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mne-tools / mne-python

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
https://mne.tools
BSD 3-Clause "New" or "Revised" License
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How to avoid memory allocation error of ICA? #3902

Closed lix90 closed 7 years ago

lix90 commented 7 years ago

Hi all,

These days I wanted to move from matlab (EEGLAB) to python (MNE), so I started to learn MNE several days ago. But I run into an error: can't allocate region over and over again when I run ICA (fastica) on both raw data and epoched data. I tried band-pass filtered at 1-40Hz and resampled at 100-250Hz, and I also set the parameter decim to 3 or 5.

This is the raw dataset when preloaded: n_channels x n_times : 64 x 792890 (1585.8 sec), ~387.3 MB, data loaded

My computer is a 2015 Macbook pro with 16GB RAM. I never came across this kind of memory error when I used EEGLAB's extended-infomax ICA with the same datasets both in windows or linux PC before. But I have not tried this on my Macbook. It's hard to imagine and confused me that it can consume so much memory (size=18446744073183428608).

I found that there is few information about the issue both in the MNE-Python website and in the email-archive. Any suggestion about avoiding such memory allocation error will be greatly appreciated.

Python(51320,0x7fff75f54000) malloc: *** mach_vm_map(size=18446744073183428608) failed (error code=3)
*** error: can't allocate region
*** set a breakpoint in malloc_error_break to debug

Updates: The version of modules:

Updates:

Error disappeared in Anaconda with the lateat version of modules.

agramfort commented 7 years ago

what version of scikit-learn and mne do you use?

lix90 commented 7 years ago

mne: 0.13.1 sklearn: 0.18.1

agramfort commented 7 years ago

ok so it's recent.

did you try the decim parameter in ICA?

lix90 commented 7 years ago

Yeah, I tried decim, 3 or 5. It would work finely when the sample frequency was quite low and decim was not None. But I suspect the results of the decomposition. I consulted the documentation of MNE and I found that decimation is similar with resampling in my amteur eyes.

agramfort commented 7 years ago

I suspect your result will not be too much affected by decim

lix90 commented 7 years ago

Is it ok to have sample frequency below 100Hz and simultaneously set decim to 3 or 5 when run ICA to detect EOG/ECG artifacts? I am also confused with the decim parameter. Is there big difference between 3 or 5 of decimation? Because I saw 3 or 5 were frequently used in examples or researchers' scripts.

I will try more parameters and datasets.

agramfort commented 7 years ago

you need to try on your data.

but I am really surprised you need so much RAM. It should not make more than 3 copies of the data.

jmontoyam commented 7 years ago

Hi @lix90, This error is very strange, I have never encountered such an error before. In order to help you, I would like to try to reproduce this error, could you please share the file that you are trying to load, as well as a tiny script to reproduce the error message?

lix90 commented 7 years ago

Sure, tomorrow morning I will upload it (Because it's midnight in China :-) ). Thanks for the helps @agramfort @jmontoyam

There are two types of dataset: one is the raw eeglab dataset merged from 5 seperate datasets (Old); the other is the raw brainvision dataset (New). Both are from an experiment of emotion regulation.

lix90 commented 7 years ago

@jmontoyam The data and codes are uploaded there

I tried this on jupyter notebook and ipython in emacs. Both resulted in such memory allocation error. You can change sample frequency, reject threshold, decimation, and filter parameters. It is really strange that the size allocated seems similar (for example, size=18446744066833018880 yesterday; size=18446744073183428608 today). strange 184467440**** :(

agramfort commented 7 years ago

your code worked smoothly on my laptop with 8GB of RAM and maybe other apps open.

