nums.numpy.logspace

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

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).

Parameters

start (BlockArray) – base ** start is the starting value of the sequence.
stop (BlockArray) – 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 (float, 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, infer the data type from the other input arguments.
axis (int, 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.

Returns

samples – num samples, equally spaced on a log scale.

Return type

BlockArray

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.

Notes

Logspace is equivalent to the code

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

Examples

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.        ])

nums.numpy.linspace

nums.numpy.tensordot