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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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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",
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"from mpl_toolkits.axes_grid1 import ImageGrid\n",
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"%matplotlib inline"
]
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9056"
]
},
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"execution_count": 2,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\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)"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8606728758791105"
]
},
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"execution_count": 3,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"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",
"\n",
"t_eff(found, lost)"
]
},
{
"cell_type": "code",
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"execution_count": 4,
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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": 4,
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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)"
]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [],
"source": [
"def weird_div(n,d):\n",
" return n/d if d else 0"
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8593328191284226"
]
},
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"execution_count": 6,
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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",
"#if any photon of an electron has an energy higher the cutoff then it is included\n",
"cutoff_energy=350\n",
"\n",
"brem_found = found[ak.any(found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\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",
"brem_lost = lost[ak.any(lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\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": 7,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean energyloss relative to initial energy (found): 0.6551043170507098\n",
"mean energyloss relative to initial energy (lost): 0.8273131179948844\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": 8,
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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": [
"#in abhängigkeit von der energie der elektronen\n",
"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",
"plt.xticks(np.arange(0,1.1,0.1), minor=True,)\n",
"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",
"\n",
"\"\"\"\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 2000x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#energyloss in abh von der energie der elektronen\n",
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
"\n",
"a0=ax0.hist2d(energyloss_found, energy_found, bins=200, cmap=plt.cm.jet, cmin=1)\n",
"ax0.set_xlabel(\"energyloss\")\n",
"ax0.set_ylabel(r\"$E_0$\")\n",
"ax0.set_title(\"found energyloss wrt electron energy\")\n",
"plt.colorbar(a0[3],ax=ax0)\n",
"\n",
"a1=ax1.hist2d(energyloss_lost, energy_lost, bins=200, cmap=plt.cm.jet, cmin=1) \n",
"ax1.set_xlabel(\"energyloss\")\n",
"ax1.set_ylabel(r\"$E_0$\")\n",
"ax1.set_title(\"lost energyloss wrt electron energy\")\n",
"plt.colorbar(a1[3],ax=ax1)\n",
"\n",
"\"\"\"\n",
"\"\"\"\n",
"plt.show()"
]
},
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{
"cell_type": "code",
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"execution_count": 10,
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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": 11,
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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",
"#should be fulfilled by all candidates\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",
"sf_energy_found = ak.to_numpy(scifi_found[\"energy\"])\n",
"sf_eph_found = ak.to_numpy(ak.sum(scifi_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
"sf_vtx_type_found = ak.to_numpy(scifi_found[\"endvtx_type\"])\n",
"brem_vtx_type_found = scifi_found[scifi_found[\"endvtx_type\"]==101]\n",
"\n",
"sf_energy_lost = ak.to_numpy(scifi_lost[\"energy\"])\n",
"sf_eph_lost = ak.to_numpy(ak.sum(scifi_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
"sf_vtx_type_lost = ak.to_numpy(scifi_lost[\"endvtx_type\"])\n",
"brem_vtx_type_lost = scifi_lost[scifi_lost[\"endvtx_type\"]==101]\n",
"\n",
"\n",
"\n",
"#ak.num(scifi_found[\"energy\"], axis=0)\n",
"#scifi_found.snapshot()"
]
},
{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9056"
]
},
