clustering/data_analysis.py

# !usr/bin/env python
# -*- coding:utf-8 _*-
"""
@Author:Lijiaxing
 
@File:data_analysis.py
@Time:2023/4/24 15:16

"""
import os.path

import pandas as pd
# from mpl_toolkits.basemap import Basemap
from scipy.signal import savgol_filter
import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import linkage, fcluster


def paint_others(y):
    """ 绘制其他数据 """
    plt.plot([j for j in range(y)], y)
    # 添加标题和标签
    plt.xlabel('x')
    plt.ylabel('y')

    # 显示图形
    plt.show()


def compute_cos_similarity(a, b):
    """
    计算两个向量的余弦相似度
    :param a: 向量a
    :param b: 向量b
    :return: 余弦相似度值
    """
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))


def compute_pearsonr(a):
    """
    计算数据皮尔逊相关系数并返回相似度矩阵
    :param a: 数据格式为n*m的矩阵，n为数据个数，m为数据维度
    :return: 返回相似度矩阵,数据格式为n*n的矩阵
    """
    return np.corrcoef(a)


def compute_distance(a, b):
    """
    计算两个向量的欧式距离
    :param a:
    :param b:
    :return: 返回两个向量的欧式距离
    """
    return np.linalg.norm(a - b)


def hierarchical_clustering(data, threshold, similarity_func):
    """
    层次聚类,使用工具包scipy.cluster.hierarchy中的linkage和fcluster函数进行层次聚类
    :param data: 二维数据，格式为n*m的矩阵，n为数据个数，m为数据维度
    :param threshold: 阈值，当两个数据的距离小于阈值时，将两个数据归为一类，阈值为根据相似度矩阵层次聚类后的类别距离阈值，可根据需求进行调整，可大于1
    :param similarity_func: 相似度计算函数，用于计算两个数据的相似度，可以进行替换，若替换为计算距离的函数需对内部进行修改
    :return: 返回聚类结果，格式为n*1的矩阵，n为数据个数，每个数据的值为该数据所属的类别
    """
    # 计算数据的相似度矩阵
    similarity_matrix = similarity_func(data)

    # 计算数据的距离矩阵
    distance_matrix = 1 - similarity_matrix

    # 进行层次聚类返回聚类结果
    Z = linkage(distance_matrix, method='ward')
    # 根据相似度阈值获取聚类结果
    clusters = fcluster(Z, t=threshold, criterion='distance')
    return clusters


class DataAnalysis:
    """
    数据分析类
    """

    def __init__(self, data_length, data_start, data_end):
        """
        初始化
        :param data_length: 分析数据段长度
        :param data_start: 分析数据段开始位置
        :param data_end: 分析数据段结束位置
        """
        # 原始风机功率数据傅里叶变换滤波后的数据
        self.ori_turbine_fft = None
        # 原始风机功率数据片段
        self.ori_turbine_pic = None
        # 聚类结果
        self.cluster = None
        # 风机功率差分平滑后的结果
        self.smooth_turbine_diff = None
        # 风机功率差分变化情况
        self.diff_change = None
        # 风机功率差分
        self.turbine_diff = None
        # 全部风机数据
        self.turbine = None
        # 风机的标号顺序
        self.turbine_id = list(range(102, 162))
        b1b4 = [142, 143, 144, 145]
        self.turbine_id = [id for id in self.turbine_id if id not in b1b4]
        # 风机功率数据15分钟级别
        self.power_15min = None
        # 风机经纬度信息
        self.info = None
        # 使用数据长度
        self.data_length = data_length
        # 使用数据开始位置
        self.data_start = data_start
        # 使用数据结束位置
        self.data_end = data_end
        # 导入数据
        self.load_data()
        # 计算风机功率差分
        self.compute_turbine_diff()

    def load_data(self, normalize=False):
        """
        加载数据
        :return:
        """
        self.info = pd.read_csv('../data-process/data/风机信息.csv', encoding='utf-8')
        # power_15min = pd.read_csv('../data/power_15min.csv')
        # for i in range(len(power_15min)):
        #     if power_15min.loc[i, 'C_REAL_VALUE'] == -9999:
        #         # 方便在曲线中看出缺失数据位置
        #         power_15min.loc[i, 'C_REAL_VALUE'] = -34.56789
        # self.power_15min = power_15min
        turbine_path = '../data-process/data/output_filtered_csv_files/turbine-{}.csv'
        self.turbine, turbines = {}, []
        for i in self.turbine_id:
            self.turbine[i] = pd.read_csv(turbine_path.format(i))[20:].reset_index(drop=True)
        if normalize:
            self.normalize()

    def normalize(self):
        turbines = [self.turbine[i].values[:, 1:].astype(np.float32) for i in self.turbine_id]
        turbines = np.vstack(turbines)
        mean, std = np.mean(turbines, axis=0), np.std(turbines, axis=0)
        for i in self.turbine_id:
            c_time = self.turbine[i]['C_TIME']
            self.turbine[i] = (self.turbine[i].iloc[:, 1:] - mean) / std
            self.turbine[i].insert(loc=0, column='C_TIME', value=c_time)
        return self.turbine

