def test_probability_classmethod(self):
file = os.path.join(tests_dir, "cNa3PS4_pda.json")
data = json.load(open(file))
diff_analyzer = DiffusionAnalyzer.from_dict(data)
# ProbabilityDensityAnalysis object
pda = ProbabilityDensityAnalysis.from_diffusion_analyzer(diffusion_analyzer=diff_analyzer, interval=0.5)
dV = pda.structure.lattice.volume / pda.lens[0] / pda.lens[1] / pda.lens[2]
Pr_tot = np.sum(pda.Pr) * dV
self.assertAlmostEqual(pda.Pr.max(), 0.0361594977596, 8)
self.assertAlmostEqual(pda.Pr.min(), 0.0, 12)
self.assertAlmostEqual(Pr_tot, 1.0, 12)
def test_site_occupancy_classmethod(self):
file = os.path.join(tests_dir, "cNa3PS4_pda.json")
data = json.load(open(file))
diff_analyzer = DiffusionAnalyzer.from_dict(data)
structure = diff_analyzer.structure
coords_ref = [ss.frac_coords for ss in structure if ss.specie.symbol == "Na"]
# SiteOccupancyAnalyzer object
socc = SiteOccupancyAnalyzer.from_diffusion_analyzer(coords_ref, diffusion_analyzer=diff_analyzer)
site_occ = socc.site_occ
self.assertAlmostEqual(np.sum(site_occ), len(coords_ref), 12)
self.assertAlmostEqual(site_occ[1], 0.98, 12)
self.assertAlmostEqual(site_occ[26], 0.97, 12)
self.assertEqual(len(coords_ref), 48)
def test_generate_stable_sites(self):
file = os.path.join(tests_dir, "cNa3PS4_pda.json")
data = json.load(open(file))
diff_analyzer = DiffusionAnalyzer.from_dict(data)
# ProbabilityDensityAnalysis object
pda = ProbabilityDensityAnalysis.from_diffusion_analyzer(diffusion_analyzer=diff_analyzer, interval=0.1)
pda.generate_stable_sites(p_ratio=0.25, d_cutoff=1.5)
self.assertEqual(len(pda.stable_sites), 50)
self.assertAlmostEqual(pda.stable_sites[1][2], 0.24113475177304966, 8)
self.assertAlmostEqual(pda.stable_sites[7][1], 0.5193661971830985, 8)
s = pda.get_full_structure()
self.assertEqual(s.num_sites, 178)
self.assertEqual(s.composition["Na"], 48)
self.assertEqual(s.composition["X"], 50)
self.assertAlmostEqual(s[177].frac_coords[2], 0.57446809)
def test_from_structure_NPT(self):
coords1 = np.array([[0.0, 0.0, 0.0], [0.5, 0.5, 0.5]])
coords2 = np.array([[0.0, 0.0, 0.0], [0.6, 0.6, 0.6]])
coords3 = np.array([[0.0, 0.0, 0.0], [0.7, 0.7, 0.7]])
lattice1 = Lattice.from_parameters(a=2.0,
b=2.0,
c=2.0,
alpha=90,
beta=90,
gamma=90)
lattice2 = Lattice.from_parameters(a=2.1,
b=2.1,
c=2.1,
alpha=90,
beta=90,
gamma=90)
lattice3 = Lattice.from_parameters(a=2.0,
b=2.0,
c=2.0,
alpha=90,
beta=90,
gamma=90)
s1 = Structure(coords=coords1, lattice=lattice1, species=["F", "Li"])
s2 = Structure(coords=coords2, lattice=lattice2, species=["F", "Li"])
s3 = Structure(coords=coords3, lattice=lattice3, species=["F", "Li"])
structures = [s1, s2, s3]
d = DiffusionAnalyzer.from_structures(
structures,
specie="Li",
temperature=500.0,
time_step=2.0,
step_skip=1,
smoothed=None,
)
self.assertArrayAlmostEqual(
d.disp[1],
np.array([[0.0, 0.0, 0.0], [0.21, 0.21, 0.21], [0.40, 0.40,
0.40]]),
)
def __init__(
self,
diffusion_analyzer: DiffusionAnalyzer,
avg_nsteps: int = 50,
ngrid: int = 101,
rmax: float = 10.0,
step_skip: int = 50,
sigma: float = 0.1,
cell_range: int = 1,
species: Union[Tuple, List] = ("Li", "Na"),
reference_species: Union[Tuple, List] = None,
indices: List = None,
):
"""
Initiation.
