.. _overview: ############## Quick overview ############## First of all, load osdyn library and set a few environment variables :: source env_osdyn_linux.csh with **env_osdyn_linux.csh** such as .. code-block:: csh # >>> conda initialize >>> # !! Contents within this block are managed by 'conda init' !! if ( -f "/appli/anaconda/versions/4.8.3/etc/profile.d/conda.csh" ) then source "/appli/anaconda/versions/4.8.3/etc/profile.d/conda.csh" else setenv PATH "/appli/anaconda/versions/4.8.3/bin:$PATH" endif # <<< conda initialize <<< # >>> PYTHON environment relative to osdyn >>> conda activate osdyn # <<< PYTHON environment relative to osdyn <<< setenv PYTHONPATH "/path/OSDYN/osdyn/lib" setenv OSDYN_MODEL mars (user favorite model outputs among nemo, croco, symphonie, ww3, mesonh...) Read data to get xarray.Datasets -------------------------------- Here are some quick examples of what you can do with :py:class:`xarray.DataArray` objects. Everything is explained in much more detail in the rest of the documentation. At first, import numpy, pandas and xarray using their customary abbreviations: .. ipython:: python import os from osdyn.utils.data.io import list_files from osdyn.config import get_config_value from osdyn.grcm.generic import moawi_dataset The main added value of osdyn, in addition to the availability of diagnostics dedicated to ocean dynamics compared to the data, is the generic reading of datasets and their small transformation (changes of variable name in particular) in order to share the same coding whathever the data source. Here, examples are shown from moawi_dataset. Note that from the **moawi** function, one gets a model class with its dataset (modelclass.ds = moawi_dataset result), plus the access to all of the methods relative to the model class, such as modelclass.get_z to get the level depths and so on. Atmosphere, ocean or wave modelling outputs ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Select the files from the database .. ipython:: python path = get_config_value("osdyn.grcm.mesonh", "path_v5_3") pattern_file = get_config_value("osdyn.grcm.mesonh", "pattern_obc_v5_3") pattern_date = eval(get_config_value("osdyn.grcm.mesonh", "period_v5_3")) infiles = list_files(os.path.join(path, pattern_file), pattern_date) pattern_file pattern_date infiles Get the common **MesoNH** fields of interest for an oceanographer .. ipython:: python moawi_dataset(infiles, model="mesonh") Let's select a few variables and a subdomain of a **WW3** output. The files of interest are : .. ipython:: python fndict = { "path": get_config_value("osdyn.grcm.ww3", "path_hindcast"), "pattern_file": get_config_value( "osdyn.grcm.ww3", "pattern_file_hindcast" ), "pattern_date": eval( get_config_value("osdyn.grcm.ww3", "period_hindcast") ), "savedbyvar": get_config_value( "osdyn.grcm.ww3", "savedbyvar_hindcast" ), } fndict and the dataset : .. ipython:: python moawi_dataset(fndict, model="ww3", varnames=["foc", "hs", "mapsta"], lons=(5.5, 10), lats=(40.0, 43.5), decode_times=True) And what about **NEMO** ? .. ipython:: python dico = { "path": get_config_value( "osdyn.grcm.nemo", "path_medrys1v1_menor" ), "pattern_file": get_config_value( "osdyn.grcm.nemo", "pattern_medrys1v1_menor" ), "pattern_date": ("2013-01-01", "2013-01-09"), } dico .. ipython:: python moawi_dataset(dico, model="nemo", decode_times=True, varnames=["z_sfc", "xcur"]) Same way for **CROCO** ! .. ipython:: python dico = { "path": get_config_value( "osdyn.grcm.croco", "path_menor" ), "pattern_file": get_config_value( "osdyn.grcm.croco", "pattern_file_menor" ), "pattern_date": ("2013-01-01", "2013-01-09"), } dico .. ipython:: python moawi_dataset(dico, model="croco", decode_times=True, varnames=["z_sfc", "xcur"]) The **default** option reads NetCDF files in which the longitude and latitude are axes .. ipython:: python dico = { "path": get_config_value("osdyn.grcm.ecmwf", "path"), "pattern_file": get_config_value("osdyn.grcm.ecmwf", "pattern_file"), "pattern_date": eval(get_config_value("osdyn.grcm.ecmwf", "period")), } dico .. ipython:: python moawi_dataset(dico, decode_times=True) So, you can select your variable of interest by using osdyn keynames and without knowing the real name in the netCDF file. Readers for MesoNH, WW3, Mars, Nemo, Croco and Symphonie, PREVIMER database,... are available. If the default reader does not fit your data, feel free to add a new reader or ask for a new one. Observations ~~~~~~~~~~~~ A reader has been developped for each type of observation. For instance, to read CTOH - Xtrack product, the use of the dedicated class allows you to read the data and and perform a few estimates or plottings. .. ipython:: python from osdyn.obs.satellite.xtrack import XTrack from osdyn.config import data_sample J222 = XTrack(data_sample('ctoh.sla.ref.J2.medsea.222.nc')) J222.modify_coords(xaxis='lat',taxis='time') J222.ds Here, you have read the NetCDF file from the XTrack class. The xarray.Dataset is available from `J222.ds`. The default Then you can estimate the distances along the track, .. ipython:: python J222.distance_along_track(J222.ds.lon, J222.ds.lat, cumulative=True) J222.ds or estimate the geostrophic velocities .. ipython:: python J222.geostrophy('sla') J222.ds and do some interanual statistics... or specific oceanic diagnostics... See dedicated notebooks for more details and other type of observations from :ref:`nbs`. Way to get the data ------------------- For each type of downloaded type of data, a descriptive .cfg file has been written and saved under $PYTHONPATH/osdyn/database/desc. It allows us to know how to get the data. The scientific reference, the handbook as well as the way to cite are also registered. .. code-block:: python [download] host = "ftp.aviso.altimetry.fr" remote_path = "regional-xtrack-coastal/version_x_track_2017_02/SLA_1Hz_201706/MEDSEA" username = aviso_username passwd = aviso_passwd [reference] ref = "Birol, F., N. Fuller, F. Lyard, M. Cancet, F. Niño, C. Delebecque, S. Fleury, F. Toublanc, A. Melet, M. Saraceno, F. Léger, 2017. “Coastal Applications from Nadir Altimetry: Example of the X-TRACK Regional Products.” Advances in Space Research, 2017, 59 (4), p.936-953. doi:10.1016/j.asr.2016.11.005." user_hdbk = "hdbk_XTRACK_CTOH.pdf" acknowledgement = "" From this file named `xtrack.cfg`, the data have been retrieved by ftp via `download_ftp_tree.py `__). At first, register to the data operational centers to get your login and password. Diagnostics ----------- To guide the user, many notebooks are available from :ref:`nbs`. The list is continuously increased. Some complete scripts are available under `osapp-tools `_ repositery and their results shown from TODO. Any **script_name.py** comes with two configuration files **script_name.ini** and **script_name.cfg**. The user is invited to parameterize the **.cfg** file and launch the script :: python script_name.py The script first reads the default parameters (in .ini file) and re-assigns them if asked thanks to the specifications in .cfg file. It saves the parameters into a **run_name.cfg** file. Then the script runs the main program. Any parameter can be modified at the launch of the script through its arguments:: python script_name.py --input.model nemo .. note:: "model" must be defined in the "input" section of one of the configuration files. Also note that new parameters can be added, both from the .cfg file (under section or not) or from arguments passed to the script.