treetime/treeanc.py
def make_reduced_alignment(self):
        """
        Create the reduced alignment from the full sequences attached to (some)
        tree nodes. The methods collects all sequences from the tree nodes, creates
        the alignment, counts the multiplicity for each column of the alignment
        ('alignment pattern'), and creates the reduced alignment, where only the
        unique patterns are present. The reduced alignment and the pattern multiplicity
        are sufficient for the GTR calculations and allow to save memory on profile
        instantiation.

        The maps from full sequence to reduced sequence and back are also stored to allow
        compressing and expanding the sequences.

        Notes
        -----

          full_to_reduced_sequence_map : (array)
             Map to reduce a sequence

          reduced_to_full_sequence_map : (dict)
             Map to restore sequence from reduced alignment

          multiplicity : (array)
            Numpy array, which stores the pattern multiplicity for each position of the reduced alignment.

          reduced_alignment : (2D numpy array)
            The reduced alignment. Shape is (N x L'), where N is number of
            sequences, L' - number of unique alignment patterns

          cseq : (array)
            The compressed sequence (corresponding row of the reduced alignment) attached to each node

        """

        self.logger("TreeAnc: making reduced alignment...", 1)

        # bind positions in real sequence to that of the reduced (compressed) sequence
        self.full_to_reduced_sequence_map = np.zeros(self.seq_len, dtype=int)

        # bind position in reduced sequence to the array of positions in real (expanded) sequence
        self.reduced_to_full_sequence_map = {}

        #if is a dict, want to be efficient and not iterate over a bunch of const_sites
        #so pre-load alignment_patterns with the location of const sites!
        #and get the sites that we want to iterate over only!
        if self.is_vcf:
            tmp_reduced_aln, alignment_patterns, positions = self.process_alignment_dict()
            seqNames = self.aln.keys() #store seqName order to put back on tree
        elif self.reduce_alignment:
            # transpose real alignment, for ease of iteration
            alignment_patterns = {}
            tmp_reduced_aln = []
            # NOTE the order of tree traversal must be the same as below
            # for assigning the cseq attributes to the nodes.
            seqs = [n.sequence for n in self.tree.find_clades() if hasattr(n, 'sequence')]
            if len(np.unique([len(x) for x in seqs]))>1:
                self.logger("TreeAnc: Sequences differ in in length! ABORTING",0, warn=True)
                aln_transpose = None
                return
            else:
                aln_transpose = np.array(seqs).T
                positions = range(aln_transpose.shape[0])
        else:
            self.multiplicity = np.ones(self.seq_len, dtype=float)
            self.full_to_reduced_sequence_map = np.arange(self.seq_len)
            self.reduced_to_full_sequence_map = {p:np.array([p]) for p in np.arange(self.seq_len)}
            for n in self.tree.find_clades():
                if hasattr(n, 'sequence'):
                    n.original_cseq = np.copy(n.sequence)
                    n.cseq = np.copy(n.sequence)
            return ttconf.SUCCESS

        for pi in positions:
            if self.is_vcf:
                pattern = [ self.aln[k][pi] if pi in self.aln[k].keys()
                            else self.ref[pi] for k,v in self.aln.items() ]
            else:
                pattern = aln_transpose[pi]

            str_pat = "".join(pattern)
            # if the column contains only one state and ambiguous nucleotides, replace
            # those with the state in other strains right away
            unique_letters = list(np.unique(pattern))
            if hasattr(self.gtr, "ambiguous"):
                if len(unique_letters)==2 and self.gtr.ambiguous in unique_letters:
                    other = [c for c in unique_letters if c!=self.gtr.ambiguous][0]
                    str_pat = str_pat.replace(self.gtr.ambiguous, other)
                    unique_letters = [other]
            # if there is a mutation in this column, give it its private pattern
            # this is required when sampling mutations from reconstructed profiles.
            # otherwise, all mutations corresponding to the same pattern will be coupled.
            if len(unique_letters)>1:
                str_pat += '_%d'%pi

            # if the pattern is not yet seen,
            if str_pat not in alignment_patterns:
                # bind the index in the reduced aln, index in sequence to the pattern string
                alignment_patterns[str_pat] = (len(tmp_reduced_aln), [pi])
                # append this pattern to the reduced alignment
                tmp_reduced_aln.append(pattern)
            else:
                # if the pattern is already seen, append the position in the real
                # sequence to the reduced aln<->sequence_pos_indexes map
                alignment_patterns[str_pat][1].append(pi)

