Box_Plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd\n",
    "from itertools import permutations, product\n",
    "from scipy.stats import norm\n",
    "\n",
    "from scipy.integrate import odeint\n",
    "from scipy import signal "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initial Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'Dataset'\n",
    "Monte = np.load(path + '/Monte.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Entropies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lorenz_entropies = pd.read_csv( path + '/lorenz_entropies.csv')\n",
    "ARMA_entropies = pd.read_csv( path + '/ARMA_entropies.csv')\n",
    "WhiteNoise_entropies = pd.read_csv( path + '/WhiteNoise_entropies.csv')\n",
    "RandomWalk_entropies = pd.read_csv( path + '/RandomWalk_entropies.csv')\n",
    "Chirp_entropies = pd.read_csv( path + '/Chirp_entropies.csv')\n",
    "SineWave_entropies = pd.read_csv( path + '/SineWave_entropies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "PeEn = pd.DataFrame()\n",
    "PeEn['Lorenz'] = lorenz_entropies['PeEn']\n",
    "PeEn['ARMA'] = ARMA_entropies['PeEn']\n",
    "PeEn['WhiteNoise'] = WhiteNoise_entropies['PeEn']\n",
    "PeEn['RandomWalk'] = RandomWalk_entropies['PeEn']\n",
    "PeEn['Chirp'] = Chirp_entropies['PeEn']\n",
    "PeEn['SineWave'] = SineWave_entropies['PeEn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ApEn = pd.DataFrame()\n",
    "ApEn['Lorenz'] = lorenz_entropies['ApEn']\n",
    "ApEn['ARMA'] = ARMA_entropies['ApEn']\n",
    "ApEn['WhiteNoise'] = WhiteNoise_entropies['ApEn']\n",
    "ApEn['RandomWalk'] = RandomWalk_entropies['ApEn']\n",
    "ApEn['Chirp'] = Chirp_entropies['ApEn']\n",
    "ApEn['SineWave'] = SineWave_entropies['ApEn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "SampEn = pd.DataFrame()\n",
    "SampEn['Lorenz'] = lorenz_entropies['SampEn']\n",
    "SampEn['ARMA'] = ARMA_entropies['SampEn']\n",
    "SampEn['WhiteNoise'] = WhiteNoise_entropies['SampEn']\n",
    "SampEn['RandomWalk'] = RandomWalk_entropies['SampEn']\n",
    "SampEn['Chirp'] = Chirp_entropies['SampEn']\n",
    "SampEn['SineWave'] = SineWave_entropies['SampEn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "FuzzyEn = pd.DataFrame()\n",
    "FuzzyEn['Lorenz'] = lorenz_entropies['FuzzyEn']\n",
    "FuzzyEn['ARMA'] = ARMA_entropies['FuzzyEn']\n",
    "FuzzyEn['WhiteNoise'] = WhiteNoise_entropies['FuzzyEn']\n",
    "FuzzyEn['RandomWalk'] = RandomWalk_entropies['FuzzyEn']\n",
    "FuzzyEn['Chirp'] = Chirp_entropies['FuzzyEn']\n",
    "FuzzyEn['SineWave'] = SineWave_entropies['FuzzyEn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "DistEn = pd.DataFrame()\n",
    "DistEn['Lorenz'] = lorenz_entropies['DistEn']\n",
    "DistEn['ARMA'] = ARMA_entropies['DistEn']\n",
    "DistEn['WhiteNoise'] = WhiteNoise_entropies['DistEn']\n",
    "DistEn['RandomWalk'] = RandomWalk_entropies['DistEn']\n",
    "DistEn['Chirp'] = Chirp_entropies['DistEn']\n",
    "DistEn['SineWave'] = SineWave_entropies['DistEn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "DispEn = pd.DataFrame()\n",
    "DispEn['Lorenz'] = lorenz_entropies['DispEn']\n",
    "DispEn['ARMA'] = ARMA_entropies['DispEn']\n",
    "DispEn['WhiteNoise'] = WhiteNoise_entropies['DispEn']\n",
    "DispEn['RandomWalk'] = RandomWalk_entropies['DispEn']\n",
    "DispEn['Chirp'] = Chirp_entropies['DispEn']\n",
    "DispEn['SineWave'] = SineWave_entropies['DispEn']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,15))\n",
    "plt.subplot(3,2,1)\n",
    "colors = ['lightsalmon', 'red', 'darkred']\n",
    "plt.boxplot(PeEn, patch_artist=True,\n",
    "            boxprops=dict(facecolor=colors[0], color=colors[1]),\n",
    "            capprops=dict(color=colors[1]),\n",
    "            whiskerprops=dict(color=colors[1]),\n",
    "            flierprops=dict(color=colors[1], markeredgecolor=colors[1]),\n",
