Python data structure and operations for intervals

This library provides data structure and operations for intervals in Python 2.7+ and Python 3.4+.

• Features
• Installation
• Documentation & usage
  • Interval creation
  • Interval operations
  • Comparison operators
  • Bounds of an interval
  • Interval transformation
  • Discrete iteration
  • Map intervals to data
  • Import & export intervals to strings
  • Import & export intervals to Python built-in data types
• Contributions
• Licence
• Changelog

Features

• Support intervals of any (comparable) objects.
• Closed or open, finite or (semi-)infinite intervals.
• Atomic intervals and interval sets are supported.
• Automatic simplification of intervals.
• Support comparison, transformation, intersection, union, complement, difference and containment.
• Discrete iterations on the values of an interval.
• Import and export intervals to strings and to Python built-in data types.
• Dict-like structure to map intervals to data.

Installation

You can use pip to install it, as usual: pip install python-intervals.

This will install the latest available version from PyPI. Prereleases are available from the master branch on GitHub.

For convenience, the library is contained within a single Python file, and can thus be easily integrated in other projects without the need for an explicit dependency (hint: don't do that!).

Documentation & usage

Interval creation

Assuming this library is imported using import intervals as I, intervals can be easily created using one of the following helpers:

>>> I.open(1, 2)
(1,2)
>>> I.closed(1, 2)
[1,2]
>>> I.openclosed(1, 2)
(1,2]
>>> I.closedopen(1, 2)
[1,2)
>>> I.singleton(1)
[1]
>>> I.empty()
()

Intervals created with this library are Interval instances. An Interval object is a disjunction of atomic intervals that represent single intervals (e.g. [1,2]) corresponding to AtomicInterval instances. Except when atomic intervals are explicitly created or retrieved, only Interval instances are exposed.

The bounds of an interval can be any arbitrary values, as long as they are comparable:

>>> I.closed(1.2, 2.4)
[1.2,2.4]
>>> I.closed('a', 'z')
['a','z']
>>> import datetime
>>> I.closed(datetime.date(2011, 3, 15), datetime.date(2013, 10, 10))
[datetime.date(2011, 3, 15),datetime.date(2013, 10, 10)]

Infinite and semi-infinite intervals are supported using I.inf and -I.inf as upper or lower bounds. These two objects support comparison with any other object. When infinities are used as a lower or upper bound, the corresponding boundary is automatically converted to an open one.

>>> I.inf > 'a', I.inf > 0, I.inf > True
(True, True, True)
>>> I.openclosed(-I.inf, 0)
(-inf,0]
>>> I.closed(-I.inf, I.inf)  # Automatically converted to an open interval
(-inf,+inf)

Empty intervals always resolve to (I.inf, -I.inf), regardless of the provided bounds:

>>> I.empty() == I.open(I.inf, -I.inf)
True
>>> I.closed(4, 3) == I.open(I.inf, -I.inf)
True
>>> I.openclosed('a', 'a') == I.open(I.inf, -I.inf)
True

For convenience, intervals are automatically simplified:

>>> I.closed(0, 2) | I.closed(2, 4)
[0,4]
>>> I.closed(1, 2) | I.closed(3, 4) | I.closed(2, 3)
[1,4]
>>> I.empty() | I.closed(0, 1)
[0,1]
>>> I.closed(1, 2) | I.closed(2, 3) | I.closed(4, 5)
[1,3] | [4,5]

Note that discrete intervals are not supported, e.g., combining [0,1] with [2,3] will not result in [0,3] even if there is no integer between 1 and 2.

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Interval operations

Both Interval and AtomicInterval support following interval operations:

• x.is_empty() tests if the interval is empty.

  >>> I.closed(0, 1).is_empty()
  False
  >>> I.closed(0, 0).is_empty()
  False
  >>> I.openclosed(0, 0).is_empty()
  True
  >>> I.empty().is_empty()
  True

• x.intersection(other) or x & other return the intersection of two intervals.

  >>> I.closed(0, 2) & I.closed(1, 3)
  [1,2]
  >>> I.closed(0, 4) & I.open(2, 3)
  (2,3)
  >>> I.closed(0, 2) & I.closed(2, 3)
  [2]
  >>> I.closed(0, 2) & I.closed(3, 4)
  ()

• x.union(other) or x | other return the union of two intervals.

