# Computation of Scalar Products

By now, we are able to construct orthogonal polynomials, and to construct quadrature rules for a given nonnegative weight function, respectively. Now we combine both ideas to solve integrals involving the orthogonal polynomials

$$$\langle \phi_{i_1} \phi_{i_2} \cdots \phi_{i_{m-1}}, \phi_{i_m} \rangle = \int \phi_{i_1}(t) \phi_{i_2}(t) \cdots \phi_{i_{m-1}}(t) \phi_{i_m}(t) w(t) \mathrm{d} t,$$$

both for the univariate and multivariate case. The integrand is a polynomial (possibly multivariate) that can be solved exactly with the appropriate Gauss quadrature rules.

Note

To simplify notation we drop the integration interval. It is clear from the context.

## Univariate Polynomials

### Classical Polynomials

Let's begin with a univariate basis for some classical orthogonal polynomial

using PolyChaos
deg, n = 4, 20
s_α, s_β = 2.1, 3.2
opq = Beta01OrthoPoly(deg, s_α, s_β; Nrec=n, addQuadrature=true)
Beta01OrthoPoly{Vector{Float64}, Beta01Measure, Quad{Float64, Vector{Float64}}}(4, [0.39622641509433965, 0.45308865339881105, 0.4732655766681396, 0.482729089351984, 0.4879233481934926, 0.49108064278342917, 0.49314292190864784, 0.4945640779897571, 0.4955849084142159, 0.4963428640512603, 0.49692106736331404, 0.4973721930243669, 0.49773093800397555, 0.4980209139779084, 0.4982586420286101, 0.4984559630451982, 0.4986215434156863, 0.4987618443402984, 0.4988817625639983, 0.4989850639437675], [1.0, 0.03797318144060756, 0.049528193464418745, 0.054496397032519545, 0.05707494398498378, 0.05858224318366976, 0.05953877522858778, 0.06018346810103027, 0.0606384719605267, 0.06097150420824787, 0.061222558056958205, 0.06141648535528466, 0.061569388295544744, 0.061692071403646374, 0.06179200321800959, 0.061874480034347076, 0.06194334217794196, 0.06200142915302492, 0.06205087705273863, 0.062093317746215744], Beta01Measure(PolyChaos.var"#102#103"{Float64, Float64}(2.1, 3.2), (0.0, 1.0), false, 2.1, 3.2), Quad{Float64, Vector{Float64}}("golubwelsch", 19, [0.00878890525374891, 0.028450106116514506, 0.05852125536671723, 0.09833813997192814, 0.14702350691009355, 0.20350504892083368, 0.266538831062297, 0.3347366435399401, 0.40659656306145764, 0.48053602819819125, 0.5549266957500689, 0.6281303108338084, 0.6985348047519305, 0.7645898365922597, 0.8248410256009169, 0.8779622119533655, 0.9227853738738273, 0.9583291958712525, 0.9838388819882817], [0.00115167784765143, 0.007007259624729887, 0.020314892689531894, 0.04140255942833847, 0.06777171117046146, 0.09471990538153915, 0.11673118518989155, 0.12913645448496422, 0.12947566141190417, 0.11812235962361355, 0.09800772591714006, 0.07359961541684255, 0.04953526816082873, 0.029389573634464723, 0.014963964676256315, 0.006252710931950127, 0.0019796669915308576, 0.00040351473743713024, 3.429268092282879e-5]))

By setting addQuadrature = true (which is default), an $n$-point Gauss quadrature rule is create relative to the underlying measure opq.measure, where $n$ is the number of recurrence coefficients stored in opq.α and opq.β.

