# xarray.ufuncs.tanh

`xarray.ufuncs.``tanh` = <xarray.ufuncs._UFuncDispatcher object>

  xarray specific variant of numpy.tanh. Handles xarray.Dataset, xarray.DataArray, xarray.Variable, numpy.ndarray and dask.array.Array objects with automatic dispatching.

  Documentation from numpy:

  Compute hyperbolic tangent element-wise.

  Equivalent to `np.sinh(x)/np.cosh(x)` or `-1j * np.tan(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.

  Returns: | y – The corresponding hyperbolic tangent values. This is a scalar if x is a scalar.
  Return type: | ndarray

  Notes

  If out is provided, the function writes the result into it, and returns a reference to out. (See Examples)

  References

  1. M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions. New York, NY: Dover, 1972, pg. 83. http://www.math.sfu.ca/~cbm/aands/
  2. Wikipedia, “Hyperbolic function”, https://en.wikipedia.org/wiki/Hyperbolic_function

  Examples

      >>> np.tanh((0, np.pi*1j, np.pi*1j/2))
      array([ 0. +0.00000000e+00j,  0. -1.22460635e-16j,  0. +1.63317787e+16j])

      >>> # Example of providing the optional output parameter illustrating
      >>> # that what is returned is a reference to said parameter
      >>> out1 = np.array([0], dtype='d')
      >>> out2 = np.tanh([0.1], out1)
      >>> out2 is out1
      True

      >>> # Example of ValueError due to provision of shape mis-matched `out`
      >>> np.tanh(np.zeros((3,3)),np.zeros((2,2)))
      Traceback (most recent call last):
        File "<stdin>", line 1, in <module>
      ValueError: operands could not be broadcast together with shapes (3,3) (2,2)