diff --git a/notebooks/clic/farouk_paper_plots.ipynb b/notebooks/clic/farouk_paper_plots.ipynb deleted file mode 100644 index f5f92fc1d..000000000 --- a/notebooks/clic/farouk_paper_plots.ipynb +++ /dev/null @@ -1,1541 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "fea2d043", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os, glob\n", - "import time\n", - "from pathlib import Path\n", - "\n", - "import awkward\n", - "import awkward as ak\n", - "import mplhep\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "import torch\n", - "import tqdm\n", - "import vector\n", - "import boost_histogram as bh\n", - "\n", - "import itertools\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import mplhep\n", - "\n", - "mplhep.set_style(mplhep.styles.CMS)\n", - "\n", - "sys.path.append(\"../mlpf/plotting/\")\n", - "\n", - "import plot_utils\n", - "from plot_utils import pid_to_text, load_eval_data, compute_jet_ratio\n", - "\n", - "from plot_utils import CLASS_LABELS_CLIC, CLASS_NAMES_CLIC" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c722929b", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 1120, - "id": "57d07f10", - "metadata": {}, - "outputs": [], - "source": [ - "def med_iqr(arr):\n", - " p25 = np.percentile(arr, 25)\n", - " p50 = np.percentile(arr, 50)\n", - " p75 = np.percentile(arr, 75)\n", - " return p50, p75 - p25\n", - "\n", - "\n", - "def flatten(arr):\n", - " return arr.reshape(-1, arr.shape[-1])\n", - "\n", - "\n", - "def binom_error(n_sig, n_tot):\n", - " \"\"\"\n", - " for an efficiency = nSig/nTrueSig or purity = nSig / (nSig + nBckgrd), this function calculates the\n", - " standard deviation according to http://arxiv.org/abs/physics/0701199 .\n", - " \"\"\"\n", - " variance = np.where(\n", - " n_tot > 0, (n_sig + 1) * (n_sig + 2) / ((n_tot + 2) * (n_tot + 3)) - (n_sig + 1) ** 2 / ((n_tot + 2) ** 2), 0\n", - " )\n", - " return np.sqrt(variance)\n", - "\n", - "\n", - "def plot_eff_and_fake_rate(icls=1, ivar=4, ielem=1, bins=np.linspace(-3, 6, 100), \n", - " log=True,\n", - " physics_process_lab=\"\",\n", - " textx=0.01,\n", - " texty=0.87,\n", - " ):\n", - "\n", - " xlabel = elemlabel[ielem][ivar]\n", - " \n", - " values = X[:, :, ivar]\n", - "\n", - " hist_X = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_gen = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_gen_pred = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_gen_cand = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_pred = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_cand = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_pred_fake = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_cand_fake = bh.Histogram(bh.axis.Variable(bins))\n", - "\n", - " eff_mlpf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", - " eff_pf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", - " fake_pf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", - " fake_mlpf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", - "\n", - " if ielem == 45: # ECAL and HCAL in CMS\n", - " msk_X = (X[:, :, 0] == 4) | (X[:, :, 0] == 5)\n", - " else:\n", - " msk_X = X[:, :, 0] == ielem\n", - "\n", - " msk_gen = yvals[\"gen_cls_id\"] == icls\n", - " msk_nogen = yvals[\"gen_cls_id\"] != icls\n", - " \n", - " msk_pred = yvals[\"pred_cls_id\"] == icls\n", - " msk_nopred = yvals[\"pred_cls_id\"] != icls\n", - "\n", - " msk_cand = yvals[\"cand_cls_id\"] == icls\n", - " msk_nocand = yvals[\"cand_cls_id\"] != icls\n", - "\n", - " hist_X.fill(awkward.flatten(values[msk_X]))\n", - " hist_gen.fill(awkward.flatten(values[msk_gen & msk_X]))\n", - " hist_pred.fill(awkward.flatten(values[msk_pred & msk_X]))\n", - " hist_cand.fill(awkward.flatten(values[msk_cand & msk_X]))\n", - "\n", - " # Genparticle exists, reco particle exists\n", - " hist_gen_pred.fill(awkward.flatten(values[msk_gen & msk_pred & msk_X]))\n", - " hist_gen_cand.fill(awkward.flatten(values[msk_gen & msk_cand & msk_X])) \n", - "\n", - " # Genparticle does not exist, reco particle exists\n", - " hist_pred_fake.fill(awkward.flatten(values[msk_nogen & msk_pred & msk_X]))\n", - " hist_cand_fake.fill(awkward.flatten(values[msk_nogen & msk_cand & msk_X]))\n", - "\n", - " eff_mlpf.values()[:] = hist_gen_pred.values() / hist_gen.values()\n", - " eff_mlpf.variances()[:] = binom_error(hist_gen_pred.values(), hist_gen.values()) ** 2\n", - "\n", - " eff_pf.values()[:] = hist_gen_cand.values() / hist_gen.values()\n", - " eff_pf.variances()[:] = binom_error(hist_gen_cand.values(), hist_gen.values()) ** 2\n", - "\n", - " fake_pf.values()[:] = hist_cand_fake.values() / hist_cand.values()\n", - " fake_pf.variances()[:] = binom_error(hist_cand_fake.values(), hist_cand.values()) ** 2\n", - "\n", - " fake_mlpf.values()[:] = hist_pred_fake.values() / hist_pred.values()\n", - " fake_mlpf.variances()[:] = binom_error(hist_pred_fake.values(), hist_pred.values()) ** 2\n", - "\n", - "# plt.figure()\n", - "# ax = plt.axes()\n", - "# mplhep.histplot(hist_X, label=\"all PFElements\", color=\"black\")\n", - "# mplhep.histplot(hist_cand, label=\"with PF\")\n", - "# mplhep.histplot(hist_pred, label=\"with MLPF reco\")\n", - "# mplhep.histplot(hist_gen, label=\"with MLPF truth\")\n", - "# plt.ylabel(\"Number of PFElements / bin\")\n", - "# plt.xlabel(xlabel)\n", - "# plt.yscale(\"log\")\n", - "\n", - "# text = physics_process_lab + \" , \" + CLASS_NAMES_CLIC[icls]\n", - "# plt.text(textx, texty+0.05, text, ha=\"left\", transform=ax.transAxes)\n", - "\n", - "# if log:\n", - "# plt.xscale(\"log\")\n", - "# plt.legend(loc=(0.6, 0.65))\n", - "# plt.ylim(10, 20 * np.max(hist_X.values()))\n", - "# plt.xlim(min(bins), max(bins))\n", - "# plt.savefig(f\"{outpath}/distr_icls{icls}_ivar{ivar}.pdf\", bbox_inches=\"tight\")\n", - "\n", - " plt.figure()\n", - " ax = plt.axes()\n", - " mplhep.histplot(eff_pf, label=\"PF\")\n", - " mplhep.histplot(eff_mlpf, label=\"MLPF\")\n", - " plt.ylim(0, 1.5)\n", - " plt.ylabel(\"Efficiency\")\n", - " plt.xlabel(xlabel)\n", - "\n", - " text = physics_process_lab + \" , \" + CLASS_NAMES_CLIC[icls]\n", - " plt.text(textx, texty, text, ha=\"left\", transform=ax.transAxes)\n", - " \n", - " if log:\n", - " plt.xscale(\"log\")\n", - " plt.legend(loc=(0.75, 0.7))\n", - " plt.xlim(min(bins), max(bins))\n", - " plt.savefig(f\"{outpath}/eff_icls{icls}_ivar{ivar}.pdf\", bbox_inches=\"tight\")\n", - "\n", - " plt.figure()\n", - " ax = plt.axes(sharex=ax)\n", - " mplhep.histplot(fake_pf, label=\"PF\")\n", - " mplhep.histplot(fake_mlpf, label=\"MLPF\")\n", - " plt.ylim(0, 1.5)\n", - " plt.ylabel(\"Fake rate\")\n", - " plt.xlabel(xlabel)\n", - " \n", - " text = physics_process_lab + \" , \" + CLASS_NAMES_CLIC[icls]\n", - " plt.text(textx, texty, text, ha=\"left\", transform=ax.transAxes)\n", - " \n", - " if log:\n", - " plt.xscale(\"log\")\n", - " plt.legend(loc=(0.75, 0.7))\n", - " plt.xlim(min(bins), max(bins))\n", - " plt.savefig(f\"{outpath}/fake_icls{icls}_ivar{ivar}.pdf\", bbox_inches=\"tight\")\n", - "\n", - " # mplhep.histplot(fake, bins=hist_gen[1], label=\"fake rate\", color=\"red\")\n", - "\n", - "\n", - "# plt.legend(frameon=False)\n", - "# plt.ylim(0,1.4)\n", - "# plt.xlabel(xlabel)\n", - "# plt.ylabel(\"Fraction of particles / bin\")" - ] - }, - { - "cell_type": "code", - "execution_count": 1007, - "id": "fa4e5878", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[34mclic_edm_qq_pf\u001b[m\u001b[m \u001b[34mclic_edm_single_pi_pf\u001b[m\u001b[m \u001b[34mclic_edm_ww_fullhad_pf\u001b[m\u001b[m\r\n", - "\u001b[34mclic_edm_single_gamma_pf\u001b[m\u001b[m \u001b[34mclic_edm_ttbar_pf\u001b[m\u001b[m \u001b[34mclic_edm_zh_tautau_pf\u001b[m\u001b[m\r\n", - "\u001b[34mclic_edm_single_kaon0l_pf\u001b[m\u001b[m \u001b[34mclic_edm_ttbar_pu10_pf\u001b[m\u001b[m\r\n" - ] - } - ], - "source": [ - "! ls evaluation/epoch_96" - ] - }, - { - "cell_type": "code", - "execution_count": 1008, - "id": "78454ad3", - "metadata": {}, - "outputs": [], - "source": [ - "sample = \"clic_edm_ttbar_pf\"\n", - "\n", - "PATH = \"evaluation/epoch_96\"\n", - "\n", - "pred_path = Path(f\"{PATH}/{sample}/test/\")\n", - "path = str(pred_path / \"*.parquet\")\n", - "\n", - "outpath = f\"paper_plots/{sample}\" # plots path\n", - "os.system(f\"mkdir -p {outpath}\");" - ] - }, - { - "cell_type": "code", - "execution_count": 1009, - "id": "5d39b09c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 4004\r\n" - ] - } - ], - "source": [ - "! ls evaluation/epoch_96/clic_edm_ttbar_pf/test/*.parquet | wc -l" - ] - }, - { - "cell_type": "markdown", - "id": "32f13854", - "metadata": {}, - "source": [ - "# Load the predictions" - ] - }, - { - "cell_type": "code", - "execution_count": 1278, - "id": "af2cdee4", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "path evaluation/epoch_96/clic_edm_ttbar_pf/test/*.parquet\n", - "['evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3708.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2202.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch175.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1168.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3670.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch994.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1989.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1010.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch165.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1178.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3718.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2212.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1000.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3660.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch984.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1999.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2457.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch720.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3025.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch658.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1645.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch730.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2447.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch648.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1655.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3035.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch446.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2731.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1523.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2649.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3343.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2721.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch456.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2659.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3353.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1533.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch213.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2164.parquet', 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'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3561.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1301.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1269.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch274.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2103.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3409.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1311.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3571.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2746.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch431.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3334.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1554.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch549.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch421.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2756.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1544.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch559.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3324.parquet']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:13<00:00, 15.13it/s]\n" - ] - } - ], - "source": [ - "yvals, X, _ = load_eval_data(path, max_files=200)" - ] - }, - { - "cell_type": "markdown", - "id": "fc9b9268", - "metadata": {}, - "source": [ - "# Make plots" - ] - }, - { - "cell_type": "code", - "execution_count": 1112, - "id": "33c5a969", - "metadata": {}, - "outputs": [], - "source": [ - "physics_process = {\n", - " \"clic_edm_ttbar_pf\": r\"$e^+e^- \\rightarrow \\mathrm{t}\\overline{\\mathrm{t}}$\",\n", - " \"clic_edm_ttbar_pu10_pf\": r\"$e^+e^- \\rightarrow \\mathrm{t}\\overline{\\mathrm{t}}$, PU10\",\n", - " \"clic_edm_qq_pf\": r\"$e^+e^- \\rightarrow \\gamma/\\mathrm{Z}^* \\rightarrow \\mathrm{hadrons}$\",\n", - " \"clic_edm_ww_fullhad_pf\": r\"$e^+e^- \\rightarrow WW \\rightarrow \\mathrm{hadrons}$\",\n", - " \"clic_edm_zh_tautau_pf\": r\"$e^+e^- \\rightarrow ZH \\rightarrow \\tau \\tau$\", \n", - "}\n", - "\n", - "elemlabel = {\n", - " 1: [\"N/A\", r\"track $p_T$\", r\"$\\eta$\", \"sin_phi\", \"cos_phi\", \"Total p\"],\n", - " 2: [\"N/A\", r\"$E_T$\", r\"$\\eta$\", \"sin_phi\", \"cos_phi\", \"cluster E [GeV]\"],\n", - "}\n", - "\n", - "# just a reminder\n", - "CLASS_NAMES_CLIC = [\n", - " r\"none\",\n", - " r\"charged hadrons\",\n", - " r\"neutral hadrons\",\n", - " r\"$\\gamma$\",\n", - " r\"$e^\\pm$\",\n", - " r\"$\\mu^\\pm$\",\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 1272, - "id": "04868971", - "metadata": {}, - "outputs": [], - "source": [ - "def med_iqr(arr):\n", - " p25 = np.percentile(arr, 25)\n", - " p50 = np.percentile(arr, 50)\n", - " p75 = np.percentile(arr, 75)\n", - " return p50, p75 - p25\n", - "\n", - "\n", - "def flatten(arr):\n", - " return arr.reshape(-1, arr.shape[-1])\n", - "\n", - "\n", - "def binom_error(n_sig, n_tot):\n", - " \"\"\"\n", - " for an efficiency = nSig/nTrueSig or purity = nSig / (nSig + nBckgrd), this function calculates the\n", - " standard deviation according to http://arxiv.org/abs/physics/0701199 .