From 3a2fdf48008537355ec381cbd77bf08e6971b8df Mon Sep 17 00:00:00 2001
From: Christoph Groth <christoph.groth@cea.fr>
Date: Mon, 17 Sep 2012 15:55:20 +0200
Subject: [PATCH] replace np.matrix with np.array in tests
np.matrix is not only slow, but it is an ill-behaved sequence:
m[0].ndim == m.ndim == 2
This causes problems when converting a numpy matrix to a tinyarray.
---
kwant/solvers/tests/test_sparse.py | 54 +++++++++++++++---------------
kwant/tests/test_system.py | 18 +++++-----
2 files changed, 36 insertions(+), 36 deletions(-)
diff --git a/kwant/solvers/tests/test_sparse.py b/kwant/solvers/tests/test_sparse.py
index 174538e2..586fafac 100644
--- a/kwant/solvers/tests/test_sparse.py
+++ b/kwant/solvers/tests/test_sparse.py
@@ -22,8 +22,8 @@ def test_output():
right_lead = kwant.Builder(kwant.TranslationalSymmetry([(1,)]))
for b, site in [(system, chain(0)), (system, chain(1)),
(left_lead, chain(0)), (right_lead, chain(0))]:
- h = np.asmatrix(np.random.rand(n, n) + 1j * np.random.rand(n, n))
- h += h.H
+ h = np.random.rand(n, n) + 1j * np.random.rand(n, n)
+ h += h.conjugate().transpose()
b[site] = h
for b, hopp in [(system, (chain(0), chain(1))),
(left_lead, (chain(0), chain(1))),
@@ -36,11 +36,11 @@ def test_output():
result1 = solve(fsys)
s, modes1 = result1
assert s.shape == 2 * (sum(i[2] for i in modes1),)
- s1 = np.asmatrix(result1.submatrix(1, 0))
+ s1 = result1.submatrix(1, 0)
s2, modes2 = solve(fsys, 0, [1], [0])
assert s2.shape == (modes2[1][2], modes2[0][2])
assert_almost_equal(s1, s2)
- assert_almost_equal(s.H * s, np.identity(s.shape[0]))
+ assert_almost_equal(s.conjugate().transpose() * s, np.identity(s.shape[0]))
assert_raises(ValueError, solve, fsys, 0, [])
modes = solve(fsys)[1]
h = fsys.leads[0].slice_hamiltonian()
@@ -60,8 +60,8 @@ def test_one_lead():
lead = kwant.Builder(kwant.TranslationalSymmetry([(-1,)]))
for b, site in [(system, chain(0)), (system, chain(1)), (system, chain(2)),
(lead, chain(0))]:
- h = np.asmatrix(np.random.rand(n, n) + 1j * np.random.rand(n, n))
- h += h.H
+ h = np.random.rand(n, n) + 1j * np.random.rand(n, n)
+ h += h.conjugate().transpose()
b[site] = h
for b, hopp in [(system, (chain(0), chain(1))),
(system, (chain(1), chain(2))),
@@ -70,8 +70,8 @@ def test_one_lead():
system.attach_lead(lead)
fsys = system.finalized()
- s = np.asmatrix(solve(fsys)[0])
- assert_almost_equal(s.H * s, np.identity(s.shape[0]))
+ s = solve(fsys)[0]
+ assert_almost_equal(s.conjugate().transpose() * s, np.identity(s.shape[0]))
# Test that a translationally invariant system with two leads has only
@@ -79,7 +79,7 @@ def test_one_lead():
def test_two_equal_leads():
def check_fsys():
s, leads = solve(fsys)[: 2]
- assert_almost_equal(s.H * s, np.identity(s.shape[0]))
+ assert_almost_equal(s.conjugate().transpose() * s, np.identity(s.shape[0]))
n_modes = leads[0][2]
assert leads[1][2] == n_modes
assert_almost_equal(s[: n_modes, : n_modes], 0)
@@ -91,10 +91,10 @@ def test_two_equal_leads():
np.random.seed(11)
