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add TensorWaves benchmark results (pytest) benchmark result for 37f8472
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Mar 5, 2024
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@@ -1,5 +1,5 @@ | ||
window.BENCHMARK_DATA = { | ||
"lastUpdate": 1707774710233, | ||
"lastUpdate": 1709637468554, | ||
"repoUrl": "https://github.com/ComPWA/tensorwaves", | ||
"entries": { | ||
"TensorWaves benchmark results": [ | ||
|
@@ -17054,6 +17054,142 @@ window.BENCHMARK_DATA = { | |
"extra": "mean: 678.8189500000101 msec\nrounds: 5" | ||
} | ||
] | ||
}, | ||
{ | ||
"commit": { | ||
"author": { | ||
"email": "[email protected]", | ||
"name": "Jonas Eschle", | ||
"username": "jonas-eschle" | ||
}, | ||
"committer": { | ||
"email": "[email protected]", | ||
"name": "GitHub", | ||
"username": "web-flow" | ||
}, | ||
"distinct": true, | ||
"id": "37f8472a35f3585ab246485a48688235f0ab8b58", | ||
"message": "DOC: fix typo in amplitude-analysis.ipynb (#520)", | ||
"timestamp": "2024-03-05T19:14:48+08:00", | ||
"tree_id": "7aa8fd71a888065744a4589f77abe1a6e47f5709", | ||
"url": "https://github.com/ComPWA/tensorwaves/commit/37f8472a35f3585ab246485a48688235f0ab8b58" | ||
}, | ||
"date": 1709637467411, | ||
"tool": "pytest", | ||
"benches": [ | ||
{ | ||
"name": "benchmarks/ampform.py::TestJPsiToGammaPiPi::test_data[10000-jax]", | ||
"value": 0.40008611133391664, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0", | ||
"extra": "mean: 2.4994619199999875 sec\nrounds: 1" | ||
}, | ||
{ | ||
"name": "benchmarks/ampform.py::TestJPsiToGammaPiPi::test_data[10000-numpy]", | ||
"value": 0.35835490853556173, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0", | ||
"extra": "mean: 2.790529656999979 sec\nrounds: 1" | ||
}, | ||
{ | ||
"name": "benchmarks/ampform.py::TestJPsiToGammaPiPi::test_data[10000-tf]", | ||
"value": 0.3938746449103913, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0", | ||
"extra": "mean: 2.5388788359999808 sec\nrounds: 1" | ||
}, | ||
{ | ||
"name": "benchmarks/ampform.py::TestJPsiToGammaPiPi::test_fit[10000-jax]", | ||
"value": 0.7111554422024714, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0", | ||
"extra": "mean: 1.4061623389999909 sec\nrounds: 1" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_data[3000-jax]", | ||
"value": 23.223513728268895, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0005726939507889583", | ||
"extra": "mean: 43.05980618181593 msec\nrounds: 11" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_data[3000-numpy]", | ||
"value": 168.5657757588275, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00011481568160812936", | ||
"extra": "mean: 5.93240232483925 msec\nrounds: 157" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_data[3000-numba]", | ||
"value": 5.43447114261426, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0011935416917279033", | ||
"extra": "mean: 184.0105456000174 msec\nrounds: 5" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_data[3000-tf]", | ||
"value": 98.56968626170966, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00011877439550371586", | ||
"extra": "mean: 10.145106857141936 msec\nrounds: 84" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-Minuit2-jax]", | ||
"value": 9.264577827493525, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0010368553888187235", | ||
"extra": "mean: 107.93799983334414 msec\nrounds: 6" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-Minuit2-numpy]", | ||
"value": 9.790406756762803, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0021447117519119976", | ||
"extra": "mean: 102.1408021999946 msec\nrounds: 10" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-Minuit2-numba]", | ||
"value": 9.949317192335952, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0011258897380526673", | ||
"extra": "mean: 100.50940990908491 msec\nrounds: 11" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-Minuit2-tf]", | ||
"value": 1.2816901379206782, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0033153449788359394", | ||
"extra": "mean: 780.2197819999833 msec\nrounds: 5" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-ScipyMinimizer-jax]", | ||
"value": 8.672951439979107, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0005134497932386331", | ||
"extra": "mean: 115.30100300001322 msec\nrounds: 5" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-ScipyMinimizer-numpy]", | ||
"value": 9.510933766604861, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00044988407304514904", | ||
"extra": "mean: 105.14214739999943 msec\nrounds: 10" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-ScipyMinimizer-numba]", | ||
"value": 9.548651586784246, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0007836717300049319", | ||
"extra": "mean: 104.7268287999998 msec\nrounds: 10" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-ScipyMinimizer-tf]", | ||
"value": 1.4792172828132986, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0026626374779111415", | ||
"extra": "mean: 676.0332046000144 msec\nrounds: 5" | ||
} | ||
] | ||
} | ||
] | ||
} | ||
|