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{
"cells": [
{
"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
"outputs": [],
"source": [
"import uproot\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",
"%matplotlib inline"
]
},
{
"cell_type": "code",
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"execution_count": 41,
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"metadata": {},
"outputs": [],
"source": [
"file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
"#file = uproot.open(\"tracking_losses_ntuple_Dst0ToD0EE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
"\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)\n",
"#ak.count(found, axis=None)\n"
]
},
{
"cell_type": "code",
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"execution_count": 42,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8606728758791105"
]
},
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"execution_count": 42,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def t_eff(found, lost):\n",
" sel = found[\"energy\"]\n",
" des = lost[\"energy\"]\n",
" return ak.count(sel,axis=None)/(ak.count(sel,axis=None)+ak.count(des,axis=None))\n",
"\n",
"t_eff(found, lost)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
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"execution_count": 43,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.96875"
]
},
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"execution_count": 43,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n",
"nobrem_found = found[found[\"brem_photons_pe_length\"]==0]\n",
"nobrem_lost = lost[lost[\"brem_photons_pe_length\"]==0]\n",
"\n",
"\"\"\"\n",
"die effizienz mit der wir elektronen finden, die keine bremsstrahlung gemacht haben, ist gut mit 0.9688.\n",
"allerdings haben wir hier nur sehr wenige teilchen (<100)\n",
"\"\"\"\n",
"\n",
"t_eff(nobrem_found, nobrem_lost)\n"
]
},
{
"cell_type": "code",
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"execution_count": 44,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8603431839847474"
]
},
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"execution_count": 44,
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"metadata": {},
"output_type": "execute_result"
}
],
"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",
"brem_found = found[found[\"brem_photons_pe_length\"]!=0]\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",
"\n",
"brem_lost = lost[lost[\"brem_photons_pe_length\"]!=0]\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",
"t_eff(brem_found,brem_lost)"
]
},
{
"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean energyloss relative to initial energy (found): 0.6475128752780828\n",
"mean energyloss relative to initial energy (lost): 0.8241268441538472\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",
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"execution_count": 46,
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"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(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",
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"plt.xticks(np.arange(0,1.1,0.1), minor=True,)\n",
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"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",
"found: elektronen verlieren durchschnittlich 0.65 ihrer anfangsenergie durch bremsstrahlung\n",
"lost: elektronen verlieren durchschnittlich 0.82 ihrer anfangsenergie durch bremsstrahlung\n",
"\n",
"-> wir können sofort erkennen, dass verlorene elektronen im schnitt mehr energie durch bremsstrahlung verlieren als gefundene, \n",
"aber auch die rate der gefundenen elektronen steigt für raten nahe 1, wenn auch wesentlich schwächer als für verlorene elektronen.\n",
"die meisten verlorenen elektronen verlieren >0.8 ihrer anfangsenergie.\n",
"\"\"\"\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
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"execution_count": 47,
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"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",
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"execution_count": 48,
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"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",
"\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",
"#ak.num(scifi_found[\"energy\"], axis=0)\n",
"#scifi_found.snapshot()"
]
},
{
"cell_type": "code",
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"execution_count": 49,
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"metadata": {},
"outputs": [],
"source": [
"#tx_lost.show()"
]
},
{
"cell_type": "code",
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"execution_count": 50,
