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LabPlot

LabPlot is a free and open-source, cross-platform software application designed for interactive scientific data visualization, analysis, and plotting, licensed under the GNU General Public License version 2.0 or later. It provides tools for creating high-quality 2D plots, managing datasets in spreadsheets and matrices, performing statistical analyses, and integrating computational notebooks with languages such as Python, R, and Julia, making it suitable for researchers, educators, and professionals handling large datasets. Originally developed as QPlot in 2001 during the 3 era and renamed to LabPlot in 2003, it underwent a major rewrite in 2008 to adopt Qt4 and KDELibs4, transitioning to its current 2.x series with and continuing active development under the Education project. Primarily written in C++ with contributions from a small core team of developers, it supports Windows, macOS, , , and operating systems, with the latest stable release, version 2.12, issued on April 28, 2025. Key capabilities include support for over 20 import/export formats such as , Excel, and files, advanced plotting options like scatter plots, histograms, and color maps, as well as analysis functions encompassing linear and non-linear regression, Fourier transforms, and statistical tools powered by libraries like the GNU Scientific Library (GSL). The software emphasizes user-friendly data organization in a project-based , interactive plot editing, and features like digitizing data from images, ensuring it remains accessible for both novice users and advanced scientific workflows.

Overview

Description and Purpose

LabPlot is a free and open-source, cross-platform application designed for interactive scientific plotting, , , and . It serves as a comprehensive tool for , educators, and professionals to and interpret efficiently, emphasizing ease of use and high performance in handling complex datasets. The primary purpose of LabPlot is to facilitate high-quality and statistical , supporting scientific workflows across , , and by enabling users to create publication-ready plots and perform reliable manipulations without requiring extensive programming knowledge. Key capabilities include through intuitive spreadsheets and matrices, which allow for organized storage and basic editing of tabular . It supports generating a variety of plots, such as scatter, line, histograms, and function plots in Cartesian or polar coordinates, to represent relationships and distributions effectively. Additionally, LabPlot provides monitoring, enabling live updates from sources like sockets or ports for dynamic . A distinctive aspect of LabPlot is its balance between accessibility for non-programmers—through a user-friendly interface, drag-and-drop functionality, and pre-built templates—and advanced options for scripting integration, allowing seamless extension with languages like or for customized computations. This approach makes it suitable for both beginners exploring data trends and experts conducting in-depth analyses.

Platforms and Licensing

LabPlot is a cross-platform application supporting as its primary development environment through integration, as well as Windows, macOS, , and operating systems. This broad compatibility is facilitated by its implementation primarily in C++ using the framework, which provides the and enables seamless operation across diverse systems. LabPlot's affiliation with the project further enhances its native integration on distributions. The software is released under the GNU General Public License or later (GPL-2.0-or-later), permitting free use, modification, and distribution for any purpose, including commercial applications, provided that any derivative works also adhere to the same open-source licensing terms and availability. Outputs generated by LabPlot, such as plots or analyzed data, are not bound by the GPL and can be licensed or distributed independently. Distribution occurs through multiple channels to accommodate various user needs and platforms. On , it is available via repositories, distribution package managers (e.g., , , ), and the official releases. Windows users can obtain installers from the KDE download site or the , while macOS supports DMG installers and Homebrew. and provide access through their respective ports and depot systems. Source code is hosted on KDE's instance at invent.kde.org/education/labplot, allowing compilation from source for custom builds.

History

Origins and Early Development

LabPlot originated in 2001 when Stefan Gerlach, a , , and IT administrator at the , developed the initial version 0.1 under the name QPlot to address the lack of suitable open-source plotting software for his research needs. Gerlach, who also created the liborigin library to enable import of files from the proprietary software, aimed to provide a free alternative integrated with the . As part of the KDE Education project, QPlot was designed to offer accessible tools for scientific users on systems. In 2003, the project was renamed LabPlot with the release of version 1.0, emphasizing deeper integration with for enhanced scientific graphing capabilities. This version shifted focus toward a more robust application for users, building on the foundational code to support interactive 2D and 3D plotting. Early iterations prioritized ease of use within the framework, targeting researchers and educators seeking non-proprietary options. The early development of LabPlot highlighted basic interactive plotting and features, such as function plotting, data import from various formats, and simple statistical tools, all tailored for /KDE environments. By version 1.5.1 in 2006, the software included support for multiple plots per worksheet and drag-and-drop functionality leveraging and protocols, establishing its role as a user-friendly tool for scientific visualization. These advancements reflected Gerlach's vision of combining advanced with high-quality, publication-ready plots under an intuitive graphical interface.

