Output files and the run log¶

Every run writes all of its files into one self-contained folder, set by output_dir (default traj/) and named <outname> (default traj). This page lists every file TOPO produces, explains the state-log format and how to parse it, and describes the run-provenance record.

The files a run produces¶

After topo-mdrun -f md.ini finishes, the run folder contains:

File	Format	Contents / use
`<outname>.dcd`	binary	Trajectory — coordinates written every `nstxout` steps. Open with VMD, MDAnalysis, or mdtraj (load alongside the `.psf`).
`<outname>.log`	text	State log — step, time, energies, temperature, speed, ETA, every `nstlog` steps. Fixed-width and machine-parsable (see below).
`<outname>.psf`	text	Topology of the CA model (atoms, masses, charges, bonds). Needed to load the `.dcd` in analysis tools.
`<outname>.chk`	binary	Checkpoint — full dynamical state (positions and velocities), every `nstchk` steps. Used to restart (Tutorial 3 — Restarting a run & understanding the outputs).
`<outname>_final.pdb`	text	Last conformation (CA PDB). Reuse as `init_position` to seed a follow-up run.
`<outname>_runinfo.log`	text (INI)	Run provenance — package versions, hardware, GPU, timing, coordinate/velocity sources. See below.

Conditional files:

<outname>_multi.psf — written when n_copies > 1: the combined multi-chain topology matching the multi-copy .dcd (the plain .psf remains the single-chain topology). See Tutorial 4 — Many copies in one run (better GPU utilization).
<outname>_quench.dcd / <outname>_quench.log — written when anneal = yes: the quench-phase trajectory and log, kept separate from the production .dcd / .log so the hot phase never contaminates your production ensemble. See Tutorial 6 — Temperature annealing & quenching.
<pdb_prefix>_stride.dat — cached STRIDE hydrogen-bond output, written next to the input PDB (not in the run folder) the first time the model is built. Reused automatically on later runs; delete to force regeneration.

Tip

Each run overwrites the traj/ folder. To keep several runs side by side, point each at its own folder (output_dir = runs/P0CX28_T300) or change outname.

The state log format¶

The .log is written by topo.reporter.topo_reporter.topoReporter, a subclass of OpenMM’s StateDataReporter tuned for readability and easy parsing. Each float column is rounded to log_precision decimals (default 4) and right-justified to log_width characters (default 14), columns are separated by two spaces, and the header is a # comment line:

#         Step       Time (ps)   Potential Energy (kJ/mole)   Kinetic Energy (kJ/mole)   ...   Temperature (K)   Speed (ns/day)   Time Remaining
           1000           15.0                   -1234.5678                    312.4567   ...          301.2345         123.4567       0:01:23
           2000           30.0                   -1241.0021                    305.1180   ...          299.8765         124.0012       0:01:10

Default columns: step, time (ps), potential energy, kinetic energy, total energy, temperature, speed (ns/day), and estimated time remaining. The energies are in kJ/mol.

``log_precision`` — decimals for float columns; None gives OpenMM’s full repr precision.
``log_width`` — minimum column width for alignment; None disables fixed-width padding.

On a restart, the reporter appends to the existing .log and .dcd (no repeated header), so a multi-stage run stays one continuous record. In an annealing run, the quench writes _quench.log and the production log’s step counter is reset to 0 (see Tutorial 6 — Temperature annealing & quenching).

Parsing the log in Python¶

Use the companion reader, which returns a {column_name: [values...]} dict (numeric columns as floats, others as strings) and respects the two-space separator so multi-word headers like Potential Energy (kJ/mole) stay intact:

import numpy as np
from topo.reporter.topo_reporter import readOpenMMReporterFile

data = readOpenMMReporterFile("traj/traj.log")
step = np.array(data["Step"])
pe   = np.array(data["Potential Energy (kJ/mole)"])
temp = np.array(data["Temperature (K)"])
print("mean T:", round(temp.mean(), 1), "K")

This is the recommended way to plot energy or temperature vs. time, or to check that a run is healthy (stable temperature near ref_t, non-exploding potential energy).

The run-provenance record¶

<outname>_runinfo.log is an INI-format side channel (written by topo.utils.runinfo) that does not affect the simulation but records everything you need to reproduce or debug it:

[run_start] — start timestamp; the control file and checkpoint paths; whether this was a restart; steps planned; initial coordinate and velocity sources (checkpoint vs. init_position vs. pdb_file, and Boltzmann velocities at ref_t); Python / NumPy / ParmEd / OpenMM versions; hostname, OS, CPU count, requested threads; and the selected OpenMM platform.
[cuda_metadata] — on GPU runs: CUDA device name, driver/runtime versions, precision, and an nvidia-smi snapshot.
[run_end] — end timestamp, wall-clock elapsed time, final step and simulated time, and the path to the final structure.

Keep this file with your trajectory: it answers “what exactly did I run, on what hardware, from which coordinates?” long after the job is gone.

Loading a trajectory for analysis¶

import MDAnalysis as mda
u = mda.Universe("traj/traj.psf", "traj/traj.dcd")     # single chain
print(u.atoms.n_atoms, "CA beads,", len(u.trajectory), "frames")

# multi-copy run: use the combined topology
u_multi = mda.Universe("traj/traj_multi.psf", "traj/traj.dcd")

From there, compute RMSD, radius of gyration, or the native-contact score Q (Native-contact analysis (the Q score)). For multi-copy runs, split the combined DCD into per-copy trajectories first with topo.split_chains() (Tutorial 4 — Many copies in one run (better GPU utilization)).