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 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 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