A good visualisation is worth millions of words. That's why our community put an emphasis on ensuring that the produced figures are informative and communicate the results clearly.
The :class:`scona` package has a great visualisation module, which enables you to produce publication-ready plots based on the results of the performed brain network analysis. You are also able to export created figures (save as a file) to include in a research paper or in a publication.
As it says the best explanation is through demonstration. That's why we have created tutorials which are jupyter notebooks that include visualisation examples of the data. In a clear and easy way, these tutorials explain how different visualisation functions can be used to produce different plots and for better data understanding.
- Global Network Measures visualisation tutorial describes how to use the following functions:
- plot_degree_dist() - tool for plotting the degree distribution
- plot_network_measures() - tool for plotting network measures values
- plot_rich_club() - tool for plotting the rich club values per degree
With the help of these functions, you can report measures relating to the whole network.
- Interactive visualisation tutorial provides examples on how to use functions like:
- view_nodes_3d() - view the nodes on a 3d plot
- view_connectome_3d() - view the edges - the connections - of the network on a 3d plot
These tools rely on the excellent nilearn.plotting library.
- Anatomical visualisation tutorial shows the usage of the following functions:
- plot_anatomical_network() - make plots of nodes and edges based on the given anatomical layout
- plot_connectome () - plot connectome on top of the brain glass schematics
These are static visualisions that you could use to report your findings in a published paper.
With proper visualization, a researcher can reveal findings easier, understand complex data relationships and describe obtained insights from analyzed data.