def test_periodic(sampler, dynamic):
# hard test of dynamic sampler with high dlogz_init and small number
# of live points
logz_true = np.log(np.sqrt(2 * np.pi) * erf(win / np.sqrt(2)) / (2 * win))
thresh = 5
rstate = get_rstate()
if dynamic:
dns = dynesty.DynamicNestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
sample=sampler,
reflective=[0],
rstate=rstate)
else:
dns = dynesty.NestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
sample=sampler,
reflective=[0],
rstate=rstate)
dns.run_nested(print_progress=printing)
assert (np.abs(dns.results.logz[-1] - logz_true) <
thresh * dns.results.logzerr[-1])
def test_overlap():
rstate = get_rstate()
ndim = 2
cen1 = np.array([0, 0])
cen2 = np.array([1, 0])
rad = 0.7
sig = np.eye(ndim) * rad**2
ell1 = db.Ellipsoid(cen1, sig)
ell2 = db.Ellipsoid(cen2, sig)
ell = db.MultiEllipsoid([ell1, ell2])
nsamp = 10000
xs = rstate.uniform(size=(nsamp, ndim))
ind1 = np.sum((xs - cen1[None, :])**2, axis=1) < rad**2
ind2 = np.sum((xs - cen2[None, :])**2, axis=1) < rad**2
for i in range(nsamp):
n1 = int(ind1[i])
n2 = int(ind2[i])
assert ell.overlap(xs[i]) == n1 + n2
assert ell.overlap(xs[i], j=0) == n2
within = ell.within(xs[i])
within2 = []
if n1 == 1:
within2.append(0)
if n2 == 1:
within2.append(1)
within2 = np.array(within2)
assert np.all(within == within2)
def test_neff():
# test of neff functionality
ndim = 2
rstate = get_rstate()
sampler = dynesty.NestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
rstate=rstate)
assert sampler.n_effective == 0
sampler.run_nested(print_progress=printing)
assert sampler.n_effective > 10
sampler = dynesty.DynamicNestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
rstate=rstate)
assert sampler.n_effective == 0
sampler.run_nested(dlogz_init=1, n_effective=1000, print_progress=printing)
assert sampler.n_effective > 1000
sampler = dynesty.DynamicNestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
rstate=rstate)
sampler.run_nested(dlogz_init=1,
n_effective=10000,
print_progress=printing)
assert sampler.n_effective > 10000
def test_pickle(dynamic, with_pool):
# test of pickling functionality
ndim = 2
rstate = get_rstate()
if with_pool:
kw = dict(pool=Pool(2), queue_size=100)
else:
kw = {}
if dynamic:
sampler = dynesty.DynamicNestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
rstate=rstate,
**kw)
else:
sampler = dynesty.NestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
rstate=rstate,
**kw)
sampler.run_nested(print_progress=printing, maxiter=100)
# i do it twice as there were issues previously
# with incorrect pool restoring
S = pickle.dumps(sampler)
sampler = pickle.loads(S)
S = pickle.dumps(sampler)
sampler = pickle.loads(S)
sampler.run_nested(print_progress=printing, maxiter=100)
if with_pool:
kw['pool'].close()
kw['pool'].join()
def test_results(dyn):
# test of various results interfaces functionality
ndim = 2
rstate = get_rstate()
if dyn:
sampler = dynesty.DynamicNestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
rstate=rstate)
else:
sampler = dynesty.NestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
rstate=rstate)
sampler.run_nested(print_progress=printing)
res = sampler.results
for k in res.keys():
pass
for k, v in res.items():
pass
for k, v in res.asdict().items():
pass
print(res)
print(str(res))
print('logl' in res)
res1 = res.copy()
# check it's pickleable
S = pickle.dumps(res)
res = pickle.loads(S)
def do_gaussians(sample='rslice',
bound='single',
nthreads=36,
ndim_min=2,
ndim_max=30,
nlive=5000):
"""
Run many tests
"""
pool = mp.Pool(nthreads)
res = []
ndims = np.arange(ndim_min, ndim_max + 1)
for ndim in ndims:
rstate = get_rstate(ndim)
co = Config(rstate, ndim)
slices = None
res.append((ndim, co,
pool.apply_async(
do_gaussian, (co, ),
dict(sample=sample,
bound=bound,
rstate=rstate,
slices=slices,
nlive=nlive))))
for ndim, co, curres in res:
curres = curres.get()
print(ndim, curres[0], curres[1], curres[2], co.logz_truth_gau)
def test_saving(dopool, maxiter):
# test saving
ndim = 2
fname = 'dynesty_test_%d.h5' % (os.getpid())
rstate = get_rstate()
kw = {}
if dopool:
pool = mp.Pool(2)
kw['pool'] = pool
kw['queue_size'] = 100
sampler = dynesty.NestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
save_history=True,
history_filename=fname,
rstate=rstate,
**kw)
sampler.run_nested(dlogz=0.01, print_progress=printing, maxiter=maxiter)
assert (os.path.exists(fname))
try:
os.unlink(fname)
except FileNotFoundError:
pass
if dopool:
try:
pool.close()
pool.join()
except Exception:
pass
def test_periodic():
