{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "82278df6",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3204608e",
   "metadata": {},
   "source": [
    "This notebook is a quick starter guide on Uproot, Awkward Array, and Vector, which are all Python packages that are part of the Scikit-HEP project. We'll briefly go through parts of each and see how they work together.\n",
    "\n",
    "Most of this notebook is adapted from my [Uproot tutorial](https://hsf-training.github.io/hsf-training-uproot-webpage/index.html) aimed at beginners in data analysis in Python. Here, I'm assuming that the reader is already familiar with at least Python, NumPy, and Matplotlib, so I've compressed the tutorial and added a bit of more advanced material, but it still just scratches the surface of these packages. See the links at the end for more extensive resources."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "474e8afe",
   "metadata": {},
   "source": [
    "## Motivation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883924cc",
   "metadata": {},
   "source": [
    "### Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "834cd090",
   "metadata": {},
   "source": [
    "For quite a while now, ROOT has been the main software used by particle physicists for data analysis. ROOT is written in C++, and at least until recently most analysis code has also been written in C++. Outside of HEP, Python is a more commonly used language for data analysis, which means that there are many popular and well-supported tools that can be utilized for common tasks. Python is also generally considered easier to learn than C++."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2145e33",
   "metadata": {},
   "source": [
    "### Uproot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3b7b1fa",
   "metadata": {},
   "source": [
    "Uproot is a Python module that can read and write files in the ROOT format. It’s part of the Scikit-HEP project, a collection of Python tools for HEP data analysis. Uproot isn’t the only way to read/write ROOT files in Python; there are other packages that allow you to do this. However, there are some significant advantages of Uproot. We’ll go through a few here.\n",
    "\n",
    "The only software required by Uproot (other than Python itself, of course) is NumPy. Awkward Array and Vector are not required but highly recommended for use with Uproot. Uproot, Awkward Array, and Vector are all part of the Scikit-HEP project. All of the above are Python modules, which can be installed with nothing more than `pip` or `conda`.\n",
    "\n",
    "Most importantly, Uproot does not require any part of the ROOT software, which is quite large and can be non-trivial to install properly.\n",
    "\n",
    "Uproot was designed for efficient reading of files. It uses compiled functions with vector optimizations to perform very fast calculations on arrays.\n",
    "\n",
    "When you import a Python package that uses ROOT, there can be a delay of several seconds while all of the required libraries are loaded. This is required every time you restart the Python interpreter or run a script. By virtue of not using ROOT itself, Uproot doesn’t have this issue."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e9ce3d",
   "metadata": {},
   "source": [
    "### Awkward"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb3685c6",
   "metadata": {},
   "source": [
    "One issue with manipulating HEP data in Python is that it is often not rectangular. That is, the length of object collections is not constant. For example, different events will have different numbers of jets, and different jets will have different numbers of associated tracks. Such arrays are called jagged arrays. Packages like NumPy are only designed to work with rectangular arrays, where each dimension has a fixed length throughout the entire array.\n",
    "\n",
    "> Jagged vs. rectangular arrays\n",
    ">\n",
    "> The name “jagged array” comes from the idea that if you have a 2D array in which there’s a different number of entries in each row and you line rows up by their first element, then the right edge of the array will be “jagged” as opposed to straight:\n",
    ">\n",
    "> Rectangular array:\n",
    ">\n",
    "> ```\n",
    "> [[0, 1],\n",
    ">  [2, 3],\n",
    ">  [4, 5],\n",
    ">  [6, 7],\n",
    ">  [8, 9]]\n",
    "> ```\n",
    ">\n",
    "> Jagged array:\n",
    ">\n",
    "> ```\n",
    "> [[0, 1],\n",
    ">  [2, 3, 4],\n",
    ">  [5],\n",
    ">  [6, 7],\n",
    ">  [8, 9]]\n",
    "> ```\n",
    "\n",
    "Awkward Array is a package used to deal with jagged arrays so that they can be manipulated in ways almost identical to standard rectangular NumPy arrays. Other Python packages for reading ROOT files usually do not support jagged arrays, which means that you cannot do operations on multiple events at once if you have jagged data. But Uproot and Awkward Array can, and this is generally much faster than operating on one event at a time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "418a4663",
   "metadata": {},
   "source": [
    "### Vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a41943ab",
   "metadata": {},
   "source": [
    "Vector is a package that provides an interface for 2D, 3D, and (3+1)D vectors and can be used with NumPy and Awkward Array to perform operations on arrays of vectors. This includes calculations for quantities like invariant mass or $\\Delta R$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20524be3",
   "metadata": {},
   "source": [
    "## Goal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f5c795",
   "metadata": {},
   "source": [
    "In order to learn how to use these packages, we’ll try to do a very short and simple analysis to look for resonances in a dimuon events. The example ROOT file is from CMS open data from proton-proton collisions in 2012. Our first goal is just to get to the data within Uproot, so we need to open the file and navigate to the muon information."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5c4131",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fa8ecb6",
   "metadata": {},
   "source": [
    "### Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03a645cb",
   "metadata": {},
   "source": [
    "These packages can be installed in the usual way if they haven't been already:\n",
    "\n",
    "`pip install uproot awkward vector`\n",
    "\n",
    "They are also available via `conda install` on the `conda-forge` channel."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c176f4",
   "metadata": {},
   "source": [
    "### Usage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51e69792",
   "metadata": {},
   "source": [
    "We'll need NumPy and Matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "9f8f3f6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.dpi'] = 200 # make figures bigger and more readable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab49dc25",
   "metadata": {},
   "source": [
    "Uproot and Vector are normally imported by their name, and Awkward Array is usually imported as `ak` as an analogy to `numpy as np`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "74b194fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import awkward as ak\n",
    "import vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94d92f7c",
   "metadata": {},
   "source": [
    "# Inspecting data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f087f3d6",
   "metadata": {},
   "source": [
    "## Files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fa26658",
   "metadata": {},
   "source": [
    "### Opening a file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdc06dc8",
   "metadata": {},
   "source": [
    "Now open the ROOT file and assign it to a variable (which I’ve named file here):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "35ac77e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = uproot.open('open-data-example.root')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0de8098",
   "metadata": {},
   "source": [
    "If you inspect file, you can see that it’s a `ReadOnlyDirectory` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "63e82638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ReadOnlyDirectory '/' at 0x7fa09e8bdd60>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48b53327",
   "metadata": {},
   "source": [
    "### File contents"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6161f777",
   "metadata": {},
   "source": [
    "Just like any other kind of directory, you can list the contents (of the file). The name of each item in the file is called a key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "36a0cdbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Events;1']"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3744b018",
   "metadata": {},
   "source": [
    "We can see that there is one key: “Events”. This doesn’t tell us what kind of object it refers to, though. ROOT files can contain many different types of objects, including subdirectories. The following function provides a way to inspect the types of each item:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "650f42cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Events;1': 'TTree'}"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file.classnames()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e838b3c",
   "metadata": {},
   "source": [
    "The output contains pairs of the form `name: type`. Therefore the key `Events` refers to a `TTree` object. This is where all the data in this file is stored. The `;1` refers to the cycle number."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8fd52e0",
   "metadata": {},
   "source": [
