TopFarmProblem

class topfarm.TopFarmProblem(design_vars, cost_comp=None, driver=<topfarm.easy_drivers.EasyScipyOptimizeDriver object>, constraints=[], plot_comp=<topfarm.plotting.NoPlot object>, record_id=None, expected_cost=1, ext_vars={}, approx_totals=False, recorder=None, additional_recorders=None, n_wt=0, grid_layout_comp=None, penalty_comp=None, reports=None, **kwargs)[source]

__init__(design_vars[, cost_comp, driver, ...])

Initialize TopFarmProblem

evaluate([state, disp])

Evaluate the cost model

optimize([state, disp, recorder_as_list])

Run the optimization problem
__init__(design_vars, cost_comp=None, driver=<topfarm.easy_drivers.EasyScipyOptimizeDriver object>, constraints=[], plot_comp=<topfarm.plotting.NoPlot object>, record_id=None, expected_cost=1, ext_vars={}, approx_totals=False, recorder=None, additional_recorders=None, n_wt=0, grid_layout_comp=None, penalty_comp=None, reports=None, **kwargs)[source]

Initialize TopFarmProblem

Parameters

  • design_vars (dict or list of key-initial_value-tuples) –

    Design variables for the problem.

    Ex: {‘x’: [1,2,3], ‘y’:([3,2,1],0,1), ‘z’:([4,5,6],[4,5,4], [6,7,6])}

    Ex: [(‘x’, [1,2,3]), (‘y’,([3,2,1],0,1)), (‘z’,([4,5,6],[4,5,4], [6,7,6]))]

    Ex: [(‘x’, ([1,2,3],0,3,’m’)), (‘y’,([3,2,1],’m’)), (‘z’,([4,5,6],[4,5,4], [6,7,6]))]

    Ex: zip(‘xy’, pos.T)

    The keys (x, y, z) are the names of the design variable.

    The values are either

    • the initial value or

    • on of the following tuples:

      (initial value, unit) (initial value, lower bound, upper bound) (initial value, lower bound, upper bound, unit)

  • cost_comp (ExplicitComponent or TopFarmProblem or TopFarmGroup) –

    Component that provides the cost function. It has to be the style of an OpenMDAO v2 ExplicitComponent. Pure python cost functions can be wrapped using CostModelComponent class in topfarm.cost_models.cost_model_wrappers.

    ExplicitComponent are wrapped into a TopFarmGroup.

    For nested problems, the cost comp_comp is typically a TopFarmProblem

  • driver (openmdao Driver, optinal) – Driver used to solve the optimization driver. For an example, see the EasyScipyOptimizeDriver class in topfarm.easy_drivers.

  • constraints (list of Constraint-objects or tuples) – Constraint-objects are e.g. XYBoundaryConstraint, SpacingConstraint Tuples have the form (variable to constrain, {dict with options passed to the the OpenMDAO method add_constraint})

  • penalty_comp (ExplicitComponent, optional) – Component that converts constraints into penalty both for drivers that do and do not support constraints. Constraints are automatically converted to penalty for drivers that do not support constraints and the default magnitude of the penalty that is added to the objective is the sum of the constraint violations + 10**10.

  • plot_comp (ExplicitComponent, optional) – OpenMDAO ExplicitComponent used to plot the state (during optimization). For no plotting, pass in the topfarm.plotting.NoPlot class.

  • record_id (string "<record_id>:<case>", optional) –

    Identifier for the optimization. Allows a user to restart an optimization where it left off.

    record_id can be name (saves as recordings/<name>.pkl), abs or relative path Case can be:

    • ””, “latest”, “-1”: Continue from latest

    • ”best”: Continue from best case (minimum cost)

    • ”0”: Start from scratch (initial position)

    • ”4”: Start from case number 4

  • expected_cost (int, float or None, optional) –

    Used to scale the cost, default is 1. This has influence on some drivers, e.g. SLSQP where it affects the step size

    If None, the value is found by evaluating the cost function

  • ext_vars (dict or list of key-initial_value tuple) –

    Used for nested problems to propagate variables from parent problem

    Ex. {‘type’: [1,2,3]}

    Ex. [(‘type’, [1,2,3])]

  • approx_totals (bool or dict) – If True, approximates the total derivative of the cost_comp group, skipping the partial ones. If it is a dictionary, it’s elements are passed to the approx_totals function of an OpenMDAO Group.

  • recorder (Main recorder) –

  • additional_recorders (list(Recorder) or None) – A list of additional recorders to be added to the problem

  • n_wt (int) – Number of wind turbines

  • grid_layout_comp (ExplicitComponent or TopFarmGroup) – Components that uses at least one of topfarm.grid_x_key or topfarm.grid_y_key as input, and provides topfarm.x_key and topfarm.y_key as output. Default values for topfarm.grid_x_key or topfarm.grid_y_key are ‘sx’ and ‘sy’ respectively. These can be overwritten in the same way as e.g. topfarm.x_key. The component is inserted before constraint components will enables the use of components relying on x and y situated before the main cost component in the workflow.

Examples

See main() in the bottom of this file

evaluate(state={}, disp=False)[source]

Evaluate the cost model

Parameters

  • state (dict, optional) –

    Initial state

    Ex: {‘x’: [1,2,3], ‘y’:[3,2,1]}

    The current state is used for unspecified variables

  • disp (bool, optional) – if True, the time used for the optimization is printed

Returns

  • Current cost (float)

  • Current state (dict)

optimize(state={}, disp=False, recorder_as_list=False)[source]

Run the optimization problem

Parameters

  • state (dict, optional) –

    Initial state

    Ex: {‘x’: [1,2,3], ‘y’:[3,2,1]}

    The current state is used to unspecified variables

  • disp (bool, optional) – if True, the time used for the optimization is printed

  • recorder_as_list (bool, optional) – if True, returns multiprocessing friendly recorder as list of class and attributes that can be pickled. Use TopFarmListRecorder().list2recorder to restore TopFarmListRecorder object

Returns

  • Optimized cost (float)

  • state (dict)

  • recorder (TopFarmListRecorder or NestedTopFarmListRecorder or [TopFarmListRecorder.__class__, attributes])