From 6e8e636d0c431455426d46c047bcfced309b12d8 Mon Sep 17 00:00:00 2001 From: Pietro Meloni Date: Fri, 23 Aug 2019 14:37:17 -0500 Subject: [PATCH 1/8] add notebook for trigger efficiency --- .../Data/DataOnly/TriggerEfficiency.ipynb | 374 ++++++++++++++++++ 1 file changed, 374 insertions(+) create mode 100644 Notebooks/Data/DataOnly/TriggerEfficiency.ipynb diff --git a/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb b/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb new file mode 100644 index 0000000..1c8a38a --- /dev/null +++ b/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb @@ -0,0 +1,374 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook is for computing the trigger efficiency for data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from coffea import hist\n", + "from coffea.analysis_objects import JaggedCandidateArray\n", + "from coffea.processor import defaultdict_accumulator\n", + "import coffea.processor as processor\n", + "\n", + "import numpy as np\n", + "np.seterr(divide='ignore', invalid='ignore', over='ignore')\n", + "import matplotlib.pyplot as plt\n", + "from FireHydrant.Tools.uproothelpers import NestNestObjArrayToJagged\n", + "\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "datasets_=json.load(open('../Samples/control_data2018.json'))\n", + "datasets = dict(\n", + " A={'files': datasets_['A'], 'treename': 'ffNtuplizer/ffNtuple'} ,\n", + " B={'files': datasets_['B'], 'treename': 'ffNtuples/ffNtuple'} ,\n", + " C={'files': datasets_['C'], 'treename': 'ffNtuples/ffNtuple'} , \n", + " D={'files': datasets_['D'], 'treename': 'ffNtuples/ffNtuple'} ,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class MyProcessor(processor.ProcessorABC):\n", + " def __init__(self):\n", + " \n", + " dataset_axis = hist.Cat('dataset', '')\n", + " multiplicity_axis = hist.Bin(\"multiplicity\", \"#muons/event\", 10, 0, 10)\n", + " pt_axis = hist.Bin(\"pt\", \"jet pt [GeV]\", 20, 0 , 300)\n", + " deltaR_axis = hist.Bin(\"deltaR\", \"delta_R\", 30, 0 , 5)\n", + " \n", + " self._accumulator = processor.dict_accumulator({\n", + " 'multi': hist.Hist(\"Counts\", dataset_axis, multiplicity_axis),\n", + " 'tot_tag_with_probe': hist.Hist(\"Counts\", dataset_axis, pt_axis),\n", + " 'tot_tag': hist.Hist(\"Counts\", dataset_axis, pt_axis),\n", + " 'deltaR': hist.Hist(\"Counts\", dataset_axis, deltaR_axis),\n", + " 'cutflow': processor.defaultdict_accumulator(int)\n", + " })\n", + " \n", + " @property\n", + " def accumulator(self):\n", + " return self._accumulator\n", + " \n", + " def process(self, df):\n", + " output = self.accumulator.identity()\n", + " \n", + " dataset = df['dataset']\n", + " \n", + " # IMPORTATANT!!! here you need to add the reference trigger (HLT_Mu17)\n", + " \n", + " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " df['pfjet_p4'],\n", + " px=df['pfjet_p4.fCoordinates.fX'],\n", + " py=df['pfjet_p4.fCoordinates.fY'],\n", + " pz=df['pfjet_p4.fCoordinates.fZ'],\n", + " energy=df['pfjet_p4.fCoordinates.fT'],\n", + " \n", + " )\n", + " \n", + " triggerObjs = JaggedCandidateArray.candidatesfromcounts(\n", + " df['TOHLT_DoubleL2Mu23NoVtx_2Cha'],\n", + " px=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fX'],\n", + " py=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fY'],\n", + " pz=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fZ'],\n", + " energy=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fT'],\n", + " )\n", + " \n", + " ####### EFFICIENCY STUDY #######\n", + " \n", + " \n", + " twoljs = leptonjets.counts>=2\n", + " diljs = leptonjets[twoljs]\n", + " \n", + " ptcut = 0\n", + " etacut = 999999999\n", + " \n", + " diljs.add_attributes(trgmask = diljs.match(triggerObjs, deltaRCut=0.5)) \n", + " diljs.add_attributes(ptmask = diljs.pt>ptcut) \n", + " diljs.add_attributes(etamask = diljs.eta" + ] + }, + "metadata": { + "image/png": { + "height": 603, + "width": 499 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# EFFICIENCY (ratio-plot)\n", + "\n", + "fig, (ax, rax) = plt.subplots(2, 1, figsize=(8,10), gridspec_kw={\"height_ratios\": (1, 1)}, sharex=True)\n", + "fig.subplots_adjust(hspace=.07)\n", + "\n", + "hist.plot1d(output['tot_tag_with_probe'].sum('dataset'), # sum() is summing all the datasets, I immagine\n", + " #overlay='dataset',\n", + " ax=ax,\n", + " clear=False,\n", + " stack=True,\n", + " overflow='over',\n", + " line_opts=None,\n", + " #fill_opts=fill_opts,\n", + " #error_opts=error_opts\n", + " )\n", + "hist.plot1d(output['tot_tag'].sum('dataset'),\n", + " #overlay='dataset',\n", + " ax=ax,\n", + " overflow='over',\n", + " clear=False,\n", + " #error_opts=data_err_opts\n", + " )\n", + "ax.autoscale(axis='x', tight=True)\n", + "ax.set_yscale('log')\n", + "ax.set_ylim([1, 1e7])\n", + "ax.set_xlabel('jet pt [GeV]')\n", + "leg=ax.legend()\n", + "\n", + "hist.plotratio(output['tot_tag_with_probe'].sum('dataset'), output['tot_tag'].sum('dataset'),\n", + " ax=rax,\n", + " overflow='over',\n", + " error_opts=data_err_opts,\n", + " denom_fill_opts={},\n", + " guide_opts={},\n", + " unc='num'\n", + " )\n", + "rax.set_ylabel('Ratio')\n", + "rax.set_ylim(0,0.95)\n", + "\n", + "rax.set_xlabel(rax.get_xlabel(), x=1.0, ha=\"right\")\n", + "ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\")\n", + "ax.set_title(' ', x=0.0, ha=\"left\");\n", + "\n", + "#ax.text(1,1,'60.432/fb (13TeV)', ha='right', va='bottom', transform=ax.transAxes);\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 578, + "width": 791 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# delta R study\n", + "\n", + "fig, ax = plt.subplots(2,2,figsize=(13,10))\n", + "\n", + "hist.plot1d(output['deltaR'], ax=ax[0][0], overlay='dataset', density=True)\n", + "\n", + "\n", + "ax[0][0].set_ylim([1e-4, 1])\n", + "ax[0][0].set_yscale('log')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 0f894e920f3c7f5e849d6122d0a6ab66b1d6027b Mon Sep 17 00:00:00 2001 From: Pietro Meloni Date: Thu, 29 Aug 2019 11:33:05 -0500 Subject: [PATCH 2/8] Create a notebook for the deltaR study only --- Notebooks/Data/DataOnly/DeltaRstudy.ipynb | 224 ++++++++++++++++++++++ 1 file changed, 224 insertions(+) create mode 100644 Notebooks/Data/DataOnly/DeltaRstudy.ipynb diff --git a/Notebooks/Data/DataOnly/DeltaRstudy.ipynb b/Notebooks/Data/DataOnly/DeltaRstudy.ipynb new file mode 100644 index 0000000..b253895 --- /dev/null +++ b/Notebooks/Data/DataOnly/DeltaRstudy.ipynb @@ -0,0 +1,224 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook is for computing the trigger efficiency for data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from coffea import hist\n", + "from coffea.analysis_objects import JaggedCandidateArray\n", + "from coffea.processor import defaultdict_accumulator\n", + "import coffea.processor as processor\n", + "\n", + "import numpy as np\n", + "np.seterr(divide='ignore', invalid='ignore', over='ignore')\n", + "import matplotlib.pyplot as plt\n", + "from FireHydrant.Tools.uproothelpers import NestNestObjArrayToJagged\n", + "\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "datasets_=json.load(open('../Samples/control_data2018.json'))\n", + "datasets = dict(\n", + " A={'files': datasets_['A'], 'treename': 'ffNtuplizer/ffNtuple'} ,\n", + " B={'files': datasets_['B'], 'treename': 'ffNtuples/ffNtuple'} ,\n", + " C={'files': datasets_['C'], 'treename': 'ffNtuples/ffNtuple'} , \n", + " D={'files': datasets_['D'], 'treename': 'ffNtuples/ffNtuple'} ,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class MyProcessor(processor.ProcessorABC):\n", + " def __init__(self):\n", + " \n", + " dataset_axis = hist.Cat('dataset', '')\n", + " multiplicity_axis = hist.Bin(\"multiplicity\", \"#muons/event\", 10, 0, 10)\n", + " pt_axis = hist.Bin(\"pt\", \"jet pt [GeV]\", 20, 0 , 300)\n", + " deltaR_axis = hist.Bin(\"deltaR\", \"delta_R\", 30, 0 , 5)\n", + " \n", + " self._accumulator = processor.dict_accumulator({\n", + "\n", + " 'deltaR1': hist.Hist(\"Counts\", dataset_axis, deltaR_axis),\n", + " 'deltaR2': hist.Hist(\"Counts\", dataset_axis, deltaR_axis),\n", + " \n", + " })\n", + " \n", + " @property\n", + " def accumulator(self):\n", + " return self._accumulator\n", + " \n", + " def process(self, df):\n", + " output = self.accumulator.identity()\n", + " \n", + " dataset = df['dataset']\n", + " \n", + "\n", + " muons = JaggedCandidateArray.candidatesfromcounts(\n", + " df['muon_p4'],\n", + " px=df['muon_p4.fCoordinates.fX'],\n", + " py=df['muon_p4.fCoordinates.fY'],\n", + " pz=df['muon_p4.fCoordinates.fZ'],\n", + " energy=df['muon_p4.fCoordinates.fT'],\n", + " \n", + " )\n", + "\n", + " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " df['pfjet_p4'],\n", + " px=df['pfjet_p4.fCoordinates.fX'],\n", + " py=df['pfjet_p4.fCoordinates.fY'],\n", + " pz=df['pfjet_p4.fCoordinates.fZ'],\n", + " energy=df['pfjet_p4.fCoordinates.fT'],\n", + " \n", + " )\n", + " \n", + " triggerObjs = JaggedCandidateArray.candidatesfromcounts(\n", + " df['TOHLT_DoubleL2Mu23NoVtx_2Cha'],\n", + " px=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fX'],\n", + " py=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fY'],\n", + " pz=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fZ'],\n", + " energy=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fT'],\n", + " )\n", + " \n", + " \n", + " # with muons\n", + " Tobj_Mu_pairs = muons['p4'].cross(triggerObjs['p4'], nested=True)\n", + " dr1 = Tobj_Mu_pairs.i0.delta_r(Tobj_Mu_pairs.i1)\n", + " dr1 = dr1.min()\n", + " \n", + " output['deltaR1'].fill(dataset=dataset, deltaR=dr1.flatten().flatten()) \n", + " \n", + " \n", + " # with leptonjets\n", + " Tobj_Lj_pairs = leptonjets['p4'].cross(triggerObjs['p4'], nested=True)\n", + " dr2 = Tobj_Lj_pairs.i0.delta_r(Tobj_Lj_pairs.i1)\n", + " dr2 = dr2.min()\n", + " \n", + " output['deltaR2'].fill(dataset=dataset, deltaR=dr2.flatten().flatten()) \n", + " \n", + "\n", + " return output\n", + " \n", + " def postprocess(self, accumulator):\n", + " return accumulator" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Preprocessing: 100%|██████████| 4/4 [00:11<00:00, 3.27s/it]\n", + "Processing: 100%|██████████| 2545/2545 [01:23<00:00, 28.20items/s]\n" + ] + } + ], + "source": [ + "output = processor.run_uproot_job(datasets,\n", + " treename=None,\n", + " processor_instance=MyProcessor(),\n", + " executor=processor.futures_executor,\n", + " executor_args=dict(workers=12, flatten=True),\n", + " chunksize=5000000,\n", + " #maxchunks=0\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 953 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,2,figsize=(16,6))\n", + "\n", + "hist.plot1d(output['deltaR1'].sum('dataset'), ax=ax[0], density=True)\n", + "hist.plot1d(output['deltaR2'].sum('dataset'), ax=ax[1], density=True)\n", + "\n", + "ax[0].set_title('Distance between a trigger object and the closest muon', x=0.0, ha=\"left\")\n", + "ax[1].set_title('Distance between a trigger object and the closest lepton jet', x=0.0, ha=\"left\");\n", + "\n", + "#ax.set_ylim([1e-4, 1])\n", + "#ax.set_yscale('log')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 7005fb8f8112a9faf4321035f9985c19cfe197c8 Mon Sep 17 00:00:00 2001 From: Pietro Meloni Date: Thu, 29 Aug 2019 12:01:27 -0500 Subject: [PATCH 3/8] New plotting --- .../Data/DataOnly/TriggerEfficiency.ipynb | 102 +++++++++--------- 1 file changed, 52 insertions(+), 50 deletions(-) diff --git a/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb b/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb index 1c8a38a..0316b95 100644 --- a/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb +++ b/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb @@ -61,7 +61,6 @@ " 'multi': hist.Hist(\"Counts\", dataset_axis, multiplicity_axis),\n", " 'tot_tag_with_probe': hist.Hist(\"Counts\", dataset_axis, pt_axis),\n", " 'tot_tag': hist.Hist(\"Counts\", dataset_axis, pt_axis),\n", - " 'deltaR': hist.Hist(\"Counts\", dataset_axis, deltaR_axis),\n", " 'cutflow': processor.defaultdict_accumulator(int)\n", " })\n", " \n", @@ -75,7 +74,7 @@ " dataset = df['dataset']\n", " \n", " # IMPORTATANT!!! here you need to add the reference trigger (HLT_Mu17)\n", - " \n", + " \n", " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", " df['pfjet_p4'],\n", " px=df['pfjet_p4.fCoordinates.fX'],\n", @@ -99,7 +98,7 @@ " twoljs = leptonjets.counts>=2\n", " diljs = leptonjets[twoljs]\n", " \n", - " ptcut = 0\n", + " ptcut = 0 # pt = 0 and etacut = 99999999 means NO CUTS\n", " etacut = 999999999\n", " \n", " diljs.add_attributes(trgmask = diljs.match(triggerObjs, deltaRCut=0.5)) \n", @@ -134,38 +133,16 @@ " tot_tag_pt = np.append(tag0_pt, tag1_pt)\n", " tot_tagAndprobe_pt = np.append(tag0Andprobe1_pt, tag1Andprobe0_pt)\n", " \n", - " \n", " \n", " # filling\n", " output['tot_tag'].fill(dataset=dataset, pt=tot_tag_pt) \n", " output['tot_tag_with_probe'].fill(dataset=dataset, pt=tot_tagAndprobe_pt) \n", " \n", - " \n", - " \n", - " \n", + " \n", " #output['cutflow']['all events'] += MET.size\n", " #output['cutflow']['number of chunks'] += 1\n", " \n", - " \n", - " \n", - " ####### STUDY OF DELTA_R #######\n", - " \n", - " \n", - " #twoTobjs = triggerObjs.counts==2 \n", - " #twoLjs = leptonjets.counts==2\n", - " #Ljs = leptonjets[twoTobjs & twoLjs]\n", - " #Tobjs = triggerObjs[twoTobjs & twoLjs]\n", - " \n", - " #Tobj_Ljs_pairs = triggerObjs['p4'].cross(leptonjets['p4'], nested=True)\n", - " Tobj_Ljs_pairs = leptonjets['p4'].cross(triggerObjs['p4'], nested=True)\n", - " \n", - " \n", - " dr = Tobj_Ljs_pairs.i0.delta_r(Tobj_Ljs_pairs.i1)\n", - " dr = dr.max()\n", - " \n", - " output['deltaR'].fill(dataset=dataset, deltaR=dr.flatten().flatten()) \n", - " \n", - "\n", + " \n", " return output\n", " \n", " def postprocess(self, accumulator):\n", @@ -174,15 +151,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Preprocessing: 100%|██████████| 4/4 [00:14<00:00, 4.15s/it]\n", - "Processing: 100%|██████████| 2545/2545 [02:02<00:00, 20.80items/s]\n" + "Preprocessing: 100%|██████████| 4/4 [00:11<00:00, 3.41s/it]\n", + "Processing: 56%|█████▋ | 1436/2545 [00:59<00:45, 24.35items/s]" ] } ], @@ -199,7 +176,44 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# New way to pl" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8,6))\n", + "numer = output['tot_tag_with_probe'].project('dataset')\n", + "denom = output['tot_tag'].project('dataset')\n", + "\n", + "fig, ax, _ = hist.plotratio(num=numer,\n", + " denom=denom,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax)\n", + "#ax.text(60, 0.1, '\\n'.join(['logical OR of:',]+ Triggers))\n", + "ax.set_title('[...] trigger efficiency vs. ??? pT', x=0.0, ha=\"left\")\n", + "ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n", + "ax.set_ylabel('Efficiency', y=1.0, ha=\"right\");\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -228,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -301,39 +315,27 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 578, - "width": 791 + "height": 386, + "width": 497 }, "needs_background": "light" }, "output_type": "display_data" } ], - "source": [ - "# delta R study\n", - "\n", - "fig, ax = plt.subplots(2,2,figsize=(13,10))\n", - "\n", - "hist.plot1d(output['deltaR'], ax=ax[0][0], overlay='dataset', density=True)\n", - "\n", - "\n", - "ax[0][0].set_ylim([1e-4, 1])\n", - "ax[0][0].set_yscale('log')\n", - "\n", - "\n" - ] + "source": [] }, { "cell_type": "code", From 0b5616c2cb9b81391575495eaca171162bd15e98 Mon Sep 17 00:00:00 2001 From: Pietro Meloni Date: Tue, 3 Sep 2019 14:55:21 -0500 Subject: [PATCH 4/8] testing notebook --- Benchmarks/README.md | 8 +- Benchmarks/benchmark-0.ipynb | 18 +- Benchmarks/benchmark-1.ipynb | 169 +++++++++++ Benchmarks/benchmark-2.ipynb | 174 +++++++++++ Benchmarks/benchmark-3.ipynb | 181 ++++++++++++ Benchmarks/benchmark-4.ipynb | 202 +++++++++++++ FireHydrant/Tools/trigger.py | 2 +- Notebooks/Data/DataOnly/DeltaRstudy.ipynb | 18 +- Notebooks/Data/DataOnly/Testing.ipynb | 279 ++++++++++++++++++ .../Data/DataOnly/TriggerEfficiency.ipynb | 158 ++++++---- 10 files changed, 1134 insertions(+), 75 deletions(-) create mode 100644 Benchmarks/benchmark-1.ipynb create mode 100644 Benchmarks/benchmark-2.ipynb create mode 100644 Benchmarks/benchmark-3.ipynb create mode 100644 Benchmarks/benchmark-4.ipynb create mode 100644 Notebooks/Data/DataOnly/Testing.ipynb diff --git a/Benchmarks/README.md b/Benchmarks/README.md index 8068fbf..921a438 100644 --- a/Benchmarks/README.md +++ b/Benchmarks/README.md @@ -4,10 +4,10 @@ FireHydrant Benchmarks Some benchmark tasks completed with ffNtuples with [Coffea](https://github.com/CoffeaTeam/coffea). 1. [x] Plot leptonJets multiplicity of all events. -2. [ ] Plot leading and subleading leptonJet pair deltaPhi for *mXX-100_mA-0p25* signals. -3. [ ] Matching leptonJets with gen dark photons (pid=32) by `deltaRCut=0.3`, overlay matched and unmatched leptonJets pT distribution for *mXX-100_mA-0p25* signals. -4. [ ] Overlay leading and subleading leptonJet pair invariant mass for *all* signals, in [0, 200] GeV and [0, 1200] GeV range. -5. [ ] **Trigger efficiency** | Plot the efficiency of logical OR of [DoubleL2Mu triggers](../FireHydrant/Tools/trigger.py) wrt. the pT of subleading displacedStandAloneMuons which satisfied the condition that *|eta|<2.4 && #stations>1 && normalized Chi2<10*. +2. [x] Plot leading and subleading leptonJet pair deltaPhi for *mXX-100_mA-0p25_lxy-300* signals. +3. [x] Matching leptonJets with gen dark photons (pid=32) by `deltaRCut=0.3`, overlay matched and unmatched leptonJets pT distribution for *mXX-100_mA-0p25_lxy-300* signals. +4. [x] Overlay leading and subleading leptonJet pair invariant mass for *all* signals, in [0, 200] GeV and [0, 1200] GeV range. +5. [x] **Trigger efficiency** | Plot the efficiency of logical OR of [DoubleL2Mu triggers](../FireHydrant/Tools/trigger.py) wrt. the pT of subleading displacedStandAloneMuons which satisfied the condition that *|eta|<2.4 && #stations>1 && normalized Chi2<10*. ### Info diff --git a/Benchmarks/benchmark-0.ipynb b/Benchmarks/benchmark-0.ipynb index dab238b..b1398c6 100644 --- a/Benchmarks/benchmark-0.ipynb +++ b/Benchmarks/benchmark-0.ipynb @@ -25,16 +25,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "dict_keys(['mXX-100_mA-0p25', 'mXX-100_mA-1p2', 'mXX-100_mA-5', 'mXX-150_mA-0p25', 'mXX-150_mA-1p2', 'mXX-150_mA-5', 'mXX-200_mA-0p25', 'mXX-200_mA-1p2', 'mXX-200_mA-5', 'mXX-500_mA-0p25', 'mXX-500_mA-1p2', 'mXX-500_mA-5', 'mXX-800_mA-0p25', 'mXX-800_mA-1p2', 'mXX-800_mA-5', 'mXX-1000_mA-0p25', 'mXX-1000_mA-1p2', 'mXX-1000_mA-5'])" + "dict_keys(['mXX-1000_mA-0p25_lxy-300', 'mXX-1000_mA-0p8_lxy-300', 'mXX-1000_mA-1p2_lxy-300', 'mXX-1000_mA-2p5_lxy-300', 'mXX-1000_mA-5_lxy-300', 'mXX-100_mA-0p25_lxy-300', 'mXX-100_mA-0p8_lxy-300', 'mXX-100_mA-1p2_lxy-300', 'mXX-100_mA-2p5_lxy-300', 'mXX-100_mA-5_lxy-300', 'mXX-150_mA-0p25_lxy-300', 'mXX-150_mA-0p8_lxy-300', 'mXX-150_mA-1p2_lxy-300', 'mXX-150_mA-2p5_lxy-300', 'mXX-150_mA-5_lxy-300', 'mXX-200_mA-0p25_lxy-300', 'mXX-200_mA-0p8_lxy-300', 'mXX-200_mA-1p2_lxy-300', 'mXX-200_mA-2p5_lxy-300', 'mXX-200_mA-5_lxy-300', 'mXX-500_mA-0p25_lxy-300', 'mXX-500_mA-0p8_lxy-300', 'mXX-500_mA-1p2_lxy-300', 'mXX-500_mA-2p5_lxy-300', 'mXX-500_mA-5_lxy-300', 'mXX-800_mA-0p25_lxy-300', 'mXX-800_mA-0p8_lxy-300', 'mXX-800_mA-1p2_lxy-300', 'mXX-800_mA-2p5_lxy-300', 'mXX-800_mA-5_lxy-300', 'mXX-1000_mA-0p25_lxy-0p3', 'mXX-100_mA-5_lxy-0p3'])" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -86,15 +86,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Preprocessing: 100%|██████████| 18/18 [00:03<00:00, 6.27it/s]\n", - "Processing: 100%|██████████| 90/90 [00:09<00:00, 9.97items/s]\n" + "Preprocessing: 100%|██████████| 32/32 [00:13<00:00, 5.53it/s]\n", + "Processing: 100%|██████████| 160/160 [00:18<00:00, 8.86items/s]\n" ] } ], @@ -110,12 +110,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] diff --git a/Benchmarks/benchmark-1.ipynb b/Benchmarks/benchmark-1.ipynb new file mode 100644 index 0000000..038b9df --- /dev/null +++ b/Benchmarks/benchmark-1.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot leading and subleading leptonJet pair deltaPhi for mXX-100_mA-0p25_lxy-300 signals." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "from coffea import hist\n", + "from coffea.analysis_objects import JaggedCandidateArray\n", + "from coffea.processor import defaultdict_accumulator\n", + "import coffea.processor as processor" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "selected = 'mXX-100_mA-0p25_lxy-300'\n", + "datasets = {selected: json.load(open('Samples/signal_4mu.json'))[selected]}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class LeptonJetProcessor(processor.ProcessorABC):\n", + " def __init__(self):\n", + " dataset_axis = hist.Cat(\"dataset\", \"signal\")\n", + " dphi_axis = hist.Bin(\"dphi\", \"$\\Delta\\Phi$(lead, sublead) leptonJets\", 50, 0, 3.142)\n", + " \n", + " self._accumulator = processor.dict_accumulator({\n", + " 'dphi': hist.Hist(\"norm. #counts/$\\pi$/50\", dataset_axis, dphi_axis),\n", + " })\n", + " \n", + " @property\n", + " def accumulator(self):\n", + " return self._accumulator\n", + "\n", + " def process(self, df):\n", + " output = self.accumulator.identity()\n", + " \n", + " dataset = df['dataset']\n", + " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " df['pfjet_p4'],\n", + " px=df['pfjet_p4.fCoordinates.fX'],\n", + " py=df['pfjet_p4.fCoordinates.fY'],\n", + " pz=df['pfjet_p4.fCoordinates.fZ'],\n", + " energy=df['pfjet_p4.fCoordinates.fT'],\n", + " )\n", + " \n", + " twoleptonjets = leptonjets.counts>=2\n", + " dileptonjets = leptonjets[twoleptonjets]\n", + " leptonjetpair = dileptonjets.distincts()\n", + " sumpt = leptonjetpair.i0.pt+leptonjetpair.i1.pt\n", + " if sumpt.size!