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
``` 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.
Let's say we want a 3-column plot: colormesh, polar, and geo plot.
UltraPlot:
import ultraplot as uplt, numpy as np
fig, ax = uplt.subplots(
ncols=3, share=0, proj="cart polar merc".split(), journal="nat2"
)
ax[0].pcolormesh(
np.random.rand(10, 10), cmap="viko", colorbar="r",
colorbar_kw=dict(title="some interesting colors")
)
angles, radii = np.random.rand(100) * 360, np.random.rand(100)
ax[1].scatter(angles, radii, c=radii, cmap="spectral_r")
x, y = np.meshgrid(np.linspace(-30, 30, 100), np.linspace(-60, 60, 100))
z = np.exp(-(x*2 + y*2) / 100)
ax[2].pcolormesh(x, y, z, cmap="Fire")
ax[2].format(landcolor="green", land=True, grid=True, lonlabels=True, latlabels=True)
ax.format(abc="[A]")
fig.show()
import matplotlib.pyplot as plt, numpy as np, cartopy.crs as ccrs
fig = plt.figure(figsize=(15, 5))
ax0 = fig.add_subplot(1, 3, 1)
pcm = ax0.pcolormesh(np.random.rand(10, 10), cmap="viridis")
cbar = plt.colorbar(pcm, ax=ax0)
cbar.set_label("some interesting colors")
cbar.ax.yaxis.label.set_color("r")
ax1 = fig.add_subplot(1, 3, 2, projection="polar")
angles = np.random.rand(100) * 2 * np.pi
radii = np.random.rand(100)
sc = ax1.scatter(angles, radii, c=radii, cmap="Spectral_r")
ax2 = fig.add_subplot(1, 3, 3, projection=ccrs.Mercator())
x, y = np.meshgrid(np.linspace(-30, 30, 100), np.linspace(-60, 60, 100))
z = np.exp(-(x*2 + y*2) / 100)
pcm2 = ax2.pcolormesh(x, y, z, cmap="magma", transform=ccrs.PlateCarree())
ax2.coastlines()
ax2.gridlines(draw_labels=True)
ax2.set_extent([-30, 30, -60, 60], crs=ccrs.PlateCarree())
import cartopy.feature as cfeature
ax2.add_feature(cfeature.LAND, facecolor="green")
for i, ax in enumerate([ax0, ax1, ax2]):
ax.set_title(f"[{chr(65+i)}]")
plt.tight_layout()
plt.show()
* 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?
- ProPlot appears to be unmaintained - I initially tried to push changes to make it compatible with matplotlib 3.9+ around mid-2024, but after repeatedly trying to contact the original owner through official and unofficial channels with no response, we decided to fork by the end of 2024. I had grown really fond of ProPlot and wanted to keep it alive.
- This is currently a friendly fork, not a reimplementation. We're carrying on the torch that ProPlot set out with, adding features along the way and refactoring when necessary.
- We implement a custom GeoAxes that allows for basemap and/or Cartopy as a backend. The GeoAxes object behaves similar to a normal axis, allowing direct plotting and manipulation without the user having to worry about projections.* Yes it seems ProPlot stagnated and no longer works with latest matplotlib versions. UltraPlot is a fork that fixes that:
https://github.com/proplot-dev/proplot/pull/459
* Yes, the documentation says that GeoAxes is from Cartopy.
(Also, typo: the project description says "succint" rather than "succinct".)
[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".
You can consider as a bunch of tools that ease the publication making process but is by no means a panacea, but offers a different flavor to the scientific plotting stack.
Check out our docs or more visual examples.