01-scientific-python-ecosystem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "# The Scientific Python Ecosystem\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em; text-align: center;\"><b>Who has ever used Python?</b> <i>(Show of hands.)</i></p>\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em; text-align: center;\"><b>Who has used Python more than C or C++?</b> <i>(Show of hands.)</i></p>\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em; text-align: center;\"><b>Who has ever used PyROOT?</b> <i>(Show of hands.)</i></p>\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em; text-align: center;\"><b>Who has ever used Numpy?</b> <i>(Show of hands.)</i></p>\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em; text-align: center;\"><b>Who has ever used Matplotlib?</b> <i>(Show of hands.)</i></p>\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em; text-align: center;\"><b>Who has ever used Pandas?</b> <i>(Show of hands.)</i></p>\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em; text-align: center;\"><b>Who has used Python for machine learning?</b> <i>(Show of hands.)</i></p>\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "### Part 1: Why Python in particle physics?\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">I could point to its broad adoption as a programming language...</p>\n",
    "\n",
    "<center><img src=\"img/pypl-2019.png\" width=\"75%\"></center>\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">But it is more relevant to point to its use in data analysis.</p>\n",
    "\n",
    "<center><img src=\"img/python-r-cpp-googletrends-dataset.png\" width=\"75%\"></center>\n",
    "\n",
    "<br>\n",
    "\n",
    "<center><img src=\"img/python-r-cpp-googletrends-machinelearning.png\" width=\"75%\"></center>\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">It's hard to overstate the scale of these communities.</p>\n",
    "\n",
    "<center><img src=\"img/root-spark-pandas-google-trends.png\" width=\"75%\"></center>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.1em\">There is value in adopting popular tools: every question/error message is googlable...</p>\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size: 1.25em\">The growth of Python in astronomy is... astronomical.</p>\n",
    "\n",
    "<table width=\"100%\"><tr style=\"background: white\"><td><img src=\"img/mentions-of-programming-languages.png\" width=\"100%\"></td><td><img src=\"img/ligo-notebook.png\" width=\"100%\"></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">It's the language of choice for some—but not all—LHC experiments.</p>\n",
    "\n",
    "<img src=\"img/github-cmssw-lin.png\" width=\"100%\">\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">It's the language of choice for some—but not all—LHC experiments.</p>\n",
    "\n",
    "<img src=\"img/github-alice-lin.png\" width=\"100%\">\n",
    "\n",
    "_(Can't measure ATLAS and LHCb because of private repos on GitLab.)_\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "<img src=\"img/commute-by-plane.png\" width=\"60%\"> _(Stolen from Jake Vanderplas.)_\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em; font-weight: bold\">Drive to the airport, then take a plane:</p>\n",
    "\n",
    "   * Not everything needs to be fast, only the part that scales with the number of events (or other large number, like number of histogram bins or MC toys).\n",
    "   \n",
    "     The rest of the analysis code is bookkeeping: convenience outweighs speed.\n",
    "     \n",
    "   * Need to step up from interactive tinkering to full-scale analysis __*in small steps*__. Scale-up \"quasistatically\" to avoid a big round of bug-hunting.\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7086.958239475886 ns per pixel\n"
     ]
    }
   ],
   "source": [
    "# Example: code to compute a fractal (calendar/mousepad/T-shirt...).\n",
    "import time, numpy\n",
    "\n",
    "def run_python(height, width, maxiterations=20):\n",
    "    y, x = numpy.ogrid[-1:0:height*1j, -1.5:0:width*1j]\n",
    "    c = x + y*1j\n",
    "    fractal = numpy.full(c.shape, maxiterations, dtype=numpy.int32)\n",
    "    for h in range(height):\n",
    "        for w in range(width):               # for each pixel (h, w)...\n",
    "            z = c[h, w]\n",
    "            for i in range(maxiterations):   # iterate at most 20 times\n",
    "                z = z**2 + c[h, w]           # applying z → z² + c\n",
    "                if abs(z) > 2:               # if it diverges (|z| > 2)\n",
    "                    fractal[h, w] = i        # color with the iteration number\n",
    "                    break                    # we're done, no need to keep iterating\n",
    "    return fractal\n",
    "\n",
    "starttime = time.time()\n",
    "fractal = run_python(800, 1200)\n",
    "print(\"{0} ns per pixel\".format(1e9 * (time.time() - starttime) / (800 * 1200)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pretty, isn't it?\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot\n",
    "fig, ax = matplotlib.pyplot.subplots(figsize=(10, 5)); ax.imshow(fractal);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# REMINDER: the original code...\n",
    "\n",
    "\n",
    "def run_python(height, width, maxiterations=20):\n",
    "    y, x = numpy.ogrid[-1:0:height*1j, -1.5:0:width*1j]\n",
    "    c = x + y*1j\n",
    "    fractal = numpy.full(c.shape, maxiterations, dtype=numpy.int32)\n",
    "\n",
    "\n",
    "\n",
    "    for h in range(height):\n",
    "        for w in range(width):               # for each pixel (h, w)...\n",
    "            z = c[h, w]\n",
    "            for i in range(maxiterations):   # iterate at most 20 times\n",
    "                z = z**2 + c[h, w]           # applying z → z² + c\n",
    "                if abs(z) > 2:               # if it diverges (|z| > 2)\n",
    "                    fractal[h, w] = i        # color with the iteration number\n",
    "                    break                    # we're done, no need to keep iterating\n",
    "    return fractal\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "144.8026392608881 ns per pixel\n"
     ]
    }
   ],
   "source": [
    "# 50× to 100× faster...\n",
    "import numba\n",
    "\n",
    "def run_numba(height, width, maxiterations=20):\n",
    "    y, x = numpy.ogrid[-1:0:height*1j, -1.5:0:width*1j]\n",
    "    c = x + y*1j\n",
    "    fractal = numpy.full(c.shape, maxiterations, dtype=numpy.int32)\n",
    "    return tight_loop(height, width, maxiterations, c, fractal)\n",
    "@numba.jit\n",
    "def tight_loop(height, width, maxiterations, c, fractal):\n",
    "    for h in range(height):\n",
    "        for w in range(width):               # for each pixel (h, w)...\n",
    "            z = c[h, w]\n",
    "            for i in range(maxiterations):   # iterate at most 20 times\n",
    "                z = z**2 + c[h, w]           # applying z → z² + c\n",
    "                if abs(z) > 2:               # if it diverges (|z| > 2)\n",
    "                    fractal[h, w] = i        # color with the iteration number\n",
    "                    break                    # we're done, no need to keep iterating\n",
    "    return fractal\n",
    "\n",
    "starttime = time.time()\n",
    "fractal = run_numba(3200, 4800)\n",
    "print(\"{0} ns per pixel\".format(1e9 * (time.time() - starttime) / (3200 * 4800)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">The \"catch\" is that code in the loop must be purely numerical: arrays and basic number types. In other words, code that doesn't take advantage of \"Pythonness,\" code that would be just as easy to write in C.</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">That's what <tt>@numba.jit</tt> does: it compiles the Python function (directly to LLVM and then machine code).</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">So... why not just write C code?</p>\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\"> If you developed your analysis in interactive Python, in a notebook or command prompt, isolating the numerical part into a function (\"<tt>tight_loop</tt>\" in the previous example) is usually easier than linking to code written in another library.</p>\n",
    "\n",
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size: 1.25em\">Fully rewriting in C++ isn't a lot faster (30% in this case), but a lot more effort.</p>\n",
    "\n",
    "| Method                                     | time (ns/px) | speedup |\n",
    "|:-------------------------------------------|-------------:|--------:|\n",
    "| Pure Python                                        | 12000   |    1× |\n",
    "| Vectorized Numpy                                   |   368   |   30× |\n",
    "| Vectorized CuPy (run on GPU)                       |    81   |  150× |\n",
    "| **Compiled by Numba**                                  |   **136**   |   **90×** |\n",
    "| Compiled & parallelized by Numba                   |    45   |  250× |\n",
    "| Compiled & run on GPU by Numba                     |     7.8 | 1500× |\n",
    "| Parallelized by Dask                               |   238   |   50× |\n",
    "| Parallelized by Dask, compiled by Numba            |    48   |  250× |\n",
