A : NumPy 2darray
Square N-by-N connection matrix of the network
coords: dict
Nodal coordinates of the graph. Format is
{0: (x, y, z),
{1: (x, y, z),
{2: (x, y, z),
Note that the dictionary has to have N keys.
colorvec : NumPy 1darray
Vector of color-values for each node. This could be nodal strength or modular information of nodes
(i.e., to which module does node i belong to). Thus colorvec has to be of length N and all its
components must be in [0,1].
sizevec : NumPy 1darray
Vector of nodal sizes. This could be degree, centrality, etc. Thus sizevec has to be of
length N and all its components must be >= 0.
labels : list or NumPy 1darray
Nodal labels. Format is [‘Name1’,’Name2’,’Name3’,...] where the ordering HAS to be the same
as in the coords dictionary. Note that the list/array has to have length N.
nodecmap : Matplotlib colormap
Colormap to use for plotting nodes
edgecmap : Matplotlib colormap
Colormap to use for plotting edges
linewidths : NumPy 2darray
Same format and nonzero-pattern as A. If no linewidhts are provided then the edge connecting
nodes v_i and v_j is plotted using the linewidth A[i,j]. By specifying, e.g.,
linewidhts = (1+A)**2, the thickness of edges in the network-plot can be scaled.
nodes3d : bool
If nodes3d=True then nodes are plotted using 3d spheres in space (with diameters = sizevec).
If nodes3d=False then the Matplotlib scatter function is used to plot nodes as flat
2d disks (faster).
viewtype : str
Camera position, viewtype can be one of the following
axial (= axial_t) : Axial view from top down
axial_t : Axial view from top down
axial_b : Axial view from bottom up
sagittal (= sagittal_l) : Sagittal view from left
sagittal_l : Sagittal view from left
sagittal_r : Sagittal view from right
coronal (= coronal_f) : Coronal view from front
coronal_f : Coronal view from front
coronal_b : Coronal view from back
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