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.tipTiltDetrend(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=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
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