    "| Partially rewritten in Cython (Python/C++ hybrid)  |  1485   |    8× |\n",
    "| **Completely rewritten in Cython (pure C++)**          |    **99**   |  **120×** |\n",
    "| **Completely rewritten in pybind11 (pure C++)**        |    **98**   |  **120×** |\n",
    "| Completely rewritten in ROOT (pure C++ with `-O0`) |   379   |   32× |\n",
    "\n",
    "_(See [misc-fractal.ipynb](misc-fractal.ipynb) for a derivation of the above.)_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\"><b>Drive/bike/skateboard on your daily commute:</b> do exploration and problem-solving in Python because it has simple data structures, doesn't seg-fault, and dumps stack traces...</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\"><b>Fly to Europe:</b> optimize the loop that scales with big numbers so that you can finish analyzing your 100 TB this year...</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em; text-align: right; margin-right: 10%\">... by replacing critical code <i><b>in small steps</b></i>.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "<center><img src=\"img/numpy-logo.png\" width=\"35%\"></center>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">Numpy is the common (in-memory) data format for scientific Python.</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">Nearly every package can input/output data as Numpy arrays.</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">Numpy also has a suite of functions for doing calculations a whole array at a time: a <b>S</b>ingle (Python) <b>I</b>nstruction on <b>M</b>ultiple <b>D</b>ata.</p>\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Names of arrays in this dict:\n",
      "\n",
      "['Type', 'Run', 'Event', 'E1', 'px1', 'py1', 'pz1', 'pt1', 'eta1', 'phi1', 'Q1', 'E2', 'px2', 'py2', 'pz2', 'pt2', 'eta2', 'phi2', 'Q2', 'M'] \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Type': <ObjectArray [b'GT' b'TT' b'GT' ... b'TT' b'GT' b'GG'] at 0x7a05daf769e8>,\n",
       " 'Run': array([148031, 148031, 148031, ..., 148029, 148029, 148029], dtype=int32),\n",
       " 'Event': array([10507008, 10507008, 10507008, ..., 99991333, 99991333, 99991333],\n",
       "       dtype=int32),\n",
       " 'E1': array([82.20186639, 62.34492895, 62.34492895, ..., 81.27013558,\n",
       "        81.27013558, 81.56621735]),\n",
       " 'px1': array([-41.19528764,  35.11804977,  35.11804977, ...,  32.37749196,\n",
       "         32.37749196,  32.48539387]),\n",
       " 'py1': array([ 17.4332439 , -16.57036233, -16.57036233, ...,   1.19940578,\n",
       "          1.19940578,   1.2013503 ]),\n",
       " 'pz1': array([-68.96496181, -48.77524654, -48.77524654, ..., -74.53243061,\n",
       "        -74.53243061, -74.80837247]),\n",
       " 'pt1': array([44.7322, 38.8311, 38.8311, ..., 32.3997, 32.3997, 32.3997]),\n",
       " 'eta1': array([-1.21769, -1.05139, -1.05139, ..., -1.57044, -1.57044, -1.57044]),\n",
       " 'phi1': array([ 2.74126  , -0.440873 , -0.440873 , ...,  0.0370275,  0.0370275,\n",
       "         0.0370275]),\n",
       " 'Q1': array([ 1, -1, -1, ...,  1,  1,  1], dtype=int32),\n",
       " 'E2': array([ 60.62187459,  82.20186639,  81.58277833, ..., 168.78012134,\n",
       "        170.58313243, 170.58313243]),\n",
       " 'px2': array([ 34.14443725, -41.19528764, -40.88332344, ..., -68.04191497,\n",
       "        -68.79413604, -68.79413604]),\n",
       " 'py2': array([-16.11952457,  17.4332439 ,  17.29929704, ..., -26.10584737,\n",
       "        -26.39840043, -26.39840043]),\n",
       " 'pz2': array([ -47.42698439,  -68.96496181,  -68.44725519, ..., -152.2350181 ,\n",
       "        -153.84760383, -153.84760383]),\n",
       " 'pt2': array([38.8311, 44.7322, 44.7322, ..., 72.8781, 72.8781, 72.8781]),\n",
       " 'eta2': array([-1.05139, -1.21769, -1.21769, ..., -1.4827 , -1.4827 , -1.4827 ]),\n",
       " 'phi2': array([-0.440873,  2.74126 ,  2.74126 , ..., -2.77524 , -2.77524 ,\n",
       "        -2.77524 ]),\n",
       " 'Q2': array([-1,  1,  1, ..., -1, -1, -1], dtype=int32),\n",
       " 'M': array([82.46269156, 83.62620401, 83.30846467, ..., 95.96547966,\n",
       "        96.49594381, 96.65672765])}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numpy arrays of physics data:\n",
    "import uproot\n",
    "arrays = uproot.open(\"data/Zmumu.root\")[\"events\"].arrays(namedecode=\"utf-8\")\n",
    "\n",
    "print(\"Names of arrays in this dict:\\n\")\n",
    "print(list(arrays), \"\\n\\n\")\n",
    "\n",
    "arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([82.20186639, 62.34492895, 62.34492895, ..., 81.27013558,\n",
       "       81.27013558, 81.56621735])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take arrays out of the dict and make each one a variable.\n",
    "for n in arrays:\n",
    "    exec(f\"{n} = arrays['{n}']\")\n",
    "\n",
    "# Example array: energy of first muon in each event\n",
    "E1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.17219282,  6.13819068,  5.81117254, ..., 43.50036668,\n",
       "       44.28500082, 44.19520441])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute pT of all muon pairs:\n",
    "\n",
    "import numpy\n",
    "\n",
    "pt = numpy.sqrt((px1 + px2)**2 + (py1 + py2)**2)\n",
    "pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BSWxjk9hjPMY90TfQ2/v8MvDRKdbxy/Q+On0VeLr7uwH4W+DZrn0nsG7CdV1Kb8/7M8BzJ/sI+GlgF/BSd3nuFPrsx4FvAz/Z1zbx/qL35nIY+D96o6ZbT9U/wEe77W0f8N4J17Wf3nzsyW3sM926v9m9vs8ATwG/PuG6Br5u0+yvrv1e4PfnrTvJ/hqUDWPfxjz9gCQ1aDVPy0iSBjDcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoP+HyiGPpN0uy9lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And __LOOK__:\n",
    "\n",
    "matplotlib.pyplot.hist(pt, bins=100, range=(0, 200));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([116.61271581, 117.90010197, 117.36645448, ..., 230.90205217,\n",
       "       232.63405045, 232.88789491])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute p of all muon pairs:\n",
    "\n",
    "p = numpy.sqrt(pt**2 + (pz1 + pz2)**2)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And __LOOK__:\n",
    "\n",
    "matplotlib.pyplot.hist(p, bins=100, range=(0, 500));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([82.46269156, 83.62620401, 83.30846467, ..., 95.96547966,\n",
       "       96.49594382, 96.65672765])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute mass of all muon pairs:\n",
    "\n",
    "mass = numpy.sqrt((E1 + E2)**2 - p**2)\n",
    "mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And __LOOK__:\n",
    "\n",
    "matplotlib.pyplot.hist(mass, bins=100, range=(0, 120));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Start adding cuts, exploring different regions, etc.\n",
    "\n",
    "matplotlib.pyplot.hist(mass[Q1 != Q2], bins=100, range=(0, 120));\n",
    "matplotlib.pyplot.hist(mass[Q1 == Q2], bins=100, range=(0, 120));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">This is the point: you calculate <i>one thing</i> and then you <i>LOOK</i> at the result.</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">Array-at-a-time logic gives you a statistical view of each <i>step</i> in your calculation as you develop it.</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">It's not always about the speed; sometimes it's about the interactivity.</p>\n",
    "\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.18/00\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As physicists, we know this. That's why we have TTree-at-a-time operations.\n",
    "\n",
    "import ROOT\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"\", 400, 300)\n",
    "file = ROOT.TFile(\"data/Zmumu.root\")\n",
    "tree = file.Get(\"events\")\n",
    "tree.Draw(\"sqrt((E1 + E2)**2 - (px1 + px2)**2 - (py1 + py2)**2 - (pz1 + pz2)**2)\")\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">The hard part is turning those <tt>TTree::Draw</tt> expressions into a full analysis.</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">How many of you have started with <tt>TTree::Draw</tt> and had to rewrite everything as a C++ loop?</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">Our goal is to do initial exploration in a convenient way and then scale up without having to change everything.</p>\n",
    "\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "### Part 2: There's an app for that\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/shells-1.png\" width=\"80%\">\n",
    "\n",
    "<i>(Stolen from Jake Vanderplas.)</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/shells-2.png\" width=\"80%\">\n",
    "\n",
    "<i>(Stolen from Jake Vanderplas.)</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/shells-3.png\" width=\"80%\">\n",
    "\n",
    "<i>(Stolen from Jake Vanderplas.)</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/shells-4.png\" width=\"80%\">\n",
    "\n",
    "<i>(Stolen from Jake Vanderplas.)</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/shells-5.png\" width=\"80%\">\n",
    "\n",
