def test_cut_remove_switch_point_on_end(self):
bts = BooleanTimeSeries([0, 1, 2], [True], 2)
cut1 = bts.cut(0, 2)
self.assertEqual([0,1], cut1.t)
self.assertEqual([True, False], cut1.y)
self.assertEqual(2, cut1.end)
def test_two_variables(self):
# A simple test: 2 variables, 2 delays.
# x1(t) = x2(t - tau1)
# x2(t) = NOT x1(t-tau2)
tau1 = 1
tau2 = 0.5
delays = [tau1, tau2]
history_a = BooleanTimeSeries([0, 1.5], [True, False], 1.8)
history_b = BooleanTimeSeries([0, 0.5], [True, False], 1.8)
x_end = 5.2
solver = BDESolver(lambda z: [z[0][1], not z[1][0]], delays,
[history_a, history_b])
[output_a, output_b] = solver.solve(x_end)
self.assertEqual([0, 1.5, 3, 4.5], output_a.t)
self.assertEqual([True, False, True, False], output_a.y)
self.assertEqual(5.2, output_a.end)
self.assertEqual([0, 0.5, 2, 3.5, 5], output_b.t)
self.assertEqual([True, False, True, False, True], output_b.y)
self.assertEqual(5.2, output_b.end)
def test_forcing_input(self):
# A test case with some forcing input.
# x1(t) = 1 if t mod 1 > 0.5, 0 otherwise <- this is the forced input
# x2(t) = x1(t-tau1) <- this is the output
tau1 = 0.5
delays = [tau1]
history = BooleanTimeSeries([0, 0.5, 1.5], [True, False, True], 1.7)
x_end = 3
input = BooleanTimeSeries([0, 0.5, 1.5, 2, 2.5, 3],
[False, True, False, True, False, True], 3)
solver = BDESolver(lambda z, z2: [z2[0][0]], delays, [history],
[input])
[output] = solver.solve(x_end)
self.assertEqual([0, 0.5, 1.5, 2, 2.5, 3], output.t)
self.assertEqual([True, False, True, False, True, False], output.y)
self.assertEqual(
x_end,
output.end,
)
def test_cut_with_no_impact(self):
bts = BooleanTimeSeries([0, 1], [True], 2)
cut1 = bts.cut(0, 2)
self.assertEqual([0,1], cut1.t)
self.assertEqual([True, False], cut1.y)
self.assertEqual(2, cut1.end)
def test_history_ends_with_switch(self):
# A switch occurs at the last point in the history but the history
# is consistent with this
# A simple test: 2 variables, 2 delays.
# x1(t) = x2(t - tau1)
# x2(t) = NOT x1(t-tau2)
tau1 = 1
tau2 = 0.5
delays = [tau1, tau2]
history_a = BooleanTimeSeries([0, 1.5], [True, False], 2.1)
history_b = BooleanTimeSeries([0, 0.5, 2], [True, False, True], 2.1)
x_end = 5.2
solver = BDESolver(lambda z: [z[0][1], not z[1][0]], delays,
[history_a, history_b])
[output_a, output_b] = solver.solve(x_end)
self.assertEqual([0, 1.5, 3, 4.5], output_a.t)
self.assertEqual([True, False, True, False], output_a.y)
self.assertEqual(5.2, output_a.end)
self.assertEqual([0, 0.5, 2, 3.5, 5], output_b.t)
self.assertEqual([True, False, True, False, True], output_b.y)
self.assertEqual(5.2, output_b.end)
def test_rounding_issues(self):
"""
This example got the wrong results due to rounding errors in initial implementation.
Explicitly keeping track what indexes are associated with candidate switch points
sorts this problem. But is it a useful regression test.
"""
delay_parameters = [0.3]
start_time = 0.5
end_time = 3
history = BooleanTimeSeries([0], [True], start_time)
input = BooleanTimeSeries([0, 0.5, 1, 1.5, 2, 2.5, 3], [False],
end_time)
my_bde_solver = BDESolver(lambda z, z2: [z2[0][0]], delay_parameters,
[history], [input])
[output] = my_bde_solver.solve(end_time)
expected_t = [0, 0.5, 0.8, 1.3, 1.8, 2.3, 2.8]
expected_y = [True, False, True, False, True, False, True]
self.assertEqual(expected_t, output.t)
self.assertEqual(expected_y, output.y)
self.assertEqual(end_time, output.end)
def test_two_variables_switch_at_switch_points(self):
