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682 lines
256 KiB
682 lines
256 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 76,
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"metadata": {},
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"outputs": [],
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"source": [
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"import uproot\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": 77,
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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": 77,
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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)\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": 78,
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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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"0.8606728758791105"
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]
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},
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"execution_count": 78,
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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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"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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"t_eff(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": 79,
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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: 32\n",
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"eff (cutoff = 0 ) = 0.96875\n",
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"sample size: 32\n",
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"eff (cutoff = 50 ) = 0.96875\n",
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"sample size: 32\n",
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"eff (cutoff = 100 ) = 0.96875\n",
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"sample size: 43\n",
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"eff (cutoff = 150 ) = 0.9767441860465116\n",
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"sample size: 65\n",
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"eff (cutoff = 200 ) = 0.9692307692307692\n",
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"sample size: 97\n",
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"eff (cutoff = 250 ) = 0.9587628865979382\n",
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"sample size: 129\n",
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"eff (cutoff = 300 ) = 0.9457364341085271\n",
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"sample size: 150\n",
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"eff (cutoff = 350 ) = 0.9533333333333334\n",
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"sample size: 169\n",
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"eff (cutoff = 400 ) = 0.9408284023668639\n",
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"sample size: 197\n",
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"eff (cutoff = 450 ) = 0.9390862944162437\n",
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"sample size: 227\n",
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"eff (cutoff = 500 ) = 0.920704845814978\n",
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"sample size: 257\n",
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"eff (cutoff = 550 ) = 0.9260700389105059\n",
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"sample size: 297\n",
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"eff (cutoff = 600 ) = 0.9326599326599326\n",
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"sample size: 334\n",
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"eff (cutoff = 650 ) = 0.9281437125748503\n",
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"sample size: 366\n",
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"eff (cutoff = 700 ) = 0.9289617486338798\n",
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"sample size: 400\n",
