def xyToRaDec(time_data, X_data, Y_data, level_data, lat, lon, Ho, X_res, Y_res, RA_d, dec_d, \
pos_angle_ref, F_scale, mag_lev, vignetting_coeff, x_poly_fwd, y_poly_fwd, station_ht):
""" A function that does the complete calibration and coordinate transformations of a meteor detection.
First, it applies field distortion on the data, then converts the XY coordinates
to altitude and azimuth. Then it converts the altitude and azimuth data to right ascension and
declination. The resulting coordinates are in J2000.0 epoch.
Arguments:
time_data: [2D ndarray] Numpy array containing time tuples of each data point (year, month, day,
hour, minute, second, millisecond).
X_data: [ndarray] 1D numpy array containing the image X component.
Y_data: [ndarray] 1D numpy array containing the image Y component.
level_data: [ndarray] Levels of the meteor centroid.
lat: [float] Latitude of the observer in degrees.
lon: [float] Longitde of the observer in degress.
Ho: [float] Reference hour angle (deg).
X_res: [int] Image size, X dimension (px).
Y_res: [int] Image size, Y dimenstion (px).
RA_d: [float] Reference right ascension of the image centre (degrees).
dec_d: [float] Reference declination of the image centre (degrees).
pos_angle_ref: [float] Field rotation parameter (degrees).
F_scale: [float] Image scale (px/deg).
mag_lev: [float] Magnitude calibration equation parameter (intercept).
vignetting_coeff: [float] Vignetting ceofficient (deg/px).
x_poly_fwd: [ndarray] 1D numpy array of 12 elements containing forward X axis polynomial parameters.
y_poly_fwd: [ndarray] 1D numpy array of 12 elements containing forward Y axis polynomial parameters.
station_ht: [float] Height above sea level of the station (m).
Return:
(JD_data, RA_data, dec_data, magnitude_data): [tuple of ndarrays]
JD_data: [ndarray] Julian date of each data point.
RA_data: [ndarray] Right ascension of each point (deg).
dec_data: [ndarray] Declination of each point (deg).
magnitude_data: [ndarray] Array of meteor's lightcurve apparent magnitudes.
"""
# Convert time to Julian date
JD_data = np.array([date2JD(*time_data_entry) for time_data_entry in time_data], dtype=np.float64)
# Convert x,y to RA/Dec using a fast cython function
RA_data, dec_data = cyXYToRADec(JD_data, np.array(X_data, dtype=np.float64), np.array(Y_data, \
dtype=np.float64), float(lat), float(lon), float(Ho), float(X_res), float(Y_res), float(RA_d), \
float(dec_d), float(pos_angle_ref), float(F_scale), x_poly_fwd, y_poly_fwd)
# Compute radiia from image centre
radius_arr = np.hypot(np.array(X_data) - X_res/2, np.array(Y_data) - Y_res/2)
# Calculate magnitudes
magnitude_data = calculateMagnitudes(level_data, radius_arr, mag_lev, vignetting_coeff)
# CURRENTLY DISABLED!
# Compute the apparent magnitudes corrected to relative atmospheric extinction
# magnitude_data -= atmosphericExtinctionCorrection(alt_data, station_ht) \
# - atmosphericExtinctionCorrection(90, station_ht)
return JD_data, RA_data, dec_data, magnitude_data
def xyToRaDecPP(time_data,
X_data,
Y_data,
level_data,
platepar,
extinction_correction=True):
""" Converts image XY to RA,Dec, but it takes a platepar instead of individual parameters.
Arguments:
time_data: [2D ndarray] Numpy array containing time tuples of each data point (year, month, day,
hour, minute, second, millisecond).
X_data: [ndarray] 1D numpy array containing the image X component.
Y_data: [ndarray] 1D numpy array containing the image Y component.
level_data: [ndarray] Levels of the meteor centroid.
platepar: [Platepar structure] Astrometry parameters.
Keyword arguments:
extinction_correction: [bool] Apply extinction correction. True by default. False is set to prevent
infinite recursion in extinctionCorrectionApparentToTrue when set to True.
Return:
(JD_data, RA_data, dec_data, magnitude_data): [tuple of ndarrays]
JD_data: [ndarray] Julian date of each data point.
RA_data: [ndarray] Right ascension of each point (deg).
dec_data: [ndarray] Declination of each point (deg).
magnitude_data: [ndarray] Array of meteor's lightcurve apparent magnitudes.
