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{ "cells": [ { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "import uproot\t\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits import mplot3d\n", "import awkward as ak\n", "from scipy.optimize import curve_fit\n", "import mplhep\n", "mplhep.style.use([\"LHCbTex2\"])\n", "plt.rcParams[\"savefig.dpi\"] = 600\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "40402 10099\n", "50501\n" ] } ], "source": [ "# file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n", "file = uproot.open(\n", " \"/work/cetin/Projektpraktikum/tracking_losses_ntuple_B_zmag.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n", ")\n", "\n", "# selektiere nur elektronen von B->K*ee\n", "allcolumns = file.arrays()\n", "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) &\n", " (allcolumns.fromB)] # B: 9056\n", "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) &\n", " (allcolumns.fromB)] # B: 1466\n", "\n", "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n", "\n", "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n", "print(ak.num(electrons, axis=0))\n", "# ak.count(found, axis=None)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre>{all_endvtx_types_length: 7,\n", " all_endvtx_types: [101, 101, 101, 101, 101, 101, 0],\n", " all_endvtx_x_length: 7,\n", " all_endvtx_x: [-6.97, -52.9, -52.9, ..., -1.71e+03, -2.14e+03, -3.57e+03],\n", " all_endvtx_y_length: 7,\n", " all_endvtx_y: [-0.89, -6.75, -6.75, -7.08, -66.1, -72.6, -39.5],\n", " all_endvtx_z_length: 7,\n", " all_endvtx_z: [112, 859, 859, 895, 8.7e+03, 9.68e+03, 1.26e+04],\n", " brem_photons_pe_length: 6,\n", " brem_photons_pe: [2.62e+03, 812, 2.54e+03, 1.86e+03, 3.12e+03, 241],\n", " brem_photons_px_length: 6,\n", " brem_photons_px: [-161, -49.7, -156, -114, -1.18e+03, -101],\n", " brem_photons_py_length: 6,\n", " brem_photons_py: [-18.9, -6.92, -21.6, -16.8, -20.9, -0.26],\n", " brem_photons_pz_length: 6,\n", " brem_photons_pz: [2.61e+03, 810, 2.54e+03, 1.86e+03, 2.89e+03, 219],\n", " brem_vtx_x_length: 6,\n", " brem_vtx_x: [-6.97, -52.9, -52.9, -55.2, -1.71e+03, -2.14e+03],\n", " brem_vtx_y_length: 6,\n", " ...}\n", "---------------------------------------------------------------------------\n", "type: {\n", " all_endvtx_types_length: int32,\n", " all_endvtx_types: var * float32,\n", " all_endvtx_x_length: int32,\n", " all_endvtx_x: var * float32,\n", " all_endvtx_y_length: int32,\n", " all_endvtx_y: var * float32,\n", " all_endvtx_z_length: int32,\n", " all_endvtx_z: var * float32,\n", " brem_photons_pe_length: int32,\n", " brem_photons_pe: var * float32,\n", " brem_photons_px_length: int32,\n", " brem_photons_px: var * float32,\n", " brem_photons_py_length: int32,\n", " brem_photons_py: var * float32,\n", " brem_photons_pz_length: int32,\n", " brem_photons_pz: var * float32,\n", " brem_vtx_x_length: int32,\n", " brem_vtx_x: var * float32,\n", " brem_vtx_y_length: int32,\n", " brem_vtx_y: var * float32,\n", " brem_vtx_z_length: int32,\n", " brem_vtx_z: var * float32,\n", " endvtx_type: int32,\n", " endvtx_x: float64,\n", " endvtx_y: float64,\n", " endvtx_z: float64,\n", " energy: float64,\n", " eta: float64,\n", " event_count: int32,\n", " fromB: bool,\n", " fromD: bool,\n", " fromDecay: bool,\n", " fromHadInt: bool,\n", " fromPV: bool,\n", " fromPairProd: bool,\n", " fromSignal: bool,\n", " fromStrange: bool,\n", " ideal_state_770_qop: float64,\n", " ideal_state_770_tx: float64,\n", " ideal_state_770_ty: float64,\n", " ideal_state_770_x: float64,\n", " ideal_state_770_y: float64,\n", " ideal_state_770_z: float64,\n", " ideal_state_9410_qop: float64,\n", " ideal_state_9410_tx: float64,\n", " ideal_state_9410_ty: float64,\n", " ideal_state_9410_x: float64,\n", " ideal_state_9410_y: float64,\n", " ideal_state_9410_z: float64,\n", " isElectron: bool,\n", " isKaon: bool,\n", " isMuon: bool,\n", " isPion: bool,\n", " isProton: bool,\n", " lost: bool,\n", " lost_in_track_fit: bool,\n", " match_chi2: float32,\n", " match_dSlope: float32,\n", " match_dSlopeY: float32,\n", " match_distX: float32,\n", " match_distY: float32,\n", " match_fraction: float32,\n", " match_teta2: float32,\n", " match_zmag: float32,\n", " mcp_idx: int32,\n", " mother_id: int32,\n", " mother_key: int32,\n", " originvtx_type: int32,\n", " originvtx_x: float64,\n", " originvtx_y: float64,\n", " originvtx_z: float64,\n", " p: float64,\n", " p_end_scifi: float64,\n", " p_end_ut: float64,\n", " p_end_velo: float64,\n", " phi: float64,\n", " pid: int32,\n", " pt: float64,\n", " px: float64,\n", " py: float64,\n", " pz: float64,\n", " rad_length_frac: float64,\n", " scifi_hit_pos_x_length: int32,\n", " scifi_hit_pos_x: var * float32,\n", " scifi_hit_pos_y_length: int32,\n", " scifi_hit_pos_y: var * float32,\n", " scifi_hit_pos_z_length: int32,\n", " scifi_hit_pos_z: var * float32,\n", " track_p: float64,\n", " track_pt: float64,\n", " tx: float64,\n", " ty: float64,\n", " ut_hit_pos_x_length: int32,\n", " ut_hit_pos_x: var * float32,\n", " ut_hit_pos_y_length: int32,\n", " ut_hit_pos_y: var * float32,\n", " ut_hit_pos_z_length: int32,\n", " ut_hit_pos_z: var * float32,\n", " velo_hit_pos_x_length: int32,\n", " velo_hit_pos_x: var * float32,\n", " velo_hit_pos_y_length: int32,\n", " velo_hit_pos_y: var * float32,\n", " velo_hit_pos_z_length: int32,\n", " velo_hit_pos_z: var * float32,\n", " velo_track_idx: int32,\n", " velo_track_tx: float64,\n", " velo_track_ty: float64,\n", " velo_track_x: float64,\n", " velo_track_y: float64,\n", " velo_track_z: float64\n", "}</pre>" ], "text/plain": [ "<Record {all_endvtx_types_length: 7, ...} type='{all_endvtx_types_length: i...'>" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "electrons[0]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre>{lost: True,\n", " rad_length_frac: 0.129,\n", " energy: 1.17e+04,\n", " photon_length: 6,\n", " brem_photons_pe: [2.62e+03, 812, 2.54e+03, 1.86e+03, 3.12e+03, 241],\n", " brem_vtx_x: [-6.97, -52.9, -52.9, -55.2, -1.71e+03, -2.14e+03],\n", " brem_vtx_z: [112, 859, 859, 895, 8.7e+03, 9.68e+03]}\n", "---------------------------------------------------------------------\n", "type: {\n", " lost: bool,\n", " rad_length_frac: float64,\n", " energy: float64,\n", " photon_length: int64,\n", " brem_photons_pe: var * float64,\n", " brem_vtx_x: var * float64,\n", " brem_vtx_z: var * float64\n", "}</pre>" ], "text/plain": [ "<Record {lost: True, rad_length_frac: ..., ...} type='{lost: bool, rad_leng...'>" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lost_e = electrons[\"lost\"]\n", "e = electrons[\"energy\"]\n", "brem_pe = electrons[\"brem_photons_pe\"]\n", "brem_z = electrons[\"brem_vtx_z\"]\n", "brem_x = electrons[\"brem_vtx_x\"]\n", "length = electrons[\"brem_vtx_z_length\"]\n", "rad_length = electrons[\"rad_length_frac\"]\n", "\n", "brem = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(electrons, axis=0)):\n", " brem.begin_record()\n", " brem.field(\"lost\").boolean(lost_e[itr])\n", " brem.field(\"rad_length_frac\").append(rad_length[itr])\n", " # [:,\"energy\"] energy\n", " brem.field(\"energy\").append(e[itr])\n", " # [:,\"photon_length\"] number