DUfkc ddlmZddlZddlZddlmZmZddlmZddl m Z m Z m Z m Z mZddlZddlZddlZddlmZmZddlmZmZdodZdpdZdqdZdqdZdrdZ dsdtdZej dej!fdud Z"dvd$Z#dwd'Z$ dxdyd1Z%dzd3Z&d{d7Z'd|d:Z(e(Z)d}d=Z*d~d@Z+ddAZ,ddDZ-ddHZ. dsddKZ/ddMZ0dsddPZ1e dddVZ2GdWdXej3Z4dd\Z5dsd]Z6dddej7fd^Z8dd_Z9 dsddgZ:GdhdaZ; dddnZ>> for lo, hi in partition(0, 9, 2): print(lo, hi) 0 2 2 4 4 6 6 8 8 9 c3BK|]}|t|zfVdSN)min).0irrs H/var/www/html/software/conda/lib/python3.11/site-packages/cooler/util.py zpartition.. s6 G GQAHd## $ G G G G G G)range)rrrs ``r partitionr!s0 H G G G GeE4.F.F G G GGrsstrtuple[str, str]c|d}t|dkr |dd}}nBt|dkr |\}}|dsd|z}ntd||fS)zW Parse a Cooler URI string e.g. /path/to/mycoolers.cool::/path/to/cooler z::rr/zInvalid Cooler URI string)splitlen startswith ValueError)r"parts file_path group_paths rparse_cooler_urir/#s GGDMME 5zzQ %a#: Uq % :$$S)) *z)J4555 j  rcHt|ddS)N,)rreplace)r"s ratoir46s qyyb!! " ""rctjd}||dd\}}}t |st |St |}|}|dvr|dz}n'|dvr|dz}n|dvr|d z}ntd |d t |S) Nz ([0-9,.]+)r1r2)KKBi)MMBi@B)GGBiʚ;zUnknown unit '') recompiler(r3r)rfloatupperstripr+)r" _NUMERIC_RE_valueunits rparse_humanizedrF:s*\**K &&qyyb'9'9::NAud t995zz %LLE ::<<    D {          1$111222 u::r"tuple[str, int | None, int | None]c(d}dfd}|d}|d}t|stdt|dkr|ddfS|||d \}}|||fS) ac Parse a UCSC-style genomic region string into a triple. Parameters ---------- s : str UCSC-style string, e.g. "chr5:10,100,000-30,000,000". Ensembl and FASTA style sequence names are allowed. End coordinate must be greater than or equal to start. Returns ------- (str, int or None, int or None) c3Kgd}dd|D}tjd|tj}||D]"}|j}|||fV#dS)N))HYPHEN-)COORDz[0-9,]+(\.[0-9]*)?(?:[a-z]+)?)OTHERz.+z|\s*c8g|]}d|dd|ddS)z(?Pr))rpairs r z:parse_region_string.._tokenize..ds4TTT <Q < <$q' < < <TTTrz\s*)joinr=r> IGNORECASEfinditer lastgroupgroup)r" token_specpattern tok_regexmatchtyps r _tokenizez&parse_region_string.._tokenize^s   ,,TTTTTUUJ/g//?? ''** ( (E/Cu{{3''' ' ' ' ' ( (rc|5tdd|||vrtd|ddS)NzExpected {} token missingz or zUnexpected token "")r+formatrT)r]tokenexpecteds r _check_tokenz)parse_region_string.._check_tokenjs] ;8?? H@U@UVVWW W("" !>e!>!>!>???#"rcDt|d\}}||dgt|}t|d\}}||dgt|d\}}||dfS||dgt|}||krtd||fS)N)NNrLrJzEnd coordinate less than start)nextrFr+)tokensr]rbrendrds r_expectz$parse_region_string.._expectqs&,// U S%'+++&&&,// U S%(,,,&,// U ;$;  S%'+++e$$ ;;=>> >czr:rzChromosome name cannot be emptyr'Nr)r(rAr)r+)r"r^rir,chromrrhrds @rparse_region_stringrlMs" ( ( (@@@& GGCLLE !HNN  E u::<:;;; 5zzA~~tT""58,,--JE3 5# rregr chromsizesdict | pd.Series | Nonerct|trt|\}}}n,|\}}}|t|n|}|t|n|} |||nd}n%#t$r}t d||d}~wwxYw|dn|}||t d|}||krt d|dks|||krt d|d|d|||fS) aU Genomic regions are represented as half-open intervals (0-based starts, 1-based ends) along the length coordinate of a contig/scaffold/chromosome. Parameters ---------- reg : str or tuple UCSC-style genomic region string, or Triple (chrom, start, end), where ``start`` or ``end`` may be ``None``. chromsizes : mapping, optional Lookup table of scaffold lengths to check against ``chrom`` and the ``end`` coordinate. Required if ``end`` is not supplied. Returns ------- A well-formed genomic region triple (str, int, int) NzUnknown sequence label: rz Cannot determine end coordinate.zEnd cannot be less than startzGenomic region out of bounds: [z, rP) isinstancer#rlrKeyErrorr+)rmrnrkrrhclenes r parse_regionrusA,#s3/44uccuc#/E U/c#hhhsD$.$:z%   DDD;E;;<.s4PPP1aPAIIKK.#a&&&QPPPr)rvr()r" _NS_REGEXs r natsort_keyr}s+ PP 8J8JPPP Q QQriterable Iterable[str] list[str]c.t|tS)N)key)sortedr})r~s r natsortedrs ( , , ,,rarray np.ndarrayctj|}t|stjgtSt t d|D}tj|dddS)Ndtypec34K|]}t|VdSr)r}rzs rrzargnatsort..s(55!{1~~555555r)npasarrayr)rrrvziplexsort)rcolss r argnatsortrso Ju  E u::'x#&&&& 55u5556 7 7D :d44R4j ! !!rz ^chr[0-9]+$z ^chr[XY]$z^chrM$F filepath_or str | IO[str] name_patternstuple[str, ...] all_namesbool pd.Seriesc t|tr+|dr|ddt j|fdddgddgdtid |}|sxg}|D]]}||dj|}|jt|d}| |^t j |d }|dj |_ |dS) at Parse a ``.chrom.sizes`` or ``.chromInfo.txt`` file from the UCSC database, where ``db`` is a genome assembly name. Parameters ---------- filepath_or : str or file-like Path or url to text file, or buffer. name_patterns : sequence, optional Sequence of regular expressions to capture desired sequence names. Each corresponding set of records will be sorted in natural order. all_names : bool, optional Whether to return all contigs listed in the file. Default is ``False``. Returns ------- :py:class:`pandas.Series` Series of integer bp lengths indexed by sequence name. References ---------- * `UCSC assembly terminology `_ * `GRC assembly terminology `_ z.gz compressiongzip rrnamelength)sepusecolsnamesraxis) rqr#endswith setdefaultpdread_csvcontainsilocrappendconcatvaluesindex)rrrkwargs chromtabler,rZparts rread_chromsizesrs@+s##1 (<( sequence record rNzNeed at least one filerT)as_rawc3,K|]}||fVdSrrQ)rrkfas rrzload_fasta...s,@@5"U),@@@@@@r)pyfaidxr)r+Fastaupdaterecordsr)rrrfilepathrrs @r load_fastars"NNN 9~~1222 9~~ ]]9Q<] 5 5! D DH IIgmmHTm::B C C C C@@@@%@@@@@G Nrbinsize pd.DataFramecfd}tjt|dd}tj|dt jd|d<|S)af Divide a genome into evenly sized bins. Parameters ---------- chromsizes : Series pandas Series indexed by chromosome name with chromosome lengths in bp. binsize : int size of bins in bp Returns ------- bins : :py:class:`pandas.DataFrame` Dataframe with columns: ``chrom``, ``start``, ``end``. c|}ttj|z }tjd|dzz}||d<t j|g|z|dd|dddgdSNrrr)rkrrhcolumns)rrceilaranger DataFrame)rkrsn_binsbinedgesrrns r_eachzbinnify.._eachDs% RWTG^,,--9Q!--7 |g&#2#xPQPRPR| T T---    rrTr ignore_indexrk categoriesordered)rrmapkeys Categoricallistr)rnrrbintables`` rbinnifyr2s$      yUJOO$5$566QTRRRHd:+;&<&._eachzsjj]51!!!45566uQC11A5s3xx?