This repository includes code for visualizing the machine learning for earth observation workflow. It was presented during the UW Madison Data Science Research Bazaar on data science for health and the environment. Compiled versions of all the notebooks are linked here. In the order that they are run, the notebooks are,
If you would like to run just one part of the pipeline, intermediate results are
saved in this box
folder. To run
this code in an interactive environment with all packages already installed,
there is a Binder environment available
(generic,
Rstudio,
JupyterLab).
If you have Docker installed and would like to run this code locally, you can
pull the krisrs1128/mappingvis image from DockerHub and launch an Rstudio or
JupyterLab environment in your browser. This can be accomplished by running,
shell > docker pull krisrs1128/mappingvis:021401
shell > mkdir ~/test-data # or whatever name you prefer
shell > docker run -p 8889:8889 -p 8787:8787 -u 0 -v ~/test-data:/home/jovyan/data/ -it krisrs1128/mappingvis:021401 bash
docker shell > jupyter lab --port 8889 --ip=0.0.0.0 --allow-root
from your home and docker shells, respectively. For those who are curious, -p xxxx:xxxx makes sure the internal docker network port is visible externally,
-u 0 lets you run docker as root (this is needed for JupyterLab), and -v
ensures that the test-data directory on your local machine is mapped to
/home/jovyan/data on the docker image.
Going to localhost:8889 in your browser and entering the token printed about
by the output from the jupyter lab command should open a JupyterLab environment
with all packages available. It's also possible to use Rstudio in a similar,
though somewhat more tedious, way. The difficulty is that Rstudio doesn't like
to be run as root, so we first have to create a user with a dummy password and
home directory,
docker shell> useradd test
docker shell> passwd test # enter a password
docker shell> mkdir -p /home/test/.rstudio/graphics-r3
docker shell> sudo chown -R test /home/test/.rstudio/
Then, we can copy over the contents of this repo to the new test user and
start Rstudio,
docker shell > cp -r ~/mappingvis /home/test/
docker shell > rstudio-server start
Navigating to localhost:8787 on your home machine will now show an Rstudio
environment. Enter the username and password you created in the previous step,
and you can start running the code.