Upgrade to numpy>=2.0.0

pyproject.toml

@@ -30,7 +30,7 @@ dependencies = [
   "healpy",
   "jax",
   "jaxlib",
-  "numpy>=1.20,<1.25",
+  "numpy>=2.0.0",
   "pyarrow>=13.0.0",
   "pandas",
   "ray",

src/adam_core/coordinates/cartesian.py

@@ -206,7 +206,7 @@ def rotate(
             Rotated Cartesian coordinates and their covariances.
         """
         # Extract coordinate values into a masked array and mask NaNss
-        masked_coords = np.ma.masked_array(self.values, fill_value=np.NaN)
+        masked_coords = np.ma.masked_array(self.values, fill_value=np.nan)
         masked_coords.mask = np.isnan(masked_coords.data)
 
         # Rotate coordinates
@@ -223,7 +223,7 @@ def rotate(
             rotation_matrix @ masked_covariances.filled() @ rotation_matrix.T
         )
         # Reset the mask to the original mask
-        covariances_rotated[masked_covariances.mask] = np.NaN
+        covariances_rotated[masked_covariances.mask] = np.nan
 
         # Check if any covariance elements are near zero, if so set them to zero
         near_zero = len(
@@ -284,7 +284,7 @@ def translate(
             raise ValueError(f"Expected vector to have shape (6,) or ({N}, 6).")
 
         # Extract coordinate values into a masked array and mask NaNss
-        masked_coords = np.ma.masked_array(self.values, fill_value=np.NaN)
+        masked_coords = np.ma.masked_array(self.values, fill_value=np.nan)
         masked_coords.mask = np.isnan(masked_coords.data)
 
         # Translate coordinates

src/adam_core/coordinates/covariances.py

@@ -11,7 +11,7 @@
 
 logger = logging.getLogger(__name__)
 
-COVARIANCE_FILL_VALUE = np.NaN
+COVARIANCE_FILL_VALUE = np.nan
 
 
 def sigmas_to_covariances(sigmas: np.ndarray) -> np.ndarray:

src/adam_core/coordinates/tests/test_covariances.py

@@ -121,13 +121,13 @@ def test_CoordinateCovariances_to_from_matrix():
     covariances = [None, np.ones((6, 6)).flatten()]
     cov = CoordinateCovariances.from_kwargs(values=covariances)
     cov_expected = np.ones((2, 6, 6))
-    cov_expected[0, :, :] = np.NaN
+    cov_expected[0, :, :] = np.nan
     np.testing.assert_equal(cov.to_matrix(), cov_expected)
 
     # Test when covariances are only None
     covariances = [None, None]
     cov = CoordinateCovariances.from_kwargs(values=covariances)
-    cov_expected = np.full((2, 6, 6), np.NaN)
+    cov_expected = np.full((2, 6, 6), np.nan)
     np.testing.assert_equal(cov.to_matrix(), cov_expected)
 
 

src/adam_core/coordinates/tests/test_residuals.py

@@ -143,8 +143,8 @@ def test_batch_coords_and_covariances_single_batch_missing_values():
     # Single batch, one dimension has no values
     coords = np.array(
         [
-            [1.0, np.NaN, 3.0],
-            [2.0, np.NaN, 4.0],
+            [1.0, np.nan, 3.0],
+            [2.0, np.nan, 4.0],
         ]
     )
     covariances = np.array(
@@ -178,8 +178,8 @@ def test_batch_coords_and_covariances_single_batch_missing_values():
     # Single batch, two dimensions have no values
     coords = np.array(
         [
-            [np.NaN, np.NaN, 3.0],
-            [np.NaN, np.NaN, 4.0],
+            [np.nan, np.nan, 3.0],
+            [np.nan, np.nan, 4.0],
         ]
     )
     covariances = np.array(
@@ -212,8 +212,8 @@ def test_batch_coords_and_covariances_multiple_batches():
     # Multiple batches, different rows have different missing values
     coords = np.array(
         [
-            [1.0, np.NaN, 3.0],
-            [np.NaN, 3.0, 4.0],
+            [1.0, np.nan, 3.0],
+            [np.nan, 3.0, 4.0],
         ]
     )
     covariances = np.array(

src/adam_core/coordinates/tests/test_spherical.py

@@ -11,10 +11,10 @@ def test_SphericalCoordinates_to_unit_sphere():
     # that have NaN values for rho will be set to 1.0 and coordinates
     # that have NaN values for vrho will be set to 0.0.
     coords = SphericalCoordinates.from_kwargs(
-        rho=[1.0, np.NaN, 3.0],
+        rho=[1.0, np.nan, 3.0],
         lon=[30.0, 45.0, 60.0],
         lat=[-60, 0.0, 60.0],
-        vrho=[np.NaN, 0.002, -0.01],
+        vrho=[np.nan, 0.002, -0.01],
         vlon=[0.005, 0.006, 0.007],
         vlat=[-0.0005, 0.0006, -0.007],
         origin=Origin.from_kwargs(code=["SUN", "SUN", "SUN"]),