1-Dimensional Interpolation

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import ndsplines
import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate
from scipy.stats import norm


def gaussian(x_in):
    z = norm.ppf(0.9995)
    x = z * (2 * x_in - 1)
    return norm.pdf(x)


def sin(x_in):
    x = np.pi * (x_in - 0.5)
    return np.sin(x)


def tanh(x_in):
    x = 2 * np.pi * (x_in - 0.5)
    return np.tanh(x)


funcs = [gaussian, sin, tanh]

x = np.linspace(0, 1, 9)
xx = np.linspace(-0.25, 1.25, 1024)
k = 3

for degree in range(0, 4):
    for func in funcs:
        fvals = func(x)
        truef = func(xx)
        plt.figure()

        plot_sel = slice(None)

        plt.gca().set_prop_cycle(None)
        test_Bspline = interpolate.make_interp_spline(x, fvals, k=degree)
        splinef = test_Bspline(xx.copy(), extrapolate=True)
        plt.plot(
            xx, splinef, "--", lw=3.0, label="scipy.interpolate.make_interp_spline"
        )

        test_NDBspline = ndsplines.make_interp_spline(x, fvals, degrees=degree)
        NDsplinef = test_NDBspline(xx.copy())
        plt.plot(xx, NDsplinef, label="ndspline.make_interp_spline")

        plt.plot(xx, truef, "k--", label="True " + func.__name__)
        plt.plot(x, fvals, "ko")
        plt.title("k=%d" % degree)

        plt.legend(loc="best")
        plt.show()