diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
index c901755..ef8d1be 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",
@@ -64,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -543,19 +521,9 @@
   },
   {
    "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",
@@ -664,7 +632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -694,7 +662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -711,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -749,7 +717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -793,7 +761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -809,7 +777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -869,32 +837,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:14: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
-      "  \n",
-      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
-      "  fig.canvas.print_figure(bytes_io, **kw)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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wyH/913+lexknLGErJFxltj/6KAgoMCKHZ80DvkKY6wqLks0S1LA4KUYH6mfmcYAqXTGQz30uZhUTxQgRDnSwx3Pfkf6Q8FC/w7O6EH0yTShRGp0oS0mhUAyaiChprpjXbaPinFK/hKzCdN8FLXddOMN5wAikZX2K2ChRUigUgyZsXdjt8SwlQXCUnFMiSpRscuC6lKU0ulCipFAoBk24gJ9Nd8a8rsn0Bzr0FvkLpxnCyjRhWkphMVKW0uhCiZJCoRg0QeuB77DHFiUdEXn4pwsZqacUHRIuBkTfeYPe9CxQERMlSmOIoy1dsWLFCk455RSqq6uprKzkxhtvpK2tbVjX+te//pWtW7fGvf7AAw/w+9//PrK+6Azhw0Uq5TS8Xi+LFi1izpw5zJo1ix/+8IeRa6tWrWLXrl3Dvs7RQHhPyWGLI0ox3GQjTSQhK+bekSYEuhQDLCVvSInSaCIlURJCXCCE2CGEqBFC3BLjulMI8aR1/X0hRFnUtW9b7TuEEOcnG1MIMc0aY5c1piPRHEKIAiHEa0KILiHEvVHjZAgh/iaE2C6E2CKEOKHqHAymdAXA448/zsaNG9m4cSNOp5NLLrlkWNeXSJSCwSBf+9rX+NKXvjSsa+hPKuU0nE4nr776Khs2bGD9+vW8+OKLvPfeewBcf/31Mc96jUXCIeHhKrP90dHSbylZa9S0XvedQIsEOoQ/gy/oS88CFTFJKkpCCB24D7gQmAlcLoSY2a/bNUCrlLIcuBu43bp3JrAKmAVcANwvhNCTjHk7cLeUsgJotcaOOwfgBb4PfDPG8u+UUlYC84ClQogLk33e0chwlq7oT/gg7f79+2MeXO3q6uLqq69m9uzZVFdX86c//QkwUxzNnj2bqqqqPg/zrKwsvvvd7zJnzhyWLFnCkSNHeOedd3j22Wf51re+FVnfihUr+M53vsMZZ5zBL3/5S370ox9x5513RsZ57LHHOO2006iqqmLt2rUAvPHGG8ydO5e5c+cyb968hCmQUiGVchpCCLKysgAzq0QgEIikR1q+fDkvv/wyweDY3zgPyiSWEoKQSK8oReo5Wf//2KzSFWGxDO8lKUtpdJHKOaVFQI2Ucg+AEGI1cAkQ/TP3EuBH1uungXuF+V/qJcBqKaUP2CuEqLHGI9aYQohtwFnAF6w+j1rj/jreHFJKD/CWEKI8etFSym7gNeu1XwjxEVCawudNyO1rb2d7y8AMCsdC5bhKbl4Uv8gdDF/piljous6cOXPYvn07c+bM6XPtf//3f8nNzWXTJrN8R2trK4cOHeLmm2/mww8/JD8/n/POO4+//vWvXHrppXg8HpYsWcJtt93Gf//3f/PQQw/xve99j4svvphPfvKTfdbR1tbGG2+8AcCPfvSjPvN6PB7eeecd3nzzTb7yla+wefNm7rzzTu677z6WLl1KV1cXLtfA8OThKKcRCoVYsGABNTU13HDDDZEzX5qmUV5ezoYNG1iwYEHcv+9YwDBCCCnjWkqa1AiRXnE2IntK5mNO14S1rr7uO79VRVcxOkjFfTcJOBD1vs5qi9lHShkE2oGCBPfGay8A2qwx+s8Vb46kCCHygE8Br8S5fp0QYp0QYl1jY2MqQ444/UtXvPXWW2zfvj1SukIIwZVXXpnSWKmUrohXcO/ll1/mhhtuiLzPz8/ngw8+YMWKFRQVFWGz2bjiiisi+1UOhyOyz7VgwYK4Zc+BAW7GaC6//HIATj/9dDo6Omhra2Pp0qXcdNNN3HPPPbS1tcVMOTSYJLGpltPQdZ3169dTV1fH2rVr+xxsPlHKaQRlEBugxcnooAkt7ZaSpG85dLDcd0LtKY1mUrGUYtVG6P9ti9cnXnssMUzUP9V1DEAIYQOeAO4JW2YDBpHyQeBBgIULFyYcM5lFM1wMV+mKWIRCITZt2sSMGTO47777eOihhwBYs2YNUsoBcyaqGGu32yP9dV1P6NrKzMyMey3W57/lllu46KKLWLNmDUuWLOHll1+msrKyT7/hKKcRJi8vjxUrVvDiiy9GspWfKOU0DGlgkxI93jklqaW5cEX0OSU90mYGYJioPaXRSSqWUh0wOep9KdD/p2CkjyUCuUBLgnvjtTcBedYY/eeKN0cyHgR2SSl/kULfUctwla7oTyAQ4Nvf/jaTJ0+murqaG264IWJdTJw4kfPOO497743Ek9Da2srixYt54403aGpqIhQK8cQTT3DGGWckXEOyMhj9CUcQvvXWW+Tm5pKbm8vu3buZPXs2N998MwsXLoyZmHaoy2k0NjZGIhN7enoGCOHOnTuZNWtWyp/reCUkg9gk6PECHYQesUjSRfjHkhb1mNPQCAnzWthS8oWUKI0mUhGlD4AKKyrOgRm48Gy/Ps8CV1mvVwKvSvMb8SywyoqcmwZUAGvjjWnd85o1BtaYzySZIy5CiFsxxes/U/ico5rhLF0BcMUVV1BdXU1VVRUej4dnnnkm5r3f+973aG1tpaqqijlz5vDaa69RUlLC//t//48zzzyTOXPmMH/+/KTRe6tWreJnP/sZ8+bNGxCIEYv8/HxOO+00vva1r/HII48A8Itf/CKyDrfbzYUXHlscSyrlNOrr6znzzDOprq7m1FNP5dxzz424J48cOYLb7aakpOSY1nE8YMgQNiS6rse8rqGleUcpuhBh72MuXBo9KIMErb0kdU5plCGlTPoP+ASwE9gNfNdq+zFwsfXaBTwF1GCKzvSoe79r3bcDuDDRmFb7dGuMGmtMZwpz1GJaTV2YFtVMTCtLAtuA9da/ryb7rAsWLJD92bp164C2kWTv3r1y1qxZQzrm0qVLZWtr65COeaJz1113yYcffjjmtXR/h4aaG35zrjzj4RnSHwzFvP4fvz5DLvrN0H5nB8vql38lq35XJZ/6x92Rtu/etVhW/a5Kdge65Y1PXSSrflcllz+2OI2rHDsA62QKepLsX0pZwqWUa4A1/dp+EPXaC8RM2yylvA24LZUxrfY99EboRbcnmqMsztIHv+FyghAuXZGXl5fupYwZ8vLy+OIXv5juZYwIpqVkZt6OhS70tFtKvdF3MSwlI0jA1wGA11DRd6MJldHhOGC4SldUV1cP6ZgnOldfffUJU3QwJENoEjQttihp6ATT/JPQkGH3Xa+LMZzdIWgECVli5TOCCYN1hooPDn/AiidXsKc9ZryVwkKJUoqMxJdWMTYZi98dA4ktwceyCR1DiEhNo3QQSTMUFX2nRVlKYUvOQI5IpvDndj9Hs7eZf9b9c9jnOp5RopQCLpeL5ubmMflwUQwvUkqam5tjHuw9ngkRSvjwCB9YDcr0OfHC9ZR0LTr6zlxXwAhE0g3ByJxVave1A3Coa+yfYzsWTgxfwzFSWlpKXV0do/VgrWJ043K5KC095mQio4qQNNBlfP9cRJRCAZxxylsMN6EYe0palPsuWpR8IR/ZZA/reuo99X3+VxEbJUopYLfbmTZtWrqXoVCMGkIYCS0lW1iUgj5wZI3MovoRrvmka9GPuag9pWhLaQTCwlu85rHKhu6GYZ/reEa57xQKxaAxklhKuiVKgWD3SC1pAOE9pWhR0qJKoAdkb2mNkThA2x0w/xZtvuEtC3O8o0RJEZedtZu4Z/XX070MxSjEtJQSiJJmph8KBHtGakkDCKcRsunR0Xe9e11BGcJpCddw7ylJKekKdAFKlJKhREkRl+/84woe8r3Gjr3r070UxSgjhEwoSpowRcnnT1+2hHDpij6WUvSekjTItIKXhjtTeE+wB4kky56FJ+AhEFIl2OOhREkRlxY9nLBSpWFR9MUgsfvObllKXn8a3XexRCnsVgwFCGGQGbaUhvk7HraSJmWZRQ+UtRQfJUoKhWLQJLOUwkLgDaTvB00osqcUlSUcy60YChCUkizDtJSGe0/JE/AASpRSQYmSQqEYNIZIJkpm9nC/L317SmFLyab1ltfQLbeiP9htuu9GaE8pIkrZSpSSoURJoVAMmhASPcHjw6abouQdBYEOWh9LybTgfIFu/FF7SsNdU0m571JHiZIiOVGhswoFWO47mVyU/Gl034UzOtij8hHqmnmQ1xf0EkRGLKWRct+VZpmHqJUoxUeJkkKhGDQh0bd4Xn8cuplWKa17SuFAB9ErSjZhiWWwh1CUKA13oENYlCZmTQR6Uw4pBqJESZEcob4mir4EkegJ9pTstrAoeUZqSQMwLEvJpkdH34VFyYsBIxbo0OU33XcF7gKculOJUgLU00aRHOW+U/QjKMBG7KqzAE5bJgC+QPr2lGTYUooSJZsW3uvqxhDgkhIh5bAHOnRbmS2y7FnkOnOV+y4BSpQUCsWgCQjQE4iSwxEWpfSdUwqFLaWoQAebtafUbVlwNilxyhEIdPB3YdfsOHQHec48JUoJUKKkSI5y3yn6ERBmddl4uCxR8qbRUoq476JCwtFcCCnxWO40GxLXCFhKXYEuMu3m3yTPmafcdwlQTxtFcpT7ThGFlJIgid13bmcOAL40hoQbWIdno9x30ubEKWXEUtIlOKUx7JaSJ+CJiFKuM1eJUgKUKCkUikERlEGkEH2i2vqT4TJrE/nTmKIqvKdkjxYlzYFDysgeT9hS8g1zNnNPwEOW3SzhofaUEpOSKAkhLhBC7BBC1Aghbolx3SmEeNK6/r4Qoizq2ret9h1CiPOTjSmEmGaNscsa05FoDiFEgRDiNSFElxDi3n7rWiCE2GTdc48QIn64kCI+yn2niCKcTDShpeSyLKURKAkRj4j7ztZbZNDQTUupx7LgbBKchsQ7zHtf0ZZSnjOPDl+HqmQdh6RPG2EWuL8PuBCYCVwuhJjZr9s1QKuUshy4G7jduncmsAqYBVwA3C+E0JOMeTtwt5SyAmi1xo47B+AFvg98M8byfw1cB1RY/y5I9nkVMVD/8SiiCBimKIVT9sTC5cpClxJ/WkXJSjNki1qn7sAhoTsiSmFLaXjdjP33lIIyGMnyoOhLKj+BFwE1Uso9Uko/sBq4pF+fS4BHrddPA2dbVsklwGoppU9KuReoscaLOaZ1z1nWGFhjXppoDimlR0r5FqY4RRBClAA5Usp3pfmT5PdRYykUiqMknKVB1+KLkt3pwiElfmN4S0IkQkbOKUWVY7eZ6/JYIiSlRpZhDLtAdAe6++wpgcrqEI9URGkScCDqfZ3VFrOPlDIItAMFCe6N114AtFlj9J8r3hyJ1l2XZN0ACCGuE0KsE0Ksa2xsTDCkQqEIV5MNZ0eIhcOZgVNKAmkUJUOGEFJii0ozJMKBDla0XQg7WYZBp79zWNcSbSnlOkxR6vB1DOucxyupiFKsfZj+/px4fYaqPdV1pLKmgY1SPiilXCilXFhUVJRgyBMTtRGniMZvWRWaFl+UnE47DklaLSUDAx0Q0Rkd7Kal1G25Ff2Gg2xD0jnMmSeiAx3yXHmAspTikYoo1QGTo96XAofi9RFC2IBcoCXBvfHam4A8a4z+c8WbI9G6S5OsW5ECEhUSrujF57MOniYQJYeuYZcQiDg9Rh4pQ2gSRFSRP93htETJFMsgDrINg65hjL4LGSF6gj3KfZciqYjSB0CFFRXnwAxceLZfn2eBq6zXK4FXrX2cZ4FVVuTcNMxgg7XxxrTuec0aA2vMZ5LMERMpZT3QKYRYYu1VfSlqLMWgULaSopceS5T06L2afjhtpigF0yhKhjTQkRB1yFezuUz3nWXB+aUpSl4jMGwlyj1B8+8VHegASpTikVSUrP2bG4G/A9uAP0optwghfiyEuNjq9ghQIISoAW4CbrHu3QL8EdgKvAjcIKUMxRvTGutm4CZrrAJr7LhzAAghaoG7gC8LIeqiIvmuBx7GDLDYDbwwmD+OwkQoUVJE0eMPW0rxRUkIkX5LCcN8wEXXU3K4cEtJyPLk+6WTbCtTeGdgePaVuq1w80zDgHfvI0czk9WqA7SxiX/6LQop5RpgTb+2H0S99gKfi3PvbcBtqYxpte/BjM7r355ojrI47euAqljXFArF0eG1LKVweYp42KUgYIVlpwMpLVGKOp6o211kGL3uaL90khUWJX8n41zjhnwd4QzhmXvehI//hC1nEtn2bCVKcVCnIhVJkQnjSRQnGj1+M5zabk8sSjapESB9omSELaUobHcgMjoAACAASURBVA4nGVFef590khMlSsNBxH3XbgUDH96osjokQImSIimGyn2niMJrue/sNnfCfjYpCKQxSMa0lPr+oLLbbLiidNJvuCM1lYZNlKy/V1a3JUJtB1RS1gQoUVIoFIMiXCPJ6chI2E+XGgGRRlHCQOtn5DttGs6oMu5ewx3ZU+rwD8+5obClZOtqNedp2EeuS1lK8VCipEiKodIMKaIIi1JSSwk9rZaSIQe67xw2DYfRu8fkkxmMM0zTqdXbOizrCO8p5XpNy8horyPXoUQpHkqUFHFRUqSIRbhGksuZlbCfU+r4RPq+RQZywAPOrmvYjd5WqbnJCpqHHpq9zcOyjnBG8hwrZ6DL10K+K3/YRPB4R4mSIjmG2lNS9OK3ag857Yndd3Z0vGk8TWBgoPfTRNNS6g0Rd2ouQtjJFQ6ae4ZHlCLRd4bBHmMCLtlDkTOf7mA3nmHOJHE8okRJEZfw80S57xTR+KwaSeGaSfGw4cCrpS9QJoSB3u+MnUPXsEWJksPmphsX+diGTZQ8AQ9OzY4d2C3N9JvFuun6bOxWuTb7o0RJERclRYpYhEuHZ1jpcuJhF2bIuDdNhf5CMralhOxNj+S0u+iQGeQb2rC579p8beRaIrRbTgSgyDoi2tDdMCxzHs8oUVIkRabxrIli9OEL+dCkJMOVmbCfXZgZH7qHuaprPELCwNZflHQNR7A3E4XTnkEXbsYZctgspXZfO7nWQeNDWgkAWT5zYQ09SpT6o0RJoVAMCr/hwyUldmeSPSXNtA56/OkpZheSckBtXIdNwxnsjRp0OJ10SjfjgqFhtZSyrRzTgbwyADJ7zH055b4biBIlRVKkoRx5il58hh+XlDhciTM6OHVTtLq7h+dhnwwz0KHfnpJNwy971+1wOOkkg/GBAD3BnmE50NruaydbmvJoLzwJAL2zjQxbhnLfxUCJkiIphipdoYjCbwRwGRKnM0nuO5sZMt6dptDnkJADAx1sGt3SxQOHG3j80GGcThdd0s1Eq5ruoa6hr27T7m8nMwQBqZM/fgqGFPg7myjOKFaiFAMlSgqFYlD4DT8OCQ574nzOLrsZndfRnajs2fARYqD7zq4LPLhY2uOl2ufH6TQtpSk+c9/rYNfBIV2DlNJ034WgEzdlxTm0kYnRpUQpHkqUFEmRKvedIoqADJiipCd+fDgdOQB0dKfHUjKQA913ukY3vRaeyxKlqT4z791Qi1J3sJugESQrEKRTZjCtMIsWmYPobqYks2RYLLPjHSVKCoViUPhlELsU2JKIUjhkvKMnPel0TEuprygJIQhovYEOdlcWndJNrmGQbc+irrPuqOYypBGpmxRNOJAhPxigkwwKsxy0iVxs3hYmZ0+moachbSHzoxUlSoqkJCjwqzgB8RPEbiRP1eB25QPQ5R2eRKfJiLWnBBCMytknHBn0aGZo+6SMYuq6Bi9KhjS4cs2VnP+n8znsOdznWtg9V+Tz0ykzyHXb6bLl4fK3UJpdCnDUQjhWUaKkSIrK6KCIxk8Im0z+6Mh0m6Lk8Q1PSYhkhJBoMpYo9YayC0cGPisgY1rGBHa37R70PFuatrCpaRNtvjYe2/pYn2tHuo8AUOzvoQs3mQ4bPfZ8MoOtTM6eDMCBzgODnnMso0RJoVAMigAGNtk/hGAgmRkFQG89oZHGEBI9xiMuZItKJOvIoNtmiufJzgLqPfWDrqv0UcNHAMwtmssLtS/0SasUtpRKfB68eiaaJgi4CsgyOpicaWZ3OBrrbCyjREmRHBXooIjCLwxsA+LaBuLKzCHTMPAE03R4FokmBq6z29Fb8lyzuQhaJdBP1k2xqmmrGdQ8e9v3Ms41js+f8nkauhvY1LQpcq3eU0+2PZu8oAe/JYaGuwANSV7IIMuepSylfihRUsRFOe0UsfAjsZE4HBzAmZFDjmHQmbY0Q8S0lHzOwshrm64RcpvvK6QdgJ0tOwc1z972vUzLncYZk8/Aptl4qfalyLXa9lqm5ZbhMjwErRB5PasIAOlpYnL2ZPZ17BvcBxvjpCRKQogLhBA7hBA1QohbYlx3CiGetK6/L4Qoi7r2bat9hxDi/GRjCiGmWWPsssZ0HMMc3xBCbBFCbBZCPCGESHzaTxETFRKuiMYnJDbsSftlZGSTHTLwhNIkSoAW4xHndxVEXjvtOlrGOEJoTPD1kOvMZXPz5kHNs6d9D9Nyp5HjyGHpxKW8UPsCIatw4J72PZRlTUbHIOQwRcmeUwxAZ0s9J+WdNGjLbKyTVJSEEDpwH3AhMBO4XAgxs1+3a4BWKWU5cDdwu3XvTGAVMAu4ALhfCKEnGfN24G4pZQXQao19NHNMAr4OLJRSVgG61U8xSKSymRQWhjTwC7CL5KLkdtrINAQeo2cEVjaQoJCIGO473Sq5cVAW4LRpZGc4aCMH0dPMvKJ5rG9Yn/IcXf4u2nxtkaCFT07/JA3dDaw7so7G7kYaexo5OcssVyGd5rktV/54ADwtR6jIr6Chu2FY0hsdr6RiKS0CaqSUe6SUfmA1cEm/PpcAj1qvnwbOFkIIq321lNInpdwL1FjjxRzTuucsawysMS89yjkAbIBbCGEDMgB1Uu0oUMF3ijA9wR6kENhxJu2b6dRxhTQ80j8CKxuIAegx9r4y7DorfD/n074f47Rp5LjsNMsc8DQxb/w8ajtqU84YXu+pB2CiFbRwxuQzyLRn8tTOp1h7eC0AC7KnAyCcphhm5U8AoKf9MBV5FQDsat119B90jJGKKE0Confi6qy2mH2klEGgHShIcG+89gKgzRqj/1yDmkNKeRC4E9gP1APtUsp/xPqAQojrhBDrhBDrGhtV1t7+KPedIky4UqpTS+4Jd9t1nIYND8GkfYeDoAAthii5HTq1soQG8nHadHLddhqMbGRXA/OL5wPwccPHKc0RFqWSLLMkhdvm5guVX+DvtX/nznV3Mj5jPDNc5h6S7jYPE+cVTMCQgkBHAxX5lii1KVEKk4ooxTol1/+3c7w+Q9U+6DmEEPmYVtQ0YCKQKYS4MkZfpJQPSikXSikXFhUVxeqiUCjoPXPkjMqKEA8hBHZpxyNGvh5XyAgREgKbGBiQ4XZElUO3a+S47TSQj+w4xKyCWWTZs3jr4FspzRM+LFuSWRJp+0rVV5hZMJMWbwv/Mf8/CHWbrjlbRh4ARTkZtJKF7GpkfMZ4sh3ZylKKIhVRqgMmR70vZaAbLNLHcpXlAi0J7o3X3gTkWWP0n2uwc5wD7JVSNkopA8CfgdNS+LyKfihLSRHGY7m1nFriWkphHNKJT0AgFBjOZQ3Ab5guw1iilBktSjaNXLedOlmI6KzHjmDZpGW8fuD1lMq4H+o6hE2zUejujejLcmTxh0/8gX+u+iefOulTdHeYfzN7lnkeKsdto4VcRHczQghmjJvB1uatSefa07aHI54jSfsd76QiSh8AFVZUnAMzWODZfn2eBa6yXq8EXpVmbppngVVW5Nw0oAJYG29M657XrDGwxnzmKOfYDywRQmRYe09nA9tS+7MoolFbSoowXVYZCpc9cdXZMA5M8Wr3j+xGvj9kilKsgIzcjKhy6DadHJeNg7IIIUPQcZAVk1fQ7G1mY+PGpPPUe+qZkDEBTfR9lOqaTo6VkNbX2WTOlW0KlxCCDi0Ph88Uq+qiana07EiYA6870M2qv61i1d/GfqxWUlGy9m9uBP6O+VD/o5RyixDix0KIi61ujwAFQoga4CbgFuveLcAfga3Ai8ANUspQvDGtsW4GbrLGKrDGPpo53scMiPgI2GR91geP4m+kUJEOCot2j/WAteem1N9h5ZXr8I9s/rvwAz6WKI2LFiW7RmG2kzppWTrtB1heuhyn7uT5Pc8nnafeUx/ZT4pHsNMUn4zcXmuqy15Alt/M9lBdWE1QBtnWEv83c01bDT3BHpp6msZ8ZvHkJ+AAKeUaYE2/th9EvfYCn4tz723AbamMabXvoTd6Lrr9aOb4IfDDWPcoUkflvlOE6fCYD1iXIz+l/k49XL6iCXKnD9u6+tPlMwMy7JpjwLX8zF6hynTYKM52