# Data objects¶

Or: How do I see the trajectory I generated?

OpenPathSampling stores everything it generates in a single file. This includes the data generated by the simulation, as well as the objects describing the simulation itself (enabling easy restarts). When we refer to storage, we mean the storage subsystem, which deals with how these things are written to a file. Most users are probably more interested in our data objects, which are needed for performing custom analysis. The data objects are described here.

Hierarchical data structure of the MCStep data object. The attribute names are shown in fixed-width font, and the type is provided in parentheses.

The data structures used by OPS allow one to replay the entire simulation, and this is generally the way we suggest performing analysis: loop over the steps, in order, and extract the necessary information. The MCStep object contains information about both the state of the simulation (the trajectories being sampled) and details on the steps taken during the simulation. We thing of these as “what is sampled” and “how sampling happens,” respectively.

The MCStep has two important attributes: .active, which describes the current (active) state of the simulation at the end of the given step, and .change, which describes the process that occurred during this step. These will be discussed in detail below.

Note

Despite the name, MCStep is not only used for Monte Carlo. The same object is also used for other PathSimulator types, such as CommittorSimulation. Other simulation types still generate multiple trajectories, and so the split of “what was the state after this step” and “how was this step performed” still applies.

## Objects describing how sampling happens¶

• The MoveChange contains a record of what happened during the simulation step. Because the simulation move itself generally consists of several nested decisions (type of move, which ensemble to sample, etc.), the MoveChange object can contain subchanges, which record this entire sequence of decisions. In addition, it includes a pointer to its PathMover, a list of the trial Samples generated during the step, and a boolean as to whether the trial move was accepted.

• The MoveChange also contains a Details object, which is essentially a dictionary to store additional metadata about a move. This metadata will vary depending on the type of move. For example, with a shooting move, it would include the shooting point. In principle, all the additional information that might be of interest for analysis should be stored in the Details.

## Getting details for the move of interest¶

The change attribute of an MCStep covers the entire move, including all the structural elements involved in making the decision. As such, its details are very general, and not the details (such as shooting point) that you are probably most interested in.

You can walk through the structural elements using the .subchanges attribute of a MoveChange, but in order to skip to the details that you are most likely to be interested in, one MoveChange is designated “canonical.” For one-way shooting, the change from either the forward or backward shot is canonical. The change from the replica exchange mover or path reversal mover or minus mover is canonical. The canonical change is always within the nested subchanges of the MoveChange, but can be accessed directly with change.canonical. Note that this returns a MoveChange; to get the associated PathMover, use change.canonical.mover. The change.canonical.details dictionary is where you can find the details of what happened during this move.

## Getting coordinates (etc.) from snapshots¶

Of course, each Snapshot is a record consisting of several fields, or as there are referred to in OPS, “features.” Because OPS is independent of the underlying engine (indeed, the engine need not represent molecular dynamics at all), these features are engine-dependent. However, we recommend that particle-based simulation engines use consistent feature names in order to facilitate integration with tools in OPS and to simplify communication between engines. These are the features we include for all particle-based engines in OPS:

• coordinates: Positions of the particles with units attached (for engines that have explicit units, such as OpenMM). List of list: the outer list loops over the atoms, while the inner list loops over spatial dimension (typically 3).

• xyz: Positions of the particles without units attached. Same shape as coordinates.

• velocities: Velocities of the particles with units attached (for engines that have explicit units). Same shape as coordinates.

• masses: The masses of the system. Units (whether implicit or explicit) should be of actual mass, not mass/mole (as is often done in cases where energies are reported per mole). This may be used to calculate kinetic energy. Shape is the length of a

• box_vectors: Box vectors for a periodic system, or None if system is not periodic. This is usually a 3x3 matrix. OPS uses the same format as MDTraj.

• engine: The engine instance that created this snapshot. Useful for checking provenance of data.

Note that implementation of these may by such that a single instance is used by all snapshots. For example, all snapshots generated by a given engine may share the same list of masses (in order to prevent redundant storage). However, these are still accessible from the snapshot itself.

Engines with specific needs may include other features. For example, wavefunction information might be included for an engine based on ab initio dynamics. For other features, see the documentation for the specific OPS engine wrapper.

For OPS engines that support it (including the OpenMM engine), trajectories can be easily converted to MDTraj trajectories with mdtraj_trajectory = trajectory.to_mdtraj(). From there, one can use all analysis tools in MDTraj, as well is its ability to write trajectories to many file formats for input to other analysis programs. In addition, you can use MDTraj as a gateway to other libraries: for example, its integration with nglview can be used for molecular structure visualization.