python class which combine Numpy and Networkx for create a graph from a n3_array (suppose n=5)
->Every edge has n (5) attributes, which are defined by the matrix itself. <-
Then a dynamic is applied on the graph trough the method move(plane) which evaluate the weighted probability of each edge, the variable "plane" set on which layer of the 3d matrix your acting the dynamic.
The class Grapher manage both the np.array and the nx.Graph. it s possible to generate a normalized random 3d-matrix dim is a 3-tuple (i,h,j)
#class Grapher(object): __Matrix=None __Graph=None __Dim=None __Position=0
def casualfill(dim)
# generate a graph from a random matrix
def setmatrix(MAT)
# generate a graph from a np.array of dimension 3 (dim is MAT.shape)
def move (plane)
# extract a node among neighbors() then change self.__Position to the new node, the variable "plane" set on which layer of the 3d matrix the weight is choosed.
def graph_generator()
#pretty useless