With these boundary conditions met, an entire function can be constructed in a piece-wise manner. The second derivative of each cubic function is set equal to zero. Cubic Spline This method splits the input data into a given number of pieces, and fits each segment with a cubic polynomial.The resulting point may not be an accurate estimation of the missing data. Linear Linear interpolation is a fast method of estimating a data point by constructing a line between two neighboring data points.If multi-XY are selected, each set of XY will be used as reference to interpolate the same X column and output the corresponding Y column and the coefficient value.įor help with range controls, see: Specifying Your Input Data The reference XY column(s) by which to interpolate Y from specify X column. menu command.Ĭontrols recalculation of analysis resultsįor more information, see: Recalculating Analysis Results Note: To generate uniform linearly spaced interpolated values, use the Interpolate/Extrapolate. The interp1 X-Function is called to perform the calculation. Select Analysis: Mathematics:Interpolate/Extrapolate Y from X.Create a new worksheet with input data.Cubic B-Splines allow the accurate modeling of more general classes of geometry. Similar to Cubic spline interpolation, Cubic B-spline interpolation also fits the data in a piecewise fashion, but it uses 3 rd order Bezier splines to approximate the data. Spline interpolation incurs less error than linear interpolation, and the interpolant is smoother. The Cubic spline method uses 3 rd order polynomials, and executes data-fitting in a piecewise fashion. These disadvantages can be avoided by using low-order polynomial fitting, or spline interpolation. Polynomial interpolation requires much more computation power than linear interpolation and when the polynomial order is high, the fit of the data oscillates wildly. The generalization of linear interpolation is polynomial interpolation. Linear interpolation is also useful for extremely large data sets, because the calculations are not time- or computation-power intensive. This method is useful in situations where low precision can be tolerated. In linear interpolation, the arithmetic mean of two adjacent data points is calculated. Linear interpolation is the simplest and fastest data interpolation method. Origin provides four options for data interpolation: Linear, Cubic spline, Cubic B-spline, Akima Spline. Given an X vector, this function interpolates a vector Y based on the input curve (XY Range). Interpolation is a method of estimating and constructing new data points from a discrete set of known data points. Y = sin(2*pi*f*t) % Test signal sampled accurately In addition to providing a better value for the distinct peak frequency in the spectrum (compare plots 1 & 3), the interpolation method also results in a lower mean squared error. It supposes a 5 Hz sine wave test signal, with a nominal sample rate of 16 Hz but with jitter added to the sample times. I have found, to my surprise, that interpolating the data by using the Matlab function interp1() gives more accurate results than resampling by using the Matlab function resample().īelow is a simple illustration. Without changing sample rates, I need to resample the data uniformly. I have a task that requires that I deal with nonuniformly sampled signals (not plain vanilla time series data).
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