def svd_tolerance():
# matrix = np.load('../data/converted_matrix.npy')
matrix = ca_data_utils.load_vid()
print('done loading data')
U, s, V = randomized_svd(matrix, n_components=500)
n_oversamples = [50, 100, 150, 200] # from 5 - 20
n_iters = [40, 70, 100] # from 4, 7,
norms = []
for n in n_oversamples:
print('start doing n_oversample = ', n)
norm = check_n_oversamples(matrix, n)
norms.append(norm)
print('done doing n_oversample = ', n)
# plt.plot(n_oversamples, norms)
# plt.savefig('n_oversamples')
np.save('n_oversamples', norms)
norms = []
for n in n_iters:
print('starting doing n_iters = ', n)
norm = check_n_iters(matrix, n)
norms.append(norm)
print('done doing n_iters = ', n)
# plt.plot(n_iters, norms)
# plt.savefig('n_iters')
np.save('n_iters', norms)
def make_svd_matrices():
matrix = ca_data_utils.load_vid()
print('done loading data')
U, s, V = randomized_svd(matrix,
n_oversamples=10,
n_iter=20,
n_components=1000)
np.save('umat', U)
np.save('svmat', np.dot(np.diag(s), V))
def main():
flatten = ca_data_utils.load_vid()
X1 = flatten[:,:-1]
X2 = flatten[:,1:]
eigenvectors_high, omega, eigenvalues_high, b, Xdmd = dmd(X1, X2, 10, 1)
# plot_utils.plot_eigenvalues(eigenvalues_high)
# plot_utils.plot_modes(eigenvectors_high, vid[0].shape, eigenvalues_high)
print(Xdmd.shape)
np.save('reconstructed', Xdmd)
np.save('eigenvalue_high', eigenvalues_high)
np.save('eigenvectors_high', eigenvectors_high)
def svd_tune_rank():
matrix = ca_data_utils.load_vid()
print('done loading data')
ranks = np.linspace(500, 1000, 5)
ranks = ranks.astype(int)
norms = []
for r in ranks:
print('starting rank = ', r)
U, s, V = randomized_svd(matrix,
n_oversamples=150,
n_iter=30,
n_components=r)
diff = matrix - np.dot(U, np.dot(np.diag(s), V))
norms.append(np.linalg.norm(diff))
print('done rank = ', r)
norms = np.array(norms) / 15452.521
np.save('ranks', norms)
def main():
# when on cylon to test on full vid fdir = '../data/vid.tif'
# when local test use 'short_vid.tif'
vid = ca_data_utils.load_vid()
X1 = flatten[:, :-1]
X2 = flatten[:, 1:]
print('start dmd')
dmd = DMD(svd_rank=10)
dmd.fit(flatten.T)
print('finished fitting data')
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)
self_re = np.load('reconstructed.npy')
diff = dmd.reconstructed_data.T - self_re
print(len(np.where(np.abs(diff) > 1e-3)[0]))
print(diff.max())
print(diff.min())
import scipy.io
import numpy as np
import skimage.io
import ca_data_utils
def trunk_data_by_states(vid, labels):
state_1 = np.where(labels == 1)
state_2 = np.where(labels == 2)
state_3 = np.where(labels == 3)
print(state_1[0].shape)
print(state_2[0].shape)
print(state_3[0].shape)
sleep = vid[:, state_1[0]]
wake_1 = vid[:, state_2[0]]
wake_2 = vid[:, state_3[0]]
print(sleep.shape)
np.save('../data/byState/sleep', sleep)
np.save('../data/byState/wake_1', wake_1)
np.save('../data/byState/wake_2', wake_2)
if __name__ == '__main__':
labels = ca_data_utils.load_labels()
vid = ca_data_utils.load_vid()
print(vid.shape)
trunk_data_by_states(vid, labels)