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 update_figure(self, i=0):
cur_cmap = cm.RdGy_r
self.f1.clf()
self.init_figure()
wh = sp.where(abs(self.object.data[0]) > 0.1 * (abs(self.object.data[0]).max()))
try:
x1, x2 = min(wh[0]), max(wh[0])
y1, y2 = min(wh[1]), max(wh[1])
except (ValueError, IndexError):
x1, x2 = 0, self.ob_p
y1, y2 = 0, self.ob_p
# Plot magnitude of object
s1 = pylab.subplot(231)
s1_im = s1.imshow(abs(self.object).data[0][x1:x2, y1:y2], cmap=cm.Greys_r)
s1.set_title('|object|')
plt.axis('off')
pylab.colorbar(s1_im)
# Plot phase of object
s2 = pylab.subplot(232)
s2_im = s2.imshow(sp.angle(self.object.data[0][x1:x2, y1:y2]), cmap=cm.hsv)
s2.set_title('phase(object)')
plt.axis('off')
pylab.colorbar(s2_im)
# Complex HSV plot of object
s3 = pylab.subplot(233)
h = ((angle(self.object).data[0][x1:x2, y1:y2] + np.pi) / (2*np.pi)) % 1.0
s = np.ones_like(h)
l = abs(self.object).data[0][x1:x2, y1:y2]
l-=l.min()
l/=l.max()
s3_im = s3.imshow(np.dstack(v_hls_to_rgb(h,l,s)))
s3.set_title('Complex plot of Object')
plt.axis('off')
# Plot probe mode 0
s4 = pylab.subplot(234)
s4_im = s4.imshow(abs(self.probe.modes[0].data[0]), cmap=cur_cmap)
s4.set_title('|probe0|')
plt.axis('off')
pylab.colorbar(s4_im)
if CXP.reconstruction.probe_modes>1:
s5 = pylab.subplot(235)
s5_im = s5.imshow(abs(self.probe.modes[1].data[0]), cmap=cur_cmap)
s5.set_title('|probe1|')
plt.axis('off')
pylab.colorbar(s5_im)
else:
pass
if self.ppc:
s6 = self.f1.add_subplot(236)
s6_im = s6.scatter(self.positions.data[0], self.positions.data[1], s=10,
c='b', marker='o', alpha=0.5, edgecolors='none', label='current')
patches = []
for m in range(self.positions.total):
patches.append(Circle((self.positions.initial[0][m], self.positions.initial[1][m]),
radius=CXP.reconstruction.ppc_search_radius))
collection = PatchCollection(patches, color='tomato', alpha=0.2, edgecolors=None)
s4.add_collection(collection)
if CXP.measurement.simulate_data:
s4_im = s4.scatter(self.positions.correct[0], self.positions.correct[1], s=10,
c='g', marker='o', alpha=0.5, edgecolors='none', label='correct')
CXP.log.info('RMS position deviation from correct: [x:{:3.2f},y:{:3.2f}] pixels'.format(
sp.sqrt(sp.mean((self.positions.data[0] - self.positions.correct[0])**2.)),
sp.sqrt(sp.mean((self.positions.data[1] - self.positions.correct[1])**2.))))
lines=[]
for m in range(self.positions.total):
lines.append(((self.positions.correct[0][m], self.positions.correct[1][m]),
(self.positions.data[0][m], self.positions.data[1][m])))
for element in lines:
x, y = zip(*element)
s4.plot(x, y, 'g-')
else:
lines = []
for m in range(self.positions.total):
lines.append(((self.positions.initial[0][m], self.positions.initial[1][m]),
(self.positions.data[0][m], self.positions.data[1][m])))
for element in lines:
x, y = zip(*element)
s6.plot(x, y, 'g-')
CXP.log.info('RMS position deviation from initial: [x:{:3.2f},y:{:3.2f}] pixels'.format(
sp.sqrt(sp.mean((self.positions.data[0] - self.positions.initial[0])**2.)),
sp.sqrt(sp.mean((self.positions.data[1] - self.positions.initial[1])**2.))))
s6.legend(prop={'size': 6})
s6.set_title('Position Correction')
s6.set_aspect('equal')
extent = s6.get_window_extent().transformed(self.f1.dpi_scale_trans.inverted())
pylab.savefig(self._cur_sequence_dir + '/ppc_{:d}.png'.format(self.total_its), bbox_inches=extent.expanded(1.2, 1.2), dpi=100)
s6.set_aspect('auto')
else:
s6 = pylab.subplot(236)
if CXP.measurement.simulate_data:
s6_im = s6.imshow(abs(self.input_probe[1].data[0]), cmap = cur_cmap)
s6.set_title('|input_probe1|')
else:
s6_im = s6.imshow(nlog(fftshift(self.det_mod[np.mod(i,self.positions.total)])).data[0], cmap=cur_cmap)
s6.set_title('Diff Patt: {:d}'.format(i))
plt.axis('off')
pylab.colorbar(s6_im)
pylab.draw()
pylab.savefig(self._cur_sequence_dir + '/recon_{:d}.png'.format(self.total_its), dpi=60)
