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{
"cells": [
{
"cell_type": "code",
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"execution_count": 19,
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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": 20,
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"metadata": {},
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"outputs": [
{
"data": {
"text/plain": [
"9056"
]
},
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"execution_count": 20,
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"metadata": {},
"output_type": "execute_result"
}
],
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"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",
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"ak.num(found, axis=0)\n",
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"#ak.count(found, axis=None)\n"
]
},
{
"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8606728758791105"
]
},
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"execution_count": 21,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"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",
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"\n",
"t_eff(found, lost)"
]
},
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{
"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sample size: 32\n",
"eff (cutoff = 0 ) = 0.96875\n",
"sample size: 32\n",
"eff (cutoff = 50 ) = 0.96875\n",
"sample size: 32\n",
"eff (cutoff = 100 ) = 0.96875\n",
"sample size: 43\n",
"eff (cutoff = 150 ) = 0.9767441860465116\n",
"sample size: 65\n",
"eff (cutoff = 200 ) = 0.9692307692307692\n",
"sample size: 97\n",
"eff (cutoff = 250 ) = 0.9587628865979382\n",
"sample size: 129\n",
"eff (cutoff = 300 ) = 0.9457364341085271\n",
"sample size: 150\n",
"eff (cutoff = 350 ) = 0.9533333333333334\n",
"sample size: 169\n",
"eff (cutoff = 400 ) = 0.9408284023668639\n",
"sample size: 197\n",
"eff (cutoff = 450 ) = 0.9390862944162437\n",
"sample size: 227\n",
"eff (cutoff = 500 ) = 0.920704845814978\n",
"sample size: 257\n",
"eff (cutoff = 550 ) = 0.9260700389105059\n",
"sample size: 297\n",
"eff (cutoff = 600 ) = 0.9326599326599326\n",
"sample size: 334\n",
"eff (cutoff = 650 ) = 0.9281437125748503\n",
"sample size: 366\n",
"eff (cutoff = 700 ) = 0.9289617486338798\n",
"sample size: 400\n",
"eff (cutoff = 750 ) = 0.925\n",
"sample size: 436\n",
"eff (cutoff = 800 ) = 0.9151376146788991\n",
"sample size: 468\n",
"eff (cutoff = 850 ) = 0.9102564102564102\n",
"sample size: 500\n",
"eff (cutoff = 900 ) = 0.912\n",
"sample size: 533\n",
"eff (cutoff = 950 ) = 0.9136960600375235\n",
"sample size: 562\n",
"eff (cutoff = 1000 ) = 0.9163701067615658\n",
"\n",
"sample size: 150\n"
]
},
{
"data": {
"text/plain": [
"0.9533333333333334"
]
},
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"execution_count": 22,
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"metadata": {},
"output_type": "execute_result"
}
],
"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",
"for cutoff_energy in range(0,1050,50):\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)))\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(\"\\nsample size: \",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n",
"t_eff(nobrem_found, nobrem_lost)"
]
},
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
"outputs": [
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{
"name": "stdout",
"output_type": "stream",
"text": [
"31\n"
]
},
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{
"data": {
"text/plain": [
"0.96875"
]
},
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"execution_count": 23,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"#hier wird ohne rücksicht auf energie der photonen getrennt\n",
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"\n",
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"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",
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"print(ak.num(nobrem_found,axis=0))\n",
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"t_eff(nobrem_found, nobrem_lost)\n"
]
},
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{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
"outputs": [],
"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"
]
},
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{
"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8603431839847474"
]
},
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"execution_count": 25,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"#keine rücksicht auf energie der photonen\n",
