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1339 lines
451 KiB
1339 lines
451 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"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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"import mplhep\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": 28,
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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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" \"/work/cetin/Projektpraktikum/tracking_losses_ntuple_B_zmag.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[(allcolumns.isElectron) & (~allcolumns.lost) &\n",
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" (allcolumns.fromB)] # B: 9056\n",
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"lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) &\n",
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" (allcolumns.fromB)] # 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": 29,
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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_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_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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" 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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" 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": 29,
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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": 30,
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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": 30,
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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": 31,
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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 (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",
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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 (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",
|
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" 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)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {},
|
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"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
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"63793\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",
|
|
" lost: True,\n",
|
|
" rad_length_frac: 0.129,\n",
|
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" energy: 1.17e+04,\n",
|
|
" brem_photons_pe: [2.62e+03, 2.54e+03, 1.86e+03, 3.12e+03],\n",
|
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" 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",
|
|
" 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",
|
|
" 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": 32,
|
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"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
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"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": 33,
|
|
"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": 34,
|
|
"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": 35,
|
|
"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, 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": 35,
|
|
"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": 36,
|
|
"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": 37,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1200x900 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.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": 38,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
|
|
"found: 44660 , lost: 19133\n",
|
|
"0.4284146887595163\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<pre>[-3.61,\n",
|
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" -61.5,\n",
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|
" -33.8,\n",
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|
" -133,\n",
|
|
" -799,\n",
|
|
" 65.2,\n",
|
|
" -5.73,\n",
|
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" -26.6,\n",
|
|
" -4.26,\n",
|
|
" -396,\n",
|
|
" ...,\n",
|
|
" -13.1,\n",
|
|
" -25.6,\n",
|
|
" -140,\n",
|
|
" -4.27,\n",
|
|
" -4.27,\n",
|
|
" -103,\n",
|
|
" 8.82,\n",
|
|
" 12.8,\n",
|
|
" -17.8]\n",
|
|
"---------------------\n",
|
|
"type: 44660 * float64</pre>"
|
|
],
|
|
"text/plain": [
|
|
"<Array [-3.61, -61.5, -33.8, ..., 8.82, 12.8, -17.8] type='44660 * float64'>"
|
|
]
|
|
},
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"tuple_found = ntuple[~ntuple.lost]\n",
|
|
"tuple_lost = ntuple[ntuple.lost]\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": 57,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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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 = 350\n",
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"nbins = 200\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",
|
|
"\n",
|
|
"a1 = ax1.hist2d(\n",
|
|
" brem_z_lost,\n",
|
|
" brem_x_lost,\n",
|
|
" density=False,\n",
|
|
" bins=nbins,\n",
|
|
" cmin=1,\n",
|
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" vmax=vmax * stretch_factor,\n",
|
|
" 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",
|
|
"ax1.set_xlabel(\"z [mm]\")\n",
|
|
"ax1.set_ylabel(\"x [mm]\")\n",
|
|
"ax1.set_title(\"lost\")\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()\n",
|
|
"plt.savefig(\n",
|
|
" \"/work/cetin/Projektpraktikum/thesis/brem_vtx_found_lost_hist2d.pdf\",\n",
|
|
