import numpy as np
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt


def fft_anlyies(data, time_step=0.0002) -> pd.DataFrame:
    """ 生成FFT数据 """
    frequencies = np.fft.fftfreq(data.shape[0], d=time_step)  # 计算频率轴
    result = pd.DataFrame([], index=frequencies)
    for col_name in data:
        """ 分列处理 """
        fft_result = np.fft.fft(data[col_name])
        fft_magnitude = np.abs(fft_result)  # 获取幅度谱
        result[col_name + '频谱'] = fft_magnitude
    return result

def RecordingAnalysis(data_origin: pd.DataFrame):
    """ 光伏逆变器录波数据分析 """
    graph_map = {
       '电网电压': ['A相电网电压', 'B相电网电压', 'C相电网电压'], 
       '滤波电容电压': ['A相滤波电容电压', 'B相滤波电容电压', 'C相滤波电容电压'], 
       '母线电压': ['上母线电压', '下母线电压'], 
       '母线电流': ['上母线电流', '下母线电流'], 
       '逆变电流': ['A相逆变电流', 'B相逆变电流', 'C相逆变电流'], 
       '负载电流': ['A相负载电流', 'B相负载电流', 'C相负载电流'], 
       '调制比': ['A相调制比', 'B相调制比', 'C相调制比'], 
       '电压前馈': ['A相电压前馈', 'B相电压前馈', 'C相电压前馈'], 
       '逆变电流参考值': ['A相逆变电流参考值', 'B相逆变电流参考值', 'C相逆变电流参考值'], 
       '谐振控制输出': ['谐振控制输出'], 
       '有源阻尼': ['有源阻尼'], 
       '零序电压': ['零序电压'], 
       '相角': ['相角'], 
       '故障状态': ['状态标签', '故障录波标志位'], 
    }
    data_origin.index = data_origin.index * time_step
    for key, value in graph_map.items():
        ax = data_origin[value].plot(title=key)
        if key == '故障状态':
            threshold = 1e-5
            diff = data_origin[value].iloc[:-1].reset_index(drop=True) - data_origin[value].iloc[1:].reset_index(drop=True)
            diff = diff.replace(0, pd.NA).dropna(how='all')
            for x, d in diff.T.items():
                
                if threshold is None or d > threshold:
                    Y = data_origin.loc[x, value]
                    for y in Y:
                        ax.annotate(f'{y:.2f}', (x, y), textcoords="jump points", xytext=(0,5), ha='center')

        if key == '电网电压' or key == '滤波电容电压':
            """ 三相运行数据 """
            data_fft = fft_anlyies(data_origin[value], time_step=time_step)
            data_fft.plot(title=(key + ' FFT频谱图'), xlabel = '频率 (Hz)', ylabel = '幅度')
            ax_fft = data_fft.plot(title=(key + ' FFT频谱图'), xlabel = '频率 (Hz)', ylabel = '幅度', xlim=(-200, 200))




if __name__ == "__main__":
    # plt中文显示
    plt.rcParams['font.sans-serif'] = ['SimHei']
    # 坐标轴负数显示
    plt.rcParams['axes.unicode_minus'] = False 

    time_step = 1 / 5000
    data = pd.read_excel(Path(r"D:\WorkSpace\UserTool\SelfTool\FrameParser\test\pv_inviter\19__24_12_06_15_19_03.xlsx"))
    # Index([
    #    'A相电网电压', 'B相电网电压', 'C相电网电压', 
    #    'A相滤波电容电压', 'B相滤波电容电压', 'C相滤波电容电压',
    #    '上母线电压', '下母线电压', 
    #    '上母线电流', '下母线电流', 
    #    'A相逆变电流', 'B相逆变电流', 'C相逆变电流',
    #    'A相负载电流', 'B相负载电流', 'C相负载电流', 
    #    'A相调制比', 'B相调制比', 'C相调制比', 
    #    'A相电压前馈', 'B相电压前馈', 'C相电压前馈', 
    #    'A相逆变电流参考值', 'B相逆变电流参考值', 'C相逆变电流参考值',
    #    '谐振控制输出', 
    #    '有源阻尼', 
    #    '零序电压', 
    #    '相角',
    #    '状态标签', '故障录波标志位'
    # ],
    #   dtype='object')

    RecordingAnalysis(data)

    if not hasattr(__builtins__,"__IPYTHON__"):
        plt.waitforbuttonpress()