def createCube_forTestDataInOpdImages(self):
tt = '20191210_143625'
fold = os.path.join(Configuration.OPD_DATA_FOLDER,
'OPDImages', tt, 'hdf5')
hduList = pyfits.open(os.path.join(fold, 'ampVect.fits'))
self._cmdAmplitude = hduList[0].data
hduList = pyfits.open(os.path.join(fold, 'modeRange.fits'))
self._actsVector = hduList[0].data
hduList = pyfits.open(os.path.join(fold, 'template.fits'))
vector_of_push_pull = hduList[0].data
cube = None
tip_til_det = TipTiltDetrend()
r = ROI()
# for i in range(self._actsVector.shape[0]):
for i in range(7):
k = i * vector_of_push_pull.shape[0]
imaList = []
for j in range(vector_of_push_pull.shape[0]):
l = k + j
file_name = os.path.join(fold, 'img_%04d.h5') %l
img = self._ic.from4D(file_name)
roi = r.roiGenerator(img)
ima_tt = tip_til_det.tipTiltRemover(img, roi, 3)
imaList.append(ima_tt)
image = imaList[0] + imaList[1] - imaList[2]
if cube is None:
cube = image
else:
cube = np.ma.dstack((cube, image))
return cube
def _zernikeCoeffCalculator(self, img):
"""
Returns:
final_coef = zernike coeff on the image
(zernike modes 2,3,4,7,8)
final_coef_selected = zernike selected using intMatModesVector (zernike2control)
"""
if fold_name.simulated == 1:
mask_index = OtherParameters.MASK_INDEX_SIMULATORE
else:
mask_index = OtherParameters.MASK_INDEX_TOWER
r = ROI()
roi = r.roiGenerator(img)
mask = roi[mask_index]
mm = np.ma.mask_or(img.mask, mask)
new_image = np.ma.masked_array(img, mask=mm)
coef, mat = zernike.zernikeFit(new_image, np.arange(10) + 1)
z = np.array([1, 2, 3, 6, 7])
all_final_coef = coef[z]
if self._intMatModesVector is None:
final_coef_selected = all_final_coef
else:
final_coef_selected = np.zeros(self._intMatModesVector.size)
for i in range(self._intMatModesVector.size):
final_coef_selected[i] = all_final_coef[
self._intMatModesVector[i]]
return all_final_coef, final_coef_selected
def createCubeFromImageFolder(self, data_file_path, tiptilt_detrend=None, phase_ambiguity=None):
cube_all_act = None
where = self._indexReorganization()
ampl_reorg = self._amplitudeReorganization(self._actsVector,
self._indexingList,
self._cmdAmplitude,
self._nPushPull)
for i in range(self._actsVector.shape[0]):
for k in range(self._nPushPull):
p = self._nPushPull * i + k
n = where[p][0][0]
mis_amp = k* self._indexingList.shape[1] + n
mis = k * self._indexingList.shape[1] * self._template.shape[0] \
+ n * self._template.shape[0]
name = 'img_%04d.h5' %mis
file_name = os.path.join(data_file_path, name)
image_for_dim = self._ic.from4D(file_name)
image_sum = np.zeros((image_for_dim.shape[0],
image_for_dim.shape[1]))
for l in range(1, self._template.shape[0]):
name = 'img_%04d.h5' %(mis+l)
file_name = os.path.join(data_file_path, name)
ima = self._ic.from4D(file_name)
image = image_sum + ima * self._template[l]
img_if = image / (2 * ampl_reorg[mis_amp] * (self._template.shape[0] - 1))
if tiptilt_detrend is None:
img_if = img_if
else:
r = ROI()
roi = r.roiGenerator(img_if)
tt = TipTiltDetrend()
img_if = tt.tipTiltRemover(img_if, roi, 3)
if_push_pull_kth = img_if-np.ma.median(img_if)
if k == 0:
all_push_pull_act_jth = if_push_pull_kth
else:
all_push_pull_act_jth = np.ma.dstack((all_push_pull_act_jth,
if_push_pull_kth))
if self._nPushPull == 1:
