`numpy:logspace`

source: /dev/numpy/build-install/usr/lib/python3.14/site-packages/numpy/_core/function_base.py:196

Signature

def logspace(start, stop, num = 50, endpoint = True, base = 10.0, dtype = None, axis = 0)

Summary

Return numbers spaced evenly on a log scale.

Extended Summary

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 : 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 : array_like, 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, the data type is inferred from start and stop. The inferred type 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, stop, or base 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.

Returns

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

Notes

If base is a scalar, 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

import numpy as np
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)
np.logspace(2.0, 3.0, num=4, base=2.0)

✗

array([4.        , 5.0396842 , 6.34960421, 8.        ])

np.logspace(2.0, 3.0, num=4, base=[2.0, 3.0], axis=-1)

✓

array([[ 4.        ,  5.0396842 ,  6.34960421,  8.        ],
       [ 9.        , 12.98024613, 18.72075441, 27.        ]])

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')

✓

[<matplotlib.lines.Line2D object at 0x7f22049a82f0>]

plt.ylim([-0.5, 1])

✗

(-0.5, 1.0)

plt.show()

✓

Aliases

numpy.logspace

Signature

Summary

Extended Summary

Parameters

Returns

Notes

Examples

See also

Aliases