Merge branch 'master' into colourbay_initial_demo

20 files changed, 2951 insertions, 282 deletions

diff --git a/README.md b/README.md
index 06d30fe..bfbdbbd 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,10 @@
-# CCPi-PythonFramework
+
+| master build | pull request build |
+|--------|-------------|
+| [![Build Status](https://anvil.softeng-support.ac.uk/jenkins/buildStatus/icon?job=CILsingle/CCPi-Framework)](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Framework/) | [![Build Status](https://anvil.softeng-support.ac.uk/jenkins/buildStatus/icon?job=CILsingle/CCPi-Framework-dev)](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Framework-dev/) |
+
+# CCPi-Framework
+
 Basic Python Framework for CIL
 
 This package aims at ensuring a longer life and easy extensibility of the CIL software. This package provides a common framework, hence the name, for the analysis of data in the CT pipeline and quick development of novel reconstruction algorithms.
diff --git a/Wrappers/Python/ccpi/framework.py b/Wrappers/Python/ccpi/framework.py
index f4cd406..9938fb7 100644
--- a/Wrappers/Python/ccpi/framework.py
+++ b/Wrappers/Python/ccpi/framework.py
@@ -17,24 +17,31 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
+from __future__ import absolute_import
 from __future__ import division
-import abc
+from __future__ import print_function
+from __future__ import unicode_literals
+
 import numpy
 import sys
 from datetime import timedelta, datetime
 import warnings
 
-if sys.version_info[0] >= 3 and sys.version_info[1] >= 4:
-    ABC = abc.ABC
-else:
-    ABC = abc.ABCMeta('ABC', (), {})
-
 def find_key(dic, val):
     """return the key of dictionary dic given the value"""
     return [k for k, v in dic.items() if v == val][0]
 
+def message(cls, msg, *args):
+    msg = "{0}: " + msg
+    for i in range(len(args)):
+        msg += " {%d}" %(i+1)
+    args = list(args)
+    args.insert(0, cls.__name__ )
+    
+    return msg.format(*args )
+
 
-class ImageGeometry:
+class ImageGeometry(object):
     
     def __init__(self, 
                  voxel_num_x=0, 
@@ -105,7 +112,7 @@ class ImageGeometry:
         return repres
     
     
-class AcquisitionGeometry:
+class AcquisitionGeometry(object):
     
     def __init__(self, 
                  geom_type, 
@@ -211,7 +218,7 @@ class DataContainer(object):
         
         if type(array) == numpy.ndarray:
             if deep_copy:
-                self.array = array[:]
+                self.array = array.copy()
             else:
                 self.array = array    
         else:
@@ -335,12 +342,17 @@ class DataContainer(object):
     def fill(self, array, **dimension):
         '''fills the internal numpy array with the one provided'''
         if dimension == {}:
-            if numpy.shape(array) != numpy.shape(self.array):
-                raise ValueError('Cannot fill with the provided array.' + \
-                                 'Expecting {0} got {1}'.format(
-                                         numpy.shape(self.array),
-                                         numpy.shape(array)))
-            self.array = array[:]
+            if issubclass(type(array), DataContainer) or\
+               issubclass(type(array), numpy.ndarray):
+                if array.shape != self.shape:
+                    raise ValueError('Cannot fill with the provided array.' + \
+                                     'Expecting {0} got {1}'.format(
+                                     self.shape,array.shape))
+                if issubclass(type(array), DataContainer):
+                    numpy.copyto(self.array, array.array)
+                else:
+                    #self.array[:] = array
+                    numpy.copyto(self.array, array)
         else:
             
             command = 'self.array['
@@ -360,8 +372,9 @@ class DataContainer(object):
         
     def check_dimensions(self, other):
         return self.shape == other.shape
-        
-    def __add__(self, other):
+    
+    ## algebra 
+    def __add__(self, other , out=None, *args, **kwargs):
         if issubclass(type(other), DataContainer):    
             if self.check_dimensions(other):
                 out = self.as_array() + other.as_array()
@@ -407,7 +420,6 @@ class DataContainer(object):
         return self.__div__(other)
     
     def __div__(self, other):
-        print ("calling __div__")
         if issubclass(type(other), DataContainer):    
             if self.check_dimensions(other):
                 out = self.as_array() / other.as_array()
@@ -470,33 +482,6 @@ class DataContainer(object):
                             type(other)))
     # __mul__
     
-    
-    #def __abs__(self):
-    #    operation = FM.OPERATION.ABS
-    #    return self.callFieldMath(operation, None, self.mask, self.maskOnValue)
-    # __abs__
-    
-    def abs(self):
-        out = numpy.abs(self.as_array() )
-        return type(self)(out,
-                       deep_copy=True, 
-                       dimension_labels=self.dimension_labels,
-                       geometry=self.geometry)
-    
-    def maximum(self,otherscalar):
-        out = numpy.maximum(self.as_array(),otherscalar)
-        return type(self)(out,
-                       deep_copy=True, 
-                       dimension_labels=self.dimension_labels,
-                       geometry=self.geometry)
-    
-    def sign(self):
-        out = numpy.sign(self.as_array() )
-        return type(self)(out,
-                       deep_copy=True, 
-                       dimension_labels=self.dimension_labels,
-                       geometry=self.geometry)
-    
     # reverse operand
     def __radd__(self, other):
         return self + other
@@ -523,7 +508,7 @@ class DataContainer(object):
             return type(self)(fother ** self.array , 
                            dimension_labels=self.dimension_labels,
                            geometry=self.geometry)
-        elif issubclass(other, DataContainer):
+        elif issubclass(type(other), DataContainer):
             if self.check_dimensions(other):
                 return type(self)(other.as_array() ** self.array , 
                            dimension_labels=self.dimension_labels,
@@ -532,27 +517,55 @@ class DataContainer(object):
                 raise ValueError('Dimensions do not match')
     # __rpow__
     
-    def sum(self):
-        return self.as_array().sum()
-    
     # in-place arithmetic operators:
     # (+=, -=, *=, /= , //=,
+    # must return self
+    
+    
     
     def __iadd__(self, other):
-        return self + other
+        if isinstance(other, (int, float)) :
+            numpy.add(self.array, other, out=self.array)
+        elif issubclass(type(other), DataContainer):
+            if self.check_dimensions(other):
+                numpy.add(self.array, other.array, out=self.array)
+            else:
+                raise ValueError('Dimensions do not match')
+        return self
     # __iadd__
     
     def __imul__(self, other):
-        return self * other
+        if isinstance(other, (int, float)) :
+            arr = self.as_array()
+            numpy.multiply(arr, other, out=arr)
+        elif issubclass(type(other), DataContainer):
+            if self.check_dimensions(other):
+                numpy.multiply(self.array, other.array, out=self.array)
+            else:
+                raise ValueError('Dimensions do not match')
+        return self
     # __imul__
     
     def __isub__(self, other):
-        return self - other
+        if isinstance(other, (int, float)) :
+            numpy.subtract(self.array, other, out=self.array)
+        elif issubclass(type(other), DataContainer):
+            if self.check_dimensions(other):
+                numpy.subtract(self.array, other.array, out=self.array)
+            else:
+                raise ValueError('Dimensions do not match')
+        return self
     # __isub__
     
     def __idiv__(self, other):
-        print ("call __idiv__")
-        return self / other
+        if isinstance(other, (int, float)) :
+            numpy.divide(self.array, other, out=self.array)
+        elif issubclass(type(other), DataContainer):
+            if self.check_dimensions(other):
+                numpy.divide(self.array, other.array, out=self.array)
+            else:
+                raise ValueError('Dimensions do not match')
+        return self
     # __idiv__
     
     def __str__ (self, representation=False):
@@ -607,13 +620,112 @@ class DataContainer(object):
                                  .format(len(self.shape),len(new_order)))
         
                 
-                    
-                
+    def copy(self):
+        '''alias of clone'''    
+        return self.clone()
+    
+    ## binary operations
+            
+    def pixel_wise_binary(self,pwop, x2 , out=None, *args,  **kwargs):    
+
+        if out is None:
+            if isinstance(x2, (int, float, complex)):
+                out = pwop(self.as_array() , x2 , *args, **kwargs )
+            elif issubclass(type(x2) , DataContainer):
+                out = pwop(self.as_array() , x2.as_array() , *args, **kwargs )
+            return type(self)(out,
+                   deep_copy=False, 
+                   dimension_labels=self.dimension_labels,
+                   geometry=self.geometry)
+            
+        
+        elif issubclass(type(out), DataContainer) and issubclass(type(x2), DataContainer):
+            if self.check_dimensions(out) and self.check_dimensions(x2):
+                pwop(self.as_array(), x2.as_array(), out=out.as_array(), *args, **kwargs )
+                #return type(self)(out.as_array(),
+                #       deep_copy=False, 
+                #       dimension_labels=self.dimension_labels,
+                #       geometry=self.geometry)
+                return out
+            else:
+                raise ValueError(message(type(self),"Wrong size for data memory: ", out.shape,self.shape))
+        elif issubclass(type(out), DataContainer) and isinstance(x2, (int,float,complex)):
+            if self.check_dimensions(out):
+
+                pwop(self.as_array(), x2, out=out.as_array(), *args, **kwargs )
+                return out
+            else:
+                raise ValueError(message(type(self),"Wrong size for data memory: ", out.shape,self.shape))
+        elif issubclass(type(out), numpy.ndarray):
+            if self.array.shape == out.shape and self.array.dtype == out.dtype:
+                pwop(self.as_array(), x2 , out=out, *args, **kwargs)
+                #return type(self)(out,
+                #       deep_copy=False, 
+                #       dimension_labels=self.dimension_labels,
+                #       geometry=self.geometry)
+        else:
+            raise ValueError (message(type(self),  "incompatible class:" , pwop.__name__, type(out)))
+    
+    def add(self, other , out=None, *args, **kwargs):
+        return self.pixel_wise_binary(numpy.add, other, out=out, *args, **kwargs)
+    
+    def subtract(self, other, out=None , *args, **kwargs):
+        return self.pixel_wise_binary(numpy.subtract, other, out=out, *args, **kwargs)
+
+    def multiply(self, other , out=None, *args, **kwargs):
+        return self.pixel_wise_binary(numpy.multiply, other, out=out, *args, **kwargs)
+    
+    def divide(self, other , out=None ,*args, **kwargs):
+        return self.pixel_wise_binary(numpy.divide, other, out=out, *args, **kwargs)
+    
+    def power(self, other , out=None, *args, **kwargs):
+        return self.pixel_wise_binary(numpy.power, other, out=out, *args, **kwargs)
+    
+    def maximum(self,x2, out=None, *args, **kwargs):
+        return self.pixel_wise_binary(numpy.maximum, x2=x2, out=out, *args, **kwargs)
+    
+    ## unary operations
+    def pixel_wise_unary(self,pwop, out=None, *args,  **kwargs):
+        if out is None:
+            out = pwop(self.as_array() , *args, **kwargs )
+            return type(self)(out,
+                       deep_copy=False, 
+                       dimension_labels=self.dimension_labels,
+                       geometry=self.geometry)
+        elif issubclass(type(out), DataContainer):
+            if self.check_dimensions(out):
+                pwop(self.as_array(), out=out.as_array(), *args, **kwargs )
+            else:
+                raise ValueError(message(type(self),"Wrong size for data memory: ", out.shape,self.shape))
+        elif issubclass(type(out), numpy.ndarray):
+            if self.array.shape == out.shape and self.array.dtype == out.dtype:
+                pwop(self.as_array(), out=out, *args, **kwargs)
+        else:
+            raise ValueError (message(type(self),  "incompatible class:" , pwop.__name__, type(out)))
+    
+    def abs(self, out=None, *args,  **kwargs):
+        return self.pixel_wise_unary(numpy.abs, out=out, *args,  **kwargs)
+    
+    def sign(self, out=None, *args,  **kwargs):
+        return self.pixel_wise_unary(numpy.sign , out=out, *args,  **kwargs)
+    
+    def sqrt(self, out=None, *args,  **kwargs):
+        return self.pixel_wise_unary(numpy.sqrt, out=out, *args,  **kwargs)
+    
+    #def __abs__(self):
+    #    operation = FM.OPERATION.ABS
+    #    return self.callFieldMath(operation, None, self.mask, self.maskOnValue)
+    # __abs__
+    
+    ## reductions
+    def sum(self, out=None, *args, **kwargs):
+        return self.as_array().sum(*args, **kwargs)
+    
 class ImageData(DataContainer):
     '''DataContainer for holding 2D or 3D DataContainer'''
     def __init__(self, 
                  array = None, 
-                 deep_copy=True, 
+                 deep_copy=False, 
                  dimension_labels=None, 
                  **kwargs):
         
@@ -671,7 +783,7 @@ class ImageData(DataContainer):
                 raise ValueError('Please pass either a DataContainer, ' +\
                                  'a numpy array or a geometry')
         else:
-            if type(array) == DataContainer:
+            if issubclass(type(array) , DataContainer):
                 # if the array is a DataContainer get the info from there
                 if not ( array.number_of_dimensions == 2 or \
                          array.number_of_dimensions == 3 or \
@@ -683,7 +795,7 @@ class ImageData(DataContainer):
                 #                 array.dimension_labels, **kwargs)
                 super(ImageData, self).__init__(array.as_array(), deep_copy,
                                  array.dimension_labels, **kwargs)
-            elif type(array) == numpy.ndarray:
+            elif issubclass(type(array) , numpy.ndarray):
                 if not ( array.ndim == 2 or array.ndim == 3 or array.ndim == 4 ):
                     raise ValueError(
                             'Number of dimensions are not 2 or 3 or 4 : {0}'\
@@ -780,7 +892,7 @@ class AcquisitionData(DataContainer):
                                  dimension_labels, **kwargs)
         else:
             
-            if type(array) == DataContainer:
+            if issubclass(type(array) ,DataContainer):
                 # if the array is a DataContainer get the info from there
                 if not ( array.number_of_dimensions == 2 or \
                          array.number_of_dimensions == 3 or \
@@ -792,7 +904,7 @@ class AcquisitionData(DataContainer):
                 #                 array.dimension_labels, **kwargs)
                 super(AcquisitionData, self).__init__(array.as_array(), deep_copy,
                                  array.dimension_labels, **kwargs)
-            elif type(array) == numpy.ndarray:
+            elif issubclass(type(array) ,numpy.ndarray):
                 if not ( array.ndim == 2 or array.ndim == 3 or array.ndim == 4 ):
                     raise ValueError(
                             'Number of dimensions are not 2 or 3 or 4 : {0}'\
@@ -860,8 +972,9 @@ class DataProcessor(object):
         raise NotImplementedError('Implement basic checks for input DataContainer')
         
     def get_output(self):
-        if None in self.__dict__.values():
-            raise ValueError('Not all parameters have been passed')
+        for k,v in self.__dict__.items():
+            if v is None:
+                raise ValueError('Key {0} is None'.format(k))
         shouldRun = False
         if self.runTime == -1:
             shouldRun = True
@@ -1120,4 +1233,4 @@ if __name__ == '__main__':
                                        pixel_num_h=5 , channels=2)
     sino = AcquisitionData(geometry=sgeometry)
     sino2 = sino.clone()
-    
\ No newline at end of file
+    
diff --git a/Wrappers/Python/ccpi/io/reader.py b/Wrappers/Python/ccpi/io/reader.py
index b0b5414..856f5e0 100644
--- a/Wrappers/Python/ccpi/io/reader.py
+++ b/Wrappers/Python/ccpi/io/reader.py
@@ -22,6 +22,11 @@ This is a reader module with classes for loading 3D datasets.
 

 @author: Mr. Srikanth Nagella

 '''

+from __future__ import absolute_import

+from __future__ import division

+from __future__ import print_function

+from __future__ import unicode_literals

+

 from ccpi.framework import AcquisitionGeometry

 from ccpi.framework import AcquisitionData

 import numpy as np

@@ -53,7 +58,18 @@ class NexusReader(object):
         self.angles = None

         self.geometry = None

         self.filename = nexus_filename

-        

+        self.key_path = 'entry1/tomo_entry/instrument/detector/image_key'

+        self.data_path = 'entry1/tomo_entry/data/data'

+        self.angle_path = 'entry1/tomo_entry/data/rotation_angle'

+    

+    def get_image_keys(self):

+        try:

+            with NexusFile(self.filename,'r') as file:    

+                return np.array(file[self.key_path])

+        except KeyError as ke:

+            raise KeyError("get_image_keys: " , ke.args[0] , self.key_path)

+            

+    

     def load(self, dimensions=None, image_key_id=0):  

         '''

         This is generic loading function of flat field, dark field and projection data.