    "### Accessing contents"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3d0a775",
   "metadata": {},
   "source": [
    "Now we want to actually access the object inside the file. You can do this just as you would to get an item in an array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "40650c39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TTree 'Events' (6 branches) at 0x7fa09d6f0760>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file['Events']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb49da0",
   "metadata": {},
   "source": [
    "This expression refers to the actual TTree object, which we will look at next."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6001fbc",
   "metadata": {},
   "source": [
    "## Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49db36bd",
   "metadata": {},
   "source": [
    "Trees in ROOT are basically just tables of information. Trees are composed of branches, which are the columns of the table. The rows usually represent events (individual bunch crossings).\n",
    "\n",
    "First we assign the tree to a variable (named `tree` here):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "ab2e5722",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = file['Events']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4d4acd2",
   "metadata": {},
   "source": [
    "In order to find out what information is in the tree, we need to know what the branches (columns) are. The term key is used (again) here to refer to the names of the branches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "7c487f4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nMuon', 'Muon_pt', 'Muon_eta', 'Muon_phi', 'Muon_mass', 'Muon_charge']"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e53ff8f7",
   "metadata": {},
   "source": [
    "The above output is a list of the branch names. So we can see that for each event, we will have the number of muons in the event (nMuon) and the pT, eta, phi, mass, and charge of each muon. We can also see these keys with the types included using `.show()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "2c8e0897",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name                 | typename                 | interpretation                \n",
      "---------------------+--------------------------+-------------------------------\n",
      "nMuon                | uint32_t                 | AsDtype('>u4')\n",
      "Muon_pt              | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "Muon_eta             | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "Muon_phi             | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "Muon_mass            | float[]                  | AsJagged(AsDtype('>f4'))\n",
      "Muon_charge          | int32_t[]                | AsJagged(AsDtype('>i4'))\n"
     ]
    }
   ],
   "source": [
    "tree.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be0b4a4c",
   "metadata": {},
   "source": [
    "But how do we get the actual data from the table? There are several ways with Uproot, but the simplest is with the arrays() function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "f1dc0dc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [{nMuon: 2, Muon_pt: [10.8, ... -1, 1]}] type='100000 * {\"nMuon\": uint32,...'>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.arrays()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "427fef42",
   "metadata": {},
   "source": [
    "You can see some numbers in there, which indeed are from the data in the tree."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6ac7add",
   "metadata": {},
   "source": [
    "## Branches"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "270b0d95",
   "metadata": {},
   "source": [
    "Now we assign this object (which contains both the names and contents of the branches) to another variable (branches):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "345980b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "branches = tree.arrays()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8558647b",
   "metadata": {},
   "source": [
    "Next let’s just look at each branch individually. You can access a single branch from branches in a similar way to getting an item from a ROOT file object (array-like notation):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "5749ec65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [2, 2, 1, 4, 4, 3, ... 0, 3, 2, 3, 2, 3] type='100000 * uint32'>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branches['nMuon']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f09ba863",
   "metadata": {},
   "source": [
    "You can also access branches with attribute/property notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "34831be1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [2, 2, 1, 4, 4, 3, ... 0, 3, 2, 3, 2, 3] type='100000 * uint32'>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branches.nMuon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "522de7b3",
   "metadata": {},
   "source": [
    "You can see the partial list of numbers in the output that represents the number of muons in each event. It’s abbreviated with an ellipsis (...) so that it doesn’t take up the whole page.\n",
    "\n",
    "These Array objects are a special type provided by the Awkward Array package. The type=100000 * uint32 means that there are 100,000 entries and that each entry is a 32-bit unsigned integer. Each entry corresponds to one event.\n",
    "\n",
    "Let’s look at another branch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "7dc89064",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[10.8, 15.7], ... 11.4, 3.08, 4.97]] type='100000 * var * float32'>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branches['Muon_pt']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c548efee",
   "metadata": {},
   "source": [
    "This is a jagged array because the number of entries is different for different events (because each event can have a different number of muons). Note that there are square brackets [] surrounding the list of entries for each event. The type='100000 * var * float32' means that there are 100,000 rows, each containing a variable number of 32-bit floating point numbers. This is basically an array of arrays (or a 2D array)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cecbfa41",
   "metadata": {},
   "source": [
    "### Events"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c91d6db",
   "metadata": {},
   "source": [
    "If we want to focus on a particular event, we can index it just like a normal array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "1750d35b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [10.8, 15.7] type='2 * float32'>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branches['Muon_pt'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e8d69b1",
   "metadata": {},
   "source": [
    "From the above output, the first event has two muons, and the two numbers in the list are the muons’ $p_\\mathrm{T}$. It’s not specified anywhere in the file, but the units are GeV. Let’s look at the third event:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "dfbbbf49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [3.28] type='1 * float32'>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branches['Muon_pt'][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c38c12",
   "metadata": {},
   "source": [
    "It only has one muon."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "441b2a03",
   "metadata": {},
   "source": [
    "What if we want to get all of the information about a single event? So far we’ve accessed data in branches by providing a branch name, but we can also just use an event index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "19de6212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Record ... 0.106], Muon_charge: [-1, -1]} type='{\"nMuon\": uint32, \"Muon_pt\": va...'>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branches[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7229dc31",
   "metadata": {},
   "source": [
    "This is a `Record` object, which is another special type provided by Awkward Array. It functions basically the same way as a standard Python dictionary (dict). Unfortunately, most of the interesting information is still hidden in the above output to save space. A little trick we can use to force printing all the data is adding `.tolist()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "8cd72f21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'nMuon': 2,\n",
       " 'Muon_pt': [10.763696670532227, 15.736522674560547],\n",
       " 'Muon_eta': [1.0668272972106934, -0.563786506652832],\n",
       " 'Muon_phi': [-0.03427272289991379, 2.5426154136657715],\n",
       " 'Muon_mass': [0.10565836727619171, 0.10565836727619171],\n",
       " 'Muon_charge': [-1, -1]}"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branches[0].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c68234a",
   "metadata": {},
   "source": [
    "There we go. Now we can see the whole picture for an individual event.\n",
    "\n",
    "`.tolist()` is a NumPy function that has been extended to Awkward Array objects. As the name suggests, it converts NumPy arrays to Python lists. In the case of trees, which have named branches, it actually converts to a dictionary of lists. It can be very useful when you want to understand exactly what’s in an Array or Record. Be careful when using it, though–trying to print out an entire branch or tree could cause Python to crash if it’s large enough. Therefore it’s best to only use `tolist()` on one or a few events at a time to be safe."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79af7e75",
   "metadata": {},
   "source": [
