Fix NumPy 2.0 compatibility by replacing removed np.alltrue with np.all

README.md

@@ -10,8 +10,17 @@ This python3 project provides tools for computing quality indicators for multipo
 The methods can also be applied to 2D or 3D images.
 
 Currently the module provides :
-- An Image class, with various import/export/conversion methods to different data types
-- Functions for evaluating connectivity, histograms and variograms of 2D and 3D categorical images.
+- **An `Image` class** (`mpstool.img`) for handling 2D and 3D categorical or continuous grids, with:
+  - Import/export to GSLIB, raw text, PNG, MagicaVoxel (`.vox`), VTK, PGM and PPM formats
+  - Transformations: thresholding (continuous → categorical), automatic categorization (1D k-means), normalization, axis flips/permutations, and random sub-sampling
+  - Visualization: 2D plots and 3D orthogonal cross-section cuts
+- **Spatial statistics** (`mpstool.stats`): histograms (facies proportions) and indicator/continuous variograms, computed with a spatial-shift method
+- **Connectivity analysis** (`mpstool.connectivity`): connectivity functions and maps describing how categories connect across distance, plus a threshold-based connectivity index (`gamma`) for continuous fields
+- **FFT-accelerated variogram maps** (`mpstool.variogram`): full 2D variogram maps computed via FFT (Marcotte, 1996), which natively handle missing data (NaN) and are much faster than the spatial-shift method on large grids; includes a variogram-comparison metric for scoring simulation quality against a reference image
+- **Cross-validation metrics** (`mpstool.cv_metrics`): probabilistic scoring rules (Brier score, CRPS, 0-1 score, linear score, and their class-balanced/skill-score variants) implementing the scikit-learn scorer interface, for cross-validating spatial simulators
+- **Command-line tools** (`tools/`): `gslib-plot.py` for quickly visualizing a `.gslib` file, and `geone_cv.py` for cross-validating the `geone`/DeeSSe multi-point simulator via `GridSearchCV`, driven by a JSON configuration file
+
+Note: `mpstool.variogram` and `mpstool.cv_metrics` are not imported automatically with `import mpstool` — import them explicitly if you need them.
 
 ## Example: connectivity function
 Connectivity function describes how different categories are connected depending on distance. It is given by: ![connectivity](assets/connectivity.png)

mpstool/img.py

@@ -104,7 +104,7 @@ def __eq__(self, other):
         for k in self._data.keys():
             if self._data[k].shape != other._data[k].shape:
                 return False
-            if not np.alltrue(self._data[k] == other._data[k]):
+            if not np.all(self._data[k] == other._data[k]):
                 return False
         return True
 

tests/test_connectivity.py

@@ -56,7 +56,7 @@ def test_threshold():
     thresholds = np.array([1.0, 2.0])
     image.threshold(thresholds)
     labels = mpstool.img.labelize(image)
-    assert np.alltrue(labels == categorical_ref)
+    assert np.all(labels == categorical_ref)
 
 
 def test_connected_component(cube):
@@ -69,7 +69,7 @@ def test_connected_component(cube):
                                         [[0, 2, 2],
                                          [0, 2, 2],
                                          [0, 0, 0]]])
-    assert np.alltrue(mpstool.connectivity.get_components(
+    assert np.all(mpstool.connectivity.get_components(
         cube, background=0) == connectivity_array_cube)
 
 
@@ -85,7 +85,7 @@ def test_get_map(array, image):
         real_map = mpstool.connectivity.get_map(ar)
         assert real_map.keys() == expected_map.keys()
         for k in expected_map.keys():
-            assert np.alltrue(real_map[k] == expected_map[k])
+            assert np.all(real_map[k] == expected_map[k])
 
 
 def test_function_2D(array, image):
@@ -97,9 +97,9 @@ def test_function_2D(array, image):
         axis1_result = mpstool.connectivity.get_function(ar, axis=1)
 
