import math

from numpy import *

from scipy import stats

def i4_bit_hi1 ( n ):
#*****************************************************************************80
#
## I4_BIT_HI1 returns the position of the high 1 bit base 2 in an integer.
#
#  Example:
#
#       N    Binary     BIT
#    ----    --------  ----
#       0           0     0
#       1           1     1
#       2          10     2
#       3          11     2 
#       4         100     3
#       5         101     3
#       6         110     3
#       7         111     3
#       8        1000     4
#       9        1001     4
#      10        1010     4
#      11        1011     4
#      12        1100     4
#      13        1101     4
#      14        1110     4
#      15        1111     4
#      16       10000     5
#      17       10001     5
#    1023  1111111111    10
#    1024 10000000000    11
#    1025 10000000001    11
#
#  	Licensing:
#
#    		This code is distributed under the GNU LGPL license.
#
#  	Modified:
#
#    		22 February 2011
#
#	Author:
#
#		Original MATLAB version by John Burkardt.
#		PYTHON version by Corrado Chisari
#
#  	Parameters:
#
#    		Input, integer N, the integer to be measured.
#    		N should be nonnegative.  If N is nonpositive, the value will always be 0.
#
#    		Output, integer BIT, the number of bits base 2.
#
	i = math.floor ( n )
	bit = 0
	while ( 1 ):
		if ( i <= 0 ):
			break
		bit += 1
		i = math.floor ( i / 2. )
	return bit
def i4_bit_lo0 ( n ):
#*****************************************************************************80
#
## I4_BIT_LO0 returns the position of the low 0 bit base 2 in an integer.
#
#  Example:
#
#       N    Binary     BIT
#    ----    --------  ----
#       0           0     1
#       1           1     2
#       2          10     1
#       3          11     3 
#       4         100     1
#       5         101     2
#       6         110     1
#       7         111     4
#       8        1000     1
#       9        1001     2
#      10        1010     1
#      11        1011     3
#      12        1100     1
#      13        1101     2
#      14        1110     1
#      15        1111     5
#      16       10000     1
#      17       10001     2
#    1023  1111111111     1
#    1024 10000000000     1
#    1025 10000000001     1
#
#  	Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  	Modified:
#
#    		22 February 2011
#
#	Author:
#
#		Original MATLAB version by John Burkardt.
#		PYTHON version by Corrado Chisari
#
#  Parameters:
#
#    		Input, integer N, the integer to be measured.
#    		N should be nonnegative.
#
#    		Output, integer BIT, the position of the low 1 bit.
#
	bit = 0
	i = math.floor ( n )
	while ( 1 ):
		bit = bit + 1
		i2 = math.floor ( i / 2. )
		if ( i == 2 * i2 ):
			break

		i = i2
	return bit
	
def i4_sobol_generate ( m, n, skip ):
#*****************************************************************************80
#
## I4_SOBOL_GENERATE generates a Sobol dataset.
#
#	Licensing:
#
#		This code is distributed under the GNU LGPL license.
#
#  	Modified:
#
#    		22 February 2011
#
#	Author:
#
#		Original MATLAB version by John Burkardt.
#		PYTHON version by Corrado Chisari
#
#	Parameters:
#
#		Input, integer M, the spatial dimension.
#
#		Input, integer N, the number of points to generate.
#
#		Input, integer SKIP, the number of initial points to skip.
#
#		Output, real R(M,N), the points.
#
	r=zeros((m,n))
	for j in xrange (1, n+1):
		seed = skip + j - 2
		[ r[0:m,j-1], seed ] = i4_sobol ( m, seed )
	return r
def i4_sobol ( dim_num, seed ):
#*****************************************************************************80
#
## I4_SOBOL generates a new quasirandom Sobol vector with each call.
#
#	Discussion:
#
#		The routine adapts the ideas of Antonov and Saleev.
#
#	Licensing:
#
#		This code is distributed under the GNU LGPL license.
#
#	Modified:
#
#    		22 February 2011
#
#	Author:
#
#		Original FORTRAN77 version by Bennett Fox.
#		MATLAB version by John Burkardt.
#		PYTHON version by Corrado Chisari
#
#	Reference:
#
#		Antonov, 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.
#
#		Ilya Sobol,
#		USSR Computational Mathematics and Mathematical Physics,
#		Volume 16, pages 236-242, 1977.
#
#		Ilya Sobol, 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 number of spatial dimensions.
#		DIM_NUM must satisfy 1 <= DIM_NUM <= 40.
#
#		Input/output, integer SEED, the "seed" for the sequence.
#		This is essentially the index in the sequence of the quasirandom
#		value to be generated.	On output, SEED has been set to the
#		appropriate next value, usually simply SEED+1.
#		If SEED is less than 0 on input, it is treated as though it were 0.
#		An input value of 0 requests the first (0-th) element of the sequence.
#
#		Output, real QUASI(DIM_NUM), the next quasirandom vector.
#
	global atmost
	global dim_max
	global dim_num_save
	global initialized
	global lastq
	global log_max
	global maxcol
	global poly
	global recipd
	global seed_save
	global v

