From 85875648e3980d99ab7b325e1195d2be8fc2064a Mon Sep 17 00:00:00 2001 From: rkansal47 Date: Mon, 8 Apr 2024 20:51:15 -0500 Subject: [PATCH] nonres bias test --- .../VBF_binder/VBFKinematicsStudyRK.ipynb | 8 +- src/HHbbVV/combine/binder/BiasTest.ipynb | 140 +++++++++++++----- src/HHbbVV/combine/binder/PlotScan.ipynb | 52 +++++-- src/HHbbVV/combine/nonres_scan.sh | 24 ++- src/HHbbVV/combine/run_blinded.sh | 4 +- src/HHbbVV/combine/submit_bias_nonres_loop.sh | 24 +++ src/HHbbVV/postprocessing/PlotFits.ipynb | 77 ++++++++-- src/HHbbVV/postprocessing/plotting.py | 7 + 8 files changed, 252 insertions(+), 84 deletions(-) create mode 100755 src/HHbbVV/combine/submit_bias_nonres_loop.sh diff --git a/src/HHbbVV/VBF_binder/VBFKinematicsStudyRK.ipynb b/src/HHbbVV/VBF_binder/VBFKinematicsStudyRK.ipynb index d72c2910..2112f2ca 100644 --- a/src/HHbbVV/VBF_binder/VBFKinematicsStudyRK.ipynb +++ b/src/HHbbVV/VBF_binder/VBFKinematicsStudyRK.ipynb @@ -339,10 +339,10 @@ "source": [ "sel = ak.fill_none(\n", " (\n", - " (txbb[bb_mask] > 0.97)\n", - " * (fatjets.particleNet_H4qvsQCD[~bb_mask] > 0.6)\n", - " * (fatjets.pt[:, 0] > 500)\n", - " * (fatjets.pt[:, 1] > 400)\n", + " # (txbb[bb_mask] > 0.97)\n", + " # * (fatjets.particleNet_H4qvsQCD[~bb_mask] > 0.6)\n", + " (fatjets.pt[:, 0] > 300)\n", + " * (fatjets.pt[:, 1] > 300)\n", " * (np.abs(fatjets[:, 0].delta_phi(fatjets[:, 1])) > 2.6)\n", " * (np.abs(fatjets[:, 0].eta - fatjets[:, 1].eta) < 2.0)\n", " ),\n", diff --git a/src/HHbbVV/combine/binder/BiasTest.ipynb b/src/HHbbVV/combine/binder/BiasTest.ipynb index f7957677..1facfebe 100644 --- a/src/HHbbVV/combine/binder/BiasTest.ipynb +++ b/src/HHbbVV/combine/binder/BiasTest.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import mplhep as hep\n", "import matplotlib.ticker as mticker\n", - "import os\n", + "from pathlib import Path\n", "\n", "plt.style.use(hep.style.CMS)\n", "hep.style.use(\"CMS\")\n", @@ -23,35 +23,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "MAIN_DIR = \"../../../../\"\n", + "MAIN_DIR = Path(\"../../../../\")\n", "\n", - "plot_dir = f\"{MAIN_DIR}/plots/BiasTest/23Sep22_increase_rbounds\"\n", - "_ = os.system(f\"mkdir -p {plot_dir}\")" + "plot_dir = MAIN_DIR / \"plots/BiasTest/24Apr8NonresVBF\"\n", + "plot_dir.mkdir(exist_ok=True, parents=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "cards_dir = \"23Sep22Scale100_bgs_only_scaleqcdsqrt\"\n", - "bias_dir = \"23Sep22\"\n", - "\n", - "# r_bounds = [-1, 20]\n", - "r_bounds = [-15, 15]\n", - "\n", - "samples = [\n", - " # \"NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190\",\n", - " \"NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-125\",\n", - " # \"NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250\",\n", - "]\n", - "\n", - "biases = [0.0, 0.15, 0.3, 1.0]\n", + "resonant = False\n", + "\n", + "if not resonant:\n", + " cards_dir = \"24Apr4VBFBDTScan/k2v0/txbb_HP_bdt_0.999_lepton_veto_Hbb\"\n", + " r_bounds = [-15, 15]\n", + " biases = [1.0]\n", + " samples = [\"k2v0\"]\n", + "else:\n", + " cards_dir = \"23Sep22Scale100_bgs_only_scaleqcdsqrt\"\n", + " bias_dir = \"23Sep22\"\n", + "\n", + " # r_bounds = [-1, 20]\n", + " r_bounds = [-15, 15]\n", + "\n", + " samples = [\n", + " # \"NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190\",\n", + " \"NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-125\",\n", + " # \"NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250\",\n", + " ]\n", + "\n", + " biases = [0.0, 0.15, 0.3, 1.0]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k2v0 1.0\n" + ] + } + ], + "source": [ "r_dict = {}\n", "\n", "for sample in samples:\n", @@ -63,9 +87,11 @@ " continue\n", "\n", " print(sample, bias)\n", - " file = uproot.concatenate(\n", - " f\"/uscms/home/rkansal/hhcombine/cards/biastests/{cards_dir}/{sample}/bias/{bias_dir}/higgsCombinebias{bias}.FitDiagnostics.mH125.*.root\"\n", - " )\n", + " if not resonant:\n", + " file_name = f\"/uscms/home/rkansal/hhcombine/cards/{cards_dir}/higgsCombinebias1.FitDiagnostics.mH125.42.root\"\n", + " else:\n", + " file_name = f\"/uscms/home/rkansal/hhcombine/cards/biastests/{cards_dir}/{sample}/bias/{bias_dir}/higgsCombinebias{bias}.FitDiagnostics.mH125.*.root\"\n", + " file = uproot.concatenate(file_name)\n", "\n", " r = np.array(file.limit)[::4]\n", " neg_lim = np.array(file.limit)[1::4]\n", @@ -86,26 +112,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k2v0\n", + "For r = 1.0, # of successful fits: = 96, 9% of these with r- = -15, 11% with r+ = 15\n" + ] + } + ], "source": [ + "# checking in how many fits the ±r values are at the parameter boundary i.e. they are unreliable\n", "for sample in samples:\n", " print(sample)\n", " for i, bias in enumerate(biases):\n", " num_toys = len(r_dict[sample][bias][\"r\"])\n", "\n", " print(\n", - " f\"For r = {bias}, # of successful fits: = {num_toys}, {np.sum(r_dict[sample][bias]['neg_lim'] == r_bounds[0]) / num_toys * 100:.0f}% of these with r- = -5, {np.sum(r_dict[sample][bias]['pos_lim'] == r_bounds[1]) / num_toys * 100 :.0f}% with r+ = 40\"\n", + " f\"For r = {bias}, # of successful fits: = {num_toys}, {np.sum(r_dict[sample][bias]['neg_lim'] == r_bounds[0]) / num_toys * 100:.0f}% of these with r- = {r_bounds[0]}, {np.sum(r_dict[sample][bias]['pos_lim'] == r_bounds[1]) / num_toys * 100 :.0f}% with r+ = {r_bounds[1]}\"\n", " )" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k2v0\n", + "For r = 1.0, # of successful fits: = 96, 10% of these with r-lim at boundary\n" + ] + } + ], "source": [ + "# checking in how many fits the ±r values are at the parameter boundary AND that side is the one we care about\n", "for sample in samples:\n", " print(sample)\n", " for i, bias in enumerate(biases):\n", @@ -129,9 +175,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from scipy import stats\n", "\n", @@ -142,7 +199,7 @@ "for sample in samples:\n", " # if sample != \"NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250\":\n", " # continue\n", - " fig, ax = plt.subplots(len(biases), 1, figsize=(12, 40))\n", + " fig, axs = plt.subplots(len(biases), 1, figsize=(12, len(biases) * 10))\n", "\n", " for i, bias in enumerate(biases):\n", " # fit_fail = r_dict[sample][bias][\"pos_lim\"] == 40\n", @@ -165,21 +222,24 @@ " reldiff = reldiff[(reldiff > -xrange) * (reldiff < xrange)]\n", "\n", " mu, sigma = np.mean(reldiff), np.std(reldiff)\n", - " ax[i].hist(reldiff, np.linspace(-xrange, xrange, bins + 1), histtype=\"step\")\n", - " ax[i].plot(\n", + "\n", + " ax = axs[i] if len(biases) > 1 else axs\n", + "\n", + " ax.hist(reldiff, np.linspace(-xrange, xrange, bins + 1), histtype=\"step\")\n", + " ax.plot(\n", " x,\n", " # scale by bin width\n", " stats.norm.pdf(x, loc=mu, scale=sigma) * len(r) * (2 * xrange / bins),\n", " label=rf\"$\\mu = {mu:.2f}, \\sigma = {sigma:.2f}$\",\n", " )\n", - " ax[i].set_xlabel(rf\"$\\frac{{\\hat{{r}} - {bias}}}{{\\Delta \\hat r}}$\")\n", - " ax[i].set_ylabel(\"Number of toys\")\n", - " ax[i].set_title(f\"r = {bias}\")\n", - " ax[i].legend()\n", + " ax.set_xlabel(rf\"$\\frac{{\\hat{{r}} - {bias}}}{{\\Delta \\hat r}}$\")\n", + " ax.set_ylabel(\"Number of toys\")\n", + " ax.set_title(f\"r = {bias}\")\n", + " ax.legend()\n", "\n", " hep.cms.label(\n", - " \"Work in Progress\",\n", - " ax=ax[i],\n", + " \"Preliminary\",\n", + " ax=ax,\n", " data=True,\n", " lumi=138,\n", " year=None,\n", @@ -347,7 +407,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.9.15" }, "orig_nbformat": 4 }, diff --git a/src/HHbbVV/combine/binder/PlotScan.ipynb b/src/HHbbVV/combine/binder/PlotScan.ipynb index cf563034..3434206a 100644 --- a/src/HHbbVV/combine/binder/PlotScan.ipynb +++ b/src/HHbbVV/combine/binder/PlotScan.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -53,19 +53,19 @@ "# scan_bdt_wps = [0.6, 0.9, 0.96, 0.99, 0.997, 0.998, 0.999]\n", "\n", "scan_txbb_wps = [\"MP\", \"HP\"]\n", - "scan_bdt_wps = [0.99, 0.997, 0.998, 0.999]\n", + "scan_bdt_wps = [0.99, 0.997, 0.998, 0.999, 0.9997, 0.9999]\n", "\n", "scan_lepton_veto = [\"Hbb\"]\n", "scan_thww_wps = [0.4, 0.6, 0.8, 0.9, 0.94, 0.96, 0.98]\n", "scan_leadingpt_wps = [300.0, 350.0, 400.0, 450.0]\n", "scan_subleadingpt_wps = [300.0, 350.0, 400.0, 450.0]\n", "\n", - "plot_dir = Path(\"../../../../plots/Scans/24Apr2NonresggFk2v0Scan\")\n", + "plot_dir = Path(\"../../../../plots/Scans/24Apr4VBFBDTScan\")\n", "plot_dir.mkdir(parents=True, exist_ok=True)\n", "\n", "\n", "# nonresonat\n", - "cards_dir = Path(\"/uscms/home/rkansal/hhcombine/cards/24Apr2ggFk2v0Scan/\")\n", + "cards_dir = Path(\"/uscms/home/rkansal/hhcombine/cards/24Apr4VBFBDTScan/\")\n", "# cards_dir = \"/uscms/home/rkansal/hhcombine/cards/23May14NonresScan/\"\n", "\n", "# resonant\n", @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -116,7 +116,8 @@ " scan_wps = list(itertools.product(scan_txbb_wps, scan_bdt_wps))\n", " scan1_wps, scan2_wps = scan_txbb_wps, scan_bdt_wps\n", " scan_cuts = nonres_scan_cuts\n", - " scan_samples = [\"SM\", \"k2v0\"]" + " scan_samples = [\"SM\", \"k2v0\"]\n", + " scan_samples = [\"k2v0\"]" ] }, { @@ -134,9 +135,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 13.78it/s]\n" + ] + } + ], "source": [ "limits = {}\n", "extrastr = \"_lepton_veto_Hbb\" if not resonant else \"\"\n", @@ -177,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -201,7 +210,10 @@ " if resonant:\n", " plt.colorbar(im, ax=ax, label=\"Expected exclusion limit (fb)\")\n", " else:\n", - " plt.colorbar(im, ax=ax, label=r\"Expected exclusion limit $\\times$SM\")\n", + " if