numpy. linspace (start, stop, num = 50, endpoint = True, retstep = False, dtype = None, axis = 0, *, device = None) [source] # Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [ start , stop ].

numpy. linspace (start, stop, num = 50, endpoint = True, retstep = False, dtype = None, axis = 0, *, device = None) [source] # Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [ start , stop ].

In this case, you should use numpy.linspace instead. Use numpy.linspace if you want the endpoint to be included in the result, or if you are using a non-integer step size. numpy.linspace can include the endpoint and determines step size from the num argument, which specifies the number of elements in the returned array.

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 ].

>>> import matplotlib.pyplot as plt >>> N = 10 >>> x1 = np. logspace (0.1, 1, N, endpoint = True) >>> x2 = np. logspace (0.1, 1, N, endpoint = False) >>> y = np. zeros (N) >>> 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 ...

>>> xv, yv = np. meshgrid (x, y, sparse = True) >>> xv array([[0. , 0.5, 1. ]]) >>> yv array([[0.], [1.]]) meshgrid is very useful to evaluate functions on a grid. If the function depends on all coordinates, both dense and sparse outputs can be used.

Jun 10, 2017 · numpy.linspace (start, stop, num=50, endpoint=True, retstep=False, dtype=None) [source] ¶ Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [ start , stop ].

For integer arguments the function is roughly equivalent to the Python built-in range, but returns an ndarray rather than a range instance. When using a non-integer step, such as 0.1, it is often better to use numpy.linspace. See the Warning sections below for more information. Parameters: start integer or real, optional. Start of interval.

You can also use np.linspace() to create an array with values that are spaced linearly in a specified interval: >>> np . linspace ( 0 , 10 , num = 5 ) array([ 0. , 2.5, 5. , 7.5, 10. Specifying your data type

>>> from numpy import pi >>> np. linspace (0, 2, 9) # 9 numbers from 0 to 2 array([0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ]) >>> x = np. linspace (0, 2 * pi, 100) # useful to evaluate function at lots of points >>> f = np. sin (x)