2-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(0.995)
    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)


def dist(x_in, y_in):
    return np.sqrt((x_in - 0.25) ** 2 + (y_in - 0.25) ** 2)


funcs = [gaussian, sin, tanh]


def wrap2d(funcx, funcy):
    def func2d(x_in, y_in):
        return funcx(x_in) * funcy(y_in)

    func2d.__name__ = "_".join([funcx.__name__, funcy.__name__])
    return func2d


funcs = [
    wrap2d(*funcs_to_wrap)
    for funcs_to_wrap in itertools.combinations_with_replacement(funcs, r=2)
]
funcs.append(dist)

x = np.linspace(0, 1, 7)
y = np.linspace(0, 1, 7)

xx = np.linspace(0, 1, 64)
yy = np.linspace(0, 1, 64)

xx = np.linspace(-0.25, 1.25, 64)
yy = np.linspace(-0.25, 1.25, 64)
k = 3


meshx, meshy = np.meshgrid(x, y, indexing="ij")
gridxy = np.stack((meshx, meshy), axis=-1)


meshxx, meshyy = np.meshgrid(xx, yy, indexing="ij")
gridxxyy = np.stack((meshxx, meshyy), axis=-1)

for func in funcs:
    fvals = func(meshx, meshy)
    truef = func(meshxx, meshyy)
    test_NDBspline = ndsplines.make_interp_spline(
        gridxy,
        fvals,
    )
    test_RectSpline = interpolate.RectBivariateSpline(x, y, fvals)

    fig = plt.figure()
    ax = fig.add_subplot(111, projection="3d")

    ax.plot_wireframe(meshxx, meshyy, truef, alpha=0.25, color="C0")
    ax.plot_wireframe(meshxx, meshyy, test_NDBspline(gridxxyy), color="C1")
    ax.plot_wireframe(
        meshxx, meshyy, test_RectSpline(meshxx, meshyy, grid=False), color="C2"
    )
    plt.show()