from itertools import product
import warnings
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.colors import same_color

import pytest
from numpy.testing import assert_array_equal

from ..palettes import color_palette

from ..relational import (
    _RelationalPlotter,
    _LinePlotter,
    _ScatterPlotter,
    relplot,
    lineplot,
    scatterplot
)


@pytest.fixture(params=[
    dict(x="x", y="y"),
    dict(x="t", y="y"),
    dict(x="a", y="y"),
    dict(x="x", y="y", hue="y"),
    dict(x="x", y="y", hue="a"),
    dict(x="x", y="y", size="a"),
    dict(x="x", y="y", style="a"),
    dict(x="x", y="y", hue="s"),
    dict(x="x", y="y", size="s"),
    dict(x="x", y="y", style="s"),
    dict(x="x", y="y", hue="a", style="a"),
    dict(x="x", y="y", hue="a", size="b", style="b"),
])
def long_semantics(request):
    return request.param


class Helpers:

    # TODO Better place for these?

    def scatter_rgbs(self, collections):
        rgbs = []
        for col in collections:
            rgb = tuple(col.get_facecolor().squeeze()[:3])
            rgbs.append(rgb)
        return rgbs

    def paths_equal(self, *args):

        equal = all([len(a) == len(args[0]) for a in args])

        for p1, p2 in zip(*args):
            equal &= np.array_equal(p1.vertices, p2.vertices)
            equal &= np.array_equal(p1.codes, p2.codes)
        return equal


class TestRelationalPlotter(Helpers):

    def test_wide_df_variables(self, wide_df):

        p = _RelationalPlotter()
        p.assign_variables(data=wide_df)
        assert p.input_format == "wide"
        assert list(p.variables) == ["x", "y", "hue", "style"]
        assert len(p.plot_data) == np.product(wide_df.shape)

        x = p.plot_data["x"]
        expected_x = np.tile(wide_df.index, wide_df.shape[1])
        assert_array_equal(x, expected_x)

        y = p.plot_data["y"]
        expected_y = wide_df.values.ravel(order="f")
        assert_array_equal(y, expected_y)

        hue = p.plot_data["hue"]
        expected_hue = np.repeat(wide_df.columns.values, wide_df.shape[0])
        assert_array_equal(hue, expected_hue)

        style = p.plot_data["style"]
        expected_style = expected_hue
        assert_array_equal(style, expected_style)

        assert p.variables["x"] == wide_df.index.name
        assert p.variables["y"] is None
        assert p.variables["hue"] == wide_df.columns.name
        assert p.variables["style"] == wide_df.columns.name

    def test_wide_df_with_nonnumeric_variables(self, long_df):

        p = _RelationalPlotter()
        p.assign_variables(data=long_df)
        assert p.input_format == "wide"
        assert list(p.variables) == ["x", "y", "hue", "style"]

        numeric_df = long_df.select_dtypes("number")

        assert len(p.plot_data) == np.product(numeric_df.shape)

        x = p.plot_data["x"]
        expected_x = np.tile(numeric_df.index, numeric_df.shape[1])
        assert_array_equal(x, expected_x)

        y = p.plot_data["y"]
        expected_y = numeric_df.values.ravel(order="f")
        assert_array_equal(y, expected_y)

        hue = p.plot_data["hue"]
        expected_hue = np.repeat(
            numeric_df.columns.values, numeric_df.shape[0]
        )
        assert_array_equal(hue, expected_hue)

        style = p.plot_data["style"]
        expected_style = expected_hue
        assert_array_equal(style, expected_style)

        assert p.variables["x"] == numeric_df.index.name
        assert p.variables["y"] is None
        assert p.variables["hue"] == numeric_df.columns.name
        assert p.variables["style"] == numeric_df.columns.name

    def test_wide_array_variables(self, wide_array):

        p = _RelationalPlotter()
        p.assign_variables(data=wide_array)
        assert p.input_format == "wide"
        assert list(p.variables) == ["x", "y", "hue", "style"]
        assert len(p.plot_data) == np.product(wide_array.shape)

        nrow, ncol = wide_array.shape

        x = p.plot_data["x"]
        expected_x = np.tile(np.arange(nrow), ncol)
        assert_array_equal(x, expected_x)

        y = p.plot_data["y"]
        expected_y = wide_array.ravel(order="f")
        assert_array_equal(y, expected_y)

        hue = p.plot_data["hue"]
        expected_hue = np.repeat(np.arange(ncol), nrow)
        assert_array_equal(hue, expected_hue)

        style = p.plot_data["style"]
        expected_style = expected_hue
        assert_array_equal(style, expected_style)

        assert p.variables["x"] is None
        assert p.variables["y"] is None
        assert p.variables["hue"] is None
        assert p.variables["style"] is None

    def test_flat_array_variables(self, flat_array):

        p = _RelationalPlotter()
        p.assign_variables(data=flat_array)
        assert p.input_format == "wide"
        assert list(p.variables) == ["x", "y"]
        assert len(p.plot_data) == np.product(flat_array.shape)

        x = p.plot_data["x"]
        expected_x = np.arange(flat_array.shape[0])
        assert_array_equal(x, expected_x)

        y = p.plot_data["y"]
        expected_y = flat_array
        assert_array_equal(y, expected_y)

        assert p.variables["x"] is None
        assert p.variables["y"] is None

    def test_flat_list_variables(self, flat_list):

        p = _RelationalPlotter()
        p.assign_variables(data=flat_list)
        assert p.input_format == "wide"
        assert list(p.variables) == ["x", "y"]
        assert len(p.plot_data) == len(flat_list)

        x = p.plot_data["x"]
        expected_x = np.arange(len(flat_list))
        assert_array_equal(x, expected_x)

        y = p.plot_data["y"]
        expected_y = flat_list
        assert_array_equal(y, expected_y)

        assert p.variables["x"] is None
        assert p.variables["y"] is None

    def test_flat_series_variables(self, flat_series):

        p = _RelationalPlotter()
        p.assign_variables(data=flat_series)
        assert p.input_format == "wide"
        assert list(p.variables) == ["x", "y"]
        assert len(p.plot_data) == len(flat_series)

        x = p.plot_data["x"]
        expected_x = flat_series.index
        assert_array_equal(x, expected_x)

        y = p.plot_data["y"]
        expected_y = flat_series
        assert_array_equal(y, expected_y)

        assert p.variables["x"] is flat_series.index.name
        assert p.variables["y"] is flat_series.name

    def test_wide_list_of_series_variables(self, wide_list_of_series):

        p = _RelationalPlotter()
        p.assign_variables(data=wide_list_of_series)
        assert p.input_format == "wide"
        assert list(p.variables) == ["x", "y", "hue", "style"]

