def extinctionCorrectionApparentToTrue(mags, x_data, y_data, jd, platepar):
""" Compute true magnitudes by applying extinction correction to apparent magnitudes.
Arguments:
mags: [list] A list of apparent magnitudes.
x_data: [list] A list of pixel columns.
y_data: [list] A list of pixel rows.
jd: [float] Julian date.
platepar: [Platepar object]
Return:
corrected_mags: [list] A list of extinction corrected mangitudes.
"""
### Compute star elevations above the horizon (epoch of date, true) ###
# Compute RA/Dec in J2000
_, ra_data, dec_data, _ = xyToRaDecPP(len(x_data)*[jd2Date(jd)], x_data, y_data, len(x_data)*[1], \
platepar, extinction_correction=False)
# Compute elevation above the horizon
elevation_data = []
for ra, dec in zip(ra_data, dec_data):
# Precess to epoch of date
ra, dec = equatorialCoordPrecession(J2000_JD.days, jd, np.radians(ra),
np.radians(dec))
# Compute elevation
_, elev = raDec2AltAz(np.degrees(ra), np.degrees(dec), jd,
platepar.lat, platepar.lon)
if elev < 0:
elev = 0
elevation_data.append(elev)
### ###
# Correct catalog magnitudes for extinction
extinction_correction = atmosphericExtinctionCorrection(np.array(elevation_data), platepar.elev) \
- atmosphericExtinctionCorrection(90, platepar.elev)
corrected_mags = np.array(
mags) - platepar.extinction_scale * extinction_correction
return corrected_mags
def extinctionCorrectionTrueToApparent(catalog_mags, ra_data, dec_data, jd,
platepar):
""" Compute apparent magnitudes by applying extinction correction to catalog magnitudes.
Arguments:
catalog_mags: [list] A list of catalog magnitudes.
ra_data: [list] A list of catalog right ascensions (J2000) in degrees.
dec_data: [list] A list of catalog declinations (J2000) in degrees.
jd: [float] Julian date.
platepar: [Platepar object]
Return:
corrected_catalog_mags: [list] Extinction corrected catalog magnitudes.
"""
### Compute star elevations above the horizon (epoch of date, true) ###
# Compute elevation above the horizon
elevation_data = []
for ra, dec in zip(ra_data, dec_data):
# Precess to epoch of date
ra, dec = equatorialCoordPrecession(J2000_JD.days, jd, np.radians(ra),
np.radians(dec))
# Compute elevation
_, elev = raDec2AltAz(np.degrees(ra), np.degrees(dec), jd,
platepar.lat, platepar.lon)
if elev < 0:
elev = 0
elevation_data.append(elev)
### ###
# Correct catalog magnitudes for extinction
extinction_correction = atmosphericExtinctionCorrection(np.array(elevation_data), platepar.elev) \
- atmosphericExtinctionCorrection(90, platepar.elev)
corrected_catalog_mags = np.array(
catalog_mags) + platepar.extinction_scale * extinction_correction
return corrected_catalog_mags
def applyPlateparToCentroids(ff_name,
fps,
meteor_meas,
platepar,
add_calstatus=False):
""" Given the meteor centroids and a platepar file, compute meteor astrometry and photometry (RA/Dec,
alt/az, mag).
Arguments:
ff_name: [str] Name of the FF file with the meteor.
fps: [float] Frames per second of the video.
meteor_meas: [list] A list of [calib_status, frame_n, x, y, ra, dec, azim, elev, inten, mag].
platepar: [Platepar instance] Platepar which will be used for astrometry and photometry.
Keyword arguments:
add_calstatus: [bool] Add a column with calibration status at the beginning. False by default.
Return:
meteor_picks: [ndarray] A numpy 2D array of: [frames, X_data, Y_data, RA_data, dec_data, az_data,
alt_data, level_data, magnitudes]
"""
meteor_meas = np.array(meteor_meas)
# Add a line which is indicating the calibration status
if add_calstatus:
meteor_meas = np.c_[np.ones((meteor_meas.shape[0], 1)), meteor_meas]
# Remove all entries where levels are equal to or smaller than 0, unless all are zero
level_data = meteor_meas[:, 8]
if np.any(level_data):
meteor_meas = meteor_meas[level_data > 0, :]
# Extract frame number, x, y, intensity
frames = meteor_meas[:, 1]
X_data = meteor_meas[:, 2]
Y_data = meteor_meas[:, 3]
level_data = meteor_meas[:, 8]
# Get the beginning time of the FF file
time_beg = filenameToDatetime(ff_name)
# Calculate time data of every point
time_data = []
for frame_n in frames:
t = time_beg + datetime.timedelta(seconds=frame_n / fps)
time_data.append([
t.year, t.month, t.day, t.hour, t.minute, t.second,
int(t.microsecond / 1000)
])
# Convert image cooredinates to RA and Dec, and do the photometry
JD_data, RA_data, dec_data, magnitudes = xyToRaDecPP(np.array(time_data), X_data, Y_data, \
level_data, platepar)
# Compute azimuth and altitude of centroids
az_data = np.zeros_like(RA_data)
alt_data = np.zeros_like(RA_data)
for i in range(len(az_data)):
jd = JD_data[i]
ra_tmp = RA_data[i]
dec_tmp = dec_data[i]
# Precess RA/Dec to epoch of date
ra_tmp, dec_tmp = equatorialCoordPrecession(J2000_JD.days, jd, np.radians(ra_tmp), \
np.radians(dec_tmp))
# Alt/Az are apparent (in the epoch of date, corresponding to geographical azimuths)
az_tmp, alt_tmp = raDec2AltAz(np.degrees(ra_tmp), np.degrees(dec_tmp),
jd, platepar.lat, platepar.lon)
az_data[i] = az_tmp
alt_data[i] = alt_tmp
# print(ff_name, cam_code, meteor_No, fps)
# print(X_data, Y_data)
# print(RA_data, dec_data)
# print('------------------------------------------')
# Construct the meteor measurements array
meteor_picks = np.c_[frames, X_data, Y_data, RA_data, dec_data, az_data, alt_data, level_data, \
magnitudes]
return meteor_picks