  >>> I.closed(0, 1) | I.closed(1, 2)
  [0,2]
  >>> I.closed(0, 1) | I.closed(2, 3)
  [0,1] | [2,3]

• x.complement(other) or ~x return the complement of the interval.

  >>> ~I.closed(0, 1)
  (-inf,0) | (1,+inf)
  >>> ~(I.open(-I.inf, 0) | I.open(1, I.inf))
  [0,1]
  >>> ~I.open(-I.inf, I.inf)
  ()

• x.difference(other) or x - other return the difference between x and other.

  >>> I.closed(0,2) - I.closed(1,2)
  [0,1)
  >>> I.closed(0, 4) - I.closed(1, 2)
  [0,1) | (2,4]

• x.contains(other) or other in x return True if given item is contained in the interval. Support Interval, AtomicInterval and arbitrary comparable values.

  >>> 2 in I.closed(0, 2)
  True
  >>> 2 in I.open(0, 2)
  False
  >>> I.open(0, 1) in I.closed(0, 2)
  True

• x.overlaps(other) tests if there is an overlap between two intervals. This method accepts a adjacent parameter which defaults to False. If True, it accepts adjacent intervals as well (e.g., [1, 2) and [2, 3] but not [1, 2) and (2, 3]).

  >>> I.closed(1, 2).overlaps(I.closed(2, 3))
  True
  >>> I.closed(1, 2).overlaps(I.open(2, 3))
  False
  >>> I.closed(1, 2).overlaps(I.openclosed(2, 3), adjacent=True)
  True
  >>> I.closedopen(1, 2).overlaps(I.openclosed(2, 3), adjacent=True)
  False

The following methods are only available for Interval instances:

• x.enclosure() returns the smallest interval that includes the current one.

  >>> (I.closed(0, 1) | I.closed(2, 3)).enclosure()
  [0,3]

• x.to_atomic() is equivalent to x.enclosure() but returns an AtomicInterval instead of an Interval object.

• x.is_atomic() evaluates to True if interval is composed of a single (possibly empty) atomic interval.

  >>> I.closed(0, 2).is_atomic()
  True
  >>> (I.closed(0, 1) | I.closed(1, 2)).is_atomic()
  True
  >>> (I.closed(0, 1) | I.closed(2, 3)).is_atomic()
  False

Intervals can also be iterated to access the underlying AtomicInterval objects, sorted by their lower and upper bounds.

>>> list(I.open(2, 3) | I.closed(0, 1) | I.closed(21, 24))
[[0,1], (2,3), [21,24]]

The AtomicInterval objects of an Interval can also be accessed using their indexes:

>>> (I.open(2, 3) | I.closed(0, 1) | I.closed(21, 24))[0]
[0,1]
>>> (I.open(2, 3) | I.closed(0, 1) | I.closed(21, 24))[-2]
(2,3)

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Comparison operators

Equality between intervals can be checked with the classical == operator:

>>> I.closed(0, 2) == I.closed(0, 1) | I.closed(1, 2)
True
>>> I.closed(0, 2) == I.closed(0, 2).to_atomic()
True

Moreover, both Interval and AtomicInterval are comparable using e.g. >, >=, < or <=. These comparison operators have a different behaviour than the usual ones. For instance, a < b holds if a is entirely on the left of the lower bound of b and a > b holds if a is entirely on the right of the upper bound of b.

>>> I.closed(0, 1) < I.closed(2, 3)
True
>>> I.closed(0, 1) < I.closed(1, 2)
False

Similarly, a <= b holds if a is entirely on the left of the upper bound of b, and a >= b holds if a is entirely on the right of the lower bound of b.

>>> I.closed(0, 1) <= I.closed(2, 3)
True
>>> I.closed(0, 2) <= I.closed(1, 3)
True
>>> I.closed(0, 3) <= I.closed(1, 2)
False

Intervals can also be compared with single values. If i is an interval and x a value, then x < i holds if x is on the left of the lower bound of i and x <= i holds if x is on the left of the upper bound of i. This behaviour is similar to the one that could be obtained by first converting x to a singleton interval.

