Loading sas_temper/sas_temper_engine.py +4 −4 Original line number Diff line number Diff line Loading @@ -468,8 +468,8 @@ def est_uncerts(d, f, modconf, best_model): # and this is where things get ugly JT = [] for w in range(0,len(stepped)): for a,m in enumerate(stepped[w].params): print("stepped["+str(w)+"] parameters " + str(m.val)) #for a,m in enumerate(stepped[w].params): # print("stepped["+str(w)+"] parameters " + str(m.val)) #calculate the profiles if d.dx is None: Loading @@ -494,8 +494,8 @@ def est_uncerts(d, f, modconf, best_model): # This is an approximation of the Hessian Hess = np.matmul(J_T,J) # print("Hessian") # print(Hess) print("Hessian") print(Hess) # Invert it to get the covariance matrix Cov = np.linalg.inv(Hess) Loading Loading
sas_temper/sas_temper_engine.py +4 −4 Original line number Diff line number Diff line Loading @@ -468,8 +468,8 @@ def est_uncerts(d, f, modconf, best_model): # and this is where things get ugly JT = [] for w in range(0,len(stepped)): for a,m in enumerate(stepped[w].params): print("stepped["+str(w)+"] parameters " + str(m.val)) #for a,m in enumerate(stepped[w].params): # print("stepped["+str(w)+"] parameters " + str(m.val)) #calculate the profiles if d.dx is None: Loading @@ -494,8 +494,8 @@ def est_uncerts(d, f, modconf, best_model): # This is an approximation of the Hessian Hess = np.matmul(J_T,J) # print("Hessian") # print(Hess) print("Hessian") print(Hess) # Invert it to get the covariance matrix Cov = np.linalg.inv(Hess) Loading