    "<i>(Stolen from Jake Vanderplas.)</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">Much of what we do, as physicists, are not new problems. You can learn a lot by attempting to write an algorithm yourself, but eventually you'll want to plug together functions from established libraries (that you understand!).</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\"><b>Wikipedia</b> (to learn the names of things) + <b>StackOverflow</b> (to find common solutions) is a good way to develop analysis code.</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">Most of these solutions come in Numpy-shaped pieces.</p>\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "<p style=\"font-size: 100px\"><img src=\"img/scipy-logo.png\" width=\"45%\" style=\"vertical-align: middle\"> = <img src=\"img/numerical-recipes.jpg\" width=\"25%\" style=\"vertical-align: middle\"></p>\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">SciPy was originally the \"all in one\" scientific package, but today, much of the development is beyond SciPy.</p>\n",
    "\n",
    "| Date | Development |\n",
    "|:----:|:-----|\n",
    "| 1994 | **Python** 1.0 released. |\n",
    "| 1995 | **Numeric** was the first array package (a.k.a. Numerical, Numerical Python, Numpy). |\n",
    "| 2001 | **SciPy** gathered scientific functions into one codebase, including **Numeric**. |\n",
    "| 2003 | **Matplotlib** released (at that time, one of many plotters... R.I.P. **Biggles**). |\n",
    "| 2003 | **Numarray** introduced as a competitor to **Numeric** with more features (memory-mapped files, alignment, record arrays). |\n",
    "| 2005 | **Numpy** unified features of **Numeric** and **Numarray** and became the common array library. |\n",
    "| 2008 | **Pandas** first released. |\n",
    "| 2010 | **Scikit-Learn** first released. |\n",
    "| 2011 | **AstroPy** first released. |\n",
    "| 2012 | **Anaconda** first released. |\n",
    "| 2014 | **Jupyter** first released. |\n",
    "| 2015 | **Keras** first released. |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><img src=\"img/scipy-docs.png\" width=\"80%\"></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mType:\u001b[0m        module\n",
       "\u001b[0;31mString form:\u001b[0m <module 'scipy.stats' from '/home/jpivarski/miniconda3/lib/python3.7/site-packages/scipy/stats/__init__.py'>\n",
       "\u001b[0;31mFile:\u001b[0m        ~/miniconda3/lib/python3.7/site-packages/scipy/stats/__init__.py\n",
       "\u001b[0;31mDocstring:\u001b[0m  \n",
       "==========================================\n",
       "Statistical functions (:mod:`scipy.stats`)\n",
       "==========================================\n",
       "\n",
       ".. currentmodule:: scipy.stats\n",
       "\n",
       "This module contains a large number of probability distributions as\n",
       "well as a growing library of statistical functions.\n",
       "\n",
       "Each univariate distribution is an instance of a subclass of `rv_continuous`\n",
       "(`rv_discrete` for discrete distributions):\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   rv_continuous\n",
       "   rv_discrete\n",
       "   rv_histogram\n",
       "\n",
       "Continuous distributions\n",
       "========================\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   alpha             -- Alpha\n",
       "   anglit            -- Anglit\n",
       "   arcsine           -- Arcsine\n",
       "   argus             -- Argus\n",
       "   beta              -- Beta\n",
       "   betaprime         -- Beta Prime\n",
       "   bradford          -- Bradford\n",
       "   burr              -- Burr (Type III)\n",
       "   burr12            -- Burr (Type XII)\n",
       "   cauchy            -- Cauchy\n",
       "   chi               -- Chi\n",
       "   chi2              -- Chi-squared\n",
       "   cosine            -- Cosine\n",
       "   crystalball       -- Crystalball\n",
       "   dgamma            -- Double Gamma\n",
       "   dweibull          -- Double Weibull\n",
       "   erlang            -- Erlang\n",
       "   expon             -- Exponential\n",
       "   exponnorm         -- Exponentially Modified Normal\n",
       "   exponweib         -- Exponentiated Weibull\n",
       "   exponpow          -- Exponential Power\n",
       "   f                 -- F (Snecdor F)\n",
       "   fatiguelife       -- Fatigue Life (Birnbaum-Saunders)\n",
       "   fisk              -- Fisk\n",
       "   foldcauchy        -- Folded Cauchy\n",
       "   foldnorm          -- Folded Normal\n",
       "   frechet_r         -- Deprecated. Alias for weibull_min\n",
       "   frechet_l         -- Deprecated. Alias for weibull_max\n",
       "   genlogistic       -- Generalized Logistic\n",
       "   gennorm           -- Generalized normal\n",
       "   genpareto         -- Generalized Pareto\n",
       "   genexpon          -- Generalized Exponential\n",
       "   genextreme        -- Generalized Extreme Value\n",
       "   gausshyper        -- Gauss Hypergeometric\n",
       "   gamma             -- Gamma\n",
       "   gengamma          -- Generalized gamma\n",
       "   genhalflogistic   -- Generalized Half Logistic\n",
       "   gilbrat           -- Gilbrat\n",
       "   gompertz          -- Gompertz (Truncated Gumbel)\n",
       "   gumbel_r          -- Right Sided Gumbel, Log-Weibull, Fisher-Tippett, Extreme Value Type I\n",
       "   gumbel_l          -- Left Sided Gumbel, etc.\n",
       "   halfcauchy        -- Half Cauchy\n",
       "   halflogistic      -- Half Logistic\n",
       "   halfnorm          -- Half Normal\n",
       "   halfgennorm       -- Generalized Half Normal\n",
       "   hypsecant         -- Hyperbolic Secant\n",
       "   invgamma          -- Inverse Gamma\n",
       "   invgauss          -- Inverse Gaussian\n",
       "   invweibull        -- Inverse Weibull\n",
       "   johnsonsb         -- Johnson SB\n",
       "   johnsonsu         -- Johnson SU\n",
       "   kappa4            -- Kappa 4 parameter\n",
       "   kappa3            -- Kappa 3 parameter\n",
       "   ksone             -- Kolmogorov-Smirnov one-sided (no stats)\n",
       "   kstwobign         -- Kolmogorov-Smirnov two-sided test for Large N (no stats)\n",
       "   laplace           -- Laplace\n",
       "   levy              -- Levy\n",
       "   levy_l\n",
       "   levy_stable\n",
       "   logistic          -- Logistic\n",
       "   loggamma          -- Log-Gamma\n",
       "   loglaplace        -- Log-Laplace (Log Double Exponential)\n",
       "   lognorm           -- Log-Normal\n",
       "   lomax             -- Lomax (Pareto of the second kind)\n",
       "   maxwell           -- Maxwell\n",
       "   mielke            -- Mielke's Beta-Kappa\n",
       "   moyal             -- Moyal\n",
       "   nakagami          -- Nakagami\n",
       "   ncx2              -- Non-central chi-squared\n",
       "   ncf               -- Non-central F\n",
       "   nct               -- Non-central Student's T\n",
       "   norm              -- Normal (Gaussian)\n",
       "   norminvgauss      -- Normal Inverse Gaussian\n",
       "   pareto            -- Pareto\n",
       "   pearson3          -- Pearson type III\n",
       "   powerlaw          -- Power-function\n",
       "   powerlognorm      -- Power log normal\n",
       "   powernorm         -- Power normal\n",
       "   rdist             -- R-distribution\n",
       "   reciprocal        -- Reciprocal\n",
       "   rayleigh          -- Rayleigh\n",
       "   rice              -- Rice\n",
       "   recipinvgauss     -- Reciprocal Inverse Gaussian\n",
       "   semicircular      -- Semicircular\n",
       "   skewnorm          -- Skew normal\n",
       "   t                 -- Student's T\n",
       "   trapz              -- Trapezoidal\n",
       "   triang            -- Triangular\n",
       "   truncexpon        -- Truncated Exponential\n",
       "   truncnorm         -- Truncated Normal\n",
       "   tukeylambda       -- Tukey-Lambda\n",
       "   uniform           -- Uniform\n",
       "   vonmises          -- Von-Mises (Circular)\n",
       "   vonmises_line     -- Von-Mises (Line)\n",
       "   wald              -- Wald\n",
       "   weibull_min       -- Minimum Weibull (see Frechet)\n",
       "   weibull_max       -- Maximum Weibull (see Frechet)\n",
       "   wrapcauchy        -- Wrapped Cauchy\n",
       "\n",
       "Multivariate distributions\n",
       "==========================\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   multivariate_normal   -- Multivariate normal distribution\n",
       "   matrix_normal         -- Matrix normal distribution\n",
       "   dirichlet             -- Dirichlet\n",
       "   wishart               -- Wishart\n",
       "   invwishart            -- Inverse Wishart\n",
       "   multinomial           -- Multinomial distribution\n",
       "   special_ortho_group   -- SO(N) group\n",
       "   ortho_group           -- O(N) group\n",
       "   unitary_group         -- U(N) group\n",
       "   random_correlation    -- random correlation matrices\n",
       "\n",
       "Discrete distributions\n",
       "======================\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   bernoulli         -- Bernoulli\n",
       "   binom             -- Binomial\n",
       "   boltzmann         -- Boltzmann (Truncated Discrete Exponential)\n",
       "   dlaplace          -- Discrete Laplacian\n",
       "   geom              -- Geometric\n",