To compute the squared norms

$$$\| \phi_k \|^2 = \langle \phi_k, \phi_k \rangle = \int \phi_k(t) \phi_k(t) w(t) \mathrm{d} t$$$

of the basis we call computeSP2()

normsq = computeSP2(opq)
5-element Vector{Float64}:
1.0
0.03797318144060756
0.0018807430768498865
0.00010249372143217383
5.849823409553853e-6

For the general case

$$$\langle \phi_{i_1} \phi_{i_2} \cdots \phi_{i_{m-1}}, \phi_{i_m} \rangle = \int \phi_{i_1}(t) \phi_{i_2}(t) \cdots \phi_{i_{m-1}}(t) \phi_{i_m}(t) w(t) \mathrm{d} t,$$$

there exists a type Tensor that requires only two arguments: the dimension $m \geq 1$, and an AbstractOrthoPoly

m = 3
t = Tensor(3,opq)
Tensor{Beta01OrthoPoly{Vector{Float64}, Beta01Measure, Quad{Float64, Vector{Float64}}}}(3,   [1 ]  =  1.0
[6 ]  =  0.0379732
[11]  =  0.00188074
[16]  =  0.000102494
[21]  =  5.84982e-6
[22]  =  0.00215924
[23]  =  0.00188074
⋮
[45]  =  5.84982e-6
[48]  =  7.80614e-6
[49]  =  7.09802e-7
[53]  =  4.73123e-7
[64]  =  9.40423e-7
[65]  =  5.08904e-7
[69]  =  6.53232e-8
[85]  =  3.55404e-8, PolyChaos.var"#getfun#45"{Int64, Beta01OrthoPoly{Vector{Float64}, Beta01Measure, Quad{Float64, Vector{Float64}}}, SparseArrays.SparseVector{Float64, Int64}}(3, Beta01OrthoPoly{Vector{Float64}, Beta01Measure, Quad{Float64, Vector{Float64}}}(4, [0.39622641509433965, 0.45308865339881105, 0.4732655766681396, 0.482729089351984, 0.4879233481934926, 0.49108064278342917, 0.49314292190864784, 0.4945640779897571, 0.4955849084142159, 0.4963428640512603, 0.49692106736331404, 0.4973721930243669, 0.49773093800397555, 0.4980209139779084, 0.4982586420286101, 0.4984559630451982, 0.4986215434156863, 0.4987618443402984, 0.4988817625639983, 0.4989850639437675], [1.0, 0.03797318144060756, 0.049528193464418745, 0.054496397032519545, 0.05707494398498378, 0.05858224318366976, 0.05953877522858778, 0.06018346810103027, 0.0606384719605267, 0.06097150420824787, 0.061222558056958205, 0.06141648535528466, 0.061569388295544744, 0.061692071403646374, 0.06179200321800959, 0.061874480034347076, 0.06194334217794196, 0.06200142915302492, 0.06205087705273863, 0.062093317746215744], Beta01Measure(PolyChaos.var"#102#103"{Float64, Float64}(2.1, 3.2), (0.0, 1.0), false, 2.1, 3.2), Quad{Float64, Vector{Float64}}("golubwelsch", 19, [0.00878890525374891, 0.028450106116514506, 0.05852125536671723, 0.09833813997192814, 0.14702350691009355, 0.20350504892083368, 0.266538831062297, 0.3347366435399401, 0.40659656306145764, 0.48053602819819125, 0.5549266957500689, 0.6281303108338084, 0.6985348047519305, 0.7645898365922597, 0.8248410256009169, 0.8779622119533655, 0.9227853738738273, 0.9583291958712525, 