Quasi-Monte Carlo Tools in OpenCL & C
The qmctoolscl package provides a Python interface to tools in OpenCL and C for performing Quasi-Monte Carlo (QMC). Routines are written as OpenCL kernels. By replacing a few code snippets, these OpenCL kernels are automatically translated to C functions. Python functions provide access to both the OpenCL kernels and C functions in a unified interface. Code is available on GitHub.
Installation
pip install qmctoolscl
To use OpenCL features, please install PyOpenCL. Commands used to install PyOpenCL on Linux, MacOS, and Windows for our automated tests can be found here. Note that Apple has deprecated support for OpenCL (see this post), but installation is still possible in many cases. If you cannot install PyOpenCL, the C backend to this package will still work independently.
Full Doctests for qmctoolscl
Setup for OpenCL vs C
Let's start by importing the relevant packages
>>> import qmctoolscl
>>> import numpy as np
and seeding a random number generator used for reproducibility
>>> rng = np.random.Generator(np.random.SFC64(7))
To use the C backend supply the following keyword arguments to function calls
>>> kwargs = {
... "backend": "C",
... }
To use the OpenCL backend supply the following keyword arguments to function calls
>>> kwargs = {
... "backend": "CL",
... "wait": True, ## required for accurate timing
... "global_size": (2,2,2), ## global size
... }
Lattice Sequences
linear order
>>> print(qmctoolscl.lat_gen_linear.__doc__)
Lattice points in linear order
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimension
g (np.ndarray of np.uint64): pointer to generating vector of size r*d
x (np.ndarray of np.double): pointer to point storage of size r*n*d
>>> g = np.array([
... [1,433461,315689,441789,501101],
... [1,182667,469891,498753,110745]],
... dtype=np.uint64)
>>> r = np.uint64(g.shape[0])
>>> n = np.uint64(8)
>>> d = np.uint64(g.shape[1])
>>> x = np.empty((r,n,d),dtype=np.float64)
>>> time_perf,time_process = qmctoolscl.lat_gen_linear(r,n,d,g,x,**kwargs)
>>> x
array([[[0. , 0. , 0. , 0. , 0. ],
[0.125, 0.625, 0.125, 0.625, 0.625],
[0.25 , 0.25 , 0.25 , 0.25 , 0.25 ],
[0.375, 0.875, 0.375, 0.875, 0.875],
[0.5 , 0.5 , 0.5 , 0.5 , 0.5 ],
[0.625, 0.125, 0.625, 0.125, 0.125],
[0.75 , 0.75 , 0.75 , 0.75 , 0.75 ],
[0.875, 0.375, 0.875, 0.375, 0.375]],
[[0. , 0. , 0. , 0. , 0. ],
[0.125, 0.375, 0.375, 0.125, 0.125],
[0.25 , 0.75 , 0.75 , 0.25 , 0.25 ],
[0.375, 0.125, 0.125, 0.375, 0.375],
[0.5 , 0.5 , 0.5 , 0.5 , 0.5 ],
[0.625, 0.875, 0.875, 0.625, 0.625],
[0.75 , 0.25 , 0.25 , 0.75 , 0.75 ],
[0.875, 0.625, 0.625, 0.875, 0.875]]])
natural order
>>> print(qmctoolscl.lat_gen_natural.__doc__)
Lattice points in natural order
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimension
n_start (np.uint64): starting index in sequence
g (np.ndarray of np.uint64): pointer to generating vector of size r*d
x (np.ndarray of np.double): pointer to point storage of size r*n*d
>>> n_start = np.uint64(2)
>>> n = np.uint64(6)
>>> x = np.empty((r,n,d),dtype=np.float64)
>>> time_perf,time_process = qmctoolscl.lat_gen_natural(r,n,d,n_start,g,x,**kwargs)
>>> x
array([[[0.25 , 0.25 , 0.25 , 0.25 , 0.25 ],
[0.75 , 0.75 , 0.75 , 0.75 , 0.75 ],
[0.125, 0.625, 0.125, 0.625, 0.625],
[0.625, 0.125, 0.625, 0.125, 0.125],
[0.375, 0.875, 0.375, 0.875, 0.875],
[0.875, 0.375, 0.875, 0.375, 0.375]],
[[0.25 , 0.75 , 0.75 , 0.25 , 0.25 ],
[0.75 , 0.25 , 0.25 , 0.75 , 0.75 ],
[0.125, 0.375, 0.375, 0.125, 0.125],
[0.625, 0.875, 0.875, 0.625, 0.625],
[0.375, 0.125, 0.125, 0.375, 0.375],
[0.875, 0.625, 0.625, 0.875, 0.875]]])
Gray code order
>>> print(qmctoolscl.lat_gen_gray.__doc__)
Lattice points in Gray code order
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimension
n_start (np.uint64): starting index in sequence
g (np.ndarray of np.uint64): pointer to generating vector of size r*d
x (np.ndarray of np.double): pointer to point storage of size r*n*d
>>> time_perf,time_process = qmctoolscl.lat_gen_gray(r,n,d,n_start,g,x,**kwargs)
>>> x
array([[[0.75 , 0.75 , 0.75 , 0.75 , 0.75 ],
[0.25 , 0.25 , 0.25 , 0.25 , 0.25 ],
[0.375, 0.875, 0.375, 0.875, 0.875],
[0.875, 0.375, 0.875, 0.375, 0.375],
[0.625, 0.125, 0.625, 0.125, 0.125],
[0.125, 0.625, 0.125, 0.625, 0.625]],
[[0.75 , 0.25 , 0.25 , 0.75 , 0.75 ],
[0.25 , 0.75 , 0.75 , 0.25 , 0.25 ],
[0.375, 0.125, 0.125, 0.375, 0.375],
[0.875, 0.625, 0.625, 0.875, 0.875],
[0.625, 0.875, 0.875, 0.625, 0.625],
[0.125, 0.375, 0.375, 0.125, 0.125]]])
shift mod 1
>>> print(qmctoolscl.lat_shift_mod_1.__doc__)
Shift mod 1 for lattice points
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimension
r_x (np.uint64): replications in x
x (np.ndarray of np.double): lattice points of size r_x*n*d
shifts (np.ndarray of np.double): shifts of size r*d
xr (np.ndarray of np.double): pointer to point storage of size r*n*d
>>> r_x = r
>>> r = np.uint64(2*r_x)
>>> print(qmctoolscl.lat_get_shifts.__doc__)
Get random shifts
Args:
rng (np.random._generator.Generator): random number generator
r (np.uint64): replications
d (np.uint64): dimension
>>> shifts = qmctoolscl.lat_get_shifts(rng,r,d)
>>> xr = np.empty((r,n,d),dtype=np.float64)
>>> time_perf,time_process = qmctoolscl.lat_shift_mod_1(r,n,d,r_x,x,shifts,xr,**kwargs)
>>> xr
array([[[0.79386058, 0.33727432, 0.1191824 , 0.40212985, 0.44669968],
[0.29386058, 0.83727432, 0.6191824 , 0.90212985, 0.94669968],
[0.41886058, 0.46227432, 0.7441824 , 0.52712985, 0.57169968],
[0.91886058, 0.96227432, 0.2441824 , 0.02712985, 0.07169968],
[0.66886058, 0.71227432, 0.9941824 , 0.77712985, 0.82169968],
[0.16886058, 0.21227432, 0.4941824 , 0.27712985, 0.32169968]],
[[0.85605352, 0.88025643, 0.38630282, 0.3468363 , 0.8076251 ],
[0.35605352, 0.38025643, 0.88630282, 0.8468363 , 0.3076251 ],
[0.48105352, 0.75525643, 0.26130282, 0.9718363 , 0.4326251 ],
[0.98105352, 0.25525643, 0.76130282, 0.4718363 , 0.9326251 ],
[0.73105352, 0.50525643, 0.01130282, 0.2218363 , 0.6826251 ],
[0.23105352, 0.00525643, 0.51130282, 0.7218363 , 0.1826251 ]],
[[0.9528797 , 0.97909681, 0.8866783 , 0.50220658, 0.59501765],
[0.4528797 , 0.47909681, 0.3866783 , 0.00220658, 0.09501765],
[0.5778797 , 0.10409681, 0.5116783 , 0.62720658, 0.72001765],
[0.0778797 , 0.60409681, 0.0116783 , 0.12720658, 0.22001765],
[0.8278797 , 0.35409681, 0.7616783 , 0.87720658, 0.97001765],
[0.3278797 , 0.85409681, 0.2616783 , 0.37720658, 0.47001765]],
[[0.31269008, 0.29826852, 0.96308655, 0.55983568, 0.60383675],
[0.81269008, 0.79826852, 0.46308655, 0.05983568, 0.10383675],
[0.93769008, 0.17326852, 0.83808655, 0.18483568, 0.22883675],
[0.43769008, 0.67326852, 0.33808655, 0.68483568, 0.72883675],
[0.18769008, 0.92326852, 0.58808655, 0.43483568, 0.47883675],
[0.68769008, 0.42326852, 0.08808655, 0.93483568, 0.97883675]]])
Digital Net Base 2
LSB to MSB integer representations
convert generating matrices from least significant bit (LSB) order to most significant bit (MSB) order
>>> print(qmctoolscl.dnb2_gmat_lsb_to_msb.__doc__)
Convert base 2 generating matrices with integers stored in Least Significant Bit order to Most Significant Bit order
Args:
r (np.uint64): replications
d (np.uint64): dimension
mmax (np.uint64): columns in each generating matrix
tmaxes (np.ndarray of np.uint64): length r vector of bits in each integer of the resulting MSB generating matrices
C_lsb (np.ndarray of np.uint64): original generating matrices of size r*d*mmax
C_msb (np.ndarray of np.uint64): new generating matrices of size r*d*mmax
>>> C_lsb = np.array([
... [1,2,4,8],
... [1,3,5,15],
... [1,3,6,9],
... [1,3,4,10]],
... dtype=np.uint64)
>>> r = np.uint64(1)
>>> d = np.uint64(C_lsb.shape[0])
>>> mmax = np.uint64(C_lsb.shape[1])
>>> tmax = 4
>>> tmaxes = np.tile(np.uint64(tmax),int(r))
>>> C = np.empty((d,mmax),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.dnb2_gmat_lsb_to_msb(r,d,mmax,tmaxes,C_lsb,C,**kwargs)
>>> C
array([[ 8, 4, 2, 1],
[ 8, 12, 10, 15],
[ 8, 12, 6, 9],
[ 8, 12, 2, 5]], dtype=uint64)
linear matrix scrambling (LMS)
>>> print(qmctoolscl.dnb2_linear_matrix_scramble.__doc__)
Linear matrix scrambling for base 2 generating matrices
Args:
r (np.uint64): replications
d (np.uint64): dimension
mmax (np.uint64): columns in each generating matrix
r_C (np.uint64): original generating matrices
tmax_new (np.uint64): bits in the integers of the resulting generating matrices
S (np.ndarray of np.uint64): scrambling matrices of size r*d*tmax_new
C (np.ndarray of np.uint64): original generating matrices of size r_C*d*mmax
C_lms (np.ndarray of np.uint64): resulting generating matrices of size r*d*mmax
>>> r_C = r
>>> r = np.uint64(2*r_C)
>>> tmax_new = np.uint64(6)
>>> print(qmctoolscl.dnb2_get_linear_scramble_matrix.__doc__)
Return a scrambling matrix for linear matrix scrambling
Args:
rng (np.random._generator.Generator): random number generator
r (np.uint64): replications
d (np.uint64): dimension
tmax (np.uint64): bits in each integer
tmax_new (np.uint64): bits in each integer of the generating matrix after scrambling
print_mats (np.uint8): flag to print the resulting matrices
>>> print_mats = np.uint8(True)
>>> S = qmctoolscl.dnb2_get_linear_scramble_matrix(rng,r,d,tmax,tmax_new,print_mats)
S with shape (r=2, d=4, tmax_new=6)
l = 0
j = 0
1000
1100
1010
1011
0000
1011
j = 1
1000
1100
1010
1111
0101
0111
j = 2
1000
0100
0010
1001
0001
0111
j = 3
1000
1100
1010
1001
0010
0010
l = 1
j = 0
1000
0100
0010
0101
1101
1000
j = 1
1000
0100
0110
1001
1010
1001
j = 2
1000
0100
0110
0111
1101
1110
j = 3
1000
0100
0110
0011
1000
0000
>>> C_lms = np.empty((r,d,mmax),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.dnb2_linear_matrix_scramble(r,d,mmax,r_C,tmax_new,S,C,C_lms,**kwargs)
>>> C_lms
array([[[61, 16, 13, 5],
[60, 43, 49, 33],
[36, 53, 24, 35],
[60, 44, 11, 20]],
[[35, 22, 8, 6],
[39, 63, 45, 48],
[35, 60, 18, 37],
[34, 58, 12, 28]]], dtype=uint64)
digital interlacing
Digital interlacing is used to create higher order digital nets.