       "   hypergeom         -- Hypergeometric\n",
       "   logser            -- Logarithmic (Log-Series, Series)\n",
       "   nbinom            -- Negative Binomial\n",
       "   planck            -- Planck (Discrete Exponential)\n",
       "   poisson           -- Poisson\n",
       "   randint           -- Discrete Uniform\n",
       "   skellam           -- Skellam\n",
       "   zipf              -- Zipf\n",
       "   yulesimon         -- Yule-Simon\n",
       "\n",
       "An overview of statistical functions is given below.\n",
       "Several of these functions have a similar version in\n",
       "`scipy.stats.mstats` which work for masked arrays.\n",
       "\n",
       "Summary statistics\n",
       "==================\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   describe          -- Descriptive statistics\n",
       "   gmean             -- Geometric mean\n",
       "   hmean             -- Harmonic mean\n",
       "   kurtosis          -- Fisher or Pearson kurtosis\n",
       "   mode              -- Modal value\n",
       "   moment            -- Central moment\n",
       "   skew              -- Skewness\n",
       "   kstat             --\n",
       "   kstatvar          --\n",
       "   tmean             -- Truncated arithmetic mean\n",
       "   tvar              -- Truncated variance\n",
       "   tmin              --\n",
       "   tmax              --\n",
       "   tstd              --\n",
       "   tsem              --\n",
       "   variation         -- Coefficient of variation\n",
       "   find_repeats\n",
       "   trim_mean\n",
       "   gstd              -- Geometric Standard Deviation\n",
       "   iqr\n",
       "   sem\n",
       "   bayes_mvs\n",
       "   mvsdist\n",
       "   entropy\n",
       "   median_absolute_deviation\n",
       "\n",
       "Frequency statistics\n",
       "====================\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   cumfreq\n",
       "   itemfreq\n",
       "   percentileofscore\n",
       "   scoreatpercentile\n",
       "   relfreq\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   binned_statistic     -- Compute a binned statistic for a set of data.\n",
       "   binned_statistic_2d  -- Compute a 2-D binned statistic for a set of data.\n",
       "   binned_statistic_dd  -- Compute a d-D binned statistic for a set of data.\n",
       "\n",
       "Correlation functions\n",
       "=====================\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   f_oneway\n",
       "   pearsonr\n",
       "   spearmanr\n",
       "   pointbiserialr\n",
       "   kendalltau\n",
       "   weightedtau\n",
       "   linregress\n",
       "   siegelslopes\n",
       "   theilslopes\n",
       "\n",
       "Statistical tests\n",
       "=================\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   ttest_1samp\n",
       "   ttest_ind\n",
       "   ttest_ind_from_stats\n",
       "   ttest_rel\n",
       "   kstest\n",
       "   chisquare\n",
       "   power_divergence\n",
       "   ks_2samp\n",
       "   epps_singleton_2samp\n",
       "   mannwhitneyu\n",
       "   tiecorrect\n",
       "   rankdata\n",
       "   ranksums\n",
       "   wilcoxon\n",
       "   kruskal\n",
       "   friedmanchisquare\n",
       "   brunnermunzel\n",
       "   combine_pvalues\n",
       "   jarque_bera\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   ansari\n",
       "   bartlett\n",
       "   levene\n",
       "   shapiro\n",
       "   anderson\n",
       "   anderson_ksamp\n",
       "   binom_test\n",
       "   fligner\n",
       "   median_test\n",
       "   mood\n",
       "   skewtest\n",
       "   kurtosistest\n",
       "   normaltest\n",
       "\n",
       "Transformations\n",
       "===============\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   boxcox\n",
       "   boxcox_normmax\n",
       "   boxcox_llf\n",
       "   yeojohnson\n",
       "   yeojohnson_normmax\n",
       "   yeojohnson_llf\n",
       "   obrientransform\n",
       "   sigmaclip\n",
       "   trimboth\n",
       "   trim1\n",
       "   zmap\n",
       "   zscore\n",
       "\n",
       "Statistical distances\n",
       "=====================\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   wasserstein_distance\n",
       "   energy_distance\n",
       "\n",
       "Random variate generation\n",
       "=========================\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   rvs_ratio_uniforms\n",
       "\n",
       "Circular statistical functions\n",
       "==============================\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   circmean\n",
       "   circvar\n",
       "   circstd\n",
       "\n",
       "Contingency table functions\n",
       "===========================\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   chi2_contingency\n",
       "   contingency.expected_freq\n",
       "   contingency.margins\n",
       "   fisher_exact\n",
       "\n",
       "Plot-tests\n",
       "==========\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   ppcc_max\n",
       "   ppcc_plot\n",
       "   probplot\n",
       "   boxcox_normplot\n",
       "   yeojohnson_normplot\n",
       "\n",
       "\n",
       "Masked statistics functions\n",
       "===========================\n",
       "\n",
       ".. toctree::\n",
       "\n",
       "   stats.mstats\n",
       "\n",
       "\n",
       "Univariate and multivariate kernel density estimation\n",
       "=====================================================\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   gaussian_kde\n",
       "\n",
       "Warnings used in :mod:`scipy.stats`\n",
       "===================================\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   PearsonRConstantInputWarning\n",
       "   PearsonRNearConstantInputWarning\n",
       "\n",
       "For many more stat related functions install the software R and the\n",
       "interface package rpy.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats\n",
    "\n",
    "?scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m       \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrystalball\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mType:\u001b[0m            crystalball_gen\n",
       "\u001b[0;31mString form:\u001b[0m     <scipy.stats._continuous_distns.crystalball_gen object at 0x7a05be4821d0>\n",
       "\u001b[0;31mFile:\u001b[0m            ~/miniconda3/lib/python3.7/site-packages/scipy/stats/_continuous_distns.py\n",
       "\u001b[0;31mDocstring:\u001b[0m      \n",
       "Crystalball distribution\n",
       "\n",
       "As an instance of the `rv_continuous` class, `crystalball` object inherits from it\n",
       "a collection of generic methods (see below for the full list),\n",
       "and completes them with details specific for this particular distribution.\n",
       "\n",
       "Methods\n",
       "-------\n",
       "rvs(beta, m, loc=0, scale=1, size=1, random_state=None)\n",
       "    Random variates.\n",
       "pdf(x, beta, m, loc=0, scale=1)\n",
       "    Probability density function.\n",
       "logpdf(x, beta, m, loc=0, scale=1)\n",
       "    Log of the probability density function.\n",
       "cdf(x, beta, m, loc=0, scale=1)\n",
       "    Cumulative distribution function.\n",
       "logcdf(x, beta, m, loc=0, scale=1)\n",
       "    Log of the cumulative distribution function.\n",
       "sf(x, beta, m, loc=0, scale=1)\n",
       "    Survival function  (also defined as ``1 - cdf``, but `sf` is sometimes more accurate).\n",
       "logsf(x, beta, m, loc=0, scale=1)\n",
       "    Log of the survival function.\n",
       "ppf(q, beta, m, loc=0, scale=1)\n",
       "    Percent point function (inverse of ``cdf`` --- percentiles).\n",
       "isf(q, beta, m, loc=0, scale=1)\n",
       "    Inverse survival function (inverse of ``sf``).\n",
       "moment(n, beta, m, loc=0, scale=1)\n",
       "    Non-central moment of order n\n",
       "stats(beta, m, loc=0, scale=1, moments='mv')\n",
       "    Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').\n",
       "entropy(beta, m, loc=0, scale=1)\n",
       "    (Differential) entropy of the RV.\n",
       "fit(data, beta, m, loc=0, scale=1)\n",
       "    Parameter estimates for generic data.\n",
       "expect(func, args=(beta, m), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds)\n",
       "    Expected value of a function (of one argument) with respect to the distribution.\n",
       "median(beta, m, loc=0, scale=1)\n",
       "    Median of the distribution.\n",
       "mean(beta, m, loc=0, scale=1)\n",
       "    Mean of the distribution.\n",
       "var(beta, m, loc=0, scale=1)\n",
       "    Variance of the distribution.\n",
       "std(beta, m, loc=0, scale=1)\n",
       "    Standard deviation of the distribution.\n",
       "interval(alpha, beta, m, loc=0, scale=1)\n",
       "    Endpoints of the range that contains alpha percent of the distribution\n",
       "\n",
       "Notes\n",
       "-----\n",
       "The probability density function for `crystalball` is:\n",
       "\n",
       ".. math::\n",
       "\n",
       "    f(x, \\beta, m) =  \\begin{cases}\n",