0.9838388819882817], [0.00115167784765143, 0.007007259624729887, 0.020314892689531894, 0.04140255942833847, 0.06777171117046146, 0.09471990538153915, 0.11673118518989155, 0.12913645448496422, 0.12947566141190417, 0.11812235962361355, 0.09800772591714006, 0.07359961541684255, 0.04953526816082873, 0.029389573634464723, 0.014963964676256315, 0.006252710931950127, 0.0019796669915308576, 0.00040351473743713024, 3.429268092282879e-5])),   [1 ]  =  1.0
[6 ]  =  0.0379732
[11]  =  0.00188074
[16]  =  0.000102494
[21]  =  5.84982e-6
[22]  =  0.00215924
[23]  =  0.00188074
⋮
[45]  =  5.84982e-6
[48]  =  7.80614e-6
[49]  =  7.09802e-7
[53]  =  4.73123e-7
[64]  =  9.40423e-7
[65]  =  5.08904e-7
[69]  =  6.53232e-8
[85]  =  3.55404e-8), Beta01OrthoPoly{Vector{Float64}, Beta01Measure, Quad{Float64, Vector{Float64}}}(4, [0.39622641509433965, 0.45308865339881105, 0.4732655766681396, 0.482729089351984, 0.4879233481934926, 0.49108064278342917, 0.49314292190864784, 0.4945640779897571, 0.4955849084142159, 0.4963428640512603, 0.49692106736331404, 0.4973721930243669, 0.49773093800397555, 0.4980209139779084, 0.4982586420286101, 0.4984559630451982, 0.4986215434156863, 0.4987618443402984, 0.4988817625639983, 0.4989850639437675], [1.0, 0.03797318144060756, 0.049528193464418745, 0.054496397032519545, 0.05707494398498378, 0.05858224318366976, 0.05953877522858778, 0.06018346810103027, 0.0606384719605267, 0.06097150420824787, 0.061222558056958205, 0.06141648535528466, 0.061569388295544744, 0.061692071403646374, 0.06179200321800959, 0.061874480034347076, 0.06194334217794196, 0.06200142915302492, 0.06205087705273863, 0.062093317746215744], Beta01Measure(PolyChaos.var"#102#103"{Float64, Float64}(2.1, 3.2), (0.0, 1.0), false, 2.1, 3.2), Quad{Float64, Vector{Float64}}("golubwelsch", 19, [0.00878890525374891, 0.028450106116514506, 0.05852125536671723, 0.09833813997192814, 0.14702350691009355, 0.20350504892083368, 0.266538831062297, 0.3347366435399401, 0.40659656306145764, 0.48053602819819125, 0.5549266957500689, 0.6281303108338084, 0.6985348047519305, 0.7645898365922597, 0.8248410256009169, 0.8779622119533655, 0.9227853738738273, 0.9583291958712525, 0.9838388819882817], [0.00115167784765143, 0.007007259624729887, 0.020314892689531894, 0.04140255942833847, 0.06777171117046146, 0.09471990538153915, 0.11673118518989155, 0.12913645448496422, 0.12947566141190417, 0.11812235962361355, 0.09800772591714006, 0.07359961541684255, 0.04953526816082873, 0.029389573634464723, 0.014963964676256315, 0.006252710931950127, 0.0019796669915308576, 0.00040351473743713024, 3.429268092282879e-5])))