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"execution_count": 12,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"ak.num(scifi_found[\"energy\"], axis=0)\n",
"scifi_found[\"\"]"
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]
},
{
"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
"outputs": [],
"source": [
"scifi_fitpars_found = ak.ArrayBuilder()\n",
"\n",
"for i in range(0,ak.num(scifi_found, 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",
" #[:,4] -> energy \n",
" scifi_fitpars_found.real(sf_energy_found[i])\n",
" #[:,5] -> photon energy\n",
" scifi_fitpars_found.real(sf_eph_found[i])\n",
" #[:,6] -> endvtx_type\n",
" scifi_fitpars_found.real(sf_vtx_type_found[i])\n",
" scifi_fitpars_found.end_list()\n",
"\n",
"scifi_fitpars_lost = ak.ArrayBuilder()\n",
"\n",
"for i in range(0,ak.num(scifi_lost, 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",
" #[:,4] -> energy \n",
" scifi_fitpars_lost.real(sf_energy_lost[i])\n",
" #[:,5] -> photon energy\n",
" scifi_fitpars_lost.real(sf_eph_lost[i])\n",
" #[:,6] -> endvtx_type\n",
" scifi_fitpars_lost.real(sf_vtx_type_lost[i])\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"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(2)"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
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"source": []
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},
{
"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 2000x600 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#b parameter des fits [:,1] hat für lost eine breitere Verteilung. Warum?\n",
"#evtl multiple scattering candidates (lost); findet man einen gewissen endvtx_type (mult scattering)\n",
"#steiler velo winkel (eta)? vertex type? evtl bremsstrahlung?\n",
"\n",
"#isolate b parameters for analysis\n",
"b_found = scifi_fitpars_found[:,1]\n",
"b_lost = scifi_fitpars_lost[:,1]\n",
"\n",
"brem_energy_found = scifi_fitpars_found[:,5]\n",
"brem_energy_lost = scifi_fitpars_lost[:,5]\n",
"\n",
"\n",
"vtx_type_found = scifi_fitpars_found[:,6]\n",
"vtx_type_lost = scifi_fitpars_lost[:,6]\n",
"\n",
"\n",
"#Erste Annahme ist Bremsstrahlung\n",
"\n",
"fig = plt.figure(figsize=(20,6))\n",
"axes = ImageGrid(fig, 111, # similar to subplot(111)\n",
" nrows_ncols=(1, 2), # creates 2x2 grid of axes\n",
" axes_pad=1, # pad between axes in inch.\n",
" cbar_mode=\"single\",\n",
" cbar_location=\"right\",\n",
" cbar_pad=0.1,\n",
" aspect=False\n",
" )\n",
"\n",
"\n",
"h0 = axes[0].hist2d(b_found, brem_energy_found, bins=200, cmap=plt.cm.jet, cmin=1,vmax=30)\n",
"axes[0].set_xlim(-1,1)\n",
"axes[0].set_xlabel(\"b parameter [mm]\")\n",
"axes[0].set_ylabel(r\"$E_{ph}$\")\n",
"axes[0].set_title(\"found photon energy wrt b parameter\")\n",
"\n",
"h1 = axes[1].hist2d(b_lost, brem_energy_lost, bins=200, cmap=plt.cm.jet, cmin=1,vmax=30)\n",
"axes[1].set_xlim(-1,1)\n",
"axes[1].set_xlabel(\"b parameter [mm]\")\n",
"axes[1].set_ylabel(r\"$E_{ph}$\")\n",
"axes[1].set_title(\"lost photon energy wrt b parameter\")\n",
"\n",
"fig.colorbar(h0[3], cax=axes.cbar_axes[0], orientation='vertical')\n",
"\n",
"\"\"\"\n",
"\"\"\"\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"array([], dtype=float64)"
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]
},
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"execution_count": 24,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"vtx_type_found[vtx_type_found!=0]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1800x600 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n",
"\n",
"a0=ax[0].hist2d(b_found, vtx_type_found, bins=50, cmap=plt.cm.jet, cmin=1)\n",
"ax[0].set_xlabel(\"b\")\n",
"ax[0].set_ylabel(\"endvtx id\")\n",
"ax[0].set_title(\"found endvtx id wrt b parameter\")\n",
"\n",
"a1=ax[1].hist2d(b_lost, vtx_type_lost, bins=50, cmap=plt.cm.jet, cmin=1) \n",
"ax[1].set_xlabel(\"b\")\n",
"ax[1].set_ylabel(\"endvtx id\")\n",
"ax[1].set_title(\"lost endvtx id wrt b paraneter\")\n",
"\n",
"\"\"\"\n",
"B:\n",
"\n",
"\"\"\"\n",
"fig.colorbar(a0[3], ax=ax.ravel().tolist(), orientation='vertical')\n",
"plt.show()"
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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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",
"#evtl multiple scattering candidates (lost); findet man einen gewissen endvtx_type (mult scattering)\n",
"#steiler velo winkel (eta)? vertex type? evtl bremsstrahlung?\n",
"\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()"
]
},
2023-09-28 15:50:32 +02:00
{
"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": "env1",
"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",
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"nbformat_minor": 2
}