" format=\"PDF\",\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 58,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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ZS2A0l8sNHW8Yhly/fl1EDp93o4KPbh/3/nMa9nMFAADgJFhaWhJN04b+E9RJuVyWTCYjc3NzommaLC0tSS6X8/UP+3HMOTYmAEf5fN4UkYGvdDpttlotx/vv7u6a9XrdLBQKZiKRGJinVquNvG+r1Rq6bxExd3d3j3Q8yWRy6HyVSsXTPIVCYeg8yWTSrNfrrubY3d01S6WSGY/He+fU6bjU/cbjccf99J/HdDrtam2qSqUy9Hiz2axZKBTMSqVi5vN5y2Ps9jmiPiajnhO7u7sD53qUbDbr6vGt1+uWcfl8fuS+u4+T+hWPx3uPef+YZDJptlqtgcfU7drUcf1fpVLJNM3hv5/ZbNYyz6jn6rBjdftYdNVqtd4xx+Nxs1KpmK1Wy2y1WmalUuk9H7rrHcbtY6COO+pzGQAAAO70v490el/Yr16vWz7j1Ot1s9VqmbVarff+0M1nn3HPOW4EnQAbW1tbth96g/oa9kdhd3fXrFQqIz/ki4iZSCTMSqXi+o/K1taWmU6nbddSKBRcBUm66vX6yCBWPB43s9msWalUzHq9bm5tbZn1et2sVCpmqVSy3C8ej7v+Iz4sEJdIJEYGuvqDAmowwovuH/larWbWajUzn8+b6XTaTCaTlsepe9xOgbfu82vUY5xMJnuBCruxiUTCzGazveeBGvhSgxTdOev1+sjnQyKRGBr42N3dtcyfTCbNQqFgGdP9mfo82t3ddTzeYfsslUq9/SUSCbNQKFie87u7u73j6D9nqv6gbzKZtDzfvDwWo1QqFTOdTlsCUN37jfodtfud7H8MWq2W7TintQEAAMA79R9+bj+v9N9v1D8Tu5+FEomEq89z45hzEjTTNE0BYFGtVsfSxHqYZDIpW1tblp9pmuZ5nng8Lru7u0Nvy+VyR0oFrdfrrkvQNjY25ObNm72SQbcSiYSsrq5KPp/31Oem2z9IPa54PC7Ly8sSj8fFMAzZ3Nzs9c2q1WqeS+oAAAAAnDyGYcgLL7xgaWdQq9UcL0TUf79EImG5gnE/Xdd7vWzT6bRtO45xzDkpBJ0ABMowDGk0GrK+vi66rvd654g8vex9IpGQl19+WbLZrOcG6k776+6zu5/l5WXJ5XIEmwAAAAC4lsvlpNlsyp07d3qBJzdBp0wm0+utVKlUJJ/P2+6j+090u7HjmHNSCDoBAAAAAAB8S7fyZWtrq3fFcRHnoFN/ppGIyO7urm01x8bGhuRyOREZXbkyjjkniavXAQAAAAAAiPSucFwoFAaupuyk/8rG6XTasX1IfwDLMIyhLVHGMeckEXQCAAAAAACQw/K0ZDJpCfa4Va1We9+7DVj1txtZX1+fyJyTRNAJAAAAAACceMViUZrNptRqNc/3VS+o9PLLL7u6X38gSc1KGseck0bQCQAAAAAAnGjNZlPK5bJUKpUjXeyo2+i7y+0c6rj+QNM45pw0gk4AAAAAAOBEW1lZkWw2e+Srvd2+fduy7dR7qevy5cuW7c3NzbHOOWkEnQAAAAAAwInVvdrba6+9duQ5dF23bB81K6nVao11zkk7FdqeAQAAAAAAQrSxsSEbGxtSr9ddZxINowaIjsowjLHOOWlkOgEAAAAAgBPHMAzJ5XKSz+clnU77nuso1EDXnTt3xjrnpJHpBCjOnz8vDx48ENM0JRazj8s+++yzcuHChQmtDAAAAECU3Lt3T9577z3bMZ1ORzRNk3Pnzsn9+/cntLLgLS8vy9e+9jVfc7g5X249++yz8r3f+72++hWtrKxIIpGQSqUSyJqCMI6spDAznQg6AYqHDx+KaZoicvgCYefu3bty9+7dSSwLAAAAwJQyTVMePHgQ9jJ8+drXviY7OzthL6Pn7t27voJg5XJZms2mbG1tBbKeeDweSHCnP0tpHHNOGkEnQDEzM9MLNh0106ndbkun05FYLCYLCwtjWacfY1mfaYr8wR8cfv9d3yWiab7n+QMR0SJ6DkWi/ziLnIA1BvW8cxD18xj19YmwxqCwRv+ivj4R1hiIvteHr8Vi8p1RXKNE/zxGfX0i4a/RbaaTiPT+sT3tYjGRhe84Wijh3r2OvHff/h/7bjjkBjhqNptSLBalVCpJMpn0vR4Rkfn5+UACRPPz82Odc9IIOgGKb//2b5ednR25cuWKbG9vH2mOxcVF2dnZkYWFhSPPMU5jWd/9+yLdANyXvyxy/rzvec6LyFxEz6FI9B9nkROwxqCedw6ifh6jvj4R1hgU1uhf1NcnwhoD0ff68OJ3fIf8fhTXKNE/j1Ffn8h0rLH7T22nf2hPi4XvOCVfbb4Q6hq+J/mW7LT3j3z/XC4nyWRSCoVCYGs6ajaRGlRSM52CnnPSCDoBAAAAAACXTOmI/2wlv2s4qnK5LLquSzqdllwu5zi+P4Bz8+ZNWV9f721fu3ZNstmsiBz2vGo2m5b7uQn2qE2+l5aWet+PY85JCzXo9OKLL4a5ewtN0+TLX/5y2MsAAAAAAABj8u6774qISKPR8HzfZrNpCQIlEole0CmVSlnG6rruqnSv1WpZtvuvojeOOSct1KBTq9USTdNCrW3t7l8bUx8QAAAAAACOkwMz7Eyn6FleXrZsuw0Q9WdSxeNxSSQSY51z0o5HUakPx6WZGwAAAAAAsFcqlcQ0Tddf/QGbWq1mua1UKvVuSyaTltK327dvu1rP5uZm73s1yDSOOSct9J5OpmlKNpsNLfJmGIZUq9VQ9g0AAAAAAI6Hq1ev9uIL/WV4dvrHFYvFicw5SaEHnarVqnziE58IdQ3ZbFY++MEPhroGJ7quS6lUkkajIbqu91LklpeXpVgsHjloVy6XpV6vy+bmphiGIYlEQpLJpKyurh657nNa5gQAAAAAeGOKSMdHI++g1hBFq6urvQCRm55R/WMSicTQz7bjmHOSQi+vC/sEiIi8/PLLYS/BVrlclqWlJalWq6LruogcZmg1m02pVquytLQk5XLZ05yNRkPm5uZ6Uc9arSatVktKpZI0m03JZDKSyWQGLrV4HOYEAAAAACBoyWTSEuPY2NiwHV+r1Xrfj8pIGseckxR6ptP8/HzYS4i0TCYjjUZD4vG4pNNpSSQSouu6NJvNXgBKRHrZTt3O+XYajYZkMhkREcnn81KpVHq3dedIpVLSaDQklUrJ1taW42UZp2VOHFpbW5O9vT25ePFi2Eux9bM3bsi5554LexkjTct5jLppOI9RX2PU1zctpuE8ssaTYRrO4TSssetv/I2/EfYSptY0PM7TsMZnn31W7t69K88++2zYS8EEVCoVWVpaEhGRmzdvjvyM3t/qJ51OSz6fn+icE2OGKBaLmXfv3g1zCaZpmqZhGGYsFgt7GQMKhYIpImapVBp6e6lUMuUws7D35WR3d9eMx+OmiJiJRGLkuFar1ZsznU4fizndunLliiki5pUrV0KdY+rcu2eaIodf9+6FPw9M0zwBz0WeL1Pj2D8XMTV4Lp4QU/L6wPPxZDguj3P3OL5rYca89wffE+rXdy3MTOycJhKJ3mfOWq3m6j61Wq13n1Gf55PJpCkiZjweN3d3d0OZcxJCLa8zI3TluCitReSwh1O3j1GhUBg6plAoDEQunRqL5XK5XimaXapdf9ZUo9GwbbY+LXMCAAAAADBu2WxW6vW6xONxKRaLksvlpNlsimEYvUqdZrMpyWRS3nrrLVcVO+OYcxJCDTqVSqVIpEFeunTJcqnDKCgWi1IqlRx7Xqnrtmsspuu65farV6/azn3t2jXLeqZ5TgAAAAAAvGq1WmKappim6aqdTVc6nZbd3V0plUqi67qsrKzI3Nyc5HI5mZ+fl1qt5rlFzDjmHLdQezp98pOfnOj+3n77bfnABz4QibU4MQxjZIZTv+5V7Lr9neyeXP0BqnQ67fhE7P+FMgxDNjY2Bn7JpmVOAAAAAAhKJpazbNc7tREjjyFT5CDsSqFoFSrZKhQKrj7bhz3nuIR+9bpJWl1dDXsJrtXrdddj79y50/t+eXl55Lj+0rNkMulq7kQi0ft+fX19aucEAAAAAACTdaKCTpubm2EvIXCGYfR6H6XT6ZFBGrXX08svv+xq/v751EszTsucAAAAAABg8kItrzuqt99+uxdocUPXdalUKp7uMy1ef/11ETnM9KnVRqd0qr2e+jOD7Kjjuo3JpmlOAAAAAEBwOmOsb/tHlffkf6zesx3zta8fjG3/CNZUBJ3efvttKZVK0mg0er2LvDJNUzRNC3hl4TIMQ1ZXVyWZTEqtVrPtfXT79m3LttvGYpcvX7Zsb25u9oI50zInAAAAAGA6vHevI3/wNYJKx0Xkg043btyQcrksIoeBIxzSdV0ymYzE43G5deuWY3BGDdYdNYOo1WpN3ZxhWFtbk729vUhcnREnG89FRAXPRUQFz0VECc9HTCNTTDkYY6bT+QuaLHynfSegP/x6RzqdsS0BAYp00OlXf/VXe1cy0zRNNE0j8CSHPYtyuadXS5ibm5NSqWTbvf6oGWKq/hLFaZkzDGtra6HuH+jiuYio4LmIqOC5iCjh+QgM+unVZ+WnV5+1HfNHUm1pf42o0zSIdNDp5s2bIiK9YFMikZBkMtnLalFLqoZ59913xTAMef311+Xu3btjXe84GYYh1WpVKpXK0MBMsViU27dvj+zrdNQgjJpB1X+lvGmZ86ja7bYsLi76nmdtbY03FAAAAECEZGI5y3a9M/g56jOf+Yx85jOfERGRb+7Yf76w+9zQbrePsELgeIh00KnZbPb6MNXrdVlZWTnyXIVCQV588cWgljZxjUZDWq2WpNNp0XV9oOG2yGEGVLlcts148mscGURRnbPT6cjOzo7vefb29nzPAQAAAGCy9vb2XH8eCOJzwzQZZyNxHC+RDjrF43G5e/euFAoFXwEnkcOePy+88EJAK5u8bDYr2WzW8rNqtSrFYtESYCkWi5LP5wcyf+LxeCCBmP55p2XOo4rFYrKwsOB7Hmr0AQAAgOlz8eJFuXLliog4Zzo9d2V+5G3tdls6NCDCCRXpoFMikZA333xTXn755UDmq1QqgcwTFfl8XtLptKRSKUugplqtDmQ7zc/PBxLMmZ+ft3w/DXMe1cLCgmxvb/ueBwAAAMD06W+ToZbjqerbw9uciByW3p20TCigK9JBp5WVFXnzzTcD6c/Tne+4SSQScuvWLUmlUr2f3b59e2DcUTN/1ACQmpU0DXMCAAAAgBeOQaYhPaBOkgMu8AWX7K9DGLKf/dmfFdM0pdlsBjLfL/zCLwQyT9Qkk0lL6d2wRuPLy8uWbbfZRGrAb2lpaermBAAAAAAAkxfpoNOlS5fk537u52R9fV3ee+893/Mdt/K6fteuXet9PyxQ058JJTI8MDVMq9WybKfT6ambEwAAAAAATF6kg04ih1edSyaTksvZpzc6eeuttwLLmIqiZDLZ+35YaZmaQeQ2mNMfwIrH45JIJKZuTgAAAABAMEwR6YT8RXHf9Ih0T6euer0uy8vL8uKLL0qxWHTdJPrOnTtiGIa0Wi15/fXXx7zK6FADNyKHQan+K8Pdvn174Gp4w2xubo6cd1rmBAAAAIB+Tj2Z1NvVHk/q9knv8QSMMhVBp9/8zd8UkcMSqtXV1SPNYZqmaJoW5LIipT8jKJPJDB1z9epVqVarIiKus776xxWLxamdEwAAAAAATFbky+t+6qd+SjKZjLz55puiaZqYpun56yToBl3i8fjIzKD+gF2j0XCcs39MIpEY2idpWuYEAAAAAATjQMxQvzA9Ip3p9Nprr/Waf3cDTkdxEgJPN2/eFJHDczZKMpmUdDrdC9JsbGzYlq7Vak9TREdlD03LnAAAAADQpZbHqQbK5TRrvkb9YD3oJQHHUqSDTmrAKZ1OSyaTkXg87rmv06uvvipvv/32GFcbrHK5LOvr65JOp+XGjRtDm4N3FYtFMQxDCoWCY/+jSqUiS0tLInIYqBo13jCMXolbOp2WfD4/9XMCAAAAAPw7OP55HQhIpINOzWZTNE2TeDwum5ub8sILLxx5ruvXr8vly5cDXN34GIbRy9hpNptSLpelUChIqVQaGJvL5WRjY0NKpZIUCgXHuROJhNRqNcnlcpa5VSsrKyJyWK7Xn0k0zXMCAAAAAIDJiXRPp252z40bN3wFnLpzvfTSSwGsavzi8bgkEgnLz8rlsszNzUkul5NisSiZTEbm5uZE5LDBupuAU1c2m5V6vS7xeFyKxWIvsGMYhjQaDUmlUtJsNiWZTMpbb71lm2U1bXMCAAAAAKLrF6r35U++/HXbr69/vRP2MuFSpDOdUqmU/OZv/uZAAOao7PodRc3W1pbcvHlTGo2G6LouhmGIyNOG4blcTmq12pEDLel0WnZ3d3tlfCsrK2IYhsTjcVleXpZareZYqjetcwIAAABAP7WHk9rzyevtx904Qz7v3evI175GUOm4iHTQaXV1VW7duiW6rgcy37RkOokcZjsNK6cLWqFQ8JQldZzmBAAAAABEy4ULMfnO77Qvyvr61zvSIS41FSIddMpms/LSSy/J+vq6/MzP/Izv+T7/+c/LRz7ykQBWBgAAAAAAgvZf58/Lf50/bzvmT7/8dbKhpkSkg04iIrVaTb73e