def test_10(self):
dsz = ((8, 8, 16), (12, 12, 32))
u = np.zeros((24, 24, 48))
u[0:8, 0:8, 0:16] = 1.0
u[0:12, 0:12, 16:] = 1.0
v = cnvrep.zeromean(u, dsz)
assert np.sum(np.abs(v)) == 0.0
def test_13(self):
N = 64
M = 4
Nd = 8
D0 = cr.normalise(cr.zeromean(
np.random.randn(Nd, Nd, M), (Nd, Nd, M), dimN=2), dimN=2)
X = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X[xp] = np.random.randn(X[xp].size)
S = np.sum(sl.ifftn(sl.fftn(D0, (N, N), (0, 1)) *
sl.fftn(X, None, (0, 1)), None, (0, 1)).real,
axis=2)
L = 0.5
opt = ccmod.ConvCnstrMOD.Options(
{'Verbose': False, 'MaxMainIter': 3000, 'ZeroMean': True,
'RelStopTol': 0., 'L': L, 'BackTrack': {'Enabled': True}})
Xr = X.reshape(X.shape[0:2] + (1, 1,) + X.shape[2:])
Sr = S.reshape(S.shape + (1,))
c = ccmod.ConvCnstrMOD(Xr, Sr, D0.shape, opt)
c.solve()
D1 = cr.bcrop(c.X, D0.shape).squeeze()
assert sl.rrs(D0, D1) < 1e-4
assert np.array(c.getitstat().Rsdl)[-1] < 1e-5
def test_18(self):
N = 64
M = 4
Nd = 8
D0 = cr.normalise(cr.zeromean(np.random.randn(Nd, Nd, M), (Nd, Nd, M),
dimN=2),
dimN=2)
X = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X[xp] = np.random.randn(X[xp].size)
S = np.sum(ifftn(
fftn(D0, (N, N), (0, 1)) * fftn(X, None, (0, 1)), None,
(0, 1)).real,
axis=2)
L = 50.0
opt = ccmod.ConvCnstrMOD.Options({
'Verbose': False,
'MaxMainIter': 3000,
'ZeroMean': True,
'RelStopTol': 0.,
'L': L,
'Monotone': True
})
Xr = X.reshape(X.shape[0:2] + (
1,
1,
) + X.shape[2:])
Sr = S.reshape(S.shape + (1, ))
c = ccmod.ConvCnstrMOD(Xr, Sr, D0.shape, opt)
c.solve()
D1 = cr.bcrop(c.X, D0.shape).squeeze()
assert rrs(D0, D1) < 1e-4
assert np.array(c.getitstat().Rsdl)[-1] < 1e-5
def test_10(self):
dsz = ((8, 8, 16), (12, 12, 32))
u = np.zeros((24, 24, 48))
u[0:8, 0:8, 0:16] = 1.0
u[0:12, 0:12, 16:] = 1.0
v = cnvrep.zeromean(u, dsz)
assert np.sum(np.abs(v)) == 0.0
def test_02(self):
N = 32
M = 4
Nd = 5
D0 = cr.normalise(cr.zeromean(
np.random.randn(Nd, Nd, M), (Nd, Nd, M), dimN=2), dimN=2)
X = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X[xp] = np.random.randn(X[xp].size)
S = np.sum(sl.ifftn(sl.fftn(D0, (N, N), (0,1)) *
sl.fftn(X, None, (0,1)), None, (0,1)).real, axis=2)
rho = 1e-1
opt = ccmod.ConvCnstrMOD_CG.Options({'Verbose': False,
'MaxMainIter': 500, 'LinSolveCheck': True,
'ZeroMean': True, 'RelStopTol': 1e-5, 'rho': rho,
'AutoRho': {'Enabled': False},
'CG': {'StopTol': 1e-5}})
Xr = X.reshape(X.shape[0:2] + (1,1,) + X.shape[2:])
Sr = S.reshape(S.shape + (1,))
c = ccmod.ConvCnstrMOD_CG(Xr, Sr, D0.shape, opt)
c.solve()
D1 = cr.bcrop(c.Y, D0.shape).squeeze()
assert(sl.rrs(D0, D1) < 1e-4)
assert(np.array(c.getitstat().XSlvRelRes).max() < 1e-3)
def test_02(self):
N = 32
M = 4
Nd = 5
D0 = cr.normalise(cr.zeromean(
np.random.randn(Nd, Nd, M), (Nd, Nd, M), dimN=2), dimN=2)
X = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X[xp] = np.random.randn(X[xp].size)