        chunks = len(wide_list_of_series)
        chunk_size = max(len(l) for l in wide_list_of_series)

        assert len(p.plot_data) == chunks * chunk_size

        index_union = np.unique(
            np.concatenate([s.index for s in wide_list_of_series])
        )

        x = p.plot_data["x"]
        expected_x = np.tile(index_union, chunks)
        assert_array_equal(x, expected_x)

        y = p.plot_data["y"]
        expected_y = np.concatenate([
            s.reindex(index_union) for s in wide_list_of_series
        ])
        assert_array_equal(y, expected_y)

        hue = p.plot_data["hue"]
        series_names = [s.name for s in wide_list_of_series]
        expected_hue = np.repeat(series_names, chunk_size)
        assert_array_equal(hue, expected_hue)

        style = p.plot_data["style"]
        expected_style = expected_hue
        assert_array_equal(style, expected_style)

        assert p.variables["x"] is None
        assert p.variables["y"] is None
        assert p.variables["hue"] is None
        assert p.variables["style"] is None

    def test_wide_list_of_arrays_variables(self, wide_list_of_arrays):

        p = _RelationalPlotter()
        p.assign_variables(data=wide_list_of_arrays)
        assert p.input_format == "wide"
        assert list(p.variables) == ["x", "y", "hue", "style"]

        chunks = len(wide_list_of_arrays)
        chunk_size = max(len(l) for l in wide_list_of_arrays)

        assert len(p.plot_data) == chunks * chunk_size

        x = p.plot_data["x"]
        expected_x = np.tile(np.arange(chunk_size), chunks)
        assert_array_equal(x, expected_x)

        y = p.plot_data["y"].dropna()
        expected_y = np.concatenate(wide_list_of_arrays)
        assert_array_equal(y, expected_y)

        hue = p.plot_data["hue"]
        expected_hue = np.repeat(np.arange(chunks), chunk_size)
        assert_array_equal(hue, expected_hue)

        style = p.plot_data["style"]
        expected_style = expected_hue
        assert_array_equal(style, expected_style)

        assert p.variables["x"] is None
        assert p.variables["y"] is None
        assert p.variables["hue"] is None
        assert p.variables["style"] is None

    def test_wide_list_of_list_variables(self, wide_list_of_lists):

        p = _RelationalPlotter()
        p.assign_variables(data=wide_list_of_lists)
        assert p.input_format == "wide"
        assert list(p.variables) == ["x", "y", "hue", "style"]

        chunks = len(wide_list_of_lists)
        chunk_size = max(len(l) for l in wide_list_of_lists)

        assert len(p.plot_data) == chunks * chunk_size

        x = p.plot_data["x"]
        expected_x = np.tile(np.arange(chunk_size), chunks)
        assert_array_equal(x, expected_x)

        y = p.plot_data["y"].dropna()
        expected_y = np.concatenate(wide_list_of_lists)
        assert_array_equal(y, expected_y)

        hue = p.plot_data["hue"]
        expected_hue = np.repeat(np.arange(chunks), chunk_size)
        assert_array_equal(hue, expected_hue)

        style = p.plot_data["style"]
        expected_style = expected_hue
        assert_array_equal(style, expected_style)

        assert p.variables["x"] is None
        assert p.variables["y"] is None
        assert p.variables["hue"] is None
        assert p.variables["style"] is None

    def test_wide_dict_of_series_variables(self, wide_dict_of_series):

        p = _RelationalPlotter()
        p.assign_variables(data=wide_dict_of_series)
        assert p.input_format == "wide"
        assert list(p.variables) == ["x", "y", "hue", "style"]

        chunks = len(wide_dict_of_series)
        chunk_size = max(len(l) for l in wide_dict_of_series.values())

        assert len(p.plot_data) == chunks * chunk_size

        x = p.plot_data["x"]
        expected_x = np.tile(np.arange(chunk_size), chunks)
        assert_array_equal(x, expected_x)

        y = p.plot_data["y"].dropna()
        expected_y = np.concatenate(list(wide_dict_of_series.values()))
        assert_array_equal(y, expected_y)

        hue = p.plot_data["hue"]
        expected_hue = np.repeat(list(wide_dict_of_series), chunk_size)
        assert_array_equal(hue, expected_hue)

        style = p.plot_data["style"]
        expected_style = expected_hue
        assert_array_equal(style, expected_style)

        assert p.variables["x"] is None
        assert p.variables["y"] is None
        assert p.variables["hue"] is None
        assert p.variables["style"] is None

    def test_wide_dict_of_arrays_variables(self, wide_dict_of_arrays):

        p = _RelationalPlotter()
        p.assign_variables(data=wide_dict_of_arrays)
        assert p.input_format == "wide"
        assert list(p.variables) == ["x", "y", "hue", "style"]

        chunks = len(wide_dict_of_arrays)
        chunk_size = max(len(l) for l in wide_dict_of_arrays.values())

        assert len(p.plot_data) == chunks * chunk_size

        x = p.plot_data["x"]
        expected_x = np.tile(np.arange(chunk_size), chunks)
        assert_array_equal(x, expected_x)

        y = p.plot_data["y"].dropna()
        expected_y = np.concatenate(list(wide_dict_of_arrays.values()))
        assert_array_equal(y, expected_y)

        hue = p.plot_data["hue"]
        expected_hue = np.repeat(list(wide_dict_of_arrays), chunk_size)
        assert_array_equal(hue, expected_hue)

        style = p.plot_data["style"]
        expected_style = expected_hue
        assert_array_equal(style, expected_style)

        assert p.variables["x"] is None
        assert p.variables["y"] is None
        assert p.variables["hue"] is None
        assert p.variables["style"] is None

    def test_wide_dict_of_lists_variables(self, wide_dict_of_lists):

        p = _RelationalPlotter()
        p.assign_variables(data=wide_dict_of_lists)
        assert p.input_format == "wide"
        assert list(p.variables) == ["x", "y", "hue", "style"]

        chunks = len(wide_dict_of_lists)
        chunk_size = max(len(l) for l in wide_dict_of_lists.values())

        assert len(p.plot_data) == chunks * chunk_size

        x = p.plot_data["x"]
        expected_x = np.tile(np.arange(chunk_size), chunks)
        assert_array_equal(x, expected_x)

        y = p.plot_data["y"].dropna()
        expected_y = np.concatenate(list(wide_dict_of_lists.values()))
        assert_array_equal(y, expected_y)

        hue = p.plot_data["hue"]
        expected_hue = np.repeat(list(wide_dict_of_lists), chunk_size)
        assert_array_equal(hue, expected_hue)

