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Projektpraktikum/notebooks/trackinglosses_photons.ipynb

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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": {
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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": []
}
],
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