def preprocessing(self): """.. method:: preprocessing() Collects together all the preprocessing functions that are required to begin phase retrieval. """ # Get the scan positions self.positions = CXData(name='positions', data=[]) self.ptycho_mesh() if CXP.measurement.simulate_data: self.simulate_data() else: # Read in raw data self.det_mod = CXData(name = 'det_mod') if CXP.actions.preprocess_data: self.det_mod.read_in_data() else: self.det_mod.load() if CXP.io.whitefield_filename: self.probe_det_mod = CXData(name='probe_det_mod') self.probe_det_mod.preprocess_data() self.object = CXData(name='object', data=[sp.zeros((self.ob_p, self.ob_p), complex)]) self.probe_intensity = CXData(name='probe_intensity', data=[sp.zeros((self.p, self.p))]) self.probe = CXModal(modes=[]) self.psi = CXModal(modes=[]) for i in range(CXP.reconstruction.probe_modes): self.probe.modes.append(CXData(name='probe{:d}'.format(i), data=[sp.zeros((self.p, self.p), complex)])) self.psi.modes.append(CXData(name='psi{:d}'.format(i), data=[sp.zeros((self.p, self.p), complex) for i in xrange(self.det_mod.len())])) self.init_probe() # Calculate STXM image if this is a ptycho scan if len(self.det_mod.data) > 1: self.calc_stxm_image() if CXP.actions.process_dpc: self.process_dpc()
def simulate_data(self): CXP.log.info('Simulating diffraction patterns.') self.sample = CXData() self.sample.load(CXP.io.simulation_sample_filename[0]) self.sample.data[0] = self.sample.data[0].astype(float) self.sample.normalise(val=0.8) self.sample.data[0]+=0.2 self.input_probe = CXModal() if len(CXP.io.simulation_sample_filename)>1: ph = CXData() ph.load(CXP.io.simulation_sample_filename[1]) ph.data[0] = ph.data[0].astype(float) ph.normalise(val=np.pi/3) self.sample.data[0] = self.sample.data[0]*exp(complex(0., 1.)*ph.data[0]) p = self.sample.data[0].shape[0] ham_window = sp.hamming(p)[:,np.newaxis]*sp.hamming(p)[np.newaxis,:] sample_large = CXData(data=sp.zeros((CXP.ob_p, CXP.ob_p), complex)) sample_large.data[0][CXP.ob_p/2-p/2:CXP.ob_p/2+p/2, CXP.ob_p/2-p/2:CXP.ob_p/2+p/2] = self.sample.data[0]*ham_window ker = sp.arange(0, p) fwhm = p/3.0 radker = sp.hypot(*sp.ogrid[-p/2:p/2,-p/2:p/2]) gaussian = exp(-1.0*(fwhm/2.35)**-2. * radker**2.0 ) ortho_modes = lambda n1, n2 : gaussian*np.sin(n1*math.pi*ker/p)[:,np.newaxis]*np.sin(n2*math.pi*ker/p)[np.newaxis, :] mode_generator = lambda : sp.floor(4*sp.random.random(2))+1 used_modes = [] self.input_psi = CXModal() for mode in range(CXP.reconstruction.probe_modes): if mode==0: new_mode = [1,1] else: new_mode = list(mode_generator()) while new_mode in used_modes: new_mode = list(mode_generator()) used_modes.append(new_mode) CXP.log.info('Simulating mode {:d}: [{:d}, {:d}]'.format(mode, int(new_mode[0]), int(new_mode[1]))) ph_func = gauss_smooth(np.random.random((p,p)), 10) self.input_probe.modes.append(CXData(name='probe{:d}'.format(mode), data=ortho_modes(new_mode[0], new_mode[1])*exp(complex(0.,np.pi)*ph_func/ph_func.max()))) self.input_probe.normalise() self.input_probe.orthogonalise() for mode in range(CXP.reconstruction.probe_modes): p2 = p/2 x, y = self.positions.correct self.input_psi.modes.append(CXData(name='input_psi_mode{:d}'.format(mode), data=[])) for i in xrange(len(x)): if i%(len(x)/10)==0.: CXP.log.info('Simulating diff patt {:d}'.format(i)) tmp = (CXData.shift(sample_large, -1.0*(x[i]-CXP.ob_p/2), -1.0*(y[i]-CXP.ob_p/2)) [CXP.ob_p/2-p2:CXP.ob_p/2+p2, CXP.ob_p/2-p2:CXP.ob_p/2+p2]* self.input_probe[mode][0]) self.input_psi[mode].data.append(tmp.data[0]) # Add modes incoherently self.det_mod = CXModal.modal_sum(abs(fft2(self.input_psi))) self.det_mod.save(path=CXP.io.base_dir+'/'+CXP.io.scan_id+'/raw_data/{:s}.npy'.format('det_mod'))