       "                        N \\exp(-x^2 / 2),  &\\text{for } x > -\\beta\\\\\n",
       "                        N A (B - x)^{-m}  &\\text{for } x \\le -\\beta\n",
       "                      \\end{cases}\n",
       "\n",
       "where :math:`A = (m / |\\beta|)^n  \\exp(-\\beta^2 / 2)`,\n",
       ":math:`B = m/|\\beta| - |\\beta|` and :math:`N` is a normalisation constant.\n",
       "\n",
       "`crystalball` takes :math:`\\beta > 0` and :math:`m > 1` as shape\n",
       "parameters.  :math:`\\beta` defines the point where the pdf changes\n",
       "from a power-law to a Gaussian distribution.  :math:`m` is the power\n",
       "of the power-law tail.\n",
       "\n",
       "References\n",
       "----------\n",
       ".. [1] \"Crystal Ball Function\",\n",
       "       https://en.wikipedia.org/wiki/Crystal_Ball_function\n",
       "\n",
       "The probability density above is defined in the \"standardized\" form. To shift\n",
       "and/or scale the distribution use the ``loc`` and ``scale`` parameters.\n",
       "Specifically, ``crystalball.pdf(x, beta, m, loc, scale)`` is identically\n",
       "equivalent to ``crystalball.pdf(y, beta, m) / scale`` with\n",
       "``y = (x - loc) / scale``.\n",
       "\n",
       ".. versionadded:: 0.19.0\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> from scipy.stats import crystalball\n",
       ">>> import matplotlib.pyplot as plt\n",
       ">>> fig, ax = plt.subplots(1, 1)\n",
       "\n",
       "Calculate a few first moments:\n",
       "\n",
       ">>> beta, m = 2, 3\n",
       ">>> mean, var, skew, kurt = crystalball.stats(beta, m, moments='mvsk')\n",
       "\n",
       "Display the probability density function (``pdf``):\n",
       "\n",
       ">>> x = np.linspace(crystalball.ppf(0.01, beta, m),\n",
       "...                 crystalball.ppf(0.99, beta, m), 100)\n",
       ">>> ax.plot(x, crystalball.pdf(x, beta, m),\n",
       "...        'r-', lw=5, alpha=0.6, label='crystalball pdf')\n",
       "\n",
       "Alternatively, the distribution object can be called (as a function)\n",
       "to fix the shape, location and scale parameters. This returns a \"frozen\"\n",
       "RV object holding the given parameters fixed.\n",
       "\n",
       "Freeze the distribution and display the frozen ``pdf``:\n",
       "\n",
       ">>> rv = crystalball(beta, m)\n",
       ">>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')\n",
       "\n",
       "Check accuracy of ``cdf`` and ``ppf``:\n",
       "\n",
       ">>> vals = crystalball.ppf([0.001, 0.5, 0.999], beta, m)\n",
       ">>> np.allclose([0.001, 0.5, 0.999], crystalball.cdf(vals, beta, m))\n",
       "True\n",
       "\n",
       "Generate random numbers:\n",
       "\n",
       ">>> r = crystalball.rvs(beta, m, size=1000)\n",
       "\n",
       "And compare the histogram:\n",
       "\n",
       ">>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)\n",
       ">>> ax.legend(loc='best', frameon=False)\n",
       ">>> plt.show()\n",
       "\u001b[0;31mClass docstring:\u001b[0m\n",
       "Crystalball distribution\n",
       "\n",
       "%(before_notes)s\n",
       "\n",
       "Notes\n",
       "-----\n",
       "The probability density function for `crystalball` is:\n",
       "\n",
       ".. math::\n",
       "\n",
       "    f(x, \\beta, m) =  \\begin{cases}\n",
       "                        N \\exp(-x^2 / 2),  &\\text{for } x > -\\beta\\\\\n",
       "                        N A (B - x)^{-m}  &\\text{for } x \\le -\\beta\n",
       "                      \\end{cases}\n",
       "\n",
       "where :math:`A = (m / |\\beta|)^n  \\exp(-\\beta^2 / 2)`,\n",
       ":math:`B = m/|\\beta| - |\\beta|` and :math:`N` is a normalisation constant.\n",
       "\n",
       "`crystalball` takes :math:`\\beta > 0` and :math:`m > 1` as shape\n",
       "parameters.  :math:`\\beta` defines the point where the pdf changes\n",
       "from a power-law to a Gaussian distribution.  :math:`m` is the power\n",
       "of the power-law tail.\n",
       "\n",
       "References\n",
       "----------\n",
       ".. [1] \"Crystal Ball Function\",\n",
       "       https://en.wikipedia.org/wiki/Crystal_Ball_function\n",
       "\n",
       "%(after_notes)s\n",
       "\n",
       ".. versionadded:: 0.19.0\n",
       "\n",
       "%(example)s\n",
       "\u001b[0;31mCall docstring:\u001b[0m \n",
       "Freeze the distribution for the given arguments.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "arg1, arg2, arg3,... : array_like\n",
       "    The shape parameter(s) for the distribution.  Should include all\n",
       "    the non-optional arguments, may include ``loc`` and ``scale``.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "rv_frozen : rv_frozen instance\n",
       "    The frozen distribution.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?scipy.stats.crystalball"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = numpy.linspace(-10, 10, 100)\n",
    "y = scipy.stats.crystalball.pdf(x, beta=0.5, m=3)     # logpdf, cdf\n",
    "\n",
    "matplotlib.pyplot.plot(x, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.06825790e+01, -1.45972955e+00, -2.09113813e+00, -9.90478404e+00,\n",
       "        5.35567212e-03,  4.21874471e-01, -5.72754162e-02, -2.88567853e+00,\n",
       "       -7.81294620e+00, -1.84375854e-02, -6.47577977e+00, -4.62231697e+00,\n",
       "       -3.72719018e+00,  4.95874471e-01,  1.21497137e-01, -1.31600078e+00,\n",
       "       -1.95263403e-01, -1.82181185e+00, -1.56141363e-01, -1.26933504e+00,\n",
       "        2.78124744e-01, -1.26049960e+00, -1.94236393e+00,  6.62756160e-01,\n",
       "        1.12885196e+00,  1.06631507e+00,  3.42902124e-01,  2.12678802e+00,\n",
       "        1.21514862e+00,  1.06356770e+00,  7.98688658e-01, -4.12273558e+00,\n",
       "        1.88165219e-01, -3.55387002e+00,  2.68836552e-01, -1.75429718e-01,\n",
       "        5.05242363e-02, -3.71298932e+00, -1.40226596e+00,  1.51460279e+00,\n",
       "        1.03613214e-01,  2.55890098e-01, -2.80985233e+00,  3.12537301e+00,\n",
       "       -2.61731451e+00, -6.26536310e+00,  1.26201953e+00, -5.72053439e+00,\n",
       "       -8.33566875e-01, -1.60350704e+01, -8.15812528e+00, -3.34211388e-01,\n",
       "       -2.46369591e+00, -2.96186438e+00, -5.79044290e-01, -1.37271425e+00,\n",
       "       -7.55232791e+00, -3.20144010e+00, -1.86915303e+00,  4.21761163e-01,\n",
       "       -3.23055661e+00,  3.54418864e-01, -7.84622370e-01, -3.62523492e+00,\n",
       "       -3.45027614e+00,  1.18144938e-01, -4.40315185e+00,  5.64214706e-01,\n",
       "       -1.16637405e+00, -3.04554286e+00, -7.81917678e+00, -3.08678006e+01,\n",
       "       -1.49089665e+00, -2.95516936e+00, -1.56660417e+00, -1.80990909e+00,\n",
       "       -4.75671792e-01, -2.93338292e+00,  2.56393240e-01,  1.06420874e+00,\n",
       "       -8.57188080e+00, -1.80114347e+00,  4.23226488e-01, -7.63329637e-01,\n",
       "       -1.51004702e+00, -2.82726269e-01, -2.59748278e+01, -6.96595566e+00,\n",
       "        8.23971214e-01,  1.91002346e+00, -6.22209754e+00, -3.13750308e-01,\n",
       "        1.45843036e+00, -1.12223604e+00, -5.91008896e-02,  1.08792481e-01,\n",
       "        1.76715458e+00, -4.39708318e+00,  1.29707624e+00,  2.05695699e-01,\n",
       "        1.26584028e-01, -5.94060478e+00, -3.96790898e+00, -7.09179996e+00,\n",
       "       -1.02998028e+01, -1.70810806e-01, -1.47109755e-01, -4.62618702e-01,\n",
       "       -2.81332106e+00, -7.99587666e-01, -6.32205861e+00, -8.54990170e-01,\n",
       "       -3.66354859e-01, -1.10215683e+00, -2.19479237e-01,  1.46837233e+00,\n",
       "       -1.63639887e+00, -1.68398667e-01,  2.55610513e-01, -2.44652899e+00,\n",
       "       -1.39132526e-01,  7.80363630e-01, -4.26055291e-01, -3.88765306e+00,\n",
       "       -8.61903426e-01, -9.86982320e-01, -4.50945668e-01,  5.83001010e-01,\n",
       "       -7.70786374e+00,  1.90088473e+00, -2.40138446e+00, -5.01995209e+00,\n",
       "       -5.49800470e+00,  1.17046704e+00, -3.72144997e+00, -2.41650847e+00,\n",
       "       -5.77181077e+00, -1.28628577e+00,  1.30317196e+00, -2.78531960e-01,\n",
       "       -2.43600727e+00, -7.88962337e+00,  5.76134279e-01, -1.46996064e+00,\n",
       "       -1.07335954e+01,  4.41941969e-01, -5.90403904e+00, -3.14969330e+00,\n",
       "       -2.65257851e+00, -2.06075169e+01,  1.39121053e+00, -1.02850387e+01,\n",
       "        2.49272296e+00, -6.88032610e-01, -5.94040997e+00, -8.18883679e-01,\n",
       "       -3.26719086e+01,  3.76975198e-02, -8.99461459e-03,  9.34947301e-01,\n",
       "       -1.05438132e+00, -4.15558482e-01, -7.84569428e-01, -3.95773764e-01,\n",
       "       -2.01075182e+00, -1.98459933e+00, -1.77997566e+00, -2.13617772e+00,\n",
       "       -4.31251318e+01,  7.49072766e-01,  4.43621044e-01, -8.84080808e-01,\n",
       "       -8.35419411e-02, -2.85663125e-01, -4.31617405e+00, -3.66860323e+01,\n",
       "       -6.88928781e-01, -3.19024805e-01, -3.18782558e+00,  2.03127941e+00,\n",
       "       -9.61604155e+00, -1.50419086e+00,  1.01698216e+00, -3.70025087e+00,\n",
       "       -3.78408411e+01, -4.41869092e+00, -6.73459995e+00, -1.47860896e+01,\n",
       "       -1.00103658e+00, -6.59158208e-01, -7.65465289e-01,  7.07467878e-01,\n",
