如何标记seaborn等高线图

Question

所以我使用seaborn与kdeplot制作sns.kdeplot(x, y, ax=plt.gca(), cmap="coolwarm")。

我可以使用levels kwarg更改级别，但我希望能够标记轮廓。在matplotlib中你只需plt.clabel(CS, CS.levels, inline=True)，但是seaborn不会返回轮廓集合CS。

我该怎么做？或者我是否必须自己从头开始做这一切？

编辑：有没有办法制作一个也会返回CS的包装器？我怎么看不出......

Answer 1

不幸的是，seaborn会尽一切努力让countourset对用户保密。除了从数据中绘制plt.contour图之外，实际上并不太难，你可以通过猴子修补seaborn _bivariate_kdeplot并让它返回countourset以供进一步使用。

这可能如下所示：

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()