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        "description": "==================================================\npyfasta: pythonic access to fasta sequence files.\n==================================================\n\n\n:Author: Brent Pedersen (brentp)\n:Email: bpederse@gmail.com\n:License: MIT\n\n.. contents ::\n\nImplementation\n==============\n\nRequires Python >= 2.6. Stores a flattened version of the fasta file without\nspaces or headers and uses either a mmap of numpy binary format or fseek/fread so the\n*sequence data is never read into memory*. Saves a pickle (.gdx) of the start, stop \n(for fseek/mmap) locations of each header in the fasta file for internal use.\n\nUsage\n=====\n::\n  \n    >>> from pyfasta import Fasta\n\n    >>> f = Fasta('tests/data/three_chrs.fasta')\n    >>> sorted(f.keys())\n    ['chr1', 'chr2', 'chr3']\n\n    >>> f['chr1']\n    NpyFastaRecord(0..80)\n\n\n\nSlicing\n-------\n::\n\n    # get full the sequence:\n    >>> a = str(f['chr1'])\n    >>> b = f['chr1'][:]\n    >>> a == b\n    True\n\n    >>> f['chr1'][:10]\n    'ACTGACTGAC'\n\n    # get the 1st basepair in every codon (it's python yo)\n    >>> f['chr1'][::3]\n    'AGTCAGTCAGTCAGTCAGTCAGTCAGT'\n\n    # can query by a 'feature' dictionary (note this is one based coordinates)\n    >>> f.sequence({'chr': 'chr1', 'start': 2, 'stop': 9})\n    'CTGACTGA'\n\n    # same as:\n    >>> f['chr1'][1:9]\n    'CTGACTGA'\n\n    # use python, zero based coords\n    >>> f.sequence({'chr': 'chr1', 'start': 2, 'stop': 9}, one_based=False)\n    'TGACTGA'\n\n    # with reverse complement (automatic for - strand)\n    >>> f.sequence({'chr': 'chr1', 'start': 2, 'stop': 9, 'strand': '-'})\n    'TCAGTCAG'\n\nKey Function\n------------\nSometimes your fasta will have a long header like: \"AT1G51370.2 | Symbols:  | F-box family protein | chr1:19045615-19046748 FORWARD\" when you only want to key off: \"AT1G51370.2\". In this case, specify the key_fn argument to the constructor:\n\n::\n\n    >>> fkey = Fasta('tests/data/key.fasta', key_fn=lambda key: key.split()[0])\n    >>> sorted(fkey.keys())\n    ['a', 'b', 'c']\n\nNumpy\n=====\n\nThe default is to use a memmaped numpy array as the backend. In which case it's possible to\nget back an array directly...\n::\n\n    >>> f['chr1'].as_string = False\n    >>> f['chr1'][:10] # doctest: +NORMALIZE_WHITESPACE\n    memmap(['A', 'C', 'T', 'G', 'A', 'C', 'T', 'G', 'A', 'C'], dtype='|S1')\n\n    >>> import numpy as np\n    >>> a = np.array(f['chr2'])\n    >>> a.shape[0] == len(f['chr2'])\n    True\n\n    >>> a[10:14] # doctest: +NORMALIZE_WHITESPACE\n    array(['A', 'A', 'A', 'A'], dtype='|S1')\n\nmask a sub-sequence\n::\n\n    >>> a[11:13] = np.array('N', dtype='S1')\n    >>> a[10:14].tostring()\n    'ANNA'\n\n\nBackends (Record class)\n=======================\nIt's also possible to specify another record class as the underlying work-horse\nfor slicing and reading. Currently, there's just the default: \n\n  * NpyFastaRecord which uses numpy memmap\n  * FastaRecord, which uses using fseek/fread\n  * MemoryRecord which reads everything into memory and must reparse the original\n    fasta every time.\n  * TCRecord which is identical to NpyFastaRecord except that it saves the index\n    in a TokyoCabinet hash database, for cases when there are enough records that\n    loading the entire index from a pickle into memory is unwise. (NOTE: that the\n    sequence is not loaded into memory in either case).\n\nIt's possible to specify the class used with the `record_class` kwarg to the `Fasta`\nconstructor:\n::\n\n    >>> from pyfasta import FastaRecord # default is NpyFastaRecord\n    >>> f = Fasta('tests/data/three_chrs.fasta', record_class=FastaRecord)\n    >>> f['chr1']\n    FastaRecord('tests/data/three_chrs.fasta.flat', 0..80)\n\nother than the repr, it should behave exactly like the Npy record class backend\n\nit's possible to create your own using a sub-class of FastaRecord. see the source \nin pyfasta/records.py for details.