To get the desired entries, Tensor comes with a get() function that is called for some index $a \in \mathbb{N}_0^m$ that has the entries $a = [i_1, i_2, \dots, i_m]$. For example

t.get([1,2,3])
0.00010249372143217403

Or using comprehension

T = [ t.get([i1,i2,i3]) for i1=0:dim(opq)-1, i2=0:dim(opq)-1, i3=0:dim(opq)-1]
5×5×5 Array{Float64, 3}:
[:, :, 1] =
1.0  0.0        0.0         0.0          0.0
0.0  0.0379732  0.0         0.0          0.0
0.0  0.0        0.00188074  0.0          0.0
0.0  0.0        0.0         0.000102494  0.0
0.0  0.0        0.0         0.0          5.84982e-6

[:, :, 2] =
0.0        0.0379732   0.0          0.0          0.0
0.0379732  0.00215924  0.00188074   0.0          0.0
0.0        0.00188074  0.000144891  0.000102494  0.0
0.0        0.0         0.000102494  8.86598e-6   5.84982e-6
0.0        0.0         0.0          5.84982e-6   5.36411e-7

[:, :, 3] =
0.0         0.0          0.00188074   0.0          0.0
0.0         0.00188074   0.000144891  0.000102494  0.0
0.00188074  0.000144891  0.000127149  1.0934e-5    5.84982e-6
0.0         0.000102494  1.0934e-5    7.80614e-6   7.09802e-7
0.0         0.0          5.84982e-6   7.09802e-7   4.73123e-7

[:, :, 4] =
0.0          0.0          0.0          0.000102494  0.0
0.0          0.0          0.000102494  8.86598e-6   5.84982e-6
0.0          0.000102494  1.0934e-5    7.80614e-6   7.09802e-7
0.000102494  8.86598e-6   7.80614e-6   9.40423e-7   5.08904e-7
0.0          5.84982e-6   7.09802e-7   5.08904e-7   6.53232e-8

[:, :, 5] =
0.0         0.0         0.0         0.0         5.84982e-6
0.0         0.0         0.0         5.84982e-6  5.36411e-7
0.0         0.0         5.84982e-6  7.09802e-7  4.73123e-7
0.0         5.84982e-6  7.09802e-7  5.08904e-7  6.53232e-8
5.84982e-6  5.36411e-7  4.73123e-7  6.53232e-8  3.55404e-8

Notice that we can cross-check the results.

using LinearAlgebra
normsq == diag(T[:,:,1]) == diag(T[:,1,:]) == diag(T[1,:,:])
true

Also, normsq can be computed analogously in Tensor format

t2 = Tensor(2, opq)
normsq == [ t2.get([i, i]) for i in 0:dim(opq)-1]
true

### Arbitrary Weights

Of course, the type OrthoPoly can be constructed for arbitrary weights $w(t)$. In this case we have to compute the orthogonal basis and the respective quadrature rule. Let's re-work the above example by hand.

using SpecialFunctions
supp = (0, 1)
w(t) = (t^(s_α-1)*(1-t)^(s_β-1)/SpecialFunctions.beta(s_α,s_β))
my_meas = Measure("my_meas", w, supp, false)
my_opq = OrthoPoly("my_op", deg, my_meas; Nrec=n, addQuadrature = true)
OrthoPoly{Vector{Float64}, Measure, Quad{Float64, Vector{Float64}}}("my_op", 4, [0.3962264150944814, 0.45308865339922266, 0.4732655766690031, 0.4827290893535499, 0.48792334819610145, 0.4910806427875326, 0.49314292191483644, 0.49456407799878116, 0.49558490842701997, 0.4963428640690021, 0.49692106738740716, 0.497372193056498, 0.4977309380461538, 0.498020914032483, 0.4982586420983253, 0.49845596313321316, 0.4986215435256337, 0.4987618444763099, 0.49888176273076457, 0.49898506414657223], [0.9999999999996537, 0.03797318144056479, 0.04952819346429694, 0.05449639703227426, 0.05707494398455888, 0.05858224318299421, 0.059538775227572845, 0.060183468099566075, 0.06063847195847913, 0.06097150420545559, 0.06122255805322868, 0.061416485350391216, 0.061569388289222635, 0.06169207139558928, 0.06179200320786562, 0.061874480021715195, 0.06194334216236786, 0.06200142913399658, 0.06205087702968266, 0.062093317718492524], Measure("my_meas", Main.w, (0.0, 1.0), false, Dict{Any, Any}()), Quad{Float64, Vector{Float64}}("golubwelsch", 19, [0.008788905307716617, 0.028450106179070535, 0.05852125542991019, 0.09833814003385195, 0.14702350696982952, 0.2035050489777793, 0.26653883111600984, 0.334736643590094, 0.4065965631078264, 0.48053602824064445, 0.5549266957885691, 0.6281303108684109, 0.6985348047827785, 0.7645898366195808, 0.8248410256250177, 0.877962211974623, 0.9227853738926779, 0.9583291958881766, 0.9838388820037521], [0.0011516778570367094, 0.007007259641384781, 0.020314892710314256, 0.04140255944907823, 0.06777171118706973, 0.09471990539100661, 0.1167311851909107, 0.12913645447805253, 0.1294756613990475, 0.11812235960758918, 0.09800772590075404, 0.07359961540231341, 0.049535268149441064, 0.029389573626546133, 0.014963964671407341, 0.0062527109293853324, 0.001979666990405446, 0.0004035147370623089, 3.429268084906005e-5]))

