use lower_case and new naming

1 files changed, 30 insertions, 29 deletions

diff --git a/Wrappers/Python/ccpi/processors.py b/Wrappers/Python/ccpi/processors.py
index 87acf92..3010053 100755
--- a/Wrappers/Python/ccpi/processors.py
+++ b/Wrappers/Python/ccpi/processors.py
@@ -17,7 +17,8 @@
 #   See the License for the specific language governing permissions and

 #   limitations under the License

 

-from ccpi.framework import DataSetProcessor, DataSet, VolumeData, SinogramData, ImageGeometry, AcquisitionGeometry

+from ccpi.framework import DataSetProcessor, DataSet, AcquisitionData,\

+ AcquisitionGeometry

 import numpy

 import h5py

 from scipy import ndimage

@@ -25,13 +26,13 @@ from scipy import ndimage
 class Normalizer(DataSetProcessor):

     '''Normalization based on flat and dark

     

-    This processor read in a SinogramDataSet and normalises it based on 

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

     the instrument reading with and without incident photons or neutrons.

     

-    Input: SinogramDataSet

+    Input: AcquisitionData

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

                2D projection with dark field (or stack)

-    Output: SinogramDataSetn

+    Output: AcquisitionDataSetn

     '''

     

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

@@ -45,37 +46,37 @@ class Normalizer(DataSetProcessor):
         #DataSetProcessor.__init__(self, **kwargs)

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

         if not flat_field is None:

-            self.setFlatField(flat_field)

+            self.set_flat_field(flat_field)

         if not dark_field is None:

-            self.setDarkField(dark_field)

+            self.set_dark_field(dark_field)

     

-    def checkInput(self, dataset):

+    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 setDarkField(self, df):

+    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.setDarkField(df.as_array())

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

     

-    def setFlatField(self, df):

+    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.setDarkField(df.as_array())

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

     

     @staticmethod

-    def normalizeProjection(projection, flat, dark, tolerance):

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

         a = (projection - dark)

         b = (flat-dark)

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

@@ -85,7 +86,7 @@ class Normalizer(DataSetProcessor):
     

     def process(self):

         

-        projections = self.getInput()

+        projections = self.get_input()

         dark = self.dark_field

         flat = self.flat_field

         

@@ -95,7 +96,7 @@ class Normalizer(DataSetProcessor):
             

                

         a = numpy.asarray(

-                [ Normalizer.normalizeProjection(

+                [ Normalizer.normalize_projection(

                         projection, flat, dark, self.tolerance) \

                  for projection in projections.as_array() ]

                 )

@@ -108,10 +109,10 @@ class Normalizer(DataSetProcessor):
 class CenterOfRotationFinder(DataSetProcessor):

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

     

-    This processor read in a SinogramDataSet and finds the center of rotation 

+    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: SinogramDataSet

+    Input: AcquisitionDataSet

     

     Output: float. center of rotation in pixel coordinate

     '''

@@ -124,7 +125,7 @@ class CenterOfRotationFinder(DataSetProcessor):
         #DataSetProcessor.__init__(self, **kwargs)

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

     

-    def checkInput(self, dataset):

+    def check_input(self, dataset):

         if dataset.number_of_dimensions == 3:

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

                 return True

@@ -378,7 +379,7 @@ class CenterOfRotationFinder(DataSetProcessor):
     

     def process(self):

         

-        projections = self.getInput()

+        projections = self.get_input()

         

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

         

@@ -436,25 +437,25 @@ if __name__ == '__main__':
                                  pixel_num_h=numpy.shape(proj)[2],

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

                                  )    

-    sino = SinogramData( proj , geometry=parallelbeam)

+    sino = AcquisitionData( proj , geometry=parallelbeam)

     

     normalizer = Normalizer()

-    normalizer.setInput(sino)

-    normalizer.setFlatField(flat)

-    normalizer.setDarkField(dark)

-    norm = normalizer.getOutput()

+    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.normalizeProjection( p, flat, dark, 1e-5 ) 

+            [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.setInput(sino)

-    cor = cor_finder.getOutput()

+    cor_finder.set_input(sino)

+    cor = cor_finder.get_output()

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

     

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

@@ -462,9 +463,9 @@ if __name__ == '__main__':
                                  pixel_num_h=numpy.shape(proj)[2],

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

                                  )    

-    sino = SinogramData( proj , geometry=conebeam)

+    sino = AcquisitionData( proj , geometry=conebeam)

     try:

-        cor_finder.setInput(sino)

-        cor = cor_finder.getOutput()

+        cor_finder.set_input(sino)

+        cor = cor_finder.get_output()

     except ValueError as err:

         print (err)
\ No newline at end of file