Interpolation for 1-D, 2-D, 3-D, and N-D gridded datain ndgrid format (2024)

Interpolation for 1-D, 2-D, 3-D, and N-D gridded datain ndgrid format

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Syntax

Vq = interpn(X1,X2,...,Xn,V,Xq1,Xq2,...,Xqn)

Vq = interpn(V,Xq1,Xq2,...,Xqn)

Vq = interpn(V)

Vq = interpn(V,k)

Vq = interpn(___,method)

Vq = interpn(___,method,extrapval)

Description

example

Vq = interpn(X1,X2,...,Xn,V,Xq1,Xq2,...,Xqn) returnsinterpolated values of a function of n variablesat specific query points using linear interpolation. The results alwayspass through the original sampling of the function. X1,X2,...,Xn containthe coordinates of the sample points. V containsthe corresponding function values at each sample point. Xq1,Xq2,...,Xqn containthe coordinates of the query points.

Vq = interpn(V,Xq1,Xq2,...,Xqn) assumes a default grid of sample points. The default grid consists of the points, 1,2,3,...n_i in each dimension. The value of n_i is the length of the ith dimension in V. Use this syntax when you want to conserve memory and are not concerned about the absolute distances between points.

Vq = interpn(V) returnsthe interpolated values on a refined grid formed by dividing the intervalbetween sample values once in each dimension.

example

Vq = interpn(V,k) returnsthe interpolated values on a refined grid formed by repeatedly halvingthe intervals k times in each dimension. This resultsin 2^k-1 interpolated points between sample values.

example

Vq = interpn(___,method) specifies an alternative interpolation method: 'linear', 'nearest', 'pchip','cubic', 'makima', or 'spline'. The default method is 'linear'.

example

Vq = interpn(___,method,extrapval) alsospecifies extrapval, a scalar value that is assignedto all queries that lie outside the domain of the sample points.

If you omit the extrapval argument for queriesoutside the domain of the sample points, then based on the method argument interpn returnsone of the following:

The extrapolated values for the 'spline' and 'makima' methods
NaN values for other interpolation methods

Examples

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1-D Interpolation

Open Live Script

Define the sample points and values.

x = [1 2 3 4 5];v = [12 16 31 10 6];

Define the query points, xq, and interpolate.

xq = (1:0.1:5);vq = interpn(x,v,xq,'cubic');

Plot the result.

figureplot(x,v,'o',xq,vq,'-');legend('Samples','Cubic Interpolation');

2-D Interpolation

Open Live Script

Create a set of grid points and corresponding sample values.

[X1,X2] = ndgrid((-5:1:5));R = sqrt(X1.^2 + X2.^2)+ eps;V = sin(R)./(R);

Interpolate over a finer grid using ntimes=1.

Vq = interpn(V,'cubic');mesh(Vq);

Interpolate Two Sets of 2-D Sample Values

Open Live Script

Create a grid of 2-D sample points using ndgrid.

[x,y] = ndgrid(0:10,0:5);

Create two different sets of sample values at the sample points and concatenate them as pages in a 3-D array. Plot the two sets of sample values against the sample points. Because surf uses meshgrid format for grids, transpose the inputs for plotting.

v1 = sin(x.*y)./(x+1);v2 = x.*erf(y);V = cat(3,v1,v2);tiledlayout(1,2)nexttilesurf(x',y',V(:,:,1)')view(2)nexttilesurf(x',y',V(:,:,2)')view(2)

Create a set of query points for interpolation using ndgrid and then use interpn to find the values of each function at the query points. Plot the interpolated values against the query points.

