def exprl2alignment(exprl):
"""
@summary: Converts experiments into alignments
@param exprl: The list of experiments to be converted into an alignment
objects
@type exprl: ListType
@author: Vladimir Likic
"""
if not is_list(exprl):
error("the argument is not a list")
algts = []
for item in exprl:
if not isinstance(item, Experiment):
error("list items must be 'Experiment' instances")
else:
algt = Class.Alignment(item)
algts.append(algt)
return algts
def merge_alignments(A1, A2, traces):
"""
@summary: Merges two alignments with gaps added in from DP traceback
@param A1: First alignment
@param A2: Second alignment
@param traces: DP traceback
@return: A single alignment from A1 and A2
@author: Woon Wai Keen
@author: Vladimir Likic
@author: Qiao Wang
"""
# Create object to hold new merged alignment and fill in its expr_codes
ma = Class.Alignment(None)
ma.expr_code = A1.expr_code + A2.expr_code
# create empty lists of dimension |A1| + |A2|
dimension = len(A1.peakpos) + len(A2.peakpos)
merged = [ [] for _ in range(dimension) ]
A1 = A1.peakpos
A2 = A2.peakpos
idx1 = idx2 = 0
# trace can either be 0, 1, or 2
# if it is 0, there are no gaps. otherwise, if it is 1 or 2,
# there is a gap in A2 or A1 respectively.
for trace in traces:
if trace == 0:
for i in range(len(A1)):
merged[i].append(A1[i][idx1])
for j in range(len(A2)):
merged[1+i+j].append(A2[j][idx2])
idx1 = idx1 + 1
idx2 = idx2 + 1
elif trace == 1:
for i in range(len(A1)):
merged[i].append(A1[i][idx1])
for j in range(len(A2)):
merged[1+i+j].append(None)
idx1 = idx1 + 1
elif trace == 2:
for i in range(len(A1)):
merged[i].append(None)
for j in range(len(A2)):
merged[1+i+j].append(A2[j][idx2])
idx2 = idx2 + 1
ma.peakalgt = numpy.transpose(merged)
# sort according to average peak
ma.peakalgt = list(ma.peakalgt)
ma.peakalgt.sort(Utils.alignment_compare)
ma.peakpos = numpy.transpose(ma.peakalgt)
return ma
