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> Instead of wrestling with subplot positioning and styling, you can write: This would be more convincing if you showed the equivalent Matplotlib code and demo

> 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 1y 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 1y 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.

cvanelteren 1y 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 1y 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.