# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import pytest
from matplotlib import gridspec
from matplotlib.collections import PolyCollection
from matplotlib.colors import same_color
from mpl_toolkits.axes_grid1.parasite_axes import HostAxes  # spatial_colors
from numpy.testing import assert_allclose

import mne
from mne import (
    Epochs,
    compute_covariance,
    compute_proj_evoked,
    make_fixed_length_events,
    read_cov,
    read_events,
)
from mne._fiff.constants import FIFF
from mne.datasets import testing
from mne.io import read_raw_fif
from mne.stats.parametric import _parametric_ci
from mne.utils import _record_warnings, catch_logging
from mne.viz import plot_compare_evokeds, plot_evoked_white
from mne.viz.utils import _fake_click, _get_cmap

base_dir = Path(__file__).parents[2] / "io" / "tests" / "data"
evoked_fname = base_dir / "test-ave.fif"
raw_fname = base_dir / "test_raw.fif"
raw_sss_fname = base_dir / "test_chpi_raw_sss.fif"
cov_fname = base_dir / "test-cov.fif"
event_name = base_dir / "test-eve.fif"
event_id, tmin, tmax = 1, -0.1, 0.1
ctf_fname = testing.data_path(download=False) / "CTF" / "testdata_ctf.ds"

# Use a subset of channels for plotting speed
# make sure we have a magnetometer and a pair of grad pairs for topomap.
default_picks = (
    0,
    1,
    2,
    3,
    4,
    6,
    7,
    61,
    122,
    183,
    244,
    305,
    315,
    316,
    317,
    318,
)  # EEG channels
sel = (0, 7)


def _get_epochs(picks=default_picks):
    """Get epochs."""
    raw = read_raw_fif(raw_fname)
    raw.add_proj([], remove_existing=True)
    events = read_events(event_name)
    epochs = Epochs(
        raw, events[:5], event_id, tmin, tmax, picks=picks, decim=10, verbose="error"
    )
    epochs.info["bads"] = [epochs.ch_names[-5], epochs.ch_names[-1]]  # MEG  # EEG
    epochs.info.normalize_proj()
    return epochs


def _get_epochs_delayed_ssp():
    """Get epochs with delayed SSP."""
    raw = read_raw_fif(raw_fname)
    events = read_events(event_name)
    reject = dict(mag=4e-12)
    epochs_delayed_ssp = Epochs(
        raw,
        events[:10],
        event_id,
        tmin,
        tmax,
        picks=default_picks,
        proj="delayed",
        reject=reject,
        verbose="error",
    )
    epochs_delayed_ssp.info.normalize_proj()
    return epochs_delayed_ssp


def test_plot_evoked_cov():
    """Test plot_evoked with noise_cov."""
    evoked = _get_epochs().average()
    cov = read_cov(cov_fname)
    cov["projs"] = []  # avoid warnings
    with pytest.warns(RuntimeWarning, match="No average EEG reference"):
        evoked.plot(noise_cov=cov, time_unit="s")
    with pytest.raises(TypeError, match="Covariance"):
        evoked.plot(noise_cov=1.0, time_unit="s")
    with pytest.raises(FileNotFoundError, match="File does not exist"):
        evoked.plot(noise_cov="nonexistent-cov.fif", time_unit="s")
    raw = read_raw_fif(raw_sss_fname)
    events = make_fixed_length_events(raw)
    epochs = Epochs(raw, events, picks=default_picks)
    cov = compute_covariance(epochs)
    evoked_sss = epochs.average()
    with _record_warnings(), pytest.warns(RuntimeWarning, match="relative scaling"):
        evoked_sss.plot(noise_cov=cov, time_unit="s")
    plt.close("all")


