Merge branch 'master' of https://github.com/vais-ral/CCPi-FISTA_Reconstruction

35 files changed, 6276 insertions, 0 deletions

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
new file mode 100644
index 0000000..cbe2fec
--- /dev/null
+++ b/src/CMakeLists.txt
@@ -0,0 +1,14 @@
+#   Copyright 2017 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.
+add_subdirectory(Python)
\ No newline at end of file
diff --git a/src/Python/CMakeLists.txt b/src/Python/CMakeLists.txt
new file mode 100644
index 0000000..1b73380
--- /dev/null
+++ b/src/Python/CMakeLists.txt
@@ -0,0 +1,181 @@
+#   Copyright 2017 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.
+
+# variables that must be set for conda compilation
+
+#PREFIX=C:\Apps\Miniconda2\envs\cil\Library
+#LIBRARY_INC=C:\\Apps\\Miniconda2\\envs\\cil\\Library\\include
+set (NUMPY_VERSION 1.12)
+
+## Tries to parse the output of conda env list to determine the current
+## active conda environment
+message ("Trying to determine your active conda environment...")
+execute_process(COMMAND "conda" "env" "list"
+					OUTPUT_VARIABLE _CONDA_ENVS
+					RESULT_VARIABLE _CONDA_RESULT
+					ERROR_VARIABLE _CONDA_ERR)
+			if(NOT _CONDA_RESULT)
+				string(REPLACE "\n" ";" ENV_LIST ${_CONDA_ENVS})
+				foreach(line ${ENV_LIST})
+				  string(REGEX MATCHALL "(.+)[*](.+)" match ${line})
+				  if (NOT ${match} EQUAL "")
+				    #message("MATCHED " ${CMAKE_MATCH_0})
+					#message("MATCHED " ${CMAKE_MATCH_1})
+					#message("MATCHED " ${CMAKE_MATCH_2})
+					string(STRIP ${CMAKE_MATCH_1} CONDA_ENVIRONMENT)
+					string(STRIP ${CMAKE_MATCH_2} CONDA_ENVIRONMENT_PATH)   
+				  endif()
+				endforeach()
+			else()
+				message(FATAL_ERROR "ERROR with conda command " ${_CONDA_ERR})
+			endif()
+
+if (${CONDA_ENVIRONMENT} AND ${CONDA_ENVIRONMENT_PATH})
+  message (FATAL_ERROR "CONDA NOT FOUND")
+else()
+  message("**********************************************************")
+  message("Using current conda environmnet " ${CONDA_ENVIRONMENT})
+  message("Using current conda environmnet path " ${CONDA_ENVIRONMENT_PATH})
+endif()
+
+message("CIL VERSION " ${CIL_VERSION})
+
+# set the Python variables for the Conda environment
+include(FindAnacondaEnvironment.cmake)
+findPythonForAnacondaEnvironment(${CONDA_ENVIRONMENT_PATH})
+
+message("Python found " ${PYTHON_VERSION_STRING})
+message("Python found Major " ${PYTHON_VERSION_MAJOR})
+message("Python found Minor " ${PYTHON_VERSION_MINOR})
+
+findPythonPackagesPath()
+message("PYTHON_PACKAGES_FOUND " ${PYTHON_PACKAGES_PATH})
+
+## CACHE SOME VARIABLES ##
+set (CONDA_ENVIRONMENT ${CONDA_ENVIRONMENT} CACHE INTERNAL "active conda environment" FORCE)
+set (CONDA_ENVIRONMENT_PATH ${CONDA_ENVIRONMENT_PATH} CACHE INTERNAL "active conda environment" FORCE)
+
+set (PYTHON_VERSION_STRING ${PYTHON_VERSION_STRING} CACHE INTERNAL "conda environment Python version string" FORCE)
+set (PYTHON_VERSION_MAJOR ${PYTHON_VERSION_MAJOR} CACHE INTERNAL "conda environment Python version major" FORCE)
+set (PYTHON_VERSION_MINOR ${PYTHON_VERSION_MINOR} CACHE INTERNAL "conda environment Python version minor" FORCE)
+set (PYTHON_VERSION_PATCH ${PYTHON_VERSION_PATCH} CACHE INTERNAL "conda environment Python version patch" FORCE)
+set (PYTHON_PACKAGES_PATH ${PYTHON_PACKAGES_PATH} CACHE INTERNAL "conda environment Python packages path" FORCE)
+
+if (WIN32)
+  #set (CONDA_ENVIRONMENT_PATH "C:\\Apps\\Miniconda2\\envs\\${CONDA_ENVIRONMENT}" CACHE PATH "Main environment directory")
+  set (CONDA_ENVIRONMENT_PREFIX "${CONDA_ENVIRONMENT_PATH}\\Library" CACHE PATH "env dir")
+  set (CONDA_ENVIRONMENT_LIBRARY_INC "${CONDA_ENVIRONMENT_PREFIX}\\include" CACHE PATH "env dir")
+elseif (UNIX)
+  #set (CONDA_ENVIRONMENT_PATH "/apps/anaconda/2.4/envs/${CONDA_ENVIRONMENT}" CACHE PATH "Main environment directory")
+  set (CONDA_ENVIRONMENT_PREFIX "${CONDA_ENVIRONMENT_PATH}/lib/python${PYTHON_VERSION_MAJOR}.${PYTHON_VERSION_MINOR}" CACHE PATH "env dir")
+  set (CONDA_ENVIRONMENT_LIBRARY_INC "${CONDA_ENVIRONMENT_PREFIX}/include" CACHE PATH "env dir")
+endif()
+
+######### CONFIGURE REGULARIZER PACKAGE #############
+
+# copy the Pyhon files of the package regularizer
+file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/ccpi/imaging/)
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/__init__.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi)
+# regularizers
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/imaging/__init__.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/imaging)
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/imaging/Regularizer.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/imaging)
+
+# Copy and configure the relative conda build and recipes
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup.py.in ${CMAKE_CURRENT_BINARY_DIR}/setup.py)
+file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe)
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/conda-recipe/meta.yaml DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe)
+
+if (WIN32)
+	
+  file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/conda-recipe/bld.bat DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe/) 
+  configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile.bat.in ${CMAKE_CURRENT_BINARY_DIR}/compile.bat)
+
+elseif(UNIX)
+  
+  message ("We are on UNIX")
+  file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/conda-recipe/build.sh DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe/)
+  # assumes we will use bash
+  configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile.sh.in ${CMAKE_CURRENT_BINARY_DIR}/compile.sh)
+
+endif()
+
+########## CONFIGURE FISTA RECONSTRUCTOR PACKAGE
+# fista reconstructor
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/reconstruction/FISTAReconstructor.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/reconstruction)
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/reconstruction/__init__.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/reconstruction)
+
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup-fista.py.in ${CMAKE_CURRENT_BINARY_DIR}/setup-fista.py)
+file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/fista-recipe)
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/fista-recipe/meta.yaml DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/fista-recipe)
+
+if (WIN32)
+
+  file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/fista-recipe/bld.bat DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/fista-recipe/)
+  configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile-fista.bat.in ${CMAKE_CURRENT_BINARY_DIR}/compile-fista.bat)
+
+elseif(UNIX)
+  message ("We are on UNIX")
+  file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/fista-recipe/build.sh DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/fista-recipe/)
+  # assumes we will use bash
+  configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile-fista.sh.in ${CMAKE_CURRENT_BINARY_DIR}/compile-fista.sh)
+endif()
+
+#############################  TARGETS
+
+##########################  REGULARIZER PACKAGE ###############################
+
+# runs cmake on the build tree to update the code from source
+add_custom_target(update_code 
+		COMMAND ${CMAKE_COMMAND} 
+		ARGS ${CMAKE_SOURCE_DIR}
+		WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+		) 
+
+
+add_custom_target(fista
+		COMMAND bash
+		compile-fista.sh
+		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+		DEPENDS ${update_code}
+		)
+
+add_custom_target(regularizers
+		COMMAND bash
+		compile.sh
+		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+		DEPENDS update_code
+		)
+
+add_custom_target(install-fista
+		COMMAND conda
+		install --force --use-local ccpi-fista=${CIL_VERSION} -c ccpi -c conda-forge
+		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+		)
+
+add_custom_target(install-regularizers
+		COMMAND conda
+		install --force --use-local ccpi-regularizers=${CIL_VERSION} -c ccpi -c conda-forge
+		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+		)
+### add tests
+
+#add_executable(RegularizersTest )
+#find_package(tiff)
+#if (TIFF_FOUND)
+#  message("LibTIFF Found")
+#  message("TIFF_INCLUDE_DIR " ${TIFF_INCLUDE_DIR})
+#  message("TIFF_LIBRARIES" ${TIFF_LIBRARIES})
+#else()
+#  message("LibTIFF not found")
+#endif()
diff --git a/src/Python/FindAnacondaEnvironment.cmake b/src/Python/FindAnacondaEnvironment.cmake
new file mode 100644
index 0000000..fa4637a
--- /dev/null
+++ b/src/Python/FindAnacondaEnvironment.cmake
@@ -0,0 +1,149 @@
+#   Copyright 2017 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.
+
+# #.rst:
+# FindAnacondaEnvironment
+# --------------
+#
+# Find Python executable and library for a specific Anaconda environment
+#
+# This module finds the Python interpreter for a specific Anaconda enviroment, 
+# if installed and determines where the include files and libraries are.  
+# This code sets the following variables:
+#
+# ::
+#   PYTHONINTERP_FOUND         - if the Python interpret has been found
+#   PYTHON_EXECUTABLE          - the Python interpret found
+#   PYTHON_LIBRARY             - path to the python library
+#   PYTHON_INCLUDE_PATH        - path to where Python.h is found (deprecated)
+#   PYTHON_INCLUDE_DIRS        - path to where Python.h is found
+#   PYTHONLIBS_VERSION_STRING  - version of the Python libs found (since CMake 2.8.8)
+#   PYTHON_VERSION_MAJOR       - major Python version
+#   PYTHON_VERSION_MINOR       - minor Python version
+#   PYTHON_VERSION_PATCH       - patch Python version
+
+
+
+function (findPythonForAnacondaEnvironment env)
+	if (WIN32)
+	  file(TO_CMAKE_PATH ${env}/python.exe PYTHON_EXECUTABLE)
+        elseif (UNIX)
+  	  file(TO_CMAKE_PATH ${env}/bin/python PYTHON_EXECUTABLE)
+	endif()
+
+	
+	message("findPythonForAnacondaEnvironment Found Python Executable" ${PYTHON_EXECUTABLE})
+	####### FROM FindPythonInterpr ########
+	# determine python version string
+	if(PYTHON_EXECUTABLE)
+		execute_process(COMMAND "${PYTHON_EXECUTABLE}" -c
+								"import sys; sys.stdout.write(';'.join([str(x) for x in sys.version_info[:3]]))"
+						OUTPUT_VARIABLE _VERSION
+						RESULT_VARIABLE _PYTHON_VERSION_RESULT
+						ERROR_QUIET)
+		if(NOT _PYTHON_VERSION_RESULT)
+			string(REPLACE ";" "." _PYTHON_VERSION_STRING "${_VERSION}")
+			list(GET _VERSION 0 _PYTHON_VERSION_MAJOR)
+			list(GET _VERSION 1 _PYTHON_VERSION_MINOR)
+			list(GET _VERSION 2 _PYTHON_VERSION_PATCH)
+			if(PYTHON_VERSION_PATCH EQUAL 0)
+				# it's called "Python 2.7", not "2.7.0"
+				string(REGEX REPLACE "\\.0$" "" _PYTHON_VERSION_STRING "${PYTHON_VERSION_STRING}")
+			endif()
+		else()
+			# sys.version predates sys.version_info, so use that
+			execute_process(COMMAND "${PYTHON_EXECUTABLE}" -c "import sys; sys.stdout.write(sys.version)"
+							OUTPUT_VARIABLE _VERSION
+							RESULT_VARIABLE _PYTHON_VERSION_RESULT
+							ERROR_QUIET)
+			if(NOT _PYTHON_VERSION_RESULT)
+				string(REGEX REPLACE " .*" "" _PYTHON_VERSION_STRING "${_VERSION}")
+				string(REGEX REPLACE "^([0-9]+)\\.[0-9]+.*" "\\1" _PYTHON_VERSION_MAJOR "${PYTHON_VERSION_STRING}")
+				string(REGEX REPLACE "^[0-9]+\\.([0-9])+.*" "\\1" _PYTHON_VERSION_MINOR "${PYTHON_VERSION_STRING}")
+				if(PYTHON_VERSION_STRING MATCHES "^[0-9]+\\.[0-9]+\\.([0-9]+)")
+					set(PYTHON_VERSION_PATCH "${CMAKE_MATCH_1}")
+				else()
+					set(PYTHON_VERSION_PATCH "0")
+				endif()
+			else()
+				# sys.version was first documented for Python 1.5, so assume
+				# this is older.
+				set(PYTHON_VERSION_STRING "1.4" PARENT_SCOPE)
+				set(PYTHON_VERSION_MAJOR "1" PARENT_SCOPE)
+				set(PYTHON_VERSION_MINOR "4" PARENT_SCOPE)
+				set(PYTHON_VERSION_PATCH "0" PARENT_SCOPE)
+			endif()
+		endif()
+		unset(_PYTHON_VERSION_RESULT)
+		unset(_VERSION)
+	endif()
+	###############################################
+	
+	set (PYTHON_EXECUTABLE ${PYTHON_EXECUTABLE} PARENT_SCOPE)
+	set (PYTHONINTERP_FOUND "ON" PARENT_SCOPE)
+	set (PYTHON_VERSION_STRING ${_PYTHON_VERSION_STRING} PARENT_SCOPE)
+	set (PYTHON_VERSION_MAJOR ${_PYTHON_VERSION_MAJOR} PARENT_SCOPE)
+	set (PYTHON_VERSION_MINOR ${_PYTHON_VERSION_MINOR} PARENT_SCOPE)
+	set (PYTHON_VERSION_PATCH ${_PYTHON_VERSION_PATCH} PARENT_SCOPE)
+	message("My version found " ${PYTHON_VERSION_STRING})
+
+endfunction()
+
+
+
+set(Python_ADDITIONAL_VERSIONS 3.5)
+
+find_package(PythonInterp)
+if (PYTHONINTERP_FOUND)
+  
+  message("Found interpret " ${PYTHON_EXECUTABLE})
+  message("Python Library " ${PYTHON_LIBRARY})
+  message("Python Include Dir " ${PYTHON_INCLUDE_DIR})
+  message("Python Include Path " ${PYTHON_INCLUDE_PATH})
+  
+  foreach(pv ${PYTHON_VERSION_STRING})
+    message("Found interpret " ${pv})
+  endforeach()
+endif()
+
+
+
+find_package(PythonLibs)
+if (PYTHONLIB_FOUND) 
+  message("Found PythonLibs PYTHON_LIBRARIES " ${PYTHON_LIBRARIES})
+  message("Found PythonLibs PYTHON_INCLUDE_PATH " ${PYTHON_INCLUDE_PATH})
+  message("Found PythonLibs PYTHON_INCLUDE_DIRS " ${PYTHON_INCLUDE_DIRS})
+  message("Found PythonLibs PYTHONLIBS_VERSION_STRING " ${PYTHONLIBS_VERSION_STRING}  )
+else()
+  message("No PythonLibs Found")  
+endif()
+
+
+
+
+function(findPythonPackagesPath)
+   execute_process(COMMAND ${PYTHON_EXECUTABLE} -c "from distutils.sysconfig import *; print (get_python_lib())"
+                      RESULT_VARIABLE PYTHON_CVPY_PROCESS
+                      OUTPUT_VARIABLE PYTHON_STD_PACKAGES_PATH
+                      OUTPUT_STRIP_TRAILING_WHITESPACE)
+   #message("STD_PACKAGES " ${PYTHON_STD_PACKAGES_PATH})
+   if("${PYTHON_STD_PACKAGES_PATH}" MATCHES "site-packages")
+        set(_PYTHON_PACKAGES_PATH "python${PYTHON_VERSION_MAJOR_MINOR}/site-packages")
+   endif()
+
+    SET(PYTHON_PACKAGES_PATH "${PYTHON_STD_PACKAGES_PATH}" PARENT_SCOPE)
+
+endfunction()
+
+
diff --git a/src/Python/Matlab2Python_utils.cpp b/src/Python/Matlab2Python_utils.cpp
new file mode 100644
index 0000000..ee76bc7
--- /dev/null
+++ b/src/Python/Matlab2Python_utils.cpp
@@ -0,0 +1,276 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazanteev
+Copyright 2017 Srikanth Nagella, 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.
+*/
+
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+
+#include <iostream>
+#include <cmath>
+
+#include <boost/python.hpp>
+#include <boost/python/numpy.hpp>
+#include "boost/tuple/tuple.hpp"
+
+#if defined(_WIN32) || defined(_WIN32) || defined(__WIN32__) || defined(_WIN64)
+#include <windows.h>
+// this trick only if compiler is MSVC
+__if_not_exists(uint8_t) { typedef __int8 uint8_t; }
+__if_not_exists(uint16_t) { typedef __int8 uint16_t; }
+#endif
+
+namespace bp = boost::python;
+namespace np = boost::python::numpy;
+
+/*! in the Matlab implementation this is called as
+void mexFunction(
+int nlhs, mxArray *plhs[],
+int nrhs, const mxArray *prhs[])
+where:
+prhs Array of pointers to the INPUT mxArrays
+nrhs int number of INPUT mxArrays
+
+nlhs Array of pointers to the OUTPUT mxArrays
+plhs int number of OUTPUT mxArrays
+
+***********************************************************
+	
+***********************************************************
+double mxGetScalar(const mxArray *pm);
+args: pm Pointer to an mxArray; cannot be a cell mxArray, a structure mxArray, or an empty mxArray.
+Returns: Pointer to the value of the first real (nonimaginary) element of the mxArray.	In C, mxGetScalar returns a double.
+***********************************************************
+char *mxArrayToString(const mxArray *array_ptr);
+args: array_ptr Pointer to mxCHAR array.
+Returns: C-style string. Returns NULL on failure. Possible reasons for failure include out of memory and specifying an array that is not an mxCHAR array.
+Description: Call mxArrayToString to copy the character data of an mxCHAR array into a C-style string.
+***********************************************************
+mxClassID mxGetClassID(const mxArray *pm);
+args: pm Pointer to an mxArray
+Returns: Numeric identifier of the class (category) of the mxArray that pm points to.For user-defined types,
+mxGetClassId returns a unique value identifying the class of the array contents.
+Use mxIsClass to determine whether an array is of a specific user-defined type.
+
+mxClassID Value	  MATLAB Type   MEX Type	 C Primitive Type
+mxINT8_CLASS 	  int8	        int8_T	     char, byte
+mxUINT8_CLASS	  uint8	        uint8_T	     unsigned char, byte
+mxINT16_CLASS	  int16	        int16_T	     short
+mxUINT16_CLASS	  uint16	    uint16_T	 unsigned short
+mxINT32_CLASS	  int32	        int32_T	     int
+mxUINT32_CLASS	  uint32	    uint32_T	 unsigned int
+mxINT64_CLASS	  int64	        int64_T	     long long
+mxUINT64_CLASS	  uint64	    uint64_T 	 unsigned long long
+mxSINGLE_CLASS	  single	    float	     float
+mxDOUBLE_CLASS	  double	    double	     double
+
+****************************************************************
+double *mxGetPr(const mxArray *pm);
+args: pm Pointer to an mxArray of type double
+Returns: Pointer to the first element of the real data. Returns NULL in C (0 in Fortran) if there is no real data.
+****************************************************************
+mxArray *mxCreateNumericArray(mwSize ndim, const mwSize *dims,
+mxClassID classid, mxComplexity ComplexFlag);
+args: ndimNumber of dimensions. If you specify a value for ndim that is less than 2, mxCreateNumericArray automatically sets the number of dimensions to 2.
+dims Dimensions array. Each element in the dimensions array contains the size of the array in that dimension.
+For example, in C, setting dims[0] to 5 and dims[1] to 7 establishes a 5-by-7 mxArray. Usually there are ndim elements in the dims array.
+classid Identifier for the class of the array, which determines the way the numerical data is represented in memory.
+For example, specifying mxINT16_CLASS in C causes each piece of numerical data in the mxArray to be represented as a 16-bit signed integer.
+ComplexFlag  If the mxArray you are creating is to contain imaginary data, set ComplexFlag to mxCOMPLEX in C (1 in Fortran). Otherwise, set ComplexFlag to mxREAL in C (0 in Fortran).
+Returns: Pointer to the created mxArray, if successful. If unsuccessful in a standalone (non-MEX file) application, returns NULL in C (0 in Fortran).
+If unsuccessful in a MEX file, the MEX file terminates and returns control to the MATLAB prompt. The function is unsuccessful when there is not
+enough free heap space to create the mxArray.
+*/
+
+void mexErrMessageText(char* text) {
+	std::cerr << text << std::endl;
+}
+
+/*
+double mxGetScalar(const mxArray *pm);
+args: pm Pointer to an mxArray; cannot be a cell mxArray, a structure mxArray, or an empty mxArray.
+Returns: Pointer to the value of the first real (nonimaginary) element of the mxArray.	In C, mxGetScalar returns a double.
+*/
+
+template<typename T>
+double mxGetScalar(const np::ndarray plh) {
+	return (double)bp::extract<T>(plh[0]);
+}
+
+
+
+template<typename T>
+T * mxGetData(const np::ndarray pm) {
+    //args: pm Pointer to an mxArray; cannot be a cell mxArray, a structure mxArray, or an empty mxArray.
+	//Returns: Pointer to the value of the first real(nonimaginary) element of the mxArray.In C, mxGetScalar returns a double.
+	/*Access the numpy array pointer:
+	char * get_data() const;
+	Returns:	Array’s raw data pointer as a char
+	Note:	This returns char so stride math works properly on it.User will have to reinterpret_cast it.
+	probably this would work.
+	A = reinterpret_cast<float *>(prhs[0]);
+	*/
+	//return reinterpret_cast<T *>(prhs[0]);
+}
+
+template<typename T>
+np::ndarray zeros(int dims , int * dim_array, T el) {
+	bp::tuple shape;
+	if (dims == 3)
+		shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]);
+	else if (dims == 2)
+		shape = bp::make_tuple(dim_array[0], dim_array[1]);
+	np::dtype dtype = np::dtype::get_builtin<T>();
+	np::ndarray zz = np::zeros(shape, dtype);
+	return zz;
+}
+
+
+bp::list mexFunction( np::ndarray input ) {
+	int number_of_dims = input.get_nd();
+	int dim_array[3];
+
+	dim_array[0] = input.shape(0);
+	dim_array[1] = input.shape(1);
+	if (number_of_dims == 2) {
+		dim_array[2] = -1;
+	}
+	else {
+		dim_array[2] = input.shape(2);
+	}
+
+	/**************************************************************************/
+	np::ndarray zz = zeros(3, dim_array, (int)0);
+	np::ndarray fzz = zeros(3, dim_array, (float)0);
+	/**************************************************************************/
+	
+	int * A = reinterpret_cast<int *>( input.get_data() );
+	int * B = reinterpret_cast<int *>( zz.get_data() );
+	float * C = reinterpret_cast<float *>(fzz.get_data());
+
+	//Copy data and cast
+	for (int i = 0; i < dim_array[0]; i++) {
+		for (int j = 0; j < dim_array[1]; j++) {
+			for (int k = 0; k < dim_array[2]; k++) {
+				int index = k + dim_array[2] * j + dim_array[2] * dim_array[1] * i;
+				int val = (*(A + index));
+				float fval = sqrt((float)val);
+				std::memcpy(B + index , &val, sizeof(int));
+				std::memcpy(C + index , &fval, sizeof(float));
+			}
+		}
+	}
+
+
+	bp::list result;
+
+	result.append<int>(number_of_dims);
+	result.append<int>(dim_array[0]);
+	result.append<int>(dim_array[1]);
+	result.append<int>(dim_array[2]);
+	result.append<np::ndarray>(zz);
+	result.append<np::ndarray>(fzz);
+
+	//result.append<bp::tuple>(tup);
+	return result;
+
+}
+bp::list doSomething(np::ndarray input, PyObject *pyobj , PyObject *pyobj2) {
+
+	boost::python::object output(boost::python::handle<>(boost::python::borrowed(pyobj)));
+	int isOutput = !(output == boost::python::api::object());
+
+	boost::python::object calculate(boost::python::handle<>(boost::python::borrowed(pyobj2)));
+	int isCalculate = !(calculate == boost::python::api::object());
+
+	int number_of_dims = input.get_nd();
+	int dim_array[3];
+
+	dim_array[0] = input.shape(0);
+	dim_array[1] = input.shape(1);
+	if (number_of_dims == 2) {
+		dim_array[2] = -1;
+	}
+	else {
+		dim_array[2] = input.shape(2);
+	}
+
+	/**************************************************************************/
+	np::ndarray zz = zeros(3, dim_array, (int)0);
+	np::ndarray fzz = zeros(3, dim_array, (float)0);
+	/**************************************************************************/
+
+	int * A = reinterpret_cast<int *>(input.get_data());
+	int * B = reinterpret_cast<int *>(zz.get_data());
+	float * C = reinterpret_cast<float *>(fzz.get_data());
+
+	//Copy data and cast
+	for (int i = 0; i < dim_array[0]; i++) {
+		for (int j = 0; j < dim_array[1]; j++) {
+			for (int k = 0; k < dim_array[2]; k++) {
+				int index = k + dim_array[2] * j + dim_array[2] * dim_array[1] * i;
+				int val = (*(A + index));
+				float fval = sqrt((float)val);
+				std::memcpy(B + index, &val, sizeof(int));
+				std::memcpy(C + index, &fval, sizeof(float));
+				// if the PyObj is not None evaluate the function 
+				if (isOutput)	
+					output(fval);
+				if (isCalculate) {
+					float nfval = (float)bp::extract<float>(calculate(val));
+					if (isOutput)
+						output(nfval);
+					std::memcpy(C + index, &nfval, sizeof(float));
+				}
+			}
+		}
+	}
+
+
+	bp::list result;
+
+	result.append<int>(number_of_dims);
+	result.append<int>(dim_array[0]);
+	result.append<int>(dim_array[1]);
+	result.append<int>(dim_array[2]);
+	result.append<np::ndarray>(zz);
+	result.append<np::ndarray>(fzz);
+
+	//result.append<bp::tuple>(tup);
+	return result;
+
+}
+
+
+BOOST_PYTHON_MODULE(prova)
+{
+	np::initialize();
+
+	//To specify that this module is a package
+	bp::object package = bp::scope();
+	package.attr("__path__") = "prova";
+
+	np::dtype dt1 = np::dtype::get_builtin<uint8_t>();
+	np::dtype dt2 = np::dtype::get_builtin<uint16_t>();
+	
+	//import_array();
+	//numpy_boost_python_register_type<float, 1>();
+	//numpy_boost_python_register_type<float, 2>();
+	//numpy_boost_python_register_type<float, 3>();
+	//numpy_boost_python_register_type<double, 3>();
+	def("mexFunction", mexFunction);
+	def("doSomething", doSomething);
+}
diff --git a/src/Python/Regularizer.py b/src/Python/Regularizer.py
new file mode 100644
index 0000000..15dbbb4
--- /dev/null
+++ b/src/Python/Regularizer.py
@@ -0,0 +1,322 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug  8 14:26:00 2017
+
+@author: ofn77899
+"""
+
+import regularizers
+import numpy as np
+from enum import Enum
+import timeit
+
+class Regularizer():
+    '''Class to handle regularizer algorithms to be used during reconstruction
+    
+    Currently 5 CPU (OMP) regularization algorithms are available:
+        
+    1) SplitBregman_TV
+    2) FGP_TV
+    3) LLT_model
+    4) PatchBased_Regul
+    5) TGV_PD
+    
+    Usage:
+        the regularizer can be invoked as object or as static method
+        Depending on the actual regularizer the input parameter may vary, and 
+        a different default setting is defined.
