Dimuon_Python_Distributed_Spark.ipynb

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   "source": [
    "# Physics Analysis: Dimuon spectrum\n",
    "\n",
    "This tutorial shows you how to analyze datasets using `RDataFrame` from a Python notebook, offloading computations to a [Spark](https://spark.apache.org/) cluster. The example analysis performs the following steps:\n",
    "\n",
    "1. Connect a ROOT dataframe to a dataset containing 61 mio. events recorded by CMS in 2012\n",
    "2. Filter the events being relevant for your analysis\n",
    "3. Compute the invariant mass of the selected dimuon candidates\n",
    "4. Plot the invariant mass spectrum showing resonances up to the Z mass\n",
    "\n",
    "This material is based on the analysis done by Stefan Wunsch, available [here](http://opendata.web.cern.ch/record/12342) in CERN's Open Data portal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prelude: Using a Distributed RDataFrame\n",
    "\n",
    "While ROOT is implemented in C++, it provides automatic Python bindings, called [PyROOT](https://root.cern/manual/python/). This means that all the ROOT C++ functionality is also available from Python, including `RDataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.25/01\n"
     ]
    }
   ],
   "source": [
    "# This import gives access to all the ROOT C++ code from Python\n",
    "import ROOT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An `RDataFrame` analysis written in Python can be executed both *locally* - possibly in parallel on the cores of the machine - and *distributedly* by offloading computations to external resources, including Spark and [Dask](https://dask.org/) clusters. This feature is enabled by the architecture depicted below, which shows that RDataFrame computation graphs can be mapped to different kinds of resources via backends. In this notebook we will exercise the Spark backend, which divides an `RDataFrame` input dataset in logical ranges and submits computations for each of those ranges to Spark resources.\n",
    "\n",
    "<img src=\"DistRDF_architecture.png\" alt=\"Distributed RDataFrame\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Spark context\n",
    "\n",
    "In order to work with a Spark cluster we need a `SparkContext` object, which represents the connection to that cluster and allows to configure execution-related parameters (e.g. number of cores, memory). When running this notebook from [SWAN](https://swan.cern.ch), a `SparkContext` object is already created for us when connecting to the selected cluster via the graphical interface. Alternatively, we could create a `SparkContext` as described in the [Spark documentation](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.SparkContext.html)."
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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       "\n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://swan005.cern.ch:5119\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v3.1.2</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>k8s://https://188.184.209.142:6443</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>pyspark_shell_swan</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        "