@pytest.mark.slowtest
def test_plot_evoked():
    """Test evoked.plot."""
    epochs = _get_epochs()
    evoked = epochs.average()
    assert evoked.proj is False
    fig = evoked.plot(
        proj=True, hline=[1], exclude=[], window_title="foo", time_unit="s"
    )
    amplitudes = _get_amplitudes(fig)
    assert len(amplitudes) == len(default_picks)
    assert evoked.proj is False
    assert evoked.info["bads"] == ["MEG 2641", "EEG 004"]
    eeg_lines = fig.axes[2].lines
    n_eeg = sum(ch_type == "eeg" for ch_type in evoked.get_channel_types())
    assert len(eeg_lines) == n_eeg == 4
    n_bad = sum(same_color(line.get_color(), "0.5") for line in eeg_lines)
    assert n_bad == 1
    # Test a click
    ax = fig.get_axes()[0]
    line = ax.lines[0]
    _fake_click(fig, ax, [line.get_xdata()[0], line.get_ydata()[0]], "data")
    _fake_click(fig, ax, [ax.get_xlim()[0], ax.get_ylim()[1]], "data")
    # plot with bad channels excluded & spatial_colors & zorder
    evoked.plot(exclude="bads", time_unit="s")

    # test selective updating of dict keys is working.
    evoked.plot(hline=[1], units=dict(mag="femto foo"), time_unit="s")
    evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
    evoked_delayed_ssp.plot(proj="interactive", time_unit="s")
    evoked_delayed_ssp.apply_proj()
    pytest.raises(
        RuntimeError, evoked_delayed_ssp.plot, proj="interactive", time_unit="s"
    )
    with evoked_delayed_ssp.info._unlock():
        evoked_delayed_ssp.info["projs"] = []
    pytest.raises(
        RuntimeError, evoked_delayed_ssp.plot, proj="interactive", time_unit="s"
    )
    pytest.raises(
        RuntimeError,
        evoked_delayed_ssp.plot,
        proj="interactive",
        axes="foo",
        time_unit="s",
    )
    plt.close("all")

    # test `gfp='only'`: GFP (EEG) and RMS (MEG)
    fig, ax = plt.subplots(3)
    evoked.plot(gfp="only", time_unit="s", axes=ax)

    assert len(ax[0].lines) == len(ax[1].lines) == len(ax[2].lines) == 1

    assert ax[0].get_title() == "EEG (3 channels)"
    assert ax[0].texts[0].get_text() == "GFP"

    assert ax[1].get_title() == "Gradiometers (9 channels)"
    assert ax[1].texts[0].get_text() == "RMS"

    assert ax[2].get_title() == "Magnetometers (2 channels)"
    assert ax[1].texts[0].get_text() == "RMS"

    plt.close("all")

    # Test invalid `gfp`
    with pytest.raises(ValueError):
        evoked.plot(gfp="foo", time_unit="s")

    # plot with bad channels excluded, spatial_colors, zorder & pos. layout
    evoked.rename_channels({"MEG 0133": "MEG 0000"})
    evoked.plot(
        exclude=evoked.info["bads"],
        spatial_colors=True,
        gfp=True,
        zorder="std",
        time_unit="s",
    )
    evoked.plot(exclude=[], spatial_colors=True, zorder="unsorted", time_unit="s")
    pytest.raises(TypeError, evoked.plot, zorder="asdf", time_unit="s")
    plt.close("all")

    evoked.plot_sensors()  # Test plot_sensors
    plt.close("all")

    evoked.pick(evoked.ch_names[:4])
    with catch_logging() as log_file:
        evoked.plot(verbose=True, time_unit="s")
    assert "Need more than one" in log_file.getvalue()

    # Test highlight
    for highlight in [(0, 0.1), [(0, 0.1), (0.1, 0.2)]]:
        fig = evoked.plot(time_unit="s", highlight=highlight)
        regular_axes = [ax for ax in fig.axes if not isinstance(ax, HostAxes)]
        for ax in regular_axes:
            highlighted_areas = [
                child
                for child in ax.get_children()
                if isinstance(child, PolyCollection)
            ]
            assert len(highlighted_areas) == len(np.atleast_2d(highlight))

    with pytest.raises(ValueError, match="must be reshapable into a 2D array"):
        fig = evoked.plot(time_unit="s", highlight=0.1)

