def exp1_posterior():
cfg_filename = os.path.join(streamspath, "config", "exp1_8.yml")
config = read_config(cfg_filename)
model = StreamModel.from_config(config)
hdf5_filename = os.path.join(streamspath, "plots", "yeti", "exper1_8", "cache",
"combined_inference.hdf5")
print(hdf5_filename)
if not os.path.exists(hdf5_filename): raise IOError("Path doesn't exist!")
with h5py.File(hdf5_filename, "r") as f:
chain = f["chain"].value
_flatchain = np.vstack(chain)
flatchain = np.zeros_like(_flatchain)
params = OrderedDict(model.parameters['potential'].items() + \
model.parameters['satellite'].items())
truths = []
bounds = []
for ii,p in enumerate(params.values()):
if p.name == 'alpha':
truths.append(np.nan)
bounds.append((1., 2.0))
flatchain[:,ii] = _unit_transform[p.name](_flatchain[:,ii])
continue
truth = _unit_transform[p.name](p.truth)
print(p.name, truth)
truths.append(truth)
bounds.append((0.95*truth, 1.05*truth))
flatchain[:,ii] = _unit_transform[p.name](_flatchain[:,ii])
# bounds = [(0.7,2.),(0.7,2.),(52,142),(100,200),(5,30),(1.1,2.5)]
#bounds = None
fig = triangle.corner(flatchain, plot_datapoints=False,
truths=truths, extents=potential_bounds, labels=potential_labels)
fig.subplots_adjust(wspace=0.13, hspace=0.13)
fig.savefig(os.path.join(plot_path, "exp1_posterior.{}".format(ext)))
def exp_posteriors(exp_num):
matplotlib.rc('xtick', labelsize=16)
matplotlib.rc('ytick', labelsize=16)
cfg_filename = os.path.join(streamspath, "config", "exp{}.yml".format(exp_num))
config = read_config(cfg_filename)
model = StreamModel.from_config(config)
cache_path = os.path.join(streamspath, "plots", "yeti",
"exper{}_marg_tub".format(exp_num), "cache")
filename = os.path.join(cache_path, "combined_inference.hdf5")
with h5py.File(filename, "r") as f:
chain = f["chain"].value
_flatchain = np.vstack(chain)
d = model.label_flatchain(_flatchain)
# Potential
this_flatchain = np.zeros((_flatchain.shape[0], len(d["potential"])))
truths = []
labels = []
for ii,pname in enumerate(d["potential"].keys()):
this_flatchain[:,ii] = _unit_transform[pname](np.squeeze(d["potential"][pname]))
p = model.parameters["potential"][pname]
truth = _unit_transform[pname](p.truth)
truths.append(truth)
labels.append(_label_map[pname])
q16,q50,q84 = np.array(np.percentile(this_flatchain, [16, 50, 84], axis=0))
q_m, q_p = q50-q16, q84-q50
for ii,pname in enumerate(d["potential"].keys()):
print("{} \n\t truth={:.2f}\n\t measured={:.2f}+{:.2f}-{:.2f}"\
.format(pname,truths[ii],q50[ii],q_p[ii],q_m[ii]))
fig = triangle.corner(this_flatchain, plot_datapoints=False,
truths=truths, extents=potential_bounds, labels=potential_labels)
fig.subplots_adjust(wspace=0.13, hspace=0.13)
fig.savefig(os.path.join(plot_path, "exp{}_potential.{}".format(exp_num, ext)))
# Particle
p_idx = 2
this_flatchain = np.zeros((_flatchain.shape[0], len(d["particles"])))
truths = []
bounds = []
labels = []
for ii,pname in enumerate(d["particles"].keys()):
this_flatchain[:,ii] = _unit_transform[pname](d["particles"][pname][:,p_idx])
p = model.parameters["particles"][pname]
truth = _unit_transform[pname](p.truth[p_idx])
truths.append(truth)
if pname == "tub":
bounds.append((model.lnpargs[1], model.lnpargs[0]))
else:
sig = model.particles.errors[pname].value[p_idx]
mu = model.particles[pname].value[p_idx]
bounds.append((mu-3*sig, mu+3*sig))
labels.append(_label_map[pname])
q16,q50,q84 = np.array(np.percentile(this_flatchain, [16, 50, 84], axis=0))
q_m, q_p = q50-q16, q84-q50
for ii,pname in enumerate(d["particles"].keys()):
print("{} \n\t truth={:.2f}\n\t measured={:.2f}+{:.2f}-{:.2f}"\