    "If you only want to see a description of the structure of the array and types within it (not the data itself), you can use `.type`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "0c347029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000 * {\"nMuon\": uint32, \"Muon_pt\": var * float32, \"Muon_eta\": var * float32, \"Muon_phi\": var * float32, \"Muon_mass\": var * float32, \"Muon_charge\": var * int32}"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branches.type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "625f92bc",
   "metadata": {},
   "source": [
    "# Transforming data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f07b7154",
   "metadata": {},
   "source": [
    "Uproot is designed for columnar analysis, which means performing operations on entire columns (branches) at a time, rather than operating on every event individually."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dfc5c37",
   "metadata": {},
   "source": [
    "## Counting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5368fe1",
   "metadata": {},
   "source": [
    "The simplest task we need for analysis is counting (i.e., cutflow). To count the total number of events, we can use the Python built-in function `len()` on any of the following:\n",
    "\n",
    "```python\n",
    "len(branches)\n",
    "len(branches['nMuon'])\n",
    "len(branches['Muon_pt']) # or any of the other branches...\n",
    "```\n",
    "\n",
    "which will output `100000`. So there are 100,000 events."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6041f605",
   "metadata": {},
   "source": [
    "Note that `len` will only tell you the length of the outermost dimension. The `Muon_pt` branch has more than 100,000 numbers in it. We can see this by flattening the array. `np.ravel` is a simple function to completely flatten an array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "af56159e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [10.8, 15.7, 10.5, ... 11.4, 3.08, 4.97] type='235286 * float32'>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ravel(branches['Muon_pt'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9619a20a",
   "metadata": {},
   "source": [
    "Note that I've used a NumPy function here on an `ak.Array`. Most of the standard NumPy functions work fine on Awkward `Array`s, although there are also equivalent Awkward functions like `ak.ravel`. Generally speaking, the default behavior between these NumPy and Awkward functions is the same, but the `ak` versions have optional parameters for funcationality that is specific to jagged arrays. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e858587d",
   "metadata": {},
   "source": [
    "Awkward also has functions that wouldn't make sense in NumPy. For counting the number of entries in each event for a branch, there's `ak.num`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "951e6c9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [2, 2, 1, 4, 4, 3, ... 0, 3, 2, 3, 2, 3] type='100000 * int64'>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.num(branches['Muon_pt'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b1215c",
   "metadata": {},
   "source": [
    "which in this case is equal to already existing `branches['nMuon']`. In NumPy, there's no need for this function, since it would be the same number over and over, and you could just get it from `np.ndarray.shape`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b884745d",
   "metadata": {},
   "source": [
    "## Selections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "778c0338",
   "metadata": {},
   "source": [
    "### Selections from 1D arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13dcffa9",
   "metadata": {},
   "source": [
    "To do more interesting counting or plotting, we want to be able to select only events or muons that pass some criteria. Just as you can compare two numbers, like `1 < 2` and get a `True` or `False` value, you can put arrays in comparisons as well. Let’s look at this case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "fce11527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [False, False, True, ... False, False] type='100000 * bool'>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branches['nMuon'] == 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b902fc60",
   "metadata": {},
   "source": [
    "This is checking for equality between an array and the number 1. Of course these are different types (array vs. scalar), so the Python objects are certainly not identical, but you can see the return value is not just False. What happens is that each element in the array is compared to the scalar value, and the return value is a new array (of the same shape) filled with all these comparison results. So we can interpret the output as the result of testing whether each event has exactly one muon or not. The first two events do not, the third does, and so on.\n",
    "\n",
    "This array of Boolean values is called a mask because we can use it to pick out only the elements in the array that satisfy some criteria (like having exactly one muon). This is very useful, and we will save it to a variable to save typing later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "bb806bcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "single_muon_mask = branches['nMuon'] == 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75bfea57",
   "metadata": {},
   "source": [
    "### Counting with selections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78312779",
   "metadata": {},
   "source": [
    "Now let’s say we want to know how many of these single-muon events there are. Note that `len()` won’t work because the length of the array is still 100,000. That is, there’s a value for every event (even if that value is `False`). We need the number of `True`s in the array. There are multiple ways to do this, but my favorite is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "9ab66fc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13447"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(single_muon_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ba4e076",
   "metadata": {},
   "source": [
    "`sum()` adds all the array values together. `True` is interpreted as `1` and `False` is interpreted as `0`, thus `sum()` is just the number of True entries. So there are 13,447 events with exactly one muon."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f052e4",
   "metadata": {},
   "source": [
    "### Applying a mask to an array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90385fc7",
   "metadata": {},
   "source": [
    "If we want to apply some selection to an array (i.e., cut out events or muons that don’t pass), we just act like the selection mask is an index. For example, if we want the pT of only those muons in events with exactly one muon (so they’re the only muon in that event):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "b8f08d4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[3.28], [3.84], ... [13.3], [9.48]] type='13447 * var * float32'>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branches['Muon_pt'][single_muon_mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "309ea63a",
   "metadata": {},
   "source": [
    "We can check that we really are only looking at the events that pass the selection by looking the number of rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "22f1a7db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13447"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(branches['Muon_pt'][single_muon_mask])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77242ed1",
   "metadata": {},
   "source": [
    "Yep, this matches the counting from single_muon_mask.sum() above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f79ad1a",
   "metadata": {},
   "source": [
    "### Plotting with selections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eba1a5b",
   "metadata": {},
   "source": [
    "We can also use masks to plot quantities after some selection. For example, let’s plot the muon $p_\\mathrm{T}$ for only the single-muon events. Note that, just like for multidimensional NumPy arrays, `Array`s must be flattened before histogramming."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "6db0683d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o8XdJzk5y8SQvrqrD9ufkqrpUkhcmuUSSc8brAQAAAFzE3EON7j43yROTVJJbJXl7Vd10M+dW1fcleWuS22bopXHSeD0AAACAi1jI1J7d/eSq+sEMr5DcLMn7qurNSV6d5N8zDP55bpIjklw5yfcnuUuSO2UIQ5Lkn7v7SYuoDzh4G42HAQAAsGgLCTVGP5vk6UlOGLfvMH7WsxJoPD/JwxdTFgAAALATLCzU6O7zk/xCVb0yye8kueUmTvv3JH/c3S9fVF3A7rKZHiVmWQEAgOW0yJ4aSZIxoHh5Vd04Q0+NmyY5Msllknw1yReTvD/Jm7v7Q4uuBwAAANgZFh5qrBgDC6EFAAAAMBeLmNIVAAAAYOEm66kBsL+MhwEAAKxHTw0AAABgKQk1AAAAgKXk9ZNdoKpOXbXLzx0AAIClp6cGAAAAsJT8xn4X6O5jZrer6qgkZ25ROQAAADAXemoAAAAAS0moAQAAACwloQYAAACwlIQaAAAAwFLatgOFVtVVktwwSbr7LVtcDgAAALDNbNtQI8n/SPLcJBdme9cJLLk9J56y7vEzTjp+okoAAID9sQyvn9RWFwAAAABsP8sQagAAAAB8F6EGAAAAsJTmPlZFVX1sTpe69JyuA9vGRmM3JMZvAAAA2KxFDMC5J0nn4MfC6IMvBQAAANipFjmryLlJvngQ5186yZXmVAsAAACwwywi1DgjyXWS/Gt33+VAL1JVD8wwpSsAAADAd1nEQKHvzvDqya0WcG0AAACAJIsJNd41Lq9QVddbwPUBAAAAFhpqJMmtF3B9AAAAgIWMqfHvSd48rh/MtKxvS/Kggy8Hlstmpn1lL39fAACwe8091Oju85LcaQ7X+X9J/t/BVwQAAADsRIuc0pVtoqpOXbXLzx0AAIClt4gxNQAAAAAWzm/sd4HuPmZ2u6qOSnLmFpUDAAAAcyHUANjAZgYjPeOk4yeoBAAAmOX1EwAAAGApCTUAAACApSTUAAAAAJaSUAMAAABYSkINAAAAYCkJNQAAAIClNPdQo6puOe9rAgAAAKy2iJ4a766qT1XVX1fV3arq0AXcAwAAANjlFvX6yTWSPCTJPyX5YlW9sqoeVlXXWND9AAAAgF1mEaHGUUkenuTVSb6R5LAkxyd5epIzq+q9VfVYr6kAAAAAB+OQeV+wuz+T5JlJnllVhyX50SR3yxBsXCPJLZLcPMnvV9XnkpyS5JVJXt/dX593PQBT2HPiKeseP+Ok4yeqBAAAdo+Fzn7S3V/v7ld29y9291FJjk3y+CTvS1JJrp7kwUlekeQsr6kAAAAAmzXplK7d/d7ufmx33ypeUwEAAAAOwqShxqzu/kx3P7O7757kSkl+Ismzknw2Qy+OWyT5/STvqqpPj7Op3Gyr6gUAAAC2l7mPqXEguvsbSV41fjL2zrhbkrtnCDeunmE2lU8nef