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"metadata": {},
"outputs": [],
"source": [
"scifi_fitpars_found = ak.ArrayBuilder()\n",
"\n",
"for i in range(0,ak.num(scifi_found[\"energy\"], 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",
" scifi_fitpars_found.end_list()\n",
"\n",
"scifi_fitpars_lost = ak.ArrayBuilder()\n",
"\n",
"for i in range(0,ak.num(scifi_lost[\"energy\"], 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",
" scifi_fitpars_lost.end_list()\n",
"\n",
"\n",
"scifi_fitpars_lost = scifi_fitpars_lost.to_numpy()\n",
"scifi_fitpars_found = scifi_fitpars_found.to_numpy()\n",
"\n",
"\n",
"\n",
"dtx_found = scifi_fitpars_found[:,1] - tx_found\n",
"dtx_lost = scifi_fitpars_lost[:,1] - tx_lost\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
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"execution_count": 51,
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"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_xlabel(\"b\")\n",
"ax1.set_ylabel(\"normed\")\n",
"ax1.set_title(\"fitparameter b der scifi track\")\n",
"ax1.legend()\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()"
]
},
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{
"cell_type": "code",
2023-09-25 11:39:04 +02:00
"execution_count": 52,
2023-09-25 11:05:16 +02:00
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"found\n",
"a = -0.6718207391527037\n",
"b = 0.0013778237292529144\n",
"c = 3.3126998287416195e-08\n",
"d = -1.0330674442255529e-10\n",
"lost\n",
"a = -36.98764338200992\n",
"b = -0.015685137956233643\n",
"c = -8.265859479503501e-07\n",
"d = -1.541510766903436e-11\n"
]
}
],
"source": [
"print(\"found\")\n",
"print(\"a = \", str(np.mean(scifi_fitpars_found[:,0])))\n",
"print(\"b = \", str(np.mean(scifi_fitpars_found[:,1])))\n",
"print(\"c = \", str(np.mean(scifi_fitpars_found[:,2])))\n",
"print(\"d = \", str(np.mean(scifi_fitpars_found[:,3])))\n",
"\n",
"print(\"lost\")\n",
"print(\"a = \", str(np.mean(scifi_fitpars_lost[:,0])))\n",
"print(\"b = \", str(np.mean(scifi_fitpars_lost[:,1])))\n",
"print(\"c = \", str(np.mean(scifi_fitpars_lost[:,2])))\n",
"print(\"d = \", str(np.mean(scifi_fitpars_lost[:,3])))"
]
},
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{
"cell_type": "code",
2023-09-25 11:39:04 +02:00
"execution_count": 53,
2023-09-22 09:21:27 +02:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-4.6785491318157854e-07"
]
},
2023-09-25 11:39:04 +02:00
"execution_count": 53,
2023-09-22 09:21:27 +02:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.min(scifi_fitpars_found[:,3])"
]
},
{
"cell_type": "code",
2023-09-25 11:39:04 +02:00
"execution_count": 54,
2023-09-22 09:21:27 +02:00
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(15,6))\n",
"\n",
"for i in range(0,ak.num(scifi_found[\"energy\"], axis=0)):\n",
" z_coord = np.linspace(scifi_z_found[i,0],12000,300)\n",
" fit = scifi_track(z_coord, *scifi_fitpars_found[i])\n",
" ax0.plot(z_coord, fit, \"-\", lw=0.5)\n",
" ax0.errorbar(ak.to_numpy(scifi_z_found[i,:]),ak.to_numpy(scifi_x_found[i,:]),fmt=\".\",ms=2)\n",
"\n",
"#ax0.legend()\n",
"ax0.set_xlabel(\"z [mm]\")\n",
"ax0.set_ylabel(\"x [mm]\")\n",
"ax0.set_title(\"found tracks of scifi hits\")\n",
"ax0.set(xlim=(7e3,12000), ylim=(-4000,4000))\n",
"ax0.grid()\n",
"\n",
"for i in range(0,ak.num(scifi_lost[\"energy\"], axis=0)):\n",
" z_coord = np.linspace(scifi_z_lost[i,0],12000,300)\n",
" fit = scifi_track(z_coord, *scifi_fitpars_lost[i])\n",
" ax1.plot(z_coord, fit, \"-\", lw=0.5)\n",
" ax1.errorbar(ak.to_numpy(scifi_z_lost[i,:]),ak.to_numpy(scifi_x_lost[i,:]),fmt=\".\",ms=2)\n",
"\n",
"#ax1.legend()\n",
"ax1.set_xlabel(\"z [mm]\")\n",
"ax1.set_ylabel(\"x [mm]\")\n",
"ax1.set_title(\"lost tracks of scifi hits\")\n",
"ax1.set(xlim=(7e3,12000), ylim=(-4000,4000))\n",
"ax1.grid()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
2023-09-25 11:39:04 +02:00
"execution_count": 55,
2023-09-22 09:21:27 +02:00
"metadata": {},
2023-09-25 11:39:04 +02:00
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"found \n",
"zmag = 5215.5640412342\n",
"lost \n",
"zmag = 5450.484726770035\n"
]
}
],
2023-09-22 09:21:27 +02:00
"source": [
"#vergleich der zmag werte\n",
"zmag_found = z_mag(xv_found, zv_found, tx_found, scifi_fitpars_found[:,0], scifi_fitpars_found[:,1])\n",
"zmag_lost = z_mag(xv_lost, zv_lost, tx_lost, scifi_fitpars_lost[:,0], scifi_fitpars_lost[:,1])\n",
"zmag_lost = zmag_lost[~np.isnan(zmag_lost)]\n",
2023-09-25 11:39:04 +02:00
"zmag_found = zmag_found[~np.isnan(zmag_found)]\n",
"\n",
"print(\"found \\nzmag = \", str(np.mean(zmag_found)))\n",
"print(\"lost \\nzmag = \", str(np.mean(zmag_lost)))"
2023-09-22 09:21:27 +02:00
]
},
{
