Unfortunately, Seaworth does everything to keep the countourset secret from the user. In addition to drawing a plt.contour graph from the data, which is actually not too complicated, you have an indication of the monkey patch on the _bivariate_kdeplot sea ship and let it return the counter for future use.
It might look like this:
import matplotlib.pyplot as plt import numpy as np; np.random.seed(10) import seaborn as sns import seaborn.distributions as sd from seaborn.palettes import color_palette, blend_palette from six import string_types def _bivariate_kdeplot(x, y, filled, fill_lowest, kernel, bw, gridsize, cut, clip, axlabel, cbar, cbar_ax, cbar_kws, ax, **kwargs): """Plot a joint KDE estimate as a bivariate contour plot.""" # Determine the clipping if clip is None: clip = [(-np.inf, np.inf), (-np.inf, np.inf)] elif np.ndim(clip) == 1: clip = [clip, clip] # Calculate the KDE if sd._has_statsmodels: xx, yy, z = sd._statsmodels_bivariate_kde(x, y, bw, gridsize, cut, clip) else: xx, yy, z = sd._scipy_bivariate_kde(x, y, bw, gridsize, cut, clip) # Plot the contours n_levels = kwargs.pop("n_levels", 10) cmap = kwargs.get("cmap", "BuGn" if filled else "BuGn_d") if isinstance(cmap, string_types): if cmap.endswith("_d"): pal = ["#333333"] pal.extend(color_palette(cmap.replace("_d", "_r"), 2)) cmap = blend_palette(pal, as_cmap=True) else: cmap = plt.cm.get_cmap(cmap) kwargs["cmap"] = cmap contour_func = ax.contourf if filled else ax.contour cset = contour_func(xx, yy, z, n_levels, **kwargs) if filled and not fill_lowest: cset.collections[0].set_alpha(0) kwargs["n_levels"] = n_levels if cbar: cbar_kws = {} if cbar_kws is None else cbar_kws ax.figure.colorbar(cset, cbar_ax, ax, **cbar_kws) # Label the axes if hasattr(x, "name") and axlabel: ax.set_xlabel(x.name) if hasattr(y, "name") and axlabel: ax.set_ylabel(y.name) return ax, cset # monkey patching sd._bivariate_kdeplot = _bivariate_kdeplot # some data mean, cov = [0, 2], [(1, .5), (.5, 1)] x, y = np.random.multivariate_normal(mean, cov, size=50).T # plot fig, ax = plt.subplots() _, cs = sns.kdeplot(x, y, ax=ax, cmap="coolwarm") # label the contours plt.clabel(cs, cs.levels, inline=True) # add a colorbar fig.colorbar(cs) plt.show()
