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1213 lines
390 KiB
1213 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": 22,
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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": 23,
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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": 23,
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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": 24,
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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": 25,
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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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" photon_length: 10,\n",
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" brem_photons_pe: [3.26e+03, 4.45e+03, 178, ..., 825, 8.99e+03, 3.48e+03],\n",
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" brem_vtx_z: [162, 187, 387, 487, ..., 9.49e+03, 1.21e+04, 1.21e+04, 1.21e+04]}\n",
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"-------------------------------------------------------------------------------\n",
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"type: {\n",
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" energy: float64,\n",
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" photon_length: int64,\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, photon_length: int6...'>"
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]
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},
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"execution_count": 25,
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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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"#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)\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"brem_f[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": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"acc_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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" acc_brem_found.begin_record()\n",
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" acc_brem_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
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" \n",
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" acc_brem_found.field(\"brem_photons_pe\")\n",
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" acc_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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" acc_brem_found.real(brem_f[itr,\"brem_photons_pe\", jentry])\n",
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" \n",
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" #acc_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
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" acc_brem_found.end_list()\n",
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" \n",
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" acc_brem_found.field(\"brem_vtx_z\")\n",
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" acc_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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" acc_brem_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
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" acc_brem_found.end_list()\n",
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" \n",
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"\n",
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" \n",
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" acc_brem_found.end_record()\n",
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"\n",
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"acc_brem_found = ak.Array(acc_brem_found)\n",
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"\n",
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"\n",
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"\n",
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"acc_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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" acc_brem_lost.begin_record()\n",
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" acc_brem_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n",
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" \n",
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" acc_brem_lost.field(\"brem_photons_pe\")\n",
|
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" acc_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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" acc_brem_lost.real(brem_l[itr,\"brem_photons_pe\", jentry])\n",
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" \n",
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" #acc_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
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" acc_brem_lost.end_list()\n",
