Mathematical functions

Available mathematical functions are:
• Integral (node to node, continuous, from one value to another value)
• Differential, first, second, third,
• FFT (Spectrum Analysis) (options: Amplitude or Power (dB), window to be used, Supported window filters: Rectangular, Hanning, Hamming, Blackman, Blackman Harris, Flat Top.
• RMS
• Average
• Adding two curve functions F1(x) + F2(x)
• Subtracting two curve functions F1(x) – F2(x)
• Square wave function
• All standard mathematical functions supported: sin, cos, tan, asin, acos, atan sh, ch, th, asinh, acosh, atanh, log2, log10, ln, exp, sqrt, abs, ...
• Linear Regression
• Interpolation
• Approximation

Interpolations are:
• Linear
• Hermite – polynomial interpolation with C1 smoothness using polynoms of order 3 between nodes (algorithm based on Hermite Interpolation).
• Akima Spline – another polynomial interpolation with C1 smoothness using polynoms of order 3 between nodes (well known algorithm by Hiroshi Akima).
• Cubic Spline - polynomial interpolation with C2 smoothness using polynoms of order 3 between nodes.

Approximation:
• Cubic B-Spline (unconditional approximation)
• Linear Regression (straight line fitting: a + b * x)

• Average
• Variances and Covariance
• Standard Deviation
• Correlation Coefficient

• Polynomial Regression (polynomial fitting, supported polynomial order from 2 to 20)
• Nonlinear Regression (you can type in any function with coefficients used for curve fitting)

• Power Law Fitting (nonlinear regression, fitting: a * x^b)
• Exponential Law Fitting (nonlinear regression, fitting: a * exp(b*x))
• Logarithmic Law Fitting (nonlinear regression, fitting: a + b*ln(x))
• Normal (Gauss) Distribution Fitting (nonlinear regression, fitting the Normal Distribution)

Liner Regression
• Average (signal mean)
• Variances (xx and yy), Covariance
• Standard deviation, percentage of the data points that are within one standard deviation of the mean.
• Correlation Coefficient (r^2)