Curve Fitting

MatDeck’s Curve Fitting software facilities form a series of data points, in which the user can apply various interpolations and regressions.

Our users benefit from two different software technologies for curve fittings:

The classical curve fitting approach – where the user is using a standard set of curve fitting functions and the commands showed in the MatDeck document is curve fitting.

The Curve Fitting Toolbox, shown in MatDeck’s Curve Fitting Toolkit document. Due to MatDeck’s parallel processing, Excel, Database interface and other features of ours, users gain extremely sophisticated yet simple solutions for curve and function fitting. Generated curve fitting data and functions are available as a curve plotting graph, curve function or an array of curve data.

Curve fitting Examples

Curve Fitting Interpolations

The interpolation curve fitting available in MatDeck gives the user and exact fit tot ha available series of data points. Interpolation estimation of a value within a sequence of known values.

Available interpolations are:

  • Linear interpolation
  • Polynomial interpolation
  • Ration interpolation
  • Cubic spline
  • Akima spline
  • Hermite spline
  • Cubic B spline
  • Bezier interpolation
MatDeck's curve fitting 1
Interpolation Method Form for 2D Graph

Curve Fitting Regressions

MatDeck’s regression curve fitting forms a curve based on the statistical processes for estimating the relationship among a range of data points.

Available regressions:

  • Linear
  • Exponential
  • Logarithmic
  • Power regression
  • Polynomial regression
MatDeck's curve fitting 2
Regression Method Form for 2D Graph

Curve Fitting Functions

MatDeck has a variety of unique curve fitting features, methods and functions.  Since curve fitting is done in a canvas, it allows the user to utilize the nearly all MatDeck Features. 2000+ functions, GUIs, 2D Graphs, various toolkits, formula templates, 3D Graphs, databases and other specialized features such as ArrayFire acceleration functions which allows the users to compute enormous amounts of data. All of this can be done in a single document. Curve fitting variables are also visible in Python and MatDeck scripts as code so functions from canvas can be merged with the code to give the user complete freedom over their work.

curve fitting functions

Curve Fitting Toolkits

Three curve fitting toolkits are available:

  • Curve Fitting Form
  • Curve Fitting Results
  • Curve Regression Table

Curve fitting toolkits and other functions in MatDeck allow you to compare fitting models, perform regression analysis, visualize data and fitting curves, create models in symbolic form and use this symbolic form for approximate numerical functions all in a simple and hassle free interface. We can divide the MatDeck curve fitting set in the following areas with their accompanying functions that are shown in MatDeck’s document screenshots:

matdeck curve fitting functions

There are a plethora of advantages of the fit functions that provide as an excellent tool which can used determine which model best represents the data you are working with:

matdeck curve fitting dialog

MatDeck summarizes all the mentioned functions and techniques into the Curve Fitting Toolkit for better, faster and easier use. You can visualize every method or compare it with other methods, create predictions based on selected method, etc.