S = np.sum(sl.ifftn(sl.fftn(D0, (N, N), (0, 1)) *
sl.fftn(X, None, (0, 1)), None, (0, 1)).real, axis=2)
rho = 1e-1
opt = ccmod.ConvCnstrMOD_CG.Options({'Verbose': False,
'MaxMainIter': 500, 'LinSolveCheck': True,
'ZeroMean': True, 'RelStopTol': 1e-5, 'rho': rho,
'AutoRho': {'Enabled': False},
'CG': {'StopTol': 1e-5}})
Xr = X.reshape(X.shape[0:2] + (1, 1,) + X.shape[2:])
Sr = S.reshape(S.shape + (1,))
c = ccmod.ConvCnstrMOD_CG(Xr, Sr, D0.shape, opt)
c.solve()
D1 = cr.bcrop(c.Y, D0.shape).squeeze()
assert sl.rrs(D0, D1) < 1e-4
assert np.array(c.getitstat().XSlvRelRes).max() < 1e-3
def test_11(self):
dsz = (((5, 5, 2, 8), (7, 7, 1, 8)), ((9, 9, 2, 16), (10, 10, 1, 16)))
u = np.zeros((16, 16, 3, 24))
u[0:5, 0:5, 0:2, 0:8] = 1.0
u[0:7, 0:7, 2, 0:8] = 1.0
u[0:9, 0:9, 0:2, 8:] = 1.0
u[0:10, 0:10, 2, 8:] = 1.0
v = cnvrep.zeromean(u, dsz)
assert np.sum(np.abs(v)) == 0.0
def test_11(self):
dsz = (((5, 5, 2, 8), (7, 7, 1, 8)),
((9, 9, 2, 16), (10, 10, 1, 16)))
u = np.zeros((16, 16, 3, 24))
u[0:5, 0:5, 0:2, 0:8] = 1.0
u[0:7, 0:7, 2, 0:8] = 1.0
u[0:9, 0:9, 0:2, 8:] = 1.0
u[0:10, 0:10, 2, 8:] = 1.0
v = cnvrep.zeromean(u, dsz)
assert np.sum(np.abs(v)) == 0.0
def setup_method(self, method):
np.random.seed(12345)
N = 32
M = 4
Nd = 5
self.D0 = cr.normalise(cr.zeromean(np.random.randn(Nd, Nd, M),
(Nd, Nd, M),
dimN=2),
dimN=2)
self.X = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
self.X[xp] = np.random.randn(self.X[xp].size)
self.S = np.sum(fftconv(self.X, self.D0, axes=(0, 1)).real, axis=2)
d0c = np.random.randn(Nd, Nd, M) + 1j * np.random.randn(Nd, Nd, M)
self.D0c = cr.normalise(cr.zeromean(d0c, (Nd, Nd, M), dimN=2), dimN=2)
self.Xc = np.zeros((N, N, M)) + 1j * np.zeros((N, N, M))
self.Xc[xp] = (np.random.randn(self.Xc[xp].size) +
1j * np.random.randn(self.Xc[xp].size))
self.Sc = np.sum(fftconv(self.Xc, self.D0c, axes=(0, 1)), axis=2)
def test_02(self):
N = 32
M = 4
Nd = 5
D0 = cr.normalise(cr.zeromean(
np.random.randn(Nd, Nd, M), (Nd, Nd, M), dimN=2), dimN=2)
X = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X[xp] = np.random.randn(X[xp].size)
S = np.sum(sl.ifftn(sl.fftn(D0, (N, N), (0,1)) *
sl.fftn(X, None, (0,1)), None, (0,1)).real, axis=2)
L = 1e1
opt = ccmod.ConvCnstrMOD.Options({'Verbose': False,
'MaxMainIter': 3000,
'ZeroMean': True, 'RelStopTol': 1e-6, 'L': L,
'BackTrack': {'Enabled': True}})
Xr = X.reshape(X.shape[0:2] + (1,1,) + X.shape[2:])
Sr = S.reshape(S.shape + (1,))
c = ccmod.ConvCnstrMOD(Xr, Sr, D0.shape, opt)
c.solve()
D1 = cr.bcrop(c.X, D0.shape).squeeze()
assert(sl.rrs(D0, D1) < 1e-4)
assert(np.array(c.getitstat().Rsdl)[-1] < 1e-5)
def test_09(self):
dsz = (8, 8, 32)
u = np.zeros((16, 16, 32))
u[0:8, 0:8, 0:16] = 1.0
v = cnvrep.zeromean(u, dsz)
assert np.sum(np.abs(v)) == 0.0
def test_09(self):
dsz = (8, 8, 32)
u = np.zeros((16, 16, 32))
u[0:8, 0:8, 0:16] = 1.0
v = cnvrep.zeromean(u, dsz)
assert np.sum(np.abs(v)) == 0.0