>>> print(qmctoolscl.dnb2_interlace.__doc__)
Interlace generating matrices or transpose of point sets to attain higher order digital nets in base 2
Args:
r (np.uint64): replications
d_alpha (np.uint64): dimension of resulting generating matrices
mmax (np.uint64): columns of generating matrices
d (np.uint64): dimension of original generating matrices
tmax (np.uint64): bits in integers of original generating matrices
tmax_alpha (np.uint64): bits in integers of resulting generating matrices
alpha (np.uint64): interlacing factor
C (np.ndarray of np.uint64): original generating matrices of size r*d*mmax
C_alpha (np.ndarray of np.uint64): resulting interlaced generating matrices of size r*d_alpha*mmax
>>> alpha = np.uint64(2)
>>> d_alpha = np.uint64(d//alpha)
>>> tmax = tmax_new
>>> tmax_alpha = min(alpha*tmax_new,64)
>>> C_alpha = np.empty((r,d_alpha,mmax),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.dnb2_interlace(r,d_alpha,mmax,d,tmax,tmax_alpha,alpha,C_lms,C_alpha,**kwargs)
>>> C_alpha
array([[[4082, 1605, 1443, 1059],
[3440, 3698, 709, 2330]],
[[3103, 1917, 1233, 1320],
[3086, 4068, 600, 2418]]], dtype=uint64)
undo digital interlacing
>>> print(qmctoolscl.dnb2_undo_interlace.__doc__)
Undo interlacing of generating matrices in base 2
Args:
r (np.uint64): replications
d (np.uint64): dimension of resulting generating matrices
mmax (np.uint64): columns in generating matrices
d_alpha (np.uint64): dimension of interlaced generating matrices
tmax (np.uint64): bits in integers of original generating matrices
tmax_alpha (np.uint64): bits in integers of interlaced generating matrices
alpha (np.uint64): interlacing factor
C_alpha (np.ndarray of np.uint64): interlaced generating matrices of size r*d_alpha*mmax
C (np.ndarray of np.uint64): original generating matrices of size r*d*mmax
>>> C_lms_cp = np.empty((r,d,mmax),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.dnb2_undo_interlace(r,d,mmax,d_alpha,tmax,tmax_alpha,alpha,C_alpha,C_lms_cp,**kwargs)
>>> print((C_lms_cp==C_lms).all())
True
natural order
>>> print(qmctoolscl.dnb2_gen_natural.__doc__)
Binary representation of digital net in base 2 in natural order
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimension
n_start (np.uint64): starting index in sequence
mmax (np.uint64): columns in each generating matrix
C (np.ndarray of np.uint64): generating matrices of size r*d*mmax
xb (np.ndarray of np.uint64): binary digital net points of size r*n*d
>>> C = C_alpha
>>> d = d_alpha
>>> n_start = np.uint64(2)
>>> n = np.uint64(14)
>>> xb = np.empty((r,n,d),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.dnb2_gen_natural(r,n,d,n_start,mmax,C,xb,**kwargs)
>>> xb
array([[[1605, 3698],
[2487, 770],
[1443, 709],
[2641, 4021],
[ 998, 3255],
[3092, 455],
[1059, 2330],
[3025, 1130],
[ 614, 1896],
[3476, 2584],
[ 384, 3039],
[3698, 1711],
[1989, 1453],
[2103, 2269]],
[[1917, 4068],
[2914, 1002],
[1233, 600],
[2254, 3670],
[ 940, 3516],
[4019, 434],
[1320, 2418],
[2359, 1404],
[ 597, 1686],
[3658, 2712],
[ 505, 2858],
[3558, 1828],
[1668, 1230],
[2715, 2240]]], dtype=uint64)
Gray code order
>>> print(qmctoolscl.dnb2_gen_gray.__doc__)
Binary representation of digital net in base 2 in Gray code order
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimension
n_start (np.uint64): starting index in sequence
mmax (np.uint64): columns in each generating matrix
C (np.ndarray of np.uint64): generating matrices of size r*d*mmax
xb (np.ndarray of np.uint64): binary digital net points of size r*n*d
>>> time_perf,time_process = qmctoolscl.dnb2_gen_gray(r,n,d,n_start,mmax,C,xb,**kwargs)
>>> xb
array([[[2487, 770],
[1605, 3698],
[ 998, 3255],
[3092, 455],
[2641, 4021],
[1443, 709],
[ 384, 3039],
[3698, 1711],
[2103, 2269],
[1989, 1453],
[ 614, 1896],
[3476, 2584],
[3025, 1130],
[1059, 2330]],
[[2914, 1002],
[1917, 4068],
[ 940, 3516],
[4019, 434],
[2254, 3670],
[1233, 600],
[ 505, 2858],
[3558, 1828],
[2715, 2240],
[1668, 1230],
[ 597, 1686],
[3658, 2712],
[2359, 1404],
[1320, 2418]]], dtype=uint64)
digital shift
>>> print(qmctoolscl.dnb2_digital_shift.__doc__)
Digital shift base 2 digital net
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimension
r_x (np.uint64): replications of xb
lshifts (np.ndarray of np.uint64): left shift applied to each element of xb
xb (np.ndarray of np.uint64): binary base 2 digital net points of size r_x*n*d
shiftsb (np.ndarray of np.uint64): digital shifts of size r*d
xrb (np.ndarray of np.uint64): digital shifted digital net points of size r*n*d
>>> tmax = tmax_alpha
>>> tmax_new = np.uint64(64)
>>> lshifts = np.tile(tmax_new-tmax,int(r))
>>> r_x = r
>>> r = np.uint64(2*r_x)
>>> print(qmctoolscl.dnb2_get_digital_shifts.__doc__)
Get random shifts
Args:
rng (np.random._generator.Generator): random number generator
r (np.uint64): replications
d (np.uint64): dimension
tmax_new (np.uint64): bits in each integer
>>> shiftsb = qmctoolscl.dnb2_get_digital_shifts(rng,r,d,tmax_new)
>>> shiftsb
array([[ 5310653692329262186, 13368902947290702765],
[14338104955770306630, 858526716083693676],
[ 8506091904275275003, 16209688535125722604],
[17447177601860792486, 9021077006476608718]], dtype=uint64)
>>> xrb = np.empty((r,n,d),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.dnb2_digital_shift(r,n,d,r_x,lshifts,xb,shiftsb,xrb,**kwargs)
>>> xrb
array([[[15187047675152759914, 9919145632724902829],
[ 3306551858149391466, 6820669089094001581],
[ 8634310217328688234, 8284338967989412781],
[ 9868296515228204138, 11959276263923737517],
[17051537920884145258, 4816567254914130861],
[ 1406032815399042154, 10797347560062149549],
[ 5887114444632685674, 321974826798375853],
[12579463490905242730, 15237896792649458605],
[14610586922849336426, 3771732141364175789],
[ 3883012610452814954, 16381811098001564589],
[ 8057849465025264746, 14918141219106153389],
[10444757267531627626, 1731601510165341101],
[17627998673187568746, 18385912932181435309],
[ 829572063095618666, 2893530214026929069]],
[[ 8132144669253763142, 3839909669402962028],
[12766348685818003526, 17701989322449348716],
[18175171838289969222, 14999829546027051116],
[ 4452703773692067910, 1209807487018592364],
[ 5339912900284055622, 17188578964929112172],
[10082203307905187910, 3344513710392207468],
[15666666845844602950, 13351512082409449580],
[ 1773061995406622790, 8766847661746284652],
[ 8019554678569500742, 9793668376786757740],
[12590708300350554182, 5118931963576182892],
[16405257184733364294, 7100515799619201132],
[ 2475623537276420166, 11703194618791848044],
[ 6164071632092856390, 6641148637627410540],
[10699196456854945862, 11297870652328503404]],
[[17112470792180292859, 15047759831264134636],