if_act_jth = all_push_pull_act_jth
else:
if_act_jth = np.ma.mean(all_push_pull_act_jth, axis=2)
if cube_all_act is None:
cube_all_act = if_act_jth
else:
cube_all_act = np.ma.dstack((cube_all_act, if_act_jth))
self._cube = cube_all_act
return self._cube
def _findMask(self, cube):
ima = cube[:, :, 0]
from m4.utils.roi import ROI
r = ROI()
roi = r.roiGenerator(ima)
if fold_name.simulated == 1:
mask_index = OtherParameters.MASK_INDEX_SIMULATORE
else:
mask_index = OtherParameters.MASK_INDEX_TOWER
mask = roi[mask_index]
return mask
def analyzerCalibrationMeasurement(self, tt, mask_index):
'''
args:
tt = tracking number of the measures to be analysed
mask_index = int the reference mirror mask index
returns:
self_intMat = interation matrix
self._rec = reconstructor
'''
a = opt_calibration.loadCommandMatrixFromFits(tt)
a.createCube(tt)
cube = a.getCube()
ima = cube[:, :, 0]
from m4.utils.roi import ROI
r = ROI()
roi = r.roiGenerator(ima)
mask = roi[mask_index]
dove = os.path.join(self._storageFolder(), tt)
self._saveMask(dove, mask)
self._intMat = a.getInteractionMatrix(mask)
self._rec = a.getReconstructor(mask)
self._saveIntMatAndRec(dove)
return self._intMat, self._rec
def createCube(self, tiptilt_detrend=None, phase_ambiguity=None):
'''
Args:
ttDetrend = in the creation of the cube the images are reduced
removing tip tilt on the central segment
phaseSolve=
Returns:
self._cube = cube
'''
cube_all_act = None
self._ttData()
self._logCubeCreation(tiptilt_detrend, phase_ambiguity) #modRB here add the Zernike removal or best decide how to manage the Zernike shapes, if to remove them in flattening only
where = self._indexReorganization() #modRB here add the image immersion in the full frame
#misure_pos, misure_neg = self._splitMeasureFromFits(self._cubeMeasure)
ampl_reorg = self._amplitudeReorganization(self._actsVector,
self._indexingList,
self._cmdAmplitude,
self._nPushPull)
for i in range(self._actsVector.shape[0]):
for k in range(self._nPushPull):
p = self._nPushPull * i + k
n = where[p][0][0]
mis_amp = k* self._indexingList.shape[1] + n
mis = k * self._indexingList.shape[1] * self._template.shape[0] \
+ n * self._template.shape[0]
#img_pos = misure_pos[:,:,mis]
#img_neg = misure_neg[:,:,mis]
image_sum = np.zeros((self._cubeMeasure.shape[0],
self._cubeMeasure.shape[1]))
for l in range(1, self._template.shape[0]):
image = image_sum + self._cubeMeasure[:,:, mis+l] * self._template[l]
img_if = image / (2 * ampl_reorg[mis_amp] * (self._template.shape[0] - 1))
if tiptilt_detrend is None:
img_if = img_if
else:
r = ROI()
roi = r.roiGenerator(img_if)
tt = TipTiltDetrend()
img_if = tt.tipTiltRemover(img_if, roi, 3)
if_push_pull_kth = img_if-np.ma.median(img_if)
if k == 0:
all_push_pull_act_jth = if_push_pull_kth
else:
all_push_pull_act_jth = np.ma.dstack((all_push_pull_act_jth,
if_push_pull_kth))
if self._nPushPull == 1:
if_act_jth = all_push_pull_act_jth
else:
if_act_jth = np.ma.mean(all_push_pull_act_jth, axis=2)
if cube_all_act is None:
cube_all_act = if_act_jth
else:
cube_all_act = np.ma.dstack((cube_all_act, if_act_jth))
self._cube = cube_all_act
return self._cube
def createCubeFromImageFolder(self, data_file_path=None, tiptilt_detrend=None,
phase_ambiguity=None):
'''
Parameters
----------