Merger with SciDAVis and Revival

In 2008, LabPlot underwent a significant rewrite using Qt4 and KDELibs4, transitioning from its 1.x series to the foundations of version 2.x, with Alexander Semke joining as a key developer alongside founder Stefan Gerlach. This period also saw the initiation of close cooperation with SciDAVis, a Qt-based fork of QtiPlot started in by developers including Tilman Benkert, Knut Franke, and Gadiou. The collaboration, formally announced in October 2009, focused on merging their backends to share common code for and , thereby reducing duplication of effort while maintaining distinct frontends: LabPlot's KDE-integrated for enhanced and SciDAVis's standalone Qt application for broader cross-platform accessibility. This backend unification accelerated development and positioned both projects as leading open-source alternatives for scientific plotting on . Following a period of limited activity, LabPlot experienced a revival in 2011 under Semke's leadership, evolving into a full project within the KDE Education umbrella to emphasize modernizing its architecture for improved cross-platform compatibility beyond . This shift integrated LabPlot more deeply into KDE's ecosystem, leveraging frameworks for better maintainability and community support. By 2014, the project released its first stable , introducing core features like spreadsheet-based as the primary structure for handling datasets, columns, and rows, which became central to plotting and analysis workflows. Subsequent releases expanded platform support, with official builds for Windows and macOS enabled through Qt's cross-platform capabilities, allowing users to run LabPlot on diverse operating systems without KDE dependencies. Key advancements continued through the mid-2010s, including enhancements to tools. By 2018, with the release of version 2.5, LabPlot incorporated advanced via linear and non-linear models (such as , , and Gaussian functions) and expanded statistics capabilities for goodness-of-fit measures like sum of squared errors and coefficients. These updates built on the shared backend from the SciDAVis , resolving earlier redundancies in data handling while preserving LabPlot's KDE-specific enhancements, such as seamless with KDE's file dialogs and theming. The revival not only stabilized the project but also fostered ongoing contributions from participants and a small core team, growing the codebase to approximately 100,000 lines of primarily C++ code by 2019.

Recent Developments and Funding

In recent years, LabPlot has seen steady advancements through a series of major releases, building on its foundation as an open-source data visualization and analysis tool. Version 2.8, released in September 2020 with patches extending into 2021, introduced support for live streams, allowing updates from sources like and files, alongside a library of over 2,000 datasets from collections such as the Journal of Statistics Education archive and datasets for easy import and analysis. This version also added reference lines as a new worksheet object for highlighting key values and patterns in plots. Subsequent releases continued this momentum: Version 2.9 in May 2022 expanded import capabilities with support for (.mat), , , and formats, enabling broader compatibility with scientific workflows, while introducing box plots for summarizing dataset distributions. Version 2.10, launched in March 2023, focused on performance optimizations, including reduced memory usage for database imports and exports to , as well as faster handling of large-column files. Further enhancements in and marked Versions 2.11 and 2.12. The 2.11 release in July 2024 added new plot types such as lollipop, Q-Q, and (KDE) charts, along with for bar plots and legend controls across all supported visualizations, improving interpretive flexibility. Version 2.12, released on April 28, 2025, broadened data format support with MCAP for multimodal logs and direct dataset downloads, while incorporating example projects for , including validation of nonlinear models against NIST-certified datasets to ensure accuracy in . A minor patch, 2.12.1, followed on August 18, 2025, addressing bug fixes and tweaks like improved shortcut handling for analysis curves. Funding has played a key role in these developments, particularly through a from the NLnet Foundation's NGI0 Fund, awarded in April 2024 under the European Commission's Next Generation Internet program (Grant Agreement No. 101092990). This support targets enhancements in scripting via bindings, advanced statistical tools like testing on live data, and notebook improvements for reproducible analysis. As of October 2025, progress on the funded work includes initial implementations of the for scripting integration, basic testing features for statistical validation, and enhancements to notebooks for better support of reproducible workflows with languages like and . Ongoing efforts emphasize performance improvements, such as optimized handling of large datasets, and new options including sparklines integrated into spreadsheets for quick trend overviews, especially with live sources. Multi-language scripting is also advancing, building on Cantor backend support for systems like Maxima and to enable seamless computational workflows within LabPlot projects.