UietveS2/eSULePsKWezZs8avrnwm7hs8YM66j31LJow4HHVh5CnmQ7pJiez1+XpcY0nv+NtMAyqi6oB2NCwgXnF8+LOE2Z7y3YmZk1MOOfxjMrooFAoUqbdKtjndo9L0tMk0272a+4Y2fxuHq+Z2sgRQ5TGZfa26ZqgMMtJvSxAIqBtPwCfqfgMnYFOXtr30oD7wwSNIA3dDX2CHGIhu1tok1nkZfSKoT9zInaC0N1EgbuA0qxSNjRuiDtGdPbxve17E853vKNESZEUFeigCNPpNfeGMqI29hOR5TCzFzSPsMup2xIluz7wPFW0+w7AZddxuzNodZRA4w4ATp1wKtNzp/Obzb+JG/BQ76nHkAaTsvqfkOmL7m2hlWxy3b2ipOeZZSt8zaYIziuex0cNHyWcK9OeSaG7UImSQqH2lBRhPL4OdCnJyEhNlLIzTCuioXNoK7omw2O57xwxRKkwy2zLdvXuXhRlO6mzTYHG7QBoQuOrs79KTVsNbxx4I+Ycte21AJTlliVci83XSpvMItvVK0ruwqkAtB02x1gycQkt3hZ2tsbOu1ffVU9JZgnTc6ezt0OJkuIER6UZUoTp8neRYUjc2Xkp9bfnlJAfCnGk83DyzkNIt89y38UQJU0T/OHaxTz1tX+JtBVnO9nFZGjaBVb4+oXTLqQ0q5R7199L0Bh4ALi2oxaAqTlTE67F4W+nS89B13qPVOZOKAPA02iOsaRkCQDvHXov5hj1nnomZE5gSs4U9nfsTzjf8Y4SJUVSlCgpwniCHrKkgTs7tUAHPaeEglCIxp6mYV5ZX3rCohQnSOG0kwqpnJATeT8hx8Vm/0QwAtBsFvqzaTa+seAb7GzdyZ92/mnAGLXtteQ4csh3JvhbSEl2oJEOW0Gf5gkTJuGVdvwtZjKa4oxiTso9iXfr3405zGHPYUoySyjLKaPN1zamc+UpUVIoFCnTGfSQEzLIzsxK3hnIyB9PQdCgZaRDwv2WKKV4nmpKQQbvd5s56TjcWw/p3KnncuqEU/nV+l/R1E9YtzRvoXJcJUIIeOtu819/V3dPK3YZwOMs7tM8Ic9NvSxAtPcGgCyZuIQPj3yIL+Tr07c70E2rr5WSzBKmZE8BGNPlLpQoKZIi1Z6SwqLL8JIVEmS7k2cJByjMySA7JGgJjWz1WZ9V7dbpSE08pxZksMMoxbBnQt3aSLsQgu8u/i7eoJfvvf29yH8LPcEedrTsMMO56z6El39k/qt5pe/AHWaARyBjQp9mu65Rr08k09MrLssmLcMX8g1w4R3uNl2fEzInRFyFSpQUCoUC6MKP2xBkOVM64khRlhN3yEGr9I/ojxtvwLSUXCmLUiYhdNrGVcP+vqJwUt5J/NfC/+Ltg2/z8KaHAXjn0DsEZZBTJ5wKB963egr48Ld9B7ZEiZyBYeMt7skU+g5ErKvFExaTZc/ilf19he1wV29+vdLsUjShjWlRSu2bpTihUSHhijBdBHEbNmx6ar9n8zMduAMu/MJHs7e5T4644aTbct9lulLb+yorMN18+zJmM672YfB1gbNX0Fadsor1Deu55+N76An28O6hdyl0F5oHZz98EtzjYO4X4P0HwNMMmeYektF2AA3Q8yYPmDOQOx1Xtxc6D0NOCXbdzvLS5bx+4HWCRhCbZj6eD3lMYSvJKsGhOyjJLBnTwQ7KUlIkRbnvFGB+D7qEgdMYeCA1HnZdwx0y6wiNZHqcnqDpvst0pxYlmJ9hJ8dl42MxA6QB+97uc10Iwa3LbuWi6Rfx0KaH2NK8hW8t/JYpHI07oKgS5qwCIwhb/hy5z3t4G93SibugdMCc9uKTAeg6tD3SdvaUs2n1tfYpnXGo6xC60BmfYRYHLMspi0T+jUWUKCkUipTwBDyEBNhJXkspGpcwU/gc7By5rA49QQ9Ow8BpVZlNhhCCmRNz+FvHdLBnws6/D+hj1+z8dPlP+dPFf+K5Tz/HJ6Z/AqRENm7n3c4Crny+m1DRTNiwOnJP6MgOdssSinIGZlXPmzwDgOb9vRnCl08y8+79vbZ3/kOeQ4zPGB+xnKbkTGF/5/4x+2NRiZIiKRLlvlP0Zvq2k9o+TRi73dycP9gS+2DocNAT6iFTSuzO1KLvAKom5rL5cA/G9DNNUYrz0D85/+Tes0meJkRPKy815PFWTRPvZJ4DB9dBUw1IiaNpKztlKZPHDaw/VTq1nG7pxFffm4g1w57BWZPP4oW9L0Si8Oq76vvkupuaMxVPwEOzN7XquMcbSpQUcRmbv8MUR0ubtw0Au5aTpGdfZGYZ+aEQB1trhmNZMekJ9pBhGLgyUhelWZNy8AUNjpScCR11cOij5DdZGSB2yUksmJrPbQeqkEKDjauhuQanr4kPjEqmFgxcx+SCLHYyGWfTlj7tl1ZcSoe/g9cOvIaUktqOWqbkTIlcH+sReEqUFEkxDGUpKaDdShXktKUWPBBG5k1hUjDIgc6R25zvNnxkGhKXO3VRmj3J3H96x74EdGcfN1xcmsxcea0Z0/j62RVs92RxpPh0eP9BeOMODASbXQtiRivqmqDOdTKFXTv6WGWLJyxmQuYEnt75NA3dDbR4Wzgl/5TI9anZpiiN1WAHJUqKuISToqiMDgqAxnbzl3lWv4OgycgsnsZ0f4Aaz8ilGvJKH5nSICMztT0lgJOKMinKdvLG/gDM+CRsegqCvsQ3Ne6gR7jJKZ7K8vJCphVm8r+BL5gBD5v+yDvOpWQWT4t7u69gFpnSg2ytjbTpms4XKr/A+/Xv89CmhwCoKqyKXC/JKsGm2cZssIMSJUVclBQpojliWUq5mQPDmxNRXFhIqV+j2eih1apcO9z4pB+nIdB1PXlnCyEEp51UwDu7m5Fzr4CeVtj854T3yMYd1BgTKR+fjaYJrlwylb8dymLXJc8SuOiX3ND1VeZOiR8BmFG2AICmXR/0aV9VuYpidzFP7niSCZkT+oiSTbNRmlWqLCXFictYjfJRDI7DHfVkGAbZMQ6CJqI0302WzwyOqGkbmX2lLvxkhQb/eFtaXkhTl4+t7vlQNAPeuSdhlnyjYTs7jElUFJufb+WCUtx2nYe223k75xO0hxycOjV+7anJpyzAL3Xad/c9sOu2ubn/nPu5+KSLuX357QPKrY/lsHAlSgqFIiUaupsoCoVw5RYN6r7SfDdBnylku1p3DcfSBtAlgmQYg88NcHZlMbom+NumI7D0P6Bha8zwcAB6WtE9R6gxJnKSJUq5bjsrF5Typ48O8oNntpDrtrOsIv6B4ZMnFbJJnoTr0NoB104Zdwq3LbuN+ePnD7g2JWcKBzoPEDJCg/6Mox0lSorkqIwOCqDJ10phMIR73OAspVy3nWajjHGhEFuiDoUOFz3BHnxC4jZSy88XTUGWk6XlhTy38RCy6rOQX2bmtAsNLF1BgxnKvV1OpqK4d+/qvy84hdmTcqlv7+GHn5qJyx7fheiwaezLmsuErq3g7064ti1/+zUfv266EyvyK/CFfOzrHHsReEqUFElR7jsFQEuoi7wQ5OcVJO8chRCCtqyTmev18fHhD4dpdb2EQ9fdcnCHfMNcPGciB1p6eHdfB5z7Y2jcBh//38CODeah10OO6RRm9Wa5yHbZ+cu/ncbm/zmfz8wfmMlhAFNPw0YIz57YZSsA2ur3MuuDW5j3+tV093QzY5x58HZ78/a49xyvpCRKQogLhBA7hBA1QohbYlx3CiGetK6/L4Qoi7r2bat9hxDi/GRjCiGmWWPsssZ0HMMceUKIp4UQ24UQ24QQvVW9FApFykgpaZVeMoIO8jMHb4FQPIP5Xh8HvI00djcO/QKjaPWZwRROBmZRSIVPVpcwLtPBb96qhRkXw5TT4JUfQ+eRvh0Pb8IjMskpnmqWr4hCCIHTllqQRWn1mYSk4MjGV+L2ObCtd89p54evMz1vOnbNzvaWE1CUhBA6cB9wITATuFwIMbNft2uAVillOXA3cLt170xgFTALuAC4XwihJxnzduBuKWUF0GqNPeg5rHt+CbwopawE5gC9R6cVKaNCwhVtvja8QuIKusnPSD33XZjCSdOp8JqPmw+PDK+1FBa9TC31cPBoXHadKxZP4ZXtR6hp7IJP/QL8HnjuP/oGPex7h4/kKZSPP7p5wlSXT2Yj5ThrX43bx3tkd+R1x94PsWt2yvPK2dYy9h5pqVhKi4AaKeUeKaUfWA1c0q/PJcCj1uungbOF+dPhEmC1lNInpdwL1FjjxRzTuucsawysMS89mjmEEDnA6cAjAFJKv5SyLbU/i0KhiOZglxkO7g7lYk8xQ3g0FeNz8PSUkWvA63WvD/Hq+nKoy8yxl20b3HmqaL58WhmZDhu3v7gDik6Bs38AO1+Ad35ldmjdB007eTNQycnHKEouu05N3jImdW9DdtTH7tS6l07pppVc9CObAZhRMIPtLdvHnHs9lW/XJOBA1Ps6qy1mHyllEGgHChLcG6+9AGizxug/12DnmA40Ar8VQnwshHhYCBHzeLcQ4johxDohxLrGxuF1LRyPGCrQ4YSnzkqmmqEf3YP+5PFZfGycwpmeLt488AaBUGAol9eHQ617cBgSl3ti8s5xKMhycv2Kk3hp6xHe3d0MS/4NZl4KL/0A3vs1/PNOJIK/hZZwyoRjEyUAV9VFADR89FzM6+7OfRzWSzicUUGhx8whWDmukjZfG0e6j8S853glFVESMdr6S3O8PkPVfjRz2ID5wK+llPMADzBgPwxASvmglHKhlHJhUdHgwl1PBMbaLzHF4AknU810DO7gbJiywkw2iErO9vTQGejinUPvDOXy+lDXXktJKIiwSj0cLV9ZOo0p4zK45c8b6Q4a8OkHoOI8ePEW+Oj3bJt8GYcoPGZLCWDRomUclAV4Nj0f83qe7yBt7lK8+acw1ThAR7c3EuywpXlLzHuOV1IRpTog+ptYCvQvjBLpI4SwAblAS4J747U3AXnWGP3nOpo56qSU4bKQT2OKlEKhGCQHW3aRFwphz51+VPfbdY1AyQIWeKFIOFi9I4W8ckdJTUct0/wBtJxjEyW3Q+dnK6vZ19zNrX/bBnY3XL4arngaLnuMBzOuozjb2Sfy7mgZn+vm44ylTGp+F7wdfa7JUIDxoSP4sqfinDQblwhQu3MTleMqsQkbmxo3HfP8o4lUROkDoMKKinNgBhU826/Ps8BV1uuVwKvS/Hn9LLDKipybBlQAa+ONad3zmjUG1pjPHM0cUsrDwAEhRDiT4dlAb+ESRcooS0mxr72W0mAQreDoRAmgemoxb4eqWdnVzVsH32J32+7kNw2SnmAP+72NVPoD6LmDO08Vi8XTC/jX06fzh/f38+QH+0HToOJcZOUneW9vG4umjRsQeXe0BGZ8Bid+GtY+1ae9tX4vdhFCjJtOUfk8AFr2rMdlc1E5rpINjRuGZP7RQlJRsvZvbgT+jhm99kcp5RYhxI+FEBdb3R4BCoQQNcBNWG4yKeUW4I+YYvAicIOUMhRvTGusm4GbrLEKrLEHPYd1z78DjwshNgJzgZ8M9g90IqOkSBGmpucwJ/kDuIuPXpTmT83nxeACLm86TJbu5q4P7xrCFZpsa96GgaTCF8RRUDYkY37r/FNYXlHI9/66mVe2mfs36w+0cbjDy+kVQ+fuP+2MC6iV4+n+sK8V2XzAzETuGl9OYVk1IQTBw2aww5ziOWxp3kLQiHG49zglpTwcUso1wJp+bT+Ieu0FPhfn3tuA21IZ02rfgxmd17/9aOZYDyyMdY8idaQKdDihafO20WJ4Ge+3M7Ewfh63ZCwsy+ebxkLuEG6u1Yu5u+5N1uxZY1ZwHSLePvQ2moQJPdk48lIvW5EIm65x7+Xz+eJv3udf/+9DbjiznHd3N5PltHHB7AlDMgeYLry/5p7Dp9qfwGg/hJZrBmp46s39vHGTKxGODI7YJpHZZgpVdWE1j297nJ2tO5lZ0P+kzvGJyuigiEtv6QolSicy4SSqGb4cygqP7kAqQHG2i5MmjecVx1l8qWYt8wqq+J93/4ctTUOzUW9Igxf2vkB1ABpCJUzIObqMDrHIzbDz+FcXc+7M8fzylV2s29fCjy6eRY7rKA4SJyB70RXoGOx/9aFIm9FUQ490UFJqWqnt2RWU+PYSDBnMKZ4DwMbGjUO6jnSiREkRF+W+UwDUhJOoBkooynIe01hnVhbzs/YV6EaQnxkF5Lvyufala3mz7s1jXueavWs40HmAL7Q1sYkKct1DLBguO7++cgFvfGsFb99yFisXpJBCaJAsXbyEd6kmd/PvI/n2nB21HNRKcDlMx5ZRNJMpHGHf4UYmZk6k0F04pvaVlCgpkqICHU5sth75kLxQCK9rxjFv6p83czx7jBJ2ln6W8euf4Ddzv8mkrEnc8MoN3PT6TWxq3HRU37d3D73Lre/dyuzMUs7zdFObMXvIAhD6M7Ugk5Jc97CM7bLrHD7lS+SHmmj44E8AFHbX0OiaGumTM3UOmpDU7fwYIQTVhdVjSpQGn9tdoVCcUGxsWE+1z49v3LHvWVRNymXWxBx+2HkpqzPfYuKam3nsy8/zyO6/8OiWR3lp30tMzJzInKI5lOeXU5xRTL4zH7tmx6bZkEh6gj10B7rp8Hewr2EjH+/4C1scNspyyrirx0mnyKW18Pg9/bH0wi+wf/sd2F+/g0DlMsYbDWwqXBW5Pr5iPrwEnv0bgQuYUzyHVw+8SnNPMwXuwSXLHY0oUVKkgLKUTlQ6/B3s7jnChT4fwbKq5DekwKpTJ/P9Z7aw7dK7mPHSl3A+cTn/dvmTXDnzSl7Z9wpv1r3Jx40f80LtC0nHcqNTYYT4ZnuAy4pn4trwAPfJz1NWHL/a62inOC+TJ6b+G5fv/xFtD3+KPMB98orIdUfhdLw40JvMvHfzis0w8fWN6zl7lhvM1gAAIABJREFUytlpWPHQokRJkRRDue9OWMIHMyf2uHBMGZo9lJULJvPLV2r48cYsnvj8/8FTX4b/bzk5593Kp6tW8umKTwPgDXpp7Gmk3ddO0AgSMMzURBm2DNxoZG19lsLXf4ZmBCCjEN5/AO/JF/PLjRfx3aKsIVlrujjvc9fz2s+f58yudayTM5i36PTei5pOg2sa+V3mXt/MgpnYNTvrG5QoKRSKMc67h97BLiX+njLmluQMyZhuh84NZ57E/zy3lTX/Mp9PXPsK/OVr8OdrzYJ6Mz4Fpafiyi9jcsY4Jgs3SB90NUHLXjjwPtS8Aj0tUHE+fOZBcGRBwMPbe734N66jcgjy0aWTgmwXBV95krv/8RwLFq8gw9k3aKMnv5Lph16jqdNLYbaLWQWz+HgECiiOBEqUFCmgQsJPVN4+8AYLvF52aFV8Jn/oNve/uGQqf/7oIN/762bmfX0ZJde9Adufg48fhw9/B+8/EP/mrPFQfg7M/yKULYdwQIOey7p99dh1wZzJx6/7Lkz1lEKqv3p1zGu20nkU1j/D2pptFM6bx9ziuTy+7XF8IR9O/dgiJNONEiVFfIYneElxnHDYc5iazn1c2u1le9GCIY1ms+kad31+Dp++/x2u/u0HrL5uCXkzL4GZl0AoAI3boeMQdLeYoqPbIXsi5E+F7JJeIerHmzsbqS7NS1iCfCxQPPMM+ABat/8TLFH63ZbfsbV5a2SP6XhFhYQrkqJCwk9M/l77dwDmdVsRX0NMxfhs7r9iPnsaPXzm1+9Q09BlXtDtMGE2nHw+zL0c5qyCqs/C1H+BnIlxBammoZMthzq4sGrosiyMVrKnzqEbN1qdmW96btFcgDHhwlOipIiPpUVKlE5M/rbneWb5Q+zxzWbR9KMvmJeI008u4v+uWUSLx88n7vknd720k7Zu/1GN9atXa3DZNS6Z27/c2xhE06nPrmJy10b8QYMCdwFTc6YqUVKMcZT77oRlZ+tOtrVs51Od7bwh5zNvSv6wzbV4egH/+MbpnDtzPPe8sovTfvoq33hyPS9uPkyrJ7lASSn53dt7eWb9Ia5dPp2i7ON7TyVVjMmLOZkDbNlr1jidWzSXDQ0bjvsfkWpPSZGU4/1Lrhg8j219DBca53b5eGPKWbgdw7tHU5zt4r4vzOffz+rgt2/V8uKWw/zlY7MEe1lBBuXF2UwZl8HEPBdZThtuh443EKK+3ctr2xvYUNfO2ZXFfP3simFd52hifNWZaFvvpW7D68yruIp5xfN4Zvcz1HbUMi13WrqXd9QoUVIoFH1o6mni+T3Pc6nHy9rAApbNPmnE5q6ckMPtK6u59dNVfLivlY/2t7LhQBv7mrt5Z3cT3f7QgHtmluRw66VVXL5oCrp24pj3ORVL8WNHr30DuKr3EG3DeiVKirFJOBBcZQk/sXhgwwMYRpAvtzTx/dBV/HTmsVVwPRrsusaS6QUsmd6bNkdKSYc3SLc/iMcXwmXXGJfpIMNxgj7G7G7qsudwUscHeAMhynLLyHHksL5xfeQA8vGI2lNSJEW5704c9rTt4emdT/O5oB0RKiIw9YxhSz46WIQQ5LrtlOS6KS/OojQ/48QVpDAnnckpYj8fbdmOJjTmFs897oMdlCgpFAoAQkaIH77zQzI0B9cf3MM9/k+xasnx6wY6EZg0/0IADq9/ETDz4O1t30ubty2dyzomlCgp4qLcdycWv93yW9Y3ruc7XUECoph/us7i/Fkj77pTpI6zdB6dWg4ZdW/9/+3deXxU1d348c93ZrIRtkBYQiBsCUJYEiCAAiL7IpsLtlhR2tJqrd186k+rfbSPtvb10D5VW7W1FG2r1lLFQpFFQEEFBcK+JDEQNlkCISvZZzLz/f0xV4gxIZMYmMnkvF+vvHLvmXPPOffA3G/uveeeC1x+XmnfhX3+bNZXYoKSYRh8cuYTnt/7PFNb92HmuaP8vPwuvjuhP2GO4J4Zodmz2cjtdD3Jrr18llvKoOhBOGyOZn0JzwQlo04eayCTuaUU3A4XHOahDx8iPrI7T2ZsIzVkJAda3cBdo+L83TTDB5EDp9NVCjiwZyvhjnASOySyLyfIz5REZLqIZIpIloj8rJbPw0TkX9bnO0SkV7XPHrXSM0VkWn1likhvq4wjVpmhja3D+swuIntFZLXv3WLA5bcomYEOwSszP5NF6xcR4Qjj+exziK0N9xd/m1/MGRT088cFi05DZwLgSl8LQHLnZA7lHsLldvmzWY1Wb1ASETvwIjADSATuFJGar6BcBBSoajzwLLDY2jYRmA8MBKYDf7SCxJXKXAw8q6oJQIFVdoPrqNa2HwMZvnWHYbQcu8/vZtGGRYTZQ/lriYOu+Z/x3bIHGNo/npsHB//8ccFC2nTldKsB9C74mMoqN8mdk3F6nKTnp/u7aY3iy5nSSCBLVY+pqhNYBsytkWcu8HdreTkwSbxTCs8FlqlqpaoeB7Ks8mot09pmolUGVpm3NLIORKQ7MBNY6lt3GNV5Ls0zZM6Ugs2KIyv4zobvEBXajr+WRxD3WSr/Y/8hJ1sn89s7kpp0RnDj6nP2mcwQstibceQLD9E2R74EpVjgVLX101ZarXlUtQooAjpeYdu60jsChVYZNetqaB0AzwEPU88LgUTkXhHZJSK7Lly4cKWsLYoJRcGn1FXK4x8/zhOfPMGI6GRez6+g+/GP+V3ED1leOZI/LRhGh8hQfzfTaKBuI27BJkr27jVER0TTvXX3ZjvYwZegVNufTDWPV3Xlaar0BtchIrOAHFXdXcvnX8ysukRVU1Q1pVOnTvVlb3E8aoaEB4N9OfuYt2oeq46u4ru9ZvPHjFTaZh/kqYiHWVI8hqULUxjSvfm/HK8lCu8xjEJbFO1ObwK8zyvtzdnbLO8H+xKUTgM9qq13B87WlUdEHEA7IP8K29aVngu0t8qoWVdD6xgDzBGRE3gvD04Ukdd92F/DorUsGc1PUWURT257knvW3YOqh792m8mPPlyC2wP3yC9ZXjaMv35zBKP7Rvu7qUZj2WzkdL2JFNceTuYUktw5mfyKfE4Vn6p/2wDjS1DaCSRYo+JC8Q4qWFUjzypgobU8D9ik3hC9CphvjZzrDSQAqXWVaW2z2SoDq8z/NKYOVX1UVburai+r/E2qusDHfjG4PCTcaJ486mHFkRXMXjGbFUdWcHevm3k7v4JhW1/kaNQYbsj/BSdCE/j3/aMZHW8CUnPXPmkWbaWMjNT3Lt9XaoYP0dY7cZSqVonID4D1gB14RVXTROQpYJeqrgJeBl4TkSy8Zy/zrW3TRORNIB2oAh5QVTdAbWVaVT4CLBORXwF7rbJpTB1G0/A0w0sALZmqsvXMVp7b8xyHCw6T3HEQ/62Due7DpVSFtuG3bR7hxTNDmJscyy9vGUTb8BB/N9loAp2Tp+Na58BzeD19Z95Gm5A27M3Zy5y+c/zdtAbxaTZDVV0LrK2R9kS15Qrgjjq2fRp42pcyrfRjWKPnaqQ3uI5qn38AfFDX50btTChqfvZf2M9zu59j1/lddG8dy+KYKUzf8zZSeZFdUTO5L3smEtmJ388f0DLe0NqShLXhVJtkEoo+wVmlDOk8pFmOwGvhU+waV+KpZckITPty9vGXg3/ho9Mf0SG8A491ncC89M2EFG3jZLuR/KRsHvuzu7NwdC8enNLPnB0FKU2YSsKeX7PtwD6GdhrKC2deoKiyiHZh7fzdNJ+ZoGTUST9/VsWcMgUkVWV79nb+cvAv7Dy3k6iw9vwo+nru+nQrrTL+zrk2g3iSx1h3fiAzB3fjN5MTSOjSxt/NNq6i7qNuhT2/Jn/vaobO9M4gvv/CfsZ1H+fnlvnOBCWjXmqiUkDxqIcPTn3A0oNLOZh7kM7hHXk4KoXbM7fSqvQAJyOH8JT7W7x/IZFJ/buwZmo/BnZrPn8pG40X1qUf5x3daH/2IwZ2fAi72NmXs88EJSPImIEOAaHUVcrKrJW8kfEGnxV/RmxEZ56ISGBO5lbC3Hs51GokTzu/yy7XQOYmd2fd2N4MiGnr72Yb11hBt3EMPbmCc3nl9O/Qv9mNwDNByTAC3Oni0/zz03/y7yP/psRVQlLrnvyQTkxO3wW2cNbZJ/Bc2SRypRcLborj9zf0onPbcH832/CTjkkzafXZMjJTNzA0ZijLDy/H5XERYmse9xFNUDLqZYY5XHuqyp6cPbye/jqbTm3ChjAlogd35V4k6fgWCh2deM59J69XjKdnjx7cNzWOWUkx5vXgBp0GT8b5Tggc2Uhy0nxez3idzPxMBkUP8nfTfGL+Bxs+