def _payoff_heatmap(
self,
data: dataType,
names: namesType,
title: titleType = None,
ax: matplotlib.axes.SubplotBase = None
) -> matplotlib.figure.Figure:
"""Generic heatmap plot"""
if ax is None:
_, ax = plt.subplots()
else:
ax = ax
figure = ax.get_figure()
width = max(self.nplayers / 4, 12)
height = width
figure.set_size_inches(width, height)
matplotlib_version = matplotlib.__version__
cmap = default_cmap(matplotlib_version)
mat = ax.matshow(data, cmap=cmap)
ax.set_xticks(range(self.result_set.nplayers))
ax.set_yticks(range(self.result_set.nplayers))
ax.set_xticklabels(names, rotation=90)
ax.set_yticklabels(names)
ax.tick_params(axis='both', which='both', labelsize=16)
if title:
ax.set_xlabel(title)
figure.colorbar(mat, ax=ax)
plt.tight_layout()
return figure
def _payoff_heatmap(
self,
data: dataType,
names: namesType,
title: titleType = None,
ax: matplotlib.axes.SubplotBase = None,
) -> matplotlib.figure.Figure:
"""Generic heatmap plot"""
if ax is None:
_, ax = plt.subplots()
else:
ax = ax
figure = ax.get_figure()
width = max(self.num_players / 4, 12)
height = width
figure.set_size_inches(width, height)
matplotlib_version = matplotlib.__version__
cmap = default_cmap(matplotlib_version)
mat = ax.matshow(data, cmap=cmap)
ax.set_xticks(range(self.result_set.num_players))
ax.set_yticks(range(self.result_set.num_players))
ax.set_xticklabels(names, rotation=90)
ax.set_yticklabels(names)
ax.tick_params(axis="both", which="both", labelsize=16)
if title:
ax.set_xlabel(title)
figure.colorbar(mat, ax=ax)
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 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_2_1_ci(ax: matplotlib.axes.SubplotBase, df_pred_str: pd.DataFrame, df_pred_str_null: pd.DataFrame,
rows_set: np.ndarray) -> None:
mu, sigma = ClusteringUtils.calc_distribution(df_pred_str.loc[rows_set])
mu_null, sigma_null = ClusteringUtils.calc_distribution(df_pred_str_null.loc[rows_set])
y = pd.DataFrame([
mu - sigma > mu_null + sigma_null
])
ax.imshow(y)
ax.set_xlabel("k")
ax.set_ylabel("CI intersection")
ax.set_yticks([])
ax.xaxis.set_major_locator(ticker.FixedLocator((np.arange(len(y.columns)))))
ax.xaxis.set_major_formatter(ticker.FixedFormatter(y.columns))
