finesse.analysis.actions.optimisation module

Collection of Actions that deal linear time invariant (LTI) modelling tasks.

class finesse.analysis.actions.optimisation.Maximize(detector, parameter, name='maximize', *args, **kwargs)[source]

Bases: Optimize

An action that maximizes some detector output by applying some feedback to multiple targets in a model. Extra keyword arguments are passed on to the Scipy method:

minimize

This action offers a simplified interface that allows an optimization to be done during a simulation. By default the the Nelder-Mead optimization method is used but can be overridden. The user should read the Scipy documentation to determine which options should be used which are method dependant.

Notes

Default optimizer used is nelder-mead. To set the absolute and relative error targets use (From the scipy documentation: https://docs.scipy.org/doc/scipy/reference/optimize.minimize-neldermead.html)

xatolfloat, optional: Absolute error in xopt between iterations that is acceptable for convergence. Defaults to 1e-4.
fatolfloat, optional: Absolute error in func(xopt) between iterations that is acceptable for convergence. Defaults to 1e-4.

These can be set as keyword arguments to the action.

Parameters

detectorstr: The name of the detector output to maximize / minimize.
parameter[Parameter | str | tuple]: The parameter or name of the parameter to optimize, or a tuple of parameters when using multiple targets to optimize over.
boundslist, optional: A pair of (lower, upper) bounds on the parameter value. Requires a method that uses bounds.
offsetfloat, optional: An offset applied to the detector output when optimizing, defaults to 0.
kindstr, optional: Either ‘max’ for maximization or ‘min’ for minimization, defaults to ‘max’.
max_iterationsint, optional: Maximum number of solver iterations, defaults to 10000.
methodstr, optional: Optimisation method to use, see Scipy documentation for options.
namestr, optional: The name of this action, defaults to ‘maximize’.
update_mapsbool, optional: If you are changing some parameter or variable that a Map depends on then setting this flag to True will recompute the Map data for each iteration of the optimiser.
pre_stepAction, optional: Action to run on each step of the optimisation.
kwargs: Optional parameters passed to the Scipy optimisation routine as the options input. See Scipy method documentation to determine what is available.

Examples

Simple example that maximizes the power in a coupled cavity solution by moving multiple mirrors

model = finesse.Model()
model.parse('''
l l1 P=1
m m1 R=0.98 T=0.02 phi=10
m m2 R=0.99 T=0.01
m m3 R=1 T=0 phi=-20
link(l1, m1, m2, m3)
pd P m3.p1.i
''')
sol = model.run("maximize(P, [m1.phi, m3.phi], xatol=1e-7)")
print(sol.result)

class finesse.analysis.actions.optimisation.Minimize(detector, parameter, name='minimize', *args, **kwargs)[source]

Bases: Optimize

An action that minimizes some detector output by applying some feedback to multiple targets in a model. Extra keyword arguments are passed on to the Scipy method:

minimize

This action offers a simplified interface that allows an optimization to be done during a simulation. By default the the Nelder-Mead optimization method is used but can be overridden. The user should read the Scipy documentation to determine which options should be used which are method dependant.

Notes

Default optimizer used is nelder-mead. To set the absolute and relative error targets use (From the scipy documentation: https://docs.scipy.org/doc/scipy/reference/optimize.minimize-neldermead.html)

xatolfloat, optional: Absolute error in xopt between iterations that is acceptable for convergence. Defaults to 1e-4.
fatolfloat, optional: Absolute error in func(xopt) between iterations that is acceptable for convergence. Defaults to 1e-4.

These can be set as keyword arguments to the action.

Parameters

detectorstr: The name of the detector output to maximize / minimize.
parameter[Parameter | str | tuple]: The parameter or name of the parameter to optimize, or a tuple of parameters when using multiple targets to optimize over.
boundslist, optional: A pair of (lower, upper) bounds on the parameter value. Requires a method that uses bounds.
offsetfloat, optional: An offset applied to the detector output when optimizing, defaults to 0.
kindstr, optional: Either ‘max’ for maximization or ‘min’ for minimization, defaults to ‘max’.
max_iterationsint, optional: Maximum number of solver iterations, defaults to 10000.
methodstr, optional: Optimisation method to use, see Scipy documentation for options.
namestr, optional: The name of this action, defaults to ‘maximize’.
update_mapsbool, optional: If you are changing some parameter or variable that a Map depends on then setting this flag to True will recompute the Map data for each iteration of the optimiser.
pre_stepAction, optional: Action to run on each step of the optimisation.
kwargs: Optional parameters passed to the Scipy optimisation routine as the options input. See Scipy method documentation to determine what is available.

Examples

Simple example that minimizes some measured power by feeding back to the laser power

model = finesse.Model()
model.parse('''
l l1 P=1
pd P l1.p1.o
''')
sol = model.run("minimize(P, l1.P)")
print(sol.result)

exception finesse.analysis.actions.optimisation.OptimizationWarning[source]: Bases: RuntimeWarning

class finesse.analysis.actions.optimisation.Optimize(detector, parameters, bounds=None, offset=0, kind='max', max_iterations=10000, tol=None, verbose=False, method='nelder-mead', opfunc=None, update_maps=False, pre_step=None, name='optimize', **kwargs)[source]

Bases: Action

property parameter_names[source]

class finesse.analysis.actions.optimisation.OptimizeSolution(*args, **kwargs)[source]

Bases: BaseSolution

Solution for an optimization action.

Attributes

resultscipy.optimize.optimize.OptimizeResult: Result from the scipy optimization method that contains the results and some extra data about the process and any errors that might happen.
parameters[Parameter | tuple]: Name or names of parameters that were optimized over
x[numeric | ndarray]: The final minimized values for the parameters requested.