numpy.linspace¶

numpy.
linspace
(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)[source]¶ Return evenly spaced numbers over a specified interval.
Returns num evenly spaced samples, calculated over the interval [start, stop].
The endpoint of the interval can optionally be excluded.
Changed in version 1.16.0: Nonscalar start and stop are now supported.
 Parameters
 startarray_like
The starting value of the sequence.
 stoparray_like
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 nonnegative.
 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.New in version 1.9.0.
 axisint, optional
The axis in the result to store the samples. Relevant only if start or stop are arraylike. 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
 samplesndarray
There are num equally spaced samples in the closed interval
[start, stop]
or the halfopen interval[start, stop)
(depending on whether endpoint is True or False). stepfloat, optional
Only returned if retstep is True
Size of spacing between samples.
See also
Examples
>>> np.linspace(2.0, 3.0, num=5) array([2. , 2.25, 2.5 , 2.75, 3. ]) >>> np.linspace(2.0, 3.0, num=5, endpoint=False) array([2. , 2.2, 2.4, 2.6, 2.8]) >>> np.linspace(2.0, 3.0, num=5, retstep=True) (array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import matplotlib.pyplot as plt >>> N = 8 >>> y = np.zeros(N) >>> x1 = np.linspace(0, 10, N, endpoint=True) >>> x2 = np.linspace(0, 10, N, endpoint=False) >>> 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()