# numpy.geomspace

numpy.geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0)

  Return numbers spaced evenly on a log scale (a geometric progression).

  This is similar to `logspace`, but with endpoints specified directly. Each output sample is a constant multiple of the previous.

  Changed in version 1.16.0: Non-scalar start and stop are now supported.

  Parameters
    start array_like

      The starting value of the sequence.

    stop array_like

      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.

    dtype dtype

      The type of the output array. If `dtype` is not given, the data type is inferred from start and stop. The inferred dtype will never be an integer; `float` is chosen even if the arguments would produce an array of integers.

    axis int, optional

      The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end.

      New in version 1.16.0.

  Returns
    samples ndarray

      num samples, equally spaced on a log scale.

  See also

  `logspace`

    Similar to geomspace, but with endpoints specified using log and base.

  `linspace`

    Similar to geomspace, but with arithmetic instead of geometric progression.

  `arange`

    Similar to linspace, with the step size specified instead of the number of samples.

  Notes

  If the inputs or dtype are complex, the output will follow a logarithmic spiral in the complex plane. (There are an infinite number of spirals passing through two points; the output will follow the shortest such path.)

  Examples

      >>> np.geomspace(1, 1000, num=4)
      array([    1.,    10.,   100.,  1000.])
      >>> np.geomspace(1, 1000, num=3, endpoint=False)
      array([   1.,   10.,  100.])
      >>> np.geomspace(1, 1000, num=4, endpoint=False)
      array([   1.        ,    5.62341325,   31.6227766 ,  177.827941  ])
      >>> np.geomspace(1, 256, num=9)
      array([   1.,    2.,    4.,    8.,   16.,   32.,   64.,  128.,  256.])

  Note that the above may not produce exact integers:

      >>> np.geomspace(1, 256, num=9, dtype=int)
      array([  1,   2,   4,   7,  16,  32,  63, 127, 256])
      >>> np.around(np.geomspace(1, 256, num=9)).astype(int)
      array([  1,   2,   4,   8,  16,  32,  64, 128, 256])

  Negative, decreasing, and complex inputs are allowed:

      >>> np.geomspace(1000, 1, num=4)
      array([1000.,  100.,   10.,    1.])
      >>> np.geomspace(-1000, -1, num=4)
      array([-1000.,  -100.,   -10.,    -1.])
      >>> np.geomspace(1j, 1000j, num=4)  # Straight line
      array([0.   +1.j, 0.  +10.j, 0. +100.j, 0.+1000.j])
      >>> np.geomspace(-1+0j, 1+0j, num=5)  # Circle
      array([-1.00000000e+00+1.22464680e-16j, -7.07106781e-01+7.07106781e-01j,
              6.12323400e-17+1.00000000e+00j,  7.07106781e-01+7.07106781e-01j,
              1.00000000e+00+0.00000000e+00j])

  Graphical illustration of endpoint parameter:

      >>> import matplotlib.pyplot as plt
      >>> N = 10
      >>> y = np.zeros(N)
      >>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=True), y + 1, 'o')
      [<matplotlib.lines.Line2D object at 0x...>]
      >>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=False), y + 2, 'o')
      [<matplotlib.lines.Line2D object at 0x...>]
      >>> plt.axis([0.5, 2000, 0, 3])
      [0.5, 2000, 0, 3]
      >>> plt.grid(True, color='0.7', linestyle='-', which='both', axis='both')
      >>> plt.show()

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