syntax: SMOOTH( x,y,xout{,weights } )
Default: weights[1:#] = 1
This function calculates a smooth curve using the method of cubic
splines under tension. The data is contained in vectors x, the
independent variable, and y, the dependent variable. A vector is
returned with the same length as xout, the output locations vector.
Vectors x and xout must be strictly monotonically increasing, with
x[1] <= xout[j] <= x[#] for j=1,2,...,LEN(xout)
See also the INTERP, SPLINTERP, SPLSMOOTH and SAVGOL functions.
Additional Information on:
syntax: SPLSMOOTH( x,y,n{,weights } ) ! returns a n by 2 matrix
Default: weights[1:#] = 1
This function calculates a smooth curve using splines by parameterizing
the data contained in vectors x, the independent variable, and y, the
dependent variable. The vector x need not be monotonically increasing.
The number of output locations is given in scalar n. The data points are
parameterized in terms of normalized arc length, then a spline under
tension is calculated for x versus arc length and y versus arc length.
For equally spaced arclength, the x and y values are interpolated
separately and then combined to form output points. The output of this
function is a matrix with n rows and 2 columns. The first column will
contain the locations and the second column the smoothed values.
See also the INTERP, SPLINTERP, SMOOTH and SAVGOL functions.
Additional Information on:
syntax: SAVGOL( n,m,y ) This function calculates a smooth curve through the data contained in vector y, the dependent variable, using the Savitzky-Golay smoothing filter method. The order of the filter is given in scalar n, and can be 2 or 4. The filter width is given in scalar m. These filters preserve the area under the data, the zero_th moment, but also the higher moments. Note that there is no input or output x variable, as the data is assumed to be equally spaced. See also the INTERP, SPLINTERP, SMOOTH and SPLSMOOTH functions.