.format(pname,truths[ii],q50[ii],q_p[ii],q_m[ii]))
# HACK
bounds = [(20.,29.), (-9.5, -7.), (0.,2.), (-55,-5)]
# OLD: bounds = [(22.,26.), (-8.6, -8.), (1.0,1.5), (-50,-10)]
# bounds = None
fig = triangle.corner(this_flatchain, plot_datapoints=False,
truths=truths, labels=labels, extents=bounds)
fig.subplots_adjust(wspace=0.13, hspace=0.13)
fig.savefig(os.path.join(plot_path, "exp{}_particle.{}".format(exp_num, ext)))
# Satellite
this_flatchain = np.zeros((_flatchain.shape[0], len(d["satellite"])))
truths = []
bounds = []
labels = []
#for ii,pname in enumerate(keys):
for ii,pname in enumerate(d["satellite"].keys()):
this_flatchain[:,ii] = _unit_transform[pname](d["satellite"][pname][:,0])
p = model.parameters["satellite"][pname]
truth = _unit_transform[pname](p.truth)
if pname == "alpha":
bounds.append((1., 2.5))
truths.append(np.nan)
else:
truths.append(truth)
sig = model.satellite.errors[pname].value[0]
mu = model.satellite[pname].value[0]
bounds.append((mu-3*sig, mu+3*sig))
labels.append(_label_map[pname])
# HACK
bounds = [(28.5,33.), (-2.6,-1.5), (1.3,2.0), (120,175), bounds[-1]]
# bounds = None
if len(d["satellite"]) > len(bounds):
bounds = [(0,10), (-20,5)] + bounds
#bounds = None
fig = triangle.corner(this_flatchain, plot_datapoints=False,
truths=truths, labels=labels, extents=bounds)
fig.subplots_adjust(wspace=0.13, hspace=0.13)
fig.savefig(os.path.join(plot_path, "exp{}_satellite.{}".format(exp_num, ext)))
def trace_plots():
cfg_filename = os.path.join(streamspath, "config", "exp1_8.yml")
config = read_config(cfg_filename)
model = StreamModel.from_config(config)
hdf5_filename = os.path.join(streamspath, "plots", "yeti", "exper1_8", "cache", "combined_inference_all.hdf5")
if not os.path.exists(hdf5_filename): raise IOError("Path doesn't exist!")
print(hdf5_filename)
with h5py.File(hdf5_filename, "r") as f:
chain = f["chain"].value
acor = f["acor"].value
labels = ["$q_1$", "$q_z$", r"$\phi$", "$v_h$", "$r_h$", r"$\alpha$"]
bounds = [(1.2,1.5),(1.2,1.5),(80,110),(111,131),(5,20),(0.5,2.5)]
ticks = [(1.25,1.35,1.45),(1.25,1.35,1.45),(85,95,105),(115,120,125),(7,12,17),(1.,1.5,2.)]
# plot individual walkers
fig,axes = plt.subplots(6,1,figsize=(8.5,11),sharex=True)
k = 0
for gname,group in model.parameters.items():
for pname,p in group.items():
thischain = _unit_transform[pname](chain[...,k])
for ii in range(config['walkers']):
axes.flat[k].plot(thischain[ii,:],
alpha=0.1, marker=None,
drawstyle='steps', color='k', zorder=0)
#axes.flat[k].set_ylabel(labels[k], rotation='horizontal')
axes[k].text(-0.02, 0.5, labels[k],
horizontalalignment='right',
fontsize=22,
transform=axes[k].transAxes)
if pname == "phi":
axes[k].text(1.07, 0.475, "deg",
horizontalalignment='left',
fontsize=18,
transform=axes[k].transAxes)
elif pname == "v_halo":
axes[k].text(1.07, 0.475, "km/s",
horizontalalignment='left',
fontsize=18,
transform=axes[k].transAxes)
elif pname == "log_R_halo":
axes[k].text(1.07, 0.475, "kpc",
horizontalalignment='left',
fontsize=18,
transform=axes[k].transAxes)
axes[k].text(0.25, 0.1, r"$t_{\rm acor}$=" + "{}".format(int(acor[k])),
horizontalalignment='right',
fontsize=18,
transform=axes[k].transAxes)
axes.flat[k].set_yticks(ticks[k])
axes.flat[k].set_xlim(0,10000)
axes.flat[k].set_ylim(bounds[k])
axes.flat[k].yaxis.tick_right()
#axes.flat[k].yaxis.set_label_position("right")
axes.flat[k].set_rasterization_zorder(1)
k += 1
axes.flat[-1].set_xlabel("Step number")
fig.tight_layout()
fig.subplots_adjust(hspace=0.04, left=0.14, right=0.86)
fig.savefig(os.path.join(plot_path, "mcmc_trace.{}".format(ext)))
