What kind of noise does numpy.random.random((NX,NY))
create? White noise? If it makes a difference, I sometimes instead make 3D or 1D noise (argument is (NX,NY,NZ)
or (N,)
).
>>> help(numpy.random.random)
Help on built-in function random_sample:
random_sample(...)
random_sample(size=None)
Return random floats in the half-open interval [0.0, 1.0).
Results are from the "continuous uniform" distribution over the
stated interval. To sample :math:`Unif[a, b), b > a` multiply
the output of `random_sample` by `(b-a)` and add `a`::
(b - a) * random_sample() + a
...
As the help says, numpy.random.random()
supplies a "continuous uniform" distribution.
For a "Gaussian/white noise" distribution use numpy.random.normal()
.