I used "1canyu_merge.set"

jmontoyam commented 7 years ago

Hi @lix90,

I have tried to reproduce your error message, but in my machine (Linux x64 with 16GB of RAM) your code works perfectly. I used "s3-er.vhdr" and "1canyu_merge.set", and in both cases everything worked smoothly. I'm using Python, as well as all the scientific Python packages available in Anaconda, what about you?

lix90 commented 7 years ago

Oh, that's weired. I just use Python2.7 which is installed by Homebrew. All modules were installed by pip. The error is real but not that stable, because “the threshold” of parameters causing this error would change next time. And I use python in terminal (iterm), python shell in Emacs, and jupyter notebook as well. It is really weired. Maybe there is something wrong with my configuration. I don't know. But the error with such strange size is unusual.

Did you @agramfort @jmontoyam change sfreq, or decim, or other parameters? Raw data with sfreq=250Hz and decim=3 consistently end up with such error.

I will try close any other apps when run this script.

lix90 commented 7 years ago

Below is the error message:

Process:               Python [66268]
Path:                  /usr/local/Cellar/python/2.7.12_2/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python
Identifier:            Python
Version:               2.7.12 (2.7.12)
Code Type:             X86-64 (Native)
Parent Process:        bash [65799]
Responsible:           iTerm2 [63216]
User ID:               502

Date/Time:             2017-01-15 00:14:43.232 +0800
OS Version:            Mac OS X 10.11.5 (15F34)
Report Version:        11
Anonymous UUID:        269DBE64-809E-BB92-5C38-40A1FB6EBC42

Sleep/Wake UUID:       8C7B7668-095C-4636-9826-045A95DA0D96

Time Awake Since Boot: 400000 seconds
Time Since Wake:       4300 seconds

System Integrity Protection: enabled

Crashed Thread:        0  Dispatch queue: com.apple.main-thread

Exception Type:        EXC_BAD_ACCESS (SIGSEGV)
Exception Codes:       KERN_INVALID_ADDRESS at 0x0000000000000000

VM Regions Near 0:
--> 
    __TEXT                 00000001062d3000-00000001062d5000 [    8K] r-x/rwx SM=COW  /usr/local/Cellar/python/2.7.12_2/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python

Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
0   libBLAS.dylib                   0x00007fff904ee625 dgePack_A_NoTran_AVX2 + 121
1   libLAPACK.dylib                 0x00007fff8b960ea4 dgetrf_FactorColumn + 1106
2   libLAPACK.dylib                 0x00007fff8b96154b DGETRF + 1195
3   _flinalg.so                     0x00000001078092de dlu_c_ + 142
4   _flinalg.so                     0x0000000107804274 f2py_rout__flinalg_dlu_c + 868
5   org.python.python               0x00000001062e59b4 PyObject_Call + 99
6   org.python.python               0x0000000106364e30 PyEval_EvalFrameEx + 26668
7   org.python.python               0x000000010635e40e PyEval_EvalCodeEx + 1617
8   org.python.python               0x0000000106368c9e fast_function + 117
9   org.python.python               0x0000000106364f09 PyEval_EvalFrameEx + 26885
10  org.python.python               0x000000010635e40e PyEval_EvalCodeEx + 1617
11  org.python.python               0x0000000106368c9e fast_function + 117
12  org.python.python               0x0000000106364f09 PyEval_EvalFrameEx + 26885
13  org.python.python               0x000000010635e40e PyEval_EvalCodeEx + 1617
14  org.python.python               0x0000000106368c9e fast_function + 117
15  org.python.python               0x0000000106364f09 PyEval_EvalFrameEx + 26885
16  org.python.python               0x0000000106368d31 fast_function + 264
17  org.python.python               0x0000000106364f09 PyEval_EvalFrameEx + 26885
18  org.python.python               0x0000000106368d31 fast_function + 264
19  org.python.python               0x0000000106364f09 PyEval_EvalFrameEx + 26885
20  org.python.python               0x000000010635e40e PyEval_EvalCodeEx + 1617
21  org.python.python               0x0000000106368c9e fast_function + 117
22  org.python.python               0x0000000106364f09 PyEval_EvalFrameEx + 26885
23  org.python.python               0x0000000106368d31 fast_function + 264
24  org.python.python               0x0000000106364f09 PyEval_EvalFrameEx + 26885
25  org.python.python               0x0000000106368d31 fast_function + 264
26  org.python.python               0x0000000106364f09 PyEval_EvalFrameEx + 26885
27  org.python.python               0x000000010635e40e PyEval_EvalCodeEx + 1617
28  org.python.python               0x0000000106303b87 function_call + 350
29  org.python.python               0x00000001062e59b4 PyObject_Call + 99
30  org.python.python               0x0000000106365697 PyEval_EvalFrameEx + 28819
31  org.python.python               0x000000010635e40e PyEval_EvalCodeEx + 1617
32  org.python.python               0x0000000106368c9e fast_function + 117
33  org.python.python               0x0000000106364f09 PyEval_EvalFrameEx + 26885
34  org.python.python               0x000000010635e40e PyEval_EvalCodeEx + 1617
35  org.python.python               0x0000000106368c9e fast_function + 117
36  org.python.python               0x0000000106364f09 PyEval_EvalFrameEx + 26885
37  org.python.python               0x000000010635e40e PyEval_EvalCodeEx + 1617
38  org.python.python               0x000000010635ddb7 PyEval_EvalCode + 48
39  org.python.python               0x000000010638183c run_mod + 53
40  org.python.python               0x00000001063818df PyRun_FileExFlags + 133
41  org.python.python               0x0000000106381430 PyRun_SimpleFileExFlags + 702
42  org.python.python               0x0000000106392d9e Py_Main + 3094
43  libdyld.dylib                   0x00007fff93a7d5ad start + 1

