def _clean_rerp_input(X, data, reject, flat, decim, info, tstep):
    # find only those positions where at least one predictor isn't 0
    has_val = np.unique(X.nonzero()[0])

    # reject positions based on extreme steps in the data
    if reject is not None:
        _, inds = _reject_data_segments(data, reject, flat, decim=None,
                                        info=info, tstep=tstep)
        for t0, t1 in inds:
            has_val = np.setdiff1d(has_val, range(t0, t1))

    return X.tocsr()[has_val], data[:, has_val]
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