[ 1322850498619333883, 564183429640619500],
[ 5218464176294812923, 3135738816869172716],
[13207849915250072827, 18195775970796111340],
[15211951749429943547, 1991824511517066732],
[ 3187340744350719227, 14746018656230311404],
[ 7929631151971851515, 6702589721746605548],
[10460654142554070267, 9945181453453362668],
[17688931544483716347, 7864518425608193516],
[ 746389746315910395, 13412953166528644588],
[ 5794924928598236411, 10841397779300091372],
[12631389162946649339, 4716502286076216812],
[14635490997126520059, 11985312084652197356],
[ 3763801496654142715, 8166259600642016748]],
[[ 4900149040006590630, 4868758150041011406],
[ 9651446646882463910, 9471436969213658318],
[14474801847796265126, 12029481557560100046],
[ 653254591396212902, 7354745144349525198],
[ 9133532689734856870, 10975639244755403982],
[13776743905553838246, 6390974824092239054],
[17127422028317487270, 14956821315350922446],
[ 3188781181605802150, 1112756060814017742],
[ 6598006099525267622, 17379757914876249294],
[11124123725032616102, 3589735855867790542],
[15524140560973590694, 1464036831748916430],
[ 1621528511280869542, 15326116484795303118],
[ 7012337265243353254, 3094339896857035982],
[11574483687769665702, 16866347557356012750]]], dtype=uint64)
convert digits to doubles
>>> print(qmctoolscl.dnb2_integer_to_float.__doc__)
Convert base 2 binary digital net points to floats
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimension
tmaxes (np.ndarray of np.uint64): bits in integers of each generating matrix of size r
xb (np.ndarray of np.uint64): binary digital net points of size r*n*d
x (np.ndarray of np.double): float digital net points of size r*n*d
>>> x = np.empty((r,n,d),dtype=np.float64)
>>> tmaxes_new = np.tile(tmax_new,int(r))
>>> time_perf,time_process = qmctoolscl.dnb2_integer_to_float(r,n,d,tmaxes_new,xrb,x,**kwargs)
>>> x
array([[[0.8232915 , 0.53771796],
[0.17924854, 0.36974921],
[0.46806689, 0.44909492],
[0.53496143, 0.64831367],
[0.92436572, 0.26110663],
[0.07622119, 0.58532538],
[0.31914111, 0.01745429],
[0.68193408, 0.82604804],
[0.7920415 , 0.20446601],
[0.21049854, 0.88805976],
[0.43681689, 0.80871406],
[0.56621143, 0.09387031],
[0.95561572, 0.99670234],
[0.04497119, 0.15685859]],
[[0.44084445, 0.20816192],
[0.69206515, 0.95962676],
[0.98527804, 0.81314239],
[0.24138156, 0.0655838 ],
[0.28947726, 0.93179473],
[0.54655734, 0.18130645],
[0.84929171, 0.72378692],
[0.09611788, 0.47525176],
[0.43474093, 0.53091583],
[0.68254366, 0.27749786],
[0.88933077, 0.38491973],
[0.13420382, 0.63443145],
[0.33415499, 0.36001739],
[0.5800046 , 0.6124588 ]],
[[0.9276689 , 0.8157407 ],
[0.07171187, 0.03058445],
[0.28289351, 0.16998874],
[0.71599898, 0.98639499],
[0.82464156, 0.10797702],
[0.17278609, 0.79938327],
[0.42986617, 0.36334812],
[0.5670732 , 0.53912937],
[0.9589189 , 0.4263364 ],
[0.04046187, 0.72711765],
[0.31414351, 0.58771335],
[0.68474898, 0.2556821 ],
[0.79339156, 0.64972507],
[0.20403609, 0.44269382]],
[[0.26563761, 0.26393591],
[0.52320597, 0.51344763],
[0.78468058, 0.6521195 ],
[0.035413 , 0.39870153],
[0.4951298 , 0.5949906 ],
[0.74683878, 0.34645544],
[0.92847941, 0.81081091],
[0.17286417, 0.06032263],
[0.35767863, 0.94215856],
[0.60303996, 0.19459997],
[0.84156535, 0.0793656 ],
[0.08790324, 0.83083044],
[0.38013956, 0.1677445 ],
[0.62745402, 0.91432653]]])
nested uniform scrambling
>>> r = np.uint64(1)
>>> n_start = np.uint64(0)
>>> n = np.uint64(8)
>>> d = np.uint64(4)
>>> C = np.array([
... [ 8, 4, 2, 1],
... [ 8, 12, 10, 15],
... [ 8, 12, 6, 9],
... [ 8, 12, 2, 5]],
... dtype=np.uint64)
>>> mmax = np.uint64(C.shape[1])
>>> tmax = np.uint64(np.ceil(np.log2(np.max(C))))
>>> xb = np.empty((r,n,d),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.dnb2_gen_natural(r,n,d,n_start,mmax,C,xb,**kwargs)
>>> print(qmctoolscl.dnb2_nested_uniform_scramble.__doc__)
Nested uniform scramble of digital net b2
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimensions
r_x (np.uint64): replications of xb
tmax (np.uint64): maximum number of bits in each integer
tmax_new (np.uint64): maximum number of bits in each integer after scrambling
rngs (np.ndarray of numpy.random._generator.Generator): random number generators of size r*d
root_nodes (np.ndarray of NUSNode_dnb2): root nodes of size r*d
xb (np.ndarray of np.uint64): array of unrandomized points of size r*n*d
xrb (np.ndarray of np.uint64): array to store scrambled points of size r*n*d
>>> tmax_new = np.uint64(2*tmax)
>>> r_x = r
>>> r = np.uint64(2*r_x)
>>> base_seed_seq = np.random.SeedSequence(7)
>>> seeds = base_seed_seq.spawn(r*d)
>>> rngs = np.array([np.random.Generator(np.random.SFC64(seed)) for seed in seeds]).reshape(r,d)
>>> root_nodes = np.array([qmctoolscl.NUSNode_dnb2() for i in range(r*d)]).reshape(r,d)
>>> xrb = np.zeros((r,n,d),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.dnb2_nested_uniform_scramble(r,n,d,r_x,tmax,tmax_new,rngs,root_nodes,xb,xrb)
>>> xrb
array([[[ 94, 54, 235, 30],
[174, 243, 26, 129],
[ 30, 172, 85, 222],
[254, 127, 143, 68],
[ 96, 207, 171, 46],
[131, 21, 125, 165],
[ 38, 79, 47, 237],
[199, 133, 199, 99]],
[[ 37, 152, 170, 143],
[219, 56, 68, 29],
[ 65, 98, 34, 111],
[181, 211, 196, 220],
[ 26, 9, 242, 180],
[245, 167, 2, 42],
[116, 225, 105, 93],
[138, 93, 139, 230]]], dtype=uint64)
Generalized Digital Net
Accommodates both Halton sequences and digital nets in any prime base
linear Matrix Scramble
>>> print(qmctoolscl.gdn_linear_matrix_scramble.__doc__)
Linear matrix scramble for generalized digital net
Args:
r (np.uint64): replications
d (np.uint64): dimension
mmax (np.uint64): columns in each generating matrix
r_C (np.uint64): number of replications of C
r_b (np.uint64): number of replications of bases
tmax (np.uint64): number of rows in each generating matrix
tmax_new (np.uint64): new number of rows in each generating matrix
bases (np.ndarray of np.uint64): bases for each dimension of size r*d
S (np.ndarray of np.uint64): scramble matrices of size r*d*tmax_new*tmax
C (np.ndarray of np.uint64): generating matrices of size r_C*d*mmax*tmax
C_lms (np.ndarray of np.uint64): new generating matrices of size r*d*mmax*tmax_new
>>> bases = np.array([[2,3],[5,7]],dtype=np.uint64)
>>> r_C = r_b = np.uint64(bases.shape[0])
>>> d = np.uint64(bases.shape[1])
>>> mmax = np.uint64(5)
>>> tmax = mmax
>>> print(qmctoolscl.gdn_get_halton_generating_matrix.__doc__)
Return the identity matrices comprising the Halton generating matrices
Arg:
r (np.uint64): replications
d (np.uint64): dimension
mmax (np.uint64): maximum number rows and columns in each generating matrix
>>> C = qmctoolscl.gdn_get_halton_generating_matrix(r_C,d,mmax)
>>> tmax_new = np.uint64(2*tmax)
