Add rendering performance example notebook#8
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Port the `speed_up_illustration` notebook from spatialdata-notebooks into the gallery as examples/performance.ipynb, adapted to repo conventions: - Uses the cached `squidpy` visium_hne_sdata loader (its multiscale `hne` image drives the image sections) instead of a manually-downloaded zarr. - Sections: automatic rasterization (single-scale images), automatic scale selection (multi-scale images), and the datashader backend for shapes/points. - Slims the synthetic-data helper to build only the benchmarked element, and adds an untimed warmup so numba JIT / datashader setup isn't measured. - Keeps the shape crossovers (~250 polygons/circles, ~225 multi-polygons) and shows that points are comparable across backends (no clean crossover). Points benchmark starts at n=10: a single point has zero extent and crashes the datashader backend (scverse/spatialdata-plot#724). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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📖 Docs preview: https://scverse.org/spatialdata-plot-notebooks/pr-8/gallery.html Built from 498f380; redeployed on every push. |
- Compute the datashader/matplotlib crossover from the measured medians
instead of hardcoding it, so the red line is correct after CI re-execution
on different hardware (the multi-polygon line was previously wrong). No line
is drawn for points, where datashader never pulls ahead.
- Move the plot legend inside (upper left) for a better thumbnail aspect ratio.
- Replace the multiscale scale="full" render with a verbal note (the dataset's
full image is small enough that the demo no longer makes its point).
- benchmark(): warm up once instead of per-count, and plt.close("all") each
iteration so figures don't accumulate (memory spike / silenced
max_open_warning on CI re-execution).
- Add a Dataset citation to the intro; drop the unsampled "~50k points" claim;
mention points in the gallery card.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Add the 10,000,000-point step so the reversed inflection is obvious: for points datashader is faster for small sets but matplotlib wins at scale, crossing around 100k and staying ahead out to 10 million. - crossover() now detects the switch in either direction (so points get a marked line too) and rounds to 2 significant figures for a clean label. - Regenerate the gallery thumbnail at a ~1.1 aspect ratio to match the other cards (was a wide 7x4 benchmark figure). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
It teaches rendering performance end-to-end on a real dataset, so it fits Tutorials rather than the single-feature Examples gallery. Moves the gallery card and toctree entry accordingly. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Adds the gallery card + thumbnail for the new rendering-performance tutorial and advances the docs/notebooks submodule to the commit that introduces it. Depends on scverse/spatialdata-plot-notebooks#8 — the submodule pointer here references that PR's head commit so the docs build/preview renders the new tutorial. Once #8 merges, the pointer should be advanced to notebooks `main`. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
timtreis
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scverse/spatialdata-plot-notebooks#8 is merged, so the tutorial is on main. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Ports the
speed_up_illustrationnotebook fromspatialdata-notebooksinto the gallery asexamples/performance.ipynb, adapted to this repo's conventions.