from distutils.version import LooseVersion
from operator import methodcaller

import numpy as np
import pytest

import pandas.util._test_decorators as td

import pandas as pd
from pandas import MultiIndex, Series, date_range
import pandas._testing as tm

from .test_generic import Generic

try:
    import xarray

    _XARRAY_INSTALLED = True
except ImportError:
    _XARRAY_INSTALLED = False


class TestSeries(Generic):
    _typ = Series
    _comparator = lambda self, x, y: tm.assert_series_equal(x, y)

    def setup_method(self):
        self.ts = tm.makeTimeSeries()  # Was at top level in test_series
        self.ts.name = "ts"

        self.series = tm.makeStringSeries()
        self.series.name = "series"

    def test_rename_mi(self):
        s = Series(
            [11, 21, 31],
            index=MultiIndex.from_tuples([("A", x) for x in ["a", "B", "c"]]),
        )
        s.rename(str.lower)

    def test_set_axis_name(self):
        s = Series([1, 2, 3], index=["a", "b", "c"])
        funcs = ["rename_axis", "_set_axis_name"]
        name = "foo"
        for func in funcs:
            result = methodcaller(func, name)(s)
            assert s.index.name is None
            assert result.index.name == name

    def test_set_axis_name_mi(self):
        s = Series(
            [11, 21, 31],
            index=MultiIndex.from_tuples(
                [("A", x) for x in ["a", "B", "c"]], names=["l1", "l2"]
            ),
        )
        funcs = ["rename_axis", "_set_axis_name"]
        for func in funcs:
            result = methodcaller(func, ["L1", "L2"])(s)
            assert s.index.name is None
            assert s.index.names == ["l1", "l2"]
            assert result.index.name is None
            assert result.index.names, ["L1", "L2"]

    def test_set_axis_name_raises(self):
        s = pd.Series([1])
        with pytest.raises(ValueError):
            s._set_axis_name(name="a", axis=1)

    def test_get_numeric_data_preserve_dtype(self):

        # get the numeric data
        o = Series([1, 2, 3])
        result = o._get_numeric_data()
        self._compare(result, o)

        o = Series([1, "2", 3.0])
        result = o._get_numeric_data()
        expected = Series([], dtype=object, index=pd.Index([], dtype=object))
        self._compare(result, expected)

        o = Series([True, False, True])
        result = o._get_numeric_data()
        self._compare(result, o)

        o = Series([True, False, True])
        result = o._get_bool_data()
        self._compare(result, o)

        o = Series(date_range("20130101", periods=3))
        result = o._get_numeric_data()
        expected = Series([], dtype="M8[ns]", index=pd.Index([], dtype=object))
        self._compare(result, expected)

    def test_nonzero_single_element(self):

        # allow single item via bool method
        s = Series([True])
        assert s.bool()

        s = Series([False])
        assert not s.bool()

        msg = "The truth value of a Series is ambiguous"
        # single item nan to raise
        for s in [Series([np.nan]), Series([pd.NaT]), Series([True]), Series([False])]:
            with pytest.raises(ValueError, match=msg):
                bool(s)

        msg = "bool cannot act on a non-boolean single element Series"
        for s in [Series([np.nan]), Series([pd.NaT])]:
            with pytest.raises(ValueError, match=msg):
                s.bool()

        # multiple bool are still an error
        msg = "The truth value of a Series is ambiguous"
        for s in [Series([True, True]), Series([False, False])]:
            with pytest.raises(ValueError, match=msg):
                bool(s)
            with pytest.raises(ValueError, match=msg):
                s.bool()

        # single non-bool are an error
        for s in [Series([1]), Series([0]), Series(["a"]), Series([0.0])]:
            msg = "The truth value of a Series is ambiguous"
            with pytest.raises(ValueError, match=msg):
                bool(s)
            msg = "bool cannot act on a non-boolean single element Series"
            with pytest.raises(ValueError, match=msg):
                s.bool()

    def test_metadata_propagation_indiv(self):
        # check that the metadata matches up on the resulting ops

        o = Series(range(3), range(3))
        o.name = "foo"
        o2 = Series(range(3), range(3))
        o2.name = "bar"

        result = o.T
        self.check_metadata(o, result)

