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test_interpolation.py
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test_interpolation.py
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# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from itertools import compress
from pathlib import Path
import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_array_equal
from mne import Epochs, pick_channels, pick_types, read_events
from mne._fiff.constants import FIFF
from mne._fiff.proj import _has_eeg_average_ref_proj
from mne.channels import make_dig_montage
from mne.channels.interpolation import _make_interpolation_matrix
from mne.datasets import testing
from mne.io import RawArray, read_raw_ctf, read_raw_fif, read_raw_nirx
from mne.preprocessing.nirs import (
beer_lambert_law,
optical_density,
scalp_coupling_index,
)
from mne.utils import _record_warnings
base_dir = Path(__file__).parents[2] / "io" / "tests" / "data"
raw_fname = base_dir / "test_raw.fif"
event_name = base_dir / "test-eve.fif"
raw_fname_ctf = base_dir / "test_ctf_raw.fif"
testing_path = testing.data_path(download=False)
event_id, tmin, tmax = 1, -0.2, 0.5
event_id_2 = 2
def _load_data(kind):
"""Load data."""
# It is more memory efficient to load data in a separate
# function so it's loaded on-demand
raw = read_raw_fif(raw_fname)
events = read_events(event_name)
# subselect channels for speed
if kind == "eeg":
picks = pick_types(raw.info, meg=False, eeg=True, exclude=[])[:15]
epochs = Epochs(
raw,
events,
event_id,
tmin,
tmax,
picks=picks,
preload=True,
reject=dict(eeg=80e-6),
)
else:
picks = pick_types(raw.info, meg=True, eeg=False, exclude=[])[1:200:2]
assert kind == "meg"
with pytest.warns(RuntimeWarning, match="projection"):
epochs = Epochs(
raw,
events,
event_id,
tmin,
tmax,
picks=picks,
preload=True,
reject=dict(grad=1000e-12, mag=4e-12),
)
return raw, epochs
@pytest.mark.parametrize("offset", (0.0, 0.1))
@pytest.mark.parametrize(
"avg_proj, ctol",
[
(True, (0.86, 0.93)),
(False, (0.97, 0.99)),
],
)
@pytest.mark.parametrize(
"method, atol",
[
pytest.param(None, 3e-6, marks=pytest.mark.slowtest), # slow on Azure
(dict(eeg="MNE"), 4e-6),
],
)
@pytest.mark.filterwarnings("ignore:.*than 20 mm from head frame origin.*")
def test_interpolation_eeg(offset, avg_proj, ctol, atol, method):
"""Test interpolation of EEG channels."""
raw, epochs_eeg = _load_data("eeg")
epochs_eeg = epochs_eeg.copy()
assert not _has_eeg_average_ref_proj(epochs_eeg.info)
# Offsetting the coordinate frame should have no effect on the output
for inst in (raw, epochs_eeg):
for ch in inst.info["chs"]:
if ch["kind"] == FIFF.FIFFV_EEG_CH:
ch["loc"][:3] += offset
ch["loc"][3:6] += offset
for d in inst.info["dig"]:
d["r"] += offset
# check that interpolation does nothing if no bads are marked
epochs_eeg.info["bads"] = []
evoked_eeg = epochs_eeg.average()
kw = dict(method=method)
with pytest.warns(RuntimeWarning, match="Doing nothing"):
evoked_eeg.interpolate_bads(**kw)
# create good and bad channels for EEG
epochs_eeg.info["bads"] = []
goods_idx = np.ones(len(epochs_eeg.ch_names), dtype=bool)
goods_idx[epochs_eeg.ch_names.index("EEG 012")] = False
bads_idx = ~goods_idx
pos = epochs_eeg._get_channel_positions()
evoked_eeg = epochs_eeg.average()
if avg_proj:
evoked_eeg.set_eeg_reference(projection=True).apply_proj()
assert_allclose(evoked_eeg.data.mean(0), 0.0, atol=1e-20)