    def compute_turbine_diff(self):
        """
        计算风机功率差分
        :return:
        """
        turbine_diff = []
        ori_turbine_pic = []
        for turbine_i in self.turbine_id:
            diff_array = np.diff(
                np.array(self.turbine[turbine_i]['C_ACTIVE_POWER'].values[self.data_start:self.data_end + 1]))
            turbine_diff.append(diff_array)
            ori_turbine_pic.append(self.turbine[turbine_i]['C_ACTIVE_POWER'].values[self.data_start:self.data_end])
        self.ori_turbine_pic = ori_turbine_pic
        self.turbine_diff = turbine_diff

        diff_change = []
        for diff_i in turbine_diff:
            single_diff_change = []
            for diff_i_i in diff_i:
                if diff_i_i > 0:
                    single_diff_change.append(1)
                elif diff_i_i < 0:
                    single_diff_change.append(-1)
                else:
                    single_diff_change.append(0)
            diff_change.append(single_diff_change)
        self.diff_change = diff_change
        self.ori_turbine_fft = [self.turbine_fft(i + 1) for i in range(len(self.ori_turbine_pic))]

        # 平滑
        self.turbine_smooth(window_size=21)

    def paint_map(self):
        """
        绘制经纬度地图
        :return:
        """
        lats = self.info['纬度'].values
        lons = self.info['经度'].values
        map = Basemap()

        # 绘制海岸线和国家边界
        map.drawcoastlines()
        map.drawcountries()

        # 绘制经纬度坐标
        map.drawmeridians(range(0, 360, 30))
        map.drawparallels(range(-90, 90, 30))

        # 绘制点

        x, y = map(lons, lats)
        map.plot(x, y, 'bo', markersize=10)

        # 显示图表
        plt.show()

    def paint_power15min(self):
        """
        绘制15分钟功率曲线
        :return:
        """

        plt.plot(self.power_15min['C_REAL_VALUE'])

        # 设置图表标题和轴标签
        plt.title('Data Time Change Curve')
        plt.xlabel('Date')
        plt.ylabel('Value')

        # 显示图表
        plt.show()

    def paint_lats_lons(self):
        """
        绘制经纬度图
        :return:
        """
        x = self.info['纬度'].values
        y = self.info['经度'].values

        # 绘制散点图
        fig, ax = plt.subplots()
        plt.scatter(x, y)

        for i, txt in enumerate(self.info['id'].values):
            ax.annotate(txt, (x[i], y[i]))

        # 设置图表标题和轴标签
        plt.xlabel('lats')
        plt.ylabel('lons')

        # 显示图表
        plt.show()

    def similarity_score(self, turbine_diff, threshold=0.5):
        """
        使用余弦相似度计算相似度分数并返回相似度大于阈值的index矩阵
        :param turbine_diff: 需要计算相似的矩阵，数据格式n*m,n为数据条数，m为数据维数
        :param threshold: 相似度阈值
        :return: 返回相似计算后的矩阵
        """
        similarity = {i: [] for i in range(49)}
        similarity_index = {i: [] for i in range(49)}
        for turbine_i in range(49):
            for turbine_j in range(49):
                cos_similarity = compute_cos_similarity(turbine_diff[turbine_i], turbine_diff[turbine_j])
                similarity[turbine_i].append(cos_similarity)
                if cos_similarity > threshold:
                    similarity_index[turbine_i].append(turbine_j)
        return similarity_index

    def paint_turbine(self, paint_default=True):
        """
        绘制风机地理位置图
        :param paint_default:默认True，绘制聚类后每个类别的数据折线图
        :return: None
        """

        # y = self.info['纬度'].values
        # x = self.info['经度'].values
        #
        # fig, ax = plt.subplots(figsize=(15, 15))
        #
        # plt.scatter(x, y, c=self.cluster)
        # for i, txt in enumerate(self.info['C_ID'].values):
        #     ax.annotate(txt, (x[i], y[i]))

        # 设置图表标题和轴标签
        # plt.xlabel('lons')
        # plt.ylabel('lats')
        # plt.legend()
        #
        # # 显示图表
        # plt.savefig('analysis_img/turbine_cluster.png')
        # plt.show()
        if paint_default:
            for i in range(max(self.cluster)):
                self.paint_turbine_k(i + 1)

    def turbine_smooth(self, window_size=50):
        """
        使用滑动平均平滑数据。

        参数：
        data -- 需要平滑的数据，numpy数组类型
        window_size -- 滑动窗口大小，整数类型

        返回值：
        smooth_data -- 平滑后的数据，numpy数组类型
        """