data_file_path: string
measurement data file path
Other Parameters
----------
ttDetrend: optional
in the creation of the cube the images are reduced
removing tip tilt on the central segment
phaseSolve: optional
Returns
-------
cube = masked array [pixels, pixels, number of images]
cube from analysis
'''
if data_file_path is None:
data_file_path = self._h5Folder #viene da loadInfoFromIFFsTtFolder
else:
data_file_path = data_file_path
where = self._indexReorganization()
ampl_reorg = self._amplitudeReorganization(self._actsVector,
self._indexingList,
self._cmdAmplitude,
self._nPushPull)
cube_all_act = []
for i in range(self._actsVector.shape[0]):
print(i)
for k in range(self._nPushPull):
p = self._nPushPull * i + k
n = where[p]
mis_amp = k* self._indexingList.shape[1] + n
mis_push = (k * self._indexingList.shape[1]+ n) * 2*self._template.shape[0]
mis_pull = self._template.shape[0] + mis_push
mis_list = [mis_push, mis_pull]
pp_images = []
for mis in mis_list:
name = 'img_%04d.h5' %mis
file_name = os.path.join(data_file_path, name)
image0 = self._ic.from4D(file_name)
# image_sum = np.zeros((image_for_dim.shape[0],
# image_for_dim.shape[1]))
image_list = [image0]
for l in range(1, self._template.shape[0]):
name = 'img_%04d.h5' %(mis+l)
file_name = os.path.join(data_file_path, name)
ima = self._ic.from4D(file_name)
image_list.append(ima)
image = np.zeros((image0.shape[0], image0.shape[1]))
for p in range(1, len(image_list)):
#opd2add = opdtemp[*,*,p]*form[p]+opdtemp[*,*,p-1]*form[p-1]
#opd += opd2add
opd2add = image_list[p] * self._template[p] + image_list[p-1] * self._template[p-1]
master_mask2add = np.ma.mask_or(image_list[p].mask, image_list[p-1].mask)
if p==1:
master_mask = master_mask2add
else:
master_mask = np.ma.mask_or(master_mask, master_mask2add)
image += opd2add
image = np.ma.masked_array(image, mask=master_mask)
#image = image + ima * self._template[l] #sbagliato
pp_images.append(image)
image = pp_images[0] + pp_images[1] #da rivedere molto
img_if = image / (2 * ampl_reorg[mis_amp] * (self._template.shape[0] - 1))
if tiptilt_detrend is None:
img_if = img_if
else:
r = ROI()
roi = r.roiGenerator(img_if)
tt = TipTiltDetrend()
img_if = tt.tipTiltDetrend(img_if, roi, 3)
if_push_pull_kth = img_if
if k == 0:
all_push_pull_act_jth = if_push_pull_kth
else:
all_push_pull_act_jth = np.ma.dstack((all_push_pull_act_jth,
if_push_pull_kth))
if self._nPushPull == 1:
if_act_jth = all_push_pull_act_jth
else:
if_act_jth = np.ma.mean(all_push_pull_act_jth, axis=2)
cube_all_act.append(if_act_jth)
self._cube = np.ma.dstack(cube_all_act)
return self._cube
class Alignment():
"""
Class to be used for alignment of the optical tower
and the deformable mirror
HOW TO USE IT:
from m4.alignment import Alignment
a = Alignment()
#for the optical tower
tt = a.ott_calibration(commandAmpVector, nPushPull, maskIndex)
cmd = a.ott_alignment(tt)
#for deformable mirror
tt, zCoefComa, comaSurface = a.m4_calibration(...)
cmd = a.m4_alignement(zCoefComa)
"""
def __init__(self):
"""The constructor """
self._cal = opt_calibration()
self._roi = ROI()
self._zOnM4 = ZernikeOnM4()
def ott_calibration(
self, command_amp_vector, n_push_pull,
mask_index): #modRB calibration forse non è il nome migliore??
