example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "rough-voltage",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import SelTable as tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fatty-anderson",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change example.csv to the \n",
    "with open('example.csv') as _file:\n",
    "    df = pd.read_csv(_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "golden-performer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>No Cut</th>\n",
       "      <th>Cut A</th>\n",
       "      <th>Cut B</th>\n",
       "      <th>Full Selection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NueCC</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NumuCC</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NC</td>\n",
       "      <td>800.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cosmic</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sample   No Cut  Cut A  Cut B  Full Selection\n",
       "0   NueCC    100.0   80.0   70.0            60.0\n",
       "1  NumuCC   1000.0  400.0   20.0            10.0\n",
       "2      NC    800.0   30.0  100.0             5.0\n",
       "3  Cosmic  10000.0  100.0  300.0            80.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "intelligent-asian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sample', 'No Cut', 'Cut A', 'Cut B', 'Full Selection'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "homeless-tradition",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "surprising-jefferson",
   "metadata": {},
   "outputs": [],
   "source": [
    "# BELOW HERE IS WHERE YOU MAKE THE TABLE YOU WANT BY GIVING IT A SET OF CUTS AND SET OPTIONS\n",
    "\n",
    "# This is the function that is being used:\n",
    "#   def makeSelectionPlot(inDF,cuts=[],signalLabel='NueCC',norm=False,grid=False):\n",
    "\n",
    "# Note - this assumes you are using the standard precision of output of 6 digits, so values > 100000\n",
    "#        have the decimal point removed in the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "improving-temperature",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VoEEDffzxx8HcFaBIqV69upxzxz26dOniH/Paa6/51+YFF1yguXPnnnDe2bNn64ILLlBMTIxq1qyp0aNHB3M3gCJp69at6t27txITExUTE6MGDRpo9uzZ/n7e90oYMyvSj4xdQGG76qqrrFu3bv7nCxcutNjYWNu/f/9JzdO5c2erUqWK7du377i+PXv2nPQ4FB7WXmh89tln9tBDD9nEiRMtNjbWxo0bl63/mWeesfj4eJs0aZKtXLnSunbtameccYbt3bvXP+byyy+3Bg0a2Lx582zevHnWoEEDu/LKK/Pcrpd5UThYe+HhqaeestNPP92mTJliqamp9u9//9vKlStnw4YN84/Jz7qZN2+eRURE2JNPPmkpKSn25JNPWkREhH333XeFsVs4AdZf6G3fvt22bt3qfyxdutScczZ+/HgzM/vggw8sMjLSxowZYykpKda/f3+Li4uzzZs35zrnxo