def test_max_magnitude():
from all_sky_cloud_detection.catalog import read_catalog
catalog = read_catalog(max_magnitude=6)
assert np.all(catalog['v_mag'] <= 6)
catalog = read_catalog(max_magnitude=3)
assert np.all(catalog['v_mag'] <= 3)
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
from astropy.coordinates import concatenate, match_coordinates_sky
import astropy.units as u
import pandas as pd
from tqdm import tqdm
from all_sky_cloud_detection.cameras import magic_2018
from all_sky_cloud_detection.catalog import read_catalog, transform_catalog, get_planets
from all_sky_cloud_detection.star_detection import find_stars
from all_sky_cloud_detection.plotting import add_blobs, add_zenith_lines
parser = ArgumentParser()
parser.add_argument('inputdir')
args = parser.parse_args()
catalog = read_catalog(
max_magnitude=2.5,
max_variability=None,
)
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]
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()
# coding: utf-8
from all_sky_cloud_detection.cameras import magic_2018
from all_sky_cloud_detection.catalog import read_catalog
from all_sky_cloud_detection.plotting import add_direction_labels, add_zenith_lines
from astropy.coordinates import SkyCoord, AltAz
import astropy.units as u
import matplotlib.pyplot as plt
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('inputfile')
parser.add_argument('-o', '--output')
args = parser.parse_args()
c = read_catalog(
max_magnitude=magic_2018.max_magnitude,
max_variability=None,
)
img = magic_2018.read(args.inputfile)
big_dipper = [54061, 53910, 58001, 59774, 62956, 65378, 67301]
dipper_mask = [row['HIP'] in big_dipper for row in c]
stars = SkyCoord(ra=c['ra'], dec=c['dec'])
altaz = AltAz(obstime=img.timestamp, location=magic_2018.location)
stars_altaz = stars.transform_to(altaz)
mask = (stars_altaz.alt.deg > 15) & (c['v_mag'] <= 3.5)
fig = plt.figure(figsize=(12, 9))
ax = fig.add_axes([0, 0, 1, 1])
ax.set_axis_off()
def test_read():
from all_sky_cloud_detection.catalog import read_catalog
catalog = read_catalog()
assert catalog.colnames == ['HIP', 'ra', 'dec', 'v_mag', 'variability']
def test_max_variability():
from all_sky_cloud_detection.catalog import read_catalog
catalog = read_catalog(max_variability=2)
assert np.any(catalog['variability'] >= 1)
assert np.all(catalog['variability'] <= 2)
# coding: utf-8
from all_sky_cloud_detection.cameras import cta_la_palma
from all_sky_cloud_detection.catalog import read_catalog
from astropy.coordinates import SkyCoord, AltAz
import astropy.units as u
import matplotlib.pyplot as plt
c = read_catalog(
max_magnitude=cta_la_palma.max_magnitude,
max_variability=None,
)
img = cta_la_palma.read('tests/resources/cta_images/starry.fits.gz')
big_dipper = [54061, 53910, 58001, 59774, 62956, 65378, 67301]
dipper_mask = [row['HIP'] in big_dipper for row in c]
stars = SkyCoord(ra=c['ra'], dec=c['dec'])
altaz = AltAz(obstime=img.timestamp, location=cta_la_palma.location)
stars_altaz = stars.transform_to(altaz)
mask = (stars_altaz.alt.deg > 15) & (c['v_mag'] <= 5)
fig = plt.figure(figsize=(8, 8))
ax = fig.add_axes([0, 0, 1, 1])
ax.set_axis_off()
ax.imshow(img.data, cmap='gray', vmax=0.2)
for m in (mask, dipper_mask):
row, col = cta_la_palma.horizontal2pixel(stars_altaz[m])
ax.plot(col, row, 'o', ms=8, mfc='none')
from all_sky_cloud_detection.cameras import iceact
from all_sky_cloud_detection.catalog import read_catalog
from astropy.coordinates import SkyCoord, AltAz
import astropy.units as u
import matplotlib.pyplot as plt
c = read_catalog(
max_magnitude=iceact.max_magnitude,
max_variability=None,
)
img = iceact.read('tests/resources/iceact_images/starry.fits')
stars = SkyCoord(ra=c['ra'], dec=c['dec'])
altaz = AltAz(obstime=img.timestamp, location=iceact.location)
stars_altaz = stars.transform_to(altaz)
mask = (stars_altaz.alt.deg > 15) & (c['v_mag'] <= 2.5)
fig = plt.figure(figsize=(8, 8))
ax = fig.add_axes([0, 0, 1, 1])
ax.set_axis_off()
ax.imshow(img.data, cmap='gray', vmax=0.2)
row, col = iceact.horizontal2pixel(stars_altaz[mask])
ax.plot(
col,
row,
'o',
ms=8,
