def test_get_lnoverlaps():
"""
Confirms that star-component overlaps get smaller as stars get further
away.
First generates a component `sphere_comp`. Then generates three stars.
The first one is identical to `sphere_comp` in mean and covmatrix.
The other two share the same covmatrix yet are separated in X.
We check that the overlap integral is smaller for the more separated
stars.
"""
dim = 6
mean = np.zeros(dim)
covmatrix = np.identity(dim)
age = 1e-10
sphere_comp = SphereComponent(attributes={
'mean': mean,
'covmatrix': covmatrix,
'age': age,
})
dx_offsets = [0., 1., 10.]
star_comps = []
for dx_offset in dx_offsets:
star = SphereComponent(
attributes={
'mean':
sphere_comp.get_mean() +
np.array([dx_offset, 0., 0., 0., 0., 0.]),
'covmatrix':
sphere_comp.get_covmatrix(),
'age':
sphere_comp.get_age(),
})
star_comps.append(star)
nstars = len(star_comps)
dummy_table = Table(data=np.arange(nstars).reshape(nstars, 1),
names=['name'])
tabletool.append_cart_cols_to_table(dummy_table)
for star_comp, row in zip(star_comps, dummy_table):
tabletool.insert_data_into_row(
row,
star_comp.get_mean(),
star_comp.get_covmatrix(),
cartesian=True,
)
dummy_data = tabletool.build_data_dict_from_table(dummy_table)
ln_overlaps = likelihood.get_lnoverlaps(sphere_comp, data=dummy_data)
# Checks that ln_overlaps is descending
assert np.allclose(ln_overlaps, sorted(ln_overlaps)[::-1])
def test_stationary_component():
"""
Integrated test which fits a single component to a synthetic association.
Runtime on my mac (single thread) is ~ 20 mins. Check logs/compfitter.log
and temp_plots/*.png for progress.
Takes about 10 mins single thread with C implementation of overlap
or ~40 mins with python implementation of overlap
"""
# log_filename = 'logs/compfitter_stationary.log'
# synth_data_savefile = 'temp_data/compfitter_stationary_synthdata.fits'
short_burnin_step = 200
true_comp_mean = np.zeros(6)
true_comp_dx = 2.
true_comp_dv = 2.
true_comp_covmatrix = np.identity(6)
true_comp_covmatrix[:3,:3] *= true_comp_dx**2
true_comp_covmatrix[3:,3:] *= true_comp_dv**2
true_comp_age = 1e-10
true_comp = SphereComponent(attributes={
'mean':true_comp_mean,
'covmatrix':true_comp_covmatrix,
'age':true_comp_age,
})
nstars = 100
measurement_error = 1e-10
best_comp, chain, lnprob = run_fit_helper(
true_comp=true_comp, starcounts=nstars,
measurement_error=measurement_error,
run_name='stationary',
burnin_step=short_burnin_step,
trace_orbit_func=dummy_trace_orbit_func,
)
np.save('temp_data/{}_compfitter_stationary_' \
'true_and_best_comp.npy'.format(PY_VERS),
[true_comp, best_comp],)
assert np.allclose(true_comp.get_mean(), best_comp.get_mean(),
atol=1.0)
assert np.allclose(true_comp.get_age(), best_comp.get_age(),
atol=1.0)
assert np.allclose(true_comp.get_covmatrix(),
best_comp.get_covmatrix(),
atol=2.0)
def test_get_lnoverlaps():
"""
Confirms that star-component overlaps get smaller as stars get further
away.
First generates a component `sphere_comp`. Then generates three stars.
The first one is identical to `sphere_comp` in mean and covmatrix.
The other two share the same covmatrix yet are separated in X.
We check that the overlap integral is smaller for the more separated
stars.
"""
dim = 6
mean = np.zeros(dim)
covmatrix = np.identity(dim)
age = 1e-10
sphere_comp = SphereComponent(attributes={
'mean':mean,
'covmatrix':covmatrix,
'age':age,
})
dx_offsets = [0., 1., 10.]
star_comps = []
for dx_offset in dx_offsets:
star = SphereComponent(attributes={
'mean':sphere_comp.get_mean()+np.array([dx_offset,0.,0.,0.,0.,0.]),
'covmatrix':sphere_comp.get_covmatrix(),
'age':sphere_comp.get_age(),
})
star_comps.append(star)
nstars = len(star_comps)
dummy_table = Table(data=np.arange(nstars).reshape(nstars,1),
names=['name'])
tabletool.append_cart_cols_to_table(dummy_table)
for star_comp, row in zip(star_comps, dummy_table):
tabletool.insert_data_into_row(row,
star_comp.get_mean(),
star_comp.get_covmatrix(),
cartesian=True,
)
dummy_data = tabletool.build_data_dict_from_table(dummy_table)
ln_overlaps = likelihood.get_lnoverlaps(sphere_comp, data=dummy_data)
# Checks that ln_overlaps is descending
assert np.allclose(ln_overlaps, sorted(ln_overlaps)[::-1])
def test_stationary_component():
"""
Integrated test which fits a single component to a synthetic association.
Runtime on my mac (single thread) is ~ 20 mins. Check logs/compfitter.log
and temp_plots/*.png for progress.
