[Mne_analysis] Python - problem with PSD if n_jobs exceeds number of available CPUs?
Alexandre Gramfort
alexandre.gramfort at telecom-paristech.fr
Sat May 24 03:41:02 EDT 2014
hi Jon,
should be fixed:
https://github.com/mne-tools/mne-python/commit/cb9ab461243f3764edf3036267440818b0fd2a4b
thanks for the bug report.
Best,
Alex
On Sat, May 24, 2014 at 1:50 AM, Jon Houck <jhouck at unm.edu> wrote:
> Hi Alex,
>
> Sure, it's at https://gist.github.com/jhouck/0a61a4f420c80356d54e
>
> Thanks,
>
> Jon
>
>
>
>
> On Fri, May 23, 2014 at 3:35 PM, Alexandre Gramfort
> <alexandre.gramfort at telecom-paristech.fr> wrote:
>>
>> Hi Jon,
>>
>> can you share a code snippet to reproduce
>> the problem? Ideally using the MNE sample
>> data.
>>
>> Please use: https://gist.github.com/
>>
>> thanks
>> Alex
>>
>>
>> On Fri, May 23, 2014 at 9:34 PM, Jon Houck <jhouck at unm.edu> wrote:
>> > Hi all,
>> >
>> > I ran into what is probably a rare error using compute_source_psd_epochs
>> > with adaptive=True. For certain labels, if n_jobs is greater than the
>> > number
>> > of CPUs, the process will crash. In my case it was n_jobs=32, for a
>> > condition with 20 epochs, in G_front_inf-Orbital-rh from aparc.a2009s.
>> > For
>> > several other labels the process ran without error. Using n_jobs =19 or
>> > 20
>> > was fine, 21 or higher failed reliably.
>> >
>> > This may not affect much else. My workaround was to set n_jobs and test
>> > the
>> > value against multiprocessing.cpu_count() early in the script. Other
>> > options would be for the user to pay attention to which machine they are
>> > using, or to be less greedy with CPU usage...
>> >
>> > The traceback is below, for those who are into that kind of thing. This
>> > was
>> > using the master downloaded on May 19 (0.8.git).
>> >
>> > Jon
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> > Computing PSD for offset event 8192 label G_front_inf-Orbital-rh at
>> > index 10
>> > Considering frequencies 4 ... 20 Hz
>> > Preparing the inverse operator for use...
>> > Scaled noise and source covariance from nave = 1 to nave = 1
>> > Created the regularized inverter
>> > Created an SSP operator (subspace dimension = 7)
>> > Created the whitener using a full noise covariance matrix (239 small
>> > eigenvalues omitted)
>> > Picked 306 channels from the data
>> > Computing inverse...
>> > (eigenleads need to be weighted)...