Now we can compute the squared norms $\| \phi_k \|^2$

my_normsq = computeSP2(my_opq)
5-element Vector{Float64}:
0.9999999999996537
0.03797318144055164
0.0018807430768424914
0.00010249372143130952
5.849823409460973e-6

And the tensor

my_t = Tensor(m, my_opq)
my_T = [ my_t.get([i1,i2,i3]) for i1=0:dim(opq)-1,i2=0:dim(opq)-1,i3=0:dim(opq)-1]
5×5×5 Array{Float64, 3}:
[:, :, 1] =
1.0  0.0        0.0         0.0          0.0
0.0  0.0379732  0.0         0.0          0.0
0.0  0.0        0.00188074  0.0          0.0
0.0  0.0        0.0         0.000102494  0.0
0.0  0.0        0.0         0.0          5.84982e-6

[:, :, 2] =
0.0        0.0379732   0.0          0.0          0.0
0.0379732  0.00215924  0.00188074   0.0          0.0
0.0        0.00188074  0.000144891  0.000102494  0.0
0.0        0.0         0.000102494  8.86598e-6   5.84982e-6
0.0        0.0         0.0          5.84982e-6   5.36411e-7

[:, :, 3] =
0.0         0.0          0.00188074   0.0          0.0
0.0         0.00188074   0.000144891  0.000102494  0.0
0.00188074  0.000144891  0.000127149  1.0934e-5    5.84982e-6
0.0         0.000102494  1.0934e-5    7.80614e-6   7.09802e-7
0.0         0.0          5.84982e-6   7.09802e-7   4.73123e-7

[:, :, 4] =
0.0          0.0          0.0          0.000102494  0.0
0.0          0.0          0.000102494  8.86598e-6   5.84982e-6
0.0          0.000102494  1.0934e-5    7.80614e-6   7.09802e-7
0.000102494  8.86598e-6   7.80614e-6   9.40423e-7   5.08904e-7
0.0          5.84982e-6   7.09802e-7   5.08904e-7   6.53232e-8

[:, :, 5] =
0.0         0.0         0.0         0.0         5.84982e-6
0.0         0.0         0.0         5.84982e-6  5.36411e-7
0.0         0.0         5.84982e-6  7.09802e-7  4.73123e-7
0.0         5.84982e-6  7.09802e-7  5.08904e-7  6.53232e-8
5.84982e-6  5.36411e-7  4.73123e-7  6.53232e-8  3.55404e-8

Let's compare the results:

abs.(normsq-my_normsq)
5-element Vector{Float64}:
3.462785613805863e-13
5.5920545971588353e-14
7.395126178089129e-15
8.643124193782881e-16
9.28797037652758e-17
norm(T-my_T)
3.601084272277955e-13
Note

The possibility to create quadrature rules for arbitrary weights should be reserved to cases different from classical ones.