@@ -64,10 +80,10 @@ class NexusReader(object):
             return        

         try:

             with NexusFile(self.filename,'r') as file:                

-                image_keys = np.array(file['entry1/tomo_entry/instrument/detector/image_key'])                

+                image_keys = np.array(file[self.key_path])                

                 projections = None

                 if dimensions == None:

-                    projections = np.array(file['entry1/tomo_entry/data/data'])

+                    projections = np.array(file[self.data_path])

                     result = projections[image_keys==image_key_id]

                     return result

                 else:

@@ -76,8 +92,8 @@ class NexusReader(object):
                     projection_indexes = index_array[0][dimensions[0]]

                     new_dimensions = list(dimensions)

                     new_dimensions[0]= projection_indexes

-                    new_dimensions = tuple(new_dimensions)              

-                    result = np.array(file['entry1/tomo_entry/data/data'][new_dimensions])

+                    new_dimensions = tuple(new_dimensions)

+                    result = np.array(file[self.data_path][new_dimensions])

                     return result

         except:

             print("Error reading nexus file")

@@ -88,6 +104,12 @@ class NexusReader(object):
         Loads the projection data from the nexus file.

         returns: numpy array with projection data

         '''

+        try:

+            if 0 not in self.get_image_keys():

+                raise ValueError("Projections are not in the data. Data Path " , 

+                                 self.data_path)

+        except KeyError as ke:

+            raise KeyError(ke.args[0] , self.data_path)

         return self.load(dimensions, 0)

     

     def load_flat(self, dimensions=None):

@@ -95,6 +117,12 @@ class NexusReader(object):
         Loads the flat field data from the nexus file.

         returns: numpy array with flat field data

         '''        

+        try:

+            if 1 not in self.get_image_keys():

+                raise ValueError("Flats are not in the data. Data Path " , 

+                                 self.data_path)

+        except KeyError as ke:

+            raise KeyError(ke.args[0] , self.data_path)

         return self.load(dimensions, 1)

     

     def load_dark(self, dimensions=None):

@@ -102,6 +130,12 @@ class NexusReader(object):
         Loads the Dark field data from the nexus file.

         returns: numpy array with dark field data

         '''        

+        try:

+            if 2 not in self.get_image_keys():

+                raise ValueError("Darks are not in the data. Data Path " , 

+                             self.data_path)

+        except KeyError as ke:

+            raise KeyError(ke.args[0] , self.data_path)

         return self.load(dimensions, 2)

     

     def get_projection_angles(self):

@@ -114,11 +148,11 @@ class NexusReader(object):
             return        

         try:

             with NexusFile(self.filename,'r') as file:                

-                angles = np.array(file['entry1/tomo_entry/data/rotation_angle'],np.float32)

-                image_keys = np.array(file['entry1/tomo_entry/instrument/detector/image_key'])                

+                angles = np.array(file[self.angle_path],np.float32)

+                image_keys = np.array(file[self.key_path])                

                 return angles[image_keys==0]

         except:

-            print("Error reading nexus file")

+            print("get_projection_angles Error reading nexus file")

             raise        

 

     

@@ -132,8 +166,8 @@ class NexusReader(object):
             return

         try:

             with NexusFile(self.filename,'r') as file:                

-                projections = file['entry1/tomo_entry/data/data']

-                image_keys = np.array(file['entry1/tomo_entry/instrument/detector/image_key'])

+                projections = file[self.data_path]

+                image_keys = np.array(file[self.key_path])

                 dims = list(projections.shape)

                 dims[0] = dims[1]

                 dims[1] = np.sum(image_keys==0)

@@ -151,15 +185,25 @@ class NexusReader(object):
         if self.filename is None:

             return

         try:

-            with NexusFile(self.filename,'r') as file:                

-                projections = file['entry1/tomo_entry/data/data']

-                image_keys = np.array(file['entry1/tomo_entry/instrument/detector/image_key'])

+            with NexusFile(self.filename,'r') as file:

+                try:                

+                    projections = file[self.data_path]

+                except KeyError as ke:

+                    raise KeyError('Error: data path {0} not found\n{1}'\

+                                   .format(self.data_path, 

+                                           ke.args[0]))

+                #image_keys = np.array(file[self.key_path])

+                image_keys = self.get_image_keys()

                 dims = list(projections.shape)

                 dims[0] = np.sum(image_keys==0)

                 return tuple(dims)

         except:

-            print("Error reading nexus file")

-            raise  

+            print("Warning: Error reading image_keys trying accessing data on " , self.data_path)

+            with NexusFile(self.filename,'r') as file:

+                dims = file[self.data_path].shape

+                return tuple(dims)

+            

+              

         

     def get_acquisition_data(self, dimensions=None):

         '''

@@ -179,6 +223,160 @@ class NexusReader(object):
         return AcquisitionData(data, geometry=geometry, 

                                dimension_labels=['angle','vertical','horizontal'])   

     

+    def get_acquisition_data_subset(self, ymin=None, ymax=None):

+        '''

+        This method load the acquisition data and given dimension and returns an AcquisitionData Object

+        '''

+        if not h5pyAvailable:

+            raise Exception("Error: h5py is not installed")

+        if self.filename is None:

+            return        

+        try:

+            

+                

+            with NexusFile(self.filename,'r') as file:    

+                try:

+                    dims = self.get_projection_dimensions()

+                except KeyError:

+                    pass

+                dims = file[self.data_path].shape

+                if ymin is None and ymax is None:

+                    data = np.array(file[self.data_path])

+                else:

+                    if ymin is None:

+                        ymin = 0

+                        if ymax > dims[1]:

+                            raise ValueError('ymax out of range')

+                        data = np.array(file[self.data_path][:,:ymax,:])

+                    elif ymax is None:        

+                        ymax = dims[1]

+                        if ymin < 0:

+                            raise ValueError('ymin out of range')

+                        data = np.array(file[self.data_path][:,ymin:,:])

+                    else:

+                        if ymax > dims[1]:

+                            raise ValueError('ymax out of range')

+                        if ymin < 0:

+                            raise ValueError('ymin out of range')

+                        

+                        data = np.array(file[self.data_path]

+                            [: , ymin:ymax , :] )

+                

+        except:

+            print("Error reading nexus file")

+            raise

+        

+        

+        try:

+            angles = self.get_projection_angles()

+        except KeyError as ke:

+            n = data.shape[0]

+            angles = np.linspace(0, n, n+1, dtype=np.float32)

+        

+        if ymax-ymin > 1:        

+            

+            geometry = AcquisitionGeometry('parallel', '3D', 

+                                       angles,

+                                       pixel_num_h          = dims[2],

+                                       pixel_size_h         = 1 ,

+                                       pixel_num_v          = ymax-ymin,

+                                       pixel_size_v         = 1,

+                                       dist_source_center   = None, 

+                                       dist_center_detector = None, 

+                                       channels             = 1)

+            return AcquisitionData(data, False, geometry=geometry, 

+                               dimension_labels=['angle','vertical','horizontal']) 

+        elif ymax-ymin == 1:

+            geometry = AcquisitionGeometry('parallel', '2D', 

+                                       angles,

+                                       pixel_num_h          = dims[2],

+                                       pixel_size_h         = 1 ,

+                                       dist_source_center   = None, 

+                                       dist_center_detector = None, 

+                                       channels             = 1)

+            return AcquisitionData(data.squeeze(), False, geometry=geometry, 

+                               dimension_labels=['angle','horizontal']) 

+    def get_acquisition_data_slice(self, y_slice=0):

+        return self.get_acquisition_data_subset(ymin=y_slice , ymax=y_slice+1)

+    def get_acquisition_data_whole(self):

+        with NexusFile(self.filename,'r') as file:    

+            try:

+                dims = self.get_projection_dimensions()

+            except KeyError:

+                print ("Warning: ")

+                dims = file[self.data_path].shape

+                

+            ymin = 0 

+            ymax = dims[1] - 1

+            

+            return self.get_acquisition_data_subset(ymin=ymin, ymax=ymax)

+            

+        

+    

+    def list_file_content(self):

+        try:

+            with NexusFile(self.filename,'r') as file:                

+                file.visit(print)

+        except:

+            print("Error reading nexus file")

+            raise  

+    def get_acquisition_data_batch(self, bmin=None, bmax=None):

+        if not h5pyAvailable:

+            raise Exception("Error: h5py is not installed")

+        if self.filename is None:

+            return        

+        try:

+            

+                

+            with NexusFile(self.filename,'r') as file:    

+                try:

+                    dims = self.get_projection_dimensions()

+                except KeyError:

+                    dims = file[self.data_path].shape

+                if bmin is None or bmax is None:

+                    raise ValueError('get_acquisition_data_batch: please specify fastest index batch limits')

+                    

+                if bmin >= 0 and bmin < bmax and bmax <= dims[0]:

+                    data = np.array(file[self.data_path][bmin:bmax])

+                else:

+                    raise ValueError('get_acquisition_data_batch: bmin {0}>0 bmax {1}<{2}'.format(bmin, bmax, dims[0]))

+                    

+        except:

+            print("Error reading nexus file")

+            raise

+        

+        

+        try:

+            angles = self.get_projection_angles()[bmin:bmax]

+        except KeyError as ke:

+            n = data.shape[0]

+            angles = np.linspace(0, n, n+1, dtype=np.float32)[bmin:bmax]

+        

+        if bmax-bmin > 1:        

+            

+            geometry = AcquisitionGeometry('parallel', '3D', 

+                                       angles,

+                                       pixel_num_h          = dims[2],

+                                       pixel_size_h         = 1 ,

+                                       pixel_num_v          = bmax-bmin,

+                                       pixel_size_v         = 1,

+                                       dist_source_center   = None, 

+                                       dist_center_detector = None, 

+                                       channels             = 1)

+            return AcquisitionData(data, False, geometry=geometry, 

+                               dimension_labels=['angle','vertical','horizontal']) 

+        elif bmax-bmin == 1:

+            geometry = AcquisitionGeometry('parallel', '2D', 

+                                       angles,

+                                       pixel_num_h          = dims[2],

+                                       pixel_size_h         = 1 ,

+                                       dist_source_center   = None, 

+                                       dist_center_detector = None, 

+                                       channels             = 1)

+            return AcquisitionData(data.squeeze(), False, geometry=geometry, 

+                               dimension_labels=['angle','horizontal']) 

+        

+    

           

 class XTEKReader(object):

     '''

diff --git a/Wrappers/Python/ccpi/optimisation/algs.py b/Wrappers/Python/ccpi/optimisation/algs.py
index a45100c..a736277 100755
--- a/Wrappers/Python/ccpi/optimisation/algs.py
+++ b/Wrappers/Python/ccpi/optimisation/algs.py
@@ -21,6 +21,12 @@ import numpy
 import time
 
 from ccpi.optimisation.funcs import Function
+from ccpi.optimisation.funcs import ZeroFun
+from ccpi.framework import ImageData 
+from ccpi.framework import AcquisitionData
+from ccpi.optimisation.spdhg import spdhg 
+from ccpi.optimisation.spdhg import KullbackLeibler
+from ccpi.optimisation.spdhg import KullbackLeiblerConvexConjugate
 
 def FISTA(x_init, f=None, g=None, opt=None):
     '''Fast Iterative Shrinkage-Thresholding Algorithm
@@ -37,27 +43,27 @@ def FISTA(x_init, f=None, g=None, opt=None):
       opt: additional algorithm 
     '''
     # default inputs
-    if f   is None: f = Function()
-    if g   is None: g = Function()
+    if f   is None: f = ZeroFun()
+    if g   is None: g = ZeroFun()
     
     # algorithmic parameters
     if opt is None: 
-        opt = {'tol': 1e-4, 'iter': 1000}
-    else:
-        try:
-            max_iter = opt['iter']
-        except KeyError as ke:
-            opt[ke] = 1000
-        try:
-            opt['tol'] = 1000
-        except KeyError as ke:
-            opt[ke] = 1e-4
-    tol = opt['tol']
-    max_iter = opt['iter']
+        opt = {'tol': 1e-4, 'iter': 1000, 'memopt':False}
     
+    max_iter = opt['iter'] if 'iter' in opt.keys() else 1000
+    tol = opt['tol'] if 'tol' in opt.keys() else 1e-4
+    memopt = opt['memopt'] if 'memopt' in opt.keys() else False
+        
+        
     # initialization
-    x_old = x_init
-    y = x_init;
+    if memopt:
+        y = x_init.clone()
+        x_old = x_init.clone()
+        x = x_init.clone()
+        u = x_init.clone()
+    else:
+        x_old = x_init
+        y = x_init;
     
     timing = numpy.zeros(max_iter)
     criter = numpy.zeros(max_iter)
@@ -65,22 +71,44 @@ def FISTA(x_init, f=None, g=None, opt=None):
     invL = 1/f.L
     
     t_old = 1
+    
+    c = f(x_init) + g(x_init)
 
     # algorithm loop
     for it in range(0, max_iter):
     
         time0 = time.time()
-        
-        u = y - invL*f.grad(y)
-        
-        x = g.prox(u,invL)
-        
-        t = 0.5*(1 + numpy.sqrt(1 + 4*(t_old**2)))
-        
-        y = x + (t_old-1)/t*(x-x_old)
-        
-        x_old = x
-        t_old = t
+        if memopt:
+            # u = y - invL*f.grad(y)
+            # store the result in x_old
+            f.gradient(y, out=u)
+            u.__imul__( -invL )
+            u.__iadd__( y )
+            # x = g.prox(u,invL)
+            g.proximal(u, invL, out=x)
+            
+            t = 0.5*(1 + numpy.sqrt(1 + 4*(t_old**2)))
+            
+            # y = x + (t_old-1)/t*(x-x_old)
+            x.subtract(x_old, out=y)
+            y.__imul__ ((t_old-1)/t)
+            y.__iadd__( x )
+            
+            x_old.fill(x)
+            t_old = t
+            
+            
+        else:
+            u = y - invL*f.grad(y)
+            
+            x = g.prox(u,invL)
+            
+            t = 0.5*(1 + numpy.sqrt(1 + 4*(t_old**2)))
+            
+            y = x + (t_old-1)/t*(x-x_old)
+            
+            x_old = x.copy()
+            t_old = t
         
         # time and criterion
         timing[it] = time.time() - time0
@@ -92,12 +120,13 @@ def FISTA(x_init, f=None, g=None, opt=None):
     
         #print(it, 'out of', 10, 'iterations', end='\r');
 
-    criter = criter[0:it+1];
+    #criter = criter[0:it+1];
     timing = numpy.cumsum(timing[0:it+1]);
     
     return x, it, timing, criter
 
-def FBPD(x_init, f=None, g=None, h=None, opt=None):
+def FBPD(x_init, operator=None, constraint=None, data_fidelity=None,\
+         regulariser=None, opt=None):
     '''FBPD Algorithm
     
     Parameters:
@@ -108,9 +137,9 @@ def FBPD(x_init, f=None, g=None, h=None, opt=None):
       opt: additional algorithm 
     '''
     # default inputs
-    if f   is None: f = Function()
-    if g   is None: g = Function()
-    if h   is None: h = Function()
+    if constraint    is None: constraint    = ZeroFun()
+    if data_fidelity is None: data_fidelity = ZeroFun()
+    if regulariser   is None: regulariser   = ZeroFun()
     
     # algorithmic parameters
     if opt is None: 
@@ -126,20 +155,24 @@ def FBPD(x_init, f=None, g=None, h=None, opt=None):
             opt[ke] = 1e-4
     tol = opt['tol']
     max_iter = opt['iter']
+    memopt = opt['memopts'] if 'memopts' in opt.keys() else False
     
     # step-sizes
-    tau   = 2 / (g.L + 2);
-    sigma = (1/tau - g.L/2) / h.L;
+    tau   = 2 / (data_fidelity.L + 2);
+    sigma = (1/tau - data_fidelity.L/2) / regulariser.L;
     
     inv_sigma = 1/sigma
 
     # initialization
     x = x_init
-    y = h.op.direct(x);
+    y = operator.direct(x);
     
     timing = numpy.zeros(max_iter)
     criter = numpy.zeros(max_iter)
 
+    
+    
+        
     # algorithm loop
     for it in range(0, max_iter):
     
@@ -147,23 +180,23 @@ def FBPD(x_init, f=None, g=None, h=None, opt=None):
     
         # primal forward-backward step
         x_old = x;
-        x = x - tau * ( g.grad(x) + h.op.adjoint(y) );
-        x = f.prox(x, tau);
+        x = x - tau * ( data_fidelity.grad(x) + operator.adjoint(y) );
+        x = constraint.prox(x, tau);
     
         # dual forward-backward step
-        y = y + sigma * h.op.direct(2*x - x_old);
-        y = y - sigma * h.prox(inv_sigma*y, inv_sigma);   
+        y = y + sigma * operator.direct(2*x - x_old);
+        y = y - sigma * regulariser.prox(inv_sigma*y, inv_sigma);   
 
         # time and criterion
         timing[it] = time.time() - t
-        criter[it] = f(x) + g(x) + h(h.op.direct(x));
+        criter[it] = constraint(x) + data_fidelity(x) + regulariser(operator.direct(x))
            
         # stopping rule
         #if np.linalg.norm(x - x_old) < tol * np.linalg.norm(x_old) and it > 10:
         #   break
 
-    criter = criter[0:it+1];
-    timing = numpy.cumsum(timing[0:it+1]);
+    criter = criter[0:it+1]
+    timing = numpy.cumsum(timing[0:it+1])
     
     return x, it, timing, criter
 
@@ -191,8 +224,8 @@ def CGLS(x_init, operator , data , opt=None):
     tol = opt['tol']
     max_iter = opt['iter']
     
-    r = data.clone()
-    x = x_init.clone()
+    r = data.copy()
+    x = x_init.copy()
     
     d = operator.adjoint(r)
     
@@ -222,4 +255,66 @@ def CGLS(x_init, operator , data , opt=None):
         criter[it] = (r**2).sum()
     
     return x, it, timing, criter
+
+def SIRT(x_init, operator , data , opt=None, constraint=None):
+    '''Simultaneous Iterative Reconstruction Technique
+    
+    Parameters:
+      x_init: initial guess
+      operator: operator for forward/backward projections
+      data: data to operate on
+      opt: additional algorithm 
+      constraint: func of Indicator type specifying convex constraint.
+    '''
+    
+    if opt is None: 
+        opt = {'tol': 1e-4, 'iter': 1000}
+    else:
+        try:
+            max_iter = opt['iter']
+        except KeyError as ke:
+            opt[ke] = 1000
+        try:
+            opt['tol'] = 1000
+        except KeyError as ke:
+            opt[ke] = 1e-4
+    tol = opt['tol']
+    max_iter = opt['iter']
+    
+    # Set default constraint to unconstrained
+    if constraint==None:
+        constraint = Function()
+    
+    x = x_init.clone()
+    
+    timing = numpy.zeros(max_iter)
+    criter = numpy.zeros(max_iter)
+    
+    # Relaxation parameter must be strictly between 0 and 2. For now fix at 1.0
+    relax_par = 1.0
+    
+    # Set up scaling matrices D and M.
+    im1 = ImageData(geometry=x_init.geometry)
+    im1.array[:] = 1.0
+    M = 1/operator.direct(im1)
+    del im1
+    aq1 = AcquisitionData(geometry=M.geometry)
+    aq1.array[:] = 1.0
+    D = 1/operator.adjoint(aq1)
+    del aq1
+    
+    # algorithm loop
+    for it in range(0, max_iter):
+        t = time.time()
+        r = data - operator.direct(x)
+        
+        x = constraint.prox(x + relax_par * (D*operator.adjoint(M*r)),None)
+        
+        timing[it] = time.time() - t
+        if it > 0:
+            criter[it-1] = (r**2).sum()
+    
+    r = data - operator.direct(x)
+    criter[it] = (r**2).sum()
+    return x, it, timing,  criter
     
diff --git a/Wrappers/Python/ccpi/optimisation/funcs.py b/Wrappers/Python/ccpi/optimisation/funcs.py
index f5463a3..c7a6143 100755
--- a/Wrappers/Python/ccpi/optimisation/funcs.py
+++ b/Wrappers/Python/ccpi/optimisation/funcs.py
@@ -19,14 +19,32 @@
 
 from ccpi.optimisation.ops import Identity, FiniteDiff2D
 import numpy
+from ccpi.framework import DataContainer
 
