diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
index 720368e..773a860 100644
--- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
+++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
@@ -9,31 +9,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py:57: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`\n",
-      "  warnings.warn(\"Not running on Linux. Determining available cpus for thread can fail\"\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-      "For more information, please see:\n",
-      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-      "  * https://github.com/tensorflow/addons\n",
-      "If you depend on functionality not listed there, please file an issue.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "\n",
@@ -66,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -280,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -335,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -545,19 +523,9 @@
   },
   {
    "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": [
     "# formfactors\n",
     "\n",
@@ -666,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -696,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -713,7 +681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -751,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -795,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -811,7 +779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -871,22 +839,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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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",
@@ -912,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -928,7 +883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -944,7 +899,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -958,7 +913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1015,7 +970,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1099,7 +1054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1115,7 +1070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1251,7 +1206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1330,7 +1285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1368,18 +1323,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "36668\n",
-      "5404696\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# for param in total_f_fit.get_dependents():\n",
     "#     if param.floating:\n",
@@ -1405,33 +1351,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.         0.00025263 0.00040526 0.00055789 0.00071053 0.00086316\n",
-      " 0.00101579 0.00116842 0.00132105 0.00147368 0.00162632 0.00177895\n",
-      " 0.00193158 0.00208421 0.00223684 0.00238947 0.00254211 0.00269474\n",
-      " 0.00284737 0.003     ]\n",
-      "[0.         0.24525574 0.31063037 0.36446136 0.41130637 0.45333628\n",
-      " 0.49178719 0.5274424  0.5608354  0.59234888 0.62226847 0.65081403\n",
-      " 0.67815909 0.70444347 0.72978178 0.75426939 0.77798661 0.80100188\n",
-      " 0.82337407 0.84515425]\n",
-      "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:163: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.cast instead.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# zfit.run.numeric_checks = False   \n",
     "\n",
-    "load = True\n",
+    "load = False\n",
     "\n",
     "bo = True\n",
     "\n",
@@ -1471,6 +1399,8 @@
     "\n",
     "Ctt_steps = np.sqrt(BR_steps/4.2*1000)\n",
     "\n",
+    "Ctt_steps[0] = 0.74\n",
+    "\n",
     "print(Ctt_steps)\n",
     "\n",
     "# total_samp = []\n",
@@ -1484,15 +1414,15 @@
     "\n",
     "__ = -1\n",
     "\n",
-    "# newset = True\n",
+    "pause = False\n",
     "\n",
     "#-----------------------------------------------------\n",
     "\n",
     "if not load:\n",
     "    for Ctt_step in Ctt_steps:\n",
     "\n",
-    "#         if not newset:\n",
-    "#             break\n",
+    "        if pause:\n",
+    "            break\n",
     "        \n",
     "        __ += 1\n",
     "        \n",
@@ -1507,8 +1437,8 @@
     "        \n",
     "        for toy in range(nr_of_toys):        \n",
     "            \n",
-    "#             if not newset:\n",
-    "#                 break\n",
+    "            if pause:\n",
+    "                break\n",
     "            \n",
     "            newset = True\n",
     "            \n",
@@ -1517,7 +1447,7 @@
     "                for floaty in [True, False]:\n",
     "                    Ctt.floating = floaty\n",
     "                    \n",
-    "                    if not floaty:\n",
+    "                    if pause and not floaty:\n",
     "                        break\n",
     "                    \n",
     "                    for bo_step in range(bo_set):\n",
@@ -1566,6 +1496,8 @@
     "                        if result.converged:\n",
     "                            \n",
     "                            save_pulls(step = _step)\n",
+    "                            \n",
+    "                            pause = True\n",
     "\n",
     "                            if floaty:\n",
     "                                Nll_list[-2].append(result.fmin)\n",
@@ -1600,17 +1532,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of jobs: 1000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if load:\n",
     "    \n",
@@ -1687,7 +1611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1748,7 +1672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1764,39 +1688,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(20, 1000)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 216x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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": [
     "l = []\n",
     "sensitivity = []\n",
@@ -1844,75 +1738,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BR: 0.0003\n",
-      "0.7253086419753086\n",
-      "\n",
-      "BR: 0.0004\n",
-      "0.6373456790123457\n",
-      "\n",
-      "BR: 0.0006\n",
-      "0.5509259259259259\n",
-      "\n",
-      "BR: 0.0007\n",
-      "0.4367283950617284\n",
-      "\n",
-      "BR: 0.0009\n",
-      "0.345679012345679\n",
-      "\n",
-      "BR: 0.0010\n",
-      "0.2993827160493827\n",
-      "\n",
-      "BR: 0.0012\n",
-      "0.24228395061728394\n",
-      "\n",
-      "BR: 0.0013\n",
-      "0.21141975308641975\n",
-      "\n",
-      "BR: 0.0015\n",
-      "0.16820987654320987\n",
-      "\n",
-      "BR: 0.0016\n",
-      "0.1419753086419753\n",
-      "\n",
-      "BR: 0.0018\n",
-      "0.10185185185185185\n",
-      "\n",
-      "BR: 0.0019\n",
-      "0.09104938271604938\n",
-      "\n",
-      "BR: 0.0021\n",
-      "0.09259259259259259\n",
-      "\n",
-      "BR: 0.0022\n",
-      "0.05864197530864197\n",
-      "\n",
-      "BR: 0.0024\n",
-      "0.040123456790123455\n",
-      "\n",
-      "BR: 0.0025\n",
-      "0.033950617283950615\n",
-      "\n",
-      "BR: 0.0027\n",
-      "0.026234567901234566\n",
-      "\n",
-      "BR: 0.0028\n",
-      "0.037037037037037035\n",
-      "\n",
-      "BR: 0.0030\n",
-      "0.029320987654320986\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for s in range(len(l)):\n",
     "    print('BR: {:.4f}'.format(BR_steps[s+1]))\n",
@@ -1931,7 +1761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1942,7 +1772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1953,14 +1783,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4 min, 1 \n"
+      "56 s\n"
      ]
     }
    ],
@@ -1969,34 +1799,93 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 34,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "integrate = False\n",
+    "# Smearing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_q = np.linspace(x_min, x_max, int(2e6))\n",
+    "\n",
+    "probs = total_f_fit.pdf(test_q, norm_range=False)\n",
+    "\n",
+    "calcs_test = zfit.run(probs)\n",
+    "\n",
+    "plt.clf()\n",
+    "# plt.plot(x_part, calcs, '.')\n",
+    "plt.title('Fitted curve before smearing')\n",
+    "plt.plot(test_q, calcs_test)#, label = 'pdf (Ctt = 0.0)')\n",
+    "# plt.plot(test_q, calcs_test1, label = 'pdf (Ctt = 0.5)')\n",
+    "# plt.plot(test_q, calcs_test2, label = 'pdf (D-contribs = 0.3)')\n",
+    "# plt.plot(test_q, f0_y, label = '0')\n",
+    "# plt.plot(test_q, fT_y, label = 'T')\n",
+    "# plt.plot(test_q, fplus_y, label = '+')\n",
+    "# plt.plot(test_q, res_y, label = 'res')\n",
+    "plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "# plt.legend()\n",
+    "plt.ylim(0.0, 1.5e-6)\n",
+    "plt.xlabel(r'$q^2 [MeV^2]$')\n",
+    "# plt.yscale('log')\n",
+    "# plt.xlim(770, 785)\n",
+    "plt.savefig('fitted_before_smearing.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ixEzFGecA4RfPAqQFhslmzUpkWG1oOu98oG31XWumlMS4lMioKCmKEjO7L/giEL7QAQIP/+RmJI2XXwG0Hb5LDRQ6aKbUZVFRUhQlZs6dPBiIVOggVkm4w5xTIhiU1wdou6NDqicl6XEpkVFRUhQlZhat2wU4i5LHYw+TOZ8uE3f2HLQOnE4JyZS8fn9S41Iio6KkKErMDH7VqmyLVBLe4vPDCy8kMqw2ZCyYDzisU0piXEpkVJQURYmZQW+9ATjPKaV67If/okWJDKsNma9ZJ+O0Gb4LFDokMS4lMipKiqLEzHv3/RaIMHyXkmKVhCcxI6mc+yfrhWZK3QoVJUVRYua0m78OQKpjSbi9HuiiixIZVhsGXHUZELKjQ4pd6JDEuJTIqCgpihIzWy6+EiDsybMQtB4oiUdE1H7FEs42G7IGMiU9uqLLoqKkKErM7Jp6IhAhUwoUOsyYkcCo2tJw2ueAkHVKHrv6LolxKZFRUVIUJWa+NPsUwHlOqbXQYejQRIbVhhFHjwNCh++SH5cSGVeiJCLniUiJiJSKyB1hrmeIyHz7+koRKQq6dqfdXiIiM6P5FJHRto+tts/0SH2ISIGILBOROhF5PMhPloi8KiIfi8hGEflF7F+Poijh+NOSdUDkHR1afAZqaxMZVhs+/XQPEFoSbu9eXlvLjgP13PDCWmoaW5IUoRKOqKIkIh7gCeB8YBJwpYhMCjG7DqgyxowFHgEetO+dBFwBTAbOA54UEU8Unw8CjxhjxgFVtm/HPoBG4CfAbWHC/7UxZgJwHHCaiJwf7fMqihKd4pdfBCKsU+oCR0TkPvvHdm2pgZ0m5s7lmXe38ep/9/D6xn1JiE5xwk2mdCJQaowpM8Y0A/OA2SE2s4Fn7dcvAWeLtQvibGCeMabJGPMpUGr7C+vTvucs2we2z0si9WGMOWSMWYElTq0YY+qNMcvs183AWmC4i8+rKEoUBny8HohUEi7WnNLq1YkMqw0Z6z4E2g7feQJzSqtXt2ZQBw41JSM8xQE3ojQM2BH0fqfdFtbGGOMFqoGCCPc6tRcAB20foX059REVEekHXAS86XB9joisFpHV5eXlblwqSq/m3zf9DHA+TyktsM1QEjOlfb9+DHDYkHXuXNJTrcdfi0+3HOpKuBGlcL91of8XnWw6q91tHO0QkVTgReAxY0xZOBtjzFxjzHRjzPTCwsJoLhWl13PZbdeQItbR5+FITbGH75JY5Tb80guAkEwpUKo+Y0brfJhXRalL4UaUdgIjgt4PB3Y72dgikAcciHCvU3sF0M/2EdqXUx/RmAtsNcY86sJWURQXvHP1jY7l4GBlJC0+P9x9d+KCCmH/LVb9VNjqu7vvbm336easXQo3orQKGGdXxaVjFS4sDLFZCFxrv74MWGqMMXb7FXbl3GhgHPCBk0/7nmW2D2yfL0fpwxERuQ9LvG5y8TkVRXFJxZBRjvNJEFQSXlycwKja0jj6KCBkl3CPXX1XXNyaKfn9KkpdiaiiZM/f3Ai8BmwGFhhjNorIvSJysW32NFAgIqXALcAd9r0bgQXAJmAJcIMxxufk0/Z1O3CL7avA9u3YB4CIbAMeBr4mIjtFZJKIDAd+hFXdt1ZEPhKRb8T+FSmKEsrXb7o8oii17n03fXoCo2rLURecCThkStOnt479e1WUuhSp0U3AGLMYWBzSdlfQ60bgcod77wfud+PTbi/Dqs4LbY/UR5FD6HqSl6LEgYeeWYbnw12O19MCVW67Q0f6E8fG1ZvhiXfbiKcnRfD6rLi8r5cAWMOMSpdBd3RQFCVmTvrHM44LZ8EudPAZeOihBEbVloI/WDuZh62+e+ghmu0ChyavLxnhKQ6oKCmKEjM5lfujzim1JDlTStu7FyAkU7LnlHbvtjImoLFFM6WuhIqSoigxs/Art0bJlA5nJMnisx9Za6lC55S8dlyBuaQmr4pSV0JFSVGUmPne7Vc6rlGCQEm4wUyblsCo2jLpIqvQIdxx6GbaNJpbMyUdvutKqCgpihIz8677UeRMyWM9Wny/+32iQmrHJ/c9DLTddSIQs+93vw8avlNR6kqoKCmKEjN16X1at+kJR6rHfvhn5yQqpHa0ZGUDbXedaBXL7JzWnRx0+K5roaKkONLQ7GPDrupkh6F0Qb790E0Rd3RIs6+lXnKxo028mXT91UD7OSWAtEtmtw7fNWmm1KVQUVIcue2v65j12xVUHWpOdihKF+P2B14iLUKmFMiiqj7ckKiQ2rFyyXtA6I4O1puadRs0U+qiqCgpjqz9rAqABv1LUgnh4n/MJS3CnFJAlDLuuzdRIbVj1GO/BNpmSoG40n52r7W4F51T6mqoKCmKEjN+Y0jzRMiUAnM3SdzCJ9Bz8DqltKC4mjVT6pKoKCmKEjPPn///XA3f7b/1zkSF1I7S71iHUQeXhGfYcVXcdqdW33VRVJQURYmZJ+65wtXw3ahTjktUSO04/cLTgJDhOztTGn7yVJ1T6qKoKCmKEjM/ue4XkYfvbFHa+vSLiQqpHaseexYIP3z36TPz2yyejXIKjpJAVJQURYmZtIa61kq2cGTYD39/TU2iQmqHHKoD2lbfBcTSX1PdWujgN3p8RVdCRUlRlJi55aWHWofCwhF4+I/+0S2JCqkdx/7sh0Db4btApjTijpvbHIOu80pdBxUlRVFi5trv/M7V8N37C15PVEjtWPa8dVxbuJLwNX9/o3X4DnReqSuhoqQoSszc+OrvIg7fBR7+R/3ip4kKqR2TH7LWSAWLUqD6ruiBn2im1EVRUVIUJWb2Zue7Wqd0aEBhokJqR/2AQQAE74YUiPlQwSC8Pn+rSGmm1HVQUVKiolPASih/PPmLpEUqdEjzALDpym8mKqR2fHyV1Xe44bst18yh2WfIzUwFNFPqSqgoKYoSM8seudpVpnTRBSckKqR2XHyh1XdwSXhAlGZ+4Xi8fj+5mWmAZkpdCVeiJCLniUiJiJSKyB1hrmeIyHz7+koRKQq6dqfdXiIiM6P5FJHRto+tts/0SH2ISIGILBOROhF5PCSuaSKy3r7nMRFx/tNOcUS/NCUYYwwXffURV4UO/5z7z0SF1Y5//fEVoO2ODoHs7tU/voLXZ8jOsDI6zZS6DlFFSUQ8wBPA+cAk4EoRmRRidh1QZYwZCzwCPGjfOwm4ApgMnAc8KSKeKD4fBB4xxowDqmzfjn0AjcBPgNvChP87YA4wzv45L9rnVdqjw3dKMF6/YUzVrsjDd7YoZW0vS1RY7cj5zOq7TaGDxxKhPts+odnnJyfDGr5ratFMqavgJlM6ESg1xpQZY5qBecDsEJvZwLP265eAs+2sZDYwzxjTZIz5FCi1/YX1ad9zlu0D2+clkfowxhwyxqzAEqdWRGQI0NcY856xlms/F+RLUZQO0uLzc9OKF1wN301/5rFEhdWOY596FGh78mwggzvu6Ufx+vzkZASG7zRT6iq4EaVhwI6g9zvttrA2xhgvUA0URLjXqb0AOGj7CO3LqY9Ice+MEjcAIjJHRFaLyOry8vIILnsnOnynBNPiM1xxVeRthlJShNQU4YUHn3W0iTev/tba4qjt4lnr9YJf/xm/IajQQTOlroIbUQr3TAod0XGy6ax2t3G4ial9ozFzjTHTjTHTCwuTV8KqKN2BFp+fB5b8NuLwHVhZyTmP/iRBUbXntF9aO5RL0FMu1ZNCisAZv/4xwOHhO82UugxuRGknMCLo/XBgt5ONiKQCecCBCPc6tVcA/WwfoX059REp7uFR4lYUJUa8PsP6weMiZkpgidKOMaHTz4ljf/EUoO3wHVhrlbaPnghAToIzJd34NTpuRGkVMM6uikvHKlxYGGKzELjWfn0ZsNSex1kIXGFXzo3GKjb4wMmnfc8y2we2z5ej9BEWY8weoFZETrbnqr4a5EuJAf1npATT4vPz4tTzSI0mSp4U