def test_ener_shift(self):
np.random.seed(0)
data = DeepmdDataSystem(['system_0', 'system_1'], 5, 10, 1.0)
data.add('energy', 1, must=True)
ener_shift0 = data.compute_energy_shift(rcond=1)
all_stat = make_all_stat(data, 4, merge_sys=False)
ener_shift1 = EnerFitting._compute_output_stats(all_stat, rcond=1)
for ii in range(len(ener_shift0)):
self.assertAlmostEqual(ener_shift0[ii], ener_shift1[ii])
def test_ntypes(self):
batch_size = 3
test_size = 2
ds = DeepmdDataSystem(self.sys_name, batch_size, test_size, 2.0)
ds.add('test', self.test_ndof, atomic=True, must=True)
ds.add('null', self.test_ndof, atomic=True, must=False)
self.assertEqual(ds.get_ntypes(), 3)
self.assertEqual(ds.get_nbatches(), [2, 4, 3, 2])
self.assertEqual(ds.get_nsystems(), self.nsys)
self.assertEqual(list(ds.get_batch_size()), [batch_size] * 4)
def test_get_test(self):
batch_size = 3
test_size = 2
ds = DeepmdDataSystem(self.sys_name, batch_size, test_size, 2.0)
ds.add('test', self.test_ndof, atomic=True, must=True)
ds.add('null', self.test_ndof, atomic=True, must=False)
sys_idx = 0
data = ds.get_test(sys_idx=sys_idx)
self.assertEqual(list(data['type'][0]),
list(np.sort(self.atom_type[sys_idx])))
self._in_array(np.load('sys_0/set.002/coord.npy'),
ds.get_sys(sys_idx).idx_map, 3, data['coord'])
self._in_array(np.load('sys_0/set.002/test.npy'),
ds.get_sys(sys_idx).idx_map, self.test_ndof,
data['test'])
self.assertAlmostEqual(
np.linalg.norm(
np.zeros([
self.nframes[sys_idx] + 2, self.natoms[sys_idx] *
self.test_ndof
]) - data['null']), 0.0)
sys_idx = 2
data = ds.get_test(sys_idx=sys_idx)
self.assertEqual(list(data['type'][0]),
list(np.sort(self.atom_type[sys_idx])))
self._in_array(np.load('sys_2/set.002/coord.npy'),
ds.get_sys(sys_idx).idx_map, 3, data['coord'])
self._in_array(np.load('sys_2/set.002/test.npy'),
ds.get_sys(sys_idx).idx_map, self.test_ndof,
data['test'])
self.assertAlmostEqual(
np.linalg.norm(
np.zeros([
self.nframes[sys_idx] + 2, self.natoms[sys_idx] *
self.test_ndof
]) - data['null']), 0.0)
def test_merge_all_stat(self):
np.random.seed(0)
data0 = DeepmdDataSystem(['system_0', 'system_1'], 5, 10, 1.0)
data0.add('energy', 1, must=True)
np.random.seed(0)
data1 = DeepmdDataSystem(['system_0', 'system_1'], 5, 10, 1.0)
data1.add('force', 3, atomic=True, must=True)
np.random.seed(0)
data2 = DeepmdDataSystem(['system_0', 'system_1'], 5, 10, 1.0)
data2.add('force', 3, atomic=True, must=True)
np.random.seed(0)
all_stat_0 = make_all_stat(data0, 10, merge_sys=False)
np.random.seed(0)
all_stat_1 = make_all_stat(data1, 10, merge_sys=True)
all_stat_2 = merge_sys_stat(all_stat_0)
np.random.seed(0)
all_stat_3 = _make_all_stat_ref(data2, 10)
####################################
# only check if the energy is concatenated correctly
####################################
dd = 'energy'
# if 'find_' in dd: continue
# if 'natoms_vec' in dd: continue
# if 'default_mesh' in dd: continue
# print(all_stat_2[dd])
# print(dd, all_stat_1[dd])
d1 = np.array(all_stat_1[dd])
d2 = np.array(all_stat_2[dd])
d3 = np.array(all_stat_3[dd])
# print(dd)
# print(d1.shape)
# print(d2.shape)
# self.assertEqual(all_stat_2[dd], all_stat_1[dd])
self._comp_data(d1, d2)
self._comp_data(d1, d3)
