# coding=utf-8
# pylint: disable-msg=E1101,W0612

from datetime import datetime, timedelta
import operator

import numpy as np
import pytest

import pandas.compat as compat
from pandas.compat import range

import pandas as pd
from pandas import (
    Categorical, DataFrame, Index, Series, bdate_range, date_range, isna)
from pandas.core import ops
import pandas.core.nanops as nanops
import pandas.util.testing as tm
from pandas.util.testing import (
    assert_almost_equal, assert_frame_equal, assert_series_equal)

from .common import TestData


class TestSeriesLogicalOps(object):
    @pytest.mark.parametrize('bool_op', [operator.and_,
                                         operator.or_, operator.xor])
    def test_bool_operators_with_nas(self, bool_op):
        # boolean &, |, ^ should work with object arrays and propagate NAs
        ser = Series(bdate_range('1/1/2000', periods=10), dtype=object)
        ser[::2] = np.nan

        mask = ser.isna()
        filled = ser.fillna(ser[0])

        result = bool_op(ser < ser[9], ser > ser[3])

        expected = bool_op(filled < filled[9], filled > filled[3])
        expected[mask] = False
        assert_series_equal(result, expected)

    def test_operators_bitwise(self):
        # GH#9016: support bitwise op for integer types
        index = list('bca')

        s_tft = Series([True, False, True], index=index)
        s_fff = Series([False, False, False], index=index)
        s_tff = Series([True, False, False], index=index)
        s_empty = Series([])

        # TODO: unused
        # s_0101 = Series([0, 1, 0, 1])

        s_0123 = Series(range(4), dtype='int64')
        s_3333 = Series([3] * 4)
        s_4444 = Series([4] * 4)

        res = s_tft & s_empty
        expected = s_fff
        assert_series_equal(res, expected)

        res = s_tft | s_empty
        expected = s_tft
        assert_series_equal(res, expected)

        res = s_0123 & s_3333
        expected = Series(range(4), dtype='int64')
        assert_series_equal(res, expected)

        res = s_0123 | s_4444
        expected = Series(range(4, 8), dtype='int64')
        assert_series_equal(res, expected)

        s_a0b1c0 = Series([1], list('b'))

        res = s_tft & s_a0b1c0
        expected = s_tff.reindex(list('abc'))
        assert_series_equal(res, expected)

        res = s_tft | s_a0b1c0
        expected = s_tft.reindex(list('abc'))
        assert_series_equal(res, expected)

        n0 = 0
        res = s_tft & n0
        expected = s_fff
        assert_series_equal(res, expected)

        res = s_0123 & n0
        expected = Series([0] * 4)
        assert_series_equal(res, expected)

        n1 = 1
        res = s_tft & n1
        expected = s_tft
        assert_series_equal(res, expected)

        res = s_0123 & n1
        expected = Series([0, 1, 0, 1])
        assert_series_equal(res, expected)

        s_1111 = Series([1] * 4, dtype='int8')
        res = s_0123 & s_1111
        expected = Series([0, 1, 0, 1], dtype='int64')
        assert_series_equal(res, expected)

        res = s_0123.astype(np.int16) | s_1111.astype(np.int32)
        expected = Series([1, 1, 3, 3], dtype='int32')
        assert_series_equal(res, expected)

        with pytest.raises(TypeError):
            s_1111 & 'a'
        with pytest.raises(TypeError):
            s_1111 & ['a', 'b', 'c', 'd']
        with pytest.raises(TypeError):
            s_0123 & np.NaN
        with pytest.raises(TypeError):
            s_0123 & 3.14
        with pytest.raises(TypeError):
            s_0123 & [0.1, 4, 3.14, 2]

        # s_0123 will be all false now because of reindexing like s_tft
        exp = Series([False] * 7, index=[0, 1, 2, 3, 'a', 'b', 'c'])
        assert_series_equal(s_tft & s_0123, exp)

        # s_tft will be all false now because of reindexing like s_0123
        exp = Series([False] * 7, index=[0, 1, 2, 3, 'a', 'b', 'c'])
        assert_series_equal(s_0123 & s_tft, exp)

        assert_series_equal(s_0123 & False, Series([False] * 4))
        assert_series_equal(s_0123 ^ False, Series([False, True, True, True]))
        assert_series_equal(s_0123 & [False], Series([False] * 4))
        assert_series_equal(s_0123 & (False), Series([False] * 4))
        assert_series_equal(s_0123 & Series([False, np.NaN, False, False]),
                            Series([False] * 4))

        s_ftft = Series([False, True, False, True])
        assert_series_equal(s_0123 & Series([0.1, 4, -3.14, 2]), s_ftft)

        s_abNd = Series(['a', 'b', np.NaN, 'd'])
        res = s_0123 & s_abNd
        expected = s_ftft
        assert_series_equal(res, expected)

    def test_scalar_na_logical_ops_corners(self):
        s = Series([2, 3, 4, 5, 6, 7, 8, 9, 10])

        with pytest.raises(TypeError):
            s & datetime(2005, 1, 1)

        s = Series([2, 3, 4, 5, 6, 7, 8, 9, datetime(2005, 1, 1)])
        s[::2] = np.nan

        expected = Series(True, index=s.index)
        expected[::2] = False
        result = s & list(s)
        assert_series_equal(result, expected)

        d = DataFrame({'A': s})
        # TODO: Fix this exception - needs to be fixed! (see GH5035)
        # (previously this was a TypeError because series returned
        # NotImplemented

        # this is an alignment issue; these are equivalent
        # https://github.com/pandas-dev/pandas/issues/5284

        with pytest.raises(TypeError):
            d.__and__(s, axis='columns')

        with pytest.raises(TypeError):
            s & d

        # this is wrong as its not a boolean result
        # result = d.__and__(s,axis='index')

    @pytest.mark.parametrize('op', [
        operator.and_,
        operator.or_,
        operator.xor,

