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1368 lines
412 KiB
1368 lines
412 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import uproot\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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"import mplhep\n",
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"\n",
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"mplhep.style.use([\"LHCbTex2\"])\n",
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"plt.rcParams[\"savefig.dpi\"] = 600\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": 4,
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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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"31836 6689\n",
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"38525\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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" \"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_B_upstream.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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre>{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_5000_qop: float64,\n",
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" ideal_state_5000_tx: float64,\n",
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" ideal_state_5000_ty: float64,\n",
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" ideal_state_5000_x: float64,\n",
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" ideal_state_5000_y: float64,\n",
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" ideal_state_5000_z: float64,\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_chi2: float32,\n",
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" match_dSlope: float32,\n",
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" match_dSlopeY: float32,\n",
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" match_distX: float32,\n",
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" match_distY: float32,\n",
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" match_fraction: float32,\n",
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" match_teta2: float32,\n",
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" match_yCorr: float32,\n",
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" match_yCorr_def: float32,\n",
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" match_zMag: float32,\n",
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" match_zMag_def: 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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" p_end_scifi: float64,\n",
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" p_end_ut: float64,\n",
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" p_end_velo: float64,\n",
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" p_upstream: 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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" 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": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"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": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre>{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": 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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"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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"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": 7,
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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",
|
|
" for jentry in range(brem[itr, \"photon_length\"]):\n",
|
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" if (brem[itr, \"brem_vtx_z\", jentry] > 9410\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\",\n",
|
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" jentry] < photon_cut_ratio * tmp_energy):\n",
|
|
" ph_length -= 1\n",
|
|
" continue\n",
|
|
" else:\n",
|
|
" cut_brem.real(brem[itr, \"brem_photons_pe\", jentry])\n",
|
|
" tmp_energy -= brem[itr, \"brem_photons_pe\", jentry]\n",
|
|
" cut_brem.end_list()\n",
|
|
"\n",
|
|
" tmp_energy = brem[itr, \"energy\"]\n",
|
|
"\n",
|
|
" cut_brem.field(\"brem_vtx_x\")\n",
|
|
" cut_brem.begin_list()\n",
|
|
" for jentry in range(brem[itr, \"photon_length\"]):\n",
|
|
" if (brem[itr, \"brem_vtx_z\", jentry] > 9410\n",
|
|
" or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
|
|
" or brem[itr, \"brem_photons_pe\",\n",
|
|
" jentry] < photon_cut_ratio * tmp_energy):\n",
|
|
" continue\n",
|
|
" else:\n",
|
|
" cut_brem.real(brem[itr, \"brem_vtx_x\", jentry])\n",
|
|
" tmp_energy -= brem[itr, \"brem_photons_pe\", jentry]\n",
|
|
" cut_brem.end_list()\n",
|
|
"\n",
|
|
" tmp_energy = brem[itr, \"energy\"]\n",
|
|
"\n",
|
|
" cut_brem.field(\"brem_vtx_z\")\n",
|
|
" cut_brem.begin_list()\n",
|
|
" for jentry in range(brem[itr, \"photon_length\"]):\n",
|
|
" if (brem[itr, \"brem_vtx_z\", jentry] > 9410\n",
