Projektpraktikum/B_rework.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\t\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "import itertools\n",
    "import awkward as ak\n",
    "from scipy.optimize import curve_fit\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10522"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "\n",
    "#selektiere nur elektronen von B->K*ee und nur solche mit einem momentum von ueber 5 GeV \n",
    "allcolumns = file.arrays()\n",
    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 9056\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 1466\n",
    "\n",
    "ak.num(found, axis=0) + ak.num(lost, axis=0)\n",
    "#ak.count(found, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eff all =  0.8606728758791105 +/- 0.003375885792719708\n"
     ]
    }
   ],
   "source": [
    "def t_eff(found, lost, axis = 0):\n",
    "    sel = ak.num(found, axis=axis)\n",
    "    des = ak.num(lost, axis=axis)\n",
    "    return sel/(sel + des)\n",
    "\n",
    "def eff_err(found, lost):\n",
    "    n_f = ak.num(found, axis=0)\n",
    "    n_all = ak.num(found, axis=0) + ak.num(lost,axis=0)\n",
    "    return 1/n_all * np.sqrt(np.abs(n_f*(1-n_f/n_all)))\n",
    "\n",
    "\n",
    "print(\"eff all = \", t_eff(found, lost), \"+/-\", eff_err(found, lost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample size:  32\n",
      "eff (cutoff =  0 ) =  0.96875 +/- 0.030757843257858637\n",
      "sample size:  32\n",
      "eff (cutoff =  100 ) =  0.96875 +/- 0.030757843257858637\n",
      "sample size:  65\n",
      "eff (cutoff =  200 ) =  0.9692307692307692 +/- 0.021419791425796485\n",
      "sample size:  129\n",
      "eff (cutoff =  300 ) =  0.9457364341085271 +/- 0.019945474377053428\n",
      "sample size:  169\n",
      "eff (cutoff =  400 ) =  0.9408284023668639 +/- 0.018149660480088193\n",
      "sample size:  227\n",
      "eff (cutoff =  500 ) =  0.920704845814978 +/- 0.017933729291194522\n",
      "\n",
      "cutoff energy = 350MeV, sample size: 150\n",
      "eff =  0.9533333333333334 +/- 0.017221863795553384\n"
     ]
    }
   ],
   "source": [
    "#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n",
    "#von energie der photonen abmachen\n",
    "#scan ab welcher energie der photonen die effizienz abfällt\n",
    "\n",
    "#abhängigkeit vom ort der emission untersuchen   <- noch nicht gemacht\n",
    "\n",
    "\n",
    "\n",
    "#idea: we make an event cut st all events that contain a photon of energy > cutoff_energy are not included\n",
    "\"\"\"\n",
    "ph_e = found[\"brem_photons_pe\"]\n",
    "event_cut = ak.all(ph_e<cutoff_energy,axis=1)\n",
    "ph_e = ph_e[event_cut]\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for cutoff_energy in range(0,550,100):\n",
    "\tnobrem_f = found[ak.all(found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "\tnobrem_l = lost[ak.all(lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "\tprint(\"sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
    "\tprint(\"eff (cutoff = \",str(cutoff_energy),\") = \",str(t_eff(nobrem_f,nobrem_l)), \"+/-\", eff_err(nobrem_f, nobrem_l))\n",
    "\n",
    "\"\"\"\n",
    "we see that a cutoff energy of 350MeV is ideal because the efficiency drops significantly for higher values\n",
    "\"\"\"\n",
    "cutoff_energy = 350.0 #MeV\n",
    "\n",
    "\"\"\"\n",
    "better statistics: cutoff=350MeV - sample size: 150 events and efficiency=0.9533\n",
    "\"\"\"\n",