[xq,yq] = ndgrid(0:0.2:10);Vq = interpn(x,y,V,xq,yq);tiledlayout(1,2)nexttilesurf(xq',yq',Vq(:,:,1)')view(2)nexttilesurf(xq',yq',Vq(:,:,2)')view(2)

Evaluate Outside Domain of 3-D Function

Open Live Script

Create the grid vectors, x1, x2, and x3. These vectors define the points associated with the values in V.

x1 = 1:100;x2 = 1:50;x3 = 1:30;

Define the sample values to be a 100-by-50-by-30 array of random numbers, V.

rng defaultV = rand(100,50,30);

Evaluate V at three points outside the domain of x1, x2, and x3. Specify extrapval = -1.

xq1 = [0 0 0];xq2 = [0 0 51];xq3 = [0 101 102];vq = interpn(x1,x2,x3,V,xq1,xq2,xq3,'linear',-1)

vq = 1×3 -1 -1 -1

All three points evaluate to -1 because they are outside the domain of x1, x2, and x3.

4-D Interpolation

Open Script

Define an anonymous function that represents .

f = @(x,y,z,t) t.*exp(-x.^2 - y.^2 - z.^2);

Create a grid of points in . Then, pass the points through the function to create the sample values, V.

[x,y,z,t] = ndgrid(-1:0.2:1,-1:0.2:1,-1:0.2:1,0:2:10);V = f(x,y,z,t);

Now, create the query grid.

[xq,yq,zq,tq] = ...ndgrid(-1:0.05:1,-1:0.08:1,-1:0.05:1,0:0.5:10);

Interpolate V at the query points.

Vq = interpn(x,y,z,t,V,xq,yq,zq,tq);

Create a movie to show the results.

figure;nframes = size(tq, 4);for j = 1:nframes slice(yq(:,:,:,j),xq(:,:,:,j),zq(:,:,:,j),... Vq(:,:,:,j),0,0,0); clim([0 10]); M(j) = getframe;endmovie(M);

Input Arguments

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`X1,X2,...,Xn` — Sample grid points
arrays | vectors

Sample grid points, specified as real arrays or vectors. Thesample grid points must be unique.

If X1,X2,...,Xn are arrays, thenthey contain the coordinates of a full grid (in ndgrid format).Use the ndgrid function tocreate the X1,X2,...,Xn arrays together. Thesearrays must be the same size.
If X1,X2,...,Xn are vectors, then they are treated as grid vectors. The values in these vectors must be strictly monotonic, either increasing or decreasing.

Example: [X1,X2,X3,X4] = ndgrid(1:30,-10:10,1:5,10:13)

Data Types: single | double

`V` — Sample values
array

Sample values, specified as a real or complex array. The size requirements for V depend on the size of the grid of sample points defined by X1,X2,...,Xn. The sample points X1,X2,...,Xn can be arrays or grid vectors, but in both cases they define an n-dimensional grid. V must be an array that at least has the same n dimension sizes, but it also can have extra dimensions beyond n:

If V also has n dimensions, then the size of V must match the size of the n-dimensional grid defined by X1,X2,...,Xn. In this case, V contains one set of sample values at the sample points. For example, if X1,X2,X3 are 3-by-3-by-3 arrays, then V can also be a 3-by-3-by-3 array.
If V has more than n dimensions, then the first n dimensions of V must match the size of the n-dimensional grid defined by X1,X2,...,Xn. The extra dimensions in V define extra sets of sample values at the sample points. For example, if X1,X2,X3 are 3-by-3-by-3 arrays, then V can be a 3-by-3-by-3-by-2 array to define two sets of sample values at the sample points.

If V contains complex numbers, then interpn interpolatesthe real and imaginary parts separately.

Example: rand(10,5,3,2)

Data Types: single | double
Complex Number Support: Yes

`Xq1,Xq2,...,Xqn` — Query points
scalars | vectors | arrays

Query points, specified as real scalars, vectors, or arrays.

If Xq1,Xq2,...,Xqn are scalars,then they are the coordinates of a single query point in Rⁿ.
If Xq1,Xq2,...,Xqn are vectorsof different orientations, then Xq1,Xq2,...,Xqn aretreated as grid vectors in Rⁿ.
If Xq1,Xq2,...,Xqn are vectorsof the same size and orientation, then Xq1,Xq2,...,Xqn aretreated as scattered points in Rⁿ.
If Xq1,Xq2,...,Xqn are arrays ofthe same size, then they represent either a full grid of query points(in ndgrid format) or scattered points in Rⁿ.

