add np 2.0 deprecation

.github/workflows/ci.yml

@@ -9,7 +9,7 @@ jobs:
 
     strategy:
       matrix:
-        python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
+        python-version: ['3.9', '3.10', '3.11', '3.12']
         os: [ubuntu-latest, macos-latest]
 
     steps:

pyamg/aggregation/adaptive.py

@@ -504,7 +504,7 @@ def relax(A, x):
         if symmetry == 'symmetric':
             A_l = P_l.T.asformat(P_l.format) * A_l * P_l
         elif symmetry == 'hermitian':
-            A_l = P_l.H.asformat(P_l.format) * A_l * P_l
+            A_l = P_l.T.conjugate().asformat(P_l.format) * A_l * P_l
         else:
             raise ValueError(f'aSA not implemented for symmetry={symmetry}.')
 
@@ -666,7 +666,7 @@ def expand_candidates(B_old, nodesize):  # pylint: disable=unused-variable
         if symmetry == 'symmetric':  # R should reflect A's structure
             levels[i].R = levels[i].P.T.asformat(levels[i].P.format)
         elif symmetry == 'hermitian':
-            levels[i].R = levels[i].P.H.asformat(levels[i].P.format)
+            levels[i].R = levels[i].P.T.conjugate().asformat(levels[i].P.format)
 
         # construct coarse A
         levels[i+1].A = levels[i].R * levels[i].A * levels[i].P
@@ -701,7 +701,7 @@ def expand_candidates(B_old, nodesize):  # pylint: disable=unused-variable
         if symmetry == 'symmetric':  # R should reflect A's structure
             levels[i+1].R = levels[i+1].P.T.asformat(levels[i+1].P.format)
         elif symmetry == 'hermitian':
-            levels[i+1].R = levels[i+1].P.H.asformat(levels[i+1].P.format)
+            levels[i+1].R = levels[i+1].P.T.conjugate().asformat(levels[i+1].P.format)
 
         # run solver on candidate
         solver = MultilevelSolver(levels[i+1:], coarse_solver=coarse_solver)

pyamg/aggregation/aggregation.py

@@ -301,8 +301,11 @@ def unpack_arg(v):
 
     A = levels[-1].A
     B = levels[-1].B
+    AH = None
+    BH = None
+    TH = None
     if A.symmetry == 'nonsymmetric':
-        AH = A.H.asformat(A.format)
+        AH = A.T.conjugate().asformat(A.format)
         BH = levels[-1].BH
 
     # Compute the strength-of-connection matrix C, where larger
@@ -391,23 +394,25 @@ def unpack_arg(v):
     # based on A.H.  Otherwise R = P.H or P.T.
     symmetry = A.symmetry
     if symmetry == 'hermitian':
-        R = P.H
+        R = P.T.conjugate()
     elif symmetry == 'symmetric':
         R = P.T
     elif symmetry == 'nonsymmetric':
         fn, kwargs = unpack_arg(smooth[len(levels)-1])
         if fn == 'jacobi':
-            R = jacobi_prolongation_smoother(AH, TH, C, BH, **kwargs).H
+            R = jacobi_prolongation_smoother(AH, TH, C, BH, **kwargs).T.conjugate()
         elif fn == 'richardson':
-            R = richardson_prolongation_smoother(AH, TH, **kwargs).H
+            R = richardson_prolongation_smoother(AH, TH, **kwargs).T.conjugate()
         elif fn == 'energy':
             R = energy_prolongation_smoother(AH, TH, C, BH, None, (False, {}),
                                              **kwargs)
-            R = R.H
+            R = R.T.conjugate()
         elif fn is None:
-            R = T.H
+            R = T.T.conjugate()
         else:
             raise ValueError(f'Unrecognized prolongation smoother method {str(fn)}')
+    else:
+        raise ValueError('Unrecognized symmetry.')
 
     if keep:
         levels[-1].C = C  # strength of connection matrix

pyamg/aggregation/pairwise.py

@@ -129,7 +129,7 @@ def unpack_arg(v):
     # Compute pairwise interpolation and restriction matrices, R=P^*
     _, kwargs = unpack_arg(aggregate[len(levels)-1])
     P = pairwise_aggregation(A, **kwargs, compute_P=True)[0]
-    R = P.H
+    R = P.T.conjugate()
     if isspmatrix_csr(P):
         # In this case, R will be CSC, which must be changed
         R = R.tocsr()

pyamg/aggregation/rootnode.py

@@ -323,7 +323,7 @@ def unpack_arg(v):
     A = levels[-1].A
     B = levels[-1].B
     if A.symmetry == 'nonsymmetric':
-        AH = A.H.asformat(A.format)
+        AH = A.T.conjugate().asformat(A.format)
         BH = levels[-1].BH
 