79Xfu3Xfk1+/Md/3NdcN2/eJOgEAAAAALDlVE6nOknldqaIHIgW+howHSLdSFzk8Cpmr776qnziE5/wNc/du3d7/ZAAAAAAAAAwXpHOdPr85z8vIiLPPfeczM3NyYsvvnikptGGYcjrr78e9PIAAAAAAAAwQqSDTp/+9KflzTff7G2bpinlcvlIc5mmKZoWbgogAAAAAADTrkN9G1yKdNDp6tWrvZI4TdMIGgEAAAAA/NOsnWYGejZp9p1ojnPPJiBIkQ46ra6uyqc+9SnRNE1Mk1AqJqvdbsvi4qLtmLW1NVlbW5vQigAAAABEyWc+8xn5zGc+Yzum3W5PaDVA9EQ66HTp0iVJJpPy5ptvSqlUkmQyKfPz857n0XVdPv3pT8sXv/jF4BeJY6vT6cjOzo7tmL29vQmtBgAAAEDU7O3tOX5mOI7Cvnodpkekg04iIteuXZOlpSX55Cc/eeQ5XnrpJfmJn/iJIwWscHLFYjFZWFiwHXPx4sUJrQYAAABAYMyOr9u75XjvmF+WM3JOnrsy+rNmu92WTsdhf8AxFfmgUzqdltu3bwcy1/LyciDz4GRYWFiQ7e3tsJcBAAAAIKKe175Pnpfvk/r26B5Pi4uLJzIbChCZgqDTSy+9JKVSKZC5/ubf/JuBzAMAAAAAwElkSvjldXR8nh72Lfkj4oUXXghknn/4D/9hIPMAAAAAAADAXuQznYK0ubkZ9hIAAAAAACGrd0aXww3T7eF01PsfL5p0zLAbiYe9f7g1lUGnt99+WwzDcD1e13WpVCqe7gMAAAAAAICjm4qg09tvvy2lUkkajYboun6kOUzTFE0jGgoAAAAAADAJkQ863bhxQ8rlsogcBo4AAAAAAEB4xtlI/LOvvSf//Bfesx3zza8fjG3/CFakg06/+qu/2rtynaZpomkagScAAAAAgC+ZmWuW7frBurf70+NpbO7f68jXv0ZQ6biIdNDp5s2bIiK9YFMikZBkMimJREJERC5fvuw4x7vvviuGYcjrr78ud+/eHet6AQAAAADA0Z2/EJNv/84Z2zHf/PqBdDoTWhB8iXTQqdls9vow1et1WVlZOfJchUJBXnzxxaCWBgAAAADAiXQgsbHN/V9dvyT/1fVLtmP+j//ZNtlQUyLSQad4PC53796VQqHgK+AkIpJIJOSFF14IaGUAAAAAcLL5LVELk7pWtVwOQDDGF54MQLeM7uWXXw5kvkqlEsg8AAAAAAAAsBfpoFM3u+nOnTuBzgcAAAAAALwzRaRjaqF+cXmx6RHpoNPP/uzPimma0mw2A5nvF37hFwKZBwAAAAAAAPYi3dPp0qVL8nM/93NSKpWkVCrJs88+62u+SqUin/jEJwJaHQAAAACcXNPUw8lJvVOzbNPjCQhGpDOdRA6vOpdMJiWX8/dL/9ZbbwWWMQUAAAAAwEl1IFqoX5gekc506qrX67K8vCwvvviiFItFmZ+fd3W/O3fuiGEY0mq15PXXXx/zKgEAAAAAANA1FUGn3/zN3xQRkVarJaurq0eawzRN0TQiogAAAAAwDk4laWoJW5g8l89pSpGQ2bGdL0rHOg4HZuSLphARkQ86/dRP/ZRUq1UREdE0TUzTe596gk0AAAAAAACTFemg02uvvSaVSkVEjh5wEpEj3w8nW7vdlsXFRdsxa2trsra2NqEVAQAAAIiSd8wvy1fl9wd+3v85ot1uT3JJQKREOuikBpzS6bRkMhmJx+Oe+zq9+uqr8vbbb49xtThuOp2O7Ozs2I7Z29ub0GoAAAAARM2+PJFH8mDg506fI6aZKZp0Qr4mmUkz8akR6aBTs9kUTdMkHo/L5uamvPDCC0ee6/r163L58uUAV4fjLhaLycLCgu2YixcvTmg1AAAAwGR57lOk9D2qH6wHvaTgOPRoUo91VA+oUzIrZ+TcwM+fu/I0SaLdbkun0xkYA5wEkQ46xeNxuXv3rty4ccNXwKk710svvRTQynASLCwsyPb2dtjLAAAAABBRz2vfJ8/L9w38vL79NGi1uLh4rDOfADuRbjmfSqVERCSRSAQy32uvvRbIPAAAAAAAnFQHooX6hekR6Uyn1dVVuXXrlui6Hsh8ZDoBAAAAABBdtV/YlY1f3LUdc+fr+xNaDfyKdNApm83KSy+9JOvr6/IzP/Mzvuf7/Oc/Lx/5yEcCWBkAAAAAHG+OPZzU8VHu4aTwulanHk9ezxVGe//egXzzawSVjotIl9eJiNRqNdna2pJf+7Vf8z3XzZs3A1gRAAAAAAAn14EZG9vX2fOn5LnvtP+KRT6Sga5IZzqJHPZzevXVV+UTn/iE/PiP//iR57l79640m80AVwYAAAAAAIKU/cS8ZD8xbzvmY3/qK2RDTYlIB50+//nPi4jIc889J3Nzc/Liiy9KNpv1PI9hGPL6668HvTwAAAAAOLacSsjU21VRLjnzunbK6Y43XdelVCpJo9EQXdclHo9LIpGQ5eVlKRaLgV3cbNS+V1dXpV6vuxpfLpelXq/L5uamGIYhiURCksmkrK6uSjqdHts6jyrSQadPf/rT8uabb/a2TdOUcrl8pLlM0xRNo8s9AAAAAAB+dI7RFeTK5bIUi0XLzwzDkGazKc1mU6rVqpRKJSkUCp7mNQxD5ubmXI3N5/OOYxqNhuRyOTEMQ9LptNRqNUkkEtJsNqVYLEomk+n9PB6Pe1rrOEU66HT16tVeSZymaQSNAAAAAABAIDKZjDQaDYnH45JOpyWRSIiu69JsNkXX9d64braTl8qrarXqeqwa9FI1Gg3JZDIichigqlQqvdu660qlUtJoNCSVSsnW1lZkAk+Rbr+1uroqIocBJ9M0fX0BAAAAAAB/TBE5kFioX0F8wi8Wi9JoNKRUKsnu7q7UajUplUpSq9Wk1WpJqVSyjM/l7EsyVW4vZNYNdo1iGEZv34lEwhJw6lerHZZ86rruea3jFOlMp0uXLkkymZQ333xTSqWSJJNJmZ+3byg2jK7r8ulPf1q++MUvBr9IAAAAAIggv32Igu7hFKm+SJqSf2F2LJuZmWuWbXo8HS+6rvd6I43qg1QoFKTValkylprNpiSTScf5q9WqGIYhhUKhl6E0yvLysu3t3ZI6EfuMqG7G08bGhjQaDalWq67K9sYt0kEnEZFr167J0tKSfPKTnzzyHC+99JL8xE/8xJECVgAAAAAA4PgoFotSKpUcG2+XSiVL0KnRaLgKOpVKJUkkEgPZUl7pui6NRqO3ffXqVdvx165dk42NDRE5PMYoBJ0iXV4ncphqFlR5nFMEEQAAAAAA2DswY6F++dXNQnLSvYpd/7aTjY0N0XXdsU+TG/1Bq3Q67bj//p5ThmH0AlBhinym00svveQ7OtgV1DwAAAAAELSgS7b83t9riZnT/R33F6GStfrBumU7SmuDf/V63fXYO3fu9L53k8hy8+ZNicfjjllJbvRnWbnJsBKRXjN0EZH19XVPzc/HIfKZTiIiL7zwQiDzvPTSS4HMAwAAAAAAjjfDMHr9lNLptGPgp9lsSrPZFMMwZG5uTpaWlmR1dfVIGUfNZtOy/fLLL7u6X/8ao5DpFGrQKUqNvaO0FgAAAAAAokmTjsRC/RLRJnKkr7/+uogcZg91rw5nRy2p03VdqtWq5HI50TRNcrncQDBplP5eTt01uKGOc7u/cQk16JRKpWRvby/MJYiIyN27dyWVSoW9DAAAAAAAEAGGYcjq6qokk0mp1+uO/ZTUpt/DbGxsSCqVktXVVcf9375927Ltpp+UiMjly5ct25ubm67uNy6h9nQKqkF4EKK0FgAAAADHX+T7BJkdX3dX+yI5jp/k8fs8Nhxvuq5LJpOReDwut27dchXwSSQSUqlUxDAMabVa0mg0er2VVNVqVTY3N2Vra8t2Der8bqjjWq2Wq/uNS6hBJ02bTEqcG1FaCwAAAAAAUWSaIgdmuJ+fx5kzsrGxIbnc04Dw3NyclEolV1e7y+fzlm3DMKRarcrNmzd7vaG6ms2mZDKZkU3NRwWsvFL3O2mhlteRXQQAAAAAAMJkGIaUy2VZWlqyBJy6isXi0J87icfjUigUZHd3V2q12kDGVKPRkHK5PHJNR6Huo//qe2EINdNJROStt96SP/pH/2ioawi7xhHR1G63ZXFx0XbM2tqarK2tTWhFAAAAAKLkM5/5jHzmM5+xHdNutye0muj7l7/4dfnCP/mG73l2v/EkgNU81Wg0pNVqSTqdHtmbaWNjQ8rlsquMp2Gy2ayk02lZWVmxNPe+efPmked0I+xMp9CDTtevX5dPfepTEo/HZX5+fqL7vnPnjui6PtYHGNOr0+nIzs6O7ZgoNMIHAADAdIpcDyeF0/qcbo98z6o+6trUtY+yt7fn+JnhODo4YtHU/XsdufOHwQaMgpDNZiWbzVp+Vq1WpVgsWoI2xWJR8vm866beqng8LltbW5JKpXqBJ8MwpNFoSDqdHhgbRMDoqGsNSuhBp62trSOlqQXJNE16OmFALBaThYUF2zEXL16c0GoAAAAARM3FixflypUrtmPa7bZ0OjQuFxF55sKMzH/HrO95dr/xZOy94PP5vKTTaUmlUpbgT7Va9Z248tprr0kqlept1+v1gaDT/Px8IEGnSSf3qEIPOnWF1d+JYBNGWVhYkO3t7bCXAQAAACCi3LTbWFxcPHbZUB3zaJlOf/avLcif/Wv2/9h346d/+N9NJGMqkUjIrVu3LAGi27dv+543mUxKOp3ulfENaxp+1AwlNVB14jOdwm4mHvb+AQAAABxP01ReNozT+h2PTwv1ulWeOJbTTdGxIFjJZFKy2axsbGyISHBXlctkMkN7R3UtLy9bej8ZhuEqgKQ2Dl9aWjryGoMQatBpd3c3zN0DAAAAAADYunbtWi/oFFRj7kQi0ft+WAlcf3aVyGGwK5lMOs7barUs22rZ3qSFGnS6dOlSmLsHAAAAAAAemHL0RuJBrmGS+oM9QZWr9Qedhs25vLxs2XYbdOoPisXjcct+wkCOIAAAAAAAgAtqMOioNjc3e99nMpmB25PJpCUY5baXVP+8Qa3Vj9B7OgEAAADAtOrvBaT2NHLq4RT5nk8OfYwGejzNXLMOGPPlxezOvVeOj5VybJF/7BCo/j5OwwJER9FfBjeqBO7q1atSrVZFRCz9nez0jysWiz5WGAwynQAAAAAAgGsHphbq16R1AznxeFyy2Wwgc3Z7RBUKhZFjVldXe9/bNR0fNiaRSITez0mEoBMAAAAAAMBIN2/eFBGR1157LZD5NjY2RNd1icfjcuPGjZHjksmkJXDUDVSNUqs9zbiLQpaTCOV1AAAAAHBkfsqqol6SVT9Yt71dLTFTy/HGfXxBnvuBY8GxVi6XZX19XdLptNy4ccO2OXixWBTDMKRQKIzMcmo0GpLL5cQwDEmn01IqlUY2/dZ1Xa5fvy4iIrdu3XJsTF6pVGRpaUlEDoNfo9ZgGEavFC+dTks+n7edd1LIdAIAAAAAAC5p0pFYqF8iRy+xMwxDisWiNJtNKZfLMjc3NzIrKJfLSblcllKpJKVSaeSctVqtd9W4RqMhqVTKUhrX1b1tfn5eWq2Wq6vRJRKJXgZTd83DrKysiMhhCWB/xlPYCDoBAAAAAIATIR6PSyKRsPysG3zK5XJSLBYlk8nI3NyciBw2/LbruyRyGJxSVavV3pyrq6uSSqUkk8lIPp+Xra2tgTXYyWazUq/XJR6PS7FYlFwuJ81mUwzD6AWyms2mJJNJeeuttxyzpyaJoBMAAAAAADgxtra2pFAoSDKZtARoms2m6LouuVxO3nrrLanVaq6CQ+l0WlqtluTzeUkkEgNz3rlzR27cuCG7u7tSKpWOFBRKp9O9++u6LisrK72g1vz8vNRqNdna2opUwEmEnk4AAAAAcGT9vYCcegypfYOi3tPJab1OfZGifLxOa1Nv12ZmLNvmwcF4FjYlDszpzl+Jx+O25XJHkUgkpFKpBDrnMIVCwTHzKkqm+5kCAAAAAACASCLTCQAAAAAAuGKKSMdHI++g1oDpQNAJAAAAAL7Fa0mYl5KxKJWXHUWUy+U807wV/Zgd+zDHsTo3IXvjn+5I/Z/u2I65+43HE1oN/CLoBAAAAAAAIuHhvX0x/pCg0nFxooJOb7/9tnzgAx8IexkAAAAAAEytcTYSP3N+VuLfcdp2zN1vPBazM7YlIEAnKuiUy+Xk9u3bYS8DAAAAAAAMkf6ri5L+q4u2Yz