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176 | def radial_distribution(
*,
transitions: Transitions,
floating_specie: str,
max_dist: float = 5.0,
resolution: float = 0.1,
) -> dict[str, list[RDFData]]:
"""Calculate and sum RDFs for the floating species in the given sites data.
Parameters
----------
transitions: Transitions
Input transitions data
floating_specie : str
Name of the floating specie
max_dist : float, optional
Max distance for rdf calculation
resolution : float, optional
Width of the bins
Returns
-------
rdfs : dict[str, np.ndarray]
Dictionary with rdf arrays per symbol
"""
# note: needs trajectory with ALL species
trajectory = transitions.trajectory
sites = transitions.sites
base_structure = trajectory.get_structure(0)
lattice = trajectory.get_lattice()
coords = trajectory.positions
sp_coords = trajectory.filter(floating_specie).positions
states2str = _get_states(sites.labels)
states_array = _get_states_array(transitions, sites.labels)
symbol_indices = _get_symbol_indices(base_structure)
bins = np.arange(0, max_dist + resolution, resolution)
length = len(bins) + 1
rdfs: dict[tuple[str, str],
np.ndarray] = defaultdict(lambda: np.zeros(length, dtype=int))
n_steps = len(trajectory)
for i in track(range(n_steps), transient=True):
t_coords = coords[i]
t_sp_coords = sp_coords[i]
dists = lattice.get_all_distances(t_sp_coords, t_coords)
rdf = np.digitize(dists, bins, right=True)
states = np.unique(states_array[i], axis=0)
t_states = states_array[i]
for state in states:
k_idx = np.argwhere(t_states == state)
state_str = states2str[state]
for symbol, symbol_idx in symbol_indices.items():
rdf_state = rdf[k_idx, symbol_idx].flatten()
rdfs[state_str, symbol] += np.bincount(rdf_state,
minlength=length)
ret: dict[str, list[RDFData]] = {}
for (state, symbol), values in rdfs.items():
rdf_data = RDFData(
x=bins,
# Drop last element with distance > max_dist
y=values[:-1],
symbol=symbol,
state=state,
)
ret.setdefault(state, [])
ret[state].append(rdf_data)
return ret
|