         for key in axis0_connectivity:
-            assert np.alltrue(axis0_result[key] == axis0_connectivity[key])
+            assert np.all(axis0_result[key] == axis0_connectivity[key])
         for key in axis1_connectivity:
-            assert np.alltrue(axis1_result[key] == axis1_connectivity[key])
+            assert np.all(axis1_result[key] == axis1_connectivity[key])
 
 
 def test_truncated_function_2D(array, image):
@@ -111,9 +111,9 @@ def test_truncated_function_2D(array, image):
         axis1_result = mpstool.connectivity.get_function(ar, axis=1, max_lag=1)
 
         for key in axis0_connectivity:
-            assert np.alltrue(axis0_result[key] == axis0_connectivity[key])
+            assert np.all(axis0_result[key] == axis0_connectivity[key])
         for key in axis1_connectivity:
-            assert np.alltrue(axis1_result[key] == axis1_connectivity[key])
+            assert np.all(axis1_result[key] == axis1_connectivity[key])
 
 
 def test_function_3D(extruded_array):
@@ -126,9 +126,9 @@ def test_function_3D(extruded_array):
     axis2_result = mpstool.connectivity.get_function(extruded_array, axis=2)
 
     for key in axis0_connectivity:
-        assert np.alltrue(axis0_result[key] == axis0_connectivity[key])
-        assert np.alltrue(axis1_result[key] == axis1_connectivity[key])
-        assert np.alltrue(axis2_result[key] == axis2_connectivity[key])
+        assert np.all(axis0_result[key] == axis0_connectivity[key])
+        assert np.all(axis1_result[key] == axis1_connectivity[key])
+        assert np.all(axis2_result[key] == axis2_connectivity[key])
 
 
 @pytest.fixture
@@ -149,9 +149,9 @@ def test_apply_threshold(c_image):
 
     result2 = np.ones_like(c_image)
 
-    assert np.alltrue(
+    assert np.all(
         result1 == mpstool.connectivity._apply_threshold(c_image, 0.2))
-    assert np.alltrue(
+    assert np.all(
         result2 == mpstool.connectivity._apply_threshold(c_image, 0.95))
 
 
@@ -168,5 +168,5 @@ def test_gamma(c_image):
 
 
 def test_gamma_function(c_image):
-    assert np.alltrue(mpstool.connectivity.gamma_function(
+    assert np.all(mpstool.connectivity.gamma_function(
         c_image, [0, 0.01, 0.92], True) == [1, 17/25, 0])

tests/test_image.py

@@ -41,15 +41,15 @@ def test_saturate(img):
                          [0, 0, 255],
                          [250, 0, 0]]).reshape((3, 3, 1))
     assert saturated.shape == expected.shape
-    assert np.alltrue(saturated == expected)
+    assert np.all(saturated == expected)
 
 
 def test_labelize(img):
     expected = np.array([[4, 6, 2],
                          [3, 1, 6],
                          [5, 3, 0]])
     labels = labelize(img)
-    assert np.alltrue(labels == expected)
+    assert np.all(labels == expected)
 
 
 def test_from_list():
@@ -60,7 +60,7 @@ def test_from_list():
     input_data = [data1, data2]
     expected = np.array(input_data)
     img = Image.fromArray(input_data).asArray()
-    assert np.alltrue(img == expected)
+    assert np.all(img == expected)
 
 
 # ------ Test of conversion functions ------
@@ -76,48 +76,48 @@ def test_conversion_txt_gslib():
     img = Image.fromTxt("tests/data/test_img.txt", (3, 3))
     img.exportAsGslib("tests/data/test_img.gslib")
     img_test = Image.fromGslib("tests/data/test_img.gslib")
-    assert np.alltrue(img_test == img)
+    assert np.all(img_test == img)
 
 
 def test_gslib_to_vtk():
     img = Image.fromGslib("tests/data/test_img.gslib")
     img.exportAsVtk("tests/data/test_img.vtk")
     img2 = img.fromVtk("tests/data/test_img.vtk")
-    assert np.alltrue(img == img2)
+    assert np.all(img == img2)
 
 
 def test_vtk_pgm():
     img = Image.fromVtk("tests/data/test_img.vtk")
     img.exportAsPgm("tests/data/test_img.pgm")
     img2 = img.fromPgm("tests/data/test_img.pgm")
-    assert np.alltrue(img == img2)
+    assert np.all(img == img2)
 