Takes about 10 mins single thread with C implementation of overlap
or ~40 mins with python implementation of overlap
"""
# log_filename = 'logs/compfitter_stationary.log'
# synth_data_savefile = 'temp_data/compfitter_stationary_synthdata.fits'
short_burnin_step = 200
true_comp_mean = np.zeros(6)
true_comp_dx = 2.
true_comp_dv = 2.
true_comp_covmatrix = np.identity(6)
true_comp_covmatrix[:3, :3] *= true_comp_dx**2
true_comp_covmatrix[3:, 3:] *= true_comp_dv**2
true_comp_age = 1e-10
true_comp = SphereComponent(
attributes={
'mean': true_comp_mean,
'covmatrix': true_comp_covmatrix,
'age': true_comp_age,
})
nstars = 100
measurement_error = 1e-10
best_comp, chain, lnprob = run_fit_helper(
true_comp=true_comp,
starcounts=nstars,
measurement_error=measurement_error,
run_name='stationary',
burnin_step=short_burnin_step,
trace_orbit_func=dummy_trace_orbit_func,
)
np.save('temp_data/{}_compfitter_stationary_' \
'true_and_best_comp.npy'.format(PY_VERS),
[true_comp, best_comp],)
assert np.allclose(true_comp.get_mean(), best_comp.get_mean(), atol=1.0)
assert np.allclose(true_comp.get_age(), best_comp.get_age(), atol=1.0)
assert np.allclose(true_comp.get_covmatrix(),
best_comp.get_covmatrix(),
atol=2.0)
def test_lcc_like():
"""
Takes about 40 mins
"""
mean_now = np.array([50., -100., 25., 1.1, -7.76, 2.25])
age = 10.
mean = trace_cartesian_orbit(mean_now, times=-age)
dx = 5.
dv = 2.
covmatrix = np.identity(6)
covmatrix[:3,:3] *= dx**2
covmatrix[3:,3:] *= dv**2
true_comp = SphereComponent(attributes={
'mean':mean,
'covmatrix':covmatrix,
'age':age,
})
nstars = 100
tiny_measurement_error = 1e-10
short_burnin_step = 200
best_comp, chain, lnprob = run_fit_helper(
true_comp=true_comp, starcounts=nstars,
measurement_error=tiny_measurement_error,
burnin_step=short_burnin_step,
run_name='lcc_like',
)
np.save('temp_data/{}_compfitter_lcc_like_'\
'true_and_best_comp.npy'.format(PY_VERS),
[true_comp, best_comp],)
assert np.allclose(true_comp.get_mean(), best_comp.get_mean(),
atol=3.0)
assert np.allclose(true_comp.get_age(), best_comp.get_age(),
atol=1.0)
assert np.allclose(true_comp.get_covmatrix(),
best_comp.get_covmatrix(),
atol=5.0)
def test_lcc_like():
"""
Takes about 40 mins
"""
mean_now = np.array([50., -100., 25., 1.1, -7.76, 2.25])
age = 10.
mean = trace_cartesian_orbit(mean_now, times=-age)
dx = 5.
dv = 2.
covmatrix = np.identity(6)
covmatrix[:3, :3] *= dx**2
covmatrix[3:, 3:] *= dv**2
true_comp = SphereComponent(attributes={
'mean': mean,
'covmatrix': covmatrix,
'age': age,
})
nstars = 100
tiny_measurement_error = 1e-10
short_burnin_step = 200
best_comp, chain, lnprob = run_fit_helper(
true_comp=true_comp,
starcounts=nstars,
measurement_error=tiny_measurement_error,
burnin_step=short_burnin_step,
run_name='lcc_like',
)
np.save('temp_data/{}_compfitter_lcc_like_'\
'true_and_best_comp.npy'.format(PY_VERS),
[true_comp, best_comp],)
assert np.allclose(true_comp.get_mean(), best_comp.get_mean(), atol=3.0)
assert np.allclose(true_comp.get_age(), best_comp.get_age(), atol=1.0)
assert np.allclose(true_comp.get_covmatrix(),
best_comp.get_covmatrix(),
atol=5.0)
comp_now=True,
comp_then=True,
comp_orbit=True,
alpha=0.3)
sub_ax.set_xlabel('{} [{}]'.format(labels[dim1], units[dim1]))
sub_ax.set_ylabel('{} [{}]'.format(labels[dim2], units[dim2]))
sub_ax.set_xlim(lims[dim1])
# print('x set fine: {}'.format(lims))
sub_ax.set_ylim(lims[dim2])
# print('y set fine {}'.format(lims))
# Put in some annotation
first_ax = ax[0, 0]
first_ax.text(0.8,
0.9,
'{:>5.1f} Myr '.format(best_comp.get_age()),
horizontalalignment='right',
transform=first_ax.transAxes)
first_ax.text(0.8,
0.8,
'{:>6} steps'.format(step_ix),
horizontalalignment='right',
transform=first_ax.transAxes)
plt.savefig('../plots/explanation_movie/{:04}_explanation.png'.\
format(
plot_ix,
# labels[dim1],
# labels[dim2]
))