Fix render_images(norm=LogNorm()) crash on zero/non-positive data#672
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render_images(norm=LogNorm()) crashed with an opaque matplotlib "Invalid vmin or vmax" when the image data was all zeros (or otherwise left LogNorm's vmin/vmax non-positive after autoscale). The default colorbar="auto" hit the crash without unusual args. Add a narrow guard at the start of _draw_colorbar: when the mappable norm is LogNorm and its (already autoscaled) bounds are invalid, emit a UserWarning pointing to colorbar=False / data clipping and skip the colorbar. The image artist itself still renders. Guard is class-specific so SymLogNorm (which legitimately supports zero/negative values) is unaffected. Closes #604
vmax <= 0 is implied by vmin <= 0 combined with vmin >= vmax (if vmin > 0 and vmin < vmax, then vmax > 0).
Drop the mixed-positives and SymLogNorm tests — they guarded against hypothetical future regressions rather than the actual bug. Keep just the zeros-only LogNorm regression test.
Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #672 +/- ##
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+ Coverage 77.63% 77.64% +0.01%
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Files 11 11
Lines 3612 3619 +7
Branches 850 853 +3
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+ Hits 2804 2810 +6
Misses 485 485
- Partials 323 324 +1
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Summary
render_images(norm=LogNorm())crashed with an opaque matplotlibValueError: Invalid vmin or vmaxwhenever the autoscaledvmin/vmaxended up non-positive (e.g. all-zero data, all-negative data, user-suppliedLogNorm(vmin=0,…)). The defaultcolorbar="auto"made this hit the standard call path._draw_colorbar(basic.py): if the mappable's norm isLogNormand its bounds are invalid, emit aUserWarningpointing tocolorbar=False/ data clipping and skip the colorbar. The image artist itself still draws.SymLogNorm(which legitimately handles zero/negative data via its linear region) is unaffected.Closes #604