def test_compare_outputs():
"""Compare the python output with the matlab output.
They should be identical."""
channel_mat = 'matlab/codes/channel/channel.mat'
mat_dict = scipy.io.loadmat(channel_mat)
UstarX1 = mat_dict['UstarX1']
S = mat_dict['S']
V = mat_dict['V']
# Sparsity-promoting parameter gamma
# Lower and upper bounds relevant for this flow type
gamma_grd = 20
gammaval = np.logspace(np.log10(0.15), np.log10(160), gamma_grd)
Fdmd, Edmd, Ydmd, xdmd, py_answer = sparse_dmd.run_dmdsp(UstarX1,
S,
V,
gammaval)
# convert class attributes to dict
py_answer = py_answer.__dict__
## matlab output created by running matlab:
## [Fdmd, Edmd, Ydmd, xdmd, answer] = run_dmdsp;
## output = struct('Fdmd', Fdmd, 'Edmd', Edmd, 'Ydmd', Ydmd, 'xdmd', xdmd);
## save('tests/answer.mat', '-struct', 'answer')
## save('tests/output.mat', '-struct', 'output')
## using channel=1 and 20 gridpoints for gamma
# using two different .mat because scipy doesn't seem to like
# nested structs
mat_answer = scipy.io.loadmat('tests/answer.mat')
mat_output = scipy.io.loadmat('tests/output.mat')
for k in py_answer:
nt.assert_array_almost_equal(py_answer[k].squeeze(),
mat_answer[k].squeeze(),
decimal=5)
py_output = {'Fdmd': Fdmd, 'Edmd': Edmd, 'Ydmd': Ydmd, 'xdmd': xdmd}
for k in py_output:
nt.assert_array_almost_equal(py_output[k].squeeze(),
mat_output[k].squeeze(),
decimal=5)
def test_compare_outputs():
"""Compare the python output with the matlab output.
They should be identical."""
channel_mat = 'matlab/codes/channel/channel.mat'
mat_dict = scipy.io.loadmat(channel_mat)
UstarX1 = mat_dict['UstarX1']
S = mat_dict['S']
V = mat_dict['V']
# Sparsity-promoting parameter gamma
# Lower and upper bounds relevant for this flow type
gamma_grd = 20
gammaval = np.logspace(np.log10(0.15), np.log10(160), gamma_grd)
Fdmd, Edmd, Ydmd, xdmd, py_answer = sparse_dmd.run_dmdsp(
UstarX1, S, V, gammaval)
# convert class attributes to dict
py_answer = py_answer.__dict__
## matlab output created by running matlab:
## [Fdmd, Edmd, Ydmd, xdmd, answer] = run_dmdsp;
## output = struct('Fdmd', Fdmd, 'Edmd', Edmd, 'Ydmd', Ydmd, 'xdmd', xdmd);
## save('tests/answer.mat', '-struct', 'answer')
## save('tests/output.mat', '-struct', 'output')
## using channel=1 and 20 gridpoints for gamma
# using two different .mat because scipy doesn't seem to like
# nested structs
mat_answer = scipy.io.loadmat('tests/answer.mat')
mat_output = scipy.io.loadmat('tests/output.mat')
for k in py_answer:
nt.assert_array_almost_equal(py_answer[k].squeeze(),
mat_answer[k].squeeze(),
decimal=5)
py_output = {'Fdmd': Fdmd, 'Edmd': Edmd, 'Ydmd': Ydmd, 'xdmd': xdmd}
for k in py_output:
nt.assert_array_almost_equal(py_output[k].squeeze(),
mat_output[k].squeeze(),
decimal=5)
def create_answer(n=200):
answer = sparse_dmd.run_dmdsp(gamma_grd=n)
scipy.io.savemat('tests/answer.mat', answer)
def create_answer(n=200):
answer = sparse_dmd.run_dmdsp(gamma_grd=n)
scipy.io.savemat('tests/answer.mat', answer)
import time import numpy as np import scipy.io from sparse_dmd import run_dmdsp channel_mat = 'matlab/codes/channel/channel.mat' mat_dict = scipy.io.loadmat(channel_mat) UstarX1 = mat_dict['UstarX1'] S = mat_dict['S'] V = mat_dict['V'] # Sparsity-promoting parameter gamma # Lower and upper bounds relevant for this flow type gamma_grd = 200 gammaval = np.logspace(np.log10(0.15), np.log10(160), gamma_grd) tic = time.time() Fdmd, Edmd, Ydmd, xdmd, py_answer = run_dmdsp(UstarX1, S, V, gammaval) toc = time.time() print "time elapsed: ", toc - tic