def test_wav_op():
wav = 'dirac'
levels = 3
shape = (128, )
op = linear_operators.db_wavelets(wav, levels, shape)
inp = np.random.normal(0, 10., shape)
out = op.dir_op(inp)
forward_operator(op, inp, out)
inp = np.random.normal(0, 10., out.shape)
out = op.adj_op(inp)
adjoint_operator(op, inp, out)
buff1 = op.adj_op(op.dir_op(out))
assert np.allclose(out, buff1, 1e-6)
wav = 'db1'
levels = 3
shape = (128, )
op = linear_operators.db_wavelets(wav, levels, shape)
inp = np.random.normal(0, 10., shape)
out = op.dir_op(inp)
forward_operator(op, inp, out)
inp = np.random.normal(0, 10., out.shape)
out = op.adj_op(inp)
adjoint_operator(op, inp, out)
buff1 = op.adj_op(op.dir_op(out))
assert np.allclose(out, buff1, 1e-6)
wav = ['db1', 'db2', 'dirac']
levels = 3
shape = (128, )
op = linear_operators.dictionary(wav, levels, shape)
inp = np.random.normal(0, 10., shape)
out = op.dir_op(inp)
forward_operator(op, inp, out)
inp = np.random.normal(0, 10., out.shape)
out = op.adj_op(inp)
adjoint_operator(op, inp, out)
buff1 = op.adj_op(op.dir_op(out))
assert np.allclose(out, buff1, 1e-6)
wav = 'db2'
levels = 3
shape = (128, 128)
op = linear_operators.db_wavelets(wav, levels, shape)
inp = np.random.normal(0, 10., shape)
out = op.dir_op(inp)
forward_operator(op, inp, out)
inp = np.random.normal(0, 10., out.shape)
out = op.adj_op(inp)
adjoint_operator(op, inp, out)
buff1 = op.adj_op(op.dir_op(out))
assert np.allclose(out, buff1, 1e-6)
wav = ['db1', 'db2', 'dirac']
levels = 3
shape = (128, 128)
op = linear_operators.dictionary(wav, levels, shape)
inp = np.random.normal(0, 10., shape)
out = op.dir_op(inp)
forward_operator(op, inp, out)
inp = np.random.normal(0, 10., out.shape)
out = op.adj_op(inp)
adjoint_operator(op, inp, out)
buff1 = op.adj_op(op.dir_op(out))
def test_l1_constrained():
options = {
'tol': 1e-5,
'iter': 5000,
'update_iter': 50,
'record_iters': False
}
ISNR = 40.
sigma = 10**(-ISNR / 20.)
size = 1024
epsilon = np.sqrt(size + 2. * np.sqrt(size)) * sigma
x = np.linspace(0, 1 * np.pi, size)
W = np.ones((size, ))
y = W * x + np.random.normal(0, sigma, size)
p = prox_operators.l2_ball(epsilon, y,
linear_operators.diag_matrix_operator(W))
wav = ['db1', 'db4']
levels = 6
shape = (size, )
psi = linear_operators.dictionary(wav, levels, shape)
h = prox_operators.l1_norm(np.max(np.abs(psi.dir_op(y))) * 1e-3, psi)
h.beta = 1.
f = prox_operators.real_prox()
z, diag = primal_dual.FBPD(y, options, None, f, h, p)
assert (np.linalg.norm(z - W * y) < epsilon * 1.05)
assert (diag['max_iter'] < 500)
#testing warm start
z1, diag1 = primal_dual.FBPD_warm_start(z, diag['y'], diag['z'], diag['w'],
options, None, f, h, p)
assert (diag1['max_iter'] < diag['max_iter'])
def test_l1_unconstrained():
options = {
'tol': 1e-5,
'iter': 500,
'update_iter': 50,
'record_iters': False
}
ISNR = 20.
sigma = 10**(-ISNR / 20.)
size = 1024
epsilon = np.sqrt(size + 2. * np.sqrt(size)) * sigma
x = np.linspace(0, 1 * np.pi, size)
W = np.ones((size, ))
y = W * x + np.random.normal(0, sigma, size)
g = grad_operators.l2_norm(sigma, y,
linear_operators.diag_matrix_operator(W))
wav = ['db1', 'db4']
levels = 6
shape = (size, )
psi = linear_operators.dictionary(wav, levels, shape)
h = prox_operators.l1_norm(np.max(np.abs(psi.dir_op(y))) * 5e-3, psi)
h.beta = 1.
