<div dir="ltr"><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)">​Hi, thanks I will try that,  I understand therefore that the number of features per subject need not be equal across subjects for searchlight hyperalignment - but please correct me if am wrong.</div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)">best</div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)">david</div></div><div class="gmail_extra"><br><div class="gmail_quote">On 29 July 2016 at 16:17, Swaroop Guntupalli <span dir="ltr"><<a href="mailto:swaroopgj@gmail.com" target="_blank">swaroopgj@gmail.com</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 David,<div><br></div><div>If you are using searchlight hyperalignment, it is advisable to align the data across subjects using anatomy first. Simplest would be to be align them to an MNI template and then run the searchlight hyperalignment.</div><div>Our tutorial dataset is affine aligned to MNI template.</div><div><br></div><div>Best,</div><div>Swaroop</div></div><div class="HOEnZb"><div class="h5"><div class="gmail_extra"><br><div class="gmail_quote">On Thu, Jul 28, 2016 at 10:51 AM, David Soto <span dir="ltr"><<a href="mailto:d.soto.b@gmail.com" target="_blank">d.soto.b@gmail.com</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"><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)">Thanks Swaroop, I managed to get the dataset in the right format as per the hyperaligmentsearchlight tutorial</div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)">however when I run the hyperaligment I get the following error (<span style="font-size:13px;font-family:arial,sans-serif;color:rgb(34,34,34)">IndexError: index 46268 is out of bounds for axis 1 with size 43506, </span>see further below)...to recap the dataset is a concatenation of each subject data, each in individual native space, so number of features are different across subjects... </div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)">The code I use is the same as in the tutorial, namely, any feedback would be great, thanks, david</div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">cv = CrossValidation(clf, NFoldPartitioner(attr='</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">subject'),</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">                     errorfx=mean_match_accuracy)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><br><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">for test_run in range(nruns):</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    ds_train = [sd[sd.sa.chunks != test_run, :] for sd in ds_all]</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    ds_test = [sd[sd.sa.chunks == test_run, :] for sd in ds_all]</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    slhyper = SearchlightHyperalignment(</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">radius=3, featsel=0.4, sparse_radius=3)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    slhypmaps = slhyper(ds_train)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    ds_hyper = [h.forward(sd) for h, sd in zip(slhypmaps, ds_test)]</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><br></span></div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    ds_hyper = vstack(ds_hyper)</span><br><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    zscore(ds_hyper, chunks_attr='subject')</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    res_cv = cv(ds_hyper)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    bsc_slhyper_results.append(</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">res_cv)</span><br></div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)"><br></div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)">OUTPUT MESSAGE.........</div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">Performing classification analyses...</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">  between-subject (searchlight hyperaligned)...</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">------------------------------</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">------------------------------</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">---------------</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">IndexError                                Traceback (most recent call last)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><ipython-input-191-</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">85bdb873d4f1> in <module>()</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">     24     # Searchlight Hyperalignment returns a list of mappers corresponding to</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">     25     # subjects in the same order as the list of datasets we passed in.</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">---> 26     slhypmaps = slhyper(ds_train)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">     27</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">     28     # Applying hyperalignment parameters is similar to applying any mapper in</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">/usr/local/lib/python2.7/site-</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">packages/mvpa2/algorithms/</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">searchlight_hyperalignment.pyc in __call__(self, datasets)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    626             node_blocks = np.array_split(roi_ids, params.nblocks)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    627             p_results = [self._proc_block(block, datasets, hmeasure, queryengines)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">--> 628                          for block in node_blocks]</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    629         results_ds = self.__handle_all_results(p_</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">results)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    630         # Dummy iterator for, you know, iteration</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">/usr/local/lib/python2.7/site-</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">packages/mvpa2/algorithms/</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">searchlight_hyperalignment.pyc in _proc_block(self, block, datasets, featselhyper, queryengines, seed, iblock)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    387                 continue</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    388             # selecting neighborhood for all subject for hyperalignment</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">--> 389             ds_temp = [sd[:, ids] for sd, ids in zip(datasets, roi_feature_ids_all)]</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    390             if self.force_roi_seed:</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    391                 roi_seed = np.array(roi_feature_ids_all[</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">self.params.ref_ds]) == node_id</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">/usr/local/lib/python2.7/site-</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">packages/mvpa2/datasets/base.</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">pyc in __getitem__(self, args)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    139</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    140         # let the base do the work</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">--> 141         ds = super(Dataset, self).__getitem__(args)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    142</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    143         # and adjusting the mapper (if any)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">/usr/local/lib/python2.7/site-</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">packages/mvpa2/base/dataset.