HKML Data Access & Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: Workshop Data \n",
    "In this notebook we go over how to interactively (python) access workshop data and visualize using common python libraries, `matplotlib` and `plotly`. \n",
    "\n",
    "## Opening a data file\n",
    "Data files are stored as a numpy array binary format with an extension `.npz`. Let's open and check what's in the file. Opening is as simple as `numpy.load` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.lib.npyio.NpzFile'>\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from __future__ import print_function\n",
    "\n",
    "# Change if you want to open a different file\n",
    "DATA_FILE_PATH='/data/hkml_data/IWCDgrid/varyE/e-/IWCDgrid_varyE_e-_R0cm_20-2000MeV_100evts_1.npz'\n",
    "\n",
    "# Open file\n",
    "f = np.load(DATA_FILE_PATH)\n",
    "\n",
    "# Print the object type\n",
    "print (type(f))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like any python object, you can try `help` function call (i.e. `help(f)`) to see what are the attributes (functions and variables). A `NpzFile` instance stores attribute objects by a strig key. You can check what are stored in a pythonic way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['energies', 'positions', 'labels', 'directions', 'pids', 'event_data']\n"
     ]
    }
   ],
   "source": [
    "print (f.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we got two objects: `labels` and `event_data`. You probably have a good guess what they are, but let's just look at what `event_data` is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print (type(f['event_data']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`numpy` array! so the next natural thing is to check its dimension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 88, 168, 2)\n"
     ]
    }
   ],
   "source": [
    "print (f['event_data'].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you probably guessed, this is a `numpy` array that contains 100 event images where each event image is 88x168 pixels. Each pixel has 2 channels that stores PMT \"PE\" and \"Time\" information in respective order. \n",
    "\n",
    "So what about the labels?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100,)\n"
     ]
    }
   ],
   "source": [
    "print (f['labels'].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's right: these are labels for 100 events, prepared for an **image classification** task (so 1 integer value as a \"label\" per event).\n",
    "\n",
    "## Visualizing data\n",
    "Let's try visualizing 2D event image. Actually, to be pedantic, the stored data is really 3D since time coordinate can be seen as yet another spatial dimension. So, for 2D image visualization, we will ignore the time information and simply plot PE/pmt as a single channel 2D image using `matplotlib`, which is by far the most popular and 101 plotting library in python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you wonder what `%matplotlib inline` means, it's called a _magic_ command in a notebook and this one sets to make matplotlib drawing in the web browser (as opposed to starting a canvas thread and drawing on it in its own window)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f29202b5f90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.imshow(f['event_data'][0,:,:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, maybe make it a bit pretier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,8),facecolor='w')\n",
    "plt.imshow(f['event_data'][0,:,:,0],cmap='jet',origin='lower')\n",
    "ax.set_title('Event Data',fontsize=20,fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So this image i an overlay of all PMT hits for all time period. Let's try to understand a bit about timing distribution. First, let's just plot time in 1D histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time in 1D array (14784,) which length should be 88*168 (= 14784 )\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time min, max, mean are: 0.0999755859375 840.300048828125 10.311539145503176\n"
