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883 lines
390 KiB
883 lines
390 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",
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" 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",
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" 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",
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" 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",
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"downstream_lost = ak.Array(downstream_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": 9,
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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: 4.62e+04,\n",
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" 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",
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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: 4.62e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
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]
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},
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"execution_count": 9,
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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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"upstream_found[0]"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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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",
|
|
"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",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"for cutoff_energy in range(50,1050,50):\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\tcontinue\n",
|
|
"\n",
|
|
"\teff = t_eff(up_nobrem_f,up_nobrem_l)\n",
|
|
"\tup_efficiencies.append(eff)\n",
|
|
"\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",
|
|
"for cutoff_energy in range(50,1050,50):\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\tcontinue\n",
|
|
"\teff = t_eff(down_nobrem_f,down_nobrem_l)\n",
|
|
"\tdown_efficiencies.append(eff)\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": {
|
|
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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(50,1050,step=50)\n",
|
|
"ax[0].scatter(x_,up_efficiencies)\n",
|
|
"ax[0].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"upstream\", ylim=[0.85,0.95])\n",
|
|
"ax[0].set_yticks(np.arange(0.85,0.96,step=0.01),minor=False)\n",
|
|
"ax[0].set_xticks(np.arange(0,1100,step=50),minor=True)\n",
|
|
"ax[0].grid()\n",
|
|
"\n",
|
|
"ax[1].scatter(x_,down_efficiencies)\n",
|
|
"ax[1].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"downstream\", ylim=[0.85,0.95])\n",
|
|
"ax[1].set_yticks(np.arange(0.85,0.96,step=0.01),minor=False)\n",
|
|
"ax[1].set_xticks(np.arange(0,1100,step=50),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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"text/plain": [
|
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"<Figure size 1800x600 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
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"#in abhängigkeit von der energie der elektronen\n",
|
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"fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n",
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"\n",
|
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"\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": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
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{
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|
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"text/plain": [
|
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"<Figure size 2000x600 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#energyloss in abh von der energie der elektronen\n",
|
|
"#upstream\n",
|
|
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
|
|
"\n",
|
|
"a0=ax0.hist2d(up_energyloss_found, up_energy_found, bins=(np.linspace(0,1,80), np.linspace(0,1.5e5,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\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(up_energyloss_lost, up_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,1.5e5,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\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\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, Upstream photons w/ brem_vtx_z$<9500$mm\")\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"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(0,1.5e5,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\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(down_energyloss_lost, down_energy_lost, bins=(np.linspace(0,1,50), np.linspace(0,1.5e5,50)), cmap=plt.cm.jet, cmin=1, vmax=15)\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\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, Downstream photons w/ brem_vtx_z$<9500$mm\")\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"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(0,1.5e5,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20)\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\"$E_0-E_\\gamma$\")\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(0,1.5e5,nbins)), cmap=plt.cm.jet, cmin=1, vmax=20) \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\"$E_0-E_\\gamma$\")\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": 19,
|
|
"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_residual_found, bins=(np.linspace(0,1,60), np.linspace(0,1.5e5,60)), cmap=plt.cm.jet, cmin=1, vmax=25)\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\"$E_0-E_\\gamma$\")\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(0,1.5e5,60)), cmap=plt.cm.jet, cmin=1, vmax=20) \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\"$E_0-E_\\gamma$\")\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": "3.11.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|