Argonne AI-Driven Adaptive Sampling System
AIDAS is the Argonne AI-Driven Adaptive Sampling System, a Python package for AI-guided instrument tasking over the Argonne Testbed for Multiscale Observational Studies (ATMOS). It detects mesoscale boundaries in NEXRAD radar data with deep learning models and uses those detections to cue ground-based instruments – closing the loop from radar observation to lidar scan strategy.
The current release contains a model that determines the lake breeze front location from the 0.5 degree scan of the NEXRAD radar, utilities for deriving an optimal instrument pointing direction from the resulting mask, and scan triggering for Halo Photonics Doppler lidars.
Installation
The recommended way to install AIDAS is via pip. This will ensure you get the latest stable release and all required dependencies:
pip install aidas-atmosGetting Started
After installation, you can import AIDAS in your Python scripts or notebooks:
import aidasSee the documentation and example notebooks for usage details.
Features
Lake breeze detection from NEXRAD radar data using deep learning models.
Easy-to-use API for loading and processing radar data.
Instrument pointing utilities for adaptive sampling.
Scan triggering for Halo Photonics Doppler lidars.
Example notebooks demonstrating functionality.
Links
Documentation: https://rcjackson.github.io/AIDAS/
Source code: https://github.com/rcjackson/AIDAS
Free software: BSD license
Project history
AIDAS was previously released on PyPI as adam-atmos (the ATMOS Analogue Digital Twin, or ADAM) through version 0.5.0. The adam-atmos distribution is deprecated; see Installation for migration notes.
Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
1 maintainer
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