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Module « numpy.matlib »

Fonction logspace - module numpy.matlib

Signature de la fonction logspace

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

Description

help(numpy.matlib.logspace)

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:: 1.25.0
    Non-scalar 'base` is 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 : 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.

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.
:ref:`how-to-partition`

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)
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.        ])
>>> 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')
[<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()

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