 
+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 Function(object):
     def __init__(self):
-        self.op = Identity()
-    def __call__(self,x):      return 0
-    def grad(self,x):     return 0
-    def prox(self,x,tau): return x
+        self.L = None
+    def __call__(self,x, out=None):       raise NotImplementedError 
+    def grad(self, x):                    raise NotImplementedError
+    def prox(self, x, tau):               raise NotImplementedError
+    def gradient(self, x, out=None):      raise NotImplementedError
+    def proximal(self, x, tau, out=None): raise NotImplementedError
+
 
 class Norm2(Function):
     
@@ -37,10 +55,25 @@ class Norm2(Function):
         self.gamma     = gamma;
         self.direction = direction; 
     
-    def __call__(self, x):
+    def __call__(self, x, out=None):
         
-        xx = numpy.sqrt(numpy.sum(numpy.square(x.as_array()), self.direction,
+        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
@@ -53,6 +86,29 @@ class Norm2(Function):
         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):
     
@@ -79,23 +135,53 @@ class Norm2sq(Function):
     
     '''
     
-    def __init__(self,A,b,c=1.0):
+    def __init__(self,A,b,c=1.0,memopt=False):
+        super(Norm2sq, self).__init__()
+    
         self.A = A  # Should be an operator, default identity
         self.b = b  # Default zero DataSet?
         self.c = c  # Default 1.
+        self.memopt = memopt
+        if memopt:
+            #self.direct_placehold = A.adjoint(b)
+            self.direct_placehold = A.allocate_direct()
+            self.adjoint_placehold = A.allocate_adjoint()
+            
+        
+        # Compute the Lipschitz parameter from the operator if possible
+        # Leave it initialised to None otherwise
+        try:
+            self.L = 2.0*self.c*(self.A.get_max_sing_val()**2)
+        except AttributeError as ae:
+            pass
         
-        # Compute the Lipschitz parameter from the operator.
-        # Initialise to None instead and only call when needed.
-        self.L = 2.0*self.c*(self.A.get_max_sing_val()**2)
-        super(Norm2sq, self).__init__()
-    
     def grad(self,x):
         #return 2*self.c*self.A.adjoint( self.A.direct(x) - self.b )
-        return 2.0*self.c*self.A.adjoint( self.A.direct(x) - self.b )
+        return (2.0*self.c)*self.A.adjoint( self.A.direct(x) - self.b )
     
     def __call__(self,x):
         #return self.c* np.sum(np.square((self.A.direct(x) - self.b).ravel()))
-        return self.c*( ( (self.A.direct(x)-self.b)**2).sum() )
+        #if out is None:
+        #    return self.c*( ( (self.A.direct(x)-self.b)**2).sum() )
+        #else:
+        y = self.A.direct(x)
+        y.__isub__(self.b)
+        y.__imul__(y)
+        return y.sum() * self.c
+    
+    def gradient(self, x, out = None):
+        if self.memopt:
+            #return 2.0*self.c*self.A.adjoint( self.A.direct(x) - self.b )
+            
+            self.A.direct(x, out=self.adjoint_placehold)
+            self.adjoint_placehold.__isub__( self.b )
+            self.A.adjoint(self.adjoint_placehold, out=self.direct_placehold)
+            self.direct_placehold.__imul__(2.0 * self.c)
+            # can this be avoided?
+            out.fill(self.direct_placehold)
+        else:
+            return self.grad(x)
+            
 
 
 class ZeroFun(Function):
@@ -109,21 +195,103 @@ class ZeroFun(Function):
         return 0
     
     def prox(self,x,tau):
-        return x
+        return x.copy()
+    
+    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):    
+                    out.fill(x) 
+                            
+                elif issubclass(type(out) , numpy.ndarray): 
+                    out[:] = x  
+            else:   
+                raise ValueError ('Wrong size: x{0} out{1}'
+                                    .format(x.shape,out.shape) )
 
 # 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):
-        # Do nothing
         self.gamma = gamma
         self.L = 1
+        self.sign_x = None
         super(Norm1, self).__init__()
     
-    def __call__(self,x):
-        return self.gamma*(x.abs().sum())
+    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) )
+
+# Box constraints indicator function. Calling returns 0 if argument is within 
+# the box. The prox operator is projection onto the box. Only implements one 
+# scalar lower and one upper as constraint on all elements. Should generalise 
+# to vectors to allow different constraints one elements.
+class IndicatorBox(Function):
+    
+    def __init__(self,lower=-numpy.inf,upper=numpy.inf):
+        # Do nothing
+        self.lower = lower
+        self.upper = upper
+        super(IndicatorBox, self).__init__()
+    
+    def __call__(self,x):
+        
+        if (numpy.all(x.array>=self.lower) and 
+            numpy.all(x.array <= self.upper) ):
+            val = 0
+        else:
+            val = numpy.inf
+        return val
+    
+    def prox(self,x,tau=None):
+        return  (x.maximum(self.lower)).minimum(self.upper)
+    
+    def proximal(self, x, tau, out=None):
+        if out is None:
+            return self.prox(x, tau)
+        else:
+            if not x.shape == out.shape:
+                raise ValueError('Norm1 Incompatible size:',
+                                 x.shape, out.shape)
+            #(x.abs() - tau*self.gamma).maximum(0) * x.sign()
+            x.abs(out = out)
+            out.__isub__(tau*self.gamma)
+            out.maximum(0, out=out)
+            if self.sign_x is None or not x.shape == self.sign_x.shape:
+                self.sign_x = x.sign()
+            else:
+                x.sign(out=self.sign_x)
+                
+            out.__imul__( self.sign_x )
diff --git a/Wrappers/Python/ccpi/optimisation/ops.py b/Wrappers/Python/ccpi/optimisation/ops.py
index 668b07e..450b084 100755
--- a/Wrappers/Python/ccpi/optimisation/ops.py
+++ b/Wrappers/Python/ccpi/optimisation/ops.py
@@ -19,69 +19,126 @@
 
 import numpy
 from scipy.sparse.linalg import svds
+from ccpi.framework import DataContainer
+from ccpi.framework import AcquisitionData
+from ccpi.framework import ImageData
+from ccpi.framework import ImageGeometry
+from ccpi.framework import AcquisitionGeometry
 
 # Maybe operators need to know what types they take as inputs/outputs
 # to not just use generic DataContainer
 
 
 class Operator(object):
-    def direct(self,x):
+    def direct(self,x, out=None):
         return x
-    def adjoint(self,x):
+    def adjoint(self,x, out=None):
         return x
     def size(self):
         # To be defined for specific class
         raise NotImplementedError
     def get_max_sing_val(self):
         raise NotImplementedError
+    def allocate_direct(self):
+        raise NotImplementedError
+    def allocate_adjoint(self):
+        raise NotImplementedError
 
 class Identity(Operator):
     def __init__(self):
         self.s1 = 1.0
+        self.L = 1
         super(Identity, self).__init__()
         
-    def direct(self,x):
-        return x
+    def direct(self,x,out=None):
+        if out is None:
+            return x.copy()
+        else:
+            out.fill(x)
     
-    def adjoint(self,x):
-        return x
+    def adjoint(self,x, out=None):
+        if out is None:
+            return x.copy()
+        else:
+            out.fill(x)
+    
+    def size(self):
+        return NotImplemented
+    
+    def get_max_sing_val(self):
+        return self.s1
+
+class TomoIdentity(Operator):
+    def __init__(self, geometry, **kwargs):
+        self.s1 = 1.0
+        self.geometry = geometry
+        super(TomoIdentity, self).__init__()
+        
+    def direct(self,x,out=None):
+        if out is None:
+            return x.copy()
+        else:
+            out.fill(x)
+    
+    def adjoint(self,x, out=None):
+        if out is None:
+            return x.copy()
+        else:
+            out.fill(x)
     
     def size(self):
         return NotImplemented
     
     def get_max_sing_val(self):
         return self.s1
+    def allocate_direct(self):
+        if issubclass(type(self.geometry), ImageGeometry):
+            return ImageData(geometry=self.geometry)
+        elif issubclass(type(self.geometry), AcquisitionGeometry):
+            return AcquisitionData(geometry=self.geometry)
+        else:
+            raise ValueError("Wrong geometry type: expected ImageGeometry of AcquisitionGeometry, got ", type(self.geometry))
+    def allocate_adjoint(self):
+        return self.allocate_direct()
+    
+    
 
 class FiniteDiff2D(Operator):
     def __init__(self):
         self.s1 = 8.0
         super(FiniteDiff2D, self).__init__()
         
-    def direct(self,x):
+    def direct(self,x, out=None):
         '''Forward differences with Neumann BC.'''
+        # FIXME this seems to be working only with numpy arrays
+        
         d1 = numpy.zeros_like(x.as_array())
         d1[:,:-1] = x.as_array()[:,1:] - x.as_array()[:,:-1]
         d2 = numpy.zeros_like(x.as_array())
         d2[:-1,:] = x.as_array()[1:,:] - x.as_array()[:-1,:]
         d = numpy.stack((d1,d2),0)
-        
-        return type(x)(d,geometry=x.geometry)
+        #x.geometry.voxel_num_z = 2
+        return type(x)(d,False,geometry=x.geometry)
     
-    def adjoint(self,x):
+    def adjoint(self,x, out=None):
         '''Backward differences, Neumann BC.'''
         Nrows = x.get_dimension_size('horizontal_x')
-        Ncols = x.get_dimension_size('horizontal_x')
+        Ncols = x.get_dimension_size('horizontal_y')
         Nchannels = 1
         if len(x.shape) == 4:
             Nchannels = x.get_dimension_size('channel')
         zer = numpy.zeros((Nrows,1))
         xxx = x.as_array()[0,:,:-1]
-        h = numpy.concatenate((zer,xxx), 1) - numpy.concatenate((xxx,zer), 1)
+        #
+        h = numpy.concatenate((zer,xxx), 1) 
+        h -= numpy.concatenate((xxx,zer), 1)
         
         zer = numpy.zeros((1,Ncols))
         xxx = x.as_array()[1,:-1,:]
-        v = numpy.concatenate((zer,xxx), 0) - numpy.concatenate((xxx,zer), 0)
-        return type(x)(h + v,geometry=x.geometry)
+        #
+        v  = numpy.concatenate((zer,xxx), 0) 
+        v -= numpy.concatenate((xxx,zer), 0)
+        return type(x)(h + v, False, geometry=x.geometry)
     
     def size(self):
         return NotImplemented
@@ -129,10 +186,10 @@ def PowerMethodNonsquareOld(op,numiters):
 #    return s, x0
     
 
-def PowerMethodNonsquare(op,numiters):
+def PowerMethodNonsquare(op,numiters , x0=None):
     # Initialise random
     # Jakob's
-    #inputsize = op.size()[1]
+    # inputsize , outputsize = op.size()
     #x0 = ImageContainer(numpy.random.randn(*inputsize)
     # Edo's
     #vg = ImageGeometry(voxel_num_x=inputsize[0],
@@ -143,13 +200,16 @@ def PowerMethodNonsquare(op,numiters):
     #print (x0)
     #x0.fill(numpy.random.randn(*x0.shape))
     
-    x0 = op.create_image_data()
+    if x0 is None:
+        #x0 = op.create_image_data()
+        x0 = op.allocate_direct()
+        x0.fill(numpy.random.randn(*x0.shape))
     
     s = numpy.zeros(numiters)
     # Loop
     for it in numpy.arange(numiters):
         x1 = op.adjoint(op.direct(x0))
-        x1norm = numpy.sqrt((x1**2).sum())
+        x1norm = numpy.sqrt((x1*x1).sum())
         #print ("x0 **********" ,x0)
         #print ("x1 **********" ,x1)
         s[it] = (x1*x0).sum() / (x0*x0).sum()
@@ -162,11 +222,19 @@ class LinearOperatorMatrix(Operator):
         self.s1 = None   # Largest singular value, initially unknown
         super(LinearOperatorMatrix, self).__init__()
         
-    def direct(self,x):
-        return type(x)(numpy.dot(self.A,x.as_array()))
+    def direct(self,x, out=None):
+        if out is None:
+            return type(x)(numpy.dot(self.A,x.as_array()))
+        else:
+            numpy.dot(self.A, x.as_array(), out=out.as_array())
+            
     
-    def adjoint(self,x):
-        return type(x)(numpy.dot(self.A.transpose(),x.as_array()))
+    def adjoint(self,x, out=None):
+        if out is None:
+            return type(x)(numpy.dot(self.A.transpose(),x.as_array()))
+        else:
+            numpy.dot(self.A.transpose(),x.as_array(), out=out.as_array())
+            
     
     def size(self):
         return self.A.shape
@@ -178,3 +246,15 @@ class LinearOperatorMatrix(Operator):
             return self.s1
         else:
             return self.s1
+    def allocate_direct(self):
+        '''allocates the memory to hold the result of adjoint'''
+        #numpy.dot(self.A.transpose(),x.as_array())
+        M_A, N_A = self.A.shape
+        out = numpy.zeros((N_A,1))
+        return DataContainer(out)
+    def allocate_adjoint(self):
+        '''allocate the memory to hold the result of direct'''
+        #numpy.dot(self.A.transpose(),x.as_array())
+        M_A, N_A = self.A.shape
+        out = numpy.zeros((M_A,1))
+        return DataContainer(out)
diff --git a/Wrappers/Python/ccpi/optimisation/spdhg.py b/Wrappers/Python/ccpi/optimisation/spdhg.py
new file mode 100755
index 0000000..263a7cd
--- /dev/null
+++ b/Wrappers/Python/ccpi/optimisation/spdhg.py
@@ -0,0 +1,338 @@
+#   Copyright 2018 Matthias Ehrhardt, Edoardo Pasca

+

+#   Licensed under the Apache License, Version 2.0 (the "License");

+#   you may not use this file except in compliance with the License.

+#   You may obtain a copy of the License at

+

+#       http://www.apache.org/licenses/LICENSE-2.0

+

+#   Unless required by applicable law or agreed to in writing, software

+#   distributed under the License is distributed on an "AS IS" BASIS,

+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

+#   See the License for the specific language governing permissions and

+#   limitations under the License.

+

+from __future__ import absolute_import

+from __future__ import division

+from __future__ import print_function

+from __future__ import unicode_literals

+

+import numpy

+

+from ccpi.optimisation.funcs import Function

+from ccpi.framework import ImageData 

+from ccpi.framework import AcquisitionData

+

+

+class spdhg():

+    """Computes a saddle point with a stochastic PDHG.

+

+    This means, a solution (x*, y*), y* = (y*_1, ..., y*_n) such that

+

+    (x*, y*) in arg min_x max_y sum_i=1^n <y_i, A_i> - f*[i](y_i) + g(x)

+

+    where g : X -> IR_infty and f[i] : Y[i] -> IR_infty are convex, l.s.c. and

+    proper functionals. For this algorithm, they all may be non-smooth and no

+    strong convexity is assumed.

+

+    Parameters

+    ----------

+    f : list of functions

+        Functionals Y[i] -> IR_infty that all have a convex conjugate with a

+        proximal operator, i.e.

+        f[i].convex_conj.prox(sigma[i]) : Y[i] -> Y[i].

+    g : function

+        Functional X -> IR_infty that has a proximal operator, i.e.

+        g.prox(tau) : X -> X.

+    A : list of functions

+        Operators A[i] : X -> Y[i] that possess adjoints: A[i].adjoint

+    x : primal variable, optional

+        By default equals 0.

+    y : dual variable, optional

+        Part of a product space. By default equals 0.

+    z : variable, optional

+        Adjoint of dual variable, z = A^* y. By default equals 0 if y = 0.

+    tau : scalar / vector / matrix, optional

+        Step size for primal variable. Note that the proximal operator of g

+        has to be well-defined for this input.

+    sigma : scalar, optional

+        Scalar / vector / matrix used as step size for dual variable. Note that

+        the proximal operator related to f (see above) has to be well-defined

+        for this input.

+    prob : list of scalars, optional

+        Probabilities prob[i] that a subset i is selected in each iteration.

+        If fun_select is not given, then the sum of all probabilities must

+        equal 1.

+    A_norms : list of scalars, optional

+        Norms of the operators in A. Can be used to determine the step sizes

+        tau and sigma and the probabilities prob.

+    fun_select : function, optional

+        Function that selects blocks at every iteration IN -> {1,...,n}. By

+        default this is serial sampling, fun_select(k) selects an index

+        i \in {1,...,n} with probability prob[i].

+

+    References

+    ----------

+    [CERS2018] A. Chambolle, M. J. Ehrhardt, P. Richtarik and C.-B. Schoenlieb,

+    *Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling

+    and Imaging Applications*. SIAM Journal on Optimization, 28(4), 2783-2808

+    (2018) http://doi.org/10.1007/s10851-010-0251-1 

+

+    [E+2017] M. J. Ehrhardt, P. J. Markiewicz, P. Richtarik, J. Schott,

+    A. Chambolle and C.-B. Schoenlieb, *Faster PET reconstruction with a

+    stochastic primal-dual hybrid gradient method*. Wavelets and Sparsity XVII,

+    58 (2017) http://doi.org/10.1117/12.2272946.

