def updateaoprms(self, mjd, lapserate=0.0065, xpm=0.0, ypm=0.0):
"""
Pre-compute the set of apparent to observed place parameters.
See http://star-www.rl.ac.uk/docs/sun67.htx/sun67ss8.html
Parameters
----------
lapserate : float
ta mere
xpm : float
polar motion coordinate x (radians)
ypm : float
polar motion coordinate y (radians)
"""
wavelength = (299792458.0 / 150.0e9) * 1e6
self.aoprms = slalib.sla_aoppa(
mjd,
self.ut1utc,
self.lon,
self.lat,
self.height,
xpm,
ypm,
self.temp,
self.pressure,
self.humidity,
wavelength,
lapserate,
)
def hor2cel(coord, time, site, copy=True): coord = np.array(coord, copy=copy) trepr = time[len(time)/2] info = iers.lookup(trepr) ao = slalib.sla_aoppa(trepr, info.dUT, site.lon*utils.degree, site.lat*utils.degree, site.alt, info.pmx*utils.arcsec, info.pmy*utils.arcsec, site.T, site.P, site.hum, 299792.458/site.freq, site.lapse) am = slalib.sla_mappa(2000.0, trepr) # This involves a transpose operation, which is not optimal pyfsla.aomulti(time, coord.T, ao, am) return coord
def calculate_airmass_at_times(times, target, obs_latitude, obs_longitude,
obs_height):
""" Returns the airmass values at each of the times passed in for the given target
Returns the airmass values for a target and observer specified at each time value
in the input times list. This uses the speedier slalib aop quick function which caches the object lat/lon/height and
refraction parameters.
Args:
times (list): A list of datetimes during which to calculate the airmass of the target
target (dict): A dictionary of target details in the rise-set library format
obs_latitude (Angle): The site/observer latitude within a rise-set Angle
obs_longitude (Angle): The site/observer longitude within a rise-set Angle
obs_height (float): The site/observer altitude in meters
Returns:
list: A list of airmass values that correspond to the input list of datetimes
"""
airmasses = []
aop_params = None
# Assume standard atmosphere
temp_k = 273.15 # local ambient temperature (K; std=273.15)
pres_mb = 1013.25 # local atmospheric pressure (mb; std=1013.25D0)
rel_humid = 0.3 # local relative humidity (in the range 0D0-1D0)
wavelen = 0.55 # effective wavelength (in microns e.g. 0.55D0 (approx V band))
tlr = 0.0065 # tropospheric lapse rate (K per metre, e.g. 0.0065D0)
# Assume no polar motion
xp = yp = 0.0
# Assume UT1-UTC
dut = 0.0
site = {
'longitude': obs_longitude,
'latitude': obs_latitude,
'altitude': obs_height
}
for time in times:
mjd_utc = gregorian_to_ut_mjd(time)
if aop_params is None:
aop_params = sla.sla_aoppa(mjd_utc, dut,
obs_longitude.in_radians(),
obs_latitude.in_radians(), obs_height,
xp, yp, temp_k, pres_mb, rel_humid,
wavelen, tlr)
else:
aop_params = sla.sla_aoppat(mjd_utc, aop_params)
# Convert datetime to MJD_TDB
tdb = ut_mjd_to_tdb(mjd_utc) #not TDB but good enough
# Convert catalog mean RA, Dec at J2000 to apparent of date
if is_moving_object(target):
ra_apparent, dec_apparent = elem_to_topocentric_apparent(
time, target, site, target_to_jform(target))
else:
ra_apparent, dec_apparent = mean_to_apparent(target, tdb)
airmass = apparent_to_airmass(ra_apparent, dec_apparent, aop_params)
airmasses.append(airmass)
return airmasses