m4.utils package

m4.utils.geometrical_transforms module

m4.utils.img_redux module

m4.utils.influence_functions_maker module

Authors
    1. Selmi: written in 2019
class m4.utils.influence_functions_maker.DMtest

Bases: object

getNActs()
setActsCommand(cmd)
class m4.utils.influence_functions_maker.IFFunctionsMaker(deformable_mirror, interf)

Bases: object

This class is responsible for the application of zonal or global commands to the deformable mirror in order to collect the functions of influence. Data are saving in the folder corresponding to the tracking number generated

HOW TO USE IT:

creazione oggetto dm e interf
from m4.influence_functions_maker import IFFunctionsMaker
IFF = IFFunctionsMaker(dm, interf)
tt = IFF.acq_IFFunctions(modesVectorFitsFileName, nPushPull,
                        amplitudeFitsFileName, cmdMatrixFitsFileName,
                        shuffle=None, template=None)
acq_IFFunctions(n_push_pull, amplitude_fits_file_name, cmd_matrix_fits_file_name, shuffle=None, template=None)

Performs the process of acquiring interferograms

Parameters:
  • n_push_pull (int) – number of push pull on the actuator
  • amplitude_fits_file_name (string) – fits file name (Example = amp.fits)
  • cmd_matrix_fits_file_name (int) – fits file name (Example = modalBase.fits)
Other Parameters:
 
  • shuffle (optional) – if not indicated, the function create the tidy command history matrix
  • template (numpy array, optional) – vector composed by 1 and -1 if not indicated, the function create the vector [1, -1, 1]
Returns:

tt – tracking number of measurements made
Return type:

string
static loadInfo(tt, fits_or_h5=0)

Reload information contained in fits Info file

Parameters:fits_file_name (string) – info file path
Returns:
  • who (string) – which segment
  • tt_cmdH (string) – CommandHistory tracking number
  • acts_vector (numpy array) – vector of actuators
  • cmd_matrix (matrix) – modal base
  • cmd_ampl (numpy array) – amplitude vector
  • n_push_pull (int) – number of push pull
  • indexingList (list) – list of index order using in command history

m4.utils.optical_alignment module

Authors
    1. Selmi: written in 2019
      modified in 2021
class m4.utils.optical_alignment.OpticalAlignment(tt_cal, ott, interf)

Bases: object

Class for the optical alignment

HOW TO USE IT:

from m4.utils.optical_alignment import OpticalAlignment
al = OpticalAlignment(tt, ott, interf)
par_command, rm_command, dove = al.opt_aligner(n_images, delay, zernike_to_be_corrected, dof_command_id)
getReorganizatedCommandForParAndRm(cmd, commandId=None)

Function that returns the command reorganized according to the selected degrees of freedom

getZernikeWhitAlignerObjectOptions(image)
Returns:
  • all_final_coef (numpy array) – zernike five coefficients on the image (zernike modes 2,3,4,7,8)
  • final_coef_selected (numpy array) – zernike selected using intMatModesVector (zernike2control)
static loadAlignmentObjectFromFits(tt)

Creates the object using information contained in alignment fits file

Parameters:tt (string) – tracking number
Returns:theObject – opt_alignment class object
Return type:ibjecct
opt_aligner(n_images, delay, zernike_to_be_corrected=None, dof_command_id=None)
Parameters:
  • n_images (int) – number of interferometers frames
  • delay (int [s]) – delay between images
Other Parameters:
 
  • zernike_to_be_corrected (numpy array) – None is equal to np.array([0,1,2,3,4]) for tip, tilt, fuoco, coma, coma
  • commandId (numpy array) – array containing the number of degrees of freedom to be commanded
nota: gli zernike possono essere [0,1], [0,1,3,4], [0,1,2,3,4]
e vanno in coppia con i dof [3,4], [1,2,3,4], [0,1,2,3,4]
Returns:
  • cmd (numpy array) – final delta command for the optical alignment
  • dove (string) – file path containing measurements
selectModesInIntMatAndRecConstruction(zernike2control=None, commandId=None)
Other Parameters:
 
