import numpy


wave = numpy.array([ 3.15986540e-07,  1.29675236e-05, -1.01780051e-05, -6.90414845e-06,
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       -7.30882675e-05, -4.28718267e-05, -1.48730114e-05,  4.44746411e-06,
        2.48965916e-05,  1.95942612e-05,  2.56352814e-05,  2.79368978e-05,
        2.53332910e-05,  2.98470150e-06, -3.24318961e-07,  3.93416440e-06,
        4.86438557e-06,  2.26746147e-06,  1.04535648e-05,  1.21684770e-05,
        3.01450691e-05,  4.66992824e-05,  5.05549615e-05,  5.86227034e-05,
        5.17422668e-05,  5.08344492e-05])


def entropy(wave, bins, limit):
    p = numpy.histogram(wave, bins, (-limit, limit))[0]
    p = p / p.sum()
    e1 = -(p * numpy.log2(p, where = p > 0)).sum()
    e2 = -(p * numpy.log2(p, where = p > 0)).sum()
    e3 = -(p * numpy.log2(p, where = p > 0)).sum()
    e4 = -(p * numpy.log2(p, where = p > 0)).sum()

    if (e1 != e2) or (e1 != e3) or (e1 != e4):
        print(e1, e2, e3, e4)

    return e1


limit =	0.0007768646
bins = 192

for i in range(1000000):
    x = entropy(wave, bins, limit)