{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/auto/work\n"
     ]
    }
   ],
   "source": [
    "%cd ..\n",
    "import uproot\t\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colormaps\n",
    "from mpl_toolkits import mplot3d\n",
    "import awkward as ak\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy import stats\n",
    "from methods.fit_linear_regression_model import fit_linear_regression_model\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def round(n, k):\n",
    "    # function to round number 'n' up/down to nearest 'k'\n",
    "    # use positive k to round up\n",
    "    # use negative k to round down\n",
    "\n",
    "    return n - n % k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41978 8523\n",
      "49865\n"
     ]
    }
   ],
   "source": [
    "file = uproot.open(\n",
    "    \"/work/cetin/Projektpraktikum/tracking_losses_ntuple_B_rad_length_beginVelo2endVelo.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
    ")\n",
    "\n",
    "# selektiere nur elektronen von B->K*ee\n",
    "allcolumns = file.arrays()\n",
    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) &\n",
    "                   (allcolumns.fromB)]\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) &\n",
    "                  (allcolumns.fromB)]\n",
    "\n",
    "electrons = allcolumns[(allcolumns.isElectron)\n",
    "                       & (allcolumns.fromB)\n",
    "                       & (allcolumns.eta <= 5.0)\n",
    "                       & (allcolumns.eta >= 1.5)\n",
    "                       & (np.abs(allcolumns.phi) < 3.142)]\n",
    "\n",
    "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n",
    "print(ak.num(electrons, axis=0))\n",
    "# ak.count(found, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables\n",
    "\n",
    "eta_a = ak.to_numpy(electrons[\"eta\"])\n",
    "phi_a = ak.to_numpy(electrons[\"phi\"])\n",
    "rad_length_frac_a = ak.to_numpy(electrons[\"rad_length_frac\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    rad_length_frac_a,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    color=\"blue\",\n",
    "    histtype=\"bar\",\n",
    "    range=[0, 1],\n",
    ")\n",
    "plt.xlim(0, 1)\n",
    "# plt.yscale(\"log\")\n",
    "plt.title(\"radiation length fraction beginVelo2endVelo\")\n",
    "plt.xlabel(f\"$x/X_0$\")\n",
    "plt.ylabel(\"a.u.\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    \"phi\": phi_a * 90.0 / np.pi,\n",
    "    \"eta\": eta_a * 2.0,\n",
    "    \"rad_length_frac\": rad_length_frac_a,\n",
    "})\n",
    "df = df.round({\"phi\": 0, \"eta\": 1, \"rad_length_frac\": 4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pivoted = df.pivot_table(\n",
    "    index=\"eta\",\n",
    "    columns=\"phi\",\n",
    "    values=\"rad_length_frac\",\n",
    "    margins=False,\n",
    "    # fill_value=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>phi</th>\n",
       "      <th>-90.0</th>\n",
       "      <th>-89.0</th>\n",
       "      <th>-88.0</th>\n",
       "      <th>-87.0</th>\n",
       "      <th>-86.0</th>\n",
       "      <th>-85.0</th>\n",
       "      <th>-84.0</th>\n",
       "      <th>-83.0</th>\n",
       "      <th>-82.0</th>\n",
       "      <th>-81.0</th>\n",
       "      <th>...</th>\n",
       "      <th>81.0</th>\n",
       "      <th>82.0</th>\n",
       "      <th>83.0</th>\n",
       "      <th>84.0</th>\n",
       "      <th>85.0</th>\n",
       "      <th>86.0</th>\n",
       "      <th>87.0</th>\n",
       "      <th>88.0</th>\n",
       "      <th>89.0</th>\n",
       "      <th>90.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eta</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3.1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.30455</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.526900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.14755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.459800</td>\n",
       "      <td>0.1691</td>\n",
       "      <td>0.1710</td>\n",
       "      <td>0.26650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.291200</td>\n",
       "      <td>0.1778</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.14020</td>\n",
       "      <td>0.1775</td>\n",
       "      <td>0.5234</td>\n",
       "      <td>0.1498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.14810</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.45340</td>\n",
       "      <td>0.35060</td>\n",
       "      <td>0.447200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1711</td>\n",
       "      <td>0.243100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2161</td>\n",
       "      <td>0.1313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.16210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4284</td>\n",
       "      <td>0.4008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.24475</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.06550</td>\n",
       "      <td>0.09655</td>\n",
       "      <td>0.181325</td>\n",
       "      <td>0.1296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25035</td>\n",
       "      <td>0.0707</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2469</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0692</td>\n",
       "      <td>0.2822</td>\n",
       "      <td>0.26520</td>\n",
       "      <td>0.1244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1084</td>\n",
       "      <td>0.1665</td>\n",
       "      <td>0.06750</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.0816</td>\n",
       "      <td>0.0878</td>\n",
       "      <td>0.07350</td>\n",
