nemo_eos

f2py'd versions of NEMO Fortran routines coded by Gurvan Madec, Fabien Roquet and others used to calculate density, thermal coefficient of expansion etc that can be called by a python script or by ipython or jupyter lab. Depending on how it is initialised, uses:

• (neos=2) polynomial TEOS-10 EOS (Roquet et al., 2015) inputs T and S are Conservative Temperature and Absolute Salinity

• (neos=-1) MacDougall and Jackett 1994; Macdougall 1987 inputs T and S are potential temperature and practical salinity (psu)

• (neos=0) polynomial approximation to EOS-80 (Roquet, pers. comm.) inputs T and S are potential temperature and practical salinity (psu)

• (neos=1) Simplified EOS (Vallis, 2006)

Currently requires to be installed into an already existing conda/mamba python environment. If this environment does not exist, setup as described in Pre-installation, below.

Pre-installation

I suggest that you use a conda or mamba installation of python. If you have neither, mamba is the one to install at is it faster and more reliable. First make sure that you have a ~/.bash_profile file that reads your ~/.bashrc i.e the ~/.bash_profile file includes lines like

	if [ -f ~/.bashrc ]; then
        . ~/.bashrc   # --> Read ~/.bashrc, if present.
	fi

If the file~/.bash_profile does not include such lines, then add the above, ideally immediately after /etc/bashrc is read

If you have no ~/.bash_profile file then create one: Edit ~/.bash_profile with whatever editor you use, E.g. open the file with vi:

	vi ~/.bash_profile
	i

Paste the code below into vi

	# not necessary but probably useful
	if [ -f /etc/bashrc ]; then
	 . /etc/bashrc   # --> Read /etc/bashrc, if present.
	fi
	# essential
	if [ -f ~/.bashrc ]; then
	 . ~/.bashrc   # --> Read ~/.bashrc, if present.
	fi

Hit esc and then :wq Then download the miniforge installer, and run it:

	cd
	# Remove any old python setups
	rm -rf miniconda3 anaconda mambaforge miniforge
	wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
	sh Miniforge3-Linux-x86_64.sh

Follow prompts; say yes to modifying .bashrc so that base environment is activated on login

Then create environment that you will use for diagnosis and into which the nemo_eos package will go. I suggest xarray, ipython and gsw(for an alternative TEOS-10 EOs library to check against); you will wish to add others.

   mamba create -n big python=3.10 xarray ipython

and move into this environment

   mamba activate big

Installation

First go into parent directory into which you want to download the package.

    cd </path/to/parent>

Then activate big environment if not previously done

    mamba activate big

Then git clone the source from github and move into new directory

   git clone https://github.com/gnurser/nemo_eos.git
   cd nemo_eos

Build and install

   . install.bash

This first installs some required mamba packages into the big environment at ~/miniforge3/envs/big/lib/python3.10/site-packages and then also installs the shared library nemo_rho into big.

This enables any python program operating in the big environment to use routines as in NEMO that calculate the equation of state. Many of the routines are accelerated by openMP threads.

Available routines

• eos_init(neos): Set EOS type

• set_eos_threads(nthreads): Specify number nthreads of threads that routines use for OpenMP.

• get_eos_threads(): returns number of threads that routines use for OpenMP.

• get_r0(depth) : For TEOS10 EOS and new EOS-80 calculate float64 depth-varying offset r0=rho-rfrom float64 depth in m. (Roquet et al., 2015)

• eos_insitu4(T, S, depth): float32 in situ density for 1D float32 arrays of T, S, depth

• eos_insitu4_m(fillvalue, mask, T, S, depth): returns in situ density for 1D float32 arrays of T, S,depth where 1D Boolean array mask=False and returns float32 value fill_value where mask=True.

• eos_sigman4(T, S, depth_km) : float32 potential density referenced to float32 depth depth_km km for 1D float32 arrays T, S

• eos_sigman4_m(fillvalue, mask, T, S, depth_km): masked version of eos_sigman4

• eos_sigma04(T, S, depth_km) : float32 potential density referenced to the surface for 1D float32 arrays T, S

• eos_sigma04(T, S, depth_km) : masked version of eos_sigma04.

