diff --git a/demos/getting_started_1.py b/demos/getting_started_1.py
index 4fe211864d9f60edb04e4cfe192ea2b088ff5cd1..54399816fce70cab4b2adf12df390cd3e9fc543e 100644
--- a/demos/getting_started_1.py
+++ b/demos/getting_started_1.py
@@ -6,19 +6,19 @@ def make_chess_mask():
mask = np.ones(position_space.shape)
for i in range(4):
for j in range(4):
- if (i+j)%2 == 0:
+ if (i+j) % 2 == 0:
mask[i*128//4:(i+1)*128//4, j*128//4:(j+1)*128//4] = 0
return mask
def make_random_mask():
- mask = ift.from_random('pm1',position_space)
+ mask = ift.from_random('pm1', position_space)
mask = (mask+1)/2
return mask.val
if __name__ == '__main__':
- ## describtion of the tutorial ###
+ # # description of the tutorial ###
# Choose problem geometry and masking
@@ -34,11 +34,11 @@ if __name__ == '__main__':
# position_space = ift.HPSpace(128)
# mask = make_random_mask()
-
harmonic_space = position_space.get_default_codomain()
HT = ift.HarmonicTransformOperator(harmonic_space, target=position_space)
- # set correlation structure with a power spectrum and build prior correlation covariance
+ # set correlation structure with a power spectrum and build
+ # prior correlation covariance
def power_spectrum(k):
return 100. / (20.+k**3)
power_space = ift.PowerSpace(harmonic_space)
@@ -47,9 +47,10 @@ if __name__ == '__main__':
S = ift.DiagonalOperator(prior_correlation_structure)
- # build instrument response consisting of a discretization, mask and harmonic transformaion
+ # build instrument response consisting of a discretization, mask
+ # and harmonic transformaion
GR = ift.GeometryRemover(position_space)
- mask = ift.Field(position_space,val=mask)
+ mask = ift.Field.from_global_data(position_space, mask)
Mask = ift.DiagonalOperator(mask)
R = GR * Mask * HT
@@ -69,10 +70,10 @@ if __name__ == '__main__':
D_inv = R.adjoint * N.inverse * R + S.inverse
# make it invertible
IC = ift.GradientNormController(iteration_limit=500, tol_abs_gradnorm=1e-3)
- D = ift.InversionEnabler(D_inv,IC,approximation=S.inverse).inverse
+ D = ift.InversionEnabler(D_inv, IC, approximation=S.inverse).inverse
# WIENER FILTER
m = D(j)
- ##PLOTTING
- #Truth, data, reconstruction, residuals
+ # PLOTTING
+ # Truth, data, reconstruction, residuals
diff --git a/demos/getting_started_2.py b/demos/getting_started_2.py
index c0f2b773adfab51903d18f643931101ca1e3c630..0fa75285be5ab744056a74761b1eb8d8b15d76e3 100644
--- a/demos/getting_started_2.py
+++ b/demos/getting_started_2.py
@@ -6,14 +6,14 @@ def get_2D_exposure():
x_shape, y_shape = position_space.shape
exposure = np.ones(position_space.shape)
- exposure[x_shape/3:x_shape/2,:] *= 2.
- exposure[x_shape*4/5:x_shape,:] *= .1
- exposure[x_shape/2:x_shape*3/2,:] *=3.
- exposure[:,x_shape / 3:x_shape / 2] *= 2.
- exposure[:,x_shape * 4 / 5:x_shape] *= .1
- exposure[:,x_shape / 2:x_shape * 3 / 2] *= 3.
-
- exposure = ift.Field(position_space, val=exposure)
+ exposure[x_shape/3:x_shape/2, :] *= 2.
+ exposure[x_shape*4/5:x_shape, :] *= .1
+ exposure[x_shape/2:x_shape*3/2, :] *= 3.
+ exposure[:, x_shape/3:x_shape/2] *= 2.
+ exposure[:, x_shape*4/5:x_shape] *= .1
+ exposure[:, x_shape/2:x_shape*3/2] *= 3.
