def test_Condition_save_load(test_A, test_alpha):
condition = hs.Condition()
condition.add(test_alpha, test_A)
condition.save(temp_file.name)
loaded_condition = hs.Condition()
loaded_condition.load(temp_file.name)
np.testing.assert_allclose(loaded_condition.alpha.value, test_alpha.value)
np.testing.assert_allclose(loaded_condition.A, test_A)
def test_model_conditions():
test_alpha_1 = hs.Alpha()
test_alpha_1.add(np.ones((2, 2)))
test_alpha_2 = hs.Alpha()
test_alpha_2.add(np.ones((2, 2)) * 2)
test_A_1 = np.ones((2, 1))
test_A_2 = np.ones((2, 1)) * 2
condition_1 = hs.Condition()
condition_1.add(test_alpha_1, test_A_1)
condition_2 = hs.Condition()
condition_2.add(test_alpha_2, test_A_2)
model = hs.LeastSquares(conditions=[condition_1, condition_2])
np.testing.assert_allclose(model.ALPHA,
np.array([[1, 1], [1, 1], [2, 2], [2, 2]]))
np.testing.assert_allclose(model.A, np.array([[1], [1], [2], [2]]))
def test_Condition(test_A, test_alpha):
condition = hs.Condition()
condition.add(test_alpha, test_A)
assert condition.alpha == test_alpha
np.testing.assert_allclose(condition.A, test_A)
with pytest.raises(IndexError) as e_info:
row_A = np.random.rand(5)
condition.add(test_alpha, row_A)
assert 'A should be column vector of Mx1 dimension' in str(e_info)
with pytest.raises(IndexError) as e_info:
square_A = np.random.rand(5, 5)
condition.add(test_alpha, square_A)
assert 'A should be column vector of Mx1 dimension' in str(e_info)
with pytest.raises(IndexError) as e_info:
mismatch_A = np.random.rand(3, 1)
condition.add(test_alpha, mismatch_A)
assert 'A and alpha should have the same 0 dimension(M).' in str(e_info)
with pytest.raises(TypeError) as e_info:
A = [[3], [3], [3]]
condition.add(test_alpha, A)
assert 'numpy array' in str(e_info)
with pytest.raises(IndexError) as e_info:
alpha = hs.Alpha()
alpha.add(np.random.rand(5, 5))
wrong_first_dim_A = np.random.rand(3, 1)
condition.add(alpha, wrong_first_dim_A)
print(condition.alpha.value, condition.A)
assert 'same 0 dimension(M)' in str(e_info)
def test_info_model():
# Set random data
np.random.seed(42)
m = 3
n = 3
real = np.random.rand(m, n)
imag = np.random.rand(m, n)
comp = real + imag * 1j
alpha1 = hs.Alpha()
alpha1.add(comp)
real = np.random.rand(m, n)
imag = np.random.rand(m, n)
comp = real + imag * 1j
alpha2 = hs.Alpha()
alpha2.add(comp)
# Condition logging and printing
condition1 = hs.Condition(name='Speed 1300')
condition1.add(alpha2, A=np.random.rand(m, 1))
condition2 = hs.Condition(name='Speed 2500')
condition2.add(alpha2, A=np.random.rand(m, 1))
model = hs.LeastSquares(conditions=[condition1, condition2])
model.solve()
angles = np.arange(100, 300, 10) # angles
split1 = model.create_split()
split1.split_setup(0,
max_number_weights_per_hole=1,
holes_available=[angles],
weights_available=[0.1])
split2 = model.create_split()
split2.split_setup(1,
max_number_weights_per_hole=1,
holes_available=[angles],
weights_available=[0.2])
split1.split_solve()
split2.split_solve()
split1.update(confirm=True)
split2.update(confirm=True)
print(model.info())
def test_info():
# Set random data
np.random.seed(42)
alpha1 = hs.Alpha()
alpha1.name = 'Turbine'
m = 3
n = 3
real = np.random.rand(m, n)
imag = np.random.rand(m, n)
comp = real + imag * 1j
# alpha1 from A, B, U
alpha1.add(A=np.random.rand(3, 1) + np.random.rand(3, 1) * 1j,
B=np.random.rand(3, 5) + np.random.rand(3, 5) * 1j,
U=np.random.rand(5) + np.random.rand(5) * 1j)
# alpha2 from direct_matrix
alpha2 = hs.Alpha()
alpha2.add(comp)
# Condition logging and printing
condition1 = hs.Condition(name='Speed 2500')
condition1.add(alpha2, A=np.random.rand(m, 1))
print(alpha1, alpha2, condition1)