Args:
diffusion_analyzer (DiffusionAnalyzer): A
pymatgen.analysis.diffusion.analyzer.DiffusionAnalyzer object
avg_nsteps (int): Number of t0 used for statistical average
ngrid (int): Number of radial grid points
rmax (float): Maximum of radial grid (the minimum is always set zero)
step_skip (int): # of time steps skipped during analysis. It defines
the resolution of the reduced time grid
sigma (float): Smearing of a Gaussian function
cell_range (int): Range of translational vector elements associated
with supercell. Default is 1, i.e. including the adjacent image
cells along all three directions.
species ([string]): a list of specie symbols of interest.
reference_species ([string]): Set this option along with 'species'
parameter to calculate the distinct-part of van Hove function.
Note that the self-part of van Hove function is always computed
only for those in "species" parameter.
indices (list of int): If not None, only a subset of atomic indices
will be selected for the analysis. If this is given, "species"
parameter will be ignored.
"""
# initial check
if step_skip <= 0:
raise ValueError("skip_step should be >=1!")
n_ions, nsteps, ndim = diffusion_analyzer.disp.shape
if nsteps <= avg_nsteps:
raise ValueError("Number of timesteps is too small!")
ntsteps = nsteps - avg_nsteps
if ngrid - 1 <= 0:
raise ValueError("Ntot should be greater than 1!")
if sigma <= 0.0:
raise ValueError("sigma should be > 0!")
dr = rmax / (ngrid - 1)
interval = np.linspace(0.0, rmax, ngrid)
reduced_nt = int(ntsteps / float(step_skip)) + 1
lattice = diffusion_analyzer.structure.lattice
structure = diffusion_analyzer.structure
if indices is None:
indices = [
j for j, site in enumerate(structure)
if site.specie.symbol in species
]
ref_indices = indices
if reference_species:
ref_indices = [
j for j, site in enumerate(structure)
if site.specie.symbol in reference_species
]
rho = float(len(indices)) / lattice.volume
# reduced time grid
rtgrid = np.arange(0.0, reduced_nt)
# van Hove functions
gsrt = np.zeros((reduced_nt, ngrid), dtype=np.double)
gdrt = np.zeros((reduced_nt, ngrid), dtype=np.double)
tracking_ions = [] # type: ArrayLike
ref_ions = [] # type: ArrayLike
# auxiliary factor for 4*\pi*r^2
aux_factor = 4.0 * np.pi * interval**2
aux_factor[0] = np.pi * dr**2
for i, ss in enumerate(
diffusion_analyzer.get_drift_corrected_structures()):
all_fcoords = np.array(ss.frac_coords)
tracking_ions.append(all_fcoords[indices, :])
ref_ions.append(all_fcoords[ref_indices, :])
tracking_ions = np.array(tracking_ions)
ref_ions = np.array(ref_ions)
gaussians = norm.pdf(interval[:, None], interval[None, :],
sigma) / float(avg_nsteps) / float(
len(ref_indices))
# calculate self part of van Hove function
image = np.array([0, 0, 0])
for it in range(reduced_nt):
dns = Counter() # type: ignore
it0 = min(it * step_skip, ntsteps)
for it1 in range(avg_nsteps):
dists = [
lattice.get_distance_and_image(tracking_ions[it1][u],
