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{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "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": 2, "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": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "41978 8523\n", "49865\n" ] } ], "source": [ "file = uproot.open(\n", " \"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) & (allcolumns.fromB)]\n", "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)]\n", "\n", "electrons = allcolumns[\n", " (allcolumns.isElectron)\n", " & (allcolumns.fromB)\n", " & (allcolumns.eta <= 5.0)\n", " & (allcolumns.eta >= 1.5)\n", " & (np.abs(allcolumns.phi) < 3.142)\n", "]\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": 4, "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": 5, "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": 7, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(\n", " {\n", " \"phi\": phi_a * 90.0 / np.pi,\n", " \"eta\": eta_a * 2.0,\n", " \"rad_length_frac\": rad_length_frac_a,\n", " }\n", ")\n", "df = df.round({\"phi\": 0, \"eta\": 1, \"rad_length_frac\": 4})" ] }, { "cell_type": "code", "execution_count": 8, "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": 9, "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": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pivoted" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.heatmap(\n", " df_pivoted,\n", " robust=False,\n", " square=False,\n", " cmap=colormaps[\"rainbow\"],\n", " xticklabels=False,\n", " yticklabels=False,\n", " vmax=0.8,\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])\n", "# ax.set_xticks([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()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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}
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