diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
index 9140c86..ce13a76 100644
--- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
+++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
@@ -51,7 +51,8 @@
     "from itertools import compress\n",
     "import tensorflow as tf\n",
     "import zfit\n",
-    "from zfit import ztf"
+    "from zfit import ztf\n",
+    "from IPython.display import clear_output"
    ]
   },
   {
@@ -64,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -303,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -347,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,19 +376,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#jpsi\n",
     "\n",
@@ -428,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -448,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,29 +479,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.09\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.clf()\n",
     "# plt.plot(x_part, calcs, '.')\n",
@@ -533,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -545,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -557,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -589,24 +560,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5404695.652173913\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(36000*(1+ pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"] + pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -627,20 +590,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(108,)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "nevents = pdg[\"number_of_decays\"]\n",
-    "event_stack = 50000\n",
+    "nevents = int(pdg[\"number_of_decays\"])\n",
+    "event_stack = 100000\n",
     "\n",
     "calls = int(nevents/event_stack + 1)\n",
     "\n",
@@ -652,9 +607,16 @@
     "    samp = total_f.sample(n=event_stack)\n",
     "    sam = samp.unstack_x()\n",
     "    sam = zfit.run(sam)\n",
-    "    total_samp = np.append(total_samp, sam)\n",
-    "\n",
-    "print(total_samp.shape)"
+    "    clear_output(wait=True)\n",
+    "    \n",
+    "    print(\"{0}/{1}\".format(call + 1, calls))\n",
+    "    print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n",
+    "    c = call + 1\n",
+    "    print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/c*(calls-c)))))\n",
+    "    \n",
+    "    \n",
+    "    with open(\"data/zfit_toys/toy_1/{}.pkl\".format(call), \"wb\") as f:\n",
+    "        pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)"
    ]
   },
   {
@@ -663,8 +625,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "with open(\"data/zfit_toys/test_toy.pkl\", \"wb\") as f:\n",
-    "    pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)\n",
+    "# with open(\"data/zfit_toys/test_toy.pkl\", \"wb\") as f:\n",
+    "#     pkl.dump(total_samp, f, pkl.HIGHEST_PROTOCOL)\n",
     "    \n",
     "print(\"Time to generate full toy: {} s\".format(int(time.time()-start)))"
    ]
@@ -675,15 +637,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "total_samp = []\n",
+    "\n",
+    "for call in range(calls):\n",
+    "    with open(r\"data/zfit_toys/toy_1/{}.pkl\".format(call), \"rb\") as input_file:\n",
+    "        sam = pkl.load(input_file)\n",
+    "        total_samp = np.append(total_samp, sam)\n",
+    "\n",
+    "print(total_samp[:nevents].shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "bins = int((x_max-x_min)/7)\n",
     "\n",
     "calcs = zfit.run(total_test_tf(samp))\n",
     "\n",
-    "plt.hist(sam, bins = bins, range = (x_min,x_max))\n",
+    "plt.hist(total_samp, bins = bins, range = (x_min,x_max))\n",
     "\n",
     "# plt.plot(sam, calcs, '.')\n",
     "# plt.plot(test_q, calcs_test)\n",
-    "# plt.ylim(0, 0.0000007)\n",
+    "# plt.ylim(0, 200)\n",
     "# plt.xlim(3000, 3750)\n",
     "\n",
     "plt.savefig('test.png')"
@@ -785,6 +763,33 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'5 h, 55 min'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display_time(int(395*pdg[\"number_of_decays\"]/100000))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
diff --git a/__pycache__/pdg_const.cpython-37.pyc b/__pycache__/pdg_const.cpython-37.pyc
index af89e87..7366c9a 100644
--- a/__pycache__/pdg_const.cpython-37.pyc
+++ b/__pycache__/pdg_const.cpython-37.pyc
Binary files differ
diff --git a/data/zfit_toys/test_toy.pkl b/data/zfit_toys/test_toy.pkl
index e2b15d9..6d897d9 100644
--- a/data/zfit_toys/test_toy.pkl
+++ b/data/zfit_toys/test_toy.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_1/0.pkl b/data/zfit_toys/toy_1/0.pkl
new file mode 100644
index 0000000..a5a5584
--- /dev/null
+++ b/data/zfit_toys/toy_1/0.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_1/1.pkl b/data/zfit_toys/toy_1/1.pkl
new file mode 100644
index 0000000..0fe3188
--- /dev/null
+++ b/data/zfit_toys/toy_1/1.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_1/10.pkl b/data/zfit_toys/toy_1/10.pkl
new file mode 100644
index 0000000..32c251f
--- /dev/null
+++ b/data/zfit_toys/toy_1/10.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_1/2.pkl b/data/zfit_toys/toy_1/2.pkl
new file mode 100644
index 0000000..75587e5
--- /dev/null
+++ b/data/zfit_toys/toy_1/2.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_1/3.pkl b/data/zfit_toys/toy_1/3.pkl
new file mode 100644
index 0000000..0ee2612
--- /dev/null
