# Log LAMMPS Reader
Log LAMMPS Reader is a high-performance Rust library and Python extension for reading LAMMPS log files and converting them into DataFrames using the [Polars](https://pola.rs/) library. This project leverages [PyO3](https://pyo3.rs/) to create a Python module that interfaces with Rust code, ensuring both speed and efficiency.
This package returns a polars DataFrame allowing the user to use powerful data manipulations (e.g filters) provided through polars. The user can specify which specific thermo output given by `run` or `mimimize` that is required.
It also has the ability to get the lines in the log file that start with a certain string prefix, like `fix` or `print` extremely quickly using rust backend. This can be parsed using python to get information about the parameters set for the simulation.
## Features
- **High-speed** reading of LAMMPS log files
- Converts log data into Polars DataFrames
- Easily convert DataFrame into other formats like json, csv, parquet etc using polars
- Gets thermo data for multiple thermo runs
- Better data parsing, skips rows if they are invalid (e.g missing newline, non-numeric characters in the log)
- Only stores the needed thermo run data specified by user
- Also able to get lines in the log file which starts with a certain string prefix (e.g 'fix ...')
- Compiled code ensures that it does not any other dependencies at execution.
## Installation
Using pip:
```bash
pip install log-lammps-reader
```
Alternatively look at build instructions to build the project.
## Usage Examples
- Note the `required_thermo_run_id = 0` gives the first data output which might include the minimization run.
- To exclude minimization data, start with `required_thermo_run_id = 1`.
```python
import log_lammps_reader
thermo_number = 0 # Choose the nth number of thermo run
df = log_lammps_reader.new('log.lammps') # polars DataFrame for 1st thermo run
# usually the minimization run
# Or choose the nth number of thermo run (default n = 0)
# n = 0 might consider the MPI minimization data, so in most cases
# start with n = 1
df = log_lammps_reader.new('log.lammps', n)
time = df.get_column('Time') # Get any thermo column
time_squared = time ** 2 # use broadcasting operations similar to numpy
# Use polars to filter the results.
import polars as pl
equilibrated_df = df.filter(pl.col('Time') > 1)
# Convert data to numpy if needed
import numpy as np
step = np.array(df.get_column('Step'))
# Get lines in the log that start with a prefix string
fixes_list = log_lammps_reader.log_starts_with('log.lammps', 'fix')
```
Example of a DataFrame for a LAMMPS log file.
```python
>>> import log_lammps_reader
>>> df = log_lammps_reader.new('log.lammps', 1)
>>> df
shape: (10_000_002, 10)
┌──────────────┬───────────┬───────────┬───────────┬───┬───────┬────────────┬───────────┬───────────┐
│ Step ┆ Time ┆ Temp ┆ Press ┆ … ┆ Atoms ┆ PotEng ┆ KinEng ┆ TotEng │
│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════════╪═══════════╪═══════════╪═══════════╪═══╪═══════╪════════════╪═══════════╪═══════════╡
│ 61.0 ┆ 0.0 ┆ 298.0 ┆ 57.20028 ┆ … ┆ 519.0 ┆ -14.776112 ┆ 19.953113 ┆ 5.1770012 │
│ 70.0 ┆ 0.009 ┆ 296.73074 ┆ 60.840723 ┆ … ┆ 519.0 ┆ -14.721924 ┆ 19.868128 ┆ 5.1462039 │
│ 80.0 ┆ 0.019 ┆ 292.56952 ┆ 72.565657 ┆ … ┆ 519.0 ┆ -14.530972 ┆ 19.589506 ┆ 5.0585341 │
│ 90.0 ┆ 0.029 ┆ 285.36347 ┆ 92.936408 ┆ … ┆ 519.0 ┆ -14.18668 ┆ 19.107012 ┆ 4.9203316 │
│ 100.0 ┆ 0.039 ┆ 275.29149 ┆ 121.91127 ┆ … ┆ 519.0 ┆ -13.681587 ┆ 18.432625 ┆ 4.7510379 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 1.0000003e8 ┆ 99999.969 ┆ 301.90216 ┆ 225.03035 ┆ … ┆ 519.0 ┆ -11.279288 ┆ 20.214389 ┆ 8.9351011 │
│ 1.0000004e8 ┆ 99999.979 ┆ 301.99266 ┆ 220.86566 ┆ … ┆ 519.0 ┆ -11.33326 ┆ 20.220449 ┆ 8.8871881 │
│ 1.0000005e8 ┆ 99999.989 ┆ 302.04158 ┆ 215.55467 ┆ … ┆ 519.0 ┆ -11.406581 ┆ 20.223724 ┆ 8.8171428 │
│ 1.0000006e8 ┆ 99999.999 ┆ 301.61379 ┆ 210.565 ┆ … ┆ 519.0 ┆ -11.471215 ┆ 20.195081 ┆ 8.723866 │
│ 1.00000061e8 ┆ 100000.0 ┆ 301.52726 ┆ 210.15164 ┆ … ┆ 519.0 ┆ -11.475823 ┆ 20.189287 ┆ 8.7134637 │
└──────────────┴───────────┴───────────┴───────────┴───┴───────┴────────────┴───────────┴───────────┘
>>> df.get_column('Time')
shape: (10_000_002,)
Series: 'Time' [f64]
[
0.0
0.009
0.019
0.029
0.039
…
99999.969
99999.979
99999.989
99999.999
100000.0
]
>>> df.get_column('Time').mean()
50000.00399999919
>>> df.get_column('Time').std()
