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927 lines
353 KiB
927 lines
353 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import uproot\t\n",
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"import numpy as np\n",
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"import sys\n",
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"import os\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"from mpl_toolkits import mplot3d\n",
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"import itertools\n",
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"import awkward as ak\n",
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"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"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"10522"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
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"\n",
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"#selektiere nur elektronen von B->K*ee und nur solche mit einem momentum von ueber 5 GeV \n",
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"allcolumns = file.arrays()\n",
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"found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 9056\n",
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"lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 1466\n",
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"\n",
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"ak.num(found, axis=0) + ak.num(lost, axis=0)\n",
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"#ak.count(found, axis=None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"eff all = 0.8606728758791105 +/- 0.003375885792719708\n"
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]
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}
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],
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"source": [
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"def t_eff(found, lost, axis = 0):\n",
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" sel = ak.num(found, axis=axis)\n",
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" des = ak.num(lost, axis=axis)\n",
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" return sel/(sel + des)\n",
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"\n",
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"def eff_err(found, lost):\n",
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" n_f = ak.num(found, axis=0)\n",
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" n_all = ak.num(found, axis=0) + ak.num(lost,axis=0)\n",
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" return 1/n_all * np.sqrt(np.abs(n_f*(1-n_f/n_all)))\n",
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"\n",
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"\n",
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"print(\"eff all = \", t_eff(found, lost), \"+/-\", eff_err(found, lost))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"#try excluding all photons that originate from a vtx @ z>9500mm\n",
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"#ignore all brem vertices @ z>9500mm \n",
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"\n",
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"#found\n",
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"\n",
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"brem_e_f = found[\"brem_photons_pe\"]\n",
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"brem_z_f = found[\"brem_vtx_z\"]\n",
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"e_f = found[\"energy\"]\n",
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"length_f = found[\"brem_vtx_z_length\"]\n",
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"\n",
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"brem_f = ak.ArrayBuilder()\n",
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"\n",
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"for itr in range(ak.num(found,axis=0)):\n",
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" brem_f.begin_record()\n",
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" #[:,\"energy\"] energy\n",
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" brem_f.field(\"energy\").append(e_f[itr])\n",
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" #[:,\"photon_length\"] number of vertices\n",
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" brem_f.field(\"photon_length\").integer(length_f[itr])\n",
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" #[:,\"brem_photons_pe\",:] photon energy \n",
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" brem_f.field(\"brem_photons_pe\").append(brem_e_f[itr])\n",
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" #[:,\"brem_vtx_z\",:] brem vtx z\n",
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" brem_f.field(\"brem_vtx_z\").append(brem_z_f[itr])\n",
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" brem_f.end_record()\n",
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"\n",
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"brem_f = ak.Array(brem_f)\n",
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"\n",
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"#lost\n",
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"\n",
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"brem_e_l = lost[\"brem_photons_pe\"]\n",
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"brem_z_l = lost[\"brem_vtx_z\"]\n",
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"e_l = lost[\"energy\"]\n",
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"length_l = lost[\"brem_vtx_z_length\"]\n",
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"\n",
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"brem_l = ak.ArrayBuilder()\n",
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"\n",
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"for itr in range(ak.num(lost,axis=0)):\n",
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" brem_l.begin_record()\n",
