1dba0aaea818505ac4e22cd779995b46454207bb,cnvlib/segmentation/__init__.py,,do_segmentation,#Any#Any#Any#Any#Any#Any#Any#Any#Any#,19

Before Change

                                skip_low, skip_outliers,
                                save_dataframe, rlibpath)

    chroms = cnarr.chromosome.unique()
    rets = []
    // TODO: handle save_dataframe=True
    with futures.ProcessPoolExecutor(processes) as pool:
        for ret in pool.map(_ds, ((cnarr[cnarr.chromosome == c], method,
                                   threshold, variants, skip_low, skip_outliers,
                                   False, rlibpath) for c in chroms)):
            rets.append(ret)
    data = pd.concat([r.data for r in rets])
    meta = rets[0].meta
    for r in rets[1:]:
        meta.update(r.meta)

After Change

    rets = []
    // TODO: handle save_dataframe=True
    with futures.ProcessPoolExecutor(processes) as pool:
        rets = list(pool.map(_ds, ((ca, method,
                                   threshold, variants, skip_low, skip_outliers,
                                   save_dataframe, rlibpath) for _, ca in cnarr.by_chromosome())))
    if save_dataframe:
        rstr = [rets[0][1]]
        for ret in rets[1:]:
            r = ret[1]
            rstr.append(r[r.index("\n") + 1:])
        rets = [ret[0] for ret in rets]

    data = pd.concat([r.data for r in rets])
    meta = rets[0].meta
    for r in rets[1:]:
        meta.update(r.meta)
    cna = CNA(data, meta)
    if save_dataframe:
        return cna, "".join(rstr)
    return cna

Instances