## Multivariate Polynomials

For multivariate polynomials the syntax for Tensor is very much alike, except that we are dealing with the type MultiOrthoPoly now.

mop = MultiOrthoPoly([opq, my_opq], deg)
MultiOrthoPoly{ProductMeasure, Quad{Float64, Vector{Float64}}, Vector{AbstractOrthoPoly{M, Quad{Float64, Vector{Float64}}} where M<:AbstractMeasure}}(["Beta01OrthoPoly{Vector{Float64}, Beta01Measure, Quad{Float64, Vector{Float64}}}", "my_op"], 4, 15, [0 0; 1 0; … ; 1 3; 0 4], ProductMeasure(PolyChaos.var"#w#39"{Vector{AbstractOrthoPoly{M, Quad{Float64, Vector{Float64}}} where M<:AbstractMeasure}}(AbstractOrthoPoly{M, Quad{Float64, Vector{Float64}}} where M<:AbstractMeasure[Beta01OrthoPoly{Vector{Float64}, Beta01Measure, Quad{Float64, Vector{Float64}}}(4, [0.39622641509433965, 0.45308865339881105, 0.4732655766681396, 0.482729089351984, 0.4879233481934926, 0.49108064278342917, 0.49314292190864784, 0.4945640779897571, 0.4955849084142159, 0.4963428640512603, 0.49692106736331404, 0.4973721930243669, 0.49773093800397555, 0.4980209139779084, 0.4982586420286101, 0.4984559630451982, 0.4986215434156863, 0.4987618443402984, 0.4988817625639983, 0.4989850639437675], [1.0, 0.03797318144060756, 0.049528193464418745, 0.054496397032519545, 0.05707494398498378, 0.05858224318366976, 0.05953877522858778, 0.06018346810103027, 0.0606384719605267, 0.06097150420824787, 0.061222558056958205, 0.06141648535528466, 0.061569388295544744, 0.061692071403646374, 0.06179200321800959, 0.061874480034347076, 0.06194334217794196, 0.06200142915302492, 0.06205087705273863, 0.062093317746215744], Beta01Measure(PolyChaos.var"#102#103"{Float64, Float64}(2.1, 3.2), (0.0, 1.0), false, 2.1, 3.2), Quad{Float64, Vector{Float64}}("golubwelsch", 19, [0.00878890525374891, 0.028450106116514506, 0.05852125536671723, 0.09833813997192814, 0.14702350691009355, 0.20350504892083368, 0.266538831062297, 0.3347366435399401, 0.40659656306145764, 0.48053602819819125, 0.5549266957500689, 0.6281303108338084, 0.6985348047519305, 0.7645898365922597, 0.8248410256009169, 0.8779622119533655, 0.9227853738738273, 0.9583291958712525, 0.9838388819882817], [0.00115167784765143, 0.007007259624729887, 0.020314892689531894, 0.04140255942833847, 0.06777171117046146, 0.09471990538153915, 0.11673118518989155, 0.12913645448496422, 0.12947566141190417, 0.11812235962361355, 0.09800772591714006, 0.07359961541684255, 0.04953526816082873, 0.029389573634464723, 0.014963964676256315, 0.006252710931950127, 0.0019796669915308576, 0.00040351473743713024, 3.429268092282879e-5])), OrthoPoly{Vector{Float64}, Measure, Quad{Float64, Vector{Float64}}}("my_op", 4, [0.3962264150944814, 0.45308865339922266, 0.4732655766690031, 0.4827290893535499, 0.48792334819610145, 0.4910806427875326, 0.49314292191483644, 0.49456407799878116, 0.49558490842701997, 0.4963428640690021, 0.49692106738740716, 0.497372193056498, 0.4977309380461538, 0.498020914032483, 0.4982586420983253, 0.49845596313321316, 0.4986215435256337, 0.4987618444763099, 0.49888176273076457, 0.49898506414657223], [0.9999999999996537, 0.03797318144056479, 0.04952819346429694, 0.05449639703227426, 0.05707494398455888, 0.05858224318299421, 0.059538775227572845, 