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{ "cells": [ { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import uproot\t\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", "from mpl_toolkits.axes_grid1 import ImageGrid\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10522" ] }, "execution_count": 30, "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) + ak.num(lost, axis=0)\n", "#ak.count(found, axis=None)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "eff all = 0.8606728758791105 +/- 0.003375885792719708\n" ] } ], "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", "def eff_err(found, lost):\n", " n_f = ak.num(found, axis=0)\n", " n_all = ak.num(found, axis=0) + ak.num(lost,axis=0)\n", " return 1/n_all * np.sqrt(np.abs(n_f*(1-n_f/n_all)))\n", "\n", "\n", "print(\"eff all = \", t_eff(found, lost), \"+/-\", eff_err(found, lost))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre>{energy: 4.62e+04,\n", " photon_length: 10,\n", " brem_photons_pe: [3.26e+03, 4.45e+03, 178, ..., 825, 8.99e+03, 3.48e+03],\n", " brem_vtx_z: [162, 187, 387, 487, ..., 9.49e+03, 1.21e+04, 1.21e+04, 1.21e+04]}\n", "-------------------------------------------------------------------------------\n", "type: {\n", " energy: float64,\n", " photon_length: int64,\n", " brem_photons_pe: var * float64,\n", " brem_vtx_z: var * float64\n", "}</pre>" ], "text/plain": [ "<Record {energy: 4.62e+04, ...} type='{energy: float64, photon_length: int6...'>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#try excluding all photons that originate from a vtx @ z>9500mm\n", "#ignore all brem vertices @ z>9500mm \n", "\n", "#found\n", "\n", "brem_e_f = found[\"brem_photons_pe\"]\n", "brem_z_f = found[\"brem_vtx_z\"]\n", "e_f = found[\"energy\"]\n", "length_f = found[\"brem_vtx_z_length\"]\n", "\n", "brem_f = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(found,axis=0)):\n", " brem_f.begin_record()\n", " #[:,\"energy\"] energy\n", " brem_f.field(\"energy\").append(e_f[itr])\n", " #[:,\"photon_length\"] number of vertices\n", " brem_f.field(\"photon_length\").integer(length_f[itr])\n", " #[:,\"brem_photons_pe\",:] photon energy \n", " brem_f.field(\"brem_photons_pe\").append(brem_e_f[itr])\n", " #[:,\"brem_vtx_z\",:] brem vtx z\n", " brem_f.field(\"brem_vtx_z\").append(brem_z_f[itr])\n", " brem_f.end_record()\n", "\n", "brem_f = ak.Array(brem_f)\n", "\n", "#lost\n", "\n", "brem_e_l = lost[\"brem_photons_pe\"]\n", "brem_z_l = lost[\"brem_vtx_z\"]\n", "e_l = lost[\"energy\"]\n", "length_l = lost[\"brem_vtx_z_length\"]\n", "\n", "brem_l = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(lost,axis=0)):\n", " brem_l.begin_record()\n", " #[:,\"energy\"] energy\n", " brem_l.field(\"energy\").append(e_l[itr])\n", " #[:,\"photon_length\"] number of vertices\n", " brem_l.field(\"photon_length\").integer(length_l[itr])\n", " #[:,\"brem_photons_pe\",:] photon energy \n", " brem_l.field(\"brem_photons_pe\").append(brem_e_l[itr])\n", " #[:,\"brem_vtx_z\",:] brem vtx z\n", " brem_l.field(\"brem_vtx_z\").append(brem_z_l[itr])\n", " brem_l.end_record()\n", "\n", "brem_l = ak.Array(brem_l)\n", "\n", "\n", "\n", "\n", "brem_f[0]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "acc_brem_found = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(brem_f, axis=0)):\n", " acc_brem_found.begin_record()\n", " acc_brem_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n", " \n", " acc_brem_found.field(\"brem_photons_pe\")\n", " acc_brem_found.begin_list()\n", " for jentry in range(brem_f[itr, \"photon_length\"]):\n", " if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n", " continue\n", " else:\n", " acc_brem_found.real(brem_f[itr,\"brem_photons_pe\", jentry])\n", " \n", " #acc_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n", " acc_brem_found.end_list()\n", " \n", " acc_brem_found.field(\"brem_vtx_z\")\n", " acc_brem_found.begin_list()\n", " for jentry in range(brem_f[itr, \"photon_length\"]):\n", " if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n", " continue\n", " else:\n", " acc_brem_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n", " acc_brem_found.end_list()\n", " \n", "\n", " \n", " acc_brem_found.end_record()\n", "\n", "acc_brem_found = ak.Array(acc_brem_found)\n", "\n", "\n", "\n", "acc_brem_lost = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(brem_l, axis=0)):\n", " acc_brem_lost.begin_record()\n", " acc_brem_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n", " \n", " acc_brem_lost.field(\"brem_photons_pe\")\n", " acc_brem_lost.begin_list()\n", " for jentry in range(brem_l[itr, \"photon_length\"]):\n", " if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n", " continue\n", " else:\n", " acc_brem_lost.real(brem_l[itr,\"brem_photons_pe\", jentry])\n", " \n", " #acc_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n", " acc_brem_lost.end_list()\n", " \n", " acc_brem_lost.field(\"brem_vtx_z\")\n", " acc_brem_lost.begin_list()\n", " for jentry in range(brem_l[itr, \"photon_length\"]):\n", " if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n", " continue\n", " else:\n", " acc_brem_lost.real(brem_l[itr, \"brem_vtx_z\",jentry])\n", " acc_brem_lost.end_list()\n", " \n", " acc_brem_lost.end_record()\n", "\n", "acc_brem_lost = ak.Array(acc_brem_lost)\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9056" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ak.num(acc_brem_found,axis=0)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'\\nph_e = found[\"brem_photons_pe\"]\\nevent_cut = ak.all(ph_e<cutoff_energy,axis=1)\\nph_e = ph_e[event_cut]\\n'" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\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" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sample size: 322\n", "eff (cutoff = 0 ) = 0.9379 +/- 0.0135\n", "sample size: 322\n", "eff (cutoff = 25 ) = 0.9379 +/- 0.0135\n", "sample size: 322\n", "eff (cutoff = 50 ) = 0.9379 +/- 0.0135\n", "sample size: 322\n", "eff (cutoff = 75 ) = 0.9379 +/- 0.0135\n", "sample size: 322\n", "eff (cutoff = 100 ) = 0.9379 +/- 0.0135\n", "sample size: 384\n", "eff (cutoff = 125 ) = 0.9453 +/- 0.0116\n", "sample size: 433\n", "eff (cutoff = 150 ) = 0.9423 +/- 0.0112\n", "sample size: 485\n", "eff (cutoff = 175 ) = 0.9443 +/- 0.0104\n", "sample size: 529\n", "eff (cutoff = 200 ) = 0.949 +/- 0.0096\n", "sample