diff --git a/demo/Constrained multi-output min-max.ipynb b/demo/Constrained multi-output min-max.ipynb
index 7347362..dade947 100644
--- a/demo/Constrained multi-output min-max.ipynb	
+++ b/demo/Constrained multi-output min-max.ipynb	
@@ -143,7 +143,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from obsidian.constraints import InConstraint_Generic"
+    "from obsidian.constraints import Linear_Constraint"
    ]
   },
   {
@@ -163,7 +163,7 @@
    "source": [
     "X_suggest, eval_suggest = campaign.optimizer.suggest(acquisition = [{'NEHVI':{'ref_point':[-350, -20]}}, 'SF'],\n",
     "                                                     # X1 + X2 <= 6, written as -X1 - X2 >= -6\n",
-    "                                                     ineq_constraints = [InConstraint_Generic(X_space, indices=[0,1], coeff=[-1,-1], rhs=-6)])"
+    "                                                     ineq_constraints = [Linear_Constraint(X_space, ind=[0,1], weights=[-1,-1], rhs=-6, equality=True)])"
    ]
   },
   {
@@ -210,7 +210,7 @@
     "for iter in range(5):\n",
     "    campaign.fit()\n",
     "    X_suggest, eval_suggest = campaign.optimizer.suggest(acquisition = [{'NEHVI':{'ref_point':[-350, -20]}}, 'SF'],\n",
-    "                                                         ineq_constraints = [InConstraint_Generic(X_space, indices=[0,1], coeff=[-1,-1], rhs=-6)])\n",
+    "                                                         ineq_constraints = [Linear_Constraint(X_space, ind=[0,1], weights=[-1,-1], rhs=-6, equality=True)])\n",
     "    y_iter = pd.DataFrame(simulator.simulate(X_suggest))\n",
     "    Z_iter = pd.concat([X_suggest, y_iter, eval_suggest], axis=1)\n",
     "    campaign.add_data(Z_iter)"
diff --git a/obsidian/plotting/plotly.py b/obsidian/plotting/plotly.py
index d7fc8cf..e753252 100644
--- a/obsidian/plotting/plotly.py
+++ b/obsidian/plotting/plotly.py
@@ -280,13 +280,12 @@ def factor_plot(optimizer: Optimizer,
     # Create a dataframe of test samples for plotting
     n_samples = 100
     if X_ref is None:
-        df_mean = optimizer.X_best_f
-        X_test = pd.concat([df_mean]*n_samples, axis=0).reset_index(drop=True)
+        X_ref = optimizer.X_best_f
     else:
         if not isinstance(X_ref, pd.DataFrame):
             raise TypeError('X_ref must be a DataFrame')
-        X_test = pd.concat([X_ref]*n_samples, axis=0).reset_index(drop=True)
-
+    X_test = pd.concat([X_ref]*n_samples, axis=0).reset_index(drop=True)
+    
     # Vary the indicated column
     X_name = X_test.columns[feature_id]
     param_i = optimizer.X_space.params[feature_id]
@@ -316,7 +315,7 @@ def factor_plot(optimizer: Optimizer,
                              line={'color': obsidian_colors.teal},
                              name='Mean'),
                   )
-    if (X_ref is not None) and plotRef:
+    if plotRef:
         Y_pred_ref = optimizer.predict(X_ref, return_f_inv=not f_transform)
         Y_mu_ref = Y_pred_ref[y_name+('_t (pred)' if f_transform else ' (pred)')].values
         fig.add_trace(go.Scatter(x=X_ref.iloc[:, feature_id].values, y=Y_mu_ref,
@@ -382,8 +381,7 @@ def surface_plot(optimizer: Optimizer,
 
     # Create a dataframe of test samples for plotting
     n_grid = 100
-    df_mean = optimizer.X_best_f
-    X_test = pd.concat([df_mean]*(n_grid**2), axis=0).reset_index(drop=True)
+    X_test = pd.concat([optimizer.X_best_f]*(n_grid**2), axis=0).reset_index(drop=True)
     
     # Create a mesh grid which is necessary for the 3D plot
     X0_name = X_test.columns[feature_ids[0]]