octp-pp
Post-processing toolkit for OCTP (Order-n Transport Properties) output files from LAMMPS molecular dynamics simulations.
The computationally intensive work — file parsing, MSD fitting, and all physical-property models — is compiled to native code via a PyO3/maturin Rust extension. Interactive visualisations use Plotly.
Features
- Rust core — parsing, diffusivity fitting, Laliberte density/viscosity, Dewane and Gilliam conductivity models, and concentration conversions all run natively; the Brent–Dekker molarity→molality inversion is ~570× faster than an equivalent
scipy.fsolveloop - Automatic run averaging — pass a list of parallel-run folders; means and standard deviations are computed automatically
- Biased Random Walk (BRW) correction — built-in correction for proton-hopping contributions (OH⁻ / H₃O⁺) matched against experimental conductivity
- Configurable constants — ion charges, molecular weights, hop distance, etc. live in a JSONL file and can be overridden at runtime without touching code
Installation
pip install octp-pp
From source
git clone https://github.com/JelleLagerweij/octp-pp
cd octp-pp
pip install maturin
maturin develop # debug build (fast compile, slower runtime)
maturin develop --release # release build (slow compile, fast runtime)
maturin build(andpip install .) always use the release profile.maturin developdefaults to debug — ideal during active development.
Quick start
import octp_pp as octp
import octp_pp.helpers as hf
# Five parallel NVT runs stored under data/run_1 … data/run_5
mixture = octp.PP_OCTP(
"data",
[f"run_{i}" for i in range(1, 6)],
groups=["wat", "Na", "OH", "SCN"],
dt=2, # timestep in fs
plotting=True, # interactive Plotly figures
)
# Configure non-default file names (if needed)
mixture.filenames(Diff_Onsag="diffonsag.dat", Diff_self="diffself.dat")
mixture.changefit(Minc=7, Mmax=45, er_max=0.05)
# State properties
mixture.density()
mixture.molality("OH", "wat", hf.MOLECULAR_WEIGHTS["WAT"])
mixture.molality("SCN", "wat", hf.MOLECULAR_WEIGHTS["WAT"])
# Transport properties
mixture.viscosity()
mixture.self_diffusivity(YH_correction=True)
mixture.onsager_coeff()
# Structural properties with new features
mixture.coord_number(solvent="wat", interpolate_spline=3) # RDF with interpolation
mixture.bond_angles(smoothing_width=5.0) # Angular smoothing in degrees
# Conductivity
mixture.electrical_conductivity(ion_names=["Na", "OH", "SCN"], ion_charges=[1, -1, -1], method="nernst-einstein")
mixture.electrical_conductivity(ion_names=["Na", "OH", "SCN"], ion_charges=[1, -1, -1], method="direct")
# BRW correction (requires experimental Excel file)
octp.biassed_random_walk(mixture, "Experiments_total.xlsx")
mixture.store() # writes postprocessed.csv next to the data folder
Physical-property helpers (octp_pp.helpers)
All functions are vectorised and delegate to the Rust core.
| Function | Model | Output |
|---|---|---|
statepoint_Laliberte(T, m, salt, fits) |
Laliberte (2004) | density (kg/m³), molarity (mol/L) |
viscosity_Laliberte(T, m, salt, fits) |
Laliberte (2007) | dynamic viscosity (Pa·s) |
dewane_conductivity(T, m, salt, ...) |
Dewane and Hammer | conductivity (S/m) |
KOH_conductivity_Gilliam_combined(T, m, salt, fits) |
Gilliam | conductivity (S/m) |
molarity_to_molality(c, T, salt, fits) |
Brent–Dekker root finder | molality (mol/kg) |
molality_to_mass_fraction(m, salt) |
exact | mass fraction |
mass_fraction_to_molality(w, salt) |
exact | molality (mol/kg) |
molality_to_mol_fraction(m, solvent) |
exact | mole fraction |
mol_fraction_to_molality(x, solvent) |
exact | molality (mol/kg) |
tau_calculator(sig_exp, sig_MD, T, L, N, δ) |
Lagerweij | lifetime τ (s) |
diffusion_adjustment(tau) |
Lagerweij | D_hop (m²/s) |
Fit parameters are stored in a user-provided JSON file (see test/fits_laliberte.json for the expected structure).
Global constants
Loaded from octp_pp/constants.jsonl at import time. Override at runtime without editing any file:
# Add a custom salt for this session
hf.load_constants(updates={"MOLECULAR_WEIGHTS": {"MYMAT": 123.45}})
# Inspect what is loaded
print(hf.MOLECULAR_WEIGHTS)
print(hf.DELTA_HOP)
API reference
PP_OCTP class
| Method | Description |
|---|---|
filenames(**kwargs) |
Set LAMMPS output file names |
changefit(margin, Minc, Mmax, er_max) |
Tune MSD log-log fitting |
check_successful(T_min) |
Drop runs shorter than T_min ns |
mandatory_properties() |
Volume, box size, temperature, particle counts |
pressure(plotting, filter_time) |
Pressure (Pa) with time-based filtering |
density() |
Density (kg/m³) from NPT run |
molarity(group) |
Molarity (mol/L) |
molality(group, solvent, MW_solvent) |
Molality (mol/kg) |
viscosity(plotting) |
Shear + bulk viscosity (Pa·s) |
thermal_conductivity(plotting) |
Thermal conductivity (W/m/K) |
self_diffusivity(YH_correction, ...) |
Self-diffusion coefficients (m²/s) |
onsager_coeff(box_size_check, ...) |
Onsager cross-coefficients (m²/s) |
coord_number(solvent, interpolate_spline, ...) |
Coordination number from RDF with interpolation |
bond_angles(start, stop, smoothing_width) |
Bond-angle distribution with angular smoothing |
read_thermoprop(property, ensemble, plotting, filter_time) |
Read thermo properties with time-based filtering |
electrical_conductivity(ion_names, ion_charges, method, ...) |
Electrical conductivity (S/m) with Nernst-Einstein or direct methods |
store(location, name, for_excel) |
Save results DataFrame to CSV / Excel |
All optional methods call mandatory_properties() automatically if it has not yet been run.
Time-based Filtering
Several methods support filter_time parameter for exponential weighted moving average filtering of time-series data. The filter window is specified in femtoseconds (fs) and is automatically converted to the appropriate number of data points based on the actual time spacing in the data:
# Apply 500 fs moving average filter to pressure data
mixture.pressure(filter_time=500)
# Filter thermodynamic property data
mixture.read_thermoprop('v_TotEn', 'nvt', filter_time=5000)
# Filter energy data
mixture.tot_energy(filter_time=1000)
mixture.pot_energy(filter_time=1000)
Filtering is applied only to plotted data, not to the computed averages stored in results.
RDF Interpolation
The coord_number method supports Akima1DInterpolator-based interpolation for smoother RDF analysis:
# No interpolation (default)
mixture.coord_number(solvent="wat")
# Interpolate with 3 subdivisions per interval (4x denser grid)
mixture.coord_number(solvent="wat", interpolate_spline=3)
# High-density interpolation (50x denser grid)
mixture.coord_number(solvent="wat", interpolate_spline=50)
interpolate_spline=None,0, or1: No interpolationinterpolate_spline>1: Interpolate with that many subdivisions per original interval
Module-level functions
| Function | Description |
|---|---|
biassed_random_walk(mixture, exp_file) |
BRW correction; writes back to mixture.results |
read(datafile, export) |
Read OCTP MSD file → DataFrame |
local_maximum_finder(data, start, n) |
First local maximum in g(r) |
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for octp-pp 0.2.3
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