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" #[:,\"energy\"] energy\n",
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" brem_l.field(\"energy\").append(e_l[itr])\n",
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" #[:,\"photon_length\"] number of vertices\n",
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" brem_l.field(\"photon_length\").integer(length_l[itr])\n",
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" #[:,\"brem_photons_pe\",:] photon energy \n",
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" brem_l.field(\"brem_photons_pe\").append(brem_e_l[itr])\n",
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" #[:,\"brem_vtx_z\",:] brem vtx z\n",
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" brem_l.field(\"brem_vtx_z\").append(brem_z_l[itr])\n",
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" brem_l.end_record()\n",
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"\n",
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"brem_l = ak.Array(brem_l)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"cut_brem_found = ak.ArrayBuilder()\n",
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"\n",
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"for itr in range(ak.num(brem_f, axis=0)):\n",
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" cut_brem_found.begin_record()\n",
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" cut_brem_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
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" \n",
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" cut_brem_found.field(\"brem_photons_pe\")\n",
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" cut_brem_found.begin_list()\n",
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" for jentry in range(brem_f[itr, \"photon_length\"]):\n",
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" if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n",
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" continue\n",
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" else:\n",
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" cut_brem_found.real(brem_f[itr,\"brem_photons_pe\", jentry])\n",
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" \n",
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" #cut_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
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" cut_brem_found.end_list()\n",
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" \n",
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" cut_brem_found.field(\"brem_vtx_z\")\n",
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" cut_brem_found.begin_list()\n",
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" for jentry in range(brem_f[itr, \"photon_length\"]):\n",
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" if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n",
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" continue\n",
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" else:\n",
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" cut_brem_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
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" cut_brem_found.end_list()\n",
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" \n",
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"\n",
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" \n",
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" cut_brem_found.end_record()\n",
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"\n",
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"cut_brem_found = ak.Array(cut_brem_found)\n",
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"\n",
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"\n",
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"\n",
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"cut_brem_lost = ak.ArrayBuilder()\n",
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"\n",
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"for itr in range(ak.num(brem_l, axis=0)):\n",
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" cut_brem_lost.begin_record()\n",
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" cut_brem_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n",
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" \n",
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" \n",
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" cut_brem_lost.field(\"brem_photons_pe\")\n",
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" cut_brem_lost.begin_list()\n",
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" for jentry in range(brem_l[itr, \"photon_length\"]):\n",
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" if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n",
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" continue\n",
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" else:\n",
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" cut_brem_lost.real(brem_l[itr,\"brem_photons_pe\", jentry])\n",
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" \n",
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" #cut_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
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" cut_brem_lost.end_list()\n",
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" \n",
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" cut_brem_lost.field(\"brem_vtx_z\")\n",
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" cut_brem_lost.begin_list()\n",
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" for jentry in range(brem_l[itr, \"photon_length\"]):\n",
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" if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n",
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" continue\n",
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" else:\n",