        style = p.plot_data["style"]
        expected_style = expected_hue
        assert_array_equal(style, expected_style)

        assert p.variables["x"] is None
        assert p.variables["y"] is None
        assert p.variables["hue"] is None
        assert p.variables["style"] is None

    def test_long_df(self, long_df, long_semantics):

        p = _RelationalPlotter(data=long_df, variables=long_semantics)
        assert p.input_format == "long"
        assert p.variables == long_semantics

        for key, val in long_semantics.items():
            assert_array_equal(p.plot_data[key], long_df[val])

    def test_long_df_with_index(self, long_df, long_semantics):

        p = _RelationalPlotter(
            data=long_df.set_index("a"),
            variables=long_semantics,
        )
        assert p.input_format == "long"
        assert p.variables == long_semantics

        for key, val in long_semantics.items():
            assert_array_equal(p.plot_data[key], long_df[val])

    def test_long_df_with_multiindex(self, long_df, long_semantics):

        p = _RelationalPlotter(
            data=long_df.set_index(["a", "x"]),
            variables=long_semantics,
        )
        assert p.input_format == "long"
        assert p.variables == long_semantics

        for key, val in long_semantics.items():
            assert_array_equal(p.plot_data[key], long_df[val])

    def test_long_dict(self, long_dict, long_semantics):

        p = _RelationalPlotter(
            data=long_dict,
            variables=long_semantics,
        )
        assert p.input_format == "long"
        assert p.variables == long_semantics

        for key, val in long_semantics.items():
            assert_array_equal(p.plot_data[key], pd.Series(long_dict[val]))

    @pytest.mark.parametrize(
        "vector_type",
        ["series", "numpy", "list"],
    )
    def test_long_vectors(self, long_df, long_semantics, vector_type):

        variables = {key: long_df[val] for key, val in long_semantics.items()}
        if vector_type == "numpy":
            # Requires pandas >= 0.24
            # {key: val.to_numpy() for key, val in variables.items()}
            variables = {
                key: np.asarray(val) for key, val in variables.items()
            }
        elif vector_type == "list":
            # Requires pandas >= 0.24
            # {key: val.to_list() for key, val in variables.items()}
            variables = {
                key: val.tolist() for key, val in variables.items()
            }

        p = _RelationalPlotter(variables=variables)
        assert p.input_format == "long"

        assert list(p.variables) == list(long_semantics)
        if vector_type == "series":
            assert p.variables == long_semantics

        for key, val in long_semantics.items():
            assert_array_equal(p.plot_data[key], long_df[val])

    def test_long_undefined_variables(self, long_df):

        p = _RelationalPlotter()

        with pytest.raises(ValueError):
            p.assign_variables(
                data=long_df, variables=dict(x="not_in_df"),
            )

        with pytest.raises(ValueError):
            p.assign_variables(
                data=long_df, variables=dict(x="x", y="not_in_df"),
            )

        with pytest.raises(ValueError):
            p.assign_variables(
                data=long_df, variables=dict(x="x", y="y", hue="not_in_df"),
            )

    @pytest.mark.parametrize(
        "arg", [[], np.array([]), pd.DataFrame()],
    )
    def test_empty_data_input(self, arg):

        p = _RelationalPlotter(data=arg)
        assert not p.variables

        if not isinstance(arg, pd.DataFrame):
            p = _RelationalPlotter(variables=dict(x=arg, y=arg))
            assert not p.variables

    def test_units(self, repeated_df):

        p = _RelationalPlotter(
            data=repeated_df,
            variables=dict(x="x", y="y", units="u"),
        )
        assert_array_equal(p.plot_data["units"], repeated_df["u"])

    def test_relplot_simple(self, long_df):

        g = relplot(data=long_df, x="x", y="y", kind="scatter")
        x, y = g.ax.collections[0].get_offsets().T
        assert_array_equal(x, long_df["x"])
        assert_array_equal(y, long_df["y"])

        g = relplot(data=long_df, x="x", y="y", kind="line")
        x, y = g.ax.lines[0].get_xydata().T
        expected = long_df.groupby("x").y.mean()
        assert_array_equal(x, expected.index)
        assert y == pytest.approx(expected.values)

        with pytest.raises(ValueError):
            g = relplot(data=long_df, x="x", y="y", kind="not_a_kind")

    def test_relplot_complex(self, long_df):

        for sem in ["hue", "size", "style"]:
            g = relplot(data=long_df, x="x", y="y", **{sem: "a"})
            x, y = g.ax.collections[0].get_offsets().T
            assert_array_equal(x, long_df["x"])
            assert_array_equal(y, long_df["y"])

        for sem in ["hue", "size", "style"]:
            g = relplot(
                data=long_df, x="x", y="y", col="c", **{sem: "a"}
            )
            grouped = long_df.groupby("c")
            for (_, grp_df), ax in zip(grouped, g.axes.flat):
                x, y = ax.collections[0].get_offsets().T
                assert_array_equal(x, grp_df["x"])
                assert_array_equal(y, grp_df["y"])

        for sem in ["size", "style"]:
            g = relplot(
                data=long_df, x="x", y="y", hue="b", col="c", **{sem: "a"}
            )
            grouped = long_df.groupby("c")
            for (_, grp_df), ax in zip(grouped, g.axes.flat):
                x, y = ax.collections[0].get_offsets().T
                assert_array_equal(x, grp_df["x"])
                assert_array_equal(y, grp_df["y"])

        for sem in ["hue", "size", "style"]:
            g = relplot(
                data=long_df.sort_values(["c", "b"]),
                x="x", y="y", col="b", row="c", **{sem: "a"}
            )
            grouped = long_df.groupby(["c", "b"])
            for (_, grp_df), ax in zip(grouped, g.axes.flat):
                x, y = ax.collections[0].get_offsets().T
                assert_array_equal(x, grp_df["x"])
                assert_array_equal(y, grp_df["y"])

