{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d563033c",
   "metadata": {},
   "source": [
    "# Import tools/models be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc9e6e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from math import pi\n",
    "import os\n",
    "import errno"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d7a2187",
   "metadata": {},
   "source": [
    "# Create \"Plots\" and storage folders if they don't exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f95e27c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    os.mkdir('Plots')\n",
    "# if folder exists do nothing\n",
    "except OSError as e:\n",
    "    if e.errno != errno.EEXIST:\n",
    "        raise\n",
    "        \n",
    "try:\n",
    "    os.mkdir('storage')\n",
    "# if folder exists do nothing\n",
    "except OSError as e:\n",
    "    if e.errno != errno.EEXIST:\n",
    "        raise        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313e157e",
   "metadata": {},
   "source": [
    "# Read LO data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bdc08ed0",
   "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>$M_{inv}$</th>\n",
       "      <th>$rapidity$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>121.079631</td>\n",
       "      <td>-0.304619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>196.219429</td>\n",
       "      <td>-0.401002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>478.422633</td>\n",
       "      <td>-1.010334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99.018601</td>\n",
       "      <td>0.097534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>423.950224</td>\n",
       "      <td>2.223391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>128.145991</td>\n",
       "      <td>0.246759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>576.782956</td>\n",
       "      <td>-0.554320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>35.437849</td>\n",
       "      <td>0.628855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>189.229229</td>\n",
       "      <td>0.462469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>443.127754</td>\n",
       "      <td>-1.858528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10001 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        $M_{inv}$  $rapidity$\n",
       "0      121.079631   -0.304619\n",
       "1      196.219429   -0.401002\n",
       "2      478.422633   -1.010334\n",
       "3       99.018601    0.097534\n",
       "4      423.950224    2.223391\n",
       "...           ...         ...\n",
       "9996   128.145991    0.246759\n",
       "9997   576.782956   -0.554320\n",
       "9998    35.437849    0.628855\n",
       "9999   189.229229    0.462469\n",
       "10000  443.127754   -1.858528\n",
       "\n",
       "[10001 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directory = '../data/'\n",
    "filein    = 'checkLO.txt'\n",
    "file      = directory+filein\n",
    "data = np.genfromtxt(file)\n",
    "\n",
    "m        = data[:,0:1]\n",
    "rap      = data[:,1:2]\n",
    "x2       = data[:,2:3]\n",
    "\n",
    "m = np.concatenate((m),axis=0) \n",
    "rap = np.concatenate((rap),axis=0) \n",
    "x2 = np.concatenate((x2),axis=0) \n",
    "\n",
    "Data_ave = np.stack((m,rap),axis=-1)\n",
    "df = pd.DataFrame(Data_ave)\n",
    "df.columns = ['$M_{inv}$','$rapidity$']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d99167a1",
   "metadata": {},
   "source": [
    "# Plot LO data to get a feeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f84ecfd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "ax1.scatter(m, x2,color='blue')\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$x_2^{LO}$',fontsize=16)\n",
    "\n",
    "ax2.scatter(rap, x2,color='green')\n",
    "ax2.set_xlabel(xlabel='rapidity',fontsize=16)\n",
    "\n",
    "plt.savefig('Plots/x2_LO_to_features.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ac2555",
   "metadata": {},
   "source": [
    "# Read NLO data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f56b91c",
   "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>$M_{inv}$</th>\n",
       "      <th>$rapidity$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>512.889687</td>\n",
       "      <td>-1.698364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>349.604335</td>\n",
       "      <td>0.649795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102.015218</td>\n",
       "      <td>0.935912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>475.693746</td>\n",
       "      <td>1.691695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>422.624040</td>\n",
       "      <td>1.233733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8604</th>\n",
       "      <td>310.392511</td>\n",
       "      <td>1.763102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8605</th>\n",
       "      <td>322.772764</td>\n",
       "      <td>1.343101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8606</th>\n",
       "      <td>373.867518</td>\n",
       "      <td>-0.255696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8607</th>\n",
       "      <td>275.877980</td>\n",
       "      <td>0.169265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8608</th>\n",
       "      <td>440.485176</td>\n",
       "      <td>0.265116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8609 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       $M_{inv}$  $rapidity$\n",
       "0     512.889687   -1.698364\n",
       "1     349.604335    0.649795\n",
       "2     102.015218    0.935912\n",
       "3     475.693746    1.691695\n",
       "4     422.624040    1.233733\n",
       "...          ...         ...\n",
       "8604  310.392511    1.763102\n",
       "8605  322.772764    1.343101\n",
       "8606  373.867518   -0.255696\n",
       "8607  275.877980    0.169265\n",
       "8608  440.485176    0.265116\n",
       "\n",
       "[8609 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directory = '../data/'\n",
    "filein    = 'checkNLO.txt'\n",
    "file      = directory+filein\n",
    "data = np.genfromtxt(file)\n",
    "\n",
    "m        = data[:,0:1]\n",
    "rap      = data[:,1:2]\n",
    "x2       = data[:,2:3]\n",
    "\n",
    "m = np.concatenate((m),axis=0) \n",
    "rap = np.concatenate((rap),axis=0) \n",
    "x2 = np.concatenate((x2),axis=0) \n",
    "\n",
    "Data_ave = np.stack((m,rap),axis=-1)\n",
    "df = pd.DataFrame(Data_ave)\n",
    "df.columns = ['$M_{inv}$','$rapidity$']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32dd5041",
   "metadata": {},
   "source": [
    "# Plot NLO data to get a feeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c0a1ae45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "ax1.scatter(m, x2,color='blue')\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$x_2^{NLO}$',fontsize=16)\n",
    "\n",
    "ax2.scatter(rap, x2,color='green')\n",
    "ax2.set_xlabel(xlabel='rapidity',fontsize=16)\n",
    "\n",
    "plt.savefig('Plots/x2_NLO_to_features.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65ea48f7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}