ROP_Graphs_and_Plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d8584e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = [\n",
    "    (2, 1, 53.75, 56.75),\n",
    "    (2, 2, 3.75, 142.00),\n",
    "    (2, 3, 0.25, 42.00),\n",
    "    (2, 4, 190.25, 15.25),\n",
    "    (4, 1, 66.75, 53.50),\n",
    "    (4, 2, 29, 25.75),\n",
    "    (4, 3, 97, 2.75),\n",
    "    (4, 4, 55.25, 174.00),\n",
    "    (6, 1, 109.5, 7.00),\n",
    "    (6, 2, 15.25, 173.00),\n",
    "    (6, 3, 23.25, 25.75),\n",
    "    (6, 4, 100, 50.25),\n",
    "    (8, 1, 53.25, 120.25),\n",
    "    (8, 2, 54.75, 1.75),\n",
    "    (8, 3, 111.5, 17.50),\n",
    "    (8, 4, 28.5, 116.50),\n",
    "    (10, 1, 103.75, 131.00),\n",
    "    (10, 2, 44, 76.50),\n",
    "    (10, 3, 41, 11.50),\n",
    "    (10, 4, 59.25, 37.00)\n",
    "]\n",
    "\n",
    "body_parts = [2, 4, 6, 8, 10]\n",
    "clusters = [1, 2, 3, 4]\n",
    "labels = [f'{bp}' for bp in body_parts]\n",
    "\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['figure.figsize'] = [8, 4]\n",
    "\n",
    "num_clusters = len(clusters)\n",
    "bar_width = 0.2\n",
    "bar_offsets = np.arange(len(body_parts)) * (num_clusters + 1) * bar_width\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red']\n",
    "\n",
    "legend_labels = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4']\n",
    "\n",
    "for i, cluster in enumerate(clusters):\n",
    "    size_data = [item[2] for item in data if item[1] == cluster]\n",
    "    central_data = [item[3] for item in data if item[1] == cluster]\n",
    "    \n",
    "    size_rects = ax.bar(bar_offsets + i * bar_width, size_data, bar_width, label=f'Cluster {cluster} - Size', color=colors[i])\n",
    "    central_rects = ax.bar(bar_offsets + i * bar_width, central_data, bar_width, bottom=size_data, color=colors[i], alpha=0.5)\n",
    "    \n",
    "#     legend_labels.append(f'Cluster {cluster} - Size')\n",
    "#     legend_labels.append(f'Cluster {cluster} - Central')\n",
    "\n",
    "# Create custom legend with correct colors\n",
    "custom_legend = [plt.Line2D([0], [0], color=color, lw=4) for color in colors]\n",
    "ax.legend(custom_legend, legend_labels, loc='upper center', ncol=4, bbox_to_anchor=(0.5, -0.15))\n",
    "\n",
    "ax.set_ylabel('Size')\n",
    "ax.set_xlabel('Number of Body Parts')\n",
    "ax.set_title('CNN-extracted Features\\' Cluster Sizes and Composition')\n",
    "ax.set_xticks(bar_offsets + (num_clusters - 1) * bar_width / 2)\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a41b4d44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = data = [\n",
    "    (2, 1, 66.25, 93.25),\n",
    "    (2, 2, 31.25, 111.5),\n",
    "    (2, 3, 14.75, 42.75),\n",
    "    (2, 4, 135.75, 8.5),\n",
    "    (4, 1, 80.25, 102.25),\n",
    "    (4, 2, 79.75, 106.25),\n",
    "    (4, 3, 64, 28),\n",
    "    (4, 4, 24, 19.5),\n",
    "    (6, 1, 81.25, 111.25),\n",
    "    (6, 2, 38.75, 48),\n",
    "    (6, 3, 33.5, 23.25),\n",
    "    (6, 4, 91.75, 76.25),\n",
    "    (8, 1, 97.25, 123.25),\n",
    "    (8, 2, 50.75, 23.25),\n",
    "    (8, 3, 89, 97.75),\n",
    "    (8, 4, 11, 11),\n",
    "    (10, 1, 55.75, 15.75),\n",
    "    (10, 2, 91.25, 126.25),\n",
    "    (10, 3, 6.75, 16.5),\n",
    "    (10, 4, 94.25, 97.5)\n",
    "]\n",
    "\n",
    "body_parts = [2, 4, 6, 8, 10]\n",
    "clusters = [1, 2, 3, 4]\n",