\n", - " \"\"\"\n", - " variance = np.where(\n", - " n_tot > 0, (n_sig + 1) * (n_sig + 2) / ((n_tot + 2) * (n_tot + 3)) - (n_sig + 1) ** 2 / ((n_tot + 2) ** 2), 0\n", - " )\n", - " return np.sqrt(variance)\n", - "\n", - "\n", - "def plot_eff_isolation(icls=1, ivar=4, ielem=1, bins=np.linspace(-3, 6, 100), \n", - " log=True,\n", - " physics_process_lab=\"\",\n", - " textx=0.01,\n", - " texty=0.87,\n", - " apply_isolation=\"Isolated\",\n", - " ):\n", - "\n", - " xlabel = elemlabel[ielem][ivar]\n", - " \n", - " values = X[:, :, ivar]\n", - "\n", - " hist_X = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_gen = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_gen_pred = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_gen_cand = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_pred = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_cand = bh.Histogram(bh.axis.Variable(bins))\n", - "\n", - " eff_mlpf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", - " eff_pf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", - "\n", - " xlabel = elemlabel[ielem][ivar]\n", - " \n", - " values = X[:, :, ivar]\n", - "\n", - " hist_X = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_gen = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_gen_pred = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_gen_cand = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_pred = bh.Histogram(bh.axis.Variable(bins))\n", - " hist_cand = bh.Histogram(bh.axis.Variable(bins))\n", - "\n", - " eff_mlpf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", - " eff_pf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", - "\n", - " msk_X = (X[:, :, 0] == ielem) \n", - " msk_gen = (yvals[\"gen_cls_id\"] == icls) \n", - " msk_pred = (yvals[\"pred_cls_id\"] == icls)\n", - " msk_cand = (yvals[\"cand_cls_id\"] == icls)\n", - "\n", - " if apply_isolation == \"Isolated\":\n", - " msk_iso = get_isolation(msk_gen)<0.15\n", - " else:\n", - " msk_iso = get_isolation(msk_gen)>0.15\n", - " \n", - " hist_X.fill(awkward.flatten(values[msk_X & msk_gen][msk_iso])) \n", - " hist_gen.fill(awkward.flatten(values[msk_X & msk_gen][msk_iso]))\n", - " \n", - " if apply_isolation == \"Isolated\":\n", - " msk_iso = get_isolation(msk_gen & msk_pred)<0.15\n", - " else:\n", - " msk_iso = get_isolation(msk_gen & msk_pred)>0.15\n", - " \n", - " # Genparticle exists, reco particle exists\n", - " hist_gen_pred.fill(awkward.flatten(values[msk_X & msk_gen & msk_pred][msk_iso]))\n", - " \n", - " if apply_isolation == \"Isolated\":\n", - " msk_iso = get_isolation(msk_gen & msk_cand)<0.15\n", - " else:\n", - " msk_iso = get_isolation(msk_gen & msk_cand)>0.15 \n", - " hist_gen_cand.fill(awkward.flatten(values[msk_X & msk_gen & msk_cand][msk_iso]))\n", - " \n", - " eff_mlpf.values()[:] = hist_gen_pred.values() / hist_gen.values()\n", - " eff_mlpf.variances()[:] = binom_error(hist_gen_pred.values(), hist_gen.values()) ** 2\n", - "\n", - " eff_pf.values()[:] = hist_gen_cand.values() / hist_gen.values()\n", - " eff_pf.variances()[:] = binom_error(hist_gen_cand.values(), hist_gen.values()) ** 2\n", - " \n", - " plt.figure()\n", - " ax = plt.axes()\n", - " mplhep.histplot(eff_pf, label=\"PF\")\n", - " mplhep.histplot(eff_mlpf, label=\"MLPF\")\n", - " plt.ylim(0, 1.5)\n", - " plt.ylabel(\"Efficiency\")\n", - " plt.xlabel(xlabel)\n", - "\n", - " text = physics_process_lab + \" , \" + CLASS_NAMES_CLIC[icls]\n", - " if apply_isolation:\n", - " text = physics_process_lab + \" , \" + apply_isolation + \" \" + CLASS_NAMES_CLIC[icls]\n", - " \n", - " plt.text(textx, texty, text, ha=\"left\", transform=ax.transAxes)\n", - " \n", - " if log:\n", - " plt.xscale(\"log\")\n", - " plt.legend(loc=(0.75, 0.7))\n", - " plt.xlim(min(bins), max(bins))\n", - " plt.savefig(f\"{outpath}/{apply_isolation}_eff_icls{icls}_ivar{ivar}.pdf\", bbox_inches=\"tight\")\n", - "\n", - " # mplhep.histplot(fake, bins=hist_gen[1], label=\"fake rate\", color=\"red\")\n", - "\n", - "\n", - "# plt.legend(frameon=False)\n", - "# plt.ylim(0,1.4)\n", - "# plt.xlabel(xlabel)\n", - "# plt.ylabel(\"Fraction of particles / bin\")" - ] - }, - { - "cell_type": "code", - "execution_count": 1279, - "id": "e949677b", - "metadata": {}, - "outputs": [], - "source": [ - "# import awkward as ak\n", - "\n", - "# # Example: Two leptons in the first event, first one not isolated, second one is isolated\n", - "# # No lepton in second event, but still some other particles\n", - "# # Third event has a good lepton with a stray hadron/photon/... around it\n", - "# leptons = ak.Array([[10., 20.], [], [30.]])\n", - "# others = ak.Array([[1., 2., 3.], [4., 5.], [1.5]])\n", - "# dR = ak.Array([[[0.25, 0.3, 0.35], [0.6, 0.7, 0.8]],\n", - "# [],\n", - "# [[0.2]]])\n", - "\n", - "# cartesian_product = ak.cartesian({\"leptons\": leptons, \"others\": others}, nested=True) # Create a nested Cartesian product of leptons and others\n", - "# dR_mask = ak.any(dR < 0.3, axis=-1) # Use ak.any to construct the dR mask\n", - "# events_extended = ak.mask(cartesian_product, dR_mask) # Apply the mask to the Cartesian product\n", - "# isolation_sum = ak.fill_none(ak.sum(events_extended.others, axis=-1), 0) # Fill None values with 0 after the sum (if no other particle nearby)\n", - "# rel_iso = isolation_sum / leptons\n", - "# print(rel_iso) # [[0.6, 0], [], [0.1]]\n", - "\n", - "def get_isolation(msk=yvals[\"gen_cls_id\"]==4):\n", - " \n", - " import numpy as np\n", - " import awkward as ak\n", - " from coffea.nanoevents.methods import vector \n", - " \n", - " typ = \"gen\"\n", - "\n", - " gen_array = {}\n", - " gen_array[\"cls\"] = yvals[f\"{typ}_cls\"]\n", - " gen_array[\"charge\"] = yvals[f\"{typ}_charge\"]\n", - " gen_array[\"pt\"] = yvals[f\"{typ}_pt\"]\n", - " gen_array[\"eta\"] = yvals[f\"{typ}_eta\"]\n", - " gen_array[\"sin_phi\"] = yvals[f\"{typ}_sin_phi\"]\n", - " gen_array[\"cos_phi\"] = yvals[f\"{typ}_cos_phi\"]\n", - " # gen_array[\"phi\"] = np.arctan2(yvals[f\"{typ}_sin_phi\"], yvals[f\"{typ}_cos_phi\"])\n", - " gen_array[\"cls_id\"] = yvals[f\"{typ}_cls_id\"]\n", - "\n", - " gen_array[\"px\"] = gen_array[\"pt\"] * gen_array[\"cos_phi\"]\n", - " gen_array[\"py\"] = gen_array[\"pt\"] * gen_array[\"sin_phi\"]\n", - " gen_array[\"pz\"] = gen_array[\"pt\"] * np.sinh(gen_array[\"eta\"])\n", - "\n", - " gen_array = ak.zip(gen_array, depth_limit=1)\n", - "\n", - " # define particle selections\n", - " indices_chhadrons = gen_array.cls_id == 1\n", - " indices_nhadrons = gen_array.cls_id == 2\n", - " indices_photons = gen_array.cls_id == 3\n", - " \n", - " particle_in_question = ak.zip(\n", - " {\n", - " \"x\": gen_array.px[msk],\n", - " \"y\": gen_array.py[msk],\n", - " \"z\": gen_array.pz[msk],\n", - " },\n", - " with_name=\"LorentzVector\",\n", - " behavior=vector.behavior,\n", - " )\n", - " others = ak.zip(\n", - " {\n", - " \"x\": gen_array.px[indices_chhadrons | indices_nhadrons | indices_photons],\n", - " \"y\": gen_array.py[indices_chhadrons | indices_nhadrons | indices_photons],\n", - " \"z\": gen_array.pz[indices_chhadrons | indices_nhadrons | indices_photons],\n", - " },\n", - " with_name=\"LorentzVector\",\n", - " behavior=vector.behavior,\n", - " )\n", - "\n", - " mval = particle_in_question.metric_table(others)\n", - " \n", - "# mval.type # 5000 * NLeptons * NHadrons\n", - "# others.type # 5000 * NHadrons \n", - "# particle_in_question.type # 5000 * NLeptons\n", - "\n", - " dR_threshold = 0.3\n", - "\n", - " isolation = [] # initialize\n", - " counts = []\n", - "\n", - " for iev in range(len(particle_in_question)):\n", - " counts.append(0)\n", - " for iprtkl, prtkl in enumerate(particle_in_question[iev]):\n", - " counts[iev] += 1 \n", - "\n", - " mask = (mval" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "var = 1\n", - "log_ = True\n", - "\n", - "if log_:\n", - " bins = np.logspace(0, 2, 41)\n", - "else:\n", - " bins = np.linspace(1, 100, 41)\n", - "\n", - "plot_eff_isolation(icls=5, ivar=var, ielem=ielem, bins=bins,\n", - " log=log_, \n", - " physics_process_lab=physics_process[sample],\n", - " textx=0.05,\n", - " texty=0.87,\n", - " apply_isolation=\"Isolated\",\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 1283, - "id": "d48a5844", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/d7/p4v84gls67vdp9_q2rkkvkch0000gn/T/ipykernel_88631/3821530621.py:86: RuntimeWarning: invalid value encountered in divide\n", - " eff_mlpf.values()[:] = hist_gen_pred.values() / hist_gen.values()\n", - "/var/folders/d7/p4v84gls67vdp9_q2rkkvkch0000gn/T/ipykernel_88631/3821530621.py:89: RuntimeWarning: invalid value encountered in divide\n", - " eff_pf.values()[:] = hist_gen_cand.values() / hist_gen.values()\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "var = 1\n", - "log_ = True\n", - "\n", - "if log_:\n", - " bins = np.logspace(0, 2, 41)\n", - "else:\n", - " bins = np.linspace(1, 100, 41)\n", - "\n", - "plot_eff_isolation(icls=5, ivar=var, ielem=ielem, bins=bins,\n", - " log=log_, \n", - " physics_process_lab=physics_process[sample],\n", - " textx=0.05,\n", - " texty=0.87,\n", - " apply_isolation=\"Non-isolated\",\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 1232, - "id": "d2746096", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "var = 1\n", - "log_ = True\n", - "\n", - "if log_:\n", - " bins = np.logspace(0, 2, 41)\n", - "else:\n", - " bins = np.linspace(1, 100, 41)\n", - "\n", - "plot_eff_and_fake_rate(icls=4, ivar=var, ielem=ielem, bins=bins,\n", - " log=log_, \n", - " physics_process_lab=physics_process[sample],\n", - " textx=0.05,\n", - " texty=0.87,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efea299b", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "4c5ef298", - "metadata": {}, - "source": [ - "## c.hadrons" - ] - }, - { - "cell_type": "code", - "execution_count": 1070, - "id": "6c2e60f3", - "metadata": {}, - "outputs": [], - "source": [ - "pid = 4\n", - "ielem = 1 # must plot tracks" - ] - }, - { - "cell_type": "code", - "execution_count": 1071, - "id": "8dd5bc0a", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "ename": "ValueError", - "evalue": "in ListArray64 attempting to get 1, index out of range\n\n(https://github.com/scikit-hep/awkward-1.0/blob/1.10.3/src/cpu-kernels/awkward_ListArray_getitem_jagged_apply.cpp#L43)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1071], line 9\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 7\u001b[0m bins \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m100\u001b[39m, \u001b[38;5;241m41\u001b[39m)\n\u001b[0;32m----> 9\u001b[0m \u001b[43mplot_eff_and_fake_rate\u001b[49m\u001b[43m(\u001b[49m\u001b[43micls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mivar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mielem\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mielem\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mphysics_process_lab\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mphysics_process\u001b[49m\u001b[43m[\u001b[49m\u001b[43msample\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mtextx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.05\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mtexty\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.87\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[43mapply_isolation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43misolated\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[1064], line 76\u001b[0m, in \u001b[0;36mplot_eff_and_fake_rate\u001b[0;34m(icls, ivar, ielem, bins, log, physics_process_lab, textx, texty, apply_isolation)\u001b[0m\n\u001b[1;32m 73\u001b[0m hist_cand\u001b[38;5;241m.\u001b[39mfill(awkward\u001b[38;5;241m.\u001b[39mflatten(values[msk_cand \u001b[38;5;241m&\u001b[39m msk_X]))\n\u001b[1;32m 75\u001b[0m \u001b[38;5;66;03m# Genparticle exists, reco particle exists\u001b[39;00m\n\u001b[0;32m---> 76\u001b[0m hist_gen_pred\u001b[38;5;241m.\u001b[39mfill(awkward\u001b[38;5;241m.\u001b[39mflatten(\u001b[43mvalues\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmsk_gen\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m&\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmsk_pred\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m&\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmsk_X\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmsk_iso\u001b[49m\u001b[43m]\u001b[49m))\n\u001b[1;32m 77\u001b[0m hist_gen_cand\u001b[38;5;241m.\u001b[39mfill(awkward\u001b[38;5;241m.\u001b[39mflatten(values[msk_gen \u001b[38;5;241m&\u001b[39m msk_cand \u001b[38;5;241m&\u001b[39m msk_X][msk_iso])) \n\u001b[1;32m 79\u001b[0m \u001b[38;5;66;03m# Genparticle does not exist, reco particle exists\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/awkward/highlevel.py:991\u001b[0m, in \u001b[0;36mArray.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m 579\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 580\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m 581\u001b[0m \u001b[38;5;124;03m where (many types supported; see below): Index of positions to\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 988\u001b[0m \u001b[38;5;124;03mhave the same dimension as the array being indexed.\u001b[39;00m\n\u001b[1;32m 989\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 990\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_tracers\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 991\u001b[0m tmp \u001b[38;5;241m=\u001b[39m ak\u001b[38;5;241m.\u001b[39m_util\u001b[38;5;241m.\u001b[39mwrap(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayout\u001b[49m\u001b[43m[\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m]\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_behavior)\n\u001b[1;32m 992\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 993\u001b[0m tmp \u001b[38;5;241m=\u001b[39m ak\u001b[38;5;241m.\u001b[39m_connect\u001b[38;5;241m.\u001b[39m_jax\u001b[38;5;241m.\u001b[39mjax_utils\u001b[38;5;241m.\u001b[39m_jaxtracers_getitem(\u001b[38;5;28mself\u001b[39m, where)\n", - "\u001b[0;31mValueError\u001b[0m: in ListArray64 attempting to get 1, index out of range\n\n(https://github.com/scikit-hep/awkward-1.0/blob/1.10.3/src/cpu-kernels/awkward_ListArray_getitem_jagged_apply.cpp#L43)" - ] - } - ], - "source": [ - "var = 1\n", - "log_ = True\n", - "\n", - "if log_:\n", - " bins = np.logspace(0, 2, 41)\n", - "else:\n", - " bins = np.linspace(1, 100, 41)\n", - "\n", - "plot_eff_and_fake_rate(icls=pid, ivar=var, ielem=ielem, bins=bins,\n", - " log=log_, \n", - " physics_process_lab=physics_process[sample],\n", - " textx=0.05,\n", - " texty=0.87,\n", - " apply_isolation=\"isolated\",\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0de656c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a41ae86c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19deaadd", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c8e7ed0c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 1074, - "id": "17ed6b3d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1074, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "msk_gen" - ] - }, - { - "cell_type": "code", - "execution_count": 1075, - "id": "7fac3d5e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1075, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "msk_pred" - ] - }, - { - "cell_type": "code", - "execution_count": 1076, - "id": "f13f4abb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1076, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "msk_X" - ] - }, - { - "cell_type": "code", - "execution_count": 1078, - "id": "6af8eb7d", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "in ListArray64 attempting to get 1, index out of range\n\n(https://github.com/scikit-hep/awkward-1.0/blob/1.10.3/src/cpu-kernels/awkward_ListArray_getitem_jagged_apply.cpp#L43)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1078], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mvalues\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmsk_gen\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m&\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmsk_pred\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmsk_iso\u001b[49m\u001b[43m]\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/awkward/highlevel.py:991\u001b[0m, in \u001b[0;36mArray.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m 579\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 580\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m 581\u001b[0m \u001b[38;5;124;03m where (many types supported; see below): Index of positions to\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 988\u001b[0m \u001b[38;5;124;03mhave the same dimension as the array being indexed.\u001b[39;00m\n\u001b[1;32m 989\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 990\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_tracers\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 991\u001b[0m tmp \u001b[38;5;241m=\u001b[39m ak\u001b[38;5;241m.\u001b[39m_util\u001b[38;5;241m.\u001b[39mwrap(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayout\u001b[49m\u001b[43m[\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m]\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_behavior)\n\u001b[1;32m 992\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 993\u001b[0m tmp \u001b[38;5;241m=\u001b[39m ak\u001b[38;5;241m.\u001b[39m_connect\u001b[38;5;241m.\u001b[39m_jax\u001b[38;5;241m.\u001b[39mjax_utils\u001b[38;5;241m.