This repository provides a set of Jupyter notebooks (examples and exercises) for the DIVAnd
user workshops and training sessions organised in the frame of H2020 SeaDataCloud project. The notebooks are also used in the FAIR-EASE project.
DIVA
and DIVAnd
are software tools designed to generate gridded fields from in-situ observations.
Event | Location | Dates |
---|---|---|
1st workshop | Liège 🇧🇪 | 3-6 April 2018 |
2nd SeaDataCloud training course | Ostend 🇧🇪 | 19-26 June 2019 |
2nd workshop | Bologna 🇮🇹 | 27-30 January 2020 |
DIVAnd
is not a new release of DIVA
, it is another software tool with different
algorithms,
functionalities and
language.
- Äpfel mit Birnen vergleichen
- Comparer des choux et des carottes
- Paragonare cavoli e patate (compare cabbages and potatoes)
For a single 2D analysis (surface salinity in the Black Sea) on Intel Xeon CPU E5-2650.
DIVA was compiled with the Intel Fortran Compiler.
DIVA - Fortran | DIVAnd - julia | |
---|---|---|
mesh | triangular | structured |
deg. of freedom | 236296 | 236317 |
correlation length | 0.19 | 0.19 |
CPU time | 43.8 s | 8.7 s |
- However, a triangular mesh is greatly more flexible than a structured mesh and has
$C_1$ continuity - The main advantage of
DIVAnd
is that it can work on more than just 2 dimensions (but the requirements of RAM memory increase also).
DIVAnd
has been made available in Virtual Research Environments (VRE) in the frame of European projects.
The deployment is performed using a Docker container.
For instance DIVAnd
can used in projects such as:
- FAIR-EASE: https://fairease.eu/
- Blue-Cloud 2026: https://blue-cloud.org/
DIVAndrun
: Implements the DIVA algorithm in N dimensions on a structured grid.DIVAndgo
: Split the domain in overlapping subdomains and callsDIVAndrun
on every subdomain (to reduce the memory consumption).diva
: High-level function which selects the appropriate data.
Jupyter has to be installed in order to have a notebook interface.
It can be installed and launched (in Julia) with the following commands
using Pkg
Pkg.add("IJulia")
using IJulia
notebook()
It is also recommended to install the following modules which allow, for example, to have the sections automatically numbered:
- https://github.com/ipython-contrib/jupyter_contrib_nbextensions
- https://github.com/Jupyter-contrib/jupyter_nbextensions_configurator
This repository aims to store the notebooks and the instructions to produce the EMODnet Chemistry products (climatologies).
Most notebooks need more resources that what is can currently available on Binder. The introduction notebooks (introduction to OI and variationa analysis) however work .