SciDAVis

SciDAVis is a free, open-source data analysis and visualization program for scientists, engineers and students. It started in 2007 as a fork of QtiPlot, created by Tilman Benkert and Knut Franke, and is now maintained by a small team that includes Russell Standish. SciDAVis is designed to provide an intuitive and interactive environment for data analysis and visualization, with tables, matrices, graphs and notes kept together in one project file.

The main features of SciDAVis are:

Data Import: SciDAVis imports data from ASCII text files such as CSV and tab-separated tables, and data can also be typed or pasted into its spreadsheet-like tables. Builds with the optional import module can open OriginLab project files, and it is a point-and-click alternative to script-based plotting tools such as Gnuplot.

SciDAVis provides an intuitive graphical user interface for data analysis, manipulation, and visualization.

Data Analysis: SciDAVis offers a wide range of data analysis tools such as column and row statistics, correlation, convolution and deconvolution, FFT and FFT-based filters, differentiation and integration. Curve fitting with built-in and user-defined functions is based on the GNU Scientific Library.

Data Visualization: SciDAVis allows users to easily create publication-quality, customizable graphs and charts. It includes 2D plot types such as line, scatter, bar and pie charts, as well as 3D surface plots and multi-layer graphs.

Data Export: SciDAVis exports graphs as bitmap images, PDF, EPS and SVG, and tables can be saved as ASCII text.

Programming Interface: SciDAVis evaluates mathematical expressions in table columns with muParser, and builds with Python support add a full scripting interface for automating tasks and creating custom analysis tools.

Documentation: SciDAVis has an online user handbook, and its source code and releases are published on GitHub and SourceForge under the GNU General Public License.

These features make SciDAVis a versatile data analysis and visualization tool for Windows, macOS and Linux. It is free of charge, familiar to users of similar programs, and suitable for a variety of data analysis tasks.