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766 lines
150 KiB
766 lines
150 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 matplotlib.pyplot as plt\n",
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"from mpl_toolkits import mplot3d\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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"%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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"31630"
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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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"file = uproot.open(\n",
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" \"tracking_losses_ntuple_B_default.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
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")\n",
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"\n",
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"# selektiere nur elektronen von B->K*ee\n",
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"allcolumns = file.arrays()\n",
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"found = allcolumns[\n",
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" (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal)\n",
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"] # B: 9056\n",
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"lost = allcolumns[\n",
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" (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal)\n",
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"] # 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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"source": [
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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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"brem_x_f = found[\"brem_vtx_x\"]\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_x\").append(brem_x_f[itr])\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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"brem_x_l = lost[\"brem_vtx_x\"]\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_x\").append(brem_x_l[itr])\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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"photon_cut = 0\n",
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"photon_cut_ratio = 0.1\n",
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"\n",
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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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" tmp_energy = 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 (\n",
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" brem_f[itr, \"brem_vtx_z\", jentry] > 5000\n",
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" or brem_f[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
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" or brem_f[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
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" ):\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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" tmp_energy -= brem_f[itr, \"brem_photons_pe\", jentry]\n",
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" cut_brem_found.end_list()\n",
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"\n",
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" tmp_energy = brem_f[itr, \"energy\"]\n",
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"\n",
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" cut_brem_found.field(\"brem_vtx_x\")\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 (\n",
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" brem_f[itr, \"brem_vtx_z\", jentry] > 5000\n",
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" or brem_f[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
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" or brem_f[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
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" ):\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_x\", jentry])\n",
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" tmp_energy -= brem_f[itr, \"brem_photons_pe\", jentry]\n",
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" cut_brem_found.end_list()\n",
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"\n",
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" tmp_energy = brem_f[itr, \"energy\"]\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 (\n",
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" brem_f[itr, \"brem_vtx_z\", jentry] > 5000\n",
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" or brem_f[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
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" or brem_f[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
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" ):\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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" tmp_energy -= brem_f[itr, \"brem_photons_pe\", jentry]\n",
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" cut_brem_found.end_list()\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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"tuple_found = ak.Array(cut_brem_found)\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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" tmp_energy = brem_l[itr, \"energy\"]\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 (\n",
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" brem_l[itr, \"brem_vtx_z\", jentry] > 5000\n",
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" or brem_l[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
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" or brem_l[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
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" ):\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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" tmp_energy -= brem_l[itr, \"brem_photons_pe\", jentry]\n",
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" cut_brem_lost.end_list()\n",
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"\n",
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" tmp_energy = brem_l[itr, \"energy\"]\n",
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"\n",
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" cut_brem_lost.field(\"brem_vtx_x\")\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 (\n",
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" brem_l[itr, \"brem_vtx_z\", jentry] > 5000\n",
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" or brem_l[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
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" or brem_l[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
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" ):\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_x\", jentry])\n",
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" tmp_energy -= brem_l[itr, \"brem_photons_pe\", jentry]\n",
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" cut_brem_lost.end_list()\n",
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"\n",
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" tmp_energy = brem_l[itr, \"energy\"]\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 (\n",
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" brem_l[itr, \"brem_vtx_z\", jentry] > 5000\n",
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" or brem_l[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
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" or brem_l[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
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" ):\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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" tmp_energy -= brem_l[itr, \"brem_photons_pe\", 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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"tuple_lost = ak.Array(cut_brem_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": 5,
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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: 3.26e+04,\n",
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" brem_photons_pe: [1.26e+04, 4.49e+03],\n",
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" brem_vtx_x: [-33.8, -133],\n",
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" brem_vtx_z: [601, 2.33e+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_x: 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: 3.26e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
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]
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},
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"execution_count": 5,
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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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"length_found = ak.num(tuple_found[\"brem_photons_pe\"], axis=-1)\n",
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"length_lost = ak.num(tuple_lost[\"brem_photons_pe\"], axis=-1)\n",
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"\n",
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"tuple_found[1]"
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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: 2.12e+04,\n",
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" brem_photons_pe: [],\n",
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" brem_vtx_x: [],\n",
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" brem_vtx_z: []}\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_x: 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: 2.12e+04, ...} 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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"tuple_found[3]"
