| Class | Description |
|---|---|
| AkimaSpline |
DataProcessor that interpolates incoming data using an Akima spline
interpolation scheme.
|
| AkimaSplineSmoother |
Optimization algorithm which attempts to smooth data without moving any
point far from its original location.
|
| AkimaSplineSmootherApp |
App to drive AkimaSplineSmoother
|
| AkimaSplineSmootherApp.Applet | |
| AkimaSplineSmootherApp.IntegratorSmoother | |
| AkimaSplineSmootherDy |
Optimization algorithm which attempts to smooth data without moving any
point far from its original location.
|
| AkimaSplineSmootherDyApp |
App to drive AkimaSplineSmoother
|
| AkimaSplineSmootherDyApp.Applet | |
| AkimaSplineSmootherDyApp.IntegratorSmoother | |
| AkimaSplineSmootherMain |
Main method to drive AkimaSplineSmoother
|
| ArrayReader1D |
Reads in lots of 1D arrays from a file.
|
| CalcGradientDifferentiable |
Uses finite difference methods to determine the second order differential of the potential (i.e.
|
| FastFourierTransform |
This utility receives a set of data points, either Real, Imaginary or Both
and performs a Discrete Fourier Transform on them using the Fast Fourier
Transform algorithm.
|
| FiniteDifferenceDerivative | |
| FittingFunctionNonLinear |
This class is coded for specific nonlinear model that decribes the Equation:
f(x) = polynomialA + exp(-Kx^L)*polynomialB
M and N are the input parameters that determine the order of polynomialA
and polynomialB respectively
polynomialA is given as:
M
polynomialA = sum [ a_m * x^m ]
m=1
for example, when M=3; polynomialA = a_1 * x + a_2 * x^2 + a_3 * x^3
Note: polynomialB has same form as polynomialA
|
| LinearFit |
Class that performs a linear fit to x,y data, optionally taking weights
associated with each data point.
|
| NewtonMinimization | |
| PadeApproximation |
Class to approximate a power series with rational function (Pade Approximation)
Pade[K/L] = A_K/ C_L = B_M
with Kth, Lth and Mth order, where K + L = M
a0 + a1*x + a2*x^2 + ...
|
| PadeApproximation.VirialParam | |
| PolynomialFit |
Class that performs a polynomial fit to x,y data, optionally taking weights
associated with each data point.
|
| PolynomialFit.FitResult | |
| SineTransform |
3D Fourier transforms of a function, f, simplify to 1D sine transforms of the auxiliary function F(r) = r*f(r)
when f is spherically symmetric.
|
| SteepestDescent | |
| VirialOptimizer |
Class to determine B9 value that best fit the simulation results after
Pade Approximation [K/L]
|
| VirialOptimizer.VirialParam |