\u001b[39m_jaxtracers_getitem(\u001b[38;5;28mself\u001b[39m, where)\n", - "\u001b[0;31mValueError\u001b[0m: in ListArray64 attempting to get 1, index out of range\n\n(https://github.com/scikit-hep/awkward-1.0/blob/1.10.3/src/cpu-kernels/awkward_ListArray_getitem_jagged_apply.cpp#L43)" - ] - } - ], - "source": [ - "values[msk_gen & msk_pred][msk_iso]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "49c1345f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "642c4b57", - "metadata": {}, - "source": [ - "## n.hadrons" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c2b4a504", - "metadata": {}, - "outputs": [], - "source": [ - "pid = 2\n", - "ielem = 2 # must plot clusters" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "9d77eb6b", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "var = 5\n", - "log_ = True\n", - "\n", - "if log_:\n", - " bins = np.logspace(0, 2, 41)\n", - "else:\n", - " bins = np.linspace(1, 100, 41)\n", - "\n", - "plot_eff_and_fake_rate(icls=pid, ivar=var, ielem=ielem, bins=bins,\n", - " log=True, \n", - " physics_process_lab=physics_process[sample],\n", - " textx=0.05,\n", - " texty=0.87,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "3264d7b1", - "metadata": {}, - "source": [ - "## Gamma" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "5e04bae8", - "metadata": {}, - "outputs": [], - "source": [ - "pid = 3\n", - "ielem = 2 # must plot clusters" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "0e630e22", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Yck8iAAAAACA8EBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABg2PacRAAAgEBwuVw1as/C7QDgH0YSAQAAAAAGI4kAACCkMTIIAMHFSCIAAAAAwCAkAgAAAAAMQiIAIKyMHz9eLpdLixcvdroUAABqJe5JBAAAjiu7gin3IAKAswISEr/99lstWrRIX375pXbt2qXDhw/r8OHD2rlzpySpuLhY77zzjoYMGaL69esHogQAABCGCJMAEHi2hsRffvlFf/nLXzR79uxyr1uWVeEf9euuu04NGzbUVVddpUmTJqlJkyZ2llLruN1upaene9yXmZmpzMzMIFcEAAAAwG5ZWVnKysryuM/tdge5Gs9sC4lr167V2WefrYMHD3r9l70jR47o5Zdf1ty5c7Vw4UJ17NjRrnJqnebNm2vNmjVOlwEAAAAggKoaAEpJSVFOTk6QK6rIloVr9u7dq0GDBunAgQNm1PC6667TCy+8oJYtW1Y8aUSEzj//fBMmd+3apUGDBqmgoMCOcgAAdcTixYvlcrnKfUyYMEGS1L9//wr7tm3b5mzBAADUAraMJP773//W9u3b5XK5lJiYqJkzZ6pv376SpMcee6xCe5fLpf/+979atmyZBg8erH379mn79u16+umnNWrUKDtKAgCEMLfbrSVLlignJ0cnTpzQKaecorS0NKWnp5e7PSFU2Fnv4sWL1b9/f6/b5+bmqnHjxjWsGAAA3/kdEo8eParnn3/efP7cc8+ZgFid3//+95o3b5569OghSXr00Ud11113KTY21t+yAAAhaPXq1fr73/+uTz/91OOtCb/73e80cuRIXXPNNV6Fr379+lXoZ/z48ZowYYI+//xz9evXL6TqlaS4uDilpaV5XUNERPg+rar0e/VbZb+WW7duVWpqahCr8t27776rK664QpLUsWNHrVmzxuufixEjRpjfpzIzM/XMM88ErE4AqI7fV57vv/9e+/fvl8vlUq9evXTJJZfU6Pjf/e53ysjIkCQdPHhQ69ev97ckAEAImj59urp376758+dXeu/6999/r+uuu0433HCD47cgBKrenj17at26dV5/xMfH2/m2bPP+++/r0UcfDcq5Sp99+dvAFWpTiS+++GI1a9ZMkrRu3TotWbLEq+MOHz6sGTNmmM9vu+22gNQHAN7yeyRxy5YtZrt3794+9dGrVy/NmTNHUsk/ql26dPG3LABACHn33Xc1fPhw83njxo2VkZGh0047TfXq1dOqVas0f/5886ikN998U3l5eZo1a5Yj009rW73B9swzz+iuu+6Sy+VSz549NXDgQJ/6SUhIULt27SRJmzdvNq+XviZJUVG155HOMTExGj58uP75z39Kkp5//nmvZle99dZbysvLk1TyO1Hnzp0DWicAVMfvf3nLrr7Tvn17n/oo+/iLrVu3+lsSACCEuN3uciMj1157rf79738rMTGxXLvDhw/rwQcf1JQpUyRJH374oT788EMNGTIkmOXWunqdMGTIEI0bN0779+/X9ddfr1WrVpkRtJq4++67dffdd0sqP8V006ZN5drVpjD55z//2YTE999/X3v27Knws1OWZVnlbtthFBFAKPB7umnZgLd9+3af+tiwYYPZDtVpNQAA30ybNk25ubmSpIEDB+q1117z+EvzSSedpH//+9+6+eabzWv3339/0OosVdvqdUJycrKmT58uSfr111914403qri4OGDnu/vuu7Vp06YK4bH0tU2bNiklJSVg56+JU045Reedd54kqbCwUK+++mqV7b/99lutXLlSUsmIdek9jQDgJL9DYtnRwxUrVvjUxzfffGO2a8vN6QCA6lmWpZdeesl8/uyzzyoyMrLKY5566imzgNm6devMlE5vjR8/XpZl+bRojRP11laDBw82z/maN2+e/v3vfztcUegoOxr4wgsvVBmgy44i3nDDDapfv35AawMAb/gdEv/whz+oUaNGsixLn332mb7++usaHf/OO+/oyy+/lFQyl9/blVEBAKFv06ZNJjS1a9fOq9sSGjRooDPOOMN8/tVXXwWsvt+qbfU67V//+pe5f27MmDH67rvvHK4oNGRkZKhFixaSSn6mPv/8c4/tDh48qHfeecd8fuuttwalPgCojt8T+KOjozVixAjzPMSbbrpJc+bM0amnnlrtsR9//LFuv/12SSX3Ilx77bWKi4vztyQAQIgoO8Nk586dXt+7/uuvv5rtXbt22V5XZWpbvU6rX7++3n77bfXo0UPHjh3TVVddpe+//77O3zoSHR2tm2++WY888oikkseDDRgwoEK7N954Q/n5+ZJK/uh+2mmnBbVOAKiMLXd5P/DAA3r33Xe1detWbdq0ST169NA999yj888/X0VFRabdvn37tHHjRm3cuFGvv/66Fi1aZPadfPLJeuihh+woBwAQIvbs2WO2jx49Wm7REW8dPnzYzpKqVNvq9VZhYWGNZ/rUxFVXXaVXX31Vmzdv1u23364333yzTqzyWpVbbrlFjz76qCzL0ocffqhff/3VjC5KLFgDILTZEhIbNmyozz77TH379lV2draOHDmihx9+WA8//LBpY1mWx5XPLMtSw4YN9fHHH6tly5Z2lAMACBEHDx70u49Dhw7ZUIl3alu93tq/f7/69+8flHO99dZbOvfcczVs2LCgnC9Upaam6oILLtB///tfnThxQtOnT9eYMWPM/v/973/66aefJJWs3nrZZZc5VSoAVOD3PYml2rZtq1WrVumaa66RVBL+Sh8+XPqg29LXyn788Y9/1MqVK/X73//erlIAACGi7C0EF154ocfrQHUfpbczUG/t0aBBA6dLCAlVLWDz3HPPme0bb7xR9erVC2ptAFAVWx8qdPLJJ+vNN9/UhAkTNH36dC1evFgrVqxQQUGBaeNyuXTaaafp7LPP1g033KBevXrZWQIAIIQkJCSY7d8+viAU1bZ6vdWkSRN9++23Aev/ueee08svvyypZPGVyy+/PGDnqk0uuugiJSUlaefOndq2bZs+/fRTXXDBBdq/f7/+85//mHYsWAMg1ATkybPt2rUrN9U0Ly9PBw8eVFxcnBo1aqSICNsGMAEAIax05UtJ2rp1q06cOOHVQ89PnDhhtiMjI4N2f1ttq9db0dHR6tGjR0D6Xrlypd544w1JUnp6Oo/CKCMqKkq33HKLJk6cKKnkcRcXXHCBXn/9dfMH9L59+6pjx45OlgkAFQQlrTVs2FDJyck6+eSTqwyI27ZtC0Y5AIAg6dKli5l6eOLECb399tvVHrNjxw7FxcUpOjpaJ598so4ePRroMo3aVq/T8vLydNVVV6mwsFD16tXTzJkzWaX8N2655Rbzu8+cOXOUnZ1dbsGaESNGOFUaAFTKlpBYWFjo1/EnTpzQpEmTWPoZAMJMdHS0uVddKnmW3pEjR6o85pFHHtHx48clSZdffnlQQ0dtq9dpd999tzZs2CBJmjJlik4//XRb+q3q4fO1TatWrXTRRRdJkoqKijR8+HCtW7dOktS0aVMNGTLEyfIAwCNbQuIll1zic1D86quv1L17d/3jH//QsWPH7CgHABBC7rnnHkVHR0uScnJydO6553p8tERRUZEeffTRcqMsN910U9DqLFXb6nXK22+/renTp0uSLr30Ulvvq/P20SN2hMlu3bqZBfYCNU247AI2n332mdm+6aabFBsbG5BzAoA/XFbpEqR+iIiI0IUXXqgPPvhAMTExXh2Tm5ur0aNH65VXXpFUshqqy+Uq91zFuiAlJUU5OTlKTk5Wdna20+UAQEA8/vjjuvfee83nDRo00EUXXaQuXbqoadOm2r59u9577z1t3LjRtBk+fLhZDCXYalu9wbZ161Z169ZNhw4dUuvWrfXDDz/o5JNP9qvPJk2aaP/+/ZJKFhDq0qWLDh8+rA8//FApKSmmXdkgt2HDBnXo0MGv83br1k2rVq0yn9vwa1EFRUVFatu2rXbs2FHu9Y0bN6p9+/a2nw9A7RUy2cCygcvlsiIiIqxBgwZZBQUF1bZ//fXXrWbNmlkRERGWy+UyHzfddJMd5dQqycnJliQrOTnZ6VIAIGCKi4ut++67z5Lk1cfVV1/t1fWEep3x/PPPW5KsyMhI66uvvrKlzz//+c8ev7Zbt24t1y4hIcHsS0hIsPr162edccYZ1o4dO3w6b9euXcudL1AmTpxY7jwDBgwI2LkA1F6hkg1smW5a+tfDefPmaciQIZVOPd2wYYPOOeccDRs2THv27DGvp6Wl6fPPPzejigCA8OJyuTRp0iTNnz9fZ5xxRqXtfve732nhwoV66623vJ6ZEgi1rd5gu/XWW/Xpp59qypQp6t27ty19TpkyRffff7/atWun2NhYNWvWTN27d6/w/MBLL73UbO/fv988bqvsCrOh6Oabb1ZkZKT5vOwUVAAINbZMN/3hhx80cOBA5ebmSpIuuOCCclNPCwoK9Oijj+qxxx7T8ePHzVSO2NhYPfDAAxo9enSduriWFTJDygAQRFu3btVXX30lt9utiIgIdezYUR07dlSbNm1C8jFJta3ecJafn69HHnlEM2fOVHZ2tho1aqTk5GR98sknatGihc/93njjjXrrrbfMIkSB0KZNG/3yyy+SpC1btqht27YBOxeA2ilUsoEtIVGSVq1apYEDB5r7CUqD4tKlS3X77bdr8+bN5r5Dy7I0cOBATZs2rc7PxQ+VHwQAAOqyoUOH6ocfftCWLVsCdo7U1FRt375dUskfHlJTUwN2LgC1U6hkA9v+/Nm1a1ctWLBACQkJkkqmnnbs2FHnnXeeNm3aZNolJiZqxowZ+vTTT+t8QAQAAKFhy5YtSk9Pd7oMAAgJUXZ21rVrVy1cuFADBgzQ/v37tW3btnL7R4wYoUcffVSNGze287Rhwe12V3pxyszMVGZmZpArAgAg/OXn5+u9997TqlWr9MQTTzhdDoA6ICsrS1lZWR73ud3uIFfjmW3TTcv68ccfNXDgQO3bt0+WZSkhIUGffPKJevbsafepar1QGVIGAKAu6tatm3755RdNnjw54M+5ZLopgOqESjYIyN32Xbp00cKFC9WkSRNJJc9EnD9/fiBOBQAA4LPPPvtM+/btC3hABIDaJGBLsnXu3Fmff/65EhMTZVmWxo8fr0ceeSRQpwMAAKixxMREuVwup8sAgJDi1T2Jr7/+us8nuOGGGzR58mRZlqUHH3xQ+/fvV9euXatsDwAAAABwhlf3JEZERNjyV7bSR2BUWozLFfIPw7VbqMw7BgAAAOCsUMkGXq9uatf6NgFYJwcAAAAAYBOvQuKNN94Y6DoAAAAAACHAq5A4ffr0QNcBAAAAAAgBAVvdFAAAAABQ+3h9T2Igvfzyyzpy5Ig6dOigCy+80OlyAAAAAKDOcjwkFhUV6W9/+5sOHz6sHj16EBIBAAAAwEEBCYlut1sFBQXVtjt+/LjeeOMNHTp0SJK0du3aQJQDAAAAAPCSbSFxy5YtGjNmjJYsWaLdu3fX6NjSZye2a9fOrnIAAAAAAD6wJSRmZ2erd+/e2rNnj8/PQYyOjtbjjz9uRzkAAAAAAB/Zsrrp5MmTy40eRkREqHXr1oqNjZVUMlKYkJCgNm3aqE2bNoqJiTGvu1wuXXDBBfrqq6907rnn2lEOAAAAAMBHfofEvLw8TZ8+3UwZve2223TgwAFt27ZN+/fvV//+/SVJ1113nbZu3aqtW7dq//79eu+999S4cWNJ0rZt25SSkuJvKQAAAAAAP/kdEletWmUWnklNTdWzzz6rhg0bSpLq16+vMWPGyLIsffLJJ+aYuLg4DR06VIsWLVL9+vW1fv16XXTRRTpx4oS/5QAAAAAA/OB3SMzJyTHbl112WYX9Z555piRp8+bNOnbsWLl9Xbt21fDhw2VZln744Qc99thj/pYDAAAAAPCDrSGxdevWFfY3atRISUlJkqRNmzZV2D9ixAhJkmVZmjp1KqOJAAAAAOAgv0Ni2dVMo6OjPbbp0KGDJGn9+vUV9p166qmKjIyUy+XS3r179fnnn/tbEgAAAADAR36HxFatWpntsqOKZZWGxJUrV1bYFxUVpRYtWpiwuXHjRn9LAgAAAAD4yNaQOHfuXI9tOnToIMuy9N1333ncf/DgQbM66m/vWwQAAAAABI/fITE9Pd08D3HlypV66qmnKrTp3r27JGnRokXatWtXuX0//fST8vLyzEhi+/bt/S0JAAAAAOAjv0NifHy8LrvsMhPyRo4cqbPOOkuzZs0ybfr06aMGDRqoqKhIt956qxktPHLkiO65555y/aWlpflbEgAAAADARy6r7MozPsrJydEZZ5yhPXv2yLIsuVwuZWZmaurUqabNX//6V02dOlUul0txcXFq166dNm7cWG566cCBAzV//nx/y6lVUlJSlJOTo6ioKHPv5m9lZmYqMzMzyJUBAAAAsFtWVpaysrI87tu4caNOnDih5ORkZWdnB7my/2dLSJRKVi4dPny4/ve//0mS7rzzznIh8cCBA/rd736nbdu2lZzY5TKB0rIs1a9fX19//bW6du1qRzm1RmlIdPoHAQAAAICzQiUb+D3dtFRaWpq++uor/fTTT3rllVc0ZMiQcvsbN26sL7/8Uueee66k/390hmVZ6tq1q7777rs6FxABAAAAINRE2d1henq60tPTPe5LSkrS/PnztXnzZq1Zs0YRERHq1q2bkpOT7S4DAAAAAOAD20OiN9q1a6d27do5cWoAAAAAQBVsm24KAAAAAKj9vAqJCQkJSkhIUI8ePQJdDwAAAADAQV5NNz1w4IAk6dChQ151+uOPP0qSYmJi1LFjR98qAwAAAAAEndf3JLpcLq877datm1wul9q3b6/169f7VBgAAAAAIPgCdk+iZVmy6RGMAAAAAIAgCVhIrMnIIwAAAAAgNLC6KQAAAADAICQCAAAAAAxCIgAAAOBBfuEJpd43V6n3zVV+4QmnywGChpAIAAAAADAIiQAAAAAAg5AIAAAAADDqVEjMysqSy+XS+PHjbenv4MGDmjx5soYOHarTTz9dcXFx6tChg4YMGaInn3xSx44ds+U8AAAAABAsdSokvvnmm7b19d133yk9PV2jRo3SBx98oJ9//llHjx7Vpk2b9OGHH+pvf/ubOnfurAULFth2TgAAAMBXixcvlsvlqvajVatWGjhwoP76179qzZo1fvXl6WPKlCnBfeOosToTEqdPn65ly5bZ0pfb7daf/vQn7dy5U5LUp08fPfHEE3r33Xf12GOP6fe//70kadOmTRo0aJCWLl1qy3kBAACAQMvOztbChQv11FNPqXPnznr88cedLglBFlWTxm63W8OHDw9Ye5fLpZdffrkmJVXp4MGD+vHHHzV9+nRbRxGffPJJud1uSdLf//53/fOf/1RExP/n7VGjRunxxx/XmDFjdPz4cd1222366aefyrUBAAAAnHLttdfquuuuq/B6fn6+1q9fr1mzZum7775TcXGx7r33XiUnJ+vaa6+tUV+V6dixo891IzhqFBLz8vL02muvVdvO5XLVqL0kWZZla0js2bOnvv32W1v6+q133nlHktSyZUtNmDChQviLiIjQvffeq8WLF2v+/Plau3atli1bpt69ewekHgAAAKAm2rdvrwsuuKDS/aNHj9bYsWM1adIkSdKYMWN0xRVXKDo6usZ9ofap0dCWZVkB+7Db7t27be9TKhkd/eWXXyRJGRkZql+/vsd2LpdLQ4YMMZ9///33AakHAAAAsFtkZKQmTpyozp07S5J27NihTZs2OVwVgsWrkcQ+ffqY0cHaYv369eXC5/bt220Z2i6dZipJbdq0qbJty5YtzfbRo0f9PjcAAAAQLFFRUTr//PO1evVqSdLatWvVqVMnh6tCMHgVEhcvXhzgMuwXGxtb5ee+atGihaZPny5J6tWrV5Vty053PfXUU205PwAAABAsqampZnvz5s3OFYKgqtE9iZCaNWumYcOGVdsuJydHWVlZkqS4uDidffbZVba3LEuHDh3yua7Y2FjbgjAAAAAgSdu2bTPbKSkpzhVSSxQUFKigoMDn4wNxG54vCIkBsHnzZmVkZCg3N1eSlJmZqaZNm1Z5zM6dO9WoUSOfzzlu3DiNHz/e5+MBAABqK8uydPR4UZVt8gtP1Ljfo4X/3+e+vALlx9S8j7iY6n/drh8dGZK3dp04cULz5883n5fen4jKTZo0SRMmTHC6DL8REm1UUFCgp59+WuPGjVN+fr4kqV+/fpo4cWK1xyYlJWnt2rU+n5tRRAAAUFcdPV6k9AfnV9/QD398fHHA+l4z8XyvwmQwFRcXa/z48eZ+xI4dO1a6vsemTZs0b948r/oN91VQx4wZo5EjR/p8fKdOncyz2J0UWj+NtZRlWXrnnXd0//33lxuSz8jI0Jtvvql69epV24fL5VJ8fHwAqwQAAABKVBbsjh49qg0bNmjWrFlavny5ef35559XVJTn6DBjxgzNmDHDq/OGynTKQPH3FrBQGVEmJPppy5YtuvXWW7Vw4ULzWtOmTTV58mRdf/31IfONBgAACFf1oyO1ZuL5Vbbxdbpp6Qji0tH9VD8mssZ9eDvdNNi8DXZxcXH697//rT59+gShKoQKQqKPLMvStGnTNHr0aDO1NC4uTiNHjtTf//53RgUBAACCxOVyVRvGfJnOWTZYNmkYG3JTQgOlXr166ty5s7p3766///3vat++fZXtWRsj/NSNn/QAeOyxxzRmzBjz+TXXXKMnnnii3LMRAQAAgFBEsENVCIk+eOONN0xAbNCggd566y0NHjzY4aoAAAAAwH+ExBoqKirSgw8+KEmKjo7WwoULddZZZzlcFQAAAADYI8LpAmqbTz/91Kxges899xAQAQAAAIQVRhI9GDZsmF577TVJFedrL1myxGw3bdrU62fCdO7cWcnJybbWCQAAAAB2IyTWkNvtNtujR4/2+rjp06dr2LBhAagIAAAAAOzDdNMaKhsSAQAAACDc1JmRxNTUVFmW5VXbV199Va+++qrHfXPnzrWxKgAAAAAILYwkAgAAAACMOjOSCAAAANRl/fr183pmXTD7QuhhJBEAAAAAYBASAQAAAAAG000BAAAAD+JiorTtnxc5XQYQdIwkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAAAAwIhyugCUcLvdSk9P97gvMzNTmZmZQa4IAAAAgN2ysrKUlZXlcZ/b7Q5yNZ65LMuynC6iLktJSVFOTo6Sk5OVnZ3tdDkAAAAAHBIq2YDppgAAAAAAg5AIAAAAADAIiQAAAIAnhUek8Y1KPgqPOF0NEDSERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAACDM9evXTy6Xy3zMmzevRsevW7eu3PGpqanl9g8bNszs27Ztm081jh8/vtw5PH1ERUXp1FNP1eDBg/Xwww9rz549fvVX2ccPP/zg03sIF4REAAAAoI6ZOXNmjdq/++67AaqkZoqKirRx40bNmTNHY8eOVVpamj777DOnywo7UU4XAAAAACC4PvjgAz333HOKjY31qn2wQ+Jjjz2mLl26VHh9//79WrNmjV555RXt2rVLubm5GjJkiFasWKG0tLQa91eZU045xae6wwUhEQAAAKgjYmJiVFhYqIMHD+rTTz9VRkZGtcesW7dOq1evliTFxsaqoKAg0GWqZ8+e6tevX6X7R48ercsuu0yfffaZjhw5ogkTJuitt97yuT+Ux3RTAAAAoI5o2bKlunbtKsn7Kaelo4hNmjRRjx49AlZbTcTHx+ull14yI6ELFy50uKLwQkgEAAAA6pArr7xSkvTRRx/p6NGj1bb/z3/+I0m69NJLFRUVOhMRW7durc6dO0uSdu/erf379ztcUfggJAIAAAB1yOWXXy5JysvL03//+98q265bt04//fSTJOmKK64IeG01VXaV1c2bNztXSJghJAIAAAB1SPv27XX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GIREAAAAAYEQ5XQBKuN1upaene9yXmZmpzMzMIFcEAAAAwG5ZWVnKysryuM/tdge5Gs9clmVZThdRl6WkpCgnJ0fJycnKzs52uhwAAAAADgmVbMB0UwAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAEeV0ASjhdruVnp7ucV9mZqYyMzODXBEAAAAAu2VlZSkrK8vjPrfbHeRqPHNZlmU5XURdlpKSopycHCUnJys7O9vpcgAAAAA4JFSyAdNNAQAAAAAGIREAAAAAYBASAQAAAAAGIREAAAAAYBASAQAAAAAGIREAAAAAYBASAQAAAAAGIREAAAAAYBASAQAAAAAGIREAAAAAYBASAQAAAAAGIREAAAAAYBASAQAAAAAGIREAAAAAYBASAQAAAAAGIREAAAAAYBASAQAAAAAGIREAAAAAYBASAQAAAAAGIREAAAAAYBASAQAAAAAGIREAAAAAYBASAQAAAAAGIREAAAAAYEQ5XQBKuN1upaene9yXmZmpzMzMIFcEAAAAwG5ZWVnKysryuM/tdge5Gs9clmVZThdRl6WkpCgnJ0fJycnKzs52uhwAAAAADgmVbMB0UwAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABiERAAAAACAQUgEAAAAABhRThcQDLm5udqyZYsOHz6spKQktW/fXhER5GMAAAAA+K2wTkobNmxQRkaGEhMT1aNHD/Xv319paWlKTU3V5MmTVVRUZPs5LcvS0KFD5XK5NGzYMNv7BwAAAIBACtuQuHTpUnXv3l0ff/xxhTC4Y8cOjRo1SkOHDrU9KD733HP64IMPbO0TAAAAAIIlLEPi3r17NWTIEOXn5ysiIkITJ07Ujh07lJeXp0WLFql79+6SpNmzZ2vixIm2nfenn37SyJEjbesPAAAAAIItLEPi448/rn379kmSpk6dqrFjxyolJUUNGjRQ//79tXjxYqWmpkqSJk+erD179vh9zvz8fF111VU6duyY330BAAAAgFPCLiQWFRXplVdekSQ1a9ZMI0aMqNAmPj5eo0aNkiQdOXJEM2fO9Pu8I0eO1M8//6yWLVv63RcAAAAAOCXsQuKyZcvMKGJGRoYiIyM9ths8eLDZnjt3rl/nfP/99/X888/L5XLpjTfe8KsvAAAAAHBS2IXE9evXm+1BgwZV2q5Vq1bq0qWLJOn777/3+Xzbt2/XLbfcIkkaPXq0BgwY4HNfAAAAAOC0sAuJu3btMttt2rSpsm2rVq0kSbt379aBAwdqfK4TJ07o2muv1YEDB3TmmWfauggOAAAAADghyukC7Pbrr7+a7YSEhCrbNmnSxGzv2rVLjRs3rtG5Jk6cqK+++koNGzbUW2+9pZiYmBodX5ZlWTp06JDPx8fGxio2Ntbn4wEAAAD4p6CgQAUFBT4fb1mWjdX4LuxCYtmRxLIh0JOy+48cOVKj8yxevFgPP/ywJGnatGlq3759jY7/rZ07d6pRo0Y+Hz9u3DiNHz/erxoAAAAA+G7SpEmaMGGC02X4LexCYtnRuPr161fZtuzI29GjR70+x759+3TdddfJsixde+21uv7662te6G8kJSVp7dq1Ph/PKCIAAADgrDFjxvj13PROnTpp586dNlbkm7ALiYmJiWb7wIED5T7/rbL3IVYXKEtZlqXhw4crJydHbdu21bRp03yutSyXy6X4+Hhb+gIAAAAQfP7eAuZyuWysxndht3BN2ecU7t+/v8q2Zfc3bNjQq/6zsrI0e/ZsRUZG6u233ybYAQAAAAgrYTeS2KJFC7NdXUjMzc0128nJyV71P2rUKEklj9fIzc3VvHnzKm2bk5Nj9p900kk6++yzvToHAAAAADgl7EJi2ZHEVatWqVevXh7bFRcXa/Xq1ZKk1q1b66STTvKq/9LViubMmaM5c+ZU2XbBggVasGCBJKlr16764YcfvDoHAAAAADgl7Kab9ujRw2xXFeJWrFhhHpfRu3fvgNcFAAAAALVB2IXEtLQ0paWlSZIWLlxYbkppWbNmzTLbQ4YM8bp/y7Kq/Sh14403mtcYRQQAAABQG4RdSJRklp0tKCjQXXfdpeLi4nL7V65cqSlTpkiS2rZtq0suuSTIFQIAAABAaArLkHjTTTepZ8+ekqQZM2aof//+mj59ut5//33de++96tOnj44dOyaXy6WnnnpKMTEx5Y4fNmyYXC6XXC4XD6gHAAAAUKeE3cI1khQdHa2PPvpIgwYN0sqVK7VkyRItWbKkQpupU6cqIyPDoSoBAAAAIPSE5UiiVPIojGXLlunpp59Wr169lJCQoJiYGKWmpuqWW27RihUrNGLECKfLBAAAAICQ4rLKrrSCoEtJSVFOTo6Sk5OVnZ3tdDkAAAAAHBIq2SBsRxIBAAAAADVHSAQAAAAAGIREAAAAAIBBSAQAAAAAGIREAAAAAIBBSAQAAAAAGIREAAAAAIBBSAQAAAAAGIREAAAAAIBBSAQAAAAAGIREAAAAAIBBSAQAAAAAGFFOF4ASbrdb6enpHvdlZmYqMzMzyBUBAAAAsFtWVpaysrI87nO73UGuxjOXZVmW00XUZSkpKcrJyVFycrKys7OdLgcAAACAQ0IlGzDdFAAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAEaU0wWghNvtVnp6usd9mZmZyszMDHJFAAAAAOyWlZWlrKwsj/vcbneQq/HMZVmW5XQRdVlKSopycnKUnJys7Oxsp8sBAAAA4JBQyQZMNwUAAAAAGIREAAAAAIBBSAQAAAAAGIREAAAAAAig/MITSr1vrlLvm6v8whNOl1MtQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAACMKKcLQAm326309HSP+zIzM5WZmRnkigAAAADY7fnnX9CLzz7jcZ/b7Q5yNZ4REkNE8+bNtWbNGqfLAAAAABBAt912q+656w6P+1JSUpSTkxPkiipiuikAAAAAwCAkAgAAAAAMQiIAAAAAwOCeRAAAAADwgWVZOnq8qNp2+YUnPG5X6M+WqvxHSAQAAAAAHxw9XqT0B+fX6JgeDy+sdJ/74DF/S7IF000BAAAAAAYjiQAAAADgp+/+MVBxMZEe9+UXnjAjiN/9Y4DiYirGsPzCIrXICmiJXiMkAgAAAICf4mIiPYa/iu2ivGrnJKabAgAAAAAMQiIAAAAAwCAkAgAAAKj18gtPKPW+uUq9b26Vj5lA9QiJAAAAAACDkAgAAAAAMEJ7WR2b5ObmasuWLTp8+LCSkpLUvn17RUSQjwEAAADgt8I6KW3YsEEZGRlKTExUjx491L9/f6WlpSk1NVWTJ09WUVGRX/0fPHhQkydP1tChQ3X66acrLi5OHTp00JAhQ/Tkk0/q2LFjNr0TAAAAAAiOsB1JXLp0qS644ALl5+dX2Ldjxw6NGjVKS5Ys0axZsxQZ6fmhl1X57rvvdPHFF2vnzp3lXt+0aZM2bdqkDz/8UM8++6yeffZZDRw40Of3AQAAAADBFJYjiXv37tWQIUOUn5+viIgITZw4UTt27FBeXp4WLVqk7t27S5Jmz56tiRMn1rh/t9utP/3pTyYg9unTR0888YTeffddPfbYY/r9738vqSQwDho0SEuXLrXvzQEAAABAAIVlSHz88ce1b98+SdLUqVM1duxYpaSkqEGDBurfv78WL16s1NRUSdLkyZO1Z8+eGvX/5JNPyu12S5L+/ve/6/PPP9ff/vY3XXbZZRo9erS++uorTZo0SZJ0/Phx3XbbbSouLrbvDQIAAABAgIRdSCwqKtIrr7wiSWrWrJlGjBhRoU18fLxGjRolSTpy5IhmzpxZo3O88847kqSWLVtqwoQJFRbBiYiI0L333qvzzz9fkrR27VotW7asxu8FAAAAAIIt7ELismXLzChiRkZGpfcbDh482GzPnTvX6/7dbrd++eUX03/9+vU9tnO5XBoyZIj5/Pvvv/f6HAAAAADglLBbuGb9+vVme9CgQZW2a9Wqlbp06aIff/yxRgGudJqpJLVp06bKti1btjTbR48e9focAAAAAEpYlqWjx6t/KkF+4QmP21WpHx0pl8vlc23hKuxC4q5du8x2dSGuVatW+vHHH7V7924dOHBAjRs3rrb/Fi1aaPr06ZKkXr16Vdn222+/NdunnnpqtX0DAAAAKO/o8SKlPzi/Rsf0eHihV+3WTDxfcTFhF4n8FnZfkV9//dVsJyQkVNm2SZMmZnvXrl1ehcRmzZpp2LBh1bbLyclRVlaWJCkuLk5nn312le0ty9KhQ4eq7bcysbGxio2N9fl4AAAAAP4pKChQQUFBhdfzC/9/JPTQocM6EVPxlriS0U8rkOV5LexCYtmRxLIh0JOy+48cOWJbDZs3b1ZGRoZyc3MlSZmZmWratGmVx+zcuVONGjXy+Zzjxo3T+PHjfT4eAAAACHXf/WOg4jwELKkkZJWOIH73jwGVjhDmFxapx8MLAlLfpEmTNGHChAqvu6Jj1Xrk+5KkpKSWso5XDJKhJOxCYtnRuMoWlSlVduTNjnsGCwoK9PTTT2vcuHHKz8+XJPXr18+rZzEmJSVp7dq1Pp+bUUQAAACEu7iYSK+mh8bFRDkyjXTMmDEaOXJkhdfzC4v0+ye+liTt3LnLY