Fixes to support numpy>=2.4

environment-dev.yml

@@ -5,7 +5,7 @@ channels:
 dependencies:
   - python >= 3.8.0
   - pip
-  - numpy>=1.15.0,<=2.3.5
+  - numpy>=1.15.0
   - scipy>=1.8.0
   - mpi4py
   - pylops

environment.yml

@@ -4,7 +4,7 @@ channels:
   - conda-forge
 dependencies:
   - python>=3.8.0
-  - numpy>=1.15.0,<=2.3.5
+  - numpy>=1.15.0
   - scipy>=1.8.0
   - pylops>=2.0
   - matplotlib

pylops_mpi/basicoperators/BlockDiag.py

@@ -170,8 +170,8 @@ def __init__(self, ops: Sequence[MPILinearOperator], base_comm: MPI.Comm = MPI.C
                  dtype: Optional[DTypeLike] = None):
         self.ops = ops
         dtype = _get_dtype(self.ops) if dtype is None else np.dtype(dtype)
-        shape = (int(np.sum(op.shape[0] for op in ops)),
-                 int(np.sum(op.shape[1] for op in ops)))
+        shape = (int(sum(op.shape[0] for op in ops)),
+                 int(sum(op.shape[1] for op in ops)))
         super().__init__(shape=shape, dtype=dtype, base_comm=base_comm)
 
     def _matvec(self, x: StackedDistributedArray) -> StackedDistributedArray:

pylops_mpi/basicoperators/VStack.py

@@ -183,7 +183,7 @@ def __init__(self, ops: Sequence[MPILinearOperator],
         self.ops = ops
         if len(set(op.shape[1] for op in ops)) > 1:
             raise ValueError("Operators have different number of columns")
-        shape = (int(np.sum(op.shape[0] for op in ops)), ops[0].shape[1])
+        shape = (int(sum(op.shape[0] for op in ops)), ops[0].shape[1])
         dtype = _get_dtype(self.ops) if dtype is None else np.dtype(dtype)
         super().__init__(shape=shape, dtype=dtype, base_comm=base_comm)
 

pylops_mpi/optimization/cls_basic.py

@@ -92,7 +92,7 @@ def setup(
         self.r = self.y - self.Op.matvec(x)
         self.rank = x.rank
         self.c = self.r.copy()
-        self.kold = float(np.abs(self.r.dot(self.r.conj())))
+        self.kold = float(np.abs(self.r.dot(self.r.conj())).item())
 
         # create variables to track the residual norm and iterations
         self.cost: List = []
@@ -127,10 +127,10 @@ def step(self, x: Union[DistributedArray, StackedDistributedArray],
         """
         Opc = self.Op.matvec(self.c)
         cOpc = np.abs(self.c.dot(Opc.conj()))
-        a = float(self.kold / cOpc)
+        a = float((self.kold / cOpc).item())
         x += a * self.c
         self.r -= a * Opc
-        k = float(np.abs(self.r.dot(self.r.conj())))
+        k = float(np.abs(self.r.dot(self.r.conj())).item())
         b = float(k / self.kold)
         self.c = self.r + b * self.c
         self.kold = k
@@ -349,13 +349,13 @@ def setup(self,
         self.rank = x.rank
         self.c = r.copy()
         self.q = self.Op.matvec(self.c)
-        self.kold = float(np.abs(r.dot(r.conj())))
+        self.kold = float(np.abs(r.dot(r.conj())).item())
 
         # create variables to track the residual norm and iterations
         self.cost = []
         self.cost1 = []
-        self.cost.append(float(self.s.norm()))
-        self.cost1.append(np.sqrt(float(self.cost[0] ** 2 + damp * np.abs(x.dot(x.conj())))))
+        self.cost.append(float(self.s.norm().item()))
+        self.cost1.append(np.sqrt(float(self.cost[0] ** 2 + damp * np.abs(x.dot(x.conj())).item())))
         self.iiter = 0
 
         # print setup
@@ -386,19 +386,19 @@ def step(self, x: Union[DistributedArray, StackedDistributedArray],
 
         """
 
-        a = float(np.abs(self.kold / (self.q.dot(self.q.conj()) + self.damp * self.c.dot(self.c.conj()))))
+        a = float(np.abs(self.kold / (self.q.dot(self.q.conj()) + self.damp * self.c.dot(self.c.conj()))).item())
         x += a * self.c
         self.s -= a * self.q
         damped_x = self.damp * x
         r = self.Op.rmatvec(self.s) - damped_x
-        k = float(np.abs(r.dot(r.conj())))
+        k = float(np.abs(r.dot(r.conj())).item())
         b = float(k / self.kold)
         self.c = r + b * self.c
         self.q = self.Op.matvec(self.c)
         self.kold = k
         self.iiter += 1
-        self.cost.append(float(self.s.norm()))
-        self.cost1.append(np.sqrt(float(self.cost[self.iiter] ** 2 + self.damp * np.abs(x.dot(x.conj())))))
+        self.cost.append(float(self.s.norm().item()))
+        self.cost1.append(np.sqrt(float(self.cost[self.iiter] ** 2 + self.damp * np.abs(x.dot(x.conj())).item())))
         if show and self.rank == 0:
             self._print_step(x)
         return x

pyproject.toml

@@ -29,7 +29,7 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Mathematics",
 ]
 dependencies = [
-    "numpy>=1.15.0,<=2.3.5",
+    "numpy>=1.15.0",
     "scipy >= 1.4.0",
     "pylops >= 2.0",
     "mpi4py",

requirements-dev.txt

@@ -1,4 +1,4 @@
-numpy>=1.15.0,<=2.3.5
+numpy>=1.15.0
 scipy>=1.8.0
 pylops
 mpi4py