Comments URL: https://news.ycombinator.com/item?id=47697535

Points: 1

# Comments: 1

UltraPlot is a Matplotlib wrapper aimed at keeping Matplotlib’s flexibility while making common plotting workflows faster and more consistent.

v2.x focuses on semantic legends (categorical/numeric/size/geo), more reliable layout + axis-sharing in complex grids, guide architecture cleanup, CI hardening, and much faster import times via lazy loading.

We also launched a new docs site with a gallery: https://ultraplot.readthedocs.io/

Code: https://github.com/Ultraplot/UltraPlot

Feedback is very welcome, especially on legend API ergonomics and layout behavior in real figures.

Comments URL: https://news.ycombinator.com/item?id=47066090

Points: 3

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=45625700

Points: 2

# Comments: 1

  - 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.

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.

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()

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!

Comments URL: https://news.ycombinator.com/item?id=45202315

Points: 34

# Comments: 13