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()