     ]
    }
   ],
   "source": [
    "# For convenience, let's just make 1D array\n",
    "time_1d = f['event_data'][0,:,:,1].reshape([-1])\n",
    "print ('Time in 1D array', time_1d.shape, 'which length should be 88*168 (=', 88*168,')')\n",
    "\n",
    "# Further, let's filter out to keep only non-zero elements\n",
    "time_1d = time_1d[time_1d>0]\n",
    "\n",
    "# Make 1D histogram\n",
    "_ = plt.hist(time_1d,bins=50)\n",
    "plt.show()\n",
    "\n",
    "# Let's print out min, max, mean\n",
    "print ('Time min, max, mean are:', time_1d.min(), time_1d.max(), time_1d.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram range is automatically set, unless specified, to the min and max value of the data array. The max value 1790 is clearly an outlier, so let's just plot within the range of 950 to 1150."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "_ = plt.hist(time_1d,bins=50,range=(0,100))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretier?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(12,8),facecolor='w')\n",
    "\n",
    "ax.hist(time_1d,bins=50,range=(0,100),color='blue',label='PMT Hit Timing')\n",
    "ax.set_title(\"Timing Distribution\",fontsize=24,fontweight='bold')\n",
    "ax.set_xlabel('Time [ns]',fontsize=24,fontweight='bold')\n",
    "ax.tick_params('x',labelsize=20)\n",
    "ax.tick_params('y',labelsize=20)\n",
    "ax.grid()\n",
    "\n",
    "leg=plt.legend(fontsize=16,loc=1)\n",
    "leg_frame=leg.get_frame()\n",
    "leg_frame.set_facecolor('white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, so the most meaty part of the timing distribution is around 958-ish. There's an interesting bump at 980! Really interesting and makes me want to look at this in 3D. So here comes plotly.\n",
    "\n",
    "## 3D Visualization\n",
    "Plotly is a nice tool for an interactive data visualization. The documentation is rich and you can google most of it. Here, I give you my way of visualizing 3D data with my style. Hopefully you can tweak and find the style you like the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.offline import init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(data):\n",
    "    \"\"\"\n",
    "        Args: data is 3 dimensional numpy array, the shape should be [Nx,Ny,2] where Nx and Ny are number of pixels along x and y axis of 2D image.\n",
    " \n",
    "        Returns: \"trace\" object that can be visualized by plotly\n",
    "    \"\"\"    \n",
    "    # Separate PE and Time into 2 separate arrays\n",
    "    pe = data[:,:,0]\n",
    "    t  = data[:,:,1]\n",
    "    # Get non-zero element location\n",
    "    coords_xy = np.where(pe>0)\n",
    "    # Get values at non-zero element location\n",
    "    vals = pe[coords_xy]\n",
    "    # Define \"z\" coordinate, which we use Time\n",
    "    coords_z  = (t[coords_xy]).astype(np.int32)\n",
    "    # Create a trace object with x, y, and z location values + PE\n",
    "    trace_3d = go.Scatter3d(x=coords_xy[:][0],y=coords_xy[:][1],z=coords_z,\n",
    "                            mode='markers',\n",
    "                            marker = dict(\n",
    "                                size = 4,\n",
    "                                color = np.log(vals*100),\n",
    "                                colorscale='Viridis',\n",
    "                                opacity=0.7\n",
    "                            )\n",
    "                           )\n",
    "    return trace_3d\n",
    "\n",
    "def get_layout_3d(ranges,titles):\n",
    "    layout = go.Layout(\n",
    "        showlegend=False,\n",
    "        #xaxis=titles[0], yaxis=titles[1], zaxis=titles[2],\n",
    "        margin=dict(l=0,r=0,b=0,t=0),        \n",
    "        scene = dict(\n",
    "            xaxis = dict(nticks=10, range = ranges[0], showticklabels=True, title=titles[0],\n",
    "                         backgroundcolor=\"lightgray\", gridcolor=\"rgb(255, 255, 255)\",\n",
    "                         showbackground=True,\n",
    "                        ),\n",
