具有周期性边界条件的2D插值

Question

I'm running a simulation on a 2D space with periodic boundary conditions. A continuous function is represented by its values on a grid. I need to be able to evaluate the function and its gradient at any point in the space. Fundamentally, this isn't a hard problem -- or to be precise, it's an almost already solved problem. The function can be interpolated using a cubic spline with scipy.interpolate.RectBivariateSpline. The reason it's almost solved is that RectBivariateSpline cannot handle periodic boundary conditions, nor can anything else in scipy.interpolate, as far as I can figure out from the documentation.

Is there a python package that can do this? If not, can I adapt scipy.interpolate to handle periodic boundary conditions? For instance, would it be enough to put a border of, say, four grid elements around the entire space and explicitly represent the periodic condition on it?

[ADDENDUM] A little more detail, in case it matters: I am simulating the motion of animals in a chemical gradient. The continuous function I mentioned above is the concentration of a chemical that they are attracted to. It changes with time and space according to a straightforward reaction/diffusion equation. Each animal has an x,y position (which cannot be assumed to be at a grid point). They move up the gradient of attractant. I'm using periodic boundary conditions as a simple way of imitating an unbounded space.

Answer 1

It appears that the python function that comes closest is scipy.signal.cspline2d. This is exactly what I want, except that it assumes mirror-symmetric boundary conditions. Thus, it appears that I have three options:

1. Write my own cubic spline interpolation function that works with periodic boundary conditions, perhaps using the cspline2d sources (which are based on functions written in C) as a starting point.

2. The kludge: the effect of data at i on the spline coefficient at j goes as r^|ij|, with r = -2 + sqrt(3) ~ -0.26. So the effect of the edge is down to r^20 ~ 10^-5 if I nest the grid within a border of width 20 all the way around that replicates the periodic values, something like this:

   bzs1 = np.array( [zs1[i%n,j%n] for i in range(-20, n+20) for j in range(-20, n+20)] )
   bzs1 = bzs1.reshape((n + 40, n + 40))

   Then I call cspline2d on the whole array, but use only the middle. This should work, but it's ugly.

3. Use Hermite interpolation instead. In a 2D regular grid, this corresponds to bicubic interpolation . The disadvantage is that the interpolated function has a discontinuous second derivative. The advantages are it is (1) relatively easy to code, and (2) for my application, computationally efficient. At the moment, this is the solution I'm favoring.

I did the math for interpolation with trig functions rather than polynomials, as @mdurant suggested. It turns out to be very similar to the cubic spline, but requires more computation and produces worse results, so I won't be doing that.

EDIT: A colleague told me of a fourth solution:

1. The GNU Scientific Library (GSL) has interpolation functions that can handle periodic boundary conditions. There are two (at least) python interfaces to GSL: PyGSL and CythonGSL . Unfortunately, GSL interpolation seems to be restricted to one dimension, so it's not a lot of use to me, but there's lots of good stuff in GSL.

Answer 2

Another function that could work is scipy.ndimage.interpolation.map_coordinates . It does spline interpolation with periodic boundary conditions. It does not not directly provide derivatives, but you could calculate them numerically.

Answer 3

These functions can be found at my github, master/hmc/lattice.py :

• Periodic boundary conditions The Periodic_Lattice() class is described here in full.
• Lattice Derivatives In the repository you will find a laplacian function, a squared gradient (for the gradient just take the square root) and and overloaded version of np.ndarray
• Unit Tests The test cases can be found in same repo in tests/test_lattice.py

Answer 4

I have been using the following function which augments the input to create data with effective periodic boundary conditions. Augmenting the data has a distinct advantage over modifying an existing algorithm: the augmented data can easily be interpolated using any algorithm. See below for an example.

def augment_with_periodic_bc(points, values, domain):
    """
    Augment the data to create periodic boundary conditions.

    Parameters
    ----------
    points : tuple of ndarray of float, with shapes (m1, ), ..., (mn, )
        The points defining the regular grid in n dimensions.
    values : array_like, shape (m1, ..., mn, ...)
        The data on the regular grid in n dimensions.
    domain : float or None or array_like of shape (n, )
        The size of the domain along each of the n dimenions
        or a uniform domain size along all dimensions if a 
        scalar. Using None specifies aperiodic boundary conditions.

    Returns
    -------
    points : tuple of ndarray of float, with shapes (m1, ), ..., (mn, )
        The points defining the regular grid in n dimensions with 
        periodic boundary conditions.
    values : array_like, shape (m1, ..., mn, ...)
        The data on the regular grid in n dimensions with periodic
        boundary conditions.
    """
    # Validate the domain argument
    n = len(points)
    if np.ndim(domain) == 0:
        domain = [domain] * n
    if np.shape(domain) != (n,):
        raise ValueError("`domain` must be a scalar or have the same "
                         "length as `points`")

    # Pre- and append repeated points
    points = [x if d is None else np.concatenate([x - d, x, x + d]) 
              for x, d in zip(points, domain)]

    # Tile the values as necessary
    reps = [1 if d is None else 3 for d in domain]
    values = np.tile(values, reps)

    return points, values

Example

The example below shows interpolation with periodic boundary conditions in one dimension but the function above can be applied in arbitrary dimensions.

rcParams['figure.dpi'] = 144
fig, axes = plt.subplots(2, 2, True, True)

np.random.seed(0)
x = np.linspace(0, 1, 10, endpoint=False)
y = np.sin(2 * np.pi * x)
ax = axes[0, 0]
ax.plot(x, y, marker='.')
ax.set_title('Points to interpolate')

sampled = np.random.uniform(0, 1, 100)
y_sampled = interpolate.interpn([x], y, sampled, bounds_error=False)
valid = ~np.isnan(y_sampled)
ax = axes[0, 1]
ax.scatter(sampled, np.where(valid, y_sampled, 0), marker='.', c=np.where(valid, 'C0', 'C1'))
ax.set_title('interpn w/o periodic bc')

[x], y = augment_with_periodic_bc([x], y, domain=1.0)
y_sampled_bc = interpolate.interpn([x], y, sampled)
ax = axes[1, 0]
ax.scatter(sampled, y_sampled_bc, marker='.')
ax.set_title('interpn w/ periodic bc')

y_sampled_bc_cubic = interpolate.interp1d(x, y, 'cubic')(sampled)
ax = axes[1, 1]
ax.scatter(sampled, y_sampled_bc_cubic, marker='.')
ax.set_title('cubic interp1d w/ periodic bc')

fig.tight_layout()