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"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": 26,
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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": 27,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
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"#in abhängigkeit von der energie der elektronen\n",
"\n",
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"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",
"#plt.yticks(np.arange(0,10,1), minor=True)\n",
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"plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
"plt.ylabel(\"counts (normed)\")\n",
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"plt.title(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\")\n",
"plt.legend(title=\"LHCb Simulation\", title_fontsize=15)\n",
"#plt.grid()\n",
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"\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": 28,
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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": 29,
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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",
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"#should be fulfilled by all candidates\n",
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"\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": 30,
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"metadata": {},
"outputs": [],
"source": [
"#tx_lost.show()"
]
},
{
"cell_type": "code",
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"execution_count": 31,
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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",
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"execution_count": 32,
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"metadata": {},
"outputs": [],
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"source": [
"#from methods.adashof import move_sn_y"
]
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},
{
"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
"outputs": [
{
"data": {
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"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",
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"#fig.tight_layout()\n",
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"\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",
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"#evtl multiple scattering candidates (lost); findet man einen gewissen endvtx_type (mult scattering)\n",
"#steiler velo winkel (eta)? vertex type? evtl bremsstrahlung?\n",
"\n",
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"\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",
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"\n",
"\n",
"#locs = move_sn_y(offs=-.05, side=\"right\")\n",
"\n",
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"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",
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"execution_count": 34,
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"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",
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"execution_count": 35,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-4.6785491318157854e-07"
]
},
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"execution_count": 35,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.min(scifi_fitpars_found[:,3])"
]
},
{
"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAABQYAAAImCAYAAAABqFcVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd5wd5WHv/8/pvZ89Z3sv0q7qripCgBrdFCOBwXbsxLGIr3OdOM5FdpL7S7+2cHq7RnZyE9vYgGRMB6EVqKCu3VXZ3vvu2dN7n/n9oUhGIEBgQBLM+/XihfacmWeemWdW89UzM88jE0VRRCKRSCQSiUQikUgkEolEIpF8qsivdAUkEolEIpFIJBKJRCKRSCQSycdP6hiUSCQSiUQikUgkEolEIpFIPoWkjkGJRCKRSCQSiUQikUgkEonkU0jqGJRIJBKJRCKRSCQSiUQikUg+haSOQYlEIpFIJBKJRCKRSCQSieRTSOoYlEgkEolEIpFIJBKJRCKRSD6FpI5BiUQikUgkEolEIpFIJBKJ5FNI6hiUSCQSiUQikUgkEolEIpFIPoWkjkGJRHJNaG9vZ8eOHVe6Gu+qtbWVhx9+mF27dl3pqnyihEIhduzYwfDw8JWuikQikUgkkk8gKcN9NKQMJ5FcG6SOQYlEQnt7O9u2bWPLli3U1NTw6KOPXukqXTA8PMyWLVtoaWnhscceu9LVeUe7du1i27Zt7Nixg0AgcKWrA3z87To8PIzNZnvXDtxt27bx6KOPsmXLFmw2G9u2bXvXMnfs2EFVVRUPP/wwoVDo196+RCKRSCSSc87nhJaWFlpaWq50da6YT3uG+yjyG0gZTiK5liivdAUkEsmV1d7ezoYNGwgGg8C5C//Q0NAVrtWvVFdXs3PnTmQy2WWvMzw8jN1ux2q1fnQVe4vNmzcDsGXLlo9tm+/mSrRrKBQiFApdcjuhUIiWlhZ27txJc3MzcC4wbt++ne985zvv2FZbt25laGjosgLxu23/SpwTEolEIpFczc5fjx999NELf/4ovJ9rsJThPv4M91HkN5AynERyLZE6BiWST7nvfve72O32Cz9v3779Ctbmw7FlyxZ27tz5sQeIqymwXIl2bW5uRhTFS363bds2rFbrRf/w2Lp1K1u3bn3Pch0Ox6+9/