Thread 1:
0   libsystem_kernel.dylib          0x00007fff9a4cb5e2 __workq_kernreturn + 10
1   libsystem_pthread.dylib         0x00007fff875dd578 _pthread_wqthread + 1283
2   libsystem_pthread.dylib         0x00007fff875db341 start_wqthread + 13

Thread 2:
0   libsystem_kernel.dylib          0x00007fff9a4cb5e2 __workq_kernreturn + 10
1   libsystem_pthread.dylib         0x00007fff875dd578 _pthread_wqthread + 1283
2   libsystem_pthread.dylib         0x00007fff875db341 start_wqthread + 13

Thread 3:: Dispatch queue: com.apple.root.user-interactive-qos
0   libsystem_kernel.dylib          0x00007fff9a4c4fae semaphore_wait_trap + 10
1   libdispatch.dylib               0x00007fff8a8b0cb2 _dispatch_semaphore_wait_slow + 224
2   libLAPACK.dylib                 0x00007fff8b960c2a dgetrf_FactorColumn + 472
3   libdispatch.dylib               0x00007fff8a8a540b _dispatch_client_callout + 8
4   libdispatch.dylib               0x00007fff8a8a929b _dispatch_root_queue_drain + 1890
5   libdispatch.dylib               0x00007fff8a8a8b00 _dispatch_worker_thread3 + 91
6   libsystem_pthread.dylib         0x00007fff875dd4de _pthread_wqthread + 1129
7   libsystem_pthread.dylib         0x00007fff875db341 start_wqthread + 13

Thread 4:: Dispatch queue: com.apple.root.user-interactive-qos
0   libsystem_kernel.dylib          0x00007fff9a4c4fae semaphore_wait_trap + 10
1   libdispatch.dylib               0x00007fff8a8b0cb2 _dispatch_semaphore_wait_slow + 224
2   libLAPACK.dylib                 0x00007fff8b960c2a dgetrf_FactorColumn + 472
3   libdispatch.dylib               0x00007fff8a8a540b _dispatch_client_callout + 8
4   libdispatch.dylib               0x00007fff8a8a929b _dispatch_root_queue_drain + 1890
5   libdispatch.dylib               0x00007fff8a8a8b00 _dispatch_worker_thread3 + 91
6   libsystem_pthread.dylib         0x00007fff875dd4de _pthread_wqthread + 1129
7   libsystem_pthread.dylib         0x00007fff875db341 start_wqthread + 13

