diff --git a/demos/critical_filtering.py b/demos/critical_filtering.py
index 78b06079c9ce94e1a6eda3bd95b4972185079072..a296521ed651a15adf7c40ad9de088f80ee1cd37 100644
--- a/demos/critical_filtering.py
+++ b/demos/critical_filtering.py
@@ -64,7 +64,7 @@ if __name__ == "__main__":
# Choose the measurement instrument
# Instrument = SmoothingOperator(s_space, sigma=0.01)
- Instrument = ift.DiagonalOperator(s_space, diagonal=1.)
+ Instrument = ift.DiagonalOperator(ift.Field(s_space, 1.))
# Instrument._diagonal.val[200:400, 200:400] = 0
# Instrument._diagonal.val[64:512-64, 64:512-64] = 0
@@ -72,7 +72,7 @@ if __name__ == "__main__":
R = AdjointFFTResponse(fft, Instrument)
noise = 1.
- N = ift.DiagonalOperator(s_space, ift.Field(s_space,noise).weight(1))
+ N = ift.DiagonalOperator(ift.Field(s_space,noise).weight(1))
n = ift.Field.from_random(domain=s_space,
random_type='normal',
std=np.sqrt(noise),
@@ -101,7 +101,7 @@ if __name__ == "__main__":
def ps0(k):
return (1./(1.+k)**2)
- t0 = ift.Field(p_space, val=np.log(1./(1+p_space.k)_lengths)**2))
+ t0 = ift.Field(p_space, val=np.log(1./(1+p_space.k_lengths)**2))
for i in range(500):
S0 = ift.create_power_operator(h_space, power_spectrum=ps0)
diff --git a/demos/log_normal_wiener_filter.py b/demos/log_normal_wiener_filter.py
index 17c74b1e6ffb2bdb5a1688bbc04ce3adb5e92686..6a47088485498dc7dd5bc11a8e46640106e8a029 100644
--- a/demos/log_normal_wiener_filter.py
+++ b/demos/log_normal_wiener_filter.py
@@ -39,7 +39,7 @@ if __name__ == "__main__":
# Setting up the noise covariance and drawing a random noise realization
ndiag = ift.Field(data_domain, mock_signal.var()/signal_to_noise).weight(1)
- N = ift.DiagonalOperator(data_domain, ndiag)
+ N = ift.DiagonalOperator(ndiag)
noise = ift.Field.from_random(domain=data_domain, random_type='normal',
std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0)
data = R(ift.exp(mock_signal)) + noise #|\label{code:wf_mock_data}|
diff --git a/demos/paper_demos/cartesian_wiener_filter.py b/demos/paper_demos/cartesian_wiener_filter.py
index 2aee523a2293a58ac7efe97df7e06657f8fc96f3..c49887b47f034015697c60a3e9903a980165a570 100644
--- a/demos/paper_demos/cartesian_wiener_filter.py
+++ b/demos/paper_demos/cartesian_wiener_filter.py
@@ -58,8 +58,7 @@ if __name__ == "__main__":
diagonal = mock_power.power_synthesize_special(spaces=(0, 1))**2
diagonal = diagonal.real
- S = ift.DiagonalOperator(domain=(harmonic_space_1, harmonic_space_2),
- diagonal=diagonal)
+ S = ift.DiagonalOperator(diagonal)
np.random.seed(10)
@@ -82,7 +81,7 @@ if __name__ == "__main__":
# Setting up the noise covariance and drawing a random noise realization
ndiag = ift.Field(data_domain, mock_signal.var()/signal_to_noise).weight(1)
- N = ift.DiagonalOperator(data_domain, ndiag)
+ N = ift.DiagonalOperator(ndiag)
noise = ift.Field.from_random(domain=data_domain, random_type='normal',
std=mock_signal.std()/np.sqrt(signal_to_noise),
mean=0)
diff --git a/demos/paper_demos/wiener_filter.py b/demos/paper_demos/wiener_filter.py
index 6c60d23ab27f10b4d8d4e630eec546eb79e7e330..abafd70fa6b20c58155acbe6a76e3c6d170e676d 100644
--- a/demos/paper_demos/wiener_filter.py
+++ b/demos/paper_demos/wiener_filter.py
@@ -38,14 +38,14 @@ if __name__ == "__main__":
# Setting up the noise covariance and drawing a random noise realization
ndiag = ift.Field(data_domain, mock_signal.var()/signal_to_noise).weight(1)
- N = ift.DiagonalOperator(data_domain, ndiag)
+ N = ift.DiagonalOperator(ndiag)
noise = ift.Field.from_random(domain=data_domain, random_type='normal',
std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0)
data = R(mock_signal) + noise #|\label{code:wf_mock_data}|
# Wiener filter
j = R_harmonic.adjoint_times(N.inverse_times(data))
- ctrl = ift.DefaultIterationController(verbose=True,tol_abs_gradnorm=0.1,iteration_limit=10)
+ ctrl = ift.DefaultIterationController(verbose=True,tol_abs_gradnorm=0.1)
inverter = ift.ConjugateGradient(controller=ctrl)
wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N, R=R_harmonic,inverter=inverter)
