Hi
I have just downloaded the SIMA package and tried to run your example dataset from
www.losonczylab.org/workflow_data.zip.
I've got the following error:
Traceback (most recent call last):
File "C:\Users\eltes.timea\Desktop\Sima trial\sima_trial.py", line 107, in <module>
dataset.segment(segmentation_approach, 'auto_ROIs')
File "C:\Python27\lib\site-packages\sima\imaging.py", line 886, in segment
rois = strategy.segment(self)
File "C:\Python27\lib\site-packages\sima\segment\segment.py", line 46, in segment
rois = self._segment(dataset)
File "C:\Python27\lib\site-packages\sima\segment\segment.py", line 88, in checked_func
return func(self, dataset)
File "C:\Python27\lib\site-packages\sima\segment\ca1pc.py", line 267, in _segment
return self._normcut_method.segment(dataset)
File "C:\Python27\lib\site-packages\sima\segment\segment.py", line 46, in segment
rois = self._segment(dataset)
File "C:\Python27\lib\site-packages\sima\segment\segment.py", line 88, in checked_func
return func(self, dataset)
File "C:\Python27\lib\site-packages\sima\segment\normcut.py", line 503, in _segment
params['cut_min_size'], params['cut_max_size'])
File "C:\Python27\lib\site-packages\sima\segment\normcut.py", line 223, in itercut
cuts, penalty = cut.split()
File "C:\Python27\lib\site-packages\sima\segment\normcut.py", line 154, in split
C = normcut_vectors(self.affinity_matrix, 1)
File "C:\Python27\lib\site-packages\sima\segment\normcut.py", line 67, in normcut_vectors
which='LM') # Get the largest eigenvalues.
File "C:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1545, in eigsh
symmetric=True, tol=tol)
File "C:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 1038, in get_OPinv_matvec
return SpLuInv(A.tocsc()).matvec
File "C:\Python27\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 899, in __init__
self.M_lu = splu(M)
File "C:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 257, in splu
ilu=False, options=_options)
MemoryError
Any suggestions or advice you might have would be greatly appreciated.
Thanks,
Timea