3jv7fxIUVXs2zroCaDt8B5ZYvjXjUiCxmdKSDXuYeu+/2V55KO59dWeiipI9f3Mj8BqwGVhgjNkoIveKyMW22dNAgYiUArcAd9j3bgQWAJuAJcANxhifk0/b1+3ALbavAtu3Yx8AIrINeBj4mojsDKrk+zbwFFaBxSfAv2L5chQLnVNSgmn2+dnwyOWuhu/uvuaUBEXVnusuOBZou6MDWJWBP/+addZSIueU/rp6J9UNLby9tSLufXVnUqObgDFmMbA4pO2uoNeNwOUO994P3O/Gp91ehlWdF9oeqY8ih/bVwNHhrimK0jFafH5O+s6z/DpKppSRmsL//nYJDycorlCeWfAOrNjdLlPKSPXw7QcXwu5GMlM9pKZIQjKlykPNAHxarplSJHRHByUqOnynBNPU4ufkHevJtLcScqJPuodRG1YlKKr2DPnwA6D98F1WuodR66240lKFzDRPQjKlvdXWqpU91Q1x76s7o6KkOKJzsko4mrx+rvxoSesCWSf6pHk4fenfExRVeya/Oh9oe8gfWGJ5zgprejk1JYWsdA/1zd7Q2zudgw1WprS7ujGKZe9GRUmJilYMKcE0eX1847KfkpEWRZTSU7n3+gcj2sSTv9/zJKkpQujuYn3SPHz7y3cDViVeTmYqtY3xFaXGFl9rNlZR2xTXvro7KkqKI4F/y6pJSjCNLX5+s/BXZKRGGb5LS+Gmp5N3ntIFP7+tzWasAbLSPdy34OcApKcKuRmp1DXFV5SqG1oASxCr6pvj2ld3R0VJcUTFSAlHk9fHm2NPcDV89874kxMUVXu2TP88qWFFKZU3x1o7iKemWJlSXZwzpYP1liiNKcymvtmnG8BGQEVJUZSYaGrxs3DSjOiZUnoqCyfPSExQYVj3+QvDrqXKTPOwcNIMwB6+S0CmdNDOjkYPyAbgwCHNlpxQUVIUJSaavH62PTgr+pxSmofVPzk3QVG15+7ZRztkSh62PTgLsIbvcjLS4j6ndLAhkCnlACpKkVBRUqKiw3hKME1eH0W3L4o6fJeV7mHMHYuSVihz59/WOc4pFd2+CLAypdzMBMwp2cN3RxVqphQNFSUlKkZXKilBNHn9XLxpuat1SrM2LqfZl5wduI9+69WwmVKfdA8Xb1oO2HNK9vBdPMUzUA5eVGCJkhY7OKOipChKTDS1+Dm7dFXrmUlOZKZ5OLt0FY3NyRGliavfxhNmK6Q+dlxgHWWRk5mKz29oiGPxQU2DlxSBEflZAFTWqSg5oaKkREWH75Rgmrw+fnDp7aSEyUKCyUr38P2Lf0B9S/wXpobjTzc+QGroxnccjgss4QxsyhrPCry6Ji85Gan065NGiujwXSRUlBRHdNhOCUeT18/cv94T1a5PmoenXrqHhubklD9/81ffDzunlJ2RylMvWfFnpXtaN2WtiaMo1TS2kJuZRkqK0D8rnQM6fOeIipISFZUmJZgmr4+XT7wgql2fdA8vTj2P+iSJ0ltnXBJ2Tql/VjovTj0PsNYs9ctKBw6XbceD2kZvq/jlZ6dzQIfvHFFRUhwR+9AK3WZICaaxxc+GccdHteuT5uH9EVOStlB0c/HxpIaZU8rPTuf9EdYBgJlpKRRkW6JUGcchtdrGFvpmprX2r8N3zqgoKY7o8J0Sjiavn5d//qWodn3SPax88tq4FhBE4tc3X4gnzJxSfnY6K5+0zgsVEfJtUYqnUNQ2eltPuc3P1uG7SKgoKVFRaVKCaWrx8cVfLIlql5Xu4eib/8qhpiTNKT2+NOzwXX52Okff/Nc27yG+olTXFDJ8p5mSIypKiqLERJPXz0UrF0W1y81I48qPllDb2JKAqNpzxvK/Oy6evfKjJWSlW+usMtM8ZKd74lqmHTqnVFXfjM+vf+6FQ0VJiYpOKSnBNLT4mLCzJKpdbmYqU/ZujfsWPk6M3v5x2ExJRPjBgFpe/d7nWtvyc9I5cCg+R0oYY6i1q+/AEiVj4ltY0Z1RUVJcoKqkHKa+2csL1/04ql1OZir/e953kyZKj1/xw7CZEkD+X/7UujkqQH52RtwKHZq8flp8pnU9VGC4UHd1CI+KkqIoMVHf7OOOX347ql2aJ4UFL96ZtOG7nz16Y9hMCYAZM9q8LchOj9vwXUCU+2a2FSXd1SE8rkRJRM4TkRIRKRWRO8JczxCR+fb1lSJSFHTtTru9ROGcHSMAACAASURBVERmRvMpIqNtH1ttn+lH0MfNIrJRRDaIyIsikhnb16OADt8pbalv8rH0y9FFCeCP51ybtEzpzzO/HvboCgDuvrvN28F5meyric8x5QFRDh6+A93VwYmooiQiHuAJ4HxgEnCliEwKMbsOqDLGjAUeAR60750EXAFMBs4DnhQRTxSfDwKPGGPGAVW27470MQz4HjDdGHM04LHtFEU5AuqbvdSNHOPK9sCwUdQ2JSdT+mzAMOdMqbi4zdth/fpQeag5LmuqAqIcKHQoyM4A0LJwB9xkSicCpcaYMmNMMzAPmB1iMxt41n79EnC2iIjdPs8Y02SM+RQotf2F9Wnfc5btA9vnJR3sAyAV6CMiqUAWsNvF51VC0ERJCaa+2cf1t33Zle3vf/Ot5M0pPXK98/5806e3eTskzxpE2X2wodPjOCxKVqbUP9v6r+7qEB43ojQM2BH0fqfdFtbGGOMFqoGCCPc6tRcAB20foX3F1IcxZhfwa+AzYA9QbYx5PdwHFJE5IrJaRFaXl5c7fhG9FR2+UwI0e/14/YZnX/qPK/vvP/xqXPeUi8Ql/7uADKfhu91t/z4d2q+P1Xyw84fwAsN3gUKHjFRrE9h47iDRnXEjSuH+1Ah9TDnZdFZ7zH2ISH+sLGo0MBTIFpFrwthijJlrjJlujJleWFgYzkRRFKyhO4AT/vaMK/tLl85LWqHDZcvnk+50EOFDD7V5O6xVlDo/U6q2T53Ny0prbQusVVLa40aUdgIjgt4Pp/0wWKuNPVSWBxyIcK9TewXQz/YR2lesfZwDfGqMKTfGtAB/B0518XmVEHS7ISVAYHPVvKr9ruwLayuTNnyXX13hLEohmdKgvpmIwM6q+k6Po8o+dTbf3vgVoL/u6uCIG1FaBYyzq+LSsYoFFobYLASutV9fBiw11i6eC4Er7Mq50cA44AMnn/Y9y2wf2D5f7mAfnwEni0iWPfd0NrDZ3deigA7bKe0JZEpbfhj96AqAt6+/I2mZ0i/O+abzQYQhmVJ6agrD+/ehrOJQp8dRVd9MRmoKfdIPn9Q7MDeD/TXxWazb3YkqSvb8zY3Aa1gP9QXGmI0icq+IXGybPQ0UiEgpcAtwh33vRmABsAlYAtxgjPE5+bR93Q7cYvsqsH13pI+VWAURa4H19med24HvqNej4qQECGRKZ1x1viv7b978JRpb/EnZKXzB/93onClNm9auaWxhDqX76zo9jqpDza1l4AGG5GWyp7rzhwp7AqnRTcAYsxhYHNJ2V9DrRuByh3vvB+5349NuL+Nw9Vxwe0f6+Cnw03D3KNERe7ZORUkJENhcddsDD3OMC/u1P3oQNltrcgLFBInA7zfcMfO7nOMkSnPb/306blAu735Sic9vHHeC6AhV9c2tZzYFGJyXSU2jl0NNXrIzXD2Gew26o4PiiIqREkqDfbR5Wr88V/bZA/oBiV8o2uzzcyi9j3OmlJvbrmlsYQ7NXn+nzytV1beQn53Wpm1oniXQe6rjs2C3O6OipERFCx2UAHV2pnTU/3O3Dv2EG61p4ESXPzd5/Tz90j3Oc0qzZrVrGjsoB4At+zp3CM8pUwLYq6LUDhUlRVFcU2OXNx/8cIMr+90ffARAZV1iJ/WbvX7OmjOXDKdMacuWdk0TB/fFkyL8d+fBTo2lsq65TeUdHF6sq/NK7VFRUqKiw3hKgBq7ki7/Vw+4sh/66INAcobvblrxvPPwXcjed2CdlDt+UC4f7YhNlLw+P998bjU3zfsQf8gZSY0tPqobWlozowCD+mqm5ISKkqIorqlp8JLuSXFdCJCRmkJqiiR8+K7Z6wdwFiUHpo7sx0c7DrYTl0i8taWcf2/axz8/2s3rm/a1uRYQncF924pSZpqHgux0dqsotUNFSVEU19Q0ttC3Typyj7t1SnLPPdZC0QTv89bs9fPo6VeT7vGENwiTKQFMHdGP2kYvpeXu55U27a4BIDcjlb+t3dnmWqCQYUhe+wMKhudnseNA5y/W7e6oKClR0eE7JUBNQwt9M9Pa7bLtSHGxdVZRnE51daLZ62fp3DnOmZJD/KeMKQBgxdYK131t3V/HsH59+NIJI1hesp/q+sOLhffWWHNGg8KI0uiCLD6Nw2Ld7o6KkqIorqlp9JLbJw0WLXJ3w6JFDOqbyd44nVXkRJPXx3WX/dRZlBziH5GfxZgB2by91f3GzKX76xg7MIeLjx1Ki8/w2qa9rde2VdQjcnhvvWCKBmSzu7ohKQuLuzIqSkpUtCRcCVDb2GKdoFpb6/KGWob2y2RPHHbfjkR9s4/s5gay0x2G7yLE//niQt4vq3QlFj6/4ZPyOsYNzOGY4XmMzM/ilXWH99UrLa9jRP8sMtPaxzF6QDbGwGc6hNcGFSUlKjp8pwSoaWihb580mDPH3Q1z5jA0L34H6DlR3+zj50t+22a/udC4nJgxvpDGFj/vuBjC21XVQJPXz9iBOYgIFx07hP98UkmFXQK/dV8tYwfmhL23qCAbQIfwQlBRUhTFNTWNXitTWrPG3Q1r1rRuL5TI3Qvqm71c9LXfkJ3usIVPhPhPGzuA/llpLFwX/UzQ0nIr4woIz0XHDsXnN/xrw16q61vYur+OY4aH3/2iaICKUjhUlJSoaKKkABhjqA4UOtx6q7ubbr2VIf3shaJxOKvIifpmHz9a+hRZTplShPjTPClcMGUIb2zax6GmyMduBDZwDYjShMF9GT8olz+/t41lJfsx5nDxRCh5fdIYmJvBlr0uh0J7CSpKiiMqRkowh5p9NHv9FOSkw9Ch7m4aOrR1kn9XAkWpodnHvpx85+G7KPFfctwwGlp8LPpv5Gxp6746BuRktNlG6OZzi9myr46b5n/EiPw+TC/Kd7x/8tC+bNpTE7GP3oaKkhIVo5NKCoe3CsrPzogpUxqcFzhAL3GidKjZy1MnfpEsp+G7KPFPH9WfCYNzeebdbRF//0vL6xg7MLtN28zJg/jBzPEcN7IfD39pasSFxpOG9qV0f51W4AWhoqQ4EvinpJKkAFTYC2BjzZQyUj2M6J8VlwP0nGho9vHBE191FoQo8YsIXz+tiI/31vJ+2YGwNsYYSvfVMW5gbrt7bzhzLP/4zmmcECFLApg0JA+v38TlHKfuioqS4oiKkRJMYP+6AdkZsHq1u5tsu6MKsxP64K1v9nHNt55wNnAR/+ypwyjITufJ5aVhr++raaK2yetYXeeGSUP7ArBxd3WHffQ0VJSUqOjonQKHh+8KctLD7rIdFtvuqMIcysrrYtpT7kioa/IyvibCfJCL+DPTPHx7xlG8s7WC/3zSvjy8ZJ9VoDB+cPuzmdwyKj+LvpmpfPhZ5+5M3p1RUVIUxRWBTVXzs9Md945rh203dmAOTV5/woodquqbmbP0z1HjisY1J49iSF4mv1xS0k5QA1VzE45AlFJShOlF+XywLfwQYW9ERUlxgaZKClTUNZGTkWrtTrB8ububbLvAEFdJgsqfD9a38Ms7fu9s4DL+zDQPt35hPB/tOMj81TvaXNuwu5rBfTPbHeAXKycU5VNWfqh1wa0T75dV8kkMG8V2V1SUlKjo8J0C1mF1BTn2AziGHR3AmjvxpAjrOvkAPScO1jfzrecfjBqXG/7n+GGcPCafBxZvZp+9h58xhpVlB5he1P9IQ+XE0ZaP1RGypfLaJq6Y+z4X/OadhA2BJgtXoiQi54lIiYiUisgdYa5niMh8+/pKESkKunan3V4iIjOj+RSR0baPrbbP9CPoo5+IvCQiH4vIZhE5JbavR1GUAHuqGw6fCzR9urubbLus9FSKO3CAXkc52NBCxYQpUeNyg4jwwKVT8PoMNzy/lmavn427a9hb08hpYwcccaxThvUjMy3FscoPrKwMrGPeA3NZPZWooiQiHuAJ4HxgEnCliEwKMbsOqDLGjAUeAR60750EXAFMBs4DnhQRTxSfDwKPGGPGAVW275j7sO/5DbDEGDMBOBbY7PaLUQ7Ts/8uU9yy+2Bj65ZBsWZKYJ1VtC7GA/Q6gs9v7TxReslVruJyw5jCHH51+TGs3l7FN55bzf2vbiYzLYULjh5yhNFaBxGePKaA5SX7HW22B5XTb9jVsyv13GRKJwKlxpgyY0wzMA+YHWIzG3jWfv0ScLaIiN0+zxjTZIz5FCi1/YX1ad9zlu0D2+clHelDRPoCnweeBjDGNBtjtMRFUTqAz2/YW9PIUHvLIHJdTu4H2U0b1Z+aRi8fx3leqaKuCWPge5dOcxWXW2YdM5QHLp3C+59U8l5ZJT+6YCJ5WWlHEOlhzpowkG2V9Y774G2rrKdPmofMtBQ27+nZmZLDcuc2DAOCZ/h2Aic52RhjvCJSDRTY7e+H3DvMfh3OZwFw0BjjDWMfax8NQDnwjIgcC6wBvm+Mafd/XUTmAHMARo4c6fQ99Fp0Tkkpr23C5zcMybMzpd3RNysNtfvcOGuoa/mW/a3rc+LBbrvCb8XydZzpIq5YuOqkkVw4ZQgNLT4Ghzm4r6OcOX4gsJGlH+/nutNHt7u+vfIQowdkk5aawuYevi2Rm0wp3JLo0MeUk01ntXekj1TgeOB3xpjjgENAu/kwAGPMXGPMdGPM9MLCwnAmvRIVIyXA7mrrQd+aKcVYfQcwqG8mk4b0ZXmJ+wP0OkJgN/KjNkZYIOs2/jDkZaV1qiCBdbjg2IE5LP14X9jr2yvrKRqQxYRBuWzp7XNKWJnHiKD3w4HQPzNabUQkFcgDDkS416m9Auhn+wjtqyN97DTGrLTbX8ISKSVGdO87JZB9tGZKc+e6uzHE7uyJA1m97UBrFVs82FllHZo3ZMFzruPqCsycPIj3yw60Kw33+vzsqKpnVEE2xYNzqTzUHLV8vDvjRpRWAePsqrh0rKKChSE2C4Fr7deXAUuN9SRbCFxhV86NBsYBHzj5tO9ZZvvA9vlyR/owxuwFdojIePues4FNLj6vYiN2DqqSpGyvtB70I/OzrIZXXnF3Y4jdF48fjt/A39bu7Mzw2lCyt46BuRmkLX7VdVxdgYuPHYbPb1i8fk+b9j3VjbT4DEUFWYwfZM2F9eTjLqKKkj2/cyPwGlb12gJjzEYRuVdELrbNngYKRKQUuAV7mMwYsxFYgCUGS4AbjDE+J5+2r9uBW2xfBbbvmPuw7/ku8LyI/BeYCjwQ6xfUm9EESQnwSXkdg/tmkp1hD2JcFaGyLZgQu9EDsjlxdD4vfvAZLT7/Ece1s6qeY+5+jdL9hx/Sm/bUMGFI38gxuo0/gYwfnMv4Qbks/KjtQNS2Smsa3MqU7EXIPXgIz02hA8aYxcDikLa7gl43Apc73Hs/cL8bn3Z7GVZ1Xmh7R/r4CHC/IEEJi4qTUlZ+iDGFQUc0zJrl7sYwdtd/fgzXPbuav6/dyZdPOLLColfW7aGm0csf3irjV5cfS2VdEx/vrWHm5HGRY3Qbf4K5eOpQfvVaCTsO1DPCzkq32RV5RQXZFOZk0D8rrUfPK+mODoqiRMQYQ1l5XVtR6mCmBFb589QR/fjVa1tadx7vKLsOWsOK/968j8q6Jhau240xcM7EQd0uUwKYPXUoIjB/1eHi5LKKQ2SlexjUNwMRoXhQbsK2a0oGKkpKVIzOKvVqDhxqpqbRy+gBQUc0iPPBdW0IYyci/PyLU6huaOYHf12Ht4PDeBt2VTN/1Q6KB+XQ0OzjC4+8zS/+9TEnj8nn6GF5kWN0G3+CGd4/i7MnDGTeqs9o9lrfy7aKQxQVZCN2zBMG57JlX12PLUBSUVKi0zN/9xWXBI7rbrMbttsHooPdxCF9uWvWJN78eD83zf+Ihmb3J68aY1iyYQ9XP7WSgbmZPP+Nk5l//SkcP6o/MycP5rErjoseYxd+oF9z8igq6pr51war4GHLvjqOCjqzqXhwLnVN3oQeL59IVJQURYnIhl2WKB09NO9w4wsvuLs5gt1XTinijvMn8Or6Pcz67Tss2bA3YvFDZV0TC1bt4JIn/8O3/rKWEfl9ePGbJ1OYm8HUEf34v69O57Erj2NgYH++SDG6jT8JfH5cIWMKs3l8aSm7Dzaw62ADU0f0a73eWoHXQ+eVXBU6KL2brvs3pZIINuyqZkR+n7Zb6ixa5G5eJordt844iolD+nLPwo186y9r6JeVxtQR/RjRP4s+6R7qm72U1zZRsreWbXZZelFBFvdfejSXTxtBemqEv6sj9e02/iSQkiL8cOZ4vvWXtVz9lLXM8rSxBa3Xx9miVLK3jrMmDEpKjPFERUlRlIhs2F3NlGF5bRs7IVMKcEZxIa/d/HmWl5Tz2sa9bNhVzbodB6lv9pGdkUr/rDQmDunL5dNHMGN8IZOG9G2dX+lw3104UwKYOXkwF0wZzOL1ezl97AAmDD68LVNenzSG5GVqpqT0Xrrw8LsSZ/bXNLK9sp6rTwop3b7oIncLUF3apXlSOHfSIM6d1Il/+Ufq2238SUJE+O2Vx/PNzx1k4pD2+wQWD8qN+8a2yULnlJSoaPVd7+W9skoAThkTcm5QB46uSDiR+k5mXC7xpAjHjexvnfQbwpRheWzZV0t9szfMnd0bFSVFURx575NK+mamtt/Ve8YMdw7c2sWDSH0nM65OYNqo/vj8hnU7et7ZSipKSlR0+K53Yozhna0VnDSmAE9KyBzO0KHunLi1iweR+k5mXJ3AcSOtary1n1UlOZLOR0VJiYCqUW9m/a5qdh1sCD/PU+tyPsOtXTyI1Hcy4+oE+mWlc1RhNmu3qygpvRCVpt7JvzbsxZMinDsxjCh18OiKhBKp7y54dEWsHD+yP2s/q+pxOzuoKClR6Wm/9Ep0fH7Dwo92c+pRBfTPTm9vsDrCAXodsYsHkfpOZlydxPSi/lTVt/BJeV2yQ+lUVJQURWnH8pL97DrYwJUnOuzirZlS0jn1KKsi8t3SyiRH0rmoKCmOBBIkzZN6H8+9t52BuRnO64a0+i7pjMjPYkR+H1aUViQ7lE5FRUlxRMWod7L2syre2lLOtacWkeZxeETcfbc7Z27t4kGkvpMZVydy2lEDeL+sssM7rXdFVJQUR/yaKvU6jDE8/PoW8rPT+dqpRc6GxcXuHLq1iweR+k5mXJ3IaWMHUNvoZcPummSH0mmoKCmOHNYkVaXewqvr97CitILvnjX28NHn4Zju8kBnt3bxIFLfyYyrEzn1KGuj1nd70BCeipLiiFbd9S4OHGrm7oWbmDIsj6+eUhTZePdud07d2sWDSH0nM65OpCAng4lD+vL2lvJkh9JpqCgpjrRmSqpNPR6f3/D9eR9S09jCL/5nSvsdHEJ56CF3jt3axYNIfSczrk5mxvhC1myvoqaxJdmhdAquRElEzhOREhEpFZE7wlzPEJH59vWVIlIUdO1Ou71ERGZG8ykio20fW22f6R3tw77mEZEPRWSR+69FAZ1K6k38csnHvLO1gnsvnszkoXnRb9BMqctw1oSBeP2GFVt7xhBeVFESEQ/wBHA+MAm4UkQmhZhdB1QZY8YCjwAP2vdOAq4AJgPnAU/aIhHJ54PAI8aYcUCV7TvmPoJi+z6w2d3XoQQTGL7TTKln88SyUv7wdhlfOXkUXz5hhLubNFPqMhw3oh95fdJY+vH+ZIfSKbjJlE4ESo0xZcaYZmAeMDvEZjbwrP36JeBssU7hmg3MM8Y0GWM+BUptf2F92vecZfvA9nlJB/tARIYDFwJPufs6lGBMyH+VnoUxhkf+vYVfvVbCJVOHcs/Fk90dngcwbVrn2sWDSH0nM65OJtWTwueLC1leUo7f3/3/tboRpWHAjqD3O+22sDbGGC9QDRREuNepvQA4aPsI7SvWPgAeBX4IRCziF5E5IrJaRFaXl/ecCcMjRTOknkuT18ftf/svv3lzK5dNG86vLj+WlGjzSMHojg5dijPHF1JR18SG3d3/KAs3ohTuNzX0ceVk01ntMfchIrOA/caYNWGutzU2Zq4xZroxZnphYWE0816Dv3X4TtWpJ7G98hCX/e49FqzeyffOHsevLjvGeZGsE7m5nWsXDyL1ncy44sAZxYWI0COG8Nz8Ju4EggeahwOhs4StNiKSCuQBByLc69ReAfSzfYT2FWsfpwEXi8g2rOHBs0TkLy4+r2KjUtSzMMbw0pqdXPjYCj47UM/cr0zjlnOL3Q/ZBTNrVufaxYNIfSczrjhQkJPB1BH9WFbS/Ud63IjSKmCcXRWXjlVUsDDEZiFwrf36MmCpsf68XghcYVfOjQbGAR84+bTvWWb7wPb5ckf6MMbcaYwZbowpsv0vNcZc4/J7UQgqdEhyHMqRs63iEFc/tZLb/rqOiUNyefV7p/OFyYM77nDLls61iweR+k5mXHHizPED+e/Og1TUNSU7lCMiqijZ8zc3Aq9hVbEtMMZsFJF7ReRi2+xpoEBESoFbgDvsezcCC4BNwBLgBmOMz8mn7et24BbbV4HtO+Y+OvqFKIfRdUrdn7omLw+/XsLMR99m/c5q7r/0aObPOYXh/bOOzLHufdflOGvCQIyB5d08W4qwj8hhjDGLgcUhbXcFvW4ELne4937gfjc+7fYy7Oq5kPaY+wi6vhxY7nRdCY9qUfelxedn3qod/OaNLVTUNXPRsUP58YUTGdQ3M9mhKXFi0pC+FOZmsLxkP5dNG57scDqMK1FSeieHCxxUnroLXp+fRf/dw2NLt1JWfogTR+fz1LUTmTqiX+d2pJlSlyMlRZhRXMhrG/fi9flJjbV4pYvQPaNWEoJKUfeh2etnwaodnPPwW9w0/yPSUlL4v69OZ/6ckztfkEB3Ce+izBg/kJpGLx/tOJjsUDqMZkqKIzqn1PVpaPbx0pod/P6tMnYdbGDKsDz+8JVpnDtxUGzrjmJlkctdu9zaxYNIfSczrjhy+rgBeFKEZSX7mV6Un+xwOoSKkhIV1aSux57qBp57bzsvrPyM6oYWjh/Zj/suPZoZxYUdK/GOldrazrWLB5H6TmZccSSvTxrTRvVn2cfl/GDmhGSH0yFUlBSlG/HhZ1X88d1tLF6/B2MM5x09mK+fNprpo/onRowCzJkDa6KuS3dvFw8i9Z3MuOLMjPGF/HJJCftqGrtlYYuKkhIVHb5LLo0tPpZs2Mtz721j7WcHyc1I5f+dVsRXTyliRP4RlnZ3FLcP9GQ++CP13UMFCaz1Sr9cUsJbJeV8ye0Gu10ILXRQlC7KJ+V13LdoE6f8/E1umv+RdQjfRZN473/P5kcXTkqeIAHcemvn2sWDSH0nM644M2FwLoP7ZrJ8S/fcckgzJSUqehx64mjy+nht4z5eWLmd98sOkJoifGHyIK46cRSnHlUQ3+KFWBg6tHPt4kGkvpMZV5wREWaML+TV/+6hxeePfV/DJKOipERFh+/iT1l5HfNX7eCva3Zy4FAzI/L78IOZ47l8+nAG5nbBeQHNlLo0M8YXMm/VDtZur+KkMQXJDicmVJQUJUnUNLbw6n/38NKanazZXoUnRThn4kCuOmkUnxs7oOtkReEYOtTd6a1u7eJBpL6TGVcCOG3sAFJThGUl5SpKSs9DE6XOw+c3vFtawUtrdvLaxr00ef2MHZjDHedP4NLjhnWfaqnVqzvXLh5E6juZcSWA3Mw0TijKZ3nJfu44v3uVhqsoKUoCKN1fx9/W7uQfa3ext6aRvD5pfGn6CC6bNpxjhucltpy7M9iyxd28jFu7eBCp72TGlSBmjC/k5//6mD3VDQzJ65PscFzTvWbAlKSgh/x1jIq6Jp57bxuXPvku5zz8FnPfLmPS0L48efXxfPCjs/nZJUdz7Ih+3U+QQPe+6wacOWEg0P12DddMSVE6kbomL69v3MvLH+1mRWkFPr9h/KBcfnTBRGYfN7RrFi10hOXLO9cuHkTqO5lxJYhxA3MY3DeTFaUVXHniyGSH4xoVJUU5Qpq8Pt4qKefldbt5Y9M+mrx+hvXrw/WfH8PFU4cyYXDfZIfY+cyZA3Pndp5dPIjUdzLjShAiwmljB7D04334/aZrF84EoaKkhCV4yE5H79rj8xtWllWycN1uFq/fQ02jl4LsdL58wghmTx3K8SMTvO1Popk+vXPt4kGkvpMZVwI5fVwBf1u7k017ajh6WF6yw3GFipISFr8KUTv8fsOaz6pYvH4Pi9fvYV9NE9npHmYePZiLjx3KaWMHdLuFih1mzpzOtYsHkfpOZlwJ5LSjBgDwbmmFipLSvWnx+Vtf9+YdHXx+w+ptB1i8fg//2rCX/bVNpKemMKO4kNlTh3H2xIFkpnmSHWbiyc11t9O2W7t4EKnvZMaVQAb2zWTcwBxWlFZw/RlHJTscV6goKWHx+nvv8J3X5+cDW4iWbNhHRV0TGakpnDl+IOdPGczZEweRk9HL/+m4XXiazAWqkfruwQtnQzlt7ADmrfqMJq+PjNSu/wdUL/+X1fN56p0ypo7oF/OBX96gTKk34PX5eb/sAIs37OG1DXupPNRMZloKZ00YyAVThnDm+IFk93YhCmb5crjoos6