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General Features

      • 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 functionality

      • Autosave 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

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

      • 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

      • Reading of Live Data with the support for Unix/UDP/TCP sockets and a serial port

      • 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