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AlexandreDecan / portion

portion, a Python library providing data structure and operations for intervals.
GNU Lesser General Public License v3.0
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datastructures interval interval-set operations python range

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portion - data structure and operations for intervals

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The portion library provides data structure and operations for intervals in Python.

Table of contents

Installation

You can use pip to install it, as usual: pip install portion. This will install the latest available version from PyPI. Pre-releases are available from the master branch on GitHub and can be installed with pip install git+https://github.com/AlexandreDecan/portion.

You can install portion and its development environment using pip install -e .[test] at the root of this repository. This automatically installs pytest (for the test suites) and ruff (for code style).

Documentation & usage

Interval creation

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

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

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

>>> P.closed(1.2, 2.4)
[1.2,2.4]
>>> P.closed('a', 'z')
['a','z']
>>> import datetime
>>> P.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 P.inf and -P.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.

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

Intervals created with this library are Interval instances. An Interval instance is a disjunction of atomic intervals each representing a single interval (e.g. [1,2]). Intervals can be iterated to access the underlying atomic intervals, sorted by their lower and upper bounds.

>>> list(P.open(10, 11) | P.closed(0, 1) | P.closed(20, 21))
[[0,1], (10,11), [20,21]]
>>> list(P.empty())
[]

Nested (sorted) intervals can also be retrieved with a position or a slice:

>>> (P.open(10, 11) | P.closed(0, 1) | P.closed(20, 21))[0]
[0,1]
>>> (P.open(10, 11) | P.closed(0, 1) | P.closed(20, 21))[-2]
(10,11)
>>> (P.open(10, 11) | P.closed(0, 1) | P.closed(20, 21))[:2]
[0,1] | (10,11)

For convenience, intervals are automatically simplified:

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

Note that, by default, simplification of discrete intervals is not supported by portion (but it can be simulated though, see #24). For example, combining [0,1] with [2,3] will not result in [0,3] even if there is no integer between 1 and 2. Refer to Specialize & customize intervals to see how to create and use specialized discrete intervals.

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Interval bounds & attributes

An Interval defines the following properties:

The left and right boundaries, and the lower and upper bounds of an interval can be respectively accessed with its left, right, lower and upper attributes. The left and right bounds are either P.CLOSED or P.OPEN. By definition, P.CLOSED == ~P.OPEN and vice-versa.

>> P.CLOSED, P.OPEN
CLOSED, OPEN
>>> x = P.closedopen(0, 1)
>>> x.left, x.lower, x.upper, x.right
(CLOSED, 0, 1, OPEN)

By convention, empty intervals resolve to (P.inf, -P.inf):

>>> i = P.empty()
>>> i.left, i.lower, i.upper, i.right
(OPEN, +inf, -inf, OPEN)

If the interval is not atomic, then left and lower refer to the lower bound of its enclosure, while right and upper refer to the upper bound of its enclosure:

>>> x = P.open(0, 1) | P.closed(3, 4)
>>> x.left, x.lower, x.upper, x.right
(OPEN, 0, 4, CLOSED)

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

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

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Interval operations

Interval instances support the following operations:

Finally, intervals are hashable as long as their bounds are hashable (and we have defined a hash value for P.inf and -P.inf).

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Comparison operators

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

>>> P.closed(0, 2) == P.closed(0, 1) | P.closed(1, 2)
True
>>> P.closed(0, 2) == P.open(0, 2)
False

Moreover, intervals are comparable using >, >=, < or <=. These comparison operators have a different behaviour than the usual ones. For instance, a < b holds if all values in a are lower than the minimal value of b (i.e., a is entirely on the left of the lower bound of b).

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

Similarly, a <= b if all values in a are lower than the maximal value of b (i.e., a is entirely on the left of the upper bound of b).

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

If an interval needs to be compared against a single value, convert the value to a singleton interval first:

>>> P.singleton(0) < P.closed(0, 10)
False
>>> P.singleton(0) <= P.closed(0, 10)
True
>>> P.singleton(5) <= P.closed(0, 10)
True
>>> P.closed(0, 1) < P.singleton(2)
True

Note that all these semantics differ from classical comparison operators. As a consequence, the empty interval is never <, <=, > nor >= than any other interval, and no interval is <, >, <= or >= when compared to the empty interval.

