deleted old PoissonEnergy

nifty5/library/__init__.py

 from .los_response import LOSResponse
 from .nonlinear_wiener_filter_energy import NonlinearWienerFilterEnergy
 from .nonlinearities import Exponential, Linear, PositiveTanh, Tanh
-from .poisson_energy import PoissonEnergy
 from .unit_log_gauss import UnitLogGauss
 from .poisson_log_likelihood import PoissonLogLikelihood
 from .wiener_filter_curvature import WienerFilterCurvature

nifty5/library/poisson_energy.py

-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-# Copyright(C) 2013-2018 Max-Planck-Society
-#
-# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
-# and financially supported by the Studienstiftung des deutschen Volkes.
-
-from ..minimization.energy import Energy
-from ..operators.diagonal_operator import DiagonalOperator
-from ..operators.sandwich_operator import SandwichOperator
-from ..operators.inversion_enabler import InversionEnabler
-from ..sugar import log
-
-
-class PoissonEnergy(Energy):
-    def __init__(self, position, d, Instrument, nonlinearity, ht, Phi_h,
-                 iteration_controller=None):
-        super(PoissonEnergy, self).__init__(position=position)
-        self._ic = iteration_controller
-        self._d = d
-        self._Instrument = Instrument
-        self._nonlinearity = nonlinearity
-        self._ht = ht
-        self._Phi_h = Phi_h
-        htpos = ht(position)
-        lam = Instrument(nonlinearity(htpos))
-        Rho = DiagonalOperator(nonlinearity.derivative(htpos))
-        eps = 1e-100  # to catch harmless 0/0 where data is zero
-        W = DiagonalOperator((d+eps)/(lam**2+eps))
-
-        phipos = Phi_h.inverse_adjoint_times(position)
-        prior_energy = 0.5*position.vdot(phipos)
-        likel_energy = lam.sum()-d.vdot(log(lam+eps))
-        self._value = prior_energy + likel_energy
-
-        R1 = Instrument*Rho*ht
-        self._grad = (phipos + R1.adjoint_times((lam-d)/(lam+eps))).lock()
-        self._curv = Phi_h.inverse + SandwichOperator.make(R1, W)
-
-    def at(self, position):
-        return self.__class__(position, self._d, self._Instrument,
-                              self._nonlinearity, self._ht, self._Phi_h,
-                              self._ic)
-
-    @property
-    def value(self):
-        return self._value
-
-    @property
-    def gradient(self):
-        return self._grad
-
-    @property
-    def curvature(self):
-        return InversionEnabler(self._curv, self._ic, self._Phi_h.inverse)

nifty5/library/poisson_log_likelihood.py

@@ -26,7 +26,7 @@ from ..sugar import log, makeOp
 class PoissonLogLikelihood(Energy):
     def __init__(self, lamb, d):
         """
-        s: Sky model object
+        lamb: Sky model object
 
         value = 0.5 * s.vdot(s), i.e. a log-Gauss distribution with unit
         covariance