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# numpy.ma.arange

ma.arange([start, ]stop, [step, ]dtype=None, *, device=None, like=None)

  Return evenly spaced values within a given interval.

  `arange` can be called with a varying number of positional arguments:

  - `arange(stop)`: Values are generated within the half-open interval `[0, stop)` (in other words, the interval including start but excluding stop).

  - `arange(start, stop)`: Values are generated within the half-open interval `[start, stop)`.

  - `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.

  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. 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, optional

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

    device str, optional

      The device on which to place the created array. Default: `None`. For Array-API interoperability only, so must be `"cpu"` if passed.

      New in version 2.0.0.

    like array_like, optional

      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=np.int_)
      array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
      >>> np.arange(-3, 3, 0.5, dtype=np.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.

  The built-in `range` generates Python built-in integers that have arbitrary size, while `numpy.arange` produces `numpy.int32` or `numpy.int64` numbers. This may result in incorrect results for large integer values:

      >>> power = 40
      >>> modulo = 10000
      >>> x1 = [(n ** power) % modulo for n in range(8)]
      >>> x2 = [(n ** power) % modulo for n in np.arange(8)]
      >>> print(x1)
      [0, 1, 7776, 8801, 6176, 625, 6576, 4001]  # correct
      >>> print(x2)
      [0, 1, 7776, 7185, 0, 5969, 4816, 3361]  # incorrect

  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.

  How to create arrays with regularly-spaced values

  Examples

      >>> import numpy as np
      >>> 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])