from datetime import datetime, timedelta

import numpy as np
from numpy.random import randn
import pytest

import pandas.util._test_decorators as td

from pandas import DataFrame, Series, bdate_range, notna


@pytest.fixture(params=[True, False])
def raw(request):
    return request.param


@pytest.fixture(
    params=[
        "triang",
        "blackman",
        "hamming",
        "bartlett",
        "bohman",
        "blackmanharris",
        "nuttall",
        "barthann",
    ]
)
def win_types(request):
    return request.param


@pytest.fixture(params=["kaiser", "gaussian", "general_gaussian", "exponential"])
def win_types_special(request):
    return request.param


@pytest.fixture(
    params=["sum", "mean", "median", "max", "min", "var", "std", "kurt", "skew"]
)
def arithmetic_win_operators(request):
    return request.param


@pytest.fixture(params=["right", "left", "both", "neither"])
def closed(request):
    return request.param


@pytest.fixture(params=[True, False])
def center(request):
    return request.param


@pytest.fixture(params=[None, 1])
def min_periods(request):
    return request.param


@pytest.fixture(params=[True, False])
def parallel(request):
    """parallel keyword argument for numba.jit"""
    return request.param


@pytest.fixture(params=[True, False])
def nogil(request):
    """nogil keyword argument for numba.jit"""
    return request.param


@pytest.fixture(params=[True, False])
def nopython(request):
    """nopython keyword argument for numba.jit"""
    return request.param


@pytest.fixture(
    params=[pytest.param("numba", marks=td.skip_if_no("numba", "0.46.0")), "cython"]
)
def engine(request):
    """engine keyword argument for rolling.apply"""
    return request.param


@pytest.fixture(
    params=[
        pytest.param(("numba", True), marks=td.skip_if_no("numba", "0.46.0")),
        ("cython", True),
        ("cython", False),
    ]
)
def engine_and_raw(request):
    """engine and raw keyword arguments for rolling.apply"""
    return request.param


# create the data only once as we are not setting it
def _create_consistency_data():
    def create_series():
        return [
            Series(dtype=object),
            Series([np.nan]),
            Series([np.nan, np.nan]),
            Series([3.0]),
            Series([np.nan, 3.0]),
            Series([3.0, np.nan]),
            Series([1.0, 3.0]),
            Series([2.0, 2.0]),
            Series([3.0, 1.0]),
            Series(
                [5.0, 5.0, 5.0, 5.0, np.nan, np.nan, np.nan, 5.0, 5.0, np.nan, np.nan]
            ),
            Series(
                [
                    np.nan,
                    5.0,
                    5.0,
                    5.0,
                    np.nan,
                    np.nan,
                    np.nan,
                    5.0,
                    5.0,
                    np.nan,
                    np.nan,
                ]
            ),
            Series(
                [
                    np.nan,
                    np.nan,
                    5.0,
                    5.0,
                    np.nan,
                    np.nan,
                    np.nan,
                    5.0,
                    5.0,
                    np.nan,
                    np.nan,
                ]
            ),
            Series(
                [
                    np.nan,
                    3.0,
                    np.nan,
                    3.0,
                    4.0,
                    5.0,
                    6.0,
                    np.nan,
                    np.nan,
                    7.0,
                    12.0,
                    13.0,
                    14.0,
                    15.0,
                ]
            ),
            Series(
                [
                    np.nan,
                    5.0,
                    np.nan,
                    2.0,
                    4.0,
                    0.0,
                    9.0,
                    np.nan,
                    np.nan,
                    3.0,
                    12.0,
                    13.0,
                    14.0,
                    15.0,
                ]
            ),
            Series(
                [
                    2.0,
                    3.0,
                    np.nan,
                    3.0,
                    4.0,
                    5.0,
                    6.0,
                    np.nan,
                    np.nan,
                    7.0,
                    12.0,
                    13.0,
                    14.0,
                    15.0,
                ]
            ),
            Series(
                [
                    2.0,
                    5.0,
                    np.nan,
                    2.0,
                    4.0,
                    0.0,
                    9.0,
                    np.nan,
                    np.nan,
                    3.0,
                    12.0,
                    13.0,
                    14.0,
                    15.0,
                ]
            ),
            Series(range(10)),
            Series(range(20, 0, -2)),
        ]

    def create_dataframes():
        return [
            DataFrame(),
            DataFrame(columns=["a"]),
            DataFrame(columns=["a", "a"]),
            DataFrame(columns=["a", "b"]),
            DataFrame(np.arange(10).reshape((5, 2))),
            DataFrame(np.arange(25).reshape((5, 5))),
            DataFrame(np.arange(25).reshape((5, 5)), columns=["a", "b", 99, "d", "d"]),
        ] + [DataFrame(s) for s in create_series()]

    def is_constant(x):
        values = x.values.ravel("K")
        return len(set(values[notna(values)])) == 1

    def no_nans(x):
        return x.notna().all().all()

    # data is a tuple(object, is_constant, no_nans)
    data = create_series() + create_dataframes()

    return [(x, is_constant(x), no_nans(x)) for x in data]


@pytest.fixture(params=_create_consistency_data())
def consistency_data(request):
    """Create consistency data"""
    return request.param


def _create_arr():
    """Internal function to mock an array."""
    arr = randn(100)
    locs = np.arange(20, 40)
    arr[locs] = np.NaN
    return arr


def _create_rng():
    """Internal function to mock date range."""
    rng = bdate_range(datetime(2009, 1, 1), periods=100)
    return rng


def _create_series():
    """Internal function to mock Series."""
    arr = _create_arr()
    series = Series(arr.copy(), index=_create_rng())
    return series


def _create_frame():
    """Internal function to mock DataFrame."""
    rng = _create_rng()
    return DataFrame(randn(100, 10), index=rng, columns=np.arange(10))


@pytest.fixture
def nan_locs():
    """Make a range as loc fixture."""
    return np.arange(20, 40)


@pytest.fixture
def arr():
    """Make an array as fixture."""
    return _create_arr()


@pytest.fixture
def frame():
    """Make mocked frame as fixture."""
    return _create_frame()


@pytest.fixture
def series():
    """Make mocked series as fixture."""
    return _create_series()


@pytest.fixture(params=[_create_series(), _create_frame()])
def which(request):
    """Turn parametrized which as fixture for series and frame"""
    return request.param


@pytest.fixture(params=["1 day", timedelta(days=1)])
def halflife_with_times(request):
    """Halflife argument for EWM when times is specified."""
    return request.param