def xyz_rdf(name):
os.chdir(
'/home/jinho93/oxides/cluster/zno/cp2k/1.aimd/3.16A/30/3.fix/2.again')
mole = IMolecule.from_file('tail.xyz')
# mole = IMolecule.from_str(out, fmt='xyz')
s = mole.get_boxed_structure(1.6027345000000000E+01,
1.6671211000000000E+01,
6.0678892000000005E+01)
structures = [s]
ind = []
ref = []
print(name)
for i, sp in enumerate(s.sites):
print(sp.z)
up = .7
down = .66
if sp.species_string == name and up > sp.c > down:
ind.append(i)
elif sp.species_string == 'O' and up + 0.05 > sp.c > down - 0.05:
ref.append(i)
r = RadialDistributionFunction(structures,
ind,
reference_indices=ref,
ngrid=301,
sigma=.1)
print(r.peak_r[0])
r.get_rdf_plot(plt=plt, ylim=[-0.005, max(r.rdf)], label=f'{name}-ref')
r.export_rdf('rdf.dat')
plt.show()
def test_rdf(self):
# Parse the DiffusionAnalyzer object from json file directly
obj = loadfn(os.path.join(tests_dir, "cNa3PS4_pda.json"))
structure_list = []
for i, s in enumerate(obj.get_drift_corrected_structures()):
structure_list.append(s)
if i == 9: break
species = ["Na", "P", "S"]
# Test from_species
obj = RadialDistributionFunction.from_species(
structures=structure_list, ngrid=101, rmax=10.0, cell_range=1,
sigma=0.1, species=species, reference_species=species)
check = np.shape(obj.rdf)[0] == 101 and np.argmax(obj.rdf) == 34
self.assertTrue(check)
self.assertAlmostEqual(obj.rdf.max(), 1.634448, 4)
# Test init
s = structure_list[0]
indices = [i for (i, site) in enumerate(s) if
site.species_string in species]
obj = RadialDistributionFunction(
structures=structure_list, ngrid=101, rmax=10.0, cell_range=1,
sigma=0.1, indices=indices, reference_indices=indices)
check = np.shape(obj.rdf)[0] == 101 and np.argmax(obj.rdf) == 34
self.assertTrue(check)
self.assertAlmostEqual(obj.rdf.max(), 1.634448, 4)
# Test init using structures w/ different lattices
s0 = deepcopy(structure_list[0])
sl_1 = [s0, s0, s0 * [1, 2, 1]]
sl_2 = [s0 * [2, 1, 1], s0, s0]
obj_1 = RadialDistributionFunction(
structures=sl_1, ngrid=101, rmax=10.0, cell_range=1,
sigma=0.1, indices=indices, reference_indices=indices)
obj_2 = RadialDistributionFunction(
structures=sl_2, ngrid=101, rmax=10.0, cell_range=1,
sigma=0.1, indices=indices, reference_indices=indices)
self.assertEqual(obj_1.rho, obj_2.rho)
self.assertEqual(obj_1.rdf[0], obj_2.rdf[0])
def xdat_rdf_speices(name):
import matplotlib.pyplot as plt
from pymatgen_diffusion.aimd.van_hove import RadialDistributionFunction
s = Xdatcar('XDATCAR').structures[-1:]
ind = []
ref = []
for i, sp in enumerate(s[0].species):
if sp.name == name:
ind.append(i)
elif sp.name == 'O':
ref.append(i)
r = RadialDistributionFunction(s, ind, reference_indices=ref, ngrid=501, sigma=.1)
print(r.peak_r[0])
r.get_rdf_plot(plt=plt, ylim=[-0.005, max(r.rdf)], label=f'{name}-ref')
r.export_rdf('/home/jinho93/rdf.dat')
plt.show()
def test_rdf(self):
data_file = os.path.join(tests_dir, "cNa3PS4_pda.json")
data = json.load(open(data_file, "r"))
obj = DiffusionAnalyzer.from_dict(data)
structure_list = []
for i, s in enumerate(obj.get_drift_corrected_structures()):
structure_list.append(s)
if i == 9: break
obj = RadialDistributionFunction(structures=structure_list, ngrid=101, rmax=10.0,
cellrange=1, sigma=0.1, species=["Na", "P", "S"])
check = np.shape(obj.rdf)[0] == 101 and np.argmax(obj.rdf) == 34
self.assertTrue(check)
self.assertAlmostEqual(obj.rdf.max(), 1.6831, 4)
def test_rdf_coordination_number(self):
# create a simple cubic lattice
coords = np.array([[0.5, 0.5, 0.5]])
atom_list = ['S']
lattice = Lattice.from_parameters(a=1.0,
b=1.0,
c=1.0,
alpha=90,
beta=90,
gamma=90)
structure = Structure(lattice, atom_list, coords)
rdf = RadialDistributionFunction(structures=[structure],
species=['S'],
rmax=5.0,
sigma=0.1,
ngrid=500)
self.assertEqual(rdf.coordination_number[100], 6.0)
def test_rdf(self):
data_file = os.path.join(tests_dir, "cNa3PS4_pda.json")
with open(data_file, "r") as j:
data = json.load(j)
obj = DiffusionAnalyzer.from_dict(data)
structure_list = []
for i, s in enumerate(obj.get_drift_corrected_structures()):
structure_list.append(s)
if i == 9: break
species = ["Na", "P", "S"]
# Test from_species
obj = RadialDistributionFunction.from_species(
structures=structure_list,
ngrid=101,
rmax=10.0,
cell_range=1,
sigma=0.1,
species=species,
reference_species=species)
check = np.shape(obj.rdf)[0] == 101 and np.argmax(obj.rdf) == 34
self.assertTrue(check)
self.assertAlmostEqual(obj.rdf.max(), 1.634448, 4)
# Test init
s = structure_list[0]
indices = [