+        reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+
+        out = reg(input=u0, regularization_parameter=10., number_of_iterations=30,
+          tolerance_constant=1e-4, 
+          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+
+        out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10.,
+          number_of_iterations=30, tolerance_constant=1e-4, 
+          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+        
+        A number of optional parameters can be passed or skipped
+        out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
+
+    '''
+    class Algorithm(Enum):
+        SplitBregman_TV = regularizers.SplitBregman_TV
+        FGP_TV = regularizers.FGP_TV
+        LLT_model = regularizers.LLT_model
+        PatchBased_Regul = regularizers.PatchBased_Regul
+        TGV_PD = regularizers.TGV_PD
+    # Algorithm
+    
+    class TotalVariationPenalty(Enum):
+        isotropic = 0
+        l1 = 1
+    # TotalVariationPenalty
+        
+    def __init__(self , algorithm, debug = True):
+        self.setAlgorithm ( algorithm )
+        self.debug = debug
+    # __init__
+    
+    def setAlgorithm(self, algorithm):
+        self.algorithm = algorithm
+        self.pars = self.getDefaultParsForAlgorithm(algorithm)
+    # setAlgorithm
+        
+    def getDefaultParsForAlgorithm(self, algorithm):
+        pars = dict()
+        
+        if algorithm == Regularizer.Algorithm.SplitBregman_TV :
+            pars['algorithm'] = algorithm
+            pars['input'] = None
+            pars['regularization_parameter'] = None
+            pars['number_of_iterations'] = 35
+            pars['tolerance_constant'] = 0.0001
+            pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
+            
+        elif algorithm == Regularizer.Algorithm.FGP_TV :
+            pars['algorithm'] = algorithm
+            pars['input'] = None
+            pars['regularization_parameter'] = None
+            pars['number_of_iterations'] = 50
+            pars['tolerance_constant'] = 0.001
+            pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
+            
+        elif algorithm == Regularizer.Algorithm.LLT_model:
+            pars['algorithm'] = algorithm
+            pars['input'] = None
+            pars['regularization_parameter'] = None
+            pars['time_step'] = None
+            pars['number_of_iterations'] = None
+            pars['tolerance_constant'] = None
+            pars['restrictive_Z_smoothing'] = 0
+            
+        elif algorithm == Regularizer.Algorithm.PatchBased_Regul:
+            pars['algorithm'] = algorithm
+            pars['input'] = None
+            pars['searching_window_ratio'] = None
+            pars['similarity_window_ratio'] = None
+            pars['PB_filtering_parameter'] = None
+            pars['regularization_parameter'] = None
+            
+        elif algorithm == Regularizer.Algorithm.TGV_PD:
+            pars['algorithm'] = algorithm
+            pars['input'] = None
+            pars['first_order_term'] = None
+            pars['second_order_term'] = None
+            pars['number_of_iterations'] = None
+            pars['regularization_parameter'] = None
+            
+        else:
+            raise Exception('Unknown regularizer algorithm')
+            
+        return pars
+    # parsForAlgorithm
+    
+    def setParameter(self, **kwargs):
+        '''set named parameter for the regularization engine
+        
+        raises Exception if the named parameter is not recognized
+        Typical usage is:
+            
+        reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+        reg.setParameter(input=u0)    
+        reg.setParameter(regularization_parameter=10.)
+        
+        it can be also used as
+        reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+        reg.setParameter(input=u0 , regularization_parameter=10.)
+        '''
+        
+        for key , value in kwargs.items():
+            if key in self.pars.keys():
+                self.pars[key] = value
+            else:
+                raise Exception('Wrong parameter {0} for regularizer algorithm'.format(key))
+    # setParameter
+	
+    def getParameter(self, **kwargs):
+        ret = {}
+        for key , value in kwargs.items():
+            if key in self.pars.keys():
+                ret[key] = self.pars[key]
+        else:
+            raise Exception('Wrong parameter {0} for regularizer algorithm'.format(key))
+    # setParameter
+	
+        
+    def __call__(self, input = None, regularization_parameter = None, **kwargs):
+        '''Actual call for the regularizer. 
+        
+        One can either set the regularization parameters first and then call the
+        algorithm or set the regularization parameter during the call (as 
+        is done in the static methods). 
+        '''
+        
+        if kwargs is not None:
+            for key, value in kwargs.items():
+                #print("{0} = {1}".format(key, value))                        
+                self.pars[key] = value
+                    
+        if input is not None: 
+            self.pars['input'] = input
+        if regularization_parameter is not None:
+            self.pars['regularization_parameter'] = regularization_parameter
+            
+        if self.debug:
+            print ("--------------------------------------------------")
+            for key, value in self.pars.items():
+                if key== 'algorithm' :
+                    print("{0} = {1}".format(key, value.__name__))
+                elif key == 'input':
+                    print("{0} = {1}".format(key, np.shape(value)))
+                else:
+                    print("{0} = {1}".format(key, value))
+        
+            
+        if None in self.pars:
+                raise Exception("Not all parameters have been provided")
+        
+        input = self.pars['input']
+        regularization_parameter = self.pars['regularization_parameter']
+        if self.algorithm == Regularizer.Algorithm.SplitBregman_TV :
+            return self.algorithm(input, regularization_parameter,
+                              self.pars['number_of_iterations'],
+                              self.pars['tolerance_constant'],
+                              self.pars['TV_penalty'].value )    
+        elif self.algorithm == Regularizer.Algorithm.FGP_TV :
+            return self.algorithm(input, regularization_parameter,
+                              self.pars['number_of_iterations'],
+                              self.pars['tolerance_constant'],
+                              self.pars['TV_penalty'].value )
+        elif self.algorithm == Regularizer.Algorithm.LLT_model :
+            #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
+            # no default
+            return self.algorithm(input, 
+                              regularization_parameter,
+                              self.pars['time_step'] , 
+                              self.pars['number_of_iterations'],
+                              self.pars['tolerance_constant'],
+                              self.pars['restrictive_Z_smoothing'] )
+        elif self.algorithm == Regularizer.Algorithm.PatchBased_Regul :
+            #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
+            # no default
+            return self.algorithm(input, regularization_parameter,
+                                  self.pars['searching_window_ratio'] , 
+                                  self.pars['similarity_window_ratio'] , 
+                                  self.pars['PB_filtering_parameter'])
+        elif self.algorithm == Regularizer.Algorithm.TGV_PD :
+            #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
+            # no default
+            if len(np.shape(input)) == 2:
+                return self.algorithm(input, regularization_parameter,
+                                  self.pars['first_order_term'] , 
+                                  self.pars['second_order_term'] , 
+                                  self.pars['number_of_iterations'])
+            elif len(np.shape(input)) == 3:
+                #assuming it's 3D
+                # run independent calls on each slice
+                out3d = input.copy()
+                for i in range(np.shape(input)[2]):
+                    out = self.algorithm(input, regularization_parameter,
+                                 self.pars['first_order_term'] , 
+                                 self.pars['second_order_term'] , 
+                                 self.pars['number_of_iterations'])
+                    # copy the result in the 3D image
+                    out3d.T[i] = out[0].copy()
+                # append the rest of the info that the algorithm returns
+                output = [out3d]
+                for i in range(1,len(out)):
+                    output.append(out[i])
+                return output
+                
+                
+            
+            
+        
+    # __call__
+    
+    @staticmethod
+    def SplitBregman_TV(input, regularization_parameter , **kwargs):
+        start_time = timeit.default_timer()
+        reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+        out = list( reg(input, regularization_parameter, **kwargs) )
+        out.append(reg.pars)
+        txt = reg.printParametersToString()
+        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+        out.append(txt)
+        return out
+        
+    @staticmethod
+    def FGP_TV(input, regularization_parameter , **kwargs):
+        start_time = timeit.default_timer()
+        reg = Regularizer(Regularizer.Algorithm.FGP_TV)
+        out = list( reg(input, regularization_parameter, **kwargs) )
+        out.append(reg.pars)
+        txt = reg.printParametersToString()
+        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+        out.append(txt)
+        return out
+    
+    @staticmethod
+    def LLT_model(input, regularization_parameter , time_step, number_of_iterations,
+                  tolerance_constant, restrictive_Z_smoothing=0):
+        start_time = timeit.default_timer()
+        reg = Regularizer(Regularizer.Algorithm.LLT_model)
+        out = list( reg(input, regularization_parameter, time_step=time_step, 
+                        number_of_iterations=number_of_iterations,
+                        tolerance_constant=tolerance_constant, 
+                        restrictive_Z_smoothing=restrictive_Z_smoothing) )
+        out.append(reg.pars)
+        txt = reg.printParametersToString()
+        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+        out.append(txt)
+        return out
+    
+    @staticmethod
+    def PatchBased_Regul(input, regularization_parameter,
+                        searching_window_ratio, 
+                        similarity_window_ratio,
+                        PB_filtering_parameter):
+        start_time = timeit.default_timer()
+        reg = Regularizer(Regularizer.Algorithm.PatchBased_Regul)   
+        out = list( reg(input, 
+                        regularization_parameter,
+                        searching_window_ratio=searching_window_ratio, 
+                        similarity_window_ratio=similarity_window_ratio,
+                        PB_filtering_parameter=PB_filtering_parameter )
+            )
+        out.append(reg.pars)
+        txt = reg.printParametersToString()
+        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+        out.append(txt)
+        return out
+    
+    @staticmethod
+    def TGV_PD(input, regularization_parameter , first_order_term, 
+               second_order_term, number_of_iterations):
+        start_time = timeit.default_timer()
+        
+        reg = Regularizer(Regularizer.Algorithm.TGV_PD)
+        out = list( reg(input, regularization_parameter, 
+                        first_order_term=first_order_term, 
+                        second_order_term=second_order_term,
+                        number_of_iterations=number_of_iterations) )
+        out.append(reg.pars)
+        txt = reg.printParametersToString()
+        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+        out.append(txt)
+        
+        return out
+    
+    def printParametersToString(self):
+        txt = r''
+        for key, value in self.pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+        
diff --git a/src/Python/ccpi/__init__.py b/src/Python/ccpi/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/src/Python/ccpi/__init__.py
diff --git a/src/Python/ccpi/imaging/Regularizer.py b/src/Python/ccpi/imaging/Regularizer.py
new file mode 100644
index 0000000..fb9ae08
--- /dev/null
+++ b/src/Python/ccpi/imaging/Regularizer.py
@@ -0,0 +1,322 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug  8 14:26:00 2017
+
+@author: ofn77899
+"""
+
+from ccpi.imaging import cpu_regularizers
+import numpy as np
+from enum import Enum
+import timeit
+
+class Regularizer():
+    '''Class to handle regularizer algorithms to be used during reconstruction
+    
+    Currently 5 CPU (OMP) regularization algorithms are available:
+        
+    1) SplitBregman_TV
+    2) FGP_TV
+    3) LLT_model
+    4) PatchBased_Regul
+    5) TGV_PD
+    
+    Usage:
+        the regularizer can be invoked as object or as static method
+        Depending on the actual regularizer the input parameter may vary, and 
+        a different default setting is defined.
+        reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+
+        out = reg(input=u0, regularization_parameter=10., number_of_iterations=30,
+          tolerance_constant=1e-4, 
+          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+
+        out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10.,
+          number_of_iterations=30, tolerance_constant=1e-4, 
+          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+        
+        A number of optional parameters can be passed or skipped
+        out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
+
+    '''
+    class Algorithm(Enum):
+        SplitBregman_TV = cpu_regularizers.SplitBregman_TV
+        FGP_TV = cpu_regularizers.FGP_TV
+        LLT_model = cpu_regularizers.LLT_model
+        PatchBased_Regul = cpu_regularizers.PatchBased_Regul
+        TGV_PD = cpu_regularizers.TGV_PD
+    # Algorithm
+    
+    class TotalVariationPenalty(Enum):
+        isotropic = 0
+        l1 = 1
+    # TotalVariationPenalty
+        
+    def __init__(self , algorithm, debug = True):
+        self.setAlgorithm ( algorithm )
+        self.debug = debug
+    # __init__
+    
+    def setAlgorithm(self, algorithm):
+        self.algorithm = algorithm
+        self.pars = self.getDefaultParsForAlgorithm(algorithm)
+    # setAlgorithm
+        
+    def getDefaultParsForAlgorithm(self, algorithm):
+        pars = dict()
+        
+        if algorithm == Regularizer.Algorithm.SplitBregman_TV :
+            pars['algorithm'] = algorithm
+            pars['input'] = None
+            pars['regularization_parameter'] = None
+            pars['number_of_iterations'] = 35
+            pars['tolerance_constant'] = 0.0001
+            pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
+            
+        elif algorithm == Regularizer.Algorithm.FGP_TV :
+            pars['algorithm'] = algorithm
+            pars['input'] = None
+            pars['regularization_parameter'] = None
+            pars['number_of_iterations'] = 50
+            pars['tolerance_constant'] = 0.001
+            pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
+            
+        elif algorithm == Regularizer.Algorithm.LLT_model:
+            pars['algorithm'] = algorithm
+            pars['input'] = None
+            pars['regularization_parameter'] = None
+            pars['time_step'] = None
+            pars['number_of_iterations'] = None
+            pars['tolerance_constant'] = None
+            pars['restrictive_Z_smoothing'] = 0
+            
+        elif algorithm == Regularizer.Algorithm.PatchBased_Regul:
+            pars['algorithm'] = algorithm
+            pars['input'] = None
+            pars['searching_window_ratio'] = None
+            pars['similarity_window_ratio'] = None
+            pars['PB_filtering_parameter'] = None
+            pars['regularization_parameter'] = None
+            
+        elif algorithm == Regularizer.Algorithm.TGV_PD:
+            pars['algorithm'] = algorithm
+            pars['input'] = None
+            pars['first_order_term'] = None
+            pars['second_order_term'] = None
+            pars['number_of_iterations'] = None
+            pars['regularization_parameter'] = None
+            
+        else:
+            raise Exception('Unknown regularizer algorithm')
+            
+        return pars
+    # parsForAlgorithm
+    
+    def setParameter(self, **kwargs):
+        '''set named parameter for the regularization engine
+        
+        raises Exception if the named parameter is not recognized
+        Typical usage is:
+            
+        reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+        reg.setParameter(input=u0)    
+        reg.setParameter(regularization_parameter=10.)
+        
+        it can be also used as
+        reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+        reg.setParameter(input=u0 , regularization_parameter=10.)
+        '''
+        
+        for key , value in kwargs.items():
+            if key in self.pars.keys():
+                self.pars[key] = value
+            else:
+                raise Exception('Wrong parameter {0} for regularizer algorithm'.format(key))
+    # setParameter
+	
+    def getParameter(self, **kwargs):
+        ret = {}
+        for key , value in kwargs.items():
+            if key in self.pars.keys():
+                ret[key] = self.pars[key]
+        else:
+            raise Exception('Wrong parameter {0} for regularizer algorithm'.format(key))
+    # setParameter
+	
+        
+    def __call__(self, input = None, regularization_parameter = None, **kwargs):
+        '''Actual call for the regularizer. 
+        
+        One can either set the regularization parameters first and then call the
+        algorithm or set the regularization parameter during the call (as 
+        is done in the static methods). 
+        '''
+        
+        if kwargs is not None:
+            for key, value in kwargs.items():
+                #print("{0} = {1}".format(key, value))                        
+                self.pars[key] = value
+                    
+        if input is not None: 
+            self.pars['input'] = input
+        if regularization_parameter is not None:
+            self.pars['regularization_parameter'] = regularization_parameter
+            
+        if self.debug:
+            print ("--------------------------------------------------")
+            for key, value in self.pars.items():
+                if key== 'algorithm' :
+                    print("{0} = {1}".format(key, value.__name__))
+                elif key == 'input':
+                    print("{0} = {1}".format(key, np.shape(value)))
+                else:
+                    print("{0} = {1}".format(key, value))
+        
+            
+        if None in self.pars:
+                raise Exception("Not all parameters have been provided")
+        
+        input = self.pars['input']
+        regularization_parameter = self.pars['regularization_parameter']
+        if self.algorithm == Regularizer.Algorithm.SplitBregman_TV :
+            return self.algorithm(input, regularization_parameter,
+                              self.pars['number_of_iterations'],
+                              self.pars['tolerance_constant'],
+                              self.pars['TV_penalty'].value )    
+        elif self.algorithm == Regularizer.Algorithm.FGP_TV :
+            return self.algorithm(input, regularization_parameter,
+                              self.pars['number_of_iterations'],
+                              self.pars['tolerance_constant'],
+                              self.pars['TV_penalty'].value )
+        elif self.algorithm == Regularizer.Algorithm.LLT_model :
+            #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
+            # no default
+            return self.algorithm(input, 
+                              regularization_parameter,
+                              self.pars['time_step'] , 
+                              self.pars['number_of_iterations'],
+                              self.pars['tolerance_constant'],
+                              self.pars['restrictive_Z_smoothing'] )
+        elif self.algorithm == Regularizer.Algorithm.PatchBased_Regul :
+            #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
+            # no default
+            return self.algorithm(input, regularization_parameter,
+                                  self.pars['searching_window_ratio'] , 
+                                  self.pars['similarity_window_ratio'] , 
+                                  self.pars['PB_filtering_parameter'])
+        elif self.algorithm == Regularizer.Algorithm.TGV_PD :
+            #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
+            # no default
+            if len(np.shape(input)) == 2:
+                return self.algorithm(input, regularization_parameter,
+                                  self.pars['first_order_term'] , 
+                                  self.pars['second_order_term'] , 
+                                  self.pars['number_of_iterations'])
+            elif len(np.shape(input)) == 3:
+                #assuming it's 3D
+                # run independent calls on each slice
+                out3d = input.copy()
+                for i in range(np.shape(input)[2]):
+                    out = self.algorithm(input, regularization_parameter,
+                                 self.pars['first_order_term'] , 
+                                 self.pars['second_order_term'] , 
+                                 self.pars['number_of_iterations'])
+                    # copy the result in the 3D image
+                    out3d.T[i] = out[0].copy()
+                # append the rest of the info that the algorithm returns
+                output = [out3d]
+                for i in range(1,len(out)):
+                    output.append(out[i])
+                return output
+                
+                
+            
+            
+        
+    # __call__
+    
+    @staticmethod
+    def SplitBregman_TV(input, regularization_parameter , **kwargs):
+        start_time = timeit.default_timer()
+        reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+        out = list( reg(input, regularization_parameter, **kwargs) )
+        out.append(reg.pars)
+        txt = reg.printParametersToString()
+        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+        out.append(txt)
+        return out
+        
+    @staticmethod
+    def FGP_TV(input, regularization_parameter , **kwargs):
+        start_time = timeit.default_timer()
+        reg = Regularizer(Regularizer.Algorithm.FGP_TV)
+        out = list( reg(input, regularization_parameter, **kwargs) )
+        out.append(reg.pars)
+        txt = reg.printParametersToString()
+        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+        out.append(txt)
+        return out
+    
+    @staticmethod
+    def LLT_model(input, regularization_parameter , time_step, number_of_iterations,
+                  tolerance_constant, restrictive_Z_smoothing=0):
+        start_time = timeit.default_timer()
+        reg = Regularizer(Regularizer.Algorithm.LLT_model)
+        out = list( reg(input, regularization_parameter, time_step=time_step, 
+                        number_of_iterations=number_of_iterations,
+                        tolerance_constant=tolerance_constant, 
+                        restrictive_Z_smoothing=restrictive_Z_smoothing) )
+        out.append(reg.pars)
+        txt = reg.printParametersToString()
+        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+        out.append(txt)
+        return out
+    
+    @staticmethod
+    def PatchBased_Regul(input, regularization_parameter,
+                        searching_window_ratio, 
+                        similarity_window_ratio,
+                        PB_filtering_parameter):
+        start_time = timeit.default_timer()
+        reg = Regularizer(Regularizer.Algorithm.PatchBased_Regul)   
+        out = list( reg(input, 
+                        regularization_parameter,
+                        searching_window_ratio=searching_window_ratio, 
+                        similarity_window_ratio=similarity_window_ratio,
+                        PB_filtering_parameter=PB_filtering_parameter )
+            )
+        out.append(reg.pars)
+        txt = reg.printParametersToString()
+        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+        out.append(txt)
+        return out
+    
+    @staticmethod
+    def TGV_PD(input, regularization_parameter , first_order_term, 
+               second_order_term, number_of_iterations):
+        start_time = timeit.default_timer()
+        
+        reg = Regularizer(Regularizer.Algorithm.TGV_PD)
+        out = list( reg(input, regularization_parameter, 
+                        first_order_term=first_order_term, 
+                        second_order_term=second_order_term,
+                        number_of_iterations=number_of_iterations) )
+        out.append(reg.pars)
+        txt = reg.printParametersToString()
+        txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+        out.append(txt)
+        
+        return out
+    
+    def printParametersToString(self):
+        txt = r''
+        for key, value in self.pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+        
diff --git a/src/Python/ccpi/imaging/__init__.py b/src/Python/ccpi/imaging/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/src/Python/ccpi/imaging/__init__.py
diff --git a/src/Python/ccpi/reconstruction/FISTAReconstructor.py b/src/Python/ccpi/reconstruction/FISTAReconstructor.py
new file mode 100644
index 0000000..c903712
--- /dev/null
+++ b/src/Python/ccpi/reconstruction/FISTAReconstructor.py
@@ -0,0 +1,612 @@
+# -*- coding: utf-8 -*-
+###############################################################################
+#This work is part of the Core Imaging Library developed by
+#Visual Analytics and Imaging System Group of the Science Technology
+#Facilities Council, STFC
+#
+#Copyright 2017 Edoardo Pasca, Srikanth Nagella
+#Copyright 2017 Daniil Kazantsev
+#
+#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.
+###############################################################################
+
+
+
+import numpy
+#from ccpi.reconstruction.parallelbeam import alg
+
+#from ccpi.imaging.Regularizer import Regularizer
+from enum import Enum
+
+import astra
+
+   
+    
+class FISTAReconstructor():
+    '''FISTA-based reconstruction algorithm using ASTRA-toolbox
+    
+    '''
+    # <<<< FISTA-based reconstruction algorithm using ASTRA-toolbox >>>>
+    # ___Input___:
+    # params.[] file:
+    #       - .proj_geom (geometry of the projector) [required]
+    #       - .vol_geom (geometry of the reconstructed object) [required]
+    #       - .sino (vectorized in 2D or 3D sinogram) [required]
+    #       - .iterFISTA (iterations for the main loop, default 40)
+    #       - .L_const (Lipschitz constant, default Power method)                                                                                                    )
+    #       - .X_ideal (ideal image, if given)
+    #       - .weights (statisitcal weights, size of the sinogram)
+    #       - .ROI (Region-of-interest, only if X_ideal is given)
+    #       - .initialize (a 'warm start' using SIRT method from ASTRA)
+    #----------------Regularization choices------------------------
+    #       - .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
+    #       - .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
+    #       - .Regul_Lambda_TVLLT (Higher order SB-LLT regularization parameter)
+    #       - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
+    #       - .Regul_Iterations (iterations for the selected penalty, default 25)
+    #       - .Regul_tauLLT (time step parameter for LLT term)
+    #       - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal)
+    #       - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1)
+    #----------------Visualization parameters------------------------
+    #       - .show (visualize reconstruction 1/0, (0 default))
+    #       - .maxvalplot (maximum value to use for imshow[0 maxvalplot])
+    #       - .slice (for 3D volumes - slice number to imshow)
+    # ___Output___:
+    # 1. X - reconstructed image/volume
+    # 2. output - a structure with
+    #    - .Resid_error - residual error (if X_ideal is given)
+    #    - .objective: value of the objective function
+    #    - .L_const: Lipshitz constant to avoid recalculations
+    
+    # References:
+    # 1. "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse
+    # Problems" by A. Beck and M Teboulle
+    # 2. "Ring artifacts correction in compressed sensing..." by P. Paleo
+    # 3. "A novel tomographic reconstruction method based on the robust
+    # Student's t function for suppressing data outliers" D. Kazantsev et.al.
+    # D. Kazantsev, 2016-17
+    def __init__(self, projector_geometry, output_geometry, input_sinogram,
+                 **kwargs):
+        # handle parmeters:
+        # obligatory parameters
+        self.pars = dict()
+        self.pars['projector_geometry'] = projector_geometry # proj_geom
+        self.pars['output_geometry'] = output_geometry       # vol_geom
+        self.pars['input_sinogram'] = input_sinogram         # sino
+        sliceZ, nangles, detectors = numpy.shape(input_sinogram)
+        self.pars['detectors'] = detectors
+        self.pars['number_of_angles'] = nangles
+        self.pars['SlicesZ'] = sliceZ
+        self.pars['output_volume'] = None
+
+        print (self.pars)
+        # handle optional input parameters (at instantiation)
+        
+        # Accepted input keywords
+        kw = (
+              # mandatory fields
+              'projector_geometry',
+              'output_geometry',
+              'input_sinogram',
+              'detectors',
+              'number_of_angles',
+              'SlicesZ',
+              # optional fields
+              'number_of_iterations', 
+              'Lipschitz_constant' , 
+              'ideal_image' ,
+              'weights' , 
+              'region_of_interest' , 
+              'initialize' , 
+              'regularizer' , 
+              'ring_lambda_R_L1',
+              'ring_alpha',
+              'subsets',
+              'output_volume',
+              'os_subsets',
+              'os_indices',
+              'os_bins')
+        self.acceptedInputKeywords = list(kw)
+        
+        # handle keyworded parameters
+        if kwargs is not None:
+            for key, value in kwargs.items():
+                if key in kw:
+                    #print("{0} = {1}".format(key, value))                        
+                    self.pars[key] = value
+                    
+        # set the default values for the parameters if not set
+        if 'number_of_iterations' in kwargs.keys():
+            self.pars['number_of_iterations'] = kwargs['number_of_iterations']
+        else:
+            self.pars['number_of_iterations'] = 40
+        if 'weights' in kwargs.keys():
+            self.pars['weights'] = kwargs['weights']
+        else:
+            self.pars['weights'] = \
+                                 numpy.ones(numpy.shape(
+                                     self.pars['input_sinogram']))
+        if 'Lipschitz_constant' in kwargs.keys():
+            self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
+        else:
+            self.pars['Lipschitz_constant'] = None
+        
+        if not 'ideal_image' in kwargs.keys():
+            self.pars['ideal_image'] = None
+        
+        if not 'region_of_interest'in kwargs.keys() :
+            if self.pars['ideal_image'] == None:
+                self.pars['region_of_interest'] = None
+            else:
+                ## nonzero if the image is larger than m
+                fsm = numpy.frompyfunc(lambda x,m: 1 if x>m else 0, 2,1)
+                
+                self.pars['region_of_interest'] = fsm(self.pars['ideal_image'], 0)
+                
+        # the regularizer must be a correctly instantiated object    
+        if not 'regularizer' in kwargs.keys() :
+            self.pars['regularizer'] = None
+
+        #RING REMOVAL
+        if not 'ring_lambda_R_L1' in kwargs.keys():
+            self.pars['ring_lambda_R_L1'] = 0
+        if not 'ring_alpha' in kwargs.keys():
+            self.pars['ring_alpha'] = 1
+
+        # ORDERED SUBSET
+        if not 'subsets' in kwargs.keys():
+            self.pars['subsets'] = 0
+        else:
+            self.createOrderedSubsets()
+
+        if not 'initialize' in kwargs.keys():
+            self.pars['initialize'] = False
+
+        
+            
+            
+    def setParameter(self, **kwargs):
+        '''set named parameter for the reconstructor engine
+        
+        raises Exception if the named parameter is not recognized
+        
+        '''
+        for key , value in kwargs.items():
+            if key in self.acceptedInputKeywords:
+                self.pars[key] = value
+            else:
+                raise Exception('Wrong parameter {0} for '.format(key) +
+                                'reconstructor')
+    # setParameter
+
+    def getParameter(self, key):
+        if type(key) is str:
+            if key in self.acceptedInputKeywords:
+                return self.pars[key]
+            else:
+                raise Exception('Unrecongnised parameter: {0} '.format(key) )
+        elif type(key) is list:
+            outpars = []
+            for k in key:
+                outpars.append(self.getParameter(k))
+            return outpars
+        else:
+            raise Exception('Unhandled input {0}' .format(str(type(key))))
+            
+    
+    def calculateLipschitzConstantWithPowerMethod(self):
+        ''' using Power method (PM) to establish L constant'''
+        
+        N = self.pars['output_geometry']['GridColCount']
+        proj_geom = self.pars['projector_geometry']
+        vol_geom = self.pars['output_geometry']
+        weights = self.pars['weights']
+        SlicesZ = self.pars['SlicesZ']
+        
+            
+                               
+        if (proj_geom['type'] == 'parallel') or \
+           (proj_geom['type'] == 'parallel3d'):
+            #% for parallel geometry we can do just one slice
+            #print('Calculating Lipshitz constant for parallel beam geometry...')