'''
Calibration of the optical tower #modRB trovare un modo per identificare in maniera automatica la maschera del componente voluto. es.: prodotto con maschere note
args:
command_amp_vector = vector containing the movement values
of the 5 degrees of freedom
n_push_pull = number of push pull for each degree of freedom
mask_index = int (3 for the RM mask)
returns:
tt = tracking number of measurement
'''
self._moveRM()
self._tt = self._cal.measureCalibrationMatrix(0, command_amp_vector,
n_push_pull)
int_mat, rec = self._cal.analyzerCalibrationMeasurement(
self._tt, mask_index)
return self._tt
def ott_alignement(self, tt=None):
"""
Args:
tt = tracking number of measurement of which you want to use the
interaction matrix and reconstructor
None for the last measurement made
Returns:
cmd = vector of command to apply to PAR+RM dof
"""
if tt is None:
al = opt_alignment(self._tt)
else:
al = opt_alignment(tt)
cmd = al.opt_align()
self._applyCmd()
return cmd
def m4_calibration(self, commandAmpVector_ForParRmAlignement,
nPushPull_ForParRmAlignement,
maskIndex_ForParRmAlignement,
commandAmpVector_ForM4Calibration,
nPushPull_ForM4Calibration, maskIndex_ForM4Alignement):
"""
Calibration of the deformable mirror
Args:
commandAmpVector_ForParRmAlignement = amplitude to be applied to par+rm
nPushPull_ForParRmAlignemen = number of push pull for par+rm dof
maskIndex_ForParRmAlignement = number of mask index to use
commandAmpVector_ForM4Calibration = amplitude to be applied to m4
nPushPull_ForM4Calibration = number of push pull for m4 dof
maskIndex_ForM4Alignement = number of mask index to use
Returns:
zernike_coef_coma = zernike coefficient value for coma calculated
by the function _measureComaOnSegmentMask
coma_surface = reconstructed surface
"""
self._moveSegmentView()
tt = self.ott_calibration(commandAmpVector_ForParRmAlignement,
nPushPull_ForParRmAlignement,
maskIndex_ForParRmAlignement)
cmd = self.ott_alignement(tt)
self._moveRM()
zernike_coef_coma, coma_surface = self._measureComaOnSegmentMask()
self._tt = self._cal.measureCalibrationMatrix(
3, commandAmpVector_ForM4Calibration, nPushPull_ForM4Calibration)
intMat, rec = self._cal.analyzerCalibrationMeasurement(
self._tt, maskIndex_ForM4Alignement)
return self._tt, zernike_coef_coma, coma_surface
def m4_alignement(self, zernike_coef_coma, tt=None):
"""
Args:
zernike_coef_coma = zernike coefficient value for coma
tt = tracking number of measurement of which you want to use the
interaction matrix and reconstructor
None for the last measurement made
Returns:
cmd = vector of command to apply to M4 dof
"""
if tt is None:
al = opt_alignment(self._tt)
else:
al = opt_alignment(tt)
cmd = al.opt_align(zernike_coef_coma)
return cmd
def _moveRM(self):
pass
def _moveSegmentView(self):
pass
def _applyCmd(self):
pass
def _measureComaOnSegmentMask(self):
ima = obj.readImageFromFitsFileName(
'Allineamento/20191001_081344/img.fits')
roi = self._roi.roiGenerator(ima)
segment_ima = np.ma.masked_array(ima.data, mask=roi[11])
coef, mat = self._zOnM4.zernikeFit(segment_ima, np.arange(2, 11))
coma = coef[5]
coma_surface = self._zOnM4.zernikeSurface(np.array([coma]),
segment_ima.mask, mat,
np.array([5]))
return coma, coma_surface
def createCubeFromImageFolder(self,
data_file_path,
tiptilt_detrend=None,
phase_ambiguity=None):
'''
Parameters
----------
data_file_path: string
measurement data file path
Other Parameters
----------
ttDetrend: optional
in the creation of the cube the images are reduced
removing tip tilt on the central segment
phaseSolve: optional