>> > Reducing data rank to 21
>> > Using 5 tapers with bandwidth 4.0Hz
>> > Processing epoch : 1
>> > [Parallel(n_jobs=32)]: Done 1 out of 32 | elapsed: 0.0s remaining:
>> > 0.1s
>> > [Parallel(n_jobs=32)]: Done 6 out of 32 | elapsed: 0.0s remaining:
>> > 0.1s
>> > [Parallel(n_jobs=32)]: Done 13 out of 32 | elapsed: 0.0s remaining:
>> > 0.1s
>> > [Parallel(n_jobs=32)]: Done 20 out of 32 | elapsed: 0.0s remaining:
>> > 0.0s
>> > An unexpected error occurred while tokenizing input
>> > The following traceback may be corrupted or invalid
>> > The error message is: ('EOF in multi-line statement', (3, 0))
>> > An unexpected error occurred while tokenizing input
>> > The following traceback may be corrupted or invalid
>> > The error message is: ('EOF in multi-line statement', (3, 0))
>> > An unexpected error occurred while tokenizing input
>> > The following traceback may be corrupted or invalid
>> > The error message is: ('EOF in multi-line statement', (3, 0))
>> >
>> >
>> >
>> > An unexpected error occurred while tokenizing input
>> > The following traceback may be corrupted or invalid
>> > The error message is: ('EOF in multi-line statement', (3, 0))
>> >
>> > An unexpected error occurred while tokenizing input
>> > The following traceback may be corrupted or invalid
>> > The error message is: ('EOF in multi-line statement', (3, 0))
>> >
>> > An unexpected error occurred while tokenizing input
>> > The following traceback may be corrupted or invalid
>> > The error message is: ('EOF in multi-line statement', (3, 0))
>> >
>> > An unexpected error occurred while tokenizing input
>> > The following traceback may be corrupted or invalid
>> > The error message is: ('EOF in multi-line statement', (3, 0))
>> >
>> > An unexpected error occurred while tokenizing input
>> > The following traceback may be corrupted or invalid
>> > The error message is: ('EOF in multi-line statement', (3, 0))
>> >
>> > An unexpected error occurred while tokenizing input
>> > The following traceback may be corrupted or invalid
>> > The error message is: ('EOF in multi-line statement', (3, 0))
>> >
>> > An unexpected error occurred while tokenizing input
>> > The following traceback may be corrupted or invalid
>> > The error message is: ('EOF in multi-line statement', (3, 0))
>> >
>> > An unexpected error occurred while tokenizing input
>> > The following traceback may be corrupted or invalid
>> > The error message is: ('EOF in multi-line statement', (3, 0))
>> >
>> > Traceback (most recent call last):
>> > File "<stdin>", line 15, in <module>
>> > File
>> >
>> > "/export/research/analysis/human/jhouck/namaste_20628/tools/anaconda/lib/python2.7/site-packages/mne-0.8.git-py2.7.egg/mne/minimum_norm/time_frequency.py",
>> > line 572, in _compute_source_psd_epochs
>> > for x in np.array_split(x_mt_src, n_jobs))
>> > File
>> >
>> > "/export/research/analysis/human/jhouck/namaste_20628/tools/anaconda/lib/python2.7/site-packages/joblib-0.8.0a3-py2.7.egg/joblib/parallel.py",
>> > line 651, in __call__
>> > self.retrieve()
>> > File
>> >
>> > "/export/research/analysis/human/jhouck/namaste_20628/tools/anaconda/lib/python2.7/site-packages/joblib-0.8.0a3-py2.7.egg/joblib/parallel.py",
>> > line 534, in retrieve
>> > raise exception_type(report)
>> >
>> > joblib.my_exceptions.JoblibValueError/export/research/analysis/human/jhouck/namaste_20628/tools/anaconda/lib/python2.7/site-packages/joblib-0.8.0a3-py2.7.egg/joblib/my_exceptions.py:26:
>> > DeprecationWarning: BaseException.message has been deprecated as of
>> > Python
>> > 2.6
>> > self.message,
>> > : JoblibValueError
>> >
>> > ___________________________________________________________________________
>> > Multiprocessing exception:
>> >
>> >
>> > ...........................................................................
>> > /na/homes/jhouck/docs/<stdin> in <module>()
>> > 10
>> > 11
>> > 12
>> > 13
>> > 14
>> > ---> 15
>> > 16
>> > 17
>> > 18
>> > 19
>> >
>> >
>> > ...........................................................................
