QtiPlot is a cross-platform scientific application for data analysis and visualisation. Thanks to its multilingual support, QtiPlot is actively used for teaching in academic institutions all over the world. Numerous research scientists trust QtiPlot for analysing their data and publishing the results of their work.

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This document is a handbook for using QtiPlot, a program for two- and three-dimensional graphical presentation of data sets and for data analysis. This manual is organized in several chapters: -The first chapter describes the main concepts and terms which are used in QtiPlot.

QtiPlot is a dynamic tool: plots created from data sets and the tables and matrices owning that data are interconnected. When a data table is modified, all objects in dependent plot windows (curves, axes scales, legends) are automatically updated.

QtiPlot - Data Analysis and Scientific Visualisation: Try for free: Home: Download: Buy. Pricing Invoice Payment. Contact: Support. Manual FAQ Tutorials. News. Latest Archive Subscribe. Trial version. The trial version has the following limitations: saving to project files as well as Python scripting are disabled and it can be used for maximum ...

QtiPlot draws a pseudo 3D colored pie chart using the selected column in the active table window (only one column allowed). The 3D geometry settings of the pie chart can be easily customized via a double-click on the drawing area which opens the plot details dialog.

The multi-peak fit tool in QtiPlot allows to fit your data points to a sum of Gaussian, Lorentzian or Pseudo-Voigt functions. The first step is to specify the number of peaks. Then you must define the position of each peak on the curve.

QtiPlot can plot 3D vectors from two different data structures: XYZ XYZ and XYZ dXdYdZ. The six XYZ XYZ data columns define the start and end points for the vectors, while XYZ dXdYdZ data columns provide the vector start points and the distances between the projections of the start and end points of the vectors on the three axes, X, Y and Z ...

QtiPlot uses least-squares algorithms for linear and nonlinear fitting of experimental data. For nonlinear fitting the following iterative algorithms are available: the Levenberg-Marquardt algorithm, provided by the MPFIT library. the Nelder-Mead algorithm (or downhill simplex method), as implemented in this public GitHub repository.

If the column selection does not include a X type column, the values for the X axis are automatically calculated by QtiPlot using row indices. Bars Makes a 3D plot of the selected data columns in the active table window using Bars style.