	if ( not 'initialized' in globals().keys() ):
		initialized = 0
		dim_num_save = -1

	if ( not initialized or dim_num != dim_num_save ):
		initialized = 1
		dim_max = 40
		dim_num_save = -1
		log_max = 30
		seed_save = -1
#
#	Initialize (part of) V.
#
		v = zeros((dim_max,log_max))
		v[0:40,0] = transpose([ \
			1, 1, 1, 1, 1, 1, 1, 1, 1, 1, \
			1, 1, 1, 1, 1, 1, 1, 1, 1, 1, \
			1, 1, 1, 1, 1, 1, 1, 1, 1, 1, \
			1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ])

		v[2:40,1] = transpose([ \
			1, 3, 1, 3, 1, 3, 3, 1, \
			3, 1, 3, 1, 3, 1, 1, 3, 1, 3, \
			1, 3, 1, 3, 3, 1, 3, 1, 3, 1, \
			3, 1, 1, 3, 1, 3, 1, 3, 1, 3 ])

		v[3:40,2] = transpose([ \
			7, 5, 1, 3, 3, 7, 5, \
			5, 7, 7, 1, 3, 3, 7, 5, 1, 1, \
			5, 3, 3, 1, 7, 5, 1, 3, 3, 7, \
			5, 1, 1, 5, 7, 7, 5, 1, 3, 3 ])

		v[5:40,3] = transpose([ \
			1, 7, 9,13,11, \
			1, 3, 7, 9, 5,13,13,11, 3,15, \
			5, 3,15, 7, 9,13, 9, 1,11, 7, \
			5,15, 1,15,11, 5, 3, 1, 7, 9 ])
	
		v[7:40,4] = transpose([ \
			9, 3,27, \
			15,29,21,23,19,11,25, 7,13,17, \
			1,25,29, 3,31,11, 5,23,27,19, \
			21, 5, 1,17,13, 7,15, 9,31, 9 ])

		v[13:40,5] = transpose([ \
							37,33, 7, 5,11,39,63, \
		 27,17,15,23,29, 3,21,13,31,25, \
			9,49,33,19,29,11,19,27,15,25 ])

		v[19:40,6] = transpose([ \
			13, \
			33,115, 41, 79, 17, 29,119, 75, 73,105, \
			7, 59, 65, 21,	3,113, 61, 89, 45,107 ])

		v[37:40,7] = transpose([ \
			7, 23, 39 ])
#
#	Set POLY.
#
		poly= [ \
			1,	 3,	 7,	11,	13,	19,	25,	37,	59,	47, \
			61,	55,	41,	67,	97,	91, 109, 103, 115, 131, \
			193, 137, 145, 143, 241, 157, 185, 167, 229, 171, \
			213, 191, 253, 203, 211, 239, 247, 285, 369, 299 ]

		atmost = 2**log_max - 1
#
#	Find the number of bits in ATMOST.
#
		maxcol = i4_bit_hi1 ( atmost )
#
#	Initialize row 1 of V.
#
		v[0,0:maxcol] = 1

