Merge pull request #55 from alexdaniel654/rel/v1.0.1

rel/v1.0.1

Author: alexdaniel654

.github/workflows/python_ci.yml

@@ -14,7 +14,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: [3.8, 3.9, "3.10"]
+        python-version: [3.9, "3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v2

CHANGELOG.md

@@ -1,5 +1,11 @@
 # Changelog
 
+## [1.0.1] - 2023-09-05
+
+### Fixed
+* A lot of dependency updates
+* Patched away from the security issue with scipy 1.10.0
+
 ## [1.0.0] - 2022-06-09
 
 ### Added

CITATION.cff

@@ -5,7 +5,7 @@ authors:
   given-names: "Alexander J"
   orcid: "https://orcid.org/0000-0003-2353-3283"
 title: "Segmentation Metrics"
-version: 1.0.0
+version: 1.0.1
 doi: 10.5281/zenodo.6576199
-date-released: 2022-06-09
+date-released: 2023-09-05
 url: "https://github.com/alexdaniel654/Segmentation_Metrics"

README.md

@@ -1,6 +1,7 @@
 # Segmentation Metrics
 [![Python CI](https://github.com/alexdaniel654/Segmentation_Metrics/actions/workflows/python_ci.yml/badge.svg)](https://github.com/alexdaniel654/Segmentation_Metrics/actions/workflows/python_ci.yml) 
 [![PyPI version](https://badge.fury.io/py/segmentationmetrics.svg)](https://badge.fury.io/py/segmentationmetrics)
+[![Downloads](https://static.pepy.tech/badge/segmentationmetrics)](https://pepy.tech/project/segmentationmetrics)
 [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 [![DOI](https://zenodo.org/badge/494534661.svg)](https://zenodo.org/badge/latestdoi/494534661)
 ### Volumetric binary mask segmentation accuracy metrics

requirements.txt

@@ -1,6 +1,6 @@
-absl-py==1.1.0
-numpy==1.22.4
-pandas==1.4.2
-pytest==7.1.2
-scikit-image==0.19.2
-scipy==1.8.1
+absl-py==1.4.0
+numpy==1.25.2
+pandas==2.1.0
+pytest==7.4.1
+scikit-image==0.21.0
+scipy==1.11.2

segmentationmetrics/surface_distance/metrics.py

@@ -46,7 +46,7 @@ def _check_3d_numpy_array(name, array):
 
 def _assert_is_bool_numpy_array(name, array):
   _assert_is_numpy_array(name, array)
-  if array.dtype != np.bool:
+  if array.dtype != bool:
     raise ValueError("The argument {!r} should be a numpy array of type bool, "
                      "not {}".format(name, array.dtype))
 

segmentationmetrics/tests/test_surface_distance.py

@@ -81,16 +81,16 @@ def _assert_metrics(self,
         places=places)
 
   @parameterized.parameters((
-      np.zeros([2, 2, 2], dtype=np.bool),
-      np.zeros([2, 2], dtype=np.bool),
+      np.zeros([2, 2, 2], dtype=bool),
+      np.zeros([2, 2], dtype=bool),
       [1, 1],
   ), (
-      np.zeros([2, 2], dtype=np.bool),
-      np.zeros([2, 2, 2], dtype=np.bool),
+      np.zeros([2, 2], dtype=bool),
+      np.zeros([2, 2, 2], dtype=bool),
       [1, 1],
   ), (
-      np.zeros([2, 2], dtype=np.bool),
-      np.zeros([2, 2], dtype=np.bool),
+      np.zeros([2, 2], dtype=bool),
+      np.zeros([2, 2], dtype=bool),
       [1, 1, 1],
   ))
   def test_compute_surface_distances_raises_on_incompatible_shapes(
@@ -100,12 +100,12 @@ def test_compute_surface_distances_raises_on_incompatible_shapes(
       surface_distance.compute_surface_distances(mask_gt, mask_pred, spacing_mm)
 
