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 get_peak_coords(spectrum):
# TODO: must select by position not by intensity
shape = spectrum.shape
center = [dim / 2. for dim in shape]
intensity = get_intensity(spectrum)
circles = [(-get_distance(center, circle[1]), circle[0], circle[1])
for circle in get_circles(intensity, 2, 20)]
circles.sort()
peak = circles[0][2]
peaks_row = (peak[0] - center[0]) / float(center[0])
peaks_col = (peak[1] - center[1]) / float(center[1])
return peaks_row, peaks_col
def get_peak_coords(spectrum):
# TODO: must select by position not by intensity
shape = spectrum.shape
center = [dim / 2. for dim in shape]
intensity = get_intensity(spectrum)
circles = [(-get_distance(center, circle[1]), circle[0], circle[1])
for circle in get_circles(intensity, 2, 20)]
circles.sort()
peak = circles[0][2]
peaks_row = (peak[0] - center[0]) / float(center[0])
peaks_col = (peak[1] - center[1]) / float(center[1])
return peaks_row, peaks_col
def get_auto_mask(spectrum,
softness=0,
radious_scale=.8,
zero_scale=1.3,
cuttop=0,
centered=True):
"""
Try to filter spurious data out.
"""
shape = spectrum.shape
rows, cols = shape
center = (rows / 2., cols / 2.)
intensity = get_intensity(spectrum)
circles = get_circles(intensity, 3, 50)
circles = sorted([(circle[1], circle[0], circle[2]) for circle in circles
if get_distance(center, circle[0]) > 10])[-2:]
circles = sorted([(circle[1], circle[0], circle[2]) for circle in circles])
virtual_order, real_order = circles
zero_order = (center, 0, 0)
peak_center, peak_height, peak_radious = real_order
peak_radious = min([(abs(shape[0] / 3.5 - peak[2]), peak[2])
for peak in circles])[1]
windowmaker = lambda x: np.kaiser(x, softness)
window = get_holed_window(windowmaker, peak_radious * radious_scale)
mask = get_mask(shape, window, peak_center)
zerowindow = get_holed_window(windowmaker, peak_radious * zero_scale)
zeromask = 1 - get_mask(shape, zerowindow, zero_order[0])
mask *= zeromask
masked_intensity = mask * intensity
cutoff = masked_intensity > (masked_intensity.max() -
masked_intensity.ptp() * cuttop)
mask[cutoff] = 0
masked = mask * spectrum
if centered:
masked = get_centered(masked, peak_center)
mask = get_centered(mask, peak_center)
centered = get_centered(intensity, peak_center)
return mask, masked, centered
def get_auto_mask(spectrum, softness=0, radious_scale=.8, zero_scale=1.3,
cuttop=0, centered=True):
"""
Try to filter spurious data out.
"""
shape = spectrum.shape
rows, cols = shape
center = (rows / 2., cols / 2.)
intensity = get_intensity(spectrum)
circles = get_circles(intensity, 3, 50)
circles = sorted([(circle[1], circle[0], circle[2])
for circle in circles
if get_distance(center, circle[0]) > 10])[-2:]
circles = sorted([(circle[1], circle[0], circle[2])
for circle in circles])
virtual_order, real_order = circles
zero_order = (center, 0, 0)
peak_center, peak_height, peak_radious = real_order
peak_radious = min([(abs(shape[0] / 3.5 - peak[2]), peak[2])
for peak in circles])[1]
windowmaker = lambda x: np.kaiser(x, softness)
window = get_holed_window(windowmaker, peak_radious * radious_scale)
mask = get_mask(shape, window, peak_center)
zerowindow = get_holed_window(windowmaker, peak_radious * zero_scale)
zeromask = 1 - get_mask(shape, zerowindow, zero_order[0])
mask *= zeromask
masked_intensity = mask * intensity
cutoff = masked_intensity > (masked_intensity.max()
- masked_intensity.ptp() * cuttop)
mask[cutoff] = 0
masked = mask * spectrum
if centered:
masked = get_centered(masked, peak_center)
mask = get_centered(mask, peak_center)
centered = get_centered(intensity, peak_center)
return mask, masked, centered