9DNLzyhlLYdVJS3P+B1VifsQmJiYqLZPnDgQLnPf+vAgQNmu7pAWRXLsvTOO+/o/vvv17Zt28zrGRkZevPNN1WvXr1q+3C5XIqPj/e5BgAAAADOquwWsKgyC+nEx5/kMcCWtAmNRXTC7hEYZVcU3b+/6hRedn/Dhg19Ot+WLVt07rnn6pprrjEBsWnTpnrttdf00UcfEfwAAAAA1CphFxJbtGhhtqsLiaX3DEpScnJyjc5jWZaysrLUuXNnLVxYMvc5Li5O//jHP7R582bdcMMNLKcLAAAAoNYJu+mmZUcSV61aVeljKoqLi7V69WpJUuvWrXXSSSfV6DyPPfaYxowZYz6/5ppr9MQTT5Q7PwAAAADExURp2z8vcroMr4XdSGKPHj3M9pw5cyptt2LFCvO4jN69e9foHG+88YYJiA0aNNBHH32kGTNmEBABAAAA1HphFxLT0tKUlpYmSVq4cGG5KaVlzZo1y2wPGTLE6/6Lior04IMPSpKio6O1cOFCDR482I+KAQAAACB0hF1IlGSWnS0oKNBdd92l4uLicvtXrlypKVOmSJLatm2rSy65xOu+P/30U7NAzT333KOzzjrLjpIBAAAAICSE3T2JknTTTTfp5Zdf1vLlyzVjxgzt2LFDw4YNU3x8vJYvX65p06bp2LFjcrlceuqppxQTE1Pu+GHDhum1116TVPEh9UuWLDHbTZs21bx587yqqXPnzjVeHAcAAAAAgi0sQ2J0dLQ++ugjDRo0SCtXrtSSJUvKhbvSNlOnTlVGRkaN+na73WZ79OjRXh83ffp0DRs2rEbnAgAAAOCd2rY4TCgLy+mmUsmjMJYtW6ann35avXr1UkJCgmJiYpSamqpbbrlFK1as0IgRI2rcb9mQCAAAAADhJixHEkvFxMTozjvv1J133lmj41599VW9+uqrHvfNnTvXhsoAAAAAIDSF7UgiAAAAECz5hSeUet9cpd43V/mFJ5wuB/ALIREAAAAAYIT1dFOEJ8uydPR4kdNlVKt+dKRcLpfTZQAAAD94+3tH2dFDb0cS+V0BoYqQiFrn6PEipT843+kyqrVm4vmKi+F/MQAAajNffu/o8fBCr9rxuwJCFT+VQIDkF9o32slfGgEAsF9+4QkTAAlswP/j/wTUat/9Y6DiYiKdLsPILyxSj4cXSJL5rx24cAEA4Lyqfu/ILzxhRhC/+8eASq/bZX9XqIqdAZYwjJriJwQBZ/c9hGVH6OJiIuvEP3SMSgIA4D077yP05feOuJioOvH7iR0IsKGJ7wICrrbcQ2iH+tGRWjPxfFv6YlQSAADfBPI+wsrExURp2z8v8qsPIFTwmyJgI5fLFfIBzM5RSYmRSQCAPWoy+lfdtE67r3WBUlWdNV0tlesx7BTav80i7Nh9D2H96NC5H9FutWFUUrL3e8oFDgDqrkCN/tlxH2Epu3/v8PaabMf79LRdGa7HICQiqOrKPYR2qA2jkpK9oZPACQCwW125j9DOwFnVrSl16R7CuvRef6vuvFOgDrNzVFLyfmW2muLeSwCAFLzRPyfuI/T2muzttNpAXI8BfoMC6gC7RyUDNRU21Ni9Mm8pRjkBoGrhPPrnyzW5svcZyoEzUCvMIjhq1/9VYcztdqtTp3SP+24dMUK33X5HkCuyD/9jhx87Q2egAqcdP3eBCrCBuDc3VEJnoIK1FFrvE4BzWEX0/9kZOO3mxAqztUVWVpaysrIqvG5ZUlH+geAX5AEhMURY9Rrp6MX/8rjvqV3SU3XkERKoewJ172Wojk5Kob14kL8COTLMFGIgtNi5GmlJO/6oXJaTYdiOVVf5flYuMzNTmZmZFV7PLzyh+CYtVJS3z4GqyuNqCwBe8DeIBTI8hXIgtpOdv3AwKgn4j5Gi8GXnIjhSaK8wC88IiSGiWXw9WxcWCVX8j41AsnuBnt/27U+oqC2LB9nJjhHOQD2+JZRGX3+LAAvAbk5P0Q3ne0zDFd+FEOFyif8pAD+F8mNDQnnxoEAJ5bATygGbR8GgrEDe51uVmj6w3s6RIok/KjvBzkVwPPWN2iU0f5sCAFQplAOxnerKSrpl8SiY2s/Oe/VC4efWmymFVY0UOT2KBe+E8iI4CD6+qwCAkBWqK+naLVBBIJQXjvB3lDOUV9LlXj0AtR0hEQBQJ4Ty6GugRkydHoGqir+jnL4EMW/ZsVBVINg9pbMqLCYC1G2hebUEAKAOCeUAGyj+BqlAjpKG6iJJVY1w2j2lkymiQN1Wt65IAACEudoyrTbUgligpvx6u6ojUFvwB4m6gX+1AAAII3VxVNKOIBaolR2ZhgmgNqpbVxEAAOCYQI1y2hHEvA3XjHoA4aWqqev5hSc8bnt7fG1GSAQAAEFRF0c5AYQ2b6eZ17UViCOcLgAAAAAAEDr4cx4AAACAOoN7kKtHSAQAAABQZ/gy9T0uJqpOTZdnuikAAAAAwCAkAgAAAAAMQiIAAAAABFLhEWl8o5KPwiNOV1MtQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAACA2q+WPWYilEU5XQBK7Ha7lZ6e7nFfZmamMjMzg1wRAAAAALs9/8ILemraixVetyypKP9A8AvygJAYIpo1b641a9Y4XQYAAAAASXExUdr2z4ts7/e2W2/VbXfeU+H1/MITim/SQkV5+2w/Z00x3RQAAAAAYBASAQAAADiD+whDEiERAAAAAH6rDgdYQiIAAAAAwCAkAgAAAP6qw6NOCD+sbhoqLMvef1Ci4ySXy77+/GFZ0vF8p6uoXih9zQAAQGjw9veYwnzP21Xhdw/v8D0IOkJiiHAd3iU9mmRfh6M2STFx9vXnj8J86Yn2TldRPbu/ZvyjAwCA/QqP/P/vTPfvlGIaBPZ8x/Nr/juat7/3BKN+p9gZ7Hz5XZLvgV8IieGqNoSyUGP31yyUgnogEYYBAHYI1GhRVdepYAdOpzjxPgMZrhFwhMQQYTVsId2/0b9OasOIXagFp0B+zUL9e2EXO7+nBE4AqD1qEupKr4lVXTMCNVpU3Tk9bVfWxtv67X6f+H98D4KCkBgqXC7//6oTHVfy16FQFmohwO6vWV38R8fO9xtqgTNQ99OG2v8HAOCL2jJS5O05vWkXE+fd72vetvOXE6OvvrAj2NW0tmB9D8IUITGc2BE06xq7v2a1IajbIVBhONQCZ6DeZ6iNqNcWhGug7vA3VDjxR9uYBtL4g8E9Z6CCut1TUkMtXKNahETATnUlqNsZhmtD4LRbKNcWysL5fiGgtrNzpEiq+o9C3oQxb69TvoxiBUtduV8SIYmQCKDm7AzDtSFwSv6P/tXFqch283Y5c2/4OyrJo32cV1e+B069T29H60pVNQLkxAibt9cpJ2orq6p/14J9vyTXKd/YuorrCblk2VOXnwiJAJwVqoHTU9/+/KJXV6Yi263sLy2hNB25tvwyFc6jD75Ms3OCv9+DUHifteFnvbYK5/sl6wobp/zGSWruOqwc/6vyGyERQPgI5em+oVxbXVRXfum1c/TVbuE8ylmWv9+DUP4eAnYIp9HcMEJIBACErtowHTnUFiIK1Oir3ewYzbWrL7uF6gh4IAXzXr3azon7JZ0OYsHiy/u0czTXzym/+UcOSU8me1dPgBESAQChqzZMR64rI2J2szM81ZXpc3XlfYY7X/5dC6XvPQvqVM7fKb+FJ+yvyUeERABA3VBXpvyG8v2vteVeTn8F8g8SqDvqyuifE8Jh9dsAIyQCABBOQjkM15XwFMrfAwC1fzQ3CAiJAAAgOAhPQO3g72Iu1e1DyCMkAgAAAPh/di7mglopwukCAAAAAAChg5FEAAAAoK4L1GIupX2jViEkhojdu3crPT3d477MzExlZmYGuSIAAADUGSzmEjTPv/CCnpr2YoXXreJiuY9YDlRUESExRDRr1kxr1qxxugwAAAAAAXTbrbfqtjvvqfB6ft5BnZp0snIOOx8UCYkAAAAAvMPzG+sEQiIAAAAA/FYdDsSsbgoAAAAAMAiJAAAAAACDkAgAAAAAMLgnEQAAAAACqZbd38hIIgAAAADAICSGCMty/nkoAADnFBQUaPz48SooKHC6FABAHUdIdJgJh4REAKjTCgoKNGHCBEIiANRhpYnA6QEkQiIAAAAAwCAkAgAAAAAMQiIAAAAAwCAkAgAAAAAMQiIAAAAAwIhyuoBgyM3N1ZYtW3T48GElJSWpffv2ioiwLx/n5eVp06ZNys3NVfPmzdWhQwdFR0fb1j8AAAAABEtYjyRu2LBBGRkZSkxMVI8ePdS/f3+lpaUpNTVVkydPVlFRkV/979q1S9ddd50SExPVvXt3nXPOOTrttNOUlJSkBx54QMeOHbPpnQAAAABAcIRtSFy6dKm6d++ujz/+uEIY3LFjh0aNGqWhQ4f6HBTXr1+vLl26aMaMGRXC4N69e/Xoo4+qb9++OnLkiM/vAbVXVlaW0yWElHD4eoTie3CypmCdO1DnsbvfUPz5gPP4uaiotn9NQrV+p+qq7deCQPQdqj8jNWaFoT179lhNmjSxJFkRERHWxIkTrR07dlh5eXnWokWLrO7du1sqeVal9eCDD9a4/2PHjlmnnnqq6eOuu+6yNm3aZOXn51vffPONNWDAALPvhhtuqLKvpKQkS5LVskULX98uQlCnTp2cLiGkhMPXIxTfg5M1BevcgTqP3f3a0d/BgwctSdbBgwdtqAihIBT/3XBabf+ahGr9TtVV268Fgejbn/6OHD5gJZ3ksiRZSUlJNlZVc2E5kvj4449r3759kqSpU6dq7NixSklJUYMGDdS/f38tXrxYqampkqTJkydrz549Ner/5Zdf1oYNGyRJo0aN0tSpU9WuXTvVr19fPXv21H//+1/17NlTkvTGG2/o559/tu/NAQAAAEAAhV1ILCoq0iuvvCJJatasmUaMGFGhTXx8vEaNGiVJOnLkiGbOnFmjc7z44ouSpKioKD3wwAMV9kdHR+vBBx+UJFmWpenTp9eofwAAAABwStiFxGXLlplRxIyMDEVGRnpsN3jwYLM9d+5cr/vPycnRDz/8IEnq27evGjdu7LHdgAED1KBBgxr3DwAAAABOCruQuH79erM9aNCgStu1atVKXbp0kSR9//33tvdfr149DRgwQJK0bt06FrABAAAAUCuEXUjctWuX2W7Tpk2VbVu1aiVJ2r17tw4cOBCw/iVp48aNXvUPAAAAAE6KcroAu/36669mOyEhocq2TZo0Mdu7du2qdOqoXf1369atQpvSRXPcu3crOTm52vNXxuVy+Xws7Od2u5WSkuJ0GSEjHL4eofgenKwpWOcO1Hns7teO/izLkiR16tSJf9PDRCj+u+G02v41CdX6naqrtl8LAtG32+32+Xd6q7hY7rySa0FNF9a0W9iFxLIjfWVDmidl93s7HdTu/kuf01hcXKydO3d6VQNqh5ycHKdLCCnh8PUIxffgZE3BOnegzmN3v3b1x7UgvITivxtOq+1fk1Ct36m6avu1IBB92/HvuK/PcrdL2IXEQ4cOme369etX2TY2NtZsHz161JH+69Wrp2PHjikyMlKJiYle1eAJf3UGAAAAnFc6M8QXe/bsUVFRkerVq2djRTUXdiGxbNA6cOBAlcGr7H2I1QW+yvqvijf9s6ANAAAAgFASdgvXtGzZ0mzv37+/yrZl9zds2DAk+gcAAAAAJ4VdSGzRooXZri7E5ebmmm1vbzD1tf+yK50CAAAAQKgKu5BYdqRv1apVlbYrLi7W6tWrJUmtW7fWSSedZGv/kvTjjz9KkiIjI9WhQwev+gcAAAAAJ4VdSOzRo4fZnjNnTqXtVqxYYR5n0bt3b6/779y5s6Kjo6vt3+12a/ny5ZKkM888UzExMV6fAwAAAACcEnYhMS0tTWlpaZKkhQsXlpvyWdasWbPM9pAhQ7zuPz4+Xuecc44kac2aNVq3bp3Hdh9++KFZ2agm/XvLsiw999xz6tq1q+rXr69mzZrpqquu0pYtW2w/FwCg9ti7d6+io6P13nvvOV0KACBICgsL9eijj6pnz55q1KiRkpOTdeGFF2rRokU+9Rd2IVGSRo4cKUkqKCjQXXfdpeLi4nL7V65cqSlTpkiS2rZtq0suucSn/iXp9ttv17Fjx8rt3759ux588EFJJaHy5ptvruE7qN4999yj22+/XTk5OfrTn/6ktm3baubMmTrzzDO1adMm288HAKgdpk2bphMnTjhdBgAgSIqKitSnTx898MAD2rt3r/70pz+pe/fu+uKLLzRgwAA9/PDDNe/UCkOFhYVWz549LUmWJKtPnz7WK6+8Yr333nvW6NGjrYYNG1qSLJfLZc2ePbvC8TfeeKM5dty4cRX2FxcXW0OHDjVtunbtaj377LPWrFmzrAkTJliJiYlm3zPPPGP7+9u4caMlyerQoYO1Z88e8/qUKVMsSdYNN9xg+zkBAKFr37591hdffGHdddddVkREhCXJevfdd50uCwAQBM8995wlybrkkkusY8eOmde3bt1qtW3b1oqIiLC+++67GvUZds9JlKTo6Gh99NFHGjRokFauXKklS5ZoyZIlFdpMnTpVGRkZNe7f5XLptdde06FDh7RgwQKtWrVKt99+e4V2DzzwgO644w6f30dlXnrpJUnSY489pqZNm5rX//KXv+jll1/Wu+++q6efflrx8fG2nxsAEHrOOeecahdTAwCEpw8//FBSSTaIjY01r6empmrSpEm66qqrNHv2bJ1xxhle9xmW002lkkdVLFu2TE8//bR69eqlhIQExcTEKDU1VbfccotWrFihESNG+Nx/w4YNNX/+fL322ms655xzlJiYqOjoaKWkpOjqq6/W0qVL9fDDD8vlclXbV25urlasWKHFixdrw4YNFabH/tZHH32kevXq6bzzzquw7+KLL9bRo0f12Wef+fzeAADOqek1QZL+/e9/64MPPtAHH3ygK6+8MghVAgACpabXgU2bNql+/foen6Zw2mmnSVKl66hUyu7hzrrsmWeeqXSKqifr16+3/vSnP1mRkZFmeqokq1WrVtYTTzxhnThxwuNx8fHxVlpamsd9M2bMsCRZU6dO9fVtAABsEKxrwm+NGzeO6aYAEAKCdR1YunSp9fXXX3vc9+KLL1qSrJEjR9ao9rAdSXTCm2++6XXbpUuXqnv37vr4449VVFRUbt+OHTs0atQoDR06tMK+/Px8HTp0SAkJCR77LZ1+Wvp4DwCAM4JxTQAAhK5gXQf+8Ic/qFevXhVe/+KLLzR69GhJ0vXXX1+j2gmJNpk+fbqWLVvmVdu9e/dqyJAhys/PV0REhCZOnKgdO3YoLy9PixYtUvfu3SVJs2fP1sSJE8sdW/pIj5NOOslj36Wv79mzx9e3AgDwU7CuCQCA0OTkdaCgoEBPPPGEzj33XOXm5uqBBx5Qt27dalQ/IdEPBw8e1NKlSzV8+HDddtttXh/3+OOPa9++fZKkqVOnauzYsUpJSVGDBg3Uv39/LV68WKmpqZKkyZMnlwt8pSOIhw4dqrQmSTr55JN9eUsAAB85cU0AAISOULgOfPLJJ0pPT9ff//53RURE6IknntBDDz1U4/dCSPRRz5491bhxY/Xp00fTp0/X8ePHvTquqKhIr7zyiiSpWbNmHhfPiY+P16hRoyRJR44c0cyZM82++vXrq1GjRtq/f7/H/kt/wJKSkmr0fgAAvnPqmgAACA1OXwcOHTqkG2+8URdddJG2bNmiSy65RD/++KP+9re/ebWQ5m8REn20e/dun45btmyZCXIZGRmKjIz02G7w4MFme+7cueX2JScna9u2bcrLy6tw3Jo1ayQREgEgmJy8JgAAnOfkdeDo0aPKyMjQ66+/rhYtWmjBggX64IMPdOqpp/pUk0RI9Nn69et19OhR8+HtsrLr168324MGDaq0XatWrdSlSxdJ0vfff19u38UXX6zCwkLNnz+/wnFz5sxRvXr1NHDgQK/qAQD4z8lrAgDAeU5eBx566CEtWbJEf/jDH7Ry5UoNGDDAh3dQHiHRR7GxsapXr575KPvgyqrs2rXLbLdp06bKtq1atZJU8peJAwcOmNdvvvlmSdKYMWPKTTudOnWqVq9erauuuop7EgEgiJy8JgAAnOfUdeDEiRN65ZVXFBcXp1mzZqlFixa+vYHfiLKlF3it7KMpKnuMRakmTZqY7V27dqlx48aSpHbt2umvf/2rpkyZoo4dO6p///7avn27li9frqZNm2rs2LEBqR0AYC87rgkAgNrL3+tATk6O3G63mjdvrgkTJlR67FlnnVWjx2AQEoOs7F8Lyn6jPSm7/8iRI+X2TZ48WaeeeqqmTZum2bNnq23btrr55pt1//33q23btvYWDQAICLuuCQCA2snf60DpvZBut1tZWVmVHpuXl0dIDGVlH11Rv379KtuWHaY+evRouX0RERG6/fbbdfvtt9tbIAAgaOy6JpQ1fvx4jR8/3u/aAACB5+914Mwzz5RlWbbXxT2JQZaYmGi2q7unpOz+6n5oAAC1D9cEAKjbQvU6QEgMspYtW5rtyp516Gl/w4YNA1YTAMAZXBMAoG4L1esAITHIyq44VN0PQm5urtlOTk4OWE0AAGdwTQCAui1UrwOExCAr+9eCVatWVdquuLhYq1evliS1bt1aJ510UsBrAwAEF9cEAKjbQvU6QEgMsh49epjtOXPmVNpuxYoVZknc3r17B7wuAEDwcU0AgLotVK8DhMQgS0tLU1pamiRp4cKF5YaNy5o1a5bZHjJkSFBqAwAEF9cEAKjbQvU6QEh0wMiRIyVJBQUFuuuuu1RcXFxu/8qVKzVlyhRJUtu2bXXJJZcEuUIAQLBwTQCAui0UrwM8J9EBN910k15++WUtX75cM2bM0I4dOzRs2DDFx8dr+fLlmjZtmo4dOyaXy6WnnnpKMTExTpcMAAgQrgkAULeF4nWAkOiA6OhoffTRRxo0aJBWrlypJUuWaMmSJRXaTJ06VRkZGQ5VCQAIBq4JAFC3heJ1gOmmDmnRooWWLVump59+Wr169VJCQoJiYmKUmpqqW265RStWrNCIESOcLhMAEARcEwCgbgu164DLsiwraGcDAAAAAIQ0RhIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAAYhEQAAAABgEBIBAAAAAEaU0wUAAAAAqDu2b9+utWvXaufOnfr111/VoEEDNWnSRF27dtVpp52miAjGsZzGdwAAAAAVjB8/Xi6XSy6XS+PHj3e6nDqt7PfCn49+/fr5XMOrr75aab8ffvhhtcfn5eXpscceU48ePZSamqoLL7xQN998sx544AH99a9/1fXXX68uXbqoSZMmuvvuu7Vhwwafa/XV1q1by72vCRMm+NxX165dTT99+/aVVPXX8NVXX7XpXdiDkAgAAAAgYN5991117NhR9913n1asWFFl2wMHDujpp5/W6aefrokTJ+r48eNBqlJq27atevXqZT7/z3/+41M/mzZt0o8//mg+v/LKK/2uLdiYbgoAAICwkpqaqu3bt0sqGR1KTU11tiAbRUZG+vx+kpOTbamhYcOGat68ufm8QYMGHttZlqWxY8fqkUceKff6ySefrIyMDKWlpSkxMVHZ2dlau3atli1bph07dkiSjh8/rnHjxmn16tV66623FB0dbUvt1bnmmmv0v//9T5K0Zs0a/fTTTzr99NNr1McHH3xgtiMiInTppZdKkk466SS1a9fO7HO73crLy7OhavsREgEAAIBaIiUlRZs2bXK0hksvvdSr6ZH33XefHn/8cfN5QkKCHn/8cV1//fWKiYmp0L6wsFAvvviiJk6cqN27d0uS3nvvPTVv3lzPPPOMbfVX5fLLL9df//pXFRUVSSoZTaxpSJw1a5bZ7tevnwnUl156qQmMkjRs2DC99tprNlRtP6abAgAAALDVJ598Ui4gnn766fr555918803ewyIkhQTE6PMzEytWrVKrVu3Nq9nZWVp4cKFAa9Zkpo3b64BAwaYz2fOnCnLsrw+fufOnVq2bJn5vDZONZUIiQAAAABsdODAAd1yyy3m81NOOUWLFi1SixYtvDq+RYsW+uSTTxQfH29ee+ihh2yvszLXXHON2d6wYUO5+wurU3YRn8jISA0dOtTO0oKGkAgAAADANi+99JJ27dplPn/llVeUmJhYoz5OO+003XHHHebzL774QmvXrrWtxqoMGTJEsbGx5vOZM2d6fWzZqaYDBgxQ06ZNba0tWLgnEQAAIIwVFRXp66+/1rp167Rv3z61atVKp556qjp27KiTTjrJ6fI8Kioq0saNG7V582Zt3LhRkZGROuWUU9SuXTulpaXJ5XIF5LwHDhzQ4sWLlZ2drfz8fKWmpqpdu3bq3r27z8/uKygo0CeffKINGzaoZcuWuuGGG2yuOrQUFRVp2rRp5vOhQ4eaR0DU1LBhw/TPf/7TfL5gwQJ16tTJqxq++eYbrV27Vrt371azZs3Url07devWTY0bN672+Pj4eGVkZOi9996TVHJf4iOPPFLtz93+/fu1ePFi83ltnWoqSbIAAAAQdgoLC60nn3zSSkxMtCRV+KhXr551xx13WAcOHPB4/Lhx40zbcePG1Xi/L+2Lioqsd99910pPT/dYsySrd+/e1hdffFHh2OnTp1d6TOnH559/7vG8W7duta644gorKirK43Gnnnqq9cwzz1iFhYWVvr/PP/+8wvv78ssvrdatW5vXu3btWu3XyZOyX7s2bdr41Ie/yn59b7zxxkrbLV26tNzXbuHChX6d9+OPP7beffdd691337W+++67KtsWFBRYTzzxhNWsWTOP38e4uDgrMzPT2rp1a7XnnTVrVrljV6xYUe0xr776qmkfFRVl7d+/v8r2N954o2k/ffr0avsPJqabAgAAhJlDhw5p4MCBGjlypPbs2eOxzbFjxzRt2jSdccYZ5aYGOqWwsFBDhgzR5ZdfrjVr1lTa7uuvv1bfvn01depUW847b948paen6z//+Y9OnDjhsc2GDRt05513atCgQTp48KBX/X7++ecaOHCgfvnlF1vqrC2WLl1qthMSEtSvXz+/+rvooot02WWX6bLLLtMZZ5xRabu9e/fqD3/4g0aNGmVWRv2t/Px8ZWVl6YwzzihXpycXXnhhuXsivZlyWnaq6XnnnaeTTz652mNCFdNNAQAAwkhxcbGGDh2qJUuWmNfOPPNMXXzxxWrWrJm2bt2qefPmaeXKlZKkzZs368orr9QXX3wRsGmc3pg0aZJmz55tPk9JSdFVV12ltm3b6vjx4/r555/15ptv6ujRo5KkUaNG6aKLLjLPnSv7DLpt27aZRxi0bt3aPGOvfv365c759ddfa/DgweaB7fXq1dPFF1+s008/XSeffLJWr16thQsXmkdOLFiwQAMHDtT//vc/RUVV/mt0Tk6OrrzySh07dkxNmjTRH/7wB7Vv315nnnmmHV+qkPbll1+a7d69e/s8TbcmCgoKdN5555mf6dJz9+rVS+3bt9cvv/yib7/9VgsWLJBUMi104MCBWrJkic466yyPfdarV0+XXnqppk+fLqlkyuk///nPSv8fycvL0/z5883ntXqqqQiJAAAAYeX5558v97iArKws3X777eV+uX3ooYf0r3/9S2PGjJFUMvozZ84cDR48OOj1SiWjiE899ZT5/Oqrr9Zrr71W4QHqY8aM0RlnnKHc3FwdP35cc+fO1d133y2p/DPoUlNTtX37dkklC554evj80aNHdcMNN5iAeP755+u5556r0LawsFCTJ0/W2LFjVVRUpO+++05PP/207rnnnkrfz0svvSRJuu222/TEE0+oYcOGNfuC1GJbtmwx2507dw7KOR966CETEJs1a6YXXnhBgwcPrhDovvjiC918883avHmzCgsLdfvtt2v58uWVBv5rrrnGhMRt27bp22+/Vc+ePT22nTdvngoKCiSVPMrj4osvtuvtOYLppgAAAGGiqKio3EIff/nLX3THHXdU+GU5MjJS9957ry655BLz2ltvvRWsMitYv369cnNzJUkul0vPPPNMhYAoSW3bti33aIWaPJrgt9555x1t3rxZUskz/GbNmuUxTMbExGjMmDH6v/buPabq8o8D+BuRuARIXBQL0CGXg0m6RpQhMegMiYtoVNqxJc3LltWGl+WtJbqWUzegqNEaqWCTcTk19QybBWW4GicuhmNkRJw8gqzQmlwEOYfz+4Px/L5Hzo2LIaf3a2P7fj3Peb7POd/jdj7neZ7PJzs7W/zb/v37cefOHYv9JyUloaCgYMoDxD/++AMODg4T+rt06dKUjsWUmzdvimMfH597fr1bt26JHxhmzZqF0tJSpKenm5zxi4uLg0qlgpubGwCgsbERSqXSbN/x8fGYN2+eOC8rKzPbVrrUNCkpCXPmzBn3a7mfMEgkIiIishNVVVViD5yjoyO2b99utq2DgwM2b94sztVq9T0fnzmjMzDAyJJQS1+wN23ahJMnT+LkyZNYt27dhK85OtsHALm5uSJwMGfPnj1YsGABAKCnpwf19fUW2+/bt29al+9OF2mQKN3TZ8ns2bNtDnTvVlpait7eXgAjM9DW9kDKZDKj/xfV1dVm2zo6OhotGy0rK8Pw8PCYdoODg1CpVOJ8pi81BbjclIiIiMhuSPchpqWlISgoyGL7+Ph45ObmAhiZhTEYDNMS2ISGhorj/v5+5OXlYfv27SbHEhYWhrCwsEldr7+/XwTFrq6uiI2NtfocR0dHxMTEiGWsFy9exPLly822NffYZDk6Opqc8bTFAw88MLWDMcHFxUUEbf39/ff8etKSE0lJSTY955lnnhHH0j2UpigUCpEkSavVora2dsy9raqqQk9PD4CR15+WlmbTOO5nDBKJiIiI7ERdXZ04fvTRR622d3V1RVZW1j0ckW3mzJkDuVwuEovs3LkTKpUKmZmZSEpKMlryNxWamppEJlOdTmfTewUAN27cEMeWMsL6+/vD0dFxcoM0IyAgQCTSuR/5+vqKIHF0CbE1ixYtEomGTBldFmyKdEZ39+7dRsuCzZEuFbaW2Tc6OhrBwcFir2VpaemYIPHLL78Ux8nJyfdt/dHxYJBIREREZCek5S4CAwOncSTjd+zYMTz99NO4du0agJEZotFZovDwcMTGxiIhIQEpKSk2L2M0R/o+DQ0NWQxCzBmdOTJl/vz5ExqXPfDz84NGowEwUjrEFleuXDH7mF6vt5hJVnovOzo6bBukRE9Pj8UZdAcHBygUCrz33nsAgPLycuTk5IisrXq9HqdPnxbt7WGpKcA9iURERER2Q1rD7+GHH57GkYxfYGAgWlpasHPnTjz44INGj125cgWFhYVQKBTw8/NDeno6WlpaJnwtW2sdWnLr1i2zj909/v+SqKgocfzjjz9Ouj/p7K0pk72XOp0OAwMDFtu8/PLL4rizsxM//PCDOL948aIIVN3c3JCSkjKp8dwvGCQSERER2QlpHcB//vln+gZigjQ5jTnu7u44evQouru7cfbsWWzcuNFkSYozZ85g2bJlKCoqmtBYpElqIiIiYDAYxv1nS3H1/6K4uDhxrNFo0NzcPKn+rC2tld7L2traCd3Lu+tn3m3x4sVYunSpOJfee+lS09TUVLv5gYBBIhEREZGd8Pb2FsejCVbuF+3t7Ta3dXFxQWpqKgoLC9He3g6tVovPP/8c69atE6Ux7ty5g61btxrV5bOV9H36/fffTWaspIlJSEgwSpBTUFAwqf7OnTtn8XHpvbyXezUVCoU4rqiogF6vh8FgMCp9YS9LTQEGiURERER2Q5qAxdb9YNnZ2cjKykJWVha6urru1dAmtO9vVEBAANavX4+SkhI0NzfD19cXwEj2TGnpAVstWbJEHA8ODkKr1dr0PL1eD51OB51Ox8DSDD8/P6OA6rPPPrO459CS3t5efPrppxbbREZGiuPW1lab+h0eHhb30VLCHClpuZWuri7U1NSgvr5efHbc3d3x3HPP2dTXTMAgkYiIiMhOxMTEiGOlUmmU1MMUjUaDAwcO4IMPPkBhYeGEi59b2zem0WgsFnJ/5513EBISgpCQEKvZKUNDQ/Hiiy+K84kEIL6+vggPDxfnxcXFVp/T19eHefPmwcnJCU5OTiLBDo21Y8cOkQhmYGAAGzZswO3bt8fdz549e/Dnn39abCP9zJ86dQpDQ0NW+33jjTfEfTx06JBNYwkKCjIqlVJWVmY0i7hq1Sqry1ZnEgaJRERERHZi1apVIv1+f38/cnJyLLaXFpSPiYkRSzltId17pVarYTAYzLY9ePCgKDlhyvDwMNra2tDW1oZTp05Z7AuAUYDm7u5usa25Gb9XX31VHB8+fBidnZ0W+8nPzxfBcHx8vNUalP9lS5Yswd69e8V5bW0tUlNTRWkMawwGA95//3189NFHVtsqFAqRafTXX3+1urxVo9Hg+PHj4vyVV16xaUyAcQKbiooKKJVKcW5PS00BBolEREREdsPDwwOvv/66OD9y5AgKCwtNBl1nzpwxmkVJT08f17WWLVsmjtVqtdEX71EGgwGHDh3CiRMnLPYlrTvX2tqKgwcPmm1bUlKCyspKcS4tjG6KuWWuW7ZsgZeXF4CRWcJnn33WqObeKIPBgKKiIuzbt0/822uvvWbxmgTs37/faJavuroaixcvRllZmcUfDJqbm5Geni7e77CwMDg7O5ttHxQUhPXr14vzt99+G3l5eSZ/HGhvb4dcLhdJlBISEsYkRrLkhRdeEPUv//rrL7Gk29PTEytXrrS5n5mAdRKJiIiI7Eh2djbOnj2LlpYWDA8PY/PmzcjPz8eKFSsQGRmJ7u5u1NTU4Pz58+I5kZGR2LRp07iu88QTT8DLy0tkUd24cSO++eYbJCcnY+7cufj5559x7tw5fPvttwCAbdu2ITc312RfiYmJCAoKwtWrV41eQ1paGvz9/WEwGNDR0YGvvvoKdXV14nmPP/64yS/nPj4+InHPmjVrEBUVhdu3b+PDDz/Ek08+CWBkyeknn3wi9pr98ssveOqpp5CSkoKlS5fikUceQUdHB1QqFRoaGkTfcrncKCj5t127dg0hISETfv7hw4eRkZExhSMyzcnJCZWVlVi9erX4DGi1Wqxduxa+vr5ITU1FaGgo5s6di4GBAWi1Wnz33Xf46aefxI8aUVFRqKysREZGBmpqasxeKy8vD1VVVejs7MTg4CC2bduG4uJirFixAhEREejt7UVDQwMqKipEgOrm5oaPP/54XK/Jz88PK1euNPqRAgBWr15tMZCdiRgkEhEREdkRV1dXnD9/HnK5XOzXa2pqQlNTk8n2ixYtgkqlMspIaQsvLy8cO3YMzz//vPi3kpISlJSUjGl74MABJCcnmw0SnZ2dUVxcjKSkJFGzrr6+3uTM3qjg4GCUl5ebLLSekZEhAru+vj5cuHABAMbsi1u7di1u3LiBN998EwaDATqdDqdPnzYqji4VHx8PpVIpljdOB71eP6kkQD09PVM4Gss8PT1RWVmJvXv3Ij8/XwRo3d3dFmeXZ82ahS1btuDIkSPw8PBAbGysxSDR29sb1dXVSExMFD80NDY2orGx0Wz7L774AjKZbNyvSaFQjAkS7W2pKcDlpkRERER2JyAgAGq1Grt37za7Z8/V1RVvvfUW6uvrJ7y/bs2aNVCpVGa/bAcFBaGiogLvvvuu1b7i4uKgVqshl8sttlu4cCHy8/Nx+fJlBAcHm2yza9cuHD16FBEREXB1dYW3tzcee+wxsbxUauvWraitrUV8fLzZa4aEhKC8vBxVVVXw9PS0+lro/1xcXJCTk4PLly8jMzPTqGTF3dzd3fHSSy+hrq4OBQUFYn+ttSXFABAeHo5Lly5hx44dZmsVOjs7Y9euXWhrazOq5zge6enpRglqHnroIauf2ZnIwWBtZzARERERzViDg4O4cOEC2tracPPmTXh4eCAsLAwxMTHiS/hk6XQ6NDQ0oLW1FVevXoWPjw/Cw8MRGxs7oVm3rq4utLa2QqvVQqvVwt3dHQsXLsSCBQsgk8lMzh5OhevXr+P7779HZ2cn9Ho9wsLCIJPJEBwcfM+uOVOcOHFC7MXcsGGD1X2m5gwNDUGtVkOj0eD69esYHh6Gv78/AgMDsXz5cri4uEx6rIODg6ipqcFvv/2Gv//+GwEBAZDJZJDJZFP2mZ8KmZmZKCoqAgAcP34cmZmZ0zsgif/2p52IiIjIzjk7OyMxMfGeXmP27NmIjo5GdHT0lPTn7+8Pf3//KelrPObPn2+XSwfvJ05OToiJiTFKajPVnJ2dIZfL7XKG79/C5aZEREREREQkMEgkIiIiIiIigUEiERERERERCQwSiYiIiIjIZkqlEiEhIeLv66+/nu4hzQh3v29KpXK6h2QWE9cQEREREZHNent70dvbK877+vqmcTQzR09Pz6RqXP6bOJNIREREREREAuskEhERERERkcCZRCIiIiIiIhIYJBIREREREZHAIJGIiIiIiIgEBolEREREREQkMEgkIiIiIiIigUEiERERERERCQwSiYiIiIiISGCQSERERERERML/AH2ANYv7bKIPAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "var = 5\n", - "log_ = True\n", - "\n", - "if log_:\n", - " bins = np.logspace(0, 2, 41)\n", - "else:\n", - " bins = np.linspace(1, 100, 41)\n", - "\n", - "plot_eff_and_fake_rate(icls=pid, ivar=var, ielem=ielem, bins=bins,\n", - " log=True, \n", - " physics_process_lab=physics_process[sample],\n", - " textx=0.4,\n", - " texty=0.87,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "a59fa4f0", - "metadata": {}, - "source": [ - "# Distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 692, - "id": "d8014df8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "path evaluation/epoch_96/clic_edm_ttbar_pf/test/*.parquet\n", - "['evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3708.