def main():
""" Exercise 4: Clusters """
lib_fits.init()
print(lib_fits.file_name)
header, pixels = lib_fits.read_first_image(lib_fits.file_name)
thres = lib_background.threshold(pixels)
peaks = lib_conv.complete_peaks_search(pixels)
lums = lib_cluster.peak_lum(pixels, peaks)
clusters = []
for i in range(len(peaks)):
clust = lib_cluster.build_cluster(pixels, peaks[i], thres)
if clust == None:
continue
else:
clusters.append(clust)
signature_fmt_1 = 'RESULT: clusters_number={:d}'.format(len(clusters))
signature_fmt_2 = 'RESULT: cluster_max_top={:d}'.format(max(lums))
print(signature_fmt_1)
print(signature_fmt_2)
sort_clus = lib_cluster.sort_clusters(clusters)
bcfe = sort_clus[0]
fig, main_axes = plt.subplots()
main_axes.imshow(pixels)
# Draws clusters arround peaks.
for i in range(len(clusters)):
xleft = clusters[i].coord[0] - clusters[i].ext
xright = clusters[i].coord[0] + clusters[i].ext
ybottom = clusters[i].coord[1] - clusters[i].ext
ytop = clusters[i].coord[1] + clusters[i].ext
squarex = [xleft, xright, xright, xleft, xleft]
squarey = [ybottom, ybottom, ytop, ytop, ybottom]
plt.plot(squarey, squarex, 'r--')
plt.show()
signature_fmt_3 = 'RESULT: cluster_max_integral={:d}'.format(bcfe.lum)
signature_fmt_4 = 'RESULT: cluster_max_column={:d}'.format(bcfe.coord[1])
signature_fmt_5 = 'RESULT: cluster_max_row={:d}'.format(bcfe.coord[0])
signature_fmt_6 = 'RESULT: cluster_max_extension={:d}'.format(bcfe.ext)
print(signature_fmt_3)
print(signature_fmt_4)
print(signature_fmt_5)
print(signature_fmt_6)
return 0
def main():
""" Exercise 6: Display stars """
lib_fits.init()
print(lib_fits.file_name)
global pixels
global header
header, pixels = lib_fits.read_first_image(lib_fits.file_name)
clusters = lib_cluster.find_clusters(pixels)
for i in range(6):
rad = lib_stars.cluster_radec(clusters, i, header, pixels)
lib_stars.celestial_objects(rad, i)
return 0
def main():
""" Exercise 5: Stars """
lib_fits.init()
print(lib_fits.file_name)
global pixels
global header
header, pixels = lib_fits.read_first_image(lib_fits.file_name)
clusters = lib_cluster.find_clusters(pixels)
clusters_radec = []
for i in range(len(clusters)):
clusters_radec.append(
lib_stars.get_radec(clusters[i].coord[0], clusters[i].coord[1],
header, pixels))
signature_fmt_1 = 'RESULT: right_ascension = {:.3f}'.format(
clusters_radec[0][0])
signature_fmt_2 = 'RESULT: declination = {:.3f}'.format(
clusters_radec[0][1])
print(signature_fmt_1)
print(signature_fmt_2)
celestial_objects = stars.get_celestial_objects(clusters_radec[0])
#print(celestial_objects)
for i in range(len(celestial_objects[0])):
signature_fmt_3 = 'RESULT: celestial_object_{:02d} = {:s}'.format(
i,
list(celestial_objects[0].keys())[i])
signature_fmt_4 = 'RESULT: dist_{:02d} = {:5.1f}'.format(
i,
list(celestial_objects[0].values())[i])
print(signature_fmt_3)
print(signature_fmt_4)
return 0
def main():
""" Exercise 1: Read Image """
#taking file name
lib_fits.init()
print(lib_fits.file_name)
header, pixels = lib_fits.read_first_image(lib_fits.file_name)
# show figure
if lib_fits.interactive:
fig, main_axes = plt.subplots()
main_axes.imshow(pixels)
plt.show()
#signature
signature_fmt_1 = 'RESULT: CRPIX1 = {:.0f}'.format(header.get("CRPIX1"))
signature_fmt_2 = 'RESULT: CRPIX2 = {:.0f}'.format(header.get("CRPIX2"))
print(signature_fmt_1)
print(signature_fmt_2)
return 0
def main():
""" Exercise 3: Peaks """
lib_fits.init()
print(lib_fits.file_name)
header, pixels = lib_fits.read_first_image(lib_fits.file_name)
# Build Gaussian pattern
pattern = lib_conv.pattern(9)
plt.imshow(pattern)
#plt.show()
sum_pat = np.sum(pattern)
pattern = pattern/sum_pat
sum_pat_norm = np.sum(pattern)
max_pat_norm = np.max(pattern)
signature_fmt_1 = 'RESULT: pattern_sum={:.5f}'.format(sum_pat_norm)
signature_fmt_2 = 'RESULT: pattern_max={:.5f}'.format(max_pat_norm)
print(signature_fmt_1)
print(signature_fmt_2)
# Extend the image
extended = lib_conv.extend(pixels, 4)
plt.imshow(extended)
plt.show()
height = len(extended)
width = len(extended[0])
sum = np.sum(extended)
signature_fmt_3 = 'RESULT: extended_image_width={:d}'.format(width)
signature_fmt_4 = 'RESULT: extended_image_height={:d}'.format(height)
signature_fmt_5 = 'RESULT: extended_image_sum={:.0f}'.format(sum)
print(signature_fmt_3)
print(signature_fmt_4)
print(signature_fmt_5)
# Convoluted
conv = lib_conv.scan(extended, 4, pattern)
plt.imshow(conv)
plt.show()
conv_height = len(conv)
conv_width = len(conv[0])
conv_sum = np.sum(conv)
signature_fmt_6 = 'RESULT: convoluted_image_width={:d}'.format(conv_width)
signature_fmt_7 = 'RESULT: convoluted_image_height={:d}'.format(conv_height)
signature_fmt_8 = 'RESULT: convoluted_image_sum={:.0f}'.format(conv_sum)
print(signature_fmt_6)
print(signature_fmt_7)
print(signature_fmt_8)
# Extend the convolution image
conv_ext = lib_conv.extend(conv, 1)
conv_ext_height = len(conv_ext)
conv_ext_width = len(conv_ext[0])
conv_ext_sum = np.sum(conv_ext)
signature_fmt_9 = 'RESULT: extended_convoluted_image_width={:d}'.format(conv_ext_width)
signature_fmt_10 = 'RESULT: extended_convoluted_image_height={:d}'.format(conv_ext_height)
signature_fmt_11 = 'RESULT: extended_convoluted_image_sum={:.0f}'.format(conv_ext_sum)
print(signature_fmt_9)
print(signature_fmt_10)
print(signature_fmt_11)
# Identify the Peaks
threshold = lib_background.threshold(pixels)
print(threshold)
peaks = lib_conv.peaks(conv_ext, 1, threshold)
signature_fmt_12 = 'RESULT: peaks_number={:d}'.format(len(peaks))
print(signature_fmt_12)
# end
return 0