""" Benchmarks for vec / val summation routine

Run benchmarks with::

    import dipy.reconst as dire
    dire.bench()

If you have doctests enabled by default in nose (with a noserc file or
environment variable), and you have a numpy version <= 1.6.1, this will also run
the doctests, let's hope they pass.
"""
import numpy as np
from numpy.random import randn

from ..vec_val_sum import vec_val_vect

from numpy.testing import measure, dec

try:
    np.einsum
except AttributeError:
    with_einsum = dec.skipif(True, "Need einsum for benchmark")
else:
    with_einsum = lambda f : f

@with_einsum
def bench_vec_val_vect():
    # nosetests -s --match '(?:^|[\\b_\\.//-])[Bb]ench'
    repeat = 100
    shape = (100, 100)
    evecs, evals = randn(*(shape + (3, 3))), randn(*(shape + (3,)))
    etime = measure("np.einsum('...ij,...j,...kj->...ik', evecs, evals, evecs)",
                    repeat)
    vtime = measure("vec_val_vect(evecs, evals)", repeat)
    print("einsum %4.2f; vec_val_vect %4.2f" % (etime, vtime))
