jax-ml/jax · error · ValueError

The length of f along the last axis must be at least 2; got

Error message

The length of f along the last axis must be at least 2; got shape {f_arr.shape}.

What it means

jax.scipy.linalg.leslie builds a Leslie population-projection matrix from fecundity coefficients f and survival rates s. A Leslie matrix needs at least two age classes (len(f) >= 2), so f with fewer than 2 elements along its last axis is rejected. Inputs are promoted with atleast_1d first, so scalars become length-1 and fail here.

Source

Thrown at jax/_src/scipy/linalg.py:2645

      coefficients.
    s: array of shape ``(..., N - 1)`` containing the survival coefficients.

  Returns:
    A Leslie matrix of shape ``(..., N, N)``.

  Examples:
    >>> jax.scipy.linalg.leslie(jnp.array([0.1, 2.0, 1.0, 0.1]),
    ...                         jnp.array([0.2, 0.8, 0.7]))
    Array([[0.1, 2. , 1. , 0.1],
           [0.2, 0. , 0. , 0. ],
           [0. , 0.8, 0. , 0. ],
           [0. , 0. , 0.7, 0. ]], dtype=float32)
  """
  check_arraylike("leslie", f, s)
  f_arr = jnp.atleast_1d(f)
  s_arr = jnp.atleast_1d(s)
  if f_arr.shape[-1] < 2:
    raise ValueError(
        "The length of f along the last axis must be at least 2; "
        f"got shape {f_arr.shape}.")
  if s_arr.shape[-1] != f_arr.shape[-1] - 1:
    raise ValueError(
        "Incorrect lengths for f and s. The length of s along the last axis "
        f"must be one less than the length of f; got f shape {f_arr.shape} "
        f"and s shape {s_arr.shape}.")
  return _leslie(f_arr, s_arr)

@partial(jnp_vectorize.vectorize, signature="(n),(m)->(n,n)")
def _leslie(f: Array, s: Array) -> Array:
  f, s = promote_dtypes(f, s)
  return jnp.diag(s, k=-1).at[0].set(f)


def companion(a: ArrayLike) -> Array:
  r"""Construct a companion matrix.

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Provide f with at least 2 entries along the last axis
2. Check argument order: f = fecundities (length n), s = survivals (length n-1)
3. Validate f_arr.shape[-1] >= 2 in a wrapper before calling

Example fix

# before
L = leslie(jnp.array([0.5]), jnp.array([]))
# after
L = leslie(jnp.array([0.5, 1.0]), jnp.array([0.8]))

f_arr = jnp.atleast_1d(f)
if f_arr.shape[-1] < 2:
    raise ValueError('leslie needs at least 2 fecundity coefficients')
L = leslie(f, s)

try:
    leslie(f, s)
except ValueError as e:
    if 'must be at least 2' in str(e):
        raise ValueError('model needs >= 2 age classes') from e
    raise

When it happens

Trigger: Calling leslie(f, s) where f has shape (1,), f is a scalar, or a batched f whose last axis has length 1.

Common situations: Testing with toy single-age-class data; passing wrong argument order (a single survival rate as f); degenerate batches where the last axis was squeezed.

Related errors

• numpy masked arrays are not supported as direct inputs to JA
• Invalid compute type {c_type}. Current supported values are
• numpy masked arrays are not supported as direct inputs to JA
• key cannot be empty
• Mesh axis names cannot be None. Got: {axis_names}

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/c6747a0238b69d11. Report an issue: GitHub.