     # Compute the strength-of-connection matrix C, where larger
@@ -423,17 +423,17 @@ def unpack_arg(v):
     # based on A.H.  Otherwise R = P.H or P.T.
     symmetry = A.symmetry
     if symmetry == 'hermitian':
-        R = P.H
+        R = P.T.conjugate()
     elif symmetry == 'symmetric':
         R = P.T
     elif symmetry == 'nonsymmetric':
         fn, kwargs = unpack_arg(smooth[len(levels)-1])
         if fn == 'energy':
             R = energy_prolongation_smoother(AH, TH, C, BH, levels[-1].BH,
                                              Cpt_params=Cpt_params, **kwargs)
-            R = R.H
+            R = R.T.conjugate()
         elif fn is None:
-            R = T.H
+            R = T.T.conjugate()
         else:
             raise ValueError(f'Unrecognized prolongation smoother method: {str(fn)}')
 

pyamg/aggregation/smooth.py

@@ -495,7 +495,7 @@ def cgnr_prolongation_smoothing(A, T, B, BtBinv, pattern, maxiter,
 
     # For non-SPD system, apply CG on Normal Equations with Diagonal
     # Preconditioning (requires transpose)
-    Ah = A.H
+    Ah = A.T.conjugate()
     Ah.sort_indices()
 
     # Preallocate

pyamg/aggregation/tests/test_tentative.py

@@ -182,4 +182,4 @@ def mask_candidate(AggOp, candidates):
 
             # each fine level candidate should be fit (almost) exactly
             assert_almost_equal(fine_candidates, Q * coarse_candidates)
-            assert_almost_equal(Q * (Q.H * fine_candidates), fine_candidates)
+            assert_almost_equal(Q * (Q.T.conjugate() * fine_candidates), fine_candidates)

pyamg/gallery/diffusion.py

@@ -148,6 +148,8 @@ def diffusion_stencil_2d(epsilon=1.0, theta=0.0, type='FE'):
         stencil = np.array([[a, b, c],
                             [d, e, d],
                             [c, b, a]])
+    else:
+        raise ValueError('only stencil types "FE" and "FD" are supported')
 
     return stencil
 

pyamg/gallery/elasticity.py

@@ -260,13 +260,12 @@ def linear_elasticity_p1(vertices, elements, E=1e5, nu=0.3, format=None):
     if elements.shape[1] != D + 1:
         raise ValueError('dimension mismatch')
 
-    if D not in (2, 3):
-        raise ValueError('only dimension 2 and 3 are supported')
-
     if D == 2:
         local_K = p12d_local
     elif D == 3:
         local_K = p13d_local
+    else:
+        raise ValueError('only dimension 2 and 3 are supported')
 
     row = elements.repeat(D).reshape(-1, D)
     row *= D

pyamg/gallery/fem.py

@@ -319,10 +319,11 @@ def l2norm(u, mesh):
     if mesh.degree == 1:
         V = mesh.V
         E = mesh.E
-
-    if mesh.degree == 2:
+    elif mesh.degree == 2:
         V = mesh.V2
         E = mesh.E2
+    else:
+        raise ValueError('only mesh.degree 1 or 2 supported')
 
     if not isinstance(u, np.ndarray):
         raise ValueError('u must be ndarray')
@@ -361,8 +362,7 @@ def basis1(x, y):
                              x,
                              y])
         basis = basis1
-
-    if mesh.degree == 2:
+    elif mesh.degree == 2:
         I = np.arange(6)
 
         def basis2(x, y):
@@ -373,6 +373,8 @@ def basis2(x, y):
                              4*x*y,
                              4*y*(1-x-y)])
         basis = basis2
+    else:
+        raise ValueError('only mesh.degree 1 or 2 supported')
 
     for e in E:
         x = V[e, 0]
@@ -663,11 +665,12 @@ def kappa(_x, _y):
         E = mesh.E
         X = mesh.X
         Y = mesh.Y
-
-    if degree == 2:
+    elif degree == 2:
         E = mesh.E2
         X = mesh.X2
         Y = mesh.Y2
+    else:
+        raise ValueError('only mesh.degree 1 or 2 supported')
 
