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update combine setup
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@rkansal47
rkansal47 committed Mar 29, 2024
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5 changes: 4 additions & 1 deletion README.md
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Expand Up @@ -407,11 +407,14 @@ python PlotFits.py --fit-file "cards/test_tied_stats/fitDiagnosticsBlindedBkgOnl

### CMSSW + Combine Quickstart

**Warning: this should be done outside of your conda/mamba environment!**

```bash
source /cvmfs/cms.cern.ch/cmsset_default.sh
cmsrel CMSSW_11_3_4
cd CMSSW_11_3_4/src
cmsenv
# git clone -b regex-float-parameters https://github.com/rkansal47/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit
# git clone -b main https://github.com/rkansal47/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit
# float regex PR was merged so we should be able to switch to the main branch now:
git clone -b v9.2.0 https://github.com/cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit
git clone -b v2.0.0 https://github.com/cms-analysis/CombineHarvester.git CombineHarvester
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107 changes: 107 additions & 0 deletions src/HHbbVV/corrections/checkJMR.ipynb
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@@ -0,0 +1,107 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as mticker\n",
"import mplhep as hep\n",
"\n",
"plt.style.use(hep.style.CMS)\n",
"hep.style.use(\"CMS\")\n",
"formatter = mticker.ScalarFormatter(useMathText=True)\n",
"formatter.set_powerlimits((-3, 3))\n",
"plt.rcParams.update({\"font.size\": 24})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Smearing using TWiki formula"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rng = np.random.default_rng()\n",
"N = 100_000\n",
"x = rng.normal(125, 11.4, N)\n",
"\n",
"jmr = 1.03\n",
"jmr_up = 1.07\n",
"\n",
"smearing = np.random.normal(scale=0.09, size=N)\n",
"# gaussian with\n",
"jmr_nom = (smearing * max(np.sqrt(jmr**2 - 1), 0)) + 1\n",
"jmr_up = (smearing * max(np.sqrt(jmr_up**2 - 1), 0)) + 1\n",
"\n",
"plt.hist(x, bins=np.linspace(100, 150, 20), histtype=\"step\", label=r\"$N(\\mu=0, \\sigma=1)$\")\n",
"plt.hist(x * jmr_nom, bins=np.linspace(100, 150, 20), histtype=\"step\", label=r\"1.03 smearing\")\n",
"plt.hist(x * jmr_up, bins=np.linspace(100, 150, 20), histtype=\"step\", label=r\"1.07 smearing\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Smearing using current Gaussian method"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rng = np.random.default_rng()\n",
"N = 100_000\n",
"x = rng.normal(125, 11.4, N)\n",
"\n",
"jmr = 1.03\n",
"jmr_up = 1.07\n",
"\n",
"smearing = np.random.normal(size=N)\n",
"# gaussian with\n",
"jmr_nom = (smearing * max(jmr - 1, 0)) + 1\n",
"jmr_up = (smearing * max(jmr_up - 1, 0)) + 1\n",
"\n",
"plt.hist(x, bins=np.linspace(100, 150, 20), histtype=\"step\", label=r\"$N(\\mu=0, \\sigma=1)$\")\n",
"plt.hist(x * jmr_nom, bins=np.linspace(100, 150, 20), histtype=\"step\", label=r\"1.03 smearing\")\n",
"plt.hist(x * jmr_up, bins=np.linspace(100, 150, 20), histtype=\"step\", label=r\"1.07 smearing\")\n",
"plt.legend()\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python310",
"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": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
87 changes: 59 additions & 28 deletions src/HHbbVV/postprocessing/PostProcess.ipynb
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Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -13,7 +13,7 @@
"from collections import OrderedDict\n",
"\n",
"from utils import CUT_MAX_VAL, ShapeVar\n",
"from hh_vars import (\n",
"from HHbbVV.hh_vars import (\n",
" years,\n",
" data_key,\n",
" qcd_key,\n",
