Run benchmarks for module using nose.
Parameters : | label : {‘fast’, ‘full’, ‘’, attribute identifier}, optional
verbose : int, optional
extra_argv : list, optional
|
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Returns : | success : bool
|
Notes
Benchmarks are like tests, but have names starting with “bench” instead of “test”, and can be found under the “benchmarks” sub-directory of the module.
Each NumPy module exposes bench in its namespace to run all benchmarks for it.
Examples
>>> success = np.lib.bench()
Running benchmarks for numpy.lib
...
using 562341 items:
unique:
0.11
unique1d:
0.11
ratio: 1.0
nUnique: 56230 == 56230
...
OK
>>> success
True