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step float, optional. Only returned if retstep is True. Size of spacing between samples.
arange(start, stop, step) Values are generated within the half-open interval [start, stop), with spacing between values given by step. For integer arguments the function is roughly equivalent to the Python built-in range , but returns an ndarray rather than a range instance.
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, *, device = None) [source] # Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [ start , stop ].
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.
The default NumPy behavior is to create arrays in either 32 or 64-bit signed integers (platform dependent and matches C long size) or double precision floating point numbers. If you expect your integer arrays to be a specific type, then you need to specify the dtype while you create the array.
Similar to linspace, but uses a step size (instead of the number of samples). logspace Samples uniformly distributed in log space.
Similar to linspace, with the step size specified instead of the number of samples. Note that, when used with a float endpoint, the endpoint may or may not be included. linspace
step: float, optional. Only returned if retstep is True. Size of spacing between samples.
An item extracted from an array, e.g., by indexing, is represented by a Python object whose type is one of the array scalar types built in NumPy. The array scalars allow easy manipulation of also more complicated arrangements of data.