>>> r = np.uint64(2*r_C)
>>> print(qmctoolscl.gdn_get_linear_scramble_matrix.__doc__)
Return a scrambling matrix for linear matrix scrambling
Args:
rng (np.random._generator.Generator): random number generator
r (np.uint64): replications
d (np.uint64): dimension
tmax (np.uint64): bits in each integer
tmax_new (np.uint64): bits in each integer of the generating matrix after scrambling
r_b (np.uint64): replications of bases
bases (np.ndarray of np.uint64): bases of size r_b*d
>>> S = qmctoolscl.gdn_get_linear_scramble_matrix(rng,r,d,tmax,tmax_new,r_b,bases)
>>> S
array([[[[1, 0, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[1, 0, 0, 0, 1],
[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[1, 0, 0, 1, 1],
[1, 0, 1, 0, 1],
[1, 1, 0, 1, 0]],
[[2, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[1, 0, 1, 0, 0],
[2, 0, 1, 2, 0],
[2, 1, 1, 0, 1],
[2, 0, 2, 2, 0],
[2, 2, 1, 0, 2],
[2, 0, 2, 0, 1],
[0, 2, 2, 0, 1],
[2, 0, 0, 1, 0]]],
[[[3, 0, 0, 0, 0],
[1, 1, 0, 0, 0],
[4, 2, 4, 0, 0],
[4, 2, 1, 2, 0],
[3, 4, 4, 3, 1],
[3, 3, 1, 1, 4],
[2, 2, 0, 4, 2],
[3, 3, 3, 1, 3],
[3, 3, 1, 2, 0],
[4, 3, 1, 1, 0]],
[[3, 0, 0, 0, 0],
[6, 1, 0, 0, 0],
[1, 0, 2, 0, 0],
[1, 4, 4, 1, 0],
[4, 4, 1, 2, 1],
[3, 4, 1, 6, 5],
[1, 3, 5, 1, 5],
[2, 0, 4, 3, 1],
[4, 0, 0, 1, 3],
[6, 0, 4, 3, 6]]],
[[[1, 0, 0, 0, 0],
[1, 1, 0, 0, 0],
[1, 1, 1, 0, 0],
[0, 1, 0, 1, 0],
[0, 0, 0, 1, 1],
[1, 0, 0, 0, 0],
[1, 0, 0, 0, 1],
[1, 1, 0, 0, 0],
[1, 0, 0, 1, 1],
[1, 0, 1, 1, 1]],
[[1, 0, 0, 0, 0],
[2, 1, 0, 0, 0],
[2, 1, 2, 0, 0],
[0, 0, 1, 1, 0],
[2, 2, 2, 1, 1],
[1, 1, 2, 2, 0],
[2, 1, 0, 2, 2],
[0, 0, 2, 0, 2],
[2, 1, 0, 2, 0],
[2, 0, 1, 0, 2]]],
[[[2, 0, 0, 0, 0],
[1, 4, 0, 0, 0],
[1, 3, 3, 0, 0],
[1, 3, 1, 1, 0],
[1, 3, 1, 1, 4],
[0, 2, 3, 3, 0],
[1, 3, 3, 3, 4],
[4, 1, 2, 4, 2],
[1, 4, 0, 1, 3],
[1, 3, 3, 1, 4]],
[[3, 0, 0, 0, 0],
[1, 5, 0, 0, 0],
[5, 2, 6, 0, 0],
[1, 4, 4, 4, 0],
[4, 3, 4, 6, 3],
[5, 6, 6, 1, 3],
[1, 1, 5, 2, 3],
[5, 6, 0, 3, 6],
[0, 2, 5, 5, 6],
[1, 1, 1, 2, 0]]]], dtype=uint64)
>>> C_lms = np.empty((r,d,mmax,tmax_new),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.gdn_linear_matrix_scramble(r,d,mmax,r_C,r_b,tmax,tmax_new,bases,S,C,C_lms,**kwargs)
>>> C_lms
array([[[[1, 1, 0, 0, 1, 1, 0, 1, 1, 1],
[0, 1, 0, 0, 0, 0, 1, 0, 0, 1],
[0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 1, 0, 0, 0, 1, 0, 1],
[0, 0, 0, 0, 1, 0, 0, 1, 1, 0]],
[[2, 0, 1, 2, 2, 2, 2, 2, 0, 2],
[0, 1, 0, 0, 1, 0, 2, 0, 2, 0],
[0, 0, 1, 1, 1, 2, 1, 2, 2, 0],
[0, 0, 0, 2, 0, 2, 0, 0, 0, 1],
[0, 0, 0, 0, 1, 0, 2, 1, 1, 0]]],
[[[3, 1, 4, 4, 3, 3, 2, 3, 3, 4],
[0, 1, 2, 2, 4, 3, 2, 3, 3, 3],
[0, 0, 4, 1, 4, 1, 0, 3, 1, 1],
[0, 0, 0, 2, 3, 1, 4, 1, 2, 1],
[0, 0, 0, 0, 1, 4, 2, 3, 0, 0]],
[[3, 6, 1, 1, 4, 3, 1, 2, 4, 6],
[0, 1, 0, 4, 4, 4, 3, 0, 0, 0],
[0, 0, 2, 4, 1, 1, 5, 4, 0, 4],
[0, 0, 0, 1, 2, 6, 1, 3, 1, 3],
[0, 0, 0, 0, 1, 5, 5, 1, 3, 6]]],
[[[1, 1, 1, 0, 0, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 1, 1, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 1, 0, 1, 0, 1, 1]],
[[1, 2, 2, 0, 2, 1, 2, 0, 2, 2],
[0, 1, 1, 0, 2, 1, 1, 0, 1, 0],
[0, 0, 2, 1, 2, 2, 0, 2, 0, 1],
[0, 0, 0, 1, 1, 2, 2, 0, 2, 0],
[0, 0, 0, 0, 1, 0, 2, 2, 0, 2]]],
[[[2, 1, 1, 1, 1, 0, 1, 4, 1, 1],
[0, 4, 3, 3, 3, 2, 3, 1, 4, 3],
[0, 0, 3, 1, 1, 3, 3, 2, 0, 3],
[0, 0, 0, 1, 1, 3, 3, 4, 1, 1],
[0, 0, 0, 0, 4, 0, 4, 2, 3, 4]],
[[3, 1, 5, 1, 4, 5, 1, 5, 0, 1],
[0, 5, 2, 4, 3, 6, 1, 6, 2, 1],
[0, 0, 6, 4, 4, 6, 5, 0, 5, 1],
[0, 0, 0, 4, 6, 1, 2, 3, 5, 2],
[0, 0, 0, 0, 3, 3, 3, 6, 6, 0]]]], dtype=uint64)
natural order
>>> print(qmctoolscl.gdn_gen_natural.__doc__)
Generalized digital net where the base can be different for each dimension e.g. for the Halton sequence
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimension
r_b (np.uint64): number of replications of bases
mmax (np.uint64): columns in each generating matrix
tmax (np.uint64): rows of each generating matrix
n_start (np.uint64): starting index in sequence
bases (np.ndarray of np.uint64): bases for each dimension of size r_b*d
C (np.ndarray of np.uint64): generating matrices of size r*d*mmax*tmax
xdig (np.ndarray of np.uint64): generalized digital net sequence of digits of size r*n*d*tmax
>>> tmax = tmax_new
>>> C = C_lms
>>> n_start = np.uint64(2)
>>> n = np.uint64(6)
>>> xdig = np.empty((r,n,d,tmax),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.gdn_gen_natural(r,n,d,r_b,mmax,tmax,n_start,bases,C,xdig,**kwargs)
>>> xdig
array([[[[0, 1, 0, 0, 0, 0, 1, 0, 0, 1],
[1, 0, 2, 1, 1, 1, 1, 1, 0, 1]],
[[1, 0, 0, 0, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 0, 1, 0, 2, 0, 2, 0]],
[[0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
[2, 1, 1, 2, 0, 2, 1, 2, 2, 2]],
[[1, 1, 1, 0, 1, 1, 0, 1, 0, 1],
[1, 1, 2, 1, 2, 1, 0, 1, 2, 1]],
[[0, 1, 1, 0, 0, 0, 1, 0, 1, 1],
[0, 2, 0, 0, 2, 0, 1, 0, 1, 0]],
[[1, 0, 1, 0, 1, 1, 1, 1, 0, 0],
[2, 2, 1, 2, 1, 2, 0, 2, 1, 2]]],
[[[1, 2, 3, 3, 1, 1, 4, 1, 1, 3],
[6, 5, 2, 2, 1, 6, 2, 4, 1, 5]],
[[4, 3, 2, 2, 4, 4, 1, 4, 4, 2],
[2, 4, 3, 3, 5, 2, 3, 6, 5, 4]],
[[2, 4, 1, 1, 2, 2, 3, 2, 2, 1],
[5, 3, 4, 4, 2, 5, 4, 1, 2, 3]],
[[0, 1, 2, 2, 4, 3, 2, 3, 3, 3],
[1, 2, 5, 5, 6, 1, 5, 3, 6, 2]],
[[3, 2, 1, 1, 2, 1, 4, 1, 1, 2],
[4, 1, 6, 6, 3, 4, 6, 5, 3, 1]],
[[1, 3, 0, 0, 0, 4, 1, 4, 4, 1],
[0, 1, 0, 4, 4, 4, 3, 0, 0, 0]]],
[[[0, 1, 1, 1, 0, 0, 0, 1, 0, 0],
[2, 1, 1, 0, 1, 2, 1, 0, 1, 1]],
[[1, 0, 0, 1, 0, 1, 1, 0, 1, 1],
[0, 1, 1, 0, 2, 1, 1, 0, 1, 0]],
[[0, 0, 1, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 1, 2, 0, 0, 0, 2]],
[[1, 1, 0, 0, 0, 1, 1, 1, 1, 0],
[2, 2, 2, 0, 0, 0, 2, 0, 2, 1]],
[[0, 1, 0, 1, 0, 0, 0, 1, 0, 1],
[0, 2, 2, 0, 1, 2, 2, 0, 2, 0]],
[[1, 0, 1, 1, 0, 1, 1, 0, 1, 0],
[1, 1, 1, 0, 0, 0, 1, 0, 1, 2]]],
[[[4, 2, 2, 2, 2, 0, 2, 3, 2, 2],
[6, 2, 3, 2, 1, 3, 2, 3, 0, 2]],
[[1, 3, 3, 3, 3, 0, 3, 2, 3, 3],
[2, 3, 1, 3, 5, 1, 3, 1, 0, 3]],
[[3, 4, 4, 4, 4, 0, 4, 1, 4, 4],
[5, 4, 6, 4, 2, 6, 4, 6, 0, 4]],
[[0, 4, 3, 3, 3, 2, 3, 1, 4, 3],
[1, 5, 4, 5, 6, 4, 5, 4, 0, 5]],
[[2, 0, 4, 4, 4, 2, 4, 0, 0, 4],
[4, 6, 2, 6, 3, 2, 6, 2, 0, 6]],
[[4, 1, 0, 0, 0, 2, 0, 4, 1, 0],
[0, 5, 2, 4, 3, 6, 1, 6, 2, 1]]]], dtype=uint64)
digital shift
>>> print(qmctoolscl.gdn_digital_shift.__doc__)
Digital shift a generalized digital net
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimension
r_x (np.uint64): replications of xdig
r_b (np.uint64): replications of bases
tmax (np.uint64): rows of each generating matrix
tmax_new (np.uint64): rows of each new generating matrix
bases (np.ndarray of np.uint64): bases for each dimension of size r_b*d
shifts (np.ndarray of np.uint64): digital shifts of size r*d*tmax_new
xdig (np.ndarray of np.uint64): binary digital net points of size r_x*n*d*tmax
