# Interpolate transition guide

This notebook contains three sets of demonstrations:

- lower-level FITPACK replacements for `scipy.interpolate.interp2d` for legacy bug-for-bug compatible `scipy.interpolate.interp2d` replacements;

- recommended replacements for `scipy.interpolate.interp2d` for use in new code;

- a demonstration of failure modes of 2D FITPACK-based linear interpolation and recommended replacements.

Note: Since this notebook shows usage of `interp2d` (which is marked for deprecation), we will silence deprecation warnings for simplicity:

    import warnings
    warnings.filterwarnings('ignore')

## 1. How to transition away from using `interp2d`

`interp2d` silently switches between interpolation on a 2D regular grid and interpolatin 2D scattered data. The switch is based on the lengths of the (raveled) `x`, `y`, and `z` arrays. In short, for regular grid use `scipy.interpolate.RectBivariateSpline`; for scattered interpolation, use the `bisprep/bisplev` combo. Below we give examples of the literal point-for-point transition, which should preserve the `interp2d` results exactly.

### 1.1 `interp2d` on a regular grid

We start from the (slightly modified) docstring example.

    import numpy as np
    import matplotlib.pyplot as plt
    from scipy.interpolate import interp2d, RectBivariateSpline

    x = np.arange(-5.01, 5.01, 0.25)
    y = np.arange(-5.01, 7.51, 0.25)
    xx, yy = np.meshgrid(x, y)
    z = np.sin(xx**2 + 2.*yy**2)
    f = interp2d(x, y, z, kind='cubic')

This is the “regular grid” code path, because

    z.size == len(x) * len(y)

    True

Also, note that `x.size != y.size`:

    x.size, y.size

    (41, 51)

Now, let’s build a convenience function to construct the interpolator and plot it.

    def plot(f, xnew, ynew):
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
        znew = f(xnew, ynew)

        ax1.plot(x, z[0, :], 'ro-', xnew, znew[0, :], 'b-')

        im = ax2.imshow(znew)
        plt.colorbar(im, ax=ax2)

        plt.show()
        return znew

Plotting:

    xnew = np.arange(-5.01, 5.01, 1e-2)
    ynew = np.arange(-5.01, 7.51, 1e-2)
    znew_i = plot(f, xnew, ynew)

#### Replacement: Use `RectBivariateSpline`, the result is identical

Note the transposes: first, in the constructor, second, you need to transpose the result of the evaluation. This is to undo the transposes `interp2d` does.

    r = RectBivariateSpline(x, y, z.T)

    rt = lambda xnew, ynew: r(xnew, ynew).T
    znew_r = plot(rt, xnew, ynew)

    from numpy.testing import assert_allclose
    assert_allclose(znew_i, znew_r, atol=1e-14)

### 1.2. `interp2d` with full coordinates of points (scattered interpolation)

Here, we flatten the meshgrid from the previous exercise to illustrate the functionality.

    xxr = xx.ravel()
    yyr = yy.ravel()
    zzr = z.ravel()

    f = interp2d(xxr, yyr, zzr, kind='cubic')

Note that this the “not regular grid” code path, meant for scattered data, with `len(x) == len(y) == len(z)`.

    len(xxr) == len(yyr) == len(zzr)

    True

    xnew = np.arange(-5.01, 5.01, 1e-2)
    ynew = np.arange(-5.01, 7.51, 1e-2)
    znew_i = plot(f, xnew, ynew)

#### Replacement: Use `scipy.interpolate.bisplrep` / `scipy.interpolate.bisplev` directly

    from scipy.interpolate import bisplrep, bisplev
    tck = bisplrep(xxr, yyr, zzr, kx=3, ky=3, s=0)
    # convenience: make up a callable from bisplev
    ff = lambda xnew, ynew: bisplev(xnew, ynew, tck).T   # Note the transpose, to mimic what interp2d does

    znew_b = plot(ff, xnew, ynew)

    assert_allclose(znew_i, znew_b, atol=1e-15)

## 2. Alternative to `interp2d`: regular grid

For new code, the recommended alternative is `RegularGridInterpolator`. It is an independent implementation, not based on FITPACK. Supports nearest, linear interpolation and odd-order tensor product splines.

