def __save_and_load_test (self, name, A ): save_scipy_matrix_as_mat ( data_path, name, A ) A_load = load_scipy_matrix_from_mat ( data_path, name ) np.testing.assert_equal ( A_load.shape, A.shape ) if type(A) != np.ndarray: np.testing.assert_equal( A_load.data, A.data ) np.testing.assert_equal( A_load.indices, A.indices ) np.testing.assert_equal( A_load.indptr, A.indptr ) else: np.testing.assert_equal( A_load, A )
def __save_and_load_test(self, name, A): save_scipy_matrix_as_mat(data_path, name, A) A_load = load_scipy_matrix_from_mat(data_path, name) np.testing.assert_equal(A_load.shape, A.shape) if type(A) != np.ndarray: np.testing.assert_equal(A_load.data, A.data) np.testing.assert_equal(A_load.indices, A.indices) np.testing.assert_equal(A_load.indptr, A.indptr) else: np.testing.assert_equal(A_load, A)
def save_matrices(path, id, A, b): """ Save the system matrix and the excitation vector. The matrices will be saved in <path>/<id>/A.mat and <path>/<id>/b.mat @param path: the folder where the matrices are to be saved @param id: a unique problem identifier @param A: the system matrix @param b: the excitation vector """ full_path = os.path.join(path, id) save_scipy_matrix_as_mat(full_path, 'A', A) save_scipy_matrix_as_mat(full_path, 'b', b) dofs = A.shape[0] f = file(os.path.join(full_path, '%dDOFs.txt' % dofs), 'w') f.write('DOFs = %d\n' % dofs) f.close()
def test_save_scipy_matrix ( self ): import scipy.sparse N = 1000; A = scipy.sparse.rand ( N, N, format='csr' ) save_scipy_matrix_as_mat ( data_path, 'A', A )
def test_save_scipy_matrix(self): import scipy.sparse N = 1000 A = scipy.sparse.rand(N, N, format='csr') save_scipy_matrix_as_mat(data_path, 'A', A)