def main():
args = parser.parse_args()
catalog = read_catalog(
max_magnitude=args.max_magnitude,
max_variability=None,
)
img = cam.read(args.inputfile)
# create catalog of possibly visible stars and planets
stars_catalog, magnitude = transform_catalog(
catalog, img.timestamp, cam, min_altitude=args.min_altitude)
# find blobs in the image
r, c, s = find_stars(img.data, threshold=args.threshold)
found_altaz = cam.pixel2horizontal(r, c, img.timestamp)
# only use blobs with alt > min_altitude
mask = found_altaz.alt.deg > args.min_altitude
idx, found = find_matching_stars(stars_catalog, found_altaz[mask])
print('Simple: ', calculate_cloudiness_simple(found))
print('Weighted:', calculate_cloudiness_weighted(magnitude, found))
# plot that stuff
fig, ax, plot = plot_img(img)
add_blobs(
*cam.horizontal2pixel(stars_catalog[found]),
np.full(len(stars_catalog), 1.5),
color='C2',
ax=ax,
)
add_blobs(
*cam.horizontal2pixel(stars_catalog[~found]),
np.full(len(stars_catalog), 1.5),
color='C1',
ax=ax,
)
plt.legend([
plt.Circle((0, 0), 0, edgecolor='C2', fill=False),
plt.Circle((0, 0), 0, edgecolor='C1', fill=False),
], ['Found Catalog Stars', 'Catalog stars without match'])
add_zenith_lines(cam, ax=ax)
add_direction_labels(cam, ax=ax, color='crimson', weight='bold', size=14)
plt.show()
def test_images():
from all_sky_cloud_detection.cameras import cta_la_palma as cam
from all_sky_cloud_detection.catalog import read_catalog, transform_catalog
from all_sky_cloud_detection.star_detection import find_stars, find_matching_stars
from all_sky_cloud_detection.calculate_cloudiness import calculate_cloudiness_simple
images = [
'tests/resources/cta_images/starry_nomoon.fits.gz',
'tests/resources/cta_images/starry.fits.gz',
'tests/resources/cta_images/partly_cloudy.fits.gz',
'tests/resources/cta_images/cloudy.fits.gz',
]
limits = [
(0, 0.1),
(0, 0.2),
(0.3, 0.7),
(0.9, 1.0),
]
catalog = read_catalog(
max_magnitude=cam.max_magnitude,
max_variability=None,
)
for (a, b), path in zip(limits, images):
img = cam.read(path)
# create catalog of possibly visible stars and planets
stars_catalog, magnitude = transform_catalog(
catalog, img.timestamp, cam, min_altitude=20,
)
r, c, s = find_stars(img.data, threshold=cam.threshold)
found_altaz = cam.pixel2horizontal(r, c, img.timestamp)
mask = found_altaz.alt.deg > 20
idx, found = find_matching_stars(
stars_catalog, found_altaz[mask], max_sep=0.25 * u.deg
)
cl = calculate_cloudiness_simple(found)
assert a <= cl <= b
def find_blobs(img_name, format, threshold):
if format == 'fits':
scale = 2**16
else:
scale = 2**10
image = normalize_image(read_fits(img_name), scale=scale)
row, col, size = find_stars(image, threshold=threshold)
fig, ax = plt.subplots(1, 1)
ax.imshow(
image,
cmap='gray',
vmin=np.nanpercentile(image, 1),
vmax=np.nanpercentile(image, 99),
)
add_blobs(row, col, size, ax=ax)
fig.tight_layout()
plt.savefig(img_name + '_blobs.pdf', dpi=600)
return row, col, size
)
write_header = True
for path in tqdm(sorted(os.listdir(args.inputdir))):
img = magic_2018.read(os.path.join(args.inputdir, path))
if img.data.mean() > 0.030:
continue
stars_altaz = transform_catalog(catalog, img.timestamp, magic_2018)
objects = concatenate(
[stars_altaz, get_planets(img.timestamp, magic_2018)])
objects = objects[objects.alt.deg > 5]
r, c, s = find_stars(img.data, 0.04)
found_altaz = magic_2018.pixel2horizontal(r, c, img.timestamp)
mask = found_altaz.alt.deg > 5
r, c, s = r[mask], c[mask], s[mask]
found_altaz = found_altaz[mask]
idx, d2d, d3d = match_coordinates_sky(objects, found_altaz)
mask = d2d < 1.0 * u.deg
idx = idx[mask]
pd.DataFrame(
dict(
row=r[idx],
col=c[idx],
alt=objects[mask].alt.deg,
def main():
args = parser.parse_args()
catalog = read_catalog(
max_magnitude=args.max_magnitude,
max_variability=None,
)
for path in images:
img = cam.read(path)
# create catalog of possibly visible stars and planets
stars_catalog, magnitude = transform_catalog(
catalog, img.timestamp, cam, min_altitude=args.min_altitude
)
# img.smoothed = gaussian(img.data, sigma=1.5)
# find blobs in the image
r, c, s = find_stars(img.data, threshold=args.threshold)
found_altaz = cam.pixel2horizontal(r, c, img.timestamp)
# only use blobs with alt > min_altitude
mask = found_altaz.alt.deg > args.min_altitude
idx, found = find_matching_stars(
stars_catalog, found_altaz[mask], max_sep=0.25 * u.deg
)
print('Simple: ', calculate_cloudiness_simple(found))
print('Weighted:', calculate_cloudiness_weighted(magnitude, found))
img.data[np.isnan(img.data)] = np.nanmin(img.data)
img.data = adjust_gamma(rescale_intensity(img.data, (0, 1)), gamma=0.7)
# plot that stuff
fig, ax, plot = plot_img(img)
fig.colorbar(plot)
add_blobs(
r[mask], c[mask], s[mask],
color='C0',
ax=ax,
)
add_blobs(
*cam.horizontal2pixel(stars_catalog[found]),
np.full(len(stars_catalog), 1.5),
color='C2',
ax=ax,
)
add_blobs(
*cam.horizontal2pixel(stars_catalog[~found]),
np.full(len(stars_catalog), 1.5),
color='C1',
ax=ax,
)
plt.legend([
plt.Circle((0, 0), 0, edgecolor='C2', fill=False),
plt.Circle((0, 0), 0, edgecolor='C1', fill=False),
], [
'Found Catalog Stars',
'Catalog stars without match'
])
add_zenith_lines(cam, ax=ax)
add_direction_labels(cam, ax=ax, color='crimson', weight='bold', size=14)
plt.show()