:>"^X@sfdZddlZddlZddlmZddlmZddlmZddl m Z ddl m Z m Z dd ZGd d d eZGd d d eZeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZdS)z*Classes and methods for tree construction.N)BaseTree)MultipleSeqAlignment) MatrixInfo)_py3k)ziprangecCs"tj|p!t|ttfS)zReturn True if is numeric.)rZ_is_int_or_long isinstancefloatcomplex)xr ?/tmp/pip-build-ww9dw3qa/biopython/Bio/Phylo/TreeConstruction.py _is_numericsrc@s|eZdZdZdddZddZddZd 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|tratdd|Dratt|t|krR||_qmtdn td|dkrddtdt|dD}||_ nt|trmtd d|Drmtd dd d|DDrmt|t|kr^d d|Dttdt|dkrO||_ qjtd qytdn tddS)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)rstr).0sr r r `sz#_Matrix.__init__..zDuplicate names foundz#'names' should be a list of stringsNcSsg|]}dg|qS)rr )rir r r ks z$_Matrix.__init__..css|]}t|tVqdS)N)rlist)rlr r r rqscss|]}t|VqdS)N)r)rnr r r rsscSs#g|]}|D] }|qqSr r )rZsublistitemr r r rts cSsg|]}t|qSr )len)rmr r r rzs z+'matrix' should be in lower triangle formatz,'names' and 'matrix' should be the same sizez,'matrix' should be a list of numerical lists) rrallrsetnames ValueError TypeErrorrmatrix)selfrr"r r r __init__Ys&(   &  2 z_Matrix.__init__cs/t|ttfrdt|tr3|nNt|tru|jkrfjj|qtdn tdtdkrtdfddt dDfd dt tDSt|d krd}d}t d d |Dr5|\}}nt d d |Dr|\}}|jkr|jkrjj|}jj|}qtdn td|tdks|tdkrtd||kr j ||Sj ||Sn tddS)amAccess value(s) by the index(s) or name(s). For a _Matrix object 'dm':: dm[i] get a value list from the given 'i' to others; dm[i, j] get the value between 'i' and 'j'; dm['name'] map name to index first dm['name1', 'name2'] map name to index first NzItem not found.zInvalid index type.rzIndex out of range.cs!g|]}j|qSr )r")rr)indexr#r r rs z'_Matrix.__getitem__..rcs!g|]}j|qSr )r")rr)r%r#r r rs css|]}t|tVqdS)N)rint)rrr r r rsz&_Matrix.__getitem__..css|]}t|tVqdS)N)rr)rrr r r rs) rr'rrr%r r!r IndexErrorrrr")r#r row_index col_indexrow_namecol_namer )r%r#r __getitem__s>    "*  ,  z_Matrix.__getitem__c Cst|ttfrhd}t|tr3|}nNt|tru||jkrf|jj|}qtdn td|t|dkrtdt|t rYt dd|DrYt|t|krJx,t d|D]}|||j ||.rzValue not the same size.zInvalid value type.r&css|]}t|tVqdS)N)rr')rrr r r rscss|]}t|tVqdS)N)rr)rrr r r rs)rr'rrr%r r!rr(rrrr"r) r#rvaluer%rr)r*r+r,r r r __setitem__sN    (  ,   z_Matrix.__setitem__cCsd}t|tr|}n0t|trB|jj|}n tdx/t|dt|D]}|j||=qhW|j|=|j|=dS)z.Delete related distances by the index or name.NzInvalid index type.r) rr'rrr%r!rrr")r#rr%rr r r __delitem__s    z_Matrix.__delitem__cCst|tr|dkr't|}t|tsBtd|jj|||jj|dg|x4t|t|D]}|j|j|dqW|||..)rjoinr")rr)r#r r r2s z#_Matrix.__str__..rz r;)r<rrr)r#Z matrix_stringr )r#r __str__.s z_Matrix.__str__) r5 __module__ __qualname____doc__r$r-r/r0r1r3r9r=r r r r rs ?* 6 @   rc@sIeZdZdZdddZddZddZd 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. NcCs!tj||||jdS)zInitialize the class.N)rr$_set_zero_diagonal)r#rr"r r r r$@szDistanceMatrix.