# hard test of dynamic sampler with high dlogz_init and small number
# of live points
logz_true = np.log(np.sqrt(2 * np.pi) * erf(win / np.sqrt(2)) / (2 * win))
thresh = 5
ndim = 2
rstate = get_rstate()
sampler = dynesty.DynamicNestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
periodic=[0],
rstate=rstate)
sampler.run_nested(dlogz_init=1, print_progress=printing)
assert (np.abs(sampler.results.logz[-1] - logz_true) <
thresh * sampler.results.logzerr[-1])
sampler = dynesty.NestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
periodic=[0],
rstate=rstate)
sampler.run_nested(dlogz=1, print_progress=printing)
assert (np.abs(sampler.results.logz[-1] - logz_true) <
thresh * sampler.results.logzerr[-1])
def test_sample():
# test sampling of two overlapping ellipsoids that samples are uniform
# within
rad = 1
shift = 0.75
ndim = 10
cen1 = np.zeros(ndim)
cen2 = np.zeros(ndim)
cen2[0] = shift
sig = np.eye(10) * rad**2
ells = [db.Ellipsoid(cen1, sig), db.Ellipsoid(cen2, sig)]
mu = db.MultiEllipsoid(ells)
R = []
nsim = 100000
rstate = get_rstate()
for i in range(nsim):
R.append(mu.sample(rstate=rstate)[0])
R = np.array(R)
assert (all([mu.contains(_) for _ in R]))
# here I'm checking that all the points are uniformly distributed
# within each ellipsoid
for curc in [cen1, cen2]:
dist1 = (np.sqrt(np.sum((R - curc)**2, axis=1)) / rad)
# radius from 0 to 1
xdist1 = dist1**ndim
# should be uniformly distributed from 0 to 1
xdist1 = xdist1[xdist1 < 1]
pval = scipy.stats.kstest(xdist1,
scipy.stats.uniform(loc=0.0, scale=1).cdf)[1]
assert ((pval > 0.003) & (pval < 0.997))
nhalf = (R[:, 0] > shift / 2.).sum()
print(nhalf, nsim)
assert (np.abs(nhalf - 0.5 * nsim) < 5 * np.sqrt(0.5 * nsim))
def test_periodic(sampler, dynamic):
# hard test of dynamic sampler with high dlogz_init and small number
# of live points
logz_true = np.log(np.sqrt(2 * np.pi) * erf(win / np.sqrt(2)) / (2 * win))
thresh = 8
# This is set up to higher level
# becasue of failures at ~5ssigma level
# this needs to be investigated
rstate = get_rstate()
if dynamic:
dns = dynesty.DynamicNestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
periodic=[0],
rstate=rstate,
sample=sampler)
dns.run_nested(dlogz_init=1, print_progress=printing)
else:
dns = dynesty.NestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
periodic=[0],
rstate=rstate,
sample=sampler)
dns.run_nested(dlogz=1, print_progress=printing)
assert (np.abs(dns.results.logz[-1] - logz_true) <
thresh * dns.results.logzerr[-1])
def test_error():
rstate = get_rstate()
with pytest.raises(ValueError):
dynesty.DynamicNestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
reflective=[22],
rstate=rstate)
def test_slice_nograd():
rstate = get_rstate()
g = Gaussian()
sampler = dynesty.NestedSampler(g.loglikelihood,
g.prior_transform,
g.ndim,
nlive=nlive,
sample='hslice',
rstate=rstate)
sampler.run_nested(print_progress=printing)
check_results_gau(sampler.results, g, rstate)
def test_exc():
# Test of exceptions that the exception is reraised
ndim = 2
rstate = get_rstate()
with pytest.raises(MyException):
sampler = dynesty.NestedSampler(loglike_exc,
prior_transform,
ndim,
nlive=nlive,
rstate=rstate)
sampler.run_nested(print_progress=printing)
def test_dynamic():
# check dynamic nested sampling behavior
logz_tol = 1
rstate = get_rstate()
dsampler = dynesty.DynamicNestedSampler(loglikelihood_gau,
prior_transform_gau,
ntotdim,
ncdim=ndim_gau,
rstate=rstate)
dsampler.run_nested(print_progress=printing)
check_results_gau(dsampler.results, rstate, logz_tol)
def test_transform_tuple():
# This is to test that the logzerr calculation is all right
# if there are very large (negative) logl vaues
# See bug #360
ndim = 2
rstate = get_rstate()
sampler = dynesty.NestedSampler(loglike_large_logl,
prior_transform_large_logl,
ndim,
rstate=rstate)
sampler.run_nested(print_progress=printing, maxiter=50)
def test_run(ndim, sample):
rstate = get_rstate()
co = Config(rstate, ndim)
nlive = 5000
bound = 'multi'
res = do_gaussian(co,
sample=sample,
bound=bound,
rstate=rstate,
nlive=nlive)
assert (np.abs(co.logz_truth_gau - res[1]) < 5 * res[2])
return res[3]
def test_cube_overlap():
rstate = get_rstate()
ndim = 10
cen = np.zeros(ndim) + .5
cen[0] = 0
sig = np.eye(ndim) * .5**2
ell = db.Ellipsoid(cen, sig)
nsamp = 10000
frac = ell.unitcube_overlap(nsamp, rstate=rstate)
true_answer = 0.5
uncertainty = np.sqrt(true_answer * (1 - true_answer) / nsamp)
assert ((frac - true_answer) < 5 * uncertainty)
def test_mc_logvol():
def capvol(n, r, h):
# see https://en.wikipedia.org/wiki/Spherical_cap
Cn = np.pi**(n / 2.) / scipy.special.gamma(1 + n / 2.)
