Commit 33bcbf5d authored by Heller, William T.'s avatar Heller, William T.
Browse files

Replace sas_temper_engine.py

parent 2ea9d307

sas_temper/sas_temper_engine.py

+7 −15
Original line number Diff line number Diff line
@@ -402,13 +402,13 @@ 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

        #else:

        #    eps.params[i].val = 0.00

            dof = dof + 1

        

        tmp = copy.deepcopy(f)

        tmp.params[i].val = f.params[i].val + eps.params[i].val
@@ -424,13 +424,12 @@ 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

            #else:

            #    eps.sqp.params[j].val = 0.00

                dof = dof + 1

                

            tmp = copy.deepcopy(f)

            tmp.sq.params[j].val = f.sq.params[j].val + eps.sq.params[j].val

            #if tmp.sq.params[j].val >= modconf.sq.params[j].max:

            #    tmp.sq.params[j].val = f.sq.params[j].val - eps.sq.params[j].val

            if tmp.sq.params[j].val >= modconf.sq.params[j].max:

                tmp.sq.params[j].val = f.sq.params[j].val - eps.sq.params[j].val

                

            stepped.append(tmp)

            steps.append(eps.sq.params[j].val)
@@ -441,8 +440,6 @@ def est_uncerts(d, f, modconf, best_model): 
            eps.params[i].polydispersity.val = step*(p.polydispersity.max-p.polydispersity.min)

            if eps.params[i].polydispersity.val == 0.00:

                eps.params[i].polydispersity.val = step

            #else:

            #    eps.params[i].polydispersity.val = 0.00

            

            tmp = copy.deepcopy(f)

            tmp.params[i].polydispersity.val = f.params[i].polydispersity.val + eps.params[i].polydispersity.val
@@ -458,8 +455,6 @@ def est_uncerts(d, f, modconf, best_model): 
                eps.sq.params[j].polydispersity.val = step*(modconf.sq.params[j].polydispersity.max-modconf.sq.params[j].polydispersity.min)

                if eps.sq.params[j].polydispersity.val == 0.00:

                    eps.sq.params[j].polydispersity.val = step

                #else:

                #    eps.sq.params[j].polydispersity.val = 0.00

                    

                tmp = copy.deepcopy(f)

                tmp.sq.params[j].polydispersity.val = f.sq.params[j].polydispersity.val + eps.sq.params[j].polydispersity.val
@@ -479,10 +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)

        

        if abs(steps[w]) > 0.0:

        JT.append((lprof.y-best_model.y)/steps[w])

        else:

            JT.append(0.00*lprof.y)

    

    #this is the matrix that we want

    J_T = np.vstack(JT)
@@ -500,7 +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))

    errs = np.sqrt(np.diag(Cov))/f.chisq



    # 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