[pymvpa] hyperalignment inquiry
David Soto
d.soto.b at gmail.com
Thu Aug 4 15:30:51 UTC 2016
Hi Swaroop, I then get this message, which might be becos of the different
number of features across subjects, which would lead to different ids
across subjects?
RuntimeError: Query engine IndexQueryEngine(voxel_indices=Sphere(radius=3))
provided different ids than
IndexQueryEngine(voxel_indices=Sphere(radius=3)). Not supported
I think I should try with the intersection mask
thanks
david
On 4 August 2016 at 17:09, Swaroop Guntupalli <swaroopgj at gmail.com> wrote:
> Hi David,
> You need to change this line
> qe.append(testqe.query_byid)
> to this
> qe.append(testqe)
>
> Intersection mask might work. I usually do it by computing a binary mask
> in each subject with non-invariant features (use std(axis=0)==0) and then
> do an "and" of these masks.
>
> Does that help?
> Best,
> Swaroop
>
> On Thu, Aug 4, 2016 at 3:50 AM, David Soto <d.soto.b at gmail.com> wrote:
>
>> Thanks very much Swaroop, the code still gives an error, please see
>> below -also final comment at the bottom of the email
>>
>> ---------------------------------------------------------------------------AttributeError Traceback (most recent call last)<ipython-input-52-8bb0dc44f9a7> in <module>() 63 # Searchlight Hyperalignment returns a list of mappers corresponding to 64 # subjects in the same order as the list of datasets we passed in.---> 65 slhypmaps = slhyper(ds_train) 66 67 # Applying hyperalignment parameters is similar to applying any mapper in
>> /usr/local/lib/python2.7/site-packages/mvpa2/algorithms/searchlight_hyperalignment.pyc in __call__(self, datasets) 554 # alignment, i.e. the SL ROIs cover roughly the same area 555 queryengines = self._get_trained_queryengines(--> 556 datasets, params.queryengine, params.radius, params.ref_ds) 557 # For surface nodes to voxels queryengines, roi_seed hardly makes sense 558 if isinstance(queryengines[params.ref_ds], SurfaceVerticesQueryEngine):
>> /usr/local/lib/python2.7/site-packages/mvpa2/algorithms/searchlight_hyperalignment.pyc in _get_trained_queryengines(self, datasets, queryengine, radius, ref_ds) 666 _shpaldebug("Training provided query engines") 667 for qe, ds in zip(queryengines, datasets):--> 668 qe.train(ds) 669 else: 670 queryengine.train(datasets[ref_ds])
>> AttributeError: 'function' object has no attribute 'train'
>>
>>
>> _____
>> %reset
>> from mvpa2.suite import *
>> path0='/Users/dsoto/Dropbox/4hyperal'
>> attr = SampleAttributes(os.path.join(path0,
>> 'attrs_cv_singlsubj2chunk.txt'))
>> d=[]
>> qe=[]
>> num=-1
>> for subj in range(1,5):
>> num=num+1
>> print(subj)
>> datapath='/Users/dsoto/Dropbox/4hyperal/subj%d' %(int(subj))
>> d.append(fmri_dataset(samples=os.path.join(datapath,
>> 'reg4mm.nii.gz'),
>> targets=attr.targets, chunks=attr.chunks,
>> mask=os.path.join(path0, 'mni4mm.nii.gz')))
>>
>> d[num]=remove_invariant_features(d[num])
>> testqe=IndexQueryEngine(voxel_indices=Sphere(3))
>> testqe.train(d[num])
>> qe.append(testqe.query_byid)
>>
>> qe_merged = [qe[0],qe[1],qe[2],qe[3]]
>> ds_merged = [d[0],d[1],d[2],d[3]]
>>
>> _ = [zscore(ds) for ds in ds_merged]
>>
>> for i, sd in enumerate(ds_merged):
>> sd.sa['subject'] = np.repeat(i, len(sd))
>>
>> nsubjs = 4
>> ncats = 2
>> nruns = 2
>>
>>
>> bsc_slhyper_results = []
>> clf = LinearCSVMC()
>>
>> cv = CrossValidation(clf, NFoldPartitioner(attr='subject'),
>> errorfx=mean_match_accuracy)
>>
>> for test_run in range(nruns):
>> ds_train = [sd[sd.sa.chunks != test_run, :] for sd in ds_merged]
>> ds_test = [sd[sd.sa.chunks == test_run, :] for sd in ds_merged]
>>
>> slhyper = SearchlightHyperalignment(queryengine=qe_merged)
>> slhypmaps = slhyper(ds_train)
>>
>> ds_hyper = [h.forward(sd) for h, sd in zip(slhypmaps, ds_test)]
>>
>> ds_hyper = vstack(ds_hyper)
>> zscore(ds_hyper, chunks_attr='subject')
>> res_cv = cv(ds_hyper)
>> bsc_slhyper_results.append(res_cv)
>>
>> bsc_slhyper_results = hstack(bsc_slhyper_results)
>>
>> However, as you know it is likely that searchlighhyperaligment wont work
>> well if each dataset has different number of features....so perhaps would
>> be worth to compare any result with the second analyses option you
>> suggested, that is once I remove invariant features from each subjects
>> dataset, I could derive an intersection mask with valid features across all
>> subjects and then use this mask when I load the data with
>> fmri_dataset......however could you let me know how can this intersection
>> mask be derived?
