[vectorarrays] remove some unneeded copy() calls

src/pymor/algorithms/genericsolvers.py

@@ -170,7 +170,7 @@ def apply_inverse(op, rhs, options=None):
 
     if options['type'] == 'generic_lgmres':
         for i in range(len(rhs)):
-            r, info = lgmres(op, rhs[i].copy(),
+            r, info = lgmres(op, rhs[i],
                              tol=options['tol'],
                              maxiter=options['maxiter'],
                              inner_m=options['inner_m'],
@@ -181,7 +181,7 @@ def apply_inverse(op, rhs, options=None):
             R.append(r)
     elif options['type'] == 'least_squares_generic_lsmr':
         for i in range(len(rhs)):
-            r, info, itn, _, _, _, _, _ = lsmr(op, rhs[i].copy(),
+            r, info, itn, _, _, _, _, _ = lsmr(op, rhs[i],
                                                damp=options['damp'],
                                                atol=options['atol'],
                                                btol=options['btol'],
@@ -195,7 +195,7 @@ def apply_inverse(op, rhs, options=None):
             R.append(r)
     elif options['type'] == 'least_squares_generic_lsqr':
         for i in range(len(rhs)):
-            r, info, itn, _, _, _, _, _, _ = lsqr(op, rhs[i].copy(),
+            r, info, itn, _, _, _, _, _, _ = lsqr(op, rhs[i],
                                                   damp=options['damp'],
                                                   atol=options['atol'],
                                                   btol=options['btol'],

src/pymor/algorithms/image.py

@@ -221,7 +221,7 @@ def estimate_image_hierarchical(operators=(), vectors=(), domain=None, extends=N
                                        orthonormalize=False, product=product,
                                        riesz_representatives=riesz_representatives)
         else:
-            new_image = estimate_image(operators, [], domain[i].copy(), extends=True,
+            new_image = estimate_image(operators, [], domain[i], extends=True,
                                        orthonormalize=False, product=product,
                                        riesz_representatives=riesz_representatives)
 

src/pymor/operators/basic.py

@@ -101,7 +101,7 @@ def apply_inverse(self, V, mu=None, least_squares=False):
                     self.logger.warn('Least squares solver selected but "least_squares == False"')
 
             try:
-                return genericsolvers.apply_inverse(assembled_op, V.copy(), options=options)
+                return genericsolvers.apply_inverse(assembled_op, V, options=options)
             except InversionError as e:
                 if least_squares and options:
                     solver_type = options if isinstance(options, str) else options['type']

src/pymor/operators/numpy.py

@@ -272,7 +272,7 @@ def projected_to_subbasis(self, dim_range=None, dim_source=None, name=None):
 
         should be the same as ::
 
-            op.projected(r_basis[:dim_range].copy(), s_basis[:dim_source].copy(), prod)
+            op.projected(r_basis[:dim_range], s_basis[:dim_source], prod)
 
         For a |NumpyMatrixOperator| this amounts to extracting the upper-left
         (dim_range, dim_source) corner of the matrix it wraps.

src/pymor/parallel/basic.py

@@ -21,7 +21,7 @@ def scatter_array(self, U, copy=True):
         else:
             slices = [U.empty() for _ in range(len(self))]
             for s in slices:
-                s.append(U[:min(slice_len, len(U))].copy(), remove_from_other=True)
+                s.append(U[:min(slice_len, len(U))], remove_from_other=True)
         remote_U = self.push(U.empty())
         del U
         self.map(_append_array_slice, slices, U=remote_U)