of vertices\n", " brem.field(\"photon_length\").integer(length[itr])\n", " # [:,\"brem_photons_pe\",:] photon energy\n", " brem.field(\"brem_photons_pe\").append(brem_pe[itr])\n", " # [:,\"brem_vtx_z\",:] brem vtx z\n", " brem.field(\"brem_vtx_x\").append(brem_x[itr])\n", " brem.field(\"brem_vtx_z\").append(brem_z[itr])\n", " brem.end_record()\n", "\n", "brem = ak.Array(brem)\n", "brem[0]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "photon_cut = 0\n", "photon_cut_ratio = 0.1\n", "\n", "cut_brem = ak.ArrayBuilder()\n", "\n", "for itr in range(ak.num(brem, axis=0)):\n", " cut_brem.begin_record()\n", " cut_brem.field(\"event_id\").integer(itr)\n", " cut_brem.field(\"lost\").boolean(brem[itr, \"lost\"])\n", " cut_brem.field(\"rad_length_frac\").real(brem[itr, \"rad_length_frac\"])\n", " cut_brem.field(\"energy\").real(brem[itr, \"energy\"])\n", "\n", " ph_length = brem[itr, \"photon_length\"]\n", "\n", " tmp_energy = brem[itr, \"energy\"]\n", "\n", " cut_brem.field(\"brem_photons_pe\")\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", " ph_length -= 1\n", " continue\n", " else:\n", " cut_brem.real(brem[itr, \"brem_photons_pe\", jentry])\n", " tmp_energy -= brem[itr, \"brem_photons_pe\", jentry]\n", " cut_brem.end_list()\n", "\n", " tmp_energy = brem[itr, \"energy\"]\n", "\n", " cut_brem.field(\"brem_vtx_x\")\n", " cut_brem.begin_list()\n", " for jentry in range(brem[itr, \"photon_length\"]):\n", " if (brem[itr, \"brem_vtx_z\", jentry] > 9410\n", " or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n", " or brem[itr, \"brem_photons_pe\",\n", " jentry] < photon_cut_ratio * tmp_energy):\n", " continue\n", " else:\n", " cut_brem.real(brem[itr, \"brem_vtx_x\", jentry])\n", " tmp_energy -= brem[itr, \"brem_photons_pe\", jentry]\n", " cut_brem.end_list()\n", "\n", " tmp_energy = brem[itr, \"energy\"]\n", "\n", " cut_brem.field(\"brem_vtx_z\")\n", " cut_brem.begin_list()\n", " for jentry in range(brem[itr, \"photon_length\"]):\n", " if (brem[itr, \"brem_vtx_z\", jentry] > 9410\n", " or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n", " or brem[itr, \"brem_photons_pe\",\n", " jentry] < photon_cut_ratio * tmp_energy):\n", " continue\n", " else:\n", " cut_brem.real(brem[itr, \"brem_vtx_z\", jentry])\n", " tmp_energy -= brem[itr, \"brem_photons_pe\", jentry]\n", " cut_brem.end_list()\n", "\n", " cut_brem.field(\"photon_length\").integer(ph_length)\n", "\n", " cut_brem.end_record()\n", "\n", "ntuple = ak.Array(cut_brem)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "63793\n", "50501\n" ] }, { "data": { "text/html": [ "<pre>{event_id: 0,\n", " lost: True,\n", " rad_length_frac: 0.129,\n", " energy: 1.17e+04,\n", " brem_photons_pe: [2.62e+03, 2.54e+03, 1.86e+03, 3.12e+03],\n", " brem_vtx_x: [-6.97, -52.9, -55.2, -1.71e+03],\n", " brem_vtx_z: [112, 859, 895, 8.7e+03],\n", " photon_length: 4}\n", "-----------------------------------------------------------\n", "type: {\n", " event_id: int64,\n", " lost: bool,\n", " rad_length_frac: float64,\n", " energy: float64,\n", " 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: 0, lost: True, ...} type='{event_id: int64, lost: bool, ...'