Example: [X1,X2,X3,X4] = ndgrid(1:10,1:5,7:9,10:11)

Data Types: single | double

`k` — Refinement factor
`1` (default) | real, nonnegative, integer scalar

Refinement factor, specified as a real, nonnegative, integerscalar. This value specifies the number of times to repeatedly dividethe intervals of the refined grid in each dimension. This resultsin 2^k-1 interpolated points between sample values.

If k is 0, then Vq isthe same as V.

interpn(V,1) is the same as interpn(V).

The following illustration depicts k=2 in R².There are 72 interpolated values in red and 9 sample values in black.

Example: interpn(V,2)

Data Types: single | double

`method` — Interpolation method
`'linear'` (default) | `'nearest'` | `'pchip'` | `'cubic'` | `'spline'` | `'makima'`

Interpolation method, specified as one of the options in this table.

Method	Description	Continuity	Comments
`'linear'`	The interpolated value at a query point is based on linear interpolation of the values at neighboring grid points in each respective dimension. This is the default interpolation method.	C⁰	Requires at least two grid points in each dimension Requires more memory than `'nearest'`
`'nearest'`	The interpolated value at a query point is the value at the nearest sample grid point.	Discontinuous	Requires two grid points in each dimension. Fastest computation with modest memory requirements
`'pchip'`	Shape-preserving piecewise cubic interpolation (for 1-D only). The interpolated value at a query point is based on a shape-preserving piecewise cubic interpolation of the values at neighboring grid points.	C¹	Requires at least four points Requires more memory and computation time than `'linear'`
`'cubic'`	The interpolated value at a query point is based on a cubic interpolation of the values at neighboring grid points in each respective dimension. The interpolation is based on a cubic convolution.	C¹	Grid must have uniform spacing in each dimension, but the spacing does not have to be the same for all dimensions Requires at least four points in each dimension Requires more memory and computation time than `'linear'`
`'makima'`	Modified Akima cubic Hermite interpolation. The interpolated value at a query point is based on a piecewise function of polynomials with degree at most three evaluated using the values of neighboring grid points in each respective dimension. The Akima formula is modified to avoid overshoots.	C¹	Requires at least 2 points in each dimension Produces fewer undulations than `'spline'` Computation time is typically less than `'spline'`, but the memory requirements are similar
`'spline'`	The interpolated value at a query point is based on a cubic interpolation of the values at neighboring grid points in each respective dimension. The interpolation is based on a cubic spline using not-a-knot end conditions.	C²	Requires four points in each dimension Requires more memory and computation time than `'cubic'`

`extrapval` — Function value outside domain of `X1,X2,...,Xn`
scalar

Function value outside domain of X1,X2,...,Xn,specified as a real or complex scalar. interpn returnsthis constant value for all points outside the domain of X1,X2,...,Xn.

Example: 5

Example: 5+1i

Data Types: single | double
Complex Number Support: Yes

Output Arguments

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`Vq` — Interpolated values
scalar | vector | array

Interpolated values, returned as a real or complex scalar, vector, or array. The size and shape of Vq depends on the syntax you use and, in some cases, the size and value of the input arguments.

