橡皮筋基线校正（Rubberband）修正

前文介绍：橡皮筋基线校正

提取修正范围

def get_portion_interest(x,y,bir):

    birlen = np.array(bir.shape[0])#用于获取 bir 的行数，也就是要提取信号的区域数量

    sp = np.transpose(np.vstack((x.reshape(-1),y.reshape(-1))))
    ### selection of bir data
    for i in range(birlen):
        if i == 0:
            yafit = sp[np.where((sp[:,0]> bir[i,0]) & (sp[:,0] < bir[i,1]))]
        else:
            je = sp[np.where((sp[:,0]> bir[i,0]) & (sp[:,0] < bir[i,1]))]
            yafit = np.vstack((yafit,je))

    return yafit

参数
    ----------
    x : ndarray
        x 轴
    y : ndarray
        y 值
    bir : n x 2 数组
        需要提取信号的区域的 x 值、
        必须是一个 n x 2 维数组，其中 n 是要提取的区域数。
        第 0 列为低边界，第 1 列为高边界。

返回值
    -------
    yafit : ndarray
        一个 2 列 x-y 数组，包含 bir 中的信号。

Rubberband_baseline

import numpy as np
from scipy.spatial import ConvexHull
def Rubberband_baseline (x_input,y_input,bir):
     # we get the signals in the bir
    yafit_unscaled = get_portion_interest(x_input,y_input,bir)

    # signal standard standardization with sklearn
    # this helps for polynomial fitting
    X_scaler = preprocessing.StandardScaler().fit(x_input.reshape(-1, 1))
    Y_scaler = preprocessing.StandardScaler().fit(y_input.reshape(-1, 1))

    # transformation
    x = X_scaler.transform(x_input.reshape(-1, 1))
    y = Y_scaler.transform(y_input.reshape(-1, 1))

    yafit = np.copy(yafit_unscaled)
    yafit[:,0] = X_scaler.transform(yafit_unscaled[:,0].reshape(-1, 1))[:,0]
    yafit[:,1] = Y_scaler.transform(yafit_unscaled[:,1].reshape(-1, 1))[:,0]

    y = y.reshape(len(y_input))
    
    x=x.flatten()
    # Find the convex hull
    v = ConvexHull(np.array(list(zip(x, y))), incremental=True).vertices
    #v = ConvexHull(np.vstack((x.ravel(), y.ravel())).T).vertices

    # Rotate convex hull vertices until they start from the lowest one
    v = np.roll(v, -v.argmin())
    # Leave only the ascending part
    v = v[:v.argmax()]

    # Create baseline using linear interpolation between vertices
    baseline_fitted = np.interp(x, x[v], y[v])
    return y_input.reshape(500,1)-Y_scaler.inverse_transform(baseline_fitted.reshape(500, 1)), Y_scaler.inverse_transform(baseline_fitted.reshape(500, 1))
    
    
    

图像绘制

plt.figure(dpi=150)
plt.plot(x, y, "k-", label="Raw")
plt.plot(x, baseline_fit.T, "-", color="r", label="baseline")
plt.plot(x, y-baseline_fit[1,:], "-", color="g", label="Rubberband_baseline")
plt.xlabel("X")
plt.ylabel("Y")
plt.legend()