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       "<SparkContext master=k8s://https://188.184.209.142:6443 appName=pyspark_shell_swan>"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provided by SWAN, but it could be created manually\n",
    "sc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a ROOT dataframe\n",
    "\n",
    "Second we will create a ROOT dataframe that is connected to a dataset named `Events` stored in a ROOT file. The file is pulled in via [XRootD](http://xrootd.org/) from EOS public, but note how it could also be stored in your CERNBox space or in any other EOS repository accessible from SWAN (e.g. the experiment ones).\n",
    "\n",
    "The dataset `Events` is a `TTree` (a \"table\" in first order) and has the following branches (also referred to as \"columns\"):\n",
    "\n",
    "| Branch name | Data type | Description |\n",
    "|-------------|-----------|-------------|\n",
    "| `nMuon` | `unsigned int` | Number of muons in this event |\n",
    "| `Muon_pt` | `float[nMuon]` | Transverse momentum of the muons stored as an array of size `nMuon` |\n",
    "| `Muon_eta` | `float[nMuon]` | Pseudo-rapidity of the muons stored as an array of size `nMuon` |\n",
    "| `Muon_phi` | `float[nMuon]` | Azimuth of the muons stored as an array of size `nMuon` |\n",
    "| `Muon_charge` | `int[nMuon]` | Charge of the muons stored as an array of size `nMuon` and either -1 or 1 |\n",
    "| `Muon_mass` | `float[nMuon]` | Mass of the muons stored as an array of size `nMuon` |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The constructor of a Spark `RDataFrame` receives two extra parameters: the number of partitions to apply to the dataset (`npartitions`) and the `SparkContext` object (`sparkcontext`). Besides that detail, a Spark `RDataFrame` is not different from a local `RDataFrame`: the analysis presented in this notebook would not change if we wanted to execute it locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a Spark RDataFrame\n",
    "RDataFrame = ROOT.RDF.Experimental.Distributed.Spark.RDataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = RDataFrame(\"Events\",\n",
    "                \"root://eospublic.cern.ch//eos/opendata/cms/derived-data/AOD2NanoAODOutreachTool/Run2012BC_DoubleMuParked_Muons.root\",\n",
    "                npartitions=16,\n",
    "                sparkcontext=sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter relevant events for this analysis\n",
    "\n",
    "Physics datasets are often general purpose datasets and therefore need extensive filtering of the events for the actual analysis. Here, we implement only a simple selection based on the number of muons and the charge to cut down the dataset in events that are relevant for our study.\n",
    "\n",