m_k = wiener_curvature.inverse_times(j) #|\label{code:wf_wiener_filter}|
diff --git a/demos/probing.py b/demos/probing.py
index 5bd282e62de80dc85595df2dff256cf78d3ae70a..b27dbea39af4e739d0a98afb9dc3a0ba4687a50c 100644
--- a/demos/probing.py
+++ b/demos/probing.py
@@ -17,7 +17,7 @@ class MultiProber(ift.DiagonalProberMixin, ift.TraceProberMixin, ift.Prober):
x = ift.RGSpace((8, 8))
f = ift.Field.from_random(domain=x, random_type='normal')
-diagOp = ift.DiagonalOperator(domain=x, diagonal=f)
+diagOp = ift.DiagonalOperator(f)
diagProber = DiagonalProber(domain=x)
diagProber(diagOp)
diff --git a/demos/wiener_filter_via_curvature.py b/demos/wiener_filter_via_curvature.py
index 252cffc4df37dfa96055466ae370d4bdac2ac9ae..03e585bc7c194e09501329fb94d644fd90d34e12 100644
--- a/demos/wiener_filter_via_curvature.py
+++ b/demos/wiener_filter_via_curvature.py
@@ -47,8 +47,7 @@ if __name__ == "__main__":
data_domain = R.target[0]
R_harmonic = ift.ComposedOperator([fft, R], default_spaces=[0, 0])
- N = ift.DiagonalOperator(data_domain,
- diagonal=ift.Field(data_domain,mock_signal.var()/signal_to_noise).weight(1))
+ N = ift.DiagonalOperator(ift.Field(data_domain,mock_signal.var()/signal_to_noise).weight(1))
noise = ift.Field.from_random(domain=data_domain,
random_type='normal',
std=mock_signal.std()/np.sqrt(signal_to_noise),
diff --git a/demos/wiener_filter_via_hamiltonian.py b/demos/wiener_filter_via_hamiltonian.py
index ee19bc324395717c17ceb44f97ff708060a8c89f..bafd2950b5617b024d42e1a3e107b71591f6e32e 100644
--- a/demos/wiener_filter_via_hamiltonian.py
+++ b/demos/wiener_filter_via_hamiltonian.py
@@ -56,14 +56,14 @@ if __name__ == "__main__":
# Choosing the measurement instrument
# Instrument = SmoothingOperator(s_space, sigma=0.05)
- Instrument = ift.DiagonalOperator(s_space, diagonal=1.)
+ Instrument = ift.DiagonalOperator(ift.Field(s_space, 1.))
# Instrument._diagonal.val[200:400, 200:400] = 0
# Adding a harmonic transformation to the instrument
R = AdjointFFTResponse(fft, Instrument)
signal_to_noise = 1.
ndiag = ift.Field(s_space, ss.var()/signal_to_noise).weight(1)
- N = ift.DiagonalOperator(s_space, ndiag)
+ N = ift.DiagonalOperator(ndiag)
n = ift.Field.from_random(domain=s_space,
random_type='normal',
std=ss.std()/np.sqrt(signal_to_noise),
diff --git a/nifty/data_objects/numpy_do.py b/nifty/data_objects/numpy_do.py
index de04b230e9c027a9de751579fa94bbda14e73cf2..8c6eca28e61f13e8aed5dab3da80445390152cda 100644
--- a/nifty/data_objects/numpy_do.py
+++ b/nifty/data_objects/numpy_do.py
@@ -2,7 +2,7 @@
import numpy as np
from numpy import ndarray as data_object
-from numpy import full, empty, sqrt, ones, zeros, vdot, abs, bincount
+from numpy import full, empty, sqrt, ones, zeros, vdot, abs, bincount, exp, log
from ..nifty_utilities import cast_iseq_to_tuple, get_slice_list
from functools import reduce
diff --git a/nifty/library/critical_filter/critical_power_curvature.py b/nifty/library/critical_filter/critical_power_curvature.py
index d1c0c820b7c0d51e793bb0d3dab2980e27e90bf1..9e57920e4665f196f3c3be34d8e7cba9649b7601 100644
--- a/nifty/library/critical_filter/critical_power_curvature.py
+++ b/nifty/library/critical_filter/critical_power_curvature.py
@@ -22,7 +22,7 @@ class CriticalPowerCurvature(InvertibleOperatorMixin, EndomorphicOperator):
# ---Overwritten properties and methods---
def __init__(self, theta, T, inverter, preconditioner=None, **kwargs):
- self.theta = DiagonalOperator(theta.domain, diagonal=theta)
+ self.theta = DiagonalOperator(theta)
self.T = T
if preconditioner is None:
preconditioner = self.theta.inverse_times
diff --git a/nifty/sugar.py b/nifty/sugar.py
index 27b607aae12320d5ee8df564ab208e96e7618f01..9902bc55c7b23c3f8af6abb0be33a65c4b8cdce3 100644
--- a/nifty/sugar.py
+++ b/nifty/sugar.py
@@ -66,7 +66,7 @@ def create_power_operator(domain, power_spectrum, dtype=None):
f = f.real
f **= 2
- return DiagonalOperator(domain, diagonal=Field(domain,f).weight(1))
+ return DiagonalOperator(Field(domain,f).weight(1))
def generate_posterior_sample(mean, covariance):