tracking_ions[it0 + it1][u],
jimage=image)[0]
for u in range(len(indices))
]
r_indices = [
int(dist / dr)
for dist in filter(lambda e: e < rmax, dists)
]
dns.update(r_indices) # type: ignore
for indx, dn in dns.most_common(ngrid):
gsrt[it, :] += gaussians[indx, :] * dn
# calculate distinct part of van Hove function of species
r = np.arange(-cell_range, cell_range + 1)
arange = r[:, None] * np.array([1, 0, 0])[None, :]
brange = r[:, None] * np.array([0, 1, 0])[None, :]
crange = r[:, None] * np.array([0, 0, 1])[None, :]
images = arange[:, None, None] + brange[None, :,
None] + crange[None, None, :]
images = images.reshape((len(r)**3, 3))
# find the zero image vector
zd = np.sum(images**2, axis=1)
indx0 = np.argmin(zd)
for it in range(reduced_nt):
dns = Counter()
it0 = min(it * step_skip, ntsteps)
for it1 in range(avg_nsteps):
dcf = (tracking_ions[it0 + it1, :, None, None, :] +
images[None, None, :, :] -
ref_ions[it1, None, :, None, :])
dcc = lattice.get_cartesian_coords(dcf)
d2 = np.sum(dcc**2, axis=3)
dists = [
d2[u, v, j]**0.5 for u in range(len(indices))
for v in range(len(ref_indices)) for j in range(len(r)**3)
if u != v or j != indx0
]
r_indices = [
int(dist / dr)
for dist in filter(lambda e: e < rmax, dists)
]
dns.update(r_indices)
for indx, dn in dns.most_common(ngrid):
gdrt[it, :] += gaussians[indx, :] * dn / aux_factor[indx] / rho
self.obj = diffusion_analyzer
self.avg_nsteps = avg_nsteps
self.step_skip = step_skip
self.rtgrid = rtgrid
self.interval = interval
self.gsrt = gsrt
self.gdrt = gdrt
# time interval (in ps) in gsrt and gdrt.
self.timeskip = self.obj.time_step * self.obj.step_skip * step_skip / 1000.0
def test_init(self):
# Diffusion vasprun.xmls are rather large. We are only going to use a
# very small preprocessed run for testing. Note that the results are
# unreliable for short runs.
with open(os.path.join(module_dir, "DiffusionAnalyzer.json")) as f:
dd = json.load(f)
d = DiffusionAnalyzer.from_dict(dd)
# large tolerance because scipy constants changed between 0.16.1 and 0.17
self.assertAlmostEqual(d.conductivity, 74.165372613735684, 4)
self.assertAlmostEqual(d.chg_conductivity, 232.8278799754324, 4)
self.assertAlmostEqual(d.diffusivity, 1.16083658794e-06, 7)
self.assertAlmostEqual(d.chg_diffusivity, 3.64565578208e-06, 7)
self.assertAlmostEqual(d.conductivity_std_dev,
0.0097244677795984488, 7)
self.assertAlmostEqual(d.diffusivity_std_dev,
9.1013023085561779e-09, 7)
self.assertAlmostEqual(d.chg_diffusivity_std_dev,
7.20911399729e-10, 5)
self.assertAlmostEqual(d.haven_ratio, 0.31854161048867402, 7)
self.assertArrayAlmostEqual(d.conductivity_components,
[45.7903694, 26.1651956, 150.5406140],
3)
self.assertArrayAlmostEqual(
d.diffusivity_components,
[7.49601236e-07, 4.90254273e-07, 2.24649255e-06],
)
self.assertArrayAlmostEqual(d.conductivity_components_std_dev,
[0.0063566, 0.0180854, 0.0217918])
self.assertArrayAlmostEqual(
d.diffusivity_components_std_dev,
