Distribution -> Distributor

demos/critical_filtering.py

@@ -106,7 +106,7 @@ if __name__ == "__main__":
         power0_energy = ift.library.NonlinearPowerEnergy(
             position=t0, d=d, N=N, xi=m0, D=D0, ht=HT,
             Instrument=MeasurementOperator, nonlinearity=nonlinearity,
-            Distribution=Distributor, sigma=1., samples=2, inverter=inverter)
+            Distributor=Distributor, sigma=1., samples=2, inverter=inverter)
 
         power0_energy = minimizer(power0_energy)[0]
 

demos/nonlinear_critical_filter.py

@@ -100,7 +100,7 @@ if __name__ == "__main__":
         power0_energy = ift.library.NonlinearPowerEnergy(
             position=t0, d=d, N=N, xi=m0, D=D0, ht=HT,
             Instrument=MeasurementOperator, nonlinearity=nonlinearity,
-            Distribution=Distributor, sigma=1., samples=2, inverter=inverter)
+            Distributor=Distributor, sigma=1., samples=2, inverter=inverter)
 
         power0_energy = minimizer(power0_energy)[0]
 

nifty4/library/noise_energy.py

@@ -25,7 +25,7 @@ from ..minimization.energy import Energy
 
 class NoiseEnergy(Energy):
     def __init__(self, position, d, xi, D, t, ht, Instrument,
-                 nonlinearity, alpha, q, Distribution, samples=3,
+                 nonlinearity, alpha, q, Distributor, samples=3,
                  xi_sample_list=None, inverter=None):
         super(NoiseEnergy, self).__init__(position=position)
         self.xi = xi
@@ -40,8 +40,8 @@ class NoiseEnergy(Energy):
 
         self.alpha = alpha
         self.q = q
-        self.Distribution = Distribution
-        self.power = self.Distribution(exp(0.5 * self.t))
+        self.Distributor = Distributor
+        self.power = self.Distributor(exp(0.5 * self.t))
         if xi_sample_list is None:
             if samples is None or samples == 0:
                 xi_sample_list = [xi]
@@ -51,7 +51,7 @@ class NoiseEnergy(Energy):
         self.xi_sample_list = xi_sample_list
         self.inverter = inverter
 
-        A = Distribution(exp(.5*self.t))
+        A = Distributor(exp(.5*self.t))
 
         self._gradient = None
         for sample in self.xi_sample_list:
@@ -81,7 +81,7 @@ class NoiseEnergy(Energy):
         return self.__class__(
             position, self.d, self.xi, self.D, self.t, self.ht,
             self.Instrument, self.nonlinearity, self.alpha, self.q,
-            self.Distribution, xi_sample_list=self.xi_sample_list,
+            self.Distributor, xi_sample_list=self.xi_sample_list,
             samples=self.samples, inverter=self.inverter)
 
     @property

nifty4/library/nonlinear_power_curvature.py

@@ -20,12 +20,12 @@ from ..operators.inversion_enabler import InversionEnabler
 from .response_operators import LinearizedPowerResponse
 
 
-def NonlinearPowerCurvature(tau, ht, Instrument, nonlinearity, Distribution, N,
+def NonlinearPowerCurvature(tau, ht, Instrument, nonlinearity, Distributor, N,
                             T, xi_sample_list, inverter):
     result = None
     for xi_sample in xi_sample_list:
         LinearizedResponse = LinearizedPowerResponse(
-            Instrument, nonlinearity, ht, Distribution, tau, xi_sample)
+            Instrument, nonlinearity, ht, Distributor, tau, xi_sample)
         op = LinearizedResponse.adjoint*N.inverse*LinearizedResponse
         result = op if result is None else result + op
     result = result*(1./len(xi_sample_list)) + T

