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 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))