ave_before = evoked_eeg.data[bads_idx]
# interpolate bad channels for EEG
epochs_eeg.info["bads"] = ["EEG 012"]
evoked_eeg = epochs_eeg.average()
if avg_proj:
evoked_eeg.set_eeg_reference(projection=True).apply_proj()
good_picks = pick_types(evoked_eeg.info, meg=False, eeg=True)
assert_allclose(evoked_eeg.data[good_picks].mean(0), 0.0, atol=1e-20)
evoked_eeg_bad = evoked_eeg.copy()
bads_picks = pick_channels(
epochs_eeg.ch_names, include=epochs_eeg.info["bads"], ordered=True
)
evoked_eeg_bad.data[bads_picks, :] = 1e10
# Test first the exclude parameter
evoked_eeg_2_bads = evoked_eeg_bad.copy()
evoked_eeg_2_bads.info["bads"] = ["EEG 004", "EEG 012"]
evoked_eeg_2_bads.data[
pick_channels(evoked_eeg_bad.ch_names, ["EEG 004", "EEG 012"])
] = 1e10
evoked_eeg_interp = evoked_eeg_2_bads.interpolate_bads(
origin=(0.0, 0.0, 0.0), exclude=["EEG 004"], **kw
)
assert evoked_eeg_interp.info["bads"] == ["EEG 004"]
assert np.all(evoked_eeg_interp.get_data("EEG 004") == 1e10)
assert np.all(evoked_eeg_interp.get_data("EEG 012") != 1e10)
# Now test without exclude parameter
evoked_eeg_bad.info["bads"] = ["EEG 012"]
evoked_eeg_interp = evoked_eeg_bad.copy().interpolate_bads(
origin=(0.0, 0.0, 0.0), **kw
)
if avg_proj:
assert_allclose(evoked_eeg_interp.data.mean(0), 0.0, atol=1e-6)
interp_zero = evoked_eeg_interp.data[bads_idx]
if method is None: # using
pos_good = pos[goods_idx]
pos_bad = pos[bads_idx]
interpolation = _make_interpolation_matrix(pos_good, pos_bad)
assert interpolation.shape == (1, len(epochs_eeg.ch_names) - 1)
interp_manual = np.dot(interpolation, evoked_eeg_bad.data[goods_idx])
assert_array_equal(interp_manual, interp_zero)
del interp_manual, interpolation, pos, pos_good, pos_bad
assert_allclose(ave_before, interp_zero, atol=atol)
assert ctol[0] < np.corrcoef(ave_before, interp_zero)[0, 1] < ctol[1]
interp_fit = evoked_eeg_bad.copy().interpolate_bads(**kw).data[bads_idx]
assert_allclose(ave_before, interp_fit, atol=2.5e-6)
assert ctol[1] < np.corrcoef(ave_before, interp_fit)[0, 1] # better
# check that interpolation fails when preload is False
epochs_eeg.preload = False
with pytest.raises(RuntimeError, match="requires epochs data to be load"):
epochs_eeg.interpolate_bads(**kw)
epochs_eeg.preload = True
# check that interpolation changes the data in raw
raw_eeg = RawArray(data=epochs_eeg._data[0], info=epochs_eeg.info)
raw_before = raw_eeg._data[bads_idx]
raw_after = raw_eeg.interpolate_bads(**kw)._data[bads_idx]
assert not np.all(raw_before == raw_after)
# check that interpolation fails when preload is False
for inst in [raw, epochs_eeg]:
assert hasattr(inst, "preload")
inst.preload = False
inst.info["bads"] = [inst.ch_names[1]]
with pytest.raises(RuntimeError, match="requires.*data to be loaded"):
inst.interpolate_bads(**kw)
# check that interpolation works with few channels
raw_few = raw.copy().crop(0, 0.1).load_data()
raw_few.pick(raw_few.ch_names[:1] + raw_few.ch_names[3:4])
assert len(raw_few.ch_names) == 2
raw_few.del_proj()
raw_few.info["bads"] = [raw_few.ch_names[-1]]
orig_data = raw_few[1][0]
with _record_warnings() as w:
raw_few.interpolate_bads(reset_bads=False, **kw)
assert len([ww for ww in w if "more than" not in str(ww.message)]) == 0
new_data = raw_few[1][0]
assert (new_data == 0).mean() < 0.5
assert np.corrcoef(new_data, orig_data)[0, 1] > 0.2
@pytest.mark.slowtest
def test_interpolation_meg():
"""Test interpolation of MEG channels."""