>> >
>> > /export/research/analysis/human/jhouck/namaste_20628/tools/anaconda/lib/python2.7/site-packages/mne-0.8.git-py2.7.egg/mne/minimum_norm/time_frequency.py
>> > in _compute_source_psd_epochs(epochs=<Epochs | n_events : 20 (all
>> > good),
>> > tmin : -1.5 (s), tmax : 0.1 (s), baseline : (None, 0)>,
>> > inverse_operator={'coord_frame': array([4], dtype=int32), 'depth_prior':
>> > {'bads': [], 'data': array([ 0.03416839, 0.03416839, 0.03416839, ...,
>> > 0.21104597,
>> > 0.21104597, 0.21104597]), 'diag': True, 'dim': 24588, 'eig':
>> > None,
>> > 'eigvec': None, 'kind': 5, 'names': [], 'nfree': 1, 'projs': []},
>> > 'eigen_fields': {'col_names': [u'MEG0113', u'MEG0112', u'MEG0111',
>> > u'MEG0122', u'MEG0123', u'MEG0121', u'MEG0132', u'MEG0133', u'MEG0131',
>> > u'MEG0143', u'MEG0142', u'MEG0141', u'MEG0213', u'MEG0212', u'MEG0211',
>> > u'MEG0222', u'MEG0223', u'MEG0221', u'MEG0232', u'MEG0233', ...],
>> > 'data':
>> > array([[ 0.00000000e+00, 6.42533777e-08, 4.....00000000e+00,
>> > 0.00000000e+00]], dtype=float32), 'ncol': 306, 'nrow': 306, 'row_names':
>> > []}, 'eigen_leads': {'col_names': [], 'data': array([[ -2.40336126e-03,
>> > 1.22079987e-03, 7.....37850686e-05, -7.04127597e-05]],
>> > dtype=float32),
>> > 'ncol': 306, 'nrow': 24588, 'row_names': []}, 'eigen_leads_weighted':
>> > False,
>> > 'fmri_prior': None, 'info': {'bads': [], 'ch_names': ['MEG0113',
>> > 'MEG0112',
>> > 'MEG0111', 'MEG0122', 'MEG0123', 'MEG0121', 'MEG0132', 'MEG0133',
>> > 'MEG0131',
>> > 'MEG0143', 'MEG0142', 'MEG0141', 'MEG0213', 'MEG0212', 'MEG0211',
>> > 'MEG0222',
>> > 'MEG0223', 'MEG0221', 'MEG0232', 'MEG0233', ...], 'chs': [{'cal':
>> > 3.250000046861601e-09, 'ch_name': 'MEG0113', 'coil_trans':
>> > array([[-0.01938988, -0.18108977, -0.98325199, -... , 0. ,
>> > 0.
>> > , 1. ]]), 'coil_type': 3012, 'coord_frame': 1, 'eeg_loc': None,
>> > 'kind': 1, 'loc': array([-0.1066 , 0.0464 , -0.0604 , -0...
>> > 0.18097401, 0.020333 ], dtype=float32), 'logno': 113, 'range':
>> > 1.0, ...}, {'cal': 3.250000046861601e-09, 'ch_name': 'MEG0112',
>> > 'coil_trans': array([[-0.18108977, 0.01938988, -0.98325199, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3012,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.1066 , 0.0464 ,
>> > -0.0604 , -0...
>> > 0.18097401, 0.020333 ], dtype=float32), 'logno': 112, 'range':
>> > 1.0, ...}, {'cal': 4.1400001482561066e-11, 'ch_name': 'MEG0111',
>> > 'coil_trans': array([[-0.01938988, -0.18108977, -0.98325199, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3024,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.1066 , 0.0464 ,
>> > -0.0604 , -0...
