OICController¶

class lsst.ts.ofc.OICController(ofc_data: OFCData, log: logging.Logger | None = None)¶

Bases: BaseController

Optimal Integral Controller (OIC)

Attributes Summary

`ETA`
`FWHM_ATM`
`aggregated_state`	Returns the aggregated state.
`derivative_filter_coeff`	Derivative filter coefficient.
`kd`	Derivative gain.
`ki`	Integral gain.
`kp`	Proportional gain.

Methods Summary

`aggregate_state`(dof, dof_idx)	Aggregate the calculated degree of freedom (DOF) in the state.
`authority`()	Compute the authority of the system.
`calc_uk_0`(mat_f, qx, mat_h)	Calculate uk by referencing to "0".
`calc_uk_x0`(mat_f, qx, **kwargs)	Calculate uk by referencing to "x0".
`calc_uk_x00`(mat_f, qx, mat_h)	Calculate uk by referencing to "x00".
`calculate_pid_step`(state)	Calculate the control signal using PID controller.
`control_step`(filter_name, dof_state[, ...])	Estimate uk in the basis of degree of freedom (DOF) with gain compensation.
`effective_fwhm_g4`(pssn, sensor_names)	Calculate the effective FWHM by Gaussian quadrature.
`fwhm_to_pssn`(fwhm)	Convert the FWHM data to PSSN.
`reset_dof_state`()	Initialize the state to the state 0 in the basis of degree of freedom (DOF).
`reset_history`()	Reset the history of the controller.
`set_fwhm_data`(fwhm, sensor_names)	Set the list of FWHMSensorData of each CCD of camera. Parameters ---------- fwhm : `np.ndarray[float]` Array of arrays (e.g. 2-d array) which contains the FWHM data. Each element contains an array of fwhm (in arcsec) measurements for a particular sensor. sensor_names : `list` [`string`] List of sensor names. Raises ------ RuntimeError If size of `fwhm` and `sensor_names` are different.
`set_pssn_gain`()	Set the gain value based on the PSSN, which comes from the FWHM by DM team.
`uk`(filter_name, dof_state[, sensor_names])	Estimate the offset (`uk`) of degree of freedom (DOF) at time `k+1` based on the wavefront error (`yk`) at time `k`.

Attributes Documentation

ETA = 1.086¶

FWHM_ATM = 0.6¶

aggregated_state¶

Returns the aggregated state.

Returns¶

np.ndarray[float]: Aggregated state.

derivative_filter_coeff¶

Derivative filter coefficient.

Returns¶

float: Derivative filter coefficient.

kd¶

Derivative gain.

Returns¶

float: Derivative gain.

ki¶

Integral gain.

Returns¶

float: Integral gain.

kp¶: Proportional gain.

Methods Documentation

aggregate_state(dof: numpy.ndarray[float] | list, dof_idx: numpy.ndarray[int] | list[int]) → None¶

Aggregate the calculated degree of freedom (DOF) in the state.

Parameters¶

dofnumpy.ndarray or list: Calculated DOF.
dof_idxnumpy.ndarray or list[int]: Index array of degree of freedom.

authority() → ndarray[float]¶: Compute the authority of the system.

Returns¶

authority : np.array

calc_uk_0(mat_f: ndarray[float], qx: ndarray[float], mat_h: ndarray[float]) → ndarray[float]¶

Calculate uk by referencing to “0”.

The offset will trace the real value and target for 0.

uk = -F’ * (QX + rho**2 * H * S).

Parameters¶

mat_fnumpy.ndarray: Matrix F.
qxnumpy.ndarray: qx array.
mat_hnumpy.ndarray: The H matrix (see equation above).

Returns¶

uknumpy.ndarray: Calculated uk in the basis of degree of freedom (DOF).

calc_uk_x0(mat_f: ndarray[float], qx: ndarray[float], **kwargs: dict[str, Any]) → ndarray[float]¶

Calculate uk by referencing to “x0”.

The offset will only trace the previous one.

uk = -F’ * QX.

Parameters¶

mat_fnumpy.ndarray: Matrix F.
qxnumpy.ndarray: qx array.
kwargsdict[str, typing.Any]: Additional keyword arguments. This is mainly added to provide similar interaface to other calc_uk_* methods.