    "            yaxis = dict(nticks=10, range = ranges[1], showticklabels=True, title=titles[1],\n",
    "                         backgroundcolor=\"lightgray\", gridcolor=\"rgb(255, 255, 255)\",\n",
    "                         showbackground=True\n",
    "                        ),\n",
    "            zaxis = dict(nticks=10, range = ranges[2], showticklabels=True, title=titles[2],\n",
    "                         backgroundcolor=\"lightgray\", gridcolor=\"rgb(255, 255, 255)\",\n",
    "                         showbackground=True,\n",
    "                        ),\n",
    "            aspectmode='cube',\n",
    "        ),  \n",
    "    )\n",
    "    return layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
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2, 4, 3, 5, 6, 0, 3, 1, 3, 3, 9, 14, 4, 3, 6, 4, 5, 5, 7, 13, 29, 31, 31, 23, 5, 1, 4, 1, 4, 5, 4, 3, 4, 3, 3, 3, 4, 4, 3, 1, 4, 3, 1, 1, 4, 1, 6, 0, 3, 3, 1, 0, 3, 2, 5, 4, 5, 58, 3, 4, 34, 10, 23, 24, 7, 4, 3, 6, 3, 5, 5, 3, 8, 5, 4, 4, 4, 5, 4, 6, 3, 6, 2, 3, 3, 1, 3, 3, 5, 5, 3, 5, 3, 2, 4, 3, 4, 6, 4, 6, 1, 3, 5, 2, 9, 8, 10, 14, 10, 19, 11, 8, 4, 5, 4, 3, 2, 5, 6, 4, 3, 4, 4, 2, 5, 3, 2, 3, 6, 3, 2, 5, 1, 4, 3, 4, 6, 2, 5, 3, 4, 5, 6, 6, 4, 41, 6, 6, 12, 25, 31, 35, 39, 25, 14, 4, 21, 6, 4, 4, 5, 6, 4, 3, 4, 4, 2, 3, 5, 4, 4, 5, 3, 3, 4, 5, 5, 2, 4, 1, 4, 3, 5, 3, 5, 4, 5, 5, 3, 8, 4, 7, 4, 23, 13, 13, 14, 29, 21, 27, 28, 8, 5, 7, 3, 6, 6, 2, 6, 5, 4, 5, 3, 5, 6, 4, 3, 5, 5, 3, 3, 3, 1, 6, 4, 4, 5, 4, 4, 5, 4, 2, 4, 5, 4, 7, 78, 5, 24, 27, 47, 4, 4, 4, 4, 7, 6, 6, 5, 5, 7, 6, 5, 5, 3, 3, 4, 4, 3, 6, 5, 4, 3, 5, 6, 5, 5, 3, 5, 5, 7, 1, 8, 18, 12, 32, 32, 8, 6, 8, 3, 6, 5, 6, 5, 5, 3, 5, 3, 4, 4, 4, 6, 5, 6, 3, 5, 7, 7, 5, 4, 5, 6, 6, 3, 5, 7, 5, 6, 8, 11, 8, 27, 32, 32, 27, 19, 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10, 15, 9, 17, 12, 9, 11, 51, 48, 12, 13, 9, 11, 11, 11, 14, 52, 33, 37, 12, 12, 13, 32, 16, 10, 14, 16, 11, 13, 16, 13, 12, 17, 12, 9, 14, 55, 24, 7, 15, 16, 16, 11, 13, 13, 49, 19, 14, 15, 20, 50, 12, 13, 12, 12, 18, 14, 17, 29, 29, 14, 14, 15, 28, 15, 20, 50, 26, 13, 23, 30, 12, 37, 12, 16, 19, 28, 42, 16, 13, 15, 15, 12, 15, 12, 32, 39, 37, 48, 21, 18, 840, 17, 20, 23, 22, 17, 26, 32, 17, 20, 13, 26, 18, 17, 18, 24, 18, 24], \"type\": \"scatter3d\"}], {\"showlegend\": false, \"margin\": {\"r\": 0, \"b\": 0, \"l\": 0, \"t\": 0}, \"scene\": {\"aspectmode\": \"cube\", \"zaxis\": {\"showticklabels\": true, \"title\": {\"text\": \"Time\"}, \"showbackground\": true, \"range\": [0.0, 100.0], \"gridcolor\": \"rgb(255, 255, 255)\", \"backgroundcolor\": \"lightgray\", \"nticks\": 10}, \"xaxis\": {\"showticklabels\": true, \"title\": {\"text\": \"PMT (vert)\"}, \"showbackground\": true, \"range\": [0.0, 88.0], \"gridcolor\": \"rgb(255, 255, 255)\", \"backgroundcolor\": \"lightgray\", \"nticks\": 10}, \"yaxis\": {\"showticklabels\": true, \"title\": {\"text\": \"PMT (hori)\"}, \"showbackground\": true, \"range\": [0.0, 168.0], \"gridcolor\": \"rgb(255, 255, 255)\", \"backgroundcolor\": \"lightgray\", \"nticks\": 10}}}, {\"plotlyServerURL\": \"https://plot.ly\", \"linkText\": \"Export to plot.ly\", \"showLink\": false}); \n",
       "}\n",
       "});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){if (document.getElementById(\"e9f25f5d-b6eb-4f37-90e0-bd3f32161831\")) {window._Plotly.Plots.resize(document.getElementById(\"e9f25f5d-b6eb-4f37-90e0-bd3f32161831\"));};})</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get plotly traces\n",
    "trace3d = plot(f['event_data'][0])\n",
    "\n",
    "# visualize 3d\n",
    "ranges=((0.,88.),(0.,168.),(0.,100.))\n",
    "layout = get_layout_3d(ranges,['PMT (vert)','PMT (hori)','Time'])\n",
    "fig = go.Figure(data=[trace3d],layout=layout)\n",
    "iplot(fig,filename='my 3d plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see a nice convex surface in 3D! This is the end of data access and visualization tutorial. If you have concerns/questions/suggestions, [please share](mailto:kterao@slac.stanford.edu)."
   ]
  }
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