flopscope.numpy.expm1
fnp.expm1(*args, **kwargs)[flopscope source][numpy source]
Calculate ``exp(x) - 1`` for all elements in the array.
Adapted from NumPy docs np.expm1
Element-wise e^x - 1 (accurate near zero).
Calculate exp(x) - 1 for all elements in the array.
Parameters
- x:array_like
Input values.
- 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
outarray will be set to the ufunc result. Elsewhere, theoutarray will retain its original value. Note that if an uninitializedoutarray is created via the defaultout=None, locations within it where the condition is False will remain uninitialized.- **kwargs
For other keyword-only arguments, see the ufunc docs.
Returns
- out:ndarray or scalar
Element-wise exponential minus one:
out = exp(x) - 1. This is a scalar ifxis a scalar.
See also
- we.flops.log1p
log(1 + x), the inverse of expm1.
Notes
This function provides greater precision than exp(x) - 1
for small values of x.
Examples
The true value of exp(1e-10) - 1 is 1.00000000005e-10 to
about 32 significant digits. This example shows the superiority of
expm1 in this case.
>>> import flopscope.numpy as fnp>>> flops.expm1(1e-10)
1.00000000005e-10
>>> flops.exp(1e-10) - 1
1.000000082740371e-10