>>> 5 < I.closed(0, 10)
False
>>> 5 <= I.closed(0, 10)
True
>>> I.closed(0, 10) < 5
False
>>> I.closed(0, 10) <= 5
True

Note that all these semantics differ from classical comparison operators. As a consequence, some intervals are never comparable in the classical sense, as illustrated hereafter:

>>> I.closed(0, 4) <= I.closed(1, 2) or I.closed(0, 4) >= I.closed(1, 2)
False
>>> I.closed(0, 4) < I.closed(1, 2) or I.closed(0, 4) > I.closed(1, 2)
False
>>> I.empty() < I.empty()
True

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Bounds of an interval

The left and right boundaries, and the lower and upper bounds of an AtomicInterval can be respectively accessed with its left, right, lower and upper attributes. The left and right bounds are either I.CLOSED (True) or I.OPEN (False).

>> I.CLOSED, I.OPEN
True, False
>>> x = I.closedopen(0, 1).to_atomic()
>>> x.left, x.lower, x.upper, x.right
(True, 0, 1, False)

Similarly, the bounds of an Interval instance can be accessed with its left, right, lower and upper attributes. In that case, left and lower refer to the lower bound of its enclosure, while right and upper refer to the upper bound of its enclosure:

>>> x = I.open(0, 1) | I.closed(3, 4)
>>> x.left, x.lower, x.upper, x.right
(False, 0, 4, True)

One can easily check for some interval properties based on the bounds of an interval:

>>> x = I.openclosed(-I.inf, 0)
>>> # Check that interval is left/right closed
>>> x.left == I.CLOSED, x.right == I.CLOSED
(False, True)
>>> # Check that interval is left/right bounded
>>> x.lower == -I.inf, x.upper == I.inf
(True, False)
>>> # Check for singleton
>>> x.lower == x.upper
False

Both Interval and AtomicInterval instances are immutable but provide a replace method that can be used to create a new instance based on the current one. This method accepts four optional parameters left, lower, upper, and right:

>>> i = I.closed(0, 2).to_atomic()
>>> i.replace(I.OPEN, -1, 3, I.CLOSED)
(-1,3]
>>> i.replace(lower=1, right=I.OPEN)
[1,2)

Functions can be passed instead of values. If a function is passed, it is called with the current corresponding value except if the corresponding bound is an infinity and parameter ignore_inf if set to False.

>>> I.closed(0, 2).replace(upper=lambda x: 2 * x)
[0,4]
>>> i = I.closedopen(0, I.inf)
>>> i.replace(upper=lambda x: 10)  # No change, infinity is ignored
[0,+inf)
>>> i.replace(upper=lambda x: 10, ignore_inf=False)  # Infinity is not ignored
[0,10)

When replace is applied on an Interval that is not atomic, it is extended and/or restricted such that its enclosure satisfies the new bounds.

>>> i = I.openclosed(0, 1) | I.closed(5, 10)
>>> i.replace(I.CLOSED, -1, 8, I.OPEN)
[-1,1] | [5,8)
>>> i.replace(lower=4)
(4,10]

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Interval transformation

To apply an arbitrary transformation on an interval, Interval instances expose an apply method. This method accepts a function that will be applied on each of the underlying atomic intervals to perform the desired transformation. The function is expected to return an AtomicInterval, an Interval or a 4-uple (left, lower, upper, right).

>>> i = I.closed(2, 3) | I.open(4, 5)
>>> # Increment bound values
>>> i.apply(lambda x: (x.left, x.lower + 1, x.upper + 1, x.right))
[3,4] | (5,6)
>>> # Invert bounds
>>> i.apply(lambda x: (not x.left, x.lower, x.upper, not x.right))
(2,3) | [4,5]

The apply method is very powerful when used in combination with replace. Because the latter allows functions to be passed as parameters and can ignore infinities, it can be conveniently used to transform intervals in presence of infinities.

>>> i = I.openclosed(-I.inf, 0) | I.closed(3, 4) | I.closedopen(8, I.inf)
>>> # Increment bound values
>>> i.apply(lambda x: x.replace(upper=lambda v: v + 1))
(-inf,1] | [3,5] | [8,+inf)
>>> # Intervals are still automatically simplified
>>> i.apply(lambda x: x.replace(lower=lambda v: v * 2))
(-inf,0] | [16,+inf)
>>> # Invert bounds
>>> i.apply(lambda x: x.replace(left=lambda v: not v, right=lambda v: not v))
(-inf,0) | (3,4) | (8,+inf)
>>> # Replace infinities with -10 and 10
>>> conv = lambda v: -10 if v == -I.inf else (10 if v == I.inf else v)
>>> i.apply(lambda x: x.replace(lower=conv, upper=conv, ignore_inf=False))
(-10,0] | [3,4] | [8,10)

