__________ 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