engine

BalanceEngine

BalanceEngine(
    cable_array: CableArray,
    section_array: SectionArray,
    balance_model_type: Type[IBalanceModel] = BalanceModel,
    span_model_type: Type[ISpan] = CatenarySpan,
    deformation_model_type: Type[
        IDeformation
    ] = DeformationRte,
)

Bases: Notifier

Engine for solving insulator chains positions.

After solving any situation, many attributes are updated in the models.

Most interesting ones are

• self.L_ref for solve_adjustment()

• self.balance_model.nodes.dxdydz and self.span_model.parameter for solve_change_state().

Examples:

    >>> balance_engine = BalanceEngine(cable_array, section_array)
    >>> balance_engine.solve_adjustment()
    >>> wind_pressure = np.array([...])  # in Pa
    >>> ice_thickness = np.array([...])  # in m
    >>> new_temperature = np.array([...])  # in °C
    >>> balance_engine.solve_change_state(
    ...     wind_pressure, ice_thickness, new_temperature
    ... )

Parameters:

Name	Type	Description	Default
cable_array	CableArray	Cable data	required
section_array	SectionArray	Section data	required
span_model_type	Type[Span]	Span model to use. Defaults to CatenarySpan.	CatenarySpan
deformation_model_type	Type[IDeformation]	Deformation model to use. Defaults to DeformationRte.	DeformationRte

Source code in src/mechaphlowers/core/models/balance/engine.py

def __init__(
    self,
    cable_array: CableArray,
    section_array: SectionArray,
    balance_model_type: Type[IBalanceModel] = BalanceModel,
    span_model_type: Type[ISpan] = CatenarySpan,
    deformation_model_type: Type[IDeformation] = DeformationRte,
) -> None:
    # TODO: find a better way to initialize objects
    self.section_array = section_array
    self.cable_array = cable_array
    self.balance_model_type = balance_model_type
    self.span_model_type = span_model_type
    self.deformation_model_type = deformation_model_type
    self._adjustment_blocked: bool = False

    self.reset(full=True)

add_loads

add_loads(
    load_position_distance: ndarray | list,
    load_mass: ndarray | list,
) -> None

Calls preferred method set_loads.

Kept for compatibility.

Expected length for load_position_distance and load_mass is the number of pylons. Last array elements should be nan or zero.

Raises:

Type	Description
ValueError	if at least one load_position_distance is not in [0, span_length] or if the arguments don't have the right lengths.

Source code in src/mechaphlowers/core/models/balance/engine.py

def add_loads(
    self,
    load_position_distance: np.ndarray | list,
    load_mass: np.ndarray | list,
) -> None:
    """Calls preferred method [`set_loads`](mechaphlowers.core.models.balance.engine.BalanceEngine.set_loads).

    Kept for compatibility.

    Expected length for load_position_distance and load_mass is the number of pylons.
    Last array elements should be nan or zero.

    Raises:
        ValueError: if at least one load_position_distance is not in [0, span_length]
            or if the arguments don't have the right lengths.
    """
    warnings.warn(
        "add_loads is deprecated, use set_loads instead."
        "Caution: expected argument length for set_loads is the number of spans.",
        category=DeprecationWarning,
    )
    load_position_distance = np.array(load_position_distance)
    load_mass = np.array(load_mass)
    self.set_loads(arr.decr(load_position_distance), arr.decr(load_mass))

get_data_spans

get_data_spans() -> dict[str, list]

Fetch data from BalanceEngine about spans.

This data is stored as a dictionary containing lists.

Returns:

Name	Type	Description
dict	dict[str, list]	dictionnary contains following fields: span_length elevation parameter tension_sup tension_inf slope_left slope_right L0 horizontal_distance arc_length T_h sag sag_s2

Source code in src/mechaphlowers/core/models/balance/engine.py

def get_data_spans(self) -> dict[str, list]:
    """Fetch data from BalanceEngine about spans.