sample == \"SM\":\n", + " plt.colorbar(im, ax=ax, label=r\"Expected exclusion limit $\\times$SM\")\n", + " else:\n", + " plt.colorbar(im, ax=ax, label=r\"Expected exclusion limit $\\times$ theory\")\n", "\n", " for vals in lim:\n", " if pt_scan:\n", @@ -239,9 +251,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, axs = plt.subplots(\n", " len(limits),\n", @@ -250,7 +273,8 @@ " gridspec_kw={\"hspace\": 0.4},\n", ")\n", "for i, (sample, lim) in enumerate(limits.items()):\n", - " plot_limits(axs[i], lim[\"50.0\"], sample, 0.8)\n", + " ax = axs if len(scan_samples) == 1 else axs[i]\n", + " plot_limits(ax, lim[\"50.0\"], sample, 0.8)\n", "\n", "plt.savefig(f\"{plot_dir}/limscan.pdf\", bbox_inches=\"tight\")\n", "plt.show()" diff --git a/src/HHbbVV/combine/nonres_scan.sh b/src/HHbbVV/combine/nonres_scan.sh index d6bbb1fd..45337906 100755 --- a/src/HHbbVV/combine/nonres_scan.sh +++ b/src/HHbbVV/combine/nonres_scan.sh @@ -15,30 +15,28 @@ # ) # done +templates_tag="24Apr8VBFBDTScanNodEta" -templates_tag="24Apr2ggFk2v0Scan" - -# for bdt_cut in 0.99 0.997 0.998 0.999 -for bdt_cut in 0.9995 0.9999 +for bdt_cut in 0.99 0.997 0.998 0.999 0.9997 0.9999 do - for txbb_cut in "HP" + for txbb_cut in "MP" "HP" do cutstr="txbb_${txbb_cut}_bdt_${bdt_cut}_lepton_veto_Hbb" echo $cutstr - python3 -u postprocessing/CreateDatacard.py --templates-dir templates/$templates_tag/$cutstr \ - --model-name $templates_tag/SM/$cutstr --no-do-jshifts --nTF 0 --only-sm + # python3 -u postprocessing/CreateDatacard.py --templates-dir templates/$templates_tag/$cutstr \ + # --model-name $templates_tag/SM/$cutstr --no-do-jshifts --nTF 0 --only-sm - ( - cd cards/$templates_tag/SM/$cutstr || exit - /uscms/home/rkansal/nobackup/HHbbVV/src/HHbbVV/combine/run_blinded.sh -wbl - ) + # ( + # cd cards/$templates_tag/SM/$cutstr || exit + # /uscms/home/rkansal/nobackup/HHbbVV/src/HHbbVV/combine/run_blinded.sh -wbl + # ) python3 -u postprocessing/CreateDatacard.py --templates-dir templates/$templates_tag/$cutstr \ - --model-name $templates_tag/k2v0/$cutstr --no-do-jshifts --nTF 0 --sig-sample qqHH_CV_1_C2V_0_kl_1_HHbbVV + --model-name $templates_tag/$cutstr --no-do-jshifts --nTF 1 --sig-sample qqHH_CV_1_C2V_0_kl_1_HHbbVV ( - cd cards/$templates_tag/k2v0/$cutstr || exit + cd cards/$templates_tag/$cutstr || exit /uscms/home/rkansal/nobackup/HHbbVV/src/HHbbVV/combine/run_blinded.sh -wbl ) done diff --git a/src/HHbbVV/combine/run_blinded.sh b/src/HHbbVV/combine/run_blinded.sh index 5df27474..92694799 100755 --- a/src/HHbbVV/combine/run_blinded.sh +++ b/src/HHbbVV/combine/run_blinded.sh @@ -298,7 +298,7 @@ if [ $dfit = 1 ]; then echo "Fit Shapes" PostFitShapesFromWorkspace --dataset "$dataset" -w ${wsm}.root --output FitShapes.root \ - -m 125 -f fitDiagnosticsBlinded.root:fit_b --postfit --print 2>&1 | tee $outsdir/FitShapes.txt + -m 125 -f fitDiagnosticsBlinded.root:fit_b --postfit --sampling --print 2>&1 | tee $outsdir/FitShapes.txt fi @@ -395,7 +395,7 @@ if [ "$bias" != -1 ]; then combine -M FitDiagnostics --trackParameters r --trackErrors r --justFit \ -m 125 -n "bias${bias}" -d ${wsm_snapshot}.root --rMin "-15" --rMax 15 \ --snapshotName