8000 Adapt test tolerances to true Float64 precision by ddahlbom · Pull Request #360 · SunnySuite/Sunny.jl · GitHub
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Adapt test tolerances to true Float64 precision #360

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Merged
merged 2 commits into from
Apr 18, 2025
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Adapt test tolerances to true Float64 precision #360

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d2cd25f
Loosen test tolerances
ddahlbom Apr 18, 2025
00e52a6
Include check on intensities with kT
kbarros Apr 18, 2025
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7 changes: 6 additions & 1 deletion src/SampledCorrelations/DataRetrieval.jl
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Expand Up @@ -97,7 +97,12 @@ function intensities(sc::SampledCorrelations, qpts; energies, kernel=nothing, kT
qpts = Base.convert(AbstractQPoints, qpts)
qs_reshaped = [to_reshaped_rlu(sc, q) for q in qpts.qs]

ffs = sc.measure.formfactors[1, :] # FIXME
# Check that form factors are uniform for each observable.
for col in eachcol(sc.measure.formfactors)
@assert allequal(col) "Observable-dependent form factors not yet supported."
end
ffs = sc.measure.formfactors[1, :]

intensities = zeros(eltype(sc.measure), isnan(sc.Δω) ? 1 : length(ωs), length(qpts.qs)) # N.B.: Inefficient indexing order to mimic LSWT
q_idx_info = pruned_wave_vector_info(sc, qs_reshaped)
crystal = @something sc.origin_crystal sc.crystal
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6 changes: 3 additions & 3 deletions test/test_entangled_units.jl
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Expand Up @@ -73,9 +73,9 @@ end

set_field!(esys, [0, 0, 0])
randomize_spins!(esys)
minimize_energy!(esys; g_abstol=1e-14)
@test norm(esys.sys_origin.dipoles[1]) < 1e-14
@test norm(esys.sys_origin.dipoles[2]) < 1e-14
minimize_energy!(esys)
@test norm(esys.sys_origin.dipoles[1]) < 1e-10
@test norm(esys.sys_origin.dipoles[2]) < 1e-10

# Test inter-bond exchange
pc = Sunny.as_general_pair_coupling(interactions.pair[1], esys.sys)
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22 changes: 11 additions & 11 deletions test/test_lswt.jl
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Expand Up @@ -36,22 +36,22 @@

sys.coherents .= reinterpret(reshape, Sunny.CVec{6}, ground_state)
minimize_energy!(sys)
@assert energy_per_site(sys) ≈ -328.38255
@test energy_per_site(sys) ≈ -328.38255

# Verify that this is a local minimum of energy
@test norm(Sunny.proj.(Sunny.energy_grad_coherents(sys), sys.coherents)) < 1e-8
@test norm(Sunny.proj.(Sunny.energy_grad_coherents(sys), sys.coherents)) < 1e-7

# Test energies at an arbitrary wave vector
qs = [[0.24331089495721447, 0.2818361515716459, 0.21954858411037714]]
swt = SpinWaveTheory(sys; measure=ssf_perp(sys; apply_g=false))

