03_atlas_xAOD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ATLAS xAOD\n",
    "\n",
    "ATLAS has several internal binary formats. These are not accessible in ROOT unless you load a full set of release libraries, and while they are often stored in a columnar format (not easily accessible!), the software presents and forces interactions on a per-event view.\n",
    "\n",
    "ServiceX's job is to take any input and transform it into columnar data. It has a transformer that is designed to do this for the ATLAS xAOD format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mplhep as hep\n",
    "import awkward as ak\n",
    "from hist import Hist\n",
    "import matplotlib.pyplot as plt\n",
    "import vector\n",
    "\n",
    "hep.style.use(hep.style.ATLAS)\n",
    "vector.register_awkward()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting a dataset\n",
    "\n",
    "For this demo lets use an ATLAS release 21 dataset that is available on the GRID: `rucio://mc16_13TeV:mc16_13TeV.361106.PowhegPythia8EvtGen_AZNLOCTEQ6L1_Zee.deriv.DAOD_PHYS.e3601_e5984_s3126_s3136_r10724_r10726_p4164`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from func_adl_servicex_xaodr21 import SXDSAtlasxAODR21, calib_tools\n",
    "\n",
    "ds_name = \"rucio://mc16_13TeV:mc16_13TeV.361106.PowhegPythia8EvtGen_AZNLOCTEQ6L1_Zee.deriv.DAOD_PHYS.e3601_e5984_s3126_s3136_r10724_r10726_p4164\"\n",
    "ds = SXDSAtlasxAODR21(f'{ds_name}?files=20&get=available')\n",
    "\n",
    "# I'll come back to this step\n",
    "ds = calib_tools.query_update(ds,\n",
    "    perform_overlap_removal=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jets\n",
    "\n",
    "Lets do the same query as we did before - but note we no longer have to build the EDM - it is already baked into the xAOD. This does mean, of course, you need to use the baked-in xAOD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_jets = (ds\n",
    "    .Select(lambda e: e.Jets())\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_jets = (all_jets\n",
    "    .Select(lambda jets: jets.Where(lambda j: j.pt()/1000.0 > 30 and abs(j.eta()) < 2.4))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94ccf03342cf470689a67b9ce1fb22d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rucio://mc16_13TeV:m...:   0%|          | 0/9000000000.0 [00:00]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a485a40d3d64f61bc059b2ddf28a999",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "        rucio://mc16_13TeV:m... Downloaded:   0%|          | 0/9000000000.0 [00:00]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jet_momenta = (filtered_jets\n",
    "    .Select(lambda jets: {\n",
    "        'px': jets.Select(lambda j: j.px()/1000.0),\n",
    "        'py': [j.py()/1000.0 for j in jets],\n",
    "        'pz': [j.pz()/1000.0 for j in jets],\n",
    "    })\n",
    "    .AsAwkwardArray()\n",
    "    .value()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can do the same thing we've done before to plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "jet_v = ak.Array(jet_momenta, with_name='Momentum3D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\gordo\\Code\\iris-hep\\agc-workshop-2022-servicex\\.venv\\lib\\site-packages\\mplhep\\utils.py:211: UserWarning: Integer weights indicate poissonian data. Will calculate Garwood interval if ``scipy`` is installed. Otherwise errors will be set to ``sqrt(w2)``.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\gordo\\Code\\iris-hep\\agc-workshop-2022-servicex\\.venv\\lib\\site-packages\\mplhep\\utils.py:211: UserWarning: Integer weights indicate poissonian data. Will calculate Garwood interval if ``scipy`` is installed. Otherwise errors will be set to ``sqrt(w2)``.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\gordo\\Code\\iris-hep\\agc-workshop-2022-servicex\\.venv\\lib\\site-packages\\mplhep\\utils.py:211: UserWarning: Integer weights indicate poissonian data. Will calculate Garwood interval if ``scipy`` is installed. Otherwise errors will be set to ``sqrt(w2)``.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = (\n",
    "  Hist.new\n",
    "  .Reg(100, 0 , 100, name=\"pt\", label=\"$p_T$ [GeV]\")\n",
    "  .Int64()\n",
    ")\n",
    "h.fill(ak.flatten(jet_v.pt))\n",
    "h.plot()\n",
    "plt.show()\n",
    "\n",
    "h = (\n",
    "  Hist.new\n",
    "  .Reg(100, -3.0 , 3.0, name=\"eta\", label=r\"$\\eta$\")\n",
    "  .Int64()\n",
    ")\n",
    "h.fill(ak.flatten(jet_v.eta))\n",
    "h.plot()\n",
    "plt.show()\n",
    "\n",
    "h = (\n",
    "  Hist.new\n",
    "  .Reg(10, 0, 10.0, name=\"njet\", label=r\"Number of Central Jets Per Event\")\n",
    "  .Int64()\n",
    ")\n",
    "h.fill(ak.count(jet_v.eta, axis=1))\n",
    "h.plot()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}