def test_polar (args) :
if args.rand_seed is not None :
np.random.seed(args.rand_seed % (2**32))
dp = DeepPolar(args.model)
data = DeepmdData(args.system, args.set_prefix, shuffle_test = args.shuffle_test)
data.add('polarizability', 9, atomic=True, must=True, high_prec=False, type_sel = dp.get_sel_type())
test_data = data.get_test ()
numb_test = args.numb_test
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = min(nframes, numb_test)
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
box = test_data["box"][:numb_test]
atype = test_data["type"][0]
polar = dp.eval(coord, box, atype)
polar = polar.reshape([numb_test,-1])
l2f = (l2err (polar - test_data["polarizability"] [:numb_test]))
print ("# number of test data : %d " % numb_test)
print ("Polarizability L2err : %e eV/A" % l2f)
detail_file = args.detail_file
if detail_file is not None :
pe = np.concatenate((np.reshape(test_data["polarizability"][:numb_test], [-1,9]),
np.reshape(polar, [-1,9])),
axis = 1)
np.savetxt(detail_file+".out", pe,
header = 'data_pxx data_pxy data_pxz data_pyx data_pyy data_pyz data_pzx data_pzy data_pzz pred_pxx pred_pxy pred_pxz pred_pyx pred_pyy pred_pyz pred_pzx pred_pzy pred_pzz')
def test_dipole (args) :
if args.rand_seed is not None :
np.random.seed(args.rand_seed % (2**32))
dp = DeepDipole(args.model)
data = DeepmdData(args.system, args.set_prefix, shuffle_test = args.shuffle_test)
data.add('dipole', 3, atomic=True, must=True, high_prec=False, type_sel = dp.get_sel_type())
test_data = data.get_test ()
numb_test = args.numb_test
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = min(nframes, numb_test)
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
box = test_data["box"][:numb_test]
atype = test_data["type"][0]
dipole = dp.eval(coord, box, atype)
dipole = dipole.reshape([numb_test,-1])
l2f = (l2err (dipole - test_data["dipole"] [:numb_test]))
print ("# number of test data : %d " % numb_test)
print ("Dipole L2err : %e eV/A" % l2f)
detail_file = args.detail_file
if detail_file is not None :
pe = np.concatenate((np.reshape(test_data["dipole"][:numb_test], [-1,3]),
np.reshape(dipole, [-1,3])),
axis = 1)
np.savetxt(detail_file+".out", pe,
header = 'data_x data_y data_z pred_x pred_y pred_z')
def test_wfc (args) :
if args.rand_seed is not None :
np.random.seed(args.rand_seed % (2**32))
dp = DeepWFC(args.model)
data = DeepmdData(args.system, args.set_prefix, shuffle_test = args.shuffle_test)
data.add('wfc', 12, atomic=True, must=True, high_prec=False, type_sel = dp.get_sel_type())
test_data = data.get_test ()
numb_test = args.numb_test
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = min(nframes, numb_test)
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
box = test_data["box"][:numb_test]
atype = test_data["type"][0]
wfc = dp.eval(coord, box, atype)
wfc = wfc.reshape([numb_test,-1])
l2f = (l2err (wfc - test_data["wfc"] [:numb_test]))
print ("# number of test data : %d " % numb_test)
print ("WFC L2err : %e eV/A" % l2f)
detail_file = args.detail_file
if detail_file is not None :
pe = np.concatenate((np.reshape(test_data["wfc"][:numb_test], [-1,12]),
np.reshape(wfc, [-1,12])),
axis = 1)
np.savetxt(detail_file+".out", pe,
header = 'ref_wfc(12 dofs) predicted_wfc(12 dofs)')
def test_avg(self):
dd = DeepmdData(self.data_name)\
.add('test_frame', 5, atomic=False, must=True)
favg = dd.avg('test_frame')
fcmp = np.average(np.concatenate(
(self.test_frame, self.test_frame_bar), axis=0),
axis=0)
for ii in range(favg.size):
self.assertAlmostEqual((favg[ii]), (fcmp[ii]), places=places)
def test_reduce(self):
dd = DeepmdData(self.data_name)\
.add('test_atomic', 7, atomic=True, must=True)
