{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical clustering basics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn in this tutorial how to use the `cnnclustering.cnn` module for step-wise hierarchical clusterings, where\n",
    "one cluster step does not deliver a satisfactory result. We will also show how to use a data set of reduced size for cluster exploration and how we can transfer the result to the original full size data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Pre-requirements\" data-toc-modified-id=\"Pre-requirements-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Pre-requirements</a></span><ul class=\"toc-item\"><li><span><a href=\"#Import-dependencies\" data-toc-modified-id=\"Import-dependencies-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Import dependencies</a></span></li><li><span><a href=\"#Notebook-configuration\" data-toc-modified-id=\"Notebook-configuration-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Notebook configuration</a></span></li><li><span><a href=\"#Package-configuration\" data-toc-modified-id=\"Package-configuration-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Package configuration</a></span></li></ul></li><li><span><a href=\"#Dissimilar-blobs-showcase\" data-toc-modified-id=\"Dissimilar-blobs-showcase-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Dissimilar blobs showcase</a></span><ul class=\"toc-item\"><li><span><a href=\"#Getting-started\" data-toc-modified-id=\"Getting-started-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Getting started</a></span></li><li><span><a href=\"#Parameter-scan\" data-toc-modified-id=\"Parameter-scan-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Parameter scan</a></span></li><li><span><a href=\"#Hierarchical-clustering\" data-toc-modified-id=\"Hierarchical-clustering-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Hierarchical clustering</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-requirements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.076407Z",
     "start_time": "2020-06-11T17:56:23.338439Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import pandas as pd                # Optional dependency\n",
    "from sklearn import datasets       # For sklearn test data set creation\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import cnnclustering.cnn as cnn    # CNN clustering\n",
    "import cnnclustering.cmsm as cmsm  # Core-set MSM estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook was created using Python 3.8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.082769Z",
     "start_time": "2020-06-11T17:56:24.078683Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.8.5 | packaged by conda-forge | (default, Aug 21 2020, 18:21:27) \n",
      "[GCC 7.5.0]\n"
     ]
    }
   ],
   "source": [
    "# Version information\n",
    "print(sys.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notebook configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `matplotlib` to create plots. A `\"matplotlibrc\"` file can be used to customise the appearance of the plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.099478Z",
     "start_time": "2020-06-11T17:56:24.085834Z"
    }
   },
   "outputs": [],
   "source": [
    "# Matplotlib configuration\n",
    "mpl.rc_file(\n",
    "    \"../matplotlibrc\",\n",
    "    use_default_template=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.114674Z",
     "start_time": "2020-06-11T17:56:24.104169Z"
    }
   },
   "outputs": [],
   "source": [
    "# Axis property defaults for the plots\n",
    "ax_props = {\n",
    "    \"xlabel\": None,\n",
    "    \"ylabel\": None,\n",
    "    \"xlim\": (-2.5, 2.5),\n",
    "    \"ylim\": (-2.5, 2.5),\n",
    "    \"xticks\": (),\n",
    "    \"yticks\": (),\n",
    "    \"aspect\": \"equal\"\n",
    "}\n",
    "\n",
    "# Line plot property defaults\n",
    "line_props = {\n",
    "    \"linewidth\": 0,\n",
    "    \"marker\": '.',\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optionally, clustering results can be nicely collected in a `pandas` data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.133954Z",
     "start_time": "2020-06-11T17:56:24.116172Z"
    }
   },
   "outputs": [],
   "source": [
    "# Pandas DataFrame print options\n",
    "pd.set_option('display.max_rows', 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Package configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.153314Z",
     "start_time": "2020-06-11T17:56:24.141047Z"
    }
   },
   "outputs": [],
   "source": [
    "# Configuration file found?\n",
    "cnn.settings.cfgfile  # If None, no file is provided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.178373Z",
     "start_time": "2020-06-11T17:56:24.160759Z"
    },
    "run_control": {
     "marked": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'default_cnn_cutoff': '1',\n",
       " 'default_cnn_offset': '0',\n",
       " 'default_radius_cutoff': '1',\n",
       " 'default_member_cutoff': '2',\n",
       " 'default_fit_policy': 'conservative',\n",
       " 'default_predict_policy': 'conservative',\n",
       " 'float_precision': 'sp',\n",
       " 'int_precision': 'sp'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display default settings\n",
    "cnn.settings.defaults"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-11T14:38:11.051577Z",
     "start_time": "2019-09-11T14:38:11.034144Z"
    }
   },
   "source": [
    "## Dissimilar blobs showcase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will generate a sample data set of three clusters that have very different point densities and are spatially not very well separated. As we will see,\n",
    "it can be non-trivial to extract all three clusters with a single set of cluster parameters. We will solve the problem by extracting the clusters in a two step procedure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-19T10:35:00.015683Z",
     "start_time": "2019-09-19T10:34:59.968481Z"
    }
   },
   "source": [
    "### Getting started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.246003Z",
     "start_time": "2020-06-11T17:56:24.183929Z"
    }
   },
   "outputs": [],
   "source": [
    "# Generate blobs with quite different point densities\n",
    "dblobs, _ = datasets.make_blobs(\n",
    "    n_samples=int(1e5),\n",
    "    cluster_std=[3.5, 0.32, 1.8],\n",
    "    random_state=1\n",
    "    )\n",
    "\n",
    "dblobs = StandardScaler().fit_transform(dblobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.304863Z",
     "start_time": "2020-06-11T17:56:24.250178Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initialise clustering\n",
    "cobj = cnn.CNN(points=dblobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:24.317903Z",
     "start_time": "2020-06-11T17:56:24.307037Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "CNN cluster object\n",
      "--------------------------------------------------------------------------------\n",
      "Alias :                         root\n",
      "Hierachy level :                0\n",
      "\n",
      "Data point shape :              Parts      - 1\n",
      "                                Points     - 100000\n",
      "                                Dimensions - 2\n",
      "\n",
      "Distance matrix calculated :    None\n",
      "Neighbourhoods calculated :     None\n",
      "Density graph calculated :      None\n",
      "\n",
      "Clustered :                     False\n",
      "Children :                      False\n",
      "================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get basic information about the cnn.CNN instance via cobj.__str__()\n",
    "print(cobj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:25.539681Z",
     "start_time": "2020-06-11T17:56:24.320552Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the original data\n",
    "_ = cobj.evaluate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the 2D plot of the generated points above we can already tell that this is probably not the easiest\n",
    "of all clustering problems. One of the three clusters is hardly visible. We also have generated a fairly large amount of data points. If we have a big amount of computer-memory or enough time, we can attempt to cluster the 100,000 points directly. In terms of speed and for data exploration it might be, however, a good idea to perform the clustering on a reduced data set. We can predict the clustering later for the full sized set on the basis of the reduced result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:25.549285Z",
     "start_time": "2020-06-11T17:56:25.541609Z"
    }
   },
   "outputs": [],
   "source": [
    "# To make the clustering easier, reduce the number of points\n",
    "# (start, stop, stride)\n",
    "robj = cobj.cut(points=(None, None, 100))\n",
    "# Equivalent to:\n",
    "#     >>> robj = cnn.CNN(points=cobj.data.points[slice(None, None, 100), :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:25.573629Z",
     "start_time": "2020-06-11T17:56:25.551165Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "CNN cluster object\n",
      "--------------------------------------------------------------------------------\n",
      "Alias :                         root\n",
      "Hierachy level :                0\n",
      "\n",
      "Data point shape :              Parts      - 1\n",
      "                                Points     - 1000\n",
      "                                Dimensions - 2\n",
      "\n",
      "Distance matrix calculated :    None\n",
      "Neighbourhoods calculated :     None\n",
      "Density graph calculated :      None\n",
      "\n",
      "Clustered :                     False\n",
      "Children :                      False\n",
      "================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(robj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We created a cluster object holding a smaller data set by using a point stride of 100, leaving us with only 1000 points that can be clustered very fast. Note, that using a slice on the original data like this does not require a copy. A plot of the reduced set also shows that we made the clustering easier just judging by visual inspection. We can now clearly spot three point clouds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:25.919311Z",
     "start_time": "2020-06-11T17:56:25.578031Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '1000 points')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the reduced data\n",
    "fig, Ax = plt.subplots(1, 2)\n",
    "_ = cobj.evaluate(ax=Ax[0], ax_props=ax_props)\n",
    "_ = robj.evaluate(ax=Ax[1], ax_props=ax_props)\n",
    "Ax[0].set_title(f\"{cobj.data.points.shape[0]} points\", fontsize=10, pad=4)\n",
    "Ax[1].set_title(f\"{robj.data.points.shape[0]} points\", fontsize=10, pad=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we pre-calculate pairwise point distances, we can plot the distribution of distances. This can give us a very basic estimate for a reasonable radius cutoff as one of the cluster parameters. For globular clusters, each cluster should be visible as a peak in the distance distribution around a value that is very roughly equivalent to the radius of the point cloud. For more complicated data sets this approximation is not valid, but we can still get a feeling for the value range of the radius cutoff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:26.071139Z",
     "start_time": "2020-06-11T17:56:25.922176Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looking at the distance distribution between points, can help to find\n",
    "# an initial value for the radius cutoff\n",
    "robj.calc_dist()  # Pre-calculate distances\n",
    "fig, ax, *_ = robj.dist_hist(maxima=True, maxima_props={\"order\": 5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-19T10:35:37.964861Z",
     "start_time": "2019-09-19T10:35:37.962263Z"
