What’s new?

v1.70.0: 🚀 UltraPlot v1.70.0: Smart Layouts, Better Maps, and Scientific Publishing Support (2026-01-04)

High-Level Overview: This release focuses on intelligent layout management, geographic plotting enhancements, and publication-ready features. Geographic plots receive improved boundary label handling and rotation capabilities, while new Copernicus Publications standard widths support scientific publishing workflows. Various bug fixes and documentation improvements round out this release.

Major Changes:

1. Geographic Plot Enhancements

image

# Improved boundary labels and rotation
fig, ax = uplt.subplots(projection="cyl")
ax.format(
    lonlim=(-180, 180),
    latlim=(-90, 90),
    lonlabelrotation=45, # new parameter
    labels=True,
    land=True,
)
# Boundary labels now remain visible and can be rotated

2. Copernicus Publications Support

# New standard figure widths for scientific publishing
fig = uplt.figure(journal = "cop1")
# Automatically sets appropriate width for Copernicus Publications

3. Legend Placement Improvements

test

import numpy as np

import ultraplot as uplt

np.random.seed(0)
fig, ax = uplt.subplots(ncols=2, nrows=2)
handles = []
for idx, axi in enumerate(ax):
    noise = np.random.randn(100) * idx
    angle = np.random.rand() * 2 * np.pi
    t = np.linspace(0, 2 * np.pi, noise.size)
    y = np.sin(t * angle) + noise[1]
    (h,) = axi.plot(t, y, label=f"$f_{idx}$")
    handles.append(h)

# New: spanning legends
fig.legend(handles=handles, ax=ax[0, :], span=(1, 2), loc="b")
fig.show()
What’s Changed
New Contributors

Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/v1.66.0…v1.70.0

v1.66.0: New feature: External Contexts, and bug splats 🐛 (2025-11-22)

Release Notes

This release introduces two key improvements to enhance compatibility and consistency.

UltraPlot provides sensible defaults by controlling matplotlib’s internal mechanics and applying overrides when needed. While this approach works well in isolation, it can create conflicts when integrating with external libraries.

We’ve introduced a new external context that disables UltraPlot-specific features when working with third-party libraries. Currently, this context prevents conflicts with internally generated labels in Seaborn plots. We plan to extend this functionality to support broader library compatibility in future releases.

Example usage with Seaborn:

import seaborn as sns
import ultraplot as uplt

# Load example dataset
tips = sns.load_dataset("tips")

# Use external context to avoid label conflicts
fig, ax = uplt.subplots()
with ax.external():
    sns.lineplot(data=tips, x="size", y="total_bill", hue="day", ax = ax)

We’ve standardized the default aggregation function across all binning operations to use sum. This change affects hexbin, which previously defaulted to averaging values. All binning functions now consistently use sum as the default, though you can specify any custom aggregation function via the reduce_C_function parameter.

Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/v1.65.1…v1.66.0

v1.65.1: Hot-fix: add minor issue where boxpct was not parsed properly (2025-11-02)

Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/v1.65.0…v1.65.1

v1.65.0: Enhanced Grid Layouts and Multi-Span Colorbars (2025-10-31)

art:

UltraPlot v1.65 release notes

This release introduces substantial improvements to subplot layout flexibility and configuration management for scientific visualization.

Non-Rectangular Grid Layouts with Side Labels (#376)
 Asymmetric subplot arrangements now support proper axis labeling, enabling complex multi-panel figures without manual positioning workarounds.

Multi-Span Colorbars (#394)
 Colorbars can span multiple subplots, eliminating redundant color scales in comparative visualizations.

RC-Configurable Color Cycles (#378)
 Cycle objects can be set via rc configuration, enabling consistent color schemes across figures and projects.

Improved Label Sharing (#372, #387)
 Enhanced logic for axis label sharing in complex grid configurations with expanded test coverage.

  • Automatic version checking (#377). Users can now get informed when a new version is available by setting uplt.rc["ultraplot.check_for_latest_version"] = True which will drop a warning if a newer version is available.