</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">pyc in __getitem__(self, args)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    445         if isinstance(self.samples, np.ndarray):</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    446             if np.any([isinstance(a, slice) for a in args]):</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">--> 447                 samples = self.samples[args[0], args[1]]</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    448             else:</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    449                 # works even with bool masks (although without</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">IndexError: index 46268 is out of bounds for axis 1 with size 43506</span><br></div></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">On 28 July 2016 at 00:25, Swaroop Guntupalli <span dir="ltr"><<a href="mailto:swaroopgj@gmail.com" target="_blank">swaroopgj@gmail.com</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 David, <div><br></div><div>If you have limited data, you can use a part of it (however you split the data for training and testing)<div>to train hyperalignment, and also use the same part to train the classifier and then apply hyperalignment and test classifier on the left-out part. Yes, you can artificially create 2 chunks (or more if you prefer). </div><div><br></div></div></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Jul 27, 2016 at 3:17 PM, David Soto <span dir="ltr"><<a href="mailto:d.soto.b@gmail.com" target="_blank">d.soto.b@gmail.com</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"><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)"><div class="gmail_default">sounds great thanks, a further thing is that I have seen that in order to preclude  circularity issues, hyperalinment is implemented on a subset of training chunks and then the transformation is applied to the full datasets prior to classification analyses.  Given that I have no proper chunks/runs here, but only 56 betas across trials, would it be okay to train hyperaligment just on half of the 56 betas, eg artificially split the data set in 2 chunks  each containing 14 betas of class A and 14 of class B? Or would it be just OK to train hyperaligment on the 56 betas in the first instance?</div><div class="gmail_default">thanks!</div><span><font color="#888888"><div class="gmail_default">david</div></font></span></div></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">On 28 July 2016 at 00:00, Swaroop Guntupalli <span dir="ltr"><<a href="mailto:swaroopgj@gmail.com" target="_blank">swaroopgj@gmail.com</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">The hyperalignment example on PyMVPA uses one beta map for each category per run.</div><div><div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Jul 27, 2016 at 2:57 PM, Swaroop Guntupalli <span dir="ltr"><<a href="mailto:swaroopgj@gmail.com" target="_blank">swaroopgj@gmail.com</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 David,<div><br></div><div>Beta maps should work fine for hyperalignment. The more maps (or TRs) there are, better the estimate.</div><div>We used within-subject hyperalignment in Haxby et al. 2011, which uses maps from 6 categories (we used 3 successive betas per condition I think).</div><div><br></div><div>vstack() merges multiple datasets into a single dataset, and if there is any voxel count (nfeatures) mismatch across subjects, it won't work (as evidenced by the error). </div><div>Hyperalignment takes in a list of datasets, one per each subject. </div><div>So, you can make that a list as </div><div>ds_all =[ds1, ds2, ...., ds16]</div><div>and use for Hyperalignment()</div><div><br></div><div>Best,</div><div>Swaroop</div><div><br></div></div><div class="gmail_extra"><br><div class="gmail_quote"><div><div>On Wed, Jul 27, 2016 at 2:28 PM, David Soto <span dir="ltr"><<a href="mailto:d.soto.b@gmail.com" target="_blank">d.soto.b@gmail.com</a>></span> wrote:<br></div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div><div dir="ltr"><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)">hi, </div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)"><br></div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)">in my experiment I have 28 betas in condition A and 28 parameter estimate images and 28  in condition B for each subject (N=16 in total).</div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)"> </div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)">i have performed across-subjects SVM-based searchlight classification using MNI-registered individual beta images and I would like to repeat and confirm my results using searchlight based on hyperaligned data.</div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)"><br></div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)">i am not aware of any paper using hyperaligment on  beta images but I think this should be possible, any advise please would be nice</div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)"><br></div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)">i've created individual datasets concatenating the 28 betas in condition A and the 28 in condition (in the actual experiment condition A and B can appear randomly on each trial). I have 16 nifti datasets, one per subject, with each in individual native anatomical space. In trying to get a dataset in the same format as in the hyperlignment tutorial I use fmri_dataset on each individual wholebrain 48 betas  and then try to merged then all i.e. <span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">ds_merged = vstack((d1, d2, d3, d4, d5, d6, d7, d8, d9, d10, d11, d12, d13, d14, d15,d16)) but this gives the following error pasted at the end,</span></div><div class="gmail_default" style="font-family:garamond,serif;font-size:large;color:rgb(0,0,0)">which I think it is becos the number of voxels is different across subjects. This is one issue.</div><div class="gmail_default"><font color="#000000" face="garamond, serif" size="4"><br></font></div><div class="gmail_default"><font color="#000000" face="garamond, serif" size="4">Another is that the function vstack does appear to produce the list of individual datasets that is in the hyperligment tutorial dataset, but a list of individual betas, I would be grateful to receive some tips.</font></div><div class="gmail_default"><font color="#000000" face="garamond, serif" size="4"><br></font></div><div class="gmail_default"><font color="#000000" face="garamond, serif" size="4">thanks!</font></div><div class="gmail_default"><font color="#000000" face="garamond, serif" size="4">david</font></div><div class="gmail_default"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">------------------------------</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">------------------------------</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">---------------</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">ValueError                                Traceback (most recent call last)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><ipython-input-64-</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">2fef46542bfc> in <module>()</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">     19 h5save('/home/dsoto/dsoto/</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">fmri/wmlearning/h5.hdf5', [d1,d2])</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">     20 #ds_merged = vstack((d1, d2, d3, d4, d5, d6, d7,d8,d9, d10, d11, d12, d13, d14, d15, d16))</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">---> 21 ds_merged = vstack((d1, d2))</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">/usr/local/lib/python2.7/site-</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">packages/mvpa2/base/dataset.</span><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">pyc in vstack(datasets, a)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    687                              "datasets have varying attributes.")</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    688     # will puke if not equal number of features</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">--> 689     stacked_samp = np.concatenate([ds.samples for ds in datasets], axis=0)</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    690</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">    691     stacked_sa = {}</span><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><br style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:13px">ValueError: all the input array dimensions except for the concatenation axis must match exactly</span><br></div></div>
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