dd.reduce('redu', 'test_atomic')
data = dd._load_set(os.path.join(self.data_name, 'set.foo'))
self.assertEqual(data['find_test_atomic'], 1)
self._comp_np_mat2(data['test_atomic'], self.test_atomic)
self.assertEqual(data['find_redu'], 1)
self._comp_np_mat2(data['redu'], self.redu_atomic)
def test_load_set_2(self):
dd = DeepmdData(self.data_name)\
.add('value_2', 1, atomic=True, must=True, type_sel = [1])
data = dd._load_set(os.path.join(self.data_name, 'set.foo'))
self.assertEqual(data['value_2'].shape, (self.nframes, 4))
for ii in range(self.nframes):
for jj in range(4):
self.assertAlmostEqual(data['value_2'][ii][jj],
self.value_2[ii][jj])
def test_reduce_null(self):
dd = DeepmdData(self.data_name)\
.add('test_atomic_1', 7, atomic=True, must=False)
dd.reduce('redu', 'test_atomic_1')
data = dd._load_set(os.path.join(self.data_name, 'set.foo'))
self.assertEqual(data['find_test_atomic_1'], 0)
self._comp_np_mat2(data['test_atomic_1'],
np.zeros([self.nframes, self.natoms * 7]))
self.assertEqual(data['find_redu'], 0)
self._comp_np_mat2(data['redu'], np.zeros([self.nframes, 7]))
def test_shuffle(self):
dd = DeepmdData(self.data_name)\
.add('test_atomic', 7, atomic=True, must=True)\
.add('test_frame', 5, atomic=False, must=True)
data = dd._load_set(os.path.join(self.data_name, 'set.foo'))
data_bk = copy.deepcopy(data)
data, idx = dd._shuffle_data(data)
self._comp_np_mat2(data_bk['coord'][idx, :], data['coord'])
self._comp_np_mat2(data_bk['test_atomic'][idx, :], data['test_atomic'])
self._comp_np_mat2(data_bk['test_frame'][idx, :], data['test_frame'])
def test_init_type_map(self):
dd = DeepmdData(self.data_name, type_map=['bar', 'foo', 'tar'])
self.assertEqual(dd.idx_map[0], 0)
self.assertEqual(dd.idx_map[1], 1)
self.assertEqual(dd.atom_type[0], 0)
self.assertEqual(dd.atom_type[1], 1)
self.assertEqual(dd.type_map, ['bar', 'foo'])
def test_init(self):
dd = DeepmdData(self.data_name)
self.assertEqual(dd.idx_map[0], 1)
self.assertEqual(dd.idx_map[1], 0)
self.assertEqual(dd.type_map, ['foo', 'bar'])
self.assertEqual(dd.test_dir, 'test_data/set.tar')
self.assertEqual(dd.train_dirs,
['test_data/set.bar', 'test_data/set.foo'])
def test_polar(dp, args, global_polar=False):
if args.rand_seed is not None:
np.random.seed(args.rand_seed % (2**32))
data = DeepmdData(args.system,
args.set_prefix,
shuffle_test=args.shuffle_test)
if not global_polar:
data.add('polarizability',
9,
atomic=True,
must=True,
high_prec=False,
type_sel=dp.get_sel_type())
else:
data.add('polarizability',
9,
atomic=False,
must=True,
high_prec=False,
type_sel=dp.get_sel_type())
test_data = data.get_test()
numb_test = args.numb_test
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = min(nframes, numb_test)
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
box = test_data["box"][:numb_test]
atype = test_data["type"][0]
polar = dp.eval(coord, box, atype)
sel_type = dp.get_sel_type()
sel_natoms = 0
for ii in sel_type:
sel_natoms += sum(atype == ii)
polar = polar.reshape([numb_test, -1])
l2f = (l2err(polar - test_data["polarizability"][:numb_test]))
l2fs = l2f / np.sqrt(sel_natoms)
l2fa = l2f / sel_natoms
print("# number of test data : %d " % numb_test)
print("Polarizability L2err : %e eV/A" % l2f)
if global_polar:
print("Polarizability L2err/sqrtN : %e eV/A" % l2fs)
print("Polarizability L2err/N : %e eV/A" % l2fa)
detail_file = args.detail_file
if detail_file is not None:
pe = np.concatenate(
(np.reshape(test_data["polarizability"][:numb_test],
[-1, 9]), np.reshape(polar, [-1, 9])),
axis=1)
np.savetxt(
detail_file + ".out",
pe,
header=
'data_pxx data_pxy data_pxz data_pyx data_pyy data_pyz data_pzx data_pzy data_pzz pred_pxx pred_pxy pred_pxz pred_pyx pred_pyy pred_pyz pred_pzx pred_pzy pred_pzz'
)
return [l2f], [polar.size]
def test_get_batch(self):
dd = DeepmdData(self.data_name)
data = dd.get_batch(5)
self._comp_np_mat2(np.sort(data['coord'], axis=0),
np.sort(self.coord_bar, axis=0))
data = dd.get_batch(5)
self._comp_np_mat2(np.sort(data['coord'], axis=0),
np.sort(self.coord, axis=0))
data = dd.get_batch(5)
self._comp_np_mat2(np.sort(data['coord'], axis=0),
np.sort(self.coord_bar, axis=0))
data = dd.get_batch(5)
self._comp_np_mat2(np.sort(data['coord'], axis=0),
np.sort(self.coord, axis=0))
def test_load_set(self):
dd = DeepmdData(self.data_name)\
.add('test_atomic', 7, atomic=True, must=True)\
.add('test_frame', 5, atomic=False, must=True)\
.add('test_null', 2, atomic=True, must=False)
data = dd._load_set(os.path.join(self.data_name, 'set.foo'))
nframes = data['coord'].shape[0]
self.assertEqual(dd.get_numb_set(), 2)
self.assertEqual(dd.get_type_map(), ['foo', 'bar'])
self.assertEqual(dd.get_natoms(), 2)
self.assertEqual(list(dd.get_natoms_vec(3)), [2, 2, 1, 1, 0])
for ii in range(nframes):
self.assertEqual(data['type'][ii][0], 0)
self.assertEqual(data['type'][ii][1], 1)
self.assertEqual(data['find_coord'], 1)
self._comp_np_mat2(data['coord'], self.coord)
self.assertEqual(data['find_test_atomic'], 1)
self._comp_np_mat2(data['test_atomic'], self.test_atomic)
self.assertEqual(data['find_test_frame'], 1)
self._comp_np_mat2(data['test_frame'], self.test_frame)
self.assertEqual(data['find_test_null'], 0)
self._comp_np_mat2(data['test_null'], self.test_null)
def test_load_null_must(self):
dd = DeepmdData(self.data_name)\
.add('test_atomic_1', 7, atomic=True, must=True)
with self.assertRaises(RuntimeError):
data = dd._load_set(os.path.join(self.data_name, 'set.foo'))
def __init__(self,
systems,
batch_size,
test_size,
rcut,
set_prefix='set',
shuffle_test=True,
type_map=None,
modifier=None):
# init data
self.rcut = rcut
self.system_dirs = systems
self.nsystems = len(self.system_dirs)
self.data_systems = []
for ii in self.system_dirs:
self.data_systems.append(
DeepmdData(ii,
set_prefix=set_prefix,
shuffle_test=shuffle_test,
type_map=type_map,
modifier=modifier))
# batch size
self.batch_size = batch_size
if isinstance(self.batch_size, int):
self.batch_size = self.batch_size * np.ones(self.nsystems,
dtype=int)
elif isinstance(self.batch_size, str):
words = self.batch_size.split(':')
if 'auto' == words[0]:
rule = 32
if len(words) == 2:
rule = int(words[1])
else:
raise RuntimeError('unknown batch_size rule ' + words[0])
self.batch_size = self._make_auto_bs(rule)
elif isinstance(self.batch_size, list):
pass
else:
raise RuntimeError('invalid batch_size')
assert (isinstance(self.batch_size, (list, np.ndarray)))
assert (len(self.batch_size) == self.nsystems)
# natoms, nbatches
ntypes = []
for ii in self.data_systems:
ntypes.append(ii.get_ntypes())
self.sys_ntypes = max(ntypes)
self.natoms = []
self.natoms_vec = []
self.nbatches = []
type_map_list = []
for ii in range(self.nsystems):
self.natoms.append(self.data_systems[ii].get_natoms())
self.natoms_vec.append(self.data_systems[ii].get_natoms_vec(
self.sys_ntypes).astype(int))
self.nbatches.append(self.data_systems[ii].get_sys_numb_batch(
self.batch_size[ii]))
type_map_list.append(self.data_systems[ii].get_type_map())
self.type_map = self._check_type_map_consistency(type_map_list)
# prob of batch, init pick idx
self.prob_nbatches = [float(i)
for i in self.nbatches] / np.sum(self.nbatches)
self.pick_idx = 0
# check batch and test size
for ii in range(self.nsystems):
chk_ret = self.data_systems[ii].check_batch_size(
self.batch_size[ii])