# Two variables switch at each of the switch points.
# x1(t) = x2(t - tau1)
# x2(t) = x1(t - tau2)
tau1 = 1
tau2 = 1
delays = [tau1, tau2]
history_a = BooleanTimeSeries([0, 0.5, 1.0, 1.5],
[True, False, True, False], 1.8)
history_b = BooleanTimeSeries([0, 0.5, 1.0, 1.5],
[True, False, True, False], 1.8)
x_end = 3.2
solver = BDESolver(lambda z: [z[0][1], z[1][0]], delays,
[history_a, history_b])
[output_a, output_b] = solver.solve(x_end)
self.assertEqual([0, 0.5, 1.0, 1.5, 2, 2.5, 3], output_a.t)
self.assertEqual([True, False, True, False, True, False, True],
output_a.y)
self.assertEqual(3.2, output_a.end)
self.assertEqual([0, 0.5, 1.0, 1.5, 2, 2.5, 3], output_b.t)
self.assertEqual([True, False, True, False, True, False, True],
output_b.y)
self.assertEqual(x_end, output_b.end)
def test_last_output_is_a_switch_point(self):
# Same as test_two_variables except the end point has been moved to be on a switch point
# A simple test: 2 variables, 2 delays.
# x1(t) = x2(t - tau1)
# x2(t) = NOT x1(t-tau2)
tau1 = 1
tau2 = 0.5
delays = [tau1, tau2]
history_a = BooleanTimeSeries([0, 1.5], [True, False], 1.8)
history_b = BooleanTimeSeries([0, 0.5], [True, False], 1.8)
x_end = 5
solver = BDESolver(lambda z: [z[0][1], not z[1][0]], delays,
[history_a, history_b])
[output_a, output_b] = solver.solve(x_end)
self.assertEqual([0, 1.5, 3, 4.5], output_a.t)
self.assertEqual([True, False, True, False], output_a.y)
self.assertEqual(5, output_a.end)
self.assertEqual([0, 0.5, 2, 3.5, 5], output_b.t)
self.assertEqual([True, False, True, False, True], output_b.y)
self.assertEqual(x_end, output_b.end)
def test_forcing_input_2_2(self):
# test case with 2 variables and 2 forcing inputs
tau1 = 1
tau2 = 0.5
tau3 = 1
tau4 = 0.25
delays = [tau1, tau2, tau3, tau4]
history_a = BooleanTimeSeries([0], [True], 1)
history_b = BooleanTimeSeries([0], [False], 1)
x_end = 1.4
input_a = BooleanTimeSeries([0, 0.25, 0.75, 1.25], [False], 1.4)
input_b = BooleanTimeSeries([0, 0.1, 0.9, 1.1], [False], 1.4)
solver = BDESolver(
lambda z, z2: [z[0][1] or not z2[3][1], (not z[1][0]) or z2[2][0]],
delays, [history_a, history_b], [input_a, input_b])
[output_a, output_b] = solver.solve(x_end)
self.assertEqual([0, 1, 1.15, 1.35], output_a.t)
self.assertEqual([True, False, True, False], output_a.y)
self.assertEqual(1.4, output_a.end)
self.assertEqual([0, 1.25], output_b.t)
self.assertEqual([False, True], output_b.y)
self.assertEqual(1.4, output_b.end)
def test_basic_merge(self):
in1 = BooleanTimeSeries([0, 1, 2, 3], [True, False, True, False], 4)
in2 = BooleanTimeSeries([0, 1, 2.5, 3], [True, False, True, False], 4)
expected_t = [0, 1, 2, 2.5, 3]
expected_y = [[True, True], [False, False], [True, False], [True, True], [False, False]]
res_t, res_y = BooleanTimeSeries.merge([in1, in2])
self.assertEqual(expected_t, res_t)
self.assertEqual(expected_y, res_y)
def test_error_when_history_inputs_end_at_different_times(self):
tau1 = 1
tau2 = 0.5
delays = [tau1, tau2]
history_a = BooleanTimeSeries([0, 1.5], [True, False], 2.2)
history_b = BooleanTimeSeries([0, 0.5, 2], [True, False, True], 2.1)
x_end = 5.2
with self.assertRaises(ValueError):
BDESolver(lambda z: [z[0][1], not z[1][0]], delays,
[history_a, history_b])
def test_raise_exception_if_end_before_start(self):
solver = BDESolver(lambda z: [not z[0][0]], [1],
[BooleanTimeSeries([0], [True], 2)])
with self.assertRaises(ValueError):
solver.solve(1.7)
def test_absolute_threshold_all_on_plateau(self):
t = [0, 1, 2, 3]
y = [5, 5, 5, 5]
sp = BooleanTimeSeries.absolute_threshold(t, y, 5)
self.assertEqual([0], sp.t)
self.assertEqual([False], sp.y)
self.assertEqual(3, sp.end)