0brXTp0ta/f39LSUmxMWPGWGRkpE2aNKmwdgsnwNoLvT179liNGjWsV69etmDBAtu4caN9+eWXlpKS4h/D+17x41t7gbOY3DqKyoN/WEKjb9++1rJly2xtFStWtE8++cTzHLt27TLnnD311FMFMg6Fi7UXenFxcdnCpPT0dEtKSrInn3zS33bw4EGLj4+30aNHm5lZSkqKSbJvvvnGP2bu3LkmydasWRNwO17mReFh7YWHLl262E033ZSt7aabbrIuXbqYWf7XTbdu3axDhw7Z2tq3b2833HBDAVaP/GL9hZ8nn3zSypYtawcPHjQzs2bNmtmtt96abUzt2rXtwQcfzHWO+++/32rXrp2t7ZZbbrGLL7644AtGvrD2Qu+hhx6yFi1a5NrP+17xlFeYxGluyJfk5GRt2bIlW1t8fLx27NjheY7169fLzFS/fv0CGQeUdKmpqUpLS9Nll13mb4uNjVXr1q01b948SdL8+fMVHx+vFi1a+Me0bNlScXFx/jH5mRcoaS655BLNnDlTa9askSSlpKTo66+/1hVXXCEp/+tm/vz52V4jSZ06dWKtAQGYmcaOHas///nPio2N1eHDh7VkyZLj1tBll12Wr3W3ePFiHTlyJCi1A0XNJ598oosuukjdu3dXpUqV1KRJE7366quZB3jwvlcCESYhX5KTk7V161b/Px7r1q3T5s2b1apVK89zZL62oMYBJV1aWpokqXLlytnaK1eu7O9LS0tTYmKinHP+fuecKlWq5B+Tn3mBkuaBBx5Qr1691KBBA5UqVUrnnHOOevfurX79+knK/7pJS0tjrQEezZgxQ6mpqbrtttskSTt37tSxY8cKbN0dPXpUO3fuLPjCgSJo48aNeu2111SzZk1Nnz5dAwYM0IMPPqhRo0ZJ4n2vJCJMQr4kJyfr8OHD2rFjh8xMAwcO1FVXXaV69epp3bp16tKliy644AK1bt1a27dvDzhHnTp15JzT6tWr89yW13EAABSWDz/8UO+++64mTJigpUuX6t1339Vrr72msWPHhro0oMR48803deGFF+rcc88NdSlAsZeenq7zzz9fw4cP13nnnaebb75Zf/vb3/xhEkoewiTkS3JysiRpy5YtGjp0qFatWqW33npLf/zxh/r166c33nhDS5YsUY8ePTRmzJiAc5QvX16dOnXSq6++qv379x/Xn3l3Ka/jgJIuKSlJkrRt27Zs7du2bfP3JSUl+UPgTGam7du3+8fkZ16gpLnvvvt077336oYbblCjRo3Uq1cvDRo0SMOHD5eU/3WTlJTEWgM82L59u/7973/7j0qSpIoVKyoiIqLA1l1kZKQqVqxYsIUDRdQZZ5yhBg0aZGurX7++fvzxR0m875VEhEnIl8ww6ZFHHtHbb7+tr7/+WomJifrkk0+0atUqXXnllWrSpIlGjhypUqVK5TrPqFGjZGZq2rSpJk6cqLVr12rNmjV6/fXX1bhx45MeB5RkNWrUUFJSkmbMmOFvO3TokObOneu/RlLz5s21f/9+zZ8/3z9m/vz5OnDgQLbrKJ3svEBJc/DgQUVERGRri4iIUHp6uqT8r5vmzZtne42UcSoPaw3Ibvz48YqOjtaNN97ob4uKitIFF1xw0msot3XXtGnTPD/HAiVJy5YttXbt2mxtP/zwg6pVqyaJ970SKbcrcxeVh7iyf8iULl3aateubRs3bvS3DR482CZMmHBS8/zyyy/Wv39/q1GjhkVFRdkZZ5xhl19+uX3++ef5GofCwdoLjX379tmyZcts2bJlFhsba48//rgtW7bMf8vjZ555xsqUKWOTJ0+2lStXWvfu3Y+7Jevll19uDRs2tHnz5tm8efOsYcOGduWVV/r7t2zZYvXq1bOPPvrI3+ZlXhQO1l546N27tyUnJ9vUqVMtNTXVPvroI6tYsaINGjTIP8bLumnXrl22u0x9++23FhERYcOHD7fVq1fb008/bZGRkdwiOUyw/sJDenq61alT57i7tpmZffDBB1aqVCl78803LSUlxf72t79ZXFycbdq0yT+mV69e1qtXL//zjRs3WunSpW3AgAGWkpJib775ppUqVcomTZpUKPuDE2Pthd7ChQstMjLSnnzySVu3bp3961//sjJlytirr77qH8P7XvGjPO7mFvIw6FQf/MMSXl555RXr3bu3//n3338fumIQVKy90Jg5c6ZJOu6Rue7S09NtyJAhlpSUZNHR0da6dWtbuXJltjl2795tPXv2tISEBEtISLCePXvanj17/P2pqakmycaNG+dv8zIvCgdrLzzs3bvXBgwYYFWrVrWYmBirUaOGPfTQQ/b777/7x3hZN9WqVcv2vmlmNnHiRKtXr56VKlXKzj77bJs8eXJh7BI8YP2Fh6+//tok2YIFCwL2jxo1yqpVq2ZRUVF2/vnn2+zZs7P1t2nTxtq0aZOtbdasWXbeeedZVFSUVa9e3V5//fVglY98YO2Fh6lTp1rjxo0tOjra6tSpYyNHjrT09HR/P+97xU9eYZKzIn6nLOecFfV9KE4OHDignj17as2aNYqOjlanTp307LPPhrosBIFzjjvtASHA2gNCh/UHhAZrDwgN39pzAfuK+qIkTAJCgzd1IDRYe0DosP6A0GDtAaGRV5jEBbgBAAAAAADgGWESAAAAAAAAPCNMAgAAAAAAgGeESQAAAAAAAPCMMAkAAAAAAACeESYBAAAAAADAM8IkAAAAAAAAeEaYBAAAAAAAAM8IkwAAAAAAAOBZZKgLOFUxMTHpzjlCMaCQxcTEyDkX6jKAEoe1B4QO6w8IDdYeEBoxMTHpufU5MyvMWgqcc86K+j4ARZFzTqw9oPCx9oDQYf0BocHaA0LDt/YCJrkc0QMAAAAAAADPCJMAAAAAAADgGWESAAAAAAAAPCNMAgAAAAAAgGeESQAAAAAAAPCMMAkht23bNg0YMEC1atVSdHS0kpOT1blzZ02bNi1f4wAAAAAAQPBEhroAlGybNm1Sy5YtlZCQoOHDh+vcc89Venq6vvrqK/Xt21c//vjjSY0DAAAAAADBxZFJyJerr75a3bt39z9ftGiRSpcurQMHDpzUPP369ZMkLV68WN26dVO9evVUv3599e/fXytWrDjpcUBxMmfOHF199dVKTk6Wc07jx4/P1m9mGjp0qKpUqaLY2Fi1bdtWq1atyjZmz5496tWrl8qWLauyZcuqV69e+vXXX7ONWblypdq0aaPY2FglJydr2LBhMrM8a/MyL1BcDR8+XM459e/f399WUOsxJy/zAiXJ8OHDdeGFF6pMmTJKTEzUVVddpf/+97/ZxuR33UyePFkNGjRQdHS0GjRooI8//jhYuwEUSQXx2TQQ1l7RRJiEfElOTtbPP//sf37hhRcqLi5OX375pec5du/erS+++EJ33nmn4uPjj+svV67cSY0Dipv9+/erYcOGGjlypGJjY4/rf/bZZ/X888/rlVde0aJFi1SpUiV17NhR+/bt84/p0aOHli5dqi+++EJffPGFli5dql69evn79+7dq44dO6py5cpatGiRRo4cqeeee04vvPBCnrWdaF6guPruu+80ZswYNW7cOFt7QazHQLzMC5Qks2bNUr9+/TRv3jx9/fXXioyMVIcOHbR7927/mPysm/nz56t79+7q2bOnli9frp49e6pr165asGBBYewWUCQUxGfTnFh7RZiZFelHxi6gsD3xxBNWrVq1bG3Vq1e3N9980/McCxYsMEn20UcfFcg4FC7WXuGKi4uzcePG+Z+np6dbUlKSPfnkk/62gwcPWnx8vI0ePdrMzFJSUkySffPNN/4xc+fONUm2Zs0aMzN77bXXLCEhwQ4ePOgf88QTT1iVKlUsPT09YC1e5kXwsPZC59dff7WaNWva119/bW3atLE777zTzApuPebkZV4ULtZf+Nm3b5+ddtppNmXKFDPL/7rp1q2bdejQIVtb+/bt7YYbbghO4TgprL3wk5/PpoGw9sKbb+0FzGI4Mgn5kpycrK1bt/pPhVm3bp02b96sVq1aeZ4j87UFNQ4oSVJTU5WWlqbLLrvM3xYbG6vWrVtr3rx5kjL+0hMfH68WLVr4x7Rs2VJxcXHZxrRq1SrbX5c6deqkX375RZs2bQq4bS/zAsXR7bffruuvv16XXnpptvaCWo85eZkXKOn27dun9PR0nX766ZLyv27mz5+f7TVSxvshaw3whrVX8hAmIV+Sk5N1+PBh7dixQ2amgQMH6qqrrlK9evXUvHlzpaWlSZKWLl2q6667LuAcderUkXNOq1evznNbXscBJUnmGqtcuXK29sqVK/v70tLSlJiYKOecv985p0qVKmUbE2iOrNsItO0TzQsUN2+++abWr1+vJ5988ri+glqP+ZkXKOkGDBigJk2aqHnz5pLyv25yez9krQHesPZKHsIk5EtycrIkacuWLRo6dKhWrVqlt956S2am7du3KykpSVLGhX1zXlciU/ny5dWpUye9+uqr2r9//3H9mRcl9ToOAIBgWLt2rR5++GFNmDBBpUqVCnU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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tbl.makeSelectionPlot(df,cuts=['No Cut','Cut A','Cut B','Full Selection'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "mathematical-cabinet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tbl.makeSelectionPlot(df,cuts=['No Cut','Cut A','Cut B','Full Selection'],norm=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "adjusted-distributor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tbl.makeSelectionPlot(df,cuts=['No Cut','Cut A','Cut B','Full Selection'],signalLabel='NumuCC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "spread-douglas",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}