+    

+    [EMS2018] M. J. Ehrhardt, P. J. Markiewicz and C.-B. Schoenlieb, *Faster 

+    PET Reconstruction with Non-Smooth Priors by Randomization and 

+    Preconditioning*. (2018) ArXiv: http://arxiv.org/abs/1808.07150 

+    """

+    

+    def __init__(self, f, g, A, x=None, y=None, z=None, tau=None, sigma=None, 

+                 prob=None, A_norms=None, fun_select=None):

+        # fun_select is optional and by default performs serial sampling

+                

+        if x is None:

+            x = A[0].allocate_direct(0)

+            

+        if y is None:

+            if z is not None:

+                raise ValueError('y and z have to be defaulted together')

+                       

+            y = [Ai.allocate_adjoint(0) for Ai in A]

+            z = 0 * x.copy()

+            

+        else:

+            if z is None:

+                raise ValueError('y and z have to be defaulted together')

+                            

+        if A_norms is not None:

+            if tau is not None or sigma is not None or prob is not None:

+                raise ValueError('Either A_norms or (tau, sigma, prob) must '

+                                 'be given')

+                

+            tau = 1 / sum(A_norms)

+            sigma = [1 / nA for nA in A_norms]

+            prob = [nA / sum(A_norms) for nA in A_norms]

+

+            #uniform prob, needs different sigma and tau

+            #n = len(A)

+            #prob = [1./n] * n

+                        

+        if fun_select is None:

+            if prob is None:

+                raise ValueError('prob was not determined')

+                

+            def fun_select(k):

+                return [int(numpy.random.choice(len(A), 1, p=prob))]

+

+        self.iter = 0

+        self.x = x

+        

+        self.y = y

+        self.z = z

+        

+        self.f = f

+        self.g = g

+        self.A = A

+        self.tau = tau

+        self.sigma = sigma

+        self.prob = prob

+        self.fun_select = fun_select

+

+        # Initialize variables

+        self.z_relax = z.copy()

+        self.tmp = self.x.copy()

+        

+    def update(self):

+        # select block

+        selected = self.fun_select(self.iter)

+

+        # update primal variable

+        #tmp = (self.x - self.tau * self.z_relax).as_array()

+        #self.x.fill(self.g.prox(tmp, self.tau))

+        self.tmp = - self.tau * self.z_relax

+        self.tmp += self.x

+        self.x = self.g.prox(self.tmp, self.tau)

+

+        # update dual variable and z, z_relax

+        self.z_relax = self.z.copy()

+        for i in selected:

+            # save old yi

+            y_old = self.y[i].copy()

+

+            # y[i]= prox(tmp)

+            tmp = y_old + self.sigma[i] * self.A[i].direct(self.x)

+            self.y[i] = self.f[i].convex_conj.prox(tmp, self.sigma[i])

+

+            # update adjoint of dual variable

+            dz = self.A[i].adjoint(self.y[i] - y_old)

+            self.z += dz

+

+            # compute extrapolation

+            self.z_relax += (1 + 1 / self.prob[i]) * dz

+

+        self.iter += 1            

+

+

+## Functions

+    

+class KullbackLeibler(Function):

+    def __init__(self, data, background):

+        self.data = data

+        self.background = background

+        self.__offset = None

+        

+    def __call__(self, x):

+        """Return the KL-diveregnce in the point ``x``.

+

+        If any components of ``x`` is non-positive, the value is positive

+        infinity.

+

+        Needs one extra array of memory of the size of `prior`.

+        """

+

+        # define short variable names

+        y = self.data

+        r = self.background

+

+        # Compute

+        #   sum(x + r - y + y * log(y / (x + r)))

+        # = sum(x - y * log(x + r)) + self.offset

+        # Assume that

+        #   x + r > 0

+

+        # sum the result up

+        obj = numpy.sum(x - y * numpy.log(x + r)) + self.offset()

+

+        if numpy.isnan(obj):

+            # In this case, some element was less than or equal to zero

+            return numpy.inf

+        else:

+            return obj

+    

+    @property

+    def convex_conj(self):

+        """The convex conjugate functional of the KL-functional."""

+        return KullbackLeiblerConvexConjugate(self.data, self.background)

+    

+    def offset(self):

+        """The offset which is independent of the unknown."""

+    

+        if self.__offset is None:

+            tmp = self.domain.element()

+    

+            # define short variable names

+            y = self.data

+            r = self.background

+    

+            tmp = self.domain.element(numpy.maximum(y, 1))

+            tmp = r - y + y * numpy.log(tmp)

+    

+            # sum the result up

+            self.__offset = numpy.sum(tmp)

+    

+        return self.__offset

+

+#     def __repr__(self):

+#         """to be added???"""

+#         """Return ``repr(self)``."""

+        # return '{}({!r}, {!r}, {!r})'.format(self.__class__.__name__,

+          ##                                    self.domain, self.data,

+           #                                   self.background)

+

+    

+class KullbackLeiblerConvexConjugate(Function):

+    """The convex conjugate of Kullback-Leibler divergence functional.

+

+    Notes

+    -----

+    The functional :math:`F^*` with prior :math:`g>0` is given by:

+

+    .. math::

+        F^*(x)

+        =

+        \\begin{cases}

+            \\sum_{i} \left( -g_i \ln(1 - x_i) \\right)

+            & \\text{if } x_i < 1 \\forall i

+            \\\\

+            +\\infty & \\text{else}

+        \\end{cases}

+

+    See Also

+    --------

+    KullbackLeibler : convex conjugate functional

+    """

+

+    def __init__(self, data, background):

+        self.data = data

+        self.background = background

+

+    def __call__(self, x):

+        y = self.data

+        r = self.background

+

+        tmp = numpy.sum(- x * r - y * numpy.log(1 - x))

+

+        if numpy.isnan(tmp):

+            # In this case, some element was larger than or equal to one

+            return numpy.inf

+        else:

+            return tmp

+

+

+    def prox(self, x, tau, out=None):

+        # Let y = data, r = background, z = x + tau * r

+        # Compute 0.5 * (z + 1 - sqrt((z - 1)**2 + 4 * tau * y))

+        # Currently it needs 3 extra copies of memory.

+

+        if out is None:

+            out = x.copy()

+

+        # define short variable names

+        try: # this should be standard SIRF/CIL mode

+            y = self.data.as_array()

+            r = self.background.as_array()

+            x = x.as_array()

+            

+            try:

+                taua = tau.as_array()

+            except:

+                taua = tau

+    

+            z = x + tau * r

+                        

+            out.fill(0.5 * (z + 1 - numpy.sqrt((z - 1) ** 2 + 4 * taua * y)))

+            

+            return out

+            

+        except: # e.g. for NumPy

+            y = self.data

+            r = self.background

+

+            try:

+                taua = tau.as_array()

+            except:

+                taua = tau

+    

+            z = x + tau * r

+                        

+            out[:] = 0.5 * (z + 1 - numpy.sqrt((z - 1) ** 2 + 4 * taua * y))

+            

+            return out 

+   

+    @property

+    def convex_conj(self):

+        return KullbackLeibler(self.data, self.background)

+

+

+def mult(x, y):

+    try:

+        xa = x.as_array()

+    except:

+        xa = x

+        

+    out = y.clone()

+    out.fill(xa * y.as_array())

+

+    return out

diff --git a/Wrappers/Python/ccpi/processors.py b/Wrappers/Python/ccpi/processors.py
index a2d0446..6a9057a 100755
--- a/Wrappers/Python/ccpi/processors.py
+++ b/Wrappers/Python/ccpi/processors.py
@@ -19,6 +19,7 @@
 

 from ccpi.framework import DataProcessor, DataContainer, AcquisitionData,\

  AcquisitionGeometry, ImageGeometry, ImageData

+from ccpi.reconstruction.parallelbeam import alg as pbalg

 import numpy

 from scipy import ndimage

 

@@ -39,8 +40,8 @@ class Normalizer(DataProcessor):
     

     def __init__(self, flat_field = None, dark_field = None, tolerance = 1e-5):

         kwargs = {

-                  'flat_field'  : None, 

-                  'dark_field'  : None,

+                  'flat_field'  : flat_field, 

+                  'dark_field'  : dark_field,

                   # very small number. Used when there is a division by zero

                   'tolerance'   : tolerance

                   }

@@ -53,7 +54,8 @@ class Normalizer(DataProcessor):
             self.set_dark_field(dark_field)

     

     def check_input(self, dataset):

-        if dataset.number_of_dimensions == 3:

+        if dataset.number_of_dimensions == 3 or\

+           dataset.number_of_dimensions == 2:

                return True

         else:

             raise ValueError("Expected input dimensions is 2 or 3, got {0}"\

@@ -65,7 +67,7 @@ class Normalizer(DataProcessor):
                 raise ValueError('Dark Field should be 2D')

             elif len(numpy.shape(df)) == 2:

                 self.dark_field = df

-        elif issubclass(type(df), DataSet):

+        elif issubclass(type(df), DataContainer):

             self.dark_field = self.set_dark_field(df.as_array())

     

     def set_flat_field(self, df):

@@ -74,7 +76,7 @@ class Normalizer(DataProcessor):
                 raise ValueError('Flat Field should be 2D')

             elif len(numpy.shape(df)) == 2:

                 self.flat_field = df

-        elif issubclass(type(df), DataSet):

+        elif issubclass(type(df), DataContainer):

             self.flat_field = self.set_flat_field(df.as_array())

     

     @staticmethod

@@ -86,22 +88,40 @@ class Normalizer(DataProcessor):
             c[ ~ numpy.isfinite( c )] = tolerance # set to not zero if 0/0 

         return c

     

+    @staticmethod

+    def estimate_normalised_error(projection, flat, dark, delta_flat, delta_dark):

+        '''returns the estimated relative error of the normalised projection

+        

+        n = (projection - dark) / (flat - dark)

+        Dn/n = (flat-dark + projection-dark)/((flat-dark)*(projection-dark))*(Df/f + Dd/d)

+        ''' 

+        a = (projection - dark)

+        b = (flat-dark)

+        df = delta_flat / flat

+        dd = delta_dark / dark

+        rel_norm_error = (b + a) / (b * a) * (df + dd)

+        return rel_norm_error

+        

     def process(self):

         

         projections = self.get_input()

         dark = self.dark_field

         flat = self.flat_field

         

-        if not (projections.shape[1:] == dark.shape and \

-           projections.shape[1:] == flat.shape):

-            raise ValueError('Flats/Dark and projections size do not match.')

-            

-               

-        a = numpy.asarray(

-                [ Normalizer.normalize_projection(

-                        projection, flat, dark, self.tolerance) \

-                 for projection in projections.as_array() ]

-                )

+        if projections.number_of_dimensions == 3:

+            if not (projections.shape[1:] == dark.shape and \

+               projections.shape[1:] == flat.shape):

+                raise ValueError('Flats/Dark and projections size do not match.')

+                

+                   

+            a = numpy.asarray(

+                    [ Normalizer.normalize_projection(

+                            projection, flat, dark, self.tolerance) \

+                     for projection in projections.as_array() ]

+                    )

+        elif projections.number_of_dimensions == 2:

+            a = Normalizer.normalize_projection(projections.as_array(), 

+                                                flat, dark, self.tolerance)

         y = type(projections)( a , True, 

                     dimension_labels=projections.dimension_labels,

                     geometry=projections.geometry)

@@ -388,3 +408,107 @@ class CenterOfRotationFinder(DataProcessor):
         

         return cor

 

+            

+class AcquisitionDataPadder(DataProcessor):

+    '''Normalization based on flat and dark

+    

+    This processor read in a AcquisitionData and normalises it based on 

+    the instrument reading with and without incident photons or neutrons.

+    

+    Input: AcquisitionData

+    Parameter: 2D projection with flat field (or stack)

+               2D projection with dark field (or stack)

+    Output: AcquisitionDataSetn

+    '''

+    

+    def __init__(self, 

+                 center_of_rotation   = None,

+                 acquisition_geometry = None,

+                 pad_value            = 1e-5):

+        kwargs = {

+                  'acquisition_geometry' : acquisition_geometry, 

+                  'center_of_rotation'   : center_of_rotation,

+                  'pad_value'            : pad_value

+                  }

+        

+        super(AcquisitionDataPadder, self).__init__(**kwargs)

+        

+    def check_input(self, dataset):

+        if self.acquisition_geometry is None:

+            self.acquisition_geometry = dataset.geometry

+        if dataset.number_of_dimensions == 3:

+               return True

+        else:

+            raise ValueError("Expected input dimensions is 2 or 3, got {0}"\

+                             .format(dataset.number_of_dimensions))

+

+    def process(self):

+        projections = self.get_input()

+        w = projections.get_dimension_size('horizontal')

+        delta = w - 2 * self.center_of_rotation

+               

+        padded_width = int (

+                numpy.ceil(abs(delta)) + w

+                )

+        delta_pix = padded_width - w

+        

+        voxel_per_pixel = 1

+        geom = pbalg.pb_setup_geometry_from_acquisition(projections.as_array(),

+                                            self.acquisition_geometry.angles,

+                                            self.center_of_rotation,

+                                            voxel_per_pixel )

+        

+        padded_geometry = self.acquisition_geometry.clone()

+        

+        padded_geometry.pixel_num_h = geom['n_h']

+        padded_geometry.pixel_num_v = geom['n_v']

+        

+        delta_pix_h = padded_geometry.pixel_num_h - self.acquisition_geometry.pixel_num_h

+        delta_pix_v = padded_geometry.pixel_num_v - self.acquisition_geometry.pixel_num_v

+        

+        if delta_pix_h == 0:

+            delta_pix_h = delta_pix

+            padded_geometry.pixel_num_h = padded_width

+        #initialize a new AcquisitionData with values close to 0

+        out = AcquisitionData(geometry=padded_geometry)

+        out = out + self.pad_value

+        

+        

+        #pad in the horizontal-vertical plane -> slice on angles

+        if delta > 0:

+            #pad left of middle

+            command = "out.array["

+            for i in range(out.number_of_dimensions):

+                if out.dimension_labels[i] == 'horizontal':

+                    value = '{0}:{1}'.format(delta_pix_h, delta_pix_h+w)

+                    command = command + str(value)

+                else:

+                    if out.dimension_labels[i] == 'vertical' :

+                        value = '{0}:'.format(delta_pix_v)

+                        command = command + str(value)

+                    else:

+                        command = command + ":"

+                if i < out.number_of_dimensions -1:

+                    command = command + ','

+            command = command + '] = projections.array'

+            #print (command)    

+        else:

+            #pad right of middle

+            command = "out.array["

+            for i in range(out.number_of_dimensions):

+                if out.dimension_labels[i] == 'horizontal':

+                    value = '{0}:{1}'.format(0, w)

+                    command = command + str(value)

+                else:

+                    if out.dimension_labels[i] == 'vertical' :

+                        value = '{0}:'.format(delta_pix_v)

+                        command = command + str(value)

+                    else:

+                        command = command + ":"

+                if i < out.number_of_dimensions -1:

+                    command = command + ','

+            command = command + '] = projections.array'

+            #print (command)    

+            #cleaned = eval(command)

+        exec(command)

+        return out
\ No newline at end of file
diff --git a/Wrappers/Python/conda-recipe/bld.bat b/Wrappers/Python/conda-recipe/bld.bat
index 4b4c7f5..97a4e62 100644
--- a/Wrappers/Python/conda-recipe/bld.bat
+++ b/Wrappers/Python/conda-recipe/bld.bat
@@ -1,8 +1,8 @@
+
 IF NOT DEFINED CIL_VERSION (
 ECHO CIL_VERSION Not Defined.
 exit 1
 )
-
 ROBOCOPY /E "%RECIPE_DIR%\.." "%SRC_DIR%"
 
 %PYTHON% setup.py build_py
diff --git a/Wrappers/Python/conda-recipe/build.sh b/Wrappers/Python/conda-recipe/build.sh
index 5dd97b0..43e85d5 100644
--- a/Wrappers/Python/conda-recipe/build.sh
+++ b/Wrappers/Python/conda-recipe/build.sh
@@ -1,7 +1,7 @@
 if [ -z "$CIL_VERSION" ]; then
     echo "Need to set CIL_VERSION"
     exit 1
-fi  
+fi
 mkdir ${SRC_DIR}/ccpi
 cp -r "${RECIPE_DIR}/../../../" ${SRC_DIR}/ccpi
 
diff --git a/Wrappers/Python/conda-recipe/conda_build_config.yaml b/Wrappers/Python/conda-recipe/conda_build_config.yaml
new file mode 100644
index 0000000..96a211f
--- /dev/null
+++ b/Wrappers/Python/conda-recipe/conda_build_config.yaml
@@ -0,0 +1,8 @@
+python:
+  - 2.7 # [not win]
+  - 3.5
+  - 3.6
+numpy:
+  # TODO investigage, as it doesn't currently build with cvxp, requires >1.14
+  #- 1.12
+  - 1.15
diff --git a/Wrappers/Python/conda-recipe/meta.yaml b/Wrappers/Python/conda-recipe/meta.yaml
index f72c980..1b7cae6 100644
--- a/Wrappers/Python/conda-recipe/meta.yaml
+++ b/Wrappers/Python/conda-recipe/meta.yaml
@@ -2,24 +2,31 @@ package:
   name: ccpi-framework
   version: {{ environ['CIL_VERSION'] }}
 
-
 build:
   preserve_egg_dir: False
   script_env:
     - CIL_VERSION   
-#  number: 0
+  #number: 0  
   
+test:
+  requires:
+    - python-wget
+    - cvxpy # [not win]
+    
 requirements:
   build:
     - python
-    - numpy
+    - numpy {{ numpy }}
     - setuptools
 
   run:
+    - {{ pin_compatible('numpy', max_pin='x.x') }}
     - python
     - numpy
+    - scipy
     - matplotlib
-	
+    - h5py
+  
 about:
   home: http://www.ccpi.ac.uk
   license:  Apache 2.0 License
diff --git a/Wrappers/Python/conda-recipe/run_test.py b/Wrappers/Python/conda-recipe/run_test.py
index 6a20d05..5bf6538 100755
--- a/Wrappers/Python/conda-recipe/run_test.py
+++ b/Wrappers/Python/conda-recipe/run_test.py
@@ -1,160 +1,883 @@
 import unittest

 import numpy

-from ccpi.framework import DataContainer, ImageData, AcquisitionData, \

-  ImageGeometry, AcquisitionGeometry

+import numpy as np

+from ccpi.framework import DataContainer

+from ccpi.framework import ImageData

+from ccpi.framework import AcquisitionData

+from ccpi.framework import ImageGeometry

+from ccpi.framework import AcquisitionGeometry

 import sys

+from timeit import default_timer as timer

+from ccpi.optimisation.algs import FISTA

+from ccpi.optimisation.algs import FBPD

+from ccpi.optimisation.funcs import Norm2sq

+from ccpi.optimisation.funcs import ZeroFun

+from ccpi.optimisation.funcs import Norm1

+from ccpi.optimisation.funcs import TV2D

+from ccpi.optimisation.funcs import Norm2

+

+from ccpi.optimisation.ops import LinearOperatorMatrix

+from ccpi.optimisation.ops import TomoIdentity

+from ccpi.optimisation.ops import Identity

+

+

+import numpy.testing

+import wget

+import os

+from ccpi.io.reader import NexusReader

+

+

+try:

+    from cvxpy import *

+    cvx_not_installable = False

+except ImportError:

+    cvx_not_installable = True

+

+

+def aid(x):

+    # This function returns the memory

+    # block address of an array.