#
#	Things to do only if the dimension changed.
#
	if ( dim_num != dim_num_save ):
#
#	Check parameters.
#
		if ( dim_num < 1 or dim_max < dim_num ):
			print 'I4_SOBOL - Fatal error!' 
			print '	The spatial dimension DIM_NUM should satisfy:' 
			print '		1 <= DIM_NUM <= %d'%dim_max
			print '	But this input value is DIM_NUM = %d'%dim_num
			return

		dim_num_save = dim_num
#
#	Initialize the remaining rows of V.
#
		for i in xrange(2 , dim_num+1):
#
#	The bits of the integer POLY(I) gives the form of polynomial I.
#
#	Find the degree of polynomial I from binary encoding.
#
			j = poly[i-1]
			m = 0
			while ( 1 ):
				j = math.floor ( j / 2. )
				if ( j <= 0 ):
					break
				m = m + 1
#
#	Expand this bit pattern to separate components of the logical array INCLUD.
#
			j = poly[i-1]
			includ=zeros(m)
			for k in xrange(m, 0, -1):
				j2 = math.floor ( j / 2. )
				includ[k-1] =  (j != 2 * j2 )
				j = j2
#
#	Calculate the remaining elements of row I as explained
#	in Bratley and Fox, section 2.
#
			for j in xrange( m+1, maxcol+1 ):
				newv = v[i-1,j-m-1]
				l = 1
				for k in xrange(1, m+1):
					l = 2 * l
					if ( includ[k-1] ):
						newv = bitwise_xor ( int(newv), int(l * v[i-1,j-k-1]) )
				v[i-1,j-1] = newv
#
#	Multiply columns of V by appropriate power of 2.
#
		l = 1
		for j in xrange( maxcol-1, 0, -1):
			l = 2 * l
			v[0:dim_num,j-1] = v[0:dim_num,j-1] * l
#
#	RECIPD is 1/(common denominator of the elements in V).
#
		recipd = 1.0 / ( 2 * l )
		lastq=zeros(dim_num)

	seed = int(math.floor ( seed ))

	if ( seed < 0 ):
		seed = 0

	if ( seed == 0 ):
		l = 1
		lastq=zeros(dim_num)

	elif ( seed == seed_save + 1 ):
#
#	Find the position of the right-hand zero in SEED.
#
		l = i4_bit_lo0 ( seed )

	elif ( seed <= seed_save ):

		seed_save = 0
		l = 1
		lastq=zeros(dim_num)

		for seed_temp in xrange( int(seed_save), int(seed)):
			l = i4_bit_lo0 ( seed_temp )
			for i in xrange(1 , dim_num+1):
				lastq[i-1] = bitwise_xor ( int(lastq[i-1]), int(v[i-1,l-1]) )

		l = i4_bit_lo0 ( seed )

	elif ( seed_save + 1 < seed ):

		for seed_temp in xrange( int(seed_save + 1), int(seed) ):
			l = i4_bit_lo0 ( seed_temp )
			for i in xrange(1, dim_num+1):
				lastq[i-1] = bitwise_xor ( int(lastq[i-1]), int(v[i-1,l-1]) )

		l = i4_bit_lo0 ( seed )
#
#	Check that the user is not calling too many times!
#
	if ( maxcol < l ):
		print 'I4_SOBOL - Fatal error!'
		print '	Too many calls!'
		print '	MAXCOL = %d\n'%maxcol
		print '	L =			%d\n'%l
		return
#
#	Calculate the new components of QUASI.
#
	quasi=zeros(dim_num)
	for i in xrange( 1, dim_num+1):
		quasi[i-1] = lastq[i-1] * recipd
		lastq[i-1] = bitwise_xor ( int(lastq[i-1]), int(v[i-1,l-1]) )

	seed_save = seed
	seed = seed + 1

	return [ quasi, seed ]
def i4_uniform ( a, b, seed ):
#*****************************************************************************80
#
## I4_UNIFORM returns a scaled pseudorandom I4.
#
#	Discussion:
#
#		The pseudorandom number will be scaled to be uniformly distributed
#		between A and B.
#
#	Licensing:
#
#		This code is distributed under the GNU LGPL 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 A, B, the minimum and maximum acceptable values.
#
#		Input, integer SEED, a seed for the random number generator.
#
#		Output, integer C, the randomly chosen integer.
#
#		Output, integer SEED, the updated seed.
#
	if ( seed == 0 ):
		print 'I4_UNIFORM - Fatal error!' 
		print '	Input SEED = 0!'