     # allocate sparse matrix arrays
     m = 3 if degree == 1 else 6
@@ -918,6 +921,8 @@ def applybc(A, b, mesh, bc):
         elif c['degree'] == 2:
             X = mesh.X2
             Y = mesh.Y2
+        else:
+            raise ValueError('only mesh.degree 1 or 2 supported')
         u0[idx] = c['g'](X[idx], Y[idx])
 
     # lift (2 of 3)

pyamg/gallery/tests/test_laplacian.py

@@ -76,7 +76,7 @@ def test_gaugelaplacian(self):
 
         for A, beta in cases:
             # Check Hermitian
-            diff = A - A.H
+            diff = A - A.T.conjugate()
             assert_equal(diff.data, np.array([]))
 
             # Check for definiteness

pyamg/krylov/_cgne.py

@@ -85,7 +85,7 @@ def cgne(A, b, x0=None, tol=1e-5, criteria='rr',
     """
     # Store the conjugate transpose explicitly as it will be used much later on
     if sparse.isspmatrix(A):
-        AH = A.H
+        AH = A.T.conjugate()
     else:
         # avoid doing this since A may be a different sparse type
         AH = aslinearoperator(np.asarray(A).conj().T)

pyamg/krylov/_cgnr.py

@@ -85,7 +85,7 @@ def cgnr(A, b, x0=None, tol=1e-5, criteria='rr',
     """
     # Store the conjugate transpose explicitly as it will be used much later on
     if sparse.isspmatrix(A):
-        AH = A.H
+        AH = A.T.conjugate()
     else:
         # avoid doing this since A may be a different sparse type
         AH = aslinearoperator(np.asarray(A).conj().T)

pyamg/krylov/_gmres.py

@@ -117,5 +117,7 @@ def gmres(A, b, x0=None, tol=1e-5, restart=None, maxiter=None,
         (x, flag) = gmres_mgs(A, b, x0=x0, tol=tol, restart=restart,
                               maxiter=maxiter, M=M,
                               callback=callback, residuals=residuals, **kwargs)
+    else:
+        raise ValueError('orthog must be either "mgs" or "householder"')
 
     return (x, flag)

pyamg/krylov/tests/test_scipy.py

@@ -1,5 +1,4 @@
 """Test scipy methods."""
-import inspect
 from functools import partial
 
 import numpy as np
@@ -37,6 +36,7 @@ def cb(x, normb):
             b = case['b']
             x0 = case['x0']
             tol = case['tol']
+            rtol = tol
 
             kwargs = dict(tol=tol, restart=3, maxiter=2)
 
@@ -52,8 +52,8 @@ def cb(x, normb):
 
             # check if scipy gmres has rtol
             kwargs['atol'] = 0
-            if 'rtol' in inspect.getfullargspec(sla.gmres).args:
-                kwargs['rtol'] = kwargs.pop(tol)
+            kwargs['rtol'] = rtol
+            del kwargs['tol']
 
             _ = sla.gmres(A, b, x0, callback=callback, callback_type='pr_norm', **kwargs)
 

pyamg/multilevel.py

@@ -1,6 +1,4 @@
 """Generic AMG solver."""
-import inspect
-
 from warnings import warn
 
 import scipy as sp
@@ -173,7 +171,7 @@ def __init__(self, levels, coarse_solver='pinv'):
 
         for level in levels[:-1]:
             if not hasattr(level, 'R'):
-                level.R = level.P.H
+                level.R = level.P.T.conjugate()
 
     def __repr__(self):
         """Print basic statistics about the multigrid hierarchy."""
@@ -486,10 +484,8 @@ def callback_wrapper(x):
                     callback_wrapper = callback
 
                 # for scipy solvers, see if rtol is available
-                kwargs['tol'] = tol
+                kwargs['rtol'] = tol
                 kwargs['atol'] = 0
-                if 'rtol' in inspect.getfullargspec(accel).args:
-                    kwargs['rtol'] = kwargs.pop(tol)
 
                 x, info = accel(A, b, x0=x0, maxiter=maxiter, M=M,
                                 callback=callback_wrapper, **kwargs)