restructure iteration controllers to match those in nightly branch

demos/critical_filtering.py

@@ -88,11 +88,11 @@ if __name__ == "__main__":
     d_data = d.val.real
     ift.plotting.plot(d.real, name="data.pdf")
 
-    IC1 = ift.DefaultIterationController(verbose=True,iteration_limit=100,tol_abs_gradnorm=0.1)
+    IC1 = ift.GradientNormController(verbose=True,iteration_limit=100,tol_abs_gradnorm=0.1)
     minimizer1 = ift.RelaxedNewton(IC1)
-    IC2 = ift.DefaultIterationController(verbose=True,iteration_limit=100,tol_abs_gradnorm=0.1)
+    IC2 = ift.GradientNormController(verbose=True,iteration_limit=100,tol_abs_gradnorm=0.1)
     minimizer2 = ift.VL_BFGS(IC2, max_history_length=20)
-    IC3 = ift.DefaultIterationController(verbose=True,iteration_limit=100,tol_abs_gradnorm=0.1)
+    IC3 = ift.GradientNormController(verbose=True,iteration_limit=100,tol_abs_gradnorm=0.1)
     minimizer3 = ift.SteepestDescent(IC3)
 
     # Set starting position
@@ -107,14 +107,14 @@ if __name__ == "__main__":
         S0 = ift.create_power_operator(h_space, power_spectrum=ps0)
 
         # Initialize non-linear Wiener Filter energy
-        ICI = ift.DefaultIterationController(verbose=False,iteration_limit=500,tol_abs_gradnorm=0.1)
+        ICI = ift.GradientNormController(verbose=False,iteration_limit=500,tol_abs_gradnorm=0.1)
         map_inverter = ift.ConjugateGradient(controller=ICI)
         map_energy = ift.library.WienerFilterEnergy(position=m0, d=d, R=R, N=N, S=S0, inverter=map_inverter)
         # Solve the Wiener Filter analytically
         D0 = map_energy.curvature
         m0 = D0.inverse_times(j)
         # Initialize power energy with updated parameters
-        ICI2 = ift.DefaultIterationController(name="powI",verbose=True,iteration_limit=200,tol_abs_gradnorm=1e-5)
+        ICI2 = ift.GradientNormController(name="powI",verbose=True,iteration_limit=200,tol_abs_gradnorm=1e-5)
         power_inverter = ift.ConjugateGradient(controller=ICI2)
         power_energy = ift.library.CriticalPowerEnergy(position=t0, m=m0, D=D0,
                                            smoothness_prior=10., samples=3, inverter=power_inverter)

demos/log_normal_wiener_filter.py

@@ -46,8 +46,8 @@ if __name__ == "__main__":
 
     # Wiener filter
     m0 = ift.Field(harmonic_space, val=0.)
-    ctrl = ift.DefaultIterationController(verbose=False,tol_abs_gradnorm=1)
-    ctrl2 = ift.DefaultIterationController(verbose=True,tol_abs_gradnorm=0.1, name="outer")
+    ctrl = ift.GradientNormController(verbose=False,tol_abs_gradnorm=1)
+    ctrl2 = ift.GradientNormController(verbose=True,tol_abs_gradnorm=0.1, name="outer")
     inverter = ift.ConjugateGradient(controller=ctrl)
     energy = ift.library.LogNormalWienerFilterEnergy(m0, data, R_harmonic, N, S, inverter=inverter)
     minimizer1 = ift.VL_BFGS(controller=ctrl2,max_history_length=20)

demos/paper_demos/cartesian_wiener_filter.py

@@ -90,7 +90,7 @@ if __name__ == "__main__":
 
     # Wiener filter
     j = R_harmonic.adjoint_times(N.inverse_times(data))
-    ctrl = ift.DefaultIterationController(verbose=True, iteration_limit=100)
+    ctrl = ift.GradientNormController(verbose=True, iteration_limit=100)
     inverter = ift.ConjugateGradient(controller=ctrl)
     wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N, R=R_harmonic, inverter=inverter)
 

demos/paper_demos/wiener_filter.py

@@ -45,7 +45,7 @@ if __name__ == "__main__":
 
     # 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.GradientNormController(verbose=True,tol_abs_gradnorm=0.1,iteration_limit=10)
     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}|

demos/wiener_filter_via_curvature.py

@@ -58,7 +58,7 @@ if __name__ == "__main__":
     # Wiener filter
 
     j = R_harmonic.adjoint_times(N.inverse_times(data))
-    ctrl = ift.DefaultIterationController(verbose=True,tol_abs_gradnorm=1e-2)
+    ctrl = ift.GradientNormController(verbose=True,tol_abs_gradnorm=1e-2)
     inverter = ift.ConjugateGradient(controller=ctrl)
     wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N, R=R_harmonic, inverter=inverter)
 

demos/wiener_filter_via_hamiltonian.py

@@ -75,7 +75,7 @@ if __name__ == "__main__":
 
     # Choosing the minimization strategy
 
-    ctrl = ift.DefaultIterationController(verbose=True,tol_abs_gradnorm=0.1)
+    ctrl = ift.GradientNormController(verbose=True,tol_abs_gradnorm=0.1)
     inverter = ift.ConjugateGradient(controller=ctrl)
     # Setting starting position
     m0 = ift.Field(h_space, val=.0)

nifty/minimization/__init__.py

@@ -17,8 +17,7 @@
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
 from .line_searching import *
-from .iteration_controller import IterationController
-from .default_iteration_controller import DefaultIterationController
+from .iteration_controlling import *
 from .minimizer import Minimizer
 from .conjugate_gradient import ConjugateGradient
 from .nonlinear_cg import NonlinearCG

nifty/minimization/iteration_controlling/__init__.py

+from .iteration_controller import IterationController
+from .gradient_norm_controller import GradientNormController

nifty/minimization/default_iteration_controller.py → nifty/minimization/iteration_controlling/gradient_norm_controller.py

@@ -20,11 +20,11 @@ from __future__ import print_function
 from .iteration_controller import IterationController
 
 
-class DefaultIterationController(IterationController):
+class GradientNormController(IterationController):
     def __init__(self, tol_abs_gradnorm=None, tol_rel_gradnorm=None,
                  convergence_level=1, iteration_limit=None,
                  name=None, verbose=None):
-        super(DefaultIterationController, self).__init__()
+        super(GradientNormController, self).__init__()
         self._tol_abs_gradnorm = tol_abs_gradnorm
         self._tol_rel_gradnorm = tol_rel_gradnorm
         self._convergence_level = convergence_level

nifty/minimization/iteration_controller.py → nifty/minimization/iteration_controlling/iteration_controller.py

@@ -18,7 +18,7 @@
 
 from builtins import range
 import abc
-from ..nifty_meta import NiftyMeta
+from ...nifty_meta import NiftyMeta
 from future.utils import with_metaclass
 
 

test/test_minimization/test_minimizers.py

@@ -24,7 +24,7 @@ class Test_Minimizers(unittest.TestCase):
         covariance = ift.DiagonalOperator(space, diagonal=covariance_diagonal)
         required_result = ift.Field(space, val=1.)
 
-        IC = ift.DefaultIterationController(tol_abs_gradnorm=1e-5)
+        IC = ift.GradientNormController(tol_abs_gradnorm=1e-5)
         minimizer = minimizer_class(controller=IC)
         energy = ift.QuadraticEnergy(A=covariance, b=required_result,
                                      position=starting_point)