|
| 1 | +""" |
| 2 | +Create movie from MEG inverse solution |
| 3 | +======================================= |
| 4 | +
|
| 5 | +Data were computed using mne-python (http://martinos.org/mne) |
| 6 | +
|
| 7 | +""" |
| 8 | +print __doc__ |
| 9 | + |
| 10 | +import os |
| 11 | +import numpy as np |
| 12 | + |
| 13 | +from surfer import Brain |
| 14 | +from surfer.io import read_stc |
| 15 | + |
| 16 | +""" |
| 17 | +create Brain object for visualization |
| 18 | +""" |
| 19 | +brain = Brain('fsaverage', 'split', 'inflated', |
| 20 | + config_opts=dict(width=800, height=400)) |
| 21 | + |
| 22 | +""" |
| 23 | +read MNE dSPM inverse solution |
| 24 | +""" |
| 25 | +for hemi in ['lh', 'rh']: |
| 26 | + stc_fname = os.path.join('example_data', |
| 27 | + 'meg_source_estimate-' + hemi + '.stc') |
| 28 | + stc = read_stc(stc_fname) |
| 29 | + data = stc['data'] |
| 30 | + |
| 31 | + """ |
| 32 | + time points in milliseconds |
| 33 | + """ |
| 34 | + time = 1e3 * np.linspace(stc['tmin'], |
| 35 | + stc['tmin'] + data.shape[1] * stc['tstep'], |
| 36 | + data.shape[1]) |
| 37 | + |
| 38 | + brain.add_data(data, colormap='hot', vertices=stc['vertices'], |
| 39 | + smoothing_steps=10, time=time, time_label='time=%0.2f ms', |
| 40 | + hemi=hemi) |
| 41 | + |
| 42 | +""" |
| 43 | +scale colormap |
| 44 | +""" |
| 45 | +brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True) |
| 46 | + |
| 47 | +""" |
| 48 | +Save movies with different combinations of views |
| 49 | +""" |
| 50 | +brain.save_movie('example_current.mov') |
| 51 | +brain.save_movie('example_single.mov', montage='single') |
| 52 | +brain.save_movie('example_h.mov', montage=['lat', 'med'], orientation='h') |
| 53 | +brain.save_movie('example_v.mov', montage=[['lat'], ['med']]) |
| 54 | + |
| 55 | +brain.close() |
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