  • Demo gallery unit tests (#386)

  • Optimized CI/CD workflow (#388, #389, #390, #391)

These changes address common pain points in creating publication-quality multi-panel figures, particularly for comparative analyses requiring consistent styling and efficient use of figure space.

Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/v1.63.0…v1.65.0

v1.63.0: 🌀 New Feature: Curved Quiver (2025-10-14)

This release introduces curved_quiver, a new plotting primitive that renders compact, curved arrows following the local direction of a vector field. It’s designed to bridge the gap between quiver (straight, local glyphs) and streamplot (continuous, global trajectories): you retain the discrete arrow semantics of quiver, but you gain local curvature that more faithfully communicates directional change.

streamplot_quiver_curvedquiver

What it does

Under the hood, the implementation follows the same robust foundations as matplotlib’s streamplot, adapted to generate short, curved arrow segments instead of full streamlines. As such it can be seen as in between streamplot and quiver plots, see figure below and above.

curved_quiver_comparison

The core types live in ultraplot/axes/plot_types/curved_quiver.py and are centered on CurvedQuiverSolver, which coordinates grid/coordinate mapping, seed point generation, trajectory integration, and spacing control:

  • _CurvedQuiverGrid validates and models the input grid. It ensures the x grid is rectilinear with equal rows and the y grid with equal columns, computes dx/dy, and exposes grid shape and extent. This means curved_quiver is designed for rectilinear grids where rows/columns of x/y are consistent, matching the expectations of stream/line-based vector plotting.

  • _DomainMap maintains transformations among data-, grid-, and mask-coordinates. Velocity components are rescaled into grid-coordinates for integration, and speed is normalized to axes-coordinates so that step sizes and error metrics align with the visual output (this is important for smooth curves at different figure sizes and grid densities). It also owns bookkeeping for the spacing mask.

  • _StreamMask enforces spacing between trajectories at a coarse mask resolution, much like streamplot spacing. As a trajectory advances, the mask is filled where the curve passes, preventing new trajectories from entering already-occupied cells. This avoids over-plotting and stabilizes density in a way that feels consistent with streamplot output while still generating discrete arrows.

  • Integration is handled by a second-order Runge–Kutta method with adaptive step sizing, implemented in CurvedQuiverSolver.integrate_rk12. This “improved Euler” approach is chosen for a balance of speed and visual smoothness. It uses an error metric in axes-coordinates to adapt the step size ds. A maximum step (maxds) is also enforced to prevent skipping mask cells. The integration proceeds forward from each seed point, terminating when any of the following hold: the curve exits the domain, an intermediate integration step would go out of bounds (in which case a single Euler step to the boundary is taken for neatness), a local zero-speed region is detected, or the path reaches the target arc length set by the visual resolution. Internally, that arc length is bounded by a threshold proportional to the mean of the sampled magnitudes along the curve, which is how scale effectively maps to a “how far to bend” control in physical units.

  • Seed points are generated uniformly over the data extent via CurvedQuiverSolver.gen_starting_points, using grains × grains positions. Increasing grains increases the number of potential arrow locations and produces smoother paths because more micro-steps are used to sample curvature. During integration, the solver marks the mask progressively via _DomainMap.update_trajectory, and very short trajectories are rejected with _DomainMap.undo_trajectory() to avoid clutter.

  • The final artist returned to you is a CurvedQuiverSet (a small dataclass aligned with matplotlib.streamplot.StreamplotSet) exposing lines (the curved paths) and arrows (the arrowheads). This mirrors familiar streamplot ergonomics. For example, you can attach a colorbar to .lines, as shown in the figures.

From a user perspective, you call ax.curved_quiver(X, Y, U, V, ...) just as you would quiver, optionally passing color as a scalar field to map magnitude, cmap for color mapping, arrow_at_end=True and arrowsize to emphasize direction, and the two most impactful shape controls: grains and scale. Use curved_quiver when you want to reveal local turning behavior—vortices, shear zones, near saddles, or flow deflection around obstacles—without committing to global streamlines. If your field is highly curved in localized pockets where straight arrows are misleading but streamplot feels too continuous or dense, curved_quiver is the right middle ground.

