Modularity

Functions used for the modularity computation

ComputeNetList

class neuropype_graph.nodes.modularity.ComputeNetList(from_file=None, **inputs)

Description:

Format correlation matrix to a list: format i j weight (integer = float * 1000)

Inputs:

Z_cor_mat_file:

type = File, exists=True, desc=’Normalized correlation matrix’, mandatory=True

coords_file:

type = File, exists=True, desc=’Corresponding coordiantes’, mandatory=False

threshold:

type = Float, xor = [‘density’], mandatory = False

density:

type = Float, xor = [‘threshold’], mandatory = False

Outputs:

net_List_file:

type = File, exists=True, desc=”net list for radatools”

ComputeNodeRoles

class neuropype_graph.nodes.modularity.ComputeNodeRoles(from_file=None, **inputs)

Description:

Compute node roles from lol modular partition and original network

Inputs:

rada_lol_file:

type = File, exists=True,desc=’lol file, describing modular structure of the network’, mandatory=True

Pajek_net_file:

type = File(exists=True, desc=’net description in Pajek format’, mandatory=True

role_type:

One of Enum(‘Amaral_roles’, ‘4roles’), desc=’definition of node roles, Amaral_roles = original 7 roles defined for transport network (useful for big network), 4_roles = defines only provincial/connecteur from participation coeff’, usedefault=True

Outputs:

node_roles_file:

type = File, exists=True, desc=”node roles with an integer code”

all_Z_com_degree_file:

type = File,exists=True, desc=”value of quantity, describing the hub/non-hub role of the nodes”

all_participation_coeff_file

type = File, exists=True, desc=”value of quality, descibing the provincial/connector role of the nodes”