Python library for building PyPhi network graphs
Utilities for building PyPhi networks that need to be large and/or malleable.
You can install graphiit from PyPi:
pip install graphiit
Or the latest development version from Github:
pip install git+https://github.com/grahamfindlay/graphiit@develop
At the core of graphiit is the Graph object.
>>> from graphiit import Graph
Graph takes a graph configuration as a parameter:
>>> graph_config = [ ('A', 'OR', 'B', 'C'), ('B', 'AND', 'A', 'C'), ('C', 'XOR', 'A', 'B')] >>> graph = Graph(graph_config)
Each line in the configuration specifies the name of a node, the mechanism that the node implements, and the nodes in the network which input to the node. For example, in the above configuration the line ('A', 'OR', 'B', 'C') specifies that node A is an OR-gate over the inputs B and C.
Once we’ve initialized a Graph object, we can create a PyPhi Network:
>>> network = graph.pyphi_network()
That’s it! You can now perform all IIT computations using this network.
While graphiit has a number of builtin mechanisms, you can easily specify your own by defining a function which takes the states of the input nodes as a parameter and returns True if the mechanism should be on and False if it should be off.
>>> def ALWAYS_ON(inputs): ... return True
You can then use this function in the graph configuration:
>>> graph_config = [('A', ALWAYS_ON, 'A')] >>> graph = Graph(graph_config)
See graphiit.micro_mechanisms for implementations of the basic mechanisms.
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