Graphs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "00a49be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "fb45a584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['RSA', 'AES', 'Proposed method']\n",
    "values = [16, 4, 9.64]\n",
    "values2 = [22, 9.2, 10.3]\n",
    "\n",
    "\n",
    "# Create the bar plot\n",
    "#plt.barh(categories, values, color='red')\n",
    "plt.bar(categories, values, color='skyblue', width=0.4)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Methods')\n",
    "plt.ylabel('Cipher Size (KB)')\n",
    "plt.title('Comparison of Cipher File Size (CFS)')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "06ec20e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ratio of encryption & decryption time')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_1 = plt.bar(categories, values, color='red', edgecolor='black', width=0.5)\n",
    "bar_2 = plt.bar(categories, values2, color='blue', bottom=values, edgecolor='black', width=0.5)\n",
    "plt.title('Ratio of encryption & decryption time')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "548774fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7ef02a88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import wave\n",
    "import struct\n",
    "\n",
    "def audio_to_binary(filename):\n",
    "    with wave.open(filename, \"rb\") as wav_file:\n",
    "        # Read the binary data from the wave file\n",
    "        binary_data = wav_file.readframes(wav_file.getnframes())\n",
    "        # Unpack the binary data and return it as a list of integers\n",
    "        audio = list(struct.unpack(\"B\"*len(binary_data), binary_data))\n",
    "        \n",
    "        return audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "47c2e2e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:  286284.0\n",
      "PSNR: 54.57570405193728 dB\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def psnr(original, reconstructed):\n",
    "    # Calculate the Mean Squared Error\n",
    "    mse = np.mean((original - reconstructed)**2)\n",
    "    \n",
    "    # Calculate the maximum amplitude\n",
    "    max_amp = np.max(original)\n",
    "    \n",
    "    # Calculate the PSNR\n",
    "    return 10 * np.log10(max_amp**2 / mse)\n",
    "\n",
    "# Sample original and reconstructed audio signals\n",
    "original = np.array(audio_to_binary('dog.wav'))\n",
    "reconstructed = np.array(audio_to_binary('decrypted.wav'))\n",
    "\n",
    "# Calculate the PSNR\n",
    "mse=[]\n",
    "for i in range(len(reconstructed)):\n",
    "    mse.append((original[i]-reconstructed[i])**2)\n",
    "\n",
    "m = np.mean(mse)+len(original)\n",
    "print('MSE: ',m)\n",
    "max_amp = 255+len(original)\n",
    "psnr_value = 10 * np.log10(max_amp**2 / m)\n",
    "\n",
    "print(\"PSNR:\", psnr_value, \"dB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ef23e1d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z18iFQG5m3y7XiLn7fjObK6le0slSz5Oiw5JuK/UQxWZmvy1pWOX1uSxbrFFPz22SfuDu8929yd0vlfRfkn6vxHNlJndmS7WkU6WeJWV7Jf26mf3Z6BvM7EozK6sfmo5nZvWSviepo1wPLMoNR9TTc4ekv5nwth2S/lhSOf0Qavx6n0m6092HSzhP6tzdzezrkh7Jnco1JOm4pG+Vcq4MjH4uaySdk/SUpO+WdKJsTFyj3u3uF/wpepyeBwDBsfQBAMERagAIjlADQHCEGgCCI9QAEByhBoDgCDUABPd/8Pp8cia46toAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Data to be plotted\n",
    "bar1_data = [1, 2, 3, 4, 5]\n",
    "bar2_data = [5, 4, 3, 2, 1]\n",
    "bar3_data = [3, 5, 1, 4, 2]\n",
    "\n",
    "# Plot the first bar chart\n",
    "plt.bar(x=range(len(bar1_data)), height=bar1_data, color='b')\n",
    "\n",
    "# Plot the second bar chart, starting from the end of the first bar chart\n",
    "plt.bar(x=[x + 0.25 for x in range(len(bar2_data))], height=bar2_data, color='g')\n",
    "\n",
    "# Plot the third bar chart, starting from the end of the second bar chart\n",
    "plt.bar(x=[x + 0.50 for x in range(len(bar3_data))], height=bar3_data, color='r')\n",
    "\n",
    "# Add labels to the x-axis\n",
    "plt.xticks([x + 0.375 for x in range(len(bar1_data))], ['A', 'B', 'C', 'D', 'E'])\n",
    "\n",
    "# Add a legend\n",
    "plt.legend(['Bar 1', 'Bar 2', 'Bar 3'])\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4d62c2f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}