diff --git a/priorConditionedAnnealing/pca.py b/priorConditionedAnnealing/pca.py
index e46c228..8a8234d 100644
--- a/priorConditionedAnnealing/pca.py
+++ b/priorConditionedAnnealing/pca.py
@@ -60,11 +60,12 @@ def cast_to_enum(inp, Class):
 
 
 def cast_to_kernel(inp):
-    if isinstance(inp, function):
+    if callable(inp):
         return inp
     else:
         func, *pars = inp.split('_')
-        return Avaible_Kernel_Funcs[func](*pars)
+        pars = [float(par) for par in pars]
+        return Avaible_Kernel_Funcs[func].get_func()(*pars)
 
 
 class PCA_Distribution(SB3_Distribution):
@@ -84,7 +85,7 @@ class PCA_Distribution(SB3_Distribution):
 
         self.action_dim = action_dim
         self.kernel_func = cast_to_kernel(kernel_func)
-        self.par_strength = cast_to_enum(Par_Strength, par_strength)
+        self.par_strength = cast_to_enum(par_strength, Par_Strength)
         self.init_std = init_std
         self.window = window
         self.epsilon = epsilon
@@ -138,11 +139,10 @@ class PCA_Distribution(SB3_Distribution):
         return eta.detach()
 
     def _pad_and_cut_trajectory(self, traj, value=0):
-        cut = traj[:self.window]
         if traj.shape[-2] < self.window:
             missing = self.window - traj.shape[-2]
-            return F.pad(input=cut, pad=(missing, 0), value=value)
-        return cut
+            return F.pad(input=traj, pad=(0, 0, missing, 0, 0, 0), value=value)
+        return traj[:, :self.window, :]
 
     def _conditioning_engine(self, trajectory, pi_mean, pi_std):
         traj = self._pad_and_cut_trajectory(trajectory)
@@ -188,6 +188,7 @@ class PCA_Distribution(SB3_Distribution):
         # https://martin-thoma.com/images/2012/07/cholesky-zerlegung-numerik.png
         # This way conditioning of the GP can be done in O(dim(A)) time.
         if not self.is_contextual():
+            # TODO: fix, this does not work
             # safe inplace
             self.conditioner[-1, -
                              1] = np.sqrt(pi_std**2 + self.Sig22 - self.adapt_norm)