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756 lines
353 KiB
756 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": "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": 5,
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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(brem_f, axis=0)):\n",
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" upstream_found.begin_record()\n",
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" upstream_found.field(\"energy\").real(brem_f[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(brem_f[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(brem_f[itr, \"photon_length\"]):\n",
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" if (brem_f[itr, \"brem_vtx_z\", jentry]>5000):\n",
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" if brem_f[itr, \"brem_vtx_z\", jentry]<9500:\n",
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" downstream_found.real(brem_f[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(brem_f[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(brem_f[itr, \"photon_length\"]):\n",
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" if brem_f[itr, \"brem_vtx_z\", jentry]>5000:\n",
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" if brem_f[itr,\"brem_vtx_z\",jentry]<9500:\n",
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" downstream_found.real(brem_f[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(brem_f[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(brem_l, axis=0)):\n",
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" upstream_lost.begin_record()\n",
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" upstream_lost.field(\"energy\").real(brem_l[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(brem_l[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(brem_l[itr, \"photon_length\"]):\n",
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" if (brem_l[itr, \"brem_vtx_z\", jentry]>5000):\n",
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" if brem_l[itr, \"brem_vtx_z\", jentry]<9500:\n",
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" downstream_lost.real(brem_l[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(brem_l[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(brem_l[itr, \"photon_length\"]):\n",
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" if brem_l[itr, \"brem_vtx_z\", jentry]>5000:\n",
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" if brem_l[itr,\"brem_vtx_z\",jentry]<9500:\n",
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" downstream_lost.real(brem_l[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(brem_l[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": 15,
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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": 15,
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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": 6,
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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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"\n",
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"cutoff = 0 MeV, sample size: 880\n",
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"eff = 0.9125 +/- 0.0095\n",
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"\n",
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"cutoff = 100 MeV, sample size: 880\n",
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"eff = 0.9125 +/- 0.0095\n",
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"\n",
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"cutoff = 200 MeV, sample size: 1279\n",
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"eff = 0.921 +/- 0.0075\n",
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"\n",
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"cutoff = 300 MeV, sample size: 1587\n",
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"eff = 0.9175 +/- 0.0069\n",
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"\n",
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"cutoff = 400 MeV, sample size: 1827\n",
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"eff = 0.9152 +/- 0.0065\n",
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"\n",
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"cutoff = 500 MeV, sample size: 2072\n",
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"eff = 0.9146 +/- 0.0061\n",
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"\n",
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"cutoff = 600 MeV, sample size: 2266\n",
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"eff = 0.9144 +/- 0.0059\n",
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"\n",
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"cutoff = 700 MeV, sample size: 2445\n",
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"eff = 0.9121 +/- 0.0057\n",
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"\n",
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"cutoff = 800 MeV, sample size: 2615\n",
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"eff = 0.9117 +/- 0.0055\n",
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"\n",
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"cutoff = 900 MeV, sample size: 2765\n",
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"eff = 0.9114 +/- 0.0054\n",
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"\n",
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"cutoff = 1000 MeV, sample size: 2910\n",
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"eff = 0.9096 +/- 0.0053\n",
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"\n",
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"upstream: cutoff energy = 200MeV, sample size: 1279\n",
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"eff = 0.921 +/- 0.008\n"
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]
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}
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],
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"source": [
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"up_efficiencies = ak.ArrayBuilder()\n",
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"\n",
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"\n",
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"\n",
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"for cutoff_energy in range(0,1050,100):\n",
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"\tup_nobrem_f = upstream_found[ak.all(upstream_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
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"\tup_nobrem_l = upstream_lost[ak.all(upstream_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
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"\n",
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"\n",
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"\n",
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"\tprint(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(up_nobrem_f,axis=0)+ak.num(up_nobrem_l,axis=0))\n",
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"\tprint(\"eff = \",np.round(t_eff(up_nobrem_f,up_nobrem_l),4), \"+/-\", np.round(eff_err(up_nobrem_f, up_nobrem_l),4))\n",
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"\n",
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"\"\"\"\n",
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"we see that a cutoff energy of xxxMeV is ideal because the efficiency drops significantly for higher values\n",
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"\"\"\"\n",
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"cutoff_energy = 200.0 #MeV\n",
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"\n",
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"\"\"\"\n",
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"better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n",
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"\"\"\"\n",
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"up_nobrem_found = upstream_found[ak.all(upstream_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
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"up_nobrem_lost = upstream_lost[ak.all(upstream_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
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"\n",
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"print(\"\\nupstream: cutoff energy = 200MeV, sample size:\",ak.num(up_nobrem_found,axis=0)+ak.num(up_nobrem_lost,axis=0))\n",
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"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"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"cutoff = 0 MeV, sample size: 3971\n",
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"eff = 0.893 +/- 0.0049\n",
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"\n",
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"cutoff = 100 MeV, sample size: 3971\n",
