finesse.analysis.actions.sensing module

Collection of Actions that deal with sensing tasks such as computing sensing matrices, optimising RF readouts, etc.

class finesse.analysis.actions.sensing.CheckLinearity(*locks, num_points=10, plot_results=True, xlim=None, name='run locks')[source]

Bases: Action

An action that shows the relationships between all DOFs and all error signals, to check whether they are related linearly. Plotted for DOFs starting at their initial values and up until their initial values + 2*gain*intial error signal.

Parameters

*lockslist, optional: A list of locks to use in each RunLocks step. Acts like *locks parameter in RunLocks: if not provided, all locks in model are used.
num_pointsint: Number of points to plot in the DOF range.
plot_resultsboolean: Whether or not to plot results (requires matplotlib)
xlimlist or None: Defines (half of) the range of DOF values over which to plot the error signals. If not specified, gains are used to find a useful range of DOF values to plot over.
namestr: Name of the action.

class finesse.analysis.actions.sensing.CheckLinearitySolution(*args, **kwargs)[source]: Bases: BaseSolution

class finesse.analysis.actions.sensing.GetErrorSignals(*locks, name='get error signals')[source]

Bases: Action

An action that quickly calculates the current error signals for all or a subset of locks in a model.

Parameters

*lockslist, optional: A list of lock names to compute the error signals for. If not provided, all locks in model are used.
namestr: Name of the action.

class finesse.analysis.actions.sensing.GetErrorSignalsSolution(*args, **kwargs)[source]: Bases: BaseSolution

class finesse.analysis.actions.sensing.OptimiseRFReadoutPhaseDC(*args, d_dof=1e-10, name='optimise_demod_phases_dc', verbose=False)[source]

Bases: Action

This optimises the demodulation phase of ReadoutRF elements relative to some DegreeOfFreedom or driven Parameter in the model. The phases are optimised by calculating the DC response of the readouts. This Action changes the state of the model by varying the readout demodulation phases. If no arguments are given it will try to automatically optimise any lock element in the model that is using an RF readout with respect to the lock feedback parameter.

Parameters

args: Pairs of DegreeOfFreedom or Parameter and ReadoutRF elements, or pairs of their names. If none are provided OptimiseRFReadoutPhaseDC will automatically search for Lock elements which have ReadoutRF error signal and optimise them. Every readout can only be optimised to a single DegreeOfFreedom or Parameter. If a readout is paired with multiple drives, its phase is optimised to the first and the others are ignored.
d_doffloat, optional: A small offset applied to the DOFs to compute the gradients of the error signals
verbosebool: If True this will print the initial and optimised readout phases. Defaults to False

Examples

Take a typicaly Pound-Drever-Hall lock of a cavity. Here is some KatScript to setup such a model:

>>> import finesse
>>> from finesse.analysis.actions import OptimiseRFReadoutPhaseDC
>>>
>>> model = finesse.Model()
>>> model.parse('''
>>> l l1
>>> mod mod1 10M 0.1 mod_type=pm
>>> readout_rf PD f=mod1.f phase=33 output_detectors=True optical_node=m1.p1.o
>>> m m1 R=0.99 T=0.01
>>> m m2 R=1 T=0
>>> link(l1, mod1, m1, 1, m2)
>>> lock cav_lock PD_I m2.phi 0.01 1e-3
>>> ''')

We have defined a lock above using the I quadrature RF demodulation and feeding back to the m2 mirror position. We can optimise this demodulation phase by running. Here we manually provied which drives and readouts to use:

>>> sol = model.run(OptimiseRFReadoutPhaseDC("m2.phi", 'PD_I'))
>>> print(sol.phases)
{'PD': 181.3535303754581}
>>> print(model.PD.phase)
181.3535303754581

Alternatively, PD_Q could also be optimised for above. You can also just optimise all locks that are using RF readouts by providing no arguments:

>>> sol = model.run(OptimiseRFReadoutPhaseDC())
>>> print(sol.phases)
{'PD': 181.3535303754581}

To tell what was optimised, see the sol.phases dictionary.

class finesse.analysis.actions.sensing.OptimiseRFReadoutPhaseDCSolution[source]: Bases: BaseSolution

class finesse.analysis.actions.sensing.SensingMatrixAC(dofs, readouts, f=0.001, name='sensing_matrix_ac')[source]

Bases: Action

Computes the sensing matrix elements for various degrees of freedom and readouts that should be present in the model. The solution object for this action then contains all the information on the sensing matrix. This can be plotted in polar coordinates, displayed in a table, or directly accessed.

