fastlogfileparser 1.0.0a7

Parse computational chemistry log files, but fast-ly.

FastLogfileParser

Parse logfiles from computational chemistry software, but fast-ly

Install with pip install fastlogfileparser or conda (forthcoming!).

1. ~10x faster than cclib
2. zero dependencies, supports all modern Python version
3. Supports linked jobs, returns a separate result dictionary for each job
4. Retrieves values at every step, not just convergence

Usage

The best way to see how fastlogfileparser works is to check out the tests! They show the syntax for importing, calling, and then accessing the values. A brief summary of overall workflow and usage is provided below.

Design

There is a single function fast_{software}_logfile_parser inside fastlogfileparser.{software} (where {software} is the name of the corresponding package like gaussian or orca) which reads log files and returns the result as a namedtuple (which prevents accidentally changing the values and allows using . syntax to access them).

Usage Example

from fastlogfileparser.gaussian import fast_gaussian_logfile_parser as fglp

# read all jobs from the logfile
job_1, job_2, ..., job_n = fglp("data/result.log")

# access results
print(job_1.frequency_modes)

# show all available values retrieved from the file
print(job_1._fields)

# can also be accessed via
from fastlogfileparser.gaussian import ALL_FIELDS

Fast logfile parser is fastest when you ask it to retrieve only the fields you want, i.e.:

job_1, job_2, job_3 = fglp(FNAME, get=("gibbs", "scf"))

Retrieved Values

Gaussian

Quantity	Key	Type	Frequency
Route Section	route_section	string	1/job
Normal Termination	normal_termination	boolean	1/job
Error	error_string	str	1/job
Maximum Allowed Steps	max_steps	int	1/job
CPU Time	cpu_time	float	1/job
Wall Time	wall_time	float	1/job
Gibbs free energy at 298K	gibbs	float	1/job
Gibbs free energy at 0K	e0_zpe	float	1/job
Enthalpy at 298K	e0_h	float	1/job
HF $^1$	hf	float	1/job
Per-atom Zero Point Energy	zpe_per_atom	float	1/job
Wavefunction Energy $^3$	wavefunction_energy	float	1/job
SCF Energy	scf	list[float]	1/job
Vibrational Frequencies	frequencies	list[float]	1/job
Frequency Modes	frequency_modes	list[list[float]]	1/job
Standardized xyz coords	std_xyz	list[list[float]]	1/step/job
Input xyz coords	xyz	list[list[float]]	1/step/job
Standardized forces	std_forces	list[list[float]]	1/step/job
Mulliken Charges (Summed into Heavy)	mulliken_charges_summed	list[list[float]]	2/job
Charge and Multiplicity	charge_and_multiplicity	list[int]	1/job
Number of Atoms $^2$	number_of_atoms	int	1/job
Number of Optimization Steps $^2$	number_of_optimization_steps	int	1/job

$1$ equals E0 only for non-wavefunction methods
$2$ requires std_xyz to be parsed to find these values
$3$ E0 for wavefunction methods

Orca

Quantity	Key	Type	Frequency
Route Section	route_section	string	1/job
Total Run Time $^1$	run_time	float	1/job
Charge and Multiplicity	charge_and_multiplicity	list[int]	1/job
Final Single Point Energy	energy	float	1/job
Input xyz coords	input_coordinates	list[list[float]]	1/job

$1$ ignores milliseconds

How much fast-ly-er?

FastLogfileParser uses REGEX and only REGEX to retrieve data from logfiles, spending as much time in Python's excellent C-based REGEX library as possible.

See comparison.py to run for yourself (install with pip install .[demos]), but in short:

• compared to cclib, fastlogfileparser is ~10x as fast and returns all values for intermediate steps in simulation (but cclib supports retrieving a different set of values)
• compared to ase, fastlogfileparser is ~2x slower, but returns far more values and in a more readily accessible format

Development Notes

FastLogfileParser is written in a purely functional style.

Running Tests

Install FastLogfileParser with the optional [dev] dependencies, i.e. from a local clone run `pip install -e ".[dev]"

Rather than keep the gigantic log files saved in the git repo directly, they are compressed to make cloning easier. Before running tests, navigate to test and run python data_loader.py decompress to prepare the needed logfiles.

To add new data for the tests to the repo, perform the previous step and then run python data_loader.py compress. This may take some time to finish executing (a minute or so).