def test_different_component_forms():
"""Check component forms can be different"""
tiny_age = 1e-10
mean1 = np.zeros(6)
covmatrix1 = np.eye(6) * 4
comp1 = SphereComponent(attributes={
'mean':mean1,
'covmatrix':covmatrix1,
'age':tiny_age,
})
mean2 = np.zeros(6) + 10.
covmatrix2 = np.eye(6) * 9
comp2 = EllipComponent(attributes={
'mean':mean2,
'covmatrix':covmatrix2,
'age':tiny_age,
})
starcounts = [100,100]
synth_data = SynthData(pars=[comp1.get_pars(), comp2.get_pars()],
starcounts=starcounts,
Components=[SphereComponent, EllipComponent])
synth_data.synthesise_everything()
assert len(synth_data.table) == np.sum(starcounts)
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_lnprob_func():
"""
Generates two components. Generates a synthetic data set based on the
first component. Confrims that the lnprob is larger for the first
component than the second.
"""
measurement_error = 1e-10
star_count = 500
tiny_age = 1e-10
dim = 6
comp_covmatrix = np.identity(dim)
comp_means = {
'comp1': np.zeros(dim),
'comp2': 10 * np.ones(dim)
}
comps = {}
data = {}
for comp_name in comp_means.keys():
comp = SphereComponent(attributes={
'mean':comp_means[comp_name],
'covmatrix':comp_covmatrix,
'age':tiny_age
})
synth_data = SynthData(pars=[comp.get_pars()], starcounts=star_count,
measurement_error=measurement_error)
synth_data.synthesise_everything()
tabletool.convert_table_astro2cart(synth_data.table)
data[comp_name] = tabletool.build_data_dict_from_table(synth_data.table)
comps[comp_name] = comp
lnprob_comp1_data1 = likelihood.lnprob_func(pars=comps['comp1'].get_pars(),
data=data['comp1'])
lnprob_comp2_data1 = likelihood.lnprob_func(pars=comps['comp2'].get_pars(),
data=data['comp1'])
lnprob_comp1_data2 = likelihood.lnprob_func(pars=comps['comp1'].get_pars(),
data=data['comp2'])
lnprob_comp2_data2 = likelihood.lnprob_func(pars=comps['comp2'].get_pars(),
data=data['comp2'])
print(lnprob_comp1_data1)
print(lnprob_comp2_data1)
print(lnprob_comp1_data2)
print(lnprob_comp2_data2)
assert lnprob_comp1_data1 > lnprob_comp2_data1
assert lnprob_comp2_data2 > lnprob_comp1_data2
# Check that the different realisations only differ by 20%
assert np.isclose(lnprob_comp1_data1, lnprob_comp2_data2, rtol=2e-1)
assert np.isclose(lnprob_comp1_data2, lnprob_comp2_data1, rtol=2e-1)
def test_lnprob_func():
"""
Generates two components. Generates a synthetic data set based on the
first component. Confrims that the lnprob is larger for the first
component than the second.
