def align(s1, s2, gapOpen, gapExtend):
# instantiate the data type to hand to the wraped C code
t1 = dpAlign.doubleArray(len(s1))
t2 = dpAlign.doubleArray(len(s2))
# populate the sequences with the correct values
for i in range(len(s1)):
t1[i] = s1[i]
for i in range(len(s2)):
t2[i] = s2[i]
# Calculate the alignment with DP
alignStruct = dpAlign.CalcGlobalAlignment(t1, len(s1), t2, len(s2), \
gapOpen, gapExtend)
score = dpAlign.getDPVal(alignStruct)
lenSeq1 = dpAlign.getSeqLen(alignStruct, 1)
lenSeq2 = dpAlign.getSeqLen(alignStruct, 2)
seq1 = {}
seq2 = {}
for i in range(lenSeq1):
seq1[i] = dpAlign.getSeq(alignStruct, 1, i)
for i in range(lenSeq2):
seq2[i] = dpAlign.getSeq(alignStruct, 2, i)
dpAlign.freeAlignInfo(alignStruct)
return score, seq1, lenSeq1, seq2, lenSeq2
def assessAlignment(Prot, alignment):
familyID = dpAlign.intArray(len(Prot))
alignScore = dpAlign.doubleArray(len(Prot)**2)
# build a dictionary of protein names to ensure consistency
i = 0
nameList = {}
for prtName in Prot:
nameList[i] = prtName
i = i + 1
# build the data structures for the wrapped C code
for i in range(len(Prot)):
familyID[i] = int(Prot[nameList[i]].familyID)
for j in range(len(Prot)):
if i != j:
alignScore[i*len(Prot)+j] = \
alignment[(nameList[i], nameList[j], 'score')]
else:
alignScore[i*len(Prot)+j] = 0
assessmentStruct = dpAlign.assessAlignment(familyID, alignScore, len(Prot))
numCorrect = dpAlign.getNumCorrect(assessmentStruct)
ROCarea = dpAlign.getROCarea(assessmentStruct)
partitionSize = dpAlign.getPartitionSize(assessmentStruct)
TPR = zeros((partitionSize, 1))
FPR = zeros((partitionSize, 1))
for i in range(partitionSize):
TPR[i] = dpAlign.getTPR(assessmentStruct, i)
FPR[i] = dpAlign.getFPR(assessmentStruct, i)
maxScore = dpAlign.getMaxScore(assessmentStruct)
minScore = dpAlign.getMinScore(assessmentStruct)
bestDiscernmentVal = dpAlign.getBestDiscernmentVal(assessmentStruct)
bestTPR = dpAlign.getBestTPR(assessmentStruct)
bestFPR = dpAlign.getBestFPR(assessmentStruct)
dpAlign.freeAssessInfo(assessmentStruct)
plot(FPR, TPR, linewidth=2.0)
plot(arange(0.0,1.01,0.01), arange(0.0,1.01,0.01),\
linewidth=1.0, linestyle=':')
title('ROC Curve')
axis([-0.1, 1.1, -0.1, 1.1])
xlabel('False Positive Rate (FPR)')
ylabel('True Positive Rate (TPR)')
if not os.path.exists('../tmp/'):
os.makedirs('../tmp/')
savefig("../tmp/ROC.png")
return numCorrect, TPR, FPR, ROCarea, partitionSize,\
maxScore, minScore, bestDiscernmentVal, bestTPR, bestFPR
