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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_proteome_correct_percentages(prots_filtered_feathers, outpath, length_filter_pid=None, copynum_scale=False, copynum_df=None, force_rerun=False):
 """Get counts and normalize by number of proteins, providing percentages"""

    if ssbio.utils.force_rerun(flag=force_rerun, outfile=outpath):
        prot_tracker = defaultdict(int)
        big_strain_counts_df = pd.DataFrame()
        first = True
        for feather in prots_filtered_feathers:
            loaded = load_feather(protein_feather=feather, length_filter_pid=length_filter_pid,
                                  copynum_scale=copynum_scale,
                                  copynum_df=copynum_df)

            if first:
                big_strain_counts_df = pd.DataFrame(columns=loaded.columns)
                first = False
            tmp_df = pd.DataFrame(columns=loaded.columns)
            for strain in loaded.columns:
                prot_tracker[strain] += 1
                totals = list(filter(lambda x: x.endswith('total'), loaded[strain].index))
                for t in totals:
                    counts = t.rsplit('_', 1)[0]
                    aa_counts = list(
                        filter(lambda x: (x.startswith(counts) and x not in totals), loaded[strain].index))
                    for aa_count in aa_counts:
                        tmp_df.at[aa_count.replace('count', '%'), strain] = loaded[strain][aa_count] / \
                                                                            loaded[strain][t]
            big_strain_counts_df = big_strain_counts_df.add(tmp_df, fill_value=0)

        for c, total in prot_tracker.items():
            big_strain_counts_df.loc[:, c] /= total

        if len(big_strain_counts_df) > 0:
            big_strain_counts_df.astype(float).reset_index().to_feather(outpath)
        return big_strain_counts_df
    else:
        return pd.read_feather(outpath).set_index('index')