xdig_new (np.ndarray of np.uint64): float digital net points of size r*n*d*tmax_new
>>> r_x = r
>>> tmax_new = np.uint64(12)
>>> print(qmctoolscl.gdn_get_digital_shifts.__doc__)
Return digital shifts for gdn
Args:
rng (np.random._generator.Generator): random number generator
r (np.uint64): replications
d (np.uint64): dimension
tmax_new (np.uint64): number of bits in each shift
r_b (np.uint64): replications of bases
bases (np.ndarray of np.uint64): bases of size r_b*d
>>> shifts = qmctoolscl.gdn_get_digital_shifts(rng,r,d,tmax_new,r_b,bases)
>>> shifts
array([[[0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1],
[1, 2, 0, 2, 2, 0, 0, 0, 1, 0, 0, 1]],
[[4, 0, 0, 1, 2, 0, 0, 1, 0, 3, 2, 1],
[1, 3, 6, 5, 0, 2, 5, 2, 1, 6, 6, 6]],
[[1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0],
[1, 0, 1, 0, 2, 0, 2, 2, 2, 0, 1, 1]],
[[0, 4, 1, 0, 2, 2, 2, 2, 1, 1, 1, 1],
[5, 5, 1, 3, 0, 4, 5, 4, 3, 0, 3, 2]]], dtype=uint64)
>>> xdig_new = np.empty((r,n,d,tmax_new),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.gdn_digital_shift(r,n,d,r_x,r_b,tmax,tmax_new,bases,shifts,xdig,xdig_new,**kwargs)
>>> xdig_new
array([[[[0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1],
[2, 2, 2, 0, 0, 1, 1, 1, 1, 1, 0, 1]],
[[1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 1]],
[[0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1],
[0, 0, 1, 1, 2, 2, 1, 2, 0, 2, 0, 1]],
[[1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1],
[2, 0, 2, 0, 1, 1, 0, 1, 0, 1, 0, 1]],
[[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],
[1, 1, 0, 2, 1, 0, 1, 0, 2, 0, 0, 1]],
[[1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1],
[0, 1, 1, 1, 0, 2, 0, 2, 2, 2, 0, 1]]],
[[[0, 2, 3, 4, 3, 1, 4, 2, 1, 1, 2, 1],
[0, 1, 1, 0, 1, 1, 0, 6, 2, 4, 6, 6]],
[[3, 3, 2, 3, 1, 4, 1, 0, 4, 0, 2, 1],
[3, 0, 2, 1, 5, 4, 1, 1, 6, 3, 6, 6]],
[[1, 4, 1, 2, 4, 2, 3, 3, 2, 4, 2, 1],
[6, 6, 3, 2, 2, 0, 2, 3, 3, 2, 6, 6]],
[[4, 1, 2, 3, 1, 3, 2, 4, 3, 1, 2, 1],
[2, 5, 4, 3, 6, 3, 3, 5, 0, 1, 6, 6]],
[[2, 2, 1, 2, 4, 1, 4, 2, 1, 0, 2, 1],
[5, 4, 5, 4, 3, 6, 4, 0, 4, 0, 6, 6]],
[[0, 3, 0, 1, 2, 4, 1, 0, 4, 4, 2, 1],
[1, 4, 6, 2, 4, 6, 1, 2, 1, 6, 6, 6]]],
[[[1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0],
[0, 1, 2, 0, 0, 2, 0, 2, 0, 1, 1, 1]],
[[0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0],
[1, 1, 2, 0, 1, 1, 0, 2, 0, 0, 1, 1]],
[[1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0],
[2, 0, 1, 0, 0, 2, 2, 2, 2, 2, 1, 1]],
[[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 2, 0, 0, 2, 0, 1, 2, 1, 1, 1, 1]],
[[1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0],
[1, 2, 0, 0, 0, 2, 1, 2, 1, 0, 1, 1]],
[[0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[2, 1, 2, 0, 2, 0, 0, 2, 0, 2, 1, 1]]],
[[[4, 1, 3, 2, 4, 2, 4, 0, 3, 3, 1, 1],
[4, 0, 4, 5, 1, 0, 0, 0, 3, 2, 3, 2]],
[[1, 2, 4, 3, 0, 2, 0, 4, 4, 4, 1, 1],
[0, 1, 2, 6, 5, 5, 1, 5, 3, 3, 3, 2]],
[[3, 3, 0, 4, 1, 2, 1, 3, 0, 0, 1, 1],
[3, 2, 0, 0, 2, 3, 2, 3, 3, 4, 3, 2]],
[[0, 3, 4, 3, 0, 4, 0, 3, 0, 4, 1, 1],
[6, 3, 5, 1, 6, 1, 3, 1, 3, 5, 3, 2]],
[[2, 4, 0, 4, 1, 4, 1, 2, 1, 0, 1, 1],
[2, 4, 3, 2, 3, 6, 4, 6, 3, 6, 3, 2]],
[[4, 0, 1, 0, 2, 4, 2, 1, 2, 1, 1, 1],
[5, 3, 3, 0, 3, 3, 6, 3, 5, 1, 3, 2]]]], dtype=uint64)
digital permutation
>>> print(qmctoolscl.gdn_digital_permutation.__doc__)
Permutation of digits for a generalized digital net
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimension
r_x (np.uint64): replications of xdig
r_b (np.uint64): replications of bases
tmax (np.uint64): rows of each generating matrix
tmax_new (np.uint64): rows of each new generating matrix
bmax (np.uint64): common permutation size, typically the maximum basis
perms (np.ndarray of np.uint64): permutations of size r*d*tmax_new*bmax
xdig (np.ndarray of np.uint64): binary digital net points of size r_x*n*d*tmax
xdig_new (np.ndarray of np.uint64): float digital net points of size r*n*d*tmax_new
>>> print(qmctoolscl.gdn_get_digital_permutations.__doc__)
Return permutations for gdn
Args:
rng (np.random._generator.Generator): random number generator
r (np.uint64): replications
d (np.uint64): dimension
tmax_new (np.uint64): number of bits in each shift
r_b (np.uint64): replications of bases
bases (np.ndarray of np.uint64): bases of size r_b*d
>>> perms = qmctoolscl.gdn_get_digital_permutations(rng,r,d,tmax_new,r_b,bases)
>>> perms
array([[[[1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0]],
[[0, 1, 2, 0, 0, 0, 0],
[1, 0, 2, 0, 0, 0, 0],
[0, 1, 2, 0, 0, 0, 0],
[2, 1, 0, 0, 0, 0, 0],
[0, 2, 1, 0, 0, 0, 0],
[2, 0, 1, 0, 0, 0, 0],
[2, 0, 1, 0, 0, 0, 0],
[1, 0, 2, 0, 0, 0, 0],
[1, 2, 0, 0, 0, 0, 0],
[2, 1, 0, 0, 0, 0, 0],
[2, 0, 1, 0, 0, 0, 0],
[2, 1, 0, 0, 0, 0, 0]]],
[[[3, 4, 0, 2, 1, 0, 0],
[3, 0, 1, 4, 2, 0, 0],
[1, 3, 2, 0, 4, 0, 0],
[1, 0, 3, 4, 2, 0, 0],
[3, 2, 1, 4, 0, 0, 0],
[1, 0, 3, 4, 2, 0, 0],
[1, 3, 0, 4, 2, 0, 0],
[0, 4, 1, 2, 3, 0, 0],
[2, 4, 3, 1, 0, 0, 0],
[2, 1, 4, 0, 3, 0, 0],
[2, 0, 4, 1, 3, 0, 0],
[1, 4, 2, 3, 0, 0, 0]],
[[5, 0, 1, 3, 2, 6, 4],
[4, 2, 3, 1, 6, 0, 5],
[6, 2, 0, 5, 1, 4, 3],
[2, 0, 3, 5, 6, 4, 1],
[0, 6, 2, 1, 4, 3, 5],
[3, 2, 5, 4, 6, 0, 1],
[1, 4, 3, 6, 5, 0, 2],
[5, 6, 4, 2, 3, 0, 1],
[3, 0, 1, 4, 2, 5, 6],
[3, 2, 6, 1, 0, 4, 5],
[1, 4, 3, 2, 5, 0, 6],
[6, 0, 4, 1, 5, 3, 2]]],
[[[1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0]],
[[1, 2, 0, 0, 0, 0, 0],
[1, 2, 0, 0, 0, 0, 0],
[2, 0, 1, 0, 0, 0, 0],
[1, 0, 2, 0, 0, 0, 0],
[2, 0, 1, 0, 0, 0, 0],
[2, 0, 1, 0, 0, 0, 0],
[0, 1, 2, 0, 0, 0, 0],
[0, 2, 1, 0, 0, 0, 0],
[2, 1, 0, 0, 0, 0, 0],
[1, 2, 0, 0, 0, 0, 0],
[0, 1, 2, 0, 0, 0, 0],
[0, 1, 2, 0, 0, 0, 0]]],
[[[1, 3, 0, 2, 4, 0, 0],
[4, 3, 2, 1, 0, 0, 0],
[0, 4, 1, 3, 2, 0, 0],
[1, 4, 2, 3, 0, 0, 0],
[1, 2, 0, 4, 3, 0, 0],
[1, 0, 2, 4, 3, 0, 0],
[3, 2, 0, 4, 1, 0, 0],
[2, 4, 0, 1, 3, 0, 0],
[2, 3, 1, 4, 0, 0, 0],
[4, 0, 2, 1, 3, 0, 0],
[0, 1, 3, 4, 2, 0, 0],
[2, 0, 1, 3, 4, 0, 0]],
[[1, 0, 4, 5, 6, 2, 3],
[4, 5, 0, 3, 1, 6, 2],
[4, 2, 1, 5, 3, 0, 6],
[3, 1, 4, 5, 6, 0, 2],
[0, 1, 3, 6, 2, 4, 5],
[4, 0, 2, 6, 1, 5, 3],
[0, 6, 4, 2, 1, 3, 5],
[5, 1, 3, 4, 0, 6, 2],
[4, 6, 0, 1, 3, 5, 2],
[3, 2, 6, 5, 0, 4, 1],
[3, 4, 2, 6, 1, 5, 0],
[2, 6, 1, 3, 0, 4, 5]]]], dtype=uint64)
>>> bmax = bases.max().astype(np.uint64)
>>> time_perf,time_process = qmctoolscl.gdn_digital_permutation(r,n,d,r_x,r_b,tmax,tmax_new,bmax,perms,xdig,xdig_new,**kwargs)
>>> xdig_new
array([[[[1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1],
[1, 1, 2, 1, 2, 0, 0, 0, 1, 1, 2, 2]],
[[0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1],
[0, 0, 0, 2, 2, 2, 1, 1, 0, 2, 2, 2]],
[[1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1],