MGHpWil2FvPO0Xd46/BbZBVm0c7Rim9LR7528gAxVcfZ7Ujme855bNeRzBwWxz9GxZn7RcYXhbbidNthJBTtwN7O+6TM3py9JigZQcTcUrrq0nLTePPwm6w7vo7yqnIGhnbkFyUeZuZm4pR2vOGawT+rJtA+uh93To3jd0ndiAwzX1+jdpIwmfjdT7Ml6xzdIruxN2cvdyfe7e9m+cT8rzbqZUbfXR1lrjLWHl/LW4ffIj0vnQhbCDM0gjvOnmBQ5WdsZzA/dd7GgcgxzBrRi6XDYulnhnQbPogdMQd2P03+vjUkX5fMznM7UdVm8UoSE5QM4xpSVdLy0liZtZLVx1ZT6iol3hbJo4VlzC7Mo4yOvOmawU9lAomDkrlzWCzP943Gbgv8g4kROEK7XEeuoytR2R8xZMw3WXt8LTllOXSJ7OLvptXLBCWjXuZM6avLLc9lzbE1rMxaSVZhFmFiZ3KFh6/nnaN/pbDWPYL73DchvcZy6/A43h7Uldbm8pzRWCIUdLuJYSf/Q5H1EoX0vHQTlIwgYWJSo7g8Lj46/RErs1ay5fQW3OpmkIbyeF4+00tLSa/qzz/c3+Jkl0lMTornt0ndiG0f4e9mG0GiY/LNtP7sXzgzT2ATG2l5aUyIm+DvZtXLBCWjXuZMqWEOFxxmZdZK1hxdTX5lAdE4WFBUxK3FF3E4O/B21XS+HzWVkUOH8f0hMfTp1NrfTTaCUIeBk3GuchCa9QF9r+tLel66v5vkExOUjHqpefNsvXLKclh3fB1rjq0mI/9THAjjyiq47WIR/crCWee+nt9ETiT+hnHMTo7lwS5tmsVNZ6MZC2vN2bbJxBft4rr2s/kke2uzGOxggpJhNFKxs5j3Tr7HmmNrSD2XiqIMcLp5pPgiNxYr21wpvBU+npiUycxK7sG3urcL+AOCEVy09zgG7H+GNq5o8ivyOV92nq6RXf3drCsyQcmolcdd7T2JZu67S5xuJ1vObGHNsTV8eGozTk8VsVXKvcUXmVzi5EhlElvCxnMieTpTh/Tkaz2jsJmRc4afxCRPg/3P0PZsHgBpeWkmKBnNU2VVxaXllh6SPOph9/ndrDm6ho0n1nGxqowot3J7SQnTSsopKO/PtlbzWJ08m8lJfVlszoiMABEel0KZtKLXmUzs3eyk56UzKW6Sv5t1RSYoGbWqrCyvttbywpJHPRy4cID1x99lw7E15DgLifAoE8vKmFpcgbP0OvZHzmN78iwmJPfjv2PMPSIjANkdnI1KYXDePnq3HUxaXpq/W1QvE5SMWpU7yy4tt5TRd58Hog1WIDrvLCRElTFl5fywpBJbyXV82mY8R4bNYlJyAtM7m1FzRuBz9B1Pz/yP6CRdychLC/jBDiYoBbl5S1MY1jqJx+a/3KDtKipKr1KLAouqciD3ABuOrWX9sTWcdxZdCkT3l7qgpB+ftZ9C3vCbmZLclzkdW/m7yYbRIN2GTYOdTxGdX8E2Wz7nSs8R0zrG382qk09BSUSmA78H7MBSVf3fGp+HAa8Cw4E84OuqesL67FFgEeAGfqSq669Upoj0BpYBHYA9wN2q6mxoHSLSw8rfFe8010tU9fcN7aDmLjOkkszKVB5r4HYXy/IvLWuQDXRQVQ7mHmTDkZWsO/YuOe5iQlQZXV7BfSUubGUDKOw2g7AJMxmbGEf7VqH+brJhNFpo14EU2jqQkHsaOntndmjWQUlE7MCLwBTgNLBTRFapavUnsRYBBaoaLyLzgcXA10UkEZgPDAS6Ae+JSD9rm7rKXAw8q6rLROQlq+w/NaKOKuCnqrpHRNoAu0VkY412B7WKyrL6M9XhYklBE7bE/1xuFzvPpfL+p2+z+cxWLmg5DlXGlFdwT4mN8Kpk6DWDXlOmM7R3Fxx2m7+bbBhNQ4Tz0aOYkvMJz0kH0vLSmNQzcAc7+HKmNBLIUtVjACKyDJgLVD+4zwX+x1peDrwg3ouWc4FlqloJHBeRLKs8aitTRDKAicA3rDx/t8r9U0PrUNVtQDaAqhZbZcfWaHdQu1B4vtHbllYUVltrnmdKF50X2XJyExvT32JbQRpl4ibc4+GG8goSStsQFTKatgPmMjRlLD06Rvq7uYZx1YT3m0D3nHXEhCQG/GAHX4JSLHCq2vppYFRdeVS1SkSKgI5W+vYa28Zay7WV2REoVNWqWvI3pg4ARKQXMBTYUdsOisi9wL0AcXFxtWVplgqKshu9bUnFxUvLnmZ0+e5MyRk2H1nF+sxVHKw4jVugY5WbyWWVdC3vRnTUVGJG3cqIQf3N+4iMFiN22HTY+jAx5d5h4YE82MGXb2VtLa95lKorT13ptV0buVL+xtTh3UikNfA28BNVvVhLXlR1CbAEICUlpfkcgetRVJLb6G3LKmvtqoCjqqTnprEx/V+8f3ITJ6x/4j5OF7eXKtHu/nTpcSvXTZ7BgB6dzYOsRovk6NCTHEcM8ReL2GUvCujBDr4EpdNgzX3u1R04W0ee0yLiANoB+fVsW1t6LtBeRBzW2VL1/A2uQ0RC8Aakf6jqv33Y16BSVG2wQkOVBfDlO6fbSerpraw78A+25u0jX5zYVEmuqGR0WSSdQ0fRPXEeQ1PG0rmtmXXbMADyokcwPv9DlkW1C+jBDr4EpZ1AgjUq7gzeQQXfqJFnFbAQ2AbMAzapqorIKuANEXkG7yCEBCAV79nNl8q0ttlslbHMKvM/janDut/0MpChqs80tGOCQYkVlKQRl98Kyy80dXO+kqLKIj488g5r05azu/wYFaJEeDyMLHcSV9GF7lFT6DP6awxN7EeYw+7v5hpGwAnreyPDz7+DjaiAHuxQb1Cy7t/8AFiPd/j2K6qaJiJPAbtUdRXeg/9r1iCDfLxBBivfm3gHF1QBD6iqG6C2Mq0qHwGWicivgL1W2TS0DhEZC9wNHBSRfVYZj6nq2sZ1VfNTUlEEQGgjTnQuVhZUu8jqnzOlUxdPsf7A67x3fAMZ7lw8AtFVbiaWeejqTqBv3G0MnDabPjEdA/b6uGEEitjkyYR9rHTWNqTnB+54L5/u9FoH8rU10p6otlwB3FHHtk8DT/tSppV+jMsj9KqnN6gOVd1K7febWoyySm9QCmnEmVKp++KloHStnlPyqIdD5/awes/f2JqTyimbd6qjvk4Xs8vC6RGaQkLinQwfcSPtzLNDhtEgYdG9ybVF07usioy8jIAd7GCGHwWxUqf3vlBjzpQKPEW0dnsoucrP61S6K9matY41+95gV9lhCmxubKokVboYVRlN3/YTGXzjAgb164fdDFIwjMYTIadDCinlu9lWERGwr7EwQSmIFVbmgR1aacMP5hdsFXStspNl1yaf+66wvIC1e1/l/aw1HHBnU2GDVh4Pw8s8xHl6kxh3G8PH3E5sdFST1msYLZ2jz1hG7t8ERATsayxMUApixe4isHvnWGqIopJ8sh3KqMo2ZFHcJG05VZDF29v+zMfnPuaw7SIeETpVVTG2LJReYckMH3g3w1PGE2GeHTKMqyY2eTK2nY8jCBl5GQH5GgtzBAhi+VoCgLuBZzrv7ngNtwjxrQfySdX2Rt1TUlUOnNrOitS/kFq0n1MOJwDxVS5mVHbguqhxjBr9bQbEJwTkdW3DCEaRMf0poB0xrhDS8wJzsIMJSkFKPR7OOioBG1UNPOZvPr6CVnYPNwy4hVcPbvf58p1HPXyYtorV+15jT2UWuQ4PNlUGudyMqOxBUo+5jLtxAdHt2zV8hwzD+OpEyG4/jIEVGewJ0JkdTFAKUh/sWUGB3YZDtUFBaVfGJnY4chlT2YGO7eu/3qyq7MrazFupfyK1MpM8uxLqUZKcwhT3dVzffwGjr59JeGjIV9gbwzCaTK+xDD+aysaKEHLKcugS2cXfLfoCE5SCUG7hWZbs+TURIR6SnG05FOLblEGncrJ4/OMf08qm3D/xGWziHXmn+uW7UiXlRby88Zesz3mPUyFuHKokV9qYFT6cSSn3kzxoZMD9BWYYBsQmTcKZ7n37UHpeuglKxtVzLu8zXljzEJuq0igOs3G3/QbOu09RJfUPVlix5c/84fDzFNvhwU4LGZgwglPZRwCocjsv5SsuK+R3q37Eu+V7KLUJ8R43812DmDr8AVKSx5lAZBgBrl3cEGJcYYhCen46E+Im+LtJX2CCUhA4mZ3Ji+/+Pz7kKGU2G8Nc4czr831mj1/Ewy/PvOLlu407l7F03/+RHlpJjCpP9Pwv5kxcBEBkK++9H6fHG5TWbH+V36X9lgsOSKlwMLnbfG6b9hMiwsOv+j4ahtFEbDZyWyfTw3WctNzAG+xgglIzlnF8N0s2PcYW2xmcAiMrIrlj8I+YNmbBpTx2cVAlgsftxma/PCfcxwfX8uftT7E3tJT2dg93uBP53i1/oHP05UkaIyPaAlDlcfL39b/m2ew36KrKI+3v4hsLfobNvAjPMJolT9xoks6lszXnoL+b8iUmKDVDH+9fy2upi9kR4n049vrKttw5/BHGpcz9Ul67eP+JK1wVtLJHcuhYKr/f9BA7HPlE2pU5rj58b+Yf6BHT+0vbhoWG41Al25XN+rNv0Ncl/HbGcvr06H/V99EwjKunW/IUElf9kXdcBVwou0CnVp383aRLTFBqJopK8vjbu79kS/4HZIa5CXN4GOfswsIxv2DYwJvq3M5h8456K6u4yOK3vsM77oPY7DDV2Y3vTXuW+J4Dr1hviCo7w0po7VaeGLfEBCTDCAId+wynZ6X3ykl6Xjo3tar7GHKtmaAUwIpK8nj7gxfYcfY99ofkU2qzEWvzcLsOZMHEx4nvObjeMuw275QOD795OzvDihlZEcEDN/6OYQPH+dSGcpv3Et2Nnp4k9R/9lfbHMIwAYbMRETYI0ZMcyk3jph4mKBl1yMk7zfIPn2dn7oekhRRTbrPRxuEh2dmeCb2/xryJD2B3+P7P5rCFgAd2hhUzw9Wd//3u6i/cW/LVwvFPNngbwzACV2ivm+id9wq7PtsBQ7/v7+ZcYoJSADiZncnbH/2BvYU7SAurwCVClN3DSFcnru9xM7eOv5/IiDaNKjvE5h0ZF+ZRnlywvMEB6aHo+RSUnmdg/IhG1W8YRmCKSZ5G4rqX2FL4qb+b8gUmKPnJp8d2s2Lbi+wv3UtGqAuPCF3tHsZXxTK2z63MGvttQkPDvnI9o/rPYtm+rdwTOZmI8MgGb79w5s+/chsMwwg8nXoPoZfTzmrKyC3PJToi2t9NAkxQuqb2H97Kqh0vsb/8IJlh3lkSeogyw92bCQPuZMqorzfq0tqV3Dh0FlsTJxEeFtGk5RqG0cyJ0N4RD5zm0IU0xscFxn0lE5SusoKiC7y64Zd8UvAR6WFuAHoLzPX0Y3rSNxk7bPZVb4MJSIZh1KZH7ATk4qt8nPm+CUrB7uSZTF5a751lodhuo5tNmav9mTP8e4wcPNnfzTMMwyB++Gx6bniFjOxUfzflEhOUmpjTWcn/vXUf/3HupMxuI6WiNTfH3c3tE+5v8ktzhmEYX0XnngOIdzrY7jiL2+P2PkLiZz7NEyMi00UkU0SyRORntXweJiL/sj7fISK9qn32qJWeKSLT6itTRHpbZRyxygxt6jqullPZR1j4txv4Z9Vu+rrCeGHQr/jrfTu4Y/IPTEAyDCMg9Y0YTIlN2Xpks7+bAvgQlETEDrwIzAASgTtFJLFGtkVAgarGA88Ci61tE4H5wEBgOvBHEbHXU+Zi4FlVTQAKrLKbuo4md/LsYe5bfSuZoU6+GTaW1xft5KbhX572xzAMI5CMHfotbKqsSV3q76YAvp0pjQSyVPWYqjqBZUDNo+1c4O/W8nJgknjfYTAXWKaqlap6HMiyyqu1TGubiVYZWGXe0pR1+NYtDVNaVsyDq+dx3gGPxH6Hn87/kzkzMgyjWUhKmsyQSgcfVB3i4JHt/m6OT/eUYoFT1dZPA6PqyqOqVSJSBHS00rfX2DbWWq6tzI5AoapW1ZK/qer4EhG5F7jXWi0RkTwgt7a89ZnPg8znwcZsGoiiaWQ/BCHTF5eZvvAKun4Ywg2N3TQa6NkUbfAlKNX2Nh71MU9d6bWdoV0pf1PW8eVE1SXAks/XRWSXqqbUlrclMf1wmemLy0xfeJl+uMzqi15NUZYvl+9OAz2qrXcHztaVR0QcQDsg/wrb1pWeC7S3yqhZV1PVYRiGYQQoX4LSTiDBGhUXindQwaoaeVYBC63lecAmVVUrfb41cq43kACk1lWmtc1mqwysMv/TlHX41i2GYRiGP9R7+c66f/MDYD1gB15R1TQReQrYpaqrgJeB10QkC+/Zy3xr2zQReRNIB6qAB1TVDVBbmVaVjwDLRORXwF6rbJq4jvosqT9Li2D64TLTF5eZvvAy/XBZk/WFeE82DMMwDMP/fHp41jAMwzCuBROUDMMwjIBhglI113paIn8QkVdEJEdEDlVL6yAiG62pnTaKSJSVLiLyB6s/DojIsGrbLLTyHxGRhbXVFchEpIeIbBaRDBFJE5EfW+ktsS/CRSRVRPZbffGkld5kU341J9aMMHtFZLW13lL74YSIHBSRfSKyy0q7+t8PVTU/3vtqduAo0AcIBfYDif5u11XYz3HAMOBQtbTfAD+zln8GLLaWbwbW4X0W7Hpgh5XeAThm/Y6ylqP8vW8N7IdMVJ2IAAAC5klEQVQYYJi13AY4jHc6qpbYFwK0tpZDgB3WPr4JzLfSXwLut5a/D7xkLc8H/mUtJ1rfmzCgt/V9svt7/xrRH/8FvAGsttZbaj+cAKJrpF3174c5U7rsmk1L5E+q+hHe0YvVVZ/CqebUTq+q13a8z5DFANOAjaqar6oFwEa88w42G6qarap7rOViIAPvTCAtsS9UVUus1RDrR2m6Kb+aDRHpDswEllrrTTn1WTC46t8PE5Quq206pdg68gabLqqaDd6DNdDZSq+rT4Kqr6zLLkPxniG0yL6wLlntA3LwHjiO4uOUX0D1Kb+ae188BzwMeKx1n6c+I7j6Abx/mGwQkd3inYoNrsH3w7xP6TJfplNqaRo6tVOzIyKtgbeBn6jqRe8furVnrSUtaPpCvc/2JYtIe2AFMKC2bNbvoOwLEZkF5KjqbhEZ/3lyLVmDuh+qGaOqZ0WkM7BRRD69Qt4m6wtzpnRZS56W6Lx1qo31O8dKD+opnEQkBG9A+oeq/ttKbpF98TlVLQQ+wHtfoKmm/GouxgBzROQE3sv3E/GeObW0fgBAVc9av3Pw/qEykmvw/TBB6bKWPC1R9Smcak7tdI81suZ6oMg6ZV8PTBWRKGv0zVQrrdmwrv2/DGSo6jPVPmqJfdHJOkNCRCKAyXjvsTXVlF/Ngqo+qqrd1Tux6Hy8+3UXLawfAEQkUkTafL6M9//1Ia7F98PfIzwC6QfvCJLDeK+n/9zf7blK+/hPIBtw4f0rZhHe6+DvA0es3x2svIL3RYlHgYNASrVyvo33Bm4W8C1/71cj+mEs3ssIB4B91s/NLbQvhuCd0uuAdeB5wkrvg/dgmgW8BYRZ6eHWepb1eZ9qZf3c6qNMYIa/9+0r9Ml4Lo++a3H9YO3zfusn7fPj4bX4fphphgzDMIyAYS7fGYZhGAHDBCXDMAwjYJigZBiGYQQME5QMwzCMgGGCkmEYhhEwTFAyDMMwAoYJSoZhGEbA+P8z9lQSUWvHZgAAAABJRU5ErkJggg==\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",
@@ -915,7 +860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -931,7 +876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -947,7 +892,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -960,7 +905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1017,7 +962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1101,7 +1046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1110,7 +1055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1119,7 +1064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1128,7 +1073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
@@ -1176,7 +1121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1193,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1217,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1244,7 +1189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1267,7 +1212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1276,7 +1221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1292,7 +1237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1322,7 +1267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1336,7 +1281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1354,7 +1299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1368,7 +1313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1389,7 +1334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1399,7 +1344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1430,7 +1375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1447,7 +1392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1583,7 +1528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1662,7 +1607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
@@ -1873,7 +1818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1885,7 +1830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1897,7 +1842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1912,7 +1857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1921,7 +1866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1930,7 +1875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1939,16 +1884,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# BR_steps = np.linspace(0.0, 1e-3, 11)\n",
     "pull_dic = {}\n",
     "\n",
-    "mi = 0.0\n",
-    "ma = 1e-2\n",
-    "ste = 11\n",
+    "mi = 1e-4\n",
+    "ma = 3e-3\n",
+    "ste = 20\n",
     "\n",
     "for param in total_f_fit.get_dependents():\n",
     "    if param.floating:\n",
@@ -1973,7 +1918,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1993,27 +1938,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.         0.48795004 0.69006556 0.84515425 0.97590007 1.09108945\n",
-      " 1.19522861 1.29099445 1.38013112 1.46385011 1.5430335 ]\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",
@@ -2041,12 +1974,16 @@
     "# nevents *= 41\n",
     "# zfit.settings.set_verbosity(10)\n",
     "\n",
-    "# mi = 0.0\n",
-    "# ma = 1e-3\n",
-    "# ste = 11\n",
+    "# mi = 1e-4\n",
+    "# ma = 3e-3\n",
+    "# ste = 13\n",
     "\n",
     "BR_steps = np.linspace(mi, ma, ste)\n",
     "\n",
+    "BR_steps[0] = 0.0\n",
+    "\n",
+    "print(BR_steps)\n",
+    "\n",
     "Ctt_steps = np.sqrt(BR_steps/4.2*1000)\n",
     "\n",
     "print(Ctt_steps)\n",
@@ -2118,8 +2055,6 @@
     "                            _step = 2*__\n",
     "                        else:\n",
     "                            _step = 2*__+1\n",
-    "                                \n",
