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# numpy.piecewise

numpy.piecewise(x, condlist, funclist, *args, **kw)

  Evaluate a piecewise-defined function.

  Given a set of conditions and corresponding functions, evaluate each function on the input data wherever its condition is true.

  Parameters:
    x ndarray or scalar

      The input domain.

    condlist list of bool arrays or bool scalars

      Each boolean array corresponds to a function in funclist. Wherever condlist[i] is True, funclist[i](x) is used as the output value.

      Each boolean array in condlist selects a piece of x, and should therefore be of the same shape as x.

      The length of condlist must correspond to that of funclist. If one extra function is given, i.e. if `len(funclist) == len(condlist) + 1`, then that extra function is the default value, used wherever all conditions are false.

    funclist list of callables, f(x,*args,**kw), or scalars

      Each function is evaluated over x wherever its corresponding condition is True. It should take a 1d array as input and give a 1d array or a scalar value as output. If, instead of a callable, a scalar is provided then a constant function (`lambda x: scalar`) is assumed.

    args tuple, optional

      Any further arguments given to `piecewise` are passed to the functions upon execution, i.e., if called `piecewise(..., ..., 1, 'a')`, then each function is called as `f(x, 1, 'a')`.

    kw dict, optional

      Keyword arguments used in calling `piecewise` are passed to the functions upon execution, i.e., if called `piecewise(..., ..., alpha=1)`, then each function is called as `f(x, alpha=1)`.

  Returns:
    out ndarray

      The output is the same shape and type as x and is found by calling the functions in funclist on the appropriate portions of x, as defined by the boolean arrays in condlist. Portions not covered by any condition have a default value of 0.

  See also

  `choose`, `select`, `where`

  Notes

  This is similar to choose or select, except that functions are evaluated on elements of x that satisfy the corresponding condition from condlist.

  The result is:

            |--
            |funclist[0](x[condlist[0]])
      out = |funclist[1](x[condlist[1]])
            |...
            |funclist[n2](x[condlist[n2]])
            |--

  Examples

      >>> import numpy as np

  Define the signum function, which is -1 for `x < 0` and +1 for `x >= 0`.

      >>> x = np.linspace(-2.5, 2.5, 6)
      >>> np.piecewise(x, [x < 0, x >= 0], [-1, 1])
      array([-1., -1., -1.,  1.,  1.,  1.])

  Define the absolute value, which is `-x` for `x <0` and `x` for `x >= 0`.

      >>> np.piecewise(x, [x < 0, x >= 0], [lambda x: -x, lambda x: x])
      array([2.5,  1.5,  0.5,  0.5,  1.5,  2.5])

  Apply the same function to a scalar value.

      >>> y = -2
      >>> np.piecewise(y, [y < 0, y >= 0], [lambda x: -x, lambda x: x])
      array(2)