nums.numpy.logspace

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

Return numbers spaced evenly on a log scale.

This docstring was copied from numpy.logspace.

Some inconsistencies with the NumS version may exist.

In linear space, the sequence starts at base ** start (base to the power of start) and ends with base ** stop (see endpoint below).

startBlockArray

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

stopBlockArray

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.

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.

basefloat, 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.

dtypedtype

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

axisint, optional

The axis in the result to store the samples. Relevant only if start or stop 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.

New in version 1.16.0.

samplesBlockArray

num samples, equally spaced on a log scale.

arangeSimilar 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.

linspaceSimilar to logspace, but with the samples uniformly distributed

in linear space, instead of log space.

Logspace is equivalent to the code

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

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.logspace(2.0, 3.0, num=4).get()  
array([ 100.        ,  215.443469  ,  464.15888336, 1000.        ])
>>> nps.logspace(2.0, 3.0, num=4, base=2.0).get()  
array([4.        ,  5.0396842 ,  6.34960421,  8.        ])