+            niter = 5;# % number of iteration for the PM
+            #N = params.vol_geom.GridColCount;
+            #x1 = rand(N,N,1);
+            x1 = numpy.random.rand(1,N,N)
+            #sqweight = sqrt(weights(:,:,1));
+            sqweight = numpy.sqrt(weights[0])
+            proj_geomT = proj_geom.copy();
+            proj_geomT['DetectorRowCount'] = 1;
+            vol_geomT = vol_geom.copy();
+            vol_geomT['GridSliceCount'] = 1;
+            
+            #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
+            
+            
+            for i in range(niter):
+            #        [id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geomT, vol_geomT);
+            #            s = norm(x1(:));
+            #            x1 = x1/s;
+            #            [sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
+            #            y = sqweight.*y;
+            #            astra_mex_data3d('delete', sino_id);
+            #            astra_mex_data3d('delete', id);
+                #print ("iteration {0}".format(i))
+                                
+                sino_id, y = astra.creators.create_sino3d_gpu(x1,
+                                                          proj_geomT,
+                                                          vol_geomT)
+                
+                y = (sqweight * y).copy() # element wise multiplication
+                
+                #b=fig.add_subplot(2,1,2)
+                #imgplot = plt.imshow(x1[0])
+                #plt.show()
+                
+                #astra_mex_data3d('delete', sino_id);
+                astra.matlab.data3d('delete', sino_id)
+                del x1
+                    
+                idx,x1 = astra.creators.create_backprojection3d_gpu((sqweight*y).copy(), 
+                                                                    proj_geomT,
+                                                                    vol_geomT)
+                del y
+                
+                                                                    
+                s = numpy.linalg.norm(x1)
+                ### this line?
+                x1 = (x1/s).copy();
+                
+            #        ### this line?
+            #        sino_id, y = astra.creators.create_sino3d_gpu(x1, 
+            #                                                      proj_geomT, 
+            #                                                      vol_geomT);
+            #        y = sqweight * y;
+                astra.matlab.data3d('delete', sino_id);
+                astra.matlab.data3d('delete', idx)
+                print ("iteration {0} s= {1}".format(i,s))
+                
+            #end
+            del proj_geomT
+            del vol_geomT
+            #plt.show()
+        else:
+            #% divergen beam geometry
+            print('Calculating Lipshitz constant for divergen beam geometry...')
+            niter = 8; #% number of iteration for PM
+            x1 = numpy.random.rand(SlicesZ , N , N);
+            #sqweight = sqrt(weights);
+            sqweight = numpy.sqrt(weights[0])
+            
+            sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
+            y = sqweight*y;
+            #astra_mex_data3d('delete', sino_id);
+            astra.matlab.data3d('delete', sino_id);
+            
+            for i in range(niter):
+                #[id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
+                idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, 
+                                                                    proj_geom, 
+                                                                    vol_geom)
+                s = numpy.linalg.norm(x1)
+                ### this line?
+                x1 = x1/s;
+                ### this line?
+                #[sino_id, y] = astra_create_sino3d_gpu(x1, proj_geom, vol_geom);
+                sino_id, y = astra.creators.create_sino3d_gpu(x1, 
+                                                              proj_geom, 
+                                                              vol_geom);
+                
+                y = sqweight*y;
+                #astra_mex_data3d('delete', sino_id);
+                #astra_mex_data3d('delete', id);
+                astra.matlab.data3d('delete', sino_id);
+                astra.matlab.data3d('delete', idx);
+            #end
+            #clear x1
+            del x1
+
+        
+        return s
+    
+    
+    def setRegularizer(self, regularizer):
+        if regularizer is not None:
+            self.pars['regularizer'] = regularizer
+        
+
+    def initialize(self):
+        # convenience variable storage
+        proj_geom = self.pars['projector_geometry']
+        vol_geom = self.pars['output_geometry']
+        sino = self.pars['input_sinogram']
+        
+        # a 'warm start' with SIRT method
+        # Create a data object for the reconstruction
+        rec_id = astra.matlab.data3d('create', '-vol',
+                                    vol_geom);
+        
+        #sinogram_id = astra_mex_data3d('create', '-proj3d', proj_geom, sino);
+        sinogram_id = astra.matlab.data3d('create', '-proj3d',
+                                          proj_geom,
+                                          sino)
+
+        sirt_config = astra.astra_dict('SIRT3D_CUDA')
+        sirt_config['ReconstructionDataId' ] = rec_id
+        sirt_config['ProjectionDataId'] = sinogram_id
+
+        sirt = astra.algorithm.create(sirt_config)
+        astra.algorithm.run(sirt, iterations=35)
+        X = astra.matlab.data3d('get', rec_id)
+
+        # clean up memory
+        astra.matlab.data3d('delete', rec_id)
+        astra.matlab.data3d('delete', sinogram_id)
+        astra.algorithm.delete(sirt)
+
+        
+
+        return X
+
+    def createOrderedSubsets(self, subsets=None):
+        if subsets is None:
+            try:
+                subsets = self.getParameter('subsets')
+            except Exception():
+                subsets = 0
+            #return subsets
+        else:
+            self.setParameter(subsets=subsets)
+            
+
+        angles = self.getParameter('projector_geometry')['ProjectionAngles'] 
+        
+        #binEdges = numpy.linspace(angles.min(),
+        #                          angles.max(),
+        #                          subsets + 1)
+        binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
+        # get rearranged subset indices
+        IndicesReorg = numpy.zeros((numpy.shape(angles)), dtype=numpy.int32)
+        counterM = 0
+        for ii in range(binsDiscr.max()):
+            counter = 0
+            for jj in range(subsets):
+                curr_index = ii + jj  + counter
+                #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
+                if binsDiscr[jj] > ii:
+                    if (counterM < numpy.size(IndicesReorg)):
+                        IndicesReorg[counterM] = curr_index
+                    counterM = counterM + 1
+                    
+                counter = counter + binsDiscr[jj] - 1    
+                
+        # store the OS in parameters
+        self.setParameter(os_subsets=subsets,
+                          os_bins=binsDiscr,
+                          os_indices=IndicesReorg)
+            
+
+    def prepareForIteration(self):
+        print ("FISTA Reconstructor: prepare for iteration")
+        
+        self.residual_error = numpy.zeros((self.pars['number_of_iterations']))
+        self.objective = numpy.zeros((self.pars['number_of_iterations']))
+
+        #2D array (for 3D data) of sparse "ring" 
+        detectors, nangles, sliceZ  = numpy.shape(self.pars['input_sinogram'])
+        self.r = numpy.zeros((detectors, sliceZ), dtype=numpy.float)
+        # another ring variable
+        self.r_x = self.r.copy()
+
+        self.residual = numpy.zeros(numpy.shape(self.pars['input_sinogram']))
+        
+        if self.getParameter('Lipschitz_constant') is None:
+            self.pars['Lipschitz_constant'] = \
+                            self.calculateLipschitzConstantWithPowerMethod()
+        # errors vector (if the ground truth is given)
+        self.Resid_error = numpy.zeros((self.getParameter('number_of_iterations')));
+        # objective function values vector
+        self.objective = numpy.zeros((self.getParameter('number_of_iterations')));      
+        
+
+    # prepareForIteration
+
+    def iterate(self, Xin=None):
+        print ("FISTA Reconstructor: iterate")
+        
+        if Xin is None:    
+            if self.getParameter('initialize'):
+                X = self.initialize()
+            else:
+                N = vol_geom['GridColCount']
+                X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
+        else:
+            # copy by reference
+            X = Xin
+        # store the output volume in the parameters
+        self.setParameter(output_volume=X)
+        X_t = X.copy()
+        # convenience variable storage
+        proj_geom , vol_geom, sino , \
+          SlicesZ  = self.getParameter([ 'projector_geometry' ,
+                                                'output_geometry',
+                                                'input_sinogram',
+                                                'SlicesZ' ])
+                   
+        t = 1
+        
+        for i in range(self.getParameter('number_of_iterations')):
+            X_old = X.copy()
+            t_old = t
+            r_old = self.r.copy()
+            if self.getParameter('projector_geometry')['type'] == 'parallel' or \
+               self.getParameter('projector_geometry')['type'] == 'fanflat' or \
+               self.getParameter('projector_geometry')['type'] == 'fanflat_vec':
+                # if the geometry is parallel use slice-by-slice
+                # projection-backprojection routine
+                #sino_updt = zeros(size(sino),'single');
+                proj_geomT = proj_geom.copy()
+                proj_geomT['DetectorRowCount'] = 1
+                vol_geomT = vol_geom.copy()
+                vol_geomT['GridSliceCount'] = 1;
+                self.sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
+                for kkk in range(SlicesZ):
+                    sino_id, self.sino_updt[kkk] = \
+                             astra.creators.create_sino3d_gpu(
+                                 X_t[kkk:kkk+1], proj_geomT, vol_geomT)
+                    astra.matlab.data3d('delete', sino_id)
+            else:
+                # for divergent 3D geometry (watch the GPU memory overflow in
+                # ASTRA versions < 1.8)
+                #[sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
+                sino_id, self.sino_updt = astra.creators.create_sino3d_gpu(
+                    X_t, proj_geom, vol_geom)
+
+
+            ## RING REMOVAL
+            self.ringRemoval(i)
+            ## Projection/Backprojection Routine
+            self.projectionBackprojection(X, X_t)
+            astra.matlab.data3d('delete', sino_id)
+            ## REGULARIZATION
+            X = self.regularize(X)
+            ## Update Loop
+            X , X_t, t = self.updateLoop(i, X, X_old, r_old, t, t_old)
+            self.setParameter(output_volume=X)
+        return X
+    ## iterate
+    
+    def ringRemoval(self, i):
+        print ("FISTA Reconstructor: ring removal")
+        residual = self.residual
+        lambdaR_L1 , alpha_ring , weights , L_const , sino= \
+                   self.getParameter(['ring_lambda_R_L1',
+                                      'ring_alpha' , 'weights',
+                                      'Lipschitz_constant',
+                                      'input_sinogram'])
+        r_x = self.r_x
+        sino_updt = self.sino_updt
+        
+        SlicesZ, anglesNumb, Detectors = \
+                    numpy.shape(self.getParameter('input_sinogram'))
+        if lambdaR_L1 > 0 :
+             for kkk in range(anglesNumb):
+                 
+                 residual[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
+                                       ((sino_updt[:,kkk,:]).squeeze() - \
+                                        (sino[:,kkk,:]).squeeze() -\
+                                        (alpha_ring * r_x)
+                                        )
+             vec = residual.sum(axis = 1)
+             #if SlicesZ > 1:
+             #    vec = vec[:,1,:].squeeze()
+             self.r = (r_x - (1./L_const) * vec).copy()
+             self.objective[i] = (0.5 * (residual ** 2).sum())
+
+    def projectionBackprojection(self, X, X_t):
+        print ("FISTA Reconstructor: projection-backprojection routine")
+        
+        # a few useful variables
+        SlicesZ, anglesNumb, Detectors = \
+                    numpy.shape(self.getParameter('input_sinogram'))
+        residual = self.residual
+        proj_geom , vol_geom , L_const = \
+                  self.getParameter(['projector_geometry' ,
+                                                  'output_geometry',
+                                                  'Lipschitz_constant'])
+        
+        
+        if self.getParameter('projector_geometry')['type'] == 'parallel' or \
+           self.getParameter('projector_geometry')['type'] == 'fanflat' or \
+           self.getParameter('projector_geometry')['type'] == 'fanflat_vec':
+            # if the geometry is parallel use slice-by-slice
+            # projection-backprojection routine
+            #sino_updt = zeros(size(sino),'single');
+            proj_geomT = proj_geom.copy()
+            proj_geomT['DetectorRowCount'] = 1
+            vol_geomT = vol_geom.copy()
+            vol_geomT['GridSliceCount'] = 1;
+            x_temp = numpy.zeros(numpy.shape(X),dtype=numpy.float32)
+            
+            for kkk in range(SlicesZ):
+                
+                x_id, x_temp[kkk] = \
+                         astra.creators.create_backprojection3d_gpu(
+                             residual[kkk:kkk+1],
+                             proj_geomT, vol_geomT)
+                astra.matlab.data3d('delete', x_id)
+        else:
+            x_id, x_temp = \
+                  astra.creators.create_backprojection3d_gpu(
+                      residual, proj_geom, vol_geom)            
+
+        X = X_t - (1/L_const) * x_temp
+        #astra.matlab.data3d('delete', sino_id)
+        astra.matlab.data3d('delete', x_id)
+
+    def regularize(self, X):
+        print ("FISTA Reconstructor: regularize")
+        
+        regularizer = self.getParameter('regularizer')
+        if regularizer is not None:
+            return regularizer(input=X)
+        else:
+            return X
+
+    def updateLoop(self, i, X, X_old, r_old, t, t_old):
+        print ("FISTA Reconstructor: update loop")
+        lambdaR_L1 = self.getParameter('ring_lambda_R_L1')
+        if lambdaR_L1 > 0:
+            self.r = numpy.max(
+                numpy.abs(self.r) - lambdaR_L1 , 0) * \
+                numpy.sign(self.r)
+        t = (1 + numpy.sqrt(1 + 4 * t**2))/2
+        X_t = X + (((t_old -1)/t) * (X - X_old))
+
+        if lambdaR_L1 > 0:
+            self.r_x = self.r + \
+                             (((t_old-1)/t) * (self.r - r_old))
+
+        if self.getParameter('region_of_interest') is None:
+            string = 'Iteration Number {0} | Objective {1} \n'
+            print (string.format( i, self.objective[i]))
+        else:
+            ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
+                                                    'ideal_image')
+            
+            Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
+            string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
+            print (string.format(i,Resid_error[i], self.objective[i]))
+        return (X , X_t, t)
+
+    def os_iterate(self, Xin=None):
+        print ("FISTA Reconstructor: iterate")
+        
+        if Xin is None:    
+            if self.getParameter('initialize'):
+                X = self.initialize()
+            else:
+                N = vol_geom['GridColCount']
+                X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
+        else:
+            # copy by reference
+            X = Xin
+        # store the output volume in the parameters
+        self.setParameter(output_volume=X)
+        X_t = X.copy()
+
+        # some useful constants
+        proj_geom , vol_geom, sino , \
+          SlicesZ, weights , alpha_ring ,\
+          lambdaR_L1 , L_const = self.getParameter(
+            ['projector_geometry' , 'output_geometry',
+             'input_sinogram', 'SlicesZ' ,  'weights', 'ring_alpha' ,
+             'ring_lambda_R_L1', 'Lipschitz_constant'])
diff --git a/src/Python/ccpi/reconstruction/Reconstructor.py b/src/Python/ccpi/reconstruction/Reconstructor.py
new file mode 100644
index 0000000..ba67327
--- /dev/null
+++ b/src/Python/ccpi/reconstruction/Reconstructor.py
@@ -0,0 +1,598 @@
+# -*- coding: utf-8 -*-
+###############################################################################
+#This work is part of the Core Imaging Library developed by
+#Visual Analytics and Imaging System Group of the Science Technology
+#Facilities Council, STFC
+#
+#Copyright 2017 Edoardo Pasca, Srikanth Nagella
+#Copyright 2017 Daniil Kazantsev
+#
+#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.
+###############################################################################
+
+
+
+import numpy
+import h5py
+from ccpi.reconstruction.parallelbeam import alg
+
+from Regularizer import Regularizer
+from enum import Enum
+
+import astra
+
+
+class Reconstructor:
+    
+    class Algorithm(Enum):
+        CGLS = alg.cgls
+        CGLS_CONV = alg.cgls_conv
+        SIRT = alg.sirt
+        MLEM = alg.mlem
+        CGLS_TICHONOV = alg.cgls_tikhonov
+        CGLS_TVREG = alg.cgls_TVreg
+        FISTA = 'fista'
+        
+    def __init__(self, algorithm = None, projection_data = None,
+                 angles = None, center_of_rotation = None , 
+                 flat_field = None, dark_field = None, 
+                 iterations = None, resolution = None, isLogScale = False, threads = None, 
+                 normalized_projection = None):
+    
+        self.pars = dict()
+        self.pars['algorithm'] = algorithm
+        self.pars['projection_data'] = projection_data
+        self.pars['normalized_projection'] = normalized_projection
+        self.pars['angles'] = angles
+        self.pars['center_of_rotation'] = numpy.double(center_of_rotation)
+        self.pars['flat_field'] = flat_field
+        self.pars['iterations'] = iterations
+        self.pars['dark_field'] = dark_field
+        self.pars['resolution'] = resolution
+        self.pars['isLogScale'] = isLogScale
+        self.pars['threads'] = threads
+        if (iterations != None):
+            self.pars['iterationValues'] = numpy.zeros((iterations)) 
+        
+        if projection_data != None and dark_field != None and flat_field != None:
+            norm = self.normalize(projection_data, dark_field, flat_field, 0.1)
+            self.pars['normalized_projection'] = norm
+            
+    
+    def setPars(self, parameters):
+        keys = ['algorithm','projection_data' ,'normalized_projection', \
+                'angles' , 'center_of_rotation' , 'flat_field', \
+                'iterations','dark_field' , 'resolution', 'isLogScale' , \
+                'threads' , 'iterationValues', 'regularize']
+        
+        for k in keys:
+            if k not in parameters.keys():
+                self.pars[k] = None
+            else:
+                self.pars[k] = parameters[k]
+                
+        
+    def sanityCheck(self):
+        projection_data = self.pars['projection_data']
+        dark_field = self.pars['dark_field']
+        flat_field = self.pars['flat_field']
+        angles = self.pars['angles']
+        
+        if projection_data != None and dark_field != None and \
+            angles != None and flat_field != None:
+            data_shape =  numpy.shape(projection_data)
+            angle_shape = numpy.shape(angles)
+            
+            if angle_shape[0] != data_shape[0]:
+                #raise Exception('Projections and angles dimensions do not match: %d vs %d' % \
+                #                (angle_shape[0] , data_shape[0]) )
+                return (False , 'Projections and angles dimensions do not match: %d vs %d' % \
+                                (angle_shape[0] , data_shape[0]) )
+            
+            if data_shape[1:] != numpy.shape(flat_field):
+                #raise Exception('Projection and flat field dimensions do not match')
+                return (False , 'Projection and flat field dimensions do not match')
+            if data_shape[1:] != numpy.shape(dark_field):
+                #raise Exception('Projection and dark field dimensions do not match')
+                return (False , 'Projection and dark field dimensions do not match')
+            
+            return (True , '' )
+        elif self.pars['normalized_projection'] != None:
+            data_shape =  numpy.shape(self.pars['normalized_projection'])
+            angle_shape = numpy.shape(angles)
+            
+            if angle_shape[0] != data_shape[0]:
+                #raise Exception('Projections and angles dimensions do not match: %d vs %d' % \
+                #                (angle_shape[0] , data_shape[0]) )
+                return (False , 'Projections and angles dimensions do not match: %d vs %d' % \
+                                (angle_shape[0] , data_shape[0]) )
+            else:
+                return (True , '' )
+        else:
+            return (False , 'Not enough data')
+            
+    def reconstruct(self, parameters = None):
+        if parameters != None:
+            self.setPars(parameters)
+        
+        go , reason = self.sanityCheck()
+        if go:
+            return self._reconstruct()
+        else:
+            raise Exception(reason)
+            
+            
+    def _reconstruct(self, parameters=None):
+        if parameters!=None:
+            self.setPars(parameters)
+        parameters = self.pars
+        
+        if parameters['algorithm'] != None and \
+           parameters['normalized_projection'] != None and \
+           parameters['angles'] != None and \
+           parameters['center_of_rotation'] != None and \
+           parameters['iterations'] != None and \
+           parameters['resolution'] != None and\
+           parameters['threads'] != None and\
+           parameters['isLogScale'] != None:
+               
+               
+           if parameters['algorithm'] in (Reconstructor.Algorithm.CGLS,
+                        Reconstructor.Algorithm.MLEM, Reconstructor.Algorithm.SIRT):
+               #store parameters
+               self.pars = parameters
+               result = parameters['algorithm'](
+                           parameters['normalized_projection'] ,
+                           parameters['angles'],
+                           parameters['center_of_rotation'],
+                           parameters['resolution'],
+                           parameters['iterations'],
+                           parameters['threads'] ,
+                           parameters['isLogScale']
+                           )
+               return result
+           elif parameters['algorithm'] in (Reconstructor.Algorithm.CGLS_CONV,
+                          Reconstructor.Algorithm.CGLS_TICHONOV, 
+                          Reconstructor.Algorithm.CGLS_TVREG) :
+               self.pars = parameters
+               result = parameters['algorithm'](
+                           parameters['normalized_projection'] ,
+                           parameters['angles'],
+                           parameters['center_of_rotation'],
+                           parameters['resolution'],
+                           parameters['iterations'],
+                           parameters['threads'] ,
+                           parameters['regularize'],
+                           numpy.zeros((parameters['iterations'])),
+                           parameters['isLogScale']
+                           )
+               
+           elif parameters['algorithm'] == Reconstructor.Algorithm.FISTA:
+               pass
+             
+        else:
+           if parameters['projection_data'] != None and \
+                     parameters['dark_field'] != None and \
+                     parameters['flat_field'] != None:
+               norm = self.normalize(parameters['projection_data'],
+                                   parameters['dark_field'], 
+                                   parameters['flat_field'], 0.1)
+               self.pars['normalized_projection'] = norm
+               return self._reconstruct(parameters)
+              
+                
+                
+    def _normalize(self, projection, dark, flat, def_val=0):
+        a = (projection - dark)
+        b = (flat-dark)
+        with numpy.errstate(divide='ignore', invalid='ignore'):
+            c = numpy.true_divide( a, b )
+            c[ ~ numpy.isfinite( c )] = def_val  # set to not zero if 0/0 
+        return c
+    
+    def normalize(self, projections, dark, flat, def_val=0):
+        norm = [self._normalize(projection, dark, flat, def_val) for projection in projections]
+        return numpy.asarray (norm, dtype=numpy.float32)
+        
+    
+    
+class FISTA():
+    '''FISTA-based reconstruction algorithm using ASTRA-toolbox
+    
+    '''
+    # <<<< FISTA-based reconstruction algorithm using ASTRA-toolbox >>>>
+    # ___Input___:
+    # params.[] file:
+    #       - .proj_geom (geometry of the projector) [required]
+    #       - .vol_geom (geometry of the reconstructed object) [required]
+    #       - .sino (vectorized in 2D or 3D sinogram) [required]
+    #       - .iterFISTA (iterations for the main loop, default 40)
+    #       - .L_const (Lipschitz constant, default Power method)                                                                                                    )
+    #       - .X_ideal (ideal image, if given)
+    #       - .weights (statisitcal weights, size of the sinogram)
+    #       - .ROI (Region-of-interest, only if X_ideal is given)
+    #       - .initialize (a 'warm start' using SIRT method from ASTRA)
+    #----------------Regularization choices------------------------
+    #       - .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
+    #       - .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
+    #       - .Regul_Lambda_TVLLT (Higher order SB-LLT regularization parameter)
+    #       - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
+    #       - .Regul_Iterations (iterations for the selected penalty, default 25)
+    #       - .Regul_tauLLT (time step parameter for LLT term)
+    #       - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal)
+    #       - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1)
+    #----------------Visualization parameters------------------------
+    #       - .show (visualize reconstruction 1/0, (0 default))
+    #       - .maxvalplot (maximum value to use for imshow[0 maxvalplot])
+    #       - .slice (for 3D volumes - slice number to imshow)
+    # ___Output___:
+    # 1. X - reconstructed image/volume
+    # 2. output - a structure with
+    #    - .Resid_error - residual error (if X_ideal is given)
+    #    - .objective: value of the objective function
+    #    - .L_const: Lipshitz constant to avoid recalculations
+    
+    # References:
+    # 1. "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse
+    # Problems" by A. Beck and M Teboulle
+    # 2. "Ring artifacts correction in compressed sensing..." by P. Paleo
+    # 3. "A novel tomographic reconstruction method based on the robust
+    # Student's t function for suppressing data outliers" D. Kazantsev et.al.
