对一个给定的文本文件，对其进行哈夫曼编码，并计算压缩率。

哈夫曼编码是一种无损数据压缩算法，它根据不同字符出现的频率，分配不同长度的编码来压缩数据。压缩率取决于文本中不同字符出现的频率。如果文本中存在大量重复的字符，则压缩率会更高。

下面是对文本文件进行哈夫曼编码的步骤：

1. 统计文本中不同字符出现的频率，并按照频率从小到大排序。

2. 构建哈夫曼树。将频率最小的两个字符合并成一个节点，其权重为它们的频率之和。重复此过程，直到所有节点合并为一棵树。

3. 给哈夫曼树的左分支编码为0，右分支编码为1。从根节点开始，沿着树走到每个叶子节点，记录下路径上的编码，即为该字符的哈夫曼编码。

4. 将文本中每个字符替换为它的哈夫曼编码，并将多个编码拼接成一个二进制字符串。

5. 将二进制字符串转换为字节数组，并将其写入到输出文件中。

6. 计算压缩率。压缩率 = 原始文件大小 / 压缩后文件大小。

下面是 Python 实现：

import heapq, os

class HuffmanCoding:
    def __init__(self, file_path):
        self.file_path = file_path
        self.heap = []
        self.codes = {}
        self.reverse_mapping = {}

    class HeapNode:
        def __init__(self, char, freq):
            self.char = char
            self.freq = freq
            self.left = None
            self.right = None

        def __lt__(self, other):
            return self.freq < other.freq

        def __eq__(self, other):
            if(other == None):
                return False
            if(not isinstance(other, HeapNode)):
                return False
            return self.freq == other.freq

    def make_frequency_dict(self, text):
        frequency = {}
        for char in text:
            if char not in frequency:
                frequency[char] = 0
            frequency[char] += 1
        return frequency

    def make_heap(self, frequency):
        for key in frequency:
            node = self.HeapNode(key, frequency[key])
            heapq.heappush(self.heap, node)

    def merge_nodes(self):
        while(len(self.heap)>1):
            node1 = heapq.heappop(self.heap)
            node2 = heapq.heappop(self.heap)

            merged = self.HeapNode(None, node1.freq + node2.freq)
            merged.left = node1
            merged.right = node2

            heapq.heappush(self.heap, merged)

    def make_codes_helper(self, root, current_code):
        if(root == None):
            return

        if(root.char != None):
            self.codes[root.char] = current_code
            self.reverse_mapping[current_code] = root.char
            return

        self.make_codes_helper(root.left, current_code + "0")
        self.make_codes_helper(root.right, current_code + "1")

    def make_codes(self):
        root = heapq.heappop(self.heap)
        current_code = ""
        self.make_codes_helper(root, current_code)

    def get_encoded_text(self, text):
        encoded_text = ""
        for char in text:
            encoded_text += self.codes[char]
        return encoded_text

    def pad_encoded_text(self, encoded_text):
        extra_padding = 8 - len(encoded_text) % 8
        for i in range(extra_padding):
            encoded_text += "0"

        padded_info = "{0:08b}".format(extra_padding)
        encoded_text = padded_info + encoded_text
        return encoded_text

    def get_byte_array(self, padded_encoded_text):
        if(len(padded_encoded_text) % 8 != 0):
            print("Encoded text not padded properly")
            exit(0)

        b = bytearray()
        for i in range(0, len(padded_encoded_text), 8):
            byte = padded_encoded_text[i:i+8]
            b.append(int(byte, 2))
        return b

    def compress(self):
        file_name, file_extension = os.path.splitext(self.file_path)
        output_path = file_name + ".bin"

        with open(self.file_path, 'r+') as file, open(output_path, 'wb') as output:
            text = file.read()
            text = text.rstrip()

            frequency = self.make_frequency_dict(text)
            self.make_heap(frequency)
            self.merge_nodes()
            self.make_codes()

            encoded_text = self.get_encoded_text(text)
            padded_encoded_text = self.pad_encoded_text(encoded_text)

            b = self.get_byte_array(padded_encoded_text)
            output.write(bytes(b))

        print("Compressed file path: " + output_path)
        return output_path

    """ functions for decompression """

    def remove_padding(self, padded_encoded_text):
        padded_info = padded_encoded_text[:8]
        extra_padding = int(padded_info, 2)

        padded_encoded_text = padded_encoded_text[8:]
        encoded_text = padded_encoded_text[:-1*extra_padding]

        return encoded_text

    def decode_text(self, encoded_text):
        current_code = ""
        decoded_text = ""

        for bit in encoded_text:
            current_code += bit
            if(current_code in self.reverse_mapping):
                character = self.reverse_mapping[current_code]
                decoded_text += character
                current_code = ""

        return decoded_text

    def decompress(self, input_path):
        file_name, file_extension = os.path.splitext(self.file_path)
        output_path = file_name + "_decompressed" + ".txt"

        with open(input_path, 'rb') as file, open(output_path, 'w') as output:
            bit_string = ""

            byte = file.read(1)
            while(byte != b""):
                byte = ord(byte)
                bits = bin(byte)[2:].rjust(8, '0')
                bit_string += bits
                byte = file.read(1)

            encoded_text = self.remove_padding(bit_string)
            decoded_text = self.decode_text(encoded_text)

            output.write(decoded_text)

        print("Decompressed file path: " + output_path)
        return output_path

使用示例：

h = HuffmanCoding("example.txt")
output_path = h.compress()
h.decompress(output_path)

其中 `example.txt` 是需要压缩的文本文件。该程序会在同一目录下生成一个名为 `example.bin` 的压缩文件，以及一个名为 `example_decompressed.txt` 的解压缩文件。压缩率可以通过比较原始文件大小和压缩文件大小来计算。