"""
SQLAlchemy-Utils provides wide variety of range data types. All range data
types return Interval objects of intervals_ package. In order to use range data
types you need to install intervals_ with:

::

    pip install intervals


Intervals package provides good chunk of additional interval operators that for
example psycopg2 range objects do not support.



Some good reading for practical interval implementations:

https://wiki.postgresql.org/images/f/f0/Range-types.pdf


Range type initialization
-------------------------

::



    from sqlalchemy_utils import IntRangeType


    class Event(Base):
        __tablename__ = 'user'
        id = sa.Column(sa.Integer, autoincrement=True)
        name = sa.Column(sa.Unicode(255))
        estimated_number_of_persons = sa.Column(IntRangeType)



You can also set a step parameter for range type. The values that are not
multipliers of given step will be rounded up to nearest step multiplier.


::


    from sqlalchemy_utils import IntRangeType


    class Event(Base):
        __tablename__ = 'user'
        id = sa.Column(sa.Integer, autoincrement=True)
        name = sa.Column(sa.Unicode(255))
        estimated_number_of_persons = sa.Column(IntRangeType(step=1000))


    event = Event(estimated_number_of_persons=[100, 1200])
    event.estimated_number_of_persons.lower  # 0
    event.estimated_number_of_persons.upper  # 1000


Range type operators
--------------------

SQLAlchemy-Utils supports many range type operators. These operators follow the
`intervals` package interval coercion rules.

So for example when we make a query such as:

::

    session.query(Car).filter(Car.price_range == 300)


It is essentially the same as:

::

    session.query(Car).filter(Car.price_range == DecimalInterval([300, 300]))


Comparison operators
^^^^^^^^^^^^^^^^^^^^

All range types support all comparison operators (>, >=, ==, !=, <=, <).

::

    Car.price_range < [12, 300]

    Car.price_range == [12, 300]

    Car.price_range < 300

    Car.price_range > (300, 500)

    # Whether or not range is strictly left of another range
    Car.price_range << [300, 500]

    # Whether or not range is strictly right of another range
    Car.price_range >> [300, 500]



Membership operators
^^^^^^^^^^^^^^^^^^^^

::

    Car.price_range.contains([300, 500])

    Car.price_range.contained_by([300, 500])

    Car.price_range.in_([[300, 500], [800, 900]])

    ~ Car.price_range.in_([[300, 400], [700, 800]])


Length
^^^^^^

SQLAlchemy-Utils provides length property for all range types. The
implementation of this property varies on different range types.

In the following example we find all cars whose price range's length is more
than 500.

::

    session.query(Car).filter(
        Car.price_range.length > 500
    )



.. _intervals: https://github.com/kvesteri/intervals
"""
from collections.abc import Iterable
from datetime import timedelta

import sqlalchemy as sa
from sqlalchemy import types
from sqlalchemy.dialects.postgresql import (
    DATERANGE,
    INT4RANGE,
    INT8RANGE,
    NUMRANGE,
    TSRANGE
)

from ..exceptions import ImproperlyConfigured
from .scalar_coercible import ScalarCoercible

intervals = None
try:
    import intervals
except ImportError:
    pass


class RangeComparator(types.TypeEngine.Comparator):
    @classmethod
    def coerced_func(cls, func):
        def operation(self, other, **kwargs):
            other = self.coerce_arg(other)
            return getattr(types.TypeEngine.Comparator, func)(
                self, other, **kwargs
            )
        return operation

    def coerce_arg(self, other):
        coerced_types = (
            self.type.interval_class.type,
            tuple,
            list,
            str,
        )

        if isinstance(other, coerced_types):
            return self.type.interval_class(other)
        return other

    def in_(self, other):
        if (
            isinstance(other, Iterable) and
            not isinstance(other, str)
        ):
            other = map(self.coerce_arg, other)
        return super(RangeComparator, self).in_(other)

    def notin_(self, other):
        if (
            isinstance(other, Iterable) and
            not isinstance(other, str)
        ):
            other = map(self.coerce_arg, other)
        return super(RangeComparator, self).notin_(other)

    def __rshift__(self, other, **kwargs):
        """
        Returns whether or not given interval is strictly right of another
        interval.

        [a, b] >> [c, d]        True, if a > d
        """
        other = self.coerce_arg(other)
        return self.op('>>')(other)

    def __lshift__(self, other, **kwargs):
        """
        Returns whether or not given interval is strictly left of another
        interval.

        [a, b] << [c, d]        True, if b < c
        """
        other = self.coerce_arg(other)
        return self.op('<<')(other)

    def contains(self, other, **kwargs):
        other = self.coerce_arg(other)
        return self.op('@>')(other)

    def contained_by(self, other, **kwargs):
        other = self.coerce_arg(other)
        return self.op('<@')(other)


class DiscreteRangeComparator(RangeComparator):
    @property
    def length(self):
        return sa.func.upper(self.expr) - self.step - sa.func.lower(self.expr)


class IntRangeComparator(DiscreteRangeComparator):
    step = 1


class DateRangeComparator(DiscreteRangeComparator):
    step = timedelta(days=1)


class ContinuousRangeComparator(RangeComparator):
    @property
    def length(self):
        return sa.func.upper(self.expr) - sa.func.lower(self.expr)


funcs = [
    '__eq__',
    '__ne__',
    '__lt__',
    '__le__',
    '__gt__',
    '__ge__',
]


for func in funcs:
    setattr(
        RangeComparator,
        func,
        RangeComparator.coerced_func(func)
    )


class RangeType(ScalarCoercible, types.TypeDecorator):
    comparator_factory = RangeComparator