        # resample
        ts = Series(
            np.random.rand(1000),
            index=date_range("20130101", periods=1000, freq="s"),
            name="foo",
        )
        result = ts.resample("1T").mean()
        self.check_metadata(ts, result)

        result = ts.resample("1T").min()
        self.check_metadata(ts, result)

        result = ts.resample("1T").apply(lambda x: x.sum())
        self.check_metadata(ts, result)

        _metadata = Series._metadata
        _finalize = Series.__finalize__
        Series._metadata = ["name", "filename"]
        o.filename = "foo"
        o2.filename = "bar"

        def finalize(self, other, method=None, **kwargs):
            for name in self._metadata:
                if method == "concat" and name == "filename":
                    value = "+".join(
                        [getattr(o, name) for o in other.objs if getattr(o, name, None)]
                    )
                    object.__setattr__(self, name, value)
                else:
                    object.__setattr__(self, name, getattr(other, name, None))

            return self

        Series.__finalize__ = finalize

        result = pd.concat([o, o2])
        assert result.filename == "foo+bar"
        assert result.name is None

        # reset
        Series._metadata = _metadata
        Series.__finalize__ = _finalize

    @pytest.mark.skipif(
        not _XARRAY_INSTALLED
        or _XARRAY_INSTALLED
        and LooseVersion(xarray.__version__) < LooseVersion("0.10.0"),
        reason="xarray >= 0.10.0 required",
    )
    @pytest.mark.parametrize(
        "index",
        [
            "FloatIndex",
            "IntIndex",
            "StringIndex",
            "UnicodeIndex",
            "DateIndex",
            "PeriodIndex",
            "TimedeltaIndex",
            "CategoricalIndex",
        ],
    )
    def test_to_xarray_index_types(self, index):
        from xarray import DataArray

        index = getattr(tm, f"make{index}")
        s = Series(range(6), index=index(6))
        s.index.name = "foo"
        result = s.to_xarray()
        repr(result)
        assert len(result) == 6
        assert len(result.coords) == 1
        tm.assert_almost_equal(list(result.coords.keys()), ["foo"])
        assert isinstance(result, DataArray)

        # idempotency
        tm.assert_series_equal(
            result.to_series(), s, check_index_type=False, check_categorical=True
        )

    @td.skip_if_no("xarray", min_version="0.7.0")
    def test_to_xarray(self):
        from xarray import DataArray

        s = Series([], dtype=object)
        s.index.name = "foo"
        result = s.to_xarray()
        assert len(result) == 0
        assert len(result.coords) == 1
        tm.assert_almost_equal(list(result.coords.keys()), ["foo"])
        assert isinstance(result, DataArray)

        s = Series(range(6))
        s.index.name = "foo"
        s.index = pd.MultiIndex.from_product(
            [["a", "b"], range(3)], names=["one", "two"]
        )
        result = s.to_xarray()
        assert len(result) == 2
        tm.assert_almost_equal(list(result.coords.keys()), ["one", "two"])
        assert isinstance(result, DataArray)
        tm.assert_series_equal(result.to_series(), s)

    @pytest.mark.parametrize(
        "s",
        [
            Series([np.arange(5)]),
            pd.date_range("1/1/2011", periods=24, freq="H"),
            pd.Series(range(5), index=pd.date_range("2017", periods=5)),
        ],
    )
    @pytest.mark.parametrize("shift_size", [0, 1, 2])
    def test_shift_always_copy(self, s, shift_size):
        # GH22397
        assert s.shift(shift_size) is not s

    @pytest.mark.parametrize("move_by_freq", [pd.Timedelta("1D"), pd.Timedelta("1M")])
    def test_datetime_shift_always_copy(self, move_by_freq):
        # GH22397
        s = pd.Series(range(5), index=pd.date_range("2017", periods=5))
        assert s.shift(freq=move_by_freq) is not s
