portalcasting's functions are also designed to be portable, allowing
users to set up a fully-functional replica repository on a local or
remote machine. This facilitates development and testing of new models
via a sandbox
Status: Deployed, Active Development
portalcasting package is deployed for use within the Portal Predictions
the underlying R code to populate the directory with up-to-date data,
analyze the data, produce new forecasts, generate new output figures,
and render a new version of the website.
All of the code underlying the forecasting functionality has been migrated
over from the predictions repository,
which contains the code executed by the continuous integration.
Having relocated the code here, the
portalcasting package is the location
for active development of the model set and additional functionality.
The current master branch code is not necessarily always being executed within
the predictions repository.
This is a desired result of our use of a software
in the repository,
which enables reproducibility. Presently, we use a
Docker image of
the software environment to create a container for the code. The
image update (i.e. the integration of the current master branch of
portalcasting into the predictions
lags behind updates to the master branch of
ideally not long behind. The
latest image is built using
The API is moderately well defined at this point, but is still evolving.
You can install the R package from github:
If you wish to spin up a local container from the Portal Predictions
image (to ensure that you are using a copy of the production environment
for implementation of the
portalcasting pipeline), you can run
sudo docker pull weecology/portal_predictions
from a shell on a computer with Docker installed
(Windows users need not include
sudo). A tutorial on using the image
to spin up a container is forthcoming.
Get started with the "how to set up a Portal Predictions directory" vignette
The motivating study—the Portal Project—has been funded nearly continuously since 1977 by the National Science Foundation, most recently by DEB-1622425 to S. K. M. Ernest. Much of the computational work was supported by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through Grant GBMF4563 to E. P. White.
We thank Henry Senyondo for help with continuous integration, Heather Bradley for logistical support, John Abatzoglou for assistance with climate forecasts, and James Brown for establishing the Portal Project.
All authors conceived the ideas, designed methodology, and developed the
automated forecasting system. J. L. Simonis led the transition of code from
the Portal Predictions repo