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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 mpl_toolkits import mplot3d\n", "import awkward as ak\n", "from scipy.optimize import curve_fit\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": [ "file = uproot.open(\n", " \"/work/cetin/Projektpraktikum/trackinglosses_B_photon_cuts.root\")\n", "\n", "# selektiere nur elektronen von B->K*ee\n", "allcolumns = []\n", "for i in range(11):\n", " allcolumns.append(file[\"Tree\" + str(i)].arrays())" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre>{oneCut_event_id: 1,\n", " oneCut_lost: False,\n", " oneCut_rad_length_frac: 0.148,\n", " oneCut_energy: 1.28e+04,\n", " noneCut_brem_photons_pe: 1,\n", " oneCut_brem_photons_pe: [7.42e+03],\n", " noneCut_brem_vtx_x: 1,\n", " oneCut_brem_vtx_x: [-3.61],\n", " noneCut_brem_vtx_z: 1,\n", " oneCut_brem_vtx_z: [35.6],\n", " oneCut_photon_length: 1}\n", "------------------------------------------\n", "type: {\n", " oneCut_event_id: int64,\n", " oneCut_lost: bool,\n", " oneCut_rad_length_frac: float64,\n", " oneCut_energy: float64,\n", " noneCut_brem_photons_pe: int32,\n", " oneCut_brem_photons_pe: var * float64,\n", " noneCut_brem_vtx_x: int32,\n", " oneCut_brem_vtx_x: var * float64,\n", " noneCut_brem_vtx_z: int32,\n", " oneCut_brem_vtx_z: var * float64,\n", " oneCut_photon_length: int64\n", "}</pre>" ], "text/plain": [ "<Record {oneCut_event_id: 1, ...} type='{oneCut_event_id: int64, oneCut_los...'>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "allcolumns[1][1]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def cutdict():\n", " basedict = {\n", " \"0\": {},\n", " \"1\": {},\n", " \"2\": {},\n", " \"3\": {},\n", " \"4\": {},\n", " \"5\": {},\n", " \"6\": {},\n", " \"7\": {},\n", " \"8\": {},\n", " \"9\": {},\n", " \"10\": {},\n", " }\n", "\n", " basedict[\"0\"] = \"no\"\n", " basedict[\"1\"] = \"one\"\n", " basedict[\"2\"] = \"two\"\n", " basedict[\"3\"] = \"three\"\n", " basedict[\"4\"] = \"four\"\n", " basedict[\"5\"] = \"five\"\n", " basedict[\"6\"] = \"six\"\n", " basedict[\"7\"] = \"seven\"\n", " basedict[\"8\"] = \"eight\"\n", " basedict[\"9\"] = \"nine\"\n", " basedict[\"10\"] = \"ten\"\n", "\n", " return basedict\n", "\n", "\n", "Cuts = cutdict()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# electrons = []\n", "# for jcut in range(11):\n", "\n", "jcut = 4 # cut 0.2*E\n", "\n", "energy_emissions = ak.ArrayBuilder()\n", "\n", "for jelec in range(ak.num(allcolumns[jcut], axis=0)):\n", " energy_emissions.begin_record()\n", " energy_emissions.field(\"lost\").boolean(\n", " allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"lost\"])\n", " energy_emissions.field(\"rad_length_frac\").real(\n", " allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"rad_length_frac\"])\n", " energy_emissions.field(\"energy\").real(\n", " allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"energy\"])\n", "\n", " tmp_velo = 0\n", " tmp_richut = 0\n", " tmp_neither = 0\n", " tmp_velo_length = 0\n", " tmp_richut_length = 0\n", " tmp_neither_length = 0\n", "\n", " for jphoton in range(\n", " ak.num(\n", " allcolumns[jcut][jelec][Cuts[str(jcut)] + \"Cut_\" +\n", " \"brem_photons_pe\"],\n", " axis=0,\n", " )):\n", " if (allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"brem_vtx_z\",\n", " jphoton] <= 770):\n", " tmp_velo += allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" +\n", " \"brem_photons_pe\", jphoton]\n", " tmp_velo_length += 1\n", " elif (allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"brem_vtx_z\",\n", " jphoton]\n", " > 770) and (allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" +\n", " \"brem_vtx_z\", jphoton] <= 