        # add constant alignment column not in the alignment. We don't know where they
        # are, so just add them to the end. First, determine sequence composition.
        if self.additional_constant_sites:
            character_counts = {c:np.sum(aln_transpose==c) for c in self.gtr.alphabet
                                if c not in [self.gtr.ambiguous, '-']}
            total = np.sum(list(character_counts.values()))
            additional_columns = [(c,int(np.round(self.additional_constant_sites*n/total)))
                                  for c, n in character_counts.items()]
            columns_left = self.additional_constant_sites
            pi = len(positions)
            for c,n in additional_columns:
                if c==additional_columns[-1][0]:  # make sure all additions add up to the correct number to avoid rounding
                    n = columns_left
                str_pat = c*len(self.aln)
                pos_list = list(range(pi, pi+n))

                if str_pat in alignment_patterns:
                    alignment_patterns[str_pat][1].extend(pos_list)
                else:
                    alignment_patterns[str_pat] = (len(tmp_reduced_aln), pos_list)
                    tmp_reduced_aln.append(np.array(list(str_pat)))
                pi += n
                columns_left -= n


        # count how many times each column is repeated in the real alignment
        self.multiplicity = np.zeros(len(alignment_patterns))
        for p, pos in alignment_patterns.values():
            self.multiplicity[p]=len(pos)

        # create the reduced alignment as np array
        self.reduced_alignment = np.array(tmp_reduced_aln).T

        # create map to compress a sequence
        for p, pos in alignment_patterns.values():
            self.full_to_reduced_sequence_map[np.array(pos)]=p

        # create a map to reconstruct full sequence from the reduced (compressed) sequence
        for p, val in alignment_patterns.items():
            self.reduced_to_full_sequence_map[val[0]]=np.array(val[1], dtype=int)

        # assign compressed sequences to all nodes of the tree, which have sequence assigned
        # for dict we cannot assume this is in the same order, as it does below!
        # so do it explicitly
        #
        # sequences are overwritten during reconstruction and
        # ambiguous sites change. Keep orgininals for reference
        if self.is_vcf:
            seq_reduce_align = {n:self.reduced_alignment[i]
                                for i, n in enumerate(seqNames)}
            for n in self.tree.find_clades():
                if hasattr(n, 'sequence'):
                    n.original_cseq = seq_reduce_align[n.name]
                    n.cseq = np.copy(n.original_cseq)
        else:
            # NOTE the order of tree traversal must be the same as above to catch the
            # index in the reduced alignment correctly
            seq_count = 0
            for n in self.tree.find_clades():
                if hasattr(n, 'sequence'):
                    n.original_cseq = self.reduced_alignment[seq_count]
                    n.cseq = np.copy(n.original_cseq)
                    seq_count+=1

        self.logger("TreeAnc: constructed reduced alignment...", 1)
        return ttconf.SUCCESS
def make_reduced_alignment(self):
        """
        Create the reduced alignment from the full sequences attached to (some)
        tree nodes. The methods collects all sequences from the tree nodes, creates
        the alignment, counts the multiplicity for each column of the alignment
        ('alignment pattern'), and creates the reduced alignment, where only the
        unique patterns are present. The reduced alignment and the pattern multiplicity
        are sufficient for the GTR calculations and allow to save memory on profile
        instantiation.

        The maps from full sequence to reduced sequence and back are also stored to allow
        compressing and expanding the sequences.

        Notes
        -----

          full_to_reduced_sequence_map : (array)
             Map to reduce a sequence

          reduced_to_full_sequence_map : (dict)
             Map to restore sequence from reduced alignment

          multiplicity : (array)
            Numpy array, which stores the pattern multiplicity for each position of the reduced alignment.

          reduced_alignment : (2D numpy array)
            The reduced alignment. Shape is (N x L'), where N is number of
            sequences, L' - number of unique alignment patterns

          cseq : (array)
            The compressed sequence (corresponding row of the reduced alignment) attached to each node

        """

        self.logger("TreeAnc: making reduced alignment...", 1)

        # bind positions in real sequence to that of the reduced (compressed) sequence
        self.full_to_reduced_sequence_map = np.zeros(self.seq_len, dtype=int)

        # bind position in reduced sequence to the array of positions in real (expanded) sequence
        self.reduced_to_full_sequence_map = {}

        #if is a dict, want to be efficient and not iterate over a bunch of const_sites
        #so pre-load alignment_patterns with the location of const sites!
        #and get the sites that we want to iterate over only!
        if self.is_vcf:
            tmp_reduced_aln, alignment_patterns, positions = self.process_alignment_dict()
            seqNames = self.aln.keys() #store seqName order to put back on tree
        elif self.reduce_alignment:
            # transpose real alignment, for ease of iteration
            alignment_patterns = {}
            tmp_reduced_aln = []
            # NOTE the order of tree traversal must be the same as below
            # for assigning the cseq attributes to the nodes.
            seqs = [n.sequence for n in self.tree.find_clades() if hasattr(n, 'sequence')]
            if len(np.unique([len(x) for x in seqs]))>1:
                self.logger("TreeAnc: Sequences differ in in length! ABORTING",0, warn=True)
                aln_transpose = None
                return
            else:
                aln_transpose = np.array(seqs).T
                positions = range(aln_transpose.shape[0])
        else:
            self.multiplicity = np.ones(self.seq_len, dtype=float)
            self.full_to_reduced_sequence_map = np.arange(self.seq_len)
            self.reduced_to_full_sequence_map = {p:np.array([p]) for p in np.arange(self.seq_len)}
            for n in self.tree.find_clades():
                if hasattr(n, 'sequence'):
                    n.original_cseq = np.copy(n.sequence)
                    n.cseq = np.copy(n.sequence)
            return ttconf.SUCCESS

        for pi in positions:
            if self.is_vcf:
                pattern = [ self.aln[k][pi] if pi in self.aln[k].keys()
                            else self.ref[pi] for k,v in self.aln.items() ]
            else:
                pattern = aln_transpose[pi]

            str_pat = "".join(pattern)
            # if the column contains only one state and ambiguous nucleotides, replace
            # those with the state in other strains right away
            unique_letters = list(np.unique(pattern))
            if hasattr(self.gtr, "ambiguous"):
                if len(unique_letters)==2 and self.gtr.ambiguous in unique_letters:
                    other = [c for c in unique_letters if c!=self.gtr.ambiguous][0]
                    str_pat = str_pat.replace(self.gtr.ambiguous, other)
                    unique_letters = [other]
            # if there is a mutation in this column, give it its private pattern
            # this is required when sampling mutations from reconstructed profiles.
            # otherwise, all mutations corresponding to the same pattern will be coupled.
            if len(unique_letters)>1:
                str_pat += '_%d'%pi

            # if the pattern is not yet seen,
            if str_pat not in alignment_patterns:
                # bind the index in the reduced aln, index in sequence to the pattern string
                alignment_patterns[str_pat] = (len(tmp_reduced_aln), [pi])
                # append this pattern to the reduced alignment
                tmp_reduced_aln.append(pattern)
            else:
                # if the pattern is already seen, append the position in the real
                # sequence to the reduced aln<->sequence_pos_indexes map
                alignment_patterns[str_pat][1].append(pi)

        # add constant alignment column not in the alignment. We don't know where they
        # are, so just add them to the end. First, determine sequence composition.
        if self.additional_constant_sites:
            character_counts = {c:np.sum(aln_transpose==c) for c in self.gtr.alphabet
                                if c not in [self.gtr.ambiguous, '-']}
            total = np.sum(list(character_counts.values()))
            additional_columns = [(c,int(np.round(self.additional_constant_sites*n/total)))
                                  for c, n in character_counts.items()]
            columns_left = self.additional_constant_sites
            pi = len(positions)
            for c,n in additional_columns:
                if c==additional_columns[-1][0]:  # make sure all additions add up to the correct number to avoid rounding
                    n = columns_left
                str_pat = c*len(self.aln)
                pos_list = list(range(pi, pi+n))

                if str_pat in alignment_patterns:
                    alignment_patterns[str_pat][1].extend(pos_list)
                else:
                    alignment_patterns[str_pat] = (len(tmp_reduced_aln), pos_list)
                    tmp_reduced_aln.append(np.array(list(str_pat)))
                pi += n
                columns_left -= n


        # count how many times each column is repeated in the real alignment
        self.multiplicity = np.zeros(len(alignment_patterns))
        for p, pos in alignment_patterns.values():
            self.multiplicity[p]=len(pos)

        # create the reduced alignment as np array
        self.reduced_alignment = np.array(tmp_reduced_aln).T

        # create map to compress a sequence
        for p, pos in alignment_patterns.values():
            self.full_to_reduced_sequence_map[np.array(pos)]=p

