def predicition(dmddataset, days):
hodmd = HODMD(svd_rank=0, exact=True, opt=True,
d=len(dmddataset) / days).fit(dmddataset)
hodmd.reconstructed_data.shape
hodmd.plot_eigs()
hodmd.dmd_time['tend'] = len(dmddataset) / days * (
days + 1) - 1 # since it starts from zero
dmd_output = hodmd.reconstructed_data[0].real
dmd_prediction = dmd_output[-len(dmddataset) / days:]
return dmd_prediction, dmd_output
print('Computing DMD...')
# ----------------------------------------------------------------------------------------------------------------------
# Compute DMD on displacement
d = 5
dmd = HODMD(svd_rank=rank, opt=True, d=d)
# dmd = MrDMD(svd_rank=rank, max_level = 3, max_cycles = 1)
dmd.fit(dataGrid.T)
# Show eigenvalues
for eig in dmd.eigs:
print('Eigenvalue {}: distance from unit circle {}'.format(eig, np.abs(eig.imag**2+eig.real**2 - 1)))
dmd.plot_eigs(show_axes=True, show_unit_circle=True)
# Show modes
fig = plt.figure(figsize=(8,3))
fig.subplots_adjust(top=0.8, bottom=0.2)
plt.subplot(121)
for mode in dmd.modes.T:
plt.plot(xVector, mode.real)
plt.title('Modes')
plt.xlabel('$x$')
# fig.savefig('DMDResults/DMDModes_'+fileName+'_Rank-%i_d-%i.png'%(rank,d))
# plt.show()
# Show dynamics
# fig = plt.figure(figsize=(10,6))
plt.subplot(122)
from pydmd import HODMD
x = int(input("predicted timestep-\n"))
z = int(input("data cutoff-\n"))
b = slice(0, z)
A = np.loadtxt('dmd.txt', delimiter=",")
B = A.T
C = B[:, b]
original = C
print(C.shape)
hodmd = HODMD(svd_rank=0, exact=True, opt=True, d=30).fit(C)
print(hodmd.reconstructed_data.shape)
hodmd.plot_eigs()
hodmd.original_time['dt'] = hodmd.dmd_time['dt']
hodmd.original_time['t0'] = hodmd.dmd_time['t0']
hodmd.original_time['tend'] = hodmd.dmd_time['tend']
plt.plot(hodmd.original_timesteps, C[0, :], '.', label='snapshots')
plt.plot(hodmd.original_timesteps,
original[0, :],
'-',
label='original function')
plt.plot(hodmd.dmd_timesteps,
hodmd.reconstructed_data[0].real,
'--',
label='DMD output')
plt.legend()
plt.show()