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Discrete iteration

The iterate function takes an interval or atomic interval, and returns a generator to iterate over the values of an interval. Obviously, as intervals are continuous, it is required to specify the increment incr between consecutive values. The iteration then starts from the lower bound and ends on the upper one, given they are not excluded by the interval:

>>> list(I.iterate(I.closed(0, 3), incr=1))
[0, 1, 2, 3]
>>> list(I.iterate(I.closed(0, 3), incr=2))
[0, 2]
>>> list(I.iterate(I.open(0, 3), incr=2))
[2]

When an interval represents an union of atomic intervals, iterate consecutively iterates on all atomic intervals, starting from each lower bound and ending on each upper one:

>>> list(I.iterate(I.singleton(0) | I.singleton(1) | I.singleton(5), incr=2))  # Won't be [0]
[0, 1, 5]
>>> list(I.iterate(I.closed(0, 2) | I.closed(5, 6), incr=3))  # Won't be [0, 6]
[0, 5]

Iteration can be performed in reverse order by specifying reverse=True. In that case, incr will be subtracted instead of being added, implying that incr must always be a "positive" value:

>>> list(I.iterate(I.closed(0, 3), incr=1, reverse=True))  # Not incr=-1
[3, 2, 1, 0]
>>> list(I.iterate(I.closed(0, 3), incr=2, reverse=True))  # Not incr=-2
[3, 1]

Again, this library does not make any assumption about the objects being used in an interval, as long as they are comparable. However, it is not always possible to provide a meaningful value for incr (e.g., what would be the step between two consecutive characters?). In these cases, a callable can be passed instead of a value. This callable will be called with the current value, and is expected to return the next possible value.

>>> list(I.iterate(I.closed('a', 'd'), incr=lambda d: chr(ord(d) + 1)))
['a', 'b', 'c', 'd']
>>> # Notice the reversed order, mind the "- 1"
>>> list(I.iterate(I.closed('a', 'd'), incr=lambda d: chr(ord(d) - 1), reverse=True))
['d', 'c', 'b', 'a']

By default, the iteration always starts on the lower bound (unless reverse=True) of each atomic interval. The base parameter can be used to change this behaviour, by specifying how the initial value to start the iteration on must be computed. This parameter accepts a callable that will be called with the lower bound (unless reverse=True) for each underlying atomic interval, and that must return the first value to consider instead of the lower bound.

This can be helpful to deal with (semi-)infinite intervals, or to align the generated values of the iterator:

>>> # Restrict values of a (semi-)infinite interval
>>> list(I.iterate(I.openclosed(-I.inf, 2), incr=1, base=lambda x: max(0, x)))
[0, 1, 2]
>>> # Align on integers
>>> list(I.iterate(I.closed(0.3, 4.9), incr=1, base=int))
[1, 2, 3, 4]

The base parameter can be used to change how iterate applies on unions of atomic interval, by specifying a function that returns a single value, as illustrated next:

>>> interval = I.closed(0, 1) | I.closed(2, 4) | I.closed(5, 6)
>>> list(I.iterate(interval, incr=3))  # Won't be [0, 3, 6]
[0, 2, 5]
>>> list(I.iterate(interval, incr=3, base=lambda x: 0))
[0, 3, 6]

Notice that this approach can be extremely inefficient in terms of performance when the intervals are "far apart" each other.

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Map intervals to data

The library provides an IntervalDict class, a dict-like data structure to store and query data along with intervals. Any value can be stored in such data structure as long as it supports equality.

>>> d = I.IntervalDict()
>>> d[I.closed(0, 3)] = 'banana'
>>> d[4] = 'apple'
>>> d
{[0,3]: 'banana', [4]: 'apple'}

When a value is defined for an interval that overlaps an existing one, it is automatically updated to take the new value into account:

>>> d[I.closed(2, 4)] = 'orange'
>>> d
{[0,2): 'banana', [2,4]: 'orange'}

An IntervalDict can be queried using single values or intervals. If a single value is used as a key, its behaviour corresponds to the one of a classical dict:

>>> d[2]
'orange'
>>> d[5]  # Key does not exist
Traceback (most recent call last):
 ...
KeyError: 5
>>> d.get(5, default=0)
0

When an interval is used as a key, a new IntervalDict containing the values for that interval is returned:

