dask.array.log1p
dask.array.log1p¶
- dask.array.log1p(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature]) = <ufunc 'log1p'>¶
This docstring was copied from numpy.log1p.
Some inconsistencies with the Dask version may exist.
Return the natural logarithm of one plus the input array, element-wise.
Calculates
log(1 + x)
.- Parameters
- xarray_like
Input values.
- outndarray, 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.
- wherearray_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.
- Returns
- yndarray
Natural logarithm of 1 + x, element-wise. This is a scalar if x is a scalar.
See also
expm1
exp(x) - 1
, the inverse of log1p.
Notes
For real-valued input, log1p is accurate also for x so small that 1 + x == 1 in floating-point accuracy.
Logarithm is a multivalued function: for each x there is an infinite number of z such that exp(z) = 1 + x. The convention is to return the z whose imaginary part lies in [-pi, pi].
For real-valued input data types, log1p always returns real output. For each value that cannot be expressed as a real number or infinity, it yields
nan
and sets the invalid floating point error flag.For complex-valued input, log1p is a complex analytical function that has a branch cut [-inf, -1] and is continuous from above on it. log1p handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard.
References
- 1
M. Abramowitz and I.A. Stegun, “Handbook of Mathematical Functions”, 10th printing, 1964, pp. 67. https://personal.math.ubc.ca/~cbm/aands/page_67.htm
- 2
Wikipedia, “Logarithm”. https://en.wikipedia.org/wiki/Logarithm
Examples
>>> import numpy as np >>> np.log1p(1e-99) 1e-99 >>> np.log(1 + 1e-99) 0.0