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Mutable, self-balancing interval tree

Project description

NB: This is a straight fork of PyIntervalTree by Chaim-Leib Halbert. This fork adds some tests, fixes some bugs, registers the package at PyPI as PyIntervalTree, adds a intervaltree.bio package with some utilities for bioinformatics needs (see below). Some further maintenance or updates might still be possible, and eventually the fork could merge into the original (depending on the original author’s opinion).

A mutable, self-balancing interval tree. Queries may be by point, by range overlap, or by range envelopment.

This library was designed to allow tagging text and time intervals, where the intervals include the lower bound but not the upper bound.

Installation

The easiest way to install most Python packages is via easy_install or pip:

$ pip install PyIntervalTree

Features

  • Initialize blank or from an iterable of Intervals in O(n * log n).
  • Insertions
    • tree[begin:end] = data
    • tree.add(interval)
    • tree.addi(begin, end, data)
    • tree.extend(list_of_interval_objs)
  • Deletions
    • tree.remove(interval) (raises ValueError if not present)
    • tree.discard(interval) (quiet if not present)
    • tree.removei(begin, end, data)
    • tree.discardi(begin, end, data)
    • tree.remove_overlap(point)
    • tree.remove_overlap(begin, end) (removes all overlapping the range)
    • tree.remove_envelop(begin, end) (removes all enveloped in the range)
  • Overlap queries:
    • tree[point]
    • tree[begin:end]
    • tree.search(point)
    • tree.search(begin, end)
  • Envelop queries:
    • tree.search(begin, end, strict = True)
  • Membership queries:
    • interval_obj in tree (this is fastest, O(1))
    • tree.containsi(begin, end, data)
    • tree.overlaps(point)
    • tree.overlaps(begin, end)
  • Iterable:
    • for interval_obj in tree:
    • tree.items()
  • Sizing:
    • len(tree)
    • tree.is_empty()
    • not tree
    • tree.begin() (the smallest coordinate of the leftmost interval)
    • tree.end() (the end coordinate of the rightmost interval)
  • Restructuring
    • split_overlaps()
  • Copy- and typecast-able:
    • IntervalTree(tree) (Interval objects are same as those in tree)
    • tree.copy() (Interval objects are shallow copies of those in tree)
    • set(tree) (can later be fed into IntervalTree())
    • list(tree) (ditto)
  • Equal-able
  • Pickle-friendly
  • Automatic AVL balancing

Examples

  • Getting started:

    from intervaltree import Interval, IntervalTree
    t = IntervalTree()
    
  • Adding intervals - you don’t have to use strings!:

    t[1:2] = "1-2"
    t[4:7] = "4-7"
    t[5:9] = "5-9"
    
  • Query by point:

    ivs = t[6]            # set([Interval(4, 7, '4-7'), Interval(5, 9, '5-9')])
    iv = sorted(ivs)[0]   # Interval(4, 7, '4-7')
    
  • Accessing an Interval object:

    iv.begin  # 4
    iv.end    # 7
    iv.data   # "4-7"
    
  • Query by range:

    Note that ranges are inclusive of the lower limit, but non-inclusive of the upper limit. So:

    t[2:4]    # set([])
    

    But:

    t[1:5]    # set([Interval(1, 2, '1-2'), Interval(4, 7, '4-7')])
    
  • Constructing from lists of Interval’s:

    We could have made the same tree this way:

    ivs = [ [1,2], [4,7], [5,9] ]
    ivs = map( lambda begin,end: Interval(begin, end, "%d-%d" % (begin,end),
               *zip(*ivs) )
    
    t = IntervalTree(ivs)
    
  • Removing intervals:

    t.remove( Interval(1, 2, "1-2") )
    list(t)     # [Interval(4, 7, '4-7'), Interval(5, 9, '5-9')]
    
    t.remove( Interval(500, 1000, "Doesn't exist") # raises ValueError
    t.discard(Interval(500, 1000, "Doesn't exist") # quietly does nothing
    
    t.remove_overlap(5)
    list(t)     # []
    

    We could also empty a tree by removing all intervals, from the lowest bound to the highest bound of the IntervalTree:

    t.remove_overlap(t.begin(), t.end())
    

Usage with Genomic Data

Interval trees are especially commonly used in bioinformatics, where intervals correspond to genes or various features along the genome. Such intervals are commonly stored in BED-format files. To simplify working with such data, the package intervaltree.bio provides a GenomeIntervalTree class.

GenomeIntervalTree is essentially a dict of IntervalTree-s, indexed by chromosome names:

gtree = GenomeIntervalTree()
gtree['chr1'].addi(10000, 20000)

There is a convenience function for adding intervals:

gtree.addi('chr2', 20000, 30000)

You can create a GenomeIntervalTree instance from a BED file:

test_url = 'http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeAwgTfbsUniform/wgEncodeAwgTfbsBroadDnd41Ezh239875UniPk.narrowPeak.gz'
data = zlib.decompress(urlopen(test_url).read(), 16+zlib.MAX_WBITS)
gtree = GenomeIntervalTree.from_bed(StringIO(data))

In addition, special functions are offered to read in UCSC tables of gene positions:

  • Load the UCSC knownGene table with each interval corresponding to gene’s transcribed region:

    knownGene = GenomeIntervalTree.from_table()
    
  • Load the UCSC refGene table with each interval corresponding to gene’s coding region:

    url = 'http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/refGene.txt.gz'
    refGene = GenomeIntervalTree.from_table(url=url, parser=UCSCTable.REF_GENE, mode='cds')
    
  • Load the UCSC ensGene table with each interval corresponding to a gene’s exon:

    url = 'http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/ensGene.txt.gz'
    ensGene = GenomeIntervalTree.from_table(url=url, parser=UCSCTable.ENS_GENE, mode='exons')
    

You may add methods for parsing your own tabular files with genomic intervals, see the documentation for GenomeIntervalTree.from_table.

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