\n\nFlattening\n==========\nIn order to efficiently access the sequence content, pyfasta saves a separate, flattened file with all newlines and headers removed from the sequence. In the case of large fasta files, one may not wish to save 2 copies of a 5GG+ file. In that case, it's possible to flatten the file \"inplace\", keeping all the headers, and retaining the validity of the fasta file -- with the only change being that the new-lines are removed from each sequence. This can be specified via `flatten_inplace` = True\n::\n    \n    >>> import os\n    >>> os.unlink('tests/data/three_chrs.fasta.gdx') # cleanup non-inplace idx\n    >>> f = Fasta('tests/data/three_chrs.fasta', flatten_inplace=True)\n    >>> f['chr1']  # note the difference in the output from above.\n    NpyFastaRecord(6..86)\n\n    # sequence from is same as when requested from non-flat file above.\n    >>> f['chr1'][1:9]\n    'CTGACTGA'\n\n    # the flattened file is kept as a place holder without the sequence data.\n    >>> open('tests/data/three_chrs.fasta.flat').read()\n    '@flattened@'\n\n\nCommand Line Interface\n======================\nthere's also a command line interface to manipulate / view fasta files.\nthe `pyfasta` executable is installed via setuptools, running it will show\nhelp text.\n\nsplit a fasta file into 6 new files of relatively even size:\n\n  $ pyfasta **split** -n 6 original.fasta\n\nsplit the fasta file into one new file per header with \"%(seqid)s\" being filled into each filename.:\n  \n  $ pyfasta **split** --header \"%(seqid)s.fasta\" original.fasta\n\ncreate 1 new fasta file with the sequence split into 10K-mers:\n\n  $ pyfasta **split** -n 1 -k 10000 original.fasta\n\n2 new fasta files with the sequence split into 10K-mers with 2K overlap:\n\n  $ pyfasta **split** -n 2 -k 10000 -o 2000 original.fasta\n\n\nshow some info about the file (and show gc content):\n\n  $ pyfasta **info** --gc test/data/three_chrs.fasta\n\n\n**extract** sequence from the file. use the header flag to make\na new fasta file. the args are a list of sequences to extract.\n\n  $ pyfasta **extract** --header --fasta test/data/three_chrs.fasta seqa seqb seqc\n\n**extract** sequence from a file using a file containing the headers *not* wanted in the new file:\n\n  $ pyfasta extract --header --fasta input.fasta --exclude --file seqids_to_exclude.txt\n\n**extract** sequence from a fasta file with complex keys where we only want to lookup based on the part before the space.\n\n  $ pyfasta extract --header --fasta input.with.keys.fasta --space --file seqids.txt\n\n**flatten** a file inplace, for faster later use by pyfasta, and without creating another copy. (`Flattening`_)\n\n  $ pyfasta flatten input.fasta \n\ncleanup \n=======\n(though for real use these will remain for faster access)\n::\n\n    >>> os.unlink('tests/data/three_chrs.fasta.gdx')\n    >>> os.unlink('tests/data/three_chrs.fasta.flat')\n\nTesting\n=======\nthere is currently > 99% test coverage for the 2 modules and all included \nrecord classes. to run the tests:\n::\n\n  $ python setup.py nosetests\n\nChanges\n=======\n0.5.2\n-----\nfix complement (@mruffalo)\n\n0.5.0\n-----\npython 3 compatibility thanks to mruffalo\n\n0.4.5\n-----\npyfasta split can handle > 52 files. (thanks Devtulya)\n\n0.4.4\n-----\nfix pyfasta extract\n\n0.4.3\n-----\nAdd 0 or 1-based intervals in sequence() thanks @jamescasbon\n\n0.4.2\n-----\nupdate for latest numpy (can't close memmap)\n\n0.4.1\n-----\ncheck for duplicate headers.\n\n0.4.0\n-----\n* add key_fn kwarg to constuctor\n\n0.3.9\n-----\n* only require 'r' (not r+) for memory map.\n\n0.3.8\n-----\n* clean up logic for mixing inplace/non-inplace flattened files.\n  if the inplace is available, it is always used. \n\n0.3.6/7\n-------\n* dont re-flatten the file every time!\n* allow spaces before and after the header in the orginal fasta.\n\n0.3.5\n-----\n\n* update docs in README.txt for new CLI stuff.\n* allow flattening inplace.\n* get rid of memmap (results in faster parsing).\n\n0.3.4\n-----\n\n* restore python2.5 compatiblity.\n* CLI: add ability to exclude sequence from extract\n* CLI: allow spliting based on header.\n\n0.3.3\n-----\n\n* include this file in the tar ball (thanks wen h.)\n\n0.3.2\n-----\n\n* separate out backends into records.py\n\n* use nosetests (python setup.py nosetests)\n\n* add a TCRecord backend for next-gen sequencing availabe if tc is (easy-)installed.\n\n* improve test coverage.",
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