if mask[y][x] == 1 and (x,y) not in points:
points.append((x,y))
mask[y][x] = 0
else:
return []
new_points = []
if x+1 < mask.shape[1]:
new_points += recurrent_point_search(x+1, y, points, mask)
if y+1 < mask.shape[0]:
new_points += recurrent_point_search(x, y+1, points, mask)
if x-1 >= 0:
new_points += recurrent_point_search(x-1, y, points, mask)
if y-1 >= 0:
new_points += recurrent_point_search(x, y-1, points, mask)
for point in new_points:
if point not in points:
points.append(point)
return points
image = read_fits_file('M27_R_60s-001.fit')
# image = np.array([
# [0,0,0,0],
# [0,1000,1000,0],
# [0,1000,1000,0],
# [0,0,0,0],
# ])
create_psf_objects(image)
# show_3d_data(image)
if args.show_object_fit:
show_object_fit = True
show_object_fit_separate = False
if args.show_object_fit_separate:
show_object_fit_separate = True
show_3d = False
if args.show_3d:
show_3d = True
if args.file is None or not os.path.isfile(args.file):
raise FileNotFoundError('target file not given')
if args.o is None:
raise FileNotFoundError('output file not given')
image = read_fits_file(args.file)
# image = np.flip(image, axis=0)
headers = read_fits_file_headers(args.file)
if show_3d:
show_3d_data(image)
segmentation_options = ['fit_threshold', 'sobel']
extracted_point_clusters = None
if not args.s:
raise AttributeError('need segmentation method, -s')
if args.s in segmentation_options:
if args.s == 'fit_threshold':
if not args.sigma_threshold:
sigma_threshold = 2
else:
bigger.shape[1] - smaller.shape[1])
first = difference[0] // 2
second = difference[1] // 2
print('Fixing sizes of outputs with shapes {} and {}'.format(
str(bigger.shape), str(smaller.shape)))
print('Crop {} from edges in 1 dimension and {} in second'.format(
str(first), str(second)))
if bigger is a1:
return a1[first:-first, second:-second], a2
else:
return a1, a2[first:-first, second:-second]
for i in range(1, 10):
input_file = 'generated/Comb_' + str(i) + '/Comb/Comb_' + str(i) + '.fits'
image = read_fits_file(input_file)
original_background_input_file = 'generated/Comb_' + str(
i) + '/Noise/Noise_' + str(i) + '.fits'
original_background = read_fits_file(original_background_input_file)
# show_3d_data(original_background, method='matplotlib')
file_name, extension = os.path.basename(input_file).split('.')
# extracted_background = sigma_clipper(image, 10, 10)
extracted_background = sigma_clipper(image)
# show_3d_data(image, method='matplotlib')
# show_3d_data(extracted_background, method='matplotlib')
difference_between_backgrounds = abs(original_background -
extracted_background)
action="store_true")
parser.add_argument("-o",
help="name of background file(default = file + _bg)")
args = parser.parse_args()
if '/' not in args.file:
directory = os.getcwd()
input_file = directory + '/' + args.file.split('/')[-1]
else:
if args.a:
input_file = args.file
directory = '/'.join(input_file.split('/')[:-1])
else:
directory = os.getcwd()
input_file = directory + '/' + args.file
image = read_fits_file(input_file)
file_name, extension = os.path.basename(input_file).split('.')
if args.i:
extracted_background = sigma_clipper(image, iterations=args.i)
my_comment = "Extracted background with {} iterations".format(args.i)
else:
extracted_background = sigma_clipper(image)
my_comment = "Extracted background with {} iterations".format(5)
if args.o:
if '.' in args.o:
edit_fits_data(input_file,
extracted_background,
args.o,
comment=my_comment)
init_vals = [1000., 0., 1000.]
best_vals, covar = curve_fit(gaussian, [x for x in range(len(hist))], hist, p0=init_vals)
# Get the fitted curve
hist_fit = gaussian([x for x in range(len(hist))], *best_vals)
center = int(best_vals[1])
sigma = int(best_vals[2])
if sigma_only:
return sigma
threshold = int(center + sigma*threshold_sigma)
if show:
fig = plt.figure()
ax = fig.add_subplot(111)
# smoothed = smooth(hist,15) ax.bar([x for x in range(len(hist))], hist)
ax.axvline(threshold, color='green', label='threshold')
ax.axvline(center, color='brown', label='center')
ax.axvline(center+sigma, color='yellow', label='center+sigma')
ax.plot([x for x in range(len(hist))], hist, label='Test data', color='red')
ax.plot([x for x in range(len(hist))], hist_fit, label='Fitted data', color='blue')
plt.show()
return bins[threshold]
else:
return bins[threshold]
if __name__ == '__main__':
image = read_fits_file('data/M27_R_60s-001.fit')
histogram_threshold(image, True)
found_spot = False
for i, point_mesh in enumerate(joined):
if found_spot:
break
for u, point_mesh_point in enumerate(point_mesh):
if found_spot:
break
if neighbor_check(point_mesh_point, point):
found_spot = True
joined[i].append(point)
if not found_spot:
joined.append([point])
return joined
def neighbor_check(first_point, second_point):
dist = np.linalg.norm(np.array(first_point) - np.array(second_point))
if dist == 1 or dist == math.sqrt(2):
return True
return False
# image = read_fits_file('data/M27_R_60s-001.fit')
image = read_fits_file('data/AGO_2017_PR25_R-005.fit')
# image = read_fits_file('data/STREAK_test_1-003.fit')
extracted_point_spread_meshes = []
extract_point_spread_meshes(image)
for point_mash in extracted_point_spread_meshes:
params = point_mash.fit_curve()
show_image(point_mash.squared_data, 'point mash')