if chk_ret is not None:
warnings.warn("system %s required batch size is larger than the size of the dataset %s (%d > %d)" % \
(self.system_dirs[ii], chk_ret[0], self.batch_size[ii], chk_ret[1]))
chk_ret = self.data_systems[ii].check_test_size(test_size)
if chk_ret is not None:
warnings.warn("system %s required test size is larger than the size of the dataset %s (%d > %d)" % \
(self.system_dirs[ii], chk_ret[0], test_size, chk_ret[1]))
def test_get_nbatch(self):
dd = DeepmdData(self.data_name)
nb = dd.get_numb_batch(1, 0)
self.assertEqual(nb, 5)
nb = dd.get_numb_batch(2, 0)
self.assertEqual(nb, 2)
def test_check_test_size(self):
dd = DeepmdData(self.data_name)
ret = dd.check_test_size(10)
self.assertEqual(ret, (os.path.join(self.data_name, 'set.tar'), 2))
ret = dd.check_test_size(2)
self.assertEqual(ret, None)
def test_check_batch_size(self):
dd = DeepmdData(self.data_name)
ret = dd.check_batch_size(10)
self.assertEqual(ret, (os.path.join(self.data_name, 'set.bar'), 5))
ret = dd.check_batch_size(5)
self.assertEqual(ret, None)
sum_err[ii] += ee * ee * ss
sum_siz[ii] += ss
for ii in range(nitems):
sum_err[ii] = np.sqrt(sum_err[ii] / sum_siz[ii])
return sum_err
def test_ener (dp, args) :
if args.rand_seed is not None :
np.random.seed(args.rand_seed % (2**32))
<<<<<<< HEAD
dp = DeepPot(args.model)
=======
>>>>>>> 93aa90768089e265465f93fb3d9f99b494d56b09
data = DeepmdData(args.system, args.set_prefix, shuffle_test = args.shuffle_test, type_map = dp.get_type_map())
data.add('energy', 1, atomic=False, must=False, high_prec=True)
data.add('force', 3, atomic=True, must=False, high_prec=False)
data.add('virial', 9, atomic=False, must=False, high_prec=False)
if dp.get_dim_fparam() > 0:
data.add('fparam', dp.get_dim_fparam(), atomic=False, must=True, high_prec=False)
if dp.get_dim_aparam() > 0:
data.add('aparam', dp.get_dim_aparam(), atomic=True, must=True, high_prec=False)
test_data = data.get_test ()
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = args.numb_test
numb_test = min(nframes, numb_test)
<<<<<<< HEAD
=======
def __init__(self,
systems,
batch_size,
test_size,
rcut,
set_prefix='set',
shuffle_test=True,
type_map=None,
modifier=None):
# init data
self.rcut = rcut
self.system_dirs = systems
self.nsystems = len(self.system_dirs)
self.data_systems = []
for ii in self.system_dirs:
self.data_systems.append(
DeepmdData(ii,
set_prefix=set_prefix,
shuffle_test=shuffle_test,
type_map=type_map,
modifier=modifier))
# batch size
self.batch_size = batch_size
if isinstance(self.batch_size, int):
self.batch_size = self.batch_size * np.ones(self.nsystems,
dtype=int)
elif isinstance(self.batch_size, str):
words = self.batch_size.split(':')
if 'auto' == words[0]:
rule = 32
if len(words) == 2:
rule = int(words[1])
else:
raise RuntimeError('unknown batch_size rule ' + words[0])
self.batch_size = self._make_auto_bs(rule)
elif isinstance(self.batch_size, list):
pass
else:
raise RuntimeError('invalid batch_size')
assert (isinstance(self.batch_size, (list, np.ndarray)))
assert (len(self.batch_size) == self.nsystems)
# natoms, nbatches
ntypes = []
for ii in self.data_systems:
ntypes.append(ii.get_ntypes())
self.sys_ntypes = max(ntypes)
self.natoms = []
self.natoms_vec = []
self.nbatches = []
type_map_list = []
for ii in range(self.nsystems):
self.natoms.append(self.data_systems[ii].get_natoms())
self.natoms_vec.append(self.data_systems[ii].get_natoms_vec(
self.sys_ntypes).astype(int))
self.nbatches.append(self.data_systems[ii].get_sys_numb_batch(
self.batch_size[ii]))
type_map_list.append(self.data_systems[ii].get_type_map())
self.type_map = self._check_type_map_consistency(type_map_list)
