Loading sas_temper/sas_temper_engine.py +5 −4 Original line number Diff line number Diff line Loading @@ -403,13 +403,13 @@ def est_uncerts(d, f, modconf, best_model): # preparation work for calculating the Jacobian matrix from the derivative step = 0.01 dof = 0 for i,p in enumerate(modconf.params): if p.kind not in ["fixed"]: eps.params[i].val = step*(p.max - p.min) if eps.params[i].val == 0.0: eps.params[i].val = step dof = dof + 1 else: eps.params[i].val = step tmp = copy.deepcopy(f) tmp.params[i].val = f.params[i].val + eps.params[i].val Loading @@ -425,7 +425,8 @@ def est_uncerts(d, f, modconf, best_model): eps.sq.params[j].val = step*(sqp.max-sqp.min) if eps.sq.params[j].val == 0.0: eps.sqp.params[j].val = step dof = dof + 1 else: eps.sqp.params[j].val = step tmp = copy.deepcopy(f) tmp.sq.params[j].val = f.sq.params[j].val + eps.sq.params[j].val Loading Loading @@ -477,7 +478,7 @@ def est_uncerts(d, f, modconf, best_model): for z in range(0,len(best_model.y)): if z==0: print(str(lprof.y[z]) + " " + str(best_model.y[z]) + " " + str(d.dy[z]*steps[w])) print(str(best_model.y[z]) + " " + str(lprof.y[z]) + " " + str(d.dy[z]*steps[w])) tprof.y[z] = 0.5*(lprof.y[z]-best_model.y[z])/(d.dy[z]*steps[w]) JT.append(tprof.y) Loading Loading
sas_temper/sas_temper_engine.py +5 −4 Original line number Diff line number Diff line Loading @@ -403,13 +403,13 @@ def est_uncerts(d, f, modconf, best_model): # preparation work for calculating the Jacobian matrix from the derivative step = 0.01 dof = 0 for i,p in enumerate(modconf.params): if p.kind not in ["fixed"]: eps.params[i].val = step*(p.max - p.min) if eps.params[i].val == 0.0: eps.params[i].val = step dof = dof + 1 else: eps.params[i].val = step tmp = copy.deepcopy(f) tmp.params[i].val = f.params[i].val + eps.params[i].val Loading @@ -425,7 +425,8 @@ def est_uncerts(d, f, modconf, best_model): eps.sq.params[j].val = step*(sqp.max-sqp.min) if eps.sq.params[j].val == 0.0: eps.sqp.params[j].val = step dof = dof + 1 else: eps.sqp.params[j].val = step tmp = copy.deepcopy(f) tmp.sq.params[j].val = f.sq.params[j].val + eps.sq.params[j].val Loading Loading @@ -477,7 +478,7 @@ def est_uncerts(d, f, modconf, best_model): for z in range(0,len(best_model.y)): if z==0: print(str(lprof.y[z]) + " " + str(best_model.y[z]) + " " + str(d.dy[z]*steps[w])) print(str(best_model.y[z]) + " " + str(lprof.y[z]) + " " + str(d.dy[z]*steps[w])) tprof.y[z] = 0.5*(lprof.y[z]-best_model.y[z])/(d.dy[z]*steps[w]) JT.append(tprof.y) Loading