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
 import itertools


 def gaussian(x_in):
     z = norm.ppf(.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(-.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()