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 atbase**start(base to the power ofstart) and ends withbase**stop(seeendpoint below).

Changed in version 1.25.0:Non-scalar ‘base` is now supported

Parameters:

startarray_like

base**start is the starting value of the sequence.

stoparray_like

base**stop is the final value of the sequence, unlessendpointis False. In that case,num+1 values are spaced over theinterval in log-space, of which all but the last (a sequence oflengthnum) are returned.

numinteger, optional

Number of samples to generate. Default is 50.

endpointboolean, optional

If true,stop is the last sample. Otherwise, it is not included.Default is True.

basearray_like, optional

The base of the log space. The step size between the elements inln(samples)/ln(base) (orlog_base(samples)) is uniform.Default is 10.0.

dtypedtype

The type of the output array. Ifdtype is not given, the data typeis inferred fromstart andstop. The inferred type will never bean integer;float is chosen even if the arguments would produce anarray of integers.

axisint, 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 bealong a new axis inserted at the beginning. Use -1 to get an axis atthe end.

Returns:

samplesndarray

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.

How to create arrays with regularly-spaced values

Notes

If base is a scalar, logspace is equivalent to the code

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

Examples

Try it in your browser!

>>>importnumpyasnp>>>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:

>>>importmatplotlib.pyplotasplt>>>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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