Features

Data Management and Import/Export

LabPlot employs a project-based to organize , utilizing a tree-like structure in the Project Explorer for hierarchical navigation and management of objects such as folders, spreadsheets, matrices, and notes. This allows users to create nested structures for complex datasets, with features like quick searching, filtering, and drag-and-drop reorganization to maintain efficient workflows. Spreadsheets serve as primary containers, supporting column-based with customizable types (numeric, text, date/time) and formats (e.g., decimal, scientific), while matrices handle two-dimensional numerical for specialized applications. For data import, LabPlot supports a wide array of formats to facilitate seamless integration from various sources, including , Excel (.xlsx), (.mat), , , , , projects, HDF5, , , , , , BLF, MCAP, and spreadsheets (.ods). Users can import data via the dedicated dialog, which provides previews, options for row/column selection, and filters to handle delimiters, headers, and encoding. Additionally, it includes nearly 2000 bundled real-world datasets for educational and testing purposes, accessible directly within projects. Live data import is enabled from files, named pipes, network sockets (/), local sockets, or ports, with configurable update frequencies, data types (ASCII, binary, , ), and options to keep historical samples or link files without copying. Import from SQL databases is supported through direct querying and table selection, allowing real-time data retrieval without intermediate files. Export capabilities in LabPlot emphasize flexibility for sharing and further processing, supporting output to PDF, , , , , , , Excel (.xlsx), LaTeX tables, text, , and . Data from spreadsheets or matrices can be exported with customizable separators, decimal locales, and header inclusion, while drag-and-drop functionality enables quick transfer to other applications or desktops. Projects maintain autosave recovery and extensive undo history to safeguard data integrity during management. Basic data processing tools aid in preparing imported data for , including support for tidy data structures through column masking and dropping to exclude unwanted entries without deletion. Sorting is available by column values, and follows distributions such as , , , , and Weibull for creation. Transformations encompass filtering via search/replace with regular expressions, , , sampling, and flattening to restructure datasets efficiently. These features ensure data remains organized and ready for integration into visualization workflows.

Plotting and Visualization

LabPlot supports a wide range of plotting types for graphical representation of imported data from spreadsheets or external sources. These include scatter plots, line plots, histograms, plots, plots, rug plots, () plots, Q-Q plots, lollipop plots, and process behavior charts such as XmR, XbarR, p, np, c, and u charts. Each plot type can accommodate multiple datasets simultaneously, enabling overlay and comparison visualizations within the same plot area. Function plotting extends these capabilities, allowing users to generate curves defined by mathematical equations in Cartesian, polar, or coordinates directly within the software. Support for multiple axes, including freely positionable options with inverse scales and multiple ranges, facilitates complex representations such as dual-axis charts. Interactive features like smooth zooming, panning, and cursor tools for measuring positions and distances enhance navigation and exploration of plots, even with large datasets. Advanced tools include color maps drawn from established palettes such as ColorBrewer, ColorCET, Scientific Colour Maps, cocean, and viridis, which can be applied to represent data intensity or categories. Sparklines provide miniature inline plots in headers for quick trend overviews, while lines and ranges allow of key thresholds or regions. syntax is supported for rendering mathematical expressions in labels, titles, and legends, ensuring precise notation for scientific outputs. Conditional formatting, primarily through heatmap-style color mapping in data views, aids in highlighting patterns before plotting. Customization options enable tailored plot appearances, with configurable legends, text labels, info elements, and embedded images. Users can apply predefined themes, such as ’s "Maximal Data, Minimal Ink" style, or create custom templates for consistent styling across projects. The Dynamic Presenter Mode offers full-screen viewing with navigation controls, ideal for demonstrations or reports. An arbitrary number of plots can be arranged in multi-plot layouts within a single worksheet for comparative analysis. LabPlot includes plot tools to extract data from existing images, supporting manual point placement or automated curve detection in Cartesian, polar, logarithmic, and coordinate systems. This feature reverses the process, converting graphical representations back into editable datasets. For output, plots can be exported in high-resolution formats including PDF, , , JPG, , , and XBM, preserving publication-quality details and theme applications.

