# scatteredInterpolant

Interpolate 2-D or 3-D scattered data

## Description

Use `scatteredInterpolant` to perform interpolation on a 2-D or 3-D data set of scattered data. `scatteredInterpolant` returns the interpolant `F` for the given data set. You can evaluate `F` at a set of query points, such as `(xq,yq)` in 2-D, to produce interpolated values `vq = F(xq,yq)`.

Use `griddedInterpolant` to perform interpolation with gridded data.

## Creation

### Syntax

``F = scatteredInterpolant``
``F = scatteredInterpolant(x,y,v)``
``F = scatteredInterpolant(x,y,z,v)``
``F = scatteredInterpolant(P,v)``
``F = scatteredInterpolant(___,Method)``
``F = scatteredInterpolant(___,Method,ExtrapolationMethod)``

### Description

````F = scatteredInterpolant` creates an empty scattered data interpolant object.```

example

````F = scatteredInterpolant(x,y,v)` creates an interpolant that fits a surface of the form v = F(x,y). Vectors `x` and `y` specify the `(x,y)` coordinates of the sample points. `v` contains the sample values associated with the points `(x,y)`.```

example

````F = scatteredInterpolant(x,y,z,v)` creates a 3-D interpolant of the form v = F(x,y,z).```

example

````F = scatteredInterpolant(P,v)` specifies the coordinates of the sample points as an array. The rows of `P` contain the (x, y) or (x, y, z) coordinates for the values in `v`.```

example

````F = scatteredInterpolant(___,Method)` specifies an interpolation method `'nearest'`, `'linear'`, or `'natural'` as the last input argument in any of the first three syntaxes.```

example

````F = scatteredInterpolant(___,Method,ExtrapolationMethod)` specifies both the interpolation and extrapolation methods. Pass `Method` and `ExtrapolationMethod` together as the last two input arguments in any of the first three syntaxes.```

### Input Arguments

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Sample points, specified as column vectors with the same number of rows as `v`. The sample points should be unique. However, if the sample points contain duplicates, `scatteredInterpolant` displays a warning and merges the duplicates into a single point.

The `x`, `y`, and `z` arguments set the value of the `Points` property of the `scatteredInterpolant` object.

Data Types: `double`

Sample points array, specified as an `m`-by-`n` matrix, where `m` is the number of points and `n` is the dimension of the space where the points reside. Each row of `P` contains the (x, y) or (x, y, z) coordinates of a sample point. The sample points should be unique. However, if the sample points contain duplicates, `scatteredInterpolant` displays a warning and merges the duplicates into a single point.

This argument sets the `Points` property of the `scatteredInterpolant` object.

Data Types: `double`

Function values at sample points, specified as a vector or matrix. For 2-D data, v = F(x,y). For 3-D data, v = F(x,y,z).

• To interpolate using a single set of values, specify `v` as a vector, where the number of rows is the same as the number of sample points.

• To interpolate using multiple sets of values, specify `v` as a matrix, where the number of rows is the same as the number of sample points. Each column in `v` represents the values of a different function at the sample points. For example, if `x` and `y` are column vectors with 10 elements, you can specify `v` as a 10-by-4 matrix to interpolate using four different sets of values.

`scatteredInterpolant` does not ignore `NaN` values in `v`, so interpolation results near those sample points are also `NaN`.

This argument sets the `Values` property of the `scatteredInterpolant` object.

Data Types: `double`

Interpolation method, specified as one of these options.

MethodDescriptionContinuity
`'linear'` (default)

Linear interpolation

C0
`'nearest'`

Nearest neighbor interpolation

Discontinuous
`'natural'`

Natural neighbor interpolation

C1 (except at sample points)

This argument sets the `Method` property of the `scatteredInterpolant` object.

Extrapolation method, specified as one of these options.

Extrapolation MethodDescription
`'linear'`

Linear extrapolation based on boundary gradients. This method is the default when `Method` is `'linear'` or `'natural'`.

`'nearest'`

Nearest neighbor extrapolation. This method evaluates to the value of the nearest neighbor. This method is the default when `Method` is `'nearest'`.

`'boundary'` (since R2024a)

Boundary extrapolation. This method extends the values on the interpolation boundary into the extrapolation domain.

`'none'`

No extrapolation. Any queries outside the convex hull of `Points` return `NaN`.

This argument sets the `ExtrapolationMethod` property of the `scatteredInterpolant` object.

## Properties

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Sample points, specified as a matrix. The size of the matrix is `m`-by-`2` or `m`-by-`3` to represent `m` points in 2-D or 3-D space. Each row of `Points` contains the (x, y) or (x, y, z) coordinates of a unique sample point. The rows in `Points` correspond to the function values in `Values`.