If you specify sample points with X1,X2,...,Xn, or use the default grid, and V has the same number of dimensions as the n-dimensional grid of sample points, then Vq contains a single set of interpolated values at the query points defined by Xq1,Xq2,...,Xqn.
- If Xq1,Xq2,...,Xqn are scalars, then Vq is a scalar.
- If Xq1,Xq2,...,Xqn are vectors of the same size and orientation, then Vq is a vector with the same size and orientation.
- If Xq1,Xq2,...,Xqn are grid vectors of mixed orientation, then Vq is an array with the same size as the grid implicitly defined by the grid vectors.
- If Xq1,Xq2,...,Xqn are arrays of the same size, then Vq is an array with the same size.
If you specify sample points with X1,X2,...,Xn, or use the default grid, and V has more dimensions than the n-dimensional grid of sample points, then Vq contains multiple sets of interpolated values at the query points defined by Xq1,Xq2,...,Xqn. In this case, the first n dimensions of Vq follow the size rules for a single set of interpolated values above, but Vq also has the same extra dimensions as V with the same sizes.
With the syntaxes interpn(V) and interpn(V,k), the interpolation is performed by subdividing the default grid k times (where k=1 for interpn(V)). In this case, Vq is an array with the same number of dimensions as V where the size of the ith dimension is 2^k * (size(V,i)-1)+1.

More About

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Strictly Monotonic

A set of values that are always increasingor decreasing, without reversals. For example, the sequence, a= [2 4 6 8] is strictly monotonic and increasing. The sequence, b= [2 4 4 6 8] is not strictly monotonic because there isno change in value between b(2) and b(3).The sequence, c = [2 4 6 8 6] contains a reversalbetween c(4) and c(5), so itis not monotonic at all.

Full Grid (in ndgrid Format)

For interpn, the fullgrid consists of n arrays, X1,X2,...,Xn,whose elements represent a grid of points in Rⁿ.The ith array, X_i, contains strictly monotonic,increasing values that vary most rapidly along the ith dimension.

Use the ndgrid functionto create a full grid that you can pass to interpn.For example, the following code creates a full grid in R² forthe region, 1 ≤ X1 ≤ 3, 1≤ X2 ≤4.

[X1,X2] = ndgrid(-1:3,(1:4))

X1 = -1 -1 -1 -1 0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3X2 = 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

Grid Vectors

For interpn, grid vectorsconsist of n vectors of mixed-orientation thatdefine the points of a grid in Rⁿ.

For example, the following code creates the grid vectors in R³ forthe region, 1 ≤ x1 ≤ 3, 4 ≤ x2 ≤5, and 6 ≤x3≤ 8:

x1 = 1:3;x2 = (4:5)';x3 = 6:8;

Scattered Points

For interpn, scatteredpoints consist of n arrays or vectors, Xq1,Xq2,...,Xqn,that define a collection of points scattered in Rⁿ.The ith array, Xi, containsthe coordinates in the ith dimension.

For example, the following code specifies the points, (1, 19,10), (6, 40, 1), (15, 33, 22), and (0, 61, 13) in R³.

Xq1 = [1 6; 15 0];Xq2 = [19 40; 33 61];Xq3 = [10 1; 22 13];

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

For best results, provide X1,X2,...,Xn as vectors. The values in these vectors must be strictly monotonic and increasing.
Code generation does not support the 'makima' interpolation method.
The interpolation method must be a constant charactervector.

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

A maximum of five dimensions is supported.
X1,X2,...,Xn must have dimensions consistent with V.
method must be 'linear' or'nearest'.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays
Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

Usage notes and limitations:

X1,X2,...,Xn must have dimensions consistent with V.

For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced before R2006a

expand all

R2021a: Interpolate multiple data sets simultaneously

Support added to interpolate multiple data sets on the same grid at the same query points. For example, if you specify a 2-D grid, a 3-D array of values at the grid points, and a 2-D collection of query points, then interpn returns the interpolated values at the query points for each 2-D page in the 3-D array of values.

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Interpolation for 1-D, 2-D, 3-D, and N-D gridded data
in ndgrid format (2024)

FAQs

What is the best way to interpolate data? ›

Linear interpolation is the most straightforward and commonly used interpolation method. It comes naturally when we have two points, we connect them with a straight line to fill out the missing information in between. By doing so, we made our assumption that the points on the line represent the unobserved values.

Learn More Now ›

How do you interpolate between charts? ›

How to interpolate

Identify your data. Use a table to list your data. ...
Create a line of best fit. After using the values to plot a graph, you can draw a line of best fit. ...
Determine your value for interpolation. ...
Use the linear interpolation equation. ...
Solve the equation.

