o Nrfkc@sddlmZddlZddlZddlmZmZddlmZddl m Z m Z m Z m Z mZddlZddlZddlZddlmZmZddlmZmZdddZdddZdddZdddZdddZ ddd"d#Ze d$ej!fdd&d'Z"dd+d,Z#dd/d0Z$ 1 2ddd:d;Z%dd=d>Z&ddBdCZ'ddFdGZ(e(Z)ddJdKZ*ddNdOZ+ddPdQZ,ddTdUZ-ddYdZZ. ddd]d^Z/dd`daZ0dddddeZ1e fdddkdlZ2Gdmdndnej3Z4ddrdsZ5ddtduZ6dddej7fdvdwZ8ddxdyZ9 ddddZ:Gdd{d{Z; ddddZ>> for lo, hi in partition(0, 9, 2): print(lo, hi) 0 2 2 4 4 6 6 8 8 9 c3s"|] }|t|fVqdSN)min.0irrT/var/www/html/software/conda/envs/catlas/lib/python3.10/site-packages/cooler/util.py s zpartition..)range)rrrrrr partitionsr sstrtuple[str, str]cCsb|d}t|dkr|dd}}||fSt|dkr-|\}}|ds)d|}||fStd)zW Parse a Cooler URI string e.g. /path/to/mycoolers.cool::/path/to/cooler z::r r/zInvalid Cooler URI string)splitlen startswith ValueError)r!parts file_pathZ group_pathrrrparse_cooler_uri#s    r,cCst|ddS)N,)rreplace)r!rrratoi6sr0cCstd}||dd\}}}t|st|St|}|}|dvr/|d9}t|S|dvr;|d9}t|S|dvrG|d 9}t|St d |d ) Nz ([0-9,.]+)r-r.)KZKBi)MMBi@B)GGBiʚ;zUnknown unit '') recompiler&r/r'rfloatupperstripr))r!Z _NUMERIC_RE_valueunitrrrparse_humanized:s  r?"tuple[str, int | None, int | None]csvdd}ddfdd}|d}|d}t|s!td t|d kr,|d d fS|||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) css\gd}ddd|D}td|tj}||D] }|j}|||fVqdS)N))HYPHEN-)COORDz[0-9,]+(\.[0-9]*)?(?:[a-z]+)?)OTHERz.+z|\s*cSs&g|]}d|dd|ddqS)z(?Pr )r)rpairrrr ds&z:parse_region_string.._tokenize..z\s*)joinr7r8 IGNORECASEfinditer lastgroupgroup)r!Z token_specpatternZ tok_regexmatchtyprrr _tokenize^sz&parse_region_string.._tokenizecSs8|durtdd|||vrtd|ddS)NzExpected {} token missingz or zUnexpected token "")r)formatrI)rPtokenexpectedrrr _check_tokenjs z)parse_region_string.._check_tokencst|d\}}||dgt|}t|d\}}||dgt|d\}}|dur/|dfS||dgt|}||krBtd||fS)N)NNrCrAzEnd coordinate less than start)nextr?r))tokensrPrTrendrVrr_expectqsz$parse_region_string.._expect:rzChromosome name cannot be emptyr%Nr )r&r;r'r))r!rQr[r*chromrrYrrZrparse_region_stringMs      r^regr chromsizesdict | pd.Series | Nonerc Cst|tr t|\}}}n|\}}}|durt|n|}|dur$t|n|}z |dur/||nd}WntyF}ztd||d}~ww|durMdn|}|dur]|dur[td|}||kretd|dksq|dur|||kr|td|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, rF) isinstancer"r^rKeyErrorr))r_r`r]rrYclenerrr parse_regions*   rfz(\d+)tuplecCstdd||DS)NcSs$g|]}|r|rt|n|qSr)isdigitrrxrrrrHs$znatsort_key..)rgr&)r!Z _NS_REGEXrrr natsort_keysrkiterable Iterable[str] list[str]cCs t|tdS)N)key)sortedrk)rlrrr natsorteds rqarray np.ndarraycCsJt|}t|stjgtdSttdd|D}t|dddS)Ndtypecss|]}t|VqdSr)rkrirrrrszargnatsort..)npasarrayr'rrrrgzipZlexsort)rrcolsrrr argnatsorts r{z ^chr[0-9]+$z ^chr[XY]$z^chrM$F filepath_or str | IO[str] name_patternstuple[str, ...] all_namesbool pd.SeriescKst|tr|dr|ddtj|fdddgddgdtid |}|sLg}|D]}||dj|}|jt|d}| |q*tj |dd }|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 rr namelength)sepZusecolsnamesruaxis) rbr"endswith setdefaultpdZread_csvcontainsilocr{appendconcatvaluesindex)r}rrkwargs chromtabler*rNpartrrrread_chromsizess*   rdbcKstd|dfi|S)zo Download chromosome sizes from UCSC as a :py:class:`pandas.Series`, indexed by chromosome label. z*http://hgdownload.soe.ucsc.edu/goldenPath/z/database/chromInfo.txt.gz)r)rrrrrfetch_chromsizess  rr filepathsOrderedDict[str, Any]cszddl}t|dkrtdt|dkr|j|dddni|D] }|j|ddjq"tfdd|D}|S) az Load lazy FASTA records from one or multiple files without reading them into memory. Parameters ---------- names : sequence of str Names of sequence records in FASTA file or files. filepaths : str Paths to one or more FASTA files to gather records from. Returns ------- OrderedDict of sequence name -> sequence record rNzNeed at least one filer T)Zas_rawc3s|] }||fVqdSrr)rr]farrr.szload_fasta..)pyfaidxr'r)ZFastaupdaterecordsr)rrrfilepathrrrr load_fastas  rbinsize pd.DataFramecsJfdd}tjt|ddd}tj|dtjdd|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``. csf|}tt|}td|d}||d<tj|g||dd|dddgddSNrr rv)r]rrYcolumns)rrwceilZaranger DataFrame)r]rdZn_binsZbinedgesrr`rr_eachDs zbinnify.._eachrTrZ ignore_indexr] categoriesZordered)rrmapkeys Categoricallistr)r`rrZbintablerrrbinnify2s   r fasta_recordsenzymec szddlm}ddlmWn tytddw}zt||jWnty;}zt d||d}~wwfdd}t j t ||dddS) av Divide a genome into restriction fragments. Parameters ---------- fasta_records : OrderedDict Dictionary of chromosome names to sequence records. enzyme: str Name of restriction enzyme (e.g., 'DpnII'). Returns ------- frags : :py:class:`pandas.DataFrame` Dataframe with columns: ``chrom``, ``start``, ``end``. rNz4Biopython is required to find restriction fragments.zUnknown enzyme name: cst|dd}tjdt|dt|ftj}t|d}tj |g||dd|dddgdd}|Sr) Seqr"rwr_rrr'astypeZint64rr)r]seqcutsZn_fragsZfragsZbioseqZ cut_finderrrrrzs*  zdigest.._eachTr) ZBio.RestrictionZ RestrictionZBio.Seqr ImportErrorrgetattrsearchAttributeErrorr)rrr)rrZbiorstchromsrerrrrdigestZs&   rbins int | NonecCspt}|jdddD]\}}||d|djddt|dkr)dSq t|dkr6tt|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 r]T)observedrYrNrvr )setgroupbyrruniquer'rWiter)rsizesZ_chromrMrrr get_binsizes $   rcCsV|jdgddddgjddjddd d }t|dt|d}}tj||d 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 r]last)ZkeeprYT)Zdroprr)r]rYrrdata)Zdrop_duplicatesZ reset_indexrenamerrSeries)rrrlengthsrrrget_chromsizess  rpd.Series | dictregionc Cstt||\}}}||}|dks|||kr8|djj|dd}||dj|dj|dd}|j||}|S)zN Range query on a BED-like dataframe with non-overlapping intervals. rrYrightZsiderNleft)rf get_groupr searchsortedr) groupedr`rr]rrYresultlohirrrbedslices  rrjrnp.ndarray | h5py.DatasetcCst|tjr|St|Sr)rbh5pyZDatasetrwrx)rjrrrasarray_or_datasetsr chunksize)tuple[np.ndarray, np.ndarray, np.ndarray]c Cstj}t|}t|}|dkr$tjgtdtjgtdtjg|jdfS|dur*|}gg}}tj}td||D]9}||||}||dd|ddkd} |d|kr_tj d| f} | || | || |d}q8t |}t tj ||f} t |}|| |fS)aS Run length encoding. Based on http://stackoverflow.com/a/32681075, which is based on the rle function from R. Parameters ---------- x : 1D array_like Input array to encode dropna: bool, optional Drop all runs of NaNs. Returns ------- start positions, run lengths, run values rrtNr rv) rwZ flatnonzerorr'rrrrunanrrrZ concatenatediff) rrrwherenZstartsrZlast_valrrjlocsrrrrrlencodes0         rcmdcs$tfddtjdtjDS)Nc3s(|]}ttj|tjVqdSr)osaccesspathrIX_OK)rrrrrrs  zcmd_exists..PATH)anyrenvironr&pathseprrrr cmd_existss rrrc Cstt|t|||Sr)rwZmedianabs)rrrrrmadsrrfpstr | h5py.GroupmodeContextManager[h5py.Group]cost|trd}tj||g|Ri|}n-d}|dkr#|jjdkr#n|dvr1|jjdkr1td|dkr9td|d vrAtd |}z|VW|rP|d Sd S|rY|ww) 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 TFrr+)raz%File object provided is not writeablewzCannot truncate open file)zw-rjz File existsN)rbr"rFilefilerr)close)rrargsrZown_fhfhrrr open_hdf5s*   rcs<eZdZd fdd ZdddZdd d Zdd d ZZS) closing_hdf5grp h5py.Groupcst|jdSr)super__init__id)selfr __class__rrr 6szclosing_hdf5.__init__rcCs|Srrr rrr __enter__9szclosing_hdf5.__enter__NonecGs |jSrrr)r exc_inforrr__exit__<s zclosing_hdf5.__exit__cCs|jdSrrrrrrr?szclosing_hdf5.close)rr)rr)rr)__name__ __module__ __qualname__r rrr __classcell__rrr rr5s   rattrsh5py.AttributeManagerdictc Csdt|}|D]'\}}z|||<Wqty#|||<Yqty/|||<Yqw|Sr)ritemsitemr)tolistr)routkvrrrattrs_to_jsonableCs   r"c stdtdtdtdtdtdtddddfdd fd d }dfd d t|dr=|jSt |t j t j frM|j ddSt |t jrY|ddSd ur_nddt |tr{t j fdd|DdSt |trt|dkr|d|ddSt |ttfrtdd|Dstd|t j fdd|Ddd|DdSt|ds|d urz t|}|WSYt|r||Std|)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 1970-01-01r Zfoo)bVr2mSrUOZ__UNKNOWN_CATEGORIES__csh|jdvr |dS|jdkr|tddS|jvr-|j}|jdvr+||S|Std|)N)rfur cr)r&r2zCan't handle dtype: )kindtypecomplexr TypeError)ruo)_simple_fake_mappingrr_scalar_from_dtype|s     z&infer_meta.._scalar_from_dtypecs`t|tjtjtjfr |St|r%t|dr|jntt |}|St dt |j d)NruzCan't handle meta of type 'r6) rbr TimestampZ TimedeltaZPeriodrwZisscalarhasattrrur.r0r)rjru)r3rr_nonempty_scalars  