SDK

The objective of LabPlot’s Software Development Kit (SDK) is to enable the utilization of LabPlot’s core functionalities in external projects. This SDK can be employed to integrate LabPlot’s algorithms into one’s own projects or to automate tasks such as batch importing multiple files, processing and visualizing them, and exporting them as vector graphics. The SDK comprises a shared library, Python wrappers and this documentation.

Important

Note, this part of LabPlot is still considered experimental. Consequently, there is no assurance regarding the stability of the API or the ABI at present.

Below is a small demo to get an impression about what is possible showing how to import a text file, how to visualize the imported data in a histogram, how to perform a distribution fit and how to export the final result to PDF:

#include <QApplication>
#include <labplot.h>

int main(int argc, char** argv) {
   QApplication app(argc, argv);

   // create a spreadsheet and import the data into it
   auto* spreadsheet = new Spreadsheet(QStringLiteral("data"));
   AsciiFilter filter;
   filter.readDataFromFile(QStringLiteral("data.txt"), spreadsheet);

   // create a worksheet
   auto* worksheet = new Worksheet(QStringLiteral("worksheet"));

   // create a plot area and add it to the worksheet
   auto* plotArea = new CartesianPlot(QStringLiteral("plot area"));
   plotArea->setType(CartesianPlot::Type::FourAxes);
   plotArea->addLegend();
   worksheet->addChild(plotArea);

   // create a histogram for the imported data and add it to the plot area
   auto* histogram = new Histogram(QStringLiteral("histogram"));
   histogram->setNormalization(Histogram::Normalization::ProbabilityDensity);
   histogram->setDataColumn(spreadsheet->column(0));
   plotArea->addChild(histogram);

   // perform a fit to the raw data and show it
   auto* fitCurve = new XYFitCurve(QStringLiteral("fit"));
   fitCurve->setDataSourceType(XYAnalysisCurve::DataSourceType::Histogram);
   fitCurve->setDataSourceHistogram(histogram);
   plotArea->addChild(fitCurve);

   // initialize the fit
   auto fitData = fitCurve->fitData();
   fitData.modelCategory = nsl_fit_model_distribution;
   fitData.modelType = nsl_sf_stats_gaussian;
   fitData.algorithm = nsl_fit_algorithm_ml; // ML distribution fit
   XYFitCurve::initFitData(fitData);
   fitCurve->setFitData(fitData);

   // perform the actual fit
   fitCurve->recalculate();

   // apply the theme "Dracula"
   worksheet->setTheme(QStringLiteral("Dracula"));

   // export the worksheet to PDF
   worksheet->exportToFile(QStringLiteral("result.pdf"), Worksheet::ExportFormat::PDF);
}

from labplot import *

# create a spreadsheet and import the data into it
spreadsheet = Spreadsheet("data")
filter = AsciiFilter()
filter.readDataFromFile("data.txt", spreadsheet)

# create a worksheet
worksheet = Worksheet("worksheet")

# create a plot area and add it to the worksheet
plot_area = CartesianPlot("plot area")
plot_area.setType(CartesianPlot.Type.FourAxes)
plot_area.addLegend()
worksheet.addChild(plot_area)

# create a histogram for the imported data and add it to the plot area
histogram = Histogram("histogram")
histogram.setNormalization(Histogram.Normalization.ProbabilityDensity)
histogram.setDataColumn(spreadsheet.column(0))
plot_area.addChild(histogram)

# perform a fit to the raw data and show it
fit_curve = XYFitCurve("fit")
fit_curve.setDataSourceType(XYAnalysisCurve.DataSourceType.Histogram)
fit_curve.setDataSourceHistogram(histogram)
plot_area.addChild(fit_curve)

# initialize the fit
fit_data = fit_curve.fitData()
fit_data.modelCategory = nsl_fit_model_distribution
fit_data.modelType = nsl_sf_stats_gaussian
fit_data.algorithm = nsl_fit_algorithm_ml  # ML distribution fit
XYFitCurve.initFitData(fit_data)
fit_curve.setFitData(fit_data)

# perform the actual fit
fit_curve.recalculate()

# apply the theme "Dracula"
worksheet.setTheme("Dracula")

# export the worksheet to PDF
worksheet.exportToFile("result.pdf", Worksheet.ExportFormat.PDF)

The section Concepts provides an overview of the main concepts and components of the SDK. The details on the installation and the documentatio of the API for C++ and Python can be found under the C++ SDK and Python SDK sections, respectively. The section Examples contains numerous illustrative examples that demonstrate the application of the SDK.

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