>> > 0.18097401, 0.020333 ], dtype=float32), 'logno': 111, 'range':
>> > 1.0, ...}, {'cal': 3.250000046861601e-09, 'ch_name': 'MEG0122',
>> > 'coil_trans': array([[ 9.03011824e-04, -2.53265113e-01, -9....00,
>> > 0.00000000e+00,
>> > 1.00000000e+00]]), 'coil_type': 3012, 'coord_frame': 1,
>> > 'eeg_loc':
>> > None, 'kind': 1, 'loc': array([ -1.01999998e-01, 6.31000027e-02,
>> > -2.5...2.53259987e-01, 2.97999999e-04], dtype=float32), 'logno': 122,
>> > 'range': 1.0, ...}, {'cal': -3.250000046861601e-09, 'ch_name':
>> > 'MEG0123',
>> > 'coil_trans': array([[ -2.53265113e-01, -9.03011824e-04, -9....00,
>> > 0.00000000e+00,
>> > 1.00000000e+00]]), 'coil_type': 3012, 'coord_frame': 1,
>> > 'eeg_loc':
>> > None, 'kind': 1, 'loc': array([ -1.01999998e-01, 6.31000027e-02,
>> > -2.5...2.53259987e-01, 2.97999999e-04], dtype=float32), 'logno': 123,
>> > 'range': 1.0, ...}, {'cal': 4.1400001482561066e-11, 'ch_name':
>> > 'MEG0121',
>> > 'coil_trans': array([[ 9.03011824e-04, -2.53265113e-01, -9....00,
>> > 0.00000000e+00,
>> > 1.00000000e+00]]), 'coil_type': 3024, 'coord_frame': 1,
>> > 'eeg_loc':
>> > None, 'kind': 1, 'loc': array([ -1.01999998e-01, 6.31000027e-02,
>> > -2.5...2.53259987e-01, 2.97999999e-04], dtype=float32), 'logno': 121,
>> > 'range': 1.0, ...}, {'cal': 3.250000046861601e-09, 'ch_name': 'MEG0132',
>> > 'coil_trans': array([[ 0.00592455, -0.07752677, -0.99696398, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3012,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.1085 , 0.0302 ,
>> > -0.0266 , 0...
>> > 0.077537 , -0.005751 ], dtype=float32), 'logno': 132, 'range':
>> > 1.0, ...}, {'cal': -3.250000046861601e-09, 'ch_name': 'MEG0133',
>> > 'coil_trans': array([[-0.07752677, -0.00592455, -0.99696398, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3012,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.1085 , 0.0302 ,
>> > -0.0266 , -0...
>> > 0.077537 , -0.005751 ], dtype=float32), 'logno': 133, 'range':
>> > 1.0, ...}, {'cal': 4.1400001482561066e-11, 'ch_name': 'MEG0131',
>> > 'coil_trans': array([[ 0.00592455, -0.07752677, -0.99696398, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3024,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.1085 , 0.0302 ,
>> > -0.0266 , 0...
>> > 0.077537 , -0.005751 ], dtype=float32), 'logno': 131, 'range':
>> > 1.0, ...}, {'cal': 3.250000046861601e-09, 'ch_name': 'MEG0143',
>> > 'coil_trans': array([[-0.05253163, -0.03200371, -0.99809802, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3012,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.1099 , 0.0131 ,
>> > -0.0627 , -0...
>> > 0.031892 , 0.052595 ], dtype=float32), 'logno': 143, 'range':
>> > 1.0, ...}, {'cal': 3.250000046861601e-09, 'ch_name': 'MEG0142',
>> > 'coil_trans': array([[-0.03200371, 0.05253163, -0.99809802, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3012,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.1099 , 0.0131 ,
>> > -0.0627 , -0...
>> > 0.031892 , 0.052595 ], dtype=float32), 'logno': 142, 'range':
>> > 1.0, ...}, {'cal': 4.1400001482561066e-11, 'ch_name': 'MEG0141',
>> > 'coil_trans': array([[-0.05253163, -0.03200371, -0.99809802, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3024,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.1099 , 0.0131 ,
>> > -0.0627 , -0...
>> > 0.031892 , 0.052595 ], dtype=float32), 'logno': 141, 'range':
>> > 1.0, ...}, {'cal': 3.250000046861601e-09, 'ch_name': 'MEG0213',
>> > 'coil_trans': array([[-0.05449655, -0.09574967, -0.99390799, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3012,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.1074 , 0.0329 ,
>> > 0.008 , -0...