    "In particular, we are applying two filters to keep:\n",
    "1. Events with exactly two muons\n",
    "2. Events with muons of opposite charge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2mu = df.Filter(\"nMuon == 2\", \"Events with exactly two muons\")\n",
    "df_oc = df_2mu.Filter(\"Muon_charge[0] != Muon_charge[1]\", \"Muons with opposite charge\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute the invariant mass of the dimuon system\n",
    "\n",
    "Since we want to see the resonances in the mass spectrum, where dimuon events are more likely, we need to compute the invariant mass from the four-vectors of the muon candidates. Because this operation is non-trivial, we will use ROOT's `ROOT::VecOps::InvariantMass` function to do the job for us.\n",
    "\n",
    "The invariant mass will be stored in a new column that we will create with the `Define` operation of `RDataFrame`. The parameters of `Define` are the name of the new column and a string with the function to be invoked, which includes the dataset columns to be used as arguments for the such function.\n",
    "\n",
    "Note how, even though `ROOT::VecOps::InvariantMass` is a C++ function, we can use it in our `RDataFrame` workflow from Python: the second parameter of `Define` is a string that contains C++ code. Such code will be just-in-time compiled by ROOT and called during the event loop in C++. This allows to benefit from C++ performance in computationally-expensive code even when programming in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mass = df_oc.Define(\"Dimuon_mass\", \"ROOT::VecOps::InvariantMass(Muon_pt, Muon_eta, Muon_phi, Muon_mass)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a histogram of the dimuon spectrum\n",
    "\n",
    "As (almost) always in physics, we have a look at the results in the form of a histogram. Let's book a histogram as one endpoint of our computation graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbins = 30000\n",
    "low = 0.25\n",
    "up = 300\n",
    "h = df_mass.Histo1D((\"Dimuon_mass\", \"Dimuon_mass\", nbins, low, up), \"Dimuon_mass\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the dimuon spectrum\n",
    "\n",
    "Now, the computation graph is set up. Next, we want to have a look at the result.\n",
    "\n",
    "Note that the event loop actually runs the first time we try to access the histogram object (results of an `RDataFrame` graph are computed lazily). Therefore, since we selected the Spark backend, the offloading of the computations happens in the cell below. You should see a monitoring display appearing in the output of the cell that tells you about the progress of the Spark job.\n",
    "\n",
    "`%%time` measures the time spend in the full cell. You can compare it with how long it takes to run locally over just 1 mio. events!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 784 ms, sys: 316 ms, total: 1.1 s\n",
      "Wall time: 59.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "ROOT.gStyle.SetOptStat(0); ROOT.gStyle.SetTextFont(42)\n",
