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Projektpraktikum/notebooks/trackinglosses_photons.ipynb

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{
"cells": [
{
"cell_type": "code",
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"execution_count": 2,
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"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",
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"import mplhep\n",
"mplhep.style.use([\"LHCbTex2\"])\n",
"\n",
"plt.rcParams[\"savefig.dpi\"] = 600\n",
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"%matplotlib inline"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [],
"source": [
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"file = uproot.open(\n",
" \"/work/cetin/Projektpraktikum/trackinglosses_B_photon_cuts.root\")\n",
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"\n",
"# selektiere nur elektronen von B->K*ee\n",
"allcolumns = []\n",
"for i in range(11):\n",
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" allcolumns.append(file[\"Tree\" + str(i)].arrays())"
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]
},
{
"cell_type": "code",
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"execution_count": 4,
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"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...'>"
]
},
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"execution_count": 4,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"allcolumns[1][1]"
]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [],
"source": [
"def cutdict():\n",
" basedict = {\n",
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" \"0\": {},\n",
" \"1\": {},\n",
" \"2\": {},\n",
" \"3\": {},\n",
" \"4\": {},\n",
" \"5\": {},\n",
" \"6\": {},\n",
" \"7\": {},\n",
" \"8\": {},\n",
" \"9\": {},\n",
" \"10\": {},\n",
" }\n",
"\n",
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" 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",
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"\n",
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" return basedict\n",
"\n",
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"\n",
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"Cuts = cutdict()"
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [],
"source": [
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"# 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",
thesis
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" allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"lost\"]\n",
" )\n",
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" energy_emissions.field(\"rad_length_frac\").real(\n",
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" allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"rad_length_frac\"]\n",
" )\n",
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" energy_emissions.field(\"energy\").real(\n",
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" allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"energy\"]\n",
" )\n",
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"\n",
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" 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",
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"\n",
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" for jphoton in range(\n",
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" ak.num(\n",
" allcolumns[jcut][jelec][Cuts[str(jcut)] + \"Cut_\" + \"brem_photons_pe\"],\n",
" axis=0,\n",
" )\n",
" ):\n",
" if (\n",
" allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"brem_vtx_z\", jphoton]\n",
" <= 770\n",
" ):\n",
" tmp_velo += allcolumns[jcut][\n",
" jelec, Cuts[str(jcut)] + \"Cut_\" + \"brem_photons_pe\", jphoton\n",
" ]\n",
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" tmp_velo_length += 1\n",
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" elif (\n",
" allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"brem_vtx_z\", jphoton]\n",
" > 770\n",
" ) and (\n",
" allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"brem_vtx_z\", jphoton]\n",
" <= 2700\n",
" ):\n",
" tmp_richut += allcolumns[jcut][\n",
" jelec, Cuts[str(jcut)] + \"Cut_\" + \"brem_photons_pe\", jphoton\n",
" ]\n",
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" tmp_richut_length += 1\n",
" else:\n",
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" tmp_neither += allcolumns[jcut][\n",
" jelec, Cuts[str(jcut)] + \"Cut_\" + \"brem_photons_pe\", jphoton\n",
" ]\n",
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" tmp_neither_length += 1\n",
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"\n",
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" energy_emissions.field(\"velo_length\").integer(tmp_velo_length)\n",
" energy_emissions.field(\"velo\").real(tmp_velo)\n",
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"\n",
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" energy_emissions.field(\"rich_length\").integer(tmp_richut_length)\n",
" energy_emissions.field(\"rich\").real(tmp_richut)\n",
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"\n",
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" energy_emissions.field(\"neither_length\").integer(tmp_neither_length)\n",
" energy_emissions.field(\"downstream\").real(tmp_neither)\n",
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"\n",
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" energy_emissions.field(\"photon_length\").integer(tmp_richut_length + tmp_velo_length)\n",
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"\n",
thesis
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" if (\n",
" (tmp_velo == 0)\n",
" and (tmp_richut == 0)\n",
" or (\n",
" allcolumns[jcut][jelec, Cuts[str(jcut)] + \"Cut_\" + \"energy\"] - tmp_velo\n",
" < 3000\n",
" )\n",
" ):\n",
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" 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)"
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]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
"outputs": [
{
"data": {
thesis
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"image/png": "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"text/plain": [
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"<Figure size 1200x900 with 1 Axes>"
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]
},
"metadata": {},
"output_type": "display_data"
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},
{
"data": {
"text/plain": [
"<Figure size 1200x900 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
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}
],
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"source": [
"velo_found = ak.to_numpy(\n",
" energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\"velo\"]\n",
")\n",
"rich_found = ak.to_numpy(\n",
" energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"]\n",
")\n",
"energy_found = ak.to_numpy(\n",
" energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
" \"energy\"\n",
" ]\n",
")\n",
"\n",
"velo_lost = ak.to_numpy(\n",
" energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\"velo\"]\n",
")\n",
"rich_lost = ak.to_numpy(\n",
" energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"]\n",
")\n",
"energy_lost = ak.to_numpy(\n",
" energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
" \"energy\"\n",
" ]\n",
")\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",
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" color=\"#2A9D8F\", # \"#107E7D\",\n",
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" label=\"found\",\n",
" range=[-xlim, xlim],\n",
")\n",
"# plt.xlim(-20000, 20000)\n",
"# plt.yscale(\"log\")\n",
"# plt.title(\"emitted energy difference\")\n",
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"plt.xlabel(r\"$\\Delta E_{VELO} - \\Delta E_{RICH1+UT}$ [MeV]\")\n",
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"plt.ylabel(\"a.u.\")\n",
"plt.legend(title=\"LHCb Simulation\")\n",
"plt.show()\n",
thesis
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"plt.savefig(\n",
" \"/work/cetin/Projektpraktikum/thesis/emitted_energy_difference.pdf\", format=\"PDF\"\n",
")"
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]
},
{
"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": {},
rad length
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"source": [
"nbins = 6\n",
thesis
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"quality_cut = electrons[jcut].quality != -1\n",
rad length
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"\n",
"### all split in velo and rich\n",
"\n",
thesis
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"fig, axs = plt.subplots(3, 3, figsize=(15, 6))\n",
rad length
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"ax = axs.ravel()\n",
thesis
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"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",
rad length
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"plt.suptitle(\"number of photons in velo and rich\")\n",
"plt.legend()\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"### found\n",
"\n",
thesis
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"fig, axs = plt.subplots(3, 3, figsize=(15, 6))\n",
rad length
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"ax = axs.ravel()\n",
thesis
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"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",
rad length
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"plt.suptitle(\"number of photons of found electrons\")\n",
"plt.legend()\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
thesis
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"### lost\n",
rad length
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"\n",
thesis
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"fig, axs = plt.subplots(3, 3, figsize=(15, 6))\n",
rad length
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"ax = axs.ravel()\n",
thesis
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"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",
rad length
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"plt.suptitle(\"number of photons of lost electrons\")\n",
"plt.legend()\n",
"plt.tight_layout()\n",
thesis
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"plt.show()\n",
rad length
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"quality_cut = electrons[jcut].quality != -1\n",
thesis
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"\n",
rad length
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"### all split in lost and found\n",
"\n",
thesis
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"fig, axs = plt.subplots(3, 3, figsize=(15, 6))\n",
rad length
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"ax = axs.ravel()\n",
thesis
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"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",
rad length
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"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": [],
thesis
2024-02-04 16:56:05 +01:00
"source": []
rad length
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},
{
"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
}
Reference in New Issue Copy Permalink