def stackplot(
self,
eco,
title: titleType = None,
logscale: bool = True,
ax: matplotlib.axes.SubplotBase = None,
) -> matplotlib.figure.Figure:
populations = eco.population_sizes
if ax is None:
_, ax = plt.subplots()
else:
ax = ax
figure = ax.get_figure()
turns = range(len(populations))
pops = [
[populations[iturn][ir] for iturn in turns]
for ir in self.result_set.ranking
]
ax.stackplot(turns, *pops)
ax.yaxis.tick_left()
ax.yaxis.set_label_position("right")
ax.yaxis.labelpad = 25.0
ax.set_ylim([0.0, 1.0])
ax.set_ylabel("Relative population size")
ax.set_xlabel("Turn")
if title is not None:
ax.set_title(title)
trans = transforms.blended_transform_factory(ax.transAxes, ax.transData)
ticks = []
for i, n in enumerate(self.result_set.ranked_names):
x = -0.01
y = (i + 0.5) * 1 / self.result_set.num_players
ax.annotate(
n,
xy=(x, y),
xycoords=trans,
clip_on=False,
va="center",
ha="right",
fontsize=5,
)
ticks.append(y)
ax.set_yticks(ticks)
ax.tick_params(direction="out")
ax.set_yticklabels([])
if logscale:
ax.set_xscale("log")
plt.tight_layout()
return figure
def stackplot(
self,
eco,
title: titleType = None,
logscale: bool = True,
ax: matplotlib.axes.SubplotBase = None,
) -> matplotlib.figure.Figure:
populations = eco.population_sizes
if ax is None:
_, ax = plt.subplots()
else:
ax = ax
figure = ax.get_figure()
turns = range(len(populations))
pops = [
[populations[iturn][ir] for iturn in turns]
for ir in self.result_set.ranking
]
ax.stackplot(turns, *pops)
ax.yaxis.tick_left()
ax.yaxis.set_label_position("right")
ax.yaxis.labelpad = 25.0
ax.set_ylim([0.0, 1.0])
ax.set_ylabel("Relative population size")
ax.set_xlabel("Turn")
if title is not None:
ax.set_title(title)
trans = transforms.blended_transform_factory(ax.transAxes, ax.transData)
ticks = []
for i, n in enumerate(self.result_set.ranked_names):
x = -0.01
y = (i + 0.5) * 1 / self.result_set.num_players
ax.annotate(
n,
xy=(x, y),
xycoords=trans,
clip_on=False,
va="center",
ha="right",
fontsize=5,
)
ticks.append(y)
ax.set_yticks(ticks)
ax.tick_params(direction="out")
ax.set_yticklabels([])
if logscale:
ax.set_xscale("log")
plt.tight_layout()
return figure
def plot_1_2(ax: matplotlib.axes.SubplotBase, diff_df: pd.DataFrame, row_id: int) -> None:
ax.bar(diff_df.columns, diff_df.loc[row_id], color="red")
ax.set_title("difference")
ax.set_xlabel("k")
ax.set_ylabel("diff")
diff_df_min = diff_df.min().min()
diff_df_max = diff_df.max().max()
if CVResultsAggregator.check_limit(diff_df_min) and CVResultsAggregator.check_limit(diff_df_max):
ax.set_ylim(diff_df_min - 1, diff_df_max + 1)
def plot_2_2(ax: matplotlib.axes.SubplotBase, df_eff_k: pd.DataFrame, rows_set: np.ndarray) -> None:
lty = ["--", "-", "--"]
lwd = [1, 2, 1]
mu, sigma = ClusteringUtils.calc_distribution(df_eff_k.loc[rows_set])
y = pd.DataFrame([mu - sigma, mu, mu + sigma]).transpose()
for col in y.columns:
ax.plot(df_eff_k.columns, y[col], color="red", linestyle=lty[col], linewidth=lwd[col])
ax.set_xlabel("k")
ax.set_ylabel("effective k")
ax.set_xlim(1, df_eff_k.columns.max())
ax.set_ylim(1, df_eff_k.columns.max())
abline_vals = np.array(ax.get_xlim())
ax.plot(abline_vals, abline_vals, color="grey", linestyle="--")
def plot_2_1(ax: matplotlib.axes.SubplotBase, pval_df: pd.DataFrame, row_id: int) -> None:
ax.bar(pval_df.columns, -np.log10(pval_df.loc[row_id]), color="red")
ax.set_title("significance of difference")
ax.set_xlabel("k")
ax.set_ylabel("-log10(p-value)")
ax.set_ylim(0, 4)