    "c = ROOT.TCanvas(\"c\", \"\", 800, 700)\n",
    "c.SetLogx();\n",
    "c.SetLogy()\n",
    "\n",
    "# This triggers the distributed computation\n",
    "h.SetTitle(\"\")\n",
    "\n",
    "h.GetXaxis().SetTitle(\"m_{#mu#mu} (GeV)\")\n",
    "h.GetXaxis().SetTitleSize(0.04)\n",
    "h.GetYaxis().SetTitle(\"N_{Events}\")\n",
    "h.GetYaxis().SetTitleSize(0.04)\n",
    "h.Draw()\n",
    "\n",
    "label = ROOT.TLatex()\n",
    "label.SetNDC(True)\n",
    "label.SetTextSize(0.040)\n",
    "label.DrawLatex(0.100, 0.920, \"#bf{CMS Open Data}\")\n",
    "label.SetTextSize(0.030)\n",
    "label.DrawLatex(0.500, 0.920, \"#sqrt{s} = 8 TeV, L_{int} = 11.6 fb^{-1}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROOT provides interactive JavaScript graphics for Jupyter, which can be activated with the `%jsroot` magic. Click and drag on the axis to zoom in and double click to reset the view."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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4,6,6,3,5,8,8,5,6,4,3,9,6,9,10,3,4,5,6,9,4,5,6,7,7,3,9,11,5,4,6,3,6,5,7,4,4,8,9,4,5,9,2,11,9,10,3,11,3,11,9,5,6,4,4,9,2,5,3,6,4,7,5,6,11,4,5,6,3,3,7,7,6,7,9,7,9,6,14,7,5,11,8,3,4,11,7,6,7,3,5,5,7,7,7,6,3,3,7,5,3,9,8,4,4,3,11,4,5,5,6,6,4,6,9,5,3,3,5,3,3,10,4,3,8,10,3,5,6,11,5,6,5,6,3,5,10,4,5,3,7,10,7,10,5,6,6,4,7,4,6,10,9,7,8,4,9,10,7,5,4,2,2,5,7,6,6,5,8,4,7,7,11,6,5,4,5,3,7,5,5,7,9,6,6,2,6,9,3,4,6,12,4,10,1,5,9,5,1,4,7,4,1,8,4,9,2,8,3,6,5,8,4,8,6,4,6,5,3,7,8,8,7,4,7,5,6,4,11,8,6,5,2,6,5,3,7,5,4,6,10,7,4,5,9,9,3,7,7,4,12,8,6,7,9,4,7,5,7,2,6,8,9,4,12,3,7,7,7,4,6,1,2,7,5,7,3,8,5,5,9,6,6,5,6,7,9,10,4,3,5,8,9,10,2,8,5,4,6,5,4,5,5,1,6,10,5,6,7,6,5,4,3,9,5,1,5,6,6,8,8,7,6,3,4,8,0,1,3,5,4,4,3,3,5,8,5,6,4,5,3,3,4,7,4,4,4,5,7,3,4,9,4,4,8,5,6,5,8,3,5,3,6,3,5,5,8,5,5,3,6,10,5,3,5,9,7,8,8,5,3,8,5,6,5,5,5,5,14,7,6,7,4,5,7,7,8,9,6,8,5,5,2,7,7,5,2,7,6,4,8,6,3,4,7,7,7,1,7,4,4,5,6,3,8,6,7,4,5,7,8,4,5,3,6,7,4,4,5,9,1,4,5,7,2,7,4,4,4,8,5,7,3,7,6,11,3,10,5,3,9,5,5,7,8,1,10,5,6,8,7,5,8,7,3,10,5,3,3,8,5,5,6,4,6,6,4,5,2,2,8,2,7,4,4,4,4,8,3,2,10,1,9,5,6,6,10,6,7,3,5,8,5,8,7,9,7,5,6,6,4,5,3,5,5,4,7,7,5,5,5,3,8,5,7,10,8,5,3,4,4,3,3,7,7,2,6,5,6,2,6,6,2,6,6,6,2,4,2,8,8,6,3,8,10,8,3,4,4,6,7,6,4,6,3,7,5,4,5,4,3,11,7,4,2,2,8,6,4,6,4,10,7,4,5,5,5,8,3,4,3,6,7,5,10,6,7,3,7,5,6,6,1,4,8,7,2,3,3,4,1,4,7,5,7,7,2,3,4,1,4,5,3,5,4,7,5,10,7,4,7,6,9,6,7,1,4,10,4,5,4,6,2,4,2,10,4,5,3,5,2,5,7,6,4,4,4,3,6,4,5,6,6,3,4,5,8,2,4,1,7,5,2,4,5,7,4,5,10,2,8,3,3,6,2,3,9,8,6,11,5,6,4,11,3,6,10,4,7,3,1,6,5,3,5,4,5,5,5,5,8,3,3,8,1,3,4,0,3,7,8,5,8,1,7,2,5,4,5,6,3,9,4,3,3,6,2,10,8,9,3,3,8,7,5,8,6,9,5,5,3,5,4,1,3,5,6,5,6,1,2,9,7,4,5,6,7,6,5,10,3,5,7,6,3,4,6,6,7,1,5,8,4,11,4,3,2,4,4,1,2,4,9,6,5,5,6,2,5,7,3,4,3,6,8,5,7,8,4,6,7,6,11,6,9,3,4,2,6,6,4,2,5,4,3,5,1,9,6,2,3,3,6,4,9,6,7,6,6,2,4,2,3,7,4,2,4,9,5,4,2,4,7,7,4,5,7,2,7,5,2,3,2,6,5,6,3,2,3,5,5,9,4,8,7,3,6,4,7,9,6,5,5,5,10,7,3,6,2,4,6,5,5,5,4,7,7,3,4,3,2,4,2,3,3,3,5,3,4,5,3,8,7,4,7,9,4,1,3,4,2,5,4,6,4,6,5,9,7,3,5,8,5,6,1,4,6,2,1,5,6,7,6,1,5,9,6,7,5,3,2,5,2,2,1,6,6,5,6,8,5,9,6,6,10,3,4,4,6,3,7,6