{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\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 utils.fit_linear_regression_model import fit_linear_regression_model\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import mplhep\n",
    "mplhep.style.use([\"LHCbTex2\"])\n",
    "\n",
    "plt.rcParams[\"savefig.dpi\"] = 600\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",
    "    \"/work/cetin/Projektpraktikum/tracking_losses_ntuple_B_EndVeloP.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
    ")\n",
    "\n",
    "# selektiere nur elektronen von B->K*ee\n",
    "allcolumns = file.arrays()\n",
    "found = allcolumns[\n",
    "    (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)\n",
    "]  # B: 9056\n",
    "lost = allcolumns[\n",
    "    (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n",
    "]  # B: 1466\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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stretch factor:  0.1943140448361755\n"
     ]
    }
   ],
   "source": [
    "rad_length_found = ak.to_numpy(\n",
    "    found[(found.eta <= 5.0) & (found.eta >= 1.5) &\n",
    "          (np.abs(found.phi) < 3.142)][\"rad_length_frac\"])\n",
    "eta_found = ak.to_numpy(found[(found.eta <= 5.0) & (found.eta >= 1.5) &\n",
    "                              (np.abs(found.phi) < 3.142)][\"eta\"])\n",
    "phi_found = ak.to_numpy(found[(found.eta <= 5.0) & (found.eta >= 1.5) &\n",
    "                              (np.abs(found.phi) < 3.142)][\"phi\"])\n",
    "rad_length_lost = ak.to_numpy(\n",
    "    lost[(lost.eta <= 5.0) & (lost.eta >= 1.5) &\n",
    "         (np.abs(lost.phi) < 3.142)][\"rad_length_frac\"])\n",
    "eta_lost = ak.to_numpy(lost[(lost.eta <= 5.0) & (lost.eta >= 1.5) &\n",
    "                            (np.abs(lost.phi) < 3.142)][\"eta\"])\n",
    "phi_lost = ak.to_numpy(lost[(lost.eta <= 5.0) & (lost.eta >= 1.5) &\n",
    "                            (np.abs(lost.phi) < 3.142)][\"phi\"])\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\"])\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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x900 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=\"#F05342\",\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=\"#2A9D8F\",\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 endVelo2endUT\")\n",
    "plt.xlabel(f\"$x/X_0$\")\n",
    "plt.ylabel(\"a.u.\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameterisation for rad_length_frac:\n",
      "intercept= 0.0\n",
      "coef= {'1': 0.19830920321074946, 'x': -4.49175976974402e-05, 'y': 0.00039490060416272056, 'tx': 0.00015102371088508598, 'ty': -0.3004315695136339, 'qop': -15.314945266490128, 'x^2': -1.8619394568578818e-05, 'x y': -4.953907513838906e-06, 'x tx': 0.021617503882699386, 'x ty': 0.03829244150062255, 'x qop': -0.41798007270055415, 'y^2': -2.4410328131494868e-05, 'y tx': -0.03443063985633742, 'y ty': 0.024201355785359608, 'y qop': 0.069823295273139, 'tx^2': -9.507076220830514, 'tx ty': -0.3980701633198789, 'tx qop': -0.04742639222342226, 'ty^2': -5.342167619183405, 'ty qop': 0.04842038611881145, 'qop^2': 0.2070268831284635, 'x^3': 1.5823479402461545e-07, 'x^2 y': -5.806838940825474e-07, 'x^2 tx': -0.00023418353598118923, 'x^2 ty': 0.0037081774556846224, 'x^2 qop': 0.01641641113222204, 'x y^2': 6.398758958085149e-08, 'x y tx': -0.002932641224303519, 'x y ty': -0.001396824762733282, 'x y qop': -0.020888196868450136, 'x tx^2': 0.09096908124129072, 'x tx ty': -2.939755357349759, 'x tx qop': -8.73057282483271, 'x ty^2': -0.15340975596199197, 'x ty qop': 9.249941815315987, 'x qop^2': 0.030205199863621846, 'y^3': 1.6478595155078324e-07, 'y^2 tx': 0.0013152209574444013, 'y^2 ty': -0.000257931039205234, 'y^2 qop': -0.0057816482028933735, 'y tx^2': 2.685350530706497, 