    @pytest.mark.parametrize(
        "vector_type",
        ["series", "numpy", "list"],
    )
    def test_relplot_vectors(self, long_df, vector_type):

        semantics = dict(x="x", y="y", hue="f", col="c")
        kws = {key: long_df[val] for key, val in semantics.items()}
        g = relplot(data=long_df, **kws)
        grouped = long_df.groupby("c")
        for (_, grp_df), ax in zip(grouped, g.axes.flat):
            x, y = ax.collections[0].get_offsets().T
            assert_array_equal(x, grp_df["x"])
            assert_array_equal(y, grp_df["y"])

    def test_relplot_wide(self, wide_df):

        g = relplot(data=wide_df)
        x, y = g.ax.collections[0].get_offsets().T
        assert_array_equal(y, wide_df.values.T.ravel())

    def test_relplot_hues(self, long_df):

        palette = ["r", "b", "g"]
        g = relplot(
            x="x", y="y", hue="a", style="b", col="c",
            palette=palette, data=long_df
        )

        palette = dict(zip(long_df["a"].unique(), palette))
        grouped = long_df.groupby("c")
        for (_, grp_df), ax in zip(grouped, g.axes.flat):
            points = ax.collections[0]
            expected_hues = [palette[val] for val in grp_df["a"]]
            assert same_color(points.get_facecolors(), expected_hues)

    def test_relplot_sizes(self, long_df):

        sizes = [5, 12, 7]
        g = relplot(
            data=long_df,
            x="x", y="y", size="a", hue="b", col="c",
            sizes=sizes,
        )

        sizes = dict(zip(long_df["a"].unique(), sizes))
        grouped = long_df.groupby("c")
        for (_, grp_df), ax in zip(grouped, g.axes.flat):
            points = ax.collections[0]
            expected_sizes = [sizes[val] for val in grp_df["a"]]
            assert_array_equal(points.get_sizes(), expected_sizes)

    def test_relplot_styles(self, long_df):

        markers = ["o", "d", "s"]
        g = relplot(
            data=long_df,
            x="x", y="y", style="a", hue="b", col="c",
            markers=markers,
        )

        paths = []
        for m in markers:
            m = mpl.markers.MarkerStyle(m)
            paths.append(m.get_path().transformed(m.get_transform()))
        paths = dict(zip(long_df["a"].unique(), paths))

        grouped = long_df.groupby("c")
        for (_, grp_df), ax in zip(grouped, g.axes.flat):
            points = ax.collections[0]
            expected_paths = [paths[val] for val in grp_df["a"]]
            assert self.paths_equal(points.get_paths(), expected_paths)

    def test_relplot_stringy_numerics(self, long_df):

        long_df["x_str"] = long_df["x"].astype(str)

        g = relplot(data=long_df, x="x", y="y", hue="x_str")
        points = g.ax.collections[0]
        xys = points.get_offsets()
        mask = np.ma.getmask(xys)
        assert not mask.any()
        assert_array_equal(xys, long_df[["x", "y"]])

        g = relplot(data=long_df, x="x", y="y", size="x_str")
        points = g.ax.collections[0]
        xys = points.get_offsets()
        mask = np.ma.getmask(xys)
        assert not mask.any()
        assert_array_equal(xys, long_df[["x", "y"]])

    def test_relplot_legend(self, long_df):

        g = relplot(data=long_df, x="x", y="y")
        assert g._legend is None

        g = relplot(data=long_df, x="x", y="y", hue="a")
        texts = [t.get_text() for t in g._legend.texts]
        expected_texts = long_df["a"].unique()
        assert_array_equal(texts, expected_texts)

        g = relplot(data=long_df, x="x", y="y", hue="s", size="s")
        texts = [t.get_text() for t in g._legend.texts]
        assert_array_equal(texts, np.sort(texts))

        g = relplot(data=long_df, x="x", y="y", hue="a", legend=False)
        assert g._legend is None

        palette = color_palette("deep", len(long_df["b"].unique()))
        a_like_b = dict(zip(long_df["a"].unique(), long_df["b"].unique()))
        long_df["a_like_b"] = long_df["a"].map(a_like_b)
        g = relplot(
            data=long_df,
            x="x", y="y", hue="b", style="a_like_b",
            palette=palette, kind="line", estimator=None,
        )
        lines = g._legend.get_lines()[1:]  # Chop off title dummy
        for line, color in zip(lines, palette):
            assert line.get_color() == color

    def test_ax_kwarg_removal(self, long_df):

        f, ax = plt.subplots()
        with pytest.warns(UserWarning):
            g = relplot(data=long_df, x="x", y="y", ax=ax)
        assert len(ax.collections) == 0
        assert len(g.ax.collections) > 0


class TestLinePlotter(Helpers):

    def test_aggregate(self, long_df):

        p = _LinePlotter(data=long_df, variables=dict(x="x", y="y"))
        p.n_boot = 10000
        p.sort = False

        x = pd.Series(np.tile([1, 2], 100))
        y = pd.Series(np.random.randn(200))
        y_mean = y.groupby(x).mean()

        def sem(x):
            return np.std(x) / np.sqrt(len(x))

        y_sem = y.groupby(x).apply(sem)
        y_cis = pd.DataFrame(dict(low=y_mean - y_sem,
                                  high=y_mean + y_sem),
                             columns=["low", "high"])

        p.ci = 68
        p.estimator = "mean"
        index, est, cis = p.aggregate(y, x)
        assert_array_equal(index.values, x.unique())
        assert est.index.equals(index)
        assert est.values == pytest.approx(y_mean.values)
        assert cis.values == pytest.approx(y_cis.values, 4)
        assert list(cis.columns) == ["low", "high"]

        p.estimator = np.mean
        index, est, cis = p.aggregate(y, x)
        assert_array_equal(index.values, x.unique())
        assert est.index.equals(index)
        assert est.values == pytest.approx(y_mean.values)
        assert cis.values == pytest.approx(y_cis.values, 4)
        assert list(cis.columns) == ["low", "high"]

        p.seed = 0
        _, _, ci1 = p.aggregate(y, x)
        _, _, ci2 = p.aggregate(y, x)
        assert_array_equal(ci1, ci2)

        y_std = y.groupby(x).std()
        y_cis = pd.DataFrame(dict(low=y_mean - y_std,
                                  high=y_mean + y_std),
                             columns=["low", "high"])

        p.ci = "sd"
        index, est, cis = p.aggregate(y, x)
        assert_array_equal(index.values, x.unique())
        assert est.index.equals(index)
        assert est.values == pytest.approx(y_mean.values)
        assert cis.values == pytest.approx(y_cis.values)
        assert list(cis.columns) == ["low", "high"]

        p.ci = None
        index, est, cis = p.aggregate(y, x)
        assert cis is None

        p.ci = 68
        x, y = pd.Series([1, 2, 3]), pd.Series([4, 3, 2])
        index, est, cis = p.aggregate(y, x)
        assert_array_equal(index.values, x)
        assert_array_equal(est.values, y)
        assert cis is None

        x, y = pd.Series([1, 1, 2]), pd.Series([2, 3, 4])
        index, est, cis = p.aggregate(y, x)
        assert cis.loc[2].isnull().all()

        p = _LinePlotter(data=long_df, variables=dict(x="x", y="y"))
        p.estimator = "mean"
        p.n_boot = 100
        p.ci = 95
        x = pd.Categorical(["a", "b", "a", "b"], ["a", "b", "c"])
        y = pd.Series([1, 1, 2, 2])
        with warnings.catch_warnings():
            warnings.simplefilter("error", RuntimeWarning)
            index, est, cis = p.aggregate(y, x)
            assert cis.loc[["c"]].isnull().all().all()

    def test_legend_data(self, long_df):

        f, ax = plt.subplots()

