Strange behaviour with parallel map and UnivariateSpline

parallelspline.py

import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate

from concurrent.futures import ThreadPoolExecutor
from os import cpu_count
executor = ThreadPoolExecutor(max_workers=cpu_count())
print(f"Processors available: {cpu_count()}")

# parallel=True uses the following map function
parallel_map = executor.map
regular_map = map

samples, length = 30, 2000

x = np.linspace(0, np.pi/2, length)

data1 = np.repeat(np.sin(x)[None, :], samples, axis=0) # data used for parallel = True
data2 = data1.copy() # data used for parallel = False
data3 = data1.copy() # sanity check checking parallel = False shows same value again

def func(d):
    spline = scipy.interpolate.UnivariateSpline(x, d)
    return spline(x)

parallel = list(parallel_map(func, data1))
regular = list(regular_map(func, data2))
sanity = list(regular_map(func, data3))


fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(9,3))

ax1.set_title('10 identical sin(x)')
ax2.set_title('10 splines on sin(x) serially')
ax3.set_title('10 splines on sin(x) in parallel')

for i in range(samples):
    ax1.plot(x, data1[i])
    ax2.plot(x, regular[i])
    ax3.plot(x, parallel[i])

The above results in the following plot. Note that the right hand plot (parallel) is not identical to the middle one (serial).