data_dict[camera][test]['diameter'].append(diameter)
data_dict[camera][test]['time'].append(obj.get_image_info('date_time'))
for prop in ['x0', 'y0', 'diameter', 'time']:
data_dict[camera][test][prop] = (np.array(data_dict[camera][test][prop])
- data_dict[camera][test][prop][0])
data_dict[camera][test]['r0'] = np.sqrt(data_dict[camera][test]['x0']**2
+ data_dict[camera][test]['y0']**2)
for i, time in enumerate(data_dict[camera][test]['time']):
data_dict[camera][test]['time'][i] = time.total_seconds()
print test
print 'r0 STDEV:', data_dict[camera][test]['r0'].std()
print 'diam STDEV:', data_dict[camera][test]['diameter'].std()
if method == 'radius':
error = 0.1
elif method == 'gaussian':
error = 0.1
error = convert_pixels_to_microns(error, pixel_size=obj.get_pixel_size(), magnification=obj.get_magnification())
plot_stability(data_dict[camera], name + ' Stability', error)
plt.savefig(SAVE_FOLDER + name + ' Stability.png')
plot_center_stability(data_dict[camera], name + ' Center Stability', error)
plt.savefig(SAVE_FOLDER + name + ' Center Stability.png')
print
unagitated_folder = base_folder + 'images/600um/unagitated/'
nf = {}
ff = {}
nf_dark = image_list(dark_folder + 'nf_dark_')
nf_ambient = image_list(ambient_folder + 'nf_ambient_')
ff_dark = image_list(dark_folder + 'ff_dark_')
ff_ambient = image_list(ambient_folder + 'ff_ambient_')
nf_agitated = FiberImage(image_list(agitated_folder + 'nf_agitated_'),
nf_dark, nf_ambient)
nf_unagitated = FiberImage(
image_list(unagitated_folder + 'nf_unagitated_'), nf_dark, nf_ambient)
nf_baseline = FiberImage(nf_agitated.get_tophat_fit(),
pixel_size=nf_agitated.get_pixel_size(),
threshold=0.1,
camera='nf')
ff_agitated = FiberImage(image_list(agitated_folder + 'ff_agitated_'),
ff_dark, ff_ambient)
ff_unagitated = FiberImage(
image_list(unagitated_folder + 'ff_unagitated_'), ff_dark, ff_ambient)
ff_baseline = FiberImage(ff_agitated.get_gaussian_fit(),
pixel_size=ff_agitated.get_pixel_size(),
magnification=1,
camera='ff')
print
modal_noise = []
data_dict[camera][test][prop] = (
np.array(data_dict[camera][test][prop]) -
data_dict[camera][test][prop][0])
data_dict[camera][test]['r0'] = np.sqrt(
data_dict[camera][test]['x0']**2 +
data_dict[camera][test]['y0']**2)
for i, time in enumerate(data_dict[camera][test]['time']):
data_dict[camera][test]['time'][i] = time.total_seconds()
print test
print 'r0 STDEV:', data_dict[camera][test]['r0'].std()
print 'diam STDEV:', data_dict[camera][test]['diameter'].std()
if method == 'radius':
error = 0.1
elif method == 'gaussian':
error = 0.1
error = convert_pixels_to_microns(
error,
pixel_size=obj.get_pixel_size(),
magnification=obj.get_magnification())
plot_stability(data_dict[camera], name + ' Stability', error)
plt.savefig(SAVE_FOLDER + name + ' Stability.png')
plot_center_stability(data_dict[camera], name + ' Center Stability',
error)
plt.savefig(SAVE_FOLDER + name + ' Center Stability.png')
print
unagitated_folder = base_folder + 'images/600um/unagitated/'
nf = {}
ff = {}
nf_dark = image_list(dark_folder + 'nf_dark_')
nf_ambient = image_list(ambient_folder + 'nf_ambient_')
ff_dark = image_list(dark_folder + 'ff_dark_')
ff_ambient = image_list(ambient_folder + 'ff_ambient_')
nf_agitated = FiberImage(image_list(agitated_folder + 'nf_agitated_'),
nf_dark, nf_ambient)
nf_unagitated = FiberImage(image_list(unagitated_folder + 'nf_unagitated_'),
nf_dark, nf_ambient)
nf_baseline = FiberImage(nf_agitated.get_tophat_fit(),
pixel_size=nf_agitated.get_pixel_size(),
threshold=0.1, camera='nf')
ff_agitated = FiberImage(image_list(agitated_folder + 'ff_agitated_'),
ff_dark, ff_ambient)
ff_unagitated = FiberImage(image_list(unagitated_folder + 'ff_unagitated_'),
ff_dark, ff_ambient)
ff_baseline = FiberImage(ff_agitated.get_gaussian_fit(),
pixel_size=ff_agitated.get_pixel_size(),
magnification=1, camera='ff')
print()
modal_noise = []
for test in [nf_agitated, nf_unagitated, nf_baseline]:
modal_noise.append(modal_noise(test, method='fft', output='array', radius_factor=1.0))