    "nobrem_found = found[ak.all(found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "nobrem_lost = lost[ak.all(lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "\n",
    "print(\"\\ncutoff energy = 350MeV, sample size:\",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n",
    "print(\"eff = \",t_eff(nobrem_found, nobrem_lost), \"+/-\", eff_err(nobrem_found, nobrem_lost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eff =  0.8593328191284226 +/- 0.003413861022128076\n"
     ]
    }
   ],
   "source": [
    "#wie viel energie relativ zur anfangsenergie verlieren die elektronen durch bremstrahlung und hat das einen einfluss darauf ob wir sie finden oder nicht?\n",
    "#if any photon of an electron has an energy higher the cutoff then it is included\n",
    "cutoff_energy=350\n",
    "\n",
    "brem_found = found[ak.any(found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n",
    "energy_found = ak.to_numpy(brem_found[\"energy\"])\n",
    "eph_found = ak.to_numpy(ak.sum(brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "energyloss_found = eph_found/energy_found\n",
    "\n",
    "brem_lost = lost[ak.any(lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n",
    "energy_lost = ak.to_numpy(brem_lost[\"energy\"])\n",
    "eph_lost = ak.to_numpy(ak.sum(brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "energyloss_lost = eph_lost/energy_lost\n",
    "\n",
    "print(\"eff = \", t_eff(brem_found,brem_lost), \"+/-\", eff_err(brem_found, brem_lost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean energyloss relative to initial energy (found):  0.6551043170507098\n",
      "mean energyloss relative to initial energy (lost):  0.8273131179948844\n"
     ]
    }
   ],
   "source": [
    "mean_energyloss_found = ak.mean(energyloss_found)\n",
    "mean_energyloss_lost = ak.mean(energyloss_lost)\n",
    "print(\"mean energyloss relative to initial energy (found): \", mean_energyloss_found)\n",
    "print(\"mean energyloss relative to initial energy (lost): \", mean_energyloss_lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#in abhängigkeit von der energie der elektronen\n",
    "plt.hist(energyloss_lost, bins=200, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
    "plt.hist(energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
    "plt.xticks(np.arange(0,1.1,0.1), minor=True,)\n",
    "plt.yticks(np.arange(0,10,1), minor=True)\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"counts (normed)\")\n",
    "plt.title(r'$E_{ph}/E_0$')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#energyloss in abh von der energie der elektronen\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0=ax0.hist2d(energyloss_found, energy_found, bins=200, cmap=plt.cm.jet, cmin=1, vmax=15)\n",
    "ax0.set_xlabel(\"energyloss\")\n",
    "ax0.set_ylabel(r\"$E_0$\")\n",
    "ax0.set_title(\"found energyloss wrt electron energy\")\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1=ax1.hist2d(energyloss_lost, energy_lost, bins=200, cmap=plt.cm.jet, cmin=1, vmax=15) \n",
    "ax1.set_xlabel(\"energyloss\")\n",
    "ax1.set_ylabel(r\"$E_0$\")\n",
    "ax1.set_title(\"lost energyloss wrt electron energy\")\n",
    "plt.colorbar(a1[3],ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ist die shape der teilspur im scifi anders? (koenntest du zum beispiel durch vergleich der verteilungen der fit parameter studieren,\n",
    "#in meiner thesis findest du das fitmodell -- ist einfach ein polynom dritten grades)\n",
    "z_ref=8520 #mm\n",
    "\n",
    "def scifi_track(z, a, b, c, d):\n",
    "    return a + b*(z-z_ref) + c*(z-z_ref)**2 + d*(z-z_ref)**3\n",
    "\n",
    "def z_mag(xv, zv, tx, a, b):\n",
    "    \"\"\" optical centre of the magnet is defined as the intersection between the trajectory tangents before and after the magnet\n",
    "\n",
    "    Args:\n",
    "        xv (double): velo x track\n",
    "        zv (double): velo z track\n",
    "        tx (double): velo x slope\n",
    "        a (double): ax parameter of track fit\n",
    "        b (double): bx parameter of track fit\n",
    "\n",
    "    Returns:\n",
    "        double: z_mag\n",
    "    \"\"\"\n",
    "    return (xv-tx*zv-a+b*z_ref)/(b-tx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "scifi_found = found[found[\"scifi_hit_pos_x_length\"]>3]\n",
    "scifi_lost = lost[lost[\"scifi_hit_pos_x_length\"]>3]\n",
    "#should be fulfilled by all candidates\n",
    "\n",
    "scifi_x_found = scifi_found[\"scifi_hit_pos_x\"]\n",
    "scifi_z_found = scifi_found[\"scifi_hit_pos_z\"]\n",
    "\n",
    "tx_found = scifi_found[\"velo_track_tx\"]\n",
    "\n",
    "scifi_x_lost = scifi_lost[\"scifi_hit_pos_x\"]\n",
    "scifi_z_lost = scifi_lost[\"scifi_hit_pos_z\"]\n",
    "\n",
    "tx_lost = scifi_lost[\"velo_track_tx\"]\n",
    "\n",
    "xv_found = scifi_found[\"velo_track_x\"]\n",
    "zv_found = scifi_found[\"velo_track_z\"]\n",
    "\n",
    "xv_lost = scifi_lost[\"velo_track_x\"]\n",
    "zv_lost = scifi_lost[\"velo_track_z\"]\n",
    "\n",
    "\n",
    "\n",
    "sf_energy_found = ak.to_numpy(scifi_found[\"energy\"])\n",
    "sf_eph_found = ak.to_numpy(ak.sum(scifi_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "sf_vtx_type_found = scifi_found[\"all_endvtx_types\"]\n",
    "\n",
    "\n",
    "brem_vtx_type_found = scifi_found[scifi_found[\"endvtx_type\"]==101]\n",
    "\n",
    "sf_energy_lost = ak.to_numpy(scifi_lost[\"energy\"])\n",
    "sf_eph_lost = ak.to_numpy(ak.sum(scifi_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "sf_vtx_type_lost = scifi_lost[\"all_endvtx_types\"]\n",
    "brem_vtx_type_lost = scifi_lost[scifi_lost[\"endvtx_type\"]==101]\n",
    "\n",
    "\n",
    "\n",
    "#ak.num(scifi_found[\"energy\"], axis=0)\n",
    "#scifi_found.snapshot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>[101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 0]\n",
       "------------------\n",
       "type: 11 * float32</pre>"
      ],
      "text/plain": [
       "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.num(scifi_found[\"energy\"], axis=0)\n",
    "scifi_found[\"all_endvtx_types\"][1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "scifi_fitpars_found = ak.ArrayBuilder()\n",
    "vtx_types_found = ak.ArrayBuilder()\n",
    "\n",
    "for i in range(0,ak.num(scifi_found, axis=0)):\n",
    "    popt, pcov = curve_fit(scifi_track,ak.to_numpy(scifi_z_found[i,:]),ak.to_numpy(scifi_x_found[i,:]))\n",
    "    scifi_fitpars_found.begin_list()\n",
    "    scifi_fitpars_found.real(popt[0])\n",
    "    scifi_fitpars_found.real(popt[1])\n",
    "    scifi_fitpars_found.real(popt[2])\n",
    "    scifi_fitpars_found.real(popt[3])\n",
    "    #[:,4] -> energy \n",
    "    scifi_fitpars_found.real(sf_energy_found[i])\n",
    "    #[:,5] -> photon energy\n",
    "    scifi_fitpars_found.real(sf_eph_found[i])\n",
    "    scifi_fitpars_found.end_list()\n",
    "    \n",
    "    vtx_types_found.begin_list()\n",
    "    #[:,0] -> endvtx_type\n",
    "    vtx_types_found.extend(sf_vtx_type_found[i,:])\n",
    "    vtx_types_found.end_list()\n",
    "    \n",
    "\n",