Performance

Performance-wise, runtime scales with the number of glyphs and the micro-steps (grains). The default values are a good balance for most grids; for very dense fields, you can either reduce grains or down-sample the input grid. The API is fully additive and doesn’t introduce any breaking changes, and it integrates with existing colorbar and colormap workflows.

Parameters

There are two main parameters that affect the plots visually. The grainsparameters controls the density of the grid by interpolating between the input grid. Setting a higher grid will fill the space with more streams. See for a full function description the documentation.

curved_quiver_grains

The size parameter will multiply the magnitude of the stream. Setting this value higher will make it look more similar to streamplot.

curved_quiver_sizes

Acknowledgements

Special thanks to @veenstrajelmer for his implementation (https://github.com/Deltares/dfm_tools) and @Yefee for his suggestion to add this to UltraPlot! And as always @beckermr for his review.

Suggestions or feedback

Do you have suggestion or feedback? Checkout our discussion on this release.

Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/v1.62.0…v1.63.0

v1.62.0: 🚀 New Release: Configurator Handler Registration (2025-10-13)

This release introduces a powerful extension point to the configuration system — Configurator.register_handler() — enabling dynamic responses to configuration changes.

✨ New Feature: register_handler

You can now register custom handlers that execute automatically when specific settings are modified.
 This is particularly useful for settings that require derived logic or side-effects, such as updating related Matplotlib parameters.

register_handler(name: str, func: Callable[[Any], Dict[str, Any]]) → None
`

Example (enabled by default):

def _cycle_handler(value):
    # Custom logic to create a cycler object from the value
    return {'axes.prop_cycle': new_cycler}

rc.register_handler('cycle', _cycle_handler)

Each handler function receives the new value of the setting and must return a dictionary mapping valid Matplotlib rc keys to their corresponding values. These updates are applied automatically to the runtime configuration.

🧩 Why It Matters

This addition:

  • Fixes an issue where cycle: entries in ultraplotrc were not properly applied.

  • Decouples configuration logic from Matplotlib internals.

  • Provides a clean mechanism for extending the configuration system with custom logic — without circular imports or hard-coded dependencies.

🔧 Internal Improvements

  • Refactored configuration update flow to support handler callbacks.

  • Simplified rc management by delegating side-effectful updates to registered handlers.

💡 Developer Note

This API is designed to be extensible. Future handlers may include dynamic color normalization, font synchronization, or interactive theme updates — all powered through the same mechanism.

v1.61.1: 🚀 Release: CFTime Support and Integration (2025-10-08)

Highlights

CFTime Axis Support:
 We’ve added robust support for CFTime objects throughout ultraplot. This enables accurate plotting and formatting of time axes using non-standard calendars (e.g., noleap, gregorian, standard), which are common in climate and geoscience datasets.

Automatic Formatter and Locator Selection:
 ultraplot now automatically detects CFTime axes and applies the appropriate formatters and locators, ensuring correct tick placement and labeling for all supported calendar types.

Seamless Integration with xarray:
 These features are designed for direct use with xarray datasets and dataarrays. When plotting data with CFTime indexes, ultraplot will handle all time axis formatting and tick generation automatically—no manual configuration required.

Intended Use

This release is aimed at users working with climate, weather, and geoscience data, where time coordinates may use non-standard calendars. The new CFTime functionality ensures that plots generated from xarray and other scientific libraries display time axes correctly, regardless of calendar type.

Example Usage

import xarray as xr
import numpy as np
import cftime
import ultraplot as uplt

# Create a sample xarray DataArray with CFTime index
times = [cftime.DatetimeNoLeap(2001, 1, i+1) for i in range(10)]
data = xr.DataArray(np.random.rand(10), coords=[times], dims=["time"])

fig, ax = uplt.subplots()
data.plot(ax=ax)

# CFTime axes are automatically formatted and labeled
ax.set_title("CFTime-aware plotting with ultraplot")
uplt.show()

Migration and Compatibility

  • No changes are required for existing code using standard datetime axes.

  • For datasets with CFTime indexes (e.g., from xarray), simply plot as usual—ultraplot will handle the rest.

We welcome feedback and bug reports as you explore these new capabilities!

Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/v1.60.2…v1.61.0

Note: v1.61.0 is yanked from pypi as it contained a debug statement. This merely removes the debug.

v1.60.2: Hotfix: double depth decorator that affected geoplots (2025-08-18)

Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/v1.60.1…v1.60.2

v1.60.1: Hotfixes for colors and colormaps (2025-08-08)

Minor bug fixes

Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/v1.60.0…v1.60.1

v1.60.0: It’s better to share! (2025-08-05)

UltraPlot extends its sharing capabilities by redefining how sharing works. As of this release, sharing will operate by looking at the subplotgrid and extending label sharing when plots are adjacent. Labels will be turned on for those subplots that

  • Face and edge of the plot

  • or face an empty plot space

In the past, sharing top and right labels would erroneously be turned off and could only be managed by turning the sharing feature off.

For example, consider a simple 2x2 layout. Turning on the top and right labels now looks like: layout=0

Similarly for more complex layouts the plots facing an edge will turn on their labels. Note that the limits are still shared for these subplots:

layout=1

Vertical inset colorbars

UltraPlot now also supports vertical inset colorbars such as

pcolormesh

Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/v1.57.2…v1.60.0

v1.57.2: Bug fixes for Geo dms coordinates and reverse colors/colormaps (2025-07-02)

Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/v1.57.1…v1.57.2

v1.57.1: Zenodo release (2025-06-24)

This PR integrates Zenodo with the UltraPlot repository to enable citation via DOI.

From now on, every GitHub release will be archived by Zenodo and assigned a unique DOI, allowing researchers and users to cite UltraPlot in a standardized, persistent way.

We’ve also added a citation file and BibTeX entry for convenience. Please refer to the GitHub “Cite this repository” section or use the provided BibTeX in your work.

This marks an important step in making UltraPlot more visible and citable in academic and scientific publications.

🔗 DOI: https://doi.org/10.5281/zenodo.15733565

Cite as

@software{vanElteren2025,
  author       = {Casper van Elteren and Matthew R. Becker},
  title        = {UltraPlot: A succinct wrapper for Matplotlib},
  year         = {2025},
  version      = {1.57.1},
  publisher    = {GitHub},
  url          = {https://github.com/Ultraplot/UltraPlot}
}

Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/v1.57…v1.57.1

v1.57: Support matplotlib 3.10 and python 3.13 (2025-06-16)

Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/v1.56…v1.57

v1.56: Feature addition: Beeswarm plot (2025-06-13)

We are introducing a new plot type with this release: a beeswarm plot. A beeswarm plot is a data visualization technique that displays individual data points in a way that prevents overlap while maintaining their relationship to categorical groups, creating a distinctive “swarm” pattern that resembles bees clustering around a hive.

Unlike traditional box plots or violin plots that aggregate data, beeswarm plots show every individual observation, making them ideal for datasets with moderate sample sizes where you want to see both individual points and overall distribution patterns, identify outliers clearly, and compare distributions across multiple categories without losing any information through statistical summaries.

This plot mimics the beeswarm from SHAP library, but lacks the more sophisticated patterns they apply such as inline group clustering. UltraPlot does not aim to add these features but instead provide an interface that is simpler that users can tweak to their hearts desires.

tmp
snippet ```python import ultraplot as uplt, numpy as np # Create mock data n_points, n_features = 50, 4 features = np.arange(n_features) data = np.empty((n_points, n_features)) feature_values = np.repeat( features, n_points, ).reshape(data.shape) for feature in features: data[:, feature] = np.random.normal(feature * 1.5, 0.6, n_points) cmap = uplt.Colormap(uplt.rc["cmap.diverging"]) # Create plot and style fig, (left, right) = uplt.subplots(ncols=2, share=0) left.beeswarm( data, orientation="vertical", alpha=0.7, cmap=cmap, ) left.format( title="Traditional Beeswarm Plot", xlabel="Category", ylabel="Value", xticks=features, xticklabels=["Group A", "Group B", "Group C", "Group D"], ) right.beeswarm( data, feature_values=feature_values, cmap=cmap, colorbar="right", ) right.format( title="Feature Value Beeswarm Plot", xlabel="SHAP Value", yticks=features, yticklabels=["A", "B", "C", "D"], ylabel="Feature", ) uplt.show(block=1) ```

Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/V1.55…v1.56

v1.55: V1.55. Bug fixes. (2025-06-04)

This release continues our ongoing mission to squash pesky bugs and make your plotting experience smoother and more intuitive.

