def test_background_component():
"""Create artificial association composed of two stars at opposite vertices
of unit 6D rectangle. Then base background density distribution on that."""
background_density = 100
# Since the background double the span of data, by setting the means as
# follows, the backbround should extend from 0 to 1 in each dimension,
# which greatly simplifies reasoning about densities and starcounts.
upper_mean = np.zeros(6) + 0.75
lower_mean = np.zeros(6) + 0.25
narrow_dx = 1e-10
narrow_dv = 1e-10
tiny_age = 1e-10
upper_pars = np.hstack((upper_mean, narrow_dx, narrow_dv, tiny_age))
lower_pars = np.hstack((lower_mean, narrow_dx, narrow_dv, tiny_age))
starcounts = [1,1]
synth_data = SynthData(pars=[upper_pars, lower_pars],
starcounts=starcounts,
background_density=background_density)
synth_data.generate_all_init_cartesian()
means = tabletool.build_data_dict_from_table(
synth_data.table[2:],
main_colnames=[el+'0' for el in 'xyzuvw'],
only_means=True,
)
assert np.allclose(0.5, np.mean(means, axis=0), atol=0.1)
assert np.allclose(1.0, np.max(means, axis=0), atol=0.1)
assert np.allclose(0.0, np.min(means, axis=0), atol=0.1)
assert len(synth_data.table) == background_density + 2
def test_measureXYZUVW():
"""Check measurements of xyzuvw_now to astrometry occur properly.
Will use extremely dense component as case study as this ensures stars
all have more or less the same true values"""
compact_comp_pars = np.copy(PARS[0])
compact_comp_pars[6] = 1e-15
compact_comp_pars[7] = 1e-15
compact_comp_pars[8] = 1e-15
starcounts = [1000]
sd = SynthData(pars=np.array([compact_comp_pars]),
starcounts=starcounts,
Components=COMPONENTS)
sd.generate_all_init_cartesian()
sd.project_stars()
sd.measure_astrometry()
for colname in SynthData.DEFAULT_ASTR_COLNAMES:
assert np.allclose(sd.GERROR[colname + '_error'],
sd.table[colname + '_error'])
# Check spread of data is similar to Gaia error, we use
# a large tolerance so a small number of stars can be used
assert np.isclose(sd.GERROR[colname + '_error'],
np.std(sd.table[colname]),
rtol=1e-1)
def test_generateInitXYZUVW():
"""Check that the mean of initial xyzuvw of stars matches that of the
initialising component"""
starcounts = (int(1e6), )
sd = SynthData(pars=PARS[:1], starcounts=starcounts, Components=COMPONENTS)
sd.generate_all_init_cartesian()
comp = SphereComponent(PARS[0])
init_xyzuvw = sd.extract_data_as_array([dim + '0' for dim in 'xyzuvw'])
assert np.allclose(comp.get_mean(), np.mean(init_xyzuvw, axis=0), atol=0.1)
def test_generateInitXYZUVW():
"""Check that the mean of initial xyzuvw of stars matches that of the
initialising component"""
starcounts = (int(1e6),)
sd = SynthData(pars=PARS[:1], starcounts=starcounts, Components=COMPONENTS)
sd.generate_all_init_cartesian()
comp = SphereComponent(PARS[0])
init_xyzuvw = sd.extract_data_as_array([dim + '0' for dim in 'xyzuvw'])
assert np.allclose(comp.get_mean(), np.mean(init_xyzuvw, axis=0),
atol=0.1)
def test_projectStars():
"""Check that the mean of stars after projection matches the mean
of the component after projection"""
starcounts = (int(1e3),)
sd = SynthData(pars=PARS[:1], starcounts=starcounts, Components=COMPONENTS)
sd.generate_all_init_cartesian()
sd.project_stars()
comp_mean_now, comp_covmatrix_now = \
sd.components[0].get_currentday_projection()
final_xyzuvw = sd.extract_data_as_array([dim + '_now' for dim in 'xzyuvw'])
assert np.allclose(comp_mean_now, final_xyzuvw.mean(axis=0), atol=1.)
def test_measureXYZUVW():
"""Check measurements of xyzuvw_now to astrometry occur properly.
Will use extremely dense component as case study as this ensures stars
all have more or less the same true values"""
compact_comp_pars = np.copy(PARS[0])
compact_comp_pars[6] = 1e-15
compact_comp_pars[7] = 1e-15
compact_comp_pars[8] = 1e-15
starcounts = [1000]
sd = SynthData(pars=np.array([compact_comp_pars]), starcounts=starcounts,
Components=COMPONENTS)
sd.generate_all_init_cartesian()
sd.project_stars()
sd.measure_astrometry()
for colname in SynthData.DEFAULT_ASTR_COLNAMES:
assert np.allclose(sd.GERROR[colname + '_error'],
sd.table[colname + '_error'])
# Check spread of data is similar to Gaia error, we use
# a large tolerance so a small number of stars can be used
assert np.isclose(sd.GERROR[colname + '_error'],
np.std(sd.table[colname]),
rtol=1e-1)