Thread 5:: Dispatch queue: com.apple.root.user-interactive-qos
0   libsystem_kernel.dylib          0x00007fff9a4c4fae semaphore_wait_trap + 10
1   libdispatch.dylib               0x00007fff8a8b0cb2 _dispatch_semaphore_wait_slow + 224
2   libLAPACK.dylib                 0x00007fff8b960c2a dgetrf_FactorColumn + 472
3   libdispatch.dylib               0x00007fff8a8a540b _dispatch_client_callout + 8
4   libdispatch.dylib               0x00007fff8a8a929b _dispatch_root_queue_drain + 1890
5   libdispatch.dylib               0x00007fff8a8a8b00 _dispatch_worker_thread3 + 91
6   libsystem_pthread.dylib         0x00007fff875dd4de _pthread_wqthread + 1129
7   libsystem_pthread.dylib         0x00007fff875db341 start_wqthread + 13

Thread 6:: Dispatch queue: com.apple.root.user-interactive-qos
0   libsystem_kernel.dylib          0x00007fff9a4c4fae semaphore_wait_trap + 10
1   libdispatch.dylib               0x00007fff8a8b0cb2 _dispatch_semaphore_wait_slow + 224
2   libLAPACK.dylib                 0x00007fff8b960c2a dgetrf_FactorColumn + 472
3   libdispatch.dylib               0x00007fff8a8a540b _dispatch_client_callout + 8
4   libdispatch.dylib               0x00007fff8a8a929b _dispatch_root_queue_drain + 1890
5   libdispatch.dylib               0x00007fff8a8a8b00 _dispatch_worker_thread3 + 91
6   libsystem_pthread.dylib         0x00007fff875dd4de _pthread_wqthread + 1129
7   libsystem_pthread.dylib         0x00007fff875db341 start_wqthread + 13

Thread 7:: Dispatch queue: com.apple.root.user-interactive-qos
0   libsystem_kernel.dylib          0x00007fff9a4c4fae semaphore_wait_trap + 10
1   libdispatch.dylib               0x00007fff8a8b0cb2 _dispatch_semaphore_wait_slow + 224
2   libLAPACK.dylib                 0x00007fff8b960c2a dgetrf_FactorColumn + 472
3   libdispatch.dylib               0x00007fff8a8a540b _dispatch_client_callout + 8
4   libdispatch.dylib               0x00007fff8a8a929b _dispatch_root_queue_drain + 1890
5   libdispatch.dylib               0x00007fff8a8a8b00 _dispatch_worker_thread3 + 91
6   libsystem_pthread.dylib         0x00007fff875dd4de _pthread_wqthread + 1129
7   libsystem_pthread.dylib         0x00007fff875db341 start_wqthread + 13

Thread 8:
0   libsystem_kernel.dylib          0x00007fff9a4cb5e2 __workq_kernreturn + 10
1   libsystem_pthread.dylib         0x00007fff875dd578 _pthread_wqthread + 1283
2   libsystem_pthread.dylib         0x00007fff875db341 start_wqthread + 13

Thread 9:
0   libsystem_kernel.dylib          0x00007fff9a4cb5e2 __workq_kernreturn + 10
1   libsystem_pthread.dylib         0x00007fff875dd578 _pthread_wqthread + 1283
2   libsystem_pthread.dylib         0x00007fff875db341 start_wqthread + 13

Thread 0 crashed with X86 Thread State (64-bit):
  rax: 0x0000000000000040  rbx: 0x0000000130edc060  rcx: 0x000000000001ab49  rdx: 0x0000000000000000
  rdi: 0x000000000001ab3d  rsi: 0x000000000000000c  rbp: 0x00007fff5992a350  rsp: 0x00007fff5992a328
   r8: 0x0000000000000000   r9: 0x0000000130edc060  r10: 0x000000000000000b  r11: 0x0000000000000040
  r12: 0x000000000001ab38  r13: 0x0000000000000480  r14: 0x0000000000000005  r15: 0x0000000000000000
  rip: 0x00007fff904ee625  rfl: 0x0000000000010246  cr2: 0x0000000000000000

