#!/usr/bin/env python
		
def r4_uniform_01 ( seed ):

#*****************************************************************************80
#
## R4_UNIFORM_01 is a uniform random number generator.
#
#  Discussion:
#
#		This routine implements the recursion
#
#			seed = 16807 * seed mod ( 2^31 - 1 )
#			r4_uniform_01 = seed / ( 2^31 - 1 )
#
#		The integer arithmetic never requires more than 32 bits,
#		including a sign bit.
#
#		If the initial seed is 12345, then the first three computations are
#
#			Input		 Output			R4_UNIFORM_01
#			SEED			SEED
#
#				 12345	 207482415	0.096616
#		 207482415	1790989824	0.833995
#		1790989824	2035175616	0.947702
#
#  Licensing:
#
#		This code is distributed under the MIT license.
#
#  Modified:
#
#    		22 February 2011
#
#  Author:
#
#		Original MATLAB version by John Burkardt.
#		PYTHON version by Corrado Chisari
#
#  Reference:
#
#		Paul Bratley, Bennett Fox, Linus Schrage,
#		A Guide to Simulation,
#		Springer Verlag, pages 201-202, 1983.
#
#		Pierre L'Ecuyer,
#		Random Number Generation,
#		in Handbook of Simulation,
#		edited by Jerry Banks,
#		Wiley Interscience, page 95, 1998.
#
#		Bennett Fox,
#		Algorithm 647:
#		Implementation and Relative Efficiency of Quasirandom
#		Sequence Generators,
#		ACM Transactions on Mathematical Software,
#		Volume 12, Number 4, pages 362-376, 1986.
#
#		Peter Lewis, Allen Goodman, James Miller,
#		A Pseudo-Random Number Generator for the System/360,
#		IBM Systems Journal,
#		Volume 8, pages 136-143, 1969.
#
#  Parameters:
#
#		Input, integer SEED, the integer "seed" used to generate
#		the output random number.	SEED should not be 0.
#
#		Output, real R, a random value between 0 and 1.
#
#		Output, integer SEED, the updated seed.	This would
#		normally be used as the input seed on the next call.
#
	seed = math.floor ( seed )

	seed = mod ( seed, 2147483647 )

	if ( seed < 0 ):
		seed = seed + 2147483647

	k = math.floor ( seed / 127773 )

	seed = 16807 * ( seed - k * 127773 ) - k * 2836

	if ( seed < 0 ):
		seed = seed + 2147483647

	r = seed * 4.656612875E-10

	return [ r, seed ]

def r8mat_write ( output_filename, m, n, table ):

#*****************************************************************************80
#
## R8MAT_WRITE writes an R8MAT file.
#
#  Discussion:
#
#		An R8MAT is an array of R8's.
#
#  Licensing:
#
#		This code is distributed under the MIT license.
#
#  Modified:
#
#    		22 February 2011
#
#  Author:
#
#		Original MATLAB version by John Burkardt.
#		PYTHON version by Corrado Chisari
#
#  Parameters:
#
#		Input, string OUTPUT_FILENAME, the output filename.
#
#		Input, integer M, the spatial dimension.
#
#		Input, integer N, the number of points.
#
#		Input, real TABLE(M,N), the points.
#

#
#	Open the file.
#
	try:
		output_unit = open ( output_filename, 'wt' )
	except:
		print 'R8MAT_WRITE - Error!' 
		print '	Could not open the output file.' 
		return
#
#	Write the data.
#
#	For smaller data files, and less precision, try:
#
#		 fprintf ( output_unit, '	#14.6f', table(i,j) )
#
	for j in xrange(0, n):
		for i in xrange (0, m):
			output_unit.write('	%24.16f'%table[i,j] )
		output_unit.write('\n' )
#
#	Close the file.
#
	output_unit.close()

	return

def tau_sobol ( dim_num ):

#*****************************************************************************80
#
## TAU_SOBOL defines favorable starting seeds for Sobol sequences.
#
#  Discussion:
#
#		For spatial dimensions 1 through 13, this routine returns
#		a "favorable" value TAU by which an appropriate starting point
#		in the Sobol sequence can be determined.
#
#		These starting points have the form N = 2**K, where
#		for integration problems, it is desirable that
#			TAU + DIM_NUM - 1 <= K
#		while for optimization problems, it is desirable that
#			TAU < K.
#
#  Licensing:
#
#		This code is distributed under the MIT license.
#
#  Modified:
#
#    		22 February 2011
#
#  Author:
#
#		Original FORTRAN77 version by Bennett Fox.
#		MATLAB version by John Burkardt.
#		PYTHON version by Corrado Chisari
#
#  Reference:
#
#		IA Antonov, VM Saleev,
#		USSR Computational Mathematics and Mathematical Physics,
#		Volume 19, 1980, pages 252 - 256.
#
#		Paul Bratley, Bennett Fox,
#		Algorithm 659:
#		Implementing Sobol's Quasirandom Sequence Generator,
#		ACM Transactions on Mathematical Software,
#		Volume 14, Number 1, pages 88-100, 1988.
#
#		Bennett Fox,
#		Algorithm 647:
#		Implementation and Relative Efficiency of Quasirandom
#		Sequence Generators,
#		ACM Transactions on Mathematical Software,
#		Volume 12, Number 4, pages 362-376, 1986.
#
#		Stephen Joe, Frances Kuo
#		Remark on Algorithm 659:
#		Implementing Sobol's Quasirandom Sequence Generator,
#		ACM Transactions on Mathematical Software,
#		Volume 29, Number 1, pages 49-57, March 2003.
#
#		Ilya Sobol,
#		USSR Computational Mathematics and Mathematical Physics,
#		Volume 16, pages 236-242, 1977.
#
#		Ilya Sobol, YL Levitan,
#		The Production of Points Uniformly Distributed in a Multidimensional
#		Cube (in Russian),
#		Preprint IPM Akad. Nauk SSSR,
#		Number 40, Moscow 1976.
#
#  Parameters:
#
#		Input, integer DIM_NUM, the spatial dimension.	Only values
#		of 1 through 13 will result in useful responses.
#
#		Output, integer TAU, the value TAU.
#
	dim_max = 13

	tau_table = [	0,	0,	1,	3,	5, \
								 8, 11, 15, 19, 23, \
								27, 31, 35 ]

	if ( 1 <= dim_num and dim_num <= dim_max ):
		tau = tau_table[dim_num]
	else:
		tau = - 1

	return tau