help numpy.linspace

Help on function linspace in module numpy:
linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)
  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.
versionchanged:: 1.16.0
Non-scalar start and stop are now supported.

Parameters

  • start : array_like
    The starting value of the sequence.
  • stop : array_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.
  • num : int, optional
    Number of samples to generate. Default is 50. Must be non-negative.
  • endpoint : bool, optional
    If True, stop is the last sample. Otherwise, it is not included.
    Default is True.
  • retstep : bool, optional
    If True, return (samples, step), where step is the spacing
    between samples.
  • dtype : dtype, optional
    The type of the output array. If dtype is not given, infer the data
    type from the other input arguments.

versionadded:: 1.9.0

  • 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.

versionadded:: 1.16.0
Returns
-------
samples : ndarray
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).
step : float, 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

>>> import numpy as np
>>> 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 numpy as np
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')
plt.plot(x2, y + 0.5, 'o')
plt.ylim([-0.5, 1])
plt.show()

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