fix: prune nan idxs in output when omitting nans
#83Comparing
prune_nans
(ffc311c
) withmain
(7e2f14a
)-56%
- Improvement
- 13
- Regression
- 48
- Untouched
- 617
Benchmarks
678 total
test_minmaxlttb_with_x[True-int32-100-100,000]
tests/benchmarks/test_downsamplers.py-56%
1.2 ms2.6 ms
test_nanminmaxlttb_with_x[True-float64-100-100,000]
tests/benchmarks/test_downsamplers.py-52%
1.3 ms2.7 ms
test_m4_with_x[True-float32-1,000-100,000]
tests/benchmarks/test_downsamplers.py-46%
1.4 ms2.6 ms
test_nanminmaxlttb_with_x[True-int32-100-100,000]
tests/benchmarks/test_downsamplers.py-41%
1.1 ms1.9 ms
test_m4_no_x[False-float32-5,000-100,000]
tests/benchmarks/test_downsamplers.py-25%
530.4 µs705.3 µs
test_nanminmax_with_x[True-int64-1,000-100,000]
tests/benchmarks/test_downsamplers.py-23%
2.3 ms3 ms
test_minmax_no_x[False-float32-5,000-100,000]
tests/benchmarks/test_downsamplers.py-21%
637.9 µs807.2 µs
test_nanminmax_with_x[True-int32-1,000-100,000]
tests/benchmarks/test_downsamplers.py-21%
1.6 ms2 ms
test_m4_no_x[False-int32-5,000-100,000]
tests/benchmarks/test_downsamplers.py-20%
652.1 µs820 µs
test_m4_no_x[True-float64-5,000-100,000]
tests/benchmarks/test_downsamplers.py-20%
789 µs986.4 µs
test_m4_no_x[True-float32-5,000-100,000]
tests/benchmarks/test_downsamplers.py-20%
591.7 µs737.5 µs
test_m4_no_x[False-float64-5,000-100,000]
tests/benchmarks/test_downsamplers.py-19%
760 µs941.8 µs
test_minmax_no_x[False-int32-5,000-100,000]
tests/benchmarks/test_downsamplers.py-19%
751 µs929.5 µs
test_m4_no_x[True-int32-5,000-100,000]
tests/benchmarks/test_downsamplers.py-19%
702.9 µs867 µs
test_minmax_no_x[True-float32-5,000-100,000]
tests/benchmarks/test_downsamplers.py-18%
714.1 µs875.5 µs
test_minmax_no_x[False-float64-5,000-100,000]
tests/benchmarks/test_downsamplers.py-18%
853 µs1,037.7 µs
test_m4_no_x[False-int64-5,000-100,000]
tests/benchmarks/test_downsamplers.py-17%
867.3 µs1,047.1 µs
test_minmax_no_x[True-int32-5,000-100,000]
tests/benchmarks/test_downsamplers.py-17%
836.2 µs1,004.7 µs
test_minmax_no_x[False-int64-5,000-100,000]
tests/benchmarks/test_downsamplers.py-16%
950.8 µs1,136.8 µs
test_m4_with_x[False-float32-5,000-100,000]
tests/benchmarks/test_downsamplers.py-16%
903.5 µs1,075 µs
test_minmax_no_x[True-float32-100-100,000]
tests/benchmarks/test_downsamplers.py-16%
493 µs583.6 µs
test_minmax_no_x[True-float64-5,000-100,000]
tests/benchmarks/test_downsamplers.py-15%
925.4 µs1,083.2 µs
test_m4_with_x[False-int32-5,000-100,000]
tests/benchmarks/test_downsamplers.py-15%
1 ms1.2 ms
test_minmax_no_x[True-int64-5,000-100,000]
tests/benchmarks/test_downsamplers.py-14%
1 ms1.2 ms
test_m4_with_x[False-float64-5,000-100,000]
tests/benchmarks/test_downsamplers.py-14%
1.1 ms1.3 ms
Commits
Click on a commit to change the comparison rangeBase
main
7e2f14a
-55.89%
fix: prune nan idxs in output when omitting nans
ffc311c
1 year ago
by jvdd