Achieve same random numbers in numpy as matlab -

i want know how generate same random (normal distribution) numbers in numpy in matlab.

as example when in matlab

randstream.setglobalstream(randstream('mt19937ar','seed',1)); rand ans =     0.417022004702574 

now can reproduce numpy:

import numpy np np.random.seed(1) np.random.rand() 0.417022004702574 

which nice, when normal distribution different numbers.

randstream.setglobalstream(randstream('mt19937ar','seed',1)); randn ans =     -0.649013765191241 

and numpy

import numpy np np.random.seed(1) np.random.randn() 1.6243453636632417 

both functions in documentation draw standard normal distribution, yet give me different results. idea how can adjust python/numpy same numbers matlab.

because marked duplicate: normal distribution, wrote in beginning , end. wrote uniform distribution works fine, normal distribution. none of answers in linked thread normal distribution.

my guess matlab , numpy may use different methods normal distribution of random numbers (which obtained uniform numbers in way).

you can avoid problem writing box-muller method generate random numbers yourself. python,

import numpy np  # box-muller normal distribution, note needs pairs of random numbers input def randn_from_rand(rand):      assert rand.size == 2      #use box-muller distributed random numbers     randn = np.zeros(2)     randn[0] = np.sqrt(-2.*np.log(rand[0]))*np.cos(2*np.pi*rand[1])     randn[1] = np.sqrt(-2.*np.log(rand[0]))*np.sin(2*np.pi*rand[1])      return randn   np.random.seed(1) r = np.random.rand(2) print(r, randn_from_rand(r)) 

which gives,

(array([ 0.417022  ,  0.72032449]), array([-0.24517852, -1.29966152])) 

and matlab,

% box-muller normal distribution, note needs pairs of random numbers input function randn = randn_from_rand(rand)      %use box-muller distributed random numbers     randn(1) = sqrt(-2*log(rand(1)))*cos(2*pi*rand(2));     randn(2) = sqrt(-2*log(rand(1)))*sin(2*pi*rand(2)); 

which call with

randstream.setglobalstream(randstream('mt19937ar','seed',1)); r = [rand, rand] rn = randn_from_rand(r) 

with answer,

r =     0.4170    0.7203 rn =    -0.2452   -1.2997 

note, can check output distributed, python,

import matplotlib.pyplot plt  ra = [] np.random.seed(1) in range(1000000):     rand = np.random.rand(2)     ra.append(randn_from_rand(rand))   plt.hist(np.array(ra).ravel(),100) plt.show() 

which gives,