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, the results will often not be consistent. It is better to use
numpy.linspace
for these cases.- Parameters
-
-
startinteger or real, optional
-
Start of interval. The interval includes this value. The default start value is 0.
-
stopinteger 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
. -
stepinteger 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. -
dtypedtype
-
The type of the output array. If
dtype
is not given, infer the data type from the other input arguments. -
likearray_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
-
-
arangendarray
-
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
.
-
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/1.21/reference/generated/numpy.ma.arange.html