Sep 30, 2022

What is the best data interpolation method? ›

In terms of the ability to fit your data and produce a smooth surface, the Multiquadric method is considered by many to be the best. All of the Radial Basis Function methods are exact interpolators, so they attempt to honor your data.

Get More Info Here ›

How to do interpolation between two numbers? ›

The interpolation equation is as follows: y − y 1 = y 2 − y 1 x 2 − x 1 ( x − x 1 ) , where ( x 1 , y 1 ) and ( x 2 , y 2 ) are two known data points and ("x," "y") represents the data point to be estimated.

Explore More ›

What is the simplest interpolation method? ›

One of the simplest methods, linear interpolation, requires knowledge of two points and the constant rate of change between them. With this information, you may interpolate values anywhere between those two points.

Discover More Details ›

Which interpolation formula is better? ›

If linear interpolation formula is concerned then it can be used to find the new value from the two given points. If we compare it to Lagrange's interpolation formula, the “n” set of numbers is needed. Thereafter Lagrange's method is to be used to find the new value.

What is an example of interpolation? ›

Example 1: Using the interpolation formula, find the value of y at x = 0, if given some set of values are (-2, 5), (1, 7)? Therefore, the value of y at (x = 0) = 6.333. Therefore, the value of y at (x = 10) is 17.

Get More Info Here ›

What is the interpolation between two sets of data? ›

If a function is only known at a discrete set of data points (xi,yi), interpolation can be used to estimate the value of that function at values of x not included in the data. An interpolation function I(x) passes through the points of a discrete data set, I(xi)=yi.

Keep Reading ›

What is the interpolation method of data? ›

Interpolation is defined to utilize the data for predicting the data within the dataset and is a method of fitting the data points to represent the value of a function. Extrapolation is the method of using the data set to estimate beyond the data set.

Find Out More ›

Which is better interpolation or extrapolation? ›

Interpolation often provides a valid estimate of an unknown value, which is why it's considered a more reliable estimation method than extrapolation.

Interpolation for 1-D, 2-D, 3-D, and N-D gridded data in ndgrid format (2024)

Syntax

Description

Examples

1-D Interpolation

2-D Interpolation

Interpolate Two Sets of 2-D Sample Values

Evaluate Outside Domain of 3-D Function

4-D Interpolation

Input Arguments

X1,X2,...,Xn — Sample grid points arrays | vectors

V — Sample values array

Xq1,Xq2,...,Xqn — Query points scalars | vectors | arrays

k — Refinement factor 1 (default) | real, nonnegative, integer scalar

method — Interpolation method 'linear' (default) | 'nearest' | 'pchip' | 'cubic' | 'spline' | 'makima'

extrapval — Function value outside domain of X1,X2,...,Xn scalar

Output Arguments

Vq — Interpolated values scalar | vector | array

More About

Strictly Monotonic

Full Grid (in ndgrid Format)

Grid Vectors

Scattered Points

Extended Capabilities

C/C++ Code Generation Generate C and C++ code using MATLAB® Coder™.

Thread-Based Environment Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

GPU Arrays Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Distributed Arrays Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

Version History

R2021a: Interpolate multiple data sets simultaneously

See Also

MATLAB-Befehl

Americas

Europe

Asia Pacific

FAQs

What is the best way to interpolate data? ›

What is an example of interpolation? ›

`X1,X2,...,Xn` — Sample grid points
arrays | vectors

`V` — Sample values
array

`Xq1,Xq2,...,Xqn` — Query points
scalars | vectors | arrays

`k` — Refinement factor
`1` (default) | real, nonnegative, integer scalar

`method` — Interpolation method
`'linear'` (default) | `'nearest'` | `'pchip'` | `'cubic'` | `'spline'` | `'makima'`

`extrapval` — Function value outside domain of `X1,X2,...,Xn`
scalar

`Vq` — Interpolated values
scalar | vector | array

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Distributed Arrays
Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.