build against NPY2

pyproject.toml

@@ -1,8 +1,9 @@
 [build-system]
 requires = [
     "Cython>=0.29.23",
-    # pin NumPy version used in the build
-    "oldest-supported-numpy",
+    # Building against NPY2 will support both NPY1 and NPY2
+    # https://numpy.org/devdocs/dev/depending_on_numpy.html#build-time-dependency
+    "numpy>=2.0.1",
     "setuptools>=65.0.0",
 ]
 build-backend = "setuptools.build_meta"
@@ -59,8 +60,8 @@ dependencies = [
     "matplotlib>=3.8",
     "monty>=2024.7.29",
     "networkx>=2.2",
-    "numpy>=1.25.0 ; platform_system != 'Windows'",
-    "numpy>=1.25.0,<2.0 ; platform_system == 'Windows'",
+    # NumPy documentation suggests pinning the current major version as the C API is used
+    # https://numpy.org/devdocs/dev/depending_on_numpy.html#runtime-dependency-version-ranges
     "palettable>=3.3.3",
     "pandas>=2",
     "plotly>=4.5.0",
@@ -73,6 +74,7 @@ dependencies = [
     "tabulate>=0.9",
     "tqdm>=4.60",
     "uncertainties>=3.1.4",
+    'numpy>=1.25.0,<3.0',
 ]
 version = "2024.7.18"
 
@@ -88,7 +90,7 @@ ase = ["ase>=3.23.0"]
 # don't depend on tblite above 3.11 since unsupported https://github.com/tblite/tblite/issues/175
 tblite = ["tblite[ase]>=0.3.0; python_version<'3.12'"]
 vis = ["vtk>=6.0.0"]
-abinit = ["netcdf4>=1.6.5"]
+abinit = ["netcdf4>=1.7.1"]
 mlp = ["chgnet>=0.3.8", "matgl>=1.1.1"]
 electronic_structure = ["fdint>=2.0.2"]
 ci = ["pytest-cov>=4", "pytest-split>=0.8", "pytest>=8"]

src/pymatgen/analysis/eos.py

@@ -15,6 +15,11 @@
 import numpy as np
 from scipy.optimize import leastsq, minimize
 
+try:
+    from numpy.exceptions import RankWarning  # NPY2
+except ImportError:
+    from numpy import RankWarning  # NPY1
+
 from pymatgen.core.units import FloatWithUnit
 from pymatgen.util.plotting import add_fig_kwargs, get_ax_fig, pretty_plot
 
@@ -420,7 +425,7 @@ def fit(self, min_ndata_factor=3, max_poly_order_factor=5, min_poly_order=2):
             min_poly_order (int): minimum order of the polynomial to be
                 considered for fitting.
         """
-        warnings.simplefilter("ignore", np.RankWarning)
+        warnings.simplefilter("ignore", RankWarning)
 
         def get_rms(x, y):
             return np.sqrt(np.sum((np.array(x) - np.array(y)) ** 2) / len(x))
@@ -490,7 +495,7 @@ def get_rms(x, y):
             norm += weight
             coeffs = np.array(val[0])
             # pad the coefficient array with zeros
-            coeffs = np.lib.pad(coeffs, (0, max(fit_poly_order - len(coeffs), 0)), "constant")
+            coeffs = np.pad(coeffs, (0, max(fit_poly_order - len(coeffs), 0)), "constant")
             weighted_avg_coeffs += weight * coeffs
 
         # normalization

src/pymatgen/optimization/linear_assignment.pyx

@@ -50,7 +50,8 @@ class LinearAssignment:
     """
 
     def __init__(self, costs, epsilon=1e-13):
-        self.orig_c = np.array(costs, dtype=np.float_, copy=False, order="C")
+        # https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword
+        self.orig_c = np.asarray(costs, dtype=np.float64, order="C")
         self.nx, self.ny = self.orig_c.shape
         self.n = self.ny
 
@@ -63,7 +64,7 @@ class LinearAssignment:
         if self.nx == self.ny:
             self.c = self.orig_c
         else:
-            self.c = np.zeros((self.n, self.n), dtype=np.float_)
+            self.c = np.zeros((self.n, self.n), dtype=np.float64)
             self.c[:self.nx] = self.orig_c
 
         # initialize solution vectors
@@ -76,15 +77,15 @@ class LinearAssignment:
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-cdef np.float_t compute(int size, np.float_t[:, :] c, np.int_t[:] x, np.int_t[:] y, np.float_t eps) nogil:
+cdef np.float_t compute(int size, np.float_t[:, :] c, np.int64_t[:] x, np.int64_t[:] y, np.float_t eps) nogil:
 
     # augment
     cdef int i, j, k, i1, j1, f, f0, cnt, low, up, z, last, nrr
     cdef int n = size
     cdef bint b
-    cdef np.int_t * col = <np.int_t *> malloc(n * sizeof(np.int_t))
-    cdef np.int_t * fre = <np.int_t *> malloc(n * sizeof(np.int_t))
-    cdef np.int_t * pred = <np.int_t *> malloc(n * sizeof(np.int_t))
+    cdef np.int64_t * col = <np.int64_t *> malloc(n * sizeof(np.int64_t))
+    cdef np.int64_t * fre = <np.int64_t *> malloc(n * sizeof(np.int64_t))
+    cdef np.int64_t * pred = <np.int64_t *> malloc(n * sizeof(np.int64_t))
     cdef np.float_t * v = <np.float_t *> malloc(n * sizeof(np.float_t))
     cdef np.float_t * d = <np.float_t *> malloc(n * sizeof(np.float_t))
     cdef np.float_t h, m, u1, u2, cost