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" \n",
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" acc_brem_lost.field(\"brem_vtx_z\")\n",
|
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" acc_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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" acc_brem_lost.real(brem_l[itr, \"brem_vtx_z\",jentry])\n",
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" acc_brem_lost.end_list()\n",
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" \n",
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" acc_brem_lost.end_record()\n",
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"\n",
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"acc_brem_lost = ak.Array(acc_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": 27,
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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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"9056"
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]
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},
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"execution_count": 27,
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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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"ak.num(acc_brem_found,axis=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": 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": 28,
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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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"'\\nph_e = found[\"brem_photons_pe\"]\\nevent_cut = ak.all(ph_e<cutoff_energy,axis=1)\\nph_e = ph_e[event_cut]\\n'"
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]
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},
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"execution_count": 28,
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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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"\n",
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"\"\"\"\n",
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"ph_e = found[\"brem_photons_pe\"]\n",
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"event_cut = ak.all(ph_e<cutoff_energy,axis=1)\n",
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"ph_e = ph_e[event_cut]\n",
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"\"\"\"\n",
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"\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": 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": 29,
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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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"sample size: 322\n",
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"eff (cutoff = 0 ) = 0.9379 +/- 0.0135\n",
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"sample size: 322\n",
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"eff (cutoff = 25 ) = 0.9379 +/- 0.0135\n",
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"sample size: 322\n",
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"eff (cutoff = 50 ) = 0.9379 +/- 0.0135\n",
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"sample size: 322\n",
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"eff (cutoff = 75 ) = 0.9379 +/- 0.0135\n",
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"sample size: 322\n",
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"eff (cutoff = 100 ) = 0.9379 +/- 0.0135\n",
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"sample size: 384\n",
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"eff (cutoff = 125 ) = 0.9453 +/- 0.0116\n",
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"sample size: 433\n",
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"eff (cutoff = 150 ) = 0.9423 +/- 0.0112\n",
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"sample size: 485\n",
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"eff (cutoff = 175 ) = 0.9443 +/- 0.0104\n",
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"sample size: 529\n",
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"eff (cutoff = 200 ) = 0.949 +/- 0.0096\n",
|
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"sample size: 581\n",
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"eff (cutoff = 225 ) = 0.9501 +/- 0.009\n",
|
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"sample size: 644\n",
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"eff (cutoff = 250 ) = 0.9519 +/- 0.0084\n",
|
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"sample size: 705\n",
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"eff (cutoff = 275 ) = 0.9475 +/- 0.0084\n",
|
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"sample size: 757\n",
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"eff (cutoff = 300 ) = 0.9498 +/- 0.0079\n",
