def _split_by_ready_regions(ext, file_key, dir_ext_fn):
    """Organize splits based on regions generated by parallel_prep_region.

    Sort splits so largest regions analyzed first, avoiding potentially lagging runs
    at end.
    """
    def _sort_by_size(region_w_bams):
        region, _ = region_w_bams
        _, start, end = region
        return end - start
    def _assign_bams_to_regions(data):
        """Ensure BAMs aligned with input regions, either global or individual.
        """
        for i, region in enumerate(data["region"]):
            work_bams = []
            for xs in data["region_bams"]:
                if len(xs) == 1:
                    work_bams.append(xs[0])
                else:
                    work_bams.append(xs[i])
            for work_bam in work_bams:
                assert os.path.exists(work_bam), work_bam
            yield region, work_bams
    def _do_work(data):
        if "region" in data:
            name = data["group"][0] if "group" in data else data["description"]
            out_dir = os.path.join(data["dirs"]["work"], dir_ext_fn(data))
            out_file = os.path.join(out_dir, "%s%s" % (name, ext))
            assert isinstance(data["region"], (list, tuple))
            out_parts = []
            for r, work_bams in sorted(_assign_bams_to_regions(data), key=_sort_by_size, reverse=True):
                out_region_dir = os.path.join(out_dir, r[0])
                out_region_file = os.path.join(out_region_dir,
                                               "%s-%s%s" % (name, pregion.to_safestr(r), ext))
                out_parts.append((r, work_bams, out_region_file))
            return out_file, out_parts
        else:
            return None, []
    return _do_work
Organize splits based on regions generated by parallel_prep_region.

    Sort splits so largest regions analyzed first, avoiding potentially lagging runs
    at end.