def test_int_ensures_the_bcf_func_is_called_corrrectly():
# Arrange
mock = MagicMock(return_value=[0, 0])
# Act
pde_solve(base_L, base_T, base_u_I, base_mx, base_mt, bcf=mock)
# Assert
assert mock.call_count == base_mt
def test_integration_ensures_that_u_I_is_called_multiple_times():
# Arrange
mock = MagicMock()
# Act
pde_solve(base_L, base_T, mock, base_mx, base_mt)
# Assert
assert mock.call_count == base_mx
def test_unit_ensures_that_u_I_is_called_with_correct_args():
# Arrange
def fake_u_I(x):
assert isinstance(x, float) == True
# Act
pde_solve(base_L, base_T, fake_u_I, base_mx, base_mt)
def test_unit_ensures_bcf_is_called_with_the_correct_params():
# Arrange
def fake_bcf(t):
assert type(t) == np.float64
return [0, 0]
# Act
pde_solve(base_L, base_T, base_u_I, base_mx, base_mt, bcf=fake_bcf)
def test_E2E_agaist_heat_equation_varying_diffusion_coefficient():
# Arrange
# set problem parameters/functions
L = 11 # length of spatial domain
T = 0.5 # total time to solve for
# set numerical parameters
mx = 10 # number of gridpoints in space
mt = 1000 # number of gridpoints in time
# define initial params
def u_I(x):
# initial temperature distribution
y = np.sin(pi * x / L)
return y
def heat_source(x, t):
res = np.piecewise(x, [x < 5, x >= 5], [-1, 1])
return res
# Act
# solve the heat equation
[u_j, x, t] = pde_solve(L, T, u_I, mx, mt, heat_source=heat_source)
print("u_j {}".format(u_j))
assert np.isclose(u_j, [
0, -863.00360932, -983.72884818, -972.80346348, -751.16896889,
753.06057621, 974.46691545, 984.96351132, 863.66058543, 0
]).all()
def test_E2E_agaist_heat_equation_varying_bcf():
# Arrange
# set problem parameters/functions
kappa = 1 # diffusion constant
L = 11 # length of spatial domain
T = 0.5 # total time to solve for
# set numerical parameters
mx = 10 # number of gridpoints in space
mt = 10 # number of gridpoints in time
# define initial params
def u_I(x):
# initial temperature distribution
y = np.sin(pi * x / L)
return y
def bcf(t):
return [t, t]
# Act
# solve the heat equation
[u_j, x, t] = pde_solve(L, T, u_I, mx, mt, bcf=bcf)
# Assert
# check solution at final value boundary conditions
assert u_j[0] == T
assert u_j[-1] == T
def test_unit_throws_if_not_passed_specified_params():
#Arrange
thrown = False
#Act
try:
res = pde_solve()
except Exception as e:
thrown = e
#assert
assert thrown != False
def test_E2E_agaist_exact_solution_to_heat_equation():
# Arrange
# set problem parameters/functions
kappa = 1 # diffusion constant
L = 11 # length of spatial domain
T = 0.5 # total time to solve for
# set numerical parameters
mx = 100 # number of gridpoints in space
mt = 1000 # number of gridpoints in time
# define initial params
def u_I(x):
# initial temperature distribution
y = np.sin(pi * x / L)
return y
# define this to compare witht the exact solution
def u_exact(x, t):
# the exact solution
y = np.exp(-kappa * (pi**2 / L**2) * t) * np.sin(pi * x / L)
return y
# Act
# solve the heat equation
[u_j, x, t] = pde_solve(L, T, u_I, mx, mt, f_kappa=lambda x: 1)
# Assert
looped = False
# compare sol vs u_exact with error threashold
for i in range(len(u_j)):
exact = u_exact(x[i], T)
assert isclose(u_j[i], exact, abs_tol=1e-3)
looped = True
assert looped == True
def test_E2E_agaist_heat_equation_varying_diffusion_coefficient():
# Arrange
# set problem parameters/functions
L = 11 # length of spatial domain
T = 0.5 # total time to solve for
# set numerical parameters
mx = 10 # number of gridpoints in space
mt = 1000 # number of gridpoints in time
# define initial params
def u_I(x):
# initial temperature distribution
y = np.sin(pi * x / L)
return y
# Act
# solve the heat equation
[u_j, x, t] = pde_solve(L, T, u_I, mx, mt, f_kappa=lambda x: x)
print("u_j {}".format(u_j))
assert np.isclose(u_j, [
0, 0.26789984, 0.40498984, 0.49342122, 0.52927611, 0.5140508,
0.45421537, 0.36018634, 0.24470462, 0.12086154, 0
]).all()
def test_unit_ensure_returns():
# Arrange
# Act
res = pde_solve(base_L, base_T, base_u_I, base_mx, base_mt)
# Assert
assert res != None