network.viz
class NetworkLayout(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)
Section titled “class NetworkLayout(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)”Bases: StrEnum, Enum
KamadaKawai = ‘kamada_kawai_layout’
Section titled “KamadaKawai = ‘kamada_kawai_layout’”Spectral = ‘spectral_layout’
Section titled “Spectral = ‘spectral_layout’”Spring = ‘spring_layout’
Section titled “Spring = ‘spring_layout’”create_cmap_from_list(array)
Section titled “create_cmap_from_list(array)”Function to create a cmap from a list of values (corresponding to a condition for each subject.) For example, subject with value = 0 will be mapped black, whereas subject = >1 will be mapped to different colors.
Parameters
Section titled “Parameters”array : Array of integers corresponding to condiditions (can be binary also.).
Returns
Section titled “Returns”cmap : List containing all colors.
creating_node_colormap(percentile_dict)
Section titled “creating_node_colormap(percentile_dict)”Function to create a colormap for a set of nodes based on a percentile dictionary.
Parameters
Section titled “Parameters”percentile_dict : Percentile dictionary with keys identifying clusters.
Returns
Section titled “Returns”cmap : List containing all colors.
membership_distribution(mat, output=‘./membership_distribution.png’)
Section titled “membership_distribution(mat, output=‘./membership_distribution.png’)”Function returning a plot of the distribution of the maximum membership values for each subject and the delta plot of the difference with the second highest membership from the fuzzy c-partitioned matrix.
Parameters
Section titled “Parameters”mat : Membership fuzzy c-partitioned matrix.
output : Output filename and path. Defaults to “./membership_distribution.png”.