def test_softminus_softplus(self):
a = pybamm.Scalar(1)
b = pybamm.StateVector(slice(0, 1))
minimum = pybamm.softminus(a, b, 50)
self.assertAlmostEqual(minimum.evaluate(y=np.array([2]))[0, 0], 1)
self.assertAlmostEqual(minimum.evaluate(y=np.array([0]))[0, 0], 0)
self.assertEqual(
str(minimum), "log(1.9287498479639178e-22 + exp(-50.0 * y[0:1])) / -50.0"
)
maximum = pybamm.softplus(a, b, 50)
self.assertAlmostEqual(maximum.evaluate(y=np.array([2]))[0, 0], 2)
self.assertAlmostEqual(maximum.evaluate(y=np.array([0]))[0, 0], 1)
self.assertEqual(
str(maximum), "log(5.184705528587072e+21 + exp(50.0 * y[0:1])) / 50.0"
)
# Test that smooth min/max are used when the setting is changed
pybamm.settings.min_smoothing = 10
pybamm.settings.max_smoothing = 10
self.assertEqual(str(pybamm.minimum(a, b)), str(pybamm.softminus(a, b, 10)))
self.assertEqual(str(pybamm.maximum(a, b)), str(pybamm.softplus(a, b, 10)))
# But exact min/max should still be used if both variables are constant
a = pybamm.Scalar(1)
b = pybamm.Scalar(2)
self.assertEqual(str(pybamm.minimum(a, b)), str(a))
self.assertEqual(str(pybamm.maximum(a, b)), str(b))
# Change setting back for other tests
pybamm.settings.min_smoothing = "exact"
pybamm.settings.max_smoothing = "exact"
def maximum(left, right):
"""
Returns the larger of two objects, possibly with a smoothing approximation.
Not to be confused with :meth:`pybamm.max`, which returns max function of child.
"""
k = pybamm.settings.max_smoothing
# Return exact approximation if that is the setting or the outcome is a constant
# (i.e. no need for smoothing)
if k == "exact" or (pybamm.is_constant(left) and pybamm.is_constant(right)):
out = Maximum(left, right)
else:
out = pybamm.softplus(left, right, k)
return pybamm.simplify_if_constant(out, keep_domains=True)