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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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"source": [
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"Z_found = ak.to_numpy(\n",
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" ak.sum(tuple_found[\"brem_photons_pe\"], axis=-1, keepdims=False)\n",
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") / ak.to_numpy(tuple_found[\"energy\"])\n",
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"Z_lost = ak.to_numpy(\n",
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" ak.sum(tuple_lost[\"brem_photons_pe\"], axis=-1, keepdims=False)\n",
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") / ak.to_numpy(tuple_lost[\"energy\"])"
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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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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.hist(\n",
|
|
" Z_lost,\n",
|
|
" bins=100,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" histtype=\"bar\",\n",
|
|
" color=\"darkorange\",\n",
|
|
" label=\"lost\",\n",
|
|
")\n",
|
|
"plt.hist(\n",
|
|
" Z_found,\n",
|
|
" bins=100,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" histtype=\"bar\",\n",
|
|
" color=\"blue\",\n",
|
|
" label=\"found\",\n",
|
|
")\n",
|
|
"plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
|
|
"plt.ylabel(\"a.u.\")\n",
|
|
"plt.title(r\"$E_{ph}/E_0$\")\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"found: 24074 , lost: 2400\n",
|
|
"0.09969261443881366\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"brem_x_found = ak.to_numpy(ak.flatten(tuple_found[\"brem_vtx_x\"]))\n",
|
|
"brem_z_found = ak.to_numpy(ak.flatten(tuple_found[\"brem_vtx_z\"]))\n",
|
|
"\n",
|
|
"brem_x_lost = ak.to_numpy(ak.flatten(tuple_lost[\"brem_vtx_x\"]))\n",
|
|
"brem_z_lost = ak.to_numpy(ak.flatten(tuple_lost[\"brem_vtx_z\"]))\n",
|
|
"\n",
|
|
"n_found = len(brem_x_found)\n",
|
|
"n_lost = len(brem_x_lost)\n",
|
|
"print(\"found: \", n_found, \", lost: \", n_lost)\n",
|
|
"stretch_factor = n_lost / n_found\n",
|
|
"print(stretch_factor)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
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|
"text/plain": [
|
|
"<Figure size 2000x800 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"vmax = 300\n",
|
|
"nbins = 100\n",
|
|
"\n",
|
|
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
|
|
"\n",
|
|
"a0 = ax0.hist2d(\n",
|
|
" brem_z_found,\n",
|
|
" brem_x_found,\n",
|
|
" density=False,\n",
|
|
" bins=nbins,\n",
|
|
" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax,\n",
|
|
" range=[[-200, 4000], [-1000, 1000]],\n",
|
|
")\n",
|
|
"ax0.vlines([770, 990, 2700], -1000, 1000)\n",
|
|
"ax0.set_ylim(-1000, 1000)\n",
|
|
"ax0.set_xlim(-200, 4000)\n",
|
|
"ax0.set_xlabel(\"z [mm]\")\n",
|
|
"ax0.set_ylabel(\"x [mm]\")\n",
|
|
"ax0.set_title(r\"$e^\\pm$ found brem vertices\")\n",
|
|
"\n",
|
|
"a1 = ax1.hist2d(\n",
|
|
" brem_z_lost,\n",
|
|
" brem_x_lost,\n",
|
|
" density=False,\n",
|
|
" bins=nbins,\n",
|
|
" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax * stretch_factor,\n",
|
|
" range=[[-200, 4000], [-1000, 1000]],\n",
|
|
")\n",
|
|
"ax1.vlines([770, 990, 2700], -1000, 1000)\n",
|
|
"ax1.set_ylim(-1000, 1000)\n",
|
|
"ax1.set_xlim(-200, 4000)\n",
|
|
"ax1.set_xlabel(\"z [mm]\")\n",
|
|
"ax1.set_ylabel(\"x [mm]\")\n",
|
|
"ax1.set_title(r\"$e^\\pm$ lost brem vertices\")\n",
|
|
"# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
|
|
"\n",
|
|
"plt.colorbar(a0[3], ax=ax1)\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"tuple_found[1]\n",
|
|
"\n",
|
|
"\n",
|
|
"def ratio(nominator, denom):\n",
|
|
" denominator = ak.num(denom[\"energy\"], axis=0)\n",
|
|
" return nominator / denominator\n",
|
|
"\n",
|
|
"\n",
|
|
"def eff(found, lost):\n",
|
|
" return found / (found + lost)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"ratio of lost electrons emitting in Velo: 0.3855211513301352\n",
|
|
"ratio of lost electrons emitting in Rich1+UT: 0.3475795900566943\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"n_elec_lost = ak.num(tuple_lost, axis=0)\n",
|
|
"\n",
|
|
"velo_lost = 0\n",
|
|
"\n",
|
|
"richut_lost = 0\n",
|
|
"\n",
|
|
"for jelec in range(ak.num(tuple_lost, axis=0)):\n",
|
|
" veloemitted = False\n",
|
|
" richemitted = False\n",
|
|
" for jphoton in range(ak.num(tuple_lost[jelec][\"brem_photons_pe\"], axis=0)):\n",
|
|
" if (tuple_lost[jelec, \"brem_vtx_z\", jphoton] <= 850) and (veloemitted == False):\n",
|
|
" velo_lost += 1\n",
|
|
" veloemitted = True\n",
|
|
" elif (\n",
|
|
" (tuple_lost[jelec, \"brem_vtx_z\", jphoton] > 850)\n",
|
|
" and (tuple_lost[jelec, \"brem_vtx_z\", jphoton] <= 3000)\n",
|
|
" and (richemitted == False)\n",
|
|
" ):\n",
|
|
" richut_lost += 1\n",
|
|
" richemitted = True\n",
|
|
"\n",
|
|
"print(\"ratio of lost electrons emitting in Velo: \", ratio(velo_lost, brem_l))\n",
|
|
"print(\"ratio of lost electrons emitting in Rich1+UT: \", ratio(richut_lost, brem_l))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"electrons_lost = ak.ArrayBuilder()\n",
