"IC_dispFact_0.65_dm_1", "IC_dispFact_0.65_dm_2",
"IC_dispFact_0.65_dm_3", "IC_dispFact_0.65_dm_4",
"IC_dispFact_0.65_dm_5", "IC_dispFact_0.65_dm_6",
"IC_dispFact_0.65_dm_7", "IC_dispFact_0.65_dm_8",
"IC_dispFact_0.65_dm_9", "IC_dispFact_0.65_dm_10"
]
distances = {}
for folder in folders:
raw_data, min_len = load_single_proc_data(options.sim_type,
os.path.join(folder, "info"))
energy_increments = process_energy_differences(raw_data)
mc = MetropolisMCSimulator(energy_increments)
who_is_accepted = mc.who_is_accepted(options.temperature)
coords = numpy.reshape(raw_data["coords_after"],
(len(raw_data["coords_after"]),
len(raw_data["coords_after"][0]) / 3, 3))
distances[folder] = calc_distances(coords, range(
len(coords))) #who_is_accepted[:150])
sns.set_style("whitegrid")
row_len = 4
col_len = 3
folders.extend(["IC_dispFact_0.65_dm_10", "IC_dispFact_0.65_dm_10"])
f, axes = prepare_subplots(row_len, col_len)
for i, folder in enumerate(folders):
ax = axes[i / row_len, i % row_len]
ax.set_title(folder)
acceptances[T][v1, v2] = mc.perform_simulation(
min(200, len(energy_increments)), 40, T)
avg_energy[T][v1, v2] = numpy.mean(energy_increments)
std_energy[T][v1, v2] = numpy.std(energy_increments)
avg_rmsd[T][v1, v2] = numpy.mean(rmsd_increments)
std_rmsd[T][v1, v2] = numpy.std(rmsd_increments)
p1_keys.append(v1)
p2_keys.append(v2)
# Rmsf calculations
rmsf_coords = numpy.reshape(
raw_data["coords_after"],
(len(raw_data["coords_after"]),
len(raw_data["coords_after"][0]) / 3, 3))
rmsf[T][v1,
v2] = coords_rmsf(rmsf_coords[mc.who_is_accepted(T)])
def default_to_regular(d):
if isinstance(d, defaultdict):
d = {k: default_to_regular(v) for k, v in d.iteritems()}
return d
pickle.dump(
(default_to_regular(acceptances), default_to_regular(avg_energy),
default_to_regular(std_energy), default_to_regular(avg_rmsd),
default_to_regular(std_rmsd), rmsf, p1, p1_keys, p2, p2_keys),
open(os.path.join(results_folder, "data"), "w"))
else:
(acceptances, avg_energy, std_energy, avg_rmsd, std_rmsd, rmsf, p1,
p1_keys, p2, p2_keys) = pickle.load(open(options.data))