DSGRN Python Interface Tutorial

This notebook shows the basics of manipulating DSGRN with the python interface.



In [1]:

    
import DSGRN

Network

The starting point of the DSGRN analysis is a network specification file. We have a network specification file "network.txt" we will load.



In [2]:

    
network = DSGRN.Network("network.txt")



In [3]:

    
print(network)









    



X1 : (X1)(X2+~X3)
X2 : X1 
X3 : (X1)(~X2)



In [4]:

    
print(network.graphviz())









    



digraph {
bgcolor = aliceblue;X1[style=filled fillcolor=beige];
X2[style=filled fillcolor=beige];
X3[style=filled fillcolor=beige];
X2 -> X1[color=black arrowhead="normal"];
X3 -> X1[color=black arrowhead="tee"];
X1 -> X1[color=darkgoldenrod arrowhead="normal"];
X1 -> X2[color=black arrowhead="normal"];
X2 -> X3[color=black arrowhead="tee"];
X1 -> X3[color=darkgoldenrod arrowhead="normal"];
}

Graphviz

Many of the objects in DSGRN provide a method "graphviz" which emits a string understood by the graphviz language. In an iPython notebook we can embed these picture easily.



In [5]:

    
import graphviz



In [6]:

    
graph = graphviz.Source(network.graphviz())



In [7]:

    
graph









    Out[7]:

ParameterGraph

Given a network, there is an associated "Parameter Graph", which is a combinatorial representation of parameter space.



In [8]:

    
parametergraph = DSGRN.ParameterGraph(network)



In [9]:

    
print("There are " + str(parametergraph.size()) + " nodes in the parameter graph.")









    



There are 326592 nodes in the parameter graph.

Parameter

The ParameterGraph class may be regarded as a factory which produces parameter nodes. In the DSGRN code, parameter nodes are referred to simply as "parameters" and are represented as "Parameter" objects.



In [10]:

    
parameterindex = 34892  # An integer in [0,32592)



In [11]:

    
parameter = parametergraph.parameter(parameterindex)



In [12]:

    
parameter









    Out[12]:





<DSGRN.libpyDSGRN.Parameter at 0x7fc258007410>



In [13]:

    
print(parameter)









    



[["X1",[3,3,"6D9000"],[0,2,1]],["X2",[1,2,"D"],[0,1]],["X3",[2,1,"8"],[0]]]

DomainGraph

Let's compute the dynamics corresponding to this parameter node. In particular, we can instruct DSGRN to create a "domaingraph" object.



In [26]:

    
domaingraph = DSGRN.DomainGraph(parameter)



In [15]:

    
domaingraph









    Out[15]:





<DSGRN.libpyDSGRN.DomainGraph at 0x7fc257fc3310>



In [16]:

    
graphviz.Source(domaingraph.graphviz())









    Out[16]:



In [17]:

    
print(domaingraph.coordinates(5)) # ... I wonder what region in phase space domain 5 corresponds to.









    



[1, 1, 0]

MorseDecomposition

Let's compute the partially ordered set of recurrent components (strongly connected components with an edge) of the domain graph.



In [25]:

    
morsedecomposition = DSGRN.MorseDecomposition(domaingraph.digraph())



In [19]:

    
graphviz.Source(morsedecomposition.graphviz())









    Out[19]:

MorseGraph

The final step in our analysis is the production of an annotated Morse graph.



In [20]:

    
morsegraph = DSGRN.MorseGraph()



In [21]:

    
morsegraph.assign(domaingraph, morsedecomposition)



In [22]:

    
morsegraph









    Out[22]:





<DSGRN.libpyDSGRN.MorseGraph at 0x7fc257fc3838>



In [23]:

    
print(morsegraph)









    



{"poset":[[1],[]],"annotations":[["XC {X1, X3}"],["XC {X1, X3}"]]}



In [24]:

    
graphviz.Source(morsegraph.graphviz())









    Out[24]:



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