ziweR+k5mXAnm9LED+NN/trF2+0FOOarr7+7gagBcRM4TkRIRKRWRO8JczxCR+fb1lSJSFHTtTru9RERmRvMpIqNtH1ttn+kd6UNERojIMhHZLCIbReT7sX893Z/7Xt3MZb9/L+b7Glp8ra97aqbU0Ozj35v2cftL/+WE+9/gmqdX8o+1uzj5qAKevPp41v7kXJ68ehqzjhmqghSK28q1ZFa4Req7h1feBXPSmHw8KcK7pRXJDsUVUf+liYgHeAI4F9gJrBKRhcaYTUFm1wFVxpixInIF8CDwZRGZBFwBTAaGAm+ISLF9j5PPB4FHjDHzROT3tu/fdaAPL3CrMWatiOQCa0Tk3yFx92j8R1Ct0NDsi27UDdlb3cibH+/jzc37ebe0giavn5yMVM6cMJALpwzmjOKB9Env+kMcSeeVVzrXLh5E6juZcSWY3Mw0jh2ex4rSCm6bOT7Z4UTFzZ9/JwKlxpgyABGZB8wGpAbP0AAAFFRJREFUgh/us4G77dcvAY+LVQ87G5hnjGkCPhWRUtsf4XyKyGbgLOAq2+ZZ2+/vYu3DGPMesAfAGFNr+x4WEneP5lCzt8P3tsmUOiOYJGGMYePuGt7YbAnR+l3VAAzv34crTxzJORMHceLofNJTe0nVXGdx1VXwwgudZxcPIvWdzLiSwOljB/D4slKqG1rI65OW7HAi4kaUhgE7gt7vBE5ysjHGeEWkGiiw298PuXeY/TqczwLgoDHGG8a+I30AYA/1HQesDPcBRWQOMAdg5Mjus/I5GoeaOp7tBGdK3W2bocYWH++VVfLGpn0s/Xg/e6obEYGpI/rxg5njOWfiIIoH5fTsdUTxZtaszrWLB5H6TmZcSeC0sQN4bGkp75dVMnPy4GSHExE3ohTuX27oU8rJxqk93J+lkew70od1k0gO8DfgJmNMTRhbjDFzgbkA06dP715P4AjUNXVOptQdqKhrYunH+3lj0z5WlFZQ3+yjT5qHz40bwM3nFnPm+IEU5mYkO8yew1VXRbeJxS4eROo7mXElgakj+5GRmsLKsgM9QpR2AsEHvQ8HQuspAzY7RSQVyAMORLk3XHsF0E9EUu1sKdg+5j5EJA1LkJ43xvzdxWftUdQfwfBdcJbVFVXaGMOWfXW8sXkfb2zex0c7DmIMDO6byaXHDeOcSYM4ZUxB71xDlAhE3FXAuLWLB5H6TmZcSSAj1cO0Uf15v6wy2aFExY0orQLGichoYBdWUUHonxkLgWuB94DLgKXGGCMiC4EXRORhrCKEccAHWNlNO5/2PctsH/Nsny93pA97vulpYLMx5uFYv5iewJFkSgfrmzsxks6h2evng08PtArRzqoGAKYMy+Oms4s5e+JAJg/tq8NyicDtAz2ZD/5IffciQQpw0ugCHn1zC9X1LeRldd15paiiZM/f3Ai8BniAPxpjNorIvcBqY8xCrIf/n+0igwNYIoNttwCruMAL3GCM8QGE82l3eTswT0TuAz60fRNrHyJyOvAVYL2IfGT7+F9jzOKOfVXdj3o72+nIJP6BYFFK4r/fqkPNLN+ynzc27eftLeXUNnnJSE3htLED+PaMozh7wiAG5/WQnbe7Ey+84G4IzK1dPIjUdzLjShInjcnHvAGrth3gnEmDkh2OI64WX9gP8sUhbXcFvW4ELne4937gfjc+7fYyDlfoBbfH1IcxZgXh55t6DYHqu4wO7Md2oO6wKCV6m6FPyut4c/M+3ti0n9XbD+A3MCAngwumDOGcSYM4bWwBWem6biipLFrk7qHu1i4eROo7mXEliakj+pGemsLKTyu7vygp3ZPaRluU0mIXpb01jaR7UmhOwM4OXp+f1durLCHavJ9PKw4BMGFwLt+ZMZZzJg3imGF53Wbr/V6B23LqZJZdR+q7F5WDB8hM83DciH68X3Yg2aFEREWpB1NR1wRYi+diZVvlIYoGZLFlX11cht9rGlt4q6ScNzfvY1lJOdUNLaR5hJPHFPC1U4s4e+JAhvfP6vyOlc7hoovcLUB1axcPIvWdzLiSyEljCnh86VZqGlvo24HnQiJQUerBBEQpVnx+Q1n5IT43bgBb9tV1WjyfVdZbi1g/3sfKsgN4/Yb+WWmcPXEg504cxOeKC3Wj0+6CHl3RLTl5dD6PGVizrar1uPSuhj4BejB7qxuBtsdQuGHT7hrqm30cP7I/r23c1+EZJWMM63dV8/rGffx70z5K9llHBYwdmMN1nxvNuRMHcdzI/nh0WK77MWNG59rFg0h9JzOuJHLcyP6keYT3P61UUVIST0AEvL7YZOW1jXtJETjVPiAsluG7Zq+f98sqeX3TXt7YtJ+9NY2kCEwvyufHF07knImDKBqQHVM8Shdk6FB35xG5tYsHkfpOZlxJpE+6h6ldfF5JRamHsvtgAzsOWOt4YsmUqutbePGDz5gRww4IPr/hvU8qWbhuF0s27KWm0UufNA+fLx7AbZPGc9aEgeRnp3focyhdFLcP9GQ++CP13QsFKcBJowv43VufUNfk7ZLD5boLZQ/EGMPv3/oEEZgxvtC1KPn8hh/+bR1V9c3ccm4xgTWofodUqay8jp8t2sRJD7zJNU+vZPH6vZwzaRBPfXU6H951Ln/4ynQumzZcBaknokdXdFtOGpOPz29Ys70q2aGEpevJpNJhjDGsKK3gsTe3smpbFV87tYjUFOGDT6On6pV1Tdy8YB1vbynnJ7MmcfSwPKoOWWuVQg/8W7P9AI++sZV3tlaQmiKcM3EQs6cO5cwJvfRo8N7I6tXuigXc2sWDSH0nM64kM21Uf1JThJVllZxRXJjscNqhotQD8PsNb2zexxPLSlm3s5rBfTO5d/ZkrjlpFL96vSTinFJtYwtPr/iUp975lGavnwcuncJVJ1k7pQd2gmjyWqJ04FAzP/7nehav38uAnAxuPbeYL584goG5uqNCr0MzpW5LVnoqxwzP67L74KkodWO8Pj+vrt/Dk8s+oWRfLSPzs3jg0in8z/9v796Do6ruAI5/fyQxolCEIMECSkBewQhIikAtICqgYqWtM9Laaq2PPrQvbX2M2sFOpy22iqO2pSrOqGN9VK0yThFTX3QGCgZBBGIggagMjxgegvJM9tc/zll2DZvNBu5mb3Z/n5lM7p4995x7z+TuL/fuvb8zug+F+e6MpaCTcLApgqp+ISfcvoNNPLGkjrlv17Jz7yGmDe/NzVMGM6i46+E6hT4oHWyMUPvJZ1zxyFJ2fO4u7V37tRLLqpDLJk1yU4oHVS8dkvWdye0KgbMHFPHIog3sPdgYuuM4XFtjUrL/UBMvvLuJv7+9gY927OX0Xl2Yc/kILjnzy+Q3SykUfd0UUfLzhENNEZ5552MefH099XsOMHHwyfxqyhDK+nY7op/8vE50Eve805XzltEYifDiT8ZzRp8j65ocM2tWsPXSIVnfmdyuEBg3oIi/vVXLO3U7Q3cJz4JSB1JTv4enl33Mi+9uYufeQ4zo2407Lh7NBcOKW0zBU+CDUmNEWbaxgTteWs3Ghs8Z078HD33nLMaU9EjaZ2F+Ho8v+ZC8TsLzPxpnAck4gwcHWy8dkvWdye0KgfL+7nmlxbUNFpRM22z9dD+vrd3K/JWbqfxwJ/mdhAtKi/nu2NMYP7Co1WkaCvLc+48vruOPr35ASdGJzLuqnMlDe6U0xUN0sr8ZI/sw6tTux75DJjuUl8Pm5tOqHUO9dEjWdya3KwROOC6fUf26s6Q2fN8rWVAKobqGz1m4ZiuvrtnKio92AS4Lwu0XDuVbo/vSs0vqM6jm+zOoPyz4gMlDe/HQd0Yd1TXk6yaUtHkdk8VS/UDP5Ad/sr5zOCBFjRtYxINvrA/d/Er2nFIIqCpVW3Yzp2Id0+5fxKQ/v8UfFnxAY5Py66lD+M9NE/jPTRP54cSBbQpIAAVxcyndP3NkmwPSXdNLufacEob2/lKb1jNZ7t57g62XDsn6zuR2hcT4gUVEFJZuDNfZkp0pZUgkoqzctIuFq90Z0Yfb9yICX+nfg7umlzJ1eHEgWbKH+Lvpbjh34FFlBb7mHDtDMgnYmVKHN/LUkzi+oBOLa7czZXjvTG/OYaI5OC1wMuXl5VpZWZmWtpsiSmXdDhas3sqrq7eydfd+CvKE8QN7Mu2M3pw/rDjl1D5tUb11D6f36mKJT40xX/C9eUup332Ahb+ccMxtichyVS0/1nbsTKkdrNu2h+fe+ZiXVm6m4bMDFOZ3YsLgk7m1bAiThxbTrXN6r+cO6d219UrGtMXo0bB8eXD10iFZ35ncrhAZN7CIe16t5pM9B9LyD/HRsKCUJpGIUlG1jblv17Lio10U5AnnDS1m+ohTOHdIL04MYSJEY1JmGR2ygpsJoJrFtQ1cOrJPpjcHsKCUFpV1O7jr5TVUbdnNqT1O4M6Lh/GNUX0oauNNCsaEVtcUz75TrZcOyfrO5HaFSFmfbvTsUshra7aFJiildPediEwTkWoRqRGR2xK8Xygiz/r3l4pI/7j3bvfl1SIytbU2RaTEt7Het3lc0H2kS2NThN//u4rL5i5h975DzLl8BG/cPJFrvzbAApLJLtOnB1svHZL1ncntCpG8TsLU4cW8WV3Pfv9MYqa1GpREJA/4C3AhUAp8W0RKm1W7BtipqqcDc4DZft1SYCYwHJgG/FVE8lppczYwR1UHATt920H3EbhDTRF+/NS7PLxoA98deyqv/XIC3xjV94i0P8ZkhXXrgq2XDsn6zuR2hcy0M3qz92ATr1fVZ3pTgNTOlMYANaq6QVUPAs8AlzarcynwuF9+HjhPXLqAS4FnVPWAqm4Eanx7Cdv060z2beDbnBFkH6kNS9tEIsotz6+iYu02Zl1Syu9mlNl3Ria7We67rDFuQBH9i07gvopqdvjpajIplU/OPsDHca83AWe3VEdVG0XkU6DIl/+v2brRC5eJ2iwCdqlqY4L6QfVxBBG5HohOrvKZiGwHGhLVbc3Vs+Hqo1kxnHpylOOQhWwsYtxY3H13arVTrZcOyfo+9u3Kur+Jol8d9ao9gdOC2IZUglKih1uaP9zUUp2WyhOdoSWrH2QfRxaqPgwcvh1HRCqDuN++o7NxiLGxiLGxcGwcYvxY9A+irVQu320C+sW97gs0fxz6cB0RyQe6ATuSrNtSeQNwkm+jeV9B9WGMMSakUglK7wCD/F1xx+FuKpjfrM584Cq/fBnwhrpUEfOBmf7OuRJgELCspTb9Om/6NvBtvhxkH6kNizHGmExo9fKd//7mRmAhkAc8pqprROS3QKWqzgfmAU+KSA3u7GWmX3eNiDwHrAUagRtUtQkgUZu+y1uBZ0Tkd8AK3zYB99Eae7LOsXGIsbGIsbFwbBxiAhsLy31njDEmNOwhGmOMMaFhQckYY0xoWFCK095piTJBRB4TkXoRWR1X1kNEKnxqpwoR6e7LRUQe8OOxSkTOilvnKl9/vYhclaivMBORfiLypohUicgaEfm5L8/FsTheRJaJyHt+LO725YGl/OpIfEaYFSLyin+dq+NQJyLvi8hKEan0Zek/PlTVftz3anlALTAAOA54DyjN9HalYT8nAGcBq+PK7gFu88u3AbP98kXAAtyzYGOBpb68B7DB/+7ul7tnet/aOA6nAGf55a7AOlw6qlwcCwG6+OUCYKnfx+eAmb58LvBjv/wTYK5fngk865dL/XFTCJT44ykv0/t3FONxE/AP4BX/OlfHoQ7o2aws7ceHnSnFtFtaokxS1UW4uxfjxadwap7a6Ql1/od7huwUYCpQoao7VHUnUIHLO9hhqOoWVX3XL+8BqnCZQHJxLFRVP/MvC/yPElzKrw5DRPoCFwOP+tdBpj7LBmk/PiwoxSRKpxSOXO7pV6yqW8B9WAO9fHlLY5JVY+Uvu4zCnSHk5Fj4S1YrgXrcB0ctKab8AuJTfnX0sbgfuAWI+Ncppz4ju8YB3D8mr4nIcnGp2KAdjg/LGhqTSjqlXNPW1E4djoh0AV4AfqGqu90/uomrJijLmrFQ92zfSBE5CfgXMCxRNf87K8dCRKYD9aq6XEQmRYsTVM3qcYjzVVXdLCK9gAoR+SBJ3cDGws6UYnI5LdE2f6qN/x3NYZ/VKZxEpAAXkJ5S1Rd9cU6ORZSq7gLewn0vEFTKr47iq8DXRaQOd/l+Mu7MKdfGAQBV3ex/1+P+URlDOxwfFpRicjktUXwKp+apna70d9aMBT71p+wLgSki0t3ffTPFl3UY/tr/PKBKVe+LeysXx+Jkf4aEiHQGzsd9xxZUyq8OQVVvV9W+6hKLzsTt1xXk2DgAiMiJItI1uoz7u15Nexwfmb7DI0w/uDtI1uGup9+R6e1J0z4+DWwBDuH+i7kGdx38dWC9/93D1xXcRIm1wPtAeVw7P8B9gVsDXJ3p/TqKcTgHdxlhFbDS/1yUo2NxJi6l1yr/wfMbXz4A92FaA/wTKPTlx/vXNf79AXFt3eHHqBq4MNP7dgxjMonY3Xc5Nw5+n9/zP2uin4ftcXxYmiFjjDGhYZfvjDHGhIYFJWOMMaFhQckYY0xoWFAyxhgTGhaUjDHGhIYFJWOMMaFhQcmYdiAiM0TkERF5WUSm5ErfxrSVBSVj2oGqvqSq1wHfBy4HlwhWRPb5RKj4sh+KiIrIxLiyG33Z+S21LyKni8j7zcoKRWQjsC5B3539PDkHRaRngLtqzDGxoGRM+7oT9+R7VK2qjox7fSYus8IwABE5AZd14xPck/It2QD0E5H4Y/p64G1VXdu8b1Xd5/vtcDnZTHazoGRMwERkkIi8JSKVInKPn41TRGQ2sED9PE4tKMOlghrqX/8Ml8omoqrbfPsl/lJcpbgZY4eoagT4COjv63QGbgZmtaFvYzLOgpIxARKRPOAJ4CZVLQc643KH/RSX6PQyEflRkiaG4WY6HSoi3XCX2xbjctJFM5s/Gtf+LNwMoOCSqEaD2Q3AfFWta0PfxmSczadkTLBmAGvjzkiqcJPEPQA8kGxFEekHbFfVDX4Om1uAB4HBuEt60faHAy/4uZ/ygf/G9TVERBbhgtJYgFT6NiYsLCgZE6xRuIzjUSNwM7mm4kxi3xvtwU0bPQY3p080yI3AZWyel2D9KtwcQD/HzRG1rW2bbkzm2eU7Y4K1HX8JTUTOBq4kdpbTmjJiQelPwI3qZoQti2tjCzA1ekODiJRJbLrcKlwQ+4Ff35gOx4KSMcF6Eij3t2d/ExekalJctwz/3ZGqvqKqS3x5KRC9g+4x3HFb5W8lv1Vj889U+zYeVtVPj3lPjMkAu3xnTIBUtQE4Gw5/RzTJ3xmXyrpXtFDeK255H7FZUJvXO4Ad06aDszMlY9JnBMkv3TUB3eIfnm0v0YdngQIgpaBpTHuwmWeNMcaEhp0pGWOMCQ0LSsYYY0LDgpIxxpjQsKBkjDEmNCwoGWOMCQ0LSsYYY0LDgpIxxpjQsKBkjDEmNCwoGWOMCY3/A4ex6sHRX9zcAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "integrate = True\n",
     "\n",
     "if integrate:\n",
     "\n",
     "    probs = total_f_fit.pdf(test_q, norm_range=False)\n",
     "\n",
-    "    calcs_test = zfit.run(probs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if integrate:\n",
-    "\n",
+    "    calcs_test1 = zfit.run(probs)\n",
+    "    \n",
     "    plt.clf()\n",
-    "    plt.plot(test_q, calcs_test)\n",
-    "    # plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
-    "    # plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
-    "    # plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
-    "    # plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.plot(test_q, calcs_test1)\n",
+    "    plt.title('Fitted curve before smearing')\n",
+    "    plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
     "    plt.ylim(0.0, 1.5e-6)\n",
     "    plt.xlabel(r'$q^2 [MeV^2]$')\n",
     "    plt.savefig('test.png')"
@@ -2004,10 +1893,92 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# if integrate:\n",
+    "\n",
+    "#     plt.clf()\n",
+    "#     plt.plot(test_q, calcs_test1)\n",
+    "#     plt.title('Fitted curve before smearing')\n",
+    "#     plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "#     plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "#     plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "#     plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "#     plt.ylim(0.0, 1.5e-6)\n",
+    "#     plt.xlabel(r'$q^2 [MeV^2]$')\n",
+    "#     plt.savefig('test.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Integration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "91404000\n",
+      "0 45\n",
+      "1 45\n",
+      "2 45\n",
+      "3 45\n",
+      "4 45\n",
+      "5 45\n",
+      "6 45\n",
+      "7 45\n",
+      "8 45\n",
+      "9 45\n",
+      "10 45\n",
+      "11 45\n",
+      "12 45\n",
+      "13 45\n",
+      "14 45\n",
+      "15 45\n",
+      "16 45\n",
+      "17 45\n",
+      "18 45\n",
+      "19 45\n",
+      "20 45\n",
+      "21 45\n",
+      "22 45\n",
+      "23 45\n",
+      "24 45\n",
+      "25 45\n",
+      "26 45\n",
+      "27 45\n",
+      "28 45\n",
+      "29 45\n",
+      "30 45\n",
+      "31 45\n",
+      "32 45\n",
+      "33 45\n",
+      "34 45\n",
+      "35 45\n",
+      "36 45\n",
+      "37 45\n",
+      "38 45\n",
+      "39 45\n",
+      "40 45\n",
+      "41 45\n",
+      "42 45\n",
+      "43 45\n",
+      "44 45\n",
+      "Full integration finished in 12 min, 30 s\n",
+      "45702\n"
+     ]
+    }
+   ],
+   "source": [
     "# total_f_fit.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n",
     "\n",
     "if integrate:\n",
@@ -2016,7 +1987,7 @@
     "\n",
     "    _max_size = 2000000\n",
     "\n",
-    "    step_size = 1000\n",
+    "    step_size = 2000\n",
     "\n",
     "    steps = np.arange(x_min, x_max, 0.1/step_size)\n",
     "\n",
@@ -2038,15 +2009,17 @@
     "\n",
     "        inte_fl = zfit.run(_c)\n",
     "\n",
-    "        for i in range(int(l/step_size)):\n",
+    "        for i in range(int(_max_size/step_size)):\n",
     "            _list.append(np.mean(inte_fl[int(i*step_size):int((i+1)*step_size)]))\n",
     "\n",
-    "    _c = total_f_fit.pdf(steps[(parts-1)*_max_size:], norm_range=False)\n",
+    "    _c = total_f_fit.pdf(steps[(parts)*_max_size:], norm_range=False)\n",
     "\n",
     "    inte_fl = zfit.run(_c)\n",
+    "    \n",
+    "    rest = l%_max_size\n",
     "\n",
-    "    for i in range(int(l/step_size)):\n",
-    "        _list.append(np.mean(steps[int(i*step_size):int((i+1)*step_size)]))\n",
+    "    for i in range(int(rest/step_size)):\n",
+    "        _list.append(np.mean(inte_fl[int(i*step_size):int((i+1)*step_size)]))\n",
     "\n",
     "    print('Full integration finished in {}'.format(display_time(int(time.time()-start))))\n",
     "    print(len(_list))"
@@ -2054,25 +2027,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if integrate:\n",
-    "\n",
-    "    dirName = 'data/CLs'\n",
-    "    with open(\"{}/inte_100keV_steps.pkl\".format(dirName), \"wb\") as f:\n",
-    "        pkl.dump(_list, f, pkl.HIGHEST_PROTOCOL)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2084,44 +2044,349 @@
     }
    ],
    "source": [
-    "for step in range(2*ste):\n",
-    "    if step%2 == 0:\n",
-    "        floaty = True\n",
-    "    else:\n",
-    "        floaty = False\n",
-    "    for key in pull_dic.keys():\n",
-    "        if not key == 'Dbar_s':\n",
-    "            continue\n",
-    "        if not os.path.exists('data/CLs/plots/{}'.format(key)):\n",
-    "            os.mkdir('data/CLs/plots/{}'.format(key))\n",
-    "        plt.clf()\n",
-    "        plt.title('Amplitude of ' + r'$\\overline{D}$' + ' contribution with ' + r'$C_{ \\tau \\tau }$' + ' = {:.2f}'.format(Ctt_steps[int(step/2)]))\n",
-    "        plt.hist(pull_dic[key][step], bins = 50, range = (-5,5))\n",
-    "        plt.xlabel('Pull')\n",
-    "        plt.savefig('data/CLs/plots/{}/{:.2f}Ctt{}s{}f.png'.format(key, Ctt_steps[int(step/2)], step, floaty))"
+    "if integrate:\n",
+    "\n",
+    "    dirName = 'data/CLs'\n",