|
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"sample size: 802\n",
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"eff (cutoff = 325 ) = 0.9451 +/- 0.008\n",
|
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"sample size: 846\n",
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"eff (cutoff = 350 ) = 0.9433 +/- 0.008\n",
|
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"sample size: 876\n",
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"eff (cutoff = 375 ) = 0.9452 +/- 0.0077\n",
|
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"sample size: 919\n",
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"eff (cutoff = 400 ) = 0.9467 +/- 0.0074\n",
|
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"sample size: 972\n",
|
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"eff (cutoff = 425 ) = 0.9475 +/- 0.0072\n",
|
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"sample size: 1019\n",
|
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"eff (cutoff = 450 ) = 0.949 +/- 0.0069\n",
|
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"sample size: 1067\n",
|
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"eff (cutoff = 475 ) = 0.9475 +/- 0.0068\n",
|
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"sample size: 1117\n",
|
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"eff (cutoff = 500 ) = 0.9418 +/- 0.007\n",
|
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"sample size: 1144\n",
|
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"eff (cutoff = 525 ) = 0.9423 +/- 0.0069\n",
|
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"sample size: 1184\n",
|
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"eff (cutoff = 550 ) = 0.9409 +/- 0.0069\n",
|
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"sample size: 1234\n",
|
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"eff (cutoff = 575 ) = 0.9408 +/- 0.0067\n",
|
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"sample size: 1268\n",
|
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"eff (cutoff = 600 ) = 0.9416 +/- 0.0066\n",
|
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"sample size: 1303\n",
|
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"eff (cutoff = 625 ) = 0.9417 +/- 0.0065\n",
|
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"sample size: 1342\n",
|
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"eff (cutoff = 650 ) = 0.9404 +/- 0.0065\n",
|
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"sample size: 1381\n",
|
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"eff (cutoff = 675 ) = 0.9399 +/- 0.0064\n",
|
|
"sample size: 1416\n",
|
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"eff (cutoff = 700 ) = 0.9407 +/- 0.0063\n",
|
|
"sample size: 1444\n",
|
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"eff (cutoff = 725 ) = 0.9418 +/- 0.0062\n",
|
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"sample size: 1484\n",
|
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"eff (cutoff = 750 ) = 0.942 +/- 0.0061\n",
|
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"sample size: 1523\n",
|
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"eff (cutoff = 775 ) = 0.9402 +/- 0.0061\n",
|
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"sample size: 1557\n",
|
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"eff (cutoff = 800 ) = 0.939 +/- 0.0061\n",
|
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"sample size: 1593\n",
|
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"eff (cutoff = 825 ) = 0.9379 +/- 0.006\n",
|
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"sample size: 1628\n",
|
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"eff (cutoff = 850 ) = 0.9373 +/- 0.006\n",
|
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"sample size: 1656\n",
|
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"eff (cutoff = 875 ) = 0.9384 +/- 0.0059\n",
|
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"sample size: 1690\n",
|
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"eff (cutoff = 900 ) = 0.9385 +/- 0.0058\n",
|
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"sample size: 1721\n",
|
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"eff (cutoff = 925 ) = 0.9378 +/- 0.0058\n",
|
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"sample size: 1745\n",
|
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"eff (cutoff = 950 ) = 0.9381 +/- 0.0058\n",
|
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"sample size: 1769\n",
|
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"eff (cutoff = 975 ) = 0.9378 +/- 0.0057\n",
|
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"sample size: 1796\n",
|
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"eff (cutoff = 1000 ) = 0.9365 +/- 0.0058\n",
|
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"sample size: 1838\n",
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"eff (cutoff = 1025 ) = 0.9374 +/- 0.0056\n",