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"eff = 0.893 +/- 0.0049\n",
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"\n",
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"cutoff = 200 MeV, sample size: 4634\n",
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"eff = 0.8869 +/- 0.0047\n",
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"\n",
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"cutoff = 300 MeV, sample size: 5096\n",
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"eff = 0.8872 +/- 0.0044\n",
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"\n",
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"cutoff = 400 MeV, sample size: 5414\n",
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"eff = 0.886 +/- 0.0043\n",
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"\n",
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"cutoff = 500 MeV, sample size: 5654\n",
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"eff = 0.885 +/- 0.0042\n",
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"\n",
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"cutoff = 600 MeV, sample size: 5881\n",
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"eff = 0.8845 +/- 0.0042\n",
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"\n",
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"cutoff = 700 MeV, sample size: 6079\n",
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"eff = 0.8842 +/- 0.0041\n",
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"\n",
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"cutoff = 800 MeV, sample size: 6251\n",
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"eff = 0.8831 +/- 0.0041\n",
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"\n",
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"cutoff = 900 MeV, sample size: 6426\n",
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"eff = 0.8828 +/- 0.004\n",
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"\n",
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"cutoff = 1000 MeV, sample size: 6561\n",
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"eff = 0.882 +/- 0.004\n",
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"\n",
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"downstream: cutoff energy = 200MeV, sample size: 4634\n",
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"eff = 0.8869 +/- 0.005\n"
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]
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}
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],
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"source": [
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"down_efficiencies = ak.ArrayBuilder()\n",
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"for cutoff_energy in range(0,1050,100):\n",
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"\tdown_nobrem_f = downstream_found[ak.all(downstream_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
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"\tdown_nobrem_l = downstream_lost[ak.all(downstream_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\tprint(\"\\ncutoff = \",str(cutoff_energy),\"MeV, sample size: \",ak.num(down_nobrem_f,axis=0)+ak.num(down_nobrem_l,axis=0))\n",
|
|
"\tprint(\"eff = \",np.round(t_eff(down_nobrem_f,down_nobrem_l),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 = 200.0 #MeV\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n",
|
|
"\"\"\"\n",
|
|
"down_nobrem_found = downstream_found[ak.all(downstream_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
|
|
"down_nobrem_lost = downstream_lost[ak.all(downstream_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
|
|
"\n",
|
|
"print(\"\\ndownstream: cutoff energy = 200MeV, 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": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"upstream eff = 0.852 +/- 0.004\n",
|
|
"downstream eff = 0.84 +/- 0.005\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"cutoff_energy=200\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.any(upstream_found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\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.any(upstream_lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\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(\"upstream 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.any(downstream_found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\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.any(downstream_lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\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": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"upstream:\n",
|
|
"mean energyloss relative to initial energy (found): 0.3207102540525612\n",
|
|
"mean energyloss relative to initial energy (lost): 0.5602258293743071\n",
|
|
"downstream:\n",
|
|
"mean energyloss relative to initial energy (found): 0.17552539358035377\n",
|
|
"mean energyloss relative to initial energy (lost): 0.2870828762276071\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": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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"text/plain": [
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"<Figure size 1800x600 with 2 Axes>"
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]
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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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"#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",
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"ax[0].hist(up_energyloss_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
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"ax[0].hist(up_energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
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"ax[0].set_xticks(np.arange(0,1.1,0.1), minor=True,)\n",
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"ax[0].set_yticks(np.arange(0,11,1), minor=True)\n",
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"ax[0].set_xlabel(r\"$E_\\gamma/E_0$\")\n",
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"ax[0].set_ylabel(\"counts (normed)\")\n",
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"ax[0].set_title(\"Upstream\")\n",
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"ax[0].legend()\n",
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"ax[0].grid()\n",
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"\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",
|
|
"electrons that lose most of their energy downstream are almost always lost\n",
|
|
"\"\"\"\n",
|
|
"fig.suptitle(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, only photons w/ brem_vtx_z$<9500$mm\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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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(0,1,70), np.linspace(0,1.5e5,70)), 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(0,1,70), np.linspace(0,1.5e5,70)), 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": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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"text/plain": [
|
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"<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(0,1,70), np.linspace(0,1.5e5,70)), 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\"$E_0$\")\n",
|
|
"ax0.set_title(\"found energyloss wrt electron energy\")\n",
|
|
"\n",
|
|
"a1=ax1.hist2d(down_energyloss_lost, down_energy_lost, bins=(np.linspace(0,1,70), np.linspace(0,1.5e5,70)), 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\"$E_0$\")\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": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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"text/plain": [
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"<Figure size 2000x600 with 3 Axes>"
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]
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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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"#plot residual energy against energyloss and try to find a good split (eg energyloss before and after the magnet)\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_residual_found, bins=(np.linspace(0,1,80), np.linspace(0,1.5e5,80)), 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(0,1,80), np.linspace(0,1.5e5,80)), 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": 14,
|
|
"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(0,1,80), np.linspace(0,1.5e5,80)), 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(down_energyloss_lost, down_residual_lost, bins=(np.linspace(0,1,80), np.linspace(0,1.5e5,80)), 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, 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
|
|
}
|