       "       -2.38430030e+00,  1.25510076e-01,  4.31856788e-01,  1.41412355e+00,\n",
       "       -3.01488298e+00,  1.38861988e+00, -3.23938586e+00, -2.26215684e-01])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.crystalball.rvs(beta=0.5, m=3, size=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "masses = uproot.open(\"data/Zmumu.root\")[\"events\"].array(\"M\")\n",
    "\n",
    "y, edges = numpy.histogram(masses, bins=100)\n",
    "yerr = numpy.sqrt(y)\n",
    "\n",
    "# the middle of each bin\n",
    "x = (edges[1:] + edges[:-1])/2\n",
    "\n",
    "matplotlib.pyplot.errorbar(x, y, yerr, fmt=\"o\", capsize=3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.optimize\n",
    "\n",
    "def f(x, a, b, c, d):\n",
    "    return a*scipy.stats.cauchy.pdf(x, b, c) + d/x**2\n",
    "\n",
    "parameters, errors = scipy.optimize.curve_fit(f, x[y > 0], y[y > 0], sigma=yerr[y > 0])  # exclude y == 0\n",
    "\n",
    "matplotlib.pyplot.plot(x, f(x, *parameters))\n",
    "matplotlib.pyplot.errorbar(x, y, yerr, fmt=\"o\", capsize=3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\"><b>Five minute challenge:</b> using only commands from the previous cell, plot the fit residuals.</p>\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/iminuit.png\" width=\"100%\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import iminuit\n",
    "\n",
    "def chi2(a, b, c, d):\n",
    "    return ((y[y > 0] - f(x[y > 0], a, b, c, d))**2 / yerr[y > 0]**2).sum()\n",
    "\n",
    "m = iminuit.Minuit(chi2, errordef=1,\n",
    "                   a=3350, b=91, c=2, d=30,\n",
    "                   error_a=100, error_b=1, error_c=0.1, error_d=10)\n",
    "m.migrad()\n",
    "m.hesse()\n",
    "m.minos()\n",
    "m.draw_mncontour(\"a\", \"b\", nsigma=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>\n",
    "\n",
    "<center><img src=\"img/scikit-learn-logo.png\" width=\"40%\"></center>\n",
    "\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">To branch out in a modular way, SciPy introduced the idea of \"SciKits\"—separate packages from SciPy that have a similar interface.</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">The most famous of these is Scikit-Learn, a package that gathered all machine learning algorithms under one roof—just before the deep learning revolution...</p>\n",
    "\n",
    "\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/scikit-learn-estimators.png\" width=\"100%\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sklearn.datasets\n",
    "X1, y1 = sklearn.datasets.make_gaussian_quantiles(\n",
    "    cov=2.0, n_samples=800, n_features=2, n_classes=2, random_state=1)\n",
    "X2, y2 = sklearn.datasets.make_gaussian_quantiles(\n",
    "    mean=(3, 3), cov=1.5, n_samples=1200, n_features=2, n_classes=2, random_state=1)\n",
    "X = numpy.concatenate((X1, X2))\n",
    "y = numpy.concatenate((y1, -y2 + 1))\n",
    "\n",
    "# Example of a hard classification problem.\n",
    "matplotlib.pyplot.scatter(X[y == 0, 0], X[y == 0, 1], c=\"deepskyblue\", edgecolor=\"k\");\n",
    "matplotlib.pyplot.scatter(X[y == 1, 0], X[y == 1, 1], c=\"orange\", edgecolor=\"k\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions:\n",
      " [0 1 1 ... 0 0 1]\n",
      "truth:\n",
      " [0 1 1 ... 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "import sklearn.tree\n",
    "\n",
    "# Example model: decision tree classifier\n",
    "model = sklearn.tree.DecisionTreeClassifier(max_depth=10)\n",
    "\n",
    "# Consistent interface: nearly every model has a fit method with this signature\n",
    "model.fit(X, y)\n",
    "\n",
    "print(\"predictions:\\n\", model.predict(X))\n",
    "print(\"truth:\\n\", y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions:\n",
      " [0 1 0 ... 1 1 1]\n",
      "truth:\n",
      " [0 1 1 ... 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "import sklearn.ensemble\n",
    "\n",
    "# Another model: boosted decision tree\n",
    "model = sklearn.ensemble.AdaBoostClassifier(\n",
    "    sklearn.tree.DecisionTreeClassifier(max_depth=2), algorithm=\"SAMME\", n_estimators=100)\n",
    "\n",
    "model.fit(X, y)\n",
    "\n",
    "print(\"predictions:\\n\", model.predict(X))\n",
    "print(\"truth:\\n\", y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx, yy = numpy.meshgrid(numpy.arange(-5, 8, 0.02), numpy.arange(-5, 8, 0.02))\n",
    "Z = model.predict(numpy.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "# Overlay the training points on the decision boundary.\n",
    "matplotlib.pyplot.contourf(xx, yy, Z);\n",
    "matplotlib.pyplot.scatter(X[y == 0, 0], X[y == 0, 1], c=\"deepskyblue\", edgecolor=\"k\", alpha=0.2);\n",
    "matplotlib.pyplot.scatter(X[y == 1, 0], X[y == 1, 1], c=\"orange\", edgecolor=\"k\", alpha=0.2);\n",
    "matplotlib.pyplot.xlim(-5, 8);\n",
    "matplotlib.pyplot.ylim(-5, 8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">Notice that we have the same Numpy interfaces everywhere, and everything acts one array at a time, rather than one value at a time.</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">The new deep learning frameworks define their own array types (e.g. PyTorch tensors, TensorFlow tensors), but these are very similar to Numpy arrays, with the addition that they can move data to and from GPUs.</p>\n",
    "\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/scikit-hep-page.png\" width=\"100%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\"><b>Example 1:</b> pyjet, a Numpythonic wrapper for FastJet</p>\n",
    "\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(0.41585193,  0.08174441,  0.33964178, -0.22559729),\n",
       "       (0.5187222 ,  0.43506647,  0.16324279,  0.23052479),\n",
       "       (0.32702403,  0.23596794,  0.18973544,  0.12354882),\n",
       "       (0.02596889, -0.00754146, -0.02367119,  0.00756204),\n",
       "       (0.4526551 ,  0.11796613,  0.35377868, -0.25655658)],\n",
       "      dtype=[('E', '<f8'), ('px', '<f8'), ('py', '<f8'), ('pz', '<f8')])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, make a fake-o Monte Carlo: three clusters at specified central values\n",
    "fourvectors = numpy.empty(1000, dtype=[(\"E\", float), (\"px\", float),\n",
    "                                       (\"py\", float), (\"pz\", float)])\n",
    "indexes = numpy.random.randint(0, 3, 1000)\n",
    "\n",
    "#                        i   pt   eta    phi\n",
    "for i, pt, eta, phi in [(0, 0.5,  0.5,     0),   # cluster center 0\n",
    "                        (1, 0.2, -0.8,  1.57),   # cluster center 1\n",
    "                        (2, 0.1,  0.3, -1.57)]:  # cluster center 2\n",
    "    this = (indexes == i)\n",
    "    pt = abs(numpy.random.normal(pt, 0.1, this.sum()))\n",
    "    eta = numpy.random.normal(eta, 0.03, this.sum())\n",
    "    phi = numpy.random.normal(phi, 0.3, this.sum())\n",
    "    fourvectors[\"px\"][this] = px = (pt*numpy.cos(phi)*numpy.cosh(eta))\n",
    "    fourvectors[\"py\"][this] = py = (pt*numpy.sin(phi)*numpy.cosh(eta))\n",
    "    fourvectors[\"pz\"][this] = pz = (pt*numpy.sinh(eta))\n",
    "    fourvectors[\"E\"][this]  = numpy.sqrt(px**2 + py**2 + pz**2)\n",
    "\n",
    "fourvectors[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PseudoJet(pt=179.614, eta=0.467, phi=0.022, mass=56.689),\n",
       " PseudoJet(pt=93.053, eta=-0.648, phi=1.567, mass=27.944),\n",
       " PseudoJet(pt=36.899, eta=0.301, phi=-1.595, mass=10.909)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyjet\n",
    "\n",
    "# This is FastJet, running in C++.\n",
    "# \n",
    "# By passing all particles, array-at-a-time, we avoid Python's slowness.\n",
    "\n",
    "clustering = pyjet.cluster(fourvectors, R=1.0, p=-1, ep=True)\n",
    "clustering.inclusive_jets()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\"><b>Example 2:</b> particle, an interface to Particle Data Tables</p>\n",
    "\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\Sigma^{-}$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Particle: name=\"Sigma-\", pdgid=3112, mass=1197.45 ± 0.03 MeV>\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\bar{\\Sigma}^{+}$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Particle: name=\"Sigma~+\", pdgid=-3112, mass=1197.45 ± 0.03 MeV>\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\Lambda$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Particle: name=\"Lambda\", pdgid=3122, mass=1115.683 ± 0.006 MeV>\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\bar{\\Lambda}$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Particle: name=\"Lambda~\", pdgid=-3122, mass=1115.683 ± 0.006 MeV>\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\Sigma^{+}$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Particle: name=\"Sigma+\", pdgid=3222, mass=1189.37 ± 0.07 MeV>\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\bar{\\Sigma}^{-}$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Particle: name=\"Sigma~-\", pdgid=-3222, mass=1189.37 ± 0.07 MeV>\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\Xi^{-}$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Particle: name=\"Xi-\", pdgid=3312, mass=1321.71 ± 0.07 MeV>\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\bar{\\Xi}^{+}$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Particle: name=\"Xi~+\", pdgid=-3312, mass=1321.71 ± 0.07 MeV>\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\Xi^{0}$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Particle: name=\"Xi0\", pdgid=3322, mass=1314.86 ± 0.20 MeV>\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\bar{\\Xi}^{0}$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Particle: name=\"Xi~0\", pdgid=-3322, mass=1314.86 ± 0.20 MeV>\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\Omega^{-}$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Particle: name=\"Omega-\", pdgid=3334, mass=1672.5 ± 0.3 MeV>\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\bar{\\Omega}^{+}$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Particle: name=\"Omega~+\", pdgid=-3334, mass=1672.5 ± 0.3 MeV>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import particle\n",