@GGQQd))a- g'$ss)DH M M---    rTr) Bio.Restriction RestrictionBio.Seqr ImportErrorrgetattrsearchAttributeErrorr+rrr)rrbiorstchromsrtrrrs` @@rdigestrZs"((((((        B       ! !FBVV,,3 BBB999::AB        9S''ad C C CCs -A A=%A88A=bins int | Noneclt}|ddD]^\}}||d|dz jddt |dkrdS_t |dkrt t|SdS) z Infer bin size from a bin DataFrame. Assumes that the last bin of each contig is allowed to differ in size from the rest. Returns ------- int or None if bins are non-uniform rkT)observedrhrNrr)setgroupbyrruniquer)rfiter)rsizes_chromrXs r get_binsizers EEEg==  eElU7^39#2#>EEGGHHH u::>>44  5zzQDKK   trc|dgdddgdddd  }t|dt|d}}t j|| S) z Infer chromsizes Series from a bin DataFrame. Assumes that the last bin of each contig is allowed to differ in size from the rest. Returns ------- int or None if bins are non-uniform rklast)keeprhT)droprr)rkrhrrdata)drop_duplicates reset_indexrenamerrSeries)rrrlengthss rget_chromsizesrs gYV44gu5EF $   &:: ; ; :f-..Z5I0J0JGF 96 0 0 00rpd.Series | dictregionc8t||\}}}||}|dks |||kr^|dj|d}||dj|d|dz}|j||}|S)zN Range query on a BED-like dataframe with non-overlapping intervals. rrhrightsiderNleft)ru get_groupr searchsortedr) groupedrnr rkrrhresultlohis rbedslicers%VZ88E5#   u % %F qyyC*U+++ E] ! . .u7 . C C &/(-::3V:LL LRU# Mrr{rnp.ndarray | h5py.Datasetcbt|tjr|ntj|Sr)rqh5pyDatasetrr)r{s rasarray_or_datasetrs%1dl++ >11A>r chunksize)tuple[np.ndarray, np.ndarray, np.ndarray]c tj}t|}t|}|dkrPtjgt tjgt tjg|jfS||}gg}}tj}td||D]}||||z}||dd|ddkdz} |d|krtj d| f} | || z| || |d}tj |}tj tj ||f} tj |}|| |fS)aS Run length encoding. Based on http://stackoverflow.com/a/32681075, which is based on the rle function from R. Parameters ---------- x : 1D array_like Input array to encode dropna: bool, optional Drop all runs of NaNs. Returns ------- start positions, run lengths, run values rrNrr) r flatnonzerorr)rrrnanr rr concatenatediff) rrwherenstartsrlast_valrr{locsrs rrlencoder(s{* NE u % %E E AAvv HRs # # # HRs # # # HRu{ + + +   FFvH 1a # # !a)m# $uQqrrUaf_%%) Q48  5D>D a$h agR5 ^F # #FgbeFAI&''G ^F # #F 7F ""rcmdctfdtjdtjDS)Nc3K|]A}tjtj|tjVBdSr)osaccesspathrTX_OK)rr.r)s rrzcmd_exists..sU  "',,tS))2733rPATH)anyr,environr(pathsep)r)s`r cmd_existsr4sR Jv&,,RZ88  rrrc |tjtj|tj||z |Sr)rmedianabs)rrs rmadr8s/ 9RVD29T4#8#88994 @ @@rrfpstr | h5py.GroupmodeContextManager[h5py.Group]c/Kt|trd}tj||g|Ri|}nfd}|dkr|jjdkrnK|dvr|jjdkrt d|dkrt d|d vrt d |} |V|r|d Sd S#|r|wwxYw) a Context manager like ``h5py.File`` but accepts already open HDF5 file handles which do not get closed on teardown. Parameters ---------- fp : str or ``h5py.File`` object If an open file object is provided, it passes through unchanged, provided that the requested mode is compatible. If a filepath is passed, the context manager will close the file on tear down. mode : str * r Readonly, file must exist * r+ Read/write, file must exist * a Read/write if exists, create otherwise * w Truncate if exists, create otherwise * w- or x Fail if exists, create otherwise TFr9r+)r?az%File object provided is not writeablewzCannot truncate open file)zw-r{z File existsN)rqr#rFilefiler<r+close)r:r<argsrown_fhfhs r open_hdf5rHs6"c Yr4 1$ 1 1 1& 1 1 3;;27<4//  [ RW\S%8%8DEE E S[[899 9 [ ]++ +    HHJJJJJ  6  HHJJJJ s B66Cc8eZdZd fd Zd dZd dZd dZxZS) closing_hdf5grp h5py.GroupcTt|jdSr)super__init__id)selfrK __class__s rrOzclosing_hdf5.