[2, 0, 1, 0, 0, 1, 0, 2, 0, 0, 2, 2]],
[[0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1],
[1, 0, 2, 1, 1, 0, 2, 0, 0, 1, 2, 2]],
[[1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1],
[0, 2, 0, 2, 1, 2, 0, 1, 2, 2, 2, 2]],
[[0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1],
[2, 2, 1, 0, 2, 1, 2, 2, 2, 0, 2, 2]]],
[[[4, 1, 0, 4, 2, 0, 2, 4, 4, 0, 2, 1],
[4, 0, 0, 3, 6, 1, 3, 3, 0, 4, 1, 6]],
[[1, 4, 2, 3, 0, 2, 3, 3, 0, 4, 2, 1],
[1, 6, 5, 5, 3, 5, 6, 1, 5, 0, 1, 6]],
[[0, 2, 3, 0, 1, 3, 4, 1, 3, 1, 2, 1],
[6, 1, 1, 6, 2, 0, 5, 6, 1, 1, 1, 6]],
[[3, 0, 2, 3, 0, 4, 0, 2, 1, 0, 2, 1],
[0, 3, 4, 4, 5, 2, 0, 2, 6, 6, 1, 6]],
[[2, 1, 3, 0, 1, 0, 2, 4, 4, 4, 2, 1],
[2, 2, 3, 1, 1, 6, 2, 0, 4, 2, 1, 6]],
[[4, 4, 1, 1, 3, 2, 3, 3, 0, 1, 2, 1],
[5, 2, 6, 6, 4, 6, 6, 5, 3, 3, 1, 6]]],
[[[1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1],
[0, 2, 0, 1, 0, 1, 1, 0, 1, 2, 0, 0]],
[[0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1],
[1, 2, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0]],
[[1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1],
[2, 1, 2, 1, 0, 1, 0, 0, 2, 0, 0, 0]],
[[0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1],
[0, 0, 1, 1, 2, 2, 2, 0, 0, 2, 0, 0]],
[[1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1],
[1, 0, 1, 1, 0, 1, 2, 0, 0, 1, 0, 0]],
[[0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1],
[2, 2, 0, 1, 2, 2, 1, 0, 1, 0, 0, 0]]],
[[[4, 2, 1, 2, 0, 1, 0, 1, 1, 2, 0, 2],
[3, 0, 5, 4, 1, 6, 4, 4, 4, 6, 3, 2]],
[[3, 1, 3, 3, 4, 1, 4, 0, 4, 1, 0, 2],
[4, 3, 2, 5, 4, 0, 2, 1, 4, 5, 3, 2]],
[[2, 0, 2, 0, 3, 1, 1, 4, 0, 3, 0, 2],
[2, 1, 6, 6, 3, 3, 1, 2, 4, 0, 3, 2]],
[[1, 0, 3, 3, 4, 2, 4, 4, 0, 1, 0, 2],
[0, 6, 3, 0, 5, 1, 3, 0, 4, 4, 3, 2]],
[[0, 4, 2, 0, 3, 2, 1, 2, 2, 3, 0, 2],
[6, 2, 1, 2, 6, 2, 5, 3, 4, 1, 3, 2]],
[[4, 3, 0, 1, 1, 2, 3, 3, 3, 4, 0, 2],
[1, 6, 1, 6, 6, 3, 6, 2, 0, 2, 3, 2]]]], dtype=uint64)
convert digits to doubles
>>> print(qmctoolscl.gdn_integer_to_float.__doc__)
Convert digits of generalized digital net to floats
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimension
r_b (np.uint64): replications of bases
tmax (np.uint64): rows of each generating matrix
bases (np.ndarray of np.uint64): bases for each dimension of size r_b*d
xdig (np.ndarray of np.uint64): binary digital net points of size r*n*d*tmax
x (np.ndarray of np.double): float digital net points of size r*n*d
>>> x = np.empty((r,n,d),dtype=np.float64)
>>> time_perf,time_process = qmctoolscl.gdn_integer_to_float(r,n,d,r_b,tmax_new,bases,xdig_new,x,**kwargs)
>>> x
array([[[0.97290039, 0.53917744],
[0.20629883, 0.03632388],
[0.60864258, 0.70539533],
[0.33618164, 0.4248148 ],
[0.84985352, 0.25407524],
[0.0793457 , 0.93686411]],
[[0.84707793, 0.57304772],
[0.38097453, 0.28219443],
[0.10456744, 0.88309157],
[0.62106168, 0.07486728],
[0.46435834, 0.33580649],
[0.97073423, 0.77539095]],
[[0.88549805, 0.23648157],
[0.02124023, 0.57254145],
[0.70288086, 0.86567088],
[0.32885742, 0.06130502],
[0.76147461, 0.38501922],
[0.14526367, 0.91271656]],
[[0.89126728, 0.44493083],
[0.67019736, 0.64080715],
[0.41704736, 0.32631978],
[0.23026955, 0.13150509],
[0.17710726, 0.90208831],
[0.92209603, 0.27111067]]])
nested uniform scramble
>>> r = np.uint64(1)
>>> n_start = np.uint64(0)
>>> n = np.uint64(9)
>>> bases = np.array([[2,3,5]],dtype=np.uint64)
>>> r_b = np.uint64(bases.shape[0])
>>> d = np.uint64(bases.shape[1])
>>> mmax = np.uint64(3)
>>> tmax = mmax
>>> C = np.tile(np.eye(mmax,dtype=np.uint64)[None,None,:,:],(int(r),int(d),1,1))
>>> xdig = np.empty((r,n,d,tmax),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.gdn_gen_natural(r,n,d,r_b,mmax,tmax,n_start,bases,C,xdig)
>>> r_x = r
>>> r = np.uint64(2)
>>> tmax_new = np.uint64(8)
>>> base_seed_seq = np.random.SeedSequence(7)
>>> seeds = base_seed_seq.spawn(r*d)
>>> rngs = np.array([np.random.Generator(np.random.SFC64(seed)) for seed in seeds]).reshape(r,d)
>>> root_nodes = np.array([qmctoolscl.NUSNode_gdn() for i in range(r*d)]).reshape(r,d)
>>> xrdig = np.zeros((r,n,d,tmax_new),dtype=np.uint64)
>>> print(qmctoolscl.gdn_nested_uniform_scramble.__doc__)
Nested uniform scramble of general digital nets
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimensions
r_x (np.uint64): replications of xb
r_b (np.uint64): replications of bases
tmax (np.uint64): maximum number digits in each point representation
tmax_new (np.uint64): maximum number digits in each point representation after scrambling
rngs (np.ndarray of numpy.random._generator.Generator): random number generators of size r*d
root_nodes (np.ndarray of NUSNode_gdn): root nodes of size r*d
bases (np.ndarray of np.uint64): array of bases of size r*d
xdig (np.ndarray of np.uint64): array of unrandomized points of size r*n*d*tmax
xrdig (np.ndarray of np.uint64): array to store scrambled points of size r*n*d*tmax_new
>>> time_perf,time_process = qmctoolscl.gdn_nested_uniform_scramble(r,n,d,r_x,r_b,tmax,tmax_new,rngs,root_nodes,bases,xdig,xrdig)
>>> xrdig
array([[[[0, 0, 1, 0, 0, 1, 0, 0],
[2, 2, 2, 2, 1, 0, 2, 1],
[3, 4, 2, 0, 3, 4, 1, 0]],
[[1, 0, 1, 0, 1, 0, 1, 1],
[1, 0, 1, 0, 1, 0, 1, 2],
[1, 0, 3, 3, 3, 0, 1, 2]],
[[0, 1, 1, 1, 1, 0, 0, 0],
[0, 2, 2, 0, 0, 2, 0, 2],
[4, 1, 2, 1, 4, 3, 0, 0]],
[[1, 1, 0, 0, 0, 1, 0, 1],
[2, 1, 1, 1, 0, 2, 2, 2],
[2, 3, 2, 4, 2, 2, 2, 0]],
[[0, 0, 0, 0, 0, 1, 0, 0],
[1, 2, 2, 0, 1, 1, 2, 0],
[0, 1, 0, 4, 3, 1, 3, 0]],
[[1, 0, 0, 0, 1, 1, 1, 1],
[0, 0, 2, 0, 0, 1, 0, 2],
[3, 2, 1, 4, 2, 1, 4, 1]],
[[0, 1, 0, 0, 0, 0, 0, 0],
[2, 0, 1, 1, 1, 0, 1, 2],
[1, 1, 4, 4, 4, 0, 1, 1]],
[[1, 1, 1, 1, 1, 0, 1, 1],
[1, 1, 1, 2, 0, 0, 0, 0],
[4, 4, 1, 4, 1, 2, 3, 0]],
[[1, 0, 1, 0, 0, 0, 0, 0],
[0, 1, 2, 0, 2, 1, 0, 1],
[2, 1, 0, 0, 1, 2, 3, 0]]],
[[[0, 0, 0, 0, 1, 1, 0, 0],
[0, 1, 1, 1, 0, 0, 2, 1],
[2, 4, 0, 0, 1, 1, 0, 0]],
[[1, 0, 1, 0, 1, 0, 0, 1],
[1, 0, 1, 2, 0, 0, 0, 1],
[1, 1, 1, 1, 3, 0, 1, 0]],
[[0, 1, 0, 1, 0, 1, 0, 1],
[2, 0, 1, 0, 0, 2, 1, 0],
[0, 0, 2, 2, 0, 0, 4, 0]],
[[1, 1, 0, 0, 1, 1, 1, 0],
[0, 2, 1, 1, 2, 1, 2, 2],
[4, 1, 4, 3, 4, 0, 1, 3]],
[[0, 0, 1, 1, 0, 1, 0, 0],
[1, 2, 2, 2, 1, 1, 0, 1],
[3, 1, 0, 4, 2, 0, 1, 2]],
[[1, 0, 0, 1, 1, 0, 0, 0],
[2, 1, 0, 2, 0, 1, 1, 1],
[2, 3, 2, 0, 4, 3, 2, 1]],
[[0, 1, 1, 0, 1, 0, 1, 1],
[0, 0, 0, 1, 2, 1, 1, 0],
[1, 3, 3, 4, 4, 4, 0, 4]],
[[1, 1, 1, 1, 0, 1, 0, 0],
[1, 1, 1, 1, 0, 1, 0, 0],
[0, 2, 1, 4, 3, 3, 2, 0]],
[[1, 0, 1, 0, 0, 0, 0, 0],
[2, 2, 2, 2, 2, 2, 2, 1],
[4, 3, 0, 0, 4, 2, 0, 4]]]], dtype=uint64)
>>> xr = np.empty((r,n,d),dtype=np.float64)
>>> time_perf,time_process = qmctoolscl.gdn_integer_to_float(r,n,d,r_b,tmax_new,bases,xrdig,xr)
>>> xr
array([[[0.140625 , 0.99283646, 0.7772288 ],
