navigate.model.features.adaptive_optics.TonyWilson

class navigate.model.features.adaptive_optics.TonyWilson(model, verbose=False)

Bases: object

Tony Wilson iterative AO routine

__init__(model, verbose=False)

Initialize the Tony Wilson iterative AO routine

Parameters:: model (Model) – Model object

Methods

`__init__`(model[, verbose])	Initialize the Tony Wilson iterative AO routine
`build_report`()
`end_func_data`()	End the data
`end_func_signal`()	End the signal
`get_steps`(ranges, step_size)	Calculate the number of steps and the position offset
`get_tw_frame_num`()	Calculate how many frames are needed: iterations x steps x num_coefs
`in_func_data`([frame_ids])	Run the data
`in_func_signal`()	Run the signal.
`pre_func_data`()	Prepare the data
`pre_func_signal`()	Prepare the signal
`process_data`(coef[, mode])	Process the data
`run`(*args)	Run the Tony Wilson iterative AO routine

Attributes

`n_modes`	Number of modes
`done_all`	True if all iterations are done, False otherwise
`done_itr`	True if the current iteration is done, False otherwise
`report`	detailed report to save as JSON after
`verbose`	True if you want to print the full output string each step
`model`	Model object
`mirror_controller`	Mirror object
`save_report`	whether to save report at the end of run
`change_coef`	List of coefficients to change
`mode_names`	List of mode names

end_func_data()

End the data

Returns:: True if the data is done, False otherwise
Return type:: bool

end_func_signal()

End the signal

Returns:: True if the signal is done, False otherwise
Return type:: bool

get_steps(ranges, step_size)

Calculate the number of steps and the position offset

Parameters:

ranges (int) – Range of the scan
step_size (int) – Step size

Returns:

int – Number of steps
int – Position offset

get_tw_frame_num(): Calculate how many frames are needed: iterations x steps x num_coefs

in_func_data(frame_ids=[])

Run the data

Parameters:: frame_ids (list, optional) – List of frame ids, by default []
Returns:: List of frame ids
Return type:: list

in_func_signal()

Run the signal.

Returns:: True if the signal is done, False otherwise
Return type:: bool

pre_func_data(): Prepare the data

pre_func_signal(): Prepare the signal

process_data(coef, mode='poly')

Process the data

Parameters:

coef (int) – Coefficient index
mode (str, optional) – Fitting mode, by default “poly”

run(*args)

Run the Tony Wilson iterative AO routine

Parameters:

args[0] (dict) – Current microscope state.
args[1] (dict) – Autofocus parameters

change_coef

List of coefficients to change

Type:: list

done_all

True if all iterations are done, False otherwise

Type:: bool

done_itr

True if the current iteration is done, False otherwise

Type:: bool

mirror_controller

Mirror object

Type:: navigate.model.devices.mirrors.mirror_imop.ImagineOpticsMirror

mode_names

List of mode names

Type:: list

model

Model object

Type:: navigate.model.Model

n_modes

Number of modes

Type:: int

report

detailed report to save as JSON after

Type:: list

save_report

whether to save report at the end of run

Type:: bool

verbose

True if you want to print the full output string each step

Type:: bool