DIVA Workshops and training

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.

Workshops

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

About DIVAnd

DIVAnd is not a new release of DIVA, it is another software tool with different

algorithms,
functionalities and
language.

Let's compare apples and oranges

• 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).

On public servers (cloud)

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/

Primary functions

• DIVAndrun: Implements the DIVA algorithm in N dimensions on a structured grid.
• DIVAndgo: Split the domain in overlapping subdomains and calls DIVAndrun on every subdomain (to reduce the memory consumption).
• diva: High-level function which selects the appropriate data.

Installation

Jupyter

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()

Extensions [optional]

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

Other relevant repositories

EMODnet-Chemistry-GriddedMaps

This repository aims to store the notebooks and the instructions to produce the EMODnet Chemistry products (climatologies).

Binder

Most notebooks need more resources that what is can currently available on Binder. The introduction notebooks (introduction to OI and variationa analysis) however work .