return (
Cn * r**n *
(1 / 2 - (r - h) / r * scipy.special.gamma(1 + n / 2) /
np.sqrt(np.pi) / scipy.special.gamma(
(n + 1.) / 2) * scipy.special.hyp2f1(1. / 2,
(1 - n) / 2, 3. / 2,
((r - h) / r)**2)))
def sphere_vol(n, r):
Cn = np.pi**(n / 2.) / scipy.special.gamma(1 + n / 2.) * r**n
return Cn
def two_sphere_vol(c1, c2, r1, r2):
D = np.sqrt(np.sum((c1 - c2)**2))
n = len(c1)
if D >= r1 + r2:
return sphere_vol(n, r1) + sphere_vol(n, r2)
# now either one is fully inside or the is overlap
if D + r1 <= r2 or D + r2 <= r1:
#fully inside
return max(sphere_vol(n, r1), sphere_vol(n, r2))
else:
x = 1. / 2 / D * np.sqrt(2 * r1**2 * r2**2 + 2 * D**2 * r1**2 +
2 * D**2 * r2**2 - r1**4 - r2**4 - D**4)
capsize1 = r1 - np.sqrt(r1**2 - x**2)
capsize2 = r2 - np.sqrt(r2**2 - x**2)
V = (sphere_vol(n, r1) + sphere_vol(n, r2) -
capvol(n, r1, capsize1) - capvol(n, r2, capsize2))
return V
rstate = get_rstate()
ndim = 10
cen1 = np.zeros(ndim)
cen2 = np.zeros(ndim)
r1 = 1
r2 = 0.5
sig1 = np.eye(ndim) * r1**2
sig2 = np.eye(ndim) * r2**2
Ds = np.linspace(0, 2, 30)
nsamp = 10000
for D in Ds:
cen2[0] = D
ell = db.MultiEllipsoid(
[db.Ellipsoid(cen1, sig1),
db.Ellipsoid(cen2, sig2)])
lv = ell.monte_carlo_logvol(nsamp, rstate=rstate)[0]
vtrue = two_sphere_vol(cen1, cen2, r1, r2)
assert (np.abs(np.log(vtrue) - lv) < 1e-2)
def test_stop_nmc():
# test stopping relying in n_mc
ndim = 2
rstate = get_rstate()
sampler = dynesty.DynamicNestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
rstate=rstate)
sampler.run_nested(dlogz_init=1,
n_effective=None,
stop_kwargs=dict(n_mc=25),
print_progress=printing)
def test_samples_multi():
rstate = get_rstate()
ndim = 10
cen = np.zeros(ndim) + .5
sig = np.eye(ndim)
ell = db.MultiEllipsoid([db.Ellipsoid(cen, sig)])
nsamp = 10000
X = ell.samples(nsamp, rstate=rstate)
R = np.sqrt(((X - cen[None, :])**2).sum(axis=1))
xdist1 = R**ndim
pval = scipy.stats.kstest(xdist1,
scipy.stats.uniform(loc=0.0, scale=1).cdf)[1]
assert ((pval > 1e-3) and (pval < 1 - 1e-3))
def test_pool():
# test pool on egg problem
rstate = get_rstate()
with mp.Pool(2) as pool:
sampler = dynesty.NestedSampler(loglike_egg,
prior_transform_egg,
ndim,
nlive=nlive,
pool=pool,
queue_size=100,
rstate=rstate)
sampler.run_nested(dlogz=0.1, print_progress=printing)
assert (abs(LOGZ_TRUTH_EGG - sampler.results.logz[-1]) <
5. * sampler.results.logzerr[-1])
def test_bounds():
rstate = get_rstate()
Ndim = 20
Ngood = 10
eigv = np.abs(rstate.normal(size=Ngood))
full_eig = np.concatenate((eigv, np.zeros(Ndim - Ngood)))
randM = rstate.normal(size=((Ndim, Ndim)))
M = randM.T @ np.diag(full_eig) @ randM
R = db.improve_covar_mat(M)[1]
assert ((scipy.linalg.eigh(R)[0] > 0).all())
M = np.zeros((Ndim, Ndim))
R = db.improve_covar_mat(M)[1]
assert ((scipy.linalg.eigh(R)[0] > 0).all())
def test_bounds(bound, sample):
# stress test various boundaries
ndim = 2
rstate = get_rstate()
sampler = dynesty.NestedSampler(loglike_egg,
prior_transform_egg,
ndim,
nlive=nlive,
bound=bound,
sample=sample,
