def matchTrees(tmData1, tmData2, columnList, threshold, resIds=False):
retList = []
if not resIds:
columnsToMean, columnsToStddev = tm3.calcColumnsMeanStddev(
columnList, [tmData1, tmData2])
revData = False
if len(tmData1.tree.keys()) < len(tmData2.tree.keys()):
tmData1, tmData2 = tmData2, tmData1
revData = True
cost = []
names1, names2 = [], []
for node2 in tmData2.tree.keys():
names2.append(node2)
for node1 in tmData1.tree.keys():
names1.append(node1)
costRow = []
for node2 in tmData2.tree.keys():
if not resIds:
score = compareColumns(
node1, node2, columnList, columnsToMean, columnsToStddev)
else:
score = compareColumnsResidues(node1, node2, columnList)
costRow.append(score)
cost.append(costRow)
matches = munkreskuhn.assignAndReturnMatches(cost)
returnConns = []
for match in matches:
if revData:
returnConns.append((names2[match[1]], names1[match[0]], match[2]))
else:
returnConns.append((names1[match[0]], names2[match[1]], match[2]))
return returnConns
def findSimilarTrees(
tmDataList,
columnListNames,
sizeColName,
sizeMin=-1.0,
sizeMax=10000000000,
outputEachPair=False,
justKeepBest=False,
):
"""does munkreskuhn matching over pocket-pocket shapes to get a score
per tree, returns table of these"""
columnList = tmDataList[0].titlesToColumns(columnListNames)
sizeCol = tmDataList[0].titleToColumn(sizeColName)
colToMean, colToStddev = calcColumnsMeanStddev(columnList, tmDataList)
totalMatrix = {}
for tmDataCount1, tmData1 in enumerate(tmDataList):
totalMatrix[tmData1] = {}
for tmDataCount2, tmData2 in enumerate(tmDataList):
if tmDataCount2 > tmDataCount1:
dotData = dot.dot([tmData1, tmData2])
rowNames, colNames, matchMatrix, tooBig = dotData.computeSearchConnections(
1e10000000,
columnList,
colToMean,
colToStddev,
False,
sizeCol,
False,
False,
sizeMin=sizeMin,
sizeMax=sizeMax,
doSelfScore=False,
returnMatrix=True,
)
if not justKeepBest:
matches = munkreskuhn.assignAndReturnMatches(matchMatrix)
sumScore = 0
for match in matches:
sumScore += match[2]
totalMatrix[tmData1][tmData2] = sumScore / float(len(matches))
if outputEachPair: # output a gdl for each pair of the munkres match
newMatches = []
justNodes = tooBig
for match in matches:
node1 = rowNames[match[0]]
node2 = colNames[match[1]]
justNodes.append(node1)
justNodes.append(node2)
newMatches.append([tmData1, tmData2, node1, node2, match[2]])
dotData.matchList = newMatches
dotData.addSearchConnections(1e1000000, remove=True)
dotData.writeGdl(
tmData1.inputFileName + "_" + tmData2.inputFileName + ".gdl",
justNodes=justNodes,
edges=True,
force=True,
)
else: # just find the best match
minMatchMatrix = 1e10000000
for row in matchMatrix:
for entry in row:
if entry < minMatchMatrix:
minMatchMatrix = entry
totalMatrix[tmData1][tmData2] = minMatchMatrix
return totalMatrix
