initial working release with simple_demo_ccpi.py

8 files changed, 1270 insertions, 0 deletions

diff --git a/Wrappers/Python/ccpi/plugins/__init__.py b/Wrappers/Python/ccpi/plugins/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/Wrappers/Python/ccpi/plugins/__init__.py
diff --git a/Wrappers/Python/ccpi/plugins/ops.py b/Wrappers/Python/ccpi/plugins/ops.py
new file mode 100755
index 0000000..0028cf1
--- /dev/null
+++ b/Wrappers/Python/ccpi/plugins/ops.py
@@ -0,0 +1,102 @@
+# -*- 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 2018 Jakob Jorgensen, Daniil Kazantsev and 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.

+

+import numpy

+from ccpi.optimisation.ops import Operator, PowerMethodNonsquare

+from ccpi.framework import ImageData, DataContainer

+from ccpi.plugins.processors import CCPiBackwardProjector, CCPiForwardProjector

+

+class LinearOperatorMatrix(Operator):

+    def __init__(self,A):

+        self.A = A

+        self.s1 = None   # Largest singular value, initially unknown

+        super(LinearOperatorMatrix, self).__init__()

+        

+    def direct(self,x):

+        return DataContainer(numpy.dot(self.A,x.as_array()))

+    

+    def adjoint(self,x):

+        return DataContainer(numpy.dot(self.A.transpose(),x.as_array()))

+    

+    def size(self):

+        return self.A.shape

+    

+    def get_max_sing_val(self):

+        # If unknown, compute and store. If known, simply return it.

+        if self.s1 is None:

+            self.s1 = svds(self.A,1,return_singular_vectors=False)[0]

+            return self.s1

+        else:

+            return self.s1

+        

+

+

+class CCPiProjectorSimple(Operator):

+    """ASTRA projector modified to use DataSet and geometry."""

+    def __init__(self, geomv, geomp):

+        super(CCPiProjectorSimple, self).__init__()

+        

+        # Store volume and sinogram geometries.

+        self.acquisition_geometry = geomp

+        self.volume_geometry = geomv

+        

+        self.fp = CCPiForwardProjector(image_geometry=geomv,

+                                       acquisition_geometry=geomp,

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

+        

+        self.bp = CCPiBackwardProjector(image_geometry=geomv,

+                                    acquisition_geometry=geomp,

+                                    output_axes_order=['horizontal_x','horizontal_y','vertical'])

+                

+        # Initialise empty for singular value.

+        self.s1 = None

+    

+    def direct(self, image_data):

+        self.fp.set_input(image_data)

+        out = self.fp.get_output()

+        return out

+    

+    def adjoint(self, acquisition_data):

+        self.bp.set_input(acquisition_data)

+        out = self.bp.get_output()

+        return out

+    

+    #def delete(self):

+    #    astra.data2d.delete(self.proj_id)

+    

+    def get_max_sing_val(self):

+        a = PowerMethodNonsquare(self,10)

+        self.s1 = a[0] 

+        return self.s1

+    

+    def size(self):

+        # Only implemented for 3D

+        return ( (self.acquisition_geometry.angles.size, \

+                  self.acquisition_geometry.pixel_num_v,

+                  self.acquisition_geometry.pixel_num_h), \

+                 (self.volume_geometry.voxel_num_x, \

+                  self.volume_geometry.voxel_num_y,

+                  self.volume_geometry.voxel_num_z) )

+    def create_image_data(self):

+        x0 = ImageData(geometry = self.volume_geometry, 

+                       dimension_labels=self.bp.output_axes_order)#\

+                       #.subset(['horizontal_x','horizontal_y','vertical'])

+        print (x0)

+        x0.fill(numpy.random.randn(*x0.shape))

+        return x0
\ No newline at end of file
diff --git a/Wrappers/Python/ccpi/plugins/processors.py b/Wrappers/Python/ccpi/plugins/processors.py
new file mode 100755
index 0000000..2fd3bf1
--- /dev/null
+++ b/Wrappers/Python/ccpi/plugins/processors.py
@@ -0,0 +1,792 @@
+# -*- 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 2018 Edoardo Pasca

+

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

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

+#   You may obtain a copy of the License at

+

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

+

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

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

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

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

+#   limitations under the License

+

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

+ AcquisitionGeometry, ImageGeometry, ImageData

+from ccpi.reconstruction.parallelbeam import alg as pbalg

+import numpy

+import h5py

+from scipy import ndimage

+

+import matplotlib.pyplot as plt

+

+

+class Normalizer(DataProcessor):

+    '''Normalization based on flat and dark

+    

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

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

+    

+    Input: AcquisitionData

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

+               2D projection with dark field (or stack)

+    Output: AcquisitionDataSetn

+    '''

+    

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

+        kwargs = {

+                  'flat_field'  : None, 

+                  'dark_field'  : None,

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

+                  'tolerance'   : tolerance

+                  }

+        

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

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

+        if not flat_field is None:

+            self.set_flat_field(flat_field)

+        if not dark_field is None:

+            self.set_dark_field(dark_field)

+    

+    def check_input(self, dataset):

+        if dataset.number_of_dimensions == 3:

+               return True

+        else:

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

+                             .format(dataset.number_of_dimensions))

+    

+    def set_dark_field(self, df):

+        if type(df) is numpy.ndarray:

+            if len(numpy.shape(df)) == 3:

+                raise ValueError('Dark Field should be 2D')

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

+                self.dark_field = df

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

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

+    

+    def set_flat_field(self, df):

+        if type(df) is numpy.ndarray:

+            if len(numpy.shape(df)) == 3:

+                raise ValueError('Flat Field should be 2D')

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

+                self.flat_field = df

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

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

+    

+    @staticmethod

+    def normalize_projection(projection, flat, dark, tolerance):

