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:orphan:
Memory-efficient I/O
====================
.. NOTE: part of this file is included in doc/overview/implementation.rst.
Changes here are reflected there. If you want to link to this content, link
to :ref:`memory` to link to that section of the implementation.rst
page. The next line is a target for :start-after: so we can omit the title
from the include:
memory-begin-content
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)
.. note:: Filtering, resampling and dropping or selecting channels does not
work with ``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`.
.. 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 :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, :]
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