+    "    with open(\"{}/inte_100keV_steps.pkl\".format(dirName), \"wb\") as f:\n",
+    "        pkl.dump(_list, f, pkl.HIGHEST_PROTOCOL)\n",
+    "\n",
+    "if integrate:\n",
+    "    \n",
+    "    center = np.arange(x_min+0.05, x_max, 0.1)\n",
+    "\n",
+    "    probs = total_f_fit.pdf(test_q, norm_range=False)\n",
+    "\n",
+    "    calcs_test1 = zfit.run(probs)\n",
+    "    \n",
+    "    plt.clf()\n",
+    "    plt.title('Integrated curve before smearing')\n",
+    "    plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.ylim(0.0, 2.5e-6)\n",
+    "    plt.plot(center,_list)\n",
+    "    plt.xlabel(r'$q^2 [MeV^2]$')\n",
+    "    plt.savefig('integrated_before_smearing.png')\n",
+    "# plt.plot(inte_fl)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Smearing"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gauss(x, area, m, sig = 7, step_s = 0.1):\n",
+    "    \n",
+    "    prefac = 1/(sig*np.sqrt(np.pi))\n",
+    "    \n",
+    "    y = np.zeros(np.shape(x))\n",
+    "    \n",
+    "    start = time.time()\n",
+    "    \n",
+    "    for i in range(len(area)):\n",
+    "        \n",
+    "        y += prefac*area[i]*np.exp(-1/2*((x-m[i])/sig)**2)\n",
+    "        \n",
+    "        t = time.time()\n",
+    "        \n",
+    "        j = i+1\n",
+    "        \n",
+    "        if i % 500 == 0:\n",
+    "            print(display_time(int((t-start)/(j)*(len(area)-j))))\n",
+    "    \n",
+    "    return y*step_s*0.7\n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "\n",
+    "#             _y += prefac*area*np.exp(-1/2*((x-_m)/sig)**2)   \n",
+    "\n",
+    "# gauss(scan_x, _list)\n",
+    "\n",
+    "# print(np.shape(_list)[0], np.shape(center)[0])\n",
+    "\n",
+    "# print(int(1.25/0.1)*0.1)\n",
+    "\n",
+    "# print((1.25-1.25%0.1)/0.1)\n",
+    "\n",
+    "# print((x_max-x_min)*10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15 min, 11 s\n",
+      "8 min, 43 s\n",
+      "8 min, 37 s\n",
+      "8 min, 24 s\n",
+      "8 min, 24 s\n",
+      "8 min, 19 s\n",
+      "8 min, 12 s\n",
+      "8 min, 5 s\n",
+      "7 min, 58 s\n",
+      "7 min, 50 s\n",
+      "7 min, 46 s\n",
+      "7 min, 39 s\n",
+      "7 min, 33 s\n",
+      "7 min, 27 s\n",
+      "7 min, 20 s\n",
+      "7 min, 14 s\n",
+      "7 min, 8 s\n",
+      "7 min, 2 s\n",
+      "6 min, 55 s\n",
+      "6 min, 51 s\n",
+      "6 min, 45 s\n",
+      "6 min, 39 s\n",
+      "6 min, 33 s\n",
+      "6 min, 27 s\n",
+      "6 min, 21 s\n",
+      "6 min, 15 s\n",
+      "6 min, 9 s\n",
+      "6 min, 3 s\n",
+      "5 min, 57 s\n",
+      "5 min, 51 s\n",
+      "5 min, 45 s\n",
+      "5 min, 39 s\n",
+      "5 min, 33 s\n",
+      "5 min, 28 s\n",
+      "5 min, 22 s\n",
+      "5 min, 16 s\n",
+      "5 min, 10 s\n",
+      "5 min, 5 s\n",
+      "4 min, 59 s\n",
+      "4 min, 54 s\n",
+      "4 min, 48 s\n",
+      "4 min, 42 s\n",
+      "4 min, 37 s\n",
+      "4 min, 32 s\n",
+      "4 min, 26 s\n",
+      "4 min, 21 s\n",
+      "4 min, 15 s\n",
+      "4 min, 9 s\n",
+      "4 min, 4 s\n",
+      "3 min, 58 s\n",
+      "3 min, 52 s\n",
+      "3 min, 46 s\n",
+      "3 min, 40 s\n",
+      "3 min, 35 s\n",
+      "3 min, 29 s\n",
+      "3 min, 23 s\n",
+      "3 min, 18 s\n",
+      "3 min, 12 s\n",
+      "3 min, 6 s\n",
+      "3 min, 1 \n",
+      "2 min, 55 s\n",
+      "2 min, 49 s\n",
+      "2 min, 44 s\n",
+      "2 min, 38 s\n",
+      "2 min, 32 s\n",
+      "2 min, 27 s\n",
+      "2 min, 21 s\n",
+      "2 min, 15 s\n",
+      "2 min, 10 s\n",
+      "2 min, 4 s\n",
+      "1 min, 59 s\n",
+      "1 min, 53 s\n",
+      "1 min, 47 s\n",
+      "1 min, 42 s\n",
+      "1 min, 36 s\n",
+      "1 min, 31 s\n",
+      "1 min, 25 s\n",
+      "1 min, 19 s\n",
+      "1 min, 14 s\n",
+      "1 min, 8 s\n",
+      "1 min, 3 s\n",
+      "57 s\n",
+      "52 s\n",
+      "46 s\n",
+      "40 s\n",
+      "35 s\n",
+      "29 s\n",
+      "24 s\n",
+      "18 s\n",
+      "13 s\n",
+      "7 s\n",
+      "2 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "scan_x = np.linspace(x_min, x_max, 1e5)\n",
+    "\n",
+    "center = np.arange(x_min+0.05, x_max, 0.1)\n",
+    "\n",
+    "# for i in range(len(steps)/step_size):\n",
+    "#     center.append(i)\n",
+    "\n",
+    "\n",
+    "sum_y = gauss(x =scan_x, m = center, area = _list)\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:24: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\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"
+    }
+   ],
+   "source": [
+    "plt.clf()\n",
+    "plt.title('Fitted curve after smearing')\n",
+    "plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.ylim(0.0, 2.5e-6)\n",
+    "plt.plot(scan_x,sum_y)\n",
+    "plt.xlabel(r'$q^2 [MeV^2]$')\n",
+    "plt.savefig('curve_after_smearing.png')\n",
+    "\n",
+    "plt.clf()\n",
+    "plt.title('Fitted curve after convolution')\n",
+    "plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.ylim(0.0, 2.5e-6)\n",
+    "plt.plot(test_q, calcs_test, label = 'inf. precision')\n",
+    "plt.plot(scan_x,sum_y*0.1, label = 'smeared')\n",
+    "plt.legend()\n",
+    "plt.xlabel(r'$q^2 [MeV^2]$')\n",
+    "# plt.xlim(jpsi_mass, psi2s_mass)\n",
+    "plt.savefig('curve_after_smearing-vs-before_smearing.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create convonluted data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[18.0, 49.0, 58.0, 80.0, 93.0, 111.0, 111.0, 107.0, 227.0, 120.0, 137.0, 154.0, 159.0, 198.0, 183.0, 216.0, 254.0, 232.0, 247.0, 274.0, 296.0, 315.0, 346.0, 370.0, 363.0, 422.0, 440.0, 459.0, 483.0, 591.0, 919.0, 2475143.0, 1186.0, 673.0, 636.0, 605.0, 715.0, 88046.0, 116824.0, 1112.0, 814.0, 769.0, 863.0, 1081.0, 844.0, 673.0, 699.0, 467.0, 306.0, 82.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "nbins = 50\n",
+    "\n",
+    "b_w = int(len(scan_x)/nbins)\n",
+    "\n",
+    "conv_data = []\n",
+    "\n",
+    "means = []\n",
+    "\n",
+    "_ = np.linspace(x_min, x_max, nbins+1)\n",
+    "\n",
+    "bin_centers = []\n",
+    "\n",
+    "_area = np.mean(sum_y)#*(x_max-x_min)\n",
+    "\n",
+    "# print(_area)\n",
+    "\n",
+    "_sum_y = sum_y/_area*pdg[\"number_of_decays\"]/100\n",
+    "\n",
+    "# print(np.mean(_sum_y))\n",
+    "\n",
+    "for i in range(nbins):\n",
+    "    bin_centers.append((_[i]+_[i+1])/2.)\n",
+    "    means.append(np.mean(_sum_y[i*b_w:(i+1)*b_w]))\n",
+    "    _width = np.sqrt(means[-1])\n",
+    "    conv_data.append(np.around(np.random.normal(means[-1], _width)))\n",
+    "    \n",
+    "print(conv_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "0.36486862499697814"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "rho_scale*rho_width/rho_mass+omega_scale*omega_width/omega_mass+phi_scale*phi_width/phi_mass"
+    "plt.clf()\n",
+    "# plt.hist(x = bin_centers, bins = nbins, range = (x_min, x_max), weights = conv_data, histtype = 'step', label = 'Example data')\n",
+    "# plt.plot(bin_centers, conv_data, '.',, color = 'r')\n",
+    "plt.plot(scan_x,_sum_y, label = 'Convoluted fit', color = 'darkorange')\n",
+    "plt.errorbar(bin_centers, conv_data,yerr = np.sqrt(conv_data), elinewidth=1, fmt = '.', ecolor = 'forestgreen', color = 'forestgreen', label = 'Example data')\n",
+    "plt.ylim(0.,1200)\n",
+    "plt.legend()\n",
+    "plt.title('Convoluted fit with possible data set')\n",
+    "# plt.xlim(jpsi_mass, psi2s_mass)\n",
+    "# print(conv_data)\n",
+    "plt.savefig('smeared_fit_with_data.png')"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
@@ -2134,18 +2399,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/chess-elo-rating-distribution.png b/chess-elo-rating-distribution.png
new file mode 100644
index 0000000..4b3c58d
--- /dev/null
+++ b/chess-elo-rating-distribution.png
Binary files differ
diff --git a/curve_after_smearing-vs-before_smearing.png b/curve_after_smearing-vs-before_smearing.png
new file mode 100644
index 0000000..23eb449
--- /dev/null
+++ b/curve_after_smearing-vs-before_smearing.png
Binary files differ
diff --git a/curve_after_smearing.png b/curve_after_smearing.png
new file mode 100644
index 0000000..ad7467e
--- /dev/null
+++ b/curve_after_smearing.png
Binary files differ
diff --git a/data/CLs/inte_100keV_steps.pkl b/data/CLs/inte_100keV_steps.pkl
index 97f6fac..b595e54 100644
--- a/data/CLs/inte_100keV_steps.pkl
+++ b/data/CLs/inte_100keV_steps.pkl
Binary files differ
diff --git a/data/CLs/plots/set_histo0.png b/data/CLs/plots/set_histo0.png
index 811b8ff..0c7da8d 100644
--- a/data/CLs/plots/set_histo0.png
+++ b/data/CLs/plots/set_histo0.png
Binary files differ
diff --git a/fitted_before_smearing.png b/fitted_before_smearing.png
new file mode 100644
index 0000000..c3bbe18
--- /dev/null
+++ b/fitted_before_smearing.png
Binary files differ
diff --git a/integrated_before_smearing.png b/integrated_before_smearing.png
new file mode 100644
index 0000000..23fd2ea
--- /dev/null
+++ b/integrated_before_smearing.png
Binary files differ
diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb
index 720368e..773a860 100644
--- a/raremodel-nb.ipynb
+++ b/raremodel-nb.ipynb
@@ -9,31 +9,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py:57: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`\n",
-      "  warnings.warn(\"Not running on Linux. Determining available cpus for thread can fail\"\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-      "For more information, please see:\n",
-      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-      "  * https://github.com/tensorflow/addons\n",
-      "If you depend on functionality not listed there, please file an issue.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "\n",
@@ -66,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -280,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -335,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -545,19 +523,9 @@
   },
   {
    "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": [
     "# formfactors\n",
     "\n",
@@ -666,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -696,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -713,7 +681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -751,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -795,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -811,7 +779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -871,22 +839,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "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",
@@ -912,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -928,7 +883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -944,7 +899,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -958,7 +913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1015,7 +970,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1099,7 +1054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1115,7 +1070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1251,7 +1206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1330,7 +1285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1368,18 +1323,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "36668\n",
-      "5404696\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# for param in total_f_fit.get_dependents():\n",
     "#     if param.floating:\n",
@@ -1405,33 +1351,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.         0.00025263 0.00040526 0.00055789 0.00071053 0.00086316\n",
-      " 0.00101579 0.00116842 0.00132105 0.00147368 0.00162632 0.00177895\n",
-      " 0.00193158 0.00208421 0.00223684 0.00238947 0.00254211 0.00269474\n",
-      " 0.00284737 0.003     ]\n",
-      "[0.         0.24525574 0.31063037 0.36446136 0.41130637 0.45333628\n",
-      " 0.49178719 0.5274424  0.5608354  0.59234888 0.62226847 0.65081403\n",
-      " 0.67815909 0.70444347 0.72978178 0.75426939 0.77798661 0.80100188\n",
-      " 0.82337407 0.84515425]\n",
-      "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:163: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.cast instead.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# zfit.run.numeric_checks = False   \n",
     "\n",
-    "load = True\n",
+    "load = False\n",
     "\n",
     "bo = True\n",
     "\n",
@@ -1471,6 +1399,8 @@
     "\n",
     "Ctt_steps = np.sqrt(BR_steps/4.2*1000)\n",
     "\n",
+    "Ctt_steps[0] = 0.74\n",
+    "\n",
     "print(Ctt_steps)\n",
     "\n",
     "# total_samp = []\n",
@@ -1484,15 +1414,15 @@
     "\n",
     "__ = -1\n",
     "\n",
-    "# newset = True\n",
+    "pause = False\n",
     "\n",
     "#-----------------------------------------------------\n",
