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Use old stride_windows implementation on 32-bit builds#29115

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Merged
QuLogic merged 1 commit intomatplotlib:mainfromQuLogic:old-strides
Jul 7, 2025

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QuLogic
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@QuLogicQuLogic commentedNov 9, 2024
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PR summary

I've long had the patch on Fedora (since#21190 (comment)), but it's now applicable to WASM as well (#29093), which is 32-bit. The older implementation doesn't OOM.

cc@anntzer as original author of that PR in case you have an alternate implementation idea.

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@anntzer
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It's not really clear to me why sliding_window_view (in the way we use it) would lead to an OOM while the manual approach wouldn't?

@QuLogicQuLogic mentioned this pull requestNov 9, 2024
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Perhaps there is a NumPy calculation bug? It ends up as:

__________ test_psd_csd[png] __________    @image_comparison(        ["psd_freqs.png", "csd_freqs.png", "psd_noise.png", "csd_noise.png"],        remove_text=True, tol=0.002)    def test_psd_csd():        n = 10000        Fs = 100.            fstims = [[Fs/4, Fs/5, Fs/11], [Fs/4.7, Fs/5.6, Fs/11.9]]        NFFT_freqs = int(1000 * Fs / np.min(fstims))        x = np.arange(0, n, 1/Fs)        ys_freqs = np.sin(2 * np.pi * np.multiply.outer(fstims, x)).sum(axis=1)            NFFT_noise = int(1000 * Fs / 11)        np.random.seed(0)        ys_noise = [np.random.standard_normal(n), np.random.rand(n)]            all_kwargs = [{"sides": "default"},                      {"sides": "onesided", "return_line": False},                      {"sides": "twosided", "return_line": True}]        for ys, NFFT in [(ys_freqs, NFFT_freqs), (ys_noise, NFFT_noise)]:            noverlap = NFFT // 2            pad_to = int(2 ** np.ceil(np.log2(NFFT)))            for ax, kwargs in zip(plt.figure().subplots(3), all_kwargs):>               ret = ax.psd(np.concatenate(ys), NFFT=NFFT, Fs=Fs,                             noverlap=noverlap, pad_to=pad_to, **kwargs)../venv-test/lib/python3.12/site-packages/matplotlib/tests/test_axes.py:5529: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../venv-test/lib/python3.12/site-packages/matplotlib/_api/deprecation.py:453: in wrapper    return func(*args, **kwargs)../venv-test/lib/python3.12/site-packages/matplotlib/__init__.py:1521: in inner    return func(../venv-test/lib/python3.12/site-packages/matplotlib/axes/_axes.py:7616: in psd    pxx, freqs = mlab.psd(x=x, NFFT=NFFT, Fs=Fs, detrend=detrend,../venv-test/lib/python3.12/site-packages/matplotlib/mlab.py:511: in psd    Pxx, freqs = csd(x=x, y=None, NFFT=NFFT, Fs=Fs, detrend=detrend,../venv-test/lib/python3.12/site-packages/matplotlib/mlab.py:567: in csd    Pxy, freqs, _ = _spectral_helper(x=x, y=y, NFFT=NFFT, Fs=Fs,../venv-test/lib/python3.12/site-packages/matplotlib/mlab.py:307: in _spectral_helper    result = np.lib.stride_tricks.sliding_window_view(../venv-test/lib/python3.12/site-packages/numpy/lib/stride_tricks.py:336: in sliding_window_view    return as_strided(x, strides=out_strides, shape=out_shape,_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ x = array([ 0.        ,  2.49169733,  1.49741725, ...,  1.04870198,        0.57119919, -0.18319108]), shape = (1988101, 11900), strides = (8, 8), subok = False, writeable = False    def as_strided(x, shape=None, strides=None, subok=False, writeable=True):        """        Create a view into the array with the given shape and strides.            .. warning:: This function has to be used with extreme care, see notes.            Parameters        ----------        x : ndarray            Array to create a new.        shape : sequence of int, optional            The shape of the new array. Defaults to ``x.shape``.        strides : sequence of int, optional            The strides of the new array. Defaults to ``x.strides``.        subok : bool, optional            .. versionadded:: 1.10                If True, subclasses are preserved.        writeable : bool, optional            .. versionadded:: 1.12                If set to False, the returned array will always be readonly.            Otherwise it will be writable if the original array was. It            is advisable to set this to False if possible (see Notes).            Returns        -------        view : ndarray            See also        --------        broadcast_to : broadcast an array to a given shape.        reshape : reshape an array.        lib.stride_tricks.sliding_window_view :            userfriendly and safe function for the creation of sliding window views.            Notes        -----        ``as_strided`` creates a view into the array given the exact strides        and shape. This means it manipulates the internal data structure of        ndarray and, if done incorrectly, the array elements can point to        invalid memory and can corrupt results or crash your program.        It is advisable to always use the original ``x.strides`` when        calculating new strides to avoid reliance on a contiguous memory        layout.            Furthermore, arrays created with this function often contain self        overlapping memory, so that two elements are identical.        Vectorized write operations on such arrays will typically be        unpredictable. They may even give different results for small, large,        or transposed arrays.            Since writing to these arrays has to be tested and done with great        care, you may want to use ``writeable=False`` to avoid accidental write        operations.            For these reasons it is advisable to avoid ``as_strided`` when        possible.        """        # first convert input to array, possibly keeping subclass        x = np.array(x, copy=False, subok=subok)        interface = dict(x.__array_interface__)        if shape is not None:            interface['shape'] = tuple(shape)        if strides is not None:            interface['strides'] = tuple(strides)    >       array = np.asarray(DummyArray(interface, base=x))E       ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.../venv-test/lib/python3.12/site-packages/numpy/lib/stride_tricks.py:105: ValueError