'y tx ty': 0.17814134491255038, 'y tx qop': 9.050929476915277, 'y ty^2': 0.10064678584510746, 'y ty qop': 4.6142369495362185, 'y qop^2': -0.00046589334175238057, 'tx^3': -0.6242025517665986, 'tx^2 ty': -0.017658603327465147, 'tx^2 qop': -0.022216794668845363, 'tx ty^2': -0.01024816705930792, 'tx ty qop': 0.024042119917448937, 'tx qop^2': 6.093129132646114e-05, 'ty^3': 0.09834545208780196, 'ty^2 qop': 0.011664187426493774, 'ty qop^2': -2.1825340747940462e-05, 'qop^3': -1.559907925017188e-06, 'x^4': -2.9483981922595603e-09, 'x^3 y': -6.13444928188045e-09, 'x^3 tx': 7.101384723817716e-06, 'x^3 ty': 7.16725431293419e-06, 'x^3 qop': 4.00953960828232e-05, 'x^2 y^2': 1.0679747086683733e-08, 'x^2 y tx': 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2.17774577667171e-07, 'y qop^4': -5.1794510300353154e-09, 'tx^5': -0.3290348306030112, 'tx^4 ty': -0.004713501192249353, 'tx^4 qop': 0.00039080511691431735, 'tx^3 ty^2': -0.02293656439194589, 'tx^3 ty qop': 1.795982117437259e-05, 'tx^3 qop^2': 1.3703545654537173e-06, 'tx^2 ty^3': -0.0016697750693905097, 'tx^2 ty^2 qop': 2.67609154249412e-05, 'tx^2 ty qop^2': -3.12791956432394e-08, 'tx^2 qop^3': -1.1594178303086993e-08, 'tx ty^4': -0.005076176679563355, 'tx ty^3 qop': 1.1834454126493736e-05, 'tx ty^2 qop^2': 1.9879319916637808e-07, 'tx ty qop^3': 4.055187311820455e-09, 'tx qop^4': 4.214995181248244e-11, 'ty^5': 0.010165548951092954, 'ty^4 qop': -1.6822916965291464e-05, 'ty^3 qop^2': -4.4801213131046415e-07, 'ty^2 qop^3': 7.260260169082666e-10, 'ty qop^4': -1.2734042962503632e-11, 'qop^5': -3.472724885502462e-13}\n",
      "r2 score= -0.008270330873300091\n",
      "RMSE = 0.10823208615961777\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "    {\n",
    "        \"rad_length_frac\": rad_length_frac,\n",
    "        \"x\": x,\n",
    "        \"y\": y,\n",
    "        \"tx\": slopex,\n",
    "        \"ty\": slopey,\n",
    "        \"qop\": qop,\n",
    "    }\n",
    ")\n",
    "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",
    ")\n",
    "\n",
    "nbins = 100\n",
    "vmax = 100\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, 0.5], [0, 0.5]],\n",
    ")\n",
    "plt.plot([0, 0.5], [0, 0.5], 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",
    "\n",
    "plt.colorbar(a0[3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 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=(15, 6))\n",
    "\n",
    "# ax0.set_aspect(\"equal\")\n",
    "\n",
    "a0 = ax0.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, 0.5], [0, 0.5]],\n",
    ")\n",
    "ax0.plot([0, 0.5], [0, 0.5], marker=\"\", alpha=0.8)\n",
    "ax0.set_box_aspect(1)\n",
    "ax0.set_xlabel(f\"True $x/X_0$\")\n",
    "ax0.set_ylabel(f\"Predicted $x/X_0$\")\n",
    "ax0.set_title(f\"found rad_length_frac\")\n",
    "plt.colorbar(a0[3], ax=ax0)\n",
    "\n",
    "ax1.hist(\n",
    "    xx0_test,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    color=\"darkorange\",\n",
    "    histtype=\"bar\",\n",
    "    label=\"test\",\n",
    "    range=[0, 0.5],\n",
    ")\n",
    "ax1.hist(\n",
    "    xx0_predicted,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    color=\"blue\",\n",
    "    histtype=\"bar\",\n",
    "    label=\"predicted\",\n",
    "    range=[0, 0.5],\n",
    ")\n",
    "ax1.set_xlim(0, 0.5)\n",
    "ax1.set_title(\"radiation length fraction endVelo\")\n",
    "ax1.set_xlabel(f\"$x/X_0$\")\n",
    "ax1.set_ylabel(\"a.u.\")\n",
    "ax1.set_box_aspect(1)\n",
    "\n",
    "ax1.legend()\n",
    "\n",
    "# plt.gca().set_aspect(\"equal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
    "    xx0_test,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    color=\"darkorange\",\n",
    "    histtype=\"bar\",\n",
    "    label=\"test\",\n",
    "    range=[0, 0.5],\n",
    ")\n",
