tf_helper.mkdir(savepath)
print('My path is: ' + savepath)
''' 1.) Read in the parameters of the dataset '''
experiments.result_fwd_bpm = '.\Data\Simulations\allAmp_simu_BPM.npy'
np_meas = np.load(experiments.savepath_simu)
# Generate Test-Object
''' File which stores the experimental parameters from the Q-PHASE setup
3.) Read in the parameters of the dataset '''
myparams = paras.MyParameter()
myparams.loadExperiment(experiments)
myparams.print()
''' MODELLING StARTS HERE'''
tf.reset_default_graph()
''' Create the Model'''
muscat = mus.MuScatModel(myparams, is_optimization=is_optimization)
muscat.zernikefactors = experiments.zernikefactors
muscat.zernikemask = experiments.zernikemask
''' Compute a first guess based on the experimental phase '''
if (is_obj_init_tikhonov):
print('Object is initialized with precomputed RI-distribution')
if (0):
print('ATTENTION: SIDELOAD a GD result!')
obj_guess_filename = 'myrefractiveindex.h5'
obj_guess = data.import_realdata_h5(
filename=obj_guess_filename,
matname='phase, abs0',
is_complex=False)
else:
if (configs.matlab_val_file.find('mat') == -1):
matlab_val = np.load(configs.matlab_val_file)
else:
matlab_val = data.import_realdata_h5(filename=configs.matlab_val_file,
matname=configs.matlab_val_name,
is_complex=True)
if (np.mod(matlab_val.shape[0], 2) == 1):
matlab_val = matlab_val[0:matlab_val.shape[0] - 1, :, :]
matlab_val = matlab_val + configs.mybackgroundval
#roisize=50
#roicenter = np.array((215,201))
#matlab_val = np.flip(matlab_val,0 )#[0:100,roicenter[0]-roisize:roicenter[0]+roisize,roicenter[1]-roisize:roicenter[1]+roisize],0)
''' Create the Model'''
muscat = mus.MuScatModel(matlab_pars, is_optimization=configs.is_display)
# Correct some values - just for the puprose of fitting in the RAM
muscat.Nx, muscat.Ny, muscat.Nz = matlab_val.shape[1], matlab_val.shape[
2], matlab_val.shape[0]
muscat.shiftIcY = configs.shiftIcY
muscat.shiftIcX = configs.shiftIcX
muscat.dn = configs.dn
muscat.NAc = configs.NAc
''' Adjust some parameters to fit it in the memory '''
muscat.mysize = (muscat.Nz, muscat.Nx, muscat.Ny
) # ordering is (Nillu, Nz, Nx, Ny)
# introduce zernike factors here
muscat.zernikefactors = configs.zernikefactors
muscat.zernikemask = configs.zernikemask
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
#muscat.NAo = .95
#muscat.dz = 0.1625*2#muscat.lambda0/4
# In[7]:
# This is the place to load data
''' 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)
print
''' 2.) Read in the parameters of the dataset '''
# matlab_val = data.import_realdata_h5(filename = matlab_val_file, matname='allAmpSimu', is_complex=True)
# # Initiate the MuScat (Multiple Scattering) Object
# In[8]:
''' Create the Model'''
mm = mus.MuScatModel(matlab_pars, optimize, my_learningrate, my_keep_prob,
tv_lambda, obj_reg_lambda,
gr_lambda) # First initiliaze it
# ## Compute the System's properties (e.g. Pupil function/Illumination Source, K-vectors, etc.)
# In[ ]:
''' Compute the systems model'''
mm.computeSys()
# Now load data which has been saved previously (Optional!)
# In[ ]:
# Load Copute the systems model
#if(load_data):
# mm.loadData()
