rayleigh(self, scale=1.0, size=None, chunks='auto', **kwargs)
This docstring was copied from numpy.random.mtrand.RandomState.rayleigh.
Some inconsistencies with the Dask version may exist.
The $\chi$ and Weibull distributions are generalizations of the Rayleigh.
New code should use the rayleigh
method of a default_rng()
instance instead; please see the :None:ref:`random-quick-start`.
The probability density function for the Rayleigh distribution is
$$P(x;scale) = \frac{x}{scale^2}e^{\frac{-x^2}{2 \cdotp scale^2}}$$The Rayleigh distribution would arise, for example, if the East and North components of the wind velocity had identical zero-mean Gaussian distributions. Then the wind speed would have a Rayleigh distribution.
Scale, also equals the mode. Must be non-negative. Default is 1.
Output shape. If the given shape is, e.g., (m, n, k)
, then m * n * k
samples are drawn. If size is None
(default), a single value is returned if scale
is a scalar. Otherwise, np.array(scale).size
samples are drawn.
Drawn samples from the parameterized Rayleigh distribution.
Draw samples from a Rayleigh distribution.
Generator.rayleigh
which should be used for new code.
Draw values from the distribution and plot the histogram
This example is valid syntax, but we were not able to check execution>>> from matplotlib.pyplot import hist # doctest: +SKIP
... values = hist(np.random.rayleigh(3, 100000), bins=200, density=True) # doctest: +SKIP
Wave heights tend to follow a Rayleigh distribution. If the mean wave height is 1 meter, what fraction of waves are likely to be larger than 3 meters?
This example is valid syntax, but we were not able to check execution>>> meanvalue = 1 # doctest: +SKIP
... modevalue = np.sqrt(2 / np.pi) * meanvalue # doctest: +SKIP
... s = np.random.rayleigh(modevalue, 1000000) # doctest: +SKIP
The percentage of waves larger than 3 meters is:
This example is valid syntax, but we were not able to check execution>>> 100.*sum(s>3)/1000000. # doctest: +SKIP 0.087300000000000003 # randomSee :
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