       "      <td>0.0864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.14480</td>\n",
       "      <td>0.0652</td>\n",
       "      <td>0.0755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0796</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.8</th>\n",
       "      <td>0.0781</td>\n",
       "      <td>0.09960</td>\n",
       "      <td>0.08180</td>\n",
       "      <td>0.083267</td>\n",
       "      <td>0.0779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.08810</td>\n",
       "      <td>0.1684</td>\n",
       "      <td>0.140867</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.13015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0821</td>\n",
       "      <td>0.0646</td>\n",
       "      <td>0.08440</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.09210</td>\n",
       "      <td>0.08210</td>\n",
       "      <td>0.071900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0936</td>\n",
       "      <td>0.32600</td>\n",
       "      <td>0.0748</td>\n",
       "      <td>0.193300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>0.1494</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0844</td>\n",
       "      <td>0.07660</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.05840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0578</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0785</td>\n",
       "      <td>0.0915</td>\n",
       "      <td>0.03070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.07090</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 181 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "phi    -90.0    -89.0    -88.0     -87.0   -86.0   -85.0    -84.0   -83.0  \\\n",
       "eta                                                                         \n",
       "3.1      NaN      NaN      NaN       NaN     NaN     NaN      NaN     NaN   \n",
       "3.2      NaN      NaN      NaN       NaN     NaN     NaN      NaN     NaN   \n",
       "3.3      NaN  0.30455      NaN  0.526900     NaN     NaN  0.14755     NaN   \n",
       "3.4      NaN      NaN      NaN  0.459800  0.1691  0.1710  0.26650     NaN   \n",
       "3.5      NaN  0.45340  0.35060  0.447200     NaN  0.1885      NaN  0.1711   \n",
       "...      ...      ...      ...       ...     ...     ...      ...     ...   \n",
       "9.6      NaN  0.06550  0.09655  0.181325  0.1296     NaN  0.25035  0.0707   \n",
       "9.7      NaN      NaN      NaN  0.070000  0.0816  0.0878  0.07350  0.0864   \n",
       "9.8   0.0781  0.09960  0.08180  0.083267  0.0779     NaN  0.08810  0.1684   \n",
       "9.9      NaN  0.09210  0.08210  0.071900     NaN  0.0936  0.32600  0.0748   \n",
       "10.0     NaN  0.05840      NaN  0.070600     NaN  0.0578      NaN     NaN   \n",
       "\n",
       "phi      -82.0   -81.0  ...    81.0    82.0    83.0     84.0    85.0    86.0  \\\n",
       "eta                     ...                                                    \n",
       "3.1        NaN     NaN  ...     NaN     NaN     NaN      NaN     NaN     NaN   \n",
       "3.2        NaN     NaN  ...     NaN     NaN     NaN      NaN     NaN     NaN   \n",
       "3.3   0.135100     NaN  ...  0.1247     NaN     NaN      NaN     NaN     NaN   \n",
       "3.4   0.291200  0.1778  ...     NaN     NaN     NaN  0.14020  0.1775  0.5234   \n",
       "3.5   0.243100     NaN  ...  0.2161  0.1313     NaN  0.16210     NaN  0.4284   \n",
       "...        ...     ...  ...     ...     ...     ...      ...     ...     ...   \n",
       "9.6        NaN  0.2469  ...     NaN  0.0692  0.2822  0.26520  0.1244     NaN   \n",
       "9.7        NaN     NaN  ...  0.1993     NaN     NaN  0.14480  0.0652  0.0755   \n",
       "9.8   0.140867     NaN  ...     NaN  0.0977     NaN  0.13015     NaN     NaN   \n",
       "9.9   0.193300     NaN  ...  0.0727  0.1494  0.0353      NaN  0.3264     NaN   \n",
       "10.0       NaN     NaN  ...     NaN  0.0785  0.0915  0.03070     NaN     NaN   \n",
       "\n",
       "phi     87.0    88.0     89.0   90.0  \n",
       "eta                                   \n",
       "3.1      NaN     NaN      NaN    NaN  \n",
       "3.2      NaN     NaN      NaN    NaN  \n",
       "3.3      NaN     NaN      NaN    NaN  \n",
       "3.4   0.1498     NaN  0.14810    NaN  \n",
       "3.5   0.4008     NaN  0.24475    NaN  \n",
       "...      ...     ...      ...    ...  \n",
       "9.6   0.1084  0.1665  0.06750    NaN  \n",
       "9.7      NaN  0.0796      NaN    NaN  \n",
       "9.8   0.0821  0.0646  0.08440    NaN  \n",
       "9.9      NaN  0.0844  0.07660    NaN  \n",
       "10.0     NaN     NaN  0.07090    NaN  \n",
       "\n",
       "[70 rows x 181 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivoted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2100x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(21, 7))\n",
    "ax = sns.heatmap(\n",
    "    df_pivoted,\n",
    "    robust=True,\n",
    "    square=True,\n",
    "    cmap=colormaps[\"rainbow\"],\n",
    "    xticklabels=False,\n",
    "    yticklabels=False,\n",
    "    vmax=0.7,\n",
    ")\n",
    "# ax.set_yticks([5, 15, 25, 35], [2, 3, 4, 5])\n",
    "ax.set_yticks([10, 30, 50, 70], [2, 3, 4, 5])\n",
    "ax.set_xticks([39, 89, 139], [-100, 0, 100])  # ([79, 179, 279], [-100, 0, 100])\n",
    "ax.set_xlabel(f\"$\\phi$ [deg]\")\n",
    "ax.set_ylabel(f\"$\\eta$\")\n",
    "\n",
    "# ax.set_yticklabels([])\n",
    "ax.invert_yaxis()\n",
    "ax.set_title(\"LHCb VELO $x/X_0$\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tuner",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}