# Copyright 2002 by Jeffrey Chang. All rights reserved. # This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. """This package implements pairwise sequence alignment using a dynamic programming algorithm. This provides functions to get global and local alignments between two sequences. A global alignment finds the best concordance between all characters in two sequences. A local alignment finds just the subsequences that align the best. When doing alignments, you can specify the match score and gap penalties. The match score indicates the compatibility between an alignment of two characters in the sequences. Highly compatible characters should be given positive scores, and incompatible ones should be given negative scores or 0. The gap penalties should be negative. The names of the alignment functions in this module follow the convention <alignment type>XX where <alignment type> is either "global" or "local" and XX is a 2 character code indicating the parameters it takes. The first character indicates the parameters for matches (and mismatches), and the second indicates the parameters for gap penalties. The match parameters are CODE DESCRIPTION x No parameters. Identical characters have score of 1, otherwise 0. m A match score is the score of identical chars, otherwise mismatch score. d A dictionary returns the score of any pair of characters. c A callback function returns scores. The gap penalty parameters are CODE DESCRIPTION x No gap penalties. s Same open and extend gap penalties for both sequences. d The sequences have different open and extend gap penalties. c A callback function returns the gap penalties. All the different alignment functions are contained in an object "align". For example: >>> from Bio import pairwise2 >>> alignments = pairwise2.align.globalxx("ACCGT", "ACG") will return a list of the alignments between the two strings. The parameters of the alignment function depends on the function called. Some examples: >>> pairwise2.align.globalxx("ACCGT", "ACG") # Find the best global alignment between the two sequences. # Identical characters are given 1 point. No points are deducted # for mismatches or gaps. >>> pairwise2.align.localxx("ACCGT", "ACG") # Same thing as before, but with a local alignment. >>> pairwise2.align.globalmx("ACCGT", "ACG", 2, -1) # Do a global alignment. Identical characters are given 2 points, # 1 point is deducted for each non-identical character. >>> pairwise2.align.globalms("ACCGT", "ACG", 2, -1, -.5, -.1) # Same as above, except now 0.5 points are deducted when opening a # gap, and 0.1 points are deducted when extending it. To see a description of the parameters for a function, please look at the docstring for the function. >>> print newalign.align.localds.__doc__ localds(sequenceA, sequenceB, match_dict, open, extend) -> alignments """ # The alignment functions take some undocumented keyword parameters: # - penalize_extend_when_opening: boolean # Whether to count an extension penalty when opening a gap. If # false, a gap of 1 is only penalize an "open" penalty, otherwise it # is penalized "open+extend". # - penalize_end_gaps: boolean # Whether to count the gaps at the ends of an alignment. By # default, they are counted for global alignments but not for local # ones. # - gap_char: string # Which character to use as a gap character in the alignment # returned. By default, uses '-'. # - force_generic: boolean # Always use the generic, non-cached, dynamic programming function. # For debugging. # - score_only: boolean # Only get the best score, don't recover any alignments. The return # value of the function is the score. # - one_alignment_only: boolean # Only recover one alignment. from types import * MAX_ALIGNMENTS = 1000 # maximum alignments recovered in traceback 00102 class align: """This class provides functions that do alignments.""" 