Projektpraktikum/trackinglosses_rad_length_endVelo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\t\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "import awkward as ak\n",
    "from scipy.optimize import curve_fit\n",
    "from methods.fit_linear_regression_model import fit_linear_regression_model\n",
    "import sklearn\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40402 10099\n",
      "50501\n"
     ]
    }
   ],
   "source": [
    "file = uproot.open(\n",
    "    \"tracking_losses_ntuple_B_default_radlength_endVelo.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)]  # B: 9056\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) &\n",
    "                  (allcolumns.fromB)]  # B: 1466\n",
    "\n",
    "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\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": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stretch factor:  0.24996287312509283\n"
     ]
    }
   ],
   "source": [
    "rad_length_found = ak.to_numpy(found[\"rad_length_frac\"])\n",
    "eta_found = ak.to_numpy(found[\"eta\"])\n",
    "rad_length_lost = ak.to_numpy(lost[\"rad_length_frac\"])\n",
    "eta_lost = ak.to_numpy(lost[\"eta\"])\n",
    "\n",
    "stretch_factor = ak.num(eta_lost, axis=0) / ak.num(eta_found, axis=0)\n",
    "print(\"stretch factor: \", stretch_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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_lost,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    color=\"darkorange\",\n",
    "    histtype=\"bar\",\n",
    "    label=\"lost\",\n",
    "    range=[0, 1],\n",
    ")\n",
    "plt.hist(\n",
    "    rad_length_found,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    color=\"blue\",\n",
    "    histtype=\"bar\",\n",
    "    label=\"found\",\n",
    "    range=[0, 1],\n",
    ")\n",
    "plt.xlim(0, 1)\n",
    "# plt.yscale(\"log\")\n",
    "plt.title(\"radiation length fraction endVelo\")\n",
    "plt.xlabel(f\"$x/X_0$\")\n",
    "plt.ylabel(\"a.u.\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 100\n",
    "vmax = 80\n",
    "\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
    "\n",
    "a0 = ax0.hist2d(\n",
    "    rad_length_found,\n",
    "    eta_found,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=vmax,\n",
    "    range=[[0, 0.6], [2, 5]],\n",
    ")\n",
    "ax0.set_xlabel(f\"$x/X_0$\")\n",
    "ax0.set_ylabel(f\"$\\eta$\")\n",
    "ax0.set_title(f\"found $\\eta$ rad_length_frac\")\n",
    "\n",
    "a1 = ax1.hist2d(\n",
    "    rad_length_lost,\n",
    "    eta_lost,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=vmax * stretch_factor,\n",
    "    range=[[0, 0.6], [2, 5]],\n",
    ")\n",
    "ax1.set_xlabel(f\"$x/X_0$\")\n",
    "ax1.set_ylabel(f\"$\\eta$\")\n",
    "ax1.set_title(f\"lost $\\eta$ rad_length_frac\")\n",
    "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
    "\n",
    "plt.suptitle(\"radiation length fraction and eta endVelo\")\n",
    "plt.colorbar(a0[3], ax=ax1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "rad_length_frac = found[\"rad_length_frac\"]\n",
    "# @ z = 9400.mm or 770.mm\n",
    "state = 1\n",
    "\n",
    "if state == 1:\n",
    "    slopex = found[\"ideal_state_770_tx\"]\n",
    "    slopey = found[\"ideal_state_770_ty\"]\n",
    "    x = found[\"ideal_state_770_x\"]\n",
    "    y = found[\"ideal_state_770_y\"]\n",
    "    qop = found[\"ideal_state_770_qop\"]\n",
    "elif state == 2:\n",
    "    slopex = found[\"ideal_state_9410_tx\"]\n",
    "    slopey = found[\"ideal_state_9410_ty\"]\n",
    "    x = found[\"ideal_state_9410_x\"]\n",
    "    y = found[\"ideal_state_9410_y\"]\n",
    "    qop = found[\"ideal_state_9410_qop\"]\n",
    "\n",
    "data = ak.zip({\n",
    "    \"rad_length_frac\": rad_length_frac,\n",
    "    \"x\": x,\n",
    "    \"y\": y,\n",
    "    \"tx\": slopex,\n",
    "    \"ty\": slopey,\n",
    "    \"qop\": qop,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameterisation for rad_length_frac:\n",
      "intercept= 0.0\n",
