main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e6671239-0e1a-4118-8fab-336680bd1cfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting compression...\n",
      "File 'sample1.txt' compressed to 'sample1_compressed.bin'\n",
      "Original Size: 5762 bytes, Compressed Size: 3344 bytes\n",
      "Compression Ratio: 41.96%\n",
      "File 'sample2.pdf' compressed to 'sample2_compressed.bin'\n",
      "Original Size: 42272 bytes, Compressed Size: 24676 bytes\n",
      "Compression Ratio: 41.63%\n",
      "File 'sample3.jpeg' compressed to 'sample3_compressed.bin'\n",
      "Original Size: 247696 bytes, Compressed Size: 246979 bytes\n",
      "Compression Ratio: 0.29%\n",
      "\n",
      "Total Compression Time: 0.07 seconds\n",
      "Overall Compression Ratio: 7.01%\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting decompression...\n",
      "File 'sample1_compressed.bin' decompressed to 'sample1_compressed_decompressed.txt'\n",
      "File 'sample2_compressed.bin' decompressed to 'sample2_compressed_decompressed.pdf'\n",
      "File 'sample3_compressed.bin' decompressed to 'sample3_compressed_decompressed.jpeg'\n",
      "\n",
      "Total Decompression Time: 0.16 seconds\n"
     ]
    }
   ],
   "source": [
    "import heapq\n",
    "import os\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import List, Dict\n",
    "\n",
    "class BinaryTree:\n",
    "    def __init__(self, value, frequ):\n",
    "        self.value = value\n",
    "        self.frequ = frequ\n",
    "        self.left = None\n",
    "        self.right = None\n",
    "\n",
    "    def __lt__(self, other):\n",
    "        return self.frequ < other.frequ\n",
    "\n",
    "    def __eq__(self, other):\n",
    "        return self.frequ == other.frequ\n",
    "\n",
    "class Huffmancode:\n",
    "    def __init__(self, files: List[str], compression_level='Balanced'):\n",
    "        self.files = files\n",
    "        self.__heap = []\n",
    "        self.__code = {}\n",
    "        self.__reverse_code = {}\n",
    "        self.compression_level = compression_level\n",
    "        self.algorithm = 'huffman'\n",
    "\n",
    "    def _frequency_from_bytes(self, byte_data):\n",
    "        frequ_dict = {}\n",
    "        for byte in byte_data:\n",
    "            if byte not in frequ_dict:\n",
    "                frequ_dict[byte] = 0\n",
    "            frequ_dict[byte] += 1\n",
    "        return frequ_dict\n",
    "\n",
    "    def _build_heap(self, frequency_dict):\n",
    "        for key in frequency_dict:\n",
    "            frequency = frequency_dict[key]\n",
    "            binary_tree_node = BinaryTree(key, frequency)\n",
    "            heapq.heappush(self.__heap, binary_tree_node)\n",
    "\n",
    "    def _build_binary_tree(self):\n",
    "        while len(self.__heap) > 1:\n",
    "            binary_tree_node_1 = heapq.heappop(self.__heap)\n",
    "            binary_tree_node_2 = heapq.heappop(self.__heap)\n",
    "            sum_of_freq = binary_tree_node_1.frequ + binary_tree_node_2.frequ\n",
    "            newnode = BinaryTree(None, sum_of_freq)\n",
    "            newnode.left = binary_tree_node_1\n",
    "            newnode.right = binary_tree_node_2\n",
    "            heapq.heappush(self.__heap, newnode)\n",
    "\n",
    "    def _build_binary_code_helper(self, root, curr_bits):\n",
    "        if root is None:\n",
    "            return\n",
    "        if root.value is not None:\n",
    "            self.__code[root.value] = curr_bits\n",
    "            self.__reverse_code[curr_bits] = root.value\n",
    "            return\n",
    "        self._build_binary_code_helper(root.left, curr_bits + '0')\n",
    "        self._build_binary_code_helper(root.right, curr_bits + '1')\n",
    "\n",
    "    def _build_binary_code(self):\n",
    "        root = heapq.heappop(self.__heap)\n",
    "        self._build_binary_code_helper(root, '')\n",
    "\n",
    "    def _build_encoded_text(self, byte_data):\n",
    "        encoded_text = ''\n",
    "        for byte in byte_data:\n",
    "            encoded_text += self.__code[byte]\n",
    "        return encoded_text\n",
    "\n",
    "    def _build_padded_text(self, encoded_text):\n",
    "        padding_value = 8 - len(encoded_text) % 8\n",
    "        for i in range(padding_value):\n",
    "            encoded_text += '0'\n",
    "        padded_info = \"{0:08b}\".format(padding_value)\n",
