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()
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 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
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 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
label = ' '.join(tokens)
self.text = plt.text(x, y, label, fontsize=14, color='white')
self.fig.canvas.draw()
# =====
# Unit test
# =====
if __name__ == '__main__':
import sys, lib_fits, lib_background, lib_wcs
filename = '../../data/fits/common.fits'
header, pixels = lib_fits.read_first_image(filename)
background, dispersion, _ = lib_background.compute_background(pixels)
clusters = lib_cluster.convolution_clustering(pixels, background,
dispersion)
wcs = lib_wcs.get_wcs(header)
fig, axis = plt.subplots()
axis.imshow(pixels, interpolation='none')
fig.canvas.mpl_connect(
'motion_notify_event',
ShowClusterProperties(fig, clusters,
lambda cl: ["{}".format(cl.integral)]))
plt.show()
sys.exit(0)