|
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"\n",
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"cutoff energy = 350MeV, sample size: 846\n",
|
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"eff = 0.9433 +/- 0.008\n"
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|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n",
|
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"#von energie der photonen abmachen\n",
|
|
"#scan ab welcher energie der photonen die effizienz abfällt\n",
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"\n",
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"#abhängigkeit vom ort der emission untersuchen <- noch nicht gemacht\n",
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"\n",
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"\n",
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"\n",
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"#idea: we make an event cut st all events that contain a photon of energy > cutoff_energy are not included\n",
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"\"\"\"\n",
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"ph_e = acc_brem_found[\"brem_photons_pe\"]\n",
|
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"event_cut = ak.all(ph_e<cutoff_energy,axis=1)\n",
|
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"ph_e = ph_e[event_cut]\n",
|
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"\"\"\"\n",
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"\n",
|
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"efficiencies_found = 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(0,1050,25):\n",
|
|
"\tnobrem_f = acc_brem_found[ak.all(acc_brem_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
|
|
"\tnobrem_l = acc_brem_lost[ak.all(acc_brem_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\tprint(\"sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
|
|
"\tprint(\"eff (cutoff = \",str(cutoff_energy),\") = \",np.round(t_eff(nobrem_f,nobrem_l),4), \"+/-\", np.round(eff_err(nobrem_f, nobrem_l),4))\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"we see that a cutoff energy of xxxMeV is ideal because the efficiency drops significantly for higher values\n",
|
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"\"\"\"\n",
|
|
"cutoff_energy = 350.0 #MeV\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n",
|
|
"\"\"\"\n",
|
|
"nobrem_found = acc_brem_found[ak.all(acc_brem_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
|
|
"nobrem_lost = acc_brem_lost[ak.all(acc_brem_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
|
|
"\n",
|
|
"print(\"\\ncutoff energy = 350MeV, sample size:\",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n",
|
|
"print(\"eff = \",np.round(t_eff(nobrem_found, nobrem_lost),4), \"+/-\", np.round(eff_err(nobrem_found, nobrem_lost),4))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"eff = 0.8535 +/- 0.0036\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<pre>[{energy: 2.58e+04, brem_photons_pe: [9.97e+03, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 8.03e+04, brem_photons_pe: [4.91e+03, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 5.6e+03, brem_photons_pe: [320, ..., 392], brem_vtx_z: [...]},\n",
|
|
" {energy: 6.36e+03, brem_photons_pe: [273, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 4.67e+04, brem_photons_pe: [8.96e+03, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 7.16e+04, brem_photons_pe: [544, ..., 142], brem_vtx_z: [...]},\n",
|
|
" {energy: 5.15e+04, brem_photons_pe: [384, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 4.07e+04, brem_photons_pe: [2.7e+04, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 2.77e+04, brem_photons_pe: [2.24e+03, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 6.4e+04, brem_photons_pe: [686, ..., 796], brem_vtx_z: [...]},\n",
|
|
" ...,\n",
|
|
" {energy: 5.59e+03, brem_photons_pe: [901, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 2.13e+04, brem_photons_pe: [787, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 9.34e+03, brem_photons_pe: [762, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 5.08e+04, brem_photons_pe: [711, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 6.41e+04, brem_photons_pe: [4.17e+03, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 1.01e+04, brem_photons_pe: [220, ..., 156], brem_vtx_z: [...]},\n",
|
|
" {energy: 1.96e+04, brem_photons_pe: [1.66e+03, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 2.98e+04, brem_photons_pe: [8.32e+03, ...], brem_vtx_z: [...]},\n",
|
|
" {energy: 3.97e+04, brem_photons_pe: [9.36e+03, ...], brem_vtx_z: [...]}]\n",
|
|
"-------------------------------------------------------------------------\n",
|
|
"type: 1418 * {\n",
|
|
" energy: float64,\n",
|
|
" brem_photons_pe: var * float64,\n",
|
|
" brem_vtx_z: var * float64\n",
|
|
"}</pre>"
|
|
],
|
|
"text/plain": [
|
|
"<Array [{energy: 2.58e+04, ...}, ..., {...}] type='1418 * {energy: float64,...'>"