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" cut_brem_lost.real(brem_l[itr, \"brem_vtx_z\",jentry])\n",
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" cut_brem_lost.end_list()\n",
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" \n",
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" cut_brem_lost.end_record()\n",
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"\n",
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"cut_brem_lost = ak.Array(cut_brem_lost)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
|
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"<pre>{energy: 9.36e+03,\n",
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" brem_photons_pe: [2.47e+03, 170, 224, 388, 3.23e+03, 809, 172, 224],\n",
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" brem_vtx_z: [400, 501, 638, 667, 677, 709, 8.58e+03, 9.28e+03]}\n",
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"---------------------------------------------------------------------\n",
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"type: {\n",
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" energy: float64,\n",
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" brem_photons_pe: var * float64,\n",
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" brem_vtx_z: var * float64\n",
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"}</pre>"
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],
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"text/plain": [
|
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"<Record {energy: 9.36e+03, ...} type='{energy: float64, brem_photons_pe: va...'>"
|
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]
|
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},
|
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#data in cut_brem_found and cut_brem_lost\n",
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"\n",
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"cut_length_found = ak.num(cut_brem_found[\"brem_photons_pe\"],axis=-1)\n",
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"cut_length_lost = ak.num(cut_brem_lost[\"brem_photons_pe\"], axis=-1)\n",
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"\n",
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"cut_brem_found[1]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"8"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"cut_length_found[1]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"### Split in Upstream and Downstream Events and analyse separately"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"#try to find a split between energy lost before and after the magnet (z~5000mm)\n",
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"\n",
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"upstream_found = ak.ArrayBuilder()\n",
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"downstream_found = ak.ArrayBuilder()\n",
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"\n",
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"for itr in range(ak.num(cut_brem_found, axis=0)):\n",
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" upstream_found.begin_record()\n",
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" upstream_found.field(\"energy\").real(cut_brem_found[itr,\"energy\"])\n",
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" \n",
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" downstream_found.begin_record()\n",
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" downstream_found.field(\"energy\").real(cut_brem_found[itr,\"energy\"])\n",
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" \n",
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" upstream_found.field(\"brem_photons_pe\")\n",
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" downstream_found.field(\"brem_photons_pe\")\n",
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" upstream_found.begin_list()\n",
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" downstream_found.begin_list()\n",
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" for jentry in range(cut_length_found[itr]):\n",
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" if (cut_brem_found[itr, \"brem_vtx_z\", jentry]>5000):\n",
|
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" if cut_brem_found[itr, \"brem_vtx_z\", jentry]<=9500:\n",
|
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" downstream_found.real(cut_brem_found[itr,\"brem_photons_pe\",jentry])\n",
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" else:\n",
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" continue\n",
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" else:\n",
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" upstream_found.real(cut_brem_found[itr,\"brem_photons_pe\", jentry]) \n",
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" upstream_found.end_list()\n",
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" downstream_found.end_list()\n",
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" \n",
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" upstream_found.field(\"brem_vtx_z\")\n",
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" downstream_found.field(\"brem_vtx_z\")\n",
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" upstream_found.begin_list()\n",
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" downstream_found.begin_list()\n",
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" for jentry in range(cut_length_found[itr]):\n",
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" if cut_brem_found[itr, \"brem_vtx_z\", jentry]>5000:\n",
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" if cut_brem_found[itr,\"brem_vtx_z\",jentry]<=9500:\n",
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" downstream_found.real(cut_brem_found[itr,\"brem_vtx_z\",jentry])\n",
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" else:\n",
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" continue\n",
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" else:\n",