>>> e = P.empty()
>>> e < e or e > e or e <= e or e >= e
False
>>> i = P.closed(0, 1)
>>> e < i or e <= i or e > i or e >= i
False

Moreover, some non-empty intervals are also not comparable in the classical sense, as illustrated hereafter:

>>> a, b = P.closed(0, 4), P.closed(1, 2)
>>> a < b or a > b
False
>>> a <= b or a >= b
False
>>> b <= a and b >= a
True

As a general rule, if a < b holds, then a <= b, b > a, b >= a, not (a > b), not (b < a), not (a >= b), and not (b <= a) hold.

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Interval transformation

Intervals are immutable but provide a replace method to create a new interval based on the current one. This method accepts four optional parameters left, lower, upper, and right:

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

Functions can be passed instead of values. If a function is passed, it is called with the current corresponding value.

>>> P.closed(0, 2).replace(upper=lambda x: 2 * x)
[0,4]

The provided function won't be called on infinities, unless ignore_inf is set to False.

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

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

>>> i = P.closed(2, 3) | P.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: (~x.left, x.lower, x.upper, ~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 ignores infinities by default, it can be conveniently used to transform (disjunction of) intervals in presence of infinities.

>>> i = P.openclosed(-P.inf, 0) | P.closed(3, 4) | P.closedopen(8, P.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: ~v, right=lambda v: ~v))
(-inf,0) | (3,4) | (8,+inf)
>>> # Replace infinities with -10 and 10
>>> conv = lambda v: -10 if v == -P.inf else (10 if v == P.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, and returns a generator to iterate over the values of an interval. Obviously, as intervals are continuous, it is required to specify the step between consecutive values. The iteration then starts from the lower bound and ends on the upper one. Only values contained by the interval are returned this way.

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

When an interval is not atomic, iterate consecutively iterates on all underlying atomic intervals, starting from each lower bound and ending on each upper one:

>>> list(P.iterate(P.singleton(0) | P.singleton(3) | P.singleton(5), step=2))  # Won't be [0]
[0, 3, 5]
>>> list(P.iterate(P.closed(0, 2) | P.closed(4, 6), step=3))  # Won't be [0, 6]
[0, 4]

By default, the iteration always starts on the lower bound of each underlying atomic interval. The base parameter can be used to change this behaviour, by specifying how the initial value to start the iteration from must be computed. This parameter accepts a callable that is called with the lower bound of each underlying atomic interval, and that returns the initial value to start the iteration from. It can be helpful to deal with (semi-)infinite intervals, or to align the generated values of the iterator:

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

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:

>>> base = lambda x: 0
>>> list(P.iterate(P.singleton(0) | P.singleton(3) | P.singleton(5), step=2, base=base))
[0]
>>> list(P.iterate(P.closed(0, 2) | P.closed(4, 6), step=3, base=base))
[0, 6]

Notice that defining base such that it returns a single value can be extremely inefficient in terms of performance when the intervals are "far apart" each other (i.e., when the gaps between atomic intervals are large).

Finally, iteration can be performed in reverse order by specifying reverse=True.

>>> list(P.iterate(P.closed(0, 3), step=-1, reverse=True))  # Mind step=-1
[3, 2, 1, 0]
>>> list(P.iterate(P.closed(0, 3), step=-2, reverse=True))  # Mind step=-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 step (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(P.iterate(P.closed('a', 'd'), step=lambda d: chr(ord(d) + 1)))
['a', 'b', 'c', 'd']
>>> # Since we reversed the order, we changed "+" to "-" in the lambda.
>>> list(P.iterate(P.closed('a', 'd'), step=lambda d: chr(ord(d) - 1), reverse=True))
['d', 'c', 'b', 'a']

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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 = P.IntervalDict()
>>> d[P.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[P.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 the key is an interval, a new IntervalDict containing the values for the specified key is returned:

>>> d[~P.empty()]  # Get all values, similar to d.copy()
{[0,2): 'banana', [2,4]: 'orange'}
>>> d[P.closed(1, 3)]
{[1,2): 'banana', [2,3]: 'orange'}
>>> d[P.closed(-2, 1)]
{[0,1]: 'banana'}
>>> d[P.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(P.closed(-2, 1), default='peach')
{[-2,0): 'peach', [0,1]: 'banana'}
>>> d.get(P.closed(-2, -1), default='peach')
{[-2,-1]: 'peach'}
>>> d.get(P.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 sorted view of disjoint intervals.

