jax-ml/jax · error · ValueError

Weights cannot be complex types.

Error message

Weights cannot be complex types.

What it means

Weighted quantiles in JAX (_quantile with weights) only accept real-valued weights; complex weights are rejected because quantile weighting has no meaningful complex interpretation.

Source

Thrown at jax/_src/numpy/reductions.py:2521

    >>> jnp.nanquantile(x, 0.5, weights=weights, method='inverted_cdf')
    Array(4.0, dtype=float32)
  """
  a, q = ensure_arraylike("nanquantile", a, q)
  if weights is not None:
    weights = ensure_arraylike("nanquantile", weights)
  if overwrite_input or out is not None:
    msg = ("jax.numpy.nanquantile does not support overwrite_input=True or "
           "out != None")
    raise ValueError(msg)
  return _quantile(a, q, axis, method, keepdims, True, weights)

def _quantile(a: Array, q: Array, axis: int | tuple[int, ...] | None,
              method: str, keepdims: bool, squash_nans: bool, weights: Array | None = None) -> Array:
  if method not in ["linear", "lower", "higher", "midpoint", "nearest", "inverted_cdf"]:
    raise ValueError("method can only be 'linear', 'lower', 'higher', 'midpoint', 'nearest' or 'inverted_cdf'")
  if weights is not None:
    if dtypes.issubdtype(weights.dtype, np.complexfloating):
      raise ValueError("Weights cannot be complex types.")
    if method != "inverted_cdf":
      raise NotImplementedError(f"{method} doesn't support weights. Only method 'inverted_cdf' supports weights.")
    a, weights = promote_dtypes_inexact(a, weights)
    if weights.shape != a.shape:
      if axis is None:
        raise ValueError("Weights shape must match 'a' shape when axis is None.")
      ax_tuple = canonicalize_axis_tuple(axis, a.ndim)
      if weights.shape != tuple(a.shape[ax] for ax in ax_tuple):
        raise ValueError(f"Weights shape {weights.shape} must match reduction axes "
                          f"{tuple(a.shape[ax] for ax in ax_tuple)}")
      weights = lax.broadcast_in_dim(weights, a.shape, broadcast_dimensions=ax_tuple)
  else:
    a, = promote_dtypes_inexact(a)
  keepdim = []
  if dtypes.issubdtype(a.dtype, np.complexfloating):
    raise ValueError("quantile does not support complex input, as the operation is poorly defined.")
  if axis is None:
    if keepdims:

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Convert weights to real: weights=jnp.abs(w) or w.real
2. Verify weight dtype before the call in pipelines that mix complex and real data

Example fix

// before
jnp.quantile(a, q, weights=complex_w, method='inverted_cdf')
// after
jnp.quantile(a, q, weights=jnp.abs(complex_w), method='inverted_cdf')

import jax.numpy as jnp, numpy as np

if np.issubdtype(weights.dtype, np.complexfloating):
    weights = jnp.abs(weights)

import numpy as np

def is_real_weights(w) -> bool:
    return not np.issubdtype(w.dtype, np.complexfloating)

When it happens

Trigger: Calling jnp.quantile(a, q, weights=w, method='inverted_cdf') where w has a complex dtype (complex64/complex128).

Common situations: Weights derived from complex spectra or FFT outputs without taking magnitudes; dtype promotion bugs producing complex weights unexpectedly.

Related errors

• {method} doesn't support weights. Only method 'inverted_cdf'
• Weights shape must match 'a' shape when axis is None.
• Weights shape {weights.shape} must match reduction axes {tup
• expected a 1-d array for weights
• expected w and y to have the same length

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/fefb2eae95a62e32. Report an issue: GitHub.