f = prox_operators.real_prox()
z, diag = primal_dual.FBPD(y, options, g, f, h)
def uncertainty_quantification(
x_sol,
data,
sigma,
weights,
wav,
levels,
gamma,
options={
'alpha': 0.99,
"top": 1e3,
"bottom": 0,
"region_size": 16,
"iters": 10,
"tol": 1e-3
}):
psi = linear_operators.dictionary(wav, levels, x_sol.shape)
W = weights
obj = lambda data_sol, data_mask, wav_sol, wav_mask: gamma * np.sum(
np.abs(wav_sol + wav_mask)) + np.sum(
np.abs(W * (data_sol + data_mask) - W * data)**2) / (2 * sigma**2)
bound = obj(x_sol, 0, psi.dir_op(x_sol), 0) + float(len(
np.ravel(x_sol))) + np.sqrt(
float(len(np.ravel(x_sol))) * 16. * np.log(3. / options['alpha']))
print(obj(x_sol, 0, psi.dir_op(x_sol), 0))
print(
np.sqrt(
float(len(np.ravel(x_sol))) * 16. * np.log(3. / options['alpha'])))
phi = linear_operators.identity()
return map_uncertainty.create_local_credible_interval_fast(
x_sol, phi, psi, options['region_size'], obj, bound, options['iters'],
options['tol'], options['bottom'], options['top'])
def separation_solver(
algo,
image,
sigma,
weights,
wav1=["dirac"],
wav2=["fourier"],
levels=6,
gamma=1,
beta=1e-3,
options={
'tol': 1e-5,
'iter': 5000,
'update_iter': 50,
'record_iters': False,
"positivity": False
},
warm_start=None,
background=None,
axes=None):
"""
Chooses algorithm and solver
"""
logger.info("Image shape %s", image.shape)
logger.info("Using wavelets %s with %s levels", wav1, levels)
logger.info("Using wavelets %s with %s levels", wav2, levels)
logger.info(
"Using an estimated noise level of %s (weighted image units, i.e. Jy/Beam)",
sigma)
psi1 = linear_operators.dictionary(wav1, levels, image.shape, axes)
psi2 = linear_operators.dictionary(wav2, levels, image.shape, axes)
data = image * weights
starting_data = data * weights
if (warm_start is not None):
logger.info("Using warm start.")
starting_data = warm_start
if algo == algorithm.l1_constrained:
logger.info("Signal separation using constrained l1 regularization")
return clearskies.core.l1_constrained_separation_solver(
data, starting_data, sigma, weights, psi1, psi2, gamma, beta,
options)
if algo == algorithm.tv_constrained:
logger.info("Signal separation using constrained tv regularization")
return clearskies.core.tv_constrained_separation_solver(
data, starting_data, sigma, weights, psi1, gamma, beta, options)
def test_constrained():
input_file = "data/lmc.fits"
x_true = open_fits(input_file)
x_true = x_true[:128, :128]
options = {
'tol': 1e-5,
'iter': 5000,
'update_iter': 50,
'record_iters': False,
'real': False,
'positivity': False
}
ISNR = 30.
sigma = 10**(-ISNR / 20.) * np.sqrt(
np.sum(np.abs(x_true)**2) / (x_true.shape[0] * x_true.shape[1]))
width, height = x_true.shape
W = np.ones(x_true.shape)
y = W * x_true + np.random.normal(0, sigma, x_true.shape)
wav = ["dirac", "db1", "db2", "db3", "db4", "db5", "db6", "db7", "db8"]
levels = 2
psi = linear_operators.dictionary(wav, levels, y.shape, None)
data = y
warm_start = data
z, diag = solver.solver(solver.algorithm.l1_constrained, y, sigma, W, wav,
levels, 1e-2, options)
z_expected, diag_expected = core.l1_constrained_solver(
data, warm_start, sigma, W, psi, 1e-2, options)
SNR = np.log10(
np.sqrt(np.sum(np.abs(x_true)**2)) /
np.sqrt(np.sum(np.abs(x_true - z)**2))) * 20.
