def test_benchmark_solvers_for_constant_diagonal_case(self):
ode = Ode(coeff_matrix=self.COEFF_MATRIX_CONSTANT_DIAGONAL, init_condition=self.INIT_CONDITION_GENERAL)
numerical = ode.calculate_numerical_solution(self.STEPS)
analytical = ode.calculate_analytical_solution(self.STEPS)
self.assertEqual(numerical.size, self.EXPECTED_SIZE_200)
self.assertEqual(analytical.size, self.EXPECTED_SIZE_200)
npt.assert_almost_equal(numerical, analytical, self.DECIMALS_6)
def test_calculate_numerical_solution_for_constant_nondiagonal_case(self):
ode = Ode(coeff_matrix=self.COEFF_MATRIX_CONSTANT_NONDIAGONAL,
init_condition=self.INIT_CONDITION_CONSTANT_NONDIAGONAL)
actual = ode.calculate_numerical_solution(self.STEPS)
self.assertEqual(actual.size, self.STEP_NUMBER * self.INIT_CONDITION_CONSTANT_NONDIAGONAL.size)
self.assertEqual(actual.shape, (self.STEP_NUMBER, self.INIT_CONDITION_CONSTANT_NONDIAGONAL.size))
for index in range(self.INIT_CONDITION_CONSTANT_NONDIAGONAL.size):
self.assertIn(type(actual[-1, index]), self.ACCEPTED_TYPES)
self.assertAlmostEqual(actual[-1, index], self.EXPECTED_RESULT_CONSTANT_NONDIAGONAL[index], self.DECIMALS_6)
def test_calculate_numerical_solution_for_constant_diagonal_case(self):
ode = Ode(coeff_matrix=self.COEFF_MATRIX_CONSTANT_DIAGONAL, init_condition=self.INIT_CONDITION_GENERAL)
actual = ode.calculate_numerical_solution(self.STEPS)
self.assertEqual(type(actual), numpy.ndarray)
self.assertEqual(actual.size, self.EXPECTED_SIZE_200)
for i in range(self.STEP_NUMBER):
for j in range(self.INIT_CONDITION_GENERAL.size):
expected = ode.calculate_exp_solution(self.INIT_CONDITION_GENERAL[j],
self.COEFF_MATRIX_CONSTANT_DIAGONAL[j, j], self.STEPS[i])
self.assertIn(type(actual[i, j]), self.ACCEPTED_TYPES)
self.assertAlmostEqual(actual[i, j], expected, self.DECIMALS_6)
def __solve_numerically(self):
if self.coefficient_matrix is None or self.initial_condition is None:
self.__initialize_ode()
ode = Ode(coeff_matrix=self.coefficient_matrix.matrix,
init_condition=self.initial_condition)
numerical = ode.calculate_numerical_solution(
self.profiles['beamlet grid']['distance']['m'])
for level in range(self.atomic_db.atomic_ceiling):
label = 'level ' + self.atomic_db.inv_atomic_dict[level]
self.profiles[label] = numerical[:, level]
return
def test_calculate_numerical_solution_for_varying_nondiagonal_case(self):
ode = Ode(coeff_matrix=self.COEFF_MATRIX_VARYING_NONDIAGONAL,
init_condition=self.INIT_CONDITION_VARYING_NONDIAGONAL)
actual = ode.calculate_numerical_solution(self.STEPS_VARYING_NONDIAGONAL)
self.assertEqual(type(actual), numpy.ndarray)
self.assertEqual(actual.size,
self.STEPS_VARYING_NONDIAGONAL.size * self.INIT_CONDITION_VARYING_NONDIAGONAL.size)
self.assertEqual(actual.shape,
(self.STEPS_VARYING_NONDIAGONAL.size, self.INIT_CONDITION_VARYING_NONDIAGONAL.size))
npt.assert_almost_equal(actual, self.EXPECTED_RESULT_VARYING_NONDIAGONAL, self.DECIMALS_6)
for i in range(self.STEPS_VARYING_NONDIAGONAL.size):
for j in range(self.INIT_CONDITION_VARYING_NONDIAGONAL.size):
self.assertIn(type(actual[i, j]), self.ACCEPTED_TYPES)
def test_benchmark_solvers_for_varying_nondiagonal_case(self):
ode = Ode(coeff_matrix=self.COEFF_MATRIX_VARYING_NONDIAGONAL,
init_condition=self.INIT_CONDITION_VARYING_NONDIAGONAL)
numerical = ode.calculate_numerical_solution(self.STEPS_VARYING_NONDIAGONAL)
analytical = ode.calculate_numerical_solution(self.STEPS_VARYING_NONDIAGONAL)
npt.assert_almost_equal(numerical, analytical, self.DECIMALS_6)