|
|
" or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
|
|
" or brem[itr, \"brem_photons_pe\",\n",
|
|
" jentry] < photon_cut_ratio * tmp_energy):\n",
|
|
" continue\n",
|
|
" else:\n",
|
|
" cut_brem.real(brem[itr, \"brem_vtx_z\", jentry])\n",
|
|
" tmp_energy -= brem[itr, \"brem_photons_pe\", jentry]\n",
|
|
" cut_brem.end_list()\n",
|
|
"\n",
|
|
" cut_brem.field(\"photon_length\").integer(ph_length)\n",
|
|
"\n",
|
|
" cut_brem.end_record()\n",
|
|
"\n",
|
|
"ntuple = ak.Array(cut_brem)"
|
|
]
|
|
},
|
|
{
|
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"cell_type": "code",
|
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"execution_count": 8,
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"metadata": {},
|
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"outputs": [
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{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
|
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"48551\n",
|
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"38525\n"
|
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]
|
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},
|
|
{
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"data": {
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"text/html": [
|
|
"<pre>{event_id: 0,\n",
|
|
" lost: True,\n",
|
|
" rad_length_frac: 0.129,\n",
|
|
" energy: 1.17e+04,\n",
|
|
" brem_photons_pe: [2.62e+03, 2.54e+03, 1.86e+03, 3.12e+03],\n",
|
|
" brem_vtx_x: [-6.97, -52.9, -55.2, -1.71e+03],\n",
|
|
" brem_vtx_z: [112, 859, 895, 8.7e+03],\n",
|
|
" photon_length: 4}\n",
|
|
"-----------------------------------------------------------\n",
|
|
"type: {\n",
|
|
" event_id: int64,\n",
|
|
" lost: bool,\n",
|
|
" rad_length_frac: float64,\n",
|
|
" energy: float64,\n",
|
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" brem_photons_pe: var * float64,\n",
|
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" brem_vtx_x: var * float64,\n",
|
|
" brem_vtx_z: var * float64,\n",
|
|
" photon_length: int64\n",
|
|
"}</pre>"
|
|
],
|
|
"text/plain": [
|
|
"<Record {event_id: 0, lost: True, ...} type='{event_id: int64, lost: bool, ...'>"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"print(ak.sum(ak.num(ntuple[\"brem_photons_pe\"], axis=1)))\n",
|
|
"print(ak.num(ntuple, axis=0))\n",
|
|
"ntuple[0]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"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": 10,
|
|
"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": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"31836\n",
|
|
"6689\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, 1.88e+03],\n",
|
|
" brem_vtx_x: [-3.61, -61.5],\n",
|
|
" brem_vtx_z: [35.6, 8.49e+03],\n",
|
|
" photon_length: 2}\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": 16,
|
|
"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)\n",
|
|
"print(length_lost)\n",
|
|
"ntuple[1]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Z_found = ak.to_numpy(\n",
|
|
" ak.sum(ntuple[~ntuple.lost][\"brem_photons_pe\"], axis=-1,\n",
|
|
" keepdims=False)) / ak.to_numpy(ntuple[~ntuple.lost][\"energy\"])\n",
|
|
"Z_lost = ak.to_numpy(\n",
|
|
" ak.sum(ntuple[ntuple.lost][\"brem_photons_pe\"], axis=-1,\n",
|
|
" keepdims=False)) / ak.to_numpy(ntuple[ntuple.lost][\"energy\"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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"text/plain": [
|
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"<Figure size 1200x900 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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|
}
|
|
],
|
|
"source": [
|
|
"xlim = 0\n",
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"\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.yscale(\"log\")\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": 33,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"found: 7000\n",
|
|
"lost: 6689\n",
|
|
"found: 7910 , lost: 12529\n",
|
|
"1.583944374209861\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<pre>[-3.61,\n",
|
|
" -61.5,\n",
|
|
" 65.2,\n",
|
|
" -5.73,\n",
|
|
" -26.6,\n",
|
|
" -4.26,\n",
|
|
" -396,\n",
|
|
" 10.7,\n",
|
|
" 26.2,\n",
|
|
" 19.6,\n",
|
|
" ...,\n",
|
|
" -38.9,\n",
|
|
" 17.6,\n",
|
|
" -168,\n",
|
|
" -74.7,\n",
|
|
" -500,\n",
|
|
" -566,\n",
|
|
" 4.2,\n",
|
|
" -20.3,\n",
|
|
" -59.9]\n",
|
|
"--------------------\n",
|
|
"type: 7910 * float64</pre>"
|
|
],
|
|
"text/plain": [
|
|
"<Array [-3.61, -61.5, 65.2, ..., 4.2, -20.3, -59.9] type='7910 * float64'>"
|
|
]
|
|
},
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"tuple_found = ntuple[~ntuple.lost]\n",
|
|
"tuple_found = tuple_found[:7000]\n",
|
|
"tuple_lost = ntuple[ntuple.lost]\n",
|
|
"\n",
|
|
"print(\"found: \", ak.num(tuple_found[\"brem_vtx_x\"], axis=0))\n",
|
|
"print(\"lost: \", ak.num(tuple_lost[\"brem_vtx_x\"], axis=0))\n",
|
|
"\n",
|
|
"brem_x = ak.to_numpy(ak.flatten(ntuple[\"brem_vtx_x\"]))\n",
|
|