+    # D. Kazantsev, 2016-17
+    def __init__(self, projector_geometry, output_geometry, input_sinogram, **kwargs):
+        self.params = dict()
+        self.params['projector_geometry'] = projector_geometry
+        self.params['output_geometry'] = output_geometry
+        self.params['input_sinogram'] = input_sinogram
+        detectors, nangles, sliceZ = numpy.shape(input_sinogram)
+        self.params['detectors'] = detectors
+        self.params['number_og_angles'] = nangles
+        self.params['SlicesZ'] = sliceZ
+        
+        # Accepted input keywords
+        kw = ('number_of_iterations', 'Lipschitz_constant' , 'ideal_image' ,
+              'weights' , 'region_of_interest' , 'initialize' , 
+              'regularizer' , 
+              'ring_lambda_R_L1',
+              'ring_alpha')
+        
+        # handle keyworded parameters
+        if kwargs is not None:
+            for key, value in kwargs.items():
+                if key in kw:
+                    #print("{0} = {1}".format(key, value))                        
+                    self.pars[key] = value
+                    
+        # set the default values for the parameters if not set
+        if 'number_of_iterations' in kwargs.keys():
+            self.pars['number_of_iterations'] = kwargs['number_of_iterations']
+        else:
+            self.pars['number_of_iterations'] = 40
+        if 'weights' in kwargs.keys():
+            self.pars['weights'] = kwargs['weights']
+        else:
+            self.pars['weights'] = numpy.ones(numpy.shape(self.params['input_sinogram']))
+        if 'Lipschitz_constant' in kwargs.keys():
+            self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
+        else:
+            self.pars['Lipschitz_constant'] = self.calculateLipschitzConstantWithPowerMethod()
+        
+        if not self.pars['ideal_image'] in kwargs.keys():
+            self.pars['ideal_image'] = None
+        
+        if not self.pars['region_of_interest'] :
+            if self.pars['ideal_image'] == None:
+                pass
+            else:
+                self.pars['region_of_interest'] = numpy.nonzero(self.pars['ideal_image']>0.0)
+            
+        if not self.pars['regularizer'] :
+            self.pars['regularizer'] = None
+        else:
+            # the regularizer must be a correctly instantiated object
+            if not self.pars['ring_lambda_R_L1']:
+                self.pars['ring_lambda_R_L1'] = 0
+            if not self.pars['ring_alpha']:
+                self.pars['ring_alpha'] = 1
+        
+            
+            
+        
+    def calculateLipschitzConstantWithPowerMethod(self):
+        ''' using Power method (PM) to establish L constant'''
+        
+        #N = params.vol_geom.GridColCount
+        N = self.pars['output_geometry'].GridColCount
+        proj_geom = self.params['projector_geometry']
+        vol_geom = self.params['output_geometry']
+        weights = self.pars['weights']
+        SlicesZ = self.pars['SlicesZ']
+        
+        if (proj_geom['type'] == 'parallel') or (proj_geom['type'] == 'parallel3d'):
+            #% for parallel geometry we can do just one slice
+            #fprintf('%s \n', 'Calculating Lipshitz constant for parallel beam geometry...');
+            niter = 15;# % number of iteration for the PM
+            #N = params.vol_geom.GridColCount;
+            #x1 = rand(N,N,1);
+            x1 = numpy.random.rand(1,N,N)
+            #sqweight = sqrt(weights(:,:,1));
+            sqweight = numpy.sqrt(weights.T[0])
+            proj_geomT = proj_geom.copy();
+            proj_geomT.DetectorRowCount = 1;
+            vol_geomT = vol_geom.copy();
+            vol_geomT['GridSliceCount'] = 1;
+            
+            
+            for i in range(niter):
+                if i == 0:
+                    #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
+                    sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
+                    y = sqweight * y # element wise multiplication
+                    #astra_mex_data3d('delete', sino_id);
+                    astra.matlab.data3d('delete', sino_id)
+                    
+                idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, proj_geomT, vol_geomT);
+                s = numpy.linalg.norm(x1)
+                ### this line?
+                x1 = x1/s;
+                ### this line?
+                sino_id, y = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
+                y = sqweight*y;
+                astra.matlab.data3d('delete', sino_id);
+                astra.matlab.data3d('delete', idx);
+            #end
+            del proj_geomT
+            del vol_geomT
+        else
+            #% divergen beam geometry
+            #fprintf('%s \n', 'Calculating Lipshitz constant for divergen beam geometry...');
+            niter = 8; #% number of iteration for PM
+            x1 = numpy.random.rand(SlicesZ , N , N);
+            #sqweight = sqrt(weights);
+            sqweight = numpy.sqrt(weights.T[0])
+            
+            sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
+            y = sqweight*y;
+            #astra_mex_data3d('delete', sino_id);
+            astra.matlab.data3d('delete', sino_id);
+            
+            for i in range(niter):
+                #[id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
+                idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, 
+                                                                    proj_geom, 
+                                                                    vol_geom)
+                s = numpy.linalg.norm(x1)
+                ### this line?
+                x1 = x1/s;
+                ### this line?
+                #[sino_id, y] = astra_create_sino3d_gpu(x1, proj_geom, vol_geom);
+                sino_id, y = astra.creators.create_sino3d_gpu(x1, 
+                                                              proj_geom, 
+                                                              vol_geom);
+                
+                y = sqweight*y;
+                #astra_mex_data3d('delete', sino_id);
+                #astra_mex_data3d('delete', id);
+                astra.matlab.data3d('delete', sino_id);
+                astra.matlab.data3d('delete', idx);
+            #end
+            #clear x1
+            del x1
+        
+        return s
+    
+    
+    def setRegularizer(self, regularizer):
+        if regularizer
+        self.pars['regularizer'] = regularizer
+        
+    
+    
+
+
+def getEntry(location):
+    for item in nx[location].keys():
+        print (item)
+
+
+print ("Loading Data")
+
+##fname = "D:\\Documents\\Dataset\\IMAT\\20170419_crabtomo\\crabtomo\\Sample\\IMAT00005153_crabstomo_Sample_000.tif"
+####ind = [i * 1049 for i in range(360)]
+#### use only 360 images
+##images = 200
+##ind = [int(i * 1049 / images) for i in range(images)]
+##stack_image = dxchange.reader.read_tiff_stack(fname, ind, digit=None, slc=None)
+
+#fname = "D:\\Documents\\Dataset\\CGLS\\24737_fd.nxs"
+fname = "C:\\Users\\ofn77899\\Documents\\CCPi\\CGLS\\24737_fd_2.nxs"
+nx = h5py.File(fname, "r")
+
+# the data are stored in a particular location in the hdf5
+for item in nx['entry1/tomo_entry/data'].keys():
+    print (item)
+
+data = nx.get('entry1/tomo_entry/data/rotation_angle')
+angles = numpy.zeros(data.shape)
+data.read_direct(angles)
+print (angles)
+# angles should be in degrees
+
+data = nx.get('entry1/tomo_entry/data/data')
+stack = numpy.zeros(data.shape)
+data.read_direct(stack)
+print (data.shape)
+
+print ("Data Loaded")
+
+
+# Normalize
+data = nx.get('entry1/tomo_entry/instrument/detector/image_key')
+itype = numpy.zeros(data.shape)
+data.read_direct(itype)
+# 2 is dark field
+darks = [stack[i] for i in range(len(itype)) if itype[i] == 2 ]
+dark = darks[0]
+for i in range(1, len(darks)):
+    dark += darks[i]
+dark = dark / len(darks)
+#dark[0][0] = dark[0][1]
+
+# 1 is flat field
+flats = [stack[i] for i in range(len(itype)) if itype[i] == 1 ]
+flat = flats[0]
+for i in range(1, len(flats)):
+    flat += flats[i]
+flat = flat / len(flats)
+#flat[0][0] = dark[0][1]
+
+
+# 0 is projection data
+proj = [stack[i] for i in range(len(itype)) if itype[i] == 0 ]
+angle_proj = [angles[i] for i in range(len(itype)) if itype[i] == 0 ]
+angle_proj = numpy.asarray (angle_proj)
+angle_proj = angle_proj.astype(numpy.float32)
+
+# normalized data are
+# norm = (projection - dark)/(flat-dark)
+
+def normalize(projection, dark, flat, def_val=0.1):
+    a = (projection - dark)
+    b = (flat-dark)
+    with numpy.errstate(divide='ignore', invalid='ignore'):
+        c = numpy.true_divide( a, b )
+        c[ ~ numpy.isfinite( c )] = def_val  # set to not zero if 0/0 
+    return c
+    
+
+norm = [normalize(projection, dark, flat) for projection in proj]
+norm = numpy.asarray (norm)
+norm = norm.astype(numpy.float32)
+
+#recon = Reconstructor(algorithm = Algorithm.CGLS, normalized_projection = norm,
+#                 angles = angle_proj, center_of_rotation = 86.2 , 
+#                 flat_field = flat, dark_field = dark, 
+#                 iterations = 15, resolution = 1, isLogScale = False, threads = 3)
+
+#recon = Reconstructor(algorithm = Reconstructor.Algorithm.CGLS, projection_data = proj,
+#                 angles = angle_proj, center_of_rotation = 86.2 , 
+#                 flat_field = flat, dark_field = dark, 
+#                 iterations = 15, resolution = 1, isLogScale = False, threads = 3)
+#img_cgls = recon.reconstruct()
+#
+#pars = dict()
+#pars['algorithm'] = Reconstructor.Algorithm.SIRT
+#pars['projection_data'] = proj
+#pars['angles'] = angle_proj
+#pars['center_of_rotation'] = numpy.double(86.2)
+#pars['flat_field'] = flat
+#pars['iterations'] = 15
+#pars['dark_field'] = dark
+#pars['resolution'] = 1
+#pars['isLogScale'] = False
+#pars['threads'] = 3
+#
+#img_sirt = recon.reconstruct(pars)
+#
+#recon.pars['algorithm'] = Reconstructor.Algorithm.MLEM
+#img_mlem = recon.reconstruct()
+
+############################################################
+############################################################
+#recon.pars['algorithm'] = Reconstructor.Algorithm.CGLS_CONV
+#recon.pars['regularize'] = numpy.double(0.1)
+#img_cgls_conv = recon.reconstruct()
+
+niterations = 15
+threads = 3
+
+img_cgls = alg.cgls(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
+img_mlem = alg.mlem(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
+img_sirt = alg.sirt(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
+
+iteration_values = numpy.zeros((niterations,))
+img_cgls_conv = alg.cgls_conv(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
+                              iteration_values, False)
+print ("iteration values %s" % str(iteration_values))
+
+iteration_values = numpy.zeros((niterations,))
+img_cgls_tikhonov = alg.cgls_tikhonov(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
+                                      numpy.double(1e-5), iteration_values , False)
+print ("iteration values %s" % str(iteration_values))
+iteration_values = numpy.zeros((niterations,))
+img_cgls_TVreg = alg.cgls_TVreg(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
+                                      numpy.double(1e-5), iteration_values , False)
+print ("iteration values %s" % str(iteration_values))
+
+
+##numpy.save("cgls_recon.npy", img_data)
+import matplotlib.pyplot as plt
+fig, ax = plt.subplots(1,6,sharey=True)
+ax[0].imshow(img_cgls[80])
+ax[0].axis('off')  # clear x- and y-axes
+ax[1].imshow(img_sirt[80])
+ax[1].axis('off')  # clear x- and y-axes
+ax[2].imshow(img_mlem[80])
+ax[2].axis('off')  # clear x- and y-axesplt.show()
+ax[3].imshow(img_cgls_conv[80])
+ax[3].axis('off')  # clear x- and y-axesplt.show()
+ax[4].imshow(img_cgls_tikhonov[80])
+ax[4].axis('off')  # clear x- and y-axesplt.show()
+ax[5].imshow(img_cgls_TVreg[80])
+ax[5].axis('off')  # clear x- and y-axesplt.show()
+
+
+plt.show()
+
+#viewer = edo.CILViewer()
+#viewer.setInputAsNumpy(img_cgls2)
+#viewer.displaySliceActor(0)
+#viewer.startRenderLoop()
+
+import vtk
+
+def NumpyToVTKImageData(numpyarray):
+    if (len(numpy.shape(numpyarray)) == 3):
+        doubleImg = vtk.vtkImageData()
+        shape = numpy.shape(numpyarray)
+        doubleImg.SetDimensions(shape[0], shape[1], shape[2])
+        doubleImg.SetOrigin(0,0,0)
+        doubleImg.SetSpacing(1,1,1)
+        doubleImg.SetExtent(0, shape[0]-1, 0, shape[1]-1, 0, shape[2]-1)
+        #self.img3D.SetScalarType(vtk.VTK_UNSIGNED_SHORT, vtk.vtkInformation())
+        doubleImg.AllocateScalars(vtk.VTK_DOUBLE,1)
+        
+        for i in range(shape[0]):
+            for j in range(shape[1]):
+                for k in range(shape[2]):
+                    doubleImg.SetScalarComponentFromDouble(
+                        i,j,k,0, numpyarray[i][j][k])
+    #self.setInput3DData( numpy_support.numpy_to_vtk(numpyarray) )
+        # rescale to appropriate VTK_UNSIGNED_SHORT
+        stats = vtk.vtkImageAccumulate()
+        stats.SetInputData(doubleImg)
+        stats.Update()
+        iMin = stats.GetMin()[0]
+        iMax = stats.GetMax()[0]
+        scale = vtk.VTK_UNSIGNED_SHORT_MAX / (iMax - iMin)
+
+        shiftScaler = vtk.vtkImageShiftScale ()
+        shiftScaler.SetInputData(doubleImg)
+        shiftScaler.SetScale(scale)
+        shiftScaler.SetShift(iMin)
+        shiftScaler.SetOutputScalarType(vtk.VTK_UNSIGNED_SHORT)
+        shiftScaler.Update()
+        return shiftScaler.GetOutput()
+        
+#writer = vtk.vtkMetaImageWriter()
+#writer.SetFileName(alg + "_recon.mha")
+#writer.SetInputData(NumpyToVTKImageData(img_cgls2))
+#writer.Write()
diff --git a/src/Python/ccpi/reconstruction/__init__.py b/src/Python/ccpi/reconstruction/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/src/Python/ccpi/reconstruction/__init__.py
diff --git a/src/Python/compile-fista.bat.in b/src/Python/compile-fista.bat.in
new file mode 100644
index 0000000..b1db686
--- /dev/null
+++ b/src/Python/compile-fista.bat.in
@@ -0,0 +1,7 @@
+set CIL_VERSION=@CIL_VERSION@
+
+set PREFIX=@CONDA_ENVIRONMENT_PREFIX@
+set LIBRARY_INC=@CONDA_ENVIRONMENT_LIBRARY_INC@
+
+REM activate @CONDA_ENVIRONMENT@
+conda build fista-recipe --python=@PYTHON_VERSION_MAJOR@.@PYTHON_VERSION_MINOR@ --numpy=@NUMPY_VERSION@ -c ccpi -c conda-forge
diff --git a/src/Python/compile-fista.sh.in b/src/Python/compile-fista.sh.in
new file mode 100644
index 0000000..267f014
--- /dev/null
+++ b/src/Python/compile-fista.sh.in
@@ -0,0 +1,9 @@
+#!/bin/sh
+# compile within the right conda environment
+#module load python/anaconda
+#source activate @CONDA_ENVIRONMENT@
+
+export CIL_VERSION=@CIL_VERSION@
+export LIBRARY_INC=@CONDA_ENVIRONMENT_LIBRARY_INC@
+
+conda build fista-recipe --python=@PYTHON_VERSION_MAJOR@.@PYTHON_VERSION_MINOR@ --numpy=@NUMPY_VERSION@ -c ccpi
diff --git a/src/Python/compile.bat.in b/src/Python/compile.bat.in
new file mode 100644
index 0000000..e5342ed
--- /dev/null
+++ b/src/Python/compile.bat.in
@@ -0,0 +1,7 @@
+set CIL_VERSION=@CIL_VERSION@
+
+set PREFIX=@CONDA_ENVIRONMENT_PREFIX@
+set LIBRARY_INC=@CONDA_ENVIRONMENT_LIBRARY_INC@
+
+REM activate @CONDA_ENVIRONMENT@
+conda build conda-recipe --python=@PYTHON_VERSION_MAJOR@.@PYTHON_VERSION_MINOR@ --numpy=@NUMPY_VERSION@ -c ccpi -c conda-forge
\ No newline at end of file
diff --git a/src/Python/compile.sh.in b/src/Python/compile.sh.in
new file mode 100644
index 0000000..93fdba2
--- /dev/null
+++ b/src/Python/compile.sh.in
@@ -0,0 +1,9 @@
+#!/bin/sh
+# compile within the right conda environment
+#module load python/anaconda
+#source activate @CONDA_ENVIRONMENT@
+
+export CIL_VERSION=@CIL_VERSION@
+export LIBRARY_INC=@CONDA_ENVIRONMENT_LIBRARY_INC@
+
+conda build conda-recipe --python=@PYTHON_VERSION_MAJOR@.@PYTHON_VERSION_MINOR@ --numpy=@NUMPY_VERSION@ -c ccpi
diff --git a/src/Python/conda-recipe/bld.bat b/src/Python/conda-recipe/bld.bat
new file mode 100644
index 0000000..69491de
--- /dev/null
+++ b/src/Python/conda-recipe/bld.bat
@@ -0,0 +1,14 @@
+IF NOT DEFINED CIL_VERSION (
+ECHO CIL_VERSION Not Defined.
+exit 1
+)
+
+mkdir "%SRC_DIR%\ccpi"
+xcopy /e "%RECIPE_DIR%\..\.." "%SRC_DIR%\ccpi"
+
+cd %SRC_DIR%\ccpi\Python
+
+%PYTHON% setup.py build_ext
+if errorlevel 1 exit 1
+%PYTHON% setup.py install
+if errorlevel 1 exit 1
diff --git a/src/Python/conda-recipe/build.sh b/src/Python/conda-recipe/build.sh
new file mode 100644
index 0000000..855047f
--- /dev/null
+++ b/src/Python/conda-recipe/build.sh
@@ -0,0 +1,14 @@
+
+if [ -z "$CIL_VERSION" ]; then
+    echo "Need to set CIL_VERSION"
+    exit 1
+fi  
+mkdir "$SRC_DIR/ccpi"
+cp -r "$RECIPE_DIR/../.." "$SRC_DIR/ccpi"
+
+cd $SRC_DIR/ccpi/Python
+
+$PYTHON setup.py build_ext
+$PYTHON setup.py install
+
+
diff --git a/src/Python/conda-recipe/meta.yaml b/src/Python/conda-recipe/meta.yaml
new file mode 100644
index 0000000..7068e9d
--- /dev/null
+++ b/src/Python/conda-recipe/meta.yaml
@@ -0,0 +1,30 @@
+package:
+  name: ccpi-regularizers
+  version: {{ environ['CIL_VERSION'] }}
+
+
+build:
+  preserve_egg_dir: False
+  script_env:
+    - CIL_VERSION   
+#  number: 0
+  
+requirements:
+  build:
+    - python
+    - numpy
+    - setuptools
+    - boost ==1.64
+    - boost-cpp ==1.64
+    - cython
+
+  run:
+    - python
+    - numpy
+    - boost ==1.64
+
+	
+about:
+  home: http://www.ccpi.ac.uk
+  license:  BSD license
+  summary: 'CCPi Core Imaging Library Quantification Toolbox'
diff --git a/src/Python/fista-recipe/build.sh b/src/Python/fista-recipe/build.sh
new file mode 100644
index 0000000..e3f3552
--- /dev/null
+++ b/src/Python/fista-recipe/build.sh
@@ -0,0 +1,10 @@
+if [ -z "$CIL_VERSION" ]; then
+    echo "Need to set CIL_VERSION"
+    exit 1
+fi  
+mkdir "$SRC_DIR/ccpifista"
+cp -r "$RECIPE_DIR/.." "$SRC_DIR/ccpifista"
+
+cd $SRC_DIR/ccpifista
+
+$PYTHON setup-fista.py install
diff --git a/src/Python/fista-recipe/meta.yaml b/src/Python/fista-recipe/meta.yaml
new file mode 100644
index 0000000..265541f
--- /dev/null
+++ b/src/Python/fista-recipe/meta.yaml
@@ -0,0 +1,29 @@
+package:
+  name: ccpi-fista
+  version: {{ environ['CIL_VERSION'] }}
+
+
+build:
+  preserve_egg_dir: False
+  script_env:
+    - CIL_VERSION   
+#  number: 0
+  
+requirements:
+  build:
+    - python
+    - numpy
+    - setuptools
+ 
+  run:
+    - python
+    - numpy
+    #- astra-toolbox
+    - ccpi-regularizers
+
+
+	
+about:
+  home: http://www.ccpi.ac.uk
+  license:  Apache v.2.0 license
+  summary: 'CCPi Core Imaging Library (Viewer)'
diff --git a/src/Python/fista_module.cpp b/src/Python/fista_module.cpp
new file mode 100644
index 0000000..aca3be0
--- /dev/null
+++ b/src/Python/fista_module.cpp
@@ -0,0 +1,1050 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, 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.
+*/
+
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+
+#include <iostream>
+#include <cmath>
+
+#include <boost/python.hpp>
+#include <boost/python/numpy.hpp>
+#include "boost/tuple/tuple.hpp"
+
+#include "SplitBregman_TV_core.h"
+#include "FGP_TV_core.h"
+#include "LLT_model_core.h"
+#include "PatchBased_Regul_core.h"
+#include "TGV_PD_core.h"
+#include "utils.h"
+
+
+
+#if defined(_WIN32) || defined(_WIN32) || defined(__WIN32__) || defined(_WIN64)
+#include <windows.h>
+// this trick only if compiler is MSVC
+__if_not_exists(uint8_t) { typedef __int8 uint8_t; }
+__if_not_exists(uint16_t) { typedef __int8 uint16_t; }
+#endif
+
+namespace bp = boost::python;
+namespace np = boost::python::numpy;
+
+/*! in the Matlab implementation this is called as
+void mexFunction(
+int nlhs, mxArray *plhs[],
+int nrhs, const mxArray *prhs[])
+where:
+prhs Array of pointers to the INPUT mxArrays
+nrhs int number of INPUT mxArrays
+
+nlhs Array of pointers to the OUTPUT mxArrays
+plhs int number of OUTPUT mxArrays
+
+***********************************************************
+
+***********************************************************
+double mxGetScalar(const mxArray *pm);
+args: pm Pointer to an mxArray; cannot be a cell mxArray, a structure mxArray, or an empty mxArray.
+Returns: Pointer to the value of the first real (nonimaginary) element of the mxArray.	In C, mxGetScalar returns a double.
+***********************************************************
+char *mxArrayToString(const mxArray *array_ptr);
+args: array_ptr Pointer to mxCHAR array.
+Returns: C-style string. Returns NULL on failure. Possible reasons for failure include out of memory and specifying an array that is not an mxCHAR array.
+Description: Call mxArrayToString to copy the character data of an mxCHAR array into a C-style string.
+***********************************************************
+mxClassID mxGetClassID(const mxArray *pm);
+args: pm Pointer to an mxArray
+Returns: Numeric identifier of the class (category) of the mxArray that pm points to.For user-defined types,
+mxGetClassId returns a unique value identifying the class of the array contents.
+Use mxIsClass to determine whether an array is of a specific user-defined type.
+
+mxClassID Value	  MATLAB Type   MEX Type	 C Primitive Type
+mxINT8_CLASS 	  int8	        int8_T	     char, byte
+mxUINT8_CLASS	  uint8	        uint8_T	     unsigned char, byte
+mxINT16_CLASS	  int16	        int16_T	     short
+mxUINT16_CLASS	  uint16	    uint16_T	 unsigned short
+mxINT32_CLASS	  int32	        int32_T	     int
+mxUINT32_CLASS	  uint32	    uint32_T	 unsigned int
+mxINT64_CLASS	  int64	        int64_T	     long long
+mxUINT64_CLASS	  uint64	    uint64_T 	 unsigned long long
+mxSINGLE_CLASS	  single	    float	     float
+mxDOUBLE_CLASS	  double	    double	     double
+
+****************************************************************
+double *mxGetPr(const mxArray *pm);
+args: pm Pointer to an mxArray of type double
+Returns: Pointer to the first element of the real data. Returns NULL in C (0 in Fortran) if there is no real data.
+****************************************************************
+mxArray *mxCreateNumericArray(mwSize ndim, const mwSize *dims,
+mxClassID classid, mxComplexity ComplexFlag);
+args: ndimNumber of dimensions. If you specify a value for ndim that is less than 2, mxCreateNumericArray automatically sets the number of dimensions to 2.
+dims Dimensions array. Each element in the dimensions array contains the size of the array in that dimension.
+For example, in C, setting dims[0] to 5 and dims[1] to 7 establishes a 5-by-7 mxArray. Usually there are ndim elements in the dims array.
+classid Identifier for the class of the array, which determines the way the numerical data is represented in memory.
+For example, specifying mxINT16_CLASS in C causes each piece of numerical data in the mxArray to be represented as a 16-bit signed integer.
+ComplexFlag  If the mxArray you are creating is to contain imaginary data, set ComplexFlag to mxCOMPLEX in C (1 in Fortran). Otherwise, set ComplexFlag to mxREAL in C (0 in Fortran).
+Returns: Pointer to the created mxArray, if successful. If unsuccessful in a standalone (non-MEX file) application, returns NULL in C (0 in Fortran).
+If unsuccessful in a MEX file, the MEX file terminates and returns control to the MATLAB prompt. The function is unsuccessful when there is not
+enough free heap space to create the mxArray.
