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1103 lines
155 KiB
1103 lines
155 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import uproot\t\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from mpl_toolkits import mplot3d\n",
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"import awkward as ak\n",
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"from scipy.optimize import curve_fit\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"40402 10099\n",
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"50501\n"
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]
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}
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],
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"source": [
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"#file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
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"file = uproot.open(\n",
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" \"tracking_losses_ntuple_B_default.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
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")\n",
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"\n",
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"# selektiere nur elektronen von B->K*ee\n",
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"allcolumns = file.arrays()\n",
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"found = allcolumns[\n",
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" (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)\n",
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"] # B: 9056\n",
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"lost = allcolumns[\n",
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" (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n",
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"] # B: 1466\n",
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"\n",
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"electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n",
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"\n",
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"print(ak.num(found, axis=0), ak.num(lost, axis=0))\n",
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"print(ak.num(electrons,axis=0))\n",
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"# ak.count(found, axis=None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre>{all_endvtx_types_length: 7,\n",
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" all_endvtx_types: [101, 101, 101, 101, 101, 101, 0],\n",
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" all_endvtx_x_length: 7,\n",
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" all_endvtx_x: [-6.97, -52.9, -52.9, ..., -1.71e+03, -2.14e+03, -3.57e+03],\n",
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" all_endvtx_y_length: 7,\n",
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" all_endvtx_y: [-0.89, -6.75, -6.75, -7.08, -66.1, -72.6, -39.5],\n",
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" all_endvtx_z_length: 7,\n",
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" all_endvtx_z: [112, 859, 859, 895, 8.7e+03, 9.68e+03, 1.26e+04],\n",
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" brem_photons_pe_length: 6,\n",
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" brem_photons_pe: [2.62e+03, 812, 2.54e+03, 1.86e+03, 3.12e+03, 241],\n",
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" brem_photons_px_length: 6,\n",
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" brem_photons_px: [-161, -49.7, -156, -114, -1.18e+03, -101],\n",
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" brem_photons_py_length: 6,\n",
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" brem_photons_py: [-18.9, -6.92, -21.6, -16.8, -20.9, -0.26],\n",
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" brem_photons_pz_length: 6,\n",
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" brem_photons_pz: [2.61e+03, 810, 2.54e+03, 1.86e+03, 2.89e+03, 219],\n",
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" brem_vtx_x_length: 6,\n",
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" brem_vtx_x: [-6.97, -52.9, -52.9, -55.2, -1.71e+03, -2.14e+03],\n",
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" brem_vtx_y_length: 6,\n",
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" ...}\n",
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"---------------------------------------------------------------------------\n",
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"type: {\n",
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" all_endvtx_types_length: int32,\n",
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" all_endvtx_types: var * float32,\n",
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" all_endvtx_x_length: int32,\n",
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" all_endvtx_x: var * float32,\n",
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" all_endvtx_y_length: int32,\n",
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" all_endvtx_y: var * float32,\n",
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" all_endvtx_z_length: int32,\n",
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" all_endvtx_z: var * float32,\n",
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" brem_photons_pe_length: int32,\n",
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" brem_photons_pe: var * float32,\n",
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" brem_photons_px_length: int32,\n",
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" brem_photons_px: var * float32,\n",
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" brem_photons_py_length: int32,\n",
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" brem_photons_py: var * float32,\n",
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" brem_photons_pz_length: int32,\n",
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" brem_photons_pz: var * float32,\n",
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" brem_vtx_x_length: int32,\n",
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" brem_vtx_x: var * float32,\n",
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" brem_vtx_y_length: int32,\n",
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" brem_vtx_y: var * float32,\n",
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" brem_vtx_z_length: int32,\n",
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" brem_vtx_z: var * float32,\n",
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" endvtx_type: int32,\n",
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" endvtx_x: float64,\n",
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" endvtx_y: float64,\n",
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" endvtx_z: float64,\n",
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" energy: float64,\n",
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" eta: float64,\n",
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" event_count: int32,\n",
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" fromB: bool,\n",
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" fromD: bool,\n",
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" fromDecay: bool,\n",
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" fromHadInt: bool,\n",
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" fromPV: bool,\n",
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" fromPairProd: bool,\n",
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" fromSignal: bool,\n",
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" fromStrange: bool,\n",
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" ideal_state_770_qop: float64,\n",