        # create a map to reconstruct full sequence from the reduced (compressed) sequence
        for p, val in alignment_patterns.items():
            self.reduced_to_full_sequence_map[val[0]]=np.array(val[1], dtype=int)

        # assign compressed sequences to all nodes of the tree, which have sequence assigned
        # for dict we cannot assume this is in the same order, as it does below!
        # so do it explicitly
        #
        # sequences are overwritten during reconstruction and
        # ambiguous sites change. Keep orgininals for reference
        if self.is_vcf:
            seq_reduce_align = {n:self.reduced_alignment[i]
                                for i, n in enumerate(seqNames)}
            for n in self.tree.find_clades():
                if hasattr(n, 'sequence'):
                    n.original_cseq = seq_reduce_align[n.name]
                    n.cseq = np.copy(n.original_cseq)
        else:
            # NOTE the order of tree traversal must be the same as above to catch the
            # index in the reduced alignment correctly
            seq_count = 0
            for n in self.tree.find_clades():
                if hasattr(n, 'sequence'):
                    n.original_cseq = self.reduced_alignment[seq_count]
                    n.cseq = np.copy(n.original_cseq)
                    seq_count+=1

        self.logger("TreeAnc: constructed reduced alignment...", 1)
        return ttconf.SUCCESS
def make_reduced_alignment ( self ) : self . logger ( "TreeAnc: making reduced alignment..." , 1 ) # bind positions in real sequence to that of the reduced (compressed) sequence self . full_to_reduced_sequence_map = np . zeros ( self . seq_len , dtype = int ) # bind position in reduced sequence to the array of positions in real (expanded) sequence self . reduced_to_full_sequence_map = { } #if is a dict, want to be efficient and not iterate over a bunch of const_sites #so pre-load alignment_patterns with the location of const sites! #and get the sites that we want to iterate over only! if self . is_vcf : tmp_reduced_aln , alignment_patterns , positions = self . process_alignment_dict ( ) seqNames = self . aln . keys ( ) #store seqName order to put back on tree elif self . reduce_alignment : # transpose real alignment, for ease of iteration alignment_patterns = { } tmp_reduced_aln = [ ] # NOTE the order of tree traversal must be the same as below # for assigning the cseq attributes to the nodes. seqs = [ n . sequence for n in self . tree . find_clades ( ) if hasattr ( n , 'sequence' ) ] if len ( np . unique ( [ len ( x ) for x in seqs ] ) ) > 1 : self . logger ( "TreeAnc: Sequences differ in in length! ABORTING" , 0 , warn = True ) aln_transpose = None return else : aln_transpose = np . array ( seqs ) . T positions = range ( aln_transpose . shape [ 0 ] ) else : self . multiplicity = np . ones ( self . seq_len , dtype = float ) self . full_to_reduced_sequence_map = np . arange ( self . seq_len ) self . reduced_to_full_sequence_map = { p : np . array ( [ p ] ) for p in np . arange ( self . seq_len ) } for n in self . tree . find_clades ( ) : if hasattr ( n , 'sequence' ) : n . original_cseq = np . copy ( n . sequence ) n . cseq = np . copy ( n . sequence ) return ttconf . SUCCESS for pi in positions : if self . is_vcf : pattern = [ self . aln [ k ] [ pi ] if pi in self . aln [ k ] . keys ( ) else self . ref [ pi ] for k , v in self . aln . items ( ) ] else : pattern = aln_transpose [ pi ] str_pat = "" . join ( pattern ) # if the column contains only one state and ambiguous nucleotides, replace # those with the state in other strains right away unique_letters = list ( np . unique ( pattern ) ) if hasattr ( self . gtr , "ambiguous" ) : if len ( unique_letters ) == 2 and self . gtr . ambiguous in unique_letters : other = [ c for c in unique_letters if c != self . gtr . ambiguous ] [ 0 ] str_pat = str_pat . replace ( self . gtr . ambiguous , other ) unique_letters = [ other ] # if there is a mutation in this column, give it its private pattern # this is required when sampling mutations from reconstructed profiles. # otherwise, all mutations corresponding to the same pattern will be coupled. if len ( unique_letters ) > 1 : str_pat += '_%d' % pi # if the pattern is not yet seen, if str_pat not in alignment_patterns : # bind the index in the reduced aln, index in sequence to the pattern string alignment_patterns [ str_pat ] = ( len ( tmp_reduced_aln ) , [ pi ] ) # append this pattern to the reduced alignment tmp_reduced_aln . append ( pattern ) else : # if the pattern is already seen, append the position in the real # sequence to the reduced aln<->sequence_pos_indexes