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| SteadyResult | feedtwin.solve.steady.solve_steady (Network network, *, float tol=1.0e-6, int max_iterations=100, float initial_flow=INITIAL_FLOW, bool raise_on_failure=True, Mapping[str, float]|None signals=None, Mapping[str, float]|None guess=None, Iterable[str]|None isolate=None, Mapping[str, tuple[float, float]]|None storage=None, bool report=True) |
| dict[str, tuple[float, float]] | feedtwin.solve.steady._stores (Network network, Mapping[str, tuple[float, float]]|None storage, list[str] branch_ids) |
| np.ndarray | feedtwin.solve.steady._initial_guess (Network network, list[str] free, list[str] branch_ids, float initial_flow) |
| np.ndarray | feedtwin.solve.steady._row_scales (Network network, list[str] free, list[str] branch_ids, float initial_flow) |
| np.ndarray | feedtwin.solve.steady._local_scales (Network network, np.ndarray x, dict[str, int] node_index, dict[str, int] branch_index, dict[str, _BranchRow] branch_rows, np.ndarray base, float tol) |
| bool | feedtwin.solve.steady._physical (np.ndarray x, list[str] free) |
| float | feedtwin.solve.steady._scaled_norm (np.ndarray residual, np.ndarray scale, np.ndarray|None ignore=None) |
| float | feedtwin.solve.steady._pressure_at (Network network, np.ndarray x, dict[str, int] node_index, str node_id) |
| _BranchRow | feedtwin.solve.steady._branch_row (Network network, str branch_id, float mdot, float p_up, float p_dn, Mapping[str, float]|None signals=None) |
| dict[str, _BranchRow] | feedtwin.solve.steady._branch_rows (Network network, np.ndarray x, dict[str, int] node_index, dict[str, int] branch_index, Mapping[str, float]|None signals=None) |
| np.ndarray | feedtwin.solve.steady._assemble_residual (Network network, np.ndarray x, dict[str, int] node_index, dict[str, int] branch_index, dict[str, _BranchRow] rows, Mapping[str, tuple[float, float]]|None stores=None) |
| np.ndarray | feedtwin.solve.steady._residuals (Network network, np.ndarray x, dict[str, int] node_index, dict[str, int] branch_index, Mapping[str, float]|None signals=None, Mapping[str, tuple[float, float]]|None stores=None) |
| None | feedtwin.solve.steady._mass_rows (Network network, np.ndarray x, dict[str, int] node_index, dict[str, int] branch_index, Mapping[str, tuple[float, float]]|None stores, np.ndarray residual) |
| tuple[list[int], list[int]] | feedtwin.solve.steady._incidence_pattern (Network network, dict[str, int] node_index, dict[str, int] branch_index, Mapping[str, tuple[float, float]]|None stores=None) |
| csc_matrix | feedtwin.solve.steady._jacobian (Network network, dict[str, int] node_index, dict[str, int] branch_index, dict[str, _BranchRow] branch_rows, list[int] rows, list[int] cols, Mapping[str, tuple[float, float]]|None stores=None) |
| np.ndarray | feedtwin.solve.steady._damped_update (Network network, np.ndarray x, np.ndarray step, np.ndarray residual, np.ndarray scale, dict[str, int] node_index, dict[str, int] branch_index, Mapping[str, float]|None signals=None, np.ndarray|None ignore=None, Mapping[str, tuple[float, float]]|None stores=None) |
| SteadyResult | feedtwin.solve.steady._result (Network network, np.ndarray x, bool converged, int iterations, float norm, float started, list[DeadEnd] stubs, set[str] isolated, Mapping[str, tuple[float, float]]|None stores=None, *, bool report=True, Mapping[str, float]|None signals=None) |
1.15.0