diff --git a/bkg_reduction.ipynb b/bkg_reduction.ipynb
index 9618180..e04f679 100644
--- a/bkg_reduction.ipynb
+++ b/bkg_reduction.ipynb
@@ -20,7 +20,8 @@
     "import numpy as np\n",
     "from array import array\n",
     "import root_numpy as rn\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import pickle"
    ]
   },
   {
@@ -76,7 +77,7 @@
     {
      "data": {
       "text/plain": [
-       "<ROOT.TTree object (\"DecayTree\") at 0x55992fc141a0>"
+       "<ROOT.TTree object (\"DecayTree\") at 0x55d5d4082200>"
       ]
      },
      "execution_count": 6,
@@ -97,7 +98,7 @@
     {
      "data": {
       "text/plain": [
-       "<ROOT.TTree object (\"DecayTree\") at 0x55992fcaafa0>"
+       "<ROOT.TTree object (\"DecayTree\") at 0x55d5d40eb6e0>"
       ]
      },
      "execution_count": 7,
@@ -244,6 +245,13 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
    "execution_count": 14,
    "metadata": {},
    "outputs": [],
@@ -329,12 +337,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "data_tuple_bkg={}\n",
     "data_tuple_bkg_under = {}\n",
     "data_tuple_bkg_over = {}\n",
     "MC_tuple_sig ={}\n",
     "\n",
     "for label in branches_needed:\n",
     "    \n",
+    "    data_tuple_bkg[label] = data_tuple_dict[label][data_bkg_indices_under+data_bkg_indices_over]\n",
     "    data_tuple_bkg_under[label] = data_tuple_dict[label][data_bkg_indices_under]\n",
     "    data_tuple_bkg_over[label] = data_tuple_dict[label][data_bkg_indices_over]\n",
     "    MC_tuple_sig[label] = MC_tuple_dict[label][MC_sig_indices]\n",
@@ -415,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -447,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -479,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -491,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -508,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -529,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -592,12 +602,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1152x576 with 1 Axes>"
       ]
@@ -624,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -636,7 +646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -653,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -663,7 +673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -684,7 +694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -714,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -746,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -778,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -790,17 +800,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "TFile::Append:0: RuntimeWarning: Replacing existing TH1: ProbNN mu MC/data comparison under (Potential memory leak).\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "h_mc= r.TH1F(\"ProbNN mu MC/data comparison under\", \"ProbNN mu MC/data under Ds mass comparison\",nbins, 0, 1)\n",
     "\n",
@@ -814,7 +816,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -844,7 +846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -876,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -908,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -920,17 +922,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 45,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "TFile::Append:0: RuntimeWarning: Replacing existing TH1: ProbNN mu MC/data comparison under (Potential memory leak).\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "h_mc= r.TH1F(\"Hlt1 TrackMVA TOS MC/data comparison under\", \"Hlt1 TrackMVA TOS MC/data under Ds mass comparison\",nbins, 0, 2)\n",
     "\n",
@@ -944,7 +938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -974,7 +968,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1006,7 +1000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -1038,7 +1032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1050,7 +1044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1066,7 +1060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -1096,7 +1090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1128,7 +1122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -1160,7 +1154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1172,7 +1166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1188,7 +1182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -1218,7 +1212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -1250,7 +1244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -1282,7 +1276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1302,25 +1296,45 @@
     "#c1.Update()\n",
     "#c1.SaveAs(\"/home/hep/davide/Rphipi/plt.pdf\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('/disk/lhcb_data/davide/Rphipi/NN_test/MC_for_NN.pickle', 'wb') as handle:\n",
+    "    pickle.dump(MC_tuple_sig, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
+    "with open('/disk/lhcb_data/davide/Rphipi/NN_test/data_for_NN.pickle', 'wb') as handle:\n",
+    "    pickle.dump(data_tuple_bkg, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.15"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,