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" ideal_state_770_tx: float64,\n",
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" ideal_state_770_ty: float64,\n",
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" ideal_state_770_x: float64,\n",
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" ideal_state_770_y: float64,\n",
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" ideal_state_770_z: float64,\n",
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" ideal_state_9410_qop: float64,\n",
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" ideal_state_9410_tx: float64,\n",
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" ideal_state_9410_ty: float64,\n",
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" ideal_state_9410_x: float64,\n",
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" ideal_state_9410_y: float64,\n",
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" ideal_state_9410_z: float64,\n",
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" isElectron: bool,\n",
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" isKaon: bool,\n",
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" isMuon: bool,\n",
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" isPion: bool,\n",
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" isProton: bool,\n",
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" lost: bool,\n",
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" lost_in_track_fit: bool,\n",
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" match_fraction: float32,\n",
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" mc_chi2: float32,\n",
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" mc_dSlope: float32,\n",
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" mc_dSlopeY: float32,\n",
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" mc_distX: float32,\n",
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" mc_distY: float32,\n",
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" mc_teta2: float32,\n",
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" mc_zMag: float32,\n",
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" mcp_idx: int32,\n",
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" mother_id: int32,\n",
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" mother_key: int32,\n",
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" originvtx_type: int32,\n",
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" originvtx_x: float64,\n",
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" originvtx_y: float64,\n",
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" originvtx_z: float64,\n",
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" p: float64,\n",
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" phi: float64,\n",
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" pid: int32,\n",
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" pt: float64,\n",
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" px: float64,\n",
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" py: float64,\n",
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" pz: float64,\n",
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" quality: int32,\n",
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" rad_length_frac: float64,\n",
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" scifi_hit_pos_x_length: int32,\n",
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" scifi_hit_pos_x: var * float32,\n",
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" scifi_hit_pos_y_length: int32,\n",
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" scifi_hit_pos_y: var * float32,\n",
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" scifi_hit_pos_z_length: int32,\n",
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" scifi_hit_pos_z: var * float32,\n",
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" track_p: float64,\n",
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" track_pt: float64,\n",
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" tx: float64,\n",
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" ty: float64,\n",
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" ut_hit_pos_x_length: int32,\n",
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" ut_hit_pos_x: var * float32,\n",
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" ut_hit_pos_y_length: int32,\n",
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" ut_hit_pos_y: var * float32,\n",
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" ut_hit_pos_z_length: int32,\n",
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" ut_hit_pos_z: var * float32,\n",
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" velo_hit_pos_x_length: int32,\n",
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" velo_hit_pos_x: var * float32,\n",
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" velo_hit_pos_y_length: int32,\n",
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" velo_hit_pos_y: var * float32,\n",
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" velo_hit_pos_z_length: int32,\n",
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" velo_hit_pos_z: var * float32,\n",
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" velo_track_idx: int32,\n",
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" velo_track_tx: float64,\n",
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" velo_track_ty: float64,\n",
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" velo_track_x: float64,\n",
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" velo_track_y: float64,\n",
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" velo_track_z: float64\n",
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"}</pre>"
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],
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"text/plain": [
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"<Record {all_endvtx_types_length: 7, ...} type='{all_endvtx_types_length: i...'>"
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]
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},
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"execution_count": 3,
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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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"electrons[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": 4,
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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>{lost: True,\n",
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" rad_length_frac: 0.129,\n",
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" energy: 1.17e+04,\n",
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" photon_length: 6,\n",
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" brem_photons_pe: [2.62e+03, 812, 2.54e+03, 1.86e+03, 3.12e+03, 241],\n",
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" brem_vtx_x: [-6.97, -52.9, -52.9, -55.2, -1.71e+03, -2.14e+03],\n",
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" brem_vtx_z: [112, 859, 859, 895, 8.7e+03, 9.68e+03]}\n",
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"---------------------------------------------------------------------\n",
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"type: {\n",
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" lost: bool,\n",
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" rad_length_frac: float64,\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_x: var * float64,\n",
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" brem_vtx_z: var * float64\n",
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"}</pre>"
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],
