LabPlot/UserGuide: Difference between revisions
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== CAS Computing == | == 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 to select them as the 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, powerfull calculations carried out inside of different CAS environments can be combined with the user-friendly visualisation and editing capabilities of LabPlot. | |||
== Import and Export == | == Import and Export == | ||
Revision as of 17:47, 3 April 2021
Interface
Data Containers
Worksheet
2D Plotting
Themes and Templates
Data Analysis
- Fitting
- Smoothing
- Interpolation
- Integration
- Differentiation
- Fourier Transformation
- 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 to select them as the 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, powerfull calculations carried out inside of different CAS environments can be combined with the user-friendly visualisation and editing capabilities of LabPlot.