diff --git a/demos/critical_filtering.py b/demos/critical_filtering.py
index 7866f04d3e290f71b5457effbb2eaf69c3f7362e..75875d0eb89369a3382c9a58e5516cfb76deb354 100644
--- a/demos/critical_filtering.py
+++ b/demos/critical_filtering.py
@@ -7,12 +7,12 @@ np.random.seed(42)
if __name__ == "__main__":
# Set up position space
- s_space = ift.RGSpace([128, 128])
- # s_space = ift.HPSpace(32)
+ #s_space = ift.RGSpace([128, 128])
+ s_space = ift.HPSpace(32)
# Define harmonic transformation and associated harmonic space
h_space = s_space.get_default_codomain()
- fft = ift.HarmonicTransformOperator(h_space, s_space)
+ HT = ift.HarmonicTransformOperator(h_space, s_space)
# Set up power space
p_space = ift.PowerSpace(h_space,
@@ -34,10 +34,10 @@ if __name__ == "__main__":
# Instrument._diagonal.val[64:512-64, 64:512-64] = 0
# Add a harmonic transformation to the instrument
- R = Instrument*fft
+ R = Instrument*HT
noise = 1.
- N = ift.DiagonalOperator(ift.Field.full(s_space, noise).weight(1))
+ N = ift.ScalingOperator(noise, s_space)
n = ift.Field.from_random(domain=s_space, random_type='normal',
std=np.sqrt(noise), mean=0)
@@ -48,7 +48,7 @@ if __name__ == "__main__":
j = R.adjoint_times(N.inverse_times(d))
realized_power = ift.log(ift.power_analyze(sh,
binbounds=p_space.binbounds))
- data_power = ift.log(ift.power_analyze(fft.adjoint_times(d),
+ data_power = ift.log(ift.power_analyze(HT.adjoint_times(d),
binbounds=p_space.binbounds))
d_data = d.val
ift.plot(d, name="data.png")
@@ -91,4 +91,4 @@ if __name__ == "__main__":
# Plot current estimate
ift.dobj.mprint(i)
if i % 50 == 0:
- ift.plot(fft(m0), name='map.png')
+ ift.plot(HT(m0), name='map.png')
diff --git a/demos/log_normal_wiener_filter.py b/demos/log_normal_wiener_filter.py
index a1a4ccac8346e2691ae834dec889a51a86f9630c..8f114ca73624b408ea9de07c1a0a0408a3def830 100644
--- a/demos/log_normal_wiener_filter.py
+++ b/demos/log_normal_wiener_filter.py
@@ -20,7 +20,7 @@ if __name__ == "__main__":
# signal_space = ift.RGSpace([N_pixels, N_pixels], distances=L/N_pixels)
signal_space = ift.HPSpace(16)
harmonic_space = signal_space.get_default_codomain()
- ht = ift.HarmonicTransformOperator(harmonic_space, target=signal_space)
+ HT = ift.HarmonicTransformOperator(harmonic_space, target=signal_space)
power_space = ift.PowerSpace(harmonic_space)
# Creating the mock signal
@@ -35,15 +35,14 @@ if __name__ == "__main__":
