python - How to specify upper and lower limits when using numpy.random.normal -

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iok want able pick values normal distribution ever fall between 0 , 1. in cases want able return random distribution, , in other cases want return values fall in shape of gaussian.

at moment using following function:

def blockedgauss(mu,sigma):     while true:         numb = random.gauss(mu,sigma)         if (numb > 0 , numb < 1):             break     return numb

it picks value normal distribution, discards if falls outside of range 0 1, feel there must better way of doing this.

it sounds want truncated normal distribution. using scipy, use scipy.stats.truncnorm generate random variates such distribution:

import matplotlib.pyplot plt import scipy.stats stats  lower, upper = 3.5, 6 mu, sigma = 5, 0.7 x = stats.truncnorm(     (lower - mu) / sigma, (upper - mu) / sigma, loc=mu, scale=sigma) n = stats.norm(loc=mu, scale=sigma)  fig, ax = plt.subplots(2, sharex=true) ax[0].hist(x.rvs(10000), normed=true) ax[1].hist(n.rvs(10000), normed=true) plt.show()

enter image description here

the top figure shows truncated normal distribution, lower figure shows normal distribution same mean mu , standard deviation sigma.


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