LabPlot/UserGuide: Difference between revisions

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* [[Special:myLanguage/LabPlot/DataAnalysis/Differentiation|Differentiation]]
* [[Special:myLanguage/LabPlot/DataAnalysis/Differentiation|Differentiation]]
* [[Special:myLanguage/LabPlot/DataAnalysis/FourierTransfromation|Fourier Transformation]]
* [[Special:myLanguage/LabPlot/DataAnalysis/FourierTransfromation|Fourier Transformation]]
* [[Special:myLanguage/LabPlot/DataAnalysis/HilbertTransform|Hilbert Transform]]
* [[Special:myLanguage/LabPlot/DataAnalysis/FourierFilter|Fourier Filter]]
* [[Special:myLanguage/LabPlot/DataAnalysis/FourierFilter|Fourier Filter]]
* [[Special:myLanguage/LabPlot/DataAnalysis/DataReduction|Data Reduction]]
* [[Special:myLanguage/LabPlot/DataAnalysis/DataReduction|Data Reduction]]

Revision as of 09:43, 24 April 2021

Interface

Data Containers

Worksheet

2D Plotting

Themes and Templates

Data Analysis

CAS Computing

LabPlot can be used as a frontend to different open-source computer algebra systems (CAS) like Maxima, Octave, R, Scilab, and Sage or programming languages providing similar capabilities like Python and Julia. LabPlot recognizes different CAS variables holding array-like data and allows selecting them as a source for curves. So, instead of providing columns of a spreadsheet as the source for x- and y-data, the user provides the names of the corresponding CAS-variables. Currently supported CAS data containers are

  • Maxima lists
  • Python lists, tuples and NumPy arrays
  • Julia vectors and tuples

With this, powerful calculations carried out inside of different CAS environments can be combined with the user-friendly visualization and editing capabilities of LabPlot.

Import and Export

LaTeX Typesetting

Curve Tracing

Tools

Command Line Options