    "        padded_text = padded_info + encoded_text\n",
    "        return padded_text\n",
    "\n",
    "    def _build_byte_array(self, padded_text):\n",
    "        array = []\n",
    "        for i in range(0, len(padded_text), 8):\n",
    "            byte = padded_text[i:i + 8]\n",
    "            array.append(int(byte, 2))\n",
    "        return array\n",
    "\n",
    "    def compression(self):\n",
    "        print(\"Starting compression...\")\n",
    "        total_compressed_size = 0\n",
    "        total_original_size = 0\n",
    "        compression_start_time = time.time()\n",
    "        compressed_file_paths = []  \n",
    "        file_metrics = []  \n",
    "\n",
    "        for file_path in self.files:\n",
    "            filename, file_extension = os.path.splitext(file_path)\n",
    "            output_path = filename + '_compressed.bin'\n",
    "            with open(file_path, 'rb') as file, open(output_path, 'wb') as output:\n",
    "                byte_data = file.read()\n",
    "                original_size = len(byte_data)\n",
    "                frequency_dict = self._frequency_from_bytes(byte_data)\n",
    "                self._build_heap(frequency_dict)\n",
    "                self._build_binary_tree()\n",
    "                self._build_binary_code()\n",
    "                encoded_text = self._build_encoded_text(byte_data)\n",
    "                padded_text = self._build_padded_text(encoded_text)\n",
    "                byte_array = self._build_byte_array(padded_text)\n",
    "                final_bytes = bytes(byte_array)\n",
    "                output.write(final_bytes)\n",
    "\n",
    "                compressed_size = len(final_bytes)\n",
    "                total_original_size += original_size\n",
    "                total_compressed_size += compressed_size\n",
    "\n",
    "                compressed_file_paths.append(output_path)  \n",
    "\n",
    "                \n",
    "                file_metrics.append({\n",
    "                    'filename': file_path,\n",
    "                    'original_size': original_size,\n",
    "                    'compressed_size': compressed_size,\n",
    "                    'compression_ratio': (original_size - compressed_size) / original_size * 100\n",
    "                })\n",
    "\n",
    "            print(f\"File '{file_path}' compressed to '{output_path}'\")\n",
    "            print(f\"Original Size: {original_size} bytes, Compressed Size: {compressed_size} bytes\")\n",
    "            print(f\"Compression Ratio: {(original_size - compressed_size) / original_size * 100:.2f}%\")\n",
    "\n",
    "        compression_time = time.time() - compression_start_time\n",
    "        print(f\"\\nTotal Compression Time: {compression_time:.2f} seconds\")\n",
    "        print(f\"Overall Compression Ratio: {(total_original_size - total_compressed_size) / total_original_size * 100:.2f}%\")\n",
    "\n",
    "        #results\n",
    "        self._visualize_results(file_metrics)\n",
    "        return compressed_file_paths  \n",
    "\n",
    "    def _visualize_results(self, file_metrics):\n",
    "        filenames = [metric['filename'] for metric in file_metrics]\n",
    "        original_sizes = [metric['original_size'] for metric in file_metrics]\n",
    "        compressed_sizes = [metric['compressed_size'] for metric in file_metrics]\n",
    "        compression_ratios = [metric['compression_ratio'] for metric in file_metrics]\n",
    "\n",
    "        \n",
    "        plt.figure(figsize=(10, 6))\n",
    "        plt.bar(filenames, original_sizes, color='blue', label='Original Size')\n",
    "        plt.bar(filenames, compressed_sizes, color='green', label='Compressed Size')\n",
    "        plt.xlabel('Files')\n",
    "        plt.ylabel('Size (bytes)')\n",
    "        plt.title('Original vs Compressed File Sizes')\n",
    "        plt.legend()\n",
    "        plt.xticks(rotation=45, ha='right')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "        \n",
    "        plt.figure(figsize=(10, 6))\n",
    "        plt.bar(filenames, compression_ratios, color='purple')\n",
    "        plt.xlabel('Files')\n",
    "        plt.ylabel('Compression Ratio (%)')\n",
    "        plt.title('Compression Ratio for Each File')\n",
    "        plt.xticks(rotation=45, ha='right')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    def decompress(self, compressed_file_paths: List[str]):\n",