+*/
+
+
+
+bp::list SplitBregman_TV(np::ndarray input, double d_mu, int iter, double d_epsil, int methTV) {
+	
+	// the result is in the following list
+	bp::list result;
+		
+	int number_of_dims, dimX, dimY, dimZ, ll, j, count;
+	//const int  *dim_array;
+	float *A, *U = NULL, *U_old = NULL, *Dx = NULL, *Dy = NULL, *Dz = NULL, *Bx = NULL, *By = NULL, *Bz = NULL, lambda, mu, epsil, re, re1, re_old;
+	
+	//number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+	//dim_array = mxGetDimensions(prhs[0]);
+
+	number_of_dims = input.get_nd();
+	int dim_array[3];
+
+	dim_array[0] = input.shape(0);
+	dim_array[1] = input.shape(1);
+	if (number_of_dims == 2) {
+		dim_array[2] = -1;
+	}
+	else {
+		dim_array[2] = input.shape(2);
+	}
+
+	// Parameter handling is be done in Python
+	///*Handling Matlab input data*/
+	//if ((nrhs < 2) || (nrhs > 5)) mexErrMsgTxt("At least 2 parameters is required: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1')");
+
+	///*Handling Matlab input data*/
+	//A = (float *)mxGetData(prhs[0]); /*noisy image (2D/3D) */
+	A = reinterpret_cast<float *>(input.get_data());
+
+	//mu = (float)mxGetScalar(prhs[1]); /* regularization parameter */
+	mu = (float)d_mu;
+
+	//iter = 35; /* default iterations number */
+	
+	//epsil = 0.0001; /* default tolerance constant */
+	epsil = (float)d_epsil;
+	//methTV = 0;  /* default isotropic TV penalty */
+	//if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5))  iter = (int)mxGetScalar(prhs[2]); /* iterations number */
+	//if ((nrhs == 4) || (nrhs == 5))  epsil = (float)mxGetScalar(prhs[3]); /* tolerance constant */
+	//if (nrhs == 5) {
+	//	char *penalty_type;
+	//	penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
+	//	if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
+	//	if (strcmp(penalty_type, "l1") == 0)  methTV = 1;  /* enable 'l1' penalty */
+	//	mxFree(penalty_type);
+	//}
+	//if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input image must be in a single precision"); }
+
+	lambda = 2.0f*mu;
+	count = 1;
+	re_old = 0.0f;
+	/*Handling Matlab output data*/
+	dimY = dim_array[0]; dimX = dim_array[1]; dimZ = dim_array[2];
+
+	if (number_of_dims == 2) {
+		dimZ = 1; /*2D case*/
+		//U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		//U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		//Dx = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		//Dy = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		//Bx = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		//By = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+		np::ndarray npU = np::zeros(shape, dtype);
+		np::ndarray npU_old = np::zeros(shape, dtype);
+		np::ndarray npDx = np::zeros(shape, dtype);
+		np::ndarray npDy = np::zeros(shape, dtype);
+		np::ndarray npBx = np::zeros(shape, dtype);
+		np::ndarray npBy = np::zeros(shape, dtype);
+
+		U = reinterpret_cast<float *>(npU.get_data());
+		U_old = reinterpret_cast<float *>(npU_old.get_data());
+		Dx = reinterpret_cast<float *>(npDx.get_data());
+		Dy = reinterpret_cast<float *>(npDy.get_data());
+		Bx = reinterpret_cast<float *>(npBx.get_data());
+		By = reinterpret_cast<float *>(npBy.get_data());
+
+
+
+		copyIm(A, U, dimX, dimY, dimZ); /*initialize */
+
+										/* begin outer SB iterations */
+		for (ll = 0; ll < iter; ll++) {
+
+			/*storing old values*/
+			copyIm(U, U_old, dimX, dimY, dimZ);
+
+			/*GS iteration */
+			gauss_seidel2D(U, A, Dx, Dy, Bx, By, dimX, dimY, lambda, mu);
+
+			if (methTV == 1)  updDxDy_shrinkAniso2D(U, Dx, Dy, Bx, By, dimX, dimY, lambda);
+			else updDxDy_shrinkIso2D(U, Dx, Dy, Bx, By, dimX, dimY, lambda);
+
+			updBxBy2D(U, Dx, Dy, Bx, By, dimX, dimY);
+
+			/* calculate norm to terminate earlier */
+			re = 0.0f; re1 = 0.0f;
+			for (j = 0; j < dimX*dimY*dimZ; j++)
+			{
+				re += pow(U_old[j] - U[j], 2);
+				re1 += pow(U_old[j], 2);
+			}
+			re = sqrt(re) / sqrt(re1);
+			if (re < epsil)  count++;
+			if (count > 4) break;
+
+			/* check that the residual norm is decreasing */
+			if (ll > 2) {
+				if (re > re_old) break;
+			}
+			re_old = re;
+			/*printf("%f %i %i \n", re, ll, count); */
+
+			/*copyIm(U_old, U, dimX, dimY, dimZ); */
+			
+		}
+		//printf("SB iterations stopped at iteration: %i\n", ll);
+		result.append<np::ndarray>(npU);
+		result.append<int>(ll);
+	}
+	if (number_of_dims == 3) {
+			/*U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			U_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			Dx = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			Dy = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			Dz = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			Bx = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			By = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			Bz = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));*/
+			bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]);
+			np::dtype dtype = np::dtype::get_builtin<float>();
+
+			np::ndarray npU     = np::zeros(shape, dtype);
+			np::ndarray npU_old = np::zeros(shape, dtype);
+			np::ndarray npDx    = np::zeros(shape, dtype);
+			np::ndarray npDy    = np::zeros(shape, dtype);
+			np::ndarray npDz    = np::zeros(shape, dtype);
+			np::ndarray npBx    = np::zeros(shape, dtype);
+			np::ndarray npBy    = np::zeros(shape, dtype);
+			np::ndarray npBz    = np::zeros(shape, dtype);
+
+			U     = reinterpret_cast<float *>(npU.get_data());
+			U_old = reinterpret_cast<float *>(npU_old.get_data());
+			Dx    = reinterpret_cast<float *>(npDx.get_data());
+			Dy    = reinterpret_cast<float *>(npDy.get_data());
+			Dz    = reinterpret_cast<float *>(npDz.get_data());
+			Bx    = reinterpret_cast<float *>(npBx.get_data());
+			By    = reinterpret_cast<float *>(npBy.get_data());
+			Bz    = reinterpret_cast<float *>(npBz.get_data());
+
+			copyIm(A, U, dimX, dimY, dimZ); /*initialize */
+
+											/* begin outer SB iterations */
+			for (ll = 0; ll<iter; ll++) {
+
+				/*storing old values*/
+				copyIm(U, U_old, dimX, dimY, dimZ);
+
+				/*GS iteration */
+				gauss_seidel3D(U, A, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda, mu);
+
+				if (methTV == 1) updDxDyDz_shrinkAniso3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda);
+				else updDxDyDz_shrinkIso3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda);
+
+				updBxByBz3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ);
+
+				/* calculate norm to terminate earlier */
+				re = 0.0f; re1 = 0.0f;
+				for (j = 0; j<dimX*dimY*dimZ; j++)
+				{
+					re += pow(U[j] - U_old[j], 2);
+					re1 += pow(U[j], 2);
+				}
+				re = sqrt(re) / sqrt(re1);
+				if (re < epsil)  count++;
+				if (count > 4) break;
+
+				/* check that the residual norm is decreasing */
+				if (ll > 2) {
+					if (re > re_old) break;
+				}
+				/*printf("%f %i %i \n", re, ll, count); */
+				re_old = re;
+			}
+			//printf("SB iterations stopped at iteration: %i\n", ll);
+			result.append<np::ndarray>(npU);
+			result.append<int>(ll);
+		}
+	return result;
+
+	}
+
+
+
+bp::list FGP_TV(np::ndarray input, double d_mu, int iter, double d_epsil, int methTV) {
+
+	// the result is in the following list
+	bp::list result;
+
+	int number_of_dims, dimX, dimY, dimZ, ll, j, count;
+	float *A, *D = NULL, *D_old = NULL, *P1 = NULL, *P2 = NULL, *P3 = NULL, *P1_old = NULL, *P2_old = NULL, *P3_old = NULL, *R1 = NULL, *R2 = NULL, *R3 = NULL;
+	float lambda, tk, tkp1, re, re1, re_old, epsil, funcval;
+
+	//number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+	//dim_array = mxGetDimensions(prhs[0]);
+
+	number_of_dims = input.get_nd();
+	int dim_array[3];
+
+	dim_array[0] = input.shape(0);
+	dim_array[1] = input.shape(1);
+	if (number_of_dims == 2) {
+		dim_array[2] = -1;
+	}
+	else {
+		dim_array[2] = input.shape(2);
+	}
+
+	// Parameter handling is be done in Python
+	///*Handling Matlab input data*/
+	//if ((nrhs < 2) || (nrhs > 5)) mexErrMsgTxt("At least 2 parameters is required: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1')");
+
+	///*Handling Matlab input data*/
+	//A = (float *)mxGetData(prhs[0]); /*noisy image (2D/3D) */
+	A = reinterpret_cast<float *>(input.get_data());
+
+	//mu = (float)mxGetScalar(prhs[1]); /* regularization parameter */
+	lambda = (float)d_mu;
+
+	//iter = 35; /* default iterations number */
+
+	//epsil = 0.0001; /* default tolerance constant */
+	epsil = (float)d_epsil;
+	//methTV = 0;  /* default isotropic TV penalty */
+	//if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5))  iter = (int)mxGetScalar(prhs[2]); /* iterations number */
+	//if ((nrhs == 4) || (nrhs == 5))  epsil = (float)mxGetScalar(prhs[3]); /* tolerance constant */
+	//if (nrhs == 5) {
+	//	char *penalty_type;
+	//	penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
+	//	if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
+	//	if (strcmp(penalty_type, "l1") == 0)  methTV = 1;  /* enable 'l1' penalty */
+	//	mxFree(penalty_type);
+	//}
+	//if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input image must be in a single precision"); }
+
+	//plhs[1] = mxCreateNumericMatrix(1, 1, mxSINGLE_CLASS, mxREAL);
+	bp::tuple shape1 = bp::make_tuple(dim_array[0], dim_array[1]);
+	np::dtype dtype = np::dtype::get_builtin<float>();
+	np::ndarray out1 = np::zeros(shape1, dtype);
+	
+	//float *funcvalA = (float *)mxGetData(plhs[1]);
+	float * funcvalA = reinterpret_cast<float *>(out1.get_data());
+	//if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input image must be in a single precision"); }
+
+	/*Handling Matlab output data*/
+	dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+
+	tk = 1.0f;
+	tkp1 = 1.0f;
+	count = 1;
+	re_old = 0.0f;
+
+	if (number_of_dims == 2) {
+		dimZ = 1; /*2D case*/
+		/*D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		D_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		P1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		P2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		P1_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		P2_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		R1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		R2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));*/
+
+		bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+
+		np::ndarray npD      = np::zeros(shape, dtype);
+		np::ndarray npD_old  = np::zeros(shape, dtype);
+		np::ndarray npP1     = np::zeros(shape, dtype);
+		np::ndarray npP2     = np::zeros(shape, dtype);
+		np::ndarray npP1_old = np::zeros(shape, dtype);
+		np::ndarray npP2_old = np::zeros(shape, dtype);
+		np::ndarray npR1     = np::zeros(shape, dtype);
+		np::ndarray npR2     = np::zeros(shape, dtype);
+
+		D      = reinterpret_cast<float *>(npD.get_data());
+		D_old  = reinterpret_cast<float *>(npD_old.get_data());
+		P1     = reinterpret_cast<float *>(npP1.get_data());
+		P2     = reinterpret_cast<float *>(npP2.get_data());
+		P1_old = reinterpret_cast<float *>(npP1_old.get_data());
+		P2_old = reinterpret_cast<float *>(npP2_old.get_data());
+		R1     = reinterpret_cast<float *>(npR1.get_data());
+		R2     = reinterpret_cast<float *>(npR2.get_data());
+
+		/* begin iterations */
+		for (ll = 0; ll<iter; ll++) {
+			/* computing the gradient of the objective function */
+			Obj_func2D(A, D, R1, R2, lambda, dimX, dimY);
+
+			/*Taking a step towards minus of the gradient*/
+			Grad_func2D(P1, P2, D, R1, R2, lambda, dimX, dimY);
+
+
+
+
+			/*updating R and t*/
+			tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
+			Rupd_func2D(P1, P1_old, P2, P2_old, R1, R2, tkp1, tk, dimX, dimY);
+
+			/* calculate norm */
+			re = 0.0f; re1 = 0.0f;
+			for (j = 0; j<dimX*dimY*dimZ; j++)
+			{
+				re += pow(D[j] - D_old[j], 2);
+				re1 += pow(D[j], 2);
+			}
+			re = sqrt(re) / sqrt(re1);
+			if (re < epsil)  count++;
+			if (count > 3) {
+				Obj_func2D(A, D, P1, P2, lambda, dimX, dimY);
+				funcval = 0.0f;
+				for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
+				//funcvalA[0] = sqrt(funcval);
+				float fv = sqrt(funcval);
+				std::memcpy(funcvalA, &fv, sizeof(float));
+				break;
+			}
+
+			/* check that the residual norm is decreasing */
+			if (ll > 2) {
+				if (re > re_old) {
+					Obj_func2D(A, D, P1, P2, lambda, dimX, dimY);
+					funcval = 0.0f;
+					for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
+					//funcvalA[0] = sqrt(funcval);
+					float fv = sqrt(funcval);
+					std::memcpy(funcvalA, &fv, sizeof(float));
+					break;
+				}
+			}
+			re_old = re;
+			/*printf("%f %i %i \n", re, ll, count); */
+
+			/*storing old values*/
+			copyIm(D, D_old, dimX, dimY, dimZ);
+			copyIm(P1, P1_old, dimX, dimY, dimZ);
+			copyIm(P2, P2_old, dimX, dimY, dimZ);
+			tk = tkp1;
+
+			/* calculating the objective function value */
+			if (ll == (iter - 1)) {
+				Obj_func2D(A, D, P1, P2, lambda, dimX, dimY);
+				funcval = 0.0f;
+				for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
+				//funcvalA[0] = sqrt(funcval);
+				float fv = sqrt(funcval);
+				std::memcpy(funcvalA, &fv, sizeof(float));
+			}
+		}
+		//printf("FGP-TV iterations stopped at iteration %i with the function value %f \n", ll, funcvalA[0]);
+		result.append<np::ndarray>(npD);
+		result.append<np::ndarray>(out1);
+		result.append<int>(ll);
+	}
+	if (number_of_dims == 3) {
+		/*D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		D_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		P1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		P2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		P3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		P1_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		P2_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		P3_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		R1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		R2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		R3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));*/
+		bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+		
+		np::ndarray npD      = np::zeros(shape, dtype);
+		np::ndarray npD_old  = np::zeros(shape, dtype);
+		np::ndarray npP1     = np::zeros(shape, dtype);
+		np::ndarray npP2     = np::zeros(shape, dtype);
+		np::ndarray npP3     = np::zeros(shape, dtype);
+		np::ndarray npP1_old = np::zeros(shape, dtype);
+		np::ndarray npP2_old = np::zeros(shape, dtype);
+		np::ndarray npP3_old = np::zeros(shape, dtype);
+		np::ndarray npR1     = np::zeros(shape, dtype);
+		np::ndarray npR2     = np::zeros(shape, dtype);
+		np::ndarray npR3     = np::zeros(shape, dtype);
+
+		D      = reinterpret_cast<float *>(npD.get_data());
+		D_old  = reinterpret_cast<float *>(npD_old.get_data());
+		P1     = reinterpret_cast<float *>(npP1.get_data());
+		P2     = reinterpret_cast<float *>(npP2.get_data());
+		P3     = reinterpret_cast<float *>(npP3.get_data());
+		P1_old = reinterpret_cast<float *>(npP1_old.get_data());
+		P2_old = reinterpret_cast<float *>(npP2_old.get_data());
+		P3_old = reinterpret_cast<float *>(npP3_old.get_data());
+		R1     = reinterpret_cast<float *>(npR1.get_data());
+		R2     = reinterpret_cast<float *>(npR2.get_data());
+		R2     = reinterpret_cast<float *>(npR3.get_data());
+		/* begin iterations */
+		for (ll = 0; ll<iter; ll++) {
+
+			/* computing the gradient of the objective function */
+			Obj_func3D(A, D, R1, R2, R3, lambda, dimX, dimY, dimZ);
+
+			/*Taking a step towards minus of the gradient*/
+			Grad_func3D(P1, P2, P3, D, R1, R2, R3, lambda, dimX, dimY, dimZ);
+
+			/* projection step */
+			Proj_func3D(P1, P2, P3, dimX, dimY, dimZ);
+
+			/*updating R and t*/
+			tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
+			Rupd_func3D(P1, P1_old, P2, P2_old, P3, P3_old, R1, R2, R3, tkp1, tk, dimX, dimY, dimZ);
+
+			/* calculate norm - stopping rules*/
+			re = 0.0f; re1 = 0.0f;
+			for (j = 0; j<dimX*dimY*dimZ; j++)
+			{
+				re += pow(D[j] - D_old[j], 2);
+				re1 += pow(D[j], 2);
+			}
+			re = sqrt(re) / sqrt(re1);
+			/* stop if the norm residual is less than the tolerance EPS */
+			if (re < epsil)  count++;
+			if (count > 3) {
+				Obj_func3D(A, D, P1, P2, P3, lambda, dimX, dimY, dimZ);
+				funcval = 0.0f;
+				for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
+				//funcvalA[0] = sqrt(funcval);
+				float fv = sqrt(funcval);
+				std::memcpy(funcvalA, &fv, sizeof(float));
+				break;
+			}
+
+			/* check that the residual norm is decreasing */
+			if (ll > 2) {
+				if (re > re_old) {
+					Obj_func3D(A, D, P1, P2, P3, lambda, dimX, dimY, dimZ);
+					funcval = 0.0f;
+					for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
+					//funcvalA[0] = sqrt(funcval);
+					float fv = sqrt(funcval);
+					std::memcpy(funcvalA, &fv, sizeof(float));
+					break;
+				}
+			}
+
+			re_old = re;
+			/*printf("%f %i %i \n", re, ll, count); */
+
+			/*storing old values*/
+			copyIm(D, D_old, dimX, dimY, dimZ);
+			copyIm(P1, P1_old, dimX, dimY, dimZ);
+			copyIm(P2, P2_old, dimX, dimY, dimZ);
+			copyIm(P3, P3_old, dimX, dimY, dimZ);
+			tk = tkp1;
+
+			if (ll == (iter - 1)) {
+				Obj_func3D(A, D, P1, P2, P3, lambda, dimX, dimY, dimZ);
+				funcval = 0.0f;
+				for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
+				//funcvalA[0] = sqrt(funcval);
+				float fv = sqrt(funcval);
+				std::memcpy(funcvalA, &fv, sizeof(float));
+			}
+
+		}
+		//printf("FGP-TV iterations stopped at iteration %i with the function value %f \n", ll, funcvalA[0]);
+		result.append<np::ndarray>(npD);
+		result.append<np::ndarray>(out1);
+		result.append<int>(ll);
+	}
+
+	return result;
+}
+
+bp::list LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher) {
+	// the result is in the following list
+	bp::list result;
+
+	int number_of_dims, dimX, dimY, dimZ, ll, j, count;
+	//const int  *dim_array;
+	float *U0, *U = NULL, *U_old = NULL, *D1 = NULL, *D2 = NULL, *D3 = NULL, lambda, tau, re, re1, epsil, re_old;
+	unsigned short *Map = NULL;
+
+	number_of_dims = input.get_nd();
+	int dim_array[3];
+
+	dim_array[0] = input.shape(0);
+	dim_array[1] = input.shape(1);
+	if (number_of_dims == 2) {
+		dim_array[2] = -1;
+	}
+	else {
+		dim_array[2] = input.shape(2);
+	}
+
+	///*Handling Matlab input data*/
+	//U0 = (float *)mxGetData(prhs[0]); /*origanal noise image/volume*/
+	//if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input in single precision is required"); }
+	//lambda = (float)mxGetScalar(prhs[1]); /*regularization parameter*/
+	//tau = (float)mxGetScalar(prhs[2]); /* time-step */
+	//iter = (int)mxGetScalar(prhs[3]); /*iterations number*/
+	//epsil = (float)mxGetScalar(prhs[4]); /* tolerance constant */
+	//switcher = (int)mxGetScalar(prhs[5]); /*switch on (1) restrictive smoothing in Z dimension*/
+	
+	U0 = reinterpret_cast<float *>(input.get_data());
+	lambda = (float)d_lambda;
+	tau = (float)d_tau;
+	// iter is passed as parameter
+	epsil = (float)d_epsil;
+	// switcher is passed as parameter
+										  /*Handling Matlab output data*/
+	dimX = dim_array[0]; dimY = dim_array[1];  dimZ = 1;
+
+	if (number_of_dims == 2) {
+		/*2D case*/
+		/*U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		D1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		D2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));*/
+
+		bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+
+		np::ndarray npU = np::zeros(shape, dtype);
+		np::ndarray npU_old = np::zeros(shape, dtype);
+		np::ndarray npD1 = np::zeros(shape, dtype);
+		np::ndarray npD2 = np::zeros(shape, dtype);
+		
+
+		U = reinterpret_cast<float *>(npU.get_data());
+		U_old = reinterpret_cast<float *>(npU_old.get_data());
+		D1 = reinterpret_cast<float *>(npD1.get_data());
+		D2 = reinterpret_cast<float *>(npD2.get_data());
+		
+		/*Copy U0 to U*/
+		copyIm(U0, U, dimX, dimY, dimZ);
+
+		count = 1;
+		re_old = 0.0f;
+
+		for (ll = 0; ll < iter; ll++) {
+
+			copyIm(U, U_old, dimX, dimY, dimZ);
+
+			/*estimate inner derrivatives */
+			der2D(U, D1, D2, dimX, dimY, dimZ);
+			/* calculate div^2 and update */
+			div_upd2D(U0, U, D1, D2, dimX, dimY, dimZ, lambda, tau);
+
+			/* calculate norm to terminate earlier */
+			re = 0.0f; re1 = 0.0f;
+			for (j = 0; j<dimX*dimY*dimZ; j++)
+			{
+				re += pow(U_old[j] - U[j], 2);
+				re1 += pow(U_old[j], 2);
+			}
+			re = sqrt(re) / sqrt(re1);
+			if (re < epsil)  count++;
+			if (count > 4) break;
+
+			/* check that the residual norm is decreasing */
+			if (ll > 2) {
+				if (re > re_old) break;
+			}
+			re_old = re;
+
+		} /*end of iterations*/
+		  //printf("HO iterations stopped at iteration: %i\n", ll);
+
+		result.append<np::ndarray>(npU);
+	}
+	else if (number_of_dims == 3) {
+		/*3D case*/
+		dimZ = dim_array[2];
+		/*U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		U_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		D1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		D2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		D3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		if (switcher != 0) {
+			Map = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(3, dim_array, mxUINT16_CLASS, mxREAL));
+		}*/
+		bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+
+		np::ndarray npU = np::zeros(shape, dtype);
+		np::ndarray npU_old = np::zeros(shape, dtype);
+		np::ndarray npD1 = np::zeros(shape, dtype);
+		np::ndarray npD2 = np::zeros(shape, dtype);
+		np::ndarray npD3 = np::zeros(shape, dtype);
+		np::ndarray npMap = np::zeros(shape, np::dtype::get_builtin<unsigned short>());
+		Map = reinterpret_cast<unsigned short *>(npMap.get_data());
+		if (switcher != 0) {
+			//Map = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(3, dim_array, mxUINT16_CLASS, mxREAL));
+			
+			Map = reinterpret_cast<unsigned short *>(npMap.get_data());
+		}
+
+		U = reinterpret_cast<float *>(npU.get_data());
+		U_old = reinterpret_cast<float *>(npU_old.get_data());
+		D1 = reinterpret_cast<float *>(npD1.get_data());
+		D2 = reinterpret_cast<float *>(npD2.get_data());
+		D3 = reinterpret_cast<float *>(npD2.get_data());
+		
+		/*Copy U0 to U*/
+		copyIm(U0, U, dimX, dimY, dimZ);
+
+		count = 1;
+		re_old = 0.0f;
+	
+
+		if (switcher == 1) {
+			/* apply restrictive smoothing */
+			calcMap(U, Map, dimX, dimY, dimZ);
+			/*clear outliers */
+			cleanMap(Map, dimX, dimY, dimZ);
+		}
+		for (ll = 0; ll < iter; ll++) {
+
+			copyIm(U, U_old, dimX, dimY, dimZ);
+
+			/*estimate inner derrivatives */
+			der3D(U, D1, D2, D3, dimX, dimY, dimZ);
+			/* calculate div^2 and update */
+			div_upd3D(U0, U, D1, D2, D3, Map, switcher, dimX, dimY, dimZ, lambda, tau);
+
+			/* calculate norm to terminate earlier */
+			re = 0.0f; re1 = 0.0f;
+			for (j = 0; j<dimX*dimY*dimZ; j++)
+			{
+				re += pow(U_old[j] - U[j], 2);
+				re1 += pow(U_old[j], 2);
+			}
+			re = sqrt(re) / sqrt(re1);
+			if (re < epsil)  count++;
+			if (count > 4) break;
+
+			/* check that the residual norm is decreasing */
+			if (ll > 2) {
+				if (re > re_old) break;
+			}
+			re_old = re;
+
+		} /*end of iterations*/
+		//printf("HO iterations stopped at iteration: %i\n", ll);
+		result.append<np::ndarray>(npU);
+		if (switcher != 0) result.append<np::ndarray>(npMap);
+
+	}
+	return result;
+}
+
+
+bp::list PatchBased_Regul(np::ndarray input, double d_lambda, int SearchW_real, int SimilW,  double d_h) {
+	// the result is in the following list
+	bp::list result;
+
+	int N, M, Z, numdims, SearchW, /*SimilW, SearchW_real,*/ padXY, newsizeX, newsizeY, newsizeZ, switchpad_crop;
+	//const int  *dims;
+	float *A, *B = NULL, *Ap = NULL, *Bp = NULL, h, lambda;
+
+	numdims = input.get_nd();
+	int dims[3];
+
+	dims[0] = input.shape(0);
+	dims[1] = input.shape(1);
+	if (numdims == 2) {
+		dims[2] = -1;
+	}
+	else {
+		dims[2] = input.shape(2);
+	}
+	/*numdims = mxGetNumberOfDimensions(prhs[0]);
+	dims = mxGetDimensions(prhs[0]);*/
+
+	N = dims[0];
+	M = dims[1];
+	Z = dims[2];
+
+	//if ((numdims < 2) || (numdims > 3)) { mexErrMsgTxt("The input should be 2D image or 3D volume"); }
+	//if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input in single precision is required"); }
+
+	//if (nrhs != 5) mexErrMsgTxt("Five inputs reqired: Image(2D,3D), SearchW, SimilW, Threshold, Regularization parameter");
+
+	///*Handling inputs*/
+	//A = (float *)mxGetData(prhs[0]);    /* the image to regularize/filter */
+	A = reinterpret_cast<float *>(input.get_data());
+	//SearchW_real = (int)mxGetScalar(prhs[1]); /* the searching window ratio */
+	//SimilW = (int)mxGetScalar(prhs[2]);  /* the similarity window ratio */
+	//h = (float)mxGetScalar(prhs[3]);  /* parameter for the PB filtering function */
+	//lambda = (float)mxGetScalar(prhs[4]); /* regularization parameter */
+
+	//if (h <= 0) mexErrMsgTxt("Parmeter for the PB penalty function should be > 0");
+	//if (lambda <= 0) mexErrMsgTxt(" Regularization parmeter should be > 0");
+
+	lambda = (float)d_lambda;
+	h = (float)d_h;
+	SearchW = SearchW_real + 2 * SimilW;
+
+	/* SearchW_full = 2*SearchW + 1; */ /* the full searching window  size */
+										/* SimilW_full = 2*SimilW + 1;  */  /* the full similarity window  size */
+
+
+	padXY = SearchW + 2 * SimilW; /* padding sizes */
+	newsizeX = N + 2 * (padXY); /* the X size of the padded array */
+	newsizeY = M + 2 * (padXY); /* the Y size of the padded array */
+	newsizeZ = Z + 2 * (padXY); /* the Z size of the padded array */
+	int N_dims[] = { newsizeX, newsizeY, newsizeZ };
+	/******************************2D case ****************************/
+	if (numdims == 2) {
+		///*Handling output*/
+		//B = (float*)mxGetData(plhs[0] = mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
+		///*allocating memory for the padded arrays */
+		//Ap = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
+		//Bp = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
+		///**************************************************************************/
+
+		bp::tuple shape = bp::make_tuple(N, M);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+		np::ndarray npB = np::zeros(shape, dtype);
+
+		shape = bp::make_tuple(newsizeX, newsizeY);
+		np::ndarray npAp = np::zeros(shape, dtype);
+		np::ndarray npBp = np::zeros(shape, dtype);
+		B = reinterpret_cast<float *>(npB.get_data());
+		Ap = reinterpret_cast<float *>(npAp.get_data());
+		Bp = reinterpret_cast<float *>(npBp.get_data());		
+
+		/*Perform padding of image A to the size of [newsizeX * newsizeY] */
+		switchpad_crop = 0; /*padding*/
+		pad_crop(A, Ap, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
+		
+		/* Do PB regularization with the padded array  */
+		PB_FUNC2D(Ap, Bp, newsizeY, newsizeX, padXY, SearchW, SimilW, (float)h, (float)lambda);
+		
+		switchpad_crop = 1; /*cropping*/
+		pad_crop(Bp, B, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
+		
+		result.append<np::ndarray>(npB);
+	}
+	else
+	{
+		/******************************3D case ****************************/
+		///*Handling output*/
+		//B = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dims, mxSINGLE_CLASS, mxREAL));
+		///*allocating memory for the padded arrays */
+		//Ap = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
+		//Bp = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
+		/**************************************************************************/
+		bp::tuple shape = bp::make_tuple(dims[0], dims[1], dims[2]);
+		bp::tuple shape_AB = bp::make_tuple(N_dims[0], N_dims[1], N_dims[2]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+		np::ndarray npB = np::zeros(shape, dtype);
+		np::ndarray npAp = np::zeros(shape_AB, dtype);
+		np::ndarray npBp = np::zeros(shape_AB, dtype);
+		B = reinterpret_cast<float *>(npB.get_data());
+		Ap = reinterpret_cast<float *>(npAp.get_data());
+		Bp = reinterpret_cast<float *>(npBp.get_data());
+		/*Perform padding of image A to the size of [newsizeX * newsizeY * newsizeZ] */
+		switchpad_crop = 0; /*padding*/
+		pad_crop(A, Ap, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
+
+		/* Do PB regularization with the padded array  */
+		PB_FUNC3D(Ap, Bp, newsizeY, newsizeX, newsizeZ, padXY, SearchW, SimilW, (float)h, (float)lambda);
+
+		switchpad_crop = 1; /*cropping*/
+		pad_crop(Bp, B, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
+
+		result.append<np::ndarray>(npB);
+	} /*end else ndims*/
+
+	return result;
+}
+
+bp::list TGV_PD(np::ndarray input, double d_lambda, double d_alpha1, double d_alpha0, int iter) {
+	// the result is in the following list
+	bp::list result;
+	int number_of_dims, /*iter,*/ dimX, dimY, dimZ, ll;
+	//const int  *dim_array;
+	float *A, *U, *U_old, *P1, *P2, *Q1, *Q2, *Q3, *V1, *V1_old, *V2, *V2_old, lambda, L2, tau, sigma, alpha1, alpha0;
+
+	//number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+	//dim_array = mxGetDimensions(prhs[0]);
+	number_of_dims = input.get_nd();
+	int dim_array[3];
+
+	dim_array[0] = input.shape(0);
+	dim_array[1] = input.shape(1);
+	if (number_of_dims == 2) {
+		dim_array[2] = -1;
+	}
+	else {
+		dim_array[2] = input.shape(2);
+	}
+	/*Handling Matlab input data*/
+	//A = (float *)mxGetData(prhs[0]); /*origanal noise image/volume*/
+	//if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input in single precision is required"); }
+	
+	A = reinterpret_cast<float *>(input.get_data());
+
+	//lambda = (float)mxGetScalar(prhs[1]); /*regularization parameter*/
+	//alpha1 = (float)mxGetScalar(prhs[2]); /*first-order term*/
+	//alpha0 = (float)mxGetScalar(prhs[3]); /*second-order term*/
+	//iter = (int)mxGetScalar(prhs[4]); /*iterations number*/
+	//if (nrhs != 5) mexErrMsgTxt("Five input parameters is reqired: Image(2D/3D), Regularization parameter, alpha1, alpha0, Iterations");
+	lambda = (float)d_lambda;
+	alpha1 = (float)d_alpha1;
+	alpha0 = (float)d_alpha0;
+
+	/*Handling Matlab output data*/
+	dimX = dim_array[0]; dimY = dim_array[1];
+
+	if (number_of_dims == 2) {
+		/*2D case*/
+		dimZ = 1;
+		bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+		np::ndarray npU = np::zeros(shape, dtype);
+		np::ndarray npP1 = np::zeros(shape, dtype);
+		np::ndarray npP2 = np::zeros(shape, dtype);
+		np::ndarray npQ1 = np::zeros(shape, dtype);
+		np::ndarray npQ2 = np::zeros(shape, dtype);
+		np::ndarray npQ3 = np::zeros(shape, dtype);
+		np::ndarray npV1 = np::zeros(shape, dtype);
+		np::ndarray npV1_old = np::zeros(shape, dtype);
+		np::ndarray npV2 = np::zeros(shape, dtype);
+		np::ndarray npV2_old = np::zeros(shape, dtype);
+		np::ndarray npU_old = np::zeros(shape, dtype);
+
+		U = reinterpret_cast<float *>(npU.get_data());
+		U_old = reinterpret_cast<float *>(npU_old.get_data());
+		P1 = reinterpret_cast<float *>(npP1.get_data());
+		P2 = reinterpret_cast<float *>(npP2.get_data());
+		Q1 = reinterpret_cast<float *>(npQ1.get_data());
+		Q2 = reinterpret_cast<float *>(npQ2.get_data());
+		Q3 = reinterpret_cast<float *>(npQ3.get_data());
+		V1 = reinterpret_cast<float *>(npV1.get_data());
+		V1_old = reinterpret_cast<float *>(npV1_old.get_data());
+		V2 = reinterpret_cast<float *>(npV2.get_data());
+		V2_old = reinterpret_cast<float *>(npV2_old.get_data());
+		//U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+
+		/*dual variables*/
+		/*P1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		P2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+
+		Q1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		Q2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		Q3 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+
+		U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+
+		V1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		V1_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		V2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		V2_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));*/
+		/*printf("%i \n", i);*/
+		L2 = 12.0; /*Lipshitz constant*/
+		tau = 1.0 / pow(L2, 0.5);
+		sigma = 1.0 / pow(L2, 0.5);
+
+		/*Copy A to U*/
+		copyIm(A, U, dimX, dimY, dimZ);
+		/* Here primal-dual iterations begin for 2D */
+		for (ll = 0; ll < iter; ll++) {
+
+			/* Calculate Dual Variable P */
+			DualP_2D(U, V1, V2, P1, P2, dimX, dimY, dimZ, sigma);
+
+			/*Projection onto convex set for P*/
+			ProjP_2D(P1, P2, dimX, dimY, dimZ, alpha1);
+
+			/* Calculate Dual Variable Q */
+			DualQ_2D(V1, V2, Q1, Q2, Q3, dimX, dimY, dimZ, sigma);
+
+			/*Projection onto convex set for Q*/
+			ProjQ_2D(Q1, Q2, Q3, dimX, dimY, dimZ, alpha0);
+
+			/*saving U into U_old*/
+			copyIm(U, U_old, dimX, dimY, dimZ);
+
+			/*adjoint operation  -> divergence and projection of P*/
+			DivProjP_2D(U, A, P1, P2, dimX, dimY, dimZ, lambda, tau);
+
+			/*get updated solution U*/
+			newU(U, U_old, dimX, dimY, dimZ);
+
+			/*saving V into V_old*/
+			copyIm(V1, V1_old, dimX, dimY, dimZ);
+			copyIm(V2, V2_old, dimX, dimY, dimZ);
+
+			/* upd V*/
+			UpdV_2D(V1, V2, P1, P2, Q1, Q2, Q3, dimX, dimY, dimZ, tau);
+
+			/*get new V*/
+			newU(V1, V1_old, dimX, dimY, dimZ);
+			newU(V2, V2_old, dimX, dimY, dimZ);
+		} /*end of iterations*/
+	
+		result.append<np::ndarray>(npU);
+	}
+	
+
+	
+	
+	return result;
+}
+
+BOOST_PYTHON_MODULE(cpu_regularizers)
+{
+	np::initialize();
+
+	//To specify that this module is a package
+	bp::object package = bp::scope();
+	package.attr("__path__") = "cpu_regularizers";
+
+	np::dtype dt1 = np::dtype::get_builtin<uint8_t>();
+	np::dtype dt2 = np::dtype::get_builtin<uint16_t>();
+
+	def("SplitBregman_TV", SplitBregman_TV);
+	def("FGP_TV", FGP_TV);
+	def("LLT_model", LLT_model);
+	def("PatchBased_Regul", PatchBased_Regul);
+	def("TGV_PD", TGV_PD);
+}
diff --git a/src/Python/setup-fista.py.in b/src/Python/setup-fista.py.in
new file mode 100644
index 0000000..c5c9f4d
--- /dev/null
+++ b/src/Python/setup-fista.py.in
@@ -0,0 +1,27 @@
+from distutils.core import setup
+#from setuptools import setup, find_packages
+import os
+
+cil_version=os.environ['CIL_VERSION']
+if  cil_version == '':
+    print("Please set the environmental variable CIL_VERSION")
+    sys.exit(1)
+
+setup(
+    name="ccpi-fista",
+    version=cil_version,
+    packages=['ccpi','ccpi.reconstruction'],
+	install_requires=['numpy'],
+
+     zip_safe = False,
+
+    # metadata for upload to PyPI
+    author="Edoardo Pasca",
+    author_email="edo.paskino@gmail.com",
+    description='CCPi Core Imaging Library - FISTA Reconstructor module',
+    license="Apache v2.0",
+    keywords="tomography interative reconstruction",
+    url="http://www.ccpi.ac.uk",   # project home page, if any
+
+    # could also include long_description, download_url, classifiers, etc.
