from scipy.linalg import cholesky, sqrtm
relavant_columns = ['Affecting homelife',
           'Affecting mobility',
           'Affecting social life/hobbies',
           'Affecting work',
           'Mood',
           'Pain Score',
           'Range of motion in Doc']

# df is a pandas dataframe containing the data frame from figure 1
mu = df[relavant_columns].mean().values
cov = df[relavant_columns].cov().values
number_of_sample = 10


# generate using affine transformation
#c2 = cholesky(cov).T
c2 = sqrtm(cov).T
s = np.matmul(c2, np.random.randn(c2.shape[0], number_of_sample)) + mu.reshape(-1, 1)

# transpose so each row is a sample
s = s.T