"""
# Convert time to Julian date
JD_data = np.array(
[date2JD(*time_data_entry) for time_data_entry in time_data],
dtype=np.float64)
# Convert x,y to RA/Dec using a fast cython function
RA_data, dec_data = cyXYToRADec(JD_data, np.array(X_data, dtype=np.float64), \
np.array(Y_data, dtype=np.float64), float(platepar.lat), float(platepar.lon), float(platepar.X_res), \
float(platepar.Y_res), float(platepar.Ho), float(platepar.RA_d), float(platepar.dec_d), \
float(platepar.pos_angle_ref), float(platepar.F_scale), platepar.x_poly_fwd, platepar.y_poly_fwd, \
unicode(platepar.distortion_type), refraction=platepar.refraction, \
equal_aspect=platepar.equal_aspect, force_distortion_centre=platepar.force_distortion_centre)
# Compute radiia from image centre
radius_arr = np.hypot(
np.array(X_data) - platepar.X_res / 2,
np.array(Y_data) - platepar.Y_res / 2)
# Calculate magnitudes
magnitude_data = calculateMagnitudes(level_data, radius_arr,
platepar.mag_lev,
platepar.vignetting_coeff)
# Extinction correction
if extinction_correction:
magnitude_data = extinctionCorrectionApparentToTrue(magnitude_data, X_data, Y_data, JD_data[0], \
platepar)
return JD_data, RA_data, dec_data, magnitude_data
def xyToRaDecPP(time_data, X_data, Y_data, level_data, platepar, extinction_correction=True, \
measurement=False):
""" Converts image XY to RA,Dec, but it takes a platepar instead of individual parameters.
Arguments:
time_data: [2D ndarray] Numpy array containing time tuples of each data point (year, month, day,
hour, minute, second, millisecond).
X_data: [ndarray] 1D numpy array containing the image X component.
Y_data: [ndarray] 1D numpy array containing the image Y component.
level_data: [ndarray] Levels of the meteor centroid.
platepar: [Platepar structure] Astrometry parameters.
Keyword arguments:
extinction_correction: [bool] Apply extinction correction. True by default. False is set to prevent
infinite recursion in extinctionCorrectionApparentToTrue when set to True.
measurement: [bool] Indicates if the given images values are image measurements. Used for correcting
celestial coordinates for refraction if the refraction was not taken into account during
plate fitting.
Return:
(JD_data, RA_data, dec_data, magnitude_data): [tuple of ndarrays]
JD_data: [ndarray] Julian date of each data point.
RA_data: [ndarray] Right ascension of each point (deg).
dec_data: [ndarray] Declination of each point (deg).
magnitude_data: [ndarray] Array of meteor's lightcurve apparent magnitudes.
"""
# Convert time to Julian date
JD_data = np.array(
[date2JD(*time_data_entry) for time_data_entry in time_data],
dtype=np.float64)
# Convert x,y to RA/Dec using a fast cython function
RA_data, dec_data = cyXYToRADec(JD_data, np.array(X_data, dtype=np.float64), \
np.array(Y_data, dtype=np.float64), float(platepar.lat), float(platepar.lon), float(platepar.X_res), \
float(platepar.Y_res), float(platepar.Ho), float(platepar.RA_d), float(platepar.dec_d), \
float(platepar.pos_angle_ref), float(platepar.F_scale), platepar.x_poly_fwd, platepar.y_poly_fwd, \
unicode(platepar.distortion_type), refraction=platepar.refraction, \
equal_aspect=platepar.equal_aspect, force_distortion_centre=platepar.force_distortion_centre, \
asymmetry_corr=platepar.asymmetry_corr)
# Correct the coordinates for refraction if it wasn't taken into account during the astrometry calibration
# procedure
if (not platepar.refraction
) and measurement and platepar.measurement_apparent_to_true_refraction:
for i, entry in enumerate(zip(JD_data, RA_data, dec_data)):
jd, ra, dec = entry
ra, dec = eqRefractionApparentToTrue(np.radians(ra), np.radians(dec), jd, \
np.radians(platepar.lat), np.radians(platepar.lon))
RA_data[i] = np.degrees(ra)
dec_data[i] = np.degrees(dec)
# Compute radiia from image centre
radius_arr = np.hypot(
np.array(X_data) - platepar.X_res / 2,
np.array(Y_data) - platepar.Y_res / 2)
# Calculate magnitudes
magnitude_data = calculateMagnitudes(level_data, radius_arr,
platepar.mag_lev,
platepar.vignetting_coeff)
# Extinction correction
if extinction_correction:
magnitude_data = extinctionCorrectionApparentToTrue(magnitude_data, X_data, Y_data, JD_data[0], \
platepar)
return JD_data, RA_data, dec_data, magnitude_data