2_eta()
# Prepare data for fitting and plotting
# av refers to an average over the 6 Hamiltonians
# s refers to svd cut imposed to regulate 0 eigenvalues of correlation matrix obtained during the averaging
# eta refers to the expansion around NM. Absence of eta implies expansion around SM.
global e_sym2_av,e_sym2_pot_av,e_sym2_pot_eff_av,s
global e_sym2_eta_av,e_sym2_eta_pot_av,e_sym2_eta_pot_eff_av,s_eta
e_sym2_av,e_sym2_pot_av,e_sym2_pot_eff_av,s,\
e_sym2_eta_av,e_sym2_eta_pot_av,e_sym2_eta_pot_eff_av,s_eta = data_preperation()
# Fit to e_sym2 obtained above
# f_esym2_c = Fit function; e_sym2_par = Best Fit parameters for delta expansion
# e_sym2_eta_par = Best Fit parameters for eta expansion
global f_esym2_c,e_sym2_par,e_sym2_eta_par
f_esym2_c,e_sym2_par,e_sym2_eta_par = Analyse_e_sym2()
# Plot e_sym2 for the two expansions and also the difference
plot_e_sym2()
# Print best fit parameter values.
print ('\n','-----Delta----')
print (e_sym2_par)
print ('\n','-----Eta-----')
print (e_sym2_eta_par)
def e_sym_results():
global EM_par_SM_1, EM_par_SM_2, EM_par_NM_1, EM_par_NM_2
EM_par_SM_1, EM_par_SM_2, EM_par_NM_1, EM_par_NM_2 = EM.EM_results()
global e_SM, e_NM, d_SM, d_NM, te_SM, te_NM, td
e_SM, e_NM, d_SM, d_NM, te_SM, te_NM, td = SM_NM.SM_NM_results()
global te_SM_av, te_NM_av, ts_SM, ts_NM, te_SM_pot_av
global te_NM_pot_av, te_SM_pot_eff_av, te_NM_pot_eff_av
global te_SM_pot_eff_1_av, te_NM_pot_eff_1_av
te_SM_av,te_NM_av,ts_SM,ts_NM,te_SM_pot_av,\
te_NM_pot_av,te_SM_pot_eff_av,te_NM_pot_eff_av,\
te_SM_pot_eff_1_av,te_NM_pot_eff_1_av = data_preparation()
global f_SM, SM3_par
f_SM, SM3_par = Analyse_SM()
global f_NM, NM3_par
f_NM, NM3_par = Analyse_NM()
return te_SM_av, te_NM_av, f_SM, SM3_par, f_NM, NM3_par
def main():
# Import results from Effective Mass module
# EM = Effective Mass; par = Best fit parameter values
# SM = Symmetric matter; NM = Neutron matter
# 1 = liner fit; 2 = quadratic fit
global EM_par_SM_1, EM_par_SM_2, EM_par_NM_1, EM_par_NM_2
EM_par_SM_1, EM_par_SM_2, EM_par_NM_1, EM_par_NM_2 = EM.EM_results()
# Import results from SM_NM module
# e_SM and e_NM are energy/particle in symmetric and neutron matter
# d_SM and d_NM are the correspondind densities
# td = target density with unifrom grid
# te_SM, te_NM = target energies corresponding to td.
global e_SM, e_NM, d_SM, d_NM, te_SM, te_NM, td
e_SM, e_NM, d_SM, d_NM, te_SM, te_NM, td = SM_NM.SM_NM_results()
# Prepare data for fitting and plotting
# av refers to an average over the 6 Hamiltonians
# s refers to svd cut imposed to regulate 0 eigenvalues of correlation matrix obtained during the averaging
global te_SM_av, te_NM_av, ts_SM, ts_NM, te_SM_pot_av
global te_NM_pot_av, te_SM_pot_eff_av, te_NM_pot_eff_av
global te_SM_pot_eff_1_av, te_NM_pot_eff_1_av
te_SM_av,te_NM_av,ts_SM,ts_NM,te_SM_pot_av,\
te_NM_pot_av,te_SM_pot_eff_av,te_NM_pot_eff_av,\
te_SM_pot_eff_1_av,te_NM_pot_eff_1_av = data_preparation()
# Fit to energy per particle in SM.
# This performs Scaling 3*
global f_SM, SM3_par
f_SM, SM3_par = Analyse_SM()
# Fit to energy per particle in NM.
# This performs Scaling 3*
global f_NM, NM3_par
f_NM, NM3_par = Analyse_NM()
# Calculate and plot e_sym
calculate_and_plot_esym()