  • zernike2control (numpy array) – None is equal to np.array([0,1,2,3,4]) for tip, tilt, fuoco, coma, coma
  • commandId (numpy array) – array containing the number of degrees of freedom to be commanded
  • nota (gli zernike possono essere [0,1], [0,1,3,4], [0,1,2,3,4]) – e vanno in coppia con i dof [3,4], [1,2,3,4], [0,1,2,3,4]

m4.utils.optical_calibration module

Authors
    1. Selmi: written in 2019
      modified in 2021
class m4.utils.optical_calibration.OpticalCalibration(ott, interf)

Bases: object

Class for the optical calibration and interaction matrix creation

HOW TO USE IT:

from m4.utils.optical_calibration import OpticalCalibration
cal = OpticalCalibration(ott, interf)
cal.measureAndAnalysisCalibrationMatrix(who, command_amp_vector,
                                    n_push_pull, n_frames, delay)
createCmatAndCmdList(command_amp_vector, dofIndex_vector)

Function to allow the creation of the matrix of commands and the decomposition of them in a list of commands to assign to devices

Parameters:
  • command_amp_vector (numpy array [mm]) – vector containing the amplitude of the commands to give degrees of freedom to calibrate
  • dofIndex_vector (numpy array) – vector containing position of Dof to move in standard vector of six position for command devices
Returns:

  • command_matrix (numpy array) – matrix 5x5 composed using command_amp_vector values and relationship between them
  • command_list (list) – decomposition of command matrix in list of command
getCommandMatrix()
Returns:commandMatrix – command matrix used for calibration
Return type:numpy array
getCube()
Returns:cube – analyzed measurements
Return type:numpy masked array
getInteractionMatrix()
Returns:intMat – interaction matrix
Return type:numpy array
getMask()
Returns:mask – mask used for interaction matrix calculation
Return type:numpy array
getWho()
static loadCalibrationObjectFromFits(tt)

Creates the object using information contained in calibration fits file

Parameters:tt (string) – tracking number
Returns:theObject – opt_calibration class object
Return type:object
measureAndAnalysisCalibrationMatrix(who, command_amp_vector, n_push_pull, n_frames, delay)
Parameters:
  • who (string) – string indicating the optical element on which to perform the calibration cal.WHO_PAR_AND_RM for parabola and reference mirror cal.WHO_PARABLE for parabola (non implemented) cal.WHO_RM for reference mirror (not implemented) cal.WHO_M4 for deformable mirror
  • command_amp_vector (numpy array [mm]) – vector containing the amplitude of the commands to give degrees of freedom to calibrate
  • n_push_pull (int) – number of push pull
  • n_frames (int) – number of frame for 4D measurement
Returns:

tt – tracking number containing measurements and IntMat from analysis
Return type:

string

m4.utils.parabola_identification module

m4.utils.roi module

Authors
    1. Selmi: written in 2019
      rewritten in 2022
class m4.utils.roi.ROI

Bases: object

Class to be used for extracting regions of interest from the image.

HOW TO USE IT:

from m4.utils.roi import ROI
roi = ROI()
automatical_roi_selection(image, segment_view, ref_mirror_in)
Parameters:
  • image (numpy masked array) – image to be analyzed
  • = boolean (RM_in) – in the ott is in segment view configuration it is True, else False
  • = boolean – if reference mirror is inside the image it is True, else False
roiGenerator(ima)
Parameters:ima (numpy masked array) – image
Returns:roiList – list of the first 12 roi found in the image
Return type:list

Note

roiList[3] = RM roi for alignement, roiList[3] = central roi for segment

m4.utils.rotation_and_optical_axis_alignment module

m4.utils.speed_time_calculator module

m4.utils.SPL_data_acquirer module

m4.utils.tip_tilt_interf_fit module