Package: seapy-ocean
State Estimation and Analysis in PYthon (SEAPY)Tools for working with ocean models and data.SEAPY requires: numpy, scipy, netCDF4, basemap, joblib, and numpy_groupiesInstallationInstall from Conda-ForgeInstall fromconda-forgewith the Conda package manager:$ conda install -c conda-forge seapyYou should also consider making conda-forge your default channel. See theconda-forge tips and tricks page.The Conda-ForgeSEAPY feedstockis maintained by Filipe Fernandes,ocefpaf. As of August 2019 there are binary packages on all the platforms that Conda-Forge supports: Python 3.6 through 3.9 on Linux, Windows and Mac OSX (all 64-bit).To remove seapy:$ conda remove seapyInstall from PyPIInstall fromPyPIwith PIP:$ pip install seapy-oceanNote that on PyPI (but nowhere else) the package name is seapy-ocean to avoid a name clash with another package. The module name is still seapy.SEAPY packages on PyPI have been built and uploaded by Mark Hadfield,hadfieldnz. There is a source distribution that should build with no problems on Linux (and Mac OSX, but we haven't tested it) and a binary distribution for Windows (64-bit).In a Conda environment, it is quite possible to install with PIP, but dependency handling and updating will be cleaner if you use the Conda package.To remove seapy-ocean$ pip uninstall seapy-oceanInstall from source code on GitHub.comThe SEAPY source code is maintained by Brian Powell, (powellb)[https://github.com/powellb]. Releases are made on themaster branchInstall fromGitHub.comwith PIP:$ pip install git+git://github.com/powellb/seapy@masterOR clone a copy of the source and install in editable mode, eg:$ git clone https://github.com/powellb/seapy.git
$ pip install -e seapyWith aneditable-modeinstallation, changes you make to your copy of the source code will take effect when you import the module.If you are building from source in a Conda environment on Windows, you should install them2w64-gccandm2w64-gfortrancompilers. On Linux and OSX the system gcc and gfortran should work OK.ContributingIf you've installed from source in editable mode, then you should definitely consider forking your own copy of the repository. This allows you to keep your changes under revision control on GitHub.com and potentially contribute them to the main project. You should follow the procedures described in thisGit Workflowdocument.Forking on GitHub.comis a lightweight process that won't complicate your workflow and keeps the relationship between your work and the original project clear, so it is strongly advised to do it early. However the immutable and unique nature of Git commits means that you can create and populate a fork later if you want to, as long as you have saved your work somewhere in Git format. To create a fork you will need aGitHub.com user account.All your changes should be committed to a branch other than "master", which is reserved for the master branch in Brian Powell's repository (or copies thereof). A common practice in theexisting SEAPY forksis to use a branch name matching your user name for your own work. However if you are developing a specific feature or bug fix to be pulled into master, it may be sensible to name the branch after that feature or bug fix.ExamplesMany of the time-saving features are in generating fields for running the ROMS model.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_vMost methods available in SEAPY require a grid, which can be specified as a "filename" or as a grid object.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.ncThis 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'.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"})Generate boundary conditions from the climatology>> seapy.roms.boundary.from_roms("my_clim.nc", "my_bry.nc")Generate initial conditions from the climatology>> seapy.roms.initial.from_roms("my_clim.nc", "my_ini.nc")You now have what you need to run your modelTo 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")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.