def test_cdmd_matrix_custom(self):
matrix = np.random.permutation(
(sample_data.shape[1] - 3) * sample_data.shape[0]).reshape(
sample_data.shape[1] - 3, sample_data.shape[0]).astype(float)
matrix /= float(np.sum(matrix))
dmd = CDMD(compression_matrix=matrix)
dmd.fit(X=sample_data)
error_norm = np.linalg.norm(dmd.reconstructed_data - sample_data, 1)
assert error_norm < 1e-10
def test_dmd_time_2(self):
dmd = CDMD()
dmd.fit(X=sample_data)
dmd.dmd_time['t0'] = 10
dmd.dmd_time['tend'] = 14
expected_data = sample_data[:, -5:]
np.testing.assert_allclose(dmd.reconstructed_data, expected_data)
def test_eigs_3(self):
dmd = CDMD(svd_rank=2)
dmd.fit(X=sample_data)
expected_eigs = np.array(
[-0.47386866 + 0.88059553j, -0.80901699 + 0.58778525j])
np.testing.assert_almost_equal(dmd.eigs, expected_eigs, decimal=6)
def test_dynamics_1(self):
dmd = CDMD(svd_rank=5)
dmd.fit(X=sample_data)
assert dmd.dynamics.shape == (5, sample_data.shape[1])
def test_Atilde_shape(self):
dmd = CDMD(svd_rank=3)
dmd.fit(X=sample_data)
assert dmd.atilde.shape == (dmd.svd_rank, dmd.svd_rank)
def test_eigs_2(self):
dmd = CDMD(svd_rank=5)
dmd.fit(X=sample_data)
assert len(dmd.eigs) == 5
def test_shape(self):
dmd = CDMD(svd_rank=-1)
dmd.fit(X=[d for d in sample_data.T])
assert dmd.modes.shape[1] == sample_data.shape[1] - 1
def test_truncation_shape(self):
dmd = CDMD(svd_rank=3)
dmd.fit(X=sample_data)
assert dmd.modes.shape[1] == 3
def test_dmd_time_1(self):
dmd = CDMD(svd_rank=2)
dmd.fit(X=sample_data)
expected_dict = {'dt': 1, 't0': 0, 'tend': 14}
np.testing.assert_equal(dmd.dmd_time, expected_dict)
def test_plot_modes_3(self):
dmd = CDMD()
snapshots = [snap.reshape(20, 20) for snap in sample_data.T]
dmd.fit(X=snapshots)
dmd.plot_modes_2D()
plt.close()
def test_cdmd_matrix_sparse(self):
dmd = CDMD(compression_matrix='sparse')
dmd.fit(X=sample_data)
error_norm = np.linalg.norm(dmd.reconstructed_data - sample_data, 1)
assert error_norm < 1e-10
def test_reconstructed_data(self):
dmd = CDMD()
dmd.fit(X=sample_data)
dmd_data = dmd.reconstructed_data
np.testing.assert_allclose(dmd_data, sample_data)
def test_tdmd_plot(self):
dmd = CDMD(tlsq_rank=3)
dmd.fit(X=sample_data)
dmd.plot_eigs(show_axes=False, show_unit_circle=False)
plt.close()
def test_plot_snapshots_5(self):
dmd = CDMD()
snapshots = [snap.reshape(20, 20) for snap in sample_data.T]
dmd.fit(X=snapshots)
dmd.plot_snapshots_2D(index_snap=2, filename='tmp.png')
self.addCleanup(os.remove, 'tmp.2.png')
def test_plot_snapshots_4(self):
dmd = CDMD()
snapshots = [snap.reshape(20, 20) for snap in sample_data.T]
dmd.fit(X=snapshots)
dmd.plot_snapshots_2D(index_snap=2)
plt.close()
def test_plot_snapshots_2(self):
dmd = CDMD(svd_rank=-1)
dmd.fit(X=sample_data)
dmd.plot_snapshots_2D((1, 2, 5), x=np.arange(20), y=np.arange(20))
plt.close()
def test_plot_snapshots_1(self):
dmd = CDMD()
dmd.fit(X=sample_data)
with self.assertRaises(ValueError):
dmd.plot_snapshots_2D()
def test_original_timesteps(self):
dmd = CDMD()
dmd.fit(X=sample_data)
np.testing.assert_allclose(dmd.original_timesteps,
np.arange(sample_data.shape[1]))
def test_sorted_eigs_default(self):
dmd = CDMD(compression_matrix='sparse')
assert dmd.operator._sorted_eigs == False
def test_sorted_eigs_param(self):
dmd = CDMD(compression_matrix='sparse', sorted_eigs='real')
assert dmd.operator._sorted_eigs == 'real'
def test_plot_eigs_2(self):
dmd = CDMD()
dmd.fit(X=sample_data)
dmd.plot_eigs(show_axes=False, show_unit_circle=False)
plt.close()