size: 581\n", "eff (cutoff = 225 ) = 0.9501 +/- 0.009\n", "sample size: 644\n", "eff (cutoff = 250 ) = 0.9519 +/- 0.0084\n", "sample size: 705\n", "eff (cutoff = 275 ) = 0.9475 +/- 0.0084\n", "sample size: 757\n", "eff (cutoff = 300 ) = 0.9498 +/- 0.0079\n", "sample size: 802\n", "eff (cutoff = 325 ) = 0.9451 +/- 0.008\n", "sample size: 846\n", "eff (cutoff = 350 ) = 0.9433 +/- 0.008\n", "sample size: 876\n", "eff (cutoff = 375 ) = 0.9452 +/- 0.0077\n", "sample size: 919\n", "eff (cutoff = 400 ) = 0.9467 +/- 0.0074\n", "sample size: 972\n", "eff (cutoff = 425 ) = 0.9475 +/- 0.0072\n", "sample size: 1019\n", "eff (cutoff = 450 ) = 0.949 +/- 0.0069\n", "sample size: 1067\n", "eff (cutoff = 475 ) = 0.9475 +/- 0.0068\n", "sample size: 1117\n", "eff (cutoff = 500 ) = 0.9418 +/- 0.007\n", "sample size: 1144\n", "eff (cutoff = 525 ) = 0.9423 +/- 0.0069\n", "sample size: 1184\n", "eff (cutoff = 550 ) = 0.9409 +/- 0.0069\n", "sample size: 1234\n", "eff (cutoff = 575 ) = 0.9408 +/- 0.0067\n", "sample size: 1268\n", "eff (cutoff = 600 ) = 0.9416 +/- 0.0066\n", "sample size: 1303\n", "eff (cutoff = 625 ) = 0.9417 +/- 0.0065\n", "sample size: 1342\n", "eff (cutoff = 650 ) = 0.9404 +/- 0.0065\n", "sample size: 1381\n", "eff (cutoff = 675 ) = 0.9399 +/- 0.0064\n", "sample size: 1416\n", "eff (cutoff = 700 ) = 0.9407 +/- 0.0063\n", "sample size: 1444\n", "eff (cutoff = 725 ) = 0.9418 +/- 0.0062\n", "sample size: 1484\n", "eff (cutoff = 750 ) = 0.942 +/- 0.0061\n", "sample size: 1523\n", "eff (cutoff = 775 ) = 0.9402 +/- 0.0061\n", "sample size: 1557\n", "eff (cutoff = 800 ) = 0.939 +/- 0.0061\n", "sample size: 1593\n", "eff (cutoff = 825 ) = 0.9379 +/- 0.006\n", "sample size: 1628\n", "eff (cutoff = 850 ) = 0.9373 +/- 0.006\n", "sample size: 1656\n", "eff (cutoff = 875 ) = 0.9384 +/- 0.0059\n", "sample size: 1690\n", "eff (cutoff = 900 ) = 0.9385 +/- 0.0058\n", "sample size: 1721\n", "eff (cutoff = 925 ) = 0.9378 +/- 0.0058\n", "sample size: 1745\n", "eff (cutoff = 950 ) = 0.9381 +/- 0.0058\n", "sample size: 1769\n", "eff (cutoff = 975 ) = 0.9378 +/- 0.0057\n", "sample size: 1796\n", "eff (cutoff = 1000 ) = 0.9365 +/- 0.0058\n", "sample size: 1838\n", "eff (cutoff = 1025 ) = 0.9374 +/- 0.0056\n", "\n", "cutoff energy = 350MeV, sample size: 846\n", "eff = 0.9433 +/- 0.008\n" ] } ], "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 = acc_brem_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", "efficiencies_found = ak.ArrayBuilder()\n", "\n", "\n", "\n", "for cutoff_energy in range(0,1050,25):\n", "\tnobrem_f = acc_brem_found[ak.all(acc_brem_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n", "\tnobrem_l = acc_brem_lost[ak.all(acc_brem_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n", "\n", "\n", "\n", "\tprint(\"sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n", "\tprint(\"eff (cutoff = \",str(cutoff_energy),\") = \",np.round(t_eff(nobrem_f,nobrem_l),4), \"+/-\", np.round(eff_err(nobrem_f, nobrem_l),4))\n", "\n", "\"\"\"\n", "we see that a cutoff energy of xxxMeV is ideal because the efficiency drops significantly for higher values\n", "\"\"\"\n", "cutoff_energy = 350.0 #MeV\n", "\n", "\"\"\"\n", "better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n", "\"\"\"\n", "nobrem_found = acc_brem_found[ak.all(acc_brem_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n", "nobrem_lost = acc_brem_lost[ak.all(acc_brem_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n", "\n", "print(\"\\ncutoff energy = 350MeV, sample size:\",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n", "print(\"eff = \",np.round(t_eff(nobrem_found, nobrem_lost),4), \"+/-\", np.round(eff_err(nobrem_found, nobrem_lost),4))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "eff = 0.8535 +/- 0.0036\n" ] }, { "data": { "text/html": [ "<pre>[{energy: 2.58e+04, brem_photons_pe: [9.97e+03, ...], brem_vtx_z: [...]},\n", " {energy: 8.03e+04, brem_photons_pe: [4.91e+03, ...], brem_vtx_z: [...]},\n", " {energy: 5.6e+03, brem_photons_pe: [320, ..., 392], brem_vtx_z: [...]},\n", " {energy: 6.36e+03, brem_photons_pe: [273, ...], brem_vtx_z: [...]},\n", " {energy: 4.67e+04, brem_photons_pe: [8.96e+03, ...], brem_vtx_z: [...]},\n", " {energy: 7.16e+04, brem_photons_pe: [544, ..., 142], brem_vtx_z: [...]},\n", " {energy: 5.15e+04, brem_photons_pe: [384, ...], brem_vtx_z: [...]},\n", " {energy: 4.07e+04, brem_photons_pe: [2.7e+04, ...], brem_vtx_z: [...]},\n", " {energy: 2.77e+04, brem_photons_pe: [2.24e+03, ...], brem_vtx_z: [...]},\n", " {energy: 6.4e+04, brem_photons_pe: [686, ..., 796], brem_vtx_z: [...]},\n", " ...,\n", " {energy: 5.59e+03, brem_photons_pe: [901, ...], brem_vtx_z: [...]},\n", " {energy: 2.13e+04, brem_photons_pe: [787, ...], brem_vtx_z: [...]},\n", " {energy: 9.34e+03, brem_photons_pe: [762, ...], brem_vtx_z: [...]},\n", " {energy: 5.08e+04, brem_photons_pe: [711, ...], brem_vtx_z: [...]},\n", " {energy: 6.41e+04, brem_photons_pe: [4.17e+03, ...], brem_vtx_z: [...]},\n", " {energy: 1.01e+04, brem_photons_pe: [220, ..., 156], brem_vtx_z: [...]},\n", " {energy: 1.96e+04, brem_photons_pe: [1.66e+03, ...], brem_vtx_z: [...]},\n", " {energy: 2.98e+04, brem_photons_pe: [8.32e+03, ...], brem_vtx_z: [...]},\n", " {energy: 3.97e+04, brem_photons_pe: [9.36e+03, ...], brem_vtx_z: [...]}]\n", "-------------------------------------------------------------------------\n", "type: 1418 * {\n", " energy: float64,\n", " brem_photons_pe: var * float64,\n", " brem_vtx_z: var * float64\n", "}</pre>" ], "text/plain": [ "<Array [{energy: 2.58e+04, ...}, ..., {...}] type='1418 * {energy: float64,...'>" ] }, "execution_count": 37, "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 = acc_brem_found[ak.any(acc_brem_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", "residual_found = energy_found - eph_found\n", "energyloss_found = eph_found/energy_found\n", "\n", "brem_lost = acc_brem_lost[ak.any(acc_brem_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", "residual_lost = energy_lost - eph_lost\n", "energyloss_lost = eph_lost/energy_lost\n", "\n", "print(\"eff = \", np.round(t_eff(brem_found,brem_lost),4), \"+/-\", np.round(eff_err(brem_found, brem_lost),4))\n", "brem_lost" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean energyloss relative to initial energy (found): 0.4107345449771658\n", "mean energyloss relative to initial energy (lost): 