Next: Multi-dimensional Interpolation, Up: Interpolation [Contents][Index]

### 29.1 One-dimensional Interpolation

Octave supports several methods for one-dimensional interpolation, most of which are described in this section. Polynomial Interpolation and Interpolation on Scattered Data describe additional methods.

Function File: yi = interp1 (x, y, xi)
Function File: yi = interp1 (y, xi)
Function File: yi = interp1 (…, method)
Function File: yi = interp1 (…, extrap)
Function File: yi = interp1 (…, "left")
Function File: yi = interp1 (…, "right")
Function File: pp = interp1 (…, "pp")

  One-dimensional interpolation.

  Interpolate input data to determine the value of yi at the points xi. If not specified, x is taken to be the indices of y. If y is a matrix or an N-dimensional array, the interpolation is performed on each column of y.

  Method is one of:

  `"nearest"`

    Return the nearest neighbor

  `"linear"`

    Linear interpolation from nearest neighbors

  `"pchip"`

    Piecewise cubic Hermite interpolating polynomial

  `"cubic"`

    Cubic interpolation (same as `pchip`)

  `"spline"`

    Cubic spline interpolation—smooth first and second derivatives throughout the curve

  Adding ’*’ to the start of any method above forces `interp1` to assume that x is uniformly spaced, and only `x(1)` and `x(2)` are referenced. This is usually faster, and is never slower. The default method is `"linear"`.

  If extrap is the string `"extrap"`, then extrapolate values beyond the endpoints using the current method. If extrap is a number, then replace values beyond the endpoints with that number. When unspecified, extrap defaults to NA.

  If the string argument `"pp"` is specified, then xi should not be supplied and `interp1` returns a piecewise polynomial object. This object can later be used with `ppval` to evaluate the interpolation. There is an equivalence, such that `ppval (interp1 (x, y, method, `"pp"`), xi) == interp1 (x, y, xi, method, `"extrap"`)`.

  Duplicate points in x specify a discontinuous interpolant. There may be at most 2 consecutive points with the same value. If x is increasing, the default discontinuous interpolant is right-continuous. If x is decreasing, the default discontinuous interpolant is left-continuous. The continuity condition of the interpolant may be specified by using the options, `"left"` or `"right"`, to select a left-continuous or right-continuous interpolant, respectively. Discontinuous interpolation is only allowed for `"nearest"` and `"linear"` methods; in all other cases, the x-values must be unique.

  An example of the use of `interp1` is

      xf = [0:0.05:10];
      yf = sin (2*pi*xf/5);
      xp = [0:10];
      yp = sin (2*pi*xp/5);
      lin = interp1 (xp, yp, xf);
      spl = interp1 (xp, yp, xf, "spline");
      cub = interp1 (xp, yp, xf, "cubic");
      near = interp1 (xp, yp, xf, "nearest");
      plot (xf, yf, "r", xf, lin, "g", xf, spl, "b",
            xf, cub, "c", xf, near, "m", xp, yp, "r*");
      legend ("original", "linear", "spline", "cubic", "nearest");

  See also: interpft, interp2, interp3, interpn.

There are some important differences between the various interpolation methods. The `"spline"` method enforces that both the first and second derivatives of the interpolated values have a continuous derivative, whereas the other methods do not. This means that the results of the `"spline"` method are generally smoother. If the function to be interpolated is in fact smooth, then `"spline"` will give excellent results. However, if the function to be evaluated is in some manner discontinuous, then `"pchip"` interpolation might give better results.

This can be demonstrated by the code

    t = -2:2;
    dt = 1;
    ti =-2:0.025:2;
    dti = 0.025;
    y = sign (t);
    ys = interp1 (t,y,ti,"spline");
    yp = interp1 (t,y,ti,"pchip");
    ddys = diff (diff (ys)./dti) ./ dti;
    ddyp = diff (diff (yp)./dti) ./ dti;
    figure (1);
    plot (ti,ys,"r-", ti,yp,"g-");
    legend ("spline", "pchip", 4);
    figure (2);
    plot (ti,ddys,"r+", ti,ddyp,"g*");
    legend ("spline", "pchip");

The result of which can be seen in Figure 29.1 and Figure 29.2.

Figure 29.1: Comparison of `"pchip"` and `"spline"` interpolation methods for a step function

Figure 29.2: Comparison of the second derivative of the `"pchip"` and `"spline"` interpolation methods for a step function

Fourier interpolation, is a resampling technique where a signal is converted to the frequency domain, padded with zeros and then reconverted to the time domain.

Function File: interpft (x, n)
Function File: interpft (x, n, dim)

  Fourier interpolation. If x is a vector, then x is resampled with n points. The data in x is assumed to be equispaced. If x is a matrix or an N-dimensional array, the interpolation is performed on each column of x. If dim is specified, then interpolate along the dimension dim.

  `interpft` assumes that the interpolated function is periodic, and so assumptions are made about the endpoints of the interpolation.

  See also: interp1.

There are two significant limitations on Fourier interpolation. Firstly, the function signal is assumed to be periodic, and so non-periodic signals will be poorly represented at the edges. Secondly, both the signal and its interpolation are required to be sampled at equispaced points. An example of the use of `interpft` is

    t = 0 : 0.3 : pi; dt = t(2)-t(1);
    n = length (t); k = 100;
    ti = t(1) + [0 : k-1]*dt*n/k;
    y = sin (4*t + 0.3) .* cos (3*t - 0.1);
    yp = sin (4*ti + 0.3) .* cos (3*ti - 0.1);
    plot (ti, yp, "g", ti, interp1 (t, y, ti, "spline"), "b", ...
          ti, interpft (y, k), "c", t, y, "r+");
    legend ("sin(4t+0.3)cos(3t-0.1)", "spline", "interpft", "data");

which demonstrates the poor behavior of Fourier interpolation for non-periodic functions, as can be seen in Figure 29.3.

Figure 29.3: Comparison of `interp1` and `interpft` for non-periodic data

In addition, the support functions `spline` and `lookup` that underlie the `interp1` function can be called directly.

Function File: pp = spline (x, y)
Function File: yi = spline (x, y, xi)

  Return the cubic spline interpolant of points x and y.

  When called with two arguments, return the piecewise polynomial pp that may be used with `ppval` to evaluate the polynomial at specific points. When called with a third input argument, `spline` evaluates the spline at the points xi. The third calling form `spline (x, y, xi)` is equivalent to `ppval (spline (x, y), xi)`.

  The variable x must be a vector of length n. y can be either a vector or array. If y is a vector it must have a length of either n or `n + 2`. If the length of y is n, then the "not-a-knot" end condition is used. If the length of y is `n + 2`, then the first and last values of the vector y are the values of the first derivative of the cubic spline at the endpoints.

  If y is an array, then the size of y must have the form `[s1, s2, …, sk, n]` or `[s1, s2, …, sk, n + 2]`. The array is reshaped internally to a matrix where the leading dimension is given by `s1 * s2 * … * sk` and each row of this matrix is then treated separately. Note that this is exactly opposite to `interp1` but is done for MATLAB compatibility.

  See also: pchip, ppval, mkpp, unmkpp.

Next: Multi-dimensional Interpolation, Up: Interpolation [Contents][Index]