def test_absolute_threshold_start_with_double_plateau(self):
x = [0, 1, 2, 3, 4]
y = [5, 5, 0, 10, 0]
sp = BooleanTimeSeries.absolute_threshold(x, y, 5)
self.assertEqual([0, 2.5, 3.5], sp.t)
self.assertEqual([False, True, False], sp.y)
self.assertEqual(4, sp.end)
def test_absolute_threshold_switch_on_multiple_plateau(self):
x = [0, 1, 2, 3, 4]
y = [10, 5, 5, 0, 1]
sp = BooleanTimeSeries.absolute_threshold(x, y, 5)
self.assertEqual([0, 1.5], sp.t)
self.assertEqual([True, False], sp.y)
self.assertEqual(4, sp.end)
def test_absolute_threshold_touch_threshold_from_above(self):
x = [0, 1, 2]
y = [10, 5, 10]
sp = BooleanTimeSeries.absolute_threshold(x, y, 5)
self.assertEqual([0], sp.t)
self.assertEqual([True], sp.y)
self.assertEqual(2, sp.end)
def test_absolute_threshold_touch_threshold_from_below(self):
x = [0, 1, 2]
y = [0, 5, 0]
sp = BooleanTimeSeries.absolute_threshold(x, y, 5)
self.assertEqual([0], sp.t)
self.assertEqual([False], sp.y)
self.assertEqual(2, sp.end)
def test_absolute_threshold(self):
x = [0, 1, 2]
y = [0, 10, 0]
sp = BooleanTimeSeries.absolute_threshold(x, y, 5)
self.assertEqual([0, 0.5, 1.5], sp.t)
self.assertEqual([False, True, False], sp.y)
self.assertEqual(2, sp.end)
def test_absolute_threshold_switch_on_threshold(self):
x = [0, 1, 2]
y = [10, 5, 0]
sp = BooleanTimeSeries.absolute_threshold(x, y, 5)
self.assertEqual([0, 1], sp.t)
self.assertEqual([True, False], sp.y)
self.assertEqual(2, sp.end)
def test_relative_threshold(self):
x = [0, 1, 2]
y = [10, 20, 10]
sp = BooleanTimeSeries.relative_threshold(x, y, 0.5)
self.assertEqual([0, 0.5, 1.5], sp.t)
self.assertEqual([False, True, False], sp.y)
self.assertEqual(2, sp.end)
def test_basic_unmerge(self):
in1 = BooleanTimeSeries([0, 1, 2, 3], [True, False, True, False], 4)
in2 = BooleanTimeSeries([0, 1, 2.5, 3], [True, False, True, False], 4)
t = [0, 1, 2, 2.5, 3]
y = [[True, True], [False, False], [True, False], [True, True], [False, False]]
[out1, out2] = BooleanTimeSeries.unmerge(t, y, 4)
self.assertEqual([0, 1, 2, 3], out1.t)
self.assertEqual([True, False, True, False], out1.y)
self.assertEqual(4, out1.end)
self.assertEqual([0, 1, 2.5, 3], out2.t)
self.assertEqual([True, False, True, False], out2.y)
self.assertEqual(4, out2.end)
def test_create_with_numpy_arrays(self):
t = np.array([0, 1, 2])
y = np.array([False, True, False])
bts = BooleanTimeSeries(t, y, 3)
self.assertEqual([0, 1, 2], bts.t)
self.assertEqual([False, True, False], bts.y)
self.assertEqual(3, bts.end)
def test_forcing_input(self):
tau1 = 0.5
delays = [tau1]
history = BooleanTimeSeries([0, 0.5, 1.5], [True, False, True], 1.7)
x_end = 3
input = BooleanTimeSeries([0, 0.5, 1.5, 2, 2.5, 3],
[False, True, False, True, False, True], 3)
solver = BDESolver(lambda z, z2: [z2[0][0]], delays, [history],
[input])
result = solver.solve(x_end)
validator = bde_solver_validator.BDESolverValidator(
lambda z, z2: [z2[0][0]], delays, result, [input])
self.assertEqual(0, validator.validate(1.7, x_end))
def test_long_two_variable(self):
tau1 = 1
tau2 = 0.5
delays = [tau1, tau2]
history_a = BooleanTimeSeries([0, 1.5], [True, False], 1.8)
history_b = BooleanTimeSeries([0, 0.5], [True, False], 1.8)
x_end = 200
solver = BDESolver(lambda z: [z[0][1], not z[1][0]], delays,
[history_a, history_b])
result = solver.solve(x_end)
validator = bde_solver_validator.BDESolverValidator(
lambda z: [z[0][1], not z[1][0]], delays, result)
self.assertEqual(0, validator.validate(1.8, x_end))
def test_error_when_first_forced_input_switch_point_is_not_zero(self):
"""
Tests that error is raised when the first forced input switch point
is not at time zero.
