import numpy as np
import pytest

from pandas import DataFrame

pytest.importorskip("jinja2")


def bar_grad(a=None, b=None, c=None, d=None):
    """Used in multiple tests to simplify formatting of expected result"""
    ret = [("width", "10em")]
    if all(x is None for x in [a, b, c, d]):
        return ret
    return ret + [
        (
            "background",
            f"linear-gradient(90deg,{','.join([x for x in [a, b, c, d] if x])})",
        )
    ]


def no_bar():
    return bar_grad()


def bar_to(x, color="#d65f5f"):
    return bar_grad(f" {color} {x:.1f}%", f" transparent {x:.1f}%")


def bar_from_to(x, y, color="#d65f5f"):
    return bar_grad(
        f" transparent {x:.1f}%",
        f" {color} {x:.1f}%",
        f" {color} {y:.1f}%",
        f" transparent {y:.1f}%",
    )


@pytest.fixture
def df_pos():
    return DataFrame([[1], [2], [3]])


@pytest.fixture
def df_neg():
    return DataFrame([[-1], [-2], [-3]])


@pytest.fixture
def df_mix():
    return DataFrame([[-3], [1], [2]])


@pytest.mark.parametrize(
    "align, exp",
    [
        ("left", [no_bar(), bar_to(50), bar_to(100)]),
        ("right", [bar_to(100), bar_from_to(50, 100), no_bar()]),
        ("mid", [bar_to(33.33), bar_to(66.66), bar_to(100)]),
        ("zero", [bar_from_to(50, 66.7), bar_from_to(50, 83.3), bar_from_to(50, 100)]),
        ("mean", [bar_to(50), no_bar(), bar_from_to(50, 100)]),
        (2.0, [bar_to(50), no_bar(), bar_from_to(50, 100)]),
        (np.median, [bar_to(50), no_bar(), bar_from_to(50, 100)]),
    ],
)
def test_align_positive_cases(df_pos, align, exp):
    # test different align cases for all positive values
    result = df_pos.style.bar(align=align)._compute().ctx
    expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]}
    assert result == expected


@pytest.mark.parametrize(
    "align, exp",
    [
        ("left", [bar_to(100), bar_to(50), no_bar()]),
        ("right", [no_bar(), bar_from_to(50, 100), bar_to(100)]),
        ("mid", [bar_from_to(66.66, 100), bar_from_to(33.33, 100), bar_to(100)]),
        ("zero", [bar_from_to(33.33, 50), bar_from_to(16.66, 50), bar_to(50)]),
        ("mean", [bar_from_to(50, 100), no_bar(), bar_to(50)]),
        (-2.0, [bar_from_to(50, 100), no_bar(), bar_to(50)]),
        (np.median, [bar_from_to(50, 100), no_bar(), bar_to(50)]),
    ],
)
def test_align_negative_cases(df_neg, align, exp):
    # test different align cases for all negative values
    result = df_neg.style.bar(align=align)._compute().ctx
    expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]}
    assert result == expected


@pytest.mark.parametrize(
    "align, exp",
    [
        ("left", [no_bar(), bar_to(80), bar_to(100)]),
        ("right", [bar_to(100), bar_from_to(80, 100), no_bar()]),
        ("mid", [bar_to(60), bar_from_to(60, 80), bar_from_to(60, 100)]),
        ("zero", [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]),
        ("mean", [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]),
        (-0.0, [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]),
        (np.nanmedian, [bar_to(50), no_bar(), bar_from_to(50, 62.5)]),
    ],
)
@pytest.mark.parametrize("nans", [True, False])
def test_align_mixed_cases(df_mix, align, exp, nans):
    # test different align cases for mixed positive and negative values
    # also test no impact of NaNs and no_bar
    expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]}
    if nans:
        df_mix.loc[3, :] = np.nan
        expected.update({(3, 0): no_bar()})
    result = df_mix.style.bar(align=align)._compute().ctx
    assert result == expected


@pytest.mark.parametrize(
    "align, exp",
    [
        (
            "left",
            {
                "index": [[no_bar(), no_bar()], [bar_to(100), bar_to(100)]],
                "columns": [[no_bar(), bar_to(100)], [no_bar(), bar_to(100)]],
                "none": [[no_bar(), bar_to(33.33)], [bar_to(66.66), bar_to(100)]],
            },
        ),
        (
            "mid",
            {
                "index": [[bar_to(33.33), bar_to(50)], [bar_to(100), bar_to(100)]],
                "columns": [[bar_to(50), bar_to(100)], [bar_to(75), bar_to(100)]],
                "none": [[bar_to(25), bar_to(50)], [bar_to(75), bar_to(100)]],
            },
        ),
        (
            "zero",
            {
                "index": [
                    [bar_from_to(50, 66.66), bar_from_to(50, 75)],
                    [bar_from_to(50, 100), bar_from_to(50, 100)],
                ],
                "columns": [
                    [bar_from_to(50, 75), bar_from_to(50, 100)],
                    [bar_from_to(50, 87.5), bar_from_to(50, 100)],
                ],
                "none": [
                    [bar_from_to(50, 62.5), bar_from_to(50, 75)],
                    [bar_from_to(50, 87.5), bar_from_to(50, 100)],
                ],
            },
        ),
        (
            2,
            {
                "index": [
                    [bar_to(50), no_bar()],
                    [bar_from_to(50, 100), bar_from_to(50, 100)],
                ],
                "columns": [
                    [bar_to(50), no_bar()],
                    [bar_from_to(50, 75), bar_from_to(50, 100)],
                ],
                "none": [
                    [bar_from_to(25, 50), no_bar()],
                    [bar_from_to(50, 75), bar_from_to(50, 100)],
                ],
            },
        ),
    ],
)
@pytest.mark.parametrize("axis", ["index", "columns", "none"])
def test_align_axis(align, exp, axis):
    # test all axis combinations with positive values and different aligns
    data = DataFrame([[1, 2], [3, 4]])
    result = (
        data.style.bar(align=align, axis=None if axis == "none" else axis)
        ._compute()
        .ctx
    )
    expected = {
        (0, 0): exp[axis][0][0],
        (0, 1): exp[axis][0][1],
        (1, 0): exp[axis][1][0],
        (1, 1): exp[axis][1][1],
    }
    assert result == expected