    }
   },
   "source": [
    "### Parameter scan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Blindly starting to cluster a data set in a happy-go-lucky attempt may already lead to a satisfactory result in some cases, but let's tackle this problem in more systematic way, to see how different cluster parameters effect the outcome. This will take a few seconds to complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:36.896239Z",
     "start_time": "2020-06-11T17:56:26.073453Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# A scan of a few parameters shows that it is difficult to extract the\n",
    "# three clusters at once with one parameter set\n",
    "for r in [0.05, 0.08, 0.1, 0.125, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.75, 1., 2.]:\n",
    "    for c in [0, 1, 2, 3, 4, 5, 10, 15, 20]:\n",
    "        robj.fit(r, c, v=False)  # fit from pre-calculated distances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T10:55:56.099867Z",
     "start_time": "2020-06-11T10:55:56.085610Z"
    }
   },
   "source": [
    "Each cluster result will be added to the `summary` attribute of our cluster object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:36.918422Z",
     "start_time": "2020-06-11T17:56:36.898721Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNNRecord(points=1000, r=0.05, c=0, min=2, max=None, clusters=Labels(111), largest=0.329, noise=0.332, time=0.010924577713012695)\n",
      "CNNRecord(points=1000, r=0.05, c=1, min=2, max=None, clusters=Labels(31), largest=0.326, noise=0.544, time=0.010438919067382812)\n",
      "CNNRecord(points=1000, r=0.05, c=2, min=2, max=None, clusters=Labels(14), largest=0.317, noise=0.627, time=0.010040998458862305)\n",
      "CNNRecord(points=1000, r=0.05, c=3, min=2, max=None, clusters=Labels(8), largest=0.315, noise=0.664, time=0.010024070739746094)\n",
      "CNNRecord(points=1000, r=0.05, c=4, min=2, max=None, clusters=Labels(3), largest=0.312, noise=0.684, time=0.01040029525756836)\n"
     ]
    }
   ],
   "source": [
    "print(*robj.summary[:5], sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have `pandas` installed, you can convert the summary to a nice table as a `pandas.DataFrame`. This makes the analysis of the cluster results more convenient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:36.957793Z",
     "start_time": "2020-06-11T17:56:36.920815Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>points</th>\n",
       "      <th>r</th>\n",
       "      <th>c</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>clusters</th>\n",
       "      <th>largest</th>\n",
       "      <th>noise</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.464</td>\n",
       "      <td>0.055377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.400</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.070035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.450</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.668</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.068890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.035683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.400</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.064144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.068568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.300</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.058881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.010400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.300</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.054430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    points      r   c  min   max  clusters  largest  noise      time\n",
       "52    1000  0.200  15    2  <NA>         3    0.334  0.464  0.055377\n",
       "86    1000  0.400   5    2  <NA>         3    0.956  0.039  0.070035\n",
       "96    1000  0.450  10    2  <NA>         3    0.668  0.058  0.068890\n",
       "35    1000  0.125  20    2  <NA>         3    0.330  0.659  0.035683\n",
       "83    1000  0.400   2    2  <NA>         3    0.981  0.015  0.064144\n",
       "81    1000  0.400   0    2  <NA>         3    0.991  0.004  0.068568\n",
       "70    1000  0.300  15    2  <NA>         3    0.629  0.272  0.058881\n",
       "4     1000  0.050   4    2  <NA>         3    0.312  0.684  0.010400\n",
       "65    1000  0.300   2    2  <NA>         3    0.950  0.042  0.054430"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary sorted by number of identified clusters\n",
    "df = robj.summary.to_DataFrame().sort_values('clusters')\n",
    "# Show cluster results where we have 3 clusters\n",
    "df[df.clusters == 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary shows indeed that we got 3 clusters (as desired) for a few parameter combinations. Apart from the number of clusters, it is, however often also of interest, how many data points ended up in the clusters and how many are considered outliers (noise). In this case we expect 3 clusters of more or less equal size (member wise) and we may be interested to keep the outliers-level low. Of the results giving 3 clusters, 3 find the largest cluster to entail one third of the data points (which is good), but the noise level is around 50 % and higher (which is probably not what we want). Let's plot a few results to see what is going on here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:37.475445Z",
     "start_time": "2020-06-11T17:56:36.961701Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 750x450 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cluster attempts in comparison\n",
    "fig, ax = plt.subplots(2, 2)\n",
    "Ax = ax.flatten()\n",
    "for i, pair in enumerate([(0.050, 4), (0.125, 20), (0.2, 15), (0.450, 10)]):\n",
    "    robj.fit(*pair, rec=False)\n",
    "    _ = robj.evaluate(ax=Ax[i], ax_props=ax_props)\n",
    "    Ax[i].set_title(f'{pair[0]}, {pair[1]}', fontsize=10, pad=4)\n",
    "\n",
    "fig.subplots_adjust(\n",
    "    left=0.2, right=0.8, bottom=0, top=1, wspace=0, hspace=0.3 \n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "None of the above attempts was able to achieve a split into 3 clusters as we wanted it. One could now try to tinker around with the parameters a bit more, or resort to hierarchical clustering. As we see in the plots above (lower left and right), two different parameter pairs are leading to a clean split of different regions of the data, so why not apply both of them, one after the other.\n",
    "\n",
    "Before we do this let's have another close look at the cluster results we obtained. Using the `smmarize` method of a cluster object, we can visualize a summary table in a 2D contour plot, to evaluate the quality measures *identified cluster number*, *members in the largest cluster*, *points classified as outliers* and *computational time of the fit* versus the input parameters *radius cutoff* (*r*) and *similarity criterion* (*c*).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:37.782322Z",
     "start_time": "2020-06-11T17:56:37.478174Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize the summary table as 2D contour plot\n",
    "# time\n",
    "fig, ax = plt.subplots()\n",
    "contour = robj.summary.summarize(ax=ax)[2][0]\n",
    "colorbar = fig.colorbar(mappable=contour, ax=ax)\n",
    "ax.set_yticks([0, 5, 10, 15, 20])\n",
    "colorbar.set_label(\"time / s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the *time vs. R/C* plot we can see how the total clustering time depends in particular on the neighbour search radius cutoff. Larger radii result in larger neighbourlists for each point increasing the processing time, so if one has the choice smaller values for *r* should be preferred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:38.146452Z",
     "start_time": "2020-06-11T17:56:37.784923Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05, 1.0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F8OWIOCsits/M+zLzHzPzr8rX8Zm5aqn3WBtXFVBvta1NQJKkRRwD7A/sDVwbEedTrNu6B8Wv3v9szvlPoKiezu1VfQ7w/oi4kaIF4T6K5bB2o8h95wLvmmSgmfkT4BUR8RKKloArIuJY4Ogq12ydjxVXNW4oE7O62N8qSZqOsjr5fOBoirB5EEVwPQHYLTOvH/FSXweOB/6T4lGyh1D8Gv87wJuB/TOzkh9Imfk1iurrUcBhFO0Dr67i2gux4ippXrYJSNJ0lYHyfeVrbeceCRw5z/4LgQurHltEbEXR27orxQSvm4B/AT6dmR+IiM8DHwVOjIi3AIdl5o+qHocVV0mSJC0oIvalmMx1GPBEinVnn03Rl3tRRKyfmT/NzFcBBwBPAn4QER+JiEr79gyuPVbVGq5dnJjVtv7WrrUJWG2VJM3yYWA18NzM3DEz9wKeDPwt8Cxm9d9m5rcoJpP9vxQrCyxp9YK1MbhKkiRpMb8DnJ2Z353ZkZkPUSyNFRRBlVnHHszMDwM7AmdXORCDqxo1lIlZXWK1VZI0x8+AZ83za/99y+2t870pM2/NzNdVORAnZ0k161qbgCRJc3wU+DjFqgGnA78CtgdeDtxFsYrBVBhcJUmStKDM/ERE3AG8B/i/KX5jvwo4E3h3Zv50WmMxuGpRTswaFtsEJEnzycwTKZa6WgZskpm3NTEOg+uULD/vpKaHIEmSNJHyUbGNhFZwclZvVbUUVp2GMDGrS/2tVlslSW1ncJUkSVInGFwlWW2VJHWCwVULcmLWZLrUJiBJUhcYXNWIIfS3SpKkahlcpYGzTUCStJiIWL/pMcwwuEo1sE1AktQjt0fEaRHxxoj4zSYH4jqu0oBZbZUkjeBC4CXAK4A1EXEpcDrwL5l5xTQHYsW1h6pYw9WJWZIkCSAzDwCeAPwxcCKwHXAM8P2I+ElEfDwiDoiIx9Y9FoOrps6JWe1gtVWSNKrMvCczv5iZrwOeCOwLfAy4HzgMOIuipeD/RMT/FRGPq2McBlepYva3SpL6LDPXZOb5mXlEZu4I7Aj8NXA58Ergn4BfRMR5EfFXEVHZr3ENrtIAWW2VJFUlM6/JzA9n5vMoqrGvB04FdgH+O/CjiHhnFfdycpYkSZIqkZm/Aj4PfL7sed0POBD4VRXXN7iqF9oyMcs2AUmSCpn5APCv5asStgroUepcUcCJWc2zTUCS1FUG156pYiksSZKkNjK4SgNitVWS1GUGV6ki9rdKklSvzgbXiNg9It4VEadExM0RkRGRI7zv0Ii4OCLujYg7I+LMiNh7GmMeOvtbm2W1VZLUdROtKhARBwEPABdn5m3VDGlk7wX+YClviIhjgcOBlcA3gPWBA4Dfj4hDMvO0ykep2rVlRQFJklSvsYNrRGwPfBG4F9gHmHZwvRC4ArikfN0ArLfQyRGxP0VovQPYKzOvLffvBZwLfC4izs3Mu+odtvrINgFJ0hCVj3bdHXgCcGNmXlDn/SZpFXgr8BjgrzPzqrWdHBHbRsSBEbHlBPf8tcz8UGa+LzPPyMyfj/CWd5TbY2ZCa3mdC4H/AWwKvLGKsUltY5uAJKlKEbF5RPwz8HPgLOALwJtmHX9T2ZL5e1Xed5Lg+kLgLuD4Ec+/ETgGuLGs1k5NRGwAvKD88uR5TpnZd+B0RlSPKpbCqnMNV0mS1H1llfUC4I+Bfwc+BcSc004BNgYOqfLekwTXbYGLMvOhUU4uzzumvOcfTXDfcexA0UZwW2bePM/xy8rtLtMb0rD0eWJW29sErLZKkir2bmA74KjM3C0z3zb3hMy8k6Klc98qbzzJ5Kx1gLuX+J5TKXpiX0ARYqdlq3I7X2glM1dExF3AZhGxcWbes9jFIuLKBQ5tN8EYNQYnZkmSNHUHAddk5pFrOe86YL8qbzxJxfUXPBwIR5KZDwIXAztOcN9xzPwO/b5FzllRbk1C6g2rrZKkGmwJXD7CeQn8RpU3nqTiegXwooh4Ymb+cgnv+wVQaaPutGXmzvPtLyuxO015OGpQ29sEJEmLW/XgYydqZ1v14PcqHE1n/CewxQjnbUfFq05NUnE9maJv9H1LfN8GwIMT3Hcc95bb5Yucs2G5XbRNQEtXV3+rbQKLs9oqSarJJcDvRsQ2C50QEc8EdgW+W+WNJwmuJwE/Av5LRBwxyhsiYh1gD4oVBqbppnL7lPkORsSGFMth/Wpt/a3jWH7eSVVfshauKCBJkkbw9xTFy1Mj4rfnHoyIpwOfp1hp4BNV3njs4Fr2q76eopr5oYg4PSLW1vP6DorS8jfHve+YfgzcD2y+wDqyu5XbK6Y3pGpVsRSWlqbNbQJWWyVJdcnMs4C/o1iN6d8j4mqKftYXRcTlwFXAM4APZOZ3qrz3JBVXMvNS4EXAnRRroF4bEV+KiNdFxE4RsVn52jMiPg18iOJxq8dNPPKljXMlcHb55avmOWVmjbEzpjOi4bBNQJKk/snMdwGvBn4IbE9RXd0C+B3gWuC1mfnequ87yeQsADLzoojYiaJs/CrgD4FXznNqAA8Ab8jM6ye97xg+CrwEeE9EfHXOI1/fQvEwhc82MC51UJurrZIkTUNmfgn4UkRsDmxNURC9OTNvqeueE1VcZ2TmbZn5x8BvAx+hSN9JEVaDop3gy8AemfnFKu4ZES+LiItmXsCycv9Fs14vmzXGb1JUeh8P/CAiTouIM4HzKAL8GzLzrirGJjXJNgFJ0jSVOfCSzPy3OkMrVFBxnS0zrwHeCb+eiPV4igB7R2ZmlfcCNqeY6DXXHnPOmT2+v4yIHwCHAQcAqyn6bY/OzAsqHl+n1DExq69tAlZbJUl6tIhYF3gTRX/rT4F/zMxfVXmPSoPrbJm5horX7ppz/ROAE6b1vjZzYpZmWG2VJNUtIt4HvB94fmaeV+5bB/g2sCfFb9sTeHNEPLvK32hX0iogDYXVVkmSOICil/W8WfsOAfaiaBd9C3A6sC3w51Xe2OBas66s4Vq1vrYJtJnVVknSlGxLseTVbK+kqLK+JjM/TTFZ/6c8vHJTJQyukiRJWorHA7fP2bcvcG1mXgVQzm26BFjbGv9LYnCVT8waUZvbBKy2SpKm6Hbg1w90KpdF/U3g3DnnraZc9akqBteOa+PELNsEJEnqtauAfSLiWeXX76BoEzhzznlbA7dWeWODqzQCq62SJP3axyhWprokIm4H3gD8BDhr5oSI2ATYHbi8yhsbXCVJkjSyzDwTeBtwC7AB8F3g4MxcPeu0PwUeC3yrynvXto6ruqHq/tY+tglYbZUk6ZEy85PAJxc55TPAP1E8PbUyBldJkiRVKjNXApVXfmwVkBZhtVWSpPaw4lqjuh8+0MYVBergagKSJDUnIs6mWDXg9Zl5c/n1qDIzX1jVWAyuqkxd/a1NsdoqSRIA+1EE1+Wzvh5VVjkQg+uA+eABSZI0gm3K7S1zvp46g6tarak2gTZXWyVJmqbMvHGxr6fJyVmqRN/aBNrMNgFJ0lBZce2ooUzMkiRJ7RQRy4A/BJ4LbFnuvgU4H/jynAcSVMLgqtayTeDRrLZKktogIvYB/hl4ChBzDr8F+GBEvCYzL6jyvgbXgapyYpZtApIkDUdEbA98DdgIuBT4AnADxQoCWwOvA3YHzoyI383Ma6u6t8G1JnWv4ap6WG2VJGmt3k0RWt+emcfNc/zjEfEXwLHluYdWdWMnZ6mVfOiAJEmt9ULg+wuEVgAy8+PA94H9q7yxwVUT6VObgNVWSZJGsjlw9QjnXQ08ocobG1w7aNIVBXzwgCRJmsAdwChhYnvgzipvbHBV6zTRJmC1VZKkkZ0DPCsi3rrQCRHxZooJWmdXeWMnZ2lsfWoTkCRJIzsGOAj4ZES8lmJZrBvKY08DXgP8HnAf8DdV3tjgqsGz2ipJ0ugy86qIeAXwv4F9gL3nnBLAL4DXZuZVVd7b4KpWcTUBSZLaLzO/FRHbAn9E8eSsJ5eHfkbx5KwvZuZ9Vd/X4FqDNq/hWtXErL60CVhtlSRpPGUwPaF8TYWTszpm0hUFJEmSusqKq1pj2m0CVlslSRpfRCwHng1sAay30HmZ+U9V3dPgqiXrS5uAJEkaT0QcBbwdWL7YaUACBlctnQ8e6AarrZKkNouI/wd4D/AQ8FXgGuCeadzb4FqxdVbcDWzS9DA6xzYBSZI6483ASuC5mXnZNG/s5CwtiW0C9bLaKknqgKcC3552aAUrrp3iigLVsNoqSZqx8qF1Jvqt38qHBlkD/DmwookbD/LfttrFhw4UrLZKkjriJGC/iNhw2jc2uA6EE7MKVlslSZrYkcBVwFci4unTvLGtAhpZHf2tVlsLVlslSR1yJkXxcz/gqoi4EbgZWDPPuZmZL6zqxgZXDUZbq62GVklSx+w3658fA2xbvuaTVd7Y4CpJkqSl2KapGxtcNZKutwlYbZUkqRqZeWNT93ZyVkdMshSWE7MkSVIfGFzVe1ZbJUnqB4Or1qrrbQKSJKkfDK5SA6y2SpK0dAZX9Vpb2wQkSdLSGVx7btKJWbYJVM9qqyRJ4zG4dsAkKwoMmdVWSZL6xeAqTZHVVkmSxmdw1YLqaBOYljZWWw2tkiRNxuCqqRp6f6skSRqfwVWaAqutkiRNzuDaY5OsKGCbgCRJahuDq6ZmqG0CVlslSaqGwbXlXApLkiSpYHBVr7StTcBqqyRJ1TG46lF8WpYkSWojg2tPTfqo1y6y2ipJUr8ZXCVJktQJBlfVbohtAlZbJUmqnsG1xVxRYHRtahMwtEqSVA+Dqx6hyw8ekCRJ/WZwVa2G1iZgtVWSpPoYXHtoaCsKtKlNQJIk1cfgKlXEaqskSfUyuKo2Q2sTkCRJ9TK46te6ODGrLW0CVlslSaqfwbWlXApLkiTpkQyuPTO0iVltYLVVkqTpGFRwjYhzIyIXeb246TH2xTT6W9vSJiBJkqZj3aYH0JAvA/fOs/+WaQ9E3Wa1VZKk6RlqcD0iM29oehBt0sWJWZIkaVgG1Sqg6RhKm4DVVkmSpsvg2kKuKCBJkvRoQ20VeGNEPB5YA1wDnJaZNzU8pokNZUUBq62SJA3TUIPre+Z8/eGIODozjx7lzRFx5QKHtptsWN3n07IkSVJdhtYqcB7wOoqAuRzYAXg38CBwVEQc3uDYGuPErKWx2ipJUjMGVXHNzPfN2XUN8IGI+B7wdeDIiPjHzFz0d9GZufN8+8tK7E6VDLaDhjIpS5LUH6seWjPRz5ZVD62pcDRam6FVXOeVmd8AvgdsCuzR5FjGnZg1lP7WplltlSSpOQbXh11bbrdodBRTVlWbgNVWSZJUN4PrwzYrtyuaGoDLYLWb1VZJkpplcAUiYnPgueWXlzU5lnE03SZgtVWSJE3DYIJrROwdEQdFxGPm7N8aOBXYEPhKZt7cwPAa4WoCo7PaKklS84a0qsD2wOeAn0fEZcBdwNOA3YH1gSuBNzc1uK62CVhtlSRJ0zKk4PpvwD9QrBrwuxQ9rSuAHwBfAv5hbctgtdG4bQJVVFuH8rABq62SJLXDYIJrZl4F/Nemx6GlsdoqSZJmDKbHtc262CZgtVWSJE2bwbXDmmwTmAarrZIkaTaDq5bMaqskSWqCwbVhXWwTmAarrZIkaS6Da0c11SZgtVWSJDXF4KqRTSu0Wm2VJEnzMbg2yDaBdrLaKklSOxlcO6iJNgGrrZIkqWkGV7VGG0Kr1VZJktrL4DoQXai2SpIkLcbg2jHjtgmMa0gtAlZbJUlqN4NrQ6Y5MavtT8oytEqSpFEYXDukr9VWSZKkURhce27caqstApIkqW0MrmqMoVWSJC2FwbUjptkmYIuAJElqI4NrA6Y1MavNk7KstkqSpKUyuOoRplFtNbRKkqRxGFw7YJw2gTZXW5tmaJUkqZsMrpqqNlRbJUlSNxlce6jtS2A1yWqrJEndZXCdsqVOzJr2Qwfq1HS11dAqSVK3GVwFDKPaKkmSus3g2mJ9mpRltVWSJE3K4Kraq62GVkmSVAWDa4+0tdraJEOrJEn9YXCdoqVMzJrWpKw+V1sNrZIk9YvBtSfaWG01tEqSpCoZXFtoqdVW122VJElDYHCdklHbBPqybqvVVkmSVDWDa8e1sdpqaJUkSXUwuLZIXyZkSZIk1cHg2jotlRsAABARSURBVGFOyHokq62SJPXbuk0PYAiWsgxW3WwRkCTpYSu5b6KfISu5r8LRaG2suLbEtFYSqEvTT8eSJEn9Z3AdkL72tlptlSRpGAyuHTROtdUWAUmS1HUG15qN0t/a5bVbbRGQJEnTYnDtmLZVW5tktVWSpGExuGpstghIkqRpMrjWqOo2gTZVW20RkCRJ02Zw7Yi2LX/VJKutkiQNkw8gaFDdk7LqqLY2XWk1tEqSNFwG15q06WlZVbGnVZIkNclWgQ5oQ2+roVWSJDXN4NqQOtsEDK2SJKmPbBWoQZVtAk1PymoqtBpYJUnSXFZcK7Zm5dr/lXal2mpolSRJbWJwbbEmq62GVkmS1Da2CvRIVdXWJkKrgVWSJK2NFdcpG7VNoKlqq6FVkiS1lcG1J6qothpaJUlSm9kqMEVtrrZOO7QaWCVJ0lJZce2BOh7tWidDqyRJGofBtWX6Xm01tEqSpHEZXKekrrVbJ622GlolSVJXGFxbZNrVVkOrJEnqEoNrh01SbTW0SpKkrjG4TkEdbQKGVkmSNDQuh1WzOpbAGje0GlglSVKXGVxrNEpoXWpf6zih1cAqSZL6wOBak6pDa9urrAZWSZJUN4NrxR5YtV4rQquBVZIk9Y3BtQF9CK0GVkmSNG0G1ykbNbS2uTXA0CpJkppgcJ0Sq6ySJEmTMbhOQddDq4FVkiS1gcG1ZnW2BhhYJUnSkAzuyVkRsUFEHBUR10TEqoj4WUQcHxFbVn2vrobWy9acY2iVJGnKqswoEbFZRBwXETdGxP3l9tiI2LSOsU/LoCquEbE+cDawJ3ArcDqwNfAG4OURsWdmXl/FveoKrXUFVoOqJEnNqTKjRMQTgAuBpwPXA6cBOwOHAy+JiL0y887Kv4kpGFRwBd5D8R/EhcDvZ+a9ABHxDuAjwPHAfpPcYNWDj+1MaDWsSpLUGlVmlGMpQuspwKsz88HyWh8H3gZ8FDi0wrFPTWRm02OYiohYBvwS2ATYLTO/P+f45cAuwLMz89Ix73HlluttutMHf+vgRc9rMrAaViVJbXTf6pvIXP2jzNx5WveMiCsjlu20fNlWY1+jinFXmVEiYgvgZuBBYKvM/MWsY+sBPwUeBzw5M3857pibMqQe130o/oO4bu5/EKWTy+2BdQ6iidA607NqaJUkqZWqzCgvpsh3588OrQCZeT9wBvAY4KXjD7c5Qwquzyy3ly1wfGb/LnUNYCmh9Xt3rawstEqSpFarMqM0nnfqNKQe15nfA9y8wPGZ/U9b24Ui4soFDu34i9X/ybuuPfVRB1Y+NPr/I6x6aM3I5y5kJfdNfA1JkqYl8wGA0Rc+r+y+q7lv9U0TvR/YbqFsMGILQWUZpeJrtc6QgutG5XahRLei3I73rNXCmgdzzYpb7r/rpxNcYwi2K7fXNToK+Tm0g59DO/g5NG9HYIMp3/M6+HX4nMSkgbvKjDKNvNOYIQXXykyzcbyPZv6v1H+PzfJzaAc/h3bwc2jeIr/NrE1mvmLa99RkhtTjem+5Xb7A8Q3L7T1TGIskSdKMKjNKr/POkILrTAPLUxY4PrP/ximMRZIkaUaVGaXXeWdIwfXycrvbAsdn9l8xhbFIkiTNqDKj9DrvDCm4fhe4m2Lm367zHD+k3J4xvSFJkiRVmlHOAtYAz42IJ84+UD6A4EDgIeDM8YfbnMEE1yymDH6i/PKTETHT4zHzOLVdgG+P+9QsSZKkcYyTUSLisIi4OiL+ds61bgVOBJYBn4qI2RPx/w7YHPhCF5+aBQN65CtARKwPnAvsAdwKnE+xjtkewG3Anpl5fWMDlCRJg7TUjBIRRwLvB/5XZh4651pPAC6iWObtOuB7wM7AM4Bry2vdWes3VJPBVFwBMnMV8HzgaIr1zQ6i+I/iBIpnAxtaJUnS1FWZUTLzduA5wN9TVF4Ppnik7MeB53Q1tMLAKq6SJEnqrkFVXCVJktRdBldJkiR1gsFVkiRJnWBwlSRJUicYXCVJktQJBldNLCI2iIijIuKaiFgVET+LiOMjYsslXGPTiPiTiDgxIn4SEasj4p6I+LeIODwiHlvn99AHVXwOC1z3tyJiZURkRHyzqvH2UdWfQURsHRH/o/wzcX9E3B4RF0bEO6see59U+TlExAER8dWIuC0iHoiIOyLiGxFxcB1j74uI2D0i3hURp0TEzeXfH2MvYxQRm0XEcRFxY/ln4caIODYiNq1y3Go/l8PSRMoFk88B9uThBZO3plg/buSHOkTEMcC7gQR+AFxD8XSPfYD1gO8AL8rM+6r/Lrqvqs9hgWufA+wLBPCtzNy/ijH3TdWfQUS8BDgZ2AC4jGLR8McDvwOsyMynVzn+vqjyc4iIvwQ+RvH30oXAT4GnAntR/Hn4QGa+u+JvoRci4jTgD+buz8wY41pPoPj3/3Tgeh5eTH9nip8Ve3V5XVItUWb68jX2CziG4i/1C4CNZu1/R7n/3BGv89+ADwFbzdn/W8CN5bU+0PT329ZXVZ/DPNd9Y/n+/1luv9n099rWV5WfAbAjsBL4JbD3nGPrAM9u+vtt66vCv5M2B1YBq4F95xx7XnlsDbBt099zG1/AXwNHAQcCTyr/feWY1/pC+dl9GVh31v6Pl/tPaPr79TW9lxVXjS0illH8YN2E4qke359z/HKK5ys/O2c9X3mM+7wG+GfghszcZoIh91Jdn0NE/CZwFUV14wMUVSwrrvOo+jOIiDOBlwAvy8wzaxhyL1X5OUTEy4EzgK9n5ovnOX468Arg1Zn5xYq+hd6KiFXAernEimtEbAHcDDxIUdj4xaxj61FUwR8HPDkzf1nhkNVS9rhqEvtQ/IC4bu4PiNLJ5fbACe9zebl98oTX6au6PofjKH5N/V8nGNtQVPYZRMRTgRcB1xtal6zKPwv3j3jPO0Y8T+N5MUVWOX92aAXIzPsp/ufiMcBLGxibGmBw1SSeWW4vW+D4zP5dJrzPtuX25xNep68q/xwi4qXAqynaM/5jgrENRZWfwX4UfzdfEBHrRsQflZNSPhERb42IzSYca59V+TlcDNwFvCAi9p19ICKeR/E/F9dS9NCqPtP6OaOOWLfpAajTtiq3Ny9wfGb/0ya8z+Hl9vQJr9NXlX4OEbEh8CngxxR9x1q7Kj+DncrtvRShaM85x/8mIg7JzHOWNsRBqOxzyMy7I+KNFG1K50TEBeX7nwLsDXwX+NPMXD3ZkLUW0/o5o46w4qpJbFRuF5rpv6LcbjzuDSLircD+FJWPD457nZ6r+nM4huKHwFv9oTyyKj+DmYrqmygmaf0JRQ/fDhSTVB4HnDrpMmc9Vemfhcw8haLX+A6KNoRXl9t7gG8At4w9Uo2q9p8z6haDq1orIp5L0WeZwJ9l5s8aHlLvRcSzgb8A/ikzz214OEM18/fyusBbMvPEzPxVZl6Tma8DLqHo47T3uGYR8VfAN4HzKH4VvVG5PZtixvwpzY1OGiaDqyZxb7ldvsDxDcvtPUu9cEQ8g6I1YBlweGaeuvThDUYln0NErAt8mqK6fUQ1QxuMKv8s3Dtr+6V5jn+u3O47z7Ghq+xziIj9gA9TrCv9qsz8YWauyMwfAoeU+19Wrrer+tT2c0bdZI+rJnFTuX3KAsdn9t+4lItGxDYUv4bbDDgyM/9+vOENRlWfw1OAXSkmwX0p4hGr1sw8nWb3iDgXIDP3W+pAe6zKPwsz59yU869XeEO5feJoQxuUKj+H15XbUzNzzewDmflQRJxC8eflecDXljpQjayWnzPqLoOrJjGzTNVuCxyf2X/FqBcs1+z7V2AL4LjM/P/GH95gVP05PKl8zWdTrPTNp8rPYGYZp4VWD3hcub13geNDVuXnMBOI7l7g+Mx+V3moV+U/Z9RttgpoEt+l+Mt7u4jYdZ7jh5TbM0a5WLnMz9eB7Sh+Hfr2KgY5AJV8Dpl5Q2bGfC/g+eVp35q1Tw+r8s/CBRSTgZ4UETvMc3zmfxzmW6d06Kr8HGaW33v2Asd/t9zeMPLoNI6zKJ5Q9tyIeMRvGcoHEBwIPAS45vFAGFw1tnLG+SfKLz9ZLqMEQES8g2ISw7dnP6EmIg6LiKsj4m9nXysilgNfpXgO+xeBNy/wa1LNUeXnoPFU+Rlk5oPAR4Eor/Ubs96zP3AoDz+GV7NU/GfhtHL72vIpWsx6zx9QrPawBrD/vgKL/Hm4FTiRYr7Dp8pe/Bl/R/Fo3i/41KzhsFVAkzqGYrmqvYFrI+J8iqWU9gBuA/5szvlPoFjWZ4s5+/8G2Ivi/5wfBD47p8cSgMw8tMKx90lVn4PGV+Vn8N8pqtz7A9dExEXl+XtSPCXo3Zl5cR3fRA9U9TmcRjE57lXAGRHxPeAnwDY8XIV9d2b+uI5vousi4mXAe2ftWlbuv2jWvqMz86vlPy/25+EvKf7b/0Pg6vKz2Bl4BsVDIN5R7ejVZgZXTSQzV0XE84H/RlGBOAi4EzgBeG9mLrRo9FwzfWKPKa+zkEPHG2m/Vfg5aExVfgaZ+UD59LK3A39K8ZSm1cC3gY9l5r9UPPzeqOpzyMyMiFdT/Kr69RTV2l0pVt04E/j7zDyr+u+gNzan+J+FufaYc85aZebtEfEc4EiKz/Ng4BfAx4H3Z+Zdkw1VXRL+NlaSJEldYI+rJEmSOsHgKkmSpE4wuEqSJKkTDK6SJEnqBIOrJEmSOsHgKkmSpE4wuEqSJKkTDK6SJEnqBIOrJEmSOsHgKkmSpE4wuEqSJKkTDK6SJEnqBIOrpN6LiGMjIue87omIKyLifRGxYdNjlCStXWRm02OQpFpFxDnAfsDXgF+Wu7cs960LfA/YKzMfbGJ8kqTRGFwl9V5E3AlsBmydmTfO2v884ByK3z69MjNPbWiIkqQR2CogqdciYiuK0HovcNPsY5l5HnBB+eUuUx6aJGmJDK6S+m7Xcnt1zv8rptvL7V1TGo8kaUwGV0l998xy+6MFju9Ybi+ewlgkSRMwuErqu5mK66OCa0QcRBFcz8zMC6c6KknSkhlcJfXdoyquEbFZRLwB+BzwFeDVTQxMkrQ0riogqbciYmPgbiAWOOWTmXnYFIckSZqAwVVSb0XEPsB3gP8EZpa6Wg48B3ha+fWrMvPkBoYnSVqidZsegCTVaKa/9YLMPHRmZ0SsC/wL8CLgbYDBVZI6wB5XSX0209966eyd5ROy3lV++XsR8ZtTHZUkaSwGV0l9NlNxvXTugcz8AfDvFH8Pvmyag5IkjcfgKqmXIuIxwDPKLx8VXEunl9uX1z8iSdKkDK6S+mp7YAPg9sy8aYFzvlJuD4iIZdMZliRpXAZXSX0109962SLnXALcCmwE7Ff3gCRJk3E5LEmSJHWCFVdJkiR1gsFVkiRJnWBwlSRJUicYXCVJktQJBldJkiR1gsFVkiRJnWBwlSRJUicYXCVJktQJBldJkiR1gsFVkiRJnWBwlSRJUicYXCVJktQJBldJkiR1gsFVkiRJnWBwlSRJUicYXCVJktQJBldJkiR1wv8PtuPig/tCaL8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize the summary table as 2D contour plot\n",
    "# noise\n",
    "fig, ax = plt.subplots()\n",
    "contour = robj.summary.summarize(ax=ax, quant=\"noise\")[2][0]\n",
    "colorbar = fig.colorbar(mappable=contour, ax=ax, ticks=(0, 0.5, 1))\n",
    "ax.set_yticks([0, 5, 10, 15, 20])\n",
    "colorbar.set_label(\"noise / %\")\n",
    "ax.set_xlim(None, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ratio of outlier-points depends on the search radius *r* as well as on the similarity criterion *c*. We can somewhat denote from this that cluster results with a similar noise-ratio (that are presumably similar qualitatively) are achieved for a variety of *r*/*c* combinations. If we increase *r* and *c* at the same time, the noise-level stays constant. The desired amount of noise depends much on the nature of the underlying data set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:38.397383Z",
     "start_time": "2020-06-11T17:56:38.149537Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05, 1.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize the summary table as 2D contour plot\n",
    "# largest cluster\n",
    "fig, ax = plt.subplots()\n",
    "contour = robj.summary.summarize(ax=ax, quant=\"largest\")[2][0]\n",
    "colorbar = fig.colorbar(mappable=contour, ax=ax, ticks=(0, 0.5, 1))\n",
    "ax.set_yticks([0, 5, 10, 15, 20])\n",
    "colorbar.set_label(\"largest / %\")\n",
    "ax.set_xlim(None, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ratio of points assigned to the largest cluster show a similar trend as the noise-ratio. This view could give a good hint towards reasonable parameter combinations if one already has an idea about the expected cluster size. It also shows for which parameters we do not observe any splitting (about 100 % of the points are in the largest cluster. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:38.728829Z",
     "start_time": "2020-06-11T17:56:38.401852Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05, 1.0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize the summary table as 2D contour plot\n",
    "# cluster number\n",
    "fig, ax = plt.subplots()\n",
    "contour = robj.summary.summarize(ax=ax, quant=\"clusters\",\n",
    "                         contour_props={\"levels\": 100})[2][0]\n",
    "colorbar = fig.colorbar(mappable=contour, ax=ax)\n",
    "ax.set_yticks([0, 5, 10, 15, 20])\n",
    "colorbar.set_label(\"# clusters\")\n",
    "ax.set_xlim(None, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:38.983621Z",
     "start_time": "2020-06-11T17:56:38.731343Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05, 1.0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize the summary table as 2D contour plot\n",
    "# cluster number with adjusted coloring\n",
    "fig, ax = plt.subplots()\n",
    "contour = robj.summary.summarize(\n",
    "    ax=ax, quant=\"clusters\",\n",
    "    contour_props={\"levels\": [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5],\n",
    "                   \"vmax\": 5}\n",
    "    )[2][0]\n",
    "colorbar = fig.colorbar(mappable=contour, ax=ax, ticks=(0, 1, 2, 3, 4, 5))\n",
    "ax.set_yticks([0, 5, 10, 15, 20])\n",
    "colorbar.set_label(\"# clusters\")\n",
    "ax.set_xlim(None, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probably most telling view is given by the *number of clusters* plots. The analysis demonstrates nicely that for this data set a splitting into 3 meaningful clusters is hard to achieve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-19T10:35:00.015683Z",
     "start_time": "2019-09-19T10:34:59.968481Z"
    }
   },
   "source": [
    "### Hierarchical clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use the hierarchical approach to clustering our data set, we will apply in a first step a pair of cluster parameters that will extract the lesser dense region of the data as an isolated cluster. That is we choose a comparably large value of the neighbour search radius *r* and a rather low value of the similarity criterion *c*. We can refer to these parameters as *soft* parameters, leaving the more dense regions of the data untouched and in one cluster. Remember that density in terms of the CNN clustering is estimated as the number of common neighbours within the neighbourhood intersection of two points with respect to a radius *r*. So more common neighbours (higher similarity cutoff *c*) and/or a smaller neighbourhood radius (smaller *r*) will result in a higher density requirement. To make this more clear let's have a look again at some clusterings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:39.665756Z",