8WlQkAAABsI9si1Fitu9+b5L1JHjeOr3G38fO1LS0MAAAA2Da2Zagxa3Y2la2uBQAAANg+tmxMDQAAAICDIdQAAAAAlpJQAwAAAFhKQg0AAABgKQk1AAAAgKUk1AAAAACWklADAAAAWEpCDQAAAGApCTUAAACApXTIVhewoqrunuQ+SY5M8vEkz+ru921tVTtDVZ26ate2+bkDAADAgZqkp0ZV3amq/ruqPllVl1/j+B8meUWSn01y5yS/mOSdVXX/KeoDAAAAls9Uv7G/a4YeGP/Q3V+aPVBVN03yO0lq3HVOkiuMtT2zqt7W3Z+YqM4dqbuPmd2uqqOSnLlF5QAAAMBcTDWmxu2SdJLXrXHslzIEGuckuWV3XynJrZOcneTQJA+fqEYAAABgiUwValxtXH5kjWN3yxB4/NXKGBrd/Z4kT8sQdvzoJBUCAAAAS2WqUOMq4/LLszur6vpJrjluvmzVOW8dl0cvsC4AAABgSU01psbKeBmXW7X/uHH55e7+j1XHvjguD19UUQDbxZ4TT9mwzRknHT9BJQAAsDym6qnxuXF5o1X7f3xcvn2Nc44Yl+cspCIAAABgqU3VU+PfkuxJ8ktV9YLu/lpVXS/JT2bfA4h+77j83BrHAJbKZnpiAAAA+2eqnhp/My5vmuRDVfUPGYKOQ5N8PcmL1jjn9uPyw4svDwAAAFg2k4Qa3f2GJE/JMLbGniT3THLkePi3u/us2fZVdWjW78UBAAAA7HJTvX6S7n5EVb0hyU9nmOL1s0mePwYeq/1Ekq9kmC1FqAEAAAB8l8lCjSTp7lcledUm2r0kyUsWXxEAAACwrKYaUwMAAABgroQaAAAAwFKa9PWTFVV1mSTXTXKZJBffqH13v2XhRQEAAABLZdJQo6oemuSXM0ztulmdLQpfAAAAgO1rkrCgqi6e5B+T3H1l1xT3BQAAAHauqXpAPDzDNK1J8vkkz03y3iRnJ7lwohoAAACAHWSqUOMB4/LDSY7r7nMmui8AAACwQ001+8mNMoyN8YcCDQAAAGAepp7S9T8nvh8AAACwQ00VavzXuLziRPcDAAAAdripQo2/yzDjyd0muh8AAACww00Vajw1yQeS/FJVHTfRPQEAAIAdbJJQo7vPT3LnDNO4vq6q/qSqbl5Vh05xfwAAAGDnmWRK16r69uxmkkeOn1TVRqd3d0819SzAtrXnxFPWPX7GScdPVAkAAGwPU4UFq5OLDZMMAAAAgPVMFWo8bqL7AAAAALvEJKFGdws1AAAAgLmaavYTAAAAgLkSagAAAABLaUtmFamqSyT5/iQ3TnLFcffZST6U5N+7+1tbURfAMttodpTEDCkAAOwsk4YaVXV4kt9P8tAkV9hHs3Oq6plJ/qi7vzZZcQAAAMBSmez1k6q6dpL/SPKoDL0zah+fKyZ5dJL3VdVRU9UHAAAALJdJemqMr5u8JsnR466PJHlukncm+VyGMOOqSW6d5IQk35fke5K8pqpu0d0XTFEnAAAAsDym6qnxkCQ3StJJ/jjJjbv7T7v7Ld19enf/57j+pCQ3TfJH43nfN54LAAAAcBFThRo/nSHQeEV3/353X7ivht19YXf/QZKXZ+jB8dMT1QgAAAAskalCjRuPy+fsxznPHpc3mXMt205VXbOqfqOqXltVn6yqb1bV56rqH6vqB7a6PgAAANiOpgo1LjcuP7Mf53x2XF52zrVsR7+W5M+TXC/J65I8Ocnbkvxkkn+tqvtsYW0AAACwLU01pevZSa6S5LpJ3rfJc643c+5O964kt+/ut87urKrjkvxLkqdX1T919/lbUh0AAABsQ1OFGv+e5H8k+ZUkL9vkOb+SYRyOzYYgS6u71/w76e63VtUbk9w5w2s475m0MGDH2XPiKeseP+Ok4yeqBAAADt5Ur5+8eFzesaqeU1VH7KthVR1RVc9Jcsdx1wsXWVhVXaWq7lZVj6+q11TVWVXV4+fk/bzWtavqSVV1WlWdV1VnV9W7quq3qurwAyzxW+PStLYAAAAwY6qeGi9M8vAkP5jkgUmOr6qXJHlnks9n6JFxtSQ/kGG2kyuP5729u1+04No+P4+LVNXxGf6cl5vZfXiSY8fPQ6rqrt39sf245rWT/GiSzyX54DzqBAAAgJ1iklCju7uq7p7klCS3yRBa/PL4Wa3G5TsyDJQ5pTOTnJbhdY9Nq6qbJXlJhhDj3CRPTPLGJIcluW+Shya5QZJTqurY7j53E9e8RJK/TXKpJI/q7m/vT00AAACw0031+km6+5wkt8sw08dpGcKLtT6nJfnVJMeN5yza45PcPcnVuvvaSX7xAK7xlAyBxgVJ7tzdT+jud3T3G7r7YUkeNba7YZJHbHSxqrpYhulvb5/kWd39twdQEwAAAOxoU71+kiTp7guT/FWSv6qqqye5cZIrjofPTvKh7v7svs5fUE2POZjzq+rY7B3/49nd/Y41mj05yYOS3CjJb1TVE7v7W2u0S1VVkmcl+bkkL8jw2g4AAACwyqShxqwxvJg0wFiQe8ysP3etBt19YVU9P8NrKVfIEIK8bnW7sYfG32QIQF6c5IQxCAIAAABWmez1kx3suHF5XpL3rtPuzTPrt1t9cFWg8fdJft44GgAAALBvW9ZTYwe50bj8aHevN+3qR9Y4J8l3Ao1nJzkhyUuT/NzBBBpVddQGTa52oNcGdrY9J56yYZszTjp+gkoAAGBjk4QaVfWcgzi9u/vBcytmjqrq0CRHjpufWq9td59TVeclOSLJtVYd/oMMgca5SU5P8nvD0BoX8Yru/o9NlnbmJtsBAADA0pqqp8YJSfoAzqvxvG0ZaiS5zMz6htO0ZnhF5Ygkl161f8+4vHSS393HuWck+Y/NlwYAAAA721ShxiezcahxRPb2eugkZyX52iKLmoNDZ9a/uYn254/Lw2Z3dvcJGYKfeVndE2S1qyV59xzvBwAAAJObJNTo7j2baVdVV0zys0kel+RLSX6yuz+y7klb6xsz65fcRPtLjcuvL6CW7+judV+FWePVFgAAAFg622r2k+4+u7uflmF2kKsmeU1VXWGLy1rPV2fWV79SspYjxuVmXlUBAAAA1rGtQo0V3X1akqcmuU6SR25xOfvU3d/I8JpMkqw748gYzqyEGgbyBAAAgIO0LUON0evG5U9taRUbO21cHl1V673Oc8M1zgEAAAAO0HYONVZe0bj2llaxsbeNyyOS3HKddneYWX/74soBAACA3WGq2U8OxC3G5be2tIqNvSLJ/xzXH5TknasbVNXFkjxg3PxSkjdOUdjM/U9dtWs7/9wBAABgU7ZlT42qum6Sx2aY2vU/trSYDXT3u5K8ddx8cFXddo1mj0xyo3H9L7p7uwc1AAAAsO1N8hv7qnrAxq1ysSRXSHKrJD+Z5PAMocYzFlhaqup2SY6e2XXkzPrRVXXCbPvuPnmNy/x6hldKDkvy2qp6QobeGIcluW+Sh43tTk/y5LkUvh+6+5jZ7ao6KgYrBQAAYMlN9RrCyRkCis2qcfnU7v77+ZdzEQ9J8sB9HPuh8TPr5NWNuvt9VfUzSV6Q5LJJnrDGtU5Pcnx3f3WNYwAAAMB+mnJshdq4SZJhzIm3JPnf3f3axZUzX939yqq6aYZeG8dnmOL1m0k+muSlSZ7W3V/bwhIB5mLPiaese/yMk46fqBIAAHa7qUKN626izYVJvtrdX1pwLRfR3SckOWFO1/pEkkeMHwAAAGCBJgk1xi/7AAAAAHOzLWc/AQAAANiIUAMAAABYSlMOFHoRVXXZJJdJcvGN2nb3Jxdf0c5VVaeu2rVlP3cAAACYl0m/3FbVnZP8UpLjklxhk6d1fAkHAAAAVpksLKiqZyR56MrmVPcl6e5jZrer6qgkZ25ROQAAADAXk4QaVfWLSR42bn41ycuTvD/JlzJM5QoAAACwX6bqqbESaJyW5Ie7+/MT3RcAAADYoaaa/eSGGcbGeKxAAwAAAJiHqUKNc8flf010PwAAAGCHmyrUOG1cXn2i+wEAAAA73FRjajwzye2T3C/Jaya6JwBbYM+Jp2zY5oyTjp+gEgAAdrpJQo3uflFV3TPJ/avqXd39tCnuy6CqTl21a7KpfAEAAGBRpvxye/8kT0rylKq6b5KXJDk9ydc2OrG737Lg2gAAAIAlM2Wo8a0k709ydpLbjp/N6OhZcFC6+5jZ7ao6KsmZW1QOAAAAzMUkYUFVHZLkxUl+amXXFPcFAAAAdq6pekA8PMm9xvUzkjwvyQeSfCnJhRPVAAAAAOwgU4UaDxmX/5bkR7r76xPdFwAAANihLjbRfY7OMDbGEwUaAAAAwDxMFWqcNy4NTgkAAADMxVSvn7w/yY8kuU6S/5jongBsU3tOPGXd42ecdPxElQAAsMym6qnxjAwznjx4ovsBAAAAO9wkoUZ3vyxDsHG3qnpSVV18ivsCAAAAO9ckr59U1QMyzHxyiyS/meTeVfWyJKcn+dpG53f38xdbIQDbyUavpyReUQEAYLoxNU7OMPvJimsl+fVNnttJhBoHoapOXbVrqp87AAAALMyUX25rwnsBAAAAO9xUocZ1J7oPa+juY2a3q+qomF4XAACAJTdJqNHdn5jiPgAAAMDuMdWUrgAAAABzJdQAAAAAlpJQAwAAAFhKQg0AAABgKQk1AAAAgKUk1AAAAACWklADAAAAWEpCDQAAAGApCTUAAACApXTIVhfA4lXVqat2+bkDAACw9Hy5BWAp7TnxlHWPn3HS8RNVAgDAVpk81KiqOyV5UJLbJrlaksOS3LS7PzzT5rgkN0nyle5+wdQ17jTdfczsdlUdleTMLSoHYBJCDwCAnW+yUKOqDk/yvCQ/tbJrXPYazb+d5GlJuqre2d3/NUGJAAAAwBKZcqDQv88QaFSSdyd50r4adve/JvnguHmvxZcGAAAALJtJQo2qumeSlX6+D+vu23T3ozY47WUZApA7LLQ4AAAAYClN1VPjgePyBd39N5s8573j8kYLqAcAAABYclONqXFshrEz/n4/zvnsuLzy/MsBgI0ZbBQAYHubqqfGlcblpw/g3CnH/QAAAACWxFQ9Nb6a5IpJLrsf51x/XH5x/uUAsNtt1AsDAIDtb6peECtTst56P85ZmfXk/XOuBQAAANgBpgo1Xp1hJpNfrqpDN2pcVXfJEGp0klctuDYAAABgCU0VajwtyZeS7Enysqq60lqNqurQqnpkhulcL5bkc0meO1GNAAAAwBKZZEyN7v5SVf1ckn9K8uNJPllVb55p8vtVdfkkP5TkiAy9Or6V5P7d/Y0pagQAAACWy2Qzi3T3q5PcNckXkhyW5C4ZXi9JkvskuXOSS2cINM5KctfuftNU9QEAAADLZdLpUrv7dUmul+TXkrw+yZczhBiV5OtJ3p7k0Umu393/MmVtAAAAwHKZakrX7+juryX5q/GTqjokycW7+/ypa9ktqurUVbsm/7kDAADAvG35l9vuviDJBVtdBwAAALBctjzUYPG6+5jZ7ao6KsmZW1QOAAAAzIVQAwAO0J4TT9mwzRknHT9BJQAAu9NcQ42qesM8rzfq7v6RBVwXAAAAWGLz7qlxxwzTtNYcrrVynd6oIQAAALD7zDvUeEuEEAAAAMAE5hpqdPcd53k9AAAAgH252FYXAAAAAHAghBoAAADAUhJqAAAAAEtp3gOFrqmqrn0Ap3WSbyT5cnd/c84lAcCOsefEUzZsc8ZJx09QCQDAtCYJNZJ8/GBOrqpPJfm3JCd392vmUxIAAACwzKYKNeogz79WkqOS3LuqXp/kvt19zsGXBQCLtVEvCj0oAAAO3FShxoPG5cOT/ECG10r+b5L3JPnCeOzKSW6V5MeTXCrJu5I8M8llk9w4yU8mOTLJjyZ5RZI7TFM6AAAAsB1NEmp09/Oq6hlJbp3kn5L8Ynf/91ptq+oqGcKMuyc5tbsfMu7/tSTPSPKAJLerqp/p7r+fon4AAABg+5lk9pOqukeShyV5R5Kf2legkSTjsXtmGEPjQVV1n3H/N5L8QpJ/H5ved5E1AwAAANvbVFO6/nKG2Uz+ort7o8Zjm6dkGIvjYTP7L0zyrHH/rRZSKQAAALAUpgo1bjouP7of56y0vcmq/R8Yl1c6qIoAAACApTZVqHHZcXnl/Thnpe1lVu3/+rj81kFVBAAAACy1qUKNM8fl/ffjnJ8fl59ctf8q4/ILAQAAAHatqaZ0/eckj0zyc1X1vu5+ynqNq+o3MwQgPZ476wfG5SfmXeROVVWnrto11c8dAAAAFmaqL7cnJXlghnEwnlxV90/y/CTvTbIyE8pVMgz++fNJvn/c94Xx3Fn3zRB2vG7BNQPAtrDnxFO2ugQAgG1pklCju79YVT+W5P8kuWqG0OL71zmlknwuyV26++zv7Ky6XpJ3jZ9/XFzFO0t3HzO7XVVHZe8rQQAAALCUphpTI939/iQ3SvKXSb6SIbhY6/OVsc0x3f2BVdf4WHc/aPz811S1AwAAANvPpGMrdPeXkvx6VT0qyS2T3DjJFcbD5yQ5Ncl7uvv8KesCgK3i1RIAgAO3JQNGjqHFv44fAAAAgP022esnAAAAAPMk1AAAAACW0uSvn1TVzZIcl+R6SS6T5OIbnNLd/eCFFwYAAAAslclCjaq6QZLnJLnN/pyWpJMINQAAAICLmCTUqKprJnlLkiMzBBVJcm6GGU8unKIGAAAAYGeZqqfG7ya5coZeF3+T5EndffpE9wYAAAB2oKlCjbtkCDSe390Pm+ieAMBoz4mnrHv8jJOOn6gSAID5mWr2k2uMy+dPdD8AAABgh5uqp8Y5Sa6S5EsT3Q8AmJjeIADA1KbqqfGecfm9E90PAAAA2OGmCjWemmHWE+NpAAAAAHMxSajR3a9L8idJ7lRVT6+qS0xxXwAAAGDnmmRMjap6QJIPJ/nXDL017l5V/5DkI0m+ttH53W2AUQAAAOAiphoo9OQMU7quuHqSX9vkuR2zpgAAAACrTBVqJMOYGgAAAABzMVWocd2J7gMAAADsEpOEGt39iSnuAwAAAOweU03pCgAAADBXQg0AAABgKQk1AAAAgKU01zE1quoN42p394+ssf9AXORaAAAAAMn8Bwq947jsNfZ39m9a15X2q68FAAAAMPdQ4y1ZO4TY134AAACAAzLXUKO777g/+wEAAAAO1Lx7arANVdWpq3b5uQMAALD0fLkFALLnxFM2bHPGScdPUAkAwOZtq1Cjqq6f5MgkZ3T357e6np2iu4+Z3a6qo5KcuUXlAAAAwFxcbIqbVNWVq+qXx8/l1jh+dFW9N8npSf41yaer6h+q6vJT1AcAAAAsn0lCjST3SvK0JL/W3V+ePVBVl0rymiQ3zzCFa4113TPJKyaqDwAAAFgyU4Uad84wpes/rnHshCTXH9f/OcmvJ3llhnDjuKq6zxQFAgAAAMtlqlDjBuPyXWscu9+4fEN336O7/7K7fzLJ6zMEG/db4xwAAABgl5sq1LjyuPzM7M6qOizJbTP04njmqnOeMy6/f7GlAQAAAMtoqlDj8uPywlX7b5PkEhlCjdevOvbxcXmVxZUFAAAALKupQo1zx+XVVu2/47j8cHefs+rYt8blBYsqCgAAAFheU4UaHxmXd1m1/14Zemm8eY1zVgKQzy+qKAAAAGB5HTLRfU7J8KrJw6rqtCRvzTDryfdlCDVetsY5K2NpfGqKAgEAAIDlMlWo8bQkv5zk6uP6rHd09xvXOOfuGQKPty64NgAAAGAJTRJqdPeXq+pHk/xtLjqbyVuzxpStVXWzJMdmCDVeN0WNAMD69px4ypbf44yTjl94DQDA8piqp0a6+7Qkt6qq62YYL+Oz3X3GOqc8aFz+66JrAwAAAJbPZKHGiu7+ePZO17qvNu9P8v5pKgIAAACW0VSznwAAAADM1eQ9NQCA3WkeY3Js5hrG3QCA3UNPDQAAAGApCTUAAACApSTUAAAAAJaSUAMAAABYSkINAAAAYCkJNQAAAIClNNdQo6r+vareW1XXXbX/2uPn4vO8HwAAALB7HTLn6908SSc5bNX+M5JcmOSmST4853sCAAAAu9C8Xz/pda5bc74XAAAAsIvNO9T48ri81pyvCwAAAHAR8w41Pjguf6+qbrjGGBq9+gQAAACAAzHvMTX+JslxSW6T5NQkqfrOWyeV5EMz25vV3T3vOgEAAIAlN9eeGt39t0melGFQ0Jr5rKgD/AAAAABcxNx7QHT3o6rqqUnulOSaSS6V5DEZXj15RpL/nvc9AQAAgN1nIa91dPenkvztynZVPWZc/avuNqUrALAwe048Zd3jZ5x0/ESVAACLNtVYFZ/M0FPjmxPdDwAAANjhJgk1unvPFPcBAAAAdo95T+kKAAAAMInJp0qtqismeVCSH01y4yRXHA+dneRDSV6f5LndffbUtQEAAADLY9JQo6p+McOUr4ev7Jo5fM0k10hy5ySPrapHdvczp6wPAAAAWB6ThRpVdWKSP87eIOPLSd6X5HPjvqsmuUWSyyU5IsnTq+ry3f0nU9W4larq55Icl+SWSW6S5JJJHtTdJ29lXQDAgTELCwAs3iShRlXdOMkfZggvPpvkt5O8tLu/tardIUl+OsmfZui18UdVdUp3nzpFnVvsj5JcJ8lZGf6OrrO15QAAAMD2NtVAob+a5OJJvpDktt39otWBRpJ09wXd/eIkt03y3+M5vzpRjVvtIUn2dPeVkzxjq4sBAACA7W6qUOOHk3SSJ3b3Jzdq3N1nJvlfGXp2/MiCa9sWuvv13f2Jra4DAAAAlsVUocY1x+W/7sc5bx+X15hzLRdRVVepqrtV1eOr6jVVdVZV9fg5eT+vde2qelJVnVZV51XV2VX1rqr6rao6fOMrAAAAAJs11UCh3z6A+620vXDOtaz2+XlcpKqOT/LCDAOdrjg8ybHj5yFVddfu/tg87gcAAAC73VShxieT3CjDqySb7a2x8trJhq+rzNGZSU7LMK3splXVzZK8JEOIcW6SJyZ5Y5LDktw3yUOT3CDJKVV1bHefO8+iAYDN22hWkmTjmUk2cw0AYPGmev3kdRnGx/itqrrJRo2r6qYZZkjpJK9dcG2PT3L3JFfr7msn+cUDuMZTMgQaFyS5c3c/obvf0d1v6O6HJXnU2O6GSR4xh5oBAABg15sq1HhKkvOTXDrJ26rqt6vqyNWNqurIqnpUkreMbc8fz12Y7n5Md7+quw/oNZSqOjbJHcfNZ3f3O9Zo9uQMPUCS5Deq6hIHci8AAABgr0lCjXFWj5UeEJdOclKSz1XVR6vq7VX1tqr6aJLPZXh147IZemn84mZmS9li95hZf+5aDbr7wiTPHzevkL0hCAAAAHCApuqpke5+fpKfSPKZDK+iXCzJ9ZLcJsltx/WLjcc+k+Tu3f23U9V3EI4bl+clee867d48s367xZUDAAAAu8NUA4UmSbr7lKq6bpJ7JvnRJDdOcsXx8NlJPpTk9Ule3t0XTFnbQbjRuPzoBjV/ZI1zFqKqjtqgydUWeX8AAACYwqShRpKMX/xfOn6WWlUdmmRlbJBPrde2u8+pqvOSHJHkWmtc6yHZ24NjZTDVh1TVHcf1V3T3KzZZ2pmbbAcAAABLa/JQY4e5zMz6ZqZpXQk1Lr3GsdsleeCqfT80fpLkjCSv2L/yAICdbKOpZTeamhYAlp1Q4+AcOrP+zU20P39cHrb6QHefkOSEgy8pyRo9QVa5WpJ3z+leAAAAsCWEGgfnGzPrl9xE+0uNy68voJbv6O51X4WpqkXeHgAAACYx2ewnO9RXZ9bXeqVktSPG5WZeVQEAAADWIdQ4CN39jSRnjZvrzjhSVVfI3lDDQJ4AAABwkIQaB++0cXl0Va33Os8N1zgHAAAAOEBCjYP3tnF5RJJbrtPuDjPrb19cOQAAALA7GCj04L0iyf8c1x+U5J2rG1TVxZI8YNz8UpI3TlHYzP1PXbXLzx0AAICl58vtQerud1XVW5Mcl+TBVfW87n7HqmaPTHKjcf0vuvtbkxYJALBAe048Zd3jZ5x0/ESVALDbTBJqVNVzxtXXdPdLp7jnZlXV7ZIcPbPryJn1o6vqhNn23X3yGpf59QyvlByW5LVV9YQMvTEOS3LfJA8b252e5MlzKXw/dPcxs9tVdVQMVgoAAMCSm6qnxgPH5d9PdL/98ZDsrW+1Hxo/s05e3ai731dVP5PkBUkum+QJa1zr9CTHd/dX1zgGAAAA7KepBgr9wrj8/ET3m1x3vzLJTZP8eYYA42sZxs94T5JHJ7lFd390ywoEAACAHWaqUOPD4/I6E91v07r7hO6uzX42uNYnuvsR3X2D7j6iu6/Q3cd2959099em+jMBAADAbjBVqPGCJJV9v+YBAAAAsF+mCjWem+RfkvxkVT2mqtbt8QAAAACwkakGCj0uyZOSXDnJHyS5b1X9fZIPJDknybfXO7m737LwCnewqjp11S5T+QIAALD0pvpy+6YkPbP9vUl+f5PndnwJBwDYb3tOPOWgr3HGScfPoZKDt5k/y3apFYDpTBkWeOVki3T3MbPbVXVUkjO3qBwAAACYi6lCjTtNdB8AAABgl5gk1OjuN09xHwAAAGD3mGr2EwAAAIC5EmoAAAAAS2nyWUWq6rJJ7p3ktkmuluTwJL/Q3Z+YaXONJJdP8o3u/tjUNQIAAADb36ShRlX9SpI/TnKZlV0Zpmw9YlXTOyR5YZJvVNVR3X32dFUCAAAAy2Cy10+q6rFJnprkskm+meS96zT/+ySfTXKpJPdaeHEAAADA0pkk1KiqWyT5/XHzBUmu1t233lf77r4wyUsz9OT4scVXuLNV1amznyT/stU1AQAAwMGa6vWTX8sQUPxrdz9gk+e8I8n/l+QmC6sKAGANe048ZatLAAA2YapQ4w4Zxs542n6cc8a4vObcq9lluvuY2e2qOirJmVtUDgAAAMzFVGNqXH1c/ud+nHP+uLzUnGsBAAAAdoCpQo1vjstL7Mc5K0HIl+ZbCgAAALATTBVqfGpcHrNuq4u687j86JxrAQAAAHaAqUKNN2QYKPRBm2lcVddL8uAM43C8boF1AQAAAEtqqlDjaUkuSPJDVfXY9RpW1a2SvDbJpTOMq/HXC68OAAAAWDqThBrdfXqSP8zQW+P3q+qdVfWomSZ3qapHV9W/JHlnkutm6KVxYnd/dooaAQAAgOUy1ZSu6e4/rKpLJPmdJMcmuVWG4CJJ/nSmaY37H9/dT52qPgAAAGC5TPX6SZKku/8gyW2SvCzJ1zMEGLOfbyV5TZLjuvtxU9YGAAAALJfJemqs6O73JLl3VR2S5PuSXCXJxZN8Mcmp3f31qWva6arq1FW7Jv+5AwAAwLxt2Zfb7r4gyQe26v4AADBve048ZcM2Z5x0/ASVAOwOfmO/C3T3MbPbVXVUkjO3qBwAAACYiy0JNarqqknumOTGSa447j47yYeSvKm7P78VdQEAAADLY9JQo6quleRJSe6xzr2/XVUvT/Lb3f3JqWoDAAAAlstks59U1XFJPpjk3kkuke+e+WTlc8jY5gNVdbup6gMAAACWyyShRlVdM8krk1w2Q3DxmiQ/neQ6SQ4dP9fJEGa8emxz2SSvrKprTFEjAAAAsFym6qlxYoaQ4ttJHtDdx3f3P3b3md39zfFzZne/rLvvluTnklw4nnPiRDUCAAAAS2SqUOOuSTrJs7r7BRs17u4XJXlmhh4b5rwCAAAAvstUocbKKyQv3Y9zVtp6/QQAAAD4LlOFGueMyy/vxzkrbc9ZtxUAAACwK00VarxnXN5kP85ZafuedVsBAAAAu9JUocZTM4yP8aiqOnyjxmObR2cYh+MvF1wbAAAAsIQmCTW6+/VJHpfkRkneVFU331fbqrpZkjcmuUGSx3X366aoEQAAAFguh8zzYlX1B+sc7gyvktwqyXur6oNJ3p3kv8djV01ybFa9dlJVf9Ddj59nnbtNVZ26atdcf+4AAACwFeb95faxGQKK9XSGV1FukrXH2Kixza3GT5IINQCAHWXPiadsdQmT2cyf9YyTjp+gEuZto5+tnyuwaIv4jX3Nod1mr8EmdPcxs9tVdVSSM7eoHAAAAJiLuYYa3T3VwKMAAADALieEAAAAAJaSUAMAAABYSkINAAAAYClt2dSeVXXZJJdJcvGN2nb3JxdfEQAAALBMJg01qurOSX4pyXFJrrDJ0zpbGL4AAAAA29NkYUFVPSPJQ1c2p7ovAAAAsDNNEmpU1S8medi4+dUkL0/y/iRfSnLhFDUAAAAAO8tUPTVWAo3Tkvxwd39+ovsCAAAAO9RUs5/cMMPYGI8VaAAAAADzMFWoce64/K+J7gcAAADscFOFGqeNy6tPdD8AAABgh5sq1HhmhhlP7jfR/QAAAIAdbpKBQrv7RVV1zyT3r6p3dffTprgvg6o6ddWuyabyBQAAgEWZ8svt/ZM8KclTquq+SV6S5PQkX9voxO5+y4JrAwAAtqE9J56y7vEzTjp+okqA7WjKUONbSd6f5Owktx0/m9HRs+CgdPcxs9tVdVSSM7eoHAAAAJiLScKCqjokyYuT/NTKrinuCwAAAOxcU/WAeHiSe43rZyR5XpIPJPlSkgsnqgEAAADYQaYKNR4yLv8tyY9099cnui8AAACwQ001pevRGcbGeKJAAwAAAJiHqUKN88alwSkBAACAuZgq1Hj/uLzORPcDAAAAdripQo1nZJjx5MET3Q8AAADY4SYJNbr7ZRmCjbtV1ZOq6uJT3BcAAADYuSaZ/aSqHpBh5pNbJPnNJPeuqpclOT3J1zY6v7ufv9gKAQAAgGUz1ZSuJ2eY/WTFtZL8+ibP7SRCDQAAAOAipgo1kmFMDQAAAIC5mCrUuO5E9wEAAAB2iUlCje7+xBT3AQAAAHaPqaZ0BQAAAJgroQYAAACwlIQaAAAAwFKaZEyNqvrYQZze3X39uRUDAAAA7AhTzX6yZz/adi46/WvPtxQAAABgJ5gq1HjeJtockeQGSW6SIch4X5IPLrKo3aKqTl21a6qfOwAAACzMVFO6Pmizbavqxkmek+SYJE/s7n9cWGEAAKxrz4mn7Jj7zOMeZ5x0/LaoYyPzqJP52+hnv5mf2zyucbA28wxP8b+V7fKcT/X3wdq23W/su/tDVfXDSf4jyfOr6tTu/sgWl7XUuvuY2e2qOirJmVtUDgAAAMzFtpz9pLvPTfJnSQ5L8ttbXA4AAACwDW3LUGP07nH5I1taBQAAALAtbedQY2UGlKtuaRUAAADAtrSdQ40fH5df3tIqAAAAgG1pW4YaVXXfJP8zw9Sub9vicgAAAIBtaJLZT6rqOZtodrEkV0jy/UmukeH1kwuSnLTA0gAAAIAlNdWUridk6HWxGStjaXwlyUO6+z0LqQgAAABYalOFGp/MxqHGhUm+muTjSd6c5AXdfdaiCwMAAACW0yShRnfvmeI+AAAAwO6xLQcKBQAAANiIUAMAAABYSkINAAAAYCkJNQAAAIClNPeBQqvq23O+ZHf3VLO0AAAAAEtiEWFBLeCaAAAAABexiFDjeQd5fiW5a5IrRUACAAAA7MPcQ43uftCBnltV90jyuAyBxoozD7YmAAAAYOfZFgOFVtVdq+o9Sf4xyY0z9ND4XJJfS/K9W1kbAAAAsD1t6QCcVfVjSR6f5NYru5L8d5L/leTp3f2NraoNAAAA2N62JNSoqjsk+cMkP7SyK8kXk/xpkqd199e2oi4AAABgeUwaalTVD2bomXGnlV1JvpTkz5I8pbvPnbIeAAAAYHlNEmpU1bEZwow7r+xK8tUkT0nyZ9395SnqAAAAAHaOhYYaVXXzDLOZ3G1lV5LzkjwtyZ9299mLvD8AAACwcy0k1KiqYzKEGfdc2ZXk60menuSk7j5rEfcFAAAAdo+5hxpV9aIk98kQZFSS85P8dYYw43Pzvh8AAACwOy2ip8Z9Z9a/kOQvknwqyZ2r6oAu2N3Pn0NdAAAAwA6yqDE1elwemWHq1oO9llDjIFTVqat2bclUvgAAADBPi/pye2BdMgAA4ADtOfGUrS5h29jM38UZJx0/QSUbW5af2xR1LsvfxWbspD8L29siQo07LeCaHITuPmZ2u6qOSnLmFpUDAAAAczH3UKO73zzvawIAAACsdrGtLgAAAADgQAg1AAAAgKUk1AAAAACWklADAAAAWEpCDQAAAGApCTUAAACApSTUAAAAAJaSUAMAAABYSkINAAAAYCkJNQAAAIClJNQAAAAAlpJQAwAAAFhKQg0AAABgKQk1AAAAgKUk1AAAAACWklADAAAAWEpCDQAAAGApCTUAAACApSTUAAAAAJaSUAMAAABYSkINAAAAYCkJNQAAAIClJNQAAAAAlpJQAwAAAFhKQg0AAABgKQk1AAAAgKUk1AAAAACWklADAAAAWEpCDQAAAGApCTUAAACApSTUAAAAAJaSUAMAAABYSkINAAAAYCkJNQAAAIClJNTYJqrq2Kp6dVWdU1XnVdW7qupnt7ouAAAA2K4O2eoCSKrqjkn+b5JvJvm7JF9O8lNJXlhVe7r7CVtXHQAAAGxPempssao6JMnfJOkkt+/uh3b3byW5WZJTkzyuqr5nK2sEAACA7UiosfV+OMn1k7you9+3srO7v5rkDzP0pnnQFtUGAAAA29auDjWq6ipVdbeqenxVvaaqzqqqHj8n7+e1rl1VT6qq08YxMc4ex8X4rao6fJ1T7zguX7vGsZV9d9ifWgAAAGA32O1janx+HhepquOTvDDJ5WZ2H57k2PHzkKq6a3d/bI3TV14t+a/VB7r7nKo6a6YNAAAAMNrVPTVWOTNr95ZYV1XdLMlLMgQa5yb53SQ/mORHkjxrbHaDJKdU1aXXuMRKEPLlfdziK7loWAIAAABET43HJ3l3knd39+erak+Sj+/nNZ6SoVfGBUnu3N3vmDn2hqr6ryR/kuSGSR4x3hMAAAA4SLu6p0Z3P6a7X9XdB/QaSlUdm71jYjx7VaCx4slJThvXf6OqLrHq+EoPjX31xrhs9t2LAwAAAHatXR1qzME9Ztafu1aD7r4wyfPHzStkbwiyYmUsje8aN6OqrpDkyKwx3gYAAADsdkKNg3PcuDwvyXvXaffmmfXb7ePYndc4786r2gAAAAAjocbBudG4/Gh3X7BOu4+scc6Kf0nysSQ/W1U3X9lZVZdJ8vsZxuo4+aArBQAAgB1mtw8UesCq6tAMr4YkyafWaztOzXpekiOSXGvVsQuq6iFJ/m+St1bVizPMePJTSa6b5Pe6+/T9rO2oDZpcbX+uBwAAANuRUOPAXWZm/dxNtF8JNb5rWtfufmNV3S7J45LcJ8klk5ya5Pe7+4UHUNuZB3AOAAAALBWhxoE7dGb9m5tof/64PGytg939riT/42CLAgCAedhz4inb4hrzsF3q2C42+vs446TjJ6rk4Ez1c53H38fB1rosP5OtINQ4cN+YWb/kJtpfalx+fQG1rHatDY5fLcm7J6gDAAAAFkaoceC+OrP+Xa+UrOGIcbmZV1UOSnevO8ZHVS26BAAAAFg4s58coO7+RpKzxs11B+asqitkb6hhvAsAAACYA6HGwTltXB5dVev1ernhGucAAAAAB0GocXDeNi6PSHLLddrdYWb97YsrBwAAAHYPocbBecXM+oPWalBVF0vygHHzS0neuNiSAAAAYHcwUOhB6O53VdVbkxyX5MFV9bzufseqZo9McqNx/S+6+1uTFpmkqk5dtcvPHQAAgKW3q7/cVtXtkhw9s+vImfWjq+qE2fbdffIal/n1DK+UHJbktVX1hAy9MQ5Lct8kDxvbnZ7kyXMpHAAAANjdoUaShyR54D6O/dD4mXXy6kbd/b6q+pkkL0hy2SRPWONapyc5vru/usaxhevuY2a3q+qomIUFAACAJWdMjTno7lcmuWmSP88QYHwtw/gZ70ny6CS36O6PblmBAAAAsAPt6p4a3X1CkhPmdK1PJHnE+AEAAAAWTE8NAAAAYCkJNQAAAIClJNQAAAAAltKuHlNjt6iqU1ft8nMHAABg6empAQAAACwlv7HfBbr7mNntqjoqyZlbVA4AAADMhZ4aAAAAwFISagAAAABLSagBAAAALCWhBgAAALCUhBoAAADAUhJqAAAAAEtJqAEAAAAspUO2ugAWr6pOXbXrOz/3z372sxNXs31d8JWztroEAGCb+9SnPrXu8WX698RO+bNs9OdIlufPMg/z+PvYKc/GvGyHv4/N/Fy3u1XfPS8+r+tWd8/rWmxTa4Qahya53lbUAgAAwK53bHe/Zx4X0lNjF+juY2a3q+pWSd69ReUAAADAXOipsQtV1aWS3GTc/EKSb29hOeu5WvaGL8cm+dwW1gL74jllu/OMst15RtnuPKNsd8vyjF48yZXH9Q929/nzuKieGrvQ+PDMpavPIlXV7Obnunv5XyRjx/Gcst15RtnuPKNsd55Rtrsle0Y/Me8Lmv0EAAAAWEpCDQAAAGApCTUAAACApSTUAAAAAJaSUAMAAABYSkINAAAAYCkJNQAAAIClVN291TUAAAAA7Dc9NQAAAIClJNQAAAAAlpJQAwAAAFhKQg0AAABgKQk1AAAAgKUk1AAAAACWklADAAAAWEpCDQAAAGApCTUAAACApSTUAAAAAJaSUINtq6quXVVPqqrTquq8qjq7qt5VVb9VVYdvdX3sPFX1/VX1O1X1mqo6s6rOr6pzq+r0qjq5qo7bz+vdpapeVlWfGq/1qXH7Lov6M7A7VdWfVFXPfO64iXM8nyxUVR1ZVY+qqrdX1efG5+wzVfXOqvrTqrrtJq7hOWUhquqSVfXgqvo/VfXZmf/m/2dVPaeqbrPJ63hG2bSqukpV3a2qHj/+e/Osmf92n3wA1zvo56+qDq+q3x6/Z509/u/gtPF72LX3t6atUN291TXAd6mq45O8MMnl9tHkP5Pctbs/Nl1V7GRV9eYkt99E079N8pDu/uY616okz0jysHWu88wkD2//J8xBqqqbJXlPkkNmdt+pu9+0j/aeTxauqn46ydOTXGmdZv/U3ffYx/meUxamqq6V5JQkN9mg6Z8neeRaz5hnlANRVes9C8/r7hM2eZ25PH9Vdf0M/1u4wT6afDnJz3b3qzdT11bRU4NtZ/wH+ksyBBrnJvndJD+Y5EeSPGtsdoMkp1TVpbekSHaia47LzyT5iyT3TnLrJLdN8ogknx6P/3ySkze41h9l739k3pfkfuO17jduZzz+h3Oom12sqi6W4f8XD0ny35s8zfPJQlXVA5L8XYZA47+TPC7JjyW5ZZLjk/x/SV6X5FvrXMZzykJU1SG5aKDxgSQnZPjv/Z2TPD7JeeOx30zyW/u4lGeUg3Vmktce4LkH/fyN36Nelb2BxrMyfN/6wQzfv87N8H3spVV10wOscxJ6arDtVNUbk9wxyQVJbt/d71h1/LeT/Mm4+Zjufvy0FbITVdWrkjw/yT9297fXOH5kkrcn+d5x1+27+61rtDs6yWkZvmS+Z2z39Znjhyd5c5JbZXjGb9jd/2/Ofxx2iar6jQy/SfxIkpcn+Z/joTV7ang+WbSqulGGf1BfKslbk9y9u7+8j7aXXKvXm+eURaqqeyX5h3HzHUmOW/3f/aq65XjsEknOSXKV7r5g5rhnlANSVY9L8u4k7+7uz1fVniQfHw9vqqfGvJ6/qnpskseMm4/q7j9ddfy2Sd4y3ueN3f3Dm/+TTktPDbaVqjo2Q6CRJM9eHWiMnpzhf8hJ8htVdYkpamNn6+67dfdL1go0xuNnJXnkzK577+NSv5m9rwH82ux/ZMbrfC3Jr42bhyT5jQMuml1t7D698huYX0qyz1eiZng+WbS/zBBonJXkp/YVaCTJOq/xeU5ZpB+aWX/iWv/d7+73ZvgNdpJcIckNVzXxjHJAuvsx3f2q7v78QVzmoJ+/8fvTr4+bp2X4frW61nckefa4eacx7NuWhBpsN/eYWX/uWg26+8IMv1FPhv/Q3HGxJcF3vGlm/fqrD47vN/7kuPmR7v63tS4y7v/PcfMe43mwv/53kktn+M3OmzZq7Plk0arqhhm6LifJ08YweH+v4Tll0S45s77e2Gyzv9m+1MqKZ5StNMfn745JLj+uP2/8frWWk2fWf2p/652KUIPtZmV2ifOSvHeddm+eWb/d4sqBi5j9h9Ba/+d/3ewdm+PNaxyftXL8qCR7Dq4sdpuquk+SuyU5O8lvb/I0zyeL9tMz6y9dWamqK1TV91TVeoOGrvCcsminz6xfb512K7+86CT/NbPfM8pWmtfzd9wa7dbynuwdY2bbfucSarDd3GhcfnT23cU1fGSNc2DR7jCz/pE1jt9og+PZx3HPMJtWVZfPMJhtkjy6u7+wyVM9nyzayhSYX05yWlXdv6renyF8Oz3JWVX1sap6zDoDfXtOWbQXJ/nKuP7oqrr46gZVdYsMg9omyd9191dmDntG2Urzev42dZ3x+9hKr6Vt+wwLNdg2qurQJEeOm59ar213n5O9qeG1FlkXJN+ZZeLEmV0vWaPZ7LO47jOcYcTrtc6DjfxJkqsl+dfsfdd1MzyfLNr3jcszMoyt8YIkq0fMv26SxyZ5R1VdY41reE5ZqDEIPiHJ1zOMr/HuqnpAVd2mqn60qh6T4TfXl0zyHxlmQJvlGWUrzev5W9k+r7u/tMnrXLmqLrVuyy0i1GA7uczM+rmbaL8SapjWlSn8ZoapspLk5d39njXa7M8zfN7MumeYTamq2yV5SIbRzNede34Nnk8W7Yrj8oZJfiXJl5I8PMlVkhya5Ngkrxnb3DjDNIGr/y3qOWXhuvvlGWaGeHaSmyd5XobZTl6XIXT7WoYw43bd/blVp3tG2Urzev5WrrM/37nWus62INRgOzl0Zn0zo/ifPy4PW0At8B1VdYckJ42b/51hpom17M8zfP7MumeYDVXVJZM8M0kl+fPu/uB+XsLzyaIdMS4vleTbSf5Hd/91d3+hu88fw+C7ZW+w8YP57oHnPKcs3Djzw88muXuG/09d7apJ7pe1B6P3jLKV5vX8rVxnf75zrXWdbUGowXbyjZn1S+6z1V4r3Z++vm4rOAhVdUySl2eYEuv8JPdZZxqu/XmGZ7vveYbZjN/J8D7rJ5M87gDO93yyaLPP2EvXGpV/HGF/dnDb+61zDc8pc1dVRyR5fZLfTXKlDK/03SjD83S5JHdO8rYMPYteWVW/vuoSnlG20ryev5Xr7M93rrWusy0INdhOvjqzvpmuTSu/EdpMtynYb1V13SSvzTB18LeT3K+71xshen+e4SNm1j3DrGucKvN/jpu/1t3nrdd+HzyfLNrsM/aafTXq7lOTfHrcPHada3hOWYTHJbn9uP7g7n50d3+ku7/Z3V/p7tcluVOSN2boxfFnVTU7NoxnlK00r+dv5Tr7851rretsC4dsdQGworu/UVVnZRgs9Kj12lbVFbL3f2BnrtcWDsQ4gN3rk1wjw3RuvzC+g7ue2QGb1n2Gc9EBmzzDbOQ3M/w25WNJDq+q+67R5sYz6z9cVVcb1185hiCeTxbtzAyD2CabG8DumhnG25jlOWVhqqqSPGjcPL27n7dWu+6+oKp+P0OPjYuN5/zmeNgzylaa1/P3qSQ/kOSIqrr8BoOFrlznC919/jrttoxQg+3mtAzzJh9dVYesM63rDVedA3NTVUdmGCxsZf76X+vu52/i1A/PrN