a8c287c0b970968feeaae325379668755760a25b,cnvlib/fix.py,,apply_weights,#Any#Any#Any#,219

Before Change

    // Weight is proportional to variance, 0--1
    variances = ref_matched["spread"] ** 2
    max_variance = variances.max()
    if max_variance == 0:
        weights = numpy.ones_like(variances)
    else:
        weights = 1.0 - (variances / max_variance)
        // Avoid 0-value bins -- CBS doesn"t like these
        weights[weights <= min_weight] = min_weight
    return cnarr.add_columns(weight=weights)

After Change

    weights = sizes / sizes.max()
    // Relative coverage depths
    if (numpy.abs(ref_matched["coverage"]) > epsilon).any():  // basically nonzero
        ratios = 2 ** ref_matched["coverage"]
        weights2 = numpy.minimum(ratios, 1.0)
        weights = (weights + weights2) / 2
    // Inverse of variance, 0--1
    if (ref_matched["spread"] > epsilon).any():  // basically nonzero

Instances