00105 class alignment_function: """This class is callable impersonates an alignment function. The constructor takes the name of the function. This class will decode the name of the function to figure out how to interpret the parameters. """ # match code -> tuple of (parameters, docstring) match2args = { 'x' : ([], ''), 'm' : (['match', 'mismatch'], """match is the score to given to identical characters. mismatch is the score given to non-identical ones."""), 'd' : (['match_dict'], """match_dict is a dictionary where the keys are tuples of pairs of characters and the values are the scores, e.g. ("A", "C") : 2.5."""), 'c' : (['match_fn'], """match_fn is a callback function that takes two characters and returns the score between them."""), } # penalty code -> tuple of (parameters, docstring) penalty2args = { 'x' : ([], ''), 's' : (['open', 'extend'], """open and extend are the gap penalties when a gap is opened and extended. They should be negative."""), 'd' : (['openA', 'extendA', 'openB', 'extendB'], """openA and extendA are the gap penalties for sequenceA, and openB and extendB for sequeneB. The penalties should be negative."""), 'c' : (['gap_A_fn', 'gap_B_fn'], """gap_A_fn and gap_B_fn are callback functions that takes 1) the index where the gap is opened, and 2) the length of the gap. They should return a gap penalty."""), } def __init__(self, name): # Check to make sure the name of the function is # reasonable. if name.startswith("global"): if len(name) != 8: raise AttributeError("function should be globalXX") elif name.startswith("local"): if len(name) != 7: raise AttributeError("function should be localXX") else: raise AttributeError(name) align_type, match_type, penalty_type = \ name[:-2], name[-2], name[-1] try: match_args, match_doc = self.match2args[match_type] except KeyError, x: raise AttributeError("unknown match type %r" % match_type) try: penalty_args, penalty_doc = self.penalty2args[penalty_type] except KeyError, x: raise AttributeError("unknown penalty type %r" % penalty_type) # Now get the names of the parameters to this function. param_names = ['sequenceA', 'sequenceB'] param_names.extend(match_args) param_names.extend(penalty_args) self.function_name = name self.align_type = align_type self.param_names = param_names self.__name__ = self.function_name # Set the doc string. doc = "%s(%s) -> alignments\n" % ( self.__name__, ', '.join(self.param_names)) if match_doc: doc += "\n%s\n" % match_doc if penalty_doc: doc += "\n%s\n" % penalty_doc doc += ( """\nalignments is a list of tuples (seqA, seqB, score, begin, end). seqA and seqB are strings showing the alignment between the sequences. score is the score of the alignment. begin and end are indexes into seqA and seqB that indicate the where the alignment occurs. """) self.__doc__ = doc def decode(self, *args, **keywds): # Decode the arguments for the _align function. keywds # will get passed to it, so translate the arguments to # this function into forms appropriate for _align. keywds = keywds.copy() if len(args) != len(self.param_names): raise TypeError("%s takes exactly %d argument (%d given)" \ % (self.function_name, len(self.param_names), len(args))) i = 0 while i < len(self.param_names): if self.param_names[i] in [ 'sequenceA', 'sequenceB', 'gap_A_fn', 'gap_B_fn', 'match_fn']: keywds[self.param_names[i]] = args[i] i += 1 elif self.param_names[i] == 'match': assert self.param_names[i+1] == 'mismatch' match, mismatch = args[i], args[i+1] keywds['match_fn'] = identity_match(match, mismatch) i += 2 elif self.param_names[i] == 'match_dict': keywds['match_fn'] = dictionary_match(args[i]) i += 1 elif self.param_names[i] == 'open': assert self.param_names[i+1] == 'extend' open, extend = args[i], args[i+1] pe = keywds.get('penalize_extend_when_opening', 0) keywds['gap_A_fn'] = affine_penalty(open, extend, pe) keywds['gap_B_fn'] = affine_penalty(open, extend, pe) i += 2 elif self.param_names[i] == 