+)
diff --git a/src/Python/setup.py b/src/Python/setup.py
new file mode 100644
index 0000000..154f979
--- /dev/null
+++ b/src/Python/setup.py
@@ -0,0 +1,64 @@
+#!/usr/bin/env python
+
+import setuptools
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Distutils import build_ext
+
+import os
+import sys
+import numpy
+import platform	
+
+cil_version=os.environ['CIL_VERSION']
+if  cil_version == '':
+    print("Please set the environmental variable CIL_VERSION")
+    sys.exit(1)
+
+library_include_path = ""
+library_lib_path = ""
+try:
+    library_include_path = os.environ['LIBRARY_INC']
+    library_lib_path = os.environ['LIBRARY_LIB']
+except:
+    library_include_path = os.environ['PREFIX']+'/include'
+    pass
+    
+extra_include_dirs = [numpy.get_include(), library_include_path]
+extra_library_dirs = [library_include_path+"/../lib", "C:\\Apps\\Miniconda2\\envs\\cil27\\Library\\lib"]
+extra_compile_args = ['-fopenmp','-O2', '-funsigned-char', '-Wall', '-std=c++0x']
+extra_libraries = []
+if platform.system() == 'Windows':
+    extra_compile_args[0:] = ['/DWIN32','/EHsc','/DBOOST_ALL_NO_LIB' , '/openmp' ]   
+    extra_include_dirs += ["..\\..\\main_func\\regularizers_CPU\\","."]
+    if sys.version_info.major == 3 :   
+        extra_libraries += ['boost_python3-vc140-mt-1_64', 'boost_numpy3-vc140-mt-1_64']
+    else:
+        extra_libraries += ['boost_python-vc90-mt-1_64', 'boost_numpy-vc90-mt-1_64']
+else:
+    extra_include_dirs += ["../../main_func/regularizers_CPU","."]
+    if sys.version_info.major == 3:
+        extra_libraries += ['boost_python3', 'boost_numpy3','gomp']
+    else:
+        extra_libraries += ['boost_python', 'boost_numpy','gomp']
+
+setup(
+    name='ccpi',
+	description='CCPi Core Imaging Library - FISTA Reconstruction Module',
+	version=cil_version,
+    cmdclass = {'build_ext': build_ext},
+    ext_modules = [Extension("ccpi.imaging.cpu_regularizers",
+                             sources=["fista_module.cpp",
+                                      "../../main_func/regularizers_CPU/FGP_TV_core.c",
+                                      "../../main_func/regularizers_CPU/SplitBregman_TV_core.c",
+                                      "../../main_func/regularizers_CPU/LLT_model_core.c",
+                                      "../../main_func/regularizers_CPU/PatchBased_Regul_core.c",
+                                      "../../main_func/regularizers_CPU/TGV_PD_core.c",
+                                      "../../main_func/regularizers_CPU/utils.c"
+                                        ],
+                             include_dirs=extra_include_dirs, library_dirs=extra_library_dirs, extra_compile_args=extra_compile_args, libraries=extra_libraries ), 
+    
+    ],
+	zip_safe = False,	
+	packages = {'ccpi','ccpi.fistareconstruction'},
+)
diff --git a/src/Python/setup.py.in b/src/Python/setup.py.in
new file mode 100644
index 0000000..12e8af1
--- /dev/null
+++ b/src/Python/setup.py.in
@@ -0,0 +1,69 @@
+#!/usr/bin/env python
+
+import setuptools
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Distutils import build_ext
+
+import os
+import sys
+import numpy
+import platform	
+
+cil_version=@CIL_VERSION@
+
+library_include_path = ""
+library_lib_path = ""
+try:
+    library_include_path = os.environ['LIBRARY_INC']
+    library_lib_path = os.environ['LIBRARY_LIB']
+except:
+    library_include_path = os.environ['PREFIX']+'/include'
+    pass
+    
+extra_include_dirs = [numpy.get_include(), library_include_path]
+extra_library_dirs = [os.path.join(library_include_path, "..", "lib")]
+extra_compile_args = ['-fopenmp','-O2', '-funsigned-char', '-Wall', '-std=c++0x']
+extra_libraries = []
+extra_include_dirs += [os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU"),
+                       	   os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_GPU") , 
+						   "@CMAKE_CURRENT_SOURCE_DIR@"]
+						   
+if platform.system() == 'Windows':
+    extra_compile_args[0:] = ['/DWIN32','/EHsc','/DBOOST_ALL_NO_LIB' , '/openmp' ]   
+    
+    if sys.version_info.major == 3 :   
+        extra_libraries += ['boost_python3-vc140-mt-1_64', 'boost_numpy3-vc140-mt-1_64']
+    else:
+        extra_libraries += ['boost_python-vc90-mt-1_64', 'boost_numpy-vc90-mt-1_64']
+else:
+    if sys.version_info.major == 3:
+        extra_libraries += ['boost_python3', 'boost_numpy3','gomp']
+    else:
+        extra_libraries += ['boost_python', 'boost_numpy','gomp']
+
+setup(
+    name='ccpi',
+	description='CCPi Core Imaging Library - Image Regularizers',
+	version=cil_version,
+    cmdclass = {'build_ext': build_ext},
+    ext_modules = [Extension("ccpi.imaging.cpu_regularizers",
+                             sources=[os.path.join("@CMAKE_CURRENT_SOURCE_DIR@" , "fista_module.cpp" ),
+                                      os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU", "FGP_TV_core.c"),
+									  os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU", "SplitBregman_TV_core.c"),
+									  os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU", "LLT_model_core.c"),
+									  os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU", "PatchBased_Regul_core.c"),
+									  os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU", "TGV_PD_core.c"),
+									  os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU", "utils.c")
+                                        ],
+                             include_dirs=extra_include_dirs, 
+							 library_dirs=extra_library_dirs, 
+							 extra_compile_args=extra_compile_args, 
+							 libraries=extra_libraries ), 
+    
+    ],
+	zip_safe = False,	
+	packages = {'ccpi','ccpi.imaging'},
+)
+
+
diff --git a/src/Python/setup_test.py b/src/Python/setup_test.py
new file mode 100644
index 0000000..7c86175
--- /dev/null
+++ b/src/Python/setup_test.py
@@ -0,0 +1,58 @@
+#!/usr/bin/env python
+
+import setuptools
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Distutils import build_ext
+
+import os
+import sys
+import numpy
+import platform	
+
+cil_version=os.environ['CIL_VERSION']
+if  cil_version == '':
+    print("Please set the environmental variable CIL_VERSION")
+    sys.exit(1)
+
+library_include_path = ""
+library_lib_path = ""
+try:
+    library_include_path = os.environ['LIBRARY_INC']
+    library_lib_path = os.environ['LIBRARY_LIB']
+except:
+    library_include_path = os.environ['PREFIX']+'/include'
+    pass
+    
+extra_include_dirs = [numpy.get_include(), library_include_path]
+extra_library_dirs = [library_include_path+"/../lib", "C:\\Apps\\Miniconda2\\envs\\cil27\\Library\\lib"]
+extra_compile_args = ['-fopenmp','-O2', '-funsigned-char', '-Wall', '-std=c++0x']
+extra_libraries = []
+if platform.system() == 'Windows':
+    extra_compile_args[0:] = ['/DWIN32','/EHsc','/DBOOST_ALL_NO_LIB']   
+    #extra_include_dirs += ["..\\ContourTree\\", "..\\win32\\" , "..\\Core\\","."]
+    if sys.version_info.major == 3 :   
+        extra_libraries += ['boost_python3-vc140-mt-1_64', 'boost_numpy3-vc140-mt-1_64']
+    else:
+        extra_libraries += ['boost_python-vc90-mt-1_64', 'boost_numpy-vc90-mt-1_64']
+else:
+    #extra_include_dirs += ["../ContourTree/", "../Core/","."]
+    if sys.version_info.major == 3:
+        extra_libraries += ['boost_python3', 'boost_numpy3','gomp']
+    else:
+        extra_libraries += ['boost_python', 'boost_numpy','gomp']
+
+setup(
+    name='ccpi',
+	description='CCPi Core Imaging Library - FISTA Reconstruction Module',
+	version=cil_version,
+    cmdclass = {'build_ext': build_ext},
+    ext_modules = [Extension("prova",
+                             sources=[  "Matlab2Python_utils.cpp",
+                                        ],
+                             include_dirs=extra_include_dirs, library_dirs=extra_library_dirs, extra_compile_args=extra_compile_args, libraries=extra_libraries ), 
+    
+    ],
+	zip_safe = False,	
+	packages = {'ccpi','ccpi.reconstruction'},
+)
diff --git a/src/Python/test.py b/src/Python/test.py
new file mode 100644
index 0000000..db47380
--- /dev/null
+++ b/src/Python/test.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Aug  3 14:08:09 2017
+
+@author: ofn77899
+"""
+
+import prova
+import numpy as np
+
+a = np.asarray([i for i in range(1*2*3)])
+a = a.reshape([1,2,3])
+print (a)
+b = prova.mexFunction(a)
+#print (b)
+print (b[4].shape)
+print (b[4])
+print (b[5])
+
+def print_element(input):
+	print ("f: {0}".format(input))
+	
+prova.doSomething(a, print_element, None)
+
+c = []
+def append_to_list(input, shouldPrint=False):
+	c.append(input)
+	if shouldPrint:
+		print ("{0} appended to list {1}".format(input, c))
+
+def element_wise_algebra(input, shouldPrint=True):
+	ret = input - 7
+	if shouldPrint:
+		print ("element_wise {0}".format(ret))
+	return ret
+		
+prova.doSomething(a, append_to_list, None)
+#print ("this is c: {0}".format(c))
+
+b = prova.doSomething(a, None, element_wise_algebra)
+#print (a)
+print (b[5])
diff --git a/src/Python/test/astra_test.py b/src/Python/test/astra_test.py
new file mode 100644
index 0000000..42c375a
--- /dev/null
+++ b/src/Python/test/astra_test.py
@@ -0,0 +1,85 @@
+import astra
+import numpy
+import filefun
+
+
+# read in the same data as the DemoRD2
+angles = filefun.dlmread("DemoRD2/angles.csv")
+darks_ar = filefun.dlmread("DemoRD2/darks_ar.csv", separator=",")
+flats_ar = filefun.dlmread("DemoRD2/flats_ar.csv", separator=",")
+
+if True:
+    Sino3D = numpy.load("DemoRD2/Sino3D.npy")
+else:
+    sino = filefun.dlmread("DemoRD2/sino_01.csv", separator=",")
+    a = map (lambda x:x, numpy.shape(sino))
+    a.append(20)
+
+    Sino3D = numpy.zeros(tuple(a), dtype="float")
+
+    for i in range(1,numpy.shape(Sino3D)[2]+1):
+        print("Read file DemoRD2/sino_%02d.csv" % i)
+        sino = filefun.dlmread("DemoRD2/sino_%02d.csv" % i, separator=",")
+        Sino3D.T[i-1] = sino.T
+    
+Weights3D = numpy.asarray(Sino3D, dtype="float")
+
+##angles_rad = angles*(pi/180); % conversion to radians
+##size_det = size(data_raw3D,1); % detectors dim
+##angSize = size(data_raw3D, 2); % angles dim
+##slices_tot = size(data_raw3D, 3); % no of slices
+##recon_size = 950; % reconstruction size
+
+
+angles_rad = angles * numpy.pi /180.
+size_det, angSize, slices_tot = numpy.shape(Sino3D)
+size_det, angSize, slices_tot = [int(i) for i in numpy.shape(Sino3D)]
+recon_size = 950
+Z_slices = 3;
+det_row_count = Z_slices;
+
+#proj_geom = astra_create_proj_geom('parallel3d', 1, 1,
+# det_row_count, size_det, angles_rad);
+
+detectorSpacingX = 1.0
+detectorSpacingY = detectorSpacingX
+proj_geom = astra.create_proj_geom('parallel3d',
+                                            detectorSpacingX,
+                                            detectorSpacingY,
+                                            det_row_count,
+                                            size_det,
+                                            angles_rad)
+
+#vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
+vol_geom = astra.create_vol_geom(recon_size,recon_size,Z_slices);
+
+sino = numpy.zeros((size_det, angSize, slices_tot), dtype="float")
+
+#weights = ones(size(sino));
+weights = numpy.ones(numpy.shape(sino))
+
+#####################################################################
+## PowerMethod for Lipschitz constant
+
+N = vol_geom['GridColCount']
+x1 = numpy.random.rand(1,N,N)
+#sqweight = sqrt(weights(:,:,1));
+sqweight = numpy.sqrt(weights.T[0]).T
+##proj_geomT = proj_geom;
+proj_geomT = proj_geom.copy()
+##proj_geomT.DetectorRowCount = 1;
+proj_geomT['DetectorRowCount'] = 1
+##vol_geomT = vol_geom;
+vol_geomT = vol_geom.copy()
+##vol_geomT.GridSliceCount = 1;
+vol_geomT['GridSliceCount'] = 1
+
+##[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
+
+#sino_id, y = astra.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
+sino_id, y = astra.create_sino(x1, proj_geomT, vol_geomT);
+
+##y = sqweight.*y;
+##astra_mex_data3d('delete', sino_id);
+        
+
diff --git a/src/Python/test/readhd5.py b/src/Python/test/readhd5.py
new file mode 100644
index 0000000..eff6c43
--- /dev/null
+++ b/src/Python/test/readhd5.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 23 16:34:49 2017
+
+@author: ofn77899
+"""
+
+import h5py
+import numpy
+
+def getEntry(nx, location):
+    for item in nx[location].keys():
+        print (item)
+        
+filename = r'/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/demos/DendrData.h5'
+nx = h5py.File(filename, "r")
+#getEntry(nx, '/')
+# I have exported the entries as children of /
+entries = [entry for entry in nx['/'].keys()]
+print (entries)
+
+Sino3D = numpy.asarray(nx.get('/Sino3D'))
+Weights3D = numpy.asarray(nx.get('/Weights3D'))
+angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
+angles_rad = numpy.asarray(nx.get('/angles_rad'))
+recon_size = numpy.asarray(nx.get('/recon_size'), dtype=int)[0]
+size_det = numpy.asarray(nx.get('/size_det'), dtype=int)[0]
+
+slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
+
+#from ccpi.viewer.CILViewer2D import CILViewer2D
+#v = CILViewer2D()
+#v.setInputAsNumpy(Weights3D)
+#v.startRenderLoop()
+
+import matplotlib.pyplot as plt
+fig = plt.figure()
+
+a=fig.add_subplot(1,1,1)
+a.set_title('noise')
+imgplot = plt.imshow(Weights3D[0].T)
+plt.show()
diff --git a/src/Python/test/simple_astra_test.py b/src/Python/test/simple_astra_test.py
new file mode 100644
index 0000000..905eeea
--- /dev/null
+++ b/src/Python/test/simple_astra_test.py
@@ -0,0 +1,25 @@
+import astra
+import numpy
+
+detectorSpacingX = 1.0
+detectorSpacingY = 1.0
+det_row_count = 128
+det_col_count = 128
+
+angles_rad = numpy.asarray([i for i in range(360)], dtype=float) / 180. * numpy.pi
+
+proj_geom = astra.creators.create_proj_geom('parallel3d',
+                                            detectorSpacingX,
+                                            detectorSpacingY,
+                                            det_row_count,
+                                            det_col_count,
+                                            angles_rad)
+
+image_size_x = 64
+image_size_y = 64
+image_size_z = 32
+
+vol_geom = astra.creators.create_vol_geom(image_size_x,image_size_y,image_size_z)
+
+x1 = numpy.random.rand(image_size_z,image_size_y,image_size_x)
+sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom)
diff --git a/src/Python/test/test_reconstructor-os.py b/src/Python/test/test_reconstructor-os.py
new file mode 100644
index 0000000..3f419cf
--- /dev/null
+++ b/src/Python/test/test_reconstructor-os.py
@@ -0,0 +1,352 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 23 16:34:49 2017
+
+@author: ofn77899
+Based on DemoRD2.m
+"""
+
+import h5py
+import numpy
+
+from ccpi.reconstruction.FISTAReconstructor import FISTAReconstructor
+import astra
+import matplotlib.pyplot as plt
+from ccpi.imaging.Regularizer import Regularizer
+
+def RMSE(signal1, signal2):
+    '''RMSE Root Mean Squared Error'''
+    if numpy.shape(signal1) == numpy.shape(signal2):
+        err = (signal1 - signal2)
+        err = numpy.sum( err * err )/numpy.size(signal1);  # MSE
+        err = sqrt(err);                                   # RMSE
+        return err
+    else:
+        raise Exception('Input signals must have the same shape')
+  
+filename = r'/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/demos/DendrData.h5'
+nx = h5py.File(filename, "r")
+#getEntry(nx, '/')
+# I have exported the entries as children of /
+entries = [entry for entry in nx['/'].keys()]
+print (entries)
+
+Sino3D = numpy.asarray(nx.get('/Sino3D'), dtype="float32")
+Weights3D = numpy.asarray(nx.get('/Weights3D'), dtype="float32")
+angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
+angles_rad = numpy.asarray(nx.get('/angles_rad'), dtype="float32")
+recon_size = numpy.asarray(nx.get('/recon_size'), dtype=int)[0]
+size_det = numpy.asarray(nx.get('/size_det'), dtype=int)[0]
+slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
+
+Z_slices = 20
+det_row_count = Z_slices
+# next definition is just for consistency of naming
+det_col_count = size_det
+
+detectorSpacingX = 1.0
+detectorSpacingY = detectorSpacingX
+
+
+proj_geom = astra.creators.create_proj_geom('parallel3d',
+                                            detectorSpacingX,
+                                            detectorSpacingY,
+                                            det_row_count,
+                                            det_col_count,
+                                            angles_rad)
+
+#vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
+image_size_x = recon_size
+image_size_y = recon_size
+image_size_z = Z_slices
+vol_geom = astra.creators.create_vol_geom( image_size_x,
+                                           image_size_y,
+                                           image_size_z)
+
+## First pass the arguments to the FISTAReconstructor and test the
+## Lipschitz constant
+
+fistaRecon = FISTAReconstructor(proj_geom,
+                                vol_geom,
+                                Sino3D ,
+                                weights=Weights3D)
+
+print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+fistaRecon.setParameter(number_of_iterations = 12)
+fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
+fistaRecon.setParameter(ring_alpha = 21)
+fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
+
+
+#reg = Regularizer(Regularizer.Algorithm.FGP_TV)
+#reg.setParameter(regularization_parameter=0.005,
+#                          number_of_iterations=50)
+reg = Regularizer(Regularizer.Algorithm.LLT_model)
+reg.setParameter(regularization_parameter=25,
+                          time_step=0.0003,
+                          tolerance_constant=0.0001,
+                          number_of_iterations=300)
+
+
+## Ordered subset
+if True:
+    subsets = 16
+    fistaRecon.setParameter(subsets=subsets)
+    fistaRecon.createOrderedSubsets()
+    angles = fistaRecon.getParameter('projector_geometry')['ProjectionAngles']
+    #binEdges = numpy.linspace(angles.min(),
+    #                          angles.max(),
+    #                          subsets + 1)
+    binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
+    # get rearranged subset indices
+    IndicesReorg = numpy.zeros((numpy.shape(angles)))
+    counterM = 0
+    for ii in range(binsDiscr.max()):
+        counter = 0
+        for jj in range(subsets):
+            curr_index = ii + jj  + counter
+            #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
+            if binsDiscr[jj] > ii:
+                if (counterM < numpy.size(IndicesReorg)):
+                    IndicesReorg[counterM] = curr_index
+                counterM = counterM + 1
+                
+            counter = counter + binsDiscr[jj] - 1
+
+
+if True:
+    print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+    print ("prepare for iteration")
+    fistaRecon.prepareForIteration()
+    
+    
+
+    print("initializing  ...")
