B b4@sdZddlZddlZddlZddlmZddlmZddlmZGdddZ Gdd d e Z e Z Gd d d Z Gd d d Z Gddde ZGdddZGdddZGdddeZGdddeZGddde ZdS)z*Classes and methods for tree construction.N)BaseTree)MultipleSeqAlignment)substitution_matricesc@sTeZdZdZdddZddZddZd d Zdd d Zd dZ ddZ ddZ dS)_MatrixaBase class for distance matrix or scoring matrix. Accepts a list of names and a lower triangular matrix.:: matrix = [[0], [1, 0], [2, 3, 0], [4, 5, 6, 0]] represents the symmetric matrix of [0,1,2,4] [1,0,3,5] [2,3,0,6] [4,5,6,0] :Parameters: names : list names of elements, used for indexing matrix : list nested list of numerical lists in lower triangular format Examples -------- >>> from Bio.Phylo.TreeConstruction import _Matrix >>> names = ['Alpha', 'Beta', 'Gamma', 'Delta'] >>> matrix = [[0], [1, 0], [2, 3, 0], [4, 5, 6, 0]] >>> m = _Matrix(names, matrix) >>> m _Matrix(names=['Alpha', 'Beta', 'Gamma', 'Delta'], matrix=[[0], [1, 0], [2, 3, 0], [4, 5, 6, 0]]) You can use two indices to get or assign an element in the matrix. >>> m[1,2] 3 >>> m['Beta','Gamma'] 3 >>> m['Beta','Gamma'] = 4 >>> m['Beta','Gamma'] 4 Further more, you can use one index to get or assign a list of elements related to that index. >>> m[0] [0, 1, 2, 4] >>> m['Alpha'] [0, 1, 2, 4] >>> m['Alpha'] = [0, 7, 8, 9] >>> m[0] [0, 7, 8, 9] >>> m[0,1] 7 Also you can delete or insert a column&row of elemets by index. >>> m _Matrix(names=['Alpha', 'Beta', 'Gamma', 'Delta'], matrix=[[0], [7, 0], [8, 4, 0], [9, 5, 6, 0]]) >>> del m['Alpha'] >>> m _Matrix(names=['Beta', 'Gamma', 'Delta'], matrix=[[0], [4, 0], [5, 6, 0]]) >>> m.insert('Alpha', [0, 7, 8, 9] , 0) >>> m _Matrix(names=['Alpha', 'Beta', 'Gamma', 'Delta'], matrix=[[0], [7, 0], [8, 4, 0], [9, 5, 6, 0]]) NcCs t|trBtdd|DrBtt|t|kr8||_qJtdntd|dkrvddtdt|dD}||_ nt|trtd d|Drtd dd d|DDrt|t|krd d|Dttdt|dkr||_ qtd ntdntddS)zInitialize matrix. Arguments are a list of names, and optionally a list of lower triangular matrix data (zero matrix used by default). css|]}t|tVqdS)N) isinstancestr).0sr 9lib/python3.7/site-packages/Bio/Phylo/TreeConstruction.py Zsz#_Matrix.__init__..zDuplicate names foundz#'names' should be a list of stringsNcSsg|]}dg|qS)rr )rir r r esz$_Matrix.__init__..css|]}t|tVqdS)N)rlist)rlr r r r kscss|]}t|tjVqdS)N)rnumbersNumber)rnr r r r mscSsg|]}|D]}|q qSr r )rZsublistitemr r r rnscSsg|] }t|qSr )len)rmr r r rtsz+'matrix' should be in lower triangle formatz,'names' and 'matrix' should be the same sizez,'matrix' should be a list of numerical lists) rrallrsetnames ValueError TypeErrorrangematrix)selfrrr r r __init__Ss&  $  z_Matrix.__init__cst|ttfrdt|tr"|n4t|trN|jkrDj|qVtdntdtdkrntdfddt dDfd dt tDSt|d krd}d}t d d |Dr|\}}n`t d d |Dr0|\}}|jkr&|jkr&j|}j|}ntdntd|tdks\|tdkrdtd||kr|j ||Sj ||SntddS)amAccess value(s) by the index(s) or name(s). For a _Matrix object 'dm':: dm[i] get a value list from the given 'i' to others; dm[i, j] get the value between 'i' and 'j'; dm['name'] map name to index first dm['name1', 'name2'] map name to index first NzItem not found.zInvalid index type.rzIndex out of range.csg|]}j|qSr )r)rr )indexrr r rsz'_Matrix.__getitem__..rcsg|]}j|qSr )r)rr )r!rr r rscss|]}t|tVqdS)N)rint)rr r r r r sz&_Matrix.