def test_get_variables(self):
client_run('A = 3; B = 4; C = 5;')
data = client_get([ 'A','B','C' ])
self.assertEqual(data['A'],3)
self.assertEqual(data['B'],4)
self.assertEqual(data['C'],5)
def test_put_and_get_variables(self):
'''Assign matrix on remote and then retrieve.'''
pyArr = np.random.random((2, 2))
client_put({'pyArr': pyArr})
client_run('matArr = pyArr')
matArr = client_get(['matArr'])['matArr']
self.assertTrue(np.allclose(pyArr, matArr))
def test_get_variables(self):
'''Assign variables on remote and then retrieve them.'''
client_run('A = 3; B = 4; C = 5;')
data = client_get(['A', 'B', 'C'])
self.assertEqual(data['A'], 3)
self.assertEqual(data['B'], 4)
self.assertEqual(data['C'], 5)
def run_ganesh_tcr(kspace,
mask,
weight_fidelity,
weight_temporal,
beta_sqrd,
noi,
reordering=None):
# start with a clean slate
client_run('clear')
if reordering is not None:
client_put({'idx_real': reordering.real, 'idx_imag': reordering.imag})
client_run("save('reordering.mat','idx_real','idx_imag')")
client_run('use_reorder = true;')
else:
client_run('use_reorder = false;')
client_run('pwd')
client_run('cd mr_utils/recon/reordering/temporal_tv')
client_put({
'Coil': kspace,
'mask_k_space_sparse': mask,
'weight_fidelity': weight_fidelity,
'weight_temporal': weight_temporal,
'beta_sqrd': beta_sqrd,
'noi': noi
})
# client.run('load Coil6.mat; load mask_k_space_sparse.mat')
client_run('reduced_k_space = Coil.*mask_k_space_sparse;')
client_run('prior = generate_prior(reduced_k_space);')
# client.run('recon_data = zeros(size(prior));')
client_run(
'recon_data = recon_tcr_reorder(prior,reduced_k_space,mask_k_space_sparse,noi,weight_fidelity,weight_temporal,beta_sqrd,use_reorder);'
)
data = client_get(['Coil', 'mask_k_space_sparse', 'recon_data'])
return (data)
def test_put_and_get_variables(self):
pyArr = np.random.random((2,2))
client_put({'pyArr':pyArr})
client_run('matArr = pyArr')
matArr = client_get([ 'matArr' ])['matArr']
self.assertTrue(np.allclose(pyArr,matArr))