"brem_z = ak.to_numpy(ak.flatten(ntuple[\"brem_vtx_z\"]))\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": 37,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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4sbq6qjNnzuipp57SwsKCxsfHfS23vb2tcrmsjY0NvfLKK11uZX9dunRJkvTSSy/VLZNMJpVOp6vB8MrKSt0LAZKUzz/MhVHvDvSo1AkAAAAAQTDlAzTlEYwSYu7miLkb1wkAAIDBkBk537yQXel+Q/pkj1HYUYdl23bod4/f/M3f1Pz8vIrFoizLaqsO27ZlWZb29vYCbl13LC0t6cqVK0qn07p48aIjx5nbwsKClpaWND8/r8XFxYb1lkolTU1NSdoPotfX143lyuWyxsbGJEnpdFqrq6uRr9OvyclJXbt2TadPn9bm5mbH9Q2V27elEyf2X9+6JR0/3t96MDzYZwAAfnC8QACC7lAPQ/xBzE3MTczdI50chziGRRufH4Bhwm9epAXVoV4bI4X9/O+gfR//eEz/5upHe7ruP/vM9/Xee5XQbhs8FPoh33/+539emUxGb775pizLkm3bLf+LmnK5rIWFBRWLRS0tLWlsbKzuXeDZbFZLS0taXFxsGthL+8OzHdxdflC/ydmzZyXtD2dXezd6lOsEAAAAADgRcxNzE3MDAAAA+yo9/ofoCHWH+ksvvaTl5eVqgN5uoB61AD8ejyuRSDjmHQT52WxWCwsLymQy1bvENzY2ND8/77v+6elpra6uKh6Pa2FhQdlsVsViUeVyWYVCQalUSsViUclkUm+//XbDO/WjVicAAAAAYB8x90PE3MTcAAAAAFBPqHOoLy8vS1L1Lvl0Oq1MJqN4PN5yTrevfOUreuedd7rY2mCtr6/r0qVLKhQKKpVKKpfLkvbvHJf275LP5/NtB7TpdFrXr1+vDnN39uxZlctlxeNxnTlzRvl8vmEOtSjXCQAAAADtinq+9FrE3MTcxNwAAABoyjXEuzsm8jVMPBBxoc6hHovFZFmW4vG41tbW9OSTT7ZdV7lc1qlTpyKTzw39E/Z8HqFGDnX0C/sMAMAPjhcIoX7GH8Tc6IehjrnJoT68+PwADBN+8yLN2DneRof66t6V6uuwn/8dtO9jH4/pt69+rKfr/gvPfE/fI4d6JIR6yPeDO8EvXrzYUWB/UNfTTz8dQKsAAAAAAIg+Ym4AAAAAAJoLdYd6KpWSJE9us3a99NJLgdQDAAAAAEDUEXMDAAAAwEMVu7f/EB2hzqE+OzurN954Q6VSKZD6uFseAAAAANCJTCzrmRfVvOrE3AAAAABq1Q7VfsA9pLs7JopqPAS0ItQd6tPT03r66ad15coV/e2//bc7ru/rX/+6fuZnfiaAlmEYbG1taXJysmGZubk5zc3N9ahFAAAAAKLk8uXLunz5csMyW1tbPWqNFzE3+omYGwAAIPretd9qek7Xz5gHCEqoO9QlKZ/P68d+7Mf0jW98Qz/90z/dUV2XLl0iuIdvlUpF165da1hmZ2enR60BAAAAEDU7OztNY4p+I+ZGvxBzAwAARN+uHoQ+5mnFnqx+NwEhFfoO9UQioa985St68cUXOwrub9y4oWKxGGDLMOhisZgmJiYalhkdHe1RawAAAABEzejoqE6fPt2wzNbWliqVSo9a5EXMjX4h5gYAAIi+Qzoc+pgHCEKoO9S//vWvS5Ief/xxjY2N6amnntL09HTL9ZTLZb3yyitBNw8DbmJiQpubm/1uBgAAAIA+cOcJPGDKD1gvr7qf4aonJyf79kQHMTf6iZgbAACg/9yxjHXosLeQXb8z/Anrk9IfeefXxk39jHlaYSvYJ9S/+tJNffWlWw3L/PD73GgQFaHuUP/Sl76kN998szpt27aWlpbaqsu2bVkWQzUAAAAAACARcwMAAABAt9y6aev779FhPihi/W5AI+fOnZNt27JtW5IIzgEAAAAACAgxNwAAAAB0x4nHLH3047GG/2Kh7qVFrVA/oT47O6svfvGLsiyrGuADAAAAAIDOEXMDAAAAwAFLFTu4m4w/++KoPvviaMMyz/4nW/r+e3uBrRPdE+oO9ZMnTyqZTOrNN9/U4uKiksmkxsfHW66nVCrpS1/6kr71rW8F30gAAAAAACKImBsAAAAAgOZC3aEuSefPn9fU1JS+8IUvtF3H008/rc985jNtXRgAAAAAAAyf1b0r/stW8l1sSXcRcwMAAADDyx3LZGLZlstEOR5y2xNpsGAW+tH50+l0YEPPnTlzJpB6AAAAAAAYBMTcAAAAAAA0FvoO9aefflqLi4uB1PW3/tbfCqQeAAAAAAAGATE3AAAAAACNhb5DXZKefPLJQOr5R//oHwVSDwAAAAAAg4KYGwAAAACkPcV6+g/REfoc6kFaW1vrdxMAAAAAABhIxNwAAACAP0HkIe9XLvN28q4DURfJDvV33nlH5XLZd/lSqaTl5eWWlgEAAAAAYBgRcwMAAAAYNrakim31fJ2Ihkh0qL/zzjtaXFxUoVBQqVRqqw7btmVZvf0iAAAAAAAQdsTcAAAAAADUF/oO9YsXL2ppaUnSfoAOAAAAAACCQcwNAAAAAPv2xE3CMAt1h/qv/dqvaXFxUZJkWZYsyyLABwAAAAD0TWbkvK9yq3tXutySzhFzAwAAAN1hyivuYMWc5Q1xRrOYIoi86wD8CXWH+qVLlySpGtQnEgklk0klEglJ0qlTp5rW8f7776tcLuuVV17RjRs3utpeAAAAAACigpgbAAAAAIDmQt2hXiwWqznYVldXdfbs2bbrmp+f11NPPRVU0wAAAAAAiDRibgAAAAB4aM+ONS+EoRTqDvV4PK4bN25ofn6+o8BekhKJhJ588smAWoZhsLW1pcnJyYZl5ubmNDc316MWAQAAAIiSy5cv6/Llyw3LbG1t9ag1XsTc6CdibgAAgOi7fPmyftv+l575ted5/Yx5gKCEukM9kUjozTff1DPPPBNIfcvLy4HUg+FQqVR07dq1hmV2dnZ61BoAAABgMJhy9rWT+y9IneYRNLU/E8tqw/53uqbGMUU/EXOjn4i5AQDAIHPHCJ6Yw644p63ePBndLPYy5XJvVGaj8m91T3c8ZZqd54WRLUsV9fYJdVtWT9eH9oW6Q/3s2bN68803tb29HVh9gF+xWEwTExMNy4yOjvaoNQAAAACi5pAO66iOOeY9fnrcMb21taVKxXUxrUeIudFPxNwAAADRd8g6rNM/crphmX7GPEBQQt2h/ou/+Iv68pe/rGKxGEh9v/zLv6wXX3wxkLow+CYmJrS5udnvZgAAAACIqCesT+oJfdIxb3XT+UTI5ORk357eIOZGPxFzAwAARN8T1o9rdfNbDcv0M+YBgtLbsQtadPLkSf3SL/2Srly5ops3b3ZcH8PPAQAAAACwj5gbAAAAAB7ak9XTf4iOUD+hLknz8/NaXV1VNpvVN7/5zbbrefvttwO76x4AAAAA0J5+50s36bRNYcwL7xcxNwAAABA8d4zQNKd6WLhzu8ubV31170rDv5vKDKP8L1/Xyj+53rDM9vd3e9QadCr0HeqStLq6qjNnzuipp57SwsKCxsfHmy8kaXt7W+VyWRsbG3rllVe63EoAAAAAAKKHmBsAAAAApD07uIG9b92s6Ifv0WE+KCLRof6bv/mbkqSNjQ3Nzs62VYdt27Ishk8AAAAAAKAWMTcAAAAABOvREyN6/OONu2G3v7+rindQAIRQ6DvUf/7nf165XE6SZFmWbNtuuQ6CegAAAAAAvIi5AQAAACB40y+Oa/rFxqN/vfDn/pCn2CMi1B3qL730kpaXlyW1H9hLans5AAAAAEB76uUEDGNu8VbyF5raH8b35AcxNwAAANAd7hjh2SMvNF7AkLu8H6xDhz3z7L09x3Ro878HoCJuFoZZqDvU3YF9Op1WJpNRPB5vOafbV77yFb3zzjtdbC0AAAAAANFBzA0AAAAAQHOh7lAvFouyLEvxeFxra2t68skn267rwoULOnXqVICtAwAAAAAguoi5AQAAAGCfLWlPsZ6vE9HQ2z2jRfF4XJJ08eLFjgL7g7qefvrpAFoFAAAAAED0EXMDAAAAANBcqDvUU6mUJCmRSARS30svvRRIPQAAAAAARB0xNwAAAAAAzYV6yPfZ2Vm98cYbKpVKgdTH3fIAAAAA0JlMLOuZt1rJ+5oXVlFqa5CIuQEAAIDucMdN1qHDfWpJa+y9Pc+81b0rjulnj7zgmH79/stdbVMv7dmhfg4ZfRTqPWN6elpPP/20rly50rywD1//+tcDqQcAAAAAgKgj5gYAAAAAoLlQd6hLUj6f1/r6ur7xjW90XNelS5cCaBEAAAAAAIOBmBsAAAAAJMlSRbGe/pOsfr9p+BT6DvVEIqGvfOUrevHFFzuq58aNGyoWiwG1CgAAAACA6CPmBgAAAACgsVDnUD8YLu7xxx/X2NiYnnrqKU1PT7dcT7lc1iuvvBJ08wAAAABg6PjNN27Ktd7K8v2WGTlv/oNd8bV8FN4nMTcAAAAGiSkGCct5uTvPuDsPuSl3eT+486VL9WO7Rn8Py3YHghLqDvUvfelLevPNN6vTtm1raWmprbps25ZlMXQCAAAAAAASMTcAAAAAHLBtac/ubUxj2z1dHToQ6g71c+fOVYeMsyyL4Bw9tbW1pcnJyYZl5ubmNDc316MWAQAAAIiSy5cv6/Llyw3LbG1t9ag1XsTc6CdibgAAgOh7135L39V3PPNrz/P6GfMAQQl1h/rs7Ky++MUvyrIs2dymgR6rVCq6du1awzI7Ozs9ag0AAACAqNnZ2WkaU/QTMTf6iZgbAAAg+nb1QPd0xzM/zHFQI3uK9bsJCKlQd6ifPHlSyWRSb775phYXF5VMJjU+Pt5yPaVSSV/60pf0rW99K/hGYmDFYjFNTEw0LDM6Otqj1gAAAADollZy/rVSdnR0VKdPn2647q2tLVUq/vKyB42YG/1EzA0AAIIWpbzd7pzqJu7Yoxfvz0+889yxz1VfH949pqO7xzzLPH76YVzRz5gHCEqoO9Ql6fz585qamtIXvvCFtut4+umn9ZnPfKatCwMYXhMTE9rc3Ox3MwAAAABElJ/hqicnJ/v69AYxN/qFmBs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"text/plain": [