    # set one channel location to nan, confirm spatial_colors still works
    evoked = _get_epochs().load_data().average("grad")  # reload data
    evoked.info["chs"][0]["loc"][:] = np.nan
    fig = evoked.plot(time_unit="s", spatial_colors=True)
    line_clr = [x.get_color() for x in fig.axes[0].get_lines()]
    assert not np.all(np.isnan(line_clr) & (line_clr == 0))


def test_constrained_layout():
    """Test that we handle constrained layouts correctly."""
    fig, ax = plt.subplots(1, 1, layout="constrained")
    assert fig.get_constrained_layout()
    evoked = mne.read_evokeds(evoked_fname)[0]
    evoked.pick(evoked.ch_names[:2])

    # smoke test that it does not break things
    evoked.plot(axes=ax)
    assert fig.get_constrained_layout()
    plt.close("all")


def _get_amplitudes(fig):
    # ignore the spatial_colors parasite axes
    regular_axes = [ax for ax in fig.axes if not isinstance(ax, HostAxes)]
    amplitudes = [line.get_ydata() for ax in regular_axes for line in ax.get_lines()]
    # this will exclude hlines, which are lists not arrays
    amplitudes = np.array([line for line in amplitudes if isinstance(line, np.ndarray)])
    return amplitudes


@pytest.mark.parametrize(
    "picks, rlims, avg_proj",
    [
        (default_picks[:-4], (0.59, 0.61), False),  # MEG
        (np.arange(340, 360), (0.56, 0.57), True),  # EEG
        (np.arange(340, 360), (0.79, 0.81), False),  # EEG
    ],
)
def test_plot_evoked_reconstruct(picks, rlims, avg_proj):
    """Test proj="reconstruct"."""
    evoked = _get_epochs(picks=picks).average()
    if avg_proj:
        evoked.set_eeg_reference(projection=True).apply_proj()
        assert len(evoked.info["projs"]) == 1
        assert evoked.proj is True
    else:
        assert len(evoked.info["projs"]) == 0
        assert evoked.proj is False
    fig = evoked.plot(
        proj=True, hline=[1], exclude=[], window_title="foo", time_unit="s"
    )
    amplitudes = _get_amplitudes(fig)
    assert len(amplitudes) == len(picks)
    assert evoked.proj is avg_proj

    fig = evoked.plot(proj="reconstruct", exclude=[])
    amplitudes_recon = _get_amplitudes(fig)
    if avg_proj is False:
        assert_allclose(amplitudes, amplitudes_recon)
    proj = compute_proj_evoked(evoked.copy().crop(None, 0).apply_proj())
    evoked.add_proj(proj)
    assert len(evoked.info["projs"]) == 2 if len(picks) == 3 else 4
    fig = evoked.plot(proj=True, exclude=[])
    amplitudes_proj = _get_amplitudes(fig)
    fig = evoked.plot(proj="reconstruct", exclude=[])
    amplitudes_recon = _get_amplitudes(fig)
    assert len(amplitudes_recon) == len(picks)
    norm = np.linalg.norm(amplitudes)
    norm_proj = np.linalg.norm(amplitudes_proj)
    norm_recon = np.linalg.norm(amplitudes_recon)
    r = np.dot(amplitudes_recon.ravel(), amplitudes.ravel()) / (norm_recon * norm)
    assert rlims[0] < r < rlims[1]
    assert 1.05 * norm_proj < norm_recon
    if not avg_proj:
        assert norm_proj < norm * 0.9

    cov = read_cov(cov_fname)
    with pytest.raises(ValueError, match='Cannot use proj="reconstruct"'):
        evoked.plot(noise_cov=cov, proj="reconstruct")
    plt.close("all")


def test_plot_evoked_image():
    """Test plot_evoked_image."""
    evoked = _get_epochs().average()
    evoked.plot_image(proj=True, time_unit="ms")

    # fail nicely on NaN
    evoked_nan = evoked.copy()
    evoked_nan.data[:, 0] = np.nan
    pytest.raises(ValueError, evoked_nan.plot)
    with np.errstate(invalid="ignore"):
        pytest.raises(ValueError, evoked_nan.plot_image)
        pytest.raises(ValueError, evoked_nan.plot_joint)