71I/+GbKgpEfmg097enly8eNH3PG+++aY0m80AVoSTot1uy+Ki/R+7tbU1WVtbm9CKAAAAMG7j7MUz9X1/lD5I6vGot2uxcJtN23JIk1Efmw+e/UnLdufxYcDjHfPL8lX5/YH793+OaLfbR10lMPUiH3RKpVLy+78/+EvsxRe/+EVZWVkJaEU4KTqdjuzs2Dew29vbm9BqAAAAAETNvjyRR/Jg4OdOnyOm3YGMr7wOx0vkg06tVkvee+89efbZZ490/5//+Z+XT33qU2KapmhahCPtiJxYLCYLCwu2Y4LIwgMAAAAwnU7JrJyRcwM/f+7KfO/7drstnQ4NiHAyRT7oJCKysrIiv/d7v+fpPm+//bbkcjlpNptimvaXtwSGWVhYkO3t7bCXAQAAACCinte+T56X7xv4eX37aXne4uLisc98AkaZiqDT1taWfOhDH5LXX3/dVWZJN7tJRHoZTgSeAAAAAIRJ7RM0bT2e6gfrlu2B9TvcHmV+1xr1xy5YmnTMsKuIwt4/3JqKQkzTNOWNN96QRCIhv/VbvzVy3Be/+EV58cUXe+V0IkLACQAAAAAAIASRDzrF43HZ2tqSV199VQ4ODiSdTstP//RPD4y7ceOGpFIpabValuymF154QZrNpty5c0deeOGFEI4AAAAAAADg5Il8ed3Vq1flpZdekpdeekmuXbsmn/jEJ+TVV1+VRqMhtVpNTNOUXC4nuq5bgk2maUqpVJJPfvKTvblWV1dDPBIAAAAAx5nXcrmTVZIVbU6lj3jKlPCvXkct0/SIfNDp1Vdf7X1/6dIlqdVqUq1W5VOf+pQkk0kRkYFSumQyKbVabSCzqT8ABQAAAAAAgPGJfHnd22+/PfCzpaUlEZFeRpOmPW0iVqlUZHNz89iV0jWbTVldXZWlpSXRNE00TZOlpSUpFotiGMaR5y2Xy5LJZGRubq43Zy6Xk0ajceznBAAAAAB41zFjoX5hekT+0crlnqY17u3tybVr1+SVV16Ru3fv9oIv3UynbDYr165dC2upY2EYhuRyOUmlUlKtVkXX9d5tuq5LuVyWubk5qVarnuZtNBoyNzcnxWJRRERqtZq0Wi0plUrSbDYlk8lIJpPxFNCaljkBAAAAAMD4Rb68bmtrS/7BP/gH8pWvfKUXWOnv3dQtpXv11Vfl53/+5+XWrVvy2muvyY//+I+HvHL/DMOQVCplCTSNsrq6KltbW1KpVBzHNhoNyWQyIiKSz+ct90kkEpLNZiWVSkmj0ZBUKiVbW1sSj8ePxZwAAAAYL699jU6SaT83Tn2Oonx8Xtc+QFPyNcyOzxUBJ0PkM51ERAqFglSr1V5Gk4j0GoV3S+m631+6dEmy2ax86EMfkvfee88yz97e3qSX7ku3QXo3sNZqtaTVakmtVpNCoTAwvlqtysbGhu2c3cwpkcPAzaggVa12+AKh67ol22ya5wQAAAAA+HcgWqhfmB5TEXQSsWY3pdNpabVaA43Bk8mktFot+cQnPiFvvPGGzM3NyS/+4i/2bp+moES1WpVGoyGFQkG2trYkm81KIpHoZfiUSiVptVq9Zupd169ft503l8v1StG6JWvDdPcjcphxZFe+Ny1zAgAAAACAydHM/vShCIrFYpa+TZVKxTGwInIYgMjlcrK3tydLS0vyEz/xE1Iul+Xg4GDcSw7E0tKSJBIJqdfrtuN0Xe81Vu+q1+uSTqcdx+7u7tqWo21sbPQCdfF4XHZ3d6d2Ti8WFxdlZ2dHrly5Itvb277mOlHu3xe5cOHw+3v3RM6fD3cenAw8XwAAw0zw9aG/LMupnCzK5WfDOJWcHbfj7Tdw7A7ldXbHdlw+X3SP49J3nJG//Vs/Mrb9/M4/e1t+9//5ju2YvW88ErMjU39OT4KpyXRKp9Oyu7vrKuDUP/4jH/mIfOUrX5FyuTzmFQan2WyKruu90jE7iURCSqXSwP2H6R+XTqcd+x91M4hEDsvdhpXuTcucAAAAAAD/TBnv1ese3juQu3/4yPaLllrTYyqCTvl8Xt544w25dOmS5/vWajV5/fXXJeIJXRbr6+uSz+ddN8VWs5refffdoeP6S8/UsrxREomEZV3TOicAAAAAIPrOnD8lF7/jjO2XmniG6Ir81evi8bj843/8j33Nkc1m5Y033pAPfvCDAa1qvK5du2YJojhRAzNquZ3IYPbTyy+/7Hru7tXz1AyiaZkTAAAAADAd/sxfeUH+zF95wXbM3/8vf0v2/vDRhFYEPyIfdBrWm+io87z00kuBzDVubrN7uroNt7uGBawajYbjmGHUcc1ms7e+aZkTAAAAGJf+Xj5OPZCmndq3yKlnk9fxYXJ87KjnsuAKcnAr8klpr7/+emBzuemRNI26GT5dwwJ1t2/ftmy7Ld27fPmyZXtzc3Pq5gQAAAAAAJMX+aBTkF54wT5Fb1r1B1jy+fzQMWpg6qgZRK1Wa+rmBAAAAAAAkxf58jo4q1Qqve+LxeLQMWow56j6S/mmZU4AAAAAQFA06Zhh569Q3jctCDpNOV3Xe823S6XSyMygowZh1PK2O3fuTN2cR9Vut2VxcdH3PGtra7K2tuZ7HgAAAPjnta+Q0/j+2516GA3MPXPN+gOlb1CUeh6JDFmvcgmx49bT6h3zy/JV+X1XY89qz1i2n7sy3/u+3W4Hui5gmhB0mnKlUklEDsvLCoXC2Pc3jgyiqM7Z6XRkZ2fH9zx7e3u+5wAAAAAwWfvyRB7JgyPdN4jPEZFlihyEnelkhrt7uEfQaYo1m02pVqsSj8elXq/bjo3H44EEYvoziqZlzqOKxWKysLDge56LFy/6ngMAAADAZJ2SWTkj5450XzXTqdPh6nc4mQg6TbHr16+LiMitW7ccG27Pz88HEsyZn5+3fD8Ncx7VwsKCbG9v+54HAAAA0eG1ZM1pvN3tUSuP88qpXNBvaWKY3JRNPq99nzwv33f4A6WUUC2FVNW3n863uLh4vDOfABsEnabU6uqqNJtNqdVqkkwmHccfNfNHDQCpWUnTMCcAAAAAIBimiHRCbuRNdd30CLvlPI6gWq1KtVqVSqUi2WzW1X2Wl5ct226zidSG3EtLS1M3JwAAAAAAmDyCTlOm0WjI6uqqVCoVyefzru+XSqUs27quu7pfq9WybKfT6ambEwAAAAAATB7ldVOk2WxKJpORUqnkKeAkMphBpOu6q7K8/kyjeDxu6R01LXMCAAAAUZCZuWb9gVNfoJB7IA2sV+lrpPZFGuh7pM4XpR5PDmsd4PBYnTShX70OU4NnypTQdV1WVlakUChIoVDwfP9kMmnpc3T79m1X99vc3Ox9rwaEpmVOAAAAAAAweQSdpoCu65JKpSSfz0upVHJ9n3K5bPnZ1atXe983m01X8/SPKxaLA7dPy5wAAAAAAGCyKK+LOMMwJJPJyNWrV10HnEREcrmcvPbaa5afra6uSrVaFZHD3lBO+sckEomhfZKmZU4AAABgHPpLxhzLxaasRKt+sG57+0B5nXJ8YZcH2lLW6rVUcNoey6B1zPFdve7f/tLvy+/98ldsx9z75sOx7R/BItMpwgzDkFQqJYlEQorFoui67vjVaDR6zbjVXkjJZNISkNnY2LDdf6329EViVPbQtMwJAAAAAIi+R/f35b2vP7T9OuExv6lCplOErays9IJJS0tLnu5bqVRG/rw7182bNyWbzQ4dZxhGL9sonU7bNi6fljkBAAAAANF25vwpefbbz9qOufdNAk/TgkyniEqlUq77GQ0zKviSSCR6mUHNZnOg71PXysqKiBxeCa4/k2ia5wQAAAAA+GOKJgcSG9vX8l/6fvnp+p+z/Tr/3LmwTwNcItMpgnK53FgCTl3ZbFbq9brkcjkpFoty+/ZtuXHjhiQSCdnc3JRisSjNZlOSyaTcunXLcjW5aZ8TAAAACJJd3yK1T1CkexwNkZm5Zv2BU2qJ0gdpoE+SItTzoaxV7V/ltcfTtD22wKQQdIqgSWTspNNp2d3dlXK5LOvr67KysiKGYUg8Hpfl5WWp1WojS9qmfU4AAAAAwNGNs5E4jheCTidcoVCQQqFwIucEAAAAAADjQ9AJAAAAAI6ovwxLLbFyKrmKevmdWnLmxLEkzWPn57GeH2Utr8x+1Hq7QzkdAHcIOgEAAAAAANc6XJMMLvFMAQAAAAAAQOAIOgEAAAAAACBwlNcBAAAAwFEd494/vnsqeezhpAqyh9NAvymFeXDgaS1O8x13B1y9Di4d37+QAAAAAAAACA2ZTsAI7XZbFhcXbcesra3J2trahFYEAAAAIEreMb8sX5Xft/3c0G63J7giIFoIOgEjdDod2dnZsR2zt7c3odUAAAAAiJp9eSKP5IHj54bjxBSRTsjldWaoe4cXBJ2AEWKxmCwsLNiOuXjx4oRWAwAAgEjq61t03Pr8OPVUcjxetd+Vzx5PfnjuyaSsfdT4UzIrZ+ScPHdlfuRU7XZbOp3wjh0IE0EnYISFhQXZ3t4OexkAAAAAIup57fvkefk+qW+PDtAtLi6eqEwooB9BJwAAAAAA4FrH5JpkcIegEwAAAICpoZY5OZWATZLfcrSoHZvTehxL1pRyurCPxw9tZsaybXboKjQub372P8gX//mXbMe8/80HE1oN/CLoBAAAAAAAXDuQ8TUSf3R/X+5/naDScUHQCQAAAAAARMLp87Ny/tvP2Y55/5sPwuxLDw8IOgEAAAAAgEj4ox//QfmjH/9B2zG/9GOfJxtqShB0AgAAADA1otYXqH89Az2NNPtmy1E7FieZmWvWH6ipJsrx1g/WrfefYM8qv/tS1/7K7Ed9r+n40KRjjq+8zu0agqTrupRKJWk0GqLrusTjcUkkErK8vCzFYlESicSR5i2Xy1Kv12Vzc1MMw5BEIiHJZFJWV1clnU5HZs5xouU8AAAAAAA4kcrlsiwtLUm1WhVd10VExDAMaTabUq1WZWlpScrlsqc5G42GzM3NSbFYFBGRWq0mrVZLSqWSNJtNyWQykslkxDCMUOecBDKdAAAAAADAiZPJZKTRaEg8Hpd0Oi2JREJ0XZdms9kLQIlIL9spm806ztloNCSTyYiISD6fl0ql0rutO0cqlZJGoyGpVEq2trYkHo9PfM5JIegEAAAAACM4lWn1l5wN3DbBcrKJcOrcrNw+UG44QZ7PtVIa6Lh2h/FT/1g76JjTXzRVLBal0WhIqVSSQqEwcHu5XO5lFYmI5HI5MU3Tdk7DMCSXO3wuJBIJS3CoX61Wk6WlJdF1XXK5nNTr9YnOOUnT/0wBAAAAAABwSdf1Xm+kYQEnEZFCoSD5fN7ys2azaTtvLpfrlbf1B6xU/VlTjUZDqtXqROecJIJOAAAAAADgxCgWi1IqlRwbb5dKJct2o9EYOVbXdcvtV69etZ372rWnWZKjgknjmHPSCDoBAAAAAABXTBHpiBbql32RmzPDMEZmOPXrXsWuf3uU/gBVOp127KnU3x/KMAzZ2NiYyJyTRk8nAAAAABjBsTePU58jG8etD5B2ataybR4cWAf4OFcTpznkZyjHMu2P3Unjpd/RnTt3et8vLy+PHNdfzpZMJl3N3W1cLiKyvr4+0Kh8HHNOGplOAAAAAAAACsMwev2U0un0yMCP2uvp5ZdfdjV//3xqVtI45gwDQScAAAAAAODagamF+jUpr7/+uogcZg/VaqOz2dReT/0leXbUcf2BpnHMGQaCTgAAAAAAAH0Mw5DV1VVJJpNSr9dt+yndvn3bsu3Ue6nr8uXLlu3Nzc2xzhkGejoBAAAAPh233jxwr/+xVp8Hal+ggdsVUX8eaTMzlm21Z9NADydFpI5H7dmk9ptSbtdi1uwas2P/2EbqWINminTMkPNX/HYSd6DrumQyGYnH43Lr1i3HgE+3h1LXUbOSWq3WWOcMA5lOAAAAAAAActgHaWlpSXRdF8MwZG5uTsrlsu191ADRUXX7R41rzjCQ6QSM0G63ZXFx0XbM2tqarK2tTWhFAAAAAKLkHfM/ylfNLw/8vP9zRLvdnuSScASGYUi1WpVKpTI02FMsFuX27dsj+zodNbCjZlD1XylvHHOGgaATMEKn05GdnR3bMXt7exNaDQAAiLJjXUqDo1NLtqaNUmL2xpPPWbYHygUdjjdKJWhquZzIzNBxTvbNJ/JIHgz83OlzxLTrHLGZ97//lf9NvvT/+v/63v+DdwfPuR+NRkNarZak02nRdX2gibfIYQZUuVyWQqEQ6L77jSMriUwnIKJisZgsLCzYjrl48eKEVgMAAAAgak5ps3LGPCfPXZkfOabdbkunM+UByIA8uf9E3v/G+2EvY0A2m5VsNmv5WbValWKxaAnaFItFyefzA9lE8Xg8kOBO/7zjmDMMBJ2AERYWFmR7ezvsZQAAAACIqOe175fnte+X+vb6yDGLi4vHPvPJrdnzs/LMtz3je54H7z4QszPebuL5fF7S6bSkUilL8KdarQ5kO83PzwcSIJqfn7d8H/ScYSDoBAAAAAAAXDFFpCNHK6/7gY/9p/IDH/tPfa/h1z78K/JgAhlTiURCbt26JalUqvez27dvD4w7ajaRGlRSM52CnjMMBJ0AAAAA4IgyM9eebig9kAZ6HCm31w+s2TEDPZKc9j3uHknK+h3X53T8EWIeHNgPUI5F7QGlbkepXxWClUwmJZvNysbGhogMv6rc8vKyNJvN3rZhGK6CPWqT76WlpbHOGYaY8xAAAAAAAICT6dq1p8HlYSVv/ZlQIsMDU8O0Wi3LdjqdHuucYSDoBAAAAAAAXNKkY4b7JUcs7zuqZDLZ+35YttHy8rJl222AqD+AFY/HJZFIjHXOMFBeBwAAAGBqhV7WZFdC5lBu5rWcTjXuY9VmZizbA42b1WN3KKdT1xvmY6edmrVsm/tPbMc7leNRTndyqMEgkcOgVP/V5m7fvj1wNbxhNjc3R847jjnDQKYTAAAAAADACP1ZRplMZuiYq1ev9r7v78Vkp39csVicyJyTRtAJAAAAAAC41jFjoX5NWjeQE4/HR2Ybra6u9r5vNBqOc/aPSSQSQ3svjWPOSSPoBAAAAAAAMMLNmzdFROS1114bOSaZTFqCPN2r3Y1Sqz0tyRyVkTSOOSeNnk4AAABAxITep2iKRPrcOPQ4UkXtWAb6GKk9qhw4Hc84j9fxd0h5bNT+VQM0bz2gjrvDZt7Tq1wuy/r6uqTTablx48bQ5uBdxWJRDMOQQqHg2FOpUqnI0tKSiBwGqkaNNwxDqtWqiBxeXS6fz090zkki0wkAAAAAAJwIhmFIsViUZrMp5XJZ5ubmRmYF5XI5KZfLUiqVpFQqOc6dSCR62Ubd+YdZWVkRkcNyvf7spEnNOUkEnQAAAAAAwIkQj8clkUhYftYNPuVyOSkWi5LJZGRubk5ERFqtlhQKBdfzZ7NZqdfrEo/HpVgsSi6Xk2azKYZhSKPRkFQqJc1mU5LJpLz11lu2WVbjnHNSCDoBAAAAAABXTBHpiBbql+nzGLa2tqRQKEgymbQEaJrNpui6LrlcTt566y2p1WoDASo30um07O7uSqlUEl3XZWVlpRfUmp+fl1qtJltbW56CQ+OYcxI00zT9Pl7AsbK4uCg7Ozty5coV2d7eDns50+P+fZELFw6/v3dP5Pz5cOfBycDzBQAwzARfHzIz155ueOzh5CTonkdee4Wp4wcoPZ7UvkhqT6j6wbrDCifH8rjJ4NrU29Vje+Pxr1jH25zb4/L5onscZ7/tvPzZf/GXQl3L//sv/JI8/Mb9qT+nJwGZTgAAAAAAAAgcV68DAAAAAACuTfvV6zA5BJ0AAAAATC2vJWNB398PdV+O5WwB78+R5lAYo5QTmvv25YVOxzfJc6+uXS2nU29/47FSfjfmx+ok+8rnvihfWf93tmMevvv+hFYDvwg6AQAAAACASHhy/7E8/Mb9sJeBgBB0AgAAAAAAro2zvO7UM2fk7LfZN/9/+O77Ih2uiTYNCDoBAAAAAIBIWProH5Olj/4x2zFvfOSfkQ01JQg6ASO0221ZXFy0HbO2tiZra2sTWhEAAABUfvsA+e4j1Nf7x6lHkzYzY3v7RHsaudm/0tfIqceTdmrWeveDA+v8B9a+SJGmHOsrsx+13vytY33n4EvyTudLA3fv/xzRbrfHsEBgOhB0AkbodDqys7NjO2Zvb29CqwEAAAAQNfvyRB7Jg4GfO32OmHZcvQ5uEXQCRojFYrKwsGA75uLFixNaDQAAAICoOSWzckbODfz8uSvzve/b7bZ0OvZX9gOOK4JOwAgLCwuyvb0d9jIAAAAwJTIz12xvN9XGx0oJ16TL7RznV9bnVB6nrn+Sx+d5bodjU8vpBh4784mIiHyPLMn3aEu2+1tcXDx2mU9kOsEt+6JcAAAAAAAA4AgIOgEAAAAAACBwlNcBAAAAAABXTBHpSLjldabzEEQEQScAAAAAOKL+Xj5OfYXUnk9OPZLCpsXsAwtOPZxU4+xR5Xlu09rY2+lY1HNhHliHT7ofFzAtKK8DAAAAAABA4Mh0AgAAAAAA7pha+FevC3v/cI2gEwAAAADXTnoZkZfjHyjZ8jFXGMyOtXOOWh7oRJuZsb2/1/LCIM+XurY3nnzOsv3K6Y9ZxyvlddqpWcv2bzz45SOvBTjOKK8DAAAAAABA4Mh0AgAAAAAAroVeXoepQaYTAAAAAAAAAkemEwAAAADXotZ3KGyWPkWa8j99s2N/Z2W8355HQVP7GKk9npyO19y3bvt97gT53BvoV+XQf0uUHk7mwWPb+/N7cnTvvN6Ur9aatmMe3bk/odXAL4JOAAAAAADAtXGW1z25/1geffPe2ObHZBF0AgAAAAAAkXDq/Gk589wF2zGP7twXUTPvEEkEnQAAAAAAgCumjDfTaTG7LIvZZdsx/+u1qjwmG2oqEHQCAAAAEJqo9cJxWo+6bRmv9jhSaDMzlm21r1DYPZy8Uter9qRSRemxVh8LUYIo5sGBdfzsKdvbw37eAlHF1esAAAAAAAAQODKdAAAAAACAa+YYy+twvBB0AgAAAMYsSmVFqrDXFvT+MrGcnDX35QuTWo9DSV2/N558zrKtnvuoUUvIVI7rV85NlJ73Ynasm+qxKmvvPHxkvVkpz1NLC6etVBIYF4JOwAjtdlsWFxdtx6ytrcna2tqEVgQAAAAgSt45+JK80/nSwM/7P0e02+1JLgmIFIJOwAidTkd2dnZsx+zt7U1oNQAAAACiZl+eyCN5MPBzp88R064jlNfBHYJOwAixWEwWFhZsx1y8eHFCqwEAAAAQNadkVs7IuYGfP3dlvvd9u92WTqczMAY4CQg6ASMsLCzI9vZ22MsAAADHwCR72Xjt0RSpPjsBqHdqIvfvi1y4EMh8Xs6n2sdHve9ADyQP/aCiQO1jpK5/oC+S0jcp6L5H/efT6/NYOzVr2TY7pig/sB//rWP9HnlRvif24sD4+vbT9SwuLh77zCdgFIJOAAAAAADAtQ5Xr4NL0xVaBwAAAAAAwFQg0wkAAOCIwr7UPJ7isXjqJB/7ODiez76yqoHyOZVaTmdOV5+fgfI5sW6r5Xdmx3q8fsvpVHaPjdPfhM6jh/aTK4+VFrNm9pj70/XYAWEh6AQAAAAAAFwzKa+DS5TXAQAAAAAAIHAEnaaIruuSyWRkY2PD1zzlclkymYzMzc2JpmmytLQkuVxOGo3GsZ8TAAAAAOBPx9RC/cL0oLxuChiGIdevX+8Fm3I5h1rxERqNhuRyOTEMQ9LptNRqNUkkEtJsNqVYLEomk+n9PB6PH6s5AQDHl59LZvtF75zo4LFAUJx6AQ30berr/aP2LMrMXFOGKn2BOlOeA6D0PRrs+WQ1yd5rjnM79Gwa6L+ljj81a9k29594Wh9Ga//q70n787dtxzy+c39Cq4FfBJ0izDAMuXnzppTLZd9zNRoNyWQyIiKSz+elUqn0bkskEpLNZiWVSkmj0ZBUKiVbW1uOAZ1pmRMAAAAAMB32338sj795L+xlICBTHlo/vsrlsqRSKWk2m77nMgyjlx2VSCQsgZx+tdrhfwN0XXfMppqWOQEAAAAAwTFFE9Mc39fMuTMy+9yztl+iZqYhsgg6RVCz2ZR0Oi2tVkvq9frI4Itb3VI1EZFisThyXDeTSOQw46harU79nAAAAACA6bHwE39ckp/967Zfs/MXwl4mXNJM0zTDXgTsNZtNSaVSve1KpSL5fN7VfXVdl6Wlpd727u6ubTnaxsZGL3soHo/L7u7u1M55VIuLi7KzsyNXrlyR7e3twOY99u7fF7nwrT/+9+6JnD8f7jw4GXi+AIGaZL+V44ZzFzETfH2w9G0yO/aDlb5Aag+oqFF7UvmmnJ9x/p4M9N5SaDMzlm2nflSx06ct250n+9YBNsd2XD5fdI9j9rlnJfnZvx7qWpof/0fy5JvvTf05PQnIdJoCfnoWlUql3vfpdNpxrm4GkchhuduwK+VNy5wAAAAAgICZEbh6HakzU4Og0zHXX3qWTCZd3SeRSPS+X18f/O/LtMwJAAAAAADCw9XrjjG1CfnLL7/s6n7JZFJ0XRcRGcggmpY5AQCAd5SEHR3n7imnsqZpP1cDx9dfMqc5/E9fKcGK+rnSlGbNZkdJL1GOZ6BkTR3vdH486j9/6rlyOndq6aA6/pXZj9ref+Dc2FfnAScWmU7HWKPRsGz3ZwbZUcf1B4WmZU4AAAAAwHiYZrhfmB4EnY6x27dvW7bd9oa6fPmyZXtzc3Pq5gQAAAAAAOEi6HSMdUvPuo6aQdRqtaZuTgAAAAAAEC56Oh1jajDnqAzDmLo5AQAAEI6w+xCNm3p8/b2BBnoa7T+x3lntaaT0RIoatSdTbNb68bHzZN86/iDYxkZqzyuvfZv87Et9rLRT1m2zYz324/68V3VEcx4ECEGnY+2oQRi1vO3OnTtTN2cQ2u22LC4u+p5nbW1N1tbWAlgRAAAAgEl5x/yP8lXzy4cbD9UgizUgZ/e5od1uB7wyYHoQdIKjcWQQTcOcnU5HdnZ2fM+zt7cXwGoAAAAATNK++UQeyQNXY4P43DBNTJNMJ7hD0OkYi8fjgQRi+jOKpmXOIMRiMVlYWPA9z8WLFwNYDYDjzKl8AMDx5+fvQNh/Q8Lef5So5WXquXhl9qPKeOv9B0r3HM7tuM997PRpy/ZvPPhl6/77SgtFZLAkLWYNTDiV34X5XIqdOWvZ7jx+LKe0WTljnht+B00JuiiXVHvuynzv+3a7LZ1OtEspgXEh6HSMzc/PBxLMmZ+ft3w/DXMGYWFhQba3twOdEwAAAMB0eF77fnle+/6ht8XOnrFsdx5YM6Lq208DZouLiycuEwroIuh0jB0180cNAKlZSdMwJwAAAABgPDqU18GlmPMQTKvl5WXLtttsIrUh99LS0tTNCQAAAAAAwkWm0zGWSqUs27quSzKZdLxfq9WybKfT6ambE8Dxl4nl5Ky5L18IeyEBOMn9T4CTKsjeNX7/hvhdy3H/G+bp/JjWvj3qfbVTs8odrD2OvD4W4z73ag8mtSeVerwDPZ2U43Xq6TTJ55JTvyltZsZ223yyP56FQb7xa/9GvvEv/q3tmCe79ya0GvhFptMxpmYQ6bru6n79mUbxeFwSicTUzQkAAAAAGA/THN/X/vuP5Mm779l+Scd0XiQigaDTMZZMJi19jm7fvu3qfpubm73v1YDQtMwJAAAAAJg+M8+ckdnLz9p+SYyeUtOC8rpj7urVq1KtVkVEpNlsurpP/7hisTi1cwI43uqdmsj9+yIXLoS9FGBsuBT98RV0SZyfuXleeTNw7vtLypTysoHyM3VbEbXHQpu1flxUr9A2UE6nBAI6jx562p/T37wg/ybGzp2zbHcePrJsO5UCOt2Oo/u2v/An5Nv+wp+wHfMf/sr/cJjxhMgj0+mYW11d7X3faDQcx/ePSSQSQ/skTcucAAAAAIBgmSJimlq4XwEfU7PZlNXVVVlaWhJN00TTNFlaWpJisej6QldHpeu6ZDIZ1+PL5bJkMhmZm5vrrTOXy7n6HB0Ggk5TwM+TPJlMWgIyGxsbtuNrtaf/LRiVPTQtcwIAAAAAMIphGJLL5SSVSkm1WrX0F9Z1XcrlsszNzfWqcrzO3Q1g2X0tLS256k/caDRkbm6u9/m3VqtJq9WSUqkkzWZTMpmMZDKZsQfJvCLoNAXUxtpen0SVSqX3/c2bN0eOMwyj98uUTqcln89P/ZwAAAAAAKgMw5BUKuWY8CByWJnTX53jhpdAlVMiRaPR6AWU8vm81Ot1SafTkkgkJJvNSqvVkmQyKY1GQ1KpVKQCT5ppmrR9j7DuL0J/4CmZTMrW1paneTY2NiSXO6yBLpVKUigUBsakUilpNpsSj8flrbfesjT3nuY5vVpcXJSdnR25cuWKbG9vBzr3sdbfW+fePZHz58OdB75FvZdMJpaTs+a+fEH+xeEPeL6cGH6em1F/Xk8a5wNR4Pd