diff --git a/demos/Wiener_Filter.ipynb b/demos/Wiener_Filter.ipynb
index c2c414ae9a81fb8935951c80daa2ab1495a95ee8..12e26c5b0e1573e11ad03a74994ea22f5d7535f9 100644
--- a/demos/Wiener_Filter.ipynb
+++ b/demos/Wiener_Filter.ipynb
@@ -93,7 +93,6 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "collapsed": true,
"slideshow": {
"slide_type": "-"
}
@@ -161,7 +160,6 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "collapsed": true,
"slideshow": {
"slide_type": "-"
}
@@ -225,9 +223,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"s_space = ift.RGSpace(N_pixels)\n",
@@ -268,7 +264,6 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "collapsed": true,
"slideshow": {
"slide_type": "-"
}
@@ -293,7 +288,6 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "collapsed": true,
"slideshow": {
"slide_type": "-"
}
@@ -414,7 +408,6 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "collapsed": true,
"slideshow": {
"slide_type": "skip"
}
@@ -448,7 +441,6 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "collapsed": true,
"slideshow": {
"slide_type": "-"
}
@@ -471,7 +463,6 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "collapsed": true,
"slideshow": {
"slide_type": "skip"
}
@@ -503,12 +494,7 @@
},
"outputs": [],
"source": [
- "sc = ift.probing.utils.StatCalculator()\n",
- "for i in range(200):\n",
- " print(i)\n",
- " sc.add(HT(curv.generate_posterior_sample()))\n",
- "\n",
- "m_var = sc.var"
+ "m_mean, m_var = ift.probe_with_posterior_samples(curv, m, HT, 200)\n"
]
},
{
@@ -526,7 +512,6 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "collapsed": true,
"slideshow": {
"slide_type": "skip"
}
@@ -601,9 +586,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"N_pixels = 256 # Number of pixels\n",
@@ -661,18 +644,7 @@
"m = D(j)\n",
"\n",
"# Uncertainty\n",
- "sc = ift.probing.utils.StatCalculator()\n",
- "\n",
- "IC = ift.GradientNormController(iteration_limit=50000,\n",
- " tol_abs_gradnorm=0.1)\n",
- "inverter = ift.ConjugateGradient(controller=IC)\n",
- "curv = ift.library.wiener_filter_curvature.WienerFilterCurvature(R,N,Sh,inverter)\n",
- "\n",
- "for i in range(20):\n",
- " print(i)\n",
- " sc.add(HT(curv.generate_posterior_sample()))\n",
- "\n",
- "m_var = sc.var\n",
+ "m_mean, m_var = ift.probe_with_posterior_samples(curv, m, HT, 20)\n",
"\n",
"# Get data\n",
"s_power = ift.power_analyze(sh)\n",
@@ -793,21 +765,21 @@
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 2",
"language": "python",
- "name": "python3"
+ "name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
- "version": 3
+ "version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.4"
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
}
},
"nbformat": 4,
diff --git a/demos/paper_demos/cartesian_wiener_filter.py b/demos/paper_demos/cartesian_wiener_filter.py
index 46e5a72881813e66e9419ca5638969bf389eb1a6..5c690f418a78b2e37aa3e62de486e537c842b661 100644
--- a/demos/paper_demos/cartesian_wiener_filter.py
+++ b/demos/paper_demos/cartesian_wiener_filter.py
@@ -108,5 +108,5 @@ if __name__ == "__main__":
ift.plot(ift.Field(plot_space,val=data.val), name='data.png', **plotdict)
ift.plot(ift.Field(plot_space,val=m.val), name='map.png', **plotdict)
# sampling the uncertainty map
- mean, variance = ift.probe_with_posterior_samples(wiener_curvature, ht, 10)
+ 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)
diff --git a/demos/paper_demos/wiener_filter.py b/demos/paper_demos/wiener_filter.py
index aa873d35a4fe8e311910aa002caf2e51c128af74..2d48e6c9b11d99b83436c19e62b6c9cec4f47f4c 100644
--- a/demos/paper_demos/wiener_filter.py
+++ b/demos/paper_demos/wiener_filter.py
@@ -72,7 +72,7 @@ if __name__ == "__main__":
sample_mean = ift.Field.zeros(signal_space)
n_samples = 10
for i in range(n_samples):
- sample = ht(wiener_curvature.generate_posterior_sample()) + m
+ sample = ht(wiener_curvature.draw_sample()) + m
sample_variance += sample**2
sample_mean += sample
variance = sample_variance/n_samples - (sample_mean/n_samples)**2
diff --git a/nifty4/library/critical_power_energy.py b/nifty4/library/critical_power_energy.py
index 2f7f289ab0ff5b3c2891e765ce100b14d7e50ef1..bc08071314556928f6baa177aa419e3d9f46473b 100644
--- a/nifty4/library/critical_power_energy.py
+++ b/nifty4/library/critical_power_energy.py
@@ -91,7 +91,7 @@ class CriticalPowerEnergy(Energy):
if self.D is not None:
w = Field.zeros(self.position.domain, dtype=self.m.dtype)
for i in range(self.samples):
- sample = self.D.generate_posterior_sample() + self.m
+ sample = self.D.draw_sample() + self.m
w += P(abs(sample)**2)
w *= 1./self.samples
diff --git a/nifty4/library/noise_energy.py b/nifty4/library/noise_energy.py
index 2ea0bcb8dd3de2d296395de842f46082c09e5eaa..465b3a621d56cae0085ae1d028dff33dd618b874 100644
--- a/nifty4/library/noise_energy.py
+++ b/nifty4/library/noise_energy.py
