jenkins errors

3 files changed, 127 insertions, 5 deletions

diff --git a/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py b/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py
index fb2bfd8..043fe38 100644
--- a/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py
+++ b/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py
@@ -6,11 +6,9 @@ Created on Mon Feb  4 16:18:06 2019
 @author: evangelos
 """
 from ccpi.optimisation.algorithms import Algorithm
-
-
 from ccpi.framework import ImageData
 import numpy as np
-import matplotlib.pyplot as plt
+#import matplotlib.pyplot as plt
 import time
 from ccpi.optimisation.operators import BlockOperator
 from ccpi.framework import BlockDataContainer
diff --git a/Wrappers/Python/ccpi/optimisation/algs.py b/Wrappers/Python/ccpi/optimisation/algs.py
index 15638a9..6b6ae2c 100755
--- a/Wrappers/Python/ccpi/optimisation/algs.py
+++ b/Wrappers/Python/ccpi/optimisation/algs.py
@@ -20,8 +20,8 @@
 import numpy
 import time
 
-from ccpi.optimisation.funcs import Function
-from ccpi.optimisation.funcs import ZeroFun
+from ccpi.optimisation.functions import Function
+from ccpi.optimisation.functions import ZeroFun
 from ccpi.framework import ImageData 
 from ccpi.framework import AcquisitionData
 from ccpi.optimisation.spdhg import spdhg 
diff --git a/Wrappers/Python/ccpi/optimisation/funcs.py b/Wrappers/Python/ccpi/optimisation/funcs.py
index 6741020..efc465c 100755
--- a/Wrappers/Python/ccpi/optimisation/funcs.py
+++ b/Wrappers/Python/ccpi/optimisation/funcs.py
@@ -22,7 +22,90 @@ import numpy
 from ccpi.framework import DataContainer
 import warnings
 from ccpi.optimisation.functions import Function
+def isSizeCorrect(data1 ,data2):
+    if issubclass(type(data1), DataContainer) and \
+       issubclass(type(data2), DataContainer):
+        # check dimensionality
+        if data1.check_dimensions(data2):
+            return True
+    elif issubclass(type(data1) , numpy.ndarray) and \
+         issubclass(type(data2) , numpy.ndarray):
+        return data1.shape == data2.shape
+    else:
+        raise ValueError("{0}: getting two incompatible types: {1} {2}"\
+                         .format('Function', type(data1), type(data2)))
+    return False
+class Norm2(Function):
+    
+    def __init__(self, 
+                 gamma=1.0, 
+                 direction=None):
+        super(Norm2, self).__init__()
+        self.gamma     = gamma;
+        self.direction = direction; 
+    
+    def __call__(self, x, out=None):
+        
+        if out is None:
+            xx = numpy.sqrt(numpy.sum(numpy.square(x.as_array()), self.direction,
+                                  keepdims=True))
+        else:
+            if isSizeCorrect(out, x):
+                # check dimensionality
+                if issubclass(type(out), DataContainer):
+                    arr = out.as_array()
+                    numpy.square(x.as_array(), out=arr)
+                    xx = numpy.sqrt(numpy.sum(arr, self.direction, keepdims=True))
+                        
+                elif issubclass(type(out) , numpy.ndarray):
+                    numpy.square(x.as_array(), out=out)
+                    xx = numpy.sqrt(numpy.sum(out, self.direction, keepdims=True))
+            else:
+                raise ValueError ('Wrong size: x{0} out{1}'.format(x.shape,out.shape) )
+        
+        p  = numpy.sum(self.gamma*xx)        
+        
+        return p
+    
+    def prox(self, x, tau):
+
+        xx = numpy.sqrt(numpy.sum( numpy.square(x.as_array()), self.direction, 
+                                  keepdims=True ))
+        xx = numpy.maximum(0, 1 - tau*self.gamma / xx)
+        p  = x.as_array() * xx
+        
+        return type(x)(p,geometry=x.geometry)
+    def proximal(self, x, tau, out=None):
+        if out is None:
+            return self.prox(x,tau)
+        else:
+            if isSizeCorrect(out, x):
+                # check dimensionality
+                if issubclass(type(out), DataContainer):
+                    numpy.square(x.as_array(), out = out.as_array())
+                    xx = numpy.sqrt(numpy.sum( out.as_array() , self.direction, 
+                                  keepdims=True ))
+                    xx = numpy.maximum(0, 1 - tau*self.gamma / xx)
+                    x.multiply(xx, out= out.as_array())
+                    
+                        
+                elif issubclass(type(out) , numpy.ndarray):
+                    numpy.square(x.as_array(), out=out)
+                    xx = numpy.sqrt(numpy.sum(out, self.direction, keepdims=True))
+                    
+                    xx = numpy.maximum(0, 1 - tau*self.gamma / xx)
+                    x.multiply(xx, out= out)
+            else:
+                raise ValueError ('Wrong size: x{0} out{1}'.format(x.shape,out.shape) )
+        
 
+class TV2D(Norm2):
+    
+    def __init__(self, gamma):
+        super(TV2D,self).__init__(gamma, 0)
+        self.op = FiniteDiff2D()
+        self.L = self.op.get_max_sing_val()
+        
 
 # Define a class for squared 2-norm
 class Norm2sq(Function):
@@ -146,3 +229,44 @@ class IndicatorBox(Function):
                 
             out.__imul__( self.sign_x )
 
+# A more interesting example, least squares plus 1-norm minimization.
+# Define class to represent 1-norm including prox function
+class Norm1(Function):
+    
+    def __init__(self,gamma):
+        super(Norm1, self).__init__()
+        self.gamma = gamma
+        self.L = 1
+        self.sign_x = None
+    
+    def __call__(self,x,out=None):
+        if out is None:
+            return self.gamma*(x.abs().sum())
+        else:
+            if not x.shape == out.shape:
+                raise ValueError('Norm1 Incompatible size:',
+                                 x.shape, out.shape)
+            x.abs(out=out)
+            return out.sum() * self.gamma
+    
+    def prox(self,x,tau):
+        return (x.abs() - tau*self.gamma).maximum(0) * x.sign()
+    
+    def proximal(self, x, tau, out=None):
+        if out is None:
+            return self.prox(x, tau)
+        else:
+            if isSizeCorrect(x,out):
+                # check dimensionality
+                if issubclass(type(out), DataContainer):
+                    v = (x.abs() - tau*self.gamma).maximum(0)
+                    x.sign(out=out)
+                    out *= v
+                    #out.fill(self.prox(x,tau))    
+                elif issubclass(type(out) , numpy.ndarray):
+                    v = (x.abs() - tau*self.gamma).maximum(0)
+                    out[:] = x.sign()
+                    out *= v
+                    #out[:] = self.prox(x,tau)
+            else:
+                raise ValueError ('Wrong size: x{0} out{1}'.format(x.shape,out.shape) )