    # test mask
    evoked.plot_image(picks=[1, 2], mask=evoked.data > 0, time_unit="s")
    evoked.plot_image(
        picks=[1, 2],
        mask_cmap=None,
        colorbar=False,
        mask=np.ones(evoked.data.shape).astype(bool),
        time_unit="s",
    )
    with _record_warnings(), pytest.warns(RuntimeWarning, match="not adding contour"):
        evoked.plot_image(picks=[1, 2], mask=None, mask_style="both", time_unit="s")
    with pytest.raises(ValueError, match="must have the same shape"):
        evoked.plot_image(mask=evoked.data[1:, 1:] > 0, time_unit="s")

    # plot with bad channels excluded
    evoked.plot_image(exclude="bads", cmap="interactive", time_unit="s")
    plt.close("all")

    with pytest.raises(ValueError, match="not unique"):
        evoked.plot_image(picks=[0, 0], time_unit="s")  # duplicates

    ch_names = evoked.ch_names[3:5]
    picks = [evoked.ch_names.index(ch) for ch in ch_names]
    fig = evoked.plot_image(show_names="all", time_unit="s", picks=picks)
    fig.canvas.draw_idle()
    yticklabels = fig.axes[0].get_yticklabels()
    assert len(yticklabels) == len(ch_names)
    for tick_target, tick_observed in zip(ch_names, yticklabels):
        assert tick_target in str(tick_observed)
    evoked.plot_image(show_names=True, time_unit="s")

    # test groupby
    evoked.plot_image(group_by=dict(sel=sel), axes=dict(sel=plt.axes()))
    plt.close("all")
    for group_by, axes in (("something", dict()), (dict(), "something")):
        pytest.raises(ValueError, evoked.plot_image, group_by=group_by, axes=axes)

    with pytest.raises(ValueError, match="`clim` must be a dict."):
        evoked.plot_image(clim=[-4, 4])


def test_plot_white():
    """Test plot_white."""
    cov = read_cov(cov_fname)
    cov["method"] = "empirical"
    cov["projs"] = []  # avoid warnings
    evoked = _get_epochs().average()
    evoked.set_eeg_reference("average")  # Avoid warnings

    # test rank param.
    with pytest.raises(ValueError, match="exceeds"):
        evoked.plot_white(cov, rank={"mag": 10})
    evoked.plot_white(cov, rank={"mag": 1, "grad": 8, "eeg": 2}, time_unit="s")
    fig = evoked.plot_white(cov, rank={"mag": 1}, time_unit="s")  # test rank
    evoked.plot_white(cov, rank={"grad": 8}, time_unit="s", axes=fig.axes[:4])
    with pytest.raises(ValueError, match=r"must have shape \(4,\), got \(2,"):
        evoked.plot_white(cov, axes=fig.axes[:2])
    with pytest.raises(ValueError, match="When not using SSS"):
        evoked.plot_white(cov, rank={"meg": 306})
    evoked.plot_white([cov, cov], time_unit="s")
    plt.close("all")

    fig = plot_evoked_white(evoked, [cov, cov])
    assert len(fig.axes) == 3 * 3
    axes = np.array(fig.axes[:6]).reshape(3, 2)
    plot_evoked_white(evoked, [cov, cov], axes=axes)
    with pytest.raises(ValueError, match=r"have shape \(3, 2\), got"):
        plot_evoked_white(evoked, [cov, cov], axes=axes[:, :1])

    # Hack to test plotting of maxfiltered data
    evoked_sss = _get_epochs(picks=("meg", "eeg")).average()
    evoked_sss.set_eeg_reference(projection=True).apply_proj()
    sss = dict(sss_info=dict(in_order=80, components=np.arange(80)))
    with evoked_sss.info._unlock():
        evoked_sss.info["proc_history"] = [dict(max_info=sss)]
    evoked_sss.plot_white([cov, cov], rank={"meg": 64})
    with pytest.raises(ValueError, match="When using SSS"):
        evoked_sss.plot_white(cov, rank={"grad": 201}, verbose="error")
    evoked_sss.plot_white(cov, rank={"meg": 302}, time_unit="s")