    "plt.hist(\n",
    "    xx0_predicted,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    color=\"blue\",\n",
    "    histtype=\"bar\",\n",
    "    label=\"predicted\",\n",
    "    range=[0, 0.5],\n",
    ")\n",
    "plt.xlim(0, 0.5)\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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameterisation for rad_length_frac:\n",
      "intercept= 0.0\n",
      "coef= {'1': 0.2484410418213911, 'x': -0.0007601095488043627, 'y': 0.0010569724392146917, 'tx': 0.6185505303064777, 'ty': -0.9394058560136732, 'qop': -9.741031889614183, 'x^2': -0.00016580416280366622, 'x y': 5.149038989659081e-05, 'x tx': 0.22996768886351043, 'x ty': -0.043161009059129354, 'x qop': -0.21658279194428842, 'y^2': 3.9826067539320166e-05, 'y tx': -0.033498957247677735, 'y ty': -0.08085122767618998, 'y qop': 0.06428923004582791, 'tx^2': -83.06687438225835, 'tx ty': 28.76266798578089, 'tx qop': -0.32072666519746007, 'ty^2': 32.80290436519906, 'ty qop': 0.29785759094660047, 'qop^2': 0.7177557091128425, 'x^3': -1.037888276319177e-06, 'x^2 y': 5.744977724613286e-07, 'x^2 tx': 0.0016261562680787358, 'x^2 ty': 0.00819223051446815, 'x^2 qop': 0.014940216048602184, 'x y^2': 1.55836456652794e-06, 'x y tx': -0.009042353485603404, 'x y ty': 0.002769481233443616, 'x y qop': 0.007035099510620806, 'x tx^2': -0.623629094925692, 'x tx ty': -0.5792857627094614, 'x tx qop': 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4.620362246071652e-05, 'y^3 ty qop': 0.013501593121770603, 'y^3 qop^2': 0.00013943149170339843, 'y^2 tx^3': -1.930662753166049, 'y^2 tx^2 ty': -0.6244835760091016, 'y^2 tx^2 qop': 9.706592754727314, 'y^2 tx ty^2': 1.567415053011399, 'y^2 tx ty qop': -1.8692848199146312, 'y^2 tx qop^2': 0.2576730572143735, 'y^2 ty^3': -0.02555256934016916, 'y^2 ty^2 qop': -5.570585900979827, 'y^2 ty qop^2': 0.4524728269219666, 'y^2 qop^3': -0.0012165533583740602, 'y tx^4': -1.705633938705921, 'y tx^3 ty': 0.9697939146444974, 'y tx^3 qop': 0.01417529417479696, 'y tx^2 ty^2': 0.24892206316288593, 'y tx^2 ty qop': 0.02112066742630421, 'y tx^2 qop^2': 0.001983588330826661, 'y tx ty^3': 1.0027301725129028, 'y tx ty^2 qop': -0.019989975028401157, 'y tx ty qop^2': 0.000703255023993435, 'y tx qop^3': -1.7780249221490402e-05, 'y ty^4': 5.039018301955912, 'y ty^3 qop': -0.03181545636834986, 'y ty^2 qop^2': 0.0013016040850667482, 'y ty qop^3': -4.788925524502292e-06, 'y qop^4': 8.952440180633341e-08, 'tx^5': 0.009735740982675399, 'tx^4 ty': -0.013441638488465971, 'tx^4 qop': 0.00015663907591491764, 'tx^3 ty^2': 0.008836930257445317, 'tx^3 ty qop': 4.602358365785842e-05, 'tx^3 qop^2': -1.2528198912759552e-06, 'tx^2 ty^3': 8.384431822878086e-05, 'tx^2 ty^2 qop': 3.2449454369351206e-05, 'tx^2 ty qop^2': 3.870208943851117e-06, 'tx^2 qop^3': 1.1062889336368561e-08, 'tx ty^4': 0.008284216728547011, 'tx ty^3 qop': -8.101830871580909e-05, 'tx ty^2 qop^2': 1.3999083905485367e-06, 'tx ty qop^3': -2.8838718241492492e-08, 'tx qop^4': -7.302022445954643e-11, 'ty^5': 0.033986172363938666, 'ty^4 qop': -0.00011487779463878022, 'ty^3 qop^2': 2.8267877400850516e-06, 'ty^2 qop^3': -1.0303917259481338e-08, 'ty qop^4': 1.343104287668482e-10, 'qop^5': -5.690015320531571e-16}\n",
      "r2 score= 0.01281806793978646\n",
      "RMSE = 0.2644569540509028\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rad_length_frac = lost[\"rad_length_frac\"]\n",
    "# @ z = 9400.mm or 770.mm\n",
    "state = 1\n",
    "\n",
    "if state == 1:\n",
    "    slopex = lost[\"ideal_state_770_tx\"]\n",
    "    slopey = lost[\"ideal_state_770_ty\"]\n",
    "    x = lost[\"ideal_state_770_x\"]\n",
    "    y = lost[\"ideal_state_770_y\"]\n",
    "    qop = lost[\"ideal_state_770_qop\"]\n",
    "elif state == 2:\n",
    "    slopex = lost[\"ideal_state_9410_tx\"]\n",
    "    slopey = lost[\"ideal_state_9410_ty\"]\n",