✨ New Features

  • Centered Labels for pcolormesh

    You can now enable center_labels when using pcolormesh, making it easier to annotate discrete diverging colormaps—especially when including zero among the label values. Ideal for visualizing data with meaningful central thresholds.

  • Direct Bar Labels for bar and hbar

    Bar labels can now be added directly via the bar and hbar commands. No more extra steps—just call the method and get your labeled bars out of the box.

🐞 Bug Fixes

Various internal improvements and minor bug fixes aimed at ensuring a more robust and predictable plotting experience.

As always, thank you for using UltraPlot! Feedback, issues, and contributions are welcome.

Full Changelog: https://github.com/Ultraplot/ultraplot/compare/v1.50.2…V1.55

v1.50.2 (2025-05-20)

Full Changelog: https://github.com/Ultraplot/ultraplot/compare/v1.50.1…v1.50.2

v1.50.1 (2025-05-12)

Full Changelog: https://github.com/Ultraplot/ultraplot/compare/v1.50…v1.50.1

v1.50: Networks, lollipops and sharing (2025-05-11)

UltraPlot v1.50

Version v1.50 is a major milestone for UltraPlot. As we become more familiar with the codebase, we’ve opened the door to new features—balancing innovation with continuous backend improvements and bug fixes.

🌍 GeoAxes Sharing

You can now share axes between subplots using GeoAxes, as long as they use the same rectilinear projection. This enables cleaner, more consistent layouts when working with geographical data.

🕸️ Network Graphs

UltraPlot now supports network visualizations out of the box. With smart defaults and simple customization options, creating beautiful network plots is easier than ever.

Network plotting code ```python import networkx as nx, ultraplot as uplt n = 100 g = nx.random_geometric_graph(n, radius=0.2) c = uplt.colormaps.get_cmap("viko") c = c(np.linspace(0, 1, n)) node = dict( node_size=np.random.rand(n) * 100, node_color=c, ) fig, ax = uplt.subplots() ax.graph(g, layout="kamada_kawai", node_kw=node) fig.show() ```

🍭 Lollipop Graphs

A sleek alternative to bar charts, lollipop graphs are now available directly through UltraPlot. They shine when visualizing datasets with many bars, reducing visual clutter while retaining clarity.

Lollipop example code ```python import ultraplot as uplt, pandas as pd, numpy as np data = np.random.rand(5, 5).cumsum(axis=0).cumsum(axis=1)[:, ::-1] data = pd.DataFrame( data, columns=pd.Index(np.arange(1, 6), name="column"), index=pd.Index(["a", "b", "c", "d", "e"], name="row idx"), ) fig, ax = uplt.subplots(ncols=2, share=0) ax[0].lollipop( data, stemcolor="green", stemwidth=2, marker="d", edgecolor="k", ) ax[1].lollipoph(data, linestyle="solid") ```

Full Changelog: https://github.com/Ultraplot/ultraplot/compare/v1.11…v1.5

v1.11: Various bug fixes (2025-04-25)

Full Changelog: https://github.com/Ultraplot/ultraplot/compare/v1.10.0…v1.11

v1.10.0: Ticks for Geoaxes (2025-03-20)

This release marks a newly added feature: ticks on GeoAxes

image

This allows for users to set ticks for the x and or y axis. These can be controlled by lonticklen, latticklen or ticklen for controlling the x, y or both axis at the same time. This works independently to the major and minor gridlines allow for optimal control over the look and feel of your plots.

