LabPlot/UserGuide: Difference between revisions
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* [[Special:myLanguage/LabPlot/FITSMetadataEditor|FITS Metadata Editor]] | * [[Special:myLanguage/LabPlot/FITSMetadataEditor|FITS Metadata Editor]] | ||
* [[Special:myLanguage/LabPlot/ColorMapsBrowser|Color Maps Browser]] | * [[Special:myLanguage/LabPlot/ColorMapsBrowser|Color Maps Browser]] | ||
* [[Special:myLanguage/LabPlot/Cursor | * [[Special:myLanguage/LabPlot/Cursor Tool|Cursor Tool]] | ||
== Command Line Options == | == Command Line Options == | ||
Revision as of 17:52, 22 February 2022
Interface
Data Containers
Worksheet
2D Plotting
Statistics
Themes and Templates
Data Analysis
- Fitting
- Smoothing
- Interpolation
- Integration
- Differentiation
- Fourier Transformation
- Hilbert Transform
- Fourier Filter
- Data Reduction
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.