def plot_summaries(trace, m, plotdir):
ar_data = az.from_pymc3(trace=trace)
# view summary
df = az.summary(ar_data, var_names=m.all_vars)
print(df)
# write summary to disk
f = open(plotdir / "summary.txt", "w")
df.to_string(f)
f.close()
with az.rc_context(rc={"plot.max_subplots": 80}):
stem = str(plotdir / "autocorr{:}.png")
efficient_autocorr(ar_data, var_names=m.all_vars, figstem=stem)
# make a traceplot
stem = str(plotdir / "trace{:}.png")
efficient_trace(ar_data, var_names=m.all_vars, figstem=stem)
'CB': r'C$_\beta$',
'C': 'C',
'N': 'N',
}
state_to_name = {
'like_o' : 'Open',
'like_fo' : 'Fully Open',
'like_c' : 'Closed',
}
for nucleus in nuclei_to_name.keys():
for method in method_to_name.keys():
for state in state_to_name.keys():
print(nucleus,method,state)
model_path = models_dir + f"{model_name}_{method}_{nucleus}.nc"
my_model = az.from_netcdf(model_path)
resids = my_model.posterior.resid
n = resids.shape[0]
fig, ax = plt.subplots(n // 6 + 1, 6, figsize=(13,15))
for i in range(6 - n % 6):
fig.delaxes(ax[-1,-i-1])
ax = fig.axes
with az.rc_context(rc={'plot.max_subplots': None}):
az.plot_ppc(my_model, flatten=['step'], var_names = [state], random_seed=RANDOM_SEED, ax=ax)
for r, a in zip(resids.to_index(), ax):
a.set_title(f'{r}', size=12)
a.set_xlabel('')
a.legend_.set_visible(False)
fig.suptitle(f'Posterior Predictive Check {method_to_name[method]} {nuclei_to_name[nucleus]} {state_to_name[state]} (ppm)', y =1.0, size =20)
fig.tight_layout()
plt.savefig(f'{reports_dir}for_print/ppc_{state_to_name[state]}_{nucleus}_{method}.png')
os.makedirs(plotdir)
trace = pm.load_trace(directory="chains", model=m.model)
if diagnostics:
ar_data = az.from_pymc3(trace=trace)
# view summary
df = az.summary(ar_data, var_names=m.all_vars)
# write summary to disk
f = open(plotdir / "summary.txt", "w")
df.to_string(f)
f.close()
with az.rc_context(rc={"plot.max_subplots": 80}):
stem = str(plotdir / "autocorr{:}.png")
efficient_autocorr(ar_data, var_names=m.all_vars, figstem=stem)
# make a traceplot
stem = str(plotdir / "trace{:}.png")
efficient_trace(ar_data, var_names=m.all_vars, figstem=stem)
# make a nice corner plot of the variables we care about
samples = pm.trace_to_dataframe(
trace,
varnames=["Mtot", "a", "P", "e", "incl", "omega", "Omega", "phi"],
)
samples["P"] /= yr
samples["incl"] /= deg
samples["omega"] /= deg