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"text/plain": [
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"<Record {lost: True, rad_length_frac: ..., ...} type='{lost: bool, rad_leng...'>"
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]
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},
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"execution_count": 4,
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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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"lost_e = electrons[\"lost\"]\n",
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"e = electrons[\"energy\"]\n",
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"brem_pe = electrons[\"brem_photons_pe\"]\n",
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"brem_z = electrons[\"brem_vtx_z\"]\n",
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"brem_x = electrons[\"brem_vtx_x\"]\n",
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"length = electrons[\"brem_vtx_z_length\"]\n",
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"rad_length = electrons[\"rad_length_frac\"]\n",
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"\n",
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"\n",
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"brem = ak.ArrayBuilder()\n",
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"\n",
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"for itr in range(ak.num(electrons, axis=0)):\n",
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" brem.begin_record()\n",
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" brem.field(\"lost\").boolean(lost_e[itr])\n",
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" brem.field(\"rad_length_frac\").append(rad_length[itr])\n",
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" # [:,\"energy\"] energy\n",
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" brem.field(\"energy\").append(e[itr])\n",
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" # [:,\"photon_length\"] number of vertices\n",
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" brem.field(\"photon_length\").integer(length[itr])\n",
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" # [:,\"brem_photons_pe\",:] photon energy\n",
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" brem.field(\"brem_photons_pe\").append(brem_pe[itr])\n",
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" # [:,\"brem_vtx_z\",:] brem vtx z\n",
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" brem.field(\"brem_vtx_x\").append(brem_x[itr])\n",
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" brem.field(\"brem_vtx_z\").append(brem_z[itr])\n",
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" brem.end_record()\n",
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"\n",
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"brem = ak.Array(brem)\n",
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"brem[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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"photon_cut = 0\n",
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"photon_cut_ratio = 0.1\n",
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"\n",
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"cut_brem = ak.ArrayBuilder()\n",
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"\n",
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"for itr in range(ak.num(brem, axis=0)):\n",
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" cut_brem.begin_record()\n",
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" cut_brem.field(\"event_id\").integer(itr)\n",
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" cut_brem.field(\"lost\").boolean(brem[itr, \"lost\"])\n",
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" cut_brem.field(\"rad_length_frac\").real(brem[itr, \"rad_length_frac\"])\n",
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" cut_brem.field(\"energy\").real(brem[itr, \"energy\"])\n",
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"\n",
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" ph_length = brem[itr, \"photon_length\"]\n",
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" \n",
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" tmp_energy = brem[itr, \"energy\"]\n",
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"\n",
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" cut_brem.field(\"brem_photons_pe\")\n",
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" cut_brem.begin_list()\n",
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" for jentry in range(brem[itr, \"photon_length\"]):\n",
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" if (\n",
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" brem[itr, \"brem_vtx_z\", jentry] > 3000\n",
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" or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
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" or brem[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
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" ):\n",
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" ph_length -= 1\n",
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" continue\n",
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" else:\n",
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" cut_brem.real(brem[itr, \"brem_photons_pe\", jentry])\n",
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" tmp_energy -= brem[itr, \"brem_photons_pe\", jentry]\n",
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" cut_brem.end_list()\n",
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"\n",
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" tmp_energy = brem[itr, \"energy\"]\n",
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"\n",
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" cut_brem.field(\"brem_vtx_x\")\n",
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" cut_brem.begin_list()\n",
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" for jentry in range(brem[itr, \"photon_length\"]):\n",
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" if (\n",
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" brem[itr, \"brem_vtx_z\", jentry] > 3000\n",
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" or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
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" or brem[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
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" ):\n",
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" continue\n",
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" else:\n",
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" cut_brem.real(brem[itr, \"brem_vtx_x\", jentry])\n",
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" tmp_energy -= brem[itr, \"brem_photons_pe\", jentry]\n",
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" cut_brem.end_list()\n",
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"\n",
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" tmp_energy = brem[itr, \"energy\"]\n",
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"\n",
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" cut_brem.field(\"brem_vtx_z\")\n",
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" cut_brem.begin_list()\n",
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" for jentry in range(brem[itr, \"photon_length\"]):\n",
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" if (\n",
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" brem[itr, \"brem_vtx_z\", jentry] > 3000\n",
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" or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