+    return x.__array_interface__['data'][0]

+

+

+def dt(steps):

+    return steps[-1] - steps[-2]

+

 

 class TestDataContainer(unittest.TestCase):

-    

+    def create_simple_ImageData(self):

+        N = 64

+        ig = ImageGeometry(voxel_num_x=N, voxel_num_y=N)

+        Phantom = ImageData(geometry=ig)

+

+        x = Phantom.as_array()

+

+        x[int(round(N/4)):int(round(3*N/4)),

+          int(round(N/4)):int(round(3*N/4))] = 0.5

+        x[int(round(N/8)):int(round(7*N/8)),

+          int(round(3*N/8)):int(round(5*N/8))] = 1

+

+        return (ig, Phantom)

+

+    def create_DataContainer(self, X,Y,Z, value=1):

+        steps = [timer()]

+        a = value * numpy.ones((X, Y, Z), dtype='float32')

+        steps.append(timer())

+        t0 = dt(steps)

+        #print("a refcount " , sys.getrefcount(a))

+        ds = DataContainer(a, False, ['X', 'Y', 'Z'])

+        return ds

+

     def test_creation_nocopy(self):

-        shape = (2,3,4,5)

+        shape = (2, 3, 4, 5)

         size = shape[0]

         for i in range(1, len(shape)):

             size = size * shape[i]

         #print("a refcount " , sys.getrefcount(a))

-        a = numpy.asarray([i for i in range( size )])

+        a = numpy.asarray([i for i in range(size)])

         #print("a refcount " , sys.getrefcount(a))

         a = numpy.reshape(a, shape)

         #print("a refcount " , sys.getrefcount(a))

-        ds = DataContainer(a, False, ['X', 'Y','Z' ,'W'])

+        ds = DataContainer(a, False, ['X', 'Y', 'Z', 'W'])

         #print("a refcount " , sys.getrefcount(a))

-        self.assertEqual(sys.getrefcount(a),3)

-        self.assertEqual(ds.dimension_labels , {0: 'X', 1: 'Y', 2: 'Z', 3: 'W'})

-        

+        self.assertEqual(sys.getrefcount(a), 3)

+        self.assertEqual(ds.dimension_labels, {0: 'X', 1: 'Y', 2: 'Z', 3: 'W'})

+

+    def testGb_creation_nocopy(self):

+        X, Y, Z = 512, 512, 512

+        X, Y, Z = 256, 512, 512

+        steps = [timer()]

+        a = numpy.ones((X, Y, Z), dtype='float32')

+        steps.append(timer())

+        t0 = dt(steps)

+        print("test clone")

+        #print("a refcount " , sys.getrefcount(a))

+        ds = DataContainer(a, False, ['X', 'Y', 'Z'])

+        #print("a refcount " , sys.getrefcount(a))

+        self.assertEqual(sys.getrefcount(a), 3)

+        ds1 = ds.copy()

+        self.assertNotEqual(aid(ds.as_array()), aid(ds1.as_array()))

+        ds1 = ds.clone()

+        self.assertNotEqual(aid(ds.as_array()), aid(ds1.as_array()))

+

+    def testInlineAlgebra(self):

+        print("Test Inline Algebra")

+        X, Y, Z = 1024, 512, 512

+        X, Y, Z = 256, 512, 512

+        steps = [timer()]

+        a = numpy.ones((X, Y, Z), dtype='float32')

+        steps.append(timer())

+        t0 = dt(steps)

+        print(t0)

+        #print("a refcount " , sys.getrefcount(a))

+        ds = DataContainer(a, False, ['X', 'Y', 'Z'])

+        #ds.__iadd__( 2 )

+        ds += 2

+        steps.append(timer())

+        print(dt(steps))

+        self.assertEqual(ds.as_array()[0][0][0], 3.)

+        #ds.__isub__( 2 )

+        ds -= 2

+        steps.append(timer())

+        print(dt(steps))

+        self.assertEqual(ds.as_array()[0][0][0], 1.)

+        #ds.__imul__( 2 )

+        ds *= 2

+        steps.append(timer())

+        print(dt(steps))

+        self.assertEqual(ds.as_array()[0][0][0], 2.)

+        #ds.__idiv__( 2 )

+        ds /= 2

+        steps.append(timer())

+        print(dt(steps))

+        self.assertEqual(ds.as_array()[0][0][0], 1.)

+

+        ds1 = ds.copy()

+        #ds1.__iadd__( 1 )

+        ds1 += 1

+        #ds.__iadd__( ds1 )

+        ds += ds1

+        steps.append(timer())

+        print(dt(steps))

+        self.assertEqual(ds.as_array()[0][0][0], 3.)

+        #ds.__isub__( ds1 )

+        ds -= ds1

+        steps.append(timer())

+        print(dt(steps))

+        self.assertEqual(ds.as_array()[0][0][0], 1.)

+        #ds.__imul__( ds1 )

+        ds *= ds1

+        steps.append(timer())

+        print(dt(steps))

+        self.assertEqual(ds.as_array()[0][0][0], 2.)

+        #ds.__idiv__( ds1 )

+        ds /= ds1

+        steps.append(timer())

+        print(dt(steps))

+        self.assertEqual(ds.as_array()[0][0][0], 1.)

+

+    def test_unary_operations(self):

+        print("Test unary operations")

+        X, Y, Z = 1024, 512, 512

+        X, Y, Z = 256, 512, 512

+        steps = [timer()]

+        a = -numpy.ones((X, Y, Z), dtype='float32')

+        steps.append(timer())

+        t0 = dt(steps)

+        print(t0)

+        #print("a refcount " , sys.getrefcount(a))

+        ds = DataContainer(a, False, ['X', 'Y', 'Z'])

+

+        ds.sign(out=ds)

+        steps.append(timer())

+        print(dt(steps))

+        self.assertEqual(ds.as_array()[0][0][0], -1.)

+

+        ds.abs(out=ds)

+        steps.append(timer())

+        print(dt(steps))

+        self.assertEqual(ds.as_array()[0][0][0], 1.)

+

+        ds.__imul__(2)

+        ds.sqrt(out=ds)

+        steps.append(timer())

+        print(dt(steps))

+        self.assertEqual(ds.as_array()[0][0][0],

+                         numpy.sqrt(2., dtype='float32'))

+

+    def test_binary_operations(self):

+        self.binary_add()

+        self.binary_subtract()

+        self.binary_multiply()

+        self.binary_divide()

+

+    def binary_add(self):

+        print("Test binary add")

+        X, Y, Z = 512, 512, 512

+        X, Y, Z = 256, 512, 512

+        steps = [timer()]

+        a = numpy.ones((X, Y, Z), dtype='float32')

+        steps.append(timer())

+        t0 = dt(steps)

+

+        #print("a refcount " , sys.getrefcount(a))

+        ds = DataContainer(a, False, ['X', 'Y', 'Z'])

+        ds1 = ds.copy()

+

+        steps.append(timer())

+        ds.add(ds1, out=ds)

+        steps.append(timer())

+        t1 = dt(steps)

+        print("ds.add(ds1, out=ds)", dt(steps))

+        steps.append(timer())

+        ds2 = ds.add(ds1)

+        steps.append(timer())

+        t2 = dt(steps)

+        print("ds2 = ds.add(ds1)", dt(steps))

+

+        self.assertLess(t1, t2)

+        self.assertEqual(ds.as_array()[0][0][0], 2.)

+

+        ds0 = ds

+        ds0.add(2, out=ds0)

+        steps.append(timer())

+        print("ds0.add(2,out=ds0)", dt(steps), 3, ds0.as_array()[0][0][0])

+        self.assertEqual(4., ds0.as_array()[0][0][0])

+

+        dt1 = dt(steps)

+        ds3 = ds0.add(2)

+        steps.append(timer())

+        print("ds3 = ds0.add(2)", dt(steps), 5, ds3.as_array()[0][0][0])

+        dt2 = dt(steps)

+        self.assertLess(dt1, dt2)

+

+    def binary_subtract(self):

+        print("Test binary subtract")

+        X, Y, Z = 512, 512, 512

+        steps = [timer()]

+        a = numpy.ones((X, Y, Z), dtype='float32')

+        steps.append(timer())

+        t0 = dt(steps)

+

+        #print("a refcount " , sys.getrefcount(a))

+        ds = DataContainer(a, False, ['X', 'Y', 'Z'])

+        ds1 = ds.copy()

+

+        steps.append(timer())

+        ds.subtract(ds1, out=ds)

+        steps.append(timer())

+        t1 = dt(steps)

+        print("ds.subtract(ds1, out=ds)", dt(steps))

+        self.assertEqual(0., ds.as_array()[0][0][0])

+

+        steps.append(timer())

+        ds2 = ds.subtract(ds1)

+        self.assertEqual(-1., ds2.as_array()[0][0][0])

+

+        steps.append(timer())

+        t2 = dt(steps)

+        print("ds2 = ds.subtract(ds1)", dt(steps))

+

+        self.assertLess(t1, t2)

+

+        del ds1

+        ds0 = ds.copy()

+        steps.append(timer())

+        ds0.subtract(2, out=ds0)

+        #ds0.__isub__( 2 )

+        steps.append(timer())

+        print("ds0.subtract(2,out=ds0)", dt(

+            steps), -2., ds0.as_array()[0][0][0])

+        self.assertEqual(-2., ds0.as_array()[0][0][0])

+

+        dt1 = dt(steps)

+        ds3 = ds0.subtract(2)

+        steps.append(timer())

+        print("ds3 = ds0.subtract(2)", dt(steps), 0., ds3.as_array()[0][0][0])

+        dt2 = dt(steps)

+        self.assertLess(dt1, dt2)

+        self.assertEqual(-2., ds0.as_array()[0][0][0])

+        self.assertEqual(-4., ds3.as_array()[0][0][0])

+

+    def binary_multiply(self):

+        print("Test binary multiply")

+        X, Y, Z = 1024, 512, 512

+        X, Y, Z = 256, 512, 512

+        steps = [timer()]

+        a = numpy.ones((X, Y, Z), dtype='float32')

+        steps.append(timer())

+        t0 = dt(steps)

+

+        #print("a refcount " , sys.getrefcount(a))

+        ds = DataContainer(a, False, ['X', 'Y', 'Z'])

+        ds1 = ds.copy()

+

+        steps.append(timer())

+        ds.multiply(ds1, out=ds)

+        steps.append(timer())

+        t1 = dt(steps)

+        print("ds.multiply(ds1, out=ds)", dt(steps))

+        steps.append(timer())

+        ds2 = ds.multiply(ds1)

+        steps.append(timer())

+        t2 = dt(steps)

+        print("ds2 = ds.multiply(ds1)", dt(steps))

+

+        self.assertLess(t1, t2)

+

+        ds0 = ds

+        ds0.multiply(2, out=ds0)

+        steps.append(timer())

+        print("ds0.multiply(2,out=ds0)", dt(

+            steps), 2., ds0.as_array()[0][0][0])

+        self.assertEqual(2., ds0.as_array()[0][0][0])

+

+        dt1 = dt(steps)

+        ds3 = ds0.multiply(2)

+        steps.append(timer())

+        print("ds3 = ds0.multiply(2)", dt(steps), 4., ds3.as_array()[0][0][0])

+        dt2 = dt(steps)

+        self.assertLess(dt1, dt2)

+        self.assertEqual(4., ds3.as_array()[0][0][0])

+        self.assertEqual(2., ds.as_array()[0][0][0])

+

+    def binary_divide(self):

+        print("Test binary divide")

+        X, Y, Z = 1024, 512, 512

+        X, Y, Z = 256, 512, 512

+        steps = [timer()]

+        a = numpy.ones((X, Y, Z), dtype='float32')

+        steps.append(timer())

+        t0 = dt(steps)

+

+        #print("a refcount " , sys.getrefcount(a))

+        ds = DataContainer(a, False, ['X', 'Y', 'Z'])

+        ds1 = ds.copy()

+

+        steps.append(timer())

+        ds.divide(ds1, out=ds)

+        steps.append(timer())

+        t1 = dt(steps)

+        print("ds.divide(ds1, out=ds)", dt(steps))

+        steps.append(timer())

+        ds2 = ds.divide(ds1)

+        steps.append(timer())

+        t2 = dt(steps)

+        print("ds2 = ds.divide(ds1)", dt(steps))

+

+        self.assertLess(t1, t2)

+        self.assertEqual(ds.as_array()[0][0][0], 1.)

+

+        ds0 = ds

+        ds0.divide(2, out=ds0)

+        steps.append(timer())

+        print("ds0.divide(2,out=ds0)", dt(steps), 0.5, ds0.as_array()[0][0][0])

+        self.assertEqual(0.5, ds0.as_array()[0][0][0])

+

+        dt1 = dt(steps)

+        ds3 = ds0.divide(2)

+        steps.append(timer())

+        print("ds3 = ds0.divide(2)", dt(steps), 0.25, ds3.as_array()[0][0][0])

+        dt2 = dt(steps)

+        self.assertLess(dt1, dt2)

+        self.assertEqual(.25, ds3.as_array()[0][0][0])

+        self.assertEqual(.5, ds.as_array()[0][0][0])

+

     def test_creation_copy(self):

-        shape = (2,3,4,5)

+        shape = (2, 3, 4, 5)

         size = shape[0]

         for i in range(1, len(shape)):

             size = size * shape[i]

         #print("a refcount " , sys.getrefcount(a))

-        a = numpy.asarray([i for i in range( size )])

+        a = numpy.asarray([i for i in range(size)])

         #print("a refcount " , sys.getrefcount(a))

         a = numpy.reshape(a, shape)

         #print("a refcount " , sys.getrefcount(a))

-        ds = DataContainer(a, True, ['X', 'Y','Z' ,'W'])

+        ds = DataContainer(a, True, ['X', 'Y', 'Z', 'W'])

         #print("a refcount " , sys.getrefcount(a))

-        self.assertEqual(sys.getrefcount(a),2)

-    

+        self.assertEqual(sys.getrefcount(a), 2)

+

     def test_subset(self):

-        shape = (4,3,2)

+        shape = (4, 3, 2)

         a = [i for i in range(2*3*4)]

         a = numpy.asarray(a)

         a = numpy.reshape(a, shape)

-        ds = DataContainer(a, True, ['X', 'Y','Z'])

+        ds = DataContainer(a, True, ['X', 'Y', 'Z'])

         sub = ds.subset(['X'])

         res = True

         try:

             numpy.testing.assert_array_equal(sub.as_array(),

-                                                 numpy.asarray([0,6,12,18]))

+                                             numpy.asarray([0, 6, 12, 18]))

         except AssertionError as err:

             res = False

-            print (err)

+            print(err)

         self.assertTrue(res)

-        

+

         sub = ds.subset(['X'], Y=2, Z=0)

         res = True

         try:

             numpy.testing.assert_array_equal(sub.as_array(),

-                                                 numpy.asarray([4,10,16,22]))

+                                             numpy.asarray([4, 10, 16, 22]))

         except AssertionError as err:

             res = False

-            print (err)

+            print(err)

         self.assertTrue(res)

-        

-        

+

         sub = ds.subset(['Y'])

         try:

             numpy.testing.assert_array_equal(

-                        sub.as_array(), numpy.asarray([0,2,4]))

+                sub.as_array(), numpy.asarray([0, 2, 4]))

             res = True

         except AssertionError as err:

             res = False

-            print (err)

+            print(err)

         self.assertTrue(res)

-            

-        

+

         sub = ds.subset(['Z'])

         try:

             numpy.testing.assert_array_equal(

-                        sub.as_array(), numpy.asarray([0,1]))

+                sub.as_array(), numpy.asarray([0, 1]))

             res = True

         except AssertionError as err:

             res = False

-            print (err)

+            print(err)

         self.assertTrue(res)

         sub = ds.subset(['Z'], X=1, Y=2)

         try:

             numpy.testing.assert_array_equal(

-                        sub.as_array(), numpy.asarray([10,11]))

+                sub.as_array(), numpy.asarray([10, 11]))

             res = True

         except AssertionError as err:

             res = False

-            print (err)

+            print(err)

         self.assertTrue(res)

-        

+

         print(a)

-        sub = ds.subset(['X', 'Y'] , Z=1)

+        sub = ds.subset(['X', 'Y'], Z=1)

         res = True

         try:

             numpy.testing.assert_array_equal(sub.as_array(),

-                                                 numpy.asarray([[ 1,  3,  5],

-       [ 7,  9, 11],

-       [13, 15, 17],

-       [19, 21, 23]]))

+                                             numpy.asarray([[1,  3,  5],

+                                                            [7,  9, 11],

+                                                            [13, 15, 17],

+                                                            [19, 21, 23]]))

         except AssertionError as err:

             res = False

-            print (err)

+            print(err)

         self.assertTrue(res)

-        

+

     def test_ImageData(self):

         # create ImageData from geometry

         vgeometry = ImageGeometry(voxel_num_x=4, voxel_num_y=3, channels=2)

         vol = ImageData(geometry=vgeometry)

-        self.assertEqual(vol.shape , (2,3,4))

-        

+        self.assertEqual(vol.shape, (2, 3, 4))

+

         vol1 = vol + 1

         self.assertNumpyArrayEqual(vol1.as_array(), numpy.ones(vol.shape))

-        

+

         vol1 = vol - 1

         self.assertNumpyArrayEqual(vol1.as_array(), -numpy.ones(vol.shape))

-        

+

         vol1 = 2 * (vol + 1)

         self.assertNumpyArrayEqual(vol1.as_array(), 2 * numpy.ones(vol.shape))

-        

-        vol1 = (vol + 1) / 2 

-        self.assertNumpyArrayEqual(vol1.as_array(), numpy.ones(vol.shape) / 2 )

-        

+

+        vol1 = (vol + 1) / 2

+        self.assertNumpyArrayEqual(vol1.as_array(), numpy.ones(vol.shape) / 2)

+

         vol1 = vol + 1

-        self.assertEqual(vol1.sum() , 2*3*4)

-        vol1 = ( vol + 2 ) ** 2

-        self.assertNumpyArrayEqual(vol1.as_array(), numpy.ones(vol.shape) * 4 )

-        

-        

-    

+        self.assertEqual(vol1.sum(), 2*3*4)

+        vol1 = (vol + 2) ** 2

+        self.assertNumpyArrayEqual(vol1.as_array(), numpy.ones(vol.shape) * 4)

+

+        self.assertEqual(vol.number_of_dimensions, 3)

+

     def test_AcquisitionData(self):

-        sgeometry = AcquisitionGeometry(dimension=2, angles=numpy.linspace(0, 180, num=10), 

-                                           geom_type='parallel', pixel_num_v=3,

-                                           pixel_num_h=5 , channels=2)

+        sgeometry = AcquisitionGeometry(dimension=2, angles=numpy.linspace(0, 180, num=10),

+                                        geom_type='parallel', pixel_num_v=3,

+                                        pixel_num_h=5, channels=2)

         sino = AcquisitionData(geometry=sgeometry)

-        self.assertEqual(sino.shape , (2,10,3,5))

-        

-    

+        self.assertEqual(sino.shape, (2, 10, 3, 5))

+

+    def assertNumpyArrayEqual(self, first, second):

+        res = True

+        try:

+            numpy.testing.assert_array_equal(first, second)

+        except AssertionError as err:

+            res = False

+            print(err)

+        self.assertTrue(res)

+

+    def assertNumpyArrayAlmostEqual(self, first, second, decimal=6):

+        res = True

+        try:

+            numpy.testing.assert_array_almost_equal(first, second, decimal)

+        except AssertionError as err:

+            res = False

+            print(err)

+        self.assertTrue(res)

+    def test_DataContainerChaining(self):

+        dc = self.create_DataContainer(256,256,256,1)

+

+        dc.add(9,out=dc)\

+          .subtract(1,out=dc)

+        self.assertEqual(1+9-1,dc.as_array().flatten()[0])

+

+

+

+

+class TestAlgorithms(unittest.TestCase):

     def assertNumpyArrayEqual(self, first, second):

         res = True

         try:

             numpy.testing.assert_array_equal(first, second)

         except AssertionError as err:

             res = False

-            print (err)

+            print(err)

+        self.assertTrue(res)

+

+    def assertNumpyArrayAlmostEqual(self, first, second, decimal=6):

+        res = True

+        try:

+            numpy.testing.assert_array_almost_equal(first, second, decimal)

+        except AssertionError as err:

+            res = False

+            print(err)

         self.assertTrue(res)

+

+    def test_FISTA_cvx(self):

+        if not cvx_not_installable:

+            # Problem data.

