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LabPlot Features

General

  • Project based management of data
  • Tree-like organization of created objects
  • Quick navigation, searching and filtering of objects using the Project Explorer
  • Easy customization of objects and methods using the Properties Explorer
  • Folders support for a better object management
  • Spreadsheet and Matrix – data-container serving as the data source used in data analysis and visualization
  • Spreadsheet linking to synchronize the number of rows across multiple spreadsheets
  • Worksheet – area for placing different visualization objects (plots, labels, images, etc) supporting different layouts, zooming and navigation mode
  • Notes – a text container which can simply be used to write comments into a project
  • The undo history dialog
  • Locale-sensitive functionalityAutosave to prevent potential data loss
  • Support for CLI parameters (e.g. to start LabPlot directly in the Presenter Mode)
  • Support for multiple application color schemes, including dark themes
  • Customizable application layouts using a full featured window docking system
  • Software Development Kit – a shared library including the core functionality of LabPlot that can be used in external projects

Data Visualization

  • High-quality, interactive and very fast data visualization optimized for large data sets
  • Arbitrary number of plots in the plot area
  • Highly configurable and publication-quality 2D Plots: scatter plots, line plots, histograms, box plots, bar plots, rug plots, KDE plots, Q-Q plots, Lollipop plots, Process Behavior Charts / Control Charts (XmR-Chart, XbarR-Chart, p-Chart, np-Chart, c-Chart, u-Chart)
  • Support for multiple, freely positionable axes, inverse axis scales and multiple ranges for plots
  • Smooth and fast zooming and navigation modes for plots
  • Function plotting with Cartesian, Polar and Parametric equations
  • Customizable and positionable plot legends, text labels, info elements, images, reference lines and reference ranges for plots
  • Color Maps Browser with an extensive support for scientific and color-vision deficiency friendly color schemes like ColorBrewer, ColorCET, Scientific Colour Maps, cocean, viridis
  • Multiple default and user-defined themes for Worksheets and plots, including Edward Tufte’s ‘Maximal Data, Minimal ink’ theme
  • User-defined plot templates that make it easy to create and customize plots that are intended to be used multiple times
  • Cursor – tool to measure positions and distances in plots
  • Dynamic Presenter Mode for worksheets with the full-screen mode and the navigation panel
  • Sparklines in the header of a spreadsheet
  • Preview panel for all available worksheets in the project
  • Support for Latex syntax in plot labels, plot titles, Computational Notebooks and multiple dialogs
  • A possibility to use multiple LaTeX engines (LuaLaTex, pdfLaTex, LaTex)

Data Analysis and Statistics

  • Column statistics spreadsheet – child spreadsheet showing various statistical properties of the parent spreadsheet
  • Linear and non-linear regression analysis and curve fitting, support for several predefined and user-defined fit models – Basic Functions like polynomial, power or exponential; Peak Functions like Gaussian, Cauchy-Lorentz, Pseudo-Voigt, hyperbolic secant, logistic; Growth Functions like Gompertz, Hill, Gudermann, inverse tangent, logistic and error functions; Statistical Functions like Gaussian, exponential, power, log-normal, binomial, Poisson, Rayleigh, Landau, Pareto, Weibull and many more
  • Maximum Likelihood estimation for fitting statistical distributions like Gaussian Poisson, Exponential, Laplace, Binomial, Cauchy-Lorentz and more
  • Baseline subtraction (background correction) with the asymmetrically re-weighted penalized least squares (arPLS) algorithm
  • Data reduction by removing data points using multiple algorithms (Douglas-Peucker, Visvalingam-Whyatt, Reumann-Witkam, Opheim, Lang and other algorithms)
  • Numerical differentiation (up to the 6th order) and numerical integration (rectangular, trapezoid and Simpson methods)
  • Smoothing of data with moving average, Savitzky-Golay and percentile filter methods
  • Interpolation of data, support for many methods (linear, polynomial, splines, piecewise cubic Hermite polynomial, etc.)
  • Fourier transform of the input data with support for many different window functions (Welch, Hann, Hamming, Blackman, etc.)
  • Fourier Filter – low-pass, high-pass, band-pass and band-reject filters of different types (Butterworth, Chebyshev I+II, Legendre, Bessel-Thomson)
  • Hilbert Transform including envelope
  • Convolution and de-convolution of data sets (sampling interval, linear/circular, normalization, wrap, standard kernel)
  • Auto-correlation and cross-correlation of data sets (sampling interval, linear/circular, normalization)
  • Quick statistical previews available in spreadsheets that consist of multiple location, dispersion and shape measures for quantitative and categorical data and statistical plots like histograms, KDE plots, Q-Q plots, box plots, Pareto plot
  • Extensive parser for mathematical expressions supporting a great number of functions and constants used for data generation in spreadsheets and further data analysis and visualization
  • Function values dialog (editor) with the syntax highlighting and support for reference to arbitrary cells of columns and other moving functions