 
 def test_pgm_vox():
     img = Image.fromPgm("tests/data/test_img.pgm")
     img.exportAsVox("tests/data/test_img.vox")
     img_test = Image.fromVox("tests/data/test_img.vox")
-    assert np.alltrue(img_test == img)
+    assert np.all(img_test == img)
 
 
 def test_conversion_vox_png():
     img = Image.fromVox("tests/data/test_img.vox")
     img.exportAsPng("tests/data/test_img.png")
     img_test = Image.fromPng("tests/data/test_img.png")
-    assert np.alltrue(img_test == img)
+    assert np.all(img_test == img)
 
 
 def test_conversion_png_txt():
     img = Image.fromPng("tests/data/test_img.png")
     img.exportAsTxt("tests/data/test_img2.txt")
     img_test = Image.fromTxt("tests/data/test_img2.txt", (3, 3))
-    assert np.alltrue(img_test == img)
+    assert np.all(img_test == img)
 
 
 def test_conversion_final():
     a = np.loadtxt("tests/data/test_img.txt").reshape((3, 3))
     b = np.loadtxt("tests/data/test_img2.txt")
-    return np.alltrue(a == b)
+    return np.all(a == b)
 
 
 def test_import_gslb2var():
@@ -126,8 +126,8 @@ def test_import_gslb2var():
     var2 = img.asArray("V1").reshape((3, 3))
     expected1 = np.array([[1, 1, 1], [1, 1, 1], [0, 0, 0]])
     expected2 = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
-    assert np.alltrue(var1 == expected1)
-    assert np.alltrue(var2 == expected2)
+    assert np.all(var1 == expected1)
+    assert np.all(var2 == expected2)
 
 
 def test_gslib_io():
@@ -145,7 +145,7 @@ def test_conversion_color():
     img.exportAsPpm("tests/data/test_color_simple.ppm")
     img2 = Image.fromPng("tests/data/test_color_simple.png")
     img3 = Image.fromPpm("tests/data/test_color_simple.ppm")
-    assert np.alltrue((img.asArray()-img2.asArray()) < 1e8)
+    assert np.all((img.asArray()-img2.asArray()) < 1e8)
     assert img == img3
 
 
@@ -156,7 +156,7 @@ def test_conversion_color2():
     img.exportAsPpm("tests/data/test_color.ppm")
     img2 = Image.fromPng("tests/data/test_color.png")
     img3 = Image.fromPpm("tests/data/test_color.ppm")
-    assert np.alltrue((img.asArray()-img2.asArray()) < 1e8)
+    assert np.all((img.asArray()-img2.asArray()) < 1e8)
     assert img == img3
 
 
@@ -192,9 +192,9 @@ def test_dimension(img):
     assert img.zmin() == 0
     assert img.zmax() == 1
     coords = np.array([0.5, 1.5, 2.5])
-    assert np.alltrue(img.x() == coords)
-    assert np.alltrue(img.y() == coords)
-    assert np.alltrue(img.z() == np.array([0.5]))
+    assert np.all(img.x() == coords)
+    assert np.all(img.y() == coords)
+    assert np.all(img.z() == np.array([0.5]))
     assert img.vmin() == 0
     assert img.vmax() == 255
     assert img.get_variables() == ["V0"]
@@ -206,12 +206,12 @@ def test_variable(img):
                      [250, 100, 0]])
     img.add_variable("test", data)
     assert set(img.get_variables()) == {"V0", "test"}
-    assert np.alltrue(img._data["test"] == data)
+    assert np.all(img._data["test"] == data)
     data = np.zeros(img.shape)
     img.set_variable("test", data)
-    assert np.alltrue(img._data["test"] == data)
+    assert np.all(img._data["test"] == data)
     img.rename_variable("test", "toto")
-    assert np.alltrue(img._data["toto"] == data)
+    assert np.all(img._data["toto"] == data)
     img.remove_variable("toto")
     assert img.get_variables() == ["V0"]