Data Analysis and Statistics

LabPlot offers comprehensive statistical tools for descriptive analysis of stored in . Column statistics compute key measures such as , , variance, standard deviation, , and , displayed in a dedicated child for easy review. These calculations provide essential summaries of data location, , and shape, enabling users to quickly assess dataset properties without advanced scripting. Visual statistical previews enhance understanding through integrated plots, including histograms for frequency distributions, kernel density estimation (KDE) plots for smooth probability density approximations, Q-Q plots for comparing data quantiles against theoretical distributions, and box plots for visualizing medians, quartiles, and outliers. These previews support customizable options, such as kernel selection for KDE and whisker definitions for box plots, and are accessible directly from the spreadsheet interface. Correlation analysis includes auto-correlation and cross-correlation functions for time series or paired datasets, with configurable parameters like sampling interval, linear or circular modes, and normalization to handle periodic signals effectively. For categorical data relationships, LabPlot supports contingency tables to summarize joint frequencies and perform basic association tests, facilitating preliminary explorations of variable dependencies. is available for fitting statistical distributions, including Gaussian, , , and others, to model data probabilistically. Curve fitting and in LabPlot accommodate both linear and nonlinear models, with a library of predefined functions spanning basic forms (e.g., , , ), peak shapes (e.g., Gaussian, Cauchy-Lorentz, Pseudo-Voigt), growth curves (e.g., Gompertz, ), and statistical distributions (e.g., log-normal, ). Users can define custom models via an expression parser, and the software provides detailed output including parameter estimates, confidence intervals, residuals, and goodness-of-fit metrics like R-squared. Baseline subtraction employs the asymmetric (arPLS) algorithm to remove trends from spectra or signals, improving accuracy in subsequent analyses. Smoothing operations utilize methods such as , Savitzky-Golay filtering (with order and window size parameters), and percentile filters to reduce while preserving signal features. Interpolation supports linear, , spline-based (cubic, Akima), and cubic Hermite methods, allowing of missing values or generation of smoother curves. Mathematical functions enable advanced and computation. Numerical integration applies the , Simpson's 1/3 rule, Simpson's 3/8 rule, or rectangular approximation, with options for adaptive step sizing to balance speed and precision. Differentiation computes derivatives up to sixth order using finite difference methods, achieving accuracy up to fourth order depending on the selected scheme. The (DFT) converts time-domain signals to the via the equation X_k = \sum_{n=0}^{N-1} x_n e^{-2\pi i k n / N}, where X_k represents the k-th frequency component, supporting window functions like Welch, Hann, Hamming, and Blackman to mitigate spectral leakage; outputs include magnitude, phase, power spectrum, or coherence. The Hilbert transform extracts instantaneous amplitude and phase for analytic signals, useful in envelope detection. Convolution and deconvolution operations facilitate filtering and peak separation, with deconvolution aiding in resolving overlapping features through iterative or direct methods. Peak detection integrates with fitting tools, employing peak-specific models (e.g., Gaussian or ) to identify and quantify peaks in noisy data, including automatic threshold-based detection and background estimation. An integrated mathematical expression parser supports custom formula creation for data transformation or analysis, incorporating operators, (e.g., sin, cos), logarithmic and exponential functions (e.g., log, exp), and constants like π and , allowing complex expressions such as baseline-corrected integrals without external dependencies. Results from these analyses can be visualized in plots for immediate interpretation.