Computational Notebooks

  • An interactive and animated front-end to powerful mathematics and statistics packages and programming languages like Maxima, Octave, R, Scilab, Sage, KAlgebra, Qalculate!, Python, Julia, Lua
  • Support for using multiple notebooks and languages at the same time
  • Notebook variables holding array-like data (Maxima lists, Python lists and tuples, etc.) can be used as the source for interactive plots
  • Ability to show variable statistics and to plot data from the context menu in the project explorer for variables created in a Notebook
  • Extensive edition capability
  • Support for plotting
  • Markdown and LaTeX syntax
  • Ability to read Jupyter and Cantor projects
  • Syntax highlighting
  • Integrated help for CAS systems and programming languages (downloading, searching, navigating documentation etc.)
  • Support for exporting Notebooks to PDF

Data Import and Export

  • Import of CSV, Origin, SAS, Stata, SPSS, MATLAB, SQL, JSON, binary, OpenDocument Spreadsheets (ods), Excel (xlsx), HDF5, MQTT, Binary Logging Format (BLF), FITS, netCDF, ROOT (CERN), LTspice, Ngspice data files, MCAP (Modular Container File Format)
  • Import of datasets directly from kaggle.com
  • Reading of Live Data with the support for Unix/UDP/TCP sockets and a serial port
  • Import filters to facilitate management of various data import configurations.
  • Export of Worksheets and plots to a file or the clipboard with the support for PDF, EPS, PNG, JPG, SVG, BMP, XMB formats
  • Printing of Notes, Worksheets and plots, Spreadsheets and Matrix data
  • Export of Spreadsheet and Matrix data to CSV, Excel (xlsx) format, SQL databases and LaTex tables
  • Support for drag&drop of files to be imported
  • Support for sharing the project via email, Nextcloud, etc. directly from the main menu
  • Templates for ASCII and Binary import filters to save and load current filter settings
  • A collection with almost 2000 real-world data sets from a variety of topics that teachers and students can use

Plot Digitization

  • Easy extraction of data from external image files
  • Cartesian, polar, logarithmic and ternary coordinate system
  • Symmetric and asymmetric error bars
  • Manual point-by-point extraction of data points or (semi-)automated extraction of curve segments
  • Multiple curves on the image can be read
  • Basic image editing capabilities to reduce the image information to the relevant minimum
  • Extracted data is added to a spreadsheet and is directly ready to use

Data Generation and Processing

  • Support for Tidy Data in spreadsheets, i.e. variables are stored in columns, each observation is stored in a row and the values for each observation is stored in its respective cell
  • Quantitative and categorical data types: Integer, Double, Big Integer (64 bit), Date and Time, Text (Categorical)
  • Data sorting
  • Extended search and replace with the support for regular expressions
  • Data transformation, normalization and standardization
  • Random number generation with support for multiple probability distributions
  • Data sampling (random and periodic methods)
  • Data ‘flattening’ – converting pivoted data to the column-based format
  • Support for dropping and masking of data in spreadsheets
  • Heatmap formatting with the support for scientific and color-vision deficiency friendly color maps

Documentation and Support

  • Extensive user guide and tutorials
  • Short, instructional video tutorials
  • Project examples and educational data sets available through LabPlot’s dialogs
  • Relation type based gallery of plots with downloadable project files
  • LabPlot is an open-source project offered in multiple languages
  • Available for Windows, macOS, Linux, FreeBSD and Haiku
  • LabPlot team offers multiple channels of communication