def loadmat(self, experiments):
# Cast the parameter-mat file into a python class
self.matpath = experiments.matlab_par_filename
self.matname = experiments.matlab_par_name
myParamter = data.import_parameters_mat(filename = self.matpath, matname = self.matname)
# ASsign variables from Experiment
self.lambda0 = np.squeeze(np.array(myParamter.get('lambda0'))) # free space wavelength (µm)
self.NAo= np.squeeze(np.array(myParamter.get('NAo'))); # Numerical aperture objective
self.NAc= np.squeeze(np.array(myParamter.get('NAc'))); # Numerical aperture condenser
try:
self.NAci = np.squeeze(np.array(myParamter.get('NAci')[0])); # Numerical aperture condenser
except:
print('No inner NA has been defined!')
self.NAci = None
if self.NAci == None:
try:
self.NAci = np.squeeze(np.array(experiments.NAci)); # Numerical aperture condenser
except:
print('No inner NA has been defined!')
self.NAci = self.NAc
# eventually decenter the illumination source - only integer!
self.shiftIcX = 0#int(np.squeeze(np.array(myParamter.get('shiftIcX'))))
self.shiftIcY = 0#int(np.squeeze(np.array(myParamter.get('shiftIcY'))))
self.nEmbb = np.squeeze(np.array(myParamter.get('nEmbb')))
self.dn=.1; # self.nImm - self.nEmbb
print('Assigned some value for dn which is not good!')
# calculate pixelsize
self.dx = np.double(np.squeeze(np.array(myParamter.get('dx'))))
self.dy = np.double(np.array(myParamter.get('dy')))
self.dz = np.double(np.array(myParamter.get('dz')))
# Sampling coordinates
self.Rsim= 0.5*np.double(np.array(myParamter.get('Nx')))*self.dx; # Radius over which simulation is performed.
self.Nz=int(np.double(np.array(myParamter.get('Nz'))))
self.Nx=np.int(np.floor((2*self.Rsim)/self.dx));
self.Ny=np.int(np.floor((2*self.Rsim)/self.dy))
self.mysize = (self.Nz,self.Nx,self.Ny) # ordering is (Nillu, Nz, Nx, Ny)
self.shiftIcY=experiments.shiftIcY
self.shiftIcX=experiments.shiftIcX
self.dn = experiments.dn
self.NAc = experiments.NAc
self.zernikefactors = experiments.zernikefactors
self.zernikemask = experiments.zernikemask
self.Nzernikes = np.squeeze(self.zernikefactors.shape)
self.is_mictype = experiments.is_mictype
def loadmat(self,
mymatpath='./Data/DROPLETS/S19_multiple/Parameter.mat',
mymatname='myParameter'):
# Cast the parameter-mat file into a python class
self.matpath = mymatpath
self.matname = mymatname
myParamter = data.import_parameters_mat(filename=self.matpath,
matname=self.matname)
# ASsign variables from Experiment
self.lambda0 = np.squeeze(np.array(
myParamter.get('lambda0'))) # free space wavelength (µm)
self.NAo = np.squeeze(np.array(myParamter.get('NAo')))
# Numerical aperture objective
self.NAc = np.squeeze(np.array(myParamter.get('NAc')))
# Numerical aperture condenser
self.NAci = np.squeeze(np.array(myParamter.get('NAci')))
# Numerical aperture condenser
# eventually decenter the illumination source - only integer!
self.shiftIcX = 0 #int(np.squeeze(np.array(myParamter.get('shiftIcX'))))
self.shiftIcY = 0 #int(np.squeeze(np.array(myParamter.get('shiftIcY'))))
self.nEmbb = np.squeeze(np.array(myParamter.get('nEmbb')))
self.dn = .1
# self.nImm - self.nEmbb
print('Assigned some value for dn which is not good!')
