Memory-efficient I/O

Preloading continuous (raw) data

MNE-Python can read data on-demand using the preload option provided in raw reading functions. For example:

from mne import io
from mne.datasets import sample
data_path = sample.data_path()
raw_fname = data_path / 'MEG' / 'sample' / 'sample_audvis_filt-0-40_raw.fif'
raw = io.read_raw_fif(raw_fname, preload=False)

Preloading epoched data

Similarly, epochs can also be be read from disk on-demand. For example:

import mne
events = mne.find_events(raw)
event_id, tmin, tmax = 1, -0.2, 0.5
picks = mne.pick_types(raw.info, meg=True, eeg=True, stim=False, eog=True)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), reject=dict(eeg=80e-6, eog=150e-6),
                    preload=False)

When preload=False, the epochs data is loaded from the disk on-demand. Note that preload=False for epochs will work even if the raw object has been loaded with preload=True. Preloading is also supported for :func:`mne.read_epochs`.

To explicitly reject artifacts with preload=False, use the function :func:`mne.Epochs.drop_bad`.

Loading data explicitly

To load the data if preload=False was initially selected, use the functions :func:`mne.io.Raw.load_data` and :func:`mne.Epochs.load_data`.

Accessing data as NumPy arrays

If you just want your raw data as a :class:`Numpy array <numpy.ndarray>` to work with it in a different framework you can use slicing syntax:

first_channel_data, times = raw[0, :]
channels_3_and_4, times = raw[3:5, :]