def test_get_best_from_chain():
# Triplicate sphere pars (are copies)
# Represents a chain with 2 walkers and 3 steps
intern_sphere_pars = SphereComponent.internalise(SPHERE_PARS)
dummy_chain = np.array(
[[intern_sphere_pars, intern_sphere_pars, intern_sphere_pars],
[intern_sphere_pars, intern_sphere_pars, intern_sphere_pars]])
dummy_lnprob = np.zeros(dummy_chain.shape[:2])
# Incorporate identifying marker at desired index
true_best_ix = (1, 1)
dummy_chain[true_best_ix][0] = 10.
dummy_lnprob[true_best_ix] = 1.
best_comp = SphereComponent.get_best_from_chain(dummy_chain, dummy_lnprob)
assert np.allclose(dummy_chain[true_best_ix], best_comp.get_emcee_pars())
if PLOT_FED_STARS:
print("Plotting fed stars)")
# Setting up file names
synth_fit = 'fed_stars'
rdir = '../../results/archive/fed_fits/20/gaia/'
origins_file = rdir + 'origins.npy'
origin_comp_file = rdir + 'origin_ellip_comp.npy'
chain_file = rdir + 'final_chain.npy'
lnprob_file = rdir + 'final_lnprob.npy'
star_pars_file = rdir + 'xyzuvw_now.fits'
init_xyzuvw_file = rdir + '../xyzuvw_init_offset.npy'
perf_xyzuvw_now = rdir + '../perf_xyzuvw.npy'
# loading in data
best_comp = SphereComponent.get_best_from_chain(chain_file, lnprob_file)
origin_comp = EllipComponent.load_raw_components(origin_comp_file)[0]
init_xyzuvw = np.load(init_xyzuvw_file)
star_pars = dt.loadXYZUVW(star_pars_file)
perf_mean_now = np.load(perf_xyzuvw_now)
original_origin = dt.loadGroups(origins_file)[0]
# assigning useful shorthands
mns = star_pars['xyzuvw']
covs = star_pars['xyzuvw_cov']
fed_xranges, fed_yranges = calcRanges(
{'xyzuvw': np.vstack((star_pars['xyzuvw'], init_xyzuvw))},
sep_axes=True,
)