• eos_rab4( T, S, depth, alpha, beta): returns float32 1-D arrays of in situ alpha and beta for 1D float32 arrays of T, S, depth.

• eos_rab4_m( fillvalue, mask, T, S, depth, alpha, beta): masked version of eos_rab_ref

• eos_rab_ref4(T, S, depth_km): returns float32 1-D arrays of alpha and beta referenced to float32 d epth depth_km for 1D float32 arrays of T, S.

• eos_rab_ref4_m: masked version of eos_rab_ref4

• eos_pen4(T, S, depth, alpha_pe, beta_be, ppen: returns float32 1-D arrays of non-linear PE anomaly driven by temperature variations (alpha_pe), salinity variations (beta_pe), and total PE (ppen) for 1D float32 arrays of T, S and depth.

• eos_insitu04(T0, S0, depth_km, depth): returns 1D float32 arrays of difference between in situ density at 1D depth array depth and potential density at depth depth_km for float32 temperature and salinity variables T0 and S0

• eos_insitu04_m(fillvalue, mask, T0, S0, depth_km, depth): masked version of eos_insitu04

Public variables accessible to python include:

• Default Boussinesq ocean density rho0, EOS type neos
• Coefficients for Vallis's simplified EOS: rn_lambda1, rn_nu, rn_lambda2, rn_mu1, rn_mu2, rn_a0, rn_b0

Test and check sensitivity of speed to number of threads

If the gsw package is installed comparisons are made with its speed and the values it produces; otherwise it is just internal comparisons.

Check test values OK

	cd test
	python set_test.py

Examine sensitivity to number of threads

	cd test
	ipython threads.ipy

Default is to test for array 1e6 long, and 1 vs 4 threads. Can do 1 vs nthreads and change array_length with the following. Note spaces and double dash. Experience so far suggests nthreads=4 is good on a laptop, but can increase to nthreads=16 on a unix server; gives a speedup of at least 10x.

	ipython -- threads.ipy --nthreads=16 --array_length=1.e7

Explore

I have suggested using ipython here for simpliciy, but of course the below will also work in a Jupyter lab window.

   ipython

   Python 3.10.13 | packaged by conda-forge | (main, Dec 23 2023, 15:36:59) [Clang| 16.0.6 ]
   Type 'copyright', 'credits' or 'license' for more information
   IPython 8.22.1 -- An enhanced Interactive Python. Type '?' for help.

   In [1]: from nemo_eos.nemo_rho import eos

   In [2]: eos.rho0
   Out[2]: array(1026.)

   In [3]: eos.set_eos_threads(16)

   In [4]: eos.get_eos_threads()
   Out[4]: 16

Type in eos.<TAB> to get list of functions, options, constants etc. Then select routine with cursor keys

   In [5]: eos.eos_insitu4_m
            eos_init        eos_insitu4_m   eos_rab_ref4    eos_sigman4     neos            rn_lambda1      rn_nu
            eos_insitu04    eos_pen4        eos_rab_ref4_m  eos_sigman4_m   rho0            rn_lambda2      set_eos
            eos_insitu04_m  eos_rab4        eos_sigma04     get_eos_threads rn_a0           rn_mu1          set_eos_threads
            eos_insitu4     eos_rab4_m      eos_sigma04_m   get_r0          rn_b0           rn_mu2

Then when desired routine is selected, type and other options will disappear

   In [5]: eos.eos_insitu4_m

Then type ?, and ipython will give description of how to call the routine:

   In [5]: eos.eos_insitu4_m?
   Call signature: eos.rho_mn4(*args, **kwargs)
   Type:           fortran
   String form:    <fortran function eos_insitu4_m>
   Docstring:
   rho = eos_insitu4_m(fillvalue,mask,theta,s,depth,[n])

   Wrapper for `eos_insitu4_m`.

   Parameters
   ----------
   fillvalue : input float
   mask : input rank-1 array('b') with bounds (n)
   theta : input rank-1 array('f') with bounds (n)
   s : input rank-1 array('f') with bounds (n)
   depth : input rank-1 array('f') with bounds (n)

   Other Parameters
   ----------------
   n : input int, optional
       Default: shape(mask, 0)

   Returns
   -------
   rho : rank-1 array('f') with bounds (n)