该函数输出的结果是一个三元组tck,这个三元组就是曲线的spline表示,用于表示曲线的节点向量、系数和spline序号,默认的spline orde是cubic,这可以通过k参数来修改。一旦确定了曲线的spline表示,就可以使用splev()函数对x进行评估: scipy.interpolate.splev(x, tck, der=0, ext=0) 1. 举个例子,使用splrep()函数来...
import numpy as np import matplotlib.pyplot as plt from scipy.interpolate import CubicSpline class Natural_cubic_spline: def __init__(self,x,y): self.x = np.array(x) #n个点的x值 self.y = np.array(y) #n个点的y值 self.h = self.x[1:] - self.x[:-1] #n-1个值 self.dy ...
ax.plot(np.cos(xs), np.sin(xs), label='true') cs = CubicSpline(theta, y, bc_type='periodic') print("ds/dx={:.1f} ds/dy={:.1f}".format(cs(0, 1)[0], cs(0, 1)[1])) #ds/dx=0.0 ds/dy=1.0 ax.plot(cs(xs)[:, 0], cs(xs)[:, 1], label='spline') #样条插值...
I'm trying to get the coefficient of cubic spline poly from scipy interpolate Cubic Spline with the point (0,0),(1,1), (2,0). The coefficients I got are different from the values from site: https://tools.timodenk.com/cubic-spline-interpolation inserting the same points. I would like...
f=interpolate.CubicSpline(x,y)y_new=f(x_new)print(y_new) 1. 2. 3. 4. 3. 插值方法选择 在实际应用中,需要根据数据的特点和需求来选择合适的插值方法。线性插值简单快速,适用于数据变化较为线性的情况;多项式插值可以更加精确地拟合数据,但容易出现过拟合;样条插值可以平滑地处理数据,适用于较为复杂的数...
When it is possible to get each individual for ai, bi, ci, di, it becomes easy to combine the definitions of the natural cubic spline interpolator function within these 2 single loops. def cubic_interpolate(x0, x, y): """ Natural cubic spline interpolate function This function is lic...
sin(x) tck = interpolate.splrep(x, y, s=0) xnew = np.arange(0, 2 * np.pi, np.pi / 50) ynew = interpolate.splev(xnew, tck, der=0) plt.figure() plt.plot(x, y, 'x', xnew, ynew, xnew, np.sin(xnew), x, y, 'b') plt.legend(['Linear', 'Cubic Spline', 'True...
参数定义:scipy.interpolate.interp1d(x, y, kind='linear', axis=- 1, copy=True, bounds_error=None, fill_value=nan, assume_sorted=False) 参数说明: kind: 指定插值函数的数据生成线性特征 ‘linear’ ‘nearest’‘nearest-up’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, ...
y_bspline=interpolate.splev(x_new, tck) plt.xlabel(u'安培/A') plt.ylabel(u'伏特/V') plt.plot(x, y,"o", label=u"原始数据") plt.plot(x_new, f_linear(x_new), label=u"线性插值") plt.plot(x_new, y_bspline, label=u"B-spline插值") ...
SciPy的interpolate模块提供了许多对数据进行插值运算的函数,范围涵盖简单的一维插值到复杂多维插值求解。当样本数据变化归因于一个独立的变量时,就使用一维插值;反之样本数据归因于多个独立变量时,使用多维插值。 classscipy.interpolate.interp1d(x,y,kind=’linear’,axis=-1,copy=True,bounds_error=None,fill_value...