+++ b/data/zfit_toys/toy_1/3.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_1/4.pkl b/data/zfit_toys/toy_1/4.pkl
new file mode 100644
index 0000000..017deb2
--- /dev/null
+++ b/data/zfit_toys/toy_1/4.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_1/5.pkl b/data/zfit_toys/toy_1/5.pkl
new file mode 100644
index 0000000..3197c30
--- /dev/null
+++ b/data/zfit_toys/toy_1/5.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_1/6.pkl b/data/zfit_toys/toy_1/6.pkl
new file mode 100644
index 0000000..269a4cc
--- /dev/null
+++ b/data/zfit_toys/toy_1/6.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_1/7.pkl b/data/zfit_toys/toy_1/7.pkl
new file mode 100644
index 0000000..27c52f1
--- /dev/null
+++ b/data/zfit_toys/toy_1/7.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_1/8.pkl b/data/zfit_toys/toy_1/8.pkl
new file mode 100644
index 0000000..68079f2
--- /dev/null
+++ b/data/zfit_toys/toy_1/8.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_1/9.pkl b/data/zfit_toys/toy_1/9.pkl
new file mode 100644
index 0000000..b3aedb0
--- /dev/null
+++ b/data/zfit_toys/toy_1/9.pkl
Binary files differ
diff --git a/pdg_const.py b/pdg_const.py
index 47c0f94..aedf951 100644
--- a/pdg_const.py
+++ b/pdg_const.py
@@ -50,7 +50,7 @@
 "alpha_ew" : 1.0/137.0,
 "Vts" : 0.0394,
 "Vtb" : 1.019,
-"number_of_decays": 8e10, #---------------> Look up real value
+"number_of_decays": 5404696,
 
 #Formfactor z coefficients
 
diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb
index 9140c86..ce13a76 100644
--- a/raremodel-nb.ipynb
+++ b/raremodel-nb.ipynb
@@ -51,7 +51,8 @@
     "from itertools import compress\n",
     "import tensorflow as tf\n",
     "import zfit\n",
-    "from zfit import ztf"
+    "from zfit import ztf\n",
+    "from IPython.display import clear_output"
    ]
   },
   {
@@ -64,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -303,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -347,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,19 +376,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#jpsi\n",
     "\n",
@@ -428,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -448,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,29 +479,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.09\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.clf()\n",
     "# plt.plot(x_part, calcs, '.')\n",
@@ -533,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -545,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -557,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -589,24 +560,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5404695.652173913\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(36000*(1+ pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"] + pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -627,20 +590,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(108,)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "nevents = pdg[\"number_of_decays\"]\n",
-    "event_stack = 50000\n",
+    "nevents = int(pdg[\"number_of_decays\"])\n",
+    "event_stack = 100000\n",
     "\n",
     "calls = int(nevents/event_stack + 1)\n",
     "\n",
@@ -652,9 +607,16 @@
     "    samp = total_f.sample(n=event_stack)\n",
     "    sam = samp.unstack_x()\n",
     "    sam = zfit.run(sam)\n",
-    "    total_samp = np.append(total_samp, sam)\n",
-    "\n",
-    "print(total_samp.shape)"
+    "    clear_output(wait=True)\n",
+    "    \n",
+    "    print(\"{0}/{1}\".format(call + 1, calls))\n",
+    "    print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n",
+    "    c = call + 1\n",
+    "    print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/c*(calls-c)))))\n",
+    "    \n",
+    "    \n",
+    "    with open(\"data/zfit_toys/toy_1/{}.pkl\".format(call), \"wb\") as f:\n",
+    "        pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)"
    ]
   },
   {
@@ -663,8 +625,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "with open(\"data/zfit_toys/test_toy.pkl\", \"wb\") as f:\n",
-    "    pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)\n",
+    "# with open(\"data/zfit_toys/test_toy.pkl\", \"wb\") as f:\n",
+    "#     pkl.dump(total_samp, f, pkl.HIGHEST_PROTOCOL)\n",
     "    \n",
     "print(\"Time to generate full toy: {} s\".format(int(time.time()-start)))"
    ]
@@ -675,15 +637,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "total_samp = []\n",
+    "\n",
+    "for call in range(calls):\n",
+    "    with open(r\"data/zfit_toys/toy_1/{}.pkl\".format(call), \"rb\") as input_file:\n",
+    "        sam = pkl.load(input_file)\n",
+    "        total_samp = np.append(total_samp, sam)\n",
+    "\n",
+    "print(total_samp[:nevents].shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "bins = int((x_max-x_min)/7)\n",
     "\n",
     "calcs = zfit.run(total_test_tf(samp))\n",
     "\n",
-    "plt.hist(sam, bins = bins, range = (x_min,x_max))\n",
+    "plt.hist(total_samp, bins = bins, range = (x_min,x_max))\n",
     "\n",
     "# plt.plot(sam, calcs, '.')\n",
     "# plt.plot(test_q, calcs_test)\n",
-    "# plt.ylim(0, 0.0000007)\n",
+    "# plt.ylim(0, 200)\n",
     "# plt.xlim(3000, 3750)\n",
     "\n",
     "plt.savefig('test.png')"
@@ -785,6 +763,33 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'5 h, 55 min'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display_time(int(395*pdg[\"number_of_decays\"]/100000))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],