def update_probe_nonmodal(self, i, psi_old, psi_new):
d1, d2 = self.positions.data
id1, id2 = d1//1, d2//1
object_intensity_max = (abs(self.object)**2.0).data[0].max()
self.probe.modes[0] += \
CXData.shift(conj(self.object[id1[i] - self.p2:id1[i] + self.p2, id2[i] - self.p2:id2[i] + self.p2]) *
(psi_new-psi_old)[0] / object_intensity_max, d1[i]%1, d2[i]%1)
self.probe.normalise()
def update_probe(self, i, psi_old, psi_new):
d1, d2 = self.positions.data
id1, id2 = d1//1, d2//1
object_intensity_max = (abs(self.object)**2.0).data[0].max()
for mode in range(len(self.probe)):
self.probe.modes[mode] += \
CXData.shift(conj(self.object[id1[i] - self.p2:id1[i] + self.p2, id2[i] - self.p2:id2[i] + self.p2]) *
(psi_new-psi_old)[mode] / object_intensity_max, d1[i]%1, d2[i]%1)
self.probe.normalise()
self.probe.orthogonalise()
def update_object(self, i, psi_old, psi_new):
"""
Update the object from a single ptycho position.
"""
then=time.time()
d1, d2 = self.positions.data
id1, id2 = d1//1, d2//1
probe_intensity_max = CXModal.modal_sum(abs(self.probe)**2.0).data[0].max()
self.object[id1[i] - self.p2:id1[i] + self.p2, id2[i] - self.p2:id2[i] + self.p2] += \
CXData.shift(CXModal.modal_sum(conj(self.probe) * (psi_new-psi_old)) / probe_intensity_max,
d1[i]%1, d2[i]%1)
if self.total_its==0 and sp.mod(i, len(self.positions.data[0]) / 10) == 0:
self.update_figure(i)
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'))