'openA': assert self.param_names[i+3] == 'extendB' openA, extendA, openB, extendB = args[i:i+4] pe = keywds.get('penalize_extend_when_opening', 0) keywds['gap_A_fn'] = affine_penalty(openA, extendA, pe) keywds['gap_B_fn'] = affine_penalty(openB, extendB, pe) i += 4 else: raise ValueError("unknown parameter %r" \ % self.param_names[i]) # Here are the default parameters for _align. Assign # these to keywds, unless already specified. pe = keywds.get('penalize_extend_when_opening', 0) default_params = [ ('match_fn', identity_match(1, 0)), ('gap_A_fn', affine_penalty(0, 0, pe)), ('gap_B_fn', affine_penalty(0, 0, pe)), ('penalize_extend_when_opening', 0), ('penalize_end_gaps', self.align_type == 'global'), ('align_globally', self.align_type == 'global'), ('gap_char', '-'), ('force_generic', 0), ('score_only', 0), ('one_alignment_only', 0) ] for name, default in default_params: keywds[name] = keywds.get(name, default) return keywds def __call__(self, *args, **keywds): keywds = self.decode(*args, **keywds) return _align(**keywds) def __getattr__(self, attr): return self.alignment_function(attr) align = align() def _align(sequenceA, sequenceB, match_fn, gap_A_fn, gap_B_fn, penalize_extend_when_opening, penalize_end_gaps, align_globally, gap_char, force_generic, score_only, one_alignment_only): if not sequenceA or not sequenceB: return [] if (not force_generic) and \ type(gap_A_fn) is InstanceType and \ gap_A_fn.__class__ is affine_penalty and \ type(gap_B_fn) is InstanceType and \ gap_B_fn.__class__ is affine_penalty: open_A, extend_A = gap_A_fn.open, gap_A_fn.extend open_B, extend_B = gap_B_fn.open, gap_B_fn.extend x = _make_score_matrix_fast( sequenceA, sequenceB, match_fn, open_A, extend_A, open_B, extend_B, penalize_extend_when_opening, penalize_end_gaps, align_globally, score_only) else: x = _make_score_matrix_generic( sequenceA, sequenceB, match_fn, gap_A_fn, gap_B_fn, penalize_extend_when_opening, penalize_end_gaps, align_globally, score_only) score_matrix, trace_matrix = x #print "SCORE"; print_matrix(score_matrix) #print "TRACEBACK"; print_matrix(trace_matrix) # Look for the proper starting point. Get a list of all possible # starting points. starts = _find_start( score_matrix, sequenceA, sequenceB, gap_A_fn, gap_B_fn, penalize_end_gaps, align_globally) # Find the highest score. best_score = max([x[0] for x in starts]) # If they only want the score, then return it. if score_only: return best_score tolerance = 0 # XXX do anything with this? # Now find all the positions within some tolerance of the best # score. i = 0 while i < len(starts): score, pos = starts[i] if rint(abs(score-best_score)) > rint(tolerance): del starts[i] else: i += 1 # Recover the alignments and return them. x = _recover_alignments( sequenceA, sequenceB, starts, score_matrix, trace_matrix, align_globally, penalize_end_gaps, gap_char, one_alignment_only) return x def _make_score_matrix_generic( sequenceA, sequenceB, match_fn, gap_A_fn, gap_B_fn, penalize_extend_when_opening, penalize_end_gaps, align_globally, score_only): # This is an implementation of the Needleman-Wunsch dynamic # programming algorithm for aligning sequences. # Create the score and traceback matrices. These should be in the # shape: # sequenceA (down) x sequenceB (across) lenA, lenB = len(sequenceA), len(sequenceB) score_matrix, trace_matrix = [], [] for i in range(lenA): score_matrix.append([None] * lenB) trace_matrix.append([[None]] * lenB) # The top and left borders of the matrices are special cases # because there are no previously aligned characters. To simplify # the main loop, handle these separately. for i in range(lenA): # Align the first residue in sequenceB to the ith residue in # sequence A. This is like opening up i gaps at the beginning # of sequence B. score = match_fn(sequenceA[i], sequenceB[0]) if penalize_end_gaps: score += gap_B_fn(0, i) score_matrix[i][0] = score for i in