def M(psi, det_mod): """.. method:: M(mode, psi_modes, det_mod) Applies modulus constraint to psi_modes(mode) for a given position. :param list psi_modes: A list of CXData instances containing all modes at a given position. :param np.ndarray det_mod: Modulus of measured diffraction pattern. """ if isinstance(psi, CXData): return ifft2(det_mod * exp(complex(0., 1.) * angle(fft2(psi)))) elif isinstance(psi, CXModal): mode_sum = CXModal.modal_sum(abs(fft2(psi))**2.0)**0.5 return ifft2((fft2(psi)/(mode_sum))*det_mod)
def init_probe(self, *args, **kwargs): if CXP.io.initial_probe_guess is not '': probe = CXData() probe.load(CXP.io.initial_probe_guess) self.probe.modes = [CXData(data=[probe.data[0]/(i+1)]) for i in range(CXP.reconstruction.probe_modes)] self.probe.normalise() else: dx_s = CXP.dx_s p, p2 = CXP.preprocessing.desired_array_shape, CXP.preprocessing.desired_array_shape/2 probe = sp.zeros((p, p), complex) if CXP.experiment.optic.lower() == 'kb': if len(CXP.experiment.beam_size)==1: bsx=bsy=np.round(CXP.experiment.beam_size[0]/dx_s) elif len(CXP.experiment.beam_size)==2: bsx, bsy = np.round(CXP.experiment.beam_size[0]/dx_s), np.round(CXP.experiment.beam_size[1]/dx_s) probe = np.sinc((np.arange(p)-p2)/bsx)[:,np.newaxis]*np.sinc((np.arange(p)-p2)/bsy)[np.newaxis,:] elif CXP.experiment.optic.lower() == 'zp': probe = np.sinc(sp.hypot(*sp.ogrid[-p2:p2, -p2:p2])/np.round(3.*CXP.experiment.beam_size[0]/(2*CXP.dx_s))) ph_func = gauss_smooth(np.random.random(probe.shape), 10) fwhm = p/2.0 radker = sp.hypot(*sp.ogrid[-p/2:p/2,-p/2:p/2]) gaussian = exp(-1.0*(fwhm/2.35)**-2. * radker**2.0 ) gaussian /= gaussian.max() probe = abs(gaussian*probe)* exp(complex(0.,np.pi)*ph_func/ph_func.max()) self.probe.modes = [CXData(data=[probe/(i+1)]) for i in range(CXP.reconstruction.probe_modes)] self.probe.normalise()
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'))