def sparseArrayPlot(indexes, dimensions, reshape=False,grid=True):
#convert sparse array into dense array
arraySize = reduce(operator.mul, dimensions, 1)
denseArray = numpy.zeros(arraySize)
denseArray[indexes]=1
denseArray = numpy.reshape(denseArray,dimensions)
#plot the dense array
plotReturn = arrayPlot(denseArray,reshape=reshape,grid=grid)
return plotReturn
def permanencePlot(permanences,numOfSources,numOfEndPoints, grid=True):
"""
permanences -> [[synapseStart(ie column), synapseEnd(ie input bit), permanence],[45,162,0.230001], ... ]
numOfSources -> e.g. number of columns in a SP
numOfEndPoints -> e.g. number of input bits in a SP
"""
#create an adjacency matrix
adjMatrix = numpy.zeros([numOfSources,numOfEndPoints])
#Set each value to be the permanence of the corresponding synapse or NaN if no synapse exists
adjMatrix.fill(numpy.nan)
for synapse in permanences:
adjMatrix[synapse[0],synapse[1]]=synapse[2]
#plot the matrix using arrayPlot
returnValues = arrayPlot(adjMatrix,grid=grid)
#make it big
returnValues[0].set_size_inches(20,20)
return returnValues