# jax.numpy.fmodÂ¶

jax.numpy.fmod(x1, x2)[source]Â¶

Return the element-wise remainder of division.

LAX-backend implementation of fmod(). Original docstring below.

fmod(x1, x2, /, out=None, *, where=True, casting=â€™same_kindâ€™, order=â€™Kâ€™, dtype=None, subok=True[, signature, extobj])

This is the NumPy implementation of the C library function fmod, the remainder has the same sign as the dividend x1. It is equivalent to the Matlab(TM) rem function and should not be confused with the Python modulus operator x1 % x2.

Parameters
• x1 (array_like) â€“ Dividend.

• x2 (array_like) â€“ Divisor. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

Returns

y â€“ The remainder of the division of x1 by x2. This is a scalar if both x1 and x2 are scalars.

Return type

array_like

remainder()

Equivalent to the Python % operator.

divide()

Notes

The result of the modulo operation for negative dividend and divisors is bound by conventions. For fmod, the sign of result is the sign of the dividend, while for remainder the sign of the result is the sign of the divisor. The fmod function is equivalent to the Matlab(TM) rem function.

Examples

>>> np.fmod([-3, -2, -1, 1, 2, 3], 2)
array([-1,  0, -1,  1,  0,  1])
>>> np.remainder([-3, -2, -1, 1, 2, 3], 2)
array([1, 0, 1, 1, 0, 1])

>>> np.fmod([5, 3], [2, 2.])
array([ 1.,  1.])
>>> a = np.arange(-3, 3).reshape(3, 2)
>>> a
array([[-3, -2],
[-1,  0],
[ 1,  2]])
>>> np.fmod(a, [2,2])
array([[-1,  0],
[-1,  0],
[ 1,  0]])