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perf: a vertex makes its coordinate with np.array, not np.r_ - #66
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np.r_[coord] is a class that parses its index and cost 1.4 us, 88% of creating a vertex. np.array(coord, ndmin=1) costs 0.16 us and gives the same array for every coordinate checked (a list, a tuple, a scalar, a numpy array of either type, a range, a 2-D list): same shape, type and values, and a copy. Vertex(coord) 1.59 -> 0.35 us; add_vertex(coord) 3.12 -> 1.72 us, where 0.6.5 took 2.18 us. Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Up to standards ✅🟢 Issues
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| Metric | Results |
|---|---|
| Complexity | 4 |
| Duplication | 0 |
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #66 +/- ##
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Coverage 97.26% 97.26%
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Files 3 3
Lines 1570 1570
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Hits 1527 1527
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np.r_[coord]costs 1.4 µs, 88% of the cost of creating a vertex (profiled: it is a class that parses its index).np.array(coord, ndmin=1)costs 0.16 µs and gives the same array.Vertex(coord)g.add_vertex(coord)Same harness, 100,000 vertices, best of 5.
Equivalence is tested, not assumed: for a list of ints, a list of floats, a tuple, an int and a float scalar, a numpy array of floats and of
int32, arangeand a 2-D list, the coordinate has the same shape, dtype and values asnp.r_[coord]; a scalar is a vector of one; and an array given is copied, not shared.758 tests pass, mypy clean.
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