__init__cCs!tj||||jdS)zSet Matrix's items to values.N)rr/rB)r#rr.r r r r/EszDistanceMatrix.__setitem__cCs5x.tdt|D]}d|j||.z{0:zs}z r:c3s |]}j|VqdS)N)r")rj)rr#r r rdsN)writeformatrrmaxr7rr"rr< enumerater itertoolschain) r#handleZ name_widthZ value_fmtsZrow_fmtr2valuesZ mirror_valuesfieldsr )rr#r format_phylipOs%)%.2zDistanceMatrix.format_phylip)r5r>r?r@r$r/rBrOr r r r rA:s   rAc@sZeZdZdZddddgZdgd+dgd,d-dgd.d/d0dggZdgd1dgd2d3dgd4d5d6dggZdd dd d d dddddddddddddddddgZdedeiZe j Z dd e DZ e ejZd!gee Zd!d"d#d$Zd%d&Zd'd(Zd)d*Zd"S)7DistanceCalculatoraClass 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_matrices`` contains the available model names for DNA sequences and ``protein_matrices`` 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:: SingleLetterAlphabet() 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 ATCGrBDEFHIKLMNPQRSVWXYZblastntranscCs"i|]}tt||qSr )getattrr)rr2r r r s zDistanceCalculator.identityNcCs|r||_n!|dkr*f|_n d|_|dkrKd|_nu||jkryt|j|j||_nG||jkr|j|j||_nt ddj |j dS)z!Initialize with a distance model.ro-*Nz'Model not supported. Available models: z, )rprq) skip_lettersscoring_matrix dna_modelsr dna_alphabet dna_matricesprotein_models_build_protein_matrixprotein_matricesr r<models)r#modelrrr r r r$s      zDistanceCalculator.__init__c s}d}d}jr)d}d}xtdt|D]}||}||} |jks7| jkrrq7|jjkrtd||j|f| jjkrtd| |j|f|j||f7}|j| | f7}|j|| f7}q7Wt||}n4tfddt ||D}t|}|dkrmdSd|d|S)zCalculate pairwise distance from two sequences (PRIVATE). Returns a value between 0 (identical sequences) and 1 (completely different, or seq1 is an empty string.) rz3Bad alphabet '%s' in sequence '%s' at position '%s'c3s?|]5\}}|jkr|jkr||kVqdS)N)rr)rl1l2)r#r r rs z/DistanceCalculator._pairwise..rg?) rsrrrrrr idrHsumr) r#seq1seq2scoreZ max_scoreZ max_score1Z max_score2rr|r}r )r#r _pairwises:     zDistanceCalculator._pairwisecCst|tstddd|D}t|}xBtj|dD].\}}|j||||j|jf.r&)rrr!rArJ combinationsrr~)r#msardmrrr r r get_distances  &zDistanceCalculator.get_distancecCsLt|j}x6|jD](\}}|\}}||||fr?r@rurkrlrrvrZavailable_matricesrwryrkeysrtrzr$rrrxr r r r rPmsH ?00   + rPc@s"eZdZdZddZdS)TreeConstructorz$Base class for all tree constructor.cCstddS)zvCaller to built the tree from a MultipleSeqAlignment object. This should be implemented in subclass. zMethod not implemented!N)NotImplementedError)r#rr r r build_tree8szTreeConstructor.build_treeN)r5r>r?r@rr r r r r5s rc@sdeZdZdZddgZddddZddZd 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'||_n tdt|tr]||jkr]||_n$td|ddj|jdS)zInitialize the class.Nz)Must provide a DistanceCalculator object.z Bad method: z. Available methods: z, )rrPdistance_calculatorr!rmethodsmethodr<)r#rrr r r r${s    z DistanceTreeConstructor.