"""
MDSuite: A Zincwarecode package.
License
-------
This program and the accompanying materials are made available under the terms
of the Eclipse Public License v2.0 which accompanies this distribution, and is
available at https://www.eclipse.org/legal/epl-v20.html
SPDX-License-Identifier: EPL-2.0
Copyright Contributors to the Zincwarecode Project.
Contact Information
-------------------
email: zincwarecode@gmail.com
github: https://github.com/zincware
web: https://zincwarecode.com/
Citation
--------
If you use this module please cite us with:
Summary
-------
Module for the spatial distribution function calculator.
"""
from __future__ import annotations
import logging
import math
from dataclasses import dataclass
from typing import TYPE_CHECKING
import numpy as np
import tensorflow as tf
from tqdm import tqdm
from mdsuite.calculators.calculator import call
from mdsuite.calculators.trajectory_calculator import TrajectoryCalculator
from mdsuite.database.mdsuite_properties import mdsuite_properties
from mdsuite.utils.linalg import (
cartesian_to_spherical_coordinates,
get2dHistogram,
spherical_to_cartesian_coordinates,
)
from mdsuite.utils.meta_functions import join_path
from mdsuite.utils.tensor_flow.layers import NLLayer
from mdsuite.visualizer.d3_data_visualizer import DataVisualizer3D
if TYPE_CHECKING:
from mdsuite import Experiment
log = logging.getLogger(__name__)
[docs]@dataclass
class Args:
"""Data class for the saved properties."""
number_of_configurations: int
data_range: int
correlation_time: int
atom_selection: np.s_
molecules: bool
species: list
r_min: float
r_max: float
n_bins: int
[docs]class SpatialDistributionFunction(TrajectoryCalculator):
"""Spatial Distribution Function Calculator based on the r_ij matrix."""
def __init__(self, experiment: Experiment, experiments=None):
"""
Constructor of the SpatialDistributionFunction.
Parameters
----------
experiment: Experiment
managed by RunComputation
experiments:
list of Experiments, managed by RunComputation
load_data: bool
managed by RunComputation
"""
super().__init__(experiment, experiments=experiments)
self.scale_function = {"quadratic": {"outer_scale_factor": 1}}
self.loaded_property = mdsuite_properties.positions
self.x_label = r"$$\text{r} / nm$$" # None
self.y_label = r"$$\text{g(r)}$$" # None
self.analysis_name = "Spatial_Distribution_Function"
self.experimental = True
self._dtype = tf.float32
@call
def __call__(
self,
molecules: bool = False,
start: int = 1,
stop: int = 10,
number_of_configurations: int = 5,
r_min: float = 4.0,
r_max: float = 4.5,
species: list = None,
n_bins: int = 100,
**kwargs,
):
"""
User Interface to the Spatial Distribution Function.
Parameters
----------
molecules : bool
If true, load molecules.
start: int
Index of the first configuration
stop: int
Index of the last configuration
number_of_configurations: int
Number of configurations to sample between start and stop
r_min: float
Minimal distance for the SDF
r_max: float
Maximal distance for the SDF
species: list
List of species to use, for computing the SDF,
if None a single SDF of all available species will be computed
kwargs
"""
if species is None:
if molecules:
species = list(self.experiment.molecules)
else:
species = list(self.experiment.species)
# choose sampled configurations
self.sample_configurations = np.linspace(
start, stop, number_of_configurations, dtype=np.int
)
self.plot = False
self.args = Args(
molecules=molecules,
species=species,
number_of_configurations=number_of_configurations,
r_min=r_min,
atom_selection=np.s_[:],
r_max=r_max,
data_range=number_of_configurations,
correlation_time=1,
n_bins=n_bins,
)
def _load_positions(self, indices: list, species: str) -> tf.Tensor:
"""
Load the positions matrix.
This function is here to optimize calculation speed
Parameters
----------
indices : list
List of indices to take from the database_path
species: str
The species to load the positions from
Returns
-------
loaded_data : tf.Tensor
tf.Tensor of tensor_values loaded from the hdf5 database_path
"""
path_list = [join_path(species, self.loaded_property.name)]
data_dict = self.database.load_data(
path_list=path_list, select_slice=np.s_[:, indices]
)
data = []
for item in path_list:
data.append(data_dict[item])
if len(self.args.species) == 1:
return tf.cast(data, dtype=self.dtype)
else:
return tf.cast(tf.concat(data, axis=0), dtype=self.dtype)
[docs] def run_calculator(self):
"""Run the computation."""