2700):\n", " tmp_richut += allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" +\n", " \"brem_photons_pe\", jphoton]\n", " tmp_richut_length += 1\n", " else:\n", " tmp_neither += allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" +\n", " \"brem_photons_pe\", jphoton]\n", " tmp_neither_length += 1\n", "\n", " energy_emissions.field(\"velo_length\").integer(tmp_velo_length)\n", " energy_emissions.field(\"velo\").real(tmp_velo)\n", "\n", " energy_emissions.field(\"rich_length\").integer(tmp_richut_length)\n", " energy_emissions.field(\"rich\").real(tmp_richut)\n", "\n", " energy_emissions.field(\"neither_length\").integer(tmp_neither_length)\n", " energy_emissions.field(\"downstream\").real(tmp_neither)\n", "\n", " energy_emissions.field(\"photon_length\").integer(tmp_richut_length +\n", " tmp_velo_length)\n", "\n", " if ((tmp_velo == 0) and (tmp_richut == 0)\n", " or (allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"energy\"] -\n", " tmp_velo < 3000)):\n", " energy_emissions.field(\"quality\").integer(0)\n", " else:\n", " energy_emissions.field(\"quality\").integer(1)\n", "\n", " energy_emissions.end_record()\n", "\n", "energy_emissions = ak.Array(energy_emissions)\n", "# electrons.append(energy_emissions)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1200x900 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "velo_found = ak.to_numpy(\n", " energy_emissions[(~energy_emissions.lost)\n", " & (energy_emissions.quality == 1)][\"velo\"])\n", "rich_found = ak.to_numpy(\n", " energy_emissions[(~energy_emissions.lost)\n", " & (energy_emissions.quality == 1)][\"rich\"])\n", "energy_found = ak.to_numpy(\n", " energy_emissions[(~energy_emissions.lost)\n", " & (energy_emissions.quality == 1)][\"energy\"])\n", "\n", "velo_lost = ak.to_numpy(\n", " energy_emissions[(energy_emissions.lost)\n", " & (energy_emissions.quality == 1)][\"velo\"])\n", "rich_lost = ak.to_numpy(\n", " energy_emissions[(energy_emissions.lost)\n", " & (energy_emissions.quality == 1)][\"rich\"])\n", "energy_lost = ak.to_numpy(\n", " energy_emissions[(energy_emissions.lost)\n", " & (energy_emissions.quality == 1)][\"energy\"])\n", "\n", "diff_found = velo_found - rich_found\n", "diff_lost = velo_lost - rich_lost\n", "\n", "xlim = 20000\n", "nbins = 60\n", "\n", "plt.hist(\n", " diff_lost,\n", " bins=nbins,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"#F05342\",\n", " label=\"lost\",\n", " range=[-xlim, xlim],\n", ")\n", "plt.hist(\n", " diff_found,\n", " bins=nbins,\n", " density=True,\n", " alpha=0.5,\n", " histtype=\"bar\",\n", " color=\"#2A9D8F\", # \"#107E7D\",\n", " label=\"found\",\n", " range=[-xlim, xlim],\n", ")\n", "# plt.xlim(-20000, 20000)\n", "# plt.yscale(\"log\")\n", "# plt.title(\"emitted energy difference\")\n", "plt.xlabel(r\"$\\Delta E_{VELO} - \\Delta E_{RICH1+UT}$ [MeV]\")\n", "plt.ylabel(\"Number of Tracks (normalised)\")\n", "plt.legend(loc=\"best\")\n", "mplhep.lhcb.text(\"Simulation\")\n", "# plt.show()\n", "plt.savefig(\n", " \"/work/cetin/Projektpraktikum/thesis/emitted_energy_difference.pdf\",\n", " format=\"PDF\")" ] }, { "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": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "nbins = 6\n", "quality_cut = electrons[jcut].quality != -1\n", "\n", "### all split in velo and rich\n", "\n", "fig, axs = plt.subplots(3, 3, figsize=(15, 6))\n", "ax = axs.ravel()\n", "for jcut, ax in enumerate(ax):\n", "ax.hist(\n", "ak.to_numpy(electrons[jcut][quality_cut][\"velo_length\"]),\n", "bins=nbins,\n", "density=True,\n", "alpha=0.5,\n", "color=\"darkorange\",\n", "histtype=\"bar\",\n", "label=\"velo\",\n", "range=[0, nbins],\n", ")\n", "ax.hist(\n", "ak.to_numpy(electrons[jcut][quality_cut][\"rich_length\"]),\n", "bins=nbins,\n", "density=True,\n", "alpha=0.5,\n", "color=\"blue\",\n", "histtype=\"bar\",\n", "label=\"rich\",\n", "range=[0, nbins],\n", ")\n", "ax.set_xlim(0, nbins)\n", "ax.set_ylim(0, 1)\n", "ax.set_title(\"Photon Cut: \" + str(np.round(jcut \\* 0.05, 2)) + f\"$E_0$\")\n", "ax.set_xlabel(\"number of photons\")\n", "ax.set_ylabel(\"a.u.