""" ===================== Visualize Evoked data ===================== In this tutorial we focus on the plotting functions of :class:`mne.Evoked`. """ import os.path as op import numpy as np import matplotlib.pyplot as plt import mne # sphinx_gallery_thumbnail_number = 9 ############################################################################### # First we read the evoked object from a file. Check out # :ref:`tut_epoching_and_averaging` to get to this stage from raw data. data_path = mne.datasets.sample.data_path() fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif') evoked = mne.read_evokeds(fname, baseline=(None, 0), proj=True) print(evoked) ############################################################################### # Notice that ``evoked`` is a list of :class:`evoked ` instances. # You can read only one of the categories by passing the argument ``condition`` # to :func:`mne.read_evokeds`. To make things more simple for this tutorial, we # read each instance to a variable. evoked_l_aud = evoked[0] evoked_r_aud = evoked[1] evoked_l_vis = evoked[2] evoked_r_vis = evoked[3] ############################################################################### # Let's start with a simple one. We plot event related potentials / fields # (ERP/ERF). The bad channels are not plotted by default. Here we explicitly # set the ``exclude`` parameter to show the bad channels in red. All plotting # functions of MNE-python return a handle to the figure instance. When we have # the handle, we can customise the plots to our liking. fig = evoked_l_aud.plot(exclude=(), time_unit='s') ############################################################################### # All plotting functions of MNE-python return a handle to the figure instance. # When we have the handle, we can customise the plots to our liking. For # example, we can get rid of the empty space with a simple function call. fig.tight_layout() ############################################################################### # Now we will make it a bit fancier and only use MEG channels. Many of the # MNE-functions include a ``picks`` parameter to include a selection of # channels. ``picks`` is simply a list of channel indices that you can easily # construct with :func:`mne.pick_types`. See also :func:`mne.pick_channels` and # :func:`mne.pick_channels_regexp`. # Using ``spatial_colors=True``, the individual channel lines are color coded # to show the sensor positions - specifically, the x, y, and z locations of # the sensors are transformed into R, G and B values. picks = mne.pick_types(evoked_l_aud.info, meg=True, eeg=False, eog=False) evoked_l_aud.plot(spatial_colors=True, gfp=True, picks=picks, time_unit='s') ############################################################################### # Notice the legend on the left. The colors would suggest that there may be two # separate sources for the signals. This wasn't obvious from the first figure. # Try painting the slopes with left mouse button. It should open a new window # with topomaps (scalp plots) of the average over the painted area. There is # also a function for drawing topomaps separately. evoked_l_aud.plot_topomap(time_unit='s') ############################################################################### # By default the topomaps are drawn from evenly spread out points of time over # the evoked data. We can also define the times ourselves. times = np.arange(0.05, 0.151, 0.05) evoked_r_aud.plot_topomap(times=times, ch_type='mag', time_unit='s') ############################################################################### # Or we can automatically select the peaks. evoked_r_aud.plot_topomap(times='peaks', ch_type='mag', time_unit='s') ############################################################################### # You can take a look at the documentation of :func:`mne.Evoked.plot_topomap` # or simply write ``evoked_r_aud.plot_topomap?`` in your python console to # see the different parameters you can pass to this function. Most of the # plotting functions also accept ``axes`` parameter. With that, you can # customise your plots even further. First we create a set of matplotlib # axes in a single figure and plot all of our evoked categories next to each # other. fig, ax = plt.subplots(1, 5, figsize=(8, 2)) kwargs = dict(times=0.1, show=False, vmin=-300, vmax=300, time_unit='s') evoked_l_aud.plot_topomap(axes=ax[0], colorbar=True, **kwargs) evoked_r_aud.plot_topomap(axes=ax[1], colorbar=False, **kwargs) evoked_l_vis.plot_topomap(axes=ax[2], colorbar=False, **kwargs) evoked_r_vis.plot_topomap(axes=ax[3], colorbar=False, **kwargs) for ax, title in zip(ax[:4], ['Aud/L', 'Aud/R', 'Vis/L', 'Vis/R']): ax.set_title(title) plt.show() ############################################################################### # Notice that we created five axes, but had only four categories. The fifth # axes was used for drawing the colorbar. You must provide room for it when you # create this kind of custom plots or turn the colorbar off with # ``colorbar=False``. That's what the warnings are trying to tell you. Also, we # used ``show=False`` for the three first function calls. This prevents the # showing of the figure prematurely. The behavior depends