""" Visualize Raw data ================== """ import os.path as op import numpy as np import mne data_path = op.join(mne.datasets.sample.data_path(), 'MEG', 'sample') raw = mne.io.read_raw_fif(op.join(data_path, 'sample_audvis_raw.fif'), preload=True) raw.set_eeg_reference('average', projection=True) # set EEG average reference ############################################################################### # The visualization module (:mod:`mne.viz`) contains all the plotting functions # that work in combination with MNE data structures. Usually the easiest way to # use them is to call a method of the data container. All of the plotting # method names start with ``plot``. If you're using Ipython console, you can # just write ``raw.plot`` and ask the interpreter for suggestions with a # ``tab`` key. # # To visually inspect your raw data, you can use the python equivalent of # ``mne_browse_raw``. raw.plot(block=True, lowpass=40) ############################################################################### # The channels are color coded by channel type. Generally MEG channels are # colored in different shades of blue, whereas EEG channels are black. The # scrollbar on right side of the browser window also tells us that two of the # channels are marked as ``bad``. Bad channels are color coded gray. By # clicking the lines or channel names on the left, you can mark or unmark a bad # channel interactively. You can use +/- keys to adjust the scale (also = works # for magnifying the data). Note that the initial scaling factors can be set # with parameter ``scalings``. If you don't know the scaling factor for # channels, you can automatically set them by passing scalings='auto'. With # ``pageup/pagedown`` and ``home/end`` keys you can adjust the amount of data # viewed at once. # # Drawing annotations # ------------------- # # You can enter annotation mode by pressing ``a`` key. In annotation mode you # can mark segments of data (and modify existing annotations) with the left # mouse button. You can use the description of any existing annotation or # create a new description by typing when the annotation dialog is active. # Notice that the description starting with the keyword ``'bad'`` means that # the segment will be discarded when epoching the data. Existing annotations # can be deleted with the right mouse button. Annotation mode is exited by # pressing ``a`` again or closing the annotation window. See also # :class:`mne.Annotations` and :ref:`marking_bad_segments`. To see all the # interactive features, hit ``?`` key or click ``help`` in the lower left # corner of the browser window. # # .. warning:: Annotations are modified in-place immediately at run-time. # Deleted annotations cannot be retrieved after deletion. # # The channels are sorted by channel type by default. You can use the # ``group_by`` parameter of :func:`raw.plot ` to group the # channels in a different way. ``group_by='selection'`` uses the same channel # groups as MNE-C's mne_browse_raw (see :ref:`CACCJEJD`). The selections are # defined in ``mne-python/mne/data/mne_analyze.sel`` and by modifying the # channels there, you can define your own selection groups. Notice that this # also affects the selections returned by :func:`mne.read_selection`. By # default the selections only work for Neuromag data, but # ``group_by='position'`` tries to mimic this behavior for any data with sensor # positions available. The channels are grouped by sensor positions to 8 evenly # sized regions. Notice that for this to work effectively, all the data # channels in the channel array must be present. The ``order`` parameter allows # to customize the order and select a subset of channels for plotting (picks). # Here we use the butterfly mode and group the channels by position. To toggle # between regular and butterfly modes, press 'b' key when the plotter window is # active. Notice that ``group_by`` also affects the channel groupings in # butterfly mode. raw.plot(butterfly=True, group_by='position') ############################################################################### # We can read events from a file (or extract them from the trigger channel) # and pass them as a parameter when calling the method. The events are plotted # as vertical lines so you can see how they align with the raw data. # # We can also pass a corresponding "event_id" to transform the event # trigger integers to strings. events = mne.read_events(op.join(data_path, 'sample_audvis_raw-eve.fif')) event_id = {'A/L': 1, 'A/R': 2, 'V/L': 3, 'V/R': 4, 'S': 5, 'B': 32} raw.plot(butterfly=True, events=events, event_id=event_id) ############################################################################### # We can check where the channels reside with ``plot_sensors``. Notice that # this method (along with many other MNE plotting functions) is callable using # any MNE data container where the channel information is available. raw.plot_sensors(kind='3d', ch_type='mag', ch_groups='position') ############################################################################### # We used ``ch_groups='position'`` to color code the different regions. It uses # the same algorithm for dividing the regions as ``order='position'`` of # :func:`raw.plot `. You can also pass a list of picks to # color any channel group with different colors. # # Now let's add some ssp projectors to the raw data. Here we read them from a # file and plot them. projs = mne.read_proj(op.join(data_path, 'sample_audvis_eog-proj.fif')) raw.add_proj(projs) raw.plot_projs_topomap() ############################################################################### # Note that the projections in ``raw.info['projs']`` can be visualized using # :meth:`raw.plot_projs_topomap ` or calling # :func:`proj.plot_topomap ` # # more examples can be found in # :ref:`sphx_glr_auto_examples_io_plot_read_proj.py` projs[0].plot_topomap() ############################################################################### # The first three projectors that we see are the SSP vectors from empty room # measurements to compensate for the noise. The fourth one is the average EEG # reference. These are already applied to the data and can no longer be # removed. The next six are the EOG projections that we added. Every data # channel type has two projection vectors each. Let's try the raw browser # again. raw.plot() ############################################################################### # Now click the ``proj`` button at the lower right corner of the browser # window. A selection dialog should appear, where you can toggle the projectors # on and off. Notice that the first four are already applied to the data and # toggling them does not change the data. However the newly added projectors # modify the data to get rid of the EOG artifacts. Note that toggling the # projectors here doesn't actually modify the data. This is purely for visually # inspecting the effect. See :func:`mne.io.Raw.del_proj` to actually remove the # projectors. # # Raw container also lets us easily plot the power spectra over the raw data. # Here we plot the data using ``spatial_colors`` to map the line colors to # channel locations (default in versions >= 0.15.0). Other option is to use the # ``average`` (default in < 0.15.0). See the API documentation for more info. raw.plot_psd(tmax=np.inf, average=False) ############################################################################### # Plotting channel-wise power spectra is just as easy. The layout is inferred # from the data by default when plotting topo plots. This works for most data, # but it is also possible to define the layouts by hand. Here we select a # layout with only magnetometer channels and plot it. Then we plot the channel # wise spectra of first 30 seconds of the data. layout = mne.channels.read_layout('Vectorview-mag') layout.plot() raw.plot_psd_topo(tmax=30., fmin=5., fmax=60., n_fft=1024, layout=layout)