def parseMods(mods, alignerArgs, indices, skipped):
mods.reverse()
while mods:
mod = mods.pop()
if mod == '-' + indices[0]:
alignerArgs.NEWICK_TREE_STRING = mods.pop()
continue
if mod == '-' + indices[1]:
alignerArgs.SEQUENCE_FILES = parseVarArgs(mods)
continue
if mod == '-' + indices[2]:
alignerArgs.OUTPUT_FILE = mods.pop()
continue
if mod == '-' + indices[3]:
alignerArgs.OUTPUT_TREE_FILE = mods.pop()
continue
if mod == '-' + indices[4]:
alignerArgs.OUTPUT_SCORE_FILE = mods.pop()
continue
if mod == '-' + indices[5]:
A = float(mods.pop())
C = float(mods.pop())
G = float(mods.pop())
T = float(mods.pop())
alignerArgs.EXPECTED_CHARACTER_FREQUENCIES = normaliseWV([ A, C, G, T ])
continue
if mod == '-' + indices[6]:
alignerArgs.EMPIRICALLY_ESTIMATE_CHARACTER_FREQUENCIES = True
continue
if mod == '-' + alignerArgs.MAKE_FINAL_ALIGNMENT_INDICE:
alignerArgs.MAKE_FINAL_ALIGNMENT = not alignerArgs.MAKE_FINAL_ALIGNMENT
continue
skipped.append(mod)
return indices[7:]
def calculateTreeNodeCosts(binaryTree, alpha=1.5):
def avg(binaryTree, i, j):
if binaryTree.internal:
return (i[binaryTree.left.traversalID.mid] - normaliseWV(j[binaryTree.left.traversalID.mid])[0]) + \
(i[binaryTree.right.traversalID.mid] - normaliseWV(j[binaryTree.right.traversalID.mid])[0])
return 0.0
def subMatrix(distance):
i = 0.5 + 0.5*math.exp(-2.0*distance*alpha)
return ( (i, 1.0 - i), (1.0 - i, i) )
binaryTreeNodes = []
getBinaryTreeNodes(binaryTree, binaryTreeNodes)
subMatrices = [ subMatrix(i.distance) for i in binaryTreeNodes ]
leaves = [ (1.0, 0.0) for i in xrange(0, binaryTree.traversalID.midEnd) ]
j = felsensteins(binaryTree, subMatrices, (0.5, 0.5), leaves, 2)
for i in j.keys():
j[i] = normaliseWV(j[i])[0]
finalCosts = [ avg(i, j, felsensteins(i, subMatrices, (0.5, 0.5), leaves, 2)) for i in binaryTreeNodes ]
return finalCosts
def calculateTreeNodeCosts(binaryTree, alpha=1.5):
def avg(binaryTree, i, j):
if binaryTree.internal:
return (i[binaryTree.left.traversalID.mid] - normaliseWV(j[binaryTree.left.traversalID.mid])[0]) + \
(i[binaryTree.right.traversalID.mid] - normaliseWV(j[binaryTree.right.traversalID.mid])[0])
return 0.0
def subMatrix(distance):
i = 0.5 + 0.5 * math.exp(-2.0 * distance * alpha)
return ((i, 1.0 - i), (1.0 - i, i))
binaryTreeNodes = []
getBinaryTreeNodes(binaryTree, binaryTreeNodes)
subMatrices = [subMatrix(i.distance) for i in binaryTreeNodes]
leaves = [(1.0, 0.0) for i in xrange(0, binaryTree.traversalID.midEnd)]
j = felsensteins(binaryTree, subMatrices, (0.5, 0.5), leaves, 2)
for i in j.keys():
j[i] = normaliseWV(j[i])[0]
finalCosts = [
avg(i, j, felsensteins(i, subMatrices, (0.5, 0.5), leaves, 2))
for i in binaryTreeNodes
]
return finalCosts
def avg(binaryTree, i, j):
if binaryTree.internal:
return (i[binaryTree.left.traversalID.mid] - normaliseWV(j[binaryTree.left.traversalID.mid])[0]) + \
(i[binaryTree.right.traversalID.mid] - normaliseWV(j[binaryTree.right.traversalID.mid])[0])
return 0.0
def empiricallyEstimateNucleotideFrequencies(seqFiles):
return normaliseWV(sumWVA([ calculateCharacterFrequencies(fastaRead(seqFile), dNAMap_IUPACToInt, 4) for seqFile in seqFiles ], 4))
def avg(binaryTree, i, j):
if binaryTree.internal:
return (i[binaryTree.left.traversalID.mid] - normaliseWV(j[binaryTree.left.traversalID.mid])[0]) + \
(i[binaryTree.right.traversalID.mid] - normaliseWV(j[binaryTree.right.traversalID.mid])[0])
return 0.0
