{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os.path\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from pathlib import Path\n",
    "\n",
    "from clusterking.plots import ClusterPlot\n",
    "from clusterking.data.data import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "d = Data(\"output/cluster/\", \"tutorial_basics\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manual Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clusters = list(df['cluster'].unique())\n",
    "colors = [\"red\", \"green\", \"blue\", \"pink\"]\n",
    "markers = [\"o\", \"v\", \"^\"]\n",
    "df = d.df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manual 3d Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ax = plt.figure().gca(projection='3d')\n",
    "ax.set_xlabel('CVL_bctaunutau')\n",
    "ax.set_ylabel('sl')\n",
    "ax.set_zlabel('CT_bctaunutau')\n",
    "\n",
    "for index, cluster in enumerate(clusters):\n",
    "    df_cluster = df[df['cluster'] == cluster]\n",
    "    ax.scatter(\n",
    "        df_cluster['CVL_bctaunutau'], \n",
    "        df_cluster['CSL_bctaunutau'], \n",
    "        df_cluster['CT_bctaunutau'], \n",
    "        color=colors[cluster % len(colors)], \n",
    "        marker=markers[cluster % len(markers)],\n",
    "        label=cluster\n",
    "    )\n",
    "\n",
    "plt.legend(loc='upper left');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manual 2d Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel('CVL_bctaunutau')\n",
    "ax.set_ylabel('CSL_bctaunutau')\n",
    "\n",
    "# fix remaining Wilson coefficients\n",
    "t_value_index = 1\n",
    "t_value = df['CT_bctaunutau'].unique()[t_value_index]\n",
    "\n",
    "for index, cluster in enumerate(clusters):\n",
    "    df_cluster = df[df['cluster'] == cluster]\n",
    "    df_cluster = df_cluster[df_cluster['CT_bctaunutau'] == t_value]\n",
    "    ax.scatter(\n",
    "        df_cluster['CVL_bctaunutau'], \n",
    "        df_cluster['CSL_bctaunutau'], \n",
    "        color=colors[cluster % len(colors)], \n",
    "        marker=markers[cluster % len(markers)],\n",
    "        label=cluster\n",
    "    )\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.2, 1.0));\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using ``plot_clusters``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the plotter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cp = ClusterPlot(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cp.draw_legend=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3D plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter plot: The list is the list of the columns on the axes. \n",
    "Changing the order of the columns will turn around the cube. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp.scatter(['CVL_bctaunutau', 'CSL_bctaunutau', 'CT_bctaunutau'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If it is still not easy to get an overview, use the ``clusters`` argument to limit ourselves to certain clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "cp.scatter(['CVL_bctaunutau', 'CSL_bctaunutau', 'CT_bctaunutau'], clusters=[0, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cp.savefig(\"/home/fuchur/scatter_3d_02.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If only two columns are given, several cuts will be presented (up to 16 by default):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2D cuts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cp.scatter(['CVL_bctaunutau', 'CSL_bctaunutau'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we can also limit ourselves on the clusters that we want to display:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "cp.scatter(['CVL_bctaunutau', 'CSL_bctaunutau'], clusters=[1,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If many wilson points are available, it is better to switch to a 'fill' plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cp.fill(['CVL_bctaunutau', 'CSL_bctaunutau'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several options to configure the the ClusterPlot object can be changed after the object has been initialized."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of plots for the 'slices' by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cp.max_subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's change that (note that no warning is issued when trying to set a non-existing property, so do be careful with your typing):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cp.max_subplots = "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And try it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp.scatter(['CVL_bctaunutau', 'CSL_bctaunutau'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp.fill(['CVL_bctaunutau', 'CSL_bctaunutau'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cp.savefig(\"/home/fuchur/fill_2d.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see all options, see the ``Attribute`` section of the help."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "help(cp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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