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# numpy.log1p

numpy.log1p(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature]) = <ufunc 'log1p'>

  Return the natural logarithm of one plus the input array, element-wise.

  Calculates `log(1 + x)`.

  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 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:
    y ndarray

      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