9wn62++7hnmI2sdP28XobpCDfy+zPr180wyJfnk0U7NXt7XnzXNJmrrBxf/d95zymLdNXsHdD2fRu0fe/M+uyz5hllK83r+ftwknvNtPuu1wWTpKoOSXL9fVxj2/D6CdvN28blEUluuU67O8ysv31x5bDbVNXlkvzf7J2a8MTu/qtNnv7xJJ8Z1++wXsPs7fr66QzTH8KieT5ZtLfMrF9/n60GK6Hxp1ft95yySLMh2ka/3L3EPs7zjLKV5vX8vW1mfb3r3Cp7e8dv2+9cQg22m1fMrD9orQbj9G8PGDe/lOGdRzhoVXV4klOSfP+464+7+39t9vxxes1/GjdvWFW32cd9bpO96fk/7ee0nOxC3X1Cd9d6n1x08NA7zRw7Y7yG55NF++ck3xrXV89q8h3jjFJXGjffOnvMc8qCnZ3kK+P6bcffQu/L7Be9j6+seEbZSnN8/t6U5Mvj+gPHV7PWcsLM+kavYW8ZoQbbSne/K3v/gfPgqrrtGs0emWGU6iT5i+7+1hptYL+M02W+PMkPjbv+ort/7wAu9ZTs/Y3OX1bVRaa+Grf/cty8YGwPU3lKPJ8sSHd/McnfjJs/ttbYL1V1mVz0ufrrNS71lHhOWYBx9p1Txs1rZJgB5buMY7fN/lLjVauaPCWeUbbOU3KQz193fzPJU8fNGyX5rdVtxu9hDx4339zd7z7YwhelhIZsN1V1iwzdmw7LMMLuEzL0xjgsyX2TPGxsenqSW3X3V9e6DuyPqvrH7P3N4huS/EaGAUL35Zvdffo+rvXEJCeOm+/L8A+j/5ehO/ajk9xiPPbE7v6dg6scBlX12CSPGTfv1N1v2kc7zycLU1VXTvKeJNfO8I/pZyR5WYbfjt8kwzO28tvDp3f3L+/jOp5TFmKcTeq9SQ4fd70yyfMyDMR8aJLbZPg3wLXH4//S3T+6xnU8o+y3qrpdkqNndh2Z5E/H9bdnbzCcJOnuk/dxnYN+/saQ+T1Jvnfc9cwkf5dh2tY7ZZhK/tLj9g92939s4o+4JYQabEtVdfckL0hy2X00OT3J8d390emqYierqv39P8NPdPeefVzrYkmeleQX1jn/2UkeNv7WCA7afoQank8WqqpulOFVlKPXafacJA/fV29LzymLVFU/mmHQ5SM3aPqGJPfu7nPWuIZnlP1WVScneeBm24+vl651nbk8f1V1dJJXJ/mefTT5SpL7d/fq3krbilCDbauqrpPk15Mcn2HKom8m+WiSlyZ5Wnd/bQvLY4eZZ6gxc827ZuhZdGyGfzidleTdSf66u19zIHXCvmw21Jhp7/lkYarqiCS/lOTeGf6xfOkk/53hN5F/3d2bGg/Lc8qiVNWVMnSt/x9Jjkly+Qy9iz6X4Rl7UZJ/3mgsDM8o+2NeocbM9Q76+Rv///pXkvx0hjD6khmmgH11htexP7HZereKUAMAAABYSgYKBQAAAJaSUAMAAABYSkINAAAAYCkJNQAAAIClJNQAAAAAlpJQAwAAAFhKQg0AAABgKQk1AAAAgKUk1AAAAACWklADAAAAWEpCDQAAAGApCTUAAACApSTUAAAAAJaSUAMAAABYSkINAAAAYCkJNQAAAIClJNQAAAAAlpJQAwAAAFhKQg0AAABgKQk1AAC2qao6uap6jc+era5tK1TVHffx9/HYra4NgK0h1ACAJbLGl7qvVtXhmzjvsKr68qpz77j4itkKVXWxqrpHVT29qt5fVZ+vqm9W1Veq6mNV9U9VdWJVfe8Ca/jrmWftTvt57o/MnPu0RdUIwPITagDAcrt0kntsot1PJrnsYkthgT6T5CYzn0/vq2FV3TXJqUlenuThSW6a5CpJLpHkMkmum+QnkjwxyX9W1Zuq6gcXUPPzZ9Z/fj/P/bmZ9b+dWX93Lvr3AMAud8hWFwAAHLBvJDk0wxfGF23QduVL5co5LJdvdfeHNmpUVY/OEFbUuOvtSV6Z5H1JvpjhZ3/VJD+U5PgkN0hyhyR/kOQu8yy4u99eVf8vyfWT3LuqfqW7v76JP8NhSe41bv5nd79z5prnJfnQTNt5lgzAEtJTAwCW1z+Pyx+rqqvtq1FVXSXJncfNf1p4VWyJqnpAkpMyBBpnJfnx7r5dd/+v7n5td7+3u9/e3S/r7kcmuVGGHjwfXmBZK701LjPeazPuMbZPLtpLAwC+i1ADAJbXa5N8LsnFk9xvnXb3y9A78/NJXjdBXUysqq6Z5Bnj5nlJbt/dr13vnB78c5JbJnn2gkr72yQ9rm/2FZSVdp3kBXOvCIAdRagBAMvr20lePK6v94XxAePyReM5a5qZaeOM9W5aVSdsZhaOqrpkVf1yVb2xqr4wDlT5uap6dVX9XFXt898hVfXYlXuM24dW1W9X1b+Pg6N+tareVVW/WlUH/TptVV115s/0i1V12ar63ap6+0ztnxwH3txnr5gt9Igkh43rv9fdp232xO7+Rne/dKN2VXXrqnpWVZ1eVedW1XlV9ZGq+quq+p59XPvjSd42bt557DW03j2umuTHxs03d/cnNvvnAGB3EmoAwHJb6Z5/i6o6ZvXBqvq+JN+/qu3CVdV1kvxHkr9KcsckR2YYqPKqSf7HWMubq+qKm7jWVZP8W5I/SXKLDIOjXjrJsUn+MsnL1gtINunmM+ud5L+S/FGSH5yp/VoZBt58Z1Vd4yDvNzc1DCyxElydmzn3uqiqQ6rqfyd5Z5KHJPmeJEckOTzDmBy/nOTUqnroPi6x8grKIVm/R1Gyt1fR7HkAsE9CDQBYYt39vuwdOHGt3hor+04d2y5cVV06yRsyjNmQJK/IMNvGrZL8dJI3j/tvl+RVVXXxDS75svFaT83wW/xbJvnZJCu9Ee6eZF9fqDfr5jPrT8owW8hLM4wDccskP5PkA+Pxayf584O83zwdkyF4SZK3dvdX53z9Zyf5pXH9NRlmJrl1hlDpoRlmWrlEkmdW1d3XOP8lSVYGCN3oFZSV419L8g8HUTMAu4RQAwCW30oPjPvP9lgYf4N//1VtpvCYJNcb1/+ou+/Z3a8cB6r8hyR3SvLC8fhtkzxsg+sdm2HQy1/v7td3979394szhCKfH9v88kHWfLOZ9cOS3Ke779Pd/zze7yWr7vdTVXX5g7znvNx0Zv3f53nhqrpX9vYCeWh337W7X9jd7+7u93T332QIq94wtnnq6teBuvsr2TtA7S2r6kZZw6peRa9YQDgDwA4k1ACA5ffCJBcmOSrD9Jwr7pjhlYkLszdEWKiqulSGVxSSYVaNx65u092dIYT44rjrVze47F9295vWuM7ZSZ47bt60qi53ACWvuPnM+qPWGmNi/JL9tHHzkAyvwmwHR86sf2G9hlV1TFXdeB+fI9Y45X+Oy5ePAcZ36e5vZO/PcE+G52612VdJ9tVbY3a/V08A2BShBgAsue7+dJI3jpuzXwxX1t/U3Z+aqJxbJrn8uH5yd685MOn42/uXjJvfV1VXX+ea6wUy751Zv+5mi5xVVYcm+d5x88MZXnPZlw/NrB+5z1bTuszM+rkbtH1/kg/u43PsbMNxRpVbjpsvyTrGgUnPGjdvu0aT1yb57Lh+/7EX0ey9ZnsVfTbJ6zf4cwBAEqEGAOwUK7/ZvndVHVZVhyW517hvyldPbjyz/s4N2s4ev/E+WyUfWefY2TPrl9lnq/XdJMO0uEnyjH0FMaNzZta/NjNjyqY+B1jfRmZf01irt8WButXM+os38WdbCXm+a3aY8e/0RePmtXPRHkXJ3l5FSfKiDX4GAPAdBz0FGgCwLbwsydMzfLH/ySSV5LIZBmj8xwnrmJ3N5PP7bDX43D7Ou4ju/to617hwZn2jAUf3ZXY8jX/eoO3sF/bPJ3ncGm0ek+TLSZ5ygPXsry/OrF95vYbdfZF/+1XVYzPUu5Z1p19dx+H72P+8JI8c138+yZtmjnn1BIADItQAgB2gu8+tqpdn6ML/8xlCjWRrB1zcqGdCbXB8Kjcfl2d19yc2aHubcXl+ktO6+z2rG1TVY5J8qbsfO7cK1/f+mfXv32er/TcbEt0/e2d/2cg5a+3s7g9W1fszhEj3rqpf7e6vr+pV9P7u3ux9AECoAQA7yPMzfPm888y+/Xn1ZKXXw0avp673isPs6yBXS3L6Om2vuo/zprbSU2PdniXjuA93Gzff0t3nLbSqzTs1Q2+NKyU5rqqOmFNtsz1Aurs/tM+Wm/e8JH+WoRfRTyT5+ww9iy47HtdLA4D9YkwNANg5/iXDIIuHjJ/PZxigcbNWenRcfoN2N1jn2OwX3x/Y4Dq33sd5kxmDipUpUff12sSKH0ty9Lj+vIUVtZ/G2WRWwoDLJDlhTpd+38z6nffZav+8KMkF4/rPr1rOjrsBAJsi1ACAHWIcXPFvM7wacX6SF+zngIsfH5eXqao1g4uqumT2viqwlvcm+dK4/sCqWnOci6q6TJL7jJsf7u7PrtVuAtfN3l4C16qqS6/VaPxz/+m4+Z9JXjxBbfvjzzKMn5IkT6iqo9drvBnd/dEMs8EkyX2r6tpzuOZs0PbjVXXj7A1MXtvdn1v7TABYm1ADAHaQ7n50dx86fn5rP09/88z6I1cfHHs1/EWSa6xz//OT/M24eUzWGIRyvM7Tsne2jKftZ53zNDtI6CFZ+899qSQnZ+jR8e0kv9TdF65ut5XGKXt/Zdy8bJK3VtUdN3HqFTY4/kfj8tAkL6uqfQ5EWlWXqqpfHqfIXc9KL5dDkvxd9r4O7dUTAPabMTUAgCRJd7+vqv4tw2CYDx17Jzwvw0we35Pk4Rmm3nxHktuuc6nHJ/mpJNdL8vvjb+Ofk+QzGXpG/Op4nYzXeua8/yz74ebj8uwMPUweW1VXT/KKDANe3jzJr2UIaJLk97r7jZNWuEnd/dyqumaGv/+rJXljVb0lw4wuH8gwRkZlmNXkZknumYu+AvT1rNLdL66qH0/ywCS3TPLhqvrrDAHYFzKMr3L9JMdl+JlfMRuHE/+c4e/68tn79/qVJP+0X39gAIhQAwC4qAdl+MJ6lQxfZB+46vifJflg1gk1uvurVfUjSV6T5IYZvjzfc42mb0/yE/v5isy8rfTU+ECSJyR5ZZJfHD+zLkjyO939p9nGuvuPxhlGnpwhiLr9+FnP25M8urvfuY/jD84wPssjM/Su+d3xs5bzMvRmWa/Gb1TVS5M8dGb3S7v7u0IVANiI108AgO/o7o9kmBb06Uk+keSbGX4j/3+SHN/d3/V6xj6uc0aGwOBXM4QkX0zyrQxfjv9PhsEhb9/dWznrSbK3p8YHuvt1GXocvDxDnednGGfkr5PcZLsHGiu6+5VJbpSh58QzM4RQX8gQzHw1w8/11Ukem+SY7r5dd799net9u7sfneT7MoQl78vQi+Xb4/VOTfLCDAHY1TcZTqweaNWrJwAckBoGzAYA2F2q6nLZO6jpQ7v7b9Zpvj/X7SSf6O49c7jWyRnCgrlcb6cZ/66T5HHd/ditrAWAreH1EwBgt7r5zPoHt6qITbrEODbJiv/s7m9tWTVbpKqOyDAuCwAkEWoAALvXynganeEViu3sGrlo8HLdJGdsTSlb6tgk23KgVgC2hjE1AIDd6ubj8mPdfe5WFgIAHBhjagAAu1JVvTfDoKiv6O61ZmcBALY5PTUAgF2nqg5Jcsy4ud3H0wAA9kFPDQAAAGAp6akBAAAALCWhBgAAALCUhBoAAADAUhJqAAAAAEtJqAEAAAAsJaEGAAAAsJSEGgAAAMBSEmoAAAAAS0moAQAAACwloQYAAACwlIQaAAAAwFISagAAAABLSagBAAAALCWhBgAAALCUhBoAAADAUhJqAAAAAEtJqAEAAAAsJaEGAAAAsJSEGgAAAMBSEmoAAAAAS+n/BwEdiwEbr6FZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ak.ravel(branches['Muon_pt'][single_muon_mask]), bins=100, range=(0, 100))\n",
    "plt.xlabel('Muon $p_{\\mathrm{T}}$ [GeV]')\n",
    "plt.ylabel('Number of single muons / 1 GeV')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65ebf643",
   "metadata": {},
   "source": [
    "### Selections from a jagged array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a292319",
   "metadata": {},
   "source": [
    "Let’s look at a comparison for a jagged array, using the absolute value of muon eta:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "1c852351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[True, True], ... True, True, True]] type='100000 * var * bool'>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eta_mask = abs(branches['Muon_eta']) < 2\n",
    "eta_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec80a106",
   "metadata": {},
   "source": [
    "Again, the mask array has the same dimensions as the original array. There’s one Boolean value for each muon, corresponding to whether its eta is less than 2 in absolute value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ea9d42",
   "metadata": {},
   "source": [
    "We can do counting and plotting just as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "50563ac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "204564"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(eta_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1f8ffcf",
   "metadata": {},
   "source": [
    "This is the number of muons that pass the eta cut."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5291da21",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48841bb4",
   "metadata": {},
   "source": [
    "Let’s plot both the original eta distribution and the one after the cut to verify its effect:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "708aecdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ak.ravel(branches['Muon_eta']), bins=50, range=(-2.5, 2.5))\n",
    "plt.title('No selection')\n",
    "plt.xlabel('Muon $\\eta$')\n",
    "plt.ylabel('Number of muons')\n",
    "plt.show()\n",
    "\n",
    "plt.hist(ak.flatten(branches['Muon_eta'][eta_mask]), bins=50, range=(-2.5, 2.5))\n",
    "plt.title('With $|\\eta| < 2$ selection')\n",
    "plt.xlabel('Muon $\\eta$')\n",
    "plt.ylabel('Number of muons')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94186dce",
   "metadata": {},
   "source": [
    "You can see the second plot just has both ends past 2 cut off, demonstrating that we’ve cut those muons out."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a0b17cb",
   "metadata": {},
   "source": [
    "### Operations on selections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bcc1b2c",
   "metadata": {},
   "source": [
    "We can invert selections with `~` (the NOT operator):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "4043ee61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [True, True, False, ... True, True] type='100000 * bool'>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "~single_muon_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80d66f19",
   "metadata": {},
   "source": [
    "This new mask is False only for events with exactly one muon and True otherwise.\n",
    "\n",
    "We can get the intersection of selections with `&` (the AND operator):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "fc8e6419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[False, False], ... False, False]] type='100000 * var * bool'>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_muon_mask & eta_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cec9900",
   "metadata": {},
   "source": [
    "This mask is True only for muons with no other muons in their event and `abs(eta) < 2`.\n",
    "\n",