"""
measurement_error = 1e-10
star_count = 500
tiny_age = 1e-10
dim = 6
comp_covmatrix = np.identity(dim)
comp_means = {
'comp1': np.zeros(dim),
'comp2': 10 * np.ones(dim)
}
comps = {}
data = {}
for comp_name in comp_means.keys():
comp = SphereComponent(attributes={
'mean':comp_means[comp_name],
'covmatrix':comp_covmatrix,
'age':tiny_age
})
synth_data = SynthData(pars=[comp.get_pars()], starcounts=star_count,
measurement_error=measurement_error)
synth_data.synthesise_everything()
tabletool.convert_table_astro2cart(synth_data.table)
data[comp_name] = tabletool.build_data_dict_from_table(synth_data.table)
comps[comp_name] = comp
lnprob_comp1_data1 = likelihood.lnprob_func(pars=comps['comp1'].get_pars(),
data=data['comp1'])
lnprob_comp2_data1 = likelihood.lnprob_func(pars=comps['comp2'].get_pars(),
data=data['comp1'])
lnprob_comp1_data2 = likelihood.lnprob_func(pars=comps['comp1'].get_pars(),
data=data['comp2'])
lnprob_comp2_data2 = likelihood.lnprob_func(pars=comps['comp2'].get_pars(),
data=data['comp2'])
assert lnprob_comp1_data1 > lnprob_comp2_data1
assert lnprob_comp2_data2 > lnprob_comp1_data2
# Check that the different realisations only differ by 10%
assert np.isclose(lnprob_comp1_data1, lnprob_comp2_data2, rtol=1e-1)
assert np.isclose(lnprob_comp1_data2, lnprob_comp2_data1, rtol=1e-1)
def test_externalise_and_internalise_pars():
"""Check that pars are successfully converted from internal form (used by
emcee) to external form (interacted with by user) successfully"""
# Check SphereComponent
internal_sphere_pars = np.copy(SPHERE_PARS)
internal_sphere_pars[6:8] = np.log(internal_sphere_pars[6:8])
sphere_comp = SphereComponent(emcee_pars=internal_sphere_pars)
external_sphere_pars = sphere_comp.get_pars()
assert np.allclose(SPHERE_PARS, external_sphere_pars)
re_internal_sphere_pars = sphere_comp.internalise(external_sphere_pars)
assert np.allclose(internal_sphere_pars, re_internal_sphere_pars)
# Check EllipComponent
internal_ellip_pars = np.copy(ELLIP_PARS)
internal_ellip_pars[6:10] = np.log(internal_ellip_pars[6:10])
ellip_comp = EllipComponent(emcee_pars=internal_ellip_pars)
external_ellip_pars = ellip_comp.get_pars()
assert np.allclose(ELLIP_PARS, external_ellip_pars)
re_internal_ellip_pars = ellip_comp.internalise(external_ellip_pars)
assert np.allclose(internal_ellip_pars, re_internal_ellip_pars)
def test_externalise_and_internalise_pars():
"""Check that pars are successfully converted from internal form (used by
emcee) to external form (interacted with by user) successfully"""
# Check SphereComponent
internal_sphere_pars = np.copy(SPHERE_PARS)
internal_sphere_pars[6:8] = np.log(internal_sphere_pars[6:8])
sphere_comp = SphereComponent(emcee_pars=internal_sphere_pars)
external_sphere_pars = sphere_comp.get_pars()
assert np.allclose(SPHERE_PARS, external_sphere_pars)
re_internal_sphere_pars = sphere_comp.internalise(external_sphere_pars)
assert np.allclose(internal_sphere_pars, re_internal_sphere_pars)
# Check EllipComponent
internal_ellip_pars = np.copy(ELLIP_PARS)
internal_ellip_pars[6:10] = np.log(internal_ellip_pars[6:10])
ellip_comp = EllipComponent(emcee_pars=internal_ellip_pars)
external_ellip_pars = ellip_comp.get_pars()
assert np.allclose(ELLIP_PARS, external_ellip_pars)
re_internal_ellip_pars = ellip_comp.internalise(external_ellip_pars)
assert np.allclose(internal_ellip_pars, re_internal_ellip_pars)
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)
from chronostar.component import SphereComponent
from chronostar.traceorbit import trace_cartesian_orbit
import numpy as np
mean_now = np.array([0., 0., 30., 5., 5., 5.])
init_dx = 5.
init_dv = 1.
age = 100.
mean_then = trace_cartesian_orbit(mean_now, times=-age)
pars1 = np.hstack((mean_then, init_dx, init_dv, age))
comp1 = SphereComponent(pars1)
print(comp1.get_pars())
labels = 'XYZUVW'
units = 3 * ['pc'] + 3 * ['km/s']
for dim1, dim2 in [(0, 3), (1, 4), (2, 5)]:
plt.clf()
comp1.plot(dim1=dim1,
dim2=dim2,
comp_now=True,
comp_then=True,
comp_orbit=True)
plt.xlabel('{} [{}]'.format(labels[dim1], units[dim1]))
plt.ylabel('{} [{}]'.format(labels[dim2], units[dim2]))
plt.savefig('../plots/simple_plot_{}{}.pdf'.format(labels[dim1],
labels[dim2]))