def test_parallax_factors_independent_of_reference_epoch(self):
astron = Astrometry('Hip1', '27321', 'htof/test/data_for_tests/Hip1', central_epoch_ra=2000,
central_epoch_dec=2000, format='jyear', fit_degree=1, use_parallax=True,
central_ra=self.cntr_ra, central_dec=self.cntr_dec)
ra_motionn = astron.fitter.parallactic_pertubations['ra_plx']
dec_motionn = astron.fitter.parallactic_pertubations['dec_plx']
assert np.allclose(to_along_scan_basis(self.ra_motion, self.dec_motion, self.scan_angle),
to_along_scan_basis(ra_motionn, dec_motionn, self.scan_angle))
def test_Hip2_fit_to_hip27321():
# Hip 27321 parameters from the Hipparcos 21 IAD
cntr_ra, cntr_dec = Angle(86.82118073, 'degree'), Angle(-51.06671341, 'degree')
plx = 51.44 # mas
pmRA = 4.65 # mas/year
pmDec = 83.10 # mas/year
# generate fitter and parse intermediate data
for datadir in ['Hip2', 'Hip21']:
# trying fits with a fresh computation of the parallax factors and without
astro = Astrometry('hip2or21', '27321', f'htof/test/data_for_tests/{datadir}', central_epoch_ra=1991.25,
central_epoch_dec=1991.25, format='jyear', fit_degree=1, use_parallax=True,
central_ra=cntr_ra, central_dec=cntr_dec,
use_catalog_parallax_factors=True)
chisq = np.sum(astro.data.residuals ** 2 / astro.data.along_scan_errs ** 2)
# generate ra and dec for each observation.
year_epochs = Time(astro.data.julian_day_epoch(), format='jd', scale='tcb').jyear - \
Time(1991.25, format='decimalyear').jyear
ra_motion = astro.fitter.parallactic_pertubations['ra_plx']
dec_motion = astro.fitter.parallactic_pertubations['dec_plx']
ra = Angle(ra_motion * plx + pmRA * year_epochs, unit='mas')
dec = Angle(dec_motion * plx + pmDec * year_epochs, unit='mas')
# add residuals
ra += Angle(astro.data.residuals.values * np.sin(astro.data.scan_angle.values), unit='mas')
dec += Angle(astro.data.residuals.values * np.cos(astro.data.scan_angle.values), unit='mas')
#
coeffs, errors, chisq_found, residuals = astro.fit(ra.mas, dec.mas, return_all=True)
residuals = to_along_scan_basis(residuals[:, 0], residuals[:, 1], astro.data.scan_angle.values)
assert np.isclose(chisq, chisq_found, atol=1E-3)
assert np.allclose([pmRA, pmDec], np.array([coeffs[3], coeffs[4]]).round(2))
assert np.isclose(plx, coeffs[0].round(2), atol=0.01)
assert np.allclose(errors.round(2), np.array([0.12, 0.10, 0.11, 0.11, 0.15]), atol=0.01)
assert np.allclose(residuals, astro.data.residuals.values, atol=0.02)
def test_Gaia_fit_to_hip27321():
# Hip 27321 central ra and dec from the gost data file.
cntr_ra, cntr_dec = Angle(1.5153157780177544, 'radian'), Angle(-0.8912787619608181, 'radian')
#
plx = 51.87 # mas
pmRA = 4.65 # mas/year
pmDec = 81.96 # mas/year
# generate fitter and parse intermediate data
for use_catalog_parallax_factors in [False, True]:
print('not '* (not use_catalog_parallax_factors) + 'using catalog parallax factors.')
# trying fits with a fresh computation of the parallax factors and without
astro = Astrometry('GaiaEDR3', '27321', 'htof/test/data_for_tests/GaiaeDR3',
central_epoch_ra=2016, central_epoch_dec=2016,
format='jyear', fit_degree=1, use_parallax=True,
central_ra=cntr_ra, central_dec=cntr_dec,
use_catalog_parallax_factors=use_catalog_parallax_factors)
chisq = np.sum(astro.data.residuals ** 2 / astro.data.along_scan_errs ** 2)
# generate ra and dec for each observation.
year_epochs = Time(astro.data.julian_day_epoch(), format='jd', scale='tcb').jyear - \
Time(2016, format='decimalyear').jyear
ra_motion = astro.fitter.parallactic_pertubations['ra_plx']
dec_motion = astro.fitter.parallactic_pertubations['dec_plx']
ra = Angle(ra_motion * plx + pmRA * year_epochs, unit='mas')
dec = Angle(dec_motion * plx + pmDec * year_epochs, unit='mas')
#
coeffs, errors, chisq_found, residuals = astro.fit(ra.mas, dec.mas, return_all=True)
residuals = to_along_scan_basis(residuals[:, 0], residuals[:, 1], astro.data.scan_angle.values)
assert np.isclose(chisq, chisq_found, atol=1E-3)
assert np.allclose([pmRA, pmDec], np.array([coeffs[3], coeffs[4]]).round(2))
assert np.isclose(plx, coeffs[0].round(2), atol=0.01)
def test_parallax_factors_backward_transform(self):
# note that this is redundant with test_basis_change_consistency()
ra_motion, dec_motion = to_ra_dec_basis(self.along_scan_parallax_factors, self.scan_angle)
# convert the calculated parallax factors to the along-scan basis and back to null out the AC component.
al_parallax_factor = to_along_scan_basis(self.ra_motion, self.dec_motion, self.scan_angle)
ra_motion_acnull, dec_motion_acnull = to_ra_dec_basis(al_parallax_factor, self.scan_angle)
assert np.allclose(ra_motion, ra_motion_acnull, atol=0.03)
assert np.allclose(dec_motion, dec_motion_acnull, atol=0.03)
def test_basis_change_consistency():
"""
Tests that to_ra_dec_basis() and to_along_scan_basis() are inverse transforms in the case where
the data being transformed has a zero across-scan component.
Note that these two functions are NOT inverse transforms if the data has a non-zero across scan component.
"""
al_value = np.array([-5, 0, 1, 5, 10] * 100) # [-5, 0, 1, 5, 10, -5, 0, 1, 5, 10, -5, 0,...]
scan_angle = np.array([[i]*5 for i in np.linspace(-2*np.pi, 2*np.pi, 100)]).flatten()
ra_val, dec_val = to_ra_dec_basis(al_value, scan_angle)
al_value_check = to_along_scan_basis(ra_val, dec_val, scan_angle)
assert np.allclose(al_value_check, al_value)
def test_new_computed_parallax_factors_agree_with_catalog(self):
assert np.allclose(self.along_scan_parallax_factors,
to_along_scan_basis(self.ra_motion, self.dec_motion, self.scan_angle), atol=0.03)
def test_new_computed_parallax_factors_agree_with_scanninglaw(self):
# test that the newly computed parallax factors agree with
# parallaxFactorAlongScan from the GOST data.
assert np.allclose(self.along_scan_parallax_factors,
to_along_scan_basis(self.ra_motion, self.dec_motion, self.scan_angle), atol=0.03)