+        a = (projection - dark)

+        b = (flat-dark)

+        with numpy.errstate(divide='ignore', invalid='ignore'):

+            c = numpy.true_divide( a, b )

+            c[ ~ numpy.isfinite( c )] = tolerance # set to not zero if 0/0 

+        return c

+    

+    def process(self):

+        

+        projections = self.get_input()

+        dark = self.dark_field

+        flat = self.flat_field

+        

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

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

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

+            

+               

+        a = numpy.asarray(

+                [ Normalizer.normalize_projection(

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

+                 for projection in projections.as_array() ]

+                )

+        y = type(projections)( a , True, 

+                    dimension_labels=projections.dimension_labels,

+                    geometry=projections.geometry)

+        return y

+    

+

+class CenterOfRotationFinder(DataProcessor):

+    '''Processor to find the center of rotation in a parallel beam experiment

+    

+    This processor read in a AcquisitionDataSet and finds the center of rotation 

+    based on Nghia Vo's method. https://doi.org/10.1364/OE.22.019078

+    

+    Input: AcquisitionDataSet

+    

+    Output: float. center of rotation in pixel coordinate

+    '''

+    

+    def __init__(self):

+        kwargs = {

+                  

+                  }

+        

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

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

+    

+    def check_input(self, dataset):

+        if dataset.number_of_dimensions == 3:

+            if dataset.geometry.geom_type == 'parallel':

+                return True

+            else:

+                raise ValueError('{0} is suitable only for parallel beam geometry'\

+                                 .format(self.__class__.__name__))

+        else:

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

+                             .format(dataset.number_of_dimensions))

+        

+    

+    # #########################################################################

+    # Copyright (c) 2015, UChicago Argonne, LLC. All rights reserved.         #

+    #                                                                         #

+    # Copyright 2015. UChicago Argonne, LLC. This software was produced       #

+    # under U.S. Government contract DE-AC02-06CH11357 for Argonne National   #

+    # Laboratory (ANL), which is operated by UChicago Argonne, LLC for the    #

+    # U.S. Department of Energy. The U.S. Government has rights to use,       #

+    # reproduce, and distribute this software.  NEITHER THE GOVERNMENT NOR    #

+    # UChicago Argonne, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR        #

+    # ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE.  If software is     #

+    # modified to produce derivative works, such modified software should     #

+    # be clearly marked, so as not to confuse it with the version available   #

+    # from ANL.                                                               #

+    #                                                                         #

+    # Additionally, redistribution and use in source and binary forms, with   #

+    # or without modification, are permitted provided that the following      #

+    # conditions are met:                                                     #

+    #                                                                         #

+    #     * Redistributions of source code must retain the above copyright    #

+    #       notice, this list of conditions and the following disclaimer.     #

+    #                                                                         #

+    #     * Redistributions in binary form must reproduce the above copyright #

+    #       notice, this list of conditions and the following disclaimer in   #

+    #       the documentation and/or other materials provided with the        #

+    #       distribution.                                                     #

+    #                                                                         #

+    #     * Neither the name of UChicago Argonne, LLC, Argonne National       #

+    #       Laboratory, ANL, the U.S. Government, nor the names of its        #

+    #       contributors may be used to endorse or promote products derived   #

+    #       from this software without specific prior written permission.     #

+    #                                                                         #

+    # THIS SOFTWARE IS PROVIDED BY UChicago Argonne, LLC AND CONTRIBUTORS     #

+    # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT       #

+    # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS       #

+    # FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL UChicago     #

+    # Argonne, LLC OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,        #

+    # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,    #

+    # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;        #

+    # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER        #

+    # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT      #

+    # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN       #

+    # ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE         #

+    # POSSIBILITY OF SUCH DAMAGE.                                             #

+    # #########################################################################

+    

+    @staticmethod

+    def as_ndarray(arr, dtype=None, copy=False):

+        if not isinstance(arr, numpy.ndarray):

+            arr = numpy.array(arr, dtype=dtype, copy=copy)

+        return arr

+    

+    @staticmethod

+    def as_dtype(arr, dtype, copy=False):

+        if not arr.dtype == dtype:

+            arr = numpy.array(arr, dtype=dtype, copy=copy)

+        return arr

+    

+    @staticmethod

+    def as_float32(arr):

+        arr = CenterOfRotationFinder.as_ndarray(arr, numpy.float32)

+        return CenterOfRotationFinder.as_dtype(arr, numpy.float32)

+    

+    

+    

+    

+    @staticmethod

+    def find_center_vo(tomo, ind=None, smin=-40, smax=40, srad=10, step=0.5,

+                       ratio=2., drop=20):

+        """

+        Find rotation axis location using Nghia Vo's method. :cite:`Vo:14`.

+    

+        Parameters

+        ----------

+        tomo : ndarray

+            3D tomographic data.

+        ind : int, optional

+            Index of the slice to be used for reconstruction.

+        smin, smax : int, optional

+            Reference to the horizontal center of the sinogram.

+        srad : float, optional

+            Fine search radius.

+        step : float, optional

+            Step of fine searching.

+        ratio : float, optional

+            The ratio between the FOV of the camera and the size of object.

+            It's used to generate the mask.

+        drop : int, optional

+            Drop lines around vertical center of the mask.

+    

+        Returns

+        -------

+        float

+            Rotation axis location.

+            

+        Notes

+        -----

+        The function may not yield a correct estimate, if:

+        

+        - the sample size is bigger than the field of view of the camera. 

+          In this case the ``ratio`` argument need to be set larger

+          than the default of 2.0.

+        

+        - there is distortion in the imaging hardware. If there's 

+          no correction applied, the center of the projection image may 

+          yield a better estimate.

