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Each subdirectory is focuses on a particular problem/question.
The subdirectories may contain R DESCRIPTION and/or python requirements.txt files to help with installing dependencies.
Subdirectories can also contain their own README files.
There are no hard rules here; it's just a quick and easy place to drop your scripts/examples.
Installation: Python
If you're munging through data in Python, you might want to consider using a virtual environment to install your dependencies in.
Virtual Environment Using pip
On a Mac or Linux machine, you can quickly create a virtual environment with the commands below.
If you're installing and using jupyter notebooks this way, you'll also want to ensure that
the user is specified so your installed packages are found properly:
python -m ipykernel install --user
Now, you should be ready to fire up Python and start exploring!
The install.sh (and install_*.sh) files in the root dir are helpers which comb through the subdirs and try to install all dependencies.
They aren't pretty.
Highlights:
data_to_dwc : Abby's example data transformations
obis_subset : Pull data out of OBIS using robis then make some plots in R & in python.
About
Bio data scripts & code samples related to the IOOS code sprint Biological Data Session