     "start_time": "2020-06-11T17:56:38.985758Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cluster attempts in comparison\n",
    "fig, ax = plt.subplots(2, 2)\n",
    "Ax = ax.flatten()\n",
    "for i, pair in enumerate([(0.18, 6), (0.2, 5), (0.6, 17), (1, 30)]):\n",
    "    robj.fit(*pair, rec=False)\n",
    "    _ = robj.evaluate(ax=Ax[i], ax_props=ax_props)\n",
    "    Ax[i].set_title(f'{pair[0]}, {pair[1]}', fontsize=10, pad=4)\n",
    "\n",
    "fig.subplots_adjust(\n",
    "    left=0.2, right=0.8, bottom=0, top=1, wspace=0, hspace=0.3 \n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Lower right__: Choosing a high radius cutoff ($r=1$, ), a soft density requirement, results in no cluster splitting. The data set as a whole forms cluster in which the point density is *at least* as high as our density requirement.\n",
    "\n",
    "__Lower left__: When we increase the density requirement ($r=0.6$, $c=17$), we observe a splitting between the broadly distributed lower density cluster and the more dense clusters. Within both the resulting clusters the point density is higher than required by the parameters but they are separated by a low density region and therefore split. This parameter set can be used for the first step of the hierarchical clustering.\n",
    "\n",
    "__Upper right__: Increasing the density requirement further ($r=0.2$, $c=5$), leads to a splitting within the broad cluster first, while the more dense clusters still remain united. Obviously, the point density in the region between the two dense clusters is higher than in parts within the low density cluster.\n",
    "\n",
    "__Upper left__: Choosing parameters hard enough ($r=0.18$, $c=6$) to achieve a splitting of the two dense clusters, does also completely split up the less dense cluster. We could use this parameter set in a second hierarchical clustering step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The central element of the hierarchical cluster functionality is the `isolate` method of a cluster object. After a clustering (with soft parameters) we can *freeze* the result before we start to re-cluster. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:39.765809Z",
     "start_time": "2020-06-11T17:56:39.668048Z"
    }
   },
   "outputs": [],
   "source": [
    "# After the first step, we need to isolate the cluster result\n",
    "robj.fit(0.6, 17, rec=False)\n",
    "robj.isolate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `isolate` method will create a new cluster object (a child cluster) for every cluster of a cluster result.\n",
    "In our case we get two new child clusters (plus one for outliers). The clusters are stored in a dictionary under the `children` attribute of the parent cluster object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:39.787963Z",
     "start_time": "2020-06-11T17:56:39.768093Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(<function cnnclustering.cnn.CNN.isolate.<locals>.<lambda>()>,\n",
       "            {1: CNN clustering object (child No. 1),\n",
       "             2: CNN clustering object (child No. 2),\n",
       "             0: CNN clustering object (child No. 0)})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The children dictionary of the data after isolation holds\n",
    "# a cluster object for each cluster found in the last step\n",
    "robj.children"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we will use different cluster parameters now for different clusters it would be nice to have something\n",
    "to keep the overview of which cluster has been identified under which parameters. This information is provided by `labels.info`. A label info tells us three things:\n",
    "\n",
    "  - `origin`: How have these labels been assigned? The entry `\"fitted\"` means, they were obtained by a regular clustering.\n",
    "  - `reference`: This is a related clustering object, i.e. the object that is associated to the data for which the labels are valid. For `\"fitted\"` labels this is a reference to the clustering object itself that carries the labels.\n",
    "  - `params`: This is a dictionary stating the cluster parameters (*r*, *c*) that led to each cluster. For `\"fitted\"` labels, each cluster has the same parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:39.821196Z",
     "start_time": "2020-06-11T17:56:39.790337Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelInfo(origin='fitted', reference=CNN clustering object (root), params={1: (0.6, 17), 2: (0.6, 17)})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Information on used cluster parameters are attached for each cluster\n",
    "robj.labels.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every single isolated child cluster is a full-fledged, completely functional cluster object itself. When we want to re-cluster a child cluster, this is no different to clustering a parent cluster. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:39.841267Z",
     "start_time": "2020-06-11T17:56:39.833641Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "CNN cluster object\n",
      "--------------------------------------------------------------------------------\n",
      "Alias :                         child No. 1\n",
      "Hierachy level :                1\n",
      "\n",
      "Data point shape :              Parts      - 1\n",
      "                                Points     - 678\n",
      "                                Dimensions - 2\n",
      "\n",
      "Distance matrix calculated :    None\n",
      "Neighbourhoods calculated :     None\n",
      "Density graph calculated :      None\n",
      "\n",
      "Clustered :                     False\n",
      "Children :                      False\n",
      "================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "robj1 = robj.children[1]  # Child cluster 1\n",
    "print(robj1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, that the `Hierarchy level` in the above overview has increased to 1. We can again plot the distance distribution within this child cluster, to see how this changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.004702Z",
     "start_time": "2020-06-11T17:56:39.844410Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distance distribution for this cluster\n",
    "robj1.calc_dist()\n",
    "fig, ax, *_ = robj1.dist_hist(maxima=True, maxima_props={\"order\": 5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we can attempt the second clustering step. For somewhat inhomogeneous data sets, a typical artifact or side effect of the CNN clustering is, that very small clusters may be appear in the result. To filter those out because they are irrelevant, we can use the `member_cutoff` keyword argument of the `fit` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.244896Z",
     "start_time": "2020-06-11T17:56:40.007634Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time for call of fit: 0 hours, 0 minutes, 0.0616 seconds\n",
      "--------------------------------------------------------------------------------\n",
      "#points   R         C         min       max       #clusters %largest  %noise    \n",
      "678       0.180     6         2         None      4         0.494     0.117     \n",
      "--------------------------------------------------------------------------------\n",
      "Execution time for call of fit: 0 hours, 0 minutes, 0.0519 seconds\n",
      "--------------------------------------------------------------------------------\n",
      "#points   R         C         min       max       #clusters %largest  %noise    \n",
      "678       0.180     6         5         None      2         0.494     0.124     \n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'member cutoff 5')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now cluster the child cluster\n",
    "fig, Ax = plt.subplots(1, 2)\n",
    "robj1.fit(0.18, 6)\n",
    "_ = robj1.evaluate(ax=Ax[0], ax_props=ax_props)\n",
    "Ax[0].set_title(f\"member cutoff 1\", fontsize=10, pad=4)\n",
    "\n",
    "# When the result is satisfying, but also a few very small clusters are found,\n",
    "# this can be surpressed\n",
    "robj1.fit(0.18, 6, member_cutoff=5)\n",
    "_ = robj1.evaluate(ax=Ax[1], ax_props=ax_props)\n",
    "Ax[1].set_title(f\"member cutoff 5\", fontsize=10, pad=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "robj.data.points.edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a little extra feature a cluster object has a `pie` method that allows to visualize the current state and splitting on the different hierarchy levels as a pie-diagram (from level 0 in the middle to lower levels to the outside)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.349062Z",
     "start_time": "2020-06-11T17:56:40.248063Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.3, 0.3)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3cgIO7QC4HufS123NJhPVJUXeNwUCb+Gcno/+/fffufTSS5kzZ47hWL7BNZrv3ayX4SRCeJ8yezkfLv6auyc+yYGCbIBWWuulOJvyhI+RIu97GgHzgXsAPvnkE6655hoyMzNNZvIpriJfPyKRUFuw4TRCeKc1mRsZNu5+lu1ahVIqFGdT3keAfNP4ECnyvuVSrfVKKqfnb7nlFl577bU6f2vciVq1ahX79+9HKcWAjhebjiOE1zpcksdDU57n82VjXNP3NwMLgSZmk4njJUXeNwQCb1I5Pb9q1Sr69+/Pr7/+ajqXT9JaM2PGDAD6tDjDcBohvJtDO/hi+Rge/Ok5DhfnAXSu7AW6xHA0cRykyHu/Rji75+8FZ/f8kCFD2Lt3r9FQvs41ZZ8cVY8Aa4DhNEJ4v+W7V3PjuPtZt2+T6576H4FXAfkG8mJS5L3bJZXT8yfl5uZy6623Sve8myxfvpxDhw5hURYub3eB6ThC+ISswmzumvgkY1dPcn3qQWA20MBcKvFfpMh7JwU8Dvx45PT87NmzTefyG3a7vWrK/vxWvQ2nEcJ32B123l/4BU/+/Kpro5tTgd+Bk8wmE/9Eirz3CcS5AMXzAF999ZVMz9cS15R9WkwKNotsrS3EiZi7bTE3j3+QrdnbAZK01r8BA8ymEn8lRd67xODcDeq6iooKnnrqKV544QWZnq8lS5Ys4fDhw1iUhYvb9DEdRwifsydvH7dPeIyF25ejlAoGxuKchZRNbryEFHnv0QTnrSm9CwoKuOWWWxg9erTpTH6tvLy86hLIha3ONpxGCN9UXF7C49NfZuzqya5PPQ98gXNPDWGYFHnvcIrWejHQKjMzk8GDBzNv3jzTmeoE15R9k7g0lAw+hKgWh3bw/sLPeeO3D7E77ADXAjOAeLPJhBR58wZorX9VSiWsX7+egQMHkp6ebjpTnTF//nwKCwuxWqyc3/JM03GE8GmTNvzCQ1OedzXknQYsBlqZTVW3SZE3RwEPA2OVUkGzZ89myJAhHDhwwHSuOqWsrKxqUaFL2p5nOI0Qvm/57tXcNuFR9ubtA2iqtV4EyPUwQ6TImxEAfAy8DPDll19y++23U1RUZDZVHeWasm8eLyt1CuEOO3J2c8v3j7AmcyNKqWit9XTgJtO56iIp8p4XBUwFhtntdp577jlefPFFHA6H6Vx11ty5cykuLsZmtXFWU9mZTgh3OFySx/2Tn+aXzb+hlLLhHNg8gXTee5QUec9qhLOD/pyioiJuu+02vvnmG8ORRHFxcVWjY//2fQ2nEcJ/lNnLeWHWO3yxfIzrU88BryGF3mOkyHtO28oO+jb79+9n8ODBsv+7F3FN2bdKbG44iRD+5/NlYxgx/1PXh/cDIwGruUR1hxR5z+igtZ6jlEratGkTAwYMYOPGjaYziSP8+uuvlJWVEWgN4JS0bqbjCOF3vl87hZdnv4fdeWnyRuBbnCt8ilokRb72da68RS5+3bp1XHvttezfv990JvEXBQUFLFiwAIABHS4ynEYI/zQtfTZPz3idcnsFwECcO9mFGg3l56TI165uWuvZSqnYVatWcd1113H48GHTmcS/cE3Zt6vX0nASIfzX3G2LeXTai5RUlAL0xbmUd5TZVP5LinztOVlrPVMpFf37779zww03kJ+fbzqT+A+zZ8+moqKCIFsQnRu0Mx1HCL+1bNcqHpj8zJGL5swGEsym8k9S5GtHL631DKVU1LJlyxg2bBiFhYWmM4ljyM3NZcmSJQBc2ekSw2mE8G9r923inkn/I6f4MEAXYC6QbDaV/5Ei736na61/VkpFLFmyhJtuukkKvA9xTdl3bNDWcBIh/N+Wgxnc9eMTHCg4CM7lb+cBKWZT+Rcp8u51ltZ6mlIqbMGCBdx8880UFxebziROwIwZM3A4HIQEBNMmsYXpOEL4vZ25e7hjwuPsPpwJ0BiYBdQzm8p/SJF3nz5a6ylKqdC5c+dy6623UlJSYjqTOEHZ2dksX74cgKs6X2o2jBB1xP6CLO6b9DT78rMAWgAzkR3s3EKKvHucr7WerJQK/vXXX7n99tspLS01nUlUk2vKvmtKB8NJhKg79hdkce+k/3Gw8BBAW+AXINpoKD8gRb7mLtJaT1RKBc2cOZM777yTsrIy05lEDbiKfGhACE1i0wynEaLu2Ju3n/smPeVqxusMTAMizKbybVLka+Z8rfUPSqnA6dOnc/fdd1NeXm46k6ih/fv3s2rVKpRSDOlymek4QtQpO3L3cN+kp8kryQfoAfyELJhTbVLkq6+H1vp7pVTA1KlTuf/++6moqDCdSbiJazTfvWEns0GEqIO2HdrBAz8967qP/nScK+MFGw3lo6TIV09rV5PdvHnzeOihh6TA+5mff/4ZgIigcFKi6htOI0Tdk561lYemPE9xeQlAH2Asstb9CZMif+IaAr+4lqq98847ZYreD+3evZv169dXTtlfbjqOEHXS+v3pPDrtRUorygAuBkYBNrOpfIsU+RMTh7PjM2Xr1q0MHz5c7oP3Y64p+1PSuhpOIkTdtXLPOp6Y/oprU5srgHeR/eiPmxT545cErABaZWZmcuONN5Kbm2s4kqhNriIfFRxJUrjcsiuEKUt3reSZGW/g0A6AW3DuSS+OgxT542MD1gJpWmvuuOMOMjMzTWcStWzbtm1s2bIFpRRXS5e9EEbNy1jC+wu/cH34GjDAXBrfIUX+2BTwPpU7JCml+Oyzz0hMTDSbSniEqwGvV6OTDCcRQoxf8xPfr5kCgNb6a6Cn2UTeT4r8sT0M3KwdDjL/70PshYVERUUxY8YMWrdubTqbqGWuKfu40BhiQmTLayFMe2/h58zPWIpSKkhrPQlobjqTN5Mi/9+uAl4C2PXCS+x9ewTpV19LedZBgoODGT9+PKeffrrhiKI2paens337dueUfWeZshfCNId28NzMt9h04A+UUnHAVGSd+38lRf7fna61/gJg/+dfkPX1KACKN2xk05WDKcnYjs1m46OPPmLQoEEmc4pa5hrNn97kFMNJhBAAJRWlPDL1BTLz9gM0AyYCIWZTeScp8v+sdeV69IE5P//C7pdfO+rJsl272XTlYApWrcZisfDMM89w7733GooqapuryCeGxxEeGGY4jRACIKf4MA9PeZ780gJwXpv/EqlpfyP/Q/4uGpislIouWLmKjAceBq3/9iJ7Ti5bht5A7uxfUUoxfPhwXnvttb+9Tvi+tWvXsmfPHpRSXNXpUtNxhBCVduTuOfIe+gFUXl4Vf5IifzQL8BXQtHT3Hrbecjv6P7aMdRQXs/X2u8gaOw6lFP369WPUqFEeCys8xzWaP6tZL8NJhBBHWrV3Pa/8+p7rw4eAGwzG8TpS5I/2CHCxo7SUbXfdQ0VOzrHfYbez84mn2DvC+Y+sW7duTJ8+neBg2UvBn7iKfP3IJEJsQYbTCCGONGPLXD5bNhoArfUHgNzzWkmK/J/O1Vo/D7DzmecoWrf+hN6c+d7/seOJ/6Htdho3bszs2bOJiYmplaDC81auXMmBAwdQSjGgYz/TcYQQf/HV8nHMy1iCUioQ+AHnKqV1nhR5pzSt9bdKKXVw3Hiyx/9QrYMcHDuerbfdiaO4mLi4OGbPnk3Tpk3dHFWYoLVmxowZAPRpIbdNCuFtNJoXZ41gR85ugGScu9YFmE1lnhR55x7F45VScYVr17HzmedrdLDDv85h87XXU5GTQ2hoKBMnTuSkk2TmyB+4puxTouoTYJGNsITwNkXlxTwx/RUKy4rAuQ/9G4YjGSdFHkYA3Spyctl21z3osrIaH7Bw9Ro2XXk1pbt2ExAQwJdffskll1xS86TCqGXLlpGTk4NFWbis/QWm4wgh/sHO3D28MOsd14d3AoMNxjGurhf5G4GbtMNBxv0PUrZnr9sOXJqxnU2DBlO0fgMWi4VXXnmFW265xW3HF55nt9urpuzPb3mW4TRCiH+zYPsyvloxDgCt9UigrdlE5tTlIt9Va/0+wN533iVv/gK3n6Di4EHShwwlb8FClFLcc889PPvss24/j/Ac15R9o9gUrMpqOI0Q4t98vmwMy3evRikVCnwPRJjOZEJdLfJxwPdKqaDc2b+y78OPa+1EjsJC/rj5VrInTkIpxaBBg/j0009r7Xyidi1evJi8vDwsysLFbfqYjiOE+BfONe7f5kBBNkBL4BOcu4rWKXWxyFuBUUBayY6dbH/wkX9c0c6ddHk52x96lH0ffwLAqaeeyuTJk7HZpHnL15SXlzN79mwALmx9juE0Qoj/klt8mKd/eZ0KRwXAQOAOw5E8ri4W+f8B5zmKi9l2593Y8/M9c1at2fP6m+x87kW0w0GLFi2YPXs2kZGRnjm/cBvXlH3TuDTDSYQQx7J+fzofLPoKAK3160B7s4k8q64V+fNwFnl2PPUMxZvSPR4g6+tv2HbP/TjKykhKSuLXX3+lYcOGHs8hqm/+/PkUFhZitVg5v2Vv03GEEMcwfs1PLNy+3LVQziict07XCXWpyMcCnwNkfTuaQz9OMhYkd/rPbLl+GBV5eYSHhzN16lQ6duxoLI84MaWlpcyZMweAS9qeZzaMEOK4vDrnfXKKD4NzJP+C4TgeU5eK/Aigfsm2DHa9/KrpLBQsW076VUMo27ePwMBAvvvuO847TwqGr3BN2beIb2I4iRDieOQUHz5yI5v7gLMNxvGYulLkLwOu1nY72x9+FF1SYjoPACVb/mDTwMEUb96C1WrlnXfe4brrrjMdSxyHuXPnUlJSgs1q48wmPU3HEUIch0U7VjBx/XTXh1/inOH1a3WhyCdqrT8E2DfyUwpXrzGd5yjl+/aRPvga8pcuQynFI488wmOPPWY6ljiGoqIi5s2bByCr3wnhQ/5v4ZfszN0DzvXtP8LPb6vz9yKvgA+UUglFm9LJfPd903n+kT0vjy033ETOtJ9RSjF06FDee++9Y79RGOWasm+d1NxwEiHE8SqpKOX5mW+7bqu7ArjGcKRa5e9FfjBwmS6vcE7Tl5ebzvOvdFkZ2+69nwNffQNAnz59GD9+PBaLv3+JfNfs2bMpLy8n0BrAKWldTccRQhyn9KytfL5sDABa6/eAxmYT1R5/riDJlV889r73PsUbN5nOc2wOB7uef5Hdr74OQPv27ZkxYwYhISGGg4l/UlBQwIIFzuWQB3S42HAaIcSJ+HblBNZkbkApFQF8jXOhNL/jr0VeAZ8opaIL16ypWmnOV+z/5DMyHnwYXV5BSkoKc+bMITEx0XQs8Q9cU/Zt67U0nEQIcSIc2sELs95xbUvbC3jYcKRa4a9FfhhwvqO0lO0PPwZ2u+k8J+zQxMlsufkW7IWFREdHM2PGDFq3bm06lviLWbNmUVFRQbAtiE4N6uxGV0L4pH35Wbw9byQAWuuncK5x71f8scg31lq/CbDnzbcp2brNdJ5qy1+wkPSrr6U86yDBwcGMHz+e008/3XQscYTc3FyWLl0KwFWdLjUbRghxwn7Z/BuLd6xwrYbnd932/lbkLcDnSqnw/GXLOfDl16bz1Fjxho1sunIwJRnbsdlsfPTRRwwaNMh0LHEE15R9xwZtDCcRQlTHW/M+pri8BOAM4HrDcdzK34r8ncAZ9qIitj/yODgcpvO4Rdmu3Wy6cjAFq1ZjsVh45plnuPfee03HEpVmzJiBw+EgJCCE1olyO50QvmZffhafLRsNVG1i4zdNUP5U5JtqrV8G2P3ya5Tt2mU6j1vZc3LZMvQGcmf/ilKK4cOH89prr5mOJYCDBw+yYsUKAAZ37m84jRCiOr5f8xObs7ahlIoB3jKdx138qci/pZQKzlu4iIOjx5jOUiscxcVsvf0ussaOQylFv379GDVqlOlYgj+n7Lsk16ldLIXwG3bt4PXfPsDunAEejHPXUp+ntNamM7hDX2CqLi9n/cWXUrotw3SeWlf/jttocNcdAGRkZHDppZdS4iVr8tdF9erV47fffkNrzQ1j72XboZ0ezxAZFE5CeDwxIVFYLRYUFixKYVEWVOXfzj8KpSxYqz6vKLNXkFeSz+GSPA6X5JNXkk9RebHH/xuEMO32ntczsOPFABlAO6DIbKKa8YciHwisBVrs+/Rz9rxSd6aw4wdeQeozT6GsVrKzs7nwwgvJyckxHavOGjt2LB07dmTmlnk8N9O9s30RQeEkhseTEBZHYngcCeFxR3zs/Ds4IMit5yy3l5NbnMfBwkNkFWZzoOAgWYXZZBVkk1WYTcahXeSXFrj1nEKYFmIL5osr36FeRALAq/j4/fP+UOQfBF4tzzrIunP74igsNJ3Ho6J6n0mTt9/AEhJCUVERV1xxBVu3bjUdq0668cYbeeihh8gryefiz4dW6xhWZaFJXBqtE5vTJqkFLROb0SAi6bgLuNaaCrumwu5Aa3BojdbOz2utcRzxOaXAohRWi8JmtWCzKWwWC0qBUsd3F1Fm3n42H9zG5qzKPwe3kevcs1sIn3VKWldevuBxtNZ2pVQ3YJXpTNXl60W+gdY6XSkVnvHwoxyaMNF0HiPCOnag2ccfYIuJoby8nBtuuKHq3m3hOSkpKcyaNQutNUO+u53dh/cd8z0JYXG0TWpB66QWtElqTov4pv9a0B0OTUmZncOFZRw8XMKB3BL2ZRexK6uAHfsK2Lo3j4LiCrf8t4QG2UhNCqNx/Qga14sgJSGMxJgQYiODiAgNIDjQivVf9lU4UHCwsuhvrfoFILtIZpiEb3nm3Ac4s2lPgGXAKYDvraqG7xf5r4BrClauIv3Kq8G3/1tqJKhxI5p/8jFBDVNwOBw88sgjTJxYN3/pMWnChAm0adOGqRtn88qco3cSDLDYaFuvJW2SWtAmsQWtk5oTH/b37awdDk1uQSk7DxSwYXsua7YeYs22Q+QWlHnqP+O4JEYHc3rH+nRpEUez5CgSooMJtFn+cRYguzCHDfs3s3DHchbtWE6OjPaFl4sNjeHrK0cQHhQGzlVUPzUcqVp8ucj3BBZoh4NNA66kaO0603mMs8XH03zkh4S2bYPWmrfffpsPP/zQdKw65dZbb+Wee+4hp+gwl355PZFB4ZyS1o2ejbpzUsNOhAYevdmQ1priUju7DhSwams2s3/PZF3GIZ/9fTUs2ErPtkmc1CaRVqnRJMeHEhRgParwO7SDDfs3s2D7MhZsX8aOnN0GEwvx7wZ0uIg7et0AsA9oAeSbTXTifLXIW4GlQJeD48az4/H/mc7jNSxhYTR97x0ie/VEa83YsWP53//k/4+nNGnShGnTpqG1Zt2+TbRJannUtLbDoTl4uIQN23OYv24/c1ZmUlTqnil2bxUVFsA53ZLp3akBrVKjCQ22HfX87sOZLNy+jPkZS1m3bxN27R+LWAnfZ7PY+HLQ26RENwB4HnjScKQT5qtF/ibg44q8PNafewEVhw6ZzuNVVEAAaS8+R9wl/QCYN28ew4YNM5zKv9WvX5/zzz+fCy64gA4dOhz1XEmZnU07c5iyaBfTl+7G7vDJ7zm3SUsKY8CZTejZLomkmJCjRvl5Jfks3rGCBduXsXTXKrmNTxh3WuOTef78h9FalyilWgA+tdKaLxb5GK31ZqVU/K7nX+TAV9+YzuOdlCL5/nuod/NNAGzevJn+/ftTUeHfo0ZPCggI4JxzzmHQoEGccsopVZ/XWmO3a9ZuO8QHEzewbnuuuZBeLizIyoDeTTi3ewqpieFYLH8W/NKKUmZumceEddPZctB3N5oSvu+dS55z7TL5NXCt4TgnxBeL/AjgzuLNW9hw6eUgRes/JVxzNQ0ffxRlsbB//34uuugi8vLyTMfyaY0aNWLgwIH079+f2Fhn45zWmqLSCpasP8Cn0zaTkelzl+6Ms1rg4p5pXNwzleYpUdisf17mWLdvExPWTee3rQspd8j3vPCsFvFNGDngddeH3YHlBuOcEF8r8u211iuVUtbN115P/uIlpvP4hOjzz6Px669gCQwkPz+f/v37s8vP1vavbQEBAfTp04dBgwbRo0ePqs/bHZr1GYf4dEo6y9IPGkzofy47PY2rz2lOvdg/p/QPFeUyecMMJm/4hazCbMMJRV3y6Fl3cn7L3gDzcO5W5xPF05eKvAJmA2fmTPuZbXfLLmwnIrx7N5p+8B62yEjKysoYMmQIq1evNh3L64WHh3P11VczdOhQ4uLiAOeoPetwCT/O286oGX9QbveZ7yGf1CgpnDsvb0v3VglVo3u7w868jKX8uG4aK/fKnTWi9iWExfHNVe+51rG4HPjBcKTj4ktF/gJgiqOkhPXnX0TZ3r2m8/ic4ObNaP7JRwTWr4/dbufee+/l559/Nh3LK8XExDB06FCGDBlCREQE4OyMX7PtECO+X8emnXKft6dZLYqh5zXnstMbERsZXPX5jEM7+XHddH7ePMe1J7gQteKG7lcytNtAgK1AG8C7Fq/4B75S5BWwBOi+b+Sn7HntDdN5fFZAvXo0/+QjQlo0R2vNSy+9xJdffmk6ltdISkrixhtvZODAgYSEOO9pL6uw88vS3bw1fj3Ffn67m6/o1DSWu65oR8uGUVVT+bnFeYxa+QM/rptOmd3rf/YKHxRiC2bU4PeJC4sBuA8f2JLWV4r8hcBP9qIi1p11rtwyV0PWyEia/t+7RJzUHa01X331FS+++KLpWEYlJydzyy230L9/fwICAgAoLC5n7JxtfDo1HYfcuu2VEmOCeXBQB3q0TaxajyCrIJsvV4xj6qZZ2B0+uRKp8GIXtDqLh3vfgdY6VynVBPDqNZt9ocgrnAvfdNv38Sfsef1N03n8ggoMpPFrrxDT17ll8owZM7jjjjsMp/K88PBwhg8fztChQwkKcq4Zn19UxidT0hk3x/+3LPYXYSE2HrqyA2d1ScZaeRvensP7+GzZaGZtmYf2jR4p4QMsysInA16naVwjgGeBp8wm+m++UOQvBibZC4tYd1YfKmQrVfexWGj42CMkXjsEgLVr1zJw4EAcdWDYarVaGTBgAHfddVdVQ11hcTkf/7RJirsPCwuy8fDgjpzVpUHVPffpWVt5b8HnrMncYDid8BdnNDmFZ897EK31YaVUIyDXcKR/5e1FXuG8H7HLvo9Hsud1r7/84ZOSht1AykMPALB7924uuugiiov9d6Wx0047jYcffpjmzZsDUF5h59uZW/lo8ibDyYS7RIUF8uwNXenWMr7qmv2crYv4cNFXZObvN5xO+DqF4rOBb9IkLg3gaeAZs4n+nbcX+X7ARHthEWvPOgd7Tq7pPH4r9pKLafTiC6gAG7m5ufTr14/9+/3rh2HTpk159NFHOe200wBnt/zPy3bzyrerKavw/9mLuqhpg0iev7ErqUnhKKUos5czfs1PfL1ivCyZK2rkzCan8IxzNJ9bOZr3yltuvLnIK2AF0Dnzo5HsfUNG8bUtoucpNH1/BNawMEpKSrjyyivZuHGj6Vg1FhgYyC233MLNN99MQEAAWms27sjl4Y+Wkp1Xajqe8ICzujTgoSs7EBkWCDi3vn11zv+xeOcKw8mEr1IoPhv0Fk1iUwH+BzxnONI/8uYifwnwo72wkLVn9ZFRvIeEtGlN85EfEZAQT0VFBbfeeitz5841HavaunbtyvPPP0+TJk0AOFxYxjNfrGDxhizDyYQJN13UkmvObV61qM7kDb/w/sIv5P56US29m/bi6XPvR2udo5RqjBeO5r21yCvgd6BT5ocfs/fNtw3HqVsCG6bQ/JOPCW7cCIfDwdNPP82YMWNMxzohISEh3H///VxzzTWAc2r+h3kZvDlWVker66LCA3jnjlNonuK8x35v3j5emv2eNOaJE2ZRFj4f+BaNYhuCcxva5w1H+htvLfL9gR/sBQXOUXyu1/1y5PesMdE0++gDwjt1RGvNRx99xFtv+cYlk+7du/Piiy+SmpoKwI59+dz3f4vJzJZrsOJPV5zRmLsub4vNasGhHYxdPZlPl35Lmb3cdDThQ85udir/63OfazTfCPCqHcC8schbgJVAh8z/+5C9b48wnafOUsHBNHn7DaLP6o3Wmp9++okHHnjAdKx/FRAQwL333suNN94IgN3h4IMfN/DtLNmmVPyz6PBARtzVk2bJkYBzidwXZo2QrW3FcbMoC18Mepu0mBSAxwGvWlnMG4v8ZcD39oIC1vbug/2wjOKNslpJfeZ/JAwcAMCyZcsYMmSI4VB/16BBA9566y06deoEwO6sQu58eyH7c2X0Lo7tyrOacNulbbBZLVQ4Kvhy+ThG/f49di13XYhjO6f5aTx5zr0Ah4BGgNfsNe1tRf7PUfz7H7D3nXdN5xGV6t9xGw3ucq6Il5GRwaWXXkpJiXc0K/Xu3ZuXX36Z6OhotNaM+XUbI75fbzqW8DHxUcG8f09PGiaGA7Bh/2ZenD2CXbmyGZb4b38ZzT8EvGY4UhVvK/LnA9NkFO+d4gdeQeozT6GsVrKzs7nwwgvJMbgCoc1m47777quani8qqeD+/1vM6q2yt4Govrsub8ug3k1QSlFaUcYbv33Iz5vnmI4lvNz5LXvz6Fl3AuwCmgBesZuVxXSAv7gD4OD4H6TAe6GDY8ez9bY7cRQXExcXx+zZs6tuTfO0Bg0a8M0331QV+HXbDnHhI9OlwIsaG/H9eoa/MZ/C4nKCbIE8dvZd3HjSVSiU6WjCi83+Yz45xYcBGuJsHvcK3lTkm2qtLwDIGvWd6SziXxz+dQ6br72eipwcQkNDmTRpEieddJJHM5x88slMmDCBzp0749CaT6ds4uY35lNaLtdPhXusy8jhokd/ZtPOXACu7TqAp/rcR5At0Gww4bXK7OVMXD/d9eHdJrMcyZuK/G1KKXV47jxKd+wwnUX8h8LVa9h05dWU7tpNQEAAX375JZdccolHzt2/f38+/fRToqOjKS2zc+c7C/l06maPnFvULaXlDm54ZS4/zM1Aa03vZr14p99zxIZEm44mvNTEdT9Tbq8A6AV0MxwH8J4iHwbcAHDgm1GGo4jjUZqxnU2DBlO0fgMWi4VXXnmFW265pVbPeffdd/Pyyy8TEBBAVm4xlz45g5Vbsmv1nEK8PmYtz321ErtD0zqpOR9e/gpNYtNMxxJe6FBxLr/+Md/1oVeM5r2lyA8Gokt37iTvt3mms4jjVHHwIOlDriVv/gKUUtxzzz08++yzbj9PYGAgr7/+OrfddhsAa7Zm0//JGRwuKHP7uYT4J9OX7uaWN+ZTWm4nKSKB9/u/SI/UrqZjCS80bs1PAGitBwH1zabxjiKvgDsBDoz6Dryr218cg6OwiD+G30b2xEkopRg0aBCffPKJ244fExPDF198wcUXX4zWmh/mZnDLmwuoA1veCy+zfnsOA56aRU5+KaGBIbzY91Eub3+h6VjCy2w+uI01mRtRSgUAt5rO4w230J0O/OYoLmbNab2x53nVioDieClF8v33UO/mmwDYvHkz/fv3p6Ki+neRpKam8sknn5CWloZDa94au47v52a4K7EQ1RJoU3z60Bk0rVwl78d10xmx4FPsDrvhZMJbuLahBbKAVMDYoiLeMJK/AyB70mQp8L5Ma/a8/hY7n3sB7XDQokULZs+eTWRkZLUO17hxY7755hvS0tKw2x088tFSKfDCK5RVaK55cQ6/rcpEa82l7c7n5b6PSee9qDIvYwn787MAEoArTWYxXeSTtdaXAWR9863hKMIdsr4exba778NRVkZSUhKzZ8+mYcOGJ3SMpk2b8tVXX5GUlER5hYPhb8xn/tr9tZRYiOp5dOQyvpy+Ba01J6V25oXzHyHQGmA6lvACdu1gwrpprg/vAXOLLJgu8rcopaz5S5dRnC63QfmL3J9/Ycv1w6jIyyMiIoKpU6fSsWPH43pv8+bN+frrr0lMTKS03M51L89hw47c2g0sRDV9/NMm3hq/Dq013Rt24ulzH8BmsZmOJbzATxtnUlJeCtAROM1UDpNFPgi4GeCAjOL9TsGy5aRfNYSyzEwCAwP57rvvOPfcc//zPS1btuTrr78mLi6O4tIKrn7+VzIyCzyUWIjqGT8ng3cqC32vRt154uy7sSrT4ydhWn5pATO3zHV9eL2pHCb/JV4BJJbt20fuzFkGY4jaUrLlDzYNuprizVuwWq2MGDGCoUOH/uNr27Rpw1dffUVMTAyFxeVc+ews9h4s8nBiIapn7JwMRny/vmrRnId73yHL4AqmbpoNgNZ6ABBuIoPJIn8HQNZ3Y6AGHdjCu5Xv20f64GvIX7oMpRSPPvoojz322FGvadWqFV988QXR0dEUlpRz5bOzycotNZRYiOoZ8+s2Ppi4Ea0157U8k/tOH246kjBs/f50duXuRSkVBlxuIoOpIt8J6OEoK+Pg2PGGIghPseflseWGm8iZ9jNKKYYOHcp7770HOG+T+/TTT4mKiqKopIKrnv2V7Dwp8MI3fTPjDz6Zko7Wmn5tz+XOXjeYjiQMm57+q+vhdSbOb+o++deAB3Km/cy2u+81cX5hgsVCw8ceIfHaIQBs2LCB8PBwUlNTKSmzc9Wzs9mfU2w4pBA1d0u/1lx7XnMAvvn9e0YukeW666qEsDjGXvMRFmefRhPAo/cCmxjJW6i8b/DQlCkGTi+McTjY9fyL7H71dcB5HT41NZUKu4Nhr82VAi/8xoeTNjJpwXYAhnS5nGu7DjAbSBiTVZjNit1rXB/+c1NSLTJR5HsBKfb8fA7PmXvMFwv/s/+LryjauAkArTVPf76CbXvzDacSwr1e/nYNc1dnAnDjSVcxsGM/w4mEKUdM2Q/Fw3XXRJG/EiBnxkx0mWwwUhelPfs0oa1bobXmgx83MntlpulIQtSKRz5expptzp0Sb+95HT0bdTecSJgwL2MJBaWFAI1wLuXuMZ4u8jZgAEDOT1M9fGrhDerdOpz4Ky5Da81nU9P5ZuYfpiMJUatufXMBuw8UAvDE2XeTFpNiOJHwtNKKMn7dusD14XWePLeni/zZQEJ5djZ5ixZ7+NTCtMgzTyf5XucWy7+t2senU2WVQ+H/tIahL82hoLicsMBQXjz/EcIDw0zHEh42bZNzyl5rfQUevGfe00X+KoCc6T+DXXZsqksCG6bQ+LVXANi5v4DHPllmOJEQnlNcZufGV+dSYXeQEt2Ap/rc5+q2FnXE+v3p7Mzd47pn3mOdmJ78VxYM9Ac4JFP1dYoKDqbpeyOwVd4Lf8Orv5mOJITH7TpQyFOf/161oc3NJ19tOpLwsJ/T57geDvTUOT1Z5C8AIsv2ZlL4+0oPnlaYlvr0k4S2boXDobn97QUUlcgsjqibfl25l/G/OW+Tvqpzf05rfLLhRMKTftu2CACt9dlAtCfO6ckifxXAoSlTnRepRJ0QP2gg8Zf1R2vN62PXkL7rsOlIQhj11rh1bNnt/D54pPcd1I9IMpxIeMqu3L1sP7QLpVQAcKEnzumpIh+ptb4IZKq+Lgnt0IGG/3scgN9W7+PHeTsMJxLCO9zyxnyKSioIDwrj6XPvJ0C2p60z5mZUNZ3398T5PFXkL1FKBZds20bxxo0eOqUwyRIWRpO338ASEMDBw8U8NlIa7YRwKS6zc/e7C3FoTavEZtzW8zrTkYSHzN22BACtdV8gtLbP56ki75yql1F8ndHwsYcJSknGbncw/I35puMI4XXWb8/l0ynpAFzW/gJ6N+1pOJHwhC0Ht5GZdwClVChwbm2fzxNFPl5r3Qfg0E+yVn1dEHnm6cQPuAKtNe/9uIHMbFmTXoh/8vm0zWzYngPAvacPJyo40nAi4QnzMpa4Htb6lL0ninw/pZStaP0GSrfLNVl/Z42OotHzzwGwaedhxszeZjiREN7trhGLKCu3ExUcwR0ybV8nzPvzunw/IKA2z+WJIn8eQO7sX4/1OuEHUp98nIDEBErL7dw1YqHpOEJ4vaLSCt4YuxatNee2PJNuKR1NRxK1bN2+dHKKD4PzNroza/NctV3krcA5AHnzFxzjpcLXRZ9/HrEXX4TWmme//J3CkgrTkYTwCZMX7mRz5W11950+nCBboOFEojY5tIP5Hpqyr+0i3xWIrcjLo3DN2lo+lTDJFhdH2jP/A+D3zQf5VXaWE+KE3PPeIirsDpKj6sn+83WAq8seZ5GvtVpc20X+XID8RYtlrXo/l/Low9hiYigtt/PQR0tNxxHC5xwuKOejSc5bjK/qdClNYtMMJxK16fc9a13bz9YDam3pw9ou8ueBTNX7u7CuXYjr55ymf3PsWopL5Rc6Iapj1Myt7DxQgNVi5YEzb5VNbPxYhaOC5btXuz6stVvpavNfUKTW+hSQIu/XLBZSn3SuarfzQAGTF+40HEgI3/bA/y3G4dC0TWrBJW3PMx1H1KJlu1a5HvaprXPUZpE/SyllLdmWQdmevbV4GmFS/MABhLZpjUNrHpZpeiFqbHdWEVOX7ALgppOvJiEsznAiUVtcI3mtdQ8gqjbOUZtF/lyAvAVyG5W/skZHkXzv3QDMWLaHnfsLDScSwj+8NGoVhSUVhAWGcvepw0zHEbVkX36Wa495K9C7Ns5Rm0XeeT1+nixp6q8a3H0XtphoiksreOEb2T5YCHfRGl4etQqtNac1OZmejbqbjiRqyfp96a6H99bG8Wtr66OmQBNHWTn5S2VjEn8U0roVCVcOBOD9HzdQYZftg4Vwp1m/72