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"<Figure size 2000x800 with 3 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"vmax = 150\n",
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"nbins = 150\n",
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"\n",
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"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
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"\n",
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"a0 = ax0.hist2d(\n",
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" brem_z_found,\n",
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" brem_x_found,\n",
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" density=False,\n",
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" bins=nbins,\n",
|
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" cmin=1,\n",
|
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" vmax=vmax,\n",
|
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" range=[[-200, 9500], [-3200, 3200]],\n",
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")\n",
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"ax0.vlines([770, 990, 2700, 7500], -3200, 3200, colors=\"red\", lw=1.5)\n",
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"ax0.set_ylim(-3200, 3200)\n",
|
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"ax0.set_xlim(-200, 9500)\n",
|
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"ax0.set_xlabel(\"z [mm]\")\n",
|
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"ax0.set_ylabel(\"x [mm]\")\n",
|
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"ax0.set_title(\"found\")\n",
|
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"\n",
|
|
"a1 = ax1.hist2d(\n",
|
|
" brem_z_lost,\n",
|
|
" brem_x_lost,\n",
|
|
" density=False,\n",
|
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" bins=nbins,\n",
|
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" cmin=1,\n",
|
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" vmax=vmax, # * stretch_factor,\n",
|
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" range=[[-200, 9500], [-3200, 3200]],\n",
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")\n",
|
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"ax1.vlines([770, 990, 2700, 7500], -3200, 3200, colors=\"red\", lw=1.5)\n",
|
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"ax1.set_ylim(-3200, 3200)\n",
|
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"ax1.set_xlim(-200, 9500)\n",
|
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"ax1.set_xlabel(\"z [mm]\")\n",
|
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"ax1.set_ylabel(\"x [mm]\")\n",
|
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"ax1.set_title(\"lost\")\n",
|
|
"# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
|
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"\n",
|
|
"# plt.suptitle(\"brem vtx of photons w/ $E>0.1E_0$\")\n",
|
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"plt.colorbar(a0[3], ax=ax1)\n",
|
|
"\n",
|
|
"plt.show()\n",
|
|
"# plt.savefig(\n",
|
|
"# \"/work/cetin/Projektpraktikum/thesis/brem_vtx_found_lost_hist2d.pdf\",\n",
|
|
"# format=\"PDF\",\n",
|
|
"# )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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ga9f7252Ty3Byx/3trolNCKojm7Z2YZpnFxfc9+nS68/9vO32uYsvSJIi1vD/q8vpPRLEYNvTYhJBw5N4PXmPhBev3ejc/l5xusbTuva9Y3rWeqovDbH/UDd5sPld//qFz7ja1yk02+733Zcf/qbrMU2C3U1g7G4kYndOTr+//QzjdnqN7cLN7UTO2IfANx8/HnlMoTbmbCM/9RaOstnswDDv1iykaDTaNUOppXe2VOdyvEHi8Xi74MVMJwAAAAAAgJCLx+Nds5Gq1Wr7rnO9SqVSuzDUuYSuU2eWk+QuJ8qu3bBL/cKCohMAAAAAADg13nzzza7n29vbfYWnarWqbDYrSSoWi1pfX7ftqzdc/KQ8p5befKidnR1X+4UNRScAAAAAAOCeFfH3MaZ4PK5yuXvJ5fb2tlZWVlSr1VQoFJRKpRSLxVSpVBwLTpL6MqFGnelUr9ddjj5cKDoBAAAAAIBTJZ1Oq1gsdm0zDEOJREK5XE6bm5uq1+tKJpMD+zkpiNwt0zQ96SdoCBIHHDQaDS0vLw9ss7GxoY2NjSmNyF/DBGz+1Auf0UeR7h8vc88+09eu+fCh98e3C8h2CN22M0zotl2QqOvQ7yH6tNt/qGBWm2PZhWE6BWzbhYaeWYz2bbO+5/71dCty4Tnb7dbTp8f/tZ4O3H/cgGrbsPshwuInEZDt53EmJUxjxcnGvfED74duQbxOQXyN+sb04IH0/PMTOZbbm5bMPdf/O8z27wRJZy78gKvjTOt3iCT7G4nYhIvbBXE7BoYP8TeZW+NeJ7u/Ue3G3/zoke3+39I39M3m17q29X6GaDQarseD6WrNYGoto+tUrVZlmuaJy+VGLRb19nvv3r2R+gk6ik6Ag2azqf39/YFtDg8PpzQaAAAAAEHzVE/0SN3/4+2kzxAzYcS71x1Wv6LDd/7V2Ic/uu/d57D19XXt7u6qVCp1ba/Varpy5YreeeedruDxSWGmE3DKzM3NaWlpaWCb+fn5KY0GAAAAQNCc1Tk9o+5ZbS9eXux63mg01Gx6P8srjJofPdKRed/vYXTJZrMqlUpaX19XtVrtWi5nmqYSiYQqlYrjMrtoNOpJwchtAHnYUHQCHCwtLWlvb8/vYQAAAAAIqJfO/Fm9dObPdm17e+8LXc+Xl5dPx+wnF+aefUZnohfH7ufo/qFkjTjdqkMqlVK1WtXm5qby+bykj4tQve2cCk+Li4ueFJ0WFxdPbhRCFJ2AU2IS2Tad6/KftZ7qS9//+ksf/IZ04UJX07/6g3+rb3e7TKefev7n2v381PM/186Gss0DcZuf5OD1536+b9tQ18TmmtrlH1l2sQZDjNOuT6dMCLfssgqczt3uOum5Z/s2RY68/z94kRcc8jgeH2dKRJqTvR/GMPlNtvu7zLEZN6PD9feHHLLIQp4J5dbYPwdn8Dq5zaYZ5jzdtg37tbMzre9vBJBN/pHd3zlzz/T//pSkowff69s2rdfe6Tj2WU397eZs8qiO7jvMZHHItByH2+tkdz6S/d9Ec+fPu9omSc1HH7k6/swZsd4zv/aTml/7ybEPv/fL//exZ0wlEgnVajUlk8l2wUmSisWiUqmUMpnun+mZTEZ3797tm5E06gyl3kLVrM504u51AAAAAADg1MhkMqrVapLUdwc76fjOdru7u12FINM0lcvl+tqurq52PXc766k3OHxlZcXVfmFD0QkAAAAAAJwKhmFoe3tbkhSPxxWLxWzbxeNx7e7udm0rlUp9RaVEItHXvxv1er3ruVNmVNhRdAIAAAAAAO5ZPj/G0DmzqXeWUq9YLNY3E2pnZ6freW8fbotOncWraDTqWPwKO4pOAAAAAADgVOgs9rhZ0ra+vt71vLeoFI/Hu5bh3blzx9U4OotXJxW/wowgcYRGoVBQpVLRzs6OTNNULBZTPB5XNpud2amIndyGvUr2gYp2YdROXr/wmb5tZ+xCIrtCL+fa/9ehMwC8vf+fe6V/TPcP+w/+5KntmNyev11Ap1PAY/Px4/7jDBHQPffcc33bmh896ttmd+3sQisdj28XOu4UxOk2oNxmf6dzn3v2mf6NZ870b3vGPmBzLHbHkaQf+P77q+nw7y1jBpaGOTh6mBD0sJzTuNwGuzu1nVYw/DDGPf5pee2n5bRcT7/f936zO1e7m27Y/a63+9tDkiJOv+98ZDd+u3EeHX7gus9xb9Axjrcff8F2u91rZ/c6nVmwv+PaqZ3FYUX8HsHIOrOUepe4OYnH4+0MKDvXrl1r3/FuULtOne3ssqJmxan9HkF4VKtVLSwstL8Ry+Wy6vW68vm8arWaUqmUUqmUJ7epBAAAAADMrs5lbG6Xwjnt35LNZttfV6vVE/vobBOLxWZ6EgVFJwRatVptF5TW19dVqVSUTCYVi8WUTqdVr9cVj8dVrVaVSCQoPAEAAAAAHF2//vHs/tYqmpN0FqfsCkTxeLxreyuo3Em5/PGMyVme5SRRdEKAmaapTOZ4+rZdgFtL6xvWMIx2ewAAAADAZEQsfx/j6CwQmaapW7duDWxfrVbbhal8Pu/YrvPz6qA+TdNsL8VLJpN9mVGzhqITAiuTybS/uQdVf1uznqTjHwitb2AAAAAAAHqVy+V2+HehUHD8DNk5sSGZTGpzc9Oxz1gs1p4QUavVVCgUbNutra1JOr5jXeeMp1lFkDgCyTCMrnWu165dG9j++vXr7SmMuVwuVNXiT83/Qt+2yCd+sG/bmcUF133+1R/8W/37X/nRvm1OwdFnlz7Rt+3gkz/Wty36hx+H8M01H0v/8/eP9UMv6mykO7j8/dVLffsv/vv/0LctctaSHn7/6+ee1dz3+7ELrrQL7bYLvbQLF5fsQyLnzveHYTuFflo2oedn5l+wbdvHITDdLrTbNpzc7XFkH/Bpdxy785GkiM01kc11frLyw33bxg3AtzuOJD388eP3qPX0sfT92c5/4wc/q0cR94H5I49pCGEO2x37tbPxqRftfzb/7nf9+58FTmO3+/loF4A7iddz3HDzsAvz980kDPN+mJbT/HpI9j8f7P5+cLppiB3r6ZOxxuTWMN9fdn97uQ08txxO/bXzn+7b5hTwbWeY3029hnnf2v7defjhyMdG8ESjUd29e1e5XE6lUknZbFblclmZTEarq6u6d++eKpVKu3CUz+cHFpxa0um0KpWKMpmMcrmc7ty5o5s3byoWi2lnZ0e5XE61Wk3xeFzvvPNO113vZhUznRBIndMWk8nkid+MrZlO0vF0xZPW0AIAAAAARmAF5DGmaDSqYrGo3d1dra+vyzAMZbNZJRIJZbNZGYahzc1NHRwcuCo4tSSTSR0cHCifz8swDK2trWlhYUGZTEaLi4sql8va3d09FQUniZlOCKjO6Y3xeNzVPrFYrB3wtrW11VWIAgAAAACgVzwed8wPHsfm5uZQxapZxUwnBE6tVut6fvXqVVf7dRanmOkEAAAAAIC/KDohcDqznKTjGUxu9LbrLV4BAAAAAIDpYXkdAufOnTtdz92udb10qTuoemdnx/XSvHH81T//y33b/ujGi33bfi/za7b7/87X3QdC2/lL/+Zn+7b9b/8X/6ZvW/HOlb5tL20t2vZ595P9Ycz/6U/+275t/+a3/nz76+ceP2oHiT/5s8t6fKY7UPOjtNm3/7X/4zf6tn3h7/016Uv/TJL0veR/rI/OHvfzfPV/7mt75kcv9w/+wwf9246a/dsk6XuRvk1DhYPbBIRaNqHfkXP9P2qbjz6y7TJytv/a24V+24WDS9KZCz/Q36dd6GezfyG8YzioXRDq4/7A07mH/dfOabm9XZinXTjo2diP2e5/7sPjY507+viYkfPnFekJEn/8v/6P+/a1C9qXJOtp//iP7t/v2zZMEOmZ55/v2zatkORxw4cnMaajewee9zkpdqHh43L72p+WkOZJBGQP02dYAsuDOKZTz+r/u+LLD3+zb5vbwHHJ+aYlXhv3/WQ7Tpvr4XgTF4e/f9yaxPeD62tvc56SNPfccx6OBpg9zHRC4LRymVpGnelUr9c9GxMAAAAAABgOM50QOL1Fp1GZpulJPwAAAACAj0U8uHscTgeKTgicUYtFvcvw7t27N9Y4Go2GlpeXT2z33T857Nv29Fc+nkR48Sd/Uhc/+ZNjjQUAAADAdH3T+iN9y/r64EYP++Malpd/r+t5o9HwclhAqFB0wswad6ZTs9nU/v7+aDt3xMBY3/tIc0+lHz07XnaTk+YXfrBv29f+i6W+bc++0J81ZJ2zX4P+f/rp/vXy//W/S/UfuyM+pzMi6Mz9j3T2TPe691c/0X8tv9fszzV49t7j7q+/HzMUeaE/G8cy+/N2IgvR/nbftS9AOmYY9e7vsNZ/zm5M9/uzlo4efK9vm13Wj1Nbu0wop7yZ185/un+cNplQdiy77CZJkeX+99PDH73Yt+2ZP+0fuxVxv4rbLs/q8H/5Cdu2L/y742yguebgHIYzH/WfU9Mu90v25z92doTL99gkjDv2obJxbHJL7N6jpz2bZlrnf5qziobpM4jXxM4ksq+mye3Ph7GP03OdnrWe6kueH8X98Z045gc55AWF1lx/IcZvTq+RXf5U63V6aj3RIz0c3LHNjJ/9/f6/iWaOFbzXGMFE0QmBE41GPVka5zaA3Mnc3JyWlro/bH9336Z4cb7/g/LT5z7+ITz3rH2QIgAAAIDgOhs5p2es7/9P2ohDkcXqrzq9eLn7Zj2NRkPN5owVFgGXKDohcBYXFz0pOi0u2t+Zza2lpSXt7e11bbP9PySJn+jbdPen++8gBgAAACA8Xor8uF6K/Lgk55njdnc+fnvvC13Pl5eXR19BcQrd/xdf0f3f/8rANkeH/REnCCaKTgicUWco9Raqxp3pBAAAAACwMcEg8eZHH+nofn+MBsKJohMCZ3V1VbVarf3cNE1XBaTe4PCVlRWvhwYAAAAAmKC5Z5/VmYv9+aGdjg4PbZc2IngoOiFwEolE13PDMBSPx0/cr16vdz1PJpO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J6XQ60vhcLhd6/Nlnn00dt474MmVOrE+xWJRt26rVaksfa0r8EJPJ02w2Q5djbzQaofd82bkGkhwzxGFyHB4eDu97nifP8+aOPz8/H96fFaemxAxxCCApSDoBS3JdN/T4yZMnkY4b/Qe/3W7HuiZsx/Pnz5c6gR//oy+TyUyMWUd8mTIn1qdarcp1XbVaraWPNSV+iMnk8TxP5XJ5+Ni2bZVKpXvNZUrMEIfJMp44Go3HaQbvfTqdVqFQmPi5KTFDHAJIEpJOwJJGv7GVZn8TNm583PgfBDCPbduRq5wkyff90ONpsbOO+DJlTqyH67qq1+v3rjAxJX6IyeQpFouhxx9//PG95zIlZojDZLFtO5RIcRxnIi4Hms3msBJq1hdKpsQMcQggSUg6AUsaLb2WFDnp8Pjx49Dji4uLuJYEQ4yX9Y9vt5PWE1+mzIn1ePr0qQqFwr0rTEyJH2IyWTzPC52wzqocicqUmCEOk+f169ehx+12eyLx5DjOsAqq0WjM/P+lKTFDHAJIEpJOwJLGEwf3/fao1+vFtiaYYfSPt1l/0K4jvkyZE/EbnFiNn3Qtw5T4ISaTpdFohB4PkuyDE/5MJqNUKqWDgwNls1nV6/WJatBRpsQMcZg8tm1PbC1ut9vKZDLDStB8Pi/LstTpdOYm6E2JGeIQQJJ8sO0FAKZZ1IQyqnl/XGM3jZ6EVavVqWPWEV+mzIl4tdtttdttdTqdpbaBjjMlfojJZBltYizdNXTOZrMT23V835frunJdV9VqVa1Wa2pFlCkxQxwmU6FQUKPRCPV08jxP2WxWklSpVCL1aDQlZohDAElCpROwpPv+Azx+0vfu3bvVFwNjjG41qdVqM791XEd8mTIn4uP7vorFokql0tRtnMvOdR/E5MPled7E5/HJJ5+oXC7r5uZGQRAoCAL1er2JqpJisTi1gbEpMUMcJlepVJqowBtwHCfSZ2dKzBCHAJKEpBOwJXx79LAMvkG1LEuVSmXtr7eO+DJlTtz1cbIsa+YJ1jaYEj/E5OrGqyzS6bQ+/fRTlUql0EntIEbH4/TFixdr+xxMiRnicD1KpdLU7XOu6+oHP/jBxhpnmxIzxCGAOJB0Apa0yjaVdcyD5HNdV81mU+l0Wp1OZ+7YdcSXKXMiHvV6Xa7rTvQwuS9T4oeYTI7xpNN4smnceCLA9329fPkyNMaUmCEOk61cLqvZbKpUKk1UHPu+r2w2O3Hlt1GmxAxxCCBJSDoBSzo8PEzUPEi+Fy9eSJLevn27sJnnOuLLlDmxukFfnFqtFrpM+CpMiR9iMjnGmw8/efJk4THjfe7q9XrosSkxQxwmVz6fV7PZVKVSUaPRmLq9czBuVuLJlJghDgEkCUknYEn3/dZnvESZb48ehnK5PKw6iZIEWEd8