nifty4/library/nonlinear_power_energy.py

@@ -52,7 +52,7 @@ class NonlinearPowerEnergy(Energy):
     """
     # MR FIXME: docstring incomplete and outdated
     def __init__(self, position, d, N, xi, D, ht, Instrument, nonlinearity,
-                 Distribution, sigma=0., samples=3, xi_sample_list=None,
+                 Distributor, sigma=0., samples=3, xi_sample_list=None,
                  inverter=None):
         super(NonlinearPowerEnergy, self).__init__(position)
         self.xi = xi
@@ -64,7 +64,7 @@ class NonlinearPowerEnergy(Energy):
         self.ht = ht
         self.Instrument = Instrument
         self.nonlinearity = nonlinearity
-        self.Distribution = Distribution
+        self.Distributor = Distributor
         self.sigma = sigma
         if xi_sample_list is None:
             if samples is None or samples == 0:
@@ -75,7 +75,7 @@ class NonlinearPowerEnergy(Energy):
         self.xi_sample_list = xi_sample_list
         self.inverter = inverter
 
-        A = Distribution(exp(.5 * position))
+        A = Distributor(exp(.5 * position))
         map_s = self.ht(A * xi)
         Tpos = self.T(position)
 
@@ -83,7 +83,7 @@ class NonlinearPowerEnergy(Energy):
         for xi_sample in self.xi_sample_list:
             map_s = self.ht(A * xi_sample)
             LinR = LinearizedPowerResponse(
-                self.Instrument, self.nonlinearity, self.ht, self.Distribution,
+                self.Instrument, self.nonlinearity, self.ht, self.Distributor,
                 self.position, xi_sample)
 
             residual = self.d - \
@@ -107,7 +107,7 @@ class NonlinearPowerEnergy(Energy):
     def at(self, position):
         return self.__class__(position, self.d, self.N, self.xi, self.D,
                               self.ht, self.Instrument, self.nonlinearity,
-                              self.Distribution, sigma=self.sigma,
+                              self.Distributor, sigma=self.sigma,
                               samples=len(self.xi_sample_list),
                               xi_sample_list=self.xi_sample_list,
                               inverter=self.inverter)
@@ -125,5 +125,5 @@ class NonlinearPowerEnergy(Energy):
     def curvature(self):
         return NonlinearPowerCurvature(
             self.position, self.ht, self.Instrument, self.nonlinearity,
-            self.Distribution, self.N, self.T, self.xi_sample_list,
+            self.Distributor, self.N, self.T, self.xi_sample_list,
             self.inverter)

nifty4/library/response_operators.py

@@ -23,9 +23,9 @@ def LinearizedSignalResponse(Instrument, nonlinearity, ht, power, m):
     return Instrument * nonlinearity.derivative(m) * ht * power
 
 
-def LinearizedPowerResponse(Instrument, nonlinearity, ht, Distribution, tau,
+def LinearizedPowerResponse(Instrument, nonlinearity, ht, Distributor, tau,
                             xi):
     power = exp(0.5*tau)
-    position = ht(Distribution(power)*xi)
+    position = ht(Distributor(power)*xi)
     linearization = nonlinearity.derivative(position)
-    return 0.5*Instrument*linearization*ht*xi*Distribution*power
+    return 0.5*Instrument*linearization*ht*xi*Distributor*power

test/test_energies/test_noise.py

@@ -86,7 +86,7 @@ class Noise_Energy_Tests(unittest.TestCase):
 
         energy0 = ift.library.NoiseEnergy(
             position=eta0, d=d, xi=xi, D=D, t=tau, Instrument=R,
-            alpha=alpha, q=q, Distribution=Dist, nonlinearity=f,
+            alpha=alpha, q=q, Distributor=Dist, nonlinearity=f,
             ht=ht, samples=10)
         energy1 = energy0.at(eta1)
 

test/test_energies/test_power.py

@@ -82,7 +82,7 @@ class Energy_Tests(unittest.TestCase):
             xi=xi,
             D=D,
             Instrument=R,
-            Distribution=Dist,
+            Distributor=Dist,
             nonlinearity=f,
             ht=ht,
             N=N,