[8.9465670e-09, 2.4931224e-08, 2.2636384e-08],
)
self.assertArrayAlmostEqual(
d.mscd[0:4], [0.69131064, 0.71794072, 0.74315283, 0.76703961])
self.assertArrayAlmostEqual(
d.max_ion_displacements,
[
1.4620659693989553,
1.2787303484445025,
3.419618540097756,
2.340104469126246,
2.6080973517594233,
1.3928579365672844,
1.3561505956708932,
1.6699242923686253,
1.0352389639563648,
1.1662520093955808,
1.2322019205885841,
0.8094210554832534,
1.9917808504954169,
1.2684148391206396,
2.392633794162402,
2.566313049232671,
1.3175030435622759,
1.4628945430952793,
1.0984921286753002,
1.2864482076554093,
0.655567027815413,
0.5986961164605746,
0.5639091444309045,
0.6166004192954059,
0.5997911580422605,
0.4374606277579815,
1.1865683960470783,
0.9017064371676591,
0.6644840367853767,
1.0346375380664645,
0.6177630142863979,
0.7952002051914302,
0.7342686123054011,
0.7858047956905577,
0.5570732369065661,
1.0942937746885417,
0.6509372395308788,
1.0876687380413455,
0.7058162184725,
0.8298306317598585,
0.7813913747621343,
0.7337655232056153,
0.9057161616236746,
0.5979093093186919,
0.6830333586985015,
0.7926500894084628,
0.6765180009988608,
0.8555866032968998,
0.713087091642237,
0.7621007695790749,
],
)
self.assertEqual(d.sq_disp_ions.shape, (50, 206))
self.assertEqual(d.lattices.shape, (1, 3, 3))
self.assertEqual(d.mscd.shape, (206, ))
self.assertEqual(d.mscd.shape, d.msd.shape)
self.assertAlmostEqual(d.max_framework_displacement, 1.18656839605)
ss = list(d.get_drift_corrected_structures(10, 1000, 20))
self.assertEqual(len(ss), 50)
n = random.randint(0, 49)
n_orig = n * 20 + 10
self.assertArrayAlmostEqual(
ss[n].cart_coords - d.structure.cart_coords +
d.drift[:, n_orig, :],
d.disp[:, n_orig, :],
)
d = DiffusionAnalyzer.from_dict(d.as_dict())
self.assertIsInstance(d, DiffusionAnalyzer)
# Ensure summary dict is json serializable.
json.dumps(d.get_summary_dict(include_msd_t=True))
d = DiffusionAnalyzer(
d.structure,
d.disp,
d.specie,
d.temperature,
d.time_step,
d.step_skip,
smoothed="max",
)
self.assertAlmostEqual(d.conductivity, 74.165372613735684, 4)
self.assertAlmostEqual(d.diffusivity, 1.14606446822e-06, 7)
self.assertAlmostEqual(d.haven_ratio, 0.318541610489, 6)
self.assertAlmostEqual(d.chg_conductivity, 232.8278799754324, 4)
self.assertAlmostEqual(d.chg_diffusivity, 3.64565578208e-06, 7)
d = DiffusionAnalyzer(
d.structure,
d.disp,
d.specie,
d.temperature,
d.time_step,
d.step_skip,
smoothed=False,
)
self.assertAlmostEqual(d.conductivity, 27.20479170406027, 4)
self.assertAlmostEqual(d.diffusivity, 4.25976905436e-07, 7)
self.assertAlmostEqual(d.chg_diffusivity, 1.6666666666666667e-17,
3)
d = DiffusionAnalyzer(
d.structure,
d.disp,
d.specie,
d.temperature,
d.time_step,
d.step_skip,
smoothed="constant",
avg_nsteps=100,
)
self.assertAlmostEqual(d.conductivity, 47.404056230438741, 4)
self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7)
self.assertAlmostEqual(d.chg_conductivity, 1.06440821953e-09, 4)
