def consensusHarness(cSize,depth):
print("^"*100)
a = ArtificialPhylogeny(size=cSize,numChromosomes=5)
evRates = []
while len( a.tree.getTips() ) < depth:
# evRate = rn.uniform(0.2,0.75)
evRate = 0.3
evRates.append(evRate)
a.evolve(evolutionRate=evRate)
print("Finished generating phylogeny. Score is {}. Rates were {}".format( a.tree.getScore(), evRates ))
u = UPGMA( [t.genome for t in a.tree.getTips()])
u.calculate()
root = Tk()
d1 = TreeDrawer(root)
d1.draw(a.tree,row=0,col=0)
d2 = TreeDrawer(root, leafs=d1.leafs)
d2.draw(u.tree,row=0,col=1)
tree = u.tree
uRF = fastRFDist(tree.toTuple(), a.tree.toTuple())
print("Finished upgma. Score is {}. RF distance is {}".format( tree.getScore(), uRF))
uRF = fastRFDist(a.tree.toTuple(), tree.toTuple())
uScore = tree.getScore()
oldScore = tree.getScore()
newScore = 0
counter = 0
n = NNI( tree )
nniTreeDrawers = []
while abs(newScore - oldScore) >= 1 and counter < 5:
counter += 1
n.calculate(deepScoring=True)
oldScore = newScore
newScore = sum([t.getScore() for t in n.trees])
nRFs = [fastRFDist(t.toTuple(), a.tree.toTuple()) for t in n.trees]
print("Running NNI. Current scores are {}. Current RF distances are {}".format([t.getScore() for t in n.trees], nRFs))
nniTreeDrawers = []
for i,tr in enumerate(n.trees):
t= TreeDrawer(root,leafs=d1.leafs)
t.draw(tr,row=1,col=i)
nRFs = [fastRFDist(t.toTuple(), a.tree.toTuple()) for t in n.trees]
print("Finished NNI. Scores are {}. RF distances are {}".format([t.getScore() for t in n.trees], nRFs))
c = ConsensusTree(n.trees)
c.calculate()
cRF = fastRFDist(tree.toTuple(), a.tree.toTuple())
print("Finished consensus. Score is {}. RF distance is {}".format( tree.getScore(), cRF))
tree = Tree( c.conTree )
t1= TreeDrawer(root,leafs=d1.leafs)
t1.draw(tree,row=2,col=0)
c.calculateNewGenomes()
cRF = fastRFDist(c.conTree.toTuple(), a.tree.toTuple())
print("Recalculated Genomes in consensusTree. Score is {}. RF distance is {}".format( tree.getScore(), cRF))
t2= TreeDrawer(root,leafs=d1.leafs)
t2.draw(c.conTree,row=2,col=1)
for x in (0,1,2):
Grid.columnconfigure(root,x,weight=1)
for y in (0,1,2):
Grid.rowconfigure(root,y,weight=1)
root.mainloop()
nScore = tree.getScore()