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

The .keys, .values and .items methods return exactly one element for each stored value (i.e., if two intervals share a value, they are merged into a disjunction), as illustrated next. See #44 to know how to obtain a sorted list of atomic intervals instead.

>>> d = P.IntervalDict()
>>> d[P.closed(0, 1)] = d[P.closed(2, 3)] = 'peach'
>>> list(d.items())
[([0,1] | [2,3], 'peach')]

Two IntervalDict instances can be combined using the .combine method. This method returns a new IntervalDict whose keys and values are taken from the two source IntervalDict. The values corresponding to intersecting keys (i.e., when the two instances overlap) are combined using the provided how function, while values corresponding to non-intersecting keys are simply copied (i.e., the how function is not called for them), as illustrated hereafter:

>>> d1 = P.IntervalDict({P.closed(0, 2): 'banana'})
>>> d2 = P.IntervalDict({P.closed(1, 3): 'orange'})
>>> concat = lambda x, y: x + '/' + y
>>> d1.combine(d2, how=concat)
{[0,1): 'banana', [1,2]: 'banana/orange', (2,3]: 'orange'}

The how function can also receive the current interval as third parameter, by enabling the pass_interval parameter of .combine. The combine method also accepts a missing parameter. When missing is set, the how function is called even for non-intersecting keys, using the value of missing to replace the missing values:

>>> d1.combine(d2, how=concat, missing='kiwi')
{[0,1): 'banana/kiwi', [1,2]: 'banana/orange', (2,3]: 'kiwi/orange'}

Resulting keys always correspond to an outer join. Other joins can be easily simulated by querying the resulting IntervalDict as follows:

>>> d = d1.combine(d2, how=concat)
>>> d[d1.domain()]  # Left join
{[0,1): 'banana', [1,2]: 'banana/orange'}
>>> d[d2.domain()]  # Right join
{[1,2]: 'banana/orange', (2,3]: 'orange'}
>>> d[d1.domain() & d2.domain()]  # Inner join
{[1,2]: 'banana/orange'}

While .combine accepts a single IntervalDict, it can be generalized to support an arbitrary number of IntervalDicts, as illustrated in #95.

Finally, similarly to a dict, an IntervalDict also supports len, in and del, and defines .clear, .copy, .update, .pop, .popitem, and .setdefault. For convenience, one can export the content of an IntervalDict to a classical Python dict using the as_dict method. This method accepts an optional atomic parameter (whose default is False). When set to True, the keys of the resulting dict instance are atomic intervals.

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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.

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

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 = P.openclosed(0, 1) | P.closed(2, P.inf)
>>> P.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.

>>> P.from_string('[0, 1]', conv=int) == P.closed(0, 1)
True
>>> P.from_string('[1.2]', conv=float) == P.singleton(1.2)
True
>>> converter = lambda s: datetime.datetime.strptime(s, '%Y/%m/%d')
>>> P.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]),
... }
>>> P.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
>>> P.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. P.CLOSED and P.OPEN are represented by Boolean values True (inclusive) and False (exclusive).

>>> P.to_data(P.openclosed(0, 2))
[(False, 0, 2, True)]

The values used to represent positive and negative infinities can be specified with pinf and ninf. They default to float('inf') and float('-inf') respectively.

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

The function to convert bounds can be specified with the conv parameter.

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

Intervals can be imported from such a list of 4-tuples 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)]
>>> P.from_data(x, conv=lambda v: datetime.date(*v))
[datetime.date(2011, 3, 15),datetime.date(2013, 10, 10))

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Specialize & customize intervals

Disclaimer: the features explained in this section are still experimental and are subject to backward incompatible changes even in minor or patch updates of portion.

The intervals provided by portion already cover a wide range of use cases. However, in some situations, it might be interesting to specialize or customize these intervals. One typical example would be to support discrete intervals such as intervals of integers.