mTInVlcsFmXbdqxbOE2JH2IyOe7znlqWNfH/yNGtTqbEDHGYTIMKplwuF2oY3mg0plaFFovFqVvLTIkZ4hBAkpB0ApZ0enoaehx1v/t4M8ZMJhPXkpBQzWZTzWZTjUZj6tWYpllHfJkyJ1ZTr9fleZ4ODw9VLBYX3kY/s5cvX4Z+NtrI2ZT4ISaT477v4fhnOLpNz5SYIQ6Tp1gsDhOY0/rcFQoFdbvdUILF9/2pV5k1JWaIQwBJ8sG2FwCYZnB53QHP8yJVsIxvN1j1ilJINsdxVC6X1Wg0ppbvz7KO+DJlTqzms88+k6S5/UhmGVyyfsCyrGGi1JT4ISaTY7w6IuoJ7/gJ7ugJsCkxQxwmi+d5wyS6bdszt7jbtq1utxuKwWazqVqtFopnU2KGOASQJFQ6AUua903sPKN/dKfT6YW9fWAu13WVz+dVq9WWSjhJ64kvU+ZEMpkSP8Rkctz3sxhPVo32kzElZojDZBmtbBr/bMZNu+LnxcVF6LEpMUMcAkgSkk7AkmzbDv1hfH5+Hum40T9cFv3hA3N5nqenT5+qUqncq6/OOuLLlDmxmlqtpiAIIt9GTyZarVboZ6M9T0yJH2IyOcYrKsarJ6IajVFTYoY4TJbRJEqUrWLjXxSNJ2tMiRniEECSkHQC7uHZs2fD+6NbUuYZHTetTwDM53mestmsSqVS6KR90THjV2laR3yZMieSyZT4ISaTY3RbTtQtn+Pb8MaTV6bEDHGYHKNbNKMmPxdtQzMlZohDAIkRAFhat9sNJA1vi3Q6neFYy7I2sEJs2s3NTWBZVlAqlZY6zrbtoNvthp5bR3yZMic2x7Ks4efRarXmjjUlfojJ5Bh9byUFNzc3C48plUrD8blcbuLnpsQMcZgclUplbkxNY9v28JhOpzPxc1NihjgEkBRUOgH3YNt26Fvc0Ss9TTN6OV6+Odo9vu8rm83KsixVq1V5nrfw5jjOsNHn+Leq64gvU+ZEMpkSP8RkcuRyudD2uJcvXy48ZrQialq1qCkxQxwmx/Pnz4f3Ly4uIjW1H91SN62RtikxQxwCSIxtZ70AU/V6veE3QrZtzxx3c3Oz9LdsMMvot6LL3hqNxtQ51xFfpsyJzVim0ikIzIkfYjI5xister3ezLGjVRbzKkZNiRniMDlyudzwPa5UKnPHjsZhrVabOc6UmCEOASQBSSdgBa1Wa+EfJ4OERDqdjrS9AGZZJeG0KO+/jvgyZU6s37JJpyAwJ36IyeRoNBqhLTvT3uvB9uRFJ8YDpsQMcZgMNzc3QTqdjvRlz2BclMSLKTFDHALYNpJOwIo6nc7wj5RCoRB0u93g5uYm6HQ6w3/EbdvmH/EdVCgUVko4Ren/tI74MmVOrNd9kk5BYE78EJPJMfpZpNPpoFarBd1uN+h2u0GtVhv+bJmeeKbEDHGYDDc3NxM9wxqNRtDtdoNOpxPq/TSvwmmcKTFDHALYJpJOQExqtVpg23boD+tcLrfUyRwwyzriy5Q5kUymxA8xmRyNRiPI5XKhz8K27aBSqczdejePKTFDHCZDt9sNSqVSKOluWVZQKBSCSqVy76SLKTFDHALYhlQQBIEAAAAAAACAGHH1OgAAAAAAAMSOpBMAAAAAAABiR9IJAAAAAAAAsSPpBAAAAAAAgNiRdAIAAAAAAEDsSDoBAAAAAAAgdiSdAAAAAAAAEDuSTgAAAAAAAIgdSScAAAAAAADEjqQTAAAAAAAAYkfSCQAAAAAAALEj6QQAAAAAAIDYkXQCAAAAAABA7Eg6AQAAAMAO831fqVRq4c113W0vNbHq9frC9y+TyWx7mUDipIIgCLa9CAAAAADAevi+r4ODg+Fj27b1+vVrWZYVGpdOpze8MrP4vj+8/+7dOzmOo3K5PHzOsiz1er0trAxILpJOAAAAALDDxpNOrVZLhUJhiyvaHcViUe12WxJJJ2AattcBAAAAwANCRVN8xqvFAISRdAIAAACAB+Tw8HDbSwDwQJB0AgAAAAAAQOxIOgEAAAAAACB2JJ0AAAAAAAAQO5JOAAAAAICZfN9XvV5XNpsdXqltoN1uK5/P6+DgQKlUStlsVs1mc+Y81WpV2Ww2NH58zqSvA0B0H2x7AQAAAACw66rVqur1+r2OtW1b3W435hXN57quHMfRmzdv5LruxM89z1OxWJz4meu6KpfL6na7ajQaw+fr9bqq1erU1ykWi6pUKqrVaoldB4D7IekEAAAAAGvm+74kybIslctlWZY1ddz5+flEcmrTSRDP8/Ty5UtJmproaTabKpfLsm1btVpNlmUNjxn8ns1mU8ViUblcTvl8XhcXFyqVSspmszo8PJz4Pev1+sT7kpR1ALi/VBAEwbYXAQAAAAC7rFwu65NPPtHNzc3ccdlsNpRgKZVKoUqd+/B9XwcHB8PH3W5Xtm1HOna8MsiyLL17906vX79WoVAIjfU8T5lMZvh48BqHh4dqtVpKp9Oh8Y7jKJ/PDx/P+12Tso5xoxVslmWp1+tFOg54KOjpBAAAAAAb8PHHH8/9eb1eDyWc0un0ygmnVU1LTt3c3EwkeqS7pEsulxs+dl1Xvu+r0+lMJHokKZfLhSqKHMdJ/DoALIekEwAAAACs2bt37+ZWF3meN9FrqNVqrXtZCx0eHoYeT0vyjBqtGJI0tX/SqNH3xPO8xK8DwHJIOgEAAADAmtVqtVD1zbhisRh6XCqV5o5PqvFKovFk0bjx3klxJXySsg7goSPpBAAAAABrNq8xdbPZnNhWZ+oV1BYldxYZNADflXUADx1JJwAAAADYEs/zVC6XQ89Na3a9qx4/frztJUhKzjqAXUPSCQAAAAC2ZDzhVCgUjNxWBwDTkHQCAAAAgC1oNpuhK6Wl02m9fv16iysCgHiRdAIAAACADfN9f+KKaq9fv34w2+oAPAwknQAAAABgw4rFYqhZdaFQUKFQ2N6CAGANSDoBAAAAwAa122221QF4EEg6AQAAAMCG+L6vFy9ehJ5jWx2AXUXSCQAAAAA25MWLF0tvq6vX62o2m2teGQDEj6QTAAAAAGyA4zhqt9vDx1G31XU6HR0eHq5zaWvz7t27bS9BUnLWATw0JJ0AAAAAYAOKxWLocZRtdb7vy3EcWZa1xpXFZzy5M1rVNc1nn3220+sAHjqSTgAAAACwZuVyOZT4yOVyka5WN+j/tK2kk+d5oceLkjfjP+/1ekuNH3+9pK0DwHJSQRAE214EAAAAAOwqx3GUz+dDzzUajamJJN/39e7dO/V6PbXb7WHyY5XTNt/3dXBwMHzc7XZl23akY7PZrFzXHT5Op9P69NNPp1Zo+b6vbDYbStik02l1u92pv6vnecpmsxPJuE6nk9h1jKtWq6rX65LuEoOLklvAQ0OlEwAAAACsUbVanXiuXC4rn89P3IrFosrlsur1+jBpsukr27muq3w+r4ODg1CiR/p1AiubzQ77U7muq2w2q4ODg6kVSZlMRtlsdtgM3XEcZTIZZTKZiQojx3F0cHCgfD6vP/3TP03EOqh6Au6PSicAAAAA2GGrVDphPiqdgPmodAIAAAAAAEDsSDoBAAAAAAAgdiSdAAAAAOABeffu3baXAOCBIOk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"text/plain": [
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"<Figure size 1200x900 with 2 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"nbins = 150\n",
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"vmax = 150\n",
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"\n",
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"a1 = plt.hist2d(\n",
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" brem_z_found,\n",
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" brem_x_found,\n",
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" density=False,\n",
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" bins=nbins,\n",
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" cmin=1,\n",
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" vmax=vmax,\n",
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" range=[[-200, 9500], [-3200, 3200]],\n",
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")\n",
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"plt.vlines([770, 990, 2700, 7500], -3200, 3200, colors=\"red\", lw=1.5)\n",
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"plt.ylim(-3200, 3200)\n",
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"plt.xlim(-200, 9500)\n",
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"plt.xlabel(\"z [mm]\")\n",
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"plt.ylabel(\"x [mm]\")\n",