__init__cCse|jrU|jj|}d}|jdkrB|j|}n|j|}|StddS)z7Construct and return a Tree, Neighbor Joining or UPGMA.Nrz)Must provide a DistanceCalculator object.)rrrrrr!)r#rrtreer r r rs z"DistanceTreeConstructor.build_treecCsdt|tstdtj|}dd|jD}d}d}d}xt|dkrM|d }xitdt|D]R}xItd|D]8} |||| fkr||| f}|}| }qWqW||} ||} |d7}tj ddt |} | j j | | j j | | j rW|dd | _n|dd |j| | _| j r|dd | _n|dd |j| | _| ||<||=xetdt|D]N} | |kr| |kr||| f||| fdd ||| f.rrNInnerg?r&)rr)rrAr!copydeepcopyrrrrrrcladesappend is_terminal branch_length _height_ofTree)r#distance_matrixrrmin_imin_j inner_countmin_distrrEclade1clade2 inner_claderr r r rsH         4  zDistanceTreeConstructor.upgmacCsmt|tstdtj|}dd|jD}dgt|}d}d}d}t|dkr|d}tj|ddSt|dkrPd}d}||} ||} |||fd | _ |||f| j | _ tj d d } | j j | | j j | | |d<|d}tj|ddSxnt|dkrx{t dt|D]d} d|| .rrrootedFr&g@Nr)rr)rr)rr)rrAr!rrrrrrrrrrrr)r#rrrZ node_distrrrrootrrrrrErtemprr r r rs               )     zDistanceTreeConstructor.njcsHd}|jr|j}n&|tfdd|jD}|S)zECalculate clade height -- the longest path to any terminal (PRIVATE).rc3s|]}j|VqdS)N)r)rc)r#r r rIsz5DistanceTreeConstructor._height_of..)rrrHr)r#cladeheightr )r#r rCs   &z"DistanceTreeConstructor._height_of) r5r>r?r@rr$rrrrr r r r r@s 7  B grc@s"eZdZdZddZdS)Scorerz(Base class for all tree scoring methods.cCstddS)ztCaller to get the score of a tree for the given alignment. This should be implemented in subclass. zMethod not implemented!N)r)r#r alignmentr r r get_scoreSszScorer.get_scoreN)r5r>r?r@rr r r r rPs rc@s"eZdZdZddZdS) TreeSearcherz*Base class for all tree searching methods.cCstddS)znCaller to search the best tree with a starting tree. This should be implemented in subclass. zMethod not implemented!N)r)r# starting_treerr r r search^szTreeSearcher.searchN)r5r>r?r@rr r r r r[s rc@sFeZdZdZddZddZddZdd 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. cCs+t|tr||_n tddS)zInitialize the class.zMust provide a Scorer object.N)rrscorerr!)r#rr r r r$ps zNNITreeSearcher.__init__cCs|j||S)a5Implement the TreeSearcher.search method. :Parameters: starting_tree : Tree starting tree of NNI method. alignment : MultipleSeqAlignment multiple sequence alignment used to calculate parsimony score of different NNI trees. )_nni)r#rrr r r rws zNNITreeSearcher.searchcCs|}xs|jj||}|}xD|j|D]3}|jj||}||kr4|}|}q4W||kr Pq W|S)zESearch for the best parsimony tree using the NNI algorithm (PRIVATE).)rr_get_neighbors)r#rrZ best_treeZ best_scorertrr r r rs   zNNITreeSearcher._nnicCsi}xb|jD]T}||jkr|j|}t|dkrY|j||r?r@r$rrrr r r r rfs   rc@s1eZdZdZdddZddZdS)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. NcCs2| st|tr"||_n tddS)zInitialize the class.zMust provide a _Matrix object.N)rrr"r!)r#r"r r r r$ s zParsimonyScorer.__init__cCs#|jstd|js+|j|j}|jddd|jtddt||Dstdd}xtt |dD]y}d}|d d |f}|t ||dkrq|j st t|d d |D}x|j d d D]Y} | j } || d} || d} | | @} | sn| | B} |d}| || }|g|}|j||}d||<||||sz+ParsimonyScorer.get_score..css'|]\}}|j|jkVqdS)N)r2r~)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 r4s z-ParsimonyScorer.get_score..rZ postorderrinf)Zis_bifurcatingr rZroot_at_midpointZ get_terminalssortrrrrr"dictrrr rr%rmin)r#rrZtermsrrZscore_iZcolumn_iZ clade_statesrZ clade_childsZ left_stateZ right_statestaterZalphabetlengthZ clade_scoresrEarrayr%Z left_scoreZ right_scorerZmin_lZmin_rrslsrr r r rsp      "  "          ##    zParsimonyScorer.get_score)r5r>r?r@r$rr r r r rs rc@s1eZdZdZdddZddZdS)ParsimonyTreeConstructora0 Parsimony tree constructor. :Parameters: searcher : TreeSearcher tree searcher to search the best parsimony tree. starting_tree : Tree starting tree provided to the searcher. Examples -------- We will load an alignment, and then load various trees which have already been computed from it:: from Bio import AlignIO, Phylo aln = AlignIO.read(open('TreeConstruction/msa.phy'), 'phylip') print(aln) Output:: SingleLetterAlphabet() alignment with 5 rows and 13 columns AACGTGGCCACAT Alpha AAGGTCGCCACAC Beta CAGTTCGCCACAA Gamma GAGATTTCCGCCT Delta GAGATCTCCGCCC Epsilon Load a starting tree:: starting_tree = Phylo.read('TreeConstruction/nj.tre', 'newick') print(starting_tree) Output:: Tree(rooted=False, weight=1.0) Clade(branch_length=0.0, name='Inner3') Clade(branch_length=0.01421, name='Inner2') Clade(branch_length=0.23927, name='Inner1') Clade(branch_length=0.08531, name='Epsilon') Clade(branch_length=0.13691, name='Delta') Clade(branch_length=0.2923, name='Alpha') Clade(branch_length=0.07477, name='Beta') Clade(branch_length=0.17523, name='Gamma') Build the Parsimony tree from the starting tree:: scorer = Phylo.TreeConstruction.ParsimonyScorer() searcher = Phylo.TreeConstruction.NNITreeSearcher(scorer) constructor = Phylo.TreeConstruction.ParsimonyTreeConstructor(searcher, starting_tree) pars_tree = constructor.build_tree(aln) print(pars_tree) Output:: Tree(rooted=True, weight=1.0) Clade(branch_length=0.0) Clade(branch_length=0.19732999999999998, name='Inner1') Clade(branch_length=0.13691, name='Delta') Clade(branch_length=0.08531, name='Epsilon') Clade(branch_length=0.04194000000000003, name='Inner2') Clade(branch_length=0.01421, name='Inner3') Clade(branch_length=0.17523, name='Gamma') Clade(branch_length=0.07477, name='Beta') Clade(branch_length=0.2923, name='Alpha') NcCs||_||_dS)zInitialize the class.N)searcherr)r#rrr r r r$s z!ParsimonyTreeConstructor.__init__cCsL|jdkr6ttdd}|j||_|jj|j|S)zBuild the tree. :Parameters: alignment : MultipleSeqAlignment multiple sequence alignment to calculate parsimony tree. Nror)rrrPrrr)r#rZdtcr r r rs z#ParsimonyTreeConstructor.build_tree)r5r>r?r@r$rr r r r rcs @r)r@rJrZ Bio.PhylorZ Bio.AlignrZ Bio.SubsMatrZBiorZ Bio._py3krrrobjectrrAZ_DistanceMatrixrPrrrrrrrr r r r s*   #0   f