Labplot: Free, Open Source and Cross-Platform Data Visualization and Analysis
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Data VisualizationOpen-Source SoftwareScientific Computing
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Scientific Computing
LabPlot, a free and open-source data visualization and analysis tool, is introduced to the HN community, sparking discussions about its features, limitations, and potential use cases.
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https://github.com/KDE/labplot
https://invent.kde.org/education/labplot
https://docs.labplot.org/en/import_export/import_export_sql....
https://doc.qt.io/archives/qt-5.15/sql-driver.html
Are you talking about something else?
They can be useful if you have other tools (e.g. measurement software) that already produces the data you want, and you just want a GUI tool to create plots, and maybe do some simple things like least squares curve fitting etc.
If you already do a lot of data wrangling in something with a programming language and plotting libraries accessible from said language, like the ones you mention, yeah, this is not the tool for you.
This is great for people who don't know nor want to learn to program.
My reaction is much more emotional than rational.
As a side note, my research ended up essentially discovering Flame Graphs before Brendan Gregg was publishing/popularizing his version. His are much better than mine, but I take some comfort knowing that the ideas I was coming up with in grad school were decent!
* Quickly cycle visually through time series graphs (often several hundred parameters). You'd have seen most of those parameters before and would quickly catch any anomalies. You can clear so much data rapidly like this.
* Quickly analyze a graph at various zoom and pan settings. May be save some as images for inclusion in documents. Like above, the zoom and pan operations often follow each other in a matter of seconds.
* Zoom into fine details, down to single bit levels or single sample intervals. There's surprising amount information you can glean even at these levels. I have run into freak, but useful single events at these levels. And since they're freak events, it's hard to predict in advance where they'd show up. So operation speed becomes a key factor again.
* Plot multiple parameters (sometimes with different units) together to assess their correlation or unusual events. We used to even have team analysis sessions where such visualizations were prepared on demand.
* Do statistical or spectral analysis (like periodograms, log or semi-log graphs, PDFs, etc)
* Add markers or notes within the graph (usually to describe events). Change the axes or plot labels. Change grid value formatting (eg: Do you want time in seconds or HMS?).
All the operations above are possible with Julia, Matlab, R or Python. And we did use almost all of them (depending on personal preference). But none of them suit the workflow described above for one simple reason - speed. You don't have enough time to select each parameter by text or GUI. There must be a way to either quickly launch a visualization or cycle through the parameters as the investigator closes each graph. You also don't have time to set zoom, pan and labels by text. It must be done using mouse (zoom & pan) and directly on the graph (labels and markers) in a WYSIWYG manner. And you don't want to run an FFT or a filter function, save the new series and then plot it - you want it done with a single menu selection. The difference is like using a C++ compiler vs Python in JupyterLab. The application we used was very similar to Labplot.
Now, Excel might seem like a better choice. In fact, LabPlot and our application all has a spreadsheet-like interface with the ability to directly import CSV, TSV, etc. But Excel just doesn't cross the finish line for our requirement. For example, to plot a time series in excel, you have to select the values (column or cells), designate the axes, optionally define the axes and graph labels, start a plot, expand it to required levels and format the print. At that rate, you wouldn't finish the analysis in a month. Those applications would do all that on their own (the labels and other metadata were embedded in the data files by means of formatted comments). But an even bigger problem was the size of the data. Some of those files on import would slow down Excel to speed of molasses. The application had disk and memory level buffering to significantly improve the responsiveness to almost instant interactivity.
I hope this gives you an idea where the tools that you mentioned are not good enough replacements for LabPlot and similar tools.
However, those people also belong to the most-of-the-world who are still leery of "open source" or anything that doesn't come from a known brand.
This thing could be an option for someone who wants to mess around with data but isn't comfortable mentioning it to the boss until they see for themselves if it's worthwhile.
For data viz, I'm absolutely smitten with R and ggplot. It works the same way as my brain, "OK I want to use the students dataset, specifically the age variable, I want to make a histogram, and I'd like to label the axes." You build the viz in that order, with one function call for each thought.
I have a Python "wrapper" for every piece of lab equipment that I touch.
I'm a physicist, and I work on developing measurement equipment. My graphing needs tend to be simplistic, with a big factor being the ability to visualize something quickly and then plan the next step (or realize I screwed up and start over). I'm often the only reader of my graphs.
My work is all secret, so I don't publish, except an occasional patent. The graphing needs for patents are their own beast, arcane, and perhaps a bit repulsive.
I noticed your comment suggests a more "life science" interest, and I think those fields may place a heavier burden on visualization. So I wouldn't be shocked if the physical and life sciences had different graphing needs. I suspect pyplot has a closer vibe to what you're using, than straight matplotlib, but maybe not close enough. There have been attempts to wrap mpl in a ggplot-like interface, but I don't know how successfully.
> LabPlot is licensed under GNU General Public License, version 2.0 or later, so to put it in a few sentences:
> You are free to use LabPlot, for any purpose
> You are free to distribute LabPlot
> You can study how LabPlot works and change it
> You can distribute changed versions of LabPlot
> In the last case you have the obligation to also publish the changed source code as GPL.
[1] https://e-m-mccormick.github.io/static/longitudinal-primer/l... [2] https://www.lesahoffman.com/ [3] https://www.lesahoffman.com/PSYC944/944_Lecture11_Alt_Time.p... [4] https://www.lesahoffman.com/Workshops/SMiP_Presentation_June... [5] https://www.tandfonline.com/doi/full/10.1080/00273171.2025.2...
[1] https://onlinelibrary.wiley.com/doi/book/10.1002/97811195134...
Tried LabPlot recently and had issues with csv import with datetime data not really recognising date and time series format even after using advanced import options and setting it myself manually. Tried to find some solutions, the LabPlot manual website is just a bunch of youtube videos [1]. That is really not helpful, I am not browsing manual to be forced to watch clips of what I already tried. Developers really need to think about making traditional manual.
There is also a AlphaPlot, a more or less alive fork of SciDavis. Still have its own issues but still has the same issue with yyyy-MM-dd hh:mm:ss.zzz dates. Other than that it is a useful bit of kit.
But when I want to do some batch processing and generate multiple plots, automate and have it reproducible I go with gnuplot. The learning curve is steep, but after writing gnuplot scripts few time you just have a personal template and know relevant parts. It is really good.
All in all I am glad there is an opensource movement in this area. It is always better to have more options.
1. https://docs.labplot.org/en/2D_plotting/2D_plotting_xycurve....