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"#wie viel energie relativ zur anfangsenergie verlieren die elektronen durch bremstrahlung und hat das einen einfluss darauf ob wir sie finden oder nicht?\n",
|
|
"#if any photon of an electron has an energy higher the cutoff then it is included\n",
|
|
"cutoff_energy=350\n",
|
|
"\n",
|
|
"brem_found = acc_brem_found[ak.any(acc_brem_found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n",
|
|
"energy_found = ak.to_numpy(brem_found[\"energy\"])\n",
|
|
"eph_found = ak.to_numpy(ak.sum(brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
|
|
"residual_found = energy_found - eph_found\n",
|
|
"energyloss_found = eph_found/energy_found\n",
|
|
"\n",
|
|
"brem_lost = acc_brem_lost[ak.any(acc_brem_lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n",
|
|
"energy_lost = ak.to_numpy(brem_lost[\"energy\"])\n",
|
|
"eph_lost = ak.to_numpy(ak.sum(brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
|
|
"residual_lost = energy_lost - eph_lost\n",
|
|
"energyloss_lost = eph_lost/energy_lost\n",
|
|
"\n",
|
|
"print(\"eff = \", np.round(t_eff(brem_found,brem_lost),4), \"+/-\", np.round(eff_err(brem_found, brem_lost),4))\n",
|
|
"brem_lost"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"mean energyloss relative to initial energy (found): 0.4107345449771658\n",
|
|
"mean energyloss relative to initial energy (lost): 0.7300783757368142\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"mean_energyloss_found = ak.mean(energyloss_found)\n",
|
|
"mean_energyloss_lost = ak.mean(energyloss_lost)\n",
|
|
"print(\"mean energyloss relative to initial energy (found): \", mean_energyloss_found)\n",
|
|
"print(\"mean energyloss relative to initial energy (lost): \", mean_energyloss_lost)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#in abhängigkeit von der energie der elektronen\n",
|
|
"plt.hist(energyloss_lost, bins=200, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
|
|
"plt.hist(energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
|
|
"plt.xticks(np.arange(0,1.1,0.1), minor=True,)\n",
|
|
"plt.yticks(np.arange(0,10,1), minor=True)\n",
|
|
"plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
|
|
"plt.ylabel(\"counts (normed)\")\n",
|
|
"plt.title(r'$E_{ph}/E_0$')\n",
|
|
"plt.legend()\n",
|
|
"plt.grid()\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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|
|
"text/plain": [
|
|
"<Figure size 2000x600 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#energyloss in abh von der energie der elektronen\n",
|
|
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
|
|
"\n",
|
|
"a0=ax0.hist2d(energyloss_found, energy_found, bins=(np.linspace(0,1,70), np.linspace(0,1.5e5,105)), cmap=plt.cm.jet, cmin=1, vmax=7)\n",
|
|
"ax0.set_ylim(0,1.5e5)\n",
|
|
"ax0.set_xlim(0,1)\n",
|
|
"ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
|
|
"ax0.set_ylabel(r\"$E_0$\")\n",
|
|
"ax0.set_title(\"found energyloss wrt electron energy\")\n",
|
|
"\n",
|
|
"a1=ax1.hist2d(energyloss_lost, energy_lost, bins=(np.linspace(0,1,70), np.linspace(0,1.5e5,105)), cmap=plt.cm.jet, cmin=1, vmax=7) \n",
|
|
"ax1.set_ylim(0,1.5e5)\n",
|
|
"ax1.set_xlim(0,1)\n",
|
|
"ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
|
|
"ax1.set_ylabel(r\"$E_0$\")\n",
|
|
"ax1.set_title(\"lost energyloss wrt electron energy\")\n",
|
|
"\n",
|
|
"fig.colorbar(a1[3],ax=ax1)\n",
|
|
"fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, only photons w/ brem_vtx_z$<9500$mm\")\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"we can see that high energy electrons are often found even though they emit a lot of their energy through bremsstrahlung\n",
|
|
"\"\"\"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 2000x600 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#plot residual energy against energyloss and try to find a good split (eg energyloss before and after the magnet)\n",
|
|
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
|
|
"\n",
|
|
"a0=ax0.hist2d(energyloss_found, residual_found, bins=(np.linspace(0,1,80), np.linspace(0,1e5,80)), cmap=plt.cm.jet, cmin=1, vmax=20)\n",
|
|
"ax0.set_ylim(0,1e5)\n",
|
|
"ax0.set_xlim(0,1)\n",
|
|
"ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
|
|
"ax0.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
|
|
"ax0.set_title(\"found energyloss wrt residual electron energy\")\n",
|
|
"\n",
|
|
"a1=ax1.hist2d(energyloss_lost, residual_lost, bins=(np.linspace(0,1,80), np.linspace(0,1e5,80)), cmap=plt.cm.jet, cmin=1, vmax=20) \n",
|
|
"ax1.set_ylim(0,1e5)\n",
|
|
"ax1.set_xlim(0,1)\n",
|
|
"ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
|
|
"ax1.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
|
|
"ax1.set_title(\"lost energyloss wrt residual electron energy\")\n",
|
|
"\n",
|
|
"fig.colorbar(a1[3],ax=ax1)\n",
|
|
"fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, only photons w/ brem_vtx_z$<9500$mm\")\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"\"\"\"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#try to find a split between energy lost before and after the magnet (z~5000mm)\n",
