Rename unitloggauss -> gaussianenergy

demos/getting_started_1.py

@@ -2,7 +2,9 @@ import nifty5 as ift
 import numpy as np
 from global_newton.models_other.apply_data import ApplyData
 from global_newton.models_energy.hamiltonian import Hamiltonian
-from nifty5.library.unit_log_gauss import UnitLogGauss
+from nifty5.library.gaussian_energy import GaussianEnergy
+
+
 if __name__ == '__main__':
     # s_space = ift.RGSpace([1024])
     s_space = ift.RGSpace([128,128])
@@ -45,7 +47,7 @@ if __name__ == '__main__':
     NWR = ApplyData(data, ift.Field(d_space,val=noise), Rs)
 
     INITIAL_POSITION = ift.from_random('normal',total_domain)
-    likelihood = UnitLogGauss(INITIAL_POSITION, NWR)
+    likelihood = GaussianEnergy(INITIAL_POSITION, NWR)
 
     IC = ift.GradientNormController(iteration_limit=500, tol_abs_gradnorm=1e-3)
     inverter = ift.ConjugateGradient(controller=IC)

nifty5/energies/hamiltonian.py

@@ -2,7 +2,7 @@ from ..minimization.energy import Energy
 from ..operators import InversionEnabler, SamplingEnabler
 from ..models.variable import Variable
 from ..utilities import memo
-from ..library.unit_log_gauss import UnitLogGauss
+from ..library.gaussian_energy import GaussianEnergy
 
 
 class Hamiltonian(Energy):
@@ -19,7 +19,7 @@ class Hamiltonian(Energy):
             self._ic_samp = iteration_controller
         else:
             self._ic_samp = iteration_controller_sampling
-        self._prior = UnitLogGauss(Variable(self.position))
+        self._prior = GaussianEnergy(Variable(self.position))
         self._precond = self._prior.curvature
 
     def at(self, position):

nifty5/library/__init__.py

@@ -2,7 +2,7 @@ from .amplitude_model import make_amplitude_model
 from .apply_data import ApplyData
 from .los_response import LOSResponse
 from .nonlinear_wiener_filter_energy import NonlinearWienerFilterEnergy
-from .unit_log_gauss import UnitLogGauss
+from .gaussian_energy import GaussianEnergy
 from .point_sources import PointSources
 from .poisson_log_likelihood import PoissonLogLikelihood
 from .smooth_sky import make_smooth_mf_sky_model, make_smooth_sky_model

nifty5/library/unit_log_gauss.py → nifty5/library/gaussian_energy.py

@@ -22,7 +22,7 @@ from ..operators.sandwich_operator import SandwichOperator
 from ..utilities import memo
 
 
-class UnitLogGauss(Energy):
+class GaussianEnergy(Energy):
     def __init__(self, s, inverter=None):
         """
         s: Sky model object
@@ -30,7 +30,7 @@ class UnitLogGauss(Energy):
         value = 0.5 * s.vdot(s), i.e. a log-Gauss distribution with unit
         covariance
         """
-        super(UnitLogGauss, self).__init__(s.position)
+        super(GaussianEnergy, self).__init__(s.position)
         self._s = s
         self._inverter = inverter
 

nifty5/library/nonlinear_wiener_filter_energy.py

@@ -17,12 +17,12 @@
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
 from ..models.constant import Constant
-from .unit_log_gauss import UnitLogGauss
+from .gaussian_energy import GaussianEnergy
 from ..energies.hamiltonian import Hamiltonian
 
 
 def NonlinearWienerFilterEnergy(measured_data, data_model, sqrtN, iteration_controller):
         d = measured_data.lock()
         residual = Constant(data_model.position, d) - data_model
-        lh = UnitLogGauss(sqrtN.inverse(residual))
+        lh = GaussianEnergy(sqrtN.inverse(residual))
         return Hamiltonian(lh, iteration_controller)