Unfortunately the only database it supports is SQLite, I really wanted to hook this up directly to a database or REST API. Going back and forth between exporting files and importing them into LabPlot is just too much work...
Obviously there is a lot of work here, but I am a bit confused. If you already have lab code in Julia, Matlab, R, Python, Excel, etc., what is the motivation to use this tool? Is this hot in a specific community?
In my experience, there are people out there who don't program, or who don't feel that it's a productive way of doing things. I'm firmly in the Python camp, but recognize that my workplace has several JMP licenses, and the majority of engineers are satisfied with Excel. And I never let anybody see how long it takes me to do things. ;-)
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.
What Python libraries do you prefer? Even after doing this for years, I have trouble making anything remotely complicated in matplotlib without at least one look at the documentation.
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 actually use straight matplotlib, or for quick-and-dirty, pyplot. Every notebook starts with the same boilerplate, turning on auto-reload, then numpy, pyplot, asdf. And then my own weird libraries, or those shared with colleagues. Occasionally OpenCV, pyserial, sympy, and other odds and ends.
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.
It's so interesting to see how much of a commodity charting/graphing has become. When we started building Deltagraph in late 1988, what we made become a kind of standard since we targeted Postscript and Illustrator output, and included almost every kind of chart we could find with ridiculous options for everything, so people used it world wide, especially if targeting print. In the mid-90's, it was sold by the publisher (we just did the dev), and it spent the next 25 years at various owners before dying during the pandemic, all still based on the original source code (C) I started. I can't imagine how bad the code looked by then...
Sure ggplot, for example, is finicky, and you need to fuss over it to get the look you are wanting, but then again, it is very flexible. Most of these solutions get frustrating as soon as you want to do, for example, spaghetti plots of within subject repeated measures using age (not time-point) of accelerated longitudinal design data, with fixed effect plots on top. e.g. this plot of mine [1]
[1] https://imgur.com/a/gw2vV7w
Would be really helpful to add support for access to S3 buckets and other clouds object store.
Iceberg support would also be super helpful as it is gaining lots of traction.
I used SciDavis a lot and before that tried QtiPlot. When I had a chance to I used Origin.
SciDavis was clunky and had some issues (liked to crash) but it worked well enough for what I wanted. Had some problems with setting plots styles, maybe it was just me but it wasn't obvious how to copy style between plots.
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.
Looks cool, but I wish there was a section explaining 'here's why it's better than matplotlib or [other popular charting tools]'. I looked through the feature list but I didn't feel like mentally constructing a comparison matrix. I see lots of things to like about it, but I would really appreciate case studies or something to explain why I might want to invest time in learning this new thing.
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[ 2.6 ms ] story [ 47.9 ms ] threadhttps://github.com/KDE/labplot
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.
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....