,3,7,6,5,5,7,4,5,8,3,6,5,4,0,2,4,8,4,5,7,2,4,5,7,2,1,6,7,5,10,7,3,2,8,6,5,4,3,4,3,3,5,5,6,7,3,3,6,2,3,4,3,4,7,6,7,4,0,5,4,5,7,6,2,0,5,2,6,2,5,4,3,2,3,5,2,6,2,4,2,4,6,2,4,3,5,2,6,3,2,1,4,5,5,6,6,2,8,1,4,3,2,4,2,2,4,5,5,9,5,4,6,7,3,4,5,6,6,1,1,2,9,3,1,2,6,4,6,4,4,5,3,9,3,2,3,5,5,2,5,7,2,2,3,5,7,1,2,12,3,4,2,4,2,5,2,4,3,5,6,1,3,5,8,4,6,7,6,4,7,1,5,4,3,2,7,8,1,1,3,0,7,11,3,7,5,2,6,5,3,6,4,4,7,5,4,3,2,4,3,9,5,3,4,3,4,5,3,4,2,7,2,5,2,6,1,5,3,2,7,1,4,4,4,6,6,8,3,6,3,6,5,3,5,7,3,4,6,5,4,3,9,3,8,1,7,3,9,3,8,6,3,8,2,4,11,4,6,8,2,5,3,2,0,4,2,7,5,5,8,7,2,8,1,7,3,5,3,3,8,4,3,5,4,6,4,4,2,2,3,0,3,5,2,0,3,2,5,3,2,7,10,6,7,6,7,5,3,5,3,5,3,5,9,6,5,7,2,2,3,6,2,3,10,9,7,5,6,7,4,5,3,4,6,6,2,6,3,4,5,5,3,3,7,7,3,3,4,7,3,8,6,4,3,3,6,2,5,4,6,4,3,5,2,4,3,6,1,0,2,4,2,2,5,2,3,11,2,2,2,1,5,4,2,6,2,3,4,4,7,5,8,3,3,3,4,5,2,2,3,4,2,6,10,3,2,4,4,3,6,5,3,6,3,4,2,7,4,4,3,8,4,4,6,1,2,2,4,3,3,3,4,6,3,6,4,5,3,1,2,3,2,2,6,6,5,7,4,2,2,2,9,3,8,4,4,3,5,6,6,2,1,4,7,3,2,9,4,4,4,3,4,4,5,5,5,4,4,2,4,7,2,4,2,1,4,3,4,4,3,6,5,6,5,4,3,3,6,5,8,3,3,5,1,3,7,3,4,2,4,1,11,4,1,7,8,8,7,7,4,3,5,8,3,3,3,2,5,7,4,6,5,2,0,3,0,2,4,4,3,4,3,0,3,3,4,3,2,6,3,3,5,5,4,1,2,4,6,4,3,2,6,7,6,4,1,5,1,2,4,4,4,5,3,3,3,7,3,5,2,2,8,5,5,1,1,5,7,5,7,2,4,2,4,2,2,2,2,4,1,2,4,8,0,3,4,5,6,2,4,7,4,7,2,5,4,7,5,3,6,4,5,2,5,2,1,3,2,0,2,11,4,6,4,3,2,4,3,7,4,4,3,4,7,4,4,1,5,2,3,8,5,2,3,3,7,4,3,3,3,4,7,4,1,5,4,7,5,3,3,6,3,2,2,4,7,1,4,1,2,1,5,4,1,3,4,1,3,3,5,4,4,5,5,5,4,3,5,2,7,2,4,8,2,3,4,5,4,4,4,2,4,7,4,3,7,3,6,4,4,5,5,1,6,3,4,7,2,4,11,3,2,4,4,5,3,1,6,2,3,9,4,6,3,0,7,1,7,1,5,1,3,6,3,2,3,3,3,1,6,8,4,2,4,6,4,0,3,3,5,8,5,3,4,1,3,3,5,6,2,6,3,8,4,2,3,5,5,3,3,6,6,3,7,7,1,5,7,4,5,2,6,6,2,5,1,2,2,5,4,1,3,4,1,5,5,3,3,3,1,4,4,3,5,0,4,5,1,4,5,6,3,1,4,7,2,2,7,6,3,6,2,7,4,1,6,3,4,3,3,2,2,4,6,3,4,4,4,5,7,8,2,5,1,1,4,3,1,2,3,2,1,4,6,8,1,3,1,2,3,4,2,3,10,1,6,6,2,1,2,1,5,4,2,5,4,5,2,3,4,1,6,1,5,4,1,6,2,6,5,5,3,1,3,2,5,11,5,4,4,6,2,5,3,2,3,5,6,6,1,7,2,2,4,8,6,3,3,5,1,2,4,3,9,4,3,4,0,2,6,4,6,3,4,5,6,3,2,3,4,3,5,6,3,5,3,2,1,2,6,3,1,7,3,2,0,2,4,3,6,1,4,3,3,8,9,3,1,4,3,2,6,4,6,4,3,5,2,4,4,2,7,6,4,2,5,2,1,4,5,3,5,3,4,1,1,2,1,10,3,4,1,1,6,5,4,4,7,6,3,4,4,0,6,4,3,8,2,4,3,1,2,7,4,2,4,6,6,2,4,7,4,3,3,3,6,4,5,6,5,9,2,3,4,2,3,3,1,4,6,2,2,3,3,8,2,1,1,1,1,2,3,5,3,4,6,4,5,1,2,2,3,2,6,3,2,3,1,1,3,1,5,3,4,3,1,4,4,4,3,4,2,2,5,2,8,3,3,0,2,3,4,5,4,5,7,4,3,5,2,3,5,6,4,4,4,2,8,5,4,3,2,7,2,0,3,2,2,3,2,5,3,2,4,2,4,6,3,1,1,3,3,2,1,4,5,3,1,5,4,4,5,5,1,4,2,1,1,4,2,5,5,6,1,3,4,8,3,4,2,2,1,2,5,1,5,2,1,4,3,4,5,4,7,3,2,3,2,3,4,2,1,3,3,5,4,5,1,2,2,4,5,4,5,6,1,6,2,6,3,3,6,3,0,0,9,1,1,4,5,2,2,4,3,3,6,3,6,3,4,5,5,4,0,5,6,5,4,7,2,10,4,4,5,3,3,4,5,5,3,5,5,9,3,5,1,5,3,3,5,5,3,4,4,5,3,1,1,4,4,6,2,4,5,3,4,1,2,2,5,9,2,1,2,5,4,10,4,4,4,0,8,0,4,2,3,0,3,1,5,2,4,5,2,3,2,3,2,6,6,2,1,1,1,0,5,2,2,2,6,4,4,3,1,0,4,4,2,3,0,5,1,1,2,1,1,6,4,3,5,2,5,4,9,3,3,5,6,8,3,5,3,2,4,5,3,3,3,6,7,3,3,3,2,7,4,2,1,6,2,4,5,3,5,4,2,4,2,2,7,3,6,4,8,4,0,4,4,2,6,2,6,3,2,4,3,5,2,3,3,2,4,3,9,4,5,3,6,3,8,5,6,5,5,2,6,5,1,4,5,1,3,1,4,5,2,3,2,3,3,5,4,5,5,3,1,3,5,1,1,3,4,7,3,5,2,4,7,3,1,3,1,2,3,7,4,4,2,6,5,5,4,6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