     "\n",
     "if not load:\n",
     "    for Ctt_step in Ctt_steps:\n",
     "\n",
-    "#         if not newset:\n",
-    "#             break\n",
+    "        if pause:\n",
+    "            break\n",
     "        \n",
     "        __ += 1\n",
     "        \n",
@@ -1507,8 +1437,8 @@
     "        \n",
     "        for toy in range(nr_of_toys):        \n",
     "            \n",
-    "#             if not newset:\n",
-    "#                 break\n",
+    "            if pause:\n",
+    "                break\n",
     "            \n",
     "            newset = True\n",
     "            \n",
@@ -1517,7 +1447,7 @@
     "                for floaty in [True, False]:\n",
     "                    Ctt.floating = floaty\n",
     "                    \n",
-    "                    if not floaty:\n",
+    "                    if pause and not floaty:\n",
     "                        break\n",
     "                    \n",
     "                    for bo_step in range(bo_set):\n",
@@ -1566,6 +1496,8 @@
     "                        if result.converged:\n",
     "                            \n",
     "                            save_pulls(step = _step)\n",
+    "                            \n",
+    "                            pause = True\n",
     "\n",
     "                            if floaty:\n",
     "                                Nll_list[-2].append(result.fmin)\n",
@@ -1600,17 +1532,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of jobs: 1000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if load:\n",
     "    \n",
@@ -1687,7 +1611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1748,7 +1672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1764,39 +1688,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(20, 1000)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 216x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEWCAYAAACdaNcBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAYMklEQVR4nO3deZhldX3n8fdHEBWQsDXKJo0jRowRNS1qTAwDjo8IKj5xQY1BgsGMGveF6EwwedSAcdyiE0NEJSMgi0Zwm4gKxrgQG0QFGgURoQGhWBpowRHwO3+cU3i7uNVdVfdWVfev3q/nqafqnnPuOd9zl8/5nd9ZKlWFJKkt91nsAiRJ42e4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHAfUZLlSSrJ5v3jc5K8bLHr0uwl2S/J6sWuY6EleUeSG5L8fOrneczL+cMkPxr3fEfRr/snFruO+WC495JckeSOJGuTXJfk40m2HvMy3p7kziS39T8/TvKhJDsPTLNfkl/3daxNsjrJqUkeP8NlfCLJO8ZZ93xKsm+SLyZZk+SmJP+Z5PB+3L3Ctn8NP7k41c5dH5pnJ7k9ySVJnrqeabdPckofuDckOTHJNgPjBz+ra5N8eYS6dgfeADyyqh481/lMM+9K8rDJx1X1jar67XEuY2BZ5yT55cBrsjbJk+ZjWZsKw31dz6yqrYHHAY8H/sc8LOOUqnogsD3wHODBwHmDAQ9c09fxQOCJwCXAN5IcMA/1rGM+WmzrWdaTgK8BXwceBuwA/HfgwIWqYQGdDHyPbh3fBpyeZNk0074D2A54KPBfgAcBb58yzTOrauv+52kj1LUHcGNVXT/CPDYWrxp4Tbauqm8vdkGLyXAfoqquBr4EPAruaSnd09IaR+uxqu6sqouAFwATdK2nqdNUVa2uqr8GPgoc2y8/Sd6X5PoktyT5QZJHJTkSeDHw5r7l8rl++qOS/KTfW7g4yXMG1uWlSb7Zz+8m7h0iDJn2H/rlXjLiBufvgROq6tiquqFf3/Oq6vlJtqJ7D3YZaIm9CHgr8IL+8feH1HhUktOnDPtAkg/2fx+eZFX/Wlye5OXrWd91Wp5T94qSHJzkgn6v41tJHj3NfB5O12A4uqruqKpPAz8E/niaRe8JfLaqbq2qW4B/BX5nujrnqv9Mn8VvXuNPDJlmlyRn9ntVlyX584Fx+yb5dr/+1/Z7oVv04/69n+z7/bxfMHVPrP9evbH//N7S763cf2D8m/v5XpPkZVPfj1ms54fS7QHfmuS7SX5/mum2THJSkhv7dfrPJDv247ZNtzd/bT+vv02yUefnRl3cYul3VZ9B19KaV1V1N3AG8IcbmPQzwOP60Hsa8BTg4cC2dBuIG6vqOOBE4N19y+WZ/XN/0s//t4C/AT45ZU/hCcDlwE7AOzdQx+S0OwJHA59Jsv1M1nVQki2BJwGnDxtfVb+ga8FfM9ASOwl4F93ez9ZVtc+Qp54MPCN9N0aSzYDnAyf1468HDga2AQ4H3pfkcXOo/3HAx4CX07XG/wk4M8n9hkz+O8DlVXXbwLDvM31gfxg4OMl2Sbaj2wh8aco0JyaZSPLlJMNehw2qqq+w7mv80iGTnQysBnYBngu8a2CDfjfwOrrPwpOAA4BX9PN+Sj/NPv28T5mmjOcDT6fboD0aeClAkqcDrweeSrdX90dzWcfeuf28t6f7vJ02zft0OLAlsBvde/oK4Jf9uE8Cd9DtSa0ADuqn32gZ7uv6bJI1wH/QdRW8a4GWew3dB29D04QuzO+k67J5BJCqWlVV1073xKo6raquqapf91+yS4F9B+ddVf9QVXdV1R0bqON64P39nscpwI/oPuiztR3d52/auueiqn4GnA8c0g/aH7i9qr7Tj/9CVf2k30v4OvBlNrxhHebPgX+qqnOr6u6qOgH4f3TdaFNtDdwyZdgtdO/hMOcDWwA39j93A/97YPyLgeV0XSpnA/+WZNs5rMN69Y2cPwDeUlW/rKoL6PYgXwLQ72V9p//cXEG3gZttCH+w/2zeBHwOeEw//PnAx6vqoqq6na5RssF59S3uNUnOnxxYVf+nqm6qqruAd9Nt2IftAdxJt6F6WP+erqyqtUl2pdtwva6qbq+qnwPvBw6d5bouKMN9XYdU1bZVtUdVvWIGQTcuuwI3zWCaAtZU1deAD9G18K5LclwGDrhNleRPB7oP1tB1N+04MMlVs6j16lr3bnM/o2vVTV3miwe6U6a2OgFuBn4N7Dxk3KhOAl7Y//0iftNqJ8mBSb7TdzOsodtD23HIPDZkD+ANA2GyBtidIa8FsJYuUAZtA9w2ZFqA04Af04X/NnR7Xvd0A1bVN/vundur6u+ANQzZQCV5yMB7sHaW6wfdutw0ZY/jZ3SfRZI8PMnn051lcytdY2i2r+XPB/6+nW5DOLnswc/lTD6jr+6/v9tW1T17Y333ziVJbqH73G01TZ2fAL4CnJrk6iTHpDsGtQdwP7rv2uR7/WG6YyEbLcN9Zn5Bt7s2aWxnFfT9ds8EvrGBSZ8DnN93V1BVH6yq36PbtX848KZ+unVu85lkD+CfgVcBO1TVtsCFdHsBDHvOBuyaZPC5D6Hbq1hHVZ040J1yrwOkfWvs20zf7zxdXTOp9TRgvyS70b1uJwH0u+KfBt4DPKh/Lb7Iuq/FoNuZ/n2/CnjnQJhsW1VbVtXJQ+ZzEfDQJIMt9X364cPsQ7dX8IuqWgt8hG4jNJ0atg5VdeXgAcb1PH861wDbT6n7IcDV/d//SHewf6+q2obueMh0r+VsXUvXPTJp97nMJMl/peve+WO6vd7t6Da2w16vX1XV26tqb7o9lufQ7SVdRfdZ2H7gvd6mqoYeY9lYGO4zcwFwaJL7JllB1/c4kn5ee9P1aT4YeO+QaZJk1yRHAy+j+/KQ5PFJnpDkvnQbnl/S7boDXEd3lsWkrei+/BP9cw+nP1A8RzsBr+7rfx6wN11AzsWbgZcmeVOSHfr69knyqX78dcAOSX5r4DnXAcvXdzCrqiaAc4CPAz+tqlX9qC3oWmATwF1JDqQ7fjGdC4AXJdms7wMe7HL4Z+Av+vchSbZKctCUIJys58f9vI5Ocv90B7QfTbehGea7wMuSPCDJA4Aj6froJ1vjT06yRT+vN9G1Qr+5nvWYk6q6CvgW8Hf9sh4NHEF3XAe6PYtbgbVJHkF3ptOgqZ/F2TgVODzJ3v3xmb+e43weCNwF3ADcl+6Ega2GTZhk/3QnJtyHbr3uBO7uX4evA+9Jsk2S+yR5WJKnDJvPxsJwn5n/SXcg5Wa6vr+T1j/5er2g30VeA5xJ16f6e1U12PrdpZ9mLd0X/XeB/apq8nzmbejC5Wa63eQb6VqjAMcDj+x3Hz9bVRcD/4uulXxdP69RguBcYC+6L8s7gedW1Y1zmVFVfYuuT3x/4PJ0Z+scR7+xqKpL6DZ+l/frswtdqxzgxsF+1SFOojsYd8971XcvvJouOG6m67I5cz3zeA3dXtUauhbcZwfmtZKu3/1D/bwuoz8YOI1D6Q7E3QwcQ/e6TW5wX5xksBX/Z3R96qvpWskPHZj3A+lazDf3454OHDjX92AGXtjXcg3dWTtHV9VZ/bg30r2Gt9F9HqceNH07cEL/3j1/Ngutqi8BH6Q7pnAZ3ecXuuMas/FFuq6WS4Er6EJ7uuM8u9CduHAr3V7VV+g+fwB/QrdRuJjutT+NMe7Bz4eU/6xDM5TkpcDLquoPFrsWLS39Xu6FwP36A6PaAFvukjZKSZ7Tdz9tR3eNx+cM9pkz3LWOJB/JupdwT/58ZLFr05LzcrrjIz+hO6Y0tU9f62G3jCQ1aIMt9yQfS3eZ+4UDw7ZPclaSS/vf2/XDk+SD6S5T/kHmcOWfJGl0G2y596f7rAX+paom77XybrqLG45JchSwXVW9JckzgL+kOyf3CcAHquoJGypixx13rOXLl4+2JpK0xJx33nk3VNXQG9Bt8A6AVfXvSZZPGfxsYL/+7xPozil+Sz/8X/orGL+T7mY7O6/v0niA5cuXs3Llyg2VIkkakORn042b6wHVB00Gdv97p374rqx7mfDqftiwoo5MsjLJyomJiTmWIUkaZtxnywy79Hhov09VHVdVK6pqxbJl093WWpI0F3MN9+vS3zK2/z15o//VrHsPiN0Yct8RSdL8mmu4nwkc1v99GN39yCeH/2l/1swTgVs21N8uSRq/DR5QTXIy3cHTHdP9F5Wj6e6NcWqSI4Argef1k3+R7kyZy+juorZR38xeklo1k7NlXjjNqHv9e7X+LJlXjlqUJGk03n5AkhpkuEtSgwx3SWrQBvvcpVEsP+oLQ4dfccxc/qe2pJmy5S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaNFK4J3ldkouSXJjk5CT3T7JnknOTXJrklCRbjKtYSdLMzDnck+wKvBpYUVWPAjYDDgWOBd5XVXsBNwNHjKNQSdLMjdotsznwgCSbA1sC1wL7A6f3408ADhlxGZKkWZpzuFfV1cB7gCvpQv0W4DxgTVXd1U+2Gth12POTHJlkZZKVExMTcy1DkjTEKN0y2wHPBvYEdgG2Ag4cMmkNe35VHVdVK6pqxbJly+ZahiRpiFG6ZZ4K/LSqJqrqTuAzwO8D2/bdNAC7AdeMWKMkaZZGCfcrgScm2TJJgAOAi4Gzgef20xwGnDFaiZKk2Rqlz/1cugOn5wM/7Od1HPAW4PVJLgN2AI4fQ52SpFnYfMOTTK+qjgaOnjL4cmDfUeYrSRqNV6hKUoMMd0lqkOEuSQ0y3CWpQYa7JDVopLNlpEnLj/rCYpcgaYAtd0lqkOEuSQ0y3CWpQYa7JDXIcJekBnm2jDYJ052Nc8UxB20S85cWmi13SWqQ4S5JDTLcJalBhrskNcgDqloUHsCU5pctd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIG/5q43KdLcCljQ7ttwlqUGGuyQ1aKRwT7JtktOTXJJkVZInJdk+yVlJLu1/bzeuYiVJMzNqy/0DwP+tqkcA+wCrgKOAr1bVXsBX+8eSpAU053BPsg3wFOB4gKr6VVWtAZ4NnNBPdgJwyKhFSpJmZ5SW+0OBCeDjSb6X5KNJtgIeVFXXAvS/dxr25CRHJlmZZOXExMQIZUiSphol3DcHHgf8Y1U9FvgFs+iCqarjqmpFVa1YtmzZCGVIkqYaJdxXA6ur6tz+8el0YX9dkp0B+t/Xj1aiJGm25hzuVfVz4Kokv90POgC4GDgTOKwfdhhwxkgVSpJmbdQrVP8SODHJFsDlwOF0G4xTkxwBXAk8b8RlSJJmaaRwr6oLgBVDRh0wynwlSaPxClVJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoNG/U9MWmKWH/WFxS5B0gzYcpekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSg7xxmLRAprvp2hXHHLTAlWgpsOUuSQ0y3CWpQYa7JDXIPndt0uzHloYbueWeZLMk30vy+f7xnknOTXJpklOSbDF6mZKk2RhHt8xrgFUDj48F3ldVewE3A0eMYRmSpFkYKdyT7AYcBHy0fxxgf+D0fpITgENGWYYkafZGbbm/H3gz8Ov+8Q7Amqq6q3+8Gth1xGVIkmZpzuGe5GDg+qo6b3DwkElrmucfmWRlkpUTExNzLUOSNMQoLfcnA89KcgXwKbrumPcD2yaZPAtnN+CaYU+uquOqakVVrVi2bNkIZUiSpppzuFfVX1XVblW1HDgU+FpVvRg4G3huP9lhwBkjVylJmpX5uIjpLcDrk1xG1wd//DwsQ5K0HmO5iKmqzgHO6f++HNh3HPOVJM2Ntx+QpAZ5+wFpkXkLBc0HW+6S1CDDXZIaZLhLUoMMd0lqkOEuSQ3ybBkNNd0ZHJI2DbbcJalBhrskNchuGTXJC4O01Nlyl6QGGe6S1CDDXZIaZLhLUoMMd0lqkGfLSGM2rgvAPONHo7DlLkkNsuUurcf6WuG2oLUxs+UuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUHefkBLyrhu6jXueUnjZstdkhpky13axHgrYM2ELXdJapDhLkkNmnO4J9k9ydlJViW5KMlr+uHbJzkryaX97+3GV64kaSZGabnfBbyhqvYGngi8MskjgaOAr1bVXsBX+8eSpAU053Cvqmur6vz+79uAVcCuwLOBE/rJTgAOGbVISdLsjKXPPcly4LHAucCDqupa6DYAwE7jWIYkaeZGDvckWwOfBl5bVbfO4nlHJlmZZOXExMSoZUiSBowU7knuSxfsJ1bVZ/rB1yXZuR+/M3D9sOdW1XFVtaKqVixbtmyUMiRJU4xytkyA44FVVfXegVFnAof1fx8GnDH38iRJczHKFapPBl4C/DDJBf2wtwLHAKcmOQK4EnjeaCVKkmZrzuFeVf8BZJrRB8x1vpLmxtsSaJBXqEpSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIa5P9QXcKmu+hF0qbPlrskNchwl6QGGe6S1CDDXZIaZLhLUoM8W2YJ8KwYDeMtgttmy12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yFMhpcZ5KuzSZMtdkhpky30TZEtM88mLm9pgy12SGmTLfSNmC13SXNlyl6QG2XLfCNhClzRuttwlqUG23EfgWQVaSuayh+l3YfHYcpekBm3yLfdxtp7HNS9b9FLH78LiseUuSQ3a5FvumxLPipE6tujnny13SWrQvIR7kqcn+VGSy5IcNR/LkCRNb+zdMkk2Az4M/DdgNfDdJGdW1cXjXtZczKVrxO4UaWHM90kN821j6laaj5b7vsBlVXV5Vf0K+BTw7HlYjiRpGvNxQHVX4KqBx6uBJ0ydKMmRwJH9w7VJfjTOInLsOOc2rR2BGxZkSRuPpbbOS219YSNc5wX4Po9lnRcodwbtMd2I+Qj3DBlW9xpQdRxw3Dwsf8EkWVlVKxa7joW01NZ5qa0vuM6tmI9umdXA7gOPdwOumYflSJKmMR/h/l1gryR7JtkCOBQ4cx6WI0maxti7ZarqriSvAv4N2Az4WFVdNO7lbCQ26W6lOVpq67zU1hdc5yak6l7d4ZKkTZxXqEpSgwx3SWqQ4T4mSd6YpJLsuNi1zKckf5/kkiQ/SPKvSbZd7Jrmy1K7jUaS3ZOcnWRVkouSvGaxa1ooSTZL8r0kn1/sWsbFcB+DJLvT3W7hysWuZQGcBTyqqh4N/Bj4q0WuZ14M3EbjQOCRwAuTPHJxq5p3dwFvqKq9gScCr1wC6zzpNcCqxS5inAz38Xgf8GaGXKzVmqr6clXd1T/8Dt11DC1acrfRqKprq+r8/u/b6MJu18Wtav4l2Q04CPjoYtcyTob7iJI8C7i6qr6/2LUsgj8DvrTYRcyTYbfRaD7oJiVZDjwWOHdxK1kQ76drnP16sQsZJ/9Zxwwk+Qrw4CGj3ga8FXjawlY0v9a3vlV1Rj/N2+h2409cyNoW0Ixuo9GiJFsDnwZeW1W3LnY98ynJwcD1VXVekv0Wu55xMtxnoKqeOmx4kt8F9gS+nwS6Lorzk+xbVT9fwBLHarr1nZTkMOBg4IBq90KJJXkbjST3pQv2E6vqM4tdzwJ4MvCsJM8A7g9sk+STVfUni1zXyLyIaYySXAGsqKqN6o5645Tk6cB7gT+qqonFrme+JNmc7oDxAcDVdLfVeFHDV1uTroVyAnBTVb12setZaH3L/Y1VdfBi1zIO9rlrtj4EPBA4K8kFST6y2AXNh/6g8eRtNFYBp7Yc7L0nAy8B9u/f2wv6Fq02QbbcJalBttwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuGtJS3J3f8rfhUlOS7LlBqb/RJLn9n+fk6Spf6qsdhjuWuruqKrHVNWjgF8Bf7HYBUnjYLhLv/EN4GFJlie5cHJgf6/+ty9eWdLsGe4S99xu4EDgh4tdizQOhruWugckuQBYSffPVo5f5HqksfCukFrq7qiqxwwOSHIX6zZ87r+wJUmjs+Uu3dt1wE5JdkhyP7rbG0ubFFvu0hRVdWeSv6X7L0Q/BS5Z5JKkWfOukJLUILtlJKlBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lq0P8HykwDvqDuligAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "l = []\n",
     "sensitivity = []\n",
@@ -1844,75 +1738,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BR: 0.0003\n",
-      "0.7253086419753086\n",
-      "\n",
-      "BR: 0.0004\n",
-      "0.6373456790123457\n",
-      "\n",
-      "BR: 0.0006\n",
-      "0.5509259259259259\n",
-      "\n",
-      "BR: 0.0007\n",
-      "0.4367283950617284\n",
-      "\n",
-      "BR: 0.0009\n",
-      "0.345679012345679\n",
-      "\n",
-      "BR: 0.0010\n",
-      "0.2993827160493827\n",
-      "\n",
-      "BR: 0.0012\n",
-      "0.24228395061728394\n",
-      "\n",
-      "BR: 0.0013\n",
-      "0.21141975308641975\n",
-      "\n",
-      "BR: 0.0015\n",
-      "0.16820987654320987\n",
-      "\n",
-      "BR: 0.0016\n",
-      "0.1419753086419753\n",
-      "\n",
-      "BR: 0.0018\n",
-      "0.10185185185185185\n",
-      "\n",
-      "BR: 0.0019\n",
-      "0.09104938271604938\n",
-      "\n",
-      "BR: 0.0021\n",
-      "0.09259259259259259\n",
-      "\n",
-      "BR: 0.0022\n",
-      "0.05864197530864197\n",
-      "\n",
-      "BR: 0.0024\n",
-      "0.040123456790123455\n",
-      "\n",
-      "BR: 0.0025\n",
-      "0.033950617283950615\n",
-      "\n",
-      "BR: 0.0027\n",
-      "0.026234567901234566\n",
-      "\n",
-      "BR: 0.0028\n",
-      "0.037037037037037035\n",
-      "\n",
-      "BR: 0.0030\n",
-      "0.029320987654320986\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for s in range(len(l)):\n",
     "    print('BR: {:.4f}'.format(BR_steps[s+1]))\n",
@@ -1931,7 +1761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1942,7 +1772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1953,14 +1783,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4 min, 1 \n"
+      "56 s\n"
      ]
     }
    ],
@@ -1969,34 +1799,93 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 34,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "integrate = False\n",
