Merge pull request #1005 from PMEAL/fix_scikit_image_deprecations

Changed `skeletonize_3d` to `skeletonize` to deal with skimage deprecation, plus a few other related fixes #maint
Updated requirements in pyproject.toml to force use of skimage 0.24 because watershed in 0.25 returns different results #maint

.gitignore

@@ -163,3 +163,4 @@ examples/networks/*.vt*
 examples/**/*.vtp
 examples/**/*.tif
 *.mphtxt
+*.DS_Store

examples/filters/reference/apply_padded.ipynb

@@ -80,7 +80,7 @@
    "metadata": {},
    "source": [
     "## `func`\n",
-    "A good use case is `skeletonize_3d`, which has edge artifacts that can be reduced if the image is pre-padded. It works on 2D or 3D images."
+    "A good use case is `skeletonize`, which has edge artifacts that can be reduced if the image is pre-padded. It works on 2D or 3D images."
    ]
   },
   {
@@ -96,7 +96,7 @@
    },
    "outputs": [],
    "source": [
-    "func = skimage.morphology.skeletonize_3d"
+    "func = skimage.morphology.skeletonize"
    ]
   },
   {

examples/filters/reference/prune_branches.ipynb

@@ -25,7 +25,7 @@
     "import porespy as ps\n",
     "import scipy.ndimage as spim\n",
     "import matplotlib.pyplot as plt\n",
-    "from skimage.morphology import skeletonize_3d\n",
+    "from skimage.morphology import skeletonize\n",
     "ps.visualization.set_mpl_style()"
    ]
   },
@@ -34,7 +34,7 @@
    "metadata": {},
    "source": [
     "## `im`\n",
-    "The function requires a skeleton such as that produced by ``skimage.mophology.skeletonize_3d``:"
+    "The function requires a skeleton such as that produced by ``skimage.mophology.skeletonize``:"
    ]
   },
   {
@@ -118,7 +118,7 @@
    ],
    "source": [
     "im = ps.generators.blobs(shape=[250, 250], blobiness=1, porosity=0.6)\n",
-    "sk = skeletonize_3d(im)\n",
+    "sk = skeletonize(im)\n",
     "sk1 = ps.filters.prune_branches(sk)\n",
     "\n",
     "fig, ax = plt.subplots(1, 2, figsize=[12, 6])\n",

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"]
@@ -125,6 +125,7 @@ packages = ["src/porespy"]
 [tool.pytest.ini_options]
 minversion = "6.0"
 addopts = [
+    "--assert=plain",
     "-ra -v",
     "--ignore=docs/conf.py",
     "--ignore=examples/generators/reference/fractal_noise.ipynb",
@@ -138,6 +139,7 @@ python_functions = "test_*"
 testpaths = ["test", "examples"]
 norecursedirs = [".git", ".github", ".ipynb_checkpoints", "build", "dist"]
 
+
 [tool.coverage.run]
 source = ["src/porespy"]
 [tool.coverage.report]

src/porespy/filters/_funcs.py

@@ -169,7 +169,7 @@ def apply_padded(im, pad_width, func, pad_val=1, **kwargs):
 
     Notes
     -----
-    A use case for this is when using ``skimage.morphology.skeletonize_3d``
+    A use case for this is when using ``skimage.morphology.skeletonize``
     to ensure that the skeleton extends beyond the edges of the image.
 
     Examples

src/porespy/metrics/_regionprops.py

@@ -7,7 +7,7 @@
     from skimage.measure import marching_cubes
 except ImportError:
     from skimage.measure import marching_cubes_lewiner as marching_cubes
-from skimage.morphology import skeletonize_3d, ball
+from skimage.morphology import skeletonize, ball
 from skimage.measure import regionprops
 from skimage.measure._regionprops import RegionProperties
 from pandas import DataFrame
@@ -191,7 +191,7 @@ def regionprops_3D(im):
             as the region to the actual surface area of the region.
 
         'skeleton'
-            The medial axis of the region obtained using the ``skeletonize_3D``
+            The medial axis of the region obtained using the ``skeletonize``
             method from **skimage**.
 
         'convex_volume'
@@ -274,7 +274,7 @@ def sphericity(self):
 
     @property
     def skeleton(self):
-        return skeletonize_3d(self.mask)
+        return skeletonize(self.mask)
 
     @property
     def surface_area(self):

src/porespy/tools/_funcs.py

@@ -62,7 +62,7 @@ def unpad(im, pad_width):
 
     Notes
     -----
-    A use case for this is when using ``skimage.morphology.skeletonize_3d``
+    A use case for this is when using ``skimage.morphology.skeletonize``
     to ensure that the skeleton extends beyond the edges of the image, but the
     padding should be subsequently removed.
 

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_3d
+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)
-        skel1 = skeletonize_3d(im)
+        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)
-        skel1 = skeletonize_3d(im)
+        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_3d(im)
-        skel2 = ps.filters.apply_padded(im=im, pad_width=20, pad_val=1,
-                                        func=skeletonize_3d)
+        skel1 = skeletonize(im)
+        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

test/unit/test_parallel_filters.py

@@ -2,7 +2,7 @@
 from edt import edt
 import porespy as ps
 import scipy.ndimage as spim
-from skimage.morphology import skeletonize_3d
+from skimage.morphology import skeletonize
 ps.settings.loglevel = "CRITICAL"
 ps.settings.tqdm['disable'] = True
 

test/unit/test_simulations.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_3d
+from skimage.morphology import disk, ball, skeletonize
 from skimage.util import random_noise
 from scipy.stats import norm