   @parameterized.parameters((
-      np.zeros([2], dtype=np.bool),
-      np.zeros([2], dtype=np.bool),
+      np.zeros([2], dtype=bool),
+      np.zeros([2], dtype=bool),
       [1],
   ), (
-      np.zeros([2, 2, 2, 2], dtype=np.bool),
-      np.zeros([2, 2, 2, 2], dtype=np.bool),
+      np.zeros([2, 2, 2, 2], dtype=bool),
+      np.zeros([2, 2, 2, 2], dtype=bool),
       [1, 1, 1, 1],
   ))
   def test_compute_surface_distances_raises_on_invalid_shapes(
@@ -118,8 +118,8 @@ def test_compute_surface_distances_raises_on_invalid_shapes(
 class SurfaceDistance2DTest(SurfaceDistanceTest, parameterized.TestCase):
 
   def test_on_2_pixels_2mm_away(self):
-    mask_gt = np.zeros((128, 128), np.bool)
-    mask_pred = np.zeros((128, 128), np.bool)
+    mask_gt = np.zeros((128, 128), bool)
+    mask_pred = np.zeros((128, 128), bool)
     mask_gt[50, 70] = 1
     mask_pred[50, 72] = 1
     surface_distances = surface_distance.compute_surface_distances(
@@ -160,7 +160,7 @@ def test_two_squares_shifted_by_one_pixel(self):
             [0, 0, 0, 0, 0, 0],
             [0, 0, 0, 0, 0, 0],
         ],
-        dtype=np.bool)
+        dtype=bool)
 
     mask_pred = np.asarray(
         [
@@ -171,7 +171,7 @@ def test_two_squares_shifted_by_one_pixel(self):
             [0, 0, 0, 0, 0, 0],
             [0, 0, 0, 0, 0, 0],
         ],
-        dtype=np.bool)
+        dtype=bool)
 
     vertical = 2
     horizontal = 1
@@ -236,8 +236,8 @@ def test_two_squares_shifted_by_one_pixel(self):
             mask_gt, mask_pred)))
 
   def test_empty_prediction_mask(self):
-    mask_gt = np.zeros((128, 128), np.bool)
-    mask_pred = np.zeros((128, 128), np.bool)
+    mask_gt = np.zeros((128, 128), bool)
+    mask_pred = np.zeros((128, 128), bool)
     mask_gt[50, 60] = 1
     surface_distances = surface_distance.compute_surface_distances(
         mask_gt, mask_pred, spacing_mm=(3, 2))
@@ -253,8 +253,8 @@ def test_empty_prediction_mask(self):
         expected_volumetric_dice=0.0)
 
   def test_empty_ground_truth_mask(self):
-    mask_gt = np.zeros((128, 128), np.bool)
-    mask_pred = np.zeros((128, 128), np.bool)
+    mask_gt = np.zeros((128, 128), bool)
+    mask_pred = np.zeros((128, 128), bool)
     mask_pred[50, 60] = 1
     surface_distances = surface_distance.compute_surface_distances(
         mask_gt, mask_pred, spacing_mm=(3, 2))
@@ -270,8 +270,8 @@ def test_empty_ground_truth_mask(self):
         expected_volumetric_dice=0.0)
 
   def test_both_empty_masks(self):
-    mask_gt = np.zeros((128, 128), np.bool)
-    mask_pred = np.zeros((128, 128), np.bool)
+    mask_gt = np.zeros((128, 128), bool)
+    mask_pred = np.zeros((128, 128), bool)
     surface_distances = surface_distance.compute_surface_distances(
         mask_gt, mask_pred, spacing_mm=(3, 2))
     self._assert_metrics(
@@ -289,8 +289,8 @@ def test_both_empty_masks(self):
 class SurfaceDistance3DTest(SurfaceDistanceTest):
 