# Can't average over 2000 steps because this is a 1000-step run.
self.assertRaises(
ValueError,
DiffusionAnalyzer,
d.structure,
d.disp,
d.specie,
d.temperature,
d.time_step,
d.step_skip,
smoothed="constant",
avg_nsteps=2000,
)
d = DiffusionAnalyzer.from_structures(
list(d.get_drift_corrected_structures()),
d.specie,
d.temperature,
d.time_step,
d.step_skip,
smoothed=d.smoothed,
avg_nsteps=100,
)
self.assertAlmostEqual(d.conductivity, 47.404056230438741, 4)
self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7)
d.export_msdt("test.csv")
with open("test.csv") as f:
data = []
for row in csv.reader(f):
if row:
data.append(row)
data.pop(0)
data = np.array(data, dtype=np.float64)
self.assertArrayAlmostEqual(data[:, 1], d.msd)
self.assertArrayAlmostEqual(data[:, -1], d.mscd)
os.remove("test.csv")
def test_init_npt(self):
# Diffusion vasprun.xmls are rather large. We are only going to use a
# very small preprocessed run for testing. Note that the results are
# unreliable for short runs.
with open(os.path.join(module_dir, "DiffusionAnalyzer_NPT.json"),
"r") as f:
dd = json.load(f)
d = DiffusionAnalyzer.from_dict(dd)
# large tolerance because scipy constants changed between 0.16.1 and 0.17
self.assertAlmostEqual(d.conductivity, 499.1504129387108, 4)
self.assertAlmostEqual(d.chg_conductivity, 1219.5959181678043, 4)
self.assertAlmostEqual(d.diffusivity, 8.40265434771e-06, 7)
self.assertAlmostEqual(d.chg_diffusivity, 2.05305709033e-05, 6)
self.assertAlmostEqual(d.conductivity_std_dev, 0.10368477696021029,
7)
self.assertAlmostEqual(d.diffusivity_std_dev,
9.1013023085561779e-09, 7)
self.assertAlmostEqual(d.chg_diffusivity_std_dev,
1.20834853646e-08, 6)
self.assertAlmostEqual(d.haven_ratio, 0.409275240679, 7)
self.assertArrayAlmostEqual(d.conductivity_components,
[455.178101, 602.252644, 440.0210014],
3)
self.assertArrayAlmostEqual(
d.diffusivity_components,
[7.66242570e-06, 1.01382648e-05, 7.40727250e-06],
)
self.assertArrayAlmostEqual(d.conductivity_components_std_dev,
[0.1196577, 0.0973347, 0.1525400])
self.assertArrayAlmostEqual(
d.diffusivity_components_std_dev,
[2.0143072e-09, 1.6385239e-09, 2.5678445e-09],
)
self.assertArrayAlmostEqual(
d.max_ion_displacements,
[
1.13147881,
0.79899554,
1.04153733,
0.96061850,
0.83039864,
0.70246715,
0.61365911,
0.67965179,
1.91973907,
1.69127386,
1.60568746,
1.35587641,
1.03280378,
0.99202692,
2.03359655,
1.03760269,
1.40228350,
1.36315080,
1.27414979,
1.26742035,
0.88199589,
0.97700804,
1.11323184,
1.00139511,
2.94164403,
0.89438909,
1.41508334,
1.23660358,
0.39322939,
0.54264064,
1.25291806,
0.62869809,
0.40846708,
1.43415505,
0.88891241,
0.56259128,
0.81712740,
0.52700441,
0.51011733,
0.55557882,
0.49131002,
0.66740277,
0.57798671,
0.63521025,
0.50277142,
0.52878021,
0.67803443,
0.81161269,
0.46486345,
0.47132761,
0.74301293,
0.79285519,
0.48789600,
0.61776836,
0.60695847,
0.67767756,
0.70972268,
1.08232442,
0.87871177,
0.84674206,
0.45694693,
0.60417985,
0.61652272,
0.66444583,
0.52211986,
0.56544134,
0.43311443,
0.43027547,
1.10730439,
0.59829728,
0.52270635,
0.72327608,
1.02919775,
0.84423208,
0.61694764,
0.72795752,
0.72957755,
0.55491631,
0.68507454,
0.76745343,
0.96346584,
0.66672645,
1.06810107,
0.65705843,
],
)
self.assertEqual(d.sq_disp_ions.shape, (84, 217))
self.assertEqual(d.lattices.shape, (1001, 3, 3))
self.assertEqual(d.mscd.shape, (217, ))
self.assertEqual(d.mscd.shape, d.msd.shape)
self.assertAlmostEqual(d.max_framework_displacement, 1.43415505156)
ss = list(d.get_drift_corrected_structures(10, 1000, 20))
self.assertEqual(len(ss), 50)
n = random.randint(0, 49)
n_orig = n * 20 + 10
self.assertArrayAlmostEqual(
ss[n].cart_coords - d.structure.cart_coords +
d.drift[:, n_orig, :],
d.disp[:, n_orig, :],
)
d = DiffusionAnalyzer.from_dict(d.as_dict())
self.assertIsInstance(d, DiffusionAnalyzer)