range(1, lenB): score = match_fn(sequenceA[0], sequenceB[i]) if penalize_end_gaps: score += gap_A_fn(0, i) score_matrix[0][i] = score # Fill in the score matrix. Each position in the matrix # represents an alignment between a character from sequenceA to # one in sequence B. As I iterate through the matrix, find the # alignment by choose the best of: # 1) extending a previous alignment without gaps # 2) adding a gap in sequenceA # 3) adding a gap in sequenceB for row in range(1, lenA): for col in range(1, lenB): # First, calculate the score that would occur by extending # the alignment without gaps. best_score = score_matrix[row-1][col-1] best_score_rint = rint(best_score) best_indexes = [(row-1, col-1)] # Try to find a better score by opening gaps in sequenceA. # Do this by checking alignments from each column in the # previous row. Each column represents a different # character to align from, and thus a different length # gap. for i in range(0, col-1): score = score_matrix[row-1][i] + gap_A_fn(i, col-1-i) score_rint = rint(score) if score_rint == best_score_rint: best_score, best_score_rint = score, score_rint best_indexes.append((row-1, i)) elif score_rint > best_score_rint: best_score, best_score_rint = score, score_rint best_indexes = [(row-1, i)] # Try to find a better score by opening gaps in sequenceB. for i in range(0, row-1): score = score_matrix[i][col-1] + gap_B_fn(i, row-1-i) score_rint = rint(score) if score_rint == best_score_rint: best_score, best_score_rint = score, score_rint best_indexes.append((i, col-1)) elif score_rint > best_score_rint: best_score, best_score_rint = score, score_rint best_indexes = [(i, col-1)] score_matrix[row][col] = best_score + \ match_fn(sequenceA[row], sequenceB[col]) if not align_globally and score_matrix[row][col] < 0: score_matrix[row][col] = 0 trace_matrix[row][col] = best_indexes return score_matrix, trace_matrix def _make_score_matrix_fast( sequenceA, sequenceB, match_fn, open_A, extend_A, open_B, extend_B, penalize_extend_when_opening, penalize_end_gaps, align_globally, score_only): first_A_gap = calc_affine_penalty(1, open_A, extend_A, penalize_extend_when_opening) first_B_gap = calc_affine_penalty(1, open_B, extend_B, penalize_extend_when_opening) # Create the score and traceback matrices. These should be in the # shape: # sequenceA (down) x sequenceB (across) lenA, lenB = len(sequenceA), len(sequenceB) score_matrix, trace_matrix = [], [] for i in range(lenA): score_matrix.append([None] * lenB) trace_matrix.append([[None]] * lenB) # The top and left borders of the matrices are special cases # because there are no previously aligned characters. To simplify # the main loop, handle these separately. for i in range(lenA): # Align the first residue in sequenceB to the ith residue in # sequence A. This is like opening up i gaps at the beginning # of sequence B. score = match_fn(sequenceA[i], sequenceB[0]) if penalize_end_gaps: score += calc_affine_penalty( i, open_B, extend_B, penalize_extend_when_opening) score_matrix[i][0] = score for i in range(1, lenB): score = match_fn(sequenceA[0], sequenceB[i]) if penalize_end_gaps: score += calc_affine_penalty( i, open_A, extend_A, penalize_extend_when_opening) score_matrix[0][i] = score # In the generic algorithm, at each row and column in the score # matrix, we had to scan all previous rows and columns to see # whether opening a gap might yield a higher score. Here, since # we know the penalties are affine, we can cache just the best # score in the previous rows and columns. Instead of scanning # through all the previous rows and cols, we can just look at the # cache for the best one. Whenever the row or col increments, the # best cached score just decreases by extending the gap longer. # The best score and indexes for each row (goes down all columns). # I don't need to store the last row because it's the end of the # sequence. row_cache_score, row_cache_index = [None]*(lenA-1), [None]*(lenA-1) # The best score and indexes for each column (goes across rows). col_cache_score, col_cache_index = [None]*(lenB-1), [None]*(lenB-1) for i in range(lenA-1): # Initialize each row to be the alignment of sequenceA[i] to # sequenceB[0], plus opening a gap in sequenceA. row_cache_score[i] = score_matrix[i][0] + first_A_gap row_cache_index[i] = [(i, 0)] for i in range(lenB-1): col_cache_score[i] = score_matrix[0][i] + first_B_gap col_cache_index[i] = [(0, i)] # Fill in the score_matrix. for row in range(1, lenA): for col in range(1, lenB): # Calculate the score that would occur by extending the # alignment without gaps. nogap_score = score_matrix[row-1][col-1] # Check the score that would occur if there were a gap in # sequence A. if col > 1: row_score = row_cache_score[row-1] else: row_score = nogap_score - 1 # Make sure it's not the best. # Check the score that would occur if there were a gap in # sequence B. if row > 1: col_score = col_cache_score[col-1] else: col_score = nogap_score - 1 best_score = max(nogap_score, row_score, col_score) best_score_rint = rint(best_score) best_index = [] if best_score_rint == rint(nogap_score): best_index.append((row-1, col-1)) if best_score_rint == rint(row_score): best_index.extend(row_cache_index[row-1]) if best_score_rint == rint(col_score): best_index.extend(col_cache_index[col-1]) # Set the score and traceback matrices. score = best_score + match_fn(sequenceA[row], sequenceB[col]) if not align_globally and score < 0: score_matrix[row][col] = 0 else: score_matrix[row][col] = score trace_matrix[row][col] = best_index # Update the cached column scores. The best score for # this can come from either extending the gap in the # previous cached score, or opening a new gap from the # most previously seen character. Compare the two scores # and keep the best one. open_score = score_matrix[row-1][col-1] + first_B_gap extend_score = col_cache_score[col-1] + extend_B open_score_rint, extend_score_rint = \ rint(open_score), rint(extend_score) if open_score_rint > extend_score_rint: col_cache_score[col-1] = open_score col_cache_index[col-1] = [(row-1, col-1)] elif extend_score_rint > open_score_rint: col_cache_score[col-1] = extend_score else: col_cache_score[col-1] = open_score if (row-1, col-1) not in col_cache_index[col-1]: col_cache_index[col-1] = col_cache_index[col-1] + \ [(row-1, col-1)] # Update the cached row scores. open_score = score_matrix[row-1][col-1] + first_A_gap extend_score = row_cache_score[row-1] + extend_A open_score_rint, extend_score_rint = \ rint(open_score), rint(extend_score) if open_score_rint > extend_score_rint: row_cache_score[row-1] = open_score row_cache_index[row-1] = [(row-1, col-1)] elif extend_score_rint > open_score_rint: row_cache_score[row-1] = extend_score else: row_cache_score[row-1] = open_score if (row-1, col-1) not in row_cache_index[row-1]: row_cache_index[row-1] = row_cache_index[row-1] + \ [(row-1, col-1)] return score_matrix, trace_matrix def _recover_alignments(sequenceA, sequenceB, starts, score_matrix, trace_matrix, align_globally, penalize_end_gaps, gap_char, one_alignment_only): # Recover the alignments by following the traceback matrix. This # is a recursive procedure, but it's implemented here iteratively # with a stack. lenA, lenB = len(sequenceA), len(sequenceB) tracebacks = [] # list of (seq1, seq2, score, begin, end) in_process = [] # list of ([same as tracebacks], prev_pos, next_pos) # sequenceA and sequenceB may be sequences, including strings, # lists, or list-like objects. In order to preserve the type of # the object, we need to use slices on the sequences instead of # indexes. For example, sequenceA[row] may return a type that's # not compatible with sequenceA, e.g. if sequenceA is a list and # sequenceA[row] is a string. Thus, avoid using indexes and use # slices, e.g. sequenceA[row:row+1]. Assume that client-defined # sequence classes preserve these semantics. # Initialize the in_process stack for score, (row, col) in starts: if align_globally: begin, end = None, None else: begin, end = None, -max(lenA-row, lenB-col)+1 if not end: end = None # Initialize the in_process list with empty sequences of the # same type as sequenceA. To do this, take empty slices of # the sequences. in_process.append( (sequenceA[0:0], sequenceB[0:0], score, begin, end, (lenA, lenB), (row, col))) if one_alignment_only: break while in_process and len(tracebacks) < MAX_ALIGNMENTS: seqA, seqB, score, begin, end, prev_pos, next_pos = in_process.pop() prevA, prevB = prev_pos if next_pos is None: prevlen = len(seqA) # add the rest of the sequences seqA = sequenceA[:prevA] + seqA seqB = sequenceB[:prevB] + seqB # add the rest of the gaps seqA, seqB = _lpad_until_equal(seqA, seqB, gap_char) # Now make sure begin is set. if begin is None: if align_globally: begin = 0 else: begin = len(seqA) - prevlen tracebacks.append((seqA, seqB, score, begin, end)) else: nextA, nextB = next_pos nseqA, nseqB = prevA-nextA, prevB-nextB maxseq = max(nseqA, nseqB) ngapA, ngapB = maxseq-nseqA, maxseq-nseqB seqA = sequenceA[nextA:nextA+nseqA] + gap_char*ngapA + seqA seqB = sequenceB[nextB:nextB+nseqB] + gap_char*ngapB + seqB prev_pos = next_pos # local alignment stops early if score falls < 0 if not align_globally and score_matrix[nextA][nextB] <= 0: begin = max(prevA, prevB) in_process.append( (seqA, seqB, score, begin, end, prev_pos, None)) else: for next_pos in trace_matrix[nextA][nextB]: in_process.append( (seqA, seqB, score, begin, end, prev_pos, next_pos)) if one_alignment_only: break return _clean_alignments(tracebacks) def _find_start(score_matrix, sequenceA, sequenceB, gap_A_fn, gap_B_fn, penalize_end_gaps, align_globally): # Return a list of (score, (row, col)) indicating every possible # place to start the tracebacks. if align_globally: if penalize_end_gaps: starts = _find_global_start( sequenceA, sequenceB, score_matrix, gap_A_fn, gap_B_fn, 1) else: starts = _find_global_start( sequenceA, sequenceB, score_matrix, None, None, 0) else: starts = _find_local_start(score_matrix) return starts def _find_global_start(sequenceA, sequenceB, score_matrix, gap_A_fn, gap_B_fn, penalize_end_gaps): # The whole sequence should be aligned, so return the positions at # the end of either one of the sequences. nrows, ncols = len(score_matrix), len(score_matrix[0]) positions = [] # Search all rows in the last column. for row in range(nrows): # Find the score, penalizing end gaps if necessary. score = score_matrix[row][ncols-1] if penalize_end_gaps: score += gap_B_fn(ncols, nrows-row-1) positions.append((score, (row, ncols-1))) # Search all columns in the last row. for col in range(ncols-1): score = score_matrix[nrows-1][col] if penalize_end_gaps: score += gap_A_fn(nrows, ncols-col-1) positions.append((score, (nrows-1, col))) return positions def _find_local_start(score_matrix): # Return every position in the matrix. positions = [] nrows, ncols = len(score_matrix), len(score_matrix[0]) for row in range(nrows): for col in range(ncols): score = score_matrix[row][col] positions.append((score, (row, col))) return positions def _clean_alignments(alignments): # Take a list of alignments and return a cleaned version. Remove # duplicates, make sure begin and end are set correctly, remove # empty alignments. unique_alignments = [] for align in alignments : if align not in unique_alignments : unique_alignments.append(align) i = 0 while i < len(unique_alignments): seqA, seqB, score, begin, end = unique_alignments[i] # Make sure end is set reasonably. if end is None: # global alignment end = len(seqA) elif end < 0: end = end + len(seqA) # If there's no alignment here, get rid of it. if begin >= end: del unique_alignments[i] continue unique_alignments[i] = seqA, seqB, score, begin, end