[0.66796875, 0.37524768, 0.22977792],
[0.46875 , 0.29934461, 0.859072 ],
[0.76953125, 0.8311233 , 0.5431936 ],
[0.015625 , 0.63603109, 0.0474624 ],
[0.55859375, 0.07575065, 0.69515776],
[0.25 , 0.72092669, 0.27969536],
[0.98046875, 0.50617284, 0.9748864 ],
[0.625 , 0.1949398 , 0.4404864 ]],
[[0.046875 , 0.16156074, 0.560384 ],
[0.66015625, 0.39521414, 0.2505728 ],
[0.33203125, 0.70690444, 0.0192512 ],
[0.8046875 , 0.28242646, 0.87810048],
[0.203125 , 0.65996037, 0.64705792],
[0.59375 , 0.80445054, 0.53750016],
[0.41796875, 0.02240512, 0.35194624],
[0.953125 , 0.4951989 , 0.0955776 ],
[0.625 , 0.99969517, 0.92141824]]])
digital interlacing
>>> print(qmctoolscl.gdn_interlace.__doc__)
Interlace generating matrices or transpose of point sets to attain higher order digital nets
Args:
r (np.uint64): replications
d_alpha (np.uint64): dimension of resulting generating matrices
mmax (np.uint64): columns of generating matrices
d (np.uint64): dimension of original generating matrices
tmax (np.uint64): rows of original generating matrices
tmax_alpha (np.uint64): rows of interlaced generating matrices
alpha (np.uint64): interlacing factor
C (np.ndarray of np.uint64): original generating matrices of size r*d*mmax*tmax
C_alpha (np.ndarray of np.uint64): resulting interlaced generating matrices of size r*d_alpha*mmax*tmax_alpha
>>> r = np.uint64(2)
>>> d = np.uint64(6)
>>> alpha = np.uint64(3)
>>> d_alpha = np.uint64(d//alpha)
>>> mmax = np.uint64(3)
>>> tmax = np.uint64(4)
>>> tmax_alpha = np.uint64(alpha*tmax)
>>> C = rng.integers(0,5,(r,d,mmax,tmax),dtype=np.uint64)
>>> C
array([[[[0, 4, 4, 0],
[0, 2, 4, 0],
[1, 2, 4, 4]],
[[2, 4, 3, 4],
[4, 0, 1, 2],
[0, 2, 0, 0]],
[[1, 1, 3, 1],
[3, 3, 1, 1],
[1, 3, 2, 0]],
[[1, 1, 4, 2],
[4, 2, 4, 4],
[1, 0, 3, 1]],
[[1, 4, 3, 0],
[2, 4, 4, 4],
[1, 4, 2, 4]],
[[0, 2, 3, 1],
[3, 1, 1, 0],
[3, 0, 1, 0]]],
[[[4, 4, 3, 2],
[3, 3, 3, 0],
[4, 3, 3, 3]],
[[2, 3, 2, 0],
[0, 4, 0, 2],
[2, 2, 1, 0]],
[[3, 0, 1, 4],
[4, 1, 3, 3],
[0, 3, 1, 0]],
[[2, 0, 3, 1],
[4, 1, 3, 1],
[3, 1, 0, 4]],
[[2, 0, 2, 0],
[4, 0, 1, 3],
[0, 1, 1, 1]],
[[4, 4, 0, 1],
[2, 3, 0, 3],
[0, 1, 4, 0]]]], dtype=uint64)
>>> C_alpha = np.empty((r,d_alpha,mmax,tmax_alpha),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.gdn_interlace(r,d_alpha,mmax,d,tmax,tmax_alpha,alpha,C,C_alpha)
>>> C_alpha
array([[[[0, 2, 1, 4, 4, 1, 4, 3, 3, 0, 4, 1],
[0, 4, 3, 2, 0, 3, 4, 1, 1, 0, 2, 1],
[1, 0, 1, 2, 2, 3, 4, 0, 2, 4, 0, 0]],
[[1, 1, 0, 1, 4, 2, 4, 3, 3, 2, 0, 1],
[4, 2, 3, 2, 4, 1, 4, 4, 1, 4, 4, 0],
[1, 1, 3, 0, 4, 0, 3, 2, 1, 1, 4, 0]]],
[[[4, 2, 3, 4, 3, 0, 3, 2, 1, 2, 0, 4],
[3, 0, 4, 3, 4, 1, 3, 0, 3, 0, 2, 3],
[4, 2, 0, 3, 2, 3, 3, 1, 1, 3, 0, 0]],
[[2, 2, 4, 0, 0, 4, 3, 2, 0, 1, 0, 1],
[4, 4, 2, 1, 0, 3, 3, 1, 0, 1, 3, 3],
[3, 0, 0, 1, 1, 1, 0, 1, 4, 4, 1, 0]]]], dtype=uint64)
undo digital interlacing
>>> print(qmctoolscl.gdn_undo_interlace.__doc__)
Undo interlacing of generating matrices
Args:
r (np.uint64): replications
d (np.uint64): dimension of resulting generating matrices
mmax (np.uint64): columns in generating matrices
d_alpha (np.uint64): dimension of interlaced generating matrices
tmax (np.uint64): rows of original generating matrices
tmax_alpha (np.uint64): rows of interlaced generating matrices
alpha (np.uint64): interlacing factor
C_alpha (np.ndarray of np.uint64): interlaced generating matrices of size r*d_alpha*mmax*tmax_alpha
C (np.ndarray of np.uint64): original generating matrices of size r*d*mmax*tmax
>>> C_cp = np.empty((r,d,mmax,tmax),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.gdn_undo_interlace(r,d,mmax,d_alpha,tmax,tmax_alpha,alpha,C_alpha,C_cp)
>>> print((C==C_cp).all())
True
natural order with the same base
>>> print(qmctoolscl.gdn_gen_natural_same_base.__doc__)
Generalized digital net with the same base for each dimension e.g. a digital net in base greater than 2
Args:
r (np.uint64): replications
n (np.uint64): points
d (np.uint64): dimension
mmax (np.uint64): columns in each generating matrix
tmax (np.uint64): rows of each generating matrix
n_start (np.uint64): starting index in sequence
b (np.uint64): common base
C (np.ndarray of np.uint64): generating matrices of size r*d*mmax*tmax
xdig (np.ndarray of np.uint64): generalized digital net sequence of digits of size r*n*d*tmax
>>> n_start = np.uint64(0)
>>> n = np.uint64(9)
>>> b = np.uint64(3)
>>> C = np.array([[
... [
... [1,1,0,0],
... [1,1,1,1],
... ],
... [
... [2,1,0,0],
... [2,1,2,1],
... ]
... ]],
... dtype=np.uint64)
>>> r = np.uint64(C.shape[0])
>>> d = np.uint64(C.shape[1])
>>> mmax = np.uint64(C.shape[2])
>>> tmax = np.uint64(C.shape[3])
>>> xdig = np.empty((r,n,d,tmax),dtype=np.uint64)
>>> time_perf,time_process = qmctoolscl.gdn_gen_natural_same_base(r,n,d,mmax,tmax,n_start,b,C,xdig)
>>> xdig
array([[[[0, 0, 0, 0],
[0, 0, 0, 0]],
[[1, 1, 0, 0],
[2, 1, 0, 0]],
[[2, 2, 0, 0],
[1, 2, 0, 0]],
[[1, 1, 1, 1],
[2, 1, 2, 1]],
[[2, 2, 1, 1],
[1, 2, 2, 1]],
[[0, 0, 1, 1],
[0, 0, 2, 1]],
[[2, 2, 2, 2],
[1, 2, 1, 2]],
[[0, 0, 2, 2],
[0, 0, 1, 2]],
[[1, 1, 2, 2],
[2, 1, 1, 2]]]], dtype=uint64)
Fast Transforms
Fast transforms require the use of a single work group for the final dimension i.e. it is required that global_size[2]==local_size[2]
>>> if kwargs["backend"]=="CL":
... kwargs_ft = kwargs.copy()
... kwargs_ft["global_size"] = (2,2,2)
... kwargs_ft["local_size"] = (1,1,2)
... else: ## kwargs["backend"]=="C"
... kwargs_ft = kwargs
(inverse) fast Fourier transform
>>> print(qmctoolscl.fft_bro_1d_radix2.__doc__)
Fast Fourier Transform for inputs in bit reversed order.
FFT is done in place along the last dimension where the size is required to be a power of 2.
Follows a decimation-in-time procedure described in https://www.cmlab.csie.ntu.edu.tw/cml/dsp/training/coding/transform/fft.html.
Args:
d1 (np.uint64): first dimension
d2 (np.uint64): second dimension
n_half (np.uint64): half of the last dimension of size n = 2n_half along which FFT is performed
twiddler (np.ndarray of np.double): size n vector used to store real twiddle factors
twiddlei (np.ndarray of np.double): size n vector used to store imaginary twiddle factors
xr (np.ndarray of np.double): real array of size d1*d2*n on which to perform FFT in place
xi (np.ndarray of np.double): imaginary array of size d1*d2*n on which to perform FFT in place
>>> print(qmctoolscl.ifft_bro_1d_radix2.__doc__)
Inverse Fast Fourier Transform with outputs in bit reversed order.
FFT is done in place along the last dimension where the size is required to be a power of 2.
Follows a procedure described in https://www.expertsmind.com/learning/inverse-dft-using-the-fft-algorithm-assignment-help-7342873886.aspx.
