navigate.model.features.adaptive_optics.TonyWilson

class navigate.model.features.adaptive_optics.TonyWilson(model)

Bases: object

Tony Wilson iterative AO routine

__init__(model)

Initialize the Tony Wilson iterative AO routine

Parameters:

model (Model) – Model object

Methods

__init__(model)	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
n_iter	Number of iterations
n_steps	Number of steps
coef_amp	Coefficient amplitude
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
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

coef_amp

Coefficient amplitude

Type:

float

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_iter

Number of iterations

Type:

int

n_modes

Number of modes

Type:

int

n_steps

Number of steps

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