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y"),
            legend="full"
        )
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert handles == []

        # --

        ax.clear()
        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a"),
            legend="full",
        )
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        colors = [h.get_color() for h in handles]
        assert labels == p._hue_map.levels
        assert colors == p._hue_map(p._hue_map.levels)

        # --

        ax.clear()
        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a", style="a"),
            legend="full",
        )
        p.map_style(markers=True)
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        colors = [h.get_color() for h in handles]
        markers = [h.get_marker() for h in handles]
        assert labels == p._hue_map.levels
        assert labels == p._style_map.levels
        assert colors == p._hue_map(p._hue_map.levels)
        assert markers == p._style_map(p._style_map.levels, "marker")

        # --

        ax.clear()
        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a", style="b"),
            legend="full",
        )
        p.map_style(markers=True)
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        colors = [h.get_color() for h in handles]
        markers = [h.get_marker() for h in handles]
        expected_labels = (
            ["a"]
            + p._hue_map.levels
            + ["b"] + p._style_map.levels
        )
        expected_colors = (
            ["w"] + p._hue_map(p._hue_map.levels)
            + ["w"] + [".2" for _ in p._style_map.levels]
        )
        expected_markers = (
            [""] + ["None" for _ in p._hue_map.levels]
            + [""] + p._style_map(p._style_map.levels, "marker")
        )
        assert labels == expected_labels
        assert colors == expected_colors
        assert markers == expected_markers

        # --

        ax.clear()
        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a", size="a"),
            legend="full"
        )
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        colors = [h.get_color() for h in handles]
        widths = [h.get_linewidth() for h in handles]
        assert labels == p._hue_map.levels
        assert labels == p._size_map.levels
        assert colors == p._hue_map(p._hue_map.levels)
        assert widths == p._size_map(p._size_map.levels)

        # --

        x, y = np.random.randn(2, 40)
        z = np.tile(np.arange(20), 2)

        p = _LinePlotter(variables=dict(x=x, y=y, hue=z))

        ax.clear()
        p.legend = "full"
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert labels == [str(l) for l in p._hue_map.levels]

        ax.clear()
        p.legend = "brief"
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert len(labels) < len(p._hue_map.levels)

        p = _LinePlotter(variables=dict(x=x, y=y, size=z))

        ax.clear()
        p.legend = "full"
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert labels == [str(l) for l in p._size_map.levels]

        ax.clear()
        p.legend = "brief"
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert len(labels) < len(p._size_map.levels)

        ax.clear()
        p.legend = "auto"
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert len(labels) < len(p._size_map.levels)

        ax.clear()
        p.legend = True
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert len(labels) < len(p._size_map.levels)

        ax.clear()
        p.legend = "bad_value"
        with pytest.raises(ValueError):
            p.add_legend_data(ax)

        ax.clear()
        p = _LinePlotter(
            variables=dict(x=x, y=y, hue=z + 1),
            legend="brief"
        )
        p.map_hue(norm=mpl.colors.LogNorm()),
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert float(labels[1]) / float(labels[0]) == 10

        ax.clear()
        p = _LinePlotter(
            variables=dict(x=x, y=y, hue=z % 2),
            legend="auto"
        )
        p.map_hue(norm=mpl.colors.LogNorm()),
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert labels == ["0", "1"]

        ax.clear()
        p = _LinePlotter(
            variables=dict(x=x, y=y, size=z + 1),
            legend="brief"
        )
        p.map_size(norm=mpl.colors.LogNorm())
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert float(labels[1]) / float(labels[0]) == 10

        ax.clear()
        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="f"),
            legend="brief",
        )
        p.add_legend_data(ax)
        expected_labels = ['0.20', '0.22', '0.24', '0.26', '0.28']
        handles, labels = ax.get_legend_handles_labels()
        assert labels == expected_labels

        ax.clear()
        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", size="f"),
            legend="brief",
        )
        p.add_legend_data(ax)
        expected_levels = ['0.20', '0.22', '0.24', '0.26', '0.28']
        handles, labels = ax.get_legend_handles_labels()
        assert labels == expected_levels

    def test_plot(self, long_df, repeated_df):

        f, ax = plt.subplots()

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y"),
            sort=False,
            estimator=None
        )
        p.plot(ax, {})
        line, = ax.lines
        assert_array_equal(line.get_xdata(), long_df.x.values)
        assert_array_equal(line.get_ydata(), long_df.y.values)

        ax.clear()
        p.plot(ax, {"color": "k", "label": "test"})
        line, = ax.lines
        assert line.get_color() == "k"
        assert line.get_label() == "test"

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y"),
            sort=True, estimator=None
        )

        ax.clear()
        p.plot(ax, {})
        line, = ax.lines
        sorted_data = long_df.sort_values(["x", "y"])
        assert_array_equal(line.get_xdata(), sorted_data.x.values)
        assert_array_equal(line.get_ydata(), sorted_data.y.values)

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a"),
        )

        ax.clear()
        p.plot(ax, {})
        assert len(ax.lines) == len(p._hue_map.levels)
        for line, level in zip(ax.lines, p._hue_map.levels):
            assert line.get_color() == p._hue_map(level)

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", size="a"),
        )

        ax.clear()
        p.plot(ax, {})
        assert len(ax.lines) == len(p._size_map.levels)
        for line, level in zip(ax.lines, p._size_map.levels):
            assert line.get_linewidth() == p._size_map(level)

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a", style="a"),
        )
        p.map_style(markers=True)

        ax.clear()
        p.plot(ax, {})
        assert len(ax.lines) == len(p._hue_map.levels)
        assert len(ax.lines) == len(p._style_map.levels)
        for line, level in zip(ax.lines, p._hue_map.levels):
            assert line.get_color() == p._hue_map(level)
            assert line.get_marker() == p._style_map(level, "marker")

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a", style="b"),
        )
        p.map_style(markers=True)

        ax.clear()
        p.plot(ax, {})
        levels = product(p._hue_map.levels, p._style_map.levels)
        expected_line_count = len(p._hue_map.levels) * len(p._style_map.levels)
        assert len(ax.lines) == expected_line_count
        for line, (hue, style) in zip(ax.lines, levels):
            assert line.get_color() == p._hue_map(hue)
            assert line.get_marker() == p._style_map(style, "marker")