Data Types: `double`

Function values at sample points, specified as a vector or matrix. This property is set by the v input argument.

Data Types: `double`

Interpolation method, specified as `'linear'`,`'nearest'` , or `'natural'`. See `Method` for descriptions of these methods.

Extrapolation method, specified as `'linear'`, `'nearest'`, `'boundary'` (since R2024a), or `'none'`. See `ExtrapolationMethod` for descriptions of these methods.

## Usage

### Syntax

``Vq = F(Pq)``
``Vq = F(Xq,Yq)``
``Vq = F(Xq,Yq,Zq)``
``Vq = F({xq,yq})``
``Vq = F({xq,yq,zq})``

### Description

Use `scatteredInterpolant` to create the interpolant, `F`. Then you can evaluate `F` at specific points using any of the following syntaxes.

````Vq = F(Pq)` evaluates `F` at the query points in the matrix `Pq`. Each row in `Pq` contains the coordinates of a query point.```

example

````Vq = F(Xq,Yq)` and `Vq = F(Xq,Yq,Zq)` specify query points as two or three arrays of equal size. `F` treats the query points as column vectors, for example, `Xq(:)`. If the `Values` property of `F` is a column vector representing one set of values at the sample points, then `Vq` is the same size as the query points.If the `Values` property of `F` is a matrix representing multiple sets of values at the sample points, then `Vq` is a matrix, and each column represents a different set of values at the query points. ```

example

````Vq = F({xq,yq})` and `Vq = F({xq,yq,zq})` specify query points as grid vectors. Use this syntax to conserve memory when you want to query a large grid of points.```

## Examples

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Define some sample points and calculate the value of a trigonometric function at those locations. These points are the sample values for the interpolant.

```t = linspace(3/4*pi,2*pi,50)'; x = [3*cos(t); 2*cos(t); 0.7*cos(t)]; y = [3*sin(t); 2*sin(t); 0.7*sin(t)]; v = repelem([-0.5; 1.5; 2],length(t));```

Create the interpolant.

`F = scatteredInterpolant(x,y,v);`

Evaluate the interpolant at query locations (`xq`,`yq`).

```tq = linspace(3/4*pi+0.2,2*pi-0.2,40)'; xq = [2.8*cos(tq); 1.7*cos(tq); cos(tq)]; yq = [2.8*sin(tq); 1.7*sin(tq); sin(tq)]; vq = F(xq,yq);```

Plot the result.

```plot3(x,y,v,'.',xq,yq,vq,'.'), grid on title('Linear Interpolation') xlabel('x'), ylabel('y'), zlabel('Values') legend('Sample data','Interpolated query data','Location','Best')```

Create an interpolant for a set of scattered sample points, then evaluate the interpolant at a set of 3-D query points.

Define 200 random points and sample a trigonometric function. These points are the sample values for the interpolant.

```rng default; P = -2.5 + 5*rand([200 3]); v = sin(P(:,1).^2 + P(:,2).^2 + P(:,3).^2)./(P(:,1).^2+P(:,2).^2+P(:,3).^2);```

Create the interpolant.

`F = scatteredInterpolant(P,v);`

Evaluate the interpolant at query locations (`xq`,`yq`,`zq`).

```[xq,yq,zq] = meshgrid(-2:0.25:2); vq = F(xq,yq,zq);```

Plot slices of the result.

```xslice = [-.5,1,2]; yslice = [0,2]; zslice = [-2,0]; slice(xq,yq,zq,vq,xslice,yslice,zslice)```

Replace the elements in the `Values` property when you want to change the values at the sample points. You get immediate results when you evaluate the new interpolant because the original triangulation does not change.

Create 50 random points and sample an exponential function. These points are the sample values for the interpolant.

```rng('default') x = -2.5 + 5*rand([50 1]); y = -2.5 + 5*rand([50 1]); v = x.*exp(-x.^2-y.^2);```

Create the interpolant.

`F = scatteredInterpolant(x,y,v)`
```F = scatteredInterpolant with properties: Points: [50x2 double] Values: [50x1 double] Method: 'linear' ExtrapolationMethod: 'linear' ```

Evaluate the interpolant at `(1.40,1.90)`.

`F(1.40,1.90)`
```ans = 0.0069 ```

Change the interpolant sample values and reevaluate the interpolant at the same point.

```vnew = x.^2 + y.^2; F.Values = vnew; F(1.40,1.90)```
```ans = 5.6491 ```

Use `groupsummary` to eliminate duplicate sample points and control how they are combined prior to calling `scatteredInterpolant`.

