.. _scil_connectivity_structural_graph_measures:

scil_connectivity_structural_graph_measures
===========================================

::

	usage: __main__.py [-h] [--length_matrix LENGTH_MATRIX]
	                   [--filtering_mask FILTERING_MASK] [--avg_node_wise]
	                   [--append_json] [--small_world] [--indent INDENT]
	                   [--sort_keys] [-v [{DEBUG,INFO,WARNING,ERROR}]] [-f]
	                   in_conn_matrix out_json
	
	Evaluate graph theory measures from structural connectivity matrices from
	outputs of diffusion MRI tractography. A length-weighted matrix is
	optional but required to extract measures such as: global efficiency,
	local efficiency, betweeness centrality, path length, edge count, and
	small-world omega/sigma. These are not computed if a length matrix is
	not provided. The other computed connectivity measures that do
	not require the length matrix are: modularity, assortativity,
	participation, clustering, nodal_strength, and rich_club.
	
	This script evaluates the measures one subject at the time. To generate a
	population dictionary (similarly to other scil_connectivity_* scripts), use
	the --append_json option as well as using the same output filename.
	>>> for i in hcp/*/; do scil_connectivity_structural_graph_measures
	    ${i}/sc_prob.npy ${i}/len_prob.npy hcp_prob.json
	    --append_json --avg_node_wise; done
	
	Some measures output one value per node, the default behavior is to list
	them all. To obtain only the average use the --avg_node_wise option.
	
	For more details about the measures, please refer to
	- https://sites.google.com/site/bctnet/
	- https://github.com/aestrivex/bctpy/wiki
	
	This script is under the GNU GPLv3 license, for more detail please refer to
	https://www.gnu.org/licenses/gpl-3.0.en.html
	
	----------------------------------------------------------------------------
	Reference:
	[1] Rubinov, Mikail, and Olaf Sporns. "Complex network measures of brain
	    connectivity: uses and interpretations." Neuroimage 52.3 (2010):
	    1059-1069.
	----------------------------------------------------------------------------
	
	positional arguments:
	  in_conn_matrix        Input structural connectivity matrix (.npy).
	  out_json              Path of the output json.
	
	options:
	  -h, --help            show this help message and exit
	  --length_matrix LENGTH_MATRIX
	                        Input length-weighted matrix (.npy).
	  --filtering_mask FILTERING_MASK
	                        Binary filtering mask to apply before computing the measures.
	  --avg_node_wise       Return a single value for node-wise measures.
	  --append_json         If the file already exists, will append to the dictionary.
	  --small_world         Compute measure related to small worldness (omega and sigma).
	                         This option is much slower.
	  -v [{DEBUG,INFO,WARNING,ERROR}]
	                        Produces verbose output depending on the provided level. 
	                        Default level is warning, default when using -v is info.
	  -f                    Force overwriting of the output files.
	
	Json options:
	  --indent INDENT       Indent for json pretty print.
	  --sort_keys           Sort keys in output json.
	
	2.2.2