"""
tau1 = 1
tau2 = 0.5
delays = [tau1, tau2]
history = BooleanTimeSeries([0.2, 0.5, 1.5], [True], 1.7)
forced_input = BooleanTimeSeries([1], [True], 1.7)
x_end = 5
with self.assertRaises(ValueError):
BDESolver(lambda z: [z[0][1], not z[1][0]], delays, [history],
[forced_input])
def test_one_variable(self):
history = BooleanTimeSeries([0], [False], 1)
solver = BDESolver(lambda z: [not z[0][0]], [1], [history])
result = solver.solve(3)
validator = bde_solver_validator.BDESolverValidator(
lambda z: [not z[0][0]], [1], result)
self.assertEqual(0, validator.validate(1, 3))
def main():
history = BooleanTimeSeries([0], [True], 1)
delay_parameters = [1]
end_time = 5
my_bde_solver = BDESolver(my_model, delay_parameters, [history])
my_bde_solver.solve(end_time)
plt.figure(figsize=(5, 2))
my_bde_solver.plot_result()
plt.savefig("C:\\Users\\ahume\\Documents\\MultiObjectiveOptimisation\\BDE\\wiki\\pybde.wiki\\images\\v1.0\\single_variable_output.png")
def test_error_when_given_negative_delay(self):
tau1 = 1
tau2 = -0.5
delays = [tau1, tau2]
history = BooleanTimeSeries([0, 0.5, 1.5], [True], 1.7)
x_end = 5
with self.assertRaises(ValueError):
BDESolver(lambda z: [z[0][1], not z[1][0]], delays, [history])
def test_forcing_input_2(self):
# A test case with 2 variables and one forcing input.
# This is equivalent to the 1 loop Neurospora model.
# This is also a useful test because all of the delays are the
# same, hence we must deal with multiple candidate switches at
# each point.
#
# This test contains a switch that is the result of a model
# variable that originates from a forcing switch.
delays = [1, 1, 1]
# NOTE: In the original Matlab test this used the line below, but the second point is inconsistent with the
# model is changed for the output. We do not yet support this functionality so change it just now.
#x = [0, 1]
#y = [[True, False], [True, False]]
history_a = BooleanTimeSeries([0], [True], 1)
history_b = BooleanTimeSeries([0], [False], 1)
x_end = 5
input = BooleanTimeSeries(
[0, 0.25, 0.75, 1.25, 1.75, 2.25, 2.75, 3.25, 3.75, 4.25, 4.75], [
False, True, False, True, False, True, False, True, False,
True, False
], 5)
solver = BDESolver(lambda z, z2: [z[0][1], (not z[1][0]) or z2[2][0]],
delays, [history_a, history_b], [input])
[output_a, output_b] = solver.solve(x_end)
self.assertEqual([0, 1, 2.25, 2.75, 3, 5], output_a.t)
self.assertEqual([True, False, True, False, True, False], output_a.y)
self.assertEqual(5, output_a.end)
self.assertEqual([0, 1.25, 1.75, 2, 4, 4.25, 4.75], output_b.t)
self.assertEqual([False, True, False, True, False, True, False],
output_b.y)
self.assertEqual(5, output_b.end)
def test_one_variable(self):
history = BooleanTimeSeries([0, 1], [False, True], 1.5)
solver = BDESolver(lambda z: [not z[0][0]], [1], [history])
[output] = solver.solve(3)
expected_t = [0, 1, 2, 3]
expected_y = [False, True, False, True]
self.assertEqual(expected_t, output.t)
self.assertEqual(expected_y, output.y)
self.assertEqual(3, output.end)
