There are three functions in SMath for spline interpolation:

**linterp**('X-vector','Y-vector','x') - Returns a linearly interpolated value at x for data vectors X-vector and Y-vector of the same size.

**cinterp**('X-vector','Y-vector','x') - Returns a cubic spline interpolated value at x for data vectors X-vector and Y-vector of the same size.

**ainterp**('X-vector','Y-vector','x') - Returns Akima-spline interpolated value at x for data vectors X-vector and Y-vector of the same size.

Here is the example of presenting these functions:

Notice how misleading all of these functions are outside the limits of the original datapoints, ie below x=1 and above x=5. Try and make sure that you only use these for interpolation, not extrapolation.

You can have the X-vector sorted in ascending order:

And also**X-vector** **not sorted **. As the main usage of the interpolation is to estimate y-value for the x-value not given in the table, this is **not advisable**:

Here is the example of presenting these functions:

Notice how misleading all of these functions are outside the limits of the original datapoints, ie below x=1 and above x=5. Try and make sure that you only use these for interpolation, not extrapolation.

You can have the X-vector sorted in ascending order:

And also