Returns¶

uknumpy.ndarray: Calculated uk in the basis of degree of freedom (DOF).

calc_uk_x00(mat_f: ndarray[float], qx: ndarray[float], mat_h: ndarray[float]) → ndarray[float]¶

Calculate uk by referencing to “x00”. The offset will only trace the relative changes of offset without regarding the real value. uk = -F’ * [QX + rho**2 * H * (S - S0)].

Parameters¶

mat_fnumpy.ndarray: Matrix F.
qxnumpy.ndarray: qx array.
mat_hnumpy.ndarray: Matrix H.

Returns¶

numpy.ndarray: Calculated uk in the basis of degree of freedom (DOF).

calculate_pid_step(state: ndarray[float]) → ndarray[float]¶

Calculate the control signal using PID controller.

Parameters¶

statenp.ndarray[float]: State of the system.

Returns¶

uknumpy.ndarray: Calculated uk in the basis of DOF.

control_step(filter_name: str, dof_state: ndarray[float], sensor_names: list[str] | None = None) → ndarray[float]¶

Estimate uk in the basis of degree of freedom (DOF) with gain compensation.

Parameters¶

filter_namestring: Name of the filter.
dof_statenumpy.ndarray: Optical state in the basis of DOF.
sensor_nameslist [string] or None: List of sensor names.

Returns¶

control_effortnumpy.ndarray: Calculated uk in the basis of DOF.

Raises¶

RuntimeError: If sensor_names is not provided for full array mode instruments.

effective_fwhm_g4(pssn: ndarray[float], sensor_names: list[str]) → float¶

Calculate the effective FWHM by Gaussian quadrature.

FWHM: Full width at half maximum. FWHM = eta * FWHM_{atm} * sqrt(1/PSSN -1). Effective GQFWHM = sum_{i} (w_{i}* FWHM_{i}).

Parameters¶

pssnnumpy.ndarray or list: Normalized point source sensitivity (PSSN).
sensor_nameslist [string]: List of sensor names.

Returns¶

float: Effective FWHM in arcsec by Gaussian quadrature.

Raises¶

ValueError: Input values are unphysical.
ValueError: Image quality weights sum is zero. Please check your weights.

fwhm_to_pssn(fwhm: ndarray[float]) → ndarray[float]¶

Convert the FWHM data to PSSN.

Take the array of FWHM values (nominally 1 per CCD) and convert it to PSSN (nominally 1 per CCD).

Parameters¶

fwhmnumpy.ndarray[x]: An array of FWHM values with sensor information.

Returns¶

pssnnumpy.ndarray[y]: An array of PSSN values.

reset_dof_state() → None¶: Initialize the state to the state 0 in the basis of degree of freedom (DOF).

reset_history() → None¶: Reset the history of the controller.

set_fwhm_data(fwhm: ndarray[float], sensor_names: list[str]) → None¶

Set the list of FWHMSensorData of each CCD of camera. Parameters ———- fwhm : np.ndarray[float]

Array of arrays (e.g. 2-d array) which contains the FWHM data. Each element contains an array of fwhm (in arcsec) measurements for a particular sensor.

sensor_nameslist [string]: List of sensor names.

Raises¶

RuntimeError: If size of fwhm and sensor_names are different.

set_pssn_gain() → None¶

Set the gain value based on the PSSN, which comes from the FWHM by DM team.

Raises¶

RuntimeError: If pssn_data is not properly set.

uk(filter_name: str, dof_state: ndarray[float], sensor_names: list[str] | None = None) → ndarray[float]¶

Estimate the offset (uk) of degree of freedom (DOF) at time k+1 based on the wavefront error (yk) at time k.

uk in the basis of degree of freedom (DOF) without gain compensation.

Parameters¶

filter_namestring: Name of the filter.
dof_statenumpy.ndarray: Optical state in the basis of DOF.
sensor_nameslist [string]: List of sensor names.

Returns¶

uknumpy.ndarray: Calculated uk in the basis of DOF.

Raises¶

RuntimeError: If xref strategy is not valid.
RuntimeError: If sensor_names is not provided for full array mode instruments.
ValueError: If image quality weights sum is zero.
RuntimeError: If Gaussian Quadrature points and weights are not provided for LSST instrument.
RuntimeError: If sensor names are not provided for full array mode instruments.

Navigation

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