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

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Sep 16, 2024
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2 changes: 1 addition & 1 deletion invesalius/data/coordinates.py
Original file line number Diff line number Diff line change
@@ -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),
4 changes: 2 additions & 2 deletions invesalius/data/imagedata_utils.py
Original file line number Diff line number Diff line change
@@ -713,10 +713,10 @@ 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
2 changes: 1 addition & 1 deletion invesalius/data/tractography.py
Original file line number Diff line number Diff line change
@@ -467,7 +467,7 @@ 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]
4 changes: 2 additions & 2 deletions invesalius/data/transformations.py
Original file line number Diff line number Diff line change
@@ -1502,7 +1502,7 @@ 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])
@@ -1816,7 +1816,7 @@ def random_vector(size):
False

"""
return numpy.random.random(size)
return numpy.random.default_rng().random(size)


def vector_product(v0, v1, axis=0):
2 changes: 1 addition & 1 deletion invesalius/gui/deep_learning_seg_dialog.py
Original file line number Diff line number Diff line change
@@ -363,7 +363,7 @@ 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))
2 changes: 1 addition & 1 deletion invesalius/gui/dialogs.py
Original file line number Diff line number Diff line change
@@ -6731,7 +6731,7 @@ 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(
6 changes: 3 additions & 3 deletions invesalius/segmentation/deep_learning/segment.py
Original file line number Diff line number Diff line change
@@ -92,7 +92,7 @@ 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):
@@ -131,7 +131,7 @@ 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(
@@ -194,7 +194,7 @@ 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")