pyTomoAO.dm_fitting.fitting

class pyTomoAO.dm_fitting.fitting(dmParams, logger=<Logger pyTomoAO.dm_fitting (INFO)>)

Bases: object

A class for handling deformable mirror fitting operations with influence function computation.

This class provides methods for computing influence functions, generating fitting matrices, and fitting optical path difference (OPD) maps for deformable mirror control. The class supports both single and double Gaussian influence functions, and can handle different grid resolutions and actuator geometries. The class forwards attribute access to dmParams when appropriate.

Parameters:

• dmParams (object) – An instance of a class containing DM geometry parameters

• logger (logging.Logger, optional) – Logger object for logging messages (default is the module-level logger)

Returns:

None – Initializes the fitting object with the specified parameters.

Notes

The class maintains several internal attributes:

• modesnumpy.ndarray

  Array containing influence function modes

• resolutionint

  Resolution of the grid for influence function computation

• _fitting_matrixnumpy.ndarray

  Matrix used for fitting OPD maps to actuator commands

• _influence_functionsnumpy.ndarray

  Matrix of influence functions for each actuator

• actuator_coordinateslist

  List of (y, x) coordinate tuples for all actuators

property F

Get the fitting matrix.

Returns:

numpy.ndarray – The fitting matrix for OPD map to actuator command conversion

property fitting_matrix

Get the fitting matrix (alias for F property).

Returns:

numpy.ndarray – The fitting matrix for OPD map to actuator command conversion

property IF

Get the influence functions matrix.

Returns:

numpy.ndarray – The influence functions matrix

property influence_functions

Get the influence functions matrix (alias for IF property).

Returns:

numpy.ndarray – The influence functions matrix

fit(opd_map)

Multiply the OPD map by the fitting matrix to obtain the command vector.

Parameters:

opd_map (numpy.ndarray) – The Optical Path Difference (OPD) map to be fitted

Returns:

numpy.ndarray – The command vector to send to the DM

Raises:

ValueError – If the fitting matrix is not set

double_gaussian_influence(x, y, center_x=0, center_y=0, w1=2, w2=-1, sigma1=0.54, sigma2=0.85)

Compute the double Gaussian influence function for a deformable mirror.

This function allows placement of a double Gaussian influence function at any position on a grid of any dimensions.

Parameters:

• x (float or numpy.ndarray) – Coordinates at which to evaluate the influence function

• y (float or numpy.ndarray) – Coordinates at which to evaluate the influence function

• center_x (float) – Center coordinates of the double Gaussian function

• center_y (float) – Center coordinates of the double Gaussian function

• w1 (float) – Weights of the two Gaussian components

• w2 (float) – Weights of the two Gaussian components

• sigma1 (float) – Standard deviations of the two Gaussian components

• sigma2 (float) – Standard deviations of the two Gaussian components

Returns:

float or numpy.ndarray – Influence function value at the given coordinates

create_influence_grid(grid_shape, actuator_pos, w1=2, w2=-1, sigma1=0.5, sigma2=0.85)

Create a grid of the specified shape with a double Gaussian placed at the given position.

Parameters:

• grid_shape (tuple) – Shape of the grid (height, width)

• actuator_pos (tuple) – Position (y, x) where the center of the double Gaussian should be placed

• w1 (float) – Weights of the two Gaussian components

• w2 (float) – Weights of the two Gaussian components

• sigma1 (float) – Standard deviations of the two Gaussian components

• sigma2 (float) – Standard deviations of the two Gaussian components

Returns:

numpy.ndarray – 2D grid with the double Gaussian influence function

extract_actuator_coordinates(valid_actuator_map)

Extract the (y, x) coordinates of all actuators from the map.

Parameters:

valid_actuator_map (numpy.ndarray) – Binary array where 1s indicate valid actuator positions

Returns:

list – List of (y, x) coordinate tuples for all actuators

map_actuators_to_new_grid(actuator_coords, original_shape, new_shape, stretch_factor=1.03)

Map actuator coordinates from original grid to a new grid size.

Maintains relative positions and stretches beyond [-1, 1] by the stretch factor.

Parameters:

• actuator_coords (list) – List of (y, x) coordinate tuples in the original grid

• original_shape (tuple) – Shape of the original grid (height, width)

• new_shape (tuple) – Shape of the new grid (height, width)

• stretch_factor (float, optional) – Factor to stretch the normalized coordinates (default: 1.03)

Returns:

list – List of (y, x) coordinate tuples in the new grid, normalized and stretched

map_actuators_to_new_grid_old(actuator_coords, original_shape, new_shape)

Map actuator coordinates from original grid to a new grid size (legacy method).

Maintains relative positions using direct scaling.

Parameters:

• actuator_coords (list) – List of (y, x) coordinate tuples in the original grid

• original_shape (tuple) – Shape of the original grid (height, width)

• new_shape (tuple) – Shape of the new grid (height, width)

Returns:

list – List of (y, x) coordinate tuples in the new grid

set_influence_function(dmParams=None, resolution=None, display=False, w1=2, w2=-1, sigma1=1.0, sigma2=1.7, stretch_factor=1.03)

Generate a deformable mirror influence function based on the provided parameters.

This method computes the influence functions for each actuator in the DM and stores them in the class.

Parameters:

• dmParams (object, optional) – Contains deformable mirror parameters. If None, uses the associated dmParams.

• resolution (int, optional) – Resolution of the output influence function grid. If None, uses the class default resolution.

• display (bool, optional) – Whether to display plots of the influence functions.

• w1 (float, optional) – Weights for the double Gaussian function.

• w2 (float, optional) – Weights for the double Gaussian function.

• sigma1 (float, optional) – Standard deviations for the double Gaussian function.

• sigma2 (float, optional) – Standard deviations for the double Gaussian function.

• stretch_factor (float, optional) – Factor to stretch the normalized coordinates (default: 1.03)

Returns:

numpy.ndarray – 2D array representing the influence function for each actuator.