SciPy

numpy.testing.Tester.benchΒΆ

Tester.bench(label='fast', verbose=1, extra_argv=None)[source]ΒΆ

Run benchmarks for module using nose.

Parameters:
label : {β€˜fast’, β€˜full’, β€˜β€™, attribute identifier}, optional

Identifies the benchmarks to run. This can be a string to pass to the nosetests executable with the β€˜-A’ option, or one of several special values. Special values are: * β€˜fast’ - the default - which corresponds to the nosetests -A

option of β€˜not slow’.

  • β€˜full’ - fast (as above) and slow benchmarks as in the β€˜no -A’ option to nosetests - this is the same as β€˜β€™.
  • None or β€˜β€™ - run all tests.

attribute_identifier - string passed directly to nosetests as β€˜-A’.

verbose : int, optional

Verbosity value for benchmark outputs, in the range 1-10. Default is 1.

extra_argv : list, optional

List with any extra arguments to pass to nosetests.

Returns:
success : bool

Returns True if running the benchmarks works, False if an error occurred.

Notes

Benchmarks are like tests, but have names starting with β€œbench” instead of β€œtest”, and can be found under the β€œbenchmarks” 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