numpy.logspace¶

numpy.
logspace
(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0)[source]¶ 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 withbase ** stop
(see endpoint below).Changed in version 1.16.0: Nonscalar 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 logspace, 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)
(orlog_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 arraylike. 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
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()