__________ 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