    This data is stored as a dictionary containing lists.

    Returns:
        dict: dictionnary contains following fields:
            <ul>
                <li>span_length</li>
                <li>elevation</li>
                <li>parameter</li>
                <li>tension_sup</li>
                <li>tension_inf</li>
                <li>slope_left</li>
                <li>slope_right</li>
                <li>L0</li>
                <li>horizontal_distance</li>
                <li>arc_length</li>
                <li>T_h</li>
                <li>sag</li>
                <li>sag_s2</li>
            </ul>
    """
    T_sup, T_inf = self.span_model.tensions_sup_inf()
    force_output_unit = options.output_units.force
    T_sup_q_array, T_inf_q_array = (
        QuantityArray(T_sup, 'N', force_output_unit),
        QuantityArray(T_inf, 'N', force_output_unit),
    )
    T_h_q_array = QuantityArray(
        self.span_model.T_h(), 'N', force_output_unit
    )
    span_slope_left = QuantityArray(
        self.span_model.slope(side="left"), 'rad', 'deg'
    )
    span_slope_right = QuantityArray(
        self.span_model.slope(side="right"), 'rad', 'deg'
    )

    result_dict = {
        "span_length": arr.decr(
            self.section_array.data["span_length"].to_numpy()
        ).tolist(),
        "elevation": arr.decr(
            self.section_array.data["elevation_difference"].to_numpy()
        ).tolist(),
        "parameter": arr.decr(self.parameter).tolist(),
        "slope_left": arr.decr(span_slope_left.value()).tolist(),
        "slope_right": arr.decr(span_slope_right.value()).tolist(),
        "tension_sup": arr.decr(T_sup_q_array.value()).tolist(),
        "tension_inf": arr.decr(T_inf_q_array.value()).tolist(),
        "L0": self.L_ref.tolist(),
        "horizontal_distance": self.balance_model.a.tolist(),
        "arc_length": arr.decr(self.span_model.compute_L()).tolist(),
        "T_h": arr.decr(T_h_q_array.value()).tolist(),
        "sag": arr.decr(self.span_model.sag()).tolist(),
        "sag_s2": arr.decr(self.span_model.sag_s2()).tolist(),
    }
    return result_dict

get_ruling_span_length

get_ruling_span_length() -> float

Compute ruling span length:

if we considered the whole section as a single span, the length would be ruling_span_length

Used for tensions computation when unfolding the cable.

\(L_{R} = \sqrt{\frac{\sum(L_n ^ 4 / C_n)}{\sum{C_n}}}\)

where \(L_n\) are the horizontal length of span n, and \(C_n\) the chord length of span n

Returns:

Name	Type	Description
float	float	span length of ruling span

Source code in src/mechaphlowers/core/models/balance/engine.py

def get_ruling_span_length(self) -> float:
    """Compute ruling span length:

    if we considered the whole section as a single span, the length would be ruling_span_length

    Used for tensions computation when unfolding the cable.

    $L_{R} = \\sqrt{\\frac{\\sum(L_n ^ 4 / C_n)}{\\sum{C_n}}}$

    where $L_n$ are the horizontal length of span n, and $C_n$ the chord length of span n

    Returns:
        float: span length of ruling span
    """
    # proto uses section_array.span_length instead of balance_model.a (called a_chain in proto)
    chord = np.sqrt(self.balance_model.a**2 + self.balance_model.b**2)
    return np.sqrt(np.sum(self.balance_model.a**4 / chord) / np.sum(chord))

reset

reset(full: bool = False) -> None

Reset the balance engine to initial state.

This method re-initializes the span model, cable loads, span loads, deformation model, balance model, and solvers. This method is useful when an error occurs during solving that may cause an inconsistent state with NaN values.

Source code in src/mechaphlowers/core/models/balance/engine.py

def reset(self, full: bool = False) -> None:
    """Reset the balance engine to initial state.

