diff --git a/kwant/solvers/sparse.py b/kwant/solvers/sparse.py
index 24d1af51b8d9ecefee1991ca45eb1105e507c9e4..edee2accee7a2577c9dcab2827444cb014bb1282 100644
--- a/kwant/solvers/sparse.py
+++ b/kwant/solvers/sparse.py
@@ -68,7 +68,7 @@ def factorized(A, piv_tol=1.0, sym_piv_tol=1.0):
return solve
-LinearSys = namedtuple('LinearSys', ['h_sys', 'rhs', 'keep_vars'])
+LinearSys = namedtuple('LinearSys', ['lhs', 'rhs', 'kept_vars'])
def make_linear_sys(sys, out_leads, in_leads, energy=0, force_realspace=False):
@@ -93,10 +93,10 @@ def make_linear_sys(sys, out_leads, in_leads, energy=0, force_realspace=False):
Returns
-------
- (h_sys, rhs, keep_vars) : LinearSys
- `h_sys` is a numpy.sparse.csc_matrix, containing the left hand side
+ (lhs, rhs, kept_vars) : LinearSys
+ `lhs` is a numpy.sparse.csc_matrix, containing the left hand side
of the system of equations, `rhs` is a list of matrices with the
- right hand side, `keep_vars` is a list of numbers of variables in the
+ right hand side, `kept_vars` is a list of numbers of variables in the
solution that have to be stored (typically a small part of the
complete solution).
lead_info : list of objects
@@ -114,12 +114,12 @@ def make_linear_sys(sys, out_leads, in_leads, energy=0, force_realspace=False):
"""
if not sys.lead_neighbor_seqs:
raise ValueError('System contains no leads.')
- h_sys, norb = sys.hamiltonian_submatrix(sparse=True)[:2]
- h_sys = h_sys.tocsc()
- h_sys = h_sys - energy * sp.identity(h_sys.shape[0], format='csc')
+ lhs, norb = sys.hamiltonian_submatrix(sparse=True)[:2]
+ lhs = lhs.tocsc()
+ lhs = lhs - energy * sp.identity(lhs.shape[0], format='csc')
# Hermiticity check.
- if np.any(np.abs((h_sys - h_sys.T.conj()).data) > 1e-13):
+ if np.any(np.abs((lhs - lhs.T.conj()).data) > 1e-13):
raise ValueError('System Hamiltonian is not Hermitian.')
offsets = np.zeros(norb.shape[0] + 1, int)
@@ -127,7 +127,7 @@ def make_linear_sys(sys, out_leads, in_leads, energy=0, force_realspace=False):
# Process the leads, generate the eigenvector matrices and lambda vectors.
# Then create blocks of the linear system and add them step by step.
- keep_vars = []
+ kept_vars = []
rhs = []
lead_info = []
for leadnum, lead_neighbors in enumerate(sys.lead_neighbor_seqs):
@@ -149,7 +149,7 @@ def make_linear_sys(sys, out_leads, in_leads, energy=0, force_realspace=False):
u, ulinv, nprop, svd = modes
if leadnum in out_leads:
- keep_vars.append(range(h_sys.shape[0], h_sys.shape[0] + nprop))
+ kept_vars.append(range(lhs.shape[0], lhs.shape[0] + nprop))
u_out, ulinv_out = u[:, nprop:], ulinv[:, nprop:]
u_in, ulinv_in = u[:, : nprop], ulinv[:, : nprop]
@@ -161,7 +161,7 @@ def make_linear_sys(sys, out_leads, in_leads, energy=0, force_realspace=False):
for i in lead_neighbors)]
coords = np.r_[[np.arange(neighbors.size)], [neighbors]]
tmp = sp.csc_matrix((np.ones(neighbors.size), coords),
- (neighbors.size, h_sys.shape[0]))
+ (neighbors.size, lhs.shape[0]))
if svd is not None:
v_sp = sp.csc_matrix(svd[2].T.conj()) * tmp
@@ -173,7 +173,7 @@ def make_linear_sys(sys, out_leads, in_leads, energy=0, force_realspace=False):
vdaguout_sp = tmp.T * sp.csr_matrix(np.dot(v.T.conj(), u_out))
lead_mat = - ulinv_out
- h_sys = sp.bmat([[h_sys, vdaguout_sp], [v_sp, lead_mat]])
+ lhs = sp.bmat([[lhs, vdaguout_sp], [v_sp, lead_mat]])
if nprop > 0 and leadnum in in_leads:
if svd:
@@ -194,19 +194,19 @@ def make_linear_sys(sys, out_leads, in_leads, energy=0, force_realspace=False):
assert sigma.shape == 2 * indices.shape
y, x = np.meshgrid(indices, indices)
sig_sparse = sp.coo_matrix((sigma.flat, [x.flat, y.flat]),
- h_sys.shape)
- h_sys = h_sys + sig_sparse # __iadd__ is not implemented in v0.7
+ lhs.shape)
+ lhs = lhs + sig_sparse # __iadd__ is not implemented in v0.7
if leadnum in out_leads:
- keep_vars.append(list(indices))
+ kept_vars.append(list(indices))
if leadnum in in_leads:
l = indices.shape[0]
rhs.append(sp.coo_matrix((-np.ones(l), [indices,
np.arange(l)])))
- return LinearSys(h_sys, rhs, keep_vars), lead_info
+ return LinearSys(lhs, rhs, kept_vars), lead_info
-def solve_linear_sys(a, b, keep_vars=None):
+def solve_linear_sys(a, b, kept_vars=None):
"""
Solve matrix system of equations a x = b with sparse input.
@@ -216,7 +216,7 @@ def solve_linear_sys(a, b, keep_vars=None):
b : a list of matrices.
Sizes of these matrices may be smaller than needed, the missing
entries at the end are padded with zeros.
- keep_vars : list of lists of integers
+ kept_vars : list of lists of integers
a list of numbers of variables to keep in the solution
Returns
@@ -230,10 +230,10 @@ def solve_linear_sys(a, b, keep_vars=None):
"""
a = sp.csc_matrix(a)
- if keep_vars == None:
- keep_vars = [range(a.shape[1])]
+ if kept_vars == None:
+ kept_vars = [range(a.shape[1])]
slv = factorized(a)
- keeptot = sum(keep_vars, [])
+ keeptot = sum(kept_vars, [])
sols = []
vec = np.empty(a.shape[0], complex)
for mat in b:
@@ -308,7 +308,7 @@ def solve(sys, energy=0, out_leads=None, in_leads=None, force_realspace=False):
raise ValueError('No output is requested.')
linsys, lead_info = make_linear_sys(sys, out_leads, in_leads, energy,
force_realspace)
- out_modes = [len(i) for i in linsys.keep_vars]
+ out_modes = [len(i) for i in linsys.kept_vars]
in_modes = [i.shape[1] for i in linsys.rhs]
result = BlockResult(solve_linear_sys(*linsys), lead_info)
result.in_leads = in_leads
@@ -407,7 +407,7 @@ def ldos(fsys, energy=0):
# TODO: fix this
msg = 'ldos only works when all leads are tight binding systems.'
raise ValueError(msg)
- (h, rhs, keep_vars), lead_info = \
+ (h, rhs, kept_vars), lead_info = \
make_linear_sys(fsys, [], xrange(len(fsys.leads)), energy)
Modes = physics.Modes
num_extra_vars = sum(li.vecs.shape[1] - li.nmodes