updating toml to force scikit-image 0.24

pyproject.toml

@@ -34,14 +34,14 @@ dependencies = [
     "pandas",
     "psutil",
     "rich",
-    "scikit-image",
+    "scikit-image<0.25.0",
     "scipy",
     "tqdm",
     "pywavelets",
     "setuptools",
 ]
 readme = "README.md"
-requires-python = ">= 3.8"
+requires-python = ">= 3.10"
 
 [project.optional-dependencies]
 build = ["hatch"]

test/unit/test_filters.py

@@ -3,7 +3,7 @@
 from edt import edt
 import porespy as ps
 import scipy.ndimage as spim
-from skimage.morphology import disk, ball, skeletonize
+from skimage.morphology import disk, ball
 from skimage.util import random_noise
 from scipy.stats import norm
 ps.settings.tqdm['disable'] = True
@@ -466,24 +466,33 @@ def test_chunked_func_w_ill_defined_filter(self):
                                     overlap=5)
 
     def test_prune_branches(self):
-        im = ps.generators.lattice_spheres(shape=[100, 100, 100], r=4)
+        from skimage.morphology import skeletonize
+        im = ps.generators.random_spheres([100, 100, 100], r=4, seed=0)
         skel1 = skeletonize(im)
         skel2 = ps.filters.prune_branches(skel1)
         assert skel1.sum() > skel2.sum()
 
     def test_prune_branches_n2(self):
-        im = ps.generators.lattice_spheres(shape=[100, 100, 100], r=4)
+        from skimage.morphology import skeletonize
+        im = ps.generators.random_spheres([100, 100, 100], r=4, seed=0)
         skel1 = skeletonize(im)
         skel2 = ps.filters.prune_branches(skel1, iterations=1)
         skel3 = ps.filters.prune_branches(skel1, iterations=2)
         assert skel1.sum() > skel2.sum()
-        assert skel2.sum() == skel3.sum()
+        assert skel2.sum() > skel3.sum()
+        skel4 = ps.filters.prune_branches(skel1, iterations=3)
+        assert skel3.sum() > skel4.sum()
 
     def test_apply_padded(self):
+        from skimage.morphology import skeletonize
         im = ps.generators.blobs(shape=[100, 100])
         skel1 = skeletonize(im)
-        skel2 = ps.filters.apply_padded(im=im, pad_width=20, pad_val=1,
-                                        func=skeletonize)
+        skel2 = ps.filters.apply_padded(
+            im=im,
+            pad_width=20,
+            pad_val=1,
+            func=skeletonize,
+        )
         assert (skel1.astype(bool)).sum() != (skel2.astype(bool)).sum()
 
     def test_trim_small_clusters(self):
@@ -514,7 +523,8 @@ def test_hold_peaks_algorithm(self):
 
     def test_nl_means_layered(self):
         im = ps.generators.blobs(shape=[50, 50, 50], blobiness=.5)
-        im2 = random_noise(im, seed=0)
+        np.random.seed(0)
+        im2 = random_noise(im)
         filt = ps.filters.nl_means_layered(im=im2)
         p1 = (filt[0, ...] > 0.5).sum()
         p2 = (im[0, ...]).sum()

test/unit/test_network_size_factor.py

@@ -5,8 +5,7 @@
 
 class NetworkSizeFactorTest():
     def setup_class(self):
-        np.random.seed(10)
-        im = ps.generators.blobs(shape=[50, 50, 50])
+        im = ps.generators.blobs(shape=[50, 50, 50], seed=10)
         self.im = im[:15, :15, :15]
         self.snow = ps.networks.snow2(self.im, boundary_width=0,
                                       parallelization=None)
@@ -15,8 +14,10 @@ def test_diffusive_size_factor_DNS(self):
         regions = self.snow.regions
         net = self.snow.network
         conns = net['throat.conns']
-        size_factors = ps.networks.diffusive_size_factor_DNS(regions,
-                                                             throat_conns=conns)
+        size_factors = ps.networks.diffusive_size_factor_DNS(
+            regions,
+            throat_conns=conns,
+        )
         values = np.array([1.43456123, 0.9612569, 1.22389664,
                            0.14359343, 0.18617079, 1.30144843,
                            0.22238891, 1.32222092])
@@ -27,9 +28,11 @@ def test_diffusive_size_factor_DNS_voxel_size(self):
         regions = self.snow.regions
         net = self.snow.network
         conns = net['throat.conns']
-        size_factors = ps.networks.diffusive_size_factor_DNS(regions,
-                                                             throat_conns=conns,
-                                                             voxel_size=voxel_size)
+        size_factors = ps.networks.diffusive_size_factor_DNS(
+            regions,
+            throat_conns=conns,
+            voxel_size=voxel_size,
+        )
         values = np.array([1.43456123, 0.9612569, 1.22389664,
                            0.14359343, 0.18617079, 1.30144843,
                            0.22238891, 1.32222092])*voxel_size