__init__6s#      rrc|SrrQrQs r __enter__zclosing_hdf5.__enter__9s rNonec4|jSrrCrD)rQexc_infos r__exit__zclosing_hdf5.__exit__<sy   rc8|jdSrrXrTs rrDzclosing_hdf5.close?s r)rKrL)rrL)rrV)__name__ __module__ __qualname__rOrUrZrD __classcell__)rRs@rrJrJ5sy!!!!!!!!!!rrJattrsh5py.AttributeManagerdictct|}|D]T\}} |||<#t$r|||<YAt $r|||<YQwxYw|Sr)rbitemsitemr+tolistr)r`outkvs rattrs_to_jsonablerjCs u++C 1 VVXXCFF   XXZZCFFF   CFFF  JsA!A6%A65A6c  tjdtjdtjdtjdtjdtjdtjddddfdfd }dfd t|d r|jSt|tj tj fr|j d d St|tjr |d d Sn d d t|tr5tj fd|DSt|t"r-t%|dkr|d |dSt|t&t"frYt)d|Dst+d|tj fd|Dd|DSt|ds)|' tj|}|S#YnxYwt/|r ||St1d|)a Extracted and modified from dask/dataframe/utils.py : make_meta (BSD licensed) Create an empty pandas object containing the desired metadata. Parameters ---------- x : dict, tuple, list, pd.Series, pd.DataFrame, pd.Index, dtype, scalar To create a DataFrame, provide a `dict` mapping of `{name: dtype}`, or an iterable of `(name, dtype)` tuples. To create a `Series`, provide a tuple of `(name, dtype)`. If a pandas object, names, dtypes, and index should match the desired output. If a dtype or scalar, a scalar of the same dtype is returned. index : pd.Index, optional Any pandas index to use in the metadata. If none provided, a `RangeIndex` will be used. Examples -------- >>> make_meta([('a', 'i8'), ('b', 'O')]) Empty DataFrame Columns: [a, b] Index: [] >>> make_meta(('a', 'f8')) Series([], Name: a, dtype: float64) >>> make_meta('i8') 1 T z 1970-01-01rfoo)bVr8mSr@UO__UNKNOWN_CATEGORIES__c,|jdvr|dS|jdkr#|tddS|jvr-|j}|jdvr||n|St d|)N)rfurcr)rpr8zCan't handle dtype: )kindtypecomplexr TypeError)ro_simple_fake_mappings r_scalar_from_dtypez&infer_meta.._scalar_from_dtype|s : ( (::a== Z3  ::gamm,, , Z/ / /$UZ0A&+jJ&>&>188E???A E:5::;; ;rcbt|tjtjtjfr|St j|rCt|dr|jn t jt|}|Stdt|j d)NrzCan't handle meta of type 'r<) rqr Timestamp TimedeltaPeriodrisscalarhasattrrrzr|r\)r{rrs r_nonempty_scalarz$infer_meta.._nonempty_scalars a", bi@ A A RH [^^ R&q'22IAGGa8I8IE%%e,, ,PT!WW=MPPPQQ QrNct|tr=|dkr7tjtjg||jddStjg|||S)Ncategory)rrr)rrr)rqr#rrrr)rrrUNKNOWN_CATEGORIESs r _empty_seriesz!infer_meta.._empty_seriessv eS ! ! ez&9&99 23444u2A2 y5t5AAAAr_metarc4i|]\}}|||SrrQrrxdrrs r zinfer_meta..s0 I I IVaQ a%000 I I Irrr'c3fK|],}t|tot|dkV-dS)r'N)rqrvr))rrs rrzinfer_meta..s;CCA:a''7CFFaKCCCCCCrz2Expected iterable of tuples of (name, dtype), got c4i|]\}}|||SrrQrs rrzinfer_meta..s0 A A AVaQ a%000 A A Arcg|]\}}|SrQrQ)rrxrs rrSzinfer_meta..s%%%41aQ%%%rrrrz'Don't know how to create metadata from r)rbool_void datetime64 timedelta64str_unicode_rrrqrrrrIndexrbrdrvr)rallr+rr r|)r{rrrrrrr~s ` @@@@r infer_metarOsBXd^^ WT]] ]< ( ( ^A   WU^^ WU^^ [     2 < < < < <RRRRRBBBBBBq'w!bi.