Computational Notebooks and Scripting

LabPlot provides an integrated for computational notebooks, enabling users to combine , execution, and in a single . This feature leverages the KDE backend to support interactive sessions in multiple programming languages, facilitating reproducible research through cell-based execution. Notebooks in LabPlot allow for and formatting to create richly documented reports, alongside executable cells that run computations directly within the application. The notebook interface supports executable cells in languages such as , , , , and Maxima, with multi-language execution possible within the same project for flexible workflows. Users can import Jupyter (.ipynb) and (.cws) files, converting them into LabPlot's native worksheets for seamless integration with existing projects that include spreadsheets and plots. For instance, can be used for custom data processing tasks like applying window functions for transforms, while enables advanced statistical modeling. This cell-based structure promotes reproducibility, as code cells execute sequentially with "Shift+Enter" and maintain session state across runs. Scripting capabilities extend to embedding plots and analyses directly in notebooks, with inline plotting for immediate visualization of results and options to display outputs in external windows. Notebook variables, such as Python lists or arrays, are recognized as data sources for LabPlot's plotting and analysis tools, allowing bidirectional sharing between the notebook environment and the main application. Built-in analysis functions from LabPlot can be called directly from scripts in supported languages, enhancing extensibility without leaving the interface. Notebooks support PDF export for sharing documented results, and features like and integrated help for languages further streamline development. Examples include simulating the Duffing oscillator in Maxima or generating Blackman windows in , demonstrating practical applications in scientific computing.

User Interface

Project Structure and Navigation

LabPlot organizes projects in a hierarchical tree structure within the Project Explorer, a dedicated panel that allows users to manage worksheets, plots, folders, sub-folders, spreadsheets, matrices, and notes as distinct objects. This tree-like layout facilitates the grouping of related elements, such as placing multiple data containers under a single workbook or worksheet, enabling efficient organization of complex analyses. The main interface includes several key views for data handling and annotation: the Spreadsheet view serves as a tabular editor for entering and editing one-dimensional or multi-column manually or via import; the Matrix view handles two-dimensional numerical arrays, supporting operations like image visualization; and integrated Notes provide a text-based container for adding , comments, or documentation directly within the project, which can be printed or exported. These s are accessible by selecting corresponding objects in the Project Explorer, with support for row across multiple spreadsheets to maintain consistency during workflows. Navigation within LabPlot relies on the dockable Project Explorer panel for browsing and selecting project elements, complemented by a search bar for quick filtering and locating objects by name or type. panels, including the Properties Explorer for inspecting and modifying selected item attributes, can be repositioned, resized, or undocked as needed to customize the layout. Additionally, LabPlot offers an unlimited /redo stack accessible via the menu or shortcuts (Ctrl+Z and Ctrl+Shift+Z), applying to all actions across the project for reliable experimentation. For batch operations, a (CLI) supports parameters to launch LabPlot in specific modes, such as directly opening projects or entering presenter view. Basic workflows begin with creating a new project through the File > New menu, which initializes an empty .lml file structure ready for adding and visualizations. Autosave is enabled by default, automatically backing up changes every five minutes to prevent data loss during extended sessions. The user interface supports multiple color schemes, configurable via , including light and dark themes to accommodate different working environments and reduce .