+    if False:
+        # if X doesn't exist
+        #N = params.vol_geom.GridColCount
+        N = vol_geom['GridColCount']
+        print ("N " + str(N))
+        X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
+    else:
+        #X = fistaRecon.initialize()
+        X = numpy.load("X.npy")
+    
+    print (numpy.shape(X))
+    X_t = X.copy()
+    print ("initialized")
+    proj_geom , vol_geom, sino , \
+        SlicesZ, weights , alpha_ring = fistaRecon.getParameter(
+            ['projector_geometry' , 'output_geometry',
+             'input_sinogram', 'SlicesZ' ,  'weights', 'ring_alpha'])
+    lambdaR_L1 , alpha_ring , weights , L_const= \
+                       fistaRecon.getParameter(['ring_lambda_R_L1',
+                                               'ring_alpha' , 'weights',
+                                               'Lipschitz_constant'])
+
+    #fistaRecon.setParameter(number_of_iterations = 3)
+    iterFISTA = fistaRecon.getParameter('number_of_iterations')
+    # errors vector (if the ground truth is given)
+    Resid_error = numpy.zeros((iterFISTA));
+    # objective function values vector
+    objective = numpy.zeros((iterFISTA)); 
+
+      
+    t = 1
+    
+
+    ## additional for 
+    proj_geomSUB = proj_geom.copy()
+    fistaRecon.residual2 = numpy.zeros(numpy.shape(fistaRecon.pars['input_sinogram']))
+    residual2 = fistaRecon.residual2
+    sino_updt_FULL = fistaRecon.residual.copy()
+    r_x = fistaRecon.r.copy()
+    
+    print ("starting iterations")
+##    % Outer FISTA iterations loop
+    for i in range(fistaRecon.getParameter('number_of_iterations')):
+##        % With OS approach it becomes trickier to correlate independent subsets, hence additional work is required
+##        % one solution is to work with a full sinogram at times
+##        if ((i >= 3) && (lambdaR_L1 > 0))
+##            [sino_id2, sino_updt2] = astra_create_sino3d_cuda(X, proj_geom, vol_geom);
+##            astra_mex_data3d('delete', sino_id2);
+##        end
+        # With OS approach it becomes trickier to correlate independent subsets,
+        # hence additional work is required one solution is to work with a full
+        # sinogram at times
+
+        r_old = fistaRecon.r.copy()
+        t_old = t
+        SlicesZ, anglesNumb, Detectors = \
+                    numpy.shape(fistaRecon.getParameter('input_sinogram'))        ## https://github.com/vais-ral/CCPi-FISTA_Reconstruction/issues/4
+        if (i > 1 and lambdaR_L1 > 0) :
+            for kkk in range(anglesNumb):
+                 
+                 residual2[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
+                                       ((sino_updt_FULL[:,kkk,:]).squeeze() - \
+                                        (sino[:,kkk,:]).squeeze() -\
+                                        (alpha_ring * r_x)
+                                        )
+            
+            vec = fistaRecon.residual.sum(axis = 1)
+            #if SlicesZ > 1:
+            #    vec = vec[:,1,:] # 1 or 0?
+            r_x = fistaRecon.r_x
+            fistaRecon.r = (r_x - (1./L_const) * vec).copy()
+
+        # subset loop
+        counterInd = 1
+        geometry_type = fistaRecon.getParameter('projector_geometry')['type']
+        if geometry_type == 'parallel' or \
+           geometry_type == 'fanflat' or \
+           geometry_type == 'fanflat_vec' :
+            
+            for kkk in range(SlicesZ):
+                sino_id, sinoT[kkk] = \
+                         astra.creators.create_sino3d_gpu(
+                             X_t[kkk:kkk+1], proj_geomSUB, vol_geom)
+                sino_updt_Sub[kkk] = sinoT.T.copy()
+                
+        else:
+            sino_id, sino_updt_Sub = \
+                astra.creators.create_sino3d_gpu(X_t, proj_geomSUB, vol_geom)
+        
+        astra.matlab.data3d('delete', sino_id)
+  
+        for ss in range(fistaRecon.getParameter('subsets')):
+            print ("Subset {0}".format(ss))
+            X_old = X.copy()
+            t_old = t
+            
+            # the number of projections per subset
+            numProjSub = fistaRecon.getParameter('os_bins')[ss]
+            CurrSubIndices = fistaRecon.getParameter('os_indices')\
+                             [counterInd:counterInd+numProjSub]
+            #print ("Len CurrSubIndices {0}".format(numProjSub))
+            mask = numpy.zeros(numpy.shape(angles), dtype=bool)
+            cc = 0
+            for j in range(len(CurrSubIndices)):
+                mask[int(CurrSubIndices[j])] = True
+            proj_geomSUB['ProjectionAngles'] = angles[mask]
+
+            shape = list(numpy.shape(fistaRecon.getParameter('input_sinogram')))
+            shape[1] = numProjSub
+            sino_updt_Sub = numpy.zeros(shape)
+
+            if geometry_type == 'parallel' or \
+               geometry_type == 'fanflat' or \
+               geometry_type == 'fanflat_vec' :
+
+                for kkk in range(SlicesZ):
+                    sino_id, sinoT = astra.creators.create_sino3d_gpu (
+                        X_t[kkk:kkk+1] , proj_geomSUB, vol_geom)
+                    sino_updt_Sub[kkk] = sinoT.T.copy()
+                    
+            else:
+                # for 3D geometry (watch the GPU memory overflow in ASTRA < 1.8)
+                sino_id, sino_updt_Sub = \
+                     astra.creators.create_sino3d_gpu (X_t, proj_geomSUB, vol_geom)
+                
+            astra.matlab.data3d('delete', sino_id)
+                
+            
+
+
+            ## RING REMOVAL
+            residual = fistaRecon.residual
+            
+            
+            if lambdaR_L1 > 0 :
+                 print ("ring removal")
+                 residualSub = numpy.zeros(shape)
+    ##             for a chosen subset
+    ##                for kkk = 1:numProjSub
+    ##                    indC = CurrSubIndeces(kkk);
+    ##                    residualSub(:,kkk,:) =  squeeze(weights(:,indC,:)).*(squeeze(sino_updt_Sub(:,kkk,:)) - (squeeze(sino(:,indC,:)) - alpha_ring.*r_x));
+    ##                    sino_updt_FULL(:,indC,:) = squeeze(sino_updt_Sub(:,kkk,:)); % filling the full sinogram
+    ##                end
+                 for kkk in range(numProjSub):
+                     #print ("ring removal indC ... {0}".format(kkk))
+                     indC = int(CurrSubIndices[kkk])
+                     residualSub[:,kkk,:] = weights[:,indC,:].squeeze() * \
+                            (sino_updt_Sub[:,kkk,:].squeeze() - \
+                             sino[:,indC,:].squeeze() - alpha_ring * r_x)
+                     # filling the full sinogram
+                     sino_updt_FULL[:,indC,:] = sino_updt_Sub[:,kkk,:].squeeze()
+
+            else:
+                #PWLS model
+                residualSub = weights[:,CurrSubIndices,:] * \
+                              ( sino_updt_Sub - sino[:,CurrSubIndices,:].squeeze() )
+                objective[i] = 0.5 * numpy.linalg.norm(residualSub)
+
+            if geometry_type == 'parallel' or \
+               geometry_type == 'fanflat' or \
+               geometry_type == 'fanflat_vec' :
+                # if geometry is 2D use slice-by-slice projection-backprojection
+                # routine
+                x_temp = numpy.zeros(numpy.shape(X), dtype=numpy.float32)
+                for kkk in range(SlicesZ):
+                    
+                    x_id, x_temp[kkk] = \
+                             astra.creators.create_backprojection3d_gpu(
+                                 residualSub[kkk:kkk+1],
+                                 proj_geomSUB, vol_geom)
+                    
+            else:
+                x_id, x_temp = \
+                      astra.creators.create_backprojection3d_gpu(
+                          residualSub, proj_geomSUB, vol_geom)
+
+            astra.matlab.data3d('delete', x_id)
+            X = X_t - (1/L_const) * x_temp
+
+        
+
+        ## REGULARIZATION
+        ## SKIPPING FOR NOW
+        ## Should be simpli
+        # regularizer = fistaRecon.getParameter('regularizer')
+        # for slices:
+        # out = regularizer(input=X)
+        print ("regularizer")
+        X = reg(input=X)[0]
+
+
+        ## FINAL
+        print ("final")
+        lambdaR_L1 = fistaRecon.getParameter('ring_lambda_R_L1')
+        if lambdaR_L1 > 0:
+            fistaRecon.r = numpy.max(
+                numpy.abs(fistaRecon.r) - lambdaR_L1 , 0) * \
+                numpy.sign(fistaRecon.r)
+            # updating r
+            r_x = fistaRecon.r + ((t_old-1)/t) * (fistaRecon.r - r_old)
+        
+
+        if fistaRecon.getParameter('region_of_interest') is None:
+            string = 'Iteration Number {0} | Objective {1} \n'
+            print (string.format( i, objective[i]))
+        else:
+            ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
+                                                    'ideal_image')
+            
+            Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
+            string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
+            print (string.format(i,Resid_error[i], objective[i]))
+
+    numpy.save("X_out_os.npy", X)
+
+else:
+    fistaRecon = FISTAReconstructor(proj_geom,
+                                vol_geom,
+                                Sino3D ,
+                                weights=Weights3D)
+
+    print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+    fistaRecon.setParameter(number_of_iterations = 12)
+    fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
+    fistaRecon.setParameter(ring_alpha = 21)
+    fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
+    fistaRecon.prepareForIteration()
+    X = fistaRecon.iterate(numpy.load("X.npy"))
diff --git a/src/Python/test/test_reconstructor.py b/src/Python/test/test_reconstructor.py
new file mode 100644
index 0000000..3342301
--- /dev/null
+++ b/src/Python/test/test_reconstructor.py
@@ -0,0 +1,309 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 23 16:34:49 2017
+
+@author: ofn77899
+Based on DemoRD2.m
+"""
+
+import h5py
+import numpy
+
+from ccpi.reconstruction.FISTAReconstructor import FISTAReconstructor
+import astra
+import matplotlib.pyplot as plt
+
+def RMSE(signal1, signal2):
+    '''RMSE Root Mean Squared Error'''
+    if numpy.shape(signal1) == numpy.shape(signal2):
+        err = (signal1 - signal2)
+        err = numpy.sum( err * err )/numpy.size(signal1);  # MSE
+        err = sqrt(err);                                   # RMSE
+        return err
+    else:
+        raise Exception('Input signals must have the same shape')
+  
+filename = r'/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/demos/DendrData.h5'
+nx = h5py.File(filename, "r")
+#getEntry(nx, '/')
+# I have exported the entries as children of /
+entries = [entry for entry in nx['/'].keys()]
+print (entries)
+
+Sino3D = numpy.asarray(nx.get('/Sino3D'), dtype="float32")
+Weights3D = numpy.asarray(nx.get('/Weights3D'), dtype="float32")
+angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
+angles_rad = numpy.asarray(nx.get('/angles_rad'), dtype="float32")
+recon_size = numpy.asarray(nx.get('/recon_size'), dtype=int)[0]
+size_det = numpy.asarray(nx.get('/size_det'), dtype=int)[0]
+slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
+
+Z_slices = 20
+det_row_count = Z_slices
+# next definition is just for consistency of naming
+det_col_count = size_det
+
+detectorSpacingX = 1.0
+detectorSpacingY = detectorSpacingX
+
+
+proj_geom = astra.creators.create_proj_geom('parallel3d',
+                                            detectorSpacingX,
+                                            detectorSpacingY,
+                                            det_row_count,
+                                            det_col_count,
+                                            angles_rad)
+
+#vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
+image_size_x = recon_size
+image_size_y = recon_size
+image_size_z = Z_slices
+vol_geom = astra.creators.create_vol_geom( image_size_x,
+                                           image_size_y,
+                                           image_size_z)
+
+## First pass the arguments to the FISTAReconstructor and test the
+## Lipschitz constant
+
+fistaRecon = FISTAReconstructor(proj_geom,
+                                vol_geom,
+                                Sino3D ,
+                                weights=Weights3D)
+
+print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+fistaRecon.setParameter(number_of_iterations = 12)
+fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
+fistaRecon.setParameter(ring_alpha = 21)
+fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
+
+reg = Regularizer(Regularizer.Algorithm.LLT_model)
+reg.setParameter(regularization_parameter=25,
+                          time_step=0.0003,
+                          tolerance_constant=0.0001,
+                          number_of_iterations=300)
+fistaRecon.setParameter(regularizer = reg)
+
+## Ordered subset
+if False:
+    subsets = 16
+    angles = fistaRecon.getParameter('projector_geometry')['ProjectionAngles']
+    #binEdges = numpy.linspace(angles.min(),
+    #                          angles.max(),
+    #                          subsets + 1)
+    binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
+    # get rearranged subset indices
+    IndicesReorg = numpy.zeros((numpy.shape(angles)))
+    counterM = 0
+    for ii in range(binsDiscr.max()):
+        counter = 0
+        for jj in range(subsets):
+            curr_index = ii + jj  + counter
+            #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
+            if binsDiscr[jj] > ii:
+                if (counterM < numpy.size(IndicesReorg)):
+                    IndicesReorg[counterM] = curr_index
+                counterM = counterM + 1
+                
+            counter = counter + binsDiscr[jj] - 1
+
+
+if False:
+    print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+    print ("prepare for iteration")
+    fistaRecon.prepareForIteration()
+    
+    
+
+    print("initializing  ...")
+    if False:
+        # if X doesn't exist
+        #N = params.vol_geom.GridColCount
+        N = vol_geom['GridColCount']
+        print ("N " + str(N))
+        X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
+    else:
+        #X = fistaRecon.initialize()
+        X = numpy.load("X.npy")
+    
+    print (numpy.shape(X))
+    X_t = X.copy()
+    print ("initialized")
+    proj_geom , vol_geom, sino , \
+                      SlicesZ = fistaRecon.getParameter(['projector_geometry' ,
+                                                            'output_geometry',
+                                                            'input_sinogram',
+                                                         'SlicesZ'])
+
+    #fistaRecon.setParameter(number_of_iterations = 3)
+    iterFISTA = fistaRecon.getParameter('number_of_iterations')
+    # errors vector (if the ground truth is given)
+    Resid_error = numpy.zeros((iterFISTA));
+    # objective function values vector
+    objective = numpy.zeros((iterFISTA)); 
+
+      
+    t = 1
+
+
+    print ("starting iterations")
+##    % Outer FISTA iterations loop
+    for i in range(fistaRecon.getParameter('number_of_iterations')):
+        X_old = X.copy()
+        t_old = t
+        r_old = fistaRecon.r.copy()
+        if fistaRecon.getParameter('projector_geometry')['type'] == 'parallel' or \
+           fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat' or \
+           fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat_vec' :
+            # if the geometry is parallel use slice-by-slice
+            # projection-backprojection routine
+            #sino_updt = zeros(size(sino),'single');
+            proj_geomT = proj_geom.copy()
+            proj_geomT['DetectorRowCount'] = 1
+            vol_geomT = vol_geom.copy()
+            vol_geomT['GridSliceCount'] = 1;
+            sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
+            for kkk in range(SlicesZ):
+                sino_id, sino_updt[kkk] = \
+                         astra.creators.create_sino3d_gpu(
+                             X_t[kkk:kkk+1], proj_geom, vol_geom)
+                astra.matlab.data3d('delete', sino_id)
+        else:
+            # for divergent 3D geometry (watch the GPU memory overflow in
+            # ASTRA versions < 1.8)
+            #[sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
+            sino_id, sino_updt = astra.creators.create_sino3d_gpu(
+                X_t, proj_geom, vol_geom)
+
+        ## RING REMOVAL
+        residual = fistaRecon.residual
+        lambdaR_L1 , alpha_ring , weights , L_const= \
+                   fistaRecon.getParameter(['ring_lambda_R_L1',
+                                           'ring_alpha' , 'weights',
+                                           'Lipschitz_constant'])
+        r_x = fistaRecon.r_x
+        SlicesZ, anglesNumb, Detectors = \
+                    numpy.shape(fistaRecon.getParameter('input_sinogram'))
+        if lambdaR_L1 > 0 :
+             print ("ring removal")
+             for kkk in range(anglesNumb):
+                 
+                 residual[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
+                                       ((sino_updt[:,kkk,:]).squeeze() - \
+                                        (sino[:,kkk,:]).squeeze() -\
+                                        (alpha_ring * r_x)
+                                        )
+             vec = residual.sum(axis = 1)
+             #if SlicesZ > 1:
+             #    vec = vec[:,1,:].squeeze()
+             fistaRecon.r = (r_x - (1./L_const) * vec).copy()
+             objective[i] = (0.5 * (residual ** 2).sum())
+##            % the ring removal part (Group-Huber fidelity)
+##            for kkk = 1:anglesNumb
+##                residual(:,kkk,:) =  squeeze(weights(:,kkk,:)).*
+##                 (squeeze(sino_updt(:,kkk,:)) -
+##                 (squeeze(sino(:,kkk,:)) - alpha_ring.*r_x));
+##            end
+##            vec = sum(residual,2);
+##            if (SlicesZ > 1)
+##                vec = squeeze(vec(:,1,:));
+##            end
+##            r = r_x - (1./L_const).*vec;
+##            objective(i) = (0.5*sum(residual(:).^2)); % for the objective function output
+
+        
+
+        # Projection/Backprojection Routine
+        if fistaRecon.getParameter('projector_geometry')['type'] == 'parallel' or \
+           fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat' or\
+           fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat_vec':
+            x_temp = numpy.zeros(numpy.shape(X),dtype=numpy.float32)
+            print ("Projection/Backprojection Routine")
+            for kkk in range(SlicesZ):
+                
+                x_id, x_temp[kkk] = \
+                         astra.creators.create_backprojection3d_gpu(
+                             residual[kkk:kkk+1],
+                             proj_geomT, vol_geomT)
+                astra.matlab.data3d('delete', x_id)
+        else:
+            x_id, x_temp = \
+                  astra.creators.create_backprojection3d_gpu(
+                      residual, proj_geom, vol_geom)            
+
+        X = X_t - (1/L_const) * x_temp
+        astra.matlab.data3d('delete', sino_id)
+        astra.matlab.data3d('delete', x_id)
+        
+
+        ## REGULARIZATION
+        ## SKIPPING FOR NOW
+        ## Should be simpli
+        # regularizer = fistaRecon.getParameter('regularizer')
+        # for slices:
+        # out = regularizer(input=X)
+        print ("skipping regularizer")
+
+
+        ## FINAL
+        print ("final")
+        lambdaR_L1 = fistaRecon.getParameter('ring_lambda_R_L1')
+        if lambdaR_L1 > 0:
+            fistaRecon.r = numpy.max(
+                numpy.abs(fistaRecon.r) - lambdaR_L1 , 0) * \
+                numpy.sign(fistaRecon.r)
+        t = (1 + numpy.sqrt(1 + 4 * t**2))/2
+        X_t = X + (((t_old -1)/t) * (X - X_old))
+
+        if lambdaR_L1 > 0:
+            fistaRecon.r_x = fistaRecon.r + \
+                             (((t_old-1)/t) * (fistaRecon.r - r_old))
+
+        if fistaRecon.getParameter('region_of_interest') is None:
+            string = 'Iteration Number {0} | Objective {1} \n'
+            print (string.format( i, objective[i]))
+        else:
+            ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
+                                                    'ideal_image')
+            
+            Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
+            string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
+            print (string.format(i,Resid_error[i], objective[i]))
+            
+##        if (lambdaR_L1 > 0)
+##            r =  max(abs(r)-lambdaR_L1, 0).*sign(r); % soft-thresholding operator for ring vector
+##        end
+##        
+##        t = (1 + sqrt(1 + 4*t^2))/2; % updating t
+##        X_t = X + ((t_old-1)/t).*(X - X_old); % updating X
+##        
+##        if (lambdaR_L1 > 0)
+##            r_x = r + ((t_old-1)/t).*(r - r_old); % updating r
+##        end
+##        
+##        if (show == 1)
+##            figure(10); imshow(X(:,:,slice), [0 maxvalplot]);
+##            if (lambdaR_L1 > 0)
+##                figure(11); plot(r); title('Rings offset vector')
+##            end
+##            pause(0.01);
+##        end
+##        if (strcmp(X_ideal, 'none' ) == 0)
+##            Resid_error(i) = RMSE(X(ROI), X_ideal(ROI));
+##            fprintf('%s %i %s %s %.4f  %s %s %f \n', 'Iteration Number:', i, '|', 'Error RMSE:', Resid_error(i), '|', 'Objective:', objective(i));
+##        else
+##            fprintf('%s %i  %s %s %f \n', 'Iteration Number:', i, '|', 'Objective:', objective(i));
+##        end
+else:
+    fistaRecon = FISTAReconstructor(proj_geom,
+                                vol_geom,
+                                Sino3D ,
+                                weights=Weights3D)
+
+    print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+    fistaRecon.setParameter(number_of_iterations = 12)
+    fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
+    fistaRecon.setParameter(ring_alpha = 21)
+    fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
+    fistaRecon.prepareForIteration()
+    X = fistaRecon.iterate(numpy.load("X.npy"))
+    numpy.save("X_out.npy", X)
diff --git a/src/Python/test/test_regularizers.py b/src/Python/test/test_regularizers.py
new file mode 100644
index 0000000..27e4ed3
--- /dev/null
+++ b/src/Python/test/test_regularizers.py
@@ -0,0 +1,412 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Aug  4 11:10:05 2017
+
+@author: ofn77899
+"""
+
+#from ccpi.viewer.CILViewer2D import Converter
+#import vtk
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os    
+from enum import Enum
+import timeit
+#from PIL import Image
+#from Regularizer import Regularizer
+from ccpi.imaging.Regularizer import Regularizer
+
+###############################################################################
+#https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956
+#NRMSE a normalization of the root of the mean squared error
+#NRMSE is simply 1 - [RMSE / (maxval - minval)]. Where maxval is the maximum
+# intensity from the two images being compared, and respectively the same for
+# minval. RMSE is given by the square root of MSE: 
+# sqrt[(sum(A - B) ** 2) / |A|],
+# where |A| means the number of elements in A. By doing this, the maximum value
+# given by RMSE is maxval.
+
+def nrmse(im1, im2):
+    a, b = im1.shape
+    rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b))
+    max_val = max(np.max(im1), np.max(im2))
+    min_val = min(np.min(im1), np.min(im2))
+    return 1 - (rmse / (max_val - min_val))
+###############################################################################
+
+###############################################################################
+#
+#  2D Regularizers
+#
+###############################################################################
+#Example:
+# figure;
+# Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
+# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+
+
+#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
+filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif"
+#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif'
+
+#reader = vtk.vtkTIFFReader()
+#reader.SetFileName(os.path.normpath(filename))
+#reader.Update()
+Im = plt.imread(filename)                     
+#Im = Image.open('/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif')/255
+#img.show()
+Im = np.asarray(Im, dtype='float32')
+
+
+
+
+#imgplot = plt.imshow(Im)
+perc = 0.05
+u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
+# map the u0 u0->u0>0
+f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = f(u0).astype('float32')
+
+## plot 
+fig = plt.figure()
+#a=fig.add_subplot(3,3,1)
+#a.set_title('Original')
+#imgplot = plt.imshow(Im)
+
+a=fig.add_subplot(2,3,1)
+a.set_title('noise')
+imgplot = plt.imshow(u0,cmap="gray")
+
+reg_output = []
+##############################################################################
+# Call regularizer
+
+####################### SplitBregman_TV #####################################
+# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+
+use_object = True
+if use_object:
+    reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+    print (reg.pars)
+    reg.setParameter(input=u0)    
+    reg.setParameter(regularization_parameter=10.)
+    # or 
+    # reg.setParameter(input=u0, regularization_parameter=10., #number_of_iterations=30,
+              #tolerance_constant=1e-4, 
+              #TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+    plotme = reg() [0]
+    pars = reg.pars
+    textstr = reg.printParametersToString() 
+    
+    #out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
+              #tolerance_constant=1e-4, 
+    #          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+    
+#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
+#          tolerance_constant=1e-4, 
+#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+
+else:
+    out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
+    pars = out2[2]
+    reg_output.append(out2)
+    plotme = reg_output[-1][0]
+    textstr = out2[-1]
+
+a=fig.add_subplot(2,3,2)
+
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(plotme,cmap="gray")
+
+###################### FGP_TV #########################################
+# u = FGP_TV(single(u0), 0.05, 100, 1e-04);
+out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.0005,
+                          number_of_iterations=50)
+pars = out2[-2]
+
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,3)
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0])
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
+
+###################### LLT_model #########################################
+# * u0 = Im + .03*randn(size(Im)); % adding noise
+# [Den] = LLT_model(single(u0), 10, 0.1, 1);
+#Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
+#input, regularization_parameter , time_step, number_of_iterations,
+#                  tolerance_constant, restrictive_Z_smoothing=0
+out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
+                          time_step=0.0003,
+                          tolerance_constant=0.0001,
+                          number_of_iterations=300)
+pars = out2[-2]
+
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,4)
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
+
+
+# ###################### PatchBased_Regul #########################################
+# # Quick 2D denoising example in Matlab:   
+# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+# #   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
+
+out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
+                       searching_window_ratio=3,
+                       similarity_window_ratio=1,
+                       PB_filtering_parameter=0.08)
+pars = out2[-2]
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,5)
+
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
+
+
+# ###################### TGV_PD #########################################
+# # Quick 2D denoising example in Matlab:   
+# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+# #   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
+
+
+out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
+                           first_order_term=1.3,
+                           second_order_term=1,
+                           number_of_iterations=550)
+pars = out2[-2]
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,6)
+
+
+textstr = out2[-1]
+
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
+
+
+plt.show()
+
+################################################################################
+##
+##  3D Regularizers
+##
+################################################################################
+##Example:
+## figure;
+## Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+## u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
+## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+#
+##filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha"
+#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha"
+#
+#reader = vtk.vtkMetaImageReader()
+#reader.SetFileName(os.path.normpath(filename))
+#reader.Update()
+##vtk returns 3D images, let's take just the one slice there is as 2D
+#Im = Converter.vtk2numpy(reader.GetOutput())
+#Im = Im.astype('float32')
+##imgplot = plt.imshow(Im)
+#perc = 0.05
+#u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
+## map the u0 u0->u0>0
+#f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+#u0 = f(u0).astype('float32')
+#converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(),
+#                                        reader.GetOutput().GetOrigin())
+#converter.Update()
+#writer = vtk.vtkMetaImageWriter()
+#writer.SetInputData(converter.GetOutput())
+#writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha")
+##writer.Write()
+#
+#
+### plot 
+#fig3D = plt.figure()
+##a=fig.add_subplot(3,3,1)
+##a.set_title('Original')
+##imgplot = plt.imshow(Im)
+#sliceNo = 32
+#
+#a=fig3D.add_subplot(2,3,1)
+#a.set_title('noise')
+#imgplot = plt.imshow(u0.T[sliceNo])
+#
+#reg_output3d = []
+#
+###############################################################################
+## Call regularizer
+#
+######################## SplitBregman_TV #####################################
+## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+#
+##reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+#
+##out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
+##          #tolerance_constant=1e-4, 
+##          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+#
+#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
+#          tolerance_constant=1e-4, 
+#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+#
+#
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### FGP_TV #########################################
+## u = FGP_TV(single(u0), 0.05, 100, 1e-04);
+#out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005,
+#                          number_of_iterations=200)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### LLT_model #########################################
+## * u0 = Im + .03*randn(size(Im)); % adding noise
+## [Den] = LLT_model(single(u0), 10, 0.1, 1);
+##Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
+##input, regularization_parameter , time_step, number_of_iterations,
+##                  tolerance_constant, restrictive_Z_smoothing=0
+#out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
+#                          time_step=0.0003,
+#                          tolerance_constant=0.0001,
+#                          number_of_iterations=300)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### PatchBased_Regul #########################################
+## Quick 2D denoising example in Matlab:   
+##   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+##   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+##   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
+#
+#out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
+#                          searching_window_ratio=3,
+#                          similarity_window_ratio=1,
+#                          PB_filtering_parameter=0.08)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+
+###################### TGV_PD #########################################
+# Quick 2D denoising example in Matlab:   
+#   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+#   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+#   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
+
+
+#out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
+#                          first_order_term=1.3,
+#                          second_order_term=1,
+#                          number_of_iterations=550)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
diff --git a/src/Python/test/test_regularizers_3d.py b/src/Python/test/test_regularizers_3d.py
new file mode 100644
index 0000000..2d11a7e
--- /dev/null
+++ b/src/Python/test/test_regularizers_3d.py
@@ -0,0 +1,425 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Aug  4 11:10:05 2017
+
+@author: ofn77899
+"""
+
+#from ccpi.viewer.CILViewer2D import Converter
+#import vtk
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os    
+from enum import Enum
+import timeit
+#from PIL import Image
+#from Regularizer import Regularizer
+from ccpi.imaging.Regularizer import Regularizer
+
+###############################################################################
+#https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956
+#NRMSE a normalization of the root of the mean squared error
+#NRMSE is simply 1 - [RMSE / (maxval - minval)]. Where maxval is the maximum
+# intensity from the two images being compared, and respectively the same for
+# minval. RMSE is given by the square root of MSE: 
+# sqrt[(sum(A - B) ** 2) / |A|],
+# where |A| means the number of elements in A. By doing this, the maximum value
+# given by RMSE is maxval.
