nparray.logspace (function)


def logspace(start, stop, num, endpoint=True, base=10.0, unit=None)

This is an nparray wrapper around the numpy function. The numpy documentation is included below. Currently most kwargs should be accepted with the exception of 'dtype'. The returned object should act exactly like the numpy array itself, but with several extra helpful methods and attributes. Call help on the resulting object for more information.

If you have astropy installed, units are supported by passing unit=astropy.unit to the instantiation functions or by multiplying an array with a unit object.

See also:

Arguments

  • start (int or float): base ** start is the starting value of the sequence.
  • stop (int or 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 (int): number of samples to generate.
  • endpoint (bool, optional, default=True): If True, stop is the last sample. Otherwise, it is not included.
  • base (float, optional, default=10.0): The base of the log space. The step size between the elements in ln(samples) / ln(base) (or log_base(samples)) is uniform.
  • unit (astropy unit or string, optional, default=None): unit corresponding to the passed values.

Returns

===============================================================

numpy documentation for underlying function: 

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).

.. versionchanged:: 1.16.0
    Non-scalar `start` and `stop` are now supported.

Parameters
----------
start : array_like
    ``base ** start`` is the starting value of the sequence.
stop : array_like
    ``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.
dtype : dtype
    The type of the output array.  If `dtype` is not given, infer the data
    type from the other input arguments.
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.

    .. versionadded:: 1.16.0


Returns
-------
samples : ndarray
    `num` samples, equally spaced on a log scale.

See Also
--------
arange : Similar 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.
geomspace : Similar to logspace, but with endpoints specified directly.

Notes
-----
Logspace is equivalent to the code

>>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
... # doctest: +SKIP
>>> power(base, y).astype(dtype)
... # doctest: +SKIP

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')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()