plots.py 文件源码

def tm_para_plot(tm_data, ordered_genomes, biotypes, para_tgt):
    """transMap paralogy plots"""
    legend_labels = ['= 1', '= 2', '= 3', u'\u2265 4']
    title_string = 'Proportion of transcripts that have multiple alignments'
    biotype_title_string = 'Proportion of {} transcripts that have multiple alignments'
    df = json_biotype_nested_counter_to_df(tm_data, 'Paralogy')
    # we want a dataframe where each row is the counts, in genome order
    # we construct the transpose first
    r = []
    df['Paralogy'] = pd.to_numeric(df['Paralogy'])
    # make sure genomes are in order
    df['genome'] = pd.Categorical(df['genome'], ordered_genomes, ordered=True)
    df = df.sort_values('genome')
    for biotype, biotype_df in df.groupby('biotype'):
        for genome, genome_df in biotype_df.groupby('genome'):
            high_para = genome_df[genome_df.Paralogy >= 4]['count'].sum()
            counts = dict(zip(genome_df['Paralogy'], genome_df['count']))
            r.append([biotype, genome, counts.get(1, 0), counts.get(2, 0), counts.get(3, 0), high_para])
    df = pd.DataFrame(r, columns=['biotype', 'genome', '1', '2', '3', u'\u2265 4'])
    sum_df = df.groupby('genome', sort=False).aggregate(sum).T

    plot_fn = generic_unstacked_barplot if len(df.columns) <= 5 else generic_stacked_barplot
    box_label = 'Number of\nalignments'
    with para_tgt.open('w') as outf, PdfPages(outf) as pdf:
        plot_fn(sum_df, pdf, title_string, legend_labels, 'Number of transcripts', ordered_genomes, box_label)
        for biotype in biotypes:
            biotype_df = biotype_filter(df, biotype)
            if biotype_df is not None:
                biotype_df = biotype_df.drop(['genome', 'biotype'], axis=1).T
                title_string = biotype_title_string.format(biotype)
                plot_fn(biotype_df, pdf, title_string, legend_labels, 'Number of transcripts', ordered_genomes,
                        box_label)