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" or brem[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
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" ):\n",
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" continue\n",
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" else:\n",
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" cut_brem.real(brem[itr, \"brem_vtx_z\", jentry])\n",
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" tmp_energy -= brem[itr, \"brem_photons_pe\", jentry]\n",
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" cut_brem.end_list()\n",
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" \n",
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" cut_brem.field(\"photon_length\").integer(ph_length)\n",
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"\n",
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" cut_brem.end_record()\n",
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"\n",
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"ntuple = ak.Array(cut_brem)\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": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"44163\n",
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"50501\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<pre>{event_id: 0,\n",
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" lost: True,\n",
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" rad_length_frac: 0.129,\n",
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" energy: 1.17e+04,\n",
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" brem_photons_pe: [2.62e+03, 2.54e+03, 1.86e+03],\n",
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" brem_vtx_x: [-6.97, -52.9, -55.2],\n",
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" brem_vtx_z: [112, 859, 895],\n",
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" photon_length: 3}\n",
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"-------------------------------------------------\n",
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"type: {\n",
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" event_id: int64,\n",
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" lost: bool,\n",
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" rad_length_frac: float64,\n",
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" energy: float64,\n",
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" brem_photons_pe: var * float64,\n",
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" brem_vtx_x: var * float64,\n",
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" brem_vtx_z: var * float64,\n",
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" photon_length: int64\n",
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"}</pre>"
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],
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"text/plain": [
|
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"<Record {event_id: 0, lost: True, ...} type='{event_id: int64, lost: bool, ...'>"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"print(ak.sum(ak.num(ntuple[\"brem_photons_pe\"], axis=1)))\n",
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"print(ak.num(ntuple,axis=0))\n",
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"ntuple[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,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# nulltuple = ntuple[:7000]\n",
|
|
"# onetuple = ntuple[7000:14000]\n",
|
|
"# twotuple = ntuple[14000:21000]\n",
|
|
"# threetuple = ntuple[21000:28000]\n",
|
|
"# fourtuple = ntuple[28000:35000]\n",
|
|
"# fivetuple = ntuple[35000:42000]\n",
|
|
"# sixtuple = ntuple[42000:49000]\n",
|
|
"# seventuple = ntuple[49000:]\n",
|
|
"\n",
|
|
"# ntuple.nbytes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# cut = \"tenCut\"\n",
|
|
"# tree = \"Tree10\"\n",
|
|
"# with uproot.update(\"trackinglosses_B_photon_cuts.root\") as outFile:\n",
|
|
"# #outFile[\"README\"] = \"The Cuts are placed on the photons. noCut: 0*E, first: 0.05*E, second: 0.1*E, etc.\"\n",
|
|
"# outFile.mktree(tree, {cut + \"_event_id\": ntuple[\"event_id\"].type, cut + \"_lost\": ntuple[\"lost\"].type, cut + \"_rad_length_frac\": ntuple[\"rad_length_frac\"].type, cut + \"_energy\": ntuple[\"energy\"].type, cut + \"_brem_photons_pe\": ntuple[\"brem_photons_pe\"].type, cut + \"_brem_vtx_x\": ntuple[\"brem_vtx_x\"].type, cut + \"_brem_vtx_z\": ntuple[\"brem_vtx_z\"].type, cut + \"_photon_length\": ntuple[\"photon_length\"].type} )\n",
|
|
"# outFile[tree].extend( {cut + \"_event_id\": nulltuple[\"event_id\"], cut + \"_lost\": nulltuple[\"lost\"], cut + \"_rad_length_frac\": nulltuple[\"rad_length_frac\"], cut + \"_energy\": nulltuple[\"energy\"], cut + \"_brem_photons_pe\": nulltuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": nulltuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": nulltuple[\"brem_vtx_z\"], cut + \"_photon_length\": nulltuple[\"photon_length\"]} )\n",
|
|
"# outFile[tree].extend( {cut + \"_event_id\": onetuple[\"event_id\"], cut + \"_lost\": onetuple[\"lost\"], cut + \"_rad_length_frac\": onetuple[\"rad_length_frac\"], cut + \"_energy\": onetuple[\"energy\"], cut + \"_brem_photons_pe\": onetuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": onetuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": onetuple[\"brem_vtx_z\"], cut + \"_photon_length\": onetuple[\"photon_length\"]} )\n",
|
|
"# outFile[tree].extend( {cut + \"_event_id\": twotuple[\"event_id\"], cut + \"_lost\": twotuple[\"lost\"], cut + \"_rad_length_frac\": twotuple[\"rad_length_frac\"], cut + \"_energy\": twotuple[\"energy\"], cut + \"_brem_photons_pe\": twotuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": twotuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": twotuple[\"brem_vtx_z\"], cut + \"_photon_length\": twotuple[\"photon_length\"]} )\n",
|
|
"# outFile[tree].extend( {cut + \"_event_id\": threetuple[\"event_id\"], cut + \"_lost\": threetuple[\"lost\"], cut + \"_rad_length_frac\": threetuple[\"rad_length_frac\"], cut + \"_energy\": threetuple[\"energy\"], cut + \"_brem_photons_pe\": threetuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": threetuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": threetuple[\"brem_vtx_z\"], cut + \"_photon_length\": threetuple[\"photon_length\"]} )\n",
|
|
"# outFile[tree].extend( {cut + \"_event_id\": fourtuple[\"event_id\"], cut + \"_lost\": fourtuple[\"lost\"], cut + \"_rad_length_frac\": fourtuple[\"rad_length_frac\"], cut + \"_energy\": fourtuple[\"energy\"], cut + \"_brem_photons_pe\": fourtuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": fourtuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": fourtuple[\"brem_vtx_z\"], cut + \"_photon_length\": fourtuple[\"photon_length\"]} )\n",
|
|
"# outFile[tree].extend( {cut + \"_event_id\": fivetuple[\"event_id\"], cut + \"_lost\": fivetuple[\"lost\"], cut + \"_rad_length_frac\": fivetuple[\"rad_length_frac\"], cut + \"_energy\": fivetuple[\"energy\"], cut + \"_brem_photons_pe\": fivetuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": fivetuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": fivetuple[\"brem_vtx_z\"], cut + \"_photon_length\": fivetuple[\"photon_length\"]} )\n",
|
|
"# outFile[tree].extend( {cut + \"_event_id\": sixtuple[\"event_id\"], cut + \"_lost\": sixtuple[\"lost\"], cut + \"_rad_length_frac\": sixtuple[\"rad_length_frac\"], cut + \"_energy\": sixtuple[\"energy\"], cut + \"_brem_photons_pe\": sixtuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": sixtuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": sixtuple[\"brem_vtx_z\"], cut + \"_photon_length\": sixtuple[\"photon_length\"]} )\n",
|
|
