Loading sas_temper/sas_temper_engine.py +2 −2 Original line number Diff line number Diff line Loading @@ -474,7 +474,7 @@ def est_uncerts(d, f, modconf, best_model): lprof_usm = sas_calc.calc_profile_usm(d, stepped[w]) lprof = sas_calc.calc_profile(d,stepped[w],lprof_usm) JT.append((lprof.y-best_model.y)/steps[w]) JT.append((lprof.y-best_model.y)*(lprof.y-best_model.y)/(d.dy*d.dy*steps[w]) #this is the matrix that we want J_T = np.vstack(JT) Loading @@ -492,7 +492,7 @@ def est_uncerts(d, f, modconf, best_model): print(Cov) # the diagonal should be only as long as the number of parameters errs = np.sqrt(np.diag(Cov))/f.chisq errs = np.sqrt(np.diag(Cov)) # these final values need to be inserted into the structure to be returned # note that fixed parameters have their uncertainty set to 0.0 here to avoid Loading Loading
sas_temper/sas_temper_engine.py +2 −2 Original line number Diff line number Diff line Loading @@ -474,7 +474,7 @@ def est_uncerts(d, f, modconf, best_model): lprof_usm = sas_calc.calc_profile_usm(d, stepped[w]) lprof = sas_calc.calc_profile(d,stepped[w],lprof_usm) JT.append((lprof.y-best_model.y)/steps[w]) JT.append((lprof.y-best_model.y)*(lprof.y-best_model.y)/(d.dy*d.dy*steps[w]) #this is the matrix that we want J_T = np.vstack(JT) Loading @@ -492,7 +492,7 @@ def est_uncerts(d, f, modconf, best_model): print(Cov) # the diagonal should be only as long as the number of parameters errs = np.sqrt(np.diag(Cov))/f.chisq errs = np.sqrt(np.diag(Cov)) # these final values need to be inserted into the structure to be returned # note that fixed parameters have their uncertainty set to 0.0 here to avoid Loading