    "        print(\"Starting decompression...\")\n",
    "        decompression_start_time = time.time()\n",
    "\n",
    "        for input_path in compressed_file_paths:\n",
    "            filename, file_extension = os.path.splitext(input_path)\n",
    "            original_file_extension = os.path.splitext(self.files[compressed_file_paths.index(input_path)])[1]\n",
    "            output_path = filename + '_decompressed' + original_file_extension\n",
    "            with open(input_path, 'rb') as file, open(output_path, 'wb') as output:\n",
    "                bit_string = ''.join(f'{byte:08b}' for byte in file.read())\n",
    "                text_after_removing_padding = self._remove_padding(bit_string)\n",
    "                actual_data = self._decode_text(text_after_removing_padding)\n",
    "                output.write(actual_data)\n",
    "\n",
    "            print(f\"File '{input_path}' decompressed to '{output_path}'\")\n",
    "\n",
    "        decompression_time = time.time() - decompression_start_time\n",
    "        print(f\"\\nTotal Decompression Time: {decompression_time:.2f} seconds\")\n",
    "\n",
    "    def _remove_padding(self, padded_text):\n",
    "        padding_info = padded_text[:8]\n",
    "        padding_value = int(padding_info, 2)\n",
    "        padded_text = padded_text[8:]  \n",
    "        return padded_text[:-padding_value]  \n",
    "\n",
    "    def _decode_text(self, text):\n",
    "        current_bits = ''\n",
    "        decoded_bytes = bytearray()\n",
    "        for bit in text:\n",
    "            current_bits += bit\n",
    "            if current_bits in self.__reverse_code:\n",
    "                decoded_bytes.append(self.__reverse_code[current_bits])\n",
    "                current_bits = ''\n",
    "        return decoded_bytes\n",
    "\n",
    "\n",
    "files_to_compress = ['sample1.txt', 'sample2.pdf', 'sample3.jpeg'] \n",
    "compression_level = 'Balanced'  # Options: 'Fast', 'Balanced', 'Max'\n",
    "huffman = Huffmancode(files=files_to_compress, compression_level=compression_level)\n",
    "\n",
    "compressed_file_paths = huffman.compression()  \n",
    "\n",
    "# Decompressing\n",
    "huffman.decompress(compressed_file_paths)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f256691",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting compression...\n",
      "File '/Users/akshitha/Downloads/competative_code project/sample2.pdf' compressed to '/Users/akshitha/Downloads/competative_code project/sample2_compressed.bin'\n",
      "Original Size: 42272 bytes, Compressed Size: 24676 bytes\n",
      "Compression Ratio: 41.63%\n",
      "\n",
      "Total Compression Time: 0.01 seconds\n",
      "Overall Compression Ratio: 41.63%\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting decompression...\n",
      "File '/Users/akshitha/Downloads/competative_code project/sample2_compressed.bin' decompressed to '/Users/akshitha/Downloads/competative_code project/sample2_compressed_decompressed.pdf'\n",
      "\n",
      "Total Decompression Time: 0.01 seconds\n"
     ]
    }
   ],
   "source": [
    "import heapq\n",
    "import os\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import List\n",
    "from tkinter import Tk, filedialog\n",
    "\n",
    "class BinaryTree:\n",
    "    def __init__(self, value, frequ):\n",
    "        self.value = value\n",
    "        self.frequ = frequ\n",
    "        self.left = None\n",
    "        self.right = None\n",
    "\n",
    "    def __lt__(self, other):\n",
    "        return self.frequ < other.frequ\n",
    "\n",
    "    def __eq__(self, other):\n",
    "        return self.frequ == other.frequ\n",
    "\n",
    "class Huffmancode:\n",
    "    def __init__(self, files: List[str], compression_level='Balanced'):\n",
    "        self.files = files\n",
    "        self.__heap = []\n",
    "        self.__code = {}\n",
    "        self.__reverse_code = {}\n",
    "        self.compression_level = compression_level\n",
    "        self.algorithm = 'huffman'\n",
    "\n",
    "    def _frequency_from_bytes(self, byte_data):\n",
    "        frequ_dict = {}\n",
    "        for byte in byte_data:\n",
    "            if byte not in frequ_dict:\n",
    "                frequ_dict[byte] = 0\n",
    "            frequ_dict[byte] += 1\n",
    "        return frequ_dict\n",
    "\n",
    "    def _build_heap(self, frequency_dict):\n",
    "        for key in frequency_dict:\n",
