def setup(self):
self.mjd_array = np.array(
[58827.15925499, 58827.15925499, 58827.15925499,
58827.15925499, 58827.15925499])
self.mjd_scalar = self.mjd_array[0].item()
self.utc2mjd = 2400000.5
paranal_long = -1.228798
paranal_lat = -0.42982
paranal_height = 2669.
self.astrom_scalar, _ = erfa.apco13(
self.utc2mjd, self.mjd_scalar, 0.0, paranal_long, paranal_lat,
paranal_height, 0.0, 0.0, 0.0, 0.0, 0.0, 2.5)
self.astrom_array, _ = erfa.apco13(
self.utc2mjd, self.mjd_array, 0.0, paranal_long, paranal_lat,
paranal_height, 0.0, 0.0, 0.0, 0.0, 0.0, 2.5)
def test_iau_fullstack(fullstack_icrs, fullstack_fiducial_altaz,
fullstack_times, fullstack_locations,
fullstack_obsconditions):
"""
Test the full transform from ICRS <-> AltAz
"""
# create the altaz frame
altazframe = AltAz(obstime=fullstack_times,
location=fullstack_locations,
pressure=fullstack_obsconditions[0],
temperature=fullstack_obsconditions[1],
relative_humidity=fullstack_obsconditions[2],
obswl=fullstack_obsconditions[3])
aacoo = fullstack_icrs.transform_to(altazframe)
# compare aacoo to the fiducial AltAz - should always be different
assert np.all(
np.abs(aacoo.alt - fullstack_fiducial_altaz.alt) > 50 *
u.milliarcsecond)
assert np.all(
np.abs(aacoo.az - fullstack_fiducial_altaz.az) > 50 * u.milliarcsecond)
# if the refraction correction is included, we *only* do the comparisons
# where altitude >5 degrees. The SOFA guides imply that below 5 is where
# where accuracy gets more problematic, and testing reveals that alt<~0
# gives garbage round-tripping, and <10 can give ~1 arcsec uncertainty
if fullstack_obsconditions[0].value == 0:
# but if there is no refraction correction, check everything
msk = slice(None)
tol = 5 * u.microarcsecond
else:
msk = aacoo.alt > 5 * u.deg
# most of them aren't this bad, but some of those at low alt are offset
# this much. For alt > 10, this is always better than 100 masec
tol = 750 * u.milliarcsecond
# now make sure the full stack round-tripping works
icrs2 = aacoo.transform_to(ICRS)
adras = np.abs(fullstack_icrs.ra - icrs2.ra)[msk]
addecs = np.abs(fullstack_icrs.dec - icrs2.dec)[msk]
assert np.all(adras < tol), 'largest RA change is {} mas, > {}'.format(
np.max(adras.arcsec * 1000), tol)
assert np.all(addecs < tol), 'largest Dec change is {} mas, > {}'.format(
np.max(addecs.arcsec * 1000), tol)
# check that we're consistent with the ERFA alt/az result
iers_tab = iers.earth_orientation_table.get()
xp, yp = u.Quantity(iers_tab.pm_xy(fullstack_times)).to_value(u.radian)
lon = fullstack_locations.geodetic[0].to_value(u.radian)
lat = fullstack_locations.geodetic[1].to_value(u.radian)
height = fullstack_locations.geodetic[2].to_value(u.m)
jd1, jd2 = get_jd12(fullstack_times, 'utc')
pressure = fullstack_obsconditions[0].to_value(u.hPa)
temperature = fullstack_obsconditions[1].to_value(u.deg_C)
# Relative humidity can be a quantity or a number.
relative_humidity = u.Quantity(fullstack_obsconditions[2], u.one).value
obswl = fullstack_obsconditions[3].to_value(u.micron)
astrom, eo = erfa.apco13(jd1, jd2, fullstack_times.delta_ut1_utc, lon, lat,
height, xp, yp, pressure, temperature,
relative_humidity, obswl)
erfadct = _erfa_check(fullstack_icrs.ra.rad, fullstack_icrs.dec.rad,
astrom)
npt.assert_allclose(erfadct['alt'], aacoo.alt.radian, atol=1e-7)
npt.assert_allclose(erfadct['az'], aacoo.az.radian, atol=1e-7)