rstate=rstate)
sampler.run_nested(dlogz=0.01, print_progress=printing)
logz_truth = 235.856
assert (abs(logz_truth - sampler.results.logz[-1]) <
5. * sampler.results.logzerr[-1])
def test_pool_dynamic():
# test pool in dynamic mode
# here for speed I do a gaussian
rstate = get_rstate()
with mp.Pool(2) as pool:
sampler = dynesty.DynamicNestedSampler(loglike_gau,
prior_transform_gau,
ndim,
nlive=nlive,
pool=pool,
queue_size=100,
rstate=rstate)
sampler.run_nested(dlogz_init=1, print_progress=printing)
assert (abs(LOGZ_TRUTH_GAU - sampler.results.logz[-1]) <
5. * sampler.results.logzerr[-1])
def test_bounding_bootstrap(bound, sample):
# check various bounding methods
rstate = get_rstate()
g = Gaussian()
sampler = dynesty.NestedSampler(g.loglikelihood,
g.prior_transform,
g.ndim,
nlive=nlive,
bound=bound,
sample=sample,
bootstrap=5,
rstate=rstate)
sampler.run_nested(print_progress=printing)
check_results_gau(sampler.results, g, rstate)
def test_generator():
# Test that we can use the sampler as a generator
rstate = get_rstate()
g = Gaussian()
sampler = dynesty.NestedSampler(g.loglikelihood,
g.prior_transform,
g.ndim,
nlive=nlive,
rstate=rstate)
for it in sampler.sample():
pass
for it in sampler.add_live_points():
pass
res = sampler.results
check_results_gau(res, g, rstate)
def test_oldstop():
# test of old stopping function functionality
ndim = 2
rstate = get_rstate()
import dynesty.utils as dyutil
stopfn = dyutil.old_stopping_function
sampler = dynesty.DynamicNestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
rstate=rstate)
sampler.run_nested(dlogz_init=1,
n_effective=None,
stop_function=stopfn,
print_progress=printing)
def test_livepoints():
# Test the providing of initial live-points to the sampler
ndim = 2
rstate = get_rstate()
live_u = rstate.uniform(size=(nlive, ndim))
live_v = np.array([prior_transform(_) for _ in live_u])
live_logl = np.array([loglike(_) for _ in live_v])
live_points = [live_u, live_v, live_logl]
sampler = dynesty.NestedSampler(loglike,
prior_transform,
ndim,
nlive=nlive,
live_points=live_points,
rstate=rstate)
sampler.run_nested(print_progress=printing)
dyutil.unravel_run(sampler.results)
def test_saving():
# test saving
ndim = 2
fname = 'xx.h5'
rstate = get_rstate()
sampler = dynesty.NestedSampler(loglike_egg,
prior_transform_egg,
ndim,
nlive=nlive,
bound='multi',
sample='unif',
save_history=True,
history_filename=fname,
rstate=rstate)
sampler.run_nested(dlogz=1, print_progress=printing)
assert (os.path.exists(fname))
def test_dyn():
# hard test of dynamic sampler with high dlogz_init and small number
# of live points
ndim = 2
THRESHOLD = 5 # in sigmas
rstate = get_rstate()
# this is expected to use unif sampler and multi bound
sampler = dynesty.DynamicNestedSampler(loglike_egg,
prior_transform_egg,
ndim,
nlive=nlive,
rstate=rstate)
sampler.run_nested(dlogz_init=1, print_progress=printing)
assert (abs(LOGZ_TRUTH - sampler.results.logz[-1]) <
THRESHOLD * sampler.results.logzerr[-1])
print(sampler.citations)
