Upgrade packages post 2.8 release

conda/meta.yaml

@@ -17,24 +17,24 @@ requirements:
   run:
     - python=3.10.*
     - pip
-    - astropy=5.0.*
-    - scipy=1.8.*
+    - astropy=6.0.*
+    - scipy=1.14.*
     - scikit-image=0.19.*
     - tifffile=2023.7.18
     - imagecodecs=2023.1.23
-    - numpy=1.23.*
+    - numpy=1.26.*
     - numexpr=2.8.*
-    - algotom=1.0.*
+    - algotom=1.6.*
     - tomopy=1.12.*
-    - cudatoolkit=10.2*
-    - cupy=10.2.*
-    - astra-toolbox=2.0.*
+    - cudatoolkit=11.8.*
+    - cupy=12.3.*
+    - astra-toolbox=2.0.0
     - requests=2.27.*
     - h5py=3.7.*
     - psutil=5.9.*
     - cil=24.0.*
     - ccpi-regulariser
-    - jenkspy=0.2.0
+    - jenkspy=0.4.*
     - pyqt=5.15.*
     - pyqtgraph=0.13.7
     - qt-material=2.14

docs/release_notes/next/dev-2268-upgrade-packages

@@ -0,0 +1 @@
+#2268: various python packages have been updated including astropy, scipy, nu,py, algotom, cudatoolkit, cupy, and jenkspy

mantidimaging/core/reconstruct/astra_recon.py

@@ -84,7 +84,8 @@ def _count_gpus() -> int:
         return num_gpus
 
     @staticmethod
-    def find_cor(images: ImageStack, slice_idx: int, start_cor: float, recon_params: ReconstructionParameters) -> float:
+    def find_cor(images: ImageStack, slice_idx: int, start_cor: float | np.ndarray,
+                 recon_params: ReconstructionParameters) -> float:
         """
         Find the best CoR for this slice by maximising the squared sum of the reconstructed slice.
 
@@ -96,7 +97,9 @@ def find_cor(images: ImageStack, slice_idx: int, start_cor: float, recon_params:
         def get_sumsq(image: np.ndarray) -> float:
             return np.sum(image**2)
 
-        def minimizer_function(cor: float):
+        def minimizer_function(cor: float | np.ndarray) -> float:
+            if isinstance(cor, np.ndarray):
+                cor = float(cor[0])
             return -get_sumsq(AstraRecon.single_sino(images.sino(slice_idx), ScalarCoR(cor), proj_angles, recon_params))
 
         return minimize(minimizer_function, start_cor, method='nelder-mead', tol=0.1).x[0]

mantidimaging/core/utility/data_containers.py

@@ -48,7 +48,7 @@ class ScalarCoR(SingleValue):
     __slots__ = 'value'
     value: float
 
-    def to_vec(self, detector_width: float) -> VectorCoR:
+    def to_vec(self, detector_width):
         return VectorCoR(detector_width / 2 - self.value)