    ])
    def test_logical_ops_with_index(self, op):
        # GH#22092, GH#19792
        ser = Series([True, True, False, False])
        idx1 = Index([True, False, True, False])
        idx2 = Index([1, 0, 1, 0])

        expected = Series([op(ser[n], idx1[n]) for n in range(len(ser))])

        result = op(ser, idx1)
        assert_series_equal(result, expected)

        expected = Series([op(ser[n], idx2[n]) for n in range(len(ser))],
                          dtype=bool)

        result = op(ser, idx2)
        assert_series_equal(result, expected)

    @pytest.mark.parametrize("op, expected", [
        (ops.rand_, pd.Index([False, True])),
        (ops.ror_, pd.Index([False, True])),
        (ops.rxor, pd.Index([])),
    ])
    def test_reverse_ops_with_index(self, op, expected):
        # https://github.com/pandas-dev/pandas/pull/23628
        # multi-set Index ops are buggy, so let's avoid duplicates...
        ser = Series([True, False])
        idx = Index([False, True])
        result = op(ser, idx)
        tm.assert_index_equal(result, expected)

    def test_logical_ops_label_based(self):
        # GH#4947
        # logical ops should be label based

        a = Series([True, False, True], list('bca'))
        b = Series([False, True, False], list('abc'))

        expected = Series([False, True, False], list('abc'))
        result = a & b
        assert_series_equal(result, expected)

        expected = Series([True, True, False], list('abc'))
        result = a | b
        assert_series_equal(result, expected)

        expected = Series([True, False, False], list('abc'))
        result = a ^ b
        assert_series_equal(result, expected)

        # rhs is bigger
        a = Series([True, False, True], list('bca'))
        b = Series([False, True, False, True], list('abcd'))

        expected = Series([False, True, False, False], list('abcd'))
        result = a & b
        assert_series_equal(result, expected)

        expected = Series([True, True, False, False], list('abcd'))
        result = a | b
        assert_series_equal(result, expected)

        # filling

        # vs empty
        result = a & Series([])
        expected = Series([False, False, False], list('bca'))
        assert_series_equal(result, expected)

        result = a | Series([])
        expected = Series([True, False, True], list('bca'))
        assert_series_equal(result, expected)

        # vs non-matching
        result = a & Series([1], ['z'])
        expected = Series([False, False, False, False], list('abcz'))
        assert_series_equal(result, expected)

        result = a | Series([1], ['z'])
        expected = Series([True, True, False, False], list('abcz'))
        assert_series_equal(result, expected)

        # identity
        # we would like s[s|e] == s to hold for any e, whether empty or not
        for e in [Series([]), Series([1], ['z']),
                  Series(np.nan, b.index), Series(np.nan, a.index)]:
            result = a[a | e]
            assert_series_equal(result, a[a])

        for e in [Series(['z'])]:
            result = a[a | e]
            assert_series_equal(result, a[a])

        # vs scalars
        index = list('bca')
        t = Series([True, False, True])

        for v in [True, 1, 2]:
            result = Series([True, False, True], index=index) | v
            expected = Series([True, True, True], index=index)
            assert_series_equal(result, expected)

        for v in [np.nan, 'foo']:
            with pytest.raises(TypeError):
                t | v

        for v in [False, 0]:
            result = Series([True, False, True], index=index) | v
            expected = Series([True, False, True], index=index)
            assert_series_equal(result, expected)

        for v in [True, 1]:
            result = Series([True, False, True], index=index) & v
            expected = Series([True, False, True], index=index)
            assert_series_equal(result, expected)

        for v in [False, 0]:
            result = Series([True, False, True], index=index) & v
            expected = Series([False, False, False], index=index)
            assert_series_equal(result, expected)
        for v in [np.nan]:
            with pytest.raises(TypeError):
                t & v

    def test_logical_ops_df_compat(self):
        # GH#1134
        s1 = pd.Series([True, False, True], index=list('ABC'), name='x')
        s2 = pd.Series([True, True, False], index=list('ABD'), name='x')

        exp = pd.Series([True, False, False, False],
                        index=list('ABCD'), name='x')
        assert_series_equal(s1 & s2, exp)
        assert_series_equal(s2 & s1, exp)

        # True | np.nan => True
        exp = pd.Series([True, True, True, False],
                        index=list('ABCD'), name='x')
        assert_series_equal(s1 | s2, exp)
        # np.nan | True => np.nan, filled with False
        exp = pd.Series([True, True, False, False],
                        index=list('ABCD'), name='x')
        assert_series_equal(s2 | s1, exp)

        # DataFrame doesn't fill nan with False
        exp = pd.DataFrame({'x': [True, False, np.nan, np.nan]},
                           index=list('ABCD'))
        assert_frame_equal(s1.to_frame() & s2.to_frame(), exp)
        assert_frame_equal(s2.to_frame() & s1.to_frame(), exp)

        exp = pd.DataFrame({'x': [True, True, np.nan, np.nan]},
                           index=list('ABCD'))
        assert_frame_equal(s1.to_frame() | s2.to_frame(), exp)
        assert_frame_equal(s2.to_frame() | s1.to_frame(), exp)

        # different length
        s3 = pd.Series([True, False, True], index=list('ABC'), name='x')
        s4 = pd.Series([True, True, True, True], index=list('ABCD'), name='x')

        exp = pd.Series([True, False, True, False],
                        index=list('ABCD'), name='x')
        assert_series_equal(s3 & s4, exp)
        assert_series_equal(s4 & s3, exp)

        # np.nan | True => np.nan, filled with False
        exp = pd.Series([True, True, True, False],