def test_lammps(otf_object, structure):
atom_types = [1, 2]
atom_masses = [108, 127]
atom_species = [1, 2] * 27
# create data file
lammps_location = 'AgI_Molten_15.txt'
data_file_name = 'tmp.data'
data_text = lammps_calculator.lammps_dat(structure, atom_types,
atom_masses, atom_species)
lammps_calculator.write_text(data_file_name, data_text)
# create lammps input
style_string = 'mgpf' # use mgpf for the new lammps executable pair_style,
# because mgp is the energy version
coeff_string = '* * {} Ag I yes yes'.format(lammps_location)
lammps_executable = '$lmp'
dump_file_name = 'tmp.dump'
input_file_name = 'tmp.in'
output_file_name = 'tmp.out'
input_text = \
lammps_calculator.generic_lammps_input(data_file_name, style_string,
coeff_string, dump_file_name)
lammps_calculator.write_text(input_file_name, input_text)
lammps_calculator.run_lammps(lammps_executable, input_file_name,
output_file_name)
lammps_forces = lammps_calculator.lammps_parser(dump_file_name)
# check that lammps agrees with gp to within 1 meV/A
forces = otf_object.force_list[-1]
assert(np.abs(lammps_forces[0, 1] - forces[0, 1]) < 1e-3)
os.system('rm tmp.in tmp.out tmp.dump tmp.data'
' log.lammps')
os.system('rm '+lammps_location)
os.system('rm grid3*.npy')
os.system('rm -r kv3')
# lmp coef file is automatically written now every time MGP is constructed
# create test structure
species = otf_object.gp_species_list[-1]
positions = otf_object.position_list[-1]
forces = otf_object.force_list[-1]
otf_cell = otf_object.header['cell']
structure = struc.Structure(otf_cell, species, positions)
atom_types = [1, 2]
atom_masses = [108, 127]
atom_species = [1, 2] * 27
# create data file
data_file_name = 'tmp.data'
data_text = lammps_calculator.lammps_dat(structure, atom_types, atom_masses,
atom_species)
lammps_calculator.write_text(data_file_name, data_text)
# create lammps input
style_string = 'mgp'
coeff_string = '* * {} Ag I yes yes'.format(lammps_location)
lammps_executable = '$lmp'
dump_file_name = 'tmp.dump'
input_file_name = 'tmp.in'
output_file_name = 'tmp.out'
input_text = \
lammps_calculator.generic_lammps_input(data_file_name, style_string,
coeff_string, dump_file_name)
lammps_calculator.write_text(input_file_name, input_text)
lammps_calculator.run_lammps(lammps_executable, input_file_name,
def test_lmp_predict(all_gp, all_mgp, bodies, multihyps):
"""
test the lammps implementation
"""
for f in os.listdir("./"):
if f in [
f'tmp{bodies}{multihyps}in', f'tmp{bodies}{multihyps}out',
f'tmp{bodies}{multihyps}dump', f'tmp{bodies}{multihyps}data',
'log.lammps'
]:
os.remove(f)
clean()
mgp_model = all_mgp[f'{bodies}{multihyps}']
gp_model = all_gp[f'{bodies}{multihyps}']
lammps_location = mgp_model.lmp_file_name
# lmp file is automatically written now every time MGP is constructed
mgp_model.write_lmp_file(lammps_location)
# create test structure
cell = np.eye(3)
nenv = 10
unique_species = gp_model.training_data[0].species
cutoffs = gp_model.cutoffs
struc_test, f = get_random_structure(cell, unique_species, nenv)
