Comment by cvanelteren

5 months ago

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.

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!

7 comments

cvanelteren

Reply
zahlman  5 months ago

> 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.

  • cvanelteren  5 months ago

    You are right. I was doubting whether to make a more complicated example -- but formatting is poor in text boxes. Let me give you a more complex one.

    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()

    Matplotlib equivalent:

      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()

    The aim isn't to replace matplotlib but make publication-ready plots with fewer keystrokes and better defaults. We also bundle plot types not available in matplotlib like graph plotting, lollipop charts, heatmaps etc.

    • zahlman  5 months ago

      > You are right. I was doubting to make a more complicated example -- but formatting is poor on txt boxes.

      I see now that you have an example in the README. I think it would be better still in the README, but as plain text rather than rendered into an SVG.

quietbritishjim  5 months ago

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?

  • cvanelteren  5 months ago

    Thanks for your questions.

      - 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.