stablefig — re-run numerical experiments without dirtying every committed figure

Re-running a numerical experiment reproduces its figures, but not bit-for-bit. Floating-point noise reorders a few least-significant digits, PDF and SVG embed a creation timestamp, and font hinting shifts a coordinate. Every committed figure then shows up as modified even though nothing about it looks different:

$ python run.py && git status --short
 M figures/cosine.pdf
 M figures/sine.pdf

stablefig wraps the image writers of matplotlib, OpenCV and Pillow. A write to a path that already exists renders to a scratch file first, compares it against what is on disk, and overwrites only when the difference would be visible. Unchanged figures keep their original bytes, mtime and git status — while a figure that genuinely changed is written as usual.

$ python -m stablefig run.py && git status --short
stablefig: 4 kept

Install

pip install -e .

Use

Guard an existing script without editing it:

python -m stablefig run.py --epochs 100

Or from inside the script, before the figures are written:

import stablefig
stablefig.install()

Or for one block only:

with stablefig.stable():
    plt.savefig("figures/convergence.pdf")

Everything downstream is covered — plt.savefig, Figure.savefig, plt.imsave, cv2.imwrite and Image.save — since only the innermost writer of each chain is patched. Writes to a buffer or an open file handle pass straight through: an explicit handle means you meant it.

How it works

install() monkey-patches four functions, reassigning attributes on the libraries' own modules and classes:

Patched	Covers
matplotlib.figure.Figure.savefig	fig.savefig, plt.savefig
matplotlib.image.imsave	mimage.imsave, plt.imsave
PIL.Image.Image.save	Image.save, matplotlib's raster paths
cv2.imwrite	cv2.imwrite

Only the innermost writer of each chain is listed, since plt.savefig delegates to Figure.savefig and matplotlib's PNG/JPEG paths end up in PIL.Image.save; patching a caller as well would just nest one guard inside another. The rendering itself is always done by the untouched library code — the wrapper only redirects where it writes and decides what to do with the result.

There are no import hooks, no sys.meta_path entries, no subclassing and nothing written outside your figures. The patches are process-local, reversible with uninstall(), and idempotent: a second install() detects its own marker and patches nothing twice. Only libraries that are importable get patched, so no OpenCV simply means no cv2.imwrite patch. Because Python resolves methods on the class at call time, patching the classes also covers objects that already existed before install() ran.

Import order

Whether you import matplotlib, OpenCV or Pillow before or after install() makes no difference — the patch lives on the shared module object, so a library imported later still picks it up and one imported earlier is mutated in place. install() also needs to run before the writes, not before the plotting; building figures early and saving late is fine.

The one thing that can slip past is a stale alias — a module that did from cv2 import imwrite at import time, before install() ran, holds a direct reference to the original function:

from cv2 import imwrite   # captured before install
stablefig.install()
imwrite("figure.png", array)   # unguarded: writes unconditionally

In practice this is only cv2.imwrite. from matplotlib.figure import Figure and from PIL.Image import Image import classes, whose methods still resolve to the patch at call time; from matplotlib.image import imsave is a stale alias but delegates to the patched PIL.Image.save, so the guard catches it one level down.

python -m stablefig run.py removes the problem entirely, since patching happens before your script's first line. Calling install() in-process, put it above your own imports:

import stablefig; stablefig.install()   # first
import helpers, plotting                # then everything else

One side effect worth knowing: install() imports the libraries it patches. It does not import pyplot, so it will not lock in a matplotlib backend.

What counts as visible

That decision is a swappable criterion, because there is no single right answer: a curve redrawn half a pixel to the left has not changed in any way a reader would notice, while a colourmap where every pixel moved one level might have. Four are built in, strictest first:

Criterion	Calls it unchanged when
exact	The bytes are identical
pixels	Every decoded pixel matches; metadata may differ
tolerance	Few enough pixels moved far enough (default)
perceptual	It looks the same after averaging tiles

stablefig.configure(criterion="perceptual")

STABLEFIG_CRITERION=pixels python -m stablefig run.py

Where that line falls, for the loosest of them: a 513×513 photograph downsampled and restored is kept at 2× and replaced at 4×.

Both look the same at a glance; only the magnified crop shows the 4× roundtrip losing the fur and the catchlights. perceptual puts the two on opposite sides of its default budget, with room to spare on each — see docs/experiments/resampling.md for the calibration, and scripts/make_resampling_figures.py to redraw the figure.

The thresholds those criteria read are policy knobs, tunable globally, per block, or from the environment:

Knob	Default	Meaning
criterion	"tolerance"	Which definition to use — a name or a callable
atol	3.0	Per-channel tolerance, 0–255
max_frac	1e-3	Fraction of pixels (or tiles) allowed to change
vtol	1e-2	Coordinate drift allowed in vector output, in points
block	8	Tile size for perceptual
enabled	True	Set False to write unconditionally

stablefig.configure(atol=8, max_frac=0.01)      # global
with stablefig.configure(criterion="exact"):    # this block only
    ...

STABLEFIG_ATOL=8 STABLEFIG_MAX_FRAC=0.01 python -m stablefig run.py
STABLEFIG_DISABLE=1 python -m stablefig run.py   # bypass entirely

You can also write your own — any callable taking two paths and returning (differs, detail), optionally registered under a name so it can be selected from the environment:

@stablefig.criteria.register("ink")
def ink(new_path, old_path):
    ...

See docs/criteria.md for what each built-in does, the helpers available for building your own, worked examples, and the guarantees a criterion can rely on.

Whatever the criterion, a resize, a container-format change, a failed render and an unreadable file on disk all count as changes — a bad file is always replaced rather than trusted as a cache.

Seeing what it decided

stablefig.stats()      # {'written': 2, 'kept': 5, 'replaced': 1}

Per-file reasons go to the stablefig logger at INFO:

logging.basicConfig(level=logging.INFO)
# stablefig kept figures/sine.png: 0.000% of pixels differ (worst channel delta 1.0)
# stablefig replaced figures/loss.png: 4.212% of pixels differ (worst channel delta 255.0)

Notes

Scratch files are written beside the destination as .<name>.<random><ext> and removed even if the render raises. They are hidden dotfiles, but if a process is killed mid-write one can survive; .gitignore already covers .*~.

Writes that never touch a path — to a buffer or an open handle — are passed through untouched, as is any write when enabled is False.

stablefig does not make your pipeline reproducible — it only keeps irreproducibility out of your history. Caching the arrays and treating plotting as a pure function of the cache is still the stronger guarantee; this is the non-invasive alternative when you would rather not restructure the experiment.

Layout

src/stablefig/
    __init__.py   public API: install, stable, configure, stats
    __main__.py   python -m stablefig run.py
    criteria.py   the definitions of "visibly changed", and the registry
    _config.py    Policy: which criterion, and with what thresholds
    _compare.py   dispatch to the chosen criterion
    _guard.py     write to a scratch file, keep or replace
    _patch.py     which writers to wrap, and how
docs/
    criteria.md   choosing a criterion, and writing your own
    experiments/
        resampling.md   how perceptual's defaults were calibrated
scripts/
    make_resampling_figures.py   redraws the figure above
assets/images/            lemur.png, and the figure derived from it
tests/
    test_matplotlib.py   float noise, real changes, png/pdf/svg
    test_cv2_pillow.py   the other backends, resize, format, failure
    test_criteria.py     each built-in, the registry, the documented examples
    test_resampling.py   perceptual's calibration, on a real photograph
    test_api.py          policy knobs, install/uninstall, the CLI

Tests

python -m pytest