Add interpolation of nirs data. Replaces with closest

mne/channels/interpolation.py

@@ -213,3 +213,55 @@ def _interpolate_bads_meg(inst, mode='accurate', origin=(0., 0., 0.04),
     info_to = pick_info(inst.info, picks_bad)
     mapping = _map_meg_channels(info_from, info_to, mode=mode, origin=origin)
     _do_interp_dots(inst, mapping, picks_good, picks_bad)
+
+
+@verbose
+def _interpolate_bads_nirs(inst, method='closest', verbose=None):
+    """Interpolate bad nirs channels. Simply replaces by closest non bad.
+
+    Parameters
+    ----------
+    inst : mne.io.Raw, mne.Epochs or mne.Evoked
+        The data to interpolate. Must be preloaded.
+    method : str
+        Only the method 'closest' is currently available. This method replaces
+        each bad channel with the closest non bad channel.
+    %(verbose)s
+    """
+    from scipy.spatial.distance import pdist, squareform
+    from mne.preprocessing.nirs import _channel_frequencies, _check_channels_ordered
+
+    freqs = np.unique(_channel_frequencies(inst))
+    # Returns pick of all nirs and ensures channels are correctly ordered
+    picks_nirs = _check_channels_ordered(inst, freqs)
+    nirs_ch_names = [inst.info['ch_names'][p] for p in picks_nirs]
+    bads_nirs = [ch for ch in inst.info['bads'] if ch in nirs_ch_names]
+
+    # select the bad meg channel to be interpolated
+    if len(bads_nirs) == 0:
+        picks_bad = []
+    else:
+        picks_bad = pick_channels(inst.info['ch_names'], bads_nirs,
+                                  exclude=[])
+    bads_mask = [p in picks_bad for p in picks_nirs]
+
+    chs = [inst.info['chs'][i] for i in picks_nirs]
+    locs3d = np.array([ch['loc'][:3] for ch in chs])
+    dist = pdist(locs3d)
+    dist = squareform(dist)
+
+    for bad in picks_bad:
+        dists_to_bad = dist[bad]
+        # Ignore distances to self
+        dists_to_bad[dists_to_bad == 0] = np.inf
+        # Ignore distances to other bad channels
+        dists_to_bad[bads_mask] = np.inf
+        # Find closest remaining channels
+        closest_idx = np.where(dists_to_bad == np.amin(dists_to_bad))[0]
+        # Return the same frequency
+        closest_idx = closest_idx[bad % 2]
+        inst._data[bad] = inst._data[closest_idx]
+
+    inst.info['bads'] = []
+
+    return inst

mne/channels/tests/test_interpolation.py

@@ -219,4 +219,23 @@ def test_interpolation_ctf_comp():
     assert raw.info['bads'] == []
 
 
+@testing.requires_testing_data
+def test_interpolation_nirs():
+    """Test interpolating bad nirs channels."""
+    from mne.preprocessing.nirs import optical_density, scalp_coupling_index
+    from mne.datasets.testing import data_path
+    from mne.io import read_raw_nirx
+    from itertools import compress
+    from mne.channels.interpolation import _interpolate_bads_nirs
+
+    fname = op.join(data_path(download=False),
+                    'NIRx', '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))
+    _interpolate_bads_nirs(raw_od)
+    assert raw_od.info['bads'] == []
+
+
 run_tests_if_main()