    "scifi_fitpars_lost = ak.ArrayBuilder()\n",
    "vtx_types_lost = ak.ArrayBuilder()\n",
    "\n",
    "for i in range(0,ak.num(scifi_lost, axis=0)):\n",
    "    popt, pcov = curve_fit(scifi_track,ak.to_numpy(scifi_z_lost[i,:]),ak.to_numpy(scifi_x_lost[i,:]))\n",
    "    scifi_fitpars_lost.begin_list()\n",
    "    scifi_fitpars_lost.real(popt[0])\n",
    "    scifi_fitpars_lost.real(popt[1])\n",
    "    scifi_fitpars_lost.real(popt[2])\n",
    "    scifi_fitpars_lost.real(popt[3])\n",
    "    #[:,4] -> energy \n",
    "    scifi_fitpars_lost.real(sf_energy_lost[i])\n",
    "    #[:,5] -> photon energy\n",
    "    scifi_fitpars_lost.real(sf_eph_lost[i])\n",
    "    scifi_fitpars_lost.end_list()\n",
    "    \n",
    "    vtx_types_lost.begin_list()\n",
    "    #endvtx_type\n",
    "    vtx_types_lost.extend(sf_vtx_type_lost[i,:])\n",
    "    vtx_types_lost.end_list()\n",
    "    \n",
    "\n",
    "\n",
    "scifi_fitpars_lost = ak.to_numpy(scifi_fitpars_lost)\n",
    "scifi_fitpars_found = ak.to_numpy(scifi_fitpars_found)\n",
    "\n",
    "vtx_types_lost = ak.Array(vtx_types_lost)\n",
    "vtx_types_found = ak.Array(vtx_types_found)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>[101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 0]\n",
       "------------------\n",
       "type: 11 * float64</pre>"
      ],
      "text/plain": [
       "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vtx_types_found[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#b parameter des fits [:,1] hat für lost eine breitere Verteilung. Warum?\n",
    "#evtl multiple scattering candidates (lost); findet man einen gewissen endvtx_type (mult scattering)\n",
    "#steiler velo winkel (eta)? vertex type? evtl bremsstrahlung?\n",
    "\n",
    "#isolate b parameters for analysis\n",
    "b_found = scifi_fitpars_found[:,1]\n",
    "b_lost = scifi_fitpars_lost[:,1]\n",
    "\n",
    "brem_energy_found = scifi_fitpars_found[:,5]\n",
    "brem_energy_lost = scifi_fitpars_lost[:,5]\n",
    "\n",
    "\n",
    "bs_found, vtx_types_found = ak.broadcast_arrays(b_found, vtx_types_found)\n",
    "bs_found = ak.to_numpy(ak.ravel(bs_found))\n",
    "vtx_types_found = ak.to_numpy(ak.ravel(vtx_types_found))\n",
    "\n",
    "bs_lost, vtx_types_lost = ak.broadcast_arrays(b_lost, vtx_types_lost)\n",
    "bs_lost = ak.to_numpy(ak.ravel(bs_lost))\n",
    "vtx_types_lost = ak.to_numpy(ak.ravel(vtx_types_lost))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#Erste Annahme ist Bremsstrahlung\n",
    "\n",
    "fig = plt.figure(figsize=(18,6))\n",
    "axes = ImageGrid(fig, 111,  # similar to subplot(111)\n",
    "                 nrows_ncols=(1, 2),  # creates 2x2 grid of axes\n",
    "                 axes_pad=1,  # pad between axes in inch.\n",
    "                 cbar_mode=\"single\",\n",
    "                 cbar_location=\"right\",\n",
    "                 cbar_pad=0.1,\n",
    "                 aspect=False\n",
    "                )\n",
    "\n",
    "\n",
    "h0 = axes[0].hist2d(b_found, brem_energy_found, bins=200, cmap=plt.cm.jet, cmin=1,vmax=30)\n",
    "axes[0].set_xlim(-1,1)\n",
    "axes[0].set_xlabel(\"b parameter [mm]\")\n",
    "axes[0].set_ylabel(r\"$E_{ph}$\")\n",
    "axes[0].set_title(\"found photon energy wrt b parameter\")\n",
    "\n",
    "h1 = axes[1].hist2d(b_lost, brem_energy_lost, bins=200, cmap=plt.cm.jet, cmin=1,vmax=30)\n",
    "axes[1].set_xlim(-1,1)\n",
    "axes[1].set_xlabel(\"b parameter [mm]\")\n",
    "axes[1].set_ylabel(r\"$E_{ph}$\")\n",
