def examples_transf():
    ##lognormal = ExpTransf(a=0.0, xa=-10.0, name = 'Log transformed normal')
    ##print(lognormal.cdf(1))
    ##print(stats.lognorm.cdf(1,1))
    ##print(lognormal.stats())
    ##print(stats.lognorm.stats(1))
    ##print(lognormal.rvs(size=10))

    print('Results for lognormal')
    lognormalg = ExpTransf_gen(stats.norm, a=0, name = 'Log transformed normal general')
    print(lognormalg.cdf(1))
    print(stats.lognorm.cdf(1,1))
    print(lognormalg.stats())
    print(stats.lognorm.stats(1))
    print(lognormalg.rvs(size=5))

    ##print('Results for loggamma')
    ##loggammag = ExpTransf_gen(stats.gamma)
    ##print(loggammag._cdf(1,10))
    ##print(stats.loggamma.cdf(1,10))

    print('Results for expgamma')
    loggammaexpg = LogTransf_gen(stats.gamma)
    print(loggammaexpg._cdf(1,10))
    print(stats.loggamma.cdf(1,10))
    print(loggammaexpg._cdf(2,15))
    print(stats.loggamma.cdf(2,15))


    # this requires change in scipy.stats.distribution
    #print(loggammaexpg.cdf(1,10))

    print('Results for loglaplace')
    loglaplaceg = LogTransf_gen(stats.laplace)
    print(loglaplaceg._cdf(2))
    print(stats.loglaplace.cdf(2,1))
    loglaplaceexpg = ExpTransf_gen(stats.laplace)
    print(loglaplaceexpg._cdf(2))
    stats.loglaplace.cdf(3,3)
    #0.98148148148148151
    loglaplaceexpg._cdf(3,0,1./3)
    #0.98148148148148151
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/sandbox/distributions/examples/ex_extras.py/examples_transf/original