-    "                        save_pulls(step = _step)\n",
     "\n",
     "                        nll = zfit.loss.UnbinnedNLL(model=total_f_fit, data=data, constraints = constraints)\n",
     "\n",
@@ -2133,6 +2068,8 @@
     "                        params = result.params\n",
     "\n",
     "                        if result.converged:\n",
+    "                            \n",
+    "                            save_pulls(step = _step)\n",
     "\n",
     "                            if floaty:\n",
     "                                Nll_list[-2].append(result.fmin)\n",
@@ -2167,7 +2104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2245,7 +2182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2306,7 +2243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2322,29 +2259,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(11, 100)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "l = []\n",
     "\n",
@@ -2386,48 +2303,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BR: 0.0010\n",
-      "0.24\n",
-      "\n",
-      "BR: 0.0020\n",
-      "0.06\n",
-      "\n",
-      "BR: 0.0030\n",
-      "0.02\n",
-      "\n",
-      "BR: 0.0040\n",
-      "0.0\n",
-      "\n",
-      "BR: 0.0050\n",
-      "0.0\n",
-      "\n",
-      "BR: 0.0060\n",
-      "0.0\n",
-      "\n",
-      "BR: 0.0070\n",
-      "0.0\n",
-      "\n",
-      "BR: 0.0080\n",
-      "0.0\n",
-      "\n",
-      "BR: 0.0090\n",
-      "0.0\n",
-      "\n",
-      "BR: 0.0100\n",
-      "0.0\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for s in range(len(l)):\n",
     "    print('BR: {:.4f}'.format(BR_steps[s+1]))\n",
@@ -2437,7 +2317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2448,7 +2328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2459,24 +2339,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 min, 19 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(display_time(int(time.time()-start)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/__pycache__/pdg_const1.cpython-37.pyc b/__pycache__/pdg_const1.cpython-37.pyc
index 9128726..3b22672 100644
--- a/__pycache__/pdg_const1.cpython-37.pyc
+++ b/__pycache__/pdg_const1.cpython-37.pyc
Binary files differ
diff --git a/data/CLs/plots/set_histo0.png b/data/CLs/plots/set_histo0.png
index 365644b..bb1d641 100644
--- a/data/CLs/plots/set_histo0.png
+++ b/data/CLs/plots/set_histo0.png
Binary files differ
diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb
index c901755..ef8d1be 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",
@@ -64,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -543,19 +521,9 @@
   },
   {
    "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",
@@ -664,7 +632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -694,7 +662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -711,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -749,7 +717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -793,7 +761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -809,7 +777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -869,32 +837,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:14: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
-      "  \n",
-      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
-      "  fig.canvas.print_figure(bytes_io, **kw)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.clf()\n",
     "# plt.plot(x_part, calcs, '.')\n",
@@ -915,7 +860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -931,7 +876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -947,7 +892,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -960,7 +905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1017,7 +962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1101,7 +1046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1110,7 +1055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1119,7 +1064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1128,7 +1073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
@@ -1176,7 +1121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1193,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1217,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1244,7 +1189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1267,7 +1212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1276,7 +1221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1292,7 +1237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1322,7 +1267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1336,7 +1281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1354,7 +1299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1368,7 +1313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1389,7 +1334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1399,7 +1344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1430,7 +1375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1447,7 +1392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1583,7 +1528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1662,7 +1607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
@@ -1873,7 +1818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1885,7 +1830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1897,7 +1842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1912,7 +1857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1921,7 +1866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1930,7 +1875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1939,16 +1884,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# BR_steps = np.linspace(0.0, 1e-3, 11)\n",
     "pull_dic = {}\n",
     "\n",
-    "mi = 0.0\n",
-    "ma = 1e-2\n",
-    "ste = 11\n",
+    "mi = 1e-4\n",
+    "ma = 3e-3\n",
+    "ste = 20\n",
     "\n",
     "for param in total_f_fit.get_dependents():\n",
     "    if param.floating:\n",
@@ -1973,7 +1918,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1993,27 +1938,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.         0.48795004 0.69006556 0.84515425 0.97590007 1.09108945\n",
-      " 1.19522861 1.29099445 1.38013112 1.46385011 1.5430335 ]\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",
@@ -2041,12 +1974,16 @@
     "# nevents *= 41\n",
     "# zfit.settings.set_verbosity(10)\n",
     "\n",
-    "# mi = 0.0\n",
-    "# ma = 1e-3\n",
-    "# ste = 11\n",
+    "# mi = 1e-4\n",
+    "# ma = 3e-3\n",
+    "# ste = 13\n",
     "\n",
     "BR_steps = np.linspace(mi, ma, ste)\n",
     "\n",
+    "BR_steps[0] = 0.0\n",
+    "\n",
+    "print(BR_steps)\n",
+    "\n",
     "Ctt_steps = np.sqrt(BR_steps/4.2*1000)\n",
     "\n",
     "print(Ctt_steps)\n",
@@ -2118,8 +2055,6 @@
     "                            _step = 2*__\n",
     "                        else:\n",
     "                            _step = 2*__+1\n",
-    "                                \n",
-    "                        save_pulls(step = _step)\n",
     "\n",
     "                        nll = zfit.loss.UnbinnedNLL(model=total_f_fit, data=data, constraints = constraints)\n",
     "\n",
@@ -2133,6 +2068,8 @@
     "                        params = result.params\n",
     "\n",
     "                        if result.converged:\n",
+    "                            \n",
+    "                            save_pulls(step = _step)\n",
     "\n",
     "                            if floaty:\n",
     "                                Nll_list[-2].append(result.fmin)\n",
@@ -2167,7 +2104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2245,7 +2182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2306,7 +2243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2322,29 +2259,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(11, 100)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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JNgAbANauXTvk6vRkNaor1Hilm59Y7HMx29WMtHIN3ANPcjRwZ1Vtmmu5qtpYVeurav3ExMSgq5MkTTPMEMqhwDFJtgCfAV6V5JMjqUqSNK+BA7yqTquqvatqHXA88NWqOmFklUmS5uRx4JLUqGF3YgJQVZcBl42iLUnSwtgDl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJatRIzkYoaXDjuozcbOv10mztsAcuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowYO8CT7JPn7JJuTXJ/k5FEWJkma2zAXdHgUeGdVXZ1kV2BTkkur6oYR1SZJmsPAPfCq2lpVV/d/3w9sBvYaVWGSpLmN5JJqSdYBLwKunGHeBmADwNq1a0exOkmLMK5LtmnpDb0TM8ka4HPAKVV13/T5VbWxqtZX1fqJiYlhVydJ6g0V4El2pAvvc6vqwtGUJElaiGGOQglwFrC5qj44upIkSQsxTA/8UOAtwKuSXNP/vGZEdUmS5jHwTsyq+jqQEdYiSVoEv4kpSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUqJFcUk3bmu0SVlvOOGos7Y+qHi/N9eS2El6fpf7fWWnsgUtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktSooQI8yZFJvpPkX5KcOqqiJEnzGzjAk+wAfBh4NfAC4I1JXjCqwiRJcxumB/4S4F+q6saqehj4DHDsaMqSJM0nVTXYHZPjgCOr6sT+9luAl1bVSdOW2wBs6G8eAHxn8HLHYg/gh+MuYpn5mLcPPuZ27FtVE9MnDnNNzMww7ac+DapqI7BxiPWMVZKrqmr9uOtYTj7m7YOPuX3DDKHcAuwz5fbewG3DlSNJWqhhAvyfgOcl2S/JTsDxwBdHU5YkaT4DD6FU1aNJTgL+FtgBOLuqrh9ZZU8ezQ7/DMHHvH3wMTdu4J2YkqTx8puYktQoA1ySGmWAL0KS30lSSfYYdy1LLckfJfl2kmuTXJRk93HXtFS2t1NCJNknyd8n2Zzk+iQnj7um5ZBkhyTfTHLxuGsZFQN8gZLsAxwB3DTuWpbJpcCBVfWLwP8DThtzPUtiOz0lxKPAO6vq+cAhwDu2g8cMcDKwedxFjJIBvnB/CryLGb6stBJV1Zer6tH+5hV0x/mvRNvdKSGqamtVXd3/fT9dqO013qqWVpK9gaOAj427llEywBcgyTHArVX1rXHXMia/AVwy7iKWyF7AzVNu38IKD7OpkqwDXgRcOd5KltyH6Dpgj4+7kFEa5qv0K0qSrwDPnmHWe4B3A7+6vBUtvbkec1V9oV/mPXSb3OcuZ23LaEGnhFiJkqwBPgecUlX3jbuepZLkaODOqtqU5JXjrmeUDPBeVR0+0/Qk/wHYD/hWEuiGEq5O8pKqun0ZSxy52R7zpCRvBY4GDquV+4WB7fKUEEl2pAvvc6vqwnHXs8QOBY5J8hpgNfC0JJ+sqhPGXNfQ/CLPIiXZAqyvqhbPaLZgSY4EPgi8oqruGnc9SyXJKrqdtIcBt9KdIuJNK/RbxQCk64mcA9xdVaeMu57l1PfAf6eqjh53LaPgGLhmcyawK3BpkmuSfGTcBS2Ffkft5CkhNgPnreTw7h0KvAV4Vf/aXtP3TtUYe+CS1Ch74JLUKANckhplgEtSowxwSWqUAS5JjTLAtV1I8lh/uNx1Sc5Psss8y38iyXH935clWTEXwtXKYYBre/FQVR1cVQcCDwO/Oe6CpGEZ4NoefQ3YP8m6JNdNTuzP9376+MqSFscA13al/+r8q4F/Hnct0rAMcG0vdk5yDXAV3UU5zhpzPdLQPBuhthcPVdXBUyckeZRtOzGrl7ckaTj2wLU9uwN4ZpKfTfJUulPnSs2wB67tVlU9kuT36K5G833g22MuSVoUz0YoSY1yCEWSGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEb9f9pegX35YIlAAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "l = []\n",
     "\n",
@@ -2386,48 +2303,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BR: 0.0010\n",
-      "0.24\n",
-      "\n",
-      "BR: 0.0020\n",
-      "0.06\n",
-      "\n",
-      "BR: 0.0030\n",
-      "0.02\n",
-      "\n",
-      "BR: 0.0040\n",
-      "0.0\n",
-      "\n",
-      "BR: 0.0050\n",
-      "0.0\n",
-      "\n",
-      "BR: 0.0060\n",
-      "0.0\n",
-      "\n",
-      "BR: 0.0070\n",
-      "0.0\n",
-      "\n",
-      "BR: 0.0080\n",
-      "0.0\n",
-      "\n",
-      "BR: 0.0090\n",
-      "0.0\n",
-      "\n",
-      "BR: 0.0100\n",
-      "0.0\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for s in range(len(l)):\n",
     "    print('BR: {:.4f}'.format(BR_steps[s+1]))\n",
@@ -2437,7 +2317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2448,7 +2328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2459,24 +2339,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 min, 19 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(display_time(int(time.time()-start)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/thesis/01-introduction.tex b/thesis/01-introduction.tex
index 9db512c..71de38d 100644
--- a/thesis/01-introduction.tex
+++ b/thesis/01-introduction.tex
@@ -17,4 +17,5 @@
 \end{figure}
 