        # weights = np.repeat(1.0, window_size) / window_size
        smooth_data = []
        for turbine_diff_i in self.turbine_diff:
            smooth_y = savgol_filter(turbine_diff_i, window_length=window_size, polyorder=3)
            smooth_data.append(smooth_y)
        #     smooth_data.append(np.convolve(turbine_diff_i, weights, 'valid'))
        self.smooth_turbine_diff = smooth_data

    def paint_turbine_k(self, k):
        """
        绘制第k聚类的风机数据折线图
        :param k:
        :return:
        """
        pic_label = []
        y = []
        plt.figure(figsize=(20, 10))
        cmap = plt.get_cmap('viridis')
        for i, item in enumerate(self.cluster):
            if item == k:
                pic_label.append('turbine-' + str(self.turbine_id[i]))
                y.append(self.ori_turbine_fft[i])
        for i in range(len(y)):
            color = cmap(i / 10)
            plt.plot([j for j in range(len(y[i]))], y[i], color=color, label=pic_label[i])
        # 添加标签和标题
        plt.xlabel('x')
        plt.ylabel('y')
        plt.title('Cluster {}'.format(k))

        # 添加图例
        plt.legend()
        # 显示图形
        plt.savefig('analysis_img/cluster/cluster_{}.png'.format(k))
        plt.show()

    def turbine_fft(self, k):
        """
        对第k台原始风机数据进行傅里叶变换，并绘制变换前后曲线
        :param k: 数据读入时的风机顺序index，从1开始
        :return: 傅里叶变换清洗后的数据，数据格式
        """
        y = self.ori_turbine_pic
        t = np.linspace(0, 1, self.data_length)
        signal = y[k - 1]

        # 进行傅里叶变换
        freq = np.fft.fftfreq(len(signal), t[1] - t[0])
        spectrum = np.fft.fft(signal)
        spectrum_abs = np.abs(spectrum)
        threshold = np.percentile(spectrum_abs, 98)
        indices = spectrum_abs > threshold
        spectrum_clean = indices * spectrum

        # 进行傅里叶逆变换
        signal_clean = np.fft.ifft(spectrum_clean)
        # plt.figure(figsize=(20, 10))
        #
        # # 绘制时域信号
        # plt.subplot(4, 1, 1)
        # plt.plot(t, signal)
        # plt.title(k)
        #
        # # 绘制频域信号
        # plt.subplot(4, 1, 2)
        # plt.plot(freq, np.abs(spectrum))
        #
        # # 绘制过滤后的频域信号
        # plt.subplot(4, 1, 3)
        # plt.plot(freq, np.abs(spectrum_clean))
        #
        # # 绘制过滤后的时域信号
        # plt.subplot(4, 1, 4)
        # plt.plot(t, signal_clean)
        #
        # plt.savefig('analysis_img/fft/{}_turbine_fft.png'.format(k))
        # plt.show()
        return signal_clean

    def paint_double(self, i, j):
        """
        绘制两台风机的数据变换对比
        :param i: 风机数据读入时数据编号，从1开始
        :param j: 风机数据读入时数据编号，从1开始
        :return:
        """
        y = self.ori_turbine_fft
        x = [index for index in range(self.data_length)]
        data_i = y[i - 1]
        data_j = y[j - 1]

        plt.figure(figsize=(20, 10))
        plt.plot(x, data_i, label='turbine {}'.format(self.turbine_id[i - 1]), linestyle='solid')
        plt.plot(x, data_j, label='turbine {}'.format(self.turbine_id[j - 1]), linestyle='dashed')

        plt.title('{} and {}'.format(i, j))
        plt.legend()
        plt.savefig('analysis_img/{}_{}_turbine.png'.format(self.turbine_id[i - 1], self.turbine_id[j - 1]))
        plt.show()

    def process_ori_data(self):
        """
        对原始数据进行处理，聚类和绘图
        :return:
        """
        self.turbine_clusters(self.ori_turbine_fft)
        self.paint_turbine()

    def turbine_clusters(self, data=None):
        """
        风机数据聚类，聚类信息保存在self.cluster中
        :param data: 默认为空，也可以使用其他数据聚类，并体现在绘图中，
        数据格式:二维数据n*m，n为数据条数，m为每条数据维数
        :return: None
        """
        if data is None:
            cluster = hierarchical_clustering(self.turbine_diff, threshold=1.4,
                                              similarity_func=compute_pearsonr)  # 层次聚类
        else:
            cluster = hierarchical_clustering(data, threshold=1.2,
                                              similarity_func=compute_pearsonr)
        self.cluster = cluster
        # 在这里保存cluster变量
        from cluster_power import cluster_power_list_file, cluster_power_list_folder

        output_path = '../data-process/data/cluster_power/'

        cluster_power_list_file(self.cluster, self.turbine_id,
                                input_path='../data-process/data/output_filtered_csv_files/', output_path=output_path)
        cluster_power_list_folder(self.cluster, self.turbine_id, input_path='../data-process/data/continuous_data/',
                                  output_path=output_path)


data_analysis = DataAnalysis(data_length=9773,
                             data_start=0,
                             data_end=9773)

data_analysis.process_ori_data()
data_analysis.paint_double(20, 21)