def make_eps(asset_array, num_samples, seed, correlation):
    """
    :param asset_array: an array of assets
    :param int num_samples: the number of ruptures
    :param int seed: a random seed
    :param float correlation: the correlation coefficient
    :returns: epsilons matrix of shape (num_assets, num_samples)
    """
    assets_by_taxo = group_array(asset_array, 'taxonomy')
    eps = numpy.zeros((len(asset_array), num_samples), numpy.float32)
    for taxonomy, assets in assets_by_taxo.items():
        shape = (len(assets), num_samples)
        logging.info('Building %s epsilons for taxonomy %s', shape, taxonomy)
        zeros = numpy.zeros(shape)
        epsilons = scientific.make_epsilons(zeros, seed, correlation)
        for asset, epsrow in zip(assets, epsilons):
            eps[asset['ordinal']] = epsrow
    return eps
:param asset_array: an array of assets
    :param int num_samples: the number of ruptures
    :param int seed: a random seed
    :param float correlation: the correlation coefficient
    :returns: epsilons matrix of shape (num_assets, num_samples)