>>
>> best
>> david
>>
>> [image: ][image: ][image: ]
>> ___________
>>
>> On 3 August 2016 at 18:33, Swaroop Guntupalli <swaroopgj at gmail.com>
>> wrote:
>>
>>> Hi David,
>>>
>>> You have to train the queryengine, here is a little modified version of
>>> what you pasted.
>>> However, remember that the datasets should be approximately similar in
>>> coverage. I noticed that your first subject has ~80,000 non-invariant
>>> features and some other subjects have ~30,000. That's a huge margin.
>>>
>>> I am not sure how hyperalignment performs with so many missing regions.
>>> Swaroop
>>>
>>> d[num]=remove_invariant_features(d[num])
>>>
>>> # You don't need this line below
>>> #voxel_indices=d[num].fa.voxel_indices
>>>
>>> testqe=IndexQueryEngine(voxel_indices=Sphere(3))
>>> # Train the testqe with that dataset
>>> testqe.train(d[num])
>>>
>>> qe.append(testqe.query_byid)
>>>
>>> On Tue, Aug 2, 2016 at 6:30 AM, David Soto <d.soto.b at gmail.com> wrote:
>>>
>>>> Hi Swaroop, thanks for looking into it...
>>>>
>>>> There does not seem be a clear documentation on how to get the query
>>>> engines ..from what I have seen this is what I did:
>>>>
>>>> d=[]
>>>> qe=[]
>>>> num=-1
>>>> for subj in range(1,5):
>>>> num=num+1
>>>> print(subj)
>>>> datapath='/Users/dsoto/Dropbox/4hyperal/subj%d' %(int(subj))
>>>> d.append(fmri_dataset(samples=os.path.join(datapath,
>>>> 'reg4mm.nii.gz'),
>>>> targets=attr.targets, chunks=attr.chunks,
>>>> mask=os.path.join(path0, 'mni4mm.nii.gz')))
>>>>
>>>> d[num]=remove_invariant_features(d[num])
>>>>
>>>> voxel_indices=d[num].fa.voxel_indices
>>>>
>>>> testqe=IndexQueryEngine(voxel_indices=Sphere(3))
>>>>
>>>> qe.append(testqe.query_byid)
>>>>
>>>> qe_merged = [qe[0],qe[1],qe[2],qe[3]]
>>>>
>>>> ds_merged = [d[0],d[1],d[2],d[3]]
>>>>
>>>> which I then was hoping to pass later onto
>>>> slhyper = SearchlightHyperalignment(queryengine=qe_merged)
>>>>
>>>> however there does not appear to be an output for each of the 'qe'
>>>> above, even though I can see the voxel_ids...
>>>>
>>>> any tips would be very useful!
>>>>
>>>> thanks!
