diff --git a/ezyrb/nnspod.py b/ezyrb/nnspod.py
index e3166b50..8531034f 100644
--- a/ezyrb/nnspod.py
+++ b/ezyrb/nnspod.py
@@ -34,7 +34,7 @@ def reshape1dto2d(self, snapshots):
         """
         return snapshots.reshape(int(np.sqrt(len(snapshots))), int(np.sqrt(len(snapshots))))
 
-    def train_interpnet(self,ref_data, interp_layers, interp_function, interp_stop_training, interp_loss, retrain = False, frequency_print = 0):
+    def train_interpnet(self,ref_data, interp_layers, interp_function, interp_stop_training, interp_loss, retrain = False, frequency_print = 0, save = True):
         """
         trains the Interpnet given 1d data:
 
@@ -58,10 +58,12 @@ def train_interpnet(self,ref_data, interp_layers, interp_function, interp_stop_t
                 print("loaded interpnet")
             except:
                 self.interp_net.fit(space, snapshots, frequency_print = frequency_print)
-                self.interp_net.save_state(self.path)
+                if save:
+                    self.interp_net.save_state(self.path)
         else:
             self.interp_net.fit(space, snapshots, frequency_print = frequency_print)
-            self.interp_net.save_state(self.path)
+            if save:
+                self.interp_net.save_state(self.path)
 
     def shift(self, x, y, shift_quantity):
         """
@@ -187,4 +189,38 @@ def train_shiftnet(self, db, shift_layers, shift_function, shift_stop_training,
                         shift)[0]
         x_ret = x_new.detach().numpy()
         return x_ret
-
+    
+    def fit(self, db, ref_point):
+
+        self.train_interpnet(db[ref_point], [20,20], nn.Sigmoid(), [0.000001], None, retrain  = False, frequency_print = 5)
+        new_x = np.zeros(shape = db.space.shape)
+        i = 0
+        while i < db.parameters.shape[0]:
+            new_x[i] = self.train_shiftnet(db[i], [20,20,20], nn.Tanh(), [1000, 0.00001], db[ref_point], preshift = True, frequency_print = 5).reshape(-1)
+            i+=1
+            if i == ref_point:
+                new_x[ref_point] = db.space[ref_point]
+                i +=1
+        db = Database(space = new_x, snapshots = db.snapshots, parameters = db.parameters)
+
+        i = 0
+        new_snapshots = np.zeros(shape = db.snapshots.shape)
+        new_space = np.zeros(shape = db.space.shape)
+        while i < db.parameters.shape[0]:
+            self.train_interpnet(db[i], [20,20], nn.Sigmoid(), [0.00001], None, retrain  = True, frequency_print = 50, save = False)
+            new_snapshots[i] = self.interp_net.model(torch.from_numpy(db.space[ref_point].reshape(-1,1)).float()).detach().numpy().reshape(-1)
+            new_space[i] = db.space[ref_point]
+            i+=1
+            if i == ref_point:
+                new_snapshots[ref_point] = db.snapshots[ref_point]
+                new_space[ref_point] =  db.space[ref_point]
+                i +=1
+
+        db = Database(space = new_space, snapshots = new_snapshots, parameters = db.parameters)
+        POD_ = POD(method = 'svd')
+        return POD_.fit(db.snapshots)
+
+        ##Ideas for fit:
+        # create an interpnet for each datapoint, and use to get snapshots
+        # and parameters at ref data positions
+        # 
\ No newline at end of file
diff --git a/tutorials/tutorial-3.ipynb b/tutorials/tutorial-3.ipynb
index be3cad5b..117ff68b 100644
--- a/tutorials/tutorial-3.ipynb
+++ b/tutorials/tutorial-3.ipynb
@@ -145,1813 +145,600 @@
      "name": "stdout",
      "output_type": "stream",
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+      "0.0017107638996094465\n",
+      "0.0004019137704744935\n",
+      "1.3126131307217292e-05\n",
+      "0.0005859642405994236\n",
+      "0.002046966226771474\n",
+      "0.004228001926094294\n",
+      "0.006900358479470015\n",
+      "0.00981244258582592\n",
+      "0.012722432613372803\n",
+      "0.015421003103256226\n",
+      "0.01774325966835022\n",
+      "0.019571620970964432\n",
+      "0.020832080394029617\n",
+      "0.021487673744559288\n",
+      "0.021531488746404648\n",
+      "0.020981982350349426\n",
+      "0.01988057978451252\n",
+      "0.018291110172867775\n",
+      "0.01629975438117981\n",
+      "0.014013787731528282\n",
+      "0.01155805867165327\n",
+      "0.009068493731319904\n",
+      "0.006683044601231813\n",
+      "0.00453118234872818\n",
+      "0.002723463112488389\n",
+      "0.0013431423576548696\n",
+      "0.00044084805995225906\n",
+      "3.309685052954592e-05\n",
+      "0.05857272446155548\n",
+      "0.056210942566394806\n",
+      "0.05348619073629379\n",
+      "0.05026448145508766\n",
+      "0.04649009555578232\n",
+      "0.042143769562244415\n",
+      "0.03725339472293854\n",
+      "0.03190308064222336\n",
+      "0.026242224499583244\n",
+      "0.020488467067480087\n",
+      "0.014917096123099327\n",
+      "0.009838476777076721\n",
+      "0.00556654017418623\n",
+      "0.002382381819188595\n",
+      "0.000497721484862268\n",
+      "0.0063920957036316395\n",
+      "0.002932596020400524\n",
+      "0.0007090680883266032\n",
+      "9.507111826678738e-06\n"
      ]
     }
    ],
    "source": [
     "i = 0\n",
     "while i < 10:\n",
-    "    x_new  = NNsPOD_tutorial.train_shiftnet(database[i], [20,20,20], nn.Tanh(), [1000, 0.00001], database[ref_data], preshift = True, frequency_print = 0, learning_rate = 0.01) \n",
+    "    x_new  = NNsPOD_tutorial.train_shiftnet(database[i], [20,20,20], nn.Tanh(), [10000, 0.00001], database[ref_data], preshift = True, frequency_print = 5, learning_rate = 0.0001) \n",
     "    db = database[i]   \n",
     "    plt.plot(db.space, db.snapshots, \"go\")\n",
     "    plt.plot(x_new, db.snapshots.reshape(-1,1), \".\")\n",
@@ -1977,6 +764,16 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pod = NNsPOD_tutorial.fit(database, 5)\n",
+    "print(pod.modes, pod.singular_values)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1988,7 +785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2013,27 +810,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.5 0.5]\n",
-      "[0.94444444 0.94444444]\n",
-      "[1.38888889 1.38888889]\n",
-      "[1.83333333 1.83333333]\n",
-      "[2.27777778 2.27777778]\n",
-      "[2.72222222 2.72222222]\n",
-      "[3.16666667 3.16666667]\n",
-      "[3.61111111 3.61111111]\n",
-      "[4.05555556 4.05555556]\n",
-      "[4.5 4.5]\n",
-      "[0 0]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "n_params = 10\n",
@@ -2064,7 +843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2080,17 +859,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loaded interpnet\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "ref_data = 5\n",
@@ -2106,7 +877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [