def test_reconstructed_data(self):
dmd = FbDMD(exact=True, svd_rank=-1)
dmd.fit(X=sample_data)
dmd_data = dmd.reconstructed_data
dmd_data_correct = np.load('tests/test_datasets/fbdmd_data.npy')
assert np.allclose(dmd_data, dmd_data_correct)
def test_plot_eigs_2(self):
dmd = FbDMD(svd_rank=-1)
dmd.fit(X=sample_data)
dmd.plot_eigs(show_axes=False, show_unit_circle=False)
plt.close()
def test_eigs_2(self):
dmd = FbDMD(svd_rank=1)
dmd.fit(X=sample_data)
assert len(dmd.eigs) == 1
def test_eigs_modulus_2(self):
dmd = FbDMD(svd_rank=-1, exact=True)
dmd.fit(X=sample_data)
np.testing.assert_almost_equal(np.linalg.norm(dmd.eigs[1]),
1.,
decimal=6)
def test_dynamics(self):
dmd = FbDMD(svd_rank=-1)
dmd.fit(X=sample_data)
assert dmd.dynamics.shape == (2, 100)
def test_truncation_shape(self):
dmd = FbDMD(svd_rank=1)
dmd.fit(X=sample_data)
assert dmd.modes.shape[1] == 1
def test_modes_shape(self):
dmd = FbDMD(svd_rank=-1)
dmd.fit(X=sample_data)
assert dmd.modes.shape[1] == 2
def test_sorted_eigs_param(self):
dmd = FbDMD(svd_rank=-1, sorted_eigs='real')
assert dmd.operator._sorted_eigs == 'real'
def test_sorted_eigs_default(self):
dmd = FbDMD(svd_rank=-1)
assert dmd.operator._sorted_eigs == False