@@ -46,7 +46,7 @@ class NoiseEnergy(Energy):
if samples is None or samples == 0:
xi_sample_list = [xi]
else:
- xi_sample_list = [D.generate_posterior_sample() + xi
+ xi_sample_list = [D.draw_sample() + xi
for _ in range(samples)]
self.xi_sample_list = xi_sample_list
self.inverter = inverter
diff --git a/nifty4/library/nonlinear_power_energy.py b/nifty4/library/nonlinear_power_energy.py
index c7a0a4616cda1f54108997d656d9ef7747fe9a0f..670757d1f64c25efa18dc471e02eafe41c3b2034 100644
--- a/nifty4/library/nonlinear_power_energy.py
+++ b/nifty4/library/nonlinear_power_energy.py
@@ -70,7 +70,7 @@ class NonlinearPowerEnergy(Energy):
if samples is None or samples == 0:
xi_sample_list = [xi]
else:
- xi_sample_list = [D.generate_posterior_sample() + xi
+ xi_sample_list = [D.draw_sample() + xi
for _ in range(samples)]
self.xi_sample_list = xi_sample_list
self.inverter = inverter
diff --git a/nifty4/library/wiener_filter_curvature.py b/nifty4/library/wiener_filter_curvature.py
index 53f0c708250944ec1c659149558b455d21b7ccd2..25f1f342f13ce661681434b46e1582c7d42bb603 100644
--- a/nifty4/library/wiener_filter_curvature.py
+++ b/nifty4/library/wiener_filter_curvature.py
@@ -59,23 +59,22 @@ class WienerFilterCurvature(EndomorphicOperator):
def apply(self, x, mode):
return self._op.apply(x, mode)
- def generate_posterior_sample(self):
- """ Generates a posterior sample from a Gaussian distribution with
- given mean and covariance.
+ def draw_sample(self):
+ """ Generates a sample from a Gaussian distribution with
+ covariance given by the operator.
This method generates samples by setting up the observation and
reconstruction of a mock signal in order to obtain residuals of the
- right correlation which are added to the given mean.
+ right correlation.
Returns
-------
sample : Field
- Returns the a sample from the Gaussian of mean zero and
- given covariance.
+ Returns the a sample from the Gaussian of given covariance.
"""
- mock_signal = self.S.generate_posterior_sample()
- mock_noise = self.N.generate_posterior_sample()
+ mock_signal = self.S.draw_sample()
+ mock_noise = self.N.draw_sample()
mock_data = self.R(mock_signal) + mock_noise
diff --git a/nifty4/operators/diagonal_operator.py b/nifty4/operators/diagonal_operator.py
index 8be3c7a6805c746cfb3f0650b13db74c1a500bc3..15c1418d432c8dc6b86a9fe3dab13d55741d3185 100644
--- a/nifty4/operators/diagonal_operator.py
+++ b/nifty4/operators/diagonal_operator.py
@@ -141,19 +141,18 @@ class DiagonalOperator(EndomorphicOperator):
return DiagonalOperator(self._diagonal.conjugate(), self._domain,
self._spaces)
- def generate_posterior_sample(self):
- """ Generates a posterior sample from a Gaussian distribution with
- given mean and covariance.
+ def draw_sample(self):
+ """ Generates a sample from a Gaussian distribution with
+ covariance given by the operator.
This method generates samples by setting up the observation and
reconstruction of a mock signal in order to obtain residuals of the
- right correlation which are added to the given mean.
+ right correlation.
Returns
-------
sample : Field
- Returns the a sample from the Gaussian of given mean and
- covariance.
+ Returns the a sample from the Gaussian of given covariance.
"""
if self._spaces is not None:
diff --git a/nifty4/operators/scaling_operator.py b/nifty4/operators/scaling_operator.py
index 678788e75614ecaa987ce0459111b9ce326867a9..e3429048e7f28b9a4537bb91092c476bc65cbdd5 100644
--- a/nifty4/operators/scaling_operator.py
+++ b/nifty4/operators/scaling_operator.py
@@ -89,19 +89,18 @@ class ScalingOperator(EndomorphicOperator):
return (self.TIMES | self.ADJOINT_TIMES |
self.INVERSE_TIMES | self.ADJOINT_INVERSE_TIMES)
- def generate_posterior_sample(self):
- """ Generates a posterior sample from a Gaussian distribution with
- given mean and covariance.
+ def draw_sample(self):
+ """ Generates a sample from a Gaussian distribution with
+ covariance given by the operator.
This method generates samples by setting up the observation and
reconstruction of a mock signal in order to obtain residuals of the
- right correlation which are added to the given mean.
+ right correlation.
Returns
-------
sample : Field
- Returns the a sample from the Gaussian of given mean and
- covariance.
+ Returns the a sample from the Gaussian of given covariance.
"""
return Field.from_random(random_type="normal",
diff --git a/nifty4/probing/utils.py b/nifty4/probing/utils.py
index a179e55dc1ad9998558e2ca18c1ef861636656af..7aa959666128273016b642cb22649ed0ea008ff1 100644
--- a/nifty4/probing/utils.py
+++ b/nifty4/probing/utils.py
@@ -50,7 +50,7 @@ class StatCalculator(object):
def probe_with_posterior_samples(op, m, post_op, nprobes):
sc = StatCalculator()
for i in range(nprobes):
- sample = post_op(op.generate_posterior_sample() + m)
+ sample = post_op(op.draw_sample() + m)
sc.add(sample)
if nprobes == 1: