# Analyzing a lammps trajectory¶

In this example, a lammps trajectory in dump-text format will be read in, and Steinhardt’s parameters will be calculated.

Earlier versions of this tutorial used traj_process module. It is now updated to work with the more efficient trajectory module.

import pyscal.core as pc
import os
from pyscal.trajectory import Trajectory
import matplotlib.pyplot as plt
import numpy as np


First, we will use the Trajectory class to load the trajectory.

traj = Trajectory("traj.light")
traj

Trajectory of 10 slices with 500 atoms


Now we can make a small function which reads slice and calculates $$q_6$$ values.

def calculate_q6(timeslice, format="lammps-dump"):
sys = timeslice.to_system()[0]
sys.find_neighbors(method="cutoff", cutoff=0)
sys.calculate_q(6)
q6 = sys.get_qvals(6)
return q6


There are a couple of things of interest in the above function. The find_neighbors method finds the neighbors of the individual atoms. Here, an adaptive method is used, but, one can also use a fixed cutoff or Voronoi tessellation. Also only the unaveraged $$q_6$$ values are calculated above. The averaged ones can be calculate using the averaged=True keyword in both calculate_q and get_qvals method. Now we can simply call the function for each file..

q6s = [calculate_q6(traj[x]) for x in range(traj.nblocks)]


We can now visualise the calculated values

plt.plot(np.hstack(q6s))

[<matplotlib.lines.Line2D at 0x7f230fdeab80>]


We will now modify the above function to also find clusters which satisfy particular $$q_6$$ value. But first, for a single file.

sys = traj[0].to_system()[0]
sys.find_neighbors(method="cutoff", cutoff=0)
sys.calculate_q(6)


Now a clustering algorithm can be applied on top using the cluster_atoms method. cluster_atoms uses a condition as argument which should give a True/False value for each atom. Lets define a condition.

def condition(atom):
return atom.get_q(6) > 0.5


The above function returns True for any atom which has a $$q_6$$ value greater than 0.5 and False otherwise. Now we can call the cluster_atoms method.

sys.cluster_atoms(condition)

16


The method returns 16, which here is the size of the largest cluster of atoms which have $$q_6$$ value of 0.5 or higher. If information about all clusters are required, that can also be accessed.

atoms = sys.atoms


atom.cluster gives the number of the cluster that each atom belongs to. If the value is -1, the atom does not belong to any cluster, that is, the clustering condition was not met.

clusters = [atom.cluster for atom in atoms if atom.cluster != -1]


Now we can see how many unique clusters are there, and what their sizes are.

unique_clusters, counts = np.unique(clusters, return_counts=True)


counts contain all the necessary information. len(counts) will give the number of unique clusters.

plt.bar(range(len(counts)), counts)
plt.ylabel("Number of atoms in cluster")
plt.xlabel("Cluster ID")

Text(0.5, 0, 'Cluster ID')


Now we can finally put all of these together into a single function and run it over our individual time slices.

def calculate_q6_cluster(timeslice, cutoff_q6 = 0.5, format="lammps-dump"):
sys = timeslice.to_system()[0]
sys.find_neighbors(method="cutoff", cutoff=0)
sys.calculate_q(6)
def _condition(atom):
return atom.get_q(6) > cutoff_q6
sys.cluster_atoms(condition)
atoms = sys.atoms
clusters = [atom.cluster for atom in atoms if atom.cluster != -1]
unique_clusters, counts = np.unique(clusters, return_counts=True)
return counts

q6clusters = [calculate_q6_cluster(traj[x]) for x in range(traj.nblocks)]


We can plot the number of clusters for each slice

plt.plot(range(len(q6clusters)), [len(x) for x in q6clusters], 'o-')
plt.xlabel("Time slice")
plt.ylabel("number of unique clusters")

Text(0, 0.5, 'number of unique clusters')


We can also plot the biggest cluster size

plt.plot(range(len(q6clusters)), [max(x) for x in q6clusters], 'o-')
plt.xlabel("Time slice")
plt.ylabel("Largest cluster size")

Text(0, 0.5, 'Largest cluster size')


## Using ASE¶

The above example can also done using ASE. The ASE read method needs to be imported.

from ase.io import read

traj = read("traj.light", format="lammps-dump-text", index=":")


In the above function, index=":" tells ase to read the complete trajectory. The individual slices can now be accessed by indexing.

traj[0]

Atoms(symbols='H500', pbc=False, cell=[18.21922, 18.22509, 18.36899], momenta=...)


We can use the same functions as above, but by specifying a different file format.

q6clusters_ase = [calculate_q6_cluster(x, format="ase") for x in traj]


We will plot and compare with the results from before,

plt.plot(range(len(q6clusters_ase)), [max(x) for x in q6clusters_ase], 'o-')
plt.xlabel("Time slice")
plt.ylabel("Largest cluster size")

Text(0, 0.5, 'Largest cluster size')


As expected, the results are identical for both calculations!