atom_num = 1
test_envi = env.AtomicEnvironment(struc_test, atom_num, cutoffs)
atom_types = [1, 2]
atom_masses = [108, 127]
atom_species = struc_test.coded_species
# create data file
data_file_name = f'tmp{bodies}{multihyps}.data'
data_text = lammps_calculator.lammps_dat(struc_test, atom_types,
atom_masses, atom_species)
lammps_calculator.write_text(data_file_name, data_text)
# create lammps input
by = 'no'
ty = 'no'
if '2' in bodies:
by = 'yes'
if '3' in bodies:
ty = 'yes'
style_string = 'mgp'
coeff_string = f'* * {lammps_location} H He {by} {ty}'
lammps_executable = os.environ.get('lmp')
dump_file_name = f'tmp{bodies}{multihyps}.dump'
input_file_name = f'tmp{bodies}{multihyps}.in'
output_file_name = f'tmp{bodies}{multihyps}.out'
input_text = \
lammps_calculator.generic_lammps_input(data_file_name, style_string,
coeff_string, dump_file_name,
newton=True)
lammps_calculator.write_text(input_file_name, input_text)
lammps_calculator.run_lammps(lammps_executable, input_file_name,
output_file_name)
lammps_forces = lammps_calculator.lammps_parser(dump_file_name)
mgp_forces = mgp_model.predict(test_envi, mean_only=True)
# check that lammps agrees with gp to within 1 meV/A
for i in range(3):
assert (np.abs(lammps_forces[atom_num, i] - mgp_forces[0][i]) < 1e-3)
for f in os.listdir("./"):
if f in [
f'tmp{bodies}{multihyps}in', f'tmp{bodies}{multihyps}out',
f'tmp{bodies}{multihyps}dump', f'tmp{bodies}{multihyps}data',
'log.lammps', lammps_location
]:
os.remove(f)
clean()
def test_parse_header():
# -------------------------------------------------------------------------
# reconstruct gp model from otf snippet
# -------------------------------------------------------------------------
file_name = 'test_files/AgI_snippet.out'
hyp_no = 2
# parse otf output
otf_object = otf_parser.OtfAnalysis(file_name)
otf_cell = otf_object.header['cell']
# reconstruct gp model
kernel = mc_simple.two_plus_three_body_mc
kernel_grad = mc_simple.two_plus_three_body_mc_grad
gp_model = otf_object.make_gp(kernel=kernel,
kernel_grad=kernel_grad,
hyp_no=hyp_no)
gp_model.par = True
gp_model.hyp_labels = ['sig2', 'ls2', 'sig3', 'ls3', 'noise']
# -------------------------------------------------------------------------
# check gp reconstruction
# -------------------------------------------------------------------------
# create test structure
species = np.array([47, 53] * 27)
positions = otf_object.position_list[-1]
forces = otf_object.force_list[-1]
structure = struc.Structure(otf_cell, species, positions)
atom = 0
environ = env.AtomicEnvironment(structure, atom, gp_model.cutoffs)
force_comp = 2
pred, _ = gp_model.predict(environ, force_comp)
assert (np.isclose(pred, forces[0][1]))
# -------------------------------------------------------------------------
# map the potential
# -------------------------------------------------------------------------
file_name = 'AgI.gp'
grid_num_2 = 64
grid_num_3 = 15
lower_cut = 2.
two_cut = 7.
three_cut = 5.
lammps_location = 'AgI_Molten_15.txt'