+        

+        - the sample contrast is weak. Paganin's filter need to be applied 

+          to overcome this. 

+       

+        - the sample was changed during the scan. 

+        """

+        tomo = CenterOfRotationFinder.as_float32(tomo)

+    

+        if ind is None:

+            ind = tomo.shape[1] // 2

+        _tomo = tomo[:, ind, :]

+    

+        

+    

+        # Reduce noise by smooth filters. Use different filters for coarse and fine search 

+        _tomo_cs = ndimage.filters.gaussian_filter(_tomo, (3, 1))

+        _tomo_fs = ndimage.filters.median_filter(_tomo, (2, 2))

+    

+        # Coarse and fine searches for finding the rotation center.

+        if _tomo.shape[0] * _tomo.shape[1] > 4e6:  # If data is large (>2kx2k)

+            #_tomo_coarse = downsample(numpy.expand_dims(_tomo_cs,1), level=2)[:, 0, :]

+            #init_cen = _search_coarse(_tomo_coarse, smin, smax, ratio, drop)

+            #fine_cen = _search_fine(_tomo_fs, srad, step, init_cen*4, ratio, drop)

+            init_cen = CenterOfRotationFinder._search_coarse(_tomo_cs, smin, 

+                                                             smax, ratio, drop)

+            fine_cen = CenterOfRotationFinder._search_fine(_tomo_fs, srad, 

+                                                           step, init_cen, 

+                                                           ratio, drop)

+        else:

+            init_cen = CenterOfRotationFinder._search_coarse(_tomo_cs, 

+                                                             smin, smax, 

+                                                             ratio, drop)

+            fine_cen = CenterOfRotationFinder._search_fine(_tomo_fs, srad, 

+                                                           step, init_cen, 

+                                                           ratio, drop)

+    

+        #logger.debug('Rotation center search finished: %i', fine_cen)

+        return fine_cen

+

+

+    @staticmethod

+    def _search_coarse(sino, smin, smax, ratio, drop):

+        """

+        Coarse search for finding the rotation center.

+        """

+        (Nrow, Ncol) = sino.shape

+        centerfliplr = (Ncol - 1.0) / 2.0

+    

+        # Copy the sinogram and flip left right, the purpose is to

+        # make a full [0;2Pi] sinogram

+        _copy_sino = numpy.fliplr(sino[1:])

+    

+        # This image is used for compensating the shift of sinogram 2

+        temp_img = numpy.zeros((Nrow - 1, Ncol), dtype='float32')

+        temp_img[:] = sino[-1]

+    

+        # Start coarse search in which the shift step is 1

+        listshift = numpy.arange(smin, smax + 1)

+        listmetric = numpy.zeros(len(listshift), dtype='float32')

+        mask = CenterOfRotationFinder._create_mask(2 * Nrow - 1, Ncol, 

+                                                   0.5 * ratio * Ncol, drop)

+        for i in listshift:

+            _sino = numpy.roll(_copy_sino, i, axis=1)

+            if i >= 0:

+                _sino[:, 0:i] = temp_img[:, 0:i]

+            else:

+                _sino[:, i:] = temp_img[:, i:]

+            listmetric[i - smin] = numpy.sum(numpy.abs(numpy.fft.fftshift(

+                #pyfftw.interfaces.numpy_fft.fft2(

+                #    numpy.vstack((sino, _sino)))

+                numpy.fft.fft2(numpy.vstack((sino, _sino)))

+                )) * mask)

+        minpos = numpy.argmin(listmetric)

+        return centerfliplr + listshift[minpos] / 2.0

+    

+    @staticmethod

+    def _search_fine(sino, srad, step, init_cen, ratio, drop):

+        """

+        Fine search for finding the rotation center.

+        """

+        Nrow, Ncol = sino.shape

+        centerfliplr = (Ncol + 1.0) / 2.0 - 1.0

+        # Use to shift the sinogram 2 to the raw CoR.

+        shiftsino = numpy.int16(2 * (init_cen - centerfliplr))

+        _copy_sino = numpy.roll(numpy.fliplr(sino[1:]), shiftsino, axis=1)

+        if init_cen <= centerfliplr:

+            lefttake = numpy.int16(numpy.ceil(srad + 1))

+            righttake = numpy.int16(numpy.floor(2 * init_cen - srad - 1))

+        else:

+            lefttake = numpy.int16(numpy.ceil(

+                init_cen - (Ncol - 1 - init_cen) + srad + 1))

+            righttake = numpy.int16(numpy.floor(Ncol - 1 - srad - 1))

+        Ncol1 = righttake - lefttake + 1

+        mask = CenterOfRotationFinder._create_mask(2 * Nrow - 1, Ncol1, 

+                                                   0.5 * ratio * Ncol, drop)

+        numshift = numpy.int16((2 * srad) / step) + 1

+        listshift = numpy.linspace(-srad, srad, num=numshift)

+        listmetric = numpy.zeros(len(listshift), dtype='float32')

+        factor1 = numpy.mean(sino[-1, lefttake:righttake])

+        num1 = 0

+        for i in listshift:

+            _sino = ndimage.interpolation.shift(

+                _copy_sino, (0, i), prefilter=False)

+            factor2 = numpy.mean(_sino[0,lefttake:righttake])

+            _sino = _sino * factor1 / factor2

+            sinojoin = numpy.vstack((sino, _sino))

+            listmetric[num1] = numpy.sum(numpy.abs(numpy.fft.fftshift(

+                #pyfftw.interfaces.numpy_fft.fft2(

+                #    sinojoin[:, lefttake:righttake + 1])

+                numpy.fft.fft2(sinojoin[:, lefttake:righttake + 1])

+                )) * mask)

+            num1 = num1 + 1

+        minpos = numpy.argmin(listmetric)

