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: Non-scalar start and stop are now supported.
The starting value of the sequence.
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.
Number of samples to generate. Default is 50. Must be non-negative.
If True, stop is the last sample. Otherwise, it is not included. Default is True.
If True, return (samples, step), where step is the spacing between samples.
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.
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.
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).
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()
