diff --git a/3SpotEcalc/run_opt.py b/3SpotEcalc/run_opt.py
index f70afe5..6b9b352 100644
--- a/3SpotEcalc/run_opt.py
+++ b/3SpotEcalc/run_opt.py
@@ -6,7 +6,7 @@
 from glob import glob
 import numpy as np
 
-from popt.loop.ensemble import Ensemble
+from popt.loop.ensemble import GaussianEnsemble
 from popt.loop.optimize import Optimize
 from subsurface.multphaseflow.opm import flow
 from input_output import read_config
@@ -29,7 +29,7 @@ def main():
 
     # Get variables
     sim = flow(kf)
-    ensemble = Ensemble(ke, sim, ecalc_npv)
+    ensemble = GaussianEnsemble(ke, sim, ecalc_npv)
     x0 = ensemble.get_state()
     cov = ensemble.get_cov()
     bounds = ensemble.get_bounds()
diff --git a/LinearModel/run_script.py b/LinearModel/run_script.py
index 88f6b56..28bf88d 100644
--- a/LinearModel/run_script.py
+++ b/LinearModel/run_script.py
@@ -50,7 +50,10 @@
         Cd = np.diag(assimilation.ensemble.cov_data)
     else:
         Cd = assimilation.ensemble.cov_data
-    dobs = assimilation.ensemble.obs_data_vector
+    try:
+        dobs = assimilation.ensemble.obs_data_vector
+    except:
+        dobs = assimilation.ensemble.vecObs  
 
 CxGT = np.dot(Cm, G.T)
 GCxGT = np.dot(G,CxGT)
@@ -64,9 +67,9 @@
 Sx_post = np.sqrt(np.diag(Cx_post))
 
 # Plot the results
-x_post_ens = assimilation.ensemble.state[param].mean(axis=1)
+x_post_ens = assimilation.ensemble.enX.mean(axis=1)
 Sx_prior = np.sqrt(assimilation.ensemble.prior_info[param]['variance'])*np.ones(x_prior.size)
-Sx_post_ens = assimilation.ensemble.state[param].std(ddof=1,axis=1)
+Sx_post_ens = assimilation.ensemble.enX.std(ddof=1,axis=1)
 plt.figure();plt.plot(x_prior,'g');plt.plot(x_post_ens);plt.plot(x_post,'r');
 plt.legend(['Prior','Posterior', 'Analytic']); plt.xlabel('Index'); plt.ylabel('State mean');
 plt.figure();plt.plot(Sx_prior,'g');plt.plot(Sx_post_ens);plt.plot(Sx_post,'r');
diff --git a/Quadratic/init_optim.toml b/Quadratic/init_optim.toml
index 0ece369..684f8cd 100644
--- a/Quadratic/init_optim.toml
+++ b/Quadratic/init_optim.toml
@@ -26,7 +26,7 @@ tol = 1e-02
 alpha = 0.25
 alpha_maxiter = 4
 resample = 0
-optimizer = 'GA'
+optimizer = 'GD'
 restartsave = true
 restart = false
 hessian = false
diff --git a/Quadratic/run_opt.py b/Quadratic/run_opt.py
index a0c700f..fe0a0cf 100644
--- a/Quadratic/run_opt.py
+++ b/Quadratic/run_opt.py
@@ -37,7 +37,7 @@ def main():
     bounds = ensemble.get_bounds()
     EnOpt(ensemble.function, x0, args=(cov,), jac=ensemble.gradient, hess=ensemble.hessian, bounds=bounds, **ko)
 
-    print('Final state: ' + str(ensemble.get_final_state()))
+    print('Final state: ' + str(ensemble.get_state()))
 
 
 if __name__ == '__main__':