plots.py 文件源码

def improvement_plot(consensus_data, ordered_genomes, improvement_tgt):
    def do_kdeplot(x, y, ax, n_levels=None, bw='scott'):
        try:
            sns.kdeplot(x, y, ax=ax, cut=0, cmap='Purples_d', shade=True, shade_lowest=False, n_levels=n_levels, bw=bw,
                        rasterized=True)
        except:
            logger.warning('Unable to do a KDE fit to AUGUSTUS improvement.')
            pass

    with improvement_tgt.open('w') as outf, PdfPages(outf) as pdf, sns.axes_style("whitegrid"):
        for genome in ordered_genomes:
            data = pd.DataFrame(consensus_data[genome]['Evaluation Improvement']['changes'])
            unchanged = consensus_data[genome]['Evaluation Improvement']['unchanged']
            if len(data) == 0:
                continue
            data.columns = ['transMap original introns',
                            'transMap intron annotation support',
                            'transMap intron RNA support',
                            'Original introns',
                            'Intron annotation support',
                            'Intron RNA support',
                            'transMap alignment goodness',
                            'Alignment goodness']
            fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2)
            for ax in [ax1, ax2, ax3]:  # goodness plots are allowed to auto-set scale
                ax.set_xlim(0, 100)
                ax.set_ylim(0, 100)
            goodness_min = min(data['Alignment goodness'])
            ax4.set_xlim(goodness_min, 100)
            ax4.set_ylim(goodness_min, 100)
            do_kdeplot(data['transMap original introns'], data['Original introns'], ax1, n_levels=25, bw=2)
            sns.regplot(x=data['transMap original introns'], y=data['Original introns'], ax=ax1,
                        color='#A9B36F', scatter_kws={"s": 3, 'alpha': 0.7, 'rasterized': True}, fit_reg=False)
            do_kdeplot(data['transMap intron annotation support'], data['Intron annotation support'], ax2,
                       n_levels=25, bw=2)
            sns.regplot(x=data['transMap intron annotation support'], y=data['Intron annotation support'], ax=ax2,
                        color='#A9B36F', scatter_kws={"s": 3, 'alpha': 0.7, 'rasterized': True}, fit_reg=False)
            do_kdeplot(data['transMap intron RNA support'], data['Intron RNA support'], ax3, n_levels=25, bw=2)
            sns.regplot(x=data['transMap intron RNA support'], y=data['Intron RNA support'], ax=ax3,
                        color='#A9B36F', scatter_kws={"s": 3, 'alpha': 0.7, 'rasterized': True}, fit_reg=False)
            do_kdeplot(data['transMap alignment goodness'], data['Alignment goodness'], ax4, n_levels=20, bw=1)
            sns.regplot(x=data['transMap alignment goodness'], y=data['Alignment goodness'], ax=ax4,
                        color='#A9B36F', scatter_kws={"s": 3, 'alpha': 0.7, 'rasterized': True}, fit_reg=False)
            fig.suptitle('AUGUSTUS metric improvements for {:,} transcripts in {}.\n'
                         '{:,} transMap transcripts were chosen.'.format(len(data), genome, unchanged))
            for ax in [ax1, ax2, ax3, ax4]:
                ax.set(adjustable='box-forced', aspect='equal')
            fig.subplots_adjust(hspace=0.3)
            multipage_close(pdf, tight_layout=False)