res = intensities_bands(swt, qs)
res = intensities_bands(swt, qs; kT=100.0)
# println(round.(vec(res.disp); digits=12))
# println(round.(vec(res.data); digits=12))
disps_golden = [1394.440092579932, 1393.728009951748, 1393.008551251227, 1392.919524974106, 1279.239919068656, 1279.094568472208, 1278.224518515369, 1277.691761482934, 1194.366336255048, 1193.750083625344, 1191.583519659752, 1189.794451340786, 1131.422439587814, 1131.202770074483, 1065.242927850645, 1065.095892446623, 1026.649340922942, 1024.028348558074, 1022.830406299683, 1020.767349639389, 945.202397530637, 944.795817851532, 835.545028394177, 832.001588695239, 827.939501409159, 827.307586947865, 821.216582166477, 820.430993567146, 820.294548776124, 818.594570998049, 810.207001090183, 808.553158273676, 766.524411071072, 766.516102750272, 766.51382585253, 766.508655558251, 758.5798541677, 754.683765885704, 750.572578891224, 750.47100625256, 665.954573008178, 662.421047653195, 651.465562549975, 651.417940124351, 581.258189152584, 568.105209800095, 559.053702296455, 558.493005822971, 552.043762736843, 550.131096070956, 539.733572947825, 530.698033192909, 499.661483510074, 494.928560823138, 435.233706061892, 427.702277064325, 408.128705853663, 399.856401749648, 370.069343063308, 369.845327686246, 365.049514240266, 363.639416669427, 354.648012591404, 346.609483927002, 341.989165167266, 339.373361067994, 318.363717384335, 276.219249203178, 263.1610538318, 257.409506246766, 230.539454193854, 229.778324172693, 203.971681278992, 197.504237153931, 193.879371534726, 189.866421875022, 189.815806967694, 167.94413443161, 154.923566498384, 146.21953884777]
data_golden = [0.00038665026, 0.0, 0.007231537047, 0.0, 0.008665001888, 0.0, 0.015340530772, 0.0, 0.0, 0.054200267869, 0.073309637341, 0.0, 0.005276014091, 0.0, 0.0, 0.026708463804, 0.0, 0.062332773978, 0.112027719874, 0.0, 0.031129659091, 0.0, 0.115569104228, 0.0, 0.0, 0.038994974901, 0.0, 0.031152494381, 0.029113142876, 0.0, 0.0, 0.00467095768, 0.000762927464, 0.000940556177, 0.0, 0.0, 0.00877327198, 0.0, 0.033263081608, 0.0, 0.066082588247, 0.0, 0.0, 0.059956690592, 0.334527718881, 0.007608761425, 0.0, 0.0, 0.0, 0.068446181755, 0.029366565904, 0.0, 0.0, 0.605679689903, 0.378667970539, 0.0, 0.858558512424, 0.0, 0.0, 0.290056738762, 0.349117318827, 0.0, 0.0, 0.955658357376, 0.0, 1.606184739756, 0.0, 0.201711065754, 0.181786511323, 0.0, 0.0, 0.313711125445, 0.769401980923, 0.0, 0.010377681107, 0.0, 0.151200728665, 0.327686235743, 0.0, 0.0]
@test isapprox(res.disp, disps_golden; atol=1e-9)
@test isapprox(res.data, data_golden; atol=1e-9)
disps_golden = [1394.440092579925, 1393.728009951747, 1393.008551251224, 1392.919524974098, 1279.239919068637, 1279.094568472202, 1278.224518515366, 1277.69176148292, 1194.366336255056, 1193.750083625346, 1191.583519659758, 1189.794451340792, 1131.422439587906, 1131.202770074575, 1065.242927850642, 1065.09589244662, 1026.649340922954, 1024.028348558092, 1022.830406299696, 1020.767349639404, 945.202397530689, 944.795817851584, 835.545028394171, 832.001588695236, 827.939501409146, 827.307586947863, 821.216582166429, 820.430993567084, 820.29454877608, 818.594570998006, 810.207001090189, 808.553158273681, 766.524411070996, 766.51610275022, 766.513825852464, 766.508655558188, 758.579854167682, 754.683765885699, 750.572578891218, 750.471006252543, 665.954573008179, 662.421047653194, 651.465562550036, 651.417940124412, 581.258189152574, 568.105209800088, 559.053702296449, 558.493005822973, 552.043762736839, 550.131096070953, 539.733572947827, 530.698033192904, 499.661483510112, 494.928560823174, 435.233706061902, 427.70227706432, 408.128705853668, 399.856401749667, 370.0693430633, 369.845327686247, 365.04951424025, 363.639416669404, 354.648012591371, 346.609483926993, 341.989165167298, 339.373361067981, 318.363717384318, 276.219249203163, 263.161053831818, 257.409506246762, 230.539454193868, 229.778324172696, 203.971681278995, 197.504237153905, 193.879371534689, 189.866421874996, 189.815806967662, 167.944134431612, 154.923566498395, 146.219538847758]
data_golden = [0.0003866506, 0.0, 0.007231543496, 0.0, 0.008665025993, 0.0, 0.015340573883, 0.0, 0.0, 0.054200622367, 0.073310127326, 0.0, 0.00527607845, 0.0, 0.0, 0.026709096225, 0.0, 0.062334999507, 0.112031767918, 0.0, 0.031132103909, 0.0, 0.115596282255, 0.0, 0.0, 0.039004932814, 0.0, 0.031161016058, 0.029121117571, 0.0, 0.0, 0.004672396604, 0.000763285324, 0.000940997393, 0.0, 0.0, 0.008777727636, 0.0, 0.033281383926, 0.0, 0.066167396885, 0.0, 0.0, 0.060045694629, 0.335530855821, 0.007634795842, 0.0, 0.0, 0.0, 0.068726684036, 0.02950016123, 0.0, 0.0, 0.610003683362, 0.383607563841, 0.0, 0.873304893237, 0.0, 0.0, 0.297421427631, 0.358428673038, 0.0, 0.0, 0.986474626382, 0.0, 1.661999771965, 0.0, 0.215308474961, 0.195882715465, 0.0, 0.0, 0.348754438732, 0.884436719117, 0.0, 0.012121723144, 0.0, 0.177850646384, 0.402799537928, 0.0, 0.0]
@test isapprox(res.disp, disps_golden; atol=1e-8)
@test isapprox(res.data, data_golden; atol=1e-8)