    def __init__(self, *args, **kwargs):
        if intervals is None:
            raise ImproperlyConfigured(
                'RangeType needs intervals package installed.'
            )
        self.step = kwargs.pop('step', None)
        super(RangeType, self).__init__(*args, **kwargs)

    def load_dialect_impl(self, dialect):
        if dialect.name == 'postgresql':
            # Use the native range type for postgres.
            return dialect.type_descriptor(self.impl)
        else:
            # Other drivers don't have native types.
            return dialect.type_descriptor(sa.String(255))

    def process_bind_param(self, value, dialect):
        if value is not None:
            return str(value)
        return value

    def process_result_value(self, value, dialect):
        if isinstance(value, str):
            factory_func = self.interval_class.from_string
        else:
            factory_func = self.interval_class
        if value is not None:
            if self.interval_class.step is not None:
                return self.canonicalize_result_value(
                    factory_func(value, step=self.step)
                )
            else:
                return factory_func(value, step=self.step)
        return value

    def canonicalize_result_value(self, value):
        return intervals.canonicalize(value, True, True)

    def _coerce(self, value):
        if value is None:
            return None
        return self.interval_class(value, step=self.step)


class IntRangeType(RangeType):
    """
    IntRangeType provides way for saving ranges of integers into database. On
    PostgreSQL this type maps to native INT4RANGE type while on other drivers
    this maps to simple string column.

    Example::


        from sqlalchemy_utils import IntRangeType


        class Event(Base):
            __tablename__ = 'user'
            id = sa.Column(sa.Integer, autoincrement=True)
            name = sa.Column(sa.Unicode(255))
            estimated_number_of_persons = sa.Column(IntRangeType)


        party = Event(name='party')

        # we estimate the party to contain minium of 10 persons and at max
        # 100 persons
        party.estimated_number_of_persons = [10, 100]

        print party.estimated_number_of_persons
        # '10-100'


    IntRangeType returns the values as IntInterval objects. These objects
    support many arithmetic operators::


        meeting = Event(name='meeting')

        meeting.estimated_number_of_persons = [20, 40]

        total = (
            meeting.estimated_number_of_persons +
            party.estimated_number_of_persons
        )
        print total
        # '30-140'
    """
    impl = INT4RANGE
    comparator_factory = IntRangeComparator
    cache_ok = True

    def __init__(self, *args, **kwargs):
        super(IntRangeType, self).__init__(*args, **kwargs)
        self.interval_class = intervals.IntInterval


class Int8RangeType(RangeType):
    """
    Int8RangeType provides way for saving ranges of 8-byte integers into
    database. On PostgreSQL this type maps to native INT8RANGE type while on
    other drivers this maps to simple string column.

    Example::


        from sqlalchemy_utils import IntRangeType


        class Event(Base):
            __tablename__ = 'user'
            id = sa.Column(sa.Integer, autoincrement=True)
            name = sa.Column(sa.Unicode(255))
            estimated_number_of_persons = sa.Column(Int8RangeType)


        party = Event(name='party')

        # we estimate the party to contain minium of 10 persons and at max
        # 100 persons
        party.estimated_number_of_persons = [10, 100]

        print party.estimated_number_of_persons
        # '10-100'


    Int8RangeType returns the values as IntInterval objects. These objects
    support many arithmetic operators::


        meeting = Event(name='meeting')

        meeting.estimated_number_of_persons = [20, 40]

        total = (
            meeting.estimated_number_of_persons +
            party.estimated_number_of_persons
        )
        print total
        # '30-140'
    """
    impl = INT8RANGE
    comparator_factory = IntRangeComparator
    cache_ok = True

    def __init__(self, *args, **kwargs):
        super(Int8RangeType, self).__init__(*args, **kwargs)
        self.interval_class = intervals.IntInterval


class DateRangeType(RangeType):
    """
    DateRangeType provides way for saving ranges of dates into database. On
    PostgreSQL this type maps to native DATERANGE type while on other drivers
    this maps to simple string column.

    Example::


        from sqlalchemy_utils import DateRangeType


        class Reservation(Base):
            __tablename__ = 'user'
            id = sa.Column(sa.Integer, autoincrement=True)
            room_id = sa.Column(sa.Integer))
            during = sa.Column(DateRangeType)
    """
    impl = DATERANGE
    comparator_factory = DateRangeComparator
    cache_ok = True

    def __init__(self, *args, **kwargs):
        super(DateRangeType, self).__init__(*args, **kwargs)
        self.interval_class = intervals.DateInterval


class NumericRangeType(RangeType):
    """
    NumericRangeType provides way for saving ranges of decimals into database.
    On PostgreSQL this type maps to native NUMRANGE type while on other drivers
    this maps to simple string column.

    Example::


        from sqlalchemy_utils import NumericRangeType


        class Car(Base):
            __tablename__ = 'car'
            id = sa.Column(sa.Integer, autoincrement=True)
            name = sa.Column(sa.Unicode(255)))
            price_range = sa.Column(NumericRangeType)
    """

    impl = NUMRANGE
    comparator_factory = ContinuousRangeComparator
    cache_ok = True

    def __init__(self, *args, **kwargs):
        super(NumericRangeType, self).__init__(*args, **kwargs)
        self.interval_class = intervals.DecimalInterval


class DateTimeRangeType(RangeType):
    impl = TSRANGE
    comparator_factory = ContinuousRangeComparator
    cache_ok = True

    def __init__(self, *args, **kwargs):
        super(DateTimeRangeType, self).__init__(*args, **kwargs)
        self.interval_class = intervals.DateTimeInterval