0.060183468099566075, 0.06063847195847913, 0.06097150420545559, 0.06122255805322868, 0.061416485350391216, 0.061569388289222635, 0.06169207139558928, 0.06179200320786562, 0.061874480021715195, 0.06194334216236786, 0.06200142913399658, 0.06205087702968266, 0.062093317718492524], Measure("my_meas", Main.w, (0.0, 1.0), false, Dict{Any, Any}()), Quad{Float64, Vector{Float64}}("golubwelsch", 19, [0.008788905307716617, 0.028450106179070535, 0.05852125542991019, 0.09833814003385195, 0.14702350696982952, 0.2035050489777793, 0.26653883111600984, 0.334736643590094, 0.4065965631078264, 0.48053602824064445, 0.5549266957885691, 0.6281303108684109, 0.6985348047827785, 0.7645898366195808, 0.8248410256250177, 0.877962211974623, 0.9227853738926779, 0.9583291958881766, 0.9838388820037521], [0.0011516778570367094, 0.007007259641384781, 0.020314892710314256, 0.04140255944907823, 0.06777171118706973, 0.09471990539100661, 0.1167311851909107, 0.12913645447805253, 0.1294756613990475, 0.11812235960758918, 0.09800772590075404, 0.07359961540231341, 0.049535268149441064, 0.029389573626546133, 0.014963964671407341, 0.0062527109293853324, 0.001979666990405446, 0.0004035147370623089, 3.429268084906005e-5]))]), AbstractMeasure[Beta01Measure(PolyChaos.var"#102#103"{Float64, Float64}(2.1, 3.2), (0.0, 1.0), false, 2.1, 3.2), Measure("my_meas", Main.w, (0.0, 1.0), false, Dict{Any, Any}())]), AbstractOrthoPoly{M, Quad{Float64, Vector{Float64}}} where M<:AbstractMeasure[Beta01OrthoPoly{Vector{Float64}, Beta01Measure, Quad{Float64, Vector{Float64}}}(4, [0.39622641509433965, 0.45308865339881105, 0.4732655766681396, 0.482729089351984, 0.4879233481934926, 0.49108064278342917, 0.49314292190864784, 0.4945640779897571, 0.4955849084142159, 0.4963428640512603, 0.49692106736331404, 0.4973721930243669, 0.49773093800397555, 0.4980209139779084, 0.4982586420286101, 0.4984559630451982, 0.4986215434156863, 0.4987618443402984, 0.4988817625639983, 0.4989850639437675], [1.0, 0.03797318144060756, 0.049528193464418745, 0.054496397032519545, 0.05707494398498378, 0.05858224318366976, 0.05953877522858778, 0.06018346810103027, 0.0606384719605267, 0.06097150420824787, 0.061222558056958205, 0.06141648535528466, 0.061569388295544744, 0.061692071403646374, 0.06179200321800959, 0.061874480034347076, 0.06194334217794196, 0.06200142915302492, 0.06205087705273863, 0.062093317746215744], Beta01Measure(PolyChaos.var"#102#103"{Float64, Float64}(2.1, 3.2), (0.0, 1.0), false, 2.1, 3.2), Quad{Float64, Vector{Float64}}("golubwelsch", 19, [0.00878890525374891, 0.028450106116514506, 0.05852125536671723, 0.09833813997192814, 0.14702350691009355, 0.20350504892083368, 0.266538831062297, 0.3347366435399401, 0.40659656306145764, 0.48053602819819125, 0.5549266957500689, 0.6281303108338084, 0.6985348047519305, 0.7645898365922597, 0.8248410256009169, 0.8779622119533655, 0.9227853738738273, 0.9583291958712525, 0.9838388819882817], [0.00115167784765143, 0.007007259624729887, 0.020314892689531894, 0.04140255942833847, 0.06777171117046146, 0.09471990538153915, 0.11673118518989155, 0.12913645448496422, 0.12947566141190417, 0.11812235962361355, 0.09800772591714006, 0.07359961541684255, 0.04953526816082873, 