+    "# Smearing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ixEzFGecA4RfPAqQFhslmzUpkWG1oOu98oG31XWumlMS4lMioKCmKEjO7L/giEL7QAQIP/+RmJI2XXwG0Hb5LDRQ6aKbUZVFRUhQlZs6dPBiIVOggVkm4w5xTIhiU1wdou6NDqicl6XEpkVFRUhQlZhat2wU4i5LHYw+TOZ8uE3f2HLQOnE4JyZS8fn9S41Iio6KkKErMDH7VqmyLVBLe4vPDCy8kMqw2ZCyYDzisU0piXEpkVJQURYmZQW+9ATjPKaV67If/okWJDKsNma9ZJ+O0Gb4LFDokMS4lMipKiqLEzHv3/RaIMHyXkmKVhCcxI6mc+yfrhWZK3QoVJUVRYua0m78OQKpjSbi9HuiiixIZVhsGXHUZELKjQ4pd6JDEuJTIqCgpihIzWy6+EiDsybMQtB4oiUdE1H7FEs42G7IGMiU9uqLLoqKkKErM7Jp6IhAhUwoUOsyYkcCo2tJw2ueAkHVKHrv6LolxKZFRUVIUJWa+NPsUwHlOqbXQYejQRIbVhhFHjwNCh++SH5cSGVeiJCLniUiJiJSKyB1hrmeIyHz7+koRKQq6dqfdXiIiM6P5FJHRto+tts/0SH2ISIGILBOROhF5PMhPloi8KiIfi8hGEflF7F+Poijh+NOSdUDkHR1afAZqaxMZVhs+/XQPEFoSbu9eXlvLjgP13PDCWmoaW5IUoRKOqKIkIh7gCeB8YBJwpYhMCjG7DqgyxowFHgEetO+dBFwBTAbOA54UEU8Unw8CjxhjxgFVtm/HPoBG4CfAbWHC/7UxZgJwHHCaiJwf7fMqihKd4pdfBCKsU+oCR0TkPvvHdm2pgZ0m5s7lmXe38ep/9/D6xn1JiE5xwk2mdCJQaowpM8Y0A/OA2SE2s4Fn7dcvAWeLtQvibGCeMabJGPMpUGr7C+vTvucs2we2z0si9WGMOWSMWYElTq0YY+qNMcvs183AWmC4i8+rKEoUBny8HohUEi7WnNLq1YkMqw0Z6z4E2g7feQJzSqtXt2ZQBw41JSM8xQE3ojQM2BH0fqfdFtbGGOMFqoGCCPc6tRcAB20foX059REVEekHXAS86XB9joisFpHV5eXlblwqSq/m3zf9DHA+TyktsM1QEjOlfb9+DHDYkHXuXNJTrcdfi0+3HOpKuBGlcL91of8XnWw6q91tHO0QkVTgReAxY0xZOBtjzFxjzHRjzPTCwsJoLhWl13PZbdeQItbR5+FITbGH75JY5Tb80guAkEwpUKo+Y0brfJhXRalL4UaUdgIjgt4PB3Y72dgikAcciHCvU3sF0M/2EdqXUx/RmAtsNcY86sJWURQXvHP1jY7l4GBlJC0+P9x9d+KCCmH/LVb9VNjqu7vvbm336easXQo3orQKGGdXxaVjFS4sDLFZCFxrv74MWGqMMXb7FXbl3GhgHPCBk0/7nmW2D2yfL0fpwxERuQ9LvG5y8TkVRXFJxZBRjvNJEFQSXlycwKja0jj6KCBkl3CPXX1XXNyaKfn9KkpdiaiiZM/f3Ai8BmwGFhhjNorIvSJysW32NFAgIqXALcAd9r0bgQXAJmAJcIMxxufk0/Z1O3CL7avA9u3YB4CIbAMeBr4mIjtFZJKIDAd+hFXdt1ZEPhKRb8T+FSmKEsrXb7o8oii17n03fXoCo2rLURecCThkStOnt479e1WUuhSp0U3AGLMYWBzSdlfQ60bgcod77wfud+PTbi/Dqs4LbY/UR5FD6HqSl6LEgYeeWYbnw12O19MCVW67Q0f6E8fG1ZvhiXfbiKcnRfD6rLi8r5cAWMOMSpdBd3RQFCVmTvrHM44LZ8EudPAZeOihBEbVloI/WDuZh62+e+ghmu0ChyavLxnhKQ6oKCmKEjM5lfujzim1JDlTStu7FyAkU7LnlHbvtjImoLFFM6WuhIqSoigxs/Art0bJlA5nJMnisx9Za6lC55S8dlyBuaQmr4pSV0JFSVGUmPne7Vc6rlGCQEm4wUyblsCo2jLpIqvQIdxx6GbaNJpbMyUdvutKqCgpihIz8677UeRMyWM9Wny/+32iQmrHJ/c9DLTddSIQs+93vw8avlNR6kqoKCmKEjN16X1at+kJR6rHfvhn5yQqpHa0ZGUDbXedaBXL7JzWnRx0+K5roaKkONLQ7GPDrupkh6F0Qb790E0Rd3RIs6+lXnKxo028mXT91UD7OSWAtEtmtw7fNWmm1KVQUVIcue2v65j12xVUHWpOdihKF+P2B14iLUKmFMiiqj7ckKiQ2rFyyXtA6I4O1puadRs0U+qiqCgpjqz9rAqABv1LUgnh4n/MJS3CnFJAlDLuuzdRIbVj1GO/BNpmSoG40n52r7W4F51T6mqoKCmKEjN+Y0jzRMiUAnM3SdzCJ9Bz8DqltKC4mjVT6pKoKCmKEjPPn///XA3f7b/1zkSF1I7S71iHUQeXhGfYcVXcdqdW33VRVJQURYmZJ+65wtXw3ahTjktUSO04/cLTgJDhOztTGn7yVJ1T6qKoKCmKEjM/ue4XkYfvbFHa+vSLiQqpHaseexYIP3z36TPz2yyejXIKjpJAVJQURYmZtIa61kq2cGTYD39/TU2iQmqHHKoD2lbfBcTSX1PdWujgN3p8RVdCRUlRlJi55aWHWofCwhF4+I/+0S2JCqkdx/7sh0Db4btApjTijpvbHIOu80pdBxUlRVFi5trv/M7V8N37C15PVEjtWPa8dVxbuJLwNX9/o3X4DnReqSuhoqQoSszc+OrvIg7fBR7+R/3ip4kKqR2TH7LWSAWLUqD6ruiBn2im1EVRUVIUJWb2Zue7Wqd0aEBhokJqR/2AQQAE74YUiPlQwSC8Pn+rSGmm1HVQUVKiolPASih/PPmLpEUqdEjzALDpym8mKqR2fHyV1Xe44bst18yh2WfIzUwFNFPqSqgoKYoSM8seudpVpnTRBSckKqR2XHyh1XdwSXhAlGZ+4Xi8fj+5mWmAZkpdCVeiJCLniUiJiJSKyB1hrmeIyHz7+koRKQq6dqfdXiIiM6P5FJHRto+tts/0SH2ISIGILBOROhF5PCSuaSKy3r7nMRFx/tNOcUS/NCUYYwwXffURV4UO/5z7z0SF1Y5//fEVoO2ODoHs7tU/voLXZ8jOsDI6zZS6DlFFSUQ8wBPA+cAk4EoRmRRidh1QZYwZCzwCPGjfOwm4ApgMnAc8KSKeKD4fBB4xxowDqmzfjn0AjcBPgNvChP87YA4wzv45L9rnVdqjw3dKMF6/YUzVrsjDd7YoZW0vS1RY7cj5zOq7TaGDxxKhPts+odnnJyfDGr5ratFMqavgJlM6ESg1xpQZY5qBecDsEJvZwLP265eAs+2sZDYwzxjTZIz5FCi1/YX1ad9zlu0D2+clkfowxhwyxqzAEqdWRGQI0NcY856xlms/F+RLUZQO0uLzc9OKF1wN301/5rFEhdWOY596FGh78mwggzvu6Ufx+vzkZASG7zRT6iq4EaVhwI6g9zvttrA2xhgvUA0URLjXqb0AOGj7CO3LqY9Ice+MEjcAIjJHRFaLyOry8vIILnsnOnynBNPiM1xxVeRthlJShNQU4YUHn3W0iTev/tba4qjt4lnr9YJf/xm/IajQQTOlroIbUQr3TAod0XGy6ax2t3G4ial9ozFzjTHTjTHTCwuTV8KqKN2BFp+fB5b8NuLwHVhZyTmP/iRBUbXntF9aO5RL0FMu1ZNCisAZv/4xwOHhO82UugxuRGknMCLo/XBgt5ONiKQCecCBCPc6tVcA/WwfoX059REp7uFR4lYUJUa8PsP6weMiZkpgidKOMaHTz4ljf/EUoO3wHVhrlbaPnghAToIzJd34NTpuRGkVMM6uikvHKlxYGGKzELjWfn0ZsNSex1kIXGFXzo3GKjb4wMmnfc8y2we2z5ej9BEWY8weoFZETrbnqr4a5EuJAf1npATT4vPz4tTzSI0mSp4U3jv7fxIUVXs2zroCaDt8B5ZYvjXjUiCxmdKSDXuYeu+/2V55KO59dWeiipI9f3Mj8BqwGVhgjNkoIveKyMW22dNAgYiUArcAd9j3bgQWAJuAJcANxhifk0/b1+3ALbavAtu3Yx8AIrINeBj4mojsDKrk+zbwFFaBxSfAv2L5chQLnVNSgmn2+dnwyOWuhu/uvuaUBEXVnusuOBZou6MDWJWBP/+addZSIueU/rp6J9UNLby9tSLufXVnUqObgDFmMbA4pO2uoNeNwOUO994P3O/Gp91ehlWdF9oeqY8ih/bVwNHhrimK0jFafH5O+s6z/DpKppSRmsL//nYJDycorlCeWfAOrNjdLlPKSPXw7QcXwu5GMlM9pKZIQjKlykPNAHxarplSJHRHByUqOnynBNPU4ufkHevJtLcScqJPuodRG1YlKKr2DPnwA6D98F1WuodR66240lKFzDRPQjKlvdXWqpU91Q1x76s7o6KkOKJzsko4mrx+rvxoSesCWSf6pHk4fenfExRVeya/Oh9oe8gfWGJ5zgprejk1JYWsdA/1zd7Q2zudgw1WprS7ujGKZe9GRUmJilYMKcE0eX1847KfkpEWRZTSU7n3+gcj2sSTv9/zJKkpQujuYn3SPHz7y3cDViVeTmYqtY3xFaXGFl9rNlZR2xTXvro7KkqKI4F/y6pJSjCNLX5+s/BXZKRGGb5LS+Gmp5N3ntIFP7+tzWasAbLSPdy34OcApKcKuRmp1DXFV5SqG1oASxCr6pvj2ld3R0VJcUTFSAlHk9fHm2NPcDV89874kxMUVXu2TP88qWFFKZU3x1o7iKemWJlSXZwzpYP1liiNKcymvtmnG8BGQEVJUZSYaGrxs3DSjOiZUnoqCyfPSExQYVj3+QvDrqXKTPOwcNIMwB6+S0CmdNDOjkYPyAbgwCHNlpxQUVIUJSaavH62PTgr+pxSmofVPzk3QVG15+7ZRztkSh62PTgLsIbvcjLS4j6ndLAhkCnlACpKkVBRUqKiw3hKME1eH0W3L4o6fJeV7mHMHYuSVihz59/WOc4pFd2+CLAypdzMBMwp2cN3RxVqphQNFSUlKkZXKilBNHn9XLxpuat1SrM2LqfZl5wduI9+69WwmVKfdA8Xb1oO2HNK9vBdPMUzUA5eVGCJkhY7OKOipChKTDS1+Dm7dFXrmUlOZKZ5OLt0FY3NyRGliavfxhNmK6Q+dlxgHWWRk5mKz29oiGPxQU2DlxSBEflZAFTWqSg5oaKkREWH75Rgmrw+fnDp7aSEyUKCyUr38P2Lf0B9S/wXpobjTzc+QGroxnccjgss4QxsyhrPCry6Ji85Gan065NGiujwXSRUlBRHdNhOCUeT18/cv94T1a5PmoenXrqHhubklD9/81ffDzunlJ2RylMvWfFnpXtaN2WtiaMo1TS2kJuZRkqK0D8rnQM6fOeIipISFZUmJZgmr4+XT7wgql2fdA8vTj2P+iSJ0ltnXBJ2Tql/VjovTj0PsNYs9ctKBw6XbceD2kZvq/jlZ6dzQIfvHFFRUhwR+9AK3WZICaaxxc+GccdHteuT5uH9EVOStlB0c/HxpIaZU8rPTuf9EdYBgJlpKRRkW6JUGcchtdrGFvpmprX2r8N3zqgoKY7o8J0Sjiavn5d//qWodn3SPax88tq4FhBE4tc3X4gnzJxSfnY6K5+0zgsVEfJtUYqnUNQ2eltPuc3P1uG7SKgoKVFRaVKCaWrx8cVfLIlql5Xu4eib/8qhpiTNKT2+NOzwXX52Okff/Nc27yG+olTXFDJ8p5mSIypKiqLERJPXz0UrF0W1y81I48qPllDb2JKAqNpzxvK/Oy6evfKjJWSlW+usMtM8ZKd74lqmHTqnVFXfjM+vf+6FQ0VJiYpOKSnBNLT4mLCzJKpdbmYqU/ZujfsWPk6M3v5x2ExJRPjBgFpe/d7nWtvyc9I5cCg+R0oYY6i1q+/AEiVj4ltY0Z1RUVJcoKqkHKa+2csL1/04ql1OZir/e953kyZKj1/xw7CZEkD+X/7UujkqQH52RtwKHZq8flp8pnU9VGC4UHd1CI+KkqIoMVHf7OOOX347ql2aJ4UFL96ZtOG7nz16Y9hMCYAZM9q8LchOj9vwXUCU+2a2FSXd1SE8rkRJRM4TkRIRKRWRO8JczxCR+fb1lSJSFHTtTru9ROGcHSMAACAASURBVERmRvMpIqNtH1ttn+lH0MfNIrJRRDaIyIsikhnb16OADt8pbalv8rH0y9FFCeCP51ybtEzpzzO/HvboCgDuvrvN28F5meyric8x5QFRDh6+A93VwYmooiQiHuAJ4HxgEnCliEwKMbsOqDLGjAUeAR60750EXAFMBs4DnhQRTxSfDwKPGGPGAVW27470MQz4HjDdGHM04LHtFEU5AuqbvdSNHOPK9sCwUdQ2JSdT+mzAMOdMqbi4zdth/fpQeag5LmuqAqIcKHQoyM4A0LJwB9xkSicCpcaYMmNMMzAPmB1iMxt41n79EnC2iIjdPs8Y02SM+RQotf2F9Wnfc5btA9vnJR3sAyAV6CMiqUAWsNvF51VC0ERJCaa+2cf1t33Zle3vf/Ot5M0pPXK98/5806e3eTskzxpE2X2wodPjOCxKVqbUP9v6r+7qEB43ojQM2BH0fqfdFtbGGOMFqoGCCPc6tRcAB20foX3F1IcxZhfwa+AzYA9QbYx5PdwHFJE5IrJaRFaXl5c7fhG9FR2+UwI0e/14/YZnX/qPK/vvP/xqXPeUi8Ql/7uADKfhu91t/z4d2q+P1Xyw84fwAsN3gUKHjFRrE9h47iDRnXEjSuH+1Ah9TDnZdFZ7zH2ISH+sLGo0MBTIFpFrwthijJlrjJlujJleWFgYzkRRFKyhO4AT/vaMK/tLl85LWqHDZcvnk+50EOFDD7V5O6xVlDo/U6q2T53Ny0prbQusVVLa40aUdgIjgt4Pp/0wWKuNPVSWBxyIcK9TewXQz/YR2lesfZwDfGqMKTfGtAB/B0518XmVEHS7ISVAYHPVvKr9ruwLayuTNnyXX13hLEohmdKgvpmIwM6q+k6Po8o+dTbf3vgVoL/u6uCIG1FaBYyzq+LSsYoFFobYLASutV9fBiw11i6eC4Er7Mq50cA44AMnn/Y9y2wf2D5f7mAfnwEni0iWPfd0NrDZ3deigA7bKe0JZEpbfhj96AqAt6+/I2mZ0i/O+abzQYQhmVJ6agrD+/ehrOJQp8dRVd9MRmoKfdIPn9Q7MDeD/TXxWazb3YkqSvb8zY3Aa1gP9QXGmI0icq+IXGybPQ0UiEgpcAtwh33vRmABsAlYAtxgjPE5+bR93Q7cYvsqsH13pI+VWAURa4H19med24HvqNej4qQECGRKZ1x1viv7b978JRpb/EnZKXzB/93onClNm9auaWxhDqX76zo9jqpDza1l4AGG5GWyp7rzhwp7AqnRTcAYsxhYHNJ2V9DrRuByh3vvB+5349NuL+Nw9Vxwe0f6+Cnw03D3KNERe7ZORUkJENhcddsDD3OMC/u1P3oQNltrcgLFBInA7zfcMfO7nOMkSnPb/306blAu735Sic9vHHeC6AhV9c2tZzYFGJyXSU2jl0NNXrIzXD2Gew26o4PiiIqREkqDfbR5Wr88V/bZA/oBiV8o2uzzcyi9j3OmlJvbrmlsYQ7NXn+nzytV1beQn53Wpm1oniXQe6rjs2C3O6OipERFCx2UAHV2pnTU/3O3Dv2EG61p4ESXPzd5/Tz90j3Oc0qzZrVrGjsoB4At+zp3CM8pUwLYq6LUDhUlRVFcU2OXNx/8cIMr+90ffARAZV1iJ/WbvX7OmjOXDKdMacuWdk0TB/fFkyL8d+fBTo2lsq65TeUdHF6sq/NK7VFRUqKiw3hKgBq7ki7/Vw+4sh/66INAcobvblrxvPPwXcjed2CdlDt+UC4f7YhNlLw+P998bjU3zfsQf8gZSY0tPqobWlozowCD+mqm5ISKkqIorqlp8JLuSXFdCJCRmkJqiiR8+K7Z6wdwFiUHpo7sx0c7DrYTl0i8taWcf2/axz8/2s3rm/a1uRYQncF924pSZpqHgux0dqsotUNFSVEU19Q0ttC3Typyj7t1SnLPPdZC0QTv89bs9fPo6VeT7vGENwiTKQFMHdGP2kYvpeXu55U27a4BIDcjlb+t3dnmWqCQYUhe+wMKhudnseNA5y/W7e6oKClR0eE7JUBNQwt9M9Pa7bLtSHGxdVZRnE51daLZ62fp3DnOmZJD/KeMKQBgxdYK131t3V/HsH59+NIJI1hesp/q+sOLhffWWHNGg8KI0uiCLD6Nw2Ld7o6KkqIorqlp9JLbJw0WLXJ3w6JFDOqbyd44nVXkRJPXx3WX/dRZlBziH5GfxZgB2by91f3GzKX76xg7MIeLjx1Ki8/w2qa9rde2VdQjcnhvvWCKBmSzu7ohKQuLuzIqSkpUtCRcCVDb2GKdoFpb6/KGWob2y2RPHHbfjkR9s4/s5gay0x2G7yLE//niQt4vq3QlFj6/4ZPyOsYNzOGY4XmMzM/ilXWH99UrLa9jRP8sMtPaxzF6QDbGwGc6hNcGFSUlKjp8pwSoaWihb580mDPH3Q1z5jA0L34H6DlR3+zj50t+22a/udC4nJgxvpDGFj/vuBjC21XVQJPXz9iBOYgIFx07hP98UkmFXQK/dV8tYwfmhL23qCAbQIfwQlBRUhTFNTWNXitTWrPG3Q1r1rRuL5TI3Qvqm71c9LXfkJ3usIVPhPhPGzuA/llpLFwX/UzQ0nIr4woIz0XHDsXnN/xrw16q61vYur+OY4aH3/2iaICKUjhUlJSoaKKkABhjqA4UOtx6q7ubbr2VIf3shaJxOKvIifpmHz9a+hRZTplShPjTPClcMGUIb2zax6GmyMduBDZwDYjShMF9GT8olz+/t41lJfsx5nDxRCh5fdIYmJvBlr0uh0J7CSpKiiMqRkowh5p9NHv9FOSkw9Ch7m4aOrR1kn9XAkWpodnHvpx85+G7KPFfctwwGlp8LPpv5Gxp6746BuRktNlG6OZzi9myr46b5n/EiPw+TC/Kd7x/8tC+bNpTE7GP3oaKkhIVo5NKCoe3CsrPzogpUxqcFzhAL3GidKjZy1MnfpEsp+G7KPFPH9WfCYNzeebdbRF//0vL6xg7MLtN28zJg/jBzPEcN7IfD39pasSFxpOG9qV0f51W4AWhoqQ4EvinpJKkAFTYC2BjzZQyUj2M6J8VlwP0nGho9vHBE191FoQo8YsIXz+tiI/31vJ+2YGwNsYYSvfVMW5gbrt7bzhzLP/4zmmcECFLApg0JA+v38TlHKfuioqS4oiKkRJMYP+6AdkZsHq1u5tsu6MKsxP64K1v9nHNt55wNnAR/+ypwyjITufJ5aVhr++raaK2yetYXeeGSUP7ArBxd3WHffQ0VJSUqOjonQKHh+8KctLD7rIdFtvuqMIcysrrYtpT7kioa/IyvibCfJCL+DPTPHx7xlG8s7WC/3zSvjy8ZJ9VoDB+cPuzmdwyKj+LvpmpfPhZ5+5M3p1RUVIUxRWBTVXzs9Md945rh203dmAOTV5/woodquqbmbP0z1HjisY1J49iSF4mv1xS0k5QA1VzE45AlFJShOlF+XywLfwQYW9ERUlxgaZKClTUNZGTkWrtTrB8ububbLvAEFdJgsqfD9a38Ms7fu9s4DL+zDQPt35hPB/tOMj81TvaXNuwu5rBfTPbHeAXKycU5VNWfqh1wa0T75dV8kkMG8V2V1SUlKjo8J0C1mF1BTn2AziGHR3AmjvxpAjrOvkAPScO1jfzrecfjBqXG/7n+GGcPCafBxZvZp+9h58xhpVlB5he1P9IQ+XE0ZaP1RGypfLaJq6Y+z4X/OadhA2BJgtXoiQi54lIiYiUisgdYa5niMh8+/pKESkKunan3V4iIjOj+RSR0baPrbbP9CPoo5+IvCQiH4vIZhE5JbavR1GUAHuqGw6fCzR9urubbLus9FSKO3CAXkc52NBCxYQpUeNyg4jwwKVT8PoMNzy/lmavn427a9hb08hpYwcccaxThvUjMy3FscoPrKwMrGPeA3NZPZWooiQiHuAJ4HxgEnCliEwKMbsOqDLGjAUeAR60750EXAFMBs4DnhQRTxSfDwKPGGPGAVW275j7sO/5DbDEGDMBOBbY7PaLUQ7Ts/8uU9yy+2Bj65ZBsWZKYJ1VtC7GA/Q6gs9v7TxReslVruJyw5jCHH51+TGs3l7FN55bzf2vbiYzLYULjh5yhNFaBxGePKaA5SX7HW22B5XTb9jVsyv13GRKJwKlxpgyY0wzMA+YHWIzG3jWfv0ScLaIiN0+zxjTZIz5FCi1/YX1ad9zlu0D2+clHelDRPoCnweeBjDGNBtjtMRFUTqAz2/YW9PIUHvLIHJdTu4H2U0b1Z+aRi8fx3leqaKuCWPge5dOcxWXW2YdM5QHLp3C+59U8l5ZJT+6YCJ5WWlHEOlhzpowkG2V9Y774G2rrKdPmofMtBQ27+nZmZLDcuc2DAOCZ/h2Aic52RhjvCJSDRTY7e+H3DvMfh3OZwFw0BjjDWMfax8NQDnwjIgcC6wBvm+Mafd/XUTmAHMARo4c6fQ99Fp0Tkkpr23C5zcMybMzpd3RNysNtfvcOGuoa/mW/a3rc+LBbrvCb8XydZzpIq5YuOqkkVw4ZQgNLT4Ghzm4r6OcOX4gsJGlH+/nutNHt7u+vfIQowdkk5aawuYevi2Rm0wp3JLo0MeUk01ntXekj1TgeOB3xpjjgENAu/kwAGPMXGPMdGPM9MLCwnAmvRIVIyXA7mrrQd+aKcVYfQcwqG8mk4b0ZXmJ+wP0OkJgN/KjNkZYIOs2/jDkZaV1qiCBdbjg2IE5LP14X9jr2yvrKRqQxYRBuWzp7XNKWJnHiKD3w4HQPzNabUQkFcgDDkS416m9Auhn+wjtqyN97DTGrLTbX8ISKSVGdO87JZB9tGZKc+e6uzHE7uyJA1m97UBrFVs82FllHZo3ZMFzruPqCsycPIj3yw60Kw33+vzsqKpnVEE2xYNzqTzUHLV8vDvjRpRWAePsqrh0rKKChSE2C4Fr7deXAUuN9SRbCFxhV86NBsYBHzj5tO9ZZvvA9vlyR/owxuwFdojIePues4FNLj6vYiN2DqqSpGyvtB70I/OzrIZXXnF3Y4jdF48fjt/A39bu7Mzw2lCyt46BuRmkLX7VdVxdgYuPHYbPb1i8fk+b9j3VjbT4DEUFWYwfZM2F9eTjLqKKkj2/cyPwGlb12gJjzEYRuVdELrbNngYKRKQUuAV7mMwYsxFYgCUGS4AbjDE+J5+2r9uBW2xfBbbvmPuw7/ku8LyI/BeYCjwQ6xfUm9EESQnwSXkdg/tmkp1hD2JcFaGyLZgQu9EDsjlxdD4vfvAZLT7/Ece1s6qeY+5+jdL9hx/Sm/bUMGFI38gxuo0/gYwfnMv4Qbks/KjtQNS2Smsa3MqU7EXIPXgIz02hA8aYxcDikLa7gl43Apc73Hs/cL8bn3Z7GVZ1Xmh7R/r4CHC/IEEJi4qTUlZ+iDGFQUc0zJrl7sYwdtd/fgzXPbuav6/dyZdPOLLColfW7aGm0csf3irjV5cfS2VdEx/vrWHm5HGRY3Qbf4K5eOpQfvVaCTsO1DPCzkq32RV5RQXZFOZk0D8rrUfPK+mODoqiRMQYQ1l5XVtR6mCmBFb589QR/fjVa1tadx7vKLsOWsOK/968j8q6Jhau240xcM7EQd0uUwKYPXUoIjB/1eHi5LKKQ2SlexjUNwMRoXhQbsK2a0oGKkpKVIzOKvVqDhxqpqbRy+gBQUc0iPPBdW0IYyci/PyLU6huaOYHf12Ht4PDeBt2VTN/1Q6KB+XQ0OzjC4+8zS/+9TEnj8nn6GF5kWN0G3+CGd4/i7MnDGTeqs9o9lrfy7aKQxQVZCN2zBMG57JlX12PLUBSUVKi0zN/9xWXBI7rbrMbttsHooPdxCF9uWvWJN78eD83zf+Ihmb3J68aY1iyYQ9XP7WSgbmZPP+Nk5l//SkcP6o/MycP5rErjoseYxd+oF9z8igq6pr51war4GHLvjqOCjqzqXhwLnVN3oQeL59IVJQURYnIhl2WKB09NO9w4wsvuLs5gt1XTinijvMn8Or6Pcz67Tss2bA3YvFDZV0TC1bt4JIn/8O3/rKWEfl9ePGbJ1OYm8HUEf34v69O57Erj2NgYH++SDG6jT8JfH5cIWMKs3l8aSm7Dzaw62ADU0f0a73eWoHXQ+eVXBU6KL2brvs3pZIINuyqZkR+n7Zb6ixa5G5eJordt844iolD+nLPwo186y9r6JeVxtQR/RjRP4s+6R7qm72U1zZRsreWbXZZelFBFvdfejSXTxtBemqEv6sj9e02/iSQkiL8cOZ4vvWXtVz9lLXM8rSxBa3Xx9miVLK3jrMmDEpKjPFERUlRlIhs2F3NlGF5bRs7IVMKcEZxIa/d/HmWl5Tz2sa9bNhVzbodB6lv9pGdkUr/rDQmDunL5dNHMGN8IZOG9G2dX+lw3104UwKYOXkwF0wZzOL1ezl97AAmDD68LVNenzSG5GVqpqT0Xrrw8LsSZ/bXNLK9sp6rTwop3b7oIncLUF3apXlSOHfSIM6d1Il/+Ufq2238SUJE+O2Vx/PNzx1k4pD2+wQWD8qN+8a2yULnlJSoaPVd7+W9skoAThkTcm5QB46uSDiR+k5mXC7xpAjHjexvnfQbwpRheWzZV0t9szfMnd0bFSVFURx575NK+mamtt/Ve8YMdw7c2sWDSH0nM65OYNqo/vj8hnU7et7ZSipKSlR0+K53Yozhna0VnDSmAE9KyBzO0KHunLi1iweR+k5mXJ3AcSOtary1n1UlOZLOR0VJiYCqUW9m/a5qdh1sCD/PU+tyPsOtXTyI1Hcy4+oE+mWlc1RhNmu3qygpvRCVpt7JvzbsxZMinDsxjCh18OiKhBKp7y54dEWsHD+yP2s/q+pxOzuoKClR6Wm/9Ep0fH7Dwo92c+pRBfTPTm9vsDrCAXodsYsHkfpOZlydxPSi/lTVt/BJeV2yQ+lUVJQURWnH8pL97DrYwJUnOuzirZlS0jn1KKsi8t3SyiRH0rmoKCmOBBIkzZN6H8+9t52BuRnO64a0+i7pjMjPYkR+H1aUViQ7lE5FRUlxRMWod7L2syre2lLOtacWkeZxeETcfbc7Z27t4