# mask.val[N10*5:N10*9, N10*5:N10*9] = 0.
R = ift.GeometryRemover(signal_space)
R = R*ift.DiagonalOperator(mask)
- R = R*ht
+ R = R*HT
R = R * ift.create_harmonic_smoothing_operator((harmonic_space,),0,response_sigma)
data_domain = R.target[0]
# Setting up the noise covariance and drawing a random noise realization
noiseless_data = R(mock_signal)
noise_amplitude = noiseless_data.val.std()/signal_to_noise
- N = ift.DiagonalOperator(
- ift.Field.full(data_domain, noise_amplitude**2))
+ N = ift.ScalingOperator(noise_amplitude**2, data_domain)
noise = ift.Field.from_random(
domain=data_domain, random_type='normal',
std=noise_amplitude, mean=0)
@@ -61,11 +60,11 @@ if __name__ == "__main__":
#minimizer = ift.SteepestDescent(controller=ctrl2)
me = minimizer(energy)
- m = ht(me[0].position)
+ m = HT(me[0].position)
# Plotting
plotdict = {"colormap": "Planck-like"}
- ift.plot(ht(mock_signal), name="mock_signal.png", **plotdict)
+ ift.plot(HT(mock_signal), name="mock_signal.png", **plotdict)
logdata = np.log(ift.dobj.to_global_data(data.val)).reshape(signal_space.shape)
ift.plot(ift.Field(signal_space, val=ift.dobj.from_global_data(logdata)),
name="log_of_data.png", **plotdict)
@@ -75,8 +74,8 @@ if __name__ == "__main__":
class Proby(ift.DiagonalProberMixin, ift.Prober):
pass
proby = Proby(signal_space, probe_count=1)
- proby(lambda z: ht(me2[0].curvature.inverse_times(ht.adjoint_times(z))))
+ proby(lambda z: HT(me2[0].curvature.inverse_times(HT.adjoint_times(z))))
sm = ift.FFTSmoothingOperator(signal_space, sigma=0.02)
- variance = sm(proby.diagonal.weight(-1))
+ variance = sm(proby.diagonal.weigHT(-1))
ift.plot(variance, name='variance.png', **plotdict)
diff --git a/demos/nonlinear_critical_filter.py b/demos/nonlinear_critical_filter.py
index 501f6f0ef1f059114eca0b1c415ebf38eee30ea9..2627fdc698fff114fc42d0bf9d89914551b833e4 100644
--- a/demos/nonlinear_critical_filter.py
+++ b/demos/nonlinear_critical_filter.py
@@ -66,8 +66,7 @@ if __name__ == "__main__":
true_sky = nonlinearity(HT(power*sh))
noiseless_data = MeasurementOperator(true_sky)
noise_amplitude = noiseless_data.val.std()*noise_level
- N = ift.DiagonalOperator(
- ift.Field.full(d_space, noise_amplitude**2))
+ N = ift.ScalingOperator(noise_amplitude**2, d_space)
n = ift.Field.from_random(
domain=d_space, random_type='normal',
std=noise_amplitude, mean=0)
diff --git a/demos/paper_demos/cartesian_wiener_filter.py b/demos/paper_demos/cartesian_wiener_filter.py
index 5c690f418a78b2e37aa3e62de486e537c842b661..faf60daf111bad7cc591341728e6c99ffeab35ee 100644
--- a/demos/paper_demos/cartesian_wiener_filter.py
+++ b/demos/paper_demos/cartesian_wiener_filter.py
@@ -83,7 +83,6 @@ if __name__ == "__main__":
noiseless_data = R(mock_signal)
noise_amplitude = noiseless_data.val.std()/signal_to_noise
# Setting up the noise covariance and drawing a random noise realization
- #ndiag = ift.Field.full(data_domain, noise_amplitude**2)
N = ift.ScalingOperator(noise_amplitude**2, data_domain)
noise = ift.Field.from_random(
domain=data_domain, random_type='normal',
@@ -100,8 +99,7 @@ if __name__ == "__main__":
m_k = wiener_curvature.inverse_times(j)
m = ht(m_k)
- plotdict = {"xlabel": "Pixel index", "ylabel": "Pixel index",
- "colormap": "Planck-like"}
+ plotdict = {"colormap": "Planck-like"}
plot_space = ift.RGSpace((N_pixels_1, N_pixels_2))
ift.plot(ift.Field(plot_space,val=ht(mock_signal).val), name='mock_signal.png',
**plotdict)
@@ -110,3 +108,4 @@ if __name__ == "__main__":
# sampling the uncertainty map
mean, variance = ift.probe_with_posterior_samples(wiener_curvature, m_k, ht, 10)
ift.plot(ift.Field(plot_space, val=ift.sqrt(variance).val), name="uncertainty.png", **plotdict)
+ ift.plot(ift.Field(plot_space, val=mean.val), name="posterior_mean.png", **plotdict)
diff --git a/demos/paper_demos/wiener_filter.py b/demos/paper_demos/wiener_filter.py
index 2d48e6c9b11d99b83436c19e62b6c9cec4f47f4c..d36218b185ac68e19c972aaebaa3ddcf56e48fc5 100644