0.7300783757368142\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", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAHMCAYAAAD7xYOxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA/vklEQVR4nO3deXxTZdr/8W/aphulBQqFApV9kUUYQbG4gWNBUNFnHFxQRHAd+YmIjyButC6D6OO4jaKigqMy4sgioyhUpQVEFgs6siiIKMUBS1naQqVNm/P7AxtJ0yVJk5zT9vN+vfKiuc997nOdq4Fc3GezGYZhCAAAwILCzA4AAACgOhQqAADAsihUAACAZVGoAAAAy6JQAQAAlkWhAgAALItCBQAAWBaFCgAAsCwKFQAAYFkUKgAAwLIoVAAAgGVRqACos2XLlslms1X7eu211wKynfLyciUlJenpp58O+bYBmCPC7AAA1H+bNm2SJL3//vtKSkryWN6rV6+AbGfVqlU6cOCA/vSnP4V82wDMQaECoM42bdqk+Ph4XXrppbLZbEHbznvvvaeBAweqQ4cOId82AHNw6AdAneXk5Khfv35BLRQMw9DixYt1xRVXhHzbAMxDoQKgTg4ePKg9e/aob9++Kisr83gZhhGQ7axdu1b79u1zK1RCtW0A5qFQAVAnFeeIvPjii7Lb7R6vbdu21TqGYRiKj49Xfn5+tX3ee+899e3bV926dfN52wcOHNDFF1+sJk2aqHv37srMzKzLLgMIIc5RAVAnOTk5kqRFixYpJSXFY7k3J7Pu3LlTrVq1UsuWLavts2jRIk2YMMGvbU+cOFFt2rTRgQMH9Mknn+jKK6/U999/r8TExFpjA2AuChUAdbJp0yZFR0dr1KhRCg8Pr7bfyy+/rH/961+y2+1av3692rZtq3fffVe9evXSpk2b1L9/f916661asGCB2rZtq6VLl6pr166SpA0bNmjPnj0e56d4s+2jR49qyZIl2rVrl2JjYzVq1Cj169dP77//vkfhA8B6OPQDoE42bdqkPn361FikSNKWLVu0ceNGTZ48Wb/88osGDRqkBx54QJK0efNmffnllxo/frwOHTqk008/XXPmzHGtu3DhQnXv3l19+vTxeds7d+5UXFyc24xL3759tXXrVn92F0CIUagA8FtBQYF++OEH9evXr9a+W7Zs0QMPPKDhw4fLbrfr2muv1Y4dOySdKFQyMjJ01llnKSwsTF26dHE7EXbhwoUesynebvvo0aOKj493a4uPj9fRo0e93U0AJuLQDwC/bdq0SYZhqEmTJlq3bp3H8nbt2rlmMrZu3ep2l9i8vDzXOSmbN2/WW2+95Vq2detWjRw5UpL01VdfadeuXVUe9vFm23FxcSosLHRbVlhYqLi4OD/3GkAoMaMCwG8VV90899xzSk1N9XitWLFC0omi5MCBA253jl28eLFGjBih3NxcRUREuC37z3/+o9NOO03SidmUDh06aMCAAX5tu1u3bjp69Kj27t3rWnfLli3q3bt3EDICINBsBjcaABBkn376qYYPH66XXnpJ48aN0z/+8Q898sgj+vrrr5Wdna0XXnhBy5cvlyQVFxerWbNmKigoUExMjHr16qURI0boqaee8nv7o0ePVkJCgp5//nl9+umnGjt2rHbu3FnjVUYArIFDPwCCbsuWLZowYYLeeecdTZkyRQMGDFBmZqYSEhK0efNm1+yJdOKwT5cuXRQTEyNJXt2HpTYvvviixo0bp8TERLVr104LFiygSAHqCWZUAATdzTffrIEDB+rWW281OxQA9QznqAAIui1btqhnz55mhwGgHmJGBUDQJSQkaMeOHWrdurXZoQCoZ0ydUSkqKtLkyZPVoUMHxcTEaPDgwdq4caOZIQEIgoKCAooUAH4xtVC56aablJmZqTfffFPffPONhg0bpgsvvFA///yzmWEBAACLMO3Qz6+//qqmTZvq/fff18UXX+xq79+/vy655BI9+uijZoQFAAAsxLTLk8vKylReXq7o6Gi39piYGK1Zs6bKdUpKSlRSUuJ673Q6dejQISUmJspmswU1XgAAEBiGYaioqEht27ZVWFgtB3cME6Wmphrnn3++8fPPPxtlZWXGm2++adhsNqN79+5V9p8xY4YhiRcvXrx48eLVAF65ubm11gqmXvWza9cuTZgwQatWrVJ4eLhOP/10de/eXZs2baryJk+VZ1QKCgp0yimnaPfu3WratGnA4nI4HFq5cqWGDh0qu90esHHhjjyHDrkODfIcOuQ6NIKV56KiInXq1ElHjhxRQkJCjX1NvTNtly5dlJ2drWPHjqmwsFDJycm66qqr1KlTpyr7R0VFKSoqyqO9RYsWHk9HrQuHw6HY2FglJibyFyCIyHPokOvQIM+hQ65DI1h5rhjLm9M2LHHDtyZNmig5OVmHDx/W8uXLddlll5kdEgAAsABTZ1SWL18uwzDUo0cPff/997rnnnvUo0cPjR8/3sywAACARZg6o1JQUKCJEyeqZ8+euv7663XOOedoxYoVTOMBAABJJs+oXHnllbryyivNDAEA0IiVl5fL4XCYHYZlORwORURE6Pjx4yovL/d6PbvdrvDw8IDEYGqhAgCAGQzD0P79+3XkyBGzQ7E0wzDUpk0b5ebm+ny/smbNmqlNmzZ1vs8ZhQoAoNGpKFKSkpIUGxvLTUOr4XQ6dfToUcXFxdV+Y7bfGIah4uJi5eXlSZKSk5PrFAOFCgCgUSkvL3cVKYmJiWaHY2lOp1OlpaWKjo72ulCRTtxlXpLy8vKUlJRUp8NAlrg8GQCAUKk4JyU2NtbkSBq2ivzW9RwgChUAQKPE4Z7gClR+KVQAAIBlUagAAFBPDBkyRJMnTzY7jJDiZFoAACqsTQ/t9gaHeHu/ycrK0tChQ3X48GE1a9bMlBi8xYwKAACwLAoVAADqocOHD+v6669X8+bNFRsbqxEjRmjnzp2u5T/99JMuvfRSNW/eXE2aNFHv3r21bNky/fjjjxo6dKgkqXnz5rLZbLrhhhtM2ovacegHAIB66IYbbtDOnTu1dOlSxcfHa9q0aRo5cqS2bdsmu92uiRMnqrS0VKtWrVKTJk20bds2xcXFKSUlRQsXLtQVV1yh7777TvHx8a77nlgRhQoAAPVMRYHy