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2202.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch175.parquet', 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"source": [ - "yvals, X, _ = load_eval_data(path, max_files=50)" - ] - }, - { - "cell_type": "code", - "execution_count": 1245, - "id": "ec35931f", - "metadata": {}, - "outputs": [], - "source": [ - "def reso_plot(pid, var, bins, physics_process_lab=\"\", textx=0.01, texty=0.87):\n", - "\n", - " fig = plt.figure()\n", - " ax = plt.axes()\n", - "\n", - " msk = (yvals[\"gen_cls_id\"] == pid) & (yvals[\"cand_cls_id\"] != 0)\n", - " \n", - " vals_gen = awkward.flatten(yvals[\"gen_\" + var][msk])\n", - " vals_cand = awkward.flatten(yvals[\"cand_\" + var][msk])\n", - " reso_1 = vals_cand / vals_gen\n", - " \n", - " msk = (yvals[\"gen_cls_id\"] == pid) & (yvals[\"pred_cls_id\"] != 0)\n", - " \n", - " vals_gen = awkward.flatten(yvals[\"gen_\" + var][msk])\n", - " vals_mlpf = awkward.flatten(yvals[\"pred_\" + var][msk])\n", - " reso_2 = vals_mlpf / vals_gen\n", - " \n", - " plt.hist(reso_1, bins=bins, histtype=\"step\", lw=2, label=\"PF, M={:.2f}, IQR={:.2f}\".format(*med_iqr(reso_1)))\n", - " plt.hist(reso_2, bins=bins, histtype=\"step\", lw=2, label=\"MLPF, M={:.2f}, IQR={:.2f}\".format(*med_iqr(reso_2)))\n", - " plt.yscale(\"log\")\n", - " if var == \"pt\":\n", - " plt.xlabel(r\"$p_\\mathrm{T,reco} / p_\\mathrm{T,gen}$\")\n", - " elif var == \"eta\":\n", - " plt.xlabel(r\"$\\eta_\\mathrm{reco} / \\eta_\\mathrm{gen}$\")\n", - " elif var == \"energy\":\n", - " plt.xlabel(r\"$E_\\mathrm{reco} / E_\\mathrm{gen}$\") \n", - " \n", - " plt.ylabel(\"Number of particles / bin\")\n", - " \n", - " text = physics_process_lab + \" , \" + CLASS_NAMES_CLIC[pid]\n", - " plt.text(textx, texty, text, ha=\"left\", transform=ax.transAxes)\n", - " plt.xlim(min(bins), max(bins))\n", - " plt.legend(loc=(0.4, 0.7))\n", - " # plt.ylim(1, 1e9)\n", - " # plt.savefig(\"{}/pt_res_ch_had.pdf\".format(outpath), bbox_inches=\"tight\")\n", - " plt.savefig(f\"{outpath}/res_{var}_{pid}.pdf\", bbox_inches=\"tight\")" - ] - }, - { - "cell_type": "code", - "execution_count": 1247, - "id": "9b176ab1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pid = 4\n", - "var = \"pt\"\n", - "bins = np.linspace(-5,50,100)\n", - "\n", - "reso_plot(pid=pid, var=var, bins=bins, physics_process_lab=physics_process[sample], textx=0.05, texty=0.87)" - ] - }, - { - "cell_type": "code", - "execution_count": 1248, - "id": "df3de7ef", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pid = 1\n", - "var = \"pt\"\n", - "bins = np.linspace(-5,50,100)\n", - "\n", - "reso_plot(pid=pid, var=var, bins=bins, physics_process_lab=physics_process[sample], textx=0.05, texty=0.87)" - ] - }, - { - "cell_type": "code", - "execution_count": 1249, - "id": "b1248042", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pid = 2\n", - "var = \"energy\"\n", - "bins = np.linspace(-5,50,100)\n", - "\n", - "reso_plot(pid=pid, var=var, bins=bins, physics_process_lab=physics_process[sample], textx=0.05, texty=0.87)" - ] - }, - { - "cell_type": "code", - "execution_count": 1250, - "id": "60ec31f9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pid = 3\n", - "var = \"energy\"\n", - "bins = np.linspace(-5,50,100)\n", - "\n", - "reso_plot(pid=pid, var=var, bins=bins, physics_process_lab=physics_process[sample], textx=0.05, texty=0.87)" - ] - }, - { - "cell_type": "markdown", - "id": "03acc850", - "metadata": {}, - "source": [ - "# Lepton Isolation resolution plots" - ] - }, - { - "cell_type": "code", - "execution_count": 1257, - "id": "15756a46", - "metadata": {}, - "outputs": [], - "source": [ - "def reso_plot_isolated(pid, var, bins, physics_process_lab=\"\", textx=0.01, texty=0.87, apply_isolation=\"Isolated\"):\n", - "\n", - " fig = plt.figure()\n", - " ax = plt.axes()\n", - "\n", - "# msk = (yvals[\"gen_cls_id\"] == pid) & (yvals[\"cand_cls_id\"] != 0) & (yvals[\"pred_cls_id\"] != 0)\n", - "# vals_gen = awkward.flatten(yvals[\"gen_\" + var][msk])\n", - "# vals_cand = awkward.flatten(yvals[\"cand_\" + var][msk])\n", - "# vals_mlpf = awkward.flatten(yvals[\"pred_\" + var][msk])\n", - "# reso_1 = vals_cand / vals_gen\n", - "# reso_2 = vals_mlpf / vals_gen\n", - "\n", - " msk = (yvals[\"gen_cls_id\"] == pid) & (yvals[\"cand_cls_id\"] != 0)\n", - " \n", - " if apply_isolation == \"Isolated\":\n", - " msk_iso = get_isolation(msk)<0.15\n", - " else:\n", - " msk_iso = get_isolation(msk)>0.15\n", - " \n", - " vals_gen = awkward.flatten(yvals[\"gen_\" + var][msk][msk_iso])\n", - " vals_cand = awkward.flatten(yvals[\"cand_\" + var][msk][msk_iso])\n", - " reso_1 = vals_cand / vals_gen\n", - " \n", - " msk = (yvals[\"gen_cls_id\"] == pid) & (yvals[\"pred_cls_id\"] != 0)\n", - " \n", - " if apply_isolation == \"Isolated\":\n", - " msk_iso = get_isolation(msk)<0.15\n", - " else:\n", - " msk_iso = get_isolation(msk)>0.15\n", - " \n", - " vals_gen = awkward.flatten(yvals[\"gen_\" + var][msk][msk_iso])\n", - " vals_mlpf = awkward.flatten(yvals[\"pred_\" + var][msk][msk_iso])\n", - " reso_2 = vals_mlpf / vals_gen\n", - " \n", - " plt.hist(reso_1, bins=bins, histtype=\"step\", lw=2, label=\"PF, M={:.2f}, IQR={:.2f}\".format(*med_iqr(reso_1)))\n", - " plt.hist(reso_2, bins=bins, histtype=\"step\", lw=2, label=\"MLPF, M={:.2f}, IQR={:.2f}\".format(*med_iqr(reso_2)))\n", - " plt.yscale(\"log\")\n", - " if var == \"pt\":\n", - " plt.xlabel(r\"$p_\\mathrm{T,reco} / p_\\mathrm{T,gen}$\")\n", - " elif var == \"eta\":\n", - " plt.xlabel(r\"$\\eta_\\mathrm{reco} / \\eta_\\mathrm{gen}$\")\n", - " elif var == \"energy\":\n", - " plt.xlabel(r\"$E_\\mathrm{reco} / E_\\mathrm{gen}$\") \n", - " \n", - " plt.ylabel(\"Number of particles / bin\")\n", - " \n", - " text = physics_process_lab + \" , \" + apply_isolation + \" \" + CLASS_NAMES_CLIC[pid]\n", - " plt.text(textx, texty, text, ha=\"left\", transform=ax.transAxes)\n", - " plt.xlim(min(bins), max(bins))\n", - " plt.legend(loc=(0.4, 0.7))\n", - " # plt.ylim(1, 1e9)\n", - " # plt.savefig(\"{}/pt_res_ch_had.pdf\".format(outpath), bbox_inches=\"tight\")\n", - " plt.savefig(f\"{outpath}/{apply_isolation}_res_{var}_{pid}.pdf\", bbox_inches=\"tight\")" - ] - }, - { - "cell_type": "code", - "execution_count": 1266, - "id": "15ee0ffc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "path evaluation/epoch_96/clic_edm_ttbar_pf/test/*.parquet\n", - "['evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3708.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2202.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch175.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1168.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3670.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch994.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1989.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1010.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch165.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1178.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3718.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2212.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1000.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3660.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch984.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1999.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2457.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch720.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch3025.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch658.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch1645.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch730.parquet', 'evaluation/epoch_96/clic_edm_ttbar_pf/test/pred_batch2447.parquet', 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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1269], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m var \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menergy\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 3\u001b[0m bins \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m30\u001b[39m,\u001b[38;5;241m100\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mreso_plot_isolated\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpid\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mphysics_process_lab\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mphysics_process\u001b[49m\u001b[43m[\u001b[49m\u001b[43msample\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mtextx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.05\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mtexty\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.87\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mapply_isolation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mIsolated\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[1257], line 35\u001b[0m, in \u001b[0;36mreso_plot_isolated\u001b[0;34m(pid, var, bins, physics_process_lab, textx, texty, apply_isolation)\u001b[0m\n\u001b[1;32m 32\u001b[0m vals_mlpf \u001b[38;5;241m=\u001b[39m awkward\u001b[38;5;241m.\u001b[39mflatten(yvals[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpred_\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m var][msk][msk_iso])\n\u001b[1;32m 33\u001b[0m reso_2 \u001b[38;5;241m=\u001b[39m vals_mlpf \u001b[38;5;241m/\u001b[39m vals_gen\n\u001b[0;32m---> 35\u001b[0m plt\u001b[38;5;241m.\u001b[39mhist(reso_1, bins\u001b[38;5;241m=\u001b[39mbins, histtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstep\u001b[39m\u001b[38;5;124m\"\u001b[39m, lw\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPF, M=\u001b[39m\u001b[38;5;132;01m{:.2f}\u001b[39;00m\u001b[38;5;124m, IQR=\u001b[39m\u001b[38;5;132;01m{:.2f}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;241m*\u001b[39m\u001b[43mmed_iqr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreso_1\u001b[49m\u001b[43m)\u001b[49m))\n\u001b[1;32m 36\u001b[0m plt\u001b[38;5;241m.\u001b[39mhist(reso_2, bins\u001b[38;5;241m=\u001b[39mbins, histtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstep\u001b[39m\u001b[38;5;124m\"\u001b[39m, lw\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMLPF, M=\u001b[39m\u001b[38;5;132;01m{:.2f}\u001b[39;00m\u001b[38;5;124m, IQR=\u001b[39m\u001b[38;5;132;01m{:.2f}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;241m*\u001b[39mmed_iqr(reso_2)))\n\u001b[1;32m 37\u001b[0m plt\u001b[38;5;241m.\u001b[39myscale(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "Cell \u001b[0;32mIn[1225], line 2\u001b[0m, in \u001b[0;36mmed_iqr\u001b[0;34m(arr)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmed_iqr\u001b[39m(arr):\n\u001b[0;32m----> 2\u001b[0m p25 \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpercentile\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m25\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m p50 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mpercentile(arr, \u001b[38;5;241m50\u001b[39m)\n\u001b[1;32m 4\u001b[0m p75 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mpercentile(arr, \u001b[38;5;241m75\u001b[39m)\n", - "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mpercentile\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/awkward/highlevel.py:1434\u001b[0m, in \u001b[0;36mArray.__array_function__\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 1417\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array_function__\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, types, args, kwargs):\n\u001b[1;32m 1418\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1419\u001b[0m \u001b[38;5;124;03m Intercepts attempts to pass this Array to those NumPy functions other\u001b[39;00m\n\u001b[1;32m 1420\u001b[0m \u001b[38;5;124;03m than universal functions that have an Awkward equivalent.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1432\u001b[0m \u001b[38;5;124;03m See also #__array_ufunc__.\u001b[39;00m\n\u001b[1;32m 1433\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1434\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mak\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_connect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_numpy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/awkward/_connect/_numpy.py:36\u001b[0m, in \u001b[0;36marray_function\u001b[0;34m(func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 34\u001b[0m args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(_to_rectilinear(x) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m args)\n\u001b[1;32m 35\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m {k: _to_rectilinear(v) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m---> 36\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 37\u001b[0m nplike \u001b[38;5;241m=\u001b[39m ak\u001b[38;5;241m.\u001b[39mnplike\u001b[38;5;241m.\u001b[39mof(out)\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, nplike\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", - "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mpercentile\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/numpy/lib/function_base.py:4166\u001b[0m, in \u001b[0;36mpercentile\u001b[0;34m(a, q, axis, out, overwrite_input, method, keepdims, interpolation)\u001b[0m\n\u001b[1;32m 4164\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _quantile_is_valid(q):\n\u001b[1;32m 4165\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPercentiles must be in the range [0, 100]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 4166\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_quantile_unchecked\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4167\u001b[0m \u001b[43m \u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverwrite_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/numpy/lib/function_base.py:4424\u001b[0m, in \u001b[0;36m_quantile_unchecked\u001b[0;34m(a, q, axis, out, overwrite_input, method, keepdims)\u001b[0m\n\u001b[1;32m 4416\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_quantile_unchecked\u001b[39m(a,\n\u001b[1;32m 4417\u001b[0m q,\n\u001b[1;32m 4418\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4421\u001b[0m method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlinear\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 4422\u001b[0m keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 4423\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Assumes that q is in [0, 1], and is an ndarray\"\"\"\u001b[39;00m\n\u001b[0;32m-> 4424\u001b[0m r, k \u001b[38;5;241m=\u001b[39m \u001b[43m_ureduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4425\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_quantile_ureduce_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4426\u001b[0m \u001b[43m \u001b[49m\u001b[43mq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4427\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4428\u001b[0m \u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4429\u001b[0m \u001b[43m \u001b[49m\u001b[43moverwrite_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverwrite_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4430\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4431\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keepdims:\n\u001b[1;32m 4432\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r\u001b[38;5;241m.