 However while the SM proves to be a robust and good description of the fundamental particles and how they interact, we know that the SM doesn't describe the full picture. Several phenomena are simply not explained by it. Examples are gravity, dark matter, dark energy, neutrino masses and the matter-antimatter asymmetry in the universe.
-Additionally there are experimental results that cannot be explained by the SM as the $R_K$
\ No newline at end of file
+Additionally there are experimental results that cannot be explained by the SM as for example the $R_{K^{(*)}}$ and $R_{D^{(*)}}$ \cite{aaij2015measurement} measurement.
+
diff --git a/thesis/bib_main.bib b/thesis/bib_main.bib
new file mode 100644
index 0000000..06e3ace
--- /dev/null
+++ b/thesis/bib_main.bib
@@ -0,0 +1,10 @@
+@article{aaij2015measurement,
+  title={Measurement of the Ratio of Branching Fractions B (B{\={}} 0→ D*+ $\tau$- $\nu${\={}} $\tau$)/B (B{\={}} 0→ D*+ $\mu$- $\nu${\={}} $\mu$)},
+  author={Aaij, Roel and Adeva, B and Adinolfi, M and Affolder, A and Ajaltouni, Z and Akar, S and Albrecht, J and Alessio, F and Alexander, M and Ali, S and others},
+  journal={Physical review letters},
+  volume={115},
+  number={11},
+  pages={111803},
+  year={2015},
+  publisher={APS}
+}
\ No newline at end of file
diff --git a/thesis/main.aux b/thesis/main.aux
index 9e6cfef..dedc3ba 100644
--- a/thesis/main.aux
+++ b/thesis/main.aux
@@ -21,11 +21,18 @@
 \providecommand\HyField@AuxAddToCoFields[2]{}
 \providecommand{\mciteSetMaxWidth}[3]{\relax}
 \providecommand{\mciteSetMaxCount}[3]{\relax}
-\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}{section.1}\protected@file@percent }
+\citation{aaij2015measurement}
+\citation{aaij2015measurement}
+\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}{section.1}}
 \newlabel{sec:Introduction}{{1}{1}{Introduction}{section.1}{}}
-\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Standardmodel of particles\relax }}{1}{figure.caption.2}\protected@file@percent }
+\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Standardmodel of particles\relax }}{1}{figure.caption.2}}
 \providecommand*\caption@xref[2]{\@setref\relax\@undefined{#1}}
 \newlabel{fig:SM-model}{{1}{1}{Standardmodel of particles\relax }{figure.caption.2}{}}
-\bibstyle{LHCb}
-\@writefile{toc}{\contentsline {section}{References}{2}{figure.caption.2}\protected@file@percent }
+\bibstyle{abbrv}
+\bibdata{bib_main.bib}
+\mciteSetMaxCount{main}{bibitem}{0}
+\mciteSetMaxCount{main}{subitem}{1}
+\mciteSetMaxWidth{main}{bibitem}{0}
+\mciteSetMaxWidth{main}{subitem}{}
+\@writefile{toc}{\contentsline {section}{References}{2}{figure.caption.2}}
 \zref@newlabel{LastPage}{\default{1}\page{2}\abspage{6}}
diff --git a/thesis/main.bbl b/thesis/main.bbl
index 655e88e..d76d8a1 100644
--- a/thesis/main.bbl
+++ b/thesis/main.bbl
@@ -2,193 +2,10 @@
 \PackageError{LHCb.bst}{mciteplus.sty has not been loaded}
 {This bibstyle requires the use of the mciteplus package.}\fi
 \providecommand{\href}[2]{#2}
-\begin{mcitethebibliography}{10}
+\begin{mcitethebibliography}{}
 \mciteSetBstSublistMode{n}
 \mciteSetBstMaxWidthForm{subitem}{\alph{mcitesubitemcount})}
 \mciteSetBstSublistLabelBeginEnd{\mcitemaxwidthsubitemform\space}
 {\relax}{\relax}
 
