❄️ Happy holidays from UltraPlot!

[cvanelteren]

It’s been a huge year of steady, focused development. Since January last year we went from 0 to 241 stars, crossed 134k+ downloads, and pushed test coverage to ~80%. Thank you to everyone who tried the library, filed issues, and helped shape the roadmap.

What shipped this year

• New plot types and capabilities: curved_quiver, beeswarm, lollipop graphs,network plotting.
• Colorbar and legend upgrades: multi-span colorbars, vertical inset colorbars, improved label handling, and legend placement tied to axes.
• Geospatial quality-of-life: better GeoAxes sharing, lon/lat label rotation, DMS fixes, zoom fixes, rasterization support, and boundary label improvements.
• Formatter and axis behavior: stronger log formatting defaults, tick/label sharing fixes, and cleaner edge-case handling.
• Scale and grid flexibility: non-rectangular grids, subplot indexing fixes, and external context mode.
• Calendar support: cftime support for non‑standard calendars.
• Docs and metadata: refreshed docs, better references, citation metadata/DOI badge, and site upgrades.
• Reliability: ~80% coverage, expanded tests, and CI improvements.

Where we’re going in 2026

• Mature the GeoAxes layout such that gives a more intuitive UI experience with feweer surprises.
• Create a UltraPlot Layout manger that keeps the distinct ultraplot look without generating UI artifacts
• Improve import times and other speed improvements
• [...] Open to new ideas and implement

Thank you for a great year of building. Here’s to an even sharper UltraPlot in 2026. 🥂

[cvanelteren]

snippet

importcartopy.crsasccrsimportcartopy.featureascfeatureimportnumpyasnpimportxarrayasxrimportultraplotasupltdefmain():
uplt.rc["formatter.log"] =Truelayout= [[1, 1, 1], [2, 3, 4]]
fig, axs=uplt.subplots(
layout,
share=0,
figsize=(10, 8),
proj=["cyl", "cartesian", "cartesian", "cartesian"],
)
axs.format(abc=True, abcloc="ul")
# Curved quiver on a geo wind map.u_url= (
"https://psl.noaa.gov/thredds/dodsC/Datasets/""ncep.reanalysis/surface/uwnd.sig995.2025.nc"
)
v_url= (
"https://psl.noaa.gov/thredds/dodsC/Datasets/""ncep.reanalysis/surface/vwnd.sig995.2025.nc"
)
ds_u=xr.open_dataset(u_url)
ds_v=xr.open_dataset(v_url)
ds=xr.merge([ds_u, ds_v])
ds=ds.assign_coords(lon=((ds.lon+180) %360) -180).sortby("lon")
when="2025-01-15T12:00"lon_bounds= (-160, -60)
lat_bounds= (15, 60)
dsi=ds.sel(
time=when,
lat=slice(lat_bounds[1], lat_bounds[0]),
lon=slice(lon_bounds[0], lon_bounds[1]),
)
U=dsi["uwnd"].coarsen(lat=2, lon=2, boundary="trim").mean()
V=dsi["vwnd"].coarsen(lat=2, lon=2, boundary="trim").mean()
ifU.lat.values[0] >U.lat.values[-1]:
U=U.sortby("lat")
V=V.sortby("lat")
speed=np.hypot(U, V)
lon1d=U.lon.valueslat1d=U.lat.valuesU2d=U.valuesV2d=V.valuesdefmake_cell_center_seed_points(x1d, y1d, grains=8):
ifx1d.size<2ory1d.size<2:
eps=1e-9xs=np.linspace(
float(x1d.min()) +eps, float(x1d.max()) -eps, max(1, grains)
)
ys=np.linspace(
float(y1d.min()) +eps, float(y1d.max()) -eps, max(1, grains)
)
Xs, Ys=np.meshgrid(xs, ys)
returnnp.c_[Xs.ravel(), Ys.ravel()]
xcent=0.5* (x1d[:-1] +x1d[1:])
ycent=0.5* (y1d[:-1] +y1d[1:])
nx=int(np.clip(grains, 1, xcent.size))
ny=int(np.clip(grains, 1, ycent.size))
xi=np.linspace(0, xcent.size-1, nx).astype(int)
yi=np.linspace(0, ycent.size-1, ny).astype(int)
Xs, Ys=np.meshgrid(xcent[xi], ycent[yi])
returnnp.c_[Xs.ravel(), Ys.ravel()]
cq_seeds=make_cell_center_seed_points(lon1d, lat1d, grains=20)
ax_geo=axs[0]
ax_geo.set_extent(
[lon_bounds[0], lon_bounds[1], lat_bounds[0], lat_bounds[1]],
crs=ccrs.PlateCarree(),
)
cq=ax_geo.curved_quiver(
lon1d,
lat1d,
U2d,
V2d,
color=speed.values,
cmap="viko",
arrow_at_end=True,
arrowsize=2,
grains=40,
scale=20,
start_points=cq_seeds,
transform=ccrs.PlateCarree(),
linewidth=3.5,
)
# Add simple map contextax_geo.format(
title="Near-surface winds",
lonlabels=True,
latlabels=True,
land=True,
coast=True,
ocean=True,
oceancolor="ocean blue",
landcolor="mushroom",
)
# Beeswarm plot.rng=np.random.default_rng(2)
n_points, n_cats=40, 4levels=np.tile(np.arange(n_cats, dtype=float), (n_points, 1))
data=rng.normal(loc=np.arange(n_cats) *1.5, scale=0.6, size=(n_points, n_cats))
feature_values=rng.standard_normal(size=(n_points, n_cats))
axs[1].beeswarm(
data,
levels=levels,
feature_values=feature_values,
ss=22,
orientation="vertical",
cmap="viko",
colorbar="lr",
colorbar_kw=dict(title="SHAP value"),
discrete=False,
)
axs[1].format(
title="Beeswarm (SHAP-style)",
xlabel="Group",
ylabel="Value",
xticks=np.arange(n_cats),
xticklabels=["A", "B", "C", "D"],
)
# Lollipop graph.labels= ["Atlas", "Nova", "Sol", "Lumen", "Echo"]
x=np.arange(len(labels))
y=np.array([2.2, 5.4, 3.1, 6.2, 4.5])
cmap=uplt.Colormap("set3")
colors=cmap(np.linspace(0.1, 0.9, len(x)))
axs[2].lollipop(
x,
y,
width=0.6,
color=colors,
marker="o",
s=140,
edgecolor="k",
linewidth=1.5,
stemwidth=2.4,
stemcolor="gray6",
)
axs[2].format(
title="Lollipop",
xlabel="Series",
ylabel="Score",
xticks=x,
xticklabels=labels,
ygrid=True,
ygridcolor="gray9",
)
# CFTime axis with log-scaled values.time=xr.cftime_range("2024-01-01", periods=49, freq="30D", calendar="360_day")
values=np.geomspace(1, 1e4, time.size)
axs[3].plot(time, values, lw=2)
axs[3].format(
title="CFTime + log y",
yscale="log",
xlabel="Date (360_day)",
ylabel="Magnitude",
)
fig.colorbar(cq.lines, ax=ax_geo, label="Near-surface wind speed (m/s)")
fig.format(suptitle="UltraPlot 2025 Showcase")
fig.savefig("showcase2025.png")
uplt.show()
if__name__=="__main__":
main()

[syrte]

UltraPlot has become part of my daily workflow.
I really appreciate the fast responses and quick fixes, and I’m excited to see how the project continues to grow!