Xo+c1plhzFTScNZtH25Wjk+8zXxYXG0DWlA11TOtAluQOJ4VWXY06pjfPVVpE/D6Dw999xFBXV0imESSkP3o+yWsnKLeaHudtNxxHCLz3+yXJG/+8smsSlcVqTk5m7bfGx3yS8SnhgKJ0atKss6u1pFNvwqOe1dv7iVrnHfBrg1vXfa6vIy/V4PxbWpTORp/ZCa82L36wyHUcIv7XrQCHL0w/SvVUCQ7sOZN62JTKa93KB1gDa1mtJ12TnaL1lQjOslj+vjGutKS4uJj09nRkzZjBu3Dg+++wz2rdvD3AaPlDkA4CzAA7L9Xi/1ODO2wHYlVXIko1ZhtMI4d+e/ep3Jr5wLs3iG3Fq45OO3MFMeAGLstA8vjFdkjvQLaUD7eu3IsgWdNRrysrK2L59O7/99htjxoxh165dRz2/dOlSV5E/E/jGnflqo8ifAkSUZ2dTvHFTLRxemBTWtQuRvXrKKF4ID8k+XMqyTVmc3DqRoV0HSJH3AilR9emW0pEuKe3p3KAdkcERRz1vt9vZu3cvixcvZsyYMaxd+9/Lui9evJgbb7wRaqHDvjaK/NkA+QsXOVtEhV9xjeK3ZeazZushw2mEqBte/HoVE17oQ/OEJvRq1J0F26Wh2ZNiQ2Pomty+qmEuMTz+qOe11hw8eJAVK1YwYcIE5syZc0LHX7FiBRUVFdhstiZAKuC2VcVqayRP/rIVtXBoYVJ4t65E9jwFrTXPfSW3zAnhKVmHS/h980G6tUzgum6DpMjXsrDAUDo2aFtV2BvHph71vNaa/Px81q5dy9SpU5k0aRJlZWXVPl9hYSHr16+nY8eO4Jyy/6om+Y/k7iJvAU4CKFy9xs2HFqbVrxzFb92bx+Zdhw2nEaJueeGblXz/bB9aJDShZ6PuLJRC7zYBFpuzWS6lA12TO9AqsRlWi7Xqea01JSUlbN68mZkzZzJ27Fhyc3PdmmHJkiWuIt8bLy7yLYEoR3ExxZs3u/nQwqSwLp2JPKUHWmte/U5+gRPC0/YfKmHVlmy6tIjnum4DpcjXgELRLL4x3Spva+tQvw3BAUc3y5WXl7N9+3bmzp3L6NGj2bmzdvflWLGiavbbrSsfubvInwxQuG69bC3rZxKvvQaAvdlFrMvIMZxGiLrp+a9/5/tn+9AyoSmnpHVl0Q65LHq8kqPqV02/d05uT9RfmuUcDkdVs9y4ceNYtWqVR/OtX78eAK11a6VUKOCWRWbcXeR7ABSuWn2s1wkfEpCUSMy55wAw8ie5Y0IIU/YdKmHttkN0aBrHdd0GSZH/D7Eh0XRJaU/X5A50SelAvYiEo57XWpOdnc3vv//OxIkTmTlzpqGkTllZWRw4cIDExEQL0AFwy8pHtVPk5Xq8X4kfNBBls1FQXM4vy/aYjiNEnfbq6DV8/diZtEpsRof6rVmTudF0JK8QGhBCxwZt6JrSka7J7WkSl3bU865mufXr1zNlyhQmT55MSUmJobT/bMOGDSQmJgJ0wQuLfJjWur1SSoq8H1EBAVVr1E+cv91sGCEE2/bmk3moiAZxYZzXsnedLfIBFhttklpU3dbWOrH5Uc1yAMXFxWzZsqWqWS4nx7svNW7YsIEzzzwToKu7junOIt9ZKWUp27+f8v373XhYYVLM+ecREB9Phd3Bx5Nlql4IbzD2123cc0V7ejftybvzP6WkotR0pFrnbJZrRJfK5WI71G9NSEDwUa8pLy9nx44dzJs3j9GjR7N9+3YzYavJdV0e50jeLdxZ5LsAFK1bf6zXCR+SMGQwAEs3ZlEuO80J4RXG/prB7Ze2JSwwlNMan8yMLXNNR6oVDSKTqqbfOye3Jzok8qjnXc1yS5cuZdy4cfz++++GkrrHhg0bXA/bAUFAjX97c3+R31A3p478UWi7toR37oTWmvcmyC9vQniTZelZ9GybRN9WZ/lNkY8JiaJLcnu6VN6vXj8y8ajnXc1yK1euZOLEicyYMcNQ0tqxd+9ecnJyiImJseEs9DXurHTrdD1A0foNx3qd8BHxA64AICu3hO37CgynEUIc6YMfN9CzbRJdUzqQFJ7A/gLf2ywqJCCYjvXb0jXFeWtb07hGRz2vtaagoID169czbdo0fvzxR69rlnO3DRs20KtXL3AOnL2myAdrrdsqpWQk7y9sNmLOPxeASQvduvOhEMINtu7NJyu3mIToEM5reSZfrRhnOtIx2Sw22iQ1r9xbvQNtEptjsx5dhkpKStiyZQuzZ89mzJgxZGdnG0prxl+KfI25q8i3V0pZyw8donzfPjcdUpgU2asntpgYHA7NqBl/mI4jhPgHUxbt4rq+LTi/ZW+vLPIKRZO4tMrlYtvToUEbQgNCjnpNRUUFO3bsYP78+Xz33XdkZGQYSusd3N18564iL1P1fib2wr6Ac5360nKH4TRCiH/yzYwtXHt+c5Kj6nnNPfP1I5Iqp9870iW5HdEhUUc973A42LdvH0uWLGH8+PEsX77cUFLvdETzXUcgACivyfHcVeSl6c6PqKAgos9xrnA3ds42w2mEEP+mqNTO9n35NKkfyfmG7pmPCo6kS3J75zrwKe1pEFnvqOe11hw6dIiVK1cyadIkZsyYgcMhA4d/s3PnTvLz84mIiAgCWgH/vRn9MbiryLcCKNm8xU2HEyZFnXkG1vAwKiocTF28y3QcIcR/+OG37TxwZQd6N+3FCA/cMx9iC6ZDgzZ0rbxfvVl8o6OedzXLbdiwgenTpzNhwgSKi4trNZM/0VqzYcMGTj75ZHAOoL2iyDcDKNlRu7v0CM+IvegCANbvyEHLrfFCeLUJ87dz74B2hAaGcEbTU/g5fY5bj2+1WGmT2JwuKR3oltKBNokt/rFZ7o8//mDWrFmMGzeOrCzf6/T3JkcU+a7AlzU5ljuKfAiQDFC6S4q8r7OEhRF15hkAfP9b3W6AEcIXaA0bd+bSrnEsZzSpeZFXKBrHplZOv3egY/02hAb+vVlu586dzJ8/n9GjR7N169YanVMcbfOfW7W3qumx3FHkGwPY8/Ox5+S64XDCpMhePbEEBVFhdzBzxV7TcYQQx2HCvB20axxL5+R2BFhslDsqTuj99SISqlaW65LSgZh/aJbbv38/S5cuZfz48SxdutSd8cVf7NhRddtys5oeyx1FvilA6U65dusPIk/rBcCO/bL4jRC+YvrSXTw2pBOhASG0q9+KlXvW/efro4IjnCvLVV5XT476e7NcTk4Oq1evZtKkSUyfPl2a5TzoiCKfBgQCZdU9ltuKvFyP9w+RzkUY+G2VjOKF8BVaw56sQlKTwjm5Yee/FflgWxAd6repul+9eUKTv7xfU1hYyMaNG5k+fTrff/+9NMsZdPDgQQoLCwkLC7MAjYDNx3jLv3LfSH6XjOR9XVDjRgSlJKO1Zvxv203HEUKcgIXr95OaFM5JqZ0ZufRbWiU0q7qu3japBQHWgKNeX1paytatW6tWljtw4ICh5OKf7Nq1i1atWoFzyt5okW8GULpTRvK+LvJU5yj+cGEZuQXVnh0SQhiwfFMWg3o3oWlcIyZf/yVhgaFHPV9RUcGuXbtYsGABY8aMObK5S3ihHTt2HFnkq02uyYsqkb16ArD6j0OGkwghjiUpJoRureLp1jKBbi3jiYv8c2/1sMBQHA4HBw4cYNmyZYwfP57FixcbTCtO1M4/B85Gi7wV5/UCKfI+TgUEEOG8L5Mpi2VWRghvExkWQNcW8XRtmUD3lvE0TAw/6nmtNQ6HA6vVytKlSxk6dKg0y/kwd3XY17TINwQCHGVllO/fX8NDCZPCOnfCGhaKw6FZsE6+lkKYFhRgpWPTWLq1jKdbqwRapERhsaiq549slvvll18YP348gwcP5sEHHyQlJUUKvI/bs2eP62FqTY5T0yL/Z9Od/IPyaeHdugKQdbhYVrkTwgCrRdEqNZrurZyj9faNYwgMsB71Glez3K+//sro0aP/1iw3fvx4HnjgARo0aEBKSgq7d+/25H+CcKPMzEzXwzRAAdX6yeyeIi9T9T4vrGMHADbuyDUbRIg6pHH9COdIvWUCnZvHER5ydAd8RUUFu3fvrmqWS09P/8/j5ebmUlhYSHh4OF27dpUi78P27q26jTkciAZyqnMcKfICgPBOHQGYv0am6oWoLYnRwXRrlUD3lgl0bRlPfFTwUc+7muWWL1/O+PHjWbRo0QmfY+fOnbRp04Y2bdowceJEd0UXHlZaWkp2djZxcXHgHM0bKfKVt89JkfdlQamp2GJi0FozZ1Xmsd8ghDguEaEBdGkeXzUFn5b092a53Nxc1qxZw+TJk5k2bRoVFSe2JO1frVmzpqrIC9+WmZnpKvKpwKrqHKOmRb4RQJkshOPTQtu3BaC41E5Rac1+wAhRlwUGWOjYNK5qCr5lw783yxUVFbFp06aqZrmCAvcuIT1v3jyuvPJK2rRpg1IKLU02PmvPnj20a9cOnCP5aqlpkY8HKM/OruFhhEmhrVsDsDe7yHASIXyL1aJomRpFt8rb2to1iSXoH5rltm3bxpw5cxg9ejT79u2r1Uxz585Fa014eDipqalH3oolfMwRzXfV7rCvaZGPBajIPVzDwwiTQts6p/XSd+WaDSKED0hLCqd7K+cCNF1axP+tWc5ut7N7924WLlzImDFj2Lhxo0fzlZWVUVRURFhYGG3atJEi78OO+IWwQXWPUZMiH4iz6w/7YSnyviy08trdyi0yIyPEXyVEB1etKtetZTwJ0Ufvra61rmqWmzBhAvPmzTOU9E979uyhRYsWtG3blmnTppmOI6opLy/P9TC6useoSZGPAdAOB/b8/BocRphki4vDFhON1prFG2SDCiEiQgLo3CKO7pWFPa1exFHPa605fPgwa9as4aeffmLKlCk1bpZzt/Xr19OiRQtpvvNxRxT5mOoeoyZFPhbAfjgPWT3FdwWlNgTAoeFQXqnhNEJ4XmCAhfZNYquKesvUaKz/0CyXnp7OL7/8wrhx49zeLOduixcvpn///jRv3tx0FFEDpkfycQAVMlXv04LSnP0cRSXlhpMI4RkWBS0bRldt7tKh6d+b5crKyti2bRu//fYb33333ZENUD5h2bJlACQmJhIaGkpRkTTV+iKvGMlX5ObW4BDCtKBUZ5HPllG88GNpSeF0bRlP95YJdG4RR2Ro4FHP2+129uzZU9Ust2HDBkNJ3WPPnj04HA4sFgsNGzY85kp5wjuZHslXTtfLSN6XBTV0TtfvySo0nEQI94mPCqpslnNOwSfG/L1ZLisrixUrVvDDDz8wd+5cQ0lrT3FxMWFhYaSlpUmR91FHFPngyj8lJ3oMN4zkpcj7Mtc1+S178o7xSiG8V3iIjS7N452j9VYJNPqHZrm8vDzWrFnDlClTmDJlCmVlZYbSekZOTg5hYWE0rPxFXviegoKCqhkZnKP5E15kQUbydZyryG/aUa1lkYUwItDmbJZzbcPa6h+a5YqLi0lPT2fGjBmMGzfuyFFRnZCVlUVKSgr169c3HUVUk9aa/Px8oqKiwHld3qNFXhrvfJwlLJQA57rIbNopX0fhvSwKWjSMqtzYJYGOTWMJCvx7s9z27duZM2cOY8aMqfM7sO3evZvOnTvToEG111ERXiAvL89V5KOr836Zrq/DAus5f8PXWnMg94Qv9QgfYrMqosODsFkVVquFopIKcvK9u9myYWJY1W5tXVvEExn292a5vXv3snDhQsaOHcu6desMJfVO27ZtA5Ai7+MOHz7suuRSrQ77mk/XS3e9z7LFRANgd8g6B/4oIiSAc7olc0bHerRvEktI0NHf7gdyilmWnsWsFXu9YiGkuMjKZrnKW9uS/qFZ7uDBg6xYsYIJEyYwZ84cM0F9REZGBgDx8fGGk4iayP9zsbno6ry/5iN5ma73WVbnFBDlFQ7DSYQ7KQUX9GjI7Ze2ITo86Min7EAZUK61Dk+MCbFc2COVC3uksmLzQUZ8v44tuz133Tos2Ebn5nF0b+UcrTepH3nU865muXXr1jFlyhQmT57s981y7uRa9zwyMvIYrxTe7PCfNdbMSF6m632XrbLIl5TZDScR7pIQHcwz13elU7M416c2AZ8BU4GNgANAKRUOnARcqrW+qWuL+ODPHz6Db2b8wUeTN9bKIpYBNgvtG8dU3dbWKi0am9VS9bzWmpKSEjZv3ly1DWuuzBRWm2sBn6CgIIKCgigt9e7LM+KfmRzJhwM4ZCUln2WNdhb5ohLvWndbVE9idDDv39OL5IQwtNZFSqmngHeAf1rOsACYDcxWSr0BvGSxqKuuPa85MRGBvPzt6hoXeouC5imV27C2iqdD01iCA4/+kVNeXs727dv57bffGD16NLt27arZSUWVAwcOoLVGKUVkZCRZWVmmI4lqqOmqdzUp8s5fwbVM9foq10g+v0iWtPV1ocE2Xr+tB8kJYQDblFJ9gG3H+fYdwGDgZ631pxf3TLPm5Jfx4aQT3yI1JSHsz21Ym8cTFX50s5zD4WDv3r0sXryYsWPHsnr16hM+hzg+DodDirwfOGIkH1Wd99ekyCtwTrEJ3+Qq8nlFcp3T1w27sCXNkiPBeR9tb2BnNQ7zpVJKA18O6dOMWb/vOeY1+tjIoMotWJ2FvV5s6FHPu5rlVq5cyYQJE5g9e3Y1YonqstvtWCwWuS7vw47Y4bBa9brGRV52oPNdrun6Qpmu92n1YkO47PRGrg+HUr0C7/IVcKHFogbefXk77nhn4VFPhgbb6Nwsjm6VK8s1afD3Zrn8/HzWrVvH1KlTmThxojTLGVRRUUFAQIAUeR/mcFTNllv+63X/puZFXm6/8lnW8HBArsn7uhsvaEmgzQrOa+y/uOGQD2mt+3VpER98Wod65BeV073ytrbW/9Ist2XLFmbOnMnYsWPJyZHVE71FaWkpISEhUuR9mN1e1Rht/a/X/Rs3XJOXIu+rlNX5b6bcLn0VvirAZuHsrlWLnfzPTYfdoZSaAlz+yvCT/vZkeXk5O3bsYN68eYwePZrt27e76bTC3VyzKKGhocd4pfBWR4zkPV7k5Zq8r3NueiCL4fiwto2iXR3r+4GFx3j5iZgPXA5/NsstXbqUcePG8fvvv7vxNKI2uQpE5QYnwgeZHMlXXpOXUaCvs8tI3mc1T6lquF0MuPO3tc+AtwB69uwpU/A+yjUIkyLvu44YSHv8mrxM1/s4pZy/p1XY5Wvoq2Ijqla0c/cN5nlABWBbvHjxkR2+wodYKy/JPf744zz22GOG04jqcP2cBrpW5/1uGMnX4AjCLNd0vRR5n1X852qFIf/1umqqGjnYbDX5USFMcxV74dOCq/MmuSZfl1X+hmiXr6HPKi6tGmGHu/nQATjXurdkjn2Eipy9bj688IT6Q97BFhbD559/zrRp00zHEdVw7rnnMmzYMIA11Xm/G26hk+u5PquyyFst6hgvFN4qO69qPfJWbj50JyDAXpRLwdqf3Xxo4Sm6wvnvY+fOnbK6oI9q27at62G1do+qSTdG5XtlFOirXJd6ggJkKs9Xrdx80PWwI9DQjYc+A6BkV7UGD8JLKOVqnZKf077qiKbJau0kVpMiLyve+Th7oXNzobAQud7qq3IKyli5parQ3+imw1qBmwEKN8930yGFCcrm3DugSDYS81lHFPlqTZvXuMhrucfaZ9krNz4IDwkwnETUxI/zdwCgtX4IaOaGQ14PNLcXHSZv1RQ3HE6YoqzO7+0jNjkRPsZ4kRe+y55fAEB4sIzkfdmM5XtYtikLpVQIMJKafV+311q/A3Bo7qfoMhkB+rTKIl9QUGA4iKiuI+6M8HiRLwKwhNTGnTvCE+yV+xSHBstI3te98u1qV6f9mTgLfXUaLdKACUqp0MI/FpG7cJQbEwoTlMX5z0CKvO8K+bPGFlbn/TUp8rkA1gh337kjPMVe+Y0fEiSNd75ub3YRL41a5Vqi+AZgClD/BA5xNrAEaFqes5d94x6T1Sz9QWXjnRR53xUeXlVjD1fn/TUp8ocBbLK7kc9yjeSDA6XI+4OZK/byxCfLKXEukHOe1joDGAGk/MtbLMCpwM/ATCCpdN9mdn9yA46iXE9EFrXJYqtaLU2KvO86oshX6xa6mlyMzQUZyfsy1zX5kEC5Ju8vfludyfA35nH/wA50aBobBNxZ+acYSAcWAEFAPNADqAegHRUcXvYDB39+C11eYii9cCdbZELVYynyvstkkT8MYI2QkbyvqsivHMnLdL1f2bI7j1venE+3lvHccEFLOjWLA+eyt50q/1RxlBaRv+4XDv32CRU5ezwfVtSaoAatAcjJyanaclb4HhnJi2or358FQKBNdqjyR8vTD7I8/SATX+hDQnQI9uI8chePRtvL0KVFlGSmU7p7LdpebjqqqAWBCU0B2Ldvn+EkoibCwsJcDw2N5OWavM8q2+McuSmliI0I5FC+/Lbvj2Iqd6rL/O4BijOWGU4jPCUwPhWQIu/rajqSr3l3fbiM5H2VPS+PisrmuzaNYgynEbWhQVwoNqvz27w0c5PhNMKTbNHOmyukyPu2iIgI10OPF3kZyfuBsj3O3cWapcjX0R+d2dn5g77s0C4cJbLqWV1ii3A23kmR923mR/JyTd6nle7eDUBaonwd/VGX5vEAlO6VUXxdYw1x/uKemZlpOImoLqXUkdfkzdwnb/1zKkH4oLLdzuvy9ePDjvFK4YuaNHB+f5bu3Wg4ifA0S5DzF3cp8r4rNDT0yLXrDU3XS5H3aaWVRT4pOthwElEb4iKdX1cp8nWLJSQKKpe03bp1q+E0orqOGMVXANVawMIN0/VS5H2Zq8M+urIDW/iPhOhgAipvjyyRprs6JbTZKSilyMnJITs723QcUU1HNN3lA9Xa8tUNy9pKkfdlJdu2Ac575WV5W//Su7Lprjw3U5aprWNCGnUFYMuWLYaTiJpISKhatfBAdY8ht9DVcaU7dmIvKEQpRY82Ccd+g/AZXVu4mu42GE4iPC2oXgsA/vjjD8NJRE0kJSW5Hu6q7jFqUuQPAaiAALmNzpdpTdFG5/Xa7q0SDYcR7tQ0OQqAEumsr3MCYhoAUuR9Xf36VRtJ7q7uMWpS5IuBLIDA5AY1OIwwrWiDs8i3Tos2G0S4VUKUs89Cmu7qHmtoNCBF3tfVq1fP9dDISB4gAyAwObmGhxEmFW1wTuc2iAs1nES4S1xk4J8r3UmRr1NsMckoq3PF8s2bNxtOI2riiCJvZCQPsB0gKEWKvC8rrhzJh4cGULn9tPBxZ3RqgFKK8sP7sRceMh1HeFBExwsByMjIICcnx3AaURMykhduUbx1G47SUixK0bFprOk4wg26uZru5Na5OiesWQ8AVq5caTiJqCkZyQv3qKigOD0dgLO6yNfSHzRLcTbdSWd93ROY0ASQIu/rgoODiYmp2jjMWJGXkbyfKFy9FoBuLeMNJxHukBjtWulORvJ1ii0QS+Wa9atWrTKbRdTIEbfPFVJ5y3p1uGkkn1LDwwjT8hYuAiBZ1rD3eVFhgX+udCdNd3VKWKszUEqRn58vnfU+7i+3z1VrtTuoeZHfAWAND8MaHVXDQwmTCpYuQ9vtBNgstG0UbTqOqIEzOtZDKUVFfhb2/CzTcYQHRbQ+C4DVq1fjcDgMpxE14Y6FcKDmRb4EyAQZzfs6e34+hWvXAXDRKamG04ia6N7KuXKhTNXXPSGNuwGwdOlSw0lETbljIRyoeZGHyil7uS7v+/IXLQaga0tZ3taXNW/oWulOpurrlMBgrOFxAMydO9dwGFFT3lTkM0BG8v7AdV2+fmyI4SSiJpKinV+/0kwp8nVJdLcrUEqRlZXFpk0yi+PrGjVq5HpYo72C3TeST5GlbX1d4cpVOEpKsFotnNxG1rH3ReHBNgIDKle62yNFvi4Jb3cuAPPmzUPravdpCS/RrFkz18P1NTmOjORFFV1WRsHyFQD0PzXNcBpRHad3rO9suivMoSJvv+k4woNcO8/NmzfPcBJRUzExMcTHV93OXKPf1uWavDjK4bnOHxCdm8v98r7opNaupjtZBKcuCWrQBktAEHa7nYULF5qOI2qoadOmrofbgYKaHMuNI/lkZOFz35c7YyYA4SE2UpPknnlf06Kha6U7uSZbl8T0ugaANWvWkJubazaMqLHmzZu7Htb4t3V3FPkdQIklOJigNLn1yteV7dlL4dp1KKW49tzmx36D8Cr1KpsmZee5uiW0RS8Apk+fbjiJcAd3XY8H9xT5CmA1QGjbNm44nDDNNZrv1S7pGK8U3iQkyEpQgBWQ2+fqkqAGbbAGRwAwbdo0w2mEOxwxkveKIg/wO0Bo69ZuOpwwKWfazwBEhgWSmihT9r7i1PbOle7sRYepyN1rOo7wkNgzbwJg2bJl7N8vzZb+4Ihr8l5T5FeCjOT9RemOHVVT9tf3bWE6jjhOJ7ua7mR72ToltOlJAEydOtVwEuEO7uysB3eP5NtIkfcXh36aAkCv9vWO8UrhLVqlRQNQIp31dUZIo25YAkOx2+1yPd5PHHE9PgPnDnQ14q4iv05rXWGLiSbgz6X4hA/LmToN7XAQHhLASa3kdjpfUD82FJDO+rok5swbAVi0aBGHDh0ynEa4gzs768F9Rb5UKbUOZMreX5TvP0DeAuf9tsP7Sa+Ftwu0WQgOdDbdSWd9HWGxEdrIuSHNpEmTDIcR7uLO6/HgviIPruvybaQg+Ius78YA0DI1mrAgm+E04r/0ap/kbLoryac8p0b7WQgfEXPa9SirjdzcXJmq9yPu7KwH9xb5yuvyUuT9xeFf51CWmYlFKYZfIl9Xb9ajcq+B0r2bQNYtrxOiTx4IwIQJEygtLTWcRriD1WqlXbt2rg9XuuOY7i/yMl3vP+x2Do4dD8D5J8neBN6sdVoMIJ31dUVQctuqbWXHjBljOI1wl+bNmxMWFgaQhxeO5NdorXVgUhK2uDg3HlaYdHDceHRFBeEhAfRsJzvTeav6ca6V7qSzvi6IP+9elFIsXryYjIwM03GEm3Tu3Nn1cDHgcMcx3VnkC5RS6SBT9v6k/EAWuTNnATD8Yvm6eqNAm4XQyp6JEums93+2IELSOgEwevRos1mEWx1R5Be565juLPIgU/Z+ydWA1yw5ksToYMNpxF+d1DoBpRSO0kLKs3eYjiNqWcJ596AsVrKyspg5c6bpOMKNOnXq5Hrotq0E3V3kpcPeD+UvWkzJtm0opXjk6k6m44i/6NnWucdAaWa6NN3VAZFdLwXg66+/pry83GwY4TaxsbGkpaWhtdbAEncdV0by4rjs++gTAE5qlUB0eIDhNOJIbRpFA7IpTV0Qc/oNWAKCKSws5LvvvjMdR7iRa6peKbUBOOyu47q9yGutHUENGxKQJE1a/iR78k+U7tqNxaJ4+KpOpuOIIzSId24iJJ31/i/m1OsAGDduHHl5eWbDCLc64nq826bqwf1FPlcp9TtARI+T3XxoYVRFBfs++hiAUzvUIyxYFsfxBhYLVV8L6az3b5EnDcQaEkF5eTmfffaZ6TjCzY64Hu+2pjtwf5EHmAUQcUqPWji0MCl7wkTK9mZitSgevLKD6TgCOLlVorPprqyYsoPSdOfP4s66BYDvv/9etpT1MwEBAbRv3971oVeP5AFmAkT2PKUWDi1M0uXl7BvpvDZ/VpcGBAXUxj8fcSJOqVy7oHTfZnDYDacRtSWy++XYwmKoqKhg5MiRpuMIN2vVqhXBwcEAh4DN7jx2bfyUXqC1Lg2sV4+gxo1q4fDCpIPjvqds/wFsVouM5r1Au0axgGxK4+/iz7sXcI7id++WvQn8zV8WwXHrLTK1UeSLlVILQEbz/kiXlbH/k08BOO+khsRIp71RyQmu7WWlyPuruD53YA0Ko7i4mPfee890HFELunbt6nro1uvxUDtFHuS6vF/LGj2W0j17sVoUzw3rbjpOnWWxQHiI85esEums90+2QGJ6XgPAF198wYEDBwwHEu5mtVo55ZSqAbHbVzeqrSI/EyDi5JOcP4mEX9Glpex57XUAOjeLo3VqlOFEdVPn5vHOprvyUsoObDMdR9SCpEueRNkCOXTokFyL91MdO3YkKioKnNfjl7n7+LVVgVcAh21RUbIwjp/KmTqd/OUrUErx7I3dTMepk05t51zprmz/FnBUGE4j3M0SGk1Eh74A/N///R+FhYWGE4nacNppp7ke/gK4vXu2toq8HZgDMmXvz3a/8BLa4SA5PowLeshWtJ7WrrFze1lZ6c4/NRj8JspiZefOnbIRjR87/fTTXQ+n18bxa3MuXW6l83NF6zeQPWEiAPde0R6lDAeqY1ISwwFpuvNHoS1OJTi1EwAvv/yyrFHvp2JjY2nXrp3rw59r4xy1WeRnAYR37YIKCqrF0wiT9rz5FvbCQsJCArhvQPtjv0G4TWSos+lOirz/qXfFCyilmD17NrNmzTIdR9SSI6bqVwL7auMctVnkNwF7LUFBhHfpfMwXC99UkXWQfR86l7vtf1ojUhLCDCeqGzo2jUUpha4op+zAVtNxhBslXPw41pBIiouLef75503HEbXoiCJfK1P1ULtFXiO30tUJ+z//kpJt27BYFG/fIV9rTzi1fT0ASvdvQdtlKtdf2GKSiep2GQDvv/8+e/bsMZxI1BaLxcKpp57q+nBarZ2ntg5caRZApBR5v6bLytj+yONoh4MG8WHcdGFL05H8XvumzqY72XnOvzQYMgJlsbBlyxa++OIL03FELWrbti0xMTEAeThXuqsVtV3kZwKEtm+HLS6ulk8lTCpctZr9n38JwLXnt6BebLDhRP4tVZru/E7MadcTlNgEgKefflqa7fzcEV31M4Ba+2LXdpHfAyxTFgvR55xdy6cSpu19ewQl2zKwWhRv39HTdBy/FhkaCMjtc/7CFpNM3Dm3A/DNN9+wfPlyw4lEbfPE9Xio/SIP8D1AzHl9PHAqYZIuLWX7o85p+9SkcK49r7npSH6pTVoUFotC2yucC+EIn5dyw0iUxUpGRgavvfaa6TiilkVHR9OhQ9UGXz5f5H8A5xK31ihZ/tTfFa5cxYEvvgLgpotakRwfajiR/zm9Y30AyrK2oSvKDKcRNZVw4cMERNenoqKCBx98kJKSEtORRC0766yzsFqtAGuBWt1W0BNFfguwVgUEEHXWmR44nTBtz9sjKMnYjtWi+PC+U2WRHDdr38S5vWzJng2Gk4iaCmrQlqiTBwLOpWvXrl1rOJHwhAsuuMD1cExtn8tTu8c4p+z7yJR9XaBLSsh48GEc5eXERQXzys0nmY7kVxrViwCks97nWQNIvu59lLKwevVqPvzwQ9OJhAfExsYeueucfxX5yNN6YQmT6du6oGjNWna/9AoAvdonccmpaYYT+Y+oMGfTnXTW+7aUG0ZiDYmiqKiIhx56CLvd7XuTCC903nnnYbPZAJYDf9T2+TxV5NcDWyxBQUSdeYaHTilMy/rmWw5NmYpSigcGdSA1UVbDq6kWKZHOpjuHndJ9m03HEdUUd84dhKR2BOCxxx5j+/btZgMJj+nbt6/roUd2HfJUkdfAWIDYCy84xkuFP9nxxP8o2bYNq0XxwX2nYvXUvzg/dVpV010GulwatHxRSJOTiTn9BgC++OILpk2rtcXOhJdJTEyke/furg/HeuKcnvyROxog8vTTsEZEePC0wiRHYRFb77wHR3ExMRFBvHm7rH5YE52aOZvu5Hq8b7KERtNgyDsopVi+fLncLlfH9O3bF4vFAjAf2OWJc3qyyK8D1lkCA4k+Vxrw6pKSLX+w46lnAOjWMoHhF7cynMh3NUqqbLqTznqflHrLN1gCgsjKyuKee+6hoqLCdCThQUd01Xtkqh48W+QBvgOIvUim7OuaQz9OImv0WJRSXHtec/qenGI6kk+KjnBu21wiI3mfk3zdhwTEJFNRUcE999xDVlaW6UjCg1JSUujUqRNaawcw3lPn9XSRHw0Q0eNkbPHxHj61MG3ns8+TN38BSikeH9KZ9o1jTEfyKY3rR2C1KLTDIdP1Piah3xOENj0ZgOeff16Wra2DXA13SqnZwH5PndfTRX4bsERZrcT0Pd/DpxbGVVSw9c57KNqUjsWiePfunrKRzQk4o7Lprjx7O7qs2HAacbyiew6p2j525MiRfPfdd4YTCRMuvPBC10OPTdWD54s8yJR9neYoLOSPm26hbN8+AgOsfPHImQQHWk3H8gmupruSvTKK9xVhrc4g/vz7UEoxZcoU3njjDdORhAFNmjShdevWaK3LgQmePLeJIj9Wa20P79yJ4GbNDJxemFa+fz9/3Hwr9oICIsMC+eoxWTvheDSuHwnIIji+IjCpBfWver2qk/6RRx5Ba206ljCgX79+ACilfgEOefLcJop8plJqEkDCVYMMnF54g+JN6Wy98x50RQUpCeF8+tBpx35THRcTISvd+YqAuEY0vOVrlMVGRkYGt99+O2VlsplQXWSz2bjiiitcH37u6fObWprkA4C4S/thCZVlbuuq/AUL2fHk0wC0Tovh4wdONRvIiyUnhGKrXEmoNDPdcBrxX2wxyaTe/h0WWyD79+9n2LBh5Obmmo4lDDn77LNJSEgA2AdM8vT5TRX5WcAf1ogIuTZfx2V//wO7nn8RgHaNY/ngvl6GE3mn3p0aAFCWvRNHaYHhNOLf2KLqkXbHOCwBwWRlZTF06FB2767VnUSFl7vyyitdDz8Byj19flNF3gF8CJAw+CpDEYS3OPDVN1WFvmPTON6/p6fhRN6nS/M4QKbqvZk1IoG0O8djCQwhOzuboUOHkpGRYTqWMKhRo0b07NnTdW/8SBMZTK4k/oXWujS0TWvCOnYwGEN4gwNffcOuF14CoHPzeN69Swr9kRo3qFzpToq8V7KExZJ21w9YgsLIzc3l+uuvZ+vWraZjCcMGDhwIgFJqKrDTRAaTRT5bKTUGIGHwlcd6ragDDnz5NbtefBmAri3jefeuU47xjrojLtK5nkCJFHmvY4tLpfG9k7EGh5OXl8f1119Perr0TdR1gYGBXHbZZa4PPzSVw/SeYB8AxFzQF2t0lOEowhsc+OIrdlXuQ9+1ZQIjHzwNpQyHMqx+bMgRTXdyj7w3CUpuS6M7xmEJCiU3N5cbbriBDRtkXwHhXOEuJiYGnCP46aZymC7yS4BVlqAg4i7rbziK8BYHPv+yqtC3bRTDmKfOJtBm+p+qOWd2qlzp7tBuHMV5htMIl9DmvWh485coWyCZmZlcddVVrF271nQs4SWOaLj7GLCbymH6J6emcjSfcOUg6vyQTVQ58PmXbH/k8cr76MOY8HyfqvvE65rOLZz7PMimNN4jotNFNLhmBMpiZdu2bVx11VVs27bNdCzhJVq2bEmXLl3QWlcAn5nMYrrIA3yrtc4LbpRGxCmy17j4U/YPE/jjtjuq9qL//tlzaJESaTqWxzVrICvdeZOY04eRdNmzKGVh7dq1DB48mMzMTNOxhBcZNMi50JtS6kfA6D8ObyjyBUqpr0Aa8MTf5c2ZS/o111OenU1woI1PHzqd0zvUMx3Lo+KinE13UuTNq3fla8SdcxtKKRYuXMjQoUPJyckxHUt4kdDQUC655BLXh8Ya7ly8ochD5f+I6LN6E5CUZDqL8DJFa9awaeBVFG/ditVq4aWbuzPswpamY3lEfFQwAZX9CKWyMY05gcGk3jGeiLbnoJTi+++/5+abb6awsNB0MuFl+vXrR3h4OMAW4FfDcbymyK8H5iqbjfhBA0xnEV6obNdu0gddTf7iJSiluL5vCz64rxcBVv/u4zizk3PWojw3E3uRjBhNCExoSpMHfyEoqSkOh4OXX36Zxx57jPJyjy9eJrycxWLh+uuvd334fzgXfjPKW4o8uBrwBg5ABdbNBivx3+x5eWy58WYOjPoOpRQdm8Yx6aXzSEsKMx2t1nRrmQDIVL0pER0vJPWOMViDI8jPz+fWW2/l8889vseI8BHnnHMOjRo1AsjBuYytcd5U5H8AdgUkJhA/4HLTWYSX0uXl7HrmOTLufwh7UTFRYYF888RZ9OuZajparWiaXNl0J531Hlf/qjdJuvy5qg76AQMGMGfOHNOxhBe76aabXA/fB7xikwlvKvJlwMsA9W4ehgoIMBxHeLNDk39i0xWDKNm2DatF8fDgjrwwrJvpWG6XUNl0V7JHRvKeEhDXiMYPzyS8TW+UUvzyyy8MGDBA1qEX/6l79+506NABrXUp8K7pPC7eVOTBeT9hZmD9+sRddqnpLMLLlfzxBxsvH8ihqdNQStG7cwN+fL4PyfH+MX0fHR74Z9NdphR5T4juMZi0u8ZjC4+jtLSUp59+mjvvvJOCAq8YlAkvNmzYMACUUp8DB8ym+ZO3FfkS4BWAejffBDab4TjC2zkKi8i45352PvcCjvJyEmNCGPPUWQy/uJXpaDV2Zqf6KKWoyDuAvSDbdBz/Zgsi+cZPiL/gAZTFytatWxkwYADfffed6WTCB7Rs2ZIzzzzTtdvcG6bzHMnbijw4t+M7ENQwhbh+F5vOInxE1tejSL9qCCXbtmGxKIae34Jxz5xN/dgQ09GqzdV0J5vS1K7w9ufR9NE5hDbqilKK8ePHc/nll8smM+K4DR8+HACl1HjgD7NpjuaNRb4IeB2g3i03g9VqOI7wFUVr1rLhksvZ98lnaIeD5Pgwxj5zDjf0bWE6WrU0bygr3dUmS1A4KcM+p96Al7AEBpObm8t9993H448/TnFxsel4wkc0btyYvn37uj58wWSWf+KNRR6ct9NlBzdKI/aCvsd8sRAuurSUPa++TvqVV1OyLQOrRTHsolaMeeoskmKCTcc7IYnRzlkI6ax3v8hul9PkkVmEpHVCKcX06dO54IILmDJliulowsfcdNNNWCwWgEnAGsNx/sZbi3wB8CZAvduGg8VbYwpvVbhqNRsuuYx9n36OdjhomBjO98/24dGrO/nEP6fwUFvVznsle2TrUnexRdUj9bbRJPZ7HGULJCsrizvuuIO7776b7GzpexAnJjk5+cglbL1uFA/eW+QB3tNa54Y0bUrMeeeaziJ8kC4tZc8rr1Veq8/AYlFc3DOVGa9f4PX31Z/RsbLpriAbe36W6Ti+z2Ij6YoXaXT/FILqt6xamvbCCy9kxowZptMJH3XTTTdhczaI/wIsNRznH3lzkc9TSr0NUO/W4bINrai2wpWr2HDxpex+5XXsBQWEBNl45OpOjH/mbFo2jDId7x+d3CoRkOvx7hDdcwhNn5hPZMe+KGXhjz/+4Prrr+exxx7j8OHDpuMJH9WgQQMuv7xq4bbnTWb5L95c5AFGaK3zQ1u1JPqcs0xnET5Ml5ez/9PPWHfuBRwc9z3a4aBBfBifPXw6b9/Rg/AQ77pdU5ruai64URcaP/gzCX3vxxIQRG5uLs899xyXXHIJCxcuNB1P+Li7776bQOcS7LOBeYbj/CultTad4VieBx4vWr+Bjf2vMJ1F+InQdm1p+PijhHftAoDd4WD2yr28OmoNhaUVhtPB7LcuJDjQyt5v76Nwo/GNrHxKUEp7kvo/TWBCY+clj4oKRo8ezbvvvktubq7peMIPtGzZkh9//NHVcNcNWGE40r/yhSIfr7XerpQK23LzLeTNmWs6j/AjMRf2JeWhBwisXx8Au0Pz26pMXh61ioISM8U+NMjGjDf6opQi4/ULqDicaSSHrwlKbktS/2cITGyCqry8N2/ePF555RW2bNliOJ3wJyNHjuT0008HGA1cZTjOf/KFIg/OVfAeKly3nk2XDwTfyCx8hAoKIn7gFdS7eRiBSUmAs9jPW53JS9+uIr/Is8X+/JOS+d/QrtgLc9j2slymOpbA+q2od9kzBCY1ryruixYt4t1332XFCq8dYAkf1aNHD7788ku01uVKqdbAVtOZ/ouvFPkErfUfSqnI7Y88TvYPE0znEX5IBQYSd3l/6g+/mcAGzpG9w6FZsG4fb4xdy4GcEo/k+N+1nTn/5IYU/rGIvV/e5pFz+qLw9ucTd/ZtBMSmVBX3JUuW8O6777Js2TLD6YQ/cq2I2K5dO3BuQnOX4UjH5CtFHuAB4LXyA1msO68vjsIi03mEn1IBAcT1v4R6w28mqGEKAFprtu8r4JMpm/h1Ze1On496ojeN60dwaO5nZM/wms2svIPFRtzZtxHV/QqsIRFVn166dCnvvfceS5YsMRhO+LsLLriAt956C611vlKqKeD197f6UpEPBNYDzTI/GsneN94ynUf4O5uNuH4XkzTsBkKaNa36dGFxOdOW7uLDiZsoqoUmvVlvXkBIkI3M0Q9SsH6m24/vi2xxqSScdy9hLU5FWZ13QpSXlzNt2jS++uor1q5dazih8HcBAQFMnTqV1NRUgCfx4tvmjuRLRR6gHzDRUVbG+r4XUbZrt+k8oo4I796NhKuvIqbPOaiAAMA5lb9+ew4fTNzIqj/cs1pacKCFWW9e6Gy6e/MiKnL2uOW4PskWROwZNxLV5RKsEQlVU/I5OTmMHj2ab7/9lgMHvGZHT+HnhgwZwpNPPgmwD2gGFJpNdHx8rcgrnCsLnZPz8y9su/Mew3FEXWNLiCd+wBUkXDmQwHr1qj5fXFrB8vSDfDk9nQ07qr/AytldGvDcjd2wF+ex7cUz3BHZ54S160PsaTcQVK8F6og1iNetW8d3333H5MmTKS0tNZhQ1DVhYWHMnDmT2NhYgFuAjwxHOm6+VuQB2mmtVyulLOlDhlKwVBpshAFWK9FnnUnCVVcScUoP1BG7JRYWl7N0UxZfTNvMlj15J3TYx4d04sJTUinauoQ9X9zi7tTeyWIhouOFRHW9jKDkNlhsgVVPZWVlMWnSJH788Uc2b95sMKSoy+6++25uu+02gM1AW8D8YhrHyReLPMD7wG1FGzc5F8hxOEznEXWYLT6emPP6EHNBX8K7dqkafWqtKSiuYNUf2Uxfsos5qzOPeffn14+dSdPkSHLmf8nBn9+u/fCmBAYT02MwER36Ohetsfz5S1JZWRmzZs1iwoQJzJ8/H7vdbjCoqOsSExP5+eefCQ0NBbgc+MFwpBPiq0U+Xmu9RSkVvePJpzg4ZpzpPEIAEJCUSMx55zoLfpfORz3ncGj25RSzIj2LCfO2s2nn36f1Z75xAaHBNjLHPkLB2p89FdsjwlqdQUSniwhJ7YQ1PK7qGjtAYWEhv/32G7NmzeK3334jPz/fYFIh/vTGG29w0UUXASwCegE+VTR9tcgD3A28XZ6dzbo+fXEUFJjOI8RRAurXJ+a8PkSe2ovwbl2xOkcCVcor7GzfV8DKLQeZs2ofG7bn8Ovbzqa77W9fQnn2TkPJa04FhhLW6kzC2/QmOKU9toiEo66vAxw4cIDZs2cza9YsFi1aRHl5uaG0Qvyznj178vnnn6O1diilugO/m850ony5yAcAa4BW+z79nD2vvGY6jxD/SgUEENapI5G9ehLR8xTC2rc76jo+OKf3lVJorcld+A2Fm+dTvH2511+OsoRGE97mbEKbnkRQvZbYopJQtqCjRuoAJSUlLF++nMWLF7No0SLWr1+PD//8EX4uMDCQyZMn06hRI4AROAeWPseXizxAX2Cqo7ycDRf0o3THDtN5hDgu1ogIwk8+ichTehDWoT0hrVthCQz82+u01uiKMhzFeVTkHaAseyelmemU7FpFyd6NUFHmkbyWsFhCGnUlOLkNgQlNCIhNxhYehyUoHCzWvxV0cN7qtnbtWpYuXcqKFStYu3atjNaFz7jjjju48847ATKBVsCJddF6CV8v8gBTgb65s2az9dY7TGcRolpUQABtJv9IcJPGpKenU1paSosWLQgODv7P92ntAHsFjopSdFkx9uJ87MW56NIi5+fKy3CUl6ArStDlxTjKy1BWGxZbECogCGULRNmCsdgCUQHBWEOjsIREYgkKxxIYjLIG/msRP9L+/ftZv349GzduZP369WzYsIHMTNlYR/imtLQ0fvrpJ9dWsgMBn2388q5NtKvnPq11n+izz7JF9OpJ/gLZJ1r4Hl1eTkBiAgCffPIJkyZNwmq10qBBA9LS0khNTT3q74YNGxIYGIhSFrAFYrUFQnAEtsjEWsuYk5PDzp072blzJ7t27ap6vH37drKz3bMYkBDe4KmnnnIV+J+B8Ybj1Ig/FPlNSqn3gbvTnn2KDRf3x1Ek69oLH2OzYQkLA5yLvgDY7XZ27drFrl27/vZyi8VCVFQUsbGxxMXFERcXR0xMDHFxccTGxhISEkJgYCBBQUFVfwcEBBAYGIjdbqesrIzy8nLKysqqHpeWlpKbm8uhQ4fIyckhJyeHQ4cOcejQIbKzsymQ5lZRB1x44YX06tULrXWpUup2fKyb/q/8YboeIBJYC6Qe+HoUu557wXQeIU5I5Jmn0/zjDyksLKRbt244vLzZTgh/FBERwdSpU0lMTAT4H/Cc4Ug1Zjn2S3xCHjAMIPGaqwnv3s1wHCFOTNSZZwKwadMmKfBCGHLPPfe4Cvxm4FXDcdzCX4o8wAxgJECjl57HEhJiOI4Qxy+sQ3sA1q9fbziJEHVT+/btGTx4sOvDWwG/2CDBn4o8OPec3xWUmkry/feaziLEcQtKbQhIkRfCBKvVytNPP43FuWDTKGC24Uhu429F/s9p+2uHyLS98A0WC9aICAA2bNhgOIwQdc+wYcNo164dWuvDwP2m87iTvxV5cG5F+wlA2osybS+8X8QpPVBKUVJSwtatW03HEaJOadOmjWvRG5RSdwH7zSZyL38s8uCctt8dnJZKg3t9ciVCUYdE9T4TcDbdyY5rQnhOUFAQr776KgEBAQDfA18bjuR2/lrkDwM3QeW0fbeuhuMI8e/CO3UE5Hq8EJ5233330bx5c4B9wC34+D3x/8RfizzAdOAzZbGQ9uLzqGMsDyqEKUFpqYBcjxfCk3r06MF1113n+vAG4KC5NLXHn4s8OBso9gQ3SiNZpu2Fl7JGRgJ/rnQnhKhdERERvPzyy64PPwSmGYxTq/y9yOfimrYfeg1hXbuYTSPEX4Sf1B2lFGVlZdJ0J4SH/O9//6N+/foAf+Ds4fJb/l7kwfkb2ufKYqGRdNsLLxN1Vm8A0tPTZRtWITygb9++9OvXD621HRgCFJrOVJvqQpEHuA/YE9y4EanPPmU6ixBVwjt3AqTpTghPSExM5OmnnwZAKfUCsMRoIA+oK0U+F7haa+2Iu6Qf8YMGmM4jBADBjdIAKfJC1DalFC+99BLR0dEAy4HnzSbyjLpS5AF+U0o9BtDwiccIadPadB4hsEZFAdJZL0RtGzJkCKeeeipa62Kc0/R14vpYXSryAK8Bky1BQTR99+2qpUSFMCGscyeUxUJ5eTnp6emm4wjhtzp27MjDDz8MgFLqIaDOfMPVtSLvAIYC24MaNqTRKy+aziPqsOizzwJgy5Yt0nQnRC2JjY1lxIgRrlXtxgHvG47kUXWtyAPkAAO01mXR55xN0g3Xm84j6qiwLp0BuR4vRG2xWq289dZb1KtXD2ATcCN+uKrdf6mLRR5guVLqHoDkB+6VZW+FEcFNGgNyPV6I2nLPPffQo0cPtNYFwGVAvulMnlZXizw4Vzn6VtlsNH7rDWxxcabziDrG5uzylZXuhKgF55xzDjfffDMASqnrgY1mE5lRl4u8BoYDGwOTEmn85mtgqcv/O4QnhbZri7JYqKiokKY7IdysUaNGvPLKK64P3wDGG4xjVF2vagXAFVrroshTelD/jttM5xF1RNQ5ZwOwdetWSktLDacRwn+EhITw7rvvEh4eDjAXeMRwJKPqepEH2KCUuhmgwR23EXn6aabziDogorIPRJruhHCv5557jhYtWgBkAoOACrOJzJIi7zQK5zV6Gr/2CgHOjQuEqDWupjsp8kK4zzXXXMPFF1+M1roCGIBzn/g6TYr8n+4FVthiomn2wXtYwkJN5xF+zBYTA0hnvRDu0qVLlyMXvLkfWGA2kXeQIv+nEpzX57NC27SmyTtvgdVqOpPwQ8EtW6KsVhwOBxs31smGXyHcqmHDhrz//vuuBW++A941HMlrSJE/2nal1MVa6+Ko008j9eknTecRfii6j7Ppbtu2bRQXFxtOI4Rvi46OZuTIkcTGxgL8DtxEHVvw5r9Ikf+7JUqpq7TWOmHQQOoNv8l0HuFnpOlOCPcIDAzk//7v/2jcuDHADuBC/Hx/+BMlRf6fTVRK3Q2QfP+9xFx0oek8wo8EN2sKSJEXoiaUUrz66qt07doVrfVh4AKk0e5vpMj/u3eBNwEavfwC4d27GY4j/EVArHN1RSnyQlTfAw88QN++fdFalyul+gPSxfoPpMj/tweB7y2BgTT9v3cJbtrEdB7h44IaN0bZnA2d0nQnRPUMHjyYYcOGAaCUugH41Wwi7yVF/r85gGuARbaoKJqN/AhbfLzpTMKHxZzbB4CMjAwKC+XSoRAnqnfv3jzxxBOuD58AvjEYx+tJkT+2YqAf8EdQSjLNPv4AS6jcQy+qJ/wk52UfmaoX4sS1a9eON998E6vz9uZPgBcNR/J6UuSPz0Ggr9b6YFi7tjR+63W5h15US0jzZoAUeSFOVHJyMh9++CGhzkHWz8BtyK1yxyRF/vj9oZTqp7Uuie59Jg2feMx0HuGDAiov98hKd0Icv6ioKD7++GMSEhIA1gADgXKzqXyDFPkTs0gpdbXWWidefRVJN91oOo/wIYENG6JsNkBG8kIcr4iICD777DOaNWsGsAfnvfB5ZlP5DinyJ+4HpdR9ACkP3k/CNUNM5xE+wtV0t3PnTvLz8w2nEcL7hYWF8cknn9CuXTu01lnAucBu07l8iRT56nmbyoaP1CcfI+Gaq82mET4h/OTugIzihTgeoaGhfPzxx3Tq1Amt9SGl1DnIvfAnTIp89T0BvAyQ+uTjJAwZbDiO8HYhLZoDUuSFOJbg4GA++OADunXrhtb6sFKqD85r8eIESZGvPg08BrwCkPq/J6TQi/8U4GwakiIvxH8IDAzkvffeo0ePHmit85VS5+HceEZUgxT5mtHAo8CrUFnor77KbCLhlWz1krA4t8GUznoh/kVAQAAjRozgtNNOQ2tdqJTqCywxncuXSZGvOQ08ArwGkPrUkyQMvtJsIuF1Ys47F4Ddu3eTm5trNowQXshms/HWW2/Ru3dvtNbFSqmLgAWmc/k6KfLuoYGHgdcBUp/+H/FXDTKbSHiVyB4nAzKKF+KfWK1WXnvtNfr06YPWulQp1Q+YYzqXP5Ai7z4aeAh4AyDtmaeIv1IKvXAKadkSkOvxQvyVxWLhpZde4oILLkBrXVa5o9xM07n8hRR599I4d657EyDt2aeIHzTQbCLhFQISnU13MpIX4k8BAQG89tprXHLJJWitK5RSA4BppnP5E5vpAH5IAw9UPr4v7bmnAc3BMePMJRJG2RLiUZVNdzKSF8IpJCSEd99919VkV66UugqYZDqXv5EiXztchV4B96Y99wygODhmrNlUwojoPn1QSrFv3z6ys7NNxxHCuOjoaD766CPXQjeFSqnLgF9M5/JHMl1fezRwP87V8Uh77mnqDb/JaCBhRuQpPQAZxQsBkJSUxKhRo45cye5spMDXGinytUsD91G5YE7y/ffS8MnHwSL/2+uS0NbSdCcEQOPGjfnuu++qNptRSp2G3Adfq6Ta1D7XffT3AiReczVN3noDFRhoNpXwmICkJECKvKjb2rZty7fffktycjLAZqAXshZ9rZMi7zlvA1dprctj+p5H808/xhoRYTqTqGXWmOiqX+iks17UVT169ODrr78mNjYWYAVwKrDDbKq6QYq8Z41WSp2vtc6POPkkWoz6qurWKuGfos85B6UUBw4c4MCBA6bjCOFxffr0YeTIkYSFhQHMBnoDWWZT1R1S5D1vtlLqdGBfaKuWtBo7muDK3cmE/4nsdQogo3hRN11xxRW88847BDpns34ALgDyzaaqW6TIm7EK6AmkBzaoT6vRo4g8tZfhSKI2hLZuDcj1eFG3KKW47777eOGFF7BarQAjgYFAqdlkdY8UeXMycBb636zh4TT7+