KernelDensity malfunctions for some inputs

Question

I am using KernelDensity with the following code to separate a 1D vector of numbers based on their spatial distance.

import numpy as np
from numpy import array, linspace
from sklearn.neighbors import KernelDensity
from matplotlib.pyplot import plot
from scipy.signal import argrelextrema

a = array(v).reshape(-1, 1)
kde = KernelDensity(kernel='gaussian', bandwidth=1).fit(a)
s = linspace(min(a),max(a))
e = kde.score_samples(s.reshape(-1,1))
plt.plot(s, e)
mi = argrelextrema(e, np.less)[0]
print ("Minima:", s[mi])

If I use v=[1,10] as input vector, according to the figure below, there is a minima and s[mi] has a value

Minima: [[5.40816327]]

However, for an input like v=[1000,2000], the figure shows that there is a minima, but s[mi] is empty which is weird.

Minima: []

I even tested that with some other inputs like v=[10,20], v=[20,60], v=[10,60] or v=[30,60] and see that s[mi] is empty while there is indeed a minima in the figure.

Any idea about that?

P.S: The version of sklearn is 1.0.2.