Use ruff and manual coding to adapt to numpy 2 (#845)

invesalius/data/coordinates.py

@@ -621,7 +621,7 @@ def dynamic_reference(probe, reference):
     # a: rotation of plane (X, Y) around Z axis (azimuth)
     # b: rotation of plane (X', Z) around Y' axis (elevation)
     # a: rotation of plane (Y', Z') around X'' axis (roll)
-    m_rot = np.mat(
+    m_rot = np.asmatrix(
         [
             [
                 cos(a) * cos(b),

invesalius/data/imagedata_utils.py

@@ -701,10 +701,10 @@ def create_spherical_grid(radius=10, subdivision=1):
 
 
 def random_sample_sphere(radius=3, size=100):
-    uvw = np.random.normal(0, 1, (size, 3))
+    uvw = np.random.default_rng().normal(0, 1, (size, 3))
     norm = np.linalg.norm(uvw, axis=1, keepdims=True)
     # Change/remove **(1./3) to make samples more concentrated around the center
-    r = np.random.uniform(0, 1, (size, 1)) ** 1.5
+    r = np.random.default_rng().uniform(0, 1, (size, 1)) ** 1.5
     scale = radius * np.divide(r, norm)
     xyz = scale * uvw
     return xyz

invesalius/data/tractography.py

@@ -465,7 +465,7 @@ def run(self):
                 # Spherical sampling of seed coordinates ---
                 # compute the samples of a sphere centered on seed coordinate offset by the grid
                 # given in the invesalius-vtk space
-                samples = np.random.choice(coord_list_sphere.shape[1], size=100)
+                samples = np.random.default_rng().choice(coord_list_sphere.shape[1], size=100)
                 m_seed[:-1, -1] = coord_offset.copy()
                 # translate the spherical grid samples to the coil location in invesalius-vtk space
                 seed_trk_r_inv = m_seed @ coord_list_sphere[:, samples]

invesalius/data/transformations.py

@@ -1499,7 +1499,7 @@ def random_quaternion(rand=None):
 
     """
     if rand is None:
-        rand = numpy.random.rand(3)
+        rand = numpy.random.default_rng().random(3)
     else:
         assert len(rand) == 3
     r1 = numpy.sqrt(1.0 - rand[0])
@@ -1813,7 +1813,7 @@ def random_vector(size):
     False
 
     """
-    return numpy.random.random(size)
+    return numpy.random.default_rng().random(size)
 
 
 def vector_product(v0, v1, axis=0):

invesalius/gui/deep_learning_seg_dialog.py

@@ -358,7 +358,7 @@ def OnTickTimer(self, evt):
                 return
 
             progress = self.ps.get_completion()
-            if progress == np.Inf:
+            if progress == np.inf:
                 progress = 1
                 self.AfterSegment()
             progress = max(0, min(progress, 1))

invesalius/gui/dialogs.py

@@ -6735,7 +6735,7 @@ def LoadRegistration(self, evt: wx.CommandEvent) -> None:
             reader = csv.reader(file, delimiter="\t")
             content = [row for row in reader]
 
-        self.matrix_tracker_to_robot = np.vstack(list(np.float_(content)))
+        self.matrix_tracker_to_robot = np.vstack(list(np.float64(content)))
 
         # Send registration to robot.
         Publisher.sendMessage(

invesalius/segmentation/deep_learning/segment.py

@@ -88,7 +88,7 @@ def segment_keras(image, weights_file, overlap, probability_array, comm_array, p
         sums[iz:ez, iy:ey, ix:ex] += 1
 
     probability_array /= sums
-    comm_array[0] = np.Inf
+    comm_array[0] = np.inf
 
 
 def download_callback(comm_array):
@@ -127,7 +127,7 @@ def segment_torch(
             sums[iz:ez, iy:ey, ix:ex] += 1
 
     probability_array /= sums
-    comm_array[0] = np.Inf
+    comm_array[0] = np.inf
 
 
 def segment_torch_jit(
@@ -190,7 +190,7 @@ def segment_torch_jit(
             probability_array, output_shape=old_shape, preserve_range=True
         )
 
-    comm_array[0] = np.Inf
+    comm_array[0] = np.inf
 
 
 ctx = multiprocessing.get_context("spawn")