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preprocessing.py
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preprocessing.py
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import logging
from collections import OrderedDict
from operator import methodcaller
from typing import Union
import mne
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import FunctionTransformer, Pipeline
log = logging.getLogger(__name__)
def _is_none_pipeline(pipeline):
"""Check if a pipeline is the result of make_pipeline(None)"""
return isinstance(pipeline, Pipeline) and pipeline.steps[0][1] is None
class ForkPipelines(TransformerMixin, BaseEstimator):
def __init__(self, transformers: list[tuple[str, Union[Pipeline, TransformerMixin]]]):
for _, t in transformers:
assert hasattr(t, "transform")
self.transformers = transformers
def transform(self, X, y=None):
return OrderedDict([(n, t.transform(X)) for n, t in self.transformers])
def fit(self, X, y=None):
for _, t in self.transformers:
t.fit(X)
class FixedTransformer(TransformerMixin, BaseEstimator):
def fit(self, X, y=None):
pass
class RawToEvents(FixedTransformer):
def __init__(self, event_id):
assert isinstance(event_id, dict) # not None
self.event_id = event_id
def transform(self, raw, y=None):
stim_channels = mne.utils._get_stim_channel(None, raw.info, raise_error=False)
if len(stim_channels) > 0:
events = mne.find_events(raw, shortest_event=0, verbose=False)
else:
events, _ = mne.events_from_annotations(
raw, event_id=self.event_id, verbose=False
)
try:
events = mne.pick_events(events, include=list(self.event_id.values()))
except RuntimeError:
# skip raw if no event found
return
return events
class RawToFixedIntervalEvents(FixedTransformer):
def __init__(
self,
length_samples,
stride_samples,
start_offset_samples,
stop_offset_samples,
marker=1,
):
self.length_samples = length_samples
self.stride_samples = stride_samples
self.start_offset_samples = start_offset_samples
self.stop_offset_samples = stop_offset_samples
self.marker = marker
def transform(self, raw: mne.io.BaseRaw, y=None):
stop_offset_samples = (
raw.n_times if self.stop_offset_samples is None else self.stop_offset_samples
)
stop_samples = stop_offset_samples - self.length_samples + raw.first_samp
onset = np.arange(
raw.first_samp + self.start_offset_samples,
stop_samples + 1,
self.window_stride_samples,
)
events = np.empty((len(onset), 3), dtype=int)
events[:, 0] = onset
events[:, 1] = self.length_samples
events[:, 2] = self.marker
return events
class EpochsToEvents(FixedTransformer):
def transform(self, epochs, y=None):
return epochs.events
class EventsToLabels(FixedTransformer):
def __init__(self, event_id):
self.event_id = event_id
def transform(self, events, y=None):
inv_events = {k: v for v, k in self.event_id.items()}
labels = [inv_events[e] for e in events[:, -1]]
return labels
class RawToEpochs(FixedTransformer):
def __init__(
self,
event_id: dict[str, int],
tmin: float,
tmax: float,
baseline: tuple[float, float],
channels: list[str] = None,
):
assert isinstance(event_id, dict) # not None
self.event_id = event_id
self.tmin = tmin
self.tmax = tmax
self.baseline = baseline
self.channels = channels
def transform(self, X, y=None):
raw = X["raw"]
events = X["events"]
if not isinstance(raw, mne.io.BaseRaw):
raise ValueError("raw must be a mne.io.BaseRaw")
if self.channels is None:
picks = mne.pick_types(raw.info, eeg=True, stim=False)
else:
picks = mne.pick_channels(
raw.info["ch_names"], include=self.channels, ordered=True
)
epochs = mne.Epochs(
raw,
events,
event_id=self.event_id,
tmin=self.tmin,
tmax=self.tmax,
proj=False,
baseline=self.baseline,
preload=True,
verbose=False,
picks=picks,
event_repeated="drop",
on_missing="ignore",
)
return epochs
def get_filter_pipeline(fmin, fmax):
return FunctionTransformer(
methodcaller(
"filter",
l_freq=fmin,
h_freq=fmax,
method="iir",
picks="eeg",
verbose=False,
),
)
def get_crop_pipeline(tmin, tmax):
return FunctionTransformer(
methodcaller("crop", tmin=tmax, tmax=tmin, verbose=False),
)
def get_resample_pipeline(sfreq):
return FunctionTransformer(
methodcaller("resample", sfreq=sfreq, verbose=False),
)