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y"),
            estimator="mean", err_style="band", ci="sd", sort=True
        )

        ax.clear()
        p.plot(ax, {})
        line, = ax.lines
        expected_data = long_df.groupby("x").y.mean()
        assert_array_equal(line.get_xdata(), expected_data.index.values)
        assert np.allclose(line.get_ydata(), expected_data.values)
        assert len(ax.collections) == 1

        # Test that nans do not propagate to means or CIs

        p = _LinePlotter(
            variables=dict(
                x=[1, 1, 1, 2, 2, 2, 3, 3, 3],
                y=[1, 2, 3, 3, np.nan, 5, 4, 5, 6],
            ),
            estimator="mean", err_style="band", ci=95, n_boot=100, sort=True,
        )
        ax.clear()
        p.plot(ax, {})
        line, = ax.lines
        assert line.get_xdata().tolist() == [1, 2, 3]
        err_band = ax.collections[0].get_paths()
        assert len(err_band) == 1
        assert len(err_band[0].vertices) == 9

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a"),
            estimator="mean", err_style="band", ci="sd"
        )

        ax.clear()
        p.plot(ax, {})
        assert len(ax.lines) == len(ax.collections) == len(p._hue_map.levels)
        for c in ax.collections:
            assert isinstance(c, mpl.collections.PolyCollection)

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a"),
            estimator="mean", err_style="bars", ci="sd"
        )

        ax.clear()
        p.plot(ax, {})
        n_lines = len(ax.lines)
        assert n_lines / 2 == len(ax.collections) == len(p._hue_map.levels)
        assert len(ax.collections) == len(p._hue_map.levels)
        for c in ax.collections:
            assert isinstance(c, mpl.collections.LineCollection)

        p = _LinePlotter(
            data=repeated_df,
            variables=dict(x="x", y="y", units="u"),
            estimator=None
        )

        ax.clear()
        p.plot(ax, {})
        n_units = len(repeated_df["u"].unique())
        assert len(ax.lines) == n_units

        p = _LinePlotter(
            data=repeated_df,
            variables=dict(x="x", y="y", hue="a", units="u"),
            estimator=None
        )

        ax.clear()
        p.plot(ax, {})
        n_units *= len(repeated_df["a"].unique())
        assert len(ax.lines) == n_units

        p.estimator = "mean"
        with pytest.raises(ValueError):
            p.plot(ax, {})

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a"),
            err_style="band", err_kws={"alpha": .5},
        )

        ax.clear()
        p.plot(ax, {})
        for band in ax.collections:
            assert band.get_alpha() == .5

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a"),
            err_style="bars", err_kws={"elinewidth": 2},
        )

        ax.clear()
        p.plot(ax, {})
        for lines in ax.collections:
            assert lines.get_linestyles() == 2

        p.err_style = "invalid"
        with pytest.raises(ValueError):
            p.plot(ax, {})

        x_str = long_df["x"].astype(str)
        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue=x_str),
        )
        ax.clear()
        p.plot(ax, {})

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y", size=x_str),
        )
        ax.clear()
        p.plot(ax, {})

    def test_axis_labels(self, long_df):

        f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)

        p = _LinePlotter(
            data=long_df,
            variables=dict(x="x", y="y"),
        )

        p.plot(ax1, {})
        assert ax1.get_xlabel() == "x"
        assert ax1.get_ylabel() == "y"

        p.plot(ax2, {})
        assert ax2.get_xlabel() == "x"
        assert ax2.get_ylabel() == "y"
        assert not ax2.yaxis.label.get_visible()

    def test_matplotlib_kwargs(self, long_df):

        kws = {
            "linestyle": "--",
            "linewidth": 3,
            "color": (1, .5, .2),
            "markeredgecolor": (.2, .5, .2),
            "markeredgewidth": 1,
        }
        ax = lineplot(data=long_df, x="x", y="y", **kws)

        line, *_ = ax.lines
        for key, val in kws.items():
            plot_val = getattr(line, f"get_{key}")()
            assert plot_val == val

    def test_lineplot_axes(self, wide_df):

        f1, ax1 = plt.subplots()
        f2, ax2 = plt.subplots()

        ax = lineplot(data=wide_df)
        assert ax is ax2

        ax = lineplot(data=wide_df, ax=ax1)
        assert ax is ax1

    def test_lineplot_vs_relplot(self, long_df, long_semantics):

        ax = lineplot(data=long_df, **long_semantics)
        g = relplot(data=long_df, kind="line", **long_semantics)

        lin_lines = ax.lines
        rel_lines = g.ax.lines

        for l1, l2 in zip(lin_lines, rel_lines):
            assert_array_equal(l1.get_xydata(), l2.get_xydata())
            assert same_color(l1.get_color(), l2.get_color())
            assert l1.get_linewidth() == l2.get_linewidth()
            assert l1.get_linestyle() == l2.get_linestyle()

    def test_lineplot_smoke(
        self,
        wide_df, wide_array,
        wide_list_of_series, wide_list_of_arrays, wide_list_of_lists,
        flat_array, flat_series, flat_list,
        long_df, missing_df, object_df
    ):

        f, ax = plt.subplots()

        lineplot(x=[], y=[])
        ax.clear()

        lineplot(data=wide_df)
        ax.clear()

        lineplot(data=wide_array)
        ax.clear()

        lineplot(data=wide_list_of_series)
        ax.clear()

        lineplot(data=wide_list_of_arrays)
        ax.clear()

        lineplot(data=wide_list_of_lists)
        ax.clear()

        lineplot(data=flat_series)
        ax.clear()

        lineplot(data=flat_array)
        ax.clear()

        lineplot(data=flat_list)
        ax.clear()

        lineplot(x="x", y="y", data=long_df)
        ax.clear()

        lineplot(x=long_df.x, y=long_df.y)
        ax.clear()

        lineplot(x=long_df.x, y="y", data=long_df)
        ax.clear()

        lineplot(x="x", y=long_df.y.values, data=long_df)
        ax.clear()

        lineplot(x="x", y="t", data=long_df)
        ax.clear()

        lineplot(x="x", y="y", hue="a", data=long_df)
        ax.clear()

        lineplot(x="x", y="y", hue="a", style="a", data=long_df)
        ax.clear()

        lineplot(x="x", y="y", hue="a", style="b", data=long_df)
        ax.clear()

        lineplot(x="x", y="y", hue="a", style="a", data=missing_df)
        ax.clear()

        lineplot(x="x", y="y", hue="a", style="b", data=missing_df)
        ax.clear()

        lineplot(x="x", y="y", hue="a", size="a", data=long_df)
        ax.clear()

        lineplot(x="x", y="y", hue="a", size="s", data=long_df)
        ax.clear()

        lineplot(x="x", y="y", hue="a", size="a", data=missing_df)
        ax.clear()

        lineplot(x="x", y="y", hue="a", size="s", data=missing_df)
        ax.clear()

        lineplot(x="x", y="y", hue="f", data=object_df)
        ax.clear()

        lineplot(x="x", y="y", hue="c", size="f", data=object_df)
        ax.clear()

        lineplot(x="x", y="y", hue="f", size="s", data=object_df)
        ax.clear()


class TestScatterPlotter(Helpers):

    def test_legend_data(self, long_df):

        m = mpl.markers.MarkerStyle("o")
        default_mark = m.get_path().transformed(m.get_transform())

        m = mpl.markers.MarkerStyle("")
        null = m.get_path().transformed(m.get_transform())

        f, ax = plt.subplots()

        p = _ScatterPlotter(
            data=long_df,
            variables=dict(x="x", y="y"),
            legend="full",
        )
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert handles == []

