Computing bivariate contrasts and time-series anomaly detection.
Code for the publication:
Stachelek J., Vachel A. Kraklow, E. Christi Thompson, L. Turin Dickman, Emily Casleton, Sanna Sevanto, Ann Junghans. In-prep. Eddy covariance towers as sentinels of abnormal radioactive material releases.
# rstats
remotes::update_packages(renv::dependencies("scripts/99_utils.R")$Package)# python
mamba env create -f environment.ymlmamba activate fluxnet
make all## "effect size"
# interaction term F-value
# quantile of F-value relative to other windows
# raw comparison in the initial variable pair screening step:
# stats.percentileofscore(fdist_compilation, f_fl, nan_policy="omit") / 100
# in the event detection flagging step:
# np.quantile(<fdist during event>, [event_quantile_effect]) -> event_effect
# other events flagged if f >= event_effect## dependent variables
# co2: carbon dioxide
# le: latent heat flux
# h: sensible heat flux
# fc: co2 flux
## independent variables
# ta: air temperature
# pa: atmospheric pressure
# ws: wind speed
# ppfd: photon flux density
# p: precipitation
# netrad: net solar radiation
# rh: relative humidity© 2024. Triad National Security, LLC. All rights reserved. This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S. Department of Energy/National Nuclear Security Administration. All rights in the program are reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear Security Administration. The Government is granted for itself and others acting on its behalf a nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.