system = kwant.Builder()
lead = kwant.Builder(kwant.TranslationalSymmetry([(1,)]))
- h = np.asmatrix(np.random.rand(n, n) + 1j * np.random.rand(n, n))
- h += h.H
+ h = np.random.rand(n, n) + 1j * np.random.rand(n, n)
+ h += h.conjugate().transpose()
h *= 0.8
- t = 4 * np.asmatrix(np.random.rand(n, n) + 4j * np.random.rand(n, n))
+ t = 4 * np.random.rand(n, n) + 4j * np.random.rand(n, n)
lead[chain(0)] = system[chain(0)] = h
lead[chain(0), chain(1)] = t
system.attach_lead(lead)
@@ -119,11 +119,11 @@ def test_graph_system():
lead = kwant.Builder(kwant.TranslationalSymmetry([(-1, 0)]))
lead.default_site_group = system.default_site_group = square
- h = np.asmatrix(np.random.rand(n, n) + 1j * np.random.rand(n, n))
- h += h.H
+ h = np.random.rand(n, n) + 1j * np.random.rand(n, n)
+ h += h.conjugate().transpose()
h *= 0.8
- t = 4 * np.asmatrix(np.random.rand(n, n) + 4j * np.random.rand(n, n))
- t1 = 4 * np.asmatrix(np.random.rand(n, n) + 4j * np.random.rand(n, n))
+ t = 4 * np.random.rand(n, n) + 4j * np.random.rand(n, n)
+ t1 = 4 * np.random.rand(n, n) + 4j * np.random.rand(n, n)
lead[0, 0] = system[[(0, 0), (1, 0)]] = h
lead[0, 1] = system[[(0, 1), (1, 1)]] = 4 * h
for builder in [system, lead]:
@@ -135,7 +135,7 @@ def test_graph_system():
fsys = system.finalized()
s, leads = solve(fsys)[: 2]
- assert_almost_equal(s.H * s, np.identity(s.shape[0]))
+ assert_almost_equal(s.conjugate().transpose() * s, np.identity(s.shape[0]))
n_modes = leads[0][2]
assert_equal(leads[1][2], n_modes)
assert_almost_equal(s[: n_modes, : n_modes], 0)
@@ -152,11 +152,11 @@ def test_singular_graph_system():
system = kwant.Builder()
lead = kwant.Builder(kwant.TranslationalSymmetry([(-1, 0)]))
lead.default_site_group = system.default_site_group = square
- h = np.asmatrix(np.random.rand(n, n) + 1j * np.random.rand(n, n))
- h += h.H
+ h = np.random.rand(n, n) + 1j * np.random.rand(n, n)
+ h += h.conjugate().transpose()
h *= 0.8
- t = 4 * np.asmatrix(np.random.rand(n, n) + 4j * np.random.rand(n, n))
- t1 = 4 * np.asmatrix(np.random.rand(n, n) + 4j * np.random.rand(n, n))
+ t = 4 * np.random.rand(n, n) + 4j * np.random.rand(n, n)
+ t1 = 4 * np.random.rand(n, n) + 4j * np.random.rand(n, n)
lead[0, 0] = system[[(0, 0), (1, 0)]] = h
lead[0, 1] = system[[(0, 1), (1, 1)]] = 4 * h
for builder in [system, lead]:
@@ -167,7 +167,7 @@ def test_singular_graph_system():
fsys = system.finalized()
s, leads = solve(fsys)[: 2]
- assert_almost_equal(s.H * s, np.identity(s.shape[0]))
+ assert_almost_equal(s.conjugate().transpose() * s, np.identity(s.shape[0]))
n_modes = leads[0][2]
assert leads[1][2] == n_modes
assert_almost_equal(s[: n_modes, : n_modes], 0)
@@ -205,8 +205,8 @@ def test_tricky_singular_hopping():
system.leads.append(kwant.builder.BuilderLead(lead, neighbors))
fsys = system.finalized()
- s = np.asmatrix(solve(fsys, -1.3)[0])
- assert_almost_equal(s.H * s, np.identity(s.shape[0]))
+ s = solve(fsys, -1.3)[0]
+ assert_almost_equal(s.conjugate().transpose() * s, np.identity(s.shape[0]))
