nums.numpy.linspace

nums.numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)

Return evenly spaced numbers over a specified interval.

This docstring was copied from numpy.linspace.

Some inconsistencies with the NumS version may exist.

Returns num evenly spaced samples, calculated over the interval [start, stop].

The endpoint of the interval can optionally be excluded.

startBlockArray

The starting value of the sequence.

stopBlockArray

The end value of the sequence, unless endpoint is set to False. In that case, the sequence consists of all but the last of num + 1 evenly spaced samples, so that stop is excluded. Note that the step size changes when endpoint is False.

numint, optional

Number of samples to generate. Default is 50. Must be non-negative.

endpointbool, optional

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

retstepbool, optional

If True, return (samples, step), where step is the spacing between samples.

dtypedtype, optional

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.

samplesBlockArray

There are num equally spaced samples in the closed interval [start, stop] or the half-open interval [start, stop) (depending on whether endpoint is True or False).

stepfloat, optional

Only returned if retstep is True

Size of spacing between samples.

arangeSimilar to linspace, but uses a step size (instead of the

number of samples).

geomspaceSimilar to linspace, but with numbers spaced evenly on a log

scale (a geometric progression).

logspaceSimilar to geomspace, but with the end points specified as

logarithms.

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

>>> nps.linspace(2.0, 3.0, num=5).get()  
array([2.  , 2.25, 2.5 , 2.75, 3.  ])