def save_fft_plot(folder, tests, cam, labels, title):
print 'saving fft plot'
fft_info_list = []
for test in tests:
im_obj = FiberImage(object_file(folder, test, cam))
fft_info_list.append(im_obj.get_modal_noise(method='fft'))
min_wavelength = im_obj.pixel_size / im_obj.magnification * 2.0
max_wavelength = im_obj.get_fiber_radius(method='edge', units='microns')
plot_fft(fft_info_list,
labels=labels,
min_wavelength=min_wavelength,
max_wavelength=max_wavelength)
save_plot(folder + 'analysis/' + title + '/' + cam.upper() + '.png',
dpi=600)
save_plot(folder + 'analysis/' + title + '/' + cam.upper() + '.pdf',
dpi=600)
def save_modal_noise_data(folder, tests, cam, methods, title):
print 'saving modal noise data'
modal_noise_info = [['cam', 'test'] + methods]
for i, test in enumerate(tests):
im_obj = FiberImage(object_file(folder, test, cam))
modal_noise_info.append([cam, test])
for method in methods:
modal_noise = im_obj.get_modal_noise(method)
im_obj.save_object(object_file(folder, test, cam))
print cam, test, method, modal_noise
modal_noise_info[i + 1].append(modal_noise)
with open(folder + 'analysis/' + title + '/' + cam.upper() + ' Data.csv',
'wb') as f:
wr = csv.writer(f)
wr.writerows(modal_noise_info)
'0.1Hz 160mm 80s',
'1.0Hz 160mm 1s',
'1.0Hz 160mm 10s',
'baseline']
# def object_file(folder, test, cam):
# return folder + test + '/' + cam + '_obj.pkl'
if __name__ == '__main__':
for cam in CAMS:
# save_modal_noise_data(FOLDERS, TESTS, cam, labels, METHODS, TITLES)
modal_noise = []
for title, folder, test in zip(TITLES, FOLDERS, TESTS):
modal_noise.append([])
for t in test:
# mn = []
# for im in xrange(10):
# im_obj = FiberImage(object_file(folder + t, cam, 1, im))
# mn.append(im_obj.get_modal_noise(method=METHOD))
# mn = np.array(mn).mean()
im_obj = FiberImage(object_file(folder + t, cam, 10, 0))
mn = im_obj.get_modal_noise(method=METHOD)
modal_noise[-1].append(mn)
print t, mn
plot_modal_noise(modal_noise, plot_type='bar', bar_labels=LABELS, labels=TITLES, method=METHOD)
save_plot(FOLDER + 'analysis/' + TITLE + ' ' + cam.upper() + ' ' + METHOD + ' SNR.png')
# save_plot(FOLDER + 'analysis/' + TITLE + ' ' + cam.upper() + ' ' + METHOD + ' SNR.pdf')
if CASE == 4:
TITLE = 'Amplitude vs Frequency NSR'
FOLDER += 'amp_freq_200um/'
TITLES = ['']
FOLDERS = [FOLDER]
TESTS = [[
'agitated_5volts_40mm_8s', 'agitated_30volts_40mm_1s',
'agitated_5volts_160mm_8s', 'agitated_30volts_160mm_1s', 'baseline'
]]
LABELS = [
'0.1Hz 40mm', '1.0Hz 40mm', '0.1Hz 160mm', '1.0Hz 160mm', 'LED source'
]
def object_file(folder, test, cam):
return folder + test + '/' + cam + '_obj.pkl'
if __name__ == '__main__':
for cam in CAMS:
modal_noise = []
for title, folder, test in zip(TITLES, FOLDERS, TESTS):
modal_noise.append([])
for t in test:
im_obj = FiberImage(object_file(folder, t, cam))
modal_noise[-1].append(im_obj.get_modal_noise(method=METHOD))
plot_modal_noise(modal_noise, LABELS, TITLES, method=METHOD)
save_plot(FOLDER + 'analysis/' + TITLE + '/' + cam.upper() + '.png')
save_plot(FOLDER + 'analysis/' + TITLE + '/' + cam.upper() + '.pdf')