def main():
file_name, interactive = lib_args.get_args()
header, pixels = lib_fits.read_first_image(file_name)
background, dispersion, _ = lib_background.compute_background(pixels)
# search for clusters
clustering = RecursiveClustering()
clusters = clustering(pixels, background, dispersion)
max_cluster = clusters[0]
wcs = lib_wcs.get_wcs(header)
pxy = lib_wcs.PixelXY(max_cluster.column, max_cluster.row)
radec = lib_wcs.xy_to_radec(wcs, pxy)
cobjects, _, _ = lib_stars.get_celestial_objects(radec)
# console output
print(
'number of clusters: {:2d}, greatest integral: {:7d}, x: {:4.1f}, y: {:4.1f}'
.format(len(clusters), max_cluster.integral, max_cluster.column,
max_cluster.row))
for cobj in cobjects.keys():
print('celestial object: {}'.format(cobj))
# graphic output
if interactive:
_, axis = plt.subplots()
axis.imshow(lib_cluster.add_crosses(pixels, clusters))
plt.show()
return 0
def main():
file_name, interactive = lib_args.get_args()
# importing image
pixels = None
pixels, header = lib_fits.read_first_image(file_name)
my_wcs = lib_wcs.get_wcs(header)
clusters = ex3_clusters.find_clusters(pixels, False)
for j in range(len(clusters)):
peak_pixel = lib_wcs.PixelXY(clusters[j].y, clusters[j].x)
cel_coord = lib_wcs.xy_to_radec(my_wcs, peak_pixel)
acc_radius = 0.001
celestial_objects, out, req = lib_stars.get_celestial_objects(
cel_coord, acc_radius)
signature_fmt_1 = 'RESULT: right_ascension_{:d} = {:.3f}'.format(
j, cel_coord[0])
signature_fmt_2 = 'RESULT: declination_{:d} = {:.3f}'.format(
j, cel_coord[1])
print(signature_fmt_1)
print(signature_fmt_2)
for i, key in enumerate(celestial_objects.keys()):
signature_fmt_3 = 'RESULT: celestial_object_{:d}_{:d} = {}'.format(
j, i, key)
print(signature_fmt_3)
# graphic output
if interactive:
fig, main_axes = plt.subplots()
handler = Handler(fig, main_axes, my_wcs)
main_axes.imshow(pixels)
#fig.canvas.mpl_connect('motion_notify_event', handler.move)
fig.canvas.mpl_connect('button_press_event', handler.on_click)
plt.show()
# end
return 0
def on_click(self, event):
if event.button == 1:
# Create a transformation matrix
axis = event.inaxes
for txt in self.texts:
txt.remove()
for patch in self.patches:
patch.remove()
self.texts = []
self.patches = []
display_to_image = axis.transData.inverted()
# Image coordinates returned as a list
image_x_y = display_to_image.transform((event.x, event.y))
peak_pixel = lib_wcs.PixelXY(*image_x_y)
cel_coord = lib_wcs.xy_to_radec(self.wcs, peak_pixel)
#text = axis.text(*image_x_y, str(cel_coord[0]) + "\n" + str(cel_coord[1]), fontsize=10, color='white')
acc_radius = 0.001
celestial_objects, out, req = lib_stars.get_celestial_objects(
cel_coord, acc_radius)
for i, key in enumerate(celestial_objects.keys()):
patch = axis.add_patch(
patches.Rectangle(image_x_y - 5,
10,
10,
fill=False,
color='white'))
text = axis.text(image_x_y[0] + 6,
image_x_y[1] - i * 5,
key,
fontsize=10,
color='white')
self.patches.append(patch)
self.texts.append(text)
print(key)
event.canvas.draw()
return
def main():
# analyse command line arguments
file_name, interactive = lib_args.get_args()
# importing image
pixels = None
pixels, header = lib_fits.read_first_image(file_name)
my_wcs = lib_wcs.get_wcs(header)
sorted_clusters = ex3_clusters.find_clusters(pixels, False)
peak_pixel = lib_wcs.PixelXY(sorted_clusters[0].y, sorted_clusters[0].x)
cel_coord = lib_wcs.xy_to_radec(my_wcs, peak_pixel)
acc_radius = 0.001
celestial_objects, out, req = lib_stars.get_celestial_objects(
cel_coord, acc_radius)
signature_fmt_1 = 'RESULT: right_ascension = {:.3f}'.format(cel_coord[0])
signature_fmt_2 = 'RESULT: declination = {:.3f}'.format(cel_coord[1])
print(signature_fmt_1)
print(signature_fmt_2)
i = 0
for key in celestial_objects.keys():
signature_fmt_3 = 'RESULT: celestial_object_{:d} = {}'.format(i, key)
print(signature_fmt_3)
i += 1
# graphic output
if interactive:
# ...
pass
# end
return 0
def get_celestial_objects(wcs, cluster):
pxy = lib_wcs.PixelXY(cluster.column, cluster.row)
radec = lib_wcs.xy_to_radec(wcs, pxy)
return lib_stars.get_celestial_objects(radec)
if ((cl.row < pattern_diameter) or
(cl.row >= (pixels.shape[0]-pattern_diameter)) or
(cl.column < pattern_diameter) or
(cl.column >= (pixels.shape[1]-pattern_diameter))):
print('cluster {:d} is in the border'.format(icl, cl))
nb_patho += 1
# check max integral clusters are not too similar
if len(clusters) > 1 and ((clusters[0].integral - clusters[1].integral) < 10.):
print('clusters 0 and 1 are too similar')
nb_patho += 1
# check lacking or multiple celestial objects for max cluster
wcs = lib_wcs.get_wcs(header)
pxy = lib_wcs.PixelXY(clusters[0].column, clusters[0].row)
radec = lib_wcs.xy_to_radec(wcs, pxy)
cobjects, _, _ = lib_stars.get_celestial_objects(radec)
if len(cobjects) < 1:
print('clusters 0 is associated with no celestial object')
nb_patho += 1
if len(cobjects) > 1:
print('clusters 0 is associated with multiple celestial objects')
for icobj, cobj in enumerate(sorted(cobjects.keys())):
print('celestial object {}: {}'.format(icobj, cobj))
nb_symptom += 1
# conclusion
print('{} pathologies'.format(nb_patho))
print('{} symptoms'.format(nb_symptom))
sys.exit(nb_patho)