# speed accuracy tradeoff: channel subselection is faster but the
# correlation drops
thresh = 0.68
raw, epochs_meg = _load_data("meg")
# check that interpolation works when non M/EEG channels are present
# before MEG channels
raw.crop(0, 0.1).load_data().pick(epochs_meg.ch_names)
raw.info.normalize_proj()
raw.set_channel_types({raw.ch_names[0]: "stim"}, on_unit_change="ignore")
raw.info["bads"] = [raw.ch_names[1]]
raw.load_data()
raw.interpolate_bads(mode="fast")
del raw
# check that interpolation works for MEG
epochs_meg.info["bads"] = ["MEG 0141"]
evoked = epochs_meg.average()
pick = pick_channels(epochs_meg.info["ch_names"], epochs_meg.info["bads"])
# MEG -- raw
raw_meg = RawArray(data=epochs_meg._data[0], info=epochs_meg.info)
raw_meg.info["bads"] = ["MEG 0141"]
data1 = raw_meg[pick, :][0][0]
raw_meg.info.normalize_proj()
data2 = raw_meg.interpolate_bads(reset_bads=False, mode="fast")[pick, :][0][0]
assert np.corrcoef(data1, data2)[0, 1] > thresh
# the same number of bads as before
assert len(raw_meg.info["bads"]) == len(raw_meg.info["bads"])
# MEG -- epochs
data1 = epochs_meg.get_data(pick).ravel()
epochs_meg.info.normalize_proj()
epochs_meg.interpolate_bads(mode="fast")
data2 = epochs_meg.get_data(pick).ravel()
assert np.corrcoef(data1, data2)[0, 1] > thresh
assert len(epochs_meg.info["bads"]) == 0
# MEG -- evoked (plus auto origin)
data1 = evoked.data[pick]
evoked.info.normalize_proj()
data2 = evoked.interpolate_bads(origin="auto").data[pick]
assert np.corrcoef(data1, data2)[0, 1] > thresh
# MEG -- with exclude
evoked.info["bads"] = ["MEG 0141", "MEG 0121"]
pick = pick_channels(evoked.ch_names, evoked.info["bads"], ordered=True)
evoked.data[pick[-1]] = 1e10
data1 = evoked.data[pick]
evoked.info.normalize_proj()
data2 = evoked.interpolate_bads(origin="auto", exclude=["MEG 0121"]).data[pick]
assert np.corrcoef(data1[0], data2[0])[0, 1] > thresh
assert np.all(data2[1] == 1e10)
def _this_interpol(inst, ref_meg=False):
from mne.channels.interpolation import _interpolate_bads_meg
_interpolate_bads_meg(inst, ref_meg=ref_meg, mode="fast")
return inst
@pytest.mark.slowtest
def test_interpolate_meg_ctf():
"""Test interpolation of MEG channels from CTF system."""
thresh = 0.85
tol = 0.05 # assert the new interpol correlates at least .05 "better"
bad = "MLC22-2622" # select a good channel to test the interpolation
raw = read_raw_fif(raw_fname_ctf).crop(0, 1.0).load_data() # 3 secs
raw.apply_gradient_compensation(3)
# Show that we have to exclude ref_meg for interpolating CTF MEG-channels
# (fixed in #5965):
raw.info["bads"] = [bad]
pick_bad = pick_channels(raw.info["ch_names"], raw.info["bads"])
data_orig = raw[pick_bad, :][0]
# mimic old behavior (the ref_meg-arg in _interpolate_bads_meg only serves
# this purpose):
data_interp_refmeg = _this_interpol(raw, ref_meg=True)[pick_bad, :][0]
# new:
data_interp_no_refmeg = _this_interpol(raw, ref_meg=False)[pick_bad, :][0]
R = dict()
R["no_refmeg"] = np.corrcoef(data_orig, data_interp_no_refmeg)[0, 1]
R["with_refmeg"] = np.corrcoef(data_orig, data_interp_refmeg)[0, 1]
print("Corrcoef of interpolated with original channel: ", R)
assert R["no_refmeg"] > R["with_refmeg"] + tol
assert R["no_refmeg"] > thresh
@testing.requires_testing_data
def test_interpolation_ctf_comp():
"""Test interpolation with compensated CTF data."""
raw_fname = testing_path / "CTF" / "somMDYO-18av.ds"
raw = read_raw_ctf(raw_fname, preload=True)
raw.info["bads"] = [raw.ch_names[5], raw.ch_names[-5]]
raw.interpolate_bads(mode="fast", origin=(0.0, 0.0, 0.04))
assert raw.info["bads"] == []
@testing.requires_testing_data
def test_interpolation_nirs():
"""Test interpolating bad nirs channels."""
pytest.importorskip("pymatreader")
fname = testing_path / "NIRx" / "nirscout" / "nirx_15_2_recording_w_overlap"
raw_intensity = read_raw_nirx(fname, preload=False)
raw_od = optical_density(raw_intensity)
sci = scalp_coupling_index(raw_od)
raw_od.info["bads"] = list(compress(raw_od.ch_names, sci < 0.5))
bad_0 = np.where([name == raw_od.info["bads"][0] for name in raw_od.ch_names])[0][0]
bad_0_std_pre_interp = np.std(raw_od._data[bad_0])
bads_init = list(raw_od.info["bads"])
raw_od.interpolate_bads(exclude=bads_init[:2])
assert raw_od.info["bads"] == bads_init[:2]
raw_od.interpolate_bads()
assert raw_od.info["bads"] == []
assert bad_0_std_pre_interp > np.std(raw_od._data[bad_0])
raw_haemo = beer_lambert_law(raw_od, ppf=6)
raw_haemo.info["bads"] = raw_haemo.ch_names[2:4]
assert raw_haemo.info["bads"] == ["S1_D2 hbo", "S1_D2 hbr"]
raw_haemo.interpolate_bads()
assert raw_haemo.info["bads"] == []
@testing.requires_testing_data
def test_interpolation_ecog():
"""Test interpolation for ECoG."""