# set struc params. cell and masses arbitrary?
mapped_cell = np.eye(3) * 100
struc_params = {
'species': [47, 53],
'cube_lat': mapped_cell,
'mass_dict': {
'0': 27,
'1': 16
}
}
# grid parameters
grid_params = {
'bounds_2': [[lower_cut], [two_cut]],
'bounds_3': [[lower_cut, lower_cut, 0], [three_cut, three_cut, np.pi]],
'grid_num_2': grid_num_2,
'grid_num_3': [grid_num_3, grid_num_3, grid_num_3],
'svd_rank_2': 64,
'svd_rank_3': 90,
'bodies': [2, 3],
'load_grid': None,
'update': True
}
mgp_model = MappedGaussianProcess(gp_model,
grid_params,
struc_params,
mean_only=True,
lmp_file_name=lammps_location)
# -------------------------------------------------------------------------
# test the mapped potential
# -------------------------------------------------------------------------
gp_pred_x = gp_model.predict(environ, 1)
mgp_pred = mgp_model.predict(environ, mean_only=True)
# check mgp is within 1 meV/A of the gp
assert (np.abs(mgp_pred[0][0] - gp_pred_x[0]) < 1e-3)
# -------------------------------------------------------------------------
# check lammps potential
# -------------------------------------------------------------------------
# mgp_model.write_lmp_file(lammps_location)
# lmp file is automatically written now every time MGP is constructed
# create test structure
species = otf_object.gp_species_list[-1]
positions = otf_object.position_list[-1]
forces = otf_object.force_list[-1]
structure = struc.Structure(otf_cell, species, positions)
atom_types = [1, 2]
atom_masses = [108, 127]
atom_species = [1, 2] * 27
# create data file
data_file_name = 'tmp.data'
data_text = lammps_calculator.lammps_dat(structure, atom_types,
atom_masses, atom_species)
lammps_calculator.write_text(data_file_name, data_text)
# create lammps input
style_string = 'mgp' #TODO: change the name of lammps
coeff_string = '* * {} 47 53 yes yes'.format(lammps_location)
lammps_executable = '$lmp'
dump_file_name = 'tmp.dump'
input_file_name = 'tmp.in'
output_file_name = 'tmp.out'
input_text = \
lammps_calculator.generic_lammps_input(data_file_name, style_string,
coeff_string, dump_file_name)
lammps_calculator.write_text(input_file_name, input_text)
lammps_calculator.run_lammps(lammps_executable, input_file_name,
output_file_name)
lammps_forces = lammps_calculator.lammps_parser(dump_file_name)
# check that lammps agrees with gp to within 1 meV/A
assert (np.abs(lammps_forces[0, 1] - forces[0, 1]) < 1e-3)
os.system('rm tmp.in tmp.out tmp.dump tmp.data AgI_Molten_15.txt'
' log.lammps')
os.system('rm grid3*.npy')
os.system('rm -r kv3')
def test_lammps():
sgp_py.write_mapping_coefficients("beta.txt", "A", 0)
new_kern = sgp_py.write_varmap_coefficients("beta_var.txt", "B", 0) # here the new kernel needs to be returned, otherwise the kernel won't be found in the current module
assert sgp_py.sparse_gp.sparse_indices[0] == sgp_py.sgp_var.sparse_indices[0], \
"the sparse_gp and sgp_var don't have the same training data"
for s in range(len(atom_types)):
org_desc = sgp_py.sparse_gp.sparse_descriptors[0].descriptors[s]
new_desc = sgp_py.sgp_var.sparse_descriptors[0].descriptors[s]
if not np.allclose(org_desc, new_desc): # the atomic order might change
assert np.allclose(org_desc.shape, new_desc.shape)
for i in range(org_desc.shape[0]):
flag = False
for j in range(new_desc.shape[0]): # seek in new_desc for matching of org_desc
if np.allclose(org_desc[i], new_desc[j]):
flag = True
break
assert flag, "the sparse_gp and sgp_var don't have the same descriptors"
# set up input and data files
data_file_name = "tmp.data"
lammps_location = "beta.txt"
style_string = "flare"
coeff_string = "* * {}".format(lammps_location)
lammps_executable = os.environ.get("lmp")
dump_file_name = "tmp.dump"
input_file_name = "tmp.in"
output_file_name = "tmp.out"
newton = True
# write data file
data_text = lammps_calculator.lammps_dat(
test_structure, atom_types, atom_masses, species
)
lammps_calculator.write_text(data_file_name, data_text)
# write input file
input_text = lammps_calculator.generic_lammps_input(
data_file_name, style_string, coeff_string, dump_file_name, newton=newton,
std_string="beta_var.txt", std_style="flare_pp",
)
lammps_calculator.write_text(input_file_name, input_text)
# run lammps
lammps_calculator.run_lammps(lammps_executable, input_file_name, output_file_name)
# read output
lmp_dump = read(dump_file_name, format="lammps-dump-text")
lmp_forces = lmp_dump.get_forces()
# lmp_var_1 = lmp_dump.get_array("c_std[1]")
# lmp_var_2 = lmp_dump.get_array("c_std[2]")
# lmp_var_3 = lmp_dump.get_array("c_std[3]")
# lmp_var = np.hstack([lmp_var_1, lmp_var_2, lmp_var_3])
lmp_std = lmp_dump.get_array("c_std")
lmp_var = lmp_std ** 2
print(lmp_var)
