def test_dmd_complex128(self):
m, n = 9, 7
a = np.array(np.fliplr(np.vander(np.random.rand(m)+1, n)) + 1j*np.fliplr(np.vander(np.random.rand(m), n)),
np.complex128, order='F')
a_gpu = gpuarray.to_gpu(a)
f_gpu, b_gpu, v_gpu, omega = linalg.dmd(a_gpu, modes='standard', return_amplitudes=True, return_vandermonde=True)
assert np.allclose(a[:,:(n-1)], np.dot(f_gpu.get(), np.dot(np.diag(b_gpu.get()), v_gpu.get()) ), atol_float64)
def test_cdmd_complex128(self):
# Define time and space discretizations
x = np.linspace(-5, 5, 50)
t = np.linspace(0, 8 * np.pi, 20)
dt = t[2] - t[1]
X, T = np.meshgrid(x, t)
# Create two patio-temporal patterns
F1 = 0.5 * np.cos(X) * (1. + 0. * T)
F2 = ((1. / np.cosh(X)) * np.tanh(X)) * (2. * np.exp(1j * 2.8 * T))
D = F1 + F2
a = np.array(D.real, np.complex128, order='F')
a_gpu = gpuarray.to_gpu(a)
dmdf_gpu, dmdb_gpu, dmdv_gpu, dmdomega = linalg.dmd(
a_gpu,
k=2,
modes='exact',
return_amplitudes=True,
return_vandermonde=True)
f_gpu, b_gpu, v_gpu, omega = rlinalg.cdmd(a_gpu,
k=2,
c=10,
modes='exact',
return_amplitudes=True,
return_vandermonde=True)
assert np.allclose(dmdomega.get().real, omega.get().real, atol_float64)
def test_cdmd_complex128(self):
# Define time and space discretizations
x=np.linspace( -5, 5, 50)
t=np.linspace(0, 8*np.pi , 20)
dt=t[2]-t[1]
X, T = np.meshgrid(x,t)
# Create two patio-temporal patterns
F1 = 0.5* np.cos(X)*(1.+0.* T)
F2 = ( (1./np.cosh(X)) * np.tanh(X)) *(2.*np.exp(1j*2.8*T))
D = F1+F2
a = np.array(D.real, np.complex128, order='F')
a_gpu = gpuarray.to_gpu(a)
dmdf_gpu, dmdb_gpu, dmdv_gpu, dmdomega = linalg.dmd(a_gpu, k=2, modes='exact', return_amplitudes=True, return_vandermonde=True)
f_gpu, b_gpu, v_gpu, omega = rlinalg.cdmd(a_gpu, k=2, c=10, modes='exact', return_amplitudes=True, return_vandermonde=True)
assert np.allclose(dmdomega.get().real, omega.get().real, atol_float64)