//vac{ Arx 1v ]EEac E!T | I I I I Iqwwyy I I IQV    !UA! }QqT1Q4u5555 Ae} % %CCCCCCC KKK | A A A A Aq A A A%%1%%%    Q Q] HQKKE%%e,, ,  D||#""" AaAA B BBs 3IIct|}ttstfdn^}tfd|D]#\}}t |r||n|}||<$||durtjg} nfd|D} t| dkr#tj| d|d} n!tj | | } | t|D]\} } || | z fd |D} tj| || S) ze Extracted and modified from pandas/io/parsers.py : _get_empty_meta (BSD licensed). c pSrrQ) default_dtypersrzget_meta..s E$:]rcSrrQ)rsrrzget_meta..sMrNFcHg|]}tjg|Srrr)rrrs rrSzget_meta..s,IIIT "E$K000IIIrrr)r)rcJi|]}|tjg| Srr)rcol_namers rrzget_meta..s.WWW8")BeHo>>>WWWrr)rrqrbrcopyrdr rrr) MultiIndex from_arrayssort enumeratepopr)rr index_columns index_namesr_dtyperhricolrrrr$col_dicts ` ` rget_metars7mmG eT " " :::::;;111122LLNN  DAq *1 4'!**1CE#JJ  6 6 IIII[III t99>>HT!W;q>:::EEM--d+-FFEm,,  DAq KKA    WWWWwWWWH <' ? ? ??rc>t|djtj}|}|s2tj|jt|jd|d<n/|dj j |jk sJ|S)NrkTr) rqrrCategoricalDtyperrrkrrcatrr)rrnis_cats r check_binsrs W +R-@ A AF 99;;D H J4 (8#9#94   W W !, 0@@EEGGGGG KrgsGenomeSegmentation n_chunk_max file_contigsloadingslist[int | float] | Nonelist[GenomicRangeTuple]c , |j}|}||}|}||j|z }|j||z }g}|D]\ } |vr |j } t t j||j z } | jj dd| } | d| krt j | | f} | fdt| dd| ddD|S)Nrc3(K|] \}}||fV dSrrQ)rrrhrks rrz%balanced_partition..s?  $.E3UE3       rr) _bins_groupedsizeidxmaxlocrnrrrrrrextendr)rrrrr chrom_nbinschrmaxconstgrangesrXrsranchorsrks @rbalanced_partitionrsAG,,..K __  F(,v..H OF #k 1EG   u  $ $ }U#2758<#667788+$VVtV, 2;$  eGTM*G    25gcrclGABBK2P2P        NrceZdZd dZd d Zd S) rrnrrrct||}|ddd|_|jj}||_t ||_t| |_ ||_ tj | tt||_t"jdt#j|f|_t"jdt#j|jf|_|j|djj|djz|_dS)NrkTF)rrrrr)rrrrrrnrrrrcontigsrrrr r)idmaprrcumsumchrom_binoffset chrom_absposrcodes start_abspos)rQrnrnbins_per_chroms rrOzGenomeSegmentation.__init__s$ ++!\\'Du\MM,1133:$"4(( JOO--..  YZ__%6%6U3z??=S=STTT !uQ /(B(B%BCE!RYz/@%A%A"AB  d7m/5 6g9M M rr rrcVt||j\}}}|j|}|dks||j|kr^|dj|d}||dj|d|dz}|j||}|S)Nrrhr r rr)rurnrrrrr)rQr rkrrhrrrs rfetchzGenomeSegmentation.fetch(s(AAuc#--e44 199doe444%225w2GGBfWo,RSS1>>s>PPPB[B'F rN)rnrrr)r rrr)r\r]r^rOrrQrrrrs<    r逖chunksIterable[pd.DataFrame]rIterator[pd.DataFrame]c#Kg}d}|D]K}|t|z }||||krtj|dVg}d}Lt|rtj|dVdSdS)a Take an incoming iterator of small data frame chunks and buffer them into an outgoing iterator of larger chunks. Parameters ---------- chunks : iterator of :py:class:`pandas.DataFrame` Each chunk should have the same column names. size : int Minimum length of output chunks. Yields ------ Larger outgoing :py:class:`pandas.DataFrame` chunks made from concatenating the incoming ones. rrN)r)rrr)rrbufr$chunks rbufferedr2s* C A SZZ 5 t88)Ca((( ( ( (CA 3xx%i!$$$$$$$$%%r)rrrrrrrr)r"r#rr$)r"r#rr)r"r#rrGr)rmrrnrorr)r"r#rrv)r~rrr)rrrr)rF)rrrrrrrr)rr#rr)rrrr#rr)rnrrrrr)rrrr#rr)rrrr)rrrr)rnr r rrr)r{rrr)rrrrrr)r)r#rr)rrrrrr)r9)r:r;r<r#rr=)r`rarrb)rrrnrrr) rrrrrrrrrr)r)rrrrrr)= __future__rr,r= collectionsrr contextlibrtypingrrr r r rnumpyrpandasrpandas.api.typesr r _typingrrr!r/r4rFrlrur>rrr}rrrrrr make_bintablerrrrrr(r4r8rHGrouprJrjrobject_rrrrrrQrrrs"""""" 00000000%%%%%%>>>>>>>>>>>>>> 22222222========HHHH$!!!!&####&>>>>F+/.....b#-"*Xrt"<"<RRRRR----""""&O2 2 2 2 2 j    D""""J +D+D+D+D\*1111&$???? !0#0#0#0#0#fAAAAA-----`     4:       nCnCnCnCdtRZ(@(@(@(@V    "*. >:%%%%%%%r