ANZHc8PBdavB0iRF3VHaGgo7777LsOHD3d96nlgOGA3l6rukiJv1iHgXOArZbOR9tzTJD/0AChlOpdwA0t4OCooCJAiL+qGevXqMWrUKNdGM2XANcCTOO8yEgZIkTevDLgO+B9AvWE30GTEW6jgYKOhRM1F9zkbpRSHDh1i3759puMIUavat2/PuHHjaNOmDVrrA0qpM4FvDMeq86TIewcNPAcM0VqXxZx3Li2//ZrAlGTTuUQNRPZy9lnIKF74u379+vHNN9+QmJgIsFYpdRKwyHAsgRR5bzNKKXWO1jo7rF1bWk8YT1TvM01nEtUU2rYNIEVe+C+r1crDDz/Ma6+9RrBz9nEyzkVu5B54LyFF3vvMU0p1BpbYoqJo9tH/0eD+e8HZoSp8SGCD+oAUeeGfoqOj+eSTT7jhhhtcn3oeuBS5Rc6rSJH3TruA04F3AeoPv4nmn43EFhdnNpU4bpbQUCyVfRVyj7zwNy1btmT8+PH07NkTrXUhcAXOBjuH4WjiL6TIe68y4C7gSq11QeQpPWj94/eEd+tqOpc4DlFnnYlSitzcXHbv3m06jhBuc8kllzB69GgaNmwIsE0pdQrwveFY4l9Ikfd+Y5RS3YENgUmJtPjqc5JuuN50JnEMkaedCsgoXviPsLAwXn31VV599VVCQ0MBZgLdgbVmk4n/IkXeN2wCTga+VTYbKY88SJP3R2AJDzedS/yLsHZtAbkeL/xDmzZt+OGHH7jkkkvQWjtwTs2fj3OtD+HFpMj7jgJgCHCb1rosps85tJ4wnpDWrUznEv8g0LmdphR54fOuvfZaxo4dS6NGjQB2KaXOwNlkJyvY+QAp8r5FAx8opU4FdgSnpdJqzLfEXX6Z6VziSMHBWEJCACnywnfFxMTwwQcf8PjjjxMQEADwI9AJmG8ylzgxUuR90zKgKzDNEhxMo5eeJ+2F57A4r5MJw6LPOB2lFPn5+ezatct0HCFO2EknncTEiRM566yz0FqXAncAlyHT8z5HirzvygYuAp7QWjviB1xOm8kTCD+pu+lcdV7U6acBzqY7rWXJbuE7rFYrd955J19++SVJSUkA6Uqpk4H3kfXnfZIUed/mAF5QSp0D7Axq2JCW33xJwycfr5ouFp4X2r4dIFP1wrfUq1ePL7/8kjvuuAOLxQLwGc4Zw9Vmk4makCLvH34F2gMfAyRec7VzVN+9m9lUdVRQijTdCd9yxRVXMHnyZLp3747WOh+4GrgRKDQcTdSQFHn/kYdzz+ZzgV1Bqam0HPUVDZ94TEb1nhQQgCUsDJB75IX3S0lJ4fPPP+eFF14gMjISYKlSqgvwreFowk2kyPufGUA7YCRA4rVDaD1pgqyU5yFRp52KUorCwkIyMjJMxxHiH1ksFq699lomT57sWpq2BHgA6An8YTiecCMp8v4pD7gZ52IVu4PTUmnxzZekPP6ojOprWdSZpwOwceNGaboTXqlJkyaMGjWKxx9/3LVy3W9KqfbAG8i9735Hirx/+xnnqP4TZbGQNPQaGdXXstAO7QG5Hi+8j81m45ZbbmHixIl06dLFde39VuD/27v/4KjrO4/jz8/mJ2ziboJAyyUQNIBJIJqlAVIsICx09AZNeiitEAPj3YjooJmxcHg62rvzpl47N3fXsaWnXK+MVSogIJSEWrC9K+hRQIbwI1DkIIIoZBNCILCbsN/745sNgYIlkPDd7L4eM9/Z5fv9JvvOTCYvPt/Pr8mo9R6zFPKxrwn4G65s1S9ehGnfJU26T4q9aYdCXqJKXl4eK1asoLKykuTkZIAqY0wBsATtHBfTFPLxI9KqX2pcLgbOrSD/vdXcNnGC03XFjoQEEtr3E1DISzRITk6msrKSlStXkp+fj2VZDUA58JfYW1pLjFPIx5cm4K+BB4DjqTlDGPb6EnJfX0LKHUMdLq33Sx9fgjGG8+fPa9CdOO6+++5j3bp1zJs3j8TERIAVxph84E20sE3cUMjHpyogH/iBZVmtnokTKFi3hqzFi0hIT3e6tl7LO2kSYA+6u3hR45fEGbm5uSxdupQlS5ZENpX5HHtJ2keAL5ysTW49hXz8OgMsbO+XW2eSkhg4t4KC96u4feYj4NKvRle57y4END9enOH1ennxxRdZu3Yt9957L5ZlhYDvA8OB1Q6XJw7RX3L5I/Ag8E1gX1JmJkP+4WXyVq/UOvhdlDJkMKD+eLm1EhMTmT17Nhs3bmT27NmRR/Or2x/NLwaana1QnGQ0l1c6SQLmWZb198YYL0Bj9UaO/fMPCR077mxl0c7lwre/BmMMDz74IAcOHHC6IokD48ePZ/HixQwbNixyqgZ4FtjsWFESVRTycjX9gO9ZlvWkMcYVDgb5YunP+Pw/3iDc0uJ0bVEpvWQcw3/+nwSDQXw+H21tbU6XJDEsJyeHRYsWMXny5MipAPAC8AagXz7poMf1cjUB4GljzD3AZldKCl+dP4+Cjb8i86HpYIzD5UUfz32TADhw4IACXnpMRkYGCxcuZP369ZG93tuAfwWGYc951y+fXEYhL1+mBvADZcDh5IEDGfqDV8lbvRLv1CkK+07cRfcA6o+XnuH1eqmsrGTTpk08/vjjJCUlAWxoX462Emh0tkKJVolOFyBRzwLWYE+7e9ayrBf65uel3fnaj2ipreXEa0s4/ev3Ic67fVI16E56gMfjYe7cuZSXl5PWvtASsBP4O6Daucqkt1CfvHRVP+ywX2CMuQ3g/B8PceLHP6GxaiOE43OFTF/tHozLRVlZmabQyU1LT09nzpw5VFRUkH5p7YpdwMvAe2gxG7lOCnm5URnAM5ZlPWuM8QBcOHyYEz/+KQ2/2gBxtBiMe7SPu95+k1AohM/no7W11emSpJdKS0ujoqKCOXPmRPZ3B9iNHe5r0Trz0kXqk5cb1Qi8bIwZArxoWVZj6h13MPSHr1JQtZ5+ZaWQGB+9Qd4p9gjngwcPKuDlhrjdbubPn8/mzZtZsGBBJOD3AA8DRdiL2SjgpcsU8nKzmoB/NMbkYC+8EUjNGULOq//EyOr19JvxLYw9SChmpfmKAPXHS9dlZmby1FNPsXnzZp555hk8Hg/APmAmcDewEoW73AQ9rpfuloa9R/V3gf4AwWPH+fynrxN4dzVWDLZ0Cz/aQlJmBi+99BLLly93uhzpBUaMGMFjjz3G9OnTSUlJiZyuBb4HrADip79LepRCXnpKX+AJYCHwFYDWQID6FauoX/4Ooc8+c7S47hQZdDdjxgxqamqcLkeilDGGSZMmUVFRQUlJSedL24F/Ad5B4S7dTCEvPa0P9va2C4EsACscpumD33LqreWc+f2WXj39rm9hIXkrl9Pa2orP5yMUCjldkkQZt9tNWVkZ5eXlkV3hsCwrbIxZhb2QzYdotLz0kPgYGSVOOg/8CPgJMB2Yb1wuv3fKZLxTJhOsq+PUW7+k/t13uXi6ydlKb4DXbw+6O3TokAJeLpOVlcWsWbN4+OGHO6bBWZZ12hjzujHmNeCosxVKPFBLXpwwApgHzAU8AOFgkMYNVZz8xXJadu92tLiuGP6LZaQXf41Vq1bx/PPPO12OOMwYw5gxY5g1axZ+v5+EhITIpYPAvwHLgLOOFShxRy15ccIB7KU4XwC+DTzlSkkp6ldWSr+yUs7t2cupt96mYf0GrAsXnK30z0i9YygAe/bscbgScVJ2djalpaWUlpaSlZXV+dL72I/kq9EoeXGAWvISDQwwBphvWdZMY0wKQFtTE4F313Dql+8QPPx/zlZ4Db79ezAJLmbOnMmuXbucLkduIbfbzf33309paSnFxcWdL50BlgP/DmhepThKIS/R5nbsx/hPAkMjJ1v27qOhqprGqmpCnx5zrLjOUvNGULB2NRcvXsTn83Ehyp86yM1zuVyMHTuWsrIypk2bRp8+fQCwLMsyxrwP/Bf2Xg/nnatS5BKFvEQrF/BN7Nb9/caYjs7Nc7t307hhIw1V1bSeOOFYgV9d8DSDnp7PwYMHmT59umN1SM/LycmhtLSUhx56iEGDBnW+VAv8HHgTiI7/fYp0oj55iVZh7J3vqowxtwPfAmZaljXJXVjochcWkvW33+Xszo9prKqmsXojrV+cvKUFpn1tNKCV7mLVkCFD8Pv9TJ06laKios6XTgNvY4f7NjT9TaKYWvLS2wwE/go78L9hjL2pvRUOc3bHzvbA/zVt9fU9Xkjhlt+R1L8/r7zyCsuWLevxz5OeZYxh5MiR+P1+/H4/ubm5Hdfa57VXYwf7e4D6ZqRXUMhLbzYImIG9zvfXIyetixdp3vYHGjdUc/o3m2gLBHrkw337azAJCTz66KPs2LGjRz5DelZSUhJjx47F7/czZcoUBgwY0HHNsqw2Y8wH2H3sqwHn+oZEbpBCXmJFNvaOXTOxR+p3aKmt5cyWD2ne+iFnt+8gfP7mx0Sl5t5JwYZ1hMNhRo8eTUtLy01/T7k10tLSmDBhAn6/n4kTJ5KWltb58llgA3awV2E/mhfptRTyEotygEfaj9GdL4RDIc7t/JgzWz/izNattOzZC+GuT1/+ypPz+IvKBXzyySc88MAD3VK09IyEhARGjRpFSUkJJSUlFBUVkZyc3PmWz7H3al8DfAAEHShTpEco5CXWDQAmA35gKjC488W2piaa/3cbZ7ZspXnrRwSPXt9Ko8N+9ga3jf8669at47nnnuv2ouXGGWMYPnw448aNY9y4cYwZM+bK1jrYo+LXtB9/QAvVSIzS6HqJdSexFyZZjr3oTi6XAn9yosfjyZg2lYxpUwEIHv+M5q1bObP1Q5o/2nbN/vzU3DsBrXQXDRITE8nLy6O4uJji4mJ8Ph9er/fK2xqxW+mbgN9gLzMrEvPUkpd4lgj4sAPfb1nWeGNMUucbgsc/o2V3Dedqaji3u4aWvXsJn2vBt283JjGR8vJytm3b5kjx8SorK4v8/Hzy8/MZNWoURUVFuN3uK287B2zBDvVNwC60javEIYW8yCVu4Bu0t/QtyxoVmaIXYYXDXDhylD7ta9Y/8cQTbN++nbNntedId3O5XAwdOpSCggLy8vIoKCjgrrvuwuPxXO32RuB/2o//Bj4GWm9huSJRSSEvcm23YQ/cK8YesV/MFX36EXV1dezfv5/a2lpqa2s5cuQIx44d0/az18ntdpOTk0NeXl5HK33EiBH07dv3areHgBpgJ3aY/x57jXj1q4tcQSEv0jUDgVLgO9iD+tKwp+/9iXA4zIkTJ/j00085evQodXV1Ha91dXWc74apfL1J//79GTx4MNnZ2QwePPiy95mZmdf6snPYj9ojgb4T2Ida6SLXRSEvcvP6AXcD97QfI7EH+KV/2RedPHmyI/iPHz9OfX09gUCAhoYGGhoaCAQCNDc393TtNy0hIQGPx0NmZuZlRyTAs7Ozyc7OvlarvLNTwG7sII+E+iHUly5ywxTyIj3DYO+ol3vFcWf7a7/r+SahUIjGxkYCgQCBQOCy96dPnyYUChEMBq/62vmInGttbSUpKYnk5OSO1z/3PjU1Fa/XS0ZGBpmZmR2vkfcejweXy3U9P04YqAM+ucpxGHuLVhHpRgp5EWdkcCnwc7EX8OnffgxoP770SUAUagDqsVvkp4AjXB7kR7D700XkFlHIi0SvVC6F/pWvA4DM9ntSvuQ18j6BP2Vhr+4WxA7fK993PhfgUnh3DvLIvwNAW/f96CLSHRTyIvEhETvwk7AHrQWx+7r1B0AkhinkRUREYtR1jZYRERGR3kchLyIiEqMU8iIiIjFKIS8iIhKjFPIiIiIxSiEvIiISoxTyIiIiMUohLyIiEqMU8iIiIjFKIS8iIhKjFPIiIiIxSiEvIiISo/4fYmxxL9kOyzAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The cluster hierarchy can be visualised as pie diagram\n",
    "fig, ax, *plotted = robj.pie()\n",
    "ax.set_xlim(-0.3, 0.3)\n",
    "ax.set_ylim(-0.3, 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have isolated one cluster in the first step and two others in a further splitting on a lower hierarchy level, the last step that remains, is to put everything back together. This can be done automatically by calling `reel` on the parent cluster object into which the child cluster results should be integrated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.358729Z",
     "start_time": "2020-06-11T17:56:40.352159Z"
    }
   },
   "outputs": [],
   "source": [
    "# Wrap up the hierarchical clustering and integrate the child clusters into\n",
    "# the parent cluster\n",
    "robj.reel()\n",
    "\n",
    "# Manually sort clusters by size (largest = 1)\n",
    "robj.labels.sort_by_size()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.566015Z",
     "start_time": "2020-06-11T17:56:40.360784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.3, 0.3)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "robj._children = None  # Remove child clusters \n",
    "fig, ax, *plotted = robj.pie()\n",
    "ax.set_xlim(-0.3, 0.3)\n",
    "ax.set_ylim(-0.3, 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.656154Z",
     "start_time": "2020-06-11T17:56:40.567696Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "_ = robj.evaluate(ax=ax, ax_props=ax_props)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.665352Z",
     "start_time": "2020-06-11T17:56:40.658563Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelInfo(origin='reeled', reference=CNN clustering object (root), params={2: (0.6, 17), 3: (0.18, 6), 1: (0.18, 6)})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now the paramater information becomes useful as they differ\n",
    "robj.labels.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:40.679191Z",
     "start_time": "2020-06-11T17:56:40.667648Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label\tr\tn\n",
      "--------------------\n",
      "1\t0.18\t6\n",
      "2\t0.6\t17\n",
      "3\t0.18\t6\n"
     ]
    }
   ],
   "source": [
    "print(\"Label\", \"r\", \"n\", sep=\"\\t\")\n",
    "print(\"-\" * 20)\n",
    "for k, v in sorted(robj.labels.info.params.items()):\n",
    "    print(k, *v, sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cluster result is satisfying. But we have clustered only a reduced set of 1000 points. We would like to predict the cluster label assignment for the full 100,000 points on the basis of the reduced set assignments. This we can do with the `predict` method of a cluster object. We call `predict` with another cluster object for which labels should be predicted as an argument. Similar to a regular `fit` we need to compute neighbourhoods for the points that need assignment, but this time we need relative neighbourhood between two data sets. We want to compute the neighbouring points in the small set `robj` for the points in the big set `cobj`. To allow the fast computation of theses neighbourhoods we could calculate a relative pairwise distance matrix for distances from points in `cobj` to points in `robj` (a so called map matrix). When we predict labels afterwards we should do the prediction separately for the clusters because the assignment parameters differ. This is where the label info comes in handy showing us the parameters used for the fit as an orientation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:53.938371Z",
     "start_time": "2020-06-11T17:56:40.681152Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance matrix calculated in 0.29 seconds\n",
      "\n",
      "Prediction in 2.74 seconds\n",
      "\n",
      "Prediction in 4.73 seconds\n",
      "\n",
      "Prediction in 2.46 seconds\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In the end the cluster assignment for the reduced (train) set can\n",
    "# be mapped onto another test set.\n",
    "# The assignment prediction can (and should) be done\n",
    "# for each cluster individually. \n",
    "go = time.time()\n",
    "cobj.calc_dist(robj)  # Distance map calculation\n",
    "print(f\"Distance matrix calculated in {time.time()-go:.2f} seconds\\n\")\n",
    "time.sleep(1)\n",
    "\n",
    "fig, Ax = plt.subplots(1, 3)\n",
    "\n",
    "go = time.time()\n",
    "r = 0.15; n = 10\n",
    "robj.predict(cobj, r, n, clusters=[1], purge=True, policy=\"progressive\")\n",
    "print(f\"Prediction in {time.time()-go:.2f} seconds\\n\")\n",
    "_ = cobj.evaluate(ax=Ax[0], ax_props=ax_props)\n",
    "Ax[2].set_title(f'1 {(r, n)}', fontsize=10, pad=4)\n",
    "\n",
    "go = time.time()\n",
    "r = 0.5; n = 17\n",
    "robj.predict(cobj, r, n, clusters=[2], policy=\"progressive\")\n",
    "print(f\"Prediction in {time.time()-go:.2f} seconds\\n\")\n",
    "time.sleep(1)\n",
    "_ = cobj.evaluate(ax=Ax[1], ax_props=ax_props)\n",
    "Ax[0].set_title(f'2 {(r, n)}', fontsize=10, pad=4)\n",
    "\n",
    "go = time.time()\n",
    "r = 0.3; n = 5\n",
    "robj.predict(cobj, r, n, clusters=[3], policy=\"progressive\")\n",
    "print(f\"Prediction in {time.time()-go:.2f} seconds\\n\")\n",
    "_ = cobj.evaluate(ax=Ax[2], ax_props=ax_props)\n",
    "Ax[1].set_title(f'3 {(r, n)}', fontsize=10, pad=4)\n",
    "\n",
    "\n",
    "fig.subplots_adjust(\n",
    "    left=0, right=1, bottom=0, top=1, wspace=0.1, hspace=0.3 \n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T17:56:53.951991Z",
     "start_time": "2020-06-11T17:56:53.940555Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label\tr\tn\n",
      "--------------------\n",
      "1\t0.15\t10\n",
      "2\t0.5\t17\n",
      "3\t0.3\t5\n"
     ]
    }
   ],
   "source": [
    "print(\"Label\", \"r\", \"n\", sep=\"\\t\")\n",
    "print(\"-\" * 20)\n",
    "for k, v in sorted(cobj.labels.info.params.items()):\n",
    "    print(k, *v, sep=\"\\t\")"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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