@anntzer
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Oh, I see, this is because the intermediate array is too large even though we slice it immediately (to compute the overlapping FTs); also it seems like numpy wants array.size * array.itemsize to be representable even though that may be much bigger than the physical array size. That seems overall related to the request for step_size atnumpy/numpy#18244.

I guess the easy way out is indeed to go back to as_strided.

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I thought mlab was being deprecated at some point. How useful is this to add this code piece, versus adding apytest.skipif(sys.maxsize < 2**32) on the failing tests and suggesting users to do this themselves if they want to do large array calculations on 32-bit systems?

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@QuLogic
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Fair enough; I don't know what the status of the deprecations are at this point. I will say that this is reverting to the pre-#21190 code, so it's not new, and I've been using the patch on Fedora without issue since that PR, so it's been stable AFAICT.

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To me, it seems more sense to put the comment about OOM in the 32-bit clause, but apart from that it makes sense to add this to simplify life for those running 32-bit systems.

@QuLogicQuLogicforce-pushed theold-strides branch 3 times, most recently frombc3a3ba toaf49409CompareMay 21, 2025 10:08
@QuLogicQuLogic added this to thev3.11.0 milestoneJun 4, 2025

x = np.asarray(x)

if n == 1 and noverlap == 0:
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I think the special-case here can go away, it should be handled by the general case below.

# np.lib.stride_tricks.as_strided easily leads to memory corruption for
# non integer shape and strides, i.e. noverlap or n. See #3845.
noverlap = int(noverlap)
n = int(n)
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Nowadays I think it is more usual to error out if noverlap or n arenot ints (or rather number.Integer).

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Indeed, both error out in the 64-bit case for floats.

@@ -210,6 +211,30 @@ def detrend_linear(y):
return y - (b*x + a)


def _stride_windows(x, n, noverlap=0):
if noverlap >= n:
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Given that this is just a backcompat function I would suggest grouping together all the checks for conciseness, something like

ifnot (isinstance(n,Integer)andisinstance(noverlap,Integer)and1<=n<=x.sizeandn<noverlap:raiseValueError(f"n ({n}) and noverlap ({noverlap}) must be positive integers with n < noverlap and n <= x.size ({x.size})")

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Oops, I merged and didn't notice the typo:#30273

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Minor nits, but nothing critical. Feel free to merge with or without them.

This was originally for i686 on Fedora, but is now applicable to WASM,which is 32-bit. The older implementation doesn't OOM.
@QuLogicQuLogic merged commit4c345b4 intomatplotlib:mainJul 7, 2025
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@QuLogicQuLogic deleted the old-strides branchJuly 7, 2025 22:40
QuLogic added a commit to QuLogic/matplotlib that referenced this pull requestJul 8, 2025
Unfortunately, I applied the change frommatplotlib#29115 (comment)directly without noticing the typo, or running full tests.So fix the swapped condition, and add a test (for `csd` only, whichshould be enough since everything goes though `_spectral_helper`.)
QuLogic added a commit to QuLogic/matplotlib that referenced this pull requestJul 9, 2025
Unfortunately, I applied the change frommatplotlib#29115 (comment)directly without noticing the typo, or running full tests.So fix the swapped condition, and add a test (for `csd` only, whichshould be enough since everything goes though `_spectral_helper`.)
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@anntzeranntzeranntzer approved these changes

@oscargusoscargusoscargus approved these changes

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