>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(ak.sum(ak.num(ntuple[\"brem_photons_pe\"], axis=1)))\n", "print(ak.num(ntuple, axis=0))\n", "ntuple[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 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", "text": [ "found: 44660 , lost: 19133\n", "0.4284146887595163\n" ] }, { "data": { "text/html": [ "<pre>[-3.61,\n", " -61.5,\n", " -33.8,\n", " -133,\n", " -799,\n", " 65.2,\n", " -5.73,\n", " -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": { "image/png": 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"text/plain": [ "<Figure size 2000x800 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vmax = 350\n", "nbins = 200\n", "\n", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n", "\n", "a0 = ax0.hist2d(\n", " brem_z_found,\n", " brem_x_found,\n", " density=False,\n", " bins=nbins,\n", " cmin=1,\n", " vmax=vmax,\n", " range=[[-200, 9500], [-3200, 3200]],\n", ")\n", "ax0.vlines([770, 990, 2700, 7500], -3200, 3200, colors=\"red\", lw=1.5)\n", "ax0.set_ylim(-3200, 3200)\n", "ax0.set_xlim(-200, 9500)\n", "ax0.set_xlabel(\"z [mm]\")\n", "ax0.set_ylabel(\"x [mm]\")\n", "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", " vmax=vmax * stretch_factor,\n", " range=[[-200, 9500], [-3200, 3200]],\n", ")\n", "ax1.vlines([770, 990, 2700, 7500], -3200, 3200, colors=\"red\", lw=1.5)\n", "ax1.set_ylim(-3200, 3200)\n", "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": { "image/png": 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"text/plain": [ "<Figure size 1200x900 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbins = 200\n", "vmax = 400\n", "\n", "a1 = plt.hist2d(\n", " brem_z_found,\n", " brem_x_found,\n", " density=False,\n", " bins=nbins,\n", " cmin=1,\n", " vmax=vmax,\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", "# plt.title(r\"$e^\\pm$ lost brem vertices\")\n", "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n", "\n", "# plt.suptitle(\"brem vtx of photons w/ $E>0.1E_0$\")\n", "plt.colorbar(a1[3])\n", "mplhep.lhcb.text(\"Simulation\", loc=0)\n", "# plt.show()\n", "plt.savefig(\n", " \"/work/cetin/Projektpraktikum/thesis/brem_vtx_hist2d_found.pdf\",\n", " format=\"PDF\",\n", ")" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1200x900 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib\n", "\n", "nbins = 200\n", "vmax = 400\n", "\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": [ "<Figure size 1200x900 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbins = 200\n", "vmax = 400\n", "\n", "a1 = plt.hist2d(\n", " brem_z,\n", " brem_x,\n", " density=False,\n", " bins=nbins,\n", " cmin=1,\n", " vmax=vmax,\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", "# plt.title(r\"$e^\\pm$ lost brem vertices\")\n", "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n", "\n", "# plt.suptitle(\"brem vtx of photons w/ $E>0.1E_0$\")\n", "plt.colorbar(a1[3])\n", "mplhep.lhcb.text(\"Simulation\", loc=0)\n", "# plt.show()\n", "plt.savefig(\n", " \"/work/cetin/Projektpraktikum/thesis/brem_vtx_hist2d.pdf\",\n", " format=\"PDF\",\n", ")" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "50501\n" ] } ], "source": [ "energy_emissions = ak.ArrayBuilder()\n", "\n", "for jelec in range(ak.num(ntuple, axis=0)):\n", " energy_emissions.begin_record()\n", " energy_emissions.field(\"lost\").boolean(ntuple[jelec, \"lost\"])\n", " energy_emissions.field(\"energy\").real(ntuple[jelec, \"energy\"])\n", "\n", " tmp_velo = 0\n", " tmp_richut = 0\n", " tmp_neither = 0\n", " tmp_velo_length = 0\n", " tmp_richut_length = 0\n", " tmp_neither_length = 0\n", "\n", " for jphoton in range(ak.num(ntuple[jelec][\"brem_photons_pe\"], axis=0)):\n", " if ntuple[jelec, \"brem_vtx_z\", jphoton] <= 770:\n", " tmp_velo += ntuple[jelec, \"brem_photons_pe\", jphoton]\n", " tmp_velo_length += 1\n", " elif (ntuple[jelec, \"brem_vtx_z\", jphoton]\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}
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