line endings

1 files changed, 513 insertions, 513 deletions

diff --git a/Wrappers/Python/ccpi/processors.py b/Wrappers/Python/ccpi/processors.py
index 6a9057a..611c8c6 100755
--- a/Wrappers/Python/ccpi/processors.py
+++ b/Wrappers/Python/ccpi/processors.py
@@ -1,514 +1,514 @@
-# -*- 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

-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'  : flat_field, 

-                  'dark_field'  : dark_field,

-                  # 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 or\

-           dataset.number_of_dimensions == 2:

-               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), DataContainer):

-            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), DataContainer):

-            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

-    

-    @staticmethod

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

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

-        

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

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

-        ''' 

-        a = (projection - dark)

-        b = (flat-dark)

-        df = delta_flat / flat

-        dd = delta_dark / dark

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

-        return rel_norm_error

-        

-    def process(self):

-        

-        projections = self.get_input()

-        dark = self.dark_field

-        flat = self.flat_field

-        

-        if projections.number_of_dimensions == 3:

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

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

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

-                

-                   

-            a = numpy.asarray(

-                    [ Normalizer.normalize_projection(

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

-                     for projection in projections.as_array() ]

-                    )

-        elif projections.number_of_dimensions == 2:

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

-                                                flat, dark, self.tolerance)

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

-                    dimension_labels=projections.dimension_labels,

-                    geometry=projections.geometry)

-        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 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)

+# -*- 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
+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'  : flat_field, 
+                  'dark_field'  : dark_field,
+                  # 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 or\
+           dataset.number_of_dimensions == 2:
+               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), DataContainer):
+            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), DataContainer):
+            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
+    
+    @staticmethod
+    def estimate_normalised_error(projection, flat, dark, delta_flat, delta_dark):
+        '''returns the estimated relative error of the normalised projection
+        
+        n = (projection - dark) / (flat - dark)
+        Dn/n = (flat-dark + projection-dark)/((flat-dark)*(projection-dark))*(Df/f + Dd/d)
+        ''' 
+        a = (projection - dark)
+        b = (flat-dark)
+        df = delta_flat / flat
+        dd = delta_dark / dark
+        rel_norm_error = (b + a) / (b * a) * (df + dd)
+        return rel_norm_error
+        
+    def process(self):
+        
+        projections = self.get_input()
+        dark = self.dark_field
+        flat = self.flat_field
+        
+        if projections.number_of_dimensions == 3:
+            if not (projections.shape[1:] == dark.shape and \
+               projections.shape[1:] == flat.shape):
+                raise ValueError('Flats/Dark and projections size do not match.')
+                
+                   
+            a = numpy.asarray(
+                    [ Normalizer.normalize_projection(
+                            projection, flat, dark, self.tolerance) \
+                     for projection in projections.as_array() ]
+                    )
+        elif projections.number_of_dimensions == 2:
+            a = Normalizer.normalize_projection(projections.as_array(), 
+                                                flat, dark, self.tolerance)
+        y = type(projections)( a , True, 
+                    dimension_labels=projections.dimension_labels,
+                    geometry=projections.geometry)
+        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 AcquisitionDataPadder(DataProcessor):
+    '''Normalization based on flat and dark
+    
+    This processor read in a AcquisitionData and normalises it based on 
+    the instrument reading with and without incident photons or neutrons.
+    
+    Input: AcquisitionData
+    Parameter: 2D projection with flat field (or stack)
+               2D projection with dark field (or stack)
+    Output: AcquisitionDataSetn
+    '''
+    
+    def __init__(self, 
+                 center_of_rotation   = None,
+                 acquisition_geometry = None,
+                 pad_value            = 1e-5):
+        kwargs = {
+                  'acquisition_geometry' : acquisition_geometry, 
+                  'center_of_rotation'   : center_of_rotation,
+                  'pad_value'            : pad_value
+                  }
+        
+        super(AcquisitionDataPadder, self).__init__(**kwargs)
+        
+    def check_input(self, dataset):
+        if self.acquisition_geometry is None:
+            self.acquisition_geometry = dataset.geometry
+        if dataset.number_of_dimensions == 3:
+               return True
+        else:
+            raise ValueError("Expected input dimensions is 2 or 3, got {0}"\
+                             .format(dataset.number_of_dimensions))
+
+    def process(self):
+        projections = self.get_input()
+        w = projections.get_dimension_size('horizontal')
+        delta = w - 2 * self.center_of_rotation
+               
+        padded_width = int (
+                numpy.ceil(abs(delta)) + w
+                )
+        delta_pix = padded_width - w
+        
+        voxel_per_pixel = 1
+        geom = pbalg.pb_setup_geometry_from_acquisition(projections.as_array(),
+                                            self.acquisition_geometry.angles,
+                                            self.center_of_rotation,
+                                            voxel_per_pixel )
+        
+        padded_geometry = self.acquisition_geometry.clone()
+        
+        padded_geometry.pixel_num_h = geom['n_h']
+        padded_geometry.pixel_num_v = geom['n_v']
+        
+        delta_pix_h = padded_geometry.pixel_num_h - self.acquisition_geometry.pixel_num_h
+        delta_pix_v = padded_geometry.pixel_num_v - self.acquisition_geometry.pixel_num_v
+        
+        if delta_pix_h == 0:
+            delta_pix_h = delta_pix
+            padded_geometry.pixel_num_h = padded_width
+        #initialize a new AcquisitionData with values close to 0
+        out = AcquisitionData(geometry=padded_geometry)
+        out = out + self.pad_value
+        
+        
+        #pad in the horizontal-vertical plane -> slice on angles
+        if delta > 0:
+            #pad left of middle
+            command = "out.array["
+            for i in range(out.number_of_dimensions):
+                if out.dimension_labels[i] == 'horizontal':
+                    value = '{0}:{1}'.format(delta_pix_h, delta_pix_h+w)
+                    command = command + str(value)
+                else:
+                    if out.dimension_labels[i] == 'vertical' :
+                        value = '{0}:'.format(delta_pix_v)
+                        command = command + str(value)
+                    else:
+                        command = command + ":"
+                if i < out.number_of_dimensions -1:
+                    command = command + ','
+            command = command + '] = projections.array'
+            #print (command)    
+        else:
+            #pad right of middle
+            command = "out.array["
+            for i in range(out.number_of_dimensions):
+                if out.dimension_labels[i] == 'horizontal':
+                    value = '{0}:{1}'.format(0, w)
+                    command = command + str(value)
+                else:
+                    if out.dimension_labels[i] == 'vertical' :
+                        value = '{0}:'.format(delta_pix_v)
+                        command = command + str(value)
+                    else:
+                        command = command + ":"
+                if i < out.number_of_dimensions -1:
+                    command = command + ','
+            command = command + '] = projections.array'
+            #print (command)    
+            #cleaned = eval(command)
+        exec(command)
         return out
\ No newline at end of file