# Optionally, tweak styles.
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/'
tf.reset_default_graph()
'''START CODE'''
# Generate Test-Object
''' File which stores the experimental parameters from the Q-PHASE setup
2.) Read in the parameters of the dataset '''
matlab_pars = paras.MyParameter()
matlab_pars.loadmat(mymatpath=experiments.matlab_par_file,
mymatname=experiments.matlab_par_name)
try:
matlab_pars.Nz, matlab_pars.Nx, matlab_pars.Ny = experiments.mysize
except:
print("We don't need to adjust the parameters here!")
matlab_pars.mysize = (matlab_pars.Nz, matlab_pars.Nx, matlab_pars.Ny
) # ordering is (Nillu, Nz, Nx, Ny)
matlab_pars.shiftIcY = experiments.shiftIcY
matlab_pars.shiftIcX = experiments.shiftIcX
matlab_pars.dn = experiments.dn
matlab_pars.NAc = experiments.NAc
''' 2.) Read in the parameters of the dataset '''
if (experiments.matlab_val_file.find('mat') == -1):
mytimestamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
savepath = experiments.mysavepath + mytimestamp + '_' + '_PSFmodel_' + psf_model + experiments.regularizer + '_' + str(
experiments.lambda_reg) + '_eps_' + str(
experiments.myepstvval) + '_' + 'Shift_x-' + str(
experiments.shiftIcX) + 'Shift_y-' + str(
experiments.shiftIcY)
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!')
tf.reset_default_graph()
'''Choose between Born (BORN) or BPM (BPM)'''
psf_modell = 'BPM' # 1st Born
#psf_modell = 'Born' # MultiSlice
#psf_modell = '3QDPC'
#psf_modell = None
is_mictype = 'BF' # BF, DF, DIC, PC
tf.reset_default_graph()
# need to figure out why this holds somehow true - at least produces reasonable results
# Generate Test-Object
''' File which stores the experimental parameters from the Q-PHASE setup
2.) Read in the parameters of the dataset '''
myparams = paras.MyParameter(Nx=128, Ny=128, NAc=.25, Nz=2, dz=3)
#myparams.loadmat(mymatpath = experiments.matlab_par_file, mymatname = experiments.matlab_par_name)
myparams.print()
''' Create the Model'''
muscat = mus.MuScatModel(myparams, is_optimization=is_optimization)
#experiments.zernikefactors = np.array((0,0,0,0, -1.2058168e-04, -2.3622499e-03, -7.7374041e-02 ,-1.4900701e-02, -6.6282146e-04 ,-4.2013789e-04 , -1.2619525e+00))
experiments.zernikemask = np.ones(muscat.zernikemask.shape)
obj_real = np.zeros(myparams.mysize)
obj_real[
0, :, :] = 1.33 + .01 * tf_go.generateSpeckle(mysize=myparams.Nx, D=20)
obj_real[1, :, :] = 1.33 + myparams.dn * (
nip.rr(mysize=(myparams.Nx, myparams.Ny)) < 10) * (nip.rr(
mysize=(myparams.Nx, myparams.Ny), freq='ftfreq'))