-\bibitem{tensorflow2015-whitepaper}
-M.~Abadi {\em et~al.}, \ifthenelse{\boolean{articletitles}}{\emph{{TensorFlow}:
-  Large-scale machine learning on heterogeneous systems}, }{} 2015.
-\newblock Software available from tensorflow.org\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{paszke2017automatic}
-A.~Paszke {\em et~al.}, \ifthenelse{\boolean{articletitles}}{\emph{Automatic
-  differentiation in pytorch}, }{}\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{hep_survey_jim}
-J.~Pivarski, \ifthenelse{\boolean{articletitles}}{\emph{Non-fork repositories
-  of github users who forked cmssw/aliphysics}, }{}
-  \url{https://github.com/jpivarski/2019-06-10-usatlas-argonne-python/blob/master/01-why-python-in-hep.ipynb},
-  2019\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{software:scipy}
-T.~E. {Oliphant}, \ifthenelse{\boolean{articletitles}}{\emph{Python for
-  scientific computing},
-  }{}\href{https://doi.org/10.1109/MCSE.2007.58}{Computing in Science
-  Engineering \textbf{9} (2007) 10}\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{software:lmfit}
-M.~Newville {\em et~al.},
-  \ifthenelse{\boolean{articletitles}}{\emph{lmfit/lmfit-py 0.9.13}, }{} 2019.
-\newblock
-  doi:~\href{https://doi.org/10.5281/zenodo.2620617}{10.5281/zenodo.2620617}\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{DBLP:journals/corr/abs-1711-10604}
-J.~V. Dillon {\em et~al.},
-  \ifthenelse{\boolean{articletitles}}{\emph{Tensorflow distributions}, }{}CoRR
-  \textbf{abs/1711.10604} (2017)
-  \href{http://arxiv.org/abs/1711.10604}{{\normalfont\ttfamily
-  arXiv:1711.10604}}\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{Verkerke:2003ir}
-W.~Verkerke and D.~P. Kirkby, \ifthenelse{\boolean{articletitles}}{\emph{{The
-  RooFit toolkit for data modeling}}, }{}eConf \textbf{C0303241} (2003)
-  MOLT007, \href{http://arxiv.org/abs/physics/0306116}{{\normalfont\ttfamily
-  arXiv:physics/0306116}}, [,186(2003)]\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{software:probfit}
-P.~Ongmongkolkul {\em et~al.},
-  \ifthenelse{\boolean{articletitles}}{\emph{scikit-hep/probfit: 1.1.0}, }{}
-  2018.
-\newblock
-  doi:~\href{https://doi.org/10.5281/zenodo.1477853}{10.5281/zenodo.1477853}\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{software:pyhf}
-Lukas {\em et~al.}, \ifthenelse{\boolean{articletitles}}{\emph{diana-hep/pyhf
-  v0.0.15}, }{} 2018.
-\newblock
-  doi:~\href{https://doi.org/10.5281/zenodo.1464139}{10.5281/zenodo.1464139}\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{higgsanalysis_combinedlimit}
-J.~Bendavid,
-  \ifthenelse{\boolean{articletitles}}{\emph{Higgsanalysis-combinedlimit}, }{}
-  \url{https://github.com/bendavid/HiggsAnalysis-CombinedLimit/tree/tensorflowfit},
-  2016\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{tensorflow_analysis}
-A.~Poluektov, \ifthenelse{\boolean{articletitles}}{\emph{Tensorflow analysis},
-  }{} \url{https://gitlab.cern.ch/poluekt/TensorFlowAnalysis/}, 2017\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{James:1975dr}
-F.~James and M.~Roos, \ifthenelse{\boolean{articletitles}}{\emph{{Minuit: A
-  System for Function Minimization and Analysis of the Parameter Errors and
-  Correlations}},
-  }{}\href{https://doi.org/10.1016/0010-4655(75)90039-9}{Comput.\ Phys.\
-  Commun.\  \textbf{10} (1975) 343}\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{kingma2014adam}
-D.~P. Kingma and J.~Ba, \ifthenelse{\boolean{articletitles}}{\emph{Adam: A
-  method for stochastic optimization}, }{} 2014\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{Aaij:2015oid}
-LHCb, R.~Aaij {\em et~al.}, \ifthenelse{\boolean{articletitles}}{\emph{{Angular
-  analysis of the $B^{0} \to K^{*0} \mu^{+} \mu^{-}$ decay using 3 fb$^{-1}$ of
-  integrated luminosity}},
-  }{}\href{https://doi.org/10.1007/JHEP02(2016)104}{JHEP \textbf{02} (2016)
-  104}, \href{http://arxiv.org/abs/1512.04442}{{\normalfont\ttfamily
-  arXiv:1512.04442}}\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{Khachatryan:2038750}
-\relax
-\mciteBstWouldAddEndPunctfalse
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{James:1968gu}
-F.~James, \ifthenelse{\boolean{articletitles}}{\emph{{Monte-Carlo phase
-  space}}, }{}\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{PhysRevLett.103.211801}
-BABAR Collaboration, B.~Aubert {\em et~al.},
-  \ifthenelse{\boolean{articletitles}}{\emph{Measurement of
-  ${D}^{0}\mathrm{\text{\ensuremath{-}}}{\overline{d}}^{0}$ mixing from a
-  time-dependent amplitude analysis of
-  ${D}^{0}\ensuremath{\rightarrow}{K}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}{\ensuremath{\pi}}^{0}$
-  decays}, }{}\href{https://doi.org/10.1103/PhysRevLett.103.211801}{Phys.\
-  Rev.\ Lett.\  \textbf{103} (2009) 211801}\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{software:lauztat}
-M.~Marinangeli, \ifthenelse{\boolean{articletitles}}{\emph{marinang/lauztat:
-  v1.1.2}, }{} 2019.
-\newblock
-  doi:~\href{https://doi.org/10.5281/zenodo.2648147}{10.5281/zenodo.2648147}\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{software:uproot}
-J.~Pivarski {\em et~al.},
-  \ifthenelse{\boolean{articletitles}}{\emph{scikit-hep/uproot: 3.6.3}, }{}
-  2019.
-\newblock
-  doi:~\href{https://doi.org/10.5281/zenodo.3239529}{10.5281/zenodo.3239529}\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{mckinney-proc-scipy-2010}
-W.~McKinney, {\em Data Structures for Statistical Computing in Python},
-  2010\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
-\bibitem{software:numpy}
-T.~Oliphant, \ifthenelse{\boolean{articletitles}}{\emph{{NumPy}: A guide to
-  {NumPy}}, }{} USA: Trelgol Publishing, 2006--.
-\newblock [Online; accessed <today>]\relax
-\mciteBstWouldAddEndPuncttrue
-\mciteSetBstMidEndSepPunct{\mcitedefaultmidpunct}
-{\mcitedefaultendpunct}{\mcitedefaultseppunct}\relax
-\EndOfBibitem
 \end{mcitethebibliography}
diff --git a/thesis/main.blg b/thesis/main.blg
index 8f47b28..8c25abc 100644
--- a/thesis/main.blg
+++ b/thesis/main.blg
@@ -1,76 +1,10 @@
-This is BibTeX, Version 0.99d (TeX Live 2019/dev/Debian)
-Capacity: max_strings=100000, hash_size=100000, hash_prime=85009
-The top-level auxiliary file: main.aux
+This is BibTeX, Version 0.99dThe top-level auxiliary file: main.aux
 The style file: LHCb.bst
-Reallocated wiz_functions (elt_size=4) to 6000 items from 3000.
-Database file #1: main.bib
-I was expecting a `,' or a `}'---line 300 of file main.bib
- :  
- :  commit = {e0020a5354b5d624f9e7a3b647e5db4d11f3a805}
-(Error may have been on previous line)
-I'm skipping whatever remains of this entry
-I was expecting a `,' or a `}'---line 309 of file main.bib
- :  
- :  commit = {0026fc9fd21cd831cc26e3c64f94cec4bcdb1be7}
-(Error may have been on previous line)
-I'm skipping whatever remains of this entry
-I was expecting a `,' or a `}'---line 318 of file main.bib
- :  
- :  commit = {ff1fe04bb7a69d119f75176a596d65437a692c8a}
-(Error may have been on previous line)
-I'm skipping whatever remains of this entry
-Your field is more than 20000 characters---line 691 of file main.bib
- :                        and Cov
- :                               arelli, Roberto and Degano, Alessandro and Demaria,
-I'm skipping whatever remains of this entry
-Database file #2: standard.bib
-Database file #3: LHCb-PAPER.bib
-Database file #4: LHCb-CONF.bib
-Database file #5: LHCb-DP.bib
-Database file #6: LHCb-TDR.bib
-Warning--empty author in Khachatryan:2038750
-Warning--empty title in Khachatryan:2038750
-Warning--can't use both author and editor fields in mckinney-proc-scipy-2010
-Warning--empty publisher in mckinney-proc-scipy-2010
-You've used 21 entries,
-            3128 wiz_defined-function locations,
-            802 strings with 19180 characters,
-and the built_in function-call counts, 988995 in all, are:
-= -- 109737
-> -- 132
-< -- 0
-+ -- 3350
-- -- 32
-* -- 109186
-:= -- 110695
-add.period$ -- 28
-call.type$ -- 21
-change.case$ -- 19
-chr.to.int$ -- 0
-cite$ -- 25
-duplicate$ -- 1099
-empty$ -- 109541
-format.name$ -- 32
-if$ -- 219456
-int.to.chr$ -- 0
-int.to.str$ -- 21
-missing$ -- 40
-newline$ -- 144
-num.names$ -- 20
-pop$ -- 317
-preamble$ -- 1
-purify$ -- 0
-quote$ -- 0
-skip$ -- 94
-stack$ -- 0
-substring$ -- 324272
-swap$ -- 2
-text.length$ -- 42
-text.prefix$ -- 0
-top$ -- 0
-type$ -- 0
-warning$ -- 4
-while$ -- 381
-width$ -- 23
-write$ -- 281
-(There were 4 error messages)
+I couldn't open database file bib_main.bib.bib
+---line 32 of file main.aux
+ : \bibdata{bib_main.bib
+ :                      }
+I'm skipping whatever remains of this command
+I found no database files---while reading file main.aux
+Warning--I didn't find a database entry for "aaij2015measurement"
+(There were 2 error messages)
diff --git a/thesis/main.log b/thesis/main.log
index 7fda2f2..f234f68 100644
--- a/thesis/main.log
+++ b/thesis/main.log
@@ -1,12 +1,12 @@
-This is pdfTeX, Version 3.14159265-2.6-1.40.20 (MiKTeX 2.9.7140 64-bit) (preloaded format=pdflatex 2019.9.16)  3 OCT 2019 12:58
+This is pdfTeX, Version 3.14159265-2.6-1.40.19 (MiKTeX 2.9.6840 64-bit) (preloaded format=pdflatex 2018.10.23)  4 OCT 2019 01:23
 entering extended mode
 **./main.tex
 (main.tex
-LaTeX2e <2018-12-01>
-("C:\Program Files\MiKTeX 2.9\tex/latex/base\article.cls"
-Document Class: article 2018/09/03 v1.4i Standard LaTeX document class
-("C:\Program Files\MiKTeX 2.9\tex/latex/base\size12.clo"
-File: size12.clo 2018/09/03 v1.4i Standard LaTeX file (size option)
+LaTeX2e <2018-04-01> patch level 5
+("C:\Program Files\MiKTeX 2.9\tex\latex\base\article.cls"
+Document Class: article 2014/09/29 v1.4h Standard LaTeX document class
+("C:\Program Files\MiKTeX 2.9\tex\latex\base\size12.clo"
+File: size12.clo 2014/09/29 v1.4h Standard LaTeX file (size option)
 )
 \c@part=\count80
 \c@section=\count81
@@ -20,24 +20,24 @@
 \belowcaptionskip=\skip42
 \bibindent=\dimen102
 )
-("C:\Program Files\MiKTeX 2.9\tex/latex/base\ifthen.sty"
+("C:\Program Files\MiKTeX 2.9\tex\latex\base\ifthen.sty"
 Package: ifthen 2014/09/29 v1.1c Standard LaTeX ifthen package (DPC)
 ) (preamble.tex
-("C:\Program Files\MiKTeX 2.9\tex/latex/geometry\geometry.sty"
+("C:\Program Files\MiKTeX 2.9\tex\latex\geometry\geometry.sty"
 Package: geometry 2018/04/16 v5.8 Page Geometry
 
