lada 1.0.0

LAMMPS Data Analysis library

LaDa

LaDa (LAMMPS Data Access) is a lightweight Python package for parsing common LAMMPS output formats. It provides simple, streaming-friendly access to dump files, log files, and LAMMPS data files, with minimal dependencies.

---

🚀 Installation

Install using pip from PyPI:

pip install lada

Note: The package is designed to be used with Python 3.8+.

---

📦 Package structure

The core parser modules are located in src/LaDa/parsers/:

• dump_parser.py - streaming parser for LAMMPS dump files (i.e., output from dump command) using iter_dump_frames()
• log_parser.py - parser for LAMMPS log files using read_lammps_log()
• data_parser.py - parser for LAMMPS data files (i.e., output form write_data command) using read_data_file()

To keep imports simple, the package exports the most common entry point:

from LaDa import iter_dump_frames, read_lammps_log, read_data_file

---

🧩 1) Parsing LAMMPS dump files (dump_parser.py)

Main API

from LaDa import iter_dump_frames

for frame in iter_dump_frames("path/to/dump_file.dump"):
    # metadata is a dict of "ITEM:" blocks
    timestep = frame.metadata["TIMESTEP"]          # int
    box_bounds = frame.metadata["BOX BOUNDS pp pp pp"]

    # get numeric columns by name
    ids = frame.get_column("id")
    xs  = frame.get_column("x")

    # convert the atom block into a pandas DataFrame
    df = frame.to_dataframe()

Notes on metadata conversion

• TIMESTEP is returned as an int.
• NUMBER OF ... entries (e.g., NUMBER OF ATOMS) are converted to int.
• BOX BOUNDS ... entries are parsed into numeric values:
  • For orthogonal boxes, you get a 2D NumPy array shape (3, 2).
  • For triclinic boxes, you get a 2D NumPy array shape (3, 3) where the 3rd column contains tilt factors (xy, xz, yz).

Column helpers

These helpers avoid manual index lookups:

ids = frame.get_column("id")
atom_ids = frame.get_column_or("id", default=None)
col_idx = frame.column_index("type")

---

📝 2) Parsing LAMMPS log files (log_parser.py)

Main API

from LaDa import read_lammps_log

thermo = read_lammps_log("log.lammps")
print(thermo.columns)        # column names (Step, Temp, E_pair, ...)
print(thermo.get("E_pair")) # numpy array of E_pair values

# Convert to pandas DataFrame
df = thermo.to_pandas()

What it parses

• It extracts the table between the Per MPI rank memory allocation marker and the Loop time marker.
• The first non-empty line in that section is treated as the header.

---

🧬 3) Parsing LAMMPS data files (data_parser.py)

Main API

from LaDa import read_data_file

lammps_data = read_data_file("data.lmp")

# Get a parsed section as a NumPy array
atoms = lammps_data.get("Atoms")

# Convert a section to pandas (smart infer columns for Atoms/Bonds/Velocities)
df_atoms = lammps_data.to_pandas(section="Atoms")

Notes on atom style detection

• The parser attempts to detect the atom style from the comment in the Atoms section header, e.g. Atoms # atomic.
• If detected, it uses the correct column layout for that style (e.g., x y z vs qx qy qz).

---

License

MIT