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"eff (cutoff = 750 ) = 0.925\n",
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"sample size: 436\n",
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"eff (cutoff = 800 ) = 0.9151376146788991\n",
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"sample size: 468\n",
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"eff (cutoff = 850 ) = 0.9102564102564102\n",
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"sample size: 500\n",
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"eff (cutoff = 900 ) = 0.912\n",
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"sample size: 533\n",
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"eff (cutoff = 950 ) = 0.9136960600375235\n",
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"sample size: 562\n",
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"eff (cutoff = 1000 ) = 0.9163701067615658\n",
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"\n",
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"sample size: 150\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"0.9533333333333334"
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]
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},
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"execution_count": 79,
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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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"#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n",
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"#von energie der photonen abmachen\n",
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"#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 = 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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"\n",
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"\n",
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"for cutoff_energy in range(0,1050,50):\n",
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"\tnobrem_f = found[ak.all(found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
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"\tnobrem_l = lost[ak.all(lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
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"\tprint(\"sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
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"\tprint(\"eff (cutoff = \",str(cutoff_energy),\") = \",str(t_eff(nobrem_f,nobrem_l)))\n",
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"\n",
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"\"\"\"\n",
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"we see that a cutoff energy of 350MeV is ideal because the efficiency drops significantly for higher values\n",
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"\"\"\"\n",
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"cutoff_energy = 350.0 #MeV\n",
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"\n",
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"\"\"\"\n",
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"better statistics: cutoff=350MeV - sample size: 150 events and efficiency=0.9533\n",
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"\"\"\"\n",
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"nobrem_found = found[ak.all(found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
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"nobrem_lost = lost[ak.all(lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
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"\n",
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"print(\"\\nsample size: \",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n",
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"t_eff(nobrem_found, nobrem_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": 80,
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"metadata": {},
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"outputs": [],
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"source": [
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"def weird_div(n,d):\n",