    "    x = lost[\"ideal_state_9410_x\"]\n",
    "    y = lost[\"ideal_state_9410_y\"]\n",
    "    qop = lost[\"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",
    "})\n",
    "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",
    ")\n",
    "\n",
    "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 * stretch_factor,\n",
    "    range=[[-0.1, 1.0], [-0.1, 1.0]],\n",
    ")\n",
    "plt.plot([-0.1, 1.0], [-0.1, 1.0], marker=\"\", alpha=0.8)\n",
    "plt.xlabel(f\"True $x/X_0$\")\n",
    "plt.ylabel(f\"Predicted $x/X_0$\")\n",
    "plt.title(f\"lost 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": 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": 20,
   "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": 21,
   "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>0.96215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.746800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7263</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.381400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3813</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>0.743500</td>\n",
       "      <td>0.37900</td>\n",
       "      <td>0.11090</td>\n",
       "      <td>1.6273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.380200</td>\n",
       "      <td>0.1115</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.38025</td>\n",
       "      <td>1.1143</td>\n",
       "      <td>0.7429</td>\n",
       "      <td>0.1109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.378400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.38280</td>\n",
       "      <td>0.33605</td>\n",
       "      <td>0.110300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.11870</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.23330</td>\n",
       "      <td>1.479350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1458</td>\n",
       "      <td>0.39860</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.39800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7175</td>\n",
       "      <td>0.7303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.243900</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>0.09270</td>\n",
       "      <td>0.09775</td>\n",
       "      <td>0.099525</td>\n",
       "      <td>0.09825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0926</td>\n",
       "      <td>0.09710</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0934</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.09000</td>\n",
       "      <td>0.1005</td>\n",
       "      <td>0.09510</td>\n",
       "      <td>0.1005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0960</td>\n",
       "      <td>0.09185</td>\n",
       "      <td>0.094067</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>0.098700</td>\n",
       "      <td>0.09340</td>\n",
       "      <td>0.09075</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.08810</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0912</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.09350</td>\n",
       "      <td>0.0962</td>\n",
       "      <td>0.0971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.09540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.8</th>\n",
       "      <td>0.0922</td>\n",
       "      <td>0.09230</td>\n",
       "      <td>0.09660</td>\n",
       "      <td>0.089067</td>\n",
       "      <td>0.09260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0802</td>\n",
       "      <td>0.09195</td>\n",
       "      <td>0.090867</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.07670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.08775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>0.08030</td>\n",
       "      <td>0.100300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.09080</td>\n",
