now working with default NIFTy_4

demos/demo.py

@@ -43,11 +43,11 @@ def generate_mock_data():
 if __name__ == '__main__':
     np.random.seed(42)
     data = generate_mock_data()
-    myStarblade = sb.build_starblade(data=data, alpha=1.5, newton_steps=200, cg_steps=5, q=1e-3)
+    myStarblade = sb.build_starblade(data=data, alpha=1.5, q=1e-3)
 
-    for i in range(3):  # not fully converged after 3 steps.
-        myStarblade = sb.starblade_iteration(myStarblade, samples=5, cg_steps=10,
-                                             newton_steps=100, sampling_steps=1000)
+    for i in range(5):  # not fully converged after 5 steps.
+        myStarblade = sb.starblade_iteration(myStarblade, samples=5, cg_steps=100,
+                                             newton_steps=50)
 
     ### PLOTS ###
     vmin = data.min()

starblade/starblade_energy.py

@@ -17,7 +17,7 @@
 # Starblade is being developed at the Max-Planck-Institut fuer Astrophysik
 
 from nifty4 import Energy, Field, log, exp, DiagonalOperator,\
-    create_power_operator, SandwichOperator, ScalingOperator, InversionEnabler, SamplingEnabler
+    create_power_operator, SandwichOperator, ScalingOperator, InversionEnabler#, SamplingEnabler
 from nifty4.library.nonlinearities import PositiveTanh, Tanh
 
 
@@ -59,7 +59,6 @@ class StarbladeEnergy(Energy):
 
         self.parameters = parameters
         self.inverter = parameters['inverter']
-        self.sampling_inverter = parameters['sampling_inverter']
         self.d = parameters['data']
         self.FFT = parameters['FFT']
         self.power_spectrum = parameters['power_spectrum']
@@ -116,5 +115,5 @@ class StarbladeEnergy(Energy):
         R = ScalingOperator(1., point.domain)
         S_p = DiagonalOperator(self.s_p)
         my_S_inv = SandwichOperator.make(self.FFT.adjoint.inverse.adjoint*S_p, self.correlation.inverse)
-        curv = InversionEnabler(SamplingEnabler(my_S_inv+N_inv, O_x, self.sampling_inverter), self.inverter)
+        curv = InversionEnabler(my_S_inv+N_inv, self.inverter,O_x)
         return curv

starblade/starblade_kl.py

@@ -46,8 +46,6 @@ class StarbladeKL(Energy):
             the minimization strategy to use for operator inversion
         newton_iterations :
             Number of Newton optimization steps.
-        sampling_inverter :
-            Inverter which is used to generate samples.
     """
 
     def __init__(self, position, samples, parameters):

starblade/sugar.py

@@ -23,8 +23,8 @@ from .starblade_energy import StarbladeEnergy
 from .starblade_kl import StarbladeKL
 
 
-def build_starblade(data, alpha=1.5, q=1e-10, cg_steps=10, newton_steps=100,
-                    manual_power_spectrum=None, sampling_steps=100):
+def build_starblade(data, alpha=1.5, q=1e-10, cg_steps=100, newton_steps=100,
+                    manual_power_spectrum=None):
     """ Setting up the StarbladeEnergy for the given data and parameters
     Parameters
     ----------
@@ -64,19 +64,16 @@ def build_starblade(data, alpha=1.5, q=1e-10, cg_steps=10, newton_steps=100,
     ICI = ift.GradientNormController(iteration_limit=cg_steps,
                                      tol_abs_gradnorm=1e-5)
     inverter = ift.ConjugateGradient(controller=ICI)
-    IC_samples = ift.GradientNormController(iteration_limit=sampling_steps,
-                                            tol_abs_gradnorm=1e-5)
-    sampling_inverter = ift.ConjugateGradient(controller=IC_samples)
 
     parameters = dict(data=data, power_spectrum=initial_spectrum,
                       alpha=alpha, q=q,
                       inverter=inverter, FFT=FFT,
-                      newton_iterations=newton_steps, sampling_inverter=sampling_inverter, update_power=update_power)
+                      newton_iterations=newton_steps, update_power=update_power)
     Starblade = StarbladeEnergy(position=initial_x, parameters=parameters)
     return Starblade
 
 
-def starblade_iteration(starblade, samples=5, cg_steps=10, newton_steps=100, sampling_steps=1000):
+def starblade_iteration(starblade, samples=5, cg_steps=100, newton_steps=100):
     """ Performing one Newton minimization step
     Parameters
     ----------
@@ -86,11 +83,9 @@ def starblade_iteration(starblade, samples=5, cg_steps=10, newton_steps=100, sam
         Number of samples drawn in order to estimate the KL. If zero the MAP is calculated (default: 5).
     cg_steps : int
         Maximum number of conjugate gradient iterations for
-        numerical operator inversion for each Newton step (default: 10).
+        numerical operator inversion (default: 100).
     newton_steps : int
         Number of consecutive Newton steps within one algorithmic step.(default: 100)
-    sampling_steps : int
-        Number of conjugate gradient steps for each sample (default: 1000).
     """
     controller = ift.GradientNormController(name="Newton",
                                             tol_abs_gradnorm=1e-8,
@@ -98,17 +93,13 @@ def starblade_iteration(starblade, samples=5, cg_steps=10, newton_steps=100, sam
     minimizer = ift.RelaxedNewton(controller=controller)
     ICI = ift.GradientNormController(iteration_limit=cg_steps, tol_abs_gradnorm=1e-5)
     inverter = ift.ConjugateGradient(controller=ICI)
-    IC_samples = ift.GradientNormController(iteration_limit=sampling_steps,
-                                            tol_abs_gradnorm=1e-5)
-    sampling_inverter = ift.ConjugateGradient(controller=IC_samples)
     para = starblade.parameters
     para['inverter'] = inverter
-    para['sampling_inverter'] = sampling_inverter
     energy = StarbladeEnergy(starblade.position, parameters=para)
 
     sample_list = []
     for i in range(samples):
-        sample = energy.curvature.inverse.draw_sample()
+        sample = energy.curvature.draw_sample(from_inverse=True)
         sample_list.append(sample)
     if len(sample_list) > 0:
         energy = StarbladeKL(starblade.position, samples=sample_list, parameters=energy.parameters)
@@ -118,7 +109,7 @@ def starblade_iteration(starblade, samples=5, cg_steps=10, newton_steps=100, sam
     energy = StarbladeEnergy(energy.position, parameters=energy.parameters)
     sample_list = []
     for i in range(samples):
-        sample = energy.curvature.inverse.draw_sample()
+        sample = energy.curvature.draw_sample(from_inverse=True)
         sample_list.append(sample)
     if len(sample_list) == 0:
         sample_list.append(energy.position)