+        return init_cen + listshift[minpos] / 2.0

+    

+    @staticmethod

+    def _create_mask(nrow, ncol, radius, drop):

+        du = 1.0 / ncol

+        dv = (nrow - 1.0) / (nrow * 2.0 * numpy.pi)

+        centerrow = numpy.ceil(nrow / 2) - 1

+        centercol = numpy.ceil(ncol / 2) - 1

+        # added by Edoardo Pasca

+        centerrow = int(centerrow)

+        centercol = int(centercol)

+        mask = numpy.zeros((nrow, ncol), dtype='float32')

+        for i in range(nrow):

+            num1 = numpy.round(((i - centerrow) * dv / radius) / du)

+            (p1, p2) = numpy.int16(numpy.clip(numpy.sort(

+                (-num1 + centercol, num1 + centercol)), 0, ncol - 1))

+            mask[i, p1:p2 + 1] = numpy.ones(p2 - p1 + 1, dtype='float32')

+        if drop < centerrow:

+            mask[centerrow - drop:centerrow + drop + 1,

+                 :] = numpy.zeros((2 * drop + 1, ncol), dtype='float32')

+        mask[:,centercol-1:centercol+2] = numpy.zeros((nrow, 3), dtype='float32')

+        return mask

+    

+    def process(self):

+        

+        projections = self.get_input()

+        

+        cor = CenterOfRotationFinder.find_center_vo(projections.as_array())

+        

+        return cor

+

+class CCPiForwardProjector(DataProcessor):

+    '''Normalization based on flat and dark

+    

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

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

+    

+    Input: AcquisitionData

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

+               2D projection with dark field (or stack)

+    Output: AcquisitionDataSetn

+    '''

+    

+    def __init__(self,

+                 image_geometry       = None, 

+                 acquisition_geometry = None,

+                 output_axes_order    = None):

+        if output_axes_order is None:

+            # default ccpi projector image storing order

+            output_axes_order = ['angle','vertical','horizontal']

+        

+        kwargs = {

+                  'image_geometry'       : image_geometry, 

+                  'acquisition_geometry' : acquisition_geometry,

+                  'output_axes_order'    : output_axes_order,

+                  'default_image_axes_order' : ['horizontal_x','horizontal_y','vertical'],

+                  'default_acquisition_axes_order' : ['angle','vertical','horizontal'] 

+                  }

+        

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

+        

+    def check_input(self, dataset):

+        if dataset.number_of_dimensions == 3 or dataset.number_of_dimensions == 2:

+            # sort in the order that this projector needs it

+            return True

+        else:

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

+                             .format(dataset.number_of_dimensions))

+

+    def process(self):

+        

+        volume = self.get_input()

+        volume_axes = volume.get_data_axes_order(new_order=self.default_image_axes_order)

+        if not volume_axes == [0,1,2]:

+            volume.array = numpy.transpose(volume.array, volume_axes)

+        pixel_per_voxel = 1 # should be estimated from image_geometry and 

+                            # acquisition_geometry

+        if self.acquisition_geometry.geom_type == 'parallel':

+            #int msize = ndarray_volume.shape(0) > ndarray_volume.shape(1) ? ndarray_volume.shape(0) : ndarray_volume.shape(1);

+        	  #int detector_width = msize;

+            # detector_width is the max between the shape[0] and shape[1]

+            

+            

+            #double rotation_center = (double)detector_width/2.;

+        	  #int detector_height = ndarray_volume.shape(2);

+        	  

+            #int number_of_projections = ndarray_angles.shape(0);

+        	

+            ##numpy_3d pixels(reinterpret_cast<float*>(ndarray_volume.get_data()),

+		     #boost::extents[number_of_projections][detector_height][detector_width]);

+

+            pixels = pbalg.pb_forward_project(volume.as_array(), 

+                                                  self.acquisition_geometry.angles, 

+                                                  pixel_per_voxel)

+            out = AcquisitionData(geometry=self.acquisition_geometry, 

+                                  label_dimensions=self.default_acquisition_axes_order)

+            out.fill(pixels)

+            out_axes = out.get_data_axes_order(new_order=self.output_axes_order)

+            if not out_axes == [0,1,2]:

+                out.array = numpy.transpose(out.array, out_axes)

+            return out

+        else:

+            raise ValueError('Cannot process cone beam')

+

+class CCPiBackwardProjector(DataProcessor):

+    '''Backward projector

+    

+    This processor reads in a AcquisitionData and performs a backward projection, 

+    i.e. project to reconstruction space.

+    Notice that it assumes that the center of rotation is in the middle

+    of the horizontal axis: in case when that's not the case it can be chained 

+    with the AcquisitionDataPadder.

+    

+    Input: AcquisitionData

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

+               2D projection with dark field (or stack)

+    Output: AcquisitionDataSetn

+    '''

+    

+    def __init__(self, 

+                 image_geometry = None, 

+                 acquisition_geometry = None,

+                 output_axes_order=None):

+        if output_axes_order is None:

+            # default ccpi projector image storing order

+            output_axes_order = ['horizontal_x','horizontal_y','vertical']

+        kwargs = {

+                  'image_geometry'       : image_geometry, 

+                  'acquisition_geometry' : acquisition_geometry,

+                  'output_axes_order'    : output_axes_order,

+                  'default_image_axes_order' : ['horizontal_x','horizontal_y','vertical'],

+                  'default_acquisition_axes_order' : ['angle','vertical','horizontal'] 

+                  }

+        

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

+        

+    def check_input(self, dataset):

+        if dataset.number_of_dimensions == 3 or dataset.number_of_dimensions == 2:

+            #number_of_projections][detector_height][detector_width

+            

+            return True

+        else:

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

+                             .format(dataset.number_of_dimensions))

+

+    def process(self):

+        projections = self.get_input()

+        projections_axes = projections.get_data_axes_order(new_order=self.default_acquisition_axes_order)

+        if not projections_axes == [0,1,2]:

+            projections.array = numpy.transpose(projections.array, projections_axes)

+        

+        pixel_per_voxel = 1 # should be estimated from image_geometry and acquisition_geometry

+        image_geometry = ImageGeometry(voxel_num_x = self.acquisition_geometry.pixel_num_h,

+                                       voxel_num_y = self.acquisition_geometry.pixel_num_h,

+                                       voxel_num_z = self.acquisition_geometry.pixel_num_v)

+        # input centered/padded acquisitiondata

+        center_of_rotation = projections.get_dimension_size('horizontal') / 2

+        #print (center_of_rotation)

+        if self.acquisition_geometry.geom_type == 'parallel':

+            back = pbalg.pb_backward_project(

+                         projections.as_array(), 

+                         self.acquisition_geometry.angles, 

+                         center_of_rotation, 

+                         pixel_per_voxel

+                         )

+            out = ImageData(geometry=self.image_geometry, 

+                            dimension_labels=self.default_image_axes_order)

+            

+            out_axes = out.get_data_axes_order(new_order=self.output_axes_order)

+            if not out_axes == [0,1,2]:

+                back = numpy.transpose(back, out_axes)

+            out.fill(back)

+            

+            return out

+            

+        else:

+            raise ValueError('Cannot process cone beam')

+            

+class AcquisitionDataPadder(DataProcessor):

+    '''Normalization based on flat and dark

+    

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

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

+    

+    Input: AcquisitionData

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

+               2D projection with dark field (or stack)

+    Output: AcquisitionDataSetn

+    '''

+    

+    def __init__(self, 

+                 center_of_rotation   = None,

+                 acquisition_geometry = None,

+                 pad_value            = 1e-5):

+        kwargs = {

+                  'acquisition_geometry' : acquisition_geometry, 

+                  'center_of_rotation'   : center_of_rotation,

+                  'pad_value'            : pad_value

+                  }

+        

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

+        

+    def check_input(self, dataset):

+        if self.acquisition_geometry is None:

+            self.acquisition_geometry = dataset.geometry

+        if dataset.number_of_dimensions == 3:

+               return True

+        else:

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

+                             .format(dataset.number_of_dimensions))

+

+    def process(self):

+        projections = self.get_input()

+        w = projections.get_dimension_size('horizontal')

+        delta = w - 2 * self.center_of_rotation

+               

+        padded_width = int (

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

+                )

+        delta_pix = padded_width - w

+        

+        voxel_per_pixel = 1

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

+                                            self.acquisition_geometry.angles,

+                                            self.center_of_rotation,

+                                            voxel_per_pixel )

+        

+        padded_geometry = self.acquisition_geometry.clone()

+        

+        padded_geometry.pixel_num_h = geom['n_h']

+        padded_geometry.pixel_num_v = geom['n_v']

+        

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

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

+        

+        if delta_pix_h == 0:

+            delta_pix_h = delta_pix

+            padded_geometry.pixel_num_h = padded_width

+        #initialize a new AcquisitionData with values close to 0

+        out = AcquisitionData(geometry=padded_geometry)

+        out = out + self.pad_value

+        

+        

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

+        if delta > 0:

+            #pad left of middle

+            command = "out.array["

+            for i in range(out.number_of_dimensions):

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

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

+                    command = command + str(value)

+                else:

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

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

+                        command = command + str(value)

+                    else:

+                        command = command + ":"

+                if i < out.number_of_dimensions -1:

+                    command = command + ','

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

+            #print (command)    

+        else:

+            #pad right of middle

+            command = "out.array["

+            for i in range(out.number_of_dimensions):

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

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

+                    command = command + str(value)

+                else:

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

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

+                        command = command + str(value)

+                    else:

+                        command = command + ":"

+                if i < out.number_of_dimensions -1:

+                    command = command + ','

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

+            #print (command)    

+            #cleaned = eval(command)

+        exec(command)

+        return out

+        

+#class FiniteDifferentiator(DataProcessor):

+#    def __init__(self):

+#        kwargs = {

+#                  

+#                  }

+#        

+#        super(FiniteDifferentiator, self).__init__(**kwargs)

+#        

+#    def check_input(self, dataset):

+#        return True

+#    

+#    def process(self):

+#        axis = 0

+#        d1 = numpy.diff(x,n=1,axis=axis)

+#        d1 = numpy.resize(d1, numpy.shape(x))

+

+        

+

+def loadNexus(filename):

+    '''Load a dataset stored in a NeXuS file (HDF5)'''

+    ###########################################################################

+    ## Load a dataset

+    nx = h5py.File(filename, "r")

+    

+    data = nx.get('entry1/tomo_entry/data/rotation_angle')

+    angles = numpy.zeros(data.shape)

+    data.read_direct(angles)

+    

+    data = nx.get('entry1/tomo_entry/data/data')

+    stack = numpy.zeros(data.shape)

+    data.read_direct(stack)

+    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)

+    

+    return angle_proj , numpy.asarray(proj) , dark, flat

+    

+    

+    

+if __name__ == '__main__':

+    angles, proj, dark, flat = loadNexus('../../../data/24737_fd.nxs')

+    

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

+                                 angles=angles, 

+                                 pixel_num_h=numpy.shape(proj)[2],

+                                 pixel_num_v=numpy.shape(proj)[1],

+                                 )    

+    

+    dim_labels = ['angles' , 'vertical' , 'horizontal']

+    sino = AcquisitionData( proj , geometry=parallelbeam, 

+                            dimension_labels=dim_labels)

+    

+    normalizer = Normalizer()

+    normalizer.set_input(sino)

+    normalizer.set_flat_field(flat)

+    normalizer.set_dark_field(dark)

+    norm = normalizer.get_output()

+    #print ("Processor min {0} max {1}".format(norm.as_array().min(), norm.as_array().max()))

+    

+    #norm1 = numpy.asarray(

+    #        [Normalizer.normalize_projection( p, flat, dark, 1e-5 ) 

+    #       for p in proj]

+    #        )

+    

+    #print ("Numpy min {0} max {1}".format(norm1.min(), norm1.max()))

+    

+    cor_finder = CenterOfRotationFinder()

+    cor_finder.set_input(sino)

+    cor = cor_finder.get_output()

+    print ("center of rotation {0} == 86.25?".format(cor))

+    

+        

+    padder = AcquisitionDataPadder(center_of_rotation=cor, 

+                                   pad_value=0.75,

+                                   acquisition_geometry=normalizer.get_output().geometry)

+    #padder.set_input(normalizer.get_output())

+    padder.set_input_processor(normalizer)

+    

+    #print ("padder ", padder.get_output())

+    

+    volume_geometry = ImageGeometry()

+    

+    back = CCPiBackwardProjector(acquisition_geometry=parallelbeam, 

+                                 image_geometry=volume_geometry)

+    back.set_input_processor(padder)

+    #back.set_input(padder.get_output())

+    #print (back.image_geometry)

+    forw = CCPiForwardProjector(acquisition_geometry=parallelbeam, 

+                                 image_geometry=volume_geometry)

+    forw.set_input_processor(back)

+    

+

+    out  = padder.get_output()

+    fig = plt.figure()

+    # projections row

+    a = fig.add_subplot(1,4,1)    

+    a.imshow(norm.array[80])

+    a.set_title('orig')

+    a = fig.add_subplot(1,4,2)    

+    a.imshow(out.array[80])

+    a.set_title('padded')

+    

+    a = fig.add_subplot(1,4,3)    

+    a.imshow(back.get_output().as_array()[15])

+    a.set_title('back')

+    

+    a = fig.add_subplot(1,4,4)    

+    a.imshow(forw.get_output().as_array()[80])

+    a.set_title('forw')

+    

+    

+    plt.show()

+

+    

+    conebeam = AcquisitionGeometry('cone', '3D' , 

+                                 angles=angles, 

+                                 pixel_num_h=numpy.shape(proj)[2],

+                                 pixel_num_v=numpy.shape(proj)[1],

+                                 )    

+    

+    try:

+        sino2 = AcquisitionData( proj , geometry=conebeam)

+        cor_finder.set_input(sino2)

+        cor = cor_finder.get_output()

+    except ValueError as err:

