cvanelteren

  - 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.

cvanelteren

hace 10 mesesdiscuss

Fair points. Behind the scenes there are changes from matplotlib. For example our `GridSpec` assumes a flat layout and does not allow for nesting. This simplifies the alignment when using (nested) panels, colorbars (which are aligned by default) and legends. Additionally it dispatches the plotting over multiple plots by default. We also attempt to make the plotting process a bit more pydanctic by moving `colorbars` to the axis or figure objects and allowing direct plotting for geo plots.

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.

cvanelteren

hace 10 mesesdiscuss

We do provide some type hinting but it is more a recent development as we are building on the legacy code from proplot which did not provide any. I am in favor of using typing more.

cvanelteren

hace 10 mesesdiscuss

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.

cvanelteren

hace 10 mesesdiscuss

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!

cvanelteren

Submissions

Are We in the Chinese Room?

Show HN: UltraPlot 2.0 – semantic legends, better layouts, faster imports

Rocks, minds, and Turing machines – what does it mean to compute?

Show HN: Ultraplot – A succint wrapper for matplotlib

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