# Test first 5 output matrices
formfactors = [1 => FormFactor("Fe2")]
Expand All @@ -60,7 +60,7 @@
res = intensities_bands(swt, qs)
data_flat = reinterpret(ComplexF64, res.data[1:5])
# println(round.(data_flat; digits=12))
data_golden = natoms * ComplexF64[0.000768755803 + 0.0im, 0.000453313199 - 4.8935387e-5im, 0.000468535469 + 8.5812793e-5im, 0.000453313199 + 4.8935387e-5im, 0.00027042076 + 0.0im, 0.000270819458 + 8.0426107e-5im, 0.000468535469 - 8.5812793e-5im, 0.000270819458 - 8.0426107e-5im, 0.000295138353 + 0.0im, 0.0 + 0.0im, 0.0 - 0.0im, 0.0 + 0.0im, 0.0 + 0.0im, 0.0 + 0.0im, 0.0 + 0.0im, 0.0 - 0.0im, 0.0 - 0.0im, 0.0 + 0.0im, 0.00021794048 + 0.0im, -0.000114399503 - 0.000211293782im, -0.000126018935 + 0.000199895684im, -0.000114399503 + 0.000211293782im, 0.000264899429 + 0.0im, -0.000127650501 - 0.000227103219im, -0.000126018935 - 0.000199895684im, -0.000127650501 + 0.000227103219im, 0.000256212415 + 0.0im, 0.0 + 0.0im, -0.0 - 0.0im, -0.0 + 0.0im, -0.0 + 0.0im, 0.0 + 0.0im, -0.0 - 0.0im, -0.0 - 0.0im, -0.0 + 0.0im, 0.0 + 0.0im, 7.5017149e-5 + 0.0im, 0.000206625046 + 0.000158601356im, 0.000240055385 - 0.00011016714im, 0.000206625046 - 0.000158601356im, 0.000904437194 + 0.0im, 0.000428286033 - 0.000810966567im, 0.000240055385 + 0.00011016714im, 0.000428286033 + 0.000810966567im, 0.000929965845 + 0.0im]
data_golden = [0.003075023211 + 0.0im, 0.001813252796 - 0.000195741551im, 0.001874141877 + 0.000343251171im, 0.001813252796 + 0.000195741551im, 0.001081683041 + 0.0im, 0.001083277833 + 0.000321704428im, 0.001874141877 - 0.000343251171im, 0.001083277833 - 0.000321704428im, 0.001180553411 + 0.0im, 0.0 + 0.0im, 0.0 - 0.0im, 0.0 + 0.0im, 0.0 + 0.0im, 0.0 + 0.0im, 0.0 + 0.0im, 0.0 - 0.0im, 0.0 - 0.0im, 0.0 + 0.0im, 0.00087176192 + 0.0im, -0.000457598014 - 0.000845175127im, -0.000504075741 + 0.000799582738im, -0.000457598014 + 0.000845175127im, 0.001059597714 + 0.0im, -0.000510602006 - 0.000908412874im, -0.000504075741 - 0.000799582738im, -0.000510602006 + 0.000908412874im, 0.001024849661 + 0.0im, 0.0 + 0.0im, -0.0 - 0.0im, -0.0 + 0.0im, -0.0 + 0.0im, 0.0 + 0.0im, -0.0 - 0.0im, -0.0 - 0.0im, -0.0 + 0.0im, 0.0 + 0.0im, 0.000300068597 + 0.0im, 0.000826500187 + 0.000634405423im, 0.000960221539 - 0.000440668562im, 0.000826500187 - 0.000634405423im, 0.003617748774 + 0.0im, 0.001713144131 - 0.003243866269im, 0.000960221539 + 0.000440668562im, 0.001713144131 + 0.003243866269im, 0.003719863381 + 0.0im]

@test isapprox(data_flat, data_golden; atol=1e-9)
end
Expand Down Expand Up @@ -608,9 +608,9 @@ end
energies = 0:0.5:5
kernel = lorentzian(fwhm=0.3)
res = intensities(swt, qs; energies, kernel)
# println(round.(res.data; digits=10))
data_ref = [0.0425516442 0.148531187 0.0425516442; 0.1237107851 0.9444077155 0.1237107851; 1.0795751088 1.2612860859 1.0795751088; 0.4927038544 0.1685055314 0.4927038544; 0.0877705066 0.0606652185 0.0877705066; 0.0344619785 0.0308251889 0.0344619785; 0.0182142627 0.0185853576 0.0182142627; 0.0112300272 0.0124102892 0.0112300272; 0.0076073202 0.0088685106 0.0076073202; 0.0054909426 0.0066513058 0.0054909426; 0.0041486157 0.005172181 0.0041486157]
@test res.data ≈ data_ref
# println(round.(res.data; digits=12))
data_ref = [0.042551644188 0.148531187027 0.042551644188; 0.123710785142 0.944407715529 0.123710785142; 1.079575108835 1.261286085876 1.079575108835; 0.492703854423 0.168505531387 0.492703854423; 0.087770506619 0.06066521855 0.087770506619; 0.034461978527 0.030825188879 0.034461978527; 0.018214262744 0.018585357642 0.018214262744; 0.011230027228 0.012410289203 0.011230027228; 0.007607320239 0.008868510563 0.007607320239; 0.005490942587 0.006651305827 0.005490942587; 0.004148615687 0.005172181047 0.004148615687]
isapprox(res.data, data_ref; atol=1e-9)
end


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