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Update values to int64 - #13548

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larsoner merged 29 commits into
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teonbrooks:cnt-overflow-fix
Apr 7, 2026
Merged

larsoner merged 29 commits into
mne-tools:mainfrom
teonbrooks:cnt-overflow-fix

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@teonbrooks

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Fix attempt as fixing the overflow issue in the read_raw_cnt reader. This error has manifested with numpy upgrade.

Reference issue

Fixes #13547.

What does this implement/fix?

This follows a pattern suggested in #12907 to cast the integer to int64.

@larsoner

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To read your file it needs a few more fixes actually... I'll push

@larsoner

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Definitely still something wrong here...

$ python -uic "import mne; raw = mne.io.read_raw_cnt('~/Desktop/945flankers_ready.cnt', data_format='int16').load_data(); raw.plot(annotation_regex='aaa')"
Traceback (most recent call last):
  File "<string>", line 1, in <module>
    import mne; raw = mne.io.read_raw_cnt('~/Desktop/945flankers_ready.cnt', data_format='int16').load_data(); raw.plot(annotation_regex='aaa')
                      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
  File "<decorator-gen-190>", line 12, in load_data
  File "/home/larsoner/python/mne-python/mne/io/base.py", line 589, in load_data
    self._preload_data(True)
    ~~~~~~~~~~~~~~~~~~^^^^^^
  File "/home/larsoner/python/mne-python/mne/io/base.py", line 601, in _preload_data
    self._data = self._read_segment(data_buffer=data_buffer)
                 ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<decorator-gen-189>", line 12, in _read_segment
  File "/home/larsoner/python/mne-python/mne/io/base.py", line 420, in _read_segment
    data = _allocate_data(data_buffer, data_shape, dtype)
  File "/home/larsoner/python/mne-python/mne/io/base.py", line 2577, in _allocate_data
    data = np.zeros(shape, dtype)
numpy._core._exceptions._ArrayMemoryError: Unable to allocate 2.06 TiB for an array with shape (66, 4294966564) and data type float64

Same error if I use data_format='int32'. If I remove the .load_data and use data_format='int32' the plot at least looks okay

image

So need to figure out the n_samples issue, 4294966564 samples for 66 channels is totally unreasonable for a 150MB file...

Comment thread mne/io/cnt/_utils.py Outdated
@larsoner

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@teonbrooks I'm done pushing/looking for now, I hope the changes I made help debugging a bit more. Something is wrong with n_samples here, it gets read as 4294966564 ...

@teonbrooks

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thanks @larsoner!

came across this post and adding it here for reference in the future
https://paulbourke.net/dataformats/eeg/

@teonbrooks

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@teonbrooks I'm done pushing/looking for now, I hope the changes I made help debugging a bit more. Something is wrong with n_samples here, it gets read as 4294966564 ...

according to the link above, it looks like this is not an uncommon occurrence:

Experience has shown that many (most) of the fields are not filled out correctly by the software. In particular, the best way to work out the number of samples is

it looks like n_samples should be calculated as:

nsamples = SETUP.EventTablePos - (900 + 75 * nchannels) / (2 * nchannels)

@larsoner

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Great, can you add some comments / links in the code for the next time we dig into this, and try the suggested fix?

@teonbrooks

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added a note. after trying it out and I look more closely at the code, it looks as though the n_samples logic is already there starting at https://github.com/mne-tools/mne-python/blob/main/mne/io/cnt/cnt.py#L339.

@teonbrooks

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I actually don't know what to do about the n_samples. it looks like the code already is trying to best handle the data without knowing the data_format and with the header not having a reliable header entry for it.

@larsoner

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@teonbrooks do you want me to take a look?

it looks like the code already is trying to best handle the data without knowing the data_format and with the header not having a reliable header entry for it.

So we have two potential sources of truth:

  1. n_samples, which is 4294966564 for this dataset
  2. SETUP.EventTablePos - (900 + 75 * nchannels) / (2 * nchannels), which is presumably correct for this dataset (right?)

In main we assume (1) is going to be more correct so we use it incorrectly for this file. Does that sound right?

If so, maybe we should prefer to use (2) if it's available, since it's more likely to be correct.

We could add some parameter to control which of these to prefer, too, if needed. We can even make it like n_samples="computed" (new default, option 2 above) | "read" (default on main, option 1 above) and change the default without a deprecation cycle since I think we can consider this a bugfix given the unreliability of "read"

@teonbrooks

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@larsoner, yes, that would be a great help if you could take a look at it. and yep, I agree with your assessment on the number of samples

* upstream/main:
  BUG: Fix bug with error message check (mne-tools#13579)
  Add QC + Full MNE Report tutorial (mne-tools#13532)
  FIX: adding kit_system_id info to forward solution (mne-tools#13520)
  MAINT: Update code credit (mne-tools#13572)
  MAINT: Fix Circle (mne-tools#13574)
  DOC: Clarify read_raw_nirx expects directory path, not file path (mne-tools#13541)
@larsoner

larsoner commented Jan 9, 2026

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Yikes this is a bit of a nightmare. Looks like in the header for event_table_offset and n_samples:

  1. Sometimes they are both correct, and consistent
  2. Sometimes event_table_offset is correct and n_samples is incorrect (like in the linked docs)
  3. Sometimes event_table_offset is incorrect and n_samples is correct (like in some of our test datasets)
  4. event_table_offset will always be wrong for file sizes > 2 GB

So when I said before:

If so, maybe we should prefer to use (2) [event_table_offset-computed value] if it's available, since it's more likely to be correct.

