if run_type == "sampling":
sampling.simulated_annealing(model,
sigma=sigma0,
equil_steps=nsteps,
sample_sigma=True,
annealing_steps=annealing_steps,
outfile_prefix="Macro",
outdir=outputdir)
###############################
### Analysis:
plots.plot_2state_fragment_avg_model_fits(model.states[0],
model.states[1],
sig=5.0,
num_best_models=num_best_models,
write_file=False,
outdir=outputdir,
show_plot=False)
plots.plot_apo_lig_dhdx(model,
show_plot=True,
save_plot=False,
outfile="dhdx.png",
outdir=outputdir,
noclobber=False)
plots.plot_fragment_chi_values(model.states[0],
sig="model",
outdir=outputdir,
show_plot=True)
if run_type == "sampling":
sampling.simulated_annealing(model,
sigma=sigma0,
equil_steps=nsteps,
sample_sigma=False,
annealing_steps=annealing_steps,
outfile_prefix="Macro",
outdir=outputdir)
###############################
### Analysis:
# Use this line if analyzing data post-run
# hdxm.import_model_deuteration_from_file(model.states[1].frags, model.states[1].modelfile)
'''
plots.plot_2state_fragment_avg_model_fits(model.states[0], model.states[1],
sig=5.0,
num_best_models=num_best_models,
write_file=True,
outdir=outputdir,
show_plot=False)
for s in model.states:
plots.plot_fragment_avg_model_fits(s,
sig=5.0,
num_best_models=num_best_models,
write_file=True,
outdir=outputdir,
show_plot=False)