isosurfaces
Construct isolines/isosurfaces over a 2D/3D scalar field defined by a function (not a uniform grid)
Decision gist · record as of 2026-08-14
Yes, if you need to extract isolines or isosurfaces from implicit functions and want an adaptive alternative to uniform grid methods. The low install friction and permissive license make it accessible. However, note that maintenance is aging (last update February 2024, no commits in over 900 days)—suitable for stable use cases, but not for projects requiring active development or rapid bug fixes.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction: pure Python wheel with only numpy as a runtime dependency.
- Maintenance status is aging—last release was in February 2024 and the repository has not been updated in over 900 days, though it remains active and unarchived.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you may use, modify, and distribute this package freely with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2024-02-26 (900 days) · last repo commit 2025-03-03 · 39 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 182,935 downloads/mo, #10,081 on PyPI
Alternatives
Verify before relying
from isosurfaces import plot_isoline
import numpy as np
def f(x, y):
return y * (x - y) ** 2 - 4 * x - 8
curves = plot_isoline(
lambda u: f(u[0], u[1]),
np.array([-8, -6]),
np.array([8, 6]),
min_depth=3,
max_quads=1000,
)
for curve in curves:
print(curve)- Whether the quadtree approach provides meaningful performance gains over grid-based methods for typical use cases.
- API stability and whether breaking changes are likely given the aging maintenance status.
- Whether 3D isosurface generation is fully implemented or still experimental.
What it is and what it does
Isosurfaces is a Python library for extracting level curves (isolines in 2D) and level surfaces (isosurfaces in 3D) from scalar fields defined by mathematical functions. Rather than sampling uniformly over a grid like traditional marching squares algorithms, it uses a quadtree-based adaptive sampling strategy to focus computational effort on regions where the implicit surface actually exists, avoiding wasted samples in empty space.
The library implements the approach from a 2010 Computer Graphics Forum paper on isosurfaces over simplicial partitions of multiresolution grids. It depends only on numpy and is typed, supporting Python 3.8 through 3.12. You provide a function f(x, y) or f(x, y, z), a bounding box, and parameters controlling sampling depth and density, and it returns the curves or surfaces as collections of points.
Use it for
- Visualizing level curves of mathematical functions for educational or exploratory analysis.
- Extracting contours from 2D scalar fields (e.g., temperature, pressure, or potential maps) more efficiently than uniform grid sampling.
- Generating 3D isosurfaces from volumetric data defined implicitly by a function.
- Creating topographic-style maps or contour plots where adaptive sampling avoids redundant computation.
- Prototyping geometric algorithms that require accurate implicit surface representation without full grid evaluation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to extract isolines or isosurfaces from implicit functions and want an adaptive alternative to uniform grid methods.
The low install friction and permissive license make it accessible. However, note that maintenance is aging (last update February 2024, no commits in over 900 days)—suitable for stable use cases, but not for projects requiring active development or rapid bug fixes.
Install
isosurfaces on PyPI
Before you install
Low friction: pure Python wheel with only numpy as a runtime dependency. Maintenance status is aging—last release was in February 2024 and the repository has not been updated in over 900 days, though it remains active and unarchived.
License in practice
MIT license is permissive; you may use, modify, and distribute this package freely with minimal restrictions, making it suitable for both open-source and commercial projects.
Quickstart
from isosurfaces import plot_isoline
import numpy as np
def f(x, y):
return y * (x - y) ** 2 - 4 * x - 8
curves = plot_isoline(
lambda u: f(u[0], u[1]),
np.array([-8, -6]),
np.array([8, 6]),
min_depth=3,
max_quads=1000,
)
for curve in curves:
print(curve)Verify before relying
- Whether the quadtree approach provides meaningful performance gains over grid-based methods for typical use cases.
- API stability and whether breaking changes are likely given the aging maintenance status.
- Whether 3D isosurface generation is fully implemented or still experimental.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Aging 900 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 182,935 / month, #10,081 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Typing :: Typed |
Evidence: isosurfaces-0.1.2-py3-none-any.whl
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