@pytest.mark.parametrize(
    "combine,vlines,title,picks",
    (
        pytest.param(None, [0.1, 0.2], "MEG 0113", "MEG 0113", id="singlepick"),
        pytest.param("mean", [], "(mean)", "mag", id="mag-mean"),
        pytest.param("gfp", "auto", "(GFP)", "eeg", id="eeg-gfp"),
        pytest.param(None, "auto", "(RMS)", ["MEG 0113", "MEG 0112"], id="meg-rms"),
        pytest.param(
            "std", "auto", "(std. dev.)", ["MEG 0113", "MEG 0112"], id="meg-std"
        ),
        pytest.param(
            lambda x: np.min(x, axis=1), "auto", "MEG 0112", [0, 1], id="intpicks"
        ),
    ),
)
def test_plot_compare_evokeds_title(evoked, picks, vlines, combine, title):
    """Test title generation by plot_compare_evokeds()."""
    # test picks, combine, and vlines (1-channel pick also shows sensor inset)
    fig = plot_compare_evokeds(evoked, picks=picks, vlines=vlines, combine=combine)
    assert fig[0].axes[0].get_title().endswith(title)


@pytest.mark.slowtest  # slow on Azure
def test_plot_compare_evokeds(evoked):
    """Test plot_compare_evokeds."""
    # test defaults
    figs = plot_compare_evokeds(evoked)
    assert len(figs) == 3
    # test passing more than one evoked
    red, blue = evoked.copy(), evoked.copy()
    red.comment = red.comment + "*" * 100
    red.data *= 1.5
    blue.data /= 1.5
    evoked_dict = {"aud/l": blue, "aud/r": red, "vis": evoked}
    huge_dict = {f"cond{i}": ev for i, ev in enumerate([evoked] * 11)}
    plot_compare_evokeds(evoked_dict)  # dict
    plot_compare_evokeds([[red, evoked], [blue, evoked]])  # list of lists
    figs = plot_compare_evokeds({"cond": [blue, red, evoked]})  # dict of list
    # test that confidence bands are plausible
    for fig in figs:
        extents = fig.axes[0].collections[0].get_paths()[0].get_extents()
        xlim, ylim = extents.get_points().T
        assert np.allclose(xlim, evoked.times[[0, -1]])
        line = fig.axes[0].lines[0]
        xvals = line.get_xdata()
        assert np.allclose(xvals, evoked.times)
        yvals = line.get_ydata()
        assert (yvals < ylim[1]).all()
        assert (yvals > ylim[0]).all()
    # test plotting eyetracking data
    plt.close("all")  # close the previous figures as to avoid a too many figs warning
    info_tmp = mne.create_info(["pupil_left"], evoked.info["sfreq"], ["pupil"])
    evoked_et = mne.EvokedArray(np.ones_like(evoked.times).reshape(1, -1), info_tmp)
    figs = plot_compare_evokeds(evoked_et, show_sensors=False)
    assert len(figs) == 1
    # test plotting only invalid channel types
    info_tmp = mne.create_info(["ias"], evoked.info["sfreq"], ["ias"])
    ev_invalid = mne.EvokedArray(np.ones_like(evoked.times).reshape(1, -1), info_tmp)
    with pytest.raises(RuntimeError, match="No valid"):
        plot_compare_evokeds(ev_invalid, picks="all")
    plt.close("all")

    # test other CI args
    def ci_func(array):
        return array.mean(axis=0, keepdims=True) * np.array([[0.5], [1.5]])

    ci_types = (None, False, 0.5, ci_func)
    for _ci in ci_types:
        fig = plot_compare_evokeds({"cond": [blue, red, evoked]}, ci=_ci)[0]
        if _ci in ci_types[2:]:
            assert np.any(
                [isinstance(coll, PolyCollection) for coll in fig.axes[0].collections]
            )
    # make sure we can get a CI even for single conditions
    fig = plot_compare_evokeds(evoked, picks="eeg", ci=ci_func)[0]
    assert np.any(
        [isinstance(coll, PolyCollection) for coll in fig.axes[0].collections]
    )