I'm no longer convinced this is a good idea, given it's not even the case for our test datasets! To make things worse, all of this stuff interacts with data_format, which we allow to be "auto". So thinking about it more, how about we:

  1. Add n_samples="header" (default) | "computed" where "computed" means "compute it using event_table_offset and data_format"
  2. Only allow data_format="auto" when n_samples="header" or data_format != "auto" and file size < 2GB, because you can't figure out both data_format and n_samples given just an event_table_pos (and event_table_pos is only even potentially usable for file sizes < 2 GB) -- you can just as easily say there are 2 bytes per sample and X samples or 4 bytes per sample and X//2 samples.

Also, looking at the docs from https://paulbourke.net/dataformats/eeg/, their example file has the data offset (SETUP+ELECTLOC) at 5550, which runs to EventTablePos=8511950 for 62 channels. They say this span is n_samples * n_channels * 2 (so data_format="int16"). But if you take their math at the top nsamples = SETUP.EventTablePos - (900 + 75 * nchannels) / (2 * nchannels) you get a very wrong value 8511950-(900+75*62)/(2*62) = 8511905.241935484. Correcting their algebra to be what I think should be correct (EventTablePos-(900+75*n_channels))/(n_channels*n_bytes) we get a more reasonable 68675.0 (note the whole value on this float div, which is good / suggests possible correctness once we convert to integer arithmetic!)... but according to the doc itself the number of samples is 68600! So somehow there are 75 extra values here. I wondered if this could be the source of #11802, but I also see this is the data @teonbrooks shared -- computing the number of samples from the event_table_offset I see ~1865 samples toward the end of the data that are almost all (but not all!) zeros. Wouldn't be surprised if this is the result of something writing un-zeroed malloc'ed rather than calloc'ed data or something... in any case, we should revisit #11802 once we work out the solutions above since maybe we'll magically fix that issue, too.

@withmywoessner you've worked on this stuff a bit recently... WDYT?

xref previous nightmares #6535 #6537 #11802 #12393

@larsoner

larsoner commented Apr 7, 2026

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I think this should be an early 1.13 PR, not a last-minute 1.12 one. I am going to look now and hopefully push a commit to have this where I think it can be merged, but given it changes some behavior it seems better to have it in dev for a few months in case we can spot new bugs earlier.

@larsoner

larsoner commented Apr 7, 2026 •

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Okay actually I think this could possibly go in for 1.12. Here's what's changed:

  1. Added a recompute_n_samples=None which means "recompute if n_samples from header is negative", and allows a user to set to True if their n_samples is wrong (but positive) and it should be recomputed from the event table offset. This makes the file from Overflow Error with read_raw_cnt reader #13547 be read correctly.
  2. Makes data_format handling more strict. data_format="auto" tries to use the header values to figure out the number of bits per sample. In main, if it couldn't do it correctly, it just chose data_format="int16". This was incorrect for some files. In this PR, we are now more strict -- if it can't be safely computed, we raise an error. The workaround for users is easy enough -- try both options and see which one looks like actual EEG data (it's usually easy to tell by eye). This seems safer than us guessing one or the other.

WDYT @drammock , safe enough for 1.12 or you want to wait? I'm okay either way\

@larsoner larsoner changed the title Update values to int64 Update values to int32 Apr 7, 2026
@larsoner larsoner changed the title Update values to int32 Update values to int64 Apr 7, 2026
@larsoner larsoner modified the milestones: 1.12, 1.13 Apr 7, 2026

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in the end it was just adding code comments and changing one var name, then it all made sense

Comment thread mne/io/cnt/_utils.py Outdated
Comment thread mne/io/cnt/_utils.py
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@larsoner
larsoner enabled auto-merge (squash) April 7, 2026 17:01
@larsoner larsoner modified the milestones: 1.13, 1.12 Apr 7, 2026
@larsoner
larsoner merged commit 77d0960 into mne-tools:main Apr 7, 2026
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@teonbrooks

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thanks @larsoner for bringing this home!

@teonbrooks
teonbrooks deleted the cnt-overflow-fix branch April 7, 2026 20:03
bruAristimunha added a commit to bruAristimunha/mne-bids that referenced this pull request Apr 13, 2026
mne-tools/mne-python#13548 removed auto-detection of data_format in
read_raw_cnt, causing the mne-main CI tests on scan41_short.cnt to fail
with "Could not automatically compute number of bytes per sample".
Pass data_format="int16" explicitly when the test wrapper sees a .cnt
file.
larsoner pushed a commit to mne-tools/mne-bids that referenced this pull request Apr 13, 2026
…te if you can (#1556)

* FIX: resolve the timezone from the historical date if you can

* TST: set data_format="int16" for CNT reader in test_write

mne-tools/mne-python#13548 removed auto-detection of data_format in
read_raw_cnt, causing the mne-main CI tests on scan41_short.cnt to fail
with "Could not automatically compute number of bytes per sample".
Pass data_format="int16" explicitly when the test wrapper sees a .cnt
file.

---------

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* FIX: Check [ci skip]

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* FIX: Make set_eeg_reference working with a string for a channel name (mne-tools#13917)

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