+
+ exposure = ift.Field.from_global_data(position_space, exposure)
return exposure
@@ -27,14 +27,13 @@ if __name__ == '__main__':
# position_space = ift.RGSpace(1024)
# exposure = np.ones(position_space.shape)
-
- # Two dimensional regular grid with inhomogeneous exposure
+ # Two-dimensional regular grid with inhomogeneous exposure
position_space = ift.RGSpace([512, 512])
exposure = get_2D_exposure()
# # Sphere with with uniform exposure
# position_space = ift.HPSpace(128)
- # exposure = np.ones(position_space.shape)
+ # exposure = ift.Field.full(position_space, 1.)
# Defining harmonic space and transform
harmonic_space = position_space.get_default_codomain()
@@ -58,7 +57,6 @@ if __name__ == '__main__':
logsky = HT(logsky_h)
sky = ift.PointwiseExponential(logsky)
- exposure = ift.Field(position_space, val=exposure)
M = ift.DiagonalOperator(exposure)
GR = ift.GeometryRemover(position_space)
# Set up instrumental response
@@ -68,14 +66,16 @@ if __name__ == '__main__':
d_space = R.target[0]
lamb = R(sky)
mock_position = ift.from_random('normal', lamb.position.domain)
- data = np.random.poisson(lamb.at(mock_position).value.val.astype(np.float64))
- data = ift.Field.from_local_data(d_space, data)
+ data = lamb.at(mock_position).value
+ data = np.random.poisson(data.to_global_data().astype(np.float64))
+ data = ift.Field.from_global_data(d_space, data)
# Compute likelihood and Hamiltonian
position = ift.from_random('normal', lamb.position.domain)
likelihood = ift.PoissonianEnergy(lamb, data)
ic_cg = ift.GradientNormController(iteration_limit=50)
- ic_newton = ift.GradientNormController(name='Newton',iteration_limit=50, tol_abs_gradnorm=1e-3)
+ ic_newton = ift.GradientNormController(name='Newton', iteration_limit=50,
+ tol_abs_gradnorm=1e-3)
minimizer = ift.RelaxedNewton(ic_newton)
# Minimize the Hamiltonian
@@ -84,6 +84,4 @@ if __name__ == '__main__':
# Plot results
result_sky = sky.at(H.position).value
- ##PLOTTING
-
-
+ # PLOTTING
diff --git a/demos/getting_started_3.py b/demos/getting_started_3.py
index e9c670e9fa0ec87c72feeb4367f529e2738e9885..1952cd0a560eb6b932529632485a5ed881c72e8b 100644
--- a/demos/getting_started_3.py
+++ b/demos/getting_started_3.py
@@ -7,18 +7,20 @@ from scipy.io import loadmat
def get_random_LOS(n_los):
- starts = list(np.random.uniform(0,1,(n_los,2)).T)
- ends = list(np.random.uniform(0,1,(n_los,2)).T)
+ starts = list(np.random.uniform(0, 1, (n_los, 2)).T)
+ ends = list(np.random.uniform(0, 1, (n_los, 2)).T)
return starts, ends
+
if __name__ == '__main__':
- ### ABOUT THIS TUTORIAL
+ # ## ABOUT THIS TUTORIAL
np.random.seed(42)
- position_space = ift.RGSpace([128,128])
+ position_space = ift.RGSpace([128, 128])
# Setting up an amplitude model
- A, amplitude_internals = make_amplitude_model(position_space,16, 1, 10, -4., 1, 0., 1.)
+ A, amplitude_internals = make_amplitude_model(
+ position_space, 16, 1, 10, -4., 1, 0., 1.)
