def test_stat_point_minimisation():
# Test that the minimisation works for very shallow minima
energies_list = [
np.array([0.0, 3.8, -9.1, -1.6, 0.3]),
np.array([0.0, 10, -20, 10, -5])
]
for energies in energies_list:
result = minimize(plotting.error_on_stationary_points,
x0=energies,
args=(energies, ),
method='BFGS',
tol=0.1)
assert result.success
spline = interpolate.CubicSpline([0, 1, 2, 3, 4],
result.x,
bc_type='clamped')
fine_zi_s = np.linspace(-0.2, 5.2, num=500)
stationary_points = plotting.get_stationary_points(
xs=fine_zi_s, dydx=spline.derivative())
assert len(stationary_points) == 5
def test_stat_points():
# y = (x-2)^2 has a stationary point at x = 2
stationary_points = plotting.get_stationary_points(xs=np.linspace(-1, 3, 100),
dydx=lambda x: 2*(x-2))
assert len(stationary_points) == 1
assert 1.9 < stationary_points[0] < 2.1