|
|
"\n",
|
|
"for jelec in range(ak.num(tuple_lost, axis=0)):\n",
|
|
" electrons_lost.begin_record()\n",
|
|
" electrons_lost.field(\"energy\").real(tuple_lost[jelec, \"energy\"])\n",
|
|
"\n",
|
|
" tmp_velo = 0\n",
|
|
" tmp_richut = 0\n",
|
|
" for jphoton in range(ak.num(tuple_lost[jelec][\"brem_photons_pe\"], axis=0)):\n",
|
|
" if tuple_lost[jelec, \"brem_vtx_z\", jphoton] <= 850:\n",
|
|
" tmp_velo += tuple_lost[jelec, \"brem_photons_pe\", jphoton]\n",
|
|
" elif (tuple_lost[jelec, \"brem_vtx_z\", jphoton] > 850) and (\n",
|
|
" tuple_lost[jelec, \"brem_vtx_z\", jphoton] <= 3000\n",
|
|
" ):\n",
|
|
" tmp_richut += tuple_lost[jelec, \"brem_photons_pe\", jphoton]\n",
|
|
"\n",
|
|
" electrons_lost.field(\"velo\").real(tmp_velo)\n",
|
|
"\n",
|
|
" electrons_lost.field(\"rich\").real(tmp_richut)\n",
|
|
"\n",
|
|
" electrons_lost.end_record()\n",
|
|
"\n",
|
|
"electrons_lost = ak.Array(electrons_lost)\n",
|
|
"\n",
|
|
"electrons_found = ak.ArrayBuilder()\n",
|
|
"\n",
|
|
"for jelec in range(ak.num(tuple_found, axis=0)):\n",
|
|
" electrons_found.begin_record()\n",
|
|
" electrons_found.field(\"energy\").real(tuple_found[jelec, \"energy\"])\n",
|
|
"\n",
|
|
" tmp_velo = 0\n",
|
|
" tmp_richut = 0\n",
|
|
" for jphoton in range(ak.num(tuple_found[jelec][\"brem_photons_pe\"], axis=0)):\n",
|
|
" if tuple_found[jelec, \"brem_vtx_z\", jphoton] <= 850:\n",
|
|
" tmp_velo += tuple_found[jelec, \"brem_photons_pe\", jphoton]\n",
|
|
" elif (tuple_found[jelec, \"brem_vtx_z\", jphoton] > 850) and (\n",
|
|
" tuple_found[jelec, \"brem_vtx_z\", jphoton] <= 3000\n",
|
|
" ):\n",
|
|
" tmp_richut += tuple_found[jelec, \"brem_photons_pe\", jphoton]\n",
|
|
"\n",
|
|
" electrons_found.field(\"velo\").real(tmp_velo)\n",
|
|
"\n",
|
|
" electrons_found.field(\"rich\").real(tmp_richut)\n",
|
|
"\n",
|
|
" electrons_found.end_record()\n",
|
|
"\n",
|
|
"electrons_found = ak.Array(electrons_found)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"VELO energy emission, eff: 0.9318135969336292\n",
|
|
"RICH1+UT energy emission, eff: 0.9119065010956903\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"num_velo_found = 0\n",
|
|
"num_rich_found = 0\n",
|
|
"for itr in range(ak.num(electrons_found, axis=0)):\n",
|
|
" if electrons_found[itr, \"velo\"] >= electrons_found[itr, \"rich\"]:\n",
|
|
" num_velo_found += 1\n",
|
|
" else:\n",
|
|
" num_rich_found += 1\n",
|
|
"\n",
|
|
"num_velo_lost = 0\n",
|
|
"num_rich_lost = 0\n",
|
|
"for itr in range(ak.num(electrons_lost, axis=0)):\n",
|
|
" if electrons_lost[itr, \"velo\"] >= electrons_lost[itr, \"rich\"]:\n",
|
|
" num_velo_lost += 1\n",
|
|
" else:\n",
|
|
" num_rich_lost += 1\n",
|
|
"\n",
|
|
"print(\"VELO energy emission, eff: \", eff(num_velo_found, num_velo_lost))\n",
|
|
"\n",
|
|
"print(\"RICH1+UT energy emission, eff: \", eff(num_rich_found, num_rich_lost))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 56,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"cut = 4000\n",
|
|
"\n",
|
|
"tf = tuple_found[ak.sum(\n",
|
|
" tuple_found[\"brem_photons_pe\"], axis=-1, keepdims=False) > cut]\n",
|
|
"tl = tuple_lost[ak.sum(tuple_lost[\"brem_photons_pe\"], axis=-1, keepdims=False)\n",
|
|
" > cut]\n",
|
|
"\n",
|
|
"cut_x_found = ak.to_numpy(ak.flatten(tf[\"brem_vtx_x\"]))\n",
|
|
"cut_z_found = ak.to_numpy(ak.flatten(tf[\"brem_vtx_z\"]))\n",
|
|
"\n",
|
|
"cut_x_lost = ak.to_numpy(ak.flatten(tf[\"brem_vtx_x\"]))\n",
|
|
"cut_z_lost = ak.to_numpy(ak.flatten(tf[\"brem_vtx_z\"]))\n",
|
|
"\n",
|
|
"# how many tracks of all are included?\n",
|
|
"ratio_f = tuple_found[ak.sum(\n",
|
|
" tuple_found[\"brem_photons_pe\"], axis=-1, keepdims=False) > cut]\n",
|
|
"ratio_l = tuple_lost[ak.sum(\n",
|
|
" tuple_lost[\"brem_photons_pe\"], axis=-1, keepdims=False) > cut]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 57,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 2000x800 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"vmax = 500\n",
|
|
"\n",
|
|
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
|
|
"\n",
|
|
"a0 = ax0.hist2d(\n",
|
|
" cut_z_found,\n",
|
|
" cut_x_found,\n",
|
|
" density=False,\n",
|
|
" bins=200,\n",
|
|
" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax,\n",
|
|
")\n",
|
|
"ax0.set_ylim(-1000, 1000)\n",
|
|
"ax0.set_xlim(-200, 4000)\n",
|
|
"ax0.set_xlabel(\"z [mm]\")\n",
|
|
"ax0.set_ylabel(\"x [mm]\")\n",
|
|
"ax0.set_title(r\"$e^\\pm$ found brem vertices\")\n",
|
|
"\n",
|
|
"a1 = ax1.hist2d(cut_z_lost,\n",
|
|
" cut_x_lost,\n",
|
|
" density=False,\n",
|
|
" bins=200,\n",
|
|
" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax)\n",
|
|
"ax1.set_ylim(-1000, 1000)\n",
|
|
"ax1.set_xlim(-200, 4000)\n",
|
|
"ax1.set_xlabel(\"z [mm]\")\n",
|
|
"ax1.set_ylabel(\"x [mm]\")\n",
|
|
"ax1.set_title(r\"$e^\\pm$ lost brem vertices\")\n",
|
|
"# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
|
|
"\n",
|
|
"plt.colorbar(a0[3], ax=ax1)\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "tuner",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|