    "from hepunits.units import cm\n",
    "\n",
    "import IPython.display\n",
    "\n",
    "# Find all strange baryons with c*tau > 1 cm\n",
    "for x in particle.Particle.findall(lambda p:\n",
    "    p.pdgid.is_baryon and p.pdgid.has_strange and p.width > 0 and p.ctau > 1 * cm):\n",
    "    \n",
    "    IPython.display.display(IPython.display.Latex(\"$\" + x.latex_name + \"$\"))\n",
    "    print(repr(x), end=\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\"><b>Example 3:</b> pyhf, limit-setting similar to HistFactory and CmsCombine</p>\n",
    "\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pyhf                                                        # (scale to 125%)\n",
    "import awkward\n",
    "\n",
    "s, b, obs = [[0.0, 0.0, 0.1, 0.2, 0.5, 1.0, 1.8, 2.7, 2.0, 0.3],   # signal peaks around bin 8\n",
    "             [6.0, 5.5, 4.5, 3.8, 3.3, 2.6, 2.0, 1.8, 1.5, 1.0],   # background steadily falls\n",
    "             [  6,   7,   4,   4,   4,   3,   4,   3,   4,   0]]   # observations perfer 3/4 signal\n",
    "model = pyhf.simplemodels.hepdata_like(signal_data=s, bkg_data=b, bkg_uncerts=numpy.sqrt(b).tolist())\n",
    "def hypotest(mu):\n",
    "    return pyhf.utils.hypotest(mu, obs + model.config.auxdata, model, return_expected_set=True)\n",
    "\n",
    "mus = numpy.linspace(0, 2, 30)\n",
    "CLs = awkward.fromiter([hypotest(mu) for mu in mus])\n",
    "CLs_observed = CLs[:, 0, 0]                                        # mucking around with indexes\n",
    "CLs_minus2, CLs_minus1, CLs_expected, CLs_plus1, CLs_plus2 = [CLs[:, 1, i, 0] for i in range(5)]\n",
    "\n",
    "matplotlib.pyplot.fill_between(mus, CLs_minus2, CLs_plus2, facecolor=\"yellow\");\n",
    "matplotlib.pyplot.fill_between(mus, CLs_minus1, CLs_plus1, facecolor=\"limegreen\");\n",
    "matplotlib.pyplot.plot(mus, CLs_expected, c=\"black\", linestyle=\"dotted\");\n",
    "matplotlib.pyplot.plot(mus, CLs_observed, c=\"black\", marker=\"o\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "### Part 3: ROOT data in Python\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ROOT                                   # PyROOT\n",
    "\n",
    "file = ROOT.TFile(\"data/HZZ-objects.root\")    # PyROOT transliterates C++ to Python\n",
    "tree = file.Get(\"events\")\n",
    "\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"\", 400, 300) # JupyROOT only: must create TCanvas\n",
    "tree.Draw(\"muonp4.Pt()\")\n",
    "canvas.Draw()                                 # and Draw it to see plots inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "event 0\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a650> 54.16810703140204\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a690> 37.74415265978988\n",
      "event 1\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a650> 24.41791248135961\n",
      "event 2\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a650> 53.58826697278532\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a690> 29.811997139120674\n",
      "event 3\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a650> 88.63194310332618\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a690> 77.95148447265454\n",
      "event 4\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a650> 81.01140452083426\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a690> 47.17504574667714\n",
      "event 5\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a650> 41.591052358050575\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a690> 30.844215085173435\n",
      "event 6\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a650> 44.35777431661225\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a690> 29.874452374029385\n",
      "event 7\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a650> 38.37087544930784\n",
      "event 8\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a650> 106.28355631699014\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a690> 12.311635885539262\n",
      "event 9\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a650> 85.53881159059787\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a690> 39.506103898783294\n",
      "event 10\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a650> 51.64914852288953\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a690> 46.49716868294783\n",
      "event 11\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a650> 75.51093887887036\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x57242462a690> 50.514888290524446\n"
     ]
    }
   ],
   "source": [
    "# PyROOT can iterate over the data directly, in a Python-friendly way.\n",
    "\n",
    "for i, event in enumerate(tree):\n",
    "    print(\"event\", i)\n",
    "    for muon in event.muonp4:\n",
    "        print(repr(muon), muon.Pt())\n",
    "    if i > 10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">But be forewarned: PyROOT was not made to be used in a loop over big data. (Actually, it's as much slower than Python as Python is from C++.)</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">You can start this way, but if you'll be analyzing TB of data, you'll have to rewrite your code.</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">We want to explore data in a way that doesn't have to be completely rewritten for speed.</p>\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00927101],\n",
       "       [0.00033064],\n",
       "       [0.00507963],\n",
       "       ...,\n",
       "       [0.00415307],\n",
       "       [0.00882933],\n",
       "       [0.00875541]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PyROOT's new AsMatrix method gives you Numpy arrays directly (loops run in C++).\n",
    "\n",
    "tree.AsMatrix([\"eventweight\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Reading of branch ['MET'] is not supported (branch has unsupported data-type ['TVector2']).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-83abc36b1eb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# But it's only for purely numeric data, not objects...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAsMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"MET\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/ROOT.py\u001b[0m in \u001b[0;36m_TTreeAsMatrix\u001b[0;34m(self, columns, exclude, dtype, return_labels)\u001b[0m\n\u001b[1;32m    331\u001b[0m         raise Exception(exception_template.format(\n\u001b[1;32m    332\u001b[0m             [k for k in invalid_cols_dtype], \"branch has unsupported data-type {}\".format(\n\u001b[0;32m--> 333\u001b[0;31m                 [invalid_cols_dtype[k] for k in invalid_cols_dtype])))\n\u001b[0m\u001b[1;32m    334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    335\u001b[0m     \u001b[0;31m# Check that given data-type is supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mException\u001b[0m: Reading of branch ['MET'] is not supported (branch has unsupported data-type ['TVector2'])."
     ]
    }
   ],
   "source": [
    "# But it's only for purely numeric data, not objects...\n",
    "\n",
    "tree.AsMatrix([\"MET\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Reading of branch ['muoniso'] is not supported (branch has unsupported data-type ['vector<float>']).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-4f2da9df4263>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# ... and not variable number of values per event, like vector<float>\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAsMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"muoniso\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/ROOT.py\u001b[0m in \u001b[0;36m_TTreeAsMatrix\u001b[0;34m(self, columns, exclude, dtype, return_labels)\u001b[0m\n\u001b[1;32m    331\u001b[0m         raise Exception(exception_template.format(\n\u001b[1;32m    332\u001b[0m             [k for k in invalid_cols_dtype], \"branch has unsupported data-type {}\".format(\n\u001b[0;32m--> 333\u001b[0;31m                 [invalid_cols_dtype[k] for k in invalid_cols_dtype])))\n\u001b[0m\u001b[1;32m    334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    335\u001b[0m     \u001b[0;31m# Check that given data-type is supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mException\u001b[0m: Reading of branch ['muoniso'] is not supported (branch has unsupported data-type ['vector<float>'])."
