def main():
file_name, interactive = lib_args.get_args()
header, pixels = lib_fits.read_first_image(file_name)
background, dispersion, max_x = lib_background.compute_background(pixels)
clustering = lib_cluster.Clustering()
clusters = clustering(pixels, background, dispersion)
# console output
if not interactive:
print('{} clusters'.format(len(clusters)))
else:
# graphic output
fig, axis = plt.subplots()
imgplot = axis.imshow(pixels)
axcolor = 'lightgoldenrodyellow'
ax_thresh = plt.axes([0.25, 0.92, 0.65, 0.03], axisbg=axcolor)
threshold = 6.0
slider = widgets.Slider(ax_thresh, 'Threshold', 0.0, 5*threshold, valinit=threshold)
update_slider = UpdateSlider(pixels, background, dispersion, imgplot, fig)
slider.on_changed(update_slider)
update_slider(threshold)
plt.show()
return 0
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)
clustering = lib_cluster.Clustering()
clusters = clustering(pixels, background, dispersion)
max_cluster = clusters[0]
# coordinates ra dec
wcs = lib_wcs.get_wcs(header)
for i, c in enumerate(clusters):
show_cluster(wcs, i, c)
# graphic output
if interactive:
import matplotlib.pyplot as plt
import lib_graphics
fig, axis = plt.subplots()
axis.imshow(pixels, interpolation='none')
fig.canvas.mpl_connect('motion_notify_event',
lib_graphics.ShowClusterProperties(fig,clusters,ShowCelestialObjects(wcs)))
plt.show()
return 0
def __call__(self, val):
clustering = lib_cluster.Clustering()
clusters = clustering(self.pixels, self.background, self.dispersion, val)
print('{} clusters'.format(len(clusters)))
crosses = lib_cluster.add_crosses(self.pixels, clusters)
self.imgplot.set_data(crosses)
self.fig.canvas.draw_idle()
def main():
# analyse command line arguments
file_name, interactive = lib_args.get_args()
logging.info('----------------')
logging.info('name of file: {}'.format(file_name))
# read fits file
header, pixels = lib_fits.read_first_image(file_name)
logging.info('cd1_1: {CD1_1:.10f}'.format(**header))
logging.info('cd1_2: {CD1_2:.10f}'.format(**header))
logging.info('cd2_1: {CD2_1:.10f}'.format(**header))
logging.info('cd2_2: {CD2_2:.10f}'.format(**header))
# compute background
background, dispersion, _ = lib_background.compute_background(pixels)
logging.info('background: {:d}'.format(int(background)))
logging.info('dispersion: {:d}'.format(int(dispersion)))
# clustering
clustering = lib_cluster.Clustering()
clusters = clustering(pixels, background, dispersion)
for icl, cl in enumerate(clusters):
logging.info('----------------')
logging.info('cluster {:d}: {}'.format(icl, cl))
# radec coordinates of the greatest cluster
wcs = lib_wcs.get_wcs(header)
pxy = lib_wcs.PixelXY(cl.column, cl.row)
radec = lib_wcs.xy_to_radec(wcs, pxy)
logging.info('right ascension: {:.3f}'.format(radec.ra))
logging.info('declination: {:.3f}'.format(radec.dec))
# celestial objects for the biggest cluster
cobjects, _, _ = get_celestial_objects(wcs, cl)
for icobj, cobj in enumerate(cobjects.keys()):
logging.info('celestial object {}: {}'.format(icobj, cobj))
# graphic output
if interactive:
fig, axis = plt.subplots()
axis.imshow(pixels, interpolation='none')
fig.canvas.mpl_connect(
'motion_notify_event',
lib_graphics.ShowClusterProperties(fig, clusters,
ShowCelestialObjects(wcs)))
plt.show()
logging.info('----------------')
return 0
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)
clustering = lib_cluster.Clustering()
clusters = clustering(pixels, background, dispersion)
max_cluster = clusters[0]
# coordinates ra dec
wcs = lib_wcs.get_wcs(header)
for i, c in enumerate(clusters):
show_cluster(wcs, i, c)
break
return 0
sys.path.append('../solutions')
import lib_args
import lib_fits
import lib_background
import lib_cluster
import lib_wcs
import lib_stars
# prepare clusters
file_name, interactive = lib_args.get_args()
header, pixels = lib_fits.read_first_image(file_name)
print('image shape is {}'.format(pixels.shape))
background, dispersion, _ = lib_background.compute_background(pixels)
pattern_radius = 4
clustering = lib_cluster.Clustering(pattern_radius)
clusters = clustering(pixels, background, dispersion)
nb_patho = 0
nb_symptom = 0
# print clusters details
for icl, cl in enumerate(clusters):
print('cluster {:d}: {}'.format(icl, cl))
# check no cluster
if len(clusters) == 0:
print('no cluster')
nb_patho += 1
# check single cluster (too simple)
if len(clusters) == 1: