import numpy as np
from sklearn.neighbors import LocalOutlierFactor
from .utilities import zoom_min_max
[docs]def density_contour(
ax,
data,
x,
y,
groupby=None,
c="lightgray",
single_contour_pad=1,
linewidth=1,
palette=None,
):
_data = data.copy()
if groupby is not None:
if isinstance(groupby, str):
_data["groupby"] = data[groupby]
else:
_data["groupby"] = groupby
else:
_data["groupby"] = "one group"
_contour_kws = dict(
linewidths=linewidth, levels=(-single_contour_pad,), linestyles="dashed"
)
_lof_kws = dict(n_neighbors=25, novelty=True, contamination="auto")
xmin, ymin = _data[[x, y]].min()
xmax, ymax = _data[[x, y]].max()
xmin, xmax = zoom_min_max(xmin, xmax, 1.2)
ymin, ymax = zoom_min_max(ymin, ymax, 1.2)
for group, sub_data in _data[[x, y, "groupby"]].groupby("groupby"):
xx, yy = np.meshgrid(np.linspace(xmin, xmax, 500), np.linspace(ymin, ymax, 500))
clf = LocalOutlierFactor(**_lof_kws)
clf.fit(sub_data.iloc[:, :2].values)
z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
z = z.reshape(xx.shape)
if palette is None:
_color = c
else:
_color = palette[group] if group in palette else c
# plot contour line(s)
ax.contour(xx, yy, z, colors=_color, **_contour_kws)
return