Create a 200-by-3 matrix of sample point locations. Add duplicate points in the last five rows.

```P = -2.5 + 5*rand(200,3); P(197:200,:) = repmat(P(196,:),4,1);```

Create a vector of random values at the sample points.

`V = rand(size(P,1),1);`

If you attempt to use `scatteredInterpolant` with duplicate sample points, it throws a warning and averages the corresponding values in `V` to produce a single unique point. However, you can use `groupsummary` to eliminate the duplicate points prior to creating the interpolant. This is particularly useful if you want to combine the duplicate points using a method other than averaging.

Use `groupsummary` to eliminate the duplicate sample points and preserve the maximum value in `V` at the duplicate sample point location. Specify the sample points matrix as the grouping variable and the corresponding values as the data.

`[V_unique,P_unique] = groupsummary(V,P,@max);`

Since the grouping variable has three columns, `groupsummary` returns the unique groups `P_unique` as a cell array. Convert the cell array back into a matrix.

`P_unique = [P_unique{:}];`

Create the interpolant. Since the sample points are now unique, `scatteredInterpolant` does not throw a warning.

`I = scatteredInterpolant(P_unique,V_unique);`

Compare the results of several different interpolation algorithms offered by `scatteredInterpolant`.

Create a sample data set of 50 scattered points. The number of points is artificially small to highlight the differences between the interpolation methods.

```x = -3 + 6*rand(50,1); y = -3 + 6*rand(50,1); v = sin(x).^4 .* cos(y);```

Create the interpolant and a grid of query points.

```F = scatteredInterpolant(x,y,v); [xq,yq] = meshgrid(-3:0.1:3);```

Plot the results using the `'nearest'`, `'linear'`, and `'natural'` methods. Each time the interpolation method changes, you need to requery the interpolant to get the updated results.

```F.Method = 'nearest'; vq1 = F(xq,yq); plot3(x,y,v,'mo') hold on mesh(xq,yq,vq1) title('Nearest Neighbor') legend('Sample Points','Interpolated Surface','Location','NorthWest')```

```F.Method = 'linear'; vq2 = F(xq,yq); figure plot3(x,y,v,'mo') hold on mesh(xq,yq,vq2) title('Linear') legend('Sample Points','Interpolated Surface','Location','NorthWest')```

```F.Method = 'natural'; vq3 = F(xq,yq); figure plot3(x,y,v,'mo') hold on mesh(xq,yq,vq3) title('Natural Neighbor') legend('Sample Points','Interpolated Surface','Location','NorthWest')```

Plot the exact solution.

```figure plot3(x,y,v,'mo') hold on mesh(xq,yq,sin(xq).^4 .* cos(yq)) title('Exact Solution') legend('Sample Points','Exact Surface','Location','NorthWest')```

Since R2024a

Compare the results of several different extrapolation methods offered by `scatteredInterpolant`.

Create a sample data set of 50 scattered points, and calculate the value of a trigonometric function at those locations. These points are the sample values for the interpolant.

```rng default x = -3 + 6*rand(50,1); y = -3 + 6*rand(50,1); v = sin(x).^4 .* cos(y);```

Create the interpolant.

`F = scatteredInterpolant(x,y,v)`
```F = scatteredInterpolant with properties: Points: [50x2 double] Values: [50x1 double] Method: 'linear' ExtrapolationMethod: 'linear' ```

Compute the boundary of the input data.

```C = convhull(x,y); xc = [x(C); x(C(1))]; yc = [y(C); y(C(1))]; vc = [v(C); v(C(1))];```

Evaluate the interpolant at query locations `(xq,yq)` using linear extrapolation based on boundary gradients. Then, plot the result of the interpolation and extrapolation.

```[xq,yq] = meshgrid(-4:0.1:4); vq1 = F(xq,yq); surf(xq,yq,vq1,FaceAlpha=0.5,DisplayName="Interpolation + extrapolation result",EdgeColor=[100 100 100]./256,FaceColor="interp") hold on plot3(x,y,v,"black.",MarkerSize=20,DisplayName="Sample points") plot3(xc,yc,vc,"magenta-",LineWidth=3,DisplayName="Interpolation-extrapolation boundary") title("Linear interpolation + linear extrapolation") legend(Location="NorthEast") zlim([-2.8 1.3]) colorbar colormap jet clim([-2.8 1.3]) view([-183.69 21.20]) hold off```

Modify the interpolant to use nearest neighbor extrapolation, and evaluate and visualize the interpolant.

