# -*- coding: utf-8 -*-
from __future__ import division

from datetime import timedelta
import textwrap
import warnings

import numpy as np

from pandas._libs import lib, tslibs
from pandas._libs.tslibs import NaT, Timedelta, Timestamp, iNaT
from pandas._libs.tslibs.fields import get_timedelta_field
from pandas._libs.tslibs.timedeltas import (
    array_to_timedelta64, parse_timedelta_unit, precision_from_unit)
import pandas.compat as compat
from pandas.util._decorators import Appender

from pandas.core.dtypes.common import (
    _NS_DTYPE, _TD_DTYPE, ensure_int64, is_datetime64_dtype, is_dtype_equal,
    is_float_dtype, is_integer_dtype, is_list_like, is_object_dtype, is_scalar,
    is_string_dtype, is_timedelta64_dtype, is_timedelta64_ns_dtype,
    pandas_dtype)
from pandas.core.dtypes.dtypes import DatetimeTZDtype
from pandas.core.dtypes.generic import (
    ABCDataFrame, ABCIndexClass, ABCSeries, ABCTimedeltaIndex)
from pandas.core.dtypes.missing import isna

from pandas.core import ops
from pandas.core.algorithms import checked_add_with_arr
import pandas.core.common as com

from pandas.tseries.frequencies import to_offset
from pandas.tseries.offsets import Tick

from . import datetimelike as dtl

_BAD_DTYPE = "dtype {dtype} cannot be converted to timedelta64[ns]"


def _is_convertible_to_td(key):
    return isinstance(key, (Tick, timedelta,
                            np.timedelta64, compat.string_types))


def _field_accessor(name, alias, docstring=None):
    def f(self):
        values = self.asi8
        result = get_timedelta_field(values, alias)
        if self._hasnans:
            result = self._maybe_mask_results(result, fill_value=None,
                                              convert='float64')

        return result

    f.__name__ = name
    f.__doc__ = "\n{}\n".format(docstring)
    return property(f)


def _td_array_cmp(cls, op):
    """
    Wrap comparison operations to convert timedelta-like to timedelta64
    """
    opname = '__{name}__'.format(name=op.__name__)
    nat_result = True if opname == '__ne__' else False

    def wrapper(self, other):
        if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
            return NotImplemented

        if _is_convertible_to_td(other) or other is NaT:
            try:
                other = Timedelta(other)
            except ValueError:
                # failed to parse as timedelta
                return ops.invalid_comparison(self, other, op)

            result = op(self.view('i8'), other.value)
            if isna(other):
                result.fill(nat_result)

        elif not is_list_like(other):
            return ops.invalid_comparison(self, other, op)

        elif len(other) != len(self):
            raise ValueError("Lengths must match")

        else:
            try:
                other = type(self)._from_sequence(other)._data
            except (ValueError, TypeError):
                return ops.invalid_comparison(self, other, op)

            result = op(self.view('i8'), other.view('i8'))
            result = com.values_from_object(result)

            o_mask = np.array(isna(other))
            if o_mask.any():
                result[o_mask] = nat_result

        if self._hasnans:
            result[self._isnan] = nat_result

        return result

    return compat.set_function_name(wrapper, opname, cls)


class TimedeltaArray(dtl.DatetimeLikeArrayMixin, dtl.TimelikeOps):
    """
    Pandas ExtensionArray for timedelta data.

    .. versionadded:: 0.24.0

    .. warning::

       TimedeltaArray is currently experimental, and its API may change
       without warning. In particular, :attr:`TimedeltaArray.dtype` is
       expected to change to be an instance of an ``ExtensionDtype``
       subclass.

    Parameters
    ----------
    values : array-like
        The timedelta data.

    dtype : numpy.dtype
        Currently, only ``numpy.dtype("timedelta64[ns]")`` is accepted.
    freq : Offset, optional
    copy : bool, default False
        Whether to copy the underlying array of data.
    """
    _typ = "timedeltaarray"
    _scalar_type = Timedelta
    __array_priority__ = 1000
    # define my properties & methods for delegation
    _other_ops = []
    _bool_ops = []
    _object_ops = ['freq']
    _field_ops = ['days', 'seconds', 'microseconds', 'nanoseconds']
    _datetimelike_ops = _field_ops + _object_ops + _bool_ops
    _datetimelike_methods = ["to_pytimedelta", "total_seconds",
                             "round", "floor", "ceil"]

    # Needed so that NaT.__richcmp__(DateTimeArray) operates pointwise
    ndim = 1

    @property
    def _box_func(self):
        return lambda x: Timedelta(x, unit='ns')

    @property
    def dtype(self):
        """
        The dtype for the TimedeltaArray.

        .. warning::

           A future version of pandas will change dtype to be an instance
           of a :class:`pandas.api.extensions.ExtensionDtype` subclass,
           not a ``numpy.dtype``.