"# outFile[tree].extend( {cut + \"_event_id\": seventuple[\"event_id\"], cut + \"_lost\": seventuple[\"lost\"], cut + \"_rad_length_frac\": seventuple[\"rad_length_frac\"], cut + \"_energy\": seventuple[\"energy\"], cut + \"_brem_photons_pe\": seventuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": seventuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": seventuple[\"brem_vtx_z\"], cut + \"_photon_length\": seventuple[\"photon_length\"]} )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"50501\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre>{event_id: 1,\n",
|
|
" lost: False,\n",
|
|
" rad_length_frac: 0.148,\n",
|
|
" energy: 1.28e+04,\n",
|
|
" brem_photons_pe: [7.42e+03],\n",
|
|
" brem_vtx_x: [-3.61],\n",
|
|
" brem_vtx_z: [35.6],\n",
|
|
" photon_length: 1}\n",
|
|
"-----------------------------------\n",
|
|
"type: {\n",
|
|
" event_id: int64,\n",
|
|
" lost: bool,\n",
|
|
" rad_length_frac: float64,\n",
|
|
" energy: float64,\n",
|
|
" brem_photons_pe: var * float64,\n",
|
|
" brem_vtx_x: var * float64,\n",
|
|
" brem_vtx_z: var * float64,\n",
|
|
" photon_length: int64\n",
|
|
"}</pre>"
|
|
],
|
|
"text/plain": [
|
|
"<Record {event_id: 1, lost: False, ...} type='{event_id: int64, lost: bool,...'>"
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# data in cut_brem_found and cut_brem_lost\n",
|
|
"\n",
|
|
"length_found = ak.num(ntuple[~ntuple.lost][\"brem_photons_pe\"], axis=0)\n",
|
|
"length_lost = ak.num(ntuple[ntuple.lost][\"brem_photons_pe\"], axis=0)\n",
|
|
"print(length_found+length_lost)\n",
|
|
"ntuple[1]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Z_found = ak.to_numpy(\n",
|
|
" ak.sum(ntuple[~ntuple.lost][\"brem_photons_pe\"], axis=-1, keepdims=False)\n",
|
|
") / ak.to_numpy(ntuple[~ntuple.lost][\"energy\"])\n",
|
|
"Z_lost = ak.to_numpy(\n",
|
|
" ak.sum(ntuple[ntuple.lost][\"brem_photons_pe\"], axis=-1, keepdims=False)\n",
|
|
") / ak.to_numpy(ntuple[ntuple.lost][\"energy\"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"xlim=0\n",
|
|
"\n",
|
|
"plt.hist(\n",
|
|
" Z_lost,\n",
|
|
" bins=100,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" histtype=\"bar\",\n",
|
|
" color=\"darkorange\",\n",
|
|
" label=\"lost\",\n",
|
|
" range=[xlim,1]\n",
|
|
")\n",
|
|
"plt.hist(\n",
|
|
" Z_found,\n",
|
|
" bins=100,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" histtype=\"bar\",\n",
|
|
" color=\"blue\",\n",
|
|
" label=\"found\",\n",
|
|
" range=[xlim,1]\n",
|
|
")\n",
|
|
"plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
|
|
"plt.ylabel(\"a.u.\")\n",
|
|
"plt.title(r\"$E_{ph}/E_0$\")\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"found: 32898 , lost: 11265\n",
|
|
"0.34242203173445196\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre>[-3.61,\n",
|
|
" -33.8,\n",
|
|
" -133,\n",
|
|
" 65.2,\n",
|
|
" -5.73,\n",
|
|
" -26.6,\n",
|
|
" -4.26,\n",
|
|
" 6.83,\n",
|
|
" 10.7,\n",
|
|
" 26.2,\n",
|
|
" ...,\n",
|
|
" -11.6,\n",
|
|
" -13.1,\n",
|
|
" -25.6,\n",
|
|
" -4.27,\n",
|
|
" -4.27,\n",
|
|
" -103,\n",
|
|
" 8.82,\n",
|
|
" 12.8,\n",
|
|
" -17.8]\n",
|
|
"---------------------\n",
|
|
"type: 32898 * float64</pre>"
|
|
],
|
|
"text/plain": [
|
|
"<Array [-3.61, -33.8, -133, ..., 8.82, 12.8, -17.8] type='32898 * float64'>"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"tuple_found = ntuple[~ntuple.lost]\n",
|
|
"tuple_lost = ntuple[ntuple.lost]\n",
|
|
"\n",
|
|
"brem_x_found = ak.to_numpy(ak.flatten(tuple_found[\"brem_vtx_x\"]))\n",
|
|
"brem_z_found = ak.to_numpy(ak.flatten(tuple_found[\"brem_vtx_z\"]))\n",
|
|
"\n",
|
|
"brem_x_lost = ak.to_numpy(ak.flatten(tuple_lost[\"brem_vtx_x\"]))\n",
|
|
"brem_z_lost = ak.to_numpy(ak.flatten(tuple_lost[\"brem_vtx_z\"]))\n",
|
|
"\n",
|
|
"n_found = len(brem_x_found)\n",
|
|
"n_lost = len(brem_x_lost)\n",
|
|
"print(\"found: \", n_found, \", lost: \", n_lost)\n",
|
|
"stretch_factor = n_lost / n_found\n",
|
|
"print(stretch_factor)\n",
|
|
"ak.flatten(tuple_found[\"brem_vtx_x\"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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",
|
|
"text/plain": [
|
|
"<Figure size 2000x800 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"vmax = 150\n",
|
|
"nbins = 100\n",
|
|
"\n",
|
|
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
|
|
"\n",
|
|
"a0 = ax0.hist2d(\n",
|
|
" brem_z_found,\n",
|
|
" brem_x_found,\n",
|
|
" density=False,\n",
|
|
" bins=nbins,\n",
|
|
" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax,\n",
|
|
" range=[[-200, 3000], [-1000, 1000]],\n",
|
|
")\n",
|
|
"ax0.vlines([770, 990, 2700], -1000, 1000,colors=\"red\")\n",
|
|
"ax0.set_ylim(-1000, 1000)\n",
|
|
"ax0.set_xlim(-200, 3000)\n",
|
|
"ax0.set_xlabel(\"z [mm]\")\n",
|
|
"ax0.set_ylabel(\"x [mm]\")\n",
|
|
"ax0.set_title(r\"$e^\\pm$ found brem vertices\")\n",
|
|
"\n",
|
|
"a1 = ax1.hist2d(\n",
|
|
" brem_z_lost,\n",
|
|
" brem_x_lost,\n",
|
|
" density=False,\n",
|
|
" bins=nbins,\n",
|
|
" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax * stretch_factor,\n",
|
|
" range=[[-200, 3000], [-1000, 1000]],\n",
|
|
")\n",
|
|
"ax1.vlines([770, 990, 2700], -1000, 1000, colors=\"red\")\n",
|
|
"ax1.set_ylim(-1000, 1000)\n",
|
|
"ax1.set_xlim(-200, 3000)\n",
|
|
"ax1.set_xlabel(\"z [mm]\")\n",
|
|
"ax1.set_ylabel(\"x [mm]\")\n",
|
|
"ax1.set_title(r\"$e^\\pm$ lost brem vertices\")\n",
|
|
"# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
|
|
"\n",
|
|
"plt.suptitle(\"brem vtx of photons w/ $E>0.1E_0$\")\n",
|
|
"plt.colorbar(a0[3], ax=ax1)\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"50501\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"energy_emissions = ak.ArrayBuilder()\n",
|
|
"\n",
|
|
"for jelec in range(ak.num(ntuple, axis=0)):\n",
|
|
" energy_emissions.begin_record()\n",
|
|
" energy_emissions.field(\"lost\").boolean(ntuple[jelec, \"lost\"])\n",
|
|
" energy_emissions.field(\"energy\").real(ntuple[jelec, \"energy\"])\n",
|
|
"\n",
|
|
" tmp_velo = 0\n",
|
|
" tmp_richut = 0\n",
|
|
" tmp_neither = 0\n",
|
|
" tmp_velo_length = 0\n",
|
|
" tmp_richut_length = 0\n",
|
|
" tmp_neither_length = 0\n",
|
|
" \n",
|
|
" for jphoton in range(ak.num(ntuple[jelec][\"brem_photons_pe\"], axis=0)):\n",
|
|
" if ntuple[jelec, \"brem_vtx_z\", jphoton] <= 770:\n",
|
|
" tmp_velo += ntuple[jelec, \"brem_photons_pe\", jphoton]\n",
|
|
" tmp_velo_length += 1\n",
|
|
" elif (ntuple[jelec, \"brem_vtx_z\", jphoton] > 770) and (\n",
|
|
" ntuple[jelec, \"brem_vtx_z\", jphoton] <= 2700\n",
|
|
" ):\n",
|
|
" tmp_richut += ntuple[jelec, \"brem_photons_pe\", jphoton]\n",
|
|
" tmp_richut_length += 1\n",
|
|
" else:\n",
|
|
" tmp_neither += ntuple[jelec, \"brem_photons_pe\", jphoton]\n",
|
|
" tmp_neither_length += 1\n",
|
|
"\n",
|
|
" energy_emissions.field(\"velo_length\").integer(tmp_velo_length)\n",
|
|
" energy_emissions.field(\"velo\").real(tmp_velo)\n",
|
|
"\n",
|
|
" energy_emissions.field(\"rich_length\").integer(tmp_richut_length)\n",
|
|
" energy_emissions.field(\"rich\").real(tmp_richut)\n",
|
|
" \n",
|
|
" energy_emissions.field(\"neither_length\").integer(tmp_neither_length)\n",
|
|
" energy_emissions.field(\"downstream\").real(tmp_neither)\n",
|
|
" \n",
|
|
" energy_emissions.field(\"photon_length\").integer(tmp_neither_length+tmp_richut_length+tmp_velo_length)\n",
|
|
" \n",
|
|
" if (tmp_velo==0) and (tmp_richut==0):\n",
|
|
" energy_emissions.field(\"quality\").integer(0)\n",
|
|
" else:\n",
|
|
" energy_emissions.field(\"quality\").integer(1)\n",
|
|
"\n",
|
|
" energy_emissions.end_record()\n",
|
|
"\n",
|
|
"energy_emissions = ak.Array(energy_emissions)\n",
|
|
"\n",
|
|
"print(ak.num(energy_emissions,axis=0))\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre>{lost: False,\n",
|
|
" energy: 5.09e+04,\n",
|
|
" velo_length: 0,\n",
|
|
" velo: 0,\n",
|
|
" rich_length: 0,\n",
|
|
" rich: 0,\n",
|
|
" neither_length: 0,\n",
|
|
" downstream: 0,\n",
|
|
" photon_length: 0,\n",
|
|
" quality: 0}\n",
|
|
"--------------------------\n",