    This method re-initializes the span model, cable loads, span loads, deformation model, balance model, and solvers.
    This method is useful when an error occurs during solving that may cause an inconsistent state with NaN values.
    """

    logger.debug("Resetting balance engine.")

    if full:
        self.initialized = False
        zeros_vector = np.zeros_like(
            self.section_array.data.conductor_attachment_altitude.to_numpy()
        )
        sagging_temperature = arr.decr(
            (self.section_array.data.sagging_temperature.to_numpy())
        )
        parameter = arr.decr(
            self.section_array.data.sagging_parameter.to_numpy()
        )
        self.span_model = span_model_builder(
            self.section_array, self.cable_array, self.span_model_type
        )
        self.cable_loads = CableLoads(
            np.float64(self.cable_array.data.diameter.iloc[0]),
            np.float64(self.cable_array.data.linear_weight.iloc[0]),
            zeros_vector,
            zeros_vector,
        )
        self.span_loads = SpanLoads(
            arr.decr(zeros_vector),
            arr.decr(zeros_vector),
            self.section_array.data.span_length.to_numpy(),
        )
        self.deformation_model = deformation_model_builder(
            self.cable_array,
            self.span_model,
            sagging_temperature,
            self.deformation_model_type,
        )
        if not hasattr(self, "_observers"):
            super().__init__()
        self.balance_model = self.balance_model_type(
            sagging_temperature,
            parameter,
            self.section_array,
            self.cable_array,
            self.span_model,
            self.deformation_model,
            self.cable_loads,
            self.span_loads,
        )
    else:
        self.balance_model.reset(
            cable_array=self.cable_array,
            span_model=self.span_model,
            deformation_model=self.deformation_model,
            cable_loads=self.cable_loads,
            span_loads=self.span_loads,
            full=full,
        )

    if full:
        self.solver_change_state = BalanceSolver(
            **options.solver.balance_solver_change_state_params
        )
        self.solver_adjustment = BalanceSolver(
            **options.solver.balance_solver_adjustment_params
        )
        self.L_ref: np.ndarray

    self.get_displacement: Callable[[], np.ndarray] = (
        self.balance_model.chain_displacement
    )

    self.notify()
    self.initialized = True

    logger.debug("Balance engine initialized.")

set_loads

set_loads(
    load_position_distance: ndarray | list,
    load_mass: ndarray | list,
) -> None

Adds loads to BalanceEngine.

Input for position is a distance, and will be converted into ratio.

Expected input are arrays of size matching the number of spans. Each value refers to a span.

If either load_position_distance[i] or load_mass[i] is 0 or nan, it means there is no load at span i.

Parameters:

Name	Type	Description	Default
load_position_distance	ndarray | list	Position of the loads, in meters	required
load_mass	ndarray | list	Mass of the loads	required

Raises:

Type	Description
ValueError	if at least one load_position_distance is not in [0, span_length] or if the arguments don't have the right lengths.

Examples:

>>> load_position_distance = np.array([150, 200, 0])  # 3 spans
>>> load_mass = np.array([500, 70, 0])
>>> engine.set_loads(load_position_distance, load_mass)
>>> plot_engine.reset()  # optional: only needed if cached plots must be discarded

Source code in src/mechaphlowers/core/models/balance/engine.py

def set_loads(
    self,
    load_position_distance: np.ndarray | list,
    load_mass: np.ndarray | list,
) -> None:
    """Adds loads to BalanceEngine.

    Input for position is a distance, and will be converted into ratio.

    Expected input are arrays of size matching the number of spans. Each value refers to a span.

    If either load_position_distance[i] or load_mass[i] is 0 or nan, it means there is no load at span i.

    Args:
        load_position_distance (np.ndarray | list): Position of the loads, in meters
        load_mass (np.ndarray | list): Mass of the loads

    Raises:
        ValueError: if at least one load_position_distance is not in [0, span_length]
            or if the arguments don't have the right lengths.