    "axes[1].set_title(\"lost photon energy wrt b parameter\")\n",
    "\n",
    "fig.colorbar(h0[3], cax=axes.cbar_axes[0], orientation='vertical')\n",
    "\n",
    "\"\"\"\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n",
    "\n",
    "a0=ax[0].hist2d(bs_found, vtx_types_found, bins=110, density=True, cmap=plt.cm.jet, cmin=1e-20,vmax=2)\n",
    "ax[0].set_ylim(0,110)\n",
    "ax[0].set_xlim(-1,1)\n",
    "ax[0].set_xlabel(\"b\")\n",
    "ax[0].set_ylabel(\"endvtx id\")\n",
    "ax[0].set_title(\"found endvtx id wrt b parameter\")\n",
    "ax[0].set_yticks(np.arange(0,110,1),minor=True)\n",
    "\n",
    "a1=ax[1].hist2d(bs_lost, vtx_types_lost, bins=110, density=True, cmap=plt.cm.jet, cmin=1e-20,vmax=2)\n",
    "ax[1].set_ylim(0,110)\n",
    "ax[1].set_xlim(-1,1)\n",
    "ax[1].set_xlabel(\"b\")\n",
    "ax[1].set_ylabel(\"endvtx id\")\n",
    "ax[1].set_title(\"lost endvtx id wrt b paraneter\")\n",
    "ax[1].set_yticks(np.arange(0,110,1), minor=True)\n",
    "\n",
    "\"\"\"\n",
    "vtx_id: 101 - Bremsstrahlung\n",
    "B:\n",
    "wir können nicht wirklich sagen dass bei den lost teilchen jegliche endvertex types überwiegen, im gegensatz zu den found \n",
    "\"\"\"\n",
    "fig.colorbar(a0[3], ax=ax, orientation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, figsize=(15,10))\n",
    "\n",
    "ax0.hist(scifi_fitpars_found[:,0], bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$a_x$ found\")\n",
    "ax0.hist(scifi_fitpars_lost[:,0], bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$a_x$ lost\")\n",
    "ax0.set_xlabel(\"a\")\n",
    "ax0.set_ylabel(\"normed\")\n",
    "ax0.set_title(\"fitparameter a der scifi track\")\n",
    "ax0.legend()\n",
    "\n",
    "ax1.hist(scifi_fitpars_found[:,1], bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$b_x$ found\")\n",
    "ax1.hist(scifi_fitpars_lost[:,1], bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$b_x$ lost\")\n",
    "ax1.set_xticks(np.arange(-1,1,0.1),minor=True)\n",
    "ax1.set_xlabel(\"b\")\n",
    "ax1.set_ylabel(\"normed\")\n",
    "ax1.set_title(\"fitparameter b der scifi track\")\n",
    "ax1.legend()\n",
    "#evtl multiple scattering candidates (lost); findet man einen gewissen endvtx_type (mult scattering)\n",
    "#steiler velo winkel (eta)? vertex type? evtl bremsstrahlung?\n",
    "\n",
    "\n",
    "ax2.hist(scifi_fitpars_found[:,2], bins=500, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$c_x$ found\")\n",
    "ax2.hist(scifi_fitpars_lost[:,2], bins=500, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$c_x$ lost\")\n",
    "ax2.set_xlim([-3e-5,3e-5])\n",
    "ax2.set_xticks(np.arange(-3e-5,3.5e-5,1e-5),minor=False)\n",
    "ax2.set_xlabel(\"c\")\n",
    "ax2.set_ylabel(\"normed\")\n",
    "ax2.set_title(\"fitparameter c der scifi track\")\n",
    "ax2.legend()\n",
    "\n",
    "ax3.hist(scifi_fitpars_found[:,3], bins=500, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$d_x$ found\")\n",
    "ax3.hist(scifi_fitpars_lost[:,3], bins=500, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$d_x$ lost\")\n",
    "ax3.set(xlim=(-5e-8,5e-8))\n",
    "ax3.text(-4e-8,3e8,\"d negligible <1e-7\")\n",
    "ax3.set_xlabel(\"d\")\n",
    "ax3.set_ylabel(\"normed\")\n",
    "ax3.set_title(\"fitparameter d der scifi track\")\n",
    "ax3.legend()\n",
    "\n",
    "\"\"\"\n",
    "a_x: virtual hit on the reference plane\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env1",
   "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": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}