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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 3: Data Plots\n",
    "\n",
    "This tutorial will talk about how to visualise the distributions that have been built in Tutorial 1. \n",
    "\n",
    "**NOTE FOR CONTRIBUTORS: Always clear all output before commiting (``Cell`` > ``All Output`` > ``Clear``)**!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Magic\n",
    "%matplotlib inline\n",
    "# Reload modules whenever they change\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# Make clusterking package available even without installation\n",
    "import sys\n",
    "sys.path = [\"../../\"] + sys.path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from clusterking.data.data import Data\n",
    "from clusterking.plots import BundlePlot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we load the data created in Tutorial 1 in the folder output/cluster/ with the name tutorial_basics and pass it to the Data class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "d = Data(\"output/cluster/\", \"tutorial_basics\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data is then used to create an instance of the BundlePlot class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pb = BundlePlot(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to visualise our created data: Let's start by drawing the histograms corresponding to the benchmark points of each clusters by typing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pb.plot_bundles()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also add more sample points to the plot (in addition to the benchmark point):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pb.plot_bundles(1, nlines=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save the above plot to the output/cluster folder we use the following commnad:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pb.fig.savefig(\"output/cluster/example_plot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing the minima and maxima of all clusters is achieved with the plot_minmax method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pb.plot_minmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same plot for clusters 2 and 3 only:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pb.plot_minmax([2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing the reference line leads to the follwoing output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pb.plot_minmax([2,  3], reference=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Box plots can be produced using  the box_plot method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pb.box_plot(reference=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing clusters 0 and 2 only:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pb.box_plot([0, 2])"
   ]
  }
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