def get_int_var(masked_spectrum, distance, wavelength):
propagation_array = get_propagation_array(masked_spectrum.shape, distance,
wavelength)
propagated = propagation_array * masked_spectrum
reconstructed = get_shifted_idft(propagated)
intensity = get_intensity(reconstructed)
fitness = intensity.var()
return fitness
def get_int_var(masked_spectrum, distance, wavelength):
propagation_array = get_propagation_array(masked_spectrum.shape, distance,
wavelength)
propagated = propagation_array * masked_spectrum
reconstructed = get_shifted_idft(propagated)
intensity = get_intensity(reconstructed)
fitness = intensity.var()
return fitness
def get_lowpass_var(masked_spectrum, distance):
propagation_array = get_propagation_array(masked_spectrum.shape, distance)
propagated = propagation_array * masked_spectrum
lowpass_mask = get_lowpass_mask(propagated.shape, .4)
propagated = lowpass_mask * propagated
reconstructed = get_shifted_idft(propagated)
intensity = get_intensity(reconstructed)
fitness = intensity.var()
return fitness
def get_lowpass_var(masked_spectrum, distance):
propagation_array = get_propagation_array(masked_spectrum.shape, distance)
propagated = propagation_array * masked_spectrum
lowpass_mask = get_lowpass_mask(propagated.shape, .4)
propagated = lowpass_mask * propagated
reconstructed = get_shifted_idft(propagated)
intensity = get_intensity(reconstructed)
fitness = intensity.var()
return fitness
def propagate(masked, **kw):
propagation_array = get_propagation_array(masked.shape, kw["distance"])
propagated = propagation_array * masked
return propagated
radius = round(min(shape) * (1 - radius))
window = np.kaiser(radius, softness)
mask = 1 - get_mask(shape, window)
return mask
def get_lowpass_mask(shape, radius=0.2, softness=0):
radius = round(min(shape) * radius)
window = np.kaiser(radius, softness)
mask = get_mask(shape, window)
return mask
@methods
def get_var(masked_spectrum, distance, wavelength, (dx, dy)):
propagation_array = get_propagation_array(masked_spectrum.shape, distance,
wavelength, (dx, dy))
propagated = propagation_array * masked_spectrum
reconstructed = get_shifted_idft(propagated)
module = get_module(reconstructed)
module_zone = get_best_contrast_zone(module)
# showimage(normalize(module_zone))
fitness = module_zone.var()
return fitness
@methods
def get_diff_var(masked_spectrum, distance, wavelength, (dx, dy)):
fitness = get_var(masked_spectrum, distance, wavelength, (dx, dy))
fitness -= get_var(masked_spectrum, -distance, wavelength, (dx, dy))
return fitness
def propagation(self):
print("Propagation")
return get_propagation_array(self.spectrum.shape, self.distance,
self.wavelength, (self.dx, self.dy))
def propagate(masked, **kw):
propagation_array = get_propagation_array(masked.shape, kw["distance"])
propagated = propagation_array * masked
return propagated
def propagation(self):
print("Propagation")
return get_propagation_array(self.spectrum.shape, self.distance,
self.wavelength, (self.dx, self.dy))
radius = round(min(shape) * (1 - radius))
window = np.kaiser(radius, softness)
mask = 1 - get_mask(shape, window)
return mask
def get_lowpass_mask(shape, radius=0.2, softness=0):
radius = round(min(shape) * radius)
window = np.kaiser(radius, softness)
mask = get_mask(shape, window)
return mask
@methods
def get_var(masked_spectrum, distance, wavelength, (dx, dy)):
propagation_array = get_propagation_array(masked_spectrum.shape,
distance, wavelength, (dx, dy))
propagated = propagation_array * masked_spectrum
reconstructed = get_shifted_idft(propagated)
module = get_module(reconstructed)
module_zone = get_best_contrast_zone(module)
# showimage(normalize(module_zone))
fitness = module_zone.var()
return fitness
@methods
def get_diff_var(masked_spectrum, distance, wavelength, (dx, dy)):
fitness = get_var(masked_spectrum, distance, wavelength, (dx, dy))
fitness -= get_var(masked_spectrum, -distance, wavelength, (dx, dy))
return fitness