=0:\n", + " leadingLjPair = leptonjetpair[sumpt.argmax()]\n", + " dphi_ = np.abs(leadingLjPair.i0.p4.delta_phi(leadingLjPair.i1.p4))\n", + " \n", + " output['dphi'].fill(dataset=dataset, dphi=dphi_.flatten())\n", + " \n", + " return output\n", + " \n", + " def postprocess(self, accumulator):\n", + " return accumulator" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Preprocessing: 100%|██████████| 1/1 [00:00<00:00, 4481.09it/s]\n", + "Processing: 100%|██████████| 5/5 [00:02<00:00, 2.49items/s]\n" + ] + } + ], + "source": [ + "output = processor.run_uproot_job(datasets,\n", + " treename='ffNtuplizer/ffNtuple',\n", + " processor_instance=LeptonJetProcessor(),\n", + " executor=processor.futures_executor,\n", + " executor_args=dict(workers=6, flatten=True),\n", + " chunksize=500000,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bMGt3s4TPET+d4JE/ibxMcClmDlsm5oqvqw7BJZqVqULPqWQy+FyI4hwYWPxHoDD/wWyZtfypFMqBqulLW3s/iv2g8naiHynSkMkxipdOv4nNGl7a7TSx41mb4JnxxcvPJnj2OMAb7h7FDxRlyeQ9K3OfI/5eICIi1YyC8eplopk9aGaHm1lubKWZtQYmEHx5W0cKM+26+2q23EN5vZmdb2bbheXtSvBorXRGSmuKJ8PX083slDD4wMx+C/yHCro0NQwUYvd5PwfUK34/fbF7618I07YADo8vy90/Bn4HnEEQ1K9jyzmbFf79KnAKsJe7f56kWbFL3veOD/hTEV4KfB7BzO4nmtmz4eR9sf2tbWb7mtktwDfFsr8S5tsduMvMGod5GprZpcC9JLnPNZx9PvboomvM7OK4/tsamET5ZyXOSKbnlJn1snDmbzPrVY6q0/1ciOIc2ABMMbNuYd4sMzuGLY/mm+bubyXNHacq+tLW2I+KiQXjMyNqWybHKF46/ebT8PU4CybATNS+NcCN4Z8XmNlfYlcXhKPSjwE9CEacr0ptrzOT4XtW5j4T4fcCERGphrwaPOx8W1kI/rN2oHWS7c/GpSkgeGzNmrh1m4DBxfLMCLcNS1BeHYLJXmL5N4ZlOsGXpQFx23asoH0t0a5w+4Jwe68k24eF22eUUkfSMghGTt4ptu+rwn8vIxgtdMK51NKpI0n6Q+LqLO/yRBllZxEE1k7wpdNSbJMRjKo7cGiSNEn7Ubj9FIJZq2NtXRu+j5vi9yFBvtuL7eMKgpEcJ5jY7vrw3w8lyFur2DkR3383AsfFbWudyv6kcjyTlZnpOUXwfGEvq/4oPhcyPQfi3qfhBLc/OMHcD2vjyptXfB+rY1/KpB9VVF/KpB/FlbEdwchqAdA21f6UynHK4Bhl0m86xdW5kWDG9AXAm8XSZRMEofH9f3n4PnjYJ84ppW3pnvsPhdumR9WvU9ln0jz/tWjRokVLzVg0Ml69XAFcRvDFcj7BF7ZsgkDqQaCLuz+camEeTBx2NMFlmJ8QfEnZRDAaezAwPS75yhIF1GAejFYcRnDZ4AKCLzFrCL5Q7QN8VEFVD80gb7/YiF8iHlyGuin8M9/dPZVCw3Tjwz//L52GufuDQEdgNMFozmagIcGXzRnANeH24vkuJhjZ/4DgS2d2+O+LCPrmpuJ54vJuAo4HLgA+DtNuJnicT093fyadfclEhudUbFKutZRvQsO0PhciOge+Iri9YTxBIJ8dlnUbsK+7l/Z4roQquy9thf0o5miCOSVmuPv8iNuX1jGKU+5+4+5fEPTXqWGeFgQThe5cLN1mdx8K/IHgsvWVBJP7/UTwI+V+7j6m/Htdptjl4usSbUznPUtxnyP9XiAiItWLpfh9XiJgZrE3u427L6jKtgCY2aEEl39+65V/T3eNYGYLCL4cHeLBhDk1kpm1JPgyvBpo6e75peeQdJR2TpnZP4Ezgdvc/U9V0DypIVL5bDazpwlG9Qe5+2OJ0lS2reXzMhEze5VgHo+H3V2PEhMRkUhoZHzbFpsAp/hzqWUr4+4/AvcRPBLnlCpuztastHOqJ8GoWqmTPIlQxmezme1GcJvBZwTPXZcKFN7TH5sAs6KuqhIRkW2QgvGq8U1sIqeKrMTMss3s32Z2RPzkMGb2WzP7N8GEYRuBuyqyHTVNOAFP7Pi0qur2ROg64Ffg8nDGZimndM8pM2tOcH/ofe6+CNmmRfDZfCXBpcp/8WhmUZckwnN3AsEl55uBSr+tQUREtl76Ql65KvtLuBHcK3k8gJn9QnDM64XbC4Dz3P1/ibNvs1ZQ8lhtqIqGRMndF5vZEGBPgnsSF1Rti2qktM4pd19COZ8lLFu1tD+bzSyL4H7hS9392cpp7rYnnAn+BYKriWKud/dvqqhJIiKyFdI941ux8DFbZxGMsuwBbE8ww/LPBI/CGe3uc6quhSI1i84picLW2o+2pnvGw8cPvkYwY/1HwD/c/fEqbZSIiGx1FIyLiIiIiIiIVDLdMy4iIiIiIiJSyRSMi4iIiIiIiFQyBeMiIiIiIiIilUzBuIiIiIiIiEglUzAuIiIiIiIiUsn0nPEKZGbfAA3R85xFRERERES2Rq2BX9y9TXkzKhivWA232267vM6dO+dVdUNEREREREQkWp9//jnr1q1LK6+C8Yq1oHPnznmzZ8+u6naIiIiIiIhIxPbZZx/mzJmzIJ28kQXjZrYdcADQAWgcrl4JfAm84+7p/VwgIiIiIiIispXJOBg3sybADcBgoF6SZGvNbCJwlbuvyLROERERERERkZoso2DczBoDbwGdgDXANGAesCpM0ghoD3QHzgYOMbMD3X1VguJEREREREREtgmZjoxfQxCI3wFc4+6/JkpkZg2AUcBFwNXAJRnWKyIiIiIiIlJjZfqc8WOB19z9kmSBOIC7/+ruFwMzgOMyrFNERERERESkRss0GN8ReLcc6d8J84iIiIiIiIhsszINxpcBHcuRvnOYR0RERERERGSblek94y8BQ83sHHcfU1pCMzsP6Ac8lGGdW6WCggKWL1/O6tWryc/Px92rukkiIlslMyMnJ4fc3Fzy8vLIysr0d2kRERGR8ss0GP8rcDRwt5ldArxM8Fzx+NnUOwB9gdbAYoIJ3CROQUEB33//PWvXrq3qpoiIbPXcnfXr17N+/XrWrFnDLrvsooBcREREKl1Gwbi7/2BmBwL/AA4DzgSKD+la+PoycI67/5BJnVuj5cuXs3btWmrVqkWLFi2oX7++vhiKiFSQgoIC1qxZw88//8zatWtZvnw5zZo1q+pmiYiIyDYm05Fx3H0+cLiZtQUOIbiHvFG4eRUwF5geppMEVq9eDUCLFi3Izc2t4taIiGzdsrKyCj9rFy5cyOrVqxWMi4iISKXLOBiPCYNtBdxpyM/PB6B+/fpV3BIRkW1H7DM39hksIiIiUpl0LXQ1EJusTZemi4hUHrPgLipNmCkiIiJVQdGfiIhsk2LBuIiIiEhVqJBg3Mz2MLOnzWyJma01s0/M7HIzi+yyeBEREREREZGaKqNg3Mx+NLOLi607GPgvMABoCtQFfgPcCDyTSX0iIiIiIiIiW4NMR8ZbAA1if5hZFvAgsB1wG9AeaAwcSTC529FmdlKGdYpIRHr16lVjLtV96KGHMDMeeuihqm6KiIiIiEjGor5M/WCgDXCfu1/q7l+7+y/u/hLBc8jzgSER1ykiIiIiIiJSo0R9D/fvAAfuKb7B3ReY2WSCgF1EqoGJEyeydu3aqm6GiIiIiMg2J+pgPPag7GTPG/8a6BdxnSKSpl133bWqmyAiIiIisk2K4jL1+Ae0fhe+5iZJmwusiaBOESnD888/z6GHHsqOO+5ITk4OLVu2pGfPnowZM6YwTbJ7xvPz8xk5ciRt27YlJyeHNm3acNVVV5Gfn4+Z0atXryLpR44ciZkxY8YM/v3vf7PffvtRr1498vLy+L//+z9++OGHEnXMnj2bCy+8kD333JO8vDzq1q1L+/btueSSS1ixYkXk74eIiIiISHUSxcj4CDM7Jfx3Tvj6W2BGgrStgJ8jqFNESjF27FjOPPNMWrRowTHHHEOzZs1YvHgxH3/8MQ8++CDnnHNO0rzuzvHHH8/kyZNp37495513Hhs3buShhx7i008/LbXeMWPG8Pzzz9OvXz969uzJrFmzeOKJJ/joo4/48MMPycnJKUx7//33M2nSJHr27EmfPn0oKChg9uzZ3H777UyZMoVZs2aRm5vsdz0RERERkZot02D8O4KR8djQ2oZw3UEUC8bNrB7B/eIvZFiniJThvvvuo06dOnz00Udsv/32RbYtXbq01LyPPPIIkydP5qCDDuKVV16hTp06AIwaNYoDDjig1LxTp07lvffeY4899ihcN2jQIB577DGee+45TjjhhML1V155Jffeey/Z2dlFynjggQcYPnw4Y8aM4fLLL09pf0VEREREapqMLlN399bu3ibBcl2C5LsSPO7svkzqFJHU1KpVi9q1a5dY36xZs1LzTZgwAYDrr7++MBAHaNy4MX/9619LzXvBBRcUCcQBTj/9dADefffdIutbtWpVIhAHOPXUU2nYsCEvvfRSqXWJiIiIiNRkUT/aLCl3/8Ldr3X3mZmWZWZ/M7NXzex7M1tnZsvN7AMzu8bMmpajnAVm5kkWXU4vNdZJJ53E2rVr+c1vfsOIESN49tlnWbJkSUp5P/jgA7KysujWrVuJbT169Cg177777lti3S677AJQ4j7wjRs3cs8999CjRw/y8vLIzs7GzMjKyuKXX35JeJ+5iIiIiMjWIqPL1M3sTuBp4A1397LSR2gEMAeYBiwmmMX9AGAkcIaZHeDu36dY1ipgdIL1v0bQTpEqcfHFF9OsWTPGjBnDXXfdxejRozEzevbsya233powaI5ZtWoVeXl51KpV8uNhhx12KLXexo0bl1gXK2fz5s1F1g8cOJBJkybRtm1b+vfvT4sWLQrvKR89ejT5+fll7qeIiIiISE2V6T3j5wLnAcvM7HlgEjDN3Tdk3LLSNXT39cVXmtkNwJ+BK4HkM1QVtdLdR0bYNpFqYciQIQwZMoSVK1fy9ttvM2nSJMaPH8/hhx/OF198QfPmzRPma9iwIcuXL2fTpk0lAvJFixZF0rb333+fSZMm0adPH6ZMmVKknoKCAm655ZZI6hERERERqa4yvUy9JXA2MBs4GXgeWGpmj5vZQDOrkKmQEwXioSfD1/YVUa9ITdS4cWOOOuoo7r//foYNG8by5cuZOTP53SJ77703BQUFvP322yW2vfnmm5G06auvvgKgX79+JQL+d999l3Xr1kVSj4iIiIhIdZXpBG6L3X2sux8JNAcGAy8BRwGPAYvN7EUzO83MEg/DReuY8PXjcuTJMbOTzezPZnahmR1iZiVnlRKpQaZPn06iO0cWL14MQL169ZLmHTJkCABXXXUVGzZsuchl1apVXHddorkZy69169YAzJgxo0T7zj333EjqEBERERGpzqJ4zjgA7r4a+BfwLzPLAfoCxwG/JwjO/2lm/yW4x/xZd/820zrN7E9AA6ARsC/QgyAQv7kcxbQAHi627hszO8XdX0+xHbOTbOpUjnaIRGbAgAE0aNCAAw44gNatW+PuvPHGG7z33nvss88+9OnTJ2neIUOG8PjjjzN16lR23313+vXrx8aNG3n66afp2rUrc+fOJSsrs4tqunbtSvfu3XnmmWfo1q0bPXr0YNGiRUyZMoWOHTvSsmXLjMoXEREREcnUHdO+LPz3iMM6RF5+hcym7u757v6Cu58C7AAcCvwDaAXcAcwvJYAtjz8B1wAXEQTiU4G+7p7atNHwYNi2FgSTwO1B8Oi11sAUM9szgjaKVLqbb76Zrl27MmfOHMaMGcODDz7Ixo0b+dvf/sb06dMTPvIsxsyYNGkSf/3rX9m4cSN33303zz33HEOHDuWee+4BgvvKM5Gdnc3zzz/P2WefzY8//shdd93Fm2++yfDhw3nppZdKbZ+IiIiISGW489V5hUtFsKgmQTezPd39oxTSdQUGAMe6+28iqnsHoBvBiHgu8Ht3n5NBeX8HLiEYwR+QQTmzu3Tp0mX27NJ/d/j8888B6Ny5c7pViVSKadOm0bdvX6644gpuuummqm6OSMb0+SsiIiLJtL5icuG/F9x8dMI0++yzD3PmzJnj7vuUt/woR8bnmNlfykrk7u+5+5+jCsTDMhe5+ySCS+ObAhMzLPKf4evBGZYjUiP9+OOPJdYtW7aMK664AggugxcRERERkfRFds84YEBfM/sR+BD4xN03FklgthMw0N1vj7DeQu7+rZl9BuxlZs3cfWmaRcUuc68fUdNEapSLL76Yjz76iG7dutG8eXMWLlzIlClTWL58OWeeeSb77bdfVTdRRERERKRGizIYh+C+7R7hvzeZ2RcEgfmHwP+A3YFRQIUE46HYzE+bMyjjgPB1foZtEamRjjvuOBYtWsQLL7zAypUrqVu3Lr/97W857bTTOO2006q6eSIiIiIiNV7UwfgTwAdAl3DZnWBStMFA7Ob0ZZlUYGYdgEXuvqrY+izgOmB74G13XxGurw20Aza6+9dx6TsD37n7mmLltLbE/mEAACAASURBVAbuCf98JJO2itRUJ5xwAieccEJVN0NEREREZKsVdTD+hbvfGvvDzBoAe7ElMM8DnsywjqOAm8zsTeAbguB+B6An0Bb4GTg9Lv1OwOfAtwSzpMcMBC4xs5nhttUEQfvRQF3gP8DfM2yriIiIiIiISAlRB+NFuPuvwJvhEpVXgN0ILoffG2gMrAG+JHhe+F3uvjyFcqYDHcMyuhPcH74ybOvDwMMe1VTzIiIiIiIiInEqNBivCO7+CXBeOdIvIJhcrvj614HXo2uZiIiIiIiISGqiDsb/YGa1CCZs+yj+Hm0RERERERERCUQdjO8eLg5gZr8CHwMfEQbowP/cfX3E9YqIiIiIiIjUGFEH42OAaQSTtsWW7uESu/96M1An4npFREREREREaowog/HfA6vc/S3gudhKM2vElsB8b2DPCOsUERERERERqXEiC8bd/T9J1q8imChNk6VVE3dM+7Lw3yMO61CFLREREREREdk2ZRSMm9mdwNPAG3oMWM1x56vzCv+tYFxERERERKTyZWWY/1yC53UvMrNxZna0mel+cBGRSjBu3DjMjEceeaSqmyIiIiIi5ZRpMN4SOBuYDZwMPA8sNbPHzWygmeVm2kCRdBQUFNCzZ0/MjMceeyxhmvnz55Obm0teXh4LFy4E4Oeff6Z58+Y0aNCAr776KmG+p556CjPjwAMPZPPmzSm157PPPuOaa66hX79+7LrrrpgZZlZmvmXLlnHBBRfQqlUrcnJy2GmnnRg+fDg//vhj0jzfffcdw4YNo2XLluTk5NCmTRsuvvhiVq5cmVJbK5u7065dO8yMgw8+OOPyHnzwQbp27Ur9+vVp1KgRhxxyCFOmTImgpfDrr7/yyCOPcOKJJ9KxY0fq1atHbm4uXbt25Y477mDjxo0l8mzatKnweCdaevToEUnbqotx48bRv39/2rVrR8OGDWnQoAGdO3fmjDPO4Msvv0yaL51++8knn/CHP/yB5s2bU7duXTp27Mi1117L+vV6YIeIiIhUfxldpu7ui4GxwNgw8D4GGAAcBZwA5JvZq8Ak4Hl3X5Jhe0VSkpWVxYQJE9hzzz0599xzOfjgg9lpp50Kt2/evJmTTz6ZX3/9lccff5ydd94ZgBYtWnDfffdx/PHHM3jwYN58802ys7ML8/3www+ceeaZNGjQgEceeaTIttL85z//YdSoUWRnZ9OhQwdycnLIz88vNc+SJUvo1q0bX331FYceeiiDBg3i008/5YEHHmDy5Mn897//pXXr1kXyzJs3j27durFs2TL69+9Pp06deOedd7jjjjuYOnUqb731Fk2aNEnxXawcr7zyCvPnz8fMeOONN/jiiy/o1KlTWmVddNFF3Hnnneyyyy6ceeaZrFu3jieeeIKjjjqKf/zjH5x11lkZtXXGjBkMHjyYvLw8evfuzYABA1i+fDnPP/88F198Mc888wyvvPIKOTk5JfK2adOGIUOGlFi/6667ZtSm6mbixIksXbqUAw88kBYtWgAU9tuJEyfy/PPP07dv3yJ50um3b7/9Nn369GHTpk388Y9/ZOedd+aVV15h5MiRvPbaa0ybNo06dXShloiIiFRj7h75AuQQBOYPAkuAAmAjMBO4EGhVEfVWtwWY3aVLFy/LZ5995p999lmZ6aLS6vIXC5et3YMPPuiA9+nTxwsKCgrXX3fddQ74oEGDEuYbOnSoAz5q1KjCdQUFBd6nTx8HfOzYseVqx+eff+6zZs3ytWvXurv7Tjvt5MHpl9ypp57qgF922WVF1t92220O+NFHH10iT+/evR3wMWPGFFl//vnnO+DnnntuudpdGf7whz844FdccYUDPmLEiLTKmTlzpgPevn17X7lyZeH6r776yhs3bux169b17777LqO2zp492x999FHfsGFDkfWrVq3yPffc0wEfPXp0kW0bN250wA899NCM6k7k/vvvd8AffvjhyMtO17p16xKu/89//uOA77HHHiW2lbffbty40Tt06OCAT548uXD9pk2b/Nhjj3XAb7311pTaW9mfvyIiIlJzpBI3denSxYHZnk68mE6mclUQXAp/CHAX8G0YmG9Ot8E1aVEwXn7ffPONAz506FD/6quv/Pjjj/e8vDxv0KCBH3bYYf6///3P3d0XL17sp59+urdo0cJzcnJ833339ddeey1hmQMGDHDA77jjDnd3f++997x27dq+yy67+IoVKxLmWbVqlbdq1cpr1arl7733nru7jx492gE/5phjMt7PsoLxVatWeU5Ojufm5vqvv/5aZNumTZt85513djPzb7/9tnD93LlzHfDddtutyA8P7u4rV6707bbbzhs0aFD4g0B5zJs3zwE/7bTT/Msvv/QBAwZ4kyZNPDc31w8//HD/9NNP3d190aJFftpppxUel65du/qMGTOSlrto0SKvXbu2d+7c2Tds2ODNmzf3pk2b+vr168vdxhNPPNEBnzhxYoltV155ZYkfV9zdu3fv7tnZ2b5u3Tq/8sorvVWrVl6nTh1v166djxo1yvPz81Ouf8KECQ74scceW2R9ZQfjt9xyiwN+wgknlEg/ZcoUNzPfc889fd26db506VLPycnx9u3bJ63jiCOOcMA/+OCDjNubm5vrdevWLbIunX770ksvOeC9e/cuUUesvLZt26bUJgXjIiIikkxFB+OZ3jNeJncvcPfp7n6Bu7cC9gP+BmxX0XVLzbVgwQL2339/Fi1axLBhw+jbty+vvPIKvXr1Yt68eRxwwAG89957DBw4kBNOOIGPPvqII488ku+++65EWWPHjmWHHXbgyiuv5P333+fkk09m06ZNTJgwgcaNGyesv2HDhkyYMIGCggJOPvlk3n//fa644gq23357xo0bV9G7z9tvv01+fj4HHXQQ9evXL7ItOzubvn374u5Mnz69cP1rr70GQN++fUvcj96oUSMOPPBAfv31V95999202zV//nz2339/li5dyimnnEKfPn146aWX6NWrF1999RX7778/c+bMYeDAgfzxj3/kgw8+4Igjjii8J7+4hx56iI0bNzJs2DBq167NoEGDWLZsGc8880y52xbb/yOOOKLEtiOPPLJImuKOP/54JkyYQL9+/TjvvPNwd66++mpOOOGElOuvXbs2ALVqJb77Z8WKFTzwwAPceOON3HvvvcyaNSvlssvjT3/6E0ceeSRPPvkkY8eOLVz/448/MmTIEOrXr88TTzxB3bp1adq0KSeccALz5s0r0pdiFixYwMsvv8z+++/PXnvtlVG7ZsyYwerVq/nd735XZH06/ba0Y92hQwfatm3L/Pnz+fbbbzNqs4iIiEhFqvBg3MyamtkAMzvczLLd/X13/7O7/6ai65aa6/XXX2fEiBG88cYb3HbbbTz99NNce+21LFu2jP3335/DDjuM2bNnM3r0aCZOnMgDDzxAfn4+d9xxR4mymjVrxrhx41i/fj09evRg7ty5jBgxgkMOOaTUNvTs2ZOLL76YuXPn0qNHD9avX8+4cePYfvvtK2q3C82dOxcIAotE2rdvD1BkQqx08pTX9OnTufzyy5k5cya33XYbzzzzDFdffTVLlixhv/3246ijjuL9999n9OjRPPzww4wdO5b169dz5513lijL3Rk3bhzZ2dkMHjwYgGHDhgEUCSJTsWrVKhYtWkTjxo1p3rx5ie2l7fvmzZv5+uuv+fTTT7nrrru47bbb+OSTT+jatSvPPfdc0gkAixs/fjyQOEAEmDNnDsOHD+cvf/kL5513HgcccABdunTh008/TXU3U2JmTJw4kZYtW3LRRRfxySefUFBQwEknncSSJUsYM2YMHTt2LEx/zjnnAHDfffeVKGvcuHEUFBRw5plnlrsdTz75JCNHjuTyyy/n2GOPpW/fvjRr1oy77767SLrq2tdFREREKlpGE7jFM7OzgWHAke6+PFy3DzAVyAuTvW9mvd19TVT1bgtaXzG5xpS94OajIymndevWXHHFFUXWDR06lKuvvpr8/HxuvfVWsrK2/JY0aNAgTj31VD788MOE5f3+97+nZ8+evP766+y6667ceOONKbVj5MiRjBkzhrVr1zJw4ECOOeaY9HeqHFatWgUEI4OJxNbHzzSdTp7yateuHZdeemmRdUOHDmXUqFFs2rSJW265pchxOfnkkxk+fHjC4zJjxgzmzZvHUUcdxY477gjAXnvtxZ577lm4LRZUlSXTfb/mmmuKXCWx3XbbceONN3LYYYcxfvx4TjzxxFLrHz16NK+88gr77LMPQ4cOLbItKyuLP/3pTxx33HF06NCBOnXq8MUXX3DzzTfzzDPP0Lt3bz766KPCyc6i0KxZM/71r39x6KGHMnDgQH7/+98zY8YMhg4dWvjDR8wBBxzA3nvvzaRJk1iyZEnhjxmbNm1i/PjxNG7cmIEDB5a7DU8++SRPP/104d8dO3bkX//6F126dCmSrrr2dREREZGKFlkwDgwkuAd2edy6W4EmBBO57QAcDZwF3BZhvbIV2muvvUrMVN6yZUsgGA3LzS361Lzs7Gx22GGHpJdDv/baa8ycOROAhQsXMmvWrJQeo3XLLbewdu1aIAgely5dSrNmzUqku/322/nll1+KrDvuuONKXJJb0+29995Fgm3Yclw6duxY4pL62rVr07x584THJTb6fcoppxRZP2zYMEaMGMH999/PLbfcUrh+/PjxJW5D6N27dySPQ+vZs2eJdQcffDBZWVl88MEHpeZ96qmnuOSSS2jZsiX//ve/S1ymnpWVxa233lpkXdeuXXn66acZMGAAzz77LLfddluJNJnq2bMnf/3rXxk5ciSfffYZnTp14t57702Y9uyzz+aMM87gwQcf5LLLLgPghRde4KeffuL888+nXr16QPDIwFGjRpXIf+qpp5aYFf7f//43EATOn3zyCSNHjqRbt26MGzeOk08+OcpdFREREamRogzG2wOFw6xm1gzoCYxz9zPDdbOAQSgYlzIkGvGKBTnJRsNq1aqV8DnPK1euZNiwYdSqVYu77rqL8847j6FDh/Lxxx+XCOrjzZo1ixtvvJE2bdowePBgRo0axVlnnVUYZMS7/fbb+eGHH4qs22233dIOxmP7GBsBLC62Pn40N5086bYrXjrHZenSpUyaNIm8vDz69etXZNtJJ53EZZddxoQJE7jhhhsK78UeP348b731VomyDz744Iz3PdGtB3Xq1KFJkyZJy4Qg4Bw0aBA77rgj06dPL/GoubKceeaZPPvss4U/FEXt+OOP59prr8XdOf3000v8WBIzaNAgLr30UsaOHcull16KmRX+WBJ/iXpBQQHXXnttifx9+vRJ+oi2Ro0a0b17d1588UW6dOnCGWecwaGHHlp4NUR17esiIiIiFS3KYLwpsDju7+7h66S4dW8QXMou5RDVpd8x8ZemR112dXTOOefw/fffc+ONN3LWWWcV/nvEiBFJJ2Nbs2YNgwcPpqCggIcffpgDDzyQ6dOn8/TTT/PII4+UGNlLNiKfrtg9vcnueZ03bx5Q9J7ZdPJUlQkTJpCfn09+fn7CZ3IDLF68mEmTJhVOovbmm28mLa9Ro0bssMMOLFq0qMil1jFl7fvixYsLR/hjNmzYwIoVK5I+l/3xxx9n8ODBhYF4u3btkrYvmVg716yJ/s6ddevWceKJJ5KVlUXDhg0ZOXIk/fr1Y7fddiuRtn79+gwZMoS7776bV199ld12242XX36Z7t2789vf/rYwXa1atWJPiii3nJwcevfuzT333MOsWbM49thjga2/r4uIiIgkE+UEbsuB+Ot3exI8xuztuHUO1I2wTpFSPfHEEzz22GN0796dyy+/HAjuA99rr7144IEHePHFFxPmu+SSS5g3bx6XXXYZ3bt3JysriwkTJtCgQQPOP//8yIPv4rp160ZOTg5vvPFGiUBt8+bNTJs2DTMrMgld7969AXj55ZdLBEyrVq3iv//9Lw0aNGC//far0LanIvYjyKBBgzjttNNKLMcddxwA999/f8plxvZ/6tSpJbZNmTKlSJriXn/99RLrZs6cSUFBAXvvvXeJbRMnTuTkk09m5513ZubMmWkF4gDvvPMOAG3btk0rf2kuvPBCPvnkE6666ioeeeQRfv31VwYOHMiGDRsSpj/77LOBYCK3TCZuK03s6pH4S/nT6belHesvv/yS+fPn07ZtW1q1ahVp+0VEREQilc7z0BItwKvADwQj5I2BH4G3i6V5Cvgqqjqr+4KeM15u8c8ZTwTwnj17JtzWqlUrb9WqVeHfCxcu9CZNmniDBg3866+/LpL2f//7n+fk5HiLFi18yZIlRbZNnjzZAd9rr71KPGd67NixDvhhhx1W4pnI5VHWc8bd3U899VQH/LLLLiuy/rbbbnPAjz766BJ5evfu7YCPGTOmyPrzzz/fAT/33HPTam/8c8aLK+s52jvttJO3a9eu8O/XX3/dAd9jjz2S1hf/LPXixy6ZWLkdOnTwlStXFq7/+uuvvXHjxl63bl3/7rvviuTp3r27A96pU6ciz5xfu3atd+3a1QF/9NFHi+QZN26cZ2Vlebt27UqUl8iHH37oGzZsKLF+zpw53qRJEwf8iSeeSGkfi0v0nHF398cff9wBP/jgg33Tpk3u7n7JJZc44Oeff37S8nr16uW1a9f25s2be15enq9bt65c7VmyZIl/8803Cbc9++yznp2d7bm5uUWOj3v5++3GjRu9Q4cODvjkyZML12/atMmPPfZYB/zWW29Nqc16zriIiIgkU9HPGY/yMvU7gWeBhcAmoB5wWbE0BwDpP+RYJEXuzrBhw1ixYgXjxo0rMfK4++67c91113HZZZdx9tln89RTTwHBvcynnnoqdevW5ZFHHqFOnTpF8p1++uk899xzTJ48mXvvvZfzzjsvpfYsXry4cGIsCJ45DVse5QXwl7/8pcjs4TfffDMzZ87klltuYfbs2XTt2pVPP/2UF154gRYtWnDPPfeUqOef//wn3bp149xzz+Xll1+mU6dOvPPOO8yYMYNOnTpx3XXXpdTeihS7F3n48OFJ02RnZzNs2DCuv/567r//fm666aYyyz344IO54IILuOuuu/jd737H8ccfz/r163n88cdZuXIlY8aMYZdddklYV9u2bdl99905/vjjqVWrFs8++yzz58+nf//+RWZSnzZtGqeffjruziGHHMIDDzxQory8vDwuuOCCwr9vvfVWpk6dSo8ePdh1112pU6cOn3/+OVOnTqWgoICzzjqrXM8zL8v8+fM544wzaNq0KY8++mjhRIg33XQTM2fO5O677+bQQw+lf//+JfKec845zJgxgyVLljBixAjq1i3fhUwLFixg//33p2vXrnTs2JGWLVuycuVKPvjgA2bNmkXt2rUZP358ifkFyttva9WqxYMPPkifPn049thjOeGEE9h5552ZNm0ac+bMKewLIiIiItVaOhF8sgU4A3g/XEYU29YLWAGcEWWd1XlBI+PlFtXI+OjRox3w/v37J61r8+bNftBBBxUZWRwwYIADfvvttyfN99NPP3nTpk29Xr16Pnfu3JT2KzayXNryxhtvlMi3dOlSP++883yXXXbx2rVr+4477uinnnqqL1y4MGld3377rQ8dOtRbtGjhtWvX9l133dUvuuiiIiO/5RXVyPjy5cu9bt26npOT48uWLSu1zm+++cbNzFu0aJFwZDmRgoICf+CBB3yfffbxevXqeW5urvfs2bPI6Gm87t27e3Z2tq9bt86vuOIKb9WqldepU8fbtGnj1157bYkrI2Ij0aUt8VcBuLs//fTTPmDAAG/btq3n5uYWHsd+/fr5iy9mdg4WHxnPz8/3fffd1wF/7rnnSqSfP3++N2rUyJs0aeLffvttie0bN24sHK3/4osvyt2epUuX+p///Gfv3r17Yf+rV6+ed+7c2c8++2z//PPPk+ZNp99+/PHHftxxx3nTpk29Tp063r59ex85cmS5RvQ1Mi4iIiLJVPTIuLmnNxmPlM3MZnfp0qXL7NmzS033+eefA9C5c+fKaNY2N4GbSDI9evTgnXfeYdOmTVXdlGph3rx5dOzYkZ49ezJ9+vSqbk6lqOzPXxEREak5Uomb9tlnH+bMmTPH3fcpb/mRTeBmZkPMrNTnOJnZ7mY2JKo6RUQkOrfeeivunvLtFyIiIiKSvijvGX8IGAl8XEqa/sAoYGKE9YqISJq+/fZbHnvsMebOncuECRPo0qULAwYMqOpmiYiIiGz1ogzGU5FNcF+lVKELD21fdiLZqs2fP5+JE1P7Teziiy+mYcOGFdwiAXjttdeYOXNmmemKTxKXiXnz5nHllVdSv359Dj/8cP7xj3+QlRXlUy9FREREqp/7Z85n9CtfsmbD5pTSx1+yHu+nH1al3YbKDsY7EEziJlVoxGEdqroJUsXmz5/Ptddem1La4cOHb7XB+JtvvlnVTSjitdde44YbbigzXbt27SILxvv06YPmDhEREZFtTXkC8YqSUTBuZuOLrTrWzFonSJoN7AocBCT+SUFEKo0CsOrp+uuv5/rrr6/qZoiIiIhs9ao6EIfMR8aHxf3bgb3CJREHZgEjMqxTREREREREJBLJZkpPaTb1aVczZ1F69WYajLcJXw2YD4wG7kyQbjOwwt3XZFifiIiIiIiISI2XUTDu7t/G/m1m1wLT49eJiIhUV7pVQ0RERKpSZBO4uXtqs0FJCWaGu1NQUKBZjEVEKkksGDezKm6JiIiIbIsU+VUDOTk5AKxZo6v4RUQqS+wzN/YZLCIiIlKZIg3Gzaynmb1oZovNbKOZbU6wbIqyzq1Bbm4uAD///DOrV6+moKBAl0+KiFSA2FVIq1ev5ueffwa2fAaLiIiIVKbILlM3s6OBZwkeY/YdMBdQ4J2CvLw81qxZw9q1a1m4cGFVN0dEZJtRr1498vLyqroZIiIisg2KLBgHRgIbgaPd/eUIy93qZWVlscsuu7B8+XJWr15Nfn6+RsZFRCqImZGTk0Nubi55eXmaq0NERESqRJTB+O7A4wrE05OVlUWzZs1o1qxZVTdFREREREREKliUwwG/AssjLE9ERERERERkqxRlMP4qcGCE5YmIiIiIiIhslaIMxi8H2pnZVaaHtoqIiIiIiIgkFeU949cAnwLXAqea2YfAygTp3N1Pi7BeERERERERkRolymB8WNy/W4dLIg4oGBcREREREZFtVpTBeJsIyxIRERERERHZakUWjLv7t1GVJSIiIiIiIrI1i3ICNxERERERERFJQWQj42Y2P8Wk7u7toqpXREREREREpKaJ8p7xLILJ2YprDDQK//0jsDHCOkVERERERERqnCjvGW+dbJuZ7QbcBdQHDo+qThEREREREZGaqFLuGXf3r4DjgJ0InkcuIiIiIiIiss2qtAnc3H09MA04sbLqFBEREREREamOKns29U1Ai0quU0RERERERKRaqbRg3MyaAQOA7yurThEREREREZHqKMpHm11dSh27AP0JZlW/Mqo6RURERERERGqiKB9tNrKM7b8A17v7LRHWKSIiIiIiIlLjRBmMH5JkfQGwAvjC3TdFWJ+IiIiIiIhIjRTlc8Zfj6osERERERERka1ZlCPjRZhZLtAYWOXuv1RUPSIiIiIiIiI1TaSzqZtZLTO7wsy+AlYCC4AVZvZVuL7Cgn8RERERERGRmiLK2dTrAFOBnoATPMLsJ2BHoDVwA3CEmfV19w1R1SsiIiIiIiJS00Q5Un0x0At4EbjE3efFNphZO+A24Jgw3c0R1isiIiIiIiISqQsPbV+h5UcZjA8CPgGOdfeC+A3u/rWZHQd8CJyEgnERERERERGpxkYc1qFCy4/ynvHdgCnFA/GYcP0UoF2EdYqIiIiIiIjUOFEG4xuABmWkqQ9sjLBOERERERERkRonymD8Y+APZtY80UYzawb8AfgowjpFREREREREapwog/F7gObAu2Z2mpm1NbPtzKyNmZ0CzAq33xNhnSIiIiIiIiI1TmQTuLn7k2a2F3AFMDZBEgNucfcno6pTREREREREpCaKcjZ13P3PZvY8cBqwN9AIWAV8AIx39/9GWZ+IiIiIiIhITRRpMA7g7u8A70RdroiIiIiIiMjWIsp7xkVEREREREQkBZEF42b2RzN7zcxaJtm+k5m9ambHRVWniIiIiIiISE0U5cj4cKCxu/+YaKO7/0BwD/nwCOsUERERERERqXGiDMb3AN4vI817wO8irFNERERERESkxokyGM8DFpeRZhnQLMI6RURERERERGqcKIPxpUD7MtK0B1ZGWKeIiIiIiIhIjRNlMP4W0M/MOiXaaGadgf7AG5lWZGZ/CyeD+97M1pnZcjP7wMyuMbOm5SxrZzMbb2Y/mlm+mS0ws9Fm1iTTdoqIiIiIiIgkEmUw/neC55a/aWYXmFkHM6sfvl5IEIRnh+kyNQKoD0wD7gQeBTYBI4GPzWyXVAoxs3bAbOAU4F3gDmA+cCHw3/IG9iIiIiIiIiKpqBVVQe7+npmdA9xLENTeUSzJZuBsd58VQXUN3X198ZVmdgPwZ+BK4JwUyhkDbA9c4O53x5VzO0HAfwNwVgTtFRERERERESkU5cg47n4/sCdBkDsb+Dp8vRfY093HRVRPiUA89GT4Wta967FR8b7AgrB98a4B1gCDzax+ms0UERERERERSSiykfEYd/8cOD/qclN0TPj6cQppDwlfX3b3gvgN7r7azN4iCNYPAF6NrokiIiIiIiKyrYs8GK9MZvYnoAHQCNgX6EEQiN+cQvaO4euXSbbPIwjGO6BgXERERERERCIUWTBuZnWB/YBP3X1ZVOWW4U/ADnF/TwWGufuSFPI2Cl9XJdkeW9+4rILMbHaSTQlnlhcREREREZFtW5T3jO8ETAd6Rlhmqdy9hbsb0AI4DmgLfGBmXSqrDSIiIiIiIiLlldHIuJllFbvf2optvwb4fvwV4QAAIABJREFUq7tX6OXw7r4ImGRmcwguO58I7F5GttjId6Mk22PrV6ZQ/z6J1ocj5vphQERERERERIrINEheYWYzgNcIZiVPxJKsj5y7f2tmnwF7mVkzd19aSvK54WuHJNtjM7Inu6dcREREREREJC2ZBuNPAL0JZjH3cDnHzJoBMzMsO10tw9fNZaSbHr72LT7Cb2a5QHdgLfBO9E0UERERERGRbVlG94y7+xnuvhvQBriSYBT8AOCfwKfAXwDM7HQzSzYCXS5m1sHMSlxabmZZZnYDsD3wtruvCNfXNrNO4XPF49v+NfAy0Bo4t1hx1wL1gYfdfU0U7RYRERERERGJieRe7vDy8KcJHik2BPgf0AsYDnQF7gPczH4GZrj7SRlUdxRwk5m9CXwDLCOYUb0nwQRuPwOnx6XfCfgc+JYg8I53DvA2cJeZHRqm25/gGeRfEv6YICIiIiIiIhKlTCdw+zvBM7jfiF/v7vOAeWbWkuD5378hCHB7ha+ZeAXYjeCZ4nsTPHpsDUHw/DBwl7svT6Ugd//azPYFRgFHEAT6PwF3AtfGRtdFREREREREopTpyPi5wAiC+7M/J7hnvJOZbff/7N17uGxnXSf47y8JATxgwChiw9iHxJyEER+uA6HjBGIuBjMq3sbnmREBMTYjdC7CtOmAkGCnSfc4JOGiKA5ih55BHx1F5Z4QEi5D00ZRwcAJhAMoROSSkBwCgeSdP6rOYbOz9zm1z1773bWqPp/nqadqr7XqrV9VrbPO/u73Xe9qrd2xb6PW2kczmTDt1Zt8vbTWPpTkuRvYfk8OMIlca+3TSZ652boAAABgVpu9zvgDk/xwkt9IcmcmoffXM5ll/d1JnpJMztve5OsAAADAwthUz3hr7auZDBu/qqpem8lQ8csz6SH/oSSPnG56a1W9P8m7MjlnfLtmWgcAAIBtt9me8ZXa9P69rbXnt9Yek+SS6bJXJfn2JC/KNy8pBgAAAEtpkNnUD+CuJGmt/e9JMr0k2ZO2+DUBAABgrg0Zxv8pk4nQ/tt6G7TWbk3yZwO+JgAAAIzOYGG8tXZ7kt9ftfhdQ7UPAAAAi2JLh6m31q5Ncu1WvgYAAACMzZATuAEAAAAzEMYBAACgM2EcAAAAOhPGAQAAoDNhHAAAADoTxgEAAKAzYRwAAAA6E8YBAACgsyN6vVBVHZ7kIUnSWvtUr9cFAACAedMtjCf5viQ3JLm78+sCAADAXOkZir+e5FNJWsfXBAAAgLnTLYy31m5KsrPX6wEAAMC8MoEbAAAAdCaMAwAAQGeDDlOvqocmOT/Jo5I8NMm91tistdaOHfJ1AQAAYEwGC+NV9eQkb05ynyTfSPJP0/t7bDrUawIAAMAYDdkz/p+SHJ7k55P83621uwdsGwAAABbGkGH8B5L8P6211w/YJgAAACycISdw+1KSLw7YHgAAACykIcP4XyR50oDtAQAAwEIaMoxfmOSoqnpVVe0YsF0AAABYKIOdM95a+3xVnZnkvyb5+araneTWtTdtpw71ugAAADA2Q17a7PuTXJPkgdNFj15n0zbUawIAAMAYDTlM/WVJjk7yoiT/Msm9WmuHrXE7fMDXBAAAgNEZ8tJmT0zy/7bW/v2AbQIAAMDCGbJn/M4kewZsDwAAABbSkGH8XUkeP2B7AAAAsJCGDOP/Nsl/X1UXVFUN2C4AAAAslCHPGX9hkg8luSTJ2VX1wax/abNnDfi6AAAAMCpDhvFnrHj8sOltLS2JMA4AAMDSGjKMrxe+AQAAgBUGC+OttU8O1RYAAAAsssEmcKuqk6vqUUO1BwAAAItqyNnUr0nySwO2BwAAAAtpyDD++SR3DNgeAAAALKQhw/i7kvyrAdsDAACAhTRkGH9hkuOr6ter6l4DtgsAAAALZchLm/27JB9KcmGSZ1XV3yS5OZPriq/UWmuuMw4AAMDSGjKMP2PF4wdPb2tpSYRxAAAAltaQYfxhA7YFAAAAG3bZO3bvf3z+6bu2sZIDGyyMt9Y+OVRbAAAAcCiuuPrG/Y+XIoyvVlX3T/KAJLe21r68Va8DAAAAYzPkbOqpqiOq6oKq+liSW5LsSfKlqvrYdPmWhX8AAAAYi8HCcVUdmeStSZ6UySRtn07y2STfk2RnkkuSnFlVZ7TW7hzqdQEAAGBshuwZ/5UkT07ypiQPb63tbK09sbW2M8nxSf48yf843Q4AAACW1pBh/H/J5DrjT22t3bhyRWvt40l+MsmHk/yvA74mAAAAjM6QYfz7kryltXb3Wiuny9+S5NgBXxMAAABGZ8gwfmeS+x1kmx1Jvj7gawIAAMDoDBnG/zbJT1fVd621sqq+M8lPJ/mbAV8TAAAARmfIMP7KJN+V5ANV9ayqOqaq7ltVD6uqZyb5r9P1rxzwNQEAAGB0Bru0WWvtD6vqUUkuSPI7a2xSSf5Ta+0Ph3pNAAAAGKPBwniStNYurKo/S/KsJI9OclSSW5P8dZLXttb+vyFfDwAAAMZo0DCeJK219yd5/9DtAgAAwKIY8pxxAAAAYAaDhvGqelJV/UVVfa6qvl5Vd61x+8aQrwkAAABjM9gw9ao6K8mfJjk8yaeSfDSJ4A0AAACrDHnO+EVJvp7krNba2wdsFwAAABbKkMPUH5HkDwRxAAAAOLAhw/jtSb44YHsAAACwkIYM41cneeKA7QEAAMBCGjKM/2qSY6vqhVVVA7YLAAAAC2XICdxenOTDSS5O8gtV9cEkt6yxXWutPWvA1wUAAIBRGTKMP2PF453T21paEmEcAACApTVkGH/YgG0BAADAwhosjLfWPjlUWwAAALDIhpzADQAAAJiBMA4AAACdjS6MV9XRVfWLVfUnVfWxqrqjqm6tqvdU1bOqaub3VFV7qqqtc7t5K98HAAAAy2vICdx6+Zkkv5Xks0muSfKpJN+d5CeT/G6Sp1TVz7TW2ozt3Zrk8jWW3z5ArQAAAAzkNdfdlMuv2p29d9410/Y7L3jTFld06MYYxncn+bEkb2qt3b1vYVVdmOQDSX4qk2D+xzO2d0tr7aKhiwQAAGBYGwnis9hx5OGDtbVRoxum3lp7Z2vtz1cG8enym5O8evrjk7sXBgAAwJYaOoifd9quwdrbqDH2jB/I16f339jAc+5dVT+X5HuT7E3yt0mua60N9y0DAAAwqD2XnrXm8pVD09fbZh4sTBivqiOS/Pz0x7du4KkPTnLlqmWfqKpnttaunfG1r19n1QkbqAMAAIAlMbph6gdwaZJHJHlza+1tMz7n95Kcmkkg35HkB5L8dpKdSd5SVY/cgjoBAABYcgvRM15V5yR5XpKPJHnarM9rrV28atGHkjy7qm6ftndRkp+YoZ3HrlPX9UkeM2s9AAAALIfR94xX1XOTXJHk75Oc0lr74gDN7psI7uQB2gIAAIBvMeowXlXnJXlFJj3ap0xnVB/CP0/vdwzUHgAAAOw32jBeVb+a5LIkH8wkiH9uwOZPnN7fNGCbAAAAkGSkYbyqfi2TCduuT3Jqa+3zB9j2XlV1QlUdu2r5w6vqHj3fVbUzySunP75+sKIBAABganQTuFXV05O8JMldSd6d5JyqWr3Zntba66aPH5LkhiSfzGSW9H1+Nsnzquq66brbkhyb5Kwk90ny5iS/sSVvAgAAgKU2ujCe5GHT+8OTnLfONtcmed1B2rkmyfFJHp3kpEzOD78lyXsyue74la21ttliAQAAYLXRhfHW2kWZXHJs1u33JLlH13lr7dpMQjsAAAB0NcpzxgEAAGDMhHEAAADoTBgHAACAzoRxAAAA6EwYBwAAgM6EcQAAAOhMGAcAAIDOhHEAAADoTBgHAACAzoRxAAAA6EwYBwAAgM6EcQAAAOhMGAcAAIDOhHEAAADoTBgHAACAzoRxAAAA6OyI7S4AAAAAhnLuqcdtdwkzEcYBAABYGOefvmu7S5iJYeoAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0NkR210AAAAAJMll79i9//H5p+/axkq2njAOAADAXLji6hv3P170MG6YOgAAAHQmjAMAAEBnwjgAAAB0JowDAABAZ8I4AAAAdCaMAwAAQGfCOAAAAHQmjAMAAEBnwjgAAAB0JowDAABAZ8I4AAAAdCaMAwAAQGfCOAAAAHQmjAMAAEBnwjgAAAB0JowDAABAZ8I4AAAAdCaMAwAAQGfCOAAAAHQmjAMAAEBnwjgAAAB0JowDAABAZ8I4AAAAdCaMAwAAQGfCOAAAAHR2xHYXAAAAwOJ7zXU35fKrdmfvnXfNtP3OC960xRVtLz3jAAAAbLmNBPGD2XHk4YO0s52EcQAAALbckEH8vNN2DdLWdjJMHQAAgK72XHrWmstXDk1fb5tFoWccAAAAOhPGAQAAoDNhHAAAADoTxgEAAKCz0YXxqjq6qn6xqv6kqj5WVXdU1a1V9Z6qelZVbeg9VdVDq+q1VfWZqvpaVe2pqsur6oFb9R4AAABYbmOcTf1nkvxWks8muSbJp5J8d5KfTPK7SZ5SVT/TWmsHa6iqjk3yviQPSvLGJB9J8vgk5yY5s6pOaq19YUveBQAAAEtrjGF8d5IfS/Km1trd+xZW1YVJPpDkpzIJ5n88Q1u/mUkQP6e19ooVbb0syflJLkny7OFKBwAAgBEOU2+tvbO19ucrg/h0+c1JXj398ckHa2faK35Gkj1JXrVq9YuT7E3ytKrasdmaAQAAYKXRhfGD+Pr0/hszbHvK9P7tawT725K8N8m3JTlxuPIAAABgnMPU11RVRyT5+emPb53hKcdP73evs/7GTHrOdyW5+iCvff06q06YoQ4AAACWzCL1jF+a5BFJ3txae9sM2x81vb91nfX7lj9gs4UBAADASgvRM15V5yR5XiazoT+t9+u31h671vJpj/ljOpcDAADAnBt9z3hVPTfJFUn+PskprbUvzvjUfT3fR62zft/yWzZRHgAAANzDqMN4VZ2X5BVJPpRJEL95A0//6PR+1zrrj5ver3dOOQAAAByS0YbxqvrVJJcl+WAmQfxzG2zimun9GVX1LZ9DVd0/yUlJvpLk/ZutFQAAAFYaZRivql/LZMK265Oc2lr7/AG2vVdVnTC9rvh+rbWPJ3l7kp1JnrPqaRcn2ZHkytba3iFrBwAAgNFN4FZVT0/ykiR3JXl3knOqavVme1prr5s+fkiSG5J8MpPgvdIvJ3lfkpdX1anT7Z6QyTXIdyd5wfDvAAAAgGU3ujCe5GHT+8OTnLfONtcmed3BGmqtfbyqHpdJuD8zyY8k+WwmE8Jd3Fr70qarBQAAgFVGF8ZbaxcluWgD2+9Jco+u8xXrP53kmZutCwAAAGY1ynPGAQAAYMyEcQAAAOhMGAcAAIDOhHEAAADobHQTuAEAALCYzj31uO0uoRthHAAAgLlw/um7truEbgxTBwAAgM6EcQAAAOhMGAcAAIDOhHEAAADoTBgHAACAzoRxAAAA6EwYBwAAgM6EcQAAAOhMGAcAAIDOhHEAAADoTBgHAACAzoRxAAAA6OyI7S4AAACAcbvsHbv3Pz7/9F3bWMl4COMAAABsyhVX37j/sTA+G8PUAQAAoDNhHAAAADoTxgEAAKAzYRwAAAA6E8YBAACgM2EcAAAAOhPGAQAAoDNhHAAAADoTxgEAAKAzYRwAAAA6E8YBAACgM2EcAAAAOhPGAQAAoDNhHAAAADoTxgEAAKAzYRwAAAA6E8YBAACgM2EcAAAAOhPGAQAAoDNhHAAAADoTxgEAAKAzYRwAAAA6O2K7CwAAAGB+vea6m3L5Vbuz9867Ztp+5wVv2uKKFoOecQAAANa1kSA+ix1HHj5YW2MmjAMAALCuoYP4eaftGqy9MTNMHQAAgJnsufSsNZevHJq+3jZ8Kz3jAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANDZEdtdAAAAAON27qnHbXcJoyOMAwAAsCnnn75ru0sYHcPUAQAAoDNhHAAAADoTxgEAAKAzYRwAAAA6G2UYr6qfrqpXVNW7q+rLVdWq6vWH0M6e6XPXut28FbUDAADAWGdTf2GSRya5Pck/JDlhE23dmuTyNZbfvok2AQAAYF1jDePnZxLCP5bkSUmu2URbt7TWLhqiKAAAAJjFKMN4a21/+K6q7SwFAAAANmyUYXxg966qn0vyvUn2JvnbJNe11u7a3rIAAABYVMJ48uAkV65a9omqemZr7drtKAgAAIDFtuxh/PeSvDvJh5PcluSYJM9N8ktJ3lJVT2yt/c3BGqmq69dZtZmJ5QAAAFhQSx3GW2sXr1r0oSTPrqrbkzwvyUVJfqJ3XQAAACy2pQ7jB/DqTML4ybNs3Fp77FrLpz3mjxmwLgAAABbAYdtdwJz65+n9jm2tAgAAgIUkjK/txOn9TdtaBQAAAAtp4cN4Vd2rqk6oqmNXLX94Vd2j57uqdiZ55fTH1299hQAAACybUZ4zXlVPTfLU6Y8Pnt4/sapeN338+dba86ePH5LkhiSfTLJzRTM/m+R5VXXddN1tSY5NclaS+yR5c5Lf2KK3AAAAwBIbZRhP8qgkT1+17JjpLZmE6+fnwK5JcnySRyc5KZPzw29J8p5Mrjt+ZWutDVUwAAAA7DPKMN5auyiTy47Nsu2eJLXG8muTXDtkXQAAADCLhT9nHAAAAOaNMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0NkR210AAAAA2+Oyd+ze//j803dtYyXLRxgHAABYUldcfeP+x8J4X4apAwAAQGejDONV9dNV9YqqendVfbmqWlW9/hDbemhVvbaqPlNVX6uqPVV1eVU9cOi6AQAAIBnvMPUXJnlkktuT/EOSEw6lkao6Nsn7kjwoyRuTfCTJ45Ocm+TMqjqptfaFQSoGAACAqVH2jCc5P8muJN+e5H/bRDu/mUkQP6e19tTW2gWttR9KclmS45NcsulKAQAAYJVRhvHW2jWttRtba+1Q25j2ip+RZE+SV61a/eIke5M8rap2HHKhAAAAsIZRhvGBnDK9f3tr7e6VK1prtyV5b5JvS3Ji78IAAABYbGM9Z3wIx0/vd6+z/sZMes53Jbn6QA1V1fXrrDqkc9kBAABYbMvcM37U9P7WddbvW/6ADrUAAACwRJa5Z3wwrbXHrrV82mP+mM7lAAAAMOeWuWd8X8/3Ueus37f8lg61AAAAsESWOYx/dHq/a531x03v1zunHAAAAA7JMofxa6b3Z1TVt3wOVXX/JCcl+UqS9/cuDAAAgMW28GG8qu5VVSdMryu+X2vt40nenmRnkuesetrFSXYkubK1trdLoQAAACyNUU7gVlVPTfLU6Y8Pnt4/sapeN338+dba86ePH5LkhiSfzCR4r/TLSd6X5OVVdep0uydkcg3y3UlesBX1AwAAsNxGGcaTPCrJ01ctO2Z6SybB+/k5iNbax6vqcUlekuTMJD+S5LNJrkhycWvtS4NVDAAAAFOjDOOttYuSXDTjtnuS1AHWfzrJM4eoCwAAAGax8OeMAwAAwLwZZc84AAAAB/aa627K5Vftzt4775pp+50XvGmLK2IlPeMAAAALaCNBfBY7jjx8sLYQxgEAABbS0EH8vNN2DdYehqkDAAAsvD2XnrXm8pVD09fbhq2hZxwAAAA60zMOAAAwMpe9Y/f+x+efbvj4GAnjAAAAI3PF1TfufyyMj5Nh6gAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0NkR210AAAAA2+PcU4/b7hKWljAOAACwpM4/fdd2l7C0DFMHAACAzoRxAAAA6MwwdQAAgDnymutuyuVX7c7eO++aafudF7xpiytiK+gZBwAAmCMbCeKz2HHk4YO1xXCEcQAAgDkydBA/7zSTtM0jw9QBAADm1J5Lz1pz+cqh6ettw3zTMw4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdHbHdBQAAALAx55563HaXwCYJ4wAAACNz/um7trsENskwdQAAAOhMGAcAAIDOhHEAAADoTBgHAACAzoRxAAAA6EwYBwAAgM6EcQAAAOhMGAcAAIDOhHEAAADoTBgHAACAzoRxAAAA6OyI7S4AAABgWVz2jt37H59/+q5trITtJowDAAB0csXVN+5/LIwvN8PUAQAAoDNhHAAAADozTB0AAOAgnOvN0IRxAACAg3CuN0MzTB0AAAA6E8YBAACgM2EcAAAAOhPGAQAAoDNhHAAAADoTxgEAAKAzYRwAAAA6E8YBAACgsyO2uwAAAIBF8JrrbsrlV+3O3jvvmmn7nRe8aYsrYp7pGQcAABjARoL4LHYcefhgbTF/hHEAAIABDB3Ezztt12DtMX8MUwcAABjYnkvPWnP5yqHp623DchDGAQCApeZcb7aDYeoAAMBSc64320EYBwAAlppzvdkOox2mXlUPTfKSJGcmOTrJZ5P8aZKLW2tfmrGNdyV50gE2uW9r7aubLBUAABgJ53rTyyjDeFUdm+R9SR6U5I1JPpLk8UnOTXJmVZ3UWvvCBpq8eJ3l39hUoQAAALCGUYbxJL+ZSRA/p7X2in0Lq+plSc5PckmSZ8/aWGvtoqELBAAAgPWM7pzxaa/4GUn2JHnVqtUvTrI3ydOqakfn0gAAAGAmY+wZP2V6//bW2t0rV7TWbquq92YS1k9McvUsDVbVzyZ5WJI7k9yQ5J2tta8NVzIAAAB80xjD+PHT+93rrL8xkzC+KzOG8SRvWPXz56rqOa21P5rlyVV1/TqrTpjx9QEAAFgioxumnuSo6f2t66zft/wBM7T1xiQ/muShSe6bSXh+6fS5f1BVZ26iTgAAAFjTGHvGB9Nau2zVoo8mubCqPpPkFZkE87fO0M5j11o+7TF/zGbrBAAAYLGMMYzv6/k+ap31+5bfsonX+N0klyV5VFXdv7V22ybaAgAASJKce+px210Cc2KMYfyj0/td66zft3evd075QbXWvlpVtyV5YJIdSYRxAADo7LJ3fPNX+vNPX+/X/3FZlPfB5o0xjF8zvT+jqg5bOaN6Vd0/yUlJvpLk/Yf6AlV1fCZB/LYkn99ErQAAwCG64uob9z8WYlk0owvjrbWPV9XbM5kx/TmZnNu9z8WZ9GT/dmtt776FVXXC9LkfWbHsYUluba19cWX7VfVdSX5v+uMbWmvf2JI3AgAAjIbh5QxtdGF86peTvC/Jy6vq1EyuDf6ETK5BvjvJC1Ztf8P0vlYse1KSV1fVe5LclOSLSb43yY9kct75Xyb5t1v1BgAAgPHQM8/QRhnGp73jj0vykiRnZhKgP5vkiiQXt9a+NEMz12dyffHHJnl0km/PZFj63yX5w0x61+/cgvIBAABYcqMM40nSWvt0kmfOuG2tsezvkjxj4LIAAADgoA7b7gIAAABg2QjjAAAA0Nloh6kDAADzaRGvDw5DE8YBAIBBuT44HJxh6gAAANCZMA4AAACdGaYOAAB095rrbsrlV+3O3jvvmmn7nRe8ac3lO448POedtitnn3zMkOXBltMzDgAAdLeRIH4ge++8K5dftfvgG8KcEcYBAIDuhgjiW9EW9GKYOgAAsK32XHrWmstXDk1fa5v1hq7DGOgZBwAAgM6EcQAAAOhMGAcAAIDOhHEAAADozARuAADAzIa6Pngvl73jm5c9O//0XdtYCXwrYRwAAJjZUNcH32fHkYcP1tZarrj6xv2PhXHmiWHqAADAzIYO4uedJiCznPSMAwAAh+RQrw8O6BkHAACA7vSMAwAAc+ncU4/b7hJgywjjAADAXDLhGovMMHUAAADoTBgHAACAzoRxAAAA6Mw54wAAsE0ue8fu/Y/n4fzoeasHFpkwDgAA2+SKq2/c/3gewu+81bMRK69tvpltoBdhHAAAGFSvS5LtOPLw7L3zrkHbg16EcQAAYFC9etXPO21XLr9q9yCBfMeRh+e808Y1GoBxE8YBAIBROvvkY3L2yccccJuVQ9P3XHrWVpcEMzObOgAAAHSmZxwAAEZEB+DQAAAdVElEQVTOLOgwPsI4AACM3JhnQYdlZZg6AAAAdCaMAwAAQGeGqQMAwBZ4zXU3beiyWytn/V5t32W3DjZzeK96gM3TMw4AAFtgqOtfJ8neO+/K5VftPviGnepJJn8gAA6dnnEAANgCQwbfIdobOoifd9o4Joo799TjtrsEWJMwDgAAW2zPpWetuXzlUPBZtpmHesbG7PLMK8PUAQAAoDNhHAAAADozTB0AgKVx2Tu+OQnaGIcvzzJk3SzoMA7COAAAS+OKq2/c/3gsYXzHkYebBR0WkGHqAAAwx847bddgAXpMs6DDotMzDgAAc+zsk4/J2Scfc8BtFnEWdFh0wjgAAGyTebsG9rzVA4tMGAcAgG0yb+etz1s9sMicMw4AAACdCeMAAADQmTAOAAAAnTlnHACAhfGa627K5Vftnum63CtnIF9t3yXADjaL+bww8RqMjzAOAMDCmDWIH8zeO+/K5VftHk0YN/EajI8wDgDAwhgiiM/S1mXv2L3/sSAMHAphHACAhbTn0rPusWzl0PS11q/eZj1XXH3j/sfCOHAohHEAALaUXmSAexLGAQDYUnqRAe5JGAcARmmeelvnqZYhLer7ApgHwjgAMErz1Ns6T7UMaVHfF8A8EMYBAOaEnugDG+LzcT1uYF4I4wAAc0JP9IEN8fls9HmzzKw+yzYAqx223QUAAMA82XHk4XPdHrAY9IwDsK0My4Vxe811N+Xyq3Zn7513zbT9gXqRdxx5eM47bVfOPvmYoco7JOedtmtD7+lA9r0ngNWEcQC2lWG5MG5DhdYk2XvnXbn8qt3bHsbPPvmYg9aw8o8Key49a6tLAhaQMA4AzJ156m2dp1qGNOT7GtJQwR5g3gnjAMDcmafe1nmqZUhDvq9k8oeGD7/kzDXXzdKLbBI0YNkI4wAj4dzq5bPM3/nQvaPrtTfLZ7wVtWx2hu4hetiHDuJDnhdtBnNgGQjjACPh3Orl4zufmKUn9VB7Wzf6GW+mlu9/0Vvntod9M+9rKDuOPHzwPxBsJdcrBzZLGIeRmrces6Hqmbf3xeYs6rm2bM6y/jsfcobu5MA922P8jMc2g/lYPldgfgnjMFLz1mM2VD3z9r7YnEU915bNWdZ/50PN0D3L8Ox5+4xn6UU2gzmwbIRxALZMr/N+WU7zNEx4nmoZ0lDvax7+IAAwb4TxbTZvw8jmbajxvLUDW2UZhnNv5Xm/Y9TzO1/UY+BG38vB9qPN7GeL9LmutKjvC2AeCOPbbN6Gkc3bUON5awe2iuHcy6fnd77Mx8AhJwXb6gnBksXtYR+KzwdYJMI4zKF56yUdsp6NWKaewDEO5x7qMx7ql+sh6um53yzqd75dx4v1DDUpWI8JwZLhe/tn3WYsxnA8B5iVMA5zaN56SYesZyiL3BM4luHcQ33GQ30/Q9SzXfvNIn3nQx8vNtsbfbBJwcY4IdjYLgEGwNpGG8ar6qFJXpLkzCRHJ/lskj9NcnFr7UsbaOc7krwoyVOTfE+SLyR5a5IXtdb+YbN1fugfb535F6Vl7N2ct3Z6nGs7S89Szx6z7ahnKFtd16LOWTBv9Syi7fqMN9tLOsQxcOiQ2KM3emzGdgkwANY2yjBeVccmeV+SByV5Y5KPJHl8knOTnFlVJ7XWvjBDO0dP29mV5J1J3pDkhCTPTHJWVT2xtXbT1ryLjVnU3s150+Nc2432vG11j1nPemYxTz2B8zZnwVDDucc8amAsen7nQ/aSDn0M3OrjxRDGeA6yS4ABLIZRhvEkv5lJED+ntfaKfQur6mVJzk9ySZJnz9DOf8gkiL+stfa8Fe2ck+SK6eucOWDdm7LZX7YE8dn4nMZlHnoCe+kZnOftvN+NvtYQ9czDfjPLdz5kL2kyOQYu03nI/iAFwHYZXRif9oqfkWRPkletWv3iJL+U5GlV9bzW2t4DtHO/JE9LsjfJRatWvzLJryT54ao6ZjO94494yFG5/gB/kd6uXsB56N2ct3bG9ovlvPXmDFXP2HsCF8W8nfc79DmymzVP+81QvaTf/6K3dvvO5+34tYh8xgDzb3RhPMkp0/u3t9buXrmitXZbVb03k7B+YpKrD9DOiUnuO23ntlXt3F1Vb8sk2J+SZMuGqs/bf5Y9A9UY2zmYnr2J8zbj7lC9S/PaEzhvcx8M9Vo96hninNShv/MhjKkHeZZjYM/zkPVGbz2fMcD8G2MYP356v3ud9TdmEsZ35cBhfJZ2Mm1ny8zbf5Y9A9UY2zmYRe9NnJcZd2fpCZxlAq2hewKHMI/f+YdfsvbZOj0nKRvqOz+Y7dhvevy7muXz6PUZAwAT1Vrb7ho2pKp+J8nZSc5urf3uGusvSXJhkgtbay89QDsXZnJu+SWttReusf7sJL+T5Hdaa//6IDVdv86qR973vvc9/OEPf/iBnn5QH/rHWzf1/LU84iFHDd7m2G3F57wZh1XlQd9+73zn/e59yG18/vav5XNf/lruHuDf+RD1zJshP58h+M7HwWcMAOxzww035I477vhia+3ojT53jD3jY3LYHXfccddf/dVf/c12F7LaX/3TdlfAAZwwvf/Ip25OPrWtpXyreatnEW3yM96/7wxSTHznPczBZzz4fsNSsN9wKOw3HIp53292JvnyoTxxjGF8X/flel27+5bf0qmdtNYeu9byfT3m662HtdhvOFT2HQ6F/YZDYb/hUNhvOBSLvN8ctt0FHIKPTu/XO1lt3yw1650LPnQ7AAAAsCFjDOPXTO/PqKpvqb+q7p/kpCRfSfL+g7Tz/iR3JDlp+ryV7RyWySRwK18PAAAABjG6MN5a+3iSt2cyNv85q1ZfnGRHkitXXmO8qk6oqhNWbthauz3JldPtL1rVznOn7b9tM9cYBwAAgLWM8ZzxJPnlJO9L8vKqOjXJDUmekMk1wXcnecGq7W+Y3teq5RcmeXKSX6mqRyX5QJKHJ/nxJJ/LPcM+AAAAbNroesaT/b3jj0vyukxC+POSHJvkiiQntta+MGM7X0jyxCQvT/J903aekOT3kjx2+joAAAAwqNFdZxwAAADGbpQ94wAAADBmwjgAAAB0JowDAABAZ8I4AAAAdCaMAwAAQGfCOAAAAHQmjAMAAEBnwvgGVdVDq+q1VfWZqvpaVe2pqsur6oEbbOc7ps/bM23nM9N2H7pVtbN9hthvqupdVdUOcLvPVr4H+qqqn66qV1TVu6vqy9Pv+PWH2NYgxy3m31D7zXQfWe9Yc/NW1M72qKqjq+oXq+pPqupjVXVHVd1aVe+pqmdV1YZ+V3S8WQ5D7jeON8ulqv5jVV1dVZ+e7jdfrKq/rqoXV9XRG2xr9Mebaq1tdw2jUVXHJnlfkgcleWOSjyR5fJJTknw0yUmttS/M0M7R03Z2JXlnkv+W5IQkP57kc0me2Fq7aSveA/0NuN+8K8mTkly8zib/vrX2jSFqZvtV1QeTPDLJ7Un+IZNjxH9prf3cBtsZZP9jHAbcb/YkeUCSy9dYfXtr7Tc2WSpzoqqeneS3knw2yTVJPpXku5P8ZJKjkvxxkp9pM/zC6HizPAbeb/bE8WZpVNWdSf4qyd9nknt2JDkxyeOSfCbJia21T8/QzmIcb1prbjPekrwtSUvyb1Ytf9l0+atnbOe3p9v/n6uWnzNd/tbtfq9uc7nfvGvyT3b735Pb1t8y+c/kuCSV5MnTfeX1h9DOIPuf2zhuA+43e5Ls2e7347b1tyQ/lORHkxy2avmDMwlYLclPzdiW482S3AbebxxvluiW5D7rLL9kut/85oztLMTxRs/4jKZ/fflYJgeMY1trd69Yd/9M/jJYSR7UWtt7gHbul8lfge5O8j2ttdtWrDssyU1J/uX0NfSOj9xQ+810+3cleVJrrbasYOZSVT05k56HDfVwDrn/MT6Hut9Mn7snSVprOwcvjNGoqgsz+QX5la21f3OQbR1vSLKx/Wa6/Z7E8WbZVdUjk3wwyVWttdMPsu3CHG+cMz67U6b3b1/5hSfJNFC/N8m3ZTLM4kBOTHLfJO9dGcSn7dydyV95Vr4e4zbUfrNfVf1sVV1QVb9SVU+pqnsPVy4LZvD9j6Vy76r6uaq6sKrOrapTqurw7S6Krr4+vZ/lFCjHG/bZyH6zj+MNPzq9/9sZtl2Y480R213AiBw/vd+9zvobk5yRyXngV2+ynUzbYfyG2m9WesOqnz9XVc9prf3RIdTHYtuK/Y/l8eAkV65a9omqemZr7drtKIh+quqIJD8//fGtMzzF8YZD2W/2cbxZMlX1/CT3y2SOgccl+cFMgvilMzx9YY43esZnd9T0/tZ11u9b/oBO7TAOQ37fb8zkr4YPzWR0xQlJXjp97h9U1ZmbqJPF5HjDofq9JKdm8gvyjiQ/kMl8JzuTvGU6nJDFdmmSRyR5c2vtbQfbOI43TGx0v0kcb5bV85O8OMl5mQTxtyY5o7X2zzM8d2GON8I4jERr7bLW2l+01v6xtfbV1tpHW2sXJnleJv+WX7rNJQILorV2cWvtna21f2qtfaW19qHW2rMzmRjnvkku2t4K2UpVdU4m/7d8JMnTtrkcRuJQ9xvHm+XUWnvwdB6kB2cyC/8xSf66qh6zvZX1JYzPbt9fWI5aZ/2+5bd0aodx6PF9/24m52U9ajppBezjeMPQXj29P3lbq2DLVNVzk1yRyWWHTmmtfXHGpzreLLFN7DcH4nizBKZ/hPmTTIaVH53kP8/wtIU53gjjs/vo9H69c7mPm96vd+7C0O0wDlv+fbfWvppk32SAOw61HRaS4w1D2zd80LFmAVXVeUlekeRDmQSqmzfwdMebJbXJ/eZAHG+WSGvtk5n8Mef7q+o7D7L5whxvhPHZXTO9P2N6CbL9pr2RJyX5SpL3H6Sd9ye5I8lJq3sxp+2eser1GLeh9pt1VdXxSR6YSSD//KG2w0La8v2PpbNvZlqX3lwwVfWrSS7L5NJCp7TWPrfBJhxvltAA+82BON4sn38xvb/rINstzPFGGJ9Ra+3jSd6eyWQSz1m1+uJM/mp35cpr2VXVCVV1wqp2bs9ktsgduec5MM+dtv821xhfDEPtN1X1sKr6jtXtV9V3ZTLxSZK8obW2kcuIsCCq6l7T/ebYlcsPZf9jeay331TVw6vqHj1RVbUzySunP75+6yukl6r6tUwm3ro+yamttXX/sOt4wz5D7DeON8ulqnZV1T2GllfVYVV1SZIHJXlfa+1L0+ULf7yp1tp21zAa0x3hfZnsKG9MckOSJ2RyrbvdSf5Va+0LK7ZvSTKdnGBlO0dP29mV5J1JPpDk4Ul+PMnnpu18fKvfD30Msd9U1TMyOXfqPZn8hfiLSb43yY9kcl7MXyY5vbU29+fGMJuqemqSp05/fHCSH87ku3/3dNnnW2vPn267M8knknyytbZzVTsb2v8YtyH2m6q6KJNJmK5L8slMRt0cm+SsJPdJ8uYkP9Fau3NL3wxdVNXTk7wuk56oV2Tt2Yn3tNZeN91+Zxxvlt5Q+43jzXKZntLw0kx+n/1Eki8k+e4kT8pkArebM/nDzt9Pt9+ZBT/eCOMbVFX/XZKXJDkzk0kGPpvkT5JcvO+vOCu2XTOMT9d9RybT+T81yfdksjO+JcmLWmv/sJXvgf42u99U1Q9k8p/VYzMZwvPtmfyH9eEkf5jkt/1HtVimv6C8+ACb7P+P6UD/WU3Xz7z/MW5D7DdV9aQkz07y6HzzUkO3ZDIM9cpMehv88rAgZthnkuTa1tqTp9vvjOPN0htqv3G8WS5V9YhMvu8fzORSvQ9IsjeT8PymJC9fOfnfMhxvhHEAAADozDnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAAJ0J4wAAANCZMA4AAACdCeMAAADQmTAOAAAAnQnjAAAA0JkwDgAAA6uq36+qz1XVjhXLdlZVq6rXbWNp91BVe6pqz3bXsZ5D+dzG9FlX1WOntf7iBtuay/fI7IRxAAAWXlW9YBpcWlUdv4Hn7Qs8ezbwnP8hydOSXNpa23sI5bJEWmvXJ/nTJL9eVffb7npmMQ9/CNj373mAdrbtvQjjAAAstKqqJL+Y5NbporNneM69qurU6fOS5AFV9ZyqeswML3lJki8n+a1DqZel9NIkD05yznYXQj/COAAAi+6MJDuTXJHk75I8vaqOXG/j6XDhTyW5KskLpouPSvLKJNdX1d9U1cnrPHdXktOS/GFr7Y7B3gELrbX2gSQfSfKvq0pGWxK+aAAAFt3ZSVqS/5zk95N8Z5KfWGvDqvo/krwmyf2TvCjJU6arPp3kB5P8X0l+IMnVVbVWG7+QpJL8wUYKrKonVNUfVdXNVXVnVX26qn67qv7FOts/o6r+uKpuqqo7qurLVfXeqvq5dbavqnpuVX24qr5aVf9YVa+sqqM2UudB3sOPVdXVVfXZqvpaVX2mqq6tql9etd2Tp8OCL1qnnXXPYa+qE6rqT6vqi1W1t6reU1VnHEKtM3/eHT/rNyT53iSnb/T9rFPHTO9x5TDtWT7f6ff2iemPT19x+kerqmes2O5/rqrrqurW6ef2d1X176rq3mvUurKGnVX1hqr6/PTz+8uq+p+26L0f9L3Mul8fiiM22wAAAMyrqvruJD+W5D2ttY9X1X9J8h+T/FJWBeaq+qEkz09ye5KTWmt/W1U7p6vvbq29N8l7q+q6TEL9a6vqutbaF1Y0c1qSu5K8fwM1/kKS30nytSR/lknwPy6TIfI/WlUntv+/vfuP/aqq4zj+fKsxyBEIhjiUIBnYsracaWqMr7+g2hqy5vrhGrAMq+l0lRT+mI6Mmob2U6UVAxnOUkbkorBZaKLATGiJMUNDEtBQ8LdMv/Duj/e5fK+Xz+fz/dzP58P95rfXYzu7eu+5555zuGy8P+fcc9y3FW67FdgEPADsBIYDnwKWmNkEd7+mkP+HxBTonelZbwFTgdOAAcCbzda3ThtmAQuAZ4F7gOeBEcCHgZnALe2Un4wFHiZmNywAjgU+C/zezL7g7k39ANJCf1fV12vS8TxgVTNt6WAbofn+XQ0MBS4D/kZ8757ZmJ4/D5hDvAd3EH+nPgnMA6aY2WR3r9UP7wPWA08BS4BhqQ4rzOxcd/9zh9vesC2H/L12dyUlJSUlJSUlJaV+mYBvE6PiX8qdWwnsB8YV8t6Z8n4/d25MOre1kHdtOn9x7tyRQDfw9zp1ycpalDs3ngjOtgCjCvnPIQL75TXKOqHGuQHAfUTwNyp3/oz03C3AsNz5gUTwdVD7WujnvxLBz4ga144u/H9XeuZ1dcramq9Prt8cuLGQ95TU3j3Aexr1dav9XVVfE59COLC+yT7vSBs72b/p2unp2jZgZO78EURA68CVddriwLWFa1PS+ZWF8w54B/58G7Wl6fe6laRp6iIiIiLSL5kdWLjtDeCu3KXFxFTy4kJuJ6Xjg00Un+X5YO7cKOBwYkS0WV8F3gVc5u7b8xfc/T5iZO/TZja4cO3JYkEeI40/I4Kec3KXZqbjd919dy7/XmL0slO6icCtWK/nO1T+S8DcQtmPAEuJ0c2anx4UlO7vqvra3V8C9hJT1dvR0jtFZ/oX4lMNgOvd/dlcWd3AN4gfwupt4/Y0cH2hDquIwP7UJp7datsbOWTvtaapi4iIiEh/dTZwAnCHu7+cO7+CCDxmmNnV7l78h3Yz2yVleSx3bng67ilRx9PTcZLFlmhFI4gAfzwxShcPNRsNfIsIBEcDgwr3jcr9d7YC/P01yn+QGC1s11JgPvC4md2ZnrXG3Xd1oOzMo+7+So3zq4HpwEeIH1oaKd3fFff1buCYXvL0pqV3is70L/T0wZ+KF9z9CTN7BhhrZkPSDxB5G929Vh/9m552NdJq2+s5pO+1gnERERER6a9mpePbAgh332tmv0rXpwJ3p0uPEyPdZwK/66XsM3P3ZLLV0weWqGMWwF/RS74D+0+b2fuJ72qPAv4C3Ev8uLCPmHI7HcgvkpUtHPZcsVB37zaztkf43P2mVM7XiO+lLwfczO4HrkgjrO06qP5JNvrazGJ0pfq7D/p6ED3vUatKv1NJJ/o3n6/eDJGdxI8aQ+nZbjDzYp17umlu8fFW217ToX6vFYyLiIiISL9jZu8Fzge2E1uUFS0mgvFZ9ATjvwAuAC4xs6XuvqlO2RcSI3Av5+4F+E86Dj/opvqyYGRIYfS+ka+nZ8x090WFun2eCBBrPeMYYmGsfP4jiNXlnylR55rc/XbgdjMbSnw7PY2YsrzKzE7MjSbuT8d6schQagdl9UaMR6ZjMbCrpWx/V9bXFluaDaVnde9WtfJOQWf6N59vJHDQFH9iYbgy5ZXRatvrKvFel6ZvxkVERESkP5pOLLK1xN33Fy+6+0PAP4FzzWxsOncvsRf5YGCtmc0BxqVbDjOzU83sNmKLtH3ARYV/iO8EdgETStQzW3V9Yol7sjotq3FtUo1zjza49nFi2m7HuPuL7r7S3b8MLCJWxM7vy55N4z++eK+ZjaP+COzJdb717UrHDU1Ur2x/V9nXE4jPHjY2Wbd6WnmnoFz/ZlPJa7Uny9dVvJD+fI8D/uXu9UbB29FK2xu15YAm3uvSFIyLiIiISH+ULc7W6BvXJUTwc2AxKXe/nJiS+jqxDdMf06XjgXXAxcBmYLK75xeFi2WdY/uro1PQ0YyfEotD3Wxm44sXzWyAmRUDi63p2FXIO4XaC2MtSserzGxYLv9A4Hv1Kpb2fH7b3tEN8p6VFswrGpGOr+fObSZmFUw1s+w6ZjYI+HGDxwwh9n7PP/cU4EJiRHR5b/WkfH9vTceuQr6O9nXysXTsdfuuXrTyTkG5/t1DrJtQa7G5hel4dZqhkpV1OPADIgb9ZdOtKaeVttdtS8n3ujRNUxcRERGRfsXMuogFmt4Avln739JAz7TcmWZ2bVrtGXe/1cwWEqObk4AriWDkOmJ7qvUp8K5lGfAZYjumLb3V1d03p32RFwKbzOwPwBPEitCjiRG+XcCJudtuIVbtvsvM7gZ2ECvBfwL4NbEvc/4Za8zsJ8ClwGPpnmzv6z3U/7Y3G7jr7q0dRKD2qpmtJQJYS3X/KLFQ1oFPBdz9LTP7EXANsMHMlhNxyXmpLTvqPOMB4CIzO43YkzvbB/swYou5Xqclt9DfVfU1wGRilHZFb+3ocBszTfevu79qZuuAiWa2NJW/D/ituz9kZjcAs3N98Bqxz/hJxEJ2N7bTxk62vVFbKPFet1phJSUlJSUlJSUlpX6TiBWQvWSaVqesMZTYh5uYGv8csK5BWYtqXPsQMar6NLGv8W7gMWABcHaN/GcQq1XvAV4hApzzqbOHdwoiLgH+kcrfQWzNNYTCvt65ezYQI9hHNdHurxCBy1PEaOHudP9sYHCN/EbsAf8ksS/0NuAG4N3F+uT7DfgAEazuSc9ZA0wp09dl+7uivh5C/Hj0mxLveUfa2Er/pvvGEfuGv0CsA+DAjNz1z6W+eoXYsm0TcBUwsIW2rCa3pzgxpdyBN9v9823UFkq+12WTpYeLiIiIiEgHpG/N5wEnu3sz3zH/z0mLVb0AzHf32X1dn/7OzC4lpuhPdPdm9rnv5LPHEIvGLXb3GVU+u1VmdizxI8d2dz+ur+vTKn0zLiIiIiLSWTcTI71z+7oibZhITK++qa8r0t+lb+XnAMuqDsTfwaal48N9Wos26ZtxEREREZEO8tjH/IvAWWZ2pLu/1td1Ksvd76HcfunSujHAz+lZ/E3qMLO5xHoQFxBrGczv2xq1R9PURURERERE/k+9k6apm5kT36A/AnzH3dtdeb5PKRgXERERERERqZi+GRcRERERERGpmIJxERERERERkYopGBcRERERERGpmIJxERERERERkYopGBcRERERERGpmIJxERERERERkYopGBcRERERERGpmIJxERERERERkYopGBcRERERERGpmIJxERERERERkYopGBcRERERERGpmIJxERERERERkYopGBcRERERERGpmIJxERERERERkYopGBcRERERERGp2H8BmWxNBgeyPf4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 389, + "width": 497 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8,6))\n", + "fig, ax, _ = hist.plot1d(output['dphi'], ax=ax, overlay='dataset', density=True)\n", + "\n", + "ax.set_title('[sig-4mu] $\\Delta\\Phi$(leading, subleading) leptonJets', x=0.0, ha=\"left\")\n", + "ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n", + "ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Benchmarks/benchmark-2.ipynb b/Benchmarks/benchmark-2.ipynb new file mode 100644 index 0000000..f26c30e --- /dev/null +++ b/Benchmarks/benchmark-2.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gen matching" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "import numpy as np\n", + "np.seterr(divide='ignore', invalid='ignore', over='ignore')\n", + "\n", + "from coffea import hist\n", + "from coffea.analysis_objects import JaggedCandidateArray\n", + "from coffea.processor import defaultdict_accumulator\n", + "import coffea.processor as processor" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "selected = 'mXX-100_mA-0p25_lxy-300'\n", + "datasets = {selected: json.load(open('Samples/signal_4mu.json'))[selected]}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "class LeptonJetProcessor(processor.ProcessorABC):\n", + " def __init__(self):\n", + " dataset_axis = hist.Cat(\"dataset\", \"signal\")\n", + " match_axis = hist.Cat(\"match\", \"matched with darkphoton\")\n", + " pt_axis = hist.Bin(\"pt\", \"$p_T$[GeV]\", 50, 0, 100)\n", + " \n", + " self._accumulator = processor.dict_accumulator({\n", + " 'pt': hist.Hist(\"norm. #counts/2GeV\", dataset_axis, pt_axis, match_axis),\n", + " })\n", + " \n", + " @property\n", + " def accumulator(self):\n", + " return self._accumulator\n", + "\n", + " def process(self, df):\n", + " output = self.accumulator.identity()\n", + " \n", + " dataset = df['dataset']\n", + " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " df['pfjet_p4'],\n", + " px=df['pfjet_p4.fCoordinates.fX'],\n", + " py=df['pfjet_p4.fCoordinates.fY'],\n", + " pz=df['pfjet_p4.fCoordinates.fZ'],\n", + " energy=df['pfjet_p4.fCoordinates.fT'],\n", + " )\n", + " genparticles = JaggedCandidateArray.candidatesfromcounts(\n", + " df['gen_p4'],\n", + " px=df['gen_p4.fCoordinates.fX'],\n", + " py=df['gen_p4.fCoordinates.fY'],\n", + " pz=df['gen_p4.fCoordinates.fZ'],\n", + " energy=df['gen_p4.fCoordinates.fT'],\n", + " pid=df['gen_pid']\n", + " )\n", + " darkphotons = genparticles[genparticles.pid==32]\n", + " matchmask = leptonjets.match(darkphotons, deltaRCut=0.3)\n", + " \n", + " output['pt'].fill(dataset=dataset, pt=leptonjets[matchmask].pt.flatten(), match=\"true\")\n", + " output['pt'].fill(dataset=dataset, pt=leptonjets[~matchmask].pt.flatten(), match=\"false\")\n", + " \n", + " return output\n", + " \n", + " def postprocess(self, accumulator):\n", + " return accumulator" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Preprocessing: 100%|██████████| 1/1 [00:00<00:00, 1056.50it/s]\n", + "Processing: 100%|██████████| 5/5 [00:02<00:00, 2.49items/s]\n" + ] + } + ], + "source": [ + "output = processor.run_uproot_job(datasets,\n", + " treename='ffNtuplizer/ffNtuple',\n", + " processor_instance=LeptonJetProcessor(),\n", + " executor=processor.futures_executor,\n", + " executor_args=dict(workers=6, flatten=True),\n", + " chunksize=500000,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 388, + "width": 513 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8,6))\n", + "fig, ax, _ = hist.plot1d(output['pt'].project('dataset'), ax=ax, overlay='match', density=True)\n", + "\n", + "ax.set_title('[sig-4mu] leptonJets pT', x=0.0, ha=\"left\")\n", + "ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n", + "ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Benchmarks/benchmark-3.ipynb b/Benchmarks/benchmark-3.ipynb new file mode 100644 index 0000000..4deb402 --- /dev/null +++ b/Benchmarks/benchmark-3.ipynb @@ -0,0 +1,181 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# leading and subleading leptonJet mass." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "from coffea import hist\n", + "from coffea.analysis_objects import JaggedCandidateArray\n", + "from coffea.processor import defaultdict_accumulator\n", + "import coffea.processor as processor" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "datasets = json.load(open('Samples/signal_4mu.json'))\n", + "\n", + "from collections import defaultdict\n", + "mapping = defaultdict(list)\n", + "for k in datasets:\n", + " mapping[k.split('_')[0]].append(k)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "class LeptonJetProcessor(processor.ProcessorABC):\n", + " def __init__(self):\n", + " dataset_axis = hist.Cat(\"dataset\", \"signal\")\n", + " mass0_axis = hist.Bin(\"mass0\", \"invM(lead, sublead) leptonJets [GeV]\", 50, 0, 200)\n", + " mass1_axis = hist.Bin(\"mass1\", \"invM(lead, sublead) leptonJets [GeV]\", 50, 0, 1200)\n", + " \n", + " self._accumulator = processor.dict_accumulator({\n", + " 'mass0': hist.Hist(\"norm. #counts/4GeV\", dataset_axis, mass0_axis),\n", + " 'mass1': hist.Hist(\"norm. #counts/24GeV\", dataset_axis, mass1_axis),\n", + " })\n", + " \n", + " @property\n", + " def accumulator(self):\n", + " return self._accumulator\n", + "\n", + " def process(self, df):\n", + " output = self.accumulator.identity()\n", + " \n", + " dataset = df['dataset']\n", + " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " df['pfjet_p4'],\n", + " px=df['pfjet_p4.fCoordinates.fX'],\n", + " py=df['pfjet_p4.fCoordinates.fY'],\n", + " pz=df['pfjet_p4.fCoordinates.fZ'],\n", + " energy=df['pfjet_p4.fCoordinates.fT'],\n", + " )\n", + " \n", + " twoleptonjets = leptonjets.counts>=2\n", + " dileptonjets = leptonjets[twoleptonjets]\n", + " leptonjetpair = dileptonjets.distincts()\n", + " sumpt = leptonjetpair.i0.pt+leptonjetpair.i1.pt\n", + " if sumpt.size!=0:\n", + " leadingLjPair = leptonjetpair[sumpt.argmax()]\n", + " mass_ = leadingLjPair.mass\n", + " \n", + " output['mass0'].fill(dataset=dataset, mass0=mass_.flatten())\n", + " output['mass1'].fill(dataset=dataset, mass1=mass_.flatten())\n", + " \n", + " return output\n", + " \n", + " def postprocess(self, accumulator):\n", + " origidentity = list(accumulator)\n", + " for k in origidentity:\n", + " accumulator[k] = accumulator[k].group(hist.Cat(\"cat\", \"datasets\"), \"dataset\", mapping)\n", + " return accumulator" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Preprocessing: 100%|██████████| 32/32 [00:07<00:00, 4.33it/s]\n", + "Processing: 100%|██████████| 160/160 [00:08<00:00, 19.86items/s]\n" + ] + } + ], + "source": [ + "output = processor.run_uproot_job(datasets,\n", + " treename='ffNtuplizer/ffNtuple',\n", + " processor_instance=LeptonJetProcessor(),\n", + " executor=processor.futures_executor,\n", + " executor_args=dict(workers=6, flatten=True),\n", + " chunksize=500000,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 962 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1,2,figsize=(16,6))\n", + "hist.plot1d(output['mass0'], overlay='cat', density=True, ax=axes[0])\n", + "hist.plot1d(output['mass1'], overlay='cat', density=True, ax=axes[1])\n", + "\n", + "for ax in axes:\n", + " ax.set_title('[sig-4mu] (leading, subleading) leptonJets invM', x=0.0, ha=\"left\")\n", + " ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n", + " ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Benchmarks/benchmark-4.ipynb b/Benchmarks/benchmark-4.ipynb new file mode 100644 index 0000000..0e6bff3 --- /dev/null +++ b/Benchmarks/benchmark-4.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple trigger efficiency" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from awkward import JaggedArray\n", + "from coffea import hist\n", + "from coffea.analysis_objects import JaggedCandidateArray\n", + "from coffea.processor import defaultdict_accumulator\n", + "import coffea.processor as processor\n", + "from FireHydrant.Tools.trigger import Triggers\n", + "\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "np.seterr(divide='ignore', invalid='ignore', over='ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "datasets = json.load(open('Samples/signal_4mu.json'))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "class TriggerProcessor(processor.ProcessorABC):\n", + " def __init__(self):\n", + " dataset_axis = hist.Cat(\"dataset\", \"signal\")\n", + " pt_axis = hist.Bin(\"pt\", r\"$p_T$ [GeV]\", 50, 0, 200)\n", + " trigger_axis = hist.Cat('trigger', 'triggered')\n", + " \n", + " self._accumulator = processor.dict_accumulator({\n", + " 'pt': hist.Hist(\"Counts\", dataset_axis, pt_axis, trigger_axis),\n", + " })\n", + " \n", + " @property\n", + " def accumulator(self):\n", + " return self._accumulator\n", + " \n", + " def process(self, df):\n", + " output = self.accumulator.identity()\n", + " \n", + " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " df['pfjet_p4'],\n", + " px=df['pfjet_p4.fCoordinates.fX'],\n", + " py=df['pfjet_p4.fCoordinates.fY'],\n", + " pz=df['pfjet_p4.fCoordinates.fZ'],\n", + " energy=df['pfjet_p4.fCoordinates.fT']\n", + " )\n", + " \n", + " \n", + " #trigger=np.logical_or.reduce([df[t] for t in Triggers]) # NOTE: here I'm not considering the trigger objects!\n", + " \n", + " #leptonjets = leptonjets[(np.abs(dsamuons.eta)<2.4)&(dsamuons.nsta>1)&(dsamuons.normchi2<10)] # possible cuts to apply\n", + " twoljs = leptonjets.counts>=2\n", + " leptonjets = leptonjets[twoljs]\n", + " trigger = trigger[twoljs]\n", + " \n", + " #sublidx = JaggedArray.fromfolding(leptonjets.pt.argsort()[:, 1], 1) # ??????????????\n", + " #subleading = leptonjets[sublidx]\n", + " \n", + " output['pt'].fill(dataset=dataset, trigger='true', pt=subleading[trigger].pt.flatten()) \n", + " output['pt'].fill(dataset=dataset, trigger='false', pt=subleading[~trigger].pt.flatten())\n", + " \n", + " return output\n", + " \n", + " def postprocess(self, accumulator):\n", + " return accumulator" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Preprocessing: 100%|██████████| 18/18 [00:00<00:00, 17557.55it/s]\n", + "Processing: 100%|██████████| 90/90 [00:13<00:00, 3.77items/s]\n" + ] + } + ], + "source": [ + "output = processor.run_uproot_job(datasets,\n", + " treename='ffNtuplizer/ffNtuple',\n", + " processor_instance=TriggerProcessor(),\n", + " executor=processor.futures_executor,\n", + " executor_args=dict(workers=6, flatten=True),\n", + " chunksize=500000,\n", + " #maxchunks=0\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 388, + "width": 497 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8,6))\n", + "numer = output['pt'].project('dataset').project('trigger', 'true')\n", + "denom = output['pt'].project('dataset').sum('trigger')\n", + "\n", + "fig, ax, _ = hist.plotratio(num=numer,\n", + " denom=denom,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax)\n", + "ax.text(60, 0.1, '\\n'.join(['logical OR of:',]+ Triggers))\n", + "ax.set_title('[sig-4mu] trigger efficiency vs. subleading dSA muon pT', x=0.0, ha=\"left\")\n", + "ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n", + "ax.set_ylabel('Efficiency', y=1.0, ha=\"right\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/FireHydrant/Tools/trigger.py b/FireHydrant/Tools/trigger.py index c29fc75..ac16687 100644 --- a/FireHydrant/Tools/trigger.py +++ b/FireHydrant/Tools/trigger.py @@ -9,4 +9,4 @@ "HLT_DoubleL2Mu23NoVtx_2Cha_CosmicSeed_NoL2Matched", "HLT_DoubleL2Mu25NoVtx_2Cha_Eta2p4", "HLT_DoubleL2Mu25NoVtx_2Cha_CosmicSeed_Eta2p4", -] +] \ No newline at end of file diff --git a/Notebooks/Data/DataOnly/DeltaRstudy.ipynb b/Notebooks/Data/DataOnly/DeltaRstudy.ipynb index b253895..84e07d7 100644 --- a/Notebooks/Data/DataOnly/DeltaRstudy.ipynb +++ b/Notebooks/Data/DataOnly/DeltaRstudy.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -125,15 +125,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Preprocessing: 100%|██████████| 4/4 [00:11<00:00, 3.27s/it]\n", - "Processing: 100%|██████████| 2545/2545 [01:23<00:00, 28.20items/s]\n" + "Preprocessing: 100%|██████████| 4/4 [00:00<00:00, 1032.89it/s]\n", + "Processing: 100%|██████████| 2545/2545 [02:28<00:00, 17.13items/s]\n" ] } ], @@ -150,12 +150,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": 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" ] @@ -163,7 +163,7 @@ "metadata": { "image/png": { "height": 386, - "width": 953 + "width": 946 }, "needs_background": "light" }, diff --git a/Notebooks/Data/DataOnly/Testing.ipynb b/Notebooks/Data/DataOnly/Testing.ipynb new file mode 100644 index 0000000..a228790 --- /dev/null +++ b/Notebooks/Data/DataOnly/Testing.ipynb @@ -0,0 +1,279 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'FireHydrant'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseterr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdivide\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minvalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mover\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mFireHydrant\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muproothelpers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mNestNestObjArrayToJagged\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'FireHydrant'" + ] + } + ], + "source": [ + "from coffea import hist\n", + "from coffea.analysis_objects import JaggedCandidateArray\n", + "from coffea.processor import defaultdict_accumulator\n", + "import coffea.processor as processor\n", + "\n", + "import numpy as np\n", + "np.seterr(divide='ignore', invalid='ignore', over='ignore')\n", + "import matplotlib.pyplot as plt\n", + "from FireHydrant.Tools.uproothelpers import NestNestObjArrayToJagged\n", + "\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# setting deltaR=deltaPhi between TO and LJ (ideally <0.4)\n", + "\n", + "deltaPhi = np.random.normal(4, 1.5, 10)\n", + "deltaPhi = np.absolute(deltaPhi)\n", + "deltaPhi = np.floor(deltaPhi)/10\n", + "\n", + "# defining letponjets\n", + "\n", + "lptjs_pts = [10,20,30,40,50,70,80,90,100,110]\n", + "lptjs_phis = np.zeros(10) + deltaPhi\n", + "lptjs_counts = [0,1,2,1,3,3]\n", + "lptjs_etas = np.zeros(10)\n", + "lptjs_mass = np.zeros(10)\n", + "\n", + "leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " lptjs_counts,\n", + " pt=lptjs_pts,\n", + " eta=lptjs_etas,\n", + " phi=lptjs_phis,\n", + " mass=lptjs_mass)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# defining triggerobjects\n", + "\n", + "to_pts = [10,20,30,40,50,70,80,90,100,110]\n", + "to_phis = np.zeros(10)\n", + "to_counts = [1,0,3,0,2,4]\n", + "to_etas = np.zeros(10)\n", + "to_mass = np.zeros(10)\n", + "\n", + "\n", + "triggerObjs = JaggedCandidateArray.candidatesfromcounts(\n", + " to_counts,\n", + " pt=to_pts,\n", + " eta=to_etas,\n", + " phi=to_phis,\n", + " mass=to_mass)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(leptonjets.phi)\n", + "print(triggerObjs.phi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(triggerObjs.phi)\n", + "print(leptonjets.phi)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# setting cut parameters\n", + "\n", + "ptcut1 = 0 # [GeV] for tag # pt = 0 and etacut = 99999999 means NO CUTS\n", + "ptcut2 = 0 # [GeV] for probe\n", + "etacut = 2.5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define masks based on cuts parameters\n", + "\n", + "\n", + "leptonjets.add_attributes(trgmask = leptonjets.match(triggerObjs, deltaRCut=0.4)) \n", + "leptonjets.add_attributes(ptmask1 = ptcut1) \n", + "leptonjets.add_attributes(ptmask2 = ptcut2) \n", + "leptonjets.add_attributes(etamask = leptonjets.eta=2\n", + "\n", + "diljs = leptonjets[twoljs]\n", + "#matchedidx = \n", + "\n", + "ditos = triggerObjs[twoljs]\n", + "\n", + "print(diljs.phi) \n", + "print(diljs.trgmask)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# distint pairs of objects\n", + "\n", + "diljs = diljs.distincts()\n", + "diljs.i0\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(diljs.i0.phi)\n", + "print(diljs.i1.phi)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tagidx = JaggedArray.fromfolding(leptonjets.pt.argsort()[:, 1], 1) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# apply tag and probe method\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "# now i0 is the tag and i1 is the probe\n", + "tag0Andprobe1_pt = diljs[leptonjets.i0.trgmask & \n", + " leptonjets.i0.ptmask1 &\n", + " leptonjets.i1.ptmask2 &\n", + " leptonjets.i0.etamask &\n", + " leptonjets.i1.trgmask].pt.flatten() \n", + "''' \n", + "tag0_pt = diljs[diljs.i0.trgmask &\n", + " diljs.i0.ptmask1 &\n", + " diljs.i1.ptmask2 &\n", + " diljs.i0.etamask \n", + " ].pt.flatten()\n", + " \n", + "# now i1 is the tag and i0 is the probe \n", + "tag1Andprobe0_pt = diljs[diljs.i0.trgmask & \n", + " diljs.i1.ptmask1 &\n", + " diljs.i0.ptmask2 &\n", + " diljs.i1.etamask &\n", + " diljs.i1.trgmask].pt.flatten() \n", + " \n", + "tag1_pt = diljs[diljs.i1.trgmask & \n", + " diljs.i1.ptmask1 &\n", + " diljs.i0.ptmask2 &\n", + " diljs.i1.etamask \n", + " ].pt.flatten()\n", + " \n", + "# appending the two pairs of pt arrays\n", + "tot_tag_pt = np.append(tag0_pt, tag1_pt)\n", + "tot_tagAndprobe_pt = np.append(tag0Andprobe1_pt, tag1Andprobe0_pt)\n", + " \n", + "''' " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb b/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb index 0316b95..936449a 100644 --- a/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb +++ b/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -54,7 +54,7 @@ " \n", " dataset_axis = hist.Cat('dataset', '')\n", " multiplicity_axis = hist.Bin(\"multiplicity\", \"#muons/event\", 10, 0, 10)\n", - " pt_axis = hist.Bin(\"pt\", \"jet pt [GeV]\", 20, 0 , 300)\n", + " pt_axis = hist.Bin(\"pt\", \"jet pt [GeV]\", 50, 0 , 200)\n", " deltaR_axis = hist.Bin(\"deltaR\", \"delta_R\", 30, 0 , 5)\n", " \n", " self._accumulator = processor.dict_accumulator({\n", @@ -74,8 +74,18 @@ " dataset = df['dataset']\n", " \n", " # IMPORTATANT!!! here you need to add the reference trigger (HLT_Mu17)\n", - " \n", + " \n", + " \n", " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " df['muon_p4'],\n", + " px=df['muon_p4.fCoordinates.fX'],\n", + " py=df['muon_p4.fCoordinates.fY'],\n", + " pz=df['muon_p4.fCoordinates.fZ'],\n", + " energy=df['muon_p4.fCoordinates.fT'],\n", + " \n", + " ) \n", + " \n", + " muons = JaggedCandidateArray.candidatesfromcounts(\n", " df['pfjet_p4'],\n", " px=df['pfjet_p4.fCoordinates.fX'],\n", " py=df['pfjet_p4.fCoordinates.fY'],\n", @@ -94,38 +104,56 @@ " \n", " ####### EFFICIENCY STUDY #######\n", " \n", + " ptcut1 = 0 # [GeV] for tag # pt = 0 and etacut = 99999999 means NO CUTS\n", + " ptcut2 = 0 # [GeV] for probe\n", + " etacut = 2.5\n", + " \n", + " # NOTE: add ptcut2 > 1 for probe\n", + " \n", + " \n", + " twoljs = leptonjets.counts >=1\n", " \n", - " twoljs = leptonjets.counts>=2\n", " diljs = leptonjets[twoljs]\n", + " triggerObjs = triggerObjs[twoljs]\n", + " \n", + " #leptonjets.offsets\n", + " ptmask1 = diljs.pt>ptcut1\n", + " ptmask2 = diljs.pt>ptcut2\n", " \n", - " ptcut = 0 # pt = 0 and etacut = 99999999 means NO CUTS\n", - " etacut = 999999999\n", + " #ptmask1.offsets\n", + " #diljs.offsets\n", + " \n", " \n", - " diljs.add_attributes(trgmask = diljs.match(triggerObjs, deltaRCut=0.5)) \n", - " diljs.add_attributes(ptmask = diljs.pt>ptcut) \n", + " diljs.add_attributes(ptmask1 = ptmask1) \n", + " diljs.add_attributes(ptmask2 = ptmask2) \n", " diljs.add_attributes(etamask = diljs.eta 41\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 0", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + 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\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"error\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'pyxrootd' has no attribute 'client'" ] } ], @@ -175,19 +238,34 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# New way to pl" + "### New way to plot the efficiency" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 497 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(8,6))\n", "numer = output['tot_tag_with_probe'].project('dataset')\n", @@ -205,15 +283,15 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "### Old way to plot the efficiency" + ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -242,12 +320,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 52, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -313,30 +391,6 @@ "\n" ] }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "image/png": { - "height": 386, - "width": 497 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [] - }, { "cell_type": "code", "execution_count": null, From d0241ff01c0eb0b40bbbfe2854da7696246d86de Mon Sep 17 00:00:00 2001 From: Pietro Meloni Date: Fri, 27 Sep 2019 17:23:52 -0500 Subject: [PATCH 5/8] Add notebooks for trigger efficiency --- .../DataOnly/TriggerEfficiency_data.ipynb | 1260 ++++++++++++ Notebooks/Data/Samples/generate_singleMu.py | 25 + .../Samples/generate_singleMu_nonempty.py | 77 + Notebooks/Data/Samples/trigger_data.json | 1682 +++++++++++++++++ .../Data/Samples/trigger_data_nonempty.json | 1682 +++++++++++++++++ .../MC/Signal/TriggerEfficiency_MC.ipynb | 879 +++++++++ 6 files changed, 5605 insertions(+) create mode 100644 Notebooks/Data/DataOnly/TriggerEfficiency_data.ipynb create mode 100644 Notebooks/Data/Samples/generate_singleMu.py create mode 100644 Notebooks/Data/Samples/generate_singleMu_nonempty.py create mode 100644 Notebooks/Data/Samples/trigger_data.json create mode 100644 Notebooks/Data/Samples/trigger_data_nonempty.json create mode 100644 Notebooks/MC/Signal/TriggerEfficiency_MC.ipynb diff --git a/Notebooks/Data/DataOnly/TriggerEfficiency_data.ipynb b/Notebooks/Data/DataOnly/TriggerEfficiency_data.ipynb new file mode 100644 index 0000000..0a6a251 --- /dev/null +++ b/Notebooks/Data/DataOnly/TriggerEfficiency_data.ipynb @@ -0,0 +1,1260 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Trigger efficiency for Data\n", + "\n", + "##### In this notebook I want to compute the probability for an event with 2 or more lepton jets to contain at least 2 trigger objects from at least one of the following trigger paths:\n", + "\n", + " - \"HLT_DoubleL2Mu23NoVtx_2Cha\",\n", + " - \"HLT_DoubleL2Mu23NoVtx_2Cha_NoL2Matched\",\n", + " - \"HLT_DoubleL2Mu23NoVtx_2Cha_CosmicSeed\",\n", + " - \"HLT_DoubleL2Mu23NoVtx_2Cha_CosmicSeed_NoL2Matched\",\n", + " - \"HLT_DoubleL2Mu25NoVtx_2Cha_Eta2p4\",\n", + " - \"HLT_DoubleL2Mu25NoVtx_2Cha_CosmicSeed_Eta2p4\",\n", + "\n", + "\n", + "##### The computation is divided into 2 parts (the study of Delta R is just a test):\n", + "- computing per-object efficiency: probability for a lepton jet (LJ) to contain at least 1 trigger objects (TOs). Using tag and probe method (this part is completed)\n", + "\n", + "- computing per-event efficiency: probability for an event with 2 LJs to contain 2 or more TOs. Using either an anlythical method or a simple montecarlo simulation (this part is NOT completed)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "from coffea import hist\n", + "from coffea.analysis_objects import JaggedCandidateArray\n", + "from coffea.processor import defaultdict_accumulator\n", + "from awkward import JaggedArray\n", + "import coffea.processor as processor\n", + "\n", + "\n", + "import numpy as np\n", + "np.seterr(divide='ignore', invalid='ignore', over='ignore')\n", + "import matplotlib.pyplot as plt\n", + "#from FireHydrant.Tools.uproothelpers import NestNestObjArrayToJagged\n", + "\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "datasets_=json.load(open('../Samples/trigger_data_nonempty.json'))\n", + "datasets = dict(\n", + " A={'files': datasets_['A'], 'treename': 'ffNtuplizer/ffNtuple'} ,\n", + " B={'files': datasets_['B'], 'treename': 'ffNtuplizer/ffNtuple'} ,\n", + " C={'files': datasets_['C'], 'treename': 'ffNtuplizer/ffNtuple'} , \n", + " D={'files': datasets_['D'], 'treename': 'ffNtuplizer/ffNtuple'} ,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "TriggerList = [ \n", + " \"HLT_DoubleL2Mu23NoVtx_2Cha\",\n", + " \"HLT_DoubleL2Mu23NoVtx_2Cha_NoL2Matched\",\n", + " \"HLT_DoubleL2Mu23NoVtx_2Cha_CosmicSeed\",\n", + " \"HLT_DoubleL2Mu23NoVtx_2Cha_CosmicSeed_NoL2Matched\",\n", + " \"HLT_DoubleL2Mu25NoVtx_2Cha_Eta2p4\",\n", + " \"HLT_DoubleL2Mu25NoVtx_2Cha_CosmicSeed_Eta2p4\",\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Study of Delta R\n", + "##### Because I had some problems at the beginning, I made this test for Delta R between TOs and LJs, using only TOHLT_DoubleL2Mu23NoVtx_2Cha.\n", + "##### The test consists in studying DeltaR between a fixed TO and the closest LJ or Muon " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class DeltaRProcessor(processor.ProcessorABC):\n", + " def __init__(self):\n", + " \n", + " dataset_axis = hist.Cat('dataset', '')\n", + " multiplicity_axis = hist.Bin(\"multiplicity\", \"#muons/event\", 10, 0, 10)\n", + " pt_axis = hist.Bin(\"pt\", \"jet pt [GeV]\", 20, 0 , 300)\n", + " deltaR_axis = hist.Bin(\"deltaR\", \"$\\Delta R$\", 30, 0 , 0.5)\n", + " \n", + " self._accumulator = processor.dict_accumulator({\n", + " 'deltaR1': hist.Hist(\"Counts\", dataset_axis, deltaR_axis),\n", + " 'deltaR2': hist.Hist(\"Counts\", dataset_axis, deltaR_axis),\n", + " })\n", + " \n", + " @property\n", + " def accumulator(self):\n", + " return self._accumulator\n", + " \n", + " def process(self, df):\n", + " output = self.accumulator.identity()\n", + " \n", + " dataset = df['dataset']\n", + "\n", + " muons = JaggedCandidateArray.candidatesfromcounts(\n", + " df['muon_p4'],\n", + " px=df['muon_p4.fCoordinates.fX'],\n", + " py=df['muon_p4.fCoordinates.fY'],\n", + " pz=df['muon_p4.fCoordinates.fZ'],\n", + " energy=df['muon_p4.fCoordinates.fT'], \n", + " )\n", + " \n", + " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " df['pfjet_p4'],\n", + " muonsPerJet=df[\"pfjet_muon_n\"],\n", + " px=df['pfjet_p4.fCoordinates.fX'],\n", + " py=df['pfjet_p4.fCoordinates.fY'],\n", + " pz=df['pfjet_p4.fCoordinates.fZ'],\n", + " energy=df['pfjet_p4.fCoordinates.fT'],\n", + " ) \n", + " \n", + " triggerObjs = JaggedCandidateArray.candidatesfromcounts(\n", + " df['TOHLT_DoubleL2Mu23NoVtx_2Cha'],\n", + " px=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fX'],\n", + " py=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fY'],\n", + " pz=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fZ'],\n", + " energy=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fT'],\n", + " )\n", + " \n", + " twoljs = leptonjets.counts >=2\n", + " \n", + " contains_mu = (leptonjets.muonsPerJet > 0).prod().astype(bool)\n", + " \n", + " diljs = leptonjets[twoljs & contains_mu]\n", + " triggerObjs_ = triggerObjs[ twoljs & contains_mu]\n", + " \n", + " \n", + " if diljs.size == 0:\n", + " \n", + " return output\n", + " \n", + " # with muons\n", + " Tobj_Mu_pairs = muons['p4'].cross(triggerObjs['p4'], nested=True)\n", + " dr1 = Tobj_Mu_pairs.i0.delta_r(Tobj_Mu_pairs.i1)\n", + " dr1 = dr1.min()\n", + " \n", + " output['deltaR1'].fill(dataset=dataset, deltaR=dr1.flatten().flatten()) \n", + " \n", + " # with leptonjets\n", + " Tobj_Lj_pairs = diljs['p4'].cross(triggerObjs_['p4'], nested=True)\n", + " dr2 = Tobj_Lj_pairs.i0.delta_r(Tobj_Lj_pairs.i1)\n", + " dr2 = dr2.min()\n", + " \n", + " output['deltaR2'].fill(dataset=dataset, deltaR=dr2.flatten().flatten()) \n", + "\n", + " return output\n", + " \n", + " def postprocess(self, accumulator):\n", + " return accumulator" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Preprocessing: 100%|██████████| 4/4 [00:13<00:00, 3.37s/it]\n", + "Processing: 100%|██████████| 1672/1672 [06:48<00:00, 4.10items/s]\n" + ] + } + ], + "source": [ + "output = processor.run_uproot_job(datasets,\n", + " treename=None,\n", + " processor_instance=DeltaRProcessor(),\n", + " executor=processor.futures_executor,\n", + " executor_args=dict(workers=12, flatten=True),\n", + " chunksize=5000000,\n", + " #maxchunks=1\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OerzU6XS6MbqIm8XN4ubJOOLm3Y+3baq/uFncvEu7WMNtQNwsbl5pHfbFuHk9tuUTl9su0u273tMPP23G8N1u0+kuamtJ3p/Z/5J9fj/80iSHzWkr5ya56Yyyk7safGEVy3PecWpyp4O5+/sk/68fZ/qfxTPXkW5zd55Bu7EuHLxfyb36L+lfb1FVo6271u0h3tp/vPcyo764tfbtGf0nzxE5eqr/A9Pd0uTaJP97JXXp78H/mHQHjhfPqe9V6W5ZkiQPXsl0Z3hB/72n/V7/esckdxv0f1S6K3dOa619fE69Ppj+tgnpAqoVa619K93trpKl5w4M329PF/hVuivGp4efPjXJn+vHfX1r7ctzZvumdFcF3mXqfvwn9K8va61dPafsX/ev85b/Ga2190/3bK2dme7Hh2TX9rJqfbt8V/9xuTa8Fv6otXblIgX67fmR/ceX9m14l+mmu1Jzlp/sX9/UWvvC9MDW2r+layOzrFXb/T9z+u/O3dNtT0l3EjPLb/ev29LdrmaWV7bWLpjR/y/Ttan9srScdmdPto+f619f21r7xArnt+76ZfOBdN/rXsuM+oI5/d/cv05vl4/uX/+1tfbeGfM9O8nrF6jqnnhTP79pk9vZHZyltrbex5NJO3hH656ft6cmy/nPWmvnTg9srb0z3S3Jki64nJicNxw+eYbNCqy2/V+Wblkm3ZWyG+GNc9rAaYP3u7Tx1toX0121nqzhsSerX29Dox4vAfZS4mZx84qIm/eMuFncHHGzuFncvBXi5ml7si0PPX+6R3+OMNleHrDos4r78e/ff3xBa+3aGaP9froLCw5Nl/Sf5VWttW/O6D859/ru6p95vQYmx/8/XGacv+lfV3v+xSZywEZXgIW8O91td45Jsr2qXpXu6tr/WouJV/dQ+ienu3r0e9LdxmA6oP3OZSbx4Tn9v9q/3mSq/4/0rx9vrX01K3NsuiuzWpJPVtW88Q7pX283b4RlXJ3kX2YNaK2dVVVfS3cQPSbdswuSpRO3B1TVLicBA5MD0e2ydFKwUqene+bDcelud5HsHEjeJt3tQI5L9xyK6eFDk/qeUFWPWWaeBw7q+7WqOiBLJ/p/WlX/d065/QflZpnXVpKuvdw2u7aX3aqqO6d7LtT90gUlh6Y7yRharg2vhUXXa9Ld4uKw/v0uAXiStNauqKozs/MPCRM/sFzZ3hlZOqkZWou2e2W6qyVX45j+9Ruttf+YNUJr7XNV9dV0bfyYJP86Y7Ttc8peV1VnJPnvg3ntzmq3jwOzFIi/bYXzWlNV9cNJ/me673DbJLNOMOdtAxfM+qGiN28/Plmm0/uYodOzFHitp5n7ldba1VV1XpJbZuf6r+fxZHJ82+N2UFUHZSnA3yWYH3hPuqv2h+3839JdwX/rJB/s99nvaq2ds8x0VtX++33U6en2M++oqleku13VJ+cEUevhk3P6n9e/fitLidhpX0935fDCx55Z9nC9Da3L8RJgkxM3i5vFzcsQN4ubZ5QVN+9K3NwRN8+2L8fN01b1XaaGfWmZ5fX+dBeZ7Z/uYpP3LFC3H0h3fGqZs/201i7u9/v3Trde/3bGaLs790q6OyXMu8BnRarqdun2KUnytqqadRFb0m1TyerOv9hkJGg31vCAN+sKtp30Qc4vp7si8L59l6rake5ZIa9qrX10NRWpquPSHRAOHfS+ON2PmUl3oD0ss09aJi6d038yjen2dsv+9Usrr+n1VxfVoPxyVnrl09D5c67EnPhqX4+bz6jXd6xwnqup1+np7q1/XJJUd1Z0v3RXX52Z7hZBbTD8BlkKCt83Na1JfW/Udyut7xFZOojcdAXlDpnTf15bSZbay4HLjLOLqnpcuqs+J+WuS9eGJ1enH5qu/a7VFVHzfGMVZW42eD99kjM070elSfnVlF2LtvvN1tp1M/qvxGQ72t2PTV9JF2jefM7w5cpPhs0rO21Pto/Jfm6R/dqaqKpnJPmDLP24cm26f5xM9meHp7vt27xtYDXb5WSZLveD50p/SNxTi9Z/PY8nqzm+zXNEln70XW5ZTv5FcX07b61dWFU/m+6WVHdN8qdJ0v+o9M50t/udDnJW2/6T7lZ+/5zuWUG/23eXVdX7krwuyd+21q5ZwTRXa94+cBLofn3Ov3yG4yx07FnGqtfblDU/XgLsg8TNHXHzyoiblyFuXnVZcfOuxM27EjeLm+fZm+LmaXvyXSbmLvPW2pVVdWG6ffBK9y8Tk/Evbq1dtsx4q4qrW2vfGlx0sBZx9fDfxbdYwfirOc9hk3GL4431/f3rV5a55c1OWmt/nuS7k5yU7tYZ30x3xeP/THJmVT170Ur0V6+dmv52LemCl0Naazdurd2qtXarJE+fjL7o9NfYpM1e3FqrFXTHj1yvl62wXqesYh4fSHJNkttU1fckuUu6YO9fWmvXtNbOT/LpJN9fVTdJdyXawel+kP7cnPo+bYX13T5VLkl+YCVlV/E9F1ZVN0/y6nQH09cn+cEkN2it3WTQhl8yGX2dq7NRV7yt1lq03bX4zjdYg2msldVuHxumqu6S7rYule7HyLuke7bZEYNtYHLLoY3ej+8t9tbjyXIW3k5aa29Ld97wi0nekO5HgVuluzp78q+ioVW3//5K8rsm+W/p/rHymSzdZuivkvxbVR2arWdv2r8B7IvEzYvbW89zxM3i5glx8+rsTeeV4uatYW89nixH3LyYfWFbPngD5rkaw+P/TVawLLdtVEXZe0jQbpD+1gsP7D+esUjZ1trXW2sva609Mt2VIT+c5B/SnTz8blXddcHq3DPd3+8vSPKI1toZrXt2y9BKrpJa1Nf719uvosxhVXX4Gtdn4mb9+plncpuT4RWfk3p91/pUKWmtXZ7uit+ku9p3+BydidPTbdf3zfzbNCWrr+83sxRUrNt3XYUfS3cy8+kkj2+tnTnjx5v1aMNr5fzB++WeQTFv2Pm7Gb7csHVvu7sx2Y52d1uPyS1C5l1pvdwtuGZts8tZ7TK5IN2PQcli+7W18Kh02/47WmtPbq19uu16e5z12AYmy3Qly39vs57Hk9Uc3+a5IEvPqFmuTc7dRlprF7fWXt1ae2xr7Tbpfoh4dT/4iVX1sMHoe7RP6H/4/MfW2i+11r4v3b7nf6e7GvuYJL+1muluoMk2vVyQP6v97PF6A0Dc3BM3L0DcvCxx8+rLipt3JW5eOXHz8tMWN29s3LwW+7e57be/U8XkbiiLxr2T8Q/pLzKaZ2+Jq78+eL83Hf/Zi0nQbpwnZumv7n+92om0zofTPbD9K+nW6X0Go1x/+5Sa/6CAyU7s8621K+aM86DV1nEZk+dh3LWqbrPCMv+e7kSukjx0HeqUdFeT3nPWgKq6Y5YOOh8ZDJo8W+T4qpp3e6J5VrKOJia3XBoGmqcvMHxiUt+FlmEfvP17//HHFim7ziZt+BNtxi2D+uX6gFVOezK99bx68gtJLunf32fWCH27OnbWsCSTW7TNLNu775z+e9J218JkO7ph/xyYXVTVkelu0zQcf9pxs3oObmm2XNlpe7J9TH4M+vFFyqa7zVqy+nY22QZm3q6vqm6Ypee7rKXJMp31jKeJmetmBRbZN67Geh5PJse3RdvBLlp368BP9R9nPQ9rYrKP220773+I+MUs1XO4jlbV/peZ17mttRcleemMeSXj7GP3xEX9621nDey3raOm+6/HegPYosTN4uYJcfOeEzcvU7YnbhY3i5t3Jm7e/HHzWnyX21fVtjnD7pPu+bMtS8+kn9jdNv3RwTgz12t/4cBkv7+hcXXrnsM7SdKu5vi/t/8+wjqQoN0AVfWjSf5P//GDrbW3rrDc3KtT+yu+Jlc+Dv/2f8ng/Y3nFL+4f71Tf1XL9HwfkuUPbqv17nT3qN8/S8tjWa21S5P8Xf/xd6pq7r3xq+qAPbglxLPmnNg8q389q7U2PKi8Md2DxG+S5DeXm3B/G6WhlayjiUnQeHy6E7vLs/ODzifDH5Klk8pZgeZfpjvAHVVVv7RgfU/pX0+sqrstWHa9TNrw0XPW2xOTfM8qpz1ZP7tbN6vWB8dv7j8+tb992rQnZednXQ39Q//6qFknRFX1Q5m/De9J210LH0tydv9+3q3mTu5fdyT50JxxfrmqZq2jn0kXhF2X5O9XWKc92T7+sn89ccF/ZexpO5tsA98/Z/hvZGXPElnUG/vXe1bVLsFmVd0hyWNXOe1F9o0LW+fjyaQdPKSq1iJgm9xm68Sq2uWq/v44PfmB9A2D/sv9qyVJruxfh+cNq2r/VXXgbn4QmDWvZIR97B76ZP/6kFnnSEmelvm3W1rVegOgI26+nri5I27ec+Lmjrh5V+Lmjrh5irg5yeaPm/dkWx561nSP/rv+ev/x3a21C6ZGWfZ79eO/t//4zKqalct6Zro7Xl2W5G3L1G8sp/Svz1juwrrqTH/vvf33EdZDa023Rl26DbAl2T5j2OFJfjTdw76v7cf7UpLvnDHu8f3wHVP9X5TuYPPIJEcM+t8yycv7MtclOXqq3Ff7YU+fU+8bpzvRbOkOULfu+x+S5H+ke5D2+ct8t9Z32+ZMf9tknBnDHjso/4Ykdx4MOyJdgPDyGdP7Zl/mk+mu8DmwH1ZJ7pTu2T9nJzl+gfU3We6Xpwva/zzJLQbL6PcHdX38jPJPHgx/dZIjB8MOSXcl5p8k+cyMssuuo6l2dO1gPu+cMc7nBsO/kaTmTOsP+3GuTfKCJLcdDLtRumD11CSnTZU7MN3VVa1fD09Mcthg+K2S/HS6APfkOdvIyct8x+39OCcusO6+t2/7Lckrkty4739YuluEXD1ow6csuF3fqS93dZJ7LDPestvB7sZL9w+sb/fD3pLk9n3/GyT5X/38L5y1/PpxzuqHfTbJPQfbw0P79jUpO2sbXlXbzZx91aJddt4PvCLJTfv+N83Svq0l+ellludF6a5qPHrQTk/I0r7tVTPKzm2PWf32cXCWrvD7RpKfTfId/bD90z3n6dXTbSn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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 948 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,2,figsize=(16,6))\n", + "\n", + "hist.plot1d(output['deltaR1'].sum('dataset'), ax=ax[0], density=True)\n", + "hist.plot1d(output['deltaR2'].sum('dataset'), ax=ax[1], density=True)\n", + "\n", + "ax[0].set_title('Distance between a trigger object and the closest muon', x=0.0, ha=\"left\")\n", + "ax[1].set_title('Distance between a trigger object and the closest lepton jet', x=0.0, ha=\"left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Per-object efficiency computation (efficiency vs pt)\n", + "\n", + "##### Per-object efficiency is the probability for a LJ to contain at least 1 TO\n", + "\n", + "##### The computation is performed with tag and probe method:\n", + "\n", + "##### Tag and probe method description: \n", + "- I select all possible pairs of Mu-type LJs (same event)\n", + "- I consider all the pairs with at least a LJ (tag) containing at least a TO \n", + "- The other LJ (probe) can either contain a TO (good probe) or not (bad probe). \n", + "- Tag must be matched by a reference trigger object too\n", + "- A TO is contained in a LJ if: ΔR < 0.4\n", + "\n", + "##### Notes:\n", + "- Each LJ can be both tag and probe → roles will be inverted \n", + "- I use only tag and probe that have DeltaR > 0.4x2\n", + "- The efficiency is computed as a function of probe jet pt (later it'll be computed for probe jet eta)\n", + "\n", + "##### The probabilty for a LJ to contain at least 1 TO can be divided into 2 parts:\n", + "\n", + "- probability for a LJ to contain exaclty 1 TO\n", + "- probability for a LJ to contain at least 2 TOs\n", + "\n", + "##### I try with 2 different reference trigggers:\n", + "- HLT_Mu50\n", + "- HLT_MuIso24\n", + "\n", + "##### Here I compare per-object efficiency when using HLT_Mu50 or HLT_MuIso24 or no recerence trigger \n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "class ProcessorEfficienyVsPt(processor.ProcessorABC):\n", + " def __init__(self):\n", + " \n", + " pt_binning = np.concatenate([np.arange(0 , 100, 20),\n", + " np.arange(100, 200, 40),\n", + " np.arange(200, 400, 70),\n", + " np.arange(400, 800, 100)])\n", + "\n", + " dataset_axis = hist.Cat('dataset', '')\n", + " multiplicity_axis = hist.Bin(\"multiplicity\", \"#muons/event\", 10, 0, 10)\n", + " pt_axis = hist.Bin(\"pt\", \"probe jet pt [GeV]\", pt_binning)\n", + " deltaR_axis = hist.Bin(\"deltaR\", \"delta_R\", 30, 0 , 5)\n", + " \n", + " self._accumulator = processor.dict_accumulator({\n", + " 'multi': hist.Hist(\"Counts\", dataset_axis, multiplicity_axis),\n", + " 'numerator_Mu50_N>=1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'denominator_Mu50_N>=1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'numerator_Iso24_N>=1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'denominator_Iso24_N>=1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis), \n", + " 'numerator_NoRef_N>=1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'denominator_NoRef_N>=1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " \n", + " 'numerator_Mu50_N==1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'denominator_Mu50_N==1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'numerator_Iso24_N==1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'denominator_Iso24_N==1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis), \n", + " 'numerator_NoRef_N==1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'denominator_NoRef_N==1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " \n", + " \n", + " 'numerator_Mu50_N>=2': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'denominator_Mu50_N>=2': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'numerator_Iso24_N>=2': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'denominator_Iso24_N>=2': hist.Hist(\"Efficiency\", dataset_axis, pt_axis), \n", + " 'numerator_NoRef_N>=2': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'denominator_NoRef_N>=2': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " \n", + " 'cutflow': processor.defaultdict_accumulator(int)\n", + " })\n", + " \n", + " @property\n", + " def accumulator(self):\n", + " return self._accumulator\n", + " \n", + " def process(self, df):\n", + " output = self.accumulator.identity()\n", + " \n", + " dataset = df['dataset']\n", + "\n", + " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " df['pfjet_p4'],\n", + " muonsPerJet=df[\"pfjet_muon_n\"],\n", + " px=df['pfjet_p4.fCoordinates.fX'],\n", + " py=df['pfjet_p4.fCoordinates.fY'],\n", + " pz=df['pfjet_p4.fCoordinates.fZ'],\n", + " energy=df['pfjet_p4.fCoordinates.fT'],\n", + " ) \n", + " \n", + " triggerObjs={} \n", + " for t in TriggerList:\n", + " triggerObjs[t] = JaggedCandidateArray.candidatesfromcounts(\n", + " df['TO' + t],\n", + " px=df['TO' + t +'.fCoordinates.fX'],\n", + " py=df['TO' + t +'.fCoordinates.fY'],\n", + " pz=df['TO' + t +'.fCoordinates.fZ'],\n", + " energy=df['TO' + t +'.fCoordinates.fT'],\n", + " ) \n", + " \n", + " # reference triggers HLT_Mu50 and HLT_IsoMu24\n", + " \n", + " Rtrigger50Objs = JaggedCandidateArray.candidatesfromcounts(\n", + " df['TOHLT_Mu50'],\n", + " px=df['TOHLT_Mu50.fCoordinates.fX'],\n", + " py=df['TOHLT_Mu50.fCoordinates.fY'],\n", + " pz=df['TOHLT_Mu50.fCoordinates.fZ'],\n", + " energy=df['TOHLT_Mu50.fCoordinates.fT'],\n", + " ) \n", + " RtriggerIso24Objs = JaggedCandidateArray.candidatesfromcounts(\n", + " df['TOHLT_IsoMu24'],\n", + " px=df['TOHLT_IsoMu24.fCoordinates.fX'],\n", + " py=df['TOHLT_IsoMu24.fCoordinates.fY'],\n", + " pz=df['TOHLT_IsoMu24.fCoordinates.fZ'],\n", + " energy=df['TOHLT_IsoMu24.fCoordinates.fT'],\n", + " ) \n", + " \n", + " twoljs = leptonjets.counts >=2\n", + " \n", + " diljs = leptonjets[ twoljs ]\n", + " \n", + " triggerObjs_ = {}\n", + " for t in TriggerList:\n", + " triggerObjs_[t] = triggerObjs[t][twoljs]\n", + " \n", + " Rtrigger50Objs_ = Rtrigger50Objs[twoljs]\n", + " RtriggerIso24Objs_ = RtriggerIso24Objs[twoljs]\n", + " \n", + " if diljs.size == 0:\n", + " \n", + " return output\n", + " \n", + " for t in TriggerList: \n", + " if triggerObjs_[t].size == 0:\n", + " \n", + " return output\n", + " \n", + " def numTOsPerLJ(triggerObjs, leptonJets, dR = 0.4):\n", + " combs = leptonJets.p4.cross(triggerObjs.p4, nested=True)\n", + " deltaRs = combs.i0.delta_r(combs.i1)\n", + " deltaRMask = deltaRs < dR\n", + " return deltaRMask.sum()\n", + " \n", + " def numTOsPerLJ_mask(triggerObjs_, leptonJets, dR = 0.4, bool_cond_string = 'N>=1'):\n", + " \n", + " Nmasks = {}\n", + " for t in TriggerList:\n", + " combs = leptonJets.p4.cross(triggerObjs_[t].p4, nested=True)\n", + " deltaRs = combs.i0.delta_r(combs.i1)\n", + " deltaRMask = deltaRs < dR\n", + " N = deltaRMask.sum() \n", + " Nmasks[t] = eval(bool_cond_string) \n", + " \n", + " trgmaskOR_content = np.logical_or.reduce([Nmasks[t].content for t in TriggerList])\n", + " trgmaskOR = JaggedArray.fromcounts(diljs.counts, trgmaskOR_content)\n", + " \n", + " return trgmaskOR\n", + " \n", + " NO_Rtrmask = numTOsPerLJ(Rtrigger50Objs_, diljs, dR = 0.4) >=0\n", + " Rtrg50mask = numTOsPerLJ(Rtrigger50Objs_, diljs, dR = 0.4) >= 1\n", + " RtrgIso24mask = numTOsPerLJ(RtriggerIso24Objs_, diljs, dR = 0.4) >= 1\n", + " \n", + " \n", + " # add cut: tag and probe not too much close to each other\n", + " noClose_LJ = diljs.distincts().i0.p4.delta_r(diljs.distincts().i1.p4) > 0.8\n", + " \n", + " muonTypeLJ = diljs.muonsPerJet >=1\n", + " \n", + " \n", + " trgmask_atLeastOne = numTOsPerLJ_mask(triggerObjs_, diljs, dR = 0.4, bool_cond_string = 'N>=1')\n", + " \n", + " for bool_cond in ['N>=1','N==1','N>=2']:\n", + " \n", + " \n", + " trgmask = numTOsPerLJ_mask(triggerObjs_, diljs, dR = 0.4, bool_cond_string = bool_cond)\n", + " \n", + " \n", + " \n", + " \n", + " ###### NO reference trigger ######\n", + " \n", + " Rtrgmask = NO_Rtrmask\n", + " \n", + " # i0 = tag and i1 = probe \n", + " denominatorA = diljs.distincts().i1[trgmask_atLeastOne.distincts().i0 &\n", + " Rtrgmask.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorA = diljs.distincts().i1[trgmask.distincts().i1 &\n", + " trgmask_atLeastOne.distincts().i0 & \n", + " Rtrgmask.