work with Vaggelis

4 files changed, 22 insertions, 26 deletions

diff --git a/Wrappers/Python/ccpi/framework/__init__.py b/Wrappers/Python/ccpi/framework/__init__.py
index 4683c21..66e2f56 100755
--- a/Wrappers/Python/ccpi/framework/__init__.py
+++ b/Wrappers/Python/ccpi/framework/__init__.py
@@ -22,3 +22,4 @@ from .framework import find_key, message
 from .framework import DataProcessor

 from .framework import AX, PixelByPixelDataProcessor, CastDataContainer

 from .BlockDataContainer import BlockDataContainer

+from .BlockGeometry import BlockGeometry

diff --git a/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py b/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py
index 54c947a..9267565 100644
--- a/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py
@@ -52,51 +52,43 @@ class SimpleL2NormSq(Function):
 class L2NormSq(SimpleL2NormSq):
     
     def __init__(self, **kwargs):
-                 
         super(L2NormSq, self).__init__()
         self.b = kwargs.get('b',None)              
-        
+
     def __call__(self, x):
-        
         if self.b is None:
             return SimpleL2NormSq.__call__(self, x)
         else:
             return SimpleL2NormSq.__call__(self, x - self.b) 
         
     def gradient(self, x):
-        
         if self.b is None:
             return 2 * x 
         else:
             return 2 * (x - self.b) 
-                                                                 
+                                                       
     def convex_conjugate(self, x):
-        
         ''' The convex conjugate corresponds to the simple functional i.e., 
         f(x) = alpha * ||x - b||_{2}^{2}
         '''
-        
         if self.b is None:
             return SimpleL2NormSq.convex_conjugate(self, x)
         else:
             return SimpleL2NormSq.convex_conjugate(self, x) + (self.b * x).sum()
-    
+
     def proximal(self, x, tau):
-        
+
         ''' The proximal operator corresponds to the simple functional i.e., 
         f(x) = alpha * ||x - b||_{2}^{2}
         
         argmin_x { 0.5||x - u||^{2} + tau f(x) }
-        '''
-        
+        '''        
         if self.b is None:
             return SimpleL2NormSq.proximal(self, x, tau)
         else:
             return self.b + SimpleL2NormSq.proximal(self, x - self.b , tau)
-            
     
     def proximal_conjugate(self, x, tau):
-        
         ''' The proximal operator corresponds to the simple convex conjugate 
         functional i.e., f^{*}(x^{)        
         argmin_x { 0.5||x - u||^{2} + tau f(x) }
@@ -105,5 +97,3 @@ class L2NormSq(SimpleL2NormSq):
             return SimpleL2NormSq.proximal_conjugate(self, x, tau)
         else:
             return SimpleL2NormSq.proximal_conjugate(self, x - tau * self.b, tau)
-     
-
diff --git a/Wrappers/Python/ccpi/optimisation/operators/FiniteDifferenceOperator.py b/Wrappers/Python/ccpi/optimisation/operators/FiniteDifferenceOperator.py
index 16cd215..999975c 100644
--- a/Wrappers/Python/ccpi/optimisation/operators/FiniteDifferenceOperator.py
+++ b/Wrappers/Python/ccpi/optimisation/operators/FiniteDifferenceOperator.py
@@ -24,9 +24,9 @@ class FiniteDiff(Operator):
     # Grad_order = ['channels', 'direction_z', 'direction_y', 'direction_x']
     
     def __init__(self, gm_domain, gm_range=None, direction=0, bnd_cond = 'Neumann'):
-        
+        ''''''
         super(FiniteDiff, self).__init__() 
-        
+        '''FIXME: domain and range should be geometries'''
         self.gm_domain = gm_domain
         self.gm_range = gm_range
         self.direction = direction
@@ -297,14 +297,16 @@ class FiniteDiff(Operator):
         res = out
         return res
             
-    def range_dim(self):
+    def range_geometry(self):
         return self.gm_range
     
-    def domain_dim(self):
+    def domain_geometry(self):
+        '''currently is a tuple'''
         return self.gm_domain
        
     def norm(self):
-        x0 = ImageData(np.random.random_sample(self.domain_dim()))
+        x0 = self.gm_domain.allocate()
+        x0 = np.random.random_sample(x0.shape)
         self.s1, sall, svec = PowerMethodNonsquare(self, 25, x0)
         return self.s1
     
diff --git a/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py b/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py
index 3dcc1bd..2eb77ce 100644
--- a/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py
+++ b/Wrappers/Python/ccpi/optimisation/operators/GradientOperator.py
@@ -11,6 +11,7 @@ from ccpi.optimisation.ops import PowerMethodNonsquare
 from ccpi.framework import ImageData, BlockDataContainer
 import numpy as np
 from ccpi.optimisation.operators import FiniteDiff
+from ccpi.framework import BlockGeometry
 
 #%%
 
@@ -57,11 +58,12 @@ class Gradient(Operator):
     def alloc_range_dim(self):
         return ImageData(np.zeros(self.range_dim))
     
-    def domain_dim(self):
+    def domain_geometry(self):
         return self.gm_domain
     
-    def range_dim(self):
-        return self.gm_range
+    def range_geometry(self):
+        '''fix this'''
+        return BlockGeometry(self.gm_range, self.gm_range)
                                    
     def norm(self):
 #        return np.sqrt(4*len(self.domainDim()))        
@@ -83,10 +85,10 @@ if __name__ == '__main__':
 #         DataContainer(np.random.randint(10, size=G.domain_dim()))]
 
     # domain_dim
-    print('Domain {}'.format(G.domain_dim()))
+    print('Domain {}'.format(G.domain_geometry()))
     
     # range_dim
-    print('Range {}'.format(G.range_dim()))
+    print('Range {}'.format(G.range_geometry()))
     
     # Direct
     z = G.direct(u)
@@ -104,7 +106,8 @@ if __name__ == '__main__':
     print(G.norm())
     
 #    print(G.adjoint(G.direct(u)))
-    
+
+