28867.520676357886
# Example of getting the lines that start with a certain prefix in the log file
# Returns a list of strings.
>>> log_lammps_reader.log_starts_with('log.lammps', 'fix')
['fix WALL methane wall/region/tjatjopoulos pore 0.005547314165349033 3.565 0.4824 ${radius}',
'fix WALL methane wall/region/tjatjopoulos pore 0.005547314165349033 3.565 0.4824 30',
'fix NVT all nvt temp ${temp_sim} ${temp_sim} $(100.0*dt)',
'fix NVT all nvt temp 298 ${temp_sim} $(100.0*dt)',
'fix NVT all nvt temp 298 298 $(100.0*dt)',
'fix NVT all nvt temp 298 298 0.10000000000000000555']
```
### Rust API
Clone the repo and add it to your project
```rust
use log_lammps_reader::LogLammpsReader;
fn main() {
let log_file_name = "log.lammps";
// skipping minimization
let required_thermo_run_id = Some(1);
match LogLammpsReader::new(log_file_name.into(), required_thermo_run_id) {
Ok(df) => println!("DataFrame read successfully: {:?}", df),
Err(e) => eprintln!("Error reading DataFrame: {}", e),
}
}
```
## Build From Source
Alternatively, to build the Python module, follow these steps:
### Requirements
- Rust (latest stable version recommended)
- Python 3.8 or later
- Cargo (Rust package manager)
1. Ensure you have `maturin` installed:
```bash
pip install maturin # or use conda or micromamba
```
2. Clone the repository
```bash
git clone https://github.com/GeordyJ/log_lammps_reader.git && cd log_lammps_reader
```
3. Compile the Rust packages and install the python module.
```bash
maturin develop --release
```
Raw data
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"description": "# Log LAMMPS Reader\n\nLog LAMMPS Reader is a high-performance Rust library and Python extension for reading LAMMPS log files and converting them into DataFrames using the [Polars](https://pola.rs/) library. This project leverages [PyO3](https://pyo3.rs/) to create a Python module that interfaces with Rust code, ensuring both speed and efficiency.\n\nThis package returns a polars DataFrame allowing the user to use powerful data manipulations (e.g filters) provided through polars. The user can specify which specific thermo output given by `run` or `mimimize` that is required.\n\nIt also has the ability to get the lines in the log file that start with a certain string prefix, like `fix` or `print` extremely quickly using rust backend. This can be parsed using python to get information about the parameters set for the simulation.\n\n## Features\n\n- **High-speed** reading of LAMMPS log files\n- Converts log data into Polars DataFrames\n- Easily convert DataFrame into other formats like json, csv, parquet etc using polars\n- Gets thermo data for multiple thermo runs\n- Better data parsing, skips rows if they are invalid (e.g missing newline, non-numeric characters in the log)\n- Only stores the needed thermo run data specified by user\n- Also able to get lines in the log file which starts with a certain string prefix (e.g 'fix ...')\n- Compiled code ensures that it does not any other dependencies at execution.\n\n## Installation\n\nUsing pip:\n\n```bash\npip install log-lammps-reader\n```\n\nAlternatively look at build instructions to build the project.\n\n## Usage Examples\n\n- Note the `required_thermo_run_id = 0` gives the first data output which might include the minimization run.\n- To exclude minimization data, start with `required_thermo_run_id = 1`.