def fill_cell(self, column, row):
"""Fill a cell using the Needleman-Wunsch algorithm.
In this algorithm we calculate the value of each cell i,j as the
maximum of:
1. F(i-1, j-1) + s(xi, yj) : The value of cell i-1, j-1 plus
the substitution matrix value of the two items (Amino Acids or
Nucleotides) held in the cell.
2. F(i, j-1) - d : The value of cell i, j-1 minus the gap penalty.
3. F(i-1, j) - d : The value of cell i-1, j minus the gap penalty.
Returns:
A DPMatrixCell object that has it's information filled in.
"""
# test to be sure we aren't falling off the matrix.
assert (column >= 0)
assert (row >= 0)
# most special case, if we are at (0,0) in the matrix, this
# is a zero and has no parent
if (column == 0) and (row == 0):
corner_cell = DPMatrixCell(column, row, "", "")
corner_cell.set_value(0)
return corner_cell
# flags so we know which scores to calculate
# default is to calculate them all.
calculate1 = 1
calculate2 = 1
calculate3 = 1
# two other special cases, when we are the top row or in the
# right-most column, then we only need to calculate a
# single score.
# Top Row -> only calculate score 3, and this will be our score
if (row == 0) and (column != 0):
calculate1 = 0
calculate2 = 0
# right-most column -> only calculate score 2 and that is
# our score
elif (column == 0) and (row != 0):
calculate1 = 0
calculate3 = 0
# Now calculate the three scores
# Score 1
score1 = None
while score1 is None and calculate1 == 1:
try:
score1 = self.dpmatrix[(column - 1, row - 1)].get_value() + \
self.sub_matrix.get_sub_value(self.seq1[column - 1],
self.seq2[row - 1])
# if we get either a key or index error the previous
# cell item is not filled and needs to be filled
except KeyError, IndexError:
diag_cell = self.fill_cell(column - 1, row - 1)
self.dpmatrix[(column - 1, row - 1)] = diag_cell
self.dpmatrix[(column - 1, row)] = right_cell
# determine which score is highest of the three
# first calculate the max of all the scores which are not none.
max_list = []
for n in [score1, score2, score3]:
if n:
max_list.append(n)
max_score = max(max_list)
# now set the appropriate value and previous item depending on
# which score is largest, and return the created cell
if max_score == score1:
new_cell = DPMatrixCell(column, row, self.seq1[column - 1],
self.seq2[row - 1])
new_cell.set_value(max_score)
new_cell.set_parent(self.dpmatrix[(column - 1, row - 1)])
return new_cell
elif max_score == score2:
# two cases to create the new cell.
# 1. special case (right column)
if score1 is None and score3 is None:
new_cell = DPMatrixCell(column, row, "", self.seq2[row - 1])
# 2. normal case (elsewhere in the matrix)
else:
new_cell = DPMatrixCell(column, row, self.seq1[column - 1],
self.seq2[row - 1])
new_cell.set_value(max_score)