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" return n/d if d else 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": 81,
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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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"0.8593328191284226"
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]
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},
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"execution_count": 81,
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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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"#wie viel energie relativ zur anfangsenergie verlieren die elektronen durch bremstrahlung und hat das einen einfluss darauf ob wir sie finden oder nicht?\n",
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"#if any photon of an electron has an energy higher the cutoff then it is included\n",
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"cutoff_energy=350\n",
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"\n",
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"brem_found = found[ak.any(found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n",
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"energy_found = ak.to_numpy(brem_found[\"energy\"])\n",
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"eph_found = ak.to_numpy(ak.sum(brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
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"energyloss_found = eph_found/energy_found\n",
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"\n",
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"brem_lost = lost[ak.any(lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n",
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"energy_lost = ak.to_numpy(brem_lost[\"energy\"])\n",
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"eph_lost = ak.to_numpy(ak.sum(brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
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"energyloss_lost = eph_lost/energy_lost\n",
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"\n",
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"t_eff(brem_found,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": 82,
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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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"mean energyloss relative to initial energy (found): 0.6551043170507098\n",
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"mean energyloss relative to initial energy (lost): 0.8273131179948844\n"
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]
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}
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],
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"source": [
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"mean_energyloss_found = ak.mean(energyloss_found)\n",
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"mean_energyloss_lost = ak.mean(energyloss_lost)\n",
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"print(\"mean energyloss relative to initial energy (found): \", mean_energyloss_found)\n",
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"print(\"mean energyloss relative to initial energy (lost): \", mean_energyloss_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": 83,
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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": [
|
|
"#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": 84,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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AAAAAAAAAQNWgeDgAAAAAAAAAAKga3NgAAAAAAAAAAABVgxsbAAAAAAAAAACganBjAwAAAAAAAAAAVA1ubAAAAAAAAAAAgKrBjQ0AAAAAAAAAAFA1uLEBAAAAAAAAAACqBjc2AAAAAAAAAABA1fj/AfV1DLS5s/9uAAAAAElFTkSuQmCC",
|
|
"text/plain": [
|
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"<Figure size 2000x600 with 4 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=200, cmap=plt.cm.jet, cmin=1)\n",
|
|
"ax0.set_xlabel(\"energyloss\")\n",
|
|