       "      <td>0.08490</td>\n",
       "      <td>0.076200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.08550</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.09840</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3018</td>\n",
       "      <td>0.08900</td>\n",
       "      <td>0.0889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.08160</td>\n",
       "      <td>0.088767</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.19140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.119100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.17850</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>0.10425</td>\n",
       "      <td>0.1109</td>\n",
       "      <td>0.08550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.081500</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  0.96215      NaN  0.746800      NaN      NaN  1.7263      NaN   \n",
       "3.4      NaN      NaN      NaN  0.743500  0.37900  0.11090  1.6273      NaN   \n",
       "3.5      NaN  0.38280  0.33605  0.110300      NaN  1.11870     NaN  0.23330   \n",
       "...      ...      ...      ...       ...      ...      ...     ...      ...   \n",
       "9.6      NaN  0.09270  0.09775  0.099525  0.09825      NaN  0.0926  0.09710   \n",
       "9.7      NaN      NaN      NaN  0.098700  0.09340  0.09075  0.0900  0.08810   \n",
       "9.8   0.0922  0.09230  0.09660  0.089067  0.09260      NaN  0.0802  0.09195   \n",
       "9.9      NaN  0.09080  0.08490  0.076200      NaN  0.08550  0.0900  0.09840   \n",
       "10.0     NaN  0.19140      NaN  0.119100      NaN  0.17850     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   0.381400     NaN  ...  0.3813      NaN     NaN      NaN     NaN     NaN   \n",
       "3.4   0.380200  0.1115  ...     NaN      NaN     NaN  0.38025  1.1143  0.7429   \n",
       "3.5   1.479350     NaN  ...  0.1458  0.39860     NaN  0.39800     NaN  0.7175   \n",
       "...        ...     ...  ...     ...      ...     ...      ...     ...     ...   \n",
       "9.6        NaN  0.0934  ...     NaN  0.09000  0.1005  0.09510  0.1005     NaN   \n",
       "9.7        NaN     NaN  ...  0.0912      NaN     NaN  0.09350  0.0962  0.0971   \n",
       "9.8   0.090867     NaN  ...     NaN  0.07670     NaN  0.08775     NaN     NaN   \n",
       "9.9   0.090000     NaN  ...  0.3018  0.08900  0.0889      NaN  0.0900     NaN   \n",
       "10.0       NaN     NaN  ...     NaN  0.10425  0.1109  0.08550     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   0.1109      NaN  0.378400    NaN  \n",
       "3.5   0.7303      NaN  0.243900    NaN  \n",
       "...      ...      ...       ...    ...  \n",
       "9.6   0.0960  0.09185  0.094067    NaN  \n",
       "9.7      NaN  0.09540       NaN    NaN  \n",
       "9.8   0.0920  0.08030  0.100300    NaN  \n",
       "9.9      NaN  0.08160  0.088767    NaN  \n",
       "10.0     NaN      NaN  0.081500    NaN  \n",
       "\n",
       "[70 rows x 181 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivoted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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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],\n",
    "              [-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 EndVelo to EndUT $x/X_0$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # make these smaller to increase the resolution\n",
    "# dy, dx = 0.1, 1.0\n",
    "\n",
    "# # generate 2 2d grids for the x & y bounds\n",
    "# y, x = np.mgrid[slice(1.5, 5 + dy, dy), slice(-180, 180 + dx, dx)]\n",
    "\n",
    "# plt.pcolormesh(x, y, df_pivoted, cmap=colormaps[\"jet\"], vmax=0.7)\n",
    "\n",
    "# plt.colorbar()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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