\u001b[39mreshape(q\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m k)\n", - "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/numpy/lib/function_base.py:3725\u001b[0m, in \u001b[0;36m_ureduce\u001b[0;34m(a, func, **kwargs)\u001b[0m\n\u001b[1;32m 3722\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 3723\u001b[0m keepdim \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m1\u001b[39m,) \u001b[38;5;241m*\u001b[39m a\u001b[38;5;241m.\u001b[39mndim\n\u001b[0;32m-> 3725\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3726\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r, keepdim\n", - "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/numpy/lib/function_base.py:4593\u001b[0m, in \u001b[0;36m_quantile_ureduce_func\u001b[0;34m(a, q, axis, out, overwrite_input, method)\u001b[0m\n\u001b[1;32m 4591\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 4592\u001b[0m arr \u001b[38;5;241m=\u001b[39m a\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m-> 4593\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43m_quantile\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4594\u001b[0m \u001b[43m \u001b[49m\u001b[43mquantiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4595\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4596\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4597\u001b[0m \u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4598\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/numpy/lib/function_base.py:4699\u001b[0m, in \u001b[0;36m_quantile\u001b[0;34m(arr, quantiles, axis, method, out)\u001b[0m\n\u001b[1;32m 4691\u001b[0m arr\u001b[38;5;241m.\u001b[39mpartition(\n\u001b[1;32m 4692\u001b[0m np\u001b[38;5;241m.\u001b[39munique(np\u001b[38;5;241m.\u001b[39mconcatenate(([\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 4693\u001b[0m previous_indexes\u001b[38;5;241m.\u001b[39mravel(),\n\u001b[1;32m 4694\u001b[0m next_indexes\u001b[38;5;241m.\u001b[39mravel(),\n\u001b[1;32m 4695\u001b[0m ))),\n\u001b[1;32m 4696\u001b[0m axis\u001b[38;5;241m=\u001b[39mDATA_AXIS)\n\u001b[1;32m 4697\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(arr\u001b[38;5;241m.\u001b[39mdtype, np\u001b[38;5;241m.\u001b[39minexact):\n\u001b[1;32m 4698\u001b[0m slices_having_nans \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39misnan(\n\u001b[0;32m-> 4699\u001b[0m \u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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indices, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 95\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;124;03m Take elements from an array along an axis.\u001b[39;00m\n\u001b[1;32m 97\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;124;03m [5, 7]])\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 190\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtake\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[43mbound\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 59\u001b[0m \u001b[38;5;66;03m# A TypeError occurs if the object does have such a method in its\u001b[39;00m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;66;03m# class, but its signature is not identical to that of NumPy's. This\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;66;03m# Call _wrapit from within the except clause to ensure a potential\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;66;03m# exception has a traceback chain.\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _wrapit(obj, method, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n", - "\u001b[0;31mIndexError\u001b[0m: cannot do a non-empty take from an empty axes." - ] - }, - { - "data": { - "image/png": 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6621ex19ytQsAAMrryiuvjMsuu6zdjx8xYkSsXr26A1fUdkWF1+47DkZEbNiwodXw2n3XwYiIvn37tmn+JUuWxJVXXhkRESNHjoyFCxdGnz59CudHjBgRI0aMiM9+9rPx0Y9+NNatWxcXX3xxTJgwodW1RERUVFREZWVlm9YBAAB0Pgf68Z+2fkwphaLuajhw4MDCcfOwasnGjRsLx9XV1W2a/4c//GHhfZezZs3aI7qaGzRoUMyYMSMi3v/82P3339+m+QEAAMqhqPBqfsXr5Zdf3ue4pqamWLJkSUREHHvssXH44Ye3af433nijcHzqqae2OnbMmDGFY1+iDAAAdGZFhdfo0aMLx/PmzdvnuEWLFhVuPX/66ae3ef4ePXoUjvd3B8Lm533+CgAA6MyKCq/hw4fH8OHDIyLiqaee2uPthM09/PDDheMpU6a0ef6PfvSjheMnnnii1bG/+tWvCsennHJKm58DAACg1IoKr4go3EWkoaEhLr300mhqatrj/OLFi+O2226LiIjjjz8+zj777DbPfd5550X37t0jIuLyyy+P3/72ty2Oe+yxx2LWrFkR8f4XOf/DP/xDka8CAACgdIoOrwsvvLDw+aq5c+fGuHHjYs6cOfHQQw/F17/+9Rg7dmzs2rUrKioq4rvf/e4ebx+MiJg6dWpUVFRERUXFXt+L9aEPfShmzpwZERHbtm2LMWPGxHnnnRd33nlnPPzww3HrrbfGxIkTY9KkSYXgy+VyB/T9XAAAAKkVdTv5iIju3bvHI488EhMnTozFixfH/PnzY/78+XuNuf3222PSpElFL+jrX/96VFZWxjXXXBMbNmyIuXPnxty5c/caN3jw4Lj11lvjs5/9bNHPAQAAUEpFh1fE+7eVX7BgQfzgBz+In/zkJ7Fs2bLYtm1bVFVVxac//en413/91zjppJPataCKioq45JJL4gtf+ELcdttt8cILL8SyZcti9erVcdxxx8Xw4cNj7NixcckllxT1xcwAAADlUpHt/uKsQ1RNTU3U19dHdXV11NXVlXs5AABAmZSzDYr+jBcAAADFEV4AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJdSv3Akoln8/HyJEjWzxXW1sbtbW1JV4RAADQ0XK5XORyuRbP5fP5Eq/mzyqyLMvK9uwlUFNTE/X19VFdXR11dXXlXg4AAFAm5WwDbzUEAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMS6lXsBpZLP52PkyJEtnqutrY3a2toSrwgAAOhouVwucrlci+fy+XyJV/NnFVmWZWV79hKoqamJ+vr6qK6ujrq6unIvBwAAKJNytoG3GgIAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxLqVewGlks/nY+TIkS2eq62tjdra2hKvCAAA6Gi5XC5yuVyL5/L5fIlX82cVWZZlZXv2EqipqYn6+vqorq6Ourq6ci8HAAAok3K2gbcaAgAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEut2oBNs3LgxVq5cGVu3bo2qqqoYOnRodOnSsT1XV1cXf/zjH6Nnz54xYsSIOOywwzp0fgAAgJTaXUjLly+PSZMmRf/+/WP06NExbty4GD58eAwZMiRmzZoVjY2NB7SwLMvixz/+cYwaNSoGDx4cZ5xxRowePTr69u0bEyZMiFdfffWA5gcAACiVdoXXs88+G6NGjYrHHntsr8BatWpVTJ8+Pc4555x2x1dTU1NMnTo1vvSlL8VLL720x7ksy+Lpp5+Oj370o/H444+3a34AAIBSKjq81q9fH1OmTIkdO3ZEly5dYubMmbFq1arYtm1bPP300zFq1KiIiHj00Udj5syZ7VrUddddF//3//7fiIg444wz4te//nVs3bo13nzzzfj2t78dPXv2jMbGxjj//POjrq6uXc8BAABQKkWH18033xzvvPNORETcfvvtcc0110RNTU306dMnxo0bF88880wMGTIkIiJmzZoVb7/9dlHzr1y5Mm688caIiBg7dmw8/fTTcdZZZ0Xfvn3juOOOiyuvvDJyuVxERGzYsCHuuuuuYl8CAABASRUVXo2NjXHPPfdERMSAAQNi2rRpe42prKyM6dOnR0TE9u3b44EHHihqQbNmzYr33nsvIiK++93vRo8ePfYac+GFF0ZNTU1EvH9lDQAAoDMrKrwWLFhQuNo1adKk6Nq1a4vjJk+eXDgu5nNYTU1N8fOf/zwiIk488cQ45ZRTWhzXpUuXePLJJ+O///u/4+67744sy9r8HAAAAKVW1O3kly1bVjieOHHiPscNHjw4Tj755HjllVfixRdfbPP8r732WqxZsyYiIj73uc9FRUXFPscOHz68zfMCAACUU1FXvHZHUUTEcccd1+rYwYMHR0TEunXrYtOmTW2a/3e/+13heOjQoYXj+vr6eO6552Lx4sWxa9euIlYMAABQfkWF19q1awvH/fr1a3XsUUcdVThuHmytaX5FbcCAAfHrX/86Tj311KipqYlPfepT8bGPfSz69u0bH//4x+Ppp58uZukAAABlU9RbDZsHVPOwaknz89u3b2/T/Js3by4c//KXv4xbb711rzGNjY3x4osvxoQJE2LatGmRy+WiS5f992OWZbFly5Y2raMlPXv2jJ49e7b78QAAwIFpaGiIhoaGdj++nPeGKCq8modL7969Wx3bPFJ27tzZpvm3bt1aOL711lujW7ducdlll8XnPve5+PCHPxz19fXxxBNPxDe/+c3YunVrfO9734sPfehDhbsotmb16tVxxBFHtGkdLZkxY0Zcd9117X48AABwYG688ca4/vrry72MdikqvPr371843rRp0x6//0vNP9e1v0grLKbbnst5/PHH42/+5m8Kvx8+fHgMHz48xo0bF6NGjYrGxsa44YYb4itf+Ur06dOn1bmrqqri9ddfb9M6WuJqFwAAlNeVV14Zl112WbsfP2LEiFi9enUHrqjtigqvQYMGFY43bNjQanht2LChcNy3b982zV9VVVU4/vznP79HdDV30kknxUUXXRR33313bN68OZ555pn4H//jf7Q6d0VFRVRWVrZpHQAAQOdzoB//ae2u6akVdXONgQMHFo6bh1VLNm7cWDiurq5u0/zNw27s2LGtjv3EJz5ROF6+fHmb5gcAACiHosKreRi9/PLL+xzX1NQUS5YsiYiIY489Ng4//PA2zd/8itf+7po4YMCAwvG7777bpvkBAADKoajwGj16dOF43rx5+xy3aNGiwq3nTz/99DbPP2zYsMJx8+/0asnSpUsLx229ogYAAFAORYXX7ptbREQ89dRTe7ydsLmHH364cDxlypQ2zz906NAYMWJERETMnTs3duzY0eK4xsbGeOCBBwq/P+uss9r8HAAAAKVWVHhFROEuIg0NDXHppZdGU1PTHucXL14ct912W0REHH/88XH22WcXNf9Xv/rViIhYuXJlXHLJJbFr1649zjc2NsY3v/nNWLRoUUS8H3Y1NTXFvgwAAICSqciK/BaxP/3pT/GpT30qFi5cGBHv3wRj6tSpUVlZGQsXLow777wztm3bFhUVFfHII4/EpEmT9nj81KlT4957742Ilr8b609/+lOcdtpphbAaMWJEfPGLX4yPfOQj8eabb8bPfvazeP755yPi/c95LVy4MI477rh9rrempibq6+ujuro66urqinmpAADAIaScbVDU7eQjIrp37x6PPPJITJw4MRYvXhzz58+P+fPn7zXm9ttv3yu62jr/448/HhMnTowXX3wxXn/99bjmmmv2Gjds2LD4+c9/3mp0AQAAdAZFv9Uw4v3byi9YsCBmz54dp512WvTr1y969OgRQ4YMiYsvvjgWLVoU06ZNa/eijjnmmHj++efjBz/4QYwfPz4GDBgQ3bp1i6OPPjrGjx8fd911VyxZsiROPPHEdj8HAABAqRT9VsODjbcaAgAAEeVtg3Zd8QIAAKDthBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBi3cq9gFLJ5/MxcuTIFs/V1tZGbW1tiVcEAAB0tFwuF7lcrsVz+Xy+xKv5s4osy7KyPXsJ1NTURH19fVRXV0ddXV25lwMAAJRJOdvAWw0BAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAILFu5V5AqeTz+Rg5cmSL52pra6O2trbEKwIAADpaLpeLXC7X4rl8Pl/i1fxZRZZlWdmevQRqamqivr4+qquro66urtzLAQAAyqScbeCthgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgsW7lXkCp5PP5GDlyZIvnamtro7a2tsQrAgAAOloul4tcLtfiuXw+X+LV/FlFlmVZ2Z69BGpqaqK+vj6qq6ujrq6u3MsBAADKpJxt4K2GAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxLod6AQbN26MlStXxtatW6OqqiqGDh0aXbroOQAAgN3aXUjLly+PSZMmRf/+/WP06NExbty4GD58eAwZMiRmzZoVjY2NHbnOiIjIsizOOeecqKioiKlTp3b4/AAAACm0K7yeffbZGDVqVDz22GN7BdaqVati+vTpcc4553R4fH3ve9+Ln//85x06JwAAQGpFh9f69etjypQpsWPHjujSpUvMnDkzVq1aFdu2bYunn346Ro0aFRERjz76aMycObPDFvrqq6/GZZdd1mHzAQAAlErR4XXzzTfHO++8ExERt99+e1xzzTVRU1MTffr0iXHjxsUzzzwTQ4YMiYiIWbNmxdtvv33Ai9yxY0d8/vOfj127dh3wXAAAAKVWVHg1NjbGPffcExERAwYMiGnTpu01prKyMqZPnx4REdu3b48HHnjggBd52WWXxe9+97sYNGjQAc8FAABQakWF14IFCwpXuyZNmhRdu3ZtcdzkyZMLx48//vgBLC/ioYceiu9///tRUVER99133wHNBQAAUA5FhdeyZcsKxxMnTtznuMGDB8fJJ58cEREvvvhiO5cW8dZbb8XFF18cERFXXHFFTJgwod1zAQAAlEtR4bVmzZrC8XHHHdfq2MGDB0dExLp162LTpk1FL+y9996Lc889NzZt2hSnnnpqh96oAwAAoJSKCq+1a9cWjvv169fq2KOOOqpw3DzY2mrmzJnx3HPPRd++feMnP/lJ9OjRo+g5AAAAOoNuxQxuHlDNw6olzc9v3769qEU988wzccMNN0RExJ133hlDhw4t6vEtybIstmzZ0u7H9+zZM3r27HnA6wAAANqnoaEhGhoa2v34LMs6cDXFKSq8modL7969Wx3bPFJ27tzZ5ud455134rzzzossy+Lcc8+NL33pS8UscZ9Wr14dRxxxRLsfP2PGjLjuuus6ZC0AAEDxbrzxxrj++uvLvYx2KSq8+vfvXzjetGnTHr//S80/17W/SNsty7K46KKLor6+Po4//vi48847i1leq6qqquL1119v9+Nd7QIAgPK68sor47LLLmv340eMGBGrV6/uwBW1XVHh1fx7tDZs2NBqeG3YsKFw3Ldv3zbNn8vl4tFHH42uXbvG/fffH5WVlcUsr1UVFRUdOh8AAFBaB/rxn4qKig5cTXGKCq+BAwcWjpuHVUs2btxYOK6urm7T/Lu/eHnixImxcePG+NWvfrXPsfX19YXzhx9+eJxxxhlteg4AAIBSa/cVr5dffjlOO+20Fsc1NTXFkiVLIiLi2GOPjcMPP7xN8+/+oNy8efNi3rx5rY598skn48knn4yIiFNOOSVeeumlNj0HAABAqRV1O/nRo0cXjlsLo0WLFhVuPX/66ae3c2kAAACHhqLCa/jw4TF8+PCIiHjqqaf2eDthcw8//HDheMqUKW2eP8uy/f7a7YILLij8b652AQAAnVlR4RURhbuINDQ0xKWXXhpNTU17nF+8eHHcdtttERFx/PHHx9lnn33AiwQAADiYFR1eF154YYwZMyYiIubOnRvjxo2LOXPmxEMPPRRf//rXY+zYsbFr166oqKiI7373u9GjR489Hj916tSoqKiIiooK34sFAAB8IBR1c42IiO7du8cjjzwSEydOjMWLF8f8+fNj/vz5e425/fbbY9KkSR22UAAAgINV0Ve8It6/rfyCBQti9uzZcdppp0W/fv2iR48eMWTIkLj44otj0aJFMW3atI5eKwAAwEGpImt+x4pDUE1NTdTX10d1dXXU1dWVezkAAECZlLMN2nXFCwAAgLYTXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIl1K/cCSiWfz8fIkSNbPFdbWxu1tbUlXhEAANDRcrlc5HK5Fs/l8/kSr+bPKrIsy8r27CVQU1MT9fX1UV1dHXV1deVeDgAAUCblbANvNQQAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxLqVewGlks/nY+TIkS2eq62tjdra2hKvCAAA6Gi5XC5yuVyL5/L5fIlX82cVWZZlZXv2EqipqYn6+vqorq6Ourq6ci8HAAAok3K2gbcaAgAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEupV7AaWSz+dj5MiRLZ6rra2N2traEq8IAADoaLlcLnK5XIvn8vl8iVfzZxVZlmVle/YSqKmpifr6+qiuro66urpyLwcAACiTcraBtxoCAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAAS63agE2zcuDFWrlwZW7dujaqqqhg6dGh06aLnAAAAdmt3IS1fvjwmTZoU/fv3j9GjR8e4ceNi+PDhMWTIkJg1a1Y0NjYe0MI2b94cs2bNinPOOSf+6q/+Kg477LAYNmxYTJkyJW699dbYtWvXAc0PAABQKhVZlmXFPujZZ5+Nz3zmM7Fjx459jpk8eXI8/PDD0bVr16IX9dvf/jb+4R/+IVavXr3PMUOHDo277rorPv3pT7c6V01NTdTX10d1dXXU1dUVvRYAAODQUM42KPqK1/r162PKlCmxY8eO6NKlS8ycOTNWrVoV27Zti6effjpGjRoVERGPPvpozJw5s+gF5fP5+Pu///tCdI0dOzZuueWWePDBB+Omm26KT37ykxERsWLFipg4cWI8++yzRT8HAABAKRV9xeuKK66I73znOxERcccdd0Rtbe0e57ds2RKnnHJKvPnmm9GnT5/4wx/+EP3792/z/F//+tfj5ptvjoiIyy+/PP793/99j8+MNTU1xc033xxXXnllRESMGDEiXn311X1+rswVLwAAIOIguuLV2NgY99xzT0REDBgwIKZNm7bXmMrKypg+fXpERGzfvj0eeOCBohb005/+NCIiBg0aFNdff/1eQdWlS5f4+te/Hn/7t38bERGvv/56LFiwoKjnAAAAKKWiwmvBggXxzjvvRETEpEmT9vn5rcmTJxeOH3/88TbPn8/n449//GNh/t69e7c4rqKiIqZMmVL4/Ysvvtjm5wAAACi1osJr2bJlheOJEyfuc9zgwYPj5JNPjojioiifzxeOjzvuuFbHDho0qHC8c+fONj8HAABAqRX1PV5r1qwpHO8vjAYPHhyvvPJKrFu3LjZt2hRHHnnkfucfOHBgzJkzJyIiTjvttFbHvvDCC4XjD3/4w/udGwAAoFyKCq+1a9cWjvv169fq2KOOOqpwvGbNmjaF14ABA2Lq1Kn7HVdfXx+5XC4iIg477LA444wz9vsYAACAcmn3Fa/mYdWS5ue3b99e5LL27fe//31MmjQpNm7cGBERtbW1cfTRR+/3cVmWxZYtW9r9vD179oyePXu2+/EAAMCBaWhoiIaGhnY/vh1fYdxhigqv5uGyrxtf7NY8UjriM1gNDQ0xe/bsmDFjRuGLm88666w2f1fY6tWr44gjjmj388+YMSOuu+66dj8eAAA4MDfeeGNcf/315V5GuxQVXs2/j2vTpk2tfj/Xpk2bCsf7i7TWZFkWP/3pT+Oqq66KN998s/C/T5o0KX784x9Hr1692jRPVVVVvP766+1eh6tdAABQXldeeWVcdtll7X78iBEjYvXq1R24orYrKrya30lww4YNrYbXhg0bCsd9+/Ztx9IiVq5cGf/8z/8cTz31VOF/O/roo2PWrFnxpS99KSoqKto8V0VFRVRWVrZrHQAAQPkd6Md/iumHjlbU7eQHDhxYOG4eVi3Z/RmsiIjq6uqiFpVlWeRyuTjppJMK0XXYYYfFN7/5zfj9738f559/fll/aAAAAMUoKryaX/F6+eWX9zmuqakplixZEhERxx57bBx++OFFLeqmm26Kr371q4XPcn3xi1+MFStWxLe+9S1XrQAAgINOUeE1evTowvG8efP2OW7RokWFW8+ffvrpRS3ovvvuiyuvvDIiIvr06ROPPPJIzJ07d4/oAwAAOJgUFV7Dhw+P4cOHR0TEU089tcfbCZt7+OGHC8dTpkxp8/yNjY1x7bXXRkRE9+7d46mnnorJkycXs0QAAIBOp6jwiojCXUQaGhri0ksvjaampj3OL168OG677baIiDj++OPj7LPPbvPc//Vf/1W4c+HXvva1+MQnPlHs8gAAADqdou5qGBFx4YUXxg9/+MNYuHBhzJ07N1atWhVTp06NysrKWLhwYdx5552xa9euqKioiO9+97vRo0ePPR4/derUuPfeeyNi7+/Gmj9/fuH46KOPjl/96ldtWtNJJ51U9A08AAAASqXo8OrevXs88sgjMXHixFi8eHHMnz9/j2DaPeb222+PSZMmFTV3Pp8vHF9xxRVtftycOXNi6tSpRT0XAABAqRT9VsOI928rv2DBgpg9e3acdtpp0a9fv+jRo0cMGTIkLr744li0aFFMmzat6HmbhxcAAMChoiLLsqzci0ippqYm6uvro7q6Ourq6sq9HAAAoEzK2QbtuuIFAABA2wkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxLqVewGlks/nY+TIkS2eq62tjdra2hKvCAAA6Gi5XC5yuVyL5/L5fIlX82cVWZZlZXv2EqipqYn6+vqorq6Ourq6ci8HAAAok3K2gbcaAgAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBi3cq9gFLJ5/MxcuTIFs/V1tZGbW1tiVcEAAB0tFwuF7lcrsVz+Xy+xKv5s4osy7KyPXsJ1NTURH19fVRXV0ddXV25lwMAAJRJOdvAWw0BAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgsAACAx4QUAAJCY8AIAAEhMeAEAACQmvAAAABITXgAAAIkJLwAAgMSEFwAAQGLdyr2AUsnn8zFy5MgWz9XW1kZtbW2JVwQAAHS0XC4XuVyuxXP5fL7Eq/mziizLsrI9ewnU1NREfX19VFdXR11dXbmXAwAAlEk528BbDQEAABITXgAAAIkJLwAAgMSEFwAAQGLCCwAAIDHhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDEhBcAAEBiwgs6SENDQ1x33XXR0NBQ7qVwiLPXKBV7jVKx1/ggqMiyLCv3IlKqrq6O1atXR1VVVdTX15d7ORzCtmzZEkcccURs3rw5Kisry70cDmH2GqVir1Eq9hqlUs42cMULAAAgMeEFAACQmPACAABITHgBAAAk1u1AJ9i4cWOsXLkytm7dGlVVVTF06NDo0qXjem7btm2xYsWK2LhxYxxzzDExbNiw6N69e4fNDwAAkFq7C2n58uUxadKk6N+/f4wePTrGjRsXw4cPjyFDhsSsWbOisbHxgBa2Zs2aOO+886J///4xatSoGD9+fJx44olRVVUVV199dezateuA5gcAACiVdoXXs88+G6NGjYrHHntsr8BatWpVTJ8+Pc4555x2x9eyZcvi5JNPjrlz5+4VWOvXr49vf/vbceaZZ8b27dvbNT8AAEApFR1e69evjylTpsSOHTuiS5cuMXPmzFi1alVs27Ytnn766Rg1alRERDz66KMxc+bMohfU0NAQkydPjvXr10dExKWXXhorVqyIHTt2xPPPPx8TJkyIiIiFCxfGJZdcUvT8nUkulyv3EvbJ2g4tnflnZm2Hls78M7O2Q0tn/plZ26GlM//MrO0gkxXp8ssvzyIii4jsjjvu2Ov85s2bsyFDhmQRkfXp0ydbt25dUfPncrnC/NOnT9/r/LvvvpuNGTMmi4isoqIie/XVV1udr6qqKouIrKqqqqh1lMKIESPKvYR9srbibd68OYuIbPPmzeVeyl46688sy6ytPey19rG24tlr7WNtxbPX2sfailfONijqildjY2Pcc889ERExYMCAmDZt2l5jKisrY/r06RERsX379njggQeKCsG77747IiK6desWV1999V7nu3fvHtdee21ERGRZFnPmzClqfgAAgFIrKrwWLFgQ77zzTkRETJo0Kbp27driuMmTJxeOH3/88TbPX19fHy+99FJERJx55plx5JFHtjhuwoQJ0adPn6LnBwAAKIeiwmvZsmWF44kTJ+5z3ODBg+Pkk0+OiIgXX3yxw+fv1atX4bNeS5cudZMNAACgUysqvNasWVM4Pu6441odO3jw4IiIWLduXWzatCnZ/BERb7zxRpvmBwAAKIeiwmvt2rWF4379+rU69qijjiocNw+qcs4PAABQDt2KGdw8cJqHT0uan2/rWwFTzP/2229HxPtRV11d3aZ1tKSioqLdj92XfD4fNTU1HT5vR7C24mVZFhERI0aMSLJfDkRn/ZlFWFt72GvtY23Fs9fax9qKZ6+1zwd1bbv3S3vsvtCzuxFKqajw2rJlS+G4d+/erY7t2bNn4Xjnzp1lm3/3lzg3NTXF6tWr27SOUqqvry/3EvbJ2tqnM+6ziM79M7O29rHXimdt7WOvFc/a2sdeK561tc/uRiilosKrf//+heNNmzbt8fu/1PxzXfuLqH3N35q2zt+rV6/YtWtXdO3atdX17k9n+9cXAAD4IDqQK15vv/12NDY2Rq9evTpwRW1TVHgNGjSocLxhw4ZWQ2bDhg2F4759+7Zr/ta0dX53PAQAAMqtqJtrDBw4sHC8vzDauHFj4bitn61q7/zN73AIAADQ2RQVXs2vSL388sv7HNfU1BRLliyJiIhjjz02Dj/88A6dPyLilVdeiYiIrl27xrBhw9o