eelbrain.plot.brain.SequencePlotter
- class eelbrain.plot.brain.SequencePlotter(subject=None, subjects_dir=None)
Grid of anatomical images in one figure
Notes
For plots with multiple layers, layers are added to the brain in the order they were added to the
SequencePlotter
.Examples
Plot an evoked response or TRF (i.e., a time by source
NDVar
) in 50 ms bins between 0 and 300 ms. Use theextrema()
method to plot peak activity in each time bin. This will plot the most extreme value including its sign, and thus show positive and negative effects:# Bin the data ndvar_binned = ndvar.bin(step=0.050, start=0, stop=0.3, func='extrema') # Initialize the SequencePlotter and set parameters for visualizing the brain sp = plot.brain.SequencePlotter() sp.set_brain_args(surf='smoothwm') # Add the data to be plotted sp.add_ndvar(ndvar_binned) # Generate the figure with multiple brain plots p = sp.plot_table(view='lateral') # Save as you would normally p.save('Figure.pdf')
Plot specific time points in an evoked response or TRF (
ndvar
). To do this, simply add mutiple data objects. Make sure to pass a colormap orvmax
parameter to make the color-scale consistent between plots:cmap = plot.soft_threshold_colormap('xpolar-a', 0.0001, 0.010) sp = plot.brain.SequencePlotter() sp.set_brain_args(surf='smoothwm') for t in [0.050, 0.100, 0.200]: sp.add_ndvar(ndvar.sub(time=t), cmap=cmap, label=f'{int(t*1000)} ms') p = sp.plot_table(view='lateral', orientation='vertical')
Visualize a test result, by separately adding condition means and a difference map that is masked by significance:
res = testnd.TTestRelated('srcm', 'condition', 'a', 'b', match='subject', data=data) vmax = 3 # explicitly set vmax to make sure that the color-maps agree sp = plot.brain.SequencePlotter() sp.set_brain_args(surf='inflated') sp.add_ndvar(res.c1_mean, vmax=vmax, label='a') sp.add_ndvar(res.c0_mean, vmax=vmax, label='b') sp.add_ndvar(res.masked_difference(), vmax=vmax, label='a - b') p = sp.plot_table(view='lateral', orientation='vertical')
Plot a source by time test result, showing how a difference map evolves over time:
res = testnd.TTestRelated('srcm', 'condition', 'a', 'b', match='subject', data=data) difference = res.masked_difference() binned = difference.bin(step=0.200, start=0.200, stop=1.00, func='extrema') sp = plot.brain.SequencePlotter() sp.add_ndvar(binned) p = sp.plot_table(view='lateral', title='a = b')
Methods
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Custom modification of the brain object (calls |
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Add a label to the plot. |
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Add a data layer to the brain plot |
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Add a boolean label to the brain plot |
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Create a figure with the images |
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Set the brain model on which to plot |
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Set parameters for anatomical plot (see |
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Set the order in which frames are plotted |
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Set view for all plots (see |