def hologram(self):
'''Return hologram of sphere
Returns
-------
hologram : numpy.ndarray
Computed hologram.
'''
gpufield = self.alpha * self.field(return_gpu=True)
gpufield[0, :] += 1.
gpufield = gpufield * gpufield.conj()
return np.sum(gpufield.real.get(), axis=0)
if __name__ == '__main__':
import matplotlib.pyplot as plt
from Instrument import coordinates
import time
shape = [201, 251]
# shape = [1024, 1280]
h = CudaLMHologram(coordinates=coordinates(shape))
h.particle.r_p = [125, 75, 100]
h.instrument.wavelength = 0.447
start = time.time()
img = h.hologram().reshape(shape)
print(time.time() - start)
plt.imshow(img, cmap='gray')
plt.show()
if return_gpu is False:
hologram = hologram.get()
elif self.using_numba:
field = self.field()
hologram = self.holo
fh.fasthologram(field, self.alpha, hologram.size, hologram)
else:
field = self.alpha * self.field()
field[0, :] += 1.
hologram = np.sum(np.real(field * np.conj(field)), axis=0)
return hologram
if __name__ == '__main__':
import matplotlib.pyplot as plt
from Instrument import coordinates
from time import time
shape = [201, 201]
h = LMHologram(coordinates=coordinates(shape))
h.particle.r_p = [125, 75, 100]
h.particle.a_p = 0.9
h.particle.n_p = 1.45
h.instrument.wavelength = 0.447
h.hologram()
start = time()
hol = h.hologram()
print("Time to calculate {}".format(time() - start))
plt.imshow(hol.reshape(shape), cmap='gray')
plt.show()
for key in self._keys:
params.add(key, getattr(particle, key))
self._minimizer.params = params
return self._minimizer.minimize()
if __name__ == '__main__':
from Instrument import coordinates
import numpy as np
from lmfit import report_fit
import matplotlib.pyplot as plt
a = Feature()
# Use model to generate synthetic data
shape = [201, 201]
a.model.coordinates = coordinates(shape)
p = a.model.particle
p.r_p = [100, 100, 100]
p.a_p = 0.75
p.n_p = 1.45
h = a.model.hologram()
h += np.random.normal(0., 0.05, h.size)
a.data = h
# add errors to parameters
p.r_p += np.random.normal(0., 1, 3)
p.a_p += np.random.normal(0., 0.01, 1)
p.n_p += np.random.normal(0., 0.01, 1)
# ... and now fit
result = a.optimize()
report_fit(result)
# plot residuals