# ! altered by Marián Rynik
# test size
# now test size can be set as a percentage of systems data or test size
# can be set for each system individualy in the same manner as batch
# size. This enables one to use systems with diverse number of
# structures and different number of atoms.
self.test_size = test_size
if isinstance(self.test_size, int):
self.test_size = self.test_size * np.ones(self.nsystems, dtype=int)
elif isinstance(self.test_size, str):
words = self.test_size.split('%')
try:
percent = int(words[0])
except ValueError:
raise RuntimeError('unknown test_size rule ' + words[0])
self.test_size = self._make_auto_ts(percent)
elif isinstance(self.test_size, list):
pass
else:
raise RuntimeError('invalid test_size')
assert (isinstance(self.test_size, (list, np.ndarray)))
assert (len(self.test_size) == self.nsystems)
# prob of batch, init pick idx
self.prob_nbatches = [float(i)
for i in self.nbatches] / np.sum(self.nbatches)
self.pick_idx = 0
# check batch and test size
for ii in range(self.nsystems):
chk_ret = self.data_systems[ii].check_batch_size(
self.batch_size[ii])
if chk_ret is not None:
warnings.warn("system %s required batch size is larger than the size of the dataset %s (%d > %d)" % \
(self.system_dirs[ii], chk_ret[0], self.batch_size[ii], chk_ret[1]))
chk_ret = self.data_systems[ii].check_test_size(self.test_size[ii])
if chk_ret is not None:
warnings.warn("system %s required test size is larger than the size of the dataset %s (%d > %d)" % \
(self.system_dirs[ii], chk_ret[0], self.test_size[ii], chk_ret[1]))
def test_ener(args):
if args.rand_seed is not None:
np.random.seed(args.rand_seed % (2**32))
dp = DeepPot(args.model)
data = DeepmdData(args.system,
args.set_prefix,
shuffle_test=args.shuffle_test,
type_map=dp.get_type_map())
data.add('energy', 1, atomic=False, must=False, high_prec=True)
data.add('force', 3, atomic=True, must=False, high_prec=False)
data.add('virial', 9, atomic=False, must=False, high_prec=False)
if dp.get_dim_fparam() > 0:
data.add('fparam',
dp.get_dim_fparam(),
atomic=False,
must=True,
high_prec=False)
if dp.get_dim_aparam() > 0:
data.add('aparam',
dp.get_dim_aparam(),
atomic=True,
must=True,
high_prec=False)
test_data = data.get_test()
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = args.numb_test
numb_test = min(nframes, numb_test)
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
box = test_data["box"][:numb_test]
atype = test_data["type"][0]
if dp.get_dim_fparam() > 0:
fparam = test_data["fparam"][:numb_test]
else:
fparam = None
if dp.get_dim_aparam() > 0:
aparam = test_data["aparam"][:numb_test]
else:
aparam = None
energy, force, virial, ae, av = dp.eval(coord,
box,
atype,
fparam=fparam,
aparam=aparam,
atomic=True)
energy = energy.reshape([numb_test, 1])
force = force.reshape([numb_test, -1])
virial = virial.reshape([numb_test, 9])
ae = ae.reshape([numb_test, -1])
av = av.reshape([numb_test, -1])
l2e = (l2err(energy - test_data["energy"][:numb_test].reshape([-1, 1])))
l2f = (l2err(force - test_data["force"][:numb_test]))
l2v = (l2err(virial - test_data["virial"][:numb_test]))
l2ea = l2e / natoms
l2va = l2v / natoms
# print ("# energies: %s" % energy)
print("# number of test data : %d " % numb_test)
print("Energy L2err : %e eV" % l2e)
print("Energy L2err/Natoms : %e eV" % l2ea)