        # --

        ax.clear()
        p = _ScatterPlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a"),
            legend="full",
        )
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        colors = [h.get_facecolors()[0] for h in handles]
        expected_colors = p._hue_map(p._hue_map.levels)
        assert labels == p._hue_map.levels
        assert same_color(colors, expected_colors)

        # --

        ax.clear()
        p = _ScatterPlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a", style="a"),
            legend="full",
        )
        p.map_style(markers=True)
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        colors = [h.get_facecolors()[0] for h in handles]
        expected_colors = p._hue_map(p._hue_map.levels)
        paths = [h.get_paths()[0] for h in handles]
        expected_paths = p._style_map(p._style_map.levels, "path")
        assert labels == p._hue_map.levels
        assert labels == p._style_map.levels
        assert same_color(colors, expected_colors)
        assert self.paths_equal(paths, expected_paths)

        # --

        ax.clear()
        p = _ScatterPlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a", style="b"),
            legend="full",
        )
        p.map_style(markers=True)
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        colors = [h.get_facecolors()[0] for h in handles]
        paths = [h.get_paths()[0] for h in handles]
        expected_colors = (
            ["w"] + p._hue_map(p._hue_map.levels)
            + ["w"] + [".2" for _ in p._style_map.levels]
        )
        expected_paths = (
            [null] + [default_mark for _ in p._hue_map.levels]
            + [null] + p._style_map(p._style_map.levels, "path")
        )
        assert labels == (
            ["a"] + p._hue_map.levels + ["b"] + p._style_map.levels
        )
        assert same_color(colors, expected_colors)
        assert self.paths_equal(paths, expected_paths)

        # --

        ax.clear()
        p = _ScatterPlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a", size="a"),
            legend="full"
        )
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        colors = [h.get_facecolors()[0] for h in handles]
        expected_colors = p._hue_map(p._hue_map.levels)
        sizes = [h.get_sizes()[0] for h in handles]
        expected_sizes = p._size_map(p._size_map.levels)
        assert labels == p._hue_map.levels
        assert labels == p._size_map.levels
        assert same_color(colors, expected_colors)
        assert sizes == expected_sizes

        # --

        ax.clear()
        sizes_list = [10, 100, 200]
        p = _ScatterPlotter(
            data=long_df,
            variables=dict(x="x", y="y", size="s"),
            legend="full",
        )
        p.map_size(sizes=sizes_list)
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        sizes = [h.get_sizes()[0] for h in handles]
        expected_sizes = p._size_map(p._size_map.levels)
        assert labels == [str(l) for l in p._size_map.levels]
        assert sizes == expected_sizes

        # --

        ax.clear()
        sizes_dict = {2: 10, 4: 100, 8: 200}
        p = _ScatterPlotter(
            data=long_df,
            variables=dict(x="x", y="y", size="s"),
            legend="full"
        )
        p.map_size(sizes=sizes_dict)
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        sizes = [h.get_sizes()[0] for h in handles]
        expected_sizes = p._size_map(p._size_map.levels)
        assert labels == [str(l) for l in p._size_map.levels]
        assert sizes == expected_sizes

        # --

        x, y = np.random.randn(2, 40)
        z = np.tile(np.arange(20), 2)

        p = _ScatterPlotter(
            variables=dict(x=x, y=y, hue=z),
        )

        ax.clear()
        p.legend = "full"
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert labels == [str(l) for l in p._hue_map.levels]

        ax.clear()
        p.legend = "brief"
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert len(labels) < len(p._hue_map.levels)

        p = _ScatterPlotter(
            variables=dict(x=x, y=y, size=z),
        )

        ax.clear()
        p.legend = "full"
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert labels == [str(l) for l in p._size_map.levels]

        ax.clear()
        p.legend = "brief"
        p.add_legend_data(ax)
        handles, labels = ax.get_legend_handles_labels()
        assert len(labels) < len(p._size_map.levels)

        ax.clear()
        p.legend = "bad_value"
        with pytest.raises(ValueError):
            p.add_legend_data(ax)

    def test_plot(self, long_df, repeated_df):

        f, ax = plt.subplots()

        p = _ScatterPlotter(data=long_df, variables=dict(x="x", y="y"))

        p.plot(ax, {})
        points = ax.collections[0]
        assert_array_equal(points.get_offsets(), long_df[["x", "y"]].values)

        ax.clear()
        p.plot(ax, {"color": "k", "label": "test"})
        points = ax.collections[0]
        assert same_color(points.get_facecolor(), "k")
        assert points.get_label() == "test"

        p = _ScatterPlotter(
            data=long_df, variables=dict(x="x", y="y", hue="a")
        )

        ax.clear()
        p.plot(ax, {})
        points = ax.collections[0]
        expected_colors = p._hue_map(p.plot_data["hue"])
        assert same_color(points.get_facecolors(), expected_colors)

        p = _ScatterPlotter(
            data=long_df,
            variables=dict(x="x", y="y", style="c"),
        )
        p.map_style(markers=["+", "x"])

        ax.clear()
        color = (1, .3, .8)
        p.plot(ax, {"color": color})
        points = ax.collections[0]
        assert same_color(points.get_edgecolors(), [color])

        p = _ScatterPlotter(
            data=long_df, variables=dict(x="x", y="y", size="a"),
        )

        ax.clear()
        p.plot(ax, {})
        points = ax.collections[0]
        expected_sizes = p._size_map(p.plot_data["size"])
        assert_array_equal(points.get_sizes(), expected_sizes)

        p = _ScatterPlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a", style="a"),
        )
        p.map_style(markers=True)