map alignment_patterns [ str_pat ] [ 1 ] . append ( pi ) # add constant alignment column not in the alignment. We don't know where they # are, so just add them to the end. First, determine sequence composition. if self . additional_constant_sites : character_counts = { c : np . sum ( aln_transpose == c ) for c in self . gtr . alphabet if c not in [ self . gtr . ambiguous , '-' ] } total = np . sum ( list ( character_counts . values ( ) ) ) additional_columns = [ ( c , int ( np . round ( self . additional_constant_sites * n / total ) ) ) for c , n in character_counts . items ( ) ] columns_left = self . additional_constant_sites pi = len ( positions ) for c , n in additional_columns : if c == additional_columns [ - 1 ] [ 0 ] : # make sure all additions add up to the correct number to avoid rounding n = columns_left str_pat = c * len ( self . aln ) pos_list = list ( range ( pi , pi + n ) ) if str_pat in alignment_patterns : alignment_patterns [ str_pat ] [ 1 ] . extend ( pos_list ) else : alignment_patterns [ str_pat ] = ( len ( tmp_reduced_aln ) , pos_list ) tmp_reduced_aln . append ( np . array ( list ( str_pat ) ) ) pi += n columns_left -= n # count how many times each column is repeated in the real alignment self . multiplicity = np . zeros ( len ( alignment_patterns ) ) for p , pos in alignment_patterns . values ( ) : self . multiplicity [ p ] = len ( pos ) # create the reduced alignment as np array self . reduced_alignment = np . array ( tmp_reduced_aln ) . T # create map to compress a sequence for p , pos in alignment_patterns . values ( ) : self . full_to_reduced_sequence_map [ np . array ( pos ) ] = p # create a map to reconstruct full sequence from the reduced (compressed) sequence for p , val in alignment_patterns . items ( ) : self . reduced_to_full_sequence_map [ val [ 0 ] ] = np . array ( val [ 1 ] , dtype = int ) # assign compressed sequences to all nodes of the tree, which have sequence assigned # for dict we cannot assume this is in the same order, as it does below! # so do it explicitly # # sequences are overwritten during reconstruction and # ambiguous sites change. Keep orgininals for reference if self . is_vcf : seq_reduce_align = { n : self . reduced_alignment [ i ] for i , n in enumerate ( seqNames ) } for n in self . tree . find_clades ( ) : if hasattr ( n , 'sequence' ) : n . original_cseq = seq_reduce_align [ n . name ] n . cseq = np . copy ( n . original_cseq ) else : # NOTE the order of tree traversal must be the same as above to catch the # index in the reduced alignment correctly seq_count = 0 for n in self . tree . find_clades ( ) : if hasattr ( n , 'sequence' ) : n . original_cseq = self . reduced_alignment [ seq_count ] n . cseq = np . copy ( n . original_cseq ) seq_count += 1 self . logger ( "TreeAnc: constructed reduced alignment..." , 1 ) return ttconf . SUCCESS
Create the reduced alignment from the full sequences attached to (some)
        tree nodes. The methods collects all sequences from the tree nodes, creates
        the alignment, counts the multiplicity for each column of the alignment
        ('alignment pattern'), and creates the reduced alignment, where only the
        unique patterns are present. The reduced alignment and the pattern multiplicity
        are sufficient for the GTR calculations and allow to save memory on profile
        instantiation.

        The maps from full sequence to reduced sequence and back are also stored to allow
        compressing and expanding the sequences.

        Notes
        -----

          full_to_reduced_sequence_map : (array)
             Map to reduce a sequence

          reduced_to_full_sequence_map : (dict)
             Map to restore sequence from reduced alignment

          multiplicity : (array)
            Numpy array, which stores the pattern multiplicity for each position of the reduced alignment.

          reduced_alignment : (2D numpy array)
            The reduced alignment. Shape is (N x L'), where N is number of
            sequences, L' - number of unique alignment patterns

          cseq : (array)
            The compressed sequence (corresponding row of the reduced alignment) attached to each node
Create the reduced alignment from the full sequences attached to ( some ) tree nodes . The methods collects all sequences from the tree nodes creates the alignment counts the multiplicity for each column of the alignment ( alignment pattern ) and creates the reduced alignment where only the unique patterns are present . The reduced alignment and the pattern multiplicity are sufficient for the GTR calculations and allow to save memory on profile instantiation .