print("Force L2err : %e eV/A" % l2f)
print("Virial L2err : %e eV" % l2v)
print("Virial L2err/Natoms : %e eV" % l2va)
detail_file = args.detail_file
if detail_file is not None:
pe = np.concatenate((np.reshape(test_data["energy"][:numb_test],
[-1, 1]), np.reshape(energy, [-1, 1])),
axis=1)
np.savetxt(detail_file + ".e.out", pe, header='data_e pred_e')
pf = np.concatenate((np.reshape(test_data["force"][:numb_test],
[-1, 3]), np.reshape(force, [-1, 3])),
axis=1)
np.savetxt(detail_file + ".f.out",
pf,
header='data_fx data_fy data_fz pred_fx pred_fy pred_fz')
pv = np.concatenate((np.reshape(test_data["virial"][:numb_test],
[-1, 9]), np.reshape(virial, [-1, 9])),
axis=1)
np.savetxt(
detail_file + ".v.out",
pv,
header=
'data_vxx data_vxy data_vxz data_vyx data_vyy data_vyz data_vzx data_vzy data_vzz pred_vxx pred_vxy pred_vxz pred_vyx pred_vyy pred_vyz pred_vzx pred_vzy pred_vzz'
)
def test_ener(dp,
system,
set_prefix='set',
numb_test=100000,
shuffle_test=False,
rand_seed=None,
detail_file='detail'):
if rand_seed is not None:
np.random.seed(rand_seed % (2**32))
data = DeepmdData(system,
set_prefix,
shuffle_test=shuffle_test,
type_map=dp.get_type_map())
data.add('energy', 1, atomic=False, must=False, high_prec=True)
data.add('force', 3, atomic=True, must=False, high_prec=False)
data.add('virial', 9, atomic=False, must=False, high_prec=False)
if dp.get_dim_fparam() > 0:
data.add('fparam',
dp.get_dim_fparam(),
atomic=False,
must=True,
high_prec=False)
if dp.get_dim_aparam() > 0:
data.add('aparam',
dp.get_dim_aparam(),
atomic=True,
must=True,
high_prec=False)
test_data = data.get_test()
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = min(nframes, numb_test)
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
box = test_data["box"][:numb_test]
atype = test_data["type"][0]
if dp.get_dim_fparam() > 0:
fparam = test_data["fparam"][:numb_test]
else:
fparam = None
if dp.get_dim_aparam() > 0:
aparam = test_data["aparam"][:numb_test]
else:
aparam = None
energy, force, virial, ae, av = dp.eval(coord,
box,
atype,
fparam=fparam,
aparam=aparam,
atomic=True)
energy = energy.reshape([numb_test, 1])
force = force.reshape([numb_test, -1])
virial = virial.reshape([numb_test, 9])
ae = ae.reshape([numb_test, -1])
av = av.reshape([numb_test, -1])
l2e = (l2err(energy - test_data["energy"][:numb_test].reshape([-1, 1])))
l2f = (l2err(force - test_data["force"][:numb_test]))
l2v = (l2err(virial - test_data["virial"][:numb_test]))
l2ea = l2e / natoms
l2va = l2v / natoms
if detail_file is not None:
pe = np.concatenate(
(np.reshape(test_data["energy"][:numb_test] / natoms,
[-1, 1]), np.reshape(energy / natoms, [-1, 1])),
axis=1)
np.savetxt(detail_file + ".e.out", pe, header='data_e pred_e')
pf = np.concatenate((np.reshape(test_data["force"][:numb_test],
[-1, 3]), np.reshape(force, [-1, 3])),
axis=1)
np.savetxt(detail_file + ".f.out",
pf,
header='data_fx data_fy data_fz pred_fx pred_fy pred_fz')
pv = np.concatenate((np.reshape(test_data["virial"][:numb_test],
[-1, 9]), np.reshape(virial, [-1, 9])),
axis=1)
np.savetxt(
detail_file + ".v.out",
pv,
header=
'data_vxx data_vxy data_vxz data_vyx data_vyy data_vyz data_vzx data_vzy data_vzz pred_vxx pred_vxy pred_vxz pred_vyx pred_vyy pred_vyz pred_vzx pred_vzy pred_vzz'
)
return numb_test, l2e, l2ea, l2f, l2v, l2va