     ]
    }
   ],
   "source": [
    "# ... and not variable number of values per event, like vector<float>\n",
    "\n",
    "tree.AsMatrix([\"muoniso\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROOT's new preferred way of processing TTrees is called RDataFrame.\n",
    "# You can define a dataflow in Python with C++ in strings (convenient!).\n",
    "\n",
    "rdf = ROOT.RDataFrame(\"events\", \"data/HZZ-objects.root\")\n",
    "h = (rdf.Filter(\"muonp4.size() >= 2\")\n",
    "        .Define(\"zmass\", r\"\"\"(muonp4[0] + muonp4[1]).M()\"\"\")\n",
    "        .Histo1D((\"\", \"\", 120, 0, 120), \"zmass\"))\n",
    "h.Draw(); canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'zmass': numpy.array([90.22779777, 74.74654928, 89.75736376, ..., 92.06495256,\n",
       "              85.44384208, 75.96066262])}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And you can get the result of this dataflow as Numpy arrays (even better!).\n",
    "\n",
    "array = (rdf.Filter(\"muonp4.size() >= 2\")\n",
    "            .Define(\"zmass\", r\"\"\"(muonp4[0] + muonp4[1]).M()\"\"\")\n",
    "            .AsNumpy(columns=[\"zmass\"]))\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.array([<ROOT.ROOT::VecOps::RVec<TLorentzVector> object at 0x572430da5100>,\n",
       "             <ROOT.ROOT::VecOps::RVec<TLorentzVector> object at 0x572430da5128>,\n",
       "             <ROOT.ROOT::VecOps::RVec<TLorentzVector> object at 0x572430da5150>,\n",
       "             ...,\n",
       "             <ROOT.ROOT::VecOps::RVec<TLorentzVector> object at 0x572430dbcad0>,\n",
       "             <ROOT.ROOT::VecOps::RVec<TLorentzVector> object at 0x572430dbcaf8>,\n",
       "             <ROOT.ROOT::VecOps::RVec<TLorentzVector> object at 0x572430dbcb20>],\n",
       "            dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can also get arrays of objects and arrays of vectors of objects.\n",
    "\n",
    "array = rdf.AsNumpy(columns=[\"muonp4\"])[\"muonp4\"]\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "event 0\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430c6f640> 54.16810703140204\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430c6f680> 37.74415265978988\n",
      "event 1\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430c6f6d0> 24.41791248135961\n",
      "event 2\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430a24ce0> 53.58826697278532\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430a24d20> 29.811997139120674\n",
      "event 3\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430a24d70> 88.63194310332618\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430a24db0> 77.95148447265454\n",
      "event 4\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430a24e00> 81.01140452083426\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430a24e40> 47.17504574667714\n",
      "event 5\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430a24e90> 41.591052358050575\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430a24ed0> 30.844215085173435\n",
      "event 6\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430a24f20> 44.35777431661225\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430a24f60> 29.874452374029385\n",
      "event 7\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430a24fb0> 38.37087544930784\n",
      "event 8\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430a25000> 106.28355631699014\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430a25040> 12.311635885539262\n",
      "event 9\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430c5a4f0> 85.53881159059787\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430c5a530> 39.506103898783294\n",
      "event 10\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430c5a580> 51.64914852288953\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430c5a5c0> 46.49716868294783\n",
      "event 11\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430c5a610> 75.51093887887036\n",
      "<ROOT.TLorentzVector object (\"TLorentzVector\") at 0x572430c5a650> 50.514888290524446\n"
     ]
    }
   ],
   "source": [
    "# But looping over them is back to PyROOT. The array is an array of PyROOT objects.\n",
    "\n",
    "for i, event in enumerate(array):\n",
    "    print(\"event\", i)\n",
    "    for muon in event:\n",
    "        print(repr(muon), muon.Pt())\n",
    "    if i > 10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">To get efficient processing in ROOT, you have to do the heavy work in C++. That's just how it works. What's new is that the C++ can be expressed as inline strings in Python.</p>\n",
    "\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em\">uproot is an alternative ROOT I/O implemented in Python + Numpy (i.e. it's pip-installable).</p>\n",
    "\n",
    "<br>\n",
    "\n",
    "<center><img src=\"img/abstraction-layers.png\" width=\"80%\"></center>\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<JaggedArrayMethods [[TLorentzVector(-52.899, -11.655, -8.1608, 54.779) TLorentzVector(37.738, 0.69347, -11.308, 39.402)] [TLorentzVector(-0.81646, -24.404, 20.2, 31.69)] [TLorentzVector(48.988, -21.723, 11.168, 54.74) TLorentzVector(0.82757, 29.801, 36.965, 47.489)] ... [TLorentzVector(-29.757, -15.304, -52.664, 62.395)] [TLorentzVector(1.1419, 63.61, 162.18, 174.21)] [TLorentzVector(23.913, -35.665, 54.719, 69.556)]] at 0x7a05c7a27208>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import uproot\n",
    "\n",
    "file = uproot.open(\"data/HZZ-objects.root\")\n",
    "tree = file[\"events\"]\n",
    "\n",
    "array = tree.array(\"muonp4\")\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " layout \n",
      "[     ()] JaggedArrayMethods(starts=layout[0], stops=layout[1], content=layout[2])\n",
      "[      0]   ndarray(shape=2421, dtype=dtype('int64'))\n",
      "[      1]   ndarray(shape=2421, dtype=dtype('int64'))\n",
      "[      2]   ObjectArrayMethods(content=layout[2, 0], generator=<function ArrayMethods._initObjectArray.<locals>.<lambda> at 0x7a05c781f9d8>, args=(), kwargs={})\n",
      "[   2, 0]     Table( fBits=layout[2, 0, 0],  fUniqueID=layout[2, 0, 1],  fBits2=layout[2, 0, 2],  fUniqueID2=layout[2, 0, 3], fX=layout[2, 0, 4], fY=layout[2, 0, 5], fZ=layout[2, 0, 6], fE=layout[2, 0, 7])\n",
      "[2, 0, 0]       ndarray(shape=3825, dtype=dtype('uint64'))\n",
      "[2, 0, 1]       ndarray(shape=3825, dtype=dtype('uint64'))\n",
      "[2, 0, 2]       ndarray(shape=3825, dtype=dtype('uint64'))\n",
      "[2, 0, 3]       ndarray(shape=3825, dtype=dtype('uint64'))\n",
      "[2, 0, 4]       ndarray(shape=3825, dtype=dtype('float64'))\n",
      "[2, 0, 5]       ndarray(shape=3825, dtype=dtype('float64'))\n",
      "[2, 0, 6]       ndarray(shape=3825, dtype=dtype('float64'))\n",
      "[2, 0, 7]       ndarray(shape=3825, dtype=dtype('float64'))\n",
      "\n",
      "array for TLorentzVector.fX:\n",
      "[-52.89945602  37.73778152  -0.81645936 ... -29.75678635   1.14186978\n",
      "  23.9132061 ]\n"
     ]
    }
   ],
   "source": [
    "# Although this looks like an array of variable-length arrays of TLorentzVectors,\n",
    "# it's implmented in terms of columnar arrays, not objects.\n",
    "print(array.layout)\n",
    "print(f\"\\narray for TLorentzVector.fX:\\n{array.layout[2, 0, 4].array}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good_events: [ True False  True ... False False False]\n",
      "\n",
      "first:       [TLorentzVector(-52.899, -11.655, -8.1608, 54.779) TLorentzVector(48.988, -21.723, 11.168, 54.74) TLorentzVector(22.088, -85.835, 403.85, 413.46) ... TLorentzVector(53.006, -24.486, 13.952, 60.032) TLorentzVector(55.72, 26.37, -24.588, 66.368) TLorentzVector(34.507, 28.84, -150.66, 157.23)]\n",
      "second:      [TLorentzVector(37.738, 0.69347, -11.308, 39.402) TLorentzVector(0.82757, 29.801, 36.965, 47.489) TLorentzVector(76.692, -13.956, 335.09, 344.04) ... TLorentzVector(-30.209, 19.269, 18.661, 40.399) TLorentzVector(-26.914, -9.8128, -0.38995, 28.65) TLorentzVector(-31.568, -10.424, -111.26, 116.13)]\n",
      "\n",
      "z_mass:      [90.22779777 74.74654928 89.75736376 ... 92.06495256 85.44384208\n",
      " 75.96066262]\n"
     ]
    }
   ],
   "source": [
    "# So the interface is array-at-a-time: neither for loops nor call-outs to C++.\n",
    "\n",
    "good_events = (array.counts >= 2)\n",
    "print(f\"good_events: {good_events}\")\n",
    "\n",
    "first  = array[good_events, 0]\n",
    "second = array[good_events, 1]\n",
    "print(f\"\\nfirst:       {first}\")\n",
    "print(f\"second:      {second}\")\n",
    "\n",
    "z_candidates = first + second\n",
    "print(f\"\\nz_mass:      {z_candidates.mass}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "<p style=\"font-size: 1.25em; text-align: center\">Which one should you use?</p>\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br><br>\n",
    "\n",
    "<p style=\"font-size: 2em; font-weight: bold; text-align: center\">Both!</p>\n",
    "\n",
    "<br><br><br><br><br>"
   ]
  }
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