numpy.linspace¶

numpy.linspace(start,stop,num=50,endpoint=True,retstep=False,dtype=None)[source]¶

Return evenly spaced numbers over a specified interval.

Returnsnum evenly spaced samples, calculated over theinterval [start,stop].

The endpoint of the interval can optionally be excluded.

Parameters:

start : scalar The starting value of the sequence. stop : scalar The end value of the sequence, unlessendpoint is set to False.In that case, the sequence consists of all but the last ofnum+1evenly spaced samples, so thatstop is excluded. Note that the stepsize changes whenendpoint 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), wherestep is the spacingbetween samples. dtype : dtype, optional The type of the output array. Ifdtype is not given, infer the datatype from the other input arguments. New in version 1.9.0.

Returns:

samples : ndarray There arenum equally spaced samples in the closed interval[start,stop] or the half-open interval[start,stop)(depending on whetherendpoint is True or False). step : float, optional Only returned ifretstep is True Size of spacing between samples.

See also

arange

Similar tolinspace, but uses a step size (instead of the number of samples).

logspace

Samples uniformly distributed in log space.

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:

>>>importmatplotlib.pyplotasplt>>>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()

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