5OPT+E3w/Ydm/0uOofrA+eqyIaKdmLdvm/hPr/UP+nczMXPM0XpuZsWwP9D1SelqFeXyvnP6YZVtdq9qfylSuljZwu3L//mM7Lp8vusdx6vKz8gP/5L8JdS1f+mv/vey/+56vc5rJZKTRaEgymZQbN25IMpkUkcNSu9u3b0u5XB64T61Wk2w262r+ubk5V8GfdDot9Xp95O2GYcgLL7wghmFIIpGQVqs1dJyu67K0tORqzkki0ymCuil+3TpNNdOp2WyKpmmSyWQkl8u5arydzWalXq9LPB6XYrHYu59hGL1oaLPZlGQy6TqQMy1zAgAAAADQVa1WpdFoSKFQkK2tLclms5JIJHqZQ6VSqZc91O/69euu5zcMQwqFgtTrdduv/tYxw+RyuV7wyi4jqrt2kcPMqKOUBI4DV6+LoHg87vjEO4p0Oi27u7tSLpdlfX1dVlZWxDAMicfjsry87ClqO21zAgAAAACC0TE150ERViqVJJ1OS6lUGjkmkUhIrVbrZQ+JSC8ZwulCVqVSSRKJhO38bui6bmkQfvXqVdvx165d65ULFovFSLSiIeh0AhUKhaFlaydhTgAAAADAydVsNkXXdVcta7qBo/4Mo2azaRt02tjYEF3XLT2Lj6o/aJVOpx0rffqTMwzDkI2NjdATNgg6AQCGcuqzMO5eME7zD/TswInh57kWdn8Ur8b9exbl8+F07JPuRxX237xp5urvuQ9O9//whY/LQ+3px57Az21/HyelZ9FATySl55NdH6AwqI+Vul6nPkeqgR5P+50RI8fPqZ/WAE3tRHPg7XZE1vr6uuTzedetWtQA07vvvms7/ubNmxKPxx2zktzoL5FTS/1GSSQSvRY96+vrBJ0AAAAAAMD0mObLkV27dk0SiYTr8Wqwp7/cTtVsNns9l+fm5iSRSEg6nZZMJuM5+KP2bn755Zddr7cbdHJzZb5xo5E4AAAAAAA4EZLJpKcLUqlXoLMLWKmNvnVdl2q1KrlcTjRNc30hMBGx9HJy2q/dOLf7GxcynQAARzLuEoCwSwymGWVAx8dJPrdOxz7pc8PfvKML+9i+cO+zIufPj23+/hIy06HCarDc7IllO+y/rwNlrEp5oFoOqB6PSj2+SXI6l6+c/pj1DkpppCiNsgdKC0M8NkyWejX5Uf2c1Kbfw2xsbMjGxobk83nHnk+3b9+2bLsNlF2+fNmyvbm56bo0bxwIOgEAAAAAAHdMETPsq9dNsLxvc3Oz973d1eASiYRUKhUxDENarZY0Go2BgFVXtVqVzc1N22bm6n2PmunUarVc3W9cCDoBAAAAAAAM0Z+RpJbPqdSglGEYUq1W5ebNmwNles1mUzKZjNTr9aFzjQpYeaXud9IIOgEjtNttWVxctB2ztrYma2trE1oRAAAAgCh5x/yyfFV+f+Dn/Z8j2u32JJeEAOm63uuJVCqVPDUgFzksiSsUClIoFGRjY0OuX79uCQI1Gg0pl8tSKBQG7nvUYJFahnfnzp0jzRMUgk7ACJ1OR3Z2dmzH7O3tTWg1QPSE3XcCo9F7ZnoMXJ5cwbkGovd6Y/d7q52atWy/8fhX7O+rKdd1UvsKOex70udioCdVx1Rut46PnT5t2e482fe1fy/H73hulHOtrnXg2JSeTt1zcbDfkUcHDwamd/ocMd20I5fX3fmX/6vsfuFf+17B/u4933O4USqVROSwZG1YYMiLbDYr6XRaVlZWLM29b9686XtuO2Q6AREVi8VkYWHBdszFixcntBoAAAAAUXNKZuWMnBv4+XNX5nvft9tt6XTsA4onRefBI9m/Mx3/uG82m1KtViUej48sgfMqHo/L1taWpFKpXuDJMAxpNBoDDcrj8XggASMvV+obB4JOwAgLCwuyvb0d9jIAAAAARNQHTv2gfODUD0rn8WPLz+vbTzOtFhcXj3nmk3uxc2fk1Lz/f9zv774nYo63m/j169dFROTWrVuey+qcvPbaa5JKpXrb9Xp9IOg0Pz8fSNBpfn7eedAYEXQCAESSU9mRiMhZc1++MIG1AOMSdpkQMA2i9nuirueV2Y/2vh8op5u5Zr2zUk4Xm7V+HDMPDjzt22u5ndfxA+V0+09sb1epgZgoUcvnRJRzrzxWTuV2J81Rwz1zf/5Pydyf/1O+969f/wdjzZhaXV2VZrMptVpNkslk4PMnk0lJp9PSaDREZHjT8KNmKKmBqrAznWLOQwAAAAAAAI6/arUq1WpVKpWKZLPZse0nk8nY3r68vGzZdpv1pDYOX1pa8rSuoBF0AgAAAAAArpmmFurXuDQaDVldXZVKpSL5fH5s+xERS8nesBK4/vI7keHZUMO0Wi3Ltlq2N2kEnQAAAAAAwInWbDYlk8lIqVQae8BJxBp0GlYCp2Y6uQ069WdExePxwPtReUVPJwA4IfxeZlm9v9rHIfDLOHu8jPRxEvYlsREdPBemxzQ9VkG/HkT5WCei7/XqldMfs96mvHZpp2Yt250n+7527fXcB/3arPY5ip1VPl4qx+/Us8qJl/U7Pk8H1ma9OXbG+lhpSv+tzsNH1juo71swVXRdl5WVFSkUClIoFCayz83Nzd73w0rtksmk5Qp2t2/fdlXu1z+vGrgKA78ZAAAAAADAPTPkrwDpui6pVEry+byUSiXX9ymXy772218GN6oE7urVq73vm82mq3n7xxWLxSOuLjgEnQAAAAAAwIljGIZkMhm5evWq64CTiEgul/PdK2ljY0NExDazanV1tfd990p3dvrHJBKJ0Ps5iVBeBwAAAAAAThjDMCSVSkkikZBiseiqZ5Ku673soWQyeeR9b2xsiK7rEo/H5caNGyPHJZNJSafTvWDSxsaGbYldrfa0jDQKWU4iBJ0A4MTw28dBvX9m5prteN89P05QDyfVie+Pcoz4/T2Y5ueC12Of9j5B07TeoF8Pwhb2c0eLPb2SltqzSO3h1D9WRESbOW3Z7jx6GPDq/FHXq/Y9cjGB8gN/PZ38cOpNqfanUqk9nAb6U52g9y2myFivIOd2DX6trKyIruui67osLS15um+lUrFsNxoNyeVyYhiGpNNpKZVKI4NSuq7L9evXRUTk1q1bQ5uIq/vqru/mzZsjg06GYUi1WhWRw3K9STRDd4PyOgAAAAAAcGKkUinXPZKGUQM6tVqt1/C70WhIKpWylMZ1dW+bn5+XVqvlKlsqkUj0MpiazebIXlIrKysicnjFuv6Mp7ARdAIAAAAAACdCLpcLNODUnVNVrVZlbm5OcrmcrK6uSiqVkkwmI/l8Xra2tiSRSLjeZzablXq9LvF4XIrFYu8YDMPoBbKazaYkk0l56623HLOnJonyOgDA0TikkQd9Web6wbplW02T92uc5Rlhl374Ne3rD9NJPlcnqZQQ4Qr7udN5st/7PjZr/XjVefzYsm0OlJtZeS1DdeL33KglZwMlaWqJmTJ+4HYHTsfn5XgG2gIocw8cm1JK6PQ+Z+Bc7J+c8joRETPgK8hN0jiygNLptLRaLSmVStJoNOTOnTu9zKduMOjGjRuSTqePHBBKp9Oyu7sr5XJZ1tfXZWVlRQzDkHg8LsvLy1Kr1Wz7PYWFoBMAAAAAAIAPiURioNfTOBQKBdsr3kUN5XUAAAAAAAAIHJlOAAAAAADAtXFevc74n/6V3P2f/pXtmIPd98a2fwSLoBMAnFB+L2euUu//yuxHLdtvPPmch9XJQC+FoHs4qcbZEyTsfiN+Tfv6ARHnv3knqXfZSTrWSRvoE3Rq1rqt9g1S+gJ5fWycnsdeqfePnT5t2VaPb4Dy2q0er6m0eArzuaj2ZNKUflxmX68udxNSRBSUzvsP5eDOXtjLQEAIOgEAAAAAAPfGmOkUO3dWZuYv2o452H1vuruZnyAEnQAAAAAAQCTE/9wPS/zP/bDtmHf+eolsqClB0AkYod1uy+Liou2YtbU1WVtbm9CKAH/8prA7lt/NXLP+wOFSw47UNHW/8/kU5GWcMTlBl25QljS9vJYpHWdRO9awf6+C3L+5/8SyrZZwdZ4o5XcH1nozv89Tv+dOvf8Hz/6kZdtU1qvS1HK8h4+st1tPh+fSez+P1UApovq+ReFUftd58EBERN7p/Ed5p/Mf5bkr85bb+z9HtNtt1+sEjhuCTsAInU5HdnZ2bMfs7RFdBwAAAE6qffOJPJIHjp8bjhsq2+AWQSdghFgsJgsLC7ZjLl60rzUGAAAAcHyd0mbljHluINOpX7vdlk4n3IxtICwEnYARFhYWZHt7O+xlAAAAAIio52PfL8/Hvl/e2B5dKri4uHjiMqGALoJOAHBCBH158FdmP2rZdrosslfqZZo7jx5atuudmsj9+yIXLgy9f9A9mKLWA2WSprkvUtDP+5P8PMD0CrtnkpOw1+N3/9beP0rTIqUfYUzpC6T2L1T7DNUP1n2tzbdT1vXGlPV2Hj+2bJtP9q3bag8on/0Z7f6me30c1Z5NA5T3NR2lP9XAYxfx37NAmd/6CnsNmAox5yEAAAAAAACANwSdAAAAAAAAEDjK6wAAAAAAgGumqTkPAoSgEwBM1CR74wTd00idTzs1a9k29594ms+J2idi2HrOmvvyhRG3T1PfoagL+tj9Pvf8rOckP46witrv+DjXE/axRZ3fc9/f01DtYaRuD/QRUvoG+e15pPL7XsB8ZO1jZHasjXTU49GUnlWaej72o3MFt9jZM5ZtU7m6nPnY+r7G6bEL+n0QcFxQXgcAAAAAAIDAkekEAAAAAADc4+pxcImgEwD44DUlP+iSLy/78nJfN+MH0syVkoA3nnzO0/6cDJTzHRyISOzIb3qcHruTVI4StTIjVdTWEyVRf+yiLGrnyk/ZadSOZdr4PX/9JWfqa5VosZFjRUTkwL6U3MnY34co69eUl371vUDnoVKOp5TXBc3PY6eW06nUYxsolRTr7fweAsMRdAIAAAAAAK7RSBxu0dMJAAAAAAAAgSPTCQAAAAAARMLe//y7svfr/x/bMQfGexNaDfwi6ATgRAm6/4nT/f1eqtjr/r32bfI098w1y3bnsdKHQunp9Mrpj1m233j8K9b5HNYaO3PWur9HDwdu18yYyLfaR3z4wsflofb0ZW3c/bWOE87FaE5/M8LuqcRjN1rYj40Tv+uL2vFESSaWk7PmvnxhUjvse/3rPHpiuUnt8aTFrCVJps+WR+N+rRvoa7RvPb7OY6XPkTI+Nmv9uDnw3sEjP7836n1jzzxj2e48UN5nnD5t3T57xrJtKsfyyuxHLdtB97aMnDE2Eu88eCQHu3vj2wEmiqATAAAAAACIhNi5MzIzd9F2zIHxnojJJfSmAUEnAAAAAAAQCRc/9J/LxQ/957Zjtv/PN8mGmhIEnQCcaOMunfFzCexh9/eTJu/12NRyOrV8Tk2hF81aQqBeWtjrufzg2Z+0vV3MjmVNX7j3WZHz5+3vM0ZBXr7c7/Mu6mVFqiitd+yXH484r8cf5PM+aF7LnymNfCrsc+NXvVMTuX9f5MKFI90/0ONXXjvV10rtlPW6TmbHmrkx7jJ9R+r6FWoJmvraL1rMfnuC1HOlvs9Q39eopYCxGfvH6tiX0w3g6nVwh6vXAQAAAAAAIHAEnQAAAAAAABA4yusAAAAAAIB79PCGSwSdAJwo09aXwo+g+wAN9HhS+jIM9HhSvPF43dN6VLEzZ4f99Mjz+e3nNc6eJ1HvRePXJNcftXMVen8WB+PuWxclYa89Ss/NqPeRm/S58vo3OPbMM083Hj+xDlZ6Iqk9j5xeO8N+ng681p+xfnw0lePVTlt7VqlXF9Ni/voA2b0Wez5XA/2lrI/NQL8q9bFVROl3GogSgk7ACO12WxYXF23HrK2tydra2oRWBAAAACBK3jn4krzT+dLAz/s/R7Tb7UkuCYgUgk7ACJ1OR3Z2dmzH7O1xmU4AAADgpNqXJ/JIHgz83OlzxNSjvA4uEXQCRojFYrKwsGA75uLFixNaDY7KbymL3zInp/173p9a4uZwKWMv+1LX6rT22NlnLNvmk33rtnrZZI+0c+esP1DmO7j/vpimdZ/9nB4bv2nvYabN+33eejXNJQNRKxebpnPnVdSeJ5Nej9f9hX1+7ETtWKL+2L0y+9GnG0rJltmxfjKPzVrLyzqPH1u2ncrtnAT+vFfL4fZHv+6KiMjB0d+XHIWf49NmHT4KK8cemz1j2e6+zzklZ+SM+cxAKeFzV+Z737fbbel0JntugKgg6ASMsLCwINvb22EvAwAAAEBEfWD2h+QDsz8knQfWbKf69tOA2OLi4vHLfDL99efCyXH0DqwAAAAAAADACASdAAAAAAAAEDjK6wCMldqDqH6wbr1d7T3w3j8LdP9+exl47Y3gd7zXHlCq/v157TfldPvMhQvWnSl9JwaSrB89dFitvc79963zK5dhjp0+LZoZE3nkazc9436s/axFNeleMEHvL8zLsUet71DUeXkuTvpcjrtvm1eTfr0J0rj7wI17f+Pm9Nhop0/3vlf7Gw70aDpl/filZgD47YfoldNjNXPpkmXbVErI1PXGzp213v74iWXbb8+qQGnKO5cZ66OhxZRHR1m7+VB5A6KdrHwOk0bicOlk/WYAAAAAAABgIgg6AQAAAAAAIHCU1wEAAAAAAPfGWF6398b/Iu/Vf9d2zIHx3vgWgEARdAIQuA9f+Lg81A7/vGinrH14HHtW3L8f6Fq89pXw2ufIL699gwb6BZgd+/F9Xjn9Mfu5HJgd675E3T5Qtn32NlB7OA2s5+BAxBzd+8JrzxC/PZz6b/c717ifd+PuHeO3n5gqyPVNWy+ZsHl5Lke9h1LQz/ug55vmXmVef+eD7jkY5rlXqT2LBno0qa+VSo+nNx7+c1/793qunI79gxf+smXb7FgjDbG+flaHA6y3m/vWnk7q/cfJ6djVx6bzwNqLUu1PJWq/LuV9ivm+9VhxdJ0Hj+Rgdy/sZSAgBJ0AAAAAAEAkxM6dkZm5i7ZjDoz36GY+JQg6AQAAAAAAd0zt8GtMLmZ+RC5mfsR2zM4n/74cGHfHtgYEh6ATgMBpZ85K7FvldRIb3wtSEDIz1+xv91rWpJSUqfPXD9Z97U+9v235nVJ6p6a4e9237Ktp5UpKvXJp4djMeK9VETt3VjTziciESvr9lIB5fR55LR0Julxt3KU649x/1EuogjbpMiRV1M5Hv5P0XBj34+61VN3r7X7Hj5vTevpL6jpPHllvU98HqdsH7svk3azFabzn54ZDJsnAewvltX/gvYJSojZOXh63oZRjV8vp1HI8v20FgOOK3wwAAAAAAAAEjkwnAAAAAADgmkY7JbhEphMAAAAAAAACR6YTgMD9y3d/QeT8+aG3vXL6Y57m8trbYKBHk1pfr/Q58tpjyfFS9sr8TtT7a6eUy+8ql/NVj8/20swOx+54bpRj/dDcJ8SO+cTa1yF2/hnb8U6P7cz8nHV+tXfC7CnbXhPj7ovkxbj7mYTdp8fvuQvysQp631HrLaMad/+uMPtr+b1/1H4vnOYL8m9Q2Mful9ffy7D7bZmdp6+fsXNnLbepfX80sb6uyynrxzH1df2NJ5/ztTa/v+MDfY80a08q87H1tV+L2ec0dB48sL19opT3EOpjN3Dss8pj9djmPdhJQKYTXCLTCQAAAAAAAIEj6AQAAAAAAIDAUV4HwFGQaeuxC9ayO3Xus9KRL3zr+w8/+5fEKTY+UE7ntbxNvb/CsXxP5XC5XKf7D1x62KEEzlQyuS2p4Mp9bccOuV19bGYuXBhccB/1sTXft0+hd3oe/dgH/hvrD9QU/jNnROu4/9+J53INj88tu+MJ+hLYTsZZMjXMpO8fZunjceP3+PufS5Mu6Rr38zxqvyd2wi4nnvS58rq/D1/4uDzUnn7sCbpUMnbmaVmWqZRsqa+1mlKi5VRuNu7nudP4/tJBERE5sG5rMWu5neP9nd4nTbDkt/9xExl8bNTyO/PhI+vtDsd+7Jkn/PjhGplOAAAAAAAACByZTsAI7XZbFhcXbcesra3J2trahFYEAAAAIErefvLv5Z39/zCQGdX/OaLdbk96WUBkEHQCRuh0OrKzs2M7Zm9vb0KrAQAAABA1++YTeWS+P/Bzp88RU4+r18Elgk7ACLFYTBYWFmzHXLx4cUKrCZbaq6Z+sG693amefkivm7Pmfq8X05+//Al59K3eCb/x4JctQ7Uzp23X9oX3fknkwud732ee/Su24x17OCm9A5x7D1hfQQf6OKi9CNT51T5Jao8mh/UNWZB1+KlZ6802l+d1unSv0+1Ol0mW77H+fpjtb1i3n+zbzu/Yl2HWeqzafNx6+/0HIqbN+XXoh+V1PV5vtxvrl9f+VF7XM+leOH7OrdP84+6nFfal2lWT7q3Tf7vTuVaNuxdN2D2Y/B6Pl/Pj9XfMK79/c4Lm5m9E//sSldfnntP+BnoB9TE71tdC9bXX6XW93vmcp7V5vd3xXCg9nAZeS9X+keprv/K+a+C9hcLvY2E3Vt3+4Lm/aNk2Hz+2TuD0Hu1bx3ZKm5Uz2jMDmU7PXZnvfd9ut6Wj9rcCTgiCTsAICwsLsr29HfYyAAAAAETUB2Z/SD4w+0PSURqN17ef/lN3cXHx+Gc+ASMQdAIAAAAAAO5RXgeXCDoBAAAAAIBI2Lv1O7J363dtxxzcpbfutCDoBJwAjn2JvN5foZ2aFc3URLpl/GZHREbUrcetfbC0d3ct2x++8PFeD4YPX/i4xM5esNzeUevtVQ59e9SeTbHT1h5T5qOH1vWpPZoc+yRZt9X51XOvHs9grwP73gcD++87Pqf+UrEzZ23Xop4rTT1Xyr41pcfZwR3rY+u5p4jaR2JfOblnz4h0bJ7LynMhyD4Rw26fZF+hcfdY8jvfJHvZeJ3bL7+9ccYt6OeGl+fuuJ/HkzbOXmNu7h+ksPtbjdvQ9d2/L3Lh8D3EF+59VuT8+bHt366H4UAPJ1Ppj3jK+nFsoK+QR377VTkZeG+gtn887b73ZBD8PDfVXlxqeZx2Wu0LqvSvUo/Nqc8oXOs8eCQHxt2wl4GAEHQCAAAAAADujbG8Lnb2jMzEL9mOObi7N9C8HdFE0AkAAAAAAETCxZUfkYsrP2I7Zvu//XtkQ00Jgk6YGuVyWer1umxubophGJJIJCSZTMrq6qqk0+mwlzdWXlP4Xzn9MdvbMzPXbOePPfOMZdt8bHNZevlWGZdpPi2v02K9MrIfW/jr1sFKGrk27FK6B0+/V1OXY2oqtJLSrl56WJ3fqbTQ6dLFA/MrJWwD5X/K8XYe2JfvDVBStdXx2oxS8maTxq6W06lmziuPu1ou8Ky11PHJt1m3Txv3rPM9+6xl+9fv/hPb/avPw5k/8gOWbe2ucmz7B7b/4VIfq6Avzx71Mqt+Qa993KU2Qc4fdGmhym9poN/1jXt/US6rmvRj6/V2v/NH+W/MuM+91/0Pc9bc75XrB0093g9e+Mu97wfeM6klV6fOKLdbX8fqB+viR+B/Q9T3Icr7MFHK7cz9fdvbx8nr89LsdNQfWDfVx1IpHRRNeY950qilosAI3hq7ACFoNBoyNzcnxWJRRERqtZq0Wi0plUrSbDYlk8lIJpMRwzDCXSgAAAAAYOo0m01ZXV2VpaUl0TRNNE2TpaUlKRaLvj5nlstlyWQyMjc315szl8tJo9GI1JzjRNAJkdZoNHoBpXw+L/V6XdLptCQSCclms9JqtSSZTEqj0ZBUKkXgCQAAAADgimEYksvlJJVKSbVaFV3Xe7fpui7lclnm5uakWq16mncciRPTmoxB0AmR1f0DICKSSCSkUqkMHVerHabO6rreGw8AAAAAGA/NDPcrCIZhSCqVko2NDcexq6ursrq66mrecSROTHMyBj2dEFm5XK73y9KN5g7T/UXb2NiQRqMh1WpV8vn8hFZ5NGrN+WCfHuv2qW97zjpBzD5ePPNtl21vP/Xd32XZ3v///YF1+u/8dsu2eeGcdVupYTfPnhI5eCxy+3BbO39WNO2w7n3vT79gGXvxX7+jLFbtaTTb6+kkp2ZFBsrt1Uv12p87UXo6qZciVvv+qGJnz9jertbzx05beyypl9cd6JvkdClhtVeC2mNKvZyv3dwDfSUcLhWsnrtz1nMx+w1rD6fOd8xZ538uLnacei986D/5b23vf/Dd3yadg8ci7cPtD1/4uDzUnh6TY78sh/U43u7QH6yfU9+foC+97rUXjdfeMV7396H565btA4c3QpPsDxP0uRt3rzB1vN/9OfFyfF7XHvS5C9qk+xY57S/sPkpR2beb/fvth+X1sYide/o+SX3tMR1e5kV5rQ76cXaaz3F+9bVU7dE0o7wPUd73mKKcAGXT7/N+nL3PNKWH08CxqT2hPLwvQDTkcjnRdV2SyaTcuHFDksmkiByW2t2+fVvK5bJlfLValUwmI9lsduScXhInlpaWeokT9Xp9onNOEr8ZiCRd1y01qVevXrUdf+3a08bYdgEqAAAAAMDJVq1WpdFoSKFQkK2tLclms5JIJHoJDaVSqZc91O/69esjZjzkNXFCRHqJE5Occ5IIOiGSSqVS7/t0Oi3xeNx2fH+02TAMVymSAAAAAACPzIh8+VAqlSSdTls+d6oSiUSvlUuXYRgjG3aPI3HiOCRjUF6HSOqPyqrR5VESiUSv8dv6+rpt2uOkvXL6Y5ZttVzu4PkFy/b7V6wlWO8/Z01tvvAH1ku4qqnFez/5Jy3bHzz7k5btb/7FZcv2c5/9pmXb+OPfaV3faWuZ1akH1r/ys/cORNt/Osb8ngUxZw7LzN79I9a1fyOZsGzP/+/W1OTv+JdvinyrykubnZFYzFquJuqlep9YL807UH6mpEJrFy5Y76+mhSvzDaSVqyVnDx7azqemXmuzSjmfUu43kJqt7C927rz1drWErm9+Tb2Ur3oZY6W8Tr00sKaWCiq3dy5Zn6favvVYDy5Yy/E8p8j/qT9m2VSL5R7Hz0hsf/Tlep/8ly9Ztk81mpZt9fdSLYl448nnbNenXta6/3b12H7s2/5P1vt6LA/wXR7hcb7MzDWx43V/5iNr6WbszFnL9m88+GVP83kRdKlG0KWOXuf3uz+/5YRBjT3KeK+iXr7nd392f3OCNu7nWdDqnZrI/fsi6mv+t/j9m6re/sELf7lvy+MnYY+l4Kqg/2apZp591rKtvleQA6XETH2fo7yvUkv33/D5N8hLya/TXAMtF0zlsVTL7dRzobYxQGQ1m03RdV22trYcxyYSCSmVSpYATrPZlHQ6PTA2iMQJ9TPsOOacNDKdEDnNpvWD4csvv+zqfv3BKTKdAAAAAACq9fV1yefzjgGcLjXA9O677w4dd9TEif51TWLOSSPohMhR0xX7f2nsqOPU4BUAAAAA4GS7du2abVmdSg32LC0tDYwZR+LEcUnGIOiEyLl9+7Zl220E+vJl6xXbNjc3g1oSAAAAAOAYSCaTrj9jikiviXfXsKSIcSROHJdkDHo6IXK6fZm6jvrL1Wq1AlvTQH8TpWb7YCVl2X72/7Zt2f71r443APZH/oefsmz/b3/rH1u2/8SMtZ/M7f/OevsP761atv/+p61XOnivc86y/dt7P2DZ3n5/Ts48eCLyxuH21/+zi/Jw9rCfz5Pvf2AZ+zf/2G9atv/GX/uqZfs/+ZN/WeRvHH7/5Zs/KN/zi9aeR7GvWftPyQVrjyNN7S2gUvsaKUz1UsBqPf8T+2sfqz2bzCfWen/zobW3jdpXSV2fNmPtq9S5d1+5Xbk08/7T/cWesfZcUnXee8/6A82hH5XSI6pz2rr2zkXrWmbuKcfucClh9VgOZq3jTyl9Ic5884GcPlB6YvV5eNm6vovnrH2E1H5fHfWxceClV4T6uKn89vFx4rlfyUGwqdi/cf+XLNvjvMS1k6AvQx/1S8f7HT9JfnvTeB0fdt8hvya5Xr/nPujH1q/Ae6ep7xX6qK9t6nbn/fdtbw+6p5/K6f7WflWDvTNV5n3leNRemOr7rDFyPDfKe0ZN6dk00L9K7fupvC9SHzscH+rn02H9nIJMnOhmKo1jzjAQdELkqL/UR6VGpAEAAAAA/mmTix+Grr+CJp/PDx0zjsSJKCZjHAVBJ0TOUYNFauT3zp07vtbRbrdlcXFRRES+2XGY63ffsGxqH3n6X6Clq39M5G/7WgoAAACACXt7/9/LO/tfOtxwSohWMt66nyNEDj9XYHpVKpXe9/1Xses3jsSJ45KMQdAJx5bfX65OpyM7OzvuBj+ylpDJN55+e2Hf3zrc2LevopL9c6MvKy8iYirZwGc1azrxzsGcZfvKmV3L9m/tvChnHz4tc5p5JDLzrYzlg0fWyT9wWimPU5z+5ozle01NWVdKogZSndXUZvX+SsnYQJq4kgYuB/b7105by98GyucU2nlrqaJjuZ16/1nldiVNXTtzpm9y620D5XL9Y4ftSzk2lfED1ifexbesxzKz8w3L