numpy.logspace()

numpy.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None) [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 with base ** stop (see endpoint below).

Parameters:

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. dtype : dtype The type of the output array. If `dtype` is not given, infer the data type from the other input arguments.
Returns:	samples : ndarray `num` samples, equally spaced on a log scale.

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.

dtype : dtype

The type of the output array. If dtype is not given, infer the data type from the other input arguments.

Returns:

samples : ndarray

num samples, equally spaced on a log scale.

Notes

Logspace is equivalent to the code

>>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
... 
>>> power(base, y).astype(dtype)
... 

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()

(Source code, png, pdf)

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.logspace.html

doc_NumPy

2025-01-10 15:47:30

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