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" upstream_found.real(cut_brem_found[itr, \"brem_vtx_z\",jentry])\n",
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" upstream_found.end_list()\n",
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" downstream_found.end_list()\n",
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" upstream_found.end_record()\n",
|
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" downstream_found.end_record()\n",
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" \n",
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"\n",
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"upstream_found = ak.Array(upstream_found)\n",
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"downstream_found = ak.Array(downstream_found)\n",
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"\n",
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"\n",
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"upstream_lost = ak.ArrayBuilder()\n",
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"downstream_lost = ak.ArrayBuilder()\n",
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"\n",
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"for itr in range(ak.num(cut_brem_lost, axis=0)):\n",
|
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" upstream_lost.begin_record()\n",
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" upstream_lost.field(\"energy\").real(cut_brem_lost[itr,\"energy\"])\n",
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" \n",
|
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" downstream_lost.begin_record()\n",
|
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" downstream_lost.field(\"energy\").real(cut_brem_lost[itr,\"energy\"])\n",
|
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" \n",
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" upstream_lost.field(\"brem_photons_pe\")\n",
|
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" downstream_lost.field(\"brem_photons_pe\")\n",
|
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" upstream_lost.begin_list()\n",
|
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" downstream_lost.begin_list()\n",
|
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" for jentry in range(cut_length_lost[itr]):\n",
|
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" if (cut_brem_lost[itr, \"brem_vtx_z\", jentry]>5000):\n",
|
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" if cut_brem_lost[itr, \"brem_vtx_z\", jentry]<=9500:\n",
|
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" downstream_lost.real(cut_brem_lost[itr,\"brem_photons_pe\",jentry])\n",
|
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" else:\n",
|
|
" continue\n",
|
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" else:\n",
|
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" upstream_lost.real(cut_brem_lost[itr,\"brem_photons_pe\", jentry]) \n",
|
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" upstream_lost.end_list()\n",
|
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" downstream_lost.end_list()\n",
|
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" \n",
|
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" upstream_lost.field(\"brem_vtx_z\")\n",
|
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" downstream_lost.field(\"brem_vtx_z\")\n",
|
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" upstream_lost.begin_list()\n",
|
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" downstream_lost.begin_list()\n",
|
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" for jentry in range(cut_length_lost[itr]):\n",
|
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" if cut_brem_lost[itr, \"brem_vtx_z\", jentry]>5000:\n",
|
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" if cut_brem_lost[itr,\"brem_vtx_z\",jentry]<=9500:\n",
|
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" downstream_lost.real(cut_brem_lost[itr,\"brem_vtx_z\",jentry])\n",
|
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" else:\n",
|
|
" continue\n",
|
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" else:\n",
|
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" upstream_lost.real(cut_brem_lost[itr, \"brem_vtx_z\",jentry])\n",
|
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" upstream_lost.end_list()\n",
|
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" downstream_lost.end_list()\n",
|
|
" upstream_lost.end_record()\n",
|
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" downstream_lost.end_record()\n",
|
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" \n",
|
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"\n",
|
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"upstream_lost = ak.Array(upstream_lost)\n",
|
|
"downstream_lost = ak.Array(downstream_lost)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre>{energy: 4.62e+04,\n",
|
|
" brem_photons_pe: [3.26e+03, 4.45e+03, 178, 1.45e+04, 1.1e+03, 3.79e+03],\n",
|
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" brem_vtx_z: [162, 187, 387, 487, 1.34e+03, 2.32e+03]}\n",
|
|
"-------------------------------------------------------------------------\n",
|
|
"type: {\n",
|
|
" energy: float64,\n",
|
|
" brem_photons_pe: var * float64,\n",
|
|
" brem_vtx_z: var * float64\n",
|
|
"}</pre>"
|
|
],
|
|
"text/plain": [
|
|
"<Record {energy: 4.62e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"upstream_found[0]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"upstream: cutoff energy = 350MeV, sample size: 1562\n",
|
|
"eff = 0.9181 +/- 0.007\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#plot efficiency against cutoff energy \n",
|
|
"up_efficiencies = []\n",
|
|
"up_deff = []\n",
|
|
"\n",
|
|
"\n",
|
|
"for cutoff_energy in range(0,10050,200):\n",
|
|
"\tup_nobrem_f = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
|
|
"\tup_nobrem_l = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
|
|
"\t\n",
|
|
"\tif ak.num(up_nobrem_f,axis=0)+ak.num(up_nobrem_l,axis=0)==0:\n",
|
|
"\t\tup_efficiencies.append(0)\n",
