def mol_dyn_sampling(self,md_work, i, N, T1, T2, dt, Nc, Ns, sig, gpuid):
activ = aat.moldynactivelearning(self.netdict['cnstfile'],
self.netdict['saefile'],
self.netdict['nnfprefix'],
self.netdict['num_nets'],
gpuid)
difo = open(self.ldtdir + self.datdir + '/info_data_mdso-'+str(i)+'.nfo', 'w')
Nmol = 0
dnfo = 'MD Sampler running: ' + str(md_work.size)
difo.write(dnfo + '\n')
Nmol = md_work.size
ftme_t = 0.0
for di, id in enumerate(md_work):
data = hdt.read_rcdb_coordsandnm(id)
#print(di, ') Working on', id, '...')
S = data["species"]
if "charge" in data and "multip" in data:
chg = data["charge"]
mlt = data["multip"]
else:
chg = "0"
mlt = "1"
# Set mols
activ.setmol(data["coordinates"], S)
# Generate conformations
X = activ.generate_conformations(N, T1, T2, dt, Nc, Ns, dS=sig)
ftme_t += activ.failtime
nfo = activ._infostr_
m = id.rsplit('/',1)[1].rsplit('.',1)[0]
difo.write(' -' + m + ': ' + nfo + '\n')
difo.flush()
#print(nfo)
if X.size > 0:
hdt.writexyzfilewc(self.cdir + 'mds_' + m.split('.')[0] + '_' + str(i).zfill(2) + str(di).zfill(4) + '.xyz', X, S, ' '+chg+' '+mlt)
difo.write('Complete mean fail time: ' + "{:.2f}".format(ftme_t / float(Nmol)) + '\n')
print(Nmol)
del activ
difo.close()
#(1.00,'/home/jujuman/Research/GDB-11-AL-wB97x631gd/dnnts_red/dnntsgdb11_03_red/inputs/'),
#(1.00,'/home/jujuman/Research/GDB-11-AL-wB97x631gd/dnnts_red/dnntsgdb11_04_red/inputs/'),
#(0.50,'/home/jujuman/Research/GDB-11-AL-wB97x631gd/dnnts_red/dnntsgdb11_05_red/inputs/'),
#(0.35,'/home/jujuman/Research/GDB-11-AL-wB97x631gd/dnnts_red/dnntsgdb11_06_red/inputs/'),
#(1.0, '/home/jujuman/Research/GDB-11-AL-wB97x631gd/elements_SFCl/gdb11_size1/inputs/'),
#(1.0, '/home/jujuman/Research/GDB-11-AL-wB97x631gd/elements_SFCl/gdb11_size2/inputs/'),
#(1.0, '/home/jujuman/Research/GDB-11-AL-wB97x631gd/elements_SFCl/gdb11_size3/inputs/'),
#(1.0, '/home/jujuman/Research/GDB-11-AL-wB97x631gd/elements_SFCl/gdb11_size4/inputs/'),
(1.0,
'/home/jujuman/Research/GDB-11-AL-wB97x631gd/elements_SFCl/gdb11_size5/inputs/'
),
]
#-------------------------------------------
activ = pya.moldynactivelearning(cnstfile, saefile, wkdir + 'train', 5)
difo = open(dstore + 'info_data_mdso.nfo', 'w')
Nmol = 0
ftme = 0.0
for di, id in enumerate(idir):
files = os.listdir(id[1])
random.shuffle(files)
dnfo = str(di) + ' of ' + str(
len(idir)) + ') dir: ' + str(id) + ' Selecting: ' + str(
id[0] * len(files))
print(dnfo)
Nmol += len(files)
difo.write(dnfo + '\n')
ftme_t = 0.0