Structure of tutorials and examples

[thomasrobiglio]

Following the diataxis approach to documentation, I propose the following organization of tutorials and examples.

EXPLANATION -> theoretical explanations as already present here + references (might require checking of the equations + adding some figures)

REFERENCE -> API documentation

TUTORIALS The goal of this part is to guide the user through the structure of the important functions in the toolbox.

• [ ] "Core information theoretical measures" Here the goal is to show how to compute entropy and mutual information. I think a simple case with entropy/MI of univariate Gaussians is ok.
• [x] "Higher-order information theoretical measures" Here we show for a single metric (O-info?) how the hoi.metrics class works. Something very similar to the existing example with RSI. Create a simple data, define the model, fit it, print the best multiplets, and plot the results
• [ ] "Simulation of HOI"

HOW-TO GUIDES -> Examples This consists of a gallery of examples (one per metric?) showing the computation of the given metric on simple data (similar to what is done in the corresponding tutorial but with shorter descriptions).

• [ ] Oinfo
• [ ] InfoTopo
• [ ] TC
• [ ] DTC
• [ ] Sinfo
• [ ] GradientOinfo
• [ ] RSI
• [ ] RedundancyMMI
• [ ] SynergyMMI
• [ ] InfoTot

[Mattehub]

@EtienneCmb, I am working on examples and tutorials, it is ok to have one example per metric? For sure there will be some redundancy and repetition: many times the application of different metrics is very similar. But at the same time, there are differences in all the metrics, so if you agree I will add one example per metric. Let me know what you think about that.

[EtienneCmb]

@Mattehub I agree that the code is going to be highly redundant. Maybe a comparison between metrics might be better. I planned a list of examples, I'm working on this right now. If you can, it might be better to clean your function for simulating HOI using gaussians.

[Mattehub]

OK, perfect! Thanks for telling me. So I go to clean the gaussian simulation functions