def transform(sample_size,ps=0):

  """creates uniformly distr. r.v.
  transforms them to be distributed as g(y)=y/50 (disty)
  transforms the latter to be distributed as h(u)=1./(10.*sqrt(u))
  via two transformations: either u(y)=(y-5)^2 (green)  or
                                  u'(y)=(5/100)^2*y^4 (white)
  both transformations should give (and give!) identical results.
  analytical distribution in red.  
  plots distributions as histograms.  
  """
  import numpy as np
  import my_hist
  import matplotlib.pyplot as plt

  ss=sample_size
  
  np.random.seed(5)
  
  distx=np.random.uniform(size=ss)

# create sample which is xdx-distributed
  disty=10.*np.sqrt(distx)

# transform via u'(y)
  distup=(5./100.)**2*disty**4

  bins=0.02
  umin=0.
  umax=25.

  h,xx=my_hist.hist(distup,bins,mi=umin,ma=umax,norm=1)
  plt.ylim(0.,0.3)

#;or via u(y)
  
  distu=(disty-5.)**2

  h,u=my_hist.hist(distu,bins,mi=umin,ma=umax,norm=1,oplot=1,color='g') 
 
  plt.plot(u,0.1/np.sqrt(u),'r')
  
  if ps:
     plt.savefig('transform_py.ps')