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

import itertools
from pathlib import Path

import numpy as np
import pytest
from numpy.testing import assert_array_equal

from mne import (
    compute_raw_covariance,
    make_fixed_length_epochs,
    pick_info,
    pick_types,
    read_cov,
    read_evokeds,
)
from mne._fiff.pick import _picks_by_type
from mne._fiff.proj import _has_eeg_average_ref_proj
from mne.cov import prepare_noise_cov
from mne.datasets import testing
from mne.io import read_raw_fif
from mne.proj import compute_proj_raw
from mne.rank import (
    _compute_rank_int,
    _estimate_rank_raw,
    _get_rank_sss,
    compute_rank,
    estimate_rank,
)
from mne.utils import catch_logging

base_dir = Path(__file__).parents[1] / "io" / "tests" / "data"
cov_fname = base_dir / "test-cov.fif"
raw_fname = base_dir / "test_raw.fif"
ave_fname = base_dir / "test-ave.fif"
ctf_fname = base_dir / "test_ctf_raw.fif"
hp_fif_fname = base_dir / "test_chpi_raw_sss.fif"

testing_path = testing.data_path(download=False)
data_dir = testing_path / "MEG" / "sample"
mf_fif_fname = testing_path / "SSS" / "test_move_anon_raw_sss.fif"


def test_estimate_rank():
    """Test rank estimation."""
    data = np.eye(10)
    assert_array_equal(estimate_rank(data, return_singular=True)[1], np.ones(10))
    data[0, 0] = 0
    assert estimate_rank(data) == 9
    pytest.raises(ValueError, estimate_rank, data, "foo")


@pytest.mark.slowtest
@pytest.mark.parametrize(
    "fname, ref_meg",
    ((raw_fname, False), (hp_fif_fname, False), (ctf_fname, False), (ctf_fname, True)),
)
@pytest.mark.parametrize("scalings", ("norm", dict(mag=1e11, grad=1e9, eeg=1e5)))
@pytest.mark.parametrize(
    "tol_kind, tol",
    [
        ("absolute", 1e-4),
        ("relative", 1e-6),
    ],
)
def test_raw_rank_estimation(fname, ref_meg, scalings, tol_kind, tol):
    """Test raw rank estimation."""
    if ref_meg and scalings != "norm":
        # Adjust for CTF data (scale factors are quite different)
        if tol_kind == "relative":
            scalings = dict(mag=1.0)
        else:
            scalings = dict(mag=1e31)
    raw = read_raw_fif(fname)
    raw.crop(0, min(4.0, raw.times[-1])).load_data()
    out = _picks_by_type(raw.info, ref_meg=ref_meg, meg_combined=True)
    has_eeg = "eeg" in raw
    if has_eeg:
        (_, picks_meg), (_, picks_eeg) = out
    else:
        ((_, picks_meg),) = out
        picks_eeg = []
    n_meg = len(picks_meg)
    n_eeg = len(picks_eeg)

    if len(raw.info["proc_history"]) == 0:
        expected_rank = n_meg + n_eeg
    else:
        expected_rank = _get_rank_sss(raw.info) + n_eeg
    got_rank = _estimate_rank_raw(
        raw, scalings=scalings, with_ref_meg=ref_meg, tol=tol, tol_kind=tol_kind
    )
    assert got_rank == expected_rank
    if "sss" in fname.name:
        raw.add_proj(compute_proj_raw(raw))
    raw.apply_proj()
    n_proj = len(raw.info["projs"])
    want_rank = expected_rank - (0 if "sss" in fname.name else n_proj)
    got_rank = _estimate_rank_raw(
        raw, scalings=scalings, with_ref_meg=ref_meg, tol=tol, tol_kind=tol_kind
    )
    assert got_rank == want_rank


@pytest.mark.slowtest
@pytest.mark.parametrize("meg", ("separate", "combined"))
@pytest.mark.parametrize(
    "rank_method, proj", [("info", True), ("info", False), (None, True), (None, False)]
)
def test_cov_rank_estimation(rank_method, proj, meg):
    """Test cov rank estimation."""
    # Test that our rank estimation works properly on a simple case
    evoked = read_evokeds(ave_fname, condition=0, baseline=(None, 0), proj=False)
    cov = read_cov(cov_fname)
    ch_names = [
        ch for ch in evoked.info["ch_names"] if "053" not in ch and ch.startswith("EEG")
    ]
    cov = prepare_noise_cov(cov, evoked.info, ch_names, None)
    assert cov["eig"][0] <= 1e-25  # avg projector should set this to zero
    assert (cov["eig"][1:] > 1e-16).all()  # all else should be > 0