    "            frequency = frequency_dict[key]\n",
    "            binary_tree_node = BinaryTree(key, frequency)\n",
    "            heapq.heappush(self.__heap, binary_tree_node)\n",
    "\n",
    "    def _build_binary_tree(self):\n",
    "        while len(self.__heap) > 1:\n",
    "            binary_tree_node_1 = heapq.heappop(self.__heap)\n",
    "            binary_tree_node_2 = heapq.heappop(self.__heap)\n",
    "            sum_of_freq = binary_tree_node_1.frequ + binary_tree_node_2.frequ\n",
    "            newnode = BinaryTree(None, sum_of_freq)\n",
    "            newnode.left = binary_tree_node_1\n",
    "            newnode.right = binary_tree_node_2\n",
    "            heapq.heappush(self.__heap, newnode)\n",
    "\n",
    "    def _build_binary_code_helper(self, root, curr_bits):\n",
    "        if root is None:\n",
    "            return\n",
    "        if root.value is not None:\n",
    "            self.__code[root.value] = curr_bits\n",
    "            self.__reverse_code[curr_bits] = root.value\n",
    "            return\n",
    "        self._build_binary_code_helper(root.left, curr_bits + '0')\n",
    "        self._build_binary_code_helper(root.right, curr_bits + '1')\n",
    "\n",
    "    def _build_binary_code(self):\n",
    "        root = heapq.heappop(self.__heap)\n",
    "        self._build_binary_code_helper(root, '')\n",
    "\n",
    "    def _build_encoded_text(self, byte_data):\n",
    "        encoded_text = ''\n",
    "        for byte in byte_data:\n",
    "            encoded_text += self.__code[byte]\n",
    "        return encoded_text\n",
    "\n",
    "    def _build_padded_text(self, encoded_text):\n",
    "        padding_value = 8 - len(encoded_text) % 8\n",
    "        for i in range(padding_value):\n",
    "            encoded_text += '0'\n",
    "        padded_info = \"{0:08b}\".format(padding_value)\n",
    "        padded_text = padded_info + encoded_text\n",
    "        return padded_text\n",
    "\n",
    "    def _build_byte_array(self, padded_text):\n",
    "        array = []\n",
    "        for i in range(0, len(padded_text), 8):\n",
    "            byte = padded_text[i:i + 8]\n",
    "            array.append(int(byte, 2))\n",
    "        return array\n",
    "\n",
    "    def compression(self):\n",
    "        print(\"Starting compression...\")\n",
    "        total_compressed_size = 0\n",
    "        total_original_size = 0\n",
    "        compression_start_time = time.time()\n",
    "        compressed_file_paths = []  \n",
    "        file_metrics = []  \n",
    "\n",
    "        for file_path in self.files:\n",
    "            filename, file_extension = os.path.splitext(file_path)\n",
    "            output_path = filename + '_compressed.bin'\n",
    "            with open(file_path, 'rb') as file, open(output_path, 'wb') as output:\n",
    "                byte_data = file.read()\n",
    "                original_size = len(byte_data)\n",
    "                frequency_dict = self._frequency_from_bytes(byte_data)\n",
    "                self._build_heap(frequency_dict)\n",
    "                self._build_binary_tree()\n",
    "                self._build_binary_code()\n",
    "                encoded_text = self._build_encoded_text(byte_data)\n",
    "                padded_text = self._build_padded_text(encoded_text)\n",
    "                byte_array = self._build_byte_array(padded_text)\n",
    "                final_bytes = bytes(byte_array)\n",
    "                output.write(final_bytes)\n",
    "\n",
    "                compressed_size = len(final_bytes)\n",
    "                total_original_size += original_size\n",
    "                total_compressed_size += compressed_size\n",
    "\n",
    "                compressed_file_paths.append(output_path)  \n",
    "\n",
    "                \n",
    "                file_metrics.append({\n",
    "                    'filename': file_path,\n",
    "                    'original_size': original_size,\n",
    "                    'compressed_size': compressed_size,\n",
    "                    'compression_ratio': (original_size - compressed_size) / original_size * 100\n",
    "                })\n",
    "\n",
    "            print(f\"File '{file_path}' compressed to '{output_path}'\")\n",
    "            print(f\"Original Size: {original_size} bytes, Compressed Size: {compressed_size} bytes\")\n",
    "            print(f\"Compression Ratio: {(original_size - compressed_size) / original_size * 100:.2f}%\")\n",
