# https://github.com/GISerWang/Spatio-temporal-Clustering.git
import os
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial.distance import pdist
from scipy.spatial.distance import squareform


def DBSCAN(data_, eps, minPts):
    # 获得距离矩阵
    disMat = squareform(pdist(data_, metric='euclidean'))
    # 获得数据的行和列(一共有n条数据)
    n, m = data_.shape
    # 将矩阵的中小于eps的数赋予1, 大于eps的数置0, 按行求和, 求核心点坐标的索引
    core_points_index = np.where(np.sum(np.where(disMat <= eps, 1, 0), axis=1) >= minPts)[0]
    # 初始化类别，-1代表未分类。
    labels = np.full((n,), -1)
    clusterId = 0
    # 遍历所有的核心点
    for pointId in core_points_index:
        # 如果核心点未被分类，将其作为的种子点，开始寻找相应簇集
        if labels[pointId] == -1:
            # 首先将点pointId标记为当前类别(即标识为已操作)
            labels[pointId] = clusterId
            # 然后寻找种子点的eps邻域且没有被分类的点，将其放入种子集合
            neighbour = np.where((disMat[:, pointId] <= eps) & (labels == -1))[0]
            seeds = set(neighbour)
            # 通过种子点，开始生长，寻找密度可达的数据点，一直到种子集合为空，一个簇集寻找完毕
            while len(seeds) > 0:
                # 弹出一个新种子点
                newPoint = seeds.pop()
                # 将newPoint标记为当前类
                labels[newPoint] = clusterId
                # 寻找newPoint种子点eps邻域（包含自己）
                queryResults = np.where(disMat[:, newPoint] <= eps)[0]
                # 如果newPoint属于核心点，那么newPoint是可以扩展的，即密度是可以通过newPoint继续密度可达的
                if len(queryResults) >= minPts:
                    # 将邻域内且没有被分类的点压入种子集合
                    for resultPoint in queryResults:
                        if labels[resultPoint] == -1:
                            seeds.add(resultPoint)
            # 簇集生长完毕，寻找到一个类别
            clusterId = clusterId + 1
    return labels


def plotFeature(md_keys_, data_, labels_, output_path_):
    clusterNum = len(set(labels_))
    fig = plt.figure()
    scatterColors = ['black', 'blue', 'green', 'yellow', 'red', 'purple', 'orange', 'brown']
    ax = fig.add_subplot(111, projection='3d')
    for i in range(-1, clusterNum):
        colorStyle = scatterColors[i % len(scatterColors)]
        subCluster = data_[np.where(labels_ == i)]
        ax.scatter(subCluster[:, 0], subCluster[:, 1], subCluster[:, 2], c=colorStyle, s=12)
    ax.set_xlabel(md_keys_[0], rotation=0)  # 设置标签角度
    ax.set_ylabel(md_keys_[1], rotation=-45)
    ax.set_zlabel(md_keys_[2], rotation=0)
    plt.title(output_path_.split('\\')[-1].split('.')[0])
    plt.savefig(output_path_, dpi=500)
    plt.show()


if __name__ == '__main__':
    outcome_path = r'E:\Data\Research\Outcome'
    config_dir = r'\Magellan+Smac+roberta-large-nli-stsb-mean-tokens+inter-0.5'
    dataset_name_list = [f.name for f in os.scandir(outcome_path) if f.is_dir()]
    for dataset_name in dataset_name_list:
        absolute_path = outcome_path + rf'\{dataset_name}' + config_dir + r'\mds.txt'
        md_keys = []
        with open(absolute_path, 'r') as f:
            # 读取每一行的md，加入该文件的md列表
            data = []
            for line in f.readlines():
                md_metadata = line.strip().split('\t')
                md_tuple = eval(md_metadata[1])
                md_keys = list(md_tuple[0].keys())[1:4]
                md_values = list(md_tuple[0].values())
                data.append(md_values[1:4])
                if len(data) == 10000:
                    break

        data = np.array(data, dtype=np.float32)
        labels = DBSCAN(data, 0.5, 30)
        output_path = outcome_path + rf'\{dataset_name}.png'
        plotFeature(md_keys, data, labels, output_path)