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"# plt.title(r\"$e^\\pm$ lost brem vertices\")\n",
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"# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
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"\n",
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"# plt.suptitle(\"brem vtx of photons w/ $E>0.1E_0$\")\n",
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"plt.colorbar(a1[3])\n",
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"mplhep.lhcb.text(\"Simulation\", loc=0)\n",
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"# plt.show()\n",
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"plt.savefig(\n",
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" \"/work/cetin/Projektpraktikum/thesis/brem_vtx_hist2d_found.pdf\",\n",
|
|
" format=\"PDF\",\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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|
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"text/plain": [
|
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"<Figure size 1200x900 with 2 Axes>"
|
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]
|
|
},
|
|
"metadata": {},
|
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"output_type": "display_data"
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}
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|
],
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"source": [
|
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"# import matplotlib\n",
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"\n",
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"# nbins = 200\n",
|
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"# vmax = 400\n",
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"\n",
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"a1 = plt.hist2d(\n",
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" brem_z_lost,\n",
|
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" brem_x_lost,\n",
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" density=False,\n",
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" bins=nbins,\n",
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" cmin=1,\n",
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" vmax=vmax, # * stretch_factor,\n",
|
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" # norm=matplotlib.colors.Normalize(vmin=1, vmax=vmax * stretch_factor),\n",
|
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" range=[[-200, 9500], [-3200, 3200]],\n",
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")\n",
|
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"plt.vlines([770, 990, 2700, 7500], -3200, 3200, colors=\"red\", lw=1.5)\n",
|
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"plt.ylim(-3200, 3200)\n",
|
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"plt.xlim(-200, 9500)\n",
|
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"plt.xlabel(\"z [mm]\")\n",
|
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"plt.ylabel(\"x [mm]\")\n",
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"\n",
|
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"plt.colorbar(a1[3])\n",
|
|
"mplhep.lhcb.text(\"Simulation\", loc=0)\n",
|
|
"# plt.show()\n",
|
|
"plt.savefig(\n",
|
|
" \"/work/cetin/Projektpraktikum/thesis/brem_vtx_hist2d_lost.pdf\",\n",
|
|
" format=\"PDF\",\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 55,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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"text/plain": [
|
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"<Figure size 1200x900 with 2 Axes>"
|
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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|
}
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|
],
|
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"source": [
|
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"nbins = 200\n",
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"vmax = 400\n",
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"\n",
|
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"a1 = plt.hist2d(\n",
|
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" brem_z,\n",
|
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" brem_x,\n",
|
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" density=False,\n",
|
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" bins=nbins,\n",
|
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" cmin=1,\n",
|
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" vmax=vmax,\n",
|
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" range=[[-200, 9500], [-3200, 3200]],\n",
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")\n",
|
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"plt.vlines([770, 990, 2700, 7500], -3200, 3200, colors=\"red\", lw=1.5)\n",
|
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"plt.ylim(-3200, 3200)\n",