\")\n", "plt.suptitle(\"number of photons in velo and rich\")\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "### found\n", "\n", "fig, axs = plt.subplots(3, 3, figsize=(15, 6))\n", "ax = axs.ravel()\n", "for jcut, ax in enumerate(ax):\n", "ax.hist(\n", "ak.to_numpy(\n", "electrons[jcut]~(electrons[jcut].lost) & quality_cut][\"velo_length\"]\n", "),\n", "bins=nbins,\n", "density=True,\n", "alpha=0.5,\n", "color=\"darkorange\",\n", "histtype=\"bar\",\n", "label=\"velo\",\n", "range=[0, nbins],\n", ")\n", "ax.hist(\n", "ak.to_numpy(\n", "electrons[jcut]~(electrons[jcut].lost) & quality_cut][\"rich_length\"]\n", "),\n", "bins=nbins,\n", "density=True,\n", "alpha=0.5,\n", "color=\"blue\",\n", "histtype=\"bar\",\n", "label=\"rich\",\n", "range=[0, nbins],\n", ")\n", "ax.set_xlim(0, nbins)\n", "ax.set_ylim(0, 1)\n", "ax.set_title(\"Photon Cut: \" + str(np.round(jcut \\* 0.05, 2)) + f\"$E_0$\")\n", "ax.set_xlabel(\"number of photons\")\n", "ax.set_ylabel(\"a.u.\")\n", "plt.suptitle(\"number of photons of found electrons\")\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "### lost\n", "\n", "fig, axs = plt.subplots(3, 3, figsize=(15, 6))\n", "ax = axs.ravel()\n", "for jcut, ax in enumerate(ax):\n", "ax.hist(\n", "ak.to_numpy(\n", "electrons[jcut](electrons[jcut].lost) & quality_cut][\"velo_length\"]\n", "),\n", "bins=nbins,\n", "density=True,\n", "alpha=0.5,\n", "color=\"darkorange\",\n", "histtype=\"bar\",\n", "label=\"velo\",\n", "range=[0, nbins],\n", ")\n", "ax.hist(\n", "ak.to_numpy(\n", "electrons[jcut](electrons[jcut].lost) & quality_cut][\"rich_length\"]\n", "),\n", "bins=nbins,\n", "density=True,\n", "alpha=0.5,\n", "color=\"blue\",\n", "histtype=\"bar\",\n", "label=\"rich\",\n", "range=[0, nbins],\n", ")\n", "ax.set_xlim(0, nbins)\n", "ax.set_ylim(0, 1)\n", "ax.set_title(\"Photon Cut: \" + str(np.round(jcut \\* 0.05, 2)) + f\"$E_0$\")\n", "ax.set_xlabel(\"number of photons\")\n", "ax.set_ylabel(\"a.u.\")\n", "plt.suptitle(\"number of photons of lost electrons\")\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()\n", "quality_cut = electrons[jcut].quality != -1\n", "\n", "### all split in lost and found\n", "\n", "fig, axs = plt.subplots(3, 3, figsize=(15, 6))\n", "ax = axs.ravel()\n", "for jcut, ax in enumerate(ax):\n", "ax.hist(\n", "ak.to_numpy(\n", "electrons[jcut](electrons[jcut].lost) & (quality_cut)][\"photon_length\"]\n", "),\n", "bins=10,\n", "density=True,\n", "alpha=0.5,\n", "color=\"darkorange\",\n", "histtype=\"bar\",\n", "label=\"lost\",\n", "range=[0, 10],\n", ")\n", "ax.hist(\n", "ak.to_numpy(\n", "electrons[jcut](~electrons[jcut].lost) & (quality_cut)][\"photon_length\"]\n", "),\n", "bins=10,\n", "density=True,\n", "alpha=0.5,\n", "color=\"blue\",\n", "histtype=\"bar\",\n", "label=\"found\",\n", "range=[0, 10],\n", ")\n", "ax.set_xlim(0, 10) # ax.set_ylim(0,1) # ax.set_yscale('log')\n", "ax.set_title(\"Photon Cut: \" + str(np.round(jcut \\* 0.05, 2)) + f\"$E_0$\")\n", "ax.set_xlabel(\"number of photons\")\n", "ax.set_ylabel(\"a.u.\")\n", "plt.suptitle(\"number of photons in lost and found\")\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "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": "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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