__getitem__..css|]}t|tVqdS)N)rr)rr r r r r s) rr#rrr!rrr IndexErrorrrr)rr row_index col_indexrow_namecol_namer )r!rr __getitem__}s>         $ z_Matrix.__getitem__c Cst|ttfrd}t|tr$|}n4t|trP||jkrF|j|}qXtdntd|t|dkrptdt|t rt dd|Drt|t|krx$t d|D]}|||j ||<qWx2t |t|D]}|||j ||<qWntd ntd n t|d krd}d}t d d|Dr8|\}}n`t d d|Dr|\}}||jkr||jkr|j|}|j|}ntdntd|t|dks|t|dkrtdt|t jr||kr||j ||<n||j ||<ntd ntddS)zSet value by the index(s) or name(s). Similar to __getitem__:: dm[1] = [1, 0, 3, 4] set values from '1' to others; dm[i, j] = 2 set the value from 'i' to 'j' NzItem not found.zInvalid index type.rzIndex out of range.css|]}t|tjVqdS)N)rrr)rrr r r r sz&_Matrix.__setitem__..rzValue not the same size.zInvalid value type.r"css|]}t|tVqdS)N)rr#)rr r r r r scss|]}t|tVqdS)N)rr)rr r r r r s)rr#rrr!rrrr$rrrrrr) rrvaluer!r r%r&r'r(r r r __setitem__sP           $  z_Matrix.__setitem__cCspd}t|tr|}n t|tr,|j|}ntdx&t|dt|D]}|j||=qHW|j|=|j|=dS)z.Delete related distances by the index or name.NzInvalid index type.r) rr#rrr!rrrr)rrr!r r r r __delitem__s  z_Matrix.__delitem__cCst|tr|dkrt|}t|ts,td|j|||j|dg|x(t|t|D]}|j||dq^W|||<ntddS)zInsert distances given the name and value. :Parameters: name : str name of a row/col to be inserted value : list a row/col of values to be inserted NzInvalid index type.rzInvalid name type.) rrrr#rrinsertrr)rnamer*r!r r r r r-s   z_Matrix.insertcCs t|jS)zMatrix length.)rr)rr r r __len__ sz_Matrix.__len__cCs"|jjdttt|j|jfS)zReturn Matrix as a string.z(names=%s, matrix=%s)) __class____name__tuplemapreprrr)rr r r __repr__$s z_Matrix.__repr__cs:dfddtdtD}|ddj}|S)z%Get a lower triangular matrix string. cs4g|],}j|ddddj|DqS) cSsg|] }t|qSr )r)rrr r r r.sz._Matrix.__str__...)rjoinr)rr )rr r r.sz#_Matrix.__str__..rz r7)r8rrr)rZ matrix_stringr )rr __str__*s  z_Matrix.__str__)N)N) r1 __module__ __qualname____doc__r r)r+r,r-r/r5r9r r r r rs? *6B rc@s2eZdZdZd ddZddZddZd d ZdS) DistanceMatrixzDistance matrix class that can be used for distance based tree algorithms. All diagonal elements will be zero no matter what the users provide. NcCst||||dS)zInitialize the class.N)rr _set_zero_diagonal)rrrr r r r <szDistanceMatrix.__init__cCst||||dS)zSet Matrix's items to values.N)rr+r>)rrr*r r r r+AszDistanceMatrix.__setitem__cCs*x$tdt|D]}d|j||<qWdS)z,Set all diagonal elements to zero (PRIVATE).rN)rrr)rr r r r r>Fsz!DistanceMatrix._set_zero_diagonalc s|dtjdtdtttjd}ddtdtjdD}dt|dd |d}xft t jjD]P\\}}fd dtdtjD}t |g||}||j |qWd S) aWrite data in Phylip format to a given file-like object or handle. The output stream is the input distance matrix format used with Phylip programs (e.g. 'neighbor'). See: http://evolution.genetics.washington.edu/phylip/doc/neighbor.html :Parameters: handle : file or file-like object A writeable text mode file handle or other object supporting the 'write' method, such as StringIO or sys.stdout. z r6 rcss|]}dt|dVqdS){z:.4f}N)r)rxr r r r [sz/DistanceMatrix.format_phylip..z{0:zs}z c3s|]}j|VqdS)N)r)rj)r rr r r _sN)writerrmaxr3rrrr8 enumeratezip itertoolschainformat) rZhandleZ name_widthZ value_fmtsZrow_fmtr.valuesZ mirror_valuesZfieldsr )r rr format_phylipKs  $zDistanceMatrix.format_phylip)N)r1r:r;r<r r+r>rKr r r r r=6s  r=c@seZdZdZedZgZgZe Z xRe D]JZ e e Z e dkrFdZ ne Z eee jrjee q*ee q*W[[ [ [ dgeeZd ddZd d Zd d ZdS)DistanceCalculatora}Class to calculate the