def _steadystate_LU_liouvillian(L, ss_args, has_mkl=0):
"""Creates modified Liouvillian for LU based SS methods.
"""
perm = None
perm2 = None
rev_perm = None
n = int(np.sqrt(L.shape[0]))
form = 'csr'
if has_mkl:
L = L.data + sp.csr_matrix(
(ss_args['weight']*np.ones(n), (np.zeros(n), [nn * (n + 1)
for nn in range(n)])),
shape=(n ** 2, n ** 2))
else:
form = 'csc'
L = L.data.tocsc() + sp.csc_matrix(
(ss_args['weight']*np.ones(n), (np.zeros(n), [nn * (n + 1)
for nn in range(n)])),
shape=(n ** 2, n ** 2))
if settings.debug:
old_band = sp_bandwidth(L)[0]
old_pro = sp_profile(L)[0]
logger.debug('Orig. NNZ: %i' % L.nnz)
if ss_args['use_rcm']:
logger.debug('Original bandwidth: %i' % old_band)
if ss_args['use_wbm']:
if settings.debug:
logger.debug('Calculating Weighted Bipartite Matching ordering...')
_wbm_start = time.time()
perm = weighted_bipartite_matching(L)
_wbm_end = time.time()
L = sp_permute(L, perm, [], form)
ss_args['info']['perm'].append('wbm')
ss_args['info']['wbm_time'] = _wbm_end-_wbm_start
if settings.debug:
wbm_band = sp_bandwidth(L)[0]
logger.debug('WBM bandwidth: %i' % wbm_band)
if ss_args['use_rcm']:
if settings.debug:
logger.debug('Calculating Reverse Cuthill-Mckee ordering...')
_rcm_start = time.time()
perm2 = reverse_cuthill_mckee(L)
_rcm_end = time.time()
rev_perm = np.argsort(perm2)
L = sp_permute(L, perm2, perm2, form)
ss_args['info']['perm'].append('rcm')
ss_args['info']['rcm_time'] = _rcm_end-_rcm_start
if settings.debug:
rcm_band = sp_bandwidth(L)[0]
rcm_pro = sp_profile(L)[0]
logger.debug('RCM bandwidth: %i' % rcm_band)
logger.debug('Bandwidth reduction factor: %f' %
(old_band/rcm_band))
logger.debug('Profile reduction factor: %f' %
(old_pro/rcm_pro))
L.sort_indices()
return L, perm, perm2, rev_perm, ss_args
def _steadystate_power_liouvillian(L, ss_args, has_mkl=0):
"""Creates modified Liouvillian for power based SS methods.
"""
perm = None
perm2 = None
rev_perm = None
n = L.shape[0]
if ss_args['solver'] == 'mkl':
L = L.data - (1e-15) * sp.eye(n, n, format='csr')
kind = 'csr'
else:
L = L.data.tocsc() - (1e-15) * sp.eye(n, n, format='csc')
kind = 'csc'
if settings.debug:
old_band = sp_bandwidth(L)[0]
old_pro = sp_profile(L)[0]
logger.debug('Original bandwidth: %i' % old_band)
logger.debug('Original profile: %i' % old_pro)
if ss_args['use_wbm']:
if settings.debug:
logger.debug('Calculating Weighted Bipartite Matching ordering...')
_wbm_start = time.time()
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
"qutip graph functions are deprecated",
DeprecationWarning,
)
perm = weighted_bipartite_matching(L)
_wbm_end = time.time()
L = sp_permute(L, perm, [], kind)
ss_args['info']['perm'].append('wbm')
ss_args['info']['wbm_time'] = _wbm_end - _wbm_start
if settings.debug:
wbm_band = sp_bandwidth(L)[0]
wbm_pro = sp_profile(L)[0]
logger.debug('WBM bandwidth: %i' % wbm_band)
logger.debug('WBM profile: %i' % wbm_pro)
if ss_args['use_rcm']:
if settings.debug:
logger.debug('Calculating Reverse Cuthill-Mckee ordering...')
ss_args['info']['perm'].append('rcm')
_rcm_start = time.time()
perm2 = sp.csgraph.reverse_cuthill_mckee(L)
_rcm_end = time.time()
ss_args['info']['rcm_time'] = _rcm_end - _rcm_start
rev_perm = np.argsort(perm2)
L = sp_permute(L, perm2, perm2, kind)
if settings.debug:
new_band = sp_bandwidth(L)[0]
new_pro = sp_profile(L)[0]
logger.debug('RCM bandwidth: %i' % new_band)
logger.debug('Bandwidth reduction factor: %f' %
(old_band / new_band))
logger.debug('RCM profile: %i' % new_pro)
logger.debug('Profile reduction factor: %f' % (old_pro / new_pro))
L.sort_indices()
return L, perm, perm2, rev_perm, ss_args
def _steadystate_power_liouvillian(L, ss_args, has_mkl=0):
"""Creates modified Liouvillian for power based SS methods.