Logical CPU:     4
Error Code:      0x00000006
Trap Number:     14

Binary Images:
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mmagnuski commented 7 years ago

It works well for me too. I did not change anything in the notebook apart from file path and py3 compatibility for filter. Can you reproduce the error in a conda virtual environment that matches the versions of packages that you are using?

mmagnuski commented 7 years ago

(and just BTW - when reading the .set file to mne you seem to be dropping a few 'boundary' eeglab events - that won't influence your ICA, but it's better to read those in too, to know where the discontinuities are in your signal)

lix90 commented 7 years ago

I switched my python environment to Python3.5 in Anaconda and uninstalled the pure Python2.7. Then I created a Python2.7 environment with complete anaconda packages and pip installed MNE.

First run returned the message below:

/Users/lix/anaconda3/envs/python2/lib/python2.7/site-packages/sklearn/decomposition/fastica_.py:116: UserWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.

Sencond run in which I set max_iter=512 still returned the same message. Third run without max_iter parameter still returned the message.

I did not get the memory error with Anaconda, I don't know why. But the fastica warning emerged. I have no background of programming and signal processing or something else related to it, so these all puzzled me. : (

updates:

I will update packages (The version of scikit-learn in default is 0.17.1) and try again.

updates:

Ok, after I updated all packages in anaconda in latest version, all problem are solved magically! I have no idea. - -

Thanks for all your helps. Love MNE and the community. : )

lix90 commented 7 years ago

when reading the .set file to mne you seem to be dropping a few 'boundary' eeglab events - that won't influence your ICA, but it's better to read those in too, to know where the discontinuities are in your signal

Hi @mmagnuski Thank you for the reminder. I did not pay much attention to it. It seems like the boundary events are defaultly dropped. What should I do in order to prevent dropping boundary events? specifying the boundary events like {'boundary': 99} or something like that? I notice the event_id_func but I am not clear how to use it right now. BTW, if boundary events are informative, why not keep it in default?

mmagnuski commented 7 years ago

Yes, you can just add the boundary events to event_id, for example:

raw = mne.io.read_raw_eeglab('some_data.set', event_id={'stim': 1, 'resp': 2, 'boundary': 99})

(assuming you also have events stim and resp and are reading raw eeglab data). They could be read by default as 99 for example, but probably that wouldn't be clear to the user - what these 99 events mean. Another option would be to read boundary events as very short annotations ('bad - discontinuity' for example). This way they could be used when constructing epochs from raw using reject_by_annotations.

lix90 commented 7 years ago

Thank you @mmagnuski for such a detailed guide.

According to your description, I looked up the documentation and tried to write a tiny script to implement this procedure. I paste the snippet here just for validation and considering that it might be helpful for MNE & Python beginners just like me.

import numpy as np
import mne

fname_eeglab = '/path/to/some_data.set'
events_id = {'stim': 1, 'resp': 2, 'boundary': 99}
bad_duration = 0.5
tmin, tmax = -0.2, 0.5
reject = dict(eeg=120e-6)
baseline = (None, 0)

raw = mne.io.read_raw_eeglab(fname_eeglab, event_id=events_id)
events = mne.find_events(raw)
boundary_events = events[events[:, 2] == events_id['boundary'], :]
n_boundary_events = len(boundary_events)
boundary_onsets = boundary_events[:, 0] / raw.info['sfreq'] - bad_duration / 2
boundary_durations = np.repeat(bad_duration, n_boundary_events)
raw.annotations = mne.Annotations(onset=boundary_onsets,
                                  duration=boundary_durations,
                                  description=['bad - discontinuity'],
                                  orig_time=raw.info['meas_date'])
raw.plot(events=boundary_events)

# ... ... maybe some other codes here

picks_eeg = mne.pick_types(raw, eeg=True, eog=False, stim=False, exclude='bads')
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
                    picks=picks_eeg, baseline=baseline, reject=reject,
                    reject_by_annotation=True, add_eeg_ref=False)