"ax0.set_ylabel(r\"$E_0$\")\n",
|
|
"ax0.set_title(\"found energyloss wrt electron energy\")\n",
|
|
"plt.colorbar(a0[3],ax=ax0)\n",
|
|
"\n",
|
|
"a1=ax1.hist2d(energyloss_lost, energy_lost, bins=200, cmap=plt.cm.jet, cmin=1) \n",
|
|
"ax1.set_xlabel(\"energyloss\")\n",
|
|
"ax1.set_ylabel(r\"$E_0$\")\n",
|
|
"ax1.set_title(\"lost energyloss wrt electron energy\")\n",
|
|
"plt.colorbar(a1[3],ax=ax1)\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"\"\"\"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 85,
|
|
"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": 86,
|
|
"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 = ak.to_numpy(scifi_found[\"endvtx_type\"])\n",
|
|
"brem_vtx_type_found = scifi_found[scifi_found[\"endvtx_type\"]==101]\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 = ak.to_numpy(scifi_lost[\"endvtx_type\"])\n",
|
|
"brem_vtx_type_lost = scifi_lost[scifi_lost[\"endvtx_type\"]==101]\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"#ak.num(scifi_found[\"energy\"], axis=0)\n",
|
|
"#scifi_found.snapshot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 87,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"9056"
|
|
]
|
|
},
|
|
"execution_count": 87,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"ak.num(scifi_found[\"energy\"], axis=0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 88,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"scifi_fitpars_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",
|
|
" #[:,6] -> endvtx_type\n",
|
|
" scifi_fitpars_found.real(sf_vtx_type_found[i])\n",
|
|
" scifi_fitpars_found.end_list()\n",
|
|
"\n",
|
|
"scifi_fitpars_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",
|
|
" #[:,6] -> endvtx_type\n",
|
|
" scifi_fitpars_lost.real(sf_vtx_type_lost[i])\n",
|
|
" scifi_fitpars_lost.end_list()\n",
|
|
"\n",
|
|
"\n",
|
|
"scifi_fitpars_lost = scifi_fitpars_lost.to_numpy()\n",
|
|
"scifi_fitpars_found = scifi_fitpars_found.to_numpy()\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 101,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array(2)"
|
|
]
|
|
},
|
|
"execution_count": 101,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"np.array()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 129,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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zzjvP+h7LzZkzx+M/Avj6PgEAQNNSOaGUn59vPXxS0Z49e4zT6TSpqanGGGP+85//GEnm6aef9nn8yy67zCQnJ/vdH0kmLCzM4yGtsrIyc+mll5pLLrmkSr8r3/vPnj3bSDIHDhwwxtRujHDDDTdUO+4qT3b8+c9/9lj/+OOPG0kmKyvLo//+jMeqU9sxgT/tDB482FxwwQVVEin333+/ad68uTWmKB+zXnvttR5xbrfbxMbGmt69e3us//bbb01ISIjH9SosLDQRERHmF7/4hUds165dqyT5Kvvmm2+MJPPKK68YY4xZt26dkWSmTp1q4uPjrbhBgwZ5jA3Lfwd33nlnlWN6Gzd6U35P/8wzz3isnzlzppFk1q1b53N/f8fIlbndblNaWmoeeeQR06pVK4/EYYcOHUxQUJDZsWOHz7bLj/HKK6+YoKAgj7Fieb82bdpkrTty5IgJCgoyYWFhHsmjLVu2GEnm97//vbXO39+Qr3HdpZdeanr06GFKS0s91g8bNsy0bdvW+m8Avr5PAGgMTHkHwLZ27Nih/fv3Ky0tTc2aff8/V+edd55uuukm5eTk+FVWX53+/fsrIiLC+hwTE6Po6Gh9++23kqQTJ04oNzdXI0eO9JhbOiIiQsOHD/e7nYiICI0YMcJjXWpqqk6dOqW1a9d6rL/hhhsUFBRkfe7evbskWX2qj+tRPt1Wenq6x/of/OAH6tKli1auXOmxPjY2Vj/4wQ881nXv3t3qkzfvvvuuHA6H7rjjDpWVlVlLbGysLr/88irTKPjTzrvvvqtu3brpiiuu8Djm4MGDq52a4brrrlNUVJTHujVr1ui6665T69atrXXNmjXTrbfeWuUcfvGLX2jLli36+9//Lun01BqvvvqqRo0apfPOO8/n+Q8YMEArVqyQdPpFt//97381adIktW7dWtnZ2ZKkFStWKCkpSeHh4R77jhgxQiEhIT6P76/bb7/d43Pfvn3VoUMHv6ddq7z/rbfequDgYI/9//Wvfyk1NVWxsbEKCgpSSEiI9R60ylO+SNJNN91UZd2hQ4f085//XO3bt1dwcLBCQkLUoUMHr8cYNmyYx+cuXbrI4XBo6NCh1rrg4GBdcskldfoNVefdd99V//79FRcX53GM8rbXrFnjEV+f3ycAALCHDRs2qKioqMo9dfv27XXddddZ99QtW7bUxRdfrCeeeEJz587VZ5995tfU0f4YMGCAYmJirM9BQUG67bbb9M0331SZyrryeKTyOKO2Y4TqrFq1SuHh4br55ps91pcfs/IxahqPeVPb+7ma2jl58qRWrlypH//4x2rRooXHMa+//nqdPHmyyjTkle9nd+zYoby8vCpjigsvvFBXX321x7qIiAjdddddWrhwoTVF36pVq/SPf/xD999/v89zv/jii9WxY0drnJGdna3ExETdcccd2rVrl/75z3+quLhY69at08CBA6vsX919+JmqPE5ITU2VJL/GGf6OkVetWqWBAwfK5XJZ44zf/va3OnLkiMf04tLp33Tnzp2rtPXZZ59pxIgRatWqlXWMO++8U263W19//bVHbNu2bdWzZ0/rc8uWLRUdHa0rrrhCcXFx1vouXbpIUp1+Q5V98803+uqrr6zrWvkYBw4c0I4dOzz2qc/vEwBqg4QSANs6cuSIpNM3dpXFxcXp1KlTys/PP6Njt2rVqso6p9OpoqIiSaffPXPq1CnFxsZWiatunTcVB3mV9y8/P299cjqdkmT1qT6uR03HqKlP5f0q75M3Bw8elDFGMTExCgkJ8VhycnKqvBvHn3YOHjyoL774osrxIiIiZIypcszqzvHIkSPVfifVrbvxxhvVsWNHa97r8kGfr3nNyw0cOFB79uzRzp07tWLFCvXo0UPR0dG67rrrtGLFChUVFWn9+vXVDvSq6/eZ8vb7rfw9+7t/cHCwWrVqZe1//Phx/fCHP9TGjRv16KOPavXq1crNzdWbb74pSVV+Jy1atFBkZKTHulOnTiklJUVvvvmmpk6dqpUrV+qTTz6xBl3V/dZatmzp8Tk0NFQtWrSo8mLh0NBQnTx50vpc299QdQ4ePKh33nmnyjHK57P353cIAACaNn/vqR0Oh1auXKnBgwdr9uzZuvLKK9WmTRtNmDBBx44dq1MffI1T6nuc4c+945EjRxQbG1vl/bXR0dEKDg6u13FGbe7namrnyJEjKisr07x586oc8/rrr5dU8/1d+bn5O84YP368jh07psWLF0uS5s+frwsuuEA33nijz3OXTicSy5NzK1as0KBBg5SYmKiYmBitWLFCf//731VUVNSg44zyMUFF3n571fFnjPzJJ58oJSVF0ul3U/39739Xbm6uHnzwQUlVxwjVnduePXv0wx/+UP/+97/1zDPP6OOPP1Zubq41vqt8jMpjDOn0eKK6sYcka5xxJr+hyg4ePChJmjJlSpVjjB07ttpjMM4AECjBge4AAHhTfpN64MCBKtv279+vZs2aWRUozZs3r/YllP78B+LqREVFyeFwKC8vr8q26tZ5U35jWN3+1Q1ufKnN9fDnGBdccEGVY1Ss3KmL1q1by+Fw6OOPP7YGrBVVt86fY4aFhemPf/yj1+0VVR7MSqfP39d3UlGzZs00btw4TZ8+XU8++aSee+45DRgwQAkJCTX2dcCAAZJOD/Kys7M1aNAga/1vfvMbrV27VsXFxdUO9Krr95ny9vu95JJL/N6/Xbt21ueysjIdOXLE+h2tWrVK+/fv1+rVq62qJEk6evRotcer7ty2bdumzz//XAsXLtSoUaOs9eUvN65Ptf0NeYvp3r27Zs6cWe32ik8vSvX7fQIAAHuo6b684j1Fhw4d9NJLL0mSvv76a/35z39WRkaGSkpK9Pzzz59xH3yNU+oyzjjTMUKrVq20ceNGGWM87n8OHTqksrKyeh1n1PV+rqKoqCgFBQUpLS3N64Nj8fHxHp8r39+VXz9/xxmXXHKJhg4dqmeffVZDhw7V22+/rRkzZnjMVuHNgAED9NJLL+mTTz7Rxo0b9Zvf/EbS6dkZsrOz9e233+q8885Tnz59quxbX/ellccEUu1+e/6MkZcuXaqQkBC9++67Hg+NvfXWW9Ues7pze+utt3TixAm9+eab1uwHkrRly5Ya+1gbZ/Ibqqz8dztt2jSNHDmy2pjK41DGGQAChQolALaVkJCgdu3aacmSJTLGWOtPnDihN954Q0lJSWrRooUkqWPHjjp06JDHzWlJSYk+/PDDM2o7PDxcP/jBD/Tmm296VDgcO3ZM77zzjt/HOXbsmN5++22PdUuWLFGzZs107bXX1qpPtbkelZ86LHfddddJkl577TWP9bm5ufryyy+tREhdDRs2TMYY/fvf/1avXr2qLImJiWd0zH/+859q1apVtcfs2LFjjcdITk7WqlWrPBKNp06d0l/+8pdq43/2s58pNDRUt99+u3bs2FHjNBTl2rZtq65du+qNN97Q5s2brYTSoEGDdPjwYc2dO1eRkZG66qqr/Dqet++zJuVPPZZbv369vv32W/Xr1++M9v/zn/+ssrIya//yQUzlBOELL7zgdx/r4xj+qs1vyNsTssOGDdO2bdt08cUXV3uMygklAABw9klKSlJYWFiVe+p9+/Zp1apVXu+pO3furN/85jdKTEzUp59+aq33pzKnspUrV3qMfdxut15//XVdfPHFVZJCNanNGMFbXwcMGKDjx49X+Q/+r7zyirW9PtTHmKCiFi1aqH///vrss8/UvXv3ao9ZU5IkISFBsbGx+vOf/+yxfs+ePVq/fn21+/ziF7/QF198oVGjRikoKEijR4/2q78DBgyQw+HQQw895DGmHDhwoD766CNlZ2fr2muv9XvK5foaZyxZskSS/Bpn+DNGdjgcCg4O9kiyFRUV6dVXX/W7j9WNM4wxevHFF/0+hj9q8xvydr0TEhLUqVMnff7559Xu36tXL4+pGwEgkKhQAmBbzZo10+zZs3X77bdr2LBhGjNmjIqLi/XEE0/o6NGjeuyxx6zY2267Tb/97W/1k5/8RL/85S918uRJ/f73v5fb7T7j9n/3u99pyJAhGjRokCZPniy3263HH39c4eHh+u677/w6RqtWrXTfffdpz5496ty5s95//329+OKLuu+++3ThhRfWqj+1uR7lCZtnnnlGo0aNUkhIiBISEpSQkKB7771X8+bNU7NmzTR06FDt3r1bDz30kNq3b68HHnigVn3y5uqrr9a9996ru+66S5s2bdK1116r8PBwHThwQOvWrVNiYqLuu+++Wh1z4sSJeuONN3TttdfqgQceUPfu3XXq1Cnt2bNHWVlZmjx5snr37u3zGA8++KDeeecdDRgwQA8++KDCwsL0/PPPW/OXV3w3lSSdf/75uvPOO7VgwQJ16NChVu/PGjBggObNm6ewsDBr7vT4+HjFx8crKytLI0aMUHCwf/9v+OKLL1ZYWJgWL16sLl266LzzzlNcXFyNyYtNmzbpZz/7mW655Rbt3btXDz74oNq1a2dNm1CTN998U8HBwRo0aJC2b9+uhx56SJdffrk1P3zfvn0VFRWln//853r44YcVEhKixYsX6/PPP/fr+JJ06aWX6uKLL9avf/1rGWPUsmVLvfPOO9a7pupTbX5DiYmJWr16td555x21bdtWERERSkhI0COPPKLs7Gz17dtXEyZMUEJCgk6ePKndu3fr/fff1/PPP1/r/4gDAACalvPPP18PPfSQpk+frjvvvFM//elPdeTIEc2YMUPNmzfXww8/LEn64osvdP/99+uWW25Rp06dFBoaqlWrVumLL77Qr3/9a+t4iYmJWrp0qV5//XVddNFFat68eY0PYLVu3VrXXXedHnroIYWHh+u5557TV199paVLl9b6fGozRkhMTNSbb76pBQsWqGfPnmrWrJl69eqlO++8U88++6xGjRql3bt3KzExUevWrVNmZqauv/76aivzz0R9jAkqe+aZZ3TNNdfohz/8oe677z517Nh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",
|
|
"text/plain": [
|
|
"<Figure size 2000x600 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#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",
|
|
"vtx_type_found = scifi_fitpars_found[:,6]\n",
|
|
"vtx_type_lost = scifi_fitpars_lost[:,6]\n",
|
|
"\n",
|
|
"\n",
|
|
"#Erste Annahme ist Bremsstrahlung\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(20,6))\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": 28,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([0., 0., 0., ..., 0., 0., 0.])"
|
|
]
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"vtx_type_found"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<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_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": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "env1",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.5"
|
|
},
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|