(c) 2018 Applied Geosolutions, LLC
This library provides an efficient means of flexibly performing time series analysis on stacks of gridded data. There is a core python application that breaks the processing job into pieces and launches workers to perform the processing. Each worker has a configurable sequence of processing steps. All the inputs and each step are prescribed in a user-conigured JSON files.
Authors:
- Bobby H. Braswell (rbraswell at ags.io)
- Justin Fisk
- Ian Cooke
Supported in part by NASA Interdisciplinary Science Grant (NASA-IDS) #NNX14AD31G -- Drought-induced vegetation change and fire in Amazonian forests: past, present, and future to University of New Hampshire (Michael Palace, PI)
Also see this directory
correlate.pyx
diff_ts.pyx
gapfill.pyx
interpolate.pyx
multiply.pyx
passthrough.pyx
phenology.pyx
recomposite.pyx
screen.pyx
simpletrend.pyx
summation.pyx
validmask.pyx
Build a container, set an alias to let you run tests using your host machine's working copy, then run the test suite:
$ time docker build . -t mt --no-cache
$ alias rmt="docker run --rm -it -v ${HOME}/src/multitemporal/:/multitemporal"
$ time rmt mt python3 setup.py build_ext --inplace
$ rmt mt pytest -vv -s