You may get the path that incoporates all points via a convex hull scipy.spatial.ConvexHull.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
from scipy.spatial import ConvexHull
x1, y1 = np.random.normal(loc=5, scale=2, size=(2,15))
x2, y2 = np.random.normal(loc=8, scale=2.5, size=(2,13))
plt.scatter(x1, y1)
plt.scatter(x2, y2)
def encircle(x,y, ax=None, **kw):
if not ax: ax=plt.gca()
p = np.c_[x,y]
hull = ConvexHull(p)
poly = plt.Polygon(p[hull.vertices,:], **kw)
ax.add_patch(poly)
encircle(x1, y1, ec="k", fc="gold", alpha=0.2)
encircle(x2, y2, ec="orange", fc="none")
plt.show()
Another option is to draw a circle around the mean of the point cloud.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
from scipy.spatial import ConvexHull
x1, y1 = np.random.normal(loc=5, scale=2, size=(2,15))
x2, y2 = np.random.normal(loc=8, scale=2.5, size=(2,13))
plt.scatter(x1, y1)
plt.scatter(x2, y2)
def encircle2(x,y, ax=None, **kw):
if not ax: ax=plt.gca()
p = np.c_[x,y]
mean = np.mean(p, axis=0)
d = p-mean
r = np.max(np.sqrt(d[:,0]**2+d[:,1]**2 ))
circ = plt.Circle(mean, radius=1.05*r,**kw)
ax.add_patch(circ)
encircle2(x1, y1, ec="k", fc="gold", alpha=0.2)
encircle2(x2, y2, ec="orange", fc="none")
plt.gca().relim()
plt.gca().autoscale_view()
plt.show()