+            m = 30

+            n = 20

+            np.random.seed(1)

+            Amat = np.random.randn(m, n)

+            A = LinearOperatorMatrix(Amat)

+            bmat = np.random.randn(m)

+            bmat.shape = (bmat.shape[0], 1)

+

+            # A = Identity()

+            # Change n to equal to m.

+

+            b = DataContainer(bmat)

+

+            # Regularization parameter

+            lam = 10

+            opt = {'memopt': True}

+            # Create object instances with the test data A and b.

+            f = Norm2sq(A, b, c=0.5, memopt=True)

+            g0 = ZeroFun()

+

+            # Initial guess

+            x_init = DataContainer(np.zeros((n, 1)))

+

+            f.grad(x_init)

+

+            # Run FISTA for least squares plus zero function.

+            x_fista0, it0, timing0, criter0 = FISTA(x_init, f, g0, opt=opt)

+

+            # Print solution and final objective/criterion value for comparison

+            print("FISTA least squares plus zero function solution and objective value:")

+            print(x_fista0.array)

+            print(criter0[-1])

+

+            # Compare to CVXPY

+

+            # Construct the problem.

+            x0 = Variable(n)

+            objective0 = Minimize(0.5*sum_squares(Amat*x0 - bmat.T[0]))

+            prob0 = Problem(objective0)

+

+            # The optimal objective is returned by prob.solve().

+            result0 = prob0.solve(verbose=False, solver=SCS, eps=1e-9)

+

+            # The optimal solution for x is stored in x.value and optimal objective value

+            # is in result as well as in objective.value

+            print("CVXPY least squares plus zero function solution and objective value:")

+            print(x0.value)

+            print(objective0.value)

+            self.assertNumpyArrayAlmostEqual(

+                numpy.squeeze(x_fista0.array), x0.value, 6)

+        else:

+            self.assertTrue(cvx_not_installable)

+

+    def test_FISTA_Norm1_cvx(self):

+        if not cvx_not_installable:

+            opt = {'memopt': True}

+            # Problem data.

+            m = 30

+            n = 20

+            np.random.seed(1)

+            Amat = np.random.randn(m, n)

+            A = LinearOperatorMatrix(Amat)

+            bmat = np.random.randn(m)

+            bmat.shape = (bmat.shape[0], 1)

+

+            # A = Identity()

+            # Change n to equal to m.

+

+            b = DataContainer(bmat)

+

+            # Regularization parameter

+            lam = 10

+            opt = {'memopt': True}

+            # Create object instances with the test data A and b.

+            f = Norm2sq(A, b, c=0.5, memopt=True)

+            g0 = ZeroFun()

+

+            # Initial guess

+            x_init = DataContainer(np.zeros((n, 1)))

+

+            # Create 1-norm object instance

+            g1 = Norm1(lam)

+

+            g1(x_init)

+            g1.prox(x_init, 0.02)

+

+            # Combine with least squares and solve using generic FISTA implementation

+            x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1, opt=opt)

+

+            # Print for comparison

+            print("FISTA least squares plus 1-norm solution and objective value:")

+            print(x_fista1.as_array().squeeze())

+            print(criter1[-1])

+

+            # Compare to CVXPY

+

+            # Construct the problem.

+            x1 = Variable(n)

+            objective1 = Minimize(

+                0.5*sum_squares(Amat*x1 - bmat.T[0]) + lam*norm(x1, 1))

+            prob1 = Problem(objective1)

+

+            # The optimal objective is returned by prob.solve().

+            result1 = prob1.solve(verbose=False, solver=SCS, eps=1e-9)

+

+            # The optimal solution for x is stored in x.value and optimal objective value

+            # is in result as well as in objective.value

+            print("CVXPY least squares plus 1-norm solution and objective value:")

+            print(x1.value)

+            print(objective1.value)

+

+            self.assertNumpyArrayAlmostEqual(

+                numpy.squeeze(x_fista1.array), x1.value, 6)

+        else:

+            self.assertTrue(cvx_not_installable)

+

+    def test_FBPD_Norm1_cvx(self):

+        if not cvx_not_installable:

+            opt = {'memopt': True}

+            # Problem data.

+            m = 30

+            n = 20

+            np.random.seed(1)

+            Amat = np.random.randn(m, n)

+            A = LinearOperatorMatrix(Amat)

+            bmat = np.random.randn(m)

+            bmat.shape = (bmat.shape[0], 1)

+

+            # A = Identity()

+            # Change n to equal to m.

+

+            b = DataContainer(bmat)

+

+            # Regularization parameter

+            lam = 10

+            opt = {'memopt': True}

+            # Create object instances with the test data A and b.

+            f = Norm2sq(A, b, c=0.5, memopt=True)

+            g0 = ZeroFun()

+

+            # Initial guess

+            x_init = DataContainer(np.zeros((n, 1)))

+

+            # Create 1-norm object instance

+            g1 = Norm1(lam)

+

+            # Compare to CVXPY

+

+            # Construct the problem.

+            x1 = Variable(n)

+            objective1 = Minimize(

+                0.5*sum_squares(Amat*x1 - bmat.T[0]) + lam*norm(x1, 1))

+            prob1 = Problem(objective1)

+

+            # The optimal objective is returned by prob.solve().

+            result1 = prob1.solve(verbose=False, solver=SCS, eps=1e-9)

+

+            # The optimal solution for x is stored in x.value and optimal objective value

+            # is in result as well as in objective.value

+            print("CVXPY least squares plus 1-norm solution and objective value:")

+            print(x1.value)

+            print(objective1.value)

+

+            # Now try another algorithm FBPD for same problem:

+            x_fbpd1, itfbpd1, timingfbpd1, criterfbpd1 = FBPD(x_init,

+                                                              Identity(), None, f, g1)

+            print(x_fbpd1)

+            print(criterfbpd1[-1])

+

+            self.assertNumpyArrayAlmostEqual(

+                numpy.squeeze(x_fbpd1.array), x1.value, 6)

+        else:

+            self.assertTrue(cvx_not_installable)

+        # Plot criterion curve to see both FISTA and FBPD converge to same value.

+        # Note that FISTA is very efficient for 1-norm minimization so it beats

+        # FBPD in this test by a lot. But FBPD can handle a larger class of problems

+        # than FISTA can.

+

+        # Now try 1-norm and TV denoising with FBPD, first 1-norm.

+

+        # Set up phantom size NxN by creating ImageGeometry, initialising the

+        # ImageData object with this geometry and empty array and finally put some

+        # data into its array, and display as image.

+    def test_FISTA_denoise_cvx(self):

+        if not cvx_not_installable:

+            opt = {'memopt': True}

+            N = 64

+            ig = ImageGeometry(voxel_num_x=N, voxel_num_y=N)

+            Phantom = ImageData(geometry=ig)

+

+            x = Phantom.as_array()

+

+            x[int(round(N/4)):int(round(3*N/4)),

+              int(round(N/4)):int(round(3*N/4))] = 0.5

+            x[int(round(N/8)):int(round(7*N/8)),

+              int(round(3*N/8)):int(round(5*N/8))] = 1

+

+            # Identity operator for denoising

+            I = TomoIdentity(ig)

+

+            # Data and add noise

+            y = I.direct(Phantom)

+            y.array = y.array + 0.1*np.random.randn(N, N)

+

+            # Data fidelity term

+            f_denoise = Norm2sq(I, y, c=0.5, memopt=True)

+

+            # 1-norm regulariser

+            lam1_denoise = 1.0

+            g1_denoise = Norm1(lam1_denoise)

+

+            # Initial guess

+            x_init_denoise = ImageData(np.zeros((N, N)))

+

+            # Combine with least squares and solve using generic FISTA implementation

+            x_fista1_denoise, it1_denoise, timing1_denoise, \

+                criter1_denoise = \

+                FISTA(x_init_denoise, f_denoise, g1_denoise, opt=opt)

+

+            print(x_fista1_denoise)

+            print(criter1_denoise[-1])

+

+            # Now denoise LS + 1-norm with FBPD

+            x_fbpd1_denoise, itfbpd1_denoise, timingfbpd1_denoise,\

+                criterfbpd1_denoise = \

+                FBPD(x_init_denoise, I, None, f_denoise, g1_denoise)

+            print(x_fbpd1_denoise)

+            print(criterfbpd1_denoise[-1])

+

+            # Compare to CVXPY

+

+            # Construct the problem.

+            x1_denoise = Variable(N**2, 1)

+            objective1_denoise = Minimize(

+                0.5*sum_squares(x1_denoise - y.array.flatten()) + lam1_denoise*norm(x1_denoise, 1))

+            prob1_denoise = Problem(objective1_denoise)

+

+            # The optimal objective is returned by prob.solve().

+            result1_denoise = prob1_denoise.solve(

+                verbose=False, solver=SCS, eps=1e-12)

+

+            # The optimal solution for x is stored in x.value and optimal objective value

+            # is in result as well as in objective.value

+            print("CVXPY least squares plus 1-norm solution and objective value:")

+            print(x1_denoise.value)

+            print(objective1_denoise.value)

+            self.assertNumpyArrayAlmostEqual(

+                x_fista1_denoise.array.flatten(), x1_denoise.value, 5)

+

+            self.assertNumpyArrayAlmostEqual(

+                x_fbpd1_denoise.array.flatten(), x1_denoise.value, 5)

+            x1_cvx = x1_denoise.value

+            x1_cvx.shape = (N, N)

+

+            # Now TV with FBPD

+            lam_tv = 0.1

+            gtv = TV2D(lam_tv)

+            gtv(gtv.op.direct(x_init_denoise))

+

+            opt_tv = {'tol': 1e-4, 'iter': 10000}

+

+            x_fbpdtv_denoise, itfbpdtv_denoise, timingfbpdtv_denoise,\

+                criterfbpdtv_denoise = \

+                FBPD(x_init_denoise, gtv.op, None, f_denoise, gtv, opt=opt_tv)

+            print(x_fbpdtv_denoise)

+            print(criterfbpdtv_denoise[-1])

+

+            # Compare to CVXPY

+

+            # Construct the problem.

+            xtv_denoise = Variable((N, N))

+            objectivetv_denoise = Minimize(

+                0.5*sum_squares(xtv_denoise - y.array) + lam_tv*tv(xtv_denoise))

+            probtv_denoise = Problem(objectivetv_denoise)

+

+            # The optimal objective is returned by prob.solve().

+            resulttv_denoise = probtv_denoise.solve(

+                verbose=False, solver=SCS, eps=1e-12)

+

+            # The optimal solution for x is stored in x.value and optimal objective value

+            # is in result as well as in objective.value

+            print("CVXPY least squares plus 1-norm solution and objective value:")

+            print(xtv_denoise.value)

+            print(objectivetv_denoise.value)

+

+            self.assertNumpyArrayAlmostEqual(

+                x_fbpdtv_denoise.as_array(), xtv_denoise.value, 1)

+

+        else:

+            self.assertTrue(cvx_not_installable)

+

+

+class TestFunction(unittest.TestCase):

+    def assertNumpyArrayEqual(self, first, second):

+        res = True

+        try:

+            numpy.testing.assert_array_equal(first, second)

+        except AssertionError as err:

+            res = False

+            print(err)

+        self.assertTrue(res)

+

+    def create_simple_ImageData(self):

+        N = 64

+        ig = ImageGeometry(voxel_num_x=N, voxel_num_y=N)

+        Phantom = ImageData(geometry=ig)

+

+        x = Phantom.as_array()

+

+        x[int(round(N/4)):int(round(3*N/4)),

+          int(round(N/4)):int(round(3*N/4))] = 0.5

+        x[int(round(N/8)):int(round(7*N/8)),

+          int(round(3*N/8)):int(round(5*N/8))] = 1

+

+        return (ig, Phantom)

+

+    def _test_Norm2(self):

+        print("test Norm2")

+        opt = {'memopt': True}

+        ig, Phantom = self.create_simple_ImageData()

+        x = Phantom.as_array()

+        print(Phantom)

+        print(Phantom.as_array())

+

+        norm2 = Norm2()

+        v1 = norm2(x)

+        v2 = norm2(Phantom)

+        self.assertEqual(v1, v2)

+

+        p1 = norm2.prox(Phantom, 1)

+        print(p1)

+        p2 = norm2.prox(x, 1)

+        self.assertNumpyArrayEqual(p1.as_array(), p2)

+

+        p3 = norm2.proximal(Phantom, 1)

+        p4 = norm2.proximal(x, 1)

+        self.assertNumpyArrayEqual(p3.as_array(), p2)

+        self.assertNumpyArrayEqual(p3.as_array(), p4)

+

+        out = Phantom.copy()

+        p5 = norm2.proximal(Phantom, 1, out=out)

+        self.assertEqual(id(p5), id(out))

+        self.assertNumpyArrayEqual(p5.as_array(), p3.as_array())

 # =============================================================================

 #     def test_upper(self):

 #         self.assertEqual('foo'.upper(), 'FOO')

-# 

+#

 #     def test_isupper(self):

 #         self.assertTrue('FOO'.isupper())

 #         self.assertFalse('Foo'.isupper())

-# 

+#

 #     def test_split(self):

 #         s = 'hello world'

 #         self.assertEqual(s.split(), ['hello', 'world'])

@@ -163,5 +886,84 @@ class TestDataContainer(unittest.TestCase):
 #             s.split(2)

 # =============================================================================

 

+class TestNexusReader(unittest.TestCase):

+

+    def setUp(self):

+        wget.download('https://github.com/DiamondLightSource/Savu/raw/master/test_data/data/24737_fd.nxs')

+        self.filename = '24737_fd.nxs'

+

+    def tearDown(self):

+        os.remove(self.filename)

+

+

+    def testGetDimensions(self):

+        nr = NexusReader(self.filename)

+        self.assertEqual(nr.get_sinogram_dimensions(), (135, 91, 160), "Sinogram dimensions are not correct")

+        

+    def testGetProjectionDimensions(self):

+        nr = NexusReader(self.filename)

+        self.assertEqual(nr.get_projection_dimensions(), (91,135,160), "Projection dimensions are not correct")        

+        

+    def testLoadProjectionWithoutDimensions(self):

+        nr = NexusReader(self.filename)

+        projections = nr.load_projection()        

+        self.assertEqual(projections.shape, (91,135,160), "Loaded projection data dimensions are not correct")        

+

+    def testLoadProjectionWithDimensions(self):

+        nr = NexusReader(self.filename)

+        projections = nr.load_projection((slice(0,1), slice(0,135), slice(0,160)))        

+        self.assertEqual(projections.shape, (1,135,160), "Loaded projection data dimensions are not correct")        

+            

+    def testLoadProjectionCompareSingle(self):

+        nr = NexusReader(self.filename)

+        projections_full = nr.load_projection()

+        projections_part = nr.load_projection((slice(0,1), slice(0,135), slice(0,160))) 

+        numpy.testing.assert_array_equal(projections_part, projections_full[0:1,:,:])

+        

+    def testLoadProjectionCompareMulti(self):

+        nr = NexusReader(self.filename)

+        projections_full = nr.load_projection()

+        projections_part = nr.load_projection((slice(0,3), slice(0,135), slice(0,160))) 

+        numpy.testing.assert_array_equal(projections_part, projections_full[0:3,:,:])

+        

+    def testLoadProjectionCompareRandom(self):

+        nr = NexusReader(self.filename)

+        projections_full = nr.load_projection()

+        projections_part = nr.load_projection((slice(1,8), slice(5,10), slice(8,20))) 

+        numpy.testing.assert_array_equal(projections_part, projections_full[1:8,5:10,8:20])                

+        

+    def testLoadProjectionCompareFull(self):

+        nr = NexusReader(self.filename)

+        projections_full = nr.load_projection()

+        projections_part = nr.load_projection((slice(None,None), slice(None,None), slice(None,None))) 

+        numpy.testing.assert_array_equal(projections_part, projections_full[:,:,:])

+        

+    def testLoadFlatCompareFull(self):

+        nr = NexusReader(self.filename)

+        flats_full = nr.load_flat()

+        flats_part = nr.load_flat((slice(None,None), slice(None,None), slice(None,None))) 

+        numpy.testing.assert_array_equal(flats_part, flats_full[:,:,:])

+        

+    def testLoadDarkCompareFull(self):

+        nr = NexusReader(self.filename)

+        darks_full = nr.load_dark()

+        darks_part = nr.load_dark((slice(None,None), slice(None,None), slice(None,None))) 

+        numpy.testing.assert_array_equal(darks_part, darks_full[:,:,:])

+        

+    def testProjectionAngles(self):

+        nr = NexusReader(self.filename)

+        angles = nr.get_projection_angles()

+        self.assertEqual(angles.shape, (91,), "Loaded projection number of angles are not correct")        

+        

+    def test_get_acquisition_data_subset(self):

+        nr = NexusReader(self.filename)

+        key = nr.get_image_keys()

+        sl = nr.get_acquisition_data_subset(0,10)

+        data = nr.get_acquisition_data().subset(['vertical','horizontal'])

+        

+        self.assertTrue(sl.shape , (10,data.shape[1]))

+        

+

 if __name__ == '__main__':

-    unittest.main()
\ No newline at end of file
+    unittest.main()