# Test equivalence between self-energy and scattering matrix representations.
@@ -226,8 +226,8 @@ def test_self_energy():
right_lead = kwant.Builder(kwant.TranslationalSymmetry([(1,)]))
for b, site in [(system, chain(0)), (system, chain(1)),
(left_lead, chain(0)), (right_lead, chain(0))]:
- h = np.asmatrix(np.random.rand(n, n) + 1j * np.random.rand(n, n))
- h += h.H
+ h = np.random.rand(n, n) + 1j * np.random.rand(n, n)
+ h += h.conjugate().transpose()
b[site] = h
for b, hopp in [(system, (chain(0), chain(1))),
(left_lead, (chain(0), chain(1))),
@@ -238,7 +238,7 @@ def test_self_energy():
fsys = system.finalized()
t = solve(fsys, 0, [1], [0]).data
- eig_should_be = np.linalg.eigvals(t * t.H)
+ eig_should_be = np.linalg.eigvals(t * t.conjugate().transpose())
n_eig = len(eig_should_be)
fsys.leads[1] = LeadWithOnlySelfEnergy(fsys.leads[1])
diff --git a/kwant/tests/test_system.py b/kwant/tests/test_system.py
index 893b4637..b2bb7fc4 100644
--- a/kwant/tests/test_system.py
+++ b/kwant/tests/test_system.py
@@ -16,7 +16,7 @@ def test_hamiltonian_submatrix():
assert mat.shape == (3, 3)
# Sorting is required due to unknown compression order of builder.
perm = np.argsort(sys2.onsite_hamiltonians)
- mat_should_be = np.mat('0 1j 0; -1j 0.5 2j; 0 -2j 1')
+ mat_should_be = np.array([[0, 1j, 0], [-1j, 0.5, 2j], [0, -2j, 1]])
mat = mat[perm, :]
mat = mat[:, perm]
@@ -30,27 +30,27 @@ def test_hamiltonian_submatrix():
np.testing.assert_array_equal(mat, mat_should_be)
mat = sys2.hamiltonian_submatrix(perm[[0, 1]], perm[[2]])[0]
- np.testing.assert_array_equal(mat, mat_should_be[: 2, 2])
+ np.testing.assert_array_equal(mat, mat_should_be[:2, 2:3])
mat = sys2.hamiltonian_submatrix(perm[[0, 1]], perm[[2]], sparse=True)[0]
mat = mat.todense()
- np.testing.assert_array_equal(mat, mat_should_be[: 2, 2])
+ np.testing.assert_array_equal(mat, mat_should_be[:2, 2:3])
# Test for correct treatment of matrix input.
sys = kwant.Builder()
sys.default_site_group = kwant.lattice.Chain()
- sys[(0,)] = np.mat('0 1j; -1j 0')
- sys[(1,)] = np.mat('1')
- sys[(2,)] = np.mat('2')
- sys[(1,), (0,)] = np.mat('1 2j')
- sys[(2,), (1,)] = np.mat('3j')
+ sys[(0,)] = np.array([[0, 1j], [-1j, 0]])
+ sys[(1,)] = np.array([[1]])
+ sys[(2,)] = np.array([[2]])
+ sys[(1,), (0,)] = np.array([[1, 2j]])
+ sys[(2,), (1,)] = np.array([[3j]])
sys2 = sys.finalized()
mat_dense = sys2.hamiltonian_submatrix()[0]
mat_sp = sys2.hamiltonian_submatrix(sparse=True)[0].todense()
np.testing.assert_array_equal(mat_sp, mat_dense)
# Test for shape errors.
- sys[(0,), (2,)] = np.mat('1 2')
+ sys[(0,), (2,)] = np.array([[1, 2]])
sys2 = sys.finalized()
assert_raises(ValueError, sys2.hamiltonian_submatrix)
assert_raises(ValueError, sys2.hamiltonian_submatrix, None, None, True)
--
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