first commit

main.py

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+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()

尺寸标定板/hor-1.csv

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尺寸标定板/hor-2.csv

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尺寸标定板/hor-4.csv

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尺寸标定板/ver-2.csv

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尺寸标定板/ver-3.csv

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