0PwAAQDkUFV6jR48uHM+bN2+f4xYtWlS4Y8jpp5/e5vlPOumk6N69+37nz+fzsXDhwoiIOPXUU6NHjx5tfg4AAIBSKyq8hg8fHsOHD4+IiKeeemqPt/s19/DDDxeOp0yZ0ub5KysrY/z48RER8dprr8XSpUtbHPeLX/yi8KG6YuYHAAAoh6LCKyLisssui4iIhoaGuPTSS6OpqWmP84sXL47bbrstIiKOP/74OPvss9s1f0TEV77yldi1a9ce599666249tprI+L9UPvyl79c5CsAAAAorYqsyPsx/ulPf4pPfepThbf6jR07NqZOnRqVlZWxcOHCuPPOO2Pbtm1RUVERjzzySEyaNGmPx0+dOjXuvffeiIiYMWNGXHfddXucz7IsPvvZzxaump1yyikxbdq06NOnTzz33HPxwAMPFG4lf8cdd0RtbW17Xvdetm3bFitWrIiNGzfGMcccE8OGDSu87ZEPlo0bN8bKlStj69atUVVVFUOHDo0uXYr+NwrYL3uNUinFXqurq4s//vGP0bNnzxgxYkQcdthhHTo/B4fUe62pqSneeuutWLVqVZxwwglRXV3tK39IIkkbZO2wZs2abNSoUVlEtPire/fu2V133dXiYy+44ILCuBkzZrQ4ZuvWrdmnP/3pfc4fEVllZWX2ne98J3vvvffa8xIKVq9enZ177rlZr1699pj/6KOPzq666qps586dBzQ/B49ly5Zlf//3f5917dp1j70wePDg7JZbbjngvbZp06bslltuyaZMmZKdeOKJWe/evbOhQ4dmZ599djZr1ix77QMk9V5rSVNTUzZlypQsIrILLrigw+enc0q915qamrL77rsv++hHP7rH/BUVFdn48eOzJUuWdNArobNLvddWrlyZ/eM//mPWu3fvPebv27dv9r/+1//K8vl8B70SDjZ33HFHq11RrJRt0K7wyrIsa2hoyGbPnp2ddtppWb9+/bIePXpkQ4YMyS6++OLslVde2efj2hJeWZZljY2N2VVXXZV16dKl1QCbPHlyu/+feenSpdnRRx/d6vxjxozJtm3b1q75OXjMnz8/O+yww5LttRdeeCGrqqpqdf6hQ4dm/+///b8OfmV0Nqn32r7ceeedhfmF1wdD6r3W2NiYnX/++a3O37Vr1+yxxx7r4FdGZ5N6rz344INZz5499/sP8vPnz+/gV8bB4JOf/GSHhVfqNmh3eKX29ttvZ0cddVQWEVmXLl2ymTNnZqtWrcq2bduWPf3003tccbv22muLnn/Xrl3Zhz/84cIcl156abZixYpsx44d2fPPP59NmDChcO78889P8ArpLFLvtbVr12bHHHNMYY6xY8dmt9xyS/bggw9mN910U+EPjN1Xi/2H49CVeq/ty5IlS/b4lzvhdegrxV675pprCnOcccYZ2a9//ets69at2Ztvvpl9+9vfLvxFuV+/ftmqVas6+BXSWaTea2+88UZ2+OGHZxGR9e7dO5s5c2a2bNmybNu2bdmrr76afe1rXytcZaupqcnWr1+f4FXSWd1zzz1tuqDTFqVog04bXpdffnnhxd1xxx17nd+8eXM2ZMiQLCKyPn36ZOvWrStq/lwuV5h/+vTpe51/9913szFjxhTeMvHqq6+2+7XQuaXea1dccUVh/ssvvzxrbGzc43xjY2N24403FsaMGDFirzEcGlLvtZZs3749O/HEE/f41zrhdehLvdd+//vfZ926dSv8Y1JDQ8NeY/7jP/6jsIarrrqq3a+Fzi31Xvvyl79cmH/u3Lktjvk//+f/FMZ861vfatfr4OCxadOmbP78+dmFF16Yde/evcPCqxRt0CnD67333iv868mAAQP2eWl693s6IyKbPXt2Uc+x+/3o3bp1yzZu3NjimMcee6ww///+3/+72JfBQaAUe+3YY4/NIiIbNGhQtmPHjhbHNDU1ZX/7t39beI7nnnuu6NdC51aKvdaSf/mXfynsP+H1wVCKvXbJJZcUHrt48eIWxzQ2NmY1NTVZRGR/9Vd/VezL4CBQir120kknZRGR9e/fP2tqampxzObNm/d4SyOHrlNPPXWfbwE80PAqRRt0yttnLViwIN55552IiJg0aVJ07dq1xXGTJ08uHD/++ONtnr++vj5eeumliIg488wz48gjj2xx3IQJE6JPnz5Fz8/BI/Vey+fz8cc//rEwf+/evVscV1FRscd30r344ottfg4ODqn3Wkseeuih+P73vx8VFRVx3333HdBcHDxS77Wmpqb4+c9/HhERJ554YpxyyiktjuvSpUs8+eST8d///d9x9913F75/k0NHKf5cW758eUREDB48eJ93L6ysrIx+/frtMZ5D07p165LMW6o26JThtWzZssLxxIkT9zlu8ODBcfLJJ0dEcX9Rbev8vXr1igkTJkRExNKlS2P79u1tfg4ODqn3Wj6fLxwfd9xxrY4dNGhQ4Xjnzp1tfg4ODqn32l9666234uKLL46IiCuuuKLwZxmHvtR77bXXXos1a9ZERMTnPve5Vm/lPXz48PjkJz8Zn/zkJ93y+xBUij/Xdv+3cenSpdHQ0NDimPr6+tiwYUNERFRVVRU1PweXZcuWxc6dOwu/li5d2mHz7payDTpleO3+Az1i/39ZHTx4cES8X8C7v98rxfwREW+88Uab5ufgkXqvDRw4MObMmRNz5syJ//k//2erY1944YXC8Yc//OE2zc/BI/Vea+69996Lc889NzZt2hSnnnpqzJw5s+g5OHil3mu/+93vCsdDhw4tHNfX18dzzz0Xixcvjl27dhWxYg5Wpfhz7YILLoiIiB07dsQ3vvGNva6cvvfee/Fv//Zvhd+fd955bZ6bg0/Pnj2jV69ehV89e/bskHlL1QadMrzWrl1bON596XhfjjrqqMJx8x9aOefn4JF6LwwYMCCmTp0aU6dOjeHDh+9zXH19feRyuYiIOOyww+KMM85o0/wcPEr5587MmTPjueeei759+8ZPfvKT6NGjR9FzcPBKvdea/8vwgAED4te//nWceuqpUVNTE5/61KfiYx/7WPTt2zc+/vGPx9NPP13k6jmYlOLPtauuuio+85nPRETEbbfdFmPHjo3Zs2fHww8/HLNmzYpRo0bFQw89FBERF110USHUoBil+m90t+KWVRrNX0TzF9eS5ufberkv9fwcPDrDXvj9738fkyZNio0bN0ZERG1tbRx99NEdNj+dQ6n22jPPPBM33HBDRETceeede1yR4IMh9V7bvHlz4fiXv/xl3HrrrXuNaWxsjBdffDEmTJgQ06ZNi1wuF126dMp/6+UAlOLPtR49esS8efPiqquuiu985zvxm9/8Jn7zm9/sNW727NlRW1vrLa20S6n+G90p/xTcsmVL4XhfNyPYrfklxrZ+Lib1/Bw8yrkXGhoa4pZbbomTTz45Xn/99YiIOOuss7wt7BBVir32zjvvxHnnnRdZlsW5554bX/rSl4pfKAe91Htt69atheNbb701unXrFldccUUsWrQotm7dGkuXLo3vfve7cfjhh0dExPe+970W44yDX6n+G/rAAw/s9wZBs2fPjqeeeqqoeWG3Uu3lThle/fv3Lxzv733Azc/v7wdVqvk5eJRjL2RZFvfff3985CMficsvvzx27NgREe/fEeqRRx6JXr16tXtuOq/Uey3Lsrjooouivr4+jj/++Ljzzjvbs0wOAan3Wrdue75Z5vHHH4+bbrqp8BbD4cOHx7/+67/Gc889V7jL3Q033OBdI4egUvw39Kabborzzjsv1q5dGyeccEL86Ec/ijfeeCO2b98er732Wtx+++0xYMCAWL58eXzmM5+Jn/70p8W+DCjZ3wc7ZXg1v7vb7rvU7Evz83379u0U83PwKPVeWLlyZfz1X/91fPGLX4w333wzIiKOPvrouPfee+ORRx6JysrKds1L55d6r+VyuXj00Ueja9eucf/999tLH2Cp91rzu8Z9/vOfj7/5m79pcdxJJ50UF110UUS8//bEZ555pk3zc/BIvdeWLFkSV155ZUREjBw5Ml555ZW44IILYujQoXHYYYfFiBEj4tJLL42XXnopBgwYEI2NjXHxxRfH22+/3Y5XwwdZqf4+2CnDa+DAgYXj/b343Z+LiYiorq5OOn/zu5hwaEi913bLsixyuVycdNJJhbdCHHbYYfHNb34zfv/738f555/vfemHuNR7bfr06RHx/m1wN27cGL/61a/2+rVbfX194X977rnninkZHARS77Xmf0EZO3Zsq2M/8YlPFI59v9KhJ/Ve++EPf1i4i+GsWbMK35/0lwYNGhQzZsyIiPc/c3P//fe3aX7YrVRt0ClvrtH8D/WXX345TjvttBbHNTU1xZIlSyIi4thjjy28n7zY+b/whS/sc+wrr7wSERFdu3aNYcOGtWl+Dh6p99puN910U+Ff7SIivvjFL8Ytt9yyx/NzaEu913Z/v828efNi3rx5rY598skn48knn4yIiFNOOaXwpZEcGlLvteZXvPZ3968BAwYUjt999902zc/BI/Vea36r7lNPPbXVsWPGjCkci3yKVao26JRXvEaPHl04bu0vEIsWLSrc/vH0009v8/wnnXRSdO/efb/z5/P5WLhwYUS8///wbsl86Em91yIi7rvvvkJ09enTJx555JGYO3eu6PqAKcVeg4j0e635XzSaf6dXS5p/uWmx7xSg80u915r/vav5zQ9a0vx8R323Ex8cpWqDThlew4cPL3zn0VNPPbXHJb3mHn744cLxlClT2jx/ZWVljB8/PiIiXnvttX1+6/UvfvGLwiXuYubn4JF6rzU2Nsa1114bERHdu3ePp556KiZPnnwAK+ZglXqvZVm231+7XXDBBYX/zdWuQ0/qvTZ06NAYMWJERETMnTu3cIOgv9TY2BgPPPBA4fdnnXVWm5+Dg0PqvfbRj360cPzEE0+0Orb526lPOeWUNj8HRJSwDbJO6vvf/34WEVlEZOeee27W2Ni4x/kXX3wx69WrVxYR2fHHH581NDQUNf8TTzxRmP+ss87Kdu7cucf5N998MxswYEAWEVllZWW2fv36A35NdE4p99ovf/nLwtxXXHFFRy+dg0zqP9f2Z/dzX3DBBR06L51P6r2Wy+X22E9/+d/Q9957L/vGN75RGDNlypQDfk10Tin32ooVK7Lu3btnEZH17ds3e+GFF1ocN2/evKxLly5ZRGT9+vXLNm3adECviYPHH/7wh8L+mzFjxgHNVYo26LTh9e6772Zjxowp/ADGjh2b3XPPPdnPfvaz7Iorrsj69u2bRURWUVGRPfroo3s9/oILLmj1/xBNTU3ZOeecUxhzyimnZHfddVf28MMPZ9dff33Wv3//wrk77rijBK+Yckm515r/xePmm2/O/vM//7NNv+rq6kr06iml1H+u7Y/w+uBIvdfefffd7OMf/3hhzIgRI7Jvfetb2YMPPph95zvfyT7xiU8Uzg0YMCB78803S/CqKYfUe+3GG28snK+oqMjOPffcLJfLZQ899FA2a9as7O/+7u8K5yMiu//++0vwquksigmvztAGnTa8sizL1qxZk40aNWqP/4dq/qt79+7ZXXfd1eJj2/IXlK1bt2af/vSn9zl/RGRXX3111tTUlPBV0hmk2msXXnhhq/trX7/mzJmT/kVTFqn/XGuN8PpgSb3X1q5dm33sYx9r9c+yYcOGZa+++mrCV0lnkHKvNTU1ZblcLuvXr1+re23w4MHZgw8+mPiV0tl0ZHhlWfo26JSf8dpt4MCBsWDBgpg9e3acdtpp0a9fv+jRo0cMGTIkLr744li0aFFMmzat3fP37ds3nnjiibj33ntj/Pjx0b9//+jevXvU1NTEF77whXj22WfjhhtucJvvD4BUey2fzydYLQez1H+uwW6p99oxxxwTzz//fPzgBz+I8ePHx4ABA6Jbt25x9NFHx/jx4+Ouu+6KJUuWxIknntiBr4rOKOVeq6ioiEsuuSRWrFgR1157bfzd3/1dnHDCCdGrV68YPnx4TJ48OW655ZZYtmxZfPazn+3gV8YHTeo2qMiyZp+4BgAAoMN16iteAAAAhwLhBQAAkJjwAgAASEx4AQAAJCa8AAAAEhNeAAAAiQkvAACAxIQXAABAYsILAAAgMeEFAACQmPACAABITHgBAAAkJrwAAAASE14AAACJCS8AAIDE/j/VuMC7dPIT5AAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pid = 2\n", - "var = \"energy\"\n", - "bins = np.linspace(-5,30,100)\n", - "\n", - "reso_plot_isolated(pid=pid, var=var, bins=bins, physics_process_lab=physics_process[sample], \n", - " textx=0.05, \n", - " texty=0.87,\n", - " apply_isolation=\"Isolated\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f818883", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb93fbd7", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8f76a6f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "7d6491e2", - "metadata": {}, - "source": [ - "# Isolation for different samples" - ] - }, - { - "cell_type": "code", - "execution_count": 1291, - "id": "c29aef9b", - "metadata": {}, - "outputs": [], - "source": [ - "isolation = {}\n", - "isolation[\"electrons\"] = {}\n", - "isolation[\"muons\"] = {}" - ] - }, - { - "cell_type": "code", - "execution_count": 1297, - "id": "01e572ac", - "metadata": {}, - "outputs": [], - "source": [ - "# sample = \"clic_edm_ttbar_pf\"\n", - "sample = \"clic_edm_qq_pf\"\n", - "\n", - "PATH = \"evaluation/epoch_96\"\n", - "\n", - "pred_path = Path(f\"{PATH}/{sample}/test/\")\n", - "path = str(pred_path / \"*.parquet\")\n", - "\n", - "outpath = f\"paper_plots/{sample}\" # plots path\n", - "os.system(f\"mkdir -p {outpath}\");" - ] - }, - { - "cell_type": "code", - "execution_count": 1298, - "id": "3b4f30ef", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "path evaluation/epoch_96/clic_edm_qq_pf/test/*.parquet\n", - "['evaluation/epoch_96/clic_edm_qq_pf/test/pred_batch7424.parquet', 'evaluation/epoch_96/clic_edm_qq_pf/test/pred_batch3708.parquet', 'evaluation/epoch_96/clic_edm_qq_pf/test/pred_batch2202.parquet', 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"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:03<00:00, 16.06it/s]\n" - ] - } - ], - "source": [ - "yvals, X, _ = load_eval_data(path, max_files=50)" - ] - }, - { - "cell_type": "code", - "execution_count": 1299, - "id": "5c9f8aea", - "metadata": {}, - "outputs": [], - "source": [ - "isolation[\"electrons\"][sample] = get_isolation(msk=yvals[\"gen_cls_id\"]==4)" - ] - }, - { - "cell_type": "code", - "execution_count": 1300, - "id": "255e402d", - "metadata": {}, - "outputs": [], - "source": [ - "isolation[\"muons\"][sample] = get_isolation(msk=yvals[\"gen_cls_id\"]==5)" - ] - }, - { - "cell_type": "code", - "execution_count": 1321, - "id": "8fcbd839", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for lepton in isolation:\n", - " fig, ax = plt.subplots()\n", - " \n", - " for sample in isolation[lepton]:\n", - " ax.hist(ak.flatten(isolation[lepton][sample]), \n", - " bins=np.linspace(0,3,20), \n", - " histtype=\"step\", \n", - " lw=2, \n", - " label=sample,\n", - " density=True,\n", - " )\n", - " \n", - " if lepton == \"electrons\":\n", - " title = r\"$e^\\pm$\"\n", - " elif lepton == \"muons\":\n", - " title = r\"$\\mu^\\pm$\"\n", - " \n", - " ax.legend(title=title)\n", - " ax.set_yscale(\"log\")\n", - " \n", - " ax.set_xlabel('Relative Isolation per event (cone dR=0.3)', fontsize=20)\n", - " ax.set_ylabel('Counts')\n", - " \n", - " plt.savefig(f\"isolation_{lepton}.pdf\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3323bfde", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "coffea-env", - "language": "python", - "name": "coffea-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/clic/paper_plots_2023_farouk.ipynb b/notebooks/clic/paper_plots_2023_farouk.ipynb new file mode 100644 index 000000000..153a5c34d --- /dev/null +++ b/notebooks/clic/paper_plots_2023_farouk.ipynb @@ -0,0 +1,1199 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "fea2d043", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os, glob\n", + "import time\n", + "from pathlib import Path\n", + "\n", + "import awkward\n", + "import awkward as ak\n", + "import mplhep\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import torch\n", + "import tqdm\n", + "import vector\n", + "import boost_histogram as bh\n", + "\n", + "import itertools\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import mplhep\n", + "\n", + "mplhep.set_style(mplhep.styles.CMS)\n", + "\n", + "sys.path.append(\"../mlpf/plotting/\")\n", + "\n", + "import plot_utils\n", + "from plot_utils import pid_to_text, load_eval_data, compute_jet_ratio\n", + "\n", + "from plot_utils import CLASS_LABELS_CLIC, CLASS_NAMES_CLIC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c722929b", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57d07f10", + "metadata": {}, + "outputs": [], + "source": [ + "def med_iqr(arr):\n", + " p25 = np.percentile(arr, 25)\n", + " p50 = np.percentile(arr, 50)\n", + " p75 = np.percentile(arr, 75)\n", + " return p50, p75 - p25\n", + "\n", + "\n", + "def flatten(arr):\n", + " return arr.reshape(-1, arr.shape[-1])\n", + "\n", + "\n", + "def binom_error(n_sig, n_tot):\n", + " \"\"\"\n", + " for an efficiency = nSig/nTrueSig or purity = nSig / (nSig + nBckgrd), this function calculates the\n", + " standard deviation according to http://arxiv.org/abs/physics/0701199 .\n", + " \"\"\"\n", + " variance = np.where(\n", + " n_tot > 0, (n_sig + 1) * (n_sig + 2) / ((n_tot + 2) * (n_tot + 3)) - (n_sig + 1) ** 2 / ((n_tot + 2) ** 2), 0\n", + " )\n", + " return np.sqrt(variance)\n", + "\n", + "\n", + "def plot_eff_and_fake_rate(icls=1, ivar=4, ielem=1, bins=np.linspace(-3, 6, 100), \n", + " log=True,\n", + " physics_process_lab=\"\",\n", + " textx=0.01,\n", + " texty=0.87,\n", + " ):\n", + "\n", + " xlabel = elemlabel[ielem][ivar]\n", + " \n", + " values = X[:, :, ivar]\n", + "\n", + " hist_X = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_gen = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_gen_pred = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_gen_cand = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_pred = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_cand = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_pred_fake = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_cand_fake = bh.Histogram(bh.axis.Variable(bins))\n", + "\n", + " eff_mlpf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", + " eff_pf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", + " fake_pf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", + " fake_mlpf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", + "\n", + " if ielem == 45: # ECAL and HCAL in CMS\n", + " msk_X = (X[:, :, 0] == 4) | (X[:, :, 0] == 5)\n", + " else:\n", + " msk_X = X[:, :, 0] == ielem\n", + "\n", + " msk_gen = yvals[\"gen_cls_id\"] == icls\n", + " msk_nogen = yvals[\"gen_cls_id\"] != icls\n", + " \n", + " msk_pred = yvals[\"pred_cls_id\"] == icls\n", + " msk_nopred = yvals[\"pred_cls_id\"] != icls\n", + "\n", + " msk_cand = yvals[\"cand_cls_id\"] == icls\n", + " msk_nocand = yvals[\"cand_cls_id\"] != icls\n", + "\n", + " hist_X.fill(awkward.flatten(values[msk_X]))\n", + " hist_gen.fill(awkward.flatten(values[msk_gen & msk_X]))\n", + " hist_pred.fill(awkward.flatten(values[msk_pred & msk_X]))\n", + " hist_cand.fill(awkward.flatten(values[msk_cand & msk_X]))\n", + "\n", + " # Genparticle exists, reco particle exists\n", + " hist_gen_pred.fill(awkward.flatten(values[msk_gen & msk_pred & msk_X]))\n", + " hist_gen_cand.fill(awkward.flatten(values[msk_gen & msk_cand & msk_X])) \n", + "\n", + " # Genparticle does not exist, reco particle exists\n", + " hist_pred_fake.fill(awkward.flatten(values[msk_nogen & msk_pred & msk_X]))\n", + " hist_cand_fake.fill(awkward.flatten(values[msk_nogen & msk_cand & msk_X]))\n", + "\n", + " eff_mlpf.values()[:] = hist_gen_pred.values() / hist_gen.values()\n", + " eff_mlpf.variances()[:] = binom_error(hist_gen_pred.values(), hist_gen.values()) ** 2\n", + "\n", + " eff_pf.values()[:] = hist_gen_cand.values() / hist_gen.values()\n", + " eff_pf.variances()[:] = binom_error(hist_gen_cand.values(), hist_gen.values()) ** 2\n", + "\n", + " fake_pf.values()[:] = hist_cand_fake.values() / hist_cand.values()\n", + " fake_pf.variances()[:] = binom_error(hist_cand_fake.values(), hist_cand.values()) ** 2\n", + "\n", + " fake_mlpf.values()[:] = hist_pred_fake.values() / hist_pred.values()\n", + " fake_mlpf.variances()[:] = binom_error(hist_pred_fake.values(), hist_pred.values()) ** 2\n", + "\n", + "# plt.figure()\n", + "# ax = plt.axes()\n", + "# mplhep.histplot(hist_X, label=\"all PFElements\", color=\"black\")\n", + "# mplhep.histplot(hist_cand, label=\"with PF\")\n", + "# mplhep.histplot(hist_pred, label=\"with MLPF reco\")\n", + "# mplhep.histplot(hist_gen, label=\"with MLPF truth\")\n", + "# plt.ylabel(\"Number of PFElements / bin\")\n", + "# plt.xlabel(xlabel)\n", + "# plt.yscale(\"log\")\n", + "\n", + "# text = physics_process_lab + \" , \" + CLASS_NAMES_CLIC[icls]\n", + "# plt.text(textx, texty+0.05, text, ha=\"left\", transform=ax.transAxes)\n", + "\n", + "# if log:\n", + "# plt.xscale(\"log\")\n", + "# plt.legend(loc=(0.6, 0.65))\n", + "# plt.ylim(10, 20 * np.max(hist_X.values()))\n", + "# plt.xlim(min(bins), max(bins))\n", + "# plt.savefig(f\"{outpath}/distr_icls{icls}_ivar{ivar}.pdf\", bbox_inches=\"tight\")\n", + "\n", + " plt.figure()\n", + " ax = plt.axes()\n", + " mplhep.histplot(eff_pf, label=\"PF\")\n", + " mplhep.histplot(eff_mlpf, label=\"MLPF\")\n", + " plt.ylim(0, 1.5)\n", + " plt.ylabel(\"Efficiency\")\n", + " plt.xlabel(xlabel)\n", + "\n", + " text = physics_process_lab + \" , \" + CLASS_NAMES_CLIC[icls]\n", + " plt.text(textx, texty, text, ha=\"left\", transform=ax.transAxes)\n", + " \n", + " if log:\n", + " plt.xscale(\"log\")\n", + " plt.legend(loc=(0.75, 0.7))\n", + " plt.xlim(min(bins), max(bins))\n", + " plt.savefig(f\"{outpath}/eff_icls{icls}_ivar{ivar}.pdf\", bbox_inches=\"tight\")\n", + "\n", + " plt.figure()\n", + " ax = plt.axes(sharex=ax)\n", + " mplhep.histplot(fake_pf, label=\"PF\")\n", + " mplhep.histplot(fake_mlpf, label=\"MLPF\")\n", + " plt.ylim(0, 1.5)\n", + " plt.ylabel(\"Fake rate\")\n", + " plt.xlabel(xlabel)\n", + " \n", + " text = physics_process_lab + \" , \" + CLASS_NAMES_CLIC[icls]\n", + " plt.text(textx, texty, text, ha=\"left\", transform=ax.transAxes)\n", + " \n", + " if log:\n", + " plt.xscale(\"log\")\n", + " plt.legend(loc=(0.75, 0.7))\n", + " plt.xlim(min(bins), max(bins))\n", + " plt.savefig(f\"{outpath}/fake_icls{icls}_ivar{ivar}.pdf\", bbox_inches=\"tight\")\n", + "\n", + " # mplhep.histplot(fake, bins=hist_gen[1], label=\"fake rate\", color=\"red\")\n", + "\n", + "\n", + "# plt.legend(frameon=False)\n", + "# plt.ylim(0,1.4)\n", + "# plt.xlabel(xlabel)\n", + "# plt.ylabel(\"Fraction of particles / bin\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa4e5878", + "metadata": {}, + "outputs": [], + "source": [ + "! ls evaluation/epoch_96" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78454ad3", + "metadata": {}, + "outputs": [], + "source": [ + "sample = \"clic_edm_ttbar_pf\"\n", + "\n", + "PATH = \"evaluation/epoch_96\"\n", + "\n", + "pred_path = Path(f\"{PATH}/{sample}/test/\")\n", + "path = str(pred_path / \"*.parquet\")\n", + "\n", + "outpath = f\"paper_plots/{sample}\" # plots path\n", + "os.system(f\"mkdir -p {outpath}\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d39b09c", + "metadata": {}, + "outputs": [], + "source": [ + "! ls evaluation/epoch_96/clic_edm_ttbar_pf/test/*.parquet | wc -l" + ] + }, + { + "cell_type": "markdown", + "id": "32f13854", + "metadata": {}, + "source": [ + "# Load the predictions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af2cdee4", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "yvals, X, _ = load_eval_data(path, max_files=200)" + ] + }, + { + "cell_type": "markdown", + "id": "fc9b9268", + "metadata": {}, + "source": [ + "# Make plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33c5a969", + "metadata": {}, + "outputs": [], + "source": [ + "physics_process = {\n", + " \"clic_edm_ttbar_pf\": r\"$e^+e^- \\rightarrow \\mathrm{t}\\overline{\\mathrm{t}}$\",\n", + " \"clic_edm_ttbar_pu10_pf\": r\"$e^+e^- \\rightarrow \\mathrm{t}\\overline{\\mathrm{t}}$, PU10\",\n", + " \"clic_edm_qq_pf\": r\"$e^+e^- \\rightarrow \\gamma/\\mathrm{Z}^* \\rightarrow \\mathrm{hadrons}$\",\n", + " \"clic_edm_ww_fullhad_pf\": r\"$e^+e^- \\rightarrow WW \\rightarrow \\mathrm{hadrons}$\",\n", + " \"clic_edm_zh_tautau_pf\": r\"$e^+e^- \\rightarrow ZH \\rightarrow \\tau \\tau$\", \n", + "}\n", + "\n", + "elemlabel = {\n", + " 1: [\"N/A\", r\"track $p_T$\", r\"$\\eta$\", \"sin_phi\", \"cos_phi\", \"Total p\"],\n", + " 2: [\"N/A\", r\"$E_T$\", r\"$\\eta$\", \"sin_phi\", \"cos_phi\", \"cluster E [GeV]\"],\n", + "}\n", + "\n", + "# just a reminder\n", + "CLASS_NAMES_CLIC = [\n", + " r\"none\",\n", + " r\"charged hadrons\",\n", + " r\"neutral hadrons\",\n", + " r\"$\\gamma$\",\n", + " r\"$e^\\pm$\",\n", + " r\"$\\mu^\\pm$\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04868971", + "metadata": {}, + "outputs": [], + "source": [ + "def med_iqr(arr):\n", + " p25 = np.percentile(arr, 25)\n", + " p50 = np.percentile(arr, 50)\n", + " p75 = np.percentile(arr, 75)\n", + " return p50, p75 - p25\n", + "\n", + "\n", + "def flatten(arr):\n", + " return arr.reshape(-1, arr.shape[-1])\n", + "\n", + "\n", + "def binom_error(n_sig, n_tot):\n", + " \"\"\"\n", + " for an efficiency = nSig/nTrueSig or purity = nSig / (nSig + nBckgrd), this function calculates the\n", + " standard deviation according to http://arxiv.org/abs/physics/0701199 .\n", + " \"\"\"\n", + " variance = np.where(\n", + " n_tot > 0, (n_sig + 1) * (n_sig + 2) / ((n_tot + 2) * (n_tot + 3)) - (n_sig + 1) ** 2 / ((n_tot + 2) ** 2), 0\n", + " )\n", + " return np.sqrt(variance)\n", + "\n", + "\n", + "def plot_eff_isolation(icls=1, ivar=4, ielem=1, bins=np.linspace(-3, 6, 100), \n", + " log=True,\n", + " physics_process_lab=\"\",\n", + " textx=0.01,\n", + " texty=0.87,\n", + " apply_isolation=\"Isolated\",\n", + " ):\n", + "\n", + " xlabel = elemlabel[ielem][ivar]\n", + " \n", + " values = X[:, :, ivar]\n", + "\n", + " hist_X = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_gen = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_gen_pred = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_gen_cand = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_pred = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_cand = bh.Histogram(bh.axis.Variable(bins))\n", + "\n", + " eff_mlpf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", + " eff_pf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", + "\n", + " xlabel = elemlabel[ielem][ivar]\n", + " \n", + " values = X[:, :, ivar]\n", + "\n", + " hist_X = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_gen = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_gen_pred = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_gen_cand = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_pred = bh.Histogram(bh.axis.Variable(bins))\n", + " hist_cand = bh.Histogram(bh.axis.Variable(bins))\n", + "\n", + " eff_mlpf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", + " eff_pf = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())\n", + "\n", + " msk_X = (X[:, :, 0] == ielem) \n", + " msk_gen = (yvals[\"gen_cls_id\"] == icls) \n", + " msk_pred = (yvals[\"pred_cls_id\"] == icls)\n", + " msk_cand = (yvals[\"cand_cls_id\"] == icls)\n", + "\n", + " if apply_isolation == \"Isolated\":\n", + " msk_iso = get_isolation(msk_gen)<0.15\n", + " else:\n", + " msk_iso = get_isolation(msk_gen)>0.15\n", + " \n", + " hist_X.fill(awkward.flatten(values[msk_X & msk_gen][msk_iso])) \n", + " hist_gen.fill(awkward.flatten(values[msk_X & msk_gen][msk_iso]))\n", + " \n", + " if apply_isolation == \"Isolated\":\n", + " msk_iso = get_isolation(msk_gen & msk_pred)<0.15\n", + " else:\n", + " msk_iso = get_isolation(msk_gen & msk_pred)>0.15\n", + " \n", + " # Genparticle exists, reco particle exists\n", + " hist_gen_pred.fill(awkward.flatten(values[msk_X & msk_gen & msk_pred][msk_iso]))\n", + " \n", + " if apply_isolation == \"Isolated\":\n", + " msk_iso = get_isolation(msk_gen & msk_cand)<0.15\n", + " else:\n", + " msk_iso = get_isolation(msk_gen & msk_cand)>0.15 \n", + " hist_gen_cand.fill(awkward.flatten(values[msk_X & msk_gen & msk_cand][msk_iso]))\n", + " \n", + " eff_mlpf.values()[:] = hist_gen_pred.values() / hist_gen.values()\n", + " eff_mlpf.variances()[:] = binom_error(hist_gen_pred.values(), hist_gen.values()) ** 2\n", + "\n", + " eff_pf.values()[:] = hist_gen_cand.values() / hist_gen.values()\n", + " eff_pf.variances()[:] = binom_error(hist_gen_cand.values(), hist_gen.values()) ** 2\n", + " \n", + " plt.figure()\n", + " ax = plt.axes()\n", + " mplhep.histplot(eff_pf, label=\"PF\")\n", + " mplhep.histplot(eff_mlpf, label=\"MLPF\")\n", + " plt.ylim(0, 1.5)\n", + " plt.ylabel(\"Efficiency\")\n", + " plt.xlabel(xlabel)\n", + "\n", + " text = physics_process_lab + \" , \" + CLASS_NAMES_CLIC[icls]\n", + " if apply_isolation:\n", + " text = physics_process_lab + \" , \" + apply_isolation + \" \" + CLASS_NAMES_CLIC[icls]\n", + " \n", + " plt.text(textx, texty, text, ha=\"left\", transform=ax.transAxes)\n", + " \n", + " if log:\n", + " plt.xscale(\"log\")\n", + " plt.legend(loc=(0.75, 0.7))\n", + " plt.xlim(min(bins), max(bins))\n", + " plt.savefig(f\"{outpath}/{apply_isolation}_eff_icls{icls}_ivar{ivar}.pdf\", bbox_inches=\"tight\")\n", + "\n", + " # mplhep.histplot(fake, bins=hist_gen[1], label=\"fake rate\", color=\"red\")\n", + "\n", + "\n", + "# plt.legend(frameon=False)\n", + "# plt.ylim(0,1.4)\n", + "# plt.xlabel(xlabel)\n", + "# plt.ylabel(\"Fraction of particles / bin\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e949677b", + "metadata": {}, + "outputs": [], + "source": [ + "# import awkward as ak\n", + "\n", + "# # Example: Two leptons in the first event, first one not isolated, second one is isolated\n", + "# # No lepton in second event, but still some other particles\n", + "# # Third event has a good lepton with a stray hadron/photon/... around it\n", + "# leptons = ak.Array([[10., 20.], [], [30.]])\n", + "# others = ak.Array([[1., 2., 3.], [4., 5.], [1.5]])\n", + "# dR = ak.Array([[[0.25, 0.3, 0.35], [0.6, 0.7, 0.8]],\n", + "# [],\n", + "# [[0.2]]])\n", + "\n", + "# cartesian_product = ak.cartesian({\"leptons\": leptons, \"others\": others}, nested=True) # Create a nested Cartesian product of leptons and others\n", + "# dR_mask = ak.any(dR < 0.3, axis=-1) # Use ak.any to construct the dR mask\n", + "# events_extended = ak.mask(cartesian_product, dR_mask) # Apply the mask to the Cartesian product\n", + "# isolation_sum = ak.fill_none(ak.sum(events_extended.others, axis=-1), 0) # Fill None values with 0 after the sum (if no other particle nearby)\n", + "# rel_iso = isolation_sum / leptons\n", + "# print(rel_iso) # [[0.6, 0], [], [0.1]]\n", + "\n", + "def get_isolation(msk=yvals[\"gen_cls_id\"]==4):\n", + " \n", + " import numpy as np\n", + " import awkward as ak\n", + " from coffea.nanoevents.methods import vector \n", + " \n", + " typ = \"gen\"\n", + "\n", + " gen_array = {}\n", + " gen_array[\"cls\"] = yvals[f\"{typ}_cls\"]\n", + " gen_array[\"charge\"] = yvals[f\"{typ}_charge\"]\n", + " gen_array[\"pt\"] = yvals[f\"{typ}_pt\"]\n", + " gen_array[\"eta\"] = yvals[f\"{typ}_eta\"]\n", + " gen_array[\"sin_phi\"] = yvals[f\"{typ}_sin_phi\"]\n", + " gen_array[\"cos_phi\"] = yvals[f\"{typ}_cos_phi\"]\n", + " # gen_array[\"phi\"] = np.arctan2(yvals[f\"{typ}_sin_phi\"], yvals[f\"{typ}_cos_phi\"])\n", + " gen_array[\"cls_id\"] = yvals[f\"{typ}_cls_id\"]\n", + "\n", + " gen_array[\"px\"] = gen_array[\"pt\"] * gen_array[\"cos_phi\"]\n", + " gen_array[\"py\"] = gen_array[\"pt\"] * gen_array[\"sin_phi\"]\n", + " gen_array[\"pz\"] = gen_array[\"pt\"] * np.sinh(gen_array[\"eta\"])\n", + "\n", + " gen_array = ak.zip(gen_array, depth_limit=1)\n", + "\n", + " # define particle selections\n", + " indices_chhadrons = gen_array.cls_id == 1\n", + " indices_nhadrons = gen_array.cls_id == 2\n", + " indices_photons = gen_array.cls_id == 3\n", + " \n", + " particle_in_question = ak.zip(\n", + " {\n", + " \"x\": gen_array.px[msk],\n", + " \"y\": gen_array.py[msk],\n", + " \"z\": gen_array.pz[msk],\n", + " },\n", + " with_name=\"LorentzVector\",\n", + " behavior=vector.behavior,\n", + " )\n", + " others = ak.zip(\n", + " {\n", + " \"x\": gen_array.px[indices_chhadrons | indices_nhadrons | indices_photons],\n", + " \"y\": gen_array.py[indices_chhadrons | indices_nhadrons | indices_photons],\n", + " \"z\": gen_array.pz[indices_chhadrons | indices_nhadrons | indices_photons],\n", + " },\n", + " with_name=\"LorentzVector\",\n", + " behavior=vector.behavior,\n", + " )\n", + "\n", + " mval = particle_in_question.metric_table(others)\n", + " \n", + "# mval.type # 5000 * NLeptons * NHadrons\n", + "# others.type # 5000 * NHadrons \n", + "# particle_in_question.type # 5000 * NLeptons\n", + "\n", + " dR_threshold = 0.3\n", + "\n", + " isolation = [] # initialize\n", + " counts = []\n", + "\n", + " for iev in range(len(particle_in_question)):\n", + " counts.append(0)\n", + " for iprtkl, prtkl in enumerate(particle_in_question[iev]):\n", + " counts[iev] += 1 \n", + "\n", + " mask = (mval0.15\n", + " \n", + " vals_gen = awkward.flatten(yvals[\"gen_\" + var][msk][msk_iso])\n", + " vals_cand = awkward.flatten(yvals[\"cand_\" + var][msk][msk_iso])\n", + " reso_1 = vals_cand / vals_gen\n", + " \n", + " msk = (yvals[\"gen_cls_id\"] == pid) & (yvals[\"pred_cls_id\"] != 0)\n", + " \n", + " if apply_isolation == \"Isolated\":\n", + " msk_iso = get_isolation(msk)<0.15\n", + " else:\n", + " msk_iso = get_isolation(msk)>0.15\n", + " \n", + " vals_gen = awkward.flatten(yvals[\"gen_\" + var][msk][msk_iso])\n", + " vals_mlpf = awkward.flatten(yvals[\"pred_\" + var][msk][msk_iso])\n", + " reso_2 = vals_mlpf / vals_gen\n", + " \n", + " plt.hist(reso_1, bins=bins, histtype=\"step\", lw=2, label=\"PF, M={:.2f}, IQR={:.2f}\".format(*med_iqr(reso_1)))\n", + " plt.hist(reso_2, bins=bins, histtype=\"step\", lw=2, label=\"MLPF, M={:.2f}, IQR={:.2f}\".format(*med_iqr(reso_2)))\n", + " plt.yscale(\"log\")\n", + " if var == \"pt\":\n", + " plt.xlabel(r\"$p_\\mathrm{T,reco} / p_\\mathrm{T,gen}$\")\n", + " elif var == \"eta\":\n", + " plt.xlabel(r\"$\\eta_\\mathrm{reco} / \\eta_\\mathrm{gen}$\")\n", + " elif var == \"energy\":\n", + " plt.xlabel(r\"$E_\\mathrm{reco} / E_\\mathrm{gen}$\") \n", + " \n", + " plt.ylabel(\"Number of particles / bin\")\n", + " \n", + " text = physics_process_lab + \" , \" + apply_isolation + \" \" + CLASS_NAMES_CLIC[pid]\n", + " plt.text(textx, texty, text, ha=\"left\", transform=ax.transAxes)\n", + " plt.xlim(min(bins), max(bins))\n", + " plt.legend(loc=(0.4, 0.7))\n", + " # plt.ylim(1, 1e9)\n", + " # plt.savefig(\"{}/pt_res_ch_had.pdf\".format(outpath), bbox_inches=\"tight\")\n", + " plt.savefig(f\"{outpath}/{apply_isolation}_res_{var}_{pid}.pdf\", bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15ee0ffc", + "metadata": {}, + "outputs": [], + "source": [ + "yvals, X, _ = load_eval_data(path, max_files=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49e5a6a8", + "metadata": {}, + "outputs": [], + "source": [ + "pid = 2\n", + "var = \"energy\"\n", + "bins = np.linspace(-5,30,100)\n", + "\n", + "reso_plot_isolated(pid=pid, var=var, bins=bins, physics_process_lab=physics_process[sample], \n", + " textx=0.05, \n", + " texty=0.87,\n", + " apply_isolation=\"Isolated\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f818883", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb93fbd7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8f76a6f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "7d6491e2", + "metadata": {}, + "source": [ + "# Isolation for different samples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c29aef9b", + "metadata": {}, + "outputs": [], + "source": [ + "isolation = {}\n", + "isolation[\"electrons\"] = {}\n", + "isolation[\"muons\"] = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01e572ac", + "metadata": {}, + "outputs": [], + "source": [ + "# sample = \"clic_edm_ttbar_pf\"\n", + "sample = \"clic_edm_qq_pf\"\n", + "\n", + "PATH = \"evaluation/epoch_96\"\n", + "\n", + "pred_path = Path(f\"{PATH}/{sample}/test/\")\n", + "path = str(pred_path / \"*.parquet\")\n", + "\n", + "outpath = f\"paper_plots/{sample}\" # plots path\n", + "os.system(f\"mkdir -p {outpath}\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b4f30ef", + "metadata": {}, + "outputs": [], + "source": [ + "yvals, X, _ = load_eval_data(path, max_files=50)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c9f8aea", + "metadata": {}, + "outputs": [], + "source": [ + "isolation[\"electrons\"][sample] = get_isolation(msk=yvals[\"gen_cls_id\"]==4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "255e402d", + "metadata": {}, + "outputs": [], + "source": [ + "isolation[\"muons\"][sample] = get_isolation(msk=yvals[\"gen_cls_id\"]==5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fcbd839", + "metadata": {}, + "outputs": [], + "source": [ + "for lepton in isolation:\n", + " fig, ax = plt.subplots()\n", + " \n", + " for sample in isolation[lepton]:\n", + " ax.hist(ak.flatten(isolation[lepton][sample]), \n", + " bins=np.linspace(0,3,20), \n", + " histtype=\"step\", \n", + " lw=2, \n", + " label=sample,\n", + " density=True,\n", + " )\n", + " \n", + " if lepton == \"electrons\":\n", + " title = r\"$e^\\pm$\"\n", + " elif lepton == \"muons\":\n", + " title = r\"$\\mu^\\pm$\"\n", + " \n", + " ax.legend(title=title)\n", + " ax.set_yscale(\"log\")\n", + " \n", + " ax.set_xlabel('Relative Isolation per event (cone dR=0.3)', fontsize=20)\n", + " ax.set_ylabel('Counts')\n", + " \n", + " plt.savefig(f\"isolation_{lepton}.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3323bfde", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": 5 +}