plots.py 文件源码

def tx_modes_plot(consensus_data, ordered_genomes, tx_mode_plot_tgt):
    ordered_groups = ['transMap', 'transMap+TM', 'transMap+TMR', 'transMap+TM+TMR', 'TM', 'TMR', 'TM+TMR', 'CGP', 'PB',
                      'Other']
    ordered_groups = OrderedDict([[frozenset(x.split('+')), x] for x in ordered_groups])

    def split_fn(s):
        return ordered_groups.get(frozenset(s['Transcript Modes'].replace('aug', '').split(',')), 'Other')

    modes_df = json_biotype_counter_to_df(consensus_data, 'Transcript Modes')
    df = modes_df.pivot(index='genome', columns='Transcript Modes').transpose().reset_index()
    df['Modes'] = df.apply(split_fn, axis=1)
    df = df[['Modes'] + ordered_genomes]
    ordered_values = [x for x in ordered_groups.itervalues() if x in set(df['Modes'])]
    with tx_mode_plot_tgt.open('w') as outf, PdfPages(outf) as pdf:
        title_string = 'Transcript modes in protein coding consensus gene set'
        ylabel = 'Number of transcripts'
        if len(ordered_genomes) > 1:
            df['Ordered Modes'] = pd.Categorical(df['Modes'], ordered_values, ordered=True)
            df = df.sort_values('Ordered Modes')
            df = df[['Ordered Modes'] + ordered_genomes].set_index('Ordered Modes')
            df = df.fillna(0)
            generic_stacked_barplot(df, pdf, title_string, df.index, ylabel, ordered_genomes, 'Transcript mode(s)',
                                    bbox_to_anchor=(1.25, 0.7))

        else:
            generic_barplot(pd.melt(df, id_vars='Modes'), pdf, 'Transcript mode(s)', ylabel, title_string, x='Modes',
                            y='value', order=ordered_values)