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)