Returns
-------
cube = masked array [pixels, pixels, number of images]
cube from analysis
'''
cube_all_act = None
where = self._indexReorganization()
ampl_reorg = self._amplitudeReorganization(self._actsVector,
self._indexingList,
self._cmdAmplitude,
self._nPushPull)
for i in range(self._actsVector.shape[0]):
for k in range(self._nPushPull):
p = self._nPushPull * i + k
n = where[p][0][0]
mis_amp = k * self._indexingList.shape[1] + n
mis = k * self._indexingList.shape[1] * self._template.shape[0] \
+ n * self._template.shape[0]
name = 'img_%04d.h5' % mis
file_name = os.path.join(data_file_path, name)
image_for_dim = self._ic.from4D(file_name)
image_sum = np.zeros(
(image_for_dim.shape[0], image_for_dim.shape[1]))
for l in range(1, self._template.shape[0]):
name = 'img_%04d.h5' % (mis + l)
file_name = os.path.join(data_file_path, name)
ima = self._ic.from4D(file_name)
image = image_sum + ima * self._template[l]
img_if = image / (2 * ampl_reorg[mis_amp] *
(self._template.shape[0] - 1))
if tiptilt_detrend is None:
img_if = img_if
else:
r = ROI()
roi = r.roiGenerator(img_if)
tt = TipTiltDetrend()
img_if = tt.tipTiltDetrend(img_if, roi, 3)
if_push_pull_kth = img_if - np.ma.median(img_if)
if k == 0:
all_push_pull_act_jth = if_push_pull_kth
else:
all_push_pull_act_jth = np.ma.dstack(
(all_push_pull_act_jth, if_push_pull_kth))
if self._nPushPull == 1:
if_act_jth = all_push_pull_act_jth
else:
if_act_jth = np.ma.mean(all_push_pull_act_jth, axis=2)
if cube_all_act is None:
cube_all_act = if_act_jth
else:
cube_all_act = np.ma.dstack((cube_all_act, if_act_jth))
self._cube = cube_all_act
return self._cube
def createCube(self, tiptilt_detrend=None, phase_ambiguity=None):
'''
Other Parameters
----------
ttDetrend: optional
in the creation of the cube the images are reduced
removing tip tilt on the central segment
phaseSolve: optional
Returns
-------
cube = masked array [pixels, pixels, number of images]
cube from analysis
'''
cube_all_act = None
self._ttData()
self._logCubeCreation(tiptilt_detrend, phase_ambiguity)
where = self._indexReorganization()
#misure_pos, misure_neg = self._splitMeasureFromFits(self._cubeMeasure)
ampl_reorg = self._amplitudeReorganization(self._actsVector,
self._indexingList,
self._cmdAmplitude,
self._nPushPull)
for i in range(self._actsVector.shape[0]):
for k in range(self._nPushPull):
p = self._nPushPull * i + k
n = where[p][0][0]
mis_amp = k * self._indexingList.shape[1] + n
mis = k * self._indexingList.shape[1] * self._template.shape[0] \
+ n * self._template.shape[0]
#img_pos = misure_pos[:,:,mis]
#img_neg = misure_neg[:,:,mis]
image_sum = np.zeros(
(self._cubeMeasure.shape[0], self._cubeMeasure.shape[1]))
for l in range(1, self._template.shape[0]):
image = image_sum + self._cubeMeasure[:, :, mis +
l] * self._template[l]
img_if = image / (2 * ampl_reorg[mis_amp] *
(self._template.shape[0] - 1))
if tiptilt_detrend is None:
img_if = img_if
else:
r = ROI()
roi = r.roiGenerator(img_if)
tt = TipTiltDetrend()
img_if = tt.tipTiltDetrend(img_if, roi, 3)
if_push_pull_kth = img_if - np.ma.median(img_if)
if k == 0:
all_push_pull_act_jth = if_push_pull_kth
else:
all_push_pull_act_jth = np.ma.dstack(
(all_push_pull_act_jth, if_push_pull_kth))
if self._nPushPull == 1:
if_act_jth = all_push_pull_act_jth
else:
if_act_jth = np.ma.mean(all_push_pull_act_jth, axis=2)
if cube_all_act is None:
cube_all_act = if_act_jth
else:
cube_all_act = np.ma.dstack((cube_all_act, if_act_jth))
self._cube = cube_all_act
return self._cube
class PhaseSolve():
"""
Class for ...
