Memory-efficient IO

Preloading

Raw

MNE-Python can read data on-demand using the preload option provided in IO 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)

Note

Filtering, resampling and dropping or selecting channels does not work with preload=False.

Epochs

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 mne.read_epochs().

Warning

This comes with a caveat. When preload=False, data rejection based on peak-to-peak thresholds is executed when the data is loaded from disk, not when the Epochs object is created.

To explicitly reject artifacts with preload=False, use the function mne.Epochs.drop_bad().

Loading data explicitly

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

Simplest way to access data

If you just want your raw data as a numpy array to work with it in a different framework you can use slicing syntax:

first_channel_data, times = raw[0, :]
channel_3_5, times_3_5 = raw[3:5, :]