--- a/demos/paper_demos/wiener_filter.py
+++ b/demos/paper_demos/wiener_filter.py
@@ -44,8 +44,7 @@ if __name__ == "__main__":
noiseless_data = R(mock_signal)
noise_amplitude = noiseless_data.val.std()/signal_to_noise
# Setting up the noise covariance and drawing a random noise realization
- ndiag = ift.Field.full(data_domain, noise_amplitude**2)
- N = ift.DiagonalOperator(ndiag)
+ N = ift.ScalingOperator(noise_amplitude**2, data_domain)
noise = ift.Field.from_random(
domain=data_domain, random_type='normal',
std=noise_amplitude, mean=0)
@@ -60,20 +59,13 @@ if __name__ == "__main__":
m_k = wiener_curvature.inverse_times(j)
m = ht(m_k)
- plotdict = {"xlabel": "Pixel index", "ylabel": "Pixel index",
- "colormap": "Planck-like"}
+ plotdict = {"colormap": "Planck-like"}
ift.plot(ht(mock_signal), name="mock_signal.png", **plotdict)
ift.plot(ift.Field(signal_space, val=data.val),
name="data.png", **plotdict)
ift.plot(m, name="map.png", **plotdict)
# sampling the uncertainty map
- sample_variance = ift.Field.zeros(signal_space)
- sample_mean = ift.Field.zeros(signal_space)
- n_samples = 10
- for i in range(n_samples):
- sample = ht(wiener_curvature.draw_sample()) + m
- sample_variance += sample**2
- sample_mean += sample
- variance = sample_variance/n_samples - (sample_mean/n_samples)**2
+ mean, variance = ift.probe_with_posterior_samples(wiener_curvature, m_k, ht, 5)
ift.plot(ift.sqrt(variance), name="uncertainty.png", **plotdict)
+ ift.plot(mean, name="posterior_mean.png", **plotdict)
diff --git a/demos/wiener_filter_easy.py b/demos/wiener_filter_easy.py
index 0aa9394aff24fb72a3911a0503bedc58e5370121..8e2a9d671b1f6cef3f295de426a629e6a1865250 100644
--- a/demos/wiener_filter_easy.py
+++ b/demos/wiener_filter_easy.py
@@ -43,7 +43,7 @@ if __name__ == "__main__":
noiseless_data = R(sh)
signal_to_noise = 1.
noise_amplitude = noiseless_data.val.std()/signal_to_noise
- N = ift.DiagonalOperator(ift.Field.full(s_space, noise_amplitude**2))
+ N = ift.ScalingOperator(noise_amplitude**2, s_space)
n = ift.Field.from_random(domain=s_space,
random_type='normal',
std=noise_amplitude,
diff --git a/demos/wiener_filter_via_curvature.py b/demos/wiener_filter_via_curvature.py
index 0182f87cdb06eaf0ec4a1a936a573864b0633c86..a8493e3caa31476191893863240cd70bf95e184b 100644
--- a/demos/wiener_filter_via_curvature.py
+++ b/demos/wiener_filter_via_curvature.py
@@ -59,8 +59,7 @@ if __name__ == "__main__":
noiseless_data = R(mock_signal)
noise_amplitude = noiseless_data.val.std()/signal_to_noise
- N = ift.DiagonalOperator(
- ift.Field.full(data_domain, noise_amplitude**2))
+ N = ift.ScalingOperator(noise_amplitude**2, data_domain)
noise = ift.Field.from_random(
domain=data_domain, random_type='normal',
std=noise_amplitude, mean=0)
diff --git a/demos/wiener_filter_via_hamiltonian.py b/demos/wiener_filter_via_hamiltonian.py
index 283400e878ef3a0b07a1e573b4ecb46e7a89e09d..ce1a2f05a6fe1d0c8ba6aa66fc9a79413358c37c 100644
--- a/demos/wiener_filter_via_hamiltonian.py
+++ b/demos/wiener_filter_via_hamiltonian.py
@@ -42,7 +42,7 @@ if __name__ == "__main__":
noiseless_data = R(sh)
signal_to_noise = 1.
noise_amplitude = noiseless_data.val.std()/signal_to_noise
- N = ift.DiagonalOperator(ift.Field.full(s_space, noise_amplitude**2))
+ N = ift.ScalingOperator(noise_amplitude**2, s_space)
n = ift.Field.from_random(domain=s_space,
random_type='normal',
std=noise_amplitude,
diff --git a/nifty4/field.py b/nifty4/field.py
index dd34401d2eb316ccf9446228777d6b4738786866..33435ee3db3ef03066b4a4c4fa1a4952d90617e5 100644
--- a/nifty4/field.py
+++ b/nifty4/field.py
@@ -529,13 +529,9 @@ class Field(object):
return "<nifty4.Field>"
def __str__(self):
- minmax = [self.min(), self.max()]
- mean = self.mean()
return "nifty4.Field instance\n- domain = " + \
self._domain.__str__() + \
- "\n- val = " + repr(self.val) + \
- "\n - min.,max. = " + str(minmax) + \
- "\n - mean = " + str(mean)
+ "\n- val = " + repr(self.val)
# Arithmetic functions working on Fields