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ListInterpolation—Wolfram Language Documentation

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• See Also
  • Interpolation
  • FunctionInterpolation
  • ListContourPlot
  • Quantile
• Related Guides
  • Curve Fitting & Approximate Functions
  • Creating & Importing Signals
• Tech Notes
  • Approximate Functions and Interpolation

ListInterpolation[array,{{x_min,x_max},{y_min,y_max},…}]

specifies the domain of the grid from which the values in array are assumed to come.

• You can replace {x_min,x_max} etc. by explicit lists of positions for grid lines. The grid lines are otherwise assumed to be equally spaced.
• ListInterpolation[array] assumes grid lines at integer positions in each direction.
• array can be an array in any number of dimensions, corresponding to a list with any number of levels of nesting.
• ListInterpolation[array,domain] generates an InterpolatingFunction object that returns values with the same precision as those in {array,domain}.
• ListInterpolation supports a Method option. Possible settings include "Spline" for spline interpolation and "Hermite" for Hermite interpolation.

Construct an approximate function that interpolates the data:

Apply the function to find interpolated values:

Plot the interpolation function:

Compare with the original data:

Construct an approximate function with the x values equally spaced on the interval :

Apply the function to find interpolated values:

Plot the interpolation function with the original data:

Construct an approximate function that interpolates the values from an array of values:

Plot the function with the original data:

Interpolate between points at arbitrary x values:

The x values may be included in the data directly:

Create data with Table:

Form the interpolation:

Plot the interpolated function:

Create a list of multidimensional data:

Create an approximate interpolating function:

Plot the interpolating function:

Create data including derivative values:

Construct an interpolation:

Plot the interpolation:

Create 2D data that includes a gradient vector at each point:

Compare with data that does not include gradients:

Also include tensors of second derivatives:

Make a 0-order interpolation:

Make a linear interpolation:

Make a quadratic interpolation:

Make an interpolation linear in the first dimension and quadratic in the second:

Compare splines with piecewise Hermite interpolation for random data:

The curves appear close, but the spline has a continuous derivative:

Make an interpolating function that repeats periodically:

Make an interpolating function that repeats periodically in the second dimension only:

The interpolating function always goes through the data points:

Find the integral of an interpolating function:

Plot the interpolating function and its integral:

Find a root of the integral:

Beyond the domain defined by the original data extrapolation is used:

A plot shows the inaccuracy of extrapolation:

With the default choice of order, at least 4 points are needed in each dimension:

With a lower order, fewer points are needed:

The interpolation function will always be continuous, but may not be differentiable:

If derivatives are specified, the interpolation function will have a continuous derivative:

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