MathLibX

LeastSqX: LeastSqX provides powerful linear and non-linear least squares algorithms with an easy one-minute learning curve interface. Write your own function for fitting and pass the function name to the control, or simply rely on the default general polynomial function. Freely distribute all LeastSqX runtime files with your finished application! To use LeastSqX, simply make a single call to the FitData method defined by the control. This call includes passing the number of points, degrees of freedom to fit, array of independent variables (e.g., time), array of dependent variables (e.g., measurements), array of uncertainties of these measurements (may be all set to 0 for non-weighted fitting), function name (or 0 for a general polynomial fitting), and initial guess of the coefficients (may be all 0 for linear least squares). Upon return, the coefficients are replaced by the fitted values. Also returned are the one sigma uncertainties of the fit, along with the Chisq value. Example implimentations included with the control show linear and non-linear fitting, and also a simple demonstration of how to use LeastSqX to perform multidimensional fitting (e.g., z as a function of both x and y).

FFTX: The FFT control allows one to quickly calculate the Fast Fourier Transform and the Inverse Fast Fourier Transform of a set of data. The data can be a real or complex data set. The control also includes the option of Welch windowing the data prior to FFT. The example code demonstrates how to use this control for creating a smoothing filter. Other uses for the control includes data convolution and deconvolution.

The control is extremely simple to use. To FFT data, simply make a call to the FFT method, passing a vector of data, plus the number of data. Upon return, the data vector has been replaced by its FFT. To take the inverse, make a similar call to the IFFT method. To use Welch windowing prior to FFT, just set the Welch window property to true.

For complex data sets, make a call to the complex FFT method (CFFT) passing vectors of the real and imaginary components of the complex numbers. Use the complex IFFT (ICFFT) to take the complex inverse.

SigProcX: The Digital Signal Processing Filter control gives the user the capability to filter real-time data with a number of different weighting schemes. The control includes several types of digital filters including highpass, midpass, lowpass filters, and an Alpha Beta type filter. The user only has to select the filter type, set several filter weight constants, and the control is ready to start filtering data. Call the "Data" method with each new unfiltered data point, and the lowpass, midpass, and highpass filter values are returned. Call the "alpha_beta" method to perform Alpha Beta filtering.

KalmanFtX: With this Kalman Filter control, one can easily incorporate the power of linear Kalman filtering into your application. The Kalman Filter is similar to least squares fitting, but allows for real-time updates to the fit. The control allows user entries of the Process Noise (Q) matrix, the Dynamics (Phi) matrix, the Partials (H) matrix, the measurement (Z) vector, the initial Covariance (P) matrix, and the initial State (X) vector. A single call to KalmanFtX propagates the state vector and covariance matrix, adds the Q process noise to the covariance matrix, calculates the gain, updates covariance matrix, and then updates the state vector.

A single call consists simply of "call KalmanFtX1.Kalman(Z, R, X, P, H, Q, Phi, Nx, Nz)" where Nx are the number of states and Nz are the number of measurements at this update.

SortX: The SortX control contains two types of functions, those utilized for sorting a vector (VectorSort), and those utilized for sorting an array of data based on a selected column (ColumnSort). A property called SortOrder sets whether any future call to these sorting functions will return the data sorted in ascending of descending order. The control accepts data declared as double float, single float, long integer, and integer.

System Requirements