def _bivariate_kdeplot(x, y, filled, kernel, bw, gridsize, cut, clip, axlabel,
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
try:
xx, yy, z = _statsmodels_bivariate_kde(x, y, bw, gridsize, cut, clip)
except ImportError:
xx, yy, z = _scipy_bivariate_kde(x, y, bw, gridsize, cut, clip)
# Plot the contours
n_levels = kwargs.pop("n_levels", 10)
cmap = kwargs.pop("cmap", "BuGn" if filled else "BuGn_d")
if isinstance(cmap, str):
if cmap.endswith("_d"):
pal = ["#333333"]
pal.extend(color_palette(cmap.replace("_d", "_r"), 2))
cmap = blend_palette(pal, as_cmap=True)
contour_func = ax.contourf if filled else ax.contour
contour_func(xx, yy, z, n_levels, cmap=cmap, **kwargs)
# 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
def _bivariate_kdeplot(x, y, filled, kernel, bw, gridsize, cut, clip, axlabel,
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
try:
xx, yy, z = _statsmodels_bivariate_kde(x, y, bw, gridsize, cut, clip)
except ImportError:
xx, yy, z = _scipy_bivariate_kde(x, y, bw, gridsize, cut, clip)
# Plot the contours
n_levels = kwargs.pop("n_levels", 10)
cmap = kwargs.pop("cmap", "BuGn" if filled else "BuGn_d")
if isinstance(cmap, str):
if cmap.endswith("_d"):
pal = ["#333333"]
pal.extend(color_palette(cmap.replace("_d", "_r"), 2))
cmap = blend_palette(pal, as_cmap=True)
contour_func = ax.contourf if filled else ax.contour
contour_func(xx, yy, z, n_levels, cmap=cmap, **kwargs)
# 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
def _bivariate_kde(x, y, filled, kernel, bw, gridsize, cut, clip, axlabel, 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 isinstance(bw, str):
bw_func = getattr(sm.nonparametric.bandwidths, "bw_" + bw)
x_bw = bw_func(x)
y_bw = bw_func(y)
bw = [x_bw, y_bw]
elif np.isscalar(bw):
bw = [bw, bw]
kde = sm.nonparametric.KDEMultivariate([x, y], "cc", bw)
x_support = _kde_support(x, kde.bw[0], gridsize, cut, clip[0])
y_support = _kde_support(y, kde.bw[1], gridsize, cut, clip[1])
xx, yy = np.meshgrid(x_support, y_support)
z = kde.pdf([xx.ravel(), yy.ravel()]).reshape(xx.shape)
# Plot the contours
n_levels = kwargs.pop("n_levels", 10)
cmap = kwargs.pop("cmap", "BuGn" if filled else "BuGn_d")
if isinstance(cmap, str):
if cmap.endswith("_d"):
pal = ["#333333"]
pal.extend(color_palette(cmap.replace("_d", "_r"), 2))
cmap = blend_palette(pal, as_cmap=True)
contour_func = ax.contourf if filled else ax.contour
contour_func(xx, yy, z, n_levels, cmap=cmap, **kwargs)
# 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