|
|
"\t\tup_deff.append(0)\n",
|
|
"\t\tcontinue\n",
|
|
"\n",
|
|
"\teff = t_eff(up_nobrem_f,up_nobrem_l)\n",
|
|
"\tdeff = eff_err(up_nobrem_f,up_nobrem_l)\n",
|
|
"\tup_efficiencies.append(eff)\n",
|
|
"\tup_deff.append(deff)\n",
|
|
"\n",
|
|
"\t#print(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(up_nobrem_f,axis=0)+ak.num(up_nobrem_l,axis=0))\n",
|
|
"\t#print(\"eff = \",np.round(eff,4), \"+/-\", np.round(eff_err(up_nobrem_f, up_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",
|
|
"up_nobrem_found = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
|
|
"up_nobrem_lost = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
|
|
"\n",
|
|
"print(\"\\nupstream: cutoff energy = 350MeV, sample size:\",ak.num(up_nobrem_found,axis=0)+ak.num(up_nobrem_lost,axis=0))\n",
|
|
"print(\"eff = \",np.round(t_eff(up_nobrem_found, up_nobrem_lost),4), \"+/-\", np.round(eff_err(up_nobrem_found, up_nobrem_lost),3))\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"nobrem_vertices\n",
|
|
"upstream: cutoff energy = 350MeV, sample size: 1562\n",
|
|
"eff = 0.9181 +/- 0.007\n",
|
|
"\n",
|
|
"downstream: cutoff energy = 350MeV, sample size: 5131\n",
|
|
"eff = 0.8864 +/- 0.004\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"down_efficiencies = []\n",
|
|
"down_deff = []\n",
|
|
"\n",
|
|
"for cutoff_energy in range(0,10050,200):\n",
|
|
"\tdown_nobrem_f = downstream_found[ak.sum(downstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
|
|
"\tdown_nobrem_l = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
|
|
"\n",
|
|
"\tif ak.num(down_nobrem_f,axis=0)+ak.num(down_nobrem_l,axis=0)==0:\n",
|
|
"\t\tdown_efficiencies.append(0)\n",
|
|
"\t\tdown_deff.append(0)\n",
|
|
"\t\tcontinue\n",
|
|
"\teff = t_eff(down_nobrem_f,down_nobrem_l)\n",
|
|
"\tdeff = eff_err(down_nobrem_f,down_nobrem_l)\n",
|
|
"\tdown_efficiencies.append(eff)\n",
|
|
"\tdown_deff.append(deff)\n",
|
|
"\n",
|
|
"\n",
|
|
"\t#print(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(down_nobrem_f,axis=0)+ak.num(down_nobrem_l,axis=0))\n",
|
|
"\t#print(\"eff = \",np.round(eff,4), \"+/-\", np.round(eff_err(down_nobrem_f, down_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",
|
|
"down_nobrem_found = downstream_found[ak.sum(downstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
|
|
"down_nobrem_lost = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
|
|
"\n",
|
|
"\n",
|
|
"print(\"nobrem_vertices\\nupstream: cutoff energy = 350MeV, sample size:\",ak.num(up_nobrem_found,axis=0)+ak.num(up_nobrem_lost,axis=0))\n",
|
|
"print(\"eff = \",np.round(t_eff(up_nobrem_found, up_nobrem_lost),4), \"+/-\", np.round(eff_err(up_nobrem_found, up_nobrem_lost),3))\n",
|
|
"\n",
|
|
"print(\"\\ndownstream: cutoff energy = 350MeV, sample size:\",ak.num(down_nobrem_found,axis=0)+ak.num(down_nobrem_lost,axis=0))\n",
|
|
"print(\"eff = \",np.round(t_eff(down_nobrem_found, down_nobrem_lost),4), \"+/-\", np.round(eff_err(down_nobrem_found, down_nobrem_lost),3))\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1800x600 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#plot efficiencies wrt cutoff energy\n",
|
|
"fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(18,6))\n",
|
|
"x_ = np.arange(0,10050,step=200)\n",
|
|
"\n",
|
|
"ax[0].errorbar(x_,up_efficiencies,yerr=up_deff, ls=\"\", capsize=1,fmt=\".\")\n",
|
|
"ax[0].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"upstream\", ylim=[0.8,1.0])\n",
|
|
"#ax[0].set_yticks(np.arange(0.8,1.01,step=0.02),minor=False)\n",
|
|
"#ax[0].set_xticks(np.arange(0,10100,step=200),minor=True)\n",
|
|
"#ax[0].grid()\n",
|
|
"\n",
|
|
"ax[1].errorbar(x_,down_efficiencies,yerr=down_deff, ls=\"\", capsize=1,fmt=\".\")\n",
|
|
"ax[1].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"downstream\", ylim=[0.8,1.0])\n",
|
|
"#ax[1].set_yticks(np.arange(0.8,1.01,step=0.02),minor=False)\n",
|
|
"#ax[1].set_xticks(np.arange(0,10100,step=200),minor=True)\n",
|
|
"#ax[1].grid(True)\n",
|
|
"\n",
|
|
"fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, nobrem electrons\")\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"brem vertices\n",
|
|
"upstream eff = 0.851 +/- 0.004\n",
|
|
"downstream eff = 0.836 +/- 0.005\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"cutoff_energy=350\n",
|
|
"#possibly: instead of checking if any photons exceed the cutoff, use the sum of all photon energies to separate nobrem and brem\n",
|
|
"\n",
|
|
"upstream_brem_found = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n",
|
|
"up_energy_found = ak.to_numpy(upstream_brem_found[\"energy\"])\n",
|
|
"up_eph_found = ak.to_numpy(ak.sum(upstream_brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
|
|
"up_residual_found = up_energy_found - up_eph_found\n",
|
|
"up_energyloss_found = up_eph_found/up_energy_found\n",
|
|
"\n",
|
|
"\n",
|
|
"upstream_brem_lost = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n",
|
|
"up_energy_lost = ak.to_numpy(upstream_brem_lost[\"energy\"])\n",
|
|
"up_eph_lost = ak.to_numpy(ak.sum(upstream_brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
|
|
"up_residual_lost = up_energy_lost - up_eph_lost\n",
|
|
"up_energyloss_lost = up_eph_lost/up_energy_lost\n",
|
|
"\n",
|
|
"\n",
|
|
"print(\"brem vertices\\nupstream eff = \", np.round(t_eff(upstream_brem_found,upstream_brem_lost),3), \"+/-\", np.round(eff_err(upstream_brem_found, upstream_brem_lost),3))\n",
|
|
"\n",
|
|
"\n",
|
|
"downstream_brem_found = downstream_found[ak.sum(downstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n",
|
|
"down_energy_found = ak.to_numpy(downstream_brem_found[\"energy\"])\n",
|
|
"down_eph_found = ak.to_numpy(ak.sum(downstream_brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
|
|
"down_residual_found = down_energy_found - down_eph_found\n",
|
|
"down_energyloss_found = down_eph_found/down_energy_found\n",
|
|
"\n",
|
|
"\n",
|
|
"downstream_brem_lost = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n",
|
|
"down_energy_lost = ak.to_numpy(downstream_brem_lost[\"energy\"])\n",
|
|
