Refactor framework

CHANGELOG.md

@@ -3,9 +3,12 @@
     - Added SVRG and LSVRG stochastic functions (#1625)
     - Added SAG and SAGA stochastic functions (#1624)
     - Allow `SumFunction` with 1 item (#1857)
+    - Added `labels` module with `ImageDimension`, `AcquisitionDimension`, `AcquisitionType`, `AngleUnit`, `FillType` (#1692)
   - Enhancements:
     - Use ravel instead of flat in KullbackLeibler numba backend (#1874)
     - Upgrade Python wrapper (#1873, #1875)
+    - Internal refactor: Replaced string-based label checks with enum-based checks for improved type safety and consistency (#1692)
+    - Internal refactor: Separate framework into multiple files (#1692)
   - Bug fixes:
     - `ImageData` removes dimensions of size 1 from the input array. This fixes an issue where single slice reconstructions from 3D data would fail due to shape mismatches (#1885)
     - Make Binner accept accelerated=False (#1887)
@@ -37,9 +40,6 @@
     - Merged the files `BlockGeometry.py` and `BlockDataContainer.py` in `framework` to one file `block.py`. Please use `from cil.framework import BlockGeometry, BlockDataContainer` as before (#1799)
     - Bug fix in `FGP_TV` function to set the default behaviour not to enforce non-negativity (#1826).
 
-
-
-
 * 24.0.0
   - Update to new CCPi-Regularisation toolkit v24.0.0. This is a backward incompatible release of the toolkit.
   - CIL plugin support for TIGRE version v2.6

NOTICE.txt

@@ -65,6 +65,8 @@ Ashley Gillman (2024) -12
 Zeljko Kereta (2024) - 5
 Evgueni Ovtchinnikov (2024) -1 
 Georg Schramm (2024) - 13
+Joshua Hellier (2024) - 3
+Nicholas Whyatt (2024) - 1
 
 CIL Advisory Board:
 Llion Evans - 9

Wrappers/Python/cil/framework/__init__.py

@@ -16,20 +16,14 @@
 # Authors:
 # CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
 
-import numpy
-import sys
-from datetime import timedelta, datetime
-import warnings
-from functools import reduce
-from .framework import cilacc
-from .framework import DataContainer
-from .framework import ImageData, AcquisitionData
-from .framework import ImageGeometry, AcquisitionGeometry
-from .framework import VectorData, VectorGeometry
-from .framework import find_key, message
-from .framework import DataProcessor, Processor
-from .framework import AX, PixelByPixelDataProcessor, CastDataContainer
-from .block import BlockDataContainer
-from .block import BlockGeometry
-from .framework import DataOrder
-from .framework import Partitioner
+from .cilacc import cilacc
+from .acquisition_data import AcquisitionData
+from .acquisition_geometry import AcquisitionGeometry, SystemConfiguration
+from .data_container import DataContainer
+from .image_data import ImageData
+from .image_geometry import ImageGeometry
+from .vector_data import VectorData
+from .vector_geometry import VectorGeometry
+from .processors import DataProcessor, Processor, AX, PixelByPixelDataProcessor, CastDataContainer
+from .block import BlockDataContainer, BlockGeometry
+from .partitioner import Partitioner

Wrappers/Python/cil/framework/acquisition_data.py

@@ -0,0 +1,120 @@
+#  Copyright 2018 United Kingdom Research and Innovation
+#  Copyright 2018 The University of Manchester
+#
+#  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.
+#
+# Authors:
+# CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
+import numpy
+
+from .labels import AcquisitionDimension, Backend
+from .data_container import DataContainer
+from .partitioner import Partitioner
+
+
+class AcquisitionData(DataContainer, Partitioner):
+    '''DataContainer for holding 2D or 3D sinogram'''
+    __container_priority__ = 1
+
+    @property
+    def geometry(self):
+        return self._geometry
+
+    @geometry.setter
+    def geometry(self, val):
+        self._geometry = val
+
+    @property
+    def dimension_labels(self):
+        return self.geometry.dimension_labels
+
+    @dimension_labels.setter
+    def dimension_labels(self, val):
+        if val is not None:
+            raise ValueError("Unable to set the dimension_labels directly. Use geometry.set_labels() instead")
+
+    def __init__(self,
+                 array = None,
+                 deep_copy=True,
+                 geometry = None,
+                 **kwargs):
+
+        dtype = kwargs.get('dtype', numpy.float32)
+
+        if geometry is None:
+            raise AttributeError("AcquisitionData requires a geometry")
+
+        labels = kwargs.get('dimension_labels', None)
+        if labels is not None and labels != geometry.dimension_labels:
+                raise ValueError("Deprecated: 'dimension_labels' cannot be set with 'allocate()'. Use 'geometry.set_labels()' to modify the geometry before using allocate.")
+
+        if array is None:
+            array = numpy.empty(geometry.shape, dtype=dtype)
+        elif issubclass(type(array) , DataContainer):
+            array = array.as_array()
+        elif issubclass(type(array) , numpy.ndarray):
+            # remove singleton dimensions
+            array = numpy.squeeze(array)
+        else:
+            raise TypeError('array must be a CIL type DataContainer or numpy.ndarray got {}'.format(type(array)))
+
+        if array.shape != geometry.shape:
+            raise ValueError('Shape mismatch got {} expected {}'.format(array.shape, geometry.shape))
+
+        super(AcquisitionData, self).__init__(array, deep_copy, geometry=geometry,**kwargs)
+
+
+    def get_slice(self,channel=None, angle=None, vertical=None, horizontal=None, force=False):
+        '''Returns a new dataset of a single slice in the requested direction.'''
+        try:
+            geometry_new = self.geometry.get_slice(channel=channel, angle=angle, vertical=vertical, horizontal=horizontal)
+        except ValueError:
+            if force:
+                geometry_new = None
+            else:
+                raise ValueError ("Unable to return slice of requested AcquisitionData. Use 'force=True' to return DataContainer instead.")
+
+        #get new data
+        #if vertical = 'centre' slice convert to index and subset, this will interpolate 2 rows to get the center slice value
+        if vertical == 'centre':
+            dim = self.geometry.dimension_labels.index('vertical')
+
+            centre_slice_pos = (self.geometry.shape[dim]-1) / 2.
+            ind0 = int(numpy.floor(centre_slice_pos))
+            w2 = centre_slice_pos - ind0
+            out = DataContainer.get_slice(self, channel=channel, angle=angle, vertical=ind0, horizontal=horizontal)
+
+            if w2 > 0:
+                out2 = DataContainer.get_slice(self, channel=channel, angle=angle, vertical=ind0 + 1, horizontal=horizontal)
+                out = out * (1 - w2) + out2 * w2
+        else:
+            out = DataContainer.get_slice(self, channel=channel, angle=angle, vertical=vertical, horizontal=horizontal)
+
+        if len(out.shape) == 1 or geometry_new is None:
+            return out
+        else:
+            return AcquisitionData(out.array, deep_copy=False, geometry=geometry_new, suppress_warning=True)
+
+    def reorder(self, order):
+        '''
+        Reorders the data in memory as requested. This is an in-place operation.
+
+        Parameters
+        ----------
+        order: list or str
+            Ordered list of labels from self.dimension_labels, or string 'astra' or 'tigre'.
+        '''
+        if order in Backend:
+            order = AcquisitionDimension.get_order_for_engine(order, self.geometry)
+
+        super().reorder(order)