# Building the model for a correlated signal
harmonic_space = position_space.get_default_codomain()
@@ -46,8 +48,7 @@ if __name__ == '__main__':
# specify noise
data_space = R.target
noise = .001
- N = ift.ScalingOperator(noise,data_space)
-
+ N = ift.ScalingOperator(noise, data_space)
# generate mock data
MOCK_POSITION = ift.from_random('normal', signal.position.domain)
@@ -63,28 +64,31 @@ if __name__ == '__main__':
minimizer = ift.RelaxedNewton(ic_newton)
# build model Hamiltonian
- H = ift.Hamiltonian(likelihood,ic_cg,iteration_controller_sampling=ic_sampling)
+ H = ift.Hamiltonian(likelihood, ic_cg,
+ iteration_controller_sampling=ic_sampling)
- INITIAL_POSITION = ift.from_random('normal',H.position.domain)
+ INITIAL_POSITION = ift.from_random('normal', H.position.domain)
position = INITIAL_POSITION
- ift.plot(signal.at(MOCK_POSITION).value,name='truth.pdf')
- ift.plot(R.adjoint_times(data),name='data.pdf')
- ift.plot([ A.at(MOCK_POSITION).value], name='power.pdf')
+ ift.plot(signal.at(MOCK_POSITION).value, name='truth.pdf')
+ ift.plot(R.adjoint_times(data), name='data.pdf')
+ ift.plot([A.at(MOCK_POSITION).value], name='power.pdf')
# number of samples used to estimate the KL
N_samples = 20
for i in range(5):
H = H.at(position)
- samples = [H.curvature.draw_sample(from_inverse=True) for _ in range(N_samples)]
+ samples = [H.curvature.draw_sample(from_inverse=True)
+ for _ in range(N_samples)]
KL = ift.SampledKullbachLeiblerDivergence(H, samples, ic_cg)
KL, convergence = minimizer(KL)
position = KL.position
- ift.plot(signal.at(position).value,name='reconstruction.pdf')
+ ift.plot(signal.at(position).value, name='reconstruction.pdf')
- ift.plot([A.at(position).value, A.at(MOCK_POSITION).value],name='power.pdf')
+ ift.plot([A.at(position).value, A.at(MOCK_POSITION).value],
+ name='power.pdf')
avrg = 0.
va = 0.
@@ -101,15 +105,5 @@ if __name__ == '__main__':
std = ift.sqrt(va)
ift.plot(avrg, name='avrg.pdf')
ift.plot(std, name='std.pdf')
- ift.plot([A.at(position).value, A.at(MOCK_POSITION).value]+powers, name='power.pdf')
-
-
-
-
-
-
-
-
-
-
-
+ ift.plot([A.at(position).value, A.at(MOCK_POSITION).value]+powers,
+ name='power.pdf')
diff --git a/nifty5/library/amplitude_model.py b/nifty5/library/amplitude_model.py
index a85eace4292cd3dc9bc4e08045dc86148846352b..630b1678b17e9de1c10420074e7b2c047bf46d58 100644
--- a/nifty5/library/amplitude_model.py
+++ b/nifty5/library/amplitude_model.py
@@ -22,7 +22,7 @@ def make_amplitude_model(s_space, Npixdof, ceps_a, ceps_k, sm, sv, im, iv,
Npixdof : #pix in dof_space
- ceps_a, ceps_k0 : Smoothnessparameters in ceps_kernel
+ ceps_a, ceps_k0 : Smoothness parameters in ceps_kernel
eg. ceps_kernel(k) = (a/(1+(k/k0)**2))**2
a = ceps_a, k0 = ceps_k0
@@ -48,7 +48,7 @@ def make_amplitude_model(s_space, Npixdof, ceps_a, ceps_k, sm, sv, im, iv,
phi_sig = np.array([sv, iv])
slope = SlopeOperator(param_space, logk_space, phi_sig)
- norm_phi_mean = Field(param_space, val=phi_mean/phi_sig)
+ norm_phi_mean = Field.from_global_data(param_space, phi_mean/phi_sig)
fields = {keys[0]: Field.from_random('normal', dof_space),
keys[1]: Field.from_random('normal', param_space)}
@@ -93,7 +93,7 @@ def create_cepstrum_amplitude_field(domain, cepstrum):
# Prepare q_array
q_array = np.zeros((dim,) + shape)
if dim == 1:
- ks = domain.get_k_length_array().val
+ ks = domain.get_k_length_array().to_global_data()
q_array = np.array([ks])
else:
for i in range(dim):
@@ -120,4 +120,4 @@ def create_cepstrum_amplitude_field(domain, cepstrum):
# Do summation
cepstrum_field[sl2] = np.sum(cepstrum_field[sl], axis=i)
