diff --git a/GPy/plotting/gpy_plot/gp_plots.py b/GPy/plotting/gpy_plot/gp_plots.py
index c6975326..702aeb7b 100644
--- a/GPy/plotting/gpy_plot/gp_plots.py
+++ b/GPy/plotting/gpy_plot/gp_plots.py
@@ -235,8 +235,6 @@ def plot_density(self, plot_limits=None, fixed_inputs=None,
 
     Give the Y_metadata in the predict_kw if you need it.
 
-
-
     :param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
     :type plot_limits: np.array
     :param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input dimension i should be set to value v.
diff --git a/GPy/plotting/matplot_dep/util.py b/GPy/plotting/matplot_dep/util.py
index ca129bc9..dd2291da 100644
--- a/GPy/plotting/matplot_dep/util.py
+++ b/GPy/plotting/matplot_dep/util.py
@@ -117,3 +117,41 @@ def align_subplot_array(axes,xlim=None, ylim=None):
             ax.set_xticks([])
         else:
             removeUpperTicks(ax)
+
+def fixed_inputs(model, non_fixed_inputs, fix_routine='median', as_list=True, X_all=False):
+    """
+    Convenience function for returning back fixed_inputs where the other inputs
+    are fixed using fix_routine
+    :param model: model
+    :type model: Model
+    :param non_fixed_inputs: dimensions of non fixed inputs
+    :type non_fixed_inputs: list
+    :param fix_routine: fixing routine to use, 'mean', 'median', 'zero'
+    :type fix_routine: string
+    :param as_list: if true, will return a list of tuples with (dimension, fixed_val) otherwise it will create the corresponding X matrix
+    :type as_list: boolean
+    """
+    f_inputs = []
+    if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs():
+        X = model.X.mean.values.copy()
+    elif isinstance(model.X, VariationalPosterior):
+        X = model.X.values.copy()
+    else:
+        if X_all:
+            X = model.X_all.copy()
+        else:
+            X = model.X.copy()
+    for i in range(X.shape[1]):
+        if i not in non_fixed_inputs:
+            if fix_routine == 'mean':
+                f_inputs.append( (i, np.mean(X[:,i])) )
+            if fix_routine == 'median':
+                f_inputs.append( (i, np.median(X[:,i])) )
+            else: # set to zero zero
+                f_inputs.append( (i, 0) )
+            if not as_list:
+                X[:,i] = f_inputs[-1][1]
+    if as_list:
+        return f_inputs
+    else:
+        return X