```F.ExtrapolationMethod = "nearest"; vq2 = F(xq,yq); surf(xq,yq,vq2,FaceAlpha=0.5,DisplayName="Interpolation + extrapolation result",EdgeColor=[100 100 100]./256,FaceColor="interp") hold on plot3(x,y,v,"black.",MarkerSize=20,DisplayName="Sample points") plot3(xc,yc,vc,"magenta-",LineWidth=3,DisplayName="Interpolation-extrapolation boundary") title("Linear interpolation + Nearest extrapolation") legend(Location="NorthEast") zlim([-2.8 1.3]) colorbar colormap jet clim([-2.8 1.3]) view([-183.69 21.20]) hold off```

Modify the interpolant to extend the interpolation boundary into the extrapolation domain, and evaluate and visualize the interpolant.

```F.ExtrapolationMethod = "boundary"; vq3 = F(xq,yq); surf(xq,yq,vq3,FaceAlpha=0.5,DisplayName="Interpolation + extrapolation result",EdgeColor=[100 100 100]./256,FaceColor="interp") hold on plot3(x,y,v,"black.",MarkerSize=20,DisplayName="Sample points") plot3(xc,yc,vc,"magenta-",LineWidth=3,DisplayName="Interpolation-extrapolation boundary") title("Linear interpolation + Boundary extrapolation") legend(Location="NorthEast") zlim([-2.8 1.3]) colorbar colormap jet clim([-2.8 1.3]) view([-183.69 21.20]) hold off```

Compare the extrapolation methods by examining the plotted results. Boundary extrapolation preserves continuity between the interpolation and extrapolation domain, while nearest neighbor extrapolation can be discontinuous along the boundary. Boundary extrapolation does not produce extreme values in the extrapolation domain, while linear extrapolation can produce extreme values.

Since R2023b

Interpolate multiple data sets at the same query points.

Create a sample data set with 50 scattered points represented by sample point vectors `x` and `y`.

```rng("default") x = -3 + 6*rand(50,1); y = -3 + 6*rand(50,1);```

To interpolate multiple data sets, create a matrix where each column represents the values of a different function at the sample points.

```s1 = sin(x).^4 .* cos(y); s2 = sin(x) + cos(y); s3 = x + y; s4 = x.^2 + y; v = [s1 s2 s3 s4];```

Create query point vectors, which indicate the locations to perform interpolation for each set of values in `v`.

```xq = -3:0.1:3; yq = -3:0.1:3;```

Create the interpolant `F`.

`F = scatteredInterpolant(x,y,v)`
```F = scatteredInterpolant with properties: Points: [50x2 double] Values: [50x4 double] Method: 'linear' ExtrapolationMethod: 'linear' ```

Evaluate the interpolant at the query locations. Each page of `Vq` contains the interpolated values for the corresponding data set in `v`.

```Vq = F({xq,yq}); size(Vq)```
```ans = 1×3 61 61 4 ```

Plot the interpolated values for each data set.

```tiledlayout(2,2) nexttile plot3(x,y,v(:,1),'mo') hold on mesh(xq,yq,Vq(:,:,1)') title("sin(x).^4 .* cos(y)") nexttile plot3(x,y,v(:,2),'mo') hold on mesh(xq,yq,Vq(:,:,2)') title("sin(x) + cos(y)") nexttile plot3(x,y,v(:,3),'mo') hold on mesh(xq,yq,Vq(:,:,3)') title("x + y") nexttile plot3(x,y,v(:,4),'mo') hold on mesh(xq,yq,Vq(:,:,4)') title("x.^2 + y") lg = legend("Sample Points","Interpolated Surface"); lg.Layout.Tile = "north";```

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## Tips

• It is quicker to evaluate a `scatteredInterpolant` object `F` at many different sets of query points than it is to compute the interpolations separately using the functions `griddata` or `griddatan`. For example:

```% Fast to create interpolant F and evaluate multiple times F = scatteredInterpolant(X,Y,V) v1 = F(Xq1,Yq1) v2 = F(Xq2,Yq2) % Slower to compute interpolations separately using griddata v1 = griddata(X,Y,V,Xq1,Yq1) v2 = griddata(X,Y,V,Xq2,Yq2) ```
• To change the interpolation sample values or interpolation method, it is more efficient to update the properties of the interpolant object `F` than it is to create a new `scatteredInterpolant` object. When you update `Values` or `Method`, the underlying Delaunay triangulation of the input data does not change, so you can compute new results quickly.

• Scattered data interpolation with `scatteredInterpolant` uses a Delaunay triangulation of the data, so interpolation can be sensitive to scaling issues in the sample points `x`, `y`, `z`, or `P`. When scaling issues occur, you can use `normalize` to rescale the data and improve the results. See Normalize Data with Differing Magnitudes for more information.

## Algorithms

`scatteredInterpolant` uses a Delaunay triangulation of the scattered sample points to perform interpolation [1].

## References

[1] Amidror, Isaac. “Scattered data interpolation methods for electronic imaging systems: a survey.” Journal of Electronic Imaging. Vol. 11, No. 2, April 2002, pp. 157–176.

## Version History

Introduced in R2013a

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