def new_descriptor_performance_plot(fig, max_overview, selection, method, glomeruli=[],
descriptor_plot_type='scatterplot'):
"""compare performance of different descriptors for several glomeruli"""
desc_names = max_overview[method][selection]['desc_names']
if 'p_selection' in max_overview[method][selection] and \
np.sum(max_overview[method][selection]['p_selection']) != 0.0:
print('plotting param selection instead of maximum')
data = max_overview[method][selection]['p_selection']
else:
data = max_overview[method][selection]['max']
# use only selected glomeruli
avail_glomeruli = max_overview[method][selection]['glomeruli']
if glomeruli:
glom_idx = [i for i, g in enumerate(avail_glomeruli) if g in glomeruli]
data = data[:, glom_idx]
# compute colors to color all glomeruli according to their all performance
all_idx = max_overview[method][selection]['desc_names'].index('all')
all_values = data[all_idx]
n_val = float(len(all_values)-1)
clist_all = [sorted(all_values,reverse=True).index(i) / n_val for i in all_values]
ax = fig.add_subplot(111)
if descriptor_plot_type == 'boxplot':
_descriptor_boxplot(ax, data, desc_names)
elif descriptor_plot_type == 'scatterplot':
_descriptor_scatterplot(ax, data, clist_all, desc_names)
elif descriptor_plot_type == 'curveplot':
_descriptor_curveplot(ax, data, desc_names)
if i_meth == 1:
ax.legend(prop={'size':5})
elif descriptor_plot_type == 'violinplot':
_violin_boxplot(ax, data, desc_names)
else:
assert False
utils.simple_axis(ax)
ax.set_ylabel('q2 score')
ax.plot(comp_score, ref_score, 'x', color='0.0', markersize=3)
plt.axis('scaled')
ax.annotate(example_receptor, xy=(comp_score, ref_score), xytext=(0.65, 0.465),
arrowprops=dict(facecolor='black', shrink=0.2, width=1, frac=0.2, headwidth=3))
ax.text(0.55, 0.75, 'r:{:.2f}'.format(stats.pearsonr(col_comp, col_ref)[0]))
print stats.pearsonr(col_comp, col_ref)
ax.set_yticks(xticks)
ax.set_yticklabels(xticklabels)
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels)
ax.set_xlim([-0.05, 0.9])
ax.set_ylim([-0.05, 0.9])
ax.set_xlabel('comparison descriptor (q2)')
ax.set_ylabel('ALL (q2)')
ax.legend(loc='lower right', numpoints=1, frameon=False, fancybox=True, prop={'size': 'small'})
utils.simple_axis(ax)
ax.text(-0.3,0.85, 'A)', fontsize=9, weight='bold')
ax = fig.add_subplot(1,2,2)
ax.plot([-0.1, 0.8], [-0.1, 0.8], color='0.6')
ref_predictions = res[reference][example_receptor]['predictions']
comp_predictions = res[comparison_desc][example_receptor]['predictions']
ax.plot(ref_predictions, comp_predictions, 'kx', color='0.3',
markeredgecolor='0.3',
markersize=2,
label=example_receptor)
plt.axis('scaled')
ax.set_yticks(xticks)
ax.set_yticklabels(xticklabels)
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels)