    # Now do some more comprehensive tests
    raw_sample = read_raw_fif(raw_fname)
    assert not _has_eeg_average_ref_proj(raw_sample.info)

    raw_sss = read_raw_fif(hp_fif_fname)
    assert not _has_eeg_average_ref_proj(raw_sss.info)
    raw_sss.add_proj(compute_proj_raw(raw_sss, meg=meg))

    cov_sample = compute_raw_covariance(raw_sample)
    cov_sample_proj = compute_raw_covariance(raw_sample.copy().apply_proj())

    cov_sss = compute_raw_covariance(raw_sss)
    cov_sss_proj = compute_raw_covariance(raw_sss.copy().apply_proj())

    picks_all_sample = pick_types(raw_sample.info, meg=True, eeg=True)
    picks_all_sss = pick_types(raw_sss.info, meg=True, eeg=True)

    info_sample = pick_info(raw_sample.info, picks_all_sample)
    picks_stack_sample = [("eeg", pick_types(info_sample, meg=False, eeg=True))]
    picks_stack_sample += [("meg", pick_types(info_sample, meg=True))]
    picks_stack_sample += [("all", pick_types(info_sample, meg=True, eeg=True))]

    info_sss = pick_info(raw_sss.info, picks_all_sss)
    picks_stack_somato = [("eeg", pick_types(info_sss, meg=False, eeg=True))]
    picks_stack_somato += [("meg", pick_types(info_sss, meg=True))]
    picks_stack_somato += [("all", pick_types(info_sss, meg=True, eeg=True))]

    iter_tests = list(
        itertools.product(
            [
                (cov_sample, picks_stack_sample, info_sample),
                (cov_sample_proj, picks_stack_sample, info_sample),
                (cov_sss, picks_stack_somato, info_sss),
                (cov_sss_proj, picks_stack_somato, info_sss),
            ],  # sss
            [dict(mag=1e15, grad=1e13, eeg=1e6)],
        )
    )

    for (cov, picks_list, iter_info), scalings in iter_tests:
        rank = compute_rank(cov, rank_method, scalings, iter_info, proj=proj)
        rank["all"] = sum(rank.values())
        for ch_type, picks in picks_list:
            this_info = pick_info(iter_info, picks)

            # compute subset of projs, active and inactive
            n_projs_applied = sum(
                proj["active"]
                and len(set(proj["data"]["col_names"]) & set(this_info["ch_names"])) > 0
                for proj in cov["projs"]
            )
            n_projs_info = sum(
                len(set(proj["data"]["col_names"]) & set(this_info["ch_names"])) > 0
                for proj in this_info["projs"]
            )

            # count channel types
            ch_types = this_info.get_channel_types()
            n_eeg, n_mag, n_grad = (ch_types.count(k) for k in ["eeg", "mag", "grad"])
            n_meg = n_mag + n_grad
            has_sss = n_meg > 0 and len(this_info["proc_history"]) > 0
            if has_sss:
                n_meg = _get_rank_sss(this_info)

            expected_rank = n_meg + n_eeg
            if rank_method is None:
                if meg == "combined" or not has_sss:
                    if proj:
                        expected_rank -= n_projs_info
                    else:
                        expected_rank -= n_projs_applied
            else:
                # XXX for now it just uses the total count
                assert rank_method == "info"
                if proj:
                    expected_rank -= n_projs_info

            assert rank[ch_type] == expected_rank


@pytest.mark.parametrize(
    "rank_method, proj", [("info", True), ("info", False), (None, True), (None, False)]
)
def test_rank_epochs(rank_method, proj):
    """Test that raw and epochs give the same results in a simple case."""
    # And a smoke test for epochs
    raw = read_raw_fif(raw_fname, preload=True)
    epochs = make_fixed_length_epochs(raw, preload=True, proj=False)
    rank_raw = compute_rank(raw, rank_method, proj=proj)
    with catch_logging(verbose=True) as log:
        rank_epochs = compute_rank(epochs, rank_method, proj=proj)
    log = log.getvalue()
    assert "{" not in log
    assert rank_raw == rank_epochs


@pytest.mark.slowtest  # ~3 s apiece on Azure means overall it's slow
@testing.requires_testing_data
@pytest.mark.parametrize("fname, rank_orig", ((hp_fif_fname, 120), (mf_fif_fname, 67)))
@pytest.mark.parametrize(
    "n_proj, meg", ((0, "combined"), (10, "combined"), (10, "separate"))
)
@pytest.mark.parametrize(
    "tol_kind, tol",
    [
        ("absolute", "float32"),
        ("relative", "float32"),
        ("relative", 1e-5),
    ],
)
def test_maxfilter_get_rank(n_proj, fname, rank_orig, meg, tol_kind, tol):
    """Test maxfilter rank lookup."""
    raw = read_raw_fif(fname).crop(0, 5).load_data().pick("meg")
    assert raw.info["projs"] == []
    mf = raw.info["proc_history"][0]["max_info"]
    assert mf["sss_info"]["nfree"] == rank_orig