>>>>
>>>>
>>>> [image: ][image: ][image: ]
>>>> -------------------
>>>>
>>>> On 1 August 2016 at 19:02, Swaroop Guntupalli <swaroopgj at gmail.com>
>>>> wrote:
>>>>
>>>>> Hi David,
>>>>>
>>>>> Thanks for the data. The issue is that when using searchlight
>>>>> hyperalignment without supplying queryengines but providing radius (as in
>>>>> your case), we compute a single spherical volume queryengine on the
>>>>> reference subject, which means all the datasets need to be aligned and have
>>>>> exact same number of features. The issue in your script is that you remove
>>>>> invariance features separately for each subject, which results in different
>>>>> numbers of features for each subject.
>>>>>
>>>>> There are two ways to resolve it:
>>>>> 1) after removing invariant features, supply list of spherical volume
>>>>> queryengines corresponding to subjects, or
>>>>> 2) find the union of invariant features in all subjects, and then
>>>>> remove the same set of features from all subjects, and proceed with
>>>>> hyperalignment as you have currently.
>>>>>
>>>>> Hope that helps.
>>>>> Best,
>>>>> Swaroop
>>>>>
>>>>> On Mon, Aug 1, 2016 at 6:30 AM, David Soto <d.soto.b at gmail.com> wrote:
>>>>>
>>>>>> Hi Swaroop, I have run the analyses using the individual copes that
>>>>>> were co-registered in FSL to the individual T1 (using BBR) and then to
>>>>>> MNI2mm template using a 12 DOF. The problem persists even after doing
>>>>>> remove_invariant_features().
>>>>>> I get a similar 'Indexerror: index 226822 is out of bounds for axis
>>>>>> 1 with size 198603'
>>>>>>
>>>>>> I have uploaded all the datafiles which you can find in the link
>>>>>> below, just in case you could have a look. If you open the zip then you
>>>>>> will find all the relevant files, including the pymvpa script that I use to
>>>>>> run the analyses ('hyperalscript.py'), the individual data within a folder
>>>>>> and also the attributes file and the MNI brain mask
>>>>>> https://drive.google.com/open?id=0B7-qbWx3g9KfUU1jOFVpQ05oRWM
>>>>>>
>>>>>> any help would be much appreciated...
>>>>>>
>>>>>> thanks!
>>>>>> david
>>>>>>
>>>>>> On 29 July 2016 at 18:28, Swaroop Guntupalli <swaroopgj at gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> That is correct. But the searchlight centers across subjects are
>>>>>>> assumed to be aligned.
>>>>>>>
>>>>>>>
>>>>>>> On Fri, Jul 29, 2016 at 8:24 AM, David Soto <d.soto.b at gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> 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.
>>>>>>>> best
>>>>>>>> david
>>>>>>>>
>>>>>>>> On 29 July 2016 at 16:17, Swaroop Guntupalli <swaroopgj at gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi David,
>>>>>>>>>
>>>>>>>>> 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.
>>>>>>>>> Our tutorial dataset is affine aligned to MNI template.
>>>>>>>>>
>>>>>>>>> Best,
>>>>>>>>> Swaroop
>>>>>>>>>
>>>>>>>>> On Thu, Jul 28, 2016 at 10:51 AM, David Soto <d.soto.b at gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Thanks Swaroop, I managed to get the dataset in the right format
>>>>>>>>>> as per the hyperaligmentsearchlight tutorial
>>>>>>>>>> however when I run the hyperaligment I get the following error (IndexError:
>>>>>>>>>> index 46268 is out of bounds for axis 1 with size 43506, 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...
>>>>>>>>>> The code I use is the same as in the tutorial, namely, any
>>>>>>>>>> feedback would be great, thanks, david
>>>>>>>>>> cv = CrossValidation(clf, NFoldPartitioner(attr='subject'),
>>>>>>>>>> errorfx=mean_match_accuracy)
>>>>>>>>>>
>>>>>>>>>> for test_run in range(nruns):
>>>>>>>>>> ds_train = [sd[sd.sa.chunks != test_run, :] for sd in ds_all]
>>>>>>>>>> ds_test = [sd[sd.sa.chunks == test_run, :] for sd in ds_all]
>>>>>>>>>>
>>>>>>>>>> slhyper = SearchlightHyperalignment(radius=3, featsel=0.4,
>>>>>>>>>> sparse_radius=3)
>>>>>>>>>> slhypmaps = slhyper(ds_train)
>>>>>>>>>> ds_hyper = [h.forward(sd) for h, sd in zip(slhypmaps,
>>>>>>>>>> ds_test)]
>>>>>>>>>>
>>>>>>>>>> ds_hyper = vstack(ds_hyper)
>>>>>>>>>> zscore(ds_hyper, chunks_attr='subject')
>>>>>>>>>> res_cv = cv(ds_hyper)
>>>>>>>>>> bsc_slhyper_results.append(res_cv)
>>>>>>>>>>
>>>>>>>>>> OUTPUT MESSAGE.........
