QtiPlot - Python Scripting

QtiPlot is fully scriptable via Python, watch the video bellow for a short demonstration of how to create and fully customize a plot using a very simple Python script:



The fact that you can run Python scripts from QtiPlot gives you the possibility to use powerfull existing scientific tools, like SciPy, SymPy or rpy2, thus bringing unlimited data analysis power. Here's a screenshot demonstrating how QtiPlot can become a full-featured computer algebra system (CAS), thanks to SymPy:

QtiPlot - a full-featured computer algebra system (CAS) thanks to SymPy

If you need a detailed example showing how to completely automatize tasks in QtiPlot, please download this Python script. It can be used in order to verify the accuracy of the curve fitting algorithms in QtiPlot. The data used in this example is retrieved from the Statistical Reference Datasets Project of the National Institute of Standards and Technology (NIST).

In order to run this example, you need an internet connection, since the script will try to download all the nonlinear regression test files from the Statistical Reference Datasets Project. Unzip the downloaded script and launch QtiPlot from a command interpreter:
	qtiplot -x strd_nist_fit.py
If you don't want to display the QtiPlot user interface, run the example with the following option:
	qtiplot -X strd_nist_fit.py
For more details about the scripting capabilities available in QtiPlot, via Python and PyQt, please read the user manual.