Accept NumPy axis labels in annotated heatmaps - #5695
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create_annotated_heatmap tested its optional x and y arguments for
truthiness rather than for None. A NumPy array has no unambiguous truth
value, so passing arrays as axis labels raised
ValueError: The truth value of an array with more than one element
is ambiguous. Use a.any() or a.all()
from validate_annotated_heatmap, before any heatmap was built. This is
easy to hit because z itself is documented to accept an ndarray, so
labels derived from the same data are naturally arrays too.
Compare x and y against None in the three places that gate on them:
the length validation, the choice of trace/layout with or without tick
labels, and the default axis ranges in _AnnotatedHeatmap. Sequences
that are merely falsy, such as an empty list, are now length-checked
against z instead of being silently ignored.
pandas Series and Index objects work for the same reason.
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Description
figure_factory.create_annotated_heatmap()documents NumPy arrays forz, but passing NumPy arrays for the correspondingxorylabels raises:The factory uses truthiness checks for these optional arguments in validation, trace construction, and annotation setup. This change checks explicitly for
None, allowing NumPy arrays (and other array-like labels) while still validating their dimensions.Regression tests cover NumPy arrays for both axes and a mismatched array length. The accepted types in the docstring and the changelog are updated as well.
Fixes #4160.
Tests
python -m pytest tests/test_optional/test_tools/test_figure_factory.py -q? 34 passedruff check plotly/figure_factory/_annotated_heatmap.py tests/test_optional/test_tools/test_figure_factory.pygit diff --check upstream/main...HEAD