Code from publication @eScience 2017: Adaptive Lossy Compression of Complex Environmental Indices using Seasonal Auto Regressive Integrated Moving Average Models.

Requirements

This code has been tested on following machine:

Python: 3.6.1
OS: Debian 4.11.6-1 (2017-06-19) testing (buster)
CPU: Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
MEM: 16 GiB 2400MHz DDR4

To recreate the software environment you can use the provided spec-file.txt and requirements.txt files. Currently only GNU\Linux is supported.

conda create -n ENVNAME --file spec-file.txt
conda activate ENVNAME
pip install -r requirements.txt

macOS & Windows: Conda does not support cross-plattform export of package names incl. versions. As soon as this feature is added I'll generate the appropiate macOS and Windows environmental files.

Project structure

- run.py
- data
    - arima_uncompressed : Lossily compressed and uncompressed residue data from ARIMA model output (numpy raw)
    - direct_uncompressed : Lossily compressed and uncompressed original data (numpy raw)
    - original : Daily and monthly environmental indices (numpy raw)
    - dm_weather.nc : Daily environmental indices (netcdf)
    - mm_weather.nc : Monthly environmental indices (netcdf)
- model : Parameters of used ARIMA model
- envelope.py
- manualsarima.py
- transport.py

Run

To run the experiment simply execute python run.py