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bundles / numpy 2.4.4 / numpy / logspace

_ArrayFunctionDispatcher

numpy:logspace

source: /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)
✓
np.logspace(2.0, 3.0, num=4, endpoint=False)
np.logspace(2.0, 3.0, num=4, base=2.0)
✗
np.logspace(2.0, 3.0, num=4, base=[2.0, 3.0], axis=-1)
✓
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')
✓
plt.ylim([-0.5, 1])
✗
plt.show()
✓
fig-4e42094ba3b2b192.png

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.

geomspace

Similar to logspace, but with endpoints specified directly.

how-to-partition

ref

linspace

Similar to logspace, but with the samples uniformly distributed in linear space, instead of log space.

Aliases

  • numpy.logspace

Referenced by

This package