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 includingstart
but excludingstop
). For integer arguments the function is equivalent to the Python built-inrange
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 ofout
. - 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. Ifstep
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 ofout
being greater thanstop
.
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 isdtype(start + step) - dtype(start)
and notstep
. Precision loss can occur here, due to casting or due to using floating points whenstart
is much larger thanstep
. 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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https://numpy.org/doc/1.22/reference/generated/numpy.ma.arange.html