#! /usr/bin/env python
#
def geometric_cdf_values ( n_data ):

#*****************************************************************************80
#
## GEOMETRIC_CDF_VALUES returns values of the geometric CDF.
#
#  Discussion:
#
#    The geometric or Pascal probability density function gives the 
#    probability that the first success will happen on the X-th Bernoulli 
#    trial, given that the probability of a success on a single trial is P.
#
#    The value of CDF ( X, P ) is the probability that the first success
#    will happen on or before the X-th trial.
#
#    In Mathematica, the function can be evaluated by:
#
#      Needs["Statistics`DiscreteDistributions`]
#      dist = GeometricDistribution [ p ]
#      CDF [ dist, x ]
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    14 February 2015
#
#  Author:
#
#    John Burkardt
#
#  Reference:
#
#    Stephen Wolfram,
#    The Mathematica Book,
#    Fourth Edition,
#    Wolfram Media / Cambridge University Press, 1999.
#
#    Daniel Zwillinger and Stephen Kokoska,
#    CRC Standard Probability and Statistics Tables and Formulae,
#    Chapman and Hall / CRC Press, 2000.
#
#  Parameters:
#
#    Input/output, integer N_DATA.  The user sets N_DATA to 0 before the
#    first call.  On each call, the routine increments N_DATA by 1, and
#    returns the corresponding data; when there is no more data, the
#    output value of N_DATA will be 0 again.
#
#    Output, integer X, the number of trials.
#
#    Output, real P, the probability of success 
#    on one trial.
#
#    Output, real CDF, the cumulative density function.
#
  import numpy as np

  n_max = 14

  cdf_vec = np.array ( ( \
     0.1900000000000000E+00, \
     0.2710000000000000E+00, \
     0.3439000000000000E+00, \
     0.6861894039100000E+00, \
     0.3600000000000000E+00, \
     0.4880000000000000E+00, \
     0.5904000000000000E+00, \
     0.9141006540800000E+00, \
     0.7599000000000000E+00, \
     0.8704000000000000E+00, \
     0.9375000000000000E+00, \
     0.9843750000000000E+00, \
     0.9995117187500000E+00, \
     0.9999000000000000E+00 ))

  p_vec = np.array ( ( \
     0.1E+00, \
     0.1E+00, \
     0.1E+00, \
     0.1E+00, \
     0.2E+00, \
     0.2E+00, \
     0.2E+00, \
     0.2E+00, \
     0.3E+00, \
     0.4E+00, \
     0.5E+00, \
     0.5E+00, \
     0.5E+00, \
     0.9E+00 ))

  x_vec = np.array ( ( \
    1,  2,  3, 10, 1, \
    2,  3, 10,  3, 3, \
    3,  5, 10,  3 ))

  if ( n_data < 0 ):
    n_data = 0

  if ( n_max <= n_data ):
    n_data = 0
    p = 0.0
    x = 0
    cdf = 0.0
  else:
    p = p_vec[n_data]
    x = x_vec[n_data]
    cdf = cdf_vec[n_data]
    n_data = n_data + 1

  return n_data, x, p, cdf

def geometric_cdf_values_test ( ):

#*****************************************************************************80
#
## GEOMETRIC_CDF_VALUES_TEST demonstrates the use of GEOMETRIC_CDF_VALUES.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    14 February 2015
#
#  Author:
#
#    John Burkardt
#
  import platform

  print ( '' )
  print ( 'GEOMETRIC_CDF_VALUES_TEST:' )
  print ( '  Python version: %s' % ( platform.python_version ( ) ) )
  print ( '  GEOMETRIC_CDF_VALUES stores values of the Geometric CDF.' )
  print ( '' )
  print ( '      X         P        GEOMETRIC_CDF(X,P)' )
  print ( '' )

  n_data = 0

  while ( True ):

    n_data, x, p, cdf = geometric_cdf_values ( n_data )

    if ( n_data == 0 ):
      break

    print ( '  %12d  %12g  %24.16g' % ( x, p, cdf ) )
#
#  Terminate.
#
  print ( '' )
  print ( 'GEOMETRIC_CDF_VALUES_TEST:' )
  print ( '  Normal end of execution.' )
  return

if ( __name__ == '__main__' ):
  from timestamp import timestamp
  timestamp ( )
  geometric_cdf_values_test ( )
  timestamp ( )