   def test_on_2_pixels_2mm_away(self):
-    mask_gt = np.zeros((128, 128, 128), np.bool)
-    mask_pred = np.zeros((128, 128, 128), np.bool)
+    mask_gt = np.zeros((128, 128, 128), bool)
+    mask_pred = np.zeros((128, 128, 128), bool)
     mask_gt[50, 60, 70] = 1
     mask_pred[50, 60, 72] = 1
     surface_distances = surface_distance.compute_surface_distances(
@@ -304,8 +304,8 @@ def test_on_2_pixels_2mm_away(self):
                          expected_volumetric_dice=0.0)
 
   def test_two_cubes_shifted_by_one_pixel(self):
-    mask_gt = np.zeros((100, 100, 100), np.bool)
-    mask_pred = np.zeros((100, 100, 100), np.bool)
+    mask_gt = np.zeros((100, 100, 100), bool)
+    mask_pred = np.zeros((100, 100, 100), bool)
     mask_gt[0:50, :, :] = 1
     mask_pred[0:51, :, :] = 1
     surface_distances = surface_distance.compute_surface_distances(
@@ -320,8 +320,8 @@ def test_two_cubes_shifted_by_one_pixel(self):
         expected_volumetric_dice=0.990)
 
   def test_empty_prediction_mask(self):
-    mask_gt = np.zeros((128, 128, 128), np.bool)
-    mask_pred = np.zeros((128, 128, 128), np.bool)
+    mask_gt = np.zeros((128, 128, 128), bool)
+    mask_pred = np.zeros((128, 128, 128), bool)
     mask_gt[50, 60, 70] = 1
     surface_distances = surface_distance.compute_surface_distances(
         mask_gt, mask_pred, spacing_mm=(3, 2, 1))
@@ -335,8 +335,8 @@ def test_empty_prediction_mask(self):
         expected_volumetric_dice=0.0)
 
   def test_empty_ground_truth_mask(self):
-    mask_gt = np.zeros((128, 128, 128), np.bool)
-    mask_pred = np.zeros((128, 128, 128), np.bool)
+    mask_gt = np.zeros((128, 128, 128), bool)
+    mask_pred = np.zeros((128, 128, 128), bool)
     mask_pred[50, 60, 72] = 1
     surface_distances = surface_distance.compute_surface_distances(
         mask_gt, mask_pred, spacing_mm=(3, 2, 1))
@@ -350,8 +350,8 @@ def test_empty_ground_truth_mask(self):
         expected_volumetric_dice=0.0)
 
   def test_both_empty_masks(self):
-    mask_gt = np.zeros((128, 128, 128), np.bool)
-    mask_pred = np.zeros((128, 128, 128), np.bool)
+    mask_gt = np.zeros((128, 128, 128), bool)
+    mask_pred = np.zeros((128, 128, 128), bool)
     surface_distances = surface_distance.compute_surface_distances(
         mask_gt, mask_pred, spacing_mm=(3, 2, 1))
     self._assert_metrics(

setup.py

@@ -10,14 +10,14 @@
 
 setup(
     name="segmentationmetrics",
-    version="1.0.0",
+    version="1.0.1",
     description="Binary segmentation accuracy metrics",
     long_description=long_description,
     long_description_content_type="text/markdown",
     url="https://github.com/alexdaniel654/Segmentation_Metrics",
     license="Apache-2.0",
 
-    python_requires='>=3.8, <4',
+    python_requires='>=3.9, <4',
     packages=find_packages(),
     install_requires=requirements,
     include_package_data=True,
@@ -29,9 +29,9 @@
         'Topic :: Scientific/Engineering',
         'Environment :: Console',
         'Operating System :: OS Independent',
-        'Programming Language :: Python :: 3.8',
         'Programming Language :: Python :: 3.9',
         'Programming Language :: Python :: 3.10',
+        'Programming Language :: Python :: 3.11',
         'License :: OSI Approved :: Apache Software License',
     ],
 )