on the mode you are # using for your python session. See http://matplotlib.org/users/shell.html for # more information. # # We can combine the two kinds of plots in one figure using the # :func:`mne.Evoked.plot_joint` method of Evoked objects. Called as-is # (``evoked.plot_joint()``), this function should give an informative display # of spatio-temporal dynamics. # You can directly style the time series part and the topomap part of the plot # using the ``topomap_args`` and ``ts_args`` parameters. You can pass key-value # pairs as a python dictionary. These are then passed as parameters to the # topomaps (:func:`mne.Evoked.plot_topomap`) and time series # (:func:`mne.Evoked.plot`) of the joint plot. # For an example of specific styling using these ``topomap_args`` and # ``ts_args`` arguments, here, topomaps at specific time points # (90 and 200 ms) are shown, sensors are not plotted (via an argument # forwarded to `plot_topomap`), and the Global Field Power is shown: ts_args = dict(gfp=True, time_unit='s') topomap_args = dict(sensors=False, time_unit='s') evoked_r_aud.plot_joint(title='right auditory', times=[.09, .20], ts_args=ts_args, topomap_args=topomap_args) ############################################################################### # Sometimes, you may want to compare two or more conditions at a selection of # sensors, or e.g. for the Global Field Power. For this, you can use the # function :func:`mne.viz.plot_compare_evokeds`. The easiest way is to create # a Python dictionary, where the keys are condition names and the values are # :class:`mne.Evoked` objects. If you provide lists of :class:`mne.Evoked` # objects, such as those for multiple subjects, the grand average is plotted, # along with a confidence interval band - this can be used to contrast # conditions for a whole experiment. # First, we load in the evoked objects into a dictionary, setting the keys to # '/'-separated tags (as we can do with event_ids for epochs). Then, we plot # with :func:`mne.viz.plot_compare_evokeds`. # The plot is styled with dict arguments, again using "/"-separated tags. # We plot a MEG channel with a strong auditory response. # # For move advanced plotting using :func:`mne.viz.plot_compare_evokeds`. # See also :ref:`sphx_glr_auto_tutorials_plot_metadata_epochs.py`. conditions = ["Left Auditory", "Right Auditory", "Left visual", "Right visual"] evoked_dict = dict() for condition in conditions: evoked_dict[condition.replace(" ", "/")] = mne.read_evokeds( fname, baseline=(None, 0), proj=True, condition=condition) print(evoked_dict) colors = dict(Left="Crimson", Right="CornFlowerBlue") linestyles = dict(Auditory='-', visual='--') pick = evoked_dict["Left/Auditory"].ch_names.index('MEG 1811') mne.viz.plot_compare_evokeds(evoked_dict, picks=pick, colors=colors, linestyles=linestyles, split_legend=True) ############################################################################### # We can also plot the activations as images. The time runs along the x-axis # and the channels along the y-axis. The amplitudes are color coded so that # the amplitudes from negative to positive translates to shift from blue to # red. White means zero amplitude. You can use the ``cmap`` parameter to define # the color map yourself. The accepted values include all matplotlib colormaps. evoked_r_aud.plot_image(picks=picks, time_unit='s') ############################################################################### # Finally we plot the sensor data as a topographical view. In the simple case # we plot only left auditory responses, and then we plot them all in the same # figure for comparison. Click on the individual plots to open them bigger. title = 'MNE sample data\n(condition : %s)' evoked_l_aud.plot_topo(title=title % evoked_l_aud.comment, background_color='k', color=['white']) mne.viz.plot_evoked_topo(evoked, title=title % 'Left/Right Auditory/Visual', background_color='w') ############################################################################### # We can also plot the activations as arrow maps on top of the topoplot. # The arrows represent an estimation of the current flow underneath the MEG # sensors. Here, sample number 175 corresponds to the time of the maximum # sensor space activity. evoked_l_aud_mag = evoked_l_aud.copy().pick_types(meg='mag') mne.viz.plot_arrowmap(evoked_l_aud_mag.data[:, 175], evoked_l_aud_mag.info) ############################################################################### # Visualizing field lines in 3D # ----------------------------- # We now compute the field maps to project MEG and EEG data to the MEG helmet # and scalp surface. # # To do this, we need coregistration information. See # :ref:`tut_forward` for more details. Here we just illustrate usage. subjects_dir = data_path + '/subjects' trans_fname = data_path + '/MEG/sample/sample_audvis_raw-trans.fif' maps = mne.make_field_map(evoked_l_aud, trans=trans_fname, subject='sample', subjects_dir=subjects_dir, n_jobs=1) # Finally, explore several points in time field_map = evoked_l_aud.plot_field(maps, time=.1) ############################################################################### # .. note:: # If trans_fname is set to None then only MEG estimates can be visualized.