-("C:\Program Files\MiKTeX 2.9\tex/latex/graphics\keyval.sty"
+("C:\Program Files\MiKTeX 2.9\tex\latex\graphics\keyval.sty"
 Package: keyval 2014/10/28 v1.15 key=value parser (DPC)
 \KV@toks@=\toks14
 )
-("C:\Program Files\MiKTeX 2.9\tex/generic/oberdiek\ifpdf.sty"
+("C:\Program Files\MiKTeX 2.9\tex\generic\oberdiek\ifpdf.sty"
 Package: ifpdf 2018/09/07 v3.3 Provides the ifpdf switch
 )
-("C:\Program Files\MiKTeX 2.9\tex/generic/oberdiek\ifvtex.sty"
+("C:\Program Files\MiKTeX 2.9\tex\generic\oberdiek\ifvtex.sty"
 Package: ifvtex 2016/05/16 v1.6 Detect VTeX and its facilities (HO)
 Package ifvtex Info: VTeX not detected.
 )
-("C:\Program Files\MiKTeX 2.9\tex/generic/ifxetex\ifxetex.sty"
+("C:\Program Files\MiKTeX 2.9\tex\generic\ifxetex\ifxetex.sty"
 Package: ifxetex 2010/09/12 v0.6 Provides ifxetex conditional
 )
 \Gm@cnth=\count88
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-Package: microtype 2019/02/28 v2.7b Micro-typographical refinements (RS)
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+("C:\Program Files\MiKTeX 2.9\tex\latex\microtype\microtype.sty"
+Package: microtype 2018/01/14 v2.7a Micro-typographical refinements (RS)
 \MT@toks=\toks16
 \MT@count=\count91
-LaTeX Info: Redefining \textls on input line 790.
+LaTeX Info: Redefining \textls on input line 793.
 \MT@outer@kern=\dimen111
-LaTeX Info: Redefining \textmicrotypecontext on input line 1336.
+LaTeX Info: Redefining \textmicrotypecontext on input line 1339.
 \MT@listname@count=\count92
 
