def plot(self, filename, cv_errors=None):
cols, rows = (2, 3)
plot_width, plot_height = (7, 4)
fig = plt.figure(figsize=(plot_width * cols, plot_height * rows))
ax_ids = list(np.arange(cols * rows) + 1)
ax_ids.reverse()
if cv_errors is None:
cv_errors = self.read_cv_errors()
ymin, ymax = cv_errors.describe().loc[["min", "max"], "CV_error"]
dataset_labels = cv_errors.index.get_level_values("dataset").unique()
for dataset_label in dataset_labels:
dataset = Dataset(dataset_label)
ax = fig.add_subplot(rows, cols, ax_ids.pop())
lines = []
panel_labels = cv_errors.loc[dataset.label].index\
.get_level_values("panel").unique()
palette = sns.color_palette("Dark2", len(panel_labels))
for panel in panel_labels:
data = cv_errors.loc[(dataset.label, panel)]
data.plot(ax=ax, marker="", color=palette.pop(0), zorder=1,
linestyle="solid", lw=2)
# Minimum error mark
x_min = data["CV_error"].idxmin()
y_min = data["CV_error"].min()
min_marker = ax.scatter(x_min, y_min, marker="v", zorder=2,
edgecolor="black", color="yellow",
lw=1.5, s=85)
sns.despine(ax=ax, top=True, right=True, left=True, offset=1)
lines, labels = ax.get_legend_handles_labels()
names = [Panel(label).name for label in panel_labels]
title = "{}".format(dataset.name)
ax.set_title(title, fontsize=16, y=1.1, family="serif")
ax.set_ylabel("CV Error", fontsize=16)
ax.legend_.remove()
ax.xaxis.grid(linestyle="dotted", color="grey")
ax.set_ylim([ymin * 0.99, ymax * 1.01]) # Keep the same limits across axes
# Ugly hack to get the legend in a separate subplot slot
ax = plt.subplot(rows, cols, ax_ids.pop())
legend_lines = lines + [min_marker]
legend_labels = names + ["Valor mínimo de error"]
legend_subplot(ax, legend_lines, legend_labels)
plt.tight_layout()
plt.savefig(join(PLOT_DIR, filename), bbox_inches="tight")
return ax
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")