    "\n",
    "        compression_time = time.time() - compression_start_time\n",
    "        print(f\"\\nTotal Compression Time: {compression_time:.2f} seconds\")\n",
    "        print(f\"Overall Compression Ratio: {(total_original_size - total_compressed_size) / total_original_size * 100:.2f}%\")\n",
    "\n",
    "        #results\n",
    "        self._visualize_results(file_metrics)\n",
    "        return compressed_file_paths  \n",
    "\n",
    "    def _visualize_results(self, file_metrics):\n",
    "        filenames = [metric['filename'] for metric in file_metrics]\n",
    "        original_sizes = [metric['original_size'] for metric in file_metrics]\n",
    "        compressed_sizes = [metric['compressed_size'] for metric in file_metrics]\n",
    "        compression_ratios = [metric['compression_ratio'] for metric in file_metrics]\n",
    "\n",
    "        \n",
    "        plt.figure(figsize=(10, 6))\n",
    "        plt.bar(filenames, original_sizes, color='blue', label='Original Size')\n",
    "        plt.bar(filenames, compressed_sizes, color='green', label='Compressed Size')\n",
    "        plt.xlabel('Files')\n",
    "        plt.ylabel('Size (bytes)')\n",
    "        plt.title('Original vs Compressed File Sizes')\n",
    "        plt.legend()\n",
    "        plt.xticks(rotation=45, ha='right')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "        \n",
    "        plt.figure(figsize=(10, 6))\n",
    "        plt.bar(filenames, compression_ratios, color='purple')\n",
    "        plt.xlabel('Files')\n",
    "        plt.ylabel('Compression Ratio (%)')\n",
    "        plt.title('Compression Ratio for Each File')\n",
    "        plt.xticks(rotation=45, ha='right')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    def decompress(self, compressed_file_paths: List[str]):\n",
    "        print(\"Starting decompression...\")\n",
    "        decompression_start_time = time.time()\n",
    "\n",
    "        for input_path in compressed_file_paths:\n",
    "            filename, file_extension = os.path.splitext(input_path)\n",
    "            original_file_extension = os.path.splitext(self.files[compressed_file_paths.index(input_path)])[1]\n",
    "            output_path = filename + '_decompressed' + original_file_extension\n",
    "            with open(input_path, 'rb') as file, open(output_path, 'wb') as output:\n",
    "                bit_string = ''.join(f'{byte:08b}' for byte in file.read())\n",
    "                text_after_removing_padding = self._remove_padding(bit_string)\n",
    "                actual_data = self._decode_text(text_after_removing_padding)\n",
    "                output.write(actual_data)\n",
    "\n",
    "            print(f\"File '{input_path}' decompressed to '{output_path}'\")\n",
    "\n",
    "        decompression_time = time.time() - decompression_start_time\n",
    "        print(f\"\\nTotal Decompression Time: {decompression_time:.2f} seconds\")\n",
    "\n",
    "    def _remove_padding(self, padded_text):\n",
    "        padding_info = padded_text[:8]\n",
    "        padding_value = int(padding_info, 2)\n",
    "        padded_text = padded_text[8:]  \n",
    "        return padded_text[:-padding_value]  \n",
    "\n",
    "    def _decode_text(self, text):\n",
    "        current_bits = ''\n",
    "        decoded_bytes = bytearray()\n",
    "        for bit in text:\n",
    "            current_bits += bit\n",
    "            if current_bits in self.__reverse_code:\n",
    "                decoded_bytes.append(self.__reverse_code[current_bits])\n",
    "                current_bits = ''\n",
    "        return decoded_bytes\n",
    "\n",
    "\n",
    "def select_files():\n",
    "    root = Tk()\n",
    "    root.withdraw()  \n",
    "    files = filedialog.askopenfilenames(title=\"Select Files for Compression\")\n",
    "    root.destroy()\n",
    "    return list(files)\n",
    "\n",
    "# User selects files\n",
    "files_to_compress = select_files()\n",
    "compression_level = 'Balanced'  # Options: 'Fast', 'Balanced', 'Max'\n",
    "huffman = Huffmancode(files=files_to_compress, compression_level=compression_level)\n",
    "\n",
    "# Compress the selected files\n",
    "compressed_file_paths = huffman.compression()  \n",
    "\n",
    "# Decompress the files\n",
    "huffman.decompress(compressed_file_paths)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}