Dear pymvpa-Team,
I'm new to your toolbox (running version 2.6.0) and I got stucked while data loading from my own dataset and right now I really don't know what the issue could be and what to look up. So, I sorted data analog to openfmri structure and paths are all fine since I can load one data file but not the other. My loading procedure looks as follows and it works fine for a different flavor (here, I try to load normalized data, but it works with the unnormalized ones from the same subject): of = OpenFMRIDataset(datapath) print of.get_task_descriptions() conditions = of.get_model_conditions(1) print conditions print [c['name'] for c in conditions] ds = of.get_model_bold_dataset( model_id=1, subj_id=2, flavor='iwrbadvols', mask=opj(datapath, 'sub002', 'masks', 'test', 'brain.nii.gz'), modelfx=fit_event_hrf_model, time_attr='time_coords', condition_attr='condition') print "Data loaded" When I try to load the normalized data, I always end up in the numpy file "function_base.py" in line 76 with "num = int(num)" and the issue is that "num" in this case has "Inf" as value. The error message says exactly: " The debugged program raised the exception OverflowError (cannot convert float infinity to integer)" (plus file reference to function_base.py and line 76) So, data are from same subject, look fine if I inspect them, just the normalized ones of course are resampled to the normalization template (so there are 3x more data with 770MB just this file). And I can't see anything how to set/influence this variable. I really can't figure out where I have to look now to fix this - could there be something wrong with the data or is the file size just too big? Do I have to use another option for the function call? Thanks for any advice Lydia P.S: I'm not sure if this is relevant: the toolbox is running on a virtual machine on a middle performing computer (at least the VM is using 5 GB RAM and 4 processors)
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