|
|
"\n",
|
|
"upstream_found = ak.ArrayBuilder()\n",
|
|
"downstream_found = ak.ArrayBuilder()\n",
|
|
"\n",
|
|
"for itr in range(ak.num(brem_f, axis=0)):\n",
|
|
" upstream_found.begin_record()\n",
|
|
" upstream_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
|
|
" \n",
|
|
" downstream_found.begin_record()\n",
|
|
" downstream_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
|
|
" \n",
|
|
" upstream_found.field(\"brem_photons_pe\")\n",
|
|
" downstream_found.field(\"brem_photons_pe\")\n",
|
|
" upstream_found.begin_list()\n",
|
|
" downstream_found.begin_list()\n",
|
|
" for jentry in range(brem_f[itr, \"photon_length\"]):\n",
|
|
" if (brem_f[itr, \"brem_vtx_z\", jentry]>5000):\n",
|
|
" if brem_f[itr, \"brem_vtx_z\", jentry]<9500:\n",
|
|
" downstream_found.real(brem_f[itr,\"brem_photons_pe\",jentry])\n",
|
|
" else:\n",
|
|
" continue\n",
|
|
" else:\n",
|
|
" upstream_found.real(brem_f[itr,\"brem_photons_pe\", jentry]) \n",
|
|
" upstream_found.end_list()\n",
|
|
" downstream_found.end_list()\n",
|
|
" \n",
|
|
" upstream_found.field(\"brem_vtx_z\")\n",
|
|
" downstream_found.field(\"brem_vtx_z\")\n",
|
|
" upstream_found.begin_list()\n",
|
|
" downstream_found.begin_list()\n",
|
|
" for jentry in range(brem_f[itr, \"photon_length\"]):\n",
|
|
" if brem_f[itr, \"brem_vtx_z\", jentry]>5000:\n",
|
|
" if brem_f[itr,\"brem_vtx_z\",jentry]<9500:\n",
|
|
" downstream_found.real(brem_f[itr,\"brem_vtx_z\",jentry])\n",
|
|
" else:\n",
|
|
" continue\n",
|
|
" else:\n",
|
|
" upstream_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
|
|
" upstream_found.end_list()\n",
|
|
" downstream_found.end_list()\n",
|
|
" upstream_found.end_record()\n",
|
|
" downstream_found.end_record()\n",
|
|
" \n",
|
|
"\n",
|
|
"upstream_found = ak.Array(upstream_found)\n",
|
|
"downstream_found = ak.Array(downstream_found)"
|
|
]
|
|
},
|
|
{
|
|
"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": 14,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#ist die shape der teilspur im scifi anders? (koenntest du zum beispiel durch vergleich der verteilungen der fit parameter studieren,\n",
|
|
"#in meiner thesis findest du das fitmodell -- ist einfach ein polynom dritten grades)\n",
|
|
"z_ref=8520 #mm\n",
|
|
"\n",
|
|
"def scifi_track(z, a, b, c, d):\n",
|
|
" return a + b*(z-z_ref) + c*(z-z_ref)**2 + d*(z-z_ref)**3\n",
|
|
"\n",
|
|
"def z_mag(xv, zv, tx, a, b):\n",
|
|
" \"\"\" optical centre of the magnet is defined as the intersection between the trajectory tangents before and after the magnet\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" xv (double): velo x track\n",
|
|
" zv (double): velo z track\n",
|
|
" tx (double): velo x slope\n",
|
|
" a (double): ax parameter of track fit\n",
|
|
" b (double): bx parameter of track fit\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" double: z_mag\n",
|
|
" \"\"\"\n",
|
|
" return (xv-tx*zv-a+b*z_ref)/(b-tx)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"###"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"scifi_found = found[found[\"scifi_hit_pos_x_length\"]>3]\n",
|
|
"scifi_lost = lost[lost[\"scifi_hit_pos_x_length\"]>3]\n",
|
|
"#should be fulfilled by all candidates\n",
|
|
"\n",
|
|
"scifi_x_found = scifi_found[\"scifi_hit_pos_x\"]\n",
|
|
"scifi_z_found = scifi_found[\"scifi_hit_pos_z\"]\n",
|
|
"\n",
|
|
"tx_found = scifi_found[\"velo_track_tx\"]\n",
|
|
"\n",
|
|
"scifi_x_lost = scifi_lost[\"scifi_hit_pos_x\"]\n",
|
|
"scifi_z_lost = scifi_lost[\"scifi_hit_pos_z\"]\n",
|
|
"\n",
|
|
"tx_lost = scifi_lost[\"velo_track_tx\"]\n",
|
|
"\n",
|
|
"xv_found = scifi_found[\"velo_track_x\"]\n",
|
|
"zv_found = scifi_found[\"velo_track_z\"]\n",
|
|
"\n",
|
|
"xv_lost = scifi_lost[\"velo_track_x\"]\n",
|
|
"zv_lost = scifi_lost[\"velo_track_z\"]\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"sf_energy_found = ak.to_numpy(scifi_found[\"energy\"])\n",
|
|
"sf_eph_found = ak.to_numpy(ak.sum(scifi_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
|
|
"sf_vtx_type_found = scifi_found[\"all_endvtx_types\"]\n",
|
|
"\n",
|
|
"\n",
|
|
"sf_energy_lost = ak.to_numpy(scifi_lost[\"energy\"])\n",
|
|
"sf_eph_lost = ak.to_numpy(ak.sum(scifi_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
|
|
"sf_vtx_type_lost = scifi_lost[\"all_endvtx_types\"]\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"#ak.num(scifi_found[\"energy\"], axis=0)\n",
|
|
"#scifi_found.snapshot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre>[101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 0]\n",
|
|
"------------------\n",
|
|
"type: 11 * float32</pre>"
|
|
],
|
|
"text/plain": [
|
|
"<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>"
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"ak.num(scifi_found[\"energy\"], axis=0)\n",
|
|
"scifi_found[\"all_endvtx_types\"][1,:]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"scifi_fitpars_found = ak.ArrayBuilder()\n",
