##State Estimation and Analysis in PYthon (SEAPY)##

Tools for working with ocean models and data.

SEAPY requires: numpy, scipy, netCDF4, joblib, and numpy_groupies

###Installation###

As this library is in continued development with regular updates, it is easier to install in an individual location and use git pull rather than python installation.

1. Clone the Repository into your local structure

2. Build the components (you must have gfortran installed):

    % cd seapy && make all
   

   This will build the FORTRAN library, the TAGS file, and the documentation. (you must have sphinx along with the numpydoc extension installed). The documentation contains examples and references for the package routines. The HTML documentation will be linked to doc/html.

3. If the seapy directory is not already in your python path:

    % export PYTHONPATH=`pwd`:$PYTHONPATH
   

You can then test the installation worked by:

    % python
    >> import seapy

Examples

Many of the time-saving features are in generating fields for running the ROMS model.

1. To load the meta information about a model (ROMS, HYCOM, MITgcm, POM, SODA), load an output file (history, average, climatology, grid, etc.) via:

    >> mygrid = seapy.model.asgrid(filename)
   
    >> mygrid
    C-Grid: 32x194x294
   
    >> print(mygrid)
    filename
    32x194x294: C-Grid with S-level
    Available: I,J,_isroms,_nc,angle,cgrid,cs_r,depth_rho,depth_u,depth_v,dm,dn,eta_rho,eta_u,eta_v,f,filename,h,hc,lat_rho,lat_u,lat_v,lm,ln,lon_rho,lon_u,lon_v,mask_rho,mask_u,mask_v,n,name,pm,pn,s_rho,shape,spatial_dims,tcline,theta_b,theta_s,thick_rho,thick_u,thick_v,vstretching,vtransform,xi_rho,xi_u,xi_v
   

2. Most methods available in SEAPY require a grid, which can be specified as a "filename" or as a grid object.

3. Find out how to download global HYCOM data that will span my grid from 1/1/2015 through 5/1/2015:

    >> seapy.model.hycom.load_history("hycom_file.nc", start_time=datetime(2015,1,1),
                                     end_time=datetime(2015,5,1),
                                     grid=mygrid, load_data=False)
    ncks -v water_temp,salinity,surf_el,water_v,water_u -d time,352,352 -d lat,1204,1309 -d lon,2438,2603 http://tds.hycom.org/thredds/dodsC/GLBu0.08/expt_91.1 hycom_file.nc
   

This will display the 'ncks' command necessary to download the data. If you want to have SEAPY download it (not recommended due to server-speed), use 'load_data=True'.

4. Once you have HYCOM data, interpolate it to your grid

    >> seapy.roms.interp.to_clim("hycom_file.nc", "my_clim.nc",
                      dest_grid=mygrid, nx=1/6, ny=1/6,
                      vmap={"surf_el":"zeta", "water_temp":"temp",
                      "water_u":"u", "water_v":"v", "salinity":"salt"})
   

5. Generate boundary conditions from the climatology

    >> seapy.roms.boundary.from_roms("my_clim.nc", "my_bry.nc")
   

6. Generate initial conditions from the climatology

    >> seapy.roms.initial.from_roms("my_clim.nc", "my_ini.nc")
   

7. You now have what you need to run your model

8. To set up data assimilation, download the raw observations (e.g., aviso_map_day1.nc, aviso_map_day2.nc, argo_day1.nc ). You can then process the data:

    >> dt = 400/86400       # time-step of the model in days
    >> aviso_gen = seapy.roms.obsgen.aviso_sla_map(mygrid, dt)
    >> aviso_gen.batch_files(seapy.list_files('.','aviso.*nc'), 'aviso_roms_#.nc')
    >> argo_gen = seapy.roms.obsgen.argo_ctd(mygrid, dt)
    >> obs = argo_gen.convert_file("argo_day1.nc")
    >> obs.to_netcdf("argo_roms_1.nc")
   

9. Put all of the processed observations files together into a file for a given assimilation window

    >> seapy.roms.obs.merge_files(seapy.list_files('.*roms_[0-9]+.nc'), 'roms_obs_#.nc', np.arange([0, 10.1, 5]))
   

There are many more things that can be done, but these show some of the power available via simple commands.