Full Changelog: https://github.com/Ultraplot/ultraplot/compare/v1.0.9…v1.10.0

v1.0.9: IPython 9.0.0 compatibility and numerous backend fixes. (2025-03-05)

Full Changelog: https://github.com/Ultraplot/ultraplot/compare/v1.0.8…v1.0.9

v1.0.8-2: Hotfix cycling properties (2025-02-27)

Hot fix for cycle not recognizing color argument

Full Changelog: https://github.com/Ultraplot/ultraplot/compare/v1.0.8…v1.0.8-2

v1.0.8: Minor bug fixes (2025-02-23)

Fixes an issue where ticks were not properly set when giving levels and ticks in pcolormesh and related functions in the colorbar. See more of the changes below.

Full Changelog: https://github.com/Ultraplot/ultraplot/compare/v1.0.7…v1.0.8

v1.0.7: Dev update. (2025-02-15)

Full Changelog: https://github.com/Ultraplot/ultraplot/compare/v1.0.6…v1.0.7

v1.0.6: Ensure norm fix (2025-02-15)

Full Changelog: https://github.com/Ultraplot/ultraplot/compare/v1.0.5…v1.0.6

v1.0.5 (2025-02-02)

Full Changelog: https://github.com/Ultraplot/ultraplot/compare/v1.0.4…v1.0.5

v1.0.4: Fixing Margins (2025-01-31)

A major change for this release is that the margins were not properly being set with the latest mpl. This reverts the margins back to the behavior where they are tighter as is expected from UltraPlot!

v1.0.3: Minor bug fixes (2025-01-27)

We are still experiencing some growing pains with proplot -> ultraplot conversion, however it is looking good. Some bugs were fixed regarding compatibility with mpl3.10 and we are moving towards fidelity checks. Please update when you can to this latest release.

This release is to ensure that the latest version is on pypi and conda.

Full Changelog: https://github.com/Ultraplot/ultraplot/compare/v1.0.2…v1.0.3

v1.0: Big Compatibility Release! Matplotlib >= 3.8. (2025-01-11)

  • 78367da2 (HEAD -> main, tag: v1.0, uplt/main) Matplotlib 3.10 - Compatability

  • 4e15fde2 Merge pull request #16 from cvanelteren/linter-workflow

  • 54837966 (origin/linter-workflow, linter-workflow) make repo - black compatible

  • 7aa82a2c added linter workflow

  • 97f14082 point readme badge to correct workflow

  • fb762c5b (origin/main) Merge pull request #13 from - cvanelteren/triangulation-fix

  • afa14caf (origin/triangulation-fix) removed pandas reference

  • 2d66e46b added data dict to unittest test and made - preprocessing compatible

  • 465688e7 add decorator to other trifunctions

  • ad83bfb0 ensure backwards compatibility

  • 23f65bb9 added df to unittest

  • d239bfc3 added unittest for triangulaions

  • 5bb8ac14 use mpl triangulation parser

  • 5dc8b44b move logic to internals and update input parsing - functions for tri

  • f213d870 tripoint also added

  • 9eeda2db small typo

  • a1c8894b Merge branch ‘main’ into triangulation-fix

  • 83973941 allow triangulation object in tricountour(f)

  • 6b8223a8 Merge pull request #4 from cvanelteren/conda

  • fa1f2fcc (origin/conda) removed conda recipe

  • defb219e separate build and test

  • 4cf2c940 # This is a combination of 2 commits. # This is the - 1st commit message:

  • 9c75035c separate build and test

  • 0089fe04 license revert

  • adac1c9a Merge pull request #10 from Ultraplot/revert-6-main

  • e31afe64 (uplt/revert-6-main) Revert “license update”

  • 35204ef4 renamed yml to ensure consistency

  • b243afe7 Merge pull request #8 from cvanelteren/main

  • 0c4bc1f8 replaced pplt -> uplt

  • 656a7464 Merge pull request #5 from cvanelteren/logo_square

  • 89c59cf5 Merge pull request #7 from cvanelteren/main

  • 8d01cf33 typo in readme shield

  • 7e0ec000 Merge pull request #6 from cvanelteren/main

  • 70157b33 license update

  • e6d8eca9 (origin/logo_square, logo_square) capitalization to - UltraPlot in docs

  • e99be782 square logos

  • c2a96554 separated workflows

  • 5609372c conda and pypi publish workflow

  • d04ea9d9 small changes in workflow

  • 5432bdbe add workflow for conda-forge