Here you can curve fit and surface fit your 2D and 3D data online with
a rich set of error histograms, error plots, curve plots, surface plots,
contour plots, VRML, auto-generated source code, and PDF file output.
If you're looking for quality curve fitting and surface fitting, this is the
site for you! Source code is available at the Google Code Repository.
To begin, select an equation family from the drop-down menus above or try
the "Function Finders" to help determine the best curve fit for your data.
A couple of posts ago, I talked about a simple monte carlo simulation for diffusion limited aggregation. In this post, I’m going to talk about another algorithm that, at its heart, is based on random numbers. Unlike DLA though, this algorithm isn’t about simulating a physical system. Instead, it is about a method for solving optimization problems that are generally poorly suited to traditional numerical optimization techniques. This post describes an application of a library implementing the GEP method posed by Cândida Ferreira nearly 10 years ago. I started messing with GEP shortly after the paper “Gene Expression Programming: A New Adaptive Algorithm for Solving Problems” was published in the journal Complex Systems. The paper sat in a pile for a while, and about two years ago I picked it up again and started to implement it as a Haskell library.
SciDAVis is a free application for Scientific Data Analysis and Visualization. SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. It combines a shallow learning curve and an intuitive, easy-to-use graphical user interface with powerful features such as scriptability and extensibility. SciDAVis is similar in its field of application to proprietary Windows applications like Origin and SigmaPlot as well as free applications like QtiPlot, Labplot and Gnuplot. What sets SciDAVis apart from the above is its emphasis on providing a friendly and open environment (in the software as well as the project) for new and experienced users alike. Particularly, this means that we will try to provide good documentation on all levels, ranging from user’s manual over tutorials down to and including documentation of the internal APIs We encourage users to share their experiences on our forums and on our mailing lists.