Changelog

Changelog#

Changelog#

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[1.1.0] - 2026-05-29#

Maintenance and polish release: verified bug fixes (several affecting numerical output on specific paths), documentation accuracy, test-suite hygiene, and release-process improvements. All fixes ship with regression tests.

Fixed#

  • FastNN.encode silent mis-scaling for 6-sample batches (affects output): a 2-D input of shape (6 params, 6 samples) broadcast against the parameter bounds without error and normalized along the wrong axis, producing silently corrupted SEDs/bolometric corrections only when exactly 6 valid samples were evaluated (reached via StellarPop synthesis). Now dispatches on input dimensionality explicitly.

  • los_dust kernels collapsing per-object cloud means (affects output): kernel_gauss/kernel_lorentz/kernel_tophat collapsed an array-valued mean to a scalar, so line-of-sight fits using a reddening template (template_reds, additive_foreground) evaluated every object against object 0’s cloud mean. Means are now kept broadcastable. The default uniform-cloud path is unchanged.

  • Binary companions no longer discard a valid primary SED (affects binary grid generation): StarEvolTrack.get_seds returned an all-NaN combined SED (dropping the valid primary) when the secondary’s age could not be matched to the unrealistically tight default tolerance. The default tol is relaxed 1e-6 -> 1e-2 dex and a primary-only fallback is used when the companion does not converge. Regenerate binary (smf>0) grids to benefit; single-star fitting and the shipped default grids are unaffected.

  • load_offsets no longer crashes on a single-filter offsets file.

  • logp_imf no longer raises ZeroDivisionError for a power-law slope of exactly 1.0 (flat-in-log); uses the logarithmic mass integral.

  • _fetch (data download) removes a stale/broken symlink before re-linking and falls back to a file copy on filesystems without symlink support.

  • BruteForce.logpost_grid floors the Monte-Carlo sample count at 1 (a very small mem_lim could drive it to 0 and crash).

  • BruteForce.loglike_grid guards the dimensional-prior log(Ndim) term and fails fast with a clear error on a fully-masked object (was an opaque ZeroDivisionError deep in the optimizer).

  • cornerplot no longer mutates a caller-supplied labels list in place.

  • dist_vs_red uses interpolation="none" so binned PDF images show raw bins; fixed a no-op smoothing guard in hist2d.

  • Removed a dead from scipy import polyfit import (scipy has no top-level polyfit; the code always used numpy.polyfit).

Changed#

  • Python support: dropped end-of-life Python 3.8 (requires-python>=3.9); added 3.13 to the classifiers and tooling targets.

  • Dependencies: raised the numba floor to >=0.59.0 (the old 0.53 floor predates numpy>=1.22/2.x support); numpy remains uncapped (the code is numpy-2.0 clean, verified on 2.2).

  • Documentation engine: standardized on a single NumPy-docstring processor (napoleon); removed the redundant numpydoc extension. The docs now build with zero warnings.

  • Tutorials: standardized on the committed Orion_l209.1_b-19.9 example field; replaced chi2/Nbands quality cuts with goodness-of-fit p-values.

Documentation#

  • Corrected numerous stale code examples across the docs and docstrings to match the real API (StarGrid(models, labels), get_seds 3-tuple in magnitudes, magnitude(flux, err), sample_multivariate_normal(size=), the prior call signatures, samps_dred, the data cache path/env var, real filter names, and the maggies flux convention).

  • Added summary_plot to the API reference; documented BruteForce.fit’s performance/accuracy parameters (max_models, precision_shrinkage, subsample_mode, R_solar, Z_solar); reframed fit-quality guidance around the p-value methodology.

Testing & packaging#

  • Added regression tests covering every fix above; reconciled the conftest coverage guidance with what CI actually runs (NUMBA_DISABLE_JIT=1 pytest --cov); the default pytest run no longer enables coverage (which crashed on some WSL/DrvFs paths).

  • Added a tag-triggered PyPI publish workflow (Trusted Publishing); fixed the stale MANIFEST.in data-file path.

[1.0.0] - 2025-12-08#

First stable release of brutus following code verification, testing, and documentation improvements.

Added#

  • Documentation: Scientific background pages, user guides, API documentation with examples, and ReadTheDocs hosting

  • Testing: 92% code coverage, 606 tests, GitHub Actions CI/CD with Codecov integration

  • Code verification: All functions verified for correctness; fixed IMF normalization bug, StarGrid distance reference, and other issues

Changed#

  • Development status updated to Production/Stable

  • Added tqdm as formal dependency

  • Enforced Black formatting across codebase

[0.9.0] - 2024-08-28#

Major refactoring to improve usability and maintainability while preserving scientific functionality.

Added#

  • Modern packaging: Migrated to pyproject.toml with black, isort, flake8, mypy

  • Testing: pytest framework with 100+ tests, coverage reporting, multi-platform CI

  • Modular architecture: Split into brutus.core, brutus.analysis, brutus.plotting, brutus.dust, brutus.utils, brutus.data, brutus.priors

  • Performance: Numba JIT compilation, vectorized operations, improved caching

Changed#

  • Minimum Python version: 3.8+ (dropped Python 2.7)

  • Split large modules (utils.py, plotting.py) into focused submodules

  • Updated all dependencies to modern versions

Fixed#

  • Windows/WSL compatibility documentation

  • Circular imports and module loading issues

  • Infinite loop bug in hist2d function

Migration#

Update imports:

# Old
from brutus.seds import Isochrone
from brutus.fitting import BruteForce

# New
from brutus import Isochrone, BruteForce
# or
from brutus.core import Isochrone
from brutus.analysis import BruteForce

All scientific algorithms, file formats, and core APIs remain unchanged.

[0.8.3] - Previous Release#

Final release using old project structure and Python 2 compatibility.

Features: individual star fitting, cluster modeling, 3D dust mapping, MIST support, neural network SED prediction.

For migration questions or bug reports, see the issue tracker.