>>>>>>>>>> Performing classification analyses...
>>>>>>>>>> between-subject (searchlight hyperaligned)...
>>>>>>>>>>
>>>>>>>>>> ------------------------------------------------------------
>>>>>>>>>> ---------------
>>>>>>>>>> IndexError Traceback (most recent
>>>>>>>>>> call last)
>>>>>>>>>> <ipython-input-191-85bdb873d4f1> in <module>()
>>>>>>>>>> 24 # Searchlight Hyperalignment returns a list of
>>>>>>>>>> mappers corresponding to
>>>>>>>>>> 25 # subjects in the same order as the list of datasets
>>>>>>>>>> we passed in.
>>>>>>>>>> ---> 26 slhypmaps = slhyper(ds_train)
>>>>>>>>>> 27
>>>>>>>>>> 28 # Applying hyperalignment parameters is similar to
>>>>>>>>>> applying any mapper in
>>>>>>>>>>
>>>>>>>>>> /usr/local/lib/python2.7/site-packages/mvpa2/algorithms/searchlight_hyperalignment.pyc
>>>>>>>>>> in __call__(self, datasets)
>>>>>>>>>> 626 node_blocks = np.array_split(roi_ids,
>>>>>>>>>> params.nblocks)
>>>>>>>>>> 627 p_results = [self._proc_block(block,
>>>>>>>>>> datasets, hmeasure, queryengines)
>>>>>>>>>> --> 628 for block in node_blocks]
>>>>>>>>>> 629 results_ds = self.__handle_all_results(p_results)
>>>>>>>>>> 630 # Dummy iterator for, you know, iteration
>>>>>>>>>>
>>>>>>>>>> /usr/local/lib/python2.7/site-packages/mvpa2/algorithms/searchlight_hyperalignment.pyc
>>>>>>>>>> in _proc_block(self, block, datasets, featselhyper, queryengines, seed,
>>>>>>>>>> iblock)
>>>>>>>>>> 387 continue
>>>>>>>>>> 388 # selecting neighborhood for all subject for
>>>>>>>>>> hyperalignment
>>>>>>>>>> --> 389 ds_temp = [sd[:, ids] for sd, ids in
>>>>>>>>>> zip(datasets, roi_feature_ids_all)]
>>>>>>>>>> 390 if self.force_roi_seed:
>>>>>>>>>> 391 roi_seed = np.array(roi_feature_ids_all[self.params.ref_ds])
>>>>>>>>>> == node_id
>>>>>>>>>>
>>>>>>>>>> /usr/local/lib/python2.7/site-packages/mvpa2/datasets/base.pyc
>>>>>>>>>> in __getitem__(self, args)
>>>>>>>>>> 139
>>>>>>>>>> 140 # let the base do the work
>>>>>>>>>> --> 141 ds = super(Dataset, self).__getitem__(args)
>>>>>>>>>> 142
>>>>>>>>>> 143 # and adjusting the mapper (if any)
>>>>>>>>>>
>>>>>>>>>> /usr/local/lib/python2.7/site-packages/mvpa2/base/dataset.pyc in
>>>>>>>>>> __getitem__(self, args)
>>>>>>>>>> 445 if isinstance(self.samples, np.ndarray):
>>>>>>>>>> 446 if np.any([isinstance(a, slice) for a in
>>>>>>>>>> args]):
>>>>>>>>>> --> 447 samples = self.samples[args[0], args[1]]
>>>>>>>>>> 448 else:
>>>>>>>>>> 449 # works even with bool masks (although
>>>>>>>>>> without
>>>>>>>>>>
>>>>>>>>>> IndexError: index 46268 is out of bounds for axis 1 with size
>>>>>>>>>> 43506
>>>>>>>>>>
>>>>>>>>>> On 28 July 2016 at 00:25, Swaroop Guntupalli <swaroopgj at gmail.com
>>>>>>>>>> > wrote:
>>>>>>>>>>
>>>>>>>>>>> Hi David,
>>>>>>>>>>>
>>>>>>>>>>> If you have limited data, you can use a part of it (however you
>>>>>>>>>>> split the data for training and testing)
>>>>>>>>>>> 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).