|
|
"vtx_types_found = ak.ArrayBuilder()\n",
|
|
"\n",
|
|
"for i in range(0,ak.num(scifi_found, axis=0)):\n",
|
|
" popt, pcov = curve_fit(scifi_track,ak.to_numpy(scifi_z_found[i,:]),ak.to_numpy(scifi_x_found[i,:]))\n",
|
|
" scifi_fitpars_found.begin_list()\n",
|
|
" scifi_fitpars_found.real(popt[0])\n",
|
|
" scifi_fitpars_found.real(popt[1])\n",
|
|
" scifi_fitpars_found.real(popt[2])\n",
|
|
" scifi_fitpars_found.real(popt[3])\n",
|
|
" #[:,4] -> energy \n",
|
|
" scifi_fitpars_found.real(sf_energy_found[i])\n",
|
|
" #[:,5] -> photon energy\n",
|
|
" scifi_fitpars_found.real(sf_eph_found[i])\n",
|
|
" scifi_fitpars_found.end_list()\n",
|
|
" \n",
|
|
" vtx_types_found.begin_list()\n",
|
|
" #[:,0] -> endvtx_type\n",
|
|
" vtx_types_found.extend(sf_vtx_type_found[i,:])\n",
|
|
" vtx_types_found.end_list()\n",
|
|
" \n",
|
|
"\n",
|
|
"scifi_fitpars_lost = ak.ArrayBuilder()\n",
|
|
"vtx_types_lost = ak.ArrayBuilder()\n",
|
|
"\n",
|
|
"for i in range(0,ak.num(scifi_lost, axis=0)):\n",
|
|
" popt, pcov = curve_fit(scifi_track,ak.to_numpy(scifi_z_lost[i,:]),ak.to_numpy(scifi_x_lost[i,:]))\n",
|
|
" scifi_fitpars_lost.begin_list()\n",
|
|
" scifi_fitpars_lost.real(popt[0])\n",
|
|
" scifi_fitpars_lost.real(popt[1])\n",
|
|
" scifi_fitpars_lost.real(popt[2])\n",
|
|
" scifi_fitpars_lost.real(popt[3])\n",
|
|
" #[:,4] -> energy \n",
|
|
" scifi_fitpars_lost.real(sf_energy_lost[i])\n",
|
|
" #[:,5] -> photon energy\n",
|
|
" scifi_fitpars_lost.real(sf_eph_lost[i])\n",
|
|
" scifi_fitpars_lost.end_list()\n",
|
|
" \n",
|
|
" vtx_types_lost.begin_list()\n",
|
|
" #endvtx_type\n",
|
|
" vtx_types_lost.extend(sf_vtx_type_lost[i,:])\n",
|
|
" vtx_types_lost.end_list()\n",
|
|
" \n",
|
|
"\n",
|
|
"\n",
|
|
"scifi_fitpars_lost = ak.to_numpy(scifi_fitpars_lost)\n",
|
|
"scifi_fitpars_found = ak.to_numpy(scifi_fitpars_found)\n",
|
|
"\n",
|
|
"vtx_types_lost = ak.Array(vtx_types_lost)\n",
|
|
"vtx_types_found = ak.Array(vtx_types_found)\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre>[101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 101,\n",
|
|
" 0]\n",
|
|
"------------------\n",
|
|
"type: 11 * float64</pre>"
|
|
],
|
|
"text/plain": [
|
|
"<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>"
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]
|
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},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"vtx_types_found[0]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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",
|
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"text/plain": [
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"<Figure size 1800x600 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
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"source": [
|
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"#b parameter des fits [:,1] hat für lost eine breitere Verteilung. Warum?\n",
|
|
"#evtl multiple scattering candidates (lost); findet man einen gewissen endvtx_type (mult scattering)\n",
|
|
"#steiler velo winkel (eta)? vertex type? evtl bremsstrahlung?\n",
|
|
"\n",
|
|
"#isolate b parameters for analysis\n",
|
|
"b_found = scifi_fitpars_found[:,1]\n",
|
|
"b_lost = scifi_fitpars_lost[:,1]\n",
|
|
"\n",
|
|
"brem_energy_found = scifi_fitpars_found[:,5]\n",
|
|
"brem_energy_lost = scifi_fitpars_lost[:,5]\n",
|
|
"\n",
|
|
"\n",
|
|
"bs_found, vtx_types_found = ak.broadcast_arrays(b_found, vtx_types_found)\n",
|
|
"bs_found = ak.to_numpy(ak.ravel(bs_found))\n",
|
|
"vtx_types_found = ak.to_numpy(ak.ravel(vtx_types_found))\n",
|
|
"\n",
|
|
"bs_lost, vtx_types_lost = ak.broadcast_arrays(b_lost, vtx_types_lost)\n",
|
|
"bs_lost = ak.to_numpy(ak.ravel(bs_lost))\n",
|
|
"vtx_types_lost = ak.to_numpy(ak.ravel(vtx_types_lost))\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"#Erste Annahme ist Bremsstrahlung\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(18,6))\n",
|
|
"axes = ImageGrid(fig, 111, # similar to subplot(111)\n",
|
|
" nrows_ncols=(1, 2), # creates 2x2 grid of axes\n",
|
|
" axes_pad=1, # pad between axes in inch.\n",
|
|
" cbar_mode=\"single\",\n",
|
|
" cbar_location=\"right\",\n",
|
|
" cbar_pad=0.1,\n",
|
|
" aspect=False\n",
|
|
" )\n",
|
|
"\n",
|
|
"\n",
|
|
"h0 = axes[0].hist2d(b_found, brem_energy_found, bins=200, cmap=plt.cm.jet, cmin=1,vmax=30)\n",
|
|
"axes[0].set_xlim(-1,1)\n",
|
|
"axes[0].set_xlabel(\"b parameter [mm]\")\n",
|
|
"axes[0].set_ylabel(r\"$E_{ph}$\")\n",
|
|
"axes[0].set_title(\"found photon energy wrt b parameter\")\n",
|
|
"\n",
|
|
"h1 = axes[1].hist2d(b_lost, brem_energy_lost, bins=200, cmap=plt.cm.jet, cmin=1,vmax=30)\n",
|
|
"axes[1].set_xlim(-1,1)\n",
|
|
"axes[1].set_xlabel(\"b parameter [mm]\")\n",
|
|
"axes[1].set_ylabel(r\"$E_{ph}$\")\n",
|
|
"axes[1].set_title(\"lost photon energy wrt b parameter\")\n",
|
|
"\n",
|
|
"fig.colorbar(h0[3], cax=axes.cbar_axes[0], orientation='vertical')\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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"text/plain": [
