Once you have installed freud, you can start using freud with any simulation data that you have on hand.
As an example, we’ll assume that you have run a simulation using the HOOMD-blue and used the
hoomd.dump.gsd command to output the trajectory into a file
The GSD file format provides its own convenient Python file reader that offers access to data in the form of NumPy arrays, making it immediately suitable for calculation with freud. Many other file readers and data formats are supported, see Reading Simulation Data for freud for a full list and more examples.
We start by reading the data into a NumPy array:
import gsd.hoomd traj = gsd.hoomd.open('trajectory.gsd', 'rb')
We can now immediately calculate important quantities.
Here, we will compute the radial distribution function \(g(r)\) using the
freud.density.RDF compute class.
Since the radial distribution function is in practice computed as a histogram, we must specify the histogram bin widths and the largest interparticle distance to include in our calculation.
To do so, we simply instantiate the class with the appropriate parameters and then perform a computation on the given data:
import freud rdf = freud.density.RDF(bins=50, r_max=5) rdf.compute(system=traj[-1])
We can now access the data through properties of the
r = rdf.bin_centers y = rdf.rdf
Many classes in freud natively support plotting their data using Matplotlib <https://matplotlib.org/>:
import matplotlib as plt fig, ax = plt.subplots() rdf.plot(ax=ax)
You will note that in the above example, we computed \(g(r)\) only using the final frame of the simulation trajectory,
However, in many cases, radial distributions and other similar quantities may be noisy in simulations due to the natural fluctuations present.
In general, what we are interested in are time-averaged quantities once a system has equilibrated.
To perform such a calculation, we can easily modify our original calculation to take advantage of freud’s accumulation features.
To accumulate, just add the argument
reset=False with a supported compute object (such as histogram-like computations).
Assuming that you have some method for identifying the frames you wish to include in your sample, our original code snippet would be modified as follows:
import freud rdf = freud.density.RDF(bins=50, r_max=5) for frame in traj: rdf.compute(frame, reset=False)
You can then access the data exactly as we previously did. And that’s it!
Now that you’ve seen a brief example of reading data and computing a radial distribution function, you’re ready to learn more. If you’d like a complete walkthrough please see the Tutorial. The tutorial walks through many of the core concepts in freud in greater detail, starting with the basics of the simulation systems we analyze and describing the details of the neighbor finding logic in freud. To see specific features of freud in action, look through the Examples. More detailed documentation on specific classes and functions can be found in the API documentation.