{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Magic\n",
    "%matplotlib inline\n",
    "# Reload modules whenever they change\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# Make clusterking package available even without installation\n",
    "import sys\n",
    "sys.path = [\"../../\"] + sys.path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import os.path\n",
    "\n",
    "from clusterking.plots import ClusterPlot\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [ For testing purposes we make sure that we always have a file\n",
    "#   that exists to fall back to ]\n",
    "paths = [\n",
    "    \"output/cluster/long_g10_n30_d05_data.csv\",\n",
    "    \"output/cluster/tutorial_basics_data.csv\"\n",
    "    \n",
    "]\n",
    "path = None\n",
    "for p in paths:\n",
    "    if os.path.exists(p):\n",
    "        path = p\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(path)\n",
    "\n",
    "cp = ClusterPlot(df)\n",
    "cp.debug = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BetterClusterPlot(ClusterPlot):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        \n",
    "    def voxel(self):\n",
    "        self._setup_all(cols, clusters)\n",
    "\n",
    "        for isubplot in range(self._nsubplots):\n",
    "            for cluster in self._clusters:\n",
    "                df_cluster = self.df[self.df[self.cluster_column] == cluster]\n",
    "                for col in self._dofs:\n",
    "                    df_cluster = df_cluster[df_cluster[col] ==\n",
    "                                            self._df_dofs.iloc[isubplot][col]]\n",
    "                self._axli[isubplot].scatter(\n",
    "                    *[df_cluster[col] for col in self._axis_columns],\n",
    "                    color=self.colors[cluster-1 % len(self.colors)],\n",
    "                    marker=self.markers[cluster-1 % len(self.markers)],\n",
    "                    label=cluster\n",
    "                )\n",
    "        if 'inline' not in matplotlib.get_backend():\n",
    "            return self._fig\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bcp = BetterClusterPlot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp.scatter([\"l\", \"sl\", 't'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y, z = np.indices((2, 2, 2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x, y, z = np.indices((8, 8, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# prepare some coordinates\n",
    "x, y, z = np.indices((8, 8, 8))\n",
    "\n",
    "# draw cuboids in the top left and bottom right corners, and a link between them\n",
    "cube1 = (x < 3) & (y < 3) & (z < 3)\n",
    "cube2 = (x >= 5) & (y >= 5) & (z >= 5)\n",
    "link = abs(x - y) + abs(y - z) + abs(z - x) <= 2\n",
    "\n",
    "# combine the objects into a single boolean array\n",
    "voxels = cube1 | cube2 | link\n",
    "\n",
    "# set the colors of each object\n",
    "colors = np.empty(voxels.shape, dtype=object)\n",
    "colors[link] = 'red'\n",
    "colors[cube1] = 'blue'\n",
    "colors[cube2] = 'green'\n",
    "\n",
    "# and plot everything\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "_ = ax.voxels(voxels, facecolors=colors, edgecolor='k')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = df['cluster'] == 4\n",
    "mask2 = df['cluster'] == 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_values = df['l'].unique()\n",
    "y_values = df['sl'].unique()\n",
    "z_values = df['t'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "yes = mask.values.reshape(len(x_values), len(y_values), len(z_values))\n",
    "yes2 = mask2.values.reshape(len(x_values), len(y_values), len(z_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = np.empty(yes.shape, dtype=object)\n",
    "colors[yes] = \"#FF000050\"\n",
    "colors[yes2] = \"#00FF0050\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "ax.view_init(30, 20)\n",
    "_ = ax.voxels(yes|yes2, facecolors=colors, edgecolor='')"
   ]
  }
 ],
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