import os
import pandas as pd
import numpy as np
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import matplotlib.pyplot as plt
from .analyst import Analyst
from .utils.directoryUtil import DirectoryUtil as dir
from confBusiness import ConfBusiness


class CpTrendAnalyst(Analyst):
    """
    风电机组风能利用系数时序分析
    """

    def typeAnalyst(self):
        return "cp_trend"

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

        self.cp_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, confData.density_air)

    def cp_trend(self, dataFrame, outputFilePath,  time_col, wind_speed_col, generator_speed_col, power_col, pitch_col, rotor_diameter, density_air):
        dataFrame['time_day'] = dataFrame[time_col].dt.date

        # Assign columns and calculate 'cp'
        dataFrame['wind_speed'] = dataFrame[wind_speed_col].astype(float)
        dataFrame['rotor_speed'] = dataFrame[generator_speed_col].astype(float)
        dataFrame['power'] = dataFrame[power_col]

        # Power coefficient calculation
        rotor_diameter = pd.to_numeric(rotor_diameter, errors='coerce')
        air_density = pd.to_numeric(rotor_diameter, errors='coerce')
        # Calculate cp
        dataFrame['cp'] = dataFrame['power'] * 1000 / (0.5 * np.pi * air_density * (
            rotor_diameter ** 2) / 4 * dataFrame['wind_speed'] ** 3)

        # Group by day and aggregate
        grouped = dataFrame.groupby('time_day').agg({
            time_col: 'min',  # Assuming time_col is the datetime column for minimum time
            'wind_speed': 'mean',
            'rotor_speed': 'mean',
            'cp': ['mean', 'max', 'min']
        }).reset_index()

        # Rename columns post aggregation for clarity
        grouped.columns = ['time_day', 'time_', 'wind_speed',
                           'rotor_speed', 'cp', 'cp_max', 'cp_min']

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

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

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

    def create_cp_trend_plots(self, csvFileDirOfCp,  farm_name, encoding='utf-8'):
        """
        Generates and saves error bar plots for CP trend data stored in CSV files.

        Parameters:
        - csvFileDirOfCp: Path to the directory containing the input CSV files.
        - farm_name: Name of the farm, used to format the output path.
        - encoding: str, encoding of the input CSV files. Defaults to 'utf-8'.
        """
        time_day = 'time_day'
        y_name = 'cp'
        y_min = 'cp_min'
        y_max = 'cp_max'
        split_way = '_cp_trend.csv'

        # Create the output directory if it does not exist
        if not os.path.exists(csvFileDirOfCp):
            os.makedirs(csvFileDirOfCp)

        # Walk through the input directory to process each CSV file
        for root, dir_names, file_names in dir.list_directory(csvFileDirOfCp):
            for file_name in file_names:
                if not file_name.endswith(".csv"):
                    continue

                # Read each CSV file
                data = pd.read_csv(os.path.join(
                    root, file_name), encoding=encoding)
                data.loc[:, time_day] = pd.to_datetime(data.loc[:, time_day])
                data[y_min] = data[y_name] - data[y_min]
                data[y_max] = data[y_max] - data[y_name]
                turbine_name = file_name.split(split_way)[0]

                # Generate the plot
                fig, ax = plt.subplots()
                ax.errorbar(x=data[time_day], y=data[y_name], yerr=[data[y_min], data[y_max]],
                            fmt='o', capsize=4, elinewidth=2, ecolor='lightgrey', mfc='dodgerblue')

                ax.set_xlabel('time')
                ax.set_ylabel('Cp')
                # ax.set_ylim(-0.2, 8)
                ax.set_title('{}={}'.format('turbine_name', turbine_name))
                # 旋转x轴刻度标签
                plt.xticks(rotation=45)


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