MultiDimFit --bypassFrequentistFit --toysFrequentist --expectSignal "$bias" \ - "${unblindedparams},r=$bias" --floatParameters "${freezeparamsblinded}" \ + ${unblindedparams},r=$bias --floatParameters ${freezeparamsblinded} \ --robustFit=1 -t "$numtoys" -s "$seed" \ --X-rtd MINIMIZER_MaxCalls=1000000 --cminDefaultMinimizerTolerance "$mintol" 2>&1 | tee "$outsdir/bias${bias}seed${seed}.txt" fi diff --git a/src/HHbbVV/combine/submit_bias_nonres_loop.sh b/src/HHbbVV/combine/submit_bias_nonres_loop.sh new file mode 100755 index 00000000..dae86278 --- /dev/null +++ b/src/HHbbVV/combine/submit_bias_nonres_loop.sh @@ -0,0 +1,24 @@ +#!/bin/bash +# shellcheck disable=SC2043 + +####################################################################### +# Script to submit bias jobs, needs to be run from inside the datacards directory +##################################################### + +seed=$1 +TAG=$2 + +for bias in 0.0 0.3 1.0 1.5 +do + python3 /uscms_data/d1/rkansal/HHbbVV/src/HHbbVV/combine/submit/submit_bias.py --seed "$seed" --num-jobs 100 --toys-per-job 10 --bias "$bias" --submit --tag "$TAG" --mintol 20 +done + +# # need to submit extra jobs for these because of high fit failures +# sample=NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250 +# cd $sample +# bias=0.0 +# python3 /uscms_data/d1/rkansal/HHbbVV/src/HHbbVV/combine/submit/submit_bias.py --seed $((seed + 1000)) --num-jobs 100 --toys-per-job 10 --bias $bias --submit --tag $TAG + +# bias=0.15 +# python3 /uscms_data/d1/rkansal/HHbbVV/src/HHbbVV/combine/submit/submit_bias.py --seed $((seed + 1000)) --num-jobs 50 --toys-per-job 10 --bias $bias --submit --tag $TAG +# cd - diff --git a/src/HHbbVV/postprocessing/PlotFits.ipynb b/src/HHbbVV/postprocessing/PlotFits.ipynb index 19d57fd2..3d205e0c 100644 --- a/src/HHbbVV/postprocessing/PlotFits.ipynb +++ b/src/HHbbVV/postprocessing/PlotFits.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -36,13 +36,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "MAIN_DIR = Path(\"../../../\")\n", "\n", - "plot_dir = MAIN_DIR / \"plots/PostFit/24Apr3NonresScan\"\n", + "# plot_dir = MAIN_DIR / \"plots/PostFit/24Apr4VBFBDTScan/txbb_MP_bdt_0.9999_lepton_veto_Hbb/\"\n", + "plot_dir = MAIN_DIR / \"plots/PostFit/24Apr8Uncs1st/\"\n", "plot_dir.mkdir(exist_ok=True, parents=True)" ] }, @@ -52,8 +53,8 @@ "metadata": {}, "outputs": [], "source": [ - "cards_dir = \"24Mar15UpdateData\"\n", - "cards_dir = \"24Apr2ggFk2v0Scan/SM/txbb_HP_bdt_0.9995_lepton_veto_Hbb\"\n", + "cards_dir = \"24Mar15UpdateDatanTF1\"\n", + "# cards_dir = \"24Apr4VBFBDTScan/k2v0/txbb_MP_bdt_0.9999_lepton_veto_Hbb\"\n", "asimov = False\n", "\n", "asimov_label = \"Asimov\" if asimov else \"\"\n", @@ -69,7 +70,8 @@ "metadata": {}, "outputs": [], "source": [ - "templates_dir = Path(\"templates/24Mar15UpdateData\")\n", + "templates_dir = Path(f\"templates/{cards_dir[:-4]}\")\n", + "# templates_dir = Path(f\"templates/24Apr4VBFBDTScan/txbb_MP_bdt_0.9999_lepton_veto_Hbb\")\n", "templates_dict = {}\n", "for year in years:\n", " with (templates_dir / f\"{year}_templates.pkl\").open(\"rb\") as f:\n", @@ -103,6 +105,7 @@ "hist_label_map = {val: key for key, val