jsonfile = outfile.split(".")[0]+".json"
else:
jsonfile = outfile
print("INFO: Writing mean configuration to", jsonfile, file=stderr)
with open(jsonfile, "w") as file:
file.write(mean_file)
if outoxdna:
#save output as oxDNA .dat format
if outjson == True:
#if making both outputs, automatically set file extensions.
outname = outfile.split(".")[0]+".dat"
else:
outname = outfile
from mean2dat import make_dat
make_dat(loads(mean_file), outname)
#If requested, run compute_deviations.py using the output from this script.
if dev_file:
print("INFO: launching compute_deviations.py", file=stderr)
#fire up a subprocess running compute_deviations.py
import subprocess
from sys import executable, path
launchargs = [executable, path[0]+"/compute_deviations.py", jsonfile, traj_file, top_file, "-o {}".format(dev_file)]
if parallel:
launchargs.append("-p {}".format(n_cpus))
subprocess.run(launchargs)
#compute_deviations needs the json meanfile, but its not useful for visualization
#so we dump it
a1s = []
a3s = []
ts = []
out = parallelize_erik_onefile.fire_multiprocess(
traj_file, compute_centroid, num_confs, n_cpus, mean_structure)
[candidates.append(i[0]) for i in out]
[rmsfs.append(i[3]) for i in out]
[a1s.append(i[1]) for i in out]
[a3s.append(i[2]) for i in out]
[ts.append(i[4]) for i in out]
min_id = rmsfs.index(min(rmsfs))
centroid = candidates[min_id]
centroid_a1s = a1s[min_id]
centroid_a3s = a3s[min_id]
centroid_time = ts[min_id]
centroid_rmsf = rmsfs[min_id]
print(
"INFO: Centroid configuration found at configuration t = {}, RMSF = {}"
.format(centroid_time, centroid_rmsf),
file=stderr)
from mean2dat import make_dat
make_dat(
{
'g_mean': centroid,
'a1_mean': centroid_a1s,
'a3_mean': centroid_a3s
}, outfile)