Args:
d1 (np.uint64): first dimension
d2 (np.uint64): second dimension
n_half (np.uint64): half of the last dimension of size n = 2n_half along which FFT is performed
twiddler (np.ndarray of np.double): size n vector used to store real twiddle factors
twiddlei (np.ndarray of np.double): size n vector used to store imaginary twiddle factors
xr (np.ndarray of np.double): real array of size d1*d2*n on which to perform FFT in place
xi (np.ndarray of np.double): imaginary array of size d1*d2*n on which to perform FFT in place
>>> d1 = np.uint64(1)
>>> d2 = np.uint64(1)
>>> xr = np.array([1,0,1,0,0,1,1,0],dtype=np.double)
>>> xi = np.array([0,1,0,1,1,0,0,1],dtype=np.double)
>>> xr_og = xr.copy()
>>> xi_og = xi.copy()
>>> twiddler = np.empty_like(xr,dtype=np.double)
>>> twiddlei = np.empty_like(xr,dtype=np.double)
>>> n_half = np.uint64(len(xr)//2)
>>> time_perf,time_process = qmctoolscl.fft_bro_1d_radix2(d1,d2,n_half,twiddler,twiddlei,xr,xi,**kwargs_ft)
>>> xr
array([ 1.41421356, -0.5 , 0. , 1.20710678, 0. ,
0.5 , 0. , 0.20710678])
>>> xi
array([ 1.41421356, -0.20710678, 0. , -0.5 , 0. ,
-1.20710678, 0. , 0.5 ])
>>> time_perf,time_process = qmctoolscl.ifft_bro_1d_radix2(d1,d2,n_half,twiddler,twiddlei,xr,xi,**kwargs_ft)
>>> np.allclose(xr,xr_og,atol=1e-8)
True
>>> np.allclose(xi,xi_og,atol=1e-8)
True
>>> d1 = np.uint(5)
>>> d2 = np.uint(7)
>>> m = 8
>>> n = 2**m
>>> n_half = np.uint(n//2)
>>> bitrev = np.vectorize(lambda i,m: int('{:0{m}b}'.format(i,m=m)[::-1],2))
>>> ir = bitrev(np.arange(n),m)
>>> xr = rng.uniform(0,1,(d1,d2,int(2*n_half))).astype(np.double)
>>> xi = rng.uniform(0,1,(d1,d2,int(2*n_half))).astype(np.double)
>>> twiddler = np.empty_like(xr,dtype=np.double)
>>> twiddlei = np.empty_like(xr,dtype=np.double)
>>> x_np = xr+1j*xi
>>> x_bro_np = x_np[:,:,ir]
>>> y = xr+1j*xi
>>> yt_np = np.fft.fft(x_bro_np,norm="ortho")
>>> time_perf,time_process = qmctoolscl.fft_bro_1d_radix2(d1,d2,n_half,twiddler,twiddlei,xr,xi,**kwargs_ft)
>>> yt = xr+1j*xi
>>> np.allclose(yt,yt_np,atol=1e-8)
True
>>> time_perf,time_process = qmctoolscl.ifft_bro_1d_radix2(d1,d2,n_half,twiddler,twiddlei,xr,xi,**kwargs_ft)
>>> np.allclose(xr+1j*xi,y,atol=1e-8)
True
>>> fft_np = lambda x: np.fft.fft(x,norm="ortho")
>>> ifft_np = lambda x: np.fft.ifft(x,norm="ortho")
>>> def fft(x):
... assert x.ndim==1
... n = len(x)
... n_half = np.uint64(n//2)
... xr = x.real.copy()
... xi = x.imag.copy()
... qmctoolscl.fft_bro_1d_radix2(1,1,n_half,np.empty(n,dtype=np.double),np.empty(n,dtype=np.double),xr,xi)
... return xr+1j*xi
>>> def ifft(x):
... assert x.ndim==1
... n = len(x)
... n_half = np.uint64(n//2)
... xr = x.real.copy()
... xi = x.imag.copy()
... qmctoolscl.ifft_bro_1d_radix2(1,1,n_half,np.empty(n,dtype=np.double),np.empty(n,dtype=np.double),xr,xi)
... return xr+1j*xi
>>> # parameters
>>> m = 10
>>> n = 2**m
>>> # bit reverse used for reference solver
>>> bitrev = np.vectorize(lambda i,m: int('{:0{m}b}'.format(i,m=m)[::-1],2))
>>> ir = bitrev(np.arange(2*n),m+1)
>>> # points
>>> y1 = np.random.rand(n)+1j*np.random.rand(n)
>>> y2 = np.random.rand(n)+1j*np.random.rand(n)
>>> y = np.hstack([y1,y2])
>>> # kernel evaluations
>>> k1 = np.random.rand(n)
>>> k2 = np.random.rand(n)
>>> k = np.hstack([k1,k2])
>>> # fast transforms
>>> yt = fft(y)
>>> kt = fft(k)
>>> y1t = fft(y1)
>>> y2t = fft(y2)
>>> k1t = fft(k1)
>>> k2t = fft(k2)
>>> wt = np.exp(-np.pi*1j*np.arange(n)/n)
>>> wtsq = wt**2
>>> gammat = k1t**2-wtsq*k2t**2
>>> # matrix vector product
>>> u = ifft(yt*kt)*np.sqrt(2*n)
>>> u_np = ifft_np(fft_np(y[ir])*fft_np(k[ir]))[ir]*np.sqrt(2*n)
>>> np.allclose(u,u_np,atol=1e-10)
True
>>> u_hat = np.hstack([ifft(y1t*k1t+wtsq*y2t*k2t),ifft(y2t*k1t+y1t*k2t)])*np.sqrt(n)
>>> np.allclose(u_hat,u,atol=1e-10)
True
>>> # inverse
>>> v = ifft(yt/kt)/np.sqrt(2*n)
>>> v_np = ifft_np(fft_np(y[ir])/fft_np(k[ir]))[ir]/np.sqrt(2*n)
>>> np.allclose(v,v_np,atol=1e-10)
True
>>> v_hat = np.hstack([ifft((y1t*k1t-wtsq*y2t*k2t)/gammat),ifft((y2t*k1t-y1t*k2t)/gammat)])/np.sqrt(n)
>>> np.allclose(v,v_hat,atol=1e-10)
True
fast Walsh-Hadamard transform
>>> print(qmctoolscl.fwht_1d_radix2.__doc__)
Fast Walsh-Hadamard Transform for real valued inputs.
FWHT is done in place along the last dimension where the size is required to be a power of 2.
Follows the divide-and-conquer algorithm described in https://en.wikipedia.org/wiki/Fast_Walsh%E2%80%93Hadamard_transform
Args:
d1 (np.uint64): first dimension
d2 (np.uint64): second dimension
n_half (np.uint64): half of the last dimension along which FWHT is performed
x (np.ndarray of np.double): array of size d1*d2*2n_half on which to perform FWHT in place
>>> d1 = np.uint64(1)
>>> d2 = np.uint64(1)
>>> x = np.array([1,0,1,0,0,1,1,0],dtype=np.double)
>>> n_half = np.uint64(len(x)//2)
>>> time_perf,time_process = qmctoolscl.fwht_1d_radix2(d1,d2,n_half,x,**kwargs_ft)
>>> x
array([ 1.41421356, 0.70710678, 0. , -0.70710678, 0. ,
0.70710678, 0. , 0.70710678])
>>> d1 = np.uint(5)
>>> d2 = np.uint(7)
>>> n = 2**8
>>> n_half = np.uint(n//2)
>>> x = rng.uniform(0,1,(d1,d2,int(2*n_half))).astype(np.double)
>>> x_og = x.copy()
>>> import sympy
>>> y_sympy = np.empty_like(x,dtype=np.double)
>>> for i in range(d1):
... for j in range(d2):
... y_sympy[i,j] = np.array(sympy.fwht(x[i,j])/np.sqrt(n),dtype=np.double)
>>> time_perf,time_process = qmctoolscl.fwht_1d_radix2(d1,d2,n_half,x,**kwargs_ft)
>>> np.allclose(x,y_sympy,atol=1e-8)
True
>>> time_perf,time_process = qmctoolscl.fwht_1d_radix2(d1,d2,n_half,x,**kwargs_ft)
>>> np.allclose(x,x_og,atol=1e-8)
True
>>> def fwht_sp(x):
... assert x.ndim==1
... n = len(x)
... y = np.array(sympy.fwht(x),dtype=np.float64)
... y_ortho = y/np.sqrt(n)
... return y_ortho
>>> def fwht(x):
... assert x.ndim==1
... n = len(x)
... n_half = np.uint64(n//2)
... x_cp = x.copy()
... qmctoolscl.fwht_1d_radix2(1,1,n_half,x_cp)
... return x_cp
>>> # parameters
>>> m = 10
>>> n = int(2**m)
>>> # points
>>> y1 = np.random.rand(n)
>>> y2 = np.random.rand(n)
>>> y = np.hstack([y1,y2])
>>> # kernel evaluations
>>> k1 = np.random.rand(n)
>>> k2 = np.random.rand(n)
>>> k = np.hstack([k1,k2])
>>> # fast transforms
>>> yt = fwht(y)
>>> kt = fwht(k)
>>> y1t = fwht(y1)
>>> y2t = fwht(y2)
>>> k1t = fwht(k1)
>>> k2t = fwht(k2)
>>> gammat = k1t**2-k2t**2
>>> # matrix vector product
>>> u_sp = fwht_sp(fwht_sp(y)*fwht_sp(k))*np.sqrt(2*n)
>>> u = fwht(yt*kt)*np.sqrt(2*n)
>>> np.allclose(u_sp,u,atol=1e-8)
True
>>> u_hat = np.hstack([fwht(y1t*k1t+y2t*k2t),fwht(y2t*k1t+y1t*k2t)])*np.sqrt(n)
>>> np.allclose(u,u_hat,atol=1e-10)
True
>>> # inverse
>>> v_sp = fwht_sp(fwht_sp(y)/fwht_sp(k))/np.sqrt(2*n)
>>> v = fwht(yt/kt)/np.sqrt(2*n)
>>> np.allclose(v_sp,v,atol=1e-8)
True
>>> v_hat = np.hstack([fwht((y1t*k1t-y2t*k2t)/gammat),fwht((y2t*k1t-y1t*k2t)/gammat)])/np.sqrt(n)
>>> np.allclose(v,v_hat,atol=1e-10)
True