    "            medianprops=dict(color=colors[2]));\n",
    "plt.xticks(ticks = np.arange(1,7), labels = ['Lorenz', 'ARMA', 'WhiteNoise', 'RandomWalk',\n",
    "                                            'Chirp', 'SineWave']);\n",
    "plt.title('Permutation Entropy')\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(3,2,2)\n",
    "colors = ['lightgreen', 'green', 'darkgreen']\n",
    "plt.boxplot(ApEn, patch_artist=True,\n",
    "            boxprops=dict(facecolor=colors[0], color=colors[1]),\n",
    "            capprops=dict(color=colors[1]),\n",
    "            whiskerprops=dict(color=colors[1]),\n",
    "            flierprops=dict(color=colors[1], markeredgecolor=colors[1]),\n",
    "            medianprops=dict(color=colors[2]));\n",
    "plt.xticks(ticks = np.arange(1,7), labels = ['Lorenz', 'ARMA', 'WhiteNoise', 'RandomWalk',\n",
    "                                            'Chirp', 'SineWave']);\n",
    "plt.title('Approximation Entropy')\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(3,2,3)\n",
    "colors = ['lightblue', 'blue', 'darkblue']\n",
    "plt.boxplot(SampEn, patch_artist=True,\n",
    "            boxprops=dict(facecolor=colors[0], color=colors[1]),\n",
    "            capprops=dict(color=colors[1]),\n",
    "            whiskerprops=dict(color=colors[1]),\n",
    "            flierprops=dict(color=colors[1], markeredgecolor=colors[1]),\n",
    "            medianprops=dict(color=colors[2]));\n",
    "plt.xticks(ticks = np.arange(1,7), labels = ['Lorenz', 'ARMA', 'WhiteNoise', 'RandomWalk',\n",
    "                                            'Chirp', 'SineWave']);\n",
    "plt.title('Sample Entropy')\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(3,2,4)\n",
    "colors = ['plum', 'violet', 'magenta']\n",
    "plt.boxplot(FuzzyEn, patch_artist=True,\n",
    "            boxprops=dict(facecolor=colors[0], color=colors[1]),\n",
    "            capprops=dict(color=colors[1]),\n",
    "            whiskerprops=dict(color=colors[1]),\n",
    "            flierprops=dict(color=colors[1], markeredgecolor=colors[1]),\n",
    "            medianprops=dict(color=colors[2]));\n",
    "plt.xticks(ticks = np.arange(1,7), labels = ['Lorenz', 'ARMA', 'WhiteNoise', 'RandomWalk',\n",
    "                                            'Chirp', 'SineWave']);\n",
    "plt.title('Fuzzy Entropy')\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(3,2,5)\n",
    "colors = ['lightskyblue', 'skyblue', 'deepskyblue']\n",
    "plt.boxplot(DistEn, patch_artist=True,\n",
    "            boxprops=dict(facecolor=colors[0], color=colors[1]),\n",
    "            capprops=dict(color=colors[1]),\n",
    "            whiskerprops=dict(color=colors[1]),\n",
    "            flierprops=dict(color=colors[1], markeredgecolor=colors[1]),\n",
    "            medianprops=dict(color=colors[2]));\n",
    "plt.xticks(ticks = np.arange(1,7), labels = ['Lorenz', 'ARMA', 'WhiteNoise', 'RandomWalk',\n",
    "                                            'Chirp', 'SineWave']);\n",
    "plt.title('Distribution Entropy')\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(3,2,6)\n",
    "colors = ['khaki', 'gold', 'darkgoldenrod']\n",
    "plt.boxplot(DispEn, patch_artist=True,\n",
    "            boxprops=dict(facecolor=colors[0], color=colors[1]),\n",
    "            capprops=dict(color=colors[1]),\n",
    "            whiskerprops=dict(color=colors[1]),\n",
    "            flierprops=dict(color=colors[1], markeredgecolor=colors[1]),\n",
    "            medianprops=dict(color=colors[2]));\n",
    "plt.xticks(ticks = np.arange(1,7), labels = ['Lorenz', 'ARMA', 'WhiteNoise', 'RandomWalk',\n",
    "                                            'Chirp', 'SineWave']);\n",
    "plt.title('Dispersion Entropy')\n",
    "plt.grid()"
   ]
  }
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