|
|
"plt.xlim(-200, 9500)\n",
|
|
"plt.xlabel(\"z [mm]\")\n",
|
|
"plt.ylabel(\"x [mm]\")\n",
|
|
"# plt.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(a1[3])\n",
|
|
"mplhep.lhcb.text(\"Simulation\", loc=0)\n",
|
|
"# plt.show()\n",
|
|
"plt.savefig(\n",
|
|
" \"/work/cetin/Projektpraktikum/thesis/brem_vtx_hist2d.pdf\",\n",
|
|
" format=\"PDF\",\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 133,
|
|
"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(\n",
|
|
" tmp_neither_length + tmp_richut_length + tmp_velo_length\n",
|
|
" )\n",
|
|
"\n",
|
|
" # if (tmp_velo == 0) and (tmp_richut == 0):\n",
|
|
" if (\n",
|
|
" False # (tmp_velo >= 0.5 * ntuple[jelec, \"energy\"])\n",
|
|
" or ((tmp_velo == 0) and (tmp_richut == 0))\n",
|
|
" or (ntuple[jelec, \"energy\"] - tmp_velo < 3000)\n",
|
|
" ):\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))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 134,
|
|
"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": 134,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"energy_emissions[3]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 135,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"found: 41978\n",
|
|
"lost: 8523\n",
|
|
"50501\n",
|
|
"VELO energy emission, eff: 0.18201619769905547\n",
|
|
"RICH1+UT energy emission, eff: 0.12653214787825984\n",
|
|
"Neither, eff: 0.5226827191540762\n",
|
|
"total efficiency: 0.8312310647313914\n",
|
|
"efficiency: 0.8312310647313914\n",
|
|
"\n",
|
|
"found in velo/(found + lost in velo)\n",
|
|
"VELO energy emission, eff: 0.848831840428479\n",
|
|
"RICH1+UT energy emission, eff: 0.794479671764267\n",
|
|
"eff von e die nicht strahlen: 0.8345505706788074\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",
|
|
"denom = ak.num(electrons, axis=0)\n",
|
|
"print(denom)\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": 136,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"41978\n",
|
|
"8523\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))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 137,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# energy_emissions = energy_emissions[energy_emissions.energy >= 5e3]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 138,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# energyspektren angucken von velo und rich\n",
|
|
"\n",
|
|
"velo_found = ak.to_numpy(\n",
|
|
" energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\"velo\"]\n",
|
|
")\n",
|
|
"rich_found = ak.to_numpy(\n",
|
|
" energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"]\n",
|
|
")\n",
|
|
"energy_found = ak.to_numpy(\n",
|
|
" energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
|
|
" \"energy\"\n",
|
|
" ]\n",
|
|
")\n",
|
|
"\n",
|
|
"velo_lost = ak.to_numpy(\n",
|
|
" energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\"velo\"]\n",
|
|
")\n",
|
|
"rich_lost = ak.to_numpy(\n",
|
|
" energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"]\n",
|
|
")\n",
|
|
"energy_lost = ak.to_numpy(\n",
|
|
" energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
|
|
" \"energy\"\n",
|
|
" ]\n",
|
|
")\n",
|
|
"\n",
|
|
"diff_found = velo_found - rich_found # / energy_found\n",
|
|
"diff_lost = velo_lost - rich_lost # / energy_lost\n",
|
|
"\n",
|
|
"xlim = 20000\n",
|
|
"nbins = 80\n",
|
|
"\n",
|
|
"plt.hist(\n",
|
|
" diff_lost,\n",
|
|
" bins=nbins,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" histtype=\"bar\",\n",
|
|
" color=\"darkorange\",\n",
|
|
" label=\"lost\",\n",
|
|
" range=[-xlim, xlim],\n",
|
|
")\n",
|
|
"plt.hist(\n",
|
|
" diff_found,\n",
|
|
" bins=nbins,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" histtype=\"bar\",\n",
|
|
" color=\"blue\",\n",
|
|
" label=\"found\",\n",
|
|
" range=[-xlim, xlim],\n",
|
|
")\n",
|
|
"# plt.xlim(-20000, 20000)\n",
|
|
"# plt.yscale(\"log\")\n",
|
|
"plt.title(\"emitted energy difference\")\n",
|
|
"plt.xlabel(r\"$(E_{VELO} - E_{RICH1+UT})$ [MeV]\")\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": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 139,
|
|
"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(\n",
|
|
" energy_emissions[energy_emissions.quality == 1][\"velo_length\"]\n",
|
|
")\n",
|
|
"number_rich = ak.to_numpy(\n",
|
|
" energy_emissions[energy_emissions.quality == 1][\"rich_length\"]\n",
|
|
")\n",
|
|
"\n",
|
|
"plt.hist(\n",
|
|
" number_velo,\n",
|
|
" bins=10,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" histtype=\"bar\",\n",
|
|
" color=\"darkorange\",\n",
|
|
" label=\"velo\",\n",
|
|
" range=[0, 10],\n",
|
|
")\n",
|
|
"plt.hist(\n",
|
|
" number_rich,\n",
|
|
" bins=10,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" histtype=\"bar\",\n",
|
|
" color=\"blue\",\n",
|
|
" label=\"rich\",\n",
|
|
" range=[0, 10],\n",
|
|
")\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": 140,
|
|
"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": 140,
|
|
"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
|
|
}
|