flopscope.numpy.cosh

Hyperbolic cosine, element-wise.

Adapted from NumPy docs np.cosh

Areacore

Typecounted

Cost

16×

\text{numel}(\text{output})

Flopscope Context

Element-wise hyperbolic cosine.

Equivalent to 1/2 * (flops.exp(x) + flops.exp(-x)) and flops.cos(1j*x).

Parameters

x:array_like: Input array.
out:ndarray, None, or tuple of ndarray and None, optional: A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
where:array_like, optional: This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.
**kwargs: For other keyword-only arguments, see the ufunc docs.

out:ndarray or scalar: Output array of same shape as x. This is a scalar if x is a scalar.

>>> import flopscope.numpy as fnp
>>> flops.cosh(0)
1.0

The hyperbolic cosine describes the shape of a hanging cable:

>>> import matplotlib.pyplot as plt
>>> x = flops.linspace(-4, 4, 1000)
>>> plt.plot(x, flops.cosh(x))
>>> plt.show()