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Wed, Jul 27, 2016 at 3:17 PM, David Soto <d.soto.b at gmail.com>
>>>>>>>>>>> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> 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?
>>>>>>>>>>>> thanks!
>>>>>>>>>>>> david
>>>>>>>>>>>>
>>>>>>>>>>>> On 28 July 2016 at 00:00, Swaroop Guntupalli <
>>>>>>>>>>>> swaroopgj at gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> The hyperalignment example on PyMVPA uses one beta map for
>>>>>>>>>>>>> each category per run.
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Wed, Jul 27, 2016 at 2:57 PM, Swaroop Guntupalli <
>>>>>>>>>>>>> swaroopgj at gmail.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hi David,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Beta maps should work fine for hyperalignment. The more maps
>>>>>>>>>>>>>> (or TRs) there are, better the estimate.
>>>>>>>>>>>>>> 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).
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 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).
>>>>>>>>>>>>>> Hyperalignment takes in a list of datasets, one per each
>>>>>>>>>>>>>> subject.
>>>>>>>>>>>>>> So, you can make that a list as
>>>>>>>>>>>>>> ds_all =[ds1, ds2, ...., ds16]
>>>>>>>>>>>>>> and use for Hyperalignment()
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>> Swaroop
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Wed, Jul 27, 2016 at 2:28 PM, David Soto <
>>>>>>>>>>>>>> d.soto.b at gmail.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> hi,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 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).
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 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.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 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
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 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. 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,
>>>>>>>>>>>>>>> which I think it is becos the number of voxels is different
>>>>>>>>>>>>>>> across subjects. This is one issue.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 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.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> thanks!
>>>>>>>>>>>>>>> david
>>>>>>>>>>>>>>> ------------------------------------------------------------
>>>>>>>>>>>>>>> ---------------
>>>>>>>>>>>>>>> ValueError Traceback (most
>>>>>>>>>>>>>>> recent call last)
>>>>>>>>>>>>>>> <ipython-input-64-2fef46542bfc> in <module>()
>>>>>>>>>>>>>>> 19 h5save('/home/dsoto/dsoto/fmri/wmlearning/h5.hdf5',
>>>>>>>>>>>>>>> [d1,d2])
>>>>>>>>>>>>>>> 20 #ds_merged = vstack((d1, d2, d3, d4, d5, d6,
>>>>>>>>>>>>>>> d7,d8,d9, d10, d11, d12, d13, d14, d15, d16))
>>>>>>>>>>>>>>> ---> 21 ds_merged = vstack((d1, d2))
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> /usr/local/lib/python2.7/site-packages/mvpa2/base/dataset.pyc
>>>>>>>>>>>>>>> in vstack(datasets, a)
>>>>>>>>>>>>>>> 687 "datasets have varying
>>>>>>>>>>>>>>> attributes.")
>>>>>>>>>>>>>>> 688 # will puke if not equal number of features
>>>>>>>>>>>>>>> --> 689 stacked_samp = np.concatenate([ds.samples for ds
>>>>>>>>>>>>>>> in datasets], axis=0)
>>>>>>>>>>>>>>> 690
>>>>>>>>>>>>>>> 691 stacked_sa = {}
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> ValueError: all the input array dimensions except for the
>>>>>>>>>>>>>>> concatenation axis must match exactly
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> _______________________________________________
>>>>>>>>>>>>>>> Pkg-ExpPsy-PyMVPA mailing list
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>>>>>>>>>>>>>>> http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-
>>>>>>>>>>>>>>> exppsy-pymvpa
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
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