def plot_(self, components_df, explained_variance, title, filename,
component_pairs=[("PC1", "PC2")], plot_size=None, legend_on=True):
# + 1 axes for the one with the legend, +1 because index starts at 1
ax_ids = list(np.arange(1, len(component_pairs) + 2))
nrows, ncols, figsize = self._fig_dimensions(len(ax_ids), plot_size)
fig = plt.figure(figsize=figsize)
for components_to_compare in component_pairs:
ax_id = ax_ids.pop(0)
ax = fig.add_subplot(nrows, ncols, ax_id)
ax = self.draw_ax(ax, components_to_compare, components_df,
explained_variance, "PEL", title)
if legend_on:
# Legend subplot. It will use the handles and labels of the last ax
handles, labels = ax.get_legend_handles_labels()
populations_df = ThousandGenomes.population_names()
descriptions = populations_df.ix[labels, "description"]
legend_labels = [" - ".join([code, desc])
for code, desc in descriptions.iteritems()]
ax = fig.add_subplot(nrows, ncols, ax_ids.pop(0))
ax = legend_subplot(ax, handles, legend_labels)
# plt.tight_layout()
fig.suptitle(title, fontsize=18, position=(0.12, 1.1), ha="left",
family="serif")
plt.subplots_adjust(wspace=0.05)
if filename is not None:
makedirs(self.FIGS_DIR, exist_ok=True)
plt.savefig(join(self.FIGS_DIR, filename), facecolor="w",
bbox_inches="tight")