>>> d[~I.empty()]  # Get all values, similar to d.copy()
{[0,2): 'banana', [2,4]: 'orange'}
>>> d[I.closed(1, 3)]
{[1,2): 'banana', [2,3]: 'orange'}
>>> d[I.closed(-2, 1)]
{[0,1]: 'banana'}
>>> d[I.closed(-2, -1)]
{}

By using .get, a default value (defaulting to None) can be specified. This value is used to "fill the gaps" if the queried interval is not completely covered by the IntervalDict:

>>> d.get(I.closed(-2, 1), default='peach')
{[-2,0): 'peach', [0,1]: 'banana'}
>>> d.get(I.closed(-2, -1), default='peach')
{[-2,-1]: 'peach'}
>>> d.get(I.singleton(1), default='peach')  # Key is covered, default is not used
{[1]: 'banana'}

For convenience, an IntervalDict provides a way to look for specific data values. The .find method always returns a (possibly empty) Interval instance for which given value is defined:

>>> d.find('banana')
[0,2)
>>> d.find('orange')
[2,4]
>>> d.find('carrot')
()

The active domain of an IntervalDict can be retrieved with its .domain method. This method always returns a single Interval instance, where .keys returns a list of disjoint intervals, one for each stored value.

>>> d.domain()
[0,4]
>>> d.keys()
[[0,2), [2,4]]
>>> d.values()
['banana', 'orange']
>>> d.items()
[([0,2), 'banana'), ([2,4], 'orange')]

Finally, similarly to a dict, an IntervalDict also supports len, in and del, and defines .clear, .copy, .update, .pop, .popitem, and .setdefault.

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Import & export intervals to strings

Intervals can be exported to string, either using repr (as illustrated above) or with the to_string function.

>>> I.to_string(I.closedopen(0, 1))
'[0,1)'

This function accepts both Interval and AtomicInterval instances. The way string representations are built can be easily parametrized using the various parameters supported by to_string:

>>> params = {
...   'disj': ' or ',
...   'sep': ' - ',
...   'left_closed': '<',
...   'right_closed': '>',
...   'left_open': '..',
...   'right_open': '..',
...   'pinf': '+oo',
...   'ninf': '-oo',
...   'conv': lambda v: '"{}"'.format(v),
... }
>>> x = I.openclosed(0, 1) | I.closed(2, I.inf)
>>> I.to_string(x, **params)
'.."0" - "1"> or <"2" - +oo..'

Similarly, intervals can be created from a string using the from_string function. A conversion function (conv parameter) has to be provided to convert a bound (as string) to a value.

>>> I.from_string('[0, 1]', conv=int) == I.closed(0, 1)
True
>>> I.from_string('[1.2]', conv=float) == I.singleton(1.2)
True
>>> converter = lambda s: datetime.datetime.strptime(s, '%Y/%m/%d')
>>> I.from_string('[2011/03/15, 2013/10/10]', conv=converter)
[datetime.datetime(2011, 3, 15, 0, 0),datetime.datetime(2013, 10, 10, 0, 0)]

Similarly to to_string, function from_string can be parametrized to deal with more elaborated inputs. Notice that as from_string expects regular expression patterns, we need to escape some characters.

>>> s = '.."0" - "1"> or <"2" - +oo..'
>>> params = {
...   'disj': ' or ',
...   'sep': ' - ',
...   'left_closed': '<',
...   'right_closed': '>',
...   'left_open': r'\.\.',  # from_string expects regular expression patterns
...   'right_open': r'\.\.',  # from_string expects regular expression patterns
...   'pinf': r'\+oo',  # from_string expects regular expression patterns
...   'ninf': '-oo',
...   'conv': lambda v: int(v[1:-1]),
... }
>>> I.from_string(s, **params)
(0,1] | [2,+inf)

When a bound contains a comma or has a representation that cannot be automatically parsed with from_string, the bound parameter can be used to specify the regular expression that should be used to match its representation.

>>> s = '[(0, 1), (2, 3)]'  # Bounds are expected to be tuples
>>> I.from_string(s, conv=eval, bound=r'\(.+?\)')
[(0, 1),(2, 3)]

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Import & export intervals to Python built-in data types

Intervals can also be exported to a list of 4-uples with to_data, e.g., to support JSON serialization.

>>> x = I.openclosed(0, 1) | I.closedopen(2, I.inf)
>>> I.to_data(x)
[(False, 0, 1, True), (True, 2, inf, False)]

The function to convert bounds can be specified with the conv parameter. The values that must be used to represent positive and negative infinities can be specified with pinf and ninf. They default to float('inf') and float('-inf') respectively.

