<div dir="ltr">hi yvonne,<div><br></div><div><span style="font-size:12.8px">connectivity must be a sparse matrix not a numpy array</span><br></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">from scipy import sparse</span></div><div><span style="font-size:12.8px">connectivity = sparse.eye(</span><span style="font-size:12.8px">numChan</span><span style="font-size:12.8px">)</span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">HTH</span></div><div><span style="font-size:12.8px">Alex</span></div><div><span style="font-size:12.8px"><br></span></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Sep 20, 2017 at 10:00 PM, Yvonne Fonken <span dir="ltr"><<a href="mailto:yfonken@berkeley.edu" target="_blank">yfonken@berkeley.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hi Alex, <div><br></div><div>Thank you for your reply. </div><div><br></div><div>This is my code, with two versions of a diagonal connectivity matrix (using version 14.1 of MNE): </div><div><div># Implement cluster permutation</div><div>numChan = Data.shape[1] # Data is shaped: Trials (115) x channels (217) x timepoints (30) numpy array</div><div>#connectivity = np.diagflat( range(0,numChan), k = 0)</div><div>connectivity = np.diagflat( np.ones((1,numChan)), k = 0) # N x N diagonal matrix with N being # of electrodes</div><div>TestData = np.swapaxes(Data, 1,2) # trials x time x space (electrodes)</div><div>T_obs, clusters, p_values, H0 = mne.stats.permutation_cluster_<wbr>1samp_test(TestData, n_permutations = 500, tail = 0, connectivity = connectivity)</div></div><div><br></div><div>Both versions of the connectivity matrix now give me the following error: </div><div>'numpy.ndarray' object has no attribute 'tocsr'<br></div><div><br></div><div>Thanks,</div><div>Yvonne</div></div><div class="gmail_extra"><div><div class="h5"><br><div class="gmail_quote">On Tue, Sep 19, 2017 at 1:02 AM, Alexandre Gramfort <span dir="ltr"><<a href="mailto:alexandre.gramfort@inria.fr" target="_blank">alexandre.gramfort@inria.fr</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi Yvonne<br>
<br>
I would have said that a sparse diagonal matrix should work.<br>
<br>
can you share the code snippet you used?<br>
<br>
Alex<br>
<div><div class="m_-5055594899142456954h5"><br>
<br>
On Tue, Sep 19, 2017 at 3:17 AM, Yvonne Fonken <<a href="mailto:yfonken@berkeley.edu" target="_blank">yfonken@berkeley.edu</a>> wrote:<br>
> Hi all,<br>
><br>
> I am trying to apply the cluster permutation function in MNE on intracranial<br>
> data (electrodes by time points), either by using<br>
> mne.stats.permutation_cluster_<wbr>test, or<br>
> mne.stats.permutation_cluster_<wbr>1samp_test. In this case, I do not want to<br>
> cluster across electrodes, but I want to include this dimension in order to<br>
> correct for multiple comparisons.<br>
><br>
> My question is: how do I specify to only cluster over time, and not<br>
> electrodes? Can I do this using the connectivity matrix, and if yes, what<br>
> would a connectivity matrix look like when all electrodes do not have<br>
> neighbors? I've tried using diagonal matrices already, but I still get<br>
> clusters spanning multiple electrodes.<br>
><br>
> Thanks,<br>
> Yvonne<br>
><br>
> --<br>
> Yvonne Fonken<br>
> PhD Candidate<br>
> Helen Wills Neuroscience Institute<br>
> University of California, Berkeley<br>
><br>
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</blockquote></div><br><br clear="all"><div><br></div></div></div><span class="HOEnZb"><font color="#888888">-- <br></font></span><div class="m_-5055594899142456954gmail_signature" data-smartmail="gmail_signature"><span class="HOEnZb"><font color="#888888">Yvonne Fonken<br>Graduate student</font></span><span class=""><br>Helen Wills Neuroscience Institute<br>University of California, Berkeley<br></span></div>
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