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: Non-scalar start and stop are now supported.

Changed in version 1.20.0: Values are rounded towards -inf instead of 0 when an integer dtype is specified. The old behavior can still be obtained with np.linspace(start, stop, num).astype(int)

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 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, the data type is inferred from start and stop. The inferred dtype will never be an integer; float is chosen even if the arguments would produce an array of integers.

New in version 1.9.0.
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.

Returns

samplesndarray

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.

See also

arange: Similar to linspace, but uses a step size (instead of the number of samples).
geomspace: Similar to linspace, but with numbers spaced evenly on a log scale (a geometric progression).
logspace: Similar to geomspace, but with the end points specified as logarithms.

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()

numpy.arange

numpy.logspace