# compare with sgp_py prediction
assert len(sgp_py.training_data) > 0
# Convert coded species to 0, 1, 2, etc.
coded_species = []
for spec in test_structure.coded_species:
coded_species.append(species_map[spec])
test_cpp_struc = Structure(
test_structure.cell,
coded_species,
test_structure.positions,
sgp_py.cutoff,
sgp_py.descriptor_calculators,
)
print("GP predicting")
sgp_py.sparse_gp.predict_DTC(test_cpp_struc)
sgp_efs = test_cpp_struc.mean_efs
sgp_forces = np.reshape(sgp_efs[1:len(sgp_efs)-6], (test_structure.nat, 3))
assert np.allclose(lmp_forces, sgp_forces)
print("GP forces match LMP forces")
sgp_py.sgp_var.predict_DTC(test_cpp_struc)
#sgp_var = np.reshape(test_cpp_struc_pow1.variance_efs[1:len(sgp_efs)-6], (test_structure.nat, 3))
sgp_var = sgp_py.sgp_var.compute_cluster_uncertainties(test_cpp_struc)
# sgp_var = test_cpp_struc.variance_efs[0]
print(sgp_var)
n_descriptors = np.shape(test_cpp_struc.descriptors[0].descriptors[0])[1]
n_species = len(atom_types)
desc_en = np.zeros((n_species, n_descriptors))
py_var = 0.0
# for s in range(n_species):
# n_struc_s = test_cpp_struc_pow1.descriptors[0].n_clusters_by_type[s];
# assert np.shape(test_cpp_struc_pow1.descriptors[0].descriptors[s])[0] == n_struc_s
# for i in range(n_descriptors):
# desc_en[s, i] = np.sum(test_cpp_struc_pow1.descriptors[0].descriptors[s][:, i] / test_cpp_struc_pow1.descriptors[0].descriptor_norms[s], axis=0)
#
print("len(varmap_coeffs)", len(sgp_py.sgp_var.varmap_coeffs))
beta_matrices = []
for s1 in range(n_species):
# for s2 in range(n_species):
beta_matr = np.reshape(sgp_py.sgp_var.varmap_coeffs[s1, :], (n_descriptors, n_descriptors))
print(s1, s1, "max", np.max(np.abs(beta_matr)), beta_matr.shape)
beta_matrices.append(beta_matr)
# py_var += desc_en[s1, :].dot(desc_en[s1, :].dot(beta_matr))
# print(py_var)
struc_desc = test_cpp_struc.descriptors[0]
n_descriptors = np.shape(struc_desc.descriptors[0])[1]
n_species = len(atom_types)
# desc = np.zeros((n_atoms, 3, n_species, n_descriptors))
print("len(varmap_coeffs)", len(sgp_py.sgp_var.varmap_coeffs))
py_var = np.zeros(n_atoms)
for s in range(n_species):
n_struc = struc_desc.n_clusters_by_type[s];
beta_matr = beta_matrices[s]
for j in range(n_struc):
norm = struc_desc.descriptor_norms[s][j]
n_neigh = struc_desc.neighbor_counts[s][j]
c_neigh = struc_desc.cumulative_neighbor_counts[s][j]
atom_index = struc_desc.atom_indices[s][j]
print("atom", atom_index, "n_neigh", n_neigh)
desc = struc_desc.descriptors[s][j]
py_var[atom_index] = desc.dot(desc.dot(beta_matr)) / norm ** 2
#py_var[atom_index] = desc.dot(desc) / norm ** 2 * sgp_py.sgp_var.kernels[0].sigma ** 2
