For those unfamiliar, ProPlot was widely loved for enabling publication-quality graphics with minimal effort. UltraPlot continues that mission with active development, updated compatibility, and a focus on simplicity.
Why UltraPlot?
Key improvements over vanilla matplotlib:
- Effortless subplot management: build complex multi-panel layouts in one line
- GeoAxes support included out of the box
- Smarter aesthetics: beautiful colormaps, fonts, and styles without extra code
- Intuitive syntax: less boilerplate, more plotting
- Seamless compatibility: everything you know from matplotlib still applies
Instead of wrestling with subplot positioning and styling, you can write:
```
import ultraplot as uplt
layout = [[0, 1, 2], [3, 3, 4]]
fig, axs = uplt.subplots(layout)
axs[0].plot(x, y1, label="Data 1")
axs[1].plot(x, y2, label="Data 2")
axs.format(xlabel="Hello",
ylabel="Hacker news",
abc="[A]") # format applies to all axes
fig.legend()
```
...and get a clean, professional-looking plot in seconds.
> Instead of wrestling with subplot positioning and styling, you can write:
This would be more convincing if you showed the equivalent Matplotlib code and demonstrated that any improvements are not just a result of default settings being a closer match for what the example tries to do. The code shown here looks more or less like what I'd expect a Matplotlib hello-world to look like.
Interesting, thanks. A few questions from a newbie:
* I hadn't heard of ProPlot before. I take it that it's no longer maintained? Is there an announcement, or is it just obvious from commits drying up (like with PIL which was forked into Pillow)?
* Is this a (friendly) fork (again, as with PIL/Pillow), or a reimplementation (in which case are there big differences or does it aim to match)?
* I hadn't of GeoAxes either and that looks pretty useful. The top web search results for that term are ProPlot and Cartopy. Is the Cartopy implementation related at all? Is this a bundling of that, or a similar reimplementation, or something fairly different?
That comparison between the matplotlib and ultraplot APIs doesn't seem fair. Most of the difference in length is that the separate methods for formatting in matplotlib are bundled into a single format() function in ultraplot. But, in matplotlib, you can pass those as kwargs to add_subplot() [1] which would work out at a similar length (or even shorter).
Maybe there are much more important API differences
(I hope so, as that's a pretty trivial difference to start with.) I just mention it because that's what the screenshot seems to focus on as a justification: "Why UltraPlot? | Write Less, Create More".
7 comments
[ 4.4 ms ] story [ 24.7 ms ] threadWhy UltraPlot?
Key improvements over vanilla matplotlib:
Instead of wrestling with subplot positioning and styling, you can write:``` import ultraplot as uplt
layout = [[0, 1, 2], [3, 3, 4]]
fig, axs = uplt.subplots(layout)
axs[0].plot(x, y1, label="Data 1")
axs[1].plot(x, y2, label="Data 2")
axs.format(xlabel="Hello", ylabel="Hacker news", abc="[A]") # format applies to all axes fig.legend()
```
...and get a clean, professional-looking plot in seconds.
Get Started:
- GitHub: https://github.com/Ultraplot/ultraplot
- Docs: https://ultraplot.readthedocs.io/en/latest/
Try it out and let us know what you think — contributions and feedback are very welcome!
This would be more convincing if you showed the equivalent Matplotlib code and demonstrated that any improvements are not just a result of default settings being a closer match for what the example tries to do. The code shown here looks more or less like what I'd expect a Matplotlib hello-world to look like.
* I hadn't heard of ProPlot before. I take it that it's no longer maintained? Is there an announcement, or is it just obvious from commits drying up (like with PIL which was forked into Pillow)?
* Is this a (friendly) fork (again, as with PIL/Pillow), or a reimplementation (in which case are there big differences or does it aim to match)?
* I hadn't of GeoAxes either and that looks pretty useful. The top web search results for that term are ProPlot and Cartopy. Is the Cartopy implementation related at all? Is this a bundling of that, or a similar reimplementation, or something fairly different?
[1] https://matplotlib.org/stable/api/_as_gen/matplotlib.figure....
Maybe there are much more important API differences (I hope so, as that's a pretty trivial difference to start with.) I just mention it because that's what the screenshot seems to focus on as a justification: "Why UltraPlot? | Write Less, Create More".