The sensing gain is computed by calculating the gradient of each readout signal, which means it is a DC measurement. This will not include any suspension or radiation pressure effects.

This action does not modify the states model.

Parameters

dofsiterable[str]: String names of degrees of freedom
readoutsiterable[str]: String names of readouts
ffloat: Frequency to measure sensing matrix at

class finesse.analysis.actions.sensing.SensingMatrixDC(dofs, readouts, d_dof=1e-09, name='sensing_matrix_dc')[source]

Bases: Action

Computes the sensing matrix elements for various degrees of freedom and readouts that should be present in the model. The solution object for this action then contains all the information on the sensing matrix. This can be plotted in polar coordinates, displayed in a table, or directly accessed.

The sensing gain is computed by calculating the gradient of each readout signal, which means it is a DC measurement. This will not include any suspension or radiation pressure effects.

This action does not modify the states model.

Parameters

dofsiterable[str]: String names of degrees of freedom
readoutsiterable[str]: String names of readouts
d_doffloat, optional: Small step used to compute derivative

class finesse.analysis.actions.sensing.SensingMatrixSolution[source]

Bases: BaseSolution

Sensing matrix solution.

The raw sensing matrix information can be accessed using the SensingMatrixSolution.out member. This is a complex-valued array with dimensions (DOFs, Readouts), which are accessible via SensingMatrixSolution.dofs and SensingMatrixSolution.readouts.

A table can be printed using SensingMatrixSolution.display().

Polar plot can be generated using SensingMatrixSolution.plot()

Printing SensingMatrixSolution will show an ASCII table of the data.

display(dofs=None, readouts=None, tablefmt='pandas', numfmt='{:.2G}', highlight=None, highlight_color='#808080')[source]

Displays a HTML table of the sensing matrix, optionally highlighting the largest absolute value for each readout or dof.

Notes

Only works when called from an IPython environment with the display method available. Pandas is required for highlighting.

Parameters

dofsiterable[str], optional: Names of degrees of freedom to show, defaults to all if None
readoutsiterable[str], optional: Names of readouts to show, defaults to all if None
tablefmtstr, optional: Either ‘pandas’ for pandas formatting, or anything else to use finesse.utilities.tables.Table. Defaults to ‘pandas’ if available.
numfmtstr or func or array, optional: Either a function to format numbers or a formatting string. The function must return a string. Can also be an array with one option per row, column or cell. Defaults to ‘{:.2G}’.
highlightstr or None, optional: Either ‘dof’ to highlight the readout that gives the largest output for each dof, or ‘readout’ to highlight the dof for which each readout gives the largest output. Defaults to None (no highlighting).
highlight_colorstr, optional: Color to highlight the maximum values with. Pandas is required for this to have an effect. Defaults to pale orange.

matrix_data(dofs=None, readouts=None)[source]

Generates a sensing matrix table.

Parameters

dofsiterable[str], optional: Names of degrees of freedom to show, defaults to all if None
readoutsiterable[str], optional: Names of readouts to show, defaults to all if None

Returns

matrix : 2D numpy array, complex dofs : list of str readouts: list of str

plot(self, *args, show=True, **kwargs)[source]

Plot solution(s).

If the solution contains child solutions, they are plotted in order. Solutions without plot arguments are skipped. Positional arguments passed to this method are assumed to be dict and are unpacked into calls to each child’s plot method. Global arguments to be passed to all plot methods can be specified directly as keyword arguments. Duplicate arguments specified in a positional argument dictionaries take precendence over global arguments.

Parameters

showbool, optional: Show the figures.

Other Parameters

*args: Sequence of dict to use as parameters for the call to each child solution’s plot method.
**kwargs: Keyword arguments supported by the child solution(s).

Notes

If the Nth solution contains no plot method, it still consumes the Nth positional argument passed to this method.

finesse.analysis.actions.sensing.get_readout_workspace(readouts, output, readout_workspaces)[source]

Return the readout workspaces for a particular readout’s quadrature outputs.

Parameters

readoutsiterable: ReadoutRF elements
outputstr: Quadrature output to try and select, ‘I’, ‘Q’, ‘DC’
readout_workspacesdict: Workspaces of the readouts in the simulation