>> > 0.095467 , 0.055009 ], dtype=float32), 'logno': 213, 'range':
>> > 1.0, ...}, {'cal': 3.250000046861601e-09, 'ch_name': 'MEG0212',
>> > 'coil_trans': array([[-0.09574967, 0.05449655, -0.99390799, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3012,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.1074 , 0.0329 ,
>> > 0.008 , -0...
>> > 0.095467 , 0.055009 ], dtype=float32), 'logno': 212, 'range':
>> > 1.0, ...}, {'cal': 4.1400001482561066e-11, 'ch_name': 'MEG0211',
>> > 'coil_trans': array([[-0.05449655, -0.09574967, -0.99390799, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3024,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.1074 , 0.0329 ,
>> > 0.008 , -0...
>> > 0.095467 , 0.055009 ], dtype=float32), 'logno': 211, 'range':
>> > 1.0, ...}, {'cal': 3.250000046861601e-09, 'ch_name': 'MEG0222',
>> > 'coil_trans': array([[-0.34224641, -0.15804897, -0.92622799, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3012,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.0989 , 0.0403 ,
>> > 0.0413 , -0...
>> > 0.14645299, 0.34734601], dtype=float32), 'logno': 222, 'range':
>> > 1.0, ...}, {'cal': -3.250000046861601e-09, 'ch_name': 'MEG0223',
>> > 'coil_trans': array([[-0.15804897, 0.34224641, -0.92622799, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3012,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.0989 , 0.0403 ,
>> > 0.0413 , -0...
>> > 0.14645299, 0.34734601], dtype=float32), 'logno': 223, 'range':
>> > 1.0, ...}, {'cal': 4.1400001482561066e-11, 'ch_name': 'MEG0221',
>> > 'coil_trans': array([[-0.34224641, -0.15804897, -0.92622799, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3024,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.0989 , 0.0403 ,
>> > 0.0413 , -0...
>> > 0.14645299, 0.34734601], dtype=float32), 'logno': 221, 'range':
>> > 1.0, ...}, {'cal': 3.250000046861601e-09, 'ch_name': 'MEG0232',
>> > 'coil_trans': array([[-0.34559116, 0.04283677, -0.93739903, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3012,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.1011 , 0.0044 ,
>> > 0.0408 , -0...
>> > -0.048961 , 0.34478199], dtype=float32), 'logno': 232, 'range':
>> > 1.0, ...}, {'cal': -3.250000046861601e-09, 'ch_name': 'MEG0233',
>> > 'coil_trans': array([[ 0.04283677, 0.34559116, -0.93739903, -...
>> > ,
>> > 0. , 0. , 1. ]]), 'coil_type': 3012,
>> > 'coord_frame':
>> > 1, 'eeg_loc': None, 'kind': 1, 'loc': array([-0.1011 , 0.0044 ,
>> > 0.0408 , 0...
>> > -0.048961 , 0.34478199], dtype=float32), 'logno': 233, 'range':
>> > 1.0, ...}, ...], 'dev_head_t': {'from': 1, 'to': 4, 'trans': array([[
>> > 0.99667621, -0.07383786, 0.03442231, ... , 0. , 0.
>> > ,
>> > 1. ]])}, 'meas_file':
>> > u'M87110374_hallucinations_raw_all_tsss_mc_trans.fif', 'meas_id': None,
>> > 'mri_file':
>> > u'./M87110374_hallucinations_raw_all_tsss_mc_trans-ave-trans.fif',
>> > 'mri_head_t': {'from': 5, 'to': 4, 'trans': array([[ 0.98450035,
>> > -0.13287687, -0.11446673, ... , 0. , 0. , 1.
>> > ]])}, 'mri_id': None, 'nchan': 306}, 'methods': 1, 'mri_head_t':
>> > {'from': 5,
>> > 'to': 4, 'trans': array([[ 0.98450035, -0.13287687, -0.11446673, ...