    assert compute_rank(raw, "info")["meg"] == rank_orig
    assert compute_rank(raw.copy().pick("grad"), "info")["grad"] == rank_orig
    assert compute_rank(raw.copy().pick("mag"), "info")["mag"] == rank_orig

    mult = 1 + (meg == "separate")
    rank = rank_orig - mult * n_proj
    if n_proj > 0:
        # Let's do some projection
        raw.add_proj(
            compute_proj_raw(raw, n_mag=n_proj, n_grad=n_proj, meg=meg, verbose=True)
        )
    raw.apply_proj()
    data_orig = raw[:][0]

    # degenerate cases
    with pytest.raises(ValueError, match="tol must be"):
        _estimate_rank_raw(raw, tol="foo")
    with pytest.raises(TypeError, match="must be a string or a"):
        _estimate_rank_raw(raw, tol=None)

    allowed_rank = [rank_orig if meg == "separate" else rank]
    if fname == mf_fif_fname:
        # Here we permit a -1 because for mf_fif_fname we miss by 1, which is
        # probably acceptable. If we use the entire duration instead of 5 s
        # this problem goes away, but the test is much slower.
        allowed_rank.append(allowed_rank[0] - 1)

    # multiple ways of hopefully getting the same thing
    # default tol=1e-4, scalings='norm'
    rank_new = _estimate_rank_raw(raw, tol_kind=tol_kind)
    assert rank_new in allowed_rank

    rank_new = _estimate_rank_raw(raw, tol=tol, tol_kind=tol_kind)
    if fname == mf_fif_fname and tol_kind == "relative" and tol != "auto":
        pass  # does not play nicely with row norms of _estimate_rank_raw
    else:
        assert rank_new in allowed_rank
    rank_new = _estimate_rank_raw(raw, scalings=dict(), tol=tol, tol_kind=tol_kind)
    assert rank_new in allowed_rank
    scalings = dict(grad=1e13, mag=1e15)
    rank_new = _compute_rank_int(
        raw, None, scalings=scalings, tol=tol, tol_kind=tol_kind, verbose="debug"
    )
    assert rank_new in allowed_rank
    # XXX default scalings mis-estimate sometimes :(
    if fname == hp_fif_fname:
        allowed_rank.append(allowed_rank[0] - 2)
    rank_new = _compute_rank_int(raw, None, tol=tol, tol_kind=tol_kind, verbose="debug")
    assert rank_new in allowed_rank
    del allowed_rank

    rank_new = _compute_rank_int(raw, "info")
    assert rank_new == rank
    assert_array_equal(raw[:][0], data_orig)


def test_explicit_bads_pick():
    """Test when bads channels are explicitly passed + default picks=None."""
    raw = read_raw_fif(raw_fname).crop(0, 5).load_data()
    raw.pick(picks=["eeg", "meg", "ref_meg"])

    # Covariance
    # Default picks=None
    raw.info["bads"] = list()
    noise_cov_1 = compute_raw_covariance(raw, picks=None)
    assert noise_cov_1["bads"] == raw.info["bads"]
    rank = compute_rank(noise_cov_1, info=raw.info)
    assert rank == dict(meg=303, eeg=60)
    assert raw.info["bads"] == []

    raw.info["bads"] = ["EEG 002", "EEG 012", "EEG 015", "MEG 0122"]
    noise_cov = compute_raw_covariance(raw, picks=None)
    assert noise_cov["bads"] == []
    assert not any(bad in noise_cov["names"] for bad in raw.info["bads"])
    rank = compute_rank(noise_cov, info=raw.info)
    want_rank = dict(meg=302, eeg=57)
    assert raw.info["bads"] == ["EEG 002", "EEG 012", "EEG 015", "MEG 0122"]

    # Explicit picks
    picks = pick_types(raw.info, meg=True, eeg=True, exclude=[])
    noise_cov_2 = compute_raw_covariance(raw, picks=picks)
    assert noise_cov_2["bads"] == raw.info["bads"]  # correctly populated
    rank = compute_rank(noise_cov_2, info=raw.info)
    assert rank == want_rank
    assert raw.info["bads"] == ["EEG 002", "EEG 012", "EEG 015", "MEG 0122"]

    assert_array_equal(noise_cov_1["data"], noise_cov_2["data"])
    assert noise_cov_1["names"] == noise_cov_2["names"]

    # Raw
    raw.info["bads"] = list()
    rank = compute_rank(raw)
    assert rank == dict(meg=303, eeg=60)

    raw.info["bads"] = ["EEG 002", "EEG 012", "EEG 015", "MEG 0122"]
    rank = compute_rank(raw)
    assert rank == want_rank