"""
perm = None
perm2 = None
rev_perm = None
n = L.shape[0]
if ss_args['solver'] == 'mkl':
L = L.data - (1e-15) * sp.eye(n, n, format='csr')
kind = 'csr'
else:
L = L.data.tocsc() - (1e-15) * sp.eye(n, n, format='csc')
kind = 'csc'
orig_nnz = L.nnz
if settings.debug:
old_band = sp_bandwidth(L)[0]
old_pro = sp_profile(L)[0]
logger.debug('Original bandwidth: %i' % old_band)
logger.debug('Original profile: %i' % old_pro)
if ss_args['use_wbm']:
if settings.debug:
logger.debug('Calculating Weighted Bipartite Matching ordering...')
_wbm_start = time.time()
perm = weighted_bipartite_matching(L)
_wbm_end = time.time()
L = sp_permute(L, perm, [], kind)
ss_args['info']['perm'].append('wbm')
ss_args['info']['wbm_time'] = _wbm_end-_wbm_start
if settings.debug:
wbm_band = sp_bandwidth(L)[0]
wbm_pro = sp_profile(L)[0]
logger.debug('WBM bandwidth: %i' % wbm_band)
logger.debug('WBM profile: %i' % wbm_pro)
if ss_args['use_rcm']:
if settings.debug:
logger.debug('Calculating Reverse Cuthill-Mckee ordering...')
ss_args['info']['perm'].append('rcm')
_rcm_start = time.time()
perm2 = reverse_cuthill_mckee(L)
_rcm_end = time.time()
ss_args['info']['rcm_time'] = _rcm_end-_rcm_start
rev_perm = np.argsort(perm2)
L = sp_permute(L, perm2, perm2, kind)
if settings.debug:
new_band = sp_bandwidth(L)[0]
new_pro = sp_profile(L)[0]
logger.debug('RCM bandwidth: %i' % new_band)
logger.debug('Bandwidth reduction factor: %f'
% (old_band/new_band))
logger.debug('RCM profile: %i' % new_pro)
logger.debug('Profile reduction factor: %f'
% (old_pro/new_pro))
L.sort_indices()
return L, perm, perm2, rev_perm, ss_args
def _steadystate_power_liouvillian(L, ss_args):
"""Creates modified Liouvillian for power based SS methods.
"""
perm = None
perm2 = None
rev_perm = None
n = L.shape[0]
L = L.data.tocsc() - (1e-15) * sp.eye(n, n, format='csc')
orig_nnz = L.nnz
if settings.debug:
old_band = sp_bandwidth(L)[0]
old_pro = sp_profile(L)[0]
logger.debug('Original bandwidth: %i' % old_band)
logger.debug('Original profile: %i' % old_pro)
if ss_args['use_wbm']:
if settings.debug:
logger.debug('Calculating Weighted Bipartite Matching ordering...')
_wbm_start = time.time()
perm = weighted_bipartite_matching(L)
_wbm_end = time.time()
L = sp_permute(L, perm, [], 'csc')
ss_args['info']['perm'].append('wbm')
ss_args['info']['wbm_time'] = _wbm_end - _wbm_start
if settings.debug:
wbm_band = sp_bandwidth(L)[0]
wbm_pro = sp_profile(L)[0]
logger.debug('WBM bandwidth: %i' % wbm_band)
logger.debug('WBM profile: %i' % wbm_pro)
if ss_args['use_rcm']:
if settings.debug:
logger.debug('Calculating Reverse Cuthill-Mckee ordering...')
ss_args['info']['perm'].append('rcm')
_rcm_start = time.time()
perm2 = reverse_cuthill_mckee(L)
_rcm_end = time.time()
ss_args['info']['rcm_time'] = _rcm_end - _rcm_start
rev_perm = np.argsort(perm2)
L = sp_permute(L, perm2, perm2, 'csc')
if settings.debug:
new_band = sp_bandwidth(L)[0]
new_pro = sp_profile(L)[0]
logger.debug('RCM bandwidth: %i' % new_band)
logger.debug('Bandwidth reduction factor: %f' %
(old_band / new_band))
logger.debug('RCM profile: %i' % new_pro)
logger.debug('Profile reduction factor: %f' % (old_pro / new_pro))
L.sort_indices()
return L, perm, perm2, rev_perm, ss_args
def test_sp_profile():
"Sparse: Profile"
for kk in range(10):
A = sp.rand(1000, 1000, 0.1, format='csr')
pro = sp_profile(A)
B = A.toarray()
ans = _dense_profile(B)
assert_equal(pro, ans)
for kk in range(10):
A = sp.rand(1000, 1000, 0.1, format='csc')
pro = sp_profile(A)
B = A.toarray()
ans = _dense_profile(B)
assert_equal(pro, ans)
def test_sp_profile():
"Sparse: Profile"
for kk in range(10):
A = sp.rand(1000, 1000, 0.1, format='csr')
pro = sp_profile(A)
B = A.toarray()
ans = _dense_profile(B)
assert_equal(pro, ans)
for kk in range(10):
A = sp.rand(1000, 1000, 0.1, format='csc')
pro = sp_profile(A)
B = A.toarray()
ans = _dense_profile(B)
assert_equal(pro, ans)