+        print (err)
\ No newline at end of file
diff --git a/Wrappers/Python/conda-recipe/bld.bat b/Wrappers/Python/conda-recipe/bld.bat
new file mode 100644
index 0000000..4b4c7f5
--- /dev/null
+++ b/Wrappers/Python/conda-recipe/bld.bat
@@ -0,0 +1,10 @@
+IF NOT DEFINED CIL_VERSION (
+ECHO CIL_VERSION Not Defined.
+exit 1
+)
+
+ROBOCOPY /E "%RECIPE_DIR%\.." "%SRC_DIR%"
+
+%PYTHON% setup.py build_py
+%PYTHON% setup.py install
+if errorlevel 1 exit 1
diff --git a/Wrappers/Python/conda-recipe/build.sh b/Wrappers/Python/conda-recipe/build.sh
new file mode 100644
index 0000000..5dd97b0
--- /dev/null
+++ b/Wrappers/Python/conda-recipe/build.sh
@@ -0,0 +1,9 @@
+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 ${SRC_DIR}/ccpi/Wrappers/Python
+$PYTHON setup.py install
diff --git a/Wrappers/Python/conda-recipe/meta.yaml b/Wrappers/Python/conda-recipe/meta.yaml
new file mode 100644
index 0000000..f97de5e
--- /dev/null
+++ b/Wrappers/Python/conda-recipe/meta.yaml
@@ -0,0 +1,27 @@
+package:
+  name: ccpi-plugins
+  version: {{ environ['CIL_VERSION'] }}
+
+
+build:
+  preserve_egg_dir: False
+  script_env:
+    - CIL_VERSION   
+#  number: 0
+  
+requirements:
+  build:
+    - python
+    - setuptools
+
+  run:
+    - python
+    - numpy
+    - ccpi-framework
+    - ccpi-reconstruction
+    - matplotlib
+	
+about:
+  home: http://www.ccpi.ac.uk
+  license:  Apache 2.0 License
+  summary: 'CCPi Framework Plugins'
diff --git a/Wrappers/Python/setup.py b/Wrappers/Python/setup.py
new file mode 100644
index 0000000..f4c42e8
--- /dev/null
+++ b/Wrappers/Python/setup.py
@@ -0,0 +1,58 @@
+#!/usr/bin/env python
+# -*- 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 2018 Edoardo Pasca
+
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+
+#       http://www.apache.org/licenses/LICENSE-2.0
+
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+from distutils.core import setup
+import os
+import sys
+
+
+
+cil_version=os.environ['CIL_VERSION']
+if  cil_version == '':
+    print("Please set the environmental variable CIL_VERSION")
+    sys.exit(1)
+
+setup(
+    name="ccpi-plugins",
+    version=cil_version,
+    packages=['ccpi' , 'ccpi.plugins'],
+
+    # Project uses reStructuredText, so ensure that the docutils get
+    # installed or upgraded on the target machine
+    #install_requires=['docutils>=0.3'],
+
+#    package_data={
+#        # If any package contains *.txt or *.rst files, include them:
+#        '': ['*.txt', '*.rst'],
+#        # And include any *.msg files found in the 'hello' package, too:
+#        'hello': ['*.msg'],
+#    },
+    # zip_safe = False,
+
+    # metadata for upload to PyPI
+    author="Edoardo Pasca",
+    author_email="edoardo.pasca@stfc.ac.uk",
+    description='CCPi Core Imaging Library - Python Framework Plugins Module',
+    license="Apache v2.0",
+    keywords="Python Framework Plugins",
+    url="http://www.ccpi.ac.uk",   # project home page, if any
+
+    # could also include long_description, download_url, classifiers, etc.
+)
diff --git a/Wrappers/Python/wip/simple_demo_ccpi.py b/Wrappers/Python/wip/simple_demo_ccpi.py
new file mode 100755
index 0000000..10bb75f
--- /dev/null
+++ b/Wrappers/Python/wip/simple_demo_ccpi.py
@@ -0,0 +1,272 @@
+#import sys
+#sys.path.append("..")
+
+from ccpi.framework import ImageData , AcquisitionData, ImageGeometry, AcquisitionGeometry
+
+from ccpi.optimisation.algs import FISTA, FBPD, CGLS
+from ccpi.optimisation.funcs import Norm2sq, Norm1 , TV2D
+
+from ccpi.plugins.ops import CCPiProjectorSimple
+from ccpi.plugins.processors import CCPiForwardProjector, CCPiBackwardProjector 
+
+from ccpi.reconstruction.parallelbeam import alg as pbalg
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+test_case = 1   # 1=parallel2D, 2=cone2D, 3=parallel3D
+
+# Set up phantom
+N = 128
+vert = 1
+# Set up measurement geometry
+angles_num = 20; # angles number
+det_w = 1.0
+det_num = N
+SourceOrig = 200
+OrigDetec = 0
+
+if test_case==1:
+    angles = np.linspace(0,np.pi,angles_num,endpoint=False,dtype=np.float32)*180/np.pi
+    #nangles = angles_num
+    #angles = np.linspace(0,360, nangles, dtype=np.float32)
+
+elif test_case==2:
+    angles = np.linspace(0,2*np.pi,angles_num,endpoint=False)
+elif test_case == 3:
+    angles = np.linspace(0,np.pi,angles_num,endpoint=False)
+else:
+    NotImplemented
+
+
+def setupCCPiGeometries(voxel_num_x, voxel_num_y, voxel_num_z, angles, counter):
+    vg = ImageGeometry(voxel_num_x=voxel_num_x,voxel_num_y=voxel_num_y, voxel_num_z=voxel_num_z)
+    Phantom_ccpi = ImageData(geometry=vg,
+                        dimension_labels=['horizontal_x','horizontal_y','vertical'])
+    #.subset(['horizontal_x','horizontal_y','vertical'])
+    # ask the ccpi code what dimensions it would like
+        
+    voxel_per_pixel = 1
+    geoms = pbalg.pb_setup_geometry_from_image(Phantom_ccpi.as_array(),
+                                                angles,
+                                                voxel_per_pixel )
+    
+    pg = AcquisitionGeometry('parallel',
+                              '3D',
+                              angles,
+                              geoms['n_h'],det_w,
+                              geoms['n_v'], det_w #2D in 3D is a slice 1 pixel thick
+                              )
+    
+    center_of_rotation = Phantom_ccpi.get_dimension_size('horizontal_x') / 2
+    ad = AcquisitionData(geometry=pg,dimension_labels=['angle','vertical','horizontal'])
+    geoms_i = pbalg.pb_setup_geometry_from_acquisition(ad.as_array(),
+                                                angles,
+                                                center_of_rotation,
+                                                voxel_per_pixel )
+
+    #print (counter)
+    counter+=1
+    #print (geoms , geoms_i)
+    if counter < 4:
+        if (not ( geoms_i == geoms )):
+            print ("not equal and {0}".format(counter))
+            X = max(geoms['output_volume_x'], geoms_i['output_volume_x'])
+            Y = max(geoms['output_volume_y'], geoms_i['output_volume_y'])
