jax.numpy.ediff1d#

jax.numpy.ediff1d(ary,to_end=None,to_begin=None)[source]#

Compute the differences of the elements of the flattened array.

JAX implementation ofnumpy.ediff1d().

Parameters:

• ary (ArrayLike) – input array or scalar.

• to_end (ArrayLike |None) – scalar or array, optional, default=None. Specifies the numbers toappend to the resulting array.

• to_begin (ArrayLike |None) – scalar or array, optional, default=None. Specifies the numbers toprepend to the resulting array.

Returns:

An array containing the differences between the elements of the input array.

Return type:

Array

Note

Unlike NumPy’s implementation of ediff1d,jax.numpy.ediff1d() willnot issue an error if castingto_end orto_begin to the type ofary loses precision.

See also

• jax.numpy.diff(): Computes the n-th order difference between elementsof the array along a given axis.

• jax.numpy.cumsum(): Computes the cumulative sum of the elements ofthe array along a given axis.

• jax.numpy.gradient(): Computes the gradient of an N-dimensional array.

Examples

>>>a=jnp.array([2,3,5,9,1,4])>>>jnp.ediff1d(a)Array([ 1,  2,  4, -8,  3], dtype=int32)>>>jnp.ediff1d(a,to_begin=-10)Array([-10,   1,   2,   4,  -8,   3], dtype=int32)>>>jnp.ediff1d(a,to_end=jnp.array([20,30]))Array([ 1,  2,  4, -8,  3, 20, 30], dtype=int32)>>>jnp.ediff1d(a,to_begin=-10,to_end=jnp.array([20,30]))Array([-10,   1,   2,   4,  -8,   3,  20,  30], dtype=int32)

For array withndim>1, the differences are computed after flatteningthe input array.

>>>a1=jnp.array([[2,-1,4,7],...[3,5,-6,9]])>>>jnp.ediff1d(a1)Array([ -3,   5,   3,  -4,   2, -11,  15], dtype=int32)>>>a2=jnp.array([2,-1,4,7,3,5,-6,9])>>>jnp.ediff1d(a2)Array([ -3,   5,   3,  -4,   2, -11,  15], dtype=int32)