St1TkgkEolEcjX7KDsEz3s/12Apw338Ge6jym8gZTiJ5FohvUoskXzKtbe3X+kqfKi2bNnyidunD+JqOwatra1XbNvSOSGRSCQSyZXxfq7B0vX6nKvpGFzJ/AbSOSGRfFykjkGJ5FNqx44dbNmyheHh4Qvj+G3ZsuWiABAKhXj44YfZtm0bmzZtYtOmTRd9v2vXLmw2GzKZ7MJFu7W1lS1btiCTyS68knF+4OGWlhZ27dpFa2srLS0tFy3zZue3e/6/yx1XZdeuXRfq8fDDD18UJnbt2nWh/jt27MBms/Hwww9fWPfRRx+9sK8tLS2XHOPkzfU6fzzeLaycX/78fp4f0PrNx/Xhhx+mpqbmssdUea82uZx2fa9y36lO77X/u3bturC989rb299Wn/Prnj93LnffA4HAhfPoUufOpbb/bufEr9MOEolEIpF80r1X5njrMm+9lr7bNfitpAz3wTLctZDfQMpwEslVT5RIJJ9q1dXVYnV19ds+b2trE61Wq9jW1nbhs8cee0wExO3bt1/4bOvWrSJw0XJDQ0MiIG7evPnCz5s3bxYBcePGjeIjjzwitrW1XVj3zeUNDQ2JVqtV3LNnz4XPtm/fLgJic3Pze+7PI488IgLi0NDQhc927twpVldXi4C4detW8ZFHHhGbm5svlHd+nfP27NkjAhfVYWhoSKyurr6oXKvVKlqt1ret99hjj134bOPGjeLOnTsvquPmzZvFRx555KLj+uZj8E4ut01E8Z3b9Z28V53ea/+HhoYutNPGjRvfVv6l2u983d98vC7lzeVu3779kufOu23/UufE5eyzRCKRSCSfBpe6Rl9u5niva+k7XYMvRcpw57yfDHc15zdRlDKcRHKtkJ4YlEgkl/TVr36VZcuWvW1MkebmZrZt28bw8DBw6TFZ3jwuCpybQOSBBx4AYNOmTWzfvp3m5uYLswzv2bPnwrLbtm1j2bJlbNy48cJnjzzyyK+1L5s3b75wZ9lqtbJ9+3ba2tpoa2sDzt0VffN+LFu27G312rJlCw8//DDV1dUXPvvOd75zYbDkS9myZQvbt2+/MKj1eW+983u547Rcbpt8EO9Vp/fa/+rq6vfdTm89T97Lli1beOSRRy557nyQ7X/QdpBIJBKJ5JPucjPHR30tlTLcu7sW8tv5ekgZTiK5ekkdgxKJ5G2Gh4dpb2+/5IDU58PZ+Yv6+3WpjsRAIHBhu+dfF/mwnd/u8uXL3/bdzp07LwRMgJMnTwJcCIvnj8ebOyvhXIelKIpv26dQKHRRB+hbVVdX8+ijj170ivR7BaKPsk3eq07vd/8/KufD/nlWq/XX6gz9IO0gkUgkEskn3fvJHB/HtVTKcO/sWshvIGU4ieRqJ3UMSiSSt3m3MVfOX9h/nYv5Ozlf5pvvan7YLhWCrFYr1dXVF8Y3eev+n//5cgPUY489Rmtr6zuOCXN+ZrVt27ZRU1NzWYMqf9Rt8m51er/7f634IO0gkUgkEskn3fvJHB/ntVTKcO+vPp/U/AZShpNIPmxSx6BEInlHl3q94ny4+CCvEbyX86Ho/BOEH5fh4WFaWloYHh5m586db7vjeL5elxvaHn74YZqbm3n44YcvuU51dTUjIyNs3Ljxwrbfz8DVb/VhtMm71en97v+14tdpB4lEIpFIPukuJ3Nc6Wvppz3DfRrzG1z5804i+aSROgYlEsnbnH/V4VJ3S8+Hmpqamg99u+efFHzzKyEfh02bNmG329/xFYTzx2Pnzp2X/P6tgctqtV5Y9lKvRQ8PD2O1WtmzZ8+F5d48u9671eGjapN3q9P73f9rxQdpB4lEIpFIPuneT+a40tfST3uG+zTmN7jy551E8kkjdQxKJJ9ygUDgbU/oVVdX09zczPDw8NtCw8mTJ7FarRcG+XU4HMDF4eL8n99pQOd3cv51ih07dlxy3fdT3uUue34f3/yaxfl1zx+XN9frraFu27Ztl7zLW11dzWOPPcbw8PDbgsr27dsv/Hnz5s0XxpV5t4D2ftrkfN3fz5OX71anD7L/Hya/3w/8+k+SvvWc+CDtIJFIJBLJJ937yRyXey2VMtxHk+Gu5vwGUoaTSK4VUsegRCK5pPNjd7w5EIVCIbZv384Pf/jDCyHs/N3Ibdu20drayo4dOy5cnFtbWy/cbb2cQGC1Wi/c8W1paaG1tZXh4WG2bdsGnLvYv3mQ4Us5f8f1fKDbtWvXhbq/+f/nnQ9Fu3btYseOHezYsePC9trb2y+sfz6AbNq0iS1btrBt2zZaWlqoqam5cCzeuo2tW7eyceNGduzYcVG9n3rqqYuCy/lZ4d5rbMXLbZMP4t3qdH4WwMvd/3dq63cK+u+3A/nN6715W++0/Xc6Jz5oO0gkEolE8kl3uZnjva6l73QNvhQpw71/12J+O7+ulOEkkquIKJFIPpXa2trErVu3ioAIiFu3bhX37Nlz0TLBYFDcvHmzuHHjRnHr1q3i1q1bxba2treVtX37dtFqtYpWq1V85JFHRFEUxerqavGRRx4R29raxLa2NrG5uVkExOrqanHPnj1iMBi8aPvbt2+/UN5jjz0mVldXi4DY3NwsDg0NXShvaGjoPfetublZtFqt4tatW0VRFMWdO3deKK+6ulp87LHHLlr+scceE61W64VtiKIobt269aL9OV/O+f1obm6+6Hi9+bvq6mpx586dF8o5v4+bN28Wh4aGxI0bN17Y1iOPPCJu3rxZDAaD77lfl9Mml9Oul3I5dXq3/W9raxM3b958UXsGg0ExGAyKjzzyyNs+37Nnz9vOiUvZuXOnaLVaL2yzra3tbefO+fPsUts/763nxOXus0QikUgkn1RvzQxvvXZeTg68nGvppa7B70TKcO8vw12t+e38dqUMJ5FcG2SiKIofTZejRCKRSCQSiUQikUg
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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",
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"execution_count": 37,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"found \n",
"zmag = 5215.5640412342\n",
"lost \n",
"zmag = 5450.484726770035\n"
]
}
],
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"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",
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"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)))"
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]
},
{
"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
"outputs": [
{
"data": {
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"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",
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"plt.hist(zmag_lost, bins=400, density=True, alpha=0.5, histtype=\"bar\",color=\"darkorange\", label=\"lost\")\n",
"plt.xlabel(r\"$\\bf{z_{mag}}$ [mm]\")\n",
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"plt.ylabel(\"normed\")\n",
"plt.title(\"magnet kick position $z_{mag}$ calculated w fitparameters\")\n",
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"plt.legend(title=\"LHCb Simulation\", title_fontsize=15)\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(5050,5750)\n",
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"\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",
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"#file.show()"
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]
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},
{
"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
},
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