Eric Larson
a2dd739e3d
* FIX: Unify around time_unit="s" * FIX: Examples [circle full] * FIX: Doctest [circle full] * FIX: Bump timeout [circle full] * FIX: More examples [circle full] * FIX: More fixes [circle full]
77 lines
3.4 KiB
Python
77 lines
3.4 KiB
Python
"""
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.. _tut_evoked_objects:
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The :class:`Evoked <mne.Evoked>` data structure: evoked/averaged data
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=====================================================================
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The :class:`Evoked <mne.Evoked>` data structure is mainly used for storing
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averaged data over trials. In MNE the evoked objects are usually created by
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averaging epochs data with :func:`mne.Epochs.average`.
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"""
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import os.path as op
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import mne
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###############################################################################
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# Here for convenience we read the evoked dataset from a file.
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data_path = mne.datasets.sample.data_path()
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fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
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evokeds = mne.read_evokeds(fname, baseline=(None, 0), proj=True)
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print(evokeds)
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###############################################################################
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# Notice that the reader function returned a list of evoked instances. This is
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# because you can store multiple categories into a single file. Here we have
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# categories of
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# ``['Left Auditory', 'Right Auditory', 'Left Visual', 'Right Visual']``.
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# We can also use ``condition`` parameter to read in only one category.
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evoked = mne.read_evokeds(fname, condition='Left Auditory')
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evoked.apply_baseline((None, 0)).apply_proj()
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print(evoked)
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###############################################################################
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# If you're gone through the tutorials of raw and epochs datasets, you're
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# probably already familiar with the :class:`Info <mne.Info>` attribute.
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# There is nothing new or special with the ``evoked.info``. All the relevant
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# info is still there.
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print(evoked.info)
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print(evoked.times)
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###############################################################################
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# The evoked data structure also contains some new attributes easily
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# accessible:
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print(evoked.nave) # Number of averaged epochs.
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print(evoked.first) # First time sample.
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print(evoked.last) # Last time sample.
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print(evoked.comment) # Comment on dataset. Usually the condition.
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print(evoked.kind) # Type of data, either average or standard_error.
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###############################################################################
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# The data is also easily accessible. Since the evoked data arrays are usually
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# much smaller than raw or epochs datasets, they are preloaded into the memory
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# when the evoked object is constructed. You can access the data as a numpy
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# array.
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data = evoked.data
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print(data.shape)
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###############################################################################
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# The data is arranged in an array of shape `(n_channels, n_times)`. Notice
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# that unlike epochs, evoked object does not support indexing. This means that
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# to access the data of a specific channel you must use the data array
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# directly.
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print('Data from channel {0}:'.format(evoked.ch_names[10]))
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print(data[10])
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###############################################################################
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# If you want to import evoked data from some other system and you have it in a
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# numpy array you can use :class:`mne.EvokedArray` for that. All you need is
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# the data and some info about the evoked data. For more information, see
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# :ref:`tut_creating_data_structures`.
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evoked = mne.EvokedArray(data, evoked.info, tmin=evoked.times[0])
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evoked.plot(time_unit='s')
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###############################################################################
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# To write an evoked dataset to a file, use the :meth:`mne.Evoked.save` method.
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# To save multiple categories to a single file, see :func:`mne.write_evokeds`.
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