0.029389573634464723, 0.014963964676256315, 0.006252710931950127, 0.0019796669915308576, 0.00040351473743713024, 3.429268092282879e-5])), OrthoPoly{Vector{Float64}, Measure, Quad{Float64, Vector{Float64}}}("my_op", 4, [0.3962264150944814, 0.45308865339922266, 0.4732655766690031, 0.4827290893535499, 0.48792334819610145, 0.4910806427875326, 0.49314292191483644, 0.49456407799878116, 0.49558490842701997, 0.4963428640690021, 0.49692106738740716, 0.497372193056498, 0.4977309380461538, 0.498020914032483, 0.4982586420983253, 0.49845596313321316, 0.4986215435256337, 0.4987618444763099, 0.49888176273076457, 0.49898506414657223], [0.9999999999996537, 0.03797318144056479, 0.04952819346429694, 0.05449639703227426, 0.05707494398455888, 0.05858224318299421, 0.059538775227572845, 0.060183468099566075, 0.06063847195847913, 0.06097150420545559, 0.06122255805322868, 0.061416485350391216, 0.061569388289222635, 0.06169207139558928, 0.06179200320786562, 0.061874480021715195, 0.06194334216236786, 0.06200142913399658, 0.06205087702968266, 0.062093317718492524], Measure("my_meas", Main.w, (0.0, 1.0), false, Dict{Any, Any}()), Quad{Float64, Vector{Float64}}("golubwelsch", 19, [0.008788905307716617, 0.028450106179070535, 0.05852125542991019, 0.09833814003385195, 0.14702350696982952, 0.2035050489777793, 0.26653883111600984, 0.334736643590094, 0.4065965631078264, 0.48053602824064445, 0.5549266957885691, 0.6281303108684109, 0.6985348047827785, 0.7645898366195808, 0.8248410256250177, 0.877962211974623, 0.9227853738926779, 0.9583291958881766, 0.9838388820037521], [0.0011516778570367094, 0.007007259641384781, 0.020314892710314256, 0.04140255944907823, 0.06777171118706973, 0.09471990539100661, 0.1167311851909107, 0.12913645447805253, 0.1294756613990475, 0.11812235960758918, 0.09800772590075404, 0.07359961540231341, 0.049535268149441064, 0.029389573626546133, 0.014963964671407341, 0.0062527109293853324, 0.001979666990405446, 0.0004035147370623089, 3.429268084906005e-5]))])
mt2 = Tensor(2,mop)
mt3 = Tensor(3,mop)
mT2 = [ mt2.get([i,i]) for i=0:dim(mop)-1 ]
15-element Vector{Float64}:
0.9999999999996537
0.03797318144059441
0.03797318144055164
0.0018807430768492354
0.001441962508719179
0.0018807430768424914
0.00010249372143213834
7.14177981002821e-5
7.141779810010645e-5
0.00010249372143130952
5.8498234095518276e-6
3.892012680461293e-6
3.53719452110487e-6
3.892012680434204e-6
5.849823409460973e-6

Notice that mT2 carries the elements of the 2-dimensional tensors for the univariate bases opq and my_opq. The encoding is given by the multi-index mop.ind

mop.ind
15×2 Matrix{Int64}:
0  0
1  0
0  1
2  0
1  1
0  2
3  0
2  1
1  2
0  3
4  0
3  1
2  2
1  3
0  4

To cross-check the results we can distribute the multi-index back to its univariate indices with the help of findUnivariateIndices.

ind_opq = findUnivariateIndices(1,mop.ind)
ind_my_opq = findUnivariateIndices(2,mop.ind)
5-element Vector{Int64}:
1
3
6
10
15
mT2[ind_opq] - normsq
5-element Vector{Float64}:
-3.462785613805863e-13
-1.3149203947904198e-14
-6.51171824794794e-16
-3.54940686217442e-17
-2.025255776885032e-18
mT2[ind_my_opq] - my_normsq
5-element Vector{Float64}:
0.0
0.0
0.0
0.0
0.0