assert (SNR > ISNR)
size = z.shape[0] * z.shape[1]
assert (np.linalg.norm(W * z - y) <
np.sqrt(size + 2 * np.sqrt(size)) * sigma * 1.1)
assert (np.all(np.isclose(z, z_expected)))
size = 1024
epsilon = np.sqrt(size + 2. * np.sqrt(size)) * sigma
x = np.linspace(0, 1 * np.pi, size)
W = np.ones((size, ))
y = W * x + np.random.normal(0, sigma, size)
p = prox_operators.l2_ball(epsilon, y,
linear_operators.diag_matrix_operator(W))
p.beta = 1.
wav = ['db1', 'db4']
levels = 6
shape = (size, )
psi = linear_operators.dictionary(wav, levels, shape)
h = prox_operators.l1_norm(np.max(np.abs(psi.dir_op(y))) * 1e-3, psi)
h.beta = 1.
f = prox_operators.real_prox()
z, diag = primal_dual.FBPD(y, options, None, f, h, p, None)
plt.plot(np.real(y))
plt.plot(np.real(x))
plt.plot(np.real(z))
plt.legend(['data', 'true', 'fit'])
SNR = np.log10(
np.sqrt(np.sum(np.abs(x)**2)) / np.sqrt(np.sum(np.abs(x - z)**2))) * 20.
plt.title('SNR = ' + str(SNR))
plt.savefig(output_dir + '1d_constrained_example.png')
def solver(
algo,
image,
sigma,
weights,
wav=["dirac"],
levels=6,
beta=1e-3,
options={
'tol': 1e-5,
'iter': 5000,
'update_iter': 50,
'record_iters': False,
"positivity": False
},
warm_start=None,
background=None,
axes=None):
"""
Chooses algorithm and solver
"""
logger.info("Image shape %s", image.shape)
logger.info("Using wavelets %s with %s levels", wav, levels)
logger.info(
"Using an estimated noise level of %s (weighted image units, i.e. Jy/Beam)",
sigma)
psi = linear_operators.dictionary(wav, levels, image.shape, axes)
data = image * weights
starting_data = data * weights
if (warm_start is not None):
logger.info("Using warm start.")
starting_data = warm_start
if algo == algorithm.l1_constrained:
logger.info("Denosing using constrained l1 regularization")
return clearskies.core.l1_constrained_solver(data, starting_data,
sigma, weights, psi, beta,
options)
if algo == algorithm.l1_unconstrained:
logger.info("Denosing using unconstrained l1 regularization")
return clearskies.core.l1_unconstrained_solver(data, starting_data,
sigma, weights, psi,
beta, options)
if algo == algorithm.l2_unconstrained:
logger.info("Denosing using unconstrained l2 regularization")
return clearskies.core.l2_unconstrained_solver(data, starting_data,
sigma, weights, psi,
beta, options)
if algo == algorithm.l1_poisson_constrained:
logger.info(
"Denosing using constrained l1 regularization with poisson constraint"
)
logger.info(
"Ignoring Sigma since noise level is determined by the data")
if (background is not None):
logger.info("Using background.")
else:
background = image * 0
return clearskies.core.l1_poissonian_constrained_solver(
data, starting_data, len(np.ravel(image[image > 0])), weights,
background, psi, beta, options)
if algo == algorithm.l1_poisson_unconstrained:
logger.info(
"Denosing using unconstrained l1 regularization with poisson likelihood"
)
logger.info(
"Ignoring Sigma since noise level is determined by the data")
if (background is not None):
logger.info("Using background.")
else:
background = image * 0
return clearskies.core.l1_poissonian_unconstrained_solver(
data, starting_data, weights, background, psi, beta, options)
if algo == algorithm.tv_constrained:
logger.info("Denosing using constrained tv regularization")
return clearskies.core.tv_constrained_solver(data, starting_data,
sigma, weights, beta,
options)
if algo == algorithm.tv_unconstrained:
logger.info("Denosing using unconstrained tv regularization")
return clearskies.core.tv_unconstrained_solver(data, starting_data,
sigma, weights, beta,
options)
raise ValueError("Algorithm not reconginized.")