distance matrix from a DNA or Protein. Multiple Sequence Alignment(MSA) and the given name of the substitution model. Currently only scoring matrices are used. :Parameters: model : str Name of the model matrix to be used to calculate distance. The attribute ``dna_models`` contains the available model names for DNA sequences and ``protein_models`` for protein sequences. Examples -------- Loading a small PHYLIP alignment from which to compute distances:: from Bio.Phylo.TreeConstruction import DistanceCalculator from Bio import AlignIO aln = AlignIO.read(open('TreeConstruction/msa.phy'), 'phylip') print(aln) Output:: Alignment with 5 rows and 13 columns AACGTGGCCACAT Alpha AAGGTCGCCACAC Beta CAGTTCGCCACAA Gamma GAGATTTCCGCCT Delta GAGATCTCCGCCC Epsilon DNA calculator with 'identity' model:: calculator = DistanceCalculator('identity') dm = calculator.get_distance(aln) print(dm) Output:: Alpha 0 Beta 0.23076923076923073 0 Gamma 0.3846153846153846 0.23076923076923073 0 Delta 0.5384615384615384 0.5384615384615384 0.5384615384615384 0 Epsilon 0.6153846153846154 0.3846153846153846 0.46153846153846156 0.15384615384615385 0 Alpha Beta Gamma Delta Epsilon Protein calculator with 'blosum62' model:: calculator = DistanceCalculator('blosum62') dm = calculator.get_distance(aln) print(dm) Output:: Alpha 0 Beta 0.36904761904761907 0 Gamma 0.49397590361445787 0.25 0 Delta 0.5853658536585367 0.5476190476190477 0.5662650602409638 0 Epsilon 0.7 0.3555555555555555 0.48888888888888893 0.2222222222222222 0 Alpha Beta Gamma Delta Epsilon ZABCDEFGHIKLMNPQRSTVWXYZzNUC.4.4blastnidentityNcCsx|r ||_n|dkrd|_nd|_|dkr2d|_nB||jkr`|dkrJd}n|}t||_ntdd|jdS) z!Initialize with a distance model.rNr )-*NrMzNUC.4.4z'Model not supported. Available models: z, ) skip_lettersscoring_matrixmodelsupperrloadrr8)rZmodelrQr.r r r r s zDistanceCalculator.__init__c s<d}d}jdkr8tfddt||D}t|}nd}d}xtdt|D]}||}||} |jksP| jkrzqPy|j||f7}Wn*tk rtd||j|fdYnXy|j| | f7}Wn*tk rtd| |j|fdYnX|j|| f7}qPWt ||}|dkr,dSd|d|S)zCalculate pairwise distance from two sequences (PRIVATE). Returns a value between 0 (identical sequences) and 1 (completely different, or seq1 is an empty string.) rNc3s.|]&\}}|jkr|jkr||kVqdS)N)rQ)rl1l2)rr r r sz/DistanceCalculator._pairwise..z1Bad letter '%s' in sequence '%s' at position '%s'rg?) rRsumrFrrrQr$ridrD) rseq1seq2scoreZ max_scoreZ max_score1Z max_score2r rVrWr )rr _pairwisesB       zDistanceCalculator._pairwisecCs^t|tstddd|D}t|}x0t|dD] \}}|||||j|jf<q6W|S)zReturn a DistanceMatrix for MSA object. :Parameters: msa : MultipleSeqAlignment DNA or Protein multiple sequence alignment. z+Must provide a MultipleSeqAlignment object.cSsg|] }|jqSr )rY)rr r r r rsz3DistanceCalculator.get_distance..r")rrrr=rG combinationsr]rY)rmsardmrZr[r r r get_distances zDistanceCalculator.get_distance)rNN)r1r:r;r<rZprotein_alphabetZ dna_modelsZprotein_modelsrrUrr.rlowerissubsetalphabetappendrSr r]rar r r r rLhs*?    -rLc@seZdZdZddZdS)TreeConstructorz$Base class for all tree constructor.cCs tddS)zvCaller to built the tree from a MultipleSeqAlignment object. This should be implemented in subclass. zMethod not implemented!N)NotImplementedError)rr_r r r build_treeszTreeConstructor.build_treeN)r1r:r;r<rhr r r r rfsrfc@sBeZdZdZddgZdddZddZd d Zd d Zd dZ dS)DistanceTreeConstructora Distance based tree constructor. :Parameters: method : str Distance tree construction method, 'nj'(default) or 'upgma'. distance_calculator : DistanceCalculator The distance matrix calculator for multiple sequence alignment. It must be provided if ``build_tree`` will be called. Examples -------- Loading a small PHYLIP alignment from which to compute distances, and then build a upgma Tree:: from Bio.Phylo.TreeConstruction import DistanceTreeConstructor from Bio.Phylo.TreeConstruction import DistanceCalculator from Bio import AlignIO aln = AlignIO.read(open('TreeConstruction/msa.phy'), 'phylip') constructor = DistanceTreeConstructor() calculator = DistanceCalculator('identity') dm = calculator.get_distance(aln) upgmatree = constructor.upgma(dm) print(upgmatree) Output:: Tree(rooted=True) Clade(branch_length=0, name='Inner4') Clade(branch_length=0.18749999999999994, name='Inner1') Clade(branch_length=0.07692307692307693, name='Epsilon') Clade(branch_length=0.07692307692307693, name='Delta') Clade(branch_length=0.11057692307692304, name='Inner3') Clade(branch_length=0.038461538461538464, name='Inner2') Clade(branch_length=0.11538461538461536, name='Gamma') Clade(branch_length=0.11538461538461536, name='Beta') Clade(branch_length=0.15384615384615383, name='Alpha') Build a NJ Tree:: njtree = constructor.nj(dm) print(njtree) Output:: Tree(rooted=False) Clade(branch_length=0, name='Inner3') Clade(branch_length=0.18269230769230765, name='Alpha') Clade(branch_length=0.04807692307692307, name='Beta') Clade(branch_length=0.04807692307692307, name='Inner2') Clade(branch_length=0.27884615384615385, name='Inner1') Clade(branch_length=0.051282051282051266, name='Epsilon') Clade(branch_length=0.10256410256410259, name='Delta') Clade(branch_length=0.14423076923076922, name='Gamma') njupgmaNcCs^|dkst|tr||_ntdt|tr>||jkr>||_ntd|dd|jdS)zInitialize the class.Nz)Must provide a DistanceCalculator object.z Bad method: z. Available methods: z, )rrLdistance_calculatorrrmethodsmethodr8)rrlrnr r r r _s z DistanceTreeConstructor.__init__cCsF|jr:|j|}d}|jdkr,||}n ||}|StddS)z7Construct and return a Tree, Neighbor Joining or UPGMA.Nrkz)Must provide a DistanceCalculator object.)rlrarnrkrjr)rr_r`treer r r rhqs    z"DistanceTreeConstructor.build_treecCst|tstdt|}dd|jD}d}d}d}xt|dkr|d}xNtdt|D]<}x6td|D](} |||| fkrr||| f}|}| }qrWqbW||} ||} |d7}t ddt |} | j | | j | | r|d d | _n|d d || | _| r0|d d | _n|d d || | _| ||<||=xTtdt|D]B} | |krf| |krf||| f||| fd d ||| f<qfWdt ||j|<||=q.rr)rrNInnerg?r")rr=rcopydeepcopyrrrrrprcladesre is_terminal branch_length _height_ofTree)rdistance_matrixr`rtmin_imin_j inner_countmin_distr rBclade1clade2 inner_cladekr r r rk~sH       . zDistanceTreeConstructor.upgmacCst|tstdt|}dd|jD}dgt|}d}d}d}t|dkrh|d}tj|ddSt|dkrd}d}||} ||} |||fd | _ |||f| j | _ t d d } | j | | j | | |d<|d}tj|ddSxt|dkrxjt dt|D]X} d|| <x0t dt|D]} || || | f7<q2W|| t|d|| <qW|d |d|d}d}d}x`t dt|D]N} xFt d| D]8} || | f|| || }||kr|}| }| }qWqW||} ||} |d7}t d d t|} | j | | j | |||f||||d | _ |||f| j | _ | ||<||=x\t dt|D]J}||kr||kr|||f|||f|||fd |||f<qWd t||j|<||=qWd }|d| kr>d|d_ |d |d_ |dj |d|d}n4|d |d_ d|d_ |dj |d|d}tj|ddS) zConstruct and return a Neighbor Joining tree. :Parameters: distance_matrix : DistanceMatrix The distance matrix for tree construction. z%Must provide a DistanceMatrix object.cSsg|]}td|qS)N)rrp)rr.r r r rsz.DistanceTreeConstructor.nj..rrF)rootedr"g@Nrq)rr)rr=rrrrsrrrrxrvrprtrerr)rryr`rtZ node_distrzr{r|rootr~rrr rBr}temprr r r rjs          "    zDistanceTreeConstructor.njcs4d}|r|j}n|tfdd|jD}|S)zECalculate clade height -- the longest path to any terminal (PRIVATE).rc3s|]}|VqdS)N)rw)rc)rr r r -sz5DistanceTreeConstructor._height_of..)rurvrDrt)rcladeZheightr )rr rw's z"DistanceTreeConstructor._height_of)Nrj) r1r:r;r<rmr rhrkrjrwr r r r ri$s7  Bgric@seZdZdZddZdS)Scorerz(Base class for all tree scoring methods.cCs tddS)ztCaller to get the score of a tree for the given alignment. This should be implemented in subclass. zMethod not implemented!N)rg)rro alignmentr r r get_score7szScorer.get_scoreN)r1r:r;r<rr r r r r4src@seZdZdZddZdS) TreeSearcherz*Base class for all tree searching methods.cCs tddS)znCaller to search the best tree with a starting tree. This should be implemented in subclass. zMethod not implemented!N)rg)r starting_treerr r r searchBszTreeSearcher.searchN)r1r:r;r<rr r r r r?src@s0eZdZdZddZddZddZdd Zd S) NNITreeSearcherzTree searching with Nearest Neighbor Interchanges (NNI) algorithm. :Parameters: scorer : ParsimonyScorer parsimony scorer to calculate the parsimony score of different trees during NNI algorithm. cCst|tr||_ntddS)zInitialize the class.zMust provide a Scorer object.N)rrscorerr)rrr r r r Ts zNNITreeSearcher.__init__cCs |||S)a5Implement the TreeSearcher.search method. :Parameters: starting_tree : Tree starting tree of NNI method. alignment : MultipleSeqAlignment multiple sequence alignment used to calculate parsimony score of different NNI trees. )_nni)rrrr r r r[s zNNITreeSearcher.searchcCs\|}xR|j||}|}x0||D]"}|j||}||kr$|}|}q$W||krPqW|S)zESearch for the best parsimony tree using the NNI algorithm (PRIVATE).)rr_get_neighbors)rrrZ best_treeZ best_scorertr\r r r rhszNNITreeSearcher._nnicCsi}xH|D]<}||jkr||}t|dkr>|j||<q|d||<qWg}g}x|jddD]}||jkrv|jd}|jd}|||||s|s|jd} |jd} |jd} |jd=|jd=|j| |j| t |} || |jd=|jd=|j| |j| t |} || |jd=|jd=|j| |j d| qf||krqfqf|jd}|jd}||} || jdkr^| jd}| jd=|jd=| j||j|t |} || | jd=|jd=| j||j|t |} || | jd=|jd=| j||j d|qf| jd}| jd=|jd=| j d||j|t |} || | jd=|jd=| j d||j|t |} || | jd=|jd=| j d||j d|qfW|S)zmGet all neighbor trees of the given tree (PRIVATE). Currently only for binary rooted trees. rlevel)orderr) Z find_cladesrZget_pathrget_nonterminalsrtrerurrrsr-)rroparentsrZ node_pathZ neighborsZ root_childsleftrightZ left_rightZ right_leftZ right_rightZ temp_treeparentZsisterr r r rxs                                         zNNITreeSearcher._get_neighborsN)r1r:r;r<r rrrr r r r rJs  rc@s"eZdZdZdddZddZdS)ParsimonyScorera Parsimony scorer with a scoring matrix. This is a combination of Fitch algorithm and Sankoff algorithm. See ParsimonyTreeConstructor for usage. :Parameters: matrix : _Matrix scoring matrix used in parsimony score calculation. NcCs"|rt|tr||_ntddS)zInitialize the class.zMust provide a _Matrix object.N)rrrr)rrr r r r szParsimonyScorer.