# numpy.ma.arange

ma.arange([start, ]stop, [step, ]dtype=None, *, like=None) = <numpy.ma.core._convert2ma object>

  Return evenly spaced values within a given interval.

  Values are generated within the half-open interval `[start, stop)` (in other words, the interval including start but excluding stop). For integer arguments the function is equivalent to the Python built-in range function, but returns an ndarray rather than a list.

  When using a non-integer step, such as 0.1, it is often better to use `numpy.linspace`. See the warnings section below for more information.

  Parameters
    start integer or real, optional

      Start of interval. The interval includes this value. The default start value is 0.

    stop integer or real

      End of interval. The interval does not include this value, except in some cases where step is not an integer and floating point round-off affects the length of out.

    step integer or real, optional

      Spacing between values. For any output out, this is the distance between two adjacent values, `out[i+1] - out[i]`. The default step size is 1. If step is specified as a position argument, start must also be given.

    dtype dtype

      The type of the output array. If `dtype` is not given, infer the data type from the other input arguments.

    like array_like

      Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as `like` supports the `__array_function__` protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

      New in version 1.20.0.

  Returns
    arange MaskedArray

      Array of evenly spaced values.

      For floating point arguments, the length of the result is `ceil((stop - start)/step)`. Because of floating point overflow, this rule may result in the last element of out being greater than stop.

  Warning

  The length of the output might not be numerically stable.

  Another stability issue is due to the internal implementation of `numpy.arange`. The actual step value used to populate the array is `dtype(start + step) - dtype(start)` and not step. Precision loss can occur here, due to casting or due to using floating points when start is much larger than step. This can lead to unexpected behaviour. For example:

      >>> np.arange(0, 5, 0.5, dtype=int)
      array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
      >>> np.arange(-3, 3, 0.5, dtype=int)
      array([-3, -2, -1,  0,  1,  2,  3,  4,  5,  6,  7,  8])

  In such cases, the use of `numpy.linspace` should be preferred.

  See also

  `numpy.linspace`

    Evenly spaced numbers with careful handling of endpoints.

  `numpy.ogrid`

    Arrays of evenly spaced numbers in N-dimensions.

  `numpy.mgrid`

    Grid-shaped arrays of evenly spaced numbers in N-dimensions.

  Examples

      >>> np.arange(3)
      array([0, 1, 2])
      >>> np.arange(3.0)
      array([ 0.,  1.,  2.])
      >>> np.arange(3,7)
      array([3, 4, 5, 6])
      >>> np.arange(3,7,2)
      array([3, 5])

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