+    

diff --git a/Wrappers/Python/setup.py b/Wrappers/Python/setup.py
index b4fabbd..b584344 100644
--- a/Wrappers/Python/setup.py
+++ b/Wrappers/Python/setup.py
@@ -23,11 +23,7 @@ import os
 import sys
 
 
-
-cil_version=os.environ['CIL_VERSION']
-if  cil_version == '':
-    print("Please set the environmental variable CIL_VERSION")
-    sys.exit(1)
+cil_version='0.11.3'
 
 setup(
     name="ccpi-framework",
diff --git a/Wrappers/Python/wip/demo_compare_cvx.py b/Wrappers/Python/wip/demo_compare_cvx.py
index cbfe50e..27b1c97 100644
--- a/Wrappers/Python/wip/demo_compare_cvx.py
+++ b/Wrappers/Python/wip/demo_compare_cvx.py
@@ -1,9 +1,11 @@
 
 from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, DataContainer
 from ccpi.optimisation.algs import FISTA, FBPD, CGLS
-from ccpi.optimisation.funcs import Norm2sq, ZeroFun, Norm1, TV2D
+from ccpi.optimisation.funcs import Norm2sq, ZeroFun, Norm1, TV2D, Norm2
 
-from ccpi.optimisation.ops import LinearOperatorMatrix, Identity
+from ccpi.optimisation.ops import LinearOperatorMatrix, TomoIdentity
+from ccpi.optimisation.ops import Identity
+from ccpi.optimisation.ops import FiniteDiff2D
 
 # Requires CVXPY, see http://www.cvxpy.org/
 # CVXPY can be installed in anaconda using
@@ -33,9 +35,9 @@ b = DataContainer(bmat)
 
 # Regularization parameter
 lam = 10
-
+opt = {'memopt':True}
 # Create object instances with the test data A and b.
-f = Norm2sq(A,b,c=0.5)
+f = Norm2sq(A,b,c=0.5, memopt=True)
 g0 = ZeroFun()
 
 # Initial guess
@@ -44,7 +46,7 @@ x_init = DataContainer(np.zeros((n,1)))
 f.grad(x_init)
 
 # Run FISTA for least squares plus zero function.
-x_fista0, it0, timing0, criter0 = FISTA(x_init, f, g0)
+x_fista0, it0, timing0, criter0 = FISTA(x_init, f, g0 , opt=opt)
 
 # Print solution and final objective/criterion value for comparison
 print("FISTA least squares plus zero function solution and objective value:")
@@ -56,7 +58,7 @@ if use_cvxpy:
     
     # Construct the problem.
     x0 = Variable(n)
-    objective0 = Minimize(0.5*sum_squares(Amat*x0 - bmat) )
+    objective0 = Minimize(0.5*sum_squares(Amat*x0 - bmat.T[0]) )
     prob0 = Problem(objective0)
     
     # The optimal objective is returned by prob.solve().
@@ -80,10 +82,24 @@ plt.show()
 g1 = Norm1(lam)
 
 g1(x_init)
-g1.prox(x_init,0.02)
-
+x_rand = DataContainer(np.reshape(np.random.rand(n),(n,1)))
+x_rand2 = DataContainer(np.reshape(np.random.rand(n-1),(n-1,1)))
+v = g1.prox(x_rand,0.02)
+#vv = g1.prox(x_rand2,0.02)
+vv = v.copy() 
+vv *= 0
+print (">>>>>>>>>>vv" , vv.as_array())
+vv.fill(v)
+print (">>>>>>>>>>fill" , vv.as_array())
+g1.proximal(x_rand, 0.02, out=vv)
+print (">>>>>>>>>>v" , v.as_array())
+print (">>>>>>>>>>gradient" , vv.as_array())
+
+print (">>>>>>>>>>" , (v-vv).as_array())
+import sys
+#sys.exit(0)
 # Combine with least squares and solve using generic FISTA implementation
-x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1)
+x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1,opt=opt)
 
 # Print for comparison
 print("FISTA least squares plus 1-norm solution and objective value:")
@@ -95,7 +111,7 @@ if use_cvxpy:
     
     # Construct the problem.
     x1 = Variable(n)
-    objective1 = Minimize(0.5*sum_squares(Amat*x1 - bmat) + lam*norm(x1,1) )
+    objective1 = Minimize(0.5*sum_squares(Amat*x1 - bmat.T[0]) + lam*norm(x1,1) )
     prob1 = Problem(objective1)
     
     # The optimal objective is returned by prob.solve().
@@ -108,7 +124,7 @@ if use_cvxpy:
     print(objective1.value)
     
 # Now try another algorithm FBPD for same problem:
-x_fbpd1, itfbpd1, timingfbpd1, criterfbpd1 = FBPD(x_init, None, f, g1)
+x_fbpd1, itfbpd1, timingfbpd1, criterfbpd1 = FBPD(x_init,Identity(), None, f, g1)
 print(x_fbpd1)
 print(criterfbpd1[-1])
 
@@ -147,7 +163,7 @@ plt.title('Phantom image')
 plt.show()
 
 # Identity operator for denoising
-I = Identity()
+I = TomoIdentity(ig)
 
 # Data and add noise
 y = I.direct(Phantom)
@@ -157,8 +173,10 @@ plt.imshow(y.array)
 plt.title('Noisy image')
 plt.show()
 
+
+###################
 # Data fidelity term
-f_denoise = Norm2sq(I,y,c=0.5)
+f_denoise = Norm2sq(I,y,c=0.5,memopt=True)
 
 # 1-norm regulariser
 lam1_denoise = 1.0
@@ -168,23 +186,24 @@ g1_denoise = Norm1(lam1_denoise)
 x_init_denoise = ImageData(np.zeros((N,N)))
 
 # Combine with least squares and solve using generic FISTA implementation
-x_fista1_denoise, it1_denoise, timing1_denoise, criter1_denoise = FISTA(x_init_denoise, f_denoise, g1_denoise)
+x_fista1_denoise, it1_denoise, timing1_denoise, criter1_denoise = FISTA(x_init_denoise, f_denoise, g1_denoise, opt=opt)
 
 print(x_fista1_denoise)
 print(criter1_denoise[-1])
 
-plt.imshow(x_fista1_denoise.as_array())
-plt.title('FISTA LS+1')
-plt.show()
+#plt.imshow(x_fista1_denoise.as_array())
+#plt.title('FISTA LS+1')
+#plt.show()
 
 # Now denoise LS + 1-norm with FBPD
-x_fbpd1_denoise, itfbpd1_denoise, timingfbpd1_denoise, criterfbpd1_denoise = FBPD(x_init_denoise, None, f_denoise, g1_denoise)
+x_fbpd1_denoise, itfbpd1_denoise, timingfbpd1_denoise, \
+  criterfbpd1_denoise = FBPD(x_init_denoise, I, None, f_denoise, g1_denoise)
 print(x_fbpd1_denoise)
 print(criterfbpd1_denoise[-1])
 
-plt.imshow(x_fbpd1_denoise.as_array())
-plt.title('FBPD LS+1')
-plt.show()
+#plt.imshow(x_fbpd1_denoise.as_array())
+#plt.title('FBPD LS+1')
+#plt.show()
 
 if use_cvxpy:
     # Compare to CVXPY
@@ -206,30 +225,53 @@ if use_cvxpy:
 x1_cvx = x1_denoise.value
 x1_cvx.shape = (N,N)
 
+
+
+#plt.imshow(x1_cvx)
+#plt.title('CVX LS+1')
+#plt.show()
+
+fig = plt.figure()
+plt.subplot(1,4,1)
+plt.imshow(y.array)
+plt.title("LS+1")
+plt.subplot(1,4,2)
+plt.imshow(x_fista1_denoise.as_array())
+plt.title("fista")
+plt.subplot(1,4,3)
+plt.imshow(x_fbpd1_denoise.as_array())
+plt.title("fbpd")
+plt.subplot(1,4,4)
 plt.imshow(x1_cvx)
-plt.title('CVX LS+1')
+plt.title("cvx")
 plt.show()
 
-# Now TV with FBPD
+##############################################################
+# Now TV with FBPD and Norm2
 lam_tv = 0.1
 gtv = TV2D(lam_tv)
-gtv(gtv.op.direct(x_init_denoise))
+norm2 = Norm2(lam_tv)
+op = FiniteDiff2D()
+#gtv(gtv.op.direct(x_init_denoise))
 
 opt_tv = {'tol': 1e-4, 'iter': 10000}
 
-x_fbpdtv_denoise, itfbpdtv_denoise, timingfbpdtv_denoise, criterfbpdtv_denoise = FBPD(x_init_denoise, None, f_denoise, gtv,opt=opt_tv)
+x_fbpdtv_denoise, itfbpdtv_denoise, timingfbpdtv_denoise, \
+ criterfbpdtv_denoise = FBPD(x_init_denoise, op, None, \
+                             f_denoise, norm2 ,opt=opt_tv)
 print(x_fbpdtv_denoise)
 print(criterfbpdtv_denoise[-1])
 
 plt.imshow(x_fbpdtv_denoise.as_array())
 plt.title('FBPD TV')
-plt.show()
+#plt.show()
 
 if use_cvxpy:
     # Compare to CVXPY
     
     # Construct the problem.
-    xtv_denoise = Variable(N,N)
+    xtv_denoise = Variable((N,N))
+    #print (xtv_denoise.value.shape)
     objectivetv_denoise = Minimize(0.5*sum_squares(xtv_denoise - y.array) + lam_tv*tv(xtv_denoise) )
     probtv_denoise = Problem(objectivetv_denoise)
     
@@ -244,7 +286,21 @@ if use_cvxpy:
     
 plt.imshow(xtv_denoise.value)
 plt.title('CVX TV')
+#plt.show()
+
+fig = plt.figure()
+plt.subplot(1,3,1)
+plt.imshow(y.array)
+plt.title("TV2D")
+plt.subplot(1,3,2)
+plt.imshow(x_fbpdtv_denoise.as_array())
+plt.title("fbpd tv denoise")
+plt.subplot(1,3,3)
+plt.imshow(xtv_denoise.value)
+plt.title("CVX tv")
 plt.show()
 
+
+
 plt.loglog([0,opt_tv['iter']], [objectivetv_denoise.value,objectivetv_denoise.value], label='CVX TV')
-plt.loglog(criterfbpdtv_denoise, label='FBPD TV')
\ No newline at end of file
+plt.loglog(criterfbpdtv_denoise, label='FBPD TV')
diff --git a/Wrappers/Python/wip/demo_imat_multichan_RGLTK.py b/Wrappers/Python/wip/demo_imat_multichan_RGLTK.py
index 59d634e..8370c78 100644
--- a/Wrappers/Python/wip/demo_imat_multichan_RGLTK.py
+++ b/Wrappers/Python/wip/demo_imat_multichan_RGLTK.py
@@ -32,7 +32,6 @@ ProjAngleChannels = np.zeros((totalAngles,totChannels,n,n),dtype='float32')
 
 #########################################################################
 print ("Loading the data...")
-#MainPath = '/media/algol/336F96987817D4B4/DATA/IMAT_DATA/' # path to data
 MainPath = '/media/jakob/050d8d45-fab3-4285-935f-260e6c5f162c1/Data/neutrondata/' # path to data
 pathname0 = '{!s}{!s}'.format(MainPath,'PSI_phantom_IMAT/DATA/Sample/')
 counterFileName = 4675
diff --git a/Wrappers/Python/wip/demo_memhandle.py b/Wrappers/Python/wip/demo_memhandle.py
new file mode 100755
index 0000000..db48d73
--- /dev/null
+++ b/Wrappers/Python/wip/demo_memhandle.py
@@ -0,0 +1,193 @@
+
+from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, DataContainer
+from ccpi.optimisation.algs import FISTA, FBPD, CGLS
+from ccpi.optimisation.funcs import Norm2sq, ZeroFun, Norm1, TV2D
+
+from ccpi.optimisation.ops import LinearOperatorMatrix, Identity
+from ccpi.optimisation.ops import TomoIdentity
+
+# Requires CVXPY, see http://www.cvxpy.org/
+# CVXPY can be installed in anaconda using
+# conda install -c cvxgrp cvxpy libgcc
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Problem data.
+m = 30
+n = 20
+np.random.seed(1)
+Amat = np.random.randn(m, n)
+A = LinearOperatorMatrix(Amat)
+bmat = np.random.randn(m)
+bmat.shape = (bmat.shape[0],1)
+
+
+
+# A = Identity()
+# Change n to equal to m.
+
+b = DataContainer(bmat)
+
+# Regularization parameter
+lam = 10
+
+# Create object instances with the test data A and b.
+f = Norm2sq(A,b,c=0.5, memopt=True)
+g0 = ZeroFun()
+
+# Initial guess
+x_init = DataContainer(np.zeros((n,1)))
+
+f.grad(x_init)
+opt = {'memopt': True}
+# Run FISTA for least squares plus zero function.
+x_fista0, it0, timing0, criter0 = FISTA(x_init, f, g0)
+x_fista0_m, it0_m, timing0_m, criter0_m = FISTA(x_init, f, g0, opt=opt)
+
+iternum = [i for i in range(len(criter0))]
+# Print solution and final objective/criterion value for comparison
+print("FISTA least squares plus zero function solution and objective value:")
+print(x_fista0.array)
+print(criter0[-1])
+
+
+# Plot criterion curve to see FISTA converge to same value as CVX.
+#iternum = np.arange(1,1001)
+plt.figure()
+plt.loglog(iternum,criter0,label='FISTA LS')
+plt.loglog(iternum,criter0_m,label='FISTA LS memopt')
+plt.legend()
+plt.show()
+#%%
+# Create 1-norm object instance
+g1 = Norm1(lam)
+
+g1(x_init)
+g1.prox(x_init,0.02)
+
+# Combine with least squares and solve using generic FISTA implementation
+x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1)
+x_fista1_m, it1_m, timing1_m, criter1_m = FISTA(x_init, f, g1, opt=opt)
+iternum = [i for i in range(len(criter1))]
+# Print for comparison
+print("FISTA least squares plus 1-norm solution and objective value:")
+print(x_fista1)
+print(criter1[-1])
+
+
+# Now try another algorithm FBPD for same problem:
+x_fbpd1, itfbpd1, timingfbpd1, criterfbpd1 = FBPD(x_init, None, f, g1)
+iternum = [i for i in range(len(criterfbpd1))]
+print(x_fbpd1)
+print(criterfbpd1[-1])
+
+# Plot criterion curve to see both FISTA and FBPD converge to same value.
+# Note that FISTA is very efficient for 1-norm minimization so it beats
+# FBPD in this test by a lot. But FBPD can handle a larger class of problems 
+# than FISTA can.
+plt.figure()
+plt.loglog(iternum,criter1,label='FISTA LS+1')
+plt.loglog(iternum,criter1_m,label='FISTA LS+1 memopt')
+plt.legend()
+plt.show()
+
+plt.figure()
+plt.loglog(iternum,criter1,label='FISTA LS+1')
+plt.loglog(iternum,criterfbpd1,label='FBPD LS+1')
+plt.legend()
+plt.show()
+#%%
+# Now try 1-norm and TV denoising with FBPD, first 1-norm.
+
+# Set up phantom size NxN by creating ImageGeometry, initialising the 
+# ImageData object with this geometry and empty array and finally put some
+# data into its array, and display as image.
+N = 1000
+ig = ImageGeometry(voxel_num_x=N,voxel_num_y=N)
+Phantom = ImageData(geometry=ig)
+
+x = Phantom.as_array()
+x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5
+x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1
+
+plt.imshow(x)
+plt.title('Phantom image')
+plt.show()
+
+# Identity operator for denoising
+I = TomoIdentity(ig)
+
+# Data and add noise
+y = I.direct(Phantom)
+y.array +=  0.1*np.random.randn(N, N)
+
+plt.figure()
+plt.imshow(y.array)
+plt.title('Noisy image')
+plt.show()
+
+# Data fidelity term
+f_denoise = Norm2sq(I,y,c=0.5, memopt=True)
+
+# 1-norm regulariser
+lam1_denoise = 1.0
+g1_denoise = Norm1(lam1_denoise)
+
+# Initial guess
+x_init_denoise = ImageData(np.zeros((N,N)))
+opt = {'memopt': False, 'iter' : 50}
+# Combine with least squares and solve using generic FISTA implementation
+print ("no memopt")
+x_fista1_denoise, it1_denoise, timing1_denoise, \
+        criter1_denoise = FISTA(x_init_denoise, f_denoise, g1_denoise, opt=opt)
+opt = {'memopt': True, 'iter' : 50}        
+print ("yes memopt")
+x_fista1_denoise_m, it1_denoise_m, timing1_denoise_m, \
+      criter1_denoise_m = FISTA(x_init_denoise, f_denoise, g1_denoise, opt=opt)
+
+print(x_fista1_denoise)
+print(criter1_denoise[-1])
+
+plt.figure()
+plt.imshow(x_fista1_denoise.as_array())
+plt.title('FISTA LS+1')
+plt.show()
+
+plt.figure()
+plt.imshow(x_fista1_denoise_m.as_array())
+plt.title('FISTA LS+1 memopt')
+plt.show()
+
+plt.figure()
+plt.loglog(iternum,criter1_denoise,label='FISTA LS+1')
+plt.loglog(iternum,criter1_denoise_m,label='FISTA LS+1 memopt')
+plt.legend()
+plt.show()
+#%%
+# Now denoise LS + 1-norm with FBPD
+x_fbpd1_denoise, itfbpd1_denoise, timingfbpd1_denoise, criterfbpd1_denoise = FBPD(x_init_denoise, None, f_denoise, g1_denoise)
+print(x_fbpd1_denoise)
+print(criterfbpd1_denoise[-1])
+
+plt.figure()
+plt.imshow(x_fbpd1_denoise.as_array())
+plt.title('FBPD LS+1')
+plt.show()
+
+
+# Now TV with FBPD
+lam_tv = 0.1
+gtv = TV2D(lam_tv)
+gtv(gtv.op.direct(x_init_denoise))
+
+opt_tv = {'tol': 1e-4, 'iter': 10000}
+
+x_fbpdtv_denoise, itfbpdtv_denoise, timingfbpdtv_denoise, criterfbpdtv_denoise = FBPD(x_init_denoise, None, f_denoise, gtv,opt=opt_tv)
+print(x_fbpdtv_denoise)
+print(criterfbpdtv_denoise[-1])
+
+plt.imshow(x_fbpdtv_denoise.as_array())
+plt.title('FBPD TV')
+plt.show()
diff --git a/Wrappers/Python/wip/demo_test_sirt.py b/Wrappers/Python/wip/demo_test_sirt.py
new file mode 100644
index 0000000..6f5a44d
--- /dev/null
+++ b/Wrappers/Python/wip/demo_test_sirt.py
@@ -0,0 +1,176 @@
+# This demo illustrates how to use the SIRT algorithm without and with 
+# nonnegativity and box constraints. The ASTRA 2D projectors are used.
+
+# First make all imports
+from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, \
+    AcquisitionData
+from ccpi.optimisation.algs import FISTA, FBPD, CGLS, SIRT
+from ccpi.optimisation.funcs import Norm2sq, Norm1, TV2D, IndicatorBox
+from ccpi.astra.ops import AstraProjectorSimple
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Choose either a parallel-beam (1=parallel2D) or fan-beam (2=cone2D) test case
+test_case = 1
+
+# Set up phantom size NxN by creating ImageGeometry, initialising the 
+# ImageData object with this geometry and empty array and finally put some
+# data into its array, and display as image.
+N = 128
+ig = ImageGeometry(voxel_num_x=N,voxel_num_y=N)
+Phantom = ImageData(geometry=ig)
+
+x = Phantom.as_array()
+x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5
+x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1
+
+plt.imshow(x)
+plt.title('Phantom image')
+plt.show()
+
+# Set up AcquisitionGeometry object to hold the parameters of the measurement
+# setup geometry: # Number of angles, the actual angles from 0 to 
+# pi for parallel beam and 0 to 2pi for fanbeam, set the width of a detector 
+# pixel relative to an object pixel, the number of detector pixels, and the 
+# source-origin and origin-detector distance (here the origin-detector distance 
+# set to 0 to simulate a "virtual detector" with same detector pixel size as
+# object pixel size).
+angles_num = 20
+det_w = 1.0
+det_num = N
+SourceOrig = 200
+OrigDetec = 0
+
+if test_case==1:
+    angles = np.linspace(0,np.pi,angles_num,endpoint=False)
+    ag = AcquisitionGeometry('parallel',
+                             '2D',
+                             angles,
+                             det_num,det_w)
+elif test_case==2:
+    angles = np.linspace(0,2*np.pi,angles_num,endpoint=False)
+    ag = AcquisitionGeometry('cone',
+                             '2D',
+                             angles,
+                             det_num,
+                             det_w,
+                             dist_source_center=SourceOrig, 
+                             dist_center_detector=OrigDetec)
+else:
+    NotImplemented
+
+# Set up Operator object combining the ImageGeometry and AcquisitionGeometry
+# wrapping calls to ASTRA as well as specifying whether to use CPU or GPU.
+Aop = AstraProjectorSimple(ig, ag, 'gpu')
+
+# Forward and backprojection are available as methods direct and adjoint. Here 
+# generate test data b and do simple backprojection to obtain z.
+b = Aop.direct(Phantom)
+z = Aop.adjoint(b)
+
+plt.imshow(b.array)
+plt.title('Simulated data')
+plt.show()
+
+plt.imshow(z.array)
+plt.title('Backprojected data')
+plt.show()
+
+# Using the test data b, different reconstruction methods can now be set up as
+# demonstrated in the rest of this file. In general all methods need an initial 
+# guess and some algorithm options to be set:
+x_init = ImageData(np.zeros(x.shape),geometry=ig)
+opt = {'tol': 1e-4, 'iter': 1000}
+
+# First a CGLS reconstruction can be done:
+x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aop, b, opt)
+
+plt.imshow(x_CGLS.array)
+plt.title('CGLS')
+plt.colorbar()
+plt.show()
+
+plt.semilogy(criter_CGLS)
+plt.title('CGLS criterion')
+plt.show()
+
+# A SIRT unconstrained reconstruction can be done: similarly:
+x_SIRT, it_SIRT, timing_SIRT, criter_SIRT = SIRT(x_init, Aop, b, opt)
+
+plt.imshow(x_SIRT.array)
+plt.title('SIRT unconstrained')
+plt.colorbar()
+plt.show()
+
+plt.semilogy(criter_SIRT)
+plt.title('SIRT unconstrained criterion')
+plt.show()
+
+# A SIRT nonnegativity constrained reconstruction can be done using the 
+# additional input "constraint" set to a box indicator function with 0 as the 
+# lower bound and the default upper bound of infinity:
+x_SIRT0, it_SIRT0, timing_SIRT0, criter_SIRT0 = SIRT(x_init, Aop, b, opt,
+                                                      constraint=IndicatorBox(lower=0))
+
+plt.imshow(x_SIRT0.array)
+plt.title('SIRT nonneg')
+plt.colorbar()
+plt.show()
+
+plt.semilogy(criter_SIRT0)
+plt.title('SIRT nonneg criterion')
+plt.show()
+
+# A SIRT reconstruction with box constraints on [0,1] can also be done:
+x_SIRT01, it_SIRT01, timing_SIRT01, criter_SIRT01 = SIRT(x_init, Aop, b, opt,
+         constraint=IndicatorBox(lower=0,upper=1))
+
+plt.imshow(x_SIRT01.array)
+plt.title('SIRT box(0,1)')
+plt.colorbar()
+plt.show()
+
+plt.semilogy(criter_SIRT01)
+plt.title('SIRT box(0,1) criterion')
+plt.show()
+
+# The indicator function can also be used with the FISTA algorithm to do 
+# least squares with nonnegativity constraint.
+
+# Create least squares object instance with projector, test data and a constant 
+# coefficient of 0.5:
+f = Norm2sq(Aop,b,c=0.5)
+
+# Run FISTA for least squares without constraints
+x_fista, it, timing, criter = FISTA(x_init, f, None,opt)
+
+plt.imshow(x_fista.array)
+plt.title('FISTA Least squares')
+plt.show()
+
+plt.semilogy(criter)
+plt.title('FISTA Least squares criterion')
+plt.show()
+
+# Run FISTA for least squares with nonnegativity constraint
+x_fista0, it0, timing0, criter0 = FISTA(x_init, f, IndicatorBox(lower=0),opt)
+
+plt.imshow(x_fista0.array)
+plt.title('FISTA Least squares nonneg')
+plt.show()
+
+plt.semilogy(criter0)
+plt.title('FISTA Least squares nonneg criterion')
+plt.show()
+
+# Run FISTA for least squares with box constraint [0,1]
+x_fista01, it01, timing01, criter01 = FISTA(x_init, f, IndicatorBox(lower=0,upper=1),opt)
+
+plt.imshow(x_fista01.array)
+plt.title('FISTA Least squares box(0,1)')
+plt.show()
+
+plt.semilogy(criter01)
+plt.title('FISTA Least squares box(0,1) criterion')
+plt.show()
\ No newline at end of file
diff --git a/Wrappers/Python/wip/multifile_nexus.py b/Wrappers/Python/wip/multifile_nexus.py
new file mode 100755
index 0000000..d1ad438
--- /dev/null
+++ b/Wrappers/Python/wip/multifile_nexus.py
@@ -0,0 +1,307 @@
+# -*- coding: utf-8 -*-

