mode_frequencies = defaultdict(list)
modes_p_v = defaultdict(list)
ENERGY_LABEL = "$\Delta$ U"
RMSD_LABEL = "RMSD"
nmd_file_name = {"CC":"normalized_modes.1.nmd",
"IC":"normalized_modes_cc.1.nmd"}
v1s = []
v2s = []
for (p1,v1),(p2,v2) in pair_parameter_values(experiment_details["check"], experiment_details["parameter_values"]):
v1s.append(v1)
v2s.append(v2)
folder_name = "%s_%s_%s_%s_%s"%(experiment_details["prefix"],
experiment_details["parameter_abbv"][p1], parameter_value_to_string(v1),
experiment_details["parameter_abbv"][p2], parameter_value_to_string(v2))
if experiment_details["prefix"] == "CC":
raw_data, data_len = load_cc_data(os.path.join(workspace,
folder_name,
options.folder),
full_pele_energy = False,
skip_first = 15)
if experiment_details["prefix"] == "IC":
raw_data, data_len = load_ic_data(os.path.join(workspace,
folder_name,
options.folder),
skip_first = 15)
step_granularity = 100
max_samples = 1000
if options.data is None:
acceptances = {}
rmsf = {}
rmsd = {}
rmsf_ref = numpy.loadtxt(options.rmsf_ref)[:-1]
for dataset in datasets:
acceptances[dataset["prefix"]] = {}
rmsf[dataset["prefix"]] = {}
rmsd[dataset["prefix"]] = {}
for T, v1, v2 in dataset["parameters"]:
folder_name = "%s_%s_%s_%s_%s" % (
dataset["prefix"], "dispFact",
parameter_value_to_string(v1), "rmsg",
parameter_value_to_string(v2))
main_folder = "%s_%d" % (dataset["prefix"], T)
data_folder = os.path.join(dataset["source_folder"],
main_folder, dataset["workspace"],
folder_name, "info")
print data_folder
if dataset["prefix"] == "CC":
raw_data, min_len = load_data(
data_folder,
"perturb_energy_before.log",
"final_energy.log", # Energy of the whole step
"initial_cc.log",
"after_minimization_cc.log",
cutoff_keys = []
for (p1, v1), (p2, v2) in pair_parameter_values(
experiment_details["check"],
experiment_details["parameter_values"]):
for sim_type in ["CC", "IC", "IC_FULL"]:
if p1 == "prot":
prot_keys.append(v1)
cutoff_keys.append(v2)
key = (v1, v2)
else:
prot_keys.append(v2)
cutoff_keys.append(v1)
key = (v2, v1)
folder_name = "%s_%s_%s_%s_%s" % (
prefixes[sim_type], experiment_details["parameter_abbv"][p1],
parameter_value_to_string(v1),
experiment_details["parameter_abbv"][p2],
parameter_value_to_string(v2))
rev_folder_name = "%s_%s_%s_%s_%s" % (
prefixes[sim_type], experiment_details["parameter_abbv"][p2],
parameter_value_to_string(v2),
experiment_details["parameter_abbv"][p1],
parameter_value_to_string(v1))
nmd_file_path = os.path.join(workspace_folder[sim_type],
folder_name, "info",
nmd_file_name[sim_type])
rev_nmd_file_path = os.path.join(workspace_folder[sim_type],
rev_folder_name, "info",
nmd_file_name[sim_type])
for p1_key in p1_keys:
#Choose all elements with p1 key
p1_indices = df[plot_using[0]] == p1_key
for p2_key in p2_keys:
p2_indices = df[plot_using[1]] == p2_key
subdf = df.loc[p1_indices].loc[p2_indices]
x, y = subdf.T.loc[average_by[0]].mean(), subdf.T.loc[
average_by[1]].mean()
x_err, y_err = subdf.T.loc[average_by[0]].std(), subdf.T.loc[
average_by[1]].std()
xs.append(x)
ys.append(y)
xs_std.append(x_err)
ys_std.append(y_err)
labels.append("%s %s" % (parameter_value_to_string(p1_key),
parameter_value_to_string(p2_key)))
plt.scatter(xs, ys, marker='o', c=colors[i], label=plt_labels[folder])
# plt.errorbar(xs, ys, xerr=xs_std, yerr=ys_std, linestyle="None", c = colors[i])
for label, x, y in zip(labels, xs, ys):
plt.annotate(label,
xy=(x, y),
xytext=(5, 5),
textcoords='offset points',
ha='right',
va='bottom',
size=6)
plt.xlabel(RMSD_LABEL)
plt.ylabel(ENERGY_LABEL)
lgd = plt.legend() #(loc='upper center', ncol=4)
plt.show()