The spline knots are guaranteed to coincide with the data points.

Note that, here:

1. the tuple argument, is `(x, y)`

2. `z` array needs a transpose

3. the keyword name is method, not kind

4. `bounds_error` argument is `True` by default.

    from scipy.interpolate import RegularGridInterpolator as RGI

    r = RGI((x, y), z.T, method='linear', bounds_error=False)

Evaluation: create a 2D meshgrid. Use indexing=’ij’ and `sparse=True` to save some memory:

    xxnew, yynew = np.meshgrid(xnew, ynew, indexing='ij', sparse=True)

Evaluate, note the tuple argument:

    znew_reggrid = r((xxnew, yynew))

    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))

    # Again, note the transpose to undo the `interp2d` convention
    znew_reggrid_t = znew_reggrid.T

    ax1.plot(x, z[0, :], 'ro-', xnew, znew_reggrid_t[0, :], 'b-')

    im = ax2.imshow(znew_reggrid_t)
    plt.colorbar(im, ax=ax2)

    <matplotlib.colorbar.Colorbar at 0x7f0ecbe021f0>

## 3. Scattered 2D linear interpolation: prefer `LinearNDInterpolator` to `SmoothBivariateSpline` or `bisplrep`

For 2D scattered linear interpolation, both `SmoothBivariateSpline` and `biplrep` may either emit warnings, or fail to interpolate the data, or produce splines which with knots away from the data points. “Instead, prefer `LinearNDInterpolator`, which is based on triangulating the data via `QHull`.

    # TestSmoothBivariateSpline::test_integral
    from scipy.interpolate import SmoothBivariateSpline, LinearNDInterpolator

    x = np.array([1,1,1,2,2,2,4,4,4])
    y = np.array([1,2,3,1,2,3,1,2,3])
    z = np.array([0,7,8,3,4,7,1,3,4])

Now, use the linear interpolation over Qhull-based triangulation of data:

    xy = np.c_[x, y]   # or just list(zip(x, y))
    lut2 = LinearNDInterpolator(xy, z)

    X = np.linspace(min(x), max(x))
    Y = np.linspace(min(y), max(y))
    X, Y = np.meshgrid(X, Y)

The result is easy to understand and interpret:

    fig = plt.figure()
    ax = fig.add_subplot(projection='3d')

    ax.plot_wireframe(X, Y, lut2(X, Y))
    ax.scatter(x, y, z,  'o', color='k', s=48)

    <mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f0ecbd019a0>

Note that `bisplrep` does something different! It may place spline knots outside of the data.

For illustration, consider the same data from the previous example:

    tck = bisplrep(x, y, z, kx=1, ky=1, s=0)

    fig = plt.figure()
    ax = fig.add_subplot(projection='3d')

    xx = np.linspace(min(x), max(x))
    yy = np.linspace(min(y), max(y))
    X, Y = np.meshgrid(xx, yy)
    Z = bisplev(xx, yy, tck)
    Z = Z.reshape(*X.shape).T

    ax.plot_wireframe(X, Y, Z, rstride=2, cstride=2)
    ax.scatter(x, y, z,  'o', color='k', s=48)

    <mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f0ec82eeac0>

Also, `SmoothBivariateSpline` fails to interpolate the data. Again, use the same data from the previous example.

    lut = SmoothBivariateSpline(x, y, z, kx=1, ky=1, s=0)

    fig = plt.figure()
    ax = fig.add_subplot(projection='3d')

    xx = np.linspace(min(x), max(x))
    yy = np.linspace(min(y), max(y))
    X, Y = np.meshgrid(xx, yy)

    ax.plot_wireframe(X, Y, lut(xx, yy).T, rstride=4, cstride=4)
    ax.scatter(x, y, z,  'o', color='k', s=48)

    <mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f0ec82443d0>

Note that both `SmoothBivariateSpline` and `bisplrep` results have artifacts, unlike the `LinearNDInterpolator`’s. Issues illustrated here were reported for linear interpolation, however the FITPACK knot-selection mechanism does not guarantee to avoid either of these issues for higher-order (e.g. cubic) spline surfaces.