    "labels = [f'{bp}' for bp in body_parts]\n",
    "\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['figure.figsize'] = [8, 4]\n",
    "\n",
    "num_clusters = len(clusters)\n",
    "bar_width = 0.2\n",
    "bar_offsets = np.arange(len(body_parts)) * (num_clusters + 1) * bar_width\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red']\n",
    "\n",
    "legend_labels = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4']\n",
    "\n",
    "for i, cluster in enumerate(clusters):\n",
    "    size_data = [item[2] for item in data if item[1] == cluster]\n",
    "    central_data = [item[3] for item in data if item[1] == cluster]\n",
    "    \n",
    "    size_rects = ax.bar(bar_offsets + i * bar_width, size_data, bar_width, label=f'Cluster {cluster} - Size', color=colors[i])\n",
    "    central_rects = ax.bar(bar_offsets + i * bar_width, central_data, bar_width, bottom=size_data, color=colors[i], alpha=0.5)\n",
    "    \n",
    "#     legend_labels.append(f'Cluster {cluster} - Size')\n",
    "#     legend_labels.append(f'Cluster {cluster} - Central')\n",
    "\n",
    "# Create custom legend with correct colors\n",
    "custom_legend = [plt.Line2D([0], [0], color=color, lw=4) for color in colors]\n",
    "ax.legend(custom_legend, legend_labels, loc='upper center', ncol=4, bbox_to_anchor=(0.5, -0.15))\n",
    "\n",
    "ax.set_ylabel('Size')\n",
    "ax.set_xlabel('Number of Body Parts')\n",
    "ax.set_title('Fourier-transformed Features\\' Cluster Sizes and Composition')\n",
    "ax.set_xticks(bar_offsets + (num_clusters - 1) * bar_width / 2)\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "26259b31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = {\n",
    "    'Body Parts': [2, 4, 6, 8, 10],\n",
    "    'Coefficient of Variation (CV)': [3.133, 17.348, 12.792, 7.741, 14.558]\n",
    "}\n",
    "\n",
    "# Create a scatter plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot scatter plot\n",
    "ax.scatter(data['Body Parts'], data['Coefficient of Variation (CV)'], color='blue', label='Data')\n",
    "\n",
    "# Fit a linear trendline\n",
    "slope, intercept = np.polyfit(data['Body Parts'], data['Coefficient of Variation (CV)'], 1)\n",
    "ax.plot(data['Body Parts'], slope * np.array(data['Body Parts']) + intercept, color='red', label='Trendline')\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_xlabel('Number of Body Parts')\n",
    "ax.set_ylabel('Coefficient of Variation')\n",
    "ax.set_title('Coefficient of Variation as the Number of Body Parts Increase')\n",
    "\n",
    "# Add legend\n",
    "# ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2af86e12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared value: 0.1363602124926263\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import linregress\n",
    "\n",
    "# Data\n",
    "body_parts = np.array([2, 4, 6, 8, 10])\n",
    "cv = np.array([3.133, 17.348, 12.792, 7.741, 14.558])\n",
    "\n",
    "# Calculate the linear regression\n",
    "slope, intercept, r_value, p_value, std_err = linregress(body_parts, cv)\n",
    "\n",
    "# Calculate R-squared\n",
    "r_squared = r_value ** 2\n",
    "\n",
    "print(\"R-squared value:\", r_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1f15c237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = {\n",
    "    'Body Parts': [2, 4, 6, 8, 10],\n",
    "    'CNN Inter-Cluster Distance': [23.2425, 20.37, 22, 20.965, 19.9525],\n",