z$infer_meta.._nonempty_scalarNcsFt|tr|dkrtjtg||djddStjg|||dS)Ncategory)rrr)rurr)rbr"rrrr)rrur)UNKNOWN_CATEGORIESrr _empty_seriessz!infer_meta.._empty_series_metarc i|] \}}|||dqSrrrr,dr9rrr  zinfer_meta..r=r%css&|]}t|tot|dkVqdS)r%N)rbrgr'rrrrrs$zinfer_meta..z2Expected iterable of tuples of (name, dtype), got cr;r<rr>r@rrrArBcSsg|]\}}|qSrrr>rrrrHszinfer_meta..rrruz'Don't know how to create metadata from r)rwZbool_voidZ datetime64Z timedelta64Zstr_Zunicode_r5r:rbrrrrIndexrrrgr'rallr)rur r0)rjrr6rur)r8r9r3r2rr infer_metaOsZ!         rGcst|}ttstfddn#}tfdd|D]\}}t|r/||n|}||<q#|dus>|durDtg} n7fdd|D} t | dkr_tj| d |d d } ntj j | |d } | t |D] \} } || | qofd d |D} tj| || dS)ze Extracted and modified from pandas/io/parsers.py : _get_empty_meta (BSD licensed). cspSrrr default_dtyperurrszget_meta..csSrrr)rIrrrJsNFcsg|] }tjg|dqSrtrr)rrrtrrrHszget_meta..r r)r)rcs i|] }|tjg|dqSrKrL)rZcol_namertrrrArBzget_meta..rC)rrbrrcopyrr rrEr'Z MultiIndexZ from_arrayssort enumeratepopr)rruZ index_columnsZ index_namesrIZ_dtyper r!colrrrrZcol_dictrrHrget_metas&    rRcCs\t|djtj}|}|stj|jt|jdd|d<|S|dj j |jk s,J|S)Nr]Tr) rbrurZCategoricalDtyperMrr]rrcatrrF)rr`Zis_catrrr check_binss rTgsGenomeSegmentation n_chunk_max file_contigsloadingslist[int | float] | Nonelist[GenomicRangeTuple]c s|j}|}|dur |}|}||j|}|j||}g}|D]G\} |vr,q#|j} tt||j} | jj dd| } | d| krStj | | f} | fddt | dd| ddDq#|S)Nrvc3s|] \}}||fVqdSrr)rrrYr]rrrs z%balanced_partition..r ) _bins_groupedsizeZidxmaxlocr`rrwrrrrextendry) rUrWrXrYrZ chrom_nbinsZchrmaxconstZgrangesrMrdranchorsrr\rbalanced_partitions(    rcc@s eZdZd ddZdd d Zd S)rVr`rrrcCst||}|jdddd|_|jj}||_t||_t| |_ ||_ t j | tt|d|_tjdt|f|_tjdt|jf|_|j|djj|dj|_dS)Nr]TF)rrNrrr)rTrr]r^rr`rrrrZcontigsrrrrr'ZidmaprwrZcumsumZchrom_binoffsetZ chrom_absposrScodesZ start_abspos)r r`rZnbins_per_chromrrrr s   zGenomeSegmentation.__init__rrrcCszt||j\}}}|j|}|dks||j|kr;|djj|dd}||dj|dj|dd}|j||}|S)NrrYrrrr)rfr`r]rrrr)r rr]rrYrrrrrrfetch(s  zGenomeSegmentation.fetchN)r`rrr)rrrr)rrrr rerrrrrVs 逖chunksIterable[pd.DataFrame]r^Iterator[pd.DataFrame]ccsjg}d}|D]}|t|7}||||kr$tj|ddVg}d}qt|r3tj|ddVdSdS)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)rgr^bufrchunkrrrbuffered2s  rl)rrrrrrrr)r!r"rr#)r!r"rr)r!r"rr@r)r_rr`rarr)r!r"rrg)rlrmrrn)rrrmrrs)r|F)r}r~rrrrrr)rr"rr)rrnrr"rr)r`rrrrr)rrrr"rr)rrrr)rrrr)r`rrrrr)rjrrr)rrrsrrrr)rr"rr)rrsrrrrs)r)rrrr"rr)rrrr)rrr`rrr) rUrVrWrrXrnrYrZrr[)rf)rgrhr^rrri)= __future__rrr7 collectionsrr contextlibrtypingrrrr r rnumpyrwZpandasrZpandas.api.typesr r Z_typingrrr r,r0r?r^rfr8r(rkrqr{rrrrZ make_bintablerrrrrrrrrGrouprr"rGZobject_rRrTrcrVrlrrrrsf       C 1    5 "%  .    3 0  r +