# requires: mayavi
# dataset: mne_sample
'''
Example performes a permutation cluster test on source space data and creates
an HTML file describing he output.
'''
import eelbrain as e
# settings
n_samples = 1000
# create an HTML report in which to document results
report = e.Report("MNE Sample Dataset", author="Prof. Enid Gumby")
section = report.add_section("Introduction")
text = ("A comparison of auditory stimulation to the left vs. the right ear "
"in the MNE sample dataset. "
"Spatio-temporal clusters were formed by thresholding the comparison "
"at a t-value equivalent to an uncorrected p-value of 0.05. For each "
"cluster, a cluster value was calculated as the sum of all t-values "
"in the cluster. In order to calculate p-values for each cluster, "
"a distribution of cluster values was computed by shuffling condition "
"labels %i times and extracting each time the value of the largest "
"cluster." % n_samples)
section.append(text)
'''
use the sample dataset loader to load source space data for the mne samples
dataset. Load only auditory data.
'''
ds = e.datasets.get_mne_sample(-0.1, 0.2, src='ico', sub="modality == 'A'")
# Add a table with trial numbers to the report
'side',
'L',
'R',
ds=ds,
samples=n_samples,
tstart=0.05)
# generate parameter map thresholded at p=0.05
pmap = res.masked_parameter_map(pmin=0.05)
# the next line could be used to plot the result for inspection
# (any area that is significant at any time)
##e.plot.brain.cluster(pmap.sum('time'), surf='inflated')
# create an HTML report with the results form the test
report = e.Report("Permutation Test", author="Prof. Enid Gumby")
# add some information about the test
section = report.add_section("Introduction")
text = ("A comparison of auditory stimulation to the left vs. the right ear. "
"A distribution of t values was calculated by shuffling condition "
"labels %i times and for each test picking the largest absolute t-"
"value across time and space. P-values were then calculated for "
"every source and time point using this distribution." % n_samples)
section.append(text)
# image with significance maps in time bins
section = report.add_section("Result")
image = e.plot.brain.bin_table(pmap,
tstep=0.05,
surf='smoothwm',
views=['lat', 'med'])
section.add_figure("Significant regions in time bins.", image)