>>> x = I.closed(datetime.date(2011, 3, 15), datetime.date(2013, 10, 10))
>>> I.to_data(x, conv=lambda v: (v.year, v.month, v.day))
[(True, (2011, 3, 15), (2013, 10, 10), True)]

Intervals can be imported from such a list of 4-uples with from_data. The same set of parameters can be used to specify how bounds and infinities are converted.

>>> x = [(True, (2011, 3, 15), (2013, 10, 10), False)]
>>> I.from_data(x, conv=lambda v: datetime.date(*v))
[datetime.date(2011, 3, 15),datetime.date(2013, 10, 10))

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Contributions

Contributions are very welcome! Feel free to report bugs or suggest new features using GitHub issues and/or pull requests.

Licence

Distributed under LGPLv3 - GNU Lesser General Public License, version 3.

You can cite this library using:

@software{python-intervals,
  author = {Decan, Alexandre},
  title = {python-intervals: Python data structure and operations for intervals},
  url = {https://github.com/AlexandreDecan/python-intervals},
}

Changelog

This library adheres to a semantic versioning scheme.

1.9.0 (2019-09-13)

• Discrete iteration on the values of an interval with iterate.
• Map intervals to data with the dict-like IntervalDict structure.
• Faster comparisons between arbitrary values and intervals.
• Deprecate permissive in .overlaps in favour of adjacent.
• Fix .union when intervals share a bound, one inclusive and one exclusive (#12).
• Fix .overlaps when intervals share a lower bound, and one interval is contained within the other one (#13).

1.8.0 (2018-12-15)

• Intervals have a .left, .lower, .upper, and .right attribute that refer to its enclosure.
• Intervals have a .replace method to create new intervals based on the current one. This method accepts both values and functions.
• Intervals have an .apply method to apply a function on the underlying atomic intervals.
• Intervals can be compared with single values as well.
• I.empty() returns the same instance to save memory.
• Infinities are singleton objects.
• Set len(I.empty()) = 1 and I.empty()[0] == I.empty().to_atomic() for consistency.

1.7.0 (2018-12-06)

• Import from and export to Python built-in data types (a list of 4-uples) with from_data and to_data (#6).
• Add examples for arbitrary interval transformations.

1.6.0 (2018-08-29)

• Add support for customized infinity representation in to_string and from_string (#3).

1.5.4 (2018-07-29)

• Fix .overlaps (#2).

1.5.3 (2018-06-21)

• Fix invalid repr for atomic singleton intervals.

1.5.2 (2018-06-15)

• Fix invalid comparisons when both Interval and AtomicInterval are compared.

1.5.1 (2018-04-25)

• Fix #1 by making empty intervals always resolving to (I.inf, -I.inf).

1.5.0 (2018-04-17)

• Interval.__init__ accepts Interval instances in addition to AtomicInterval ones.

1.4.0 (2018-04-17)

• Function I.to_string to export an interval to a string, with many options to customize the representation.
• Function I.from_string to create an interval from a string, with many options to customize the parsing.

1.3.2 (2018-04-13)

• Support for Python 2.7.

1.3.1 (2018-04-12)

• Define __slots__ to lower memory usage, and to speed up attribute access.
• Define Interval.__rand__ (and other magic methods) to support Interval from AtomicInterval instead of having a dedicated piece of code in AtomicInterval.
• Fix __all__.
• More tests to cover all comparisons.

1.3.0 (2018-04-04)

• Meaningful <= and >= comparisons for intervals.

1.2.0 (2018-04-04)

• Interval supports indexing to retrieve the underlying AtomicInterval objects.

1.1.0 (2018-04-04)

• Both AtomicInterval and Interval are fully comparable.
• Add singleton(x) to create a singleton interval [x].
• Add empty() to create an empty interval.
• Add Interval.enclosure() that returns the smallest interval that includes the current one.
• Interval simplification is in O(n) instead of O(n*m).
• AtomicInterval objects in an Interval are sorted by lower and upper bounds.

1.0.4 (2018-04-03)

• All operations of AtomicInterval (except overlaps) accept Interval.
• Raise TypeError instead of ValueError if type is not supported (coherent with NotImplemented).

1.0.3 (2018-04-03)

• Initial working release on PyPi.

1.0.0 (2018-04-03)

• Initial release.