@pytest.mark.parametrize(
    "values, vmin, vmax",
    [
        ("positive", 1.5, 2.5),
        ("negative", -2.5, -1.5),
        ("mixed", -2.5, 1.5),
    ],
)
@pytest.mark.parametrize("nullify", [None, "vmin", "vmax"])  # test min/max separately
@pytest.mark.parametrize("align", ["left", "right", "zero", "mid"])
def test_vmin_vmax_clipping(df_pos, df_neg, df_mix, values, vmin, vmax, nullify, align):
    # test that clipping occurs if any vmin > data_values or vmax < data_values
    if align == "mid":  # mid acts as left or right in each case
        if values == "positive":
            align = "left"
        elif values == "negative":
            align = "right"
    df = {"positive": df_pos, "negative": df_neg, "mixed": df_mix}[values]
    vmin = None if nullify == "vmin" else vmin
    vmax = None if nullify == "vmax" else vmax

    clip_df = df.where(df <= (vmax if vmax else 999), other=vmax)
    clip_df = clip_df.where(clip_df >= (vmin if vmin else -999), other=vmin)

    result = (
        df.style.bar(align=align, vmin=vmin, vmax=vmax, color=["red", "green"])
        ._compute()
        .ctx
    )
    expected = clip_df.style.bar(align=align, color=["red", "green"])._compute().ctx
    assert result == expected


@pytest.mark.parametrize(
    "values, vmin, vmax",
    [
        ("positive", 0.5, 4.5),
        ("negative", -4.5, -0.5),
        ("mixed", -4.5, 4.5),
    ],
)
@pytest.mark.parametrize("nullify", [None, "vmin", "vmax"])  # test min/max separately
@pytest.mark.parametrize("align", ["left", "right", "zero", "mid"])
def test_vmin_vmax_widening(df_pos, df_neg, df_mix, values, vmin, vmax, nullify, align):
    # test that widening occurs if any vmax > data_values or vmin < data_values
    if align == "mid":  # mid acts as left or right in each case
        if values == "positive":
            align = "left"
        elif values == "negative":
            align = "right"
    df = {"positive": df_pos, "negative": df_neg, "mixed": df_mix}[values]
    vmin = None if nullify == "vmin" else vmin
    vmax = None if nullify == "vmax" else vmax

    expand_df = df.copy()
    expand_df.loc[3, :], expand_df.loc[4, :] = vmin, vmax

    result = (
        df.style.bar(align=align, vmin=vmin, vmax=vmax, color=["red", "green"])
        ._compute()
        .ctx
    )
    expected = expand_df.style.bar(align=align, color=["red", "green"])._compute().ctx
    assert result.items() <= expected.items()


def test_numerics():
    # test data is pre-selected for numeric values
    data = DataFrame([[1, "a"], [2, "b"]])
    result = data.style.bar()._compute().ctx
    assert (0, 1) not in result
    assert (1, 1) not in result


@pytest.mark.parametrize(
    "align, exp",
    [
        ("left", [no_bar(), bar_to(100, "green")]),
        ("right", [bar_to(100, "red"), no_bar()]),
        ("mid", [bar_to(25, "red"), bar_from_to(25, 100, "green")]),
        ("zero", [bar_from_to(33.33, 50, "red"), bar_from_to(50, 100, "green")]),
    ],
)
def test_colors_mixed(align, exp):
    data = DataFrame([[-1], [3]])
    result = data.style.bar(align=align, color=["red", "green"])._compute().ctx
    assert result == {(0, 0): exp[0], (1, 0): exp[1]}


def test_bar_align_height():
    # test when keyword height is used 'no-repeat center' and 'background-size' present
    data = DataFrame([[1], [2]])
    result = data.style.bar(align="left", height=50)._compute().ctx
    bg_s = "linear-gradient(90deg, #d65f5f 100.0%, transparent 100.0%) no-repeat center"
    expected = {
        (0, 0): [("width", "10em")],
        (1, 0): [
            ("width", "10em"),
            ("background", bg_s),
            ("background-size", "100% 50.0%"),
        ],
    }
    assert result == expected


def test_bar_value_error_raises():
    df = DataFrame({"A": [-100, -60, -30, -20]})

    msg = "`align` should be in {'left', 'right', 'mid', 'mean', 'zero'} or"
    with pytest.raises(ValueError, match=msg):
        df.style.bar(align="poorly", color=["#d65f5f", "#5fba7d"]).to_html()

    msg = r"`width` must be a value in \[0, 100\]"
    with pytest.raises(ValueError, match=msg):
        df.style.bar(width=200).to_html()

    msg = r"`height` must be a value in \[0, 100\]"
    with pytest.raises(ValueError, match=msg):
        df.style.bar(height=200).to_html()