+eefa/DgwZKkt99+WykpKVqyZIlGjx6tPXv26IorrlDfvn0lSZ07d3at36JFC0lSUlIS56gAAIDA2r59uyIiIjRo0CBXW2Jionr06KHt27dLkiZNmqRHH31UZ599tmbMmKH//Oc/ZoVbJxQqAADUM4ZhVNtecaO1m266ST/88IPGjh2rb775RgMHDtTzzz8fyjADgkIFAIB6plevXiorK9P69etdbQcPHtSOHTt06qmnutpSUlJ02223adGiRbr77rs1Z84cSVJkZKSkE889sjoKFQAA6plu3brpsssu080336w1a9bo66+/1nXXXad27drpsssukyRNnjxZy5cv1+7du7Vp0yZ99tlnriKmQ4cOstls+uCDD3TgwAEdPXrUzN2pESfTAgBQwaQbsPlj7ty5uvPOO3XJJZeotLRU5513npYtWya73S7pxGzJxIkTtXfvXsXHx+uiiy7S008/LUlq166dMjIydO+992r8+PG6/vrrNW/ePBP3pnoUKgAA1BNZWVmun5s3b65//OMf1fat7XyUBx98UA8++GCgQgsaDv0AAADLolABAACWRaECAAAsi0IFAABYFoUKAACwLAoVAABgWRQqAADAsihUAACAZVGoAAAAy6JQAQCgnjAMQ7fccotatGghm82mr776yrRYhgwZosmTJwd9O9xCHwCA36SnW3t7H3/8sebNm6esrCx17txZLVu2DEpcVkKhAgBAPbFr1y4lJydr8ODBZocSMqYe+ikrK9MDDzygTp06KSYmRp07d9bDDz8sp9NpZlgAAFjODTfcoDvuuEN79uyRzWZTx44dVVJSokmTJikpKUnR0dE655xztHHjRtc68+bNU7NmzdzGWbJkiWw2m+t9enq6+vfvrzfffFMdO3ZUQkKCrr76ahUVFbn "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", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 2000x600 with 3 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=(np.linspace(0,1,70), np.linspace(0,1.5e5,105)), cmap=plt.cm.jet, cmin=1, vmax=7)\n", "ax0.set_ylim(0,1.5e5)\n", "ax0.set_xlim(0,1)\n", "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax0.set_ylabel(r\"$E_0$\")\n", "ax0.set_title(\"found energyloss wrt electron energy\")\n", "\n", "a1=ax1.hist2d(energyloss_lost, energy_lost, bins=(np.linspace(0,1,70), np.linspace(0,1.5e5,105)), cmap=plt.cm.jet, cmin=1, vmax=7) \n", "ax1.set_ylim(0,1.5e5)\n", "ax1.set_xlim(0,1)\n", "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax1.set_ylabel(r\"$E_0$\")\n", "ax1.set_title(\"lost energyloss wrt electron energy\")\n", "\n", "fig.colorbar(a1[3],ax=ax1)\n", "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, only photons w/ brem_vtx_z$<9500$mm\")\n", "\n", "\"\"\"\n", "we can see that high energy electrons are often found even though they emit a lot of their energy through bremsstrahlung\n", "\"\"\"\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 2000x600 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot residual energy against energyloss and try to find a good split (eg energyloss before and after the magnet)\n", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", "a0=ax0.hist2d(energyloss_found, residual_found, bins=(np.linspace(0,1,80), np.linspace(0,1e5,80)), cmap=plt.cm.jet, cmin=1, vmax=20)\n", "ax0.set_ylim(0,1e5)\n", "ax0.set_xlim(0,1)\n", "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax0.set_ylabel(r\"$E_0-E_\\gamma$\")\n", "ax0.set_title(\"found energyloss wrt residual electron energy\")\n", "\n", "a1=ax1.hist2d(energyloss_lost, residual_lost, bins=(np.linspace(0,1,80), np.linspace(0,1e5,80)), cmap=plt.cm.jet, cmin=1, vmax=20) \n", "ax1.set_ylim(0,1e5)\n", "ax1.set_xlim(0,1)\n", "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax1.set_ylabel(r\"$E_0-E_\\gamma$\")\n", "ax1.set_title(\"lost energyloss wrt residual electron energy\")\n", "\n", "fig.colorbar(a1[3],ax=ax1)\n", "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, only photons w/ brem_vtx_z$<9500$mm\")\n", "\n", "\"\"\"\n", "\"\"\"\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 