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State Estimation and Analysis in PYthon

Project description

State Estimation and Analysis in PYthon (SEAPY)

Tools for working with ocean models and data.

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


The simplest way to install is:

   pip install git+git://

If you wish to have a copy of the source locally:

   git clone
   python install


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)
     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("", start_time=datetime(2015,1,1),
                                      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

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'.

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

     >> seapy.roms.interp.to_clim("", "",
                       dest_grid=mygrid, nx=1/6, ny=1/6,
                       vmap={"surf_el":"zeta", "water_temp":"temp",
                       "water_u":"u", "water_v":"v", "salinity":"salt"})
  2. Generate boundary conditions from the climatology

     >> seapy.roms.boundary.from_roms("", "")
  3. Generate initial conditions from the climatology

     >> seapy.roms.initial.from_roms("", "")
  4. You now have what you need to run your model

  5. To set up data assimilation, download the raw observations (e.g.,,, ). 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'), '')
     >> argo_gen = seapy.roms.obsgen.argo_ctd(mygrid, dt)
     >> obs = argo_gen.convert_file("")
     >> obs.to_netcdf("")
  6. 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]'), '', 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.

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