i += 1 return unique_alignments def _pad_until_equal(s1, s2, char): # Add char to the end of s1 or s2 until they are equal length. ls1, ls2 = len(s1), len(s2) if ls1 < ls2: s1 = _pad(s1, char, ls2-ls1) elif ls2 < ls1: s2 = _pad(s2, char, ls1-ls2) return s1, s2 def _lpad_until_equal(s1, s2, char): # Add char to the beginning of s1 or s2 until they are equal # length. ls1, ls2 = len(s1), len(s2) if ls1 < ls2: s1 = _lpad(s1, char, ls2-ls1) elif ls2 < ls1: s2 = _lpad(s2, char, ls1-ls2) return s1, s2 def _pad(s, char, n): # Append n chars to the end of s. return s + char*n def _lpad(s, char, n): # Prepend n chars to the beginning of s. return char*n + s _PRECISION = 1000 def rint(x, precision=_PRECISION): return int(x * precision + 0.5) 00708 class identity_match: """identity_match([match][, mismatch]) -> match_fn Create a match function for use in an alignment. match and mismatch are the scores to give when two residues are equal or unequal. By default, match is 1 and mismatch is 0. """ def __init__(self, match=1, mismatch=0): self.match = match self.mismatch = mismatch def __call__(self, charA, charB): if charA == charB: return self.match return self.mismatch 00724 class dictionary_match: """dictionary_match(score_dict[, symmetric]) -> match_fn Create a match function for use in an alignment. score_dict is a dictionary where the keys are tuples (residue 1, residue 2) and the values are the match scores between those residues. symmetric is a flag that indicates whether the scores are symmetric. If true, then if (res 1, res 2) doesn't exist, I will use the score at (res 2, res 1). """ def __init__(self, score_dict, symmetric=1): self.score_dict = score_dict self.symmetric = symmetric def __call__(self, charA, charB): if self.symmetric and (charA, charB) not in self.score_dict: # If the score dictionary is symmetric, then look up the # score both ways. charB, charA = charA, charB return self.score_dict[(charA, charB)] 00745 class affine_penalty: """affine_penalty(open, extend[, penalize_extend_when_opening]) -> gap_fn Create a gap function for use in an alignment. """ def __init__(self, open, extend, penalize_extend_when_opening=0): if open > 0 or extend > 0: raise ValueError("Gap penalties should be non-positive.") self.open, self.extend = open, extend self.penalize_extend_when_opening = penalize_extend_when_opening def __call__(self, index, length): return calc_affine_penalty( length, self.open, self.extend, self.penalize_extend_when_opening) def calc_affine_penalty(length, open, extend, penalize_extend_when_opening): if length <= 0: return 0 penalty = open + extend * length if not penalize_extend_when_opening: penalty -= extend return penalty def print_matrix(matrix): """print_matrix(matrix) Print out a matrix. For debugging purposes. """ # Transpose the matrix and get the length of the values in each column. matrixT = [[] for x in range(len(matrix[0]))] for i in range(len(matrix)): for j in range(len(matrix[i])): matrixT[j].append(len(str(matrix[i][j]))) ndigits = map(max, matrixT) for i in range(len(matrix)): for j in range(len(matrix[i])): n = ndigits[j] print "%*s " % (n, matrix[i][j]), print def format_alignment(align1, align2, score, begin, end): """format_alignment(align1, align2, score, begin, end) -> string Format the alignment prettily into a string. """ s = [] s.append("%s\n" % align1) s.append("%s%s\n" % (" "*begin, "|"*(end-begin))) s.append("%s\n" % align2) s.append(" Score=%g\n" % score) return ''.join(s) # Try and load C implementations of functions. If I can't, # then just ignore and use the pure python implementations. try: import cpairwise2 except ImportError: pass else: import sys this_module = sys.modules[__name__] for name in cpairwise2.__dict__.keys(): if not name.startswith("__"): this_module.__dict__[name] = cpairwise2.__dict__[name]

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