42, "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": 43, "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 = scifi_found[\"all_endvtx_types\"]\n", "\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 = scifi_lost[\"all_endvtx_types\"]\n", "\n", "\n", "\n", "#ak.num(scifi_found[\"energy\"], axis=0)\n", "#scifi_found.snapshot()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "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>" ], "text/plain": [ "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ak.num(scifi_found[\"energy\"], axis=0)\n", "scifi_found[\"all_endvtx_types\"][1,:]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "scifi_fitpars_found = ak.ArrayBuilder()\n", "vtx_types_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", " scifi_fitpars_found.end_list()\n", " \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", "\n", "scifi_fitpars_lost = ak.ArrayBuilder()\n", "vtx_types_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", " scifi_fitpars_lost.end_list()\n", " \n", " vtx_types_lost.begin_list()\n", " #endvtx_type\n", " vtx_types_lost.extend(sf_vtx_type_lost[i,:])\n", " vtx_types_lost.end_list()\n", " \n", "\n", "\n", "scifi_fitpars_lost = ak.to_numpy(scifi_fitpars_lost)\n", "scifi_fitpars_found = ak.to_numpy(scifi_fitpars_found)\n", "\n", "vtx_types_lost = ak.Array(vtx_types_lost)\n", "vtx_types_found = ak.Array(vtx_types_found)\n", "\n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "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>" ], "text/plain": [ "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vtx_types_found[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1800x600 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", "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", "\n", "\n", "#Erste Annahme ist Bremsstrahlung\n", "\n", "fig = plt.figure(figsize=(18,6))\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", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 48, "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(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", "ax[0].set_xlabel(\"b\")\n", "ax[0].set_ylabel(\"endvtx id\")\n", "ax[0].set_title(\"found endvtx id wrt b parameter\")\n", "ax[0].set_yticks(np.arange(0,110,1),minor=True)\n", "\n", "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", "ax[1].set_xlabel(\"b\")\n", "ax[1].set_ylabel(\"endvtx id\")\n", "ax[1].set_title(\"lost endvtx id wrt b paraneter\")\n", "ax[1].set_yticks(np.arange(0,110,1), minor=True)\n", "\n", "\"\"\"\n", "vtx_id: 101 - Bremsstrahlung\n", "B:\n", "wir können nicht wirklich sagen dass bei den lost teilchen jegliche endvertex types überwiegen, im gegensatz zu den found \n", "\"\"\"\n", "fig.colorbar(a0[3], ax=ax, orientation='vertical')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 49, "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_xticks(np.arange(-1,1,0.1),minor=True)\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()" ] }, { "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": [] }, { "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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