"down_eph_lost = ak.to_numpy(ak.sum(downstream_brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
|
|
"down_residual_lost = down_energy_lost - down_eph_lost\n",
|
|
"down_energyloss_lost = down_eph_lost/down_energy_lost\n",
|
|
"\n",
|
|
"\n",
|
|
"print(\"downstream eff = \", np.round(t_eff(downstream_brem_found,downstream_brem_lost),3), \"+/-\", np.round(eff_err(downstream_brem_found, downstream_brem_lost),3))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"upstream:\n",
|
|
"mean energyloss relative to initial energy (found): 0.33078325542598164\n",
|
|
"mean energyloss relative to initial energy (lost): 0.5708618852236069\n",
|
|
"downstream:\n",
|
|
"mean energyloss relative to initial energy (found): 0.19104090843883118\n",
|
|
"mean energyloss relative to initial energy (lost): 0.3051594568487781\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(\"upstream:\\nmean energyloss relative to initial energy (found): \",ak.mean(up_energyloss_found))\n",
|
|
"print(\"mean energyloss relative to initial energy (lost): \", ak.mean(up_energyloss_lost))\n",
|
|
"\n",
|
|
"print(\"downstream:\\nmean energyloss relative to initial energy (found): \",ak.mean(down_energyloss_found))\n",
|
|
"print(\"mean energyloss relative to initial energy (lost): \", ak.mean(down_energyloss_lost))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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tHR4ezux48zrmPI7LOV2sY3I+F+uYnM/FOuYync95HXcRz+l/+2//TX/6p38a9fewPGbZx5eW6zNqWY7JOV28Y3JOF+uY8zqf0nK8vvM4Jud08Y45rXM6aB9/aZPd4W2N/+W//Je+tziWSiWVSqWxj/X69WtJ0t27d3X79u2x2xvU2tqaLMua2fHmdcx5HJdzuljH5Hwu1jE5n4t1zGU6n/M6blLPabVaVbVa7bnu7OxMkhjGYgmF5/zf/Jt/M5P39TJ9Ri3LMTmni3dMzuliHXNe51Najtd3HsfknC7eMad9Tm/q44+c7P75z3+uZrOp4+Nj+b4vSTIMQ9lsVg8fPtS//bf/dtSmZ+rw8HAuF7MAAAAY3XVFCa7rKp1OzzgiLMr1AQAAAJJr6GT3z3/+c5XLZXmeFy0zDEOS1Ol01G63ValUZFmWvvrqKzq1AAAAwALj+gAAAABx8c4wG//Zn/2ZyuWyisWi2u22fve73+l3v/udTk5OdHJyEv3+N3/zN/rjP/5jbW1t6auvvppW7AAAAADmiOsDAAAAxMnAye4/+7M/Uzab1a9//Wv9+Mc/1gcffNB320wmo0qlol//+tf69a9/TYcWAAAAWDBcHwAAACBuBhrG5Msvv5Rt27pz587QB/jiiy+0v7+vFy9e6O7du0PvDwAAACBe4n59sLW1NfVJ6AEAADBZg0xCf5OBkt1bW1sjdWRD29vbevny5cj7YzTz6MTP68JhWS5YluWccj45ZhIty2vL+Vy84y7LOcVkxf36YNEnoV+Wz4pl+nxalteXc8oxk2xZXl/OKcecp0lMQr8SBEEw6cC+/PJL5XI5vf/++5NuemLCF6jdbk+9I/z69Wu9++67+s1vfqPbt29P9ViYDc7pYuF8LhbO52LhfC6eWZzTWfbzMJhZXR/M+tzzGbV4OKeLh3O6WDifi4dzunimdU4H7efdWNm9v7+vWq028IF935fneTo5OdFPf/rTgfcDAAAAEH9cHwAAACCubkx2b25uqlgsDt1wvV6nMwsAAAAsGK4PAAAAEFfv3LTBBx98oFwup9/97nfRo1KpqFKpdC27+CiXy2o2m7OIHwAAAMAMcX0AAACAuLox2S1JlUql63fP8/TjH/+47/bFYlH5fH68yAAAAADEEtcHAAAAiKOBkt3DzrTueZ5c1x0pIAAAAADxxvUBAAAA4migZPdlQRDov/7X/9pz3evXr1UsFmWa5liBAQAAAEgGrg8AAAAQBzdOUNnLF198IdM0de/ePWWzWZmmqZOTE7Xb7WhmdsdxJhrotGxtbWltba3nulKppFKpNOOIAAAAcJNqtapqtdpz3dnZ2YyjwSJdHwAAACC5Rkp2G4ah4+Nj2batcrmslZUVSW8rOiSpXC7rBz/4weSinKLDw0NZljXVY6yuruqzzz7T6urqVI+D2eGcLhbO52LhfC4WzufimdQ5va4owXVdpdPpsdrHcOJ2fTCrghY+oxYP53TxcE4XC+dz8XBOF88453QSBS0rQdgDHdHLly/leZ48z5Npmtrc3NS77747TpMzEV4EtdvtqSe7AQAAMDv08+ZrntcHnHsAAIDFNGg/b6Qxu0OvX7/Wy5cv9fHHH2t7e1uS1G63x2kSAAAAQEJxfQAAAIB5GjnZ/cMf/lDr6+v69//+30fLPv74Y3U6HT169GgiwQEAAABIBq4PAAAAMG8jJbt/8pOfyHEcvfvuu1duSdze3la73dbPfvaziQQIAAAAIN64PgAAAEAcjDRBZaPRUKPR0Pe+9z09ePDgyvpsNqsvvvhC3//+98cOEFft7Ay3HAAAAJimaV0fNBoNNZtNGYYRjQFeqVQmFfZU0WcHAACYvZGS3aZp6nvf+54kRTOtX/T8+XN5njdeZAAAAAASYRrXB41GQ7u7u11jfmezWdm2nZiENwAAAGZrpGFMDMOIfg6CoGvdL3/5SzUaDZmmOVZgAAAAAJJhGtcHjuNoc3Oza1k2m1Wj0Rg5TgAAACy2kZLdjx490ieffKIXL15ElRtff/21vvzyS21ubmplZUXFYnGkgBqNhtLptFZWVpRKpdRqtQbaz3Vd5fN52batYrFIJxgAAACYkWlcH5ycnFy5Fuh0OhTVAAAAoK+RhjH54IMPtLu7qx/84AdyXTdKLIdVHLZt60c/+tHQ7dZqNbXb7ei2RNu2lc1mb+zUep6ndDqtdrsty7IkSalUSicnJyoUCkPHAQAAAGBw07g+KBaLKhaLyufzqtfrcl1XBwcHevLkycTjBwAAwGIYKdktSZZl6fj4WC9fvlS73dbLly9lmqYymcyVGdgH5fu+HMeJft/f31c6nZbrutcmu4vFojKZTJTolhRVeJPsBgAAAKZv0tcHhUJB7XZbtVpNqVRKpmnq5cuXXUOm9PPmzRu9fv16hGfx1urqqlZXV0feHwAAAN3Oz891fn4+8v5v3rwZaLuRk92hO3fu6M6dO1eWf/XVV/rBD34wVFvlcrnr97AjezGJfZnv+2q1WlcmqQnH96vVaiS8AQAAgBmZ5PWB4zg6Pj6W67ryPE+tVku5XO7G/e7fvz/UcS777LPPtLOzM1YbAAAA+Nbu7q4+//zzqR9n5GT3ixcv1Gq11Ol0rqwLx9cbtjN7WaPRUKVSubaq+/j4WJKubBMmyJvNJsluAAAW0bOdeUcwmA935h0BMBPTuD7IZrMqFosyTVP5fD4a0uSmhPfTp0919+7doY51EVXdAADMCX38hfXo0SN9+umnI+//4sWLgQoaRkp2/+QnP9Gf//mfX5lp/aJwYppR2batWq2m/f39a7fzPE+S+t7OGK7vh1scAQBAnLmuq8ePH0cT9bXb7TlHNH2zusURkzON64NwQsuwcOXly5e6c+eOtre3b0x237p1S7dv3x7qeAAAALOyjH38cXOot27dGmi7d0ZpvFaraWtrS+12W6enp1cev/71r7W1tTVK05Kkvb09eZ4n3/eVz+dVq9X6bhtWjmxsbPRc7/v+tce6f/++3n333ZEfu7u7Iz9PAACAm1iWpYcPH8p13akeJ+x7xcHu7u5Y/bNxh7DA8KZxfXBwcNA1nKFhGKpUKvJ9f+p/DwAAANO0jH38WRmpsntjY0N7e3t6//33e65/9913r4yhPYxw7O5Wq6V8Pq9KpdJ3KJJUKiXp7a2RvVw3BIrELY4AACD+rpu/ZFLC4SEGmfxv2mZ1iyMmZxrXBxsbG1cuzjKZjKT+d3UCAAAkxbL18WdlpGR3sViU53l9O7OS9Jvf/GbUmCKZTEaFQkF7e3t9twmT2f2+pbgp2c0tjgAAYNnl8/lYVcrO6hZHTM40rg+KxaJ2d3dVqVSiC7RGoyHLsm7s4wMAACy7uPXxZ2WkZPePf/xj/fCHP1QqldL6+vqV9ScnJ9rd3dXjx4/HDvDevXvXdmY3NzclXR2bO/w9nU6PHQMAAEBc+b4v27ZlGEbUmbVtO6qAvbyN7/tqtVqybVuFQkGNRiPar1gsyjAMPXr0aCaVJlgc07g+KJfLMgxD+Xw+ej/6vq8nT55MLG4AAIA4oo8/upGS3a9fv1an05lJRYXneV0n8jLDMGRZlprNZjT8iaRogPcHDx5MPUYAAIB5cF1XH3/8sZ48eRJ1XGu1mrLZrCqVStQ32t7elmma0TAStVotuisul8vp+fPn2tvbk+M4VMxiJNO6PigUCn2HMwQAAFhE9PHHM9IElblcTq1WSx988IG2trauPP74j/946DbDySgbjUa0zPM8NZtNOY7TtSyVSkXJbEna399Xq9Xqqu6uVCpdtzwCAAAsmu3tbW1ubnZVaBQKBVmWJdu2o77RxX5TuA0wSdO4PgAAAFhG9PHHM1Jl9/HxsVqt1rWd1v39/aHaDEvut7e35TiOstmsTNNUs9ns2s73fZ2cnHSN0W1Zltrttmzblmma8jwvKtsHAABYRJ7nyXXdrjvbQsViUcViUY7jqFKpyDRN7e3t6b333ou277UfMKppXB8AAAAsG/r44xsp2b25uamNjY1rt9ne3h663cuJ7V4sy9Lp6WnP5fV6fehjAgAAJNF1k81cntOkXq8rnU7Ltm05jqN6vb4U4/VhdqZ1fQAAALBM6OOPb6RhTBzHuXFymZ///OcjBQQAAIDBXbzbLRQO4xYmH03T1MuXL5XJZOR5ntLptGq12gyjxKKL2/XB1taW/vAP/7Dno1qtziwOAACAUSxrH79arfbtw21tbQ3UxkiV3U+ePJHruvrhD3/Yd0zsWq2m733ve6M0DwAAgBuEVRuXx+qTvu0cp1IpSW+rP8Lh4RqNhvL5vIrFIkO+YWLidn1weHhIZRMAAEicZe/jl0ollUqlnutc11U6nb6xjZGS3QcHBz1f9ItWVlZGaRoAAAADME1TlmXJdd2ooxs6Pj6WYRhRR7dSqUQTfudyOTmOo2KxeGW/XhUkwCC4PgAAABgfffzxjZTszufzMk1TxWKx5/pvvvlGe3t7YwWG4e3sDLYMAAAshnCcvmKxGM194vu+KpWK9vf3owrbg4ODaCLvcBvTNKPfw+oQx3Fk27Zc11Uul5v9E0JicX0AAAAwGfTxxzNSsvvhw4fKZrN6//33+27z3nvvjRoTAADAzT7cmXcEM+G6blSx4bqu9vb2VCgUZBhGNE7f9va2stls1LG9PDnN5uamstls1Ln1PE/tdjtaXygU5DiODg4OJCk6HjAorg8AAMBE0Menjz+mkZLdtm3r+PhYz58/77vNBx98MHJQAAAAeMuyLDmO07dzahiG6vX6tW2EFSHXudgxBobF9QEAAMDg6ONPz0jJ7sePH0el8Em3tbWltbW1nuuuGxQdAAAA81OtVlWtVnuuOzs7m3E0WKTrAwAAACTXSMnuSqUiz/Ou3earr77SD37wg5GCmiVmagcAAEieSczUjslZpOsDAAAAJNdIyW7TNOW6rh49eqR79+5FA6OHwkHT6cwCAAAAi4/rAwAAAMTByJXdT548URAEWllZubK+33IAAAAAi4frAwAAAMTBSMnuQqEgz/NULBavVG1I0unpqWq12rixAQAAAEgArg8AAAAQByMlu3O5nFZWVrS1tdV3GyaoAQAAAJZD3K4PmIQeAAAgeSYxCf1IyW7pbQfy66+/luM48jxPGxsb+oM/+ANtb2/r9u3b13Z0AQAAACyWOF0fMAk9AABA8kxiEvqRk91ffvmlbNtWEARdy3/605/qq6++0p/8yZ+M2jQAAACAhOH6AAAAAPM2UrL7yZMnKpfLsixLxWJRm5ubMgxDvu/r+fPn+vGPf6w7d+7o7t27Ew4XAAAAQNxwfQAAAIA4GCnZXalU5DiOtre3r6z74IMP9ODBAz169Eh/8Rd/MXaAAAAAAOKN6wMAAADEwTuj7tirIxvqNQM7AAAAgMXF9QEAAADmbaRk9yCDgXueN0rTAAAAABKG6wMAAADEwUjJ7tPTU/3t3/5tz3Vff/21PvnkE6o3AAAAgCXB9QEAAADiYKQxu7/44guZpql79+7JsixJku/7arVa8jxPhmHo5cuXEw10Ge3szDsCAADiKyn/TyYlzklxXVfHx8cqFArzDgUzxPUBAACYhKT0nZMS56QkqY8/UmW3YRhqtVr6h3/4B1UqlWhCmk6now8++EDHx8e6ffv2pGMFAABYaq7ryrZt5fN5pVIp7e3tzTukiOd5yufzSqfTchxn3uFgxrg+AAAAGA19/MkaqbJbkizLUrvd1suXL+W6brTszp07EwtuFra2trS2ttZzXalUUqlUmnFEAAAAV7muq48//linp6eSJNu21el05hzVt0zTVL1e18rKykyOV61WVa1We647OzubSQzotijXBwAAALNCH3/yRk52h+7cudOzA/vll1/qRz/60bjNT93h4WF0qyUAAEBc7e7uamNjI/q9UqnMMZr5u64owXXdgSZMxHTE4fqAghYAAJAE9PG7TaKgZeRk95dffqlms6mTk5Oe613XTUSyGwAAIAnCSlkgruJ0fUBBCwAASAL6+N0mUdAyUrL74cOHqtfr126TpPJ2AACAuKrVamo2m/I8T5KUz+clScViUZlMRtLbiQBt25ZhGFGH2bbtaH2j0dD29rZ831e73ZZlWWq1WnIcR41GQ7lcTvV6Xb7v6+DgQI7j6NGjRzIMQ7Zty3XdaJuLwuOGUqnU1F8PxBPXBwAAAIOjjz89I01QWa/XVSwW9bvf/a7vY3t7e9KxAgAALJ1CoaB6vS7TNKMx8+r1etTJdV1Xd+7cUbFYVKVSUbPZVD6fVzabjSa3yeVyevDgQVe7mUzmym2SJycnajabcl1XjuOo2Wxqf39fhUJBjUaja7Icz/N0584d5fN5OY6TmAlrMB1cHwAAAAyOPv70jJTstixLxWLx2m2WfYwZAACAWdje3tbm5mbXkA2FQkGWZcm27ahaxDCMK/teHB9QejsBzcOHDyVJ2WxWlUpFlmVFndxmsxlta9u2Njc3ow65JJXL5Yk9LyQL1wcAAACTQx9/dCMluyuVih4/fnztNu12e6SAAAAAMBjP8+S6bs+xicPE46jVGL06zuFYzJ7nqdFoKJvNjtQ2Fg/XBwAAAJNBH388I43Z7fu+XNfVl19+2fNFkt52eH/1q1+NExsAAACucd2ENpubm5IUVX1MUtimaZoTbxvJxPUBAADAZNDHH89Iye7d3V25rttV5n4ZE9AAAADMhu/7V5aFCcfLtzFOQtgRDqtAAK4PAAAAJos+/mhGSnYXCgW1Wq1ovJfLvvnmG9VqtbECAwAAwPXCWxtbrdaVdWHneBqzp4fVHgxLgRDXBwAAAJNBH388IyW7Hz58qGw2qzt37vTd5t69eyMHBQAAgG69KixM05RlWXJdV57ndd1yeHx8LMMwVCgUJEnvvfeepLcVG2EHOqze6FU1cp3w9slaraZKpXJl2Iph20PycX0AAAAwPPr4kzfQBJWvX7/u+v3dd9+9tiMrSR988MG1bQAAAGB89XpdhmFEk9VIbzuilUpF+/v7USc17Pzatq1Wq6VarRZNbNNqtaKJaAa5bdEwjGhW9nQ6rVarJc/zZNu2pLcd7L29vYk9R8QP1wcAAADTQx9/dAMlux3H0ddffz3yQX7+85/r+Ph45P0BAACWleu6KhaL8n1fvu+rWCx23dJomqZevnwpwzCUzWZVLBZl27bq9bpyuVy0XSaTUaVS0cnJifL5vDqdjhzHkWmaKpfLqlQqcl036hxXKhW1Wq3omGEsYQe3UqlE22azWeXzeRWLxai9i8fG4on79cHW1pb+8A//sOejWq1O7bgAAACDoI/fW7Va7duH29raGqiNlSAIgkE2fPDggX74wx/qj/7oj4YKcn9/X7/5zW/0ox/9aKj9ps11XaXTabXb7ehbkLjZ2YlHGwAAAEmShH7eIojj9UGczn2/fjj9cwAAgOEN2s8beMzug4MDbW5uKpVK6T/8h/+gjz/+WLdv3+657ddff61GoyHHcVQsFmOX6L5oa2tLa2trPdeVSiWVSqUZRwQAAICbVKvVvhW6Z2dnM45mOS3q9QEAAACSa6gJKo+Pj2Xbtra2trSysiLDMLSxsRGNE+N5XjRYuWmaOjg4uDI2X9wcHh7OvepjHqg0AQAASXZdUUJY9YHpW8TrAwAAACTXQGN2X1SpVHR6eqrd3V2l02l98803arfbarfbCoJAW1tbOjg40K9+9Ss6sgAAAMCCm+b1QTab1crKSs/HxXEtAQAAAGnIyu7Qu+++q3K5HM3QCQAAAGB5TeP6wPM8eZ6nSqUSVYpLUqfT0d7enjKZzMSOBQAAgMUwUrIbAAAAAKap1Wqp3W53JbolkegGAABAX0MPYwIAAAAA01YoFK4kuiXp8ePHyufzsw8IAAAAsUdlNwAAAIBE8H1fruvqwYMH8w5lZEwUDwAAMD0kuwEAAAAkwsHBgSzL6lnxfdGbN2/0+vXrkY+zurqq1dXVkfcHAABAt/Pzc52fn4+8/5s3bwbajmQ3AAAAgESo1+t6+PDhjdvdv39/rON89tln2qHUGgAAYGJ2d3f1+eefT/04JLsBAAAAxJ7v+2q1WnIc58Ztnz59qrt37458LKq6AQAAJuvRo0f69NNPR97/xYsXAxU0TDzZ/fXXX+v999+fdLMAAAAAEmhS1wetVkumaco0zRu3vXXrlm7fvj32MQEAADAZ4w4Td+vWrYG2e2eUxr/66it9+eWX+vLLL6Nl+/v7+hf/4l8olUrpX//rfz3WGHkAAAAAkmMW1wePHz9WLpcbN1QAAAAssJGS3V988YUMw9CPfvQjSdIvf/lLFYtFBUGgv/zLv9T29ra2t7cnGigAAACAeJrF9UGj0RhovG4AAAAsr5GGMclkMvrBD34Q/Z7P57WysqJ6va7vfe97kqSf/OQnk4kQAAAAQKxN+/qg0WjIMAxZljV2rAAAAFhcIyW719fXo5///M//XJ7nKZvNRh1ZSVpZWRk/uhnY2trS2tpaz3WlUkmlUmnGEQEAAOAm1WpV1Wq157qzs7MZR4NpXx88fvxYDx48GCtGAAAALL6Rkt2np6d69OiRJKlSqWh9fV31ej1a//LlSzUaDe3u7k4myik6PDykQgQAACBhritKcF1X6XR6xhEtt2lfH1xsCwAAAOhnpDG7K5WKOp2OHMeRZVk6Pj7W7du39fLlS/3Zn/2Z0un0QLOkAwAAAEg+rg8AAAAQByNVdr/77rs6ODi4svzOnTv6y7/8S/3lX/7l2IEBAABghp7t9F7+YZ/lwAVcHwAAACAORqrsfvHixbXrf/7zn4/SLAAAAIAE4voAAAAAcTBSsvumsfY++OAD/fCHPxwpIAAAAADJErfrg62tLf3hH/5hz0e/iU0BAAAwX9VqtW8fbmtra6A2Bh7G5De/+Y1OT08lSb7v6+/+7u8UBMGV7Xzfl+M4Ojg40F/8xV8M2jymZGdn3hEAAABgEcX5+oBJ6AEAAJJnEpPQD5zsPjk5UT6f1y9/+UtJunaCmSAIBjo4AAAAgGTi+gAAAABxM3Cy+86dOzo+Po46tLlcru+2qVRK29vbEwlwHJ7nMes7AAAAMAVJvD4AAADAYhs42R2q1+s6PDwceJyUYTUaDe3u7sp1XVmWpUqlokwmM9C+KysrXb9blqV2uz2NMAEAAABo+tcHAAAAwKCGTnZLGqgj++WXX+pHP/rRUO3u7e2p2WyqWCyq0+lob29P2WxWzWbzxoR3rVZToVBQKpWKlg2aJAcAAAAwumldHwAAAADDGCnZLb3trDabTZ2cnPRc77ru0J3Z58+fq9lsRr8/fPhQ6XR6oOruer3etS8AAACA2ZnG9QEAAAAwjJGS3Q8fPlS9Xr92m8tDityk1WqpUql0LbMsS5ZlyfO8a/dtNBrReIHZbFaFQmGoYwMAAAAY3TSuDwAAAIBhvTPKTvV6XcViUb/73e/6PoadgCaTyfSdTPKmSSabzaZ831ej0VCxWNT6+rpardZQxwcAAAAwmmlcHwAAAADDGqmy27IsFYvFa7e5XKU9Ks/zbjyW4zhyHEeu68pxHNVqNWWzWXU6nRsT5W/evNHr169Hjm91dVWrq6sj7w8AAIBu5+fnOj8/H3n/N2/eTDAaDGKW1wcAAABAPyNVdlcqFT1+/Pjabdrt9kgBXdRoNGSa5sDDkliWJcdxolsobdu+cZ/79+/r3XffHfmxu7s71nMEAABAt93d3bH6Z/fv35/3U1g6s7o+AAAAAK4zUmW37/tyXVdffvmlDMPouU2lUtGvfvWrcWLT7u7ujWP/9ZLL5ZTL5eS67o3bPn36VHfv3h0hureo6gYAAJisR48e6dNPPx15/xcvXpDwnrFZXR8MamtrS2traz3XlUollUqlmcQBAACAwVWrVVWr1Z7rzs7OBmpjpGT37u6uXNdVs9nsu824E9DYtq39/f0bhyHpJ5vNDjRu961bt3T79u2RjgEAAIDJG3eYuFu3bk0wGgxiFtcHwzg8PJRlWTM7HgAAAMZ3XVGC67pKp9M3tjF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|
|
"text/plain": [
|
|
"<Figure size 1800x600 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#in abhängigkeit von der energie der elektronen\n",
|
|
"fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n",
|
|
"\n",
|
|
"\n",
|
|
"ax[0].hist(up_energyloss_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
|
|
"ax[0].hist(up_energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
|
|
"#ax[0].set_xticks(np.arange(0,1.1,0.1), minor=True,)\n",
|
|
"#ax[0].set_yticks(np.arange(0,11,1), minor=True)\n",
|
|
"ax[0].set_xlabel(r\"$E_\\gamma/E_0$\")\n",
|
|
"ax[0].set_ylabel(\"counts (normed)\")\n",
|
|
"ax[0].set_title(\"Upstream\")\n",
|
|
"ax[0].legend()\n",
|
|
"#ax[0].grid()\n",
|