# for k in range(n_neigh):
# ind = c_neigh + k
# neighbor_index = struc_desc.neighbor_indices[s][ind];
#
# neighbor_coord = struc_desc.neighbor_coordinates[s][ind];
# neigh_dist = np.sum((neighbor_coord - test_positions[atom_index]) ** 2)
# #print("neighbor", neighbor_index, "dist", neigh_dist)
# for comp in range(3):
# f1 = struc_desc.descriptor_force_dervs[s][3 * ind + comp] / norm
# f2 = struc_desc.descriptors[s][j] * struc_desc.descriptor_force_dots[s][3 * ind + comp] / norm ** 3
# f3 = f1 - f2
# desc[atom_index, comp, s, :] += norm #f3
# desc[neighbor_index, comp, s, :] -= norm #f3
#
#
# for i in range(n_atoms):
# for comp in range(3):
# for s1 in range(n_species):
# for s2 in range(n_species):
# beta_matr = np.reshape(sgp_py.sgp_var.varmap_coeffs[s1 * n_species + s2, :], (n_descriptors, n_descriptors))
## print(s1, s2, "max", np.max(np.abs(beta_matr)))
# py_var[i, comp] += desc[i, comp, s1].dot(desc[i, comp, s2].dot(beta_matr))
## py_var[i, comp] += np.sum(desc[i, comp, s1]) #np.sum(beta_matr[:, comp]) #np.sum(desc[i, comp, s1])
print(py_var)
def test_lmp_predict(all_gp, all_mgp, bodies, multihyps):
"""
test the lammps implementation
"""
pytest.skip()
prefix = f'{bodies}{multihyps}'
mgp_model = all_mgp[f'{bodies}{multihyps}']
gp_model = all_gp[f'{bodies}{multihyps}']
lammps_location = mgp_model.lmp_file_name
# create test structure
cell = 5 * np.eye(3)
nenv = 10
unique_species = gp_model.training_data[0].species
cutoffs = gp_model.cutoffs
struc_test, f = get_random_structure(cell, unique_species, nenv)
atom_num = 1
test_envi = env.AtomicEnvironment(struc_test,
atom_num,
cutoffs,
cutoffs_mask=gp_model.hyps_mask)
all_species = list(set(struc_test.coded_species))
atom_types = list(np.arange(len(all_species)) + 1)
atom_masses = [_Z_to_mass[spec] for spec in all_species]
atom_species = [
all_species.index(spec) + 1 for spec in struc_test.coded_species
]
specie_symbol_list = " ".join(
[_Z_to_element[spec] for spec in all_species])
# create data file
data_file_name = f'{prefix}.data'
data_text = lammps_calculator.lammps_dat(struc_test, atom_types,
atom_masses, atom_species)
lammps_calculator.write_text(data_file_name, data_text)
# create lammps input
by = 'no'
ty = 'no'
if '2' in bodies:
by = 'yes'
if '3' in bodies:
ty = 'yes'
style_string = 'mgp'
coeff_string = f'* * {lammps_location}.mgp {specie_symbol_list} {by} {ty}'
std_string = f'{lammps_location}.var {specie_symbol_list} {by} {ty}'
lammps_executable = os.environ.get('lmp')
dump_file_name = f'{prefix}.dump'
input_file_name = f'{prefix}.in'
output_file_name = f'{prefix}.out'
input_text = \
lammps_calculator.generic_lammps_input(data_file_name, style_string,
coeff_string, dump_file_name,
newton=False, std_string=std_string)
lammps_calculator.write_text(input_file_name, input_text)
lammps_calculator.run_lammps(lammps_executable, input_file_name,
output_file_name)
pred_std = True
lammps_forces, lammps_stds = lammps_calculator.lammps_parser(
dump_file_name, std=pred_std)
mgp_pred = mgp_model.predict(test_envi)
# check that lammps agrees with gp to within 1 meV/A
for i in range(3):
print("isclose? diff:", lammps_forces[atom_num, i] - mgp_pred[0][i],
"mgp value", mgp_pred[0][i])
assert np.isclose(lammps_forces[atom_num, i],
mgp_pred[0][i],
rtol=1e-2)
# check the lmp var
mgp_std = np.sqrt(mgp_pred[1])
print("isclose? diff:", lammps_stds[atom_num] - mgp_std, "mgp value",
mgp_std)
assert np.isclose(lammps_stds[atom_num], mgp_std, rtol=1e-2)
clean(prefix=prefix)