	seed = math.floor ( seed )
	a = round ( a )
	b = round ( b )

	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
#
#	Scale R to lie between A-0.5 and B+0.5.
#
	r = ( 1.0 - r ) * ( min ( a, b ) - 0.5 ) + r * ( max ( a, b ) + 0.5 )
#
#	Use rounding to convert R to an integer between A and B.
#
	value = round ( r )

	value = max ( value, min ( a, b ) )
	value = min ( value, max ( a, b ) )

	c = value

	return [ int(c), int(seed) ]
def prime_ge ( n ):
#*****************************************************************************80
#
## PRIME_GE returns the smallest prime greater than or equal to N.
#
#
#	Example:
#
#		N		 PRIME_GE
#
#		-10		2
#			1		2
#			2		2
#			3		3
#			4		5
#			5		5
#			6		7
#			7		7
#			8	 11
#			9	 11
#		 10	 11
#
#	Licensing:
#
#		This code is distributed under the GNU LGPL license.
#
#	Modified:
#
#    		22 February 2011
#
#	Author:
#
#		Original MATLAB version by John Burkardt.
#		PYTHON version by Corrado Chisari
#
#	Parameters:
#
#		Input, integer N, the number to be bounded.
#
#		Output, integer P, the smallest prime number that is greater
#		than or equal to N.	
#
	p = max ( math.ceil ( n ), 2 )
	while ( not isprime ( p ) ):
		p = p + 1

	return p

def isprime(n):
	#*****************************************************************************80
#
## IS_PRIME returns True if N is a prime number, False otherwise
#
#
#	Licensing:
#
#		This code is distributed under the GNU LGPL license.
#
#	Modified:
#
#    		22 February 2011
#
#	Author:
#
#		Corrado Chisari
#
#	Parameters:
#
#		Input, integer N, the number to be checked.
#
#		Output, boolean value, True or False
#
	if n!=int(n) or n<1:
		return False
	p=2
	while p<n:
		if n%p==0:
			return False
		p+=1
	return True

#############################################################################
# Extensions added by
# Author: Daniele Bigoni (dabi@imm.dtu.dk)
#

def sobol_generate(k,N,skip,leap):
    """
    Skip and leap sobol sequence
    
    Reference:

    .. [1] Saltelli, A., Chan, K., Scott, E.M., "Sensitivity Analysis"
    """
    
    # Generate sobol sequence
    samples = i4_sobol_generate(k,N*(leap+1),skip).T;
    
    # Remove leap values
    samples = samples[0:samples.shape[0]:(leap+1),:]
    
    return samples

def scrambled_sobol_generate(k,N,skip,leap):
    """
    Scramble function as in Owen (1997)
    
    Reference:

    .. [1] Saltelli, A., Chan, K., Scott, E.M., "Sensitivity Analysis"
    """
    
    # Generate sobol sequence
    samples = sobol_generate(k,N,skip,leap);
    
    # Scramble the sequence
    for col in range(0,k):
        samples[:,col] = scramble(samples[:,col]);
    
    return samples

def scramble(X):
    """
    Scramble function as in Owen (1997)
    
    Reference:

    .. [1] Saltelli, A., Chan, K., Scott, E.M., "Sensitivity Analysis"
    """

    N = len(X) - (len(X) % 2)
    
    idx = X[0:N].argsort()
    iidx = idx.argsort()
    
    # Generate binomial values and switch position for the second half of the array
    bi = stats.binom(1,0.5).rvs(size=N/2).astype(bool)
    pos = stats.uniform.rvs(size=N/2).argsort()
    
    # Scramble the indexes
    tmp = idx[0:N/2][bi];
    idx[0:N/2][bi] = idx[N/2:N][pos[bi]];
    idx[N/2:N][pos[bi]] = tmp;
    
    # Apply the scrambling
    X[0:N] = X[0:N][idx[iidx]];
    
    # Apply scrambling to sub intervals
    if N > 2:
        X[0:N/2] = scramble(X[0:N/2])
        X[N/2:N] = scramble(X[N/2:N])
    
    return X