"cell_type": "code",
2023-09-25 11:39:04 +02:00
"execution_count": 56,
2023-09-22 09:21:27 +02:00
"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(zmag_found, bins=5000, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
"plt.hist(zmag_lost, bins=300, density=True, alpha=0.5, histtype=\"bar\",color=\"darkorange\", label=\"lost\")\n",
"plt.xlabel(\"$z_{mag}$ [mm]\")\n",
"plt.ylabel(\"normed\")\n",
"plt.title(\"magnet kick position $z_{mag}$ calculated w fitparameters\")\n",
"plt.legend()\n",
"plt.xticks(np.arange(5100,5800,5), minor=True)\n",
"plt.yticks(np.arange(0,0.015,0.001), minor=True)\n",
"plt.xlim(5100,5800)\n",
"\n",
"\"\"\"\n",
"wir können einen radikalen unterschied für den z_mag wert erkennen, zwischen den found and lost elektronen.\n",
"\"\"\"\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
2023-09-25 10:22:31 +02:00
"execution_count": 25,
2023-09-22 09:21:27 +02:00
"metadata": {},
2023-09-25 10:22:31 +02:00
"outputs": [
{
"data": {
"text/plain": [
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" 'all_endvtx_types': [101.0,\n",
" 101.0,\n",
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" 'all_endvtx_x_length': 12,\n",
" 'all_endvtx_x': [-3.31820011138916,\n",
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" 'all_endvtx_z_length': 12,\n",
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" 'brem_photons_pz_length': 10,\n",
" 'brem_photons_pz': [1879.6300048828125,\n",
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" 'brem_vtx_z_length': 10,\n",
" 'brem_vtx_z': [184.35940551757812,\n",
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" 'endvtx_type': 0,\n",
" 'endvtx_x': nan,\n",
" 'endvtx_y': nan,\n",
" 'endvtx_z': nan,\n",
" 'energy': 134321.90632702777,\n",
" 'eta': 3.8988255511590677,\n",
" 'event_count': 2,\n",
" 'fromB': True,\n",
" 'fromD': False,\n",
" 'fromDecay': True,\n",
" 'fromHadInt': False,\n",
" 'fromPV': False,\n",
" 'fromPairProd': False,\n",
" 'fromSignal': True,\n",
" 'fromStrange': False,\n",
" 'isElectron': True,\n",
" 'isKaon': False,\n",
" 'isMuon': False,\n",
" 'isPion': False,\n",
" 'isProton': False,\n",
" 'lost': False,\n",
" 'lost_in_track_fit': False,\n",
" 'match_fraction': 1.0,\n",
" 'mcp_idx': 5524,\n",
" 'mother_id': 511,\n",
" 'mother_key': 5479,\n",
" 'originvtx_type': 2,\n",
" 'originvtx_x': -0.0663,\n",
" 'originvtx_y': -0.0023,\n",
" 'originvtx_z': 40.3966,\n",
" 'p': 134321.90632605576,\n",
" 'phi': -2.1616595746843887,\n",
" 'pid': 11,\n",
" 'pt': 5442.019481892728,\n",
" 'px': -3031.63,\n",
" 'py': -4519.38,\n",
" 'pz': 134211.62,\n",
" 'scifi_hit_pos_x_length': 13,\n",
" 'scifi_hit_pos_x': [-147.52284240722656,\n",
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" 'scifi_hit_pos_y_length': 13,\n",
" 'scifi_hit_pos_y': [-262.7794494628906,\n",
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" -267.5039367675781,\n",
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" -308.8764343261719,\n",
" -311.22991943359375,\n",
" -313.5960998535156,\n",
" -315.9485168457031],\n",
" 'scifi_hit_pos_z_length': 13,\n",
" 'scifi_hit_pos_z': [7825.15283203125,\n",
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" 9401.7626953125],\n",
" 'track_p': 111183.03747794438,\n",
" 'track_pt': 4503.0812830108725,\n",
" 'tx': -0.022588431612702388,\n",
" 'ty': -0.03367353735838968,\n",
" 'ut_hit_pos_x_length': 4,\n",
" 'ut_hit_pos_x': [-51.275360107421875,\n",
" -52.504058837890625,\n",
" -57.51994705200195,\n",
" -58.743045806884766],\n",
" 'ut_hit_pos_y_length': 4,\n",
" 'ut_hit_pos_y': [-76.70736694335938,\n",
" -78.56417083740234,\n",
" -86.16027069091797,\n",
" -88.01727294921875],\n",
" 'ut_hit_pos_z_length': 4,\n",
" 'ut_hit_pos_z': [2313.153564453125,\n",
" 2368.153564453125,\n",
" 2593.153564453125,\n",
" 2648.153564453125],\n",
" 'velo_hit_pos_x_length': 12,\n",
" 'velo_hit_pos_x': [-2.809859037399292,\n",
" -3.374659538269043,\n",
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" -4.503256320953369,\n",
" -5.067859172821045,\n",
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" -13.584664344787598,\n",
" -14.716963768005371,\n",
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" 'velo_hit_pos_y_length': 12,\n",
" 'velo_hit_pos_y': [-4.092668056488037,\n",
" -4.934769153594971,\n",
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" -18.47857666015625,\n",
" -20.166975021362305,\n",
" -21.85477638244629,\n",
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" 'velo_hit_pos_z_length': 12,\n",
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]
},
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}
],
"source": [
"\n"
]
2023-09-22 09:21:27 +02:00
},
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