The code in this repository pulls in various data sources from the LeConte dropbox folder and performs some postprocessing before compiling it into netcdf files.
This code will not work without access to the LeConte dropbox!
The instructions below are for macOS, but should mostly work for other unix systems.
Install the python package manager Miniconda.
Clone this repository.
git clone https://github.com/jessecusack/LeConte_postprocessing.git
The above can also be achieved with ssh keys but requires some initial setup (recommended, but you need a github.com account). Alternatively, download the repository directly from github.
Using the terminal from within the downloaded repository, install the conda environment.
conda env create -f environment.yml
This will create a new python environment called lcpp with all the packages needed to run the code in this repository. You can see what environments you have with conda info --env.
Activate the environment:
conda activate lcpp
Note this is not a permanent activation and environments need to be reactivated in every new terminal window.
Run the postprocessing scripts located in the code folder, for example:
cd code
python -W ignore sep2018.py
where the -W ignore option specifies that warnings should not be displayed. This is not strictly necessary but the text output to terminal may be cleaner. The code makes some assumptions about where the Dropbox folder is located, but different paths can be specified if this fails.
The processed output will be placed, by default, in the proc directory.
The processing scripts can accept arguments, for example,
python sep2018.py --help
will display the optional argument list. You should see something like,
usage: sep2018.py [-h] [-ld LECONTE] [-ad ADCP] [-sd SAVE]
optional arguments:
-h, --help show this help message and exit
-ld LECONTE, --leconte LECONTE
path to LeConte Dropbox directory
-ad ADCP, --adcp ADCP
path to LeConte ADCP directory
-sd SAVE, --save SAVE
path to save processed data
which indicates that you can specify differet paths to the data and save directories, e.g.
python sep2018.py -ad some/path/to/adcp_final
This might be useful if the defaults fail on your computer.
The development environment contains more modules for testing.
conda activate lcpp-dev
python -m ipykernel install --user --name lcpp-dev --display-name lcpp-dev
conda deactivate
The oce package in R has a lot of tools for processing oceanographic data. To make use of it we have to first install R e.g.
brew install r
Optionally install the GUI editor 'Rstudio', which is a bit like matlab (brew install rstudio).
To get oce, first start up R with the command
R
then either install the standard package which is updated infrequently,
install.packages("oce")
or install the frequently updated (but potentially buggy) development branch (recommended),
install.packages("remote")
remotes::install_github("dankelley/oce", ref="develop")
The above requires a fortran compiler such as gfortran. If you get an error, it could be because R doesn't know where to find the fortran compiler. I followed these instructions to fix the error. In summary, first install gcc using homebrew. Then create a file ~/.R/Makevars and insert the following lines,
FC = /opt/homebrew/Cellar/gcc/[version]/bin/gfortran
F77 = /opt/homebrew/Cellar/gcc/[version]/bin/gfortran
FLIBS = -L/opt/homebrew/Cellar/gcc/[version]/lib
replacing [version] with the actual directory name (in my case it was 11.2.0_3). If you update gcc this fix may no longer work and you'll have to update Makevars appropriately.
My scripts also require:
install.packages("ncdf4")
install.packages("RSQLite") # For loading rsk files
With all the above installed, it should be possible to run the processing scripts from the terminal, e.g.:
Rscript ABLE_deep_2018.R
This is a convenient way to run R from within jupyter lab. I followed the installation instructions here.
R
install.packages('IRkernel')
IRkernel::installspec()
q()
To install oce from source, first fork and clone the oce repository. From the folder that now contains the local oce repository, use:
R CMD build --no-build-vignettes oce
R CMD install <FILENAME>.tar.gz
where FILENAME is probably something like oce_1.5-0.tar.gz. Attempting to build the vignettes was throwing errors so I didn't bother.
Installing oce this was also requires gfortran be installed using the method described in the section above.
To keep my github fork of the repository up to date, do something like...
cd oce
git pull upstream develop
git push origin develop
Many different plots of adp objects can be made, see ?"plot,adp-method". Control the colorbar with zlim argument e.g. zlim = c(-0.1, 0.1). Restrict ranges with subset.
Dylan Winters at OSU has written some great code for parsing ADCP data. This requires matlab with the navigation and robotics toolboxes installed (and perhaps others, I'm not sure)
RBR provides a MATLAB toolbox for working with their instruments (such as the soloT).
Rich Pawlowicz provides several MATLAB toolboxes for reading ADCP data from teledyne RDI, as well as CTD data from Seabird among others.