def cornerbound(self, **kwargs):
dyplot.cornerbound(results=self.samples.results,
labels=self.model.parameter_labels_latex,
**kwargs)
self.output.to_figure(structure=None, auto_filename="cornerbound")
def cornerbound(self, **kwargs):
if conf.instance["general"]["test"]["test_mode"]:
return None
dyplot.cornerbound(results=self.samples.results,
labels=self.model.parameter_labels_latex,
**kwargs)
self.output.to_figure(structure=None, auto_filename="cornerbound")
self.mat_plot_1d.figure.close()
def test_gaussian():
logz_tol = 1
sampler = dynesty.NestedSampler(loglikelihood_gau,
prior_transform_gau,
ndim_gau,
nlive=nlive)
sampler.run_nested(print_progress=printing)
# add samples
# check continuation behavior
sampler.run_nested(dlogz=0.1, print_progress=printing)
# get errors
nerr = 2
for i in range(nerr):
sampler.reset()
sampler.run_nested(print_progress=False)
results = sampler.results
pos = results.samples
wts = np.exp(results.logwt - results.logz[-1])
mean, cov = dyfunc.mean_and_cov(pos, wts)
logz = results.logz[-1]
assert (np.abs(logz - logz_truth_gau) < logz_tol)
# check summary
res = sampler.results
res.summary()
# check plots
dyplot.runplot(sampler.results)
plt.close()
dyplot.traceplot(sampler.results)
plt.close()
dyplot.cornerpoints(sampler.results)
plt.close()
dyplot.cornerplot(sampler.results)
plt.close()
dyplot.boundplot(sampler.results,
dims=(0, 1),
it=3000,
prior_transform=prior_transform_gau,
show_live=True,
span=[(-10, 10), (-10, 10)])
plt.close()
dyplot.cornerbound(sampler.results,
it=3500,
prior_transform=prior_transform_gau,
show_live=True,
span=[(-10, 10), (-10, 10)])
plt.close()
def plot_status(res, labels=None, periodic=None):
# Generate a plot of the trace.
try:
fig, ax = dyplot.traceplot(res, show_titles=True, trace_cmap="viridis",\
connect=True, connect_highlight=range(5), labels=labels)
except:
# If it hasn't converged enough...
fig, ax = dyplot.traceplot(res, show_titles=True, trace_cmap="viridis",\
connect=True, connect_highlight=range(5), labels=labels, \
kde=False)
fig.savefig("traceplot.png")
plt.close(fig)
# Generate a bounds cornerplot.
fig, ax = dyplot.cornerbound(res, it=res.niter-1, periodic=periodic, \
prior_transform=sampler.prior_transform, show_live=True, \
labels=labels)
fig.savefig("boundplot.png")
plt.close(fig)
def cornerbound(self, figsize=(10,10), labels=None):
fig, axes = plt.subplots(self.ndim-1, self.ndim-1,
figsize=figsize)
fg, ax = dyplot.cornerbound(self.results, it=1000, labels=labels,
prior_transform=self.prior_tf,
show_live=True, fig=(fig, axes))
def test_gaussianx(bound):
ndim = 3
rstate = get_rstate()
g = Gaussian(ndim=ndim)
sampler = dynesty.NestedSampler(g.loglikelihood,
g.prior_transform,
g.ndim,
nlive=nlive,
rstate=rstate,
bound=bound)
sampler.run_nested(print_progress=printing)
results = sampler.results
dyplot.boundplot(results,
dims=(0, 1)[:min(ndim, 2)],
it=1000,
prior_transform=g.prior_transform,
show_live=False)
dyplot.cornerbound(results,
it=3000,
prior_transform=g.prior_transform,
show_live=False)
def draw_cornerbounds(results,
nidm,
prior_transform,
labels=None,
figsize=(10, 10),