While it is definitely possible to rely on the default intervals provided by portion to encode discrete intervals, there are a few edge cases that lead some operations to return unexpected results:

>>> P.singleton(0) | P.singleton(1)  # Case 1: should be [0,1] for discrete numbers
[0] | [1]
>>> P.open(0, 1)  # Case 2: should be empty
(0,1)
>>> P.closedopen(0, 1)  # Case 3: should be singleton [0]
[0,1)

The portion library makes its best to ease defining and using subclasses of Interval to address these situations. In particular, Interval instances always produce new intervals using self.__class__, and the class is written in a way that most of its methods can be easily extended.

To implement a class for intervals of discrete numbers and to cover the three aforementioned cases, we need to change the behaviour of the Interval._mergeable class method (to address first case) and of the Interval.from_atomic class method (for cases 2 and 3). The former is used to detect whether two atomic intervals can be merged into a single interval, while the latter is used to create atomic intervals.

Thankfully, since discrete intervals are expected to be a frequent use case, portion provides an AbstractDiscreteInterval class that already makes the appropriate changes to these two methods. As indicated by its name, this class cannot be used directly and should be inherited. In particular, one has either to provide a _step class attribute to define the step between consecutive discrete values, or to define the _incr and _decr class methods:

>>> class IntInterval(P.AbstractDiscreteInterval):
...     _step = 1

That's all! We can now use this class to manipulate intervals of discrete numbers and see it covers the three problematic cases:

>>> IntInterval.from_atomic(P.CLOSED, 0, 0, P.CLOSED) | IntInterval.from_atomic(P.CLOSED, 1, 1, P.CLOSED)
[0,1]
>>> IntInterval.from_atomic(P.OPEN, 0, 1, P.OPEN)
()
>>> IntInterval.from_atomic(P.CLOSED, 0, 1, P.OPEN)
[0]

As an example of using _incr and _decr, consider the following CharInterval subclass tailored to manipulate intervals of characters:

>>> class CharInterval(P.AbstractDiscreteInterval):
...     _incr = lambda v: chr(ord(v) + 1)
...     _decr = lambda v: chr(ord(v) - 1)
>>> CharInterval.from_atomic(P.OPEN, 'a', 'z', P.OPEN)
['b','y']

Having to call from_atomic on the subclass to create intervals is quite verbose. For convenience, all the functions that create interval instances accept an additional klass parameter to specify the class that creates intervals, circumventing the direct use of the class constructors. However, having to specify the klass parameter in each call to P.closed or other helpers that create intervals is still a bit too verbose to be convenient. Consequently, portion provides a create_api function that, given a subclass of Interval, returns a dynamically generated module whose API is similar to the one of portion but configured to use the subclass instead:

>>> D = P.create_api(IntInterval)
>>> D.singleton(0) | D.singleton(1)
[0,1]
>>> D.open(0, 1)
()
>>> D.closedopen(0, 1)
[0]

This makes it easy to use our newly defined IntInterval subclass while still benefiting from portion's API.

Let's extend our example to support intervals of natural numbers. Such intervals are quite similar to the above ones, except they cannot go over negative values. We can prevent the bounds of an interval to be negative by slightly changing the from_atomic class method as follows:

>>> class NaturalInterval(IntInterval):
...    @classmethod
...    def from_atomic(cls, left, lower, upper, right):
...        return super().from_atomic(
...            P.CLOSED if lower < 0 else left,
...            max(0, lower),
...            upper,
...            right,
...        )

We can now define and use the N module to check whether our newly defined NaturalInterval does the job:

>>> N = P.create_api(NaturalInterval)
>>> N.closed(-10, 2)
[0,2]
>>> N.open(-10, 2)
[0,1]
>>> ~N.empty()
[0,+inf)

Keep in mind that just because NaturalInterval has semantics associated with natural numbers does not mean that all possible operations on these intervals strictly comply the semantics. The following examples illustrate some of the cases where additional checks should be implemented to strictly adhere to these semantics:

>>> N.closed(1.5, 2.5)  # Bounds are not natural numbers
[1.5,2.5]
>>> 0.5 in N.closed(0, 1)  # Given value is not a natural number
True
>>> ~N.singleton(0.5)
[1.5,+inf)

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Changelog

This library adheres to a semantic versioning scheme. See CHANGELOG.md for the list of changes.

Contributions

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

License

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

You can refer to this library using:

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