pyserep.io

pyserep.io — matrix loading, export, and mesh output.

pyserep.io.enforce_symmetry(mat: spmatrix) → csc_matrix[source]

Enforce symmetry: A ← 0.5 * (A + Aᵀ).

Parameters:: mat (scipy.sparse matrix)
Returns:: Symmetric version of mat.
Return type:: scipy.sparse.csc_matrix

pyserep.io.load_frf_npz(path: str) → Dict[str, ndarray][source]

Load a saved FRF .npz file.

Returns:: freqs_hz — evaluation frequencies (Hz) rom_* — ROM FRF arrays (complex) ref_* — reference FRF arrays (complex)
Return type:: dict with keys

pyserep.io.load_matrices(stiffness_path: str, mass_path: str, verbose: bool = True, symmetry_tol: float = 1e-08) → Tuple[csc_matrix, csc_matrix][source]

Load stiffness and mass matrices from disk.

Parameters:

stiffness_path (str) – Path to the stiffness matrix file.
mass_path (str) – Path to the mass matrix file.
verbose (bool) – Print loading progress, matrix statistics, and memory usage.
symmetry_tol (float) – Symmetry tolerance forwarded to load_matrix().

Returns:

K (scipy.sparse.csc_matrix) – Stiffness matrix.
M (scipy.sparse.csc_matrix) – Mass matrix.

Raises:

FileNotFoundError – If either file does not exist.
ValueError – If K and M have incompatible shapes or are not square.

Examples

>>> K, M = load_matrices("K.mtx", "M.mtx")
>>> K.shape == M.shape
True

pyserep.io.load_matrix(path: str, symmetry_tol: float = 1e-08) → csc_matrix[source]

Load a single sparse or dense matrix from disk, returning a CSC matrix.

Parameters:

path (str) – Absolute or relative path to the matrix file.
symmetry_tol (float) – If the relative asymmetry |A - Aᵀ|_F / |A|_F exceeds this threshold, a UserWarning is issued (does not abort).

Returns:

The loaded matrix in CSC format.

Return type:

scipy.sparse.csc_matrix

Raises:

FileNotFoundError – If the file does not exist.
ValueError – If the format is unrecognised or the file cannot be parsed.

Examples

>>> K = load_matrix("StiffMatrixmm.mtx")
>>> K.shape
(66525, 66525)

pyserep.io.load_metrics(folder: str, prefix: str = 'SEREP') → Dict[str, Any][source]: Load a saved metrics JSON file.

pyserep.io.load_reduced_matrices(folder: str, prefix: str = 'SEREP') → tuple[ndarray, ndarray, ndarray][source]

Load saved Ka, Ma, and T matrices.

Returns:: Ka, Ma, T
Return type:: np.ndarray

pyserep.io.save_results(results: PipelineResults, folder: str, prefix: str = 'SEREP', save_matrices: bool = True, verbose: bool = True) → Dict[str, str][source]

Save all pipeline results to folder.

Parameters:

results (PipelineResults)
folder (str) – Output directory (created if it does not exist).
prefix (str) – Filename prefix for all output files.
save_matrices (bool) – If True, save Ka, Ma, and T matrices. These can be large; set to False to save only indices, FRF, and metrics.
verbose (bool)

Returns:

Mapping of result type → absolute file path.

Return type:

dict

pyserep.io.write_ansys_node_list(master_dofs: ndarray, path: str, component_name: str = 'MASTER_NODES', verbose: bool = True) → None[source]

Write an Ansys APDL input file that selects the master DOF nodes.

Usage in Ansys: /INPUT, master_nodes.inp

Parameters:

master_dofs (np.ndarray of int)
path (str — output .inp path)
component_name (str — Ansys component name)
verbose (bool)

pyserep.io.write_master_dofs_csv(master_dofs: ndarray, path: str, node_coords: ndarray | None = None, verbose: bool = True) → None[source]

Write master DOF indices to a CSV file.

Columns: dof_index, node_number, direction, [x, y, z]

Parameters:

master_dofs (np.ndarray of int — 0-based DOF indices)
path (str — output .csv path)
node_coords (np.ndarray, shape (N_nodes, 3), optional) – Node coordinate array (indexed by node_number − 1). If provided, x, y, z columns are written.
verbose (bool)

pyserep.io.write_master_dofs_vtk(master_dofs: ndarray, node_coords: ndarray, path: str, scalar_data: ndarray | None = None, scalar_name: str = 'eid_score', verbose: bool = True) → None[source]

Write master DOF locations as a VTK unstructured point cloud.

Opens in ParaView, MayaVi, VisIt, etc.

Parameters:

master_dofs (np.ndarray of int)
node_coords (np.ndarray, shape (N_nodes, 3))
path (str — output .vtk path)
scalar_data (np.ndarray, shape (n_master,), optional) – Per-DOF scalar field (e.g. EID score, KE contribution).
scalar_name (str)
verbose (bool)

pyserep.io.write_uff58_mode_shapes(phi: ndarray, freqs_hz: ndarray, selected_modes: ndarray, master_dofs: ndarray, node_coords: ndarray | None, path: str, zeta: float = 0.001, verbose: bool = True) → None[source]

Write mode shapes to a UFF (Universal File Format) dataset 58 file.

Compatible with SDyPy, pyFRF, ME’scopeVES, nModal, and other EMA/OMA post-processors.

Parameters:

phi (np.ndarray, shape (N, n_all_modes))
freqs_hz (np.ndarray)
selected_modes (np.ndarray of int)
master_dofs (np.ndarray of int — only these DOFs are exported)
node_coords (np.ndarray, shape (N_nodes, 3), optional)
path (str — output .uff path)
zeta (float — damping ratio for all modes)
verbose (bool)