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + "\n", + " # i1 = tag and i0 = probe \n", + " denominatorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " Rtrgmask.distincts().i1 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " trgmask.distincts().i0 & \n", + " Rtrgmask.distincts().i1 & \n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " \n", + " \n", + " # appendinding the two parts\n", + " denominator = np.append(denominatorA.pt.flatten().tolist(), denominatorB.pt.flatten().tolist())\n", + " numerator = np.append(numeratorA.pt.flatten().tolist(), numeratorB.pt.flatten().tolist()) \n", + " # filling\n", + " output['denominator_NoRef_'+ bool_cond].fill(dataset=dataset, pt=denominator) \n", + " output['numerator_NoRef_' + bool_cond].fill(dataset=dataset, pt=numerator) \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " ###### Mu50 reference trigger ######\n", + " \n", + " Rtrgmask = Rtrg50mask\n", + " \n", + " # i0 = tag and i1 = probe \n", + " denominatorA = diljs.distincts().i1[trgmask_atLeastOne.distincts().i0 &\n", + " Rtrgmask.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorA = diljs.distincts().i1[trgmask.distincts().i1 &\n", + " trgmask_atLeastOne.distincts().i0 & \n", + " Rtrgmask.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + "\n", + " # i1 = tag and i0 = probe \n", + " denominatorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " Rtrgmask.distincts().i1 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " trgmask.distincts().i0 & \n", + " Rtrgmask.distincts().i1 & \n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " \n", + " \n", + " # appendinding the two parts\n", + " denominatorMu50 = np.append(denominatorA.pt.flatten().tolist(), denominatorB.pt.flatten().tolist())\n", + " numeratorMu50 = np.append(numeratorA.pt.flatten().tolist(), numeratorB.pt.flatten().tolist()) \n", + " # filling\n", + " output['denominator_Mu50_'+ bool_cond].fill(dataset=dataset, pt=denominatorMu50) \n", + " output['numerator_Mu50_' + bool_cond].fill(dataset=dataset, pt=numeratorMu50) \n", + " \n", + " \n", + " \n", + " \n", + " ####### Iso24 reference trigger ######\n", + "\n", + " Rtrgmask = RtrgIso24mask\n", + " \n", + " # i0 = tag and i1 = probe \n", + " denominatorA = diljs.distincts().i1[trgmask_atLeastOne.distincts().i0 &\n", + " Rtrgmask.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorA = diljs.distincts().i1[trgmask.distincts().i1 &\n", + " trgmask_atLeastOne.distincts().i0 & \n", + " Rtrgmask.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + "\n", + " # i1 = tag and i0 = probe \n", + " denominatorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " Rtrgmask.distincts().i1 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " trgmask.distincts().i0 & \n", + " Rtrgmask.distincts().i1 & \n", + " noClose_LJ &\n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " \n", + " \n", + " # appendinding the two parts\n", + " denominatorIso24 = np.append(denominatorA.pt.flatten().tolist(), denominatorB.pt.flatten().tolist())\n", + " numeratorIso24 = np.append(numeratorA.pt.flatten().tolist(), numeratorB.pt.flatten().tolist()) \n", + " # filling\n", + " output['denominator_Iso24_' + bool_cond].fill(dataset=dataset, pt=denominatorIso24) \n", + " output['numerator_Iso24_' + bool_cond].fill(dataset=dataset, pt=numeratorIso24) \n", + " \n", + "\n", + " return output\n", + " \n", + " \n", + " def postprocess(self, accumulator):\n", + " return accumulator" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Preprocessing: 100%|██████████| 4/4 [00:00<00:00, 2578.33it/s]\n", + "Processing: 100%|██████████| 1672/1672 [16:49<00:00, 1.66items/s]\n" + ] + } + ], + "source": [ + "output = processor.run_uproot_job(datasets,\n", + " treename=None,\n", + " processor_instance=ProcessorEfficienyVsPt(),\n", + " executor=processor.futures_executor,\n", + " executor_args=dict(workers=12, flatten=True),\n", + " chunksize=500000,\n", + " #maxchunks=5\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison between with and without reference trigger (vs Pt)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 497 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8,6))\n", + "\n", + "numerator_Mu50_N1_ = output['numerator_Mu50_N>=1'].project('dataset')\n", + "denominator_Mu50_N1_ = output['denominator_Mu50_N>=1'].project('dataset')\n", + "\n", + "numerator_Iso24_N1_ = output['numerator_Iso24_N>=1'].project('dataset')\n", + "denominator_Iso24_N1_ = output['denominator_Iso24_N>=1'].project('dataset')\n", + "\n", + "numerator_NoRef_N1_ = output['numerator_NoRef_N>=1'].project('dataset')\n", + "denominator_NoRef_N1_ = output['denominator_NoRef_N>=1'].project('dataset')\n", + "\n", + "\n", + "fig, ax, _ = hist.plotratio(num=numerator_NoRef_N1_,\n", + " denom=denominator_NoRef_N1_,\n", + " clear = False,\n", + " error_opts={'marker': 'x'},\n", + " unc='clopper-pearson',\n", + " ax=ax)\n", + "\n", + "fig, ax, _ = hist.plotratio(num=numerator_Mu50_N1_,\n", + " denom=denominator_Mu50_N1_,\n", + " clear = False,\n", + " error_opts={'marker': '+'},\n", + " unc='clopper-pearson',\n", + " ax=ax)\n", + "\n", + "fig, ax, _ = hist.plotratio(num=numerator_Iso24_N1_,\n", + " denom=denominator_Iso24_N1_,\n", + " clear = False,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax)\n", + "\n", + "ax.set_title('[SingleMuon2018] per-object efficiency', x=0.0, ha=\"left\")\n", + "\n", + "ax.text(45, 0.4, '\\n Blue: HLT_MuIso24 \\n Orange: HLT_Mu50 \\n Green: No reference trigger');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Probability for 1 or 2+ TOs (vs Pt)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 971 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,2,figsize=(16,6))\n", + "\n", + "####### Mu50 reference trigger ######\n", + "\n", + "numerator_Mu50_N1_ = output['numerator_Mu50_N>=1'].project('dataset')\n", + "denominator_Mu50_N1_ = output['denominator_Mu50_N>=1'].project('dataset')\n", + "\n", + "numerator_Mu50_N1 = output['numerator_Mu50_N==1'].project('dataset')\n", + "denominator_Mu50_N1 = output['denominator_Mu50_N==1'].project('dataset')\n", + "\n", + "numerator_Mu50_N2_ = output['numerator_Mu50_N>=2'].project('dataset')\n", + "denominator_Mu50_N2_ = output['denominator_Mu50_N>=2'].project('dataset')\n", + "\n", + "fig, ax[0], _ = hist.plotratio(num=numerator_Mu50_N1_,\n", + " denom=denominator_Mu50_N1_,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax[0])\n", + "\n", + "fig, ax[0], _ = hist.plotratio(num=numerator_Mu50_N1,\n", + " denom=denominator_Mu50_N1,\n", + " clear = False,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax[0])\n", + "\n", + "fig, ax[0], _ = hist.plotratio(num=numerator_Mu50_N2_,\n", + " denom=denominator_Mu50_N2_,\n", + " clear = False,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax[0])\n", + "\n", + "####### Iso24 reference trigger ######\n", + "\n", + "numerator_Iso24_N1_ = output['numerator_Iso24_N>=1'].project('dataset')\n", + "denominator_Iso24_N1_ = output['denominator_Iso24_N>=1'].project('dataset')\n", + "\n", + "numerator_Iso24_N1 = output['numerator_Iso24_N==1'].project('dataset')\n", + "denominator_Iso24_N1 = output['denominator_Iso24_N==1'].project('dataset')\n", + "\n", + "numerator_Iso24_N2_ = output['numerator_Iso24_N>=2'].project('dataset')\n", + "denominator_Iso24_N2_ = output['denominator_Iso24_N>=2'].project('dataset')\n", + "\n", + "fig, ax[1], _ = hist.plotratio(num=numerator_Iso24_N1_,\n", + " denom=denominator_Iso24_N1_,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax[1])\n", + "\n", + "fig, ax[1], _ = hist.plotratio(num=numerator_Iso24_N1,\n", + " denom=denominator_Iso24_N1,\n", + " clear = False,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax[1])\n", + "\n", + "fig, ax[1], _ = hist.plotratio(num=numerator_Iso24_N2_,\n", + " denom=denominator_Iso24_N2_,\n", + " clear = False,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax[1])\n", + "\n", + "ax[0].set_title('[SingleMuon2018] per-object efficiency, reference trgger: HLT_Mu50 ', x=0.0, ha=\"left\")\n", + "ax[1].set_title('[SingleMuon2018] per-object efficiency, reference trgger: HLT_MuIso24 ', x=0.0, ha=\"left\");\n", + " \n", + "ax[0].text(45, 0.4, '\\n Blue: $\\geq $ 1 TO matched \\n Orange: 1 TO matched \\n Green: $\\geq 2$ TO matched ');\n", + "ax[1].text(45, 0.4, '\\n Blue: $\\geq $ 1 TO matched \\n Orange: 1 TO matched \\n Green: $\\geq 2$ TO matched ');" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "class ProcessorEfficienyVsEta(processor.ProcessorABC):\n", + " def __init__(self):\n", + "\n", + " dataset_axis = hist.Cat('dataset', '')\n", + " multiplicity_axis = hist.Bin(\"multiplicity\", \"#muons/event\", 10, 0, 10)\n", + " eta_axis = hist.Bin(\"eta\", \"probe jet eta [rad]\", 30, -3.5 , 3.5)\n", + " deltaR_axis = hist.Bin(\"deltaR\", \"delta_R\", 30, 0 , 5)\n", + " \n", + " self._accumulator = processor.dict_accumulator({\n", + " 'multi': hist.Hist(\"Counts\", dataset_axis, multiplicity_axis),\n", + " 'numerator_Mu50_N>=1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'denominator_Mu50_N>=1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'numerator_Iso24_N>=1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'denominator_Iso24_N>=1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis), \n", + " 'numerator_NoRef_N>=1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'denominator_NoRef_N>=1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " \n", + " 'numerator_Mu50_N==1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'denominator_Mu50_N==1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'numerator_Iso24_N==1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'denominator_Iso24_N==1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis), \n", + " 'numerator_NoRef_N==1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'denominator_NoRef_N==1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " \n", + " \n", + " 'numerator_Mu50_N>=2': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'denominator_Mu50_N>=2': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'numerator_Iso24_N>=2': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'denominator_Iso24_N>=2': hist.Hist(\"Efficiency\", dataset_axis, eta_axis), \n", + " 'numerator_NoRef_N>=2': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'denominator_NoRef_N>=2': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " \n", + " 'cutflow': processor.defaultdict_accumulator(int)\n", + " })\n", + " \n", + " @property\n", + " def accumulator(self):\n", + " return self._accumulator\n", + " \n", + " def process(self, df):\n", + " output = self.accumulator.identity()\n", + " \n", + " dataset = df['dataset']\n", + "\n", + " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " df['pfjet_p4'],\n", + " muonsPerJet=df[\"pfjet_muon_n\"],\n", + " px=df['pfjet_p4.fCoordinates.fX'],\n", + " py=df['pfjet_p4.fCoordinates.fY'],\n", + " pz=df['pfjet_p4.fCoordinates.fZ'],\n", + " energy=df['pfjet_p4.fCoordinates.fT'],\n", + " ) \n", + " \n", + " triggerObjs={} \n", + " for t in TriggerList:\n", + " triggerObjs[t] = JaggedCandidateArray.candidatesfromcounts(\n", + " df['TO' + t],\n", + " px=df['TO' + t +'.fCoordinates.fX'],\n", + " py=df['TO' + t +'.fCoordinates.fY'],\n", + " pz=df['TO' + t +'.fCoordinates.fZ'],\n", + " energy=df['TO' + t +'.fCoordinates.fT'],\n", + " ) \n", + " \n", + " # reference triggers HLT_Mu50 and HLT_IsoMu24\n", + " \n", + " Rtrigger50Objs = JaggedCandidateArray.candidatesfromcounts(\n", + " df['TOHLT_Mu50'],\n", + " px=df['TOHLT_Mu50.fCoordinates.fX'],\n", + " py=df['TOHLT_Mu50.fCoordinates.fY'],\n", + " pz=df['TOHLT_Mu50.fCoordinates.fZ'],\n", + " energy=df['TOHLT_Mu50.fCoordinates.fT'],\n", + " ) \n", + " RtriggerIso24Objs = JaggedCandidateArray.candidatesfromcounts(\n", + " df['TOHLT_IsoMu24'],\n", + " px=df['TOHLT_IsoMu24.fCoordinates.fX'],\n", + " py=df['TOHLT_IsoMu24.fCoordinates.fY'],\n", + " pz=df['TOHLT_IsoMu24.fCoordinates.fZ'],\n", + " energy=df['TOHLT_IsoMu24.fCoordinates.fT'],\n", + " ) \n", + " \n", + " twoljs = leptonjets.counts >=2\n", + " \n", + " diljs = leptonjets[ twoljs ]\n", + " \n", + " triggerObjs_ = {}\n", + " for t in TriggerList:\n", + " triggerObjs_[t] = triggerObjs[t][twoljs]\n", + " \n", + " Rtrigger50Objs_ = Rtrigger50Objs[twoljs]\n", + " RtriggerIso24Objs_ = RtriggerIso24Objs[twoljs]\n", + " \n", + " if diljs.size == 0:\n", + " \n", + " return output\n", + " \n", + " #if triggerObjs_.size == 0:\n", + " \n", + " # return output\n", + " \n", + " for t in TriggerList: \n", + " if triggerObjs_[t].size == 0:\n", + " \n", + " return output\n", + " \n", + " def numTOsPerLJ(triggerObjs, leptonJets, dR = 0.4):\n", + " combs = leptonJets.p4.cross(triggerObjs.p4, nested=True)\n", + " deltaRs = combs.i0.delta_r(combs.i1)\n", + " deltaRMask = deltaRs < dR\n", + " return deltaRMask.sum()\n", + " \n", + " def numTOsPerLJ_mask(triggerObjs_, leptonJets, dR = 0.4, bool_cond_string = 'N>=1'):\n", + " \n", + " Nmasks = {}\n", + " for t in TriggerList:\n", + " combs = leptonJets.p4.cross(triggerObjs_[t].p4, nested=True)\n", + " deltaRs = combs.i0.delta_r(combs.i1)\n", + " deltaRMask = deltaRs < dR\n", + " N = deltaRMask.sum() \n", + " Nmasks[t] = eval(bool_cond_string) \n", + " \n", + " trgmaskOR_content = np.logical_or.reduce([Nmasks[t].content for t in TriggerList])\n", + " trgmaskOR = JaggedArray.fromcounts(diljs.counts, trgmaskOR_content)\n", + " \n", + " return trgmaskOR\n", + " \n", + " NO_Rtrmask = numTOsPerLJ(Rtrigger50Objs_, diljs, dR = 0.4) >=0\n", + " Rtrg50mask = numTOsPerLJ(Rtrigger50Objs_, diljs, dR = 0.4) >= 1\n", + " RtrgIso24mask = numTOsPerLJ(RtriggerIso24Objs_, diljs, dR = 0.4) >= 1\n", + " \n", + " \n", + " # add cut: tag and probe not too much close to each other\n", + " noClose_LJ = diljs.distincts().i0.p4.delta_r(diljs.distincts().i1.p4) > 0.8\n", + " \n", + " muonTypeLJ = diljs.muonsPerJet >=1\n", + " \n", + " \n", + " trgmask_atLeastOne = numTOsPerLJ_mask(triggerObjs_, diljs, dR = 0.4, bool_cond_string = 'N>=1')\n", + " \n", + " for bool_cond in ['N>=1','N==1','N>=2']:\n", + " \n", + " \n", + " trgmask = numTOsPerLJ_mask(triggerObjs_, diljs, dR = 0.4, bool_cond_string = bool_cond)\n", + " \n", + " \n", + " \n", + " \n", + " ###### NO reference trigger ######\n", + " \n", + " Rtrgmask = NO_Rtrmask\n", + " \n", + " # i0 = tag and i1 = probe \n", + " denominatorA = diljs.distincts().i1[trgmask_atLeastOne.distincts().i0 &\n", + " Rtrgmask.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorA = diljs.distincts().i1[trgmask.distincts().i1 &\n", + " trgmask_atLeastOne.distincts().i0 & \n", + " Rtrgmask.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + "\n", + " # i1 = tag and i0 = probe \n", + " denominatorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " Rtrgmask.distincts().i1 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " trgmask.distincts().i0 & \n", + " Rtrgmask.distincts().i1 & \n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " \n", + " \n", + " # appendinding the two parts\n", + " denominator = np.append(denominatorA.eta.flatten().tolist(), denominatorB.eta.flatten().tolist())\n", + " numerator = np.append(numeratorA.eta.flatten().tolist(), numeratorB.eta.flatten().tolist()) \n", + " # filling\n", + " output['denominator_NoRef_'+ bool_cond].fill(dataset=dataset, eta=denominator) \n", + " output['numerator_NoRef_' + bool_cond].fill(dataset=dataset, eta=numerator) \n", + " \n", + " \n", + " ###### Mu50 reference trigger ######\n", + " \n", + " Rtrgmask = Rtrg50mask\n", + " \n", + " # i0 = tag and i1 = probe \n", + " denominatorA = diljs.distincts().i1[trgmask_atLeastOne.distincts().i0 &\n", + " Rtrgmask.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorA = diljs.distincts().i1[trgmask.distincts().i1 &\n", + " trgmask_atLeastOne.distincts().i0 & \n", + " Rtrgmask.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + "\n", + " # i1 = tag and i0 = probe \n", + " denominatorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " Rtrgmask.distincts().i1 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " trgmask.distincts().i0 & \n", + " Rtrgmask.distincts().i1 & \n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " \n", + " \n", + " # appendinding the two parts\n", + " denominatorMu50 = np.append(denominatorA.eta.flatten().tolist(), denominatorB.eta.flatten().tolist())\n", + " numeratorMu50 = np.append(numeratorA.eta.flatten().tolist(), numeratorB.eta.flatten().tolist()) \n", + " # filling\n", + " output['denominator_Mu50_'+ bool_cond].fill(dataset=dataset, eta=denominatorMu50) \n", + " output['numerator_Mu50_' + bool_cond].fill(dataset=dataset, eta=numeratorMu50) \n", + " \n", + " \n", + " \n", + " \n", + " ####### Iso24 reference trigger ######\n", + "\n", + " Rtrgmask = RtrgIso24mask\n", + " \n", + "\n", + " \n", + " \n", + " # i0 = tag and i1 = probe\n", + " denominatorA = diljs.distincts().i1[trgmask_atLeastOne.distincts().i0 &\n", + " Rtrgmask.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorA = diljs.distincts().i1[trgmask.distincts().i1 &\n", + " trgmask_atLeastOne.distincts().i0 & \n", + " Rtrgmask.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + "\n", + " # i1 = tag and i0 = probe \n", + " denominatorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " Rtrgmask.distincts().i1 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " trgmask.distincts().i0 & \n", + " Rtrgmask.distincts().i1 & \n", + " noClose_LJ &\n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " \n", + " \n", + " # appendinding the two parts\n", + " denominatorIso24 = np.append(denominatorA.eta.flatten().tolist(), denominatorB.eta.flatten().tolist())\n", + " numeratorIso24 = np.append(numeratorA.eta.flatten().tolist(), numeratorB.eta.flatten().tolist()) \n", + " # filling\n", + " output['denominator_Iso24_' + bool_cond].fill(dataset=dataset, eta=denominatorIso24) \n", + " output['numerator_Iso24_' + bool_cond].fill(dataset=dataset, eta=numeratorIso24) \n", + " \n", + "\n", + " return output\n", + " \n", + " \n", + " def postprocess(self, accumulator):\n", + " return accumulator" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Preprocessing: 100%|██████████| 4/4 [00:00<00:00, 3190.19it/s]\n", + "Processing: 100%|██████████| 1672/1672 [16:57<00:00, 1.64items/s]\n" + ] + } + ], + "source": [ + "output = processor.run_uproot_job(datasets,\n", + " treename=None,\n", + " processor_instance=ProcessorEfficienyVsEta(),\n", + " executor=processor.futures_executor,\n", + " executor_args=dict(workers=12, flatten=True),\n", + " chunksize=500000,\n", + " #maxchunks=100\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 971 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,2,figsize=(16,6))\n", + "\n", + "####### Mu50 reference trigger ######\n", + "\n", + "\n", + "numerator_Mu50_N1_ = output['numerator_Mu50_N>=1'].project('dataset')\n", + "denominator_Mu50_N1_ = output['denominator_Mu50_N>=1'].project('dataset')\n", + "\n", + "\n", + "numerator_Mu50_N1 = output['numerator_Mu50_N==1'].project('dataset')\n", + "denominator_Mu50_N1 = output['denominator_Mu50_N==1'].project('dataset')\n", + "\n", + "\n", + "numerator_Mu50_N2_ = output['numerator_Mu50_N>=2'].project('dataset')\n", + "denominator_Mu50_N2_ = output['denominator_Mu50_N>=2'].project('dataset')\n", + "\n", + "\n", + "fig, ax[0], _ = hist.plotratio(num=numerator_Mu50_N1_,\n", + " denom=denominator_Mu50_N1_,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax[0])\n", + "\n", + "\n", + "fig, ax[0], _ = hist.plotratio(num=numerator_Mu50_N1,\n", + " denom=denominator_Mu50_N1,\n", + " clear = False,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax[0])\n", + "\n", + "\n", + "fig, ax[0], _ = hist.plotratio(num=numerator_Mu50_N2_,\n", + " denom=denominator_Mu50_N2_,\n", + " clear = False,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax[0])\n", + "\n", + "\n", + "\n", + "\n", + "####### Iso24 reference trigger ######\n", + "\n", + "\n", + "numerator_Iso24_N1_ = output['numerator_Iso24_N>=1'].project('dataset')\n", + "denominator_Iso24_N1_ = output['denominator_Iso24_N>=1'].project('dataset')\n", + "\n", + "\n", + "numerator_Iso24_N1 = output['numerator_Iso24_N==1'].project('dataset')\n", + "denominator_Iso24_N1 = output['denominator_Iso24_N==1'].project('dataset')\n", + "\n", + "\n", + "numerator_Iso24_N2_ = output['numerator_Iso24_N>=2'].project('dataset')\n", + "denominator_Iso24_N2_ = output['denominator_Iso24_N>=2'].project('dataset')\n", + "\n", + "\n", + "\n", + "fig, ax[1], _ = hist.plotratio(num=numerator_Iso24_N1_,\n", + " denom=denominator_Iso24_N1_,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax[1])\n", + "\n", + "\n", + "fig, ax[1], _ = hist.plotratio(num=numerator_Iso24_N1,\n", + " denom=denominator_Iso24_N1,\n", + " clear = False,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax[1])\n", + "\n", + "\n", + "fig, ax[1], _ = hist.plotratio(num=numerator_Iso24_N2_,\n", + " denom=denominator_Iso24_N2_,\n", + " clear = False,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax[1])\n", + "\n", + "\n", + "\n", + "ax[0].set_title('[SingleMuon2018] per-object efficiency, reference trgger: HLT_Mu50 ', x=0.0, ha=\"left\")\n", + "ax[1].set_title('[SingleMuon2018] per-object efficiency, reference trgger: HLT_MuIso24 ', x=0.0, ha=\"left\");\n", + " \n", + "ax[0].text(0, 0.4, '\\n Blue: $\\geq $ 1 TO matched \\n Orange: 1 TO matched \\n Green: $\\geq 2$ TO matched ');\n", + "ax[1].text(0, 0.4, '\\n Blue: $\\geq $ 1 TO matched \\n Orange: 1 TO matched \\n Green: $\\geq 2$ TO matched ');\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example of computing per-event efficiency (combining per-object efficeincy) \n", + "##### Per-event efficiency is the probability for an event with 2 LJs to contain 2 or more TOs\n", + "##### Per-event efficiency can be computed in 2 ways:\n", + "- Analythical method: just using probability reasonings: eff = P(1)P(1) + P(2+)P(2+) + 2P(0)P(2+) + 2P(1)P(2+)\n", + "- Montecarlo method: simulating how many TO are contained in a LJ and summing the number of TOs in 2 LJs\n", + "##### The Montecarlo method is a convinient way for computing per-event efficiency for events with more than 2 LJs. \n", + "##### Here it is an example of the Montecarlo method for events with N LJs. But this is just a prototype (this part of code does not use variables from the above code)\n", + "##### This method should be improved to used binned histogram values, instead of plateau values" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.76\n" + ] + } + ], + "source": [ + "import random\n", + "import numpy as np\n", + "\n", + "# defining per-object efficeincy: \n", + "#\n", + "# obj_eff[0] -> probability that 1 LJ contains 0 TO (inefficiency = 1 - efficiency) \n", + "# obj_eff[1] -> probability that 1 LJ contains 1 TO \n", + "# obj_eff[2] -> probability that 1 LJ contains 2 TO (or more)\n", + "# the list can be expanded to any number of TOs\n", + "\n", + "obj_eff = [0.13, 0.80, 0.13]\n", + "# NOTES: - one can use the efficiency distribution instead of using a flat distribution\n", + "# - the overall efficiency is the sum of all elements excluding the first one (ie, inefficiency)\n", + "# - the sum of all elements (including the inefficiency) must be equal to 1\n", + "\n", + " \n", + "def TOs_in_oneLJ():\n", + " rnd = random.random()\n", + " if 0 < rnd < obj_eff[0]:\n", + " return 0\n", + " if obj_eff[0] < rnd < obj_eff[0] + obj_eff[1]:\n", + " return 1\n", + " if obj_eff[0] + obj_eff[1] < rnd < obj_eff[0] + obj_eff[1] + obj_eff[2]:\n", + " return 2\n", + " \n", + "def TOs_in_N_LJs(N=2):\n", + " TOs_in_oneLJ_array = np.array([TOs_in_oneLJ() for i in range(N)])\n", + " return np.sum(TOs_in_oneLJ_array) \n", + " \n", + "\n", + "# computing per-event efficeincy (requiring at least 2 TOs in the event with N LJs)\n", + "TOs_in_N_LJs_array = np.array([TOs_in_N_LJs(N=2) for i in range( int(100) )])\n", + "per_event_eff = np.size(TOs_in_N_LJs_array[TOs_in_N_LJs_array >= 2])/np.size(TOs_in_N_LJs_array) \n", + "\n", + "\n", + "print(per_event_eff)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Notebooks/Data/Samples/generate_singleMu.py b/Notebooks/Data/Samples/generate_singleMu.py new file mode 100644 index 0000000..56c14bb --- /dev/null +++ b/Notebooks/Data/Samples/generate_singleMu.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +"""generate data sample list, until proper sample management tool show up. +""" +import json +import concurrent.futures +import uproot +from FireHydrant.Tools.commonhelpers import eosls, eosfindfile + +# This is control region events. +EOSPATHS = dict( + A="/store/user/pimeloni/ffNtuple4trigger/2018/SingleMuon/Run2018A-17Sep2018-v2/190903_170031", + B="/store/user/pimeloni/ffNtuple4trigger/2018/SingleMuon/Run2018B-17Sep2018-v1/190903_170117", + C="/store/user/pimeloni/ffNtuple4trigger/2018/SingleMuon/Run2018C-17Sep2018-v1/190903_170204", + D="/store/user/pimeloni/ffNtuple4trigger/2018/SingleMuon/Run2018D-22Jan2019-v2/190903_170249", +) +REDIRECTOR = "root://cmseos.fnal.gov/" + +if __name__ == "__main__": + + datasets = {k: eosfindfile(v) for k, v in EOSPATHS.items()}#json.load(open("trigger_data.json")) + with open("trigger_data.json", "w") as outf: + outf.write(json.dumps(datasets, indent=4)) + + + diff --git a/Notebooks/Data/Samples/generate_singleMu_nonempty.py b/Notebooks/Data/Samples/generate_singleMu_nonempty.py new file mode 100644 index 0000000..ea2c1d9 --- /dev/null +++ b/Notebooks/Data/Samples/generate_singleMu_nonempty.