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

import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from .analyst import Analyst
from .utils.directoryUtil import DirectoryUtil as dir
from confBusiness import ConfBusiness


class TSRTrendAnalyst(Analyst):
    """
    风电机组叶尖速比时序分析
    """

    def typeAnalyst(self):
        return "tsr_trend"

    def turbineAnalysis(self,
                        dataFrame,
                        outputAnalysisDir,
                        outputFilePath,
                        confData: ConfBusiness,
                        turbineName):

        self.tsr_trend(dataFrame, outputFilePath,
                       confData.field_turbine_time, confData.field_wind_speed, confData.field_rotor_speed, confData.field_power, confData.field_pitch_angle1,
                       confData.rotor_diameter)

    def tsr_trend(self, dataFrame, output_path, field_time, field_wind_speed, field_rotor_speed, field_power_active, field_angle_pitch, rotor_diameter):
        # Convert time column to datetime and extract date
        dataFrame[field_time] = pd.to_datetime(
            dataFrame[field_time], format='%Y-%m-%d %H:%M:%S')
        dataFrame['time_day'] = dataFrame[field_time].dt.date

        # Calculate 'tsr'
        dataFrame['wind_speed'] = dataFrame[field_wind_speed].astype(float)
        dataFrame['rotor_speed'] = dataFrame[field_rotor_speed].astype(float)
        rotor_diameter = pd.to_numeric(rotor_diameter, errors='coerce')
        dataFrame['tsr'] = (dataFrame['rotor_speed'] * 0.104667 *
                            (rotor_diameter / 2)) / dataFrame['wind_speed']

        # Group by day and aggregate
        grouped = dataFrame.groupby('time_day').agg({
            field_time: 'min',
            'wind_speed': 'mean',
            'rotor_speed': 'mean',
            'tsr': ['mean', 'max', 'min']
        }).reset_index()

        # Rename columns post-aggregation
        grouped.columns = ['time_day', 'time_', 'wind_speed',
                           'rotor_speed', 'tsr', 'tsr_max', 'tsr_min']

        # Sort by day
        grouped.sort_values('time_day', inplace=True)

        # Write to CSV
        grouped.to_csv(output_path, index=False)

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

    def plot_tsr_trend(self, csvFileDirOfCp, farm_name, encoding='utf-8'):
        """  
        Plot TSR trend from CSV files in a given input path and save the plots to an output path.  

        Parameters:  
        - csvFileDirOfCp: str, path to the directory containing input CSV files.
        - farm_name: str, name of the wind farm.
        - encoding: str, encoding of the input CSV files. Defaults to 'utf-8'.
        """
        field_Name_Turbine = "turbine_name"
        file_time = 'time_day'
        y_name = 'tsr'
        y_min = 'tsr_min'
        y_max = 'tsr_max'
        split_way = '_tsr_trend.csv'

        # Set seaborn palette
        sns.set_palette('deep')

        # Iterate over the files in the input directory
        for root, dir_names, file_names in dir.list_directory(csvFileDirOfCp):
            for file_name in file_names:

                if not file_name.endswith(".csv"):
                    continue

                try:
                    print(os.path.join(root, file_name))
                    data = pd.read_csv(os.path.join(
                        root, file_name), encoding=encoding)

                    # Convert the time column to datetime
                    data.loc[:, file_time] = pd.to_datetime(
                        data.loc[:, file_time])

                    # Calculate min and max TSR values
                    data[y_min] = data[y_name] - data[y_min]
                    data[y_max] = data[y_max] - data[y_name]

                    # Split the file name to get the output name
                    turbine_name = file_name.split(split_way)[0]
                    # 添加设备名作为新列
                    data[field_Name_Turbine] = turbine_name

                    # Plot the TSR trend with error bars
                    fig, ax = plt.subplots()
                    ax.errorbar(x=data[file_time], y=data[y_name], yerr=[data[y_min], data[y_max]],
                                fmt='o', capsize=4, elinewidth=2, ecolor='lightgrey', mfc='dodgerblue')

                    # Set axis labels, limits, and title
                    ax.set_xlabel('time')
                    ax.set_ylabel('TSR')
                    # ax.set_ylim(0, 16)
                    ax.set_title('turbine_name={}'.format(turbine_name))

                    # Save the plot to the output path
                    plt.savefig(os.path.join(csvFileDirOfCp, "{}.png".format(
                        turbine_name)), bbox_inches='tight', dpi=120)
                    plt.close(fig)

                except Exception as e:
                    print(f"An error occurred while processing file {file_name}: {e}")