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# numpy.logspace

`numpy.``logspace`(start, stop, num=50, endpoint=True, base=10.0)

  Return numbers spaced evenly on a log scale.

  In linear space, the sequence starts at `base ** start` (base to the power of start) and ends with `base ** stop` (see endpoint below).

  Parameters: | start : float `base ** start` is the starting value of the sequence. stop : float `base ** stop` is the final value of the sequence, unless endpoint is False. In that case, `num + 1` values are spaced over the interval in log-space, of which all but the last (a sequence of length `num`) are returned. num : integer, optional Number of samples to generate. Default is 50. endpoint : boolean, optional If true, stop is the last sample. Otherwise, it is not included. Default is True. base : float, optional The base of the log space. The step size between the elements in `ln(samples) / ln(base)` (or `log_base(samples)`) is uniform. Default is 10.0.
  Returns: | samples : ndarray num samples, equally spaced on a log scale.

  See also

  `arange`
    Similiar to linspace, with the step size specified instead of the number of samples. Note that, when used with a float endpoint, the endpoint may or may not be included.
  `linspace`
    Similar to logspace, but with the samples uniformly distributed in linear space, instead of log space.

  Notes

  Logspace is equivalent to the code

      >>> y = linspace(start, stop, num=num, endpoint=endpoint)
      >>> power(base, y)

  Examples

      >>> np.logspace(2.0, 3.0, num=4)
          array([  100.        ,   215.443469  ,   464.15888336,  1000.        ])
      >>> np.logspace(2.0, 3.0, num=4, endpoint=False)
          array([ 100.        ,  177.827941  ,  316.22776602,  562.34132519])
      >>> np.logspace(2.0, 3.0, num=4, base=2.0)
          array([ 4.        ,  5.0396842 ,  6.34960421,  8.        ])

  Graphical illustration:

      >>> import matplotlib.pyplot as plt
      >>> N = 10
      >>> x1 = np.logspace(0.1, 1, N, endpoint=True)
      >>> x2 = np.logspace(0.1, 1, N, endpoint=False)
      >>> y = np.zeros(N)
      >>> plt.plot(x1, y, 'o')
      >>> plt.plot(x2, y + 0.5, 'o')
      >>> plt.ylim([-0.5, 1])
      >>> plt.show()

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