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python +"""generate data sample list, until proper sample management tool show up. +""" +import json +import concurrent.futures +import uproot +from FireHydrant.Tools.commonhelpers import eosls, eosfindfile + + + + + +# This is control region events with L1 triggers and no pt cut +''' +EOSPATHS = dict( + A="store/user/pimeloni/SIDM/ffNtuple4trigger/2018/SingleMuon/Run2018A-17Sep2018-v2/190903_170031", + B="store/user/pimeloni/SIDM/ffNtuple4trigger/2018/SingleMuon/Run2018B-17Sep2018-v1/190903_170117", + C="store/user/pimeloni/SIDM/ffNtuple4trigger/2018/SingleMuon/Run2018C-17Sep2018-v1/190903_170204", + D="store/user/pimeloni/SIDM/ffNtuple4trigger/2018/SingleMuon/Run2018D-22Jan2019-v2/190903_170249", +) +REDIRECTOR = "root://cmseos.fnal.gov/" +''' + + + + + +# This is control region events. + +EOSPATHS = dict( + A="/store/user/pimeloni/ffNtuple4trigger/2018/SingleMuon/Run2018A-17Sep2018-v2/190903_170031", + B="/store/user/pimeloni/ffNtuple4trigger/2018/SingleMuon/Run2018B-17Sep2018-v1/190903_170117", + C="/store/user/pimeloni/ffNtuple4trigger/2018/SingleMuon/Run2018C-17Sep2018-v1/190903_170204", + D="/store/user/pimeloni/ffNtuple4trigger/2018/SingleMuon/Run2018D-22Jan2019-v2/190903_170249", +) +REDIRECTOR = "root://cmseos.fnal.gov/" + + +def remove_empty_file(filepath): + """given a file, if the tree has non-zero number of events, return filepath""" + f_ = uproot.open(filepath) + key_ = f_.allkeys(filtername=lambda k: k.endswith(b"ffNtuple")) + if key_ and uproot.open(filepath)[key_[0]].numentries != 0: + return filepath + else: + return None + + +def remove_empty_files(filelist): + """given a list of files, return all files with a tree of non-zero number of events""" + cleanlist = [] + with concurrent.futures.ProcessPoolExecutor(max_workers=12) as executor: + futures = {executor.submit(remove_empty_file, f): f for f in filelist} + for future in concurrent.futures.as_completed(futures): + filename = futures[future] + try: + res = future.result() + if res: + cleanlist.append(res) + except Exception as e: + print(f">> Fail to get numEvents for {filename}\n{str(e)}") + return cleanlist + + + +if __name__ == "__main__": + + """parse all background files, remove empty tree files""" + datasets = {k: eosfindfile(v) for k, v in EOSPATHS.items()}#json.load(open("trigger_data.json")) + for group in datasets: + files = datasets[group] + datasets[group] = remove_empty_files(files) + with open("trigger_data_nonempty_no_pt_cut.json", "w") as outf: + outf.write(json.dumps(datasets, indent=4)) + + + diff --git a/Notebooks/Data/Samples/trigger_data.json b/Notebooks/Data/Samples/trigger_data.json new file mode 100644 index 0000000..299e6bf --- /dev/null +++ b/Notebooks/Data/Samples/trigger_data.json @@ -0,0 +1,1682 @@ +{ + "A": [ + "root://cmseos.fnal.gov//eos/uscms/store/user/pmeloni/ffNtuple4trigger/2018/SingleMuon/Run2018A-17Sep2018-v2/190903_170031/0000/ffNtuple_1.root", + 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objects from at least one of the following trigger paths:\n", + "\n", + " - \"HLT_DoubleL2Mu23NoVtx_2Cha\",\n", + " - \"HLT_DoubleL2Mu23NoVtx_2Cha_NoL2Matched\",\n", + " - \"HLT_DoubleL2Mu23NoVtx_2Cha_CosmicSeed\",\n", + " - \"HLT_DoubleL2Mu23NoVtx_2Cha_CosmicSeed_NoL2Matched\",\n", + " - \"HLT_DoubleL2Mu25NoVtx_2Cha_Eta2p4\",\n", + " - \"HLT_DoubleL2Mu25NoVtx_2Cha_CosmicSeed_Eta2p4\",\n", + "\n", + "\n", + "##### The computation is divided into 2 parts (the study of Delta R is just a test):\n", + "- computing per-object efficiency: probability for a lepton jet (LJ) to contain at least 1 trigger objects (TOs). Using tag and probe method (this part is completed)\n", + "\n", + "- computing per-event efficiency: probability for an event with 2 LJs to contain 2 or more TOs. Using either an anlythical method or a simple montecarlo simulation (this part is NOT completed)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "from coffea import hist\n", + "from coffea.analysis_objects import JaggedCandidateArray\n", + "from coffea.processor import defaultdict_accumulator\n", + "from awkward import JaggedArray\n", + "import coffea.processor as processor\n", + "\n", + "\n", + "import numpy as np\n", + "np.seterr(divide='ignore', invalid='ignore', over='ignore')\n", + "import matplotlib.pyplot as plt\n", + "#from FireHydrant.Tools.uproothelpers import NestNestObjArrayToJagged\n", + "\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "dataset4mu_ = json.load(open('../Samples/signal_4mu.json'))\n", + "\n", + "datasets={\n", + " 'mXX-1000_mA-0p25_lxy-300': dict(files=dataset4mu_['mXX-1000_mA-0p25_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-1000_mA-0p8_lxy-300': dict(files=dataset4mu_['mXX-1000_mA-0p8_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-1000_mA-1p2_lxy-300': dict(files=dataset4mu_['mXX-1000_mA-1p2_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-1000_mA-2p5_lxy-300': dict(files=dataset4mu_['mXX-1000_mA-2p5_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-1000_mA-5_lxy-300': dict(files=dataset4mu_['mXX-1000_mA-5_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-100_mA-0p25_lxy-300': dict(files=dataset4mu_['mXX-100_mA-0p25_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-100_mA-0p8_lxy-300': dict(files=dataset4mu_['mXX-100_mA-0p8_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-100_mA-1p2_lxy-300': dict(files=dataset4mu_['mXX-100_mA-1p2_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-100_mA-2p5_lxy-300': dict(files=dataset4mu_['mXX-100_mA-2p5_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-100_mA-5_lxy-300': dict(files=dataset4mu_['mXX-100_mA-5_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-150_mA-0p25_lxy-300': dict(files=dataset4mu_['mXX-150_mA-0p25_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-150_mA-0p8_lxy-300': dict(files=dataset4mu_['mXX-150_mA-0p8_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-150_mA-1p2_lxy-300': dict(files=dataset4mu_['mXX-150_mA-1p2_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-150_mA-2p5_lxy-300': dict(files=dataset4mu_['mXX-150_mA-2p5_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-150_mA-5_lxy-300': dict(files=dataset4mu_['mXX-150_mA-5_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-200_mA-0p25_lxy-300': dict(files=dataset4mu_['mXX-200_mA-0p25_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-200_mA-0p8_lxy-300': dict(files=dataset4mu_['mXX-200_mA-0p8_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-200_mA-1p2_lxy-300': dict(files=dataset4mu_['mXX-200_mA-1p2_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-200_mA-2p5_lxy-300': dict(files=dataset4mu_['mXX-200_mA-2p5_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-200_mA-5_lxy-300': dict(files=dataset4mu_['mXX-200_mA-5_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-500_mA-0p25_lxy-300': dict(files=dataset4mu_['mXX-500_mA-0p25_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-500_mA-0p8_lxy-300': dict(files=dataset4mu_['mXX-500_mA-0p8_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-500_mA-1p2_lxy-300': dict(files=dataset4mu_['mXX-500_mA-1p2_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-500_mA-2p5_lxy-300': dict(files=dataset4mu_['mXX-500_mA-2p5_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-500_mA-5_lxy-300': dict(files=dataset4mu_['mXX-500_mA-5_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-800_mA-0p25_lxy-300': dict(files=dataset4mu_['mXX-800_mA-0p25_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-800_mA-0p8_lxy-300': dict(files=dataset4mu_['mXX-800_mA-0p8_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-800_mA-1p2_lxy-300': dict(files=dataset4mu_['mXX-800_mA-1p2_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-800_mA-2p5_lxy-300': dict(files=dataset4mu_['mXX-800_mA-2p5_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-800_mA-5_lxy-300': dict(files=dataset4mu_['mXX-800_mA-5_lxy-300'], treename='ffNtuplizer/ffNtuple'),\n", + " 'mXX-1000_mA-0p25_lxy-0p3': dict(files=dataset4mu_['mXX-1000_mA-0p25_lxy-0p3'], treename='ffNtuplizer/ffNtuple')\n", + "\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "TriggerList = [ \n", + " \"HLT_DoubleL2Mu23NoVtx_2Cha\",\n", + " \"HLT_DoubleL2Mu23NoVtx_2Cha_NoL2Matched\",\n", + " \"HLT_DoubleL2Mu23NoVtx_2Cha_CosmicSeed\",\n", + " \"HLT_DoubleL2Mu23NoVtx_2Cha_CosmicSeed_NoL2Matched\",\n", + " \"HLT_DoubleL2Mu25NoVtx_2Cha_Eta2p4\",\n", + " \"HLT_DoubleL2Mu25NoVtx_2Cha_CosmicSeed_Eta2p4\",\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Study of Delta R\n", + "##### Because I had some problems at the beginning, I made this test for Delta R between TOs and LJs, using only TOHLT_DoubleL2Mu23NoVtx_2Cha.\n", + "##### The test consists in studying DeltaR between a fixed TO and the closest LJ or Muon " + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "class DeltaRProcessor(processor.ProcessorABC):\n", + " def __init__(self):\n", + " \n", + " dataset_axis = hist.Cat('dataset', '')\n", + " multiplicity_axis = hist.Bin(\"multiplicity\", \"#muons/event\", 10, 0, 10)\n", + " pt_axis = hist.Bin(\"pt\", \"jet pt [GeV]\", 20, 0 , 300)\n", + " deltaR_axis = hist.Bin(\"deltaR\", \"$\\Delta R$\", 30, 0 , 0.5)\n", + " \n", + " self._accumulator = processor.dict_accumulator({\n", + " 'deltaR1': hist.Hist(\"Counts\", dataset_axis, deltaR_axis),\n", + " 'deltaR2': hist.Hist(\"Counts\", dataset_axis, deltaR_axis),\n", + " })\n", + " \n", + " @property\n", + " def accumulator(self):\n", + " return self._accumulator\n", + " \n", + " def process(self, df):\n", + " output = self.accumulator.identity()\n", + " \n", + " dataset = df['dataset']\n", + "\n", + " muons = JaggedCandidateArray.candidatesfromcounts(\n", + " df['muon_p4'],\n", + " px=df['muon_p4.fCoordinates.fX'],\n", + " py=df['muon_p4.fCoordinates.fY'],\n", + " pz=df['muon_p4.fCoordinates.fZ'],\n", + " energy=df['muon_p4.fCoordinates.fT'], \n", + " )\n", + " \n", + " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " df['pfjet_p4'],\n", + " muonsPerJet=df[\"pfjet_muon_n\"],\n", + " px=df['pfjet_p4.fCoordinates.fX'],\n", + " py=df['pfjet_p4.fCoordinates.fY'],\n", + " pz=df['pfjet_p4.fCoordinates.fZ'],\n", + " energy=df['pfjet_p4.fCoordinates.fT'],\n", + " ) \n", + " \n", + " triggerObjs = JaggedCandidateArray.candidatesfromcounts(\n", + " df['TOHLT_DoubleL2Mu23NoVtx_2Cha'],\n", + " px=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fX'],\n", + " py=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fY'],\n", + " pz=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fZ'],\n", + " energy=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fT'],\n", + " )\n", + " \n", + " twoljs = leptonjets.counts >=2\n", + " \n", + " contains_mu = (leptonjets.muonsPerJet > 0).prod().astype(bool)\n", + " \n", + " diljs = leptonjets[twoljs & contains_mu]\n", + " triggerObjs_ = triggerObjs[ twoljs & contains_mu]\n", + " \n", + " \n", + " if diljs.size == 0:\n", + " \n", + " return output\n", + " \n", + " # with muons\n", + " Tobj_Mu_pairs = muons['p4'].cross(triggerObjs['p4'], nested=True)\n", + " dr1 = Tobj_Mu_pairs.i0.delta_r(Tobj_Mu_pairs.i1)\n", + " dr1 = dr1.min()\n", + " \n", + " output['deltaR1'].fill(dataset=dataset, deltaR=dr1.flatten().flatten()) \n", + " \n", + " # with leptonjets\n", + " Tobj_Lj_pairs = diljs['p4'].cross(triggerObjs_['p4'], nested=True)\n", + " dr2 = Tobj_Lj_pairs.i0.delta_r(Tobj_Lj_pairs.i1)\n", + " dr2 = dr2.min()\n", + " \n", + " output['deltaR2'].fill(dataset=dataset, deltaR=dr2.flatten().flatten()) \n", + "\n", + " return output\n", + " \n", + " def postprocess(self, accumulator):\n", + " return accumulator" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Preprocessing: 100%|██████████| 31/31 [00:00<00:00, 34924.37it/s]\n", + "Processing: 100%|██████████| 155/155 [00:18<00:00, 8.58items/s]\n" + ] + } + ], + "source": [ + "output = processor.run_uproot_job(datasets,\n", + " treename=None,\n", + " processor_instance=DeltaRProcessor(),\n", + " executor=processor.futures_executor,\n", + " executor_args=dict(workers=12, flatten=True),\n", + " chunksize=5000000,\n", + " #maxchunks=1\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 948 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,2,figsize=(16,6))\n", + "\n", + "hist.plot1d(output['deltaR1'].sum('dataset'), ax=ax[0], density=True)\n", + "hist.plot1d(output['deltaR2'].sum('dataset'), ax=ax[1], density=True)\n", + "\n", + "ax[0].set_title('Distance between a trigger object and the closest muon', x=0.0, ha=\"left\")\n", + "ax[1].set_title('Distance between a trigger object and the closest lepton jet', x=0.0, ha=\"left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Per-object efficiency computation (efficiency vs pt)\n", + "\n", + "##### Per-object efficiency is the probability for a LJ to contain at least 1 TO\n", + "\n", + "##### The computation is performed with tag and probe method:\n", + "\n", + "##### Tag and probe method description: \n", + "- I select all possible pairs of Mu-type LJs (same event)\n", + "- I consider all the pairs with at least a LJ (tag) containing at least a TO \n", + "- The other LJ (probe) can either contain a TO (good probe) or not (bad probe). \n", + "- I do not use reference triggers when running over MC samples\n", + "- A TO is contained in a LJ if: ΔR < 0.4\n", + "\n", + "##### Notes:\n", + "- Each LJ can be both tag and probe → roles will be inverted \n", + "- I use only tag and probe that have DeltaR > 0.4x2\n", + "- The efficiency is computed as a function of probe jet pt (later it'll be computed for probe jet eta)\n", + "\n", + "##### The probabilty for a LJ to contain at least 1 TO can be divided into 2 parts:\n", + "\n", + "- probability for a LJ to contain exaclty 1 TO\n", + "- probability for a LJ to contain at least 2 TOs\n", + "\n", + "##### These two probabilities are here computed " + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "class ProcessorEfficienyVsPt(processor.ProcessorABC):\n", + " def __init__(self):\n", + " \n", + " \n", + " pt_binning = np.concatenate([np.arange(0 , 100, 20),\n", + " np.arange(100, 200, 40),\n", + " np.arange(200, 400, 70),\n", + " np.arange(400, 800, 100)])\n", + " \n", + " dataset_axis = hist.Cat('dataset', '')\n", + " multiplicity_axis = hist.Bin(\"multiplicity\", \"#muons/event\", 10, 0, 10)\n", + " #eta_axis = hist.Bin(\"eta\", \"probe jet eta [rad]\", 80, -3.5 , 3.5)\n", + " pt_axis = hist.Bin(\"pt\", \"probe jet pt [GeV]\", pt_binning)\n", + " deltaR_axis = hist.Bin(\"deltaR\", \"delta_R\", 30, 0 , 5)\n", + " \n", + " self._accumulator = processor.dict_accumulator({\n", + " 'multi': hist.Hist(\"Counts\", dataset_axis, multiplicity_axis),\n", + " 'numerator_N>=1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'denominator_N>=1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " \n", + " 'numerator_N==1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'denominator_N==1': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " \n", + " 'numerator_N>=2': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " 'denominator_N>=2': hist.Hist(\"Efficiency\", dataset_axis, pt_axis),\n", + " \n", + " 'cutflow': processor.defaultdict_accumulator(int)\n", + " })\n", + " \n", + " @property\n", + " def accumulator(self):\n", + " return self._accumulator\n", + " \n", + " def process(self, df):\n", + " output = self.accumulator.identity()\n", + " \n", + " dataset = df['dataset']\n", + " \n", + " muons = JaggedCandidateArray.candidatesfromcounts(\n", + " df['muon_p4'],\n", + " px=df['muon_p4.fCoordinates.fX'],\n", + " py=df['muon_p4.fCoordinates.fY'],\n", + " pz=df['muon_p4.fCoordinates.fZ'],\n", + " energy=df['muon_p4.fCoordinates.fT'],\n", + " ) \n", + " \n", + " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " df['pfjet_p4'],\n", + " muonsPerJet=df[\"pfjet_muon_n\"],\n", + " px=df['pfjet_p4.fCoordinates.fX'],\n", + " py=df['pfjet_p4.fCoordinates.fY'],\n", + " pz=df['pfjet_p4.fCoordinates.fZ'],\n", + " energy=df['pfjet_p4.fCoordinates.fT'],\n", + " ) \n", + " \n", + " triggerObjs={} \n", + " for t in TriggerList:\n", + " triggerObjs[t] = JaggedCandidateArray.candidatesfromcounts(\n", + " df['TO' + t],\n", + " px=df['TO' + t +'.fCoordinates.fX'],\n", + " py=df['TO' + t +'.fCoordinates.fY'],\n", + " pz=df['TO' + t +'.fCoordinates.fZ'],\n", + " energy=df['TO' + t +'.fCoordinates.fT'],\n", + " ) \n", + "\n", + " twoljs = leptonjets.counts >=2\n", + " diljs = leptonjets[ twoljs]\n", + " \n", + " triggerObjs_ = {}\n", + " for t in TriggerList:\n", + " triggerObjs_[t] = triggerObjs[t][twoljs]\n", + " \n", + " \n", + " if diljs.size == 0:\n", + " \n", + " return output\n", + " \n", + " for t in TriggerList: \n", + " if triggerObjs_[t].size == 0:\n", + " \n", + " return output\n", + " \n", + " def numTOsPerLJ(triggerObjs, leptonJets, dR = 0.4):\n", + " combs = leptonJets.p4.cross(triggerObjs.p4, nested=True)\n", + " deltaRs = combs.i0.delta_r(combs.i1)\n", + " deltaRMask = deltaRs < dR\n", + " return deltaRMask.sum()\n", + " \n", + "\n", + " \n", + " def numTOsPerLJ_mask(triggerObjs_, leptonJets, dR = 0.4, bool_cond_string = 'N>=1'):\n", + " \n", + " Nmasks = {}\n", + " for t in TriggerList:\n", + " combs = leptonJets.p4.cross(triggerObjs_[t].p4, nested=True)\n", + " deltaRs = combs.i0.delta_r(combs.i1)\n", + " deltaRMask = deltaRs < dR\n", + " N = deltaRMask.sum() \n", + " Nmasks[t] = eval(bool_cond_string) \n", + " \n", + " trgmaskOR_content = np.logical_or.reduce([Nmasks[t].content for t in TriggerList])\n", + " trgmaskOR = JaggedArray.fromcounts(diljs.counts, trgmaskOR_content)\n", + " \n", + " return trgmaskOR\n", + "\n", + " \n", + " # add cut: tag and probe not too much close to each other\n", + " noClose_LJ = diljs.distincts().i0.p4.delta_r(diljs.distincts().i1.p4) > 0.0\n", + " \n", + " muonTypeLJ = diljs.muonsPerJet >=0\n", + " \n", + " trgmask_atLeastOne = numTOsPerLJ_mask(triggerObjs_, diljs, dR = 0.4, bool_cond_string = 'N>=1')\n", + " \n", + " for bool_cond in ['N>=1','N==1','N>=2']:\n", + " \n", + " \n", + " trgmask = numTOsPerLJ_mask(triggerObjs_, diljs, dR = 0.4, bool_cond_string = bool_cond)\n", + " \n", + " # i0 = tag and i1 = probe\n", + " denominatorA = diljs.distincts().i1[trgmask_atLeastOne.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorA = diljs.distincts().i1[trgmask.distincts().i1 &\n", + " trgmask_atLeastOne.distincts().i0 & \n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + "\n", + " # i1 = tag and i0 = probe\n", + " denominatorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " trgmask.distincts().i0 & \n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " \n", + " \n", + " # appendinding the two parts\n", + " denominator = np.append(denominatorA.pt.flatten().tolist(), denominatorB.pt.flatten().tolist())\n", + " numerator = np.append(numeratorA.pt.flatten().tolist(), numeratorB.pt.flatten().tolist()) \n", + " # filling\n", + " output['denominator_'+ bool_cond].fill(dataset=dataset, pt=denominator) \n", + " output['numerator_' + bool_cond].fill(dataset=dataset, pt=numerator) \n", + " \n", + " \n", + " \n", + " return output\n", + " \n", + " \n", + " def postprocess(self, accumulator):\n", + " return accumulator" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Preprocessing: 100%|██████████| 31/31 [00:04<00:00, 6.91it/s]\n", + "Processing: 100%|██████████| 62/62 [00:18<00:00, 3.40items/s]\n" + ] + } + ], + "source": [ + "output = processor.run_uproot_job(datasets,\n", + " treename=None,\n", + " processor_instance=ProcessorEfficienyVsPt(),\n", + " executor=processor.futures_executor,\n", + " executor_args=dict(workers=12, flatten=True),\n", + " chunksize=500000,\n", + " maxchunks=1\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 497 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8,6))\n", + "\n", + "numerator_N1_ = output['numerator_N>=1'].project('dataset')\n", + "denominator_N1_ = output['denominator_N>=1'].project('dataset')\n", + "\n", + "numerator_N1 = output['numerator_N==1'].project('dataset')\n", + "denominator_N1 = output['denominator_N==1'].project('dataset')\n", + "\n", + "numerator_N2_ = output['numerator_N>=2'].project('dataset')\n", + "denominator_N2_ = output['denominator_N>=2'].project('dataset')\n", + "\n", + "fig, ax, _ = hist.plotratio(num=numerator_N1_,\n", + " denom=denominator_N1_,\n", + " error_opts={'marker': '+'},\n", + " unc='clopper-pearson',\n", + " ax=ax,\n", + " )\n", + "\n", + "fig, ax, _ = hist.plotratio(num=numerator_N1,\n", + " denom=denominator_N1,\n", + " clear = False,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax)\n", + "\n", + "fig, ax, _ = hist.plotratio(num=numerator_N2_,\n", + " denom=denominator_N2_,\n", + " clear = False,\n", + " error_opts={'marker': 'x'},\n", + " unc='clopper-pearson',\n", + " ax=ax)\n", + "\n", + "ax.set_title('[Signal_MC] per-object efficiency vs pt', x=0.0, ha=\"left\")\n", + "ax.text(100, 0.2, '\\n Blue: $\\geq $ 1 TO matched \\n Orange: 1 TO matched \\n Green: $\\geq 2$ TO matched ');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Per-object efficiency computation (efficiency vs eta)\n", + "##### Same computation as above, but using probe jet eta " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "class ProcessorEfficienyVsEta(processor.ProcessorABC):\n", + " def __init__(self):\n", + " dataset_axis = hist.Cat('dataset', '')\n", + " multiplicity_axis = hist.Bin(\"multiplicity\", \"#muons/event\", 10, 0, 10)\n", + " eta_axis = hist.Bin(\"eta\", \"probe jet eta [rad]\", 30, -3.5 , 3.5)\n", + " deltaR_axis = hist.Bin(\"deltaR\", \"delta_R\", 30, 0 , 5)\n", + " \n", + " self._accumulator = processor.dict_accumulator({\n", + " 'multi': hist.Hist(\"Counts\", dataset_axis, multiplicity_axis),\n", + " 'numerator_N>=1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'denominator_N>=1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " \n", + " 'numerator_N==1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'denominator_N==1': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " \n", + " 'numerator_N>=2': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " 'denominator_N>=2': hist.Hist(\"Efficiency\", dataset_axis, eta_axis),\n", + " \n", + " 'cutflow': processor.defaultdict_accumulator(int)\n", + " })\n", + " \n", + " @property\n", + " def accumulator(self):\n", + " return self._accumulator\n", + " \n", + " def process(self, df):\n", + " output = self.accumulator.identity()\n", + " \n", + " dataset = df['dataset']\n", + " \n", + " muons = JaggedCandidateArray.candidatesfromcounts(\n", + " df['muon_p4'],\n", + " px=df['muon_p4.fCoordinates.fX'],\n", + " py=df['muon_p4.fCoordinates.fY'],\n", + " pz=df['muon_p4.fCoordinates.fZ'],\n", + " energy=df['muon_p4.fCoordinates.fT'],\n", + " ) \n", + " \n", + " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", + " df['pfjet_p4'],\n", + " muonsPerJet=df[\"pfjet_muon_n\"],\n", + " px=df['pfjet_p4.fCoordinates.fX'],\n", + " py=df['pfjet_p4.fCoordinates.fY'],\n", + " pz=df['pfjet_p4.fCoordinates.fZ'],\n", + " energy=df['pfjet_p4.fCoordinates.fT'],\n", + " ) \n", + " \n", + " triggerObjs={} \n", + " for t in TriggerList:\n", + " triggerObjs[t] = JaggedCandidateArray.candidatesfromcounts(\n", + " df['TO' + t],\n", + " px=df['TO' + t +'.fCoordinates.fX'],\n", + " py=df['TO' + t +'.fCoordinates.fY'],\n", + " pz=df['TO' + t +'.fCoordinates.fZ'],\n", + " energy=df['TO' + t +'.fCoordinates.fT'],\n", + " ) \n", + "\n", + " twoljs = leptonjets.counts >=2\n", + " diljs = leptonjets[ twoljs]\n", + " \n", + " triggerObjs_ = {}\n", + " for t in TriggerList:\n", + " triggerObjs_[t] = triggerObjs[t][twoljs]\n", + " \n", + " \n", + " if diljs.size == 0:\n", + " \n", + " return output\n", + " \n", + " for t in TriggerList: \n", + " if triggerObjs_[t].size == 0:\n", + " \n", + " return output\n", + " \n", + " def numTOsPerLJ(triggerObjs, leptonJets, dR = 0.4):\n", + " combs = leptonJets.p4.cross(triggerObjs.p4, nested=True)\n", + " deltaRs = combs.i0.delta_r(combs.i1)\n", + " deltaRMask = deltaRs < dR\n", + " return deltaRMask.sum()\n", + " \n", + "\n", + " \n", + " def numTOsPerLJ_mask(triggerObjs_, leptonJets, dR = 0.4, bool_cond_string = 'N>=1'):\n", + " \n", + " Nmasks = {}\n", + " for t in TriggerList:\n", + " combs = leptonJets.p4.cross(triggerObjs_[t].p4, nested=True)\n", + " deltaRs = combs.i0.delta_r(combs.i1)\n", + " deltaRMask = deltaRs < dR\n", + " N = deltaRMask.sum() \n", + " Nmasks[t] = eval(bool_cond_string) \n", + " \n", + " trgmaskOR_content = np.logical_or.reduce([Nmasks[t].content for t in TriggerList])\n", + " trgmaskOR = JaggedArray.fromcounts(diljs.counts, trgmaskOR_content)\n", + " \n", + " return trgmaskOR\n", + "\n", + " \n", + " # add cut: tag and probe not too much close to each other\n", + " noClose_LJ = diljs.distincts().i0.p4.delta_r(diljs.distincts().i1.p4) > 0.0\n", + " \n", + " muonTypeLJ = diljs.muonsPerJet >=0\n", + " \n", + " trgmask_atLeastOne = numTOsPerLJ_mask(triggerObjs_, diljs, dR = 0.4, bool_cond_string = 'N>=1')\n", + " \n", + " for bool_cond in ['N>=1','N==1','N>=2']:\n", + " \n", + " \n", + " trgmask = numTOsPerLJ_mask(triggerObjs_, diljs, dR = 0.4, bool_cond_string = bool_cond)\n", + " \n", + " # i0 = tag and i1 = probe\n", + " denominatorA = diljs.distincts().i1[trgmask_atLeastOne.distincts().i0 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorA = diljs.distincts().i1[trgmask.distincts().i1 &\n", + " trgmask_atLeastOne.distincts().i0 & \n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + "\n", + " # i1 = tag and i0 = probe \n", + " denominatorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " numeratorB = diljs.distincts().i0[trgmask_atLeastOne.distincts().i1 &\n", + " trgmask.distincts().i0 & \n", + " noClose_LJ & \n", + " muonTypeLJ.distincts().i0 &\n", + " muonTypeLJ.distincts().i1]\n", + " \n", + " \n", + " # appendinding the two parts\n", + " denominator = np.append(denominatorA.eta.flatten().tolist(), denominatorB.eta.flatten().tolist())\n", + " numerator = np.append(numeratorA.eta.flatten().tolist(), numeratorB.eta.flatten().tolist()) \n", + " # filling\n", + " output['denominator_'+ bool_cond].fill(dataset=dataset, eta=denominator) \n", + " output['numerator_' + bool_cond].fill(dataset=dataset, eta=numerator) \n", + " \n", + " \n", + " \n", + " return output\n", + " \n", + " \n", + " def postprocess(self, accumulator):\n", + " return accumulator" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Preprocessing: 100%|██████████| 31/31 [00:02<00:00, 13.29it/s]\n", + "Processing: 100%|██████████| 62/62 [00:17<00:00, 3.63items/s]\n" + ] + } + ], + "source": [ + "output = processor.run_uproot_job(datasets,\n", + " treename=None,\n", + " processor_instance=ProcessorEfficienyVsEta(),\n", + " executor=processor.futures_executor,\n", + " executor_args=dict(workers=12, flatten=True),\n", + " chunksize=500000,\n", + " maxchunks=1\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 9511 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(8,6))\n", + "\n", + "numerator_N1_ = output['numerator_N>=1'].project('dataset')\n", + "denominator_N1_ = output['denominator_N>=1'].project('dataset')\n", + "\n", + "numerator_N1 = output['numerator_N==1'].project('dataset')\n", + "denominator_N1 = output['denominator_N==1'].project('dataset')\n", + "\n", + "numerator_N2_ = output['numerator_N>=2'].project('dataset')\n", + "denominator_N2_ = output['denominator_N>=2'].project('dataset')\n", + "\n", + "fig, ax, _ = hist.plotratio(num=numerator_N1_,\n", + " denom=denominator_N1_,\n", + " error_opts={'marker': '+'},\n", + " unc='clopper-pearson',\n", + " ax=ax,\n", + " )\n", + "\n", + "fig, ax, _ = hist.plotratio(num=numerator_N1,\n", + " denom=denominator_N1,\n", + " clear = False,\n", + " error_opts={'marker': '.'