9oFybtWy1YGSA6W87snCJcv2qW/eF60zurzu7DfVyzQ7lA+q51bhq4Rhxr600Kl8wm85nV9e1+d3/5PcX9RKqqJWhqTycu69Pk5BP2+c5p/074nf8UGe66A5nfuY8nryGw//+Vj3rx7/hy98XB5qp0be7nU+9XbtlLUsy3Kb8trXefBQHWDZVN+XjLtM1OmxU49NLUFTS9QGKC0pIlWCprw2Hyil8LHZU7JvPpZH8q2SQY+ZPa4/R0wr0/7zxSh3f+t35O5v/47v3R/s7fmeww1d13s9kUql0shso3EkTkQlGcMvgk6InHg8Hkg01ktzuGFisZgsLCyIiMg3d+6oN1o2TaW3zemLTz8Inzk/KyKjPxgDAAAAiJ5TMitn5Fv/MFR6ONn18hIRee7KfO/7drstnY5DgO6E6Dx8KAd374a9DNe6V7lLJBJSKBTGvr9xZCWR6QQo5ufnA/nFmJ+fdx5kY2FhQba3DxuCq/8FmpmzZv68/6detGyn/u6WMlu4VwwAAAAA4M0HTv2gfODUD4qIiHbOmq3eUZqmD2SpbT+9KMfi4uLxz3xyKXb2rMxcuuQ80MHB3p5j4M+vZrMp1WpV4vG41Ot127HjSJyISjKGXwSdEDlH/aVQfyHD/uUCAAAAgGPpiPGeSz/6I3LpR3/E9+6/+n//u2PPmLp+/bqIiNy6dcuxifc4EieikozhF0EnRM7y8nKvblbkMJjkJoCk1qouLS0FtibP9fFKr5ofO/cnLdux7/pOy/ZArxrD2iPqD3/Y+ofi7B1reu7zv2q9nOZ/8XvXLdvP/fq/tWz/8Lurlu2LX/iiZfv/In/dul7lirGxfeurzJkLMTmz/7Sfz+Wtu/Jo5rB/w+7/4aJl7N/6nY9Ztv/e71r/DD3/K0+P5fmf+3fy+MKzltvVS+2aSm8E7azSp8ihh9PAeKU3gflEOXiFNqv0OlD7Aqm9C9TL8zr0GRpI5XZgubyv0ndBPVbzvvV5pq5d7btgKrfP3lP6SSnHtt/+mnVxSt+K+sG6ZVvNKDx1z3rutX3l3L3/QLTO0zFfuPdZkfPne9v/xQdL1vUr/b86j72VvXrqq6Qca+zcWdu5xt3rxknQfYTU+WLKf4fV/i5+z4fd+sfdf0oV9PxB92eZZK+fwC9LH/D+gjbp9fafr0mfayeTfiyc9qe+PvidT739g+f/0tMN5X2K155GA70mfXJ6rJ3+RsYuWM9bR+l75ETtaWXu27+vmiT1PVxMfQ+ovmdU3qPFlPdVB/fuBbg6hG11dVWazabUajVJJpOO48eROHFckjHsO5sCIUilUpZtt1371UtBptPpwNYEAAAAADj+qtWqVKtVqVQqks1mXd1neXnZsu02Q8kucWIcc4aBoBMiR/3lcht06v8ljMfjjimQAAAAAIAjMEP+GpNGoyGrq6tSqVQkn8+7vt84EieOSzIGQSdETjKZtKQA3r59e/TgPpubm73v1cAVAAAAAACjNJtNyWQyUiqVPAWcRMaTOHFckjHo6YRIunr1qlSrVRERS38nO/3jisXiWNblltqrRqXWy2stpa+P0v/lO//gG9bblRrzX3/8K5btH/vuv2XZ3lf6C1z8360pl7/+4Jet9//eTyrrsfZfUfsMPTMzI2cOnvbH0d7eFk07rJP/7l//AevYr7xr2e7stMVOZ89aHz/QZ0jtfaD0YLL0OJLB+nvz4SOx/kD514lyrAP7f2Dti6TSnnnGOv6Rsj+1R9VAz6mj/2+g8771qiozDn0kOuq5ULZn4tb+XDOPrb0NTu0p979wwbKp9jpw6j0zc8/ar0ti9ufiwxc+Lg+1py9rp89Yf486j6zzOfWxGHfPE7t9qcbdl8jp/l57gqg+dPGvWrYPHH5vvLLbv9dzFXZfoaj31hnn70HYPZlUYffH8vt7Z2fcz4uwz51Xvv+mWt4rqO8jlPd4Tj2blB5QqnH3BFTHv3La2otzYH1qD8Pzyvse9X2Z0l9xkgb6VSnvEwZ6W6p9OhUD75sw1XRdl5WVFSkUClIoFDzfv5s40Q343L5921Vpnl3ixDjmDAOZToik1dWnja4bjYbj+P4xiUQi9BRCAAAAADiuNDPcryDpui6pVEry+byUSiXnO3zrPuVy2fKzq1ev9r4PKnFiHHNOGkEnRFIymbQEjjY2NmzH12pP/ysThV8sAAAAAEC0GYYhmUxGrl696jrgJCKSy+UGEh3GkThxHJIxKK9DZFUqlV6n/Zs3b45MJTQMo1eKl06nPdffhmHclzbe/wPlUvVKKvTBl75ie/+Dr+5Ytl1d3td8mi5t7u+L+a1U82fe2rUOvrtn3VYvLdyfdn5wINpppUTKIZVZO/BWQjZwbGra+Cnrn8kD5VL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|
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"text/plain": [
|
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"<Figure size 1200x900 with 2 Axes>"
|
|
]
|
|
},
|
|
"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_found,\n",
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" brem_x_found,\n",
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" density=False,\n",
|
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" bins=nbins,\n",
|
|
" 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",
|
|
"plt.ylabel(\"x [mm]\")\n",
|
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"# plt.title(r\"$e^\\pm$ lost brem vertices\")\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",
|
|
"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_found.pdf\",\n",
|
|
" format=\"PDF\",\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 91,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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|
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"text/plain": [
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"<Figure size 1200x900 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"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",
|
|
"a1 = plt.hist2d(\n",
|
|
" brem_z_lost,\n",
|
|
" brem_x_lost,\n",
|
|
" density=False,\n",
|
|
" bins=nbins,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax * stretch_factor,\n",
|
|
" # norm=matplotlib.colors.Normalize(vmin=1, vmax=vmax * stretch_factor),\n",
|
|
" range=[[-200, 9500], [-3200, 3200]],\n",
|
|
")\n",
|
|
"plt.vlines([770, 990, 2700, 7500], -3200, 3200, colors=\"red\", lw=1.5)\n",
|
|
"plt.ylim(-3200, 3200)\n",
|
|
"plt.xlim(-200, 9500)\n",
|
|
"plt.xlabel(\"z [mm]\")\n",
|
|
"plt.ylabel(\"x [mm]\")\n",
|
|
"\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_lost.pdf\",\n",
|
|
"# format=\"PDF\",\n",
|
|
"# )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 55,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"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",
|
|
" 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",
|
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"\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]\n",
|
|
" > 770) and (ntuple[jelec, \"brem_vtx_z\", jphoton] <= 2700):\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 +\n",
|
|
" tmp_richut_length +\n",
|
|
" tmp_velo_length)\n",
|
|
"\n",
|
|
" # if (tmp_velo == 0) and (tmp_richut == 0):\n",
|
|
" if (False # (tmp_velo >= 0.5 * ntuple[jelec, \"energy\"])\n",
|
|
" or ((tmp_velo == 0) and (tmp_richut == 0)) or\n",
|
|
" (ntuple[jelec, \"energy\"] - tmp_velo < 3000)):\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],\n",
|
|
" 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)\n",
|
|
" & (energy_emissions.quality == 1)][\"velo\"])\n",
|
|
"rich_found = ak.to_numpy(\n",
|
|
" energy_emissions[(~energy_emissions.lost)\n",
|
|
" & (energy_emissions.quality == 1)][\"rich\"])\n",
|
|
"energy_found = ak.to_numpy(\n",
|
|
" energy_emissions[(~energy_emissions.lost)\n",
|
|
" & (energy_emissions.quality == 1)][\"energy\"])\n",
|
|
"\n",
|
|
"velo_lost = ak.to_numpy(\n",
|
|
" energy_emissions[(energy_emissions.lost)\n",
|
|
" & (energy_emissions.quality == 1)][\"velo\"])\n",
|
|
"rich_lost = ak.to_numpy(\n",
|
|
" energy_emissions[(energy_emissions.lost)\n",
|
|
" & (energy_emissions.quality == 1)][\"rich\"])\n",
|
|
"energy_lost = ak.to_numpy(\n",
|
|
" energy_emissions[(energy_emissions.lost)\n",
|
|
" & (energy_emissions.quality == 1)][\"energy\"])\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",
|
|
"number_rich = ak.to_numpy(\n",
|
|
" energy_emissions[energy_emissions.quality == 1][\"rich_length\"])\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
|
|
}
|