def appendFrac(whole, originalNext, averagedWhole=None):
if(averagedWhole is None):
averagedWhole = copy.copy(whole)
scaledNexts = interpulation.getScaledVectors(originalNext)
longestOverlap = 0
bestExtention = None
bestOverlap = None
for next in scaledNexts:
for j in reversed(xrange(1,len(next))):
if(j > len(whole)-1):
continue
match = True
gap = np.mean(whole[-j:]) - np.mean(next[:j])
for i in xrange(j):
if(not isEqual(whole[-j+i], next[i], gap)):
match = False
break
if(match and j>longestOverlap):
longestOverlap = j
bestExtention = next[j:]
bestOverlap = next[:j]
if(bestExtention is not None):
for i in xrange(len(bestOverlap)):
averagedWhole[-i] = ( whole[-i] + bestOverlap[-i])/2.0
pass
return whole + bestExtention.tolist(), averagedWhole + bestExtention.tolist()
return whole, averagedWhole
def prependFrac(whole, originalNext, m1=MEAN_COEFF, m2=STD_COEFF, epsilon=EPSILON_FACTOR, gap=GAP_FACTOR, overlap=OVERLAP_FACTOR):
scaledNexts = interpulation.getScaledVectors(originalNext)
longestOverlap = 0
bestExtention = None
bestOverlap = None
for next in scaledNexts:
for j in reversed(xrange(int(OVERLAP_FACTOR*len(next)),len(next))):
if(j > len(whole)-1):
continue
match = checkMoments(whole[:j],next[:j], m1, m2, epsilon, gap, overlap)
if(match and j>longestOverlap):
longestOverlap = j
bestExtention = next[:-j]
bestOverlap = next[-j:]
if(bestExtention is not None):
for i in xrange(len(bestOverlap)):
#whole[i] = ( whole[i] + bestOverlap[i])/2.0
pass
return whole + bestExtention.tolist()
return whole
