# Using freud Efficiently¶

The freud library is designed to be both fast and easy-to-use. In many cases, the library’s performance is good enough that users don’t need to worry about their usage patterns. However, in highly performance-critical applications (such as real-time visualization or on-the-fly calculations mid-simulation), uses can benefit from knowing the best ways to make use of freud. This page provides some guidance on this topic.

## Reusing Locality Information¶

Perhaps the most powerful method users have at their disposal for speeding up calculations is proper reuse of the data structures in freud.locality. As one example, consider using freud to calculate multiple neighbor-based quantities for the same set of data points. It is important to recognize that internally, each time such a calculation is performed using a (box, points) tuple, the compute class is internally rebuilding a neighbor-finding accelerator such a freud.locality.AABBQuery object and then using it to find neighbors:

# Behind the scenes, freud is essentially running
# freud.locality.AABBQuery(box, points).query(points, dict(r_max=5, exclude_ii=True))
# and feeding the result to the RDF calculation.
rdf = freud.density.RDF(bins=50, r_max=5)
rdf.compute(system=(box, points))


If users anticipate performing many such calculations on the same system of points, they can amortize the cost of rebuilding the AABBQuery object by constructing it once and then passing it into multiple computations:

# Now, let's instead reuse the object for a pair of calculations:
nq = freud.locality.AABBQuery(box=box, points=points)
rdf = freud.density.RDF(bins=50, r_max=5)
rdf.compute(system=nq)

r_max = 4
orientations = np.array([[1, 0, 0, 0]] * num_points)
pmft = freud.pmft.PMFTXYZ(r_max, r_max, r_max, bins=100)
pmft.compute(system=nq, orientations=orientations)


This reuse can significantly improve performance in e.g. visualization contexts where users may wish to calculate a bond order diagram and an RDF at each frame, perhaps for integration with a visualization toolkit like OVITO.

A slightly different use-case would be the calculation of multiple quantities based on exactly the same set of neighbors. If the user in fact expects to perform computations with the exact same pairs of neighbors (for example, to compute freud.order.Steinhardt for multiple $$l$$ values), then the user can further speed up the calculation by precomputing the entire freud.NeighborList and storing it for future use.

r_max = 3
nq = freud.locality.AABBQuery(box=box, points=points)
nlist = nq.query(points, dict(r_max=r_max))
q6_arrays = []
for l in range(3, 6):
ql = freud.order.Steinhardt(l=l)
q6_arrays.append(ql.compute((box, points), neighbors=nlist).particle_order)


Notably, if the user calls a compute method with compute(system=(box, points)), unlike in the examples above freud will not construct a freud.locality.NeighborQuery internally because the full set of neighbors is completely specified by the NeighborList. In all these cases, freud does the minimal work possible to find neighbors, so judicious use of these data structures can substantially accelerate your code.

## Proper Data Inputs¶

Minor speedups may also be gained from passing properly structured data to freud. The package was originally designed for analyzing particle simulation trajectories, which are typically stored in single-precision binary formats. As a result, the freud library also operates in single precision and therefore converts all inputs to single-precision. However, NumPy will typically work in double precision by default, so depending on how data is streamed to freud, the package may be performing numerous data copies in order to ensure that all its data is in single-precision. To avoid this problem, make sure to specify the appropriate data types (numpy.float32) when constructing your NumPy arrays.