i for (i, site) in enumerate(s) if site.species_string in species
]
obj = RadialDistributionFunction(structures=structure_list,
ngrid=101,
rmax=10.0,
cell_range=1,
sigma=0.1,
indices=indices,
reference_indices=indices)
check = np.shape(obj.rdf)[0] == 101 and np.argmax(obj.rdf) == 34
self.assertTrue(check)
self.assertAlmostEqual(obj.rdf.max(), 1.634448, 4)
def test_raises_ValueError_if_reference_species_not_in_structure(self):
with self.assertRaises(ValueError):
rdf = RadialDistributionFunction(structures=[self.structure],
species=['S'],
reference_species=['Cl'])
def test_raises_ValueError_if_sigma_is_not_positive(self):
with self.assertRaises(ValueError):
rdf = RadialDistributionFunction(structures=[self.structure],
sigma=0)
def test_raises_valueerror_if_ngrid_is_less_than_2(self):
with self.assertRaises(ValueError):
rdf = RadialDistributionFunction(structures=[self.structure],
ngrid=1)
import os
import numpy as np
from pymatgen.io.vasp import Xdatcar
from pymatgen_diffusion.aimd.van_hove import RadialDistributionFunction, plt
from pymatgen import Structure
if __name__ == '__main__':
structure = Structure.from_file('POSCAR')
num = 4
fig = plt.figure(0)
output = np.zeros((201, num))
output[:, 0] = np.linspace(0.0, 10, 201)
for c in range(num - 1):
oxy = []
for i, j in enumerate(structure.sites):
if j.species_string == "O" and c / num < j.c < (c + 1) / num:
oxy.append(i)
zn = []
for i, j in enumerate(structure.sites):
if j.species_string == "Zn":
zn.append(i)
ax = fig.add_subplot(221 + c)
rdf = RadialDistributionFunction([structure], oxy, zn, ngrid=201)
rdf.get_rdf_plot(ylim=(0, max(rdf.rdf)))
output[:, c + 1] = rdf.rdf
np.savetxt('output.dat', output)
plt.show()
def process_rdf(
atoms=None,
active_site_i=None,
df_coord_slab_i=None,
metal_atom_symbol=None,
custom_name=None,
TEST_MODE=False,
):
"""
"""
#| - process_rdf
if custom_name is None:
custom_name = ""
#| - Create out folders
import os
# directory = "out_data"
directory = os.path.join(os.environ["PROJ_irox_oer"],
"workflow/enumerate_adsorption", "out_data")
if not os.path.exists(directory):
os.makedirs(directory)
# assert False, "Fix os.makedirs"
directory = "out_plot/rdf_figures"
if not os.path.exists(directory):
os.makedirs(directory)
directory = "out_data/rdf_data"
if not os.path.exists(directory):
os.makedirs(directory)
#__|
AAA = AseAtomsAdaptor()
slab_structure = AAA.get_structure(atoms)
# Pickling data ###########################################
out_dict = dict()
out_dict["active_site_i"] = active_site_i
out_dict["df_coord_slab_i"] = df_coord_slab_i
out_dict["metal_atom_symbol"] = metal_atom_symbol
import os
import pickle
path_i = os.path.join(os.environ["HOME"], "__temp__", "temp.pickle")
with open(path_i, "wb") as fle:
pickle.dump(out_dict, fle)
# #########################################################
neigh_dict = get_indices_of_neigh(active_oxy_ind=active_site_i,
df_coord_slab_i=df_coord_slab_i,
metal_atom_symbol=metal_atom_symbol)
neighbor_oxy_indices = neigh_dict["neighbor_oxy_indices"]
neighbor_metal_indices = neigh_dict["neighbor_metal_indices"]
shell_2_metal_atoms = neigh_dict["shell_2_metal_atoms"]
neighbor_indices = neighbor_oxy_indices + neighbor_metal_indices + shell_2_metal_atoms
# neighbor_indices = neighbor_indices[0:1]
# print("neighbor_indices:", neighbor_indices)
#| - Get RDF
RDF = RadialDistributionFunction(
[
slab_structure,
],
[
active_site_i,
],
neighbor_indices,
# ngrid=1801,
ngrid=4801,
rmax=8.0,
cell_range=2,
# sigma=0.2,
# sigma=0.08,
sigma=0.015,
# sigma=0.008,
# sigma=0.0005,
)
# data_file = "out_data/rdf_data/rdf_out.csv"
data_file = os.path.join(
"out_data/rdf_data",
custom_name + "_" + str(active_site_i).zfill(4) + "_" + "rdf_out.csv")
RDF.export_rdf(data_file)
df_rdf = pd.read_csv(data_file)
df_rdf = df_rdf.rename(columns={" g(r)": "g"})
#__|
#| - Plotting
import plotly.graph_objs as go
x_array = df_rdf.r
# y_array = df_rdf[" g(r)"]
y_array = df_rdf["g"]
trace = go.Scatter(
x=x_array,
y=y_array,
)
data = [trace]
fig = go.Figure(data=data)
# fig.show()
from plotting.my_plotly import my_plotly_plot
if TEST_MODE:
plot_dir = "__temp__"
else:
plot_dir = "rdf_figures"
out_plot_file = os.path.join(
plot_dir, custom_name + "_" + str(active_site_i).zfill(4) + "_rdf")
my_plotly_plot(
figure=fig,
# plot_name=str(active_site_i).zfill(4) + "_rdf",
plot_name=out_plot_file,
write_html=True,
write_png=False,
png_scale=6.0,
write_pdf=False,
write_svg=False,
try_orca_write=False,
)
#__|
return (df_rdf)