        ax.clear()
        p.plot(ax, {})
        points = ax.collections[0]
        expected_colors = p._hue_map(p.plot_data["hue"])
        expected_paths = p._style_map(p.plot_data["style"], "path")
        assert same_color(points.get_facecolors(), expected_colors)
        assert self.paths_equal(points.get_paths(), expected_paths)

        p = _ScatterPlotter(
            data=long_df,
            variables=dict(x="x", y="y", hue="a", style="b"),
        )
        p.map_style(markers=True)

        ax.clear()
        p.plot(ax, {})
        points = ax.collections[0]
        expected_colors = p._hue_map(p.plot_data["hue"])
        expected_paths = p._style_map(p.plot_data["style"], "path")
        assert same_color(points.get_facecolors(), expected_colors)
        assert self.paths_equal(points.get_paths(), expected_paths)

        x_str = long_df["x"].astype(str)
        p = _ScatterPlotter(
            data=long_df, variables=dict(x="x", y="y", hue=x_str),
        )
        ax.clear()
        p.plot(ax, {})

        p = _ScatterPlotter(
            data=long_df, variables=dict(x="x", y="y", size=x_str),
        )
        ax.clear()
        p.plot(ax, {})

    def test_axis_labels(self, long_df):

        f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)

        p = _ScatterPlotter(data=long_df, variables=dict(x="x", y="y"))

        p.plot(ax1, {})
        assert ax1.get_xlabel() == "x"
        assert ax1.get_ylabel() == "y"

        p.plot(ax2, {})
        assert ax2.get_xlabel() == "x"
        assert ax2.get_ylabel() == "y"
        assert not ax2.yaxis.label.get_visible()

    def test_scatterplot_axes(self, wide_df):

        f1, ax1 = plt.subplots()
        f2, ax2 = plt.subplots()

        ax = scatterplot(data=wide_df)
        assert ax is ax2

        ax = scatterplot(data=wide_df, ax=ax1)
        assert ax is ax1

    def test_literal_attribute_vectors(self):

        f, ax = plt.subplots()

        x = y = [1, 2, 3]
        s = [5, 10, 15]
        c = [(1, 1, 0, 1), (1, 0, 1, .5), (.5, 1, 0, 1)]

        scatterplot(x=x, y=y, c=c, s=s, ax=ax)

        points, = ax.collections

        assert_array_equal(points.get_sizes().squeeze(), s)
        assert_array_equal(points.get_facecolors(), c)

    def test_linewidths(self, long_df):

        f, ax = plt.subplots()

        scatterplot(data=long_df, x="x", y="y", s=10)
        scatterplot(data=long_df, x="x", y="y", s=20)
        points1, points2 = ax.collections
        assert (
            points1.get_linewidths().item() < points2.get_linewidths().item()
        )

        ax.clear()
        scatterplot(data=long_df, x="x", y="y", s=long_df["x"])
        scatterplot(data=long_df, x="x", y="y", s=long_df["x"] * 2)
        points1, points2 = ax.collections
        assert (
            points1.get_linewidths().item() < points2.get_linewidths().item()
        )

        ax.clear()
        scatterplot(data=long_df, x="x", y="y", size=long_df["x"])
        scatterplot(data=long_df, x="x", y="y", size=long_df["x"] * 2)
        points1, points2, *_ = ax.collections
        assert (
            points1.get_linewidths().item() < points2.get_linewidths().item()
        )

        ax.clear()
        lw = 2
        scatterplot(data=long_df, x="x", y="y", linewidth=lw)
        assert ax.collections[0].get_linewidths().item() == lw

    def test_datetime_scale(self, long_df):

        ax = scatterplot(data=long_df, x="t", y="y")
        # Check that we avoid weird matplotlib default auto scaling
        # https://github.com/matplotlib/matplotlib/issues/17586
        ax.get_xlim()[0] > ax.xaxis.convert_units(np.datetime64("2002-01-01"))

    def test_scatterplot_vs_relplot(self, long_df, long_semantics):

        ax = scatterplot(data=long_df, **long_semantics)
        g = relplot(data=long_df, kind="scatter", **long_semantics)

        for s_pts, r_pts in zip(ax.collections, g.ax.collections):

            assert_array_equal(s_pts.get_offsets(), r_pts.get_offsets())
            assert_array_equal(s_pts.get_sizes(), r_pts.get_sizes())
            assert_array_equal(s_pts.get_facecolors(), r_pts.get_facecolors())
            assert self.paths_equal(s_pts.get_paths(), r_pts.get_paths())

    def test_scatterplot_smoke(
        self,
        wide_df, wide_array,
        flat_series, flat_array, flat_list,
        wide_list_of_series, wide_list_of_arrays, wide_list_of_lists,
        long_df, missing_df, object_df
    ):

        f, ax = plt.subplots()

        scatterplot(x=[], y=[])
        ax.clear()

        scatterplot(data=wide_df)
        ax.clear()

        scatterplot(data=wide_array)
        ax.clear()

        scatterplot(data=wide_list_of_series)
        ax.clear()

        scatterplot(data=wide_list_of_arrays)
        ax.clear()

        scatterplot(data=wide_list_of_lists)
        ax.clear()

        scatterplot(data=flat_series)
        ax.clear()

        scatterplot(data=flat_array)
        ax.clear()

        scatterplot(data=flat_list)
        ax.clear()

        scatterplot(x="x", y="y", data=long_df)
        ax.clear()

        scatterplot(x=long_df.x, y=long_df.y)
        ax.clear()

        scatterplot(x=long_df.x, y="y", data=long_df)
        ax.clear()

        scatterplot(x="x", y=long_df.y.values, data=long_df)
        ax.clear()

        scatterplot(x="x", y="y", hue="a", data=long_df)
        ax.clear()

        scatterplot(x="x", y="y", hue="a", style="a", data=long_df)
        ax.clear()

        scatterplot(x="x", y="y", hue="a", style="b", data=long_df)
        ax.clear()

        scatterplot(x="x", y="y", hue="a", style="a", data=missing_df)
        ax.clear()

        scatterplot(x="x", y="y", hue="a", style="b", data=missing_df)
        ax.clear()

        scatterplot(x="x", y="y", hue="a", size="a", data=long_df)
        ax.clear()

        scatterplot(x="x", y="y", hue="a", size="s", data=long_df)
        ax.clear()

        scatterplot(x="x", y="y", hue="a", size="a", data=missing_df)
        ax.clear()

        scatterplot(x="x", y="y", hue="a", size="s", data=missing_df)
        ax.clear()

        scatterplot(x="x", y="y", hue="f", data=object_df)
        ax.clear()

        scatterplot(x="x", y="y", hue="c", size="f", data=object_df)
        ax.clear()

        scatterplot(x="x", y="y", hue="f", size="s", data=object_df)
        ax.clear()