# Ensure summary dict is json serializable.
json.dumps(d.get_summary_dict(include_msd_t=True))
d = DiffusionAnalyzer(
d.structure,
d.disp,
d.specie,
d.temperature,
d.time_step,
d.step_skip,
smoothed="max",
)
self.assertAlmostEqual(d.conductivity, 499.1504129387108, 4)
self.assertAlmostEqual(d.diffusivity, 8.40265434771e-06, 7)
self.assertAlmostEqual(d.haven_ratio, 0.409275240679, 7)
self.assertAlmostEqual(d.chg_diffusivity, 2.05305709033e-05, 7)
d = DiffusionAnalyzer(
d.structure,
d.disp,
d.specie,
d.temperature,
d.time_step,
d.step_skip,
smoothed=False,
)
self.assertAlmostEqual(d.conductivity, 406.5964019770787, 4)
self.assertAlmostEqual(d.diffusivity, 6.8446082e-06, 7)
self.assertAlmostEqual(d.chg_diffusivity, 1.03585877962e-05, 6)
self.assertAlmostEqual(d.haven_ratio, 0.6607665413, 6)
d = DiffusionAnalyzer(
d.structure,
d.disp,
d.specie,
d.temperature,
d.time_step,
d.step_skip,
smoothed="constant",
avg_nsteps=100,
)
self.assertAlmostEqual(d.conductivity, 425.77884571149525, 4)
self.assertAlmostEqual(d.diffusivity, 7.167523809142514e-06, 7)
self.assertAlmostEqual(d.chg_diffusivity, 9.33480892187e-06, 6)
self.assertAlmostEqual(d.haven_ratio, 0.767827586952, 6)
self.assertAlmostEqual(d.chg_conductivity, 554.5240271992852, 6)
# Can't average over 2000 steps because this is a 1000-step run.
self.assertRaises(
ValueError,
DiffusionAnalyzer,
d.structure,
d.disp,
d.specie,
d.temperature,
d.time_step,
d.step_skip,
smoothed="constant",
avg_nsteps=2000,
)
d = DiffusionAnalyzer.from_structures(
list(d.get_drift_corrected_structures()),
d.specie,
d.temperature,
d.time_step,
d.step_skip,
smoothed=d.smoothed,
avg_nsteps=100,
)
self.assertAlmostEqual(d.conductivity, 425.7788457114952, 4)
self.assertAlmostEqual(d.diffusivity, 7.1675238091425148e-06, 7)
self.assertAlmostEqual(d.haven_ratio, 0.767827586952, 7)
self.assertAlmostEqual(d.chg_conductivity, 554.5240271992852, 6)
d.export_msdt("test.csv")
with open("test.csv") as f:
data = []
for row in csv.reader(f):
if row:
data.append(row)
data.pop(0)
data = np.array(data, dtype=np.float64)
self.assertArrayAlmostEqual(data[:, 1], d.msd)
self.assertArrayAlmostEqual(data[:, -1], d.mscd)
os.remove("test.csv")