>> > , 0. , 0. , 1. ]])}, 'nave': 1, ...},
>> > lambda2=1.0,
>> > method='MNE', fmin=4.0, fmax=20.0, pick_ori=None, label=<Label |
>> > M87110374, u'G_front_inf-Orbital-rh', rh : 298 vertices>, nave=1,
>> > pca=True,
>> > inv_split=None, bandwidth=4.0, adaptive=True, low_bias=True, n_jobs=32,
>> > verbose=None)
>> > 567 x_mt_src[:, i, :] = np.dot(K_part, x_mt[:, i,
>> > :])
>> > 568
>> > 569 # compute the psd
>> > 570 if adaptive:
>> > 571 out = parallel(my_psd_from_mt_adaptive(x,
>> > eigvals,
>> > freq_mask)
>> > --> 572 for x in np.array_split(x_mt_src,
>> > n_jobs))
>> > x_mt_src = array([[[ -6.20846446e-11 +0.00000000e+00j,
>> > ...j,
>> > 1.28781173e-11 +1.14432688e-13j]]])
>> > n_jobs = 32
>> > 573 this_psd = np.concatenate(out)
>> > 574 else:
>> > 575 x_mt_src = x_mt_src[:, :, freq_mask]
>> > 576 this_psd = _psd_from_mt(x_mt_src, weights)
>> >
>> >
>> > ...........................................................................
>> >
>> > /export/research/analysis/human/jhouck/namaste_20628/tools/anaconda/lib/python2.7/site-packages/joblib-0.8.0a3-py2.7.egg/joblib/parallel.py
>> > in __call__(self=Parallel(n_jobs=32), iterable=<generator object
>> > <genexpr>>)
>> > 646 if pre_dispatch == "all" or n_jobs == 1:
>> > 647 # The iterable was consumed all at once by the
>> > above
>> > for loop.
>> > 648 # No need to wait for async callbacks to trigger
>> > to
>> > 649 # consumption.
>> > 650 self._iterating = False
>> > --> 651 self.retrieve()
>> > self.retrieve = <bound method Parallel.retrieve of
>> > Parallel(n_jobs=32)>
>> > 652 # Make sure that we get a last message telling us we
>> > are
>> > done
>> > 653 elapsed_time = time.time() - self._start_time
>> > 654 self._print('Done %3i out of %3i | elapsed: %s
>> > finished',
>> > 655 (len(self._output),
>> >
>> >
>> >
>> > ---------------------------------------------------------------------------
>> > Sub-process traceback:
>> >
>> >
>> > ---------------------------------------------------------------------------
>> > ValueError Fri May 23
>> > 12:27:35
>> > 2014
>> > PID: 30246Python 2.7.6:
>> >
>> > /export/research/analysis/human/jhouck/namaste_20628/tools/anaconda/bin/python
>> >
>> > ...........................................................................
>> >
>> > /export/research/analysis/human/jhouck/namaste_20628/tools/anaconda/lib/python2.7/site-packages/mne-0.8.git-py2.7.egg/mne/time_frequency/multitaper.pyc
>> > in _psd_from_mt_adaptive(x_mt=array([], dtype=float64), eigvals=array([
>> > 0.99999996, 0.99999714, 0.99990416, 0.99810608, 0.97662124]),
>> > freq_mask=array([False, False, False, False, False, False,... False,
>> > False,
>> > False, False, False], dtype=bool), max_iter=150, return_weights=False)
>> > 277 -----
>> > 278
>> > 279 The weights to use for making the multitaper estimate, such
>> > that
>> > 280 :math:`S_{mt} = \sum_{k} |w_k|^2S_k^{mt} / \sum_{k} |w_k|^2`
>> > 281 """
>> > --> 282 n_signals, n_tapers, n_freqs = x_mt.shape
>> > 283
>> > 284 if len(eigvals) != n_tapers:
>> > 285 raise ValueError('Need one eigenvalue for each taper')
>> > 286
>> >
>> > ValueError: need more than 1 value to unpack
>> >
>> >
>> >
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