tracking-parametrisation-tuner/parameterisations/notebooks/polarity_check.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "input_tree_md = uproot.open({\"/work/guenther/reco_tuner/data/param_data_MD_selected.root\": \"Selected\"})\n",
    "input_tree_mu = uproot.open({\"/work/guenther/reco_tuner/data/param_data_MU_selected.root\": \"Selected\"})\n",
    "    # this is an event list of dictionaries containing awkward arrays\n",
    "array_md = input_tree_md.arrays()\n",
    "array_mu = input_tree_mu.arrays()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/work/guenther/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/IPython/core/pylabtools.py:151: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import mplhep\n",
    "mplhep.style.use([\"LHCbTex2\"])\n",
    "default_args = dict(\n",
    "    alpha=0.5\n",
    ")\n",
    "\n",
    "variables = {\n",
    "    \"z_mag_x\": dict(\n",
    "        range=(5100, 5600),\n",
    "        label=\"z_mag_x\",\n",
    "        unit=\"mm\",\n",
    "        bins=50,\n",
    "    ),\n",
    "    \"z_mag_y\": dict(\n",
    "        range=(2000, 10000),\n",
    "        label=\"z_mag_y\",\n",
    "        unit=\"mm\",\n",
    "        bins=50,\n",
    "    ),\n",
    "    \"AX\": dict(\n",
    "        range=(-3000, 3000),\n",
    "        label=\"AX\",\n",
    "        unit=\"mm\",\n",
    "        bins=50,\n",
    "    ),\n",
    "    \"BX\": dict(\n",
    "        range=(-1.2, 1.2),\n",
    "        label=\"BX\",\n",
    "        unit=\"1/mm\",\n",
    "        bins=50,\n",
    "    ),\n",
    "    \"CX\": dict(\n",
    "        range=(-7e-6, 7e-6),\n",
    "        label=\"CX\",\n",
    "        unit=\"1/mm\",\n",
    "        bins=50,\n",
    "    ),\n",
    "    \"DX\": dict(\n",
    "        range=(-7e-9, 7e-9),\n",
    "        label=\"DX\",\n",
    "        unit=\"1/mm\",\n",
    "        bins=50,\n",
    "    ),\n",
    "    \"AY\": dict(\n",
    "        range=(-2500, 2500),\n",
    "        label=\"AY\",\n",
    "        unit=\"mm\",\n",
    "        bins=50,\n",
    "    ),\n",
    "    \"BY\": dict(\n",
    "        range=(-0.4, 0.4),\n",
    "        label=\"BY\",\n",
    "        unit=\"mm\",\n",
    "        bins=50,\n",
    "    ),\n",
    "    \"CY\": dict(\n",
    "        range=(-4e-6, 4e-6),\n",
    "        label=\"CY\",\n",
    "        unit=\"mm\",\n",
    "        bins=50,\n",
    "    ),\n",
    "}\n",
    "for name in variables.keys():\n",
    "    plt.figure()\n",
    "    n, bins, patches = plt.hist(\n",
    "        array_md[name],\n",
    "        bins=variables[name][\"bins\"],\n",
    "        **default_args,\n",
    "        range=variables[name][\"range\"],\n",
    "        label=\"MagDown\"\n",
    "    )\n",
    "    plt.hist(array_mu[name], bins=variables[name][\"bins\"],\n",
    "        **default_args, \n",
    "        range=variables[name][\"range\"], label=\"MagUp\")\n",
    "\n",
    "    label = variables[name][\"label\"]\n",
    "    unit = variables[name][\"unit\"]\n",
    "    bin_width = \"%.1f\" % round(bins[1] - bins[0], 1)\n",
    "    plt.xlabel(f\"{label} [{unit}]\")\n",
    "    plt.ylabel(f\"Particles/({bin_width} {unit})\")\n",
    "    mplhep.lhcb.text(\"Simulation\")\n",
    "    plt.legend()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6",
   "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": "3.10.6"
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   "interpreter": {
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