    "    'FFT Inter-Cluster Distance': [20.385, 24.33, 22.08, 26.335, 25.365]\n",
    "}\n",
    "\n",
    "# Create subplots with two side-by-side plots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n",
    "\n",
    "# Plot CNN data on the first subplot\n",
    "axes[0].scatter(data['Body Parts'], data['CNN Inter-Cluster Distance'], color='blue', label='CNN')\n",
    "cnn_slope, cnn_intercept = np.polyfit(data['Body Parts'], data['CNN Inter-Cluster Distance'], 1)\n",
    "axes[0].plot(data['Body Parts'], cnn_slope * np.array(data['Body Parts']) + cnn_intercept, color='blue')\n",
    "axes[0].set_xlabel('Number of Body Parts', fontsize=16)\n",
    "axes[0].set_ylabel('Average Inter-cluster Distance', fontsize=16)\n",
    "axes[0].set_title('CNN-extracted Features', fontsize=16)\n",
    "# axes[0].set_ylim(0)  # Set y-axis lower limit to 0\n",
    "axes[0].set_xticks(data['Body Parts'][::1])  # Set fewer x-axis ticks\n",
    "\n",
    "# Plot FFT data on the second subplot\n",
    "axes[1].scatter(data['Body Parts'], data['FFT Inter-Cluster Distance'], color='orange', label='FFT')\n",
    "fft_slope, fft_intercept = np.polyfit(data['Body Parts'], data['FFT Inter-Cluster Distance'], 1)\n",
    "axes[1].plot(data['Body Parts'], fft_slope * np.array(data['Body Parts']) + fft_intercept, color='orange')\n",
    "axes[1].set_xlabel('Number of Body Parts', fontsize=16)\n",
    "# axes[1].set_ylabel('Average Inter-Cluster Distance', fontsize=12)\n",
    "axes[1].set_title('Fourier-transformed Features', fontsize=16)\n",
    "# axes[1].set_ylim(0)  # Set y-axis lower limit to 0\n",
    "axes[1].set_xticks(data['Body Parts'][::1])  # Set fewer x-axis ticks\n",
    "\n",
    "# # Add legend to both subplots\n",
    "# axes[0].legend()\n",
    "# axes[1].legend()\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5b517421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = {\n",
    "    'Body Parts': [2, 4, 6, 8, 10],\n",
    "    'CNN Intra-Cluster Distance': [12.365, 11.7975, 12.735, 12.92, 13.145],\n",
    "    'FFT Intra-Cluster Distance': [11.31, 13.065, 12.5975, 13.9125, 12.985]\n",
    "}\n",
    "\n",
    "# Create subplots with two side-by-side plots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n",
    "\n",
    "# Plot CNN data on the first subplot\n",
    "axes[0].scatter(data['Body Parts'], data['CNN Intra-Cluster Distance'], color='blue', label='CNN')\n",
    "cnn_slope, cnn_intercept = np.polyfit(data['Body Parts'], data['CNN Intra-Cluster Distance'], 1)\n",
    "axes[0].plot(data['Body Parts'], cnn_slope * np.array(data['Body Parts']) + cnn_intercept, color='blue')\n",
    "axes[0].set_xlabel('Number of Body Parts', fontsize=16)\n",
    "axes[0].set_ylabel('Average Intra-cluster Distance', fontsize=16)\n",
    "axes[0].set_title('CNN-extracted Features', fontsize=16)\n",
    "# axes[0].set_ylim(0)  # Set y-axis lower limit to 0\n",
    "axes[0].set_xticks(data['Body Parts'][::1])  # Set fewer x-axis ticks\n",
    "\n",
    "# Plot FFT data on the second subplot\n",
    "axes[1].scatter(data['Body Parts'], data['FFT Intra-Cluster Distance'], color='orange', label='FFT')\n",
    "fft_slope, fft_intercept = np.polyfit(data['Body Parts'], data['FFT Intra-Cluster Distance'], 1)\n",
    "axes[1].plot(data['Body Parts'], fft_slope * np.array(data['Body Parts']) + fft_intercept, color='orange')\n",
    "axes[1].set_xlabel('Number of Body Parts', fontsize=16)\n",