raw, epochs_eeg = _load_data("eeg")
bads = ["EEG 012"]
bads_mask = np.isin(epochs_eeg.ch_names, bads)
epochs_ecog = epochs_eeg.set_channel_types(
{ch: "ecog" for ch in epochs_eeg.ch_names}
)
epochs_ecog.info["bads"] = bads
# check that interpolation changes the data in raw
raw_ecog = RawArray(data=epochs_ecog._data[0], info=epochs_ecog.info)
raw_before = raw_ecog.copy()
raw_after = raw_ecog.interpolate_bads(method=dict(ecog="spline"))
assert not np.all(raw_before._data[bads_mask] == raw_after._data[bads_mask])
assert_array_equal(raw_before._data[~bads_mask], raw_after._data[~bads_mask])
@testing.requires_testing_data
def test_interpolation_seeg():
"""Test interpolation for sEEG."""
raw, epochs_eeg = _load_data("eeg")
bads = ["EEG 012"]
bads_mask = np.isin(epochs_eeg.ch_names, bads)
epochs_seeg = epochs_eeg.set_channel_types(
{ch: "seeg" for ch in epochs_eeg.ch_names}
)
epochs_seeg.info["bads"] = bads
# check that interpolation changes the data in raw
raw_seeg = RawArray(data=epochs_seeg._data[0], info=epochs_seeg.info)
raw_before = raw_seeg.copy()
montage = raw_seeg.get_montage()
pos = montage.get_positions()
ch_pos = pos.pop("ch_pos")
n0 = ch_pos[epochs_seeg.ch_names[0]]
n1 = ch_pos[epochs_seeg.ch_names[1]]
for i, ch in enumerate(epochs_seeg.ch_names[2:]):
ch_pos[ch] = n0 + (n1 - n0) * (i + 2)
raw_seeg.set_montage(make_dig_montage(ch_pos, **pos))
raw_after = raw_seeg.interpolate_bads(method=dict(seeg="spline"))
assert not np.all(raw_before._data[bads_mask] == raw_after._data[bads_mask])
assert_array_equal(raw_before._data[~bads_mask], raw_after._data[~bads_mask])
# check interpolation on epochs
epochs_seeg.set_montage(make_dig_montage(ch_pos, **pos))
epochs_before = epochs_seeg.copy()
epochs_after = epochs_seeg.interpolate_bads(method=dict(seeg="spline"))
assert not np.all(
epochs_before._data[:, bads_mask] == epochs_after._data[:, bads_mask]
)
assert_array_equal(
epochs_before._data[:, ~bads_mask], epochs_after._data[:, ~bads_mask]
)
# test shaft all bad
epochs_seeg.info["bads"] = epochs_seeg.ch_names
with pytest.raises(RuntimeError, match="Not enough good channels"):
epochs_seeg.interpolate_bads(method=dict(seeg="spline"))
# test bad not on shaft
ch_pos[bads[0]] = np.array([10, 10, 10])
epochs_seeg.info["bads"] = bads
epochs_seeg.set_montage(make_dig_montage(ch_pos, **pos))
with pytest.raises(RuntimeError, match="No shaft found"):
epochs_seeg.interpolate_bads(method=dict(seeg="spline"))
def test_nan_interpolation(raw):
"""Test 'nan' method for interpolating bads."""
ch_to_interp = [raw.ch_names[1]] # don't use channel 0 (type is IAS not MEG)
raw.info["bads"] = ch_to_interp
# test that warning appears for reset_bads = True
with pytest.warns(RuntimeWarning, match="Consider setting reset_bads=False"):
raw.interpolate_bads(method="nan", reset_bads=True)
# despite warning, interpolation still happened, make sure the channel is NaN
bad_chs = raw.get_data(ch_to_interp)
assert np.isnan(bad_chs).all()
# make sure reset_bads=False works as expected
raw.info["bads"] = ch_to_interp
raw.interpolate_bads(method="nan", reset_bads=False)
assert raw.info["bads"] == ch_to_interp
# make sure other channels are untouched
raw.drop_channels(ch_to_interp)
good_chs = raw.get_data()
assert np.isfinite(good_chs).all()
@testing.requires_testing_data
def test_method_str():
"""Test method argument types."""
raw = read_raw_fif(
testing_path / "MEG" / "sample" / "sample_audvis_trunc_raw.fif",
preload=False,
)
raw.crop(0, 1).pick(("meg", "eeg"), exclude=()).load_data()
raw.copy().interpolate_bads(method="MNE")
with pytest.raises(ValueError, match="Invalid value for the"):
raw.interpolate_bads(method="spline")
raw.pick("eeg", exclude=())
raw.interpolate_bads(method="spline")