Forest Modeling for Carbon Sequestration
This project attempts to predict and model tree growth in a novel way.
# Initial install:
sudo pip install virtualenvwrapper
echo "export WORKON_HOME=~/Envs" >> ~/.bashrc
echo "source /usr/local/bin/virtualenvwrapper.sh" >> ~/.bashrc
echo "add r" >> ~/.bashrc.mine
source ~/.bashrc
mkvirtualenv mcmc_growth
pip install -r requirements.txt
# After logging out of DebAthena, to re-initialize env:
sudo pip install virtualenvwrapper
source ~/.bashrc
workon mcmc_growth
All the command must be performed on the application folder.
To execute iterations 1-4:
python run.py [--online] state
state code is a 2-letter code for the state (e.g. ME)
state=US if you want to run the model for all contiguous states.
--online is to download the required files on the fly.
Note that this program doesn't output the model, only the RMSE.
python update.py [--online] state
Run this program after changing the read.py file.
To execute iterations 5-8:
rstudio
Open analyze.R and execute Source.
This program outputs RMSE but stores the models as mdlxy, where x is the iteration number and y represents human interaction (a) or not (b).
Sometimes, the connection the the FIA database is unstable.
To download them manually:
wget http://apps.fs.fed.us/fiadb-downloads/CSV/ME_PLOT.csv -O data/ME_PLOT.csv
wget http://apps.fs.fed.us/fiadb-downloads/CSV/ME_TREE.csv -O data/ME_TREE.csv
wget http://apps.fs.fed.us/fiadb-downloads/CSV/ME_COND.csv -O data/ME_COND.csv
For other states, replace ME with the corresponding state code.
cd ~/workspace/mcmc_growth
workon mcmc_growth
jupyter notebook
pip freeze > requirements.txt