__init__cCst|std|js||}|jddd|tddt||Ds^tdd}x tt |dD]}d}|d d |f}|t ||dkrqv|j s$t t|d d |D}xX|j d d D]H} | j } || d} || d} | | @} | s| | B} |d}| || <qWn@td}|j j}t |}i}xBtt |D]2}|g|}|||}d||<||||<qNWx|j d d D]} | j } || d}|| d}g}xt|D]}|}|}xjt|D]^}|j ||||f||}|j ||||f||}||kr&|}||kr|}qW|||qW||| <qWt|}||}qvW|S)zCalculate parsimony score using the Fitch algorithm. Calculate and return the parsimony score given a tree and the MSA using either the Fitch algorithm (without a penalty matrix) or the Sankoff algorithm (with a matrix). z(The tree provided should be bifurcating.cSs|jS)N)r.)Ztermr r r z+ParsimonyScorer.get_score..)keycss|]\}}|j|jkVqdS)N)r.rY)rrar r r r sz,ParsimonyScorer.get_score..zDTaxon names of the input tree should be the same with the alignment.rNcSsg|] }|hqSr r )rrr r r rsz-ParsimonyScorer.get_score..Z postorder)rrinf)Zis_bifurcatingrrZroot_at_midpointZ get_terminalssortrrFrrrdictrrtfloatrr!remin)rrorZtermsr\r Zscore_iZcolumn_iZ clade_statesrZ clade_childsZ left_stateZ right_statestaterrdlengthZ clade_scoresrBZarrayr!Z left_scoreZ right_scorerZmin_lZmin_rrZslsrr r r rsp         zParsimonyScorer.get_score)N)r1r:r;r<r rr r r r rs  rc@s"eZdZdZdddZddZdS)ParsimonyTreeConstructora Parsimony tree constructor. :Parameters: searcher : TreeSearcher tree searcher to search the best parsimony tree. starting_tree : Tree starting tree provided to the searcher. Examples -------- We will load an alignment, and then load various trees which have already been computed from it:: from Bio import AlignIO, Phylo aln = AlignIO.read(open('TreeConstruction/msa.phy'), 'phylip') print(aln) Output:: Alignment with 5 rows and 13 columns AACGTGGCCACAT Alpha AAGGTCGCCACAC Beta CAGTTCGCCACAA Gamma GAGATTTCCGCCT Delta GAGATCTCCGCCC Epsilon Load a starting tree:: starting_tree = Phylo.read('TreeConstruction/nj.tre', 'newick') print(starting_tree) Output:: Tree(rooted=False, weight=1.0) Clade(branch_length=0.0, name='Inner3') Clade(branch_length=0.01421, name='Inner2') Clade(branch_length=0.23927, name='Inner1') Clade(branch_length=0.08531, name='Epsilon') Clade(branch_length=0.13691, name='Delta') Clade(branch_length=0.2923, name='Alpha') Clade(branch_length=0.07477, name='Beta') Clade(branch_length=0.17523, name='Gamma') Build the Parsimony tree from the starting tree:: scorer = Phylo.TreeConstruction.ParsimonyScorer() searcher = Phylo.TreeConstruction.NNITreeSearcher(scorer) constructor = Phylo.TreeConstruction.ParsimonyTreeConstructor(searcher, starting_tree) pars_tree = constructor.build_tree(aln) print(pars_tree) Output:: Tree(rooted=True, weight=1.0) Clade(branch_length=0.0) Clade(branch_length=0.19732999999999998, name='Inner1') Clade(branch_length=0.13691, name='Delta') Clade(branch_length=0.08531, name='Epsilon') Clade(branch_length=0.04194000000000003, name='Inner2') Clade(branch_length=0.01421, name='Inner3') Clade(branch_length=0.17523, name='Gamma') Clade(branch_length=0.07477, name='Beta') Clade(branch_length=0.2923, name='Alpha') NcCs||_||_dS)zInitialize the class.N)searcherr)rrrr r r r sz!ParsimonyTreeConstructor.__init__cCs4|jdkr$ttdd}|||_|j|j|S)zBuild the tree. :Parameters: alignment : MultipleSeqAlignment multiple sequence alignment to calculate parsimony tree. NrNrk)rrirLrhrr)rrZdtcr r r rhs  z#ParsimonyTreeConstructor.build_tree)N)r1r:r;r<r rhr r r r rGs@ r)r<rGrrrZ Bio.PhylorZ Bio.Alignrrrr=Z_DistanceMatrixrLrfrirrrrrr r r r s.   &/2   f