        Returns
        -------
        numpy.dtype
        """
        return _TD_DTYPE

    # ----------------------------------------------------------------
    # Constructors
    _attributes = ["freq"]

    def __init__(self, values, dtype=_TD_DTYPE, freq=None, copy=False):
        if isinstance(values, (ABCSeries, ABCIndexClass)):
            values = values._values

        inferred_freq = getattr(values, "_freq", None)

        if isinstance(values, type(self)):
            if freq is None:
                freq = values.freq
            elif freq and values.freq:
                freq = to_offset(freq)
                freq, _ = dtl.validate_inferred_freq(freq, values.freq, False)
            values = values._data

        if not isinstance(values, np.ndarray):
            msg = (
                "Unexpected type '{}'. 'values' must be a TimedeltaArray "
                "ndarray, or Series or Index containing one of those."
            )
            raise ValueError(msg.format(type(values).__name__))
        if values.ndim != 1:
            raise ValueError("Only 1-dimensional input arrays are supported.")

        if values.dtype == 'i8':
            # for compat with datetime/timedelta/period shared methods,
            #  we can sometimes get here with int64 values.  These represent
            #  nanosecond UTC (or tz-naive) unix timestamps
            values = values.view(_TD_DTYPE)

        _validate_td64_dtype(values.dtype)
        dtype = _validate_td64_dtype(dtype)

        if freq == "infer":
            msg = (
                "Frequency inference not allowed in TimedeltaArray.__init__. "
                "Use 'pd.array()' instead."
            )
            raise ValueError(msg)

        if copy:
            values = values.copy()
        if freq:
            freq = to_offset(freq)

        self._data = values
        self._dtype = dtype
        self._freq = freq

        if inferred_freq is None and freq is not None:
            type(self)._validate_frequency(self, freq)

    @classmethod
    def _simple_new(cls, values, freq=None, dtype=_TD_DTYPE):
        assert dtype == _TD_DTYPE, dtype
        assert isinstance(values, np.ndarray), type(values)

        result = object.__new__(cls)
        result._data = values.view(_TD_DTYPE)
        result._freq = to_offset(freq)
        result._dtype = _TD_DTYPE
        return result

    @classmethod
    def _from_sequence(cls, data, dtype=_TD_DTYPE, copy=False,
                       freq=None, unit=None):
        if dtype:
            _validate_td64_dtype(dtype)
        freq, freq_infer = dtl.maybe_infer_freq(freq)

        data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)
        freq, freq_infer = dtl.validate_inferred_freq(freq, inferred_freq,
                                                      freq_infer)

        result = cls._simple_new(data, freq=freq)

        if inferred_freq is None and freq is not None:
            # this condition precludes `freq_infer`
            cls._validate_frequency(result, freq)

        elif freq_infer:
            # Set _freq directly to bypass duplicative _validate_frequency
            # check.
            result._freq = to_offset(result.inferred_freq)

        return result

    @classmethod
    def _generate_range(cls, start, end, periods, freq, closed=None):

        periods = dtl.validate_periods(periods)
        if freq is None and any(x is None for x in [periods, start, end]):
            raise ValueError('Must provide freq argument if no data is '
                             'supplied')

        if com.count_not_none(start, end, periods, freq) != 3:
            raise ValueError('Of the four parameters: start, end, periods, '
                             'and freq, exactly three must be specified')

        if start is not None:
            start = Timedelta(start)

        if end is not None:
            end = Timedelta(end)

        if start is None and end is None:
            if closed is not None:
                raise ValueError("Closed has to be None if not both of start"
                                 "and end are defined")

        left_closed, right_closed = dtl.validate_endpoints(closed)

        if freq is not None:
            index = _generate_regular_range(start, end, periods, freq)
        else:
            index = np.linspace(start.value, end.value, periods).astype('i8')

        if not left_closed:
            index = index[1:]
        if not right_closed:
            index = index[:-1]

        return cls._simple_new(index, freq=freq)

    # ----------------------------------------------------------------
    # DatetimeLike Interface

    def _unbox_scalar(self, value):
        if not isinstance(value, self._scalar_type) and value is not NaT:
            raise ValueError("'value' should be a Timedelta.")
        self._check_compatible_with(value)
        return value.value

    def _scalar_from_string(self, value):
        return Timedelta(value)

    def _check_compatible_with(self, other):
        # we don't have anything to validate.
        pass

    def _maybe_clear_freq(self):
        self._freq = None

    # ----------------------------------------------------------------
    # Array-Like / EA-Interface Methods

    @Appender(dtl.DatetimeLikeArrayMixin._validate_fill_value.__doc__)
    def _validate_fill_value(self, fill_value):
        if isna(fill_value):
            fill_value = iNaT
        elif isinstance(fill_value, (timedelta, np.timedelta64, Tick)):
            fill_value = Timedelta(fill_value).value
        else:
            raise ValueError("'fill_value' should be a Timedelta. "
                             "Got '{got}'.".format(got=fill_value))
        return fill_value

    def astype(self, dtype, copy=True):
        # We handle
        #   --> timedelta64[ns]
        #   --> timedelta64
        # DatetimeLikeArrayMixin super call handles other cases
        dtype = pandas_dtype(dtype)

        if is_timedelta64_dtype(dtype) and not is_timedelta64_ns_dtype(dtype):
            # by pandas convention, converting to non-nano timedelta64
            #  returns an int64-dtyped array with ints representing multiples
            #  of the desired timedelta unit.  This is essentially division
            if self._hasnans:
                # avoid double-copying
                result = self._data.astype(dtype, copy=False)
                values = self._maybe_mask_results(result,
                                                  fill_value=None,
                                                  convert='float64')