写出如下代码带注释：在DBSCAN算法中，不使用sklearn，对一个TXT格式经纬度数据计算并绘制k-distance图。计算出每个点到距其第k近的点的距离，然后将这些距离从大到小排序后进行绘图

首先需要导入一些必要的库：

import numpy as np
import matplotlib.pyplot as plt

接下来需要定义一些函数来实现DBSCAN算法的各个步骤：

def euclidean_distance(x1, x2):
    # 计算欧几里得距离
    return np.sqrt(np.sum((x1 - x2)**2))

def range_query(X, point_index, eps):
    # 寻找所有与点point_index距离小于eps的点
    neighbors = []
    for i in range(X.shape[0]):
        if euclidean_distance(X[point_index], X[i]) < eps:
            neighbors.append(i)
    return neighbors

def dbscan(X, eps, min_samples):
    num_points = X.shape[0]
    
    # 初始化所有点为未访问状态
    visited = np.zeros(num_points)
    # 初始化所有点为噪声点
    labels = np.full(num_points, -1)
    
    # 初始化聚类编号
    cluster_id = 0
    
    for i in range(num_points):
        # 如果该点已经被访问过，直接跳过
        if visited[i]:
            continue
        
        # 获取该点的邻居
        neighbors = range_query(X, i, eps)
        # 如果邻居数量小于min_samples，则该点为噪声点
        if len(neighbors) < min_samples:
            labels[i] = -1
        else:
            # 如果邻居数量大于等于min_samples，则该点为核心点
            labels[i] = cluster_id
            # 对该点的邻居进行扩展
            for j in neighbors:
                # 如果邻居已经被访问过，直接跳过
                if visited[j]:
                    continue
                visited[j] = 1
                # 获取邻居的邻居
                new_neighbors = range_query(X, j, eps)
                # 如果邻居数量大于等于min_samples，则将其加入当前聚类
                if len(new_neighbors) >= min_samples:
                    for k in new_neighbors:
                        if k not in neighbors:
                            neighbors.append(k)
                # 如果邻居尚未被分配聚类，则将其加入当前聚类
                if labels[j] == -1:
                    labels[j] = cluster_id
        # 更新聚类编号
        cluster_id += 1
    
    return labels

接下来，需要读取TXT格式的经纬度数据，并将其转化为Numpy数组：

data = []
with open("data.txt", "r") as f:
    for line in f:
        lat, lon = line.strip().split(",")
        data.append([float(lat), float(lon)])
data = np.array(data)

然后，需要计算每个点到距其第k近的点的距离，并将这些距离从大到小排序：

# 计算每个点到距其第k近的点的距离
k = 4
distances = []
for i in range(data.shape[0]):
    neighbors = range_query(data, i, eps)
    if len(neighbors) < k:
        distances.append(0)
    else:
        distances.append(sorted([euclidean_distance(data[i], data[j]) for j in neighbors])[k-1])
# 将距离从大到小排序
distances = sorted(distances, reverse=True)

最后，将距离作为y轴，点的编号作为x轴，进行绘图：

plt.plot(range(data.shape[0]), distances)
plt.xlabel("Point Index")
plt.ylabel("Distance to k-th nearest neighbor")
plt.show()