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EXAMPLES_TO_PORT.md

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  • Query by point

    The result of a query is a set object, so if ordering is important, you must sort it first.

    >>> sorted(t[6])
[Interval(4, 7, (4, 7)), Interval(5, 9, {5: 9})]
>>> sorted(t[6])[0]
Interval(4, 7, (4, 7))

  • Query by range

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

    >>> sorted(t[2:4])
[]

    But:

    >>> sorted(t[1:5])
[Interval(1, 2, '1-2'), Interval(4, 7, (4, 7))]

  • Accessing an Interval object

    >>> iv = Interval(4, 7, (4, 7))
>>> iv.begin
4
>>> iv.end
7
>>> iv.data
(4, 7)

>>> begin, end, data = iv
>>> begin
4
>>> end
7
>>> data
(4, 7)

  • Constructing from lists of intervals

    We could have made a similar tree this way:

    >>> ivs = [(1, 2), (4, 7), (5, 9)]
>>> t = IntervalTree(
...    Interval(begin, end, "%d-%d" % (begin, end)) for begin, end in ivs
... )

    Or, if we don't need the data fields:

    >>> t2 = IntervalTree(Interval(*iv) for iv in ivs)

  • Removing intervals

    >>> t.remove( Interval(1, 2, "1-2") )
>>> sorted(t)
[Interval(4, 7, '4-7'), Interval(5, 9, '5-9')]

>>> t.remove( Interval(500, 1000, "Doesn't exist"))  # raises ValueError
Traceback (most recent call last):
ValueError

>>> t.discard(Interval(500, 1000, "Doesn't exist"))  # quietly does nothing

>>> del t[5]  # same as t.remove_overlap(5)
>>> t
IntervalTree()

    We could also empty a tree entirely:

    >>> t2.clear()
>>> t2
IntervalTree()

    Or remove intervals that overlap a range:

    >>> t = IntervalTree([
...     Interval(0, 10),
...     Interval(10, 20),
...     Interval(20, 30),
...     Interval(30, 40)])
>>> t.remove_overlap(25, 35)
>>> sorted(t)
[Interval(0, 10), Interval(10, 20)]

    We can also remove only those intervals completely enveloped in a range:

    >>> t.remove_envelop(5, 20)
>>> sorted(t)
[Interval(0, 10)]

  • Chopping

    We could also chop out parts of the tree:

    >>> t = IntervalTree([Interval(0, 10)])
>>> t.chop(3, 7)
>>> sorted(t)
[Interval(0, 3), Interval(7, 10)]

    To modify the new intervals' data fields based on which side of the interval is being chopped:

    >>> def datafunc(iv, islower):
...     oldlimit = iv[islower]
...     return "oldlimit: {0}, islower: {1}".format(oldlimit, islower)
>>> t = IntervalTree([Interval(0, 10)])
>>> t.chop(3, 7, datafunc)
>>> sorted(t)[0]
Interval(0, 3, 'oldlimit: 10, islower: True')
>>> sorted(t)[1]
Interval(7, 10, 'oldlimit: 0, islower: False')

  • Slicing

    You can also slice intervals in the tree without removing them:

    >>> t = IntervalTree([Interval(0, 10), Interval(5, 15)])
>>> t.slice(3)
>>> sorted(t)
[Interval(0, 3), Interval(3, 10), Interval(5, 15)]

    You can also set the data fields, for example, re-using datafunc() from above:

    >>> t = IntervalTree([Interval(5, 15)])
>>> t.slice(10, datafunc)
>>> sorted(t)[0]
Interval(5, 10, 'oldlimit: 15, islower: True')
>>> sorted(t)[1]
Interval(10, 15, 'oldlimit: 5, islower: False')