    with pytest.raises(TypeError, match='"ci" must be None, bool, float or'):
        plot_compare_evokeds(evoked, ci="foo")
    # test sensor inset, legend location, and axis inversion & truncation
    plot_compare_evokeds(
        evoked_dict,
        invert_y=True,
        legend="upper left",
        show_sensors="center",
        truncate_xaxis=False,
        truncate_yaxis=False,
    )
    plot_compare_evokeds(evoked, ylim=dict(mag=(-50, 50)), truncate_yaxis=True)
    plt.close("all")
    # test styles
    plot_compare_evokeds(
        evoked_dict,
        colors=["b", "r", "g"],
        linestyles=[":", "-", "--"],
        split_legend=True,
    )
    style_dict = dict(aud=dict(alpha=0.3), vis=dict(linewidth=3, c="k"))
    plot_compare_evokeds(
        evoked_dict,
        styles=style_dict,
        colors={"aud/r": "r"},
        linestyles=dict(vis="dotted"),
        ci=False,
    )
    plot_compare_evokeds(evoked_dict, colors=list(range(3)))
    plt.close("all")
    # test colormap
    cmap = _get_cmap("viridis")
    plot_compare_evokeds(evoked_dict, cmap=cmap, colors=dict(aud=0.4, vis=0.9))
    plot_compare_evokeds(evoked_dict, cmap=cmap, colors=dict(aud=1, vis=2))
    plot_compare_evokeds(
        evoked_dict, cmap=("cmap title", "inferno"), linestyles=["-", ":", "--"]
    )
    plt.close("all")
    # test combine
    match = "combine must be an instance of None, callable, or str"
    with pytest.raises(TypeError, match=match):
        plot_compare_evokeds(evoked, combine=["mean", "gfp"])
    plt.close("all")
    # test warnings
    with pytest.warns(RuntimeWarning, match='in "picks"; cannot combine'):
        plot_compare_evokeds(evoked, picks=[0], combine="median")
    plt.close("all")
    # test errors
    with pytest.raises(TypeError, match='"evokeds" must be a dict, list'):
        plot_compare_evokeds("foo")
    with pytest.raises(ValueError, match=r'keys in "styles" \(.*\) must '):
        plot_compare_evokeds(evoked_dict, styles=dict(foo="foo", bar="bar"))
    with pytest.raises(ValueError, match="colors in the default color cycle"):
        plot_compare_evokeds(huge_dict, colors=None)
    with pytest.raises(TypeError, match='"cmap" is specified, then "colors"'):
        plot_compare_evokeds(
            evoked_dict,
            cmap="Reds",
            colors={"aud/l": "foo", "aud/r": "bar", "vis": "baz"},
        )
    plt.close("all")
    for kwargs in [dict(colors=[0, 1]), dict(linestyles=["-", ":"])]:
        match = r"but there are only \d* (colors|linestyles). Please specify"
        with pytest.raises(ValueError, match=match):
            plot_compare_evokeds(evoked_dict, **kwargs)
    for kwargs in [dict(colors="foo"), dict(linestyles="foo")]:
        match = r'"(colors|linestyles)" must be a dict, list, or None; got '
        with pytest.raises(TypeError, match=match):
            plot_compare_evokeds(evoked_dict, **kwargs)
    for kwargs in [dict(colors=dict(foo="f")), dict(linestyles=dict(foo="f"))]:
        match = r'If "(colors|linestyles)" is a dict its keys \(.*\) must '
        with pytest.raises(ValueError, match=match):
            plot_compare_evokeds(evoked_dict, **kwargs)
    for kwargs in [dict(legend="foo"), dict(show_sensors="foo")]:
        with pytest.raises(ValueError, match="not a legal MPL loc, please"):
            plot_compare_evokeds(evoked_dict, **kwargs)
    with pytest.raises(TypeError, match="an instance of list or tuple"):
        plot_compare_evokeds(evoked_dict, vlines="foo")
    with pytest.raises(ValueError, match='"truncate_yaxis" must be bool or '):
        plot_compare_evokeds(evoked_dict, truncate_yaxis="foo")
    plt.close("all")
    # test axes='topo'
    figs = plot_compare_evokeds(evoked_dict, axes="topo", legend=True)
    for fig in figs:
        assert len(fig.axes[0].lines) == len(evoked_dict)
    # test with (fake) CSD data
    csd = _get_epochs(picks=np.arange(315, 320)).average()  # 5 EEG chs
    for entry in csd.info["chs"]:
        entry["coil_type"] = FIFF.FIFFV_COIL_EEG_CSD
        entry["unit"] = FIFF.FIFF_UNIT_V_M2
    plot_compare_evokeds(csd, picks="csd", axes="topo")
    # old tests
    red.info["chs"][0]["loc"][:2] = 0  # test plotting channel at zero
    (fig,) = plot_compare_evokeds(
        [red, blue], picks=[0], ci=lambda x: [x.std(axis=0), -x.std(axis=0)]
    )
    # reasonable legend lengths
    leg_texts = [t.get_text() for t in fig.axes[0].get_legend().get_texts()]
    assert all(len(lt) < 50 for lt in leg_texts)
    plot_compare_evokeds([list(evoked_dict.values())], picks=[0], ci=_parametric_ci)
    # smoke test for tmin >= 0 (from mailing list)
    red.crop(0.01, None)
    assert len(red.times) > 2
    plot_compare_evokeds(red)
    # plot a flat channel
    red.data = np.zeros_like(red.data)
    plot_compare_evokeds(red)
    # smoke test for one time point (not useful but should not fail)
    red.crop(0.02, 0.02)
    assert len(red.times) == 1
    plot_compare_evokeds(red)
    # now that we've cropped `red`:
    with pytest.raises(ValueError, match="not contain the same time instants"):
        plot_compare_evokeds(evoked_dict)
    plt.close("all")