Editing and Customization Tools

LabPlot provides a dedicated Properties Explorer, a dockable that serves as the primary for modifying object properties within the application. This allows users to adjust a wide range of attributes, including scales, line styles, colors, and fonts, with changes applied in to offer immediate visual feedback. Edits made through the Properties Explorer are fully undoable and redoable, ensuring non-destructive workflows, and the tool supports simultaneous modification of multiple selected objects for streamlined customization. Editing in LabPlot encompasses several interactive modes to facilitate precise and adjustments. Users can perform direct on plots, such as panning, zooming via mouse wheel, and resizing elements through drag handles on plot areas and legends. In spreadsheets, formula-based column edits enable dynamic generation and transformation using mathematical expressions, supported by an extensive parser that handles functions, constants, and multivariant operations; and auto-completion assist in formula entry, while invalid expressions are masked to prevent errors. Additionally, application promotes consistent styling by allowing users to save and load predefined configurations for curves and plots—such as types, line widths, and color schemes—directly from a in the Properties Explorer, with options to set defaults for new objects. Customization options in LabPlot extend to user interface personalization and extensibility. shortcuts enhance efficiency, including Shift+Enter to apply property changes without closing dialogs and standard bindings like Ctrl+N for new projects. The application supports multi-language user interfaces through KDE's localization efforts, enabling translation into numerous languages for broader accessibility. For advanced users, an SDK provided as a shared C++ library allows the development of extensions and integration with external tools, facilitating custom functionality beyond the core interface. Usability is further supported by built-in aids such as tooltips and notifications that provide contextual guidance during interactions, along with validation mechanisms for expressions in spreadsheets and error handling features like data masking for outliers or computation failures. These elements ensure reliable editing experiences, with the displaying on operations and potential issues.

Development and Community

Core Development Team

LabPlot was originally developed by Stefan Gerlach, a and IT at the , who initiated the project in 2001 under the name QPlot and released the first version renamed to LabPlot in 2003. Gerlach also founded the related liborigin library for importing project files, which has been integrated into LabPlot. Alexander Semke, a , joined the project in and has served as the lead maintainer since , overseeing the core architecture, major feature implementations, and ongoing evolution of LabPlot2, the current codebase that began as a rewrite in for 4 compatibility. Semke's contributions include porting to modern Frameworks, enhancing tools, and ensuring cross-platform support, as evidenced by his extensive code authorship and release announcements. In 2025, Semke received the Akademy Award for Best Application for his work on LabPlot. The project is housed within the community's Education team, with its hosted on KDE Invent at invent.kde.org/education/labplot, facilitating collaborative development through . LabPlot collaborates with other projects, notably integrating Cantor's computational notebook backend since version 2.3 in 2016 to enable scripting and analysis in languages like and directly within worksheets. Occasional contributors provide support for bug fixes, feature enhancements, and translations into multiple languages, expanding the project's accessibility.

Contributing and Support Resources

LabPlot encourages contributions from users and developers through established infrastructure. Bug reports and feature requests can be submitted via the KDE Bugtracking System at bugs.kde.org, where the LabPlot component is available for targeted filing. Code submissions follow guidelines and are managed through Invent at invent.kde.org/education/labplot, with a development backlog accessible via issues on Invent for reviewing ongoing tasks. Translations for the user interface and documentation are handled via the localization platform at l10n.kde.org, supporting multiple languages to broaden accessibility. Comprehensive documentation resources aid users in getting started and advancing their skills. The official user manual at docs.labplot.org provides detailed coverage of the interface, data handling, plotting, analysis tools, notebooks, import/export options, and the software development kit (SDK), including API documentation for programmatic extensions. Video tutorials are available on the LabPlot website and YouTube channel, offering step-by-step guidance on topics from basic data import to advanced fitting and visualization techniques. Example projects, accessible via the application's dialogs or the online gallery, demonstrate practical implementations, while a collection of nearly 2000 real-world educational datasets supports onboarding and teaching scenarios. Community support for LabPlot is integrated into the KDE ecosystem. Users can seek help through the dedicated support email at [email protected] for general inquiries and troubleshooting. Real-time discussions occur in the LabPlot room on Matrix at matrix.to/#/!jDLqWTaTGNKnenBSNA:kde.org, facilitating collaboration among developers and users. Multi-language resources, including translated manuals and interfaces, enhance global participation, alongside educational datasets tailored for academic environments. LabPlot maintains an active user base particularly in , where it serves as a free alternative to proprietary tools like for scientific and . It is integrated into major distributions, including , , , and others, ensuring broad availability without additional installation hurdles. While formal reception statistics are not publicly tracked, its inclusion in educational datasets and community-driven examples underscores its role in replacing in research and teaching contexts.

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