|
|
"type: {\n",
|
|
" lost: bool,\n",
|
|
" energy: float64,\n",
|
|
" velo_length: int64,\n",
|
|
" velo: float64,\n",
|
|
" rich_length: int64,\n",
|
|
" rich: float64,\n",
|
|
" neither_length: int64,\n",
|
|
" downstream: float64,\n",
|
|
" photon_length: int64,\n",
|
|
" quality: int64\n",
|
|
"}</pre>"
|
|
],
|
|
"text/plain": [
|
|
"<Record {lost: False, energy: 5.09e+04, ...} type='{lost: bool, energy: flo...'>"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"energy_emissions[3]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"found: 40402\n",
|
|
"lost: 10099\n",
|
|
"50501\n",
|
|
"VELO energy emission, eff: 0.2624700500980179\n",
|
|
"RICH1+UT energy emission, eff: 0.17696679273677748\n",
|
|
"Neither, eff: 0.3605869190709095\n",
|
|
"total efficiency: 0.8000237619057049\n",
|
|
"efficiency: 0.8000237619057048\n",
|
|
"\n",
|
|
"found in velo/(found + lost in velo)\n",
|
|
"VELO energy emission, eff: 0.807739183424741\n",
|
|
"RICH1+UT energy emission, eff: 0.7549417131272175\n",
|
|
"eff von e die nicht strahlen: 0.8183166314654204\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# efficiency berechnen als found in velo oder rich über alle elektronen\n",
|
|
"# dann kann man zusammenrechnen mit velo, rich, und allen anderen elektronen\n",
|
|
"# expected eff = 81.19%\n",
|
|
"\n",
|
|
"electrons_found = energy_emissions[~energy_emissions.lost]\n",
|
|
"electrons_lost = energy_emissions[energy_emissions.lost]\n",
|
|
"\n",
|
|
"anz_found = ak.num(electrons[~electrons.lost],axis=0)\n",
|
|
"anz_lost = ak.num(electrons[electrons.lost],axis=0)\n",
|
|
"print(\"found: \",anz_found)\n",
|
|
"print(\"lost: \", anz_lost)\n",
|
|
"\n",
|
|
"num_velo_found = 0\n",
|
|
"num_rich_found = 0\n",
|
|
"num_no_up_rad_found = 0\n",
|
|
"for itr in range(ak.num(electrons_found, axis=0)):\n",
|
|
" if (electrons_found[itr, \"quality\"]==1):\n",
|
|
" if (electrons_found[itr, \"velo\"] >= electrons_found[itr, \"rich\"]):\n",
|
|
" num_velo_found += 1\n",
|
|
" else:\n",
|
|
" num_rich_found += 1\n",
|
|
" else:\n",
|
|
" num_no_up_rad_found += 1\n",
|
|
"\n",
|
|
"num_velo_lost = 0\n",
|
|
"num_rich_lost = 0\n",
|
|
"num_no_up_rad_lost = 0\n",
|
|
"for itr in range(ak.num(electrons_lost, axis=0)):\n",
|
|
" if (electrons_lost[itr, \"quality\"]==1):\n",
|
|
" if (electrons_lost[itr, \"velo\"] >= electrons_lost[itr, \"rich\"]):\n",
|
|
" num_velo_lost += 1\n",
|
|
" else:\n",
|
|
" num_rich_lost += 1\n",
|
|
" else:\n",
|
|
" num_no_up_rad_lost += 1\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"denom = ak.num(electrons,axis=0)\n",
|
|
"print(denom)\n",
|
|
"\n",
|
|
"\n",
|
|
"eff_velo = num_velo_found/denom\n",
|
|
"\n",
|
|
"eff_rich = num_rich_found/denom\n",
|
|
"\n",
|
|
"eff_other = ak.num(electrons_found[electrons_found.quality==0],axis=0)/denom\n",
|
|
"\n",
|
|
"print(\"VELO energy emission, eff: \", eff_velo)\n",
|
|
"\n",
|
|
"print(\"RICH1+UT energy emission, eff: \", eff_rich)\n",
|
|
"\n",
|
|
"print(\"Neither, eff: \", eff_other)\n",
|
|
"\n",
|
|
"print(\"total efficiency: \", eff_velo + eff_rich + eff_other)\n",
|
|
"\n",
|
|
"print(\"efficiency: \", anz_found/(anz_found+anz_lost))\n",
|
|
"\n",
|
|
"print(\"\\nfound in velo/(found + lost in velo)\")\n",
|
|
"\n",
|
|
"eff_velo = num_velo_found/(num_velo_found+num_velo_lost)\n",
|
|
"eff_rich = num_rich_found/(num_rich_found+num_rich_lost)\n",
|
|
"\n",
|
|
"eff_no_rad = num_no_up_rad_found/(num_no_up_rad_found+num_no_up_rad_lost)\n",
|
|
"\n",
|
|
"print(\"VELO energy emission, eff: \", eff_velo)\n",
|
|
"\n",
|
|
"print(\"RICH1+UT energy emission, eff: \", eff_rich)\n",
|
|
"\n",
|
|
"print(\"eff von e die nicht strahlen: \", eff_no_rad )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"40402\n",
|
|
"10099\n",
|
|
"50501\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(ak.num(electrons[~electrons.lost],axis=0))\n",
|
|
"print(ak.num(electrons[electrons.lost],axis=0))\n",
|
|
"print(ak.num(electrons,axis=0))\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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AZEfUdnBpxBZ1a2th24QBAIDV5DiOPvnkE/V6PUlSv99fcokQx8J2lAAAAKvJNE09fvxYjuMs9D6u645sYIDpEdQBAIARSUzYK5fLGgwGC7/PuiCoAwAAiSuXywtvCVw3CxlTBwAAssvzPNVqNeVyuTAwq9VqKhaLkWk8z1Ov11OtVlOlUlGn0wnzVatV5XI57e3tsZzXjAjqAABAbI7j6OHDh/r000/DIKzVasmyLNm2rd3dXUnSkydPZBhGuFJGq9UKx8+VSiV99tlnOjg4ULPZlGEYS6lL1tD9CgAAYnvy5Im2t7eHWtUqlYpM01StVpPrupIUzpy9mgaLRVAHAABicV1XjuNEdpNWq1VJUrPZlHS5du3BwYEODg7CNEErHhaDoA4AAMQyaWLD9va2JIUtde12W7lcTrVaTfl8nkkRCWBMXQrt7Oxoc3Mz8tykvecAAEhC1NpywX7vwdahhmHo+fPnKpfL6vV6KhQKajabK9sN22g01Gg0Is9dXFwkXJpoBHUpdHx8zAwgAEDqBJ9N18fLSb8M9PL5vKTLFjvDMNTtdtXpdFQul1WtVlc2qJvUqOI4jgqFQsIlGkX3KwAAiMUwDJmmKdd1w27WwOnpqXK5XBi0Xd0fvlQqhWPtrudjR4n5IagDAACxBWPlgokR0mVgZtu2Dg8Pw27Yo6OjoQDO8zwZhhEuXxK06DWbTbmuq06nk1wlMoruVwAA4nhnf9klSIzjOGHLmuM4Ojg4UKVSUS6XC8fKPXnyRJZlhUFau90eGjq0vb0ty7JUKpUkXbbQ9fv98HylUlGz2dTR0ZGkX86axfQI6gAAwBDTNNVsNscGWrlcTu12e+I1ut3ujfe5GuRhdnS/AgAAZABBHQAAQAakrvvVcRzV63UZhiHP84b642fNN6800uWAz3q9Lml4hs9VnU5H9Xo9XH3btu2hzY4BAADmJVVBneu6KhQK6vf74WDLfD6vwWAwcV2bOPnmlUa6XJ+n2Wyq0+mMLdfBwYG63a6q1arOzs50cHAgy7LU7XYJ7AAAwNylqvu1Wq2qWCwOzZ6p1WpD06anzTevNJJULBZvHCD62WefqdvtqlKpyLbtcDDouFY9AACAWaQmqPM8T71eT5ZlDR0P9pJrtVpT55tXmrh6vd5I8GaaZrhgIwAAwLylJqg7PT2VpHC9m0DQajZuanScfPNKE1exWBy5TmDccQAAgFmkZkxd0IIVrEQ97vw0+eaVZlau697YlSxJr1690suXL6e+z507d3Tnzp2p8wMAsE5ev36t169fT53/1atXcyzN9FIT1J2dnUmStra2Is+P2xsuTr55pZlFp9ORYRixNjJ+9913Z7rXhx9+qP39/ZmuASAhP9gfPbZGOxcAaVCv1/XRRx8tuxgzS01QF+wBNxgMIs+P67aMk29eaWZRr9dvnFwRePbsmR48eDD1vWilAwAgvr29PX3wwQdT5//8889nbpCZh9QEdUHQNK5F7KYxapPyzSvNtGq1mg4PD2Nf4+7du3rzzTenvh8AAIhv1mFLd+/enWNpppeaiRLBLNPrY9eC7wuFwtT55pVmGq1WS5ZlDS2TAgAAMG+pCepyuZxM0xyZZdrr9SRJjx49mjrfvNLcVqfTkaSRxYYdx7n1tQAAACZJTferJB0eHqpQKMh13bCr0rZt2bYdzkp1XVeWZanZbIbBUpx880oTuGniRK/XU71eV7VaHVrjrt/vq1Ao0HIHACtmVeafrUo5MX+pCupM01S/31etVpNhGHJdV7VabWjGqOd5GgwGQ0FVnHzzSiNdtrQ1m01J0tHRkSzLUrFYDAM/x3HCRYyjljB58eLFzM8KAABcchxHp6ensVaYyLIN3/f9ZRcClxzHGdl7FsAKmGVZEpY0WRmr0gI273I6jqNPPvlEruvKcRxVq1Xt7u7O9yZTChpfOp1O2DCzDGn5/E5VSx0AAEgPx3H08OHDsIepVquF67qmgWEYarfb2tjYWHZRUoGgDgAARKrX60OL8l/f1xzpkprZrwAAIF1YrWG1ENQBAIAhrVZL5XI53Bu9XC6rXC6Hy3xJlxMXq9WqarWaLMuSZVlD5zudju7du6eNjY0wOOz1eiqXy9rY2FC5XA6v02q1VCgU1Ol01Ov1VCgUhtJcFdw3+Do4OFjw01gdBHUAAGBIpVJRu90Od1xqt9tqt9vhUmKO4+jtt99WtVqVbdvqdrsql8uyLCsMskql0sgar8VicaQLdzAYqNvthitLdLtdHR4eqlKpqNPpDAVtruvq7bffVrlcVrPZDFeiwCWCuhTa2dnR7/zO70R+NRqNZRcPALDmnjx5ou3t7aGZnpVKRaZpqlarhbsxXV/jVdLQGD3pcrLD48ePJUmWZcm2bZmmGQZsVzcFqNVq2t7eHlrUP6mZuI1GY+xn887OTiJluAkTJVLo+PiYJU0AAKkULG0SFUwFXaLNZnOqSRVRQeBgMAjv2+l0ljZZ4+nTp3r69GnkuWBJk2WjpQ4AAMQ2afLEuH3U5yG4ZrDjE0YR1AEAgFuL2i4zaGm73sU6D0FQF7TcYRRBHQAAiC0YHnR1pmsgCPTy+fzc7xu00C1r14hVQFAHAAAiDQaDkZYxwzBkmma43MlVp6enyuVy4R6s9+/flzTcHRv8Paqlb5Kga7fVakXmve31soigDgAA3Eq73VYul1O1Wg2PeZ4n27Z1eHgYdsMGrXq1Wk29Xk+tViuc1drr9WRZlqR4Xaq5XC6cnFEoFNTr9cK9X6XLYHHd16xj9isAADHs7y+7BMkJ1owLWr+q1arK5XK4lIhhGHr+/LmePHkiy7LCrtF2uz20ekOwLl29Xle5XFalUlGz2VSv11OpVNLjx4/De0mX25AZhqHt7e0wWHMcRwcHB9rd3ZVt28rn87JtW5ZlyTRNtdttdTodlUollUqlBJ9S+mz4vu8vuxC4FEyJ7vf7LGkCrJIf7I8eeyfi2LzzAkiFtHx+0/0KAACQAQR1AAAAGUBQBwAAkAEEdQAAABnA7NcU2tnZ0ebmZuS5SXvPAQCAxWg0Gmo0GpHnLi4uEi5NNIK6FDo+Pmb2KwAAKTKpUSWY/bpsdL8CAABkAEEdAABABhDUAQAAZABBHQAAQAYQ1AEAAGQAQR0AAEAGENQBAABkAEEdAABABhDUAQAAZABBHQAAQAYQ1AEAAGQAe7+m0M7OjjY3NyPPTdp7DgAALEaj0VCj0Yg8d3FxkXBpohHUpdDx8bFM01x2MQAAwC9MalRxHEeFQiHhEo2i+xUAACADCOoAAAAygO5XALiNH+wvuwQAEImWOgAAgAwgqAMAAMgAgjoAAIAMSN2YOsdxVK/XZRiGPM+TZVkqlUpzyTevNJLkeZ7q9bokybbtudYFAADgtlIV1Lmuq0KhoH6/H67Tls/nNRgMVKlUZso3rzSS1Ov11Gw21el0xpZr2roAAABMI1Xdr9VqVcVicWjh3Vqtpmq1OnO+eaWRpGKxqHa7vZC6AAAATCM1QZ3neer1erIsa+j49va2JKnVak2db15pFl0XAACAaaWm+/X09FSSZBjG0PGgpavb7UZ2W8bJF5ybNU3cbtNp6xJ49eqVXr58GeteUe7cuaM7d+5MnR8AgHXy+vVrvX79eur8r169mmNpppeaoM51XUlSLpebeH6afPNKE9es13r33Xdj3yvKhx9+qP39/ZmuAQDAuqjX6/roo4+WXYyZpSaoOzs7kyRtbW1Fnvc8b+p880oT16zXevbsmR48eBD7ftfRSgcAQHx7e3v64IMPps7/+eefz9wgMw+pCery+bwkaTAYRJ6/3pV5m3zzShPXrNe6e/eu3nzzzdj3AwAA05t12NLdu3fnWJrppWaiRBDojGvFGhcIxck3rzRxzfNaAAAAcaQmqAtmhl4fbxZ8XygUps43rzSLrgsAAMC0UhPU5XI5maapbrc7dLzX60mSHj16NHW+eaVZdF0AAACmlZqgTpIODw/V6/WGWrhs25Zt2+FMUtd1lc/nwwApbr55pQncNNnhNtcCAACYVWomSkiX67j1+33VajUZhiHXdVWr1YbWdPM8T4PBYCioipNvXmmkyz1dm82mJOno6EiWZalYLA4Fa3GvBQAAMA8bvu/7yy4ELjmOM7JfLICU+cF+vHTvxEwXdb24eQGkQlo+v1PV/QoAAIDpENQBAABkAEEdAABABhDUAQAAZABBHQAAQAakakkTXNrZ2dHm5mbkuadPn+rp06cJlwhAopgRC6ROo9FQo9GIPHdxcZFwaaIR1KXQ8fExS5oAAJAikxpVgiVNlo3uVwAAgAwgqAMAAMgAgjoAAIAMYEwdAIwTd0swAEgBWuoAAAAygKAOAAAgAwjqAAAAMoCgDgAAIAMI6gAAADKAoA4AACADCOoAAAAygHXqUmhnZ0ebm5uR5ybtPQcAABaj0Wio0WhEnru4uEi4NNEI6lLo+PhYpmkuuxgAAOAXJjWqOI6jQqGQcIlG0f0KAACQAQR1AAAAGUBQBwAAkAEEdQAAABlAUAcAAJABBHUAAAAZQFAHAACQAQR1AAAAGUBQBwAAkAHsKAEAi/CD/dFj70QcA4A5oaUOAAAgA2ipS6GdnR1tbm5Gnpu09xwAAFiMRqOhRqMRee7i4iLh0kQjqEuh4+Njmaa57GIAAIBfmNSo4jiOCoVCwiUaRVAHAKuAMXoAbsCYOgAAgAwgqAMAAMgAgjoAAIAMIKgDAADIgNRNlHAcR/V6XYZhyPM8WZalUqk0l3xJppGkTqejbrerXC4n13VlGIZs257iqQAAAEyWqqDOdV0VCgX1+/1wSY98Pq/BYKBKpTJTviTTSJcBXb1eV7/fD49ZlqVarUZgBwAA5i5V3a/ValXFYnFojbZaraZqtTpzviTTSFKz2dT29vbQMcuy1Ol0JtYFAABgGqkJ6jzPU6/Xk2VZQ8eDwKjVak2dL8k0gcFgoF6vN5Tu7OxMhmGMeQIAAADTS01Qd3p6KkkjQU/QItbtdqfOl2SaQLValeu6KpfLki7H4R0dHdH1CgAAFiI1Y+pc15Uk5XK5ieenyZdkmkClUlG/31er1VI+n5dhGHr+/PnYvFe9evVKL1++vDHdOHfu3NGdO3emzg8AwDp5/fq1Xr9+PXX+V69ezbE000tNUHd2diZJ2traijzved7U+ZJMc1Wz2dTp6akcx5Hruur1erFm8r777rs3ppnkww8/1P7+/kzXAABgXdTrdX300UfLLsbMUhPU5fN5SZdj0aKMG4sWJ1+Saa6yLEvValWGYahcLqtcLqvdbt8Y2D179kwPHjyYmGYSWukAAIhvb29PH3zwwdT5P//885kbZOYhNUFdEBCNa5EbF9TFyZdkmkAwGzZY5uT58+d6++239eTJkxuDurt37+rNN9+cmAYAAMzHrMOW7t69O8fSTC81EyWCGaTXx84F3xcKhanzJZkmcHR0NLTsSS6Xk23b8jxPjuNE1gUAAGBaqQnqcrmcTNMcmeUaLAvy6NGjqfMlmSawtbU10qJXLBbDMgMAAMxTaoI6STo8PFSv1xtqCbNtW7Zth4GQ67rK5/NDa8DFyZdkGumy+/Xo6GgosOt0OjJNk7XqAADA3KVmTJ10ud5bv99XrVaTYRhyXVe1Wm1o+y3P8zQYDIaCpTj5kkwjSbu7u8rlciqXy2E3rOd5+vTTT+f92AAAALTh+76/7ELgkuM4I/vKAliiH+zP93rvRFxvlntEXQ9A4tLy+Z2q7lcAAABMh6AOAAAgAwjqAAAAMoCgDgAAIAMI6gAAADIgVUua4NLOzo42Nzcjzz19+lRPnz5NuEQAlm3/e++NHnsn+XIA66rRaKjRaESeu7i4SLg00QjqUuj4+JglTQAASJFJjSrBkibLRvcrAABABhDUAQAAZABBHQAAQAYwpg4AVlXUFmNsHQasLYI6AEiZqJmusfPuxzsGIHvofgUAAMgAgjoAAIAMoPsVAFZUZDftbyVeDAApQUsdAABABhDUAQAAZABBHQAAQAYwpg4AkhK1rhwAzAlBXQrt7Oxoc3Mz8tykDYUBAMBiNBoNNRqNyHMXFxcJlyYaQV0KHR8fyzTNZRcDAAD8wqRGFcdxVCgUEi7RKMbUAQAAZABBHQAAQAYQ1AEAAGQAQR0AAEAGENQBAABkAEEdAABABrCkCQBILAwMYOXRUgcAAJABBHUAAAAZQFAHAACQAQR1AAAAGcBEiRTa2dnR5uZm5LlJe88BWD3733tv2UUAEEOj0VCj0Yg8d3FxkXBpohHUpdDx8bFM01x2MQAAwC9MalRxHEeFQiHhEo2i+xUAACADCOoAAAAygO5XALiFqDFw+++fJF0MABhBSx0AAEAGENQBAABkQOq6Xx3HUb1el2EY8jxPlmWpVCrNJV+Saa5zXVedTkeSVKlUlMvlYjwNAACAeFIV1Lmuq0KhoH6/Hy7pkc/nNRgMVKlUZsqXZJrrZavVavI8T81mU4ZhzONRAQAADElV92u1WlWxWBxao61Wq6larc6cL8k0gWDdmq2tLXW7XQI6AKmxvz/6BWC1pSao8zxPvV5PlmUNHd/e3pYktVqtqfMlmeZquR4+fCjDMNRsNm9+AAAAADNITffr6empJI20ZgUtYt1uN7J7M06+4FwSaYIyBl2utm1PqDUALB6tcMB6SE1Q57quJI2dQBCcnyZfkmkCQatdt9tVrVaT67ra3t6ONa7u1atXevny5cQ0k9y5c0d37tyZOj+AS+zLCqyH169f6/Xr11Pnf/Xq1RxLM73UBHVnZ2eSpK2trcjznudNnS/JNNLlWDrpsgWvWq3Ktm25rivLspTP5/XixYuJs1/ffffdsefi+PDDD7XPf80BAIilXq/ro48+WnYxZpaaoC6fz0uSBoNB5PlxrVtx8iWZRvpli121Wh3qsm02m7IsS/V6fWK37LNnz/TgwYOx529CKx0AAPHt7e3pgw8+mDr/559/PnODzDykJqgLgp9xLXLjgro4+ZJMI43vni0Wi5LGdyUH7t69qzfffHNiGgAAMB+zDlu6e/fuHEszvdTMfg1mkF4PeILvC4XC1PmSTHO1TEF37XXjum8BAACmlZqgLpfLyTRNdbvdoeO9Xk+S9OjRo6nzJZkmKFOxWAyPB4IWvnEBKgAAwLRSE9RJ0uHhoXq93lBLmG3bsm077NJ0XVf5fH4oYIqTL8k0wTHHcYbK2Wq1ZJrmxN0xAAAAppGaMXXS5WzRfr+vWq0mwzDCLbauBkGe52kwGAyNa4uTL8k019O1223lcjl5nqd+v7+AJwcAANZdqoI66TIYarfbE8+/ePHi1vmSThOku95VCwAAsAip6n4FAADAdAjqAAAAMoCgDgAAIAMI6gAAADKAoA4AACADUjf7FdLOzo42Nzcjzz19+lRPnz5NuEQAAKy3RqOhRqMRee7i4iLh0kQjqEuh4+Njmaa57GIAiGn/e++NHnv/JOliAFigSY0qjuOkYrcogjoA2faD/dFj70QcA4AVx5g6AACADCCoAwAAyACCOgAAgAxgTB0AKHqyAwCsEoI6AOsnavKE3ku4EAAwX3S/AgAAZAAtdQAASdL+frxjANKJljoAAIAMIKgDAADIALpfAWQaW3gBWBcEdSm0s7Ojzc3NyHOT9p4DAJ2fjB77rfcSLgSQPY1GQ41GI/LcxcVFwqWJRlCXQsfHxzJNc9nFAJBl5yejxyKCPyZPAJcmNao4jqNCoZBwiUYxpg4AACADaKkDkC0jCwu/t4RCZButd0A60VIHAACQAbTUAQAunZ+MHmOSBbAyaKkDAADIAII6AACADCCoAwAAyADG1AFA1p2fLLsEABJASx0AAEAG0FIHYO1E7QeLZLDGHbA4tNQBAABkAEEdAABABtD9mkI7Ozva3NyMPDdpQ2EAALAYjUZDjUYj8tzFxUXCpYlGUJdCx8fHMk1z2cUAAAC/MKlRxXEcFQqFhEs0iu5XAACADKClDgAwM2a1AstHUAcAWAiCOiBZdL8CAABkAEEdAABABqSu+9VxHNXrdRmGIc/zZFmWSqXSXPIlmea6Xq+ncrmsFy9exHwSAAAA8aUqqHNdV4VCQf1+P1zSI5/PazAYqFKpzJQvyTRRqtXqLI8GAABgolR1v1arVRWLxaE12mq12o0BUZx8Saa5rlaryTCMiXUAAACYRWqCOs/z1Ov1ZFnW0PHt7W1JUqvVmjpfkmmu6/V6un//PosJAwCAhUpN9+vp6akkjbRoBcFQt9uN7N6Mky84l0Sa62VsNptqt9uq1WoRtQYwkx/sL7sEAJAaqQnqXNeVJOVyuYnnp8mXZJqrarWabNuOTDvJq1ev9PLly1vnC9y5c0d37tyZOj+wdFHB2jsRx7B45yejx37rvYQLASzW69ev9fr166nzv3r1ao6lmV5qgrqzszNJ0tbWVuR5z/OmzpdkmoDjOLp///5UY+nefffdW+e56sMPP9Q+q34CABBLvV7XRx99tOxizCw1QV0+n5ckDQaDyPPjgqM4+ZJME6jX62q325HpbvLs2TM9ePBgqrySaKUDAOAW9vb29MEHH0yd//PPP5+5QWYeUhPUBQHRuBa5cUFdnHxJppEuu10tyxrqjg3+Hvw5qQXv7t27evPNN8eeBwAA8zPrsKW7d+/OsTTTS01QF8wgvT4uLfi+UChMnS/JNNLljNeDg4PI8ubzeZmmqX6/H3keAABgGqlZ0iSXy8k0TXW73aHjvV5PkvTo0aOp8yWZRpL6/b583x/62t3dVS6Xk+/7BHQAAGDuUtNSJ0mHh4cqFApyXTfsnrRtW7ZthzNOXdeVZVlqNpsqFoux8yWZBsDy7H/vvWUXAQCWIlVBXdAtGezA4LquarXa0NpvnudpMBgMjWuLky/JNAAAAEnb8H3fX3YhcMlxnJF9ZYG1FHOduv33TxZckOnFLdtKtizOeZ06VmDCqkvL53dqxtQBAABgeqnqfgUAQIpuvaNFD5iMoA4AsFQEa8B80P0KAACQAQR1AAAAGUD3awrt7Oxoc3Mz8tzTp0/19OnThEsEAFecn0yfd8EzZ+nKxaI0Gg01Go3IcxcXFwmXJhpBXQodHx+zpAnWWtQyH/vvJF8OLMD5yeixOQd6wCJMalQJljRZNoI6AEBmMGsW64wxdQAAABlASx2AlUBrC/g3AExGSx0AAEAGENQBAABkAN2vAJbrB/sRB99LuBAAsPpoqQMAAMgAWuoArIbzk2WXAABSjaAOwFJFLTScVetUVwDJo/sVAAAgAwjqAAAAMoDu1xTa2dnR5uZm5LlJe88BAOJhOzHcVqPRUKPRiDx3cXGRcGmiEdSl0PHxsUzTXHYxAADAL0xqVHEcR4VCIeESjaL7FQAAIAMI6gAAADKAoA4AACADGFMHYGZxB50zEB0AFoegDsBC7L9/Mnrwt95LuBTLw0LDAJJGUAcAyDRaiLEuGFMHAACQAQR1AAAAGUD3KwAAYzDhB6uEljoAAIAMoKUOALAazk9Gj63RjGrgJgR1KbSzs6PNzc3Ic5P2ngNS7/xk2SVAGp2fLLsEwI0ajYYajUbkuYuLi4RLE42gLoWOj49lmuayiwEAAH5hUqOK4zgqFAoJl2gUY+oAAAAygJY6AAA026xWZskiDQjqAIz3g/3RY+9EHAMALB3drwAAABlASx2wjmiBA4DMoaUOAAAgA1LXUuc4jur1ugzDkOd5sixLpVJpLvmSTCNJnU5H9XpdjuPINE3Ztq1isTjFUwHSg8HfAJBOqQrqXNdVoVBQv98P12nL5/MaDAaqVCoz5UsyjSQdHByo2+2qWq3q7OxMBwcHsixL3W6XwA4A1gAzYpG0Dd/3/WUXImBZliSp2+2Gx1qtlqrVqiYVM06+JNNIUrlcVrvdDr8PFiYsFotDea8K0lwNGIGFiDmmbv/9k9F0cbdlOo/IC8zbtNuEnZ9Mfa15B2txr0dAmF5p+fxOTUud53nq9XqybXvo+Pb2tqTLwCmqtS5OvkePHiWWplKpRKYxTVOmacp13ZsfBrAEfGAA8fCzgrRKTVB3enoqSTIMY+h4EPF2u93IoC5OvuBcEmkqlcrE7tXreQEAMzg/Gf5+2pa7JYobJNJ6h5ukJqgLWrByudzE89PkSzLNJK7rqlqtTkwjSa9evdLLly9vTDfOnTt3dOfOnanzAwCwTl6/fq3Xr19Pnf/Vq1dzLM30UhPUnZ2dSZK2trYiz3ueN3W+JNOM0+l0ZBjGxAkfgXfffffGNJN8+OGH2ue/b5hg/3vvjR78rZiZz08i8kZcDwBWRL1e10cffbTsYswsNUFdPp+XJA0Gg8jz47ot4+RLMs049Xp9aOLEJM+ePdODBw9ipY1CKx0Sd36y7BIAi3F+MnqM/8Rkzt7enj744IOp83/++eczN8jMQ2qCuiAgGtfaNS5gipMvyTRRarWaDg8PY4+nu3v3rt58881YaQEAwGxmHbZ09+7dOZZmeqkJ6oIZpNfHpQXfFwqFqfMlmea6Vqsly7JYogQA0u78ZNklAGaSmm3CcrmcTNMcWcOt1+tJkh49ejR1viTTXNXpdCRpZDas4ziRdQFuY39/9AtAhPOT0a80XW9Z90DmpKalTpIODw9VKBTkum7YVWnbtmzbDmecuq4ry7LUbDbDYClOviTTSJeBXr1eV7VaVavVCo/3+30VCgVa7nA7UYsFK+pYRKp4yQBkBEufrK9UBXWmaarf76tWq8kwDLmuq1qtNjRj1PM8DQaDoXFtcfIlmcZxnHDniaglTF68eDGX5wUAwLQI/rInVUGddBk0TZolappmZFB0U74k05imOXFbM2BR+IUMAOsrNWPqAAAAML3UtdQBAID5ohV/PRDUAQlh/AqQgPOTZZfgl85PRo+xcDEWiKAOSJuIma4zbesFID3OT0aPzRDo8R9DXEVQB+DS+cmySwCsrvOTZZdgLuhRWG0EdSm0s7Ojzc3NyHNPnz7V06dPEy4RbotfggCybB2Dv0ajoUajEXnu4uIi4dJEI6hLoePjYxYnTql5/yKLzHv+3vQXBAAsxKRGFcdxxm5nmiSCOmCZzk8Wn5eB2QDiOj+JOPhesmXA1AjqgFnNsIUXACTi/GT0WMz/8MXtjVjHLtm0IagDZsTMVACYDQHhfLCjBAAAQAbQUofVFdXt+U7EMQBIs/OTZZdgYWZpgZtr692afF4Q1AGi6R8AsProfgUAAMgAWuqwdmiBAwBkEUEdsmVNxk0AQGLOT0aPsf5lKhHUIfNomQOAbGI89DCCOqROJn5Iz09Gj/E/WwBgwfYFIqhLoZ2dHW1ubkaem7T3XOqtWNfo3APJ85M5XzDl9wWwXs5PRo9F/Gc29oLtUdeLsWVZZMPA/3JjtvF5f3Gs0Wio0WhE5ru4uIh3gwUjqEuh4+Njmaa57GIAALLs/GQ5eVfUpEYVx3FUKBQSLtEogjoAAJBpUa2D++8kX45FI6jDSojsCj1/L17m68365ycRaSKuFTcdACzL+cmyS5CohY+vPj+JOPjeyJHr5fjhD+dflGkQ1OHSio13AwCsofOT4e9n+I/2yk3Ai4GgDmPNsj8fAABIFkHdKpilFS2BFrh5b9gMAIhwfrLsEtze+cmySzCzVfqcIqjLuHUZHDqz85NllwAAgJkQ1GF25ycRB98bORJ7baLY9wAAYAnOT4a//9F/WUYpRvzKsguAX/riiy+G/kzS/vfeG/lKypdfvtbJyb6+/PJ1YvdMgy9/9oVOnI/15c+Sf9/LRL2p9zqg3mtW75//u6TlfH5fRUtdigT/GJr/+/+j//vrPw2P778fkThirNz+P4wei5K28QE/+9lrPXv2kX73dz/QV75yZ21a5X72s3/Xs8//D/3u/1TWV371jWUXJzHUm3qvA+q9XvX++c++lERQhxgix8W9f7L4G59H3CPm9PFEyhfX+Uk6rwUAmL/zk+WkSwG6XwEAADKAlroU+vv/WNevfeWrkee++T/+r/oPv/MHCZcIAIAFOj9Zdglu9J/+89/rs3/9PyPP/fuXP408njSCuhT6g/95T//D178xMc1MM0kBAMiC85PEbvUffucPxjaq/Lcf/os+/r/+t8TKMg5BXZacn8w33bzzAgCAhWFMHQAAQAbQUoflOj+Rvvj/Lv/+//5H6Y3fWGZpAABYWbTUZcB/+s9/n6r00+ZZ9D2o92LST5tn0feg3otJP22eRd+Dei8m/bR5Fn2PrNR73gjqMmDcbJxlpZ82z6LvQb0Xk37aPIu+B/VeTPpp8yz6HtR7MemnzbPoe2Sl3vOWuu5Xx3FUr9dlGIY8z5NlWSqVSnPJl2SaWeoCAABwW6kK6lzXVaFQUL/fl2makqR8Pq/BYKBKpTJTviTTzFIXAACAaaSq+7VarapYLIZBkCTVajVVq9WZ8yWZZpa6AAAATCM1QZ3neer1erIsa+j49va2JKnVak2dL8k0s9QFAABgWqkJ6k5PTyVJhmEMHQ9aurrd7tT5kkwzS10AAACmlZoxda7rSpJyudzE89PkSzJN3DJF+elPL/eO+++Ds8jz43zx5U/13374L+H3v/KrX9FXfuXXxqb/8svX+uGP/kvs6982/W3zfPHvF5Kk//7j/6o3fm1zYeWi3otJf9s81Jt6L6Jc1Hsx6W+bZ1Xr/eXP/10//9mXQ8euf7ZOEnxuB5/jS+OnxO7uri/J7/f7I+ck+YZhTJ0vyTSz1OXjjz/2JfHFF1988cUXXyv49fHHH0d+viclNS11+XxekjQYDCLPX+/KvE2+JNPELVOUb33rW/r444/19a9/XV/96lcj08Txxhtv6I033pg6PwAA6+SLL77QF198MXX+n/70p/rhD3+ob33rW3Ms1e2lJqgLAh3P8yaenyZfkmnilinK1772NX3729+OPAcAADBJaiZKBDNDr483C74vFApT50syzSx1AQAAmFZqgrpcLifTNEdmhvZ6PUnSo0ePps6XZJpZ6gIAADC1pY7ou6bf7/uS/LOzs/CYYRi+bdvh92dnZ75hGH63271VviTT3CYdLl19TsgW3m028V7XE+893TZ83/eXGVRed3W/VNd1ZVnW0LZajuPo4cOHOjw8HNpH9aZ8SafpdDr6y7/8S/3rv/6rfvM3f1O//du/rT/8wz8cqUva9qudB8/zVK/XJUm2bUem2djYGPreNE31+/1blXfV6p3V931dFt/trFaprONk+b2u68/uuv6uli4/o+v1uhzHkWmasm1bxWJxIeVOtP7LjiqzyLZtv1gs+s1mM1zeRNJQ6+LZ2ZkvDS97YhiG32w2l5JmXrrdrl8qlXxJfqVSiUzTbDb9SqXi27Ydfl0tWxbrndX3fV0W3+2sVqms42T5va7rz+66/q72/Wx/RhPULUCpVBr6PuiKLRaL4bFisTj0ve9f/gBdjbOTTDNvk35RXC9L1Pms1Tvr7/vqvW86v+p1vK1VKus46/Be1/Vndx1/V2f5Mzo1EyWyotfrjTRjm6Yp0zTD2a9p3K82KZ1OR6enpyqXy5H3zmK91+V9r+O7vckqlXWcdX6v6/KzGyWr7z3rn9EEdXNWLBZvXFMvjfvVJqXb7crzPHU6HVWrVd27dy+cFSwl+2ySsi7vex3f7U1WqazjrPN7XZef3ShZfe9Z/4wmqEuI67oql8vh36V07VeblGazKd/31e/3ValUwgGht9k3d9XqvS7vex3f7U1WqazjrPN7XZef3Sjr9t6z8hlNUJeATqcjwzDCma9nZ5cb/25tbUWm9zwv0TTLYJqmms2m2u22JKlWq0lK9tkkZd3e9zq925usUllvso7vdd1+dqOsw3vP0mc0QV0C6vV6+AMhpXO/2mUplUoqlUpyHEdSss8mKev6vtfh3d5klcoa1zq913X92Y2S5feepc/o1Oz9mjaO44T/I7mJYRhqNpuR52q1mg4PD4deTPD3cRG4sYT9agPzqvdtWJYVjtXIYr3T/L6vm/dzSMO7XaZVKuttrMt7XaWf3SRk8b2v2mf0TQjqxoja5uu2Wq2WLMsKBzwG0rhfbWAe9Z5GUM4s1jvN7/u6RTyHZb/bZVqlst7WOrzXVfrZTUqW3vsqfkbf6NaLoCCWdrsduXBgsLigaZpj16V58eJF4mnmTRPWPrquVCr57XY7/D6L9c76+x4nS+92WqtU1riy+F7X9Wd3XX9XZ/UzmqBuAbrdrm+apt9sNoe+KpVK+I8ojfvVzsuLFy8if1H0+33fNM2h+7bb7ch0War3PMubxnoH98zyu53FKpX1unV5r+v6s7uuv6uz/Bmdur1fV53jOBObS1+8eBFOXU7bfrXz4DiOms2mWq2WcrmcDg8PVSwWlcvl5HmeyuWyTk9Ptb29LdM0ZVnWyH57Wav3MuqUVL0DWX6387BKZb1qHd7ruv7srvPv6ix/RhPUAQAAZABLmgAAAGQAQR0AAEAGENQBAABkAEEdAABABhDUAQAAZABBHQAAQAYQ1AEAAGQAQR0AAEAGENQBADLn4OBA9+7d08bGhjY2NmRZVviVz+fD41myKnV2XVcbGxvK5/Oq1Wqq1WryPE+dTkflcjksZ7VavfFaBwcHYfpCoaBWqxWrDL1eb+iZFAoFdTqdkXSdTkeFQiFM4ziOXNdVrVZTtVoNn7fnebd9DItx643FAABYAZVKxZfk7+7ujpw7OzvzDcNYQqkWaxXqfHZ25ksK91m9zjAMX1KsDe2vpr26d2ocwd63k8ri+77fbDb9XC4XeW53dzdWOZNCSx0AIJNOT08lSZZljZwzDCNyL9NVl4U653K5cP/Ver0+Nl2v15NhGOH3W1tbt77P7u6uJKndbo9N1+/3tbe3F3nu/v37t7rnohHUAQAyyXEcSRobyNi2nWRxEpGFOm9tbYVB1KTuVNu2VavVZrpXcJ9erxc+u+uOjo5UqVRmuk9SCOoAAJnT6/UkjQY3V8dNBa1BWZGlOpdKJRmGIc/zIgM713Xluu7ElkfHcVQul8MxhVEBYC6XU6lUkhTdKtjpdFQsFlfmuRHUAQAyJ+hOu9oN6XmePvnkk2UVaeGyVucgCItqXbyplc5xHNVqNbXbbXW7Xdm2rYODg8jJF8H1O53OyISHer0ea8JGWhDUAQAyJ2i1+uSTT1QoFJTP53Xv3j1985vfXHLJFidrda5UKsrlcnJdN6ybdBmo9nq9iV2iT548GQoGS6WScrmcWq3WSOB2dazh1da6OK2BaUNQBwDIFM/z5Lqucrmc+v1++GWa5tw/oDudjmq1mlzXnet1byvJOl+9Z6vVUrVaXdiSHkHgdjVAa7VaYZdpFNd15TiO6vW6yuVy+BUIJpNcFbT6Xe3qtW177ASJtPrKsgsAAMA8HR0dSRoeW5bL5VQsFmWa5lzvlcvldHBwcOsJCJ7n6eHDh7cKhtrt9tjyz7POvV5P1WpVZ2dnE9PlcjltbW3p6OhIzWbzVveIa29vTwcHB+FEBtM01Ww21e/3x+YJJjxMmtF6XbFYlGEYcl1XrVZLlUpFR0dHev78+cx1SBJBHQAgU7rdrqTRZT0W0eriOM5ULWFBi9q8zLPO29vbsYPUzz77TI8ePbr1PeIKJjJ0Oh3V63U9fvz4xokLQaup67pDS57cJFhQ2LZtbW1taXt7e2UmSATofgUAZEow/up6sHH1A/rqGK1ZdLvdyDXhkjbPOl+dERrnvvOu/2AwGPo+CEyDru6bJi4EgVzUDhHS+OdwdQzfkydPVmqCRICgDgCQGa7ryvM8GYYxtpWl1WoNteZYljU0k9KyrJGA4ODgINwy6mqXaa/XG2qp8zwvTFsul8cGFvN02zpLl+UOttVqtVq6d+9e2PWYz+eHyu26btgFWq1Ww+t4nhe2VHY6HeXz+bkFQlef8dVxgYZh3NidHKSt1Woja8/dtI3Y1ZbNuIFtmhDUAQAyIwhGorrdPM9TtVpVtVoNW7QMw1C5XB768C+Xy2Frked5KhQKKhaLKpVK8jwvPBfkCYIM13VVKBRUKpVUKpW0t7c3NEB/UW5bZ+ky8AmCM8MwtLe3p62trfB4EBgFa73t7u6qWCxqMBiE9Q52dDg9PZVhGKrVapGTEG4rmHV6VRB0X1/G5Gq64L1c3SmiUCioXC7r4OBAlmXp7OxsYnd5MDFjVRYbHrHsfcoAAJgH27bDvTwl+aZp+sVi0TdNc2iP0FKpNJSv3+8P7e1p23b491Kp5FcqFd/3L/dVvbqnqm3bfrFYDL83DGMob7/fX/i+oNPWOdj39Pqep81mc2h/WMMw/G63G3nvSqXiF4tFv9/v+74//KwmGbf3a7vd9ovFoi/Jz+VyQ8/S9/2hZx2U1TTNobpfvaZt2+EzMAxj4v6u1+sVdx/Z4PmnZe9XgjoAwFoLAhzfvww42u227/u/DMps2/Zt2w6Dl0CxWAwDhW63619vJ7keIKVJs9mMDEZKpVIYuEbV6SrDMIaCuFwuNzYAvGpcULeK0hbUMfsVALDWgnForuuq0+mEXXdB92Lw/XW9Xi9cyqPb7Y50f9q2ndq9Vrvdbrgg71W9Xi9cCsRxnLGzR4N18YLu0GBc321mAi9qbbsk/fjHP152EYYQ1AEAoMsg7Op6a8FMSM/zhv5umqYcxwnXaev1erp///5QgNRqtcJxeGl0NXgLBPXb3t4O63d1zNrV+vd6PZmmGQZ9wYSRIBCMsxRIvV4Pg6K9vb2VWT7Edd3w38lNEy+SRlAHAFh7xWJxpFWtUqmo3W6Hkx8sywpbogaDQRjQlUolFYtFdbtdHRwchIHOohbkndWkVrVcLqejoyNVKhWZpqlut6tCoaDHjx8PzUL97LPP9Pjx4zBfMIFkMBjcODvVMAz5vj/fSiXIMIzw30raWmI3/FV+sgAAAJDEkiYAAACZQFAHAACQAQR1AAAAGUBQBwAAkAEEdQAAABlAUAcAAJABBHUAAAAZQFAHAACQAQR1AAAAGfD/AwV8s8PNQ15RAAAAAElFTkSuQmCC",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# energyspektren angucken von velo und rich\n",
|
|
"diff_found = ak.to_numpy(energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality==1)][\"velo\"]) - ak.to_numpy(energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality==1)][\"rich\"])\n",
|
|
"diff_lost = ak.to_numpy(energy_emissions[(energy_emissions.lost) & (energy_emissions.quality==1)][\"velo\"]) - ak.to_numpy(energy_emissions[(energy_emissions.lost) & (energy_emissions.quality==1)][\"rich\"])\n",
|
|
"\n",
|
|
"xlim = 20000\n",
|
|
"\n",
|
|
"plt.hist(diff_lost,bins=100,density=True,alpha=0.5,histtype=\"bar\",color=\"darkorange\",label=\"lost\",range=[-xlim,xlim])\n",
|
|
"plt.hist(diff_found,bins=100,density=True,alpha=0.5,histtype=\"bar\",color=\"blue\",label=\"found\", range=[-xlim,xlim])\n",
|
|
"plt.xlim(-20000,20000)\n",
|
|
"plt.title(\"emitted energy difference\")\n",
|
|
"plt.xlabel(r\"$E_{velo} - E_{rich}$ [MeV]\")\n",
|
|
"plt.ylabel(\"a.u.\")\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# number of brem vtx with E>x*E_0\n",
|
|
"\n",
|
|
"number_velo = ak.to_numpy(energy_emissions[energy_emissions.quality==1][\"velo_length\"])\n",
|
|
"number_rich = ak.to_numpy(energy_emissions[energy_emissions.quality==1][\"rich_length\"])\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.hist(number_velo,bins=10,density=True,alpha=0.5,histtype=\"bar\",color=\"darkorange\",label=\"velo\",range=[0,10])\n",
|
|
"plt.hist(number_rich,bins=10,density=True,alpha=0.5,histtype=\"bar\",color=\"blue\",label=\"rich\",range=[0,10])\n",
|
|
"plt.xlim(0,10)\n",
|
|
"plt.title(\"number of photons emitted\")\n",
|
|
"plt.xlabel(\"number of photons\")\n",
|
|
"plt.ylabel(\"a.u.\")\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"' \\nphoton cut = x*E_0\\neffs, all photons included: x=0\\nfound in velo/(found + lost in velo)\\nVELO energy emission, eff: 0.8446167611094543\\nRICH1+UT energy emission, eff: 0.7961586121437423\\neff von e die nicht strahlen: 0.7954674220963173\\n'"
|
|
]
|
|
},
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"\"\"\" \n",
|
|
"photon cut = x*E_0\n",
|
|
"effs, all photons included: x=0\n",
|
|
"found in velo/(found + lost in velo)\n",
|
|
"VELO energy emission, eff: 0.8446167611094543\n",
|
|
"RICH1+UT energy emission, eff: 0.7961586121437423\n",
|
|
"eff von e die nicht strahlen: 0.7954674220963173\n",
|
|
"\"\"\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "tuner",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|