def test_plot_compare_evokeds_neuromag122():
    """Test topomap plotting."""
    evoked = mne.read_evokeds(evoked_fname, "Left Auditory", baseline=(None, 0))
    evoked.pick(picks="grad")
    evoked.pick(evoked.ch_names[:122])
    ch_names = [f"MEG {k:03}" for k in range(1, 123)]
    for c in evoked.info["chs"]:
        c["coil_type"] = FIFF.FIFFV_COIL_NM_122
    evoked.rename_channels(
        {c_old: c_new for (c_old, c_new) in zip(evoked.ch_names, ch_names)}
    )
    mne.viz.plot_compare_evokeds([evoked, evoked])


@testing.requires_testing_data
def test_plot_ctf():
    """Test plotting of CTF evoked."""
    raw = mne.io.read_raw_ctf(ctf_fname, preload=True)
    events = np.array([[200, 0, 1]])
    event_id = 1
    tmin, tmax = -0.1, 0.5  # start and end of an epoch in s.
    picks = mne.pick_types(
        raw.info, meg=True, stim=True, eog=True, ref_meg=True, exclude="bads"
    )[::20]
    epochs = mne.Epochs(
        raw,
        events,
        event_id,
        tmin,
        tmax,
        proj=True,
        picks=picks,
        preload=True,
        decim=10,
        verbose="error",
    )
    evoked = epochs.average()
    evoked.plot_joint(times=[0.1])
    # test plotting with invalid ylim argument
    with pytest.raises(TypeError, match="ylim must be an instance of dict or None"):
        evoked.plot_joint(times=[0.1], ts_args=dict(ylim=(-10, 10)))
    mne.viz.plot_compare_evokeds([evoked, evoked])

    # make sure axes position is "almost" unchanged
    # when axes were passed to plot_joint by the user
    times = [0.1, 0.2, 0.3]
    fig = plt.figure()

    # create custom axes for topomaps, colorbar and the timeseries
    gs = gridspec.GridSpec(3, 7, hspace=0.5, top=0.8, figure=fig)
    topo_axes = [
        fig.add_subplot(gs[0, idx * 2 : (idx + 1) * 2]) for idx in range(len(times))
    ]
    topo_axes.append(fig.add_subplot(gs[0, -1]))
    ts_axis = fig.add_subplot(gs[1:, 1:-1])

    def get_axes_midpoints(axes):
        midpoints = list()
        for ax in axes[:-1]:
            pos = ax.get_position()
            midpoints.append([pos.x0 + (pos.width * 0.5), pos.y0 + (pos.height * 0.5)])
        return np.array(midpoints)

    midpoints_before = get_axes_midpoints(topo_axes)
    evoked.plot_joint(
        times=times,
        ts_args={"axes": ts_axis},
        topomap_args={"axes": topo_axes},
        title=None,
    )
    midpoints_after = get_axes_midpoints(topo_axes)
    assert (np.linalg.norm(midpoints_before - midpoints_after) < 0.1).all()