      "coef= {'1': 0.19432666451166924, 'x': -0.00010136532791187516, 'y': -0.00022209404990607013, 'tx': 0.058480207541052064, 'ty': 0.18599542917589618, 'qop': -17.657405901936546, 'x^2': 1.2271024867047469e-05, 'x y': 5.376824972059055e-08, 'x tx': -0.025941371835257743, 'x ty': 0.014328015524776564, 'x qop': -0.38410998543244235, 'y^2': -1.8261507968709735e-05, 'y tx': -0.014951077113227147, 'y ty': 0.02154311538810164, 'y qop': -0.008987663050567057, 'tx^2': 9.180854607357592, 'tx ty': 0.1873064867954838, 'tx qop': -0.47120712928737807, 'ty^2': -6.787398610853974, 'ty qop': -0.05031041352613623, 'qop^2': 0.24778641944197533, 'x^3': -5.992321693190818e-09, 'x^2 y': -2.649404482539432e-07, 'x^2 tx': 5.42176741002125e-06, 'x^2 ty': 0.0020253771536145383, 'x^2 qop': 0.003955288970107154, 'x y^2': 2.8906882704270233e-09, 'x y tx': -0.0016493773782229844, 'x y ty': 0.0005067592007986898, 'x y qop': -0.0031813438496783404, 'x tx^2': 0.0022653308811514867, 'x tx ty': -1.5366360302916284, 'x tx qop': -0.47946313010450653, 'x ty^2': -0.16776305178984158, 'x ty qop': 2.1889500302073115, 'x qop^2': 0.008745243979922932, 'y^3': 1.4363120470761714e-08, 'y^2 tx': -0.0005062978553841023, 'y^2 ty': 1.4165440283044409e-05, 'y^2 qop': -0.005787147937546765, 'y tx^2': 1.404047645962725, 'y tx ty': 0.1643910546131908, 'y tx qop': 2.0452379823825813, 'y ty^2': -0.02140660574730952, 'y ty qop': 4.596387657867597, 'y qop^2': -0.00994088354226498, 'tx^3': -0.1303976515212055, 'tx^2 ty': -0.01339610187978276, 'tx^2 qop': -0.0009383144690666937, 'tx ty^2': 0.010330807276347866, 'tx ty qop': 0.00564141621903312, 'tx qop^2': 1.1663926820802422e-05, 'ty^3': 0.00629773549670367, 'ty^2 qop': 0.012052455601588194, 'ty qop^2': -1.60166284721317e-05, 'qop^3': -1.5639205966369518e-06, 'x^4': 1.4244960766518489e-09, 'x^3 y': -3.1096076824610464e-09, 'x^3 tx': -3.587531603760352e-06, 'x^3 ty': 2.4713133050052738e-05, 'x^3 qop': 2.5845963699160437e-05, 'x^2 y^2': -2.0323240867980985e-09, 'x^2 y tx': -1.781414931473213e-05, 'x^2 y ty': -1.0125741814137612e-05, 'x^2 y qop': 0.00010504795400130865, 'x^2 tx^2': 0.00318540624554603, 'x^2 tx ty': -0.019979832566411563, 'x^2 tx qop': 0.006687084675785277, 'x^2 ty^2': 0.01525871952521068, 'x^2 ty qop': 0.5999059816243137, 'x^2 qop^2': 8.117293800523118, 'x y^3': 7.45386197209541e-10, 'x y^2 tx': 1.539536253053475e-05, 'x y^2 ty': -1.089334137566178e-05, 'x y^2 qop': -1.95550966909791e-05, 'x y tx^2': 0.015033712066153922, 'x y tx ty': -0.02448056876178019, 'x y tx qop': -0.6716914089728931, 'x y ty^2': 0.007182752502229531, 'x y ty qop': -0.18936713869537714, 'x y qop^2': 3.611377836849899, 'x tx^3': -0.9115227647899486, 'x tx^2 ty': 0.8829467327098821, 'x tx^2 qop': 0.2695607767961495, 'x tx ty^2': 0.6012451073716732, 'x tx ty qop': -0.00930712726835479, 'x tx qop^2': 0.009680202136377838, 'x ty^3': -0.06079594416933904, 'x ty^2 qop': 0.04029084967629493, 'x ty qop^2': 0.004774793771381167, 'x qop^3': -4.554461348017879e-05, 'y^4': 4.04032363121587e-12, 'y^3 tx': 8.925743423537913e-06, 'y^3 ty': -1.0182095510069544e-06, 'y^3 qop': -2.928516988854517e-05, 'y^2 tx^2': 0.004743417924369025, 'y^2 tx ty': -0.0055290127765494496, 'y^2 tx qop': 0.2133121554871387, 'y^2 ty^2': 0.0017685009285896116, 'y^2 ty qop': 0.020383665461783013, 'y^2 qop^2': -0.90657292007639, 'y tx^3': 0.27175992018246475, 'y tx^2 ty': 0.6761073293647273, 'y tx^2 qop': -0.01248506771745212, 'y tx ty^2': -0.39076013367696105, 'y tx ty qop': 0.04349449144257623, 'y tx qop^2': 0.004752914013263597, 'y ty^3': -0.729116504589348, 'y ty^2 qop': -0.015043560943011547, 'y ty qop^2': -0.001547809754472937, 'y qop^3': -1.316621299355546e-05, 'tx^4': -0.003228436324482351, 'tx^3 ty': 0.0044775047770867545, 'tx^3 qop': 0.001098230908109032, 'tx^2 ty^2': 0.0029303145639830285, 'tx^2 ty qop': -4.337425914757845e-05, 'tx^2 qop^2': 1.134299372765626e-05, 'tx ty^3': -0.0014086661449565522, 'tx ty^2 qop': 0.0001660768862220596, 'tx ty qop^2': 6.3521706353330705e-06, 'tx qop^3': -5.1281203369915226e-08, 'ty^4': -0.0071021220988862245, 'ty^3 qop': -6.7134108016768
      "r2 score= 0.04993524171391073\n",
      "RMSE = 0.12351226258714283\n",
      "Type  <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "lin_reg, features, xx0_test, xx0_predicted = fit_linear_regression_model(\n",
    "    data,\n",
    "    \"rad_length_frac\",\n",
    "    [\"x\", \"y\", \"tx\", \"ty\", \"qop\"],\n",
    "    5,\n",
    "    include_bias=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 100\n",
    "vmax = 50\n",
    "\n",
    "a0 = plt.hist2d(\n",
    "    xx0_test,\n",
    "    xx0_predicted,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=vmax,\n",
    "    range=[[-0.1, 0.6], [-0.1, 0.6]],\n",
    ")\n",
    "plt.plot([-0.1, 0.6], [-0.1, 0.6], marker=\"\", alpha=0.8)\n",
    "plt.xlabel(f\"True $x/X_0$\")\n",
    "plt.ylabel(f\"Predicted $x/X_0$\")\n",
    "plt.title(f\"found rad_length_frac\")\n",
    "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
    "\n",
    "plt.colorbar(a0[3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}