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test_rank.py
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# 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