HOW TO USE IT::
from m4.utils.img_redux import PhaseSolve
PS = PhaseSolve()
"""
def __init__(self):
"""The constructor """
self._roi = ROI()
self._lambda = Interferometer.WAVEL
self._n = None
def n_calculator(self, spl_values):
n = np.zeros(spl_values.shape[0])
for i in range(spl_values.shape[0]):
n[i] = (2. * spl_values[i]) / self._lambda
self._n = n
return self._n
def m4PhaseSolver(self, m4_ima, spl_values):
self.n_calculator(spl_values)
roiList = self._roi.roiGenerator(m4_ima)
m4_new_image = None
media = []
imgList = []
for roi in roiList:
imgg = np.ma.masked_array(m4_ima.data, mask=roi)
m = imgg.mean()
media.append(m)
imgList.append(imgg)
aa = np.arange(self._n.shape[0])
zipped = zip(aa, imgList)
img_phase_solve_list = []
for i, imgg in zipped:
img_phase_solve = np.ma.masked_array(imgg.data - self._n[i],
mask=imgg.mask)
img_phase_solve_list.append(img_phase_solve)
img_phase_solve_list[len(img_phase_solve_list)-1] = \
np.ma.masked_array(imgList[len(imgList)-2].data,
mask= imgList[len(imgList)-2].mask)
for j in range(1, len(img_phase_solve_list)):
if m4_new_image is None:
m4_new_image = np.ma.array(img_phase_solve_list[0].filled(1) * \
img_phase_solve_list[j].filled(1),
mask=(img_phase_solve_list[0].mask * \
img_phase_solve_list[j].mask))
else:
m4_new_image = np.ma.array(m4_new_image.filled(1) * \
img_phase_solve_list[j].filled(1),
mask=(m4_new_image.mask * \
img_phase_solve_list[j].mask))
return m4_new_image, img_phase_solve_list, imgList
def masterRoiPhaseSolver(self, seg_ima, spl_value):
self.n_calculator(spl_value)
roiList = self._roi.roiGenerator(seg_ima)
imgg = np.ma.masked_array(seg_ima.data, mask=roiList[3])
img_phase_solve = np.ma.masked_array(imgg.data - self._n,
mask=imgg.mask)
return img_phase_solve
class PhaseSolve():
"""
Class...
una immagine dell'interferemotro che mette un lamba mezzi tra due superfici se non ho pistone
se ho il pistone seguo la variazione di pistone (tipo 2 nanometri, pi+ piccolo di lamba mezzi)
quindi le due immagini possono essere rimesse insieme usando questa condizione.
Devo riportare a fare differenziale zero le due immagini
deve lavorare su una coppia di immagini e confrontarle con una soglia
terzo caso: ho una misura e nessuna condizione, cioè la misura delle spl.
Vince la misura di interferometro se sono vicini, se sono lontani.
"""
def __init__(self):
"""The constructor """
self._roi = ROI()
self._lambda = Configuration.LAMBDA
self._n = None
def n_calculator(self, spl_values):
n = np.zeros(spl_values.shape[0])
for i in range(spl_values.shape[0]):
n[i] = (2. * spl_values[i]) / self._lambda
self._n = n
return self._n
def m4PhaseSolver(self, m4_ima, spl_values):
self.n_calculator(spl_values)
roiList = self._roi.roiGenerator(m4_ima)
m4_new_image = None
media = []
imgList = []
for roi in roiList:
imgg = np.ma.masked_array(m4_ima.data, mask=roi)
m = imgg.mean()
media.append(m)
imgList.append(imgg)
aa = np.arange(self._n.shape[0])
zipped = zip(aa, imgList)
img_phase_solve_list = []
for i, imgg in zipped:
img_phase_solve = np.ma.masked_array(imgg.data - self._n[i],
mask=imgg.mask)
img_phase_solve_list.append(img_phase_solve)
img_phase_solve_list[len(img_phase_solve_list)-1] = \
np.ma.masked_array(imgList[len(imgList)-2].data,
mask= imgList[len(imgList)-2].mask)
for j in range(1, len(img_phase_solve_list)):
if m4_new_image is None:
m4_new_image = np.ma.array(img_phase_solve_list[0].filled(1) * \
img_phase_solve_list[j].filled(1),
mask=(img_phase_solve_list[0].mask * \
img_phase_solve_list[j].mask))
else:
m4_new_image = np.ma.array(m4_new_image.filled(1) * \
img_phase_solve_list[j].filled(1),
mask=(m4_new_image.mask * \
img_phase_solve_list[j].mask))
return m4_new_image, img_phase_solve_list, imgList
def masterRoiPhaseSolver(self, seg_ima, spl_value):
self.n_calculator(spl_value)
roiList = self._roi.roiGenerator(seg_ima)
imgg = np.ma.masked_array(seg_ima.data, mask=roiList[3])
img_phase_solve = np.ma.masked_array(imgg.data - self._n,
mask=imgg.mask)
return img_phase_solve