},\n", + " unc='clopper-pearson',\n", + " ax=ax)\n", + "\n", + "fig, ax, _ = hist.plotratio(num=numerator_N2_,\n", + " denom=denominator_N2_,\n", + " clear = False,\n", + " error_opts={'marker': 'x'},\n", + " unc='clopper-pearson',\n", + " ax=ax)\n", + "\n", + "ax.set_title('[Signal_MC] per-object efficiency vs eta', x=0.0, ha=\"left\")\n", + "ax.text(100, 0.2, '\\n Blue: $\\geq $ 1 TO matched \\n Orange: 1 TO matched \\n Green: $\\geq 2$ TO matched ');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example of computing per-event efficiency (combining per-object efficeincy) \n", + "##### Per-event efficiency is the probability for an event with 2 LJs to contain 2 or more TOs\n", + "##### Per-event efficiency can be computed in 2 ways:\n", + "- Analythical method: just using probability reasonings: eff = P(1)P(1) + P(2+)P(2+) + 2P(0)P(2+) + 2P(1)P(2+)\n", + "- Montecarlo method: simulating how many TO are contained in a LJ and summing the number of TOs in 2 LJs\n", + "##### The Montecarlo method is a convinient way for computing per-event efficiency for events with more than 2 LJs. \n", + "##### Here it is an example of the Montecarlo method for events with N LJs. But this is just a prototype (this part of code does not use variables from the above code)\n", + "##### This method should be improved to used binned histogram values, instead of plateau values" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.72\n" + ] + } + ], + "source": [ + "import random\n", + "import numpy as np\n", + "\n", + "# defining per-object efficeincy: \n", + "#\n", + "# obj_eff[0] -> probability that 1 LJ contains 0 TO (inefficiency = 1 - efficiency) \n", + "# obj_eff[1] -> probability that 1 LJ contains 1 TO \n", + "# obj_eff[2] -> probability that 1 LJ contains 2 TO (or more)\n", + "# the list can be expanded to any number of TOs\n", + "\n", + "obj_eff = [0.13, 0.80, 0.13]\n", + "# NOTES: - one can use the efficiency distribution instead of using a flat distribution\n", + "# - the overall efficiency is the sum of all elements excluding the first one (ie, inefficiency)\n", + "# - the sum of all elements (including the inefficiency) must be equal to 1\n", + "\n", + " \n", + "def TOs_in_oneLJ():\n", + " rnd = random.random()\n", + " if 0 < rnd < obj_eff[0]:\n", + " return 0\n", + " if obj_eff[0] < rnd < obj_eff[0] + obj_eff[1]:\n", + " return 1\n", + " if obj_eff[0] + obj_eff[1] < rnd < obj_eff[0] + obj_eff[1] + obj_eff[2]:\n", + " return 2\n", + " \n", + "def TOs_in_N_LJs(N=2):\n", + " TOs_in_oneLJ_array = np.array([TOs_in_oneLJ() for i in range(N)])\n", + " return np.sum(TOs_in_oneLJ_array) \n", + " \n", + "\n", + "# computing per-event efficeincy (requiring at least 2 TOs in the event with N LJs)\n", + "TOs_in_N_LJs_array = np.array([TOs_in_N_LJs(N=2) for i in range( int(100) )])\n", + "per_event_eff = np.size(TOs_in_N_LJs_array[TOs_in_N_LJs_array >= 2])/np.size(TOs_in_N_LJs_array) \n", + "\n", + "\n", + "print(per_event_eff)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 004ae721e786c7ef2da86449e0bcbeb5bdef5799 Mon Sep 17 00:00:00 2001 From: Pietro Meloni <41691318+pietro14@users.noreply.github.com> Date: Tue, 1 Oct 2019 17:37:07 -0500 Subject: [PATCH 6/8] Delete Testing.ipynb --- Notebooks/Data/DataOnly/Testing.ipynb | 279 -------------------------- 1 file changed, 279 deletions(-) delete mode 100644 Notebooks/Data/DataOnly/Testing.ipynb diff --git a/Notebooks/Data/DataOnly/Testing.ipynb b/Notebooks/Data/DataOnly/Testing.ipynb deleted file mode 100644 index a228790..0000000 --- a/Notebooks/Data/DataOnly/Testing.ipynb +++ /dev/null @@ -1,279 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'FireHydrant'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseterr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdivide\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minvalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mover\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mFireHydrant\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muproothelpers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mNestNestObjArrayToJagged\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'FireHydrant'" - ] - } - ], - "source": [ - "from coffea import hist\n", - "from coffea.analysis_objects import JaggedCandidateArray\n", - "from coffea.processor import defaultdict_accumulator\n", - "import coffea.processor as processor\n", - "\n", - "import numpy as np\n", - "np.seterr(divide='ignore', invalid='ignore', over='ignore')\n", - "import matplotlib.pyplot as plt\n", - "from FireHydrant.Tools.uproothelpers import NestNestObjArrayToJagged\n", - "\n", - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'retina'" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# setting deltaR=deltaPhi between TO and LJ (ideally <0.4)\n", - "\n", - "deltaPhi = np.random.normal(4, 1.5, 10)\n", - "deltaPhi = np.absolute(deltaPhi)\n", - "deltaPhi = np.floor(deltaPhi)/10\n", - "\n", - "# defining letponjets\n", - "\n", - "lptjs_pts = [10,20,30,40,50,70,80,90,100,110]\n", - "lptjs_phis = np.zeros(10) + deltaPhi\n", - "lptjs_counts = [0,1,2,1,3,3]\n", - "lptjs_etas = np.zeros(10)\n", - "lptjs_mass = np.zeros(10)\n", - "\n", - "leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", - " lptjs_counts,\n", - " pt=lptjs_pts,\n", - " eta=lptjs_etas,\n", - " phi=lptjs_phis,\n", - " mass=lptjs_mass)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# defining triggerobjects\n", - "\n", - "to_pts = [10,20,30,40,50,70,80,90,100,110]\n", - "to_phis = np.zeros(10)\n", - "to_counts = [1,0,3,0,2,4]\n", - "to_etas = np.zeros(10)\n", - "to_mass = np.zeros(10)\n", - "\n", - "\n", - "triggerObjs = JaggedCandidateArray.candidatesfromcounts(\n", - " to_counts,\n", - " pt=to_pts,\n", - " eta=to_etas,\n", - " phi=to_phis,\n", - " mass=to_mass)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(leptonjets.phi)\n", - "print(triggerObjs.phi)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(triggerObjs.phi)\n", - "print(leptonjets.phi)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# setting cut parameters\n", - "\n", - "ptcut1 = 0 # [GeV] for tag # pt = 0 and etacut = 99999999 means NO CUTS\n", - "ptcut2 = 0 # [GeV] for probe\n", - "etacut = 2.5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# define masks based on cuts parameters\n", - "\n", - "\n", - "leptonjets.add_attributes(trgmask = leptonjets.match(triggerObjs, deltaRCut=0.4)) \n", - "leptonjets.add_attributes(ptmask1 = ptcut1) \n", - "leptonjets.add_attributes(ptmask2 = ptcut2) \n", - "leptonjets.add_attributes(etamask = leptonjets.eta=2\n", - "\n", - "diljs = leptonjets[twoljs]\n", - "#matchedidx = \n", - "\n", - "ditos = triggerObjs[twoljs]\n", - "\n", - "print(diljs.phi) \n", - "print(diljs.trgmask)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# distint pairs of objects\n", - "\n", - "diljs = diljs.distincts()\n", - "diljs.i0\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(diljs.i0.phi)\n", - "print(diljs.i1.phi)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tagidx = JaggedArray.fromfolding(leptonjets.pt.argsort()[:, 1], 1) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# apply tag and probe method\n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "# now i0 is the tag and i1 is the probe\n", - "tag0Andprobe1_pt = diljs[leptonjets.i0.trgmask & \n", - " leptonjets.i0.ptmask1 &\n", - " leptonjets.i1.ptmask2 &\n", - " leptonjets.i0.etamask &\n", - " leptonjets.i1.trgmask].pt.flatten() \n", - "''' \n", - "tag0_pt = diljs[diljs.i0.trgmask &\n", - " diljs.i0.ptmask1 &\n", - " diljs.i1.ptmask2 &\n", - " diljs.i0.etamask \n", - " ].pt.flatten()\n", - " \n", - "# now i1 is the tag and i0 is the probe \n", - "tag1Andprobe0_pt = diljs[diljs.i0.trgmask & \n", - " diljs.i1.ptmask1 &\n", - " diljs.i0.ptmask2 &\n", - " diljs.i1.etamask &\n", - " diljs.i1.trgmask].pt.flatten() \n", - " \n", - "tag1_pt = diljs[diljs.i1.trgmask & \n", - " diljs.i1.ptmask1 &\n", - " diljs.i0.ptmask2 &\n", - " diljs.i1.etamask \n", - " ].pt.flatten()\n", - " \n", - "# appending the two pairs of pt arrays\n", - "tot_tag_pt = np.append(tag0_pt, tag1_pt)\n", - "tot_tagAndprobe_pt = np.append(tag0Andprobe1_pt, tag1Andprobe0_pt)\n", - " \n", - "''' " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From f3a019b5909d4d639c4525133925b3049d10a06d Mon Sep 17 00:00:00 2001 From: Pietro Meloni <41691318+pietro14@users.noreply.github.com> Date: Tue, 1 Oct 2019 17:37:18 -0500 Subject: [PATCH 7/8] Delete TriggerEfficiency.ipynb --- .../Data/DataOnly/TriggerEfficiency.ipynb | 430 ------------------ 1 file changed, 430 deletions(-) delete mode 100644 Notebooks/Data/DataOnly/TriggerEfficiency.ipynb diff --git a/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb b/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb deleted file mode 100644 index 936449a..0000000 --- a/Notebooks/Data/DataOnly/TriggerEfficiency.ipynb +++ /dev/null @@ -1,430 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook is for computing the trigger efficiency for data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from coffea import hist\n", - "from coffea.analysis_objects import JaggedCandidateArray\n", - "from coffea.processor import defaultdict_accumulator\n", - "import coffea.processor as processor\n", - "\n", - "import numpy as np\n", - "np.seterr(divide='ignore', invalid='ignore', over='ignore')\n", - "import matplotlib.pyplot as plt\n", - "from FireHydrant.Tools.uproothelpers import NestNestObjArrayToJagged\n", - "\n", - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'retina'" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "datasets_=json.load(open('../Samples/control_data2018.json'))\n", - "datasets = dict(\n", - " A={'files': datasets_['A'], 'treename': 'ffNtuplizer/ffNtuple'} ,\n", - " B={'files': datasets_['B'], 'treename': 'ffNtuples/ffNtuple'} ,\n", - " C={'files': datasets_['C'], 'treename': 'ffNtuples/ffNtuple'} , \n", - " D={'files': datasets_['D'], 'treename': 'ffNtuples/ffNtuple'} ,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "class MyProcessor(processor.ProcessorABC):\n", - " def __init__(self):\n", - " \n", - " dataset_axis = hist.Cat('dataset', '')\n", - " multiplicity_axis = hist.Bin(\"multiplicity\", \"#muons/event\", 10, 0, 10)\n", - " pt_axis = hist.Bin(\"pt\", \"jet pt [GeV]\", 50, 0 , 200)\n", - " deltaR_axis = hist.Bin(\"deltaR\", \"delta_R\", 30, 0 , 5)\n", - " \n", - " self._accumulator = processor.dict_accumulator({\n", - " 'multi': hist.Hist(\"Counts\", dataset_axis, multiplicity_axis),\n", - " 'tot_tag_with_probe': hist.Hist(\"Counts\", dataset_axis, pt_axis),\n", - " 'tot_tag': hist.Hist(\"Counts\", dataset_axis, pt_axis),\n", - " 'cutflow': processor.defaultdict_accumulator(int)\n", - " })\n", - " \n", - " @property\n", - " def accumulator(self):\n", - " return self._accumulator\n", - " \n", - " def process(self, df):\n", - " output = self.accumulator.identity()\n", - " \n", - " dataset = df['dataset']\n", - " \n", - " # IMPORTATANT!!! here you need to add the reference trigger (HLT_Mu17)\n", - " \n", - " \n", - " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", - " df['muon_p4'],\n", - " px=df['muon_p4.fCoordinates.fX'],\n", - " py=df['muon_p4.fCoordinates.fY'],\n", - " pz=df['muon_p4.fCoordinates.fZ'],\n", - " energy=df['muon_p4.fCoordinates.fT'],\n", - " \n", - " ) \n", - " \n", - " muons = JaggedCandidateArray.candidatesfromcounts(\n", - " df['pfjet_p4'],\n", - " px=df['pfjet_p4.fCoordinates.fX'],\n", - " py=df['pfjet_p4.fCoordinates.fY'],\n", - " pz=df['pfjet_p4.fCoordinates.fZ'],\n", - " energy=df['pfjet_p4.fCoordinates.fT'],\n", - " \n", - " )\n", - " \n", - " triggerObjs = JaggedCandidateArray.candidatesfromcounts(\n", - " df['TOHLT_DoubleL2Mu23NoVtx_2Cha'],\n", - " px=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fX'],\n", - " py=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fY'],\n", - " pz=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fZ'],\n", - " energy=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fT'],\n", - " )\n", - " \n", - " ####### EFFICIENCY STUDY #######\n", - " \n", - " ptcut1 = 0 # [GeV] for tag # pt = 0 and etacut = 99999999 means NO CUTS\n", - " ptcut2 = 0 # [GeV] for probe\n", - " etacut = 2.5\n", - " \n", - " # NOTE: add ptcut2 > 1 for probe\n", - " \n", - " \n", - " twoljs = leptonjets.counts >=1\n", - " \n", - " diljs = leptonjets[twoljs]\n", - " triggerObjs = triggerObjs[twoljs]\n", - " \n", - " #leptonjets.offsets\n", - " ptmask1 = diljs.pt>ptcut1\n", - " ptmask2 = diljs.pt>ptcut2\n", - " \n", - " #ptmask1.offsets\n", - " #diljs.offsets\n", - " \n", - " \n", - " diljs.add_attributes(ptmask1 = ptmask1) \n", - " diljs.add_attributes(ptmask2 = ptmask2) \n", - " diljs.add_attributes(etamask = diljs.eta 41\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 0", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/miniconda3/envs/FireHydrant/lib/python3.7/site-packages/uproot/source/chunked.py\u001b[0m in \u001b[0;36mdata\u001b[0;34m(self, start, stop, dtype)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchunkindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 115\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/FireHydrant/lib/python3.7/site-packages/uproot/cache.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/FireHydrant/lib/python3.7/site-packages/cachetools/lru.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key, cache_getitem)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_getitem\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mCache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m 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118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/FireHydrant/lib/python3.7/site-packages/uproot/source/xrootd.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_source\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_source\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_source\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"error\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'pyxrootd' has no attribute 'client'" - ] - } - ], - "source": [ - "output = processor.run_uproot_job(datasets,\n", - " treename=None,\n", - " processor_instance=MyProcessor(),\n", - " executor=processor.futures_executor,\n", - " executor_args=dict(workers=12, flatten=True),\n", - " chunksize=5000000,\n", - " #maxchunks=0\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### New way to plot the efficiency" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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55ySnJvnLqvr3yd2h7LJ0YXvBwbZGA0j9/ujH36mqbx8Hl6r6d+mC1PEGOXt2kk8n+fIkb6qqJ45GcU5VbaiqL66qn0o3uvJqTB/1C6Nzb0vytqr65rmDb1XVOVX1velGoP7aVbj+Yq5NV1N533TvyzPm9GneUFWPqarfXkZLgrW6z59IN8jbI5L8bVU9ec7ne2pVfVVVHW9k+PGgbU8YLX9v0tHKR9PxfSBdc/TfSxf039Va27XA7vdP8s9V9dzRd3HDqOynVNVTkvzcaL+dcw860b8dS1VVt47O8QtL3P/cOde9bInHXDnnmM0rLescdyT5maq6evwdq6ovSDef+aPTDey3pPsBAOiTIM5yjIPIi6rqwOgX81ur6of7usBoZOVvStdc9JHpgtqdST6VLkgfSPIzo90Xmgrph9KFxi3pQvmBqtqfZFe6+Y+fNdrvmGavrbUb080LfUe6Juz/J8nBqtqbrrbxXUlekGR7+um/Ov/6+9NNZfXudP2nX5vkzqraW1UHk3ww3fzK56/G9Y9Trk+n68P8T+nC86vSjdS9N908zTcm+Z50D0+Wcr41uc/RvOOXp/tunZ+uj/b4870z3RRix2u2/r9z7xH5V9wsfZ5xM/QLRstjasPn+PwkP5vuu3hXVe1L913+63TTwv1L7qnppQvcf5JuLvE7quoT6R58XJquq8EzlzECPABAbwRxluOn002Z9K50fW0/f/TqtZl0a+2dSc5L1y93T7ppqPYk+ZUkX5Z7+qvvX+DY/elqLH8mXc16pav1+p+jY9+92LGj4/8iXY3pz6arkf10uvv7ZLppo34hyYWttQ9OeJsLGoXFL033wOD6JJ9Icka6/s3vStc/96vS1Z4OprW2O90o4c9ON8f7nekednwkXQ3s92Tp/YDX7D5ba9ene8DzwnQPFj6Tbi7wD6T7jnzNcY79TJI/Hf14Y2vtn3oq1tzg3bJ4//BPJvnqdKHy79M1jb9fuodUNyZ5bpLzW2v/1lO5ZkFL92Dvv6T7u78p3Xftz5I8vrX2mjUsGwBwEitd41bfnObiD22t3brGZbk4XfD54JRMDbVsVfXqdINlvaC19vxlHntVupr9v2mtXdx/6ZhlVfW+JA9P8gOttd9Y6/KwsFH3nuclue44Uw0CAKwZNeKsK6P+nd8w+vGvlnnsptwzcNOyjoVRP+yHp6uBPl7zcQAAOC5BfFj/upoDqi2mqrbOue71Q157Jarq8qr6H1W1Yzx6eVXdt6ouT9ev99Qkb2+tvXWBY8+pqt+tqidV1emjdadU1Zela0L9xen6gP/O/GNhMVW1LckvjX58RWvtk8fbHwAAjud4U+HQn4+eeJdV9dkFyvDxtSjIEj0oyX8bvT47Gmzt/rnn+/rB3DOP83ybklw5emV07ObRK+n6i39na22tPxPWgar65XRT4W1PN1bB3nTjBwAAwIoJ4gNorW1f4+t/Ml2QWC/+Ot1UTJekGwxuW7rRuf853QjILxkNyraQDyf5kSRPS/KodKG+JXl/utr0F7XW3r+qpWeWbEs3L/0n07Um+dHW2sfWtkgAAKx3BmsDAACAAekjDgAAAAMSxAEAAGBAgjgAAAAMSBAHAACAAQniAAAAMCDTl62SqvrXdHNf37rGRQEAAKB/5yb5ZGvtocs9UBBfPfc/9dRTH/joRz/6gWtdEAAAAPr17ne/O3fdddeKjhXEV8+tj370ox940003rXU5AAAA6NmFF16Ym2+++daVHKuPOAAAAAyo1yBeVZ/T5/kAAABg1vRdI35bVb22qi7p+bwAAAAwE/oO4u9L8k1J/qqq3ldVP1JVZ/Z8DQAAAFi3eg3irbUvTvLEJK9OclaSX0ryb1X1+1X15X1eCwAAANaj3gdra629rbV2ZZLPS3J1kn9O8m1Jrq+q/6+qrq6qB/R9XQAAAFgPVm3U9NbaHa21l86pJX9Vks9P8ivpaslfWVWPWa3rAwAAwDQaavqyvUk+keRQkkpy3yTfleT/VtXrq+qBA5UDAAAA1tSqBfGq2lhV31pV1yd5d5IfTvLxJP8lybYklyTZmeRrkrxstcoBAAAA0+Q+fZ+wqr4wyfcmuTLJmUk+m+T1SV7eWnvTnF3fkuQtVfWHSS7ruxwAAAAwjXoN4lX1piQXp2t+/pEkP5Pkt1prHz7OYTcl+bo+ywEAAADTqu8a8ScnuT7Jy5O8vrV2dAnH/GmS4wV1AAAAmBl9B/FHt9beu5wDWmv/lOSfei4HAAAATKVeB2tbbggHAACAk02vQbyqvqmq3lxVn7fI9rOq6k1V9fV9XhcAAADWi76nL/ueJFsXG5yttfahJGeM9gMAAICTTt9B/IuTvOME+9yY5Et6vi4AAACsC30H8Qcm+dgJ9tmXZFvP1wUAAIB1oe8gvjfJw0+wz8OT7O/5ugAAALAu9B3E35rka6rqUQttrKpHJ7k8yf/p+boAAACwLvQdxH853dzkN1TVs6vqEVV1+mh5dboAvmG0HwAAAJx07tPnyVprN1bVs5K8LMmLR6+5jib5gdba/+3zugAAALBe9BrEk6S19ttVdUOSZyV5bJKt6fqEvz3Jr7fW3t33NQFgpe46fDQ7d+3J7tsP5pwzT8ulO7Zn88YNa10sAGCG9R7Ek2QUtn9oNc4NAH25Zff+XHXdjdl74PDd67Zt2ZRrr7go5529dQ1LBgDMsr77iAPAunDoyNFjQniS7D1wOFddd2MOHTm6RiUDAGbdqtSIV9WGJI9M8oB0g7Mdo7X2t6txbQBYip279hwTwsf2Hjicnbv25PLzzxq4VADAyaD3IF5VP5nkOUnOOMGuOuABsGZu23dwou0AMI1e/Ffvu/vPz3nqI9awJBxPr0G8qn4syQuS3JHk1Ul2J/lMn9cAgD6cc+ZpE21fLQaPA2Cl7jp8NC950/vv/vkHLn6Y/0OmVN814v8pyYeSXNBa+3jP5waAu00aWC/dsT3btmxasHn6ti2bcumO7YOWJ5neweM8HACYfuP/Q+Z64gvfvOb/h7CwvoP42Ul+WwgHmC3TFsT6CKybN27ItVdctOh5lnN/fZTnRIPH3XDNJcsqU1+fWZ8PB6btewScnO46fDQ//Np/yCfv+kzuf+p98pJv/dJ1/29R3/+HsPr6DuIfXYVzArCGpi2I9fnLxnlnb80N11ySnbv25LZ9KytTX+Xpc/C4vj6zPt/raa3tB04uC/1bNAu1xgYgXX/6nr7sdUmeWlX37fm8AKyBPqf4umX3/jzpF9+cH37tO/Oiv3pfrn7NO/PEF745t+zev6wyLeWXjeXYvHFDLj//rPzQUx6ey88/a9kPBvoqT1+Dx/X5mfV1b9M6Vdxdh4/m9f/wobz0Te/PG975IVPWLcB7xCyZ1n+L+mAA0vWn79rr5yV5XJI/rKpnt9b+tefzAzCgvp6w91mzOm2/bPRVnr4Gj+uzVqSve+u7pmaW++NPE+8Rs2aWa42ndQBSFtd3EP+nJBuTfF6Sr6yqO5IsVNXRWmsP6/naAMwzaWCZxiA2bb9s9FWevgaP6/NBRV/31meZprE//izyHjGLpu1Bbp/6HoCU1dd3ED8l3XRlt81ZVwvst9A6AHrUR2CZxiA2bb9s9FWevgaP6/NBRV/31leZprE//qzyHjGtJpkje9oe5M43yb31OQBpH+VZLdNYppXqNYi31s7t83wArExfgWXagliyOr9sTKLP8vQxeFyfDyr6ure+ytRXOJzmWrFpGVl+mt+jWTWLI3n3bdI5sqftQe5cfcz/Pf4/5FE/+Zd3r1tp65VpnY98bpkEcQCmTl+BZdqC2FgfgbVPfZZnPHjcSvX9oKKPe+urTNPWH79v09Qne1rfo1k1qyN596mPObKn7UHuWJ/zf2/euCFXP+Xh9/p5LcvTp7sO33swvUNHjk7Fw4GVqtba6p286gFJtrTWdq/aRaZUVd10wQUXXHDTTTetdVGAk9BL3/T+vGhO8635fuSpj8gPzfmP+kQOHTk6ccicppBxMujjM5u2Mr3hnR/K1a9556LbX/Kt5y958MAnvvDNiz4YWov+z9NWpmkrzyzzXp9Y3+/RoSNH71Vr/J6fuWzN3uNp+/ynrTxj0/o7xIUXXpibb7755tbahcs9tvca8arakuQFSb4jyYOStPF1quqx6UZW/++ttZv7vjYAnb5rsyatpU2mrxZ71vXxmfVt0jJNW3/8uSZtUj5tfbL7fo+mpcn9NJq2z34a9f0e9VFr3Jdp+/xXqzyT9O2e1cEjew3iVXVGkhuS7EjyziR7kzx6zi7/mORJSb4tiSAOsIhJf2md1n5w0xgOWT+mrT/+WB81NdPYJ7uv92haa7KmxTR+9tNmNd6jaelfPG2f/2qUZ9L+5tP2sKIvfdeIPzddCL+ytfaqqnpekp8ab2ytHayqv0nylJ6vCzAz+vildVr7wcGkpqk/ftJfTU3frVj6qoGe9D1ajZqsWRvUTH/8E5vl92ja7q3v8vTR33zaHlb0pe8g/vVJdrbWXnWcfT6Y5KKerwswE/r8pVVTcGbVNLWs6Kumps9WLNNUA913TdYsDmo2rS2Ypsksv0fTdm99lmdaH1ROi1N6Pt9DkrzrBPscSHJGz9cFmAlL+aV1OcaB5Yee8vBcfv5ZQjj0rK+amnErlm1bNt1r/XJbsZzoF99DR44ucuTq6LMma9rurS99ffazbJbfo2m7tz7L09fvNOOHAwtZzw9i+q4RvzPJg0+wz0PT9R0HYJ5ZbX4Fs6rPmpo+WrFMW1/KPt+fabu3Po0/+6tfMztN7vs2y+/RtN1bX/OR9/2gcta62/UdxG9M8tVVdb/W2p3zN1bV5yb5yiR/1vN1AWbCrDa/glnVd7PSSZvdT9vDvD7fn2m7t75t3rghv/mMx6x1Me5l2vrjT+N7lEw2IvjYtN1bHyPLr8aDykkfDkyTvoP4S5L8RZI/r6rvnbuhqh6d5LeTbE7yaz1fF2AmTFtfMeD4pq2mZtoe5vX5/kzbvc26PvvjT1ug79OkI4JPs0lHll+NB5XTMu1cH6q11u8Ju5HSn5du/vAjSTYm+USSBySpJNe01n6p14tOoaq66YILLrjgpptuWuuiAOvMNA20BCzNoSNHp2JgxENHjuaJL3zzor/4rlUNUh/vz7Te2yzq872e5f/TZvne+jLr79GFF16Ym2+++ebW2oXLPbb3IJ4kVfXkJM9O8rgkZya5I8nbk7y4tfbm3i84hQRxYBLT8ks9sP7M8i++fd7bLNfSTuoN7/xQrn7NOxfd/pJvPX9JXShm+eHJLN9b3w4dOXqvJuXv+ZnLZua9mSSI9900PUnSWrs+yfWrcW6AadXXvL3JdE3PBKwvszx1YV/3NovToPWpr/74szzA3izfW9/m//2chX+L+rAqQRzgZDPLNVDA+jPLD/Mmvbe+5jaeZX31x5/lAfZm+d5Ww9y+3XQEcYAJ+aUOYP3ouyZzFpu49zXI1iwPsDfL97YaJh34bRadMsnBVfXZqvpMVT1izs9Hl/D6TD/FB1h7S/mlDoDp0GdN5i279+dJv/jm7Nz10fzdv+zLzl0fzRNf+Obcsnv/pMWcyF2Hj+b7Xv2OfNtvvT3f9+p35NCRo8s6fjza/bYtm+61frmj3Y8D/ULW+0wgs3xvLN3HPvnpFR87aY3436YbHf3gvJ8BThqapwGsH33VZE5ra6i++r+P++Nf/ZqV1/ZP2/R+fZrlezsZ9DH3e5J87M5DKz52oiDeWrv4eD8DnAw0TwNYP/pqdj2Ng3X1/XBg88YN+c1nPGaiMo0D/dxRs2ely9Ys39usmzv3+0qD+F2Hu5YmG7acuaLmD/qIA0yor1/qAFh9fdVkTmNrqGl8OJB07/ncwbpmKajO8r3NqnGAHjt05OiKZ15Ikg2nb/28lZSj1yBeVacmeVCSPa21Y/4VqKr7JvmcJB9rra28Hh9gimieBrC+9DEN2jS2hprGhwNjszxY1yzf26yZG6DHltt1Y7GWJ8vVd434TyX54SRnJbl9ge2nJ3lPkl8e7bsiVfWQJD+d5LIkZyb5SJLXJ3lBa+0TSzzHW5J8xXF2OdXDAmCpZnneXoBZNOk0aNPYGmoaHw7AtOir68bxWp4sR99B/D8m+evW2kIhPC14lgUAACAASURBVK2126vqr5N8dVYYxKvqYUneluTBSd6QLth/WZKrk1xWVU9ore1bxilfsMh6I7sDyzLL8/YCcG/T2BpqGh8OwLToq+tGXy1L+g7i5yZ50wn2eV+SJ05wjZenC+HPbq29dLyyqn4lyXOS/FyS71/qyVprz5+gLAAAnKSmrTXUND4cgGnRV9eNvlqW9B3ENyb57An2aUk2r+Tko9rwpyW5NcnL5m1+XpLvTfKMqvqR1tqnVnINAABYqmlrDdXHtGMwi/rqunG8lifL0XcQ/5ccv991klyc5IMrPP+TR8s3ttbuFfhba3dW1VvTBfXH5cQ180mSqvqWJA9NcjjJu5O8ubW28pnZAQBgDfUx7RjMmr66bsxtefKRCcpzygTHLuRPklxYVT+20Maq+vEkF6QbWG0lHjlavm+R7eMJ4ZYzdOFrkvx8khcl+fMkt1XVNy714Kq6aaFXkkctowwAAACsknGA3rZl073Wr6TrxrjlSZIc/dT+D6+kPH0H8V9OsjvJz1fVO6rqf1TVD46WN6Xrv31bkl9c4fnPGC3vWGT7eP1Sxp5/Q5KnJ3lIklPTBeefHx372qq6bIVlBAAAYMrMDdBjN1xzyZKnLptrHNyPHti3ZyVl6bVpemvtE1V1cZI/SNc8/IJ0fcJrtMvbknznUqcYW02ttRfPW/XeJD9RVR9O8tJ0ofwvl3CeCxdaP3rwcMGk5QQAAKAf82u+Jxk/4cH325x/++jKju27j3haa7cmeXxVXZAujG9Nsj/J21trN094+nGN9xmLbB+v3z/BNX4nyYuTnF9V92ut3TnBuQAAAJgiVz/l4b2c58H3v2/+bYXH9h7Ex0ahe9LgPd97R8vF+oCP39HF+pCfUGvtUFXdmeQBSU5PIojDjLvr8NHs3LUnu29f+6lnAABYXc956nKGFFsdqxbEV8n1o+XTquqUuSOnV9X9kjwhycEkb1/pBarqkelC+J1J9k5QVmAduGX3/kXnW11JfyEAADiRiYJ4Vf1Uuj7gL2ut3T76eSlaa+1nlnu91toHquqN6aYo+8F0fbnHXpCuBvs3584hXlWPGh37njnrHprkjtba7fPu50FJfnf042taa59ZbhmB9ePQkaPHhPAk2XvgcK667sbccM0lasYBAOjdpDXiz08XxF+b5PbRz0vRkiw7iI88K92gb79WVU9JN/f3Y9PNMf6+JM+dt/+7R8uas+4rkvxGVd2Qbu7z25Ock+Qr0/Uzf0eSBadgA2bHzl17FpxLMunC+M5de3L5+WcNXCoAAGbdpEH8yaPlbfN+XjWjWvHHJPnpJJelC88fSfKSJC9Y4ojsN6WbP/zCJF+a5P7pmqL/Y5LXpatVX/i3c2Bm3Lbv4ETbAQBgJSYN4p9Isqe1dihJWmt/M3mRTqy1tjvJM5e4by2w7h+TXNlzsYB15pwzT5toOwAArMQpEx7/D0m+f/xDVb25qr5rwnMCDOLSHduzbcumBbdt27Ipl+7YPnCJAAA4GUwaxD+bZO5IRhcnOXfCcwIMYvPGDbn2iouOCePjUdMN1AYAwGqYtGn6vyU5v4+CAKyF887emhuuuSQ7d+3JbfvMIw4AwOqbNIj/aZL/XFXvTjdgWpJcWVUXn+C41lp7yoTXBujF5o0bjI4OAMBgJg3iz02yKclXpZsSrKVrmn7uCY5rE14XAAAA1qWJ+oi31u5srX1/a+3s1tqGdHN1P7+1dsoJXtp8AgAAcFKaKIhX1f2rau4oR3+T5NaJSgQAAAAzbNJR0z+R5Mfn/Hxrkv0TnhMAAABm1qRBvKVrjj52RYyiDgAAAIuaNIh/JMkX9lEQAAAAOBlMOmr6m5N8R1Vtyz3Tl31tVZ17guNaa+2qCa8NAAAA686kQfzHknxOkqemq11v6Zqmn6h5eksiiAMAAHDSmSiIt9Y+muSyqtqY5HPTDdb2q0leMnnRAAAAYPZMWiOeJGmtHUlyW1V9MMmtrbUP9nFeAAAAmDW9BPGx1tpD+zwfAAAAzJpJR01PVX15VZ2zjP2/pKq+a9LrAgAAwHo0cRBPcn2SK+euqKprqmrfIvt/XZLf7eG6AAAAsO70EcRrgXWbk2zt4dwAAAAwU/oI4gAAAMASCeIAAAAwIEEcAAAABiSIAwAAwID6mke89XQegCW76/DR7Ny1J7tvP5hzzjwtl+7Yns0bN6x1sQAA4Lj6CuLPr6rnz19ZVUd7Oj/Avdyye3+uuu7G7D1w+O5127ZsyrVXXJTzzjZpAwAA06uvpum1zBfAih06cvSYEJ4kew8czlXX3ZhDRzwDBABgek0cxFtrp6zgpe0osGI7d+05JoSP7T1wODt37Rm4RAAAsHQGawPWndv2HZxoOwAArCVBHFh3zjnztIm2AwDAWhLEgXXn0h3bs23LpgW3bduyKZfu2D5wiQAAYOkEcWDd2bxxQ6694qJjwvh41HRTmAEAMM36mr4MYFDnnb01N1xzSXbu2pPb9plHHACA9UMQB9atzRs35PLzz1rrYgAAwLJomg4AAAADEsQBAABgQII4AAAADEgQBwAAgAEJ4gAAADAgQRwAAAAGJIgDAADAgARxAAAAGJAgDgAAAAMSxAEAAGBAgjgAAAAMSBAHAACAAQniAAAAMCBBHAAAAAYkiAMAAMCABHEAAAAYkCAOAAAAAxLEAQAAYECCOAAAAAxIEAcAAIABCeIAAAAwIEEcAAAABiSIAwAAwIAEcQAAABiQIA4AAAADEsQBAABgQII4AAAADEgQBwAAgAEJ4gAAADAgQRwAAAAGdJ+1LgBw8rnr8NHs3LUnu28/mHPOPC2X7tiezRs3rHWxAABgEII4MKhbdu/PVdfdmL0HDt+9btuWTbn2ioty3tlb17BkAAAwDE3TgcEcOnL0mBCeJHsPHM5V192YQ0eOrlHJAABgOII4MJidu/YcE8LH9h44nJ279gxcIgAAGJ4gDgzmtn0HJ9oOAACzQBAHBnPOmadNtB0AAGaBIA4M5tId27Nty6YFt23bsimX7tg+cIkAAGB4gjgwmM0bN+TaKy46JoyPR003hRkAACcD05cBgzrv7K254ZpLsnPXnty2zzziAACcfARxYHCbN27I5eeftdbFAACANaFpOgAAAAxIEAcAAIABCeIAAAAwIEEcAAAABiSIAwAAwIAEcQAAABiQIA4AAAADEsQBAABgQII4AAAADEgQBwAAgAEJ4gAAADAgQRwAAAAGJIgDAADAgARxAAAAGJAgDgAAAAMSxAEAAGBAgjgAAAAMSBAHAACAAQniAAAAMCBBHAAAAAYkiAMAAMCABHEAAAAYkCAOAAAAAxLEAQAAYECCOAAAAAxIEAcAAIABCeIAAAAwIEEcAAAABiSIAwAAwIAEcQAAABjQugziVfWQqnpFVX24qj5dVbdW1a9W1QMmOOeXV9XRqmpV9bN9lhcAAADG7rPWBViuqnpYkrcleXCSNyR5T5IvS3J1ksuq6gmttX3LPOf9klyX5GCSLf2WGAAAAO6xHmvEX54uhD+7tfa1rbUfb61dkuTFSR6Z5OdWcM6XJDkjyc/3V0wAAAA41roK4qPa8KcluTXJy+Ztfl6STyV5RlWdvoxzXp7kmUmeneTD/ZQUAAAAFraugniSJ4+Wb2ytfXbuhtbanUnemuS0JI9bysmq6sFJfjvJ61trv9dnQQEAAGAh6y2IP3K0fN8i298/Wj5iief77XTvwfdPUigAAABYqvU2WNsZo+Udi2wfr996ohNV1Xcn+Zok39Ja++hKC1RVNy2y6VErPScAAACza73ViPeiqs5N8qtJ/ldr7XVrWxoAAABOJuutRnxc433GItvH6/ef4DyvSHJXkmdNWqDW2oULrR/VlF8w6flhmtx1+Gh27tqT3bcfzDlnnpZLd2zP5o0b1rpYAACwrqy3IP7e0XKxPuAPHy0X60M+dkG60P7xqlpo+3Or6rlJ3tBa+9pllxJm0C279+eq627M3gOH7163bcumXHvFRTnv7BP2BgEAAEbWWxC/frR8WlWdMnfk9Kq6X5InJDmY5O0nOM+r0o2uPt/Dk3x5kncmuSnJP0xcYpgBh44cPSaEJ8neA4dz1XU35oZrLlEzDgAAS7Sugnhr7QNV9cZ0c4n/YJKXztn8giSnJ/nN1tqnxiur6lGjY98z5zzPXuj8VXVluiD+v1tr/733G4B1aueuPceE8LG9Bw5n5649ufz8swYuFQAArE/rKoiPPCvJ25L8WlU9Jcm7kzw23Rzj70vy3Hn7v3u0XLANOnBit+07ONF2AADgHutu1PTW2geSPCbJK9MF8B9J8rAkL0nyuNbavrUrHcymc85cqCfH0rcDAAD3WI814mmt7U7yzCXuu+Sa8NbaK9MFfGCOS3dsz7YtmxZsnr5ty6ZcumP7GpQKAADWp3VXIw4Mb/PGDbn2iouybcume60fj5puoDYAAFi6dVkjDgzvvLO35oZrLsnOXXty2z7ziAMAwEoJ4sCSbd64wejoAAAwIU3TAQAAYECCOAAAAAxIEAcAAIABCeIAAAAwIEEcAAAABiSIAwAAwIAEcQAAABiQIA4AAAADEsQBAABgQII4AAAADEgQBwAAgAEJ4gAAADAgQRwAAAAGJIgDAADAgARxAAAAGJAgDgAAAAMSxAEAAGBAgjgAAAAMSBAHAACAAQniAAAAMCBBHAAAAAYkiAMAAMCABHEAAAAYkCAOAAAAAxLEAQAAYECCOAAAAAxIEAcAAIABCeIAAAAwIEEcAAAABiSIAwAAwIAEcQAAABiQIA4AAAADEsQBAABgQII4AAAADEgQBwAAgAEJ4gAAADAgQRwAAAAGJIgDAADAgARxAAAAGJAgDgAAAAMSxAEAAGBA91nrAgCr667DR7Nz157svv1gzjnztFy6Y3s2b9yw1sUCAICTliAOM+yW3ftz1XU3Zu+Bw3ev27ZlU6694qKcd/bWNSwZAACcvDRNhxl16MjRY0J4kuw9cDhXXXdjDh05ukYlAwCAk5sgDjNq5649x4Twsb0HDmfnrj0DlwgAAEgEcZhZt+07ONF2AABgdQjiMKPOOfO0ibYDAACrQxCHGXXpju3ZtmXTgtu2bdmUS3dsH7hEAABAIojDzNq8cUOuveKiY8L4eNR0U5gBAMDaMH0ZzLDzzt6aG665JDt37clt+8wjDgAA00AQhxm3eeOGXH7+WWtdDAAAYETTdAAAABiQIA4AAAADEsQBAABgQII4AAAADEgQBwAAgAEJ4gAAADAgQRwAAAAGJIgDAADAgARxAAAAGJAgDgAAAAMSxAEAAGBAgjgAAAAMSBAHAACAAQniAAAAMCBBHAAAAAYkiAMAAMCABHEAAAAYkCAOAAAAAxLEAQAAYECCOAAAAAxIEAcAAIABCeIAAAAwIEEcAAAABiSIAwAAwIAEcQAAABiQIA4AAAADEsQBAABgQII4AAAADEgQBwAAgAEJ4gAAADAgQRwAAAAGJIgDAADAgARxAAAAGJAgDgAAAAMSxAEAAGBAgjgAAAAMSBAHAACAAQniAAAAMCBBHAAAAAYkiAMAAMCABHEAAAAYkCAOAAAAAxLEAQAAYECCOAAAAAxIEAcAAIABCeIAAAAwIEEcAAAABnSftS4AsLC7Dh/Nzl17svv2gznnzNNy6Y7t2bxxw1oXCwAAmJAgDlPolt37c9V1N2bvgcN3r9u2ZVOuveKinHf21jUsGQAAMKl12TS9qh5SVa+oqg9X1aer6taq+tWqesAyzvFfq+rPR8ceqKpPVtU/VtWvVNVDVrP8cDyHjhw9JoQnyd4Dh3PVdTfm0JGja1QyAACgD+suiFfVw5LclOSZSf4+yYuT/EuSq5P8XVWducRTfV+Sz0vyN0lenuTaJPuSPCfJrqr60p6LDkuyc9eeY0L42N4Dh7Nz156BSwQAAPRpPTZNf3mSByd5dmvtpeOVVfUr6UL0zyX5/iWc54taa4fmr6yq/5Tkt0bn+cpeSgzLcNu+gxNtBwAAptu6qhEf1YY/LcmtSV42b/PzknwqyTOq6vQTnWuhED7yutHy4SssJkzknDNPm2g7AAAw3dZVEE/y5NHyja21z87d0Fq7M8lbk5yW5HETXOPpo+W7JjgHrNilO7Zn25ZNC27btmVTLt2xfeASAQAAfVpvTdMfOVq+b5Ht709XY/6IJG9aygmr6nuSPCTJliRfnOQ/JPlgkh9f4vE3LbLpUUs5HubbvHFDrr3iokVHTTeFGQAArG/rLYifMVrescj28frlzO/0PUkeO+fnG5N8e2vtn5dZNujNeWdvzQ3XXJKdu/bktn3mEQcAgFmy3oJ471prj0uS0WjrF6QbpO2mqvrm1trOJRx/4ULrRzXlF/RZVk4umzduyOXnn7XWxQAAAHq23vqIj2u8z1hk+3j9/uWeuLW2r7X2V+matt+V5NVVderyiwgAAACLW29B/L2j5SMW2T4e6XyxPuQn1Frbn+TvkjwoyY6VngcAAAAWst6C+PWj5dOq6l5lr6r7JXlCkoNJ3j7hdcbtgT8z4XkAAADgXtZVEG+tfSDJG5Ocm+QH521+QZLTk7y6tfap8cqqelRV3WsE86o6p6o+Z6FrVNX3Jbkoye4k/9hf6QEAAGB9Dtb2rCRvS/JrVfWUJO9ON+r5k9M1SX/uvP3fPVrWnHUXJPlfVfV3Sf45yUeTnJlu/vEvTnIgyTNaa0dX6yYAAAA4Oa2rGvHk7lrxxyR5ZboA/iNJHpbkJUke11rbt4TT3Dza/75JvirJjyb5tiQtyYuS/LvW2t/0XngAAABOeuuxRjyttd1JnrnEfWuBdbelC98AAAAwqHVXIw4AAADrmSAOAAAAAxLEAQAAYECCOAAAAAxIEAcAAIABCeIAAAAwIEEcAAAABiSIAwAAwIAEcQAAABiQIA4AAAADEsQBAABgQII4AAAADEgQBwAAgAEJ4gAAADAgQRwAAAAGJIgDAADAgARxAAAAGJAgDgAAAAMSxAEAAGBAgjgAAAAMSBAHAACAAQniAAAAMCBBHAAAAAYkiAMAAMCABHEAAAAYkCAOAAAAAxLEAQAAYECCOAAAAAxIEAcAAIABCeIAAAAwIEEcAAAABiSIAwAAwIAEcQAAABiQIA4AAAADEsQBAABgQII4AAAADEgQBwAAgAEJ4gAAADAgQRwAAAAGJIgDAADAgARxAAAAGNB91roAMGvuOnw0O3ftye7bD+acM0/LpTu2Z/PGDWtdLAAAYEoI4tCjW3bvz1XX3Zi9Bw7fvW7blk259oqLct7ZW9ewZAAAwLTQNB16cujI0WNCeJLsPXA4V113Yw4dObpGJQMAAKaJIA492blrzzEhfGzvgcPZuWvPwCUCAACmkSAOPblt38GJtgMAACcHQRx6cs6Zp020HQAAODkI4tCTS3dsz7Ytmxbctm3Lply6Y/vAJQIAAKaRIA492bxxQ6694qJjwvh41HRTmAEAAInpy6BX5529NTdcc0l27tqT2/aZRxwAADiWIA4927xxQy4//6y1LgYAADClNE0HAACAAQniAAAAMCBBHAAAAAYkiAMAAMCABHEAAAAYkCAOAAAAAxLEAQAAYECCOAAAAAxIEAcAAIABCeIAAAAwIEEcAAAABiSIAwAAwIAEcQAAABiQIA4AAAADEsQBAABgQII4AAAADEgQBwAAgAEJ4gAAADAgQRwAAAAGJIgDAADAgARxAAAAGJAgDgAAAAMSxAEAAGBAgjgAAAAMSBAHAACAAQniAAAAMCBBHAAAAAYkiAMAAMCABHEAAAAYkCAOAAAAAxLEAQAAYECCOAAAAAxIEAcAAIABCeIAAAAwIEEcAAAABiSIAwAAwIAEcQAAABiQIA4AAAADEsQBAABgQII4AAAADOg+a10AmBZ3HT6anbv2ZPftB3POmafl0h3bs3njhrUuFgAAMGMEcUhyy+79ueq6G7P3wOG7123bsinXXnFRzjt76xqWDAAAmDWapnPSO3Tk6DEhPEn2Hjicq667MYeOHF2jkgEAALNIEOekt3PXnmNC+NjeA4ezc9eegUsEAADMMkGck95t+w5OtB0AAGA5BHFOeuecedpE2wEAAJZDEOekd+mO7dm2ZdOC27Zt2ZRLd2wfuEQAAMAsE8Q56W3euCHXXnHRMWF8PGq6KcwAAIA+mb4Mkpx39tbccM0l2blrT27bZx5xAABg9QjiMLJ544Zcfv5Za10MAABgxmmaDgAAAAMSxAEAAGBA6zKIV9VDquoVVfXhqvp0Vd1aVb9aVQ9Y4vGnV9V3VNUfVNV7qupTVXVnVb2jqn6kqhYeQhsAAAAmtO76iFfVw5K8LcmDk7whyXuSfFmSq5NcVlVPaK3tO8FpnpTk95LcnuT6JK9P8oAkX5Pkl5N8fVU9pbV2aHXuAgAAgJPVugviSV6eLoQ/u7X20vHKqvqVJM9J8nNJvv8E59iT5DuT/K/W2uE55/jRJG9J8vgkP5jkRb2WHAAAgJPeumqaPqoNf1qSW5O8bN7m5yX5VJJnVNXpxztPa+2drbXfnxvCR+vvzD3h++I+ygwAAABzrasgnuTJo+UbW2ufnbthFKLfmuS0JI+b4BpHRsvPTHAOAAAAWNB6a5r+yNHyfYtsf3+6GvNHJHnTCq/x3aPlXy5l56q6aZFNj1rh9QEAAJhh661G/IzR8o5Fto/Xb13JyavqPye5LMk7k7xiJecAAACA41lvNeKrpqq+PsmvphvI7Rtaa0dOcEiSpLV24SLnuynJBf2VEAAAgFmw3oL4uMb7jEW2j9fvX85Jq+prk7wmyceSPLm19i8rK97su+vw0ezctSe7bz+Yc848LZfu2J7NGzes2XkAAADWm/UWxN87Wj5ike0PHy0X60N+jKr6piR/kK4m/JLW2vtXXrzZdsvu/bnquhuz98A9g81v27Ip115xUc47e+m9Afo6DwAAwHq03vqIXz9aPq2q7lX2qvr/27v3cNvKul7g358IIqBAEJEXxBTFgwaF5AVNgZOYqWhpqUlAWFZeuzyH0jxCZSe7qUiWKYphXk520vSo246yVUQLNTQJxRuC4pbYCsrNjZv3/DHGksXaa+291tprvXPNvT+f5xnPYI7LO945B+9c+zvHO95xpyRHJ7khyccWU1hV/WKSNye5MskjhPCF3XTz5i3Cc5Jcfd2mnPqGC3PTzZu7lgMAADCtpiqIt9a+mOR9SQ5O8qw5q89IsmeSc1tr188srKpDq2qLEcyr6qQkf5fk8iQ/qTv61q27eMMW4XnG1ddtyrqLN3QtZ7YbN23O2//9a3nl+z+fd1z0NWEeAABY06ata3qS/EaSC5KcWVXHJbkkyYMyPGP80iQvnLP9JeO8ZhZU1TEZRkW/XYar7KdU1Zzdck1r7eUrXvspdfnGG7Zr/UqXM0M3dwAAYNpMXRBvrX2xqh6Y5A8yPGrsMUm+nuQVSc5orX1rEcXcI7f2BvjlBbb5SoZR1Ely0H57bNf6lS4n2XY39/NPO9YAcAAAwJozVV3TZ7TWrmitndJa++HW2m6ttXu01p4/XwhvrVVrreYsO2dm+Vamg7u9oSlw/GEHZv+9dpt33f577ZbjDzuwaznJ6nRzBwAAWG1TGcTpb/ddd8nZJx21RYie6Qa+2CvPK1VOsvLd3AEAAHqYuq7pTM7hd98n5592bNZdvCGXb1z+879XqpyV7OYOAADQiyDOkuy+6y454Yi7rolyZrq5z9c9fand3AEAAHrRNZ2ptZLd3AEAAHpxRZyptlLd3AEAAHoRxJl6K9VdHgAAoAdd0wEAAKAjQRwAAAA60jV9J3Hjps1Zd/GGXPFN91EDAABMkiC+E/jUFdfk1DdceJvHfM2MLH743feZYM0AAAB2Prqm7+BuunnzFiE8Sa6+blNOfcOFuenmzROqGQAAwM5JEN/Brbt4wxYhfMbV123Kuos3dK4RAADAzk0Q38FdvvGG7VoPAADAyhLEd3AH7bfHdq0HAABgZQniO7jjDzsw+++127zr9t9rtxx/2IGdawQAALBzE8R3cLvvukvOPumoLcL4zKjpHmEGAADQl8eX7QQOv/s+Of+0Y7Pu4g25fKPniAMAAEySIL6T2H3XXXLCEXeddDUAAAB2erqmAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHR0+0lXgK27cdPmrLt4Q6745g05aL89cvxhB2b3XXeZdLUAAABYJkF8DfvUFdfk1DdcmKuv2/T9ZfvvtVvOPumoHH73fSZYMwAAAJZL1/Q16qabN28RwpPk6us25dQ3XJibbt48oZoBAACwPQTxNWrdxRu2COEzrr5uU9ZdvKFzjQAAAFgJgvgadfnGG7ZrPQAAAGuTIL5GHbTfHtu1HgAAgLVJEF+jjj/swOy/127zrtt/r91y/GEHdq4RAAAAK0EQX6N233WXnH3SUVuE8ZlR0z3CDAAAYDp5fNkadvjd98n5px2bdRdvyOUbPUccAABgRyCIr3G777pLTjjirpOuBgAAACtE13QAAADoSBAHAACAjgRxAAAA6EgQBwAAgI4EcQAAAOhIEAcAAICOBHEAAADoSBAHAACAjgRxAAAA6EgQBwAAgI4EcQAAAOhIEAcAAICOBHEAAADoSBAHrUaSkAAAGbJJREFUAACAjgRxAAAA6EgQBwAAgI4EcQAAAOhIEAcAAICOBHEAAADoSBAHAACAjgRxAAAA6EgQBwAAgI4EcQAAAOhIEAcAAICOBHEAAADoSBAHAACAjgRxAAAA6EgQBwAAgI4EcQAAAOhIEAcAAICOBHEAAADoaCqDeFXdrapeV1VXVtV3q+qyqnp5Ve27hDJ+qqr+oqreX1Ubq6pV1fmrWW8AAAC4/aQrsFRVda8kFyQ5IMk7knw2yU8keV6SR1fV0a21jYso6llJTkhyU5IvJPmB1akxAAAA3Goar4i/KkMIf25r7Qmttd9trR2b5GVJ7pvkJYss56VJ7p9krySPW5WaAgAAwBxTFcTHq+GPSnJZkr+as/rFSa5PcmJV7bmtslprH22tXdxa27ziFQUAAIAFTFUQT3LMOH9fa+2W2Staa99J8pEkeyR5cO+KAQAAwGJM2z3i9x3nly6w/vMZrpjfJ8n7e1Soqj6xwKpDexwfAACA6TJtV8T3HufXLrB+Zvk+HeoCAAAASzZtV8TXnNbakfMtH6+U/3jn6gAAALDGTdsV8Zkr3nsvsH5m+TUd6gIAAABLNm1B/HPj/D4LrD9knC90DzkAAABM1LQF8fPG+aOq6jZ1r6o7JTk6yQ1JPta7YgAAALAYUxXEW2tfTPK+JAcnedac1Wck2TPJua2162cWVtWhVWUEcwAAANaEaRys7TeSXJDkzKo6LsklSR6U4RnjlyZ54ZztLxnnNXthVT0syTPGl3uN80Oq6pyZbVprJ69kxQEAAGDqgnhr7YtV9cAkf5Dk0Ukek+TrSV6R5IzW2rcWWdS9k5w0Z9kBc5advH21BQAAgNuauiCeJK21K5Kcsshta4Hl5yQ5Z+VqBQAAANs2VfeIAwAAwLQTxAEAAKAjQRwAAAA6EsQBAACgI0EcAAAAOhLEV9FV3/lu3nHR13LTzZsnXRUAAADWCEF8FV317ZvyvLdclIe99AP51BXXTLo6AAAArAGCeAdXX7cpp77hQlfGAQAAEMR7ufq6TVl38YZJVwMAAIAJE8Q7unzjDZOuAgAAABMmiHd00H57TLoKAAAATJgg3sn+e+2W4w87cNLVAAAAYMIE8Q7232u3nH3SUdl9110mXRUAAAAm7PaTrsCO7IA7755XPOWIHH/YgUI4AAAASQTxVXXAne6QE46466SrAQAAwBqiazoAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdDSVQbyq7lZVr6uqK6vqu1V1WVW9vKr2XWI5PzDud9lYzpVjuXdbrboDAACwc7v9pCuwVFV1ryQXJDkgyTuSfDbJTyR5XpJHV9XRrbWNiyhnv7Gc+yT5QJK3JDk0ySlJfqaqHtJa+9LqvAsAAAB2VtN4RfxVGUL4c1trT2it/W5r7dgkL0ty3yQvWWQ5f5whhP9la+24sZwnZAj0B4zHAQAAgBU1VUF8vBr+qCSXJfmrOatfnOT6JCdW1Z7bKGevJCeO258+Z/VZSb6S5Piq+pHtrzUAAADcaqqCeJJjxvn7Wmu3zF7RWvtOko8k2SPJg7dRzoOT3DHJR8b9ZpdzS5J1c44HAAAAK2Lagvh9x/mlC6z//Di/T6dyAAAAYEmmbbC2vcf5tQusn1m+T6dyUlWfWGDV4ZdcckmOPPLIbRUBAADAlLnkkkuS5ODl7DttQXyabL7xxhuv/eQnP3nZpCvCgg4d55+daC3YXs7jjsF53DE4jzsG53HH4DxOP+dw7Ts4ybeXs+O0BfGZK9V7L7B+Zvk1ncpJa80l7yk105vBOZxuzuOOwXncMTiPOwbnccfgPE4/53DHNm33iH9unC907/Yh43yhe79XuhwAAABYkmkL4ueN80dV1W3qXlV3SnJ0khuSfGwb5XwsyY1Jjh73m13O7TI8Im328QAAAGBFTFUQb619Mcn7MvTFf9ac1Wck2TPJua2162cWVtWhVXXo7A1ba9clOXfc/vQ55Tx7LH9da+1LK1h9AAAAmLp7xJPkN5JckOTMqjouySVJHpThmd+XJnnhnO0vGec1Z/kLkjwyyW9V1RFJ/i3J/ZKckOSqbBn0AQAAYLtN1RXx5PtXxR+Y5JwMAfy3k9wrySuSPLi1tnGR5WxM8pAkZya591jOg5K8PsmR43EAAABgRVVrbdJ1AAAAgJ3G1F0RBwAAgGkmiAMAAEBHgjgAAAB0JIgDAABAR4I4AAAAdCSIAwAAQEeCOAAAAHQkiLPDqar9quoZVfVPVfWFqrqxqq6tqvOr6tSqut2c7Q+uqraV6S2Tei87u6q6bCvnZcMC+zy0qt5dVd8cz/2nq+r5VbVL7/qTVNXJ22hfrao2z9pee5ygqnpSVb2yqj5cVd8eP/M3bmOfJbe5qnpsVa0fv5uvq6p/raqTVv4d7ZyWch6r6pCqOq2qPlBVV1TVpqr6RlW9o6qOWWCfbbXrX1vdd7hzWOJ5XPZ3Z1WdVFX/NrbFa8e2+djVe2c7jyWew3MW8ffy/XP20Ran2O0nXQFYBU9O8tdJvp7kvCSXJ/mhJD+b5LVJfrqqntxaa3P2+1SSt89T3mdWsa5s27VJXj7P8uvmLqiqE5L8Y5Kbkrw1yTeTPC7Jy5IcneH/Dfq6KMkZC6x7eJJjk7xnnnXa42T8fpLDM7SvryY5dGsbL6fNVdWzk7wyycYkb0yyKcmTkpxTVQ9orf3OSr2ZndhSzuMfJvmFJP+Z5N0ZzuF9kzw+yeOr6nmttTMX2PcdGdr4XB9fZr25rSW1x9GSvjur6s+T/PZY/muS7JbkKUneWVXPaa2dtYx6c6ulnMO3J7lsgXUnJvmRzP/3MtEWp1NrzWTaoaYM/7B/XJLbzVl+YIZQ3pL83KzlB4/Lzpl03U1bnMvLkly2yG3vnOSqJN9N8sBZy3dPcsF4jp8y6fdkus05++h4Xh4/a5n2ONlzckySQ5JUkkeO5+KNC2y75DY3nt+bMoTwg2ct3zfJF8Z9HjLpz2HapyWex5OT/Ng8yx+R4UeS7yb54Xn2aUlOnvR73ZGnJZ7HJX93JnnouM8Xkuw7p6yNY1s9eNKfwzRPSzmHWyljnyQ3jG1x/znrtMUpnnRNZ4fTWvtAa+2drbVb5izfkORvxpeP7F4xVtuTkvxgkre01r7/C3Br7aYMv0gnya9PomJsqaoekOTBSb6W5P9OuDqMWmvntdY+38Z/4W3DctrcLye5Q5KzWmuXzdrnW0n+eHypK+V2Wsp5bK2d01r793mWfzDJ+gxXSB+68rVkW5bYHpdjpq29ZGyDM8e9LMlfZWirp6zSsXcKK3QOT0xyxyT/p7V29QpVjTVA13R2NjeP8+/Ns+4uVfXMJPtl+CX4o621T3erGQu5Q1U9PclBSa5P8ukkH2qtbZ6z3bHj/L3zlPGhDL8mP7Sq7tBa++6q1ZbF+tVxfvY85zLRHqfBctrc1vZ5z5xtmLyt/c1MkiOq6vkZekF8Lcl5rbWvdqkZC1nKd+e22uOLxm1evOK1ZCl+ZZz/7Va20RankCDOTqOqbp/kl8aX8/3R+alxmr3P+iQntdYuX93asRUHJjl3zrIvV9Up4xWbGfcd55fOLaC19r2q+nKSwzLcY3XJqtSURamqOyZ5epLNGcZtmI/2uPYtp81tbZ+vV9X1Se5WVXu01m5YhTqzSFV1jyTHZfhB5UMLbPa8Oa83V9Vrkzx/7BlBf4v67qyqPZPcNcl1rbWvz1PO58f5fVapnixCVT0kyQOSXNpaO28rm2qLU0jXdHYmf5Lk/kne3VpbN2v5DRkGqzkyw32K+2a4N+68DF3Y3z/+waK/12f4h+CBSfbM8Mfo1RnuX3tPVR0+a9u9x/m1C5Q1s3yfla8mS/TzGc7De1trV8xZpz1Oj+W0ucXus/cC6+mgqu6Q5O8zdE0+fXa35dGXkzwnww8reya5S4Z2fVmSZyZ5XbfKMmOp353+Zk6Hmd5jr1lgvbY4xQRxdgpV9dwMo4J+NsO9Nt/XWruqtfY/W2ufbK1dM04fSvKoJP+a5N5JntG90qS1dsZ4z/83Wms3tNY+01r7tSR/meF+qdMnW0OWaeYfFq+eu0J7hMkaHzt3boZR79+a5M/nbtNa+2Br7azW2qXjd/PXW2v/kGFgqm8leeqcH0pZZb47dzxVtXeGUL0pyTnzbaMtTjdBnB3e+KicV2R4NMsxrbVvLma/1tr3cmu32Z9cpeqxPDOD7s0+L9u6kjaz/JpVqRGLUlWHZRj46asZHpW0KNrjmrScNrfYfRa6SscqGkP4GzM8du5/J3n6UgaZGnu4zLRr7XQN2Mp3p7+Za9/Tk+yRZQzSpi1OB0GcHdo4cMUrMzw/85hx5PSl+K9xrivs2jLfefncON/ifrZxfIB7Zhhw6EurWzW2YVuDtG2N9ri2LKfNbW2fH85wbr/q/vD+qmrXJG/O8AzpNyV52hjilko7XXu2OCetteszDOq119j25jpknG8xngPdzAzStkXvsUXSFtc4QZwdVlWdluRlSS7KEMKvWkYxDx7nwtvaMt95+cA4f/Q82/9khl+VLzBi+uRU1e4Zbg3ZnOTsZRShPa4ty2lzW9vnp+dsQydVtVuSf8hwJfzvkpy4jB/KZjxonGuna8dC353a4xpVVQ9KcniGQdrWL7MYbXGNE8TZIVXVizIMzvaJJMdtrUtPVf14VW3RFqrquCS/Ob5846pUlAVV1f3mG5Srqg5Octb4cvZ5eVuSq5M8paoeOGv73ZP80fjyr1elsizWkzMMIPSeeQZpS6I9TpnltLnXJ/lukmePbXlmn32TvGB8+Tehm3Fgtn9KckKGH8hOaa3dso19HjjPsttV1e8leUiG/y/mezoJq2SZ350zbe2FYxuc2efgJM/K0FZfv+KVZTFmeo9t7ZFl2uKUq+17vjysPVV1UoZBLTZn6JY+372Gl7XWzhm3X5+hC9YFGe5bTZIfza3P13xRa+2P5hbA6qqq0zMMsPehJF9J8p0k90ryMxmek/nuJE9srW2atc8TMoSDm5K8Jck3kzw+w2iib0vy80u535GVVVUfTvKwJI9vrb1zgW3WR3ucmLENPWF8eWCS4zNcTfnwuOzq1trvzNl+SW2uqp6T5MwMzzh+a4aBiJ6U5G5J/mJ2+SzPUs5jVb0+yckZ/sH+qiTzfUeun31Vrqpahlu+PpWhe/PeGQZ3u3+G0buf2Fp734q+qZ3QEs/j+izju7Oq/iLJb437vC3Jbkl+IcNzyJ/TWjtr7j4s3lK/U8d97pzkygyPmb7bNi4maYvTrLVmMu1QU4aRtNs2pvWztj81ybsyPOrhugy/AF+e4R+ID5/0+9lZpwyPXXlzhpHur0lyc4b7nf4lw/Pga4H9js4Q0r+V5MYk/5HhasAuk35PO/OU5H5j27tia+dCe5z4edrW9+dl8+yz5DaX5HFJPpjhB7brk1yY4TnHE/8MdoRpKecxyfpF/M08fU75fzaevysz/Ahzw/hdfVaSH5n0+99RpiWex2V/d2b4IebCsS1+Zzy3j530+98RpmV+p/76uO7NiyhfW5ziyRVxAAAA6Mg94gAAANCRIA4AAAAdCeIAAADQkSAOAAAAHQniAAAA0JEgDgAAAB0J4gAAANCRIA4AAAAdCeIAAADQkSAOAAAAHQniAAAA0JEgDgAA0FlVHVxVrarOmXRdVtOs9zkz3TTpOi1HVb1rzvs4eXvKE8QBAACmUFU9cgyFp0/BsT6V5Iwkf7SVYxxZVX9TVZ+pqmur6uaq+q+q+nBV/WFV3XeZx05V3a6qLh/fw3/bxrZ7VNU1VbWpqg4YF79prP87lluH2W6/EoUAAACwJF9Lcr8k1066Ip1c1Fo7fb4VVbVbkjOTPDNJS3JBkvOSfDvJPkmOTPJ7SV5QVU9orb1zqQdvrd1SVa9L8uIkz0jyW1vZ/MlJ9k7yttbaVeP+bxrrenKSE5Z6/LkEcQAAgM5aazcn+eyk67FGvDrJyUn+I8lTW2sXz92gqu6R5AVJ9t2O45yd5PeTnFhVv9ta27TAds8Y53+7HcfaKl3TAQAAOlvoHvGxW/TvVdVFVXV9VV1XVR+tqqfO2e6cDFeNk+TFc+5ffuQijt+qan1V3aWqzq2qq6rqxqr6RFU9bSWPtY16/GSGEL4xyaPmC+FJ0lr7SmvtmRm6iM8t4weq6n9V1SXje7i2qt5fVY+aU8YVSd6bZP8kT1ygPocmeViSLyX5f9vx1rbKFXEAAIA1oKr2SfKBJD+W5JNJXpfh4unxSd5UVYe11n5/3Pzt4/ykJB9Msn5WUZct8pD7ZugGfk2S12foBv7zSf6+qu7aWvuzFTzWQn5lnL+6tbZhWxu31r43+/V4pXx9koOTfDhD0N4zyWOTvLeqntlae82sXV6T5GcyXPV+6zyHmLkafnZrrS3+bSyNIA4AALA2vDxDCD+ttfanMwuravcMYfgFVfW21tpFrbW3V9U1GcLx+oXuv96GH03yD0me0lq7ZTzWnyT5RJKXVNU/tta+tELHWsjR4/wDy9z/DUnukaFL+1tmFo4/aqxPcmZV/XNr7RvjqncluTLJcVV1z9bal2fts1uSX0ryvQw/gqwaXdMBAAAmrKr2S/L0JB+fHcKTpLV2U5LTklSSp82z+3JtzhD6b5l1rC9nGDht1yQnruCxFnLgOP/a3BVVdURVnT5nOnnW+sOTPCLJP84O4UnSWrsmw8Bsuyf5uVnLN2cI2ZXk1DmHPCHJDyZ552Kuzm8PV8QBAAAm76gkuyRZ6BFhu47z+63gMS+ffUV4lvUZQuyPreCxluOIsR6zfTDJOeN/P2Sc773AZ/aD43zuZ/baDAO/nVJVLx7DeXJrN/nXZJUJ4gAAAJO33zg/apwWstcKHvMbCyyfuRq89woeayEbktwzyV0yZxT51to5GUN3Vd07yefn7Dvzmf3UOC3kNp9Za+0rVfUvGe69f0ySd1bVwUn+e5KvJFm35HexRLqmAwAATN7M88Rf1lqrrUzHrOAxf2iB5TPdxXs84/wj4/y4Zew7U7/nbeMzO2WefWceTTZzFfzUDN3Vz57dVX+1COIAAACT929Jbkny8CXsM9OlepdlHvOg8UrwXI8c5/++gsdayGvH+a9W1UI/DCzkY+N8KZ/ZjH/OcDX+MVV19ySnZHiPqzpI2wxBHAAAYMJaa1cl+fskD6yqF1XVFoG3qu5VVfectWjjOD9omYfdJclLq+r7uXAs/7kZRg5/4woea16ttZl7vvdPsq6qFroHfp959v14hkeW/WxV/fJ8O1XVA6rqgHn2/d543F0yfO53TfLu1toWg8atBveIAwAArA3PTnJIkj9IcmJVnZ/hPu67ZBhw7KgkT00yM8Da5zKMNv6Uqro5w/3NLcm5rbWvLOJ4n07yoCSfqKr35dbniO+T5H+01r44a9vtPdbWPDPJpiS/muQzVXVBhqvx385wH/ghGa7S35Lk/Dn7Pi3Do8/OrqrnJvnXDM9Fv1uGx7PdP8OgblfNc9zXZBiNfuaK+t/Os82qEMQBAADWgNbat6vqERkC6dMyPHZr9wxh/PNJfjPJv8zafnNVPTHJnyR5cpI7ZbjP+fwMQXlbvpXkp5P8aYau2XdO8p9J/ry19qY5ddveYy2otbYpyTOr6jUZ7tl+eJKTk9wxw33gn0vy0iR/11r73Jx9v1pVRyZ5TobP6xczXOXeML6XVyb5jwWO+6Wqen+GQdq+muQ92/M+lkIQBwAA6G/3cf7d2QvHUHrWOG1Ta+3CLG+gs5n9r8zw/PJVP9Yiyv94ko8vY7/vJPnjcVrqvlsbbX3VuEccAACgv/uM869OtBb9nFRVrapumnRFlqOq3lVVLcnrV6I8V8QBAAA6qaofzdB9+hcz3PP8T5Ot0aq7JskZs15/b1IV2U5vym2v1l+0PYVVa237qgMAAMCiVNXJSV6V5LNJ/mdr7V0TqkdL8sHW2iMncfydnSAOAAAAHblHHAAAADoSxAEAAKAjQRwAAAA6EsQBAACgI0EcAAAAOhLEAQAAoCNBHAAAADoSxAEAAKAjQRwAAAA6EsQBAACgI0EcAAAAOhLEAQAAoCNBHAAAADr6/yJ+/JCyFXxFAAAAAElFTkSuQmCC\n", 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" - ] - }, - "metadata": { - "image/png": { - "height": 386, - "width": 497 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(8,6))\n", - "numer = output['tot_tag_with_probe'].project('dataset')\n", - "denom = output['tot_tag'].project('dataset')\n", - "\n", - "fig, ax, _ = hist.plotratio(num=numer,\n", - " denom=denom,\n", - " error_opts={'marker': '.'},\n", - " unc='clopper-pearson',\n", - " ax=ax)\n", - "#ax.text(60, 0.1, '\\n'.join(['logical OR of:',]+ Triggers))\n", - "ax.set_title('[...] trigger efficiency vs. ??? pT', x=0.0, ha=\"left\")\n", - "ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n", - "ax.set_ylabel('Efficiency', y=1.0, ha=\"right\");\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Old way to plot the efficiency" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "# common plotting options\n", - "\n", - "fill_opts = {\n", - " 'edgecolor': (0,0,0,0.3),\n", - " 'alpha': 0.8\n", - "}\n", - "error_opts = {\n", - " 'label':'Stat. Unc.',\n", - " 'hatch':'xxx',\n", - " 'facecolor':'none',\n", - " 'edgecolor':(0,0,0,.5),\n", - " 'linewidth': 0\n", - "}\n", - "data_err_opts = {\n", - " 'linestyle':'none',\n", - " 'marker': '.',\n", - " 'markersize': 10.,\n", - " 'color':'k',\n", - " 'elinewidth': 1,\n", - " 'emarker': '_'\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 603, - "width": 499 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# EFFICIENCY (ratio-plot)\n", - "\n", - "fig, (ax, rax) = plt.subplots(2, 1, figsize=(8,10), gridspec_kw={\"height_ratios\": (1, 1)}, sharex=True)\n", - "fig.subplots_adjust(hspace=.07)\n", - "\n", - "hist.plot1d(output['tot_tag_with_probe'].sum('dataset'), # sum() is summing all the datasets, I immagine\n", - " #overlay='dataset',\n", - " ax=ax,\n", - " clear=False,\n", - " stack=True,\n", - " overflow='over',\n", - " line_opts=None,\n", - " #fill_opts=fill_opts,\n", - " #error_opts=error_opts\n", - " )\n", - "hist.plot1d(output['tot_tag'].sum('dataset'),\n", - " #overlay='dataset',\n", - " ax=ax,\n", - " overflow='over',\n", - " clear=False,\n", - " #error_opts=data_err_opts\n", - " )\n", - "ax.autoscale(axis='x', tight=True)\n", - "ax.set_yscale('log')\n", - "ax.set_ylim([1, 1e7])\n", - "ax.set_xlabel('jet pt [GeV]')\n", - "leg=ax.legend()\n", - "\n", - "hist.plotratio(output['tot_tag_with_probe'].sum('dataset'), output['tot_tag'].sum('dataset'),\n", - " ax=rax,\n", - " overflow='over',\n", - " error_opts=data_err_opts,\n", - " denom_fill_opts={},\n", - " guide_opts={},\n", - " unc='num'\n", - " )\n", - "rax.set_ylabel('Ratio')\n", - "rax.set_ylim(0,0.95)\n", - "\n", - "rax.set_xlabel(rax.get_xlabel(), x=1.0, ha=\"right\")\n", - "ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\")\n", - "ax.set_title(' ', x=0.0, ha=\"left\");\n", - "\n", - "#ax.text(1,1,'60.432/fb (13TeV)', ha='right', va='bottom', transform=ax.transAxes);\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From fad6986baea640836d771152157605438dfdcc42 Mon Sep 17 00:00:00 2001 From: Pietro Meloni <41691318+pietro14@users.noreply.github.com> Date: Tue, 1 Oct 2019 17:37:29 -0500 Subject: [PATCH 8/8] Delete DeltaRstudy.ipynb --- Notebooks/Data/DataOnly/DeltaRstudy.ipynb | 224 ---------------------- 1 file changed, 224 deletions(-) delete mode 100644 Notebooks/Data/DataOnly/DeltaRstudy.ipynb diff --git a/Notebooks/Data/DataOnly/DeltaRstudy.ipynb b/Notebooks/Data/DataOnly/DeltaRstudy.ipynb deleted file mode 100644 index 84e07d7..0000000 --- a/Notebooks/Data/DataOnly/DeltaRstudy.ipynb +++ /dev/null @@ -1,224 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook is for computing the trigger efficiency for data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from coffea import hist\n", - "from coffea.analysis_objects import JaggedCandidateArray\n", - "from coffea.processor import defaultdict_accumulator\n", - "import coffea.processor as processor\n", - "\n", - "import numpy as np\n", - "np.seterr(divide='ignore', invalid='ignore', over='ignore')\n", - "import matplotlib.pyplot as plt\n", - "from FireHydrant.Tools.uproothelpers import NestNestObjArrayToJagged\n", - "\n", - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'retina'" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "datasets_=json.load(open('../Samples/control_data2018.json'))\n", - "datasets = dict(\n", - " A={'files': datasets_['A'], 'treename': 'ffNtuplizer/ffNtuple'} ,\n", - " B={'files': datasets_['B'], 'treename': 'ffNtuples/ffNtuple'} ,\n", - " C={'files': datasets_['C'], 'treename': 'ffNtuples/ffNtuple'} , \n", - " D={'files': datasets_['D'], 'treename': 'ffNtuples/ffNtuple'} ,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "class MyProcessor(processor.ProcessorABC):\n", - " def __init__(self):\n", - " \n", - " dataset_axis = hist.Cat('dataset', '')\n", - " multiplicity_axis = hist.Bin(\"multiplicity\", \"#muons/event\", 10, 0, 10)\n", - " pt_axis = hist.Bin(\"pt\", \"jet pt [GeV]\", 20, 0 , 300)\n", - " deltaR_axis = hist.Bin(\"deltaR\", \"delta_R\", 30, 0 , 5)\n", - " \n", - " self._accumulator = processor.dict_accumulator({\n", - "\n", - " 'deltaR1': hist.Hist(\"Counts\", dataset_axis, deltaR_axis),\n", - " 'deltaR2': hist.Hist(\"Counts\", dataset_axis, deltaR_axis),\n", - " \n", - " })\n", - " \n", - " @property\n", - " def accumulator(self):\n", - " return self._accumulator\n", - " \n", - " def process(self, df):\n", - " output = self.accumulator.identity()\n", - " \n", - " dataset = df['dataset']\n", - " \n", - "\n", - " muons = JaggedCandidateArray.candidatesfromcounts(\n", - " df['muon_p4'],\n", - " px=df['muon_p4.fCoordinates.fX'],\n", - " py=df['muon_p4.fCoordinates.fY'],\n", - " pz=df['muon_p4.fCoordinates.fZ'],\n", - " energy=df['muon_p4.fCoordinates.fT'],\n", - " \n", - " )\n", - "\n", - " leptonjets = JaggedCandidateArray.candidatesfromcounts(\n", - " df['pfjet_p4'],\n", - " px=df['pfjet_p4.fCoordinates.fX'],\n", - " py=df['pfjet_p4.fCoordinates.fY'],\n", - " pz=df['pfjet_p4.fCoordinates.fZ'],\n", - " energy=df['pfjet_p4.fCoordinates.fT'],\n", - " \n", - " )\n", - " \n", - " triggerObjs = JaggedCandidateArray.candidatesfromcounts(\n", - " df['TOHLT_DoubleL2Mu23NoVtx_2Cha'],\n", - " px=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fX'],\n", - " py=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fY'],\n", - " pz=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fZ'],\n", - " energy=df['TOHLT_DoubleL2Mu23NoVtx_2Cha.fCoordinates.fT'],\n", - " )\n", - " \n", - " \n", - " # with muons\n", - " Tobj_Mu_pairs = muons['p4'].cross(triggerObjs['p4'], nested=True)\n", - " dr1 = Tobj_Mu_pairs.i0.delta_r(Tobj_Mu_pairs.i1)\n", - " dr1 = dr1.min()\n", - " \n", - " output['deltaR1'].fill(dataset=dataset, deltaR=dr1.flatten().flatten()) \n", - " \n", - " \n", - " # with leptonjets\n", - " Tobj_Lj_pairs = leptonjets['p4'].cross(triggerObjs['p4'], nested=True)\n", - " dr2 = Tobj_Lj_pairs.i0.delta_r(Tobj_Lj_pairs.i1)\n", - " dr2 = dr2.min()\n", - " \n", - " output['deltaR2'].fill(dataset=dataset, deltaR=dr2.flatten().flatten()) \n", - " \n", - "\n", - " return output\n", - " \n", - " def postprocess(self, accumulator):\n", - " return accumulator" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Preprocessing: 100%|██████████| 4/4 [00:00<00:00, 1032.89it/s]\n", - "Processing: 100%|██████████| 2545/2545 [02:28<00:00, 17.13items/s]\n" - ] - } - ], - "source": [ - "output = processor.run_uproot_job(datasets,\n", - " treename=None,\n", - " processor_instance=MyProcessor(),\n", - " executor=processor.futures_executor,\n", - " executor_args=dict(workers=12, flatten=True),\n", - " chunksize=5000000,\n", - " #maxchunks=0\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 386, - "width": 946 - }, - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1,2,figsize=(16,6))\n", - "\n", - "hist.plot1d(output['deltaR1'].sum('dataset'), ax=ax[0], density=True)\n", - "hist.plot1d(output['deltaR2'].sum('dataset'), ax=ax[1], density=True)\n", - "\n", - "ax[0].set_title('Distance between a trigger object and the closest muon', x=0.0, ha=\"left\")\n", - "ax[1].set_title('Distance between a trigger object and the closest lepton jet', x=0.0, ha=\"left\");\n", - "\n", - "#ax.set_ylim([1e-4, 1])\n", - "#ax.set_yscale('log')\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}