nums.numpy.expm1

nums.numpy.expm1(x, out=None, where=True, **kwargs)[source]

Calculate exp(x) - 1 for all elements in the array.

This docstring was copied from numpy.expm1.

Some inconsistencies with the NumS version may exist.

Parameters

x (BlockArray) – Input values.
out (BlockArray, None, or 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 (BlockArray, 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.

Returns

out – Element-wise exponential minus one: out = exp(x) - 1.

Return type

BlockArray or scalar

See also

Notes

This function provides greater precision than exp(x) - 1 for small values of x.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

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.

>>> nps.expm1(nps.array(1e-10)).get()  
array(1.e-10)
>>> nps.exp(nps.array(1e-10)).get() - 1  
1.000000082740371e-10

nums.numpy.exp2

nums.numpy.fabs