def test_to_astropy_time():
from fact.coordinates.utils import to_astropy_time
t = Time('2013-01-01 00:00')
assert to_astropy_time(datetime(2013, 1, 1)) == t
assert to_astropy_time(pd.Timestamp(t.unix * 1e9)) == t
assert to_astropy_time(pd.to_datetime('2013-01-01 00:00')) == t
assert to_astropy_time(np.array('2013-01-01 00:00',
dtype='datetime64[ns]')) == t
def calc_delta_delta(event, mask):
# if 'source_position_zd' not in df.columns:
frame = AltAz(location=LOCATION,
obstime=to_astropy_time(
pd.to_datetime(event.observation_info.time)))
crab_altaz = crab.transform_to(frame)
source_position_zd_phs = crab_altaz.zen.deg
source_position_az_phs = crab_altaz.az.deg
source_x, source_y = horizontal_to_camera(
source_position_zd_phs,
source_position_az_phs,
event.zd,
event.az,
)
# safe x, y and t components of Photons. shape = (#photons,3)
xyt = event.photon_stream.point_cloud
lol = event.photon_stream.list_of_lists
x, y, t = xyt.T
x = np.rad2deg(x) / camera_distance_mm_to_deg(1)
y = np.rad2deg(y) / camera_distance_mm_to_deg(1)
cleaned_photons = np.zeros(len(x), dtype=bool)
for i in range(len(lol)):
if mask[i]:
for j in range(len(lol[i])):
cleaned_photons[i] = True
cog_x = np.mean(x[cleaned_photons])
cog_y = np.mean(y[cleaned_photons])
true_delta = np.arctan2(cog_y - source_y, cog_x - source_x)
delta = calc_delta(phs2image(event.photon_stream.list_of_lists), mask)
delta_delta = true_delta - delta
if delta_delta < -np.pi / 2:
delta_delta += 2 * np.pi
# if delta_delta < -90:
# delta_delta += 360
return delta_delta
facttools.set_index(['run_id', 'event_num', 'night'], inplace=True)
df = phs.join(
facttools,
how='inner',
rsuffix='_std',
lsuffix='_phs',
on=('run', 'event', 'night'),
)
df.sort_index(axis=1, inplace=True)
if args.threshold:
phs = phs.query(f'gamma_prediction >= {args.threshold}').copy()
if 'source_position_zd' not in df.columns:
frame = AltAz(location=LOCATION,
obstime=to_astropy_time(pd.to_datetime(df['timestamp'])))
crab = SkyCoord.from_name('Crab Nebula')
crab_altaz = crab.transform_to(frame)
df['source_position_zd_phs'] = crab_altaz.zen.deg
df['source_position_az_phs'] = crab_altaz.az.deg
df['source_x'], df['source_y'] = horizontal_to_camera(
df['source_position_zd'],
df['source_position_az'],
df['pointing_position_zd_phs'],
df['pointing_position_az_phs'],
)
df['true_delta'] = np.arctan2(df['cog_y_phs'] - df['source_y'],
parser = ArgumentParser()
parser.add_argument('inputfile')
parser.add_argument('-o', '--outputfile')
parser.add_argument('-t', '--threshold', type=float)
args = parser.parse_args()
print("Reading in data...")
df = read_data(args.inputfile, key='events')
print("Done!")
if args.threshold:
df = df.query(f'gamma_prediction >= {args.threshold}').copy()
if 'source_position_zd' not in df.columns:
frame = AltAz(location=LOCATION, obstime=to_astropy_time(pd.to_datetime(df['timestamp'])))
crab = SkyCoord.from_name('Crab')
crab_altaz = crab.transform_to(frame)
df['source_position_zd'] = crab_altaz.zen.deg
df['source_position_az'] = crab_altaz.az.deg
df['source_x'], df['source_y'] = horizontal_to_camera(
df['source_position_zd'],
df['source_position_az'],
df['pointing_position_zd'],
df['pointing_position_az'],
)
df['true_delta'] = np.arctan2(df['cog_y'] - df['source_y'], df['cog_x'] - df['source_x'])