jax-ml/jax · error · NotImplementedError

overwrite_data argument not implemented.

Error message

overwrite_data argument not implemented.

What it means

jax.scipy.signal.detrend does not implement the overwrite_data parameter (in-place detrending is incompatible with JAX's immutable arrays and functional model). Passing any non-None value raises NotImplementedError.

Source

Thrown at jax/_src/scipy/signal.py:528

    >>> detrended = jax.scipy.signal.detrend(data)
    >>> with jnp.printoptions(precision=3, suppress=True):  # suppress float error
    ...   print("Detrended:", detrended)
    ...   print("Underlying trend:", data - detrended)
    Detrended: [-1. -0.  2. -0. -1.]
    Underlying trend: [ 2.  4.  6.  8. 10.]

    Removing a constant trend from the data:

    >>> detrended = jax.scipy.signal.detrend(data, type='constant')
    >>> with jnp.printoptions(precision=3):  # suppress float error
    ...   print("Detrended:", detrended)
    ...   print("Underlying trend:", data - detrended)
    Detrended: [-5. -2.  2.  2.  3.]
    Underlying trend: [6. 6. 6. 6. 6.]
  """
  if overwrite_data is not None:
    raise NotImplementedError("overwrite_data argument not implemented.")
  if type not in ['constant', 'linear']:
    raise ValueError("Trend type must be 'linear' or 'constant'.")
  data_arr, = promote_dtypes_inexact(jnp.asarray(data))
  if type == 'constant':
    return data_arr - data_arr.mean(axis, keepdims=True)
  else:
    N = data_arr.shape[axis]
    # bp is static, so we use np operations to avoid pushing to device.
    bp_arr = np.sort(np.unique(np.r_[0, bp, N]))
    if bp_arr[0] < 0 or bp_arr[-1] > N:
      raise ValueError("Breakpoints must be non-negative and less than length of data along given axis.")
    data_arr = jnp.moveaxis(data_arr, axis, 0)
    shape = data_arr.shape
    data_arr = data_arr.reshape(N, -1)
    for m in range(len(bp_arr) - 1):
      Npts = bp_arr[m + 1] - bp_arr[m]
      A = jnp.vstack([
        jnp.ones(Npts, dtype=data_arr.dtype),

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Drop the argument (call detrend(data) or overwrite_data=None) since JAX returns a new array anyway
2. Assign the result: data = jax.scipy.signal.detrend(data)

Example fix

// before
out = jax.scipy.signal.detrend(data, type='linear', overwrite_data=True)
// after
out = jax.scipy.signal.detrend(data, type='linear')

kwargs = {}  # never pass overwrite_data to JAX detrend
out = jax.scipy.signal.detrend(data, **kwargs)

When it happens

Trigger: Calling detrend(data, overwrite_data=True) as copied from scipy.signal.detrend usage.

Common situations: Porting scipy signal-preprocessing scripts wholesale; defaulting all scipy kwargs to True defensively.

Related errors

• convolve2d() only supports boundary='fill', fillvalue=0
• correlate2d() only supports boundary='fill', fillvalue=0
• Trend type must be 'linear' or 'constant'.
• Breakpoints must be non-negative and less than length of dat
• Computations for m!=n are not yet supported.

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/0d7514e1ce93f088. Report an issue: GitHub.