This repository provides an implementation of the Cost/Loss analysis in Python, as described by Owens et al. (2020).
The Cost/Loss analysis is written in Python 3.7.3 and requires numpy, matplotlib, pandas and astropy. For convenience, all required data are included. OMNI data are available from https://omniweb.gsfc81.nasa.gov/. The updated near-Earth CME list is available from http://www.srl.caltech.edu/ACE/ASC/DATA/level3/icmetable2.htm.
The simplest way to work with CostLoss in conda is to create its own environment. With the anaconda prompt, in the root directory of CostLoss, this can be done as:
>>conda env create -f environment.yml
>>conda activate costloss
Then the examples can be accessed through
>>jupyter lab CostLoss.ipynb
Please contact either Mathew Owens or Luke Barnard.
Please cite this software as Owens, M. J., Lockwood, M., & Barnard, L. A. ( 2020). The value of CME arrival‐time forecasts for space weather mitigation. Space Weather, 18, e2020SW002507. https://doi.org/10.1029/2020SW002507