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'))
