Fix for new scipy

optimism/WarmStart.py

@@ -1,8 +1,7 @@
-from scipy.sparse.linalg import cg, LinearOperator
-
 from optimism.JaxConfig import *
-import optimism.Objective as Objective
+from scipy.sparse.linalg import cg, LinearOperator
 
+
 def warm_start_increment(objective, x, pNew, index=0):
     dp = objective.p[index] - pNew[index]
 
@@ -14,7 +13,7 @@ def warm_start_increment(objective, x, pNew, index=0):
         raise('invalid warm start parameter gradient direction')
 
     sz = b.size
-    op = lambda v: objective.hessian_vec(x, v)
+    op = lambda v: objective.hessian_vec(x, np.array(v, dtype=np.float64))
 
     Lop = LinearOperator((sz,sz),
                          matvec = op)

optimism/contact/test/test_NewtonGlobalization.py

@@ -133,7 +133,7 @@ def test_newton_step(self):
         xl = np.hstack( (self.x, self.lam) )
         r0 = np.linalg.norm( residual(xl) )
 
-        xl += newton_step(residual, lambda v: linear_op(xl,v), xl)[0]
+        xl += newton_step(residual, lambda v: linear_op(xl, np.array(v, dtype=np.float64)), xl)[0]
         r1 = np.linalg.norm( residual(xl) )
 
         self.assertTrue( r1 < 10*r0 )
@@ -144,7 +144,7 @@ def test_globalized_newton_step_with_cubic(self):
         linear_op = create_linear_op(residual)
         x = np.array([0.1])
         r0 = np.linalg.norm( residual(x) )
-        x += globalized_newton_step(residual, lambda v: linear_op(x,v), x, self.etak, self.t)
+        x += globalized_newton_step(residual, lambda v: linear_op(x, np.array(v, dtype=np.float64)), x, self.etak, self.t)
         r1 = np.linalg.norm( residual(x) )
 
         self.assertTrue( r1 < r0 )
@@ -154,7 +154,7 @@ def test_globalized_newton_step_nonconvex(self):
         xl = np.hstack( (self.x, self.lam) )
         r0 = np.linalg.norm( residual(xl) )
 
-        xl += globalized_newton_step(residual, lambda v: linear_op(xl,v), xl, self.etak, self.t)
+        xl += globalized_newton_step(residual, lambda v: linear_op(xl, np.array(v, dtype=np.float64)), xl, self.etak, self.t)
         r1 = np.linalg.norm( residual(xl) )
 
         self.assertTrue( r1 < r0 )
@@ -171,7 +171,7 @@ def test_al_solver(self):
         randRhs = np.array(rand(self.x.size))
         randRhs *= 0.25 / np.linalg.norm(randRhs)
 
-        penalty0 = unconstrainedObjective.hessian_vec(self.x, randRhs) @ randRhs
+        penalty0 = unconstrainedObjective.hessian_vec(self.x, np.array(randRhs, dtype=np.float64)) @ randRhs
 
         alObjective = ConstrainedObjective(lambda x,p: objective(x),
                                            lambda x,p: constraint(x),

optimism/inverse/test/test_Hyperelastic_gradient_checks.py

@@ -164,7 +164,7 @@ def total_work_gradient_with_adjoint(self, storedState, parameters):
             n = self.dofManager.get_unknown_size()
             self.objective.p = p # have to update parameters to get precond to work
             self.objective.update_precond(Uu) # update preconditioner for use in cg (will converge in 1 iteration as long as the preconditioner is not approximate)
-            dRdu = linalg.LinearOperator((n, n), lambda V: onp.asarray(self.objective.hessian_vec(Uu, V)))
+            dRdu = linalg.LinearOperator((n, n), lambda V: onp.asarray(self.objective.hessian_vec(Uu, np.array(V, dtype=np.float64))))
             dRdu_decomp = linalg.LinearOperator((n, n), lambda V: onp.asarray(self.objective.apply_precond(V)))
             adjointVector = linalg.cg(dRdu, onp.array(adjointLoad, copy=False), rtol=1e-10, atol=0.0, M=dRdu_decomp)[0]
 

optimism/inverse/test/test_J2Plastic_gradient_checks.py

@@ -171,7 +171,7 @@ def total_work_gradient(self, storedState, parameters):
             p_objective = Objective.Params(bc_data=p.bc_data, state_data=p_prev.state_data) # remember R is a function of ivs_prev
             self.objective.p = p_objective 
             self.objective.update_precond(Uu) # update preconditioner for use in cg (will converge in 1 iteration as long as the preconditioner is not approximate)
-            dRdu = linalg.LinearOperator((n, n), lambda V: onp.asarray(self.objective.hessian_vec(Uu, V)))
+            dRdu = linalg.LinearOperator((n, n), lambda V: onp.asarray(self.objective.hessian_vec(Uu, np.array(V, dtype=np.float64))))
             dRdu_decomp = linalg.LinearOperator((n, n), lambda V: onp.asarray(self.objective.apply_precond(V)))
             adjointVector = linalg.cg(dRdu, onp.array(adjointLoad, copy=False), rtol=1e-10, atol=0.0, M=dRdu_decomp)[0]
 
@@ -253,7 +253,7 @@ def target_curve_gradient(self, storedState, parameters):
             p_objective = Objective.Params(bc_data=p.bc_data, state_data=p_prev.state_data) # remember R is a function of ivs_prev
             self.objective.p = p_objective 
             self.objective.update_precond(Uu) # update preconditioner for use in cg (will converge in 1 iteration as long as the preconditioner is not approximate)
-            dRdu = linalg.LinearOperator((n, n), lambda V: onp.asarray(self.objective.hessian_vec(Uu, V)))
+            dRdu = linalg.LinearOperator((n, n), lambda V: onp.asarray(self.objective.hessian_vec(Uu, np.array(V, np.float64))))
             dRdu_decomp = linalg.LinearOperator((n, n), lambda V: onp.asarray(self.objective.apply_precond(V)))
             adjointVector = linalg.cg(dRdu, onp.array(adjointLoad, copy=False), rtol=1e-10, atol=0.0, M=dRdu_decomp)[0]
 

setup.py

@@ -11,7 +11,7 @@
                       'matplotlib', # this is not strictly necessary
                       'metis',
                       'netcdf4',
-                      'scipy',],
+                      'scipy<1.15.0',],
     #tests_require=[], # could put chex and pytest here
     extras_require={'sparse': ['scikit-sparse'],
                     'test': ['pytest', 'pytest-cov', 'pytest-xdist'],