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Projektpraktikum/B_rework.ipynb

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rework
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{
"cells": [
{
"cell_type": "code",
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"execution_count": 21,
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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",
2d hists regarding the b parameter
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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": 22,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9056"
]
},
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"execution_count": 22,
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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": 23,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8606728758791105"
]
},
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"execution_count": 23,
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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": 24,
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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": 24,
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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",
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"\n",
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"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": null,
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"metadata": {},
"outputs": [],
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"source": []
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},
{
"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8593328191284226"
]
},
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"execution_count": 25,
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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": 26,
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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": 27,
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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": 28,
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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",
rework
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"execution_count": 29,
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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",
rework
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"execution_count": 30,
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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",
weiter
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"sf_vtx_type_found = scifi_found[\"all_endvtx_types\"]\n",
"\n",
"\n",
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"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",
weiter
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"sf_vtx_type_lost = scifi_lost[\"all_endvtx_types\"]\n",
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"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",
rework
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"execution_count": 31,
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"metadata": {},
"outputs": [
{
"data": {
weiter
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"text/html": [
"<pre>[101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 0]\n",
"------------------\n",
"type: 11 * float32</pre>"
],
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"text/plain": [
weiter
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"<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>"
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]
},
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"execution_count": 31,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
save
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"ak.num(scifi_found[\"energy\"], axis=0)\n",
weiter
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"scifi_found[\"all_endvtx_types\"][1,:]"
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]
},
{
"cell_type": "code",
rework
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"execution_count": 32,
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"metadata": {},
"outputs": [],
"source": [
"scifi_fitpars_found = ak.ArrayBuilder()\n",
weiter
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"vtx_types_found = ak.ArrayBuilder()\n",
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"\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",
" scifi_fitpars_found.end_list()\n",
weiter
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" \n",
" vtx_types_found.begin_list()\n",
" #[:,0] -> endvtx_type\n",
" vtx_types_found.extend(sf_vtx_type_found[i,:])\n",
" vtx_types_found.end_list()\n",
" \n",
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"\n",
"scifi_fitpars_lost = ak.ArrayBuilder()\n",
weiter
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"vtx_types_lost = ak.ArrayBuilder()\n",
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"\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",
" scifi_fitpars_lost.end_list()\n",
weiter
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" \n",
" vtx_types_lost.begin_list()\n",
" #[:,6] -> endvtx_type\n",
" vtx_types_lost.extend(sf_vtx_type_lost[i,:])\n",
" vtx_types_lost.end_list()\n",
" \n",
"\n",
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"\n",
weiter
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"scifi_fitpars_lost = ak.to_numpy(scifi_fitpars_lost)\n",
"scifi_fitpars_found = ak.to_numpy(scifi_fitpars_found)\n",
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"\n",
weiter
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"vtx_types_lost = ak.Array(vtx_types_lost)\n",
"vtx_types_found = ak.Array(vtx_types_found)\n",
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"\n"
]
},
{
"cell_type": "code",
rework
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"execution_count": 33,
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"metadata": {},
"outputs": [
{
"data": {
weiter
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"text/html": [
"<pre>[101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 101,\n",
" 0]\n",
"------------------\n",
"type: 11 * float64</pre>"
],
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"text/plain": [
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"<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>"
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]
},
rework
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"execution_count": 33,
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"metadata": {},
"output_type": "execute_result"
}
],
weiter
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"source": [
"vtx_types_found[0]"
]
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},
{
"cell_type": "code",
rework
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"execution_count": null,
weiter
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"metadata": {},
"outputs": [],
"source": [
"\n",
"\n"
]
},
{
"cell_type": "code",
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"execution_count": 34,
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"metadata": {},
"outputs": [
{
"data": {
rework
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"image/png": "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
2d hists regarding the b parameter
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"text/plain": [
rework
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"<Figure size 1800x600 with 4 Axes>"
2d hists regarding the b parameter
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]
},
"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",
weiter
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"bs_found, vtx_types_found = ak.broadcast_arrays(b_found, vtx_types_found)\n",
"bs_found = ak.to_numpy(ak.ravel(bs_found))\n",
"vtx_types_found = ak.to_numpy(ak.ravel(vtx_types_found))\n",
"\n",
"bs_lost, vtx_types_lost = ak.broadcast_arrays(b_lost, vtx_types_lost)\n",
"bs_lost = ak.to_numpy(ak.ravel(bs_lost))\n",
"vtx_types_lost = ak.to_numpy(ak.ravel(vtx_types_lost))\n",
"\n",
"\n",
2d hists regarding the b parameter
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"\n",
"\n",
"#Erste Annahme ist Bremsstrahlung\n",
"\n",
rework
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"fig = plt.figure(figsize=(18,6))\n",
2d hists regarding the b parameter
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"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",
rework
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"execution_count": null,
2d hists regarding the b parameter
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"metadata": {},
rework
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"outputs": [],
weiter
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"source": []
save
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},
{
"cell_type": "code",
rework
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"execution_count": 35,
save
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"metadata": {},
"outputs": [
{
"data": {
rework
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save
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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",
rework
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"a0=ax[0].hist2d(bs_found, vtx_types_found, bins=110, density=True, cmap=plt.cm.jet, cmin=1e-20,vmax=2)\n",
"ax[0].set_ylim(0,110)\n",
"ax[0].set_xlim(-1,1)\n",
save
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"ax[0].set_xlabel(\"b\")\n",
"ax[0].set_ylabel(\"endvtx id\")\n",
"ax[0].set_title(\"found endvtx id wrt b parameter\")\n",
rework
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"ax[0].set_yticks(np.arange(0,110,1),minor=True)\n",
save
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"\n",
rework
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"a1=ax[1].hist2d(bs_lost, vtx_types_lost, bins=110, density=True, cmap=plt.cm.jet, cmin=1e-20,vmax=2)\n",
"ax[1].set_ylim(0,110)\n",
"ax[1].set_xlim(-1,1)\n",
save
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"ax[1].set_xlabel(\"b\")\n",
"ax[1].set_ylabel(\"endvtx id\")\n",
"ax[1].set_title(\"lost endvtx id wrt b paraneter\")\n",
rework
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"ax[1].set_yticks(np.arange(0,110,1), minor=True)\n",
save
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"\n",
"\"\"\"\n",
rework
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"vtx_id: 101 - Bremsstrahlung\n",
save
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"B:\n",
rework
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"wir können nicht wirklich sagen dass bei den lost teilchen jegliche endvertex types überwiegen, im gegensatz zu den found \n",
save
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"\"\"\"\n",
weiter
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"fig.colorbar(a0[3], ax=ax, orientation='vertical')\n",
save
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"plt.show()"
2d hists regarding the b parameter
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
rework
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"execution_count": 36,
2d hists regarding the b parameter
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"metadata": {},
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{
"data": {
rework
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2d hists regarding the b parameter
2023-09-29 12:21:18 +02:00
"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",
rework
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"ax1.set_xticks(np.arange(-1,1,0.1),minor=True)\n",
2d hists regarding the b parameter
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"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()"
]
},
rework
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{
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