-("C:\Program Files\MiKTeX 2.9\tex/latex/microtype\microtype-pdftex.def"
-File: microtype-pdftex.def 2019/02/28 v2.7b Definitions specific to pdftex (RS)
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 ) (main.aux)
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 Package microtype Info: No adjustment of character kerning.
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 File: mt-cmr.cfg 2013/05/19 v2.2 microtype config. file: Computer Modern Roman 
 (RS)
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 Package caption Info: listings package is loaded.
 Package caption Info: longtable package is loaded.
 
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 Package: ltcaption 2018/08/26 v1.4-95 longtable captions (AR)
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 Package caption Info: End \AtBeginDocument code.
 
-("C:\Program Files\MiKTeX 2.9\tex/context/base\supp-pdf.mkii"
+("C:\Program Files\MiKTeX 2.9\tex\context\base\supp-pdf.mkii"
 [Loading MPS to PDF converter (version 2006.09.02).]
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-) ("C:\Program Files\MiKTeX 2.9\tex/latex/oberdiek\epstopdf-base.sty"
+) ("C:\Program Files\MiKTeX 2.9\tex\latex\oberdiek\epstopdf-base.sty"
 Package: epstopdf-base 2016/05/15 v2.6 Base part for package epstopdf
 
-("C:\Program Files\MiKTeX 2.9\tex/latex/oberdiek\grfext.sty"
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 Package: grfext 2016/05/16 v1.2 Manage graphics extensions (HO)
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 Package epstopdf-base Info: Redefining graphics rule for `.eps' on input line 4
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 )
-\AtBeginShipoutBox=\box50
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-("C:\Program Files\MiKTeX 2.9\tex/generic/oberdiek\se-pdfdoc.def"
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 File: se-pdfdoc.def 2016/05/16 v1.11 stringenc: PDFDocEncoding
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 File: se-utf8.def 2016/05/16 v1.11 stringenc: UTF-8
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 Package hyperref Info: Link coloring OFF on input line 44.
@@ -1001,7 +995,7 @@
 \@outlinefile=\write4
 \openout4 = `main.out'.
 
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 Package todonotes Warning: The length marginparwidth is less than 2cm and will 
@@ -1011,28 +1005,28 @@
 
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 File: umsa.fd 2013/01/14 v3.01 AMS symbols A
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 LaTeX Font Info:    Try loading font information for U+msb on input line 19.
 
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 File: umsb.fd 2013/01/14 v3.01 AMS symbols B
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 File: mt-msb.cfg 2005/06/01 v1.0 microtype config. file: AMS symbols (b) (RS)
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-("C:\Program Files\MiKTeX 2.9\tex/latex/microtype\mt-eur.cfg"
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 File: mt-eur.cfg 2006/07/31 v1.1 microtype config. file: AMS Euler Roman (RS)
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 Package epstopdf Info: Source file: <images/uzh_logo_e_pos.eps>
-(epstopdf)                    date: 2019-09-26 09:32:30
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 (epstopdf)                    size: 509333 bytes
 (epstopdf)             Output file: <images/uzh_logo_e_pos-eps-converted-to.pdf
 >
-(epstopdf)                    date: 2019-09-26 09:32:30
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@@ -1068,36 +1062,92 @@
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-)
-Package atenddvi Info: Last page = 5.
- [1
 
- <./figs/standardmodel.png>] [2]
+
+LaTeX Warning: Citation `aaij2015measurement' on page 1 undefined on input line
+ 20.
+
+[1
+
+ <./figs/standardmodel.png>]) (main.bbl
+
+LaTeX Warning: Empty `thebibliography' environment on input line 11.
+
+! Undefined control sequence.
+<write> ...subitem}{\@mcitecorrectmaxwidthsubitem 
+                                                  }
+l.11 \end{mcitethebibliography}
+                               
+The control sequence at the end of the top line
+of your error message was never \def'ed. If you have
+misspelled it (e.g., `\hobx'), type `I' and the correct
+spelling (e.g., `I\hbox'). Otherwise just continue,
+and I'll forget about whatever was undefined.
+
+! Undefined control sequence.
+\@mciteMaxWidthAuxWrite ... \mcitemaxwidthsubitem 
+                                                  =\@mcitecorrectmaxwidthsub...
+l.11 \end{mcitethebibliography}
+                               
+The control sequence at the end of the top line
+of your error message was never \def'ed. If you have
+misspelled it (e.g., `\hobx'), type `I' and the correct
+spelling (e.g., `I\hbox'). Otherwise just continue,
+and I'll forget about whatever was undefined.
+
+! Missing number, treated as zero.
+<to be read again> 
+                   =
+l.11 \end{mcitethebibliography}
+                               
+A number should have been here; I inserted `0'.
+(If you can't figure out why I needed to see a number,
+look up `weird error' in the index to The TeXbook.)
+
+! Undefined control sequence.
+\@mciteMaxWidthAuxWrite ...correctmaxwidthsubitem 
+                                                  \relax \else \@latex@warni...
+l.11 \end{mcitethebibliography}
+                               
+The control sequence at the end of the top line
+of your error message was never \def'ed. If you have
+misspelled it (e.g., `\hobx'), type `I' and the correct
+spelling (e.g., `I\hbox'). Otherwise just continue,
+and I'll forget about whatever was undefined.
+
+! Missing number, treated as zero.
+<to be read again> 
+                   \relax 
+l.11 \end{mcitethebibliography}
+                               
+A number should have been here; I inserted `0'.
+(If you can't figure out why I needed to see a number,
+look up `weird error' in the index to The TeXbook.)
+
+)
+Package atenddvi Info: Last page = 6.
+ [2]
 Package atveryend Info: Empty hook `BeforeClearDocument' on input line 105.
 Package atveryend Info: Executing hook `AfterLastShipout' on input line 105.
- (main.aux
-
-Package atenddvi Warning: Rerun LaTeX, last page has changed.
-
-)
+ (main.aux)
 Package atveryend Info: Executing hook `AtVeryEndDocument' on input line 105.
 Package atveryend Info: Executing hook `AtEndAfterFileList' on input line 105.
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 (rerunfilecheck)             Checksum: 13AAB94B85035E2DF5874A66E4C7ABA3;101.
 
 
-LaTeX Warning: Label(s) may have changed. Rerun to get cross-references right.
+LaTeX Warning: There were undefined references.
 
 Package atveryend Info: Empty hook `AtVeryVeryEnd' on input line 105.
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- 64i,12n,81p,825b,454s stack positions out of 5000i,500n,10000p,200000b,50000s
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+C:/Program Files/MiKTeX 2.9/fonts/type1/public/amsfonts/cm/cmr6.pfb><C:/Program
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+Output written on main.pdf (6 pages, 446925 bytes).
 PDF statistics:
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- 9 named destinations out of 1000 (max. 500000)
+ 149 PDF objects out of 1000 (max. 8388607)
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diff --git a/thesis/main.pdf b/thesis/main.pdf
index 217f038..c1fabed 100644
--- a/thesis/main.pdf
+++ b/thesis/main.pdf
Binary files differ
diff --git a/thesis/main.synctex.gz b/thesis/main.synctex.gz
index 6b9e008..854e162 100644
--- a/thesis/main.synctex.gz
+++ b/thesis/main.synctex.gz
Binary files differ
diff --git a/thesis/main.tex b/thesis/main.tex
index 2a13215..683c770 100644
--- a/thesis/main.tex
+++ b/thesis/main.tex
@@ -98,7 +98,7 @@
 \addcontentsline{toc}{section}{References}
 %\setboolean{inbibliography}{true}
 \bibliographystyle{LHCb}
-%\bibliography{main, standard, LHCb-PAPER,LHCb-CONF,LHCb-DP,LHCb-TDR}
+%\bibliography{bib_main.bib, LHCb-PAPER,LHCb-CONF,LHCb-DP,LHCb-TDR}
 
 
 
diff --git a/thesis/main.toc b/thesis/main.toc
index a7b544b..001bb8d 100644
--- a/thesis/main.toc
+++ b/thesis/main.toc
@@ -1,2 +1,2 @@
-\contentsline {section}{\numberline {1}Introduction}{1}{section.1}% 
-\contentsline {section}{References}{2}{figure.caption.2}% 
+\contentsline {section}{\numberline {1}Introduction}{1}{section.1}
+\contentsline {section}{References}{2}{figure.caption.2}