path_list = [
join_path(item, self.loaded_property.name) for item in self.args.species
]
self._prepare_managers(path_list)
# Iterate over batches
sdf_values = []
nllayer = NLLayer()
for idx, sample_configuration in tqdm(
enumerate(np.array_split(self.sample_configurations, self.n_batches)),
ncols=70,
):
positions_tensor = []
species_length = []
for species in self.args.species:
positions_tensor.append(
self._load_positions(sample_configuration, species)
)
species_length.append(len(positions_tensor[-1]))
log.debug(f"Got {species_length[-1]} ions of {species}")
positions_tensor = tf.concat(positions_tensor, axis=0)
# make it (configurations, n_atoms, 3)
positions_tensor = tf.transpose(positions_tensor, perm=(1, 0, 2))
cell = tf.linalg.set_diag(tf.zeros((3, 3)), self.experiment.box_array)
cell = tf.repeat(cell[None], positions_tensor.shape[0], axis=0)
r_ij = nllayer({"positions": positions_tensor, "cell": cell})
d_ij = tf.linalg.norm(r_ij, axis=-1) # shape (b, i, j)
# apply minimal and maximal distance and remove the diagonal elements of 0
mask = (d_ij > self.args.r_min) & (d_ij < self.args.r_max) # & (d_ij != 0)
# Slicing the mask to the area where only the distances i!=j occur.
# There are two such areas, so I am slicing them twice
# could also mirror them
mask_ = mask[:, species_length[0] :, : species_length[1]]
r_ij_cut = r_ij[:, species_length[0] :, : species_length[1], :]
r_ij_cut = r_ij_cut[mask_]
sdf_values.append(self.r_ij_to_bins(r_ij_cut))
# and the other half (only effective if species[0] != species[1])
mask_ = mask[:, : species_length[0], species_length[1] :]
r_ij_cut = r_ij[:, : species_length[0], species_length[1] :, :]
r_ij_cut = r_ij_cut[mask_]
sdf_values.append(self.r_ij_to_bins(r_ij_cut))
sdf_values = tf.reduce_sum(sdf_values, axis=0)
# TODO fix subjects and maybe rename
self.queue_data(
data={
"sdf": sdf_values.numpy().tolist(),
"sphere": self._get_unit_sphere().numpy().tolist(),
},
subjects=["System"],
)
coordinates = tf.reshape(self._get_unit_sphere(), [self.args.n_bins**2, 3])
colour_map = tf.reshape(sdf_values, [-1])
self._run_visualization(coordinates, colour_map)
def _get_unit_sphere(self) -> tf.Tensor:
"""Get the coordinates on the sphere for the bins.
Returns
-------
tf.Tensor:
A Tensor with shape (n_bins, n_bins, 3) where 3 represents (x,y,z)
for the coordinates of a unit sphere
"""
theta_range = [0, math.pi]
phi_range = [-math.pi, math.pi]
theta_vals = np.linspace(theta_range[0], theta_range[1], self.args.n_bins)
phi_vals = np.linspace(phi_range[0], phi_range[1], self.args.n_bins)
xx, yy = np.meshgrid(theta_vals, phi_vals)
spherical_map = tf.stack([tf.ones_like(xx), xx, yy], axis=-1)
cartesian_map = spherical_to_cartesian_coordinates(spherical_map)
return cartesian_map
[docs] def r_ij_to_bins(self, r_ij) -> tf.Tensor:
"""Compute the 2D histogram in spherical coordinates while projecting
all values of r to a unit sphere.
Parameters
----------
r_ij: tf.Tensor
any r_ij matrix with shape (..., 3)
Returns
-------
tf.Tensor:
bins with shape (n_bins, n_bins)
"""
r_ij_spherical = cartesian_to_spherical_coordinates(r_ij)
theta_phi_pairs = tf.reshape(r_ij_spherical, (-1, 3))
theta_range = [0, math.pi]
phi_range = [-math.pi, math.pi]
bins = get2dHistogram(
theta_phi_pairs[:, 1],
theta_phi_pairs[:, 2],
value_range=[theta_range, phi_range],
nbins=self.args.n_bins,
)
return bins
def _run_visualization(self, plot_data: tf.Tensor, colour_map: np.ndarray):
"""
Run the visualizer.
Parameters
----------
plot_data : tf.Tensor
Data to be plot.
colour_map : tf.Tensor
A colour map to highlight density on the unit sphere
"""
if self.args.species[0] in list(self.experiment.species):
center = self.args.species[0]
else:
center_dict = self.experiment.molecules[self.args.species[0]]["groups"]["0"]
center = {}
for item in center_dict:
for index in center_dict[item]:
center[f"{item}_{index}"] = self.database.load_data(
path_list=[join_path(item, "Positions")],
select_slice=np.s_[index, 0],
)[join_path(item, "Positions")]
visualizer = DataVisualizer3D(
data=plot_data, title="SDF", center=center, colour_map=colour_map
)
visualizer.plot()