    "Or we can get the union of selections with `|` (the OR operator):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "2192e6ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[True, True], ... True, True, True]] type='100000 * var * bool'>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_muon_mask | eta_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "598dd5e3",
   "metadata": {},
   "source": [
    "This mask is True for muons which are the only muon in their event or which have `abs(eta) < 2`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1496d871",
   "metadata": {},
   "source": [
    "### Comparing histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5add9f7",
   "metadata": {},
   "source": [
    "Now we can use these operations to compare distributions for different selections. Let’s look at the $p_\\mathrm{T}$ of single-event muons split into two groups by whether or not `abs(eta) < 2`. All we have to do is provide a list of arrays as the first argument to hist rather than just one array. Note the square brackets around the two arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "8f899eef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([ak.ravel(branches['Muon_pt'][single_muon_mask & eta_mask]),\n",
    "          ak.ravel(branches['Muon_pt'][single_muon_mask & ~eta_mask])],\n",
    "         label=['$|\\eta| < 2$', '$|\\eta| \\geq 2$'],\n",
    "         bins=25, range=(0, 50), density=True)\n",
    "plt.title('Normalized')\n",
    "plt.xlabel('Muon $p_{\\mathrm{T}}$ [GeV]')\n",
    "plt.ylabel('Number of single muons / 2 GeV')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eec00421",
   "metadata": {},
   "source": [
    "Now we can clearly see there’s a significantly higher fraction of muons with `abs(eta) >= 2` at lower pT compared to muons with `abs(eta) < 2`. This makes geometric sense, since muons at higher `abs(eta)` are traveling in a direction less perpendicular to the beam."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "493d37fc",
   "metadata": {},
   "source": [
    "# Analyzing data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "593675b1",
   "metadata": {},
   "source": [
    "Okay, we’re finally ready to look for resonances in dimuon events.\n",
    "\n",
    "We need a mask that selects events with exactly two muons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "779db89d",
   "metadata": {},
   "outputs": [],
   "source": [
    "two_muons_mask = branches['nMuon'] == 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eea05ac7",
   "metadata": {},
   "source": [
    "## Vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90295a88",
   "metadata": {},
   "source": [
    "Now we need to construct the four-momenta of each muon. Vector is a package that provides an interface to operate on 2D, 3D, and 4D vectors. We can get the four-momenta of all muons in the tree by using `vector.zip` and passing it the $p_\\mathrm{T}$, eta, phi, and mass arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "2083a679",
   "metadata": {},
   "outputs": [],
   "source": [
    "import vector\n",
    "muon_p4 = vector.zip({'pt': branches['Muon_pt'], 'eta': branches['Muon_eta'], 'phi': branches['Muon_phi'], 'mass': branches['Muon_mass']})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c42ac0ac",
   "metadata": {},
   "source": [
    "We’ll go ahead and filter out events that don’t contain exactly two muons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "6fe5e515",
   "metadata": {},
   "outputs": [],
   "source": [
    "two_muons_p4 = muon_p4[two_muons_mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c61ee263",
   "metadata": {},
   "source": [
    "Then let’s take a look at it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "f1206a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MomentumArray4D [[{rho: 10.8, ... tau: 0.106}]] type='48976 * var * Momentum4D[...'>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_muons_p4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4c5bd40",
   "metadata": {},
   "source": [
    "This is an array of 4D momenta. We can get the components back out with these properties:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "929f6b98",
   "metadata": {},
   "source": [
    "```python\n",
    "two_muons_p4.pt\n",
    "two_muons_p4.eta\n",
    "two_muons_p4.phi\n",
    "two_muons_p4.E\n",
    "two_muons_p4.mass\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e06dcffb",
   "metadata": {},
   "source": [
    "What we want is the invariant mass of the two muons in each of these events. To do that, we need the sum of their four-vectors. First, we pick out the first muon in each event with 2D slice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "8164eccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_muon_p4 = two_muons_p4[:, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5df16be",
   "metadata": {},
   "source": [
    "In the notation `[:, 0]`, : means “include every row in the first dimension” (i.e., all events in the array). The comma separates the selection along the first dimension from the selection along the second dimension. The second dimension is the muons in each event, so we want the first, or the one at the 0 index. Then we do the same to get the second muon in each event, just changing the 0 index to 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "8db0ba0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "second_muon_p4 = two_muons_p4[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ea7b24a",
   "metadata": {},
   "source": [
    "### $\\Delta R$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ee49c5b",
   "metadata": {},
   "source": [
    "Another useful feature of these four-vector arrays is being able to compute $\\Delta R$ ($= \\sqrt{{\\Delta \\eta}^2 + {\\Delta \\phi}^2}$):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "0d20ff91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [3.05, 2.92, 3.16, ... 4.47, 3.06, 2.88] type='48976 * float32'>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_muon_p4.deltaR(second_muon_p4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "46e22e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(first_muon_p4.deltaR(second_muon_p4), bins=100)\n",
    "plt.xlabel('$\\Delta R$ between muons')\n",
    "plt.ylabel('Number of two-muon events')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d9d48bd",
   "metadata": {},
   "source": [
    "In principle, we could use this to clean up our invariant mass distribution, but we’ll skip that for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab838504",
   "metadata": {},
   "source": [
    "### Dimuon mass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6abba670",
   "metadata": {},
   "source": [
    "Adding the four-vectors of the first muon and the second muon for all events is really as easy as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "7eb0c171",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MomentumArray4D [{rho: 8.79, phi: 1.83, ... tau: 16.5}] type='48976 * Momentum4...'>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_p4 = first_muon_p4 + second_muon_p4\n",
    "sum_p4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e7be208",
   "metadata": {},
   "source": [
    "This is a 1D array of the four-vector sum for each event.\n",
    "\n",
    "The last thing we need to do before we’re ready to plot the spectrum is to select only pairs with opposite charges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "df6dc54c",
   "metadata": {},
   "outputs": [],
   "source": [
    "two_muons_charges = branches['Muon_charge'][two_muons_mask]\n",
    "opposite_sign_muons_mask = two_muons_charges[:, 0] != two_muons_charges[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d379ac33",
   "metadata": {},
   "source": [
    "We apply this selection to the four-vector sums to get the dimuon four-vectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "0798e078",
   "metadata": {},
   "outputs": [],
   "source": [
    "dimuon_p4 = sum_p4[opposite_sign_muons_mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e54ea5ad",
   "metadata": {},
   "source": [
    "## Physics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29a79fdf",
   "metadata": {},
   "source": [
    "Finally, we want to pick out the opposite-charge dimuon events and look at the distribution of invariant mass. This was our original physics goal in this toy analysis.\n",
    "\n",
    "We create the necessary event mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "afc245a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "two_muons_charges = branches['Muon_charge'][two_muons_mask]\n",
    "opposite_sign_muons_mask = two_muons_charges[:, 0] != two_muons_charges[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404a7130",
   "metadata": {},
   "source": [
    "Apply it to the four-vector sums:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "74a93867",
   "metadata": {},
   "outputs": [],
   "source": [
    "dimuon_p4 = sum_p4[opposite_sign_muons_mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc3eece",
   "metadata": {},
   "source": [
    "And plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "def40429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dimuon_p4.mass, bins=np.logspace(np.log10(0.1), np.log10(1000), 200))\n",
    "plt.xlabel('Dimuon invariant mass [GeV]')\n",
    "plt.ylabel('Number of dimuon events')\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "192d0de4",
   "metadata": {},
   "source": [
    "How many dimuon events were selected in total?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "afb2c852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37183"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dimuon_p4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b50321",
   "metadata": {},
   "source": [