+"""

+Created on Wed Aug 15 16:00:53 2018

+

+@author: ofn77899

+"""

+

+import os

+from ccpi.io.reader import NexusReader

+

+from sys import getsizeof

+

+import matplotlib.pyplot as plt

+

+from ccpi.framework import DataProcessor, DataContainer

+from ccpi.processors import Normalizer

+from ccpi.processors import CenterOfRotationFinder

+import numpy

+

+

+class averager(object):

+    def __init__(self):

+        self.reset()

+        

+    def reset(self):

+        self.N = 0

+        self.avg = 0

+        self.min = 0

+        self.max = 0

+        self.var = 0

+        self.ske = 0

+        self.kur = 0

+    

+    def add_reading(self, val):

+        

+        if (self.N == 0):

+           self.avg = val;

+           self.min = val;

+           self.max = val;

+        elif (self.N == 1):

+        #//set min/max

+            self.max = val if val > self.max else self.max

+            self.min = val if val < self.min else self.min

+            

+      

+            thisavg = (self.avg + val)/2

+            #// initial value is in avg

+            self.var = (self.avg - thisavg)*(self.avg-thisavg) + (val - thisavg) * (val-thisavg)

+            self.ske = self.var * (self.avg - thisavg)

+            self.kur = self.var * self.var

+            self.avg = thisavg

+        else:

+            self.max = val if val > self.max else self.max

+            self.min = val if val < self.min else self.min

+            

+            M = self.N

+

+            #// b-factor =(<v>_N + v_(N+1)) / (N+1)

+            #float b = (val -avg) / (M+1);

+            b = (val -self.avg) / (M+1)

+            

+            self.kur = self.kur + (M *(b*b*b*b) * (1+M*M*M))- (4* b * self.ske) + (6 * b *b * self.var * (M-1))

+    

+            self.ske = self.ske + (M * b*b*b *(M-1)*(M+1)) - (3 * b * self.var * (M-1))

+    

+            #//var = var * ((M-1)/M) + ((val - avg)*(val - avg)/(M+1)) ;

+            self.var = self.var * ((M-1)/M) + (b * b * (M+1))

+            

+            self.avg = self.avg * (M/(M+1)) + val / (M+1)

+    

+        self.N += 1

+    

+    def stats(self, vector):

+        i = 0

+        while i < vector.size:

+            self.add_reading(vector[i])

+            i+=1

+

+avg = averager()

+a = numpy.linspace(0,39,40)

+avg.stats(a)

+print ("average" , avg.avg, a.mean())

+print ("variance" , avg.var, a.var())

+b = a - a.mean()

+b *= b

+b = numpy.sqrt(sum(b)/(a.size-1))

+print ("std" , numpy.sqrt(avg.var), b)

+#%%         

+

+class DataStatMoments(DataProcessor):

+    '''Normalization based on flat and dark

+    

+    This processor read in a AcquisitionData and normalises it based on 

+    the instrument reading with and without incident photons or neutrons.

+    

+    Input: AcquisitionData

+    Parameter: 2D projection with flat field (or stack)

+               2D projection with dark field (or stack)

+    Output: AcquisitionDataSetn

+    '''

+    

+    def __init__(self, axis, skewness=False, kurtosis=False, offset=0):

+        kwargs = {

+                  'axis'     : axis,

+                  'skewness' : skewness,

+                  'kurtosis' : kurtosis,

+                  'offset'   : offset,

+                  }

+        #DataProcessor.__init__(self, **kwargs)

+        super(DataStatMoments, self).__init__(**kwargs)

+        

+    

+    def check_input(self, dataset):

+        #self.N = dataset.get_dimension_size(self.axis)

+        return True

+    

+    @staticmethod

+    def add_sample(dataset, N, axis, stats=None, offset=0):

+        #dataset = self.get_input()

+        if (N == 0):

+            # get the axis number along to calculate the stats

+            

+            

+            #axs = dataset.get_dimension_size(self.axis)

+            # create a placeholder for the output

+            if stats is None:

+                ax = dataset.get_dimension_axis(axis)

+                shape = [dataset.shape[i] for i in range(len(dataset.shape)) if i != ax]

+                # output has 4 components avg, std, skewness and kurtosis + last avg+ (val-thisavg)

+                shape.insert(0, 4+2)

+                stats = numpy.zeros(shape)

+                

+            stats[0] = dataset.subset(**{axis:N-offset}).array[:]

+            

+            #avg = val

+        elif (N == 1):

+            # val

+            stats[5] = dataset.subset(**{axis:N-offset}).array

+            stats[4] = stats[0] + stats[5]

+            stats[4] /= 2         # thisavg

+            stats[5] -= stats[4]  # (val - thisavg) 

+            

+            #float thisavg = (avg + val)/2;

+            

+            #// initial value is in avg

+            #var = (avg - thisavg)*(avg-thisavg) + (val - thisavg) * (val-thisavg);

+            stats[1] = stats[5] * stats[5] + stats[5] * stats[5]

+            #skewness = var * (avg - thisavg);

+            stats[2] = stats[1] * stats[5]

+            #kurtosis = var * var;

+            stats[3] = stats[1] * stats[1]

+            #avg = thisavg;

+            stats[0] = stats[4]

+        else:

+        

+            #float M = (float)N;

+            M = N

+            #val

+            stats[4] = dataset.subset(**{axis:N-offset}).array

+            #// b-factor =(<v>_N + v_(N+1)) / (N+1)

+            stats[5] = stats[4] - stats[0]

+            stats[5] /= (M+1)    # b factor

+            #float b = (val -avg) / (M+1);

+    

+            #kurtosis = kurtosis + (M *(b*b*b*b) * (1+M*M*M))- (4* b * skewness) + (6 * b *b * var * (M-1));

+            #if self.kurtosis:

+            #    stats[3] += (M * stats[5] * stats[5] * stats[5] * stats[5]) - \

+            #                (4 * stats[5] * stats[2]) + \

+            #                (6 * stats[5] * stats[5] * stats[1] * (M-1))

+                        

+            #skewness = skewness + (M * b*b*b *(M-1)*(M+1)) - (3 * b * var * (M-1));

+            #if self.skewness:

+            #    stats[2] = stats[2] +  (M * stats[5]* stats[5] * stats[5] * (M-1)*(M-1) ) -\

+            #                3 * stats[5] * stats[1] * (M-1) 

+            #//var = var * ((M-1)/M) + ((val - avg)*(val - avg)/(M+1)) ;

+            #var = var * ((M-1)/M) + (b * b * (M+1));

+            stats[1] = ((M-1)/M) * stats[1] + (stats[5] * stats[5] * (M+1))

+           

+            #avg = avg * (M/(M+1)) + val / (M+1)

+            stats[0] = stats[0] * (M/(M+1)) + stats[4] / (M+1)

+    

+        N += 1

+        return stats , N

+    

+           

+    def process(self):

+        

+        data = self.get_input()

+        

+        #stats, i = add_sample(0)

+        N = data.get_dimension_size(self.axis)

+        ax = data.get_dimension_axis(self.axis)

+        stats = None

+        i = 0

+        while i < N:

+            stats , i = DataStatMoments.add_sample(data, i, self.axis, stats, offset=self.offset)

+        self.offset += N

+        labels = ['StatisticalMoments']

+        

+        labels += [data.dimension_labels[i] \

+                   for i in range(len(data.dimension_labels)) if i != ax]

+        y = DataContainer( stats[:4] , False, 

+                    dimension_labels=labels)

+        return y

+

+directory = r'E:\Documents\Dataset\CCPi\Nexus_test'

+data_path="entry1/instrument/pco1_hw_hdf_nochunking/data"

+

+reader = NexusReader(os.path.join( os.path.abspath(directory) , '74331.nxs'))

+

+print ("read flat")

+read_flat = NexusReader(os.path.join( os.path.abspath(directory) , '74240.nxs'))

+read_flat.data_path = data_path

+flatsslice = read_flat.get_acquisition_data_whole()

+avg = DataStatMoments('angle')

+

+avg.set_input(flatsslice)

+flats = avg.get_output()

+

+ave = averager()

+ave.stats(flatsslice.array[:,0,0])

+

+print ("avg" , ave.avg, flats.array[0][0][0])

+print ("var" , ave.var, flats.array[1][0][0])

+

+print ("read dark")

+read_dark = NexusReader(os.path.join( os.path.abspath(directory) , '74243.nxs'))

+read_dark.data_path = data_path

+

+## darks are very many so we proceed in batches

+total_size = read_dark.get_projection_dimensions()[0]

+print ("total_size", total_size)

+

+batchsize = 40

+if batchsize > total_size:

+    batchlimits = [batchsize * (i+1) for i in range(int(total_size/batchsize))] + [total_size-1]

+else:

+    batchlimits = [total_size]

+#avg.N = 0

+avg.offset = 0

+N = 0

+for batch in range(len(batchlimits)):  

+    print ("running batch " , batch)

+    bmax = batchlimits[batch]

+    bmin = bmax-batchsize

+    

+    darksslice = read_dark.get_acquisition_data_batch(bmin,bmax)

+    if batch == 0:

+        #create stats

+        ax = darksslice.get_dimension_axis('angle')

+        shape = [darksslice.shape[i] for i in range(len(darksslice.shape)) if i != ax]

+        # output has 4 components avg, std, skewness and kurtosis + last avg+ (val-thisavg)

+        shape.insert(0, 4+2)

+        print ("create stats shape ", shape)

+        stats = numpy.zeros(shape)

+    print ("N" , N)        

+    #avg.set_input(darksslice)

+    i = bmin

+    while i < bmax:

+        stats , i = DataStatMoments.add_sample(darksslice, i, 'angle', stats, bmin)

+        print ("{0}-{1}-{2}".format(bmin, i , bmax ) )

+    

+darks = stats

+#%%

+

+fig = plt.subplot(2,2,1)

+fig.imshow(flats.subset(StatisticalMoments=0).array)

+fig = plt.subplot(2,2,2)

+fig.imshow(numpy.sqrt(flats.subset(StatisticalMoments=1).array))

+fig = plt.subplot(2,2,3)

+fig.imshow(darks[0])

+fig = plt.subplot(2,2,4)

+fig.imshow(numpy.sqrt(darks[1]))

+plt.show()

+

+

+#%%

+norm = Normalizer(flat_field=flats.array[0,200,:], dark_field=darks[0,200,:])

+#norm.set_flat_field(flats.array[0,200,:])

+#norm.set_dark_field(darks.array[0,200,:])

+norm.set_input(reader.get_acquisition_data_slice(200))

+

+n = Normalizer.normalize_projection(norm.get_input().as_array(), flats.array[0,200,:], darks[0,200,:], 1e-5)

+#dn_n= Normalizer.estimate_normalised_error(norm.get_input().as_array(), flats.array[0,200,:], darks[0,200,:],

+#                                           numpy.sqrt(flats.array[1,200,:]), numpy.sqrt(darks[1,200,:]))

+#%%

+

+

+#%%

+fig = plt.subplot(2,1,1)

+

+

+fig.imshow(norm.get_input().as_array())

+fig = plt.subplot(2,1,2)

+fig.imshow(n)

+

+#fig = plt.subplot(3,1,3)

+#fig.imshow(dn_n)

+

+

+plt.show()

+

+

+

+

+

+

diff --git a/build/jenkins-build.sh b/build/jenkins-build.sh
new file mode 100644
index 0000000..009d43d
--- /dev/null
+++ b/build/jenkins-build.sh
@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+export CCPI_BUILD_ARGS="-c conda-forge -c ccpi"
+bash <(curl -L https://raw.githubusercontent.com/vais-ral/CCPi-VirtualMachine/master/scripts/jenkins-build.sh)