ppfq = [0.1, 0.5, 0.9]
xx = [0.95, 1.0, 1.1]
nxx = [-0.95, -1.0, -1.1]
print
#print(invnormalg.__doc__
print(
'\nsquare of standard normal random variable is chisquare with dof=1 distributed'
)
print('sqnorm cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx),
squarenormalg.cdf(xx, loc=l, scale=s))
print('sqnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx),
1 - squarenormalg.sf(xx, loc=l, scale=s))
print('chi2 cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx),
stats.chi2.cdf(xx, 1))
print('sqnorm pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx),
squarenormalg.pdf(xx, loc=l, scale=s))
print('chi2 pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx),
stats.chi2.pdf(xx, 1))
print('sqnorm ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx),
squarenormalg.ppf(ppfq, loc=l, scale=s))
print('chi2 ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx),
stats.chi2.ppf(ppfq, 1))
print('sqnorm cdf with loc scale', squarenormalg.cdf(xx,
loc=-10,
scale=20))
print('chi2 cdf with loc scale', stats.chi2.cdf(xx,
1,
loc=-10,
scale=20))
# print('cdf for [0.5]:', squarenormalg.cdf(0.5,loc=l, scale=s))
# print('chi square distribution')
if __name__ == '__main__':
#Examples for Transf2_gen, u- or hump shaped transformation
#copied from transformtwo.py
l,s = 0.0, 1.0
ppfq = [0.1, 0.5, 0.9]
xx = [0.95, 1.0, 1.1]
nxx = [-0.95, -1.0, -1.1]
print
#print(invnormalg.__doc__
print('\nsquare of standard normal random variable is chisquare with dof=1 distributed')
print('sqnorm cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.cdf(xx,loc=l, scale=s))
print('sqnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), 1-squarenormalg.sf(xx,loc=l, scale=s))
print('chi2 cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.cdf(xx,1))
print('sqnorm pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.pdf(xx,loc=l, scale=s))
print('chi2 pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1))
print('sqnorm ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.ppf(ppfq,loc=l, scale=s))
print('chi2 ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.ppf(ppfq,1))
print('sqnorm cdf with loc scale', squarenormalg.cdf(xx,loc=-10, scale=20))
print('chi2 cdf with loc scale', stats.chi2.cdf(xx,1,loc=-10, scale=20))
# print('cdf for [0.5]:', squarenormalg.cdf(0.5,loc=l, scale=s))
# print('chi square distribution')
# print('chi2 pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1))
# print('cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.cdf(xx,1))
print('\nabsolute value of standard normal random variable is foldnorm(0) and ')
print('halfnorm distributed:')
print('absnorm cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), absnormalg.cdf(xx,loc=l, scale=s))
print('absnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), 1-absnormalg.sf(xx,loc=l, scale=s))
print('foldn cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.foldnorm.cdf(xx,1e-5))
if __name__ == '__main__':
#Examples for Transf2_gen, u- or hump shaped transformation
#copied from transformtwo.py
l,s = 0.0, 1.0
ppfq = [0.1, 0.5, 0.9]
xx = [0.95, 1.0, 1.1]
nxx = [-0.95, -1.0, -1.1]
print
#print(invnormalg.__doc__
print('\nsquare of standard normal random variable is chisquare with dof=1 distributed')
print('sqnorm cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.cdf(xx,loc=l, scale=s))
print('sqnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), 1-squarenormalg.sf(xx,loc=l, scale=s))
print('chi2 cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.cdf(xx,1))
print('sqnorm pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.pdf(xx,loc=l, scale=s))
print('chi2 pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1))
print('sqnorm ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.ppf(ppfq,loc=l, scale=s))
print('chi2 ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.ppf(ppfq,1))
print('sqnorm cdf with loc scale', squarenormalg.cdf(xx,loc=-10, scale=20))
print('chi2 cdf with loc scale', stats.chi2.cdf(xx,1,loc=-10, scale=20))
# print('cdf for [0.5]:', squarenormalg.cdf(0.5,loc=l, scale=s))
# print('chi square distribution')
# print('chi2 pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1))
# print('cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.cdf(xx,1))
print('\nabsolute value of standard normal random variable is foldnorm(0) and ')
print('halfnorm distributed:')
print('absnorm cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), absnormalg.cdf(xx,loc=l, scale=s))
print('absnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), 1-absnormalg.sf(xx,loc=l, scale=s))
print('foldn cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.foldnorm.cdf(xx,1e-5))