    Examples:
        >>> load_position_distance = np.array([150, 200, 0])  # 3 spans
        >>> load_mass = np.array([500, 70, 0])
        >>> engine.set_loads(load_position_distance, load_mass)
        >>> plot_engine.reset()  # optional: only needed if cached plots must be discarded
    """
    span_length = self.section_array.data.span_length.to_numpy()

    self.span_loads.set_loads(
        load_position_distance, load_mass, span_length
    )

    self.reset(full=False)

    debug_loads = (
        "Loads have been added. PlotEngine will be notified automatically "
        "via the observer pattern; no manual reset is required."
    )
    logger.debug(debug_loads)

solve_adjustment

solve_adjustment() -> None

Solve the chain positions in the adjustment case, updating L_ref in the balance model. In this case, there is no weather, no loads, and temperature is the sagging temperature.

After running this method, many attributes are updated. Most interesting ones are L_ref, parameter in Span, and dxdydz in Nodes.

Raises:

Type	Description
SolverError	If the solver fails to converge.
RuntimeError	If adjustment is blocked (engine built from manipulations).

Source code in src/mechaphlowers/core/models/balance/engine.py

@check_time
def solve_adjustment(self) -> None:
    """Solve the chain positions in the adjustment case, updating L_ref in the balance model.
    In this case, there is no weather, no loads, and temperature is the sagging temperature.

    After running this method, many attributes are updated.
    Most interesting ones are `L_ref`, `parameter` in Span, and `dxdydz` in Nodes.

    Raises:
        SolverError: If the solver fails to converge.
        RuntimeError: If adjustment is blocked (engine built from manipulations).
    """
    if self._adjustment_blocked:
        raise RuntimeError(
            "solve_adjustment is blocked on this engine. "
            "L_ref was injected externally from a clean adjustment."
        )
    logger.debug("Starting adjustment.")

    self.balance_model.adjustment = True
    # reset parameter to sagging parameter
    sagging_parameter = (
        self.section_array.data.sagging_parameter.to_numpy()
    )
    self.span_model.set_parameter(sagging_parameter)
    # reset displacements to zero (hypothesis of adjustment)
    self.balance_model.nodes.dxdydz[:] = 0

    try:
        self.solver_adjustment.solve(self.balance_model)
    except SolverError as e:
        logger.error(
            "Error during solve_adjustment, resetting balance engine."
        )
        e.origin = "solve_adjustment"
        raise e

    self.initial_L_ref = self.L_ref = self.balance_model.update_L_ref()

    logger.debug(f"Output : L_ref = {str(self.L_ref)}")

solve_change_state

solve_change_state(
    wind_pressure: ndarray | float | None = None,
    ice_thickness: ndarray | float | None = None,
    new_temperature: ndarray | float | None = None,
    wind_direction: Literal[
        'clockwise', 'anticlockwise'
    ] = 'anticlockwise',
) -> None

Solve the chain positions, for a case of change of state. Updates weather conditions and/or sagging temperature if provided. Takes into account loads if any.

Parameters:

Name	Type	Description	Default
wind_pressure	ndarray | float | None	Wind pressure in Pa. Default to None	None
ice_thickness	ndarray | float | None	Ice thickness in m. Default to None	None
new_temperature	ndarray | float | None	New temperature in °C. Default to None	None
wind_direction	Literal['clockwise', 'anticlockwise']	Direction of the wind: if "clockwise": towards user (right), if "anticlockwise": away from user (left). Default to "anticlockwise".	'anticlockwise'

After running this method, many attributes are updated. Most interesting ones are L_ref, parameter in Span, and dxdydz in Nodes.

Raises:

Type	Description
SolverError	If the solver fails to converge.
TypeError	If input parameters have incorrect type.
ValueError	If input parameters have incorrect shape.