    "The plot should look like this, where the resonances have been labeled below:"
   ]
  },
  {
   "attachments": {
    "dimuon_invariant_mass_hist_2_labeled.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "508af98e",
   "metadata": {},
   "source": [
    "![dimuon_invariant_mass_hist_2_labeled.png](attachment:dimuon_invariant_mass_hist_2_labeled.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed2cb92",
   "metadata": {},
   "source": [
    "## Multiple files and large files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d460be4",
   "metadata": {},
   "source": [
    "`uproot.open()` and `tree.arrays()` works fine for looking at one file at a time, but often trees are spread across several ROOT files. There are a few options to deal with this in Uproot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "995ae176",
   "metadata": {},
   "source": [
    "The simplest is to join all the arrays:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c3fda6",
   "metadata": {},
   "source": [
    "`uproot.concatenate({'*.root': 'Events'})`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af39f698",
   "metadata": {},
   "source": [
    "Another method is to iterate through the entries across all files:\n",
    "\n",
    "```python\n",
    "for events in uproot.iterate({'*.root': 'Events'}, step_size=100):\n",
    "    # do something with events\n",
    "```\n",
    "In each iteration of the loop, `events` is a 100-row chunk of the full tree. This avoids loading all entries at the same time. This is important if the files are too large to fit into memory."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d62e9d0a",
   "metadata": {},
   "source": [
    "There are also some other techniques to reduce the memory usage. If you are only interested in looking at a few events, you can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "4c8c7d70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [{nMuon: 2, Muon_pt: [10.8, ... 1, -1]}] type='100 * {\"nMuon\": uint32, \"M...'>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.arrays(entry_stop=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c5cdc55",
   "metadata": {},
   "source": [
    "If you only need a subset of the branches from the tree, you can load each branch into an array individually, like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "a0967969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [2, 2, 1, 4, 4, 3, ... 0, 3, 2, 3, 2, 3] type='100000 * uint32'>"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree['nMuon'].array()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41c0aa73",
   "metadata": {},
   "source": [
    "or you can provide a list of branch names to `arrays` to get only those branches:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "33bd2e8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [{Muon_pt: [10.8, 15.7, ... 0.764]}] type='100000 * {\"Muon_pt\": var * flo...'>"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.arrays(['Muon_pt', 'Muon_eta'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a13727e5",
   "metadata": {},
   "source": [
    "My preference is to use `uproot.lazy()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "da0990e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "lazy_branches = uproot.lazy({'root://eospublic.cern.ch//eos/opendata/cms/derived-data/AOD2NanoAODOutreachTool/Run2012BC_DoubleMuParked_Muons.root': 'Events'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab5943dc",
   "metadata": {},
   "source": [
    "# Creating new objects and files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e899f18",
   "metadata": {},
   "source": [
    "So far, we've seen how Uproot, Awkward, and Vector can be used to read and manipulate data from already existing ROOT files. In this last section we'll look at how these packages can also be used to construct new objects and write data to files."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b254b22a",
   "metadata": {},
   "source": [
    "## Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a7eff64",
   "metadata": {},
   "source": [
    "New `Arrays` can be easily be created like NumPy's `np.array`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "a713c64a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[1], [2, 3], [], [4]] type='4 * var * int64'>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.Array([[1],\n",
    "          [2, 3],\n",
    "          [],\n",
    "          [4]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9882fb3f",
   "metadata": {},
   "source": [
    "## Vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6479e506",
   "metadata": {},
   "source": [
    "Individual vectors can be created with `vector.obj`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "21b813ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vector.obj(pt=1, phi=1, eta=1, E=2)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector.obj(pt=1, eta=1, phi=1, e=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3d55330",
   "metadata": {},
   "source": [
    "And we've already seen a way to make arrays of vectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "8e929821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MomentumArray4D [{rho: 1, phi: 1, eta: 1, t: 2}] type='1 * Momentum4D[\"rho\": in...'>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector.zip({'pt': ak.Array([1]),\n",
    "            'eta': ak.Array([1]),\n",
    "            'phi': ak.Array([1]),\n",
    "            'e': ak.Array([2])})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "497e7e4b",
   "metadata": {},
   "source": [
    "## Files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b12812b",
   "metadata": {},
   "source": [
    "To write to a ROOT file, use `uproot.create()`, `uproot.recreate()`, or `uproot.update()`, which correspond to the same modes as in the `option` parameter of ROOT's `TFile.Open()`. For example, to create or replace `new_file.root`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "22b4629d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<WritableDirectory '/' at 0x7fa0976d3c10>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uproot.recreate('new_file.root')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5598c22",
   "metadata": {},
   "source": [
    "Writable file objects allow an assignment syntax for new elements, similar to a dictionary:\n",
    "```\n",
    "file['new_object_name'] = new_object\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac12fcac",
   "metadata": {},
   "source": [
    "## Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb02015b",
   "metadata": {},
   "source": [
    "Trees can be specified by an `Array` or a dictionary of arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "2f77a2fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "with uproot.update('new_file.root') as new_file:\n",
    "    new_file['new_tree'] = {'branch1': ak.Array([1]), 'branch2': ak.Array([2])}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3079922b",
   "metadata": {},
   "source": [
    "And just to make sure we can read this back:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "f3e8d552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [{branch1: 1, branch2: 2}] type='1 * {\"branch1\": int64, \"branch2\": int64}'>"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uproot.open('new_file.root')['new_tree'].arrays()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14654436",
   "metadata": {},
   "source": [
    "## Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25d6b02d",
   "metadata": {},
   "source": [
    "Histograms from NumPy (or Hist) can be interpreted into ROOT's `TH1`. Let's save our dimuon mass histogram to this new ROOT file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "248f6aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "numpy_histogram = np.histogram(dimuon_p4.mass, bins=np.logspace(np.log10(0.1), np.log10(1000), 200))\n",
    "\n",
    "with uproot.update('new_file.root') as new_file:\n",
    "    new_file['new_histogram'] = numpy_histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a602cd4",
   "metadata": {},
   "source": [
    "And again make sure we can read it back correctly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "23197aaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist, bin_edges = uproot.open('new_file.root')['new_histogram'].to_numpy()\n",
    "plt.stairs(hist, bin_edges, fill=True)\n",
    "plt.xlabel('Dimuon invariant mass [GeV]')\n",
    "plt.ylabel('Number of dimuon events')\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0b05da",
   "metadata": {},
   "source": [
    "Of course ROOT can read these newly created files as well, which makes it easy to drop in an Uproot-based replacement for any part of an analysis, even if later steps still expect ROOT files as input."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9135208",
   "metadata": {},
   "source": [
    "# Links to more resources"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b361e138",
   "metadata": {},
   "source": [
    "- Uproot\n",
    "    - [GitHub repository](https://github.com/scikit-hep/uproot4)\n",
    "    - [Documentation](https://uproot.readthedocs.io/en/latest/)\n",
    "- Awkward\n",
    "    - [GitHub repository](https://github.com/scikit-hep/awkward-1.0)\n",
    "        - Contains several links to other presentations/tutorials\n",
    "    - [Documentation](https://awkward-array.org/quickstart.html)\n",
    "- Vector\n",
    "    - [GitHub repository](https://github.com/scikit-hep/vector)\n",
    "    - [Documentation](https://vector.readthedocs.io/en/latest/)"
   ]
  }
 ],
 "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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}