Expand Down Expand Up @@ -56,7 +56,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -66,18 +66,13 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"MAIN_DIR = \"../../../\"\n",
"# samples_dir = f\"{MAIN_DIR}/../data/skimmer/24Feb25_update_skimmer\"\n",
"# signal_samples_dir = f\"{MAIN_DIR}/../data/skimmer/Jun10\"\n",
"# bdt_preds_dir = f\"{MAIN_DIR}/../data/skimmer/Feb24/23_05_12_multiclass_rem_feats_3/inferences/\"\n",
"# samples_dir = \"/eos/uscms/store/user/rkansal/bbVV/skimmer/Feb24/\"\n",
"# signal_samples_dir = \"/eos/uscms/store/user/cmantill/bbVV/skimmer/Jun10/\"\n",
"# bdt_data_dir = \"/eos/uscms/store/user/cmantill/bbVV/skimmer/Jun10/bdt_data/\"\n",
"year = \"2017\"\n",
"samples_dir = f\"{MAIN_DIR}/../data/skimmer/24Mar14UpdateData\"\n",
"year = \"2018\"\n",
"\n",
"date = \"24Feb28\"\n",
"plot_dir = f\"../../../plots/PostProcessing/{date}/\"\n",
Expand All @@ -100,20 +95,11 @@
"outputs": [],
"source": [
"# add both VBF keys just in case (the unused one will be removed below)\n",
"nonres_samples[\"qqHH_CV_1_C2V_1_kl_1_HHbbVV\"] = \"VBF_HHTobbVV_CV_1_C2V_1_C3_1\"\n",
"\n",
"with open(f\"{bdt_preds_dir}/{year}/sample_order.txt\", \"r\") as f:\n",
" BDT_sample_order = list(eval(f.read()).keys())\n",
"\n",
"for key in nonres_sig_keys.copy():\n",
" if key not in BDT_sample_order:\n",
" del nonres_samples[key]\n",
" nonres_sig_keys.remove(key)\n",
"\n",
"for key in bg_keys.copy():\n",
" if key not in BDT_sample_order:\n",
" del samples[key]\n",
" bg_keys.remove(key)"
"nonres_samples = {\n",
" \"HHbbVV\": \"GluGluToHHTobbVV_node_cHHH1\",\n",
" \"qqHH_CV_1_C2V_0_kl_1_HHbbVV\": \"VBF_HHTobbVV_CV_1_C2V_0_C3_1\",\n",
" \"qqHH_CV_1_C2V_2_kl_1_HHbbVV\": \"VBF_HHTobbVV_CV_1_C2V_2_C3_1\",\n",
"}"
]
},
{
Expand All @@ -130,7 +116,7 @@
"metadata": {},
"outputs": [],
"source": [
"filters = postprocessing.new_filters\n",
"filters = postprocessing.load_filters\n",
"systematics = {year: {}}\n",
"\n",
"# save cutflow as pandas table\n",
Expand All @@ -140,14 +126,59 @@
"\n",
"# no HEM cleaning for now because it wasn't done for BDT samples\n",
"events_dict = postprocessing.load_samples(\n",
" samples_dir, nonres_samples, year, filters, hem_cleaning=True\n",
" samples_dir, nonres_samples, year, filters, hem_cleaning=False\n",
")\n",
"# events_dict |= utils.load_samples(samples_dir, samples, year, filters, hem_cleaning=False)\n",
"\n",
"utils.add_to_cutflow(events_dict, \"BDTPreselection\", \"weight\", cutflow)\n",
"# utils.add_to_cutflow(events_dict, \"BDTPreselection\", \"weight\", cutflow)\n",
"cutflow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Checking mass resolution and smearing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_mass(events, shift=\"\", i=0):\n",
" if shift == \"\":\n",
" mass = events[\"ak8FatJetParticleNetMass\"][i]\n",
" elif shift == \"up\":\n",
" mass = events[\"ak8FatJetParticleNetMass_JMR_up\"][i]\n",
" elif shift == \"down\":\n",
" mass = events[\"ak8FatJetParticleNetMass_JMR_down\"][i]\n",
"\n",
" mass = mass[(mass > 100) * (mass < 150)]\n",
" return mass"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"events = events_dict[\"qqHH_CV_1_C2V_0_kl_1_HHbbVV\"]\n",
"events = events_dict[\"qqHH_CV_1_C2V_2_kl_1_HHbbVV\"]\n",
"events = events_dict[\"HHbbVV\"]\n",
"for shift in [\"\", \"up\", \"down\"]:\n",
" print(shift)\n",
" mass = get_mass(events, shift)\n",
" print(\"mean\", np.mean(mass))\n",
" print(\"std\", np.std(mass))\n",
" print(\"\")\n",
"\n",
"# print(np.std(events[\"ak8FatJetParticleNetMass_JMR_up\"][0]) / np.std(events[\"ak8FatJetParticleNetMass\"][0]))\n",
"# print(np.std(events[\"ak8FatJetParticleNetMass\"][0]) / np.std(events[\"ak8FatJetParticleNetMass_JMR_down\"][0]))"
]
},
{
"attachments": {},
"cell_type": "markdown",
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