\n\n```python\nimport log_lammps_reader\n\nthermo_number = 0 # Choose the nth number of thermo run\ndf = log_lammps_reader.new('log.lammps') # polars DataFrame for 1st thermo run\n# usually the minimization run\n\n# Or choose the nth number of thermo run (default n = 0)\n# n = 0 might consider the MPI minimization data, so in most cases\n# start with n = 1\ndf = log_lammps_reader.new('log.lammps', n) \ntime = df.get_column('Time') # Get any thermo column\ntime_squared = time ** 2 # use broadcasting operations similar to numpy\n\n# Use polars to filter the results.\nimport polars as pl\nequilibrated_df = df.filter(pl.col('Time') > 1) \n\n# Convert data to numpy if needed\nimport numpy as np\nstep = np.array(df.get_column('Step'))\n\n# Get lines in the log that start with a prefix string\nfixes_list = log_lammps_reader.log_starts_with('log.lammps', 'fix')\n```\n\nExample of a DataFrame for a LAMMPS log file.\n\n```python\n>>> import log_lammps_reader\n>>> df = log_lammps_reader.new('log.lammps', 1)\n>>> df\nshape: (10_000_002, 10)\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Step \u2506 Time \u2506 Temp \u2506 Press \u2506 \u2026 \u2506 Atoms \u2506 PotEng \u2506 KinEng \u2506 TotEng \u2502\n\u2502 --- \u2506 --- \u2506 --- \u2506 --- \u2506 \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2502\n\u2502 f64 \u2506 f64 \u2506 f64 \u2506 f64 \u2506 \u2506 f64 \u2506 f64 \u2506 f64 \u2506 f64 \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 61.0 \u2506 0.0 \u2506 298.0 \u2506 57.20028 \u2506 \u2026 \u2506 519.0 \u2506 -14.776112 \u2506 19.953113 \u2506 5.1770012 \u2502\n\u2502 70.0 \u2506 0.009 \u2506 296.73074 \u2506 60.840723 \u2506 \u2026 \u2506 519.0 \u2506 -14.721924 \u2506 19.868128 \u2506 5.1462039 \u2502\n\u2502 80.0 \u2506 0.019 \u2506 292.56952 \u2506 72.565657 \u2506 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\u2506 99999.999 \u2506 301.61379 \u2506 210.565 \u2506 \u2026 \u2506 519.0 \u2506 -11.471215 \u2506 20.195081 \u2506 8.723866 \u2502\n\u2502 1.00000061e8 \u2506 100000.0 \u2506 301.52726 \u2506 210.15164 \u2506 \u2026 \u2506 519.0 \u2506 -11.475823 \u2506 20.189287 \u2506 8.7134637 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n>>> df.get_column('Time')\nshape: (10_000_002,)\nSeries: 'Time' [f64]\n[\n 0.0\n 0.009\n 0.019\n 0.029\n 0.039\n \u2026\n 99999.969\n 99999.979\n 99999.989\n 99999.999\n 100000.0\n]\n>>> df.get_column('Time').mean()\n50000.00399999919\n>>> df.get_column('Time').std()\n28867.520676357886\n# Example of getting the lines that start with a certain prefix in the log file\n# Returns a list of strings.\n>>> log_lammps_reader.log_starts_with('log.lammps', 'fix')\n['fix WALL methane wall/region/tjatjopoulos pore 0.005547314165349033 3.565 0.4824 ${radius}',\n 'fix WALL methane wall/region/tjatjopoulos pore 0.005547314165349033 3.565 0.4824 30',\n 'fix NVT all nvt temp ${temp_sim} ${temp_sim} $(100.0*dt)',\n 'fix NVT all nvt temp 298 ${temp_sim} $(100.0*dt)',\n 'fix NVT all nvt temp 298 298 $(100.0*dt)',\n 'fix NVT all nvt temp 298 298 0.10000000000000000555']\n```\n\n### Rust API\n\nClone the repo and add it to your project\n\n```rust\nuse log_lammps_reader::LogLammpsReader;\n\nfn main() {\n let log_file_name = \"log.lammps\";\n // skipping minimization\n let required_thermo_run_id = Some(1);\n\n\n match LogLammpsReader::new(log_file_name.into(), required_thermo_run_id) {\n Ok(df) => println!(\"DataFrame read successfully: {:?}\", df),\n Err(e) => eprintln!(\"Error reading DataFrame: {}\", e),\n }\n}\n```\n\n## Build From Source\n\nAlternatively, to build the Python module, follow these steps:\n\n### Requirements\n\n- Rust (latest stable version recommended)\n- Python 3.8 or later\n- Cargo (Rust package manager)\n\n1. 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