+            Z = max(geoms['output_volume_z'], geoms_i['output_volume_z'])
+            return setupCCPiGeometries(X,Y,Z,angles, counter)
+        else:
+            print ("return geoms {0}".format(geoms))
+            return geoms
+    else:
+        print ("return geoms_i {0}".format(geoms_i))
+        return geoms_i
+
+geoms = setupCCPiGeometries(N,N,vert,angles,0)
+#%%
+#geoms = {'n_v': 12, 'output_volume_y': 128, 'n_angles': 20, 
+# 'output_volume_x': 128, 'output_volume_z': 12, 'n_h': 128}
+vg = ImageGeometry(voxel_num_x=geoms['output_volume_x'],
+                   voxel_num_y=geoms['output_volume_y'], 
+                   voxel_num_z=geoms['output_volume_z'])
+Phantom = ImageData(geometry=vg,dimension_labels=['horizontal_x','horizontal_y','vertical']) + 0.1
+    
+
+#x = Phantom.as_array()
+i = 0
+while i < geoms['n_v']:
+    #x = Phantom.subset(vertical=i, dimensions=['horizontal_x','horizontal_y']).array
+    x = Phantom.subset(vertical=i).array
+    x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5
+    x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 0.98
+    Phantom.fill(x, vertical=i)
+    i += 1
+
+plt.imshow(Phantom.subset(vertical=0).as_array())
+plt.show()
+
+
+
+# Parallelbeam geometry test
+if test_case==1:
+    #Phantom_ccpi = Phantom.subset(['horizontal_x','horizontal_y','vertical'])
+    #Phantom_ccpi.geometry = vg.clone()
+    center_of_rotation = Phantom.get_dimension_size('horizontal_x') / 2
+        
+    pg = AcquisitionGeometry('parallel',
+                          '3D',
+                          angles,
+                          geoms['n_h'],det_w,
+                          geoms['n_v'], det_w #2D in 3D is a slice 1 pixel thick
+                          )
+elif test_case==2:
+    raise NotImplemented('cone beam projector not yet available')
+    pg = AcquisitionGeometry('cone',
+                          '2D',
+                          angles,
+                          det_num,
+                          det_w,
+                          vert, det_w, #2D in 3D is a slice 1 pixel thick 
+                          dist_source_center=SourceOrig, 
+                          dist_center_detector=OrigDetec)
+
+# ASTRA operator using volume and sinogram geometries
+#Aop = AstraProjectorSimple(vg, pg, 'cpu')
+Cop = CCPiProjectorSimple(vg, pg)
+
+# Try forward and backprojection
+b = Cop.direct(Phantom)
+out2 = Cop.adjoint(b)
+
+#%%
+for i in range(b.get_dimension_size('vertical')):
+    plt.imshow(b.subset(vertical=i).array)
+    #plt.imshow(Phantom.subset( vertical=i).array)
+    #plt.imshow(b.array[:,i,:])
+    plt.show()
+#%%
+
+plt.imshow(out2.subset( vertical=0).array)
+plt.show()
+
+# Create least squares object instance with projector and data.
+f = Norm2sq(Cop,b,c=0.5)
+
+# Initial guess
+x_init = ImageData(geometry=vg, dimension_labels=['horizontal_x','horizontal_y','vertical'])
+#invL = 0.5
+#g = f.grad(x_init)
+#print (g)
+#u = x_init - invL*f.grad(x_init)
+        
+#%%
+# Run FISTA for least squares without regularization
+opt = {'tol': 1e-4, 'iter': 100}
+x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None, opt=opt)
+
+plt.imshow(x_fista0.subset(vertical=0).array)
+plt.title('FISTA0')
+plt.show()
+
+# Now least squares plus 1-norm regularization
+lam = 0.1
+g0 = Norm1(lam)
+
+# Run FISTA for least squares plus 1-norm function.
+x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g0,opt=opt)
+
+plt.imshow(x_fista0.subset(vertical=0).array)
+plt.title('FISTA1')
+plt.show()
+
+plt.semilogy(criter1)
+plt.show()
+
+# Run FBPD=Forward Backward Primal Dual method on least squares plus 1-norm
+x_fbpd1, it_fbpd1, timing_fbpd1, criter_fbpd1 = FBPD(x_init,None,f,g0,opt=opt)
+
+plt.imshow(x_fbpd1.subset(vertical=0).array)
+plt.title('FBPD1')
+plt.show()
+
+plt.semilogy(criter_fbpd1)
+plt.show()
+
+# Now FBPD for least squares plus TV
+#lamtv = 1
+#gtv = TV2D(lamtv)
+
+#x_fbpdtv, it_fbpdtv, timing_fbpdtv, criter_fbpdtv = FBPD(x_init,None,f,gtv,opt=opt)
+
+#plt.imshow(x_fbpdtv.subset(vertical=0).array)
+#plt.show()
+
+#plt.semilogy(criter_fbpdtv)
+#plt.show()  
+
+
+# Run CGLS, which should agree with the FISTA0
+x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Cop, b, opt=opt)
+
+plt.imshow(x_CGLS.subset(vertical=0).array)
+plt.title('CGLS')
+plt.title('CGLS recon, compare FISTA0')
+plt.show()
+
+plt.semilogy(criter_CGLS)
+plt.title('CGLS criterion')
+plt.show()
+
+
+#%%
+
+clims = (0,1)
+cols = 3
+rows = 2
+current = 1
+fig = plt.figure()
+# projections row
+a=fig.add_subplot(rows,cols,current)
+a.set_title('phantom {0}'.format(np.shape(Phantom.as_array())))
+
+imgplot = plt.imshow(Phantom.subset(vertical=0).as_array(),vmin=clims[0],vmax=clims[1])
+
+current = current + 1
+a=fig.add_subplot(rows,cols,current)
+a.set_title('FISTA0')
+imgplot = plt.imshow(x_fista0.subset(vertical=0).as_array(),vmin=clims[0],vmax=clims[1])
+
+current = current + 1
+a=fig.add_subplot(rows,cols,current)
+a.set_title('FISTA1')
+imgplot = plt.imshow(x_fista1.subset(vertical=0).as_array(),vmin=clims[0],vmax=clims[1])
+
+current = current + 1
+a=fig.add_subplot(rows,cols,current)
+a.set_title('FBPD1')
+imgplot = plt.imshow(x_fbpd1.subset(vertical=0).as_array(),vmin=clims[0],vmax=clims[1])
+
+current = current + 1
+a=fig.add_subplot(rows,cols,current)
+a.set_title('CGLS')
+imgplot = plt.imshow(x_CGLS.subset(vertical=0).as_array(),vmin=clims[0],vmax=clims[1])
+
+plt.show()
+#%%
+#current = current + 1
+#a=fig.add_subplot(rows,cols,current)
+#a.set_title('FBPD TV')
+#imgplot = plt.imshow(x_fbpdtv.subset(vertical=0).as_array(),vmin=clims[0],vmax=clims[1])
+
+fig = plt.figure()
+# projections row
+b=fig.add_subplot(1,1,1)
+b.set_title('criteria')
+imgplot = plt.loglog(criter0 , label='FISTA0')
+imgplot = plt.loglog(criter1 , label='FISTA1')
+imgplot = plt.loglog(criter_fbpd1, label='FBPD1')
+imgplot = plt.loglog(criter_CGLS, label='CGLS')
+#imgplot = plt.loglog(criter_fbpdtv, label='FBPD TV')
+b.legend(loc='right')
+plt.show()
+#%%
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