kGkvpMZVydy2lEDeL+sssM7rXdFVJQUR/yaKvU6jDE8/PoW8rPT+dqpRc6GxcXuHLq1iweR+k5mXJ3IaWMHUNvoZcPummSH0mmoKCmOHNYkVaXewqvr97CitILvnjX28NHn4Zju8kBnt3bxIFLfyYyrEzn1KGuj1nd70BCeipLiiFbd9S4OHGrm7oWbmDIsj6+eUhTZePdud07d2sWDSH0nM65OpCAng4lD+vL2lvJkh9JpqCgpjrRmSqpNPR6f3/D9eR9S09jCL/5nSvsdHEJ56CF3jt3axYNIfSczrk5mxvhC1myvoqaxJdmhdAquRElEzhOREhEpFZE7wlzPEJH59vWVIlIUdO1Ou71ERGZG8ykio20fW22f6R3tw77mEZEPRWSR+69FAZ1K6k38csnHvLO1gnsvnszkoXnRb9BMqctw1oSBeP2GFVt7xhBeVFESEQ/wBHA+MAm4UkQmhZhdB1QZY8YCjwAP2vdOAq4AJgPnAU/aIhHJ54PAI8aYcUCV7TvmPoJi+z6w2d3XoQQTGL7TTKln88SyUv7wdhlfOXkUXz5hhLubNFPqMhw3oh95fdJY+vH+ZIfSKbjJlE4ESo0xZcaYZmAeMDvEZjbwrP36JeBssU7hmg3MM8Y0GWM+BUptf2F92vecZfvA9nlJB/tARIYDFwJPufs6lGBMyH+VnoUxhkf+vYVfvVbCJVOHcs/Fk90dngcwbVrn2sWDSH0nM65OJtWTwueLC1leUo7f3/3/tboRpWHAjqD3O+22sDbGGC9QDRREuNepvQA4aPsI7SvWPgAeBX4IRCziF5E5IrJaRFaXl/ecCcMjRTOknkuT18ftf/svv3lzK5dNG86vLj+WlGjzSMHojg5dijPHF1JR18SG3d3/KAs3ohTuNzX0ceVk01ntMfchIrOA/caYNWGutzU2Zq4xZroxZnphYWE0816Dv3X4TtWpJ7G98hCX/e49FqzeyffOHsevLjvGeZGsE7m5nWsXDyL1ncy44sAZxYWI0COG8Nz8Ju4EggeahwOhs4StNiKSCuQBByLc69ReAfSzfYT2FWsfpwEXi8g2rOHBs0TkLy4+r2KjUtSzMMbw0pqdXPjYCj47UM/cr0zjlnOL3Q/ZBTNrVufaxYNIfSczrjhQkJPB1BH9WFbS/Ud63IjSKmCcXRWXjlVUsDDEZiFwrf36MmCpsf68XghcYVfOjQbGAR84+bTvWWb7wPb5ckf6MMbcaYwZbowpsv0vNcZc4/J7UQgqdEhyHMqRs63iEFc/tZLb/rqOiUNyefV7p/OFyYM77nDLls61iweR+k5mXHHizPED+e/Og1TUNSU7lCMiqijZ8zc3Aq9hVbEtMMZsFJF7ReRi2+xpoEBESoFbgDvsezcCC4BNwBLgBmOMz8mn7et24BbbV4HtO+Y+OvqFKIfRdUrdn7omLw+/XsLMR99m/c5q7r/0aObPOYXh/bOOzLHufdflOGvCQIyB5d08W4qwj8hhjDGLgcUhbXcFvW4ELne4937gfjc+7fYy7Oq5kPaY+wi6vhxY7nRdCY9qUfelxedn3qod/OaNLVTUNXPRsUP58YUTGdQ3M9mhKXFi0pC+FOZmsLxkP5dNG57scDqMK1FSeieHCxxUnroLXp+fRf/dw2NLt1JWfogTR+fz1LUTmTqiX+d2pJlSlyMlRZhRXMhrG/fi9flJjbV4pYvQPaNWEoJKUfeh2etnwaodnPPwW9w0/yPSUlL4v69OZ/6ckztfkEB3Ce+izBg/kJpGLx/tOJjsUDqMZkqKIzqn1PVpaPbx0pod/P6tMnYdbGDKsDz+8JVpnDtxUGzrjmJlkctdu9zaxYNIfSczrjhy+rgBeFKEZSX7mV6Un+xwOoSKkhIV1aSux57qBp57bzsvrPyM6oYWjh/Zj/suPZoZxYUdK/GOldrazrWLB5H6TmZccSSvTxrTRvVn2cfl/GDmhGSH0yFUlBSlG/HhZ1X88d1tLF6/B2MM5x09mK+fNprpo/onRowCzJkDa6KuS3dvFw8i9Z3MuOLMjPGF/HJJCftqGrtlYYuKkhIVHb5LLo0tPpZs2Mtz721j7WcHyc1I5f+dVsRXTyliRP4RlnZ3FLcP9GQ++CP13UMFCaz1Sr9cUsJbJeV8ye0Gu10ILXRQlC7KJ+V13LdoE6f8/E1umv+RdQjfRZN473/P5kcXTkqeIAHcemvn2sWDSH0nM644M2FwLoP7ZrJ8S/fcckgzJSUqehx64mjy+nht4z5eWLmd98sOkJoifGHyIK46cRSnHlUQ3+KFWBg6tHPt4kGkvpMZV5wREWaML+TV/+6hxeePfV/DJKOipERFh+/iT1l5HfNX7eCva3Zy4FAzI/L78IOZ47l8+nAG5nbBeQHNlLo0M8YXMm/VDtZur+KkMQXJDicmVJQUJUnUNLbw6n/38NKanazZXoUnRThn4kCuOmkUnxs7oOtkReEYOtTd6a1u7eJBpL6TGVcCOG3sAFJThGUl5SpKSs9DE6XOw+c3vFtawUtrdvLaxr00ef2MHZjDHedP4NLjhnWfaqnVqzvXLh5E6juZcSWA3Mw0TijKZ3nJfu44v3uVhqsoKUoCKN1fx9/W7uQfa3ext6aRvD5pfGn6CC6bNpxjhucltpy7M9iyxd28jFu7eBCp72TGlSBmjC/k5//6mD3VDQzJ65PscFzTvWbAlKSgh/x1jIq6Jp57bxuXPvku5zz8FnPfLmPS0L48efXxfPCjs/nZJUdz7Ih+3U+QQPe+6wacOWEg0P12DddMSVE6kbomL69v3MvLH+1mRWkFPr9h/KBcfnTBRGYfN7RrFi10hOXLO9cuHkTqO5lxJYhxA3MY3DeTFaUVXHniyGSH4xoVJUU5Qpq8Pt4qKefldbt5Y9M+mrx+hvXrw/WfH8PFU4cyYXDfZIfY+cyZA3Pndp5dPIjUdzLjShAiwmljB7D04334/aZrF84EoaKkhCV4yE5H79rj8xtWllWycN1uFq/fQ02jl4LsdL58wghmTx3K8SMTvO1Popk+vXPt4kGkvpMZVwI5fVwBf1u7k017ajh6WF6yw3GFipISFr8KUTv8fsOaz6pYvH4Pi9fvYV9NE9npHmYePZiLjx3KaWMHdLuFih1mzpzOtYsHkfpOZlwJ5LSjBgDwbmmFipLSvWnx+Vtf9+YdHXx+w+ptB1i8fg//2rCX/bVNpKemMKO4kNlTh3H2xIFkpnmSHWbiyc11t9O2W7t4EKnvZMaVQAb2zWTcwBxWlFZw/RlHJTscV6goKWHx+nvv8J3X5+cDW4iWbNhHRV0TGakpnDl+IOdPGczZEweRk9HL/+m4XXiazAWqkfruwQtnQzlt7ADmrfqMJq+PjNSu/wdUL/+X1fN56p0ypo7oF/OBX96gTKk34PX5eb/sAIs37OG1DXupPNRMZloKZ00YyAVThnDm+IFk93YhCmb5crjoos6ziweR+k5mXAnm9LED+NN/trF2+0FOOarr7+7gagBcRM4TkRIRKRWRO8JczxCR+fb1lSJSFHTtTru9RERmRvMpIqNtH1ttn+kd6UNERojIMhHZLCIbReT7sX893Z/7Xt3MZb9/L+b7Glp8ra97aqbU0Ozj35v2cftL/+WE+9/gmqdX8o+1uzj5qAKevPp41v7kXJ68ehqzjhmqghSK28q1ZFa4Req7h1feBXPSmHw8KcK7pRXJDsUVUf+liYgHeAI4F9gJrBKRhcaYTUFm1wFVxpixInIF8CDwZRGZBFwBTAaGAm+ISLF9j5PPB4FHjDHzROT3tu/fdaAPL3CrMWatiOQCa0Tk3yFx92j8R1Ct0NDsi27UDdlb3cibH+/jzc37ebe0giavn5yMVM6cMJALpwzmjOKB9Env+kMcSeeVVzrXLh5E6juZcSWY3Mw0jh2ex4rSCm6bOT7Z4UTFzZ9/JwKlxpgyABGZB8wGpAbP0AAAFFRJREFUgh/us4G77dcvAY+LVQ87G5hnjGkCPhWRUtsf4XyKyGbgLOAq2+ZZ2+/vYu3DGPMesAfAGFNr+x4WEneP5lCzt8P3tsmUOiOYJGGMYePuGt7YbAnR+l3VAAzv34crTxzJORMHceLofNJTe0nVXGdx1VXwwgudZxcPIvWdzLiSwOljB/D4slKqG1rI65OW7HAi4kaUhgE7gt7vBE5ysjHGeEWkGiiw298PuXeY/TqczwLgoDHGG8a+I30AYA/1HQesDPcBRWQOMAdg5Mjus/I5GoeaOp7tBGdK3W2bocYWH++VVfLGpn0s/Xg/e6obEYGpI/rxg5njOWfiIIoH5fTsdUTxZtaszrWLB5H6TmZcSeC0sQN4bGkp75dVMnPy4GSHExE3ohTuX27oU8rJxqk93J+lkew70od1k0gO8DfgJmNMTRhbjDFzgbkA06dP715P4AjUNXVOptQdqKhrYunH+3lj0z5WlFZQ3+yjT5qHz40bwM3nFnPm+IEU5mYkO8yew1VXRbeJxS4eROo7mXElgakj+5GRmsLKsgM9QpR2AsEHvQ8HQuspAzY7RSQVyAMORLk3XHsF0E9EUu1sKdg+5j5EJA1LkJ43xvzdxWftUdQfwfBdcJbVFVXaGMOWfXW8sXkfb2zex0c7DmIMDO6byaXHDeOcSYM4ZUxB71xDlAhE3FXAuLWLB5H6TmZcSSAj1cO0Uf15v6wy2aFExY0orQLGichoYBdWUUHonxkLgWuB94DLgKXGGCMiC4EXRORhrCKEccAHWNlNO5/2PctsH/Nsny93pA97vulpYLMx5uFYv5iewJFkSgfrmzsxks6h2evng08PtArRzqoGAKYMy+Oms4s5e+JAJg/tq8NyicDtAz2ZD/5IffciQQpw0ugCHn1zC9X1LeRldd15paiiZM/f3Ai8BniAPxpjNorIvcBqY8xCrIf/n+0igwNYIoNttwCruMAL3GCM8QGE82l3eTswT0TuAz60fRNrHyJyOvAVYL2IfGT7+F9jzOKOfVXdj3o72+nIJP6BYFFK4r/fqkPNLN+ynzc27eftLeXUNnnJSE3htLED+PaMozh7wiAG5/WQnbe7Ey+84G4IzK1dPIjUdzLjShInjcnHvAGrth3gnEmDkh2OI64WX9gP8sUhbXcFvW4ELne4937gfjc+7fYyDlfoBbfH1IcxZgXh55t6DYHqu4wO7Md2oO6wKCV6m6FPyut4c/M+3ti0n9XbD+A3MCAngwumDOGcSYM4bWwBWem6biipLFrk7qHu1i4eROo7mXEliakj+pGemsLKTyu7vygp3ZPaRluU0mIXpb01jaR7UmhOwM4OXp+f1durLCHavJ9PKw4BMGFwLt+ZMZZzJg3imGF53Wbr/V6B23LqZJZdR+q7F5WDB8hM83DciH68X3Yg2aFEREWpB1NR1wRYi+diZVvlIYoGZLFlX11cht9rGlt4q6ScNzfvY1lJOdUNLaR5hJPHFPC1U4s4e+JAhvfP6vyOlc7hoovcLUB1axcPIvWdzLiSyEljCnh86VZqGlvo24HnQiJQUerBBEQpVnx+Q1n5IT43bgBb9tV1WjyfVdZbi1g/3sfKsgN4/Yb+WWmcPXEg504cxOeKC3Wj0+6CHl3RLTl5dD6PGVizrar1uPSuhj4BejB7qxuBtsdQuGHT7hrqm30cP7I/r23c1+EZJWMM63dV8/rGffx70z5K9llHBYwdmMN1nxvNuRMHcdzI/nh0WK77MWNG59rFg0h9JzOuJHLcyP6keYT3P61UUVIST0AEvL7YZOW1jXtJETjVPiAsluG7Zq+f98sqeX3TXt7YtJ+9NY2kCEwvyufHF07knImDKBqQHVM8Shdk6FB35xG5tYsHkfpOZlxJpE+6h6ldfF5JRamHsvtgAzsOWOt4YsmUqutbePGDz5gRww4IPr/hvU8qWbhuF0s27KWm0UufNA+fLx7AbZPGc9aEgeRnp3focyhdFLcP9GQ++CP13QsFKcBJowv43VufUNfk7ZLD5boLZQ/EGMPv3/oEEZgxvtC1KPn8hh/+bR1V9c3ccm4xgTWofodUqay8jp8t2sRJD7zJNU+vZPH6vZwzaRBPfXU6H951Ln/4ynQumzZcBaknokdXdFtOGpOPz29Ys70q2aGEpevJpNJhjDGsKK3gsTe3smpbFV87tYjUFOGDT6On6pV1Tdy8YB1vbynnJ7MmcfSwPKoOWWuVQg/8W7P9AI++sZV3tlaQmiKcM3EQs6cO5cwJvfRo8N7I6tXuigXc2sWDSH0nM64kM21Uf1JThJVllZxRXJjscNqhotQD8PsNb2zexxPLSlm3s5rBfTO5d/ZkrjlpFL96vSTinFJtYwtPr/iUp975lGavnwcuncJVJ1k7pQd2gmjyWqJ04FAzP/7nehav38uAnAxuPbeYL584goG5uqNCr0MzpW5LVnoqxwzP67L74KkodWO8Pj+vrt/Dk8s+oWRfLSPzs3jg0in8z/9v796Do6ruAI5/fyQxolCEIMECSkBewQhIikAtICqgYqWtM9Laaq2PPrQvbX2M2sFOpy22iqO2pSrOqGN9VK0yThFTX3QGCgZBBGIggagMjxgegvJM9tc/zll2DZvNBu5mb3Z/n5lM7p4995x7z+TuL/fuvb8zug+F+e6MpaCTcLApgqp+ISfcvoNNPLGkjrlv17Jz7yGmDe/NzVMGM6i46+E6hT4oHWyMUPvJZ1zxyFJ2fO4u7V37tRLLqpDLJk1yU4oHVS8dkvWdye0KgbMHFPHIog3sPdgYuuM4XFtjUrL/UBMvvLuJv7+9gY927OX0Xl2Yc/kILjnzy+Q3SykUfd0UUfLzhENNEZ5552MefH099XsOMHHwyfxqyhDK+nY7op/8vE50Eve805XzltEYifDiT8ZzRp8j65ocM2tWsPXSIVnfmdyuEBg3oIi/vVXLO3U7Q3cJz4JSB1JTv4enl33Mi+9uYufeQ4zo2407Lh7NBcOKW0zBU+CDUmNEWbaxgTteWs3Ghs8Z078HD33nLMaU9EjaZ2F+Ho8v+ZC8TsLzPxpnAck4gwcHWy8dkvWdye0KgfL+7nmlxbUNFpRM22z9dD+vrd3K/JWbqfxwJ/mdhAtKi/nu2NMYP7Co1WkaCvLc+48vruOPr35ASdGJzLuqnMlDe6U0xUN0sr8ZI/sw6tTux75DJjuUl8Pm5tOqHUO9dEjWdya3KwROOC6fUf26s6Q2fN8rWVAKobqGz1m4ZiuvrtnKio92AS4Lwu0XDuVbo/vSs0vqM6jm+zOoPyz4gMlDe/HQd0Yd1TXk6yaUtHkdk8VS/UDP5Ad/sr5zOCBFjRtYxINvrA/d/Er2nFIIqCpVW3Yzp2Id0+5fxKQ/v8UfFnxAY5Py66lD+M9NE/jPTRP54cSBbQpIAAVxcyndP3NkmwPSXdNLufacEob2/lKb1jNZ7t57g62XDsn6zuR2hcT4gUVEFJZuDNfZkp0pZUgkoqzctIuFq90Z0Yfb9yICX+nfg7umlzJ1eHEgWbKH+Lvpbjh34FFlBb7mHDtDMgnYmVKHN/LUkzi+oBOLa7czZXjvTG/OYaI5OC1wMuXl5VpZWZmWtpsiSmXdDhas3sqrq7eydfd+CvKE8QN7Mu2M3pw/rDjl1D5tUb11D6f36mKJT40xX/C9eUup332Ahb+ccMxtichyVS0/1nbsTKkdrNu2h+fe+ZiXVm6m4bMDFOZ3YsLgk7m1bAiThxbTrXN6r+cO6d219UrGtMXo0bB8eXD10iFZ35ncrhAZN7CIe16t5pM9B9LyD/HRsKCUJpGIUlG1jblv17Lio10U5AnnDS1m+ohTOHdIL04MYSJEY1JmGR2ygpsJoJrFtQ1cOrJPpjcHsKCUFpV1O7jr5TVUbdnNqT1O4M6Lh/GNUX0oauNNCsaEVtcUz75TrZcOyfrO5HaFSFmfbvTsUshra7aFJiildPediEwTkWoRqRGR2xK8Xygiz/r3l4pI/7j3bvfl1SIytbU2RaTEt7Het3lc0H2kS2NThN//u4rL5i5h975DzLl8BG/cPJFrvzbAApLJLtOnB1svHZL1ncntCpG8TsLU4cW8WV3Pfv9MYqa1GpREJA/4C3AhUAp8W0RKm1W7BtipqqcDc4DZft1SYCYwHJgG/FVE8lppczYwR1UHATt920H3EbhDTRF+/NS7PLxoA98deyqv/XIC3xjV94i0P8ZkhXXrgq2XDsn6zuR2hcy0M3qz92ATr1fVZ3pTgNTOlMYANaq6QVUPAs8AlzarcynwuF9+HjhPXLqAS4FnVPWAqm4Eanx7Cdv060z2beDbnBFkH6kNS9tEIsotz6+iYu02Zl1Syu9mlNl3Ria7We67rDFuQBH9i07gvopqdvjpajIplU/OPsDHca83AWe3VEdVG0XkU6DIl/+v2brRC5eJ2iwCdqlqY4L6QfVxBBG5HohOrvKZiGwHGhLVbc3Vs+Hqo1kxnHpylOOQhWwsYtxY3H13arVTrZcOyfo+9u3Kur+Jol8d9ao9gdOC2IZUglKih1uaP9zUUp2WyhOdoSWrH2QfRxaqPgwcvh1HRCqDuN++o7NxiLGxiLGxcGwcYvxY9A+irVQu320C+sW97gs0fxz6cB0RyQe6ATuSrNtSeQNwkm+jeV9B9WGMMSakUglK7wCD/F1xx+FuKpjfrM584Cq/fBnwhrpUEfOBmf7OuRJgELCspTb9Om/6NvBtvhxkH6kNizHGmExo9fKd//7mRmAhkAc8pqprROS3QKWqzgfmAU+KSA3u7GWmX3eNiDwHrAUagRtUtQkgUZu+y1uBZ0Tkd8AK3zYB99Eae7LOsXGIsbGIsbFwbBxiAhsLy31njDEmNOwhGmOMMaFhQckYY0xoWFCK095piTJBRB4TkXoRWR1X1kNEKnxqpwoR6e7LRUQe8OOxSkTOilvnKl9/vYhclaivMBORfiLypohUicgaEfm5L8/FsTheRJaJyHt+LO725YGl/OpIfEaYFSLyin+dq+NQJyLvi8hKEan0Zek/PlTVftz3anlALTAAOA54DyjN9HalYT8nAGcBq+PK7gFu88u3AbP98kXAAtyzYGOBpb68B7DB/+7ul7tnet/aOA6nAGf55a7AOlw6qlwcCwG6+OUCYKnfx+eAmb58LvBjv/wTYK5fngk865dL/XFTCJT44ykv0/t3FONxE/AP4BX/OlfHoQ7o2aws7ceHnSnFtFtaokxS1UW4uxfjxadwap7a6Ql1/od7huwUYCpQoao7VHUnUIHLO9hhqOoWVX3XL+8BqnCZQHJxLFRVP/MvC/yPElzKrw5DRPoCFwOP+tdBpj7LBmk/PiwoxSRKpxSOXO7pV6yqW8B9WAO9fHlLY5JVY+Uvu4zCnSHk5Fj4S1YrgXrcB0ctKab8AuJTfnX0sbgfuAWI+Ncppz4ju8YB3D8mr4nIcnGp2KAdjg/LGhqTSjqlXNPW1E4djoh0AV4AfqGqu90/uomrJijLmrFQ92zfSBE5CfgXMCxRNf87K8dCRKYD9aq6XEQmRYsTVM3qcYjzVVXdLCK9gAoR+SBJ3cDGws6UYnI5LdE2f6qN/x3NYZ/VKZxEpAAXkJ5S1Rd9cU6ORZSq7gLewn0vEFTKr47iq8DXRaQOd/l+Mu7MKdfGAQBV3ex/1+P+URlDOxwfFpRicjktUXwKp+apna70d9aMBT71p+wLgSki0t3ffTPFl3UY/tr/PKBKVe+LeysXx+Jkf4aEiHQGzsd9xxZUyq8OQVVvV9W+6hKLzsTt1xXk2DgAiMiJItI1uoz7u15Nexwfmb7DI0w/uDtI1uGup9+R6e1J0z4+DWwBDuH+i7kGdx38dWC9/93D1xXcRIm1wPtAeVw7P8B9gVsDXJ3p/TqKcTgHdxlhFbDS/1yUo2NxJi6l1yr/wfMbXz4A92FaA/wTKPTlx/vXNf79AXFt3eHHqBq4MNP7dgxjMonY3Xc5Nw5+n9/zP2uin4ftcXxYmiFjjDGhYZfvjDHGhIYFJWOMMaFhQckYY0xoWFAyxhgTGhaUjDHGhIYFJWOMMaFhQcmYdiAiM0TkERF5WUSm5ErfxrSVBSVj2oGqvqSq1wHfBy4HlwhWRPb5RKj4sh+KiIrIxLiyG33Z+S21LyKni8j7zcoKRWQjsC5B3539PDkHRaRngLtqzDGxoGRM+7oT9+R7VK2qjox7fSYus8IwABE5AZd14xPck/It2QD0E5H4Y/p64G1VXdu8b1Xd5/vtcDnZTHazoGRMwERkkIi8JSKVInKPn41TRGQ2sED9PE4tKMOlghrqX/8Ml8omoqrbfPsl/lJcpbgZY4eoagT4COjv63QGbgZmtaFvYzLOgpIxARKRPOAJ4CZVLQc643KH/RSX6PQyEflRkiaG4WY6HSoi3XCX2xbjctJFM5s/Gtf+LNwMoOCSqEaD2Q3AfFWta0PfxmSczadkTLBmAGvjzkiqcJPEPQA8kGxFEekHbFfVDX4Om1uAB4HBuEt60faHAy/4uZ/ygf/G9TVERBbhgtJYgFT6NiYsLCgZE6xRuIzjUSNwM7mm4kxi3xvtwU0bPQY3p080yI3AZWyel2D9KtwcQD/HzRG1rW2bbkzm2eU7Y4K1HX8JTUTOBq4kdpbTmjJiQelPwI3qZoQti2tjCzA1ekODiJRJbLrcKlwQ+4Ff35gOx4KSMcF6Eij3t2d/ExekalJctwz/3ZGqvqKqS3x5KRC9g+4x3HFb5W8lv1Vj889U+zYeVtVPj3lPjMkAu3xnTIBUtQE4Gw5/RzTJ3xmXyrpXtFDeK255H7FZUJvXO4Ad06aDszMlY9JnBMkv3TUB3eIfnm0v0YdngQIgpaBpTHuwmWeNMcaEhp0pGWOMCQ0LSsYYY0LDgpIxxpjQsKBkjDEmNCwoGWOMCQ0LSsYYY0LDgpIxxpjQsKBkjDEmNCwoGWOMCY3/A4ex6sHRX9zcAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_q = np.linspace(x_min, x_max, int(2e6))\n",
+    "\n",
+    "probs = total_f_fit.pdf(test_q, norm_range=False)\n",
+    "\n",
+    "calcs_test = zfit.run(probs)\n",
+    "\n",
+    "plt.clf()\n",
+    "# plt.plot(x_part, calcs, '.')\n",
+    "plt.title('Fitted curve before smearing')\n",
+    "plt.plot(test_q, calcs_test)#, label = 'pdf (Ctt = 0.0)')\n",
+    "# plt.plot(test_q, calcs_test1, label = 'pdf (Ctt = 0.5)')\n",
+    "# plt.plot(test_q, calcs_test2, label = 'pdf (D-contribs = 0.3)')\n",
+    "# plt.plot(test_q, f0_y, label = '0')\n",
+    "# plt.plot(test_q, fT_y, label = 'T')\n",
+    "# plt.plot(test_q, fplus_y, label = '+')\n",
+    "# plt.plot(test_q, res_y, label = 'res')\n",
+    "plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "# plt.legend()\n",
+    "plt.ylim(0.0, 1.5e-6)\n",
+    "plt.xlabel(r'$q^2 [MeV^2]$')\n",
+    "# plt.yscale('log')\n",
+    "# plt.xlim(770, 785)\n",
+    "plt.savefig('fitted_before_smearing.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "integrate = True\n",
     "\n",
     "if integrate:\n",
     "\n",
     "    probs = total_f_fit.pdf(test_q, norm_range=False)\n",
     "\n",
-    "    calcs_test = zfit.run(probs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if integrate:\n",
-    "\n",
+    "    calcs_test1 = zfit.run(probs)\n",
+    "    \n",
     "    plt.clf()\n",
-    "    plt.plot(test_q, calcs_test)\n",
-    "    # plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
-    "    # plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
-    "    # plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
-    "    # plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.plot(test_q, calcs_test1)\n",
