Universal Drone Anomaly Detection Python Package
Project description
Agomax v0.1.5
Universal Drone Anomaly Detection Python Package
Folder Structure
agomax/- Python package with backend logicdata/- Place your data files here (e.g.,base.csv,random1.csv, etc.)models/- Place trained model files here (kmeans.pkl,lof.pkl,svm.pkl,dbscan.pkl,optics.pkl)configs/- Place your rules config here (rules.yaml)
Installation
pip install agomax
Dashboard Usage
import agomax
agomax.dashboard() # Launches Streamlit dashboard
API Usage
from agomax.detect import agomax_detect
result = agomax_detect(
data_source='data/crash.csv',
mode='offline',
rules_path='configs/rules.yaml',
model_dir='models/'
)
print(result)
Data Files
The package includes a demo file crash.csv in the data/ folder for testing and demonstration. You can use this file in the dashboard or with the API to see how anomaly detection works out of the box.
For your own experiments, name your data files as in your prototype: base.csv, random1.csv, wind1.csv, engine1.csv, sensor1.csv, crash.csv and place them in the data/ folder.
Package Setup
To make this a pip-installable package, use the following setup.py:
from setuptools import setup, find_packages
setup(
name='agomax',
version='0.1.4',
author='shaguntembhurne',
author_email='your@email.com',
description='Universal Drone Anomaly Detection Python Package',
long_description=open('README.md').read(),
long_description_content_type='text/markdown',
url='https://github.com/shaguntembhurne/agomax',
packages=find_packages(),
install_requires=[
'pandas',
'numpy',
'scikit-learn',
'pyyaml',
'streamlit',
'plotly'
],
include_package_data=True,
package_data={
'': ['data/crash.csv', 'configs/rules.yaml', 'models/*.pkl'],
},
classifiers=[
'Programming Language :: Python :: 3',
'License :: OSI Approved :: MIT License',
'Operating System :: OS Independent',
],
python_requires='>=3.7',
)
Or use a pyproject.toml for modern builds.
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