def test_swigImplementation():
"""
Compares the swigged c implementation against the python one in
likelihood.py
"""
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
synth_data = SynthData(pars=true_comp.get_pars(), starcounts=nstars)
synth_data.synthesise_everything()
tabletool.convert_table_astro2cart(synth_data.table)
star_data = tabletool.build_data_dict_from_table(synth_data.table)
p_lnos = p_lno(true_comp.get_covmatrix(), true_comp.get_mean(),
star_data['covs'], star_data['means'])
c_lnos = c_lno(true_comp.get_covmatrix(), true_comp.get_mean(),
star_data['covs'], star_data['means'], nstars)
assert np.allclose(p_lnos, c_lnos)
assert np.isfinite(p_lnos).all()
assert np.isfinite(c_lnos).all()
def test_swigImplementation():
"""
Compares the swigged c implementation against the python one in
likelihood.py
"""
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
synth_data = SynthData(pars=true_comp.get_pars(), starcounts=nstars)
synth_data.synthesise_everything()
tabletool.convert_table_astro2cart(synth_data.table)
star_data = tabletool.build_data_dict_from_table(synth_data.table)
p_lnos = p_lno(true_comp.get_covmatrix(), true_comp.get_mean(),
star_data['covs'], star_data['means'])
c_lnos = c_lno(true_comp.get_covmatrix(), true_comp.get_mean(),
star_data['covs'], star_data['means'], nstars)
assert np.allclose(p_lnos, c_lnos)
assert np.isfinite(p_lnos).all()
assert np.isfinite(c_lnos).all()
def test_pythonFuncs():
"""
TODO: remove the requirements of file, have data stored in file?
"""
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
synth_data = SynthData(pars=true_comp.get_pars(), starcounts=nstars)
synth_data.synthesise_everything()
tabletool.convert_table_astro2cart(synth_data.table)
star_data = tabletool.build_data_dict_from_table(synth_data.table)
# star_data['means'] = star_data['means']
# star_data['covs'] = star_data['covs']
group_mean = true_comp.get_mean()
group_cov = true_comp.get_covmatrix()
# Test overlap with true component
co1s = []
co2s = []
for i, (scov, smn) in enumerate(zip(star_data['covs'], star_data['means'])):
co1s.append(co1(group_cov, group_mean, scov, smn))
co2s.append(co2(group_cov, group_mean, scov, smn))
co1s = np.array(co1s)
co2s = np.array(co2s)
co3s = np.exp(p_lno(group_cov, group_mean,
star_data['covs'], star_data['means']))
assert np.allclose(co1s, co2s)
assert np.allclose(co2s, co3s)
assert np.allclose(co1s, co3s)
# Test overlap with neighbouring star (with the aim of testing
# tiny overlap values). Note that most overlaps go to 0, but the
# log overlaps retain the information
co1s = []
co2s = []
for i, (scov, smn) in enumerate(zip(star_data['covs'], star_data['means'])):
co1s.append(co1(star_data['covs'][15], star_data['means'][15],
scov, smn))
co2s.append(co2(star_data['covs'][15], star_data['means'][15],
scov, smn))
co1s = np.array(co1s)
co2s = np.array(co2s)
lnos = p_lno(star_data['covs'][15], star_data['means'][15],
star_data['covs'], star_data['means'])
co3s = np.exp(lnos)
assert np.allclose(co1s, co2s)
assert np.allclose(co2s, co3s)
assert np.allclose(co1s, co3s)
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)
def test_pythonFuncs():
"""
TODO: remove the requirements of file, have data stored in file?
"""
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
synth_data = SynthData(pars=true_comp.get_pars(), starcounts=nstars)
synth_data.synthesise_everything()
tabletool.convert_table_astro2cart(synth_data.table)
star_data = tabletool.build_data_dict_from_table(synth_data.table)
# star_data['means'] = star_data['means']
# star_data['covs'] = star_data['covs']
group_mean = true_comp.get_mean()
group_cov = true_comp.get_covmatrix()
# Test overlap with true component
co1s = []
co2s = []
for i, (scov, smn) in enumerate(zip(star_data['covs'],
star_data['means'])):
co1s.append(co1(group_cov, group_mean, scov, smn))
co2s.append(co2(group_cov, group_mean, scov, smn))
co1s = np.array(co1s)
co2s = np.array(co2s)
co3s = np.exp(
p_lno(group_cov, group_mean, star_data['covs'], star_data['means']))
assert np.allclose(co1s, co2s)
assert np.allclose(co2s, co3s)
assert np.allclose(co1s, co3s)
# Test overlap with neighbouring star (with the aim of testing
# tiny overlap values). Note that most overlaps go to 0, but the
# log overlaps retain the information
co1s = []
co2s = []
for i, (scov, smn) in enumerate(zip(star_data['covs'],
star_data['means'])):
co1s.append(
co1(star_data['covs'][15], star_data['means'][15], scov, smn))
co2s.append(
co2(star_data['covs'][15], star_data['means'][15], scov, smn))
co1s = np.array(co1s)
co2s = np.array(co2s)
lnos = p_lno(star_data['covs'][15], star_data['means'][15],
star_data['covs'], star_data['means'])
co3s = np.exp(lnos)
assert np.allclose(co1s, co2s)
assert np.allclose(co2s, co3s)
assert np.allclose(co1s, co3s)