+
+def nrmse(im1, im2):
+    a, b = im1.shape
+    rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b))
+    max_val = max(np.max(im1), np.max(im2))
+    min_val = min(np.min(im1), np.min(im2))
+    return 1 - (rmse / (max_val - min_val))
+###############################################################################
+
+###############################################################################
+#
+#  2D Regularizers
+#
+###############################################################################
+#Example:
+# figure;
+# Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
+# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+
+
+#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
+filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif"
+#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif'
+
+#reader = vtk.vtkTIFFReader()
+#reader.SetFileName(os.path.normpath(filename))
+#reader.Update()
+Im = plt.imread(filename)                     
+#Im = Image.open('/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif')/255
+#img.show()
+Im = np.asarray(Im, dtype='float32')
+
+# create a 3D image by stacking N of this images
+
+
+#imgplot = plt.imshow(Im)
+perc = 0.05
+u_n = Im + (perc* np.random.normal(size=np.shape(Im)))
+y,z = np.shape(u_n)
+u_n = np.reshape(u_n , (1,y,z))
+
+u0 = u_n.copy()
+for i in range (19):
+    u_n = Im + (perc* np.random.normal(size=np.shape(Im)))
+    u_n = np.reshape(u_n , (1,y,z))
+
+    u0 = np.vstack ( (u0, u_n) )
+
+# map the u0 u0->u0>0
+f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = f(u0).astype('float32')
+
+print ("Passed image shape {0}".format(np.shape(u0)))
+
+## plot 
+fig = plt.figure()
+#a=fig.add_subplot(3,3,1)
+#a.set_title('Original')
+#imgplot = plt.imshow(Im)
+sliceno = 10
+
+a=fig.add_subplot(2,3,1)
+a.set_title('noise')
+imgplot = plt.imshow(u0[sliceno],cmap="gray")
+
+reg_output = []
+##############################################################################
+# Call regularizer
+
+####################### SplitBregman_TV #####################################
+# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+
+use_object = True
+if use_object:
+    reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+    print (reg.pars)
+    reg.setParameter(input=u0)    
+    reg.setParameter(regularization_parameter=10.)
+    # or 
+    # reg.setParameter(input=u0, regularization_parameter=10., #number_of_iterations=30,
+              #tolerance_constant=1e-4, 
+              #TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+    plotme = reg() [0]
+    pars = reg.pars
+    textstr = reg.printParametersToString() 
+    
+    #out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
+              #tolerance_constant=1e-4, 
+    #          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+    
+#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
+#          tolerance_constant=1e-4, 
+#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+
+else:
+    out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
+    pars = out2[2]
+    reg_output.append(out2)
+    plotme = reg_output[-1][0]
+    textstr = out2[-1]
+
+a=fig.add_subplot(2,3,2)
+
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(plotme[sliceno],cmap="gray")
+
+###################### FGP_TV #########################################
+# u = FGP_TV(single(u0), 0.05, 100, 1e-04);
+out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.0005,
+                          number_of_iterations=50)
+pars = out2[-2]
+
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,3)
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno])
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
+
+###################### LLT_model #########################################
+# * u0 = Im + .03*randn(size(Im)); % adding noise
+# [Den] = LLT_model(single(u0), 10, 0.1, 1);
+#Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
+#input, regularization_parameter , time_step, number_of_iterations,
+#                  tolerance_constant, restrictive_Z_smoothing=0
+out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
+                          time_step=0.0003,
+                          tolerance_constant=0.0001,
+                          number_of_iterations=300)
+pars = out2[-2]
+
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,4)
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
+
+
+# ###################### PatchBased_Regul #########################################
+# # Quick 2D denoising example in Matlab:   
+# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+# #   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
+
+out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
+                       searching_window_ratio=3,
+                       similarity_window_ratio=1,
+                       PB_filtering_parameter=0.08)
+pars = out2[-2]
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,5)
+
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
+
+
+# ###################### TGV_PD #########################################
+# # Quick 2D denoising example in Matlab:   
+# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+# #   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
+
+
+out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
+                           first_order_term=1.3,
+                           second_order_term=1,
+                           number_of_iterations=550)
+pars = out2[-2]
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,6)
+
+
+textstr = out2[-1]
+
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
+
+
+plt.show()
+
+################################################################################
+##
+##  3D Regularizers
+##
+################################################################################
+##Example:
+## figure;
+## Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+## u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
+## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+#
+##filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha"
+#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha"
+#
+#reader = vtk.vtkMetaImageReader()
+#reader.SetFileName(os.path.normpath(filename))
+#reader.Update()
+##vtk returns 3D images, let's take just the one slice there is as 2D
+#Im = Converter.vtk2numpy(reader.GetOutput())
+#Im = Im.astype('float32')
+##imgplot = plt.imshow(Im)
+#perc = 0.05
+#u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
+## map the u0 u0->u0>0
+#f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+#u0 = f(u0).astype('float32')
+#converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(),
+#                                        reader.GetOutput().GetOrigin())
+#converter.Update()
+#writer = vtk.vtkMetaImageWriter()
+#writer.SetInputData(converter.GetOutput())
+#writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha")
+##writer.Write()
+#
+#
+### plot 
+#fig3D = plt.figure()
+##a=fig.add_subplot(3,3,1)
+##a.set_title('Original')
+##imgplot = plt.imshow(Im)
+#sliceNo = 32
+#
+#a=fig3D.add_subplot(2,3,1)
+#a.set_title('noise')
+#imgplot = plt.imshow(u0.T[sliceNo])
+#
+#reg_output3d = []
+#
+###############################################################################
+## Call regularizer
+#
+######################## SplitBregman_TV #####################################
+## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+#
+##reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+#
+##out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
+##          #tolerance_constant=1e-4, 
+##          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+#
+#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
+#          tolerance_constant=1e-4, 
+#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+#
+#
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### FGP_TV #########################################
+## u = FGP_TV(single(u0), 0.05, 100, 1e-04);
+#out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005,
+#                          number_of_iterations=200)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### LLT_model #########################################
+## * u0 = Im + .03*randn(size(Im)); % adding noise
+## [Den] = LLT_model(single(u0), 10, 0.1, 1);
+##Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
+##input, regularization_parameter , time_step, number_of_iterations,
+##                  tolerance_constant, restrictive_Z_smoothing=0
+#out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
+#                          time_step=0.0003,
+#                          tolerance_constant=0.0001,
+#                          number_of_iterations=300)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### PatchBased_Regul #########################################
+## Quick 2D denoising example in Matlab:   
+##   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+##   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+##   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
+#
+#out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
+#                          searching_window_ratio=3,
+#                          similarity_window_ratio=1,
+#                          PB_filtering_parameter=0.08)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+
+###################### TGV_PD #########################################
+# Quick 2D denoising example in Matlab:   
+#   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+#   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+#   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
+
+
+#out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
+#                          first_order_term=1.3,
+#                          second_order_term=1,
+#                          number_of_iterations=550)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
diff --git a/src/Python/test_reconstructor.py b/src/Python/test_reconstructor.py
new file mode 100644
index 0000000..07668ba
--- /dev/null
+++ b/src/Python/test_reconstructor.py
@@ -0,0 +1,301 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 23 16:34:49 2017
+
+@author: ofn77899
+Based on DemoRD2.m
+"""
+
+import h5py
+import numpy
+
+from ccpi.fista.FISTAReconstructor import FISTAReconstructor
+import astra
+import matplotlib.pyplot as plt
+
+def RMSE(signal1, signal2):
+    '''RMSE Root Mean Squared Error'''
+    if numpy.shape(signal1) == numpy.shape(signal2):
+        err = (signal1 - signal2)
+        err = numpy.sum( err * err )/numpy.size(signal1);  # MSE
+        err = sqrt(err);                                   # RMSE
+        return err
+    else:
+        raise Exception('Input signals must have the same shape')
+  
+filename = r'/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/demos/DendrData.h5'
+nx = h5py.File(filename, "r")
+#getEntry(nx, '/')
+# I have exported the entries as children of /
+entries = [entry for entry in nx['/'].keys()]
+print (entries)
+
+Sino3D = numpy.asarray(nx.get('/Sino3D'), dtype="float32")
+Weights3D = numpy.asarray(nx.get('/Weights3D'), dtype="float32")
+angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
+angles_rad = numpy.asarray(nx.get('/angles_rad'), dtype="float32")
+recon_size = numpy.asarray(nx.get('/recon_size'), dtype=int)[0]
+size_det = numpy.asarray(nx.get('/size_det'), dtype=int)[0]
+slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
+
+Z_slices = 20
+det_row_count = Z_slices
+# next definition is just for consistency of naming
+det_col_count = size_det
+
+detectorSpacingX = 1.0
+detectorSpacingY = detectorSpacingX
+
+
+proj_geom = astra.creators.create_proj_geom('parallel3d',
+                                            detectorSpacingX,
+                                            detectorSpacingY,
+                                            det_row_count,
+                                            det_col_count,
+                                            angles_rad)
+
+#vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
+image_size_x = recon_size
+image_size_y = recon_size
+image_size_z = Z_slices
+vol_geom = astra.creators.create_vol_geom( image_size_x,
+                                           image_size_y,
+                                           image_size_z)
+
+## First pass the arguments to the FISTAReconstructor and test the
+## Lipschitz constant
+
+fistaRecon = FISTAReconstructor(proj_geom,
+                                vol_geom,
+                                Sino3D ,
+                                weights=Weights3D)
+
+print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+fistaRecon.setParameter(number_of_iterations = 12)
+fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
+fistaRecon.setParameter(ring_alpha = 21)
+fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
+
+## Ordered subset
+if False:
+    subsets = 16
+    angles = fistaRecon.getParameter('projector_geometry')['ProjectionAngles']
+    #binEdges = numpy.linspace(angles.min(),
+    #                          angles.max(),
+    #                          subsets + 1)
+    binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
+    # get rearranged subset indices
+    IndicesReorg = numpy.zeros((numpy.shape(angles)))
+    counterM = 0
+    for ii in range(binsDiscr.max()):
+        counter = 0
+        for jj in range(subsets):
+            curr_index = ii + jj  + counter
+            #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
+            if binsDiscr[jj] > ii:
+                if (counterM < numpy.size(IndicesReorg)):
+                    IndicesReorg[counterM] = curr_index
+                counterM = counterM + 1
+                
+            counter = counter + binsDiscr[jj] - 1
+
+
+if False:
+    print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+    print ("prepare for iteration")
+    fistaRecon.prepareForIteration()
+    
+    
+
+    print("initializing  ...")
+    if False:
+        # if X doesn't exist
+        #N = params.vol_geom.GridColCount
+        N = vol_geom['GridColCount']
+        print ("N " + str(N))
+        X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
+    else:
+        #X = fistaRecon.initialize()
+        X = numpy.load("X.npy")
+    
+    print (numpy.shape(X))
+    X_t = X.copy()
+    print ("initialized")
+    proj_geom , vol_geom, sino , \
+                      SlicesZ = fistaRecon.getParameter(['projector_geometry' ,
+                                                            'output_geometry',
+                                                            'input_sinogram',
+                                                         'SlicesZ'])
+
+    #fistaRecon.setParameter(number_of_iterations = 3)
+    iterFISTA = fistaRecon.getParameter('number_of_iterations')
+    # errors vector (if the ground truth is given)
+    Resid_error = numpy.zeros((iterFISTA));
+    # objective function values vector
+    objective = numpy.zeros((iterFISTA)); 
+
+      
+    t = 1
+
+
+    print ("starting iterations")
+##    % Outer FISTA iterations loop
+    for i in range(fistaRecon.getParameter('number_of_iterations')):
+        X_old = X.copy()
+        t_old = t
+        r_old = fistaRecon.r.copy()
+        if fistaRecon.getParameter('projector_geometry')['type'] == 'parallel' or \
+           fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat' or \
+           fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat_vec' :
+            # if the geometry is parallel use slice-by-slice
+            # projection-backprojection routine
+            #sino_updt = zeros(size(sino),'single');
+            proj_geomT = proj_geom.copy()
+            proj_geomT['DetectorRowCount'] = 1
+            vol_geomT = vol_geom.copy()
+            vol_geomT['GridSliceCount'] = 1;
+            sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
+            for kkk in range(SlicesZ):
+                sino_id, sino_updt[kkk] = \
+                         astra.creators.create_sino3d_gpu(
+                             X_t[kkk:kkk+1], proj_geom, vol_geom)
+                astra.matlab.data3d('delete', sino_id)
+        else:
+            # for divergent 3D geometry (watch the GPU memory overflow in
+            # ASTRA versions < 1.8)
+            #[sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
+            sino_id, sino_updt = astra.creators.create_sino3d_gpu(
+                X_t, proj_geom, vol_geom)
+
+        ## RING REMOVAL
+        residual = fistaRecon.residual
+        lambdaR_L1 , alpha_ring , weights , L_const= \
+                   fistaRecon.getParameter(['ring_lambda_R_L1',
+                                           'ring_alpha' , 'weights',
+                                           'Lipschitz_constant'])
+        r_x = fistaRecon.r_x
+        SlicesZ, anglesNumb, Detectors = \
+                    numpy.shape(fistaRecon.getParameter('input_sinogram'))
+        if lambdaR_L1 > 0 :
+             print ("ring removal")
+             for kkk in range(anglesNumb):
+                 
+                 residual[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
+                                       ((sino_updt[:,kkk,:]).squeeze() - \
+                                        (sino[:,kkk,:]).squeeze() -\
+                                        (alpha_ring * r_x)
+                                        )
+             vec = residual.sum(axis = 1)
+             #if SlicesZ > 1:
+             #    vec = vec[:,1,:].squeeze()
+             fistaRecon.r = (r_x - (1./L_const) * vec).copy()
+             objective[i] = (0.5 * (residual ** 2).sum())
+##            % the ring removal part (Group-Huber fidelity)
+##            for kkk = 1:anglesNumb
+##                residual(:,kkk,:) =  squeeze(weights(:,kkk,:)).*
+##                 (squeeze(sino_updt(:,kkk,:)) -
+##                 (squeeze(sino(:,kkk,:)) - alpha_ring.*r_x));
+##            end
+##            vec = sum(residual,2);
+##            if (SlicesZ > 1)
+##                vec = squeeze(vec(:,1,:));
+##            end
+##            r = r_x - (1./L_const).*vec;
+##            objective(i) = (0.5*sum(residual(:).^2)); % for the objective function output
+
+        
+
+        # Projection/Backprojection Routine
+        if fistaRecon.getParameter('projector_geometry')['type'] == 'parallel' or \
+           fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat' or\
+           fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat_vec':
+            x_temp = numpy.zeros(numpy.shape(X),dtype=numpy.float32)
+            print ("Projection/Backprojection Routine")
+            for kkk in range(SlicesZ):
+                
+                x_id, x_temp[kkk] = \
+                         astra.creators.create_backprojection3d_gpu(
+                             residual[kkk:kkk+1],
+                             proj_geomT, vol_geomT)
+                astra.matlab.data3d('delete', x_id)
+        else:
+            x_id, x_temp = \
+                  astra.creators.create_backprojection3d_gpu(
+                      residual, proj_geom, vol_geom)            
+
+        X = X_t - (1/L_const) * x_temp
+        astra.matlab.data3d('delete', sino_id)
+        astra.matlab.data3d('delete', x_id)
+        
+
+        ## REGULARIZATION
+        ## SKIPPING FOR NOW
+        ## Should be simpli
+        # regularizer = fistaRecon.getParameter('regularizer')
+        # for slices:
+        # out = regularizer(input=X)
+        print ("skipping regularizer")
+
+
+        ## FINAL
+        print ("final")
+        lambdaR_L1 = fistaRecon.getParameter('ring_lambda_R_L1')
+        if lambdaR_L1 > 0:
+            fistaRecon.r = numpy.max(
+                numpy.abs(fistaRecon.r) - lambdaR_L1 , 0) * \
+                numpy.sign(fistaRecon.r)
+        t = (1 + numpy.sqrt(1 + 4 * t**2))/2
+        X_t = X + (((t_old -1)/t) * (X - X_old))
+
+        if lambdaR_L1 > 0:
+            fistaRecon.r_x = fistaRecon.r + \
+                             (((t_old-1)/t) * (fistaRecon.r - r_old))
+
+        if fistaRecon.getParameter('region_of_interest') is None:
+            string = 'Iteration Number {0} | Objective {1} \n'
+            print (string.format( i, objective[i]))
+        else:
+            ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
+                                                    'ideal_image')
+            
+            Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
+            string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
+            print (string.format(i,Resid_error[i], objective[i]))
+            
+##        if (lambdaR_L1 > 0)
+##            r =  max(abs(r)-lambdaR_L1, 0).*sign(r); % soft-thresholding operator for ring vector
+##        end
+##        
+##        t = (1 + sqrt(1 + 4*t^2))/2; % updating t
+##        X_t = X + ((t_old-1)/t).*(X - X_old); % updating X
+##        
+##        if (lambdaR_L1 > 0)
+##            r_x = r + ((t_old-1)/t).*(r - r_old); % updating r
+##        end
+##        
+##        if (show == 1)
+##            figure(10); imshow(X(:,:,slice), [0 maxvalplot]);
+##            if (lambdaR_L1 > 0)
+##                figure(11); plot(r); title('Rings offset vector')
+##            end
+##            pause(0.01);
+##        end
+##        if (strcmp(X_ideal, 'none' ) == 0)
+##            Resid_error(i) = RMSE(X(ROI), X_ideal(ROI));
+##            fprintf('%s %i %s %s %.4f  %s %s %f \n', 'Iteration Number:', i, '|', 'Error RMSE:', Resid_error(i), '|', 'Objective:', objective(i));
+##        else
+##            fprintf('%s %i  %s %s %f \n', 'Iteration Number:', i, '|', 'Objective:', objective(i));
+##        end
+else:
+    fistaRecon = FISTAReconstructor(proj_geom,
+                                vol_geom,
+                                Sino3D ,
+                                weights=Weights3D)
+
+    print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+    fistaRecon.setParameter(number_of_iterations = 12)
+    fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
+    fistaRecon.setParameter(ring_alpha = 21)
+    fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
+    fistaRecon.prepareForIteration()
+    X = fistaRecon.iterate(numpy.load("X.npy"))
diff --git a/src/Python/test_regularizers.py b/src/Python/test_regularizers.py
new file mode 100644
index 0000000..e76262c
--- /dev/null
+++ b/src/Python/test_regularizers.py
@@ -0,0 +1,412 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Aug  4 11:10:05 2017
+
+@author: ofn77899
+"""
+
+#from ccpi.viewer.CILViewer2D import Converter
+#import vtk
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os    
+from enum import Enum
+import timeit
+#from PIL import Image
+#from Regularizer import Regularizer
+from ccpi.imaging.Regularizer import Regularizer
+
+###############################################################################
+#https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956
+#NRMSE a normalization of the root of the mean squared error
+#NRMSE is simply 1 - [RMSE / (maxval - minval)]. Where maxval is the maximum
+# intensity from the two images being compared, and respectively the same for
+# minval. RMSE is given by the square root of MSE: 
+# sqrt[(sum(A - B) ** 2) / |A|],
+# where |A| means the number of elements in A. By doing this, the maximum value
+# given by RMSE is maxval.
+
+def nrmse(im1, im2):
+    a, b = im1.shape
+    rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b))
+    max_val = max(np.max(im1), np.max(im2))
+    min_val = min(np.min(im1), np.min(im2))
+    return 1 - (rmse / (max_val - min_val))
+###############################################################################
+
+###############################################################################
+#
+#  2D Regularizers
+#
+###############################################################################
+#Example:
+# figure;
+# Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
+# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+
+
+#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
+filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif"
+#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif'
+
+#reader = vtk.vtkTIFFReader()
+#reader.SetFileName(os.path.normpath(filename))
+#reader.Update()
+Im = plt.imread(filename)                     
+#Im = Image.open('/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif')/255
+#img.show()
+Im = np.asarray(Im, dtype='float32')
+
+
+
+
+#imgplot = plt.imshow(Im)
+perc = 0.05
+u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
+# map the u0 u0->u0>0
+f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = f(u0).astype('float32')
+
+## plot 
+fig = plt.figure()
+#a=fig.add_subplot(3,3,1)
+#a.set_title('Original')
+#imgplot = plt.imshow(Im)
+
+a=fig.add_subplot(2,3,1)
+a.set_title('noise')
+imgplot = plt.imshow(u0,cmap="gray")
+
+reg_output = []
+##############################################################################
+# Call regularizer
+
+####################### SplitBregman_TV #####################################
+# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+
+use_object = True
+if use_object:
+    reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+    print (reg.pars)
+    reg.setParameter(input=u0)    
+    reg.setParameter(regularization_parameter=10.)
+    # or 
+    # reg.setParameter(input=u0, regularization_parameter=10., #number_of_iterations=30,
+              #tolerance_constant=1e-4, 
+              #TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+    plotme = reg() [0]
+    pars = reg.pars
+    textstr = reg.printParametersToString() 
+    
+    #out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
+              #tolerance_constant=1e-4, 
+    #          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+    
+#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
+#          tolerance_constant=1e-4, 
+#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+
+else:
+    out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
+    pars = out2[2]
+    reg_output.append(out2)
+    plotme = reg_output[-1][0]
+    textstr = out2[-1]
+
+a=fig.add_subplot(2,3,2)
+
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(plotme,cmap="gray")
+
+###################### FGP_TV #########################################
+# u = FGP_TV(single(u0), 0.05, 100, 1e-04);
+out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005,
+                          number_of_iterations=200)
+pars = out2[-2]
+
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,3)
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0])
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
+
+###################### LLT_model #########################################
+# * u0 = Im + .03*randn(size(Im)); % adding noise
+# [Den] = LLT_model(single(u0), 10, 0.1, 1);
+#Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
+#input, regularization_parameter , time_step, number_of_iterations,
+#                  tolerance_constant, restrictive_Z_smoothing=0
+out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
+                          time_step=0.0003,
+                          tolerance_constant=0.0001,
+                          number_of_iterations=300)
+pars = out2[-2]
+
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,4)
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
+
+
+# ###################### PatchBased_Regul #########################################
+# # Quick 2D denoising example in Matlab:   
+# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+# #   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
+
+out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
+                       searching_window_ratio=3,
+                       similarity_window_ratio=1,
+                       PB_filtering_parameter=0.08)
+pars = out2[-2]
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,5)
+
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
+
+
+# ###################### TGV_PD #########################################
+# # Quick 2D denoising example in Matlab:   
+# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+# #   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
+
+
+out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
+                           first_order_term=1.3,
+                           second_order_term=1,
+                           number_of_iterations=550)
+pars = out2[-2]
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,6)
+
+
+textstr = out2[-1]
+
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
+
+
+plt.show()
+
+################################################################################
+##
+##  3D Regularizers
+##
+################################################################################
+##Example:
+## figure;
+## Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+## u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
+## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+#
+##filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha"
+#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha"
+#
+#reader = vtk.vtkMetaImageReader()
+#reader.SetFileName(os.path.normpath(filename))
+#reader.Update()
+##vtk returns 3D images, let's take just the one slice there is as 2D
+#Im = Converter.vtk2numpy(reader.GetOutput())
+#Im = Im.astype('float32')
+##imgplot = plt.imshow(Im)
+#perc = 0.05
+#u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
+## map the u0 u0->u0>0
+#f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+#u0 = f(u0).astype('float32')
+#converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(),
+#                                        reader.GetOutput().GetOrigin())
+#converter.Update()
+#writer = vtk.vtkMetaImageWriter()
+#writer.SetInputData(converter.GetOutput())
+#writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha")
+##writer.Write()
+#
+#
+### plot 
+#fig3D = plt.figure()
+##a=fig.add_subplot(3,3,1)
+##a.set_title('Original')
+##imgplot = plt.imshow(Im)
+#sliceNo = 32
+#
+#a=fig3D.add_subplot(2,3,1)
+#a.set_title('noise')
+#imgplot = plt.imshow(u0.T[sliceNo])
+#
+#reg_output3d = []
+#
+###############################################################################
+## Call regularizer
+#
+######################## SplitBregman_TV #####################################
+## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+#
+##reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+#
+##out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
+##          #tolerance_constant=1e-4, 
+##          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+#
+#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
+#          tolerance_constant=1e-4, 
+#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+#
+#
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### FGP_TV #########################################
+## u = FGP_TV(single(u0), 0.05, 100, 1e-04);
+#out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005,
+#                          number_of_iterations=200)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### LLT_model #########################################
+## * u0 = Im + .03*randn(size(Im)); % adding noise
+## [Den] = LLT_model(single(u0), 10, 0.1, 1);
+##Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
+##input, regularization_parameter , time_step, number_of_iterations,
+##                  tolerance_constant, restrictive_Z_smoothing=0
+#out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
+#                          time_step=0.0003,
+#                          tolerance_constant=0.0001,
+#                          number_of_iterations=300)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### PatchBased_Regul #########################################
+## Quick 2D denoising example in Matlab:   
+##   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+##   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+##   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
+#
+#out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
+#                          searching_window_ratio=3,
+#                          similarity_window_ratio=1,
+#                          PB_filtering_parameter=0.08)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+
+###################### TGV_PD #########################################
+# Quick 2D denoising example in Matlab:   
+#   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+#   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+#   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
+
+
+#out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
+#                          first_order_term=1.3,
+#                          second_order_term=1,
+#                          number_of_iterations=550)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])