How to do cubic spline interpolation and integration in Pytorch

Question

In Pytorch, is there cubic spline interpolation similar to Scipy’s? Given 1D input tensors x and y, I want to interpolate through those points and evaluate them at xs to obtain ys. Also, I want an integrator function that finds Ys, the integral of the spline interpolation from x[0] to xs.

Asked By: chausies

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Source

Answer 1

Here is a gist I made doing this with Cubic Hermite Splines in Pytorch efficiently and with autograd support.

For convenience, I’ll also put the code here.

import torch as T

def h_poly_helper(tt):
  A = T.tensor([
      [1, 0, -3, 2],
      [0, 1, -2, 1],
      [0, 0, 3, -2],
      [0, 0, -1, 1]
      ], dtype=tt[-1].dtype)
  return [
    sum( A[i, j]*tt[j] for j in range(4) )
    for i in range(4) ]

def h_poly(t):
  tt = [ None for _ in range(4) ]
  tt[0] = 1
  for i in range(1, 4):
    tt[i] = tt[i-1]*t
  return h_poly_helper(tt)

def H_poly(t):
  tt = [ None for _ in range(4) ]
  tt[0] = t
  for i in range(1, 4):
    tt[i] = tt[i-1]*t*i/(i+1)
  return h_poly_helper(tt)

def interp_func(x, y):
  "Returns integral of interpolating function"
  if len(y)>1:
    m = (y[1:] - y[:-1])/(x[1:] - x[:-1])
    m = T.cat([m[[0]], (m[1:] + m[:-1])/2, m[[-1]]])
  def f(xs):
    if len(y)==1: # in the case of 1 point, treat as constant function
      return y[0] + T.zeros_like(xs)
    I = T.searchsorted(x[1:], xs)
    dx = (x[I+1]-x[I])
    hh = h_poly((xs-x[I])/dx)
    return hh[0]*y[I] + hh[1]*m[I]*dx + hh[2]*y[I+1] + hh[3]*m[I+1]*dx
  return f

def interp(x, y, xs):
  return interp_func(x,y)(xs)

def integ_func(x, y):
  "Returns interpolating function"
  if len(y)>1:
    m = (y[1:] - y[:-1])/(x[1:] - x[:-1])
    m = T.cat([m[[0]], (m[1:] + m[:-1])/2, m[[-1]]])
    Y = T.zeros_like(y)
    Y[1:] = (x[1:]-x[:-1])*(
        (y[:-1]+y[1:])/2 + (m[:-1] - m[1:])*(x[1:]-x[:-1])/12
        )
    Y = Y.cumsum(0)
  def f(xs):
    if len(y)==1:
      return y[0]*(xs - x[0])
    I = T.searchsorted(x[1:], xs)
    dx = (x[I+1]-x[I])
    hh = H_poly((xs-x[I])/dx)
    return Y[I] + dx*(
        hh[0]*y[I] + hh[1]*m[I]*dx + hh[2]*y[I+1] + hh[3]*m[I+1]*dx
        )
  return f

def integ(x, y, xs):
  return integ_func(x,y)(xs)

# Example
if __name__ == "__main__":
  import matplotlib.pylab as P # for plotting
  x = T.linspace(0, 6, 7)
  y = x.sin()
  xs = T.linspace(0, 6, 101)
  ys = interp(x, y, xs)
  Ys = integ(x, y, xs)
  P.scatter(x, y, label='Samples', color='purple')
  P.plot(xs, ys, label='Interpolated curve')
  P.plot(xs, xs.sin(), '--', label='True Curve')
  P.plot(xs, Ys, label='Spline Integral')
  P.plot(xs, 1-xs.cos(), '--', label='True Integral')
  P.legend()
  P.show()

Answered By: chausies

Answer 2

This was meant as a comment on @chausies answer, but was too long to post.

Just wanted to post a slightly minified version of his answer, mostly for my own future reference:

import torch

def h_poly(t):
    tt = t[None, :]**torch.arange(4, device=t.device)[:, None]
    A = torch.tensor([
        [1, 0, -3, 2],
        [0, 1, -2, 1],
        [0, 0, 3, -2],
        [0, 0, -1, 1]
    ], dtype=t.dtype, device=t.device)
    return A @ tt


def interp(x, y, xs):
    m = (y[1:] - y[:-1]) / (x[1:] - x[:-1])
    m = torch.cat([m[[0]], (m[1:] + m[:-1]) / 2, m[[-1]]])
    idxs = torch.searchsorted(x[1:], xs)
    dx = (x[idxs + 1] - x[idxs])
    hh = h_poly((xs - x[idxs]) / dx)
    return hh[0] * y[idxs] + hh[1] * m[idxs] * dx + hh[2] * y[idxs + 1] + hh[3] * m[idxs + 1] * dx

Answered By: Julius

How to do cubic spline interpolation and integration in Pytorch

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