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{ "cells": [ { "cell_type": "code", "execution_count": 1, "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", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "410" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n", "\n", "\n", "\n", "#selektiere Kaonen und Pionen aus K*->Kpi - B->K*ee - und nur solche mit einem momentum von ueber 5 GeV \n", "allcolumns = file.arrays()\n", "found = allcolumns[((allcolumns.isPion) | (allcolumns.isKaon)) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p>5e3)] #B: 8126\n", "lost = allcolumns[((allcolumns.isPion) | (allcolumns.isKaon)) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p>5e3)] #B: 410\n", "\n", "ak.num(lost, axis=0)\n", "#ak.count(found, axis=None)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9519681349578257" ] }, "execution_count": 3, "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)\n" ] }, { "cell_type": "code", "execution_count": 4, "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", "execution_count": 5, "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", "execution_count": 6, "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" ] }, { "cell_type": "code", "execution_count": 7, "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=200, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$c_x$ found\")\n", "ax2.hist(scifi_fitpars_lost[:,2], bins=200, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$c_x$ lost\")\n", "ax2.set_xlim([-1e-5,1e-5])\n", "ax2.set_xticks(np.arange(-1e-5,1.5e-5,5e-6),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=200, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$d_x$ found\")\n", "ax3.hist(scifi_fitpars_lost[:,3], bins=200, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$d_x$ lost\")\n", "ax3.set(xlim=(-2e-8,2e-8))\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()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "found\n", "a = 1.3758753561567658\n", "b = 1.4867317350363123e-05\n", "c = 1.0611928711675984e-09\n", "d = 2.524323901023556e-12\n", "lost\n", "a = 19.655460959039587\n", "b = -0.0007972008149349264\n", "c = -1.6113881305735422e-07\n", "d = 8.074893917737833e-11\n" ] } ], "source": [ "\"\"\"\n", "Electrons\n", "found\n", "a = -0.6718207391527037 b = 0.0013778237292529144 c = 3.3126998287416195e-08 d = -1.0330674442255529e-10\n", "lost\n", "a = -36.98764338200992 b = -0.015685137956233643 c = -8.265859479503501e-07 d = -1.541510766903436e-11\n", "\n", "Kaon and Pions\n", "found\n", "a = 1.3758753561567658 b = 1.4867317350363123e-05 c = 1.0611928711675984e-09 d = 2.524323901023556e-12\n", "lost\n", "a = 19.655460959039587 b = -0.0007972008149349264 c = -1.6113881305735422e-07 d = 8.074893917737833e-11\n", "\n", "Kaons\n", "found\n", "a = 1.553371699292705\t\tb = 8.625619976952656e-05\t\tc = 3.1024420569192613e-09\t\td = -2.028183118694356e-13\n", "lost\n", "a = 50.918713410777336\t\tb = 0.005102900113795297\t\tc = -7.416239379215659e-09\t\td = 5.64633728533602e-11\n", "\n", "Pions\n", "found\n", "a = 1.1512709788236055\t\tb = -7.54683774644083e-05\t\tc = -1.5218096529826036e-09\t\td = 5.975257412125118e-12\n", "lost\n", "a = -3.625684483743866\t\tb = -0.005190892995904242\t\tc = -2.7561307005171267e-07\t\td = 9.883393537611524e-11\n", "\"\"\"\n", "\n", "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])))\n" ] }, { "cell_type": "code", "execution_count": 9, "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", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "found \n", "zmag = 5196.312017664934\n", "lost \n", "zmag = 5200.71031871899\n" ] } ], "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", "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)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 12, "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=2000, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n", "plt.hist(zmag_lost, bins=500, 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 $z_{mag}$ for $K$ and $\\pi$\")\n", "plt.legend()\n", "plt.xticks(np.arange(4900,5405,5), minor=True)\n", "plt.yticks(np.arange(0,0.015,0.001), minor=True)\n", "plt.xlim(4900,5400)\n", "\n", "\"\"\"\n", "the distribution for lost K and pi has a peak at around the same zmag value as found but its less compact and seems chaotic outside the range of the peak.\n", "the distribution for found particles appears compact around the peak.\n", "\n", "\"\"\"\n", "\n", "plt.show()" ] }, { "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", "version": "3.11.5" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2}
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