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"<Figure size 1800x600 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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"fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n",
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"\n",
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"a0=ax[0].hist2d(bs_found, vtx_types_found, bins=110, density=True, cmap=plt.cm.jet, cmin=1e-20,vmax=2)\n",
|
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"ax[0].set_ylim(0,110)\n",
|
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"ax[0].set_xlim(-1,1)\n",
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"ax[0].set_xlabel(\"b\")\n",
|
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"ax[0].set_ylabel(\"endvtx id\")\n",
|
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"ax[0].set_title(\"found endvtx id wrt b parameter\")\n",
|
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"ax[0].set_yticks(np.arange(0,110,1),minor=True)\n",
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"\n",
|
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"a1=ax[1].hist2d(bs_lost, vtx_types_lost, bins=110, density=True, cmap=plt.cm.jet, cmin=1e-20,vmax=2)\n",
|
|
"ax[1].set_ylim(0,110)\n",
|
|
"ax[1].set_xlim(-1,1)\n",
|
|
"ax[1].set_xlabel(\"b\")\n",
|
|
"ax[1].set_ylabel(\"endvtx id\")\n",
|
|
"ax[1].set_title(\"lost endvtx id wrt b paraneter\")\n",
|
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"ax[1].set_yticks(np.arange(0,110,1), minor=True)\n",
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"\n",
|
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"\"\"\"\n",
|
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"vtx_id: 101 - Bremsstrahlung\n",
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"B:\n",
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"wir können nicht wirklich sagen dass bei den lost teilchen jegliche endvertex types überwiegen, im gegensatz zu den found \n",
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"\"\"\"\n",
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"fig.colorbar(a0[3], ax=ax, orientation='vertical')\n",
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"plt.show()"
|
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]
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},
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{
|
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"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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"text/plain": [
|
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"<Figure size 1500x1000 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, figsize=(15,10))\n",
|
|
"\n",
|
|
"ax0.hist(scifi_fitpars_found[:,0], bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$a_x$ found\")\n",
|
|
"ax0.hist(scifi_fitpars_lost[:,0], bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$a_x$ lost\")\n",
|
|
"ax0.set_xlabel(\"a\")\n",
|
|
"ax0.set_ylabel(\"normed\")\n",
|
|
"ax0.set_title(\"fitparameter a der scifi track\")\n",
|
|
"ax0.legend()\n",
|
|
"\n",
|
|
"ax1.hist(scifi_fitpars_found[:,1], bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$b_x$ found\")\n",
|
|
"ax1.hist(scifi_fitpars_lost[:,1], bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$b_x$ lost\")\n",
|
|
"ax1.set_xticks(np.arange(-1,1,0.1),minor=True)\n",
|
|
"ax1.set_xlabel(\"b\")\n",
|
|
"ax1.set_ylabel(\"normed\")\n",
|
|
"ax1.set_title(\"fitparameter b der scifi track\")\n",
|
|
"ax1.legend()\n",
|
|
"#evtl multiple scattering candidates (lost); findet man einen gewissen endvtx_type (mult scattering)\n",
|
|
"#steiler velo winkel (eta)? vertex type? evtl bremsstrahlung?\n",
|
|
"\n",
|
|
"\n",
|
|
"ax2.hist(scifi_fitpars_found[:,2], bins=500, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$c_x$ found\")\n",
|
|
"ax2.hist(scifi_fitpars_lost[:,2], bins=500, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$c_x$ lost\")\n",
|
|
"ax2.set_xlim([-3e-5,3e-5])\n",
|
|
"ax2.set_xticks(np.arange(-3e-5,3.5e-5,1e-5),minor=False)\n",
|
|
"ax2.set_xlabel(\"c\")\n",
|
|
"ax2.set_ylabel(\"normed\")\n",
|
|
"ax2.set_title(\"fitparameter c der scifi track\")\n",
|
|
"ax2.legend()\n",
|
|
"\n",
|
|
"ax3.hist(scifi_fitpars_found[:,3], bins=500, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$d_x$ found\")\n",
|
|
"ax3.hist(scifi_fitpars_lost[:,3], bins=500, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$d_x$ lost\")\n",
|
|
"ax3.set(xlim=(-5e-8,5e-8))\n",
|
|
"ax3.text(-4e-8,3e8,\"d negligible <1e-7\")\n",
|
|
"ax3.set_xlabel(\"d\")\n",
|
|
"ax3.set_ylabel(\"normed\")\n",
|
|
"ax3.set_title(\"fitparameter d der scifi track\")\n",
|
|
"ax3.legend()\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"a_x: virtual hit on the reference plane\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
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