import numpy as np
import pandas as pd
from pathlib import Path
from scipy.interpolate import interp1d, CubicSpline, UnivariateSpline, PchipInterpolator, Akima1DInterpolator
from statsmodels.tsa.stattools import acf
from scipy.signal import find_peaks
import matplotlib.pyplot as plt

# 1. 读取 CSV 文件并提取指定列
def read_csv_column(file_path, column_name):
    if isinstance(file_path, str):
        file_path = Path(file_path)
    df = pd.read_csv(file_path)
    if column_name not in df.columns:
        raise ValueError(f"Column '{column_name}' not found in CSV file.")
    return df[column_name].values

# 2. 计算自相关函数（ACF）
def compute_acf(data):
    n = len(data)
    nlags = n // 2
    acf_values = acf(data, nlags=nlags, fft=True)
    return acf_values[:nlags]  # 截断为原长度的一半

# 3. 插值函数
"""
- 'linear'       : 线性插值
- 'cubic'        : 三次样条插值 (CubicSpline)
- 'spline'       : 样条插值 (UnivariateSpline)
- 'pchip'        : 分段三次 Hermite 插值 (PchipInterpolator)
- 'akima'        : Akima 插值 (Akima1DInterpolator)
- 'quadratic'    : 二次插值
"""
def interpolate_data(data, method='cubic', num_points=100, **kwargs):
    x = np.arange(len(data))

    if method == 'linear':
        interp_func = interp1d(x, data, kind='linear', fill_value="extrapolate", **kwargs)
    elif method == 'cubic':
        interp_func = CubicSpline(x, data, bc_type='natural', **kwargs)
    elif method == 'spline':
        # UnivariateSpline 默认使用三次样条，但可以指定 degree=s
        interp_func = UnivariateSpline(x, data, s=kwargs.get('s', None), k=kwargs.get('k', 3), **kwargs)
    elif method == 'pchip':
        interp_func = PchipInterpolator(x, data, **kwargs)
    elif method == 'akima':
        interp_func = Akima1DInterpolator(x, data, **kwargs)
    elif method == 'quadratic':
        interp_func = interp1d(x, data, kind='quadratic', fill_value="extrapolate", **kwargs)
    else:
        raise ValueError(f"Unsupported interpolation method: {method}. "
                         f"Supported methods: linear, cubic, spline, pchip, akima, quadratic, quintic")

    x_new = np.linspace(0, len(data) - 1, num_points)
    y_new = interp_func(x_new)
    return y_new

# 4. 查找极大值点
def find_maxima(data):
    peaks, _ = find_peaks(data, height=np.mean(data))
    return peaks.tolist(), data[peaks].tolist()

# 5. 主流程
if __name__ == "__main__":
    # 示例文件路径和列名
    file_path = Path("尺寸标定板/ver-1.csv")  # 假设当前目录下有 data.csv
    column_name = "Gray_Value"

    # Step 1: 读取 CSV 文件
    data = read_csv_column(file_path, column_name)
    print(len(data))


    # Step 2: 计算自相关函数
    acf_data = compute_acf(data)
    print(len(acf_data))

    # Step 3: 插值自相关函数
    interpolated_acf = interpolate_data(acf_data, method='cubic', num_points=len(acf_data) * 1000)
    print(len(interpolated_acf))

    # Step 4: 查找极大值
    peaks_indices, peaks_values = find_maxima(interpolated_acf)

    # Step 5: 可视化
    plt.figure(figsize=(12, 10))

    # 原始数据
    plt.subplot(3, 1, 1)
    plt.plot(data, label='Original Data')
    plt.title('Original Data')
    plt.grid(True)

    # 自相关函数
    plt.subplot(3, 1, 2)
    plt.plot(acf_data, label='ACF', marker='o')
    plt.title('Autocorrelation Function (Truncated)')
    plt.grid(True)

    # 插值后的自相关函数及极大值
    plt.subplot(3, 1, 3)
    x_interpolated = np.linspace(0, len(acf_data) - 1, len(interpolated_acf))
    print(x_interpolated[peaks_indices])
    plt.plot(x_interpolated, interpolated_acf, label='Interpolated ACF', color='tab:orange')
    plt.plot(x_interpolated[peaks_indices], peaks_values, 'rx', markersize=10, label='Maxima')
    plt.title('Interpolated ACF with Maxima')
    plt.grid(True)
    plt.tight_layout()
    plt.legend()
    plt.show()