pysrw.lib.uti_math.get_dist_norm#
- pysrw.lib.uti_math.get_dist_norm(_min, _max, _scale=1.0, _size=None)[source]#
Select point using a normal (Gaussian) distribution
- param _min:
minimum possible value.
- param _max:
maximum possible value.
- param _scale = 1.0:
Standard deviation (spread or “width”) of the distribution.
- param _size = None:
(int or tuple of ints) 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 loc and scale are both scalars. Otherwise, np.broadcast(loc, scale).size samples are drawn.
The probability density for the Gaussian distribution is:
p(x) =
rac{1}{sqrt{ 2 pi sigma^2 }} e^{ - rac{ (x - mu)^2 } {2 sigma^2} }
Where mu is the mean and sigma the standard deviation. The square of the standard deviation, sigma^2, is called the variance. The function has its peak at the mean, and its “spread” increases with the standard deviation (the function reaches 0.607 times its maximum at x + sigma and x - sigma [2]). This implies that numpy.random.normal is more likely to return samples lying close to the mean, rather than those far away.