    "# axes[1].set_ylabel('Average Inter-Cluster Distance', fontsize=12)\n",
    "axes[1].set_title('Fourier-transformed Features', fontsize=16)\n",
    "# axes[1].set_ylim(0)  # Set y-axis lower limit to 0\n",
    "axes[1].set_xticks(data['Body Parts'][::1])  # Set fewer x-axis ticks\n",
    "\n",
    "# # Add legend to both subplots\n",
    "# axes[0].legend()\n",
    "# axes[1].legend()\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1380530a",
   "metadata": {},
   "source": [
    "# Edits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "778882e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = {\n",
    "    'Body Parts': [2, 4, 6, 8, 10],\n",
    "    'CNN Inter-Cluster Distance': [26.26, 20.81, 20.08, 20.965, 19.9525],\n",
    "    'FFT Inter-Cluster Distance': [21.0875, 23.2463, 22.08, 26.335, 25.365]\n",
    "}\n",
    "\n",
    "# Create subplots with two side-by-side plots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n",
    "\n",
    "# Plot CNN data on the first subplot\n",
    "axes[0].scatter(data['Body Parts'], data['CNN Inter-Cluster Distance'], color='blue', label='CNN')\n",
    "# cnn_slope, cnn_intercept = np.polyfit(data['Body Parts'], data['CNN Inter-Cluster Distance'], 1)\n",
    "# axes[0].plot(data['Body Parts'], cnn_slope * np.array(data['Body Parts']) + cnn_intercept, color='lightblue')\n",
    "axes[0].set_xlabel('Number of Body Parts', fontsize=16)\n",
    "axes[0].set_ylabel('Average Inter-cluster Distance', fontsize=16)\n",
    "axes[0].set_title('CNN-extracted Features', fontsize=16)\n",
    "axes[0].set_ylim(18)  # Set y-axis lower limit to 0\n",
    "axes[0].set_xticks(data['Body Parts'][::1])  # Set fewer x-axis ticks\n",
    "\n",
    "# Plot FFT data on the second subplot\n",
    "axes[1].scatter(data['Body Parts'], data['FFT Inter-Cluster Distance'], color='orange', label='FFT')\n",
    "# fft_slope, fft_intercept = np.polyfit(data['Body Parts'], data['FFT Inter-Cluster Distance'], 1)\n",
    "# axes[1].plot(data['Body Parts'], fft_slope * np.array(data['Body Parts']) + fft_intercept, color='bisque')\n",
    "axes[1].set_xlabel('Number of Body Parts', fontsize=16)\n",
    "# axes[1].set_ylabel('Average Inter-Cluster Distance', fontsize=12)\n",
    "axes[1].set_title('Fourier-transformed Features', fontsize=16)\n",
    "axes[1].set_ylim(15, 30)  # Set y-axis lower limit to 0\n",
    "axes[1].set_xticks(data['Body Parts'][::1])  # Set fewer x-axis ticks\n",
    "\n",
    "# # Add legend to both subplots\n",
    "# axes[0].legend()\n",
    "# axes[1].legend()\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aba617a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = {\n",
    "    'Body Parts': [2, 4, 6, 8, 10],\n",
    "    'CNN Intra-Cluster Distance': [12.52, 12.56, 11.87, 12.92, 13.145],\n",
    "    'FFT Intra-Cluster Distance': [11.5750, 12.5488, 12.5975, 13.9125, 12.985]\n",
    "}\n",
    "\n",
    "# Create subplots with two side-by-side plots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n",
    "\n",
    "# Plot CNN data on the first subplot\n",
    "axes[0].scatter(data['Body Parts'], data['CNN Intra-Cluster Distance'], color='blue', label='CNN')\n",
    "# cnn_slope, cnn_intercept = np.polyfit(data['Body Parts'], data['CNN Intra-Cluster Distance'], 1)\n",
    "# axes[0].plot(data['Body Parts'], cnn_slope * np.array(data['Body Parts']) + cnn_intercept, color='lightblue')\n",