+    "    plt.title('Fitted curve before smearing')\n",
+    "    plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
     "    plt.ylim(0.0, 1.5e-6)\n",
     "    plt.xlabel(r'$q^2 [MeV^2]$')\n",
     "    plt.savefig('test.png')"
@@ -2004,10 +1893,92 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# if integrate:\n",
+    "\n",
+    "#     plt.clf()\n",
+    "#     plt.plot(test_q, calcs_test1)\n",
+    "#     plt.title('Fitted curve before smearing')\n",
+    "#     plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "#     plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "#     plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "#     plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "#     plt.ylim(0.0, 1.5e-6)\n",
+    "#     plt.xlabel(r'$q^2 [MeV^2]$')\n",
+    "#     plt.savefig('test.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Integration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "91404000\n",
+      "0 45\n",
+      "1 45\n",
+      "2 45\n",
+      "3 45\n",
+      "4 45\n",
+      "5 45\n",
+      "6 45\n",
+      "7 45\n",
+      "8 45\n",
+      "9 45\n",
+      "10 45\n",
+      "11 45\n",
+      "12 45\n",
+      "13 45\n",
+      "14 45\n",
+      "15 45\n",
+      "16 45\n",
+      "17 45\n",
+      "18 45\n",
+      "19 45\n",
+      "20 45\n",
+      "21 45\n",
+      "22 45\n",
+      "23 45\n",
+      "24 45\n",
+      "25 45\n",
+      "26 45\n",
+      "27 45\n",
+      "28 45\n",
+      "29 45\n",
+      "30 45\n",
+      "31 45\n",
+      "32 45\n",
+      "33 45\n",
+      "34 45\n",
+      "35 45\n",
+      "36 45\n",
+      "37 45\n",
+      "38 45\n",
+      "39 45\n",
+      "40 45\n",
+      "41 45\n",
+      "42 45\n",
+      "43 45\n",
+      "44 45\n",
+      "Full integration finished in 12 min, 30 s\n",
+      "45702\n"
+     ]
+    }
+   ],
+   "source": [
     "# total_f_fit.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n",
     "\n",
     "if integrate:\n",
@@ -2016,7 +1987,7 @@
     "\n",
     "    _max_size = 2000000\n",
     "\n",
-    "    step_size = 1000\n",
+    "    step_size = 2000\n",
     "\n",
     "    steps = np.arange(x_min, x_max, 0.1/step_size)\n",
     "\n",
@@ -2038,15 +2009,17 @@
     "\n",
     "        inte_fl = zfit.run(_c)\n",
     "\n",
-    "        for i in range(int(l/step_size)):\n",
+    "        for i in range(int(_max_size/step_size)):\n",
     "            _list.append(np.mean(inte_fl[int(i*step_size):int((i+1)*step_size)]))\n",
     "\n",
-    "    _c = total_f_fit.pdf(steps[(parts-1)*_max_size:], norm_range=False)\n",
+    "    _c = total_f_fit.pdf(steps[(parts)*_max_size:], norm_range=False)\n",
     "\n",
     "    inte_fl = zfit.run(_c)\n",
+    "    \n",
+    "    rest = l%_max_size\n",
     "\n",
-    "    for i in range(int(l/step_size)):\n",
-    "        _list.append(np.mean(steps[int(i*step_size):int((i+1)*step_size)]))\n",
+    "    for i in range(int(rest/step_size)):\n",
+    "        _list.append(np.mean(inte_fl[int(i*step_size):int((i+1)*step_size)]))\n",
     "\n",
     "    print('Full integration finished in {}'.format(display_time(int(time.time()-start))))\n",
     "    print(len(_list))"
@@ -2054,25 +2027,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if integrate:\n",
-    "\n",
-    "    dirName = 'data/CLs'\n",
-    "    with open(\"{}/inte_100keV_steps.pkl\".format(dirName), \"wb\") as f:\n",
-    "        pkl.dump(_list, f, pkl.HIGHEST_PROTOCOL)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2084,44 +2044,349 @@
     }
    ],
    "source": [
-    "for step in range(2*ste):\n",
-    "    if step%2 == 0:\n",
-    "        floaty = True\n",
-    "    else:\n",
-    "        floaty = False\n",
-    "    for key in pull_dic.keys():\n",
-    "        if not key == 'Dbar_s':\n",
-    "            continue\n",
-    "        if not os.path.exists('data/CLs/plots/{}'.format(key)):\n",
-    "            os.mkdir('data/CLs/plots/{}'.format(key))\n",
-    "        plt.clf()\n",
-    "        plt.title('Amplitude of ' + r'$\\overline{D}$' + ' contribution with ' + r'$C_{ \\tau \\tau }$' + ' = {:.2f}'.format(Ctt_steps[int(step/2)]))\n",
-    "        plt.hist(pull_dic[key][step], bins = 50, range = (-5,5))\n",
-    "        plt.xlabel('Pull')\n",
-    "        plt.savefig('data/CLs/plots/{}/{:.2f}Ctt{}s{}f.png'.format(key, Ctt_steps[int(step/2)], step, floaty))"
+    "if integrate:\n",
+    "\n",
+    "    dirName = 'data/CLs'\n",
+    "    with open(\"{}/inte_100keV_steps.pkl\".format(dirName), \"wb\") as f:\n",
+    "        pkl.dump(_list, f, pkl.HIGHEST_PROTOCOL)\n",
+    "\n",
+    "if integrate:\n",
+    "    \n",
+    "    center = np.arange(x_min+0.05, x_max, 0.1)\n",
+    "\n",
+    "    probs = total_f_fit.pdf(test_q, norm_range=False)\n",
+    "\n",
+    "    calcs_test1 = zfit.run(probs)\n",
+    "    \n",
+    "    plt.clf()\n",
+    "    plt.title('Integrated curve before smearing')\n",
+    "    plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "    plt.ylim(0.0, 2.5e-6)\n",
+    "    plt.plot(center,_list)\n",
+    "    plt.xlabel(r'$q^2 [MeV^2]$')\n",
+    "    plt.savefig('integrated_before_smearing.png')\n",
+    "# plt.plot(inte_fl)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Smearing"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gauss(x, area, m, sig = 7, step_s = 0.1):\n",
+    "    \n",
+    "    prefac = 1/(sig*np.sqrt(np.pi))\n",
+    "    \n",
+    "    y = np.zeros(np.shape(x))\n",
+    "    \n",
+    "    start = time.time()\n",
+    "    \n",
+    "    for i in range(len(area)):\n",
+    "        \n",
+    "        y += prefac*area[i]*np.exp(-1/2*((x-m[i])/sig)**2)\n",
+    "        \n",
+    "        t = time.time()\n",
+    "        \n",
+    "        j = i+1\n",
+    "        \n",
+    "        if i % 500 == 0:\n",
+    "            print(display_time(int((t-start)/(j)*(len(area)-j))))\n",
+    "    \n",
+    "    return y*step_s*0.7\n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "\n",
+    "#             _y += prefac*area*np.exp(-1/2*((x-_m)/sig)**2)   \n",
+    "\n",
+    "# gauss(scan_x, _list)\n",
+    "\n",
+    "# print(np.shape(_list)[0], np.shape(center)[0])\n",
+    "\n",
+    "# print(int(1.25/0.1)*0.1)\n",
+    "\n",
+    "# print((1.25-1.25%0.1)/0.1)\n",
+    "\n",
+    "# print((x_max-x_min)*10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15 min, 11 s\n",
+      "8 min, 43 s\n",
+      "8 min, 37 s\n",
+      "8 min, 24 s\n",
+      "8 min, 24 s\n",
+      "8 min, 19 s\n",
+      "8 min, 12 s\n",
+      "8 min, 5 s\n",
+      "7 min, 58 s\n",
+      "7 min, 50 s\n",
+      "7 min, 46 s\n",
+      "7 min, 39 s\n",
+      "7 min, 33 s\n",
+      "7 min, 27 s\n",
+      "7 min, 20 s\n",
+      "7 min, 14 s\n",
+      "7 min, 8 s\n",
+      "7 min, 2 s\n",
+      "6 min, 55 s\n",
+      "6 min, 51 s\n",
+      "6 min, 45 s\n",
+      "6 min, 39 s\n",
+      "6 min, 33 s\n",
+      "6 min, 27 s\n",
+      "6 min, 21 s\n",
+      "6 min, 15 s\n",
+      "6 min, 9 s\n",
+      "6 min, 3 s\n",
+      "5 min, 57 s\n",
+      "5 min, 51 s\n",
+      "5 min, 45 s\n",
+      "5 min, 39 s\n",
+      "5 min, 33 s\n",
+      "5 min, 28 s\n",
+      "5 min, 22 s\n",
+      "5 min, 16 s\n",
+      "5 min, 10 s\n",
+      "5 min, 5 s\n",
+      "4 min, 59 s\n",
+      "4 min, 54 s\n",
+      "4 min, 48 s\n",
+      "4 min, 42 s\n",
+      "4 min, 37 s\n",
+      "4 min, 32 s\n",
+      "4 min, 26 s\n",
+      "4 min, 21 s\n",
+      "4 min, 15 s\n",
+      "4 min, 9 s\n",
+      "4 min, 4 s\n",
+      "3 min, 58 s\n",
+      "3 min, 52 s\n",
+      "3 min, 46 s\n",
+      "3 min, 40 s\n",
+      "3 min, 35 s\n",
+      "3 min, 29 s\n",
+      "3 min, 23 s\n",
+      "3 min, 18 s\n",
+      "3 min, 12 s\n",
+      "3 min, 6 s\n",
+      "3 min, 1 \n",
+      "2 min, 55 s\n",
+      "2 min, 49 s\n",
+      "2 min, 44 s\n",
+      "2 min, 38 s\n",
+      "2 min, 32 s\n",
+      "2 min, 27 s\n",
+      "2 min, 21 s\n",
+      "2 min, 15 s\n",
+      "2 min, 10 s\n",
+      "2 min, 4 s\n",
+      "1 min, 59 s\n",
+      "1 min, 53 s\n",
+      "1 min, 47 s\n",
+      "1 min, 42 s\n",
+      "1 min, 36 s\n",
+      "1 min, 31 s\n",
+      "1 min, 25 s\n",
+      "1 min, 19 s\n",
+      "1 min, 14 s\n",
+      "1 min, 8 s\n",
+      "1 min, 3 s\n",
+      "57 s\n",
+      "52 s\n",
+      "46 s\n",
+      "40 s\n",
+      "35 s\n",
+      "29 s\n",
+      "24 s\n",
+      "18 s\n",
+      "13 s\n",
+      "7 s\n",
+      "2 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "scan_x = np.linspace(x_min, x_max, 1e5)\n",
+    "\n",
+    "center = np.arange(x_min+0.05, x_max, 0.1)\n",
+    "\n",
+    "# for i in range(len(steps)/step_size):\n",
+    "#     center.append(i)\n",
+    "\n",
+    "\n",
+    "sum_y = gauss(x =scan_x, m = center, area = _list)\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:24: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\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"
+    }
+   ],
+   "source": [
+    "plt.clf()\n",
+    "plt.title('Fitted curve after smearing')\n",
+    "plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.ylim(0.0, 2.5e-6)\n",
+    "plt.plot(scan_x,sum_y)\n",
+    "plt.xlabel(r'$q^2 [MeV^2]$')\n",
+    "plt.savefig('curve_after_smearing.png')\n",
+    "\n",
+    "plt.clf()\n",
+    "plt.title('Fitted curve after convolution')\n",
+    "plt.axvline(x=jpsi_mass -70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=jpsi_mass +70,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=psi2s_mass -50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.axvline(x=psi2s_mass +50,color='red', linewidth=1.0, linestyle = 'dotted')\n",
+    "plt.ylim(0.0, 2.5e-6)\n",
+    "plt.plot(test_q, calcs_test, label = 'inf. precision')\n",
+    "plt.plot(scan_x,sum_y*0.1, label = 'smeared')\n",
+    "plt.legend()\n",
+    "plt.xlabel(r'$q^2 [MeV^2]$')\n",
+    "# plt.xlim(jpsi_mass, psi2s_mass)\n",
+    "plt.savefig('curve_after_smearing-vs-before_smearing.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create convonluted data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[18.0, 49.0, 58.0, 80.0, 93.0, 111.0, 111.0, 107.0, 227.0, 120.0, 137.0, 154.0, 159.0, 198.0, 183.0, 216.0, 254.0, 232.0, 247.0, 274.0, 296.0, 315.0, 346.0, 370.0, 363.0, 422.0, 440.0, 459.0, 483.0, 591.0, 919.0, 2475143.0, 1186.0, 673.0, 636.0, 605.0, 715.0, 88046.0, 116824.0, 1112.0, 814.0, 769.0, 863.0, 1081.0, 844.0, 673.0, 699.0, 467.0, 306.0, 82.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "nbins = 50\n",
+    "\n",
+    "b_w = int(len(scan_x)/nbins)\n",
+    "\n",
+    "conv_data = []\n",
+    "\n",
+    "means = []\n",
+    "\n",
+    "_ = np.linspace(x_min, x_max, nbins+1)\n",
+    "\n",
+    "bin_centers = []\n",
+    "\n",
+    "_area = np.mean(sum_y)#*(x_max-x_min)\n",
+    "\n",
+    "# print(_area)\n",
+    "\n",
+    "_sum_y = sum_y/_area*pdg[\"number_of_decays\"]/100\n",
+    "\n",
+    "# print(np.mean(_sum_y))\n",
+    "\n",
+    "for i in range(nbins):\n",
+    "    bin_centers.append((_[i]+_[i+1])/2.)\n",
+    "    means.append(np.mean(_sum_y[i*b_w:(i+1)*b_w]))\n",
+    "    _width = np.sqrt(means[-1])\n",
+    "    conv_data.append(np.around(np.random.normal(means[-1], _width)))\n",
+    "    \n",
+    "print(conv_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "0.36486862499697814"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "rho_scale*rho_width/rho_mass+omega_scale*omega_width/omega_mass+phi_scale*phi_width/phi_mass"
+    "plt.clf()\n",
+    "# plt.hist(x = bin_centers, bins = nbins, range = (x_min, x_max), weights = conv_data, histtype = 'step', label = 'Example data')\n",
+    "# plt.plot(bin_centers, conv_data, '.',, color = 'r')\n",
+    "plt.plot(scan_x,_sum_y, label = 'Convoluted fit', color = 'darkorange')\n",
+    "plt.errorbar(bin_centers, conv_data,yerr = np.sqrt(conv_data), elinewidth=1, fmt = '.', ecolor = 'forestgreen', color = 'forestgreen', label = 'Example data')\n",
+    "plt.ylim(0.,1200)\n",
+    "plt.legend()\n",
+    "plt.title('Convoluted fit with possible data set')\n",
+    "# plt.xlim(jpsi_mass, psi2s_mass)\n",
+    "# print(conv_data)\n",
+    "plt.savefig('smeared_fit_with_data.png')"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
@@ -2134,18 +2399,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/smeared_fit_with_data.png b/smeared_fit_with_data.png
new file mode 100644
index 0000000..6e8a598
--- /dev/null
+++ b/smeared_fit_with_data.png
Binary files differ
diff --git a/test.png b/test.png
index 3815a28..c3bbe18 100644
--- a/test.png
+++ b/test.png
Binary files differ