INTERP.1D.CUBIC.SPLINE Function

INTERP.1D.CUBIC.SPLINE

Cubic Spline Interpolation.

Syntax

=INTERP.1D.CUBIC.SPLINE(dataX, dataY, queryPoints, extrapolate)

Parameters

Name	Type	Required	Description
dataX	Double[]	Yes	Known x values.
dataY	Double[]	Yes	Known y values.
queryPoints	Object	Yes	Points to interpolate at.
extrapolate	Boolean	No	Allow extrapolation (true/false).

Returns

Calculated result based on input parameters

Examples

Error Conditions

Error	Cause	Solution

About SPLINE Interpolation

Interpolation estimates unknown values by fitting a function through known data points. The SPLINE method provides a specific approach to constructing this interpolating function.

Key Characteristics

When to Use:

• When you have discrete data points and need intermediate values
• For smoothing or curve fitting applications
• When analytical functions are not available

Practical Applications

• Data Analysis: Fill missing data points in time series
• Scientific Computing: Resample data to different grids
• Engineering: Smooth sensor readings
• Graphics: Generate smooth curves from control points

Related Functions

INTERP.1D.LINEAR

Linear Spline Interpolation.

INTERP.1D.AKIMA

Akima Spline Interpolation. A special case of cubic spline that is more robust to outliers.

INTERP.1D.PCHIP

Monotone Cubic Spline (Pchip). A special case of cubic spline that preserves monotonicity of the data.

INTERP.1D.STEP

Step Interpolation. A step function where the interpolated value is always the value of the first key which is nearest to and less than or equal to the evaluation point.

INTERP.1D.LOGLINEAR

Piece-wise Log-Linear Interpolation. This is equivalent to a linear interpolation in the log-scale.

INTERP.1D.NEVILLE

Lagrange Polynomial Interpolation using Neville's Algorithm. Supports both differentiation and integration.

See Also

• Method Comparison - Compare different methods
• Quick Start Guide - Get started with the add-in