    "axes[0].set_xlabel('Number of Body Parts', fontsize=16)\n",
    "axes[0].set_ylabel('Average Intra-cluster Distance', fontsize=16)\n",
    "axes[0].set_title('CNN-extracted Features', fontsize=16)\n",
    "axes[0].set_ylim(10,15)  # Set y-axis lower limit to 0\n",
    "axes[0].set_xticks(data['Body Parts'][::1])  # Set fewer x-axis ticks\n",
    "\n",
    "# Plot FFT data on the second subplot\n",
    "axes[1].scatter(data['Body Parts'], data['FFT Intra-Cluster Distance'], color='orange', label='FFT')\n",
    "# fft_slope, fft_intercept = np.polyfit(data['Body Parts'], data['FFT Intra-Cluster Distance'], 1)\n",
    "# axes[1].plot(data['Body Parts'], fft_slope * np.array(data['Body Parts']) + fft_intercept, color='bisque')\n",
    "axes[1].set_xlabel('Number of Body Parts', fontsize=16)\n",
    "# axes[1].set_ylabel('Average Inter-Cluster Distance', fontsize=12)\n",
    "axes[1].set_title('Fourier-transformed Features', fontsize=16)\n",
    "# axes[1].set_ylim(0)  # Set y-axis lower limit to 0\n",
    "axes[1].set_xticks(data['Body Parts'][::1])  # Set fewer x-axis ticks\n",
    "\n",
    "# # Add legend to both subplots\n",
    "# axes[0].legend()\n",
    "# axes[1].legend()\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c79e5de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = {\n",
    "    'Body Parts': [2, 4, 6, 8, 10],\n",
    "    'CNN Intra-Cluster Distance': [0.3553, 0.3001, 0.2975, 0.2835, 0.2777],\n",
    "    'FFT Intra-Cluster Distance': [0.3156, 0.2932, 0.3091, 0.3091, 0.3081]\n",
    "}\n",
    "\n",
    "# Create subplots with two side-by-side plots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n",
    "\n",
    "# Plot CNN data on the first subplot\n",
    "axes[0].scatter(data['Body Parts'], data['CNN Intra-Cluster Distance'], color='blue', label='CNN')\n",
    "# cnn_slope, cnn_intercept = np.polyfit(data['Body Parts'], data['CNN Intra-Cluster Distance'], 1)\n",
    "# axes[0].plot(data['Body Parts'], cnn_slope * np.array(data['Body Parts']) + cnn_intercept, color='lightblue')\n",
    "axes[0].set_xlabel('Number of Body Parts', fontsize=16)\n",
    "axes[0].set_ylabel('Average Silhouette Score', fontsize=16)\n",
    "axes[0].set_title('CNN-extracted Features', fontsize=16)\n",
    "axes[0].set_ylim(0, 0.5)  # Set y-axis lower limit to 0\n",
    "axes[0].set_xticks(data['Body Parts'][::1])  # Set fewer x-axis ticks\n",
    "\n",
    "# Plot FFT data on the second subplot\n",
    "axes[1].scatter(data['Body Parts'], data['FFT Intra-Cluster Distance'], color='orange', label='FFT')\n",
    "# fft_slope, fft_intercept = np.polyfit(data['Body Parts'], data['FFT Intra-Cluster Distance'], 1)\n",
    "# axes[1].plot(data['Body Parts'], fft_slope * np.array(data['Body Parts']) + fft_intercept, color='bisque')\n",
    "axes[1].set_xlabel('Number of Body Parts', fontsize=16)\n",
    "# axes[1].set_ylabel('Average Inter-Cluster Distance', fontsize=12)\n",
    "axes[1].set_title('Fourier-transformed Features', fontsize=16)\n",
    "# axes[1].set_ylim(0)  # Set y-axis lower limit to 0\n",
    "axes[1].set_xticks(data['Body Parts'][::1])  # Set fewer x-axis ticks\n",
    "\n",
    "# # Add legend to both subplots\n",
    "# axes[0].legend()\n",
    "# axes[1].legend()\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55f0b025",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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