|
"\n",
|
|
"ax[1].hist(down_energyloss_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
|
|
"ax[1].hist(down_energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
|
|
"#ax[1].set_xticks(np.arange(0,1.1,0.1), minor=True,)\n",
|
|
"#ax[1].set_yticks(np.arange(0,11,1), minor=True)\n",
|
|
"ax[1].set_xlabel(r\"$E_\\gamma/E_0$\")\n",
|
|
"ax[1].set_ylabel(\"counts (normed)\")\n",
|
|
"ax[1].set_title(\"Downstream\")\n",
|
|
"ax[1].legend()\n",
|
|
"#ax[1].grid()\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"most electrons lose little energy relative to their initial energy downstream\n",
|
|
"\"\"\"\n",
|
|
"fig.suptitle(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.hist(up_energyloss_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
|
|
"plt.hist(up_energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
|
|
"plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
|
|
"plt.ylabel(\"counts (normed)\")\n",
|
|
"plt.title(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\")\n",
|
|
"plt.legend(title=\"LHCb Simulation\", title_fontsize=15)\n",
|
|
"plt.text(0.35,2.0, \"Upstream\", size=15)\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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|
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"text/plain": [
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"<Figure size 2000x600 with 3 Axes>"
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]
|
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},
|
|
"metadata": {},
|
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"output_type": "display_data"
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}
|
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],
|
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"source": [
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"#energyloss in abh von der energie der elektronen\n",
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"#upstream\n",
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"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
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"\n",
|
|
"a0=ax0.hist2d(up_energyloss_found, up_energy_found, bins=(np.linspace(0,1,80), np.linspace(0,5e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
|
|
"ax0.set_ylim(0,5e4)\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(up_energyloss_lost, up_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,5e4,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
|
|
"ax1.set_ylim(0,5e4)\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\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, Upstream photons w/ brem_vtx_z$<9500$mm\")\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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|
|
"text/plain": [
|
|
"<Figure size 2000x600 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#downstream\n",
|
|
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
|
|
"\n",
|
|
"a0=ax0.hist2d(down_energyloss_found, down_energy_found, bins=(np.linspace(0,1,80), np.linspace(0,5e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
|
|
"ax0.set_ylim(0,5e4)\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(down_energyloss_lost, down_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,5e4,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
|
|
"ax1.set_ylim(0,5e4)\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\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, Downstream photons w/ brem_vtx_z$<9500$mm\")\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"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",
|
|
"#upstream\n",
|
|
"nbins=60\n",
|
|
"\n",
|
|
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
|
|
"\n",
|
|
"a0=ax0.hist2d(up_energyloss_found, up_residual_found, bins=(np.linspace(0,1,nbins), np.linspace(0,5e4,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20)\n",
|
|
"ax0.set_ylim(0,5e4)\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(up_energyloss_lost, up_residual_lost, bins=(np.linspace(0,1,nbins), np.linspace(0,5e4,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20) \n",
|
|
"ax1.set_ylim(0,5e4)\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, Upstream photons w/ brem_vtx_z$<9500$mm\")\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"\"\"\"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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|
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"text/plain": [
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"<Figure size 2000x600 with 3 Axes>"
|
|
]
|
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},
|
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"metadata": {},
|
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"output_type": "display_data"
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}
|
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],
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"source": [
|
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"#downstream\n",
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"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
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"\n",
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"a0=ax0.hist2d(down_energyloss_found, down_residual_found, bins=(np.linspace(0,1,60), np.linspace(0,5e4,60)), cmap=plt.cm.jet, cmin=1, vmax=25)\n",
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"ax0.set_ylim(0,5e4)\n",
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|
"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(down_energyloss_lost, down_residual_lost, bins=(np.linspace(0,1,60), np.linspace(0,5e4,60)), cmap=plt.cm.jet, cmin=1, vmax=20) \n",
|
|
"ax1.set_ylim(0,5e4)\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(a0[3],ax=ax1)\n",
|
|
"fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, Downstream photons w/ brem_vtx_z$<9500$mm\")\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": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|