def test_conj_solve(self):
x,y = np.exp(1.), np.exp(2.+1j)
d,w = {'x*y_':x*y.conjugate(), 'x':x}, {}
for k in d: w[k] = 1.
ls = linsolve.LogProductSolver(d,w,sparse=self.sparse)
sol = ls.solve()
for k in sol:
self.assertAlmostEqual(sol[k], eval(k))
def test_solve(self):
x,y,z = np.exp(1.), np.exp(2.), np.exp(3.)
keys = ['x*y*z', 'x*y', 'y*z']
d,w = {}, {}
for k in keys: d[k],w[k] = eval(k), 1.
ls = linsolve.LogProductSolver(d,w,sparse=self.sparse)
sol = ls.solve()
for k in sol:
self.assertAlmostEqual(sol[k], eval(k))
def test_solve(self):
x, y, z = np.exp(1. + 1j), np.exp(2. + 2j), np.exp(3. + 3j)
keys = ['x*y*z', 'x*y', 'y*z']
d, w = {}, {}
for k in keys:
d[k], w[k] = eval(k), 1.
ls = linsolve.LogProductSolver(d, w, sparse=self.sparse)
sol = ls.solve()
for k in sol:
np.testing.assert_almost_equal(sol[k], eval(k))
def test_init(self):
x,y,z = np.exp(1.), np.exp(2.), np.exp(3.)
keys = ['x*y*z', 'x*y', 'y*z']
d,w = {}, {}
for k in keys: d[k],w[k] = eval(k), 1.
ls = linsolve.LogProductSolver(d,w,sparse=self.sparse)
for k in ls.ls_phs.data:
np.testing.assert_equal(ls.ls_phs.data[k], 0)
x,y,z = 1.,2.,3.
for k in ls.ls_amp.data:
np.testing.assert_equal(eval(k), ls.ls_amp.data[k])
def test_dtype(self):
for dtype in (np.float32, np.float64, np.complex64, np.complex128):
x,y,z = np.exp(1.), np.exp(2.), np.exp(3.)
keys = ['x*y*z', 'x*y', 'y*z']
d,w = {}, {}
for k in keys:
d[k] = eval(k).astype(dtype)
w[k] = np.float32(1.)
ls = linsolve.LogProductSolver(d,w,sparse=self.sparse)
sol = ls.solve()
for k in sol:
self.assertEqual(sol[k].dtype, dtype)
def test_conj(self):
x,y = 1+1j, 2+2j
d,w = {}, {}
d['x*y_'] = x * y.conjugate()
d['x_*y'] = x.conjugate() * y
d['x*y'] = x * y
d['x_*y_'] = x.conjugate() * y.conjugate()
for k in d: w[k] = 1.
ls = linsolve.LogProductSolver(d,w,sparse=self.sparse)
self.assertEqual(len(ls.ls_amp.data), 4)
for k in ls.ls_amp.data:
self.assertEqual(eval(k), 3+3j) # make sure they are all x+y
self.assertTrue(k.replace('1','-1') in ls.ls_phs.data)
def test_no_abs_phs_solve(self):
x,y,z = 1.+1j, 2.+2j, 3.+3j
d,w = {'x*y_':x*y.conjugate(), 'x*z_':x*z.conjugate(), 'y*z_':y*z.conjugate()}, {}
for k in list(d.keys()): w[k] = 1.
ls = linsolve.LogProductSolver(d,w,sparse=self.sparse)
sol = ls.solve()
x,y,z = sol['x'], sol['y'], sol['z']
self.assertAlmostEqual(np.angle(x*y.conjugate()), 0.)
self.assertAlmostEqual(np.angle(x*z.conjugate()), 0.)
self.assertAlmostEqual(np.angle(y*z.conjugate()), 0.)
# check projection of degenerate mode
self.assertAlmostEqual(np.angle(x), 0.)
self.assertAlmostEqual(np.angle(y), 0.)
self.assertAlmostEqual(np.angle(z), 0.)
def test_conj(self):
x, y = 1 + 1j, 2 + 2j
d, w = {}, {}
d['x*y_'] = x * y.conjugate()
d['x_*y'] = x.conjugate() * y
d['x*y'] = x * y
d['x_*y_'] = x.conjugate() * y.conjugate()
for k in d:
w[k] = 1.
ls = linsolve.LogProductSolver(d, w, sparse=self.sparse)
assert len(ls.ls_amp.data) == 4
for k in ls.ls_amp.data:
assert eval(k) == 3 + 3j # make sure they are all x+y
assert k.replace('1', '-1') in ls.ls_phs.data
def test_dtype(self):
for dtype in (np.float32, np.float64, np.complex64, np.complex128):
x, y, z = np.exp(1.), np.exp(2.), np.exp(3.)
keys = ['x*y*z', 'x*y', 'y*z']
d, w = {}, {}
for k in keys:
d[k] = eval(k).astype(dtype)
w[k] = np.float32(1.)
ls = linsolve.LogProductSolver(d, w, sparse=self.sparse)
# some ridiculousness to avoid "The exact solution is x = 0" prints
save_stdout = sys.stdout
sys.stdout = io.StringIO()
sol = ls.solve()
sys.stdout = save_stdout
for k in sol:
assert sol[k].dtype == dtype
def test_no_abs_phs_solve(self):
x, y, z = 1. + 1j, 2. + 2j, 3. + 3j
d, w = {
'x*y_': x * y.conjugate(),
'x*z_': x * z.conjugate(),
'y*z_': y * z.conjugate()
}, {}
for k in list(d.keys()):
w[k] = 1.
ls = linsolve.LogProductSolver(d, w, sparse=self.sparse)
# some ridiculousness to avoid "The exact solution is x = 0" prints
save_stdout = sys.stdout
sys.stdout = io.StringIO()
sol = ls.solve()
sys.stdout = save_stdout
x, y, z = sol['x'], sol['y'], sol['z']
np.testing.assert_almost_equal(np.angle(x * y.conjugate()), 0.)
np.testing.assert_almost_equal(np.angle(x * z.conjugate()), 0.)
np.testing.assert_almost_equal(np.angle(y * z.conjugate()), 0.)
# check projection of degenerate mode
np.testing.assert_almost_equal(np.angle(x), 0.)
np.testing.assert_almost_equal(np.angle(y), 0.)
np.testing.assert_almost_equal(np.angle(z), 0.)