# calculate pixelsize
self.dx = np.double(np.squeeze(np.array(myParamter.get('dx'))))
self.dy = np.double(np.array(myParamter.get('dy')))
self.dz = np.double(np.array(myParamter.get('dz')))
# Sampling coordinates
self.Rsim = 0.5 * np.double(np.array(myParamter.get('Nx'))) * self.dx
# Radius over which simulation is performed.
self.Nz = int(np.double(np.array(myParamter.get('Nz'))))
self.Nx = np.int(np.floor((2 * self.Rsim) / self.dx) + 1)
self.Ny = np.int(np.floor((2 * self.Rsim) / self.dy) + 1)
is_padding = False # better don't do it, some normalization is probably incorrect
is_display = True
is_optimization = False
is_optimization_psf = False
is_flip = False
is_measurement = False
mysubsamplingIC=0
psf_modell ='corr'#'corr' # None # 'sep'
tf.reset_default_graph()
''' File which stores the experimental parameters from the Q-PHASE setup
1.) Read in the parameters of the dataset '''
matlab_par_name = 'myParameter' #'./Data/DROPLETS/myParameterNew.mat';matname='myParameterNew' #'./Data/DROPLETS/myParameterNew.mat'
matlab_par_file = './Data/DROPLETS/S19_multiple/Parameter.mat'; matname='myParameter'
matlab_pars = data.import_parameters_mat(filename = matlab_par_file, matname=matlab_par_name)
''' Create the Model'''
muscat = mus.MuScatModel(matlab_pars, is_optimization=is_optimization)
muscat.Nx,muscat.Ny = int(np.squeeze(matlab_pars['Nx'].value)), int(np.squeeze(matlab_pars['Ny'].value))
zernikefactors = np.array((0,0,0,0,0,0,0,0,0.0,0.0,0.0)) # 7: ComaX, 8: ComaY, 11: Spherical Aberration
zernikemask = np.array(np.abs(zernikefactors)>0)*1#!= np.array((0, 0, 0, 0, 0, 0, , 1, 1, 1, 1))# mask which factors should be updated
muscat.shiftIcX = 0 # has influence on the XZ-Plot - negative values shifts the input wave (coming from 0..end) to the left
muscat.shiftIcY = 0 # has influence on the YZ-Plot - negative values shifts the input wave (coming from 0..end) to the left
muscat.NAc = .1
muscat.NAo = .95
dn = .105 #(1.437-1.3326)#/np.pi
myfac = 1 #- 1e-6
my_learningrate = 1e-2 # learning rate
lambda_tv = 1e-5 # lambda for Total variation
lambda_gr = 0 # lambda for Goods Roughness
lambda_pos = 10
lambda_neg = 10
Niter = 5000
Ndisplay = 10
'''START CODE'''
tf.reset_default_graph() # just in case there was an open session
''' File which stores the experimental parameters from the Q-PHASE setup
1.) Read in the parameters of the dataset '''
matlab_pars = data.import_parameters_mat(filename = matlab_par_file, matname='myParameterNew')
''' 2.) Read in the parameters of the dataset '''
if(matlab_val_file.find('mat')==-1):
matlab_val = np.load(matlab_val_file)
else:
matlab_val = data.import_realdata_h5(filename = matlab_val_file, matname='allAmp_red', is_complex=True)
if(is_flip):
np_meas = np.flip(matlab_val,0)
print('Attention: We are flipping the data!')
else:
np_meas = matlab_val
print('do we need to flip the data?! -> Observe FFT!!')
mpl.rc('figure', figsize=(12, 9))
mpl.rc('image', cmap='gray')
#%%
'''Define some stuff related to infrastructure'''
mytimestamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
basepath = './' #'/projectnb/cislidt/diederich/muScat/Multiple-Scattering_Tensorflow/'
resultpath = 'Data/DROPLETS/RESULTS/'
# optional - reset tensorflow graph
tf.reset_default_graph()
# Generate Test-Object
''' File which stores the experimental parameters from the Q-PHASE setup
1.) Read in the parameters of the dataset '''
matlab_pars = data.import_parameters_mat(filename=experiments.matlab_par_file,
matname=experiments.matlab_par_name)
''' 2.) Read in the parameters of the dataset '''
if (experiments.matlab_val_file.find('mat') == -1):
matlab_val = np.load(experiments.matlab_val_file) + 1j
else:
matlab_val = data.import_realdata_h5(filename=experiments.matlab_val_file,
matname=experiments.matlab_val_name,
is_complex=True)
# Make sure it's even numberalong Z
if (np.mod(matlab_val.shape[0], 2) == 1):
matlab_val = matlab_val[0:matlab_val.shape[0] - 1, :, :]
matlab_val = (matlab_val) # - .6j
matlab_val = matlab_val[:, 0:50, 0:50]
''' Create the Model'''
muscat = mus.MuScatModel(matlab_pars, is_optimization=True)