DataContainer agnostic on backend

Wrappers/Python/cil/framework/acquisition_data.py

@@ -20,7 +20,8 @@
 from .labels import AcquisitionDimension, Backend
 from .data_container import DataContainer
 from .partitioner import Partitioner
-
+import array_api_compat
+from array_api_compat import array_namespace # https://data-apis.org/array-api-compat/
 
 class AcquisitionData(DataContainer, Partitioner):
     """
@@ -80,20 +81,18 @@ def __init__(self,
                 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:
-            if dtype is None:
-                dtype = geometry.dtype
-            array = numpy.empty(geometry.shape, dtype)
-
+            xp = array_api_compat.numpy
+            dtype = kwargs.get('dtype', xp.float32)
+            array = xp.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)))
-
+            # xp = array_namespace(array)
+            # idx = numpy.where(numpy.asarray(array.shape) == 1)
+            # # remove singleton dimensions
+            # for axis in idx:    
+            #     array = xp.squeeze(array, axis=axis)
+            array = array.squeeze()
         if array.shape != geometry.shape:
             raise ValueError('Shape mismatch got {} expected {}'.format(array.shape, geometry.shape))
 

Wrappers/Python/cil/framework/acquisition_geometry.py

@@ -2173,8 +2173,9 @@ def allocate(self, value=0, **kwargs):
         :type dtype: numpy type, default numpy.float32
         '''
         dtype = kwargs.get('dtype', self.dtype)
-
-        out = AcquisitionData(geometry=self.copy(),
+        ng = self.copy()
+        ng.dtype = dtype
+        out = AcquisitionData(geometry=ng,
                               dtype=dtype,
                               suppress_warning=True)
 

Wrappers/Python/cil/framework/array_api_compat.py

@@ -0,0 +1,74 @@
+# Updates to array API compatibility layer for CIL
+from array_api_compat import array_namespace
+
+def expand_dims(array, axis):
+    '''Expand dimensions of an array along specified axes.
+
+    Parameters
+    ----------
+    array : array-like
+        The input array to expand.
+    axis : int or tuple of int
+        The axis or axes along which to expand the dimensions.
+
+    Returns
+    -------
+    array-like
+        The array with expanded dimensions. It may be a new array or the same array with expanded dimensions.
+
+    Raises:
+    --------
+    IndexError If provided an invalid axis position, an IndexError should be raised.
+
+    Notes:
+    This function recursively expands the dimensions of the input array along the specified axes if a list or tuple of ints is provided.
+    '''
+    xp = array_namespace(array)
+
+    if isinstance(axis, int):
+        return xp.expand_dims(array, axis=axis)
+    if len(axis) == 1:
+        return xp.expand_dims(array, axis=axis[0])
+    axis = list(axis)
+    ax = axis.pop(0)
+    if len(axis) == 1:
+        axis = axis[0]
+    return expand_dims(xp.expand_dims(array, axis=ax), axis=axis)
+
+def squeeze(array, axis=None):
+    '''squeezes the array, removing all singleton dimensions recursively
+
+    Parameters
+    ----------
+    array : array-like
+        The array to squeeze
+    axis : int or tuple of int, optional
+        The axis or axes to squeeze. If None, all singleton dimensions are removed.
+
+    Returns
+    -------
+    array-like
+        The squeezed array with all singleton dimensions removed. If the input array has no singleton dimensions, it is returned unchanged.
+    '''
+    xp = array_namespace(array)
+    # find and remove singleton dimensions
+    if axis is None:
+        s = xp.nonzero(xp.asarray(array.shape) == 1)[0]
+        axis = s.tolist()
+        if len(axis) == 1:
+            axis = axis[0]
+        elif len(axis) == 0:
+            # nothing to do
+            return array
+    if isinstance(axis, int):
+        return xp.squeeze(array, axis=axis)
+    if len(axis) == 1:
+        return xp.squeeze(array, axis=axis[0])
+
+    # process from the largest axis to the smallest
+    axis = list(axis)
+    axis.sort(reverse=True)
+    ax = axis.pop(0)
+    if len(axis) == 1:
+        axis = axis[0]
+    return squeeze(xp.squeeze(array, axis=ax), axis=axis)