save=False,
save_dir='.',
suffix='',
**kwargs):
fig, axes = plt.subplots(ndim - 1, ndim - 1, figsize=figsize)
plot_kw = {'labels': labels, 'it': 1000, 'show_live': True}
plot_kw.update(kwargs)
fg, ax = dyplot.cornerbound(self.results,
prior_transform=self.prior_tf,
fig=(fig, axes),
**plot_kw)
if save:
plt.savefig(os.path.join(save_dir, "Cornerbound%s.png" % suffix),
dpi=120)
plt.close()
else:
plt.show()
def test_gaussian(dynamic, periodic, ndim, bound):
rstate = get_rstate()
g = Gaussian(ndim=ndim)
if periodic:
periodic = [0]
else:
periodic = None
if dynamic:
sampler = dynesty.DynamicNestedSampler(g.loglikelihood,
g.prior_transform,
g.ndim,
nlive=nlive,
rstate=rstate,
periodic=periodic,
bound=bound)
else:
sampler = dynesty.NestedSampler(g.loglikelihood,
g.prior_transform,
g.ndim,
nlive=nlive,
rstate=rstate,
periodic=periodic,
bound=bound)
sampler.run_nested(print_progress=printing)
results = sampler.results
# check plots
dyplot.runplot(results)
dyplot.runplot(results, span=[(0., 10.), 0.001, 0.2, (5., 6.)])
dyplot.runplot(results, logplot=True)
dyplot.runplot(results,
fig=(plt.gcf(), plt.gcf().axes),
max_x_ticks=0,
max_y_ticks=0)
plt.close()
dyplot.traceplot(results)
dyplot.traceplot(results, smooth=[10] * ndim)
dyplot.traceplot(results, connect=True)
dyplot.traceplot(results,
fig=(plt.gcf(), plt.gcf().axes),
show_titles=True,
truths=np.zeros(ndim),
verbose=True,
max_n_ticks=0)
plt.close()
truths = np.zeros(ndim)
truths[0] = -.1
span = [[-10, 10]] * ndim
if ndim > 1:
truths[1] = .1
span[1] = .9
dyplot.cornerplot(results, show_titles=True, truths=truths)
dyplot.cornerplot(results,
smooth=10,
verbose=True,
hist2d_kwargs=dict(plot_datapoints=True),
max_n_ticks=0)
plt.close()
if ndim != 1:
# cornerbound
dyplot.cornerbound(results,
it=500,
prior_transform=g.prior_transform,
show_live=True,
span=span)
dyplot.cornerbound(results,
it=500,
show_live=True,
span=span,
fig=(plt.gcf(), plt.gcf().axes))
plt.close()
# boundplot
dyplot.boundplot(results,
dims=(0, 1)[:min(ndim, 2)],
it=1000,
prior_transform=g.prior_transform,
show_live=True,
span=span)
dyplot.boundplot(results, dims=(0, 1)[:min(ndim, 2)], it=1000)
plt.close()
# cornerpoints
dyplot.cornerpoints(results)
plt.close()
dyplot.cornerpoints(results, span=span, truths=truths, max_n_ticks=0)
plt.close()
plt.close()
sys.stderr.write('Corner Plot (Contours)\n')
dyplot.cornerplot(sampler.results)
plt.close()
sys.stderr.write('2-D Bound Plot\n')
dyplot.boundplot(sampler.results,
dims=(0, 1),
it=3000,
prior_transform=prior_transform,
show_live=True,
span=[(-10, 10), (-10, 10)])
plt.close()
sys.stderr.write('Sub-Corner Plot (Bounds)\n')
dyplot.cornerbound(sampler.results,
it=3500,
prior_transform=prior_transform,
show_live=True,
span=[(-10, 10), (-10, 10)])
plt.close()
# check various bounding methods
for bound in ['none', 'single', 'multi', 'balls', 'cubes']:
sys.stderr.write('\n' + bound + '\n')
sampler = dynesty.NestedSampler(loglikelihood,
prior_transform,
ndim,
nlive=nlive,
bound=bound,
sample='unif')
sampler.run_nested(print_progress=printing)
check_results(sampler.results, lz_tol, m_tol, c_tol)