AshFall

Full Changelog: https://github.com/CosmicIndustries/AshFall/commits/v1.0.1

AshFall

Installation

Requires Python 3.9+.

pip install pandas numpy seaborn scikit-learn matplotlib

Clone or copy ashfall.py into your working directory.
Usage

python ashfall.py /path/to/your/logfile.log

If no file is specified, defaults to processlasso.log.
Outputs analysis to console, displays a heatmap, and writes CSV results next to the input file.

Output

Console Audit
    Shape, dtypes, null percentages, duplicates, constants.
    Anomaly rate and top anomalous processes.

CSV Files
    <logname>_anomalies.csv → rows flagged as anomalous.
    <logname>_summary.csv → aggregated anomaly counts by process.

Heatmap
    Matrix of anomalies by Basename × ActionId, red intensity indicates anomaly density.

Example

python ashfall.py logs/system_events.csv

Output:

system_events_anomalies.csv
system_events_summary.csv
Heatmap figure displayed inline.

Notes

Only numeric columns are used for anomaly detection.
If no numeric data exists, anomalies default to 0.
Designed for process-level log analysis, but adaptable to other structured log sources.

Full Changelog: https://github.com/CosmicIndustries/AshFall/commits/v1.0.0