Source code in src/mechaphlowers/core/models/balance/engine.py

@check_time
def solve_change_state(
    self,
    wind_pressure: np.ndarray | float | None = None,
    ice_thickness: np.ndarray | float | None = None,
    new_temperature: np.ndarray | float | None = None,
    wind_direction: Literal[
        "clockwise", "anticlockwise"
    ] = "anticlockwise",
) -> None:
    """Solve the chain positions, for a case of change of state.
    Updates weather conditions and/or sagging temperature if provided.
    Takes into account loads if any.

    Args:
        wind_pressure (np.ndarray | float | None): Wind pressure in Pa. Default to None
        ice_thickness (np.ndarray | float | None): Ice thickness in m. Default to None
        new_temperature (np.ndarray | float | None): New temperature in °C. Default to None
        wind_direction (Literal["clockwise", "anticlockwise"]): Direction of the wind: if "clockwise": towards user (right), if "anticlockwise": away from user (left). Default to "anticlockwise".

    After running this method, many attributes are updated.
    Most interesting ones are `L_ref`, `parameter` in Span, and `dxdydz` in Nodes.

    Raises:
        SolverError: If the solver fails to converge.
        TypeError: If input parameters have incorrect type.
        ValueError: If input parameters have incorrect shape.
    """
    logger.debug("Starting change state.")
    logger.debug(
        f"Parameters received: \nwind_pressure {str(wind_pressure)}\nice_thickness {str(ice_thickness)}\nnew_temperature {str(new_temperature)}\nwind_direction {str(wind_direction)}"
    )

    if wind_direction not in ["clockwise", "anticlockwise"]:
        raise ValueError(
            f"wind_direction should be 'clockwise' or 'anticlockwise', received {wind_direction}"
        )

    # check if adjustment has been done before
    try:
        _ = self.initial_L_ref
        logger.debug(
            f"Adjustment has been done before, initial_L_ref before shifting: {str(self.initial_L_ref)}"
        )
    except AttributeError:
        logger.warning(self._warning_no_L_ref)
        warnings.warn(self._warning_no_L_ref, BalanceEngineWarning)
        self.solve_adjustment()

    # Use current span_model (potentially rebuilt by solve_adjustment)
    span_shape = (
        self.span_model.parameter.shape
    )  # span_model holds n-sized array (same shape as span_length)

    def validate_input(input_value, name: str):
        if input_value is None:
            input_value = np.full(span_shape, self.default_value[name])
        elif isinstance(input_value, (int, float)):
            input_value = np.full(span_shape, input_value)
        elif isinstance(input_value, np.ndarray):
            if input_value.shape != span_shape:
                raise ValueError(
                    f"{name} has incorrect shape: {span_shape} is expected, received {input_value.shape}"
                )
        else:
            raise TypeError(f"{name} has incorrect type")

        return input_value

    # Set model attributes after potential solve_adjustment (which may
    # rebuild models via reset(full=True)).
    validated_wind = validate_input(wind_pressure, "wind_pressure")
    if wind_direction == "clockwise":
        validated_wind = -validated_wind

    self.balance_model.cable_loads.wind_pressure = validated_wind

    # Ice thickness input in meters
    self.balance_model.cable_loads.ice_thickness = validate_input(
        ice_thickness, "ice_thickness"
    )

    new_t = validate_input(new_temperature, "new_temperature")
    self.balance_model.current_temperature = arr.decr(new_t)
    self.deformation_model.current_temperature = new_t

    self.balance_model.adjustment = False

    self.span_model.load_coefficient = (
        self.balance_model.cable_loads.load_coefficient
    )

    try:
        self.solver_change_state.solve(self.balance_model)
    except SolverError as e:
        logger.error(
            "Error during solve_change_state, you should reset the balance engine."
        )
        e.origin = "solve_change_state"
        raise e

    logger.debug(
        f"Output : get_displacement \n{str(self.get_displacement())}"
    )
    self.balance_model.update_nodes_span_model()