WTOAAM/wtoaamapi/apps/business/algorithm/windRoseOfTurbine.py

import os
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
from windrose import WindroseAxes
from .analyst import Analyst
from .utils.directoryUtil import DirectoryUtil as dir
from confBusiness import *
from matplotlib.cm import get_cmap 
from matplotlib.colors import ListedColormap   


class WinRoseOfTurbineAnalyst(Analyst):
    """
    风电机组变桨-功率分析
    """

    def typeAnalyst(self):
        return "wind_rose_turbine"

    def turbinesAnalysis(self, dataFrameMerge, outputAnalysisDir, confData: ConfBusiness):
        self.windRoseAnalysis(dataFrameMerge, outputAnalysisDir, confData)

    def windRoseAnalysis(self, dataFrameMerge, outputAnalysisDir, confData: ConfBusiness):
        # 风速区间  
        bins = [0, 3, 6, 9, np.inf]  
        speed_labels = ['[0,3)', '[3,6)', '[6,9)', '>=9']  
        # 准备颜色映射  
        colors = plt.cm.Blues(np.linspace(0, 1, len(speed_labels)))  
        cmap = ListedColormap(colors)  
        # 将风向按照22.5度一个间隔进行分组  
        wind_directions = np.arange(0, 360, 22.5)  

        # 按设备名分组数据
        grouped = dataFrameMerge.groupby(Field_NameOfTurbine)
        print("self.ratedPower {}".format(confData.rated_power))
        # 遍历每个设备并绘制散点图
        for name, group in grouped:            
            # 对风速进行分箱处理，但不添加到DataFrame中  
            speed_bins = pd.cut(group[confData.field_wind_speed], bins=bins, labels=speed_labels)  
            
            # 将风向按照22.5度一个间隔进行分组  
            wind_directions = np.arange(0, 360, 22.5)  
            group['风向分组'] = pd.cut(group[confData.field_wind_dir], bins=wind_directions, labels=wind_directions[:-1])  
            
            # 绘制风玫瑰图  
            fig, ax = plt.subplots(figsize=(8, 8), subplot_kw={'polar': True})  
            
            # 为每个风速区间绘制风向的条形图，并添加图例  
            for i, (label, color) in enumerate(zip(speed_labels, colors)):  
                # 筛选出当前风速区间的数据  
                subset = group[speed_bins == label]  
                # 计算每个风向分组的频数  
                counts = subset['风向分组'].value_counts().reindex(wind_directions[:-1], fill_value=0)  
                # 绘制条形图，并添加标签用于图例  
                bar = ax.bar(counts.index * np.pi / 180, counts.values, color=cmap(i), alpha=0.75, width=(22.5 * np.pi / 180), label=label)  
            
            # 设置标题和标签  
            ax.set_title("Wind Rose", va='top')  
            ax.set_theta_zero_location('N')  # 设置0度位置为北  
            ax.set_theta_direction(-1)  # 设置角度方向为顺时针  
            ax.set_yticklabels([])  # 不显示y轴刻度标签  
            ax.set_xticks(wind_directions * np.pi / 180)  # 设置x轴刻度  
            ax.set_xticklabels(['N', 'NNE', 'NE', 'ENE', 'E', 'ESE', 'SE', 'SSE', 'S', 'SSW', 'SW', 'WSW', 'W', 'WNW', 'NW', 'NNW'])  # 设置x轴刻度标签为方向  
            
            # 添加图例，并设置位置以避免与图形重叠  
            ax.legend(title='Wind Speed', bbox_to_anchor=(1.1, 1), loc='center left', borderaxespad=0.)   

            # 保存图像并关闭绘图窗口
            output_file = os.path.join(outputAnalysisDir, f"{name}.png")
            plt.savefig(output_file, bbox_inches='tight', dpi=120)
            plt.close()