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.

- :
`yi`=**interp1**`(`

¶`x`,`y`,`xi`) - :
`yi`=**interp1**`(`

¶`y`,`xi`) - :
`yi`=**interp1**`(…,`

¶`method`) - :
`yi`=**interp1**`(…,`

¶`extrap`) - :
`yi`=**interp1**`(…, "left")`

¶ - :
`yi`=**interp1**`(…, "right")`

¶ - :
`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`(`1:length (`

). If`y`)`y`is a matrix or an N-dimensional array, the interpolation is performed on each column of`y`.The interpolation

`method`is one of:`"nearest"`

Return the nearest neighbor.

`"previous"`

Return the previous neighbor.

`"next"`

Return the next neighbor.

`"linear"`

(default)Linear interpolation from nearest neighbors.

`"pchip"`

Piecewise cubic Hermite interpolating polynomial—shape-preserving interpolation with smooth first derivative.

`"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

and`x`(1)

are referenced. This is usually faster, and is never slower. The default method is`x`(2)`"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`

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

**See also:**pchip, spline, 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.

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.

- :
`y`=**interpft**`(`

¶`x`,`n`) - :
`y`=**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. First,
the function signal is assumed to be periodic, and so non-periodic
signals will be poorly represented at the edges. Second, 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.

In addition, the support functions `spline`

and `lookup`

that
underlie the `interp1`

function can be called directly.

- :
`pp`=**spline**`(`

¶`x`,`y`) - :
`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 (`

is equivalent to`x`,`y`,`xi`)`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

. If the length of`n`+ 2`y`is`n`, then the`"not-a-knot"`

end condition is used. If the length of`y`is

, then the first and last values of the vector`n`+ 2`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`[`

or`s1`,`s2`, …,`sk`,`n`]`[`

. The array is reshaped internally to a matrix where the leading dimension is given by`s1`,`s2`, …,`sk`,`n`+ 2]

and each row of this matrix is then treated separately. Note that this is exactly the opposite of`s1`*`s2`* … *`sk``interp1`

but is done for MATLAB compatibility.