in hist_label_map_inverse.items()}\n", "\n", "sig_keys = [\"HHbbVV\", \"VBFHHbbVV\"]\n", + "sig_keys = [\"qqHH_CV_1_C2V_0_kl_1_HHbbVV\"]\n", "samples = bg_keys + sig_keys + [data_key]" ] }, @@ -133,6 +136,7 @@ "outputs": [], "source": [ "hists = {}\n", + "bgerrs = {}\n", "\n", "for shape in shapes:\n", " print(shape)\n", @@ -144,6 +148,7 @@ " )\n", " for region in selection_regions\n", " }\n", + " bgerrs[shape] = {}\n", "\n", " for region in selection_regions:\n", " h = hists[shape][region]\n", @@ -165,7 +170,9 @@ " data_key_index = np.where(np.array(list(h.axes[0])) == data_key)[0][0]\n", " h.view(flow=False)[data_key_index, :] = np.nan_to_num(\n", " templates[hist_label_map_inverse[data_key]].values()\n", - " )" + " )\n", + "\n", + " bgerrs[shape][region] = templates[\"TotalBkg\"].errors()" ] }, { @@ -174,9 +181,12 @@ "metadata": {}, "outputs": [], "source": [ - "pass_ylim = 22\n", + "pass_ylim = 17\n", "fail_ylim = 600000\n", "title_label = \" Asimov Dataset\" if asimov else \"\"\n", + "sig_scale_dict = {\"HHbbVV\": 100, \"VBFHHbbVV\": 2000, \"qqHH_CV_1_C2V_0_kl_1_HHbbVV\": 1}\n", + "sig_scale_dict = {key: val for key, val in sig_scale_dict.items() if key in sig_keys}\n", + "\n", "for shape, shape_label in shapes.items():\n", " for region, region_label in selection_regions.items():\n", " pass_region = region.startswith(\"pass\")\n", @@ -185,7 +195,8 @@ " \"hists\": hists[shape][region],\n", " \"sig_keys\": sig_keys,\n", " \"bg_keys\": bg_keys,\n", - " \"sig_scale_dict\": {\"HHbbVV\": 100, \"VBFHHbbVV\": 2000} if pass_region else None,\n", + " \"bg_err\": bgerrs[shape][region],\n", + " \"sig_scale_dict\": sig_scale_dict if pass_region else None,\n", " \"show\": True,\n", " \"year\": \"all\",\n", " \"ylim\": pass_ylim if pass_region else fail_ylim,\n", @@ -204,7 +215,51 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "hists[\"postfit\"][\"pass\"][\"QCD\", ...].values()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "edges = hists[\"postfit\"][\"pass\"].axes[1].edges\n", + "mps = (edges[1:] + edges[:-1]) / 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "errratio = bgerrs[\"postfit\"][\"pass\"] / sum(\n", + " [hists[\"postfit\"][\"pass\"][sample, :] for sample in bg_keys]\n", + ")\n", + "plt.scatter(mps, errratio)\n", + "plt.ylabel(\"Error / Total\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sum(hists[\"postfit\"][\"pass\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sum([hists[\"postfit\"][\"pass\"][sample, :] for sample in bg_keys])" + ] } ], "metadata": { diff --git a/src/HHbbVV/postprocessing/plotting.py b/src/HHbbVV/postprocessing/plotting.py index 1dc29948..fc3ecb55 100644 --- a/src/HHbbVV/postprocessing/plotting.py +++ b/src/HHbbVV/postprocessing/plotting.py @@ -383,6 +383,13 @@ def ratioHistPlot( ) if bg_err is not None: + if divide_bin_width: + raise NotImplementedError("Background error for divide bin width not checked yet") + + if len(np.array(bg_err).shape) == 1: + bg_tot = sum([pre_divide_hists[sample, :] for sample in bg_keys]) + bg_err = [bg_tot - bg_err, bg_tot + bg_err] + ax.fill_between( np.repeat(hists.axes[1].edges, 2)[1:-1], np.repeat(bg_err[0], 2),