- return Field(domain, val=cepstrum_field)
+ return Field.from_global_data(domain, cepstrum_field)
diff --git a/nifty5/multi/multi_field.py b/nifty5/multi/multi_field.py
index f2257337a19c4c628c9a3e9bdd3b5c6225954d8a..f8074b03c15eaa75b4ae75b8ae0d015bce4a4cce 100644
--- a/nifty5/multi/multi_field.py
+++ b/nifty5/multi/multi_field.py
@@ -67,7 +67,7 @@ class MultiField(object):
dtype = MultiField.build_dtype(dtype, domain)
return MultiField({key: Field.from_random(random_type, domain[key],
dtype[key], **kwargs)
- for key in domain.keys()})
+ for key in sorted(domain.keys())})
def fill(self, fill_value):
"""Fill `self` uniformly with `fill_value`
diff --git a/nifty5/operators/exp_transform.py b/nifty5/operators/exp_transform.py
index e6e635cea2c400b0f78932ed35ac6875a6a63ddb..d9c846519b9e7e821bfb15566cba1760a5d75786 100644
--- a/nifty5/operators/exp_transform.py
+++ b/nifty5/operators/exp_transform.py
@@ -67,7 +67,7 @@ class ExpTransform(LinearOperator):
def apply(self, x, mode):
self._check_input(x, mode)
- x = x.val
+ x = x.to_global_data()
ndim = len(self.target.shape)
idx = ()
for d in range(ndim):
@@ -90,7 +90,7 @@ class ExpTransform(LinearOperator):
x = xnew
idx = (slice(None),) + idx
- return Field(self._tgt(mode), val=x)
+ return Field.from_global_data(self._tgt(mode), x)
@property
def capability(self):
diff --git a/nifty5/operators/qht_operator.py b/nifty5/operators/qht_operator.py
index e022b990b0fc6ca2929d20ae715a6df24204c643..88da799f79c134a14c24d22b57995154884291e7 100644
--- a/nifty5/operators/qht_operator.py
+++ b/nifty5/operators/qht_operator.py
@@ -1,5 +1,6 @@
from ..domain_tuple import DomainTuple
from ..field import Field
+from .. import dobj
from ..utilities import hartley
from .linear_operator import LinearOperator
@@ -34,11 +35,13 @@ class QHTOperator(LinearOperator):
x = x.val * self.domain[0].scalar_dvol()
n = len(self.domain[0].shape)
rng = range(n) if mode == self.TIMES else reversed(range(n))
+ # MR FIXME: this needs to be fixed properly for MPI
+ x = dobj.to_global_data(x)
for i in rng:
sl = (slice(None),)*i + (slice(1, None),)
x[sl] = hartley(x[sl], axes=(i,))
- return Field(self._tgt(mode), val=x)
+ return Field.from_global_data(self._tgt(mode), x)
@property
def capability(self):
diff --git a/nifty5/operators/slope_operator.py b/nifty5/operators/slope_operator.py
index 81174405321d8f3cf2575c25e75b487ef5ac04d4..3daf567233747cd6b95472efa7b4061b002ad96c 100644
--- a/nifty5/operators/slope_operator.py
+++ b/nifty5/operators/slope_operator.py
@@ -18,7 +18,7 @@ class SlopeOperator(LinearOperator):
self.pos = np.zeros((self.ndim,) + self.target[0].shape)
if self.ndim == 1:
- self.pos[0] = self.target[0].get_k_length_array().val
+ self.pos[0] = self.target[0].get_k_length_array().to_global_data()
else:
shape = self.target[0].shape
for i in range(self.ndim):
@@ -46,18 +46,19 @@ class SlopeOperator(LinearOperator):
# Times
if mode == self.TIMES:
- inp = x.val
+ inp = x.to_global_data()
res = self.sigmas[-1] * inp[-1]
for i in range(self.ndim):
res += self.sigmas[i] * inp[i] * self.pos[i]
- return Field(self.target, val=res)
+ return Field.from_global_data(self.target, res)
# Adjoint times
res = np.zeros(self.domain[0].shape)
- res[-1] = np.sum